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
96 lines
2.4 KiB
Plaintext
96 lines
2.4 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of CUDASTF 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) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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/**
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* @file
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* @brief Example of reduction implementing using CUB kernels
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*/
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#include <thrust/device_vector.h>
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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template <int BLOCK_THREADS, typename T>
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__global__ void reduce(slice<const T> values, slice<T> partials, size_t nelems)
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{
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using namespace cub;
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typedef BlockReduce<T, BLOCK_THREADS> BlockReduceT;
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auto thread_id = BLOCK_THREADS * blockIdx.x + threadIdx.x;
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// Local reduction
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T local_sum = 0;
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for (size_t ind = thread_id; ind < nelems; ind += blockDim.x * gridDim.x)
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{
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local_sum += values(ind);
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}
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__shared__ typename BlockReduceT::TempStorage temp_storage;
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// Per-thread tile data
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T result = BlockReduceT(temp_storage).Sum(local_sum);
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if (threadIdx.x == 0)
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{
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partials(blockIdx.x) = result;
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}
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}
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template <typename Ctx>
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void run()
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{
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Ctx ctx;
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const size_t N = 1024 * 16;
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const size_t BLOCK_SIZE = 128;
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const size_t num_blocks = 32;
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int *X, ref_tot;
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X = new int[N];
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ref_tot = 0;
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for (size_t ind = 0; ind < N; ind++)
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{
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X[ind] = rand() % N;
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ref_tot += X[ind];
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}
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auto values = ctx.logical_data(X, {N});
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auto partials = ctx.logical_data(shape_of<slice<int>>(num_blocks));
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auto result = ctx.logical_data(shape_of<slice<int>>(1));
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ctx.task(values.read(), partials.write(), result.write())->*[&](auto stream, auto values, auto partials, auto result) {
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// reduce values into partials
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reduce<BLOCK_SIZE, int><<<num_blocks, BLOCK_SIZE, 0, stream>>>(values, partials, N);
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// reduce partials on a single block into result
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reduce<BLOCK_SIZE, int><<<1, BLOCK_SIZE, 0, stream>>>(partials, result, num_blocks);
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};
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ctx.host_launch(result.read())->*[&](auto p) {
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if (p(0) != ref_tot)
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{
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fprintf(stderr, "INCORRECT RESULT: p sum = %d, ref tot = %d\n", p(0), ref_tot);
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abort();
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}
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};
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ctx.finalize();
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}
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int main()
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{
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run<stream_ctx>();
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run<graph_ctx>();
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}
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