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
93 lines
2.6 KiB
Plaintext
93 lines
2.6 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/__places/partitions/tiled_partition.cuh>
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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using namespace cuda::experimental::stf;
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template <typename T>
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__global__ void stencil_kernel(slice<T> Un, slice<const T> Un1)
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{
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size_t N = Un.extent(0);
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for (size_t i = threadIdx.x + blockIdx.x * blockDim.x; i < N; i += blockDim.x * gridDim.x)
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{
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Un(i) = 0.9 * Un1(i) + 0.05 * Un1((i + N - 1) % N) + 0.05 * Un1((i + 1) % N);
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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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int NITER = 500;
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int NBLOCKS = 20;
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const size_t BLOCK_SIZE = 2048 * 1024;
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if (argc > 1)
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{
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NITER = atoi(argv[1]);
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}
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if (argc > 2)
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{
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NBLOCKS = atoi(argv[2]);
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}
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const size_t TOTAL_SIZE = NBLOCKS * BLOCK_SIZE;
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double* Un = new double[TOTAL_SIZE];
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double* Un1 = new double[TOTAL_SIZE];
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for (size_t idx = 0; idx < TOTAL_SIZE; idx++)
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{
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Un[idx] = (idx == 0) ? 1.0 : 0.0;
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Un1[idx] = Un[idx];
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}
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auto lUn = ctx.logical_data(make_slice(Un, TOTAL_SIZE));
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auto lUn1 = ctx.logical_data(make_slice(Un1, TOTAL_SIZE));
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// std::shared_ptr<execution_grid> all_devs = exec_place::all_devices();
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// use grid [ 0 0 0 0 ] for debugging purpose
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auto all_devs = exec_place::repeat(exec_place::device(0), 4);
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data_place cdp = data_place::composite(tiled_partition<BLOCK_SIZE>(), all_devs);
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for (int iter = 0; iter < NITER; iter++)
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{
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// UPDATE Un from Un1
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ctx.task(lUn.rw(cdp), lUn1.read(cdp))->*[&](auto stream, auto sUn, auto sUn1) {
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stencil_kernel<double><<<32, 128, 0, stream>>>(sUn, sUn1);
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};
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// We make sure that the total sum of elements remains constant
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if (iter % 250 == 0)
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{
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double sum = 0.0;
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ctx.task(exec_place::host(), lUn.read())->*[&](auto stream, auto sUn) {
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cuda_safe_call(cudaStreamSynchronize(stream));
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for (size_t offset = 0; offset < TOTAL_SIZE; offset++)
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{
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sum += sUn(offset);
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}
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};
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// TODO add an assertion to check whether sum is close enough to 1.0
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// fprintf(stderr, "iter %d : CHECK SUM = %e\n", iter, sum);
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}
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std::swap(lUn, lUn1);
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}
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ctx.finalize();
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}
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