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Sparse-checkout from NVIDIA/cccl main branch to complete cccl_upstream: Added: - python/cuda_cccl/ (226 files) — Python bindings for device-level algorithms Critical for muh toolchain: cuda.compute.reduce_into, scan, radix_sort, etc. Includes 204 .py files with full test coverage for all 27 algorithms - ci/ (163 files) — Build/test infrastructure build_cub.sh, test_cub.sh, build_and_test_targets.sh, matrix.yaml Directly maps to our [INFRA-CI] and [INFRA-BUILD] items - .agent/skills/ (7 files) — NVIDIA's own agent skills for CCCL cccl-style/SKILL.md, cccl-test/SKILL.md, sass-diff/SKILL.md - docs/ (491 files) — Official CCCL documentation CI references, CMake guides, Python compute docs, libcudacxx PTX docs - test/ (12 files) — Top-level integration tests (cuda_smoke, stdpar) - Root configs: .clang-format, .clang-tidy, CONTRIBUTING.md, pyproject.toml - CLAUDE.md symlink → AGENTS.md (NVIDIA's standard) cccl_upstream now mirrors full NVIDIA/cccl structure: Before: 42M (cub + thrust + libcudacxx + cudax + c + examples + benchmarks) After: 53M (+python +ci +docs +.agent +test +configs) This completes the CCCL base needed for: - [muh-bench] items: ci/util/build_and_test_targets.sh for targeted builds - [CCCL-verify] items: python/cuda_cccl/tests/ as reference implementations - [CCCL-test] items: ci/test_cub.sh, ci/test_thrust.sh - Agent workflow: .agent/skills/ for consistent style and test patterns
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35 lines
1.1 KiB
ReStructuredText
CCCL Python Libraries
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======================
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Overview
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--------
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The CUDA Core Compute Libraries (CCCL) for Python are a collection of modules
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with the shared goal of providing **high-quality, high-performance, and easy-to-use**
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abstractions for CUDA Python developers.
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* :doc:`cuda.compute <compute/index>` — Composable device-level primitives for building
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custom parallel algorithms, without writing CUDA kernels directly.
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These libraries expose the generic, highly-optimized algorithms from the
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`CCCL C++ libraries <https://nvidia.github.io/cccl/cpp.html>`_,
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which have been tuned to provide optimal performance across GPU architectures.
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Who is this for?
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----------------
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- **Library authors** building parallel algorithms that need portable performance
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across GPU architectures—without dropping to CUDA C++.
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- **Application developers** using PyTorch, CuPy, or other GPU-accelerated frameworks
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who need custom algorithms beyond what those libraries provide.
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.. toctree::
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:maxdepth: 2
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:caption: CCCL Python Libraries
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setup
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compute/index
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resources
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api_reference
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