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
60 lines
1.7 KiB
Plaintext
60 lines
1.7 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/stream/interfaces/slice_reduction_ops.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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__global__ void add(slice<int, 2> s, int val)
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{
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size_t tid = threadIdx.x + blockIdx.x * blockDim.x;
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size_t nthreads = blockDim.x * gridDim.x;
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for (size_t j = 0; j < s.extent(1); j++)
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{
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for (size_t i = tid; i < s.extent(0); i += nthreads)
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{
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s(i, j) += val;
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}
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}
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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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int array[6] = {0, 1, 2, 3, 4, 5};
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// auto handle = ctx.logical_data(slice<int, 2>(&array[0], std::tuple{ 2, 3 }, 2));
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auto handle = ctx.logical_data(make_slice(&array[0], std::tuple{2, 3}, 2));
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auto redux_op = std::make_shared<slice_reduction_op_sum<int, 2>>();
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ctx.task(handle.relaxed(redux_op))->*[](auto stream, auto s) {
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add<<<32, 32, 0, stream>>>(s, 42);
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};
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ctx.task(exec_place::host(), handle.read())->*[](auto stream, auto s) {
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cuda_safe_call(cudaStreamSynchronize(stream));
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for (size_t j = 0; j < s.extent(1); j++)
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{
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for (size_t i = 0; i < s.extent(0); i++)
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{
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// fprintf(stderr, "%d\t", s(i, j));
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}
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// fprintf(stderr, "\n");
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}
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};
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ctx.finalize();
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}
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