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
113 lines
2.4 KiB
Plaintext
113 lines
2.4 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 stream backend
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*
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*/
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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#include <mutex>
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#include <thread>
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using namespace cuda::experimental::stf;
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static __global__ void cuda_sleep_kernel(long long int clock_cnt)
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{
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long long int start_clock = clock64();
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long long int clock_offset = 0;
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while (clock_offset < clock_cnt)
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{
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clock_offset = clock64() - start_clock;
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}
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}
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void cuda_sleep(double ms, cudaStream_t stream)
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{
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int device;
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cudaGetDevice(&device);
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// cudaDevAttrClockRate: Peak clock frequency in kilohertz;
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int clock_rate;
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cudaDeviceGetAttribute(&clock_rate, cudaDevAttrClockRate, device);
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long long int clock_cnt = (long long int) (ms * clock_rate);
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cuda_sleep_kernel<<<1, 1, 0, stream>>>(clock_cnt);
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}
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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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void mytask(stream_ctx ctx, int /*id*/)
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{
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// std::cout << "Thread " << id << " is executing.\n";
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const size_t N = 16;
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double alpha = 3.14;
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auto lX = ctx.logical_data<double>(N);
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auto lY = ctx.logical_data<double>(N);
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ctx.task(lX.write())->*[](cudaStream_t, auto) {};
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ctx.task(lY.write())->*[](cudaStream_t, auto) {};
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/* Compute Y = Y + alpha X */
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for (size_t i = 0; i < 10; i++)
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{
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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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cuda_sleep(100.0, s);
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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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std::vector<std::thread> threads;
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// Launch 8 threads.
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for (int i = 0; i < 10; ++i)
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{
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threads.emplace_back(mytask, ctx, i);
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}
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// Wait for all threads to complete.
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for (auto& th : threads)
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{
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th.join();
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}
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ctx.finalize();
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}
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