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
85 lines
2.4 KiB
Plaintext
85 lines
2.4 KiB
Plaintext
/*
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* SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/*
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This is a simple example demonstrating the use of CCCL functionality from Thrust, CUB, and libcu++.
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The example computes the sum of an array of integers using a simple parallel reduction. Each thread block
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computes the sum of a subset of the array using cuB::BlockRecuce. The sum of each block is then reduced
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to a single value using an atomic add via cuda::atomic_ref from libcu++. The result is stored in a device_vector
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from Thrust. The sum is then printed to the console.
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*/
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#include <cub/block/block_reduce.cuh>
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#include <thrust/device_vector.h>
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#include <cuda/atomic>
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#include <cstdio>
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#include <iostream>
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constexpr int block_size = 256;
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__global__ void sumKernel(int const* data, int* result, std::size_t N)
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{
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using BlockReduce = cub::BlockReduce<int, block_size>;
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__shared__ typename BlockReduce::TempStorage temp_storage;
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int index = threadIdx.x + blockIdx.x * blockDim.x;
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int sum = 0;
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if (index < N)
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{
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sum += data[index];
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}
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sum = BlockReduce(temp_storage).Sum(sum);
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if (threadIdx.x == 0)
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{
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cuda::atomic_ref<int, cuda::thread_scope_device> atomic_result(*result);
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atomic_result.fetch_add(sum, cuda::memory_order_relaxed);
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}
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}
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int main()
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{
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std::size_t N = 1000;
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thrust::device_vector<int> data(N, 1);
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thrust::device_vector<int> result(1);
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int num_blocks = (N + block_size - 1) / block_size;
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sumKernel<<<num_blocks, block_size>>>(
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thrust::raw_pointer_cast(data.data()), thrust::raw_pointer_cast(result.data()), N);
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auto err = cudaDeviceSynchronize();
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if (err != cudaSuccess)
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{
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std::cout << "Error: " << cudaGetErrorString(err) << '\n';
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return -1;
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}
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std::cout << "Sum: " << result[0] << '\n';
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assert(result[0] == N);
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return 0;
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}
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