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
82 lines
1.8 KiB
Plaintext
82 lines
1.8 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 A reduction kernel written using launch
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*/
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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double X0(int i)
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{
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return sin((double) i);
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}
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int main()
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{
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context ctx;
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const size_t N = 128 * 1024 * 1024;
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std::vector<double> X(N);
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double sum = 0.0;
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double ref_sum = 0.0;
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for (size_t ind = 0; ind < N; ind++)
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{
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X[ind] = sin((double) ind);
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ref_sum += X[ind];
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}
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auto lX = ctx.logical_data(&X[0], {N});
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auto lsum = ctx.logical_data(&sum, {1});
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auto number_devices = 1; //
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auto where = exec_place::repeat(exec_place::device(0), number_devices);
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auto spec = par<16>(con<32>());
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ctx.launch(spec, where, lX.read(), lsum.rw())->*[] _CCCL_DEVICE(auto th, auto x, auto sum) {
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// Each thread computes the sum of elements assigned to it
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double local_sum = 0.0;
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for (size_t i = th.rank(); i < x.size(); i += th.size())
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{
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local_sum += x(i);
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}
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auto ti = th.inner();
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__shared__ double block_sum[th.static_width(1)];
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block_sum[ti.rank()] = local_sum;
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for (size_t s = ti.size() / 2; s > 0; s /= 2)
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{
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ti.sync();
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if (ti.rank() < s)
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{
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block_sum[ti.rank()] += block_sum[ti.rank() + s];
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}
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}
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if (ti.rank() == 0)
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{
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atomicAdd(&sum(0), block_sum[0]);
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}
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};
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ctx.finalize();
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EXPECT(fabs(sum - ref_sum) < 0.0001);
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}
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