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
70 lines
2.0 KiB
Plaintext
70 lines
2.0 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 This example illustrates how we can create temporary data from shapes, and use them in tasks
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*/
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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int main()
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{
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const int n = 4096;
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int X[n];
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int Y[n];
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for (size_t i = 0; i < n; i++)
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{
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X[i] = 3 * i;
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Y[i] = 2 * i - 3;
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}
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context ctx;
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auto lX = ctx.logical_data(X);
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auto lY = ctx.logical_data(Y);
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// Select an odd number
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int niter = 19;
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assert(niter % 2 == 1);
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for (int iter = 0; iter < niter; iter++)
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{
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// We here define a temporary vector with the same shape as X, for which there is no existing copy
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// This data handle has a limited scope, so that it is automatically destroyed at each iteration of the loop
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auto tmp = ctx.logical_data(lX.shape());
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ctx.task(lY.rw(), lX.rw(), tmp.write())->*[](cudaStream_t s, auto sY, auto sX, auto sTMP) {
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// We swap X and Y using TMP as temporary buffer
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// TMP = X
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cuda_safe_call(
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cudaMemcpyAsync(sTMP.data_handle(), sX.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
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// X = Y
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cuda_safe_call(cudaMemcpyAsync(sX.data_handle(), sY.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
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// Y = TMP
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cuda_safe_call(
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cudaMemcpyAsync(sY.data_handle(), sTMP.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
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};
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}
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ctx.finalize();
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// We have exchanged an odd number of times, so they must be inverted
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for (size_t i = 0; i < n; i++)
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{
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assert(X[i] == 2 * i - 3);
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assert(Y[i] == 3 * i);
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}
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}
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