dedf08166a
Added 863 files from NVIDIA/cccl sparse checkout: - c2h/ (27 files): Catch2 test helpers — generators, validators, runner - nvbench_helper/ (10 files): Benchmark harness utilities - cmake/ (29 files): CMake presets and build helpers - cudax/ (794 files): Experimental CUDA extensions - AGENTS.md: NVIDIA's official AI agent instructions for CCCL - CMakePresets.json: Standardized build configurations - cccl-version.json: Version tracking Also added CCCL_ASSET_MAP.md mapping all 4295 CCCL files to competition value and PRD items. cccl_upstream now covers 100% of competition-critical assets: - 27 tuning headers (SM80/90/100 benchmark data) - 32 dispatch headers (algorithm implementations) - 60 Thrust examples (correctness verification) - 217 CUB Catch2 tests (regression matrix) - 153 CUB benchmarks (parameter space search) - 18 CUB examples (API verification) - 27 test helpers + benchmark harness - 794 cudax experimental extensions
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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