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
54 lines
1.9 KiB
Plaintext
54 lines
1.9 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) 2025 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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// Example of using `cub::DeviceReduce::Reduce` with cudax environment.
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#include <cub/device/device_reduce.cuh>
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#include <cuda/experimental/container.cuh>
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#include <cuda/experimental/memory_resource.cuh>
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#include <cuda/experimental/stream.cuh>
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#include <iostream>
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namespace cudax = cuda::experimental;
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int main()
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{
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constexpr int num_items = 50000;
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// A CUDA stream on which to execute the reduction
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cuda::stream stream{cuda::devices[0]};
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cuda::device_memory_pool_ref mr = cuda::device_default_memory_pool(cuda::devices[0]);
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// Allocate input and output, but do not zero initialize output (`cuda::no_init`)
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auto d_in = cuda::make_buffer<int>(stream, mr, num_items, 1);
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auto d_out = cuda::make_buffer<float>(stream, mr, 1, cuda::no_init);
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// An environment we use to pass all necessary information to CUB
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cudax::env_t<cuda::mr::device_accessible> env{mr, stream};
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auto error = cub::DeviceReduce::Reduce(d_in.begin(), d_out.begin(), num_items, cuda::std::plus{}, 0, env);
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if (error != cudaSuccess)
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{
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std::cerr << "cub::DeviceReduce::Reduce failed: " << cudaGetErrorString(error) << "\n";
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exit(EXIT_FAILURE);
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}
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auto h_out = cuda::make_buffer<float>(stream, cuda::pinned_default_memory_pool(), d_out);
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stream.sync();
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if (h_out.get_unsynchronized(0) != num_items)
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{
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std::cerr << "Result verification failed: " << h_out.get_unsynchronized(0) << " != " << num_items << "\n";
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exit(EXIT_FAILURE);
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}
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}
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