56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
104 lines
2.5 KiB
Plaintext
104 lines
2.5 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of CUDA Experimental in CUDA C++ Core Libraries,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#ifndef COMMON_GROUP_CUH
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#define COMMON_GROUP_CUH
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#include <cuda/barrier>
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#include <cuda/std/cstddef>
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#include <cuda/std/type_traits>
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#include <cuda/warp>
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#include <cuda/experimental/group.cuh>
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#include "testing.cuh"
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namespace
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{
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template <class T, cuda::std::size_t Id>
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__device__ T global_barriers_storage;
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//! @brief Returns reference to an array of N cuda::barrier objects with suitable thread scope for level allocated in
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//! suitable address space (shared or device memory). Id parameter can be used to create unique object.
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template <cuda::std::size_t N, cuda::std::size_t Id = 0, class Level>
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__device__ auto& get_barriers(const Level& level) noexcept
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{
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constexpr auto scope = cudax::__minimum_required_scope_for<Level>();
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using Barrier = cuda::barrier<scope>;
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using BarriersStorage = cuda::std::aligned_storage_t<N * sizeof(Barrier), alignof(Barrier)>;
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if constexpr (scope >= cuda::thread_scope_block)
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{
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__shared__ BarriersStorage shared_barriers_storage;
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return reinterpret_cast<Barrier(&)[N]>(shared_barriers_storage);
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}
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else
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{
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return reinterpret_cast<Barrier(&)[N]>(global_barriers_storage<BarriersStorage, Id>);
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}
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}
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struct ThreadsInWarpMappingResult
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{
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__device__ static constexpr ::cuda::std::size_t static_group_count()
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{
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return 1;
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}
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__device__ unsigned group_count() const
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{
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return 1;
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}
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__device__ unsigned group_rank() const
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{
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return 0;
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}
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__device__ static constexpr ::cuda::std::size_t static_unit_count()
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{
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return 32;
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}
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__device__ unsigned unit_count() const
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{
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return 32;
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}
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__device__ unsigned unit_rank() const
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{
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return cuda::gpu_thread.rank_as<unsigned>(cuda::warp);
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}
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__device__ cuda::device::lane_mask lane_mask() const noexcept
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{
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return cuda::device::lane_mask::all();
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}
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__device__ bool is_valid() const
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{
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return true;
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}
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__device__ static constexpr bool is_always_exhaustive() noexcept
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{
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return true;
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}
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__device__ static constexpr bool is_always_contiguous() noexcept
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{
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return true;
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}
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};
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} // namespace
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#endif // COMMON_GROUP_CUH
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