clean msm

Fix CUDA compilation docs and update new curve script to generate correct cu(h) files. Resolves #101

.github/pull_request_template.md

@@ -4,4 +4,4 @@ This PR...
 
 ## Linked Issues
 
-Closes #
+Resolves #

.github/workflows/build.yml

@@ -1,13 +1,27 @@
 name: Build
 
-on: 
+on:
   pull_request:
-    branches: [ "main" ]
+    types: 
+      - ready_for_review
+      - opened
+    branches:
+      - main
+      - dev
     paths:
-      - "icicle/**"
-      - "src/**"
-      - "Cargo.toml"
-      - "build.rs"
+      - icicle/**
+      - src/**
+      - Cargo.toml
+      - build.rs
+  push:
+    branches-ignore:
+      - main
+      - dev
+    paths:
+      - icicle/**
+      - src/**
+      - Cargo.toml
+      - build.rs
 
 env:
   CARGO_TERM_COLOR: always

.gitignore

@@ -5,6 +5,8 @@
 *.cubin
 *.bin
 *.fatbin
+*.nsys-rep
+*.ncu-rep
 **/target
 **/.vscode
 **/.*lock*csv#

Cargo.toml

@@ -23,7 +23,13 @@ ark-std = "0.3.0"
 ark-ff = "0.3.0"
 ark-poly = "0.3.0"
 ark-ec = { version = "0.3.0", features = [ "parallel" ] }
-ark-bls12-381 = { version = "0.3.0", optional = true }
+ark-bls12-381 = "0.3.0"
+ark-bls12-377 = "0.3.0"
+ark-bn254 = "0.3.0"
+
+serde = { version = "1.0", features = ["derive"] }
+serde_derive = "1.0"
+serde_cbor = "0.11.2"
 
 rustacuda = "0.1"
 rustacuda_core = "0.1"
@@ -40,3 +46,4 @@ cc = { version = "1.0", features = ["parallel"] }
 [features]
 default = ["bls12_381"]
 bls12_381 = ["ark-bls12-381/curve"]
+g2 = []

README.md

@@ -4,6 +4,13 @@
                   
 ![image (4)](https://user-images.githubusercontent.com/2446179/223707486-ed8eb5ab-0616-4601-8557-12050df8ccf7.png)
 
+
+<div align="center">
+![Build status](https://github.com/ingonyama-zk/icicle/actions/workflows/buil.yml/badge.svg)
+![Discord server](https://img.shields.io/discord/1063033227788423299?label=Discord&logo=Discord&logoColor=%23&style=plastic)
+![Follow us on twitter](https://img.shields.io/twitter/follow/Ingo_zk?style=social)
+</div>
+
 ## Background
 
 Zero Knowledge Proofs (ZKPs) are considered one of the greatest achievements of modern cryptography. Accordingly, ZKPs are expected to disrupt a number of industries and will usher in an era of trustless and privacy preserving services and infrastructure.
@@ -23,8 +30,8 @@ ICICLE is a CUDA implementation of general functions widely used in ZKP. ICICLE
         - Affine: {x, y}
 - Curves
     - [BLS12-381]
-
-> NOTE: _Support for BN254 and BLS12-377 are planned_
+    - [BLS12-377]
+    - [BN254]
 
 ## Build and usage
 
@@ -36,7 +43,7 @@ ICICLE is a CUDA implementation of general functions widely used in ZKP. ICICLE
 
 ```sh
 mkdir -p build
-nvcc -o build/<ENTER_DIR_NAME> ./icicle/appUtils/ntt/ntt.cu ./icicle/appUtils/msm/msm.cu ./icicle/appUtils/vector_manipulation/ve_mod_mult.cu ./icicle/primitives/projective.cu -lib -arch=native
+nvcc -o build/<binary_name> ./icicle/curves/index.cu -lib -arch=native
 ```
 
 ### Testing the CUDA code
@@ -89,9 +96,70 @@ The flag `--test-threads=1` is needed because currently some tests might interfe
 
 An example of using the Rust bindings library can be found in our [fast-danksharding implementation][FDI]
 
+### Supporting Additional Curves
+
+Supporting additional curves can be done as follows:
+
+Create a JSON file with the curve parameters. The curve is defined by the following parameters: 
+- ``curve_name`` - e.g. ``bls12_381``.
+- ``modulus_p`` - scalar field modulus (in decimal).
+- ``bit_count_p`` - number of bits needed to represent `` modulus_p`` .
+- ``limb_p`` - number of bytes needed to represent `` modulus_p``  (rounded).
+- ``ntt_size`` - log of the maximal size subgroup of the scalar field.    
+- ``modulus_q`` - base field modulus (in decimal).
+- ``bit_count_q`` - number of bits needed to represent `` modulus_q`` .
+- ``limb_q`` number of bytes needed to represent `` modulus_p``  (rounded).
+- ``weierstrass_b`` - Weierstrauss constant of the curve. 
+- ``weierstrass_b_g2_re`` - Weierstrauss real constant of the g2 curve. 
+- ``weierstrass_b_g2_im`` - Weierstrauss imaginary constant of the g2 curve. 
+- ``gen_x`` - x-value of a generator element for the curve. 
+- ``gen_y`` - y-value of a generator element for the curve.
+- ``gen_x_re`` - real x-value of a generator element for the g2 curve. 
+- ``gen_x_im`` - imaginary x-value of a generator element for the g2 curve. 
+- ``gen_y_re`` - real y-value of a generator element for the g2 curve. 
+- ``gen_y_im`` - imaginary y-value of a generator element for the g2 curve. 
+
+Here's an example for BLS12-381.
+```
+{
+    "curve_name" : "bls12_381", 
+    "modulus_p" : 52435875175126190479447740508185965837690552500527637822603658699938581184513,
+    "bit_count_p" : 255,
+    "limb_p" :  8,
+    "ntt_size" : 32,
+    "modulus_q" : 4002409555221667393417789825735904156556882819939007885332058136124031650490837864442687629129015664037894272559787,
+    "bit_count_q" : 381,
+    "limb_q" : 12,
+    "weierstrass_b" : 4,
+    "weierstrass_b_g2_re":4,
+    "weierstrass_b_g2_im":4,
+    "gen_x" : 3685416753713387016781088315183077757961620795782546409894578378688607592378376318836054947676345821548104185464507,
+    "gen_y" : 1339506544944476473020471379941921221584933875938349620426543736416511423956333506472724655353366534992391756441569,
+    "gen_x_re" : 352701069587466618187139116011060144890029952792775240219908644239793785735715026873347600343865175952761926303160,
+    "gen_x_im" : 3059144344244213709971259814753781636986470325476647558659373206291635324768958432433509563104347017837885763365758,
+    "gen_y_re" : 1985150602287291935568054521177171638300868978215655730859378665066344726373823718423869104263333984641494340347905,
+    "gen_y_im" : 927553665492332455747201965776037880757740193453592970025027978793976877002675564980949289727957565575433344219582
+}
+```
+
+Save the parameters JSON file under the ``curve_parameters`` directory.
+
+Then run the Python script ``new_curve_script.py `` from the root folder:
+
+```
+python3 ./curve_parameters/new_curve_script.py ./curve_parameters/bls12_381.json
+```
+
+The script does the following:
+- Creates a folder in ``icicle/curves`` with the curve name, which contains all of the files needed for the supported operations in cuda.
+- Adds the curve's exported operations to ``icicle/curves/index.cu``. 
+- Creates a file with the curve name in ``src/curves`` with the relevant objects for the curve. 
+- Creates a test file with the curve name in ``src``. 
+
+Testing the new curve could be done by running the tests in ``tests_curve_name`` (e.g. ``tests_bls12_381``).
 ## Contributions
 
-Join our [Discord Server](https://discord.gg/Y4SkbDf2Ff) and find us on the icicle channel. We will be happy to work together to support your use case and talk features, bugs and design.
+Join our [Discord Server][DISCORD] and find us on the icicle channel. We will be happy to work together to support your use case and talk features, bugs and design.
 
 ### Hall of Fame
 
@@ -104,13 +172,16 @@ ICICLE is distributed under the terms of the MIT License.
 See [LICENSE-MIT][LMIT] for details.
 
 <!-- Begin Links -->
-[BLS12-381]: ./icicle/curves/bls12_381.cuh
+[BLS12-381]: ./icicle/curves/bls12_381/supported_operations.cu
+[BLS12-377]: ./icicle/curves/bls12_377/supported_operations.cu
+[BN254]: ./icicle/curves/bn254/supported_operations.cu
 [NVCC]: https://docs.nvidia.com/cuda/#installation-guides
 [CRV_TEMPLATE]: ./icicle/curves/curve_template.cuh
 [CRV_CONFIG]: ./icicle/curves/curve_config.cuh
 [B_SCRIPT]: ./build.rs
 [FDI]: https://github.com/ingonyama-zk/fast-danksharding
 [LMIT]: ./LICENSE
+[DISCORD]: https://discord.gg/Y4SkbDf2Ff
 [googletest]: https://github.com/google/googletest/
 
 <!-- End Links -->

benches/msm.rs

@@ -2,31 +2,51 @@ extern crate criterion;
 
 use criterion::{criterion_group, criterion_main, Criterion};
 
-use icicle_utils::{set_up_scalars, generate_random_points, commit_batch, get_rng};
-use rustacuda::prelude::*;
+use icicle_utils::test_bls12_381::{
+    commit_batch_bls12_381, generate_random_points_bls12_381, set_up_scalars_bls12_381,
+};
+use icicle_utils::utils::*;
+#[cfg(feature = "g2")]
+use icicle_utils::{commit_batch_g2, field::ExtensionField};
 
+use rustacuda::prelude::*;
 
 const LOG_MSM_SIZES: [usize; 1] = [12];
 const BATCH_SIZES: [usize; 2] = [128, 256];
 
 fn bench_msm(c: &mut Criterion) {
+    let mut group = c.benchmark_group("MSM");
     for log_msm_size in LOG_MSM_SIZES {
         for batch_size in BATCH_SIZES {
             let msm_size = 1 << log_msm_size;
-            let (scalars, _, _) = set_up_scalars(msm_size, 0, false);
+            let (scalars, _, _) = set_up_scalars_bls12_381(msm_size, 0, false);
             let batch_scalars = vec![scalars; batch_size].concat();
             let mut d_scalars = DeviceBuffer::from_slice(&batch_scalars[..]).unwrap();
-            let points = generate_random_points(msm_size, get_rng(None));
+
+            let points = generate_random_points_bls12_381(msm_size, get_rng(None));
             let batch_points = vec![points; batch_size].concat();
             let mut d_points = DeviceBuffer::from_slice(&batch_points[..]).unwrap();
 
-            c.bench_function(
+            #[cfg(feature = "g2")]
+            let g2_points = generate_random_points::<ExtensionField>(msm_size, get_rng(None));
+            #[cfg(feature = "g2")]
+            let g2_batch_points = vec![g2_points; batch_size].concat();
+            #[cfg(feature = "g2")]
+            let mut d_g2_points = DeviceBuffer::from_slice(&g2_batch_points[..]).unwrap();
+
+            group.sample_size(30).bench_function(
                 &format!("MSM of size 2^{} in batch {}", log_msm_size, batch_size),
-                |b| b.iter(|| commit_batch(&mut d_points, &mut d_scalars, batch_size))
+                |b| b.iter(|| commit_batch_bls12_381(&mut d_points, &mut d_scalars, batch_size)),
+            );
+
+            #[cfg(feature = "g2")]
+            group.sample_size(10).bench_function(
+                &format!("G2 MSM of size 2^{} in batch {}", log_msm_size, batch_size),
+                |b| b.iter(|| commit_batch_g2(&mut d_g2_points, &mut d_scalars, batch_size))
             );
         }
     }
 }
 
 criterion_group!(msm_benches, bench_msm);
-criterion_main!(msm_benches);
+criterion_main!(msm_benches);

benches/ntt.rs

@@ -2,39 +2,33 @@ extern crate criterion;
 
 use criterion::{criterion_group, criterion_main, Criterion};
 
-use icicle_utils::{interpolate_scalars_batch, interpolate_points_batch, set_up_scalars, set_up_points};
+use icicle_utils::test_bls12_381::{interpolate_scalars_batch_bls12_381, interpolate_points_batch_bls12_381, set_up_scalars_bls12_381, set_up_points_bls12_381};
 
 
 const LOG_NTT_SIZES: [usize; 1] = [15];
 const BATCH_SIZES: [usize; 2] = [8, 16];
 
-fn bench_point_ntt(c: &mut Criterion) {
+fn bench_ntt(c: &mut Criterion) {
+    let mut group = c.benchmark_group("NTT");
     for log_ntt_size in LOG_NTT_SIZES {
         for batch_size in BATCH_SIZES {
             let ntt_size = 1 << log_ntt_size;
-            let (_, mut d_evals, mut d_domain) = set_up_points(ntt_size * batch_size, log_ntt_size, true);
+            let (_, mut d_evals, mut d_domain) = set_up_scalars_bls12_381(ntt_size * batch_size, log_ntt_size, true);
+            let (_, mut d_points_evals, _) = set_up_points_bls12_381(ntt_size * batch_size, log_ntt_size, true);
 
-            c.bench_function(
-                &format!("EC NTT of size 2^{} in batch {}", log_ntt_size, batch_size),
-                |b| b.iter(|| interpolate_points_batch(&mut d_evals, &mut d_domain, batch_size))
-            );
-        }
-    }
-}
-
-fn bench_scalar_ntt(c: &mut Criterion) {
-    for log_ntt_size in LOG_NTT_SIZES {
-        for batch_size in BATCH_SIZES {
-            let ntt_size = 1 << log_ntt_size;
-            let (_, mut d_evals, mut d_domain) = set_up_scalars(ntt_size * batch_size, log_ntt_size, true);
-
-            c.bench_function(
+            group.sample_size(100).bench_function(
                 &format!("Scalar NTT of size 2^{} in batch {}", log_ntt_size, batch_size),
-                |b| b.iter(|| interpolate_scalars_batch(&mut d_evals, &mut d_domain, batch_size))
+                |b| b.iter(|| interpolate_scalars_batch_bls12_381(&mut d_evals, &mut d_domain, batch_size))
+            );
+
+            group.sample_size(10).bench_function(
+                &format!("EC NTT of size 2^{} in batch {}", log_ntt_size, batch_size),
+                |b| b.iter(|| interpolate_points_batch_bls12_381(&mut d_points_evals, &mut d_domain, batch_size))
             );
         }
     }
 }
 
-criterion_group!(ntt_benches, bench_point_ntt, bench_scalar_ntt);
+criterion_group!(ntt_benches, bench_ntt);
 criterion_main!(ntt_benches);
+

build.rs

@@ -8,22 +8,26 @@ fn main() {
     println!("cargo:rerun-if-changed=./icicle");
 
     let arch_type = env::var("ARCH_TYPE").unwrap_or(String::from("native"));
+    let stream_type = env::var("DEFAULT_STREAM").unwrap_or(String::from("legacy"));
 
     let mut arch = String::from("-arch=");
     arch.push_str(&arch_type);
+    let mut stream = String::from("-default-stream=");
+    stream.push_str(&stream_type);
 
     let mut nvcc = cc::Build::new();
 
     println!("Compiling icicle library using arch: {}", &arch);
 
+    if cfg!(feature = "g2") {
+        nvcc.define("G2_DEFINED", None);
+    }
     nvcc.cuda(true);
     nvcc.debug(false);
     nvcc.flag(&arch);
+    nvcc.flag(&stream);
     nvcc.files([
-        "./icicle/appUtils/vector_manipulation/ve_mod_mult.cu",
-        "./icicle/appUtils/ntt/lde.cu",
-        "./icicle/appUtils/msm/msm.cu",
-        "./icicle/primitives/projective.cu",
+        "./icicle/curves/index.cu",
     ]);
     nvcc.compile("ingo_icicle"); //TODO: extension??
 }

curve_parameters/bls12_377.json

@@ -0,0 +1,20 @@
+{
+    "curve_name" : "bls12_377",
+    "modulus_p" : 8444461749428370424248824938781546531375899335154063827935233455917409239041,
+    "bit_count_p" : 253,
+    "limb_p" :  8,
+    "ntt_size" : 32,
+    "modulus_q" : 258664426012969094010652733694893533536393512754914660539884262666720468348340822774968888139573360124440321458177,
+    "bit_count_q" : 377,
+    "limb_q" : 12,
+    "root_of_unity" : 5928890464389279575069867463136436689218492512582288454256978381122364252082,
+    "weierstrass_b" : 1,
+    "weierstrass_b_g2_re" : 0,
+    "weierstrass_b_g2_im" : 155198655607781456406391640216936120121836107652948796323930557600032281009004493664981332883744016074664192874906,
+    "g1_gen_x" : 81937999373150964239938255573465948239988671502647976594219695644855304257327692006745978603320413799295628339695,
+    "g1_gen_y" : 241266749859715473739788878240585681733927191168601896383759122102112907357779751001206799952863815012735208165030,
+    "g2_gen_x_re" : 233578398248691099356572568220835526895379068987715365179118596935057653620464273615301663571204657964920925606294,
+    "g2_gen_x_im" : 140913150380207355837477652521042157274541796891053068589147167627541651775299824604154852141315666357241556069118,
+    "g2_gen_y_re" : 63160294768292073209381361943935198908131692476676907196754037919244929611450776219210369229519898517858833747423,
+    "g2_gen_y_im" : 149157405641012693445398062341192467754805999074082136895788947234480009303640899064710353187729182149407503257491
+}

curve_parameters/bls12_381.json

@@ -0,0 +1,20 @@
+{
+    "curve_name" : "bls12_381",
+    "modulus_p" : 52435875175126190479447740508185965837690552500527637822603658699938581184513,
+    "bit_count_p" : 255,
+    "limb_p" :  8,
+    "ntt_size" : 32,
+    "modulus_q" : 4002409555221667393417789825735904156556882819939007885332058136124031650490837864442687629129015664037894272559787,
+    "bit_count_q" : 381,
+    "limb_q" : 12,
+    "root_of_unity" : 937917089079007706106976984802249742464848817460758522850752807661925904159,
+    "weierstrass_b" : 4,
+    "weierstrass_b_g2_re":4,
+    "weierstrass_b_g2_im":4,
+    "g1_gen_x" : 3685416753713387016781088315183077757961620795782546409894578378688607592378376318836054947676345821548104185464507,
+    "g1_gen_y" : 1339506544944476473020471379941921221584933875938349620426543736416511423956333506472724655353366534992391756441569,
+    "g2_gen_x_re" : 352701069587466618187139116011060144890029952792775240219908644239793785735715026873347600343865175952761926303160,
+    "g2_gen_x_im" : 3059144344244213709971259814753781636986470325476647558659373206291635324768958432433509563104347017837885763365758,
+    "g2_gen_y_re" : 1985150602287291935568054521177171638300868978215655730859378665066344726373823718423869104263333984641494340347905,
+    "g2_gen_y_im" : 927553665492332455747201965776037880757740193453592970025027978793976877002675564980949289727957565575433344219582
+}

curve_parameters/bn254.json

@@ -0,0 +1,20 @@
+{
+    "curve_name" : "bn254",
+    "modulus_p" : 21888242871839275222246405745257275088548364400416034343698204186575808495617,
+    "bit_count_p" : 254,
+    "limb_p" :  8,
+    "ntt_size" : 28,
+    "modulus_q" : 21888242871839275222246405745257275088696311157297823662689037894645226208583,
+    "bit_count_q" : 254,
+    "limb_q" : 8,
+    "root_of_unity" : 19103219067921713944291392827692070036145651957329286315305642004821462161904,
+    "weierstrass_b" : 3,
+    "weierstrass_b_g2_re" : 19485874751759354771024239261021720505790618469301721065564631296452457478373,
+    "weierstrass_b_g2_im" : 266929791119991161246907387137283842545076965332900288569378510910307636690,
+    "g1_gen_x" : 1,
+    "g1_gen_y" : 2,
+    "g2_gen_x_re" : 10857046999023057135944570762232829481370756359578518086990519993285655852781,
+    "g2_gen_x_im" : 11559732032986387107991004021392285783925812861821192530917403151452391805634,
+    "g2_gen_y_re" : 8495653923123431417604973247489272438418190587263600148770280649306958101930,
+    "g2_gen_y_im" : 4082367875863433681332203403145435568316851327593401208105741076214120093531
+}

curve_parameters/new_curve_script.py

@@ -0,0 +1,316 @@
+import json
+import math
+import os
+from string import Template
+import sys
+
+
+def to_hex(val: int, length):
+    x = hex(val)[2:]
+    if len(x) % 8 != 0:
+        x = "0" * (8-len(x) % 8) + x
+    if len(x) != length:
+        x = "0" * (length-len(x)) + x
+    n = 8
+    chunks = [x[i:i+n] for i in range(0, len(x), n)][::-1]
+    s = ""
+    for c in chunks:
+        s += f'0x{c}, '
+        
+    return s[:-2]
+
+
+def compute_values(modulus, modulus_bit_count, limbs):
+    limb_size = 8*limbs
+    modulus_ = to_hex(modulus,limb_size)
+    modulus_2 = to_hex(modulus*2,limb_size)
+    modulus_4 = to_hex(modulus*4,limb_size)
+    modulus_wide = to_hex(modulus,limb_size*2)
+    modulus_squared = to_hex(modulus*modulus,limb_size)
+    modulus_squared_2 = to_hex(modulus*modulus*2,limb_size)
+    modulus_squared_4 = to_hex(modulus*modulus*4,limb_size)
+    m_raw = int(math.floor(int(pow(2,2*modulus_bit_count) // modulus)))
+    m = to_hex(m_raw,limb_size)
+    one = to_hex(1,limb_size)
+    zero = to_hex(0,limb_size)
+
+    return (
+        modulus_,
+        modulus_2,
+        modulus_4,
+        modulus_wide,
+        modulus_squared,
+        modulus_squared_2,
+        modulus_squared_4,
+        m,
+        one,
+        zero
+    )
+
+
+def get_fq_params(modulus, modulus_bit_count, limbs, g1_gen_x, g1_gen_y, g2_gen_x_re, g2_gen_x_im, g2_gen_y_re, g2_gen_y_im):
+    (
+        modulus,
+        modulus_2,
+        modulus_4,
+        modulus_wide,
+        modulus_squared,
+        modulus_squared_2,
+        modulus_squared_4,
+        m,
+        one,
+        zero
+    ) = compute_values(modulus, modulus_bit_count, limbs)
+
+    limb_size = 8*limbs
+    return {
+        'fq_modulus': modulus,
+        'fq_modulus_2': modulus_2,
+        'fq_modulus_4': modulus_4,
+        'fq_modulus_wide': modulus_wide,
+        'fq_modulus_squared': modulus_squared,
+        'fq_modulus_squared_2': modulus_squared_2,
+        'fq_modulus_squared_4': modulus_squared_4,
+        'fq_m': m,
+        'fq_one': one,
+        'fq_zero': zero,
+        'fq_gen_x': to_hex(g1_gen_x, limb_size),
+        'fq_gen_y': to_hex(g1_gen_y, limb_size),
+        'fq_gen_x_re': to_hex(g2_gen_x_re, limb_size),
+        'fq_gen_x_im': to_hex(g2_gen_x_im, limb_size),
+        'fq_gen_y_re': to_hex(g2_gen_y_re, limb_size),
+        'fq_gen_y_im': to_hex(g2_gen_y_im, limb_size)
+    }
+
+
+def get_fp_params(modulus, modulus_bit_count, limbs, root_of_unity, size=0):
+    (
+        modulus_,
+        modulus_2,
+        modulus_4,
+        modulus_wide,
+        modulus_squared,
+        modulus_squared_2,
+        modulus_squared_4,
+        m,
+        one,
+        zero
+    ) = compute_values(modulus, modulus_bit_count, limbs)
+    limb_size = 8*limbs
+    if size > 0:
+        omega = ''
+        omega_inv = ''
+        inv = ''
+        omegas = []
+        omegas_inv = []
+        for k in range(size):
+            if k == 0:
+                om = root_of_unity
+            else:
+                om = pow(om, 2, modulus)
+            omegas.append(om)
+            omegas_inv.append(pow(om, -1, modulus))
+        omegas.reverse()
+        omegas_inv.reverse()
+        for k in range(size):
+            omega += "\n              {"+ to_hex(omegas[k],limb_size)+"}," if k>0 else "      {"+ to_hex(omegas[k],limb_size)+"},"
+            omega_inv += "\n              {"+ to_hex(omegas_inv[k],limb_size)+"}," if k>0 else "      {"+ to_hex(omegas_inv[k],limb_size)+"},"
+            inv += "\n              {"+ to_hex(pow(int(pow(2,k+1)), -1, modulus),limb_size)+"}," if k>0 else "      {"+ to_hex(pow(int(pow(2,k+1)), -1, modulus),limb_size)+"},"
+  
+  
+    return {
+        'fp_modulus': modulus_,
+        'fp_modulus_2': modulus_2,
+        'fp_modulus_4': modulus_4,
+        'fp_modulus_wide': modulus_wide,
+        'fp_modulus_squared': modulus_squared,
+        'fp_modulus_squared_2': modulus_squared_2,
+        'fp_modulus_squared_4': modulus_squared_4,
+        'fp_m': m,
+        'fp_one': one,
+        'fp_zero': zero,
+        'omega': omega[:-1],
+        'omega_inv': omega_inv[:-1],
+        'inv': inv[:-1],
+    }
+
+
+def get_weier_params(weierstrass_b, weierstrass_b_g2_re, weierstrass_b_g2_im, size):
+    
+    return {
+        'weier_b': to_hex(weierstrass_b, size),
+        'weier_b_g2_re': to_hex(weierstrass_b_g2_re, size),
+        'weier_b_g2_im': to_hex(weierstrass_b_g2_im, size),
+    }
+
+
+def get_params(config):
+    global ntt_size
+    curve_name = config["curve_name"]
+    modulus_p = config["modulus_p"]
+    bit_count_p = config["bit_count_p"]
+    limb_p =  config["limb_p"]
+    ntt_size = config["ntt_size"]
+    modulus_q = config["modulus_q"]
+    bit_count_q = config["bit_count_q"] 
+    limb_q = config["limb_q"]
+    root_of_unity = config["root_of_unity"]
+    if root_of_unity == modulus_p:
+        sys.exit("Invalid root_of_unity value; please update in curve parameters")
+
+    weierstrass_b = config["weierstrass_b"]
+    weierstrass_b_g2_re = config["weierstrass_b_g2_re"]
+    weierstrass_b_g2_im = config["weierstrass_b_g2_im"]
+    g1_gen_x = config["g1_gen_x"]
+    g1_gen_y = config["g1_gen_y"]
+    g2_generator_x_re = config["g2_gen_x_re"]
+    g2_generator_x_im = config["g2_gen_x_im"]
+    g2_generator_y_re = config["g2_gen_y_re"]
+    g2_generator_y_im = config["g2_gen_y_im"]
+
+    params = {
+        'curve_name_U': curve_name.upper(),
+        'fp_num_limbs': limb_p,
+        'fq_num_limbs': limb_q,
+        'fp_modulus_bit_count': bit_count_p,
+        'fq_modulus_bit_count': bit_count_q,
+        'num_omegas': ntt_size
+    }
+    
+    fp_params = get_fp_params(modulus_p, bit_count_p, limb_p, root_of_unity, ntt_size)
+    fq_params = get_fq_params(modulus_q, bit_count_q, limb_q, g1_gen_x, g1_gen_y, g2_generator_x_re, g2_generator_x_im, g2_generator_y_re, g2_generator_y_im)
+    weier_params = get_weier_params(weierstrass_b, weierstrass_b_g2_re, weierstrass_b_g2_im, 8*limb_q)
+
+    return {
+        **params,
+        **fp_params,
+        **fq_params,
+        **weier_params
+    }
+
+
+config = None
+with open(sys.argv[1]) as json_file:
+    config = json.load(json_file)
+
+curve_name_lower = config["curve_name"].lower()
+curve_name_upper = config["curve_name"].upper()
+limb_q = config["limb_q"]
+limb_p = config["limb_p"]
+
+# Create Cuda interface
+
+newpath = f'./icicle/curves/{curve_name_lower}'
+if not os.path.exists(newpath):
+    os.makedirs(newpath)
+
+with open("./icicle/curves/curve_template/params.cuh", "r") as params_file:
+    params_file_template = Template(params_file.read())
+    params = get_params(config)
+    params_content = params_file_template.safe_substitute(params)
+    with open(f'./icicle/curves/{curve_name_lower}/params.cuh', 'w') as f:
+        f.write(params_content)
+
+with open("./icicle/curves/curve_template/lde.cu", "r") as lde_file:
+    template_content = Template(lde_file.read())
+    lde_content = template_content.safe_substitute(
+        CURVE_NAME_U=curve_name_upper, 
+        CURVE_NAME_L=curve_name_lower
+    )
+    with open(f'./icicle/curves/{curve_name_lower}/lde.cu', 'w') as f:
+        f.write(lde_content)
+    
+with open("./icicle/curves/curve_template/msm.cu", "r") as msm_file:
+    template_content = Template(msm_file.read())
+    msm_content = template_content.safe_substitute(
+        CURVE_NAME_U=curve_name_upper, 
+        CURVE_NAME_L=curve_name_lower
+    )
+    with open(f'./icicle/curves/{curve_name_lower}/msm.cu', 'w') as f:
+        f.write(msm_content)
+
+with open("./icicle/curves/curve_template/ve_mod_mult.cu", "r") as ve_mod_mult_file:
+    template_content = Template(ve_mod_mult_file.read())
+    ve_mod_mult_content = template_content.safe_substitute(
+        CURVE_NAME_U=curve_name_upper, 
+        CURVE_NAME_L=curve_name_lower
+    )
+    with open(f'./icicle/curves/{curve_name_lower}/ve_mod_mult.cu', 'w') as f:
+        f.write(ve_mod_mult_content)
+    
+
+with open(f'./icicle/curves/curve_template/curve_config.cuh', 'r') as cc:
+    template_content = Template(cc.read())
+    cc_content = template_content.safe_substitute(
+        CURVE_NAME_U=curve_name_upper,
+    )
+    with open(f'./icicle/curves/{curve_name_lower}/curve_config.cuh', 'w') as f:
+        f.write(cc_content)
+    
+
+with open(f'./icicle/curves/curve_template/projective.cu', 'r') as proj:
+    template_content = Template(proj.read())
+    proj_content = template_content.safe_substitute(
+        CURVE_NAME_U=curve_name_upper, 
+        CURVE_NAME_L=curve_name_lower
+    )
+    with open(f'./icicle/curves/{curve_name_lower}/projective.cu', 'w') as f:
+        f.write(proj_content)
+
+
+with open(f'./icicle/curves/curve_template/supported_operations.cu', 'r') as supp_ops:
+    template_content = Template(supp_ops.read())
+    supp_ops_content = template_content.safe_substitute()
+    with open(f'./icicle/curves/{curve_name_lower}/supported_operations.cu', 'w') as f:
+        f.write(supp_ops_content)
+
+
+with open('./icicle/curves/index.cu', 'r+') as f:
+    index_text = f.read()
+    if index_text.find(curve_name_lower) == -1:
+        f.write(f'\n#include "{curve_name_lower}/supported_operations.cu"')
+    
+
+
+# Create Rust interface and tests
+
+if limb_p == limb_q: 
+    with open("./src/curve_templates/curve_same_limbs.rs", "r") as curve_file:
+        content = curve_file.read()
+        content = content.replace("CURVE_NAME_U",curve_name_upper)
+        content = content.replace("CURVE_NAME_L",curve_name_lower)
+        content = content.replace("_limbs_p",str(limb_p * 8 * 4))
+        content = content.replace("limbs_p",str(limb_p))
+        text_file = open("./src/curves/"+curve_name_lower+".rs", "w")
+        n = text_file.write(content)
+        text_file.close()
+else:
+    with open("./src/curve_templates/curve_different_limbs.rs", "r") as curve_file:
+        content = curve_file.read()
+        content = content.replace("CURVE_NAME_U",curve_name_upper)
+        content = content.replace("CURVE_NAME_L",curve_name_lower)
+        content = content.replace("_limbs_p",str(limb_p * 8 * 4))
+        content = content.replace("limbs_p",str(limb_p))
+        content = content.replace("_limbs_q",str(limb_q * 8 * 4))
+        content = content.replace("limbs_q",str(limb_q))
+        text_file = open("./src/curves/"+curve_name_lower+".rs", "w")
+        n = text_file.write(content)
+        text_file.close()
+
+with open("./src/curve_templates/test.rs", "r") as test_file:
+    content = test_file.read()
+    content = content.replace("CURVE_NAME_U",curve_name_upper)
+    content = content.replace("CURVE_NAME_L",curve_name_lower)
+    text_file = open("./src/test_"+curve_name_lower+".rs", "w")
+    n = text_file.write(content)
+    text_file.close()
+    
+with open('./src/curves/mod.rs', 'r+') as f:
+    mod_text = f.read()
+    if mod_text.find(curve_name_lower) == -1:
+        f.write('\npub mod ' + curve_name_lower + ';')
+
+with open('./src/lib.rs', 'r+') as f:
+    lib_text = f.read()
+    if lib_text.find(curve_name_lower) == -1:
+        f.write('\npub mod ' + curve_name_lower + ';')

icicle/CMakeLists.txt

@@ -22,10 +22,6 @@ FetchContent_Declare(
   URL https://github.com/google/googletest/archive/refs/tags/v1.13.0.zip
 )
 # For Windows: Prevent overriding the parent project's compiler/linker settings
-
-# boosting lib
-include_directories("/home/miner/include/boost_1_80_0")
-
 set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
 FetchContent_MakeAvailable(googletest)
 

icicle/appUtils/msm/Makefile

@@ -0,0 +1,9 @@
+test_msm:
+	mkdir -p work
+	nvcc -o work/test_msm -I. tests/msm_test.cu
+	work/test_msm
+
+test_msm_debug:
+	mkdir -p work
+	nvcc -o work/test_msm_debug -I. tests/msm_test.cu -g -G
+	work/test_msm_debug

icicle/appUtils/msm/msm.cuh

@@ -1,22 +1,22 @@
+#ifndef MSM_H
+#define MSM_H
 #pragma once
-#include <stdexcept>
-#include <cuda.h>
-
-#include "../../primitives/projective.cuh"
-#include "../../primitives/affine.cuh"
-#include "../../curves/curve_config.cuh"
 
 template <typename S, typename P, typename A>
-void bucket_method_msm(unsigned bitsize, unsigned c, S *scalars, A *points, unsigned size, P* final_result, bool on_device);
+void bucket_method_msm(unsigned bitsize, unsigned c, S *scalars, A *points, unsigned size, P* final_result, bool on_device, bool big_triangle, cudaStream_t stream);
 
 template <typename S, typename P, typename A>
-void batched_bucket_method_msm(unsigned bitsize, unsigned c, S *scalars, A *points, unsigned batch_size, unsigned msm_size, P* final_results, bool on_device);
+void batched_bucket_method_msm(unsigned bitsize, unsigned c, S *scalars, A *points, unsigned batch_size, unsigned msm_size, P* final_results, bool on_device, cudaStream_t stream);
 
 template <typename S, typename P, typename A>
-void batched_large_msm(S* scalars, A* points, unsigned batch_size, unsigned msm_size, P* result, bool on_device);
+void batched_large_msm(S* scalars, A* points, unsigned batch_size, unsigned msm_size, P* result, bool on_device, cudaStream_t stream);
 
 template <typename S, typename P, typename A>
-void large_msm(S* scalars, A* points, unsigned size, P* result, bool on_device);
+void large_msm(S* scalars, A* points, unsigned size, P* result, bool on_device, bool big_triangle, cudaStream_t stream);
 
 template <typename S, typename P, typename A>
-void short_msm(S *h_scalars, A *h_points, unsigned size, P* h_final_result, bool on_device);
+void short_msm(S *h_scalars, A *h_points, unsigned size, P* h_final_result, cudaStream_t stream);
+
+template <typename A, typename S, typename P>
+void reference_msm(S* scalars, A* a_points, unsigned size);
+#endif

icicle/appUtils/msm/msm_clean.cu

@@ -0,0 +1,899 @@
+#ifndef MSM
+#define MSM
+#pragma once
+#include <stdexcept>
+#include <cuda.h>
+#include <cooperative_groups.h>
+#include "../../primitives/affine.cuh"
+#include <iostream>
+#include <vector>
+#include <cub/device/device_radix_sort.cuh>
+#include <cub/device/device_run_length_encode.cuh>
+#include <cub/device/device_scan.cuh>
+#include "../../utils/cuda_utils.cuh"
+#include "../../primitives/projective.cuh"
+#include "../../primitives/field.cuh"
+#include "msm.cuh"
+
+#define MAX_TH 256
+
+// #define SIGNED_DIG //WIP
+// #define BIG_TRIANGLE
+// #define SSM_SUM  //WIP
+
+template <typename P>
+__global__ void single_stage_multi_reduction_kernel(P *v, P *v_r, unsigned block_size, unsigned write_stride, unsigned write_phase, unsigned padding) {
+	int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  int tid_p = padding? (tid/(2*padding))*padding + tid%padding: tid;
+  int jump =block_size/2;
+  int block_id = tid_p/jump;
+  int block_tid = tid_p%jump;
+  unsigned read_ind = block_size*block_id + block_tid; 
+  unsigned write_ind = tid;
+	v_r[write_stride? ((write_ind/write_stride)*2 + write_phase)*write_stride + write_ind%write_stride : write_ind] = padding? (tid%(2*padding)<padding)? v[read_ind] + v[read_ind + jump] : P::zero() :v[read_ind] + v[read_ind + jump];
+}
+
+//this kernel performs single scalar multiplication
+//each thread multilies a single scalar and point
+template <typename P, typename S>
+__global__ void ssm_kernel(S *scalars, P *points, P *results, unsigned N) {
+
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid < N) results[tid] = scalars[tid]*points[tid];
+
+}
+
+//this kernel sums all the elements in a given vector using multiple threads
+template <typename P>
+__global__ void sum_reduction_kernel(P *v, P* v_r) {
+
+	unsigned tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+	// Start at 1/2 block stride and divide by two each iteration
+	for (unsigned s = blockDim.x / 2; s > 0; s >>= 1) {
+		// Each thread does work unless it is further than the stride
+		if (threadIdx.x < s) {
+			v[tid] = v[tid] + v[tid + s];
+		}
+    __syncthreads();
+	}
+
+	// Let the thread 0 for this block write the final result
+	if (threadIdx.x == 0) {
+		v_r[blockIdx.x] = v[tid];
+	}
+}
+
+//this kernel initializes the buckets with zero points
+//each thread initializes a different bucket
+template <typename P>
+__global__ void initialize_buckets_kernel(P *buckets, unsigned N) {
+  
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid < N) buckets[tid] = P::zero(); //zero point
+
+}
+
+//this kernel splits the scalars into digits of size c
+//each thread splits a single scalar into nof_bms digits
+template <typename S>
+__global__ void split_scalars_kernel(unsigned *buckets_indices, unsigned *point_indices, S *scalars, unsigned total_size, unsigned msm_log_size, unsigned nof_bms, unsigned bm_bitsize, unsigned c, unsigned top_bm_nof_missing_bits){
+  
+  constexpr unsigned sign_mask = 0x80000000;
+  // constexpr unsigned trash_bucket = 0x80000000;
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  unsigned bucket_index;
+  unsigned bucket_index2;
+  unsigned current_index;
+  unsigned msm_index = tid >> msm_log_size;
+  unsigned borrow = 0;
+  if (tid < total_size){
+    S scalar = scalars[tid];
+    for (unsigned bm = 0; bm < nof_bms; bm++)
+    {
+      bucket_index = scalar.get_scalar_digit(bm, c);
+      #ifdef SIGNED_DIG
+      bucket_index += borrow;
+      borrow = 0;
+      unsigned sign = 0;
+      if (bucket_index > (1<<(c-1))) {
+        bucket_index = (1 << c) - bucket_index;
+        borrow = 1;
+        sign = sign_mask;
+      }
+      #endif
+      current_index = bm * total_size + tid;
+      #ifdef SIGNED_DIG
+      point_indices[current_index] = sign | tid; //the point index is saved for later
+      #else
+      buckets_indices[current_index] = (msm_index<<(c+bm_bitsize)) | (bm<<c) | bucket_index;  //the bucket module number and the msm number are appended at the msbs
+      if (scalar == S::zero() || scalar == S::one() || bucket_index==0) buckets_indices[current_index] = 0; //will be skipped
+      point_indices[current_index] = tid; //the point index is saved for later
+      #endif
+    }
+  }
+}
+
+template <typename P, typename A, typename S>
+__global__ void add_ones_kernel(A *points, S* scalars, P* results, const unsigned msm_size, const unsigned run_length){
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  const unsigned nof_threads = (msm_size + run_length - 1)/run_length; //129256
+  if (tid>=nof_threads) {
+    results[tid] = P::zero();
+    return;
+  }
+  const unsigned start_index = tid*run_length;
+  P sum = P::zero();
+  for (int i=start_index;i<min(start_index+run_length,msm_size);i++){
+    if (scalars[i] == S::one()) sum = sum + points[i];
+  }
+  results[tid] = sum;
+}
+
+__global__ void find_cutoff_kernel(unsigned *v, unsigned size, unsigned cutoff, unsigned run_length, unsigned *result){
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  const unsigned nof_threads = (size + run_length - 1)/run_length;
+  if (tid>=nof_threads) {
+    return;
+  }
+  const unsigned start_index = tid*run_length;
+  for (int i=start_index;i<min(start_index+run_length,size-1);i++){
+    if (v[i] > cutoff && v[i+1] <= cutoff) {
+      result[0] = i+1;
+      return;
+    }
+    if (i == size - 1) {
+      result[0] = 0;
+    }
+  }
+}
+
+__global__ void find_max_size(unsigned *bucket_sizes,unsigned *single_bucket_indices,unsigned c, unsigned *largest_bucket_size){
+  for (int i=0;;i++){
+    if (single_bucket_indices[i]&((1<<c)-1)){
+      largest_bucket_size[0] = bucket_sizes[i];
+      largest_bucket_size[1] = i;
+      break;
+    }
+  }
+}
+
+//this kernel adds up the points in each bucket
+// __global__ void accumulate_buckets_kernel(P *__restrict__ buckets, unsigned *__restrict__ bucket_offsets,
+  //  unsigned *__restrict__ bucket_sizes, unsigned *__restrict__ single_bucket_indices, unsigned *__restrict__ point_indices, A *__restrict__ points, unsigned nof_buckets, unsigned batch_size, unsigned msm_idx_shift){
+template <typename P, typename A>
+__global__ void accumulate_buckets_kernel(P *__restrict__ buckets, unsigned *__restrict__ bucket_offsets, unsigned *__restrict__ bucket_sizes, unsigned *__restrict__ single_bucket_indices, const unsigned *__restrict__ point_indices, A *__restrict__ points, const unsigned nof_buckets, const unsigned nof_buckets_to_compute, const unsigned msm_idx_shift, const unsigned c){
+  
+  constexpr unsigned sign_mask = 0x80000000;
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid>=nof_buckets_to_compute){ 
+    return;
+  }
+  if ((single_bucket_indices[tid]&((1<<c)-1))==0)
+  {
+    return; //skip zero buckets
+  } 
+  #ifdef SIGNED_DIG //todo - fix
+  const unsigned msm_index = single_bucket_indices[tid]>>msm_idx_shift;
+  const unsigned bm_index = (single_bucket_indices[tid]&((1<<msm_idx_shift)-1))>>c;
+  const unsigned bucket_index = msm_index * nof_buckets + bm_index * ((1<<(c-1))+1) + (single_bucket_indices[tid]&((1<<c)-1));
+  #else
+  unsigned msm_index = single_bucket_indices[tid]>>msm_idx_shift;
+  unsigned bucket_index = msm_index * nof_buckets + (single_bucket_indices[tid]&((1<<msm_idx_shift)-1));
+  #endif
+  const unsigned bucket_offset = bucket_offsets[tid];
+  const unsigned bucket_size = bucket_sizes[tid];
+
+  P bucket; //get rid of init buckets? no.. because what about buckets with no points
+  for (unsigned i = 0; i < bucket_size; i++)  //add the relevant points starting from the relevant offset up to the bucket size
+  {
+    unsigned point_ind = point_indices[bucket_offset+i];
+    #ifdef SIGNED_DIG
+    unsigned sign = point_ind & sign_mask;
+    point_ind &= ~sign_mask;
+    A point = points[point_ind];
+    if (sign) point = A::neg(point);
+    #else
+    A point = points[point_ind];
+    #endif
+    bucket = i? bucket + point : P::from_affine(point);
+  }
+  buckets[bucket_index] = bucket;
+}
+
+template <typename P, typename A>
+__global__ void accumulate_large_buckets_kernel(P *__restrict__ buckets, unsigned *__restrict__ bucket_offsets, unsigned *__restrict__ bucket_sizes, unsigned *__restrict__ single_bucket_indices, const unsigned *__restrict__ point_indices, A *__restrict__ points, const unsigned nof_buckets, const unsigned nof_buckets_to_compute, const unsigned msm_idx_shift, const unsigned c, const unsigned threads_per_bucket, const unsigned max_run_length){
+  
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  unsigned large_bucket_index = tid/threads_per_bucket;
+  unsigned bucket_segment_index = tid%threads_per_bucket;
+  if (tid>=nof_buckets_to_compute*threads_per_bucket){ 
+    return;
+  }
+  if ((single_bucket_indices[large_bucket_index]&((1<<c)-1))==0) //dont need
+  {
+    return; //skip zero buckets
+  } 
+  unsigned write_bucket_index = bucket_segment_index * nof_buckets_to_compute + large_bucket_index;
+  const unsigned bucket_offset = bucket_offsets[large_bucket_index] + bucket_segment_index*max_run_length;
+  const unsigned bucket_size = bucket_sizes[large_bucket_index] > bucket_segment_index*max_run_length? bucket_sizes[large_bucket_index] - bucket_segment_index*max_run_length :0;
+  P bucket; 
+  unsigned run_length = min(bucket_size,max_run_length);
+  for (unsigned i = 0; i < run_length; i++)  //add the relevant points starting from the relevant offset up to the bucket size
+  {
+    unsigned point_ind = point_indices[bucket_offset+i];
+    A point = points[point_ind];
+    bucket = i? bucket + point : P::from_affine(point); //init empty buckets
+  }
+  buckets[write_bucket_index] = run_length? bucket : P::zero();
+}
+
+template <typename P>
+__global__ void distribute_large_buckets_kernel(P* large_buckets, P* buckets, unsigned *single_bucket_indices, unsigned size){
+
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid>=size){ 
+    return;
+  }
+  buckets[single_bucket_indices[tid]] = large_buckets[tid];
+}
+
+//this kernel sums the entire bucket module
+//each thread deals with a single bucket module
+template <typename P>
+__global__ void big_triangle_sum_kernel(P* buckets, P* final_sums, unsigned nof_bms, unsigned c){
+
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid>=nof_bms) return;
+  #ifdef SIGNED_DIG
+  unsigned buckets_in_bm = (1<<c)+1;
+  #else
+  unsigned buckets_in_bm = (1<<c);
+  #endif
+  P line_sum = buckets[(tid+1)*buckets_in_bm-1];
+  final_sums[tid] = line_sum;
+  for (unsigned i = buckets_in_bm-2; i >0; i--)
+  {
+    line_sum = line_sum + buckets[tid*buckets_in_bm + i];  //using the running sum method
+    final_sums[tid] = final_sums[tid] + line_sum;
+  }
+}
+
+//this kernel uses single scalar multiplication to multiply each bucket by its index
+//each thread deals with a single bucket
+template <typename P, typename S>
+__global__ void ssm_buckets_kernel(P* buckets, unsigned* single_bucket_indices, unsigned nof_buckets, unsigned c){
+  
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid>nof_buckets) return;
+  unsigned bucket_index = single_bucket_indices[tid];
+  S scalar_bucket_multiplier;
+  scalar_bucket_multiplier = {bucket_index&((1<<c)-1), 0, 0, 0, 0, 0, 0, 0}; //the index without the bucket module index
+  buckets[bucket_index] = scalar_bucket_multiplier*buckets[bucket_index];
+
+}
+
+template <typename P>
+__global__ void last_pass_kernel(P*final_buckets, P*final_sums, unsigned num_sums){
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid>num_sums) return;
+  final_sums[tid] = final_buckets[2*tid+1];
+}
+
+//this kernel computes the final result using the double and add algorithm
+//it is done by a single thread
+template <typename P, typename S>
+__global__ void final_accumulation_kernel(P* final_sums, P* ones_result, P* final_results, unsigned nof_msms, unsigned nof_bms, unsigned c){
+  
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid>nof_msms) return;
+  P final_result = P::zero();
+  for (unsigned i = nof_bms; i >1; i--)
+  {
+    final_result = final_result + final_sums[i-1 + tid*nof_bms];  //add
+    for (unsigned j=0; j<c; j++)  //double
+    {
+      final_result = final_result + final_result;
+    }
+  }
+  final_results[tid] = final_result + final_sums[tid*nof_bms] + ones_result[0];
+  // final_results[tid] = final_result + final_sums[tid*nof_bms];
+
+}
+
+//this function computes msm using the bucket method
+template <typename S, typename P, typename A>
+void bucket_method_msm(unsigned bitsize, unsigned c, S *scalars, A *points, unsigned size, P* final_result, bool on_device, bool big_triangle, cudaStream_t stream) {
+  
+  S *d_scalars;
+  A *d_points;
+  if (!on_device) {
+    //copy scalars and point to gpu
+    cudaMallocAsync(&d_scalars, sizeof(S) * size, stream);
+    cudaMallocAsync(&d_points, sizeof(A) * size, stream);
+    cudaMemcpyAsync(d_scalars, scalars, sizeof(S) * size, cudaMemcpyHostToDevice, stream);
+    cudaMemcpyAsync(d_points, points, sizeof(A) * size, cudaMemcpyHostToDevice, stream);
+  }
+  else {
+    d_scalars = scalars;
+    d_points = points;
+  }
+
+  P *buckets;
+  //compute number of bucket modules and number of buckets in each module
+  unsigned nof_bms = bitsize/c;
+  unsigned msm_log_size = ceil(log2(size));
+  unsigned bm_bitsize = ceil(log2(nof_bms));
+  if (bitsize%c){
+    nof_bms++;
+  }
+  unsigned top_bm_nof_missing_bits = c*nof_bms - bitsize;
+  #ifdef SIGNED_DIG
+  unsigned nof_buckets = nof_bms*((1<<(c-1))+1); //signed digits
+  #else
+  unsigned nof_buckets = nof_bms<<c;
+  #endif
+  cudaMallocAsync(&buckets, sizeof(P) * nof_buckets, stream);
+
+  // launch the bucket initialization kernel with maximum threads
+  unsigned NUM_THREADS = 1 << 10;
+  unsigned NUM_BLOCKS = (nof_buckets + NUM_THREADS - 1) / NUM_THREADS;
+  initialize_buckets_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(buckets, nof_buckets);
+
+  //accumulate ones
+  P *ones_results; //fix whole division, in last run in kernel too
+  const unsigned nof_runs = msm_log_size > 10? (1<<(msm_log_size-6)) : 16;
+  const unsigned run_length = (size + nof_runs -1)/nof_runs;
+  cudaMallocAsync(&ones_results, sizeof(P) * nof_runs, stream);
+  NUM_THREADS = min(1 << 8,nof_runs);
+  NUM_BLOCKS = (nof_runs + NUM_THREADS - 1) / NUM_THREADS;
+  add_ones_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(d_points, d_scalars, ones_results, size, run_length);
+ 
+  for (int s=nof_runs>>1;s>0;s>>=1){
+    NUM_THREADS = min(MAX_TH,s);
+    NUM_BLOCKS = (s + NUM_THREADS - 1) / NUM_THREADS;
+    single_stage_multi_reduction_kernel<<<NUM_BLOCKS, NUM_THREADS,0,stream>>>(ones_results,ones_results,s*2,0,0,0);
+  }
+
+  unsigned *bucket_indices;
+  unsigned *point_indices;
+  cudaMallocAsync(&bucket_indices, sizeof(unsigned) * size * (nof_bms+1), stream);
+  cudaMallocAsync(&point_indices, sizeof(unsigned) * size * (nof_bms+1), stream);
+
+  //split scalars into digits
+  NUM_THREADS = 1 << 10;
+  NUM_BLOCKS = (size * (nof_bms+1) + NUM_THREADS - 1) / NUM_THREADS;
+  split_scalars_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(bucket_indices + size, point_indices + size, d_scalars, size, msm_log_size, 
+                                                    nof_bms, bm_bitsize, c, top_bm_nof_missing_bits); //+size - leaving the first bm free for the out of place sort later
+
+  //sort indices - the indices are sorted from smallest to largest in order to group together the points that belong to each bucket
+  unsigned *sort_indices_temp_storage{};
+  size_t sort_indices_temp_storage_bytes;
+  // The second to last parameter is the default value supplied explicitly to allow passing the stream
+  // See https://nvlabs.github.io/cub/structcub_1_1_device_radix_sort.html#a65e82152de448c6373ed9563aaf8af7e for more info
+  cub::DeviceRadixSort::SortPairs(sort_indices_temp_storage, sort_indices_temp_storage_bytes, bucket_indices + size, bucket_indices,
+                                 point_indices + size, point_indices, size, 0, sizeof(unsigned) * 8, stream);
+  cudaMallocAsync(&sort_indices_temp_storage, sort_indices_temp_storage_bytes, stream);
+  for (unsigned i = 0; i < nof_bms; i++) {
+    unsigned offset_out = i * size;
+    unsigned offset_in = offset_out + size;
+    // The second to last parameter is the default value supplied explicitly to allow passing the stream
+    // See https://nvlabs.github.io/cub/structcub_1_1_device_radix_sort.html#a65e82152de448c6373ed9563aaf8af7e for more info
+    cub::DeviceRadixSort::SortPairs(sort_indices_temp_storage, sort_indices_temp_storage_bytes, bucket_indices + offset_in, bucket_indices + offset_out,
+                                 point_indices + offset_in, point_indices + offset_out, size, 0, sizeof(unsigned) * 8, stream);
+  }
+  cudaFreeAsync(sort_indices_temp_storage, stream);
+
+  //find bucket_sizes
+  unsigned *single_bucket_indices;
+  unsigned *bucket_sizes;
+  unsigned *nof_buckets_to_compute;
+  cudaMallocAsync(&single_bucket_indices, sizeof(unsigned)*nof_buckets, stream);
+  cudaMallocAsync(&bucket_sizes, sizeof(unsigned)*nof_buckets, stream);
+  cudaMallocAsync(&nof_buckets_to_compute, sizeof(unsigned), stream);
+  unsigned *encode_temp_storage{};
+  size_t encode_temp_storage_bytes = 0;
+  cub::DeviceRunLengthEncode::Encode(encode_temp_storage, encode_temp_storage_bytes, bucket_indices, single_bucket_indices, bucket_sizes,
+                                        nof_buckets_to_compute, nof_bms*size, stream);
+  cudaMallocAsync(&encode_temp_storage, encode_temp_storage_bytes, stream);
+  cub::DeviceRunLengthEncode::Encode(encode_temp_storage, encode_temp_storage_bytes, bucket_indices, single_bucket_indices, bucket_sizes,
+                                        nof_buckets_to_compute, nof_bms*size, stream);
+  cudaFreeAsync(encode_temp_storage, stream);
+
+  //get offsets - where does each new bucket begin
+  unsigned* bucket_offsets;
+  cudaMallocAsync(&bucket_offsets, sizeof(unsigned)*nof_buckets, stream);
+  unsigned* offsets_temp_storage{};
+  size_t offsets_temp_storage_bytes = 0;
+  cub::DeviceScan::ExclusiveSum(offsets_temp_storage, offsets_temp_storage_bytes, bucket_sizes, bucket_offsets, nof_buckets, stream);
+  cudaMallocAsync(&offsets_temp_storage, offsets_temp_storage_bytes, stream);
+  cub::DeviceScan::ExclusiveSum(offsets_temp_storage, offsets_temp_storage_bytes, bucket_sizes, bucket_offsets, nof_buckets, stream);
+  cudaFreeAsync(offsets_temp_storage, stream);
+
+  //sort by bucket sizes
+  unsigned h_nof_buckets_to_compute;
+  cudaMemcpyAsync(&h_nof_buckets_to_compute, nof_buckets_to_compute, sizeof(unsigned), cudaMemcpyDeviceToHost, stream);
+
+  unsigned* sorted_bucket_sizes;
+  cudaMallocAsync(&sorted_bucket_sizes, sizeof(unsigned)*h_nof_buckets_to_compute, stream);
+  unsigned* sorted_bucket_offsets;
+  cudaMallocAsync(&sorted_bucket_offsets, sizeof(unsigned)*h_nof_buckets_to_compute, stream);
+  unsigned* sort_offsets_temp_storage{};
+  size_t sort_offsets_temp_storage_bytes = 0;
+  cub::DeviceRadixSort::SortPairsDescending(sort_offsets_temp_storage, sort_offsets_temp_storage_bytes, bucket_sizes,
+    sorted_bucket_sizes, bucket_offsets, sorted_bucket_offsets, h_nof_buckets_to_compute, 0, sizeof(unsigned) * 8, stream);
+  cudaMallocAsync(&sort_offsets_temp_storage, sort_offsets_temp_storage_bytes, stream);
+  cub::DeviceRadixSort::SortPairsDescending(sort_offsets_temp_storage, sort_offsets_temp_storage_bytes, bucket_sizes,
+    sorted_bucket_sizes, bucket_offsets, sorted_bucket_offsets, h_nof_buckets_to_compute, 0, sizeof(unsigned) * 8, stream);
+  cudaFreeAsync(sort_offsets_temp_storage, stream);
+       
+  unsigned* sorted_single_bucket_indices;
+  cudaMallocAsync(&sorted_single_bucket_indices, sizeof(unsigned)*h_nof_buckets_to_compute, stream);
+  unsigned* sort_single_temp_storage{};
+  size_t sort_single_temp_storage_bytes = 0;
+  cub::DeviceRadixSort::SortPairsDescending(sort_single_temp_storage, sort_single_temp_storage_bytes, bucket_sizes,
+    sorted_bucket_sizes, single_bucket_indices, sorted_single_bucket_indices, h_nof_buckets_to_compute, 0, sizeof(unsigned) * 8, stream);
+  cudaMallocAsync(&sort_single_temp_storage, sort_single_temp_storage_bytes, stream);
+  cub::DeviceRadixSort::SortPairsDescending(sort_single_temp_storage, sort_single_temp_storage_bytes, bucket_sizes,
+    sorted_bucket_sizes, single_bucket_indices, sorted_single_bucket_indices, h_nof_buckets_to_compute, 0, sizeof(unsigned) * 8, stream);
+  cudaFreeAsync(sort_single_temp_storage, stream);
+
+  //find large buckets
+  unsigned avarage_size = size/(1<<c);
+  // printf("avarage_size %u\n", avarage_size);
+  float large_bucket_factor = 10; //global param
+  unsigned bucket_th = ceil(large_bucket_factor*avarage_size);
+  // printf("bucket_th %u\n", bucket_th);
+
+  unsigned *nof_large_buckets;
+  cudaMallocAsync(&nof_large_buckets, sizeof(unsigned), stream);
+
+  unsigned TOTAL_THREADS = 129000; //todo - device dependant
+  unsigned cutoff_run_length = max(2,h_nof_buckets_to_compute/TOTAL_THREADS);
+  unsigned cutoff_nof_runs = (h_nof_buckets_to_compute + cutoff_run_length -1)/cutoff_run_length;
+  NUM_THREADS = min(1 << 5,cutoff_nof_runs);
+  NUM_BLOCKS = (cutoff_nof_runs + NUM_THREADS - 1) / NUM_THREADS;
+  find_cutoff_kernel<<<NUM_BLOCKS,NUM_THREADS,0,stream>>>(sorted_bucket_sizes,h_nof_buckets_to_compute,bucket_th,cutoff_run_length,nof_large_buckets);
+
+  unsigned h_nof_large_buckets;
+  cudaMemcpyAsync(&h_nof_large_buckets, nof_large_buckets, sizeof(unsigned), cudaMemcpyDeviceToHost, stream);
+
+  unsigned *max_res;
+  cudaMallocAsync(&max_res, sizeof(unsigned)*2, stream);
+  find_max_size<<<1,1,0,stream>>>(sorted_bucket_sizes,sorted_single_bucket_indices,c,max_res);
+ 
+  unsigned h_max_res[2];
+  cudaMemcpyAsync(h_max_res, max_res, sizeof(unsigned)*2, cudaMemcpyDeviceToHost, stream);
+  // printf("h_nof_large_buckets %u\n", h_nof_large_buckets);
+  unsigned h_largest_bucket_size = h_max_res[0];
+  unsigned h_nof_zero_large_buckets = h_max_res[1];
+  // printf("h_largest_bucket_size %u\n", h_largest_bucket_size);
+  // printf("h_nof_zero_large_buckets %u\n", h_nof_zero_large_buckets);
+
+  unsigned large_buckets_to_compute = h_nof_large_buckets>h_nof_zero_large_buckets? h_nof_large_buckets-h_nof_zero_large_buckets : 0;
+
+  cudaStream_t stream2;
+  cudaStreamCreate(&stream2);
+  P* large_buckets;
+
+  if (large_buckets_to_compute>0 && bucket_th>0){
+
+  unsigned threads_per_bucket = 1<<(unsigned)ceil(log2((h_largest_bucket_size + bucket_th - 1) / bucket_th)); //global param
+  unsigned max_bucket_size_run_length = (h_largest_bucket_size + threads_per_bucket - 1) / threads_per_bucket;
+  // printf("threads_per_bucket %u\n", threads_per_bucket);
+  // printf("max_bucket_size_run_length %u\n", max_bucket_size_run_length);
+  unsigned total_large_buckets_size = large_buckets_to_compute*threads_per_bucket;
+  cudaMallocAsync(&large_buckets, sizeof(P)*total_large_buckets_size, stream);
+  NUM_THREADS = min(1 << 8,total_large_buckets_size);
+  NUM_BLOCKS = (total_large_buckets_size + NUM_THREADS - 1) / NUM_THREADS;
+  accumulate_large_buckets_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream2>>>(large_buckets, sorted_bucket_offsets+h_nof_zero_large_buckets, sorted_bucket_sizes+h_nof_zero_large_buckets, sorted_single_bucket_indices+h_nof_zero_large_buckets, point_indices, 
+  d_points, nof_buckets, large_buckets_to_compute, c+bm_bitsize, c, threads_per_bucket, max_bucket_size_run_length);                   
+
+  //reduce
+  for (int s=total_large_buckets_size>>1;s>large_buckets_to_compute-1;s>>=1){
+    NUM_THREADS = min(MAX_TH,s);
+    NUM_BLOCKS = (s + NUM_THREADS - 1) / NUM_THREADS;
+    single_stage_multi_reduction_kernel<<<NUM_BLOCKS, NUM_THREADS,0,stream2>>>(large_buckets,large_buckets,s*2,0,0,0);
+  }
+
+  //distribute
+  NUM_THREADS = min(MAX_TH,large_buckets_to_compute);
+  NUM_BLOCKS = (large_buckets_to_compute + NUM_THREADS - 1) / NUM_THREADS;
+  distribute_large_buckets_kernel<<<NUM_BLOCKS, NUM_THREADS,0,stream2>>>(large_buckets,buckets,sorted_single_bucket_indices+h_nof_zero_large_buckets,large_buckets_to_compute);
+}
+else{
+  h_nof_large_buckets = 0;
+}
+
+  //launch the accumulation kernel with maximum threads
+  NUM_THREADS = 1 << 8;
+  NUM_BLOCKS = (h_nof_buckets_to_compute-h_nof_large_buckets + NUM_THREADS - 1) / NUM_THREADS;
+  accumulate_buckets_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(buckets, sorted_bucket_offsets+h_nof_large_buckets, sorted_bucket_sizes+h_nof_large_buckets, sorted_single_bucket_indices+h_nof_large_buckets, point_indices, 
+                                                          d_points, nof_buckets, h_nof_buckets_to_compute-h_nof_large_buckets, c+bm_bitsize, c);                   
+cudaStreamSynchronize(stream2);
+cudaStreamDestroy(stream2);
+cudaDeviceSynchronize();
+
+  #ifdef SSM_SUM
+    //sum each bucket
+    NUM_THREADS = 1 << 10;
+    NUM_BLOCKS = (nof_buckets + NUM_THREADS - 1) / NUM_THREADS;
+    ssm_buckets_kernel<fake_point, fake_scalar><<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(buckets, single_bucket_indices, nof_buckets, c);
+   
+    //sum each bucket module
+    P* final_results;
+    cudaMallocAsync(&final_results, sizeof(P) * nof_bms, stream);
+    NUM_THREADS = 1<<c;
+    NUM_BLOCKS = nof_bms;
+    sum_reduction_kernel<<<NUM_BLOCKS,NUM_THREADS, 0, stream>>>(buckets, final_results);
+  #endif
+
+  P* final_results;
+  if (big_triangle){
+    cudaMallocAsync(&final_results, sizeof(P) * nof_bms, stream);
+    //launch the bucket module sum kernel - a thread for each bucket module
+    NUM_THREADS = nof_bms;
+    NUM_BLOCKS = 1;
+    #ifdef SIGNED_DIG
+    big_triangle_sum_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(buckets, final_results, nof_bms, c-1); //sighed digits
+    #else
+    big_triangle_sum_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(buckets, final_results, nof_bms, c); 
+    #endif
+
+  }
+else{
+ 
+  unsigned source_bits_count = c;
+  bool odd_source_c = source_bits_count%2;
+  unsigned source_windows_count = nof_bms;
+  unsigned source_buckets_count = nof_buckets;
+  P *source_buckets = buckets;
+  buckets = nullptr;
+  P *target_buckets;
+  P *temp_buckets1;
+  P *temp_buckets2;
+  for (unsigned i = 0;; i++) {
+    // printf("round %u \n" ,i);
+    const unsigned target_bits_count = (source_bits_count + 1) >> 1; //c/2=8
+    // printf("target_bits_count %u \n" ,target_bits_count);
+    const unsigned target_windows_count = source_windows_count << 1; //nof bms*2 = 32
+    const unsigned target_buckets_count = target_windows_count << target_bits_count; // bms*2^c = 32*2^8
+    cudaMallocAsync(&target_buckets, sizeof(P) * target_buckets_count,stream); //32*2^8*2^7 buckets
+    cudaMallocAsync(&temp_buckets1, sizeof(P) * source_buckets_count/2,stream); //32*2^8*2^7 buckets
+    cudaMallocAsync(&temp_buckets2, sizeof(P) * source_buckets_count/2,stream); //32*2^8*2^7 buckets
+    
+    if (source_bits_count>0){
+      for(unsigned j=0;j<target_bits_count;j++){
+        unsigned last_j = target_bits_count-1;
+        NUM_THREADS = min(MAX_TH,(source_buckets_count>>(1+j)));
+        NUM_BLOCKS = ((source_buckets_count>>(1+j)) + NUM_THREADS - 1) / NUM_THREADS;
+        single_stage_multi_reduction_kernel<<<NUM_BLOCKS, NUM_THREADS,0,stream>>>(j==0?source_buckets:temp_buckets1,j==target_bits_count-1? target_buckets: temp_buckets1,1<<(source_bits_count-j),j==target_bits_count-1? 1<<target_bits_count: 0,0,0);
+        
+        unsigned nof_threads = (source_buckets_count>>(1+j));
+        NUM_THREADS = min(MAX_TH,nof_threads);
+        NUM_BLOCKS = (nof_threads + NUM_THREADS - 1) / NUM_THREADS;
+        single_stage_multi_reduction_kernel<<<NUM_BLOCKS, NUM_THREADS,0,stream>>>(j==0?source_buckets:temp_buckets2,j==target_bits_count-1? target_buckets: temp_buckets2,1<<(target_bits_count-j),j==target_bits_count-1? 1<<target_bits_count: 0,1,0);
+
+      }
+    }
+   if (target_bits_count == 1) {
+      nof_bms = bitsize;
+      cudaMallocAsync(&final_results, sizeof(P) * nof_bms, stream);
+      NUM_THREADS = 32;
+      NUM_BLOCKS = (nof_bms + NUM_THREADS - 1) / NUM_THREADS;
+      last_pass_kernel<<<NUM_BLOCKS,NUM_THREADS>>>(target_buckets,final_results,nof_bms);
+      c = 1;
+      cudaFreeAsync(source_buckets,stream);
+      cudaFreeAsync(target_buckets,stream);
+      cudaFreeAsync(temp_buckets1,stream);
+      cudaFreeAsync(temp_buckets2,stream);
+      break;
+    }
+    cudaFreeAsync(source_buckets,stream);
+    cudaFreeAsync(temp_buckets1,stream);
+    cudaFreeAsync(temp_buckets2,stream);
+    source_buckets = target_buckets;
+    target_buckets = nullptr;
+    temp_buckets1 = nullptr;
+    temp_buckets2 = nullptr;
+    source_bits_count = target_bits_count;
+    odd_source_c = source_bits_count%2;
+    source_windows_count = target_windows_count;
+    source_buckets_count = target_buckets_count;
+  }
+}
+
+  P* d_final_result;
+  if (!on_device)
+    cudaMallocAsync(&d_final_result, sizeof(P), stream);
+
+  //launch the double and add kernel, a single thread
+  final_accumulation_kernel<P, S><<<1,1,0,stream>>>(final_results, ones_results, on_device ? final_result : d_final_result, 1, nof_bms, c);
+  cudaStreamSynchronize(stream);
+  if (!on_device)
+    cudaMemcpyAsync(final_result, d_final_result, sizeof(P), cudaMemcpyDeviceToHost, stream);
+
+  //free memory
+  if (!on_device) {
+    cudaFreeAsync(d_points, stream);
+    cudaFreeAsync(d_scalars, stream);
+    cudaFreeAsync(d_final_result, stream);
+  }
+  cudaFreeAsync(buckets, stream);
+  #ifndef PHASE1_TEST
+  cudaFreeAsync(bucket_indices, stream);
+  cudaFreeAsync(point_indices, stream);
+  cudaFreeAsync(single_bucket_indices, stream);
+  cudaFreeAsync(bucket_sizes, stream);
+  cudaFreeAsync(nof_buckets_to_compute, stream);
+  cudaFreeAsync(bucket_offsets, stream);
+  #endif
+  cudaFreeAsync(sorted_bucket_sizes,stream);
+  cudaFreeAsync(sorted_bucket_offsets,stream);
+  cudaFreeAsync(sorted_single_bucket_indices,stream);
+  cudaFreeAsync(nof_large_buckets,stream);
+  cudaFreeAsync(max_res,stream);
+  if (large_buckets_to_compute>0 && bucket_th>0) cudaFreeAsync(large_buckets,stream);
+  cudaFreeAsync(final_results, stream);
+  cudaFreeAsync(ones_results, stream);
+
+  cudaStreamSynchronize(stream);
+
+
+}
+
+//this function computes multiple msms using the bucket method - currently isn't working on this branch
+template <typename S, typename P, typename A>
+void batched_bucket_method_msm(unsigned bitsize, unsigned c, S *scalars, A *points, unsigned batch_size, unsigned msm_size, P* final_results, bool on_device, cudaStream_t stream){
+
+  unsigned total_size = batch_size * msm_size;
+  S *d_scalars;
+  A *d_points;
+  if (!on_device) {
+    //copy scalars and point to gpu
+    cudaMallocAsync(&d_scalars, sizeof(S) * total_size, stream);
+    cudaMallocAsync(&d_points, sizeof(A) * total_size, stream);
+    cudaMemcpyAsync(d_scalars, scalars, sizeof(S) * total_size, cudaMemcpyHostToDevice, stream);
+    cudaMemcpyAsync(d_points, points, sizeof(A) * total_size, cudaMemcpyHostToDevice, stream);
+  }
+  else {
+    d_scalars = scalars;
+    d_points = points;
+  }
+
+  P *buckets;
+  //compute number of bucket modules and number of buckets in each module
+  unsigned nof_bms = bitsize/c;
+  if (bitsize%c){
+    nof_bms++;
+  }
+  unsigned msm_log_size = ceil(log2(msm_size));
+  unsigned bm_bitsize = ceil(log2(nof_bms));
+  unsigned nof_buckets = (nof_bms<<c);
+  unsigned total_nof_buckets = nof_buckets*batch_size;
+  cudaMallocAsync(&buckets, sizeof(P) * total_nof_buckets, stream); 
+
+  //lanch the bucket initialization kernel with maximum threads
+  unsigned NUM_THREADS = 1 << 10;
+  unsigned NUM_BLOCKS = (total_nof_buckets + NUM_THREADS - 1) / NUM_THREADS;
+  initialize_buckets_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(buckets, total_nof_buckets); 
+
+  unsigned *bucket_indices;
+  unsigned *point_indices;
+  cudaMallocAsync(&bucket_indices, sizeof(unsigned) * (total_size * nof_bms + msm_size), stream);
+  cudaMallocAsync(&point_indices, sizeof(unsigned) * (total_size * nof_bms + msm_size), stream);
+
+  //split scalars into digits
+  NUM_THREADS = 1 << 8;
+  NUM_BLOCKS = (total_size * nof_bms + msm_size + NUM_THREADS - 1) / NUM_THREADS;
+  split_scalars_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(bucket_indices + msm_size, point_indices + msm_size, d_scalars, total_size, 
+                                                    msm_log_size, nof_bms, bm_bitsize, c,0); //+size - leaving the first bm free for the out of place sort later
+
+  //sort indices - the indices are sorted from smallest to largest in order to group together the points that belong to each bucket
+  unsigned *sorted_bucket_indices;
+  unsigned *sorted_point_indices;
+  cudaMallocAsync(&sorted_bucket_indices, sizeof(unsigned) * (total_size * nof_bms), stream);
+  cudaMallocAsync(&sorted_point_indices, sizeof(unsigned) * (total_size * nof_bms), stream);
+
+  unsigned *sort_indices_temp_storage{};
+  size_t sort_indices_temp_storage_bytes;
+  // The second to last parameter is the default value supplied explicitly to allow passing the stream
+  // See https://nvlabs.github.io/cub/structcub_1_1_device_radix_sort.html#a65e82152de448c6373ed9563aaf8af7e for more info
+  cub::DeviceRadixSort::SortPairs(sort_indices_temp_storage, sort_indices_temp_storage_bytes, bucket_indices + msm_size, sorted_bucket_indices,
+                                 point_indices + msm_size, sorted_point_indices, total_size * nof_bms, 0, sizeof(unsigned)*8, stream);
+  cudaMallocAsync(&sort_indices_temp_storage, sort_indices_temp_storage_bytes, stream);
+  // The second to last parameter is the default value supplied explicitly to allow passing the stream
+  // See https://nvlabs.github.io/cub/structcub_1_1_device_radix_sort.html#a65e82152de448c6373ed9563aaf8af7e for more info
+  cub::DeviceRadixSort::SortPairs(sort_indices_temp_storage, sort_indices_temp_storage_bytes, bucket_indices + msm_size, sorted_bucket_indices,
+                                 point_indices + msm_size, sorted_point_indices, total_size * nof_bms, 0, sizeof(unsigned)*8, stream);
+  cudaFreeAsync(sort_indices_temp_storage, stream);
+
+  //find bucket_sizes
+  unsigned *single_bucket_indices;
+  unsigned *bucket_sizes;
+  unsigned *total_nof_buckets_to_compute;
+  cudaMallocAsync(&single_bucket_indices, sizeof(unsigned)*total_nof_buckets, stream);
+  cudaMallocAsync(&bucket_sizes, sizeof(unsigned)*total_nof_buckets, stream);
+  cudaMallocAsync(&total_nof_buckets_to_compute, sizeof(unsigned), stream);
+  unsigned *encode_temp_storage{};
+  size_t encode_temp_storage_bytes = 0;
+  cub::DeviceRunLengthEncode::Encode(encode_temp_storage, encode_temp_storage_bytes, sorted_bucket_indices, single_bucket_indices, bucket_sizes,
+                                        total_nof_buckets_to_compute, nof_bms*total_size, stream);  
+  cudaMallocAsync(&encode_temp_storage, encode_temp_storage_bytes, stream);
+  cub::DeviceRunLengthEncode::Encode(encode_temp_storage, encode_temp_storage_bytes, sorted_bucket_indices, single_bucket_indices, bucket_sizes,
+                                        total_nof_buckets_to_compute, nof_bms*total_size, stream);
+  cudaFreeAsync(encode_temp_storage, stream);
+
+  //get offsets - where does each new bucket begin
+  unsigned* bucket_offsets;
+  cudaMallocAsync(&bucket_offsets, sizeof(unsigned)*total_nof_buckets, stream);
+  unsigned* offsets_temp_storage{};
+  size_t offsets_temp_storage_bytes = 0;
+  cub::DeviceScan::ExclusiveSum(offsets_temp_storage, offsets_temp_storage_bytes, bucket_sizes, bucket_offsets, total_nof_buckets, stream);
+  cudaMallocAsync(&offsets_temp_storage, offsets_temp_storage_bytes, stream);
+  cub::DeviceScan::ExclusiveSum(offsets_temp_storage, offsets_temp_storage_bytes, bucket_sizes, bucket_offsets, total_nof_buckets, stream);
+  cudaFreeAsync(offsets_temp_storage, stream);
+
+  //launch the accumulation kernel with maximum threads
+  // NUM_THREADS = 1 << 8;
+  // NUM_BLOCKS = (total_nof_buckets + NUM_THREADS - 1) / NUM_THREADS;
+  // accumulate_buckets_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(buckets, bucket_offsets, bucket_sizes, single_bucket_indices, sorted_point_indices,
+  //                                                       d_points, nof_buckets, total_nof_buckets_to_compute, c+bm_bitsize,c);
+
+  // #ifdef SSM_SUM
+  //   //sum each bucket
+  //   NUM_THREADS = 1 << 10;
+  //   NUM_BLOCKS = (nof_buckets + NUM_THREADS - 1) / NUM_THREADS;
+  //   ssm_buckets_kernel<P, S><<<NUM_BLOCKS, NUM_THREADS>>>(buckets, single_bucket_indices, nof_buckets, c);
+   
+  //   //sum each bucket module
+  //   P* final_results;
+  //   cudaMalloc(&final_results, sizeof(P) * nof_bms);
+  //   NUM_THREADS = 1<<c;
+  //   NUM_BLOCKS = nof_bms;
+  //   sum_reduction_kernel<<<NUM_BLOCKS,NUM_THREADS>>>(buckets, final_results);
+  // #endif
+
+  // #ifdef BIG_TRIANGLE
+    P* bm_sums;
+    cudaMallocAsync(&bm_sums, sizeof(P) * nof_bms * batch_size, stream);
+    //launch the bucket module sum kernel - a thread for each bucket module
+    NUM_THREADS = 1<<8;
+    NUM_BLOCKS = (nof_bms*batch_size + NUM_THREADS - 1) / NUM_THREADS;
+    big_triangle_sum_kernel<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(buckets, bm_sums, nof_bms*batch_size, c);
+  // #endif
+
+  P* d_final_results;
+  if (!on_device)
+    cudaMallocAsync(&d_final_results, sizeof(P)*batch_size, stream);
+
+  //launch the double and add kernel, a single thread for each msm
+  NUM_THREADS = 1<<8;
+  NUM_BLOCKS = (batch_size + NUM_THREADS - 1) / NUM_THREADS;
+  final_accumulation_kernel<P, S><<<NUM_BLOCKS,NUM_THREADS, 0, stream>>>(bm_sums,bm_sums, on_device ? final_results : d_final_results, batch_size, nof_bms, c);
+  
+  final_accumulation_kernel<P, S><<<NUM_BLOCKS,NUM_THREADS>>>(bm_sums,bm_sums, on_device ? final_results : d_final_results, batch_size, nof_bms, c);
+
+  //copy final result to host
+  if (!on_device)
+    cudaMemcpyAsync(final_results, d_final_results, sizeof(P)*batch_size, cudaMemcpyDeviceToHost, stream);
+
+  //free memory
+  if (!on_device) {
+    cudaFreeAsync(d_points, stream);
+    cudaFreeAsync(d_scalars, stream);
+    cudaFreeAsync(d_final_results, stream);
+  }
+  cudaFreeAsync(buckets, stream);
+  cudaFreeAsync(bucket_indices, stream);
+  cudaFreeAsync(point_indices, stream);
+  cudaFreeAsync(sorted_bucket_indices, stream);
+  cudaFreeAsync(sorted_point_indices, stream);
+  cudaFreeAsync(single_bucket_indices, stream);
+  cudaFreeAsync(bucket_sizes, stream);
+  cudaFreeAsync(total_nof_buckets_to_compute, stream);
+  cudaFreeAsync(bucket_offsets, stream);
+  cudaFreeAsync(bm_sums, stream);
+
+  cudaStreamSynchronize(stream);
+}
+
+
+//this kernel converts affine points to projective points
+//each thread deals with a single point
+template <typename P, typename A>
+__global__ void to_proj_kernel(A* affine_points, P* proj_points, unsigned N){
+  
+  unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid < N) proj_points[tid] = P::from_affine(affine_points[tid]);
+}
+
+//the function computes msm using ssm
+template <typename S, typename P, typename A>
+void short_msm(S *h_scalars, A *h_points, unsigned size, P* h_final_result, cudaStream_t stream){ //works up to 2^8
+  S *scalars;
+  A *a_points;
+  P *p_points;
+  P *results;
+
+  cudaMallocAsync(&scalars, sizeof(S) * size, stream);
+  cudaMallocAsync(&a_points, sizeof(A) * size, stream);
+  cudaMallocAsync(&p_points, sizeof(P) * size, stream);
+  cudaMallocAsync(&results, sizeof(P) * size, stream);
+
+  //copy inputs to device
+  cudaMemcpyAsync(scalars, h_scalars, sizeof(S) * size, cudaMemcpyHostToDevice, stream);
+  cudaMemcpyAsync(a_points, h_points, sizeof(A) * size, cudaMemcpyHostToDevice, stream);
+
+  //convert to projective representation and multiply each point by its scalar using single scalar multiplication
+  unsigned NUM_THREADS = size;
+  to_proj_kernel<<<1,NUM_THREADS, 0, stream>>>(a_points, p_points, size);
+  ssm_kernel<<<1,NUM_THREADS, 0, stream>>>(scalars, p_points, results, size);
+
+  P *final_result;
+  cudaMallocAsync(&final_result, sizeof(P), stream);
+
+  //assuming msm size is a power of 2
+  //sum all the ssm results
+  NUM_THREADS = size;
+  sum_reduction_kernel<<<1,NUM_THREADS, 0, stream>>>(results, final_result);
+
+  //copy result to host
+  cudaStreamSynchronize(stream);
+  cudaMemcpyAsync(h_final_result, final_result, sizeof(P), cudaMemcpyDeviceToHost, stream);
+
+  //free memory
+  cudaFreeAsync(scalars, stream);
+  cudaFreeAsync(a_points, stream);
+  cudaFreeAsync(p_points, stream);
+  cudaFreeAsync(results, stream);
+  cudaFreeAsync(final_result, stream);
+
+}
+
+//the function computes msm on the host using the naive method
+template <typename A, typename S, typename P>
+void reference_msm(S* scalars, A* a_points, unsigned size){
+  
+  P *points = new P[size];
+  // P points[size];
+  for (unsigned i = 0; i < size ; i++)
+  {
+    points[i] = P::from_affine(a_points[i]);
+  }
+
+  P res = P::zero();
+  
+  for (unsigned i = 0; i < size; i++)
+  {
+    res = res + scalars[i]*points[i];
+  }
+
+  std::cout<<"reference results"<<std::endl;
+  std::cout<<P::to_affine(res)<<std::endl;
+  
+}
+
+unsigned get_optimal_c(const unsigned size) {
+  if (size < 17)
+    return 1;
+  // return 17;
+  return ceil(log2(size))-4;
+}
+
+//this function is used to compute msms of size larger than 256
+template <typename S, typename P, typename A>
+void large_msm(S* scalars, A* points, unsigned size, P* result, bool on_device, bool big_triangle, cudaStream_t stream){
+  unsigned c = 16;
+  unsigned bitsize = S::NBITS;
+  bucket_method_msm(bitsize, c, scalars, points, size, result, on_device, big_triangle, stream);
+}
+
+// this function is used to compute a batches of msms of size larger than 256 - currently isn't working on this branch
+template <typename S, typename P, typename A>
+void batched_large_msm(S* scalars, A* points, unsigned batch_size, unsigned msm_size, P* result, bool on_device, cudaStream_t stream){
+  unsigned c = get_optimal_c(msm_size);
+  unsigned bitsize = 255;
+  batched_bucket_method_msm(bitsize, c, scalars, points, batch_size, msm_size, result, on_device, stream);
+}
+#endif

icicle/appUtils/msm/tests/msm_test.cu

@@ -0,0 +1,251 @@
+#define G2_DEFINED
+
+#include <iostream>
+#include <chrono>
+#include <vector>
+#include "msm_clean.cu"
+#include "../../utils/cuda_utils.cuh"
+#include "../../primitives/projective.cuh"
+#include "../../primitives/field.cuh"
+// #include "../../curves/bls12_377/curve_config.cuh"
+#include "../../curves/bn254/curve_config.cuh"
+
+// using namespace BLS12_377;
+using namespace BN254;
+
+class Dummy_Scalar {
+  public:
+    static constexpr unsigned NBITS = 32;
+
+    unsigned x;
+    // unsigned p = 10;
+    unsigned p = 1<<30;
+
+    static HOST_DEVICE_INLINE Dummy_Scalar zero() {
+      return {0};
+    }
+
+    static HOST_DEVICE_INLINE Dummy_Scalar one() {
+      return {1};
+    }
+
+    friend HOST_INLINE std::ostream& operator<<(std::ostream& os, const Dummy_Scalar& scalar) {
+      os << scalar.x;
+      return os;
+    }
+
+    HOST_DEVICE_INLINE unsigned get_scalar_digit(unsigned digit_num, unsigned digit_width) {
+      return (x>>(digit_num*digit_width))&((1<<digit_width)-1);
+    }
+
+    friend HOST_DEVICE_INLINE Dummy_Scalar operator+(Dummy_Scalar p1, const Dummy_Scalar& p2) {   
+      return {(p1.x+p2.x)%p1.p};
+    }
+
+    friend HOST_DEVICE_INLINE bool operator==(const Dummy_Scalar& p1, const Dummy_Scalar& p2) {
+      return (p1.x == p2.x);
+    }
+
+    friend HOST_DEVICE_INLINE bool operator==(const Dummy_Scalar& p1, const unsigned p2) {
+      return (p1.x == p2);
+    }
+
+    static HOST_DEVICE_INLINE Dummy_Scalar neg(const Dummy_Scalar &scalar) { 
+      return {scalar.p-scalar.x};
+    }
+    static HOST_INLINE Dummy_Scalar rand_host() {
+      return {(unsigned)rand()%(1<<30)};
+      // return {(unsigned)rand()%10};
+      // return {(unsigned)rand()};
+    }
+};
+
+class Dummy_Projective {
+
+  public:
+    Dummy_Scalar x;
+
+    static HOST_DEVICE_INLINE Dummy_Projective zero() {
+      return {0};
+    }
+
+    static HOST_DEVICE_INLINE Dummy_Projective one() {
+      return {1};
+    }
+
+    static HOST_DEVICE_INLINE Dummy_Projective to_affine(const Dummy_Projective &point) {
+      return {point.x};
+    }
+
+    static HOST_DEVICE_INLINE Dummy_Projective from_affine(const Dummy_Projective &point) {
+      return {point.x};
+    }
+
+    static HOST_DEVICE_INLINE Dummy_Projective neg(const Dummy_Projective &point) { 
+      return {Dummy_Scalar::neg(point.x)};
+    }
+
+    friend HOST_DEVICE_INLINE Dummy_Projective operator+(Dummy_Projective p1, const Dummy_Projective& p2) {   
+      return {p1.x+p2.x};
+    }
+
+    // friend HOST_DEVICE_INLINE Dummy_Projective operator-(Dummy_Projective p1, const Dummy_Projective& p2) {   
+    //   return p1 + neg(p2);
+    // }
+
+    friend HOST_INLINE std::ostream& operator<<(std::ostream& os, const Dummy_Projective& point) {
+      os << point.x;
+      return os;
+    }
+
+    friend HOST_DEVICE_INLINE Dummy_Projective operator*(Dummy_Scalar scalar, const Dummy_Projective& point) {   
+      Dummy_Projective res = zero();
+  #ifdef CUDA_ARCH
+  #pragma unroll
+  #endif
+      for (int i = 0; i < Dummy_Scalar::NBITS; i++) {
+        if (i > 0) {
+          res = res + res;
+        }
+        if (scalar.get_scalar_digit(Dummy_Scalar::NBITS - i - 1, 1)) {
+          res = res + point;
+        }
+      }
+      return res;
+    }
+
+    friend HOST_DEVICE_INLINE bool operator==(const Dummy_Projective& p1, const Dummy_Projective& p2) {
+      return (p1.x == p2.x);
+    }
+
+    static HOST_DEVICE_INLINE bool is_zero(const Dummy_Projective &point) {
+      return point.x == 0;
+    }
+
+    static HOST_INLINE Dummy_Projective rand_host() {
+      return {(unsigned)rand()%10};
+      // return {(unsigned)rand()};
+    }
+};
+
+//switch between dummy and real:
+
+typedef scalar_t test_scalar;
+typedef projective_t test_projective;
+typedef affine_t test_affine;
+
+// typedef scalar_t test_scalar;
+// typedef g2_projective_t test_projective;
+// typedef g2_affine_t test_affine;
+
+// typedef Dummy_Scalar test_scalar;
+// typedef Dummy_Projective test_projective;
+// typedef Dummy_Projective test_affine;
+
+int main()
+{
+  bool on_device = false;
+
+  unsigned batch_size = 1;
+  unsigned msm_size = (1<<24) -1;
+  // unsigned msm_size = 9215384;
+  // unsigned msm_size = (1<<10) - 456;
+  // unsigned msm_size = 20;
+  // unsigned msm_size = 6075005;
+  unsigned N = batch_size*msm_size;
+
+  test_scalar *scalars = new test_scalar[N];
+  test_affine *points = new test_affine[N];
+  
+  for (unsigned i=0;i<N;i++){
+    // scalars[i] = (i%msm_size < 10)? test_scalar::rand_host() : scalars[i-10];
+    points[i] = (i%msm_size < 10)? test_projective::to_affine(test_projective::rand_host()): points[i-10];
+    // scalars[i] = test_scalar::rand_host();
+    // scalars[i] = i >462560? test_scalar::rand_host() :  (test_scalar::one() + test_scalar::one());
+    scalars[i] = i >20? test_scalar::rand_host() : i>50? (test_scalar::one() + test_scalar::one()) : (test_scalar::one() + test_scalar::one()+ test_scalar::one());
+    // points[i] = test_projective::to_affine(test_projective::rand_host());
+  }
+  std::cout<<"finished generating"<<std::endl;
+
+  test_scalar *d_scalars;
+  test_affine *d_points;
+  if (on_device) {
+    //copy scalars and point to gpu
+    cudaMalloc(&d_scalars, sizeof(test_scalar) * N);
+    cudaMalloc(&d_points, sizeof(test_affine) * N);
+    cudaMemcpy(d_scalars, scalars, sizeof(test_scalar) * N, cudaMemcpyHostToDevice);
+    cudaMemcpy(d_points, points, sizeof(test_affine) * N, cudaMemcpyHostToDevice);
+  }
+  // projective_t *short_res = (projective_t*)malloc(sizeof(projective_t));
+  // test_projective *large_res = (test_projective*)malloc(sizeof(test_projective));
+  test_projective large_res[batch_size*2];
+  test_projective *d_large_res;
+  cudaMalloc(&d_large_res, sizeof(test_projective) * batch_size*2);
+  // test_projective batched_large_res[batch_size];
+  // fake_point *large_res = (fake_point*)malloc(sizeof(fake_point));
+  // fake_point batched_large_res[256];
+
+
+  // short_msm<scalar_t, projective_t, affine_t>(scalars, points, N, short_res);
+  // for (unsigned i=0;i<batch_size;i++){
+    // large_msm<test_scalar, test_projective, test_affine>(scalars+msm_size*i, points+msm_size*i, msm_size, large_res+i, false);
+    // std::cout<<"final result large"<<std::endl;
+    // std::cout<<test_projective::to_affine(*large_res)<<std::endl;
+  // }
+  auto begin = std::chrono::high_resolution_clock::now();
+  // batched_large_msm<test_scalar, test_projective, test_affine>(scalars, points, batch_size, msm_size, batched_large_res, false);
+      cudaStream_t stream1;
+      cudaStream_t stream2;
+    cudaStreamCreate(&stream1);
+    cudaStreamCreate(&stream2);
+  // large_msm<test_scalar, test_projective, test_affine>(on_device? d_scalars : scalars, on_device? d_points : points, msm_size, on_device? d_large_res : large_res, on_device, true,stream1);
+  // std::cout<<test_projective::to_affine(large_res[0])<<std::endl;
+  large_msm<test_scalar, test_projective, test_affine>(on_device? d_scalars : scalars, on_device? d_points : points, msm_size, on_device? d_large_res+1 : large_res+1, on_device, false,stream2);
+  // test_reduce_triangle(scalars);
+  // test_reduce_rectangle(scalars);
+  // test_reduce_single(scalars);
+  // test_reduce_var(scalars);
+  auto end = std::chrono::high_resolution_clock::now();
+  auto elapsed = std::chrono::duration_cast<std::chrono::nanoseconds>(end - begin);
+  printf("Time measured: %.3f seconds.\n", elapsed.count() * 1e-9);
+    cudaStreamSynchronize(stream1);
+    cudaStreamSynchronize(stream2);
+    cudaStreamDestroy(stream1);
+    cudaStreamDestroy(stream2);
+
+  if (on_device)
+    cudaMemcpy(large_res, d_large_res, sizeof(test_projective) * batch_size*2, cudaMemcpyDeviceToHost);
+
+  // std::cout<<test_projective::to_affine(large_res[0])<<std::endl;
+  std::cout<<test_projective::to_affine(large_res[1])<<std::endl;
+
+  // reference_msm<test_affine, test_scalar, test_projective>(scalars, points, msm_size);
+
+  // std::cout<<"final results batched large"<<std::endl;
+  // bool success = true;
+  // for (unsigned i = 0; i < batch_size; i++)
+  // {
+  //   std::cout<<test_projective::to_affine(batched_large_res[i])<<std::endl;
+  //   if (test_projective::to_affine(large_res[i])==test_projective::to_affine(batched_large_res[i])){
+  //     std::cout<<"good"<<std::endl;
+  //   }
+  //   else{
+  //     std::cout<<"miss"<<std::endl;
+  //     std::cout<<test_projective::to_affine(large_res[i])<<std::endl;
+  //     success = false;
+  //   }
+  // }
+  // if (success){
+  //   std::cout<<"success!"<<std::endl;
+  // }
+  
+  // std::cout<<batched_large_res[0]<<std::endl;
+  // std::cout<<batched_large_res[1]<<std::endl;
+  // std::cout<<projective_t::to_affine(batched_large_res[0])<<std::endl;
+  // std::cout<<projective_t::to_affine(batched_large_res[1])<<std::endl;
+
+  // std::cout<<"final result short"<<std::endl;
+  // std::cout<<pr<<std::endl;
+
+  return 0;
+}

icicle/appUtils/ntt/lde.cu

@@ -1,7 +1,9 @@
+#ifndef LDE
+#define LDE
 #include <cuda.h>
 #include "ntt.cuh"
-#include "../vector_manipulation/ve_mod_mult.cuh"
 #include "lde.cuh"
+#include "../vector_manipulation/ve_mod_mult.cuh"
 
 
 /**
@@ -13,19 +15,20 @@
  * @param n Length of `d_domain` array, also equal to the number of evaluations of each polynomial.
  * @param batch_size The size of the batch; the length of `d_evaluations` is `n` * `batch_size`.
  */
-template <typename E, typename S> int interpolate_batch(E * d_out, E * d_evaluations, S * d_domain, unsigned n, unsigned batch_size) {
+template <typename E, typename S> int interpolate_batch(E * d_out, E * d_evaluations, S * d_domain, unsigned n, unsigned batch_size, cudaStream_t stream) {
   uint32_t logn = uint32_t(log(n) / log(2));
-  cudaMemcpy(d_out, d_evaluations, sizeof(E) * n * batch_size, cudaMemcpyDeviceToDevice);
+  cudaMemcpyAsync(d_out, d_evaluations, sizeof(E) * n * batch_size, cudaMemcpyDeviceToDevice, stream);
   
   int NUM_THREADS = min(n / 2, MAX_THREADS_BATCH);
   int NUM_BLOCKS = batch_size * max(int((n / 2) / NUM_THREADS), 1);
   for (uint32_t s = 0; s < logn; s++) //TODO: this loop also can be unrolled
   {
-    ntt_template_kernel <E, S> <<<NUM_BLOCKS, NUM_THREADS>>>(d_out, n, d_domain, n, NUM_BLOCKS, s, false);
+    ntt_template_kernel <E, S> <<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(d_out, n, d_domain, n, NUM_BLOCKS, s, false);
   }
 
   NUM_BLOCKS = (n * batch_size + NUM_THREADS - 1) / NUM_THREADS;
-  template_normalize_kernel <E, S> <<<NUM_BLOCKS, NUM_THREADS>>> (d_out, n * batch_size, scalar_t::inv_log_size(logn));
+  template_normalize_kernel <E, S> <<<NUM_BLOCKS, NUM_THREADS, 0, stream>>> (d_out, n * batch_size, S::inv_log_size(logn));
+  cudaStreamSynchronize(stream);
   return 0;
 }
 
@@ -37,8 +40,8 @@ template <typename E, typename S> int interpolate_batch(E * d_out, E * d_evaluat
  * @param d_domain Domain on which the polynomial is evaluated. Must be a subgroup.
  * @param n Length of `d_evaluations` and the size `d_domain` arrays (they should have equal length).
  */
-template <typename E, typename S> int interpolate(E * d_out, E * d_evaluations, S * d_domain, unsigned n) {
-  return interpolate_batch <E, S> (d_out, d_evaluations, d_domain, n, 1);
+template <typename E, typename S> int interpolate(E * d_out, E * d_evaluations, S * d_domain, unsigned n, cudaStream_t stream) {
+  return interpolate_batch <E, S> (d_out, d_evaluations, d_domain, n, 1, stream);
 }
 
 template < typename E > __global__ void fill_array(E * arr, E val, uint32_t n) {
@@ -60,36 +63,37 @@ template < typename E > __global__ void fill_array(E * arr, E val, uint32_t n) {
  * @param coset_powers If `coset` is true, a list of powers `[1, u, u^2, ..., u^{n-1}]` where `u` is the generator of the coset.
  */
 template <typename E, typename S>
-int evaluate_batch(E * d_out, E * d_coefficients, S * d_domain, unsigned domain_size, unsigned n, unsigned batch_size, bool coset, S * coset_powers) {
+int evaluate_batch(E * d_out, E * d_coefficients, S * d_domain, unsigned domain_size, unsigned n, unsigned batch_size, bool coset, S * coset_powers, cudaStream_t stream) {
   uint32_t logn = uint32_t(log(domain_size) / log(2));
   if (domain_size > n) {
     // allocate and initialize an array of stream handles to parallelize data copying across batches
     cudaStream_t *memcpy_streams = (cudaStream_t *) malloc(batch_size * sizeof(cudaStream_t));
-    for (int i = 0; i < batch_size; i++)
+    for (unsigned i = 0; i < batch_size; i++)
     {
       cudaStreamCreate(&(memcpy_streams[i]));
 
       cudaMemcpyAsync(&d_out[i * domain_size], &d_coefficients[i * n], n * sizeof(E), cudaMemcpyDeviceToDevice, memcpy_streams[i]);
-      int NUM_THREADS = MAX_THREADS_BATCH;
-      int NUM_BLOCKS = (domain_size - n + NUM_THREADS - 1) / NUM_THREADS;
+      uint32_t NUM_THREADS = MAX_THREADS_BATCH;
+      uint32_t NUM_BLOCKS = (domain_size - n + NUM_THREADS - 1) / NUM_THREADS;
       fill_array <E> <<<NUM_BLOCKS, NUM_THREADS, 0, memcpy_streams[i]>>> (&d_out[i * domain_size + n], E::zero(), domain_size - n);
 
       cudaStreamSynchronize(memcpy_streams[i]);
       cudaStreamDestroy(memcpy_streams[i]);
     }
   } else
-    cudaMemcpy(d_out, d_coefficients, sizeof(E) * domain_size * batch_size, cudaMemcpyDeviceToDevice);
+    cudaMemcpyAsync(d_out, d_coefficients, sizeof(E) * domain_size * batch_size, cudaMemcpyDeviceToDevice, stream);
 
   if (coset)
-    batch_vector_mult(coset_powers, d_out, domain_size, batch_size);
+    batch_vector_mult(coset_powers, d_out, domain_size, batch_size, stream);
 
   int NUM_THREADS = min(domain_size / 2, MAX_THREADS_BATCH);
   int chunks = max(int((domain_size / 2) / NUM_THREADS), 1);
   int NUM_BLOCKS = batch_size * chunks;
   for (uint32_t s = 0; s < logn; s++) //TODO: this loop also can be unrolled
   {
-    ntt_template_kernel <E, S> <<<NUM_BLOCKS, NUM_THREADS>>>(d_out, domain_size, d_domain, domain_size, batch_size * chunks, logn - s - 1, true);
+    ntt_template_kernel <E, S> <<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(d_out, domain_size, d_domain, domain_size, batch_size * chunks, logn - s - 1, true);
   }
+  cudaStreamSynchronize(stream);
   return 0;
 }
 
@@ -105,359 +109,76 @@ int evaluate_batch(E * d_out, E * d_coefficients, S * d_domain, unsigned domain_
  * @param coset_powers If `coset` is true, a list of powers `[1, u, u^2, ..., u^{n-1}]` where `u` is the generator of the coset.
  */
 template <typename E, typename S> 
-int evaluate(E * d_out, E * d_coefficients, S * d_domain, unsigned domain_size, unsigned n, bool coset, S * coset_powers) {
-  return evaluate_batch <E, S> (d_out, d_coefficients, d_domain, domain_size, n, 1, coset, coset_powers);
+int evaluate(E * d_out, E * d_coefficients, S * d_domain, unsigned domain_size, unsigned n, bool coset, S * coset_powers, cudaStream_t stream) {
+  return evaluate_batch <E, S> (d_out, d_coefficients, d_domain, domain_size, n, 1, coset, coset_powers, stream);
 }
 
-int interpolate_scalars(scalar_t* d_out, scalar_t* d_evaluations, scalar_t* d_domain, unsigned n) {
-  return interpolate(d_out, d_evaluations, d_domain, n);
+template <typename S> 
+int interpolate_scalars(S* d_out, S* d_evaluations, S* d_domain, unsigned n, cudaStream_t stream) {
+  return interpolate(d_out, d_evaluations, d_domain, n, stream);
 }
 
-int interpolate_scalars_batch(scalar_t* d_out, scalar_t* d_evaluations, scalar_t* d_domain, unsigned n, unsigned batch_size) {
-  return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size);
+template <typename S> 
+int interpolate_scalars_batch(S* d_out, S* d_evaluations, S* d_domain, unsigned n, unsigned batch_size, cudaStream_t stream) {
+  return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
 }
 
-int interpolate_points(projective_t* d_out, projective_t* d_evaluations, scalar_t* d_domain, unsigned n) {
-  return interpolate(d_out, d_evaluations, d_domain, n);
+template <typename E, typename S> 
+int interpolate_points(E* d_out, E* d_evaluations, S* d_domain, unsigned n, cudaStream_t stream) {
+  return interpolate(d_out, d_evaluations, d_domain, n, stream);
 }
 
-int interpolate_points_batch(projective_t* d_out, projective_t* d_evaluations, scalar_t* d_domain, unsigned n, unsigned batch_size) {
-  return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size);
+template <typename E, typename S> 
+int interpolate_points_batch(E* d_out, E* d_evaluations, S* d_domain, unsigned n, unsigned batch_size, cudaStream_t stream) {
+  return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
 }
 
-int evaluate_scalars(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, unsigned n) {
-  scalar_t* _null = nullptr;
-  return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null);
+template <typename S> 
+int evaluate_scalars(S* d_out, S* d_coefficients, S* d_domain, unsigned domain_size, unsigned n, cudaStream_t stream) {
+  S* _null = nullptr;
+  return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
 }
 
-int evaluate_scalars_batch(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, unsigned n, unsigned batch_size) {
-  scalar_t* _null = nullptr;
-  return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null);
+template <typename S> 
+int evaluate_scalars_batch(S* d_out, S* d_coefficients, S* d_domain, unsigned domain_size, unsigned n, unsigned batch_size, cudaStream_t stream) {
+  S* _null = nullptr;
+  return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
 }
 
-int evaluate_points(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, unsigned n) {
-  scalar_t* _null = nullptr;
-  return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null);
+template <typename E, typename S> 
+int evaluate_points(E* d_out, E* d_coefficients, S* d_domain, unsigned domain_size, unsigned n, cudaStream_t stream) {
+  S* _null = nullptr;
+  return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
 }
 
-int evaluate_points_batch(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, 
-                          unsigned domain_size, unsigned n, unsigned batch_size) {
-  scalar_t* _null = nullptr;
-  return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null);
+template <typename E, typename S> 
+int evaluate_points_batch(E* d_out, E* d_coefficients, S* d_domain, 
+                          unsigned domain_size, unsigned n, unsigned batch_size, cudaStream_t stream) {
+  S* _null = nullptr;
+  return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
 }
 
-int evaluate_scalars_on_coset(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, 
-                              unsigned domain_size, unsigned n, scalar_t* coset_powers) {
-  return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers);
+template <typename S> 
+int evaluate_scalars_on_coset(S* d_out, S* d_coefficients, S* d_domain, 
+                              unsigned domain_size, unsigned n, S* coset_powers, cudaStream_t stream) {
+  return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
 }
 
-int evaluate_scalars_on_coset_batch(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, 
-                                    unsigned n, unsigned batch_size, scalar_t* coset_powers) {
-  return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers);
+template <typename E, typename S> 
+int evaluate_scalars_on_coset_batch(S* d_out, S* d_coefficients, S* d_domain, unsigned domain_size, 
+                                    unsigned n, unsigned batch_size, S* coset_powers, cudaStream_t stream) {
+  return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
 }
 
-int evaluate_points_on_coset(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, 
-                             unsigned domain_size, unsigned n, scalar_t* coset_powers) {
-  return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers);
+template <typename E, typename S> 
+int evaluate_points_on_coset(E* d_out, E* d_coefficients, S* d_domain, 
+                             unsigned domain_size, unsigned n, S* coset_powers, cudaStream_t stream) {
+  return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
 }
 
-int evaluate_points_on_coset_batch(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, unsigned domain_size,
-                                   unsigned n, unsigned batch_size, scalar_t* coset_powers) {
-  return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers);
-}
-
-extern "C" scalar_t* build_domain_cuda(uint32_t domain_size, uint32_t logn, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        if (inverse) {
-            return fill_twiddle_factors_array(domain_size, scalar_t::omega_inv(logn));
-        } else {
-            return fill_twiddle_factors_array(domain_size, scalar_t::omega(logn));
-        }
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return nullptr;
-    }
-}
-
-extern "C" int ntt_cuda(scalar_t *arr, uint32_t n, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        return ntt_end2end(arr, n, inverse); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        
-        return -1;        
-    }
-}
-
-extern "C" int ecntt_cuda(projective_t *arr, uint32_t n, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        return ecntt_end2end(arr, n, inverse); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int ntt_batch_cuda(scalar_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        return ntt_end2end_batch(arr, arr_size, batch_size, inverse); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int ecntt_batch_cuda(projective_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        return ecntt_end2end_batch(arr, arr_size, batch_size, inverse); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int interpolate_scalars_cuda(scalar_t* d_out, scalar_t *d_evaluations, scalar_t *d_domain, unsigned n, unsigned device_id = 0)
-{
-    try
-    {
-        return interpolate_scalars(d_out, d_evaluations, d_domain, n); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int interpolate_scalars_batch_cuda(scalar_t* d_out, scalar_t* d_evaluations, scalar_t* d_domain, unsigned n,
-                                              unsigned batch_size, size_t device_id = 0)
-{
-    try
-    {
-        return interpolate_scalars_batch(d_out, d_evaluations, d_domain, n, batch_size); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int interpolate_points_cuda(projective_t* d_out, projective_t *d_evaluations, scalar_t *d_domain, unsigned n, size_t device_id = 0)
-{
-    try
-    {
-        return interpolate_points(d_out, d_evaluations, d_domain, n); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int interpolate_points_batch_cuda(projective_t* d_out, projective_t* d_evaluations, scalar_t* d_domain,
-                                             unsigned n, unsigned batch_size, size_t device_id = 0)
-{
-    try
-    {
-        return interpolate_points_batch(d_out, d_evaluations, d_domain, n, batch_size); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int evaluate_scalars_cuda(scalar_t* d_out, scalar_t *d_coefficients, scalar_t *d_domain, 
-                                     unsigned domain_size, unsigned n, unsigned device_id = 0)
-{
-    try
-    {
-        return evaluate_scalars(d_out, d_coefficients, d_domain, domain_size, n); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int evaluate_scalars_batch_cuda(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, unsigned domain_size,
-                                           unsigned n, unsigned batch_size, size_t device_id = 0)
-{
-    try
-    {
-        return evaluate_scalars_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int evaluate_points_cuda(projective_t* d_out, projective_t *d_coefficients, scalar_t *d_domain, 
-                                    unsigned domain_size, unsigned n, size_t device_id = 0)
-{
-    try
-    {
-        return evaluate_points(d_out, d_coefficients, d_domain, domain_size, n); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int evaluate_points_batch_cuda(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, unsigned domain_size,
-                                          unsigned n, unsigned batch_size, size_t device_id = 0)
-{
-    try
-    {
-        return evaluate_points_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int evaluate_scalars_on_coset_cuda(scalar_t* d_out, scalar_t *d_coefficients, scalar_t *d_domain, unsigned domain_size,
-                                              unsigned n, scalar_t *coset_powers, unsigned device_id = 0)
-{
-    try
-    {
-        return evaluate_scalars_on_coset(d_out, d_coefficients, d_domain, domain_size, n, coset_powers); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int evaluate_scalars_on_coset_batch_cuda(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, 
-                                                    unsigned n, unsigned batch_size, scalar_t *coset_powers, size_t device_id = 0)
-{
-    try
-    {
-        return evaluate_scalars_on_coset_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, coset_powers); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int evaluate_points_on_coset_cuda(projective_t* d_out, projective_t *d_coefficients, scalar_t *d_domain, unsigned domain_size,
-                                             unsigned n, scalar_t *coset_powers, size_t device_id = 0)
-{
-    try
-    {
-        return evaluate_points_on_coset(d_out, d_coefficients, d_domain, domain_size, n, coset_powers); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int evaluate_points_on_coset_batch_cuda(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, 
-                                                   unsigned n, unsigned batch_size, scalar_t *coset_powers, size_t device_id = 0)
-{
-    try
-    {
-        return evaluate_points_on_coset_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, coset_powers); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int reverse_order_scalars_cuda(scalar_t* arr, int n, size_t device_id = 0)
-{
-    try
-    {
-        uint32_t logn = uint32_t(log(n) / log(2));
-        reverse_order(arr, n, logn);
-        return 0;
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int reverse_order_scalars_batch_cuda(scalar_t* arr, int n, int batch_size, size_t device_id = 0)
-{
-    try
-    {
-        uint32_t logn = uint32_t(log(n) / log(2));
-        reverse_order_batch(arr, n, logn, batch_size);
-        return 0;
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int reverse_order_points_cuda(projective_t* arr, int n, size_t device_id = 0)
-{
-    try
-    {
-        uint32_t logn = uint32_t(log(n) / log(2));
-        reverse_order(arr, n, logn);
-        return 0;
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int reverse_order_points_batch_cuda(projective_t* arr, int n, int batch_size, size_t device_id = 0)
-{
-    try
-    {
-        uint32_t logn = uint32_t(log(n) / log(2));
-        reverse_order_batch(arr, n, logn, batch_size);
-        return 0;
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
+template <typename E, typename S> 
+int evaluate_points_on_coset_batch(E* d_out, E* d_coefficients, S* d_domain, unsigned domain_size,
+                                   unsigned n, unsigned batch_size, S* coset_powers, cudaStream_t stream) {
+  return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
 }
+#endif

icicle/appUtils/ntt/lde.cuh

@@ -1,31 +1,46 @@
+#ifndef LDE_H
+#define LDE_H
 #pragma once
 
+template <typename S> 
+int interpolate_scalars(S* d_out, S* d_evaluations, S* d_domain, unsigned n, cudaStream_t stream);
 
-int interpolate_scalars(scalar_t* d_out, scalar_t* d_evaluations, scalar_t* d_domain, unsigned n);
+template <typename S> 
+int interpolate_scalars_batch(S* d_out, S* d_evaluations, S* d_domain, unsigned n, unsigned batch_size, cudaStream_t stream);
 
-int interpolate_scalars_batch(scalar_t* d_out, scalar_t* d_evaluations, scalar_t* d_domain, unsigned n, unsigned batch_size);
+template <typename E, typename S> 
+int interpolate_points(E* d_out, E* d_evaluations, S* d_domain, unsigned n, cudaStream_t stream);
 
-int interpolate_points(projective_t* d_out, projective_t* d_evaluations, scalar_t* d_domain, unsigned n);
+template <typename E, typename S> 
+int interpolate_points_batch(E* d_out, E* d_evaluations, S* d_domain, unsigned n, unsigned batch_size, cudaStream_t stream);
 
-int interpolate_points_batch(projective_t* d_out, projective_t* d_evaluations, scalar_t* d_domain, unsigned n, unsigned batch_size);
+template <typename S> 
+int evaluate_scalars(S* d_out, S* d_coefficients, S* d_domain, unsigned domain_size, unsigned n, cudaStream_t stream);
 
-int evaluate_scalars(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, unsigned n);
+template <typename S> 
+int evaluate_scalars_batch(S* d_out, S* d_coefficients, S* d_domain, unsigned domain_size, unsigned n, unsigned batch_size, cudaStream_t stream);
 
-int evaluate_scalars_batch(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, unsigned n, unsigned batch_size);
+template <typename E, typename S> 
+int evaluate_points(E* d_out, E* d_coefficients, S* d_domain, unsigned domain_size, unsigned n, cudaStream_t stream);
 
-int evaluate_points(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, unsigned n);
+template <typename E, typename S> 
+int evaluate_points_batch(E* d_out, E* d_coefficients, S* d_domain, 
+                          unsigned domain_size, unsigned n, unsigned batch_size, cudaStream_t stream);
 
-int evaluate_points_batch(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, 
-                          unsigned domain_size, unsigned n, unsigned batch_size);
+template <typename S> 
+int evaluate_scalars_on_coset(S* d_out, S* d_coefficients, S* d_domain, 
+                              unsigned domain_size, unsigned n, S* coset_powers, cudaStream_t stream);
 
-int evaluate_scalars_on_coset(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, 
-                              unsigned domain_size, unsigned n, scalar_t* coset_powers);
+template <typename S>                               
+int evaluate_scalars_on_coset_batch(S* d_out, S* d_coefficients, S* d_domain, unsigned domain_size, 
+                                    unsigned n, unsigned batch_size, S* coset_powers, cudaStream_t stream);
 
-int evaluate_scalars_on_coset_batch(scalar_t* d_out, scalar_t* d_coefficients, scalar_t* d_domain, unsigned domain_size, 
-                                    unsigned n, unsigned batch_size, scalar_t* coset_powers);
+template <typename E, typename S> 
+int evaluate_points_on_coset(E* d_out, E* d_coefficients, S* d_domain, 
+                             unsigned domain_size, unsigned n, S* coset_powers, cudaStream_t stream);
 
-int evaluate_points_on_coset(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, 
-                             unsigned domain_size, unsigned n, scalar_t* coset_powers);
+template <typename E, typename S> 
+int evaluate_points_on_coset_batch(E* d_out, E* d_coefficients, S* d_domain, unsigned domain_size,
+                                   unsigned n, unsigned batch_size, S* coset_powers, cudaStream_t stream);
 
-int evaluate_points_on_coset_batch(projective_t* d_out, projective_t* d_coefficients, scalar_t* d_domain, unsigned domain_size,
-                                   unsigned n, unsigned batch_size, scalar_t* coset_powers);
+#endif

icicle/appUtils/ntt/ntt.cu

@@ -1,54 +0,0 @@
-#include <cuda.h>
-#include "ntt.cuh"
-
-extern "C" int ntt_cuda(scalar_t *arr, uint32_t n, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        return ntt_end2end(arr, n, inverse); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;        
-    }
-}
-
-extern "C" int ecntt_cuda(projective_t *arr, uint32_t n, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        return ecntt_end2end(arr, n, inverse); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int ntt_batch_cuda(scalar_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        return ntt_end2end_batch(arr, arr_size, batch_size, inverse); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}
-
-extern "C" int ecntt_batch_cuda(projective_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0)
-{
-    try
-    {
-        return ecntt_end2end_batch(arr, arr_size, batch_size, inverse); // TODO: pass device_id
-    }
-    catch (const std::runtime_error &ex)
-    {
-        printf("error %s", ex.what());
-        return -1;
-    }
-}

icicle/appUtils/ntt/ntt.cuh

@@ -1,22 +1,10 @@
+#ifndef NTT
+#define NTT
 #pragma once
-#include "../../curves/curve_config.cuh"
 
 const uint32_t MAX_NUM_THREADS = 1024;
 const uint32_t MAX_THREADS_BATCH = 256;
 
-/**
- * Copy twiddle factors array to device (returns a pointer to the device allocated array).
- * @param twiddles input empty array. 
- * @param n_twiddles length of twiddle factors. 
- */
- scalar_t * copy_twiddle_factors_to_device(scalar_t * twiddles, uint32_t n_twiddles) {
-  size_t size_twiddles = n_twiddles * sizeof(scalar_t);
-  scalar_t * d_twiddles;
-  cudaMalloc( & d_twiddles, size_twiddles);
-  cudaMemcpy(d_twiddles, twiddles, size_twiddles, cudaMemcpyHostToDevice);
-  return d_twiddles;
-}
-
 /**
  * Computes the twiddle factors.  
  * Outputs: d_twiddles[i] = omega^i.
@@ -24,8 +12,11 @@ const uint32_t MAX_THREADS_BATCH = 256;
  * @param n_twiddles number of twiddle factors. 
  * @param omega multiplying factor. 
  */
-__global__ void twiddle_factors_kernel(scalar_t * d_twiddles, uint32_t n_twiddles, scalar_t omega) {
-  d_twiddles[0] = scalar_t::one();
+ template < typename S > __global__ void twiddle_factors_kernel(S * d_twiddles, uint32_t n_twiddles, S omega) {
+  for (uint32_t i = 0; i < n_twiddles; i++) {
+    d_twiddles[i] = S::zero();
+  }
+  d_twiddles[0] = S::one();
   for (uint32_t i = 0; i < n_twiddles - 1; i++) {
     d_twiddles[i + 1] = omega * d_twiddles[i];
   }
@@ -37,11 +28,12 @@ __global__ void twiddle_factors_kernel(scalar_t * d_twiddles, uint32_t n_twiddle
  * @param n_twiddles number of twiddle factors. 
  * @param omega multiplying factor. 
  */
-scalar_t * fill_twiddle_factors_array(uint32_t n_twiddles, scalar_t omega) {
-  size_t size_twiddles = n_twiddles * sizeof(scalar_t);
-  scalar_t * d_twiddles;
-  cudaMalloc( & d_twiddles, size_twiddles);
-  twiddle_factors_kernel <<< 1, 1 >>> (d_twiddles, n_twiddles, omega);
+ template < typename S > S * fill_twiddle_factors_array(uint32_t n_twiddles, S omega, cudaStream_t stream) {
+  size_t size_twiddles = n_twiddles * sizeof(S);
+  S * d_twiddles;
+  cudaMallocAsync(& d_twiddles, size_twiddles, stream);
+  twiddle_factors_kernel<S> <<< 1, 1, 0, stream>>> (d_twiddles, n_twiddles, omega);
+  cudaStreamSynchronize(stream);
   return d_twiddles;
 }
 
@@ -55,7 +47,7 @@ scalar_t * fill_twiddle_factors_array(uint32_t n_twiddles, scalar_t omega) {
  */
 __device__ __host__ uint32_t reverseBits(uint32_t num, uint32_t logn) {
   unsigned int reverse_num = 0;
-  for (int i = 0; i < logn; i++) {
+  for (uint32_t i = 0; i < logn; i++) {
     if ((num & (1 << i))) reverse_num |= 1 << ((logn - 1) - i);
   }
   return reverse_num;
@@ -98,14 +90,14 @@ template < typename T > __global__ void reverse_order_kernel(T* arr, T* arr_reve
  * @param logn log(n).
  * @param batch_size the size of the batch.
  */
-template < typename T > void reverse_order_batch(T* arr, uint32_t n, uint32_t logn, uint32_t batch_size) {
+template < typename T > void reverse_order_batch(T* arr, uint32_t n, uint32_t logn, uint32_t batch_size, cudaStream_t stream) {
   T* arr_reversed;
-  cudaMalloc(&arr_reversed, n * batch_size * sizeof(T));
+  cudaMallocAsync(&arr_reversed, n * batch_size * sizeof(T), stream);
   int number_of_threads = MAX_THREADS_BATCH;
   int number_of_blocks = (n * batch_size + number_of_threads - 1) / number_of_threads;
-  reverse_order_kernel <<<number_of_blocks, number_of_threads>>> (arr, arr_reversed, n, logn, batch_size);
-  cudaMemcpy(arr, arr_reversed, n * batch_size * sizeof(T), cudaMemcpyDeviceToDevice);
-  cudaFree(arr_reversed);
+  reverse_order_kernel <<<number_of_blocks, number_of_threads, 0, stream>>> (arr, arr_reversed, n, logn, batch_size);
+  cudaMemcpyAsync(arr, arr_reversed, n * batch_size * sizeof(T), cudaMemcpyDeviceToDevice, stream);
+  cudaFreeAsync(arr_reversed, stream);
 }
 
 /**
@@ -116,8 +108,8 @@ template < typename T > void reverse_order_batch(T* arr, uint32_t n, uint32_t lo
  * @param n length of `arr`.
  * @param logn log(n).
  */
-template < typename T > void reverse_order(T* arr, uint32_t n, uint32_t logn) {
-  reverse_order_batch(arr, n, logn, 1);
+template < typename T > void reverse_order(T* arr, uint32_t n, uint32_t logn, cudaStream_t stream) {
+  reverse_order_batch(arr, n, logn, 1, stream);
 }
 
 /**
@@ -164,14 +156,15 @@ template < typename E, typename S > __global__ void template_normalize_kernel(E
  * @param d_twiddles twiddle factors of type S (scalars) array allocated on the device memory (must be a power of 2).
  * @param n_twiddles length of d_twiddles.
  */
-template < typename E, typename S > void template_ntt_on_device_memory(E * d_arr, uint32_t n, uint32_t logn, S * d_twiddles, uint32_t n_twiddles) {
+template < typename E, typename S > void template_ntt_on_device_memory(E * d_arr, uint32_t n, uint32_t logn, S * d_twiddles, uint32_t n_twiddles, cudaStream_t stream) {
   uint32_t m = 2;
+  // TODO: optimize with separate streams for each iteration
   for (uint32_t s = 0; s < logn; s++) {
     for (uint32_t i = 0; i < n; i += m) {
-        int shifted_m = m >> 1;
-        int number_of_threads = MAX_NUM_THREADS ^ ((shifted_m ^ MAX_NUM_THREADS) & -(shifted_m < MAX_NUM_THREADS));
-        int number_of_blocks = shifted_m / MAX_NUM_THREADS + 1;
-        template_butterfly_kernel < E, S > <<< number_of_threads, number_of_blocks >>> (d_arr, d_twiddles, n, n_twiddles, m, i, m >> 1);
+        uint32_t shifted_m = m >> 1;
+        uint32_t number_of_threads = MAX_NUM_THREADS ^ ((shifted_m ^ MAX_NUM_THREADS) & -(shifted_m < MAX_NUM_THREADS));
+        uint32_t number_of_blocks = shifted_m / MAX_NUM_THREADS + 1;
+        template_butterfly_kernel < E, S > <<< number_of_threads, number_of_blocks, 0, stream >>> (d_arr, d_twiddles, n, n_twiddles, m, i, m >> 1);
     }
     m <<= 1;
   }
@@ -186,96 +179,47 @@ template < typename E, typename S > void template_ntt_on_device_memory(E * d_arr
  * @param n_twiddles length of d_twiddles. 
  * @param inverse indicate if the result array should be normalized by n^(-1). 
  */
-template < typename E, typename S > E * ntt_template(E * arr, uint32_t n, S * d_twiddles, uint32_t n_twiddles, bool inverse) {
+template < typename E, typename S > E * ntt_template(E * arr, uint32_t n, S * d_twiddles, uint32_t n_twiddles, bool inverse, cudaStream_t stream) {
   uint32_t logn = uint32_t(log(n) / log(2));
   size_t size_E = n * sizeof(E);
   E * arrReversed = template_reverse_order < E > (arr, n, logn);
   E * d_arrReversed;
-  cudaMalloc( & d_arrReversed, size_E);
-  cudaMemcpy(d_arrReversed, arrReversed, size_E, cudaMemcpyHostToDevice);
-  template_ntt_on_device_memory < E, S > (d_arrReversed, n, logn, d_twiddles, n_twiddles);
+  cudaMallocAsync( & d_arrReversed, size_E, stream);
+  cudaMemcpyAsync(d_arrReversed, arrReversed, size_E, cudaMemcpyHostToDevice, stream);
+  template_ntt_on_device_memory < E, S > (d_arrReversed, n, logn, d_twiddles, n_twiddles, stream);
   if (inverse) {
     int NUM_THREADS = MAX_NUM_THREADS;
     int NUM_BLOCKS = (n + NUM_THREADS - 1) / NUM_THREADS;
-    template_normalize_kernel < E, S > <<< NUM_THREADS, NUM_BLOCKS >>> (d_arrReversed, n, S::inv_log_size(logn));
+    template_normalize_kernel < E, S > <<< NUM_THREADS, NUM_BLOCKS, 0, stream >>> (d_arrReversed, n, S::inv_log_size(logn));
   }
-  cudaMemcpy(arrReversed, d_arrReversed, size_E, cudaMemcpyDeviceToHost);
-  cudaFree(d_arrReversed);
+  cudaMemcpyAsync(arrReversed, d_arrReversed, size_E, cudaMemcpyDeviceToHost, stream);
+  cudaFreeAsync(d_arrReversed, stream);
+  cudaStreamSynchronize(stream);
   return arrReversed;
 }
 
-/**
- * Cooley-Tukey Elliptic Curve NTT. 
- * NOTE! this function assumes that d_twiddles are located in the device memory.
- * @param arr input array of type projective_t. 
- * @param n length of d_arr.
- * @param d_twiddles twiddle factors of type S (scalars) array allocated on the device memory (must be a power of 2). 
- * @param n_twiddles length of d_twiddles. 
- * @param inverse indicate if the result array should be normalized by n^(-1). 
- */
-projective_t * ecntt(projective_t * arr, uint32_t n, scalar_t * d_twiddles, uint32_t n_twiddles, bool inverse) {
-  return ntt_template < projective_t, scalar_t > (arr, n, d_twiddles, n_twiddles, inverse);
-}
-
 /**
  * Cooley-Tukey (scalar) NTT. 
- * NOTE! this function assumes that d_twiddles are located in the device memory.
- * @param arr input array of type scalar_t. 
- * @param n length of d_arr.
- * @param d_twiddles twiddle factors of type S (scalars) array allocated on the device memory (must be a power of 2). 
- * @param n_twiddles length of d_twiddles. 
- * @param inverse indicate if the result array should be normalized by n^(-1). 
- */
-scalar_t * ntt(scalar_t * arr, uint32_t n, scalar_t * d_twiddles, uint32_t n_twiddles, bool inverse) {
-  return ntt_template < scalar_t, scalar_t > (arr, n, d_twiddles, n_twiddles, inverse);
-}
-
-
-/**
- * Cooley-Tukey (scalar) NTT. 
- * @param arr input array of type scalar_t. 
+ * @param arr input array of type E (element). 
  * @param n length of d_arr.
  * @param inverse indicate if the result array should be normalized by n^(-1). 
  */
- extern "C" uint32_t ntt_end2end(scalar_t * arr, uint32_t n, bool inverse) {
-  uint32_t logn = uint32_t(log(n) / log(2));
-  uint32_t n_twiddles = n; // n_twiddles is set to 4096 as scalar_t::omega() is of that order. 
-  scalar_t * d_twiddles;
-  if (inverse){
-    d_twiddles = fill_twiddle_factors_array(n_twiddles, scalar_t::omega_inv(logn));
-  } else {
-    d_twiddles = fill_twiddle_factors_array(n_twiddles, scalar_t::omega(logn));
-  }
-  scalar_t * result = ntt_template < scalar_t, scalar_t > (arr, n, d_twiddles, n_twiddles, inverse);
-  for(int i = 0; i < n; i++){
-    arr[i] = result[i]; 
-  }
-  cudaFree(d_twiddles);
-  return 0; 
-}
-
-
-/**
- * Cooley-Tukey (scalar) NTT. 
- * @param arr input array of type projective_t. 
- * @param n length of d_arr.
- * @param inverse indicate if the result array should be normalized by n^(-1). 
- */
- extern "C" uint32_t ecntt_end2end(projective_t * arr, uint32_t n, bool inverse) {
+ template<typename E,typename S> uint32_t ntt_end2end_template(E * arr, uint32_t n, bool inverse, cudaStream_t stream) {
   uint32_t logn = uint32_t(log(n) / log(2));
   uint32_t n_twiddles = n; 
-  scalar_t * twiddles = new scalar_t[n_twiddles];
-  scalar_t * d_twiddles;
+  S * twiddles = new S[n_twiddles];
+  S * d_twiddles;
   if (inverse){
-    d_twiddles = fill_twiddle_factors_array(n_twiddles, scalar_t::omega_inv(logn));
+    d_twiddles = fill_twiddle_factors_array(n_twiddles, S::omega_inv(logn), stream);
   } else{
-    d_twiddles = fill_twiddle_factors_array(n_twiddles, scalar_t::omega(logn));
+    d_twiddles = fill_twiddle_factors_array(n_twiddles, S::omega(logn), stream);
   }
-  projective_t * result = ntt_template < projective_t, scalar_t > (arr, n, d_twiddles, n_twiddles, inverse);
+  E * result = ntt_template < E, S > (arr, n, d_twiddles, n_twiddles, inverse, stream);
   for(int i = 0; i < n; i++){
     arr[i] = result[i]; 
   }
-  cudaFree(d_twiddles);
+  cudaFreeAsync(d_twiddles, stream);
+  cudaStreamSynchronize(stream);
   return 0; // TODO add
 }
 
@@ -289,14 +233,14 @@ scalar_t * ntt(scalar_t * arr, uint32_t n, scalar_t * d_twiddles, uint32_t n_twi
  * @param logn log(n).
  * @param task log(n).
  */
- template < typename T > __device__ __host__ void reverseOrder_batch(T * arr, uint32_t n, uint32_t logn, uint32_t task) {
+template < typename T > __device__ __host__ void reverseOrder_batch(T * arr, uint32_t n, uint32_t logn, uint32_t task) {
   for (uint32_t i = 0; i < n; i++) {
-      uint32_t reversed = reverseBits(i, logn);
-      if (reversed > i) {
-          T tmp = arr[task * n + i];
-          arr[task * n + i] = arr[task * n + reversed];
-          arr[task * n + reversed] = tmp;
-      }
+    uint32_t reversed = reverseBits(i, logn);
+    if (reversed > i) {
+      T tmp = arr[task * n + i];
+      arr[task * n + i] = arr[task * n + reversed];
+      arr[task * n + reversed] = tmp;
+    }
   }
 }
 
@@ -387,101 +331,55 @@ __global__ void ntt_template_kernel_rev_ord(E *arr, uint32_t n, uint32_t logn, u
   }
 }
 
-
-//TODO: batch ntt and ecntt can be unified into batch_template
 /**
  * Cooley-Tukey (scalar) NTT.
  * This is a bached version - meaning it assumes than the input array 
  * consists of N arrays of size n. The function performs n-size NTT on each small array.
- * @param arr input array of type scalar_t. 
+ * @param arr input array of type BLS12_381::scalar_t. 
  * @param arr_size number of total elements = n * N.  
  * @param n size of batch.
  * @param inverse indicate if the result array should be normalized by n^(-1). 
  */
- extern "C" uint32_t ntt_end2end_batch(scalar_t * arr, uint32_t arr_size, uint32_t n, bool inverse) {
+ template <typename E, typename S> uint32_t ntt_end2end_batch_template(E * arr, uint32_t arr_size, uint32_t n, bool inverse, cudaStream_t stream) {
   int batches = int(arr_size / n);
   uint32_t logn = uint32_t(log(n) / log(2));
-  uint32_t n_twiddles = n; // n_twiddles is set to 4096 as scalar_t::omega() is of that order. 
-  size_t size_E = arr_size * sizeof(scalar_t);
-  scalar_t * d_twiddles;
+  uint32_t n_twiddles = n; // n_twiddles is set to 4096 as BLS12_381::scalar_t::omega() is of that order. 
+  size_t size_E = arr_size * sizeof(E);
+  S * d_twiddles;
   if (inverse){
-    d_twiddles = fill_twiddle_factors_array(n_twiddles, scalar_t::omega_inv(logn));
+    d_twiddles = fill_twiddle_factors_array(n_twiddles, S::omega_inv(logn), stream);
   } else{
-    d_twiddles = fill_twiddle_factors_array(n_twiddles, scalar_t::omega(logn));
+    d_twiddles = fill_twiddle_factors_array(n_twiddles, S::omega(logn), stream);
   }
-  scalar_t * d_arr;
-  cudaMalloc( & d_arr, size_E);
-  cudaMemcpy(d_arr, arr, size_E, cudaMemcpyHostToDevice);
+  E * d_arr;
+  cudaMallocAsync( & d_arr, size_E, stream);
+  cudaMemcpyAsync(d_arr, arr, size_E, cudaMemcpyHostToDevice, stream);
   int NUM_THREADS = MAX_THREADS_BATCH;
   int NUM_BLOCKS = (batches + NUM_THREADS - 1) / NUM_THREADS;
-  ntt_template_kernel_rev_ord<scalar_t, scalar_t><<<NUM_BLOCKS, NUM_THREADS>>>(d_arr, n, logn, batches);
+  ntt_template_kernel_rev_ord<E, S><<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(d_arr, n, logn, batches);
 
   NUM_THREADS = min(n / 2, MAX_THREADS_BATCH);
   int chunks = max(int((n / 2) / NUM_THREADS), 1);
   int total_tasks = batches * chunks;
   NUM_BLOCKS = total_tasks;
 
-  for (uint32_t s = 0; s < logn; s++) //TODO: this loop also can be unrolled
+  //TODO: this loop also can be unrolled
+  for (uint32_t s = 0; s < logn; s++)
   {
-    ntt_template_kernel<scalar_t, scalar_t><<<NUM_BLOCKS, NUM_THREADS>>>(d_arr, n, d_twiddles, n_twiddles, total_tasks, s, false);
+    ntt_template_kernel<E, S><<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(d_arr, n, d_twiddles, n_twiddles, total_tasks, s, false);
+    cudaStreamSynchronize(stream);
   }
-
   if (inverse == true)
   {
     NUM_THREADS = MAX_NUM_THREADS;
     NUM_BLOCKS = (arr_size + NUM_THREADS - 1) / NUM_THREADS;
-    template_normalize_kernel < scalar_t, scalar_t > <<< NUM_THREADS, NUM_BLOCKS >>> (d_arr, arr_size, scalar_t::inv_log_size(logn));
+    template_normalize_kernel < E, S > <<< NUM_THREADS, NUM_BLOCKS, 0, stream>>> (d_arr, arr_size, S::inv_log_size(logn));
   }
-  cudaMemcpy(arr, d_arr, size_E, cudaMemcpyDeviceToHost);
-  cudaFree(d_arr);
-  cudaFree(d_twiddles);
+  cudaMemcpyAsync(arr, d_arr, size_E, cudaMemcpyDeviceToHost, stream);
+  cudaFreeAsync(d_arr, stream);
+  cudaFreeAsync(d_twiddles, stream);
+  cudaStreamSynchronize(stream);
   return 0; 
 }
 
-/**
- * Cooley-Tukey (scalar) NTT.
- * This is a bached version - meaning it assumes than the input array 
- * consists of N arrays of size n. The function performs n-size NTT on each small array.
- * @param arr input array of type scalar_t. 
- * @param arr_size number of total elements = n * N.  
- * @param n size of batch.
- * @param inverse indicate if the result array should be normalized by n^(-1). 
- */
- extern "C" uint32_t ecntt_end2end_batch(projective_t * arr, uint32_t arr_size, uint32_t n, bool inverse) {
-  int batches = int(arr_size / n);
-  uint32_t logn = uint32_t(log(n) / log(2));
-  uint32_t n_twiddles = n; // n_twiddles is set to 4096 as scalar_t::omega() is of that order. 
-  size_t size_E = arr_size * sizeof(projective_t);
-  scalar_t * d_twiddles;
-  if (inverse){
-    d_twiddles = fill_twiddle_factors_array(n_twiddles, scalar_t::omega_inv(logn));
-  } else{
-    d_twiddles = fill_twiddle_factors_array(n_twiddles, scalar_t::omega(logn));
-  }
-  projective_t * d_arr;
-  cudaMalloc( & d_arr, size_E);
-  cudaMemcpy(d_arr, arr, size_E, cudaMemcpyHostToDevice);
-  int NUM_THREADS = MAX_THREADS_BATCH;
-  int NUM_BLOCKS = (batches + NUM_THREADS - 1) / NUM_THREADS;
-  ntt_template_kernel_rev_ord<projective_t, scalar_t><<<NUM_BLOCKS, NUM_THREADS>>>(d_arr, n, logn, batches);
-
-  NUM_THREADS = min(n / 2, MAX_THREADS_BATCH);
-  int chunks = max(int((n / 2) / NUM_THREADS), 1);
-  int total_tasks = batches * chunks;
-  NUM_BLOCKS = total_tasks;
-
-  for (uint32_t s = 0; s < logn; s++) //TODO: this loop also can be unrolled
-  {
-    ntt_template_kernel<projective_t, scalar_t><<<NUM_BLOCKS, NUM_THREADS>>>(d_arr, n, d_twiddles, n_twiddles, total_tasks, s, false);
-  }
-  if (inverse == true)
-  {
-    NUM_THREADS = MAX_NUM_THREADS;
-    NUM_BLOCKS = (arr_size + NUM_THREADS - 1) / NUM_THREADS;
-    template_normalize_kernel < projective_t, scalar_t > <<< NUM_THREADS, NUM_BLOCKS >>> (d_arr, arr_size, scalar_t::inv_log_size(logn));
-  }
-  cudaMemcpy(arr, d_arr, size_E, cudaMemcpyDeviceToHost);
-  cudaFree(d_arr);
-  cudaFree(d_twiddles);
-  return 0; 
-}
+#endif

icicle/appUtils/poseidon/constants.cuh

@@ -0,0 +1,51 @@
+#pragma once
+
+#include <map>
+#include <stdexcept>
+#include <cassert>
+
+#include "constants/constants_2.h"
+#include "constants/constants_4.h"
+#include "constants/constants_8.h"
+#include "constants/constants_11.h"
+
+uint32_t partial_rounds_number_from_arity(const uint32_t arity) {
+    switch (arity) {
+        case 2:
+            return 55;
+        case 4:
+            return 56;
+        case 8:
+            return 57;
+        case 11:
+            return 57;
+        default:
+            throw std::invalid_argument( "unsupported arity" );
+    }
+};
+
+// TO-DO: change to mapping
+const uint32_t FULL_ROUNDS_DEFAULT = 4;
+
+// TO-DO: for now, the constants are only generated in bls12_381
+template <typename S>
+S * load_constants(const uint32_t arity) {
+    unsigned char * constants;
+    switch (arity) {
+        case 2:
+            constants = constants_2;
+            break;
+        case 4:
+            constants = constants_4;
+            break;
+        case 8:
+            constants = constants_8;
+            break;
+        case 11:
+            constants = constants_11;
+            break;
+        default:
+            throw std::invalid_argument( "unsupported arity" );
+    }
+    return reinterpret_cast< S * >(constants);
+}

icicle/appUtils/poseidon/constants/constants_2.h

@@ -0,0 +1,995 @@
+unsigned char constants_2[] = {
+  0xd8, 0xd3, 0x6e, 0x9d, 0x00, 0x0a, 0x32, 0xa7, 0x36, 0x8b, 0x75, 0xa2,
+  0x92, 0xac, 0x1e, 0x50, 0x24, 0x4a, 0xbb, 0x1d, 0x86, 0x51, 0xbd, 0x23,
+  0x7a, 0xe1, 0x3a, 0xfa, 0x4b, 0x06, 0x9f, 0x66, 0x15, 0x3f, 0x9d, 0x2b,
+  0x84, 0xab, 0x72, 0x6e, 0x34, 0x27, 0xac, 0x45, 0x96, 0x7c, 0xe6, 0xee,
+  0x6c, 0xa6, 0x4f, 0xc8, 0xf2, 0x7f, 0x53, 0xe4, 0x36, 0xca, 0xac, 0xfb,
+  0xde, 0xa8, 0x61, 0x0a, 0xd5, 0x65, 0x81, 0x12, 0x71, 0x47, 0x23, 0x1e,
+  0x30, 0x49, 0xaa, 0x1a, 0x4e, 0x2b, 0x29, 0x17, 0x5b, 0x27, 0xdf, 0x45,
+  0x8a, 0x1e, 0x1b, 0xf9, 0x09, 0x9d, 0xb8, 0x24, 0xfa, 0xce, 0xe9, 0x21,
+  0xd1, 0xa0, 0x12, 0x2f, 0xca, 0x56, 0x5f, 0x2f, 0x1b, 0x40, 0x6c, 0x31,
+  0x90, 0x55, 0x2f, 0x1f, 0x2b, 0xd0, 0xd2, 0xd2, 0xc9, 0x24, 0x26, 0x38,
+  0x05, 0x18, 0x53, 0x38, 0x1d, 0x42, 0xfc, 0x0b, 0xc8, 0xc5, 0x8b, 0x5a,
+  0xf3, 0x19, 0xca, 0xff, 0xf5, 0x3b, 0xef, 0x15, 0x4e, 0xf4, 0xcc, 0xbe,
+  0xe8, 0x42, 0x69, 0x68, 0xf4, 0xfc, 0xd3, 0xc3, 0xf0, 0x5d, 0x03, 0x89,
+  0x4a, 0xae, 0xb0, 0x13, 0x43, 0x39, 0xaa, 0x45, 0xb2, 0x41, 0x38, 0xf8,
+  0x20, 0x2d, 0xd1, 0x1f, 0x3c, 0xc4, 0xaa, 0xf1, 0x40, 0xd0, 0x26, 0xe4,
+  0x81, 0x74, 0x41, 0xc1, 0xb4, 0xd0, 0x64, 0x8d, 0xf9, 0xdd, 0x9a, 0x4b,
+  0x38, 0x45, 0x02, 0xcc, 0x01, 0x65, 0x25, 0x72, 0x24, 0x2b, 0xba, 0x3f,
+  0x2c, 0x1a, 0xbf, 0xc6, 0x3c, 0xcf, 0xa1, 0xef, 0x9c, 0xda, 0x2e, 0x9b,
+  0x14, 0xc7, 0x81, 0x65, 0x85, 0xc3, 0x24, 0x2c, 0x65, 0xc6, 0x51, 0x3a,
+  0xd3, 0xc1, 0xd1, 0xd5, 0x42, 0x8f, 0x2f, 0x3c, 0x0e, 0x61, 0xaf, 0xb8,
+  0xf6, 0x3c, 0x32, 0x1a, 0x9f, 0x28, 0x91, 0x9d, 0x02, 0x18, 0x97, 0x47,
+  0x79, 0x21, 0xf9, 0x61, 0x40, 0x5c, 0x16, 0xa9, 0xc5, 0x6e, 0xca, 0x9f,
+  0x37, 0xf1, 0x2a, 0x13, 0xf1, 0xf0, 0xf0, 0xef, 0xb4, 0x56, 0xf4, 0x08,
+  0x6a, 0x47, 0x52, 0x4a, 0x32, 0xbf, 0xb3, 0xab, 0xa5, 0xdf, 0x36, 0x12,
+  0x63, 0x5f, 0x2e, 0xc2, 0xf4, 0x17, 0xa4, 0x0c, 0xfd, 0xeb, 0x3d, 0xe9,
+  0xc7, 0x1d, 0x97, 0x5e, 0x52, 0x61, 0x75, 0x96, 0xfb, 0x11, 0x60, 0xcd,
+  0xf8, 0xca, 0xa8, 0x11, 0xdc, 0x6e, 0xcd, 0x59, 0xf3, 0x37, 0x41, 0xd6,
+  0x61, 0xb3, 0x74, 0xe5, 0xa8, 0xc1, 0x51, 0xf5, 0xa2, 0x57, 0x2e, 0x32,
+  0xe4, 0x0e, 0xd2, 0xed, 0x73, 0xca, 0x58, 0x7a, 0x81, 0x16, 0x9c, 0xa0,
+  0xa0, 0xc0, 0xaa, 0x65, 0xe0, 0x3f, 0x43, 0xb7, 0x03, 0xb0, 0x35, 0x84,
+  0x61, 0xf6, 0x60, 0x0e, 0x18, 0xb3, 0x0a, 0xc0, 0x59, 0x98, 0x57, 0x80,
+  0x7e, 0x26, 0x8b, 0x26, 0x0f, 0x94, 0x44, 0xbc, 0xc9, 0x71, 0xf8, 0x19,
+  0x9a, 0x3b, 0x0a, 0xea, 0x9a, 0xc0, 0x41, 0x26, 0x9b, 0x50, 0xe7, 0x5d,
+  0x1b, 0x59, 0x22, 0x26, 0x79, 0x3a, 0xae, 0x39, 0x61, 0x13, 0x9c, 0x8f,
+  0x8e, 0xd0, 0xbf, 0x84, 0xb8, 0xca, 0x3f, 0x71, 0x41, 0x70, 0x35, 0x88,
+  0x03, 0x63, 0x0d, 0xc5, 0x1a, 0xcb, 0x63, 0x11, 0x32, 0x90, 0xb6, 0xaa,
+  0xfb, 0xdc, 0xd9, 0xc3, 0xa1, 0x93, 0x41, 0xe8, 0xa1, 0xfb, 0x2d, 0x88,
+  0x9e, 0xe6, 0x37, 0x21, 0xb2, 0xbe, 0xfc, 0x64, 0x18, 0x37, 0x87, 0xbc,
+  0x36, 0xf2, 0xe4, 0x08, 0x5e, 0x87, 0x5f, 0x78, 0xbc, 0xbd, 0x4c, 0x91,
+  0x53, 0xb5, 0xf3, 0x3c, 0xe9, 0x8c, 0x1d, 0xa7, 0x0a, 0x95, 0x90, 0x55,
+  0xfd, 0xfd, 0x61, 0xd1, 0x38, 0x21, 0xca, 0x5c, 0x8f, 0xc0, 0xc9, 0x39,
+  0x81, 0x8e, 0x2d, 0x7c, 0xa7, 0xab, 0x84, 0xef, 0x09, 0xd3, 0x1f, 0xb2,
+  0xc8, 0xe7, 0x6a, 0x9c, 0xe5, 0x0d, 0xea, 0x15, 0x0f, 0xdf, 0x55, 0x7e,
+  0x25, 0x01, 0xad, 0x36, 0xdc, 0xfe, 0x2c, 0xc2, 0xf5, 0xd1, 0x57, 0xef,
+  0xf2, 0x1d, 0xdd, 0x82, 0xb0, 0x20, 0xbf, 0xfe, 0x8a, 0xa8, 0x4d, 0xb5,
+  0xd2, 0x03, 0x0d, 0x49, 0x43, 0xaf, 0x4a, 0xac, 0x95, 0x64, 0x6b, 0x62,
+  0x6e, 0x75, 0x84, 0x85, 0x56, 0x8f, 0x99, 0x6d, 0xfa, 0xb4, 0x37, 0x30,
+  0xc4, 0x06, 0x82, 0x32, 0xf0, 0x86, 0x6e, 0x5f, 0xde, 0x62, 0xa3, 0x61,
+  0xdc, 0x17, 0x37, 0x5c, 0xc8, 0x9b, 0x78, 0x6a, 0xf1, 0xa2, 0x77, 0x76,
+  0x44, 0x93, 0xbe, 0x6b, 0x71, 0x39, 0x0a, 0x35, 0x86, 0xa3, 0x4c, 0x84,
+  0x0f, 0xb1, 0xbf, 0x51, 0x88, 0x18, 0x88, 0x57, 0x09, 0x97, 0x55, 0xdf,
+  0x29, 0xe6, 0xff, 0xaa, 0xaf, 0x7b, 0x27, 0x29, 0xca, 0xf5, 0x11, 0x64,
+  0xa2, 0x2e, 0xb9, 0x99, 0xc5, 0xc4, 0x56, 0x1b, 0x03, 0x0c, 0xf1, 0x7e,
+  0x9b, 0xf1, 0x8b, 0x57, 0xc7, 0x4c, 0x4a, 0x05, 0x84, 0x78, 0x67, 0x3c,
+  0x82, 0xee, 0xe4, 0x55, 0xfb, 0xf2, 0x2e, 0xcb, 0x3a, 0x64, 0x88, 0x44,
+  0x15, 0x1b, 0x23, 0xaa, 0xe9, 0x9a, 0x04, 0xbd, 0xb4, 0xbd, 0x34, 0x28,
+  0x84, 0x34, 0x55, 0x13, 0x2c, 0xbd, 0x43, 0x1c, 0x3b, 0xaf, 0x1d, 0x95,
+  0x28, 0x18, 0x5f, 0xe7, 0x33, 0xa2, 0x4c, 0x58, 0xca, 0x42, 0xbe, 0x9e,
+  0x8e, 0x72, 0xae, 0xf1, 0x08, 0x40, 0x8f, 0x55, 0x61, 0x68, 0xa3, 0x2e,
+  0xff, 0x75, 0xe7, 0x38, 0x44, 0x68, 0x4a, 0x40, 0x05, 0x3f, 0x64, 0xf2,
+  0xf3, 0xd7, 0x8d, 0xd4, 0x3d, 0x69, 0x2e, 0xc9, 0x94, 0x3f, 0xc8, 0x75,
+  0xa1, 0xa1, 0xe5, 0x0b, 0x26, 0xec, 0x36, 0xe9, 0x29, 0x67, 0x4b, 0xc9,
+  0x2b, 0x0f, 0x4b, 0xa0, 0x56, 0xaf, 0x8b, 0x81, 0xea, 0x11, 0xf5, 0x42,
+  0xd3, 0xf2, 0x6e, 0x91, 0xf3, 0x35, 0x60, 0xe6, 0xa0, 0x80, 0x09, 0x45,
+  0xfe, 0x29, 0x4c, 0xde, 0x96, 0x76, 0x6e, 0x27, 0xfc, 0x64, 0xd3, 0xf7,
+  0xb4, 0xbf, 0xfa, 0x8c, 0x13, 0x68, 0x52, 0xf7, 0x9c, 0x86, 0x74, 0xe1,
+  0x8a, 0x01, 0x97, 0x73, 0x69, 0x29, 0x21, 0x0d, 0xae, 0xcf, 0xa7, 0x83,
+  0xf2, 0x8b, 0x93, 0x8d, 0xef, 0xf2, 0x7c, 0xc1, 0xfd, 0x50, 0xca, 0x95,
+  0x53, 0x77, 0x46, 0xa8, 0xe0, 0xb9, 0x6f, 0x4e, 0xe5, 0x55, 0x35, 0x2b,
+  0x6b, 0x67, 0x5e, 0x4e, 0x24, 0x51, 0x85, 0xf3, 0x19, 0x3d, 0x4c, 0x5c,
+  0x94, 0x7c, 0xb4, 0xe5, 0x49, 0xe8, 0xdf, 0xdd, 0x34, 0x4c, 0x64, 0x13,
+  0x6e, 0x67, 0x6a, 0xc6, 0x5e, 0x82, 0x1f, 0xdc, 0x0e, 0xf6, 0x15, 0x2a,
+  0x6f, 0xdd, 0x3a, 0x5c, 0x7d, 0x20, 0xbf, 0xd5, 0x89, 0xa1, 0x25, 0x2f,
+  0x59, 0xe7, 0xca, 0xa2, 0xb4, 0xde, 0x72, 0x2c, 0xe8, 0xe6, 0xc5, 0x3d,
+  0x93, 0xa5, 0xe0, 0x47, 0x7d, 0xe5, 0x65, 0x58, 0x59, 0xec, 0x62, 0x79,
+  0xc5, 0x69, 0x21, 0xfb, 0x12, 0x45, 0xe7, 0xb3, 0xa0, 0x5c, 0xba, 0xfb,
+  0x70, 0x38, 0x8b, 0x80, 0x95, 0x90, 0x72, 0x85, 0xf8, 0x61, 0xb3, 0x6f,
+  0x5f, 0x9d, 0x2d, 0x36, 0x9f, 0xe0, 0xeb, 0xc2, 0xd2, 0xcd, 0x33, 0x5a,
+  0x26, 0x78, 0xa7, 0x7f, 0x24, 0x52, 0x52, 0x3a, 0xe6, 0xf6, 0xf4, 0xa0,
+  0x9c, 0x52, 0x1d, 0xd5, 0x26, 0x5d, 0x9a, 0x7b, 0x9f, 0xba, 0x63, 0x6a,
+  0xda, 0xb9, 0xed, 0xec, 0x37, 0x8b, 0x24, 0x76, 0xcf, 0x1d, 0xa0, 0x3e,
+  0x1e, 0xc7, 0x60, 0x73, 0xc5, 0x5b, 0x7f, 0x93, 0x84, 0x62, 0x9b, 0xe8,
+  0x28, 0x07, 0xac, 0x77, 0xe7, 0xb3, 0x7d, 0x6f, 0x51, 0x91, 0xc7, 0xf3,
+  0x4d, 0x17, 0xeb, 0xe7, 0xc5, 0x31, 0x1e, 0x2d, 0x75, 0x2e, 0x30, 0xd8,
+  0xe8, 0x75, 0x4c, 0x37, 0x7a, 0xd6, 0x5c, 0x75, 0x1d, 0xc0, 0xb4, 0x99,
+  0xa2, 0x49, 0xe0, 0x72, 0xe2, 0xb3, 0x30, 0xed, 0x8b, 0xa7, 0x7e, 0x07,
+  0x79, 0x36, 0x77, 0xee, 0x15, 0x71, 0x1f, 0xe0, 0x0a, 0x98, 0x0a, 0xee,
+  0xcf, 0x0c, 0x59, 0xcc, 0xc7, 0x48, 0x50, 0xd3, 0xea, 0x41, 0xe1, 0x66,
+  0xd4, 0x3b, 0x24, 0xe9, 0x63, 0x4c, 0x16, 0xec, 0x51, 0x8e, 0x06, 0xc2,
+  0x11, 0x53, 0x58, 0x35, 0xd0, 0xd1, 0x77, 0x43, 0x59, 0x7f, 0xdb, 0x35,
+  0xe6, 0xea, 0x04, 0x1b, 0x69, 0x2e, 0x03, 0x2e, 0x5e, 0xa9, 0x67, 0xc7,
+  0x24, 0x52, 0xef, 0x5e, 0x1d, 0x8c, 0xe8, 0xa3, 0xa4, 0x8e, 0xc4, 0xcb,
+  0x5d, 0x8a, 0x57, 0x31, 0xdf, 0x3c, 0x38, 0xdf, 0xe6, 0xaf, 0x21, 0x77,
+  0x49, 0x02, 0xbc, 0x32, 0xde, 0x1e, 0x9f, 0x6a, 0x95, 0x9f, 0x94, 0x3b,
+  0x84, 0xdc, 0xea, 0x0b, 0x09, 0x76, 0x2f, 0x93, 0x70, 0x12, 0x8c, 0xb6,
+  0xd0, 0x20, 0xc3, 0xe2, 0x94, 0x8a, 0xb6, 0x2f, 0x9a, 0x03, 0xef, 0x5b,
+  0xc0, 0x47, 0xbf, 0xd0, 0xa7, 0x90, 0xe6, 0x13, 0xac, 0xc9, 0x2e, 0x10,
+  0xef, 0x10, 0xd1, 0x81, 0x65, 0x5d, 0xfa, 0x50, 0x65, 0xc0, 0xd6, 0x59,
+  0x3a, 0xe0, 0x5c, 0x94, 0xbd, 0xf8, 0xc6, 0x25, 0x85, 0x61, 0x2f, 0xa5,
+  0x5c, 0x0d, 0x7e, 0xe1, 0xa8, 0x04, 0x3b, 0x1f, 0x61, 0x34, 0x4b, 0x30,
+  0xf3, 0x84, 0x8e, 0x89, 0xb1, 0x58, 0xe2, 0x48, 0xf4, 0x79, 0x7f, 0x5f,
+  0x95, 0x1d, 0xe7, 0x71, 0x47, 0x5d, 0x43, 0x69, 0xd4, 0x7b, 0xe6, 0x87,
+  0x9e, 0x11, 0x12, 0x2a, 0x4f, 0xf7, 0x0c, 0xfb, 0x3c, 0x0b, 0x1d, 0xe7,
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+  0xb2, 0xd7, 0x87, 0x03, 0x87, 0x5b, 0x26, 0x51, 0x01, 0x00, 0x00, 0x40,
+  0xff, 0xff, 0xff, 0x3f, 0xff, 0xc4, 0xfe, 0x3f, 0x02, 0x3b, 0xce, 0xfe,
+  0x03, 0x62, 0x39, 0x07, 0x06, 0x62, 0x6b, 0x26, 0xf6, 0x1d, 0x36, 0x5f,
+  0x7e, 0x3d, 0xf2, 0x56, 0x34, 0x33, 0x33, 0x33, 0xcc, 0xcc, 0xcc, 0xcc,
+  0x65, 0x6a, 0x65, 0x66, 0x9b, 0x95, 0x3e, 0x32, 0x03, 0xe8, 0x2d, 0x6c,
+  0x9e, 0x81, 0xef, 0x51, 0x2b, 0x4b, 0x2b, 0x4c, 0x98, 0x97, 0x8e, 0x45
+};
+unsigned int constants_2_len = 11904;

icicle/appUtils/poseidon/poseidon.cu

@@ -0,0 +1,273 @@
+#include "poseidon.cuh"
+
+template <typename S>
+__global__ void prepare_poseidon_states(S * states, size_t number_of_states, S domain_tag, const PoseidonConfiguration<S> config) {
+    int idx = (blockIdx.x * blockDim.x) + threadIdx.x;
+    int state_number = idx / config.t;
+    if (state_number >= number_of_states) {
+        return;
+    }
+    int element_number = idx % config.t;
+
+    S prepared_element;
+
+    // Domain separation
+    if (element_number == 0) {
+        prepared_element = domain_tag;
+    } else {
+        prepared_element = states[state_number * config.t + element_number - 1];
+    }
+
+    // Add pre-round constant
+    prepared_element = prepared_element + config.round_constants[element_number];
+
+    // Store element in state
+    states[idx] = prepared_element;
+}
+
+template <typename S>
+__device__ __forceinline__ S sbox_alpha_five(S element) {
+    S result = S::sqr(element);
+    result = S::sqr(result);
+    return result * element;
+}
+
+template <typename S>
+__device__ S vecs_mul_matrix(S element, S * matrix, int element_number, int vec_number, int size, S * shared_states) {
+    shared_states[threadIdx.x] = element;
+    __syncthreads();
+
+    element = S::zero();
+    for (int i = 0; i < size; i++) {
+        element = element + (shared_states[vec_number * size + i] * matrix[i * size + element_number]);
+    }
+    __syncthreads();
+    return element;
+}
+
+template <typename S>
+__device__ S full_round(S element,
+                        size_t rc_offset,
+                        int local_state_number,
+                        int element_number,
+                        bool multiply_by_mds,
+                        bool add_round_constant,
+                        S * shared_states,
+                        const PoseidonConfiguration<S> config) {
+    element = sbox_alpha_five(element);
+    if (add_round_constant) {
+        element = element + config.round_constants[rc_offset + element_number];
+    }
+
+    // Multiply all the states by mds matrix
+    S * matrix = multiply_by_mds ? config.mds_matrix : config.non_sparse_matrix;
+    return vecs_mul_matrix(element, matrix, element_number, local_state_number, config.t, shared_states);
+}
+
+// Execute full rounds
+template <typename S>
+__global__ void full_rounds(S * states, size_t number_of_states, size_t rc_offset, bool first_half, const PoseidonConfiguration<S> config) {
+    extern __shared__ S shared_states[];
+
+    int idx = (blockIdx.x * blockDim.x) + threadIdx.x;
+    int state_number = idx / config.t;
+    if (state_number >= number_of_states) {
+        return;
+    }
+    int local_state_number = threadIdx.x / config.t;
+    int element_number = idx % config.t;
+
+    for (int i = 0; i < config.full_rounds_half - 1; i++) {
+        states[idx] = full_round(states[idx],
+                                 rc_offset,
+                                 local_state_number,
+                                 element_number,
+                                 true,
+                                 true,
+                                 shared_states,
+                                 config);
+        rc_offset += config.t;
+    }
+
+    states[idx] = full_round(states[idx],
+                             rc_offset,
+                             local_state_number,
+                             element_number,
+                             !first_half,
+                             first_half,
+                             shared_states,
+                             config);
+}
+
+template <typename S>
+__device__ S partial_round(S * state,
+                                  size_t rc_offset,
+                                  int round_number,
+                                  const PoseidonConfiguration<S> config) {
+    S element = state[0];
+    element = sbox_alpha_five(element);
+    element = element + config.round_constants[rc_offset];
+
+    S * sparse_matrix = &config.sparse_matrices[(config.t * 2 - 1) * round_number];
+
+    state[0] = element * sparse_matrix[0];
+    for (int i = 1; i < config.t; i++) {
+        state[0] = state[0] + (state[i] * sparse_matrix[i]);
+    }
+
+    for (int i = 1; i < config.t; i++) {
+        state[i] = state[i] + (element * sparse_matrix[config.t + i - 1]);
+    }
+}
+
+// Execute partial rounds
+template <typename S>
+__global__ void partial_rounds(S * states, size_t number_of_states, size_t rc_offset, const PoseidonConfiguration<S> config) {
+    int idx = (blockIdx.x * blockDim.x) + threadIdx.x;
+    if (idx >= number_of_states) {
+        return;
+    }
+
+    S * state = &states[idx * config.t];
+
+    for (int i = 0; i < config.partial_rounds; i++) {
+        partial_round(state, rc_offset, i, config);
+        rc_offset++;
+    }
+}
+
+// These function is just doing copy from the states to the output
+template <typename S>
+__global__ void get_hash_results(S * states, size_t number_of_states, S * out, int t) {
+    int idx = (blockIdx.x * blockDim.x) + threadIdx.x;
+    if (idx >= number_of_states) {
+        return;
+    }
+
+    out[idx] = states[idx * t + 1];
+}
+
+template <typename S>
+__host__ void Poseidon<S>::hash_blocks(const S * inp, size_t blocks, S * out, HashType hash_type, cudaStream_t stream) {
+    S * states;
+
+    // allocate memory for {blocks} states of {t} scalars each
+    if (cudaMallocAsync(&states, blocks * this->t * sizeof(S), stream) != cudaSuccess) {
+        throw std::runtime_error("Failed memory allocation on the device");
+    }
+
+    // This is where the input matrix of size Arity x NumberOfBlocks is
+    // padded and coppied to device in a T x NumberOfBlocks matrix
+    cudaMemcpy2DAsync(states, this->t * sizeof(S),  // Device pointer and device pitch
+                 inp, (this->t - 1) * sizeof(S),    // Host pointer and pitch
+                 (this->t - 1) * sizeof(S), blocks, // Size of the source matrix (Arity x NumberOfBlocks)
+                 cudaMemcpyHostToDevice, stream);
+
+    size_t rc_offset = 0;
+
+    // The logic behind this is that 1 thread only works on 1 element
+    // We have {t} elements in each state, and {blocks} states total
+    int number_of_threads = (256 / this->t) * this->t;
+    int hashes_per_block = number_of_threads / this->t;
+    int total_number_of_threads = blocks * this->t;
+    int number_of_blocks = total_number_of_threads / number_of_threads +
+        static_cast<bool>(total_number_of_threads % number_of_threads);
+
+    // The partial rounds operates on the whole state, so we define
+    // the parallelism params for processing a single hash preimage per thread
+    int singlehash_block_size = 128;
+    int number_of_singlehash_blocks = blocks / singlehash_block_size + static_cast<bool>(blocks % singlehash_block_size);
+
+    // Pick the domain_tag accordinaly
+    S domain_tag;
+    switch (hash_type) {
+        case HashType::ConstInputLen:
+            domain_tag = this->const_input_no_pad_domain_tag;
+            break;
+
+        case HashType::MerkleTree:
+            domain_tag = this->tree_domain_tag;
+    }
+
+    #if !defined(__CUDA_ARCH__) && defined(DEBUG)
+    auto start_time = std::chrono::high_resolution_clock::now();
+    #endif
+
+    // Domain separation and adding pre-round constants
+    prepare_poseidon_states <<< number_of_blocks, number_of_threads, 0, stream >>> (states, blocks, domain_tag, this->config);
+    rc_offset += this->t;
+
+    #if !defined(__CUDA_ARCH__) && defined(DEBUG)
+    cudaStreamSynchronize(stream);
+    std::cout << "Domain separation: " << rc_offset << std::endl;
+    //print_buffer_from_cuda<S>(states, blocks * this->t);
+
+    auto end_time = std::chrono::high_resolution_clock::now();
+    auto elapsed_time = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
+    std::cout << "Elapsed time: " << elapsed_time.count() << " ms" << std::endl;
+    start_time = std::chrono::high_resolution_clock::now();
+    #endif
+
+    // execute half full rounds
+    full_rounds <<< number_of_blocks, number_of_threads, sizeof(S) * hashes_per_block * this->t, stream >>> (states, blocks, rc_offset, true, this->config);
+    rc_offset += this->t * this->config.full_rounds_half;
+
+    #if !defined(__CUDA_ARCH__) && defined(DEBUG)
+    cudaStreamSynchronize(stream);
+    std::cout << "Full rounds 1. RCOFFSET: " << rc_offset << std::endl;
+    // print_buffer_from_cuda<S>(states, blocks * this->t);
+
+    end_time = std::chrono::high_resolution_clock::now();
+    elapsed_time = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
+    std::cout << "Elapsed time: " << elapsed_time.count() << " ms" << std::endl;
+    start_time = std::chrono::high_resolution_clock::now();
+    #endif
+
+    // execute partial rounds
+    partial_rounds <<< number_of_singlehash_blocks, singlehash_block_size, 0, stream >>> (states, blocks, rc_offset, this->config);
+    rc_offset += this->config.partial_rounds;
+
+    #if !defined(__CUDA_ARCH__) && defined(DEBUG)
+    cudaStreamSynchronize(stream);
+    std::cout << "Partial rounds. RCOFFSET: " << rc_offset << std::endl;
+    //print_buffer_from_cuda<S>(states, blocks * this->t);
+
+    end_time = std::chrono::high_resolution_clock::now();
+    elapsed_time = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
+    std::cout << "Elapsed time: " << elapsed_time.count() << " ms" << std::endl;
+    start_time = std::chrono::high_resolution_clock::now();
+    #endif
+
+    // execute half full rounds
+    full_rounds <<< number_of_blocks, number_of_threads, sizeof(S) * hashes_per_block * this->t, stream >>> (states, blocks, rc_offset, false, this->config);
+
+    #if !defined(__CUDA_ARCH__) && defined(DEBUG)
+    cudaStreamSynchronize(stream);
+    std::cout << "Full rounds 2. RCOFFSET: " << rc_offset << std::endl;
+    //print_buffer_from_cuda<S>(states, blocks * this->t);
+    end_time = std::chrono::high_resolution_clock::now();
+    elapsed_time = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
+    std::cout << "Elapsed time: " << elapsed_time.count() << " ms" << std::endl;
+    start_time = std::chrono::high_resolution_clock::now();
+    #endif
+
+    // get output
+    S * out_device;
+    cudaMalloc(&out_device, blocks * sizeof(S));
+    get_hash_results <<< number_of_singlehash_blocks, singlehash_block_size, 0, stream >>> (states, blocks, out_device, this->config.t);
+
+    #if !defined(__CUDA_ARCH__) && defined(DEBUG)
+    cudaStreamSynchronize(stream);
+    std::cout << "Get hash results" << std::endl;
+    end_time = std::chrono::high_resolution_clock::now();
+    elapsed_time = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
+    std::cout << "Elapsed time: " << elapsed_time.count() << " ms" << std::endl;
+    #endif
+    cudaMemcpyAsync(out, out_device, blocks * sizeof(S), cudaMemcpyDeviceToHost, stream);
+    cudaFreeAsync(out_device, stream);
+    cudaFreeAsync(states, stream);
+
+    #if !defined(__CUDA_ARCH__) && defined(DEBUG)
+    cudaDeviceReset();
+    #endif
+}

icicle/appUtils/poseidon/poseidon.cuh

@@ -0,0 +1,163 @@
+#pragma once
+#include "constants.cuh"
+
+#if !defined(__CUDA_ARCH__) && defined(DEBUG)
+#include <iostream>
+#include <iomanip>
+#include <string>
+#include <sstream>
+#include <chrono>
+
+template <typename S>
+__host__ void print_buffer_from_cuda(S * device_ptr, size_t size, size_t t) {
+  S * buffer = static_cast< S * >(malloc(size * sizeof(S)));
+  cudaMemcpy(buffer, device_ptr, size * sizeof(S), cudaMemcpyDeviceToHost);
+
+  std::cout << "Start print" << std::endl;
+  for(int i = 0; i < size / t; i++) {
+    std::cout << "State #" << i << std::endl;
+    for (int j = 0; j < t; j++) {
+      std::cout << buffer[i * t + j] << std::endl;
+    }
+    std::cout << std::endl;
+  }
+  std::cout << std::endl;
+  free(buffer);
+}
+#endif
+
+#ifdef DEBUG
+template <typename S>
+__device__ void print_scalar(S element, int data) {
+    printf("D# %d, T# %d: 0x%08x%08x%08x%08x%08x%08x%08x%08x\n",
+           data,
+           threadIdx.x,
+           element.limbs_storage.limbs[0],
+           element.limbs_storage.limbs[1],
+           element.limbs_storage.limbs[2],
+           element.limbs_storage.limbs[3],
+           element.limbs_storage.limbs[4],
+           element.limbs_storage.limbs[5],
+           element.limbs_storage.limbs[6],
+           element.limbs_storage.limbs[7]
+    );
+}
+#endif
+
+template <typename S>
+struct PoseidonConfiguration {
+    uint32_t partial_rounds, full_rounds_half, t;
+    S * round_constants, * mds_matrix, * non_sparse_matrix, *sparse_matrices;
+};
+
+template <typename S>
+class Poseidon {
+  public:
+    uint32_t t;
+    PoseidonConfiguration<S> config;
+
+    enum HashType {
+        ConstInputLen,
+        MerkleTree,
+    };
+
+    Poseidon(const uint32_t arity, cudaStream_t stream) {
+        t = arity + 1;
+        this->config.t = t;
+        this->stream = stream;
+
+        // Pre-calculate domain tags
+        // Domain tags will vary for different applications of Poseidon
+        uint32_t tree_domain_tag_value = 1;
+        tree_domain_tag_value = (tree_domain_tag_value << arity) - tree_domain_tag_value;
+        tree_domain_tag = S::from(tree_domain_tag_value);
+
+        const_input_no_pad_domain_tag = S::one();
+
+        // TO-DO: implement binary shifts for scalar type
+        // const_input_no_pad_domain_tag = S::one() << 64;
+        // const_input_no_pad_domain_tag *= S::from(arity);
+
+        this->config.full_rounds_half = FULL_ROUNDS_DEFAULT;
+        this->config.partial_rounds = partial_rounds_number_from_arity(arity);
+
+        uint32_t round_constants_len = t * this->config.full_rounds_half * 2 + this->config.partial_rounds;
+        uint32_t mds_matrix_len = t * t;
+        uint32_t sparse_matrices_len = (t * 2 - 1) * this->config.partial_rounds;
+
+        // All the constants are stored in a single file
+        S * constants = load_constants<S>(arity);
+
+        S * mds_offset = constants + round_constants_len;
+        S * non_sparse_offset = mds_offset + mds_matrix_len;
+        S * sparse_matrices_offset = non_sparse_offset + mds_matrix_len;
+
+        #if !defined(__CUDA_ARCH__) && defined(DEBUG)
+        std::cout << "P: " << this->config.partial_rounds << " F: " << this->config.full_rounds_half << std::endl;
+        #endif
+
+        // Create streams for copying constants
+        cudaStream_t stream_copy_round_constants, stream_copy_mds_matrix, stream_copy_non_sparse, stream_copy_sparse_matrices;
+        cudaStreamCreate(&stream_copy_round_constants);
+        cudaStreamCreate(&stream_copy_mds_matrix);
+        cudaStreamCreate(&stream_copy_non_sparse);
+        cudaStreamCreate(&stream_copy_sparse_matrices);
+        
+        // Create events for copying constants
+        cudaEvent_t event_copied_round_constants, event_copy_mds_matrix, event_copy_non_sparse, event_copy_sparse_matrices;
+        cudaEventCreateWithFlags(&event_copied_round_constants, cudaEventDisableTiming);
+        cudaEventCreateWithFlags(&event_copy_mds_matrix, cudaEventDisableTiming);
+        cudaEventCreateWithFlags(&event_copy_non_sparse, cudaEventDisableTiming);
+        cudaEventCreateWithFlags(&event_copy_sparse_matrices, cudaEventDisableTiming);
+
+        // Malloc memory for copying constants
+        cudaMallocAsync(&this->config.round_constants, sizeof(S) * round_constants_len, stream_copy_round_constants);
+        cudaMallocAsync(&this->config.mds_matrix, sizeof(S) * mds_matrix_len, stream_copy_mds_matrix);
+        cudaMallocAsync(&this->config.non_sparse_matrix, sizeof(S) * mds_matrix_len, stream_copy_non_sparse);
+        cudaMallocAsync(&this->config.sparse_matrices, sizeof(S) * sparse_matrices_len, stream_copy_sparse_matrices);
+
+        // Copy constants
+        cudaMemcpyAsync(this->config.round_constants, constants,
+            sizeof(S) * round_constants_len,
+            cudaMemcpyHostToDevice, stream_copy_round_constants
+        );
+        cudaMemcpyAsync(this->config.mds_matrix, mds_offset,
+            sizeof(S) * mds_matrix_len,
+            cudaMemcpyHostToDevice, stream_copy_mds_matrix
+        );
+        cudaMemcpyAsync(this->config.non_sparse_matrix, non_sparse_offset,
+            sizeof(S) * mds_matrix_len,
+            cudaMemcpyHostToDevice, stream_copy_non_sparse
+        );
+        cudaMemcpyAsync(this->config.sparse_matrices, sparse_matrices_offset,
+            sizeof(S) * sparse_matrices_len,
+            cudaMemcpyHostToDevice, stream_copy_sparse_matrices
+        );
+
+        // Record finished copying event for streams
+        cudaEventRecord(event_copied_round_constants, stream_copy_round_constants);
+        cudaEventRecord(event_copy_mds_matrix, stream_copy_mds_matrix);
+        cudaEventRecord(event_copy_non_sparse, stream_copy_non_sparse);
+        cudaEventRecord(event_copy_sparse_matrices, stream_copy_sparse_matrices);
+
+        // Main stream waits for copying to finish
+        cudaStreamWaitEvent(stream, event_copied_round_constants);
+        cudaStreamWaitEvent(stream, event_copy_mds_matrix);
+        cudaStreamWaitEvent(stream, event_copy_non_sparse);
+        cudaStreamWaitEvent(stream, event_copy_sparse_matrices);
+    }
+
+    ~Poseidon() {
+        cudaFreeAsync(this->config.round_constants, this->stream);
+        cudaFreeAsync(this->config.mds_matrix, this->stream);
+        cudaFreeAsync(this->config.non_sparse_matrix, this->stream);
+        cudaFreeAsync(this->config.sparse_matrices, this->stream);
+    }
+
+    // Hash multiple preimages in parallel
+    void hash_blocks(const S * inp, size_t blocks, S * out, HashType hash_type, cudaStream_t stream);
+
+  private:
+    S tree_domain_tag, const_input_no_pad_domain_tag;
+    cudaStream_t stream;
+};

icicle/appUtils/vector_manipulation/ve_mod_mult.cu

@@ -1,63 +0,0 @@
-#include <stdio.h>
-#include <iostream>
-#include "../../primitives/field.cuh"
-#include "../../utils/storage.cuh"
-#include "../../primitives/projective.cuh"
-#include "../../curves/curve_config.cuh"
-#include "ve_mod_mult.cuh"
-
-
-extern "C" int32_t vec_mod_mult_point(projective_t *inout,
-                                      scalar_t *scalar_vec,
-                                      size_t n_elments,
-                                      size_t device_id)
-{
-  try
-  {
-    // TODO: device_id
-    vector_mod_mult<projective_t, scalar_t>(scalar_vec, inout, inout, n_elments);
-    return CUDA_SUCCESS;
-  }
-  catch (const std::runtime_error &ex)
-  {
-    printf("error %s", ex.what()); // TODO: error code and message
-    return -1;
-  }
-}
-
-extern "C" int32_t vec_mod_mult_scalar(scalar_t *inout,
-                                       scalar_t *scalar_vec,
-                                       size_t n_elments,
-                                       size_t device_id)
-{
-  try
-  {
-    // TODO: device_id
-    vector_mod_mult<scalar_t, scalar_t>(scalar_vec, inout, inout, n_elments);
-    return CUDA_SUCCESS;
-  }
-  catch (const std::runtime_error &ex)
-  {
-    printf("error %s", ex.what()); // TODO: error code and message
-    return -1;
-  }
-}
-
-extern "C" int32_t matrix_vec_mod_mult(scalar_t *matrix_flattened,
-                                       scalar_t *input,
-                                       scalar_t *output,
-                                       size_t n_elments,
-                                       size_t device_id)
-{
-  try
-  {
-    // TODO: device_id
-    matrix_mod_mult<scalar_t>(matrix_flattened, input, output, n_elments);
-    return CUDA_SUCCESS;
-  }
-  catch (const std::runtime_error &ex)
-  {
-    printf("error %s", ex.what()); // TODO: error code and message
-    return -1;
-  }
-}

icicle/appUtils/vector_manipulation/ve_mod_mult.cuh

@@ -1,3 +1,5 @@
+#ifndef VEC_MULT
+#define VEC_MULT
 #pragma once
 #include <stdexcept>
 #include <cuda.h>
@@ -17,7 +19,7 @@ __global__ void vectorModMult(S *scalar_vec, E *element_vec, E *result, size_t n
 }
 
 template <typename E, typename S>
-int vector_mod_mult(S *vec_a, E *vec_b, E *result, size_t n_elments) // TODO: in place so no need for third result vector
+int vector_mod_mult(S *vec_a, E *vec_b, E *result, size_t n_elments, cudaStream_t stream) // TODO: in place so no need for third result vector
 {
     // Set the grid and block dimensions
     int num_blocks = (int)ceil((float)n_elments / MAX_THREADS_PER_BLOCK);
@@ -26,23 +28,24 @@ int vector_mod_mult(S *vec_a, E *vec_b, E *result, size_t n_elments) // TODO: in
     // Allocate memory on the device for the input vectors, the output vector, and the modulus
     S *d_vec_a;
     E *d_vec_b, *d_result;
-    cudaMalloc(&d_vec_a, n_elments * sizeof(S));
-    cudaMalloc(&d_vec_b, n_elments * sizeof(E));
-    cudaMalloc(&d_result, n_elments * sizeof(E));
+    cudaMallocAsync(&d_vec_a, n_elments * sizeof(S), stream);
+    cudaMallocAsync(&d_vec_b, n_elments * sizeof(E), stream);
+    cudaMallocAsync(&d_result, n_elments * sizeof(E), stream);
 
     // Copy the input vectors and the modulus from the host to the device
-    cudaMemcpy(d_vec_a, vec_a, n_elments * sizeof(S), cudaMemcpyHostToDevice);
-    cudaMemcpy(d_vec_b, vec_b, n_elments * sizeof(E), cudaMemcpyHostToDevice);
+    cudaMemcpyAsync(d_vec_a, vec_a, n_elments * sizeof(S), cudaMemcpyHostToDevice, stream);
+    cudaMemcpyAsync(d_vec_b, vec_b, n_elments * sizeof(E), cudaMemcpyHostToDevice, stream);
 
     // Call the kernel to perform element-wise modular multiplication
-    vectorModMult<<<num_blocks, threads_per_block>>>(d_vec_a, d_vec_b, d_result, n_elments);
+    vectorModMult<<<num_blocks, threads_per_block, 0, stream>>>(d_vec_a, d_vec_b, d_result, n_elments);
 
-    cudaMemcpy(result, d_result, n_elments * sizeof(E), cudaMemcpyDeviceToHost);
+    cudaMemcpyAsync(result, d_result, n_elments * sizeof(E), cudaMemcpyDeviceToHost, stream);
 
-    cudaFree(d_vec_a);
-    cudaFree(d_vec_b);
-    cudaFree(d_result);
+    cudaFreeAsync(d_vec_a, stream);
+    cudaFreeAsync(d_vec_b, stream);
+    cudaFreeAsync(d_result, stream);
 
+    cudaStreamSynchronize(stream);
     return 0;
 }
 
@@ -58,12 +61,12 @@ __global__ void batchVectorMult(S *scalar_vec, E *element_vec, unsigned n_scalar
 }
 
 template <typename E, typename S>
-int batch_vector_mult(S *scalar_vec, E *element_vec, unsigned n_scalars, unsigned batch_size)
+int batch_vector_mult(S *scalar_vec, E *element_vec, unsigned n_scalars, unsigned batch_size, cudaStream_t stream)
 {
     // Set the grid and block dimensions
     int NUM_THREADS = MAX_THREADS_PER_BLOCK;
     int NUM_BLOCKS = (n_scalars * batch_size + NUM_THREADS - 1) / NUM_THREADS;
-    batchVectorMult<<<NUM_BLOCKS, NUM_THREADS>>>(scalar_vec, element_vec, n_scalars, batch_size);
+    batchVectorMult<<<NUM_BLOCKS, NUM_THREADS, 0, stream>>>(scalar_vec, element_vec, n_scalars, batch_size);
     return 0;
 }
 
@@ -81,7 +84,7 @@ __global__ void matrixVectorMult(E *matrix_elements, E *vector_elements, E *resu
 }
 
 template <typename E>
-int matrix_mod_mult(E *matrix_elements, E *vector_elements, E *result, size_t dim)
+int matrix_mod_mult(E *matrix_elements, E *vector_elements, E *result, size_t dim, cudaStream_t stream)
 {
     // Set the grid and block dimensions
     int num_blocks = (int)ceil((float)dim / MAX_THREADS_PER_BLOCK);
@@ -89,22 +92,24 @@ int matrix_mod_mult(E *matrix_elements, E *vector_elements, E *result, size_t di
 
     // Allocate memory on the device for the input vectors, the output vector, and the modulus
     E *d_matrix, *d_vector, *d_result;
-    cudaMalloc(&d_matrix, (dim * dim) * sizeof(E));
-    cudaMalloc(&d_vector, dim * sizeof(E));
-    cudaMalloc(&d_result, dim * sizeof(E));
+    cudaMallocAsync(&d_matrix, (dim * dim) * sizeof(E), stream);
+    cudaMallocAsync(&d_vector, dim * sizeof(E), stream);
+    cudaMallocAsync(&d_result, dim * sizeof(E), stream);
 
     // Copy the input vectors and the modulus from the host to the device
-    cudaMemcpy(d_matrix, matrix_elements, (dim * dim) * sizeof(E), cudaMemcpyHostToDevice);
-    cudaMemcpy(d_vector, vector_elements, dim * sizeof(E), cudaMemcpyHostToDevice);
+    cudaMemcpyAsync(d_matrix, matrix_elements, (dim * dim) * sizeof(E), cudaMemcpyHostToDevice, stream);
+    cudaMemcpyAsync(d_vector, vector_elements, dim * sizeof(E), cudaMemcpyHostToDevice, stream);
 
     // Call the kernel to perform element-wise modular multiplication
-    matrixVectorMult<<<num_blocks, threads_per_block>>>(d_matrix, d_vector, d_result, dim);
+    matrixVectorMult<<<num_blocks, threads_per_block, 0, stream>>>(d_matrix, d_vector, d_result, dim);
 
-    cudaMemcpy(result, d_result, dim * sizeof(E), cudaMemcpyDeviceToHost);
+    cudaMemcpyAsync(result, d_result, dim * sizeof(E), cudaMemcpyDeviceToHost, stream);
 
-    cudaFree(d_matrix);
-    cudaFree(d_vector);
-    cudaFree(d_result);
+    cudaFreeAsync(d_matrix, stream);
+    cudaFreeAsync(d_vector, stream);
+    cudaFreeAsync(d_result, stream);
 
+    cudaStreamSynchronize(stream);
     return 0;
 }
+#endif

icicle/curves/bls12_377/curve_config.cuh

@@ -0,0 +1,22 @@
+#pragma once
+
+#include "../../primitives/field.cuh"
+#include "../../primitives/projective.cuh"
+
+#include "params.cuh"
+
+namespace BLS12_377 {
+    typedef Field<PARAMS_BLS12_377::fp_config> scalar_field_t;
+    typedef scalar_field_t scalar_t;
+    typedef Field<PARAMS_BLS12_377::fq_config> point_field_t;
+    static constexpr point_field_t b = point_field_t{ PARAMS_BLS12_377::weierstrass_b };
+    typedef Projective<point_field_t, scalar_field_t, b> projective_t;
+    typedef Affine<point_field_t> affine_t;
+    #if defined(G2_DEFINED)
+    typedef ExtensionField<PARAMS_BLS12_377::fq_config> g2_point_field_t;
+    static constexpr g2_point_field_t b_g2 = g2_point_field_t{ point_field_t{ PARAMS_BLS12_377::weierstrass_b_g2_re },
+                                                               point_field_t{ PARAMS_BLS12_377::weierstrass_b_g2_im }};
+    typedef Projective<g2_point_field_t, scalar_field_t, b_g2> g2_projective_t;
+    typedef Affine<g2_point_field_t> g2_affine_t;
+    #endif
+}

icicle/curves/bls12_377/lde.cu

@@ -0,0 +1,327 @@
+#ifndef _BLS12_377_LDE
+#define _BLS12_377_LDE
+#include <cuda.h>
+#include "../../appUtils/ntt/lde.cu"
+#include "../../appUtils/ntt/ntt.cuh"
+#include "../../appUtils/vector_manipulation/ve_mod_mult.cuh"
+#include "curve_config.cuh"
+
+extern "C" BLS12_377::scalar_t* build_domain_cuda_bls12_377(uint32_t domain_size, uint32_t logn, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        if (inverse) {
+            return fill_twiddle_factors_array(domain_size, BLS12_377::scalar_t::omega_inv(logn), stream);
+        } else {
+            return fill_twiddle_factors_array(domain_size, BLS12_377::scalar_t::omega(logn), stream);
+        }
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return nullptr;
+    }
+}
+
+extern "C" int ntt_cuda_bls12_377(BLS12_377::scalar_t *arr, uint32_t n, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_template<BLS12_377::scalar_t,BLS12_377::scalar_t>(arr, n, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        
+        return -1;        
+    }
+}
+
+extern "C" int ecntt_cuda_bls12_377(BLS12_377::projective_t *arr, uint32_t n, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_template<BLS12_377::projective_t,BLS12_377::scalar_t>(arr, n, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int ntt_batch_cuda_bls12_377(BLS12_377::scalar_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_batch_template<BLS12_377::scalar_t,BLS12_377::scalar_t>(arr, arr_size, batch_size, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int ecntt_batch_cuda_bls12_377(BLS12_377::projective_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_batch_template<BLS12_377::projective_t,BLS12_377::scalar_t>(arr, arr_size, batch_size, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_scalars_cuda_bls12_377(BLS12_377::scalar_t* d_out, BLS12_377::scalar_t *d_evaluations, BLS12_377::scalar_t *d_domain, unsigned n, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        return interpolate(d_out, d_evaluations, d_domain, n, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_scalars_batch_cuda_bls12_377(BLS12_377::scalar_t* d_out, BLS12_377::scalar_t* d_evaluations, BLS12_377::scalar_t* d_domain, unsigned n,
+                                              unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_points_cuda_bls12_377(BLS12_377::projective_t* d_out, BLS12_377::projective_t *d_evaluations, BLS12_377::scalar_t *d_domain, unsigned n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        return interpolate(d_out, d_evaluations, d_domain, n, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_points_batch_cuda_bls12_377(BLS12_377::projective_t* d_out, BLS12_377::projective_t* d_evaluations, BLS12_377::scalar_t* d_domain,
+                                             unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_cuda_bls12_377(BLS12_377::scalar_t* d_out, BLS12_377::scalar_t *d_coefficients, BLS12_377::scalar_t *d_domain, 
+                                     unsigned domain_size, unsigned n, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BLS12_377::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_batch_cuda_bls12_377(BLS12_377::scalar_t* d_out, BLS12_377::scalar_t* d_coefficients, BLS12_377::scalar_t* d_domain, unsigned domain_size,
+                                           unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BLS12_377::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_cuda_bls12_377(BLS12_377::projective_t* d_out, BLS12_377::projective_t *d_coefficients, BLS12_377::scalar_t *d_domain, 
+                                    unsigned domain_size, unsigned n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BLS12_377::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_batch_cuda_bls12_377(BLS12_377::projective_t* d_out, BLS12_377::projective_t* d_coefficients, BLS12_377::scalar_t* d_domain, unsigned domain_size,
+                                          unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BLS12_377::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_on_coset_cuda_bls12_377(BLS12_377::scalar_t* d_out, BLS12_377::scalar_t *d_coefficients, BLS12_377::scalar_t *d_domain, unsigned domain_size,
+                                              unsigned n, BLS12_377::scalar_t *coset_powers, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_on_coset_batch_cuda_bls12_377(BLS12_377::scalar_t* d_out, BLS12_377::scalar_t* d_coefficients, BLS12_377::scalar_t* d_domain, unsigned domain_size, 
+                                                    unsigned n, unsigned batch_size, BLS12_377::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_on_coset_cuda_bls12_377(BLS12_377::projective_t* d_out, BLS12_377::projective_t *d_coefficients, BLS12_377::scalar_t *d_domain, unsigned domain_size,
+                                             unsigned n, BLS12_377::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_on_coset_batch_cuda_bls12_377(BLS12_377::projective_t* d_out, BLS12_377::projective_t* d_coefficients, BLS12_377::scalar_t* d_domain, unsigned domain_size, 
+                                                   unsigned n, unsigned batch_size, BLS12_377::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_scalars_cuda_bls12_377(BLS12_377::scalar_t* arr, int n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order(arr, n, logn, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_scalars_batch_cuda_bls12_377(BLS12_377::scalar_t* arr, int n, int batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order_batch(arr, n, logn, batch_size, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_points_cuda_bls12_377(BLS12_377::projective_t* arr, int n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order(arr, n, logn, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_points_batch_cuda_bls12_377(BLS12_377::projective_t* arr, int n, int batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order_batch(arr, n, logn, batch_size, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+#endif

icicle/curves/bls12_377/msm.cu

@@ -0,0 +1,93 @@
+#ifndef _BLS12_377_MSM
+#define _BLS12_377_MSM
+#include "../../appUtils/msm/msm.cu"
+#include <stdexcept>
+#include <cuda.h>
+#include "curve_config.cuh"
+
+
+extern "C"
+int msm_cuda_bls12_377(BLS12_377::projective_t *out, BLS12_377::affine_t points[],
+              BLS12_377::scalar_t scalars[], size_t count, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        large_msm<BLS12_377::scalar_t, BLS12_377::projective_t, BLS12_377::affine_t>(scalars, points, count, out, false, false, stream);
+        return CUDA_SUCCESS;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int msm_batch_cuda_bls12_377(BLS12_377::projective_t* out, BLS12_377::affine_t points[],
+                              BLS12_377::scalar_t scalars[], size_t batch_size, size_t msm_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+  try
+  {
+    cudaStreamCreate(&stream);
+    batched_large_msm<BLS12_377::scalar_t, BLS12_377::projective_t, BLS12_377::affine_t>(scalars, points, batch_size, msm_size, out, false, stream);
+    cudaStreamSynchronize(stream);
+
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what());
+    return -1;
+  }
+}
+
+/**
+ * Commit to a polynomial using the MSM.
+ * Note: this function just calls the MSM, it doesn't convert between evaluation and coefficient form of scalars or points.
+ * @param d_out Ouptut point to write the result to.
+ * @param d_scalars Scalars for the MSM. Must be on device.
+ * @param d_points Points for the MSM. Must be on device.
+ * @param count Length of `d_scalars` and `d_points` arrays (they should have equal length).
+ */
+ extern "C"
+ int commit_cuda_bls12_377(BLS12_377::projective_t* d_out, BLS12_377::scalar_t* d_scalars, BLS12_377::affine_t* d_points, size_t count, size_t device_id = 0, cudaStream_t stream = 0)
+ {
+     try
+     {
+         large_msm<BLS12_377::scalar_t, BLS12_377::projective_t, BLS12_377::affine_t>(d_scalars, d_points, count, d_out, true, false, stream);
+         cudaStreamSynchronize(stream);
+         return 0;
+     }
+     catch (const std::runtime_error &ex)
+     {
+         printf("error %s", ex.what());
+         return -1;
+     }
+ }
+ 
+ /**
+  * Commit to a batch of polynomials using the MSM.
+  * Note: this function just calls the MSM, it doesn't convert between evaluation and coefficient form of scalars or points.
+  * @param d_out Ouptut point to write the results to.
+  * @param d_scalars Scalars for the MSMs of all polynomials. Must be on device.
+  * @param d_points Points for the MSMs. Must be on device. It is assumed that this set of bases is used for each MSM.
+  * @param count Length of `d_points` array, `d_scalar` has length `count` * `batch_size`.
+  * @param batch_size Size of the batch.
+  */
+ extern "C"
+ int commit_batch_cuda_bls12_377(BLS12_377::projective_t* d_out, BLS12_377::scalar_t* d_scalars, BLS12_377::affine_t* d_points, size_t count, size_t batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+ {
+     try
+     {
+        cudaStreamCreate(&stream);
+         batched_large_msm(d_scalars, d_points, batch_size, count, d_out, true, stream);
+         cudaStreamSynchronize(stream);
+         return 0;
+     }
+     catch (const std::runtime_error &ex)
+     {
+         printf("error %s", ex.what());
+         return -1;
+     }
+ }
+
+#endif

icicle/curves/bls12_377/params.cuh

@@ -0,0 +1,180 @@
+#pragma once
+#include "../../utils/storage.cuh"
+
+namespace PARAMS_BLS12_377{
+  struct fp_config{
+    static constexpr unsigned limbs_count = 8;
+    static constexpr unsigned omegas_count = 32;
+
+    static constexpr storage<limbs_count> modulus = {0x00000001, 0x0a118000, 0xd0000001, 0x59aa76fe, 0x5c37b001, 0x60b44d1e, 0x9a2ca556, 0x12ab655e};
+    static constexpr storage<limbs_count> modulus_2 = {0x00000002, 0x14230000, 0xa0000002, 0xb354edfd, 0xb86f6002, 0xc1689a3c, 0x34594aac, 0x2556cabd};
+    static constexpr storage<limbs_count> modulus_4 = {0x00000004, 0x28460000, 0x40000004, 0x66a9dbfb, 0x70dec005, 0x82d13479, 0x68b29559, 0x4aad957a};
+    static constexpr storage<2*limbs_count> modulus_wide = {0x00000001, 0x0a118000, 0xd0000001, 0x59aa76fe, 0x5c37b001, 0x60b44d1e, 0x9a2ca556, 0x12ab655e, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<2*limbs_count> modulus_squared = {0x00000001, 0x14230000, 0xe0000002, 0xc7dd4d2f, 0x8585d003, 0x08ee1bd4, 0xe57fc56e, 0x7e7557e3, 0x483a709d, 0x1fdebb41, 0x5678f4e6, 0x8ea77334, 0xc19c3ec5, 0xd717de29, 0xe2340781, 0x015c8d01};
+    static constexpr storage<2*limbs_count> modulus_squared_2 = {0x00000002, 0x28460000, 0xc0000004, 0x8fba9a5f, 0x0b0ba007, 0x11dc37a9, 0xcaff8adc, 0xfceaafc7, 0x9074e13a, 0x3fbd7682, 0xacf1e9cc, 0x1d4ee668, 0x83387d8b, 0xae2fbc53, 0xc4680f03, 0x02b91a03};
+    static constexpr storage<2*limbs_count> modulus_squared_4 = {0x00000004, 0x508c0000, 0x80000008, 0x1f7534bf, 0x1617400f, 0x23b86f52, 0x95ff15b8, 0xf9d55f8f, 0x20e9c275, 0x7f7aed05, 0x59e3d398, 0x3a9dccd1, 0x0670fb16, 0x5c5f78a7, 0x88d01e07, 0x05723407};
+    static constexpr unsigned modulus_bit_count = 253;
+    static constexpr storage<limbs_count> m = {0x151e79ea, 0xf5204c21, 0x8d69e258, 0xfd0a180b, 0xfaa80548, 0xe4e51e49, 0xc40b2c9e, 0x36d9491e};
+    static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+
+    static constexpr storage<limbs_count> omega1= {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> omega2= {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> omega3= {0x00000000, 0x0a118000, 0xd0000001, 0x59aa76fe, 0x5c37b001, 0x60b44d1e, 0x9a2ca556, 0x12ab655e};
+    static constexpr storage<limbs_count> omega4= {0x00000001, 0x8f1a4000, 0xb0000001, 0xcf664765, 0x970dec00, 0x23ed1347, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> omega5= {0x0405f600, 0xfa8e7081, 0xf8a89660, 0x38b1c291, 0x6bda5fce, 0xefab9005, 0x92a3c754, 0x0b6b0756};
+    static constexpr storage<limbs_count> omega6= {0xaf0a50c8, 0xc5b2c78e, 0x4636deb3, 0x72e32a34, 0xb6f97778, 0x3d775d15, 0x2b16be6e, 0x0c4c070d};
+    static constexpr storage<limbs_count> omega7= {0x7a1ade2c, 0x3f5a4e73, 0x0120d1db, 0x71e5bca1, 0x3b2866fd, 0xbcb44162, 0x89c38db1, 0x06ed1a90};
+    static constexpr storage<limbs_count> omega8= {0xbd2cd25e, 0x61c5510e, 0x2b0d531c, 0xe2d70111, 0x94c3bd4b, 0x738f9894, 0x53182695, 0x0b1e0f1d};
+    static constexpr storage<limbs_count> omega9= {0x8cb9508c, 0xcfb2f75e, 0xf491e401, 0x4c14f244, 0x23c16afb, 0xc8f5265f, 0x70f3ff2a, 0x0cda7e27};
+    static constexpr storage<limbs_count> omega10= {0x0bdc32ee, 0xca77feb9, 0xd957f5a9, 0xf36ddfd4, 0x61ba14c4, 0x491c58f5, 0x93e8f339, 0x0618d3c9};
+    static constexpr storage<limbs_count> omega11= {0x2d89d82f, 0x68c3242e, 0x832a3729, 0xf9559645, 0xbceb62cc, 0x5c803c5e, 0x99ffa2f8, 0x1177cf5d};
+    static constexpr storage<limbs_count> omega12= {0x6932851a, 0xb6ed40f2, 0x1e0da12e, 0x79cbe7fb, 0x2a7d8f87, 0x8d408575, 0x7505d049, 0x11867341};
+    static constexpr storage<limbs_count> omega13= {0x07146cbf, 0x8cf7d87a, 0x109c4d23, 0x14ac37dc, 0x883e9660, 0x082d15f0, 0xad9ea9b8, 0x003719b1};
+    static constexpr storage<limbs_count> omega14= {0xfd0aee77, 0x2260e0dd, 0x1e33b6db, 0xc0cbbc3f, 0xfe7e1b36, 0xc8bf6747, 0x4cb802c1, 0x129e4fd5};
+    static constexpr storage<limbs_count> omega15= {0x8ac75741, 0x22f6fca2, 0xdd37b519, 0x8101b557, 0x1036226a, 0xf493bb8a, 0xfce05c2c, 0x06dbad6c};
+    static constexpr storage<limbs_count> omega16= {0x56733f8b, 0x7d246c24, 0xff70b46a, 0xbc3c4112, 0x6f13530b, 0x2c159b40, 0xc55d287b, 0x0c13137a};
+    static constexpr storage<limbs_count> omega17= {0xec8af73d, 0x8d24de3c, 0xcf722b45, 0x50f778d4, 0x15bc7dd7, 0xf4506bc3, 0xf94a16e1, 0x0e43ba91};
+    static constexpr storage<limbs_count> omega18= {0xd4405b8f, 0x0baa7b44, 0xee0f1394, 0xf8f3c7fe, 0xef0dfe6d, 0x46b153c0, 0x2dde6b95, 0x0ea2bcd9};
+    static constexpr storage<limbs_count> omega19= {0x3d1fa34e, 0x5f4dc975, 0x15af81db, 0xc28e54ee, 0x04947d99, 0x83d9a55f, 0x54a2b488, 0x08ec7ccf};
+    static constexpr storage<limbs_count> omega20= {0x0cac0ee8, 0x0d8fa7b3, 0x82ef38e4, 0x756284ed, 0xac8f90d2, 0x7014b194, 0x634e5d50, 0x092488f8};
+    static constexpr storage<limbs_count> omega21= {0x6d34ed69, 0xd85399bf, 0x09e49cef, 0x4d9012ba, 0xca00ae5d, 0x020142ee, 0x3bdfebfd, 0x12772e57};
+    static constexpr storage<limbs_count> omega22= {0x2eb41723, 0x676c8fc7, 0x5dd895bd, 0xe20380e2, 0x9bf22dde, 0x09dc8be8, 0x42638176, 0x12822f94};
+    static constexpr storage<limbs_count> omega23= {0x81a6d2de, 0x1f1df770, 0xcf29c812, 0x5d33b2da, 0x134f0e7e, 0x1bf162de, 0x1e2877a8, 0x045162c4};
+    static constexpr storage<limbs_count> omega24= {0xfecda1b6, 0x24f4503b, 0xded67d3c, 0x0e5d7ed3, 0x40cf20af, 0x2b7b7e5e, 0x4faad6af, 0x0d472650};
+    static constexpr storage<limbs_count> omega25= {0x584b9eb1, 0xcc6c474c, 0x15a8d886, 0x47670804, 0xbb8654c5, 0x07736d2f, 0xeb207a4b, 0x0d14ce7a};
+    static constexpr storage<limbs_count> omega26= {0xed25924a, 0xd1c6471c, 0x6bc312c3, 0xd98bb374, 0xfeae1a41, 0x50be0848, 0x3265c719, 0x04b07dea};
+    static constexpr storage<limbs_count> omega27= {0x618241e3, 0xab13f73e, 0x166ca902, 0x571c9267, 0x5e828a6d, 0x8586443a, 0x6daba50b, 0x093fdf2f};
+    static constexpr storage<limbs_count> omega28= {0xee11c34f, 0xe688e66b, 0xeacecf5a, 0xdc232eae, 0xb95ae685, 0x4fc35094, 0x7c1d31dc, 0x0273b5bd};
+    static constexpr storage<limbs_count> omega29= {0x1a9057bd, 0x8a8a5a77, 0x41834fbb, 0xdcbfae1d, 0xb34ede6e, 0x534f5b97, 0xb78bbd3e, 0x07313ac5};
+    static constexpr storage<limbs_count> omega30= {0x2be70731, 0x287abbb1, 0x7c35c5aa, 0x5cbcfd1e, 0x1671f4df, 0x7585b3fe, 0xb899c011, 0x08350ecf};
+    static constexpr storage<limbs_count> omega31= {0x09f7c5e2, 0x3400c14e, 0x0a649ea1, 0xc112e60c, 0x067ce95e, 0xf7510758, 0xf9daf17c, 0x040a66a5};
+    static constexpr storage<limbs_count> omega32= {0x43efecd3, 0x89d65957, 0x3bd6c318, 0x29246adc, 0xce01533c, 0xf9fb5ef6, 0x849078c3, 0x020410e4};
+
+    static constexpr storage_array<omegas_count, limbs_count> omega = {
+        omega1, omega2, omega3, omega4, omega5, omega6, omega7, omega8, 
+        omega9, omega10, omega11, omega12, omega13, omega14, omega15, omega16,
+        omega17, omega18, omega19, omega20, omega21, omega22, omega23, omega24,
+        omega25, omega26, omega27, omega28, omega29, omega30, omega31, omega32,
+    };
+
+    static constexpr storage<limbs_count> omega_inv1= {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> omega_inv2= {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> omega_inv3= {0x00000000, 0x0a118000, 0xd0000001, 0x59aa76fe, 0x5c37b001, 0x60b44d1e, 0x9a2ca556, 0x12ab655e};
+    static constexpr storage<limbs_count> omega_inv4= {0x00000000, 0x7af74000, 0x1fffffff, 0x8a442f99, 0xc529c400, 0x3cc739d6, 0x9a2ca556, 0x12ab655e};
+    static constexpr storage<limbs_count> omega_inv5= {0x29f04fbb, 0x401766f3, 0x0a4b98b2, 0x7e4e5f63, 0x9fbc28da, 0x35887f12, 0xdabe3b97, 0x045cb225};
+    static constexpr storage<limbs_count> omega_inv6= {0xac4ce534, 0xf3883827, 0x7c4940f0, 0x9f9a114f, 0x32cc3182, 0xe48527ee, 0x2877f4c2, 0x02d4450c};
+    static constexpr storage<limbs_count> omega_inv7= {0x4afbf0bb, 0xd2533833, 0x1d646d56, 0x20987ba6, 0xb8ae7d61, 0xf2c34c11, 0xb53ae995, 0x09962e74};
+    static constexpr storage<limbs_count> omega_inv8= {0x34f5271a, 0xd6aeb755, 0x493bb125, 0xc0e24cfd, 0x35cf1879, 0xc9d2a1ad, 0x19000e58, 0x0f3570fa};
+    static constexpr storage<limbs_count> omega_inv9= {0xbec3ee61, 0x2601423e, 0xb5252af1, 0x94f5ab4b, 0x205d09ca, 0xa1184628, 0x82a1fba2, 0x0e305e1e};
+    static constexpr storage<limbs_count> omega_inv10= {0x7e3320f2, 0x3cbad3a7, 0x4269c624, 0x7866653a, 0xa2fc13a2, 0xaf6d742d, 0xfe24db2a, 0x03ed8246};
+    static constexpr storage<limbs_count> omega_inv11= {0x30cff7d3, 0xcb6ab09e, 0xd88db7e6, 0x29949e69, 0x24db3cd4, 0xb9117dc6, 0xca8d11b5, 0x01b2aadd};
+    static constexpr storage<limbs_count> omega_inv12= {0x433b851c, 0x1c8fbc5d, 0x545e622f, 0x0ccc3b8c, 0x5c624e0f, 0x0fba9df2, 0x0496ddf9, 0x02d54c5d};
+    static constexpr storage<limbs_count> omega_inv13= {0x0a176838, 0x2ddbbfdd, 0xc4c77f0f, 0xb7a1e4f3, 0x41cad032, 0x645b4383, 0xbfb123c4, 0x0f3fe2e3};
+    static constexpr storage<limbs_count> omega_inv14= {0x9ff30538, 0x1d6d50fe, 0x8576b6fa, 0xca07f2d2, 0x720da6d2, 0x587839fa, 0xe9ebd753, 0x0038d5aa};
+    static constexpr storage<limbs_count> omega_inv15= {0x8e30fb24, 0xaeac713d, 0x21906459, 0xd004e9e3, 0xa60b0a33, 0x2fc54303, 0x14e545a6, 0x039063f8};
+    static constexpr storage<limbs_count> omega_inv16= {0x74d36c47, 0x112559bd, 0x4154b77a, 0x87db7016, 0x3843df80, 0x9e779ae5, 0x297077d0, 0x024424f2};
+    static constexpr storage<limbs_count> omega_inv17= {0x65953c15, 0xd649ae5e, 0x56accc60, 0x879fe571, 0xa3ba1e39, 0xba914f52, 0xd6ea78a2, 0x01b74920};
+    static constexpr storage<limbs_count> omega_inv18= {0x3d8a82b4, 0x319dea45, 0x8fc703de, 0x49468894, 0xc6b00817, 0x703f710f, 0xe862bc53, 0x007762fd};
+    static constexpr storage<limbs_count> omega_inv19= {0x5bae083f, 0x4f433336, 0x27612fe3, 0x485e079c, 0x7f8f0a07, 0xf83b6572, 0xca91a4d4, 0x06bdcaaf};
+    static constexpr storage<limbs_count> omega_inv20= {0xb2fb63eb, 0x4a0bf5e7, 0x996004d9, 0x6f64f8ec, 0x67519c5e, 0x0fecd781, 0x1cab2760, 0x04475eb3};
+    static constexpr storage<limbs_count> omega_inv21= {0xcd83d14f, 0xadbd6ce4, 0x750b194a, 0xc664d3bc, 0x89c9f437, 0x3034dfed, 0xcc2e643b, 0x03d502b8};
+    static constexpr storage<limbs_count> omega_inv22= {0x2272320b, 0xf89478a9, 0xd2e658b7, 0x3adac024, 0x94b25831, 0xf38d840f, 0x37dc6c4c, 0x04540b1f};
+    static constexpr storage<limbs_count> omega_inv23= {0xa6d411fe, 0x19d969b1, 0xf544a648, 0x973f00f7, 0xc9ed9f93, 0xb18f166c, 0xe7f21124, 0x02fba68e};
+    static constexpr storage<limbs_count> omega_inv24= {0x94921227, 0x78b96b20, 0x23b35b65, 0x07cd90db, 0xc843f1c3, 0x111f4fd9, 0xff729f23, 0x0ec4b820};
+    static constexpr storage<limbs_count> omega_inv25= {0x4879d823, 0x53eb200b, 0x93095f4a, 0x1971fac3, 0x86989a58, 0x8467ffe6, 0x306ed29d, 0x0af20231};
+    static constexpr storage<limbs_count> omega_inv26= {0xd4793454, 0x71c907bd, 0x7700defb, 0xc11aa47e, 0xbac11769, 0xf03e0873, 0x97419136, 0x0353190d};
+    static constexpr storage<limbs_count> omega_inv27= {0xa81a701c, 0x61a3deb6, 0x91bbbecf, 0xd8a4eda1, 0x6feb65df, 0x3f5339b1, 0x8b5421f2, 0x108adc5b};
+    static constexpr storage<limbs_count> omega_inv28= {0xe7bf5a41, 0x7d6c573a, 0xfa83b1f7, 0x8038b697, 0xa6718ce9, 0x2a988bee, 0x1239b708, 0x0846f362};
+    static constexpr storage<limbs_count> omega_inv29= {0xe3373548, 0x89a068a4, 0x78a6c4e5, 0xf31284cf, 0x6e9396d6, 0x9eed5c8d, 0x7e4342f9, 0x01643c65};
+    static constexpr storage<limbs_count> omega_inv30= {0x123a81f6, 0xc03a3272, 0x115b15e8, 0x377e6d2f, 0x2d6d7206, 0xed5575e4, 0x714004f2, 0x0b1e37e4};
+    static constexpr storage<limbs_count> omega_inv31= {0xdde8ffc5, 0x62a29589, 0x618c5d62, 0xfb6716e8, 0x88d61f25, 0x787e561c, 0xd2b21c7e, 0x0e351761};
+    static constexpr storage<limbs_count> omega_inv32= {0x7aca7fbe, 0xc9fea0e9, 0xb41a8854, 0x965ff314, 0x810eea7e, 0x743415d4, 0x8275bbd1, 0x0431c01b};
+    
+    static constexpr storage_array<omegas_count, limbs_count> omega_inv = {
+        omega_inv1, omega_inv2, omega_inv3, omega_inv4, omega_inv5, omega_inv6, omega_inv7, omega_inv8, 
+        omega_inv9, omega_inv10, omega_inv11, omega_inv12, omega_inv13, omega_inv14, omega_inv15, omega_inv16,
+        omega_inv17, omega_inv18, omega_inv19, omega_inv20, omega_inv21, omega_inv22, omega_inv23, omega_inv24,
+        omega_inv25, omega_inv26, omega_inv27, omega_inv28, omega_inv29, omega_inv30, omega_inv31, omega_inv32,
+    };
+
+    static constexpr storage<limbs_count> inv1= {0x00000001, 0x8508c000, 0x68000000, 0xacd53b7f, 0x2e1bd800, 0x305a268f, 0x4d1652ab, 0x0955b2af};
+    static constexpr storage<limbs_count> inv2= {0x00000001, 0xc78d2000, 0x1c000000, 0x033fd93f, 0xc529c401, 0xc88739d6, 0xf3a17c00, 0x0e008c06};
+    static constexpr storage<limbs_count> inv3= {0x00000001, 0xe8cf5000, 0xf6000000, 0x2e75281e, 0x90b0ba01, 0x949dc37a, 0xc6e710ab, 0x1055f8b2};
+    static constexpr storage<limbs_count> inv4= {0x00000001, 0xf9706800, 0xe3000000, 0x440fcf8e, 0x76743501, 0xfaa9084c, 0xb089db00, 0x1180af08};
+    static constexpr storage<limbs_count> inv5= {0x00000001, 0x01c0f400, 0xd9800001, 0x4edd2346, 0x6955f281, 0xadaeaab5, 0xa55b402b, 0x12160a33};
+    static constexpr storage<limbs_count> inv6= {0x00000001, 0x05e93a00, 0xd4c00001, 0x5443cd22, 0xe2c6d141, 0x07317be9, 0x1fc3f2c1, 0x1260b7c9};
+    static constexpr storage<limbs_count> inv7= {0x00000001, 0x07fd5d00, 0xd2600001, 0x56f72210, 0x1f7f40a1, 0xb3f2e484, 0xdcf84c0b, 0x12860e93};
+    static constexpr storage<limbs_count> inv8= {0x00000001, 0x09076e80, 0xd1300001, 0x5850cc87, 0x3ddb7851, 0x0a5398d1, 0x3b9278b1, 0x1298b9f9};
+    static constexpr storage<limbs_count> inv9= {0x00000001, 0x098c7740, 0x50980001, 0x58fda1c3, 0xcd099429, 0xb583f2f7, 0xeadf8f03, 0x12a20fab};
+    static constexpr storage<limbs_count> inv10= {0x00000001, 0x09cefba0, 0x104c0001, 0x59540c61, 0x14a0a215, 0x0b1c200b, 0x42861a2d, 0x12a6ba85};
+    static constexpr storage<limbs_count> inv11= {0x00000001, 0x09f03dd0, 0xf0260001, 0x597f41af, 0xb86c290b, 0xb5e83694, 0xee595fc1, 0x12a90ff1};
+    static constexpr storage<limbs_count> inv12= {0x00000001, 0x0a00dee8, 0x60130001, 0x5994dc57, 0x8a51ec86, 0x0b4e41d9, 0x4443028c, 0x12aa3aa8};
+    static constexpr storage<limbs_count> inv13= {0x00000001, 0x0a092f74, 0x18098001, 0xd99fa9ab, 0xf344ce43, 0x3601477b, 0x6f37d3f1, 0x12aad003};
+    static constexpr storage<limbs_count> inv14= {0x00000001, 0x0a0d57ba, 0xf404c001, 0x99a51054, 0x27be3f22, 0xcb5aca4d, 0x04b23ca3, 0x12ab1ab1};
+    static constexpr storage<limbs_count> inv15= {0x00000001, 0x0a0f6bdd, 0xe2026001, 0xf9a7c3a9, 0xc1faf791, 0x16078bb5, 0xcf6f70fd, 0x12ab4007};
+    static constexpr storage<limbs_count> inv16= {0x80000001, 0x0a1075ee, 0x59013001, 0xa9a91d54, 0x0f1953c9, 0xbb5dec6a, 0x34ce0b29, 0x12ab52b3};
+    static constexpr storage<limbs_count> inv17= {0x40000001, 0x0a10faf7, 0x94809801, 0x81a9ca29, 0x35a881e5, 0x0e091cc4, 0xe77d5840, 0x12ab5c08};
+    static constexpr storage<limbs_count> inv18= {0xa0000001, 0x0a113d7b, 0x32404c01, 0x6daa2094, 0x48f018f3, 0x375eb4f1, 0xc0d4fecb, 0x12ab60b3};
+    static constexpr storage<limbs_count> inv19= {0xd0000001, 0x0a115ebd, 0x81202601, 0x63aa4bc9, 0xd293e47a, 0xcc098107, 0x2d80d210, 0x12ab6309};
+    static constexpr storage<limbs_count> inv20= {0xe8000001, 0x0a116f5e, 0x28901301, 0xdeaa6164, 0x1765ca3d, 0x965ee713, 0xe3d6bbb3, 0x12ab6433};
+    static constexpr storage<limbs_count> inv21= {0x74000001, 0x0a1177af, 0x7c480981, 0x9c2a6c31, 0xb9cebd1f, 0xfb899a18, 0x3f01b084, 0x12ab64c9};
+    static constexpr storage<limbs_count> inv22= {0xba000001, 0x0a117bd7, 0x262404c1, 0x7aea7198, 0x8b033690, 0xae1ef39b, 0xec972aed, 0x12ab6513};
+    static constexpr storage<limbs_count> inv23= {0xdd000001, 0x0a117deb, 0x7b120261, 0xea4a744b, 0xf39d7348, 0x0769a05c, 0x4361e822, 0x12ab6539};
+    static constexpr storage<limbs_count> inv24= {0xee800001, 0x0a117ef5, 0x25890131, 0x21fa75a5, 0xa7ea91a5, 0x340ef6bd, 0xeec746bc, 0x12ab654b};
+    static constexpr storage<limbs_count> inv25= {0xf7400001, 0x0a117f7a, 0xfac48099, 0x3dd27651, 0x021120d3, 0x4a61a1ee, 0x4479f609, 0x12ab6555};
+    static constexpr storage<limbs_count> inv26= {0x7ba00001, 0x0a117fbd, 0x6562404d, 0x4bbe76a8, 0x2f24686a, 0xd58af786, 0xef534daf, 0x12ab6559};
+    static constexpr storage<limbs_count> inv27= {0xbdd00001, 0x0a117fde, 0x9ab12027, 0xd2b476d3, 0x45ae0c35, 0x1b1fa252, 0x44bff983, 0x12ab655c};
+    static constexpr storage<limbs_count> inv28= {0x5ee80001, 0x0a117fef, 0x35589014, 0x962f76e9, 0x50f2de1b, 0xbde9f7b8, 0x6f764f6c, 0x12ab655d};
+    static constexpr storage<limbs_count> inv29= {0xaf740001, 0x8a117ff7, 0x02ac480a, 0x77ecf6f4, 0x5695470e, 0x8f4f226b, 0x04d17a61, 0x12ab655e};
+    static constexpr storage<limbs_count> inv30= {0xd7ba0001, 0xca117ffb, 0x69562405, 0xe8cbb6f9, 0xd9667b87, 0xf801b7c4, 0x4f7f0fdb, 0x12ab655e};
+    static constexpr storage<limbs_count> inv31= {0xebdd0001, 0x6a117ffd, 0x1cab1203, 0xa13b16fc, 0x9acf15c4, 0x2c5b0271, 0x74d5da99, 0x12ab655e};
+    static constexpr storage<limbs_count> inv32= {0xf5ee8001, 0x3a117ffe, 0x76558902, 0xfd72c6fd, 0xfb8362e2, 0xc687a7c7, 0x87813ff7, 0x12ab655e};
+
+    static constexpr storage_array<omegas_count, limbs_count> inv = {
+        inv1, inv2, inv3, inv4, inv5, inv6, inv7, inv8, 
+        inv9, inv10, inv11, inv12, inv13, inv14, inv15, inv16,
+        inv17, inv18, inv19, inv20, inv21, inv22, inv23, inv24,
+        inv25, inv26, inv27, inv28, inv29, inv30, inv31, inv32,
+    };    
+  };
+
+  struct fq_config{
+    static constexpr unsigned limbs_count = 12;
+    static constexpr storage<limbs_count> modulus = {0x00000001, 0x8508c000, 0x30000000, 0x170b5d44, 0xba094800, 0x1ef3622f, 0x00f5138f, 0x1a22d9f3, 0x6ca1493b, 0xc63b05c0, 0x17c510ea, 0x01ae3a46};
+    static constexpr storage<limbs_count> modulus_2 = {0x00000002, 0x0a118000, 0x60000001, 0x2e16ba88, 0x74129000, 0x3de6c45f, 0x01ea271e, 0x3445b3e6, 0xd9429276, 0x8c760b80, 0x2f8a21d5, 0x035c748c};
+    static constexpr storage<limbs_count> modulus_4 = {0x00000004, 0x14230000, 0xc0000002, 0x5c2d7510, 0xe8252000, 0x7bcd88be, 0x03d44e3c, 0x688b67cc, 0xb28524ec, 0x18ec1701, 0x5f1443ab, 0x06b8e918};
+    static constexpr storage<2*limbs_count> modulus_wide = {0x00000001, 0x8508c000, 0x30000000, 0x170b5d44, 0xba094800, 0x1ef3622f, 0x00f5138f, 0x1a22d9f3, 0x6ca1493b, 0xc63b05c0, 0x17c510ea, 0x01ae3a46, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<2*limbs_count> modulus_squared = {0x00000001, 0x0a118000, 0xf0000001, 0x7338d254, 0x2e1bd800, 0x4ada268f, 0x35f1c09a, 0x6bcbfbd2, 0x58638c9d, 0x318324b9, 0x8bb70ae0, 0x460aaaaa, 0x502a4d6c, 0xc014e712, 0xb90660cd, 0x09d018af, 0x3dda4d5c, 0x1f5e7141, 0xa4aee93f, 0x4bb8b87d, 0xb361263c, 0x2256913b, 0xd0bbaffb, 0x0002d307};
+    static constexpr storage<2*limbs_count> modulus_squared_2 = {0x00000002, 0x14230000, 0xe0000002, 0xe671a4a9, 0x5c37b000, 0x95b44d1e, 0x6be38134, 0xd797f7a4, 0xb0c7193a, 0x63064972, 0x176e15c0, 0x8c155555, 0xa0549ad8, 0x8029ce24, 0x720cc19b, 0x13a0315f, 0x7bb49ab8, 0x3ebce282, 0x495dd27e, 0x977170fb, 0x66c24c78, 0x44ad2277, 0xa1775ff6, 0x0005a60f};
+    static constexpr storage<2*limbs_count> modulus_squared_4 = {0x00000004, 0x28460000, 0xc0000004, 0xcce34953, 0xb86f6001, 0x2b689a3c, 0xd7c70269, 0xaf2fef48, 0x618e3275, 0xc60c92e5, 0x2edc2b80, 0x182aaaaa, 0x40a935b1, 0x00539c49, 0xe4198337, 0x274062be, 0xf7693570, 0x7d79c504, 0x92bba4fc, 0x2ee2e1f6, 0xcd8498f1, 0x895a44ee, 0x42eebfec, 0x000b4c1f};
+    static constexpr unsigned modulus_bit_count = 377;
+    static constexpr storage<limbs_count> m = {0x5e4daffc, 0x1f9fd58c, 0x89c42a59, 0xd0ed6877, 0xd85a6d02, 0x6af2d488, 0x6776b1a0, 0x3bbad0de, 0x582ef4f7, 0x976c3ca0, 0x0cc4060e, 0x0261508d};
+    static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    // i^2, the square of the imaginary unit for the extension field
+    static constexpr uint32_t i_squared = 1;
+    // true if i^2 is negative
+    static constexpr bool i_squared_is_negative = true;
+    // G1 and G2 generators 
+    static constexpr storage<limbs_count> g1_gen_x = {0xb21be9ef, 0xeab9b16e, 0xffcd394e, 0xd5481512, 0xbd37cb5c, 0x188282c8,
+                                                         0xaa9d41bb, 0x85951e2c, 0xbf87ff54, 0xc8fc6225, 0xfe740a67, 0x008848de};
+    static constexpr storage<limbs_count> g1_gen_y = {0x559c8ea6, 0xfd82de55, 0x34a9591a, 0xc2fe3d36, 0x4fb82305, 0x6d182ad4,
+                                                         0xca3e52d9, 0xbd7fb348, 0x30afeec4, 0x1f674f5d, 0xc5102eff, 0x01914a69};
+    static constexpr storage<limbs_count> g2_gen_x_re = {0x7c005196, 0x74e3e48f, 0xbb535402, 0x71889f52, 0x57db6b9b, 0x7ea501f5, 
+                                                            0x203e5031, 0xc565f071, 0xa3841d01, 0xc89630a2, 0x71c785fe, 0x018480be};
+    static constexpr storage<limbs_count> g2_gen_x_im = {0x6ea16afe, 0xb26bfefa, 0xbff76fe6, 0x5cf89984, 0x0799c9de, 0xe7223ece, 
+                                                            0x6651cecb, 0x532777ee, 0xb1b140d5, 0x70dc5a51, 0xe7004031, 0x00ea6040};
+    static constexpr storage<limbs_count> g2_gen_y_re = {0x09fd4ddf, 0xf0940944, 0x6d8c7c2e, 0xf2cf8888, 0xf832d204, 0xe458c282, 
+                                                            0x74b49a58, 0xde03ed72, 0xcbb2efb4, 0xd960736b, 0x5d446f7b, 0x00690d66};
+    static constexpr storage<limbs_count> g2_gen_y_im = {0x85eb8f93, 0xd9a1cdd1, 0x5e52270b, 0x4279b83f, 0xcee304c2, 0x2463b01a,
+                                                            0x3d591bf1, 0x61ef11ac, 0x151a70aa, 0x9e549da3, 0xd2835518, 0x00f8169f};
+  };
+
+  static constexpr storage<fq_config::limbs_count> weierstrass_b = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
+                                                                    0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+  static constexpr storage<fq_config::limbs_count> weierstrass_b_g2_re = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
+                                                                          0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+  static constexpr storage<fq_config::limbs_count> weierstrass_b_g2_im = {0x9999999a, 0x1c9ed999, 0x1ccccccd, 0x0dd39e5c, 0x3c6bf800, 0x129207b6,
+                                                                          0xcd5fd889, 0xdc7b4f91, 0x7460c589, 0x43bd0373, 0xdb0fd6f3, 0x010222f6};
+}

icicle/curves/bls12_377/projective.cu

@@ -0,0 +1,22 @@
+
+#include <cuda.h>
+
+#include "curve_config.cuh"
+
+#include "../../primitives/projective.cuh"
+
+extern "C" bool eq_bls12_377(BLS12_377::projective_t *point1, BLS12_377::projective_t *point2)
+{
+    return (*point1 == *point2) && 
+    !((point1->x == BLS12_377::point_field_t::zero()) && (point1->y == BLS12_377::point_field_t::zero()) && (point1->z == BLS12_377::point_field_t::zero())) && 
+    !((point2->x == BLS12_377::point_field_t::zero()) && (point2->y == BLS12_377::point_field_t::zero()) && (point2->z == BLS12_377::point_field_t::zero()));
+}
+
+#if defined(G2_DEFINED)
+extern "C" bool eq_g2_bls12_377(BLS12_377::g2_projective_t *point1, BLS12_377::g2_projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == BLS12_377::g2_point_field_t::zero()) && (point1->y == BLS12_377::g2_point_field_t::zero()) && (point1->z == BLS12_377::g2_point_field_t::zero())) && 
+  !((point2->x == BLS12_377::g2_point_field_t::zero()) && (point2->y == BLS12_377::g2_point_field_t::zero()) && (point2->z == BLS12_377::g2_point_field_t::zero()));
+}
+#endif

icicle/curves/bls12_377/supported_operations.cu

@@ -0,0 +1,5 @@
+
+#include "projective.cu"
+#include "lde.cu"
+#include "msm.cu"
+#include "ve_mod_mult.cu"

icicle/curves/bls12_377/ve_mod_mult.cu

@@ -0,0 +1,69 @@
+#ifndef _BLS12_377_VEC_MULT
+#define _BLS12_377_VEC_MULT
+#include <stdio.h>
+#include <iostream>
+#include "../../primitives/field.cuh"
+#include "../../utils/storage.cuh"
+#include "../../primitives/projective.cuh"
+#include "curve_config.cuh"
+#include "../../appUtils/vector_manipulation/ve_mod_mult.cuh"
+
+
+extern "C" int32_t vec_mod_mult_point_bls12_377(BLS12_377::projective_t *inout,
+                                      BLS12_377::scalar_t *scalar_vec,
+                                      size_t n_elments,
+                                      size_t device_id, 
+                                      cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    vector_mod_mult<BLS12_377::projective_t, BLS12_377::scalar_t>(scalar_vec, inout, inout, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+
+extern "C" int32_t vec_mod_mult_scalar_bls12_377(BLS12_377::scalar_t *inout,
+                                       BLS12_377::scalar_t *scalar_vec,
+                                       size_t n_elments,
+                                       size_t device_id,
+                                       cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    vector_mod_mult<BLS12_377::scalar_t, BLS12_377::scalar_t>(scalar_vec, inout, inout, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+
+extern "C" int32_t matrix_vec_mod_mult_bls12_377(BLS12_377::scalar_t *matrix_flattened,
+                                       BLS12_377::scalar_t *input,
+                                       BLS12_377::scalar_t *output,
+                                       size_t n_elments,
+                                       size_t device_id,
+                                       cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    matrix_mod_mult<BLS12_377::scalar_t>(matrix_flattened, input, output, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+#endif

icicle/curves/bls12_381.cuh

@@ -1,176 +0,0 @@
-#pragma once
-
-#include "../utils/storage.cuh"
-
-
-struct fp_config {
-  // field structure size = 8 * 32 bit
-  static constexpr unsigned limbs_count = 8;
-  // modulus = 52435875175126190479447740508185965837690552500527637822603658699938581184513
-  static constexpr storage<limbs_count> modulus = {0x00000001, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753};
-  // modulus*2 = 104871750350252380958895481016371931675381105001055275645207317399877162369026
-  static constexpr storage<limbs_count> modulus_2 = {0x00000002, 0xfffffffe, 0xfffcb7fd, 0xa77b4805, 0x1343b00a, 0x6673b010, 0x533afa90, 0xe7db4ea6};
-  static constexpr storage<limbs_count> modulus_4 = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-
-  static constexpr storage<2 * limbs_count> modulus_wide = {0x00000001, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753,
-                                                            0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-  // modulus^2
-  static constexpr storage<2*limbs_count> modulus_sqared = {0x00000001, 0xfffffffe, 0xfffcb7fe, 0xa77e9007, 0x1cdbb005, 0x698ae002, 0x5433f7b8, 0x48aa415e, 
-                                                            0x4aa9c661, 0xc2611f6f, 0x59934a1d, 0x0e9593f9, 0xef2cc20f, 0x520c13db, 0xf4bc2778, 0x347f60f3};
-  // 2*modulus^2
-  static constexpr storage<2*limbs_count> modulus_sqared_2 = {0x00000002, 0xfffffffc, 0xfff96ffd, 0x4efd200f, 0x39b7600b, 0xd315c004, 0xa867ef70, 0x915482bc, 
-                                                              0x95538cc2, 0x84c23ede, 0xb326943b, 0x1d2b27f2, 0xde59841e, 0xa41827b7, 0xe9784ef0, 0x68fec1e7};
-  static constexpr unsigned modulus_bits_count = 255;
-  // m = floor(2^(2*modulus_bits_count) / modulus)
-  static constexpr storage<limbs_count> m = {0x830358e4, 0x509cde80, 0x2f92eb5c, 0xd9410fad, 0xc1f823b4, 0xe2d772d, 0x7fb78ddf, 0x8d54253b};
-
-  static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-  static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-
-  // static constexpr storage<limbs_count> omega[32]= { {0x00000000, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753}, {0x00000000, 0x00010000, 0x76030000, 0xec030002, 0x760304d0, 0x8d51ccce, 0x00000000, 0x00000000}, {0x688bc087, 0x8dd702cb, 0x78eaa4fe, 0xa0328240, 0x98ca5b22, 0xa733b23a, 0x25a31660, 0x3f96405d}, {0x0411fe73, 0x95df4b36, 0xebc1e1bb, 0x1ef4e672, 0x60afca4a, 0x6e92a9c4, 0x753e4fcc, 0x4f2c596e}, {0xba60eaa6, 0x9733f3a6, 0x77487ae7, 0xbd7fdf9c, 0xc8b6cc00, 0xd84f8612, 0x6162ffab, 0x476fa2fb}, {0xac5db47f, 0xd2fc5e69, 0x15d0b8e4, 0xa12a70a6, 0xbc8de5d9, 0x293b1d67, 0x57f86f5e, 0x0e4840ac}, {0xab28e208, 0xb750da4c, 0x3be95635, 0x501dff64, 0xf0b4b276, 0x8cbe2437, 0xa94a946e, 0x07d0c802}, {0x2fe322b8, 0x2cabadec, 0x15412560, 0x752c84f3, 0x1a3b0aef, 0x32a732ae, 0xa33dcbf2, 0x2e95da59}, {0xfe0c65f4, 0x33811ea1, 0x687f28a2, 0x15c1ad4c, 0x42dee7f4, 0xecfbede3, 0x9a5d88b1, 0x1bb46667}, {0x2d010ff9, 0xd58a5af4, 0x570bf109, 0x79efd6b0, 0x6350721d, 0x3ed6d55a, 0x58f43cef, 0x2f27b098}, {0x8c130477, 0x74a1f671, 0xb61e0abe, 0xa534af14, 0x620890d7, 0xeb674a1a, 0xca252472, 0x43527a8b}, {0x7ea8ee05, 0x450d9f97, 0x37d56fc0, 0x565af171, 0x93f9e9ac, 0xe155cb48, 0xc8e9101b, 0x110cebd0}, {0x59a0be92, 0x23c91599, 0x7a027759, 0x87d188ce, 0xcab3c3cc, 0x70491431, 0xb3f7f8da, 0x0ac00eb8}, {0x69583404, 0x13e96ade, 0x5306243d, 0x82c05727, 0x29ca9f2a, 0x77e48bf5, 0x1fe19595, 0x50646ac8}, {0xa97eccd4, 0xe6a354dd, 0x88fbbc57, 0x39929d2e, 0xd6e7b1c8, 0xa22ba63d, 0xf5f07f43, 0x42c22911}, {0xcfc35f7a, 0x137b458a, 0x29c01b06, 0x0caba63a, 0x7a02402c, 0x0409ee98, 0x56aa725b, 0x6709c6cd}, {0x8831e03e, 0x10251f7d, 0x7ff858ec, 0x77d85a93, 0x4fb9ac5c, 0xebe905bd, 0xf8727901, 0x05deb333}, {0xb9009408, 0xbf87b689, 0xdd3ccc96, 0x4f730e7d, 0x4610300c, 0xfd7f05ba, 0x0b8ac903, 0x5ef5e8db}, {0x17cd0c14, 0x64996884, 0x68812f7f, 0xa6728673, 0x22cc3253, 0x2e1d9a19, 0xaa0a1d80, 0x3a689e83}, {0x41144dea, 0x20b53cbe, 0xc2f0fcbd, 0x870c46fa, 0x537d6971, 0x556c35f6, 0x5f686d91, 0x3436287f}, {0x436ba2e7, 0x007e082a, 0x9116e877, 0x67c6630f, 0xfb4460f7, 0x36f8f165, 0x7e7046e0, 0x6eee34d5}, {0xa53a56d1, 0xc5b670ee, 0x53037d7b, 0x127d1f42, 0xa722c2e2, 0x57d4257e, 0x33cbd838, 0x03ae26a3}, {0x76504cf8, 0x1e914848, 0xb63edd02, 0x55bbbf1e, 0x4e55aa02, 0xbcdafec8, 0x2dc0beb0, 0x5145c4cd}, {0x1ab70e2c, 0x5b90153a, 0x75fb0ab8, 0x8deffa31, 0x46900c95, 0xc553ae23, 0x6bd3118c, 0x1d31dcdc}, {0x59a2e8eb, 0x801c894c, 0xe12fc974, 0xbc535c5c, 0x47d39803, 0x95508d27, 0xac5d094f, 0x16d9d3cd}, {0xcca1d8be, 0x810fa372, 0x82e0bfa7, 0xc67b8c28, 0xe2d35bc2, 0xdbb4edf0, 0x5087c995, 0x712d1580}, {0xfd88f133, 0xeb162203, 0xf010ea74, 0xac96c38f, 0xe64cfc70, 0x4307987f, 0x37b7a114, 0x350fe98d}, {0x42f2a254, 0xaba2f518, 0xa71efc0c, 0x4d7f3c3a, 0xd274a80a, 0x97ae418d, 0x5e3e7682, 0x2967385d}, {0x575a0b79, 0x75c55c7b, 0x74a7ded1, 0x3ba4a157, 0xa04fccf3, 0xc3974d73, 0x4a939684, 0x705aba4f}, {0x14ebb608, 0x8409a9ea, 0x66bac611, 0xfad0084e, 0x811c1dfb, 0x04287254, 0x23b30c29, 0x086d072b}, {0x67e4756a, 0xb427c9b3, 0x02ebc38d, 0xc7537fb9, 0xcd6a205f, 0x51de21be, 0x7923597d, 0x6064ab72}, {0x0b912f1f, 0x1b788f50, 0x70b3e094, 0xc4024ff2, 0xd168d6c0, 0x0fd56dc8, 0x5b416b6f, 0x0212d79e}};
-  // Quick fix for linking issue
-  static constexpr storage<limbs_count> omega1=   {0x00000000, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753};
-  static constexpr storage<limbs_count> omega2=   {0x00000000, 0x00010000, 0x76030000, 0xec030002, 0x760304d0, 0x8d51ccce, 0x00000000, 0x00000000};
-  static constexpr storage<limbs_count> omega3=   {0x688bc087, 0x8dd702cb, 0x78eaa4fe, 0xa0328240, 0x98ca5b22, 0xa733b23a, 0x25a31660, 0x3f96405d};
-  static constexpr storage<limbs_count> omega4=   {0x0411fe73, 0x95df4b36, 0xebc1e1bb, 0x1ef4e672, 0x60afca4a, 0x6e92a9c4, 0x753e4fcc, 0x4f2c596e};
-  static constexpr storage<limbs_count> omega5=   {0xba60eaa6, 0x9733f3a6, 0x77487ae7, 0xbd7fdf9c, 0xc8b6cc00, 0xd84f8612, 0x6162ffab, 0x476fa2fb};
-  static constexpr storage<limbs_count> omega6=   {0xac5db47f, 0xd2fc5e69, 0x15d0b8e4, 0xa12a70a6, 0xbc8de5d9, 0x293b1d67, 0x57f86f5e, 0x0e4840ac};
-  static constexpr storage<limbs_count> omega7=   {0xab28e208, 0xb750da4c, 0x3be95635, 0x501dff64, 0xf0b4b276, 0x8cbe2437, 0xa94a946e, 0x07d0c802};
-  static constexpr storage<limbs_count> omega8=   {0x2fe322b8, 0x2cabadec, 0x15412560, 0x752c84f3, 0x1a3b0aef, 0x32a732ae, 0xa33dcbf2, 0x2e95da59};
-  static constexpr storage<limbs_count> omega9=   {0xfe0c65f4, 0x33811ea1, 0x687f28a2, 0x15c1ad4c, 0x42dee7f4, 0xecfbede3, 0x9a5d88b1, 0x1bb46667};
-  static constexpr storage<limbs_count> omega10=  {0x2d010ff9, 0xd58a5af4, 0x570bf109, 0x79efd6b0, 0x6350721d, 0x3ed6d55a, 0x58f43cef, 0x2f27b098};
-  static constexpr storage<limbs_count> omega11=  {0x8c130477, 0x74a1f671, 0xb61e0abe, 0xa534af14, 0x620890d7, 0xeb674a1a, 0xca252472, 0x43527a8b};
-  static constexpr storage<limbs_count> omega12=  {0x7ea8ee05, 0x450d9f97, 0x37d56fc0, 0x565af171, 0x93f9e9ac, 0xe155cb48, 0xc8e9101b, 0x110cebd0};
-  static constexpr storage<limbs_count> omega13=  {0x59a0be92, 0x23c91599, 0x7a027759, 0x87d188ce, 0xcab3c3cc, 0x70491431, 0xb3f7f8da, 0x0ac00eb8};
-  static constexpr storage<limbs_count> omega14=  {0x69583404, 0x13e96ade, 0x5306243d, 0x82c05727, 0x29ca9f2a, 0x77e48bf5, 0x1fe19595, 0x50646ac8};
-  static constexpr storage<limbs_count> omega15=  {0xa97eccd4, 0xe6a354dd, 0x88fbbc57, 0x39929d2e, 0xd6e7b1c8, 0xa22ba63d, 0xf5f07f43, 0x42c22911};
-  static constexpr storage<limbs_count> omega16=  {0xcfc35f7a, 0x137b458a, 0x29c01b06, 0x0caba63a, 0x7a02402c, 0x0409ee98, 0x56aa725b, 0x6709c6cd};
-  static constexpr storage<limbs_count> omega17=  {0x8831e03e, 0x10251f7d, 0x7ff858ec, 0x77d85a93, 0x4fb9ac5c, 0xebe905bd, 0xf8727901, 0x05deb333};
-  static constexpr storage<limbs_count> omega18=  {0xb9009408, 0xbf87b689, 0xdd3ccc96, 0x4f730e7d, 0x4610300c, 0xfd7f05ba, 0x0b8ac903, 0x5ef5e8db};
-  static constexpr storage<limbs_count> omega19=  {0x17cd0c14, 0x64996884, 0x68812f7f, 0xa6728673, 0x22cc3253, 0x2e1d9a19, 0xaa0a1d80, 0x3a689e83};
-  static constexpr storage<limbs_count> omega20=  {0x41144dea, 0x20b53cbe, 0xc2f0fcbd, 0x870c46fa, 0x537d6971, 0x556c35f6, 0x5f686d91, 0x3436287f};
-  static constexpr storage<limbs_count> omega21=  {0x436ba2e7, 0x007e082a, 0x9116e877, 0x67c6630f, 0xfb4460f7, 0x36f8f165, 0x7e7046e0, 0x6eee34d5};
-  static constexpr storage<limbs_count> omega22=  {0xa53a56d1, 0xc5b670ee, 0x53037d7b, 0x127d1f42, 0xa722c2e2, 0x57d4257e, 0x33cbd838, 0x03ae26a3};
-  static constexpr storage<limbs_count> omega23=  {0x76504cf8, 0x1e914848, 0xb63edd02, 0x55bbbf1e, 0x4e55aa02, 0xbcdafec8, 0x2dc0beb0, 0x5145c4cd};
-  static constexpr storage<limbs_count> omega24=  {0x1ab70e2c, 0x5b90153a, 0x75fb0ab8, 0x8deffa31, 0x46900c95, 0xc553ae23, 0x6bd3118c, 0x1d31dcdc};
-  static constexpr storage<limbs_count> omega25=  {0x59a2e8eb, 0x801c894c, 0xe12fc974, 0xbc535c5c, 0x47d39803, 0x95508d27, 0xac5d094f, 0x16d9d3cd};
-  static constexpr storage<limbs_count> omega26=  {0xcca1d8be, 0x810fa372, 0x82e0bfa7, 0xc67b8c28, 0xe2d35bc2, 0xdbb4edf0, 0x5087c995, 0x712d1580};
-  static constexpr storage<limbs_count> omega27=  {0xfd88f133, 0xeb162203, 0xf010ea74, 0xac96c38f, 0xe64cfc70, 0x4307987f, 0x37b7a114, 0x350fe98d};
-  static constexpr storage<limbs_count> omega28=  {0x42f2a254, 0xaba2f518, 0xa71efc0c, 0x4d7f3c3a, 0xd274a80a, 0x97ae418d, 0x5e3e7682, 0x2967385d};
-  static constexpr storage<limbs_count> omega29=  {0x575a0b79, 0x75c55c7b, 0x74a7ded1, 0x3ba4a157, 0xa04fccf3, 0xc3974d73, 0x4a939684, 0x705aba4f};
-  static constexpr storage<limbs_count> omega30=  {0x14ebb608, 0x8409a9ea, 0x66bac611, 0xfad0084e, 0x811c1dfb, 0x04287254, 0x23b30c29, 0x086d072b};
-  static constexpr storage<limbs_count> omega31=  {0x67e4756a, 0xb427c9b3, 0x02ebc38d, 0xc7537fb9, 0xcd6a205f, 0x51de21be, 0x7923597d, 0x6064ab72};
-  static constexpr storage<limbs_count> omega32=  {0x0b912f1f, 0x1b788f50, 0x70b3e094, 0xc4024ff2, 0xd168d6c0, 0x0fd56dc8, 0x5b416b6f, 0x0212d79e};
-
-  // static constexpr storage<limbs_count> omega_inv[32]={ {0x00000000, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753}, {0x00000001, 0xfffeffff, 0x89fb5bfe, 0x67baa400, 0x939ed334, 0xa5e80b39, 0x299d7d47, 0x73eda753}, {0xae99502e, 0x6037fe81, 0x94b04fd8, 0x8e749036, 0xca86bf65, 0xbabc5aff, 0x5ce11044, 0x1333b22e}, {0x7dc08d74, 0x7f847ee4, 0x04eeaf5a, 0xbd433896, 0x1832fc60, 0xd66c91d6, 0x607e449b, 0x551115b4}, {0x4e7773cb, 0xee5bcecc, 0xf6dab086, 0x45593d6f, 0x4016e2bd, 0xa3a95d2d, 0xaf96816f, 0x047cb16c}, {0x982b68c5, 0xb891fa3f, 0x1d426b52, 0xa41e8501, 0x882952d6, 0x566009b5, 0x7b3c79d6, 0x199cdaee}, {0xcf28601b, 0x571ba2fc, 0xac74db12, 0x166fb582, 0x3501370b, 0x51420be4, 0x52f970ba, 0x1996fa8d}, {0x6a2f777a, 0xe9561c17, 0x2393991b, 0xc03cae03, 0x5a5bfd4f, 0x91b00023, 0x272e58ee, 0x6d64ed25}, {0xf02a116e, 0xfb350dbe, 0xb4543a3e, 0x1c510ebf, 0x37ad4eca, 0xf675522e, 0x80f82b2d, 0x1907a56e}, {0x4eb71aa6, 0xb0ad8003, 0xaa67e0be, 0x50a32c41, 0x19141f44, 0x105f0672, 0xa3dad316, 0x2bcd9508}, {0x0f6fb2ac, 0x3dc9e560, 0x9aa58ff5, 0x3cc5bb32, 0x36f376e1, 0xdeae67bc, 0x65ba213e, 0x394fda0d}, {0x60b82267, 0x09f239f7, 0x8b24f123, 0x14180e0e, 0x45625d95, 0xad5a5340, 0x6d174692, 0x58c3ba63}, {0x348b416f, 0x0acf21c2, 0xbc086439, 0x798b6bf6, 0xb1ca111d, 0x222d411f, 0x30ba1e0f, 0x044107b7}, {0x014abe84, 0xa3b861b8, 0x427ed008, 0x37c017e4, 0xae0ff4f5, 0xae51f613, 0xcb1218d3, 0x1a2d00e1}, {0x4de7eb2b, 0x48aaa3bf, 0x6772057d, 0x4a58d54d, 0x7093b551, 0xce25f16c, 0xd206337c, 0x242150ac}, {0x9ed57ae5, 0xdf3ec9ae, 0x7166577f, 0xea7df73a, 0x022fbbe4, 0x6ca8d281, 0x151e3f6b, 0x5850c003}, {0x645e1cfa, 0x903a0a0c, 0x34788c37, 0xfbac54cb, 0x8cf73d78, 0xdc127d11, 0x975d3c82, 0x6d0b5c7c}, {0x14b1ba04, 0xb49d6b05, 0xf00b84f2, 0x56e466b4, 0x0b904f22, 0x30c390cf, 0x3ee254cc, 0x3e11cfb7}, {0xbe8201ab, 0x84dfa547, 0x530715d2, 0x3887ce8b, 0x3eed4ed7, 0xa4c719c6, 0x8f8007b4, 0x18c44950}, {0x7d813cd1, 0xdaf0346d, 0xf755beb1, 0xeccf6f9a, 0xe08143e3, 0x167fce38, 0x6f5d6dfa, 0x545ad9b2}, {0x577605de, 0x973f5466, 0x974f953c, 0x0ce8986e, 0x074382f9, 0x8941cf4b, 0x6fa2672c, 0x156cd7f6}, {0x33b66141, 0x24315404, 0x1992f584, 0x5d1375ab, 0x8b20ca1a, 0xf193ffa6, 0x2701a503, 0x47880cd5}, {0xe9f7b9af, 0xf7b6847d, 0x62c83ce2, 0x9a339673, 0x6e5e6f79, 0xfabf4537, 0x35af33a3, 0x0975acd9}, {0x0eddd248, 0x4fb4204a, 0xc9e509b3, 0x8c98706a, 0x2bb27eb1, 0xd0be8987, 0xc831438b, 0x6ec5f960}, {0x20238f62, 0xa13c95b7, 0x83b476b9, 0x130aa097, 0x14860881, 0x758a04e0, 0x97066493, 0x58e2f8d6}, {0xe8bff41e, 0x65b09c73, 0x37f1c6a3, 0x8b3280e8, 0x2846fb21, 0xe17b82ce, 0xb1ae27df, 0x476534bf}, {0xd5fdb757, 0x8480c0e7, 0x365bf9fd, 0x3644eea0, 0xb776be86, 0x4ca116ca, 0x8b58390c, 0x17b6395f}, {0x252eb0db, 0x2c811e9a, 0x7479e161, 0x1b7d960d, 0xb0a89a26, 0xb3afc7c1, 0x32b5e793, 0x6a2f9533}, {0x08b8a7ad, 0xe877b2c4, 0x341652b4, 0x68b0e8f0, 0xe8b6a2d9, 0x2d44da3b, 0xfd09be59, 0x092778ff}, {0x7988f244, 0x84a1aa6f, 0x24faf63f, 0xa164b3d9, 0xc1bbb915, 0x7aae9724, 0xf386c0d2, 0x24e5d287}, {0x41a1b30c, 0xa70a7efd, 0x39f0e511, 0xc49c55a5, 0x033bb323, 0xab307a8f, 0x17acbd7f, 0x0158abd6}, {0x0f642025, 0x2c228b30, 0x01bd882b, 0xb0878e8d, 0xd7377fea, 0xd862b255, 0xf0490536, 0x18ac3666}};
-  // Quick fix for linking issue
-  static constexpr storage<limbs_count> omega_inv1=   {0x00000000, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753};
-  static constexpr storage<limbs_count> omega_inv2=   {0x00000001, 0xfffeffff, 0x89fb5bfe, 0x67baa400, 0x939ed334, 0xa5e80b39, 0x299d7d47, 0x73eda753};
-  static constexpr storage<limbs_count> omega_inv3=   {0xae99502e, 0x6037fe81, 0x94b04fd8, 0x8e749036, 0xca86bf65, 0xbabc5aff, 0x5ce11044, 0x1333b22e};
-  static constexpr storage<limbs_count> omega_inv4=   {0x7dc08d74, 0x7f847ee4, 0x04eeaf5a, 0xbd433896, 0x1832fc60, 0xd66c91d6, 0x607e449b, 0x551115b4};
-  static constexpr storage<limbs_count> omega_inv5=   {0x4e7773cb, 0xee5bcecc, 0xf6dab086, 0x45593d6f, 0x4016e2bd, 0xa3a95d2d, 0xaf96816f, 0x047cb16c};
-  static constexpr storage<limbs_count> omega_inv6=   {0x982b68c5, 0xb891fa3f, 0x1d426b52, 0xa41e8501, 0x882952d6, 0x566009b5, 0x7b3c79d6, 0x199cdaee};
-  static constexpr storage<limbs_count> omega_inv7=   {0xcf28601b, 0x571ba2fc, 0xac74db12, 0x166fb582, 0x3501370b, 0x51420be4, 0x52f970ba, 0x1996fa8d};
-  static constexpr storage<limbs_count> omega_inv8=   {0x6a2f777a, 0xe9561c17, 0x2393991b, 0xc03cae03, 0x5a5bfd4f, 0x91b00023, 0x272e58ee, 0x6d64ed25};
-  static constexpr storage<limbs_count> omega_inv9=   {0xf02a116e, 0xfb350dbe, 0xb4543a3e, 0x1c510ebf, 0x37ad4eca, 0xf675522e, 0x80f82b2d, 0x1907a56e};
-  static constexpr storage<limbs_count> omega_inv10=  {0x4eb71aa6, 0xb0ad8003, 0xaa67e0be, 0x50a32c41, 0x19141f44, 0x105f0672, 0xa3dad316, 0x2bcd9508};
-  static constexpr storage<limbs_count> omega_inv11=  {0x0f6fb2ac, 0x3dc9e560, 0x9aa58ff5, 0x3cc5bb32, 0x36f376e1, 0xdeae67bc, 0x65ba213e, 0x394fda0d};
-  static constexpr storage<limbs_count> omega_inv12=  {0x60b82267, 0x09f239f7, 0x8b24f123, 0x14180e0e, 0x45625d95, 0xad5a5340, 0x6d174692, 0x58c3ba63};
-  static constexpr storage<limbs_count> omega_inv13=  {0x348b416f, 0x0acf21c2, 0xbc086439, 0x798b6bf6, 0xb1ca111d, 0x222d411f, 0x30ba1e0f, 0x044107b7};
-  static constexpr storage<limbs_count> omega_inv14=  {0x014abe84, 0xa3b861b8, 0x427ed008, 0x37c017e4, 0xae0ff4f5, 0xae51f613, 0xcb1218d3, 0x1a2d00e1};
-  static constexpr storage<limbs_count> omega_inv15=  {0x4de7eb2b, 0x48aaa3bf, 0x6772057d, 0x4a58d54d, 0x7093b551, 0xce25f16c, 0xd206337c, 0x242150ac};
-  static constexpr storage<limbs_count> omega_inv16=  {0x9ed57ae5, 0xdf3ec9ae, 0x7166577f, 0xea7df73a, 0x022fbbe4, 0x6ca8d281, 0x151e3f6b, 0x5850c003};
-  static constexpr storage<limbs_count> omega_inv17=  {0x645e1cfa, 0x903a0a0c, 0x34788c37, 0xfbac54cb, 0x8cf73d78, 0xdc127d11, 0x975d3c82, 0x6d0b5c7c};
-  static constexpr storage<limbs_count> omega_inv18=  {0x14b1ba04, 0xb49d6b05, 0xf00b84f2, 0x56e466b4, 0x0b904f22, 0x30c390cf, 0x3ee254cc, 0x3e11cfb7};
-  static constexpr storage<limbs_count> omega_inv19=  {0xbe8201ab, 0x84dfa547, 0x530715d2, 0x3887ce8b, 0x3eed4ed7, 0xa4c719c6, 0x8f8007b4, 0x18c44950};
-  static constexpr storage<limbs_count> omega_inv20=  {0x7d813cd1, 0xdaf0346d, 0xf755beb1, 0xeccf6f9a, 0xe08143e3, 0x167fce38, 0x6f5d6dfa, 0x545ad9b2};
-  static constexpr storage<limbs_count> omega_inv21=  {0x577605de, 0x973f5466, 0x974f953c, 0x0ce8986e, 0x074382f9, 0x8941cf4b, 0x6fa2672c, 0x156cd7f6};
-  static constexpr storage<limbs_count> omega_inv22=  {0x33b66141, 0x24315404, 0x1992f584, 0x5d1375ab, 0x8b20ca1a, 0xf193ffa6, 0x2701a503, 0x47880cd5};
-  static constexpr storage<limbs_count> omega_inv23=  {0xe9f7b9af, 0xf7b6847d, 0x62c83ce2, 0x9a339673, 0x6e5e6f79, 0xfabf4537, 0x35af33a3, 0x0975acd9};
-  static constexpr storage<limbs_count> omega_inv24=  {0x0eddd248, 0x4fb4204a, 0xc9e509b3, 0x8c98706a, 0x2bb27eb1, 0xd0be8987, 0xc831438b, 0x6ec5f960};
-  static constexpr storage<limbs_count> omega_inv25=  {0x20238f62, 0xa13c95b7, 0x83b476b9, 0x130aa097, 0x14860881, 0x758a04e0, 0x97066493, 0x58e2f8d6};
-  static constexpr storage<limbs_count> omega_inv26=  {0xe8bff41e, 0x65b09c73, 0x37f1c6a3, 0x8b3280e8, 0x2846fb21, 0xe17b82ce, 0xb1ae27df, 0x476534bf};
-  static constexpr storage<limbs_count> omega_inv27=  {0xd5fdb757, 0x8480c0e7, 0x365bf9fd, 0x3644eea0, 0xb776be86, 0x4ca116ca, 0x8b58390c, 0x17b6395f};
-  static constexpr storage<limbs_count> omega_inv28=  {0x252eb0db, 0x2c811e9a, 0x7479e161, 0x1b7d960d, 0xb0a89a26, 0xb3afc7c1, 0x32b5e793, 0x6a2f9533};
-  static constexpr storage<limbs_count> omega_inv29=  {0x08b8a7ad, 0xe877b2c4, 0x341652b4, 0x68b0e8f0, 0xe8b6a2d9, 0x2d44da3b, 0xfd09be59, 0x092778ff};
-  static constexpr storage<limbs_count> omega_inv30=  {0x7988f244, 0x84a1aa6f, 0x24faf63f, 0xa164b3d9, 0xc1bbb915, 0x7aae9724, 0xf386c0d2, 0x24e5d287};
-  static constexpr storage<limbs_count> omega_inv31=  {0x41a1b30c, 0xa70a7efd, 0x39f0e511, 0xc49c55a5, 0x033bb323, 0xab307a8f, 0x17acbd7f, 0x0158abd6};
-  static constexpr storage<limbs_count> omega_inv32=  {0x0f642025, 0x2c228b30, 0x01bd882b, 0xb0878e8d, 0xd7377fea, 0xd862b255, 0xf0490536, 0x18ac3666};
-  
-  // static constexpr storage<limbs_count> inv[32]={ {0x80000001, 0x7fffffff, 0x7fff2dff, 0xa9ded201, 0x04d0ec02, 0x199cec04, 0x94cebea4, 0x39f6d3a9}, {0x40000001, 0x3fffffff, 0x3ffec4ff, 0xfece3b02, 0x07396203, 0x266b6206, 0x5f361df6, 0x56f23d7e}, {0x20000001, 0x1fffffff, 0x9ffe907f, 0xa945ef82, 0x086d9d04, 0x2cd29d07, 0xc469cd9f, 0x656ff268}, {0x10000001, 0x0fffffff, 0xcffe763f, 0xfe81c9c2, 0x8907ba84, 0xb0063a87, 0xf703a573, 0x6caeccdd}, {0x08000001, 0x07ffffff, 0xe7fe691f, 0x291fb6e2, 0xc954c945, 0xf1a00947, 0x9050915d, 0x704e3a18}, {0x04000001, 0x03ffffff, 0xf3fe628f, 0x3e6ead72, 0xe97b50a5, 0x126cf0a7, 0xdcf70753, 0x721df0b5}, {0x02000001, 0x01ffffff, 0xf9fe5f47, 0x491628ba, 0xf98e9455, 0xa2d36457, 0x834a424d, 0x7305cc04}, {0x01000001, 0x00ffffff, 0xfcfe5da3, 0x4e69e65e, 0x0198362d, 0xeb069e30, 0xd673dfca, 0x7379b9ab}, {0x00800001, 0x007fffff, 0xfe7e5cd1, 0x5113c530, 0x059d0719, 0x8f203b1c, 0x8008ae89, 0x73b3b07f}, {0x00400001, 0x003fffff, 0xff3e5c68, 0x5268b499, 0x079f6f8f, 0xe12d0992, 0x54d315e8, 0x73d0abe9}, {0x00200001, 0x801fffff, 0x7f9e5c33, 0x53132c4e, 0x08a0a3ca, 0x8a3370cd, 0x3f384998, 0x73df299e}, {0x00100001, 0x400fffff, 0xbfce5c19, 0xd3686828, 0x89213de7, 0x5eb6a46a, 0xb46ae370, 0x73e66878}, {0x00080001, 0x2007ffff, 0xdfe65c0c, 0x93930615, 0x49618af6, 0x48f83e39, 0xef04305c, 0x73ea07e5}, {0x00040001, 0x9003ffff, 0x6ff25c05, 0xf3a8550c, 0xa981b17d, 0x3e190b20, 0x8c50d6d2, 0x73ebd79c}, {0x00020001, 0x4801ffff, 0xb7f85c02, 0xa3b2fc87, 0x5991c4c1, 0x38a97194, 0xdaf72a0d, 0x73ecbf77}, {0x00010001, 0xa400ffff, 0x5bfb5c00, 0x7bb85045, 0x3199ce63, 0xb5f1a4ce, 0x824a53aa, 0x73ed3365}, {0x00008001, 0xd2007fff, 0x2dfcdbff, 0x67bafa24, 0x1d9dd334, 0x7495be6b, 0x55f3e879, 0x73ed6d5c}, {0x00004001, 0x69003fff, 0x96fd9bff, 0xddbc4f13, 0x939fd59c, 0xd3e7cb39, 0xbfc8b2e0, 0x73ed8a57}, {0x00002001, 0x34801fff, 0x4b7dfbff, 0x18bcf98b, 0xcea0d6d1, 0x8390d1a0, 0x74b31814, 0x73ed98d5}, {0x00001001, 0x1a400fff, 0x25be2bff, 0x363d4ec7, 0x6c21576b, 0x5b6554d4, 0x4f284aae, 0x73eda014}, {0x00000801, 0x0d2007ff, 0x12de43ff, 0x44fd7965, 0x3ae197b8, 0x474f966e, 0xbc62e3fb, 0x73eda3b3}, {0x00000401, 0x069003ff, 0x096e4fff, 0xcc5d8eb4, 0x2241b7de, 0xbd44b73b, 0x730030a1, 0x73eda583}, {0x00000201, 0x034801ff, 0x84b655ff, 0x100d995b, 0x95f1c7f2, 0xf83f47a1, 0x4e4ed6f4, 0x73eda66b}, {0x00000101, 0x01a400ff, 0x425a58ff, 0xb1e59eaf, 0xcfc9cffb, 0x95bc8fd4, 0x3bf62a1e, 0x73eda6df}, {0x00000081, 0x00d2007f, 0x212c5a7f, 0x82d1a159, 0x6cb5d400, 0x647b33ee, 0x32c9d3b3, 0x73eda719}, {0x00000041, 0x0069003f, 0x10955b3f, 0xeb47a2ae, 0x3b2bd602, 0xcbda85fb, 0x2e33a87d, 0x73eda736}, {0x00000021, 0x0034801f, 0x8849db9f, 0x1f82a358, 0xa266d704, 0xff8a2f01, 0xabe892e2, 0x73eda744}, {0x00000011, 0x001a400f, 0xc4241bcf, 0xb9a023ad, 0xd6045784, 0x99620384, 0xeac30815, 0x73eda74b}, {0x00000009, 0x000d2007, 0x62113be7, 0x06aee3d8, 0x6fd317c5, 0xe64dedc6, 0x8a3042ae, 0x73eda74f}, {0x00000005, 0x00069003, 0xb107cbf3, 0x2d3643ed, 0x3cba77e5, 0x8cc3e2e7, 0x59e6dffb, 0x73eda751}, {0x00000003, 0x00034801, 0x588313f9, 0x4079f3f8, 0xa32e27f5, 0xdffedd77, 0x41c22ea1, 0x73eda752}, {0x00000002, 0x0001a400, 0xac40b7fc, 0x4a1bcbfd, 0xd667fffd, 0x099c5abf, 0xb5afd5f5, 0x73eda752}};
-  // Quick fix for linking issue
-  static constexpr storage<limbs_count> inv1=   {0x80000001, 0x7fffffff, 0x7fff2dff, 0xa9ded201, 0x04d0ec02, 0x199cec04, 0x94cebea4, 0x39f6d3a9};
-  static constexpr storage<limbs_count> inv2=   {0x40000001, 0x3fffffff, 0x3ffec4ff, 0xfece3b02, 0x07396203, 0x266b6206, 0x5f361df6, 0x56f23d7e};
-  static constexpr storage<limbs_count> inv3=   {0x20000001, 0x1fffffff, 0x9ffe907f, 0xa945ef82, 0x086d9d04, 0x2cd29d07, 0xc469cd9f, 0x656ff268};
-  static constexpr storage<limbs_count> inv4=   {0x10000001, 0x0fffffff, 0xcffe763f, 0xfe81c9c2, 0x8907ba84, 0xb0063a87, 0xf703a573, 0x6caeccdd};
-  static constexpr storage<limbs_count> inv5=   {0x08000001, 0x07ffffff, 0xe7fe691f, 0x291fb6e2, 0xc954c945, 0xf1a00947, 0x9050915d, 0x704e3a18};
-  static constexpr storage<limbs_count> inv6=   {0x04000001, 0x03ffffff, 0xf3fe628f, 0x3e6ead72, 0xe97b50a5, 0x126cf0a7, 0xdcf70753, 0x721df0b5};
-  static constexpr storage<limbs_count> inv7=   {0x02000001, 0x01ffffff, 0xf9fe5f47, 0x491628ba, 0xf98e9455, 0xa2d36457, 0x834a424d, 0x7305cc04};
-  static constexpr storage<limbs_count> inv8=   {0x01000001, 0x00ffffff, 0xfcfe5da3, 0x4e69e65e, 0x0198362d, 0xeb069e30, 0xd673dfca, 0x7379b9ab};
-  static constexpr storage<limbs_count> inv9=   {0x00800001, 0x007fffff, 0xfe7e5cd1, 0x5113c530, 0x059d0719, 0x8f203b1c, 0x8008ae89, 0x73b3b07f};
-  static constexpr storage<limbs_count> inv10=  {0x00400001, 0x003fffff, 0xff3e5c68, 0x5268b499, 0x079f6f8f, 0xe12d0992, 0x54d315e8, 0x73d0abe9};
-  static constexpr storage<limbs_count> inv11=  {0x00200001, 0x801fffff, 0x7f9e5c33, 0x53132c4e, 0x08a0a3ca, 0x8a3370cd, 0x3f384998, 0x73df299e};
-  static constexpr storage<limbs_count> inv12=  {0x00100001, 0x400fffff, 0xbfce5c19, 0xd3686828, 0x89213de7, 0x5eb6a46a, 0xb46ae370, 0x73e66878};
-  static constexpr storage<limbs_count> inv13=  {0x00080001, 0x2007ffff, 0xdfe65c0c, 0x93930615, 0x49618af6, 0x48f83e39, 0xef04305c, 0x73ea07e5};
-  static constexpr storage<limbs_count> inv14=  {0x00040001, 0x9003ffff, 0x6ff25c05, 0xf3a8550c, 0xa981b17d, 0x3e190b20, 0x8c50d6d2, 0x73ebd79c};
-  static constexpr storage<limbs_count> inv15=  {0x00020001, 0x4801ffff, 0xb7f85c02, 0xa3b2fc87, 0x5991c4c1, 0x38a97194, 0xdaf72a0d, 0x73ecbf77};
-  static constexpr storage<limbs_count> inv16=  {0x00010001, 0xa400ffff, 0x5bfb5c00, 0x7bb85045, 0x3199ce63, 0xb5f1a4ce, 0x824a53aa, 0x73ed3365};
-  static constexpr storage<limbs_count> inv17=  {0x00008001, 0xd2007fff, 0x2dfcdbff, 0x67bafa24, 0x1d9dd334, 0x7495be6b, 0x55f3e879, 0x73ed6d5c};
-  static constexpr storage<limbs_count> inv18=  {0x00004001, 0x69003fff, 0x96fd9bff, 0xddbc4f13, 0x939fd59c, 0xd3e7cb39, 0xbfc8b2e0, 0x73ed8a57};
-  static constexpr storage<limbs_count> inv19=  {0x00002001, 0x34801fff, 0x4b7dfbff, 0x18bcf98b, 0xcea0d6d1, 0x8390d1a0, 0x74b31814, 0x73ed98d5};
-  static constexpr storage<limbs_count> inv20=  {0x00001001, 0x1a400fff, 0x25be2bff, 0x363d4ec7, 0x6c21576b, 0x5b6554d4, 0x4f284aae, 0x73eda014};
-  static constexpr storage<limbs_count> inv21=  {0x00000801, 0x0d2007ff, 0x12de43ff, 0x44fd7965, 0x3ae197b8, 0x474f966e, 0xbc62e3fb, 0x73eda3b3};
-  static constexpr storage<limbs_count> inv22=  {0x00000401, 0x069003ff, 0x096e4fff, 0xcc5d8eb4, 0x2241b7de, 0xbd44b73b, 0x730030a1, 0x73eda583};
-  static constexpr storage<limbs_count> inv23=  {0x00000201, 0x034801ff, 0x84b655ff, 0x100d995b, 0x95f1c7f2, 0xf83f47a1, 0x4e4ed6f4, 0x73eda66b};
-  static constexpr storage<limbs_count> inv24=  {0x00000101, 0x01a400ff, 0x425a58ff, 0xb1e59eaf, 0xcfc9cffb, 0x95bc8fd4, 0x3bf62a1e, 0x73eda6df};
-  static constexpr storage<limbs_count> inv25=  {0x00000081, 0x00d2007f, 0x212c5a7f, 0x82d1a159, 0x6cb5d400, 0x647b33ee, 0x32c9d3b3, 0x73eda719};
-  static constexpr storage<limbs_count> inv26=  {0x00000041, 0x0069003f, 0x10955b3f, 0xeb47a2ae, 0x3b2bd602, 0xcbda85fb, 0x2e33a87d, 0x73eda736};
-  static constexpr storage<limbs_count> inv27=  {0x00000021, 0x0034801f, 0x8849db9f, 0x1f82a358, 0xa266d704, 0xff8a2f01, 0xabe892e2, 0x73eda744};
-  static constexpr storage<limbs_count> inv28=  {0x00000011, 0x001a400f, 0xc4241bcf, 0xb9a023ad, 0xd6045784, 0x99620384, 0xeac30815, 0x73eda74b};
-  static constexpr storage<limbs_count> inv29=  {0x00000009, 0x000d2007, 0x62113be7, 0x06aee3d8, 0x6fd317c5, 0xe64dedc6, 0x8a3042ae, 0x73eda74f};
-  static constexpr storage<limbs_count> inv30=  {0x00000005, 0x00069003, 0xb107cbf3, 0x2d3643ed, 0x3cba77e5, 0x8cc3e2e7, 0x59e6dffb, 0x73eda751};
-  static constexpr storage<limbs_count> inv31=  {0x00000003, 0x00034801, 0x588313f9, 0x4079f3f8, 0xa32e27f5, 0xdffedd77, 0x41c22ea1, 0x73eda752};
-  static constexpr storage<limbs_count> inv32=  {0x00000002, 0x0001a400, 0xac40b7fc, 0x4a1bcbfd, 0xd667fffd, 0x099c5abf, 0xb5afd5f5, 0x73eda752};
-};
-
-struct fq_config {
-  // field structure size = 12 * 32 bit
-  static constexpr unsigned limbs_count = 12;
-  // modulus = 4002409555221667393417789825735904156556882819939007885332058136124031650490837864442687629129015664037894272559787
-  static constexpr storage<limbs_count> modulus = {0xffffaaab, 0xb9feffff, 0xb153ffff, 0x1eabfffe, 0xf6b0f624, 0x6730d2a0, 0xf38512bf, 0x64774b84, 0x434bacd7, 0x4b1ba7b6, 0x397fe69a, 0x1a0111ea};
-  // modulus*2 = 8004819110443334786835579651471808313113765639878015770664116272248063300981675728885375258258031328075788545119574
-  static constexpr storage<limbs_count> modulus_2 = {0xffff5556, 0x73fdffff, 0x62a7ffff, 0x3d57fffd, 0xed61ec48, 0xce61a541, 0xe70a257e, 0xc8ee9709, 0x869759ae, 0x96374f6c, 0x72ffcd34, 0x340223d4};
-  // modulus*4 = 16009638220886669573671159302943616626227531279756031541328232544496126601963351457770750516516062656151577090239148
-  static constexpr storage<limbs_count> modulus_4 = {0xfffeaaac, 0xe7fbffff, 0xc54ffffe, 0x7aaffffa, 0xdac3d890, 0x9cc34a83, 0xce144afd, 0x91dd2e13, 0xd2eb35d, 0x2c6e9ed9, 0xe5ff9a69, 0x680447a8};
-  
-  static constexpr storage<2*limbs_count> modulus_wide = {0xffffaaab, 0xb9feffff, 0xb153ffff, 0x1eabfffe, 0xf6b0f624, 0x6730d2a0, 0xf38512bf, 0x64774b84, 
-                                                          0x434bacd7, 0x4b1ba7b6, 0x397fe69a, 0x1a0111ea, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
-                                                          0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-
-  // modulus^2
-  static constexpr storage<2*limbs_count> modulus_sqared = {0x1c718e39, 0x26aa0000, 0x76382eab, 0x7ced6b1d, 0x62113cfd, 0x162c3383, 0x3e71b743, 0x66bf91ed, 
-                                                            0x7091a049, 0x292e85a8, 0x86185c7b, 0x1d68619c, 0x0978ef01, 0xf5314933, 0x16ddca6e, 0x50a62cfd, 
-                                                            0x349e8bd0, 0x66e59e49, 0x0e7046b4, 0xe2dc90e5, 0xa22f25e9, 0x4bd278ea, 0xb8c35fc7, 0x02a437a4};
-  // 2*modulus^2
-  static constexpr storage<2*limbs_count> modulus_sqared_2 = {0x38e31c72, 0x4d540000, 0xec705d56, 0xf9dad63a, 0xc42279fa, 0x2c586706, 0x7ce36e86, 0xcd7f23da, 
-                                                              0xe1234092, 0x525d0b50, 0x0c30b8f6, 0x3ad0c339, 0x12f1de02, 0xea629266, 0x2dbb94dd, 0xa14c59fa, 
-                                                              0x693d17a0, 0xcdcb3c92, 0x1ce08d68, 0xc5b921ca, 0x445e4bd3, 0x97a4f1d5, 0x7186bf8e, 0x05486f49};
-  // 4*modulus^2
-  static constexpr storage<2*limbs_count> modulus_sqared_4 = {0x71c638e4, 0x9aa80000, 0xd8e0baac, 0xf3b5ac75, 0x8844f3f5, 0x58b0ce0d, 0xf9c6dd0c, 0x9afe47b4, 
-                                                              0xc2468125, 0xa4ba16a1, 0x186171ec, 0x75a18672, 0x25e3bc04, 0xd4c524cc, 0x5b7729bb, 0x4298b3f4, 
-                                                              0xd27a2f41, 0x9b967924, 0x39c11ad1, 0x8b724394, 0x88bc97a7, 0x2f49e3aa, 0xe30d7f1d, 0x0a90de92};
-  static constexpr unsigned modulus_bits_count = 381;
-  // m = floor(2^(2*modulus_bits_count) / modulus)
-  static constexpr storage<limbs_count> m = {0xd59646e8, 0xec4f881f, 0x8163c701, 0x4e65c59e, 0x80a19de7, 0x2f7d1dc7, 0x7fda82a5, 0xa46e09d0, 0x331e9ae8, 0x38a0406c, 0xcf327917, 0x2760d74b};
-  static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-  static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-};
-
-struct group_generator {
-  static constexpr storage<fq_config::limbs_count> generator_x = {0xdb22c6bb, 0xfb3af00a, 0xf97a1aef, 0x6c55e83f, 0x171bac58, 0xa14e3a3f, 
-                                                                  0x9774b905, 0xc3688c4f, 0x4fa9ac0f, 0x2695638c, 0x3197d794, 0x17f1d3a7};
-  static constexpr storage<fq_config::limbs_count> generator_y = {0x46c5e7e1, 0x0caa2329, 0xa2888ae4, 0xd03cc744, 0x2c04b3ed, 0x00db18cb, 
-                                                                  0xd5d00af6, 0xfcf5e095, 0x741d8ae4, 0xa09e30ed, 0xe3aaa0f1, 0x08b3f481};
-};
-
-static constexpr unsigned weierstrass_b = 4;

icicle/curves/bls12_381/curve_config.cuh

@@ -0,0 +1,22 @@
+#pragma once
+
+#include "../../primitives/field.cuh"
+#include "../../primitives/projective.cuh"
+
+#include "params.cuh"
+
+namespace BLS12_381 {
+    typedef Field<PARAMS_BLS12_381::fp_config> scalar_field_t;
+    typedef scalar_field_t scalar_t;
+    typedef Field<PARAMS_BLS12_381::fq_config> point_field_t;
+    static constexpr point_field_t b = point_field_t{ PARAMS_BLS12_381::weierstrass_b };
+    typedef Projective<point_field_t, scalar_field_t, b> projective_t;
+    typedef Affine<point_field_t> affine_t;
+    #if defined(G2_DEFINED)
+    typedef ExtensionField<PARAMS_BLS12_381::fq_config> g2_point_field_t;
+    static constexpr g2_point_field_t b_g2 = g2_point_field_t{ point_field_t{ PARAMS_BLS12_381::weierstrass_b_g2_re },
+                                                               point_field_t{ PARAMS_BLS12_381::weierstrass_b_g2_im }};
+    typedef Projective<g2_point_field_t, scalar_field_t, b_g2> g2_projective_t;
+    typedef Affine<g2_point_field_t> g2_affine_t;
+    #endif
+}

icicle/curves/bls12_381/lde.cu

@@ -0,0 +1,327 @@
+#ifndef _BLS12_381_LDE
+#define _BLS12_381_LDE
+#include <cuda.h>
+#include "../../appUtils/ntt/lde.cu"
+#include "../../appUtils/ntt/ntt.cuh"
+#include "../../appUtils/vector_manipulation/ve_mod_mult.cuh"
+#include "curve_config.cuh"
+
+extern "C" BLS12_381::scalar_t* build_domain_cuda_bls12_381(uint32_t domain_size, uint32_t logn, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        if (inverse) {
+            return fill_twiddle_factors_array(domain_size, BLS12_381::scalar_t::omega_inv(logn), stream);
+        } else {
+            return fill_twiddle_factors_array(domain_size, BLS12_381::scalar_t::omega(logn), stream);
+        }
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return nullptr;
+    }
+}
+
+extern "C" int ntt_cuda_bls12_381(BLS12_381::scalar_t *arr, uint32_t n, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_template<BLS12_381::scalar_t,BLS12_381::scalar_t>(arr, n, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        
+        return -1;        
+    }
+}
+
+extern "C" int ecntt_cuda_bls12_381(BLS12_381::projective_t *arr, uint32_t n, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_template<BLS12_381::projective_t,BLS12_381::scalar_t>(arr, n, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int ntt_batch_cuda_bls12_381(BLS12_381::scalar_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_batch_template<BLS12_381::scalar_t,BLS12_381::scalar_t>(arr, arr_size, batch_size, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int ecntt_batch_cuda_bls12_381(BLS12_381::projective_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_batch_template<BLS12_381::projective_t,BLS12_381::scalar_t>(arr, arr_size, batch_size, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_scalars_cuda_bls12_381(BLS12_381::scalar_t* d_out, BLS12_381::scalar_t *d_evaluations, BLS12_381::scalar_t *d_domain, unsigned n, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        return interpolate(d_out, d_evaluations, d_domain, n, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_scalars_batch_cuda_bls12_381(BLS12_381::scalar_t* d_out, BLS12_381::scalar_t* d_evaluations, BLS12_381::scalar_t* d_domain, unsigned n,
+                                              unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_points_cuda_bls12_381(BLS12_381::projective_t* d_out, BLS12_381::projective_t *d_evaluations, BLS12_381::scalar_t *d_domain, unsigned n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        return interpolate(d_out, d_evaluations, d_domain, n, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_points_batch_cuda_bls12_381(BLS12_381::projective_t* d_out, BLS12_381::projective_t* d_evaluations, BLS12_381::scalar_t* d_domain,
+                                             unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_cuda_bls12_381(BLS12_381::scalar_t* d_out, BLS12_381::scalar_t *d_coefficients, BLS12_381::scalar_t *d_domain, 
+                                     unsigned domain_size, unsigned n, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BLS12_381::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_batch_cuda_bls12_381(BLS12_381::scalar_t* d_out, BLS12_381::scalar_t* d_coefficients, BLS12_381::scalar_t* d_domain, unsigned domain_size,
+                                           unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BLS12_381::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_cuda_bls12_381(BLS12_381::projective_t* d_out, BLS12_381::projective_t *d_coefficients, BLS12_381::scalar_t *d_domain, 
+                                    unsigned domain_size, unsigned n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BLS12_381::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_batch_cuda_bls12_381(BLS12_381::projective_t* d_out, BLS12_381::projective_t* d_coefficients, BLS12_381::scalar_t* d_domain, unsigned domain_size,
+                                          unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BLS12_381::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_on_coset_cuda_bls12_381(BLS12_381::scalar_t* d_out, BLS12_381::scalar_t *d_coefficients, BLS12_381::scalar_t *d_domain, unsigned domain_size,
+                                              unsigned n, BLS12_381::scalar_t *coset_powers, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_on_coset_batch_cuda_bls12_381(BLS12_381::scalar_t* d_out, BLS12_381::scalar_t* d_coefficients, BLS12_381::scalar_t* d_domain, unsigned domain_size, 
+                                                    unsigned n, unsigned batch_size, BLS12_381::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_on_coset_cuda_bls12_381(BLS12_381::projective_t* d_out, BLS12_381::projective_t *d_coefficients, BLS12_381::scalar_t *d_domain, unsigned domain_size,
+                                             unsigned n, BLS12_381::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_on_coset_batch_cuda_bls12_381(BLS12_381::projective_t* d_out, BLS12_381::projective_t* d_coefficients, BLS12_381::scalar_t* d_domain, unsigned domain_size, 
+                                                   unsigned n, unsigned batch_size, BLS12_381::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_scalars_cuda_bls12_381(BLS12_381::scalar_t* arr, int n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order(arr, n, logn, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_scalars_batch_cuda_bls12_381(BLS12_381::scalar_t* arr, int n, int batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order_batch(arr, n, logn, batch_size, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_points_cuda_bls12_381(BLS12_381::projective_t* arr, int n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order(arr, n, logn, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_points_batch_cuda_bls12_381(BLS12_381::projective_t* arr, int n, int batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order_batch(arr, n, logn, batch_size, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+#endif

icicle/curves/bls12_381/msm.cu

@@ -0,0 +1,92 @@
+#ifndef _BLS12_381_MSM
+#define _BLS12_381_MSM
+#include "../../appUtils/msm/msm.cu"
+#include <stdexcept>
+#include <cuda.h>
+#include "curve_config.cuh"
+
+
+extern "C"
+int msm_cuda_bls12_381(BLS12_381::projective_t *out, BLS12_381::affine_t points[],
+              BLS12_381::scalar_t scalars[], size_t count, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        large_msm<BLS12_381::scalar_t, BLS12_381::projective_t, BLS12_381::affine_t>(scalars, points, count, out, false, false, stream);
+        return CUDA_SUCCESS;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int msm_batch_cuda_bls12_381(BLS12_381::projective_t* out, BLS12_381::affine_t points[],
+                              BLS12_381::scalar_t scalars[], size_t batch_size, size_t msm_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+  try
+  {
+    cudaStreamCreate(&stream);
+    batched_large_msm<BLS12_381::scalar_t, BLS12_381::projective_t, BLS12_381::affine_t>(scalars, points, batch_size, msm_size, out, false, stream);
+    cudaStreamSynchronize(stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what());
+    return -1;
+  }
+}
+
+/**
+ * Commit to a polynomial using the MSM.
+ * Note: this function just calls the MSM, it doesn't convert between evaluation and coefficient form of scalars or points.
+ * @param d_out Ouptut point to write the result to.
+ * @param d_scalars Scalars for the MSM. Must be on device.
+ * @param d_points Points for the MSM. Must be on device.
+ * @param count Length of `d_scalars` and `d_points` arrays (they should have equal length).
+ */
+ extern "C"
+ int commit_cuda_bls12_381(BLS12_381::projective_t* d_out, BLS12_381::scalar_t* d_scalars, BLS12_381::affine_t* d_points, size_t count, size_t device_id = 0, cudaStream_t stream = 0)
+ {
+     try
+     {
+         large_msm(d_scalars, d_points, count, d_out, true, false, stream);
+         cudaStreamSynchronize(stream);
+         return 0;
+     }
+     catch (const std::runtime_error &ex)
+     {
+         printf("error %s", ex.what());
+         return -1;
+     }
+ }
+ 
+ /**
+  * Commit to a batch of polynomials using the MSM.
+  * Note: this function just calls the MSM, it doesn't convert between evaluation and coefficient form of scalars or points.
+  * @param d_out Ouptut point to write the results to.
+  * @param d_scalars Scalars for the MSMs of all polynomials. Must be on device.
+  * @param d_points Points for the MSMs. Must be on device. It is assumed that this set of bases is used for each MSM.
+  * @param count Length of `d_points` array, `d_scalar` has length `count` * `batch_size`.
+  * @param batch_size Size of the batch.
+  */
+ extern "C"
+ int commit_batch_cuda_bls12_381(BLS12_381::projective_t* d_out, BLS12_381::scalar_t* d_scalars, BLS12_381::affine_t* d_points, size_t count, size_t batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+ {
+     try
+     {  
+        cudaStreamCreate(&stream);
+         batched_large_msm(d_scalars, d_points, batch_size, count, d_out, true, stream);
+         cudaStreamSynchronize(stream);
+         return 0;
+     }
+     catch (const std::runtime_error &ex)
+     {
+         printf("error %s", ex.what());
+         return -1;
+     }
+ }
+
+ #endif

icicle/curves/bls12_381/params.cuh

@@ -0,0 +1,213 @@
+#pragma once
+
+#include "../../utils/storage.cuh"
+
+namespace PARAMS_BLS12_381{
+  struct fp_config {
+    // field structure size = 8 * 32 bit
+    static constexpr unsigned limbs_count = 8;
+    static constexpr unsigned omegas_count = 32;
+    // modulus = 52435875175126190479447740508185965837690552500527637822603658699938581184513
+    static constexpr storage<limbs_count> modulus = {0x00000001, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753};
+    // modulus*2 = 104871750350252380958895481016371931675381105001055275645207317399877162369026
+    static constexpr storage<limbs_count> modulus_2 = {0x00000002, 0xfffffffe, 0xfffcb7fd, 0xa77b4805, 0x1343b00a, 0x6673b010, 0x533afa90, 0xe7db4ea6};
+    static constexpr storage<limbs_count> modulus_4 = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+  
+    static constexpr storage<2 * limbs_count> modulus_wide = {0x00000001, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753,
+                                                              0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    // modulus^2
+    static constexpr storage<2*limbs_count> modulus_squared = {0x00000001, 0xfffffffe, 0xfffcb7fe, 0xa77e9007, 0x1cdbb005, 0x698ae002, 0x5433f7b8, 0x48aa415e, 
+                                                              0x4aa9c661, 0xc2611f6f, 0x59934a1d, 0x0e9593f9, 0xef2cc20f, 0x520c13db, 0xf4bc2778, 0x347f60f3};
+    // 2*modulus^2
+    static constexpr storage<2*limbs_count> modulus_squared_2 = {0x00000002, 0xfffffffc, 0xfff96ffd, 0x4efd200f, 0x39b7600b, 0xd315c004, 0xa867ef70, 0x915482bc, 
+                                                                0x95538cc2, 0x84c23ede, 0xb326943b, 0x1d2b27f2, 0xde59841e, 0xa41827b7, 0xe9784ef0, 0x68fec1e7};
+    static constexpr unsigned modulus_bit_count = 255;
+    // m = floor(2^(2*modulus_bit_count) / modulus)
+    static constexpr storage<limbs_count> m = {0x830358e4, 0x509cde80, 0x2f92eb5c, 0xd9410fad, 0xc1f823b4, 0xe2d772d, 0x7fb78ddf, 0x8d54253b};
+  
+    static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+  
+    // static constexpr storage<limbs_count> omega[32]= { {0x00000000, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753}, {0x00000000, 0x00010000, 0x76030000, 0xec030002, 0x760304d0, 0x8d51ccce, 0x00000000, 0x00000000}, {0x688bc087, 0x8dd702cb, 0x78eaa4fe, 0xa0328240, 0x98ca5b22, 0xa733b23a, 0x25a31660, 0x3f96405d}, {0x0411fe73, 0x95df4b36, 0xebc1e1bb, 0x1ef4e672, 0x60afca4a, 0x6e92a9c4, 0x753e4fcc, 0x4f2c596e}, {0xba60eaa6, 0x9733f3a6, 0x77487ae7, 0xbd7fdf9c, 0xc8b6cc00, 0xd84f8612, 0x6162ffab, 0x476fa2fb}, {0xac5db47f, 0xd2fc5e69, 0x15d0b8e4, 0xa12a70a6, 0xbc8de5d9, 0x293b1d67, 0x57f86f5e, 0x0e4840ac}, {0xab28e208, 0xb750da4c, 0x3be95635, 0x501dff64, 0xf0b4b276, 0x8cbe2437, 0xa94a946e, 0x07d0c802}, {0x2fe322b8, 0x2cabadec, 0x15412560, 0x752c84f3, 0x1a3b0aef, 0x32a732ae, 0xa33dcbf2, 0x2e95da59}, {0xfe0c65f4, 0x33811ea1, 0x687f28a2, 0x15c1ad4c, 0x42dee7f4, 0xecfbede3, 0x9a5d88b1, 0x1bb46667}, {0x2d010ff9, 0xd58a5af4, 0x570bf109, 0x79efd6b0, 0x6350721d, 0x3ed6d55a, 0x58f43cef, 0x2f27b098}, {0x8c130477, 0x74a1f671, 0xb61e0abe, 0xa534af14, 0x620890d7, 0xeb674a1a, 0xca252472, 0x43527a8b}, {0x7ea8ee05, 0x450d9f97, 0x37d56fc0, 0x565af171, 0x93f9e9ac, 0xe155cb48, 0xc8e9101b, 0x110cebd0}, {0x59a0be92, 0x23c91599, 0x7a027759, 0x87d188ce, 0xcab3c3cc, 0x70491431, 0xb3f7f8da, 0x0ac00eb8}, {0x69583404, 0x13e96ade, 0x5306243d, 0x82c05727, 0x29ca9f2a, 0x77e48bf5, 0x1fe19595, 0x50646ac8}, {0xa97eccd4, 0xe6a354dd, 0x88fbbc57, 0x39929d2e, 0xd6e7b1c8, 0xa22ba63d, 0xf5f07f43, 0x42c22911}, {0xcfc35f7a, 0x137b458a, 0x29c01b06, 0x0caba63a, 0x7a02402c, 0x0409ee98, 0x56aa725b, 0x6709c6cd}, {0x8831e03e, 0x10251f7d, 0x7ff858ec, 0x77d85a93, 0x4fb9ac5c, 0xebe905bd, 0xf8727901, 0x05deb333}, {0xb9009408, 0xbf87b689, 0xdd3ccc96, 0x4f730e7d, 0x4610300c, 0xfd7f05ba, 0x0b8ac903, 0x5ef5e8db}, {0x17cd0c14, 0x64996884, 0x68812f7f, 0xa6728673, 0x22cc3253, 0x2e1d9a19, 0xaa0a1d80, 0x3a689e83}, {0x41144dea, 0x20b53cbe, 0xc2f0fcbd, 0x870c46fa, 0x537d6971, 0x556c35f6, 0x5f686d91, 0x3436287f}, {0x436ba2e7, 0x007e082a, 0x9116e877, 0x67c6630f, 0xfb4460f7, 0x36f8f165, 0x7e7046e0, 0x6eee34d5}, {0xa53a56d1, 0xc5b670ee, 0x53037d7b, 0x127d1f42, 0xa722c2e2, 0x57d4257e, 0x33cbd838, 0x03ae26a3}, {0x76504cf8, 0x1e914848, 0xb63edd02, 0x55bbbf1e, 0x4e55aa02, 0xbcdafec8, 0x2dc0beb0, 0x5145c4cd}, {0x1ab70e2c, 0x5b90153a, 0x75fb0ab8, 0x8deffa31, 0x46900c95, 0xc553ae23, 0x6bd3118c, 0x1d31dcdc}, {0x59a2e8eb, 0x801c894c, 0xe12fc974, 0xbc535c5c, 0x47d39803, 0x95508d27, 0xac5d094f, 0x16d9d3cd}, {0xcca1d8be, 0x810fa372, 0x82e0bfa7, 0xc67b8c28, 0xe2d35bc2, 0xdbb4edf0, 0x5087c995, 0x712d1580}, {0xfd88f133, 0xeb162203, 0xf010ea74, 0xac96c38f, 0xe64cfc70, 0x4307987f, 0x37b7a114, 0x350fe98d}, {0x42f2a254, 0xaba2f518, 0xa71efc0c, 0x4d7f3c3a, 0xd274a80a, 0x97ae418d, 0x5e3e7682, 0x2967385d}, {0x575a0b79, 0x75c55c7b, 0x74a7ded1, 0x3ba4a157, 0xa04fccf3, 0xc3974d73, 0x4a939684, 0x705aba4f}, {0x14ebb608, 0x8409a9ea, 0x66bac611, 0xfad0084e, 0x811c1dfb, 0x04287254, 0x23b30c29, 0x086d072b}, {0x67e4756a, 0xb427c9b3, 0x02ebc38d, 0xc7537fb9, 0xcd6a205f, 0x51de21be, 0x7923597d, 0x6064ab72}, {0x0b912f1f, 0x1b788f50, 0x70b3e094, 0xc4024ff2, 0xd168d6c0, 0x0fd56dc8, 0x5b416b6f, 0x0212d79e}};
+    // Quick fix for linking issue
+    static constexpr storage<limbs_count> omega1=   {0x00000000, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753};
+    static constexpr storage<limbs_count> omega2=   {0x00000000, 0x00010000, 0x76030000, 0xec030002, 0x760304d0, 0x8d51ccce, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> omega3=   {0x688bc087, 0x8dd702cb, 0x78eaa4fe, 0xa0328240, 0x98ca5b22, 0xa733b23a, 0x25a31660, 0x3f96405d};
+    static constexpr storage<limbs_count> omega4=   {0x0411fe73, 0x95df4b36, 0xebc1e1bb, 0x1ef4e672, 0x60afca4a, 0x6e92a9c4, 0x753e4fcc, 0x4f2c596e};
+    static constexpr storage<limbs_count> omega5=   {0xba60eaa6, 0x9733f3a6, 0x77487ae7, 0xbd7fdf9c, 0xc8b6cc00, 0xd84f8612, 0x6162ffab, 0x476fa2fb};
+    static constexpr storage<limbs_count> omega6=   {0xac5db47f, 0xd2fc5e69, 0x15d0b8e4, 0xa12a70a6, 0xbc8de5d9, 0x293b1d67, 0x57f86f5e, 0x0e4840ac};
+    static constexpr storage<limbs_count> omega7=   {0xab28e208, 0xb750da4c, 0x3be95635, 0x501dff64, 0xf0b4b276, 0x8cbe2437, 0xa94a946e, 0x07d0c802};
+    static constexpr storage<limbs_count> omega8=   {0x2fe322b8, 0x2cabadec, 0x15412560, 0x752c84f3, 0x1a3b0aef, 0x32a732ae, 0xa33dcbf2, 0x2e95da59};
+    static constexpr storage<limbs_count> omega9=   {0xfe0c65f4, 0x33811ea1, 0x687f28a2, 0x15c1ad4c, 0x42dee7f4, 0xecfbede3, 0x9a5d88b1, 0x1bb46667};
+    static constexpr storage<limbs_count> omega10=  {0x2d010ff9, 0xd58a5af4, 0x570bf109, 0x79efd6b0, 0x6350721d, 0x3ed6d55a, 0x58f43cef, 0x2f27b098};
+    static constexpr storage<limbs_count> omega11=  {0x8c130477, 0x74a1f671, 0xb61e0abe, 0xa534af14, 0x620890d7, 0xeb674a1a, 0xca252472, 0x43527a8b};
+    static constexpr storage<limbs_count> omega12=  {0x7ea8ee05, 0x450d9f97, 0x37d56fc0, 0x565af171, 0x93f9e9ac, 0xe155cb48, 0xc8e9101b, 0x110cebd0};
+    static constexpr storage<limbs_count> omega13=  {0x59a0be92, 0x23c91599, 0x7a027759, 0x87d188ce, 0xcab3c3cc, 0x70491431, 0xb3f7f8da, 0x0ac00eb8};
+    static constexpr storage<limbs_count> omega14=  {0x69583404, 0x13e96ade, 0x5306243d, 0x82c05727, 0x29ca9f2a, 0x77e48bf5, 0x1fe19595, 0x50646ac8};
+    static constexpr storage<limbs_count> omega15=  {0xa97eccd4, 0xe6a354dd, 0x88fbbc57, 0x39929d2e, 0xd6e7b1c8, 0xa22ba63d, 0xf5f07f43, 0x42c22911};
+    static constexpr storage<limbs_count> omega16=  {0xcfc35f7a, 0x137b458a, 0x29c01b06, 0x0caba63a, 0x7a02402c, 0x0409ee98, 0x56aa725b, 0x6709c6cd};
+    static constexpr storage<limbs_count> omega17=  {0x8831e03e, 0x10251f7d, 0x7ff858ec, 0x77d85a93, 0x4fb9ac5c, 0xebe905bd, 0xf8727901, 0x05deb333};
+    static constexpr storage<limbs_count> omega18=  {0xb9009408, 0xbf87b689, 0xdd3ccc96, 0x4f730e7d, 0x4610300c, 0xfd7f05ba, 0x0b8ac903, 0x5ef5e8db};
+    static constexpr storage<limbs_count> omega19=  {0x17cd0c14, 0x64996884, 0x68812f7f, 0xa6728673, 0x22cc3253, 0x2e1d9a19, 0xaa0a1d80, 0x3a689e83};
+    static constexpr storage<limbs_count> omega20=  {0x41144dea, 0x20b53cbe, 0xc2f0fcbd, 0x870c46fa, 0x537d6971, 0x556c35f6, 0x5f686d91, 0x3436287f};
+    static constexpr storage<limbs_count> omega21=  {0x436ba2e7, 0x007e082a, 0x9116e877, 0x67c6630f, 0xfb4460f7, 0x36f8f165, 0x7e7046e0, 0x6eee34d5};
+    static constexpr storage<limbs_count> omega22=  {0xa53a56d1, 0xc5b670ee, 0x53037d7b, 0x127d1f42, 0xa722c2e2, 0x57d4257e, 0x33cbd838, 0x03ae26a3};
+    static constexpr storage<limbs_count> omega23=  {0x76504cf8, 0x1e914848, 0xb63edd02, 0x55bbbf1e, 0x4e55aa02, 0xbcdafec8, 0x2dc0beb0, 0x5145c4cd};
+    static constexpr storage<limbs_count> omega24=  {0x1ab70e2c, 0x5b90153a, 0x75fb0ab8, 0x8deffa31, 0x46900c95, 0xc553ae23, 0x6bd3118c, 0x1d31dcdc};
+    static constexpr storage<limbs_count> omega25=  {0x59a2e8eb, 0x801c894c, 0xe12fc974, 0xbc535c5c, 0x47d39803, 0x95508d27, 0xac5d094f, 0x16d9d3cd};
+    static constexpr storage<limbs_count> omega26=  {0xcca1d8be, 0x810fa372, 0x82e0bfa7, 0xc67b8c28, 0xe2d35bc2, 0xdbb4edf0, 0x5087c995, 0x712d1580};
+    static constexpr storage<limbs_count> omega27=  {0xfd88f133, 0xeb162203, 0xf010ea74, 0xac96c38f, 0xe64cfc70, 0x4307987f, 0x37b7a114, 0x350fe98d};
+    static constexpr storage<limbs_count> omega28=  {0x42f2a254, 0xaba2f518, 0xa71efc0c, 0x4d7f3c3a, 0xd274a80a, 0x97ae418d, 0x5e3e7682, 0x2967385d};
+    static constexpr storage<limbs_count> omega29=  {0x575a0b79, 0x75c55c7b, 0x74a7ded1, 0x3ba4a157, 0xa04fccf3, 0xc3974d73, 0x4a939684, 0x705aba4f};
+    static constexpr storage<limbs_count> omega30=  {0x14ebb608, 0x8409a9ea, 0x66bac611, 0xfad0084e, 0x811c1dfb, 0x04287254, 0x23b30c29, 0x086d072b};
+    static constexpr storage<limbs_count> omega31=  {0x67e4756a, 0xb427c9b3, 0x02ebc38d, 0xc7537fb9, 0xcd6a205f, 0x51de21be, 0x7923597d, 0x6064ab72};
+    static constexpr storage<limbs_count> omega32=  {0x0b912f1f, 0x1b788f50, 0x70b3e094, 0xc4024ff2, 0xd168d6c0, 0x0fd56dc8, 0x5b416b6f, 0x0212d79e};
+
+    static constexpr storage_array<omegas_count, limbs_count> omega = {
+        omega1, omega2, omega3, omega4, omega5, omega6, omega7, omega8, 
+        omega9, omega10, omega11, omega12, omega13, omega14, omega15, omega16,
+        omega17, omega18, omega19, omega20, omega21, omega22, omega23, omega24,
+        omega25, omega26, omega27, omega28, omega29, omega30, omega31, omega32,
+    };
+  
+    // static constexpr storage<limbs_count> omega_inv[32]={ {0x00000000, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753}, {0x00000001, 0xfffeffff, 0x89fb5bfe, 0x67baa400, 0x939ed334, 0xa5e80b39, 0x299d7d47, 0x73eda753}, {0xae99502e, 0x6037fe81, 0x94b04fd8, 0x8e749036, 0xca86bf65, 0xbabc5aff, 0x5ce11044, 0x1333b22e}, {0x7dc08d74, 0x7f847ee4, 0x04eeaf5a, 0xbd433896, 0x1832fc60, 0xd66c91d6, 0x607e449b, 0x551115b4}, {0x4e7773cb, 0xee5bcecc, 0xf6dab086, 0x45593d6f, 0x4016e2bd, 0xa3a95d2d, 0xaf96816f, 0x047cb16c}, {0x982b68c5, 0xb891fa3f, 0x1d426b52, 0xa41e8501, 0x882952d6, 0x566009b5, 0x7b3c79d6, 0x199cdaee}, {0xcf28601b, 0x571ba2fc, 0xac74db12, 0x166fb582, 0x3501370b, 0x51420be4, 0x52f970ba, 0x1996fa8d}, {0x6a2f777a, 0xe9561c17, 0x2393991b, 0xc03cae03, 0x5a5bfd4f, 0x91b00023, 0x272e58ee, 0x6d64ed25}, {0xf02a116e, 0xfb350dbe, 0xb4543a3e, 0x1c510ebf, 0x37ad4eca, 0xf675522e, 0x80f82b2d, 0x1907a56e}, {0x4eb71aa6, 0xb0ad8003, 0xaa67e0be, 0x50a32c41, 0x19141f44, 0x105f0672, 0xa3dad316, 0x2bcd9508}, {0x0f6fb2ac, 0x3dc9e560, 0x9aa58ff5, 0x3cc5bb32, 0x36f376e1, 0xdeae67bc, 0x65ba213e, 0x394fda0d}, {0x60b82267, 0x09f239f7, 0x8b24f123, 0x14180e0e, 0x45625d95, 0xad5a5340, 0x6d174692, 0x58c3ba63}, {0x348b416f, 0x0acf21c2, 0xbc086439, 0x798b6bf6, 0xb1ca111d, 0x222d411f, 0x30ba1e0f, 0x044107b7}, {0x014abe84, 0xa3b861b8, 0x427ed008, 0x37c017e4, 0xae0ff4f5, 0xae51f613, 0xcb1218d3, 0x1a2d00e1}, {0x4de7eb2b, 0x48aaa3bf, 0x6772057d, 0x4a58d54d, 0x7093b551, 0xce25f16c, 0xd206337c, 0x242150ac}, {0x9ed57ae5, 0xdf3ec9ae, 0x7166577f, 0xea7df73a, 0x022fbbe4, 0x6ca8d281, 0x151e3f6b, 0x5850c003}, {0x645e1cfa, 0x903a0a0c, 0x34788c37, 0xfbac54cb, 0x8cf73d78, 0xdc127d11, 0x975d3c82, 0x6d0b5c7c}, {0x14b1ba04, 0xb49d6b05, 0xf00b84f2, 0x56e466b4, 0x0b904f22, 0x30c390cf, 0x3ee254cc, 0x3e11cfb7}, {0xbe8201ab, 0x84dfa547, 0x530715d2, 0x3887ce8b, 0x3eed4ed7, 0xa4c719c6, 0x8f8007b4, 0x18c44950}, {0x7d813cd1, 0xdaf0346d, 0xf755beb1, 0xeccf6f9a, 0xe08143e3, 0x167fce38, 0x6f5d6dfa, 0x545ad9b2}, {0x577605de, 0x973f5466, 0x974f953c, 0x0ce8986e, 0x074382f9, 0x8941cf4b, 0x6fa2672c, 0x156cd7f6}, {0x33b66141, 0x24315404, 0x1992f584, 0x5d1375ab, 0x8b20ca1a, 0xf193ffa6, 0x2701a503, 0x47880cd5}, {0xe9f7b9af, 0xf7b6847d, 0x62c83ce2, 0x9a339673, 0x6e5e6f79, 0xfabf4537, 0x35af33a3, 0x0975acd9}, {0x0eddd248, 0x4fb4204a, 0xc9e509b3, 0x8c98706a, 0x2bb27eb1, 0xd0be8987, 0xc831438b, 0x6ec5f960}, {0x20238f62, 0xa13c95b7, 0x83b476b9, 0x130aa097, 0x14860881, 0x758a04e0, 0x97066493, 0x58e2f8d6}, {0xe8bff41e, 0x65b09c73, 0x37f1c6a3, 0x8b3280e8, 0x2846fb21, 0xe17b82ce, 0xb1ae27df, 0x476534bf}, {0xd5fdb757, 0x8480c0e7, 0x365bf9fd, 0x3644eea0, 0xb776be86, 0x4ca116ca, 0x8b58390c, 0x17b6395f}, {0x252eb0db, 0x2c811e9a, 0x7479e161, 0x1b7d960d, 0xb0a89a26, 0xb3afc7c1, 0x32b5e793, 0x6a2f9533}, {0x08b8a7ad, 0xe877b2c4, 0x341652b4, 0x68b0e8f0, 0xe8b6a2d9, 0x2d44da3b, 0xfd09be59, 0x092778ff}, {0x7988f244, 0x84a1aa6f, 0x24faf63f, 0xa164b3d9, 0xc1bbb915, 0x7aae9724, 0xf386c0d2, 0x24e5d287}, {0x41a1b30c, 0xa70a7efd, 0x39f0e511, 0xc49c55a5, 0x033bb323, 0xab307a8f, 0x17acbd7f, 0x0158abd6}, {0x0f642025, 0x2c228b30, 0x01bd882b, 0xb0878e8d, 0xd7377fea, 0xd862b255, 0xf0490536, 0x18ac3666}};
+    // Quick fix for linking issue
+    static constexpr storage<limbs_count> omega_inv1=   {0x00000000, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753};
+    static constexpr storage<limbs_count> omega_inv2=   {0x00000001, 0xfffeffff, 0x89fb5bfe, 0x67baa400, 0x939ed334, 0xa5e80b39, 0x299d7d47, 0x73eda753};
+    static constexpr storage<limbs_count> omega_inv3=   {0xae99502e, 0x6037fe81, 0x94b04fd8, 0x8e749036, 0xca86bf65, 0xbabc5aff, 0x5ce11044, 0x1333b22e};
+    static constexpr storage<limbs_count> omega_inv4=   {0x7dc08d74, 0x7f847ee4, 0x04eeaf5a, 0xbd433896, 0x1832fc60, 0xd66c91d6, 0x607e449b, 0x551115b4};
+    static constexpr storage<limbs_count> omega_inv5=   {0x4e7773cb, 0xee5bcecc, 0xf6dab086, 0x45593d6f, 0x4016e2bd, 0xa3a95d2d, 0xaf96816f, 0x047cb16c};
+    static constexpr storage<limbs_count> omega_inv6=   {0x982b68c5, 0xb891fa3f, 0x1d426b52, 0xa41e8501, 0x882952d6, 0x566009b5, 0x7b3c79d6, 0x199cdaee};
+    static constexpr storage<limbs_count> omega_inv7=   {0xcf28601b, 0x571ba2fc, 0xac74db12, 0x166fb582, 0x3501370b, 0x51420be4, 0x52f970ba, 0x1996fa8d};
+    static constexpr storage<limbs_count> omega_inv8=   {0x6a2f777a, 0xe9561c17, 0x2393991b, 0xc03cae03, 0x5a5bfd4f, 0x91b00023, 0x272e58ee, 0x6d64ed25};
+    static constexpr storage<limbs_count> omega_inv9=   {0xf02a116e, 0xfb350dbe, 0xb4543a3e, 0x1c510ebf, 0x37ad4eca, 0xf675522e, 0x80f82b2d, 0x1907a56e};
+    static constexpr storage<limbs_count> omega_inv10=  {0x4eb71aa6, 0xb0ad8003, 0xaa67e0be, 0x50a32c41, 0x19141f44, 0x105f0672, 0xa3dad316, 0x2bcd9508};
+    static constexpr storage<limbs_count> omega_inv11=  {0x0f6fb2ac, 0x3dc9e560, 0x9aa58ff5, 0x3cc5bb32, 0x36f376e1, 0xdeae67bc, 0x65ba213e, 0x394fda0d};
+    static constexpr storage<limbs_count> omega_inv12=  {0x60b82267, 0x09f239f7, 0x8b24f123, 0x14180e0e, 0x45625d95, 0xad5a5340, 0x6d174692, 0x58c3ba63};
+    static constexpr storage<limbs_count> omega_inv13=  {0x348b416f, 0x0acf21c2, 0xbc086439, 0x798b6bf6, 0xb1ca111d, 0x222d411f, 0x30ba1e0f, 0x044107b7};
+    static constexpr storage<limbs_count> omega_inv14=  {0x014abe84, 0xa3b861b8, 0x427ed008, 0x37c017e4, 0xae0ff4f5, 0xae51f613, 0xcb1218d3, 0x1a2d00e1};
+    static constexpr storage<limbs_count> omega_inv15=  {0x4de7eb2b, 0x48aaa3bf, 0x6772057d, 0x4a58d54d, 0x7093b551, 0xce25f16c, 0xd206337c, 0x242150ac};
+    static constexpr storage<limbs_count> omega_inv16=  {0x9ed57ae5, 0xdf3ec9ae, 0x7166577f, 0xea7df73a, 0x022fbbe4, 0x6ca8d281, 0x151e3f6b, 0x5850c003};
+    static constexpr storage<limbs_count> omega_inv17=  {0x645e1cfa, 0x903a0a0c, 0x34788c37, 0xfbac54cb, 0x8cf73d78, 0xdc127d11, 0x975d3c82, 0x6d0b5c7c};
+    static constexpr storage<limbs_count> omega_inv18=  {0x14b1ba04, 0xb49d6b05, 0xf00b84f2, 0x56e466b4, 0x0b904f22, 0x30c390cf, 0x3ee254cc, 0x3e11cfb7};
+    static constexpr storage<limbs_count> omega_inv19=  {0xbe8201ab, 0x84dfa547, 0x530715d2, 0x3887ce8b, 0x3eed4ed7, 0xa4c719c6, 0x8f8007b4, 0x18c44950};
+    static constexpr storage<limbs_count> omega_inv20=  {0x7d813cd1, 0xdaf0346d, 0xf755beb1, 0xeccf6f9a, 0xe08143e3, 0x167fce38, 0x6f5d6dfa, 0x545ad9b2};
+    static constexpr storage<limbs_count> omega_inv21=  {0x577605de, 0x973f5466, 0x974f953c, 0x0ce8986e, 0x074382f9, 0x8941cf4b, 0x6fa2672c, 0x156cd7f6};
+    static constexpr storage<limbs_count> omega_inv22=  {0x33b66141, 0x24315404, 0x1992f584, 0x5d1375ab, 0x8b20ca1a, 0xf193ffa6, 0x2701a503, 0x47880cd5};
+    static constexpr storage<limbs_count> omega_inv23=  {0xe9f7b9af, 0xf7b6847d, 0x62c83ce2, 0x9a339673, 0x6e5e6f79, 0xfabf4537, 0x35af33a3, 0x0975acd9};
+    static constexpr storage<limbs_count> omega_inv24=  {0x0eddd248, 0x4fb4204a, 0xc9e509b3, 0x8c98706a, 0x2bb27eb1, 0xd0be8987, 0xc831438b, 0x6ec5f960};
+    static constexpr storage<limbs_count> omega_inv25=  {0x20238f62, 0xa13c95b7, 0x83b476b9, 0x130aa097, 0x14860881, 0x758a04e0, 0x97066493, 0x58e2f8d6};
+    static constexpr storage<limbs_count> omega_inv26=  {0xe8bff41e, 0x65b09c73, 0x37f1c6a3, 0x8b3280e8, 0x2846fb21, 0xe17b82ce, 0xb1ae27df, 0x476534bf};
+    static constexpr storage<limbs_count> omega_inv27=  {0xd5fdb757, 0x8480c0e7, 0x365bf9fd, 0x3644eea0, 0xb776be86, 0x4ca116ca, 0x8b58390c, 0x17b6395f};
+    static constexpr storage<limbs_count> omega_inv28=  {0x252eb0db, 0x2c811e9a, 0x7479e161, 0x1b7d960d, 0xb0a89a26, 0xb3afc7c1, 0x32b5e793, 0x6a2f9533};
+    static constexpr storage<limbs_count> omega_inv29=  {0x08b8a7ad, 0xe877b2c4, 0x341652b4, 0x68b0e8f0, 0xe8b6a2d9, 0x2d44da3b, 0xfd09be59, 0x092778ff};
+    static constexpr storage<limbs_count> omega_inv30=  {0x7988f244, 0x84a1aa6f, 0x24faf63f, 0xa164b3d9, 0xc1bbb915, 0x7aae9724, 0xf386c0d2, 0x24e5d287};
+    static constexpr storage<limbs_count> omega_inv31=  {0x41a1b30c, 0xa70a7efd, 0x39f0e511, 0xc49c55a5, 0x033bb323, 0xab307a8f, 0x17acbd7f, 0x0158abd6};
+    static constexpr storage<limbs_count> omega_inv32=  {0x0f642025, 0x2c228b30, 0x01bd882b, 0xb0878e8d, 0xd7377fea, 0xd862b255, 0xf0490536, 0x18ac3666};
+    
+    static constexpr storage_array<omegas_count, limbs_count> omega_inv = {
+        omega_inv1, omega_inv2, omega_inv3, omega_inv4, omega_inv5, omega_inv6, omega_inv7, omega_inv8, 
+        omega_inv9, omega_inv10, omega_inv11, omega_inv12, omega_inv13, omega_inv14, omega_inv15, omega_inv16,
+        omega_inv17, omega_inv18, omega_inv19, omega_inv20, omega_inv21, omega_inv22, omega_inv23, omega_inv24,
+        omega_inv25, omega_inv26, omega_inv27, omega_inv28, omega_inv29, omega_inv30, omega_inv31, omega_inv32,
+    };
+    
+    // Quick fix for linking issue
+    static constexpr storage<limbs_count> inv1=   {0x80000001, 0x7fffffff, 0x7fff2dff, 0xa9ded201, 0x04d0ec02, 0x199cec04, 0x94cebea4, 0x39f6d3a9};
+    static constexpr storage<limbs_count> inv2=   {0x40000001, 0x3fffffff, 0x3ffec4ff, 0xfece3b02, 0x07396203, 0x266b6206, 0x5f361df6, 0x56f23d7e};
+    static constexpr storage<limbs_count> inv3=   {0x20000001, 0x1fffffff, 0x9ffe907f, 0xa945ef82, 0x086d9d04, 0x2cd29d07, 0xc469cd9f, 0x656ff268};
+    static constexpr storage<limbs_count> inv4=   {0x10000001, 0x0fffffff, 0xcffe763f, 0xfe81c9c2, 0x8907ba84, 0xb0063a87, 0xf703a573, 0x6caeccdd};
+    static constexpr storage<limbs_count> inv5=   {0x08000001, 0x07ffffff, 0xe7fe691f, 0x291fb6e2, 0xc954c945, 0xf1a00947, 0x9050915d, 0x704e3a18};
+    static constexpr storage<limbs_count> inv6=   {0x04000001, 0x03ffffff, 0xf3fe628f, 0x3e6ead72, 0xe97b50a5, 0x126cf0a7, 0xdcf70753, 0x721df0b5};
+    static constexpr storage<limbs_count> inv7=   {0x02000001, 0x01ffffff, 0xf9fe5f47, 0x491628ba, 0xf98e9455, 0xa2d36457, 0x834a424d, 0x7305cc04};
+    static constexpr storage<limbs_count> inv8=   {0x01000001, 0x00ffffff, 0xfcfe5da3, 0x4e69e65e, 0x0198362d, 0xeb069e30, 0xd673dfca, 0x7379b9ab};
+    static constexpr storage<limbs_count> inv9=   {0x00800001, 0x007fffff, 0xfe7e5cd1, 0x5113c530, 0x059d0719, 0x8f203b1c, 0x8008ae89, 0x73b3b07f};
+    static constexpr storage<limbs_count> inv10=  {0x00400001, 0x003fffff, 0xff3e5c68, 0x5268b499, 0x079f6f8f, 0xe12d0992, 0x54d315e8, 0x73d0abe9};
+    static constexpr storage<limbs_count> inv11=  {0x00200001, 0x801fffff, 0x7f9e5c33, 0x53132c4e, 0x08a0a3ca, 0x8a3370cd, 0x3f384998, 0x73df299e};
+    static constexpr storage<limbs_count> inv12=  {0x00100001, 0x400fffff, 0xbfce5c19, 0xd3686828, 0x89213de7, 0x5eb6a46a, 0xb46ae370, 0x73e66878};
+    static constexpr storage<limbs_count> inv13=  {0x00080001, 0x2007ffff, 0xdfe65c0c, 0x93930615, 0x49618af6, 0x48f83e39, 0xef04305c, 0x73ea07e5};
+    static constexpr storage<limbs_count> inv14=  {0x00040001, 0x9003ffff, 0x6ff25c05, 0xf3a8550c, 0xa981b17d, 0x3e190b20, 0x8c50d6d2, 0x73ebd79c};
+    static constexpr storage<limbs_count> inv15=  {0x00020001, 0x4801ffff, 0xb7f85c02, 0xa3b2fc87, 0x5991c4c1, 0x38a97194, 0xdaf72a0d, 0x73ecbf77};
+    static constexpr storage<limbs_count> inv16=  {0x00010001, 0xa400ffff, 0x5bfb5c00, 0x7bb85045, 0x3199ce63, 0xb5f1a4ce, 0x824a53aa, 0x73ed3365};
+    static constexpr storage<limbs_count> inv17=  {0x00008001, 0xd2007fff, 0x2dfcdbff, 0x67bafa24, 0x1d9dd334, 0x7495be6b, 0x55f3e879, 0x73ed6d5c};
+    static constexpr storage<limbs_count> inv18=  {0x00004001, 0x69003fff, 0x96fd9bff, 0xddbc4f13, 0x939fd59c, 0xd3e7cb39, 0xbfc8b2e0, 0x73ed8a57};
+    static constexpr storage<limbs_count> inv19=  {0x00002001, 0x34801fff, 0x4b7dfbff, 0x18bcf98b, 0xcea0d6d1, 0x8390d1a0, 0x74b31814, 0x73ed98d5};
+    static constexpr storage<limbs_count> inv20=  {0x00001001, 0x1a400fff, 0x25be2bff, 0x363d4ec7, 0x6c21576b, 0x5b6554d4, 0x4f284aae, 0x73eda014};
+    static constexpr storage<limbs_count> inv21=  {0x00000801, 0x0d2007ff, 0x12de43ff, 0x44fd7965, 0x3ae197b8, 0x474f966e, 0xbc62e3fb, 0x73eda3b3};
+    static constexpr storage<limbs_count> inv22=  {0x00000401, 0x069003ff, 0x096e4fff, 0xcc5d8eb4, 0x2241b7de, 0xbd44b73b, 0x730030a1, 0x73eda583};
+    static constexpr storage<limbs_count> inv23=  {0x00000201, 0x034801ff, 0x84b655ff, 0x100d995b, 0x95f1c7f2, 0xf83f47a1, 0x4e4ed6f4, 0x73eda66b};
+    static constexpr storage<limbs_count> inv24=  {0x00000101, 0x01a400ff, 0x425a58ff, 0xb1e59eaf, 0xcfc9cffb, 0x95bc8fd4, 0x3bf62a1e, 0x73eda6df};
+    static constexpr storage<limbs_count> inv25=  {0x00000081, 0x00d2007f, 0x212c5a7f, 0x82d1a159, 0x6cb5d400, 0x647b33ee, 0x32c9d3b3, 0x73eda719};
+    static constexpr storage<limbs_count> inv26=  {0x00000041, 0x0069003f, 0x10955b3f, 0xeb47a2ae, 0x3b2bd602, 0xcbda85fb, 0x2e33a87d, 0x73eda736};
+    static constexpr storage<limbs_count> inv27=  {0x00000021, 0x0034801f, 0x8849db9f, 0x1f82a358, 0xa266d704, 0xff8a2f01, 0xabe892e2, 0x73eda744};
+    static constexpr storage<limbs_count> inv28=  {0x00000011, 0x001a400f, 0xc4241bcf, 0xb9a023ad, 0xd6045784, 0x99620384, 0xeac30815, 0x73eda74b};
+    static constexpr storage<limbs_count> inv29=  {0x00000009, 0x000d2007, 0x62113be7, 0x06aee3d8, 0x6fd317c5, 0xe64dedc6, 0x8a3042ae, 0x73eda74f};
+    static constexpr storage<limbs_count> inv30=  {0x00000005, 0x00069003, 0xb107cbf3, 0x2d3643ed, 0x3cba77e5, 0x8cc3e2e7, 0x59e6dffb, 0x73eda751};
+    static constexpr storage<limbs_count> inv31=  {0x00000003, 0x00034801, 0x588313f9, 0x4079f3f8, 0xa32e27f5, 0xdffedd77, 0x41c22ea1, 0x73eda752};
+    static constexpr storage<limbs_count> inv32=  {0x00000002, 0x0001a400, 0xac40b7fc, 0x4a1bcbfd, 0xd667fffd, 0x099c5abf, 0xb5afd5f5, 0x73eda752};
+
+    static constexpr storage_array<omegas_count, limbs_count> inv = {
+        inv1, inv2, inv3, inv4, inv5, inv6, inv7, inv8, 
+        inv9, inv10, inv11, inv12, inv13, inv14, inv15, inv16,
+        inv17, inv18, inv19, inv20, inv21, inv22, inv23, inv24,
+        inv25, inv26, inv27, inv28, inv29, inv30, inv31, inv32,
+    }; 
+};
+  
+  struct fq_config {
+    // field structure size = 12 * 32 bit
+    static constexpr unsigned limbs_count = 12;
+    // modulus = 4002409555221667393417789825735904156556882819939007885332058136124031650490837864442687629129015664037894272559787
+    static constexpr storage<limbs_count> modulus = {0xffffaaab, 0xb9feffff, 0xb153ffff, 0x1eabfffe, 0xf6b0f624, 0x6730d2a0, 0xf38512bf, 0x64774b84, 0x434bacd7, 0x4b1ba7b6, 0x397fe69a, 0x1a0111ea};
+    // modulus*2 = 8004819110443334786835579651471808313113765639878015770664116272248063300981675728885375258258031328075788545119574
+    static constexpr storage<limbs_count> modulus_2 = {0xffff5556, 0x73fdffff, 0x62a7ffff, 0x3d57fffd, 0xed61ec48, 0xce61a541, 0xe70a257e, 0xc8ee9709, 0x869759ae, 0x96374f6c, 0x72ffcd34, 0x340223d4};
+    // modulus*4 = 16009638220886669573671159302943616626227531279756031541328232544496126601963351457770750516516062656151577090239148
+    static constexpr storage<limbs_count> modulus_4 = {0xfffeaaac, 0xe7fbffff, 0xc54ffffe, 0x7aaffffa, 0xdac3d890, 0x9cc34a83, 0xce144afd, 0x91dd2e13, 0xd2eb35d, 0x2c6e9ed9, 0xe5ff9a69, 0x680447a8};
+    
+    static constexpr storage<2*limbs_count> modulus_wide = {0xffffaaab, 0xb9feffff, 0xb153ffff, 0x1eabfffe, 0xf6b0f624, 0x6730d2a0, 0xf38512bf, 0x64774b84, 
+                                                            0x434bacd7, 0x4b1ba7b6, 0x397fe69a, 0x1a0111ea, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
+                                                            0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+  
+    // modulus^2
+    static constexpr storage<2*limbs_count> modulus_squared = {0x1c718e39, 0x26aa0000, 0x76382eab, 0x7ced6b1d, 0x62113cfd, 0x162c3383, 0x3e71b743, 0x66bf91ed, 
+                                                              0x7091a049, 0x292e85a8, 0x86185c7b, 0x1d68619c, 0x0978ef01, 0xf5314933, 0x16ddca6e, 0x50a62cfd, 
+                                                              0x349e8bd0, 0x66e59e49, 0x0e7046b4, 0xe2dc90e5, 0xa22f25e9, 0x4bd278ea, 0xb8c35fc7, 0x02a437a4};
+    // 2*modulus^2
+    static constexpr storage<2*limbs_count> modulus_squared_2 = {0x38e31c72, 0x4d540000, 0xec705d56, 0xf9dad63a, 0xc42279fa, 0x2c586706, 0x7ce36e86, 0xcd7f23da, 
+                                                                0xe1234092, 0x525d0b50, 0x0c30b8f6, 0x3ad0c339, 0x12f1de02, 0xea629266, 0x2dbb94dd, 0xa14c59fa, 
+                                                                0x693d17a0, 0xcdcb3c92, 0x1ce08d68, 0xc5b921ca, 0x445e4bd3, 0x97a4f1d5, 0x7186bf8e, 0x05486f49};
+    // 4*modulus^2
+    static constexpr storage<2*limbs_count> modulus_squared_4 = {0x71c638e4, 0x9aa80000, 0xd8e0baac, 0xf3b5ac75, 0x8844f3f5, 0x58b0ce0d, 0xf9c6dd0c, 0x9afe47b4, 
+                                                                0xc2468125, 0xa4ba16a1, 0x186171ec, 0x75a18672, 0x25e3bc04, 0xd4c524cc, 0x5b7729bb, 0x4298b3f4, 
+                                                                0xd27a2f41, 0x9b967924, 0x39c11ad1, 0x8b724394, 0x88bc97a7, 0x2f49e3aa, 0xe30d7f1d, 0x0a90de92};
+    static constexpr unsigned modulus_bit_count = 381;
+    // m = floor(2^(2*modulus_bit_count) / modulus)
+    static constexpr storage<limbs_count> m = {0xd59646e8, 0xec4f881f, 0x8163c701, 0x4e65c59e, 0x80a19de7, 0x2f7d1dc7, 0x7fda82a5, 0xa46e09d0, 0x331e9ae8, 0x38a0406c, 0xcf327917, 0x2760d74b};
+    static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    // i^2, the square of the imaginary unit for the extension field
+    static constexpr uint32_t i_squared = 1;
+    // true if i^2 is negative
+    static constexpr bool i_squared_is_negative = true;
+    // G1 and G2 generators 
+    static constexpr storage<limbs_count> g1_gen_x = {0xdb22c6bb, 0xfb3af00a, 0xf97a1aef, 0x6c55e83f, 0x171bac58, 0xa14e3a3f,
+                                                         0x9774b905, 0xc3688c4f, 0x4fa9ac0f, 0x2695638c, 0x3197d794, 0x17f1d3a7};
+    static constexpr storage<limbs_count> g1_gen_y = {0x46c5e7e1, 0x0caa2329, 0xa2888ae4, 0xd03cc744, 0x2c04b3ed, 0x00db18cb,
+                                                         0xd5d00af6, 0xfcf5e095, 0x741d8ae4, 0xa09e30ed, 0xe3aaa0f1, 0x08b3f481};
+    static constexpr storage<limbs_count> g2_gen_x_re = {0xc121bdb8, 0xd48056c8, 0xa805bbef, 0x0bac0326, 0x7ae3d177, 0xb4510b64,
+                                                            0xfa403b02, 0xc6e47ad4, 0x2dc51051, 0x26080527, 0xf08f0a91, 0x024aa2b2};
+    static constexpr storage<limbs_count> g2_gen_x_im = {0x5d042b7e, 0xe5ac7d05, 0x13945d57, 0x334cf112, 0xdc7f5049, 0xb5da61bb,
+                                                            0x9920b61a, 0x596bd0d0, 0x88274f65, 0x7dacd3a0, 0x52719f60, 0x13e02b60};
+    static constexpr storage<limbs_count> g2_gen_y_re = {0x08b82801, 0xe1935486, 0x3baca289, 0x923ac9cc, 0x5160d12c, 0x6d429a69,
+                                                            0x8cbdd3a7, 0xadfd9baa, 0xda2e351a, 0x8cc9cdc6, 0x727d6e11, 0x0ce5d527};
+    static constexpr storage<limbs_count> g2_gen_y_im = {0xf05f79be, 0xaaa9075f, 0x5cec1da1, 0x3f370d27, 0x572e99ab, 0x267492ab,
+                                                            0x85a763af, 0xcb3e287e, 0x2bc28b99, 0x32acd2b0, 0x2ea734cc, 0x0606c4a0};
+  };
+  
+  static constexpr storage<fq_config::limbs_count> weierstrass_b = {0x00000004, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
+                                                                    0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+  static constexpr storage<fq_config::limbs_count> weierstrass_b_g2_re = {0x00000004, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
+                                                                          0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+  static constexpr storage<fq_config::limbs_count> weierstrass_b_g2_im = {0x00000004, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
+                                                                          0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+}

icicle/curves/bls12_381/poseidon.cu

@@ -0,0 +1,47 @@
+#ifndef _BLS12_381_POSEIDON
+#define _BLS12_381_POSEIDON
+#include <cuda.h>
+#include <stdexcept>
+#include "../../appUtils/poseidon/poseidon.cu"
+#include "curve_config.cuh"
+
+template class Poseidon<BLS12_381::scalar_t>;
+
+extern "C" int poseidon_multi_cuda_bls12_381(BLS12_381::scalar_t input[], BLS12_381::scalar_t* out,
+                                             size_t number_of_blocks, int arity, size_t device_id = 0, cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: once we get bindings to pass a stream, we should make {stream} a required parameter and use it instead of 
+    // creating a new stream
+    if (stream == 0) {
+        cudaStreamCreate(&stream);
+    }
+    
+    cudaEvent_t start_event, end_event;
+    cudaEventCreate(&start_event);
+    cudaEventCreate(&end_event);
+    cudaEventRecord(start_event, stream);
+    Poseidon<BLS12_381::scalar_t> poseidon(arity, stream);
+    poseidon.hash_blocks(input, number_of_blocks, out, Poseidon<BLS12_381::scalar_t>::HashType::MerkleTree, stream);
+    cudaEventRecord(end_event, stream);
+    cudaEventSynchronize(end_event);
+
+    #ifdef DEBUG
+    float elapsedTime;
+    cudaEventElapsedTime(&elapsedTime, start_event, end_event);
+    printf("Time elapsed: %f", elapsedTime);
+    #endif
+
+    cudaEventDestroy(start_event);
+    cudaEventDestroy(end_event);
+
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what());
+    return -1;
+  }
+}
+#endif

icicle/curves/bls12_381/projective.cu

@@ -0,0 +1,19 @@
+#include <cuda.h>
+#include "curve_config.cuh"
+#include "../../primitives/projective.cuh"
+
+extern "C" bool eq_bls12_381(BLS12_381::projective_t *point1, BLS12_381::projective_t *point2)
+{
+    return (*point1 == *point2) && 
+    !((point1->x == BLS12_381::point_field_t::zero()) && (point1->y == BLS12_381::point_field_t::zero()) && (point1->z == BLS12_381::point_field_t::zero())) && 
+    !((point2->x == BLS12_381::point_field_t::zero()) && (point2->y == BLS12_381::point_field_t::zero()) && (point2->z == BLS12_381::point_field_t::zero()));
+}
+
+#if defined(G2_DEFINED)
+extern "C" bool eq_g2_bls12_381(BLS12_381::g2_projective_t *point1, BLS12_381::g2_projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == BLS12_381::g2_point_field_t::zero()) && (point1->y == BLS12_381::g2_point_field_t::zero()) && (point1->z == BLS12_381::g2_point_field_t::zero())) && 
+  !((point2->x == BLS12_381::g2_point_field_t::zero()) && (point2->y == BLS12_381::g2_point_field_t::zero()) && (point2->z == BLS12_381::g2_point_field_t::zero()));
+}
+#endif

icicle/curves/bls12_381/supported_operations.cu

@@ -0,0 +1,5 @@
+#include "projective.cu"
+#include "lde.cu"
+#include "msm.cu"
+#include "ve_mod_mult.cu"
+#include "poseidon.cu"

icicle/curves/bls12_381/ve_mod_mult.cu

@@ -0,0 +1,68 @@
+#ifndef _BLS12_381_VEC_MULT
+#define _BLS12_381_VEC_MULT
+#include <stdio.h>
+#include <iostream>
+#include "../../primitives/field.cuh"
+#include "../../utils/storage.cuh"
+#include "../../primitives/projective.cuh"
+#include "curve_config.cuh"
+#include "../../appUtils/vector_manipulation/ve_mod_mult.cuh"
+
+extern "C" int32_t vec_mod_mult_point_bls12_381(BLS12_381::projective_t *inout,
+                                      BLS12_381::scalar_t *scalar_vec,
+                                      size_t n_elments,
+                                      size_t device_id,
+                                      cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    vector_mod_mult<BLS12_381::projective_t, BLS12_381::scalar_t>(scalar_vec, inout, inout, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+
+extern "C" int32_t vec_mod_mult_scalar_bls12_381(BLS12_381::scalar_t *inout,
+                                       BLS12_381::scalar_t *scalar_vec,
+                                       size_t n_elments,
+                                       size_t device_id,
+                                       cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    vector_mod_mult<BLS12_381::scalar_t, BLS12_381::scalar_t>(scalar_vec, inout, inout, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+
+extern "C" int32_t matrix_vec_mod_mult_bls12_381(BLS12_381::scalar_t *matrix_flattened,
+                                       BLS12_381::scalar_t *input,
+                                       BLS12_381::scalar_t *output,
+                                       size_t n_elments,
+                                       size_t device_id,
+                                       cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    matrix_mod_mult<BLS12_381::scalar_t>(matrix_flattened, input, output, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+#endif

icicle/curves/bn254/curve_config.cuh

@@ -0,0 +1,25 @@
+#pragma once
+
+#include "../../primitives/field.cuh"
+#include "../../primitives/projective.cuh"
+#if defined(G2_DEFINED)
+#include "../../primitives/extension_field.cuh"
+#endif
+
+#include "params.cuh"
+
+namespace BN254 {
+    typedef Field<PARAMS_BN254::fp_config> scalar_field_t;
+    typedef scalar_field_t scalar_t;
+    typedef Field<PARAMS_BN254::fq_config> point_field_t;
+    static constexpr point_field_t b = point_field_t{ PARAMS_BN254::weierstrass_b };
+    typedef Projective<point_field_t, scalar_field_t, b> projective_t;
+    typedef Affine<point_field_t> affine_t;
+    #if defined(G2_DEFINED)
+    typedef ExtensionField<PARAMS_BN254::fq_config> g2_point_field_t;
+    static constexpr g2_point_field_t b_g2 = g2_point_field_t{ point_field_t{ PARAMS_BN254::weierstrass_b_g2_re },
+                                                               point_field_t{ PARAMS_BN254::weierstrass_b_g2_im }};
+    typedef Projective<g2_point_field_t, scalar_field_t, b_g2> g2_projective_t;
+    typedef Affine<g2_point_field_t> g2_affine_t;
+    #endif
+}

icicle/curves/bn254/lde.cu

@@ -0,0 +1,327 @@
+#ifndef _BN254_LDE
+#define _BN254_LDE
+#include <cuda.h>
+#include "../../appUtils/ntt/lde.cu"
+#include "../../appUtils/ntt/ntt.cuh"
+#include "../../appUtils/vector_manipulation/ve_mod_mult.cuh"
+#include "curve_config.cuh"
+
+extern "C" BN254::scalar_t* build_domain_cuda_bn254(uint32_t domain_size, uint32_t logn, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        if (inverse) {
+            return fill_twiddle_factors_array(domain_size, BN254::scalar_t::omega_inv(logn), stream);
+        } else {
+            return fill_twiddle_factors_array(domain_size, BN254::scalar_t::omega(logn), stream);
+        }
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return nullptr;
+    }
+}
+
+extern "C" int ntt_cuda_bn254(BN254::scalar_t *arr, uint32_t n, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_template<BN254::scalar_t,BN254::scalar_t>(arr, n, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        
+        return -1;        
+    }
+}
+
+extern "C" int ecntt_cuda_bn254(BN254::projective_t *arr, uint32_t n, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_template<BN254::projective_t,BN254::scalar_t>(arr, n, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int ntt_batch_cuda_bn254(BN254::scalar_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_batch_template<BN254::scalar_t,BN254::scalar_t>(arr, arr_size, batch_size, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int ecntt_batch_cuda_bn254(BN254::projective_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_batch_template<BN254::projective_t,BN254::scalar_t>(arr, arr_size, batch_size, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_scalars_cuda_bn254(BN254::scalar_t* d_out, BN254::scalar_t *d_evaluations, BN254::scalar_t *d_domain, unsigned n, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        return interpolate(d_out, d_evaluations, d_domain, n, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_scalars_batch_cuda_bn254(BN254::scalar_t* d_out, BN254::scalar_t* d_evaluations, BN254::scalar_t* d_domain, unsigned n,
+                                              unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_points_cuda_bn254(BN254::projective_t* d_out, BN254::projective_t *d_evaluations, BN254::scalar_t *d_domain, unsigned n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        return interpolate(d_out, d_evaluations, d_domain, n, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_points_batch_cuda_bn254(BN254::projective_t* d_out, BN254::projective_t* d_evaluations, BN254::scalar_t* d_domain,
+                                             unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_cuda_bn254(BN254::scalar_t* d_out, BN254::scalar_t *d_coefficients, BN254::scalar_t *d_domain, 
+                                     unsigned domain_size, unsigned n, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BN254::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_batch_cuda_bn254(BN254::scalar_t* d_out, BN254::scalar_t* d_coefficients, BN254::scalar_t* d_domain, unsigned domain_size,
+                                           unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BN254::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_cuda_bn254(BN254::projective_t* d_out, BN254::projective_t *d_coefficients, BN254::scalar_t *d_domain, 
+                                    unsigned domain_size, unsigned n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BN254::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_batch_cuda_bn254(BN254::projective_t* d_out, BN254::projective_t* d_coefficients, BN254::scalar_t* d_domain, unsigned domain_size,
+                                          unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        BN254::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_on_coset_cuda_bn254(BN254::scalar_t* d_out, BN254::scalar_t *d_coefficients, BN254::scalar_t *d_domain, unsigned domain_size,
+                                              unsigned n, BN254::scalar_t *coset_powers, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_on_coset_batch_cuda_bn254(BN254::scalar_t* d_out, BN254::scalar_t* d_coefficients, BN254::scalar_t* d_domain, unsigned domain_size, 
+                                                    unsigned n, unsigned batch_size, BN254::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_on_coset_cuda_bn254(BN254::projective_t* d_out, BN254::projective_t *d_coefficients, BN254::scalar_t *d_domain, unsigned domain_size,
+                                             unsigned n, BN254::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_on_coset_batch_cuda_bn254(BN254::projective_t* d_out, BN254::projective_t* d_coefficients, BN254::scalar_t* d_domain, unsigned domain_size, 
+                                                   unsigned n, unsigned batch_size, BN254::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_scalars_cuda_bn254(BN254::scalar_t* arr, int n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order(arr, n, logn, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_scalars_batch_cuda_bn254(BN254::scalar_t* arr, int n, int batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order_batch(arr, n, logn, batch_size, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_points_cuda_bn254(BN254::projective_t* arr, int n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order(arr, n, logn, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_points_batch_cuda_bn254(BN254::projective_t* arr, int n, int batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order_batch(arr, n, logn, batch_size, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+#endif

icicle/curves/bn254/msm.cu

@@ -0,0 +1,92 @@
+#ifndef _BN254_MSM
+#define _BN254_MSM
+#include "../../appUtils/msm/msm.cu"
+#include <stdexcept>
+#include <cuda.h>
+#include "curve_config.cuh"
+
+
+extern "C"
+int msm_cuda_bn254(BN254::projective_t *out, BN254::affine_t points[],
+              BN254::scalar_t scalars[], size_t count, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        large_msm<BN254::scalar_t, BN254::projective_t, BN254::affine_t>(scalars, points, count, out, false, false, stream);
+        return CUDA_SUCCESS;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int msm_batch_cuda_bn254(BN254::projective_t* out, BN254::affine_t points[],
+                              BN254::scalar_t scalars[], size_t batch_size, size_t msm_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+  try
+  {
+    cudaStreamCreate(&stream);
+    batched_large_msm<BN254::scalar_t, BN254::projective_t, BN254::affine_t>(scalars, points, batch_size, msm_size, out, false, stream);
+    cudaStreamSynchronize(stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what());
+    return -1;
+  }
+}
+
+/**
+ * Commit to a polynomial using the MSM.
+ * Note: this function just calls the MSM, it doesn't convert between evaluation and coefficient form of scalars or points.
+ * @param d_out Ouptut point to write the result to.
+ * @param d_scalars Scalars for the MSM. Must be on device.
+ * @param d_points Points for the MSM. Must be on device.
+ * @param count Length of `d_scalars` and `d_points` arrays (they should have equal length).
+ */
+ extern "C"
+ int commit_cuda_bn254(BN254::projective_t* d_out, BN254::scalar_t* d_scalars, BN254::affine_t* d_points, size_t count, size_t device_id = 0, cudaStream_t stream = 0)
+ {
+     try
+     {
+         large_msm(d_scalars, d_points, count, d_out, true, false, stream);
+         cudaStreamSynchronize(stream);
+         return 0;
+     }
+     catch (const std::runtime_error &ex)
+     {
+         printf("error %s", ex.what());
+         return -1;
+     }
+ }
+ 
+ /**
+  * Commit to a batch of polynomials using the MSM.
+  * Note: this function just calls the MSM, it doesn't convert between evaluation and coefficient form of scalars or points.
+  * @param d_out Ouptut point to write the results to.
+  * @param d_scalars Scalars for the MSMs of all polynomials. Must be on device.
+  * @param d_points Points for the MSMs. Must be on device. It is assumed that this set of bases is used for each MSM.
+  * @param count Length of `d_points` array, `d_scalar` has length `count` * `batch_size`.
+  * @param batch_size Size of the batch.
+  */
+ extern "C"
+ int commit_batch_cuda_bn254(BN254::projective_t* d_out, BN254::scalar_t* d_scalars, BN254::affine_t* d_points, size_t count, size_t batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+ {
+     try
+     {
+        cudaStreamCreate(&stream);
+         batched_large_msm(d_scalars, d_points, batch_size, count, d_out, true, stream);
+         cudaStreamSynchronize(stream);
+         return 0;
+     }
+     catch (const std::runtime_error &ex)
+     {
+         printf("error %s", ex.what());
+         return -1;
+     }
+ }
+
+ #endif

icicle/curves/bn254/params.cuh

@@ -0,0 +1,171 @@
+#pragma once
+#include "../../utils/storage.cuh"
+
+namespace PARAMS_BN254{
+  struct fp_config{
+    static constexpr unsigned limbs_count = 8;
+    static constexpr unsigned omegas_count = 32;
+    
+    static constexpr storage<limbs_count> modulus = {0xf0000001, 0x43e1f593, 0x79b97091, 0x2833e848, 0x8181585d, 0xb85045b6, 0xe131a029, 0x30644e72};
+    static constexpr storage<limbs_count> modulus_2 = {0xe0000002, 0x87c3eb27, 0xf372e122, 0x5067d090, 0x0302b0ba, 0x70a08b6d, 0xc2634053, 0x60c89ce5};
+    static constexpr storage<limbs_count> modulus_4 = {0xc0000004, 0x0f87d64f, 0xe6e5c245, 0xa0cfa121, 0x06056174, 0xe14116da, 0x84c680a6, 0xc19139cb};
+    static constexpr storage<2*limbs_count> modulus_wide = {0xf0000001, 0x43e1f593, 0x79b97091, 0x2833e848, 0x8181585d, 0xb85045b6, 0xe131a029, 0x30644e72, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<2*limbs_count> modulus_squared = {0xe0000001, 0x08c3eb27, 0xdcb34000, 0xc7f26223, 0x68c9bb7f, 0xffe9a62c, 0xe821ddb0, 0xa6ce1975, 0x47b62fe7, 0x2c77527b, 0xd379d3df, 0x85f73bb0, 0x0348d21c, 0x599a6f7c, 0x763cbf9c, 0x0925c4b8};
+    static constexpr storage<2*limbs_count> modulus_squared_2 = {0xc0000002, 0x1187d64f, 0xb9668000, 0x8fe4c447, 0xd19376ff, 0xffd34c58, 0xd043bb61, 0x4d9c32eb, 0x8f6c5fcf, 0x58eea4f6, 0xa6f3a7be, 0x0bee7761, 0x0691a439, 0xb334def8, 0xec797f38, 0x124b8970};
+    static constexpr storage<2*limbs_count> modulus_squared_4 = {0x80000004, 0x230fac9f, 0x72cd0000, 0x1fc9888f, 0xa326edff, 0xffa698b1, 0xa08776c3, 0x9b3865d7, 0x1ed8bf9e, 0xb1dd49ed, 0x4de74f7c, 0x17dceec3, 0x0d234872, 0x6669bdf0, 0xd8f2fe71, 0x249712e1};
+    static constexpr unsigned modulus_bit_count = 254;
+    static constexpr storage<limbs_count> m = {0xbe1de925, 0x620703a6, 0x09e880ae, 0x71448520, 0x68073014, 0xab074a58, 0x623a04a7, 0x54a47462};
+    static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+
+    static constexpr storage<limbs_count> omega1= {0xf0000000, 0x43e1f593, 0x79b97091, 0x2833e848, 0x8181585d, 0xb85045b6, 0xe131a029, 0x30644e72};
+    static constexpr storage<limbs_count> omega2= {0x8f703636, 0x23120470, 0xfd736bec, 0x5cea24f6, 0x3fd84104, 0x048b6e19, 0xe131a029, 0x30644e72};
+    static constexpr storage<limbs_count> omega3= {0xc1bd5e80, 0x948dad4a, 0xf8170a0a, 0x52627366, 0x96afef36, 0xec9b9e2f, 0xc8c14f22, 0x2b337de1};
+    static constexpr storage<limbs_count> omega4= {0xe306460b, 0xb11509c6, 0x174efb98, 0x996dfbe1, 0x94dd508c, 0x1c6e4f45, 0x16cbbf4e, 0x21082ca2};
+    static constexpr storage<limbs_count> omega5= {0x3bb512d0, 0x3eed4c53, 0x838eeb1d, 0x9c18d51b, 0x47c0b2a9, 0x9678200d, 0x306b93d2, 0x09c532c6};
+    static constexpr storage<limbs_count> omega6= {0x118f023a, 0xdb94fb05, 0x26e324be, 0x46a6cb24, 0x49bdadf2, 0xc24cdb76, 0x5b080fca, 0x1418144d};
+    static constexpr storage<limbs_count> omega7= {0xba9d1811, 0x9d0e470c, 0xb6f24c79, 0x1dcb5564, 0xe85943e0, 0xdf5ce19c, 0xad310991, 0x16e73dfd};
+    static constexpr storage<limbs_count> omega8= {0x74a57a76, 0xc8936191, 0x6750f230, 0x61794254, 0x9f36ffb0, 0xf086204a, 0xa6148404, 0x07b0c561};
+    static constexpr storage<limbs_count> omega9= {0x470157ce, 0x893a7fa1, 0xfc782d75, 0xe8302a41, 0xdd9b0675, 0xffc02c0e, 0xf6e72f5b, 0x0f1ded1e};
+    static constexpr storage<limbs_count> omega10= {0xbc2e5912, 0x11f995e1, 0xa8d2d7ab, 0x39ba79c0, 0xb08771e3, 0xebbebc2b, 0x7017a420, 0x06fd19c1};
+    static constexpr storage<limbs_count> omega11= {0x769a2ee2, 0xd00a58f9, 0x7494f0ca, 0xb8c12c17, 0xa5355d71, 0xb4027fd7, 0x99c5042b, 0x027a3584};
+    static constexpr storage<limbs_count> omega12= {0x0042d43a, 0x1c477572, 0x6f039bb9, 0x76f169c7, 0xfd5a90a9, 0x01ddd073, 0xde2fd10f, 0x0931d596};
+    static constexpr storage<limbs_count> omega13= {0x9bbdd310, 0x4aa49b8d, 0x8e3a2d76, 0xd31bf3e2, 0x78b2667b, 0x001deac8, 0xb869ae62, 0x006fab49};
+    static constexpr storage<limbs_count> omega14= {0x617c6e85, 0xadaa01c2, 0x7420aae6, 0xb4a93ee1, 0x0ddca8a8, 0x1f4e51b8, 0xcdd9e481, 0x2d965651};
+    static constexpr storage<limbs_count> omega15= {0x4e26ecfb, 0xa93458fd, 0x4115a009, 0x022a2a2d, 0x69ec2bd0, 0x017171fa, 0x5941dc91, 0x2d1ba66f};
+    static constexpr storage<limbs_count> omega16= {0xdaac43b7, 0xd1628ba2, 0xe4347e7d, 0x16c8601d, 0xe081dcff, 0x649abebd, 0x5981ed45, 0x00eeb2cb};
+    static constexpr storage<limbs_count> omega17= {0xec8af73d, 0x8d24de3c, 0xcf722b45, 0x50f778d4, 0x15bc7dd7, 0xf4506bc3, 0xf94a16e1, 0x0e43ba91};
+    static constexpr storage<limbs_count> omega18= {0xd4405b8f, 0x0baa7b44, 0xee0f1394, 0xf8f3c7fe, 0xef0dfe6d, 0x46b153c0, 0x2dde6b95, 0x0ea2bcd9};
+    static constexpr storage<limbs_count> omega19= {0x3d1fa34e, 0x5f4dc975, 0x15af81db, 0xc28e54ee, 0x04947d99, 0x83d9a55f, 0x54a2b488, 0x08ec7ccf};
+    static constexpr storage<limbs_count> omega20= {0x0cac0ee8, 0x0d8fa7b3, 0x82ef38e4, 0x756284ed, 0xac8f90d2, 0x7014b194, 0x634e5d50, 0x092488f8};
+    static constexpr storage<limbs_count> omega21= {0x6d34ed69, 0xd85399bf, 0x09e49cef, 0x4d9012ba, 0xca00ae5d, 0x020142ee, 0x3bdfebfd, 0x12772e57};
+    static constexpr storage<limbs_count> omega22= {0x2eb41723, 0x676c8fc7, 0x5dd895bd, 0xe20380e2, 0x9bf22dde, 0x09dc8be8, 0x42638176, 0x12822f94};
+    static constexpr storage<limbs_count> omega23= {0x81a6d2de, 0x1f1df770, 0xcf29c812, 0x5d33b2da, 0x134f0e7e, 0x1bf162de, 0x1e2877a8, 0x045162c4};
+    static constexpr storage<limbs_count> omega24= {0xfecda1b6, 0x24f4503b, 0xded67d3c, 0x0e5d7ed3, 0x40cf20af, 0x2b7b7e5e, 0x4faad6af, 0x0d472650};
+    static constexpr storage<limbs_count> omega25= {0x584b9eb1, 0xcc6c474c, 0x15a8d886, 0x47670804, 0xbb8654c5, 0x07736d2f, 0xeb207a4b, 0x0d14ce7a};
+    static constexpr storage<limbs_count> omega26= {0xed25924a, 0xd1c6471c, 0x6bc312c3, 0xd98bb374, 0xfeae1a41, 0x50be0848, 0x3265c719, 0x04b07dea};
+    static constexpr storage<limbs_count> omega27= {0x618241e3, 0xab13f73e, 0x166ca902, 0x571c9267, 0x5e828a6d, 0x8586443a, 0x6daba50b, 0x093fdf2f};
+    static constexpr storage<limbs_count> omega28= {0xee11c34f, 0xe688e66b, 0xeacecf5a, 0xdc232eae, 0xb95ae685, 0x4fc35094, 0x7c1d31dc, 0x0273b5bd};
+    static constexpr storage<limbs_count> omega29= {0x1a9057bd, 0x8a8a5a77, 0x41834fbb, 0xdcbfae1d, 0xb34ede6e, 0x534f5b97, 0xb78bbd3e, 0x07313ac5};
+    static constexpr storage<limbs_count> omega30= {0x2be70731, 0x287abbb1, 0x7c35c5aa, 0x5cbcfd1e, 0x1671f4df, 0x7585b3fe, 0xb899c011, 0x08350ecf};
+    static constexpr storage<limbs_count> omega31= {0x09f7c5e2, 0x3400c14e, 0x0a649ea1, 0xc112e60c, 0x067ce95e, 0xf7510758, 0xf9daf17c, 0x040a66a5};
+    static constexpr storage<limbs_count> omega32= {0x43efecd3, 0x89d65957, 0x3bd6c318, 0x29246adc, 0xce01533c, 0xf9fb5ef6, 0x849078c3, 0x020410e4};
+
+    static constexpr storage_array<omegas_count, limbs_count> omega = {
+        omega1, omega2, omega3, omega4, omega5, omega6, omega7, omega8, 
+        omega9, omega10, omega11, omega12, omega13, omega14, omega15, omega16,
+        omega17, omega18, omega19, omega20, omega21, omega22, omega23, omega24,
+        omega25, omega26, omega27, omega28, omega29, omega30, omega31, omega32,
+    };
+
+    static constexpr storage<limbs_count> omega_inv1= {0xf0000000, 0x43e1f593, 0x79b97091, 0x2833e848, 0x8181585d, 0xb85045b6, 0xe131a029, 0x30644e72};
+    static constexpr storage<limbs_count> omega_inv2= {0x608fc9cb, 0x20cff123, 0x7c4604a5, 0xcb49c351, 0x41a91758, 0xb3c4d79d, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> omega_inv3= {0x07b95a9b, 0x8b11d9ab, 0x41671f56, 0x20710ead, 0x30f81dee, 0xfb3acaee, 0x9778465c, 0x130b1711};
+    static constexpr storage<limbs_count> omega_inv4= {0x373428de, 0xb85a71e6, 0xaeb0337e, 0x74954d30, 0x303402b7, 0x2bfc85eb, 0x409556c0, 0x02e40daf};
+    static constexpr storage<limbs_count> omega_inv5= {0xf210979d, 0x8c99980c, 0x34905b4d, 0xef8f3113, 0xdf25d8e7, 0x0aeaf3e7, 0x03bfbd79, 0x27247136};
+    static constexpr storage<limbs_count> omega_inv6= {0x763d698f, 0x78ce6a0b, 0x1d3213ee, 0xd80396ec, 0x67a8a676, 0x035cdc75, 0xb2a13d3a, 0x26177cf2};
+    static constexpr storage<limbs_count> omega_inv7= {0xc64427d7, 0xdddf985f, 0xa49e95bd, 0xaa4f964a, 0x5def8b04, 0x427c045f, 0x7969b732, 0x1641c053};
+    static constexpr storage<limbs_count> omega_inv8= {0x0329f5d6, 0x692c553d, 0x8712848a, 0xa54cf8c6, 0x38e2b5e6, 0x64751ad9, 0x7422fad3, 0x204bd327};
+    static constexpr storage<limbs_count> omega_inv9= {0xaf6b3e4e, 0x52f26c0f, 0xf0bcc0c8, 0x4c277a07, 0xe4fcfcab, 0x546875d5, 0xaa9995b3, 0x09d8f821};
+    static constexpr storage<limbs_count> omega_inv10= {0xb2e5cc71, 0xcaa2e1e9, 0x6e43404e, 0xed42b68e, 0x7a2c7f0a, 0x6ed80915, 0xde3c86d6, 0x1c4042c7};
+    static constexpr storage<limbs_count> omega_inv11= {0x579d71ae, 0x20a3a65d, 0x0adc4420, 0xfd7efed8, 0xfddabf54, 0x3bb6dcd7, 0xbc73d07b, 0x0fa9bb21};
+    static constexpr storage<limbs_count> omega_inv12= {0xc79e0e57, 0xb6f70f8d, 0xa04e05ac, 0x269d3fde, 0x2ba088d9, 0xcf2e371c, 0x11b88d9c, 0x1af864d2};
+    static constexpr storage<limbs_count> omega_inv13= {0xabd95dc9, 0x3b0b205a, 0x978188ca, 0xc8df74fa, 0x6a1cb6c8, 0x08e124db, 0xbfac6104, 0x1670ed58};
+    static constexpr storage<limbs_count> omega_inv14= {0x641c8410, 0xf8eee934, 0x677771c0, 0xf40976b0, 0x558e6e8c, 0x11680d42, 0x06e7e9e9, 0x281c036f};
+    static constexpr storage<limbs_count> omega_inv15= {0xb2dbc0b4, 0xc92a742f, 0x4d384e68, 0xc3f02842, 0x2fa43d0d, 0x22701b6f, 0xe4590b37, 0x05d33766};
+    static constexpr storage<limbs_count> omega_inv16= {0x02d842d4, 0x922d5ac8, 0xc830e4c6, 0x91126414, 0x082f37e0, 0xe92338c0, 0x7fe704e8, 0x0b5d56b7};
+    static constexpr storage<limbs_count> omega_inv17= {0xec8af73d, 0x8d24de3c, 0xcf722b45, 0x50f778d4, 0x15bc7dd7, 0xf4506bc3, 0xf94a16e1, 0x0e43ba91};
+    static constexpr storage<limbs_count> omega_inv18= {0xd4405b8f, 0x0baa7b44, 0xee0f1394, 0xf8f3c7fe, 0xef0dfe6d, 0x46b153c0, 0x2dde6b95, 0x0ea2bcd9};
+    static constexpr storage<limbs_count> omega_inv19= {0x3d1fa34e, 0x5f4dc975, 0x15af81db, 0xc28e54ee, 0x04947d99, 0x83d9a55f, 0x54a2b488, 0x08ec7ccf};
+    static constexpr storage<limbs_count> omega_inv20= {0x0cac0ee8, 0x0d8fa7b3, 0x82ef38e4, 0x756284ed, 0xac8f90d2, 0x7014b194, 0x634e5d50, 0x092488f8};
+    static constexpr storage<limbs_count> omega_inv21= {0x6d34ed69, 0xd85399bf, 0x09e49cef, 0x4d9012ba, 0xca00ae5d, 0x020142ee, 0x3bdfebfd, 0x12772e57};
+    static constexpr storage<limbs_count> omega_inv22= {0x2eb41723, 0x676c8fc7, 0x5dd895bd, 0xe20380e2, 0x9bf22dde, 0x09dc8be8, 0x42638176, 0x12822f94};
+    static constexpr storage<limbs_count> omega_inv23= {0x81a6d2de, 0x1f1df770, 0xcf29c812, 0x5d33b2da, 0x134f0e7e, 0x1bf162de, 0x1e2877a8, 0x045162c4};
+    static constexpr storage<limbs_count> omega_inv24= {0xfecda1b6, 0x24f4503b, 0xded67d3c, 0x0e5d7ed3, 0x40cf20af, 0x2b7b7e5e, 0x4faad6af, 0x0d472650};
+    static constexpr storage<limbs_count> omega_inv25= {0x584b9eb1, 0xcc6c474c, 0x15a8d886, 0x47670804, 0xbb8654c5, 0x07736d2f, 0xeb207a4b, 0x0d14ce7a};
+    static constexpr storage<limbs_count> omega_inv26= {0xed25924a, 0xd1c6471c, 0x6bc312c3, 0xd98bb374, 0xfeae1a41, 0x50be0848, 0x3265c719, 0x04b07dea};
+    static constexpr storage<limbs_count> omega_inv27= {0x618241e3, 0xab13f73e, 0x166ca902, 0x571c9267, 0x5e828a6d, 0x8586443a, 0x6daba50b, 0x093fdf2f};
+    static constexpr storage<limbs_count> omega_inv28= {0xee11c34f, 0xe688e66b, 0xeacecf5a, 0xdc232eae, 0xb95ae685, 0x4fc35094, 0x7c1d31dc, 0x0273b5bd};
+    static constexpr storage<limbs_count> omega_inv29= {0x1a9057bd, 0x8a8a5a77, 0x41834fbb, 0xdcbfae1d, 0xb34ede6e, 0x534f5b97, 0xb78bbd3e, 0x07313ac5};
+    static constexpr storage<limbs_count> omega_inv30= {0x2be70731, 0x287abbb1, 0x7c35c5aa, 0x5cbcfd1e, 0x1671f4df, 0x7585b3fe, 0xb899c011, 0x08350ecf};
+    static constexpr storage<limbs_count> omega_inv31= {0x09f7c5e2, 0x3400c14e, 0x0a649ea1, 0xc112e60c, 0x067ce95e, 0xf7510758, 0xf9daf17c, 0x040a66a5};
+    static constexpr storage<limbs_count> omega_inv32= {0x43efecd3, 0x89d65957, 0x3bd6c318, 0x29246adc, 0xce01533c, 0xf9fb5ef6, 0x849078c3, 0x020410e4};
+
+    static constexpr storage_array<omegas_count, limbs_count> omega_inv = {
+        omega_inv1, omega_inv2, omega_inv3, omega_inv4, omega_inv5, omega_inv6, omega_inv7, omega_inv8, 
+        omega_inv9, omega_inv10, omega_inv11, omega_inv12, omega_inv13, omega_inv14, omega_inv15, omega_inv16,
+        omega_inv17, omega_inv18, omega_inv19, omega_inv20, omega_inv21, omega_inv22, omega_inv23, omega_inv24,
+        omega_inv25, omega_inv26, omega_inv27, omega_inv28, omega_inv29, omega_inv30, omega_inv31, omega_inv32,
+    };
+    
+    static constexpr storage<limbs_count> inv1= {0xf8000001, 0xa1f0fac9, 0x3cdcb848, 0x9419f424, 0x40c0ac2e, 0xdc2822db, 0x7098d014, 0x18322739};
+    static constexpr storage<limbs_count> inv2= {0xf4000001, 0xf2e9782e, 0x5b4b146c, 0xde26ee36, 0xe1210245, 0x4a3c3448, 0x28e5381f, 0x244b3ad6};
+    static constexpr storage<limbs_count> inv3= {0x72000001, 0x1b65b6e1, 0x6a82427f, 0x832d6b3f, 0xb1512d51, 0x81463cff, 0x850b6c24, 0x2a57c4a4};
+    static constexpr storage<limbs_count> inv4= {0xb1000001, 0x2fa3d63a, 0xf21dd988, 0x55b0a9c3, 0x196942d7, 0x1ccb415b, 0xb31e8627, 0x2d5e098b};
+    static constexpr storage<limbs_count> inv5= {0x50800001, 0xb9c2e5e7, 0x35eba50c, 0x3ef24906, 0xcd754d9a, 0x6a8dc388, 0x4a281328, 0x2ee12bff};
+    static constexpr storage<limbs_count> inv6= {0xa0400001, 0xfed26dbd, 0x57d28ace, 0xb39318a7, 0xa77b52fb, 0x116f049f, 0x15acd9a9, 0x2fa2bd39};
+    static constexpr storage<limbs_count> inv7= {0xc8200001, 0x215a31a8, 0xe8c5fdb0, 0x6de38077, 0x147e55ac, 0x64dfa52b, 0xfb6f3ce9, 0x300385d5};
+    static constexpr storage<limbs_count> inv8= {0x5c100001, 0xb29e139e, 0x313fb720, 0xcb0bb460, 0xcaffd704, 0x8e97f570, 0x6e506e89, 0x3033ea24};
+    static constexpr storage<limbs_count> inv9= {0x26080001, 0xfb400499, 0x557c93d8, 0xf99fce54, 0xa64097b0, 0xa3741d93, 0xa7c10759, 0x304c1c4b};
+    static constexpr storage<limbs_count> inv10= {0x8b040001, 0x1f90fd16, 0x679b0235, 0x10e9db4e, 0x13e0f807, 0xade231a5, 0x447953c1, 0x3058355f};
+    static constexpr storage<limbs_count> inv11= {0x3d820001, 0x31b97955, 0x70aa3963, 0x1c8ee1cb, 0xcab12832, 0xb3193bad, 0x12d579f5, 0x305e41e9};
+    static constexpr storage<limbs_count> inv12= {0x96c10001, 0x3acdb774, 0xf531d4fa, 0xa2616509, 0x26194047, 0xb5b4c0b2, 0xfa038d0f, 0x3061482d};
+    static constexpr storage<limbs_count> inv13= {0x43608001, 0xbf57d684, 0x3775a2c5, 0x654aa6a9, 0x53cd4c52, 0xb7028334, 0x6d9a969c, 0x3062cb50};
+    static constexpr storage<limbs_count> inv14= {0x19b04001, 0x819ce60c, 0xd89789ab, 0xc6bf4778, 0x6aa75257, 0x37a96475, 0xa7661b63, 0x30638ce1};
+    static constexpr storage<limbs_count> inv15= {0x04d82001, 0x62bf6dd0, 0xa9287d1e, 0x777997e0, 0xf614555a, 0x77fcd515, 0x444bddc6, 0x3063edaa};
+    static constexpr storage<limbs_count> inv16= {0xfa6c1001, 0xd350b1b1, 0x9170f6d7, 0xcfd6c014, 0x3bcad6db, 0x18268d66, 0x92bebef8, 0x30641e0e};
+    static constexpr storage<limbs_count> inv17= {0xec8af73d, 0x8d24de3c, 0xcf722b45, 0x50f778d4, 0x15bc7dd7, 0xf4506bc3, 0xf94a16e1, 0x0e43ba91};
+    static constexpr storage<limbs_count> inv18= {0xd4405b8f, 0x0baa7b44, 0xee0f1394, 0xf8f3c7fe, 0xef0dfe6d, 0x46b153c0, 0x2dde6b95, 0x0ea2bcd9};
+    static constexpr storage<limbs_count> inv19= {0x3d1fa34e, 0x5f4dc975, 0x15af81db, 0xc28e54ee, 0x04947d99, 0x83d9a55f, 0x54a2b488, 0x08ec7ccf};
+    static constexpr storage<limbs_count> inv20= {0x0cac0ee8, 0x0d8fa7b3, 0x82ef38e4, 0x756284ed, 0xac8f90d2, 0x7014b194, 0x634e5d50, 0x092488f8};
+    static constexpr storage<limbs_count> inv21= {0x6d34ed69, 0xd85399bf, 0x09e49cef, 0x4d9012ba, 0xca00ae5d, 0x020142ee, 0x3bdfebfd, 0x12772e57};
+    static constexpr storage<limbs_count> inv22= {0x2eb41723, 0x676c8fc7, 0x5dd895bd, 0xe20380e2, 0x9bf22dde, 0x09dc8be8, 0x42638176, 0x12822f94};
+    static constexpr storage<limbs_count> inv23= {0x81a6d2de, 0x1f1df770, 0xcf29c812, 0x5d33b2da, 0x134f0e7e, 0x1bf162de, 0x1e2877a8, 0x045162c4};
+    static constexpr storage<limbs_count> inv24= {0xfecda1b6, 0x24f4503b, 0xded67d3c, 0x0e5d7ed3, 0x40cf20af, 0x2b7b7e5e, 0x4faad6af, 0x0d472650};
+    static constexpr storage<limbs_count> inv25= {0x584b9eb1, 0xcc6c474c, 0x15a8d886, 0x47670804, 0xbb8654c5, 0x07736d2f, 0xeb207a4b, 0x0d14ce7a};
+    static constexpr storage<limbs_count> inv26= {0xed25924a, 0xd1c6471c, 0x6bc312c3, 0xd98bb374, 0xfeae1a41, 0x50be0848, 0x3265c719, 0x04b07dea};
+    static constexpr storage<limbs_count> inv27= {0x618241e3, 0xab13f73e, 0x166ca902, 0x571c9267, 0x5e828a6d, 0x8586443a, 0x6daba50b, 0x093fdf2f};
+    static constexpr storage<limbs_count> inv28= {0xee11c34f, 0xe688e66b, 0xeacecf5a, 0xdc232eae, 0xb95ae685, 0x4fc35094, 0x7c1d31dc, 0x0273b5bd};
+    static constexpr storage<limbs_count> inv29= {0x1a9057bd, 0x8a8a5a77, 0x41834fbb, 0xdcbfae1d, 0xb34ede6e, 0x534f5b97, 0xb78bbd3e, 0x07313ac5};
+    static constexpr storage<limbs_count> inv30= {0x2be70731, 0x287abbb1, 0x7c35c5aa, 0x5cbcfd1e, 0x1671f4df, 0x7585b3fe, 0xb899c011, 0x08350ecf};
+    static constexpr storage<limbs_count> inv31= {0x09f7c5e2, 0x3400c14e, 0x0a649ea1, 0xc112e60c, 0x067ce95e, 0xf7510758, 0xf9daf17c, 0x040a66a5};
+    static constexpr storage<limbs_count> inv32= {0x43efecd3, 0x89d65957, 0x3bd6c318, 0x29246adc, 0xce01533c, 0xf9fb5ef6, 0x849078c3, 0x020410e4};
+
+    static constexpr storage_array<omegas_count, limbs_count> inv = {
+        inv1, inv2, inv3, inv4, inv5, inv6, inv7, inv8, 
+        inv9, inv10, inv11, inv12, inv13, inv14, inv15, inv16,
+        inv17, inv18, inv19, inv20, inv21, inv22, inv23, inv24,
+        inv25, inv26, inv27, inv28, inv29, inv30, inv31, inv32,
+    }; 
+  };
+
+  struct fq_config{
+    static constexpr unsigned limbs_count = 8;
+    static constexpr storage<limbs_count> modulus = {0xd87cfd47, 0x3c208c16, 0x6871ca8d, 0x97816a91, 0x8181585d, 0xb85045b6, 0xe131a029, 0x30644e72};
+    static constexpr storage<limbs_count> modulus_2 = {0xb0f9fa8e, 0x7841182d, 0xd0e3951a, 0x2f02d522, 0x0302b0bb, 0x70a08b6d, 0xc2634053, 0x60c89ce5};
+    static constexpr storage<limbs_count> modulus_4 = {0x61f3f51c, 0xf082305b, 0xa1c72a34, 0x5e05aa45, 0x06056176, 0xe14116da, 0x84c680a6, 0xc19139cb};
+    static constexpr storage<2*limbs_count> modulus_wide = {0xd87cfd47, 0x3c208c16, 0x6871ca8d, 0x97816a91, 0x8181585d, 0xb85045b6, 0xe131a029, 0x30644e72, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<2*limbs_count> modulus_squared = {0x275d69b1, 0x3b5458a2, 0x09eac101, 0xa602072d, 0x6d96cadc, 0x4a50189c, 0x7a1242c8, 0x04689e95, 0x34c6b38d, 0x26edfa5c, 0x16375606, 0xb00b8551, 0x0348d21c, 0x599a6f7c, 0x763cbf9c, 0x0925c4b8};
+    static constexpr storage<2*limbs_count> modulus_squared_2 = {0x4ebad362, 0x76a8b144, 0x13d58202, 0x4c040e5a, 0xdb2d95b9, 0x94a03138, 0xf4248590, 0x08d13d2a, 0x698d671a, 0x4ddbf4b8, 0x2c6eac0c, 0x60170aa2, 0x0691a439, 0xb334def8, 0xec797f38, 0x124b8970};
+    static constexpr storage<2*limbs_count> modulus_squared_4 = {0x9d75a6c4, 0xed516288, 0x27ab0404, 0x98081cb4, 0xb65b2b72, 0x29406271, 0xe8490b21, 0x11a27a55, 0xd31ace34, 0x9bb7e970, 0x58dd5818, 0xc02e1544, 0x0d234872, 0x6669bdf0, 0xd8f2fe71, 0x249712e1};
+    static constexpr unsigned modulus_bit_count = 254;
+    static constexpr storage<limbs_count> m = {0x19bf90e5, 0x6f3aed8a, 0x67cd4c08, 0xae965e17, 0x68073013, 0xab074a58, 0x623a04a7, 0x54a47462};
+    static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    // i^2, the square of the imaginary unit for the extension field
+    static constexpr uint32_t i_squared = 1;
+    // true if i^2 is negative
+    static constexpr bool i_squared_is_negative = true;
+    // G1 and G2 generators 
+    static constexpr storage<limbs_count> g1_gen_x = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> g1_gen_y = {0x00000002, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+    static constexpr storage<limbs_count> g2_gen_x_re = {0xd992f6ed, 0x46debd5c, 0xf75edadd, 0x674322d4, 0x5e5c4479, 0x426a0066, 0x121f1e76, 0x1800deef};
+    static constexpr storage<limbs_count> g2_gen_x_im = {0xaef312c2, 0x97e485b7, 0x35a9e712, 0xf1aa4933, 0x31fb5d25, 0x7260bfb7, 0x920d483a, 0x198e9393};
+    static constexpr storage<limbs_count> g2_gen_y_re = {0x66fa7daa, 0x4ce6cc01, 0x0c43d37b, 0xe3d1e769, 0x8dcb408f, 0x4aab7180, 0xdb8c6deb, 0x12c85ea5};
+    static constexpr storage<limbs_count> g2_gen_y_im = {0xd122975b, 0x55acdadc, 0x70b38ef3, 0xbc4b3133, 0x690c3395, 0xec9e99ad, 0x585ff075, 0x090689d0};
+  };
+
+  static constexpr storage<fq_config::limbs_count> weierstrass_b = {0x00000003, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
+  static constexpr storage<fq_config::limbs_count> weierstrass_b_g2_re = {0x24a138e5, 0x3267e6dc, 0x59dbefa3, 0xb5b4c5e5, 0x1be06ac3, 0x81be1899, 0xceb8aaae, 0x2b149d40};
+  static constexpr storage<fq_config::limbs_count> weierstrass_b_g2_im = {0x85c315d2, 0xe4a2bd06, 0xe52d1852, 0xa74fa084, 0xeed8fdf4, 0xcd2cafad, 0x3af0fed4, 0x009713b0};
+}

icicle/curves/bn254/projective.cu

@@ -0,0 +1,19 @@
+#include <cuda.h>
+#include "curve_config.cuh"
+#include "../../primitives/projective.cuh"
+
+extern "C" bool eq_bn254(BN254::projective_t *point1, BN254::projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == BN254::point_field_t::zero()) && (point1->y == BN254::point_field_t::zero()) && (point1->z == BN254::point_field_t::zero())) && 
+  !((point2->x == BN254::point_field_t::zero()) && (point2->y == BN254::point_field_t::zero()) && (point2->z == BN254::point_field_t::zero()));
+}
+
+#if defined(G2_DEFINED)
+extern "C" bool eq_g2_bn254(BN254::g2_projective_t *point1, BN254::g2_projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == BN254::g2_point_field_t::zero()) && (point1->y == BN254::g2_point_field_t::zero()) && (point1->z == BN254::g2_point_field_t::zero())) && 
+  !((point2->x == BN254::g2_point_field_t::zero()) && (point2->y == BN254::g2_point_field_t::zero()) && (point2->z == BN254::g2_point_field_t::zero()));
+}
+#endif

icicle/curves/bn254/supported_operations.cu

@@ -0,0 +1,4 @@
+#include "projective.cu"
+#include "lde.cu"
+#include "msm.cu"
+#include "ve_mod_mult.cu"

icicle/curves/bn254/ve_mod_mult.cu

@@ -0,0 +1,75 @@
+#ifndef _BN254_VEC_MULT
+#define _BN254_VEC_MULT
+#include <stdio.h>
+#include <iostream>
+#include "../../primitives/field.cuh"
+#include "../../utils/storage.cuh"
+#include "../../primitives/projective.cuh"
+#include "curve_config.cuh"
+#include "../../appUtils/vector_manipulation/ve_mod_mult.cuh"
+
+
+extern "C" int32_t vec_mod_mult_point_bn254(BN254::projective_t *inout,
+                                      BN254::scalar_t *scalar_vec,
+                                      size_t n_elments,
+                                      size_t device_id,
+                                      cudaStream_t stream = 0)
+{
+  // TODO: use device_id when working with multiple devices
+  (void)device_id;
+  try
+  {
+    // TODO: device_id
+    vector_mod_mult<BN254::projective_t, BN254::scalar_t>(scalar_vec, inout, inout, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+
+extern "C" int32_t vec_mod_mult_scalar_bn254(BN254::scalar_t *inout,
+                                       BN254::scalar_t *scalar_vec,
+                                       size_t n_elments,
+                                       size_t device_id,
+                                       cudaStream_t stream = 0)
+{
+  // TODO: use device_id when working with multiple devices
+  (void)device_id;
+  try
+  {
+    // TODO: device_id
+    vector_mod_mult<BN254::scalar_t, BN254::scalar_t>(scalar_vec, inout, inout, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+
+extern "C" int32_t matrix_vec_mod_mult_bn254(BN254::scalar_t *matrix_flattened,
+                                       BN254::scalar_t *input,
+                                       BN254::scalar_t *output,
+                                       size_t n_elments,
+                                       size_t device_id,
+                                       cudaStream_t stream = 0)
+{
+  // TODO: use device_id when working with multiple devices
+  (void)device_id;
+  try
+  {
+    // TODO: device_id
+    matrix_mod_mult<BN254::scalar_t>(matrix_flattened, input, output, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+#endif

icicle/curves/curve_config.cuh

@@ -1,14 +0,0 @@
-#pragma once
-
-#include "../primitives/field.cuh"
-#include "../primitives/projective.cuh"
-
-#include "bls12_381.cuh"
-// #include "bn254.cuh"
-
-
-typedef Field<fp_config> scalar_field_t;
-typedef scalar_field_t scalar_t;
-typedef Field<fq_config> point_field_t;
-typedef Projective<point_field_t, scalar_field_t, group_generator, weierstrass_b> projective_t;
-typedef Affine<point_field_t> affine_t;

icicle/curves/curve_template.cuh

@@ -1,86 +0,0 @@
-#pragma once
-
-#include "../utils/storage.cuh"
-
-
-// y^2 = weierstrass_a * x^3 + weierstrass_b 
-static constexpr unsigned weierstrass_b = 4;
-
-// a generator of the elliptic curve group
-struct group_generator {
-  static constexpr storage<fq_config::limbs_count> generator_x = {0x00000004, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 
-                                                                  0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-  static constexpr storage<fq_config::limbs_count> generator_y = {0x4abe706c, 0x5ea93e35, 0x00e1de5d, 0x6346b8ed, 0x92848344, 0xda9dd85e, 
-                                                                  0xc9926b26, 0xc760f988, 0xf3763e9b, 0xb33cffc3, 0xd40d6212, 0x0a989bad};
-};
-
-/// SCALAR FIELD
-struct fp_config {
-  // field structure size = 8 * 32 bit
-  static constexpr unsigned limbs_count = 8; // array size of 32bit int fo form a field element
-  static constexpr unsigned modulus_bits_count = 255; // field bit size
-  // field modulus split into array, ordered in Little-Endian 
-  // modulus = 52435875175126190479447740508185965837690552500527637822603658699938581184513 -> 0x73eda753299d7d483339d80809a1d80553bda402fffe5bfeffffffff00000001
-  static constexpr storage<limbs_count> modulus = {0x00000001, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753};
-  // modulus*2 = 104871750350252380958895481016371931675381105001055275645207317399877162369026
-  static constexpr storage<limbs_count> modulus_2 = {0x00000002, 0xfffffffe, 0xfffcb7fd, 0xa77b4805, 0x1343b00a, 0x6673b010, 0x533afa90, 0xe7db4ea6};
-  
-  static constexpr storage<limbs_count> modulus_4 = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-
-  static constexpr storage<2 * limbs_count> modulus_wide = {0x00000001, 0xffffffff, 0xfffe5bfe, 0x53bda402, 0x09a1d805, 0x3339d808, 0x299d7d48, 0x73eda753,
-                                                            0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-  // modulus^2
-  static constexpr storage<2 * limbs_count> modulus_sqared = {0x00000001, 0xfffffffe, 0xfffcb7fe, 0xa77e9007, 0x1cdbb005, 0x698ae002, 0x5433f7b8, 0x48aa415e, 
-                                                            0x4aa9c661, 0xc2611f6f, 0x59934a1d, 0x0e9593f9, 0xef2cc20f, 0x520c13db, 0xf4bc2778, 0x347f60f3};
-  // 2*modulus^2
-  static constexpr storage<2 * limbs_count> modulus_sqared_2 = {0x00000002, 0xfffffffc, 0xfff96ffd, 0x4efd200f, 0x39b7600b, 0xd315c004, 0xa867ef70, 0x915482bc, 
-                                                              0x95538cc2, 0x84c23ede, 0xb326943b, 0x1d2b27f2, 0xde59841e, 0xa41827b7, 0xe9784ef0, 0x68fec1e7};
-  // m = floor(2^(2*modulus_bits_count) / modulus)
-  static constexpr storage<limbs_count> m = {0x830358e4, 0x509cde80, 0x2f92eb5c, 0xd9410fad, 0xc1f823b4, 0xe2d772d, 0x7fb78ddf, 0x8d54253b};
-  static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-  static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-  // Scalar specific
-  static constexpr storage<limbs_count> omega = {0xa5d36306, 0xe206da11, 0x378fbf96, 0x0ad1347b, 0xe0f8245f, 0xfc3e8acf, 0xa0f704f4, 0x564c0a11};
-  static constexpr storage<limbs_count> omega_inv = {3629396834, 2518295853, 1679307267, 1346818424, 3118225798, 1256349690, 3322524792, 958081110};
-  static constexpr storage<limbs_count> inv_2 = {2147483649,2147483647,2147429887,2849952257,80800770,429714436,2496577188,972477353};
-  static constexpr storage<limbs_count> inv_4 = {1073741825,1073741823,1073661183,4274928386,121201155,644571654,1597382134,1458716030};
-  static constexpr storage<limbs_count> inv_256 = {16777217,16777215,4244528547,1315563102,26752557,3943079472,3597918154,1937357227};
-  static constexpr storage<limbs_count> inv_512 = {8388609,8388607,4269694161,1360250160,94177049,2401254172,2148052617,1941155967};
-  static constexpr storage<limbs_count> inv_4096 = {1048577,1074790399,3217972249,3546834984,2300657127,1589027946,3026903920,1944479864};
-};
-
-
-/// BASE FIELD
-struct fq_config {
-  // field structure size = 12 * 32 bit
-  static constexpr unsigned limbs_count = 12; // array size of 32bit int fo form a field element
-  static constexpr unsigned modulus_bits_count = 381; // field bit size
-  // field modulus split into array, ordered in Little-Endian 
-  // modulus = 4002409555221667393417789825735904156556882819939007885332058136124031650490837864442687629129015664037894272559787 -> 0x1a0111ea397fe69a4b1ba7b6434bacd764774b84f38512bf6730d2a0f6b0f6241eabfffeb153ffffb9feffffffffaaab
-  static constexpr storage<limbs_count> modulus = {0xffffaaab, 0xb9feffff, 0xb153ffff, 0x1eabfffe, 0xf6b0f624, 0x6730d2a0, 0xf38512bf, 0x64774b84, 0x434bacd7, 0x4b1ba7b6, 0x397fe69a, 0x1a0111ea};
-  // modulus*2 = 8004819110443334786835579651471808313113765639878015770664116272248063300981675728885375258258031328075788545119574
-  static constexpr storage<limbs_count> modulus_2 = {0xffff5556, 0x73fdffff, 0x62a7ffff, 0x3d57fffd, 0xed61ec48, 0xce61a541, 0xe70a257e, 0xc8ee9709, 0x869759ae, 0x96374f6c, 0x72ffcd34, 0x340223d4};
-  // modulus*4 = 16009638220886669573671159302943616626227531279756031541328232544496126601963351457770750516516062656151577090239148
-  static constexpr storage<limbs_count> modulus_4 = {0xfffeaaac, 0xe7fbffff, 0xc54ffffe, 0x7aaffffa, 0xdac3d890, 0x9cc34a83, 0xce144afd, 0x91dd2e13, 0xd2eb35d, 0x2c6e9ed9, 0xe5ff9a69, 0x680447a8};
-  
-  static constexpr storage<2 * limbs_count> modulus_wide = {0xffffaaab, 0xb9feffff, 0xb153ffff, 0x1eabfffe, 0xf6b0f624, 0x6730d2a0, 0xf38512bf, 0x64774b84, 
-                                                          0x434bacd7, 0x4b1ba7b6, 0x397fe69a, 0x1a0111ea, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
-                                                          0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-
-  // modulus^2
-  static constexpr storage<2 * limbs_count> modulus_sqared = {0x1c718e39, 0x26aa0000, 0x76382eab, 0x7ced6b1d, 0x62113cfd, 0x162c3383, 0x3e71b743, 0x66bf91ed, 
-                                                            0x7091a049, 0x292e85a8, 0x86185c7b, 0x1d68619c, 0x0978ef01, 0xf5314933, 0x16ddca6e, 0x50a62cfd, 
-                                                            0x349e8bd0, 0x66e59e49, 0x0e7046b4, 0xe2dc90e5, 0xa22f25e9, 0x4bd278ea, 0xb8c35fc7, 0x02a437a4};
-  // 2*modulus^2
-  static constexpr storage<2 * limbs_count> modulus_sqared_2 = {0x38e31c72, 0x4d540000, 0xec705d56, 0xf9dad63a, 0xc42279fa, 0x2c586706, 0x7ce36e86, 0xcd7f23da, 
-                                                              0xe1234092, 0x525d0b50, 0x0c30b8f6, 0x3ad0c339, 0x12f1de02, 0xea629266, 0x2dbb94dd, 0xa14c59fa, 
-                                                              0x693d17a0, 0xcdcb3c92, 0x1ce08d68, 0xc5b921ca, 0x445e4bd3, 0x97a4f1d5, 0x7186bf8e, 0x05486f49};
-  // 4*modulus^2
-  static constexpr storage<2 * limbs_count> modulus_sqared_4 = {0x71c638e4, 0x9aa80000, 0xd8e0baac, 0xf3b5ac75, 0x8844f3f5, 0x58b0ce0d, 0xf9c6dd0c, 0x9afe47b4, 
-                                                              0xc2468125, 0xa4ba16a1, 0x186171ec, 0x75a18672, 0x25e3bc04, 0xd4c524cc, 0x5b7729bb, 0x4298b3f4, 
-                                                              0xd27a2f41, 0x9b967924, 0x39c11ad1, 0x8b724394, 0x88bc97a7, 0x2f49e3aa, 0xe30d7f1d, 0x0a90de92};
-  // m = floor(2^(2*modulus_bits_count) / modulus)
-  static constexpr storage<limbs_count> m = {0xd59646e8, 0xec4f881f, 0x8163c701, 0x4e65c59e, 0x80a19de7, 0x2f7d1dc7, 0x7fda82a5, 0xa46e09d0, 0x331e9ae8, 0x38a0406c, 0xcf327917, 0x2760d74b};
-  static constexpr storage<limbs_count> one = {0x00000001, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-  static constexpr storage<limbs_count> zero = {0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000};
-};

icicle/curves/curve_template/curve_config.cuh

@@ -0,0 +1,20 @@
+#pragma once
+#include "../../primitives/field.cuh"
+#include "../../primitives/projective.cuh"
+#include "params.cuh"
+
+namespace ${CURVE_NAME_U} {
+    typedef Field<PARAMS_${CURVE_NAME_U}::fp_config> scalar_field_t;
+    typedef scalar_field_t scalar_t;
+    typedef Field<PARAMS_${CURVE_NAME_U}::fq_config> point_field_t;
+    static constexpr point_field_t b = point_field_t{ PARAMS_${CURVE_NAME_U}::weierstrass_b };
+    typedef Projective<point_field_t, scalar_field_t, b> projective_t;
+    typedef Affine<point_field_t> affine_t;
+    #if defined(G2_DEFINED)
+    typedef ExtensionField<PARAMS_${CURVE_NAME_U}::fq_config> g2_point_field_t;
+    static constexpr g2_point_field_t b_g2 = g2_point_field_t{ point_field_t{ PARAMS_${CURVE_NAME_U}::weierstrass_b_g2_re },
+                                                               point_field_t{ PARAMS_${CURVE_NAME_U}::weierstrass_b_g2_im }};
+    typedef Projective<g2_point_field_t, scalar_field_t, b_g2> g2_projective_t;
+    typedef Affine<g2_point_field_t> g2_affine_t;
+    #endif
+}

icicle/curves/curve_template/lde.cu

@@ -0,0 +1,327 @@
+#ifndef _${CURVE_NAME_U}_LDE
+#define _${CURVE_NAME_U}_LDE
+#include <cuda.h>
+#include "../../appUtils/ntt/lde.cu"
+#include "../../appUtils/ntt/ntt.cuh"
+#include "../../appUtils/vector_manipulation/ve_mod_mult.cuh"
+#include "curve_config.cuh"
+
+extern "C" ${CURVE_NAME_U}::scalar_t* build_domain_cuda_${CURVE_NAME_L}(uint32_t domain_size, uint32_t logn, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        if (inverse) {
+            return fill_twiddle_factors_array(domain_size, ${CURVE_NAME_U}::scalar_t::omega_inv(logn), stream);
+        } else {
+            return fill_twiddle_factors_array(domain_size, ${CURVE_NAME_U}::scalar_t::omega(logn), stream);
+        }
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return nullptr;
+    }
+}
+
+extern "C" int ntt_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t *arr, uint32_t n, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_template<${CURVE_NAME_U}::scalar_t,${CURVE_NAME_U}::scalar_t>(arr, n, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        
+        return -1;        
+    }
+}
+
+extern "C" int ecntt_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t *arr, uint32_t n, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_template<${CURVE_NAME_U}::projective_t,${CURVE_NAME_U}::scalar_t>(arr, n, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int ntt_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_batch_template<${CURVE_NAME_U}::scalar_t,${CURVE_NAME_U}::scalar_t>(arr, arr_size, batch_size, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int ecntt_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t *arr, uint32_t arr_size, uint32_t batch_size, bool inverse, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return ntt_end2end_batch_template<${CURVE_NAME_U}::projective_t,${CURVE_NAME_U}::scalar_t>(arr, arr_size, batch_size, inverse, stream); // TODO: pass device_id
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_scalars_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t* d_out, ${CURVE_NAME_U}::scalar_t *d_evaluations, ${CURVE_NAME_U}::scalar_t *d_domain, unsigned n, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        return interpolate(d_out, d_evaluations, d_domain, n, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_scalars_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t* d_out, ${CURVE_NAME_U}::scalar_t* d_evaluations, ${CURVE_NAME_U}::scalar_t* d_domain, unsigned n,
+                                              unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_points_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* d_out, ${CURVE_NAME_U}::projective_t *d_evaluations, ${CURVE_NAME_U}::scalar_t *d_domain, unsigned n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        return interpolate(d_out, d_evaluations, d_domain, n, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int interpolate_points_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* d_out, ${CURVE_NAME_U}::projective_t* d_evaluations, ${CURVE_NAME_U}::scalar_t* d_domain,
+                                             unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return interpolate_batch(d_out, d_evaluations, d_domain, n, batch_size, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t* d_out, ${CURVE_NAME_U}::scalar_t *d_coefficients, ${CURVE_NAME_U}::scalar_t *d_domain, 
+                                     unsigned domain_size, unsigned n, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        ${CURVE_NAME_U}::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t* d_out, ${CURVE_NAME_U}::scalar_t* d_coefficients, ${CURVE_NAME_U}::scalar_t* d_domain, unsigned domain_size,
+                                           unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        ${CURVE_NAME_U}::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* d_out, ${CURVE_NAME_U}::projective_t *d_coefficients, ${CURVE_NAME_U}::scalar_t *d_domain, 
+                                    unsigned domain_size, unsigned n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        ${CURVE_NAME_U}::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* d_out, ${CURVE_NAME_U}::projective_t* d_coefficients, ${CURVE_NAME_U}::scalar_t* d_domain, unsigned domain_size,
+                                          unsigned n, unsigned batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        ${CURVE_NAME_U}::scalar_t* _null = nullptr;
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, false, _null, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_on_coset_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t* d_out, ${CURVE_NAME_U}::scalar_t *d_coefficients, ${CURVE_NAME_U}::scalar_t *d_domain, unsigned domain_size,
+                                              unsigned n, ${CURVE_NAME_U}::scalar_t *coset_powers, unsigned device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_scalars_on_coset_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t* d_out, ${CURVE_NAME_U}::scalar_t* d_coefficients, ${CURVE_NAME_U}::scalar_t* d_domain, unsigned domain_size, 
+                                                    unsigned n, unsigned batch_size, ${CURVE_NAME_U}::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_on_coset_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* d_out, ${CURVE_NAME_U}::projective_t *d_coefficients, ${CURVE_NAME_U}::scalar_t *d_domain, unsigned domain_size,
+                                             unsigned n, ${CURVE_NAME_U}::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate(d_out, d_coefficients, d_domain, domain_size, n, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int evaluate_points_on_coset_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* d_out, ${CURVE_NAME_U}::projective_t* d_coefficients, ${CURVE_NAME_U}::scalar_t* d_domain, unsigned domain_size, 
+                                                   unsigned n, unsigned batch_size, ${CURVE_NAME_U}::scalar_t *coset_powers, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        cudaStreamCreate(&stream);
+        return evaluate_batch(d_out, d_coefficients, d_domain, domain_size, n, batch_size, true, coset_powers, stream);
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_scalars_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t* arr, int n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order(arr, n, logn, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_scalars_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t* arr, int n, int batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order_batch(arr, n, logn, batch_size, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_points_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* arr, int n, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order(arr, n, logn, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int reverse_order_points_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* arr, int n, int batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        uint32_t logn = uint32_t(log(n) / log(2));
+        cudaStreamCreate(&stream);
+        reverse_order_batch(arr, n, logn, batch_size, stream);
+        return 0;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+#endif

icicle/curves/curve_template/msm.cu

@@ -0,0 +1,92 @@
+#ifndef _${CURVE_NAME_U}_MSM
+#define _${CURVE_NAME_U}_MSM
+#include "../../appUtils/msm/msm.cu"
+#include <stdexcept>
+#include <cuda.h>
+#include "curve_config.cuh"
+
+extern "C"
+int msm_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t *out, ${CURVE_NAME_U}::affine_t points[],
+              ${CURVE_NAME_U}::scalar_t scalars[], size_t count, size_t device_id = 0, cudaStream_t stream = 0)
+{
+    try
+    {
+        large_msm<${CURVE_NAME_U}::scalar_t, ${CURVE_NAME_U}::projective_t, ${CURVE_NAME_U}::affine_t>(scalars, points, count, out, false, false, stream);
+        return CUDA_SUCCESS;
+    }
+    catch (const std::runtime_error &ex)
+    {
+        printf("error %s", ex.what());
+        return -1;
+    }
+}
+
+extern "C" int msm_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* out, ${CURVE_NAME_U}::affine_t points[],
+                              ${CURVE_NAME_U}::scalar_t scalars[], size_t batch_size, size_t msm_size, size_t device_id = 0, cudaStream_t stream = 0)
+{
+  try
+  {
+    cudaStreamCreate(&stream);
+    batched_large_msm<${CURVE_NAME_U}::scalar_t, ${CURVE_NAME_U}::projective_t, ${CURVE_NAME_U}::affine_t>(scalars, points, batch_size, msm_size, out, false, stream);
+    cudaStreamSynchronize(stream);
+
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what());
+    return -1;
+  }
+}
+
+/**
+ * Commit to a polynomial using the MSM.
+ * Note: this function just calls the MSM, it doesn't convert between evaluation and coefficient form of scalars or points.
+ * @param d_out Ouptut point to write the result to.
+ * @param d_scalars Scalars for the MSM. Must be on device.
+ * @param d_points Points for the MSM. Must be on device.
+ * @param count Length of `d_scalars` and `d_points` arrays (they should have equal length).
+ */
+ extern "C"
+ int commit_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* d_out, ${CURVE_NAME_U}::scalar_t* d_scalars, ${CURVE_NAME_U}::affine_t* d_points, size_t count, size_t device_id = 0, cudaStream_t stream = 0)
+ {
+     try
+     {
+         large_msm(d_scalars, d_points, count, d_out, true, false, stream);
+         cudaStreamSynchronize(stream);
+         return 0;
+     }
+     catch (const std::runtime_error &ex)
+     {
+         printf("error %s", ex.what());
+         return -1;
+     }
+ }
+ 
+ /**
+  * Commit to a batch of polynomials using the MSM.
+  * Note: this function just calls the MSM, it doesn't convert between evaluation and coefficient form of scalars or points.
+  * @param d_out Ouptut point to write the results to.
+  * @param d_scalars Scalars for the MSMs of all polynomials. Must be on device.
+  * @param d_points Points for the MSMs. Must be on device. It is assumed that this set of bases is used for each MSM.
+  * @param count Length of `d_points` array, `d_scalar` has length `count` * `batch_size`.
+  * @param batch_size Size of the batch.
+  */
+ extern "C"
+ int commit_batch_cuda_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t* d_out, ${CURVE_NAME_U}::scalar_t* d_scalars, ${CURVE_NAME_U}::affine_t* d_points, size_t count, size_t batch_size, size_t device_id = 0, cudaStream_t stream = 0)
+ {
+     try
+     {
+        cudaStreamCreate(&stream);
+         batched_large_msm(d_scalars, d_points, batch_size, count, d_out, true, stream);
+         cudaStreamSynchronize(stream);
+         return 0;
+     }
+     catch (const std::runtime_error &ex)
+     {
+         printf("error %s", ex.what());
+         return -1;
+     }
+ }
+
+ #endif

icicle/curves/curve_template/params.cuh

@@ -0,0 +1,66 @@
+#pragma once
+#include "../../utils/storage.cuh"
+
+namespace PARAMS_${curve_name_U} {
+  struct fp_config {
+    static constexpr unsigned limbs_count = ${fp_num_limbs};
+    static constexpr unsigned omegas_count = ${num_omegas};
+    static constexpr unsigned modulus_bit_count = ${fp_modulus_bit_count};
+    
+    static constexpr storage<limbs_count> modulus = {${fp_modulus}};
+    static constexpr storage<limbs_count> modulus_2 = {${fp_modulus_2}};
+    static constexpr storage<limbs_count> modulus_4 = {${fp_modulus_4}};
+    static constexpr storage<2*limbs_count> modulus_wide = {${fp_modulus_wide}};
+    static constexpr storage<2*limbs_count> modulus_squared = {${fp_modulus_squared}};
+    static constexpr storage<2*limbs_count> modulus_squared_2 = {${fp_modulus_squared_2}};
+    static constexpr storage<2*limbs_count> modulus_squared_4 = {${fp_modulus_squared_4}};
+
+    static constexpr storage<limbs_count> m = {${fp_m}};
+    static constexpr storage<limbs_count> one = {${fp_one}};
+    static constexpr storage<limbs_count> zero = {${fp_zero}};
+
+    static constexpr storage_array<omegas_count, limbs_count> omega = { {
+        ${omega}
+    } };
+
+
+    static constexpr storage_array<omegas_count, limbs_count> omega_inv = { {
+        ${omega_inv}
+    } };
+    
+
+    static constexpr storage_array<omegas_count, limbs_count> inv = { {
+        ${inv}
+    } }; 
+  };
+
+  struct fq_config {
+    static constexpr unsigned limbs_count = ${fq_num_limbs};
+    static constexpr unsigned modulus_bit_count = ${fq_modulus_bit_count};
+    static constexpr storage<limbs_count> modulus = {${fq_modulus}};
+    static constexpr storage<limbs_count> modulus_2 = {${fq_modulus_2}};
+    static constexpr storage<limbs_count> modulus_4 = {${fq_modulus_4}};
+    static constexpr storage<2*limbs_count> modulus_wide = {${fq_modulus_wide}};
+    static constexpr storage<2*limbs_count> modulus_squared = {${fq_modulus_squared}};
+    static constexpr storage<2*limbs_count> modulus_squared_2 = {${fq_modulus_squared_2}};
+    static constexpr storage<2*limbs_count> modulus_squared_4 = {${fq_modulus_squared_4}};
+    static constexpr storage<limbs_count> m = {${fq_m}};
+    static constexpr storage<limbs_count> one = {${fq_one}};
+    static constexpr storage<limbs_count> zero = {${fq_zero}};
+    // i^2, the square of the imaginary unit for the extension field
+    static constexpr uint32_t i_squared = 1;
+    // true if i^2 is negative
+    static constexpr bool i_squared_is_negative = true;
+    // G1 and G2 generators 
+    static constexpr storage<limbs_count> g1_gen_x = {${fq_gen_x}};
+    static constexpr storage<limbs_count> g1_gen_y = {${fq_gen_y}};
+    static constexpr storage<limbs_count> g2_gen_x_re = {${fq_gen_x_re}};
+    static constexpr storage<limbs_count> g2_gen_x_im = {${fq_gen_x_im}};
+    static constexpr storage<limbs_count> g2_gen_y_re = {${fq_gen_y_re}};
+    static constexpr storage<limbs_count> g2_gen_y_im = {${fq_gen_y_im}};
+  };
+
+  static constexpr storage<fq_config::limbs_count> weierstrass_b = {${weier_b}};
+  static constexpr storage<fq_config::limbs_count> weierstrass_b_g2_re = {${weier_b_g2_re}};
+  static constexpr storage<fq_config::limbs_count> weierstrass_b_g2_im = {${weier_b_g2_im}};
+}

icicle/curves/curve_template/projective.cu

@@ -0,0 +1,19 @@
+#include <cuda.h>
+#include "curve_config.cuh"
+#include "../../primitives/projective.cuh"
+
+extern "C" bool eq_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t *point1, ${CURVE_NAME_U}::projective_t *point2)
+{
+    return (*point1 == *point2) && 
+    !((point1->x == ${CURVE_NAME_U}::point_field_t::zero()) && (point1->y == ${CURVE_NAME_U}::point_field_t::zero()) && (point1->z == ${CURVE_NAME_U}::point_field_t::zero())) && 
+    !((point2->x == ${CURVE_NAME_U}::point_field_t::zero()) && (point2->y == ${CURVE_NAME_U}::point_field_t::zero()) && (point2->z == ${CURVE_NAME_U}::point_field_t::zero()));
+}
+
+#if defined(G2_DEFINED)
+extern "C" bool eq_g2_${CURVE_NAME_L}(${CURVE_NAME_U}::g2_projective_t *point1, ${CURVE_NAME_U}::g2_projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == ${CURVE_NAME_U}::g2_point_field_t::zero()) && (point1->y == ${CURVE_NAME_U}::g2_point_field_t::zero()) && (point1->z == ${CURVE_NAME_U}::g2_point_field_t::zero())) && 
+  !((point2->x == ${CURVE_NAME_U}::g2_point_field_t::zero()) && (point2->y == ${CURVE_NAME_U}::g2_point_field_t::zero()) && (point2->z == ${CURVE_NAME_U}::g2_point_field_t::zero()));
+}
+#endif

icicle/curves/curve_template/supported_operations.cu

@@ -0,0 +1,4 @@
+#include "projective.cu"
+#include "lde.cu"
+#include "msm.cu"
+#include "ve_mod_mult.cu"

icicle/curves/curve_template/ve_mod_mult.cu

@@ -0,0 +1,68 @@
+#ifndef _${CURVE_NAME_U}_VEC_MULT
+#define _${CURVE_NAME_U}_VEC_MULT
+#include <stdio.h>
+#include <iostream>
+#include "../../primitives/field.cuh"
+#include "../../utils/storage.cuh"
+#include "../../primitives/projective.cuh"
+#include "curve_config.cuh"
+#include "../../appUtils/vector_manipulation/ve_mod_mult.cuh"
+
+extern "C" int32_t vec_mod_mult_point_${CURVE_NAME_L}(${CURVE_NAME_U}::projective_t *inout,
+                                      ${CURVE_NAME_U}::scalar_t *scalar_vec,
+                                      size_t n_elments,
+                                      size_t device_id,
+                                      cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    vector_mod_mult<${CURVE_NAME_U}::projective_t, ${CURVE_NAME_U}::scalar_t>(scalar_vec, inout, inout, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+
+extern "C" int32_t vec_mod_mult_scalar_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t *inout,
+                                       ${CURVE_NAME_U}::scalar_t *scalar_vec,
+                                       size_t n_elments,
+                                       size_t device_id,
+                                       cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    vector_mod_mult<${CURVE_NAME_U}::scalar_t, ${CURVE_NAME_U}::scalar_t>(scalar_vec, inout, inout, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+
+extern "C" int32_t matrix_vec_mod_mult_${CURVE_NAME_L}(${CURVE_NAME_U}::scalar_t *matrix_flattened,
+                                       ${CURVE_NAME_U}::scalar_t *input,
+                                       ${CURVE_NAME_U}::scalar_t *output,
+                                       size_t n_elments,
+                                       size_t device_id,
+                                       cudaStream_t stream = 0)
+{
+  try
+  {
+    // TODO: device_id
+    matrix_mod_mult<${CURVE_NAME_U}::scalar_t>(matrix_flattened, input, output, n_elments, stream);
+    return CUDA_SUCCESS;
+  }
+  catch (const std::runtime_error &ex)
+  {
+    printf("error %s", ex.what()); // TODO: error code and message
+    return -1;
+  }
+}
+#endif

icicle/curves/index.cu

@@ -0,0 +1,3 @@
+#include "bls12_381/supported_operations.cu"
+#include "bls12_377/supported_operations.cu"
+#include "bn254/supported_operations.cu"

icicle/primitives/extension_field.cuh

@@ -0,0 +1,149 @@
+#pragma once
+
+#include "field.cuh"
+
+#define HOST_INLINE __host__ __forceinline__
+#define DEVICE_INLINE __device__ __forceinline__
+#define HOST_DEVICE_INLINE __host__ __device__ __forceinline__
+
+template <typename CONFIG> class ExtensionField {
+  private:
+    typedef typename Field<CONFIG>::Wide FWide;
+
+    struct ExtensionWide {
+      FWide real;
+      FWide imaginary;
+      
+      ExtensionField HOST_DEVICE_INLINE get_lower() {
+        return ExtensionField { real.get_lower(), imaginary.get_lower() };
+      }
+
+      ExtensionField HOST_DEVICE_INLINE get_higher_with_slack() {
+        return ExtensionField { real.get_higher_with_slack(), imaginary.get_higher_with_slack() };
+      }
+    };
+
+    friend HOST_DEVICE_INLINE ExtensionWide operator+(ExtensionWide xs, const ExtensionWide& ys) {   
+      return ExtensionField { xs.real + ys.real, xs.imaginary + ys.imaginary };
+    }
+
+    // an incomplete impl that assumes that xs > ys
+    friend HOST_DEVICE_INLINE ExtensionWide operator-(ExtensionWide xs, const ExtensionWide& ys) {   
+      return ExtensionField { xs.real - ys.real, xs.imaginary - ys.imaginary };
+    }
+
+  public:
+    typedef Field<CONFIG> FF;
+    static constexpr unsigned TLC = 2 * CONFIG::limbs_count;
+
+    FF real;
+    FF imaginary;
+
+    static constexpr HOST_DEVICE_INLINE ExtensionField zero() {
+      return ExtensionField { FF::zero(), FF::zero() };
+    }
+
+    static constexpr HOST_DEVICE_INLINE ExtensionField one() {
+      return ExtensionField { FF::one(), FF::zero() };
+    }
+
+    static constexpr HOST_DEVICE_INLINE ExtensionField generator_x() {
+      return ExtensionField { FF { CONFIG::g2_gen_x_re }, FF { CONFIG::g2_gen_x_im } };
+    }
+
+    static constexpr HOST_DEVICE_INLINE ExtensionField generator_y() {
+      return ExtensionField { FF { CONFIG::g2_gen_y_re }, FF { CONFIG::g2_gen_y_im } };
+    }
+
+
+    static HOST_INLINE ExtensionField rand_host() {
+      return ExtensionField { FF::rand_host(), FF::rand_host() };
+    }
+
+    template <unsigned REDUCTION_SIZE = 1> static constexpr HOST_DEVICE_INLINE ExtensionField reduce(const ExtensionField &xs) {
+      return ExtensionField { FF::reduce<REDUCTION_SIZE>(&xs.real), FF::reduce<REDUCTION_SIZE>(&xs.imaginary) };
+    }
+
+    friend std::ostream& operator<<(std::ostream& os, const ExtensionField& xs) {
+      os << "{ Real: " << xs.real << " }; { Imaginary: " << xs.imaginary << " }";
+      return os;
+    }
+
+    friend HOST_DEVICE_INLINE ExtensionField operator+(ExtensionField xs, const ExtensionField& ys) {
+      return ExtensionField { xs.real + ys.real, xs.imaginary + ys.imaginary };
+    }
+
+    friend HOST_DEVICE_INLINE ExtensionField operator-(ExtensionField xs, const ExtensionField& ys) {
+      return ExtensionField { xs.real - ys.real, xs.imaginary - ys.imaginary };
+    }
+
+    template <unsigned MODULUS_MULTIPLE = 1>
+    static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const ExtensionField& xs, const ExtensionField& ys) {
+      FWide real_prod = FF::mul_wide(xs.real * ys.real);
+      FWide imaginary_prod = FF::mul_wide(xs.imaginary * ys.imaginary);
+      FWide prod_of_sums = FF::mul_wide(xs.real + xs.imaginary, ys.real + ys.imaginary);
+      FWide i_sq_times_im = FF::mul_unsigned<CONFIG::i_squared>(imaginary_prod);
+      i_sq_times_im = CONFIG::i_squared_is_negative ? FF::neg(i_sq_times_im) : i_sq_times_im;
+      return ExtensionField { real_prod + i_sq_times_im, prod_of_sums - real_prod - imaginary_prod };
+    }
+
+    friend HOST_DEVICE_INLINE ExtensionField operator*(const ExtensionField& xs, const ExtensionField& ys) {
+      FF real_prod = xs.real * ys.real;
+      FF imaginary_prod = xs.imaginary * ys.imaginary;
+      FF prod_of_sums = (xs.real + xs.imaginary) * (ys.real + ys.imaginary);
+      FF i_sq_times_im = FF::template mul_unsigned<CONFIG::i_squared>(imaginary_prod);
+      i_sq_times_im = CONFIG::i_squared_is_negative ? FF::neg(i_sq_times_im) : i_sq_times_im;
+      return ExtensionField { real_prod + i_sq_times_im, prod_of_sums - real_prod - imaginary_prod };
+    }
+
+    friend HOST_DEVICE_INLINE bool operator==(const ExtensionField& xs, const ExtensionField& ys) {
+      return (xs.real == ys.real) && (xs.imaginary == ys.imaginary);
+    }
+
+    friend HOST_DEVICE_INLINE bool operator!=(const ExtensionField& xs, const ExtensionField& ys) {
+      return !(xs == ys);
+    }
+
+    template <const ExtensionField& mutliplier>
+    static constexpr HOST_DEVICE_INLINE ExtensionField mul_const(const ExtensionField &xs) {
+      constexpr uint32_t mul_real = mutliplier.real.limbs_storage.limbs[0];
+      constexpr uint32_t mul_imaginary = mutliplier.imaginary.limbs_storage.limbs[0];
+      FF real_prod = FF::template mul_unsigned<mul_real>(xs.real);
+      FF imaginary_prod = FF::template mul_unsigned<mul_imaginary>(xs.imaginary);
+      FF re_im = FF::template mul_unsigned<mul_real>(xs.imaginary);
+      FF im_re = FF::template mul_unsigned<mul_imaginary>(xs.real);
+      FF i_sq_times_im = FF::template mul_unsigned<CONFIG::i_squared>(imaginary_prod);
+      i_sq_times_im = CONFIG::i_squared_is_negative ? FF::neg(i_sq_times_im) : i_sq_times_im;
+      return ExtensionField { real_prod + i_sq_times_im, re_im + im_re };
+    }
+
+    template <uint32_t mutliplier, unsigned REDUCTION_SIZE = 1>
+    static constexpr HOST_DEVICE_INLINE ExtensionField mul_unsigned(const ExtensionField &xs) {
+      return { FF::template mul_unsigned<mutliplier>(xs.real), FF::template mul_unsigned<mutliplier>(xs.imaginary) };
+    }
+
+    template <unsigned MODULUS_MULTIPLE = 1>
+    static constexpr HOST_DEVICE_INLINE ExtensionWide sqr_wide(const ExtensionField& xs) {
+      // TODO: change to a more efficient squaring
+      return mul_wide<MODULUS_MULTIPLE>(xs, xs);
+    }
+
+    template <unsigned MODULUS_MULTIPLE = 1>
+    static constexpr HOST_DEVICE_INLINE ExtensionField sqr(const ExtensionField& xs) {
+      // TODO: change to a more efficient squaring
+      return xs * xs;
+    }
+
+    template <unsigned MODULUS_MULTIPLE = 1>
+    static constexpr HOST_DEVICE_INLINE ExtensionField neg(const ExtensionField& xs) {
+      return ExtensionField { FF::neg(xs.real), FF::neg(xs.imaginary) };
+    }
+
+    // inverse assumes that xs is nonzero
+    static constexpr HOST_DEVICE_INLINE ExtensionField inverse(const ExtensionField& xs) {
+      ExtensionField xs_conjugate = { xs.real, FF::neg(xs.imaginary) };
+      // TODO: wide here
+      FF xs_norm_squared = FF::sqr(xs.real) + FF::sqr(xs.imaginary);
+      return xs_conjugate * ExtensionField { FF::inverse(xs_norm_squared), FF::zero() };
+    }
+};

icicle/primitives/field.cuh

@@ -5,6 +5,9 @@
 #include "../utils/host_math.cuh"
 #include <random>
 #include <iostream>
+#include <iomanip>
+#include <string>
+#include <sstream>
 
 #define HOST_INLINE __host__ __forceinline__
 #define DEVICE_INLINE __device__ __forceinline__
@@ -13,7 +16,7 @@
 template <class CONFIG> class Field {
   public:
     static constexpr unsigned TLC = CONFIG::limbs_count;
-    static constexpr unsigned NBITS = CONFIG::modulus_bits_count;
+    static constexpr unsigned NBITS = CONFIG::modulus_bit_count;
 
     static constexpr HOST_DEVICE_INLINE Field zero() {
       return Field { CONFIG::zero };
@@ -23,235 +26,73 @@ template <class CONFIG> class Field {
       return Field { CONFIG::one };
     }
 
-    static constexpr HOST_INLINE Field omega(uint32_t log_size) {
-      // Quick fix to linking issue, permanent fix will follow
-      switch (log_size) {
-        case 0:
-          return Field { CONFIG::one };
-        case 1:
-          return Field { CONFIG::omega1 };
-        case 2:
-          return Field { CONFIG::omega2 };
-        case 3:
-          return Field { CONFIG::omega3 };
-        case 4:
-          return Field { CONFIG::omega4 };
-        case 5:
-          return Field { CONFIG::omega5 };
-        case 6:
-          return Field { CONFIG::omega6 };
-        case 7:
-          return Field { CONFIG::omega7 };
-        case 8:
-          return Field { CONFIG::omega8 };
-        case 9:
-          return Field { CONFIG::omega9 };
-        case 10:
-          return Field { CONFIG::omega10 };
-        case 11:
-          return Field { CONFIG::omega11 };
-        case 12:
-          return Field { CONFIG::omega12 };
-        case 13:
-          return Field { CONFIG::omega13 };
-        case 14:
-          return Field { CONFIG::omega14 };
-        case 15:
-          return Field { CONFIG::omega15 };
-        case 16:
-          return Field { CONFIG::omega16 };
-        case 17:
-          return Field { CONFIG::omega17 };
-        case 18:
-          return Field { CONFIG::omega18 };
-        case 19:
-          return Field { CONFIG::omega19 };
-        case 20:
-          return Field { CONFIG::omega20 };
-        case 21:
-          return Field { CONFIG::omega21 };
-        case 22:
-          return Field { CONFIG::omega22 };
-        case 23:
-          return Field { CONFIG::omega23 };
-        case 24:
-          return Field { CONFIG::omega24 };
-        case 25:
-          return Field { CONFIG::omega25 };
-        case 26:
-          return Field { CONFIG::omega26 };
-        case 27:
-          return Field { CONFIG::omega27 };
-        case 28:
-          return Field { CONFIG::omega28 };
-        case 29:
-          return Field { CONFIG::omega29 };
-        case 30:
-          return Field { CONFIG::omega30 };
-        case 31:
-          return Field { CONFIG::omega31 };
-        case 32:
-          return Field { CONFIG::omega32 };        
+    static constexpr HOST_DEVICE_INLINE Field from(uint32_t value) {
+      storage<TLC> scalar;
+      scalar.limbs[0] = value;
+      for (int i = 1; i < TLC; i++) {
+        scalar.limbs[i] = 0;
       }
-      // return Field { CONFIG::omega[log_size-1] };
+      return Field { scalar };
     }
 
-    static constexpr HOST_INLINE Field omega_inv(uint32_t log_size) {
-      // Quick fix to linking issue, permanent fix will follow
-      switch (log_size) {
-        case 0:
-          return Field { CONFIG::one };
-        case 1:
-          return Field { CONFIG::omega_inv1 };
-        case 2:
-          return Field { CONFIG::omega_inv2 };
-        case 3:
-          return Field { CONFIG::omega_inv3 };
-        case 4:
-          return Field { CONFIG::omega_inv4 };
-        case 5:
-          return Field { CONFIG::omega_inv5 };
-        case 6:
-          return Field { CONFIG::omega_inv6 };
-        case 7:
-          return Field { CONFIG::omega_inv7 };
-        case 8:
-          return Field { CONFIG::omega_inv8 };
-        case 9:
-          return Field { CONFIG::omega_inv9 };
-        case 10:
-          return Field { CONFIG::omega_inv10 };
-        case 11:
-          return Field { CONFIG::omega_inv11 };
-        case 12:
-          return Field { CONFIG::omega_inv12 };
-        case 13:
-          return Field { CONFIG::omega_inv13 };
-        case 14:
-          return Field { CONFIG::omega_inv14 };
-        case 15:
-          return Field { CONFIG::omega_inv15 };
-        case 16:
-          return Field { CONFIG::omega_inv16 };
-        case 17:
-          return Field { CONFIG::omega_inv17 };
-        case 18:
-          return Field { CONFIG::omega_inv18 };
-        case 19:
-          return Field { CONFIG::omega_inv19 };
-        case 20:
-          return Field { CONFIG::omega_inv20 };
-        case 21:
-          return Field { CONFIG::omega_inv21 };
-        case 22:
-          return Field { CONFIG::omega_inv22 };
-        case 23:
-          return Field { CONFIG::omega_inv23 };
-        case 24:
-          return Field { CONFIG::omega_inv24 };
-        case 25:
-          return Field { CONFIG::omega_inv25 };
-        case 26:
-          return Field { CONFIG::omega_inv26 };
-        case 27:
-          return Field { CONFIG::omega_inv27 };
-        case 28:
-          return Field { CONFIG::omega_inv28 };
-        case 29:
-          return Field { CONFIG::omega_inv29 };
-        case 30:
-          return Field { CONFIG::omega_inv30 };
-        case 31:
-          return Field { CONFIG::omega_inv31 };
-        case 32:
-          return Field { CONFIG::omega_inv32 };        
-      }
-      // return Field { CONFIG::omega_inv[log_size-1] };
+    static constexpr HOST_DEVICE_INLINE Field generator_x() {
+      return Field { CONFIG::g1_gen_x };
     }
 
-    static constexpr HOST_INLINE Field inv_log_size(uint32_t log_size) {
-      // Quick fix to linking issue, permanent fix will follow
-      switch (log_size) {
-        case 1:
-          return Field { CONFIG::inv1 };
-        case 2:
-          return Field { CONFIG::inv2 };
-        case 3:
-          return Field { CONFIG::inv3 };
-        case 4:
-          return Field { CONFIG::inv4 };
-        case 5:
-          return Field { CONFIG::inv5 };
-        case 6:
-          return Field { CONFIG::inv6 };
-        case 7:
-          return Field { CONFIG::inv7 };
-        case 8:
-          return Field { CONFIG::inv8 };
-        case 9:
-          return Field { CONFIG::inv9 };
-        case 10:
-          return Field { CONFIG::inv10 };
-        case 11:
-          return Field { CONFIG::inv11 };
-        case 12:
-          return Field { CONFIG::inv12 };
-        case 13:
-          return Field { CONFIG::inv13 };
-        case 14:
-          return Field { CONFIG::inv14 };
-        case 15:
-          return Field { CONFIG::inv15 };
-        case 16:
-          return Field { CONFIG::inv16 };
-        case 17:
-          return Field { CONFIG::inv17 };
-        case 18:
-          return Field { CONFIG::inv18 };
-        case 19:
-          return Field { CONFIG::inv19 };
-        case 20:
-          return Field { CONFIG::inv20 };
-        case 21:
-          return Field { CONFIG::inv21 };
-        case 22:
-          return Field { CONFIG::inv22 };
-        case 23:
-          return Field { CONFIG::inv23 };
-        case 24:
-          return Field { CONFIG::inv24 };
-        case 25:
-          return Field { CONFIG::inv25 };
-        case 26:
-          return Field { CONFIG::inv26 };
-        case 27:
-          return Field { CONFIG::inv27 };
-        case 28:
-          return Field { CONFIG::inv28 };
-        case 29:
-          return Field { CONFIG::inv29 };
-        case 30:
-          return Field { CONFIG::inv30 };
-        case 31:
-          return Field { CONFIG::inv31 };
-        case 32:
-          return Field { CONFIG::inv32 };        
-      }
-      // return Field { CONFIG::inv[log_size-1] };
+    static constexpr HOST_DEVICE_INLINE Field generator_y() {
+      return Field { CONFIG::g1_gen_y };
+    }
+
+    static HOST_INLINE Field omega(uint32_t logn) {
+        if (logn == 0) {
+            return Field { CONFIG::one };
+        }
+
+        if (logn > CONFIG::omegas_count) {
+            throw std::invalid_argument( "Field: Invalid omega index" );
+        }
+
+        storage_array<CONFIG::omegas_count, TLC> const omega = CONFIG::omega;
+        return Field { omega.storages[logn-1] };
+    }      
+
+    static HOST_INLINE Field omega_inv(uint32_t logn) {
+        if (logn == 0) {
+            return Field { CONFIG::one };
+        }
+
+        if (logn > CONFIG::omegas_count) {
+            throw std::invalid_argument( "Field: Invalid omega_inv index" );
+        }
+
+        storage_array<CONFIG::omegas_count, TLC> const omega_inv = CONFIG::omega_inv;
+        return Field { omega_inv.storages[logn-1] };
+    }
+
+    static HOST_INLINE Field inv_log_size(uint32_t logn) {
+        if (logn == 0) {
+            return Field { CONFIG::one };
+        }
+
+        if (logn > CONFIG::omegas_count) {
+            throw std::invalid_argument( "Field: Invalid inv index" );
+        }
+
+        storage_array<CONFIG::omegas_count, TLC> const inv = CONFIG::inv;
+        return Field { inv.storages[logn-1] };
     }
 
     static constexpr HOST_DEVICE_INLINE Field modulus() {
       return Field { CONFIG::modulus };
     }
 
-
   // private:
     typedef storage<TLC> ff_storage;
     typedef storage<2*TLC> ff_wide_storage;
 
     static constexpr unsigned slack_bits = 32 * TLC - NBITS;
 
-    struct wide {
+    struct Wide {
       ff_wide_storage limbs_storage;
       
       Field HOST_DEVICE_INLINE get_lower() {
@@ -280,15 +121,15 @@ template <class CONFIG> class Field {
       }
     };
 
-    friend HOST_DEVICE_INLINE wide operator+(wide xs, const wide& ys) {   
-      wide rs = {};
+    friend HOST_DEVICE_INLINE Wide operator+(Wide xs, const Wide& ys) {   
+      Wide rs = {};
       add_limbs<false>(xs.limbs_storage, ys.limbs_storage, rs.limbs_storage);
       return rs;
     }
 
     // an incomplete impl that assumes that xs > ys
-    friend HOST_DEVICE_INLINE wide operator-(wide xs, const wide& ys) {   
-      wide rs = {};
+    friend HOST_DEVICE_INLINE Wide operator-(Wide xs, const Wide& ys) {   
+      Wide rs = {};
       sub_limbs<false>(xs.limbs_storage, ys.limbs_storage, rs.limbs_storage);
       return rs;
     }
@@ -337,7 +178,9 @@ template <class CONFIG> class Field {
       const uint32_t *y = ys.limbs;
       uint32_t *r = rs.limbs;
       r[0] = SUBTRACT ? ptx::sub_cc(x[0], y[0]) : ptx::add_cc(x[0], y[0]);
+    #ifdef __CUDA_ARCH__
     #pragma unroll
+    #endif
       for (unsigned i = 1; i < (CARRY_OUT ? TLC : TLC - 1); i++)
         r[i] = SUBTRACT ? ptx::subc_cc(x[i], y[i]) : ptx::addc_cc(x[i], y[i]);
       if (!CARRY_OUT) {
@@ -353,7 +196,9 @@ template <class CONFIG> class Field {
       const uint32_t *y = ys.limbs;
       uint32_t *r = rs.limbs;
       r[0] = SUBTRACT ? ptx::sub_cc(x[0], y[0]) : ptx::add_cc(x[0], y[0]);
+    #ifdef __CUDA_ARCH__
     #pragma unroll
+    #endif
       for (unsigned i = 1; i < (CARRY_OUT ? 2 * TLC : 2 * TLC - 1); i++)
         r[i] = SUBTRACT ? ptx::subc_cc(x[i], y[i]) : ptx::addc_cc(x[i], y[i]);
       if (!CARRY_OUT) {
@@ -387,14 +232,6 @@ template <class CONFIG> class Field {
       return CARRY_OUT ? carry : 0;
     }
 
-    static constexpr HOST_INLINE uint32_t sub_limbs_partial_host(uint32_t* x, uint32_t* y, uint32_t* r, uint32_t num_limbs) {
-      uint32_t carry = 0;
-      host_math::carry_chain<2 * TLC, false, true> chain;
-      for (unsigned i = 0; i < num_limbs; i++)
-        r[i] = chain.sub(x[i], y[i], carry);
-      return carry;
-    }
-
     template <bool CARRY_OUT, typename T> static constexpr HOST_DEVICE_INLINE uint32_t add_limbs(const T &xs, const T &ys, T &rs) {
     #ifdef __CUDA_ARCH__
       return add_sub_limbs_device<false, CARRY_OUT>(xs, ys, rs);
@@ -419,41 +256,17 @@ template <class CONFIG> class Field {
       }
     }
 
-    static DEVICE_INLINE void mul_n_msb(uint32_t *acc, const uint32_t *a, uint32_t bi, size_t n = TLC, size_t start_i = 0) {
-      #pragma unroll
-        for (size_t i = start_i; i < n; i += 2) {
-          acc[i] = ptx::mul_lo(a[i], bi);
-          acc[i + 1] = ptx::mul_hi(a[i], bi);
-        }
-      }
-
     static DEVICE_INLINE void cmad_n(uint32_t *acc, const uint32_t *a, uint32_t bi, size_t n = TLC) {
-      // multiply scalar by vector
-      // acc = acc + bi*A[::2]
       acc[0] = ptx::mad_lo_cc(a[0], bi, acc[0]);
       acc[1] = ptx::madc_hi_cc(a[0], bi, acc[1]);
-  #pragma unroll
+    #pragma unroll
       for (size_t i = 2; i < n; i += 2) {
         acc[i] = ptx::madc_lo_cc(a[i], bi, acc[i]);
         acc[i + 1] = ptx::madc_hi_cc(a[i], bi, acc[i + 1]);
       }
     }
 
-    static DEVICE_INLINE void cmad_n_msb(uint32_t *acc, const uint32_t *a, uint32_t bi, size_t n = TLC, size_t a_start_idx=0) {
-      // multiply scalar by vector
-      // acc = acc + bi*A[::2]
-      acc[a_start_idx] = ptx::mad_lo_cc(a[a_start_idx], bi, acc[a_start_idx]);
-      acc[a_start_idx + 1] = ptx::madc_hi_cc(a[a_start_idx], bi, acc[a_start_idx + 1]);
-  #pragma unroll
-      for (size_t i = a_start_idx + 2; i < n; i += 2) {
-        acc[i] = ptx::madc_lo_cc(a[i], bi, acc[i]);
-        acc[i + 1] = ptx::madc_hi_cc(a[i], bi, acc[i + 1]);
-      }
-    }
-
     static DEVICE_INLINE void mad_row(uint32_t *odd, uint32_t *even, const uint32_t *a, uint32_t bi, size_t n = TLC) {
-      // odd = odd + bi*A
-      // even = even + bi*A
       cmad_n(odd, a + 1, bi, n - 2);
       odd[n - 2] = ptx::madc_lo_cc(a[n - 1], bi, 0);
       odd[n - 1] = ptx::madc_hi(a[n - 1], bi, 0);
@@ -461,16 +274,6 @@ template <class CONFIG> class Field {
       odd[n - 1] = ptx::addc(odd[n - 1], 0);
     }
 
-    static DEVICE_INLINE void mad_row_msb(uint32_t *odd, uint32_t *even, const uint32_t *a, uint32_t bi, size_t n = TLC, size_t a_start_idx = 0) {
-      // odd = odd + bi*A
-      // even = even + bi*A
-      cmad_n_msb(odd, a + 1, bi, n - 2, a_start_idx - 1);
-      odd[n - 2] = ptx::madc_lo_cc(a[n - 1], bi, 0);
-      odd[n - 1] = ptx::madc_hi(a[n - 1], bi, 0);
-      cmad_n_msb(even, a, bi, n, a_start_idx);
-      odd[n - 1] = ptx::addc(odd[n - 1], 0);
-    }
-
     static DEVICE_INLINE void multiply_raw_device(const ff_storage &as, const ff_storage &bs, ff_wide_storage &rs) {
       const uint32_t *a = as.limbs;
       const uint32_t *b = bs.limbs;
@@ -492,289 +295,13 @@ template <class CONFIG> class Field {
       even[i + 1] = ptx::addc(even[i + 1], 0);
     }
 
-    static DEVICE_INLINE void mult_no_carry(uint32_t a, uint32_t b, uint32_t *r) {
-      r[0] = ptx::mul_lo(a, b);
-      r[1] = ptx::mul_hi(a, b);
-    }
-
-    static DEVICE_INLINE void ingo_multiply_raw_device(const ff_storage &as, const ff_storage &bs, ff_wide_storage &rs) {
-      const uint32_t *a = as.limbs;
-      const uint32_t *b = bs.limbs;
-      uint32_t *r = rs.limbs;
-      uint32_t i, j;
-      uint32_t *even = rs.limbs;
-      __align__(8) uint32_t odd[2 * TLC];
-      for (uint32_t i = 0; i < 2 * TLC; i++)
-      {
-        even[i] = 0;
-        odd[i] = 0;
-      }
-      // first row special case, no carry in no carry out. split to non parts, even and odd.
-      for (i = 0; i < TLC - 1; i+=2 )
-      {
-        mult_no_carry(b[0], a[i], &even[i]);
-        mult_no_carry(b[0], a[i + 1], &odd[i]);
-      }
-
-      // doing two rows at one loop
-      for (i = 1; i < TLC - 1; i+=2)
-      {
-        // odd bi's 
-        // multiply accumulate even part of new row with odd part prev row (needs a carry)
-        // // j = 0, no carry in, only carry out
-        odd[i - 1] =  ptx::mad_lo_cc(a[0], b[i], odd[i - 1]);
-        odd[i] =      ptx::madc_hi_cc(a[0], b[i], odd[i]);
-        // for loop carry in carry out  
-        for (j = 2; j < TLC; j+=2) // 2, 4, 6
-        {
-          odd[i + j - 1] =  ptx::madc_lo_cc(a[j], b[i], odd[i + j - 1]);
-          odd[i + j] =      ptx::madc_hi_cc(a[j], b[i], odd[i + j]);  
-        }
-        odd[i + j - 1] = ptx::addc(odd[i + j - 1], 0); // handling last carry
-
-        // multiply accumulate odd part of new row with even part prev row (doesnt need a carry)
-        // j = 1, no carry in, only carry out
-        even[i + 1] =  ptx::mad_lo_cc(a[1], b[i], even[i + 1]);
-        even[i + 2] =  ptx::madc_hi_cc(a[1], b[i], even[i + 2]);  
-        // for loop carry in carry out
-        for (j = 3; j < TLC; j+=2)
-        {
-          even[i + j] =  ptx::madc_lo_cc(a[j], b[i], even[i + j]);
-          even[i + j + 1] =      ptx::madc_hi_cc(a[j], b[i], even[i + j + 1]);  
-        }
-
-        // even bi's
-        // multiply accumulate even part of new row with even part of prev row // needs a carry
-        // j = 0, no carry in, only carry out
-        even[i + 1] = ptx::mad_lo_cc(a[0], b[i + 1], even[i + 1]);
-        even[i + 2] = ptx::madc_hi_cc(a[0], b[i + 1], even[i + 2]);  
-        // for loop, carry in, carry out.
-        for (j = 2; j < TLC; j+=2)
-        {
-          even[i + j + 1] = ptx::madc_lo_cc(a[j], b[i + 1], even[i + j + 1]);
-          even[i + j + 2] = ptx::madc_hi_cc(a[j], b[i + 1], even[i + j + 2]);  
-        }
-        even[i + j + 1] = ptx::addc(even[i + j + 1], 0); // handling last carry
-        
-        // multiply accumulate odd part of new row with odd part of prev row
-        // j = 1, no carry in, only carry out
-        odd[i + 1] = ptx::mad_lo_cc(a[1], b[i + 1], odd[i + 1]);
-        odd[i + 2] = ptx::madc_hi_cc(a[1], b[i + 1], odd[i + 2]);  
-        // for loop, carry in, carry out.
-        for (j = 3; j < TLC; j+=2)
-        {
-          odd[i + j]      = ptx::madc_lo_cc(a[j], b[i + 1], odd[i + j]);
-          odd[i + j + 1]  = ptx::madc_hi_cc(a[j], b[i + 1], odd[i + j + 1]);  
-        }
-        
-      }
-
-      odd[i - 1] =  ptx::mad_lo_cc(a[0], b[i], odd[i - 1]);
-      odd[i] =      ptx::madc_hi_cc(a[0], b[i], odd[i]);
-      // for loop carry in carry out  
-      for (j = 2; j < TLC; j+=2)
-      {
-        odd[i + j - 1] =  ptx::madc_lo_cc(a[j], b[i], odd[i + j - 1]);
-        odd[i + j] =      ptx::madc_hi_cc(a[j], b[i], odd[i + j]);  
-      }
-      odd[i + j - 1] = ptx::addc(odd[i + j - 1], 0); // handling last carry
-
-      // multiply accumulate odd part of new row with even part prev row
-      // j = 1, no carry in, only carry out
-      even[i + 1] =  ptx::mad_lo_cc(a[1], b[i], even[i + 1]);
-      even[i + 2] =  ptx::madc_hi_cc(a[1], b[i], even[i + 2]);  
-      // for loop carry in carry out
-      for (j = 3; j < TLC; j+=2)
-      {
-        even[i + j] =  ptx::madc_lo_cc(a[j], b[i], even[i + j]);
-        even[i + j + 1] =      ptx::madc_hi_cc(a[j], b[i], even[i + j + 1]);  
-      }
-
-      // add even and odd parts
-      even[1] = ptx::add_cc(even[1], odd[0]);
-      for (i = 1; i < 2 * TLC - 2; i++)
-        even[i + 1] = ptx::addc_cc(even[i + 1], odd[i]);
-      even[i + 1] = ptx::addc(even[i + 1], 0);
-    }
-
-
-    static DEVICE_INLINE void ingo_msb_multiply_raw_device(const ff_storage &as, const ff_storage &bs, ff_wide_storage &rs) {
-      const uint32_t *a = as.limbs;
-      const uint32_t *b = bs.limbs;
-      uint32_t *r = rs.limbs;
-      uint32_t i, j;
-      uint32_t *even = rs.limbs;
-      __align__(8) uint32_t odd[2 * TLC];
-      for (uint32_t i = 0; i < 2 * TLC; i++)
-      {
-        even[i] = 0;
-        odd[i] = 0;
-      }
-      // only last element from first row.
-      mult_no_carry(b[0], a[TLC - 1], &odd[TLC - 2]);
-      
-      // doing two rows at one loop
-      #pragma unroll
-      for (i = 1; i < TLC - 1; i+=2)
-      {
-        const uint32_t first_active_j = TLC - 1 - i;
-        const uint32_t first_active_j_odd = first_active_j + (1 - (first_active_j % 2));
-        const uint32_t first_active_j_even = first_active_j + first_active_j % 2  ;
-        // odd bi's 
-        // multiply accumulate even part of new row with odd part prev row (needs a carry)
-        // j = 0, no carry in, only carry out
-        odd[first_active_j_even + i - 1] =  ptx::mad_lo_cc(a[first_active_j_even], b[i],  odd[first_active_j_even + i - 1]);
-        odd[first_active_j_even + i]     =  ptx::madc_hi_cc(a[first_active_j_even], b[i], odd[first_active_j_even + i]);
-        // for loop carry in carry out
-        #pragma unroll  
-        for (j = first_active_j_even + 2; j < TLC; j+=2)
-        {
-          odd[i + j - 1] =  ptx::madc_lo_cc(a[j], b[i], odd[i + j - 1]);
-          odd[i + j] =      ptx::madc_hi_cc(a[j], b[i], odd[i + j]);  
-        }
-        odd[i + j - 1] = ptx::addc(odd[i + j - 1], 0); // handling last carry
-
-        // multiply accumulate odd part of new row with even part prev row (doesnt need a carry)
-        // j = 1, no carry in, only carry out
-        even[i + first_active_j_odd]      =  ptx::mad_lo_cc(a[first_active_j_odd], b[i], even[i + first_active_j_odd]);
-        even[i + first_active_j_odd + 1]  =  ptx::madc_hi_cc(a[first_active_j_odd], b[i], even[i + first_active_j_odd + 1]);  
-        // for loop carry in carry out
-        #pragma unroll
-        for (j = first_active_j_odd + 2; j < TLC; j+=2)
-        {
-          even[i + j] =  ptx::madc_lo_cc(a[j], b[i], even[i + j]);
-          even[i + j + 1] =      ptx::madc_hi_cc(a[j], b[i], even[i + j + 1]);  
-        }
-
-        // even bi's
-        uint32_t const first_active_j1 = TLC - 1 - (i + 1) ;
-        uint32_t const first_active_j_odd1 = first_active_j1 + (1 - (first_active_j1 % 2));  
-        uint32_t const first_active_j_even1 = first_active_j1 + first_active_j1 % 2;
-        // multiply accumulate even part of new row with even part of prev row // needs a carry
-        // j = 0, no carry in, only carry out
-        even[first_active_j_even1 + i + 1] = ptx::mad_lo_cc(a[first_active_j_even1], b[i + 1], even[first_active_j_even1 + i + 1]);
-        even[first_active_j_even1 + i + 2] = ptx::madc_hi_cc(a[first_active_j_even1], b[i + 1], even[first_active_j_even1 + i + 2]);  
-        // for loop, carry in, carry out.
-        #pragma unroll
-        for (j = first_active_j_even1 + 2; j < TLC; j+=2)
-        {
-          even[i + j + 1] = ptx::madc_lo_cc(a[j], b[i + 1], even[i + j + 1]);
-          even[i + j + 2] = ptx::madc_hi_cc(a[j], b[i + 1], even[i + j + 2]);  
-        }
-        even[i + j + 1] = ptx::addc(even[i + j + 1], 0); // handling last carry
-        
-        // multiply accumulate odd part of new row with odd part of prev row
-        // j = 1, no carry in, only carry out
-        odd[first_active_j_odd1 + i] = ptx::mad_lo_cc(a[first_active_j_odd1], b[i + 1], odd[first_active_j_odd1 + i]);
-        odd[first_active_j_odd1+ i + 1] = ptx::madc_hi_cc(a[first_active_j_odd1], b[i + 1], odd[first_active_j_odd1 + i + 1]);  
-        // for loop, carry in, carry out.
-        #pragma unroll
-        for (j = first_active_j_odd1 + 2; j < TLC; j+=2)
-        {
-          odd[i + j]      = ptx::madc_lo_cc(a[j], b[i + 1], odd[i + j]);
-          odd[i + j + 1]  = ptx::madc_hi_cc(a[j], b[i + 1], odd[i + j + 1]);  
-        }
-        
-      }
-
-      // last round, i = TLC - 1
-      odd[i - 1] =  ptx::mad_lo_cc(a[0], b[i], odd[i - 1]);
-      odd[i] =      ptx::madc_hi_cc(a[0], b[i], odd[i]);
-      // for loop carry in carry out
-      #pragma unroll  
-      for (j = 2; j < TLC; j+=2)
-      {
-        odd[i + j - 1] =  ptx::madc_lo_cc(a[j], b[i], odd[i + j - 1]);
-        odd[i + j] =      ptx::madc_hi_cc(a[j], b[i], odd[i + j]);  
-      }
-      odd[i + j - 1] = ptx::addc(odd[i + j - 1], 0); // handling last carry
-
-      // multiply accumulate odd part of new row with even part prev row
-      // j = 1, no carry in, only carry out
-      even[i + 1] =  ptx::mad_lo_cc(a[1], b[i], even[i + 1]);
-      even[i + 2] =  ptx::madc_hi_cc(a[1], b[i], even[i + 2]);  
-      // for loop carry in carry out
-      #pragma unroll
-      for (j = 3; j < TLC; j+=2)
-      {
-        even[i + j] =  ptx::madc_lo_cc(a[j], b[i], even[i + j]);
-        even[i + j + 1] =      ptx::madc_hi_cc(a[j], b[i], even[i + j + 1]);  
-      }
-
-      // add even and odd parts
-      even[1] = ptx::add_cc(even[1], odd[0]);
-      #pragma unroll
-      for (i = 1; i < 2 * TLC - 2; i++)
-        even[i + 1] = ptx::addc_cc(even[i + 1], odd[i]);
-      even[i + 1] = ptx::addc(even[i + 1], 0);
-    }
-
-
-
-      
-
-    static DEVICE_INLINE void multiply_lsb_raw_device(const ff_storage &as, const ff_storage &bs, ff_wide_storage &rs) {
-      // r = a * b is correcrt for the first TLC + 1 digits. (not computing from TLC + 1 to 2*TLC - 2).
-      const uint32_t *a = as.limbs;
-      const uint32_t *b = bs.limbs;
-      uint32_t *even = rs.limbs;
-      __align__(8) uint32_t odd[2 * TLC - 2];
-      mul_n(even, a, b[0]);
-      mul_n(odd, a + 1, b[0]);
-      mad_row(&even[2], &odd[0], a, b[1]);
-      size_t i;
-    #pragma unroll
-      for (i = 2; i < TLC - 1; i += 2) {
-        mad_row(&odd[i], &even[i], a, b[i], TLC - i + 2);
-        mad_row(&even[i + 2], &odd[i], a, b[i + 1], TLC - i + 2);
-      }
-
-      // merge |even| and |odd|
-      even[1] = ptx::add_cc(even[1], odd[0]);
-      for (i = 1; i < TLC + 1; i++)
-        even[i + 1] = ptx::addc_cc(even[i + 1], odd[i]);
-      even[i + 1] = ptx::addc(even[i + 1], 0);
-    }
-
-    static DEVICE_INLINE void multiply_msb_raw_device(const ff_storage &as, const ff_storage &bs, ff_wide_storage &rs) {
-      const uint32_t *a = as.limbs;
-      const uint32_t *b = bs.limbs;
-      uint32_t *even = rs.limbs;
-      __align__(8) uint32_t odd[2 * TLC - 2];
-      for (int i=0; i<2*TLC - 1; i++)
-      {
-        even[i] = 0;
-        odd[i] = 0;
-      }
-      uint32_t min_indexes_sum = TLC - 1;
-      // only diagonal
-      mul_n_msb(even, a, b[0], TLC, min_indexes_sum);
-      mul_n_msb(odd, a + 1, b[0], TLC, min_indexes_sum - 1);
-      mad_row_msb(&even[2], &odd[0], a, b[1], TLC, min_indexes_sum - 1);
-      size_t i;
-    #pragma unroll
-      for (i = 2; i < TLC - 1; i += 2) {
-        mad_row(&odd[i], &even[i], a, b[i]);
-        mad_row(&even[i + 2], &odd[i], a, b[i + 1]);
-      }
-      // merge |even| and |odd|
-      even[1] = ptx::add_cc(even[1], odd[0]);
-      for (i = 1; i < 2 * TLC - 2; i++)
-        even[i + 1] = ptx::addc_cc(even[i + 1], odd[i]);
-      even[i + 1] = ptx::addc(even[i + 1], 0);
-    }
-
-
-
-
-
     static HOST_INLINE void multiply_raw_host(const ff_storage &as, const ff_storage &bs, ff_wide_storage &rs) {
       const uint32_t *a = as.limbs;
       const uint32_t *b = bs.limbs;
       uint32_t *r = rs.limbs;
       for (unsigned i = 0; i < TLC; i++) {
         uint32_t carry = 0;
-        for (unsigned j = 0; j < TLC; j++) 
+        for (unsigned j = 0; j < TLC; j++)
           r[j + i] = host_math::madc_cc(a[j], b[i], r[j + i], carry);
         r[TLC + i] = carry;
       }
@@ -788,22 +315,6 @@ template <class CONFIG> class Field {
     #endif
     }
 
-    static HOST_DEVICE_INLINE void multiply_raw_lsb(const ff_storage &as, const ff_storage &bs, ff_wide_storage &rs) {
-      #ifdef __CUDA_ARCH__
-        return multiply_lsb_raw_device(as, bs, rs);
-      #else
-        return multiply_raw_host(as, bs, rs);
-      #endif
-      }
-
-      static HOST_DEVICE_INLINE void multiply_raw_msb(const ff_storage &as, const ff_storage &bs, ff_wide_storage &rs) {
-        #ifdef __CUDA_ARCH__
-          return ingo_msb_multiply_raw_device(as, bs, rs);
-        #else
-          return multiply_raw_host(as, bs, rs);
-        #endif
-        }
-
   public:
     ff_storage limbs_storage;
 
@@ -815,8 +326,6 @@ template <class CONFIG> class Field {
       const uint32_t limb_lsb_idx = (digit_num*digit_width) / 32;
       const uint32_t shift_bits = (digit_num*digit_width) % 32;
       unsigned rv = limbs_storage.limbs[limb_lsb_idx] >> shift_bits;
-      // printf("get_scalar_func digit %u rv %u\n",digit_num,rv);
-      // if (shift_bits + digit_width > 32) {
       if ((shift_bits + digit_width > 32) && (limb_lsb_idx+1 < TLC)) {
         rv += limbs_storage.limbs[limb_lsb_idx + 1] << (32 - shift_bits);
       }
@@ -845,20 +354,24 @@ template <class CONFIG> class Field {
     }
 
     friend std::ostream& operator<<(std::ostream& os, const Field& xs) {
-      os << "{";
-      for (int i = 0; i < TLC; i++)
-        os << xs.limbs_storage.limbs[i] << ", ";
-      os << "}";
+      std::stringstream hex_string;
+      hex_string << std::hex << std::setfill('0');
+
+      for (int i = 0; i < TLC; i++) {
+          hex_string << std::setw(8) << xs.limbs_storage.limbs[i];
+      }
+
+      os << "0x" << hex_string.str();
       return os;
     }
 
-    friend HOST_DEVICE_INLINE Field operator+(Field xs, const Field& ys) {   
+    friend HOST_DEVICE_INLINE Field operator+(Field xs, const Field& ys) {
       Field rs = {};
       add_limbs<false>(xs.limbs_storage, ys.limbs_storage, rs.limbs_storage);
       return reduce<1>(rs);
     }
 
-    friend HOST_DEVICE_INLINE Field operator-(Field xs, const Field& ys) {   
+    friend HOST_DEVICE_INLINE Field operator-(Field xs, const Field& ys) {
       Field rs = {};
       uint32_t carry = sub_limbs<true>(xs.limbs_storage, ys.limbs_storage, rs.limbs_storage);
       if (carry == 0)
@@ -869,53 +382,26 @@ template <class CONFIG> class Field {
     }
 
     template <unsigned MODULUS_MULTIPLE = 1>
-    static constexpr HOST_DEVICE_INLINE wide mul_wide(const Field& xs, const Field& ys) {
-      wide rs = {};
+    static constexpr HOST_DEVICE_INLINE Wide mul_wide(const Field& xs, const Field& ys) {
+      Wide rs = {};
       multiply_raw(xs.limbs_storage, ys.limbs_storage, rs.limbs_storage);
       return rs;
     }
 
-    static constexpr DEVICE_INLINE uint32_t sub_limbs_partial_device(uint32_t *x, uint32_t *y, uint32_t *r, uint32_t num_limbs) {
-      r[0] = ptx::sub_cc(x[0], y[0]);
-      #pragma unroll
-      for (unsigned i = 1; i < num_limbs; i++)
-        r[i] = ptx::subc_cc(x[i], y[i]);
-      return ptx::subc(0, 0);
-    }
-
-    static constexpr HOST_DEVICE_INLINE uint32_t sub_limbs_partial(uint32_t *x, uint32_t *y, uint32_t *r, uint32_t num_limbs) {
-    #ifdef __CUDA_ARCH__
-      return sub_limbs_partial_device(x, y, r, num_limbs);
-    #else
-      return sub_limbs_partial_host(x, y, r, num_limbs);
-    #endif
-    }
-
     friend HOST_DEVICE_INLINE Field operator*(const Field& xs, const Field& ys) {
-      //printf("operator* called \n");
-      wide xy = mul_wide(xs, ys); // full mult
-      Field xy_hi = xy.get_higher_with_slack(); // xy << slack_bits
-      wide l = {};
-      multiply_raw_msb(xy_hi.limbs_storage, get_m(), l.limbs_storage);      // MSB mult
+      Wide xy = mul_wide(xs, ys);
+      Field xy_hi = xy.get_higher_with_slack();
+      Wide l = {};
+      multiply_raw(xy_hi.limbs_storage, get_m(), l.limbs_storage);
       Field l_hi = l.get_higher_with_slack();
-      wide lp = {};
-      multiply_raw_lsb(l_hi.limbs_storage, get_modulus(), lp.limbs_storage); // LSB mult
-      wide r_wide = xy - lp; 
-      wide r_wide_reduced = {};
-      // uint32_t reduced = sub_limbs<true>(r_wide.limbs_storage, modulus_wide(), r_wide_reduced.limbs_storage);
-      // r_wide = reduced ? r_wide : r_wide_reduced;
-      for (unsigned i = 0; i < TLC + 1; i++)
-      {
-        uint32_t carry = sub_limbs_partial(r_wide.limbs_storage.limbs, modulus_wide().limbs, r_wide_reduced.limbs_storage.limbs, TLC + 1);
-        if (carry == 0) // continue to reduce
-          r_wide = r_wide_reduced;
-        else // done
-            break;
-      }
-      
-      // number of wrap around is bounded by TLC +  1 times.
+      Wide lp = {};
+      multiply_raw(l_hi.limbs_storage, get_modulus(), lp.limbs_storage);
+      Wide r_wide = xy - lp;
+      Wide r_wide_reduced = {};
+      uint32_t reduced = sub_limbs<true>(r_wide.limbs_storage, modulus_wide(), r_wide_reduced.limbs_storage);
+      r_wide = reduced ? r_wide : r_wide_reduced;
       Field r = r_wide.get_lower();
-      return (r);
+      return reduce<1>(r);
     }
 
     friend HOST_DEVICE_INLINE bool operator==(const Field& xs, const Field& ys) {
@@ -939,22 +425,24 @@ template <class CONFIG> class Field {
       return !(xs == ys);
     }
 
-    template <unsigned REDUCTION_SIZE = 1>
-    static constexpr HOST_DEVICE_INLINE Field mul(const unsigned scalar, const Field &xs) {
-      Field rs = {};
-      Field temp = xs;
-      unsigned l = scalar;
+    template <const Field& multiplier, class T> static constexpr HOST_DEVICE_INLINE T mul_const(const T &xs) {
+      return mul_unsigned<multiplier.limbs_storage.limbs[0], T>(xs);
+    }
+
+    template <uint32_t mutliplier, class T, unsigned REDUCTION_SIZE = 1>
+    static constexpr HOST_DEVICE_INLINE T mul_unsigned(const T &xs) {
+      T rs = {};
+      T temp = xs;
       bool is_zero = true;
   #ifdef __CUDA_ARCH__
   #pragma unroll
   #endif
       for (unsigned i = 0; i < 32; i++) {
-        if (l & 1) {
+        if (mutliplier & (1 << i)) {
           rs = is_zero ? temp : (rs + temp);
           is_zero = false;
         }
-        l >>= 1;
-        if (l == 0)
+        if (mutliplier & ((1 << (31 - i) - 1) << (i + 1)))
           break;
         temp = temp + temp;
       }
@@ -962,7 +450,7 @@ template <class CONFIG> class Field {
     }
 
     template <unsigned MODULUS_MULTIPLE = 1>
-    static constexpr HOST_DEVICE_INLINE wide sqr_wide(const Field& xs) {
+    static constexpr HOST_DEVICE_INLINE Wide sqr_wide(const Field& xs) {
       // TODO: change to a more efficient squaring
       return mul_wide<MODULUS_MULTIPLE>(xs, xs);
     }

icicle/primitives/projective.cu

@@ -1,8 +1,49 @@
 #include <cuda.h>
-#include "../curves/curve_config.cuh"
+#include "../curves/bls12_381/curve_config.cuh"
+#include "../curves/bls12_377/curve_config.cuh"
+#include "../curves/bn254/curve_config.cuh"
 #include "projective.cuh"
 
-extern "C" bool eq(projective_t *point1, projective_t *point2, size_t device_id = 0)
+extern "C" bool eq_bls12_381(BLS12_381::projective_t *point1, BLS12_381::projective_t *point2)
 {
-    return (*point1 == *point2);
-}
+    return (*point1 == *point2) && 
+    !((point1->x == BLS12_381::point_field_t::zero()) && (point1->y == BLS12_381::point_field_t::zero()) && (point1->z == BLS12_381::point_field_t::zero())) && 
+    !((point2->x == BLS12_381::point_field_t::zero()) && (point2->y == BLS12_381::point_field_t::zero()) && (point2->z == BLS12_381::point_field_t::zero()));
+}
+
+extern "C" bool eq_bls12_377(BLS12_377::projective_t *point1, BLS12_377::projective_t *point2)
+{
+    return (*point1 == *point2) && 
+    !((point1->x == BLS12_377::point_field_t::zero()) && (point1->y == BLS12_377::point_field_t::zero()) && (point1->z == BLS12_377::point_field_t::zero())) && 
+    !((point2->x == BLS12_377::point_field_t::zero()) && (point2->y == BLS12_377::point_field_t::zero()) && (point2->z == BLS12_377::point_field_t::zero()));
+}
+
+extern "C" bool eq_bn254(BN254::projective_t *point1, BN254::projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == BN254::point_field_t::zero()) && (point1->y == BN254::point_field_t::zero()) && (point1->z == BN254::point_field_t::zero())) && 
+  !((point2->x == BN254::point_field_t::zero()) && (point2->y == BN254::point_field_t::zero()) && (point2->z == BN254::point_field_t::zero()));
+}
+
+#if defined(G2_DEFINED)
+extern "C" bool eq_g2_bls12_381(BLS12_381::g2_projective_t *point1, BLS12_381::g2_projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == BLS12_381::g2_point_field_t::zero()) && (point1->y == BLS12_381::g2_point_field_t::zero()) && (point1->z == BLS12_381::g2_point_field_t::zero())) && 
+  !((point2->x == BLS12_381::g2_point_field_t::zero()) && (point2->y == BLS12_381::g2_point_field_t::zero()) && (point2->z == BLS12_381::g2_point_field_t::zero()));
+}
+
+extern "C" bool eq_g2_bls12_377(BLS12_377::g2_projective_t *point1, BLS12_377::g2_projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == BLS12_377::g2_point_field_t::zero()) && (point1->y == BLS12_377::g2_point_field_t::zero()) && (point1->z == BLS12_377::g2_point_field_t::zero())) && 
+  !((point2->x == BLS12_377::g2_point_field_t::zero()) && (point2->y == BLS12_377::g2_point_field_t::zero()) && (point2->z == BLS12_377::g2_point_field_t::zero()));
+}
+
+extern "C" bool eq_g2_bn254(BN254::g2_projective_t *point1, BN254::g2_projective_t *point2)
+{
+  return (*point1 == *point2) && 
+  !((point1->x == BN254::g2_point_field_t::zero()) && (point1->y == BN254::g2_point_field_t::zero()) && (point1->z == BN254::g2_point_field_t::zero())) && 
+  !((point2->x == BN254::g2_point_field_t::zero()) && (point2->y == BN254::g2_point_field_t::zero()) && (point2->z == BN254::g2_point_field_t::zero()));
+}
+#endif

icicle/primitives/projective.cuh

@@ -2,7 +2,7 @@
 
 #include "affine.cuh"
 
-template <class FF, class SCALAR_FF, class GEN, unsigned B_VALUE>
+template <typename FF, class SCALAR_FF, const FF& B_VALUE>
 class Projective {
   friend Affine<FF>;
 
@@ -11,10 +11,6 @@ class Projective {
     FF y;
     FF z;
 
-    static HOST_DEVICE_INLINE Projective generator() {
-      return { FF { GEN::generator_x }, FF { GEN::generator_y }, FF::one()};
-    }
-
     static HOST_DEVICE_INLINE Projective zero() {
       return {FF::zero(), FF::one(), FF::zero()};
     }
@@ -28,50 +24,56 @@ class Projective {
       return {point.x, point.y, FF::one()};
     }
 
+    static HOST_DEVICE_INLINE Projective generator() {
+      return {FF::generator_x(), FF::generator_y(), FF::one()};
+    }
+
     static HOST_DEVICE_INLINE Projective neg(const Projective &point) { 
       return {point.x, FF::neg(point.y), point.z};
     }
 
     friend HOST_DEVICE_INLINE Projective operator+(Projective p1, const Projective& p2) {   
-      const FF X1 = p1.x;                                      //                   < 2
-      const FF Y1 = p1.y;                                      //                   < 2
-      const FF Z1 = p1.z;                                      //                   < 2
-      const FF X2 = p2.x;                                      //                   < 2
-      const FF Y2 = p2.y;                                      //                   < 2
-      const FF Z2 = p2.z;                                      //                   < 2
-      const FF t00 = X1 * X2;                                  // t00 ← X1 · X2     < 2
-      const FF t01 = Y1 * Y2;                                  // t01 ← Y1 · Y2     < 2
-      const FF t02 = Z1 * Z2;                                  // t02 ← Z1 · Z2     < 2
-      const FF t03 = X1 + Y1;                                  // t03 ← X1 + Y1     < 4
-      const FF t04 = X2 + Y2;                                  // t04 ← X2 + Y2     < 4
-      const FF t05 = t03 * t04;                                // t03 ← t03 · t04   < 3
-      const FF t06 = t00 + t01;                                // t06 ← t00 + t01   < 4
-      const FF t07 = t05 - t06;                                // t05 ← t05 − t06   < 2
-      const FF t08 = Y1 + Z1;                                  // t08 ← Y1 + Z1     < 4
-      const FF t09 = Y2 + Z2;                                  // t09 ← Y2 + Z2     < 4
-      const FF t10 = t08 * t09;                                // t10 ← t08 · t09   < 3
-      const FF t11 = t01 + t02;                                // t11 ← t01 + t02   < 4
-      const FF t12 = t10 - t11;                                // t12 ← t10 − t11   < 2
-      const FF t13 = X1 + Z1;                                  // t13 ← X1 + Z1     < 4
-      const FF t14 = X2 + Z2;                                  // t14 ← X2 + Z2     < 4
-      const FF t15 = t13 * t14;                                // t15 ← t13 · t14   < 3
-      const FF t16 = t00 + t02;                                // t16 ← t00 + t02   < 4
-      const FF t17 = t15 - t16;                                // t17 ← t15 − t16   < 2
-      const FF t18 = t00 + t00;                                // t18 ← t00 + t00   < 2
-      const FF t19 = t18 + t00;                                // t19 ← t18 + t00   < 2
-      const FF t20 = FF::mul(3 * B_VALUE, t02);                // t20 ← b3 · t02    < 2
-      const FF t21 = t01 + t20;                                // t21 ← t01 + t20   < 2
-      const FF t22 = t01 - t20;                                // t22 ← t01 − t20   < 2
-      const FF t23 = FF::mul(3 * B_VALUE, t17);                // t23 ← b3 · t17    < 2
-      const FF t24 = t12 * t23;                                // t24 ← t12 · t23   < 2
-      const FF t25 = t07 * t22;                                // t25 ← t07 · t22   < 2
-      const FF X3 = t25 - t24;                                 // X3 ← t25 − t24    < 2
-      const FF t27 = t23 * t19;                                // t27 ← t23 · t19   < 2
-      const FF t28 = t22 * t21;                                // t28 ← t22 · t21   < 2
-      const FF Y3 = t28 + t27;                                 // Y3 ← t28 + t27    < 2
-      const FF t30 = t19 * t07;                                // t30 ← t19 · t07   < 2
-      const FF t31 = t21 * t12;                                // t31 ← t21 · t12   < 2
-      const FF Z3 = t31 + t30;                                 // Z3 ← t31 + t30    < 2
+      const FF X1 = p1.x;                                               //                   < 2
+      const FF Y1 = p1.y;                                               //                   < 2
+      const FF Z1 = p1.z;                                               //                   < 2
+      const FF X2 = p2.x;                                               //                   < 2
+      const FF Y2 = p2.y;                                               //                   < 2
+      const FF Z2 = p2.z;                                               //                   < 2
+      const FF t00 = X1 * X2;                                           // t00 ← X1 · X2     < 2
+      const FF t01 = Y1 * Y2;                                           // t01 ← Y1 · Y2     < 2
+      const FF t02 = Z1 * Z2;                                           // t02 ← Z1 · Z2     < 2
+      const FF t03 = X1 + Y1;                                           // t03 ← X1 + Y1     < 4
+      const FF t04 = X2 + Y2;                                           // t04 ← X2 + Y2     < 4
+      const FF t05 = t03 * t04;                                         // t03 ← t03 · t04   < 3
+      const FF t06 = t00 + t01;                                         // t06 ← t00 + t01   < 4
+      const FF t07 = t05 - t06;                                         // t05 ← t05 − t06   < 2
+      const FF t08 = Y1 + Z1;                                           // t08 ← Y1 + Z1     < 4
+      const FF t09 = Y2 + Z2;                                           // t09 ← Y2 + Z2     < 4
+      const FF t10 = t08 * t09;                                         // t10 ← t08 · t09   < 3
+      const FF t11 = t01 + t02;                                         // t11 ← t01 + t02   < 4
+      const FF t12 = t10 - t11;                                         // t12 ← t10 − t11   < 2
+      const FF t13 = X1 + Z1;                                           // t13 ← X1 + Z1     < 4
+      const FF t14 = X2 + Z2;                                           // t14 ← X2 + Z2     < 4
+      const FF t15 = t13 * t14;                                         // t15 ← t13 · t14   < 3
+      const FF t16 = t00 + t02;                                         // t16 ← t00 + t02   < 4
+      const FF t17 = t15 - t16;                                         // t17 ← t15 − t16   < 2
+      const FF t18 = t00 + t00;                                         // t18 ← t00 + t00   < 2
+      const FF t19 = t18 + t00;                                         // t19 ← t18 + t00   < 2
+      const FF t20 = FF::template mul_unsigned<3>(
+        FF::template mul_const<B_VALUE>(t02));                          // t20 ← b3 · t02    < 2
+      const FF t21 = t01 + t20;                                         // t21 ← t01 + t20   < 2
+      const FF t22 = t01 - t20;                                         // t22 ← t01 − t20   < 2
+      const FF t23 = FF::template mul_unsigned<3>(
+        FF::template mul_const<B_VALUE>(t17));                          // t23 ← b3 · t17    < 2
+      const FF t24 = t12 * t23;                                         // t24 ← t12 · t23   < 2
+      const FF t25 = t07 * t22;                                         // t25 ← t07 · t22   < 2
+      const FF X3 = t25 - t24;                                          // X3 ← t25 − t24    < 2
+      const FF t27 = t23 * t19;                                         // t27 ← t23 · t19   < 2
+      const FF t28 = t22 * t21;                                         // t28 ← t22 · t21   < 2
+      const FF Y3 = t28 + t27;                                          // Y3 ← t28 + t27    < 2
+      const FF t30 = t19 * t07;                                         // t30 ← t19 · t07   < 2
+      const FF t31 = t21 * t12;                                         // t31 ← t21 · t12   < 2
+      const FF Z3 = t31 + t30;                                          // Z3 ← t31 + t30    < 2
       return {X3, Y3, Z3};
     }
 
@@ -85,7 +87,7 @@ class Projective {
     }
 
     friend HOST_INLINE std::ostream& operator<<(std::ostream& os, const Projective& point) {
-      os << "x: " << point.x << "; y: " << point.y << "; z: " << point.z;
+      os << "Point { x: " << point.x << "; y: " << point.y << "; z: " << point.z << " }";
       return os;
     }
 
@@ -95,7 +97,7 @@ class Projective {
 
     friend HOST_DEVICE_INLINE Projective operator*(SCALAR_FF scalar, const Projective& point) {   
       Projective res = zero();
-  #ifdef CUDA_ARCH
+  #ifdef __CUDA_ARCH__
   #pragma unroll
   #endif
       for (int i = 0; i < SCALAR_FF::NBITS; i++) {
@@ -120,7 +122,7 @@ class Projective {
     static HOST_DEVICE_INLINE bool is_on_curve(const Projective &point) {
       if (is_zero(point))
         return true;
-      bool eq_holds = (FF::mul(B_VALUE, FF::sqr(point.z) * point.z) + FF::sqr(point.x) * point.x == point.z * FF::sqr(point.y));
+      bool eq_holds = (FF::template mul_const<B_VALUE>(FF::sqr(point.z) * point.z) + FF::sqr(point.x) * point.x == point.z * FF::sqr(point.y));
       return point.z != FF::zero() && eq_holds;
     }
 

icicle/primitives/test.cu

@@ -1,9 +1,8 @@
 #include <cuda_runtime.h>
 #include <gtest/gtest.h>
+
 #include "test_kernels.cuh"
-#include <iostream>
-#include <boost/multiprecision/cpp_int.hpp>
-namespace mp = boost::multiprecision;
+
 
 template <class T>
 int device_populate_random(T* d_elements, unsigned n) {
@@ -21,90 +20,92 @@ int device_set(T* d_elements, T el, unsigned n) {
     return cudaMemcpy(d_elements, h_elements, sizeof(T) * n, cudaMemcpyHostToDevice);
 }
 
-mp::int1024_t convert_to_boost_mp(uint32_t *a, uint32_t length)
-{
-  mp::int1024_t res = 0;
-  for (uint32_t i = 0; i < length; i++)
-  {
-    res += (mp::int1024_t)(a[i]) << 32 * i;
-  }
-  return res;
-}
-
 class PrimitivesTest : public ::testing::Test {
 protected:
-  static const unsigned n = 1 << 10;
+  static const unsigned n = 1 << 5;
 
   proj *points1{};
   proj *points2{};
+  g2_proj *g2_points1{};
+  g2_proj *g2_points2{};
   scalar_field *scalars1{};
   scalar_field *scalars2{};
   proj *zero_points{};
+  g2_proj *g2_zero_points{};
   scalar_field *zero_scalars{};
   scalar_field *one_scalars{};
   affine *aff_points{};
+  g2_affine *g2_aff_points{};
   proj *res_points1{};
   proj *res_points2{};
+  g2_proj *g2_res_points1{};
+  g2_proj *g2_res_points2{};
   scalar_field *res_scalars1{};
   scalar_field *res_scalars2{};
-  scalar_field::wide *res_scalars_wide{};
-  scalar_field::wide *res_scalars_wide_full{};
 
   PrimitivesTest() {
     assert(!cudaDeviceReset());
     assert(!cudaMallocManaged(&points1, n * sizeof(proj)));
     assert(!cudaMallocManaged(&points2, n * sizeof(proj)));
+    assert(!cudaMallocManaged(&g2_points1, n * sizeof(g2_proj)));
+    assert(!cudaMallocManaged(&g2_points2, n * sizeof(g2_proj)));
     assert(!cudaMallocManaged(&scalars1, n * sizeof(scalar_field)));
     assert(!cudaMallocManaged(&scalars2, n * sizeof(scalar_field)));
     assert(!cudaMallocManaged(&zero_points, n * sizeof(proj)));
+    assert(!cudaMallocManaged(&g2_zero_points, n * sizeof(g2_proj)));
     assert(!cudaMallocManaged(&zero_scalars, n * sizeof(scalar_field)));
     assert(!cudaMallocManaged(&one_scalars, n * sizeof(scalar_field)));
     assert(!cudaMallocManaged(&aff_points, n * sizeof(affine)));
+    assert(!cudaMallocManaged(&g2_aff_points, n * sizeof(g2_affine)));
     assert(!cudaMallocManaged(&res_points1, n * sizeof(proj)));
     assert(!cudaMallocManaged(&res_points2, n * sizeof(proj)));
+    assert(!cudaMallocManaged(&g2_res_points1, n * sizeof(g2_proj)));
+    assert(!cudaMallocManaged(&g2_res_points2, n * sizeof(g2_proj)));
     assert(!cudaMallocManaged(&res_scalars1, n * sizeof(scalar_field)));
     assert(!cudaMallocManaged(&res_scalars2, n * sizeof(scalar_field)));
-    assert(!cudaMallocManaged(&res_scalars_wide, n * sizeof(scalar_field::wide)));
-    assert(!cudaMallocManaged(&res_scalars_wide_full, n * sizeof(scalar_field::wide)));
-
   }
 
   ~PrimitivesTest() override {
     cudaFree(points1);
     cudaFree(points2);
+    cudaFree(g2_points1);
+    cudaFree(g2_points2);
     cudaFree(scalars1);
     cudaFree(scalars2);
     cudaFree(zero_points);
+    cudaFree(g2_zero_points);
     cudaFree(zero_scalars);
     cudaFree(one_scalars);
     cudaFree(aff_points);
+    cudaFree(g2_aff_points);
     cudaFree(res_points1);
     cudaFree(res_points2);
+    cudaFree(g2_res_points1);
+    cudaFree(g2_res_points2);
     cudaFree(res_scalars1);
     cudaFree(res_scalars2);
-
-    cudaFree(res_scalars_wide);
-    cudaFree(res_scalars_wide_full);
-
     cudaDeviceReset();
   }
 
   void SetUp() override {
     ASSERT_EQ(device_populate_random<proj>(points1, n), cudaSuccess);
     ASSERT_EQ(device_populate_random<proj>(points2, n), cudaSuccess);
+    ASSERT_EQ(device_populate_random<g2_proj>(g2_points1, n), cudaSuccess);
+    ASSERT_EQ(device_populate_random<g2_proj>(g2_points2, n), cudaSuccess);
     ASSERT_EQ(device_populate_random<scalar_field>(scalars1, n), cudaSuccess);
     ASSERT_EQ(device_populate_random<scalar_field>(scalars2, n), cudaSuccess);
     ASSERT_EQ(device_set<proj>(zero_points, proj::zero(), n), cudaSuccess);
+    ASSERT_EQ(device_set<g2_proj>(g2_zero_points, g2_proj::zero(), n), cudaSuccess);
     ASSERT_EQ(device_set<scalar_field>(zero_scalars, scalar_field::zero(), n), cudaSuccess);
     ASSERT_EQ(device_set<scalar_field>(one_scalars, scalar_field::one(), n), cudaSuccess);
     ASSERT_EQ(cudaMemset(aff_points, 0, n * sizeof(affine)), cudaSuccess);
+    ASSERT_EQ(cudaMemset(g2_aff_points, 0, n * sizeof(g2_affine)), cudaSuccess);
     ASSERT_EQ(cudaMemset(res_points1, 0, n * sizeof(proj)), cudaSuccess);
     ASSERT_EQ(cudaMemset(res_points2, 0, n * sizeof(proj)), cudaSuccess);
+    ASSERT_EQ(cudaMemset(g2_res_points1, 0, n * sizeof(g2_proj)), cudaSuccess);
+    ASSERT_EQ(cudaMemset(g2_res_points2, 0, n * sizeof(g2_proj)), cudaSuccess);
     ASSERT_EQ(cudaMemset(res_scalars1, 0, n * sizeof(scalar_field)), cudaSuccess);
     ASSERT_EQ(cudaMemset(res_scalars2, 0, n * sizeof(scalar_field)), cudaSuccess);
-    
-    ASSERT_EQ(cudaMemset(res_scalars_wide, 0, n * sizeof(scalar_field::wide)), cudaSuccess);
-    ASSERT_EQ(cudaMemset(res_scalars_wide_full, 0, n * sizeof(scalar_field::wide)), cudaSuccess);
   }
 };
 
@@ -278,188 +279,103 @@ TEST_F(PrimitivesTest, ECMixedAdditionOfNegatedPointEqSubtraction) {
     ASSERT_EQ(res_points1[i], points1[i] + res_points2[i]);
 }
 
-TEST_F(PrimitivesTest, MP_LSB_MULT) {
-  // LSB multiply, check correctness of first TLC + 1 digits result.
-  ASSERT_EQ(mp_lsb_mult(scalars1, scalars2, res_scalars_wide), cudaSuccess);
-  std::cout << "first GPU lsb mult output  = 0x";
-  for (int i=0; i<2*scalar_field::TLC; i++)
-  {
-    std::cout << std::hex << res_scalars_wide[0].limbs_storage.limbs[i];
-  }
-  std::cout << std::endl;
-
-
-  ASSERT_EQ(mp_mult(scalars1, scalars2, res_scalars_wide_full), cudaSuccess);
-  std::cout << "first GPU full mult output = 0x";
-  for (int i=0; i<2*scalar_field::TLC; i++)
-  {
-    std::cout << std::hex << res_scalars_wide_full[0].limbs_storage.limbs[i];
-  }
-  std::cout << std::endl;
-  for (int j = 0; j < n; j++)
-  {
-    for (int i=0; i<scalar_field::TLC + 1; i++)
-    {
-      ASSERT_EQ(res_scalars_wide_full[j].limbs_storage.limbs[i], res_scalars_wide[j].limbs_storage.limbs[i]);
-    }
-  }
+TEST_F(PrimitivesTest, G2ECRandomPointsAreOnCurve) {
+  for (unsigned i = 0; i < 2; i++)
+    ASSERT_PRED1(g2_proj::is_on_curve, g2_points1[i]);
 }
 
-TEST_F(PrimitivesTest, MP_MSB_MULT) {
-  // MSB multiply, take n msb bits of multiplication, assert that the error is up to 1.
-  ASSERT_EQ(mp_msb_mult(scalars1, scalars2, res_scalars_wide), cudaSuccess);
-  std::cout << "first GPU msb mult output  = 0x";
-  for (int i=2*scalar_field::TLC - 1; i >=0 ; i--)
-  {
-    std::cout << std::hex << res_scalars_wide[0].limbs_storage.limbs[i] << " ";
-  }
-  std::cout << std::endl;
-
-
-  ASSERT_EQ(mp_mult(scalars1, scalars2, res_scalars_wide_full), cudaSuccess);
-  std::cout << "first GPU full mult output = 0x";
-  for (int i=2*scalar_field::TLC - 1; i >=0 ; i--)
-  {
-    std::cout << std::hex << res_scalars_wide_full[0].limbs_storage.limbs[i] << " ";
-  }
-
-  std::cout << std::endl;
-
-  for (int i=0; i < 2*scalar_field::TLC - 1; i++)
-  {
-    if (res_scalars_wide_full[0].limbs_storage.limbs[i] == res_scalars_wide[0].limbs_storage.limbs[i])
-        std::cout << "matched word idx = " << i << std::endl;
-  }
-
+TEST_F(PrimitivesTest, G2ECPointAdditionSubtractionCancel) {
+  ASSERT_EQ(vec_add(g2_points1, g2_points2, g2_res_points1, n), cudaSuccess);
+  ASSERT_EQ(vec_sub(g2_res_points1, g2_points2, g2_res_points2, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_points1[i], g2_res_points2[i]);
 }
 
-TEST_F(PrimitivesTest, INGO_MP_MULT) {
-  // MSB multiply, take n msb bits of multiplication, assert that the error is up to 1.
-  ASSERT_EQ(ingo_mp_mult(scalars1, scalars2, res_scalars_wide), cudaSuccess);
-  std::cout << "INGO   = 0x";
-  for (int i=0; i < 2*scalar_field::TLC ; i++)
-  {
-    std::cout << std::hex << res_scalars_wide[0].limbs_storage.limbs[i] << " ";
-  }
-  std::cout << std::endl;
-
-
-  ASSERT_EQ(mp_mult(scalars1, scalars2, res_scalars_wide_full), cudaSuccess);
-  std::cout << "ZKSYNC = 0x";
-  for (int i=0; i < 2*scalar_field::TLC ; i++)
-  {
-    std::cout << std::hex << res_scalars_wide_full[0].limbs_storage.limbs[i] << " ";
-  }
-
-  std::cout << std::endl;
-
-  for (int i=0; i < 2*scalar_field::TLC - 1; i++)
-  {
-    if (res_scalars_wide_full[0].limbs_storage.limbs[i] == res_scalars_wide[0].limbs_storage.limbs[i])
-        std::cout << "matched word idx = " << i << std::endl;
-  }
-  for (int j=0; j<n; j++)
-  {
-    for (int i=0; i < 2*scalar_field::TLC - 1; i++)
-    {
-      ASSERT_EQ(res_scalars_wide_full[j].limbs_storage.limbs[i], res_scalars_wide[j].limbs_storage.limbs[i]);
-    }
-  }
-
+TEST_F(PrimitivesTest, G2ECPointZeroAddition) {
+  ASSERT_EQ(vec_add(g2_points1, g2_zero_points, g2_res_points1, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_points1[i], g2_res_points1[i]);
 }
 
-
-TEST_F(PrimitivesTest, INGO_MP_MSB_MULT) {
-  // MSB multiply, take n msb bits of multiplication, assert that the error is up to 1.
-  ASSERT_EQ(ingo_mp_msb_mult(scalars1, scalars2, res_scalars_wide, n), cudaSuccess);
-  std::cout << "INGO MSB   = 0x";
-  for (int i=2*scalar_field::TLC - 1; i >= 0  ; i--)
-  {
-    std::cout << std::hex << res_scalars_wide[0].limbs_storage.limbs[i] << " ";
-  }
-  std::cout << std::endl;
-
-  ASSERT_EQ(mp_mult(scalars1, scalars2, res_scalars_wide_full), cudaSuccess);
-  std::cout << "ZKSYNC = 0x";
-  for (int i=2*scalar_field::TLC - 1; i >= 0  ; i--)
-  {
-    std::cout << std::hex << res_scalars_wide_full[0].limbs_storage.limbs[i] << " ";
-  }
-
-  std::cout << std::endl;
-  
-  
-  // for (int i=scalar_field::TLC; i < 2*scalar_field::TLC - 1; i++)
-  // {
-  //   ASSERT_EQ(in_bound, true);
-  // }
-  // for (int j=0; j<n; j++)
-  // {
-  //   for (int i=0; i < 2*scalar_field::TLC - 1; i++)
-  //   {
-  //     ASSERT_EQ(res_scalars_wide_full[j].limbs_storage.limbs[i], res_scalars_wide[j].limbs_storage.limbs[i]);
-  //   }
-  // }
-  // mp testing
-  mp::int1024_t scalar_1_mp = 0;
-  mp::int1024_t scalar_2_mp = 0;
-  mp::int1024_t res_mp = 0;
-  mp::int1024_t res_gpu = 0;
-  uint32_t num_limbs = scalar_field::TLC;
-  
-  for (int j=0; j<n; j++)
-  {
-    uint32_t* scalar1_limbs = scalars1[j].limbs_storage.limbs;
-    uint32_t* scalar2_limbs = scalars2[j].limbs_storage.limbs;
-    scalar_1_mp = convert_to_boost_mp(scalar1_limbs, num_limbs);
-    scalar_2_mp = convert_to_boost_mp(scalar2_limbs, num_limbs);
-    res_mp = scalar_1_mp * scalar_2_mp;
-    res_mp = res_mp >> (num_limbs * 32);
-    res_gpu = convert_to_boost_mp(&(res_scalars_wide[j]).limbs_storage.limbs[num_limbs], num_limbs);
-    std::cout  << "res  mp = " << res_mp << std::endl;
-    std::cout << "res gpu = " << res_gpu << std::endl;
-    std::cout << "error = " << res_mp - res_gpu << std::endl;
-    bool upper_bound = res_gpu <= res_mp;
-    bool lower_bound = res_gpu > (res_mp - num_limbs);
-    bool in_bound = upper_bound && lower_bound;
-    
-    
-    ASSERT_EQ(in_bound, true);
-  }
+TEST_F(PrimitivesTest, G2ECPointAdditionHostDeviceEq) {
+  ASSERT_EQ(vec_add(g2_points1, g2_points2, g2_res_points1, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_points1[i] + g2_points2[i], g2_res_points1[i]);
 }
 
-TEST_F(PrimitivesTest, INGO_MP_MOD_MULT) {
-  std::cout  << " taking num limbs " <<  std::endl;
-  uint32_t num_limbs = scalar_field::TLC;
-  std::cout  << " calling gpu... = " <<  std::endl;
-  ASSERT_EQ(ingo_mp_mod_mult(scalars1, scalars2, res_scalars1, n), cudaSuccess);
-  std::cout  << " gpu call done " <<  std::endl;
-  // mp testing
-  mp::int1024_t scalar_1_mp = 0;
-  mp::int1024_t scalar_2_mp = 0;
-  mp::int1024_t res_mp = 0;
-  mp::int1024_t res_gpu = 0;
-  mp::int1024_t p = convert_to_boost_mp(scalar_field::get_modulus().limbs, num_limbs);
-  std::cout << " p = " << p << std::endl;
-  
-  
-  for (int j=0; j<n; j++)
-  {
-    uint32_t* scalar1_limbs = scalars1[j].limbs_storage.limbs;
-    uint32_t* scalar2_limbs = scalars2[j].limbs_storage.limbs;
-    scalar_1_mp = convert_to_boost_mp(scalar1_limbs, num_limbs);
-    scalar_2_mp = convert_to_boost_mp(scalar2_limbs, num_limbs);
-    // std::cout << " s1 = " << scalar_1_mp << std::endl;
-    // std::cout << " s2 = " << scalar_2_mp << std::endl;
-    res_mp = (scalar_1_mp * scalar_2_mp) % p;
-    res_gpu = convert_to_boost_mp((res_scalars1[j]).limbs_storage.limbs, num_limbs);
-    std::cout  << "res  mp = " << res_mp << std::endl;
-    std::cout << "res gpu = " << res_gpu << std::endl;
-    std::cout << "error = " << res_mp - res_gpu << std::endl;
-    ASSERT_EQ(res_gpu, res_mp);
-  }
+TEST_F(PrimitivesTest, G2ECScalarMultiplicationHostDeviceEq) {
+  ASSERT_EQ(vec_mul(scalars1, g2_points1, g2_res_points1, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(scalars1[i] * g2_points1[i], g2_res_points1[i]);
 }
 
+TEST_F(PrimitivesTest, G2ECScalarMultiplicationByOne) {
+  ASSERT_EQ(vec_mul(one_scalars, points1, res_points1, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_points1[i], g2_res_points1[i]);
+}
+
+TEST_F(PrimitivesTest, G2ECScalarMultiplicationByMinusOne) {
+  ASSERT_EQ(vec_neg(one_scalars, res_scalars1, n), cudaSuccess);
+  ASSERT_EQ(vec_mul(res_scalars1, g2_points1, g2_res_points1, n), cudaSuccess);
+  ASSERT_EQ(vec_neg(g2_points1, g2_res_points2, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_res_points1[i], g2_res_points2[i]);
+}
+
+TEST_F(PrimitivesTest, G2ECScalarMultiplicationByTwo) {
+  ASSERT_EQ(vec_add(one_scalars, one_scalars, res_scalars1, n), cudaSuccess);
+  ASSERT_EQ(vec_mul(res_scalars1, g2_points1, g2_res_points1, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ((one_scalars[i] + one_scalars[i]) * g2_points1[i], g2_res_points1[i]);
+}
+
+TEST_F(PrimitivesTest, G2ECScalarMultiplicationInverseCancel) {
+  ASSERT_EQ(vec_mul(scalars1, g2_points1, g2_res_points1, n), cudaSuccess);
+  ASSERT_EQ(field_vec_inv(scalars1, res_scalars1, n), cudaSuccess);
+  ASSERT_EQ(vec_mul(res_scalars1, g2_res_points1, g2_res_points2, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_points1[i], g2_res_points2[i]);
+}
+
+TEST_F(PrimitivesTest, G2ECScalarMultiplicationIsDistributiveOverMultiplication) {
+  ASSERT_EQ(vec_mul(scalars1, g2_points1, g2_res_points1, n), cudaSuccess);
+  ASSERT_EQ(vec_mul(scalars2, g2_res_points1, g2_res_points2, n), cudaSuccess);
+  ASSERT_EQ(vec_mul(scalars1, scalars2, res_scalars1, n), cudaSuccess);
+  ASSERT_EQ(vec_mul(res_scalars1, g2_points1, g2_res_points1, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_res_points1[i], g2_res_points2[i]);
+}
+
+TEST_F(PrimitivesTest, G2ECScalarMultiplicationIsDistributiveOverAddition) {
+  ASSERT_EQ(vec_mul(scalars1, g2_points1, g2_res_points1, n), cudaSuccess);
+  ASSERT_EQ(vec_mul(scalars2, g2_points1, g2_res_points2, n), cudaSuccess);
+  ASSERT_EQ(vec_add(scalars1, scalars2, res_scalars1, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(res_scalars1[i] * g2_points1[i], g2_res_points1[i] + g2_res_points2[i]);
+}
+
+TEST_F(PrimitivesTest, G2ECProjectiveToAffine) {
+  ASSERT_EQ(point_vec_to_affine(g2_points1, g2_aff_points, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_points1[i], g2_proj::from_affine(g2_aff_points[i]));
+}
+
+TEST_F(PrimitivesTest, G2ECMixedPointAddition) {
+  ASSERT_EQ(point_vec_to_affine(g2_points2, g2_aff_points, n), cudaSuccess);
+  ASSERT_EQ(vec_add(g2_points1, g2_aff_points, g2_res_points1, n), cudaSuccess);
+  ASSERT_EQ(vec_add(g2_points1, g2_points2, g2_res_points2, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_res_points1[i], g2_res_points2[i]);
+}
+
+TEST_F(PrimitivesTest, G2ECMixedAdditionOfNegatedPointEqSubtraction) {
+  ASSERT_EQ(point_vec_to_affine(g2_points2, g2_aff_points, n), cudaSuccess);
+  ASSERT_EQ(vec_sub(g2_points1, g2_aff_points, g2_res_points1, n), cudaSuccess);
+  ASSERT_EQ(vec_neg(g2_points2, g2_res_points2, n), cudaSuccess);
+  for (unsigned i = 0; i < n; i++)
+    ASSERT_EQ(g2_res_points1[i], g2_points1[i] + g2_res_points2[i]);
+}
 
 
 int main(int argc, char **argv) {

icicle/primitives/test_kernels.cuh

@@ -3,12 +3,17 @@
 // TODO: change the curve depending on env variable
 #include "../curves/bls12_381.cuh"
 #include "projective.cuh"
-#include "field.cuh"
+#include "extension_field.cuh"
 
 typedef Field<fp_config> scalar_field;
 typedef Field<fq_config> base_field;
 typedef Affine<base_field> affine;
-typedef Projective<base_field, scalar_field, group_generator, weierstrass_b> proj;
+static constexpr base_field b = base_field{ weierstrass_b };
+typedef Projective<base_field, scalar_field, b> proj;
+typedef ExtensionField<fq_config> base_extension_field;
+typedef Affine<base_extension_field> g2_affine;
+static constexpr base_extension_field b2 = base_extension_field{ base_field {b_re},  base_field {b_im}};
+typedef Projective<base_extension_field, scalar_field, b2> g2_proj;
 
 
 template <class T1, class T2>
@@ -93,99 +98,16 @@ int field_vec_sqr(const scalar_field *x, scalar_field *result, const unsigned co
   return error ? error : cudaDeviceSynchronize();
 }
 
-__global__ void to_affine_points_kernel(const proj *x, affine *result, const unsigned count) {
+template <class P, class A>
+__global__ void to_affine_points_kernel(const P *x, A *result, const unsigned count) {
   const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x;
   if (gid >= count)
     return;
-  result[gid] = proj::to_affine(x[gid]);
+  result[gid] = P::to_affine(x[gid]);
 }
 
-int point_vec_to_affine(const proj *x, affine *result, const unsigned count) {
-  to_affine_points_kernel<<<(count - 1) / 32 + 1, 32>>>(x, result, count);
+template <class P, class A> int point_vec_to_affine(const P *x, A *result, const unsigned count) {
+  to_affine_points_kernel<P, A><<<(count - 1) / 32 + 1, 32>>>(x, result, count);
   int error = cudaGetLastError();
   return error ? error : cudaDeviceSynchronize();
 }
-
-
-__global__ void mp_mult_kernel(const scalar_field *x, const scalar_field *y, scalar_field::wide *result) {
-  const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x;
-  scalar_field::multiply_raw_device(x[gid].limbs_storage, y[gid].limbs_storage, result[gid].limbs_storage);
-}
-
-
-int mp_mult(const scalar_field *x, scalar_field *y, scalar_field::wide *result)
-{
-  mp_mult_kernel<<<1, 32>>>(x, y, result);
-  int error = cudaGetLastError();
-  return error ? error :  cudaDeviceSynchronize();
-}
-
-
-
-__global__ void mp_lsb_mult_kernel(const scalar_field *x, const scalar_field *y, scalar_field::wide *result) {
-  const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x;
-  scalar_field::multiply_lsb_raw_device(x[gid].limbs_storage, y[gid].limbs_storage, result[gid].limbs_storage);
-}
-
-
-int mp_lsb_mult(const scalar_field *x, scalar_field *y, scalar_field::wide *result)
-{
-  mp_lsb_mult_kernel<<<1, 32>>>(x, y, result);
-  int error = cudaGetLastError();
-  return error ? error :  cudaDeviceSynchronize();
-}
-
-__global__ void mp_msb_mult_kernel(const scalar_field *x, const scalar_field *y, scalar_field::wide *result) {
-  const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x;
-  scalar_field::multiply_msb_raw_device(x[gid].limbs_storage, y[gid].limbs_storage, result[gid].limbs_storage);
-}
-
-
-int mp_msb_mult(const scalar_field *x, scalar_field *y, scalar_field::wide *result)
-{
-  mp_msb_mult_kernel<<<1, 1>>>(x, y, result);
-  int error = cudaGetLastError();
-  return error ? error :  cudaDeviceSynchronize();
-}
-
-
-__global__ void ingo_mp_mult_kernel(const scalar_field *x, const scalar_field *y, scalar_field::wide *result) {
-  const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x;
-  scalar_field::ingo_multiply_raw_device(x[gid].limbs_storage, y[gid].limbs_storage, result[gid].limbs_storage);
-}
-
-
-int ingo_mp_mult(const scalar_field *x, scalar_field *y, scalar_field::wide *result)
-{
-  ingo_mp_mult_kernel<<<1, 32>>>(x, y, result);
-  int error = cudaGetLastError();
-  return error ? error :  cudaDeviceSynchronize();
-}
-
-
-__global__ void ingo_mp_msb_mult_kernel(const scalar_field *x, const scalar_field *y, scalar_field::wide *result) {
-  const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x;
-  scalar_field::ingo_msb_multiply_raw_device(x[gid].limbs_storage, y[gid].limbs_storage, result[gid].limbs_storage);
-}
-
-
-int ingo_mp_msb_mult(const scalar_field *x, scalar_field *y, scalar_field::wide *result, const unsigned n)
-{
-  ingo_mp_msb_mult_kernel<<<1, n>>>(x, y, result);
-  int error = cudaGetLastError();
-  return error ? error :  cudaDeviceSynchronize();
-}
-
-
-__global__ void ingo_mp_mod_mult_kernel(const scalar_field *x, const scalar_field *y, scalar_field *result) {
-  const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x;
-  result[gid] = x[gid] * y[gid];
-}
-
-
-int ingo_mp_mod_mult(const scalar_field *x, scalar_field *y, scalar_field *result, const unsigned n)
-{
-  ingo_mp_mod_mult_kernel<<<1, n>>>(x, y, result);
-  int error = cudaGetLastError();
-  return error ? error :  cudaDeviceSynchronize();
-}

icicle/utils/storage.cuh

@@ -7,3 +7,7 @@ template <unsigned LIMBS_COUNT> struct __align__(LIMBS_ALIGNMENT(LIMBS_COUNT)) s
   static constexpr unsigned LC = LIMBS_COUNT;
   uint32_t limbs[LIMBS_COUNT];
 };
+
+template <unsigned OMEGAS_COUNT, unsigned LIMBS_COUNT> struct __align__(LIMBS_ALIGNMENT(LIMBS_COUNT)) storage_array {
+    storage<LIMBS_COUNT> storages[OMEGAS_COUNT];
+};

src/curve_templates/curve_different_limbs.rs

@@ -0,0 +1,334 @@
+use std::ffi::c_uint;
+
+use ark_CURVE_NAME_L::{Fq as Fq_CURVE_NAME_U, Fr as Fr_CURVE_NAME_U, G1Affine as G1Affine_CURVE_NAME_U, G1Projective as G1Projective_CURVE_NAME_U};
+
+use ark_ec::AffineCurve;
+use ark_ff::{BigInteger_limbs_q, BigInteger_limbs_p, PrimeField};
+use std::mem::transmute;
+use ark_ff::Field;
+use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}};
+
+use rustacuda_core::DeviceCopy;
+use rustacuda_derive::DeviceCopy;
+
+#[derive(Debug, PartialEq, Copy, Clone)]
+#[repr(C)]
+pub struct Field_CURVE_NAME_U<const NUM_LIMBS: usize> {
+    pub s: [u32; NUM_LIMBS],
+}
+
+unsafe impl<const NUM_LIMBS: usize> DeviceCopy for Field_CURVE_NAME_U<NUM_LIMBS> {}
+
+impl<const NUM_LIMBS: usize> Default for Field_CURVE_NAME_U<NUM_LIMBS> {
+    fn default() -> Self {
+        Field_CURVE_NAME_U::zero()
+    }
+}
+
+impl<const NUM_LIMBS: usize> Field_CURVE_NAME_U<NUM_LIMBS> {
+    pub fn zero() -> Self {
+        Field_CURVE_NAME_U {
+            s: [0u32; NUM_LIMBS],
+        }
+    }
+
+    pub fn one() -> Self {
+        let mut s = [0u32; NUM_LIMBS];
+        s[0] = 1;
+        Field_CURVE_NAME_U { s }
+    }
+
+    fn to_bytes_le(&self) -> Vec<u8> {
+        self.s
+            .iter()
+            .map(|s| s.to_le_bytes().to_vec())
+            .flatten()
+            .collect::<Vec<_>>()
+    }
+}
+
+pub const BASE_LIMBS_CURVE_NAME_U: usize = limbs_q;
+pub const SCALAR_LIMBS_CURVE_NAME_U: usize = limbs_p;
+
+pub type BaseField_CURVE_NAME_U = Field_CURVE_NAME_U<BASE_LIMBS_CURVE_NAME_U>;
+pub type ScalarField_CURVE_NAME_U = Field_CURVE_NAME_U<SCALAR_LIMBS_CURVE_NAME_U>;
+
+fn get_fixed_limbs<const NUM_LIMBS: usize>(val: &[u32]) -> [u32; NUM_LIMBS] {
+    match val.len() {
+        n if n < NUM_LIMBS => {
+            let mut padded: [u32; NUM_LIMBS] = [0; NUM_LIMBS];
+            padded[..val.len()].copy_from_slice(&val);
+            padded
+        }
+        n if n == NUM_LIMBS => val.try_into().unwrap(),
+        _ => panic!("slice has too many elements"),
+    }
+}
+
+// 
+impl BaseField_CURVE_NAME_U {
+    pub fn limbs(&self) -> [u32; BASE_LIMBS_CURVE_NAME_U] {
+        self.s
+    }
+
+    pub fn from_limbs(value: &[u32]) -> Self {
+        Self {
+            s: get_fixed_limbs(value),
+        }
+    }
+
+    pub fn to_ark(&self) -> BigInteger_limbs_q {
+        BigInteger_limbs_q::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
+    }
+
+    pub fn from_ark(ark: BigInteger_limbs_q) -> Self {
+        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
+    }
+}
+//
+
+impl ScalarField_CURVE_NAME_U {
+    pub fn limbs(&self) -> [u32; SCALAR_LIMBS_CURVE_NAME_U] {
+        self.s
+    }
+
+    pub fn to_ark(&self) -> BigInteger_limbs_p {
+        BigInteger_limbs_p::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
+    }
+
+    pub fn from_ark(ark: BigInteger_limbs_p) -> Self {
+        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
+    }
+
+    pub fn to_ark_transmute(&self) -> BigInteger_limbs_p {
+        unsafe { transmute(*self) }
+    }
+
+    pub fn from_ark_transmute(v: BigInteger_limbs_p) -> ScalarField_CURVE_NAME_U {
+        unsafe { transmute(v) }
+    }
+}
+
+#[derive(Debug, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct Point_CURVE_NAME_U {
+    pub x: BaseField_CURVE_NAME_U,
+    pub y: BaseField_CURVE_NAME_U,
+    pub z: BaseField_CURVE_NAME_U,
+}
+
+impl Default for Point_CURVE_NAME_U {
+    fn default() -> Self {
+        Point_CURVE_NAME_U::zero()
+    }
+}
+
+impl Point_CURVE_NAME_U {
+    pub fn zero() -> Self {
+        Point_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::zero(),
+            y: BaseField_CURVE_NAME_U::one(),
+            z: BaseField_CURVE_NAME_U::zero(),
+        }
+    }
+
+    pub fn infinity() -> Self {
+        Self::zero()
+    }
+
+    pub fn to_ark(&self) -> G1Projective_CURVE_NAME_U {
+        //TODO: generic conversion
+        self.to_ark_affine().into_projective()
+    }
+
+    pub fn to_ark_affine(&self) -> G1Affine_CURVE_NAME_U {
+        //TODO: generic conversion
+        use ark_ff::Field;
+        use std::ops::Mul;
+        let proj_x_field = Fq_CURVE_NAME_U::from_le_bytes_mod_order(&self.x.to_bytes_le());
+        let proj_y_field = Fq_CURVE_NAME_U::from_le_bytes_mod_order(&self.y.to_bytes_le());
+        let proj_z_field = Fq_CURVE_NAME_U::from_le_bytes_mod_order(&self.z.to_bytes_le());
+        let inverse_z = proj_z_field.inverse().unwrap();
+        let aff_x = proj_x_field.mul(inverse_z);
+        let aff_y = proj_y_field.mul(inverse_z);
+        G1Affine_CURVE_NAME_U::new(aff_x, aff_y, false)
+    }
+
+    pub fn from_ark(ark: G1Projective_CURVE_NAME_U) -> Point_CURVE_NAME_U {
+        use ark_ff::Field;
+        let z_inv = ark.z.inverse().unwrap();
+        let z_invsq = z_inv * z_inv;
+        let z_invq3 = z_invsq * z_inv;
+        Point_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::from_ark((ark.x * z_invsq).into_repr()),
+            y: BaseField_CURVE_NAME_U::from_ark((ark.y * z_invq3).into_repr()),
+            z: BaseField_CURVE_NAME_U::one(),
+        }
+    }
+}
+
+extern "C" {
+    fn eq_CURVE_NAME_L(point1: *const Point_CURVE_NAME_U, point2: *const Point_CURVE_NAME_U) -> c_uint;
+}
+
+impl PartialEq for Point_CURVE_NAME_U {
+    fn eq(&self, other: &Self) -> bool {
+        unsafe { eq_CURVE_NAME_L(self, other) != 0 }
+    }
+}
+
+#[derive(Debug, PartialEq, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct PointAffineNoInfinity_CURVE_NAME_U {
+    pub x: BaseField_CURVE_NAME_U,
+    pub y: BaseField_CURVE_NAME_U,
+}
+
+impl Default for PointAffineNoInfinity_CURVE_NAME_U {
+    fn default() -> Self {
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::zero(),
+            y: BaseField_CURVE_NAME_U::zero(),
+        }
+    }
+}
+
+impl PointAffineNoInfinity_CURVE_NAME_U {
+    // TODO: generics
+    ///From u32 limbs x,y
+    pub fn from_limbs(x: &[u32], y: &[u32]) -> Self {
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(y),
+            },
+        }
+    }
+
+    pub fn limbs(&self) -> Vec<u32> {
+        [self.x.limbs(), self.y.limbs()].concat()
+    }
+
+    pub fn to_projective(&self) -> Point_CURVE_NAME_U {
+        Point_CURVE_NAME_U {
+            x: self.x,
+            y: self.y,
+            z: BaseField_CURVE_NAME_U::one(),
+        }
+    }
+
+    pub fn to_ark(&self) -> G1Affine_CURVE_NAME_U {
+        G1Affine_CURVE_NAME_U::new(Fq_CURVE_NAME_U::new(self.x.to_ark()), Fq_CURVE_NAME_U::new(self.y.to_ark()), false)
+    }
+
+    pub fn to_ark_repr(&self) -> G1Affine_CURVE_NAME_U {
+        G1Affine_CURVE_NAME_U::new(
+            Fq_CURVE_NAME_U::from_repr(self.x.to_ark()).unwrap(),
+            Fq_CURVE_NAME_U::from_repr(self.y.to_ark()).unwrap(),
+            false,
+        )
+    }
+
+    pub fn from_ark(p: &G1Affine_CURVE_NAME_U) -> Self {
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::from_ark(p.x.into_repr()),
+            y: BaseField_CURVE_NAME_U::from_ark(p.y.into_repr()),
+        }
+    }
+}
+
+impl Point_CURVE_NAME_U {
+    // TODO: generics
+
+    pub fn from_limbs(x: &[u32], y: &[u32], z: &[u32]) -> Self {
+        Point_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(y),
+            },
+            z: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(z),
+            },
+        }
+    }
+
+    pub fn from_xy_limbs(value: &[u32]) -> Point_CURVE_NAME_U {
+        let l = value.len();
+        assert_eq!(l, 3 * BASE_LIMBS_CURVE_NAME_U, "length must be 3 * {}", BASE_LIMBS_CURVE_NAME_U);
+        Point_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U {
+                s: value[..BASE_LIMBS_CURVE_NAME_U].try_into().unwrap(),
+            },
+            y: BaseField_CURVE_NAME_U {
+                s: value[BASE_LIMBS_CURVE_NAME_U..BASE_LIMBS_CURVE_NAME_U * 2].try_into().unwrap(),
+            },
+            z: BaseField_CURVE_NAME_U {
+                s: value[BASE_LIMBS_CURVE_NAME_U * 2..].try_into().unwrap(),
+            },
+        }
+    }
+
+    pub fn to_affine(&self) -> PointAffineNoInfinity_CURVE_NAME_U {
+        let ark_affine = self.to_ark_affine();
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::from_ark(ark_affine.x.into_repr()),
+            y: BaseField_CURVE_NAME_U::from_ark(ark_affine.y.into_repr()),
+        }
+    }
+
+    pub fn to_xy_strip_z(&self) -> PointAffineNoInfinity_CURVE_NAME_U {
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: self.x,
+            y: self.y,
+        }
+    }
+}
+
+impl ScalarField_CURVE_NAME_U {
+    pub fn from_limbs(value: &[u32]) -> ScalarField_CURVE_NAME_U {
+        ScalarField_CURVE_NAME_U {
+            s: get_fixed_limbs(value),
+        }
+    }
+}
+
+
+#[cfg(test)]
+mod tests {
+    use ark_CURVE_NAME_L::{Fr as Fr_CURVE_NAME_U};
+
+    use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}, curves::CURVE_NAME_L::{Point_CURVE_NAME_U, ScalarField_CURVE_NAME_U}};
+
+    #[test]
+    fn test_ark_scalar_convert() {
+        let limbs = [0x0fffffff, 1, 0x2fffffff, 3, 0x4fffffff, 5, 0x6fffffff, 7];
+        let scalar = ScalarField_CURVE_NAME_U::from_limbs(&limbs);
+        assert_eq!(
+            scalar.to_ark(),
+            scalar.to_ark_transmute(),
+            "{:08X?} {:08X?}",
+            scalar.to_ark(),
+            scalar.to_ark_transmute()
+        )
+    }
+
+    #[test]
+    #[allow(non_snake_case)]
+    fn test_point_equality() {
+        let left = Point_CURVE_NAME_U::zero();
+        let right = Point_CURVE_NAME_U::zero();
+        assert_eq!(left, right);
+        let right = Point_CURVE_NAME_U::from_limbs(&[0; 12], &[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], &[0; 12]);
+        assert_eq!(left, right);
+        let right = Point_CURVE_NAME_U::from_limbs(
+            &[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            &[0; 12],
+            &[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        );
+        assert!(left != right);
+    }
+}

src/curve_templates/curve_same_limbs.rs

@@ -0,0 +1,312 @@
+use std::ffi::c_uint;
+
+use ark_CURVE_NAME_L::{Fq as Fq_CURVE_NAME_U, Fr as Fr_CURVE_NAME_U, G1Affine as G1Affine_CURVE_NAME_U, G1Projective as G1Projective_CURVE_NAME_U};
+
+use ark_ec::AffineCurve;
+use ark_ff::{BigInteger_limbs_p, PrimeField};
+use std::mem::transmute;
+use ark_ff::Field;
+use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}};
+
+use rustacuda_core::DeviceCopy;
+use rustacuda_derive::DeviceCopy;
+
+#[derive(Debug, PartialEq, Copy, Clone)]
+#[repr(C)]
+pub struct Field_CURVE_NAME_U<const NUM_LIMBS: usize> {
+    pub s: [u32; NUM_LIMBS],
+}
+
+unsafe impl<const NUM_LIMBS: usize> DeviceCopy for Field_CURVE_NAME_U<NUM_LIMBS> {}
+
+impl<const NUM_LIMBS: usize> Default for Field_CURVE_NAME_U<NUM_LIMBS> {
+    fn default() -> Self {
+        Field_CURVE_NAME_U::zero()
+    }
+}
+
+impl<const NUM_LIMBS: usize> Field_CURVE_NAME_U<NUM_LIMBS> {
+    pub fn zero() -> Self {
+        Field_CURVE_NAME_U {
+            s: [0u32; NUM_LIMBS],
+        }
+    }
+
+    pub fn one() -> Self {
+        let mut s = [0u32; NUM_LIMBS];
+        s[0] = 1;
+        Field_CURVE_NAME_U { s }
+    }
+
+    fn to_bytes_le(&self) -> Vec<u8> {
+        self.s
+            .iter()
+            .map(|s| s.to_le_bytes().to_vec())
+            .flatten()
+            .collect::<Vec<_>>()
+    }
+}
+
+pub const BASE_LIMBS_CURVE_NAME_U: usize = limbs_p;
+pub const SCALAR_LIMBS_CURVE_NAME_U: usize = limbs_p;
+
+pub type BaseField_CURVE_NAME_U = Field_CURVE_NAME_U<BASE_LIMBS_CURVE_NAME_U>;
+pub type ScalarField_CURVE_NAME_U = Field_CURVE_NAME_U<SCALAR_LIMBS_CURVE_NAME_U>;
+
+fn get_fixed_limbs<const NUM_LIMBS: usize>(val: &[u32]) -> [u32; NUM_LIMBS] {
+    match val.len() {
+        n if n < NUM_LIMBS => {
+            let mut padded: [u32; NUM_LIMBS] = [0; NUM_LIMBS];
+            padded[..val.len()].copy_from_slice(&val);
+            padded
+        }
+        n if n == NUM_LIMBS => val.try_into().unwrap(),
+        _ => panic!("slice has too many elements"),
+    }
+}
+
+impl ScalarField_CURVE_NAME_U {
+    pub fn limbs(&self) -> [u32; SCALAR_LIMBS_CURVE_NAME_U] {
+        self.s
+    }
+
+    pub fn to_ark(&self) -> BigInteger_limbs_p {
+        BigInteger_limbs_p::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
+    }
+
+    pub fn from_ark(ark: BigInteger_limbs_p) -> Self {
+        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
+    }
+
+    pub fn to_ark_transmute(&self) -> BigInteger_limbs_p {
+        unsafe { transmute(*self) }
+    }
+
+    pub fn from_ark_transmute(v: BigInteger_limbs_p) -> ScalarField_CURVE_NAME_U {
+        unsafe { transmute(v) }
+    }
+}
+
+#[derive(Debug, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct Point_CURVE_NAME_U {
+    pub x: BaseField_CURVE_NAME_U,
+    pub y: BaseField_CURVE_NAME_U,
+    pub z: BaseField_CURVE_NAME_U,
+}
+
+impl Default for Point_CURVE_NAME_U {
+    fn default() -> Self {
+        Point_CURVE_NAME_U::zero()
+    }
+}
+
+impl Point_CURVE_NAME_U {
+    pub fn zero() -> Self {
+        Point_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::zero(),
+            y: BaseField_CURVE_NAME_U::one(),
+            z: BaseField_CURVE_NAME_U::zero(),
+        }
+    }
+
+    pub fn infinity() -> Self {
+        Self::zero()
+    }
+
+    pub fn to_ark(&self) -> G1Projective_CURVE_NAME_U {
+        //TODO: generic conversion
+        self.to_ark_affine().into_projective()
+    }
+
+    pub fn to_ark_affine(&self) -> G1Affine_CURVE_NAME_U {
+        //TODO: generic conversion
+        use ark_ff::Field;
+        use std::ops::Mul;
+        let proj_x_field = Fq_CURVE_NAME_U::from_le_bytes_mod_order(&self.x.to_bytes_le());
+        let proj_y_field = Fq_CURVE_NAME_U::from_le_bytes_mod_order(&self.y.to_bytes_le());
+        let proj_z_field = Fq_CURVE_NAME_U::from_le_bytes_mod_order(&self.z.to_bytes_le());
+        let inverse_z = proj_z_field.inverse().unwrap();
+        let aff_x = proj_x_field.mul(inverse_z);
+        let aff_y = proj_y_field.mul(inverse_z);
+        G1Affine_CURVE_NAME_U::new(aff_x, aff_y, false)
+    }
+
+    pub fn from_ark(ark: G1Projective_CURVE_NAME_U) -> Point_CURVE_NAME_U {
+        use ark_ff::Field;
+        let z_inv = ark.z.inverse().unwrap();
+        let z_invsq = z_inv * z_inv;
+        let z_invq3 = z_invsq * z_inv;
+        Point_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::from_ark((ark.x * z_invsq).into_repr()),
+            y: BaseField_CURVE_NAME_U::from_ark((ark.y * z_invq3).into_repr()),
+            z: BaseField_CURVE_NAME_U::one(),
+        }
+    }
+}
+
+extern "C" {
+    fn eq_CURVE_NAME_L(point1: *const Point_CURVE_NAME_U, point2: *const Point_CURVE_NAME_U) -> c_uint;
+}
+
+impl PartialEq for Point_CURVE_NAME_U {
+    fn eq(&self, other: &Self) -> bool {
+        unsafe { eq_CURVE_NAME_L(self, other) != 0 }
+    }
+}
+
+#[derive(Debug, PartialEq, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct PointAffineNoInfinity_CURVE_NAME_U {
+    pub x: BaseField_CURVE_NAME_U,
+    pub y: BaseField_CURVE_NAME_U,
+}
+
+impl Default for PointAffineNoInfinity_CURVE_NAME_U {
+    fn default() -> Self {
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::zero(),
+            y: BaseField_CURVE_NAME_U::zero(),
+        }
+    }
+}
+
+impl PointAffineNoInfinity_CURVE_NAME_U {
+    // TODO: generics
+    ///From u32 limbs x,y
+    pub fn from_limbs(x: &[u32], y: &[u32]) -> Self {
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(y),
+            },
+        }
+    }
+
+    pub fn limbs(&self) -> Vec<u32> {
+        [self.x.limbs(), self.y.limbs()].concat()
+    }
+
+    pub fn to_projective(&self) -> Point_CURVE_NAME_U {
+        Point_CURVE_NAME_U {
+            x: self.x,
+            y: self.y,
+            z: BaseField_CURVE_NAME_U::one(),
+        }
+    }
+
+    pub fn to_ark(&self) -> G1Affine_CURVE_NAME_U {
+        G1Affine_CURVE_NAME_U::new(Fq_CURVE_NAME_U::new(self.x.to_ark()), Fq_CURVE_NAME_U::new(self.y.to_ark()), false)
+    }
+
+    pub fn to_ark_repr(&self) -> G1Affine_CURVE_NAME_U {
+        G1Affine_CURVE_NAME_U::new(
+            Fq_CURVE_NAME_U::from_repr(self.x.to_ark()).unwrap(),
+            Fq_CURVE_NAME_U::from_repr(self.y.to_ark()).unwrap(),
+            false,
+        )
+    }
+
+    pub fn from_ark(p: &G1Affine_CURVE_NAME_U) -> Self {
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::from_ark(p.x.into_repr()),
+            y: BaseField_CURVE_NAME_U::from_ark(p.y.into_repr()),
+        }
+    }
+}
+
+impl Point_CURVE_NAME_U {
+    // TODO: generics
+
+    pub fn from_limbs(x: &[u32], y: &[u32], z: &[u32]) -> Self {
+        Point_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(y),
+            },
+            z: BaseField_CURVE_NAME_U {
+                s: get_fixed_limbs(z),
+            },
+        }
+    }
+
+    pub fn from_xy_limbs(value: &[u32]) -> Point_CURVE_NAME_U {
+        let l = value.len();
+        assert_eq!(l, 3 * BASE_LIMBS_CURVE_NAME_U, "length must be 3 * {}", BASE_LIMBS_CURVE_NAME_U);
+        Point_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U {
+                s: value[..BASE_LIMBS_CURVE_NAME_U].try_into().unwrap(),
+            },
+            y: BaseField_CURVE_NAME_U {
+                s: value[BASE_LIMBS_CURVE_NAME_U..BASE_LIMBS_CURVE_NAME_U * 2].try_into().unwrap(),
+            },
+            z: BaseField_CURVE_NAME_U {
+                s: value[BASE_LIMBS_CURVE_NAME_U * 2..].try_into().unwrap(),
+            },
+        }
+    }
+
+    pub fn to_affine(&self) -> PointAffineNoInfinity_CURVE_NAME_U {
+        let ark_affine = self.to_ark_affine();
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: BaseField_CURVE_NAME_U::from_ark(ark_affine.x.into_repr()),
+            y: BaseField_CURVE_NAME_U::from_ark(ark_affine.y.into_repr()),
+        }
+    }
+
+    pub fn to_xy_strip_z(&self) -> PointAffineNoInfinity_CURVE_NAME_U {
+        PointAffineNoInfinity_CURVE_NAME_U {
+            x: self.x,
+            y: self.y,
+        }
+    }
+}
+
+impl ScalarField_CURVE_NAME_U {
+    pub fn from_limbs(value: &[u32]) -> ScalarField_CURVE_NAME_U {
+        ScalarField_CURVE_NAME_U {
+            s: get_fixed_limbs(value),
+        }
+    }
+}
+
+
+#[cfg(test)]
+mod tests {
+    use ark_CURVE_NAME_L::{Fr as Fr_CURVE_NAME_U};
+
+    use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}, curves::CURVE_NAME_L::{Point_CURVE_NAME_U, ScalarField_CURVE_NAME_U}};
+
+    #[test]
+    fn test_ark_scalar_convert() {
+        let limbs = [0x0fffffff, 1, 0x2fffffff, 3, 0x4fffffff, 5, 0x6fffffff, 7];
+        let scalar = ScalarField_CURVE_NAME_U::from_limbs(&limbs);
+        assert_eq!(
+            scalar.to_ark(),
+            scalar.to_ark_transmute(),
+            "{:08X?} {:08X?}",
+            scalar.to_ark(),
+            scalar.to_ark_transmute()
+        )
+    }
+
+    #[test]
+    #[allow(non_snake_case)]
+    fn test_point_equality() {
+        let left = Point_CURVE_NAME_U::zero();
+        let right = Point_CURVE_NAME_U::zero();
+        assert_eq!(left, right);
+        let right = Point_CURVE_NAME_U::from_limbs(&[0; 8], &[2, 0, 0, 0, 0, 0, 0, 0], &[0; 8]);
+        assert_eq!(left, right);
+        let right = Point_CURVE_NAME_U::from_limbs(
+            &[2, 0, 0, 0, 0, 0, 0, 0],
+            &[0; 8],
+            &[1, 0, 0, 0, 0, 0, 0, 0],
+        );
+        assert!(left != right);
+    }
+}

src/curves/bls12_377.rs

@@ -0,0 +1,332 @@
+use std::ffi::c_uint;
+
+use ark_bls12_377::{Fq as Fq_BLS12_377, Fr as Fr_BLS12_377, G1Affine as G1Affine_BLS12_377, G1Projective as G1Projective_BLS12_377};
+
+use ark_ec::AffineCurve;
+use ark_ff::{BigInteger384, BigInteger256, PrimeField};
+use std::mem::transmute;
+use ark_ff::Field;
+use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}};
+
+use rustacuda_core::DeviceCopy;
+use rustacuda_derive::DeviceCopy;
+
+#[derive(Debug, PartialEq, Copy, Clone)]
+#[repr(C)]
+pub struct Field_BLS12_377<const NUM_LIMBS: usize> {
+    pub s: [u32; NUM_LIMBS],
+}
+
+unsafe impl<const NUM_LIMBS: usize> DeviceCopy for Field_BLS12_377<NUM_LIMBS> {}
+
+impl<const NUM_LIMBS: usize> Default for Field_BLS12_377<NUM_LIMBS> {
+    fn default() -> Self {
+        Field_BLS12_377::zero()
+    }
+}
+
+impl<const NUM_LIMBS: usize> Field_BLS12_377<NUM_LIMBS> {
+    pub fn zero() -> Self {
+        Field_BLS12_377 {
+            s: [0u32; NUM_LIMBS],
+        }
+    }
+
+    pub fn one() -> Self {
+        let mut s = [0u32; NUM_LIMBS];
+        s[0] = 1;
+        Field_BLS12_377 { s }
+    }
+
+    fn to_bytes_le(&self) -> Vec<u8> {
+        self.s
+            .iter()
+            .map(|s| s.to_le_bytes().to_vec())
+            .flatten()
+            .collect::<Vec<_>>()
+    }
+}
+
+pub const BASE_LIMBS_BLS12_377: usize = 12;
+pub const SCALAR_LIMBS_BLS12_377: usize = 8;
+
+pub type BaseField_BLS12_377 = Field_BLS12_377<BASE_LIMBS_BLS12_377>;
+pub type ScalarField_BLS12_377 = Field_BLS12_377<SCALAR_LIMBS_BLS12_377>;
+
+fn get_fixed_limbs<const NUM_LIMBS: usize>(val: &[u32]) -> [u32; NUM_LIMBS] {
+    match val.len() {
+        n if n < NUM_LIMBS => {
+            let mut padded: [u32; NUM_LIMBS] = [0; NUM_LIMBS];
+            padded[..val.len()].copy_from_slice(&val);
+            padded
+        }
+        n if n == NUM_LIMBS => val.try_into().unwrap(),
+        _ => panic!("slice has too many elements"),
+    }
+}
+
+impl BaseField_BLS12_377 {
+    pub fn limbs(&self) -> [u32; BASE_LIMBS_BLS12_377] {
+        self.s
+    }
+
+    pub fn from_limbs(value: &[u32]) -> Self {
+        Self {
+            s: get_fixed_limbs(value),
+        }
+    }
+
+    pub fn to_ark(&self) -> BigInteger384 {
+        BigInteger384::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
+    }
+
+    pub fn from_ark(ark: BigInteger384) -> Self {
+        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
+    }
+}
+
+impl ScalarField_BLS12_377 {
+    pub fn limbs(&self) -> [u32; SCALAR_LIMBS_BLS12_377] {
+        self.s
+    }
+
+    pub fn to_ark(&self) -> BigInteger256 {
+        BigInteger256::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
+    }
+
+    pub fn from_ark(ark: BigInteger256) -> Self {
+        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
+    }
+
+    pub fn to_ark_transmute(&self) -> BigInteger256 {
+        unsafe { transmute(*self) }
+    }
+
+    pub fn from_ark_transmute(v: BigInteger256) -> ScalarField_BLS12_377 {
+        unsafe { transmute(v) }
+    }
+}
+
+#[derive(Debug, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct Point_BLS12_377 {
+    pub x: BaseField_BLS12_377,
+    pub y: BaseField_BLS12_377,
+    pub z: BaseField_BLS12_377,
+}
+
+impl Default for Point_BLS12_377 {
+    fn default() -> Self {
+        Point_BLS12_377::zero()
+    }
+}
+
+impl Point_BLS12_377 {
+    pub fn zero() -> Self {
+        Point_BLS12_377 {
+            x: BaseField_BLS12_377::zero(),
+            y: BaseField_BLS12_377::one(),
+            z: BaseField_BLS12_377::zero(),
+        }
+    }
+
+    pub fn infinity() -> Self {
+        Self::zero()
+    }
+
+    pub fn to_ark(&self) -> G1Projective_BLS12_377 {
+        //TODO: generic conversion
+        self.to_ark_affine().into_projective()
+    }
+
+    pub fn to_ark_affine(&self) -> G1Affine_BLS12_377 {
+        //TODO: generic conversion
+        use ark_ff::Field;
+        use std::ops::Mul;
+        let proj_x_field = Fq_BLS12_377::from_le_bytes_mod_order(&self.x.to_bytes_le());
+        let proj_y_field = Fq_BLS12_377::from_le_bytes_mod_order(&self.y.to_bytes_le());
+        let proj_z_field = Fq_BLS12_377::from_le_bytes_mod_order(&self.z.to_bytes_le());
+        let inverse_z = proj_z_field.inverse().unwrap();
+        let aff_x = proj_x_field.mul(inverse_z);
+        let aff_y = proj_y_field.mul(inverse_z);
+        G1Affine_BLS12_377::new(aff_x, aff_y, false)
+    }
+
+    pub fn from_ark(ark: G1Projective_BLS12_377) -> Point_BLS12_377 {
+        use ark_ff::Field;
+        let z_inv = ark.z.inverse().unwrap();
+        let z_invsq = z_inv * z_inv;
+        let z_invq3 = z_invsq * z_inv;
+        Point_BLS12_377 {
+            x: BaseField_BLS12_377::from_ark((ark.x * z_invsq).into_repr()),
+            y: BaseField_BLS12_377::from_ark((ark.y * z_invq3).into_repr()),
+            z: BaseField_BLS12_377::one(),
+        }
+    }
+}
+
+extern "C" {
+    fn eq_bls12_377(point1: *const Point_BLS12_377, point2: *const Point_BLS12_377) -> c_uint;
+}
+
+impl PartialEq for Point_BLS12_377 {
+    fn eq(&self, other: &Self) -> bool {
+        unsafe { eq_bls12_377(self, other) != 0 }
+    }
+}
+
+#[derive(Debug, PartialEq, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct PointAffineNoInfinity_BLS12_377 {
+    pub x: BaseField_BLS12_377,
+    pub y: BaseField_BLS12_377,
+}
+
+impl Default for PointAffineNoInfinity_BLS12_377 {
+    fn default() -> Self {
+        PointAffineNoInfinity_BLS12_377 {
+            x: BaseField_BLS12_377::zero(),
+            y: BaseField_BLS12_377::zero(),
+        }
+    }
+}
+
+impl PointAffineNoInfinity_BLS12_377 {
+    // TODO: generics
+    ///From u32 limbs x,y
+    pub fn from_limbs(x: &[u32], y: &[u32]) -> Self {
+        PointAffineNoInfinity_BLS12_377 {
+            x: BaseField_BLS12_377 {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_BLS12_377 {
+                s: get_fixed_limbs(y),
+            },
+        }
+    }
+
+    pub fn limbs(&self) -> Vec<u32> {
+        [self.x.limbs(), self.y.limbs()].concat()
+    }
+
+    pub fn to_projective(&self) -> Point_BLS12_377 {
+        Point_BLS12_377 {
+            x: self.x,
+            y: self.y,
+            z: BaseField_BLS12_377::one(),
+        }
+    }
+
+    pub fn to_ark(&self) -> G1Affine_BLS12_377 {
+        G1Affine_BLS12_377::new(Fq_BLS12_377::new(self.x.to_ark()), Fq_BLS12_377::new(self.y.to_ark()), false)
+    }
+
+    pub fn to_ark_repr(&self) -> G1Affine_BLS12_377 {
+        G1Affine_BLS12_377::new(
+            Fq_BLS12_377::from_repr(self.x.to_ark()).unwrap(),
+            Fq_BLS12_377::from_repr(self.y.to_ark()).unwrap(),
+            false,
+        )
+    }
+
+    pub fn from_ark(p: &G1Affine_BLS12_377) -> Self {
+        PointAffineNoInfinity_BLS12_377 {
+            x: BaseField_BLS12_377::from_ark(p.x.into_repr()),
+            y: BaseField_BLS12_377::from_ark(p.y.into_repr()),
+        }
+    }
+}
+
+impl Point_BLS12_377 {
+    // TODO: generics
+
+    pub fn from_limbs(x: &[u32], y: &[u32], z: &[u32]) -> Self {
+        Point_BLS12_377 {
+            x: BaseField_BLS12_377 {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_BLS12_377 {
+                s: get_fixed_limbs(y),
+            },
+            z: BaseField_BLS12_377 {
+                s: get_fixed_limbs(z),
+            },
+        }
+    }
+
+    pub fn from_xy_limbs(value: &[u32]) -> Point_BLS12_377 {
+        let l = value.len();
+        assert_eq!(l, 3 * BASE_LIMBS_BLS12_377, "length must be 3 * {}", BASE_LIMBS_BLS12_377);
+        Point_BLS12_377 {
+            x: BaseField_BLS12_377 {
+                s: value[..BASE_LIMBS_BLS12_377].try_into().unwrap(),
+            },
+            y: BaseField_BLS12_377 {
+                s: value[BASE_LIMBS_BLS12_377..BASE_LIMBS_BLS12_377 * 2].try_into().unwrap(),
+            },
+            z: BaseField_BLS12_377 {
+                s: value[BASE_LIMBS_BLS12_377 * 2..].try_into().unwrap(),
+            },
+        }
+    }
+
+    pub fn to_affine(&self) -> PointAffineNoInfinity_BLS12_377 {
+        let ark_affine = self.to_ark_affine();
+        PointAffineNoInfinity_BLS12_377 {
+            x: BaseField_BLS12_377::from_ark(ark_affine.x.into_repr()),
+            y: BaseField_BLS12_377::from_ark(ark_affine.y.into_repr()),
+        }
+    }
+
+    pub fn to_xy_strip_z(&self) -> PointAffineNoInfinity_BLS12_377 {
+        PointAffineNoInfinity_BLS12_377 {
+            x: self.x,
+            y: self.y,
+        }
+    }
+}
+
+impl ScalarField_BLS12_377 {
+    pub fn from_limbs(value: &[u32]) -> ScalarField_BLS12_377 {
+        ScalarField_BLS12_377 {
+            s: get_fixed_limbs(value),
+        }
+    }
+}
+
+
+#[cfg(test)]
+mod tests {
+    use ark_bls12_377::{Fr as Fr_BLS12_377};
+
+    use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}, curves::bls12_377::{Point_BLS12_377, ScalarField_BLS12_377}};
+
+    #[test]
+    fn test_ark_scalar_convert() {
+        let limbs = [0x0fffffff, 1, 0x2fffffff, 3, 0x4fffffff, 5, 0x6fffffff, 7];
+        let scalar = ScalarField_BLS12_377::from_limbs(&limbs);
+        assert_eq!(
+            scalar.to_ark(),
+            scalar.to_ark_transmute(),
+            "{:08X?} {:08X?}",
+            scalar.to_ark(),
+            scalar.to_ark_transmute()
+        )
+    }
+
+    #[test]
+    #[allow(non_snake_case)]
+    fn test_point_equality() {
+        let left = Point_BLS12_377::zero();
+        let right = Point_BLS12_377::zero();
+        assert_eq!(left, right);
+        let right = Point_BLS12_377::from_limbs(&[0; 12], &[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], &[0; 12]);
+        assert_eq!(left, right);
+        let right = Point_BLS12_377::from_limbs(
+            &[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            &[0; 12],
+            &[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        );
+        assert!(left != right);
+    }
+}

src/curves/bls12_381.rs

@@ -0,0 +1,352 @@
+use std::ffi::c_uint;
+
+use ark_bls12_381::{Fq as Fq_BLS12_381, Fr as Fr_BLS12_381, G1Affine as G1Affine_BLS12_381, G1Projective as G1Projective_BLS12_381};
+
+use ark_ec::AffineCurve;
+use ark_ff::{BigInteger384, BigInteger256, PrimeField};
+use serde::{Serialize, Deserialize};
+use std::mem::transmute;
+use ark_ff::Field;
+use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}};
+
+use rustacuda_core::DeviceCopy;
+use rustacuda_derive::DeviceCopy;
+
+#[derive(Debug, PartialEq, Copy, Clone)]
+#[repr(C)]
+pub struct Field_BLS12_381<const NUM_LIMBS: usize> {
+    pub s: [u32; NUM_LIMBS],
+}
+
+unsafe impl<const NUM_LIMBS: usize> DeviceCopy for Field_BLS12_381<NUM_LIMBS> {}
+
+impl<const NUM_LIMBS: usize> Default for Field_BLS12_381<NUM_LIMBS> {
+    fn default() -> Self {
+        Field_BLS12_381::zero()
+    }
+}
+
+impl<const NUM_LIMBS: usize> Field_BLS12_381<NUM_LIMBS> {
+    pub fn zero() -> Self {
+        Field_BLS12_381 {
+            s: [0u32; NUM_LIMBS],
+        }
+    }
+
+    pub fn one() -> Self {
+        let mut s = [0u32; NUM_LIMBS];
+        s[0] = 1;
+        Field_BLS12_381 { s }
+    }
+
+    fn to_bytes_le(&self) -> Vec<u8> {
+        self.s
+            .iter()
+            .map(|s| s.to_le_bytes().to_vec())
+            .flatten()
+            .collect::<Vec<_>>()
+    }
+}
+
+pub const BASE_LIMBS_BLS12_381: usize = 12;
+pub const SCALAR_LIMBS_BLS12_381: usize = 8;
+
+pub type BaseField_BLS12_381 = Field_BLS12_381<BASE_LIMBS_BLS12_381>;
+pub type ScalarField_BLS12_381 = Field_BLS12_381<SCALAR_LIMBS_BLS12_381>;
+
+impl Serialize for ScalarField_BLS12_381 {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: serde::Serializer,
+    {
+        self.s.serialize(serializer)
+    }
+}
+
+impl<'de> Deserialize<'de> for ScalarField_BLS12_381 {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: serde::Deserializer<'de>,
+    {
+        let s = <[u32; SCALAR_LIMBS_BLS12_381] as Deserialize<'de>>::deserialize(deserializer)?;
+        Ok(ScalarField_BLS12_381 { s })
+    }
+}
+
+fn get_fixed_limbs<const NUM_LIMBS: usize>(val: &[u32]) -> [u32; NUM_LIMBS] {
+    match val.len() {
+        n if n < NUM_LIMBS => {
+            let mut padded: [u32; NUM_LIMBS] = [0; NUM_LIMBS];
+            padded[..val.len()].copy_from_slice(&val);
+            padded
+        }
+        n if n == NUM_LIMBS => val.try_into().unwrap(),
+        _ => panic!("slice has too many elements"),
+    }
+}
+
+impl BaseField_BLS12_381 {
+    pub fn limbs(&self) -> [u32; BASE_LIMBS_BLS12_381] {
+        self.s
+    }
+
+    pub fn from_limbs(value: &[u32]) -> Self {
+        Self {
+            s: get_fixed_limbs(value),
+        }
+    }
+
+    pub fn to_ark(&self) -> BigInteger384 {
+        BigInteger384::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
+    }
+
+    pub fn from_ark(ark: BigInteger384) -> Self {
+        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
+    }
+}
+
+impl ScalarField_BLS12_381 {
+    pub fn limbs(&self) -> [u32; SCALAR_LIMBS_BLS12_381] {
+        self.s
+    }
+
+    pub fn to_ark(&self) -> BigInteger256 {
+        BigInteger256::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
+    }
+
+    pub fn from_ark(ark: BigInteger256) -> Self {
+        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
+    }
+
+    pub fn to_ark_transmute(&self) -> BigInteger256 {
+        unsafe { transmute(*self) }
+    }
+
+    pub fn from_ark_transmute(v: BigInteger256) -> ScalarField_BLS12_381 {
+        unsafe { transmute(v) }
+    }
+}
+
+#[derive(Debug, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct Point_BLS12_381 {
+    pub x: BaseField_BLS12_381,
+    pub y: BaseField_BLS12_381,
+    pub z: BaseField_BLS12_381,
+}
+
+impl Default for Point_BLS12_381 {
+    fn default() -> Self {
+        Point_BLS12_381::zero()
+    }
+}
+
+impl Point_BLS12_381 {
+    pub fn zero() -> Self {
+        Point_BLS12_381 {
+            x: BaseField_BLS12_381::zero(),
+            y: BaseField_BLS12_381::one(),
+            z: BaseField_BLS12_381::zero(),
+        }
+    }
+
+    pub fn infinity() -> Self {
+        Self::zero()
+    }
+
+    pub fn to_ark(&self) -> G1Projective_BLS12_381 {
+        //TODO: generic conversion
+        self.to_ark_affine().into_projective()
+    }
+
+    pub fn to_ark_affine(&self) -> G1Affine_BLS12_381 {
+        //TODO: generic conversion
+        use ark_ff::Field;
+        use std::ops::Mul;
+        let proj_x_field = Fq_BLS12_381::from_le_bytes_mod_order(&self.x.to_bytes_le());
+        let proj_y_field = Fq_BLS12_381::from_le_bytes_mod_order(&self.y.to_bytes_le());
+        let proj_z_field = Fq_BLS12_381::from_le_bytes_mod_order(&self.z.to_bytes_le());
+        let inverse_z = proj_z_field.inverse().unwrap();
+        let aff_x = proj_x_field.mul(inverse_z);
+        let aff_y = proj_y_field.mul(inverse_z);
+        G1Affine_BLS12_381::new(aff_x, aff_y, false)
+    }
+
+    pub fn from_ark(ark: G1Projective_BLS12_381) -> Point_BLS12_381 {
+        use ark_ff::Field;
+        let z_inv = ark.z.inverse().unwrap();
+        let z_invsq = z_inv * z_inv;
+        let z_invq3 = z_invsq * z_inv;
+        Point_BLS12_381 {
+            x: BaseField_BLS12_381::from_ark((ark.x * z_invsq).into_repr()),
+            y: BaseField_BLS12_381::from_ark((ark.y * z_invq3).into_repr()),
+            z: BaseField_BLS12_381::one(),
+        }
+    }
+}
+
+extern "C" {
+    fn eq_bls12_381(point1: *const Point_BLS12_381, point2: *const Point_BLS12_381) -> c_uint;
+}
+
+impl PartialEq for Point_BLS12_381 {
+    fn eq(&self, other: &Self) -> bool {
+        unsafe { eq_bls12_381(self, other) != 0 }
+    }
+}
+
+#[derive(Debug, PartialEq, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct PointAffineNoInfinity_BLS12_381 {
+    pub x: BaseField_BLS12_381,
+    pub y: BaseField_BLS12_381,
+}
+
+impl Default for PointAffineNoInfinity_BLS12_381 {
+    fn default() -> Self {
+        PointAffineNoInfinity_BLS12_381 {
+            x: BaseField_BLS12_381::zero(),
+            y: BaseField_BLS12_381::zero(),
+        }
+    }
+}
+
+impl PointAffineNoInfinity_BLS12_381 {
+    // TODO: generics
+    ///From u32 limbs x,y
+    pub fn from_limbs(x: &[u32], y: &[u32]) -> Self {
+        PointAffineNoInfinity_BLS12_381 {
+            x: BaseField_BLS12_381 {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_BLS12_381 {
+                s: get_fixed_limbs(y),
+            },
+        }
+    }
+
+    pub fn limbs(&self) -> Vec<u32> {
+        [self.x.limbs(), self.y.limbs()].concat()
+    }
+
+    pub fn to_projective(&self) -> Point_BLS12_381 {
+        Point_BLS12_381 {
+            x: self.x,
+            y: self.y,
+            z: BaseField_BLS12_381::one(),
+        }
+    }
+
+    pub fn to_ark(&self) -> G1Affine_BLS12_381 {
+        G1Affine_BLS12_381::new(Fq_BLS12_381::new(self.x.to_ark()), Fq_BLS12_381::new(self.y.to_ark()), false)
+    }
+
+    pub fn to_ark_repr(&self) -> G1Affine_BLS12_381 {
+        G1Affine_BLS12_381::new(
+            Fq_BLS12_381::from_repr(self.x.to_ark()).unwrap(),
+            Fq_BLS12_381::from_repr(self.y.to_ark()).unwrap(),
+            false,
+        )
+    }
+
+    pub fn from_ark(p: &G1Affine_BLS12_381) -> Self {
+        PointAffineNoInfinity_BLS12_381 {
+            x: BaseField_BLS12_381::from_ark(p.x.into_repr()),
+            y: BaseField_BLS12_381::from_ark(p.y.into_repr()),
+        }
+    }
+}
+
+impl Point_BLS12_381 {
+    // TODO: generics
+
+    pub fn from_limbs(x: &[u32], y: &[u32], z: &[u32]) -> Self {
+        Point_BLS12_381 {
+            x: BaseField_BLS12_381 {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_BLS12_381 {
+                s: get_fixed_limbs(y),
+            },
+            z: BaseField_BLS12_381 {
+                s: get_fixed_limbs(z),
+            },
+        }
+    }
+
+    pub fn from_xy_limbs(value: &[u32]) -> Point_BLS12_381 {
+        let l = value.len();
+        assert_eq!(l, 3 * BASE_LIMBS_BLS12_381, "length must be 3 * {}", BASE_LIMBS_BLS12_381);
+        Point_BLS12_381 {
+            x: BaseField_BLS12_381 {
+                s: value[..BASE_LIMBS_BLS12_381].try_into().unwrap(),
+            },
+            y: BaseField_BLS12_381 {
+                s: value[BASE_LIMBS_BLS12_381..BASE_LIMBS_BLS12_381 * 2].try_into().unwrap(),
+            },
+            z: BaseField_BLS12_381 {
+                s: value[BASE_LIMBS_BLS12_381 * 2..].try_into().unwrap(),
+            },
+        }
+    }
+
+    pub fn to_affine(&self) -> PointAffineNoInfinity_BLS12_381 {
+        let ark_affine = self.to_ark_affine();
+        PointAffineNoInfinity_BLS12_381 {
+            x: BaseField_BLS12_381::from_ark(ark_affine.x.into_repr()),
+            y: BaseField_BLS12_381::from_ark(ark_affine.y.into_repr()),
+        }
+    }
+
+    pub fn to_xy_strip_z(&self) -> PointAffineNoInfinity_BLS12_381 {
+        PointAffineNoInfinity_BLS12_381 {
+            x: self.x,
+            y: self.y,
+        }
+    }
+}
+
+impl ScalarField_BLS12_381 {
+    pub fn from_limbs(value: &[u32]) -> ScalarField_BLS12_381 {
+        ScalarField_BLS12_381 {
+            s: get_fixed_limbs(value),
+        }
+    }
+}
+
+
+#[cfg(test)]
+mod tests {
+    use ark_bls12_381::{Fr as Fr_BLS12_381};
+
+    use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}, curves::bls12_381::{Point_BLS12_381, ScalarField_BLS12_381}};
+
+    #[test]
+    fn test_ark_scalar_convert() {
+        let limbs = [0x0fffffff, 1, 0x2fffffff, 3, 0x4fffffff, 5, 0x6fffffff, 7];
+        let scalar = ScalarField_BLS12_381::from_limbs(&limbs);
+        assert_eq!(
+            scalar.to_ark(),
+            scalar.to_ark_transmute(),
+            "{:08X?} {:08X?}",
+            scalar.to_ark(),
+            scalar.to_ark_transmute()
+        )
+    }
+
+    #[test]
+    #[allow(non_snake_case)]
+    fn test_point_equality() {
+        let left = Point_BLS12_381::zero();
+        let right = Point_BLS12_381::zero();
+        assert_eq!(left, right);
+        let right = Point_BLS12_381::from_limbs(&[0; 12], &[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], &[0; 12]);
+        assert_eq!(left, right);
+        let right = Point_BLS12_381::from_limbs(
+            &[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            &[0; 12],
+            &[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        );
+        assert!(left != right);
+    }
+}

src/curves/bn254.rs

@@ -0,0 +1,312 @@
+use std::ffi::c_uint;
+
+use ark_bn254::{Fq as Fq_BN254, Fr as Fr_BN254, G1Affine as G1Affine_BN254, G1Projective as G1Projective_BN254};
+
+use ark_ec::AffineCurve;
+use ark_ff::{BigInteger256, PrimeField};
+use std::mem::transmute;
+use ark_ff::Field;
+use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}};
+
+use rustacuda_core::DeviceCopy;
+use rustacuda_derive::DeviceCopy;
+
+#[derive(Debug, PartialEq, Copy, Clone)]
+#[repr(C)]
+pub struct Field_BN254<const NUM_LIMBS: usize> {
+    pub s: [u32; NUM_LIMBS],
+}
+
+unsafe impl<const NUM_LIMBS: usize> DeviceCopy for Field_BN254<NUM_LIMBS> {}
+
+impl<const NUM_LIMBS: usize> Default for Field_BN254<NUM_LIMBS> {
+    fn default() -> Self {
+        Field_BN254::zero()
+    }
+}
+
+impl<const NUM_LIMBS: usize> Field_BN254<NUM_LIMBS> {
+    pub fn zero() -> Self {
+        Field_BN254 {
+            s: [0u32; NUM_LIMBS],
+        }
+    }
+
+    pub fn one() -> Self {
+        let mut s = [0u32; NUM_LIMBS];
+        s[0] = 1;
+        Field_BN254 { s }
+    }
+
+    fn to_bytes_le(&self) -> Vec<u8> {
+        self.s
+            .iter()
+            .map(|s| s.to_le_bytes().to_vec())
+            .flatten()
+            .collect::<Vec<_>>()
+    }
+}
+
+pub const BASE_LIMBS_BN254: usize = 8;
+pub const SCALAR_LIMBS_BN254: usize = 8;
+
+pub type BaseField_BN254 = Field_BN254<BASE_LIMBS_BN254>;
+pub type ScalarField_BN254 = Field_BN254<SCALAR_LIMBS_BN254>;
+
+fn get_fixed_limbs<const NUM_LIMBS: usize>(val: &[u32]) -> [u32; NUM_LIMBS] {
+    match val.len() {
+        n if n < NUM_LIMBS => {
+            let mut padded: [u32; NUM_LIMBS] = [0; NUM_LIMBS];
+            padded[..val.len()].copy_from_slice(&val);
+            padded
+        }
+        n if n == NUM_LIMBS => val.try_into().unwrap(),
+        _ => panic!("slice has too many elements"),
+    }
+}
+
+impl ScalarField_BN254 {
+    pub fn limbs(&self) -> [u32; SCALAR_LIMBS_BN254] {
+        self.s
+    }
+
+    pub fn to_ark(&self) -> BigInteger256 {
+        BigInteger256::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
+    }
+
+    pub fn from_ark(ark: BigInteger256) -> Self {
+        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
+    }
+
+    pub fn to_ark_transmute(&self) -> BigInteger256 {
+        unsafe { transmute(*self) }
+    }
+
+    pub fn from_ark_transmute(v: BigInteger256) -> ScalarField_BN254 {
+        unsafe { transmute(v) }
+    }
+}
+
+#[derive(Debug, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct Point_BN254 {
+    pub x: BaseField_BN254,
+    pub y: BaseField_BN254,
+    pub z: BaseField_BN254,
+}
+
+impl Default for Point_BN254 {
+    fn default() -> Self {
+        Point_BN254::zero()
+    }
+}
+
+impl Point_BN254 {
+    pub fn zero() -> Self {
+        Point_BN254 {
+            x: BaseField_BN254::zero(),
+            y: BaseField_BN254::one(),
+            z: BaseField_BN254::zero(),
+        }
+    }
+
+    pub fn infinity() -> Self {
+        Self::zero()
+    }
+
+    pub fn to_ark(&self) -> G1Projective_BN254 {
+        //TODO: generic conversion
+        self.to_ark_affine().into_projective()
+    }
+
+    pub fn to_ark_affine(&self) -> G1Affine_BN254 {
+        //TODO: generic conversion
+        use ark_ff::Field;
+        use std::ops::Mul;
+        let proj_x_field = Fq_BN254::from_le_bytes_mod_order(&self.x.to_bytes_le());
+        let proj_y_field = Fq_BN254::from_le_bytes_mod_order(&self.y.to_bytes_le());
+        let proj_z_field = Fq_BN254::from_le_bytes_mod_order(&self.z.to_bytes_le());
+        let inverse_z = proj_z_field.inverse().unwrap();
+        let aff_x = proj_x_field.mul(inverse_z);
+        let aff_y = proj_y_field.mul(inverse_z);
+        G1Affine_BN254::new(aff_x, aff_y, false)
+    }
+
+    pub fn from_ark(ark: G1Projective_BN254) -> Point_BN254 {
+        use ark_ff::Field;
+        let z_inv = ark.z.inverse().unwrap();
+        let z_invsq = z_inv * z_inv;
+        let z_invq3 = z_invsq * z_inv;
+        Point_BN254 {
+            x: BaseField_BN254::from_ark((ark.x * z_invsq).into_repr()),
+            y: BaseField_BN254::from_ark((ark.y * z_invq3).into_repr()),
+            z: BaseField_BN254::one(),
+        }
+    }
+}
+
+extern "C" {
+    fn eq_bn254(point1: *const Point_BN254, point2: *const Point_BN254) -> c_uint;
+}
+
+impl PartialEq for Point_BN254 {
+    fn eq(&self, other: &Self) -> bool {
+        unsafe { eq_bn254(self, other) != 0 }
+    }
+}
+
+#[derive(Debug, PartialEq, Clone, Copy, DeviceCopy)]
+#[repr(C)]
+pub struct PointAffineNoInfinity_BN254 {
+    pub x: BaseField_BN254,
+    pub y: BaseField_BN254,
+}
+
+impl Default for PointAffineNoInfinity_BN254 {
+    fn default() -> Self {
+        PointAffineNoInfinity_BN254 {
+            x: BaseField_BN254::zero(),
+            y: BaseField_BN254::zero(),
+        }
+    }
+}
+
+impl PointAffineNoInfinity_BN254 {
+    // TODO: generics
+    ///From u32 limbs x,y
+    pub fn from_limbs(x: &[u32], y: &[u32]) -> Self {
+        PointAffineNoInfinity_BN254 {
+            x: BaseField_BN254 {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_BN254 {
+                s: get_fixed_limbs(y),
+            },
+        }
+    }
+
+    pub fn limbs(&self) -> Vec<u32> {
+        [self.x.limbs(), self.y.limbs()].concat()
+    }
+
+    pub fn to_projective(&self) -> Point_BN254 {
+        Point_BN254 {
+            x: self.x,
+            y: self.y,
+            z: BaseField_BN254::one(),
+        }
+    }
+
+    pub fn to_ark(&self) -> G1Affine_BN254 {
+        G1Affine_BN254::new(Fq_BN254::new(self.x.to_ark()), Fq_BN254::new(self.y.to_ark()), false)
+    }
+
+    pub fn to_ark_repr(&self) -> G1Affine_BN254 {
+        G1Affine_BN254::new(
+            Fq_BN254::from_repr(self.x.to_ark()).unwrap(),
+            Fq_BN254::from_repr(self.y.to_ark()).unwrap(),
+            false,
+        )
+    }
+
+    pub fn from_ark(p: &G1Affine_BN254) -> Self {
+        PointAffineNoInfinity_BN254 {
+            x: BaseField_BN254::from_ark(p.x.into_repr()),
+            y: BaseField_BN254::from_ark(p.y.into_repr()),
+        }
+    }
+}
+
+impl Point_BN254 {
+    // TODO: generics
+
+    pub fn from_limbs(x: &[u32], y: &[u32], z: &[u32]) -> Self {
+        Point_BN254 {
+            x: BaseField_BN254 {
+                s: get_fixed_limbs(x),
+            },
+            y: BaseField_BN254 {
+                s: get_fixed_limbs(y),
+            },
+            z: BaseField_BN254 {
+                s: get_fixed_limbs(z),
+            },
+        }
+    }
+
+    pub fn from_xy_limbs(value: &[u32]) -> Point_BN254 {
+        let l = value.len();
+        assert_eq!(l, 3 * BASE_LIMBS_BN254, "length must be 3 * {}", BASE_LIMBS_BN254);
+        Point_BN254 {
+            x: BaseField_BN254 {
+                s: value[..BASE_LIMBS_BN254].try_into().unwrap(),
+            },
+            y: BaseField_BN254 {
+                s: value[BASE_LIMBS_BN254..BASE_LIMBS_BN254 * 2].try_into().unwrap(),
+            },
+            z: BaseField_BN254 {
+                s: value[BASE_LIMBS_BN254 * 2..].try_into().unwrap(),
+            },
+        }
+    }
+
+    pub fn to_affine(&self) -> PointAffineNoInfinity_BN254 {
+        let ark_affine = self.to_ark_affine();
+        PointAffineNoInfinity_BN254 {
+            x: BaseField_BN254::from_ark(ark_affine.x.into_repr()),
+            y: BaseField_BN254::from_ark(ark_affine.y.into_repr()),
+        }
+    }
+
+    pub fn to_xy_strip_z(&self) -> PointAffineNoInfinity_BN254 {
+        PointAffineNoInfinity_BN254 {
+            x: self.x,
+            y: self.y,
+        }
+    }
+}
+
+impl ScalarField_BN254 {
+    pub fn from_limbs(value: &[u32]) -> ScalarField_BN254 {
+        ScalarField_BN254 {
+            s: get_fixed_limbs(value),
+        }
+    }
+}
+
+
+#[cfg(test)]
+mod tests {
+    use ark_bn254::{Fr as Fr_BN254};
+
+    use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}, curves::bn254::{Point_BN254, ScalarField_BN254}};
+
+    #[test]
+    fn test_ark_scalar_convert() {
+        let limbs = [0x0fffffff, 1, 0x2fffffff, 3, 0x4fffffff, 5, 0x6fffffff, 7];
+        let scalar = ScalarField_BN254::from_limbs(&limbs);
+        assert_eq!(
+            scalar.to_ark(),
+            scalar.to_ark_transmute(),
+            "{:08X?} {:08X?}",
+            scalar.to_ark(),
+            scalar.to_ark_transmute()
+        )
+    }
+
+    #[test]
+    #[allow(non_snake_case)]
+    fn test_point_equality() {
+        let left = Point_BN254::zero();
+        let right = Point_BN254::zero();
+        assert_eq!(left, right);
+        let right = Point_BN254::from_limbs(&[0; 8], &[2, 0, 0, 0, 0, 0, 0, 0], &[0; 8]);
+        assert_eq!(left, right);
+        let right = Point_BN254::from_limbs(
+            &[2, 0, 0, 0, 0, 0, 0, 0],
+            &[0; 8],
+            &[1, 0, 0, 0, 0, 0, 0, 0],
+        );
+        assert!(left != right);
+    }
+}

src/curves/mod.rs

@@ -0,0 +1,3 @@
+pub mod bls12_381;
+pub mod bls12_377;
+pub mod bn254;

src/field.rs

@@ -1,336 +0,0 @@
-use std::ffi::c_uint;
-use std::mem::transmute;
-
-use ark_bls12_381::{Fq, G1Affine, G1Projective};
-use ark_ec::AffineCurve;
-use ark_ff::{BigInteger384, BigInteger256, PrimeField};
-
-use rustacuda_core::DeviceCopy;
-use rustacuda_derive::DeviceCopy;
-
-use crate::utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec};
-
-#[derive(Debug, PartialEq, Copy, Clone)]
-#[repr(C)]
-pub struct Field<const NUM_LIMBS: usize> {
-    pub s: [u32; NUM_LIMBS],
-}
-
-unsafe impl<const NUM_LIMBS: usize> DeviceCopy for Field<NUM_LIMBS> {}
-
-impl<const NUM_LIMBS: usize> Default for Field<NUM_LIMBS> {
-    fn default() -> Self {
-        Field::zero()
-    }
-}
-
-impl<const NUM_LIMBS: usize> Field<NUM_LIMBS> {
-    pub fn zero() -> Self {
-        Field {
-            s: [0u32; NUM_LIMBS],
-        }
-    }
-
-    pub fn one() -> Self {
-        let mut s = [0u32; NUM_LIMBS];
-        s[0] = 1;
-        Field { s }
-    }
-
-    fn to_bytes_le(&self) -> Vec<u8> {
-        self.s
-            .iter()
-            .map(|s| s.to_le_bytes().to_vec())
-            .flatten()
-            .collect::<Vec<_>>()
-    }
-}
-
-pub const BASE_LIMBS: usize = 12;
-pub const SCALAR_LIMBS: usize = 8;
-
-#[cfg(feature = "bn254")]
-pub const BASE_LIMBS: usize = 8;
-#[cfg(feature = "bn254")]
-pub const SCALAR_LIMBS: usize = 8;
-
-pub type BaseField = Field<BASE_LIMBS>;
-pub type ScalarField = Field<SCALAR_LIMBS>;
-
-fn get_fixed_limbs<const NUM_LIMBS: usize>(val: &[u32]) -> [u32; NUM_LIMBS] {
-    match val.len() {
-        n if n < NUM_LIMBS => {
-            let mut padded: [u32; NUM_LIMBS] = [0; NUM_LIMBS];
-            padded[..val.len()].copy_from_slice(&val);
-            padded
-        }
-        n if n == NUM_LIMBS => val.try_into().unwrap(),
-        _ => panic!("slice has too many elements"),
-    }
-}
-
-impl BaseField {
-    pub fn limbs(&self) -> [u32; BASE_LIMBS] {
-        self.s
-    }
-
-    pub fn from_limbs(value: &[u32]) -> Self {
-        Self {
-            s: get_fixed_limbs(value),
-        }
-    }
-
-    pub fn to_ark(&self) -> BigInteger384 {
-        BigInteger384::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
-    }
-
-    pub fn from_ark(ark: BigInteger384) -> Self {
-        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
-    }
-}
-
-impl ScalarField {
-    pub fn limbs(&self) -> [u32; SCALAR_LIMBS] {
-        self.s
-    }
-
-    pub fn to_ark(&self) -> BigInteger256 {
-        BigInteger256::new(u32_vec_to_u64_vec(&self.limbs()).try_into().unwrap())
-    }
-
-    pub fn from_ark(ark: BigInteger256) -> Self {
-        Self::from_limbs(&u64_vec_to_u32_vec(&ark.0))
-    }
-
-    pub fn to_ark_transmute(&self) -> BigInteger256 {
-        unsafe { transmute(*self) }
-    }
-
-    pub fn from_ark_transmute(v: BigInteger256) -> ScalarField {
-        unsafe { transmute(v) }
-    }
-}
-
-#[derive(Debug, Clone, Copy, DeviceCopy)]
-#[repr(C)]
-pub struct Point {
-    pub x: BaseField,
-    pub y: BaseField,
-    pub z: BaseField,
-}
-
-impl Default for Point {
-    fn default() -> Self {
-        Point::zero()
-    }
-}
-
-impl Point {
-    pub fn zero() -> Self {
-        Point {
-            x: BaseField::zero(),
-            y: BaseField::one(),
-            z: BaseField::zero(),
-        }
-    }
-
-    pub fn infinity() -> Self {
-        Self::zero()
-    }
-
-    pub fn to_ark(&self) -> G1Projective {
-        //TODO: generic conversion
-        self.to_ark_affine().into_projective()
-    }
-
-    pub fn to_ark_affine(&self) -> G1Affine {
-        //TODO: generic conversion
-        use ark_ff::Field;
-        use std::ops::Mul;
-        let proj_x_field = Fq::from_le_bytes_mod_order(&self.x.to_bytes_le());
-        let proj_y_field = Fq::from_le_bytes_mod_order(&self.y.to_bytes_le());
-        let proj_z_field = Fq::from_le_bytes_mod_order(&self.z.to_bytes_le());
-        let inverse_z = proj_z_field.inverse().unwrap();
-        let aff_x = proj_x_field.mul(inverse_z);
-        let aff_y = proj_y_field.mul(inverse_z);
-        G1Affine::new(aff_x, aff_y, false)
-    }
-
-    pub fn from_ark(ark: G1Projective) -> Point {
-        use ark_ff::Field;
-        let z_inv = ark.z.inverse().unwrap();
-        let z_invsq = z_inv * z_inv;
-        let z_invq3 = z_invsq * z_inv;
-        Point {
-            x: BaseField::from_ark((ark.x * z_invsq).into_repr()),
-            y: BaseField::from_ark((ark.y * z_invq3).into_repr()),
-            z: BaseField::one(),
-        }
-    }
-}
-
-extern "C" {
-    fn eq(point1: *const Point, point2: *const Point) -> c_uint;
-}
-
-impl PartialEq for Point {
-    fn eq(&self, other: &Self) -> bool {
-        unsafe { eq(self, other) != 0 }
-    }
-}
-
-#[derive(Debug, PartialEq, Clone, Copy, DeviceCopy)]
-#[repr(C)]
-pub struct PointAffineNoInfinity {
-    pub x: BaseField,
-    pub y: BaseField,
-}
-
-impl Default for PointAffineNoInfinity {
-    fn default() -> Self {
-        PointAffineNoInfinity {
-            x: BaseField::zero(),
-            y: BaseField::zero(),
-        }
-    }
-}
-
-impl PointAffineNoInfinity {
-    // TODO: generics
-    ///From u32 limbs x,y
-    pub fn from_limbs(x: &[u32], y: &[u32]) -> Self {
-        PointAffineNoInfinity {
-            x: BaseField {
-                s: get_fixed_limbs(x),
-            },
-            y: BaseField {
-                s: get_fixed_limbs(y),
-            },
-        }
-    }
-
-    pub fn limbs(&self) -> Vec<u32> {
-        [self.x.limbs(), self.y.limbs()].concat()
-    }
-
-    pub fn to_projective(&self) -> Point {
-        Point {
-            x: self.x,
-            y: self.y,
-            z: BaseField::one(),
-        }
-    }
-
-    pub fn to_ark(&self) -> G1Affine {
-        G1Affine::new(Fq::new(self.x.to_ark()), Fq::new(self.y.to_ark()), false)
-    }
-
-    pub fn to_ark_repr(&self) -> G1Affine {
-        G1Affine::new(
-            Fq::from_repr(self.x.to_ark()).unwrap(),
-            Fq::from_repr(self.y.to_ark()).unwrap(),
-            false,
-        )
-    }
-
-    pub fn from_ark(p: &G1Affine) -> Self {
-        PointAffineNoInfinity {
-            x: BaseField::from_ark(p.x.into_repr()),
-            y: BaseField::from_ark(p.y.into_repr()),
-        }
-    }
-}
-
-impl Point {
-    // TODO: generics
-
-    pub fn from_limbs(x: &[u32], y: &[u32], z: &[u32]) -> Self {
-        Point {
-            x: BaseField {
-                s: get_fixed_limbs(x),
-            },
-            y: BaseField {
-                s: get_fixed_limbs(y),
-            },
-            z: BaseField {
-                s: get_fixed_limbs(z),
-            },
-        }
-    }
-
-    pub fn from_xy_limbs(value: &[u32]) -> Point {
-        let l = value.len();
-        assert_eq!(l, 3 * BASE_LIMBS, "length must be 3 * {}", BASE_LIMBS);
-        Point {
-            x: BaseField {
-                s: value[..BASE_LIMBS].try_into().unwrap(),
-            },
-            y: BaseField {
-                s: value[BASE_LIMBS..BASE_LIMBS * 2].try_into().unwrap(),
-            },
-            z: BaseField {
-                s: value[BASE_LIMBS * 2..].try_into().unwrap(),
-            },
-        }
-    }
-
-    pub fn to_affine(&self) -> PointAffineNoInfinity {
-        let ark_affine = self.to_ark_affine();
-        PointAffineNoInfinity {
-            x: BaseField::from_ark(ark_affine.x.into_repr()),
-            y: BaseField::from_ark(ark_affine.y.into_repr()),
-        }
-    }
-
-    pub fn to_xy_strip_z(&self) -> PointAffineNoInfinity {
-        PointAffineNoInfinity {
-            x: self.x,
-            y: self.y,
-        }
-    }
-}
-
-impl ScalarField {
-    pub fn from_limbs(value: &[u32]) -> ScalarField {
-        ScalarField {
-            s: get_fixed_limbs(value),
-        }
-    }
-}
-
-
-#[cfg(test)]
-mod tests {
-    use ark_bls12_381::Fr;
-
-    use crate::{utils::{u32_vec_to_u64_vec, u64_vec_to_u32_vec}, field::{Point, ScalarField}};
-
-    #[test]
-    fn test_ark_scalar_convert() {
-        let limbs = [0x0fffffff, 1, 0x2fffffff, 3, 0x4fffffff, 5, 0x6fffffff, 7];
-        let scalar = ScalarField::from_limbs(&limbs);
-        assert_eq!(
-            scalar.to_ark(),
-            scalar.to_ark_transmute(),
-            "{:08X?} {:08X?}",
-            scalar.to_ark(),
-            scalar.to_ark_transmute()
-        )
-    }
-
-    #[test]
-    #[allow(non_snake_case)]
-    fn test_point_equality() {
-        let left = Point::zero();
-        let right = Point::zero();
-        assert_eq!(left, right);
-        let right = Point::from_limbs(&[0; 12], &[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], &[0; 12]);
-        assert_eq!(left, right);
-        let right = Point::from_limbs(
-            &[2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-            &[0; 12],
-            &[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-        );
-        assert!(left != right);
-    }
-}

src/utils.rs

@@ -1,3 +1,7 @@
+use rand::RngCore;
+use rand::rngs::StdRng;
+use rand::SeedableRng;
+
 pub fn from_limbs<T>(limbs: Vec<u32>, chunk_size: usize, f: fn(&[u32]) -> T) -> Vec<T> {
     let points = limbs
         .chunks(chunk_size)
@@ -29,11 +33,19 @@ pub fn u64_vec_to_u32_vec(arr_u64: &[u64]) -> Vec<u32> {
     arr_u32
 }
 
+pub fn get_rng(seed: Option<u64>) -> Box<dyn RngCore> { //TOOD: this func is universal
+    let rng: Box<dyn RngCore> = match seed {
+        Some(seed) => Box::new(StdRng::seed_from_u64(seed)),
+        None => Box::new(rand::thread_rng()),
+    };
+    rng
+}
+
 #[cfg(test)]
 mod tests {
     use ark_ff::BigInteger256;
 
-    use crate::field::ScalarField;
+    use crate::curves::bls12_381::{ScalarField_BLS12_381 as ScalarField};
 
     use super::*;
 
@@ -46,7 +58,7 @@ mod tests {
         ))
         .limbs();
 
-        assert_eq!(arr_u32, s);
+        assert_eq!(arr_u32.to_vec(), s);
 
         let arr_u64_expected = [
             0x0FFFFFFF00000001,