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
54 lines
1.7 KiB
Plaintext
54 lines
1.7 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 Illustrate how we can use frozen data to initialize constant data
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*
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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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context ctx;
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/* Create a piece of data that can be use many times without further synchronizations */
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auto buffer = ctx.logical_data(shape_of<slice<double, 2>>(128, 64)).set_symbol("buffer");
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ctx.parallel_for(buffer.shape(), buffer.write())->*[] __device__(size_t i, size_t j, auto b) {
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b(i, j) = sin(-1.0 * i) + cos(2.0 * j);
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};
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auto frozen_buffer = ctx.freeze(buffer);
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auto h_buf = frozen_buffer.get(data_place::host()).first;
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auto d_buf = frozen_buffer.get(data_place::current_device()).first;
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cuda_safe_call(cudaStreamSynchronize(ctx.fence()));
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auto lX = ctx.logical_data(buffer.shape()).set_symbol("X");
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ctx.parallel_for(lX.shape(), lX.write()).set_symbol("X=buf")->*[d_buf] __device__(size_t i, size_t j, auto x) {
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x(i, j) = d_buf(i, j);
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};
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ctx.parallel_for(exec_place::host(), lX.shape(), lX.read()).set_symbol("check buf")
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->*[h_buf](size_t i, size_t j, auto x) {
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EXPECT(fabs(x(i, j) - h_buf(i, j)) < 0.0001);
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};
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// Make sure all tasks are done before unfreezing
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frozen_buffer.unfreeze(ctx.fence());
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ctx.finalize();
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}
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