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
98 lines
2.8 KiB
Plaintext
98 lines
2.8 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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*
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* @brief Jacobi method with parallel_for and graphs
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*
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*/
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#include <cuda/experimental/stf.cuh>
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#include <iostream>
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using namespace cuda::experimental::stf;
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int main([[maybe_unused]] int argc, [[maybe_unused]] char** argv)
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{
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#if _CCCL_CTK_BELOW(12, 4)
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fprintf(stderr, "Waiving test: conditional nodes are only available since CUDA 12.4.\n");
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return 0;
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#else
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stackable_ctx ctx;
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size_t n = 4096;
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size_t m = 4096;
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double tol = 0.1;
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if (argc > 2)
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{
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n = atol(argv[1]);
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m = atol(argv[2]);
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}
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if (argc > 3)
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{
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tol = atof(argv[3]);
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}
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auto lA = ctx.logical_data(shape_of<slice<double, 2>>(m, n));
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auto lAnew = ctx.logical_data(lA.shape());
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ctx.parallel_for(lA.shape(), lA.write(), lAnew.write()).set_symbol("init")->*
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[=] __device__(size_t i, size_t j, auto A, auto Anew) {
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A(i, j) = (i == j) ? 1.0 : -1.0;
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};
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cudaEvent_t start, stop;
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cuda_safe_call(cudaEventCreate(&start));
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cuda_safe_call(cudaEventCreate(&stop));
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cuda_safe_call(cudaEventRecord(start, ctx.fence()));
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auto lconverged = ctx.logical_data(shape_of<scalar_view<bool>>());
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size_t iter = 0;
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// Creating a conditional handle but not using it in a conditional node can make the graph instantiation fail.
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cudaGraphConditionalHandle handle;
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ctx.push_while(&handle, 1, cudaGraphCondAssignDefault);
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ctx.parallel_for(inner<1>(lA.shape()), lA.read(), lAnew.write(), lconverged.reduce(reducer::logical_and<bool>{}))
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->*[tol] __device__(size_t i, size_t j, auto A, auto Anew, auto& converged) {
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Anew(i, j) = 0.25 * (A(i - 1, j) + A(i + 1, j) + A(i, j - 1) + A(i, j + 1));
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double error = fabs(A(i, j) - Anew(i, j));
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converged = converged && (error < tol);
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};
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ctx.parallel_for(inner<1>(lA.shape()), lA.rw(), lAnew.read())->*[] __device__(size_t i, size_t j, auto A, auto Anew) {
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A(i, j) = Anew(i, j);
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};
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ctx.parallel_for(box(1), lconverged.read())->*[handle] __device__(size_t, auto converged) {
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cudaGraphSetConditional(handle, !*converged);
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};
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ctx.pop();
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fprintf(stderr, "ITER %zu: converged\n", iter++);
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cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
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ctx.finalize();
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float elapsedTime;
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cudaEventElapsedTime(&elapsedTime, start, stop);
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printf("Elapsed time: %f ms\n", elapsedTime);
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#endif
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}
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