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
93 lines
3.1 KiB
Plaintext
93 lines
3.1 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of CUDA Experimental 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) 2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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/**
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* Vector addition: C = A + B.
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*
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* This sample is a very basic sample that implements element by element
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* vector addition. It is the same as the sample illustrating Chapter 2
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* of the programming guide with some additions like error checking.
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*/
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#include <thrust/execution_policy.h>
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#include <thrust/random.h>
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#include <thrust/tabulate.h>
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#include <thrust/transform.h>
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#include <cuda/experimental/container.cuh>
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#include <cuda/experimental/memory_resource.cuh>
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#include <cuda/experimental/stream.cuh>
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#include <iostream>
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namespace cudax = cuda::experimental;
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constexpr int numElements = 50000;
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struct generator
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{
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thrust::default_random_engine gen{};
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thrust::uniform_real_distribution<float> dist{-10.0f, 10.0f};
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__host__ __device__ generator(const unsigned seed)
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: gen{seed}
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{}
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__host__ __device__ float operator()(cuda::std::size_t idx) noexcept
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{
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gen.discard(idx);
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return dist(gen);
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}
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};
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int main()
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{
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// A CUDA stream on which to execute the vector addition kernel
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cudax::stream stream{cuda::device_ref{0}};
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// The execution policy we want to use to run all work on the same stream
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auto policy = thrust::cuda::par_nosync.on(stream.get());
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cuda::device_memory_pool_ref device_resource = cuda::device_default_memory_pool(cuda::device_ref{0});
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// Allocate the two inputs and output, but do not zero initialize via `cuda::no_init`
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cuda::device_buffer<float> A{stream, device_resource, numElements, cuda::no_init};
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cuda::device_buffer<float> B{stream, device_resource, numElements, cuda::no_init};
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cuda::device_buffer<float> C{stream, device_resource, numElements, cuda::no_init};
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// Fill both vectors on stream using a random number generator
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thrust::tabulate(policy, A.begin(), A.end(), generator{42});
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thrust::tabulate(policy, B.begin(), B.end(), generator{1337});
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// Add the vectors together
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thrust::transform(policy, A.begin(), A.end(), B.begin(), C.begin(), cuda::std::plus<>{});
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cuda::pinned_memory_pool_ref pinned_resource = cuda::pinned_default_memory_pool();
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// Verify that the result vector is correct, by copying it to host
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cuda::host_buffer<float> h_A{stream, pinned_resource, A};
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cuda::host_buffer<float> h_B{stream, pinned_resource, B};
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cuda::host_buffer<float> h_C{stream, pinned_resource, C};
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// Do not forget to sync afterwards
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stream.sync();
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for (int i = 0; i < numElements; ++i)
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{
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if (cuda::std::abs(h_A.get_unsynchronized(i) + h_B.get_unsynchronized(i) - h_C.get_unsynchronized(i)) > 1e-5)
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{
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std::cerr << "Result verification failed at element " << i << "\n";
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exit(EXIT_FAILURE);
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}
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}
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return 0;
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}
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