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
78 lines
2.3 KiB
Plaintext
78 lines
2.3 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 This test illustrates how we can use multiple reserved::launch in a single task on different pieces of data
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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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int X0(int i)
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{
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return i * i + 12;
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}
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int main()
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{
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stream_ctx ctx;
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const int N = 16;
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int X[N], Y[N], Z[N];
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for (size_t ind = 0; ind < N; ind++)
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{
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X[ind] = X0(ind);
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Y[ind] = 0;
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Z[ind] = 0;
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}
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auto handle_X = ctx.logical_data(X, {N});
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auto handle_Y = ctx.logical_data(Y, {N});
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auto handle_Z = ctx.logical_data(Z, {N});
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ctx.task(handle_X.read(), handle_Y.write(), handle_Z.write())
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->*[](cudaStream_t s, slice<const int> x, slice<int> y, slice<int> z) {
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std::vector<cudaStream_t> streams;
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streams.push_back(s);
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auto spec = par(1024);
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reserved::launch(spec, exec_place::current_device(), streams, std::tuple{x, y})
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->*[] _CCCL_DEVICE(auto t, slice<const int> x, slice<int> y) {
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size_t tid = t.rank();
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size_t nthreads = t.size();
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for (size_t ind = tid; ind < N; ind += nthreads)
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{
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y(ind) = 2 * x(ind);
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}
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};
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reserved::launch(spec, exec_place::current_device(), streams, std::tuple{y, z})
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->*[] _CCCL_DEVICE(auto t, slice<int> y, slice<int> z) {
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size_t tid = t.rank();
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size_t nthreads = t.size();
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for (size_t ind = tid; ind < N; ind += nthreads)
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{
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z(ind) = 3 * y(ind);
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}
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};
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};
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ctx.finalize();
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for (size_t ind = 0; ind < N; ind++)
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{
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assert(Y[ind] == 2 * X[ind]);
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assert(Z[ind] == 3 * Y[ind]);
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}
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}
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