dedf08166a
Added 863 files from NVIDIA/cccl sparse checkout: - c2h/ (27 files): Catch2 test helpers — generators, validators, runner - nvbench_helper/ (10 files): Benchmark harness utilities - cmake/ (29 files): CMake presets and build helpers - cudax/ (794 files): Experimental CUDA extensions - AGENTS.md: NVIDIA's official AI agent instructions for CCCL - CMakePresets.json: Standardized build configurations - cccl-version.json: Version tracking Also added CCCL_ASSET_MAP.md mapping all 4295 CCCL files to competition value and PRD items. cccl_upstream now covers 100% of competition-critical assets: - 27 tuning headers (SM80/90/100 benchmark data) - 32 dispatch headers (algorithm implementations) - 60 Thrust examples (correctness verification) - 217 CUB Catch2 tests (regression matrix) - 153 CUB benchmarks (parameter space search) - 18 CUB examples (API verification) - 27 test helpers + benchmark harness - 794 cudax experimental extensions
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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