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
74 lines
1.8 KiB
Plaintext
74 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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* @brief A reduction kernel written using launch and CUB
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*/
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#include <cub/cub.cuh>
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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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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int main()
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{
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context ctx;
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const size_t N = 128 * 1024 * 1024;
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std::vector<double> X(N);
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double sum = 0.0;
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double ref_sum = 0.0;
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for (size_t ind = 0; ind < N; ind++)
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{
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X[ind] = sin((double) ind);
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ref_sum += X[ind];
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}
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auto lX = ctx.logical_data(&X[0], {N});
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auto lsum = ctx.logical_data(&sum, {1});
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auto number_devices = 2;
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auto where = exec_place::repeat(exec_place::device(0), number_devices);
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auto spec = par<32>(con<128>());
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ctx.launch(spec, where, lX.read(), lsum.rw())->*[] _CCCL_DEVICE(auto th, auto x, auto sum) {
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// Each thread computes the sum of elements assigned to it
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double local_sum = 0.0;
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for (auto ind : th.apply_partition(shape(x)))
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{
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local_sum += x(ind);
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}
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using BlockReduce = cub::BlockReduce<double, th.static_width(1)>;
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__shared__ typename BlockReduce::TempStorage temp_storage;
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double block_sum = BlockReduce(temp_storage).Sum(local_sum);
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if (th.inner().rank() == 0)
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{
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atomicAdd(&sum(0), block_sum);
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}
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};
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ctx.finalize();
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EXPECT(fabs(sum - ref_sum) < 0.0001);
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}
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