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
73 lines
2.0 KiB
Plaintext
73 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 Ensure that the scalar data interface works on both stream and graph backends
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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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void test_shape_from_scalar_view()
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{
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double x = 0;
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scalar_view<double> sv(&x);
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shape_of<scalar_view<double>> s = shape(sv);
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EXPECT(s.size() == sizeof(double));
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size_t n = 0;
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scalar_view<size_t> sv_n(&n);
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shape_of<scalar_view<size_t>> s_n = shape(sv_n);
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EXPECT(s_n.size() == sizeof(size_t));
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}
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template <typename Ctx>
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void run()
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{
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Ctx ctx;
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double a = 42.0;
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double b = 12.3;
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auto la = ctx.logical_data(scalar_view<double>(&a)).set_symbol("a");
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auto lb = ctx.logical_data(scalar_view<double>(&b)).set_symbol("b");
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auto lc = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("c");
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ctx.parallel_for(box(1), la.read(), lb.read(), lc.write())->*[] __device__(size_t, auto a, auto b, auto c) {
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*c.addr = *a.addr + *b.addr;
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};
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ctx.host_launch(lc.read())->*[](auto x) {
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EXPECT(fabs(*x.addr - (42.0 + 12.3)) < 0.001);
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};
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// Exercise logical_data(la.shape()) when la is scalar_view-backed (uses shape_of from scalar_view)
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auto ld = ctx.logical_data(la.shape()).set_symbol("d");
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ctx.parallel_for(box(1), la.read(), ld.write())->*[] __device__(size_t, auto a, auto d) {
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*d.addr = *a.addr;
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};
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ctx.host_launch(ld.read())->*[](auto x) {
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EXPECT(fabs(*x.addr - 42.0) < 0.001);
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};
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ctx.finalize();
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}
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int main()
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{
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test_shape_from_scalar_view();
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run<stream_ctx>();
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run<graph_ctx>();
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}
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