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
81 lines
2.5 KiB
Plaintext
81 lines
2.5 KiB
Plaintext
#include <thrust/device_vector.h>
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#include <thrust/extrema.h>
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#include <thrust/functional.h>
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#include <thrust/host_vector.h>
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#include <thrust/iterator/zip_iterator.h>
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#include <thrust/random.h>
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#include <thrust/reduce.h>
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#include <thrust/sort.h>
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#include <thrust/unique.h>
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#include <cuda/iterator>
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#include <iostream>
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#include <iterator>
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// This example compute the mode [1] of a set of numbers. If there
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// are multiple modes, one with the smallest value it returned.
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//
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// [1] http://en.wikipedia.org/wiki/Mode_(statistics)
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int main()
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{
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const size_t N = 30;
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const size_t M = 10;
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thrust::default_random_engine rng;
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thrust::uniform_int_distribution<int> dist(0, M - 1);
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// generate random data on the host
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thrust::host_vector<int> h_data(N);
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for (auto& e : h_data)
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{
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e = dist(rng);
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}
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// transfer data to device
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thrust::device_vector<int> d_data(h_data);
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// print the initial data
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std::cout << "initial data" << '\n';
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thrust::copy(d_data.begin(), d_data.end(), std::ostream_iterator<int>(std::cout, " "));
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std::cout << '\n';
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// sort data to bring equal elements together
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thrust::sort(d_data.begin(), d_data.end());
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// print the sorted data
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std::cout << "sorted data" << '\n';
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thrust::copy(d_data.begin(), d_data.end(), std::ostream_iterator<int>(std::cout, " "));
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std::cout << '\n';
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// count number of unique keys
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size_t num_unique = thrust::unique_count(d_data.begin(), d_data.end());
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// count multiplicity of each key
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thrust::device_vector<int> d_output_keys(num_unique);
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thrust::device_vector<int> d_output_counts(num_unique);
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thrust::reduce_by_key(
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d_data.begin(), d_data.end(), cuda::constant_iterator<int>(1), d_output_keys.begin(), d_output_counts.begin());
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// print the counts
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std::cout << "values" << '\n';
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thrust::copy(d_output_keys.begin(), d_output_keys.end(), std::ostream_iterator<int>(std::cout, " "));
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std::cout << '\n';
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// print the counts
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std::cout << "counts" << '\n';
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thrust::copy(d_output_counts.begin(), d_output_counts.end(), std::ostream_iterator<int>(std::cout, " "));
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std::cout << '\n';
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// find the index of the maximum count
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thrust::device_vector<int>::iterator mode_iter;
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mode_iter = thrust::max_element(d_output_counts.begin(), d_output_counts.end());
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int mode = d_output_keys[cuda::std::distance(d_output_counts.begin(), mode_iter)];
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int occurrences = *mode_iter;
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std::cout << "Modal value " << mode << " occurs " << occurrences << " times " << '\n';
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return 0;
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}
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