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
81 lines
2.0 KiB
Plaintext
81 lines
2.0 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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#include <cuda/experimental/__stf/utility/run_once.cuh>
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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int main()
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{
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context ctx;
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const int N = 16;
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size_t niter = 12;
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int A[N];
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for (int i = 0; i < N; i++)
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{
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A[i] = 2 * i + 1;
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}
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auto lres = ctx.logical_data(A);
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for (size_t k = 0; k < niter; k++)
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{
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auto ltmp = ctx.logical_data(lres.shape());
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ctx.parallel_for(ltmp.shape(), ltmp.write())->*[] __device__(size_t i, auto tmp) {
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tmp(i) = i;
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};
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ctx.parallel_for(lres.shape(), ltmp.read(), lres.rw())->*[] __device__(size_t i, auto tmp, auto res) {
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res(i) += tmp(i);
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};
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}
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for (size_t k = 0; k < niter; k++)
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{
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auto ltmp = run_once()->*[&]() {
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// Ensure this is only done once !
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static bool done = false;
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EXPECT(!done);
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done = true;
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auto ltmp = ctx.logical_data(lres.shape());
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ctx.parallel_for(ltmp.shape(), ltmp.write())->*[] __device__(size_t i, auto tmp) {
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tmp(i) = i;
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};
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return ltmp;
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};
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auto ltmp2 = run_once(size_t(k % 4))->*[&](size_t val) {
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// fprintf(stderr, "COMPUTE FOR %ld\n", val);
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auto ltmp = ctx.logical_data(lres.shape());
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ctx.parallel_for(ltmp.shape(), ltmp.write())->*[val] __device__(size_t i, auto tmp) {
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tmp(i) = val;
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};
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return ltmp;
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};
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ctx.parallel_for(lres.shape(), ltmp.read(), lres.rw())->*[] __device__(size_t i, auto tmp, auto res) {
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res(i) += tmp(i);
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};
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}
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ctx.finalize();
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for (int i = 0; i < N; i++)
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{
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EXPECT(A[i] == (2 * i + 1) + 2 * i * niter);
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}
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}
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