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
34 lines
806 B
Plaintext
34 lines
806 B
Plaintext
#include <thrust/copy.h>
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#include <thrust/device_vector.h>
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#include <thrust/fill.h>
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#include <thrust/host_vector.h>
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#include <thrust/sequence.h>
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#include <iostream>
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int main()
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{
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// initialize all ten integers of a device_vector to 1
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thrust::device_vector<int> D(10, 1);
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// set the first seven elements of a vector to 9
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thrust::fill(D.begin(), D.begin() + 7, 9);
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// initialize a host_vector with the first five elements of D
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thrust::host_vector<int> H(D.begin(), D.begin() + 5);
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// set the elements of H to 0, 1, 2, 3, ...
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thrust::sequence(H.begin(), H.end());
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// copy all of H back to the beginning of D
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thrust::copy(H.begin(), H.end(), D.begin());
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// print D
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for (size_t i = 0; i < D.size(); i++)
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{
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std::cout << "D[" << i << "] = " << D[i] << '\n';
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}
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return 0;
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}
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