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
78 lines
2.3 KiB
Plaintext
78 lines
2.3 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 test illustrates how we can use multiple reserved::launch in a single task on different pieces of data
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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 X0(int i)
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{
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return i * i + 12;
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}
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int main()
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{
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stream_ctx ctx;
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const int N = 16;
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int X[N], Y[N], Z[N];
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for (size_t ind = 0; ind < N; ind++)
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{
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X[ind] = X0(ind);
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Y[ind] = 0;
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Z[ind] = 0;
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}
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auto handle_X = ctx.logical_data(X, {N});
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auto handle_Y = ctx.logical_data(Y, {N});
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auto handle_Z = ctx.logical_data(Z, {N});
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ctx.task(handle_X.read(), handle_Y.write(), handle_Z.write())
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->*[](cudaStream_t s, slice<const int> x, slice<int> y, slice<int> z) {
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std::vector<cudaStream_t> streams;
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streams.push_back(s);
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auto spec = par(1024);
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reserved::launch(spec, exec_place::current_device(), streams, std::tuple{x, y})
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->*[] _CCCL_DEVICE(auto t, slice<const int> x, slice<int> y) {
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size_t tid = t.rank();
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size_t nthreads = t.size();
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for (size_t ind = tid; ind < N; ind += nthreads)
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{
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y(ind) = 2 * x(ind);
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}
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};
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reserved::launch(spec, exec_place::current_device(), streams, std::tuple{y, z})
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->*[] _CCCL_DEVICE(auto t, slice<int> y, slice<int> z) {
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size_t tid = t.rank();
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size_t nthreads = t.size();
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for (size_t ind = tid; ind < N; ind += nthreads)
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{
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z(ind) = 3 * y(ind);
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}
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};
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};
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ctx.finalize();
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for (size_t ind = 0; ind < N; ind++)
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{
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assert(Y[ind] == 2 * X[ind]);
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assert(Z[ind] == 3 * Y[ind]);
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}
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}
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