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
80 lines
2.7 KiB
ReStructuredText
80 lines
2.7 KiB
ReStructuredText
.. _cub-developer-guide-block-scope:
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Block-scope
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************
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Overview
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=========
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Block-scope algorithms are provided by structures as well:
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.. code-block:: c++
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template <typename T,
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int BLOCK_DIM_X,
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BlockReduceAlgorithm ALGORITHM = BLOCK_REDUCE_WARP_REDUCTIONS,
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int BLOCK_DIM_Y = 1,
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int BLOCK_DIM_Z = 1>
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class BlockReduce {
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public:
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struct TempStorage : Uninitialized<_TempStorage> {};
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// (1) new constructor
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__device__ __forceinline__ BlockReduce()
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: temp_storage(PrivateStorage()),
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linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)) {}
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__device__ __forceinline__ BlockReduce(TempStorage &temp_storage)
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: temp_storage(temp_storage.Alias()),
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linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)) {}
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};
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While warp-scope algorithms only provide a single constructor that requires the user to provide temporary storage,
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block-scope algorithms provide two constructors:
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#. The default constructor that allocates the required shared memory internally.
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#. The constructor that requires the user to provide temporary storage as argument.
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In the case of the default constructor,
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the block-level algorithm uses the ``PrivateStorage()`` member function to allocate the required shared memory.
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This ensures that shared memory required by the algorithm is only allocated when the default constructor is actually called in user code.
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If the default constructor is never called,
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then the algorithm will not allocate superfluous shared memory.
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.. code-block:: c++
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__device__ __forceinline__ _TempStorage& PrivateStorage()
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{
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__shared__ _TempStorage private_storage;
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return private_storage;
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}
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The ``__shared__`` memory has static semantic, so it's safe to return a reference here.
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Specialization
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====================================
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Block-scope facilities usually expose algorithm selection to the user.
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The algorithm is represented by the enumeration part of the API.
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For the reduction case,
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``BlockReduceAlgorithm`` is provided.
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Specializations are stored in the ``cub/block/specializations`` directory.
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Temporary storage usage
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====================================
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For block-scope algorithms,
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it's unsafe to use temporary storage without synchronization:
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.. code-block:: c++
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using BlockReduce = cub::BlockReduce<int, 128> ;
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__shared__ BlockReduce::TempStorage temp_storage;
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int aggregate_1 = BlockReduce(temp_storage).Sum(thread_data_1);
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// illegal, has to add `__syncthreads` between the two
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int aggregate_2 = BlockReduce(temp_storage).Sum(thread_data_2);
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// illegal, has to add `__syncthreads` between the two
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foo(temp_storage);
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