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
76 lines
2.1 KiB
Plaintext
76 lines
2.1 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/blocked_partition.cuh>
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#include <cuda/experimental/__places/partitions/cyclic_shape.cuh>
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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double X0(int i)
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{
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return sin((double) i);
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}
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double Y0(int i)
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{
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return cos((double) i);
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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 = 128;
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double X[N], Y[N];
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for (int ind = 0; ind < N; ind++)
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{
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X[ind] = X0(ind);
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Y[ind] = Y0(ind);
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}
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const double alpha = 3.14;
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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 number_devices = 4;
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auto all_devs = exec_place::repeat(exec_place::device(0), number_devices);
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auto spec = par(16 * 4, par(4));
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ctx.launch(spec, all_devs, handle_X.read(), handle_Y.rw())->*[=] _CCCL_DEVICE(auto th, auto x, auto y) {
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// Blocked partition among elements in the outer most level
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auto outer_sh = blocked_partition::apply(shape(x), pos4(th.rank(0)), dim4(th.size(0)));
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// Cyclic partition among elements in the remaining levels
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auto inner_sh = cyclic_partition::apply(outer_sh, pos4(th.inner().rank()), dim4(th.inner().size()));
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for (auto ind : inner_sh)
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{
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y(ind) += alpha * x(ind);
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}
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};
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ctx.host_launch(handle_X.read(), handle_Y.read())->*[=](auto X, auto Y) {
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for (int ind = 0; ind < N; ind++)
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{
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// Y should be Y0 + alpha X0
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// fprintf(stderr, "Y[%ld] = %lf - expect %lf\n", ind, Y(ind), (Y0(ind) + alpha * X0(ind)));
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EXPECT(fabs(Y(ind) - (Y0(ind) + alpha * X0(ind))) < 0.0001);
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// X should be X0
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EXPECT(fabs(X(ind) - X0(ind)) < 0.0001);
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}
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};
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ctx.finalize();
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}
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