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
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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*
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* @brief An AXPY kernel implemented with a task of the CUDA graph backend and
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* a host callback
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*
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* The host_launch mechanism is also illustrated
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*
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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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__global__ void axpy(double a, slice<const double> x, slice<double> y)
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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 (int i = tid; i < x.size(); i += nthreads)
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{
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y(i) += a * x(i);
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}
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}
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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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double Y0(int i)
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{
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return cos((double) i);
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}
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int main()
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{
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graph_ctx ctx;
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const size_t N = 16;
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double X[N], Y[N];
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for (size_t i = 0; i < N; i++)
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{
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X[i] = X0(i);
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Y[i] = Y0(i);
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}
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double alpha = 3.14;
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auto lX = ctx.logical_data(X);
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auto lY = ctx.logical_data(Y);
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/* Compute Y = Y + alpha X */
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ctx.task(lX.read(), lY.rw())->*[&](cudaStream_t s, auto dX, auto dY) {
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axpy<<<16, 128, 0, s>>>(alpha, dX, dY);
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};
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/* Asynchronously check the result on the host */
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ctx.host_launch(lX.read(), lY.read())->*[&](auto hX, auto hY) {
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for (size_t ind = 0; ind < hX.extent(0); ind++)
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{
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// Y should be Y0 + alpha X0
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EXPECT(fabs(hY(ind) - (Y0(ind) + alpha * X0(ind))) < 0.0001);
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// X should be X0
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EXPECT(fabs(hX(ind) - X0(ind)) < 0.0001);
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}
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};
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ctx.finalize();
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}
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