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
72 lines
1.6 KiB
Plaintext
72 lines
1.6 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/graph/graph_ctx.cuh>
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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using namespace cuda::experimental::stf;
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template <typename T>
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__global__ void scal(size_t n, T a, T* x)
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{
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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int nthreads = gridDim.x * blockDim.x;
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for (size_t ind = tid; ind < n; ind += nthreads)
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{
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x[ind] = a * x[ind];
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}
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}
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double x_init(int i)
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{
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return cos((double) i);
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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 int n = 4096;
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double X[n];
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for (int ind = 0; ind < n; ind++)
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{
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X[ind] = x_init(ind);
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}
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auto handle_X = ctx.logical_data(X);
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double alpha = 2.0;
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int niter = 4;
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for (int iter = 0; iter < niter; iter++)
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{
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ctx.task(handle_X.rw())->*[&](cudaStream_t s, auto sX) {
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scal<<<16, 128, 0, s>>>(sX.size(), alpha, sX.data_handle());
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};
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}
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// Ask to use Y on the host
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ctx.host_launch(handle_X.read())->*[&](auto sX) {
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for (int ind = 0; ind < n; ind++)
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{
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EXPECT(fabs(sX(ind) - pow(alpha, niter) * (x_init(ind))) < 0.00001);
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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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