- this would not give an average runtime for scalar benchmarks and for
small precisions could give super good timings (for lucky values)
- the timings for other precisions could still be favorable or unfavorable
depending on the value that was drawn
Done to circumvent criterion limitation regarding automatic
truncation of long benchmark ID.
Using a println() call we ensure the complete name is displayed
before benchmark execution to ease manual parsing and debugging.
* Added Value type name to crate::integer::KVStore impl of Named trait
as well as a bool to check we deserialize the correct value type
(Radix vs SignedRadix)
* Add KVStore to high_level_api
* Add KVStore hlapi benches
* Remove specialized `[add,mul,sub]_to_slot` as `map` is now the
intended API.
- mul_to_slot was way slower than using `map`
- add/mul_to_slot were a bit faster (~5% latency-wise), but returned
less information (no old_value, no new_value, no boolean to check)
if the key matched
- Some known improvement can be made to map, which should result in
it being better than add/sub_to_slot
* Add FheIntegerType trait to make the KVStore generic over
FheUint/FheInt, and should make GPU integration "easy"
- update SIMD_N and min_batch_size to 12 which seems to give better
latency and ERC20 throughput
- support IOp on several lines in ami /proc file
- reduce amount of ERC_20_SIMD per batch in HLAPI bench
Ciphertext sizes are measured at HLAPI layer with several
parameters set.
Keys sizes are measured at shortint level.
This benchmark has now its dedicated GitHub workflow that would
run, at least, each 24th of the month.
Add the flip(condition: BooleanBlock, a: T, b: T) -> (T, T)
operation that homomorphically flip/swap two values if the
given encrypted boolean encrypts true
- Also, remove the lut indexes concept from the 128-bit multi-bit pbs. It's assumed not to exist by the entire backend (as it doesn't for classical PBS). So to keep it here would be a bit error prone.
This new benchmark is extracted from a use case.
From a compressed ciphertext, it measures the decompression, then noise squashes it and finally compresses again the result.
