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
68 lines
1.9 KiB
Plaintext
68 lines
1.9 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/stf.cuh>
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using namespace cuda::experimental::stf;
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template <typename T>
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void init(context& ctx, logical_data<T> l, int val)
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{
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ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
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s(i) = val;
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};
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}
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int main()
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{
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context ctx;
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auto a = ctx.logical_data<int>(size_t(1000000));
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auto b = ctx.logical_data<int>(size_t(1000000));
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auto c = ctx.logical_data<int>(size_t(1000000));
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auto d = ctx.logical_data<int>(size_t(1000000));
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init(ctx, a, 12);
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init(ctx, b, 35);
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init(ctx, c, 42);
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init(ctx, d, 17);
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auto fn1 = [](context ctx, logical_data<slice<int>> a) {
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ctx.parallel_for(a.shape(), a.rw())->*[] __device__(size_t i, auto sa) {
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sa(i) += 1;
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};
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};
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algorithm alg1;
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auto fn2 = [&alg1, &fn1](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
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alg1.run_as_task(fn1, ctx, a.rw());
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alg1.run_as_task(fn1, ctx, b.rw());
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ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
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sa(i) += sb(i);
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};
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ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
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sb(i) = sa(i);
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};
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};
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algorithm alg2;
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for (size_t i = 0; i < 100; i++)
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{
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alg2.run_as_task(fn2, ctx, a.rw(), b.rw());
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alg2.run_as_task(fn2, ctx, a.rw(), c.rw());
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alg2.run_as_task(fn2, ctx, c.rw(), d.rw());
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alg2.run_as_task(fn2, ctx, d.rw(), a.rw());
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}
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ctx.finalize();
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}
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