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
87 lines
2.1 KiB
Plaintext
87 lines
2.1 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/graph/graph_ctx.cuh>
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#include <iostream>
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/*
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* The goal of this test is to ensure that using read access modes actually
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* results in concurrent tasks
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*/
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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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int main(int argc, char** argv)
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{
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int NTASKS = 256;
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int ms = 40;
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if (argc > 1)
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{
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NTASKS = atoi(argv[1]);
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}
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if (argc > 2)
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{
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ms = atoi(argv[2]);
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}
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// cudaDevAttrClockRate: Peak clock frequency in kilohertz;
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int clock_rate;
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cuda_safe_call(cudaDeviceGetAttribute(&clock_rate, cudaDevAttrClockRate, 0));
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long long int clock_cnt = (long long int) (ms * clock_rate);
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graph_ctx ctx;
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int dummy[1];
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auto handle = ctx.logical_data(dummy);
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ctx.task(handle.rw())->*[](cudaGraph_t graph, auto /*unused*/) {
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cudaGraphNode_t n;
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cuda_safe_call(cudaGraphAddEmptyNode(&n, graph, nullptr, 0));
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};
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for (int iter = 0; iter < 10; iter++)
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{
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for (int k = 0; k < NTASKS; k++)
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{
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ctx.task(handle.read())->*[&](cudaStream_t stream, auto /*unused*/) {
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cuda_sleep_kernel<<<1, 1, 0, stream>>>(clock_cnt);
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};
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}
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ctx.task(handle.rw())->*[&](cudaGraph_t graph, auto /*unused*/) {
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cudaGraphNode_t n;
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cuda_safe_call(cudaGraphAddEmptyNode(&n, graph, nullptr, 0));
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};
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}
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ctx.submit();
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if (argc > 3)
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{
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std::cout << "Generating DOT output in " << argv[3] << '\n';
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ctx.print_to_dot(argv[3]);
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}
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ctx.finalize();
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}
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