add new page to index.md

fix review comments
fix typos
2026-01-09 22:58:17 -05:00 · 2024-04-30 12:45:13 +02:00 · 2024-04-24 15:58:49 +02:00 · 2024-04-24 13:11:21 +02:00 · 2024-04-24 11:05:01 +02:00 · 2024-04-23 16:08:38 -06:00
259 changed files with 15371 additions and 7837 deletions
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -1 +1,5 @@
-* @saadrahim @Rmalavally @amd-aakash @zhang2amd @jlgreathouse @samjwu @MathiasMagnus
+* @amd-aakash @jlgreathouse @samjwu @ROCm/rocm-documentation
+# Documentation files
+docs/* @ROCm/rocm-documentation
+*.md @ROCm/rocm-documentation
+*.rst @ROCm/rocm-documentation
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -10,3 +10,4 @@ updates:
    open-pull-requests-limit: 10
    schedule:
      interval: "daily"
+    versioning-strategy: increase
--- a/.github/workflows/issue_retrieval.yml
+++ b/.github/workflows/issue_retrieval.yml
@@ -0,0 +1,22 @@
+name: Issue retrieval
+
+on:
+  issues:
+    types: [opened]
+
+jobs:
+  auto-retrieve:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Generate a token
+        id: generate_token
+        uses: actions/create-github-app-token@v1
+        with:
+          app_id: ${{ secrets.ACTION_APP_ID }}
+          private_key: ${{ secrets.ACTION_PEM }}
+      - name: 'Retrieve Issue'
+        uses: abhimeda/rocm_issue_management@main
+        with:
+          authentication-token: ${{ steps.generate_token.outputs.token }}
+          github-organization: 'ROCm'
+          project-num: '6'
--- a/.github/workflows/linting.yml
+++ b/.github/workflows/linting.yml
@@ -5,52 +5,16 @@ on:
    branches: 
    - develop
    - main
+    - 'docs/*'
+    - 'roc**'
  pull_request:
    branches: 
    - develop
    - main
-
-concurrency:
-  group: ${{ github.ref }}-${{ github.workflow }}
-  cancel-in-progress: true
+    - 'docs/*'
+    - 'roc**'

 jobs:
-  lint-rest:
-    name: "RestructuredText"
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@v3
-    - name: Install rst-lint
-      run: pip install restructuredtext-lint
-    - name: Lint ResT files
-      run: rst-lint ${{ join(github.workspace, '/docs') }}
-
-  lint-md:
-    name: "Markdown"
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@v3
-    - name: Use markdownlint-cli2
-      uses: DavidAnson/markdownlint-cli2-action@v10.0.1
-      with:
-        globs: '**/*.md'
-
-  spelling:
-    name: "Spelling"
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@v3
-    - name: Fetch config
-      shell: sh
-      run: |
-        curl --silent --show-error --fail --location https://raw.github.com/RadeonOpenCompute/rocm-docs-core/develop/.spellcheck.yaml -O
-        curl --silent --show-error --fail --location https://raw.github.com/RadeonOpenCompute/rocm-docs-core/develop/.wordlist.txt >> .wordlist.txt
-    - name: Run spellcheck
-      uses: rojopolis/spellcheck-github-actions@0.30.0
-    - name: On fail
-      if: failure()
-      run: |
-        echo "Please check for spelling mistakes or add them to '.wordlist.txt' in either the root of this project or in rocm-docs-core."
+  call-workflow-passing-data:
+    name: Documentation
+    uses: ROCm/rocm-docs-core/.github/workflows/linting.yml@develop
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,7 @@ _doxygen/
 _readthedocs/

 # avoid duplicating contributing.md due to conf.py
-docs/contributing.md
-docs/release.md
 docs/CHANGELOG.md
+docs/contribute/index.md
+docs/about/release-notes.md
+docs/about/CHANGELOG.md
--- a/.markdownlint-cli2.yaml
+++ b/.markdownlint-cli2.yaml
@@ -1,5 +1,7 @@
 config:
  default: true
+  MD004:
+    style: asterisk
  MD013: false
  MD026:
    punctuation: '.,;:!'
@@ -8,6 +10,7 @@ config:
  MD033: false
  MD034: false
  MD041: false
+  MD051: false
 ignores:
  - CHANGELOG.md
  - "{,docs/}{RELEASE,release}.md"
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -6,9 +6,13 @@ version: 2
 sphinx:
   configuration: docs/conf.py

-formats: [htmlzip, pdf, epub]
+formats: [htmlzip, pdf]

 python:
-   version: "3.8"
   install:
   - requirements: docs/sphinx/requirements.txt
+
+build:
+   os: ubuntu-22.04
+   tools:
+      python: "3.10"
--- a/.wordlist.txt
+++ b/.wordlist.txt
@@ -1,29 +1,686 @@
-# isv_deployment_win
+AAC
 ABI
-# gpu_aware_mpi
+ACE
+ACEs
+AccVGPR
+AccVGPRs
+ALU
+AMD
+AMDGPU
+AMDGPUs
+AMDMIGraphX
+AMI
+AOCC
+AOMP
+APIC
+APIs
+APU
+ASIC
+ASICs
+ASan
+ASm
+ATI
+AddressSanitizer
+AlexNet
+Arb
+BLAS
+BMC
+BitCode
+Blit
+Bluefield
+CCD
+CDNA
+CIFAR
+CLI
+CLion
+CMake
+CMakeLists
+CMakePackage
+CP
+CPC
+CPF
+CPP
+CPU
+CPUs
+CSC
+CSE
+CSV
+CSn
+CTests
+CU
+CUDA
+CUs
+CXX
+Cavium
+CentOS
+ChatGPT
+CoRR
+Codespaces
+Commitizen
+CommonMark
+Concretized
+Conda
+ConnectX
+DGEMM
+DKMS
+DL
 DMA
+DNN
+DNNL
+DPM
+DRI
+DW
+DWORD
+Dask
+DataFrame
+DataLoader
+DataParallel
+DeepSpeed
+Dependabot
+DevCap
+Dockerfile
+Doxygen
+ELMo
+ENDPGM
+EPYC
+ESXi
+FFT
+FFTs
+FFmpeg
+FHS
+FMA
+FP
+Filesystem
+Flang
+Fortran
+Fuyu
+GALB
+GCD
+GCDs
+GCN
+GDB
+GDDR
 GDR
+GDS
+GEMM
+GEMMs
+GFortran
+GiB
+GIM
+GL
+GLXT
+GMI
+GPG
+GPR
+GPT
+GPU
+GPU's
+GPUs
+GRBM
+GenAI
+GenZ
+GitHub
+Gitpod
+HBM
 HCA
-MPI
-MVAPICH
-Mellanox's
-NIC
-OFED
-OSU
-OpenFabrics
-PeerDirect
-RDMA
-UCX
-ib_core
-# linear algebra
+HIPCC
+HIPExtension
+HIPIFY
+HPC
+HPCG
+HPE
+HPL
+HSA
+HWE
+Haswell
+Higgs
+Hyperparameters
+ICV
+IDE
+IDEs
+IMDb
+IOMMU
+IOP
+IOPM
+IOV
+IRQ
+ISA
+ISV
+ISVs
+ImageNet
+InfiniBand
+Inlines
+IntelliSense
+Intersphinx
+Intra
+Ioffe
+JSON
+Jupyter
+KFD
+KiB
+KVM
+Keras
+Khronos
 LAPACK
+LCLK
+LDS
+LLM
+LLMs
+LLVM
+LM
+LSAN
+LTS
+LoRA
+MEM
+MERCHANTABILITY
+MFMA
+MiB
+MIGraphX
+MIOpen
+MIOpenGEMM
+MIVisionX
+MLM
 MMA
+MMIO
+MMIOH
+MNIST
+MPI
+MSVC
+MVAPICH
+MVFFR
+Makefile
+Makefiles
+Matplotlib
+Megatron
+Mellanox
+Mellanox's
+Meta's
+MirroredStrategy
+Multicore
+Multithreaded
+MyEnvironment
+MyST
+NBIO
+NBIOs
+NIC
+NICs
+NLI
+NLP
+NPS
+NSP
+NUMA
+NVCC
+NVIDIA
+NVPTX
+NaN
+Nano
+Navi
+Noncoherently
+NousResearch's
+NumPy
+OAM
+OAMs
+OCP
+OEM
+OFED
+OMP
+OMPI
+OMPT
+OMPX
+ONNX
+OSS
+OSU
+OpenCL
+OpenCV
+OpenFabrics
+OpenGL
+OpenMP
+OpenSSL
+OpenVX
+PCI
+PCIe
+PEFT
+PIL
+PILImage
+PRNG
+PRs
+PaLM
+Pageable
+PeerDirect
+Perfetto
+PipelineParallel
+PnP
+PowerShell
+PyPi
+PyTorch
+Qcycles
+RAII
+RCCL
+RDC
+RDMA
+RDNA
+RHEL
+ROC
+ROCProfiler
+ROCTracer
+ROCclr
+ROCdbgapi
+ROCgdb
+ROCk
+ROCm
+ROCmCC
+ROCmSoftwarePlatform
+ROCmValidationSuite
+ROCr
+RST
+RW
+Radeon
+RelWithDebInfo
+Req
+Rickle
+RoCE
+Ryzen
+SALU
+SBIOS
+SCA
+SDK
+SDMA
+SDRAM
+SENDMSG
+SGPR
+SGPRs
+SHA
+SIGQUIT
+SIMD
+SIMDs
+SKU
+SKUs
+SLES
+SMEM
+SMI
+SMT
+SPI
+SQs
+SRAM
+SRAMECC
+SVD
+SWE
+SerDes
+Shlens
+Skylake
+Softmax
+Spack
+Supermicro
+Szegedy
+TCA
+TCC
+TCI
+TCIU
+TCP
+TCR
+TF
+TFLOPS
+TPU
+TPUs
+TensorBoard
+TensorFlow
+TensorParallel
+ToC
+TorchAudio
+TorchMIGraphX
+TorchScript
+TorchServe
+TorchVision
+TransferBench
+TrapStatus
+UAC
+UC
+UCC
+UCX
+UIF
+USM
+UTCL
+UTIL
+Uncached
+Unhandled
+VALU
+VBIOS
+VGPR
+VGPRs
+VM
+VMEM
+VMWare
+VRAM
+VSIX
+VSkipped
+Vanhoucke
+Vulkan
+WGP
+WGPs
+WX
+WikiText
+Wojna
+Workgroups
+Writebacks
+XCD
+XCDs
+XGBoost
+XGBoost's
+XGMI
+XT
+XTX
+Xeon
+Xilinx
+Xnack
+Xteam
+YAML
+YML
+YModel
+ZeRO
+ZenDNN
+accuracies
+activations
+addr
+alloc
+allocator
+allocators
+amdgpu
+api
+atmi
+atomics
+autogenerated
+avx
+awk
+backend
 backends
+benchmarking
+bfloat
+bilinear
+bitsandbytes
+blit
+boson
+bosons
+buildable
+bursty
+bzip
+cacheable
+cd
+centos
+centric
+changelog
+chiplet
+cmake
+cmd
+coalescable
+codename
+collater
+comgr
+completers
+composable
+concretization
+config
+conformant
+convolutional
+convolves
+cpp
+csn
+cuBLAS
+cuFFT
+cuLIB
+cuRAND
 cuSOLVER
 cuSPARSE
-# tuning_guides
-BMC
-DGEMM
-HPCG
-HPL
-IOPM
+dataset
+datasets
+dataspace
+datatype
+datatypes
+dbgapi
+de
+deallocation
+denoise
+denoised
+denoises
+denormalize
+deserializers
+detections
+dev
+devicelibs
+devsel
+dimensionality
+disambiguates
+distro
+el
+embeddings
+enablement
+endpgm
+encodings
+env
+epilog
+etcetera
+ethernet
+exascale
+executables
+ffmpeg
+filesystem
+fortran
+galb
+gcc
+gdb
+gfortran
+gfx
+githooks
+github
+gnupg
+grayscale
+gzip
+heterogenous
+hipBLAS
+hipBLASLt
+hipCUB
+hipFFT
+hipLIB
+hipRAND
+hipSOLVER
+hipSPARSE
+hipSPARSELt
+hipTensor
+hipamd
+hipblas
+hipcub
+hipfft
+hipfort
+hipify
+hipsolver
+hipsparse
+hpp
+hsa
+hsakmt
+hyperparameter
+ib_core
+inband
+incrementing
+inferencing
+inflight
+init
+initializer
+inlining
+installable
+interprocedural
+intra
+invariants
+invocating
+ipo
+kdb
+latencies
+libfabric
+libjpeg
+libs
+linearized
+linter
+linux
+llvm
+localscratch
+logits
+lossy
+macOS
+matchers
+microarchitecture
+migraphx
+miopen
+miopengemm
+mivisionx
+mkdir
+mlirmiopen
+mtypes
+mvffr
+namespace
+namespaces
+numref
+ocl
+opencl
+opencv
+openmp
+openssl
+optimizers
+os
+pageable
+parallelization
+parameterization
+passthrough
+perfcounter
+performant
+perl
+pragma
+pre
+prebuilt
+precompiled
+prefetch
+prefetchable
+preprocess
+preprocessed
+preprocessing
+prequantized
+prerequisites
+profiler
+protobuf
+pseudorandom
+py
+quasirandom
+queueing
+rccl
+rdc
+reStructuredText
+reformats
+repos
+representativeness
+req
+resampling
+rescaling
+reusability
+roadmap
+roc
+rocAL
+rocALUTION
+rocBLAS
+rocFFT
+rocLIB
+rocMLIR
+rocPRIM
+rocRAND
+rocSOLVER
+rocSPARSE
+rocThrust
+rocWMMA
+rocalution
+rocblas
+rocclr
+rocfft
+rocm
+rocminfo
+rocprim
+rocprof
+rocprofiler
+rocr
+rocrand
+rocsolver
+rocsparse
+rocthrust
+roctracer
+runtime
+runtimes
+sL
+scalability
+scalable
+sendmsg
+serializers
+shader
+sharding
+sigmoid
+sm
+smi
+softmax
+spack
+src
+stochastically
+strided
+subdirectory
+subexpression
+subfolder
+subfolders
+supercomputing
+tensorfloat
+th
+tokenization
+tokenize
+tokenized
+tokenizer
+tokenizes
+toolchain
+toolchains
+toolset
+toolsets
+torchvision
+tqdm
+tracebacks
+txt
+uarch
+uncached
+uncorrectable
+uninstallation
+unsqueeze
+unstacking
+unswitching
+untrusted
+untuned
+upvote
+USM
+UTCL
+UTIL
+utils
+vL
+variational
+vdi
+vectorizable
+vectorization
+vectorize
+vectorized
+vectorizer
+vectorizes
+vjxb
+walkthrough
+walkthroughs
+wavefront
+wavefronts
+whitespaces
+workgroup
+workgroups
+writeback
+writebacks
+wrreq
+wzo
+xargs
+xz
+yaml
+ysvmadyb
+zypper
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -0,0 +1,40 @@
+# MIT License
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+cmake_minimum_required(VERSION 3.18.0)
+
+project(ROCm VERSION 5.7.1 LANGUAGES NONE)
+
+option(BUILD_DOCS "Build ROCm documentation" ON)
+
+include(GNUInstallDirs)
+
+# Adding default path cmake modules
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/Modules")
+
+# Handle dependencies
+include(Dependencies)
+
+# Build docs
+if(BUILD_DOCS)
+  add_subdirectory(docs)
+endif()
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,246 +1,94 @@
-# Contributing to ROCm Docs
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="Contributing to ROCm">
+  <meta name="keywords" content="ROCm, contributing, contribute, maintainer, contributor">
+</head>

-AMD values and encourages the ROCm community to contribute to our code and
-documentation. This repository is focused on ROCm documentation and this
-contribution guide describes the recommend method for creating and modifying our
-documentation.
+# Contribute to ROCm

-While interacting with ROCm Documentation, we encourage you to be polite and
-respectful in your contributions, content or otherwise. Authors, maintainers of
-these docs act on good intentions and to the best of their knowledge.
-Keep that in mind while you engage. Should you have issues with contributing
-itself, refer to
-[discussions](https://github.com/RadeonOpenCompute/ROCm/discussions) on the
-GitHub repository.
+AMD values and encourages contributions to our code and documentation. If you want to contribute
+to our ROCm repositories, first review the following guidance. For documentation-specific information,
+see [Contributing to ROCm docs](https://rocm.docs.amd.com/en/latest/contribute/contributing.html).

-## Supported Formats
+ROCm is a software stack made up of a collection of drivers, development tools, and APIs that enable
+GPU programming from low-level kernel to end-user applications. Because some of our components
+are inherited from external projects (such as
+[LLVM](https://github.com/ROCm/llvm-project) and
+[Kernel driver](https://github.com/ROCm/ROCK-Kernel-Driver)), these use
+project-specific contribution guidelines and workflow. Refer to their repositories for more information.
+All other ROCm components follow the workflow described in the following sections.

-Our documentation includes both markdown and rst files. Markdown is encouraged
-over rst due to the lower barrier to participation. GitHub flavored markdown is preferred
-for all submissions as it will render accurately on our GitHub repositories. For existing documentation,
-[MyST](https://myst-parser.readthedocs.io/en/latest/intro.html) markdown
-is used to implement certain features unsupported in GitHub markdown. This is
-not encouraged for new documentation. AMD will transition
-to stricter use of GitHub flavored markdown with a few caveats. ROCm documentation
-also uses [sphinx-design](https://sphinx-design.readthedocs.io/en/latest/index.html)
-in our markdown and rst files. We also will use breathe syntax for doxygen documentation
-in our markdown files. Other design elements for effective HTML rendering of the documents
-may be added to our markdown files. Please see
-[GitHub](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github)'s
-guide on writing and formatting on GitHub as a starting point.
+## Development workflow

-ROCm documentation adds additional requirements to markdown and rst based files
-as follows:
+ROCm uses GitHub to host code, collaborate, and manage version control. We use pull requests (PRs)
+for all changes within our repositories. We use
+[GitHub issues](https://github.com/ROCm/ROCm/issues) to track known issues, such as
+bugs.

- Level one headers are only used for page titles. There must be only one level
-  1 header per file for both Markdown and Restructured Text.
- Pass [markdownlint](https://github.com/markdownlint/markdownlint) check via
-  our automated github action on a Pull Request (PR).
+### Issue tracking

-## Filenames and folder structure
+Before filing a new issue, search the
+[existing issues](https://github.com/ROCm/ROCm/issues) to make sure your issue isn't
+already listed.

-Please use snake case for file names. Our documentation follows pitchfork for
-folder structure. All documentation is in /docs except for special files like
-the contributing guide in the / folder. All images used in the documentation are
-place in the /docs/data folder.
+General issue guidelines:

-## How to provide feedback for for ROCm documentation
+* Use your best judgement for issue creation. If your issue is already listed, upvote the issue and
+  comment or post to provide additional details, such as how you reproduced this issue.
+* If you're not sure if your issue is the same, err on the side of caution and file your issue.
+  You can add a comment to include the issue number (and link) for the similar issue. If we evaluate
+  your issue as being the same as the existing issue, we'll close the duplicate.
+* If your issue doesn't exist, use the issue template to file a new issue.
+  * When filing an issue, be sure to provide as much information as possible, including script output so
+    we can collect information about your configuration. This helps reduce the time required to
+    reproduce your issue.
+  * Check your issue regularly, as we may require additional information to successfully reproduce the
+    issue.

-There are three standard ways to provide feedback for this repository.
+### Pull requests

-### Pull Request
+When you create a pull request, you should target the default branch.  Our repositories typically use the **develop** branch as the default integration branch.

-All contributions to ROCm documentation should arrive via the
-[GitHub Flow](https://docs.github.com/en/get-started/quickstart/github-flow)
-targetting the develop branch of the repository. If you are unable to contribute
-via the GitHub Flow, feel free to email us. TODO, confirm email address.
+When creating a PR, use the following process. Note that each repository may include additional,
+project-specific steps. Refer to each repository's PR process for any additional steps.

-### GitHub Issue
+* Identify the issue you want to fix
+* Target the default branch (usually the **develop** branch) for integration
+* Ensure your code builds successfully
+* Each component has a suite of test cases to run; include the log of the successful test run in your PR
+* Do not break existing test cases
+* New functionality is only merged with new unit tests
+  * If your PR includes a new feature, you must provide an application or test so we can ensure that the
+    feature works and continues to be valid in the future
+* Tests must have good code coverage
+* Submit your PR and work with the reviewer or maintainer to get your PR approved
+* Once approved, the PR is brought onto internal CI systems and may be merged into the component
+  during our release cycle, as coordinated by the maintainer
+* We'll inform you once your change is committed

-Issues on existing or absent docs can be filed as [GitHub issues
-](https://github.com/RadeonOpenCompute/ROCm/issues).
+:::{important}
+By creating a PR, you agree to allow your contribution to be licensed under the
+terms of the LICENSE.txt file in the corresponding repository. Different repositories may use different
+licenses.
+:::

-### Email Feedback
+You can look up each license on the [ROCm licensing](https://rocm.docs.amd.com/en/latest/about/license.html) page.

-## Language and Style
+### New feature development

-Adopting Microsoft CPP-Docs guidelines for [Voice and Tone
-](https://github.com/MicrosoftDocs/cpp-docs/blob/main/styleguide/voice-tone.md).
+Use the [GitHub Discussion forum](https://github.com/ROCm/ROCm/discussions)
+(Ideas category) to propose new features. Our maintainers are happy to provide direction and
+feedback on feature development.

-ROCm documentation templates to be made public shortly. ROCm templates dictate
-the recommended structure and flow of the documentation. Guidelines on how to
-integrate figures, equations, and tables are all based off
-[MyST](https://myst-parser.readthedocs.io/en/latest/intro.html).
+### Documentation

-Font size and selection, page layout, white space control, and other formatting
-details are controlled via rocm-docs-core, sphinx extention. Please raise issues
-in rocm-docs-core for any formatting concerns and changes requested.
+Submit ROCm documentation changes to our
+[documentation repository](https://github.com/ROCm/ROCm). You must update
+documentation related to any new feature or API contribution.

-## Building Documentation
+Note that each ROCm project uses its own repository for documentation.

-While contributing, one may build the documentation locally on the command-line
-or rely on Continuous Integration for previewing the resulting HTML pages in a
-browser.
+## Future development workflow

-### Command line documentation builds
-
-Python versions known to build documentation:
-
- 3.8
-
-To build the docs locally using Python Virtual Environment (`venv`), execute the
-following commands from the project root:
-
-```sh
-python3 -mvenv .venv
-# Windows
-.venv/Scripts/python -m pip install -r docs/sphinx/requirements.txt
-.venv/Scripts/python -m sphinx -T -E -b html -d _build/doctrees -D language=en docs _build/html
-# Linux
-.venv/bin/python     -m pip install -r docs/sphinx/requirements.txt
-.venv/bin/python     -m sphinx -T -E -b html -d _build/doctrees -D language=en docs _build/html
-```
-
-Then open up `_build/html/index.html` in your favorite browser.
-
-### Pull Requests documentation builds
-
-When opening a PR to the `develop` branch on GitHub, the page corresponding to
-the PR (`https://github.com/RadeonOpenCompute/ROCm/pull/<pr_number>`) will have
-a summary at the bottom. This requires the user be logged in to GitHub.
-
- There, click `Show all checks` and `Details` of the Read the Docs pipeline. It
-  will take you to `https://readthedocs.com/projects/advanced-micro-devices-rocm/
-  builds/<some_build_num>/`
-  - The list of commands shown are the exact ones used by CI to produce a render
-    of the documentation.
- There, click on the small blue link `View docs` (which is not the same as the
-  bigger button with the same text). It will take you to the built HTML site with
-  a URL of the form `https://
-  advanced-micro-devices-demo--<pr_number>.com.readthedocs.build/projects/alpha/en
-  /<pr_number>/`.
-
-### Build the docs using VS Code
-
-One can put together a productive environment to author documentation and also
-test it locally using VS Code with only a handful of extensions. Even though the
-extension landscape of VS Code is ever changing, here is one example setup that
-proved useful at the time of writing. In it, one can change/add content, build a
-new version of the docs using a single VS Code Task (or hotkey), see all errors/
-warnings emitted by Sphinx in the Problems pane and immediately see the
-resulting website show up on a locally serving web server.
-
-#### Configuring VS Code
-
-1. Install the following extensions:
-
-    - Python (ms-python.python)
-    - Live Server (ritwickdey.LiveServer)
-
-2. Add the following entries in `.vscode/settings.json`
-
-    ```json
-    {
-      "liveServer.settings.root": "/.vscode/build/html",
-      "liveServer.settings.wait": 1000,
-      "python.terminal.activateEnvInCurrentTerminal": true
-    }
-    ```
-
-    The settings in order are set for the following reasons:
-    - Sets the root of the output website for live previews. Must be changed
-      alongside the `tasks.json` command.
-    - Tells live server to wait with the update to give time for Sphinx to
-      regenerate site contents and not refresh before all is don. (Empirical value)
-    - Automatic virtual env activation is a nice touch, should you want to build
-      the site from the integrated terminal.
-
-3. Add the following tasks in `.vscode/tasks.json`
-
-    ```json
-    {
-      "version": "2.0.0",
-      "tasks": [
-        {
-          "label": "Build Docs",
-          "type": "process",
-          "windows": {
-            "command": "${workspaceFolder}/.venv/Scripts/python.exe"
-          },
-          "command": "${workspaceFolder}/.venv/bin/python3",
-          "args": [
-            "-m",
-            "sphinx",
-            "-j",
-            "auto",
-            "-T",
-            "-b",
-            "html",
-            "-d",
-            "${workspaceFolder}/.vscode/build/doctrees",
-            "-D",
-            "language=en",
-            "${workspaceFolder}/docs",
-            "${workspaceFolder}/.vscode/build/html"
-          ],
-          "problemMatcher": [
-            {
-              "owner": "sphinx",
-              "fileLocation": "absolute",
-              "pattern": {
-                "regexp": "^(?:.*\\.{3}\\s+)?(\\/[^:]*|[a-zA-Z]:\\\\[^:]*):(\\d+):\\s+(WARNING|ERROR):\\s+(.*)$",
-                "file": 1,
-                "line": 2,
-                "severity": 3,
-                "message": 4
-              },
-            },
-            {
-              "owner": "sphinx",
-              "fileLocation": "absolute",
-              "pattern": {
-                "regexp": "^(?:.*\\.{3}\\s+)?(\\/[^:]*|[a-zA-Z]:\\\\[^:]*):{1,2}\\s+(WARNING|ERROR):\\s+(.*)$",
-                "file": 1,
-                "severity": 2,
-                "message": 3
-              }
-            }
-          ],
-          "group": {
-            "kind": "build",
-            "isDefault": true
-          }
-        },
-      ],
-    }
-    ```
-
-    > (Implementation detail: two problem matchers were needed to be defined,
-    > because VS Code doesn't tolerate some problem information being potentially
-    > absent. While a single regex could match all types of errors, if a capture
-    > group remains empty (the line number doesn't show up in all warning/error
-    > messages) but the `pattern` references said empty capture group, VS Code
-    > discards the message completely.)
-
-4. Configure Python virtual environment (venv)
-
-    - From the Command Palette, run `Python: Create Environment`
-      - Select `venv` environment and the `docs/sphinx/requirements.txt` file.
-      _(Simply pressing enter while hovering over the file from the dropdown is
-      insufficient, one has to select the radio button with the 'Space' key if
-      using the keyboard.)_
-
-5. Build the docs
-
-    - Launch the default build Task using either:
-      - a hotkey _(default is 'Ctrl+Shift+B')_ or
-      - by issuing the `Tasks: Run Build Task` from the Command Palette.
-
-6. Open the live preview
-
-    - Navigate to the output of the site within VS Code, right-click on
-    `.vscode/build/html/index.html` and select `Open with Live Server`. The
-    contents should update on every rebuild without having to refresh the
-    browser.
-
-<!-- markdownlint-restore -->
+The current ROCm development workflow is GitHub-based. If, in the future, we change this platform,
+the tools and links may change. In this instance, we will update contribution guidelines accordingly.
--- a/GOVERNANCE.md
+++ b/GOVERNANCE.md
@@ -0,0 +1,60 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="ROCm governance model">
+  <meta name="keywords" content="ROCm, governance">
+</head>
+
+# Governance model
+
+ROCm is a software stack made up of a collection of drivers, development tools, and APIs that enable
+GPU programming from the low-level kernel to end-user applications.
+
+Components of ROCm that are inherited from external projects (such as
+[LLVM](https://github.com/ROCm/llvm-project) and
+[Kernel driver](https://github.com/ROCm/ROCK-Kernel-Driver)) follow their own
+governance model and code of conduct. All other components of ROCm are governed by this
+document.
+
+## Governance
+
+ROCm is led and managed by AMD.
+
+We welcome contributions from the community. Our maintainers review all proposed changes to
+ROCm.
+
+## Roles
+
+* **Maintainers** are responsible for their designated component and repositories.
+* **Contributors** provide input and suggest changes to existing components.
+
+### Maintainers
+
+Maintainers are appointed by AMD. They are able to approve changes and can commit to our
+repositories. They must use pull requests (PRs) for all changes.
+
+You can find the list of maintainers in the CODEOWNERS file of each repository. Code owners differ
+between repositories.
+
+### Contributors
+
+If you're not a maintainer, you're a contributor. We encourage the ROCm community to contribute in
+several ways:
+
+* Help other community members by posting questions or solutions on our
+  [GitHub discussion forums](https://github.com/ROCm/ROCm/discussions)
+* Notify us of a bugs by filing an issue report on
+  [GitHub Issues](https://github.com/ROCm/ROCm/issues)
+* Improve our documentation by submitting a PR to our
+  [repository](https://github.com/ROCm/ROCm/)
+* Improve the code base (for smaller or contained changes) by submitting a PR to the component
+* Suggest larger features by adding to the *Ideas* category in the
+  [GitHub discussion forum](https://github.com/ROCm/ROCm/discussions)
+
+For more information, refer to our [contribution guidelines](CONTRIBUTING.md).
+
+## Code of conduct
+
+To engage with any AMD ROCm component that is hosted on GitHub, you must abide by the
+[GitHub community guidelines](https://docs.github.com/en/site-policy/github-terms/github-community-guidelines)
+and the
+[GitHub community code of conduct](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct).
--- a/README.md
+++ b/README.md
@@ -1,54 +1,99 @@
-# AMD ROCm™ Platform
+# AMD ROCm Software

-ROCm™ is an open-source stack for GPU computation. ROCm is primarily Open-Source
-Software (OSS) that allows developers the freedom to customize and tailor their
-GPU software for their own needs while collaborating with a community of other
-developers, and helping each other find solutions in an agile, flexible, rapid
-and secure manner.
+ROCm is an open-source stack, composed primarily of open-source software, designed for graphics
+processing unit (GPU) computation. ROCm consists of a collection of drivers, development tools, and
+APIs that enable GPU programming from low-level kernel to end-user applications.

-ROCm is a collection of drivers, development tools and APIs enabling GPU
-programming from the low-level kernel to end-user applications. ROCm is powered
-by AMD’s Heterogeneous-computing Interface for Portability (HIP), an OSS C++ GPU
-programming environment and its corresponding runtime. HIP allows ROCm
-developers to create portable applications on different platforms by deploying
-code on a range of platforms, from dedicated gaming GPUs to exascale HPC
-clusters. ROCm supports programming models such as OpenMP and OpenCL, and
-includes all the necessary OSS compilers, debuggers and libraries. ROCm is fully
-integrated into ML frameworks such as PyTorch and TensorFlow. ROCm can be
-deployed in many ways, including through the use of containers such as Docker,
-Spack, and your own build from source.
+With ROCm, you can customize your GPU software to meet your specific needs. You can develop,
+collaborate, test, and deploy your applications in a free, open source, integrated, and secure software
+ecosystem. ROCm is particularly well-suited to GPU-accelerated high-performance computing (HPC),
+artificial intelligence (AI), scientific computing, and computer aided design (CAD).

-ROCm’s goal is to allow our users to maximize their GPU hardware investment.
-ROCm is designed to help develop, test and deploy GPU accelerated HPC, AI,
-scientific computing, CAD, and other applications in a free, open-source,
-integrated and secure software ecosystem.
+ROCm is powered by AMD’s
+[Heterogeneous-computing Interface for Portability (HIP)](https://github.com/ROCm/HIP),
+an open-source software C++ GPU programming environment and its corresponding runtime. HIP
+allows ROCm developers to create portable applications on different platforms by deploying code on a
+range of platforms, from dedicated gaming GPUs to exascale HPC clusters.

-This repository contains the manifest file for ROCm™ releases, changelogs, and
-release information. The file default.xml contains information for all
-repositories and the associated commit used to build the current ROCm release.
+ROCm supports programming models, such as OpenMP and OpenCL, and includes all necessary open
+source software compilers, debuggers, and libraries. ROCm is fully integrated into machine learning
+(ML) frameworks, such as PyTorch and TensorFlow.

-The default.xml file uses the repo Manifest format.
+## Getting the ROCm Source Code

-The develop branch of this repository contains content for the next
-ROCm release.
+AMD ROCm is built from open source software. It is, therefore, possible to modify the various components of ROCm by downloading the source code and rebuilding the components. The source code for ROCm components can be cloned from each of the GitHub repositories using git.  For easy access to download the correct versions of each of these tools, the ROCm repository contains a repo manifest file called [default.xml](./default.xml). You can use this manifest file to download the source code for ROCm software.

-## ROCm Documentation
+### Installing the repo tool

-ROCm Documentation is available online at
-[rocm.docs.amd.com](https://rocm.docs.amd.com). Source code for the documenation
-is located in the docs folder of most repositories that are part of ROCm.
+The repo tool from Google allows you to manage multiple git repositories simultaneously. Run the following commands to install the repo tool:

-### How to build documentation via Sphinx
+```bash
+mkdir -p ~/bin/
+curl https://storage.googleapis.com/git-repo-downloads/repo > ~/bin/repo
+chmod a+x ~/bin/repo
+```
+
+**Note:** The ```~/bin/``` folder is used as an example. You can specify a different folder to install the repo tool into if you desire.
+
+### Installing git-lfs
+
+Some ROCm projects use the Git Large File Storage (LFS) format that may require you to install git-lfs. Refer to [Git Large File Storage](https://github.com/git-lfs/git-lfs/blob/main/INSTALLING.md) for more information. For example, to install git-lfs for Ubuntu, use the following command:
+
+```bash
+sudo apt-get install git-lfs
+```
+
+### Downloading the ROCm source code
+
+The following example shows how to use the repo tool to download the ROCm source code. If you choose a directory other than ~/bin/ to install the repo tool, you must use that chosen directory in the code as shown below:
+
+```bash
+mkdir -p ~/ROCm/
+cd ~/ROCm/
+~/bin/repo init -u http://github.com/ROCm/ROCm.git -b roc-6.0.x
+~/bin/repo sync
+```
+
+**Note:** Using this sample code will cause the repo tool to download the open source code associated with the specified ROCm release. Ensure that you have ssh-keys configured on your machine for your GitHub ID prior to the download as explained at [Connecting to GitHub with SSH](https://docs.github.com/en/authentication/connecting-to-github-with-ssh).
+
+### Building the ROCm source code
+
+Each ROCm component repository contains directions for building that component, such as the rocSPARSE documentation [Installation and Building for Linux](https://rocm.docs.amd.com/projects/rocSPARSE/en/latest/install/Linux_Install_Guide.html). Refer to the specific component documentation for instructions on building the repository.
+
+Each release of the ROCm software supports specific hardware and software configurations. Refer to [System requirements (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html) for the current supported hardware and OS.
+
+## ROCm documentation
+
+This repository contains the [manifest file](https://gerrit.googlesource.com/git-repo/+/HEAD/docs/manifest-format.md)
+for ROCm releases, changelogs, and release information.
+
+The `default.xml` file contains information for all repositories and the associated commit used to build
+the current ROCm release; `default.xml` uses the [Manifest Format repository](https://gerrit.googlesource.com/git-repo/).
+
+Source code for our documentation is located in the `/docs` folder of most ROCm repositories. The
+`develop` branch of our repositories contains content for the next ROCm release.
+
+The ROCm documentation homepage is [rocm.docs.amd.com](https://rocm.docs.amd.com).
+
+### Building the documentation
+
+For a quick-start build, use the following code. For more options and detail, refer to
+[Building documentation](./docs/contribute/building.md).

 ```bash
 cd docs
-
 pip3 install -r sphinx/requirements.txt
-
 python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
 ```

-## Older ROCm™ Releases
+Alternatively, CMake build is supported.

-For release information for older ROCm™ releases, refer to
+```bash
+cmake -B build
+cmake --build build --target=doc
+```
+
+## Older ROCm releases
+
+For release information for older ROCm releases, refer to the
 [CHANGELOG](./CHANGELOG.md).
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,7 +1,4 @@
-# Release Notes
-<!-- Do not edit this file! This file is autogenerated with -->
-<!--   tools/autotag/tag_script.py                          -->
-
+# ROCm 6.1 release highlights
 <!-- Disable lints since this is an auto-generated file.    -->
 <!-- markdownlint-disable blanks-around-headers             -->
 <!-- markdownlint-disable no-duplicate-header               -->
@@ -11,44 +8,245 @@

 <!-- spellcheck-disable -->

-The release notes for the ROCm platform.
+The ROCm™ 6.1 release consists of new features and fixes to improve the stability and
+performance of AMD Instinct™ MI300 GPU applications. Notably, we've added:

-------------------
+* Full support for Ubuntu 22.04.4.

-## ROCm 5.5.1
-<!-- markdownlint-disable first-line-h1 -->
-<!-- markdownlint-disable no-duplicate-header -->
-### What's New in This Release
+* **rocDecode**, a new ROCm component that provides high-performance video decode support for
+  AMD GPUs. With rocDecode, you can decode compressed video streams while keeping the resulting
+  YUV frames in video memory. With decoded frames in video memory, you can run video
+  post-processing using ROCm HIP, avoiding unnecessary data copies via the PCIe bus.

-#### HIP API Change
+  To learn more, refer to the rocDecode 
+  [documentation](https://rocm.docs.amd.com/projects/rocDecode/en/latest/).

-The following HIP API is updated in the ROCm v5.5.1 release,
+## OS and GPU support changes

-##### `hipDeviceSetCacheConfig`
+ROCm 6.1 adds the following operating system support:

- The return value for `hipDeviceSetCacheConfig` is updated from `hipErrorNotSupported` to `hipSuccess`
+* MI300A: Ubuntu 22.04.4 and RHEL 9.3
+* MI300X: Ubuntu 22.04.4

-### Library Changes in ROCM 5.5.1
+Future releases will add additional operating systems to match the general offering. For older
+generations of supported AMD Instinct products, we’ve added Ubuntu 22.04.4 support.

-| Library | Version |
-|---------|---------|
-| hipBLAS | [0.54.0](https://github.com/ROCmSoftwarePlatform/hipBLAS/releases/tag/rocm-5.5.1) |
-| hipCUB | [2.13.1](https://github.com/ROCmSoftwarePlatform/hipCUB/releases/tag/rocm-5.5.1) |
-| hipFFT | [1.0.11](https://github.com/ROCmSoftwarePlatform/hipFFT/releases/tag/rocm-5.5.1) |
-| hipSOLVER | [1.7.0](https://github.com/ROCmSoftwarePlatform/hipSOLVER/releases/tag/rocm-5.5.1) |
-| hipSPARSE | [2.3.5](https://github.com/ROCmSoftwarePlatform/hipSPARSE/releases/tag/rocm-5.5.1) |
-| rccl | [2.15.5](https://github.com/ROCmSoftwarePlatform/rccl/releases/tag/rocm-5.5.1) |
-| rocALUTION | [2.1.8](https://github.com/ROCmSoftwarePlatform/rocALUTION/releases/tag/rocm-5.5.1) |
-| rocBLAS | [2.47.0](https://github.com/ROCmSoftwarePlatform/rocBLAS/releases/tag/rocm-5.5.1) |
-| rocFFT | [1.0.22](https://github.com/ROCmSoftwarePlatform/rocFFT/releases/tag/rocm-5.5.1) |
-| rocPRIM | [2.13.0](https://github.com/ROCmSoftwarePlatform/rocPRIM/releases/tag/rocm-5.5.1) |
-| rocRAND | [2.10.17](https://github.com/ROCmSoftwarePlatform/rocRAND/releases/tag/rocm-5.5.1) |
-| rocSOLVER | [3.21.0](https://github.com/ROCmSoftwarePlatform/rocSOLVER/releases/tag/rocm-5.5.1) |
-| rocSPARSE | [2.5.1](https://github.com/ROCmSoftwarePlatform/rocSPARSE/releases/tag/rocm-5.5.1) |
-| rocThrust | [2.17.0](https://github.com/ROCmSoftwarePlatform/rocThrust/releases/tag/rocm-5.5.1) |
-| rocWMMA | [1.0](https://github.com/ROCmSoftwarePlatform/rocWMMA/releases/tag/rocm-5.5.1) |
-| Tensile | [4.36.0](https://github.com/ROCmSoftwarePlatform/Tensile/releases/tag/rocm-5.5.1) |
+```{tip}
+To view the complete list of supported GPUs and operating systems, refer to the system requirements
+page for
+[Linux](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html)
+and
+[Windows](https://rocm.docs.amd.com/projects/install-on-windows/en/latest/reference/system-requirements.html).
+```

-## Older versions
+## Installation packages

-The release notes for older versions can be found in [the changelog](./CHANGELOG.md).
+This release includes a new set of packages for every module (all libraries and binaries default to
+`DT_RPATH`). Package names have the suffix `rpath`; for example, the `rpath` variant of `rocminfo` is
+`rocminfo-rpath`.
+
+```{warning}
+The new `rpath` packages will conflict with the default packages; they are meant to be used only in
+environments where legacy `DT_RPATH` is the preferred form of linking (instead of `DT_RUNPATH`). We
+do **not** recommend installing both sets of packages.
+```
+
+## ROCm components
+
+The following sections highlight select component-specific changes. For additional details, refer to the
+[Changelog](https://rocm.docs.amd.com/en/develop/about/CHANGELOG.html).
+
+### AMD System Management Interface (SMI) Tool
+
+* **New monitor command for GPU metrics**.
+  Use the monitor command to customize, capture, collect, and observe GPU metrics on
+  target devices.
+
+* **Integration with E-SMI**.
+  The EPYC™ System Management Interface In-band Library is a Linux C-library that provides in-band
+  user space software APIs to monitor and control your CPU’s power, energy, performance, and other
+  system management functionality. This integration enables access to CPU metrics and telemetry
+  through the AMD SMI API and CLI tools.
+
+### Composable Kernel (CK)
+
+* **New architecture support**.
+  CK now supports to the following architectures to enable efficient image denoising on the following
+  AMD GPUs: gfx1030, gfx1100, gfx1031, gfx1101, gfx1032, gfx1102, gfx1034, gfx1103, gfx1035,
+  gfx1036
+
+* **FP8 rounding logic is replaced with stochastic rounding**.
+  Stochastic rounding mimics a more realistic data behavior and improves model convergence.
+
+### HIP
+
+* **New environment variable to enable kernel run serialization**.
+  The default `HIP_LAUNCH_BLOCKING` value is `0` (disable); which causes kernels to run as defined in
+  the queue. When set to `1` (enable), the HIP runtime serializes the kernel queue, which behaves the
+  same as `AMD_SERIALIZE_KERNEL`.
+
+### hipBLASLt
+
+* **New GemmTuning extension parameter** GemmTuning allows you to set a split-k value for each solution, which is more feasible for
+  performance tuning.
+
+### hipFFT
+
+* **New multi-GPU support for single-process transforms** Multiple GPUs can be used to perform a transform in a single process. Note that this initial
+  implementation is a functional preview.
+
+### HIPIFY
+
+* **Skipped code blocks**: Code blocks that are skipped by the preprocessor are no longer hipified under the
+  `--default-preprocessor` option. To hipify everything, despite conditional preprocessor directives
+  (`#if`, `#ifdef`, `#ifndef`, `#elif`, or `#else`), don't use the `--default-preprocessor` or `--amap` options.
+
+### hipSPARSELt
+
+* **Structured sparsity matrix support extensions**
+  Structured sparsity matrices help speed up deep-learning workloads. We now support `B` as the
+  sparse matrix and `A` as the dense matrix in Sparse Matrix-Matrix Multiplication (SPMM). Prior to this
+  release, we only supported sparse (matrix A) x dense (matrix B) matrix multiplication. Structured
+  sparsity matrices help speed up deep learning workloads.
+
+### hipTensor
+
+* **4D tensor permutation and contraction support**.
+  You can now perform tensor permutation on 4D tensors and 4D contractions for F16, BF16, and
+  Complex F32/F64 datatypes.
+
+### MIGraphX
+
+* **Improved performance for transformer-based models**.
+  We added support for FlashAttention, which benefits models like BERT, GPT, and Stable Diffusion.
+
+* **New Torch-MIGraphX driver**.
+  This driver calls MIGraphX directly from PyTorch. It provides an `mgx_module` object that you can
+  invoke like any other Torch module, but which utilizes the MIGraphX inference engine internally.
+  Torch-MIGraphX supports FP32, FP16, and INT8 datatypes.
+
+  * **FP8 support**. We now offer functional support for inference in the FP8E4M3FNUZ datatype. You
+  can load an ONNX model in FP8E4M3FNUZ using C++ or Python APIs, or `migraphx-driver`.
+  You can quantize a floating point model to FP8 format by using the `--fp8` flag with `migraphx-driver`.
+  To accelerate inference, MIGraphX uses hardware acceleration on MI300 for FP8 by leveraging FP8
+  support in various backend kernel libraries.
+
+### MIOpen
+
+* **Improved performance for inference and convolutions**.
+  Inference support now provided for Find 2.0 fusion plans. Additionally, we've enhanced the Number of
+  samples, Height, Width, and Channels (NHWC) convolution kernels for heuristics. NHWC stores data
+  in a format where the height and width dimensions come first, followed by channels.
+
+### OpenMP
+
+* **Implicit Zero-copy is triggered automatically in XNACK-enabled MI300A systems**.
+  Implicit Zero-copy behavior in `non unified_shared_memory` programs is triggered automatically in
+  XNACK-enabled MI300A systems (for example, when using the `HSA_XNACK=1` environment
+  variable). OpenMP supports the 'requires `unified_shared_memory`' directive to support programs
+  that don’t want to copy data explicitly between the CPU and GPU. However, this requires that you add
+  these directives to every translation unit of the program.
+
+* **New MI300 FP atomics**. Application performance can now improve by leveraging fast floating-point atomics on MI300 (gfx942).
+  
+
+### RCCL
+
+* **NCCL 2.18.6 compatibility**.
+  RCCL is now compatible with NCCL 2.18.6, which includes increasing the maximum IB network interfaces to 32 and fixing network device ordering when creating communicators with only one GPU
+  per node.
+
+* **Doubled simultaneous communication channels**.
+  We improved MI300X performance by increasing the maximum number of simultaneous
+  communication channels from 32 to 64.
+
+### rocALUTION
+
+* **New multiple node and GPU support**.
+  Unsmoothed and smoothed aggregations and Ruge-Stueben AMG now work with multiple nodes
+  and GPUs. For more information, refer to the 
+  [API documentation](https://rocm.docs.amd.com/projects/rocALUTION/en/latest/usermanual/solvers.html#unsmoothed-aggregation-amg).
+
+### rocDecode
+
+* **New ROCm component**.
+  rocDecode ROCm's newest component, providing high-performance video decode support for AMD
+  GPUs. To learn more, refer to the 
+  [documentation](https://rocm.docs.amd.com/projects/rocDecode/en/latest/).
+
+### ROCm Compiler
+
+* **Combined projects**. ROCm Device-Libs, ROCm Compiler Support, and hipCC are now located in
+  the `llvm-project/amd` subdirectory of AMD's fork of the LLVM project. Previously, these projects
+  were maintained in separate repositories. Note that the projects themselves will continue to be
+  packaged separately.
+
+* **Split the 'rocm-llvm' package**. This package has been split into a required and an optional package: 
+
+  * **rocm-llvm(required)**: A package containing the essential binaries needed for compilation.
+  
+  * **rocm-llvm-dev(optional)**: A package containing binaries for compiler and application developers.
+    
+
+### ROCm Data Center Tool (RDC)
+
+* **C++ upgrades**.
+  RDC was upgraded from C++11 to C++17 to enable a more modern C++ standard when writing RDC plugins.
+
+### ROCm Performance Primitives (RPP)
+
+* **New backend support**.
+  Audio processing support added for the `HOST` backend and 3D Voxel kernels support
+  for the `HOST` and `HIP` backends.
+
+### ROCm Validation Suite
+
+* **New datatype support**.
+Added BF16 and FP8 datatypes based on General Matrix Multiply(GEMM) operations in the GPU Stress Test (GST) module. This provides additional performance benchmarking and stress testing based on the newly supported datatypes.
+
+### rocSOLVER
+
+* **New EigenSolver routine**.
+Based on the Jacobi algorithm, a new EigenSolver routine was added to the library. This routine computes the eigenvalues and eigenvectors of a matrix with improved performance.
+
+### ROCTracer
+
+* **New versioning and callback enhancements**.
+Improved to match versioning changes in HIP Runtime and supports runtime API callbacks and activity record logging. The APIs of different runtimes at different levels are considered different API domains with assigned domain IDs.
+
+## Upcoming changes
+
+* ROCm SMI will be deprecated in a future release. We advise **migrating to AMD SMI** now to
+  prevent future workflow disruptions.
+
+* hipCC supports, by default, the following compiler invocation flags:
+
+  * `-mllvm -amdgpu-early-inline-all=true`
+  * `-mllvm -amdgpu-function-calls=false`
+
+  In a future ROCm release, hipCC will no longer support these flags. It will, instead, use the Clang
+  defaults:
+
+  * `-mllvm -amdgpu-early-inline-all=false`
+  * `-mllvm -amdgpu-function-calls=true`
+
+  To evaluate the impact of this change, include `--hipcc-func-supp` in your hipCC invocation.
+
+  For information on these flags, and the differences between hipCC and Clang, refer to
+  [ROCm Compiler Interfaces](https://rocm.docs.amd.com/en/latest/reference/rocmcc.html#rocm-compiler-interfaces).
+
+*  Future ROCm releases will not provide `clang-ocl`. For more information, refer to the
+  [`clang-ocl` README](https://github.com/ROCm/clang-ocl).
+
+* The following operating systems will be supported in a future ROCm release. They are currently
+  only available in beta.
+
+  * RHEL 9.4
+  * RHEL 8.10
+  * SLES 15 SP6
+
+* As of ROCm 6.2, we’ve planned for **end-of-support** for:
+
+  * Ubuntu 20.04.5
+  * SLES 15 SP4
+  * RHEL/CentOS 7.9
--- a/cmake/Modules/Dependencies.cmake
+++ b/cmake/Modules/Dependencies.cmake
@@ -0,0 +1,47 @@
+# MIT License
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# ###########################
+# ROCm dependencies
+# ###########################
+
+include(FetchContent)
+
+if(BUILD_DOCS)
+  find_package(ROCM 0.11.0 CONFIG QUIET PATHS "${ROCM_PATH}") # First version with Sphinx doc gen improvement
+  if(NOT ROCM_FOUND)
+    message(STATUS "ROCm CMake not found. Fetching...")
+    set(rocm_cmake_tag
+      "c044bb52ba85058d28afe2313be98d9fed02e293" # develop@2023.09.12. (move to 6.0 tag when released)
+      CACHE STRING "rocm-cmake tag to download")
+    FetchContent_Declare(
+      rocm-cmake
+      GIT_REPOSITORY https://github.com/ROCm/rocm-cmake.git
+      GIT_TAG        ${rocm_cmake_tag}
+      SOURCE_SUBDIR "DISABLE ADDING TO BUILD" # We don't really want to consume the build and test targets of ROCm CMake.
+    )
+    FetchContent_MakeAvailable(rocm-cmake)
+    find_package(ROCM CONFIG REQUIRED NO_DEFAULT_PATH PATHS "${rocm-cmake_SOURCE_DIR}")
+  else()
+    find_package(ROCM 0.11.0 CONFIG REQUIRED PATHS "${ROCM_PATH}")
+  endif()
+endif()
--- a/default.xml
+++ b/default.xml
@@ -1,79 +1,71 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <manifest>
-    <remote name="roc-github"
-fetch="https://github.com/RadeonOpenCompute/" />
-    <remote name="rocm-devtools"
-fetch="https://github.com/ROCm-Developer-Tools/" />
-    <remote name="rocm-swplat"
-fetch="https://github.com/ROCmSoftwarePlatform/" />
-    <remote name="gpuopen-libs"
-fetch="https://github.com/GPUOpen-ProfessionalCompute-Libraries/" />
-    <remote name="gpuopen-tools"
-fetch="https://github.com/GPUOpen-Tools/" />
-    <remote name="KhronosGroup"
-fetch="https://github.com/KhronosGroup/" />
-    <default revision="refs/tags/rocm-5.5.1"
-     remote="roc-github"
+    <remote name="rocm-org" fetch="https://github.com/ROCm/" />
+    <remote name="KhronosGroup" fetch="https://github.com/KhronosGroup/" />
+    <default revision="refs/tags/rocm-6.1.0"
+     remote="rocm-org"
     sync-c="true"
     sync-j="4" />
-<!--list of projects for ROCM-->
+<!--list of projects for ROCm-->
+    <project path="ROCm-OpenCL-Runtime/api/opencl/khronos/icd" name="OpenCL-ICD-Loader" remote="KhronosGroup" />
    <project name="ROCK-Kernel-Driver" />
-    <project name="ROCT-Thunk-Interface" />
    <project name="ROCR-Runtime" />
+    <project name="ROCT-Thunk-Interface" />
+    <project name="amdsmi" />
+    <project name="clang-ocl" />
+    <project name="rdc" />
+    <project name="rocm_bandwidth_test" />
    <project name="rocm_smi_lib" />
    <project name="rocm-core" />
-    <project name="rocm-cmake" />
    <project name="rocminfo" />
-    <project name="rocprofiler" remote="rocm-devtools" />
-    <project name="roctracer" remote="rocm-devtools" />
-    <project name="ROCm-OpenCL-Runtime" />
-    <project path="ROCm-OpenCL-Runtime/api/opencl/khronos/icd" name="OpenCL-ICD-Loader" remote="KhronosGroup" revision="6c03f8b58fafd9dd693eaac826749a5cfad515f8" />
-    <project name="clang-ocl" />
+    <project name="rocprofiler" />
+    <project name="rocprofiler-register" />
+    <project name="roctracer" />
 <!--HIP Projects-->
-    <project name="HIP" remote="rocm-devtools" />
-    <project name="hipamd" remote="rocm-devtools" />
-    <project name="HIP-Examples" remote="rocm-devtools" />
-    <project name="ROCclr" remote="rocm-devtools" />
-    <project name="HIPIFY" remote="rocm-devtools" />
-    <project name="HIPCC" remote="rocm-devtools" />
+    <project name="HIP" />
+    <project name="HIP-Examples" />
+    <project name="HIPIFY" />
+    <project name="clr" />
+    <project name="hipother" />
 <!-- The following projects are all associated with the AMDGPU LLVM compiler -->
+    <project name="half" />
    <project name="llvm-project" />
-    <project name="ROCm-Device-Libs" />
-    <project name="atmi" />
-    <project name="ROCm-CompilerSupport" />
-    <project name="rocr_debug_agent" remote="rocm-devtools" />
-    <project name="rocm_bandwidth_test" />
-    <project name="half" remote="rocm-swplat" revision="37742ce15b76b44e4b271c1e66d13d2fa7bd003e" />
-    <project name="RCP" remote="gpuopen-tools" revision="3a49405a1500067c49d181844ec90aea606055bb" />
 <!-- gdb projects -->
-    <project name="ROCgdb" remote="rocm-devtools" />
-    <project name="ROCdbgapi" remote="rocm-devtools" />
+    <project name="ROCdbgapi" />
+    <project name="ROCgdb" />
+    <project name="rocr_debug_agent" />
 <!-- ROCm Libraries -->
-    <project name="rdc" />
-    <project groups="mathlibs" name="rocBLAS" remote="rocm-swplat" />
-    <project groups="mathlibs" name="Tensile" remote="rocm-swplat" />
-    <project groups="mathlibs" name="hipBLAS" remote="rocm-swplat" />
-    <project groups="mathlibs" name="rocFFT" remote="rocm-swplat" />
-    <project groups="mathlibs" name="hipFFT" remote="rocm-swplat" />
-    <project groups="mathlibs" name="rocRAND" remote="rocm-swplat" />
-    <project groups="mathlibs" name="rocSPARSE" remote="rocm-swplat" />
-    <project groups="mathlibs" name="rocSOLVER" remote="rocm-swplat" />
-    <project groups="mathlibs" name="hipSOLVER" remote="rocm-swplat" />
-    <project groups="mathlibs" name="hipSPARSE" remote="rocm-swplat" />
-    <project groups="mathlibs" name="rocALUTION" remote="rocm-swplat" />
-    <project name="MIOpenGEMM" remote="rocm-swplat" />
-    <project name="MIOpen" remote="rocm-swplat" />
-    <project groups="mathlibs" name="rccl" remote="rocm-swplat" />
-    <project name="MIVisionX" remote="gpuopen-libs" />
-    <project groups="mathlibs" name="rocThrust" remote="rocm-swplat" />
-    <project groups="mathlibs" name="hipCUB" remote="rocm-swplat" />
-    <project groups="mathlibs" name="rocPRIM" remote="rocm-swplat" />
-    <project groups="mathlibs" name="rocWMMA" remote="rocm-swplat" />
-    <project name="hipfort" remote="rocm-swplat" />
-    <project name="AMDMIGraphX" remote="rocm-swplat" />
-    <project name="ROCmValidationSuite" remote="rocm-devtools" />
+    <project groups="mathlibs" name="AMDMIGraphX" />
+    <project groups="mathlibs" name="MIOpen" />
+    <project groups="mathlibs" name="MIVisionX" />
+    <project groups="mathlibs" name="ROCmValidationSuite" />
+    <project groups="mathlibs" name="Tensile" />
+    <project groups="mathlibs" name="composable_kernel" />
+    <project groups="mathlibs" name="hipBLAS" />
+    <project groups="mathlibs" name="hipBLASLt" />
+    <project groups="mathlibs" name="hipCUB" />
+    <project groups="mathlibs" name="hipFFT" />
+    <project groups="mathlibs" name="hipRAND" />
+    <project groups="mathlibs" name="hipSOLVER" />
+    <project groups="mathlibs" name="hipSPARSE" />
+    <project groups="mathlibs" name="hipSPARSELt" />
+    <project groups="mathlibs" name="hipTensor" />
+    <project groups="mathlibs" name="hipfort" />
+    <project groups="mathlibs" name="rccl" />
+    <project groups="mathlibs" name="rocALUTION" />
+    <project groups="mathlibs" name="rocBLAS" />
+    <project groups="mathlibs" name="rocDecode" />
+    <project groups="mathlibs" name="rocFFT" />
+    <project groups="mathlibs" name="rocPRIM" />
+    <project groups="mathlibs" name="rocRAND" />
+    <project groups="mathlibs" name="rocSOLVER" />
+    <project groups="mathlibs" name="rocSPARSE" />
+    <project groups="mathlibs" name="rocThrust" />
+    <project groups="mathlibs" name="rocWMMA" />
+    <project groups="mathlibs" name="rocm-cmake" />
+    <project groups="mathlibs" name="rpp" />
 <!-- Projects for OpenMP-Extras -->
-    <project name="aomp" path="openmp-extras/aomp" remote="rocm-devtools" />
-    <project name="aomp-extras" path="openmp-extras/aomp-extras" remote="rocm-devtools" />
-    <project name="flang" path="openmp-extras/flang" remote="rocm-devtools" />
+    <project name="aomp" path="openmp-extras/aomp" />
+    <project name="aomp-extras" path="openmp-extras/aomp-extras" />
+    <project name="flang" path="openmp-extras/flang" />
 </manifest>
--- a/docs/404.md
+++ b/docs/404.md
@@ -1,6 +0,0 @@
-# 404 Page Not Found
-
-Page could not be found.
-
-Return to [home](./index) or please use the links from the sidebar to find what
-you are looking for.
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -0,0 +1,33 @@
+# MIT License
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+include(ROCMSphinxDoc)
+
+rocm_add_sphinx_doc(
+    "${CMAKE_CURRENT_SOURCE_DIR}"
+  OUTPUT_DIR html
+  BUILDER html
+)
+  
+install(
+  DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/html"
+  DESTINATION "${CMAKE_INSTALL_DOCDIR}")
--- a/docs/about.md
+++ b/docs/about.md
@@ -1,74 +0,0 @@
-# About ROCm Documentation
-
-ROCm documentation is made available under open source [licenses](licensing.md).
-Documentation is built using open source toolchains. Contributions to our
-documentation is encouraged and welcome. As a contributor, please familiarize
-yourself with our documentation toolchain.
-
-## ReadTheDocs
-
-[ReadTheDocs](https://docs.readthedocs.io/en/stable/) is our front end for the
-our documentation. By front end, this is the tool that serves our HTML based
-documentation to our end users.
-
-## Doxygen
-
-[Doxygen](https://www.doxygen.nl/) is the most common inline code documentation
-standard. ROCm projects are use Doxygen for public API documentation (unless the
-upstream project is using a different tool).
-
-## Sphinx
-
-[Sphinx](https://www.sphinx-doc.org/en/master/) is a documentation generator
-originally used for python. It is now widely used in the Open Source community.
-Originally, sphinx supported RST based documentation. Markdown support is now
-available. ROCm documentation plans to default to markdown for new projects.
-Existing projects using RST are under no obligation to convert to markdown. New
-projects that believe markdown is not suitable should contact the documentation
-team prior to selecting RST.
-
-### MyST
-
-[Markedly Structured Text (MyST)](https://myst-tools.org/docs/spec) is an extended
-flavor of Markdown ([CommonMark](https://commonmark.org/)) influenced by reStructuredText (RST) and Sphinx.
-It is integrated via [`myst-parser`](https://myst-parser.readthedocs.io/en/latest/).
-A cheat sheet that showcases how to use the MyST syntax is available over at [the Jupyter
-reference](https://jupyterbook.org/en/stable/reference/cheatsheet.html).
-
-### Sphinx Theme
-
-ROCm is using the
-[Sphinx Book Theme](https://sphinx-book-theme.readthedocs.io/en/latest/). This
-theme is used by Jupyter books. ROCm documentation applies some customization
-include a header and footer on top of the Sphinx Book Theme. A future custom
-ROCm theme will be part of our documentation goals.
-
-### Sphinx Design
-
-Sphinx Design is an extension for sphinx based websites that add design
-functionality. Please see the documentation
-[here](https://sphinx-design.readthedocs.io/en/latest/index.html). ROCm
-documentation uses sphinx design for grids, cards, and synchronized tabs.
-Other features may be used in the future.
-
-### Sphinx External TOC
-
-ROCm uses the
-[sphinx-external-toc](https://sphinx-external-toc.readthedocs.io/en/latest/intro.html)
-for our navigation. This tool allows a YAML file based left navigation menu. This
-tool was selected due to its flexibility that allows scripts to operate on the
-YAML file. Please transition to this file for the project's navigation. You can
-see the `_toc.yml.in` file in this repository in the docs/sphinx folder for an
-example.
-
-### Breathe
-
-Sphinx uses [Breathe](https://www.breathe-doc.org/) to integrate Doxygen
-content.
-
-## `rocm-docs-core` pip package
-
-[rocm-docs-core](https://github.com/RadeonOpenCompute/rocm-docs-core) is an AMD
-maintained project that applies customization for our documentation. This
-project is the tool most ROCm repositories will use as part of the documentation
-build.
--- a/docs/about/compatibility/openmp.md
+++ b/docs/about/compatibility/openmp.md
@@ -1,4 +1,10 @@
-# OpenMP Support in ROCm
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="OpenMP support in ROCm">
+  <meta name="keywords" content="OpenMP, LLVM, OpenMP toolchain">
+</head>
+
+# OpenMP support in ROCm

 ## Introduction

@@ -9,7 +15,13 @@ Along with host APIs, the OpenMP compilers support offloading code and data onto
 GPU devices. This document briefly describes the installation location of the
 OpenMP toolchain, example usage of device offloading, and usage of `rocprof`
 with OpenMP applications. The GPUs supported are the same as those supported by
-this ROCm release. See the list of supported GPUs in {doc}`/release/gpu_os_support`.
+this ROCm release. See the list of supported GPUs for {doc}`Linux<rocm-install-on-linux:reference/system-requirements>` and
+{doc}`Windows<rocm-install-on-windows:reference/system-requirements>`.
+
+The ROCm OpenMP compiler is implemented using LLVM compiler technology.
+The following image illustrates the internal steps taken to translate a user’s application into an executable that can offload computation to the AMDGPU. The compilation is a two-pass process. Pass 1 compiles the application to generate the CPU code and Pass 2 links the CPU code to the AMDGPU device code.
+
+![OpenMP toolchain](../../data/reference/openmp/openmp-toolchain.svg "OpenMP toolchain")

 ### Installation

@@ -17,17 +29,13 @@ The OpenMP toolchain is automatically installed as part of the standard ROCm
 installation and is available under `/opt/rocm-{version}/llvm`. The
 sub-directories are:

-bin: Compilers (`flang` and `clang`) and other binaries.
+* bin: Compilers (`flang` and `clang`) and other binaries.
+* examples: The usage section below shows how to compile and run these programs.
+* include: Header files.
+* lib: Libraries including those required for target offload.
+* lib-debug: Debug versions of the above libraries.

- examples: The usage section below shows how to compile and run these programs.
-
- include: Header files.
-
- lib: Libraries including those required for target offload.
-
- lib-debug: Debug versions of the above libraries.
-
-## OpenMP: Usage
+## OpenMP: usage

 The example programs can be compiled and run by pointing the environment
 variable `ROCM_PATH` to the ROCm install directory.
@@ -53,11 +61,10 @@ that are required for target offload from an OpenMP program:
 ```

 :::{note}
-The Makefile in the example above uses a more classical and verbose set of flags
-which can also be used:
+The compiler also accepts the alternative offloading notation:

 ```bash
-fopenmp -fopenmp-targets=amdgcn-amd-amdhsa -Xopenmp-target=amdgcn-amd-amdhsa
+-fopenmp -fopenmp-targets=amdgcn-amd-amdhsa -Xopenmp-target=amdgcn-amd-amdhsa -march=<gpu-arch>
 ```

 :::
@@ -71,7 +78,7 @@ Obtain the value of `gpu-arch` by running the following command:
 [//]: # (dated link below, needs updating)

 See the complete list of compiler command-line references
-[here](https://github.com/RadeonOpenCompute/llvm-project/blob/amd-stg-open/clang/docs/CommandGuide/clang.rst).
+[here](https://github.com/ROCm/llvm-project/blob/amd-stg-open/clang/docs/CommandGuide/clang.rst).

 ### Using `rocprof` with OpenMP

@@ -108,10 +115,9 @@ code compiled with AOMP:
   options --list-basic and --list-derived. `rocprof` accepts either a text or
   an XML file as an input.

-For more details on `rocprof`, refer to the ROCm Profiling Tools document on
-{doc}`rocprofiler:rocprof`.
+For more details on `rocprof`, refer to the {doc}`ROCProfilerV1 User Manual <rocprofiler:rocprofv1>`.

-### Using Tracing Options
+### Using tracing options

 **Prerequisite:** When using the `--sys-trace` option, compile the OpenMP
 program with:
@@ -122,10 +128,10 @@ program with:

 The following tracing options are widely used to generate useful information:

- **`--hsa-trace`**: This option is used to get a JSON output file with the HSA
+* **`--hsa-trace`**: This option is used to get a JSON output file with the HSA
  API execution traces and a flat profile in a CSV file.

- **`--sys-trace`**: This allows programmers to trace both HIP and HSA calls.
+* **`--sys-trace`**: This allows programmers to trace both HIP and HSA calls.
  Since this option results in loading ``libamdhip64.so``, follow the
  prerequisite as mentioned above.

@@ -135,30 +141,46 @@ Google Chrome at chrome://tracing/ or [Perfetto](https://perfetto.dev/).
 Navigate to Chrome or Perfetto and load the JSON file to see the timeline of the
 HSA calls.

-For more details on tracing, refer to the ROCm Profiling Tools document on
-{doc}`rocprofiler:rocprof`.
+For more details on tracing, refer to the {doc}`ROCProfilerV1 User Manual <rocprofiler:rocprofv1>`.

-### Environment Variables
+### Environment variables

 :::{table}
 :widths: auto
-| Environment Variable        | Description |
-| --------------------------- | ----------- |
-| `OMP_NUM_TEAMS`             | The implementation chooses the number of teams for kernel launch. The user can change this number for performance tuning using this environment variable, subject to implementation limits. |
-| `LIBOMPTARGET_KERNEL_TRACE` | This environment variable is used to print useful statistics for device operations. Setting it to 1 and running the program emits the name of every kernel launched, the number of teams and threads used, and the corresponding register usage. Setting it to 2 additionally emits timing information for kernel launches and data transfer operations between the host and the device. |
-| `LIBOMPTARGET_INFO`         | This environment variable is used to print informational messages from the device runtime as the program executes. Users can request fine-grain information by setting it to the value of 1 or higher and can set the value of -1 for complete information. |
-| `LIBOMPTARGET_DEBUG`        | If a debug version of the device library is present, setting this environment variable to 1 and using that library emits further detailed debugging information about data transfer operations and kernel launch. |
-| `GPU_MAX_HW_QUEUES`         | This environment variable is used to set the number of HSA queues in the OpenMP runtime. |
+| Environment Variable        | Purpose                  |
+| --------------------------- | ---------------------------- |
+| `OMP_NUM_TEAMS`             | To set the number of teams for kernel launch, which is otherwise chosen by the implementation by default. You can set this number (subject to implementation limits) for performance tuning. |
+| `LIBOMPTARGET_KERNEL_TRACE` | To print useful statistics for device operations. Setting it to 1 and running the program emits the name of every kernel launched, the number of teams and threads used, and the corresponding register usage. Setting it to 2 additionally emits timing information for kernel launches and data transfer operations between the host and the device. |
+| `LIBOMPTARGET_INFO`         | To print informational messages from the device runtime as the program executes. Setting it to a value of 1 or higher, prints fine-grain information and setting it to -1 prints complete information. |
+| `LIBOMPTARGET_DEBUG`        | To get detailed debugging information about data transfer operations and kernel launch when using a debug version of the device library. Set this environment variable to 1 to get the detailed information from the library. |
+| `GPU_MAX_HW_QUEUES`         | To set the number of HSA queues in the OpenMP runtime. The HSA queues are created on demand up to the maximum value as supplied here. The queue creation starts with a single initialized queue to avoid unnecessary allocation of resources. The provided value is capped if it exceeds the recommended, device-specific value. |
+| `LIBOMPTARGET_AMDGPU_MAX_ASYNC_COPY_BYTES` | To set the threshold size up to which data transfers are initiated asynchronously. The default threshold size is 1*1024*1024 bytes (1MB). |
+| `OMPX_FORCE_SYNC_REGIONS` | To force the runtime to execute all operations synchronously, i.e., wait for an operation to complete immediately. This affects data transfers and kernel execution. While it is mainly designed for debugging, it may have a minor positive effect on performance in certain situations. |
 :::

-## OpenMP: Features
+## OpenMP: features

 The OpenMP programming model is greatly enhanced with the following new features
 implemented in the past releases.

 (openmp_usm)=

-### Unified Shared Memory
+### Asynchronous behavior in OpenMP target regions
+
+* Controlling Asynchronous Behavior
+
+The OpenMP offloading runtime executes in an asynchronous fashion by default, allowing multiple data transfers to start concurrently. However, if the data to be transferred becomes larger than the default threshold of 1MB, the runtime falls back to a synchronous data transfer. The buffers that have been locked already are always executed asynchronously.
+You can overrule this default behavior by setting `LIBOMPTARGET_AMDGPU_MAX_ASYNC_COPY_BYTES` and `OMPX_FORCE_SYNC_REGIONS`. See the [Environment Variables](#environment-variables) table for details.
+
+* Multithreaded Offloading on the Same Device
+
+The `libomptarget` plugin for GPU offloading allows creation of separate configurable HSA queues per chiplet, which enables two or more threads to concurrently offload to the same device.
+
+* Parallel Memory Copy Invocations
+
+Implicit asynchronous execution of single target region enables parallel memory copy invocations.
+
+### Unified shared memory

 Unified Shared Memory (USM) provides a pointer-based approach to memory
 management. To implement USM, fulfill the following system requirements along
@@ -166,53 +188,46 @@ with Xnack capability.

 #### Prerequisites

- Linux Kernel versions above 5.14
-
- Latest KFD driver packaged in ROCm stack
-
- Xnack, as USM support can only be tested with applications compiled with Xnack
+* Linux Kernel versions above 5.14
+* Latest KFD driver packaged in ROCm stack
+* Xnack, as USM support can only be tested with applications compiled with Xnack
  capability

-#### Xnack Capability
+#### Xnack capability

 When enabled, Xnack capability allows GPU threads to access CPU (system) memory,
 allocated with OS-allocators, such as `malloc`, `new`, and `mmap`. Xnack must be
 enabled both at compile- and run-time. To enable Xnack support at compile-time,
-the programmer should use
+use:

 ```bash
 --offload-arch=gfx908:xnack+
 ```

-Or, equivalently
+Or use another functionally equivalent option Xnack-any:

 ```bash
 --offload-arch=gfx908
 ```

-:::{note}
-The second case is called Xnack-any and it is functionally equivalent to the
-first case.
-:::
-
-At runtime, programmers enable Xnack functionality on a per-application basis
-using an environment variable:
+To enable Xnack functionality at runtime on a per-application basis,
+use environment variable:

 ```bash
 HSA_XNACK=1
 ```

-When Xnack support is not needed, then applications can be built to maximize
-resource utilization using:
+When Xnack support is not needed:
+
+* Build the applications to maximize resource utilization using:

 ```bash
 --offload-arch=gfx908:xnack-
 ```

-At runtime, the `HSA_XNACK` environment variable can be set to 0, as Xnack
-functionality is not needed.
+* At runtime, set the `HSA_XNACK` environment variable to 0.

-#### Unified Shared Memory Pragma
+#### Unified shared memory pragma

 This OpenMP pragma is available on MI200 through `xnack+` support.

@@ -264,9 +279,9 @@ The difference between the memory pages pointed to by these two variables is
 that the pages pointed by “a” are in fine-grain memory, while the pages pointed
 to by “b” are in coarse-grain memory during and after the execution of the
 target region. This is accomplished in the OpenMP runtime library with calls to
-the ROCR runtime to set the pages pointed by “b” as coarse grain.
+the ROCr runtime to set the pages pointed by “b” as coarse grain.

-### OMPT Target Support
+### OMPT target support

 The OpenMP runtime in ROCm implements a subset of the OMPT device APIs, as
 described in the OpenMP specification document. These APIs allow first-party
@@ -291,7 +306,7 @@ The file `veccopy-ompt-target-tracing.c` simulates how a tool initiates device
 activity tracing. The file `callbacks.h` shows the callbacks registered and
 implemented by the tool.

-### Floating Point Atomic Operations
+### Floating point atomic operations

 The MI200-series GPUs support the generation of hardware floating-point atomics
 using the OpenMP atomic pragma. The support includes single- and
@@ -315,8 +330,10 @@ double a = 0.0;
 a = a + 1.0;
 ```

-NOTE `AMD_unsafe_fp_atomics` is an alias for `AMD_fast_fp_atomics`, and
+:::{note}
+`AMD_unsafe_fp_atomics` is an alias for `AMD_fast_fp_atomics`, and
 `AMD_safe_fp_atomics` is implemented with a compare-and-swap loop.
+:::

 To disable the generation of fast floating-point atomic instructions at the file
 level, build using the option `-msafe-fp-atomics` or use a hint clause on a
@@ -349,44 +366,36 @@ double b = 0.0;
 b = b + 1.0;
 ```

-### Address Sanitizer (ASan) Tool
+### AddressSanitizer tool

-Address Sanitizer is a memory error detector tool utilized by applications to
+AddressSanitizer (ASan) is a memory error detector tool utilized by applications to
 detect various errors ranging from spatial issues such as out-of-bound access to
 temporal issues such as use-after-free. The AOMP compiler supports ASan for AMD
 GPUs with applications written in both HIP and OpenMP.

-**Features Supported on Host Platform (Target x86_64):**
+**Features supported on host platform (Target x86_64):**

- Use-after-free
+* Use-after-free
+* Buffer overflows
+* Heap buffer overflow
+* Stack buffer overflow
+* Global buffer overflow
+* Use-after-return
+* Use-after-scope
+* Initialization order bugs

- Buffer overflows
+**Features supported on AMDGPU platform (`amdgcn-amd-amdhsa`):**

- Heap buffer overflow
+* Heap buffer overflow
+* Global buffer overflow

- Stack buffer overflow
-
- Global buffer overflow
-
- Use-after-return
-
- Use-after-scope
-
- Initialization order bugs
-
-**Features Supported on AMDGPU Platform (`amdgcn-amd-amdhsa`):**
-
- Heap buffer overflow
-
- Global buffer overflow
-
-**Software (Kernel/OS) Requirements:** Unified Shared Memory support with Xnack
+**Software (kernel/OS) requirements:** Unified Shared Memory support with Xnack
 capability. See the section on [Unified Shared Memory](#unified-shared-memory)
 for prerequisites and details on Xnack.

 **Example:**

- Heap buffer overflow
+* Heap buffer overflow

 ```bash
 void  main() {
@@ -404,9 +413,9 @@ void  main() {
 ```

 See the complete sample code for heap buffer overflow
-[here](https://github.com/ROCm-Developer-Tools/aomp/blob/aomp-dev/examples/tools/asan/heap_buffer_overflow/openmp/vecadd-HBO.cpp).
+[here](https://github.com/ROCm/aomp/blob/aomp-dev/examples/tools/asan/heap_buffer_overflow/openmp/vecadd-HBO.cpp).

- Global buffer overflow
+* Global buffer overflow

 ```bash
 #pragma omp declare target
@@ -429,45 +438,46 @@ for(int i=0; i<N; i++){
 ```

 See the complete sample code for global buffer overflow
-[here](https://github.com/ROCm-Developer-Tools/aomp/blob/aomp-dev/examples/tools/asan/global_buffer_overflow/openmp/vecadd-GBO.cpp).
+[here](https://github.com/ROCm/aomp/blob/aomp-dev/examples/tools/asan/global_buffer_overflow/openmp/vecadd-GBO.cpp).

-### No-loop Kernel Generation
+### Clang compiler option for kernel optimization

-The No-loop kernel generation feature optimizes the compiler performance by
-generating a specialized kernel for certain OpenMP Target Constructs such as
-target teams distribute parallel for. The specialized kernel generation assumes
-that every thread executes a single iteration of the user loop, which implies
-that the runtime launches a total number of GPU threads equal to or greater than
-the iteration space size of the target region loop. This allows the compiler to
-generate code for the loop body without an enclosing loop, resulting in reduced
-control-flow complexity and potentially better performance.
+You can use the clang compiler option `-fopenmp-target-fast` for kernel optimization if certain constraints implied by its component options are satisfied. `-fopenmp-target-fast` enables the following options:

-To enable the generation of the specialized kernel, follow these guidelines:
+* `-fopenmp-target-ignore-env-vars`: It enables code generation of specialized kernels including no-loop and Cross-team reductions.

- Do not specify teams, threads, and schedule-related environment variables. The
-  `num_teams` or a `thread_limit` clause in an OpenMP target construct acts as
-  an override and prevents the generation of the specialized kernel. As the user
-  is unable to specify the number of teams and threads used within target
-  regions in the absence of the above-mentioned environment variables, the
-  runtime will select the best values for the launch configuration based on
-  runtime knowledge of the program.
+* `-fopenmp-assume-no-thread-state`: It enables the compiler to assume that no thread in a parallel region modifies an Internal Control Variable (`ICV`), thus potentially reducing the device runtime code execution.

- Assert the absence of the above-mentioned environment variables by adding the
-  command-line option `-fopenmp-target-ignore-env-vars`. This option also allows
-  programmers to enable the No-loop functionality at lower optimization levels.
+* `-fopenmp-assume-no-nested-parallelism`: It enables the compiler to assume that no thread in a parallel region encounters a parallel region, thus potentially reducing the device runtime code execution.

- Also, the No-loop functionality is automatically enabled when `-O3` or
-  `-Ofast` is used for compilation. To disable this feature, use
-  `-fno-openmp-target-ignore-env-vars`.
+* `-O3` if no `-O*` is specified by the user.

-Note The compiler might not generate the No-loop kernel in certain scenarios
-where the performance improvement is not substantial.
+### Specialized kernels

-### Cross-Team Optimized Reductions
+Clang will attempt to generate specialized kernels based on compiler options and OpenMP constructs. The following specialized kernels are supported:

-In scenarios where a No-loop kernel is generated but the OpenMP construct has a
-reduction clause, the compiler may generate optimized code utilizing efficient
-Cross-Team (Xteam) communication. No separate user option is required, and there
-is a significant performance improvement with Xteam reduction. New APIs for
-Xteam reduction are implemented in the device runtime, and clang generates these
-APIs automatically.
+* No-loop
+* Big-jump-loop
+* Cross-team reductions
+
+To enable the generation of specialized kernels, follow these guidelines:
+
+* Do not specify teams, threads, and schedule-related environment variables. The `num_teams` clause in an OpenMP target construct acts as an override and prevents the generation of the no-loop kernel. If the specification of `num_teams` clause is a user requirement then clang tries to generate the big-jump-loop kernel instead of the no-loop kernel.
+
+* Assert the absence of the teams, threads, and schedule-related environment variables by adding the command-line option `-fopenmp-target-ignore-env-vars`.
+
+* To automatically enable the specialized kernel generation, use `-Ofast` or `-fopenmp-target-fast` for compilation.
+
+* To disable specialized kernel generation, use `-fno-openmp-target-ignore-env-vars`.
+
+#### No-loop kernel generation
+
+The no-loop kernel generation feature optimizes the compiler performance by generating a specialized kernel for certain OpenMP target constructs such as target teams distribute parallel for. The specialized kernel generation feature assumes every thread executes a single iteration of the user loop, which leads the runtime to launch a total number of GPU threads equal to or greater than the iteration space size of the target region loop. This allows the compiler to generate code for the loop body without an enclosing loop, resulting in reduced control-flow complexity and potentially better performance.
+
+#### Big-jump-loop kernel generation
+
+A no-loop kernel is not generated if the OpenMP teams construct uses a `num_teams` clause. Instead, the compiler attempts to generate a different specialized kernel called the big-jump-loop kernel. The compiler launches the kernel with a grid size determined by the number of teams specified by the OpenMP `num_teams` clause and the `blocksize` chosen either by the compiler or specified by the corresponding OpenMP clause.
+
+#### Cross-team optimized reduction kernel generation
+
+If the OpenMP construct has a reduction clause, the compiler attempts to generate optimized code by utilizing efficient cross-team communication. New APIs for cross-team reduction are implemented in the device runtime and are automatically generated by clang.
--- a/docs/about/compatibility/precision-support.rst
+++ b/docs/about/compatibility/precision-support.rst
@@ -0,0 +1,565 @@
+.. meta::
+  :description: Supported data types in ROCm
+  :keywords: int8, float8, float8 (E4M3), float8 (E5M2), bfloat8, float16, half, bfloat16, tensorfloat32, float,
+   float32, float64, double, AMD, ROCm, AMDGPU
+
+*************************************************************
+Precision support
+*************************************************************
+
+Use the following sections to identify data types and HIP types ROCm™ supports.
+
+Integral types
+==========================================
+
+The signed and unsigned integral types that are supported by ROCm are listed in the following table,
+together with their corresponding HIP type and a short description.
+
+
+.. list-table::
+    :header-rows: 1
+    :widths: 15,35,50
+
+    *
+      - Type name
+      - HIP type
+      - Description
+    *
+      - int8
+      - ``int8_t``, ``uint8_t``
+      - A signed or unsigned 8-bit integer
+    *
+      - int16
+      - ``int16_t``, ``uint16_t``
+      - A signed or unsigned 16-bit integer
+    *
+      - int32
+      - ``int32_t``, ``uint32_t``
+      - A signed or unsigned 32-bit integer
+    *
+      - int64
+      - ``int64_t``, ``uint64_t``
+      - A signed or unsigned 64-bit integer
+
+Floating-point types
+==========================================
+
+The floating-point types that are supported by ROCm are listed in the following table, together with
+their corresponding HIP type and a short description.
+
+.. image:: ../../data/about/compatibility/floating-point-data-types.png
+    :alt: Supported floating-point types
+
+.. list-table::
+    :header-rows: 1
+    :widths: 15,15,70
+
+    *
+      - Type name
+      - HIP type
+      - Description
+    *
+      - float8 (E4M3)
+      - ``-``
+      - An 8-bit floating-point number that mostly follows IEEE-754 conventions and **S1E4M3** bit layout, as described in `8-bit Numerical Formats for Deep Neural Networks <https://arxiv.org/abs/2206.02915>`_ , with expanded range and with no infinity or signed zero. NaN is represented as negative zero.
+    *
+      - float8 (E5M2)
+      - ``-``
+      - An 8-bit floating-point number mostly following IEEE-754 conventions and **S1E5M2** bit layout, as described in `8-bit Numerical Formats for Deep Neural Networks <https://arxiv.org/abs/2206.02915>`_ , with expanded range and with no infinity or signed zero. NaN is represented as negative zero.
+    *
+      - float16
+      - ``half``
+      - A 16-bit floating-point number that conforms to the IEEE 754-2008 half-precision storage format.
+    *
+      - bfloat16
+      - ``bfloat16``
+      - A shortened 16-bit version of the IEEE 754 single-precision storage format.
+    *
+      - tensorfloat32
+      - ``-``
+      - A floating-point number that occupies 32 bits or less of storage, providing improved range compared to half (16-bit) format, at (potentially) greater throughput than single-precision (32-bit) formats.
+    *
+      - float32
+      - ``float``
+      - A 32-bit floating-point number that conforms to the IEEE 754 single-precision storage format.
+    *
+      - float64
+      - ``double``
+      - A 64-bit floating-point number that conforms to the IEEE 754 double-precision storage format.
+
+.. note::
+
+  * The float8 and tensorfloat32 types are internal types used in calculations in Matrix Cores and can be stored in any type of the same size.
+  * The encodings for FP8 (E5M2) and FP8 (E4M3) that are natively supported by MI300 differ from the FP8 (E5M2) and FP8 (E4M3) encodings used in H100 (`FP8 Formats for Deep Learning <https://arxiv.org/abs/2209.05433>`_).
+  * In some AMD documents and articles, float8 (E5M2) is referred to as bfloat8.
+
+ROCm support icons
+==========================================
+
+In the following sections, we use icons to represent the level of support. These icons, described in the
+following table, are also used on the library data type support pages.
+
+.. list-table::
+    :header-rows: 1
+
+    *
+      -  Icon
+      - Definition
+    *
+      - ❌
+      - Not supported
+
+    *
+      - ⚠️
+      - Partial support
+
+    *
+      - ✅
+      - Full support
+
+.. note::
+
+  * Full support means that the type is supported natively or with hardware emulation.
+  * Native support means that the operations for that type are implemented in hardware. Types that are not natively supported are emulated with the available hardware. The performance of non-natively supported types can differ from the full instruction throughput rate. For example, 16-bit integer operations can be performed on the 32-bit integer ALUs at full rate; however, 64-bit integer operations might need several instructions on the 32-bit integer ALUs.
+  * Any type can be emulated by software, but this page does not cover such cases.
+
+Hardware type support
+==========================================
+
+AMD GPU hardware support for data types is listed in the following tables.
+
+Compute units support
+-------------------------------------------------------------------------------
+
+The following table lists data type support for compute units.
+
+.. tab-set::
+
+  .. tab-item:: Integral types
+    :sync: integral-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Type name
+        - int8
+        - int16
+        - int32
+        - int64
+      *
+        - MI100
+        - ✅
+        - ✅
+        - ✅
+        - ✅
+      *
+        - MI200 series
+        - ✅
+        - ✅
+        - ✅
+        - ✅
+      *
+        - MI300 series
+        - ✅
+        - ✅
+        - ✅
+        - ✅
+
+  .. tab-item:: Floating-point types
+    :sync: floating-point-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Type name
+        - float8 (E4M3)
+        - float8 (E5M2)
+        - float16
+        - bfloat16
+        - tensorfloat32
+        - float32
+        - float64
+      *
+        - MI100
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+        - ❌
+        - ✅
+        - ✅
+      *
+        - MI200 series
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+        - ❌
+        - ✅
+        - ✅
+      *
+        - MI300 series
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+        - ❌
+        - ✅
+        - ✅
+
+Matrix core support
+-------------------------------------------------------------------------------
+
+The following table lists data type support for AMD GPU matrix cores.
+
+.. tab-set::
+
+  .. tab-item:: Integral types
+    :sync: integral-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Type name
+        - int8
+        - int16
+        - int32
+        - int64
+      *
+        - MI100
+        - ✅
+        - ❌
+        - ❌
+        - ❌
+      *
+        - MI200 series
+        - ✅
+        - ❌
+        - ❌
+        - ❌
+      *
+        - MI300 series
+        - ✅
+        - ❌
+        - ❌
+        - ❌
+
+  .. tab-item:: Floating-point types
+    :sync: floating-point-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Type name
+        - float8 (E4M3)
+        - float8 (E5M2)
+        - float16
+        - bfloat16
+        - tensorfloat32
+        - float32
+        - float64
+      *
+        - MI100
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+        - ❌
+        - ✅
+        - ❌
+      *
+        - MI200 series
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+        - ❌
+        - ✅
+        - ✅
+      *
+        - MI300 series
+        - ✅
+        - ✅
+        - ✅
+        - ✅
+        - ✅
+        - ✅
+        - ✅
+
+Atomic operations support
+-------------------------------------------------------------------------------
+
+The following table lists data type support for atomic operations.
+
+.. tab-set::
+
+  .. tab-item:: Integral types
+    :sync: integral-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Type name
+        - int8
+        - int16
+        - int32
+        - int64
+      *
+        - MI100
+        - ❌
+        - ❌
+        - ✅
+        - ❌
+      *
+        - MI200 series
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+      *
+        - MI300 series
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+
+  .. tab-item:: Floating-point types
+    :sync: floating-point-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Type name
+        - float8 (E4M3)
+        - float8 (E5M2)
+        - float16
+        - bfloat16
+        - tensorfloat32
+        - float32
+        - float64
+      *
+        - MI100
+        - ❌
+        - ❌
+        - ✅
+        - ❌
+        - ❌
+        - ✅
+        - ❌
+      *
+        - MI200 series
+        - ❌
+        - ❌
+        - ✅
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+      *
+        - MI300 series
+        - ❌
+        - ❌
+        - ✅
+        - ❌
+        - ❌
+        - ✅
+        - ✅
+
+.. note::
+
+  For cases that are not natively supported, you can emulate atomic operations using software.
+  Software-emulated atomic operations have high negative performance impact when they frequently
+  access the same memory address.
+
+Data Type support in ROCm Libraries
+==========================================
+
+ROCm library support for int8, float8 (E4M3), float8 (E5M2), int16, float16, bfloat16, int32,
+tensorfloat32, float32, int64, and float64 is listed in the following tables.
+
+Libraries input/output type support
+-------------------------------------------------------------------------------
+
+The following tables list ROCm library support for specific input and output data types. For a detailed
+description, refer to the corresponding library data type support page.
+
+.. tab-set::
+
+  .. tab-item:: Integral types
+    :sync: integral-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Library input/output data type name
+        - int8
+        - int16
+        - int32
+        - int64
+      *
+        - hipSPARSELt (:doc:`details <hipsparselt:reference/data-type-support>`)
+        - ✅/✅
+        - ❌/❌
+        - ❌/❌
+        - ❌/❌
+      *
+        - rocRAND (:doc:`details <rocrand:data-type-support>`)
+        - -/✅
+        - -/✅
+        - -/✅
+        - -/✅
+      *
+        - hipRAND (:doc:`details <hiprand:data-type-support>`)
+        - -/✅
+        - -/✅
+        - -/✅
+        - -/✅
+      *
+        - rocPRIM (:doc:`details <rocprim:reference/data-type-support>`)
+        - ✅/✅
+        - ✅/✅
+        - ✅/✅
+        - ✅/✅
+      *
+        - hipCUB (:doc:`details <hipcub:data-type-support>`)
+        - ✅/✅
+        - ✅/✅
+        - ✅/✅
+        - ✅/✅
+      *
+        - rocThrust (:doc:`details <rocthrust:data-type-support>`)
+        - ✅/✅
+        - ✅/✅
+        - ✅/✅
+        - ✅/✅
+
+  .. tab-item:: Floating-point types
+    :sync: floating-point-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Library input/output data type name
+        - float8 (E4M3)
+        - float8 (E5M2)
+        - float16
+        - bfloat16
+        - tensorfloat32
+        - float32
+        - float64
+      *
+        - hipSPARSELt (:doc:`details <hipsparselt:reference/data-type-support>`)
+        - ❌/❌
+        - ❌/❌
+        - ✅/✅
+        - ✅/✅
+        - ❌/❌
+        - ❌/❌
+        - ❌/❌
+      *
+        - rocRAND (:doc:`details <rocrand:data-type-support>`)
+        - -/❌
+        - -/❌
+        - -/✅
+        - -/❌
+        - -/❌
+        - -/✅
+        - -/✅
+      *
+        - hipRAND (:doc:`details <hiprand:data-type-support>`)
+        - -/❌
+        - -/❌
+        - -/✅
+        - -/❌
+        - -/❌
+        - -/✅
+        - -/✅
+      *
+        - rocPRIM (:doc:`details <rocprim:reference/data-type-support>`)
+        - ❌/❌
+        - ❌/❌
+        - ✅/✅
+        - ✅/✅
+        - ❌/❌
+        - ✅/✅
+        - ✅/✅
+      *
+        - hipCUB (:doc:`details <hipcub:data-type-support>`)
+        - ❌/❌
+        - ❌/❌
+        - ✅/✅
+        - ✅/✅
+        - ❌/❌
+        - ✅/✅
+        - ✅/✅
+      *
+        - rocThrust (:doc:`details <rocthrust:data-type-support>`)
+        - ❌/❌
+        - ❌/❌
+        - ⚠️/⚠️
+        - ⚠️/⚠️
+        - ❌/❌
+        - ✅/✅
+        - ✅/✅
+
+
+Libraries internal calculations type support
+-------------------------------------------------------------------------------
+
+The following tables list ROCm library support for specific internal data types. For a detailed
+description, refer to the corresponding library data type support page.
+
+.. tab-set::
+
+  .. tab-item:: Integral types
+    :sync: integral-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Library internal data type name
+        - int8
+        - int16
+        - int32
+        - int64
+      *
+        - hipSPARSELt (:doc:`details <hipsparselt:reference/data-type-support>`)
+        - ❌
+        - ❌
+        - ✅
+        - ❌
+
+
+  .. tab-item:: Floating-point types
+    :sync: floating-point-type
+
+    .. list-table::
+      :header-rows: 1
+
+      *
+        - Library internal data type name
+        - float8 (E4M3)
+        - float8 (E5M2)
+        - float16
+        - bfloat16
+        - tensorfloat32
+        - float32
+        - float64
+      *
+        - hipSPARSELt (:doc:`details <hipsparselt:reference/data-type-support>`)
+        - ❌
+        - ❌
+        - ❌
+        - ❌
+        - ❌
+        - ✅
+        - ❌
--- a/docs/about/license.md
+++ b/docs/about/license.md
@@ -0,0 +1,142 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="ROCm licensing terms">
+  <meta name="keywords" content="license, licensing terms">
+</head>
+
+# ROCm license
+
+```{include} ../../LICENSE
+```
+
+:::{note}
+The preceding license applies to the [ROCm repository](https://github.com/ROCm/ROCm), which
+primarily contains documentation. For licenses related to other ROCm components, refer to the
+following section.
+:::
+
+## ROCm component licenses
+
+ROCm is released by Advanced Micro Devices, Inc. and is licensed per component separately.
+The following table is a list of ROCm components with links to their respective license
+terms. These components may include third party components subject to
+additional licenses. Please review individual repositories for more information.
+
+<!-- spellcheck-disable -->
+| Component | License |
+|:---------------------|:-------------------------|
+| [AMDMIGraphX](https://github.com/ROCm/AMDMIGraphX/) | [MIT](https://github.com/ROCm/AMDMIGraphX/blob/develop/LICENSE) |
+| [HIPCC](https://github.com/ROCm/HIPCC/blob/develop/LICENSE.txt) | [MIT](https://github.com/ROCm/HIPCC/blob/develop/LICENSE.txt) |
+| [HIPIFY](https://github.com/ROCm/HIPIFY/) | [MIT](https://github.com/ROCm/HIPIFY/blob/amd-staging/LICENSE.txt) |
+| [HIP](https://github.com/ROCm/HIP/) | [MIT](https://github.com/ROCm/HIP/blob/develop/LICENSE.txt) |
+| [MIOpenGEMM](https://github.com/ROCm/MIOpenGEMM/) | [MIT](https://github.com/ROCm/MIOpenGEMM/blob/master/LICENSE.txt) |
+| [MIOpen](https://github.com/ROCm/MIOpen/) | [MIT](https://github.com/ROCm/MIOpen/blob/master/LICENSE.txt) |
+| [MIVisionX](https://github.com/ROCm/MIVisionX/) | [MIT](https://github.com/ROCm/MIVisionX/blob/master/LICENSE.txt) |
+| [RCP](https://github.com/GPUOpen-Tools/radeon_compute_profiler/) | [MIT](https://github.com/GPUOpen-Tools/radeon_compute_profiler/blob/master/LICENSE) |
+| [ROCK-Kernel-Driver](https://github.com/ROCm/ROCK-Kernel-Driver/) | [GPL 2.0 WITH Linux-syscall-note](https://github.com/ROCm/ROCK-Kernel-Driver/blob/master/COPYING) |
+| [ROCR-Runtime](https://github.com/ROCm/ROCR-Runtime/) | [The University of Illinois/NCSA](https://github.com/ROCm/ROCR-Runtime/blob/master/LICENSE.txt) |
+| [ROCT-Thunk-Interface](https://github.com/ROCm/ROCT-Thunk-Interface/) | [MIT](https://github.com/ROCm/ROCT-Thunk-Interface/blob/master/LICENSE.md) |
+| [ROCclr](https://github.com/ROCm/ROCclr/) | [MIT](https://github.com/ROCm/ROCclr/blob/develop/LICENSE.txt) |
+| [ROCdbgapi](https://github.com/ROCm/ROCdbgapi/) | [MIT](https://github.com/ROCm/ROCdbgapi/blob/amd-master/LICENSE.txt) |
+| [ROCgdb](https://github.com/ROCm/ROCgdb/) | [GNU General Public License v2.0](https://github.com/ROCm/ROCgdb/blob/amd-master/COPYING) |
+| [ROCm-CompilerSupport](https://github.com/ROCm/ROCm-CompilerSupport/) | [The University of Illinois/NCSA](https://github.com/ROCm/ROCm-CompilerSupport/blob/amd-stg-open/LICENSE.txt) |
+| [ROCm-Device-Libs](https://github.com/ROCm/ROCm-Device-Libs/) | [The University of Illinois/NCSA](https://github.com/ROCm/ROCm-Device-Libs/blob/amd-stg-open/LICENSE.TXT) |
+| [ROCm-OpenCL-Runtime/api/opencl/khronos/icd](https://github.com/KhronosGroup/OpenCL-ICD-Loader/) | [Apache 2.0](https://github.com/KhronosGroup/OpenCL-ICD-Loader/blob/main/LICENSE) |
+| [ROCm-OpenCL-Runtime](https://github.com/ROCm/ROCm-OpenCL-Runtime/) | [MIT](https://github.com/ROCm/ROCm-OpenCL-Runtime/blob/develop/LICENSE.txt) |
+| [ROCmValidationSuite](https://github.com/ROCm/ROCmValidationSuite/) | [MIT](https://github.com/ROCm/ROCmValidationSuite/blob/master/LICENSE) |
+| [Tensile](https://github.com/ROCm/Tensile/) | [MIT](https://github.com/ROCm/Tensile/blob/develop/LICENSE.md) |
+| [aomp-extras](https://github.com/ROCm/aomp-extras/) | [MIT](https://github.com/ROCm/aomp-extras/blob/aomp-dev/LICENSE) |
+| [aomp](https://github.com/ROCm/aomp/) | [Apache 2.0](https://github.com/ROCm/aomp/blob/aomp-dev/LICENSE) |
+| [atmi](https://github.com/ROCm/atmi/) | [MIT](https://github.com/ROCm/atmi/blob/master/LICENSE.txt) |
+| [clang-ocl](https://github.com/ROCm/clang-ocl/) | [MIT](https://github.com/ROCm/clang-ocl/blob/master/LICENSE) |
+| [flang](https://github.com/ROCm/flang/) | [Apache 2.0](https://github.com/ROCm/flang/blob/master/LICENSE.txt) |
+| [half](https://github.com/ROCm/half/) | [MIT](https://github.com/ROCm/half/blob/master/LICENSE.txt) |
+| [hipBLAS](https://github.com/ROCm/hipBLAS/) | [MIT](https://github.com/ROCm/hipBLAS/blob/develop/LICENSE.md) |
+| [hipCUB](https://github.com/ROCm/hipCUB/) | [Custom](https://github.com/ROCm/hipCUB/blob/develop/LICENSE.txt) |
+| [hipFFT](https://github.com/ROCm/hipFFT/) | [MIT](https://github.com/ROCm/hipFFT/blob/develop/LICENSE.md) |
+| [hipSOLVER](https://github.com/ROCm/hipSOLVER/) | [MIT](https://github.com/ROCm/hipSOLVER/blob/develop/LICENSE.md) |
+| [hipSPARSELt](https://github.com/ROCm/hipSPARSELt/) | [MIT](https://github.com/ROCm/hipSPARSELt/blob/develop/LICENSE.md) |
+| [hipSPARSE](https://github.com/ROCm/hipSPARSE/) | [MIT](https://github.com/ROCm/hipSPARSE/blob/develop/LICENSE.md) |
+| [hipTensor](https://github.com/ROCm/hipTensor) | [MIT](https://github.com/ROCm/hipTensor/blob/develop/LICENSE) |
+| [hipamd](https://github.com/ROCm/hipamd/) | [MIT](https://github.com/ROCm/hipamd/blob/develop/LICENSE.txt) |
+| [hipfort](https://github.com/ROCm/hipfort/) | [MIT](https://github.com/ROCm/hipfort/blob/master/LICENSE) |
+| [llvm-project](https://github.com/ROCm/llvm-project/) | [Apache](https://github.com/ROCm/llvm-project/blob/main/LICENSE.TXT) |
+| [rccl](https://github.com/ROCm/rccl/) | [Custom](https://github.com/ROCm/rccl/blob/develop/LICENSE.txt) |
+| [rdc](https://github.com/ROCm/rdc/) | [MIT](https://github.com/ROCm/rdc/blob/master/LICENSE) |
+| [rocALUTION](https://github.com/ROCm/rocALUTION/) | [MIT](https://github.com/ROCm/rocALUTION/blob/develop/LICENSE.md) |
+| [rocBLAS](https://github.com/ROCm/rocBLAS/) | [MIT](https://github.com/ROCm/rocBLAS/blob/develop/LICENSE.md) |
+| [rocFFT](https://github.com/ROCm/rocFFT/) | [MIT](https://github.com/ROCm/rocFFT/blob/develop/LICENSE.md) |
+| [rocPRIM](https://github.com/ROCm/rocPRIM/) | [MIT](https://github.com/ROCm/rocPRIM/blob/develop/LICENSE.txt) |
+| [rocRAND](https://github.com/ROCm/rocRAND/) | [MIT](https://github.com/ROCm/rocRAND/blob/develop/LICENSE.txt) |
+| [rocSOLVER](https://github.com/ROCm/rocSOLVER/) | [BSD-2-Clause](https://github.com/ROCm/rocSOLVER/blob/develop/LICENSE.md) |
+| [rocSPARSE](https://github.com/ROCm/rocSPARSE/) | [MIT](https://github.com/ROCm/rocSPARSE/blob/develop/LICENSE.md) |
+| [rocThrust](https://github.com/ROCm/rocThrust/) | [Apache 2.0](https://github.com/ROCm/rocThrust/blob/develop/LICENSE) |
+| [rocWMMA](https://github.com/ROCm/rocWMMA/) | [MIT](https://github.com/ROCm/rocWMMA/blob/develop/LICENSE.md) |
+| [rocm-cmake](https://github.com/ROCm/rocm-cmake/) | [MIT](https://github.com/ROCm/rocm-cmake/blob/develop/LICENSE) |
+| [rocm_bandwidth_test](https://github.com/ROCm/rocm_bandwidth_test/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocm_bandwidth_test/blob/master/LICENSE.txt) |
+| [rocm_smi_lib](https://github.com/ROCm/rocm_smi_lib/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocm_smi_lib/blob/master/License.txt) |
+| [rocminfo](https://github.com/ROCm/rocminfo/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocminfo/blob/master/License.txt) |
+| [rocprofiler](https://github.com/ROCm/rocprofiler/) | [MIT](https://github.com/ROCm/rocprofiler/blob/amd-master/LICENSE) |
+| [rocr_debug_agent](https://github.com/ROCm/rocr_debug_agent/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocr_debug_agent/blob/master/LICENSE.txt) |
+| [roctracer](https://github.com/ROCm/roctracer/) | [MIT](https://github.com/ROCm/roctracer/blob/amd-master/LICENSE) |
+| rocm-llvm-alt | [AMD Proprietary License](https://www.amd.com/en/support/amd-software-eula)
+
+Open sourced ROCm components are released via public GitHub
+repositories, packages on https://repo.radeon.com and other distribution channels.
+Proprietary products are only available on https://repo.radeon.com. Currently, only
+one component of ROCm, rocm-llvm-alt is governed by a proprietary license.
+Proprietary components are organized in a proprietary subdirectory in the package
+repositories to distinguish from open sourced packages.
+
+```{note}
+The following additional terms and conditions apply to your use of ROCm technical documentation.
+```
+
+©2023 Advanced Micro Devices, Inc. All rights reserved.
+
+The information presented in this document is for informational purposes only
+and may contain technical inaccuracies, omissions, and typographical errors. The
+information contained herein is subject to change and may be rendered inaccurate
+for many reasons, including but not limited to product and roadmap changes,
+component and motherboard version changes, new model and/or product releases,
+product differences between differing manufacturers, software changes, BIOS
+flashes, firmware upgrades, or the like. Any computer system has risks of
+security vulnerabilities that cannot be completely prevented or mitigated. AMD
+assumes no obligation to update or otherwise correct or revise this information.
+However, AMD reserves the right to revise this information and to make changes
+from time to time to the content hereof without obligation of AMD to notify any
+person of such revisions or changes.
+
+THIS INFORMATION IS PROVIDED “AS IS.” AMD MAKES NO REPRESENTATIONS OR WARRANTIES
+WITH RESPECT TO THE CONTENTS HEREOF AND ASSUMES NO RESPONSIBILITY FOR ANY
+INACCURACIES, ERRORS, OR OMISSIONS THAT MAY APPEAR IN THIS INFORMATION. AMD
+SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTIES OF NON-INFRINGEMENT,
+MERCHANTABILITY, OR FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT WILL AMD BE
+LIABLE TO ANY PERSON FOR ANY RELIANCE, DIRECT, INDIRECT, SPECIAL, OR OTHER
+CONSEQUENTIAL DAMAGES ARISING FROM THE USE OF ANY INFORMATION CONTAINED HEREIN,
+EVEN IF AMD IS EXPRESSLY ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+AMD, the AMD Arrow logo, ROCm, and combinations thereof are trademarks of
+Advanced Micro Devices, Inc. Other product names used in this publication are
+for identification purposes only and may be trademarks of their respective
+companies.
+
+### Package licensing
+
+:::{attention}
+AQL Profiler and AOCC CPU optimization are both provided in binary form, each
+subject to the license agreement enclosed in the directory for the binary and is
+available here: `/opt/rocm/share/doc/rocm-llvm-alt/EULA`. By using, installing,
+copying or distributing AQL Profiler and/or AOCC CPU Optimizations, you agree to
+the terms and conditions of this license agreement. If you do not agree to the
+terms of this agreement, do not install, copy or use the AQL Profiler and/or the
+AOCC CPU Optimizations.
+:::
+
+For the rest of the ROCm packages, you can find the licensing information at the
+following location: `/opt/rocm/share/doc/<component-name>/`
+
+For example, you can fetch the licensing information of the `_amd_comgr_`
+component (Code Object Manager) from the `amd_comgr` folder. A file named
+`LICENSE.txt` contains the license details at:
+`/opt/rocm-5.4.3/share/doc/amd_comgr/LICENSE.txt`
--- a/docs/conceptual/More-about-how-ROCm-uses-PCIe-Atomics.rst
+++ b/docs/conceptual/More-about-how-ROCm-uses-PCIe-Atomics.rst
@@ -0,0 +1,156 @@
+.. meta::
+   :description: How ROCm uses PCIe atomics
+   :keywords: PCIe, PCIe atomics, atomics, BAR memory, AMD, ROCm
+
+*****************************************************************************
+How ROCm uses PCIe atomics
+*****************************************************************************
+
+ROCm PCIe feature and overview of BAR memory
+================================================================
+
+ROCm is an extension of HSA platform architecture, so it shares the queuing model, memory model,
+signaling and synchronization protocols. Platform atomics are integral to perform queuing and
+signaling memory operations where there may be multiple-writers across CPU and GPU agents.
+
+The full list of HSA system architecture platform requirements are here:
+`HSA Sys Arch Features <http://hsafoundation.com/wp-content/uploads/2021/02/HSA-SysArch-1.2.pdf>`_.
+
+AMD ROCm Software uses the new PCI Express 3.0 (Peripheral Component Interconnect Express [PCIe]
+3.0) features for atomic read-modify-write transactions which extends inter-processor synchronization
+mechanisms to IO to support the defined set of HSA capabilities needed for queuing and signaling
+memory operations.
+
+The new PCIe atomic operations operate as completers for ``CAS`` (Compare and Swap), ``FetchADD``,
+``SWAP`` atomics. The atomic operations are initiated by the I/O device which support 32-bit, 64-bit and
+128-bit operand which target address have to be naturally aligned to operation sizes.
+
+For ROCm the Platform atomics are used in ROCm in the following ways:
+
+  * Update HSA queue's read_dispatch_id: 64 bit atomic add used by the command processor on the
+    GPU agent to update the packet ID it processed.
+  * Update HSA queue's write_dispatch_id: 64 bit atomic add used by the CPU and GPU agent to
+    support multi-writer queue insertions.
+  * Update HSA Signals -- 64bit atomic ops are used for CPU & GPU synchronization.
+
+The PCIe 3.0 atomic operations feature allows atomic transactions to be requested by, routed through
+and completed by PCIe components. Routing and completion does not require software support.
+Component support for each is detectable via the Device Capabilities 2 (DevCap2) register. Upstream
+bridges need to have atomic operations routing enabled or the atomic operations will fail even though
+PCIe endpoint and PCIe I/O devices has the capability to atomic operations.
+
+To do atomic operations routing capability between two or more Root Ports, each associated Root Port
+must indicate that capability via the atomic operations routing supported bit in the DevCap2 register.
+
+If your system has a PCIe Express Switch it needs to support atomic operations routing. Atomic
+operations requests are permitted only if a component's ``DEVCTL2.ATOMICOP_REQUESTER_ENABLE``
+field is set. These requests can only be serviced if the upstream components support atomic operation
+completion and/or routing to a component which does. Atomic operations routing support=1, routing
+is supported; atomic operations routing support=0, routing is not supported.
+
+An atomic operation is a non-posted transaction supporting 32-bit and 64-bit address formats, there
+must be a response for Completion containing the result of the operation. Errors associated with the
+operation (uncorrectable error accessing the target location or carrying out the atomic operation) are
+signaled to the requester by setting the Completion Status field in the completion descriptor, they are
+set to to Completer Abort (CA) or Unsupported Request (UR).
+
+To understand more about how PCIe atomic operations work, see
+`PCIe atomics <https://pcisig.com/specifications/pciexpress/specifications/ECN_Atomic_Ops_080417.pdf>`_
+
+`Linux Kernel Patch to pci_enable_atomic_request <https://patchwork.kernel.org/project/linux-pci/patch/1443110390-4080-1-git-send-email-jay@jcornwall.me/>`_
+
+There are also a number of papers which talk about these new capabilities:
+
+  * `Atomic Read Modify Write Primitives by Intel <https://www.intel.es/content/dam/doc/white-paper/atomic-read-modify-write-primitives-i-o-devices-paper.pdf>`_
+  * `PCI express 3 Accelerator White paper by Intel <https://www.intel.sg/content/dam/doc/white-paper/pci-express3-accelerator-white-paper.pdf>`_
+  * `PCIe Generation 4 Base Specification includes atomic operations <https://astralvx.com/storage/2020/11/PCI_Express_Base_4.0_Rev0.3_February19-2014.pdf>`_
+  * `Xilinx PCIe Ultrascale White paper <https://docs.xilinx.com/v/u/8OZSA2V1b1LLU2rRCDVGQw>`_
+
+Other I/O devices with PCIe atomics support:
+
+  * Mellanox ConnectX-5 InfiniBand Card
+  * Cray Aries Interconnect
+  * Xilinx 7 Series Devices
+
+Future bus technology with richer I/O atomics operation Support
+
+  * GenZ
+
+New PCIe Endpoints with support beyond AMD Ryzen and EPYC CPU; Intel Haswell or newer CPUs
+with PCIe Generation 3.0 support.
+
+  * Mellanox Bluefield SOC
+  * Cavium Thunder X2
+
+In ROCm, we also take advantage of PCIe ID based ordering technology for P2P when the GPU
+originates two writes to two different targets:
+
+* Write to another GPU memory
+* Write to system memory to indicate transfer complete
+
+They are routed off to different ends of the computer but we want to make sure the write to system
+memory to indicate transfer complete occurs AFTER P2P write to GPU has complete.
+
+BAR memory overview
+----------------------------------------------------------------------------------------------------
+On a Xeon E5 based system in the BIOS we can turn on above 4GB PCIe addressing, if so he need to set
+memory-mapped input/output (MMIO) base address (MMIOH base) and range (MMIO high size) in the BIOS.
+
+In the Supermicro system in the system bios you need to see the following
+
+  * Advanced->PCIe/PCI/PnP configuration-\> Above 4G Decoding = Enabled
+  * Advanced->PCIe/PCI/PnP Configuration-\>MMIOH Base = 512G
+  * Advanced->PCIe/PCI/PnP Configuration-\>MMIO High Size = 256G
+
+When we support Large Bar Capability there is a Large Bar VBIOS which also disable the IO bar.
+
+For GFX9 and Vega10 which have Physical Address up 44 bit and 48 bit Virtual address.
+
+  * BAR0-1 registers: 64bit, prefetchable, GPU memory. 8GB or 16GB depending on Vega10 SKU. Must
+    be placed < 2^44 to support P2P  	access from other Vega10.
+  * BAR2-3 registers: 64bit, prefetchable, Doorbell. Must be placed \< 2^44 to support P2P access from
+    other Vega10.
+  * BAR4 register: Optional, not a boot device.
+  * BAR5 register: 32bit, non-prefetchable, MMIO. Must be placed \< 4GB.
+
+Here is how our base address register (BAR) works on GFX 8 GPUs with 40 bit Physical Address Limit ::
+
+  11:00.0 Display controller: Advanced Micro Devices, Inc. [AMD/ATI] Fiji [Radeon R9 FURY / NANO
+  Series] (rev c1)
+
+  Subsystem: Advanced Micro Devices, Inc. [AMD/ATI] Device 0b35
+
+  Flags: bus master, fast devsel, latency 0, IRQ 119
+
+  Memory at bf40000000 (64-bit, prefetchable) [size=256M]
+
+  Memory at bf50000000 (64-bit, prefetchable) [size=2M]
+
+  I/O ports at 3000 [size=256]
+
+  Memory at c7400000 (32-bit, non-prefetchable) [size=256K]
+
+  Expansion ROM at c7440000 [disabled] [size=128K]
+
+Legend:
+
+1 : GPU Frame Buffer BAR -- In this example it happens to be 256M, but typically this will be size of the
+GPU memory (typically 4GB+). This BAR has to be placed \< 2^40 to allow peer-to-peer access from
+other GFX8 AMD GPUs. For GFX9 (Vega GPU) the BAR has to be placed \< 2^44 to allow peer-to-peer
+access from other GFX9 AMD GPUs.
+
+2 : Doorbell BAR -- The size of the BAR is typically will be \< 10MB (currently fixed at 2MB) for this
+generation GPUs. This BAR has to be placed \< 2^40 to allow peer-to-peer access from other current
+generation AMD GPUs.
+
+3 : IO BAR -- This is for legacy VGA and boot device support, but since this the GPUs in this project are
+not VGA devices (headless), this is not a concern even if the SBIOS does not setup.
+
+4 : MMIO BAR -- This is required for the AMD Driver SW to access the configuration registers. Since the
+reminder of the BAR available is only 1 DWORD (32bit), this is placed \< 4GB. This is fixed at 256KB.
+
+5 : Expansion ROM -- This is required for the AMD Driver SW to access the GPU video-bios. This is
+currently fixed at 128KB.
+
+For more information, you can review
+`Overview of Changes to PCI Express 3.0 <https://www.mindshare.com/files/resources/PCIe%203-0.pdf>`_.
--- a/docs/examples/machine_learning/migraphx_optimization.md
+++ b/docs/examples/machine_learning/migraphx_optimization.md
@@ -1,36 +1,37 @@
-# Inference Optimization with MIGraphX
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="Inference optimization with MIGraphX">
+  <meta name="keywords" content="Inference optimization, MIGraphX, deep-learning, MIGraphX
+  installation, AMD, ROCm">
+</head>

-The following sections cover inferencing and introduces MIGraphX.
+# Inference optimization with MIGraphX
+
+The following sections cover inferencing and introduces [MIGraphX](https://rocm.docs.amd.com/projects/AMDMIGraphX/en/latest/).

 ## Inference

-The inference is where capabilities learned during Deep Learning training are put to work. It refers to using a fully trained neural network to make conclusions (predictions) on unseen data that the model has never interacted with before. Deep Learning inferencing is achieved by feeding new data, such as new images, to the network, giving the Deep Neural Network a chance to classify the image.
+The inference is where capabilities learned during deep-learning training are put to work. It refers to using a fully trained neural network to make conclusions (predictions) on unseen data that the model has never interacted with before. Deep-learning inferencing is achieved by feeding new data, such as new images, to the network, giving the Deep Neural Network a chance to classify the image.

 Taking our previous example of MNIST, the DNN can be fed new images of handwritten digit images, allowing the neural network to classify digits. A fully trained DNN should make accurate predictions about what an image represents, and inference cannot happen without training.

-## MIGraphX Introduction
+## MIGraphX introduction

-MIGraphX is a graph compiler focused on accelerating the Machine Learning inference that can target AMD GPUs and CPUs. MIGraphX accelerates the Machine Learning models by leveraging several graph-level transformations and optimizations. These optimizations include:
+MIGraphX is a graph compiler focused on accelerating the machine-learning inference that can target AMD GPUs and CPUs. MIGraphX accelerates the machine-learning models by leveraging several graph-level transformations and optimizations. These optimizations include:

- Operator fusion
-
- Arithmetic simplifications
-
- Dead-code elimination
-
- Common subexpression elimination (CSE)
-
- Constant propagation
+* Operator fusion
+* Arithmetic simplifications
+* Dead-code elimination
+* Common subexpression elimination (CSE)
+* Constant propagation

 After doing all these transformations, MIGraphX emits code for the AMD GPU by calling to MIOpen or rocBLAS or creating HIP kernels for a particular operator. MIGraphX can also target CPUs using DNNL or ZenDNN libraries.

-MIGraphX provides easy-to-use APIs in C++ and Python to import machine models in ONNX or TensorFlow. Users can compile, save, load, and run these models using MIGraphX's C++ and Python APIs. Internally, MIGraphX parses ONNX or TensorFlow models into internal graph representation where each operator in the model gets mapped to an operator within MIGraphX. Each of these operators defines various attributes such as:
+MIGraphX provides easy-to-use APIs in C++ and Python to import machine models in ONNX or TensorFlow. Users can compile, save, load, and run these models using the MIGraphX C++ and Python APIs. Internally, MIGraphX parses ONNX or TensorFlow models into internal graph representation where each operator in the model gets mapped to an operator within MIGraphX. Each of these operators defines various attributes such as:

- Number of arguments
-
- Type of arguments
-
- Shape of arguments
+* Number of arguments
+* Type of arguments
+* Shape of arguments

 After optimization passes, all these operators get mapped to different kernels on GPUs or CPUs.

@@ -40,7 +41,7 @@ After importing a model into MIGraphX, the model is represented as `migraphx::pr

 There are three options to get started with MIGraphX installation. MIGraphX depends on ROCm libraries; assume that the machine has ROCm installed.

-### Option 1: Installing Binaries
+### Option 1: installing binaries

 To install MIGraphX on Debian-based systems like Ubuntu, use the following command:

@@ -50,28 +51,28 @@ sudo apt update && sudo apt install -y migraphx

 The header files and libraries are installed under `/opt/rocm-\<version\>`, where \<version\> is the ROCm version.

-### Option 2: Building from Source
+### Option 2: building from source

 There are two ways to build the MIGraphX sources.

- [Use the ROCm build tool](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX#use-the-rocm-build-tool-rbuild) - This approach uses [rbuild](https://github.com/RadeonOpenCompute/rbuild) to install the prerequisites and build the libraries with just one command.
+* [Use the ROCm build tool](https://github.com/ROCm/AMDMIGraphX#use-the-rocm-build-tool-rbuild) - This approach uses `[rbuild](https://github.com/ROCm/rbuild)` to install the prerequisites and build the libraries with just one command.

  or

- [Use CMake](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX#use-cmake-to-build-migraphx) - This approach uses a script to install the prerequisites, then uses CMake to build the source.
+* [Use CMake](https://github.com/ROCm/AMDMIGraphX#use-cmake-to-build-migraphx) - This approach uses a script to install the prerequisites, then uses CMake to build the source.

 For detailed steps on building from source and installing dependencies, refer to the following `README` file:

-[https://github.com/ROCmSoftwarePlatform/AMDMIGraphX#building-from-source](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX#building-from-source)
+[https://github.com/ROCm/AMDMIGraphX#building-from-source](https://github.com/ROCm/AMDMIGraphX#building-from-source)

-### Option 3: Use Docker
+### Option 3: use docker

 To use Docker, follow these steps:

 1. The easiest way to set up the development environment is to use Docker. To build Docker from scratch, first clone the MIGraphX repository by running:

    ```bash
-    git clone --recursive https://github.com/ROCmSoftwarePlatform/AMDMIGraphX
+    git clone --recursive https://github.com/ROCm/AMDMIGraphX
    ```

 2. The repository contains a Dockerfile from which you can build a Docker image as:
@@ -88,7 +89,7 @@ To use Docker, follow these steps:

 The Docker image contains all the prerequisites required for the installation, so users can go to the folder `/code/AMDMIGraphX` and follow the steps mentioned in [Option 2: Building from Source](#option-2-building-from-source).

-## MIGraphX Example
+## MIGraphX example

 MIGraphX provides both C++ and Python APIs. The following sections show examples of both using the Inception v3 model. To walk through the examples, fetch the Inception v3 ONNX model by running the following:

@@ -311,7 +312,7 @@ MIGraphX introduces a feature, known as YModel, that stores the kernel config pa

 The YModel feature is available starting from ROCm 5.4.1 and UIF 1.1.

-#### YModel Example
+#### YModel example

 Through the `migraphx-driver` functionality, you can generate `.mxr` files with tuning information stored inside it by passing additional `--binary --output model.mxr` to `migraphx-driver` along with the rest of the necessary flags.

@@ -327,12 +328,6 @@ To run generated `.mxr` files through `migraphx-driver`, use the following:
 ./path/to/migraphx-driver run --migraphx resnet50.mxr --enable-offload-copy
 ```

-Alternatively, you can use MIGraphX's C++ or Python API to generate `.mxr` file. Refer to {numref}`image018` for an example.
+Alternatively, you can use the MIGraphX C++ or Python API to generate `.mxr` files.

-```{figure} ../../data/understand/deep_learning/image.018.png
-:name: image018
---
-align: center
---
-Generating a `.mxr` File
-```
+![Generating an MXR file](../data/conceptual/image018.png "Generating an MXR file")
--- a/docs/examples/machine_learning/pytorch_inception.md
+++ b/docs/examples/machine_learning/pytorch_inception.md
@@ -1,20 +1,28 @@
-# Inception V3 with PyTorch
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="Inception V3 with PyTorch">
+  <meta name="keywords" content="PyTorch, Inception V3, deep-learning, training data, optimization
+  algorithm, AMD, ROCm">
+</head>

-## Deep Learning Training
+# Deep learning: Inception V3 with PyTorch

-Deep Learning models are designed to capture the complexity of the problem and the underlying data. These models are "deep," comprising multiple component layers. Training is finding the best parameters for each model layer to achieve a well-defined objective.
+## Deep learning training
+
+Deep-learning models are designed to capture the complexity of the problem and the underlying data. These models are "deep," comprising multiple component layers. Training is finding the best parameters for each model layer to achieve a well-defined objective.

 The training data consists of input features in supervised learning, similar to what the learned model is expected to see during the evaluation or inference phase. The target output is also included, which serves to teach the model. A loss metric is defined as part of training that evaluates the model's performance during the training process.

 Training also includes the choice of an optimization algorithm that reduces the loss by adjusting the model's parameters. Training is an iterative process where training data is fed in, usually split into different batches, with the entirety of the training data passed during one training epoch. Training usually is run for multiple epochs.

-## Training Phases
+## Training phases

-Training occurs in multiple phases for every batch of training data. {numref}`TypesOfTrainingPhases` provides an explanation of the types of training phases.
+Training occurs in multiple phases for every batch of training data. the following table provides an explanation of the types of training phases.

 :::{table} Types of Training Phases
-:name: TypesOfTrainingPhases
+:name: training-phases
 :widths: auto
+
 | Types of Phases   |     |
 | ----------------- | --- |
 | Forward Pass      | The input features are fed into the model, whose parameters may be randomly initialized initially. Activations (outputs) of each layer are retained during this pass to help in the loss gradient computation during the backward pass. |
@@ -23,11 +31,12 @@ Training occurs in multiple phases for every batch of training data. {numref}`Ty
 | Optimization Pass | The optimization algorithm updates the model parameters using the stored error gradients. |
 :::

-Training is different from inference, particularly from the hardware perspective. {numref}`TrainingVsInference` shows the contrast between training and inference.
+Training is different from inference, particularly from the hardware perspective. The following table shows the contrast between training and inference.

 :::{table} Training vs. Inference
-:name: TrainingVsInference
+:name: training-inference
 :widths: auto
+
 | Training | Inference |
 | ----------- | ----------- |
 | Training is measured in hours/days. | The inference is measured in minutes. |
@@ -36,27 +45,27 @@ Training is different from inference, particularly from the hardware perspective
 | Data for training is available on the disk before the training process and is generally significant. The training performance is measured by how fast the data batches can be processed. | Inference data usually arrive stochastically, which may be batched to improve performance. Inference performance is generally measured in throughput speed to process the batch of data and the delay in responding to the input (latency). |
 :::

-Different quantization data types are typically chosen between training (FP32, BF16) and inference (FP16, INT8). The computation hardware has different specializations from other datatypes, leading to improvement in performance if a faster datatype can be selected for the corresponding task.
+Different quantization data types are typically chosen between training (FP32, BF16) and inference (FP16, INT8). The computation hardware has different specializations from other data types, leading to improvement in performance if a faster datatype can be selected for the corresponding task.

-## Case Studies
+## Case studies

-The following sections contain case studies for the Inception v3 model.
+The following sections contain case studies for the Inception V3 model.

-### Inception v3 with PyTorch
+### Inception V3 with PyTorch

-Convolution Neural Networks are forms of artificial neural networks commonly used for image processing. One of the core layers of such a network is the convolutional layer, which convolves the input with a weight tensor and passes the result to the next layer. Inception v3[^inception_arch] is an architectural development over the ImageNet competition-winning entry, AlexNet, using more profound and broader networks while attempting to meet computational and memory budgets.
+Convolution Neural Networks are forms of artificial neural networks commonly used for image processing. One of the core layers of such a network is the convolutional layer, which convolves the input with a weight tensor and passes the result to the next layer. Inception V3[^inception_arch] is an architectural development over the ImageNet competition-winning entry, AlexNet, using more profound and broader networks while attempting to meet computational and memory budgets.

-The implementation uses PyTorch as a framework. This case study utilizes `torchvision`[^torch_vision], a repository of popular datasets and model architectures, for obtaining the model. `torchvision` also provides pre-trained weights as a starting point to develop new models or fine-tune the model for a new task.
+The implementation uses PyTorch as a framework. This case study utilizes [TorchVision](https://pytorch.org/vision/stable/index.html), a repository of popular datasets and model architectures, for obtaining the model. TorchVision also provides pre-trained weights as a starting point to develop new models or fine-tune the model for a new task.

-#### Evaluating a Pre-Trained Model
+#### Evaluating a pre-trained model

-The Inception v3 model introduces a simple image classification task with the pre-trained model. This does not involve training but utilizes an already pre-trained model from `torchvision`.
+The Inception V3 model introduces a simple image classification task with the pre-trained model. This does not involve training but utilizes an already pre-trained model from TorchVision.

-This example is adapted from the PyTorch research hub page on Inception v3[^torch_vision_inception].
+This example is adapted from the PyTorch research hub page on [Inception V3](https://pytorch.org/vision/master/models/inception.html).

 Follow these steps:

-1. Run the PyTorch ROCm-based Docker image or refer to the section [Installing PyTorch](/how_to/pytorch_install/pytorch_install.md) for setting up a PyTorch environment on ROCm.
+1. Run the PyTorch ROCm-based Docker image or refer to the section {doc}`Installing PyTorch <rocm-install-on-linux:how-to/3rd-party/pytorch-install>` for setting up a PyTorch environment on ROCm.

    ```dockerfile
    docker run -it -v $HOME:/data --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 8G rocm/pytorch:latest
@@ -85,7 +94,7 @@ Follow these steps:
    except: urllib.request.urlretrieve(url, filename)
    ```

-5. Import `torchvision` and `PIL.Image` support libraries.
+5. Import torchvision and PILImage support libraries.

    ```py
    from PIL import Image
@@ -140,13 +149,13 @@ Follow these steps:
        print(categories[top5_catid[i]], top5_prob[i].item())
    ```

-#### Training Inception v3
+#### Training Inception V3

-The previous section focused on downloading and using the Inception v3 model for a simple image classification task. This section walks through training the model on a new dataset.
+The previous section focused on downloading and using the Inception V3 model for a simple image classification task. This section walks through training the model on a new dataset.

 Follow these steps:

-1. Run the PyTorch ROCm Docker image or refer to the section [Installing PyTorch](how_to/pytorch_install/pytorch_install.md) for setting up a PyTorch environment on ROCm.
+1. Run the PyTorch ROCm Docker image or refer to the section {doc}`Installing PyTorch <rocm-install-on-linux:how-to/3rd-party/pytorch-install>` for setting up a PyTorch environment on ROCm.

    ```dockerfile
    docker pull rocm/pytorch:latest
@@ -196,7 +205,7 @@ Follow these steps:

 5. Open a Python shell.

-6. Import dependencies, including `torch`, `os`, and `torchvision`.
+6. Import dependencies, including Torch, OS, and [TorchVision](https://github.com/pytorch/vision).

    ```py
    import torch
@@ -222,7 +231,7 @@ Follow these steps:
    data_path = "tiny-imagenet-200"
    ```

-    The training image size is cropped for input into Inception v3.
+    The training image size is cropped for input into Inception V3.

    ```py
    train_crop_size = 299
@@ -241,7 +250,7 @@ Follow these steps:
    val_resize_size = 342
    ```

-    The pre-trained Inception v3 model is chosen to be downloaded from `torchvision`.
+    The pre-trained Inception V3 model is chosen to be downloaded from torchvision.

    ```py
    model_name = "inception_v3"
@@ -334,7 +343,7 @@ Follow these steps:
    ```

    :::{note}
-    Use `torchvision` to obtain the Inception v3 model. Use the pre-trained model weights to speed up training.
+    Use torchvision to obtain the Inception V3 model. Use the pre-trained model weights to speed up training.
    :::

    ```py
@@ -343,7 +352,7 @@ Follow these steps:
    model = torchvision.models.__dict__[model_name](pretrained=pretrained)
    ```

-11. Adapt Inception v3 for the current dataset. `tiny-imagenet-200` contains only 200 classes, whereas Inception v3 is designed for 1,000-class output. The last layer of Inception v3 is replaced to match the output features required.
+11. Adapt Inception V3 for the current dataset. `tiny-imagenet-200` contains only 200 classes, whereas Inception V3 is designed for 1,000-class output. The last layer of Inception V3 is replaced to match the output features required.

    ```py
    model.fc = torch.nn.Linear(model.fc.in_features, len(dataset.classes))
@@ -461,23 +470,17 @@ Follow these steps:
 torch.save(model.state_dict(), "trained_inception_v3.pt")
 ```

-Plotting the train and test loss shows both metrics reducing over training epochs. This is demonstrated in {numref}`inceptionV3`.
+Plotting the train and test loss shows both metrics reducing over training epochs. This is demonstrated in the following image.

-```{figure} ../../data/understand/deep_learning/inception_v3.png
-:name: inceptionV3
---
-align: center
---
-Inception v3 Train and Loss Graph
-```
+![Inception V3 train and loss graph](../data/conceptual/inception-v3.png "Inception V3 train and loss")

-### Custom Model with CIFAR-10 on PyTorch
+### Custom model with CIFAR-10 on PyTorch

-The CIFAR-10 (Canadian Institute for Advanced Research) dataset is a subset of the Tiny Images dataset (which contains 80 million images of 32x32 collected from the Internet) and consists of 60,000 32x32 color images. The images are labeled with one of 10 mutually exclusive classes: airplane, motor car, bird, cat, deer, dog, frog, cruise ship, stallion, and truck (but not pickup truck). There are 6,000 images per class, with 5,000 training and 1,000 testing images per class. Let us prepare a custom model for classifying these images using the PyTorch framework and go step-by-step as illustrated below.
+The Canadian Institute for Advanced Research (CIFAR)-10 dataset is a subset of the Tiny Images dataset (which contains 80 million images of 32x32 collected from the Internet) and consists of 60,000 32x32 color images. The images are labeled with one of 10 mutually exclusive classes: airplane, motor car, bird, cat, deer, dog, frog, cruise ship, stallion, and truck (but not pickup truck). There are 6,000 images per class, with 5,000 training and 1,000 testing images per class. Let us prepare a custom model for classifying these images using the PyTorch framework and go step-by-step as illustrated below.

 Follow these steps:

-1. Import dependencies, including `torch`, `os`, and `torchvision`.
+1. Import dependencies, including Torch, OS, and [TorchVision](https://github.com/pytorch/vision).

    ```py
    import torch
@@ -487,7 +490,7 @@ Follow these steps:
    import numpy as np
    ```

-2. The output of `torchvision` datasets is `PILImage` images of range [0, 1]. Transform them to Tensors of normalized range [-1, 1].
+2. The output of torchvision datasets is `PILImage` images of range [0, 1]. Transform them to Tensors of normalized range [-1, 1].

    ```py
    transform = transforms.Compose(
@@ -668,17 +671,17 @@ Follow these steps:
        print("Accuracy for class {:5s} is: {:.1f} %".format(classname,accuracy))
    ```

-### Case Study: TensorFlow with Fashion MNIST
+### Case study: TensorFlow with Fashion-MNIST

-Fashion MNIST is a dataset that contains 70,000 grayscale images in 10 categories.
+Fashion-MNIST is a dataset that contains 70,000 grayscale images in 10 categories.

 Implement and train a neural network model using the TensorFlow framework to classify images of clothing, like sneakers and shirts.

-The dataset has 60,000 images you will use to train the network and 10,000 to evaluate how accurately the network learned to classify images. The Fashion MNIST dataset can be accessed via TensorFlow internal libraries.
+The dataset has 60,000 images you will use to train the network and 10,000 to evaluate how accurately the network learned to classify images. The Fashion-MNIST dataset can be accessed via TensorFlow internal libraries.

 Access the source code from the following repository:

-[https://github.com/ROCmSoftwarePlatform/tensorflow_fashionmnist/blob/main/fashion_mnist.py](https://github.com/ROCmSoftwarePlatform/tensorflow_fashionmnist/blob/main/fashion_mnist.py)
+[https://github.com/ROCm/tensorflow_fashionmnist/blob/main/fashion_mnist.py](https://github.com/ROCm/tensorflow_fashionmnist/blob/main/fashion_mnist.py)

 To understand the code step by step, follow these steps:

@@ -696,7 +699,7 @@ To understand the code step by step, follow these steps:
    print(tf._version__) r
    ```

-3. Load the dataset from the available internal libraries to analyze and train a neural network upon the MNIST Fashion Dataset. Loading the dataset returns four NumPy arrays. The model uses the training set arrays, train_images and train_labels, to learn.
+3. Load the dataset from the available internal libraries to analyze and train a neural network upon the Fashion-MNIST dataset. Loading the dataset returns four NumPy arrays. The model uses the training set arrays, train_images and train_labels, to learn.

 4. The model is tested against the test set, test_images, and test_labels arrays.

@@ -741,11 +744,7 @@ To understand the code step by step, follow these steps:
    plt.show()
    ```

-    ```{figure} ../../data/understand/deep_learning/mnist_1.png
-    ---
-    align: center
-    ---
-    ```
+    ![ ](../data/conceptual/mnist-1.png)

 10. From the above picture, you can see that values are from zero to 255. Before training this on the neural network, you must bring them in the range of zero to one. Hence, divide the values by 255.

@@ -769,13 +768,9 @@ To understand the code step by step, follow these steps:
    plt.show()
    ```

-    ```{figure} ../../data/understand/deep_learning/mnist_2.png
-    ---
-    align: center
-    ---
-    ```
+    ![ ](../data/conceptual/mnist-2.png)

-    The basic building block of a neural network is the layer. Layers extract representations from the data fed into them. Deep Learning consists of chaining together simple layers. Most layers, such as `tf.keras.layers.Dense`, have parameters that are learned during training.
+    The basic building block of a neural network is the layer. Layers extract representations from the data fed into them. Deep learning consists of chaining together simple layers. Most layers, such as `tf.keras.layers.Dense`, have parameters that are learned during training.

    ```py
    model = tf.keras.Sequential([
@@ -785,9 +780,9 @@ To understand the code step by step, follow these steps:
    ])
    ```

-    - The first layer in this network `tf.keras.layers.Flatten` transforms the format of the images from a two-dimensional array (of 28 x 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.
+    * The first layer in this network `tf.keras.layers.Flatten` transforms the format of the images from a two-dimensional array (of 28 x 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.

-    - After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected or fully connected neural layers. The first Dense layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with a length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.
+    * After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected or fully connected neural layers. The first Dense layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with a length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.

 12. You must add the Loss function, Metrics, and Optimizer at the time of model compilation.

@@ -797,11 +792,11 @@ To understand the code step by step, follow these steps:
                metrics=['accuracy'])
    ```

-    - Loss function —This measures how accurate the model is during training when you are looking to minimize this function to "steer" the model in the right direction.
+    * Loss function —This measures how accurate the model is during training when you are looking to minimize this function to "steer" the model in the right direction.

-    - Optimizer —This is how the model is updated based on the data it sees and its loss function.
+    * Optimizer —This is how the model is updated based on the data it sees and its loss function.

-    - Metrics —This is used to monitor the training and testing steps.
+    * Metrics —This is used to monitor the training and testing steps.

    The following example uses accuracy, the fraction of the correctly classified images.

@@ -883,7 +878,7 @@ To understand the code step by step, follow these steps:
        thisplot[true_label].set_color('blue')
        ```

-    9. With the model trained, you can use it to make predictions about some images. Review the 0-th image predictions and the prediction array. Correct prediction labels are blue, and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label.
+    9. With the model trained, you can use it to make predictions about some images. Review the 0<sup>th</sup> image predictions and the prediction array. Correct prediction labels are blue, and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label.

        ```py
        i = 0
@@ -895,11 +890,7 @@ To understand the code step by step, follow these steps:
        plt.show()
        ```

-        ```{figure} ../../data/understand/deep_learning/mnist_3.png
-        ---
-        align: center
-        ---
-        ```
+        ![ ](../data/conceptual/mnist-3.png)

        ```py
        i = 12
@@ -911,11 +902,7 @@ To understand the code step by step, follow these steps:
        plt.show()
        ```

-        ```{figure} ../../data/understand/deep_learning/mnist_4.png
-        ---
-        align: center
-        ---
-        ```
+        ![ ](../data/conceptual/mnist-4.png)

    10. Use the trained model to predict a single image.

@@ -946,11 +933,7 @@ To understand the code step by step, follow these steps:
        plt.show()
        ```

-        ```{figure} ../../data/understand/deep_learning/mnist_5.png
-        ---
-        align: center
-        ---
-        ```
+        ![ ](../data/conceptual/mnist-5.png)

    13. `tf.keras.Model.predict` returns a list of lists—one for each image in the batch of data. Grab the predictions for our (only) image in the batch.

@@ -958,7 +941,7 @@ To understand the code step by step, follow these steps:
        np.argmax(predictions_single[0])
        ```

-### Case Study: TensorFlow with Text Classification
+### Case study: TensorFlow with text classification

 This procedure demonstrates text classification starting from plain text files stored on disk. You will train a binary classifier to perform sentiment analysis on an IMDB dataset. At the end of the notebook, there is an exercise for you to try in which you will train a multi-class classifier to predict the tag for a programming question on Stack Overflow.

@@ -988,7 +971,7 @@ Follow these steps:
                                        cache_subdir='')
    ```

-    ```py
+    ```bash
    Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
    84131840/84125825 [==============================] – 1s 0us/step
    84149932/84125825 [==============================] – 1s 0us/step
@@ -1115,11 +1098,7 @@ To prepare the data for training, follow these steps:
    print("Vectorized review", vectorize_text(first_review, first_label))
    ```

-    ```{figure} ../../data/understand/deep_learning/TextClassification_3.png
-    ---
-    align: center
-    ---
-    ```
+    ![ ](../data/conceptual/TextClassification-3.png)

 5. As you can see above, each token has been replaced by an integer. Look up the token (string) that each integer corresponds to by calling get_vocabulary() on the layer.

@@ -1158,11 +1137,7 @@ To prepare the data for training, follow these steps:
    model.summary()
    ```

-    ```{figure} ../../data/understand/deep_learning/TextClassification_4.png
-    ---
-    align: center
-    ---
-    ```
+    ![ ](../data/conceptual/TextClassification-4.png)

 8. A model needs a loss function and an optimizer for training. Since this is a binary classification problem and the model outputs a probability (a single-unit layer with a sigmoid activation), use [`losses.BinaryCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy) loss function.

@@ -1178,11 +1153,7 @@ To prepare the data for training, follow these steps:
    history = model.fit(train_ds,validation_data=val_ds,epochs=epochs)
    ```

-    ```{figure} ../../data/understand/deep_learning/TextClassification_5.png
-    ---
-    align: center
-    ---
-    ```
+    ![ ](../data/conceptual/TextClassification-5.png)

 10. See how the model performs. Two values are returned: loss (a number representing our error; lower values are better) and accuracy.

@@ -1194,7 +1165,8 @@ To prepare the data for training, follow these steps:
    ```

    :::{note}
-    model.fit() returns a History object that contains a dictionary with everything that happened during training.
+    `model.fit()` returns a History object that contains a dictionary with everything that happened during
+    training.
    :::

    ```py
@@ -1224,23 +1196,11 @@ To prepare the data for training, follow these steps:
    plt.show()
    ```

-    {numref}`TextClassification6` and {numref}`TextClassification7` illustrate the training and validation loss and the training and validation accuracy.
+    The following images illustrate the training and validation loss and the training and validation accuracy.

-    ```{figure} ../../data/understand/deep_learning/TextClassification_6.png
-    :name: TextClassification6
-    ---
-    align: center
-    ---
-    Training and Validation Loss
-    ```
+    ![Training and validation loss](../data/conceptual/TextClassification-6.png "Training and validation loss")

-    ```{figure} ../../data/understand/deep_learning/TextClassification_7.png
-    :name: TextClassification7
-    ---
-    align: center
-    ---
-    Training and Validation Accuracy
-    ```
+    ![Training and validation accuracy](../data/conceptual/TextClassification-7.png "Training and validation accuracy")

 12. Export the model.

@@ -1271,15 +1231,3 @@ To prepare the data for training, follow these steps:

    export_model.predict(examples)
    ```
-
-## References
-
-[^inception_arch]: C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," CoRR, p. abs/1512.00567, 2015
-
-[^torch_vision]: PyTorch, \[Online\]. Available: [https://pytorch.org/vision/stable/index.html](https://pytorch.org/vision/stable/index.html)
-
-[^torch_vision_inception]: PyTorch, \[Online\]. Available: [https://pytorch.org/hub/pytorch_vision_inception_v3/](https://pytorch.org/hub/pytorch_vision_inception_v3/)
-
-[^Stanford_deep_learning]: Stanford, \[Online\]. Available: [http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)
-
-[^cross_entropy]: Wikipedia, \[Online\]. Available: [https://en.wikipedia.org/wiki/Cross_entropy](https://en.wikipedia.org/wiki/Cross_entropy)
--- a/docs/conceptual/cmake-packages.rst
+++ b/docs/conceptual/cmake-packages.rst
@@ -1,48 +1,54 @@
-***********
+.. meta::
+   :description: Using CMake
+   :keywords: CMake, dependencies, HIP, C++, AMD, ROCm
+
+*********************************
 Using CMake
-***********
+*********************************

 Most components in ROCm support CMake. Projects depending on header-only or
 library components typically require CMake 3.5 or higher whereas those wanting
-to make use of CMake's HIP language support will require CMake 3.21 or higher.
+to make use of the CMake HIP language support will require CMake 3.21 or higher.

-Finding Dependencies
+Finding dependencies
 ====================

 .. note::
-   For a complete
-   reference on how to deal with dependencies in CMake, refer to the CMake docs
-   on `find_package
-   <https://cmake.org/cmake/help/latest/command/find_package.html>`_ and the
-   `Using Dependencies Guide
-   <https://cmake.org/cmake/help/latest/guide/using-dependencies/index.html>`_
-   to get an overview of CMake's related facilities.
+
+  For a complete
+  reference on how to deal with dependencies in CMake, refer to the CMake docs
+  on `find_package
+  <https://cmake.org/cmake/help/latest/command/find_package.html>`_ and the
+  `Using Dependencies Guide
+  <https://cmake.org/cmake/help/latest/guide/using-dependencies/index.html>`_
+  to get an overview of CMake related facilities.

 In short, CMake supports finding dependencies in two ways:

-  In Module mode, it consults a file ``Find<PackageName>.cmake`` which tries to
-   find the component in typical install locations and layouts. CMake ships a
-   few dozen such scripts, but users and projects may ship them as well.
-  In Config mode, it locates a file named ``<packagename>-config.cmake`` or
-   ``<PackageName>Config.cmake`` which describes the installed component in all
-   regards needed to consume it.
+* In Module mode, it consults a file ``Find<PackageName>.cmake`` which tries to find the component
+  in typical install locations and layouts. CMake ships a few dozen such scripts, but users and projects
+  may ship them as well.
+
+* In Config mode, it locates a file named ``<packagename>-config.cmake`` or
+  ``<PackageName>Config.cmake`` which describes the installed component in all regards needed to
+  consume it.

 ROCm predominantly relies on Config mode, one notable exception being the Module
-driving the compilation of HIP programs on Nvidia runtimes. As such, when
+driving the compilation of HIP programs on NVIDIA runtimes. As such, when
 dependencies are not found in standard system locations, one either has to
 instruct CMake to search for package config files in additional folders using
-the ``CMAKE_PREFIX_PATH`` variable (a semi-colon separated list of filesystem
+the ``CMAKE_PREFIX_PATH`` variable (a semi-colon separated list of file system
 paths), or using ``<PackageName>_ROOT`` variable on a project-specific basis.

 There are nearly a dozen ways to set these variables. One may be more convenient
 over the other depending on your workflow. Conceptually the simplest is adding
-it to your CMake configuration command on the command-line via
+it to your CMake configuration command on the command line via
 ``-D CMAKE_PREFIX_PATH=....`` . AMD packaged ROCm installs can typically be
 added to the config file search paths such as:

-  Windows: ``-D CMAKE_PREFIX_PATH=${env:HIP_PATH}``
+*  Windows: ``-D CMAKE_PREFIX_PATH=${env:HIP_PATH}``

-  Linux: ``-D CMAKE_PREFIX_PATH=/opt/rocm``
+*  Linux: ``-D CMAKE_PREFIX_PATH=/opt/rocm``

 ROCm provides the respective *config-file* packages, and this enables
 ``find_package`` to be used directly. ROCm does not require any Find module as
@@ -50,13 +56,16 @@ the *config-file* packages are shipped with the upstream projects, such as
 rocPRIM and other ROCm libraries.

 For a complete guide on where and how ROCm may be installed on a system, refer
-to the installation guides in these docs (`Linux <../deploy/linux/index.html>`_).
+to the installation guides for
+`Linux <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html>`_
+and
+`Windows <https://rocm.docs.amd.com/projects/install-on-windows/en/latest/index.html>`_.

 Using HIP in CMake
 ==================

-ROCm componenents providing a C/C++ interface support being consumed using any
-C/C++ toolchain that CMake knows how to drive. ROCm also supports CMake's HIP
+ROCm components providing a C/C++ interface support consumption via any
+C/C++ toolchain that CMake knows how to drive. ROCm also supports the CMake HIP
 language features, allowing users to program using the HIP single-source
 programming model. When a program (or translation-unit) uses the HIP API without
 compiling any GPU device code, HIP can be treated in CMake as a simple C/C++
@@ -69,22 +78,22 @@ Source code written in the HIP dialect of C++ typically uses the `.hip`
 extension. When the HIP CMake language is enabled, it will automatically
 associate such source files with the HIP toolchain being used.

-::
+.. code-block:: cmake

-    cmake_minimum_required(VERSION 3.21) # HIP language support requires 3.21
-    cmake_policy(VERSION 3.21.3...3.27)
-    project(MyProj LANGUAGES HIP)
-    add_executable(MyApp Main.hip)
+  cmake_minimum_required(VERSION 3.21) # HIP language support requires 3.21
+  cmake_policy(VERSION 3.21.3...3.27)
+  project(MyProj LANGUAGES HIP)
+  add_executable(MyApp Main.hip)

 Should you have existing CUDA code that is from the source compatible subset of
 HIP, you can tell CMake that despite their `.cu` extension, they're HIP sources.
 Do note that this mostly facilitates compiling kernel code-only source files,
 as host-side CUDA API won't compile in this fashion.

-::
+.. code-block:: cmake

-    add_library(MyLib MyLib.cu)
-    set_source_files_properties(MyLib.cu PROPERTIES LANGUAGE HIP)
+  add_library(MyLib MyLib.cu)
+  set_source_files_properties(MyLib.cu PROPERTIES LANGUAGE HIP)

 CMake itself only hosts part of the HIP language support, such as defining
 HIP-specific properties, etc. while the other half ships with the HIP
@@ -96,12 +105,16 @@ there's a catch-all, last resort variable consulted locating this file,
 ``-D CMAKE_HIP_COMPILER_ROCM_ROOT:PATH=`` which should be set the root of the
 ROCm installation.

+.. note::
+    Imported targets defined by `hip-lang-config.cmake` are for internal use
+    only.
+
 If the user doesn't provide a semi-colon delimited list of device architectures
 via ``CMAKE_HIP_ARCHITECTURES``, CMake will select some sensible default. It is
 advised though that if a user knows what devices they wish to target, then set
 this variable explicitly.

-Consuming ROCm C/C++ Libraries
+Consuming ROCm C/C++ libraries
 ------------------------------

 Libraries such as rocBLAS, rocFFT, MIOpen, etc. behave as C/C++ libraries.
@@ -109,45 +122,57 @@ Illustrated in the example below is a C++ application using MIOpen from CMake.
 It calls ``find_package(miopen)``, which provides the ``MIOpen`` imported
 target. This can be linked with ``target_link_libraries``

-::
+.. code-block:: cmake

-    cmake_minimum_required(VERSION 3.5) # find_package(miopen) requires 3.5
-    cmake_policy(VERSION 3.5...3.27)
-    project(MyProj LANGUAGES CXX)
-    find_package(miopen)
-    add_library(MyLib ...)
-    target_link_libraries(MyLib PUBLIC MIOpen)
+  cmake_minimum_required(VERSION 3.5) # find_package(miopen) requires 3.5
+  cmake_policy(VERSION 3.5...3.27)
+  project(MyProj LANGUAGES CXX)
+  find_package(miopen)
+  add_library(MyLib ...)
+  target_link_libraries(MyLib PUBLIC MIOpen)

 .. note::
-    Most libraries are designed as host-only API, so using a GPU device
-    compiler is not necessary for downstream projects unless they use GPU device
-    code.
+
+  Most libraries are designed as host-only API, so using a GPU device
+  compiler is not necessary for downstream projects unless they use GPU device
+  code.

 Consuming the HIP API in C++ code
 ---------------------------------

-Use the HIP API without compiling the GPU device code. As there is no GPU code,
-any C or C++ compiler can be used. The ``find_package(hip)`` provides the
-``hip::host`` imported target to use HIP in this context.
+Consuming the HIP API without compiling single-source GPU device code can be
+done using any C++ compiler. The ``find_package(hip)`` provides the
+``hip::host`` imported target to use HIP in this scenario.

-::
+.. code-block:: cmake

-    cmake_minimum_required(VERSION 3.5) # find_package(hip) requires 3.5
-    cmake_policy(VERSION 3.5...3.27)
-    project(MyProj LANGUAGES CXX)
-    find_package(hip REQUIRED)
-    add_executable(MyApp ...)
-    target_link_libraries(MyApp PRIVATE hip::host)
+  cmake_minimum_required(VERSION 3.5) # find_package(hip) requires 3.5
+  cmake_policy(VERSION 3.5...3.27)
+  project(MyProj LANGUAGES CXX)
+  find_package(hip REQUIRED)
+  add_executable(MyApp ...)
+  target_link_libraries(MyApp PRIVATE hip::host)
+
+When mixing such ``CXX`` sources with ``HIP`` sources holding device-code, link
+only to `hip::host`. If HIP sources don't have `.hip` as their extension, use
+`set_source_files_properties(<hip_sources>... PROPERTIES LANGUAGE HIP)` on them.
+Linking to `hip::host` will set all the necessary flags for the ``CXX`` sources
+while ``HIP`` sources inherit all flags from the built-in language support.
+Having HIP sources in a target will turn the |LINK_LANG|_ into ``HIP``.
+
+.. |LINK_LANG| replace:: ``LINKER_LANGUAGE``
+.. _LINK_LANG: https://cmake.org/cmake/help/latest/prop_tgt/LINKER_LANGUAGE.html

 Compiling device code in C++ language mode
 ------------------------------------------

 .. attention::
-    The workflow detailed here is considered legacy and is shown for
-    understanding's sake. It pre-dates the existence of HIP language support in
-    CMake. If source code has HIP device code in it, it is a HIP source file
-    and should be compiled as such. Only resort to the method below if your
-    HIP-enabled CMake codepath can't mandate CMake version 3.21.
+
+  The workflow detailed here is considered legacy and is shown for
+  understanding's sake. It pre-dates the existence of HIP language support in
+  CMake. If source code has HIP device code in it, it is a HIP source file
+  and should be compiled as such. Only resort to the method below if your
+  HIP-enabled CMake code path can't mandate CMake version 3.21.

 If code uses the HIP API and compiles GPU device code, it requires using a
 device compiler. The compiler for CMake can be set using either the
@@ -159,36 +184,36 @@ compiler that supports AMD GPU targets, which is usually Clang.
 The ``find_package(hip)`` provides the ``hip::device`` imported target to add
 all the flags necessary for device compilation.

-::
+.. code-block:: cmake

-    cmake_minimum_required(VERSION 3.8) # cxx_std_11 requires 3.8
-    cmake_policy(VERSION 3.8...3.27)
-    project(MyProj LANGUAGES CXX)
-    find_package(hip REQUIRED)
-    add_library(MyLib ...)
-    target_link_libraries(MyLib PRIVATE hip::device)
-    target_compile_features(MyLib PRIVATE cxx_std_11)
+  cmake_minimum_required(VERSION 3.8) # cxx_std_11 requires 3.8
+  cmake_policy(VERSION 3.8...3.27)
+  project(MyProj LANGUAGES CXX)
+  find_package(hip REQUIRED)
+  add_library(MyLib ...)
+  target_link_libraries(MyLib PRIVATE hip::device)
+  target_compile_features(MyLib PRIVATE cxx_std_11)

 .. note::
-    Compiling for the GPU device requires at least C++11.

-This project can then be configured with for eg.
+  Compiling for the GPU device requires at least C++11.

-  Windows: ``cmake -D CMAKE_CXX_COMPILER:PATH=${env:HIP_PATH}\bin\clang++.exe``
+This project can then be configured with the following CMake commands:

-  Linux: ``cmake -D CMAKE_CXX_COMPILER:PATH=/opt/rocm/bin/amdclang++``
+*  Windows: ``cmake -D CMAKE_CXX_COMPILER:PATH=${env:HIP_PATH}\bin\clang++.exe``
+*  Linux: ``cmake -D CMAKE_CXX_COMPILER:PATH=/opt/rocm/bin/amdclang++``

 Which use the device compiler provided from the binary packages of
-`ROCm HIP SDK <https://www.amd.com/en/developer/rocm-hub.html>`_ and
+`ROCm HIP SDK <https://www.amd.com/en/developer/resources/rocm-hub/hip-sdk.html>`_ and
 `repo.radeon.com <https://repo.radeon.com>`_ respectively.

-When using the CXX language support to compile HIP device code, selecting the
+When using the ``CXX`` language support to compile HIP device code, selecting the
 target GPU architectures is done via setting the ``GPU_TARGETS`` variable.
 ``CMAKE_HIP_ARCHITECTURES`` only exists when the HIP language is enabled. By
 default, this is set to some subset of the currently supported architectures of
-AMD ROCm. It can be set to eg. ``-D GPU_TARGETS="gfx1032;gfx1035"``.
+AMD ROCm. It can be set to the CMake option ``-D GPU_TARGETS="gfx1032;gfx1035"``.

-ROCm CMake Packages
+ROCm CMake packages
 -------------------

 +-----------+----------+--------------------------------------------------------+
@@ -229,10 +254,10 @@ ROCm CMake Packages
 |           |          | ``migraphx::migraphx_onnx``, ``migraphx::migraphx_tf`` |
 +-----------+----------+--------------------------------------------------------+

-Using CMake Presets
+Using CMake presets
 ===================

-CMake command-lines depending on how specific users like to be when compiling
+CMake command lines depending on how specific users like to be when compiling
 code can grow to unwieldy lengths. This is the primary reason why projects tend
 to bake script snippets into their build definitions controlling compiler
 warning levels, changing CMake defaults (``CMAKE_BUILD_TYPE`` or
@@ -251,13 +276,12 @@ options.

 IDEs supporting CMake (Visual Studio, Visual Studio Code, CLion, etc.) all came
 up with their own way to register command-line fragments of different purpose in
-a setup'n'forget fashion for quick assembly using graphical front-ends. This is
+a setup-and-forget fashion for quick assembly using graphical front-ends. This is
 all nice, but configurations aren't portable, nor can they be reused in
-Continuous Intergration (CI) pipelines. CMake has condensed existing practice
+Continuous Integration (CI) pipelines. CMake has condensed existing practice
 into a portable JSON format that works in all IDEs and can be invoked from any
-command-line. This is
-`CMake Presets <https://cmake.org/cmake/help/latest/manual/cmake-presets.7.html>`_
-.
+command line. This is
+`CMake Presets <https://cmake.org/cmake/help/latest/manual/cmake-presets.7.html>`_.

 There are two types of preset files: one supplied by the project, called
 ``CMakePresets.json`` which is meant to be committed to version control,
@@ -274,109 +298,110 @@ Following is an example ``CMakeUserPresets.json`` file which actually compiles
 the `amd/rocm-examples <https://github.com/amd/rocm-examples>`_ suite of sample
 applications on a typical ROCm installation:

-::
+.. code-block:: json

-    {
-      "version": 3,
-      "cmakeMinimumRequired": {
-        "major": 3,
-        "minor": 21,
-        "patch": 0
+  {
+    "version": 3,
+    "cmakeMinimumRequired": {
+      "major": 3,
+      "minor": 21,
+      "patch": 0
+    },
+    "configurePresets": [
+      {
+        "name": "layout",
+        "hidden": true,
+        "binaryDir": "${sourceDir}/build/${presetName}",
+        "installDir": "${sourceDir}/install/${presetName}"
      },
-      "configurePresets": [
-        {
-          "name": "layout",
-          "hidden": true,
-          "binaryDir": "${sourceDir}/build/${presetName}",
-          "installDir": "${sourceDir}/install/${presetName}"
-        },
-        {
-          "name": "generator-ninja-multi-config",
-          "hidden": true,
-          "generator": "Ninja Multi-Config"
-        },
-        {
-          "name": "toolchain-makefiles-c/c++-amdclang",
-          "hidden": true,
-          "cacheVariables": {
-            "CMAKE_C_COMPILER": "/opt/rocm/bin/amdclang",
-            "CMAKE_CXX_COMPILER": "/opt/rocm/bin/amdclang++",
-            "CMAKE_HIP_COMPILER": "/opt/rocm/bin/amdclang++"
-          }
-        },
-        {
-          "name": "clang-strict-iso-high-warn",
-          "hidden": true,
-          "cacheVariables": {
-            "CMAKE_C_FLAGS": "-Wall -Wextra -pedantic",
-            "CMAKE_CXX_FLAGS": "-Wall -Wextra -pedantic",
-            "CMAKE_HIP_FLAGS": "-Wall -Wextra -pedantic"
-          }
-        },
-        {
-          "name": "ninja-mc-rocm",
-          "displayName": "Ninja Multi-Config ROCm",
-          "inherits": [
-            "layout",
-            "generator-ninja-multi-config",
-            "toolchain-makefiles-c/c++-amdclang",
-            "clang-strict-iso-high-warn"
-          ]
+      {
+        "name": "generator-ninja-multi-config",
+        "hidden": true,
+        "generator": "Ninja Multi-Config"
+      },
+      {
+        "name": "toolchain-makefiles-c/c++-amdclang",
+        "hidden": true,
+        "cacheVariables": {
+          "CMAKE_C_COMPILER": "/opt/rocm/bin/amdclang",
+          "CMAKE_CXX_COMPILER": "/opt/rocm/bin/amdclang++",
+          "CMAKE_HIP_COMPILER": "/opt/rocm/bin/amdclang++"
        }
-      ],
-      "buildPresets": [
-        {
-          "name": "ninja-mc-rocm-debug",
-          "displayName": "Debug",
-          "configuration": "Debug",
-          "configurePreset": "ninja-mc-rocm"
-        },
-        {
-          "name": "ninja-mc-rocm-release",
-          "displayName": "Release",
-          "configuration": "Release",
-          "configurePreset": "ninja-mc-rocm"
-        },
-        {
-          "name": "ninja-mc-rocm-debug-verbose",
-          "displayName": "Debug (verbose)",
-          "configuration": "Debug",
-          "configurePreset": "ninja-mc-rocm",
-          "verbose": true
-        },
-        {
-          "name": "ninja-mc-rocm-release-verbose",
-          "displayName": "Release (verbose)",
-          "configuration": "Release",
-          "configurePreset": "ninja-mc-rocm",
-          "verbose": true
+      },
+      {
+        "name": "clang-strict-iso-high-warn",
+        "hidden": true,
+        "cacheVariables": {
+          "CMAKE_C_FLAGS": "-Wall -Wextra -pedantic",
+          "CMAKE_CXX_FLAGS": "-Wall -Wextra -pedantic",
+          "CMAKE_HIP_FLAGS": "-Wall -Wextra -pedantic"
        }
-      ],
-      "testPresets": [
-        {
-          "name": "ninja-mc-rocm-debug",
-          "displayName": "Debug",
-          "configuration": "Debug",
-          "configurePreset": "ninja-mc-rocm",
-          "execution": {
-            "jobs": 0
-          }
-        },
-        {
-          "name": "ninja-mc-rocm-release",
-          "displayName": "Release",
-          "configuration": "Release",
-          "configurePreset": "ninja-mc-rocm",
-          "execution": {
-            "jobs": 0
-          }
+      },
+      {
+        "name": "ninja-mc-rocm",
+        "displayName": "Ninja Multi-Config ROCm",
+        "inherits": [
+          "layout",
+          "generator-ninja-multi-config",
+          "toolchain-makefiles-c/c++-amdclang",
+          "clang-strict-iso-high-warn"
+        ]
+      }
+    ],
+    "buildPresets": [
+      {
+        "name": "ninja-mc-rocm-debug",
+        "displayName": "Debug",
+        "configuration": "Debug",
+        "configurePreset": "ninja-mc-rocm"
+      },
+      {
+        "name": "ninja-mc-rocm-release",
+        "displayName": "Release",
+        "configuration": "Release",
+        "configurePreset": "ninja-mc-rocm"
+      },
+      {
+        "name": "ninja-mc-rocm-debug-verbose",
+        "displayName": "Debug (verbose)",
+        "configuration": "Debug",
+        "configurePreset": "ninja-mc-rocm",
+        "verbose": true
+      },
+      {
+        "name": "ninja-mc-rocm-release-verbose",
+        "displayName": "Release (verbose)",
+        "configuration": "Release",
+        "configurePreset": "ninja-mc-rocm",
+        "verbose": true
+      }
+    ],
+    "testPresets": [
+      {
+        "name": "ninja-mc-rocm-debug",
+        "displayName": "Debug",
+        "configuration": "Debug",
+        "configurePreset": "ninja-mc-rocm",
+        "execution": {
+          "jobs": 0
        }
-      ]
-    }
+      },
+      {
+        "name": "ninja-mc-rocm-release",
+        "displayName": "Release",
+        "configuration": "Release",
+        "configurePreset": "ninja-mc-rocm",
+        "execution": {
+          "jobs": 0
+        }
+      }
+    ]
+  }

 .. note::
-    Getting presets to work reliably on Windows requires some CMake improvements
-    and/or support from compiler vendors. (Refer to 
-    `Add support to the Visual Studio generators <https://gitlab.kitware.com/cmake/cmake/-/issues/24245>`_
-    and `Sourcing environment scripts <https://gitlab.kitware.com/cmake/cmake/-/issues/21619>`_
-    .)
+
+  Getting presets to work reliably on Windows requires some CMake improvements
+  and/or support from compiler vendors. (Refer to
+  `Add support to the Visual Studio generators <https://gitlab.kitware.com/cmake/cmake/-/issues/24245>`_
+  and `Sourcing environment scripts <https://gitlab.kitware.com/cmake/cmake/-/issues/21619>`_
+  .)
--- a/docs/conceptual/compiler-disambiguation.md
+++ b/docs/conceptual/compiler-disambiguation.md
@@ -1,15 +1,21 @@
-# ROCm Compilers Disambiguation
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="ROCm compilers disambiguation">
+  <meta name="keywords" content="compilers, compiler naming, AMD, ROCm">
+</head>
+
+# ROCm compilers disambiguation

 ROCm ships multiple compilers of varying origins and purposes. This article
 disambiguates compiler naming used throughout the documentation.

-## Compiler Terms
+## Compiler terms

 | Term | Description |
 | - | - |
-| `amdclang++` | Clang/LLVM-based compiler that is part of `rocm-llvm` package. The source code is available at <a href="https://github.com/RadeonOpenCompute/llvm-project" target="_blank">https://github.com/RadeonOpenCompute/llvm-project</a>. |
+| `amdclang++` | Clang/LLVM-based compiler that is part of `rocm-llvm` package. The source code is available at <a href="https://github.com/ROCm/llvm-project" target="_blank">https://github.com/ROCm/llvm-project</a>. |
 | AOCC | Closed-source clang-based compiler that includes additional CPU optimizations. Offered as part of ROCm via the `rocm-llvm-alt` package. See for details, <a href="https://developer.amd.com/amd-aocc/" target="_blank">https://developer.amd.com/amd-aocc/</a>. |
 | HIP-Clang | Informal term for the `amdclang++` compiler |
-| HIPify | Tools including `hipify-clang` and `hipify-perl`, used to automatically translate CUDA source code into portable HIP C++. The source code is available at <a href="https://github.com/ROCm-Developer-Tools/HIPIFY" target="_blank">https://github.com/ROCm-Developer-Tools/HIPIFY</a> |
-| `hipcc` | HIP compiler driver. A utility that invokes `clang` or `nvcc` depending on the target and passes the appropriate include and library options for the target compiler and HIP infrastructure. The source code is available at <a href="https://github.com/ROCm-Developer-Tools/HIPCC" target="_blank">https://github.com/ROCm-Developer-Tools/HIPCC</a>. |
+| HIPIFY | Tools including `hipify-clang` and `hipify-perl`, used to automatically translate CUDA source code into portable HIP C++. The source code is available at <a href="https://github.com/ROCm/HIPIFY" target="_blank">https://github.com/ROCm/HIPIFY</a> |
+| `hipcc` | HIP compiler driver. A utility that invokes `clang` or `nvcc` depending on the target and passes the appropriate include and library options for the target compiler and HIP infrastructure. The source code is available at <a href="https://github.com/ROCm/HIPCC" target="_blank">https://github.com/ROCm/HIPCC</a>. |
 | ROCmCC | Clang/LLVM-based compiler. ROCmCC in itself is not a binary but refers to the overall compiler. |
--- a/docs/conceptual/file-reorg.md
+++ b/docs/conceptual/file-reorg.md
@@ -1,10 +1,19 @@
-# Linux Folder Structure Reorganization
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="ROCm Linux Filesystem Hierarchy Standard reorganization">
+  <meta name="keywords" content="FHS, Linux Filesystem Hierarchy Standard, directory structure,
+  AMD, ROCm">
+</head>
+
+# ROCm Linux Filesystem Hierarchy Standard reorganization

 ## Introduction

-ROCm™ packages have adopted the Linux foundation file system hierarchy standard
-to ensure ROCm components follow open source conventions for Linux-based
-distributions. Following is the ROCm proposed file structure.
+The ROCm Software has adopted the Linux Filesystem Hierarchy Standard (FHS) [https://refspecs.linuxfoundation.org/FHS_3.0/fhs/index.html](https://refspecs.linuxfoundation.org/FHS_3.0/fhs/index.html) in order to to ensure ROCm is consistent with standard open source conventions. The following sections specify how current and future releases of ROCm adhere to FHS, how the previous ROCm file system is supported, and how improved versioning specifications are applied to ROCm.
+
+## Adopting the FHS
+
+In order to standardize ROCm directory structure and directory content layout ROCm has adopted the [FHS](https://refspecs.linuxfoundation.org/FHS_3.0/fhs/index.html), adhering to open source conventions for Linux-based distribution. FHS ensures internal consistency within the ROCm stack, as well as external consistency with other systems and distributions. The ROCm proposed file structure is outlined below:

 ```none
 /opt/rocm-<ver>
@@ -44,12 +53,11 @@ distributions. Following is the ROCm proposed file structure.

 ## Changes from earlier ROCm versions

-ROCm with the file reorganization is going to have a lean structure. Following
-table gives the comparison with new and old folder structure.
+The following table provides a brief overview of the new ROCm FHS layout, compared to the layout of earlier ROCm versions. Note that /opt/ is used to denote the default rocm-installation-path and should be replaced in case of a non-standard installation location of the ROCm distribution.

 ```none
 ______________________________________________________
-|  New File Structure         |  Old File Structure    |
+|  New ROCm Layout            |  Previous ROCm Layout  |
 |_____________________________|________________________|
 | /opt/rocm-<ver>             | /opt/rocm-<ver>        |
 |     | -- bin                |     | -- bin           |
@@ -72,39 +80,28 @@ table gives the comparison with new and old folder structure.
 |______________________________________________________|
 ```

-## ROCm File reorganization transition plan
+## ROCm FHS reorganization: backward compatibility

-New file organization for ROCm was first introduced ROCm v5.2 release. Backward
-compatibility was in place to make sure users had a chance to change their
-applications using ROCm. ROCm has moved header files and libraries to its new
-location as indicated in the above structure and included symbolic-link and
-wrapper header files in its old location for backward compatibility.
+The FHS file organization for ROCm was first introduced in the release of ROCm 5.2 . Backward compatibility was implemented to make sure users could still run their ROCm applications while transitioning to the new FHS. ROCm has moved header files and libraries to their new locations as indicated in the above structure, and included symbolic-links and wrapper header files in their old location for backward compatibility. The following sections detail ROCm backward compatibility implementation for wrapper header files, executable files, library files and CMake config files.

 ### Wrapper header files

 Wrapper header files are placed in the old location (
-`/opt/rocm-xxx/<component>/include`) with a warning message to include files
-from the new location (`/opt/rocm-xxx/include`) as shown in the example below.
+`/opt/rocm-<ver>/<component>/include`) with a warning message to include files
+from the new location (`/opt/rocm-<ver>/include`) as shown in the example below.

 ```cpp
 #pragma message "This file is deprecated. Use file from include path /opt/rocm-ver/include/ and prefix with hip."
-#include "hip/hip_runtime.h"
+#include <hip/hip_runtime.h>
 ```

-The deprecation plan for backward compatibility wrapper header files is as
-follows
-
- `#pragma` message announcing deprecation – ROCm v5.2 release.
- `#pragma` message changed to `#warning` – Future release, tentatively ROCm
-  v5.5.
- `#warning` changed to `#error` – Future release, tentatively ROCm v5.6.
- Backward compatibility wrappers removed – Future release, tentatively ROCm
-  v6.0.
+* Starting at ROCm 5.2 release, the deprecation for backward compatibility wrapper header files is: `#pragma` message announcing `#warning`.
+* Starting from ROCm 6.0 (tentatively) backward compatibility for wrapper header files will be removed, and the `#pragma` message will be announcing `#error`.

 ### Executable files

-Executable files are available in the `/opt/rocm-xxx/bin` folder. For backward
-compatibility, the old library location (`/opt/rocm-xxx/<component>/bin`) has a
+Executable files are available in the `/opt/rocm-<ver>/bin` folder. For backward
+compatibility, the old library location (`/opt/rocm-<ver>/<component>/bin`) has a
 soft link to the library at the new location. Soft links will be removed in a
 future release, tentatively ROCm v6.0.

@@ -115,8 +112,8 @@ lrwxrwxrwx 1 root root   24 Jan 1 23:32 hipcc -> ../../bin/hipcc

 ### Library files

-Library files are available in the `/opt/rocm-xxx/lib` folder. For backward
-compatibility, the old library location (`/opt/rocm-xxx/<component>/lib`) has a
+Library files are available in the `/opt/rocm-<ver>/lib` folder. For backward
+compatibility, the old library location (`/opt/rocm-<ver>/<component>/lib`) has a
 soft link to the library at the new location. Soft links will be removed in a
 future release, tentatively ROCm v6.0.

@@ -126,11 +123,11 @@ drwxr-xr-x 4 root root 4096 Jan 1 10:45 cmake
 lrwxrwxrwx 1 root root   24 Jan 1 23:32 libamdhip64.so -> ../../lib/libamdhip64.so
 ```

-### CMake Config files
+### CMake config files

 All CMake configuration files are available in the
-`/opt/rocm-xxx/lib/cmake/<component>` folder. For backward compatibility, the
-old CMake locations (`/opt/rocm-xxx/<component>/lib/cmake`) consist of a soft
+`/opt/rocm-<ver>/lib/cmake/<component>` folder. For backward compatibility, the
+old CMake locations (`/opt/rocm-<ver>/<component>/lib/cmake`) consist of a soft
 link to the new CMake config. Soft links will be removed in a future release,
 tentatively ROCm v6.0.

@@ -142,7 +139,7 @@ lrwxrwxrwx 1 root root 42 Jan 1 23:32 hip-config.cmake -> ../../../../lib/cmake/
 ## Changes required in applications using ROCm

 Applications using ROCm are advised to use the new file paths. As the old files
-will be deprecated in a future release. Application have to make sure to include
+will be deprecated in a future release. Applications have to make sure to include
 correct header file and use correct search paths.

 1. `#include<header_file.h>` needs to be changed to
@@ -158,10 +155,18 @@ correct header file and use correct search paths.
   `VAR2=/opt/rocm/hsa` needs to be changed to `VAR2=/opt/rocm`

 3. Any reference to `/opt/rocm/<component>/bin` or `/opt/rocm/<component>/lib`
-   needs to be changed to `/opt/rocm/bin` and `/opt/rocm/lib/` respectively.
+   needs to be changed to `/opt/rocm/bin` and `/opt/rocm/lib/`, respectively.

-## References
+## Changes in versioning specifications

-{ref}`ROCm deprecation warning <5_4_0_filesystem_reorg_deprecation_notice>`
+In order to better manage ROCm dependencies specification and allow smoother releases of ROCm while avoiding dependency conflicts, ROCm software shall adhere to the following scheme when numbering and incrementing ROCm files versions:

-[Linux File System Standard](https://refspecs.linuxfoundation.org/fhs.shtml)
+rocm-\<ver\>, where \<ver\> = \<x.y.z\>
+
+x.y.z denote: MAJOR.MINOR.PATCH
+
+z: PATCH - increment z when implementing backward compatible bug fixes.
+
+y: MINOR - increment y when implementing minor changes that add functionality but are still backward compatible.
+
+x: MAJOR - increment x when implementing major changes that are not backward compatible.
--- a/docs/conceptual/gpu-arch.md
+++ b/docs/conceptual/gpu-arch.md
@@ -0,0 +1,72 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="GPU architecture">
+  <meta name="keywords" content="GPU architecture, architecture support, MI200, MI250, RDNA,
+  MI100, AMD Instinct">
+</head>
+
+(gpu-arch-documentation)=
+
+# GPU architecture documentation
+
+:::::{grid} 1 1 2 2
+:gutter: 1
+
+:::{grid-item-card}
+**AMD Instinct MI300 series**
+
+Review hardware aspects of the AMD Instinct™ MI300 series of GPU accelerators and the CDNA™ 3
+architecture.
+
+* [AMD Instinct™ MI300 microarchitecture](./gpu-arch/mi300.md)
+* [AMD Instinct MI300/CDNA3 ISA](https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/instruction-set-architectures/amd-instinct-mi300-cdna3-instruction-set-architecture.pdf)
+* [White paper](https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/white-papers/amd-cdna-3-white-paper.pdf)
+* [Performance counters](./gpu-arch/mi300-mi200-performance-counters.rst)
+:::
+
+:::{grid-item-card}
+**AMD Instinct MI200 series**
+
+Review hardware aspects of the AMD Instinct™ MI200 series of GPU accelerators and the CDNA™ 2
+architecture.
+
+* [AMD Instinct™ MI250 microarchitecture](./gpu-arch/mi250.md)
+* [AMD Instinct MI200/CDNA2 ISA](https://www.amd.com/system/files/TechDocs/instinct-mi200-cdna2-instruction-set-architecture.pdf)
+* [White paper](https://www.amd.com/system/files/documents/amd-cdna2-white-paper.pdf)
+* [Performance counters](./gpu-arch/mi300-mi200-performance-counters.rst)
+
+:::
+
+:::{grid-item-card}
+**AMD Instinct MI100**
+
+Review hardware aspects of the AMD Instinct™ MI100 series of GPU accelerators and the CDNA™ 1
+architecture.
+
+* [AMD Instinct™ MI100 microarchitecture](./gpu-arch/mi100.md)
+* [AMD Instinct MI100/CDNA1 ISA](https://www.amd.com/system/files/TechDocs/instinct-mi100-cdna1-shader-instruction-set-architecture%C2%A0.pdf)
+* [White paper](https://www.amd.com/system/files/documents/amd-cdna-whitepaper.pdf)
+
+:::
+
+:::{grid-item-card}
+**RDNA**
+
+* [AMD RDNA3 ISA](https://www.amd.com/system/files/TechDocs/rdna3-shader-instruction-set-architecture-feb-2023_0.pdf)
+* [AMD RDNA2 ISA](https://www.amd.com/system/files/TechDocs/rdna2-shader-instruction-set-architecture.pdf)
+* [AMD RDNA ISA](https://www.amd.com/system/files/TechDocs/rdna-shader-instruction-set-architecture.pdf)
+* [AMD RDNA Architecture White Paper](https://www.amd.com/system/files/documents/rdna-whitepaper.pdf)
+
+:::
+
+:::{grid-item-card}
+**Older architectures**
+
+* [AMD Instinct MI50/Vega 7nm ISA](https://www.amd.com/system/files/TechDocs/vega-7nm-shader-instruction-set-architecture.pdf)
+* [AMD Instinct MI25/Vega ISA](https://www.amd.com/system/files/TechDocs/vega-shader-instruction-set-architecture.pdf)
+* [AMD GCN3 ISA](https://www.amd.com/system/files/TechDocs/gcn3-instruction-set-architecture.pdf)
+* [AMD Vega Architecture White Paper](https://en.wikichip.org/w/images/a/a1/vega-whitepaper.pdf)
+
+:::
+
+:::::
--- a/docs/conceptual/gpu-arch/mi100.md
+++ b/docs/conceptual/gpu-arch/mi100.md
@@ -1,12 +1,12 @@
-# AMD Instinct™ MI100 Hardware
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="AMD Instinct MI100 microarchitecture">
+  <meta name="keywords" content="Instinct, MI100, microarchitecture, AMD, ROCm">
+</head>

-In this chapter, we are going to briefly review hardware aspects of the AMD
-Instinct™ MI100 accelerators and the CDNA architecture that is the foundation of
-these GPUs.
+# AMD Instinct™ MI100 microarchitecture

-## System Architecture
-
-{numref}`mi100-arch` shows the node-level architecture of a system that
+The following image shows the node-level architecture of a system that
 comprises two AMD EPYC™ processors and (up to) eight AMD Instinct™ accelerators.
 The two EPYC processors are connected to each other with the AMD Infinity™
 fabric which provides a high-bandwidth (up to 18 GT/sec) and coherent links such
@@ -17,12 +17,7 @@ available to connect the processors plus one PCIe Gen 4 x16 link per processor
 can attach additional I/O devices such as the host adapters for the network
 fabric.

-:::{figure-md} mi100-arch
-
-<img src="../../data/reference/gpu_arch/image.004.png" alt="Node-level system architecture with two AMD EPYC™ processors and eight AMD Instinct™ accelerators.">
-
-Structure of a single GCD in the AMD Instinct MI250 accelerator.
-:::
+![Structure of a single GCD in the AMD Instinct MI100 accelerator](../../data/conceptual/gpu-arch/image004.png "Node-level system architecture with two AMD EPYC™ processors and eight AMD Instinct™ accelerators.")

 In a typical node configuration, each processor can host up to four AMD
 Instinct™ accelerators that are attached using PCIe Gen 4 links at 16 GT/sec,
@@ -34,34 +29,29 @@ links. This inter-GPU link can be established in certified server systems if the
 GPUs are mounted in neighboring PCIe slots by installing the AMD Infinity
 Fabric™ bridge for the AMD Instinct™ accelerators.

-## Micro-architecture
+## Microarchitecture

-The micro-architecture of the AMD Instinct accelerators is based on the AMD CDNA
+The microarchitecture of the AMD Instinct accelerators is based on the AMD CDNA
 architecture, which targets compute applications such as high-performance
 computing (HPC) and AI & machine learning (ML) that run on everything from
 individual servers to the world's largest exascale supercomputers. The overall
 system architecture is designed for extreme scalability and compute performance.

-:::{figure-md} mi100-block
+![Structure of the AMD Instinct accelerator (MI100 generation)](../../data/conceptual/gpu-arch/image005.png "Structure of the AMD Instinct accelerator (MI100 generation)")

-<img src="../../data/reference/gpu_arch/image.005.png" alt="Structure of the AMD Instinct accelerator (MI100 generation).">
-
-Structure of the AMD Instinct accelerator (MI100 generation).
-:::
-
-{numref}`mi100-block` shows the AMD Instinct accelerator with its PCIe Gen 4 x16
+The above image shows the AMD Instinct accelerator with its PCIe Gen 4 x16
 link (16 GT/sec, at the bottom) that connects the GPU to (one of) the host
 processor(s). It also shows the three AMD Infinity Fabric ports that provide
 high-speed links (23 GT/sec, also at the bottom) to the other GPUs of the local
-hive as shown in {numref}`mi100-arch`.
+hive.

 On the left and right of the floor plan, the High Bandwidth Memory (HBM)
-attaches via the GPU's memory controller.  The MI100 generation of the AMD
+attaches via the GPU memory controller.  The MI100 generation of the AMD
 Instinct accelerator offers four stacks of HBM generation 2 (HBM2) for a total
 of 32GB with a 4,096bit-wide memory interface. The peak memory bandwidth of the
 attached HBM2 is 1.228 TB/sec at a memory clock frequency of 1.2 GHz.

-The execution units of the GPU are depicted in {numref}`mi100-block` as Compute
+The execution units of the GPU are depicted in the above image as Compute
 Units (CU). There are a total 120 compute units that are physically organized
 into eight Shader Engines (SE) with fifteen compute units per shader engine.
 Each compute unit is further sub-divided into four SIMD units that process SIMD
@@ -70,15 +60,9 @@ instructions of 16 data elements per instruction. This enables the CU to process
 Therefore, the theoretical maximum FP64 peak performance is 11.5 TFLOPS
 (`4 [SIMD units] x 16 [elements per instruction] x 120 [CU] x 1.5 [GHz]`).

-:::{figure-md} mi100-gcd
+![Block diagram of an MI100 compute unit with detailed SIMD view of the AMD CDNA architecture](../../data/conceptual/gpu-arch/image006.png "An MI100 compute unit with detailed SIMD view of the AMD CDNA architecture")

-<img src="../../data/reference/gpu_arch/image.006.png" alt="Block diagram of an MI100 compute unit with detailed SIMD view of the AMD CDNA architecture">
-
-Block diagram of an MI100 compute unit with detailed SIMD view of the AMD CDNA
-architecture
-:::
-
-{numref}`mi100-gcd` shows the block diagram of a single CU of an AMD Instinct™
+The preceding image shows the block diagram of a single CU of an AMD Instinct™
 MI100 accelerator and summarizes how instructions flow through the execution
 engines. The CU fetches the instructions via a 32KB instruction cache and moves
 them forward to execution via a dispatcher. The CU can handle up to ten
--- a/docs/conceptual/gpu-arch/mi250.md
+++ b/docs/conceptual/gpu-arch/mi250.md
@@ -1,19 +1,19 @@
-# AMD Instinct Hardware
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="AMD Instinct MI250 microarchitecture">
+  <meta name="keywords" content="Instinct, MI250, microarchitecture, AMD, ROCm">
+</head>

-This chapter briefly reviews hardware aspects of the AMD Instinct MI250
-accelerators and the CDNA™ 2 architecture that is the foundation of these GPUs.
+# AMD Instinct™ MI250 microarchitecture

-## AMD CDNA 2 Micro-architecture
-
-The micro-architecture of the AMD Instinct MI250 accelerators is based on the
+The microarchitecture of the AMD Instinct MI250 accelerators is based on the
 AMD CDNA 2 architecture that targets compute applications such as HPC,
-artificial intelligence (AI), and Machine Learning (ML) and that run on
+artificial intelligence (AI), and machine learning (ML) and that run on
 everything from individual servers to the world’s largest exascale
 supercomputers. The overall system architecture is designed for extreme
 scalability and compute performance.

-{numref}`mi250-gcd` shows the components of a single Graphics Compute Die (GCD
-) of the CDNA 2 architecture. On the top and the bottom are AMD Infinity Fabric™
+The following image shows the components of a single Graphics Compute Die (GCD) of the CDNA 2 architecture. On the top and the bottom are AMD Infinity Fabric™
 interfaces and their physical links that are used to connect the GPU die to the
 other system-level components of the node (see also Section 2.2). Both
 interfaces can drive four AMD Infinity Fabric links. One of the AMD Infinity
@@ -28,7 +28,7 @@ To the left and the right are memory controllers that attach the High Bandwidth
 Memory (HBM) modules to the GCD. AMD Instinct MI250 GPUs use HBM2e, which offers
 a peak memory bandwidth of 1.6 TB/sec per GCD.

-The execution units of the GPU are depicted in {numref}`mi250-gcd` as Compute
+The execution units of the GPU are depicted in the following image as Compute
 Units (CU). The MI250 GCD has 104 active CUs. Each compute unit is further
 subdivided into four SIMD units that process SIMD instructions of 16 data
 elements per instruction (for the FP64 data type). This enables the CU to
@@ -39,16 +39,11 @@ execution units (also called matrix cores), which are geared toward executing
 matrix operations like matrix-matrix multiplications. For FP64, the peak
 performance of these units amounts to 90.5 TFLOPS.

-:::{figure-md} mi250-gcd
-
-<img src="../../data/reference/gpu_arch/image.001.png" alt="Structure of a single GCD in the AMD Instinct MI250 accelerator.">
-
-Figure 1: Structure of a single GCD in the AMD Instinct MI250 accelerator.
-:::
+![Structure of a single GCD in the AMD Instinct MI250 accelerator.](../../data/conceptual/gpu-arch/image001.png "Structure of a single GCD in the AMD Instinct MI250 accelerator.")

 ```{list-table} Peak-performance capabilities of the MI250 OAM for different data types.
 :header-rows: 1
-:name: mi250-perf
+:name: mi250-perf-table

 *
  - Computation and Data Type
@@ -88,7 +83,7 @@ Figure 1: Structure of a single GCD in the AMD Instinct MI250 accelerator.
  - 362.1
 ```

-{numref}`mi250-perf` summarizes the aggregated peak performance of the AMD
+The above table summarizes the aggregated peak performance of the AMD
 Instinct MI250 OCP Open Accelerator Modules (OAM, OCP is short for Open Compute
 Platform) and its two GCDs for different data types and execution units. The
 middle column lists the peak performance (number of data elements processed in a
@@ -97,23 +92,18 @@ is being retired in each clock cycle. The third column lists the theoretical
 peak performance of the OAM module. The theoretical aggregated peak memory
 bandwidth of the GPU is 3.2 TB/sec (1.6 TB/sec per GCD).

-:::{figure-md} mi250-arch
+![Dual-GCD architecture of the AMD Instinct MI250 accelerators](../../data/conceptual/gpu-arch/image002.png "Dual-GCD architecture of the AMD Instinct MI250 accelerators")

-<img src="../../data/reference/gpu_arch/image.002.png" alt="Dual-GCD architecture of the AMD Instinct MI250 accelerators.">
-
-Dual-GCD architecture of the AMD Instinct MI250 accelerators.
-:::
-
-{numref}`mi250-arch` shows the block diagram of an OAM package that consists
+The following image shows the block diagram of an OAM package that consists
 of two GCDs, each of which constitutes one GPU device in the system. The two
 GCDs in the package are connected via four AMD Infinity Fabric links running at
 a theoretical peak rate of 25 GT/sec, giving 200 GB/sec peak transfer bandwidth
 between the two GCDs of an OAM, or a bidirectional peak transfer bandwidth of
 400 GB/sec for the same.

-## Node-level Architecture
+## Node-level architecture

-{numref}`mi250-block` shows the node-level architecture of a system that is
+The following image shows the node-level architecture of a system that is
 based on the AMD Instinct MI250 accelerator. The MI250 OAMs attach to the host
 system via PCIe Gen 4 x16 links (yellow lines). Each GCD maintains its own PCIe
 x16 link to the host part of the system. Depending on the server platform, the
@@ -121,15 +111,9 @@ GCD can attach to the AMD EPYC processor directly or via an optional PCIe switch
 . Note that some platforms may offer an x8 interface to the GCDs, which reduces
 the available host-to-GPU bandwidth.

-:::{figure-md} mi250-block
+![Block diagram of AMD Instinct MI250 Accelerators with 3rd Generation AMD EPYC processor](../../data/conceptual/gpu-arch/image003.png "Block diagram of AMD Instinct MI250 Accelerators with 3rd Generation AMD EPYC processor")

-<img src="../../data/reference/gpu_arch/image.003.png" alt="Block diagram of AMD Instinct MI250 Accelerators with 3rd Generation AMD EPYC processor.">
-
-Block diagram of AMD Instinct MI250 Accelerators with 3rd Generation
-AMD EPYC processor.
-:::
-
-{numref}`mi250-block` shows the node-level architecture of a system with AMD
+The preceding image shows the node-level architecture of a system with AMD
 EPYC processors in a dual-socket configuration and four AMD Instinct MI250
 accelerators. The MI250 OAMs attach to the host processors system via PCIe Gen 4
 x16 links (yellow lines). Depending on the system design, a PCIe switch may
@@ -146,4 +130,4 @@ two GPU dies in the MI250 OAM and operates at 25 GT/sec, which corresponds to a
 theoretical peak transfer rate of 50 GB/sec per link (or 100 GB/sec
 bidirectional peak transfer bandwidth). The GCD pairs 2 and 6 as well as GCDs 0
 and 4 connect via two XGMI links, which is indicated by the thicker red line in
-{numref}`mi250-block`.
+the preceding image.
--- a/docs/conceptual/gpu-arch/mi300-mi200-performance-counters.rst
+++ b/docs/conceptual/gpu-arch/mi300-mi200-performance-counters.rst
@@ -0,0 +1,758 @@
+.. meta::
+  :description: MI300 and MI200 series performance counters and metrics
+  :keywords: MI300, MI200, performance counters, command processor counters
+
+***************************************************************************************************
+MI300 and MI200 series performance counters and metrics
+***************************************************************************************************
+
+This document lists and describes the hardware performance counters and derived metrics available
+for the AMD Instinct™ MI300 and MI200 GPU. You can also access this information using the
+:doc:`ROCProfiler tool <rocprofiler:rocprofv1>`.
+
+MI300 and MI200 series performance counters
+===============================================================
+
+Series performance counters include the following categories:
+
+* :ref:`command-processor-counters`
+* :ref:`graphics-register-bus-manager-counters`
+* :ref:`spi-counters`
+* :ref:`compute-unit-counters`
+* :ref:`l1i-and-sl1d-cache-counters`
+* :ref:`vector-l1-cache-subsystem-counters`
+* :ref:`l2-cache-access-counters`
+
+The following sections provide additional details for each category.
+
+.. note::
+
+  Preliminary validation of all MI300 and MI200 series performance counters is in progress. Those with
+  an asterisk (*) require further evaluation.
+
+.. _command-processor-counters:
+
+Command processor counters
+---------------------------------------------------------------------------------------------------------------
+
+Command processor counters are further classified into command processor-fetcher and command
+processor-compute.
+
+Command processor-fetcher counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``CPF_CMP_UTCL1_STALL_ON_TRANSLATION``", "Cycles", "Number of cycles one of the compute unified translation caches (L1) is stalled waiting on translation"
+  "``CPF_CPF_STAT_BUSY``", "Cycles", "Number of cycles command processor-fetcher is busy"
+  "``CPF_CPF_STAT_IDLE``", "Cycles", "Number of cycles command processor-fetcher is idle"
+  "``CPF_CPF_STAT_STALL``", "Cycles", "Number of cycles command processor-fetcher is stalled"
+  "``CPF_CPF_TCIU_BUSY``", "Cycles", "Number of cycles command processor-fetcher texture cache interface unit interface is busy"
+  "``CPF_CPF_TCIU_IDLE``", "Cycles", "Number of cycles command processor-fetcher texture cache interface unit interface is idle"
+  "``CPF_CPF_TCIU_STALL``", "Cycles", "Number of cycles command processor-fetcher texture cache interface unit interface is stalled waiting on free tags"
+
+The texture cache interface unit is the interface between the command processor and the memory
+system.
+
+Command processor-compute counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``CPC_ME1_BUSY_FOR_PACKET_DECODE``", "Cycles", "Number of cycles command processor-compute micro engine is busy decoding packets"
+  "``CPC_UTCL1_STALL_ON_TRANSLATION``", "Cycles", "Number of cycles one of the unified translation caches (L1) is stalled waiting on translation"
+  "``CPC_CPC_STAT_BUSY``", "Cycles", "Number of cycles command processor-compute is busy"
+  "``CPC_CPC_STAT_IDLE``", "Cycles", "Number of cycles command processor-compute is idle"
+  "``CPC_CPC_STAT_STALL``", "Cycles", "Number of cycles command processor-compute is stalled"
+  "``CPC_CPC_TCIU_BUSY``", "Cycles", "Number of cycles command processor-compute texture cache interface unit interface is busy"
+  "``CPC_CPC_TCIU_IDLE``", "Cycles", "Number of cycles command processor-compute texture cache interface unit interface is idle"
+  "``CPC_CPC_UTCL2IU_BUSY``", "Cycles", "Number of cycles command processor-compute unified translation cache (L2) interface is busy"
+  "``CPC_CPC_UTCL2IU_IDLE``", "Cycles", "Number of cycles command processor-compute unified translation cache (L2) interface is idle"
+  "``CPC_CPC_UTCL2IU_STALL``", "Cycles", "Number of cycles command processor-compute unified translation cache (L2) interface is stalled"
+  "``CPC_ME1_DC0_SPI_BUSY``", "Cycles", "Number of cycles command processor-compute micro engine processor is busy"
+
+The micro engine runs packet-processing firmware on the command processor-compute counter.
+
+.. _graphics-register-bus-manager-counters:
+
+Graphics register bus manager counters
+---------------------------------------------------------------------------------------------------------------
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``GRBM_COUNT``", "Cycles","Number of free-running GPU cycles"
+  "``GRBM_GUI_ACTIVE``", "Cycles", "Number of GPU active cycles"
+  "``GRBM_CP_BUSY``", "Cycles", "Number of cycles any of the command processor blocks are busy"
+  "``GRBM_SPI_BUSY``", "Cycles", "Number of cycles any of the shader processor input is busy in the shader engines"
+  "``GRBM_TA_BUSY``", "Cycles", "Number of cycles any of the texture addressing unit is busy in the shader engines"
+  "``GRBM_TC_BUSY``", "Cycles", "Number of cycles any of the texture cache blocks are busy"
+  "``GRBM_CPC_BUSY``", "Cycles", "Number of cycles the command processor-compute is busy"
+  "``GRBM_CPF_BUSY``", "Cycles", "Number of cycles the command processor-fetcher is busy"
+  "``GRBM_UTCL2_BUSY``", "Cycles", "Number of cycles the unified translation cache (Level 2 [L2]) block is busy"
+  "``GRBM_EA_BUSY``", "Cycles", "Number of cycles the efficiency arbiter block is busy"
+
+Texture cache blocks include:
+
+* Texture cache arbiter
+* Texture cache per pipe, also known as vector Level 1 (L1) cache
+* Texture cache per channel, also known as known as L2 cache
+* Texture cache interface
+
+.. _spi-counters:
+
+Shader processor input counters
+---------------------------------------------------------------------------------------------------------------
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SPI_CSN_BUSY``", "Cycles", "Number of cycles with outstanding waves"
+  "``SPI_CSN_WINDOW_VALID``", "Cycles", "Number of cycles enabled by ``perfcounter_start`` event"
+  "``SPI_CSN_NUM_THREADGROUPS``", "Workgroups", "Number of dispatched workgroups"
+  "``SPI_CSN_WAVE``", "Wavefronts", "Number of dispatched wavefronts"
+  "``SPI_RA_REQ_NO_ALLOC``", "Cycles", "Number of arbiter cycles with requests but no allocation"
+  "``SPI_RA_REQ_NO_ALLOC_CSN``", "Cycles", "Number of arbiter cycles with compute shader (n\ :sup:`th` pipe) requests but no compute shader (n\ :sup:`th` pipe) allocation"
+  "``SPI_RA_RES_STALL_CSN``", "Cycles", "Number of arbiter stall cycles due to shortage of compute shader (n\ :sup:`th` pipe) pipeline slots"
+  "``SPI_RA_TMP_STALL_CSN``", "Cycles", "Number of stall cycles due to shortage of temp space"
+  "``SPI_RA_WAVE_SIMD_FULL_CSN``", "SIMD-cycles", "Accumulated number of single instruction, multiple data (SIMD) per cycle affected by shortage of wave slots for compute shader (n\ :sup:`th` pipe) wave dispatch"
+  "``SPI_RA_VGPR_SIMD_FULL_CSN``", "SIMD-cycles", "Accumulated number of SIMDs per cycle affected by shortage of vector general-purpose register (VGPR) slots for compute shader (n\ :sup:`th` pipe) wave dispatch"
+  "``SPI_RA_SGPR_SIMD_FULL_CSN``", "SIMD-cycles", "Accumulated number of SIMDs per cycle affected by shortage of scalar general-purpose register (SGPR) slots for compute shader (n\ :sup:`th` pipe) wave dispatch"
+  "``SPI_RA_LDS_CU_FULL_CSN``", "CU", "Number of compute units affected by shortage of local data share (LDS) space for compute shader (n\ :sup:`th` pipe) wave dispatch"
+  "``SPI_RA_BAR_CU_FULL_CSN``", "CU", "Number of compute units with compute shader (n\ :sup:`th` pipe) waves waiting at a BARRIER"
+  "``SPI_RA_BULKY_CU_FULL_CSN``", "CU", "Number of compute units with compute shader (n\ :sup:`th` pipe) waves waiting for BULKY resource"
+  "``SPI_RA_TGLIM_CU_FULL_CSN``", "Cycles", "Number of compute shader (n\ :sup:`th` pipe) wave stall cycles due to restriction of ``tg_limit`` for thread group size"
+  "``SPI_RA_WVLIM_STALL_CSN``", "Cycles", "Number of cycles compute shader (n\ :sup:`th` pipe) is stalled due to ``WAVE_LIMIT``"
+  "``SPI_VWC_CSC_WR``", "Qcycles", "Number of quad-cycles taken to initialize VGPRs when launching waves"
+  "``SPI_SWC_CSC_WR``", "Qcycles", "Number of quad-cycles taken to initialize SGPRs when launching waves"
+
+.. _compute-unit-counters:
+
+Compute unit counters
+---------------------------------------------------------------------------------------------------------------
+
+The compute unit counters are further classified into instruction mix, matrix fused multiply-add (FMA)
+operation counters, level counters, wavefront counters, wavefront cycle counters, and LDS counters.
+
+Instruction mix
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SQ_INSTS``", "Instr", "Number of instructions issued"
+  "``SQ_INSTS_VALU``", "Instr", "Number of vector arithmetic logic unit (VALU) instructions including matrix FMA issued"
+  "``SQ_INSTS_VALU_ADD_F16``", "Instr", "Number of VALU half-precision floating-point (F16) ``ADD`` or ``SUB`` instructions issued"
+  "``SQ_INSTS_VALU_MUL_F16``", "Instr", "Number of VALU F16 Multiply instructions issued"
+  "``SQ_INSTS_VALU_FMA_F16``", "Instr", "Number of VALU F16 FMA or multiply-add instructions issued"
+  "``SQ_INSTS_VALU_TRANS_F16``", "Instr", "Number of VALU F16 Transcendental instructions issued"
+  "``SQ_INSTS_VALU_ADD_F32``", "Instr", "Number of VALU full-precision floating-point (F32) ``ADD`` or ``SUB`` instructions issued"
+  "``SQ_INSTS_VALU_MUL_F32``", "Instr", "Number of VALU F32 Multiply instructions issued"
+  "``SQ_INSTS_VALU_FMA_F32``", "Instr", "Number of VALU F32 FMAor multiply-add instructions issued"
+  "``SQ_INSTS_VALU_TRANS_F32``", "Instr", "Number of VALU F32 Transcendental instructions issued"
+  "``SQ_INSTS_VALU_ADD_F64``", "Instr", "Number of VALU F64 ``ADD`` or ``SUB`` instructions issued"
+  "``SQ_INSTS_VALU_MUL_F64``", "Instr", "Number of VALU F64 Multiply instructions issued"
+  "``SQ_INSTS_VALU_FMA_F64``", "Instr", "Number of VALU F64 FMA or multiply-add instructions issued"
+  "``SQ_INSTS_VALU_TRANS_F64``", "Instr", "Number of VALU F64 Transcendental instructions issued"
+  "``SQ_INSTS_VALU_INT32``", "Instr", "Number of VALU 32-bit integer instructions (signed or unsigned) issued"
+  "``SQ_INSTS_VALU_INT64``", "Instr", "Number of VALU 64-bit integer instructions (signed or unsigned) issued"
+  "``SQ_INSTS_VALU_CVT``", "Instr", "Number of VALU Conversion instructions issued"
+  "``SQ_INSTS_VALU_MFMA_I8``", "Instr", "Number of 8-bit Integer matrix FMA instructions issued"
+  "``SQ_INSTS_VALU_MFMA_F16``", "Instr", "Number of F16 matrix FMA instructions issued"
+  "``SQ_INSTS_VALU_MFMA_F32``", "Instr", "Number of F32 matrix FMA instructions issued"
+  "``SQ_INSTS_VALU_MFMA_F64``", "Instr", "Number of F64 matrix FMA instructions issued"
+  "``SQ_INSTS_MFMA``", "Instr", "Number of matrix FMA instructions issued"
+  "``SQ_INSTS_VMEM_WR``", "Instr", "Number of vector memory write instructions (including flat) issued"
+  "``SQ_INSTS_VMEM_RD``", "Instr", "Number of vector memory read instructions (including flat) issued"
+  "``SQ_INSTS_VMEM``", "Instr", "Number of vector memory instructions issued, including both flat and buffer instructions"
+  "``SQ_INSTS_SALU``", "Instr", "Number of scalar arithmetic logic unit (SALU) instructions issued"
+  "``SQ_INSTS_SMEM``", "Instr", "Number of scalar memory instructions issued"
+  "``SQ_INSTS_SMEM_NORM``", "Instr", "Number of scalar memory instructions normalized to match ``smem_level`` issued"
+  "``SQ_INSTS_FLAT``", "Instr", "Number of flat instructions issued"
+  "``SQ_INSTS_FLAT_LDS_ONLY``", "Instr", "**MI200 series only** Number of FLAT instructions that read/write only from/to LDS issued. Works only if ``EARLY_TA_DONE`` is enabled."
+  "``SQ_INSTS_LDS``", "Instr", "Number of LDS instructions issued **(MI200: includes flat; MI300: does not include flat)**"
+  "``SQ_INSTS_GDS``", "Instr", "Number of global data share instructions issued"
+  "``SQ_INSTS_EXP_GDS``", "Instr", "Number of EXP and global data share instructions excluding skipped export instructions issued"
+  "``SQ_INSTS_BRANCH``", "Instr", "Number of branch instructions issued"
+  "``SQ_INSTS_SENDMSG``", "Instr", "Number of ``SENDMSG`` instructions including ``s_endpgm`` issued"
+  "``SQ_INSTS_VSKIPPED``", "Instr", "Number of vector instructions skipped"
+
+Flat instructions allow read, write, and atomic access to a generic memory address pointer that can
+resolve to any of the following physical memories:
+
+* Global Memory
+* Scratch ("private")
+* LDS ("shared")
+* Invalid - ``MEM_VIOL`` TrapStatus
+
+Matrix fused multiply-add operation counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SQ_INSTS_VALU_MFMA_MOPS_I8``", "IOP", "Number of 8-bit integer matrix FMA ops in the unit of 512"
+  "``SQ_INSTS_VALU_MFMA_MOPS_F16``", "FLOP", "Number of F16 floating matrix FMA ops in the unit of 512"
+  "``SQ_INSTS_VALU_MFMA_MOPS_BF16``", "FLOP", "Number of BF16 floating matrix FMA ops in the unit of 512"
+  "``SQ_INSTS_VALU_MFMA_MOPS_F32``", "FLOP", "Number of F32 floating matrix FMA ops in the unit of 512"
+  "``SQ_INSTS_VALU_MFMA_MOPS_F64``", "FLOP", "Number of F64 floating matrix FMA ops in the unit of 512"
+
+Level counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. note::
+
+  All level counters must be followed by ``SQ_ACCUM_PREV_HIRES`` counter to measure average latency.
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SQ_ACCUM_PREV``", "Count", "Accumulated counter sample value where accumulation takes place once every four cycles"
+  "``SQ_ACCUM_PREV_HIRES``", "Count", "Accumulated counter sample value where accumulation takes place once every cycle"
+  "``SQ_LEVEL_WAVES``", "Waves", "Number of inflight waves"
+  "``SQ_INST_LEVEL_VMEM``", "Instr", "Number of inflight vector memory (including flat) instructions"
+  "``SQ_INST_LEVEL_SMEM``", "Instr", "Number of inflight scalar memory instructions"
+  "``SQ_INST_LEVEL_LDS``", "Instr", "Number of inflight LDS (including flat) instructions"
+  "``SQ_IFETCH_LEVEL``", "Instr", "Number of inflight instruction fetch requests from the cache"
+
+Use the following formulae to calculate latencies:
+
+* Vector memory latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_INSTS_VMEM``
+* Wave latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_WAVE``
+* LDS latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_INSTS_LDS``
+* Scalar memory latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_INSTS_SMEM_NORM``
+* Instruction fetch latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_IFETCH``
+
+Wavefront counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SQ_WAVES``", "Waves", "Number of wavefronts dispatched to sequencers, including both new and restored wavefronts"
+  "``SQ_WAVES_SAVED``", "Waves", "Number of context-saved waves"
+  "``SQ_WAVES_RESTORED``", "Waves", "Number of context-restored waves sent to sequencers"
+  "``SQ_WAVES_EQ_64``", "Waves", "Number of wavefronts with exactly 64 active threads sent to sequencers"
+  "``SQ_WAVES_LT_64``", "Waves", "Number of wavefronts with less than 64 active threads sent to sequencers"
+  "``SQ_WAVES_LT_48``", "Waves", "Number of wavefronts with less than 48 active threads sent to sequencers"
+  "``SQ_WAVES_LT_32``", "Waves", "Number of wavefronts with less than 32 active threads sent to sequencers"
+  "``SQ_WAVES_LT_16``", "Waves", "Number of wavefronts with less than 16 active threads sent to sequencers"
+
+Wavefront cycle counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SQ_CYCLES``", "Cycles", "Clock cycles"
+  "``SQ_BUSY_CYCLES``", "Cycles", "Number of cycles while sequencers reports it to be busy"
+  "``SQ_BUSY_CU_CYCLES``", "Qcycles", "Number of quad-cycles each compute unit is busy"
+  "``SQ_VALU_MFMA_BUSY_CYCLES``", "Cycles", "Number of cycles the matrix FMA arithmetic logic unit (ALU) is busy"
+  "``SQ_WAVE_CYCLES``", "Qcycles", "Number of quad-cycles spent by waves in the compute units"
+  "``SQ_WAIT_ANY``", "Qcycles", "Number of quad-cycles spent waiting for anything"
+  "``SQ_WAIT_INST_ANY``", "Qcycles", "Number of quad-cycles spent waiting for any instruction to be issued"
+  "``SQ_ACTIVE_INST_ANY``", "Qcycles", "Number of quad-cycles spent by each wave to work on an instruction"
+  "``SQ_ACTIVE_INST_VMEM``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a vector memory instruction"
+  "``SQ_ACTIVE_INST_LDS``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on an LDS instruction"
+  "``SQ_ACTIVE_INST_VALU``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a VALU instruction"
+  "``SQ_ACTIVE_INST_SCA``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a SALU or scalar memory instruction"
+  "``SQ_ACTIVE_INST_EXP_GDS``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on an ``EXPORT`` or ``GDS`` instruction"
+  "``SQ_ACTIVE_INST_MISC``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a ``BRANCH`` or ``SENDMSG`` instruction"
+  "``SQ_ACTIVE_INST_FLAT``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a flat instruction"
+  "``SQ_INST_CYCLES_VMEM_WR``", "Qcycles", "Number of quad-cycles spent to send addr and cmd data for vector memory write instructions"
+  "``SQ_INST_CYCLES_VMEM_RD``", "Qcycles", "Number of quad-cycles spent to send addr and cmd data for vector memory read instructions"
+  "``SQ_INST_CYCLES_SMEM``", "Qcycles", "Number of quad-cycles spent to execute scalar memory reads"
+  "``SQ_INST_CYCLES_SALU``", "Qcycles", "Number of quad-cycles spent to execute non-memory read scalar operations"
+  "``SQ_THREAD_CYCLES_VALU``", "Qcycles", "Number of quad-cycles spent to execute VALU operations on active threads"
+  "``SQ_WAIT_INST_LDS``", "Qcycles", "Number of quad-cycles spent waiting for LDS instruction to be issued"
+
+``SQ_THREAD_CYCLES_VALU`` is similar to ``INST_CYCLES_VALU``, but it's multiplied by the number of
+active threads.
+
+LDS counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SQ_LDS_ATOMIC_RETURN``", "Cycles", "Number of atomic return cycles in LDS"
+  "``SQ_LDS_BANK_CONFLICT``", "Cycles", "Number of cycles LDS is stalled by bank conflicts"
+  "``SQ_LDS_ADDR_CONFLICT``", "Cycles", "Number of cycles LDS is stalled by address conflicts"
+  "``SQ_LDS_UNALIGNED_STALL``", "Cycles", "Number of cycles LDS is stalled processing flat unaligned load or store operations"
+  "``SQ_LDS_MEM_VIOLATIONS``", "Count", "Number of threads that have a memory violation in the LDS"
+  "``SQ_LDS_IDX_ACTIVE``", "Cycles", "Number of cycles LDS is used for indexed operations"
+
+Miscellaneous counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SQ_IFETCH``", "Count", "Number of instruction fetch requests from L1i, in 32-byte width"
+  "``SQ_ITEMS``", "Threads", "Number of valid items per wave"
+
+.. _l1i-and-sl1d-cache-counters:
+
+L1 instruction cache (L1i) and scalar L1 data cache (L1d) counters
+---------------------------------------------------------------------------------------------------------------
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition"
+
+  "``SQC_ICACHE_REQ``", "Req", "Number of L1 instruction (L1i) cache requests"
+  "``SQC_ICACHE_HITS``", "Count", "Number of L1i cache hits"
+  "``SQC_ICACHE_MISSES``", "Count", "Number of non-duplicate L1i cache misses including uncached requests"
+  "``SQC_ICACHE_MISSES_DUPLICATE``", "Count", "Number of duplicate L1i cache misses whose previous lookup miss on the same cache line is not fulfilled yet"
+  "``SQC_DCACHE_REQ``", "Req", "Number of scalar L1d requests"
+  "``SQC_DCACHE_INPUT_VALID_READYB``", "Cycles", "Number of cycles while sequencer input is valid but scalar L1d is not ready"
+  "``SQC_DCACHE_HITS``", "Count", "Number of scalar L1d hits"
+  "``SQC_DCACHE_MISSES``", "Count", "Number of non-duplicate scalar L1d misses including uncached requests"
+  "``SQC_DCACHE_MISSES_DUPLICATE``", "Count", "Number of duplicate scalar L1d misses"
+  "``SQC_DCACHE_REQ_READ_1``", "Req", "Number of constant cache read requests in a single 32-bit data word"
+  "``SQC_DCACHE_REQ_READ_2``", "Req", "Number of constant cache read requests in two 32-bit data words"
+  "``SQC_DCACHE_REQ_READ_4``", "Req", "Number of constant cache read requests in four 32-bit data words"
+  "``SQC_DCACHE_REQ_READ_8``", "Req", "Number of constant cache read requests in eight 32-bit data words"
+  "``SQC_DCACHE_REQ_READ_16``", "Req", "Number of constant cache read requests in 16 32-bit data words"
+  "``SQC_DCACHE_ATOMIC``", "Req", "Number of atomic requests"
+  "``SQC_TC_REQ``", "Req", "Number of texture cache requests that were issued by instruction and constant caches"
+  "``SQC_TC_INST_REQ``", "Req", "Number of instruction requests to the L2 cache"
+  "``SQC_TC_DATA_READ_REQ``", "Req", "Number of data Read requests to the L2 cache"
+  "``SQC_TC_DATA_WRITE_REQ``", "Req", "Number of data write requests to the L2 cache"
+  "``SQC_TC_DATA_ATOMIC_REQ``", "Req", "Number of data atomic requests to the L2 cache"
+  "``SQC_TC_STALL``", "Cycles", "Number of cycles while the valid requests to the L2 cache are stalled"
+
+.. _vector-l1-cache-subsystem-counters:
+
+Vector L1 cache subsystem counters
+---------------------------------------------------------------------------------------------------------------
+
+The vector L1 cache subsystem counters are further classified into texture addressing unit, texture data
+unit, vector L1d or texture cache per pipe, and texture cache arbiter counters.
+
+Texture addressing unit counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
+
+  "``TA_TA_BUSY[n]``", "Cycles", "Texture addressing unit busy cycles", "0-15"
+  "``TA_TOTAL_WAVEFRONTS[n]``", "Instr", "Number of wavefronts processed by texture addressing unit", "0-15"
+  "``TA_BUFFER_WAVEFRONTS[n]``", "Instr", "Number of buffer wavefronts processed by texture addressing unit", "0-15"
+  "``TA_BUFFER_READ_WAVEFRONTS[n]``", "Instr", "Number of buffer read wavefronts processed by texture addressing unit", "0-15"
+  "``TA_BUFFER_WRITE_WAVEFRONTS[n]``", "Instr", "Number of buffer write wavefronts processed by texture addressing unit", "0-15"
+  "``TA_BUFFER_ATOMIC_WAVEFRONTS[n]``", "Instr", "Number of buffer atomic wavefronts processed by texture addressing unit", "0-15"
+  "``TA_BUFFER_TOTAL_CYCLES[n]``", "Cycles", "Number of buffer cycles (including read and write) issued to texture cache", "0-15"
+  "``TA_BUFFER_COALESCED_READ_CYCLES[n]``", "Cycles", "Number of coalesced buffer read cycles issued to texture cache", "0-15"
+  "``TA_BUFFER_COALESCED_WRITE_CYCLES[n]``", "Cycles", "Number of coalesced buffer write cycles issued to texture cache", "0-15"
+  "``TA_ADDR_STALLED_BY_TC_CYCLES[n]``", "Cycles", "Number of cycles texture addressing unit address path is stalled by texture cache", "0-15"
+  "``TA_DATA_STALLED_BY_TC_CYCLES[n]``", "Cycles", "Number of cycles texture addressing unit data path is stalled by texture cache", "0-15"
+  "``TA_ADDR_STALLED_BY_TD_CYCLES[n]``", "Cycles", "Number of cycles texture addressing unit address path is stalled by texture data unit", "0-15"
+  "``TA_FLAT_WAVEFRONTS[n]``", "Instr", "Number of flat opcode wavefronts processed by texture addressing unit", "0-15"
+  "``TA_FLAT_READ_WAVEFRONTS[n]``", "Instr", "Number of flat opcode read wavefronts processed by texture addressing unit", "0-15"
+  "``TA_FLAT_WRITE_WAVEFRONTS[n]``", "Instr", "Number of flat opcode write wavefronts processed by texture addressing unit", "0-15"
+  "``TA_FLAT_ATOMIC_WAVEFRONTS[n]``", "Instr", "Number of flat opcode atomic wavefronts processed by texture addressing unit", "0-15"
+
+Texture data unit counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
+
+  "``TD_TD_BUSY[n]``", "Cycle", "Texture data unit busy cycles while it is processing or waiting for data", "0-15"
+  "``TD_TC_STALL[n]``", "Cycle", "Number of cycles texture data unit is stalled waiting for texture cache data", "0-15"
+  "``TD_SPI_STALL[n]``", "Cycle", "Number of cycles texture data unit is stalled by shader processor input", "0-15"
+  "``TD_LOAD_WAVEFRONT[n]``", "Instr", "Number of wavefront instructions (read, write, atomic)", "0-15"
+  "``TD_STORE_WAVEFRONT[n]``", "Instr", "Number of write wavefront instructions", "0-15"
+  "``TD_ATOMIC_WAVEFRONT[n]``", "Instr", "Number of atomic wavefront instructions", "0-15"
+  "``TD_COALESCABLE_WAVEFRONT[n]``", "Instr", "Number of coalescable wavefronts according to texture addressing unit", "0-15"
+
+Texture cache per pipe counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
+
+  "``TCP_GATE_EN1[n]``", "Cycles", "Number of cycles vector L1d interface clocks are turned on", "0-15"
+  "``TCP_GATE_EN2[n]``", "Cycles", "Number of cycles vector L1d core clocks are turned on", "0-15"
+  "``TCP_TD_TCP_STALL_CYCLES[n]``", "Cycles", "Number of cycles texture data unit stalls vector L1d", "0-15"
+  "``TCP_TCR_TCP_STALL_CYCLES[n]``", "Cycles", "Number of cycles texture cache router stalls vector L1d", "0-15"
+  "``TCP_READ_TAGCONFLICT_STALL_CYCLES[n]``", "Cycles", "Number of cycles tag RAM conflict stalls on a read", "0-15"
+  "``TCP_WRITE_TAGCONFLICT_STALL_CYCLES[n]``", "Cycles", "Number of cycles tag RAM conflict stalls on a write", "0-15"
+  "``TCP_ATOMIC_TAGCONFLICT_STALL_CYCLES[n]``", "Cycles", "Number of cycles tag RAM conflict stalls on an atomic", "0-15"
+  "``TCP_PENDING_STALL_CYCLES[n]``", "Cycles", "Number of cycles vector L1d is stalled due to data pending from L2 Cache", "0-15"
+  "``TCP_TCP_TA_DATA_STALL_CYCLES``", "Cycles", "Number of cycles texture cache per pipe stalls texture addressing unit data interface", "NA"
+  "``TCP_TA_TCP_STATE_READ[n]``", "Req", "Number of state reads", "0-15"
+  "``TCP_VOLATILE[n]``", "Req", "Number of L1 volatile pixels or buffers from texture addressing unit", "0-15"
+  "``TCP_TOTAL_ACCESSES[n]``", "Req", "Number of vector L1d accesses. Equals ``TCP_PERF_SEL_TOTAL_READ`+`TCP_PERF_SEL_TOTAL_NONREAD``", "0-15"
+  "``TCP_TOTAL_READ[n]``", "Req", "Number of vector L1d read accesses", "0-15"
+  "``TCP_TOTAL_WRITE[n]``", "Req", "Number of vector L1d write accesses", "0-15"
+  "``TCP_TOTAL_ATOMIC_WITH_RET[n]``", "Req", "Number of vector L1d atomic requests with return", "0-15"
+  "``TCP_TOTAL_ATOMIC_WITHOUT_RET[n]``", "Req", "Number of vector L1d atomic without return", "0-15"
+  "``TCP_TOTAL_WRITEBACK_INVALIDATES[n]``", "Count", "Total number of vector L1d writebacks and invalidates", "0-15"
+  "``TCP_UTCL1_REQUEST[n]``", "Req", "Number of address translation requests to unified translation cache (L1)", "0-15"
+  "``TCP_UTCL1_TRANSLATION_HIT[n]``", "Req", "Number of unified translation cache (L1) translation hits", "0-15"
+  "``TCP_UTCL1_TRANSLATION_MISS[n]``", "Req", "Number of unified translation cache (L1) translation misses", "0-15"
+  "``TCP_UTCL1_PERMISSION_MISS[n]``", "Req", "Number of unified translation cache (L1) permission misses", "0-15"
+  "``TCP_TOTAL_CACHE_ACCESSES[n]``", "Req", "Number of vector L1d cache accesses including hits and misses", "0-15"
+  "``TCP_TCP_LATENCY[n]``", "Cycles", "**MI200 series only** Accumulated wave access latency to vL1D over all wavefronts", "0-15"
+  "``TCP_TCC_READ_REQ_LATENCY[n]``", "Cycles", "**MI200 series only** Total vL1D to L2 request latency over all wavefronts for reads and atomics with return", "0-15"
+  "``TCP_TCC_WRITE_REQ_LATENCY[n]``", "Cycles", "**MI200 series only** Total vL1D to L2 request latency over all wavefronts for writes and atomics without return", "0-15"
+  "``TCP_TCC_READ_REQ[n]``", "Req", "Number of read requests to L2 cache", "0-15"
+  "``TCP_TCC_WRITE_REQ[n]``", "Req", "Number of write requests to L2 cache", "0-15"
+  "``TCP_TCC_ATOMIC_WITH_RET_REQ[n]``", "Req", "Number of atomic requests to L2 cache with return", "0-15"
+  "``TCP_TCC_ATOMIC_WITHOUT_RET_REQ[n]``", "Req", "Number of atomic requests to L2 cache without return", "0-15"
+  "``TCP_TCC_NC_READ_REQ[n]``", "Req", "Number of non-coherently cached read requests to L2 cache", "0-15"
+  "``TCP_TCC_UC_READ_REQ[n]``", "Req", "Number of uncached read requests to L2 cache", "0-15"
+  "``TCP_TCC_CC_READ_REQ[n]``", "Req", "Number of coherently cached read requests to L2 cache", "0-15"
+  "``TCP_TCC_RW_READ_REQ[n]``", "Req", "Number of coherently cached with write read requests to L2 cache", "0-15"
+  "``TCP_TCC_NC_WRITE_REQ[n]``", "Req", "Number of non-coherently cached write requests to L2 cache", "0-15"
+  "``TCP_TCC_UC_WRITE_REQ[n]``", "Req", "Number of uncached write requests to L2 cache", "0-15"
+  "``TCP_TCC_CC_WRITE_REQ[n]``", "Req", "Number of coherently cached write requests to L2 cache", "0-15"
+  "``TCP_TCC_RW_WRITE_REQ[n]``", "Req", "Number of coherently cached with write write requests to L2 cache", "0-15"
+  "``TCP_TCC_NC_ATOMIC_REQ[n]``", "Req", "Number of non-coherently cached atomic requests to L2 cache", "0-15"
+  "``TCP_TCC_UC_ATOMIC_REQ[n]``", "Req", "Number of uncached atomic requests to L2 cache", "0-15"
+  "``TCP_TCC_CC_ATOMIC_REQ[n]``", "Req", "Number of coherently cached atomic requests to L2 cache", "0-15"
+  "``TCP_TCC_RW_ATOMIC_REQ[n]``", "Req", "Number of coherently cached with write atomic requests to L2 cache", "0-15"
+
+Note that:
+
+* ``TCP_TOTAL_READ[n]`` = ``TCP_PERF_SEL_TOTAL_HIT_LRU_READ`` + ``TCP_PERF_SEL_TOTAL_MISS_LRU_READ`` + ``TCP_PERF_SEL_TOTAL_MISS_EVICT_READ``
+* ``TCP_TOTAL_WRITE[n]`` = ``TCP_PERF_SEL_TOTAL_MISS_LRU_WRITE``+ ``TCP_PERF_SEL_TOTAL_MISS_EVICT_WRITE``
+* ``TCP_TOTAL_WRITEBACK_INVALIDATES[n]`` = ``TCP_PERF_SEL_TOTAL_WBINVL1``+ ``TCP_PERF_SEL_TOTAL_WBINVL1_VOL``+ ``TCP_PERF_SEL_CP_TCP_INVALIDATE``+ ``TCP_PERF_SEL_SQ_TCP_INVALIDATE_VOL``
+
+Texture cache arbiter counters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+  :header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
+
+  "``TCA_CYCLE[n]``", "Cycles", "Number of texture cache arbiter cycles", "0-31"
+  "``TCA_BUSY[n]``", "Cycles", "Number of cycles texture cache arbiter has a pending request", "0-31"
+
+.. _l2-cache-access-counters:
+
+L2 cache access counters
+---------------------------------------------------------------------------------------------------------------
+
+L2 cache is also known as texture cache per channel.
+
+.. tab-set::
+
+    .. tab-item:: MI300 hardware counter
+
+      .. csv-table::
+        :header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
+
+        "``TCC_CYCLE[n]``", "Cycles", "Number of L2 cache free-running clocks", "0-31"
+        "``TCC_BUSY[n]``", "Cycles", "Number of L2 cache busy cycles", "0-31"
+        "``TCC_REQ[n]``", "Req", "Number of L2 cache requests of all types (measured at the tag block)", "0-31"
+        "``TCC_STREAMING_REQ[n]``", "Req", "Number of L2 cache streaming requests (measured at the tag block)", "0-31"
+        "``TCC_NC_REQ[n]``", "Req", "Number of non-coherently cached requests (measured at the tag block)", "0-31"
+        "``TCC_UC_REQ[n]``", "Req", "Number of uncached requests. This is measured at the tag block", "0-31"
+        "``TCC_CC_REQ[n]``", "Req", "Number of coherently cached requests. This is measured at the tag block", "0-31"
+        "``TCC_RW_REQ[n]``", "Req", "Number of coherently cached with write requests. This is measured at the tag block", "0-31"
+        "``TCC_PROBE[n]``", "Req", "Number of probe requests", "0-31"
+        "``TCC_PROBE_ALL[n]``", "Req", "Number of external probe requests with ``EA_TCC_preq_all == 1``", "0-31"
+        "``TCC_READ[n]``", "Req", "Number of L2 cache read requests (includes compressed reads but not metadata reads)", "0-31"
+        "``TCC_WRITE[n]``", "Req", "Number of L2 cache write requests", "0-31"
+        "``TCC_ATOMIC[n]``", "Req", "Number of L2 cache atomic requests of all types", "0-31"
+        "``TCC_HIT[n]``", "Req", "Number of L2 cache hits", "0-31"
+        "``TCC_MISS[n]``", "Req", "Number of L2 cache misses", "0-31"
+        "``TCC_WRITEBACK[n]``", "Req", "Number of lines written back to the main memory, including writebacks of dirty lines and uncached write or atomic requests", "0-31"
+        "``TCC_EA0_WRREQ[n]``", "Req", "Number of 32-byte and 64-byte transactions going over the ``TC_EA_wrreq`` interface (doesn't include probe commands)", "0-31"
+        "``TCC_EA0_WRREQ_64B[n]``", "Req", "Total number of 64-byte transactions (write or ``CMPSWAP``) going over the ``TC_EA_wrreq`` interface", "0-31"
+        "``TCC_EA0_WR_UNCACHED_32B[n]``", "Req", "Number of 32 or 64-byte write or atomic going over the ``TC_EA_wrreq`` interface due to uncached traffic", "0-31"
+        "``TCC_EA0_WRREQ_STALL[n]``", "Cycles", "Number of cycles a write request is stalled", "0-31"
+        "``TCC_EA0_WRREQ_IO_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of input-output (IO) credits", "0-31"
+        "``TCC_EA0_WRREQ_GMI_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of GMI credits", "0-31"
+        "``TCC_EA0_WRREQ_DRAM_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of DRAM credits", "0-31"
+        "``TCC_TOO_MANY_EA_WRREQS_STALL[n]``", "Cycles", "Number of cycles the L2 cache is unable to send an efficiency arbiter write request due to it reaching its maximum capacity of pending efficiency arbiter write requests", "0-31"
+        "``TCC_EA0_WRREQ_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter write requests in flight", "0-31"
+        "``TCC_EA0_ATOMIC[n]``", "Req", "Number of 32-byte or 64-byte atomic requests going over the ``TC_EA_wrreq`` interface", "0-31"
+        "``TCC_EA0_ATOMIC_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter atomic requests in flight", "0-31"
+        "``TCC_EA0_RDREQ[n]``", "Req", "Number of 32-byte or 64-byte read requests to efficiency arbiter", "0-31"
+        "``TCC_EA0_RDREQ_32B[n]``", "Req", "Number of 32-byte read requests to efficiency arbiter", "0-31"
+        "``TCC_EA0_RD_UNCACHED_32B[n]``", "Req", "Number of 32-byte efficiency arbiter reads due to uncached traffic. A 64-byte request is counted as 2", "0-31"
+        "``TCC_EA0_RDREQ_IO_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of IO credits", "0-31"
+        "``TCC_EA0_RDREQ_GMI_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of GMI credits", "0-31"
+        "``TCC_EA0_RDREQ_DRAM_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of DRAM credits", "0-31"
+        "``TCC_EA0_RDREQ_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter read requests in flight", "0-31"
+        "``TCC_EA0_RDREQ_DRAM[n]``", "Req", "Number of 32-byte or 64-byte efficiency arbiter read requests to High Bandwidth Memory (HBM)", "0-31"
+        "``TCC_EA0_WRREQ_DRAM[n]``", "Req", "Number of 32-byte or 64-byte efficiency arbiter write requests to HBM", "0-31"
+        "``TCC_TAG_STALL[n]``", "Cycles", "Number of cycles the normal request pipeline in the tag is stalled for any reason", "0-31"
+        "``TCC_NORMAL_WRITEBACK[n]``", "Req", "Number of writebacks due to requests that are not writeback requests", "0-31"
+        "``TCC_ALL_TC_OP_WB_WRITEBACK[n]``", "Req", "Number of writebacks due to all ``TC_OP`` writeback requests", "0-31"
+        "``TCC_NORMAL_EVICT[n]``", "Req", "Number of evictions due to requests that are not invalidate or probe requests", "0-31"
+        "``TCC_ALL_TC_OP_INV_EVICT[n]``", "Req", "Number of evictions due to all ``TC_OP`` invalidate requests", "0-31"
+
+    .. tab-item:: MI200 hardware counter
+
+      .. csv-table::
+        :header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
+
+        "``TCC_CYCLE[n]``", "Cycles", "Number of L2 cache free-running clocks", "0-31"
+        "``TCC_BUSY[n]``", "Cycles", "Number of L2 cache busy cycles", "0-31"
+        "``TCC_REQ[n]``", "Req", "Number of L2 cache requests of all types (measured at the tag block)", "0-31"
+        "``TCC_STREAMING_REQ[n]``", "Req", "Number of L2 cache streaming requests (measured at the tag block)", "0-31"
+        "``TCC_NC_REQ[n]``", "Req", "Number of non-coherently cached requests (measured at the tag block)", "0-31"
+        "``TCC_UC_REQ[n]``", "Req", "Number of uncached requests. This is measured at the tag block", "0-31"
+        "``TCC_CC_REQ[n]``", "Req", "Number of coherently cached requests. This is measured at the tag block", "0-31"
+        "``TCC_RW_REQ[n]``", "Req", "Number of coherently cached with write requests. This is measured at the tag block", "0-31"
+        "``TCC_PROBE[n]``", "Req", "Number of probe requests", "0-31"
+        "``TCC_PROBE_ALL[n]``", "Req", "Number of external probe requests with ``EA_TCC_preq_all == 1``", "0-31"
+        "``TCC_READ[n]``", "Req", "Number of L2 cache read requests (includes compressed reads but not metadata reads)", "0-31"
+        "``TCC_WRITE[n]``", "Req", "Number of L2 cache write requests", "0-31"
+        "``TCC_ATOMIC[n]``", "Req", "Number of L2 cache atomic requests of all types", "0-31"
+        "``TCC_HIT[n]``", "Req", "Number of L2 cache hits", "0-31"
+        "``TCC_MISS[n]``", "Req", "Number of L2 cache misses", "0-31"
+        "``TCC_WRITEBACK[n]``", "Req", "Number of lines written back to the main memory, including writebacks of dirty lines and uncached write or atomic requests", "0-31"
+        "``TCC_EA_WRREQ[n]``", "Req", "Number of 32-byte and 64-byte transactions going over the ``TC_EA_wrreq`` interface (doesn't include probe commands)", "0-31"
+        "``TCC_EA_WRREQ_64B[n]``", "Req", "Total number of 64-byte transactions (write or ``CMPSWAP``) going over the ``TC_EA_wrreq`` interface", "0-31"
+        "``TCC_EA_WR_UNCACHED_32B[n]``", "Req", "Number of 32 write or atomic going over the ``TC_EA_wrreq`` interface due to uncached traffic. A 64-byte request will be counted as 2", "0-31"
+        "``TCC_EA_WRREQ_STALL[n]``", "Cycles", "Number of cycles a write request is stalled", "0-31"
+        "``TCC_EA_WRREQ_IO_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of input-output (IO) credits", "0-31"
+        "``TCC_EA_WRREQ_GMI_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of GMI credits", "0-31"
+        "``TCC_EA_WRREQ_DRAM_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of DRAM credits", "0-31"
+        "``TCC_TOO_MANY_EA_WRREQS_STALL[n]``", "Cycles", "Number of cycles the L2 cache is unable to send an efficiency arbiter write request due to it reaching its maximum capacity of pending efficiency arbiter write requests", "0-31"
+        "``TCC_EA_WRREQ_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter write requests in flight", "0-31"
+        "``TCC_EA_ATOMIC[n]``", "Req", "Number of 32-byte or 64-byte atomic requests going over the ``TC_EA_wrreq`` interface", "0-31"
+        "``TCC_EA_ATOMIC_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter atomic requests in flight", "0-31"
+        "``TCC_EA_RDREQ[n]``", "Req", "Number of 32-byte or 64-byte read requests to efficiency arbiter", "0-31"
+        "``TCC_EA_RDREQ_32B[n]``", "Req", "Number of 32-byte read requests to efficiency arbiter", "0-31"
+        "``TCC_EA_RD_UNCACHED_32B[n]``", "Req", "Number of 32-byte efficiency arbiter reads due to uncached traffic. A 64-byte request is counted as 2", "0-31"
+        "``TCC_EA_RDREQ_IO_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of IO credits", "0-31"
+        "``TCC_EA_RDREQ_GMI_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of GMI credits", "0-31"
+        "``TCC_EA_RDREQ_DRAM_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of DRAM credits", "0-31"
+        "``TCC_EA_RDREQ_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter read requests in flight", "0-31"
+        "``TCC_EA_RDREQ_DRAM[n]``", "Req", "Number of 32-byte or 64-byte efficiency arbiter read requests to High Bandwidth Memory (HBM)", "0-31"
+        "``TCC_EA_WRREQ_DRAM[n]``", "Req", "Number of 32-byte or 64-byte efficiency arbiter write requests to HBM", "0-31"
+        "``TCC_TAG_STALL[n]``", "Cycles", "Number of cycles the normal request pipeline in the tag is stalled for any reason", "0-31"
+        "``TCC_NORMAL_WRITEBACK[n]``", "Req", "Number of writebacks due to requests that are not writeback requests", "0-31"
+        "``TCC_ALL_TC_OP_WB_WRITEBACK[n]``", "Req", "Number of writebacks due to all ``TC_OP`` writeback requests", "0-31"
+        "``TCC_NORMAL_EVICT[n]``", "Req", "Number of evictions due to requests that are not invalidate or probe requests", "0-31"
+        "``TCC_ALL_TC_OP_INV_EVICT[n]``", "Req", "Number of evictions due to all ``TC_OP`` invalidate requests", "0-31"
+
+Note the following:
+
+* ``TCC_REQ[n]`` may be more than the number of requests arriving at the texture cache per channel,
+  but it's a good indication of the total amount of work that needs to be performed.
+* For ``TCC_EA0_WRREQ[n]``, atomics may travel over the same interface and are generally classified as
+  write requests.
+* CC mtypes can produce uncached requests, and those are included in
+  ``TCC_EA0_WR_UNCACHED_32B[n]``
+* ``TCC_EA0_WRREQ_LEVEL[n]`` is primarily intended to measure average efficiency arbiter write latency.
+
+  * Average write latency = ``TCC_PERF_SEL_EA0_WRREQ_LEVEL`` divided by ``TCC_PERF_SEL_EA0_WRREQ``
+
+* ``TCC_EA0_ATOMIC_LEVEL[n]`` is primarily intended to measure average efficiency arbiter atomic
+  latency
+
+  * Average atomic latency = ``TCC_PERF_SEL_EA0_WRREQ_ATOMIC_LEVEL`` divided by ``TCC_PERF_SEL_EA0_WRREQ_ATOMIC``
+
+* ``TCC_EA0_RDREQ_LEVEL[n]`` is primarily intended to measure average efficiency arbiter read latency.
+
+  * Average read latency = ``TCC_PERF_SEL_EA0_RDREQ_LEVEL`` divided by ``TCC_PERF_SEL_EA0_RDREQ``
+
+* Stalls can occur regardless of the need for a read to be performed
+* Normally, stalls are measured exactly at one point in the pipeline however in the case of
+  ``TCC_TAG_STALL[n]``, probes can stall the pipeline at a variety of places. There is no single point that
+  can accurately measure the total stalls
+
+MI300 and MI200 series derived metrics list
+==============================================================
+
+.. csv-table::
+  :header: "Hardware counter", "Definition"
+
+  "``ALUStalledByLDS``", "Percentage of GPU time ALU units are stalled due to the LDS input queue being full or the output queue not being ready (value range: 0% (optimal) to 100%)"
+  "``FetchSize``", "Total kilobytes fetched from the video memory; measured with all extra fetches and any cache or memory effects taken into account"
+  "``FlatLDSInsts``", "Average number of flat instructions that read from or write to LDS, run per work item (affected by flow control)"
+  "``FlatVMemInsts``", "Average number of flat instructions that read from or write to the video memory, run per work item (affected by flow control). Includes flat instructions that read from or write to scratch"
+  "``GDSInsts``", "Average number of global data share read or write instructions run per work item (affected by flow control)"
+  "``GPUBusy``", "Percentage of time GPU is busy"
+  "``L2CacheHit``", "Percentage of fetch, write, atomic, and other instructions that hit the data in L2 cache (value range: 0% (no hit) to 100% (optimal))"
+  "``LDSBankConflict``", "Percentage of GPU time LDS is stalled by bank conflicts (value range: 0% (optimal) to 100%)"
+  "``LDSInsts``", "Average number of LDS read or write instructions run per work item (affected by flow control). Excludes flat instructions that read from or write to LDS."
+  "``MemUnitBusy``", "Percentage of GPU time the memory unit is active, which is measured with all extra fetches and writes and any cache or memory effects taken into account (value range: 0% to 100% (fetch-bound))"
+  "``MemUnitStalled``", "Percentage of GPU time the memory unit is stalled (value range: 0% (optimal) to 100%)"
+  "``MemWrites32B``", "Total number of effective 32B write transactions to the memory"
+  "``TCA_BUSY_sum``", "Total number of cycles texture cache arbiter has a pending request, over all texture cache arbiter instances"
+  "``TCA_CYCLE_sum``", "Total number of cycles over all texture cache arbiter instances"
+  "``SALUBusy``", "Percentage of GPU time scalar ALU instructions are processed (value range: 0% to 100% (optimal))"
+  "``SALUInsts``", "Average number of scalar ALU instructions run per work item (affected by flow control)"
+  "``SFetchInsts``", "Average number of scalar fetch instructions from the video memory run per work item (affected by flow control)"
+  "``VALUBusy``", "Percentage of GPU time vector ALU instructions are processed (value range: 0% to 100% (optimal))"
+  "``VALUInsts``", "Average number of vector ALU instructions run per work item (affected by flow control)"
+  "``VALUUtilization``", "Percentage of active vector ALU threads in a wave, where a lower number can mean either more thread divergence in a wave or that the work-group size is not a multiple of 64 (value range: 0%, 100% (optimal - no thread divergence))"
+  "``VFetchInsts``", "Average number of vector fetch instructions from the video memory run per work-item (affected by flow control); excludes flat instructions that fetch from video memory"
+  "``VWriteInsts``", "Average number of vector write instructions to the video memory run per work-item (affected by flow control); excludes flat instructions that write to video memory"
+  "``Wavefronts``", "Total wavefronts"
+  "``WRITE_REQ_32B``", "Total number of 32-byte effective memory writes"
+  "``WriteSize``", "Total kilobytes written to the video memory; measured with all extra fetches and any cache or memory effects taken into account"
+  "``WriteUnitStalled``", "Percentage of GPU time the write unit is stalled (value range: 0% (optimal) to 100%)"
+
+You can lower ``ALUStalledByLDS`` by reducing LDS bank conflicts or number of LDS accesses.
+You can lower ``MemUnitStalled`` by reducing the number or size of fetches and writes.
+``MemUnitBusy`` includes the stall time (``MemUnitStalled``).
+
+Hardware counters by and over all texture addressing unit instances
+---------------------------------------------------------------------------------------------------------------
+
+The following table shows the hardware counters *by* all texture addressing unit instances.
+
+.. csv-table::
+  :header: "Hardware counter", "Definition"
+
+  "``TA_BUFFER_WAVEFRONTS_sum``", "Total number of buffer wavefronts processed"
+  "``TA_BUFFER_READ_WAVEFRONTS_sum``", "Total number of buffer read wavefronts processed"
+  "``TA_BUFFER_WRITE_WAVEFRONTS_sum``", "Total number of buffer write wavefronts processed"
+  "``TA_BUFFER_ATOMIC_WAVEFRONTS_sum``", "Total number of buffer atomic wavefronts processed"
+  "``TA_BUFFER_TOTAL_CYCLES_sum``", "Total number of buffer cycles (including read and write) issued to texture cache"
+  "``TA_BUFFER_COALESCED_READ_CYCLES_sum``", "Total number of coalesced buffer read cycles issued to texture cache"
+  "``TA_BUFFER_COALESCED_WRITE_CYCLES_sum``", "Total number of coalesced buffer write cycles issued to texture cache"
+  "``TA_FLAT_READ_WAVEFRONTS_sum``", "Sum of flat opcode reads processed"
+  "``TA_FLAT_WRITE_WAVEFRONTS_sum``", "Sum of flat opcode writes processed"
+  "``TA_FLAT_WAVEFRONTS_sum``", "Total number of flat opcode wavefronts processed"
+  "``TA_FLAT_READ_WAVEFRONTS_sum``", "Total number of flat opcode read wavefronts processed"
+  "``TA_FLAT_ATOMIC_WAVEFRONTS_sum``", "Total number of flat opcode atomic wavefronts processed"
+  "``TA_TOTAL_WAVEFRONTS_sum``", "Total number of wavefronts processed"
+
+The following table shows the hardware counters *over* all texture addressing unit instances.
+
+.. csv-table::
+  :header: "Hardware counter", "Definition"
+
+  "``TA_ADDR_STALLED_BY_TC_CYCLES_sum``", "Total number of cycles texture addressing unit address path is stalled by texture cache"
+  "``TA_ADDR_STALLED_BY_TD_CYCLES_sum``", "Total number of cycles texture addressing unit address path is stalled by texture data unit"
+  "``TA_BUSY_avr``", "Average number of busy cycles"
+  "``TA_BUSY_max``", "Maximum number of texture addressing unit busy cycles"
+  "``TA_BUSY_min``", "Minimum number of texture addressing unit busy cycles"
+  "``TA_DATA_STALLED_BY_TC_CYCLES_sum``", "Total number of cycles texture addressing unit data path is stalled by texture cache"
+  "``TA_TA_BUSY_sum``", "Total number of texture addressing unit busy cycles"
+
+Hardware counters over all texture cache per channel instances
+---------------------------------------------------------------------------------------------------------------
+
+.. csv-table::
+  :header: "Hardware counter", "Definition"
+
+  "``TCC_ALL_TC_OP_WB_WRITEBACK_sum``", "Total number of writebacks due to all ``TC_OP`` writeback requests."
+  "``TCC_ALL_TC_OP_INV_EVICT_sum``", "Total number of evictions due to all ``TC_OP`` invalidate requests."
+  "``TCC_ATOMIC_sum``", "Total number of L2 cache atomic requests of all types."
+  "``TCC_BUSY_avr``", "Average number of L2 cache busy cycles."
+  "``TCC_BUSY_sum``", "Total number of L2 cache busy cycles."
+  "``TCC_CC_REQ_sum``", "Total number of coherently cached requests."
+  "``TCC_CYCLE_sum``", "Total number of L2 cache free running clocks."
+  "``TCC_EA0_WRREQ_sum``", "Total number of 32-byte and 64-byte transactions going over the ``TC_EA0_wrreq`` interface. Atomics may travel over the same interface and are generally classified as write requests. This does not include probe commands."
+  "``TCC_EA0_WRREQ_64B_sum``", "Total number of 64-byte transactions (write or `CMPSWAP`) going over the ``TC_EA0_wrreq`` interface."
+  "``TCC_EA0_WR_UNCACHED_32B_sum``", "Total Number of 32-byte write or atomic going over the ``TC_EA0_wrreq`` interface due to uncached traffic. Note that coherently cached mtypes can produce uncached requests, and those are included in this. A 64-byte request is counted as 2."
+  "``TCC_EA0_WRREQ_STALL_sum``", "Total Number of cycles a write request is stalled, over all instances."
+  "``TCC_EA0_WRREQ_IO_CREDIT_STALL_sum``", "Total number of cycles an efficiency arbiter write request is stalled due to the interface running out of IO credits, over all instances."
+  "``TCC_EA0_WRREQ_GMI_CREDIT_STALL_sum``", "Total number of cycles an efficiency arbiter write request is stalled due to the interface running out of GMI credits, over all instances."
+  "``TCC_EA0_WRREQ_DRAM_CREDIT_STALL_sum``", "Total number of cycles an efficiency arbiter write request is stalled due to the interface running out of DRAM credits, over all instances."
+  "``TCC_EA0_WRREQ_LEVEL_sum``", "Total number of efficiency arbiter write requests in flight."
+  "``TCC_EA0_RDREQ_LEVEL_sum``", "Total number of efficiency arbiter read requests in flight."
+  "``TCC_EA0_ATOMIC_sum``", "Total Number of 32-byte or 64-byte atomic requests going over the ``TC_EA0_wrreq`` interface."
+  "``TCC_EA0_ATOMIC_LEVEL_sum``", "Total number of efficiency arbiter atomic requests in flight."
+  "``TCC_EA0_RDREQ_sum``", "Total number of 32-byte or 64-byte read requests to efficiency arbiter."
+  "``TCC_EA0_RDREQ_32B_sum``", "Total number of 32-byte read requests to efficiency arbiter."
+  "``TCC_EA0_RD_UNCACHED_32B_sum``", "Total number of 32-byte efficiency arbiter reads due to uncached traffic."
+  "``TCC_EA0_RDREQ_IO_CREDIT_STALL_sum``", "Total number of cycles there is a stall due to the read request interface running out of IO credits."
+  "``TCC_EA0_RDREQ_GMI_CREDIT_STALL_sum``", "Total number of cycles there is a stall due to the read request interface running out of GMI credits."
+  "``TCC_EA0_RDREQ_DRAM_CREDIT_STALL_sum``", "Total number of cycles there is a stall due to the read request interface running out of DRAM credits."
+  "``TCC_EA0_RDREQ_DRAM_sum``", "Total number of 32-byte or 64-byte efficiency arbiter read requests to HBM."
+  "``TCC_EA0_WRREQ_DRAM_sum``", "Total number of 32-byte or 64-byte efficiency arbiter write requests to HBM."
+  "``TCC_HIT_sum``", "Total number of L2 cache hits."
+  "``TCC_MISS_sum``", "Total number of L2 cache misses."
+  "``TCC_NC_REQ_sum``", "Total number of non-coherently cached requests."
+  "``TCC_NORMAL_WRITEBACK_sum``", "Total number of writebacks due to requests that are not writeback requests."
+  "``TCC_NORMAL_EVICT_sum``", "Total number of evictions due to requests that are not invalidate or probe requests."
+  "``TCC_PROBE_sum``", "Total number of probe requests."
+  "``TCC_PROBE_ALL_sum``", "Total number of external probe requests with ``EA0_TCC_preq_all == 1``."
+  "``TCC_READ_sum``", "Total number of L2 cache read requests (including compressed reads but not metadata reads)."
+  "``TCC_REQ_sum``", "Total number of all types of L2 cache requests."
+  "``TCC_RW_REQ_sum``", "Total number of coherently cached with write requests."
+  "``TCC_STREAMING_REQ_sum``", "Total number of L2 cache streaming requests."
+  "``TCC_TAG_STALL_sum``", "Total number of cycles the normal request pipeline in the tag is stalled for any reason."
+  "``TCC_TOO_MANY_EA0_WRREQS_STALL_sum``", "Total number of cycles L2 cache is unable to send an efficiency arbiter write request due to it reaching its maximum capacity of pending efficiency arbiter write requests."
+  "``TCC_UC_REQ_sum``", "Total number of uncached requests."
+  "``TCC_WRITE_sum``", "Total number of L2 cache write requests."
+  "``TCC_WRITEBACK_sum``", "Total number of lines written back to the main memory including writebacks of dirty lines and uncached write or atomic requests."
+  "``TCC_WRREQ_STALL_max``", "Maximum number of cycles a write request is stalled."
+
+Hardware counters by, for, or over all texture cache per pipe instances
+----------------------------------------------------------------------------------------------------------------
+
+The following table shows the hardware counters *by* all texture cache per pipe instances.
+
+.. csv-table::
+  :header: "Hardware counter", "Definition"
+
+  "``TCP_TA_TCP_STATE_READ_sum``", "Total number of state reads by ATCPPI"
+  "``TCP_TOTAL_CACHE_ACCESSES_sum``", "Total number of vector L1d accesses (including hits and misses)"
+  "``TCP_UTCL1_PERMISSION_MISS_sum``", "Total number of unified translation cache (L1) permission misses"
+  "``TCP_UTCL1_REQUEST_sum``", "Total number of address translation requests to unified translation cache (L1)"
+  "``TCP_UTCL1_TRANSLATION_MISS_sum``", "Total number of unified translation cache (L1) translation misses"
+  "``TCP_UTCL1_TRANSLATION_HIT_sum``", "Total number of unified translation cache (L1) translation hits"
+
+The following table shows the hardware counters *for* all texture cache per pipe instances.
+
+.. csv-table::
+  :header: "Hardware counter", "Definition"
+
+  "``TCP_TCC_READ_REQ_LATENCY_sum``", "Total vector L1d to L2 request latency over all wavefronts for reads and atomics with return"
+  "``TCP_TCC_WRITE_REQ_LATENCY_sum``", "Total vector L1d to L2 request latency over all wavefronts for writes and atomics without return"
+  "``TCP_TCP_LATENCY_sum``", "Total wave access latency to vector L1d over all wavefronts"
+
+The following table shows the hardware counters *over* all texture cache per pipe instances.
+
+.. csv-table::
+  :header: "Hardware counter", "Definition"
+
+  "``TCP_ATOMIC_TAGCONFLICT_STALL_CYCLES_sum``", "Total number of cycles tag RAM conflict stalls on an atomic"
+  "``TCP_GATE_EN1_sum``", "Total number of cycles vector L1d interface clocks are turned on"
+  "``TCP_GATE_EN2_sum``", "Total number of cycles vector L1d core clocks are turned on"
+  "``TCP_PENDING_STALL_CYCLES_sum``", "Total number of cycles vector L1d cache is stalled due to data pending from L2 Cache"
+  "``TCP_READ_TAGCONFLICT_STALL_CYCLES_sum``", "Total number of cycles tag RAM conflict stalls on a read"
+  "``TCP_TCC_ATOMIC_WITH_RET_REQ_sum``", "Total number of atomic requests to L2 cache with return"
+  "``TCP_TCC_ATOMIC_WITHOUT_RET_REQ_sum``", "Total number of atomic requests to L2 cache without return"
+  "``TCP_TCC_CC_READ_REQ_sum``", "Total number of coherently cached read requests to L2 cache"
+  "``TCP_TCC_CC_WRITE_REQ_sum``", "Total number of coherently cached write requests to L2 cache"
+  "``TCP_TCC_CC_ATOMIC_REQ_sum``", "Total number of coherently cached atomic requests to L2 cache"
+  "``TCP_TCC_NC_READ_REQ_sum``", "Total number of non-coherently cached read requests to L2 cache"
+  "``TCP_TCC_NC_WRITE_REQ_sum``", "Total number of non-coherently cached write requests to L2 cache"
+  "``TCP_TCC_NC_ATOMIC_REQ_sum``", "Total number of non-coherently cached atomic requests to L2 cache"
+  "``TCP_TCC_READ_REQ_sum``", "Total number of read requests to L2 cache"
+  "``TCP_TCC_RW_READ_REQ_sum``", "Total number of coherently cached with write read requests to L2 cache"
+  "``TCP_TCC_RW_WRITE_REQ_sum``", "Total number of coherently cached with write write requests to L2 cache"
+  "``TCP_TCC_RW_ATOMIC_REQ_sum``", "Total number of coherently cached with write atomic requests to L2 cache"
+  "``TCP_TCC_UC_READ_REQ_sum``", "Total number of uncached read requests to L2 cache"
+  "``TCP_TCC_UC_WRITE_REQ_sum``", "Total number of uncached write requests to L2 cache"
+  "``TCP_TCC_UC_ATOMIC_REQ_sum``", "Total number of uncached atomic requests to L2 cache"
+  "``TCP_TCC_WRITE_REQ_sum``", "Total number of write requests to L2 cache"
+  "``TCP_TCR_TCP_STALL_CYCLES_sum``", "Total number of cycles texture cache router stalls vector L1d"
+  "``TCP_TD_TCP_STALL_CYCLES_sum``", "Total number of cycles texture data unit stalls vector L1d"
+  "``TCP_TOTAL_ACCESSES_sum``", "Total number of vector L1d accesses"
+  "``TCP_TOTAL_READ_sum``", "Total number of vector L1d read accesses"
+  "``TCP_TOTAL_WRITE_sum``", "Total number of vector L1d write accesses"
+  "``TCP_TOTAL_ATOMIC_WITH_RET_sum``", "Total number of vector L1d atomic requests with return"
+  "``TCP_TOTAL_ATOMIC_WITHOUT_RET_sum``", "Total number of vector L1d atomic requests without return"
+  "``TCP_TOTAL_WRITEBACK_INVALIDATES_sum``", "Total number of vector L1d writebacks and invalidates"
+  "``TCP_VOLATILE_sum``", "Total number of L1 volatile pixels or buffers from texture addressing unit"
+  "``TCP_WRITE_TAGCONFLICT_STALL_CYCLES_sum``", "Total number of cycles tag RAM conflict stalls on a write"
+
+Hardware counter over all texture data unit instances
+--------------------------------------------------------
+
+.. csv-table::
+  :header: "Hardware counter", "Definition"
+
+  "``TD_ATOMIC_WAVEFRONT_sum``", "Total number of atomic wavefront instructions"
+  "``TD_COALESCABLE_WAVEFRONT_sum``", "Total number of coalescable wavefronts according to texture addressing unit"
+  "``TD_LOAD_WAVEFRONT_sum``", "Total number of wavefront instructions (read, write, atomic)"
+  "``TD_SPI_STALL_sum``", "Total number of cycles texture data unit is stalled by shader processor input"
+  "``TD_STORE_WAVEFRONT_sum``", "Total number of write wavefront instructions"
+  "``TD_TC_STALL_sum``", "Total number of cycles texture data unit is stalled waiting for texture cache data"
+  "``TD_TD_BUSY_sum``", "Total number of texture data unit busy cycles while it is processing or waiting for data"
--- a/docs/conceptual/gpu-arch/mi300.md
+++ b/docs/conceptual/gpu-arch/mi300.md
@@ -0,0 +1,122 @@
+# AMD Instinct™ MI300 series microarchitecture
+
+The AMD Instinct MI300 series accelerators are based on the AMD CDNA 3
+architecture which was designed to deliver leadership performance for HPC, artificial intelligence (AI), and machine
+learning (ML) workloads. The AMD Instinct MI300 series accelerators are well-suited for extreme scalability and compute performance, running
+on everything from individual servers to the world’s largest exascale supercomputers.
+
+With the MI300 series, AMD is introducing the Accelerator Complex Die (XCD), which contains the
+GPU computational elements of the processor along with the lower levels of the cache hierarchy.
+
+The following image depicts the structure of a single XCD in the AMD Instinct MI300 accelerator series.
+
+```{figure} ../../data/conceptual/gpu-arch/image007.png
+---
+name: mi300-xcd
+align: center
+---
+XCD-level system architecture showing 40 Compute Units, each with 32 KB L1 cache, a Unified Compute System with 4 ACE Compute Accelerators, shared 4MB of L2 cache and an HWS Hardware Scheduler.
+```
+
+On the XCD, four Asynchronous Compute Engines (ACEs) send compute shader workgroups to the
+Compute Units (CUs). The XCD has 40 CUs: 38 active CUs at the aggregate level and 2 disabled CUs for
+yield management. The CUs all share a 4 MB L2 cache that serves to coalesce all memory traffic for the
+die. With less than half of the CUs of the AMD Instinct MI200 Series compute die, the AMD CDNA™ 3
+XCD die is a smaller building block. However, it uses more advanced packaging and the processor
+can include 6 or 8 XCDs for up to 304 CUs, roughly 40% more than MI250X.
+
+The MI300 Series integrate up to 8 vertically stacked XCDs, 8 stacks of
+High-Bandwidth Memory 3 (HBM3) and 4 I/O dies (containing system
+infrastructure) using the AMD Infinity Fabric™ technology as interconnect.
+
+The Matrix Cores inside the CDNA 3 CUs have significant improvements, emphasizing AI and machine
+learning, enhancing throughput of existing data types while adding support for new data types.
+CDNA 2 Matrix Cores support FP16 and BF16, while offering INT8 for inference. Compared to MI250X
+accelerators, CDNA 3 Matrix Cores triple the performance for FP16 and BF16, while providing a
+performance gain of 6.8 times for INT8. FP8 has a performance gain of 16 times compared to FP32,
+while TF32 has a gain of 4 times compared to FP32.
+
+```{list-table} Peak-performance capabilities of the MI300X for different data types.
+:header-rows: 1
+:name: mi300x-perf-table
+
+*
+  - Computation and Data Type
+  - FLOPS/CLOCK/CU
+  - Peak TFLOPS
+*
+  - Matrix FP64
+  - 256
+  - 163.4
+*
+  - Vector FP64
+  - 128
+  - 81.7
+*
+  - Matrix FP32
+  - 256
+  - 163.4
+*
+  - Vector FP32
+  - 256
+  - 163.4
+*
+  - Vector TF32
+  - 1024
+  - 653.7
+*
+  - Matrix FP16
+  - 2048
+  - 1307.4
+*
+  - Matrix BF16
+  - 2048
+  - 1307.4
+*
+  - Matrix FP8
+  - 4096
+  - 2614.9
+*
+  - Matrix INT8
+  - 4096
+  - 2614.9
+```
+
+The above table summarizes the aggregated peak performance of the AMD Instinct MI300X Open
+Compute Platform (OCP) Open Accelerator Modules (OAMs) for different data types and command
+processors. The middle column lists the peak performance (number of data elements processed in a
+single instruction) of a single compute unit if a SIMD (or matrix) instruction is submitted in each clock
+cycle. The third column lists the theoretical peak performance of the OAM. The theoretical aggregated
+peak memory bandwidth of the GPU is 5.3 TB per second.
+
+The following image shows the block diagram of the APU (left) and the OAM package (right) both
+connected via AMD Infinity Fabric™ network on-chip.
+
+```{figure} ../../data/conceptual/gpu-arch/image008.png
+---
+name: mi300-arch
+alt:
+align: center
+---
+MI300 series system architecture showing MI300A (left) with 6 XCDs and 3 CCDs, while the MI300X (right) has 8 XCDs.
+```
+
+## Node-level architecture
+
+```{figure} ../../data/conceptual/gpu-arch/image009.png
+---
+name: mi300-node
+
+align: center
+---
+MI300 series node-level architecture showing 8 fully interconnected MI300X OAM modules connected to (optional) PCIEe switches via retimers and HGX connectors.
+```
+
+The image above shows the node-level architecture of a system with AMD EPYC processors in a
+dual-socket configuration and eight AMD Instinct MI300X accelerators. The MI300X OAMs attach to the
+host system via PCIe Gen 5 x16 links (yellow lines). The GPUs are using seven high-bandwidth,
+low-latency AMD Infinity Fabric™ links (red lines) to form a fully connected 8-GPU system.
+
+<!---
+We need performance data about the P2P communication here.
+-->
--- a/docs/conceptual/gpu-isolation.md
+++ b/docs/conceptual/gpu-isolation.md
@@ -1,4 +1,11 @@
-# GPU Isolation Techniques
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="GPU isolation techniques">
+  <meta name="keywords" content="GPU isolation techniques, UUID, universally unique identifier,
+  environment variables, virtual machines, AMD, ROCm">
+</head>
+
+# GPU isolation techniques

 Restricting the access of applications to a subset of GPUs, aka isolating
 GPUs allows users to hide GPU resources from programs. The programs by default
@@ -8,7 +15,7 @@ There are multiple ways to achieve isolation of GPUs in the ROCm software stack,
 differing in which applications they apply to and the security they provide.
 This page serves as an overview of the techniques.

-## Environment Variables
+## Environment variables

 The runtimes in the ROCm software stack read these environment variables to
 select the exposed or default device to present to applications using them.
@@ -22,7 +29,7 @@ A list of device indices or {abbr}`UUID (universally unique identifier)`s
 that will be exposed to applications.

 Runtime
-: ROCm Platform Runtime. Applies to all applications using the user mode ROCm
+: ROCm Software Runtime. Applies to all applications using the user mode ROCm
  software stack.

 ```{code-block} shell
@@ -43,12 +50,13 @@ Runtime
 export GPU_DEVICE_ORDINAL="0,2"
 ```

+(hip_visible_devices)=
+
 ### `HIP_VISIBLE_DEVICES`

 Device indices exposed to HIP applications.

-Runtime
-: HIP Runtime. Applies only to applications using HIP on the AMD platform.
+Runtime: HIP runtime. Applies only to applications using HIP on the AMD platform.

 ```{code-block} shell
 :caption: Example to expose the 1. and 3. devices in the system.
@@ -90,7 +98,7 @@ to all programs that use the `amdgpu` kernel module interfaces.
 Even programs that don't use the ROCm runtime, like graphics applications
 using OpenGL or Vulkan, can only access the GPUs exposed to the container.

-## GPU Passthrough to Virtual Machines
+## GPU passthrough to virtual machines

 Virtual machines achieve the highest level of isolation, because even the kernel
 of the virtual machine is isolated from the host. Devices physically installed
--- a/docs/conceptual/gpu-memory.md
+++ b/docs/conceptual/gpu-memory.md
@@ -0,0 +1,241 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="GPU memory">
+  <meta name="keywords" content="GPU memory, VRAM, video random access memory, pageable
+  memory, pinned memory, managed memory, AMD, ROCm">
+</head>
+
+# GPU memory
+
+For the HIP reference documentation, see:
+
+* {doc}`hip:doxygen/html/group___memory`
+* {doc}`hip:doxygen/html/group___memory_m`
+
+Host memory exists on the host (e.g. CPU) of the machine in random access memory (RAM).
+
+Device memory exists on the device (e.g. GPU) of the machine in video random access memory (VRAM).
+Recent architectures use graphics double data rate (GDDR) synchronous dynamic random-access memory (SDRAM)such as GDDR6, or high-bandwidth memory (HBM) such as HBM2e.
+
+## Memory allocation
+
+Memory can be allocated in two ways: pageable memory, and pinned memory.
+The following API calls with result in these allocations:
+
+| API                | Data location | Allocation |
+|--------------------|---------------|------------|
+| System allocated   | Host          | Pageable   |
+| `hipMallocManaged` | Host          | Managed    |
+| `hipHostMalloc`    | Host          | Pinned     |
+| `hipMalloc`        | Device        | Pinned     |
+
+:::{tip}
+`hipMalloc` and `hipFree` are blocking calls, however, HIP recently added non-blocking versions `hipMallocAsync` and `hipFreeAsync` which take in a stream as an additional argument.
+:::
+
+### Pageable memory
+
+Pageable memory is usually gotten when calling `malloc` or `new` in a C++ application.
+It is unique in that it exists on "pages" (blocks of memory), which can be migrated to other memory storage.
+For example, migrating memory between CPU sockets on a motherboard, or a system that runs out of space in RAM and starts dumping pages of RAM into the swap partition of your hard drive.
+
+### Pinned memory
+
+Pinned memory (or page-locked memory, or non-pageable memory) is host memory that is mapped into the address space of all GPUs, meaning that the pointer can be used on both host and device.
+Accessing host-resident pinned memory in device kernels is generally not recommended for performance, as it can force the data to traverse the host-device interconnect (e.g. PCIe), which is much slower than the on-device bandwidth (>40x on MI200).
+
+Pinned host memory can be allocated with one of two types of coherence support:
+
+:::{note}
+In HIP, pinned memory allocations are coherent by default (`hipHostMallocDefault`).
+There are additional pinned memory flags (e.g. `hipHostMallocMapped` and `hipHostMallocPortable`).
+On MI200 these options do not impact performance.
+<!-- TODO: link to programming_manual#memory-allocation-flags -->
+For more information, see the section *memory allocation flags* in the HIP Programming Guide: {doc}`hip:user_guide/programming_manual`.
+:::
+
+Much like how a process can be locked to a CPU core by setting affinity, a pinned memory allocator does this with the memory storage system.
+On multi-socket systems it is important to ensure that pinned memory is located on the same socket as the owning process, or else each cache line will be moved through the CPU-CPU interconnect, thereby increasing latency and potentially decreasing bandwidth.
+
+In practice, pinned memory is used to improve transfer times between host and device.
+For transfer operations, such as `hipMemcpy` or `hipMemcpyAsync`, using pinned memory instead of pageable memory on host can lead to a ~3x improvement in bandwidth.
+
+:::{tip}
+If the application needs to move data back and forth between device and host (separate allocations), use pinned memory on the host side.
+:::
+
+### Managed memory
+
+Managed memory refers to universally addressable, or unified memory available on the MI200 series of GPUs.
+Much like pinned memory, managed memory shares a pointer between host and device and (by default) supports fine-grained coherence, however, managed memory can also automatically migrate pages between host and device.
+The allocation will be managed by AMD GPU driver using the Linux HMM (Heterogeneous Memory Management) mechanism.
+
+If heterogenous memory management (HMM) is not available, then `hipMallocManaged` will default back to using system memory and will act like pinned host memory.
+Other managed memory API calls will have undefined behavior.
+It is therefore recommended to check for managed memory capability with: `hipDeviceGetAttribute` and `hipDeviceAttributeManagedMemory`.
+
+HIP supports additional calls that work with page migration:
+
+* `hipMemAdvise`
+* `hipMemPrefetchAsync`
+
+:::{tip}
+If the application needs to use data on both host and device regularly, does not want to deal with separate allocations, and is not worried about maxing out the VRAM on MI200 GPUs (64 GB per GCD), use managed memory.
+:::
+
+:::{tip}
+If managed memory performance is poor, check to see if managed memory is supported on your system and if page migration (XNACK) is enabled.
+:::
+
+## Access behavior
+
+Memory allocations for GPUs behave as follow:
+
+| API                | Data location | Host access  | Device access        |
+|--------------------|---------------|--------------|----------------------|
+| System allocated   | Host          | Local access | Unhandled page fault |
+| `hipMallocManaged` | Host          | Local access | Zero-copy            |
+| `hipHostMalloc`    | Host          | Local access | Zero-copy*           |
+| `hipMalloc`        | Device        | Zero-copy    | Local access         |
+
+Zero-copy accesses happen over the Infinity Fabric interconnect or PCI-E lanes on discrete GPUs.
+
+:::{note}
+While `hipHostMalloc` allocated memory is accessible by a device, the host pointer must be converted to a device pointer with `hipHostGetDevicePointer`.
+
+Memory allocated through standard system allocators such as `malloc`, can be accessed a device by registering the memory via `hipHostRegister`.
+The device pointer to be used in kernels can be retrieved with `hipHostGetDevicePointer`.
+Registered memory is treated like `hipHostMalloc` and will have similar performance.
+
+On devices that support and have [](#xnack) enabled, such as the MI250X, `hipHostRegister` is not required as memory accesses are handled via automatic page migration.
+:::
+
+### XNACK
+
+Normally, host and device memory are separate and data has to be transferred manually via `hipMemcpy`.
+
+On a subset of GPUs, such as the MI200, there is an option to automatically migrate pages of memory between host and device.
+This is important for managed memory, where the locality of the data is important for performance.
+Depending on the system, page migration may be disabled by default in which case managed memory will act like pinned host memory and suffer degraded performance.
+
+*XNACK* describes the GPUs ability to retry memory accesses that failed due a page fault (which normally would lead to a memory access error), and instead retrieve the missing page.
+
+This also affects memory allocated by the system as indicated by the following table:
+
+| API                | Data location | Host after device access | Device after host access |
+|--------------------|---------------|--------------------------|--------------------------|
+| System allocated   | Host          | Migrate page to host     | Migrate page to device   |
+| `hipMallocManaged` | Host          | Migrate page to host     | Migrate page to device   |
+| `hipHostMalloc`    | Host          | Local access             | Zero-copy                |
+| `hipMalloc`        | Device        | Zero-copy                | Local access             |
+
+To check if page migration is available on a platform, use `rocminfo`:
+
+```sh
+$ rocminfo | grep xnack
+      Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
+```
+
+Here, `xnack-` means that XNACK is available but is disabled by default.
+Turning on XNACK by setting the environment variable `HSA_XNACK=1` and gives the expected result, `xnack+`:
+
+```sh
+$ HSA_XNACK=1 rocminfo | grep xnack
+Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack+
+```
+
+`hipcc`by default will generate code that runs correctly with both XNACK enabled or disabled.
+Setting the `--offload-arch=`-option with `xnack+` or `xnack-` forces code to be only run with XNACK enabled or disabled respectively.
+
+```sh
+# Compiled kernels will run regardless if XNACK is enabled or is disabled. 
+hipcc --offload-arch=gfx90a
+
+# Compiled kernels will only be run if XNACK is enabled with XNACK=1.
+hipcc --offload-arch=gfx90a:xnack+
+
+# Compiled kernels will only be run if XNACK is disabled with XNACK=0.
+hipcc --offload-arch=gfx90a:xnack-
+```
+
+:::{tip}
+If you want to make use of page migration, use managed memory. While pageable memory will migrate correctly, it is not a portable solution and can have performance issues if the accessed data isn't page aligned.
+:::
+
+### Coherence
+
+* *Coarse-grained coherence* means that memory is only considered up to date at kernel boundaries, which can be enforced through `hipDeviceSynchronize`, `hipStreamSynchronize`, or any blocking operation that acts on the null stream (e.g. `hipMemcpy`).
+For example, cacheable memory is a type of coarse-grained memory where an up-to-date copy of the data can be stored elsewhere (e.g. in an L2 cache).
+* *Fine-grained coherence* means the coherence is supported while a CPU/GPU kernel is running.
+This can be useful if both host and device are operating on the same dataspace using system-scope atomic operations (e.g. updating an error code or flag to a buffer).
+Fine-grained memory implies that up-to-date data may be made visible to others regardless of kernel boundaries as discussed above.
+
+| API                     | Flag                         | Coherence      |
+|-------------------------|------------------------------|----------------|
+| `hipHostMalloc`         | `hipHostMallocDefault`       | Fine-grained   |
+| `hipHostMalloc`         | `hipHostMallocNonCoherent`   | Coarse-grained |
+
+| API                     | Flag                         | Coherence      |
+|-------------------------|------------------------------|----------------|
+| `hipExtMallocWithFlags` | `hipDeviceMallocDefault`     | Coarse-grained |
+| `hipExtMallocWithFlags` | `hipDeviceMallocFinegrained` | Fine-grained   |
+
+| API                     | `hipMemAdvise` argument      | Coherence      |
+|-------------------------|------------------------------|----------------|
+| `hipMallocManaged`      |                              | Fine-grained   |
+| `hipMallocManaged`      | `hipMemAdviseSetCoarseGrain` | Coarse-grained |
+| `malloc`                |                              | Fine-grained   |
+| `malloc`                | `hipMemAdviseSetCoarseGrain` | Coarse-grained |
+
+:::{tip}
+Try to design your algorithms to avoid host-device memory coherence (e.g. system scope atomics). While it can be a useful feature in very specific cases, it is not supported on all systems, and can negatively impact performance by introducing the host-device interconnect bottleneck.
+:::
+
+The availability of fine- and coarse-grained memory pools can be checked with `rocminfo`:
+
+```sh
+$ rocminfo
+...
+*******
+Agent 1
+*******
+Name:                    AMD EPYC 7742 64-Core Processor
+...
+Pool Info:
+Pool 1
+Segment:                 GLOBAL; FLAGS: FINE GRAINED
+...
+Pool 3
+Segment:                 GLOBAL; FLAGS: COARSE GRAINED
+...
+*******
+Agent 9
+*******
+Name:                    gfx90a
+...
+Pool Info:
+Pool 1
+Segment:                 GLOBAL; FLAGS: COARSE GRAINED
+...
+```
+
+## System direct memory access
+
+In most cases, the default behavior for HIP in transferring data from a pinned host allocation to device will run at the limit of the interconnect.
+However, there are certain cases where the interconnect is not the bottleneck.
+
+The primary way to transfer data onto and off of a GPU, such as the MI200, is to use the onboard System Direct Memory Access engine, which is used to feed blocks of memory to the off-device interconnect (either GPU-CPU or GPU-GPU).
+Each GCD has a separate SDMA engine for host-to-device and device-to-host memory transfers.
+Importantly, SDMA engines are separate from the computing infrastructure, meaning that memory transfers to and from a device will not impact kernel compute performance, though they do impact memory bandwidth to a limited extent.
+The SDMA engines are mainly tuned for PCIe-4.0 x16, which means they are designed to operate at bandwidths up to 32 GB/s.
+
+:::{note}
+An important feature of the MI250X platform is the Infinity Fabric™ interconnect between host and device.
+The Infinity Fabric interconnect supports improved performance over standard PCIe-4.0 (usually ~50% more bandwidth); however, since the SDMA engine does not run at this speed, it will not max out the bandwidth of the faster interconnect.
+:::
+
+The bandwidth limitation can be countered by bypassing the SDMA engine and replacing it with a type of copy kernel known as a "blit" kernel.
+Blit kernels will use the compute units on the GPU, thereby consuming compute resources, which may not always be beneficial.
+The easiest way to enable blit kernels is to set an environment variable `HSA_ENABLE_SDMA=0`, which will disable the SDMA engine.
+On systems where the GPU uses a PCIe interconnect instead of an Infinity Fabric interconnect, blit kernels will not impact bandwidth, but will still consume compute resources.
+The use of SDMA vs blit kernels also applies to MPI data transfers and GPU-GPU transfers.
--- a/docs/conceptual/setting-cus.rst
+++ b/docs/conceptual/setting-cus.rst
@@ -0,0 +1,47 @@
+.. meta::
+    :description: Setting the number of CUs
+    :keywords: AMD, ROCm, cu, number of cus
+
+.. _env-variables-reference:
+
+*************************************************************
+Setting the number of CUs
+*************************************************************
+
+When using GPUs to accelerate compute workloads, it sometimes becomes necessary
+to configure the usage of Compute Units (CU) of the hardware. This is a more advanced
+option, so please read this page before experimentation.
+
+The GPU driver provides two environment variables to set the number of CUs used. The
+first one is ``HSA_CU_MASK`` and the second one is ``ROC_GLOBAL_CU_MASK``. The main
+difference is, that ``ROC_GLOBAL_CU_MASK`` sets the CU mask on queues created by
+the HIP or the OpenCL runtimes. While ``HSA_CU_MASK`` sets the mask on a lower level of
+queue creation in the driver, this mask will also be set for queues being profiled.
+
+The environment variables have the following syntax:
+
+::
+
+    ID = [0-9][0-9]*                         ex. base 10 numbers
+    ID_list = (ID | ID-ID)[, (ID | ID-ID)]*  ex. 0,2-4,7
+    GPU_list = ID_list                       ex. 0,2-4,7
+    CU_list = 0x[0-F]* | ID_list             ex. 0x337F OR 0,2-4,7
+    CU_Set = GPU_list : CU_list              ex. 0,2-4,7:0-15,32-47 OR 0,2-4,7:0x337F
+    HSA_CU_MASK = CU_Set [; CU_Set]*         ex. 0,2-4,7:0-15,32-47; 3-9:0x337F
+
+The GPU indices are taken post ``ROCR_VISIBLE_DEVICES`` reordering. For GPUs listed,
+the listed or masked CUs will be enabled, the rest disabled. Unlisted GPUs will not
+be affected, their CUs will all be enabled.
+
+The parsing of the variable is stopped when a syntax error occurs. The erroneous set
+and the ones following will be ignored. Repeating GPU or CU IDs are a syntax error.
+Specifying a mask with no usable CUs (CU_list is 0x0) is a syntax error. For excluding
+GPU devices use ``ROCR_VISIBLE_DEVICES``.
+
+These environment variables only affect ROCm software, not graphics applications.
+
+It's important to know that not all CU configurations are valid on all devices. For
+instance, on devices where two CUs can be combined into a WGP (for kernels running in
+WGP mode), it is not valid to disable only a single CU in a WGP. `This paper
+<https://www.cs.unc.edu/~otternes/papers/rtsj2022.pdf>`_ can provide more information
+about what to expect, when disabling CUs.
--- a/docs/conceptual/using-gpu-sanitizer.md
+++ b/docs/conceptual/using-gpu-sanitizer.md
@@ -0,0 +1,413 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="Using the LLVM ASan on a GPU">
+  <meta name="keywords" content="LLVM, ASan, address sanitizer, AddressSanitizer, instrumented
+  libraries, instrumented applications, AMD, ROCm">
+</head>
+
+# Using the LLVM ASan on a GPU (beta release)
+
+The LLVM AddressSanitizer (ASan) provides a process that allows developers to detect runtime addressing errors in applications and libraries. The detection is achieved using a combination of compiler-added instrumentation and runtime techniques, including function interception and replacement.
+Until now, the LLVM ASan process was only available for traditional purely CPU applications. However, ROCm has extended this mechanism to additionally allow the detection of some addressing errors on the GPU in heterogeneous applications. Ideally, developers should treat heterogeneous HIP and OpenMP applications exactly like pure CPU applications. However, this simplicity has not been achieved yet.
+This document provides documentation on using ROCm ASan.
+
+For information about LLVM ASan, see the [LLVM documentation](https://clang.llvm.org/docs/AddressSanitizer.html).
+
+**Note:** The beta release of LLVM ASan for ROCm is currently tested and validated on Ubuntu 20.04.
+
+## Compiling for ASan
+
+The ASan process begins by compiling the application of interest with the ASan instrumentation.
+
+Recommendations for doing this are:
+
+* Compile as many application and dependent library sources as possible using an AMD-built clang-based compiler such as `amdclang++`.
+* Add the following options to the existing compiler and linker options:
+  
+  * `-fsanitize=address` - enables instrumentation
+
+  * `-shared-libsan` - use shared version of runtime
+
+  * `-g` - add debug info for improved reporting
+
+* Explicitly use `xnack+` in the offload architecture option. For example, `--offload-arch=gfx90a:xnack+`
+
+Other architectures are allowed, but their device code will not be instrumented and a warning will be emitted.
+
+**Note:** It is not an error to compile some files without ASan instrumentation, but doing so reduces the ability of the process to detect addressing errors. However, if the main program "`a.out`" does not directly depend on the ASan runtime (`libclang_rt.asan-x86_64.so`) after the build completes (check by running `ldd` (List Dynamic Dependencies) or `readelf`), the application will immediately report an error at runtime as described in the next section.
+
+**Note:** When compiling OpenMP programs with ASan instrumentation, it is currently necessary to set the environment variable `LIBRARY_PATH` to `/opt/rocm-<version>/lib/llvm/lib/asan:/opt/rocm-<version>/lib/asan`. At runtime, it may be necessary to add `/opt/rocm-<version>/lib/llvm/lib/asan` to `LD_LIBRARY_PATH`.
+
+### About compilation time
+
+When `-fsanitize=address` is used, the LLVM compiler adds instrumentation code around every memory operation. This added code must be handled by all of the downstream components of the compiler toolchain and results in increased overall compilation time. This increase is especially evident in the AMDGPU device compiler and has in a few instances raised the compile time to an unacceptable level.
+
+There are a few options if the compile time becomes unacceptable:
+
+* Avoid instrumentation of the files which have the worst compile times. This will reduce the effectiveness of the ASan process.
+* Add the option `-fsanitize-recover=address` to the compiles with the worst compile times. This option simplifies the added instrumentation resulting in faster compilation. See below for more information.
+* Disable instrumentation on a per-function basis by adding `__attribute__`((no_sanitize("address"))) to functions found to be responsible for the large compile time. Again, this will reduce the effectiveness of the process.
+
+## Installing ROCm GPU ASan packages
+
+For a complete ROCm GPU Sanitizer installation, including packages, instrumented HSA and HIP runtimes, tools, and math libraries, use the following instruction,
+
+```bash
+    sudo apt-get install rocm-ml-sdk-asan
+
+```
+
+## Using AMD-supplied ASan instrumented libraries
+
+ROCm releases have optional packages that contain additional ASan instrumented builds of the ROCm libraries (usually found in `/opt/rocm-<version>/lib`). The instrumented libraries have identical names to the regular uninstrumented libraries, and are located in `/opt/rocm-<version>/lib/asan`.
+These additional libraries are built using the `amdclang++` and `hipcc` compilers, while some uninstrumented libraries are built with `g++`. The preexisting build options are used but, as described above, additional options are used: `-fsanitize=address`, `-shared-libsan` and `-g`.
+
+These additional libraries avoid additional developer effort to locate repositories, identify the correct branch, check out the correct tags, and other efforts needed to build the libraries from the source. And they extend the ability of the process to detect addressing errors into the ROCm libraries themselves.
+
+When adjusting an application build to add instrumentation, linking against these instrumented libraries is unnecessary. For example, any `-L` `/opt/rocm-<version>/lib` compiler options need not be changed. However, the instrumented libraries should be used when the application is run. It is particularly important that the instrumented language runtimes, like `libamdhip64.so` and `librocm-core.so`, are used; otherwise, device invalid access detections may not be reported.
+
+## Running ASan instrumented applications
+
+### Preparing to run an instrumented application
+
+Here are a few recommendations to consider before running an ASan instrumented heterogeneous application.
+
+* Ensure the Linux kernel running on the system has Heterogeneous Memory Management (HMM) support. A kernel version of 5.6 or higher should be sufficient.
+* Ensure XNACK is enabled
+  * For `gfx90a` (MI-2X0) or `gfx940` (MI-3X0) use environment `HSA_XNACK = 1`.
+  * For `gfx906` (MI-50) or `gfx908` (MI-100) use environment `HSA_XNACK = 1` but also ensure the amdgpu kernel module is loaded with module argument `noretry=0`.
+This requirement is due to the fact that the XNACK setting for these GPUs is system-wide.
+
+* Ensure that the application will use the instrumented libraries when it runs. The output from the shell command `ldd <application name>` can be used to see which libraries will be used.
+If the instrumented libraries are not listed by `ldd`, the environment variable `LD_LIBRARY_PATH` may need to be adjusted, or in some cases an `RPATH` compiled into the application may need to be changed and the application recompiled.
+
+* Ensure that the application depends on the ASan runtime. This can be checked by running the command `readelf -d <application name> | grep NEEDED` and verifying that shared library: `libclang_rt.asan-x86_64.so` appears in the output.
+If it does not appear, when executed the application will quickly output an ASan error that looks like:
+
+```bash
+==3210==ASan runtime does not come first in initial library list; you should either link runtime to your application or manually preload it with LD_PRELOAD.
+```
+
+* Ensure that the application `llvm-symbolizer` can be executed, and that it is located in `/opt/rocm-<version>/llvm/bin`. This executable is not strictly required, but if found is used to translate ("symbolize") a host-side instruction address into a more useful function name, file name, and line number (assuming the application has been built to include debug information).
+
+There is an environment variable, `ASAN_OPTIONS`, that can be used to adjust the runtime behavior of the ASAN runtime itself. There are more than a hundred "flags" that can be adjusted (see an old list at [flags](https://github.com/google/sanitizers/wiki/AddressSanitizerFlags)) but the default settings are correct and should be used in most cases. It must be noted that these options only affect the host ASAN runtime. The device runtime only currently supports the default settings for the few relevant options.
+
+There are two `ASAN_OPTION` flags of particular note.
+
+* `halt_on_error=0/1 default 1`.
+
+This tells the ASan runtime to halt the application immediately after detecting and reporting an addressing error. The default makes sense because the application has entered the realm of undefined behavior. If the developer wishes to have the application continue anyway, this option can be set to zero. However, the application and libraries should then be compiled with the additional option `-fsanitize-recover=address`. Note that the ROCm optional ASan instrumented libraries are not compiled with this option and if an error is detected within one of them, but halt_on_error is set to 0, more undefined behavior will occur.
+
+* `detect_leaks=0/1 default 1`.
+
+This option directs the ASan runtime to enable the [Leak Sanitizer](https://clang.llvm.org/docs/LeakSanitizer.html) (LSAN). Unfortunately, for heterogeneous applications, this default will result in significant output from the leak sanitizer when the application exits due to allocations made by the language runtime which are not considered to be to be leaks. This output can be avoided by adding `detect_leaks=0` to the `ASAN_OPTIONS`, or alternatively by producing an LSAN suppression file (syntax described [here](https://github.com/google/sanitizers/wiki/AddressSanitizerLeakSanitizer)) and activating it with environment variable `LSAN_OPTIONS=suppressions=/path/to/suppression/file`. When using a suppression file, a suppression report is printed by default. The suppression report can be disabled by using the `LSAN_OPTIONS` flag `print_suppressions=0`.
+
+## Runtime overhead
+
+Running an ASan instrumented application incurs
+overheads which may result in unacceptably long runtimes
+or failure to run at all.
+
+### Higher execution time
+
+ASan detection works by checking each address at runtime
+before the address is actually accessed by a load, store, or atomic
+instruction.
+This checking involves an additional load to "shadow" memory which
+records whether the address is "poisoned" or not, and additional logic
+that decides whether to produce an detection report or not.
+
+This extra runtime work can cause the application to slow down by
+a factor of three or more, depending on how many memory accesses are
+executed.
+For heterogeneous applications, the shadow memory must be accessible by all devices
+and this can mean that shadow accesses from some devices may be more costly
+than non-shadow accesses.
+
+### Higher memory use
+
+The address checking described above relies on the compiler to surround
+each program variable with a red zone and on ASan
+runtime to surround each runtime memory allocation with a red zone and
+fill the shadow corresponding to each red zone with poison.
+The added memory for the red zones is additional overhead on top
+of the 13% overhead for the shadow memory itself.
+
+Applications which consume most one or more available memory pools when
+run normally are likely to encounter allocation failures when run with
+instrumentation.
+
+## Runtime reporting
+
+It is not the intention of this document to provide a detailed explanation of all of the types of reports that can be output by the ASan runtime. Instead, the focus is on the differences between the standard reports for CPU issues, and reports for GPU issues.
+
+An invalid address detection report for the CPU always starts with
+
+```bash
+==<PID>==ERROR: AddressSanitizer: <problem type> on address <memory address> at pc <pc> bp <bp> sp <sp> <access> of size <N> at <memory address> thread T0
+```
+
+and continues with a stack trace for the access, a stack trace for the allocation and deallocation, if relevant, and a dump of the shadow near the <memory address>.
+
+In contrast, an invalid address detection report for the GPU always starts with
+
+```bash
+==<PID>==ERROR: AddressSanitizer: <problem type> on amdgpu device <device> at pc <pc> <access> of size <n> in workgroup id (<X>,<Y>,<Z>)
+```
+
+Above, `<device>` is the integer device ID, and `(<X>, <Y>, <Z>)` is the ID of the workgroup or block where the invalid address was detected.
+
+While the CPU report include a call stack for the thread attempting the invalid access, the GPU is currently to a call stack of size one, i.e. the (symbolized) of the invalid access, e.g.
+
+```bash
+#0 <pc> in <fuction signature> at /path/to/file.hip:<line>:<column>
+```
+
+This short call stack is followed by a GPU unique section that looks like
+
+```bash
+Thread ids and accessed addresses:
+<lid0> <maddr 0> : <lid1> <maddr1> : ...
+```
+
+where each `<lid j> <maddr j>` indicates the lane ID and the invalid memory address held by lane `j` of the wavefront attempting the invalid access.
+
+Additionally, reports for invalid GPU accesses to memory allocated by GPU code via `malloc` or new starting with, for example,
+
+```bash
+==1234==ERROR: AddressSanitizer: heap-buffer-overflow on amdgpu device 0 at pc 0x7fa9f5c92dcc
+```
+
+or
+
+```bash
+==5678==ERROR: AddressSanitizer: heap-use-after-free on amdgpu device 3 at pc 0x7f4c10062d74
+```
+
+currently may include one or two surprising CPU side tracebacks mentioning :`hostcall`". This is due to how `malloc` and `free` are implemented for GPU code and these call stacks can be ignored.
+
+### Running with `rocgdb`
+
+`rocgdb` can be used to further investigate ASan detected errors, with some preparation.
+
+Currently, the ASan runtime complains when starting `rocgdb` without preparation.
+
+```bash
+$ rocgdb my_app
+==1122==ASan` runtime does not come first in initial library list; you should either link runtime to your application or manually preload it with LD_PRELOAD.
+```
+
+This is solved by setting environment variable `LD_PRELOAD` to the path to the ASan runtime, whose path can be obtained using the command
+
+```bash
+amdclang++ -print-file-name=libclang_rt.asan-x86_64.so
+```
+
+It is also recommended to set the environment variable `HIP_ENABLE_DEFERRED_LOADING=0` before debugging HIP applications.
+
+After starting `rocgdb` breakpoints can be set on the ASan runtime error reporting entry points of interest. For example, if an ASan error report includes
+
+```bash
+WRITE of size 4 in workgroup id (10,0,0)
+```
+
+the `rocgdb` command needed to stop the program before the report is printed is
+
+```bash
+(gdb) break __asan_report_store4
+```
+
+Similarly, the appropriate command for a report including
+
+```bash
+READ of size <N> in workgroup ID (1,2,3)
+```
+
+is
+
+```bash
+(gdb) break __asan_report_load<N>
+```
+
+It is possible to set breakpoints on all ASan report functions using these commands:
+
+```bash
+$ rocgdb <path to application>
+(gdb) start <commmand line arguments>
+(gdb) rbreak ^__asan_report
+(gdb) c
+```
+
+### Using ASan with a short HIP application
+
+Consider the following simple and short demo of using the Address Sanitizer with a HIP application:
+
+```C++
+
+#include <cstdlib>
+#include <hip/hip_runtime.h>
+
+__global__ void
+set1(int *p)
+{
+    int i = blockDim.x*blockIdx.x + threadIdx.x;
+    p[i] = 1;
+}
+
+int
+main(int argc, char **argv)
+{
+    int m = std::atoi(argv[1]);
+    int n1 = std::atoi(argv[2]);
+    int n2 = std::atoi(argv[3]);
+    int c = std::atoi(argv[4]);
+    int *dp;
+    hipMalloc(&dp, m*sizeof(int));
+    hipLaunchKernelGGL(set1, dim3(n1), dim3(n2), 0, 0, dp);
+    int *hp = (int*)malloc(c * sizeof(int));
+    hipMemcpy(hp, dp, m*sizeof(int), hipMemcpyDeviceToHost);
+    hipDeviceSynchronize();
+    hipFree(dp);
+    free(hp);
+    std::puts("Done.");
+    return 0;
+}
+```
+
+This application will attempt to access invalid addresses for certain command line arguments. In particular, if `m < n1 * n2` some device threads will attempt to access
+unallocated device memory.
+
+Or, if `c < m`, the `hipMemcpy` function will copy past the end of the `malloc` allocated memory.
+
+**Note**: The `hipcc` compiler is used here for simplicity.
+
+Compiling without XNACK results in a warning.
+
+```bash
+$ hipcc -g --offload-arch=gfx90a:xnack- -fsanitize=address -shared-libsan mini.hip -o mini
+clang++: warning: ignoring` `-fsanitize=address' option for offload arch 'gfx90a:xnack-`, as it is not currently supported there. Use it with an offload arch containing 'xnack+' instead [-Woption-ignored]`.
+```
+
+The binary compiled above will run, but the GPU code will not be instrumented and the `m < n1 * n2` error will not be detected. Switching to `--offload-arch=gfx90a:xnack+` in the command above results in a warning-free compilation and an instrumented application. After setting `PATH`, `LD_LIBRARY_PATH` and `HSA_XNACK` as described earlier, a check of the binary with `ldd` yields the following,
+
+```bash
+$ ldd mini
+        linux-vdso.so.1 (0x00007ffd1a5ae000)
+        libclang_rt.asan-x86_64.so => /opt/rocm-6.1.0-99999/llvm/lib/clang/17.0.0/lib/linux/libclang_rt.asan-x86_64.so (0x00007fb9c14b6000)
+        libamdhip64.so.5 => /opt/rocm-6.1.0-99999/lib/asan/libamdhip64.so.5 (0x00007fb9bedd3000)
+        libstdc++.so.6 => /lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007fb9beba8000)
+        libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007fb9bea59000)
+        libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007fb9bea3e000)
+        libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fb9be84a000)
+        libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007fb9be844000)
+        libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007fb9be821000)
+        librt.so.1 => /lib/x86_64-linux-gnu/librt.so.1 (0x00007fb9be817000)
+        libamd_comgr.so.2 => /opt/rocm-6.1.0-99999/lib/asan/libamd_comgr.so.2 (0x00007fb9b4382000)
+        libhsa-runtime64.so.1 => /opt/rocm-6.1.0-99999/lib/asan/libhsa-runtime64.so.1 (0x00007fb9b3b00000)
+        libnuma.so.1 => /lib/x86_64-linux-gnu/libnuma.so.1 (0x00007fb9b3af3000)
+        /lib64/ld-linux-x86-64.so.2 (0x00007fb9c2027000)
+        libz.so.1 => /lib/x86_64-linux-gnu/libz.so.1 (0x00007fb9b3ad7000)
+        libtinfo.so.6 => /lib/x86_64-linux-gnu/libtinfo.so.6 (0x00007fb9b3aa7000)
+        libelf.so.1 => /lib/x86_64-linux-gnu/libelf.so.1 (0x00007fb9b3a89000)
+        libdrm.so.2 => /opt/amdgpu/lib/x86_64-linux-gnu/libdrm.so.2 (0x00007fb9b3a70000)
+        libdrm_amdgpu.so.1 => /opt/amdgpu/lib/x86_64-linux-gnu/libdrm_amdgpu.so.1 (0x00007fb9b3a62000)
+
+```
+
+This confirms that the address sanitizer runtime is linked in, and the ASAN instrumented version of the runtime libraries are used.
+Checking the `PATH` yields
+
+```bash
+$ which llvm-symbolizer
+/opt/rocm-6.1.0-99999/llvm/bin/llvm-symbolizer
+```
+
+Lastly, a check of the OS kernel version yields
+
+```bash
+$ uname -rv
+5.15.0-73-generic #80~20.04.1-Ubuntu SMP Wed May 17 14:58:14 UTC 2023
+```
+
+which indicates that the required HMM support (kernel version > 5.6) is available. This completes the necessary setup. Running with `m = 100`, `n1 = 11`, `n2 = 10` and `c = 100` should produce
+a report for an invalid access by the last 10 threads.
+
+```bash
+=================================================================
+==3141==ERROR: AddressSanitizer: heap-buffer-overflow on amdgpu device 0 at pc 0x7fb1410d2cc4
+WRITE of size 4 in workgroup id (10,0,0)
+  #0 0x7fb1410d2cc4 in set1(int*) at /home/dave/mini/mini.cpp:0:10
+
+Thread ids and accessed addresses:
+00 : 0x7fb14371d190 01 : 0x7fb14371d194 02 : 0x7fb14371d198 03 : 0x7fb14371d19c 04 : 0x7fb14371d1a0 05 : 0x7fb14371d1a4 06 : 0x7fb14371d1a8 07 : 0x7fb14371d1ac
+08 : 0x7fb14371d1b0 09 : 0x7fb14371d1b4
+
+0x7fb14371d190 is located 0 bytes after 400-byte region [0x7fb14371d000,0x7fb14371d190)
+allocated by thread T0 here:
+    #0 0x7fb151c76828 in hsa_amd_memory_pool_allocate /work/dave/git/compute/external/llvm-project/compiler-rt/lib/asan/asan_interceptors.cpp:692:3
+    #1 ...
+
+    #12 0x7fb14fb99ec4 in hipMalloc /work/dave/git/compute/external/clr/hipamd/src/hip_memory.cpp:568:3
+    #13 0x226630 in hipError_t hipMalloc<int>(int**, unsigned long) /opt/rocm-6.1.0-99999/include/hip/hip_runtime_api.h:8367:12
+    #14 0x226630 in main /home/dave/mini/mini.cpp:19:5
+    #15 0x7fb14ef02082 in __libc_start_main /build/glibc-SzIz7B/glibc-2.31/csu/../csu/libc-start.c:308:16
+
+Shadow bytes around the buggy address:
+  0x7fb14371cf00: ...
+
+=>0x7fb14371d180: 00 00[fa]fa fa fa fa fa fa fa fa fa fa fa fa fa
+  0x7fb14371d200: ...
+
+Shadow byte legend (one shadow byte represents 8 application bytes):
+  Addressable:           00
+  Partially addressable: 01 02 03 04 05 06 07
+  Heap left redzone:       fa
+  ...
+==3141==ABORTING
+```
+
+Running with `m = 100`, `n1 = 10`, `n2 = 10` and `c = 99` should produce a report for an invalid copy.
+
+```shell
+=================================================================
+==2817==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x514000150dcc at pc 0x7f5509551aca bp 0x7ffc90a7ae50 sp 0x7ffc90a7a610
+WRITE of size 400 at 0x514000150dcc thread T0
+    #0 0x7f5509551ac9 in __asan_memcpy /work/dave/git/compute/external/llvm-project/compiler-rt/lib/asan/asan_interceptors_memintrinsics.cpp:61:3
+    #1 ...
+
+    #9 0x7f5507462a28 in hipMemcpy_common(void*, void const*, unsigned long, hipMemcpyKind, ihipStream_t*) /work/dave/git/compute/external/clr/hipamd/src/hip_memory.cpp:637:10
+    #10 0x7f5507464205 in hipMemcpy /work/dave/git/compute/external/clr/hipamd/src/hip_memory.cpp:642:3
+    #11 0x226844 in main /home/dave/mini/mini.cpp:22:5
+    #12 0x7f55067c3082 in __libc_start_main /build/glibc-SzIz7B/glibc-2.31/csu/../csu/libc-start.c:308:16
+    #13 0x22605d in _start (/home/dave/mini/mini+0x22605d)
+
+0x514000150dcc is located 0 bytes after 396-byte region [0x514000150c40,0x514000150dcc)
+allocated by thread T0 here:
+    #0 0x7f5509553dcf in malloc /work/dave/git/compute/external/llvm-project/compiler-rt/lib/asan/asan_malloc_linux.cpp:69:3
+    #1 0x226817 in main /home/dave/mini/mini.cpp:21:21
+    #2 0x7f55067c3082 in __libc_start_main /build/glibc-SzIz7B/glibc-2.31/csu/../csu/libc-start.c:308:16
+
+SUMMARY: AddressSanitizer: heap-buffer-overflow /work/dave/git/compute/external/llvm-project/compiler-rt/lib/asan/asan_interceptors_memintrinsics.cpp:61:3 in __asan_memcpy
+Shadow bytes around the buggy address:
+  0x514000150b00: ...
+
+=>0x514000150d80: 00 00 00 00 00 00 00 00 00[04]fa fa fa fa fa fa
+  0x514000150e00: ...
+
+Shadow byte legend (one shadow byte represents 8 application bytes):
+  Addressable:           00
+  Partially addressable: 01 02 03 04 05 06 07
+  Heap left redzone:       fa
+  ...
+==2817==ABORTING
+```
+
+### Known issues with using GPU sanitizer
+
+* Red zones must have limited size and it is possible for an invalid access to completely miss a red zone and not be detected.
+
+* Lack of detection or false reports can be caused by the runtime not properly maintaining red zone shadows.
+
+* Lack of detection on the GPU might also be due to the implementation not instrumenting accesses to all GPU specific address spaces. For example, in the current implementation accesses to "private" or "stack" variables on the GPU are not instrumented, and accesses to HIP shared variables (also known as "local data store" or "LDS") are also not instrumented.
+
+* It can also be the case that a memory fault is hit for an invalid address even with the instrumentation. This is usually caused by the invalid address being so wild that its shadow address is outside of any memory region, and the fault actually occurs on the access to the shadow address. It is also possible to hit a memory fault for the `NULL` pointer. While address 0 does have a shadow location, it is not poisoned by the runtime.
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -5,65 +5,105 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html

 import shutil
+import jinja2
+import os

-from rocm_docs import ROCmDocs
+# Environment to process Jinja templates.
+jinja_env = jinja2.Environment(loader=jinja2.FileSystemLoader("."))

+# Jinja templates to render out.
+templates = []

-shutil.copy2('../CONTRIBUTING.md','./contributing.md')
-shutil.copy2('../RELEASE.md','./release.md')
+# Render templates and output files without the last extension.
+# For example: 'install.md.jinja' becomes 'install.md'.
+for template in templates:
+    rendered = jinja_env.get_template(template).render()
+    with open(os.path.splitext(template)[0], 'w') as file:
+        file.write(rendered)
+
+shutil.copy2('../RELEASE.md','./about/release-notes.md')
 # Keep capitalization due to similar linking on GitHub's markdown preview.
-shutil.copy2('../CHANGELOG.md','./CHANGELOG.md')
+shutil.copy2('../CHANGELOG.md','./about/CHANGELOG.md')

+latex_engine = "xelatex"
+latex_elements = {
+    "fontpkg": r"""
+\usepackage{tgtermes}
+\usepackage{tgheros}
+\renewcommand\ttdefault{txtt}
+"""
+}
+
+# configurations for PDF output by Read the Docs
+project = "ROCm Documentation"
+author = "Advanced Micro Devices, Inc."
+copyright = "Copyright (c) 2024 Advanced Micro Devices, Inc. All rights reserved."
+version = "6.0.1"
+release = "6.0.1"
 setting_all_article_info = True
-all_article_info_os = ["linux"]
+all_article_info_os = ["linux", "windows"]
 all_article_info_author = ""

 # pages with specific settings
 article_pages = [
-    {"file":"deploy/linux/index", "os":["linux"]},
-    {"file":"deploy/linux/install_overview", "os":["linux"]},
-    {"file":"deploy/linux/prerequisites", "os":["linux"]},
-    {"file":"deploy/linux/quick_start", "os":["linux"]},
-    {"file":"deploy/linux/install", "os":["linux"]},
-    {"file":"deploy/linux/upgrade", "os":["linux"]},
-    {"file":"deploy/linux/uninstall", "os":["linux"]},
-    {"file":"deploy/linux/package_manager_integration", "os":["linux"]},
-    {"file":"deploy/docker", "os":["linux"]},
-    
-    {"file":"release/gpu_os_support", "os":["linux"]},
-    {"file":"release/docker_support_matrix", "os":["linux"]},
-    
-    {"file":"reference/gpu_libraries/communication", "os":["linux"]},
-    {"file":"reference/ai_tools", "os":["linux"]},
-    {"file":"reference/management_tools", "os":["linux"]},
-    {"file":"reference/validation_tools", "os":["linux"]},
-    {"file":"reference/framework_compatibility/framework_compatibility", "os":["linux"]},
-    {"file":"reference/computer_vision", "os":["linux"]},
-    
-    {"file":"how_to/deep_learning_rocm", "os":["linux"]},
-    {"file":"how_to/gpu_aware_mpi", "os":["linux"]},
-    {"file":"how_to/magma_install/magma_install", "os":["linux"]},
-    {"file":"how_to/pytorch_install/pytorch_install", "os":["linux"]},
-    {"file":"how_to/system_debugging", "os":["linux"]},
-    {"file":"how_to/tensorflow_install/tensorflow_install", "os":["linux"]},
+    {
+        "file":"about/release-notes",
+        "os":["linux", "windows"],
+        "date":"2024-01-31"
+    },
+    {
+        "file":"about/CHANGELOG",
+        "os":["linux", "windows"],
+        "date":"2024-01-31"
+    },

-    {"file":"examples/machine_learning", "os":["linux"]},
-    {"file":"examples/inception_casestudy/inception_casestudy", "os":["linux"]},
-    
-    {"file":"understand/file_reorg", "os":["linux"]},
+    {"file":"install/windows/install-quick", "os":["windows"]},
+    {"file":"install/linux/install-quick", "os":["linux"]},

-    {"file":"understand/isv_deployment_win", "os":["windows"]},
+    {"file":"install/linux/install", "os":["linux"]},
+    {"file":"install/linux/install-options", "os":["linux"]},
+    {"file":"install/linux/prerequisites", "os":["linux"]},
+
+    {"file":"install/docker", "os":["linux"]},
+    {"file":"install/magma-install", "os":["linux"]},
+    {"file":"install/pytorch-install", "os":["linux"]},
+    {"file":"install/tensorflow-install", "os":["linux"]},
+
+    {"file":"install/windows/install", "os":["windows"]},
+    {"file":"install/windows/prerequisites", "os":["windows"]},
+    {"file":"install/windows/cli/index", "os":["windows"]},
+    {"file":"install/windows/gui/index", "os":["windows"]},
+
+    {"file":"about/compatibility/docker-image-support-matrix", "os":["linux"]},
+    {"file":"about/compatibility/user-kernel-space-compat-matrix", "os":["linux"]},
+
+    {"file":"reference/library-index", "os":["linux"]},
+
+    {"file":"how-to/deep-learning-rocm", "os":["linux"]},
+    {"file":"how-to/gpu-enabled-mpi", "os":["linux"]},
+    {"file":"how-to/system-debugging", "os":["linux"]},
+    {"file":"how-to/tuning-guides", "os":["linux", "windows"]},
+
+    {"file":"rocm-a-z", "os":["linux", "windows"]},
 ]

+exclude_patterns = ['temp']
+
 external_toc_path = "./sphinx/_toc.yml"

-docs_core = ROCmDocs("ROCm Documentation Home")
-docs_core.setup()
+extensions = ["rocm_docs", "sphinx_reredirects"]

 external_projects_current_project = "rocm"

-for sphinx_var in ROCmDocs.SPHINX_VARS:
-    globals()[sphinx_var] = getattr(docs_core, sphinx_var)
+html_theme = "rocm_docs_theme"
+html_theme_options = {"flavor": "rocm-docs-home"}
+
+html_title = "ROCm Documentation"
+
 html_theme_options = {
    "link_main_doc": False
 }
+
+redirects = {
+     "reference/openmp/openmp": "../../about/compatibility/openmp.html"
+}
--- a/docs/contribute/building.md
+++ b/docs/contribute/building.md
@@ -0,0 +1,150 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="Building ROCm documentation">
+  <meta name="keywords" content="documentation, Visual Studio Code, GitHub, command line,
+  AMD, ROCm">
+</head>
+
+# Building documentation
+
+You can build our documentation via GitHub (in a pull request) or locally (using the command line or
+Visual Studio (VS) Code.
+
+## GitHub
+
+If you open a pull request on the `develop` branch of a ROCm repository and scroll to the bottom of
+the page, there is a summary panel. Next to the line
+`docs/readthedocs.com:advanced-micro-devices-demo`, there is a `Details` link. If you click this, it takes
+you to the Read the Docs build for your pull request.
+
+![Screenshot of the GitHub documentation build link](../data/contribute/github-docs-build.png)
+
+If you don't see this line, click `Show all checks` to get an itemized view.
+
+## Command line
+
+You can build our documentation via the command line using Python. We use Python 3.8; other
+versions may not support the build.
+
+Use the Python Virtual Environment (`venv`) and run the following commands from the project root:
+
+```sh
+python3 -mvenv .venv
+
+.venv/bin/python     -m pip install -r docs/sphinx/requirements.txt
+.venv/bin/python     -m sphinx -T -E -b html -d _build/doctrees -D language=en docs _build/html
+```
+
+Navigate to `_build/html/index.html` and open this file in a web browser.
+
+## Visual Studio Code
+
+With the help of a few extensions, you can create a productive environment to author and test
+documentation locally using Visual Studio (VS) Code. Follow these steps to configure VS Code:
+
+1. Install the required extensions:
+
+   * Python: `(ms-python.python)`
+   * Live Server: `(ritwickdey.LiveServer)`
+
+2. Add the following entries to `.vscode/settings.json`.
+
+    ```json
+      {
+        "liveServer.settings.root": "/.vscode/build/html",
+        "liveServer.settings.wait": 1000,
+        "python.terminal.activateEnvInCurrentTerminal": true
+      }
+    ```
+
+    * `liveServer.settings.root`: Sets the root of the output website for live previews. Must be changed
+      alongside the `tasks.json` command.
+    * `liveServer.settings.wait`: Tells the live server to wait with the update in order to give Sphinx time to
+      regenerate the site contents and not refresh before the build is complete.
+    * `python.terminal.activateEnvInCurrentTerminal`: Activates the automatic virtual environment, so you
+      can build the site from the integrated terminal.
+
+3. Add the following tasks to `.vscode/tasks.json`.
+
+    ```json
+      {
+        "version": "2.0.0",
+        "tasks": [
+          {
+            "label": "Build Docs",
+            "type": "process",
+            "windows": {
+              "command": "${workspaceFolder}/.venv/Scripts/python.exe"
+            },
+            "command": "${workspaceFolder}/.venv/bin/python3",
+            "args": [
+              "-m",
+              "sphinx",
+              "-j",
+              "auto",
+              "-T",
+              "-b",
+              "html",
+              "-d",
+              "${workspaceFolder}/.vscode/build/doctrees",
+              "-D",
+              "language=en",
+              "${workspaceFolder}/docs",
+              "${workspaceFolder}/.vscode/build/html"
+            ],
+            "problemMatcher": [
+              {
+                "owner": "sphinx",
+                "fileLocation": "absolute",
+                "pattern": {
+                  "regexp": "^(?:.*\\.{3}\\s+)?(\\/[^:]*|[a-zA-Z]:\\\\[^:]*):(\\d+):\\s+(WARNING|ERROR):\\s+(.*)$",
+                  "file": 1,
+                  "line": 2,
+                  "severity": 3,
+                  "message": 4
+                }
+              },
+              {
+              "owner": "sphinx",
+                "fileLocation": "absolute",
+                "pattern": {
+                  "regexp": "^(?:.*\\.{3}\\s+)?(\\/[^:]*|[a-zA-Z]:\\\\[^:]*):{1,2}\\s+(WARNING|ERROR):\\s+(.*)$",
+                  "file": 1,
+                  "severity": 2,
+                  "message": 3
+                }
+              }
+            ],
+            "group": {
+              "kind": "build",
+              "isDefault": true
+            }
+          }
+        ]
+      }
+    ```
+
+    > Implementation detail: two problem matchers were needed to be defined,
+    > because VS Code doesn't tolerate some problem information being potentially
+    > absent. While a single regex could match all types of errors, if a capture
+    > group remains empty (the line number doesn't show up in all warning/error
+    > messages) but the `pattern` references said empty capture group, VS Code
+    > discards the message completely.
+
+4. Configure the Python virtual environment (`venv`).
+
+    From the Command Palette, run `Python: Create Environment`. Select `venv` environment and
+    `docs/sphinx/requirements.txt`.
+
+5. Build the docs.
+
+    Launch the default build task using one of the following options:
+
+    * A hotkey (the default is `Ctrl+Shift+B`)
+    * Issuing the `Tasks: Run Build Task` from the Command Palette
+
+6. Open the live preview.
+
+    Navigate to the site output within VS Code: right-click on `.vscode/build/html/index.html` and
+    select `Open with Live Server`. The contents should update on every rebuild without having to
+    refresh the browser.
--- a/docs/contribute/contributing.md
+++ b/docs/contribute/contributing.md
@@ -0,0 +1,112 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="Contributing to ROCm">
+  <meta name="keywords" content="ROCm, contributing, contribute, maintainer, contributor">
+</head>
+
+# Contribute to ROCm documentation
+
+All ROCm projects are GitHub-based, so if you want to contribute, you can do so by:
+
+* [Submitting a pull request in the appropriate GitHub repository](#submit-a-pull-request)
+* [Creating an issue in the appropriate GitHub repository](#create-an-issue)
+* [Suggesting a new feature](#suggest-a-new-feature)
+
+```{important}
+By creating a pull request (PR), you agree to allow your contribution to be licensed under the terms of the
+LICENSE.txt file in the corresponding repository. Different repositories may use different licenses.
+```
+
+## Submit a pull request
+
+To make edits to our documentation via PR, follow these steps:
+
+1. Identify the repository and the file you want to update. For example, to update this page, you would
+  need to modify content located in this file:
+  `https://github.com/ROCm/ROCm/blob/develop/docs/contribute/contributing.md`
+
+2. (optional, but recommended) Fork the repository.
+
+3. Clone the repository locally and (optionally) add your fork. Select the green 'Code' button and copy
+   the URL (e.g., `git@github.com:ROCm/ROCm.git`).
+
+   * From your terminal, run:
+
+      ```bash
+      git clone git@github.com:ROCm/ROCm.git
+      ```
+
+   * Optionally add your fork to this local copy of the repository by running:
+
+      ```bash
+      git add remote <name-of-my-fork> <git@github.com:my-username/ROCm.git>
+      ```
+
+      To get the URL of your fork, go to your GitHub profile, select the fork and click the green 'Code'
+      button (the same process you followed to get the main GitHub repository URL).
+
+4. Change directory into your local copy of the repository, and run ``git pull`` (or ``git pull origin develop``) to ensure your local copy has the most recent content.
+
+5. Create and checkout a new branch using the following command:
+
+    ```bash
+    git checkout -b <branch_name>
+    ```
+
+6. Change directory into the `./docs` folder and make any documentation changes locally using your preferred code editor. Follow the guidelines listed on the
+   [documentation structure](./doc-structure.md) page.
+
+7. Optionally run a local test build of the documentation to ensure the content builds and looks as expected. In your terminal, run the following commands from within the `./docs` folder of your cloned repository:
+
+     ```bash
+     pip3 install -r sphinx/requirements.txt  # You only need to run this command once
+     python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
+     ```
+
+    The build output files are located in the `docs/_build` folder. To preview your build, open the index file
+    (`docs/_build/html/index.html`) file. For more information, see [Building documentation](building.md). To learn
+    more about our build tools, see [Documentation toolchain](toolchain.md).
+
+8. Commit your changes and push them to GitHub by running:
+
+    ```bash
+    git add <path-to-my-modified-file> # To add all modified files, you can use: git add .
+    git commit -m "my-updates"
+    git push <name-of-my-fork>
+    ```
+
+    After pushing, you will get a GitHub link in the terminal output. Copy this link and paste it into a
+    browser to create your PR.
+
+## Create an issue
+
+1. To create a new GitHub issue, select the 'Issues' tab in the appropriate repository
+  (e.g., https://github.com/ROCm/ROCm/issues).
+2. Use the search bar to make sure the issue doesn't already exist.
+3. If your issue is not already listed, select the green 'New issue' button to the right of the page. Select
+  the type of issue and fill in the resulting template.
+
+### General issue guidelines
+
+* Use your best judgement for issue creation. If your issue is already listed, upvote the issue and
+  comment or post to provide additional details, such as how you reproduced this issue.
+* If you're not sure if your issue is the same, err on the side of caution and file your issue.
+  You can add a comment to include the issue number (and link) for the similar issue. If we evaluate
+  your issue as being the same as the existing issue, we'll close the duplicate.
+* If your issue doesn't exist, use the issue template to file a new issue.
+  * When filing an issue, be sure to provide as much information as possible, including script output so
+    we can collect information about your configuration. This helps reduce the time required to
+    reproduce your issue.
+  * Check your issue regularly, as we may require additional information to successfully reproduce the
+    issue.
+
+## Suggest a new feature
+
+Use the [GitHub Discussion forum](https://github.com/ROCm/ROCm/discussions)
+(Ideas category) to propose new features. Our maintainers are happy to provide direction and
+feedback on feature development.
+
+## Future development workflow
+
+The current ROCm development workflow is GitHub-based. If, in the future, we change this platform,
+the tools and links may change. In this instance, we will update contribution guidelines accordingly.
--- a/docs/contribute/doc-structure.md
+++ b/docs/contribute/doc-structure.md
@@ -0,0 +1,219 @@
+# Documentation structure
+
+Our documentation follows the Pitchfork folder structure. Most documentation files are stored in the
+`/docs` folder. Some special files (such as release, contributing, and changelog) are stored in the root
+(`/`) folder.
+
+All images are stored in the `/docs/data` folder. An image's file path mirrors that of the documentation
+file where it is used.
+
+Our naming structure uses kebab case; for example, `my-file-name.rst`.
+
+## Supported formats and syntax
+
+Our documentation includes both Markdown and RST files. We are gradually transitioning existing
+Markdown to RST in order to more effectively meet our documentation needs. When contributing,
+RST is preferred; if you must use Markdown, use GitHub-flavored Markdown.
+
+We use [Sphinx Design](https://sphinx-design.readthedocs.io/en/latest/index.html) syntax and compile
+our API references using [Doxygen](https://www.doxygen.nl/).
+
+The following table shows some common documentation components and the syntax convention we
+use for each:
+
+<table>
+<tr>
+<th>Component</th>
+<th>RST syntax</th>
+</tr>
+<tr>
+<td>Code blocks</td>
+<td>
+
+```rst
+
+.. code-block:: language-name
+
+  My code block.
+
+
+```
+
+</td>
+</tr>
+<tr>
+<td>Cross-referencing internal files</td>
+<td>
+
+```rst
+
+:doc:`Title <../path/to/file/filename>`
+
+```
+
+</td>
+</tr>
+<tr>
+<td>External links</td>
+<td>
+
+```rst
+
+`link name  <URL>`_
+
+```
+
+</td>
+</tr>
+<tr>
+<tr>
+<td>Headings</td>
+<td>
+
+```rst
+
+******************
+Chapter title (H1)
+******************
+
+Section title (H2)
+===============
+
+Subsection title (H3)
+---------------------
+
+Sub-subsection title (H4)
+^^^^^^^^^^^^^^^^^^^^
+
+
+```
+
+</td>
+</tr>
+<tr>
+<td>Images</td>
+<td>
+
+```rst
+
+.. image:: image1.png
+
+```
+
+</td>
+</tr>
+<tr>
+<td>Internal links</td>
+<td>
+
+```rst
+
+1. Add a tag to the section you want to reference:
+
+.. _my-section-tag: section-1
+
+Section 1
+==========
+
+2. Link to your tag:
+
+As shown in :ref:`section-1`.
+
+```
+
+</td>
+</tr>
+<tr>
+<tr>
+<td>Lists</td>
+<td>
+
+```rst
+
+# Ordered (numbered) list item
+
+* Unordered (bulleted) list item
+
+```
+
+</td>
+</tr>
+<tr>
+<tr>
+<td>Math (block)</td>
+<td>
+
+```rst
+
+.. math::
+
+  A = \begin{pmatrix}
+          0.0 & 1.0 & 1.0 & 3.0 \\
+          4.0 & 5.0 & 6.0 & 7.0 \\
+        \end{pmatrix}
+
+```
+
+</td>
+</tr>
+<tr>
+<td>Math (inline)</td>
+<td>
+
+```rst
+
+:math:`2 \times 2 `
+
+```
+
+</td>
+</tr>
+<tr>
+<td>Notes</td>
+<td>
+
+```rst
+
+.. note::
+
+  My note here.
+
+```
+
+</td>
+</tr>
+<tr>
+<td>Tables</td>
+<td>
+
+```rst
+
+.. csv-table::  Optional title here
+  :widths: 30, 70  #optional column widths
+  :header: "entry1 header", "entry2 header"
+
+   "entry1", "entry2"
+
+```
+
+</td>
+</tr>
+</table>
+
+## Language and style
+
+We use the
+[Google developer documentation style guide](https://developers.google.com/style/highlights) to
+guide our content.
+
+Font size and type, page layout, white space control, and other formatting
+details are controlled via
+[rocm-docs-core](https://github.com/ROCm/rocm-docs-core). If you want to notify us
+of any formatting issues, create a pull request in our
+[rocm-docs-core](https://github.com/ROCm/rocm-docs-core) GitHub repository.
+
+## Building our documentation
+
+<!--  % TODO: Fix the link to be able to work at every files  -->
+To learn how to build our documentation, refer to
+[Building documentation](./building.md).
--- a/docs/contribute/feedback.md
+++ b/docs/contribute/feedback.md
@@ -0,0 +1,36 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="Providing feedback for ROCm documentation">
+  <meta name="keywords" content="documentation, pull request, GitHub, AMD, ROCm">
+</head>
+
+# Providing feedback
+
+There are four standard ways to provide feedback on this repository.
+
+## Pull request
+
+All contributions to ROCm documentation should arrive via the
+[GitHub Flow](https://docs.github.com/en/get-started/quickstart/github-flow)
+targeting the develop branch of the repository. If you are unable to contribute
+via the GitHub Flow, feel free to email us at [rocm-feedback@amd.com](mailto:rocm-feedback@amd.com?subject=Documentation%20Feedback).
+
+For more in-depth information on creating a pull request (PR), see
+[Contributing](./contributing.md).
+
+## GitHub discussions
+
+To ask questions or view answers to frequently asked questions, refer to
+[GitHub Discussions](https://github.com/ROCm/ROCm/discussions).
+On GitHub Discussions, in addition to asking and answering questions,
+members can share updates, have open-ended conversations,
+and follow along on via public announcements.
+
+## GitHub issue
+
+Issues on existing or absent documentation can be filed in
+[GitHub Issues](https://github.com/ROCm/ROCm/issues).
+
+## Email
+
+Send other feedback or questions to [rocm-feedback@amd.com](mailto:rocm-feedback@amd.com?subject=Documentation%20Feedback).
--- a/docs/contribute/toolchain.md
+++ b/docs/contribute/toolchain.md
@@ -0,0 +1,65 @@
+<head>
+  <meta charset="UTF-8">
+  <meta name="description" content="ROCm documentation toolchain">
+  <meta name="keywords" content="documentation, toolchain, Sphinx, Doxygen, MyST, AMD, ROCm">
+</head>
+
+# ROCm documentation toolchain
+
+Our documentation relies on several open source toolchains and sites.
+
+## `rocm-docs-core`
+
+[rocm-docs-core](https://github.com/ROCm/rocm-docs-core) is an AMD-maintained
+project that applies customization for our documentation. This project is the tool most ROCm
+repositories use as part of the documentation build. It is also available as a
+[pip package on PyPI](https://pypi.org/project/rocm-docs-core/).
+
+See the user and developer guides for rocm-docs-core at
+{doc}`rocm-docs-core documentation<rocm-docs-core:index>`.
+
+## Sphinx
+
+[Sphinx](https://www.sphinx-doc.org/en/master/) is a documentation generator originally used for
+Python. It is now widely used in the open source community.
+
+### Sphinx External ToC
+
+[Sphinx External ToC](https://sphinx-external-toc.readthedocs.io/en/latest/intro.html) is a Sphinx
+extension used for ROCm documentation navigation. This tool generates a navigation menu on the left
+based on a YAML file (`_toc.yml.in`) that contains the table of contents.
+
+### Sphinx-book-theme
+
+[Sphinx-book-theme](https://sphinx-book-theme.readthedocs.io/en/latest/) is a Sphinx theme that
+defines the base appearance for ROCm documentation. ROCm documentation applies some
+customization, such as a custom header and footer on top of the Sphinx Book Theme.
+
+### Sphinx Design
+
+[Sphinx design](https://sphinx-design.readthedocs.io/en/latest/index.html) is a Sphinx extension that
+adds design functionality. ROCm documentation uses Sphinx Design for grids, cards, and synchronized
+tabs.
+
+## Doxygen
+
+[Doxygen](https://www.doxygen.nl/) is a documentation generator that extracts information from inline
+code. ROCm projects typically use Doxygen for public API documentation (unless the upstream project
+uses a different tool).
+
+## Breathe
+
+[Breathe](https://www.breathe-doc.org/) is a Sphinx plugin to integrate Doxygen content.
+
+## MyST
+
+[Markedly Structured Text (MyST)](https://myst-tools.org/docs/spec) is an extended flavor of
+Markdown ([CommonMark](https://commonmark.org/)) influenced by reStructuredText (RST) and
+Sphinx. It's integrated into ROCm documentation by the Sphinx extension
+[`myst-parser`](https://myst-parser.readthedocs.io/en/latest/).
+A MyST syntax cheat sheet is available on the [Jupyter reference](https://jupyterbook.org/en/stable/reference/cheatsheet.html) site.
+
+## Read the Docs
+
+[Read the Docs](https://docs.readthedocs.io/en/stable/) is the service that builds and hosts the HTML
+documentation generated using Sphinx to our end users.
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+++ b/docs/data/contribute/github-docs-build.png
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+++ b/docs/data/deploy/quick_start_windows/AMD-Display-Driver.png
--- a/docs/data/deploy/quick_start_windows/AMD-Logo.png
+++ b/docs/data/deploy/quick_start_windows/AMD-Logo.png
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+++ b/docs/data/deploy/quick_start_windows/BitCode-Profiler.png
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+++ b/docs/data/deploy/quick_start_windows/DeSelectAll.png
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+++ b/docs/data/deploy/quick_start_windows/HIP-Libraries.png
--- a/docs/data/deploy/quick_start_windows/HIP-Ray-Tracing.png
+++ b/docs/data/deploy/quick_start_windows/HIP-Ray-Tracing.png
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+++ b/docs/data/deploy/quick_start_windows/HIP-Runtime-Compiler.png
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+++ b/docs/data/deploy/quick_start_windows/HIP-SDK-Core.png
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+++ b/docs/data/deploy/quick_start_windows/Installation-Complete.png
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+++ b/docs/data/deploy/quick_start_windows/Uninstallation.png
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@@ -1 +0,0 @@
-
--- a/docs/data/how-to/gpu-enabled-mpi-1.png
+++ b/docs/data/how-to/gpu-enabled-mpi-1.png
--- a/docs/data/how_to/magma_install/image.005.png
+++ b/docs/data/how_to/magma_install/image.005.png
--- a/docs/data/how-to/tuning-guides/tuning001.png
+++ b/docs/data/how-to/tuning-guides/tuning001.png
--- a/Show More
+++ b/Show More