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
106 lines
2.9 KiB
ReStructuredText
106 lines
2.9 KiB
ReStructuredText
.. _cuda_compute-module:
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``cuda.compute`` API Reference
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==============================
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.. warning::
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``cuda.compute`` is in public beta.
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The API is subject to change without notice.
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Algorithms
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----------
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.. automodule:: cuda.compute.algorithms
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:members:
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:undoc-members:
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:imported-members:
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Iterators
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---------
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.. automodule:: cuda.compute.iterators
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:members:
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:undoc-members:
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:imported-members:
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Operators
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---------
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.. py:currentmodule:: cuda.compute.op
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.. Unfortunately, we need to manually document the OpKind enum here because
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.. the `._bindings` module, where OpKind is defined, is mocked out when building
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.. docs. The mock out is needed to avoid the need for CUDA to be installed
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.. at docs build time.
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.. py:class:: OpKind
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Enumeration of operator kinds for CUDA parallel algorithms.
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This enum defines the types of operations that can be performed
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in parallel algorithms, including arithmetic, logical, and bitwise operations.
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.. py:attribute:: PLUS
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.. py:attribute:: MINUS
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.. py:attribute:: MULTIPLIES
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.. py:attribute:: DIVIDES
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.. py:attribute:: MODULUS
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.. py:attribute:: EQUAL_TO
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.. py:attribute:: NOT_EQUAL_TO
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.. py:attribute:: GREATER
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.. py:attribute:: LESS
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.. py:attribute:: GREATER_EQUAL
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.. py:attribute:: LESS_EQUAL
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.. py:attribute:: LOGICAL_AND
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.. py:attribute:: LOGICAL_OR
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.. py:attribute:: LOGICAL_NOT
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.. py:attribute:: BIT_AND
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.. py:attribute:: BIT_OR
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.. py:attribute:: BIT_XOR
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.. py:attribute:: BIT_NOT
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.. py:attribute:: IDENTITY
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.. py:attribute:: NEGATE
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.. py:attribute:: MINIMUM
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.. py:attribute:: MAXIMUM
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.. autoclass:: cuda.compute.op.RawOp
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:members:
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:undoc-members:
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Ahead-of-Time Compilation
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-------------------------
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The :func:`serialize <cuda.compute.algorithms.serialize>` and
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:func:`deserialize <cuda.compute.algorithms.deserialize>` functions (listed under
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`Algorithms`_ above) persist and restore built algorithms. To build ahead of time
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for architectures other than the current device's—or with no GPU present—pass the
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following dtype-only placeholders to a ``make_*`` factory in place of real arrays
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and scalars:
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.. py:class:: cuda.compute.ProxyArray(dtype)
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A dtype-only placeholder for a device array. Use in place of a real device
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array when calling a ``make_*`` factory to compile an algorithm without
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allocating GPU memory. Satisfies the ``DeviceArrayLike`` protocol; accessing
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its data pointer raises ``RuntimeError``. See
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:ref:`cuda.compute.ahead_of_time_compilation`.
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.. py:class:: cuda.compute.ProxyValue(dtype)
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A dtype-only placeholder for a scalar or initial-value argument (such as
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``h_init``). Use in place of a real numpy scalar or array to compile an
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algorithm without real data; accessing its data raises ``RuntimeError``. See
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:ref:`cuda.compute.ahead_of_time_compilation`.
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Utilities
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---------
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.. automodule:: cuda.compute.struct
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:members:
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Typing
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------
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.. automodule:: cuda.compute.typing
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:members:
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