2a7ca101d7
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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Resources
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=========
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Examples
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--------
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For recipes and patterns, see our examples:
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* ``cuda.compute`` `examples <https://github.com/NVIDIA/cccl/tree/main/python/cuda_cccl/tests/compute/examples>`_
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CUB and Thrust Documentation
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----------------------------
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The CCCL Python libraries are built on top of the CUB and Thrust libraries.
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See the `CUB documentation <https://nvlabs.github.io/cub/>`_ and `Thrust documentation <https://thrust.github.io/>`_
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for more information regarding the underlying libraries.
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Asking for Help
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---------------
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If you have a question, run into an issue, or have a feature request,
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please raise an issue or start a discussion on our `GitHub repository <https://github.com/NVIDIA/cccl/issues>`_.
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Contributing
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------------
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We welcome contributions! Please see the
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`contributing guide <https://github.com/NVIDIA/cccl/blob/main/CONTRIBUTING.md>`_
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for instructions on how to set up a development environment and submit a pull request.
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Once you have a development environment set up, see :doc:`setup` for instructions
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on how to install `cuda.cccl` in development mode.
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License
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-------
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The CCCL Python libraries are licensed under the `Apache License 2.0 <https://www.apache.org/licenses/LICENSE-2.0>`_.
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