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
28 lines
597 B
C++
28 lines
597 B
C++
#include <thrust/generate.h>
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#include <thrust/host_vector.h>
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#include <thrust/random.h>
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#include <thrust/sort.h>
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#include <cstdlib>
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#include <iostream>
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#include <iterator>
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// defines the function prototype
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#include "device.h"
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int main()
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{
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// generate 20 random numbers on the host
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thrust::host_vector<int> h_vec(20);
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thrust::default_random_engine rng;
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thrust::generate(h_vec.begin(), h_vec.end(), rng);
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// interface to CUDA code
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sort_on_device(h_vec);
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// print sorted array
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thrust::copy(h_vec.begin(), h_vec.end(), std::ostream_iterator<int>(std::cout, "\n"));
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return 0;
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}
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