56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
73 lines
1.9 KiB
Plaintext
73 lines
1.9 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.cuh>
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using namespace cuda::experimental::stf;
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__global__ void swap_kernel(slice<double> dst, slice<double> src)
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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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size_t n = dst.size();
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for (size_t i = tid; i < n; i += nthreads)
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{
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double tmp = dst(i);
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dst(i) = src(i);
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src(i) = tmp;
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}
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}
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int main(int argc, char** argv)
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{
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stream_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] = 1.0;
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Y[i] = 2.0;
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}
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auto lX = ctx.logical_data(X);
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auto lY = ctx.logical_data(Y);
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#ifdef NDEBUG
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size_t iter_cnt = 10000000;
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#else
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size_t iter_cnt = 10000;
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fprintf(stderr, "Warning: Running with small problem size in debug mode, should use DEBUG=0.\n");
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#endif
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if (argc > 1)
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{
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iter_cnt = atol(argv[1]);
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}
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std::chrono::steady_clock::time_point start, stop;
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start = std::chrono::steady_clock::now();
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for (size_t iter = 0; iter < iter_cnt; iter++)
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{
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ctx.task(lX.rw(), lY.rw())->*[&](cudaStream_t s, auto dX, auto dY) {
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swap_kernel<<<4, 16, 0, s>>>(dY, dX);
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};
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ctx.task(lY.rw(), lX.rw())->*[&](cudaStream_t s, auto dY, auto dX) {
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swap_kernel<<<4, 16, 0, s>>>(dX, dY);
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};
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}
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stop = std::chrono::steady_clock::now();
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ctx.finalize();
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std::chrono::duration<double> duration = stop - start;
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fprintf(stderr, "Elapsed: %.2lf us per task pair\n", duration.count() * 1000000.0 / (iter_cnt));
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}
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