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nvidia-cuda

nvidia-cuda

github.com/fandhe-aiDevelopment4357 installsUpdated 1d ago
Repository

About nvidia-cuda

nvidia-cuda is a reference skill for NVIDIA CUDA programming, aimed at developers writing GPU kernels in CUDA C++ or Python. It organizes material on programming models, PTX, Blackwell tuning, and CUTLASS/CuTe through task-based indexes.

Key features

  1. 01CUDA C++ and Python kernel development
  2. 02PTX instruction set reference
  3. 03Blackwell performance tuning
  4. 04CUTLASS and CuTe GEMM guidance
  5. 05Task-based documentation navigation

Use cases

  1. 01Finding guidance for writing and building CUDA kernels
  2. 02Looking up PTX instructions and memory models
  3. 03Tuning workloads for Blackwell GPUs
  4. 04Implementing GEMM with CUTLASS or CuTe

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/fandhe-ai/agent-reference-skills --skill nvidia-cuda

After installing, type/nvidia-cudain Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/advanced/appendices/
compute-capabilities.md
cpp-language-extensions.md
cpp-language-support.md
cuda-cpp-execution-model.md
cuda-cpp-memory-model.md
device-callable-apis.md
environment-variables.md
mathematical-functions.md
README.md
references/advanced/core/
advanced-host-programming.md
advanced-kernel-programming.md
driver-api.md
feature-survey.md
multi-gpu-systems.md
README.md
references/advanced/features/
async-barriers.md
async-copies.md
cluster-launch-control.md
compute-fabric-transport.md
cooperative-groups.md
cuda-graphs.md
driver-entry-point-access.md
dynamic-parallelism.md
error-log-management.md
extended-gpu-memory.md
graphics-interop.md
green-contexts.md
inter-process-communication.md
l2-cache-control.md
lazy-loading.md

Details

From the project README

nvidia-cuda

NVIDIA CUDA — GPU 上で汎用並列計算を行うためのプログラミングモデル・ツールチェーン・ライブラリ群。 CUDA C++/Python によるカーネル記述、PTX ISA 命令セット、Blackwell アーキテクチャ向けチューニング、 CUTLASS/CuTe による高性能 GEMM 実装をカバーする。

ディレクトリ構成

skills/nvidia-cuda/
  SKILL.md
  references/
    programming-model/
      README.md
      introduction.md
      programming-model.md
      cuda-platform.md
      intro-to-cuda-cpp.md
      intro-to-cuda-python.md
      writing-cuda-kernels.md
      writing-tile-kernels.md
      asynchronous-execution.md
      understanding-memory.md
      nvcc.md
    advanced/
      README.md
      core/
        README.md
        advanced-host-programming.md
        advanced-kernel-programming.md
        driver-api.md
        multi-gpu-systems.md
        feature-survey.md
      features/
        README.md
        unified-memory.md
        cuda-graphs.md
        stream-ordered-memory-allocation.md
        cooperative-groups.md
        programmatic-dependent-launch.md
        green-contexts.md
        locality-domains.md
        compute-fabric-transport.md
        lazy-loading.md
        error-log-management.md
        async-barriers.md
        pipelines.md
        async-copies.md
        cluster-launch-control.md
        l2-cache-control.md
        memory-sync-domains.md
        inter-process-communication.md
        virtual-memory-management.md
        extended-gpu-memory.md
        dynamic-parallelism.md
        graphics-interop.md
        driver-entry-point-access.md
      appendices/
        README.md
        compute-capabilities.md
        environment-variables.md
        cpp-language-support.md
        cpp-language-extensions.md
        mathematical-functions.md
        device-callable-apis.md
        cuda-cpp-memory-model.md
        cuda-cpp-execution-model.md
    ptx-isa/
      README.md
      introduction.md
      programming-model.md
      machine-model.md
      syntax.md
      state-spaces-types-variables.md
      instruction-operands.md
      abi.md
      memory-consistency-model.md
      instruction-set-overview.md
      instructions-arithmetic.md
      instructions-comparison-logic.md
      instructions-data-movement.md
      instructions-texture-surface.md
      instructions-control-flow.md
      instructions-sync-communication.md
      instructions-matrix-multiply.md
      instructions-video-misc.md
      special-registers.md
      directives.md
      pragma-strings.md
      release-notes.md
    blackwell-tuning/
      README.md
      architecture.md
      best-practices.md
      streaming-multiprocessor.md
      memory-system.md
      nvlink.md
    cutlass/
      README.md
      cute-dsl/
        README.md
        overview.md
        functionality.md
        quick-start.md
        cute-dsl.md
        dsl-introduction.md
        dsl-code-generation.md
        dsl-control-flow.md
        dsl-jit-arg-generation.md
        dsl-dynamic-layout.md
        dsl-struct-types.md
        dsl-jit-caching.md
        dsl-jit-compilation-options.md
        dsl-types.md
        framework-integration.md
        debugging.md
        iket-profiling.md
        primitives-api.md
        task-scheduling.md
        ts-introduction.md
        ts-resources.md
        ts-pipelines.md
        ts-schedules.md
        ts-patterns.md
        ts-validation.md
        ts-printout.md
        ts-allocators.md
        ts-pipeline-groups.md
        ts-pdl.md
        ts-api-task-scheduling.md
        ts-api-resources.md
        ts-api-task.md
        ts-api-task-manager.md
        ts-api-schedule-builder.md
        ts-api-memory.md
        ts-api-pipeline-group.md
        ts-api-pipeline.md
        ts-api-enums.md
        autotuning-gemm.md
        compile-with-tvm-ffi.md
        dsl-ahead-of-time-compilation.md
        naming-conventions.md
        deprecation-policy.md
        mma-intro.md
        mma-wmma-programming.md
        mma-wgmma-programming.md
        mma-tcgen05-programming.md
        limitations.md
        faqs.md
        api-overview.md
        api-cute.md
        api-cute-arch.md
        api-cute-runtime.md
        api-cute-nvgpu.md
        api-cute-nvgpu-common.md
        api-cute-nvgpu-warp.md
        api-cute-nvgpu-warpgroup.md
        api-cute-nvgpu-cpasync.md
        api-cute-nvgpu-tcgen05.md
        api-pipeline.md
        api-utils.md
        api-utils-sm90.md
        api-utils-sm100.md
      operator-api/
        README.md
        overview.md
        tutorials.md
        tutorial-000-gemm.md
        tutorial-001-gemm-fused-epilogue.md
        tutorial-002-bring-your-own-kernel.md
        tutorial-003-host-latency-best-practices.md
        tutorial-004-fake-tensors.md
        tutorial-005-grouped-gemm-contiguous-offset.md
        tutorial-006-block-scaled-gemm.md
        tutorial-007-heuristics.md
        api-reference.md
        api-operator.md
        api-arguments.md
        api-discovery.md
        api-heuristics.md
        api-metadata.md
        api-misc.md
      cpp/
        README.md
        overview.md
        getting-started.md
        quickstart.md
        ide-setup.md
        build.md
        build-windows-visual-studio.md
        build-clang-host-compiler.md
        functionality.md
        terminology.md
        fundamental-types.md
        programming-guidelines.md
        gemm-heuristics.md
        efficient-gemm.md
        pipeline.md
        profiler.md
        gemm-performance-measurement.md
        dependent-kernel-launch.md
        blackwell.md
        blackwell-sm100-gemm.md
        blackwell-cluster-launch-control.md
        code-organization.md
        cute.md
        cute-quickstart.md
        cute-layout.md
        cute-layout-algebra.md
        cute-tensor.md
        cute-algorithms.md
        cute-mma-atom.md
        cute-gemm-tutorial.md
        cute-predication.md
        cute-tma-tensors.md
        cutlass-3x.md
        cutlass-3x-design.md
        cutlass-3x-backwards-compatibility.md
        gemm-api-3x.md
        cutlass-2x.md
        layout-2x.md
        gemm-api-2x.md
        tile-iterator-concept.md
        utilities.md
        grouped-scheduler.md
        implicit-gemm-convolution.md
  samples/
    README.md
    async-copy-pipeline.md
    cooperative-groups-reduction.md
    cuda-graph-explicit-nodes.md
    cuda-graph-stream-capture.md
    cute-dsl-gemm.md
    cutlass-cpp-device-gemm.md
    cutlass-operator-api-gemm.md
    explicit-memory-transfer.md
    multi-gpu-peer-to-peer.md
    shared-memory-tiled-matmul.md
    stream-ordered-allocation.md
    stream-overlap.md
    tile-kernel-vector-add.md
    unified-memory.md
    vector-add-kernel.md
  scripts/
    README.md
    install.md
    nvcc-build.md
    binary-utilities.md
    sanitize.md
    debug.md
    nsight-systems.md
    nsight-compute.md
    gpu-query.md
    cutlass-build.md

探索手順

タスクからカテゴリを引き、カテゴリの README.md で目的のページを特定する:

  1. 下記マッピング表でタスクに対応するカテゴリを探す
  2. そのカテゴリの references/{category}/README.md(advanced/ cutlass/ はサブディレクトリの README.md)を参照して目的のページを特定する
  3. 該当ページの .md を Read して詳細を確認する

GB10 Grace Blackwell 実機のハードウェア仕様・DGX OS・PXE/fleet 運用・playbook 起動手順は dgx-spark スキル、 リモート接続管理・VS Code 起動等のデスクトップアプリ操作は nvidia-sync スキルを参照。 本スキルは CUDA C++/Python の書き方・PTX 命令・CUTLASS API を担当する。

タスク → カテゴリ マッピング

タスクカテゴリ参照 README
CUDA とは何か・GPU 階層モデル・CUDA プラットフォームの全体像を知りたいprogramming-modelreferences/programming-model/README.md
CUDA C++/Python でカーネルを書く、nvcc でビルドする、非同期実行・ストリームを扱うprogramming-modelreferences/programming-model/README.md
advanced カテゴリ全体の構成(core / features / appendices)を確認したいadvancedreferences/advanced/README.md
Driver API・マルチ GPU・advanced host/kernel programming を調べたいadvanced/corereferences/advanced/core/README.md
CUDA Graphs / Cooperative Groups / Unified Memory / VMM / IPC / Green Contexts / Locality Domains / Compute Fabric Transport 等の機能 API を調べたいadvanced/featuresreferences/advanced/features/README.md
Compute Capability・環境変数・C++ 言語拡張・メモリ/実行モデルの技術付録を調べたいadvanced/appendicesreferences/advanced/appendices/README.md
PTX 命令セット・state space・レジスタ・memory consistency model を調べたいptx-isareferences/ptx-isa/README.md
Blackwell アーキテクチャ・SM occupancy・NVLink・メモリシステムのチューニングを知りたいblackwell-tuningreferences/blackwell-tuning/README.md
CUTLASS/CuTe 全体のカテゴリ構成(CuTe DSL / Operator API / C++)を確認したいcutlassreferences/cutlass/README.md
CuTe DSL (Python) で GEMM・MMA・レイアウト・Task Scheduling (TS) を書きたいcutlass/cute-dslreferences/cutlass/cute-dsl/README.md
CUTLASS Operator API(Python、コンパイル済みカーネル呼び出し・nvMatmulHeuristics による operator ランキング)を使いたいcutlass/operator-apireferences/cutlass/operator-api/README.md
CUTLASS C++ / CuTe C++ でテンプレートベースの GEMM カーネルを実装したいcutlass/cppreferences/cutlass/cpp/README.md
典型的な使い方を知りたい(vector add, tiled matmul, GEMM, CUDA Graphs 等)samplessamples/README.md
インストール・nvcc ビルド・nsight プロファイリング・compute-sanitizer コマンドを知りたいscriptsscripts/README.md

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