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
92 lines
2.7 KiB
Plaintext
92 lines
2.7 KiB
Plaintext
#include <thrust/copy.h>
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#include <thrust/device_vector.h>
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#include <thrust/scan.h>
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#include <iostream>
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// BinaryPredicate for the head flag segment representation
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// equivalent to cuda::std::not_fn(thrust::project2nd<int,int>()));
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template <typename HeadFlagType>
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struct head_flag_predicate
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{
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__host__ __device__ bool operator()(HeadFlagType, HeadFlagType right) const
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{
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return !right;
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}
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};
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template <typename Vector>
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void print(const Vector& v)
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{
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for (const auto& e : v)
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{
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std::cout << e << " ";
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}
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std::cout << '\n';
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}
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int main()
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{
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int keys[] = {0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 4, 4, 5, 5, 5}; // segments represented with keys
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int flags[] = {1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0}; // segments represented with head flags
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int values[] = {2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2}; // values corresponding to each key
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int N = sizeof(keys) / sizeof(int); // number of elements
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// copy input data to device
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thrust::device_vector<int> d_keys(keys, keys + N);
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thrust::device_vector<int> d_flags(flags, flags + N);
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thrust::device_vector<int> d_values(values, values + N);
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// allocate storage for output
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thrust::device_vector<int> d_output(N);
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// inclusive scan using keys
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thrust::inclusive_scan_by_key(d_keys.begin(), d_keys.end(), d_values.begin(), d_output.begin());
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std::cout << "Inclusive Segmented Scan w/ Key Sequence\n";
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std::cout << " keys : ";
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print(d_keys);
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std::cout << " input values : ";
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print(d_values);
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std::cout << " output values : ";
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print(d_output);
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// inclusive scan using head flags
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thrust::inclusive_scan_by_key(
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d_flags.begin(), d_flags.end(), d_values.begin(), d_output.begin(), head_flag_predicate<int>());
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std::cout << "\nInclusive Segmented Scan w/ Head Flag Sequence\n";
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std::cout << " head flags : ";
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print(d_flags);
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std::cout << " input values : ";
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print(d_values);
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std::cout << " output values : ";
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print(d_output);
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// exclusive scan using keys
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thrust::exclusive_scan_by_key(d_keys.begin(), d_keys.end(), d_values.begin(), d_output.begin());
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std::cout << "\nExclusive Segmented Scan w/ Key Sequence\n";
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std::cout << " keys : ";
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print(d_keys);
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std::cout << " input values : ";
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print(d_values);
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std::cout << " output values : ";
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print(d_output);
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// exclusive scan using head flags
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thrust::exclusive_scan_by_key(
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d_flags.begin(), d_flags.end(), d_values.begin(), d_output.begin(), 0, head_flag_predicate<int>());
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std::cout << "\nExclusive Segmented Scan w/ Head Flag Sequence\n";
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std::cout << " head flags : ";
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print(d_flags);
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std::cout << " input values : ";
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print(d_values);
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std::cout << " output values : ";
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print(d_output);
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return 0;
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}
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