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
99 lines
2.2 KiB
Plaintext
99 lines
2.2 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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/*
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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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/**
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* @brief Call `__nanosleep` (potentially repeatedly) to sleep `nanoseconds` nanoseconds. Supports sleep times longer
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* than 4 billion nanoseconds (i.e. 4 seconds).
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*
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* @param nanoseconds how many nanoseconds to sleep
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* @return void
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*/
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__global__ void nano_sleep(unsigned long long nanoseconds)
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{
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#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 700)
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static constexpr auto m = std::numeric_limits<unsigned int>::max();
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for (;;)
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{
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if (nanoseconds > m)
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{
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__nanosleep(m);
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nanoseconds -= m;
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}
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else
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{
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__nanosleep(static_cast<unsigned int>(nanoseconds));
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break;
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}
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}
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#else
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const clock_t end = clock() + nanoseconds / (1000000000ULL / CLOCKS_PER_SEC);
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while (clock() < end)
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{
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// busy wait
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}
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#endif
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}
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void run(context& ctx, int NTASKS, int ms)
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{
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int dummy[1];
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auto handle = ctx.logical_data(dummy);
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ctx.task().add_deps(handle.rw())->*[](cudaStream_t stream) {
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nano_sleep<<<1, 1, 0, stream>>>(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().add_deps(handle.read())->*[&](cudaStream_t stream) {
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nano_sleep<<<1, 1, 0, stream>>>(ms * 1000ULL * 1000ULL);
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};
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}
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ctx.task().add_deps(handle.rw())->*[&](cudaStream_t stream) {
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nano_sleep<<<1, 1, 0, stream>>>(0);
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};
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}
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ctx.finalize();
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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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context ctx;
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run(ctx, NTASKS, ms);
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ctx = graph_ctx();
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run(ctx, NTASKS, ms);
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}
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