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
79 lines
2.0 KiB
Plaintext
79 lines
2.0 KiB
Plaintext
#include <thrust/host_vector.h>
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#include <thrust/random.h>
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#include <thrust/remove.h>
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#include <iostream>
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// This example generates random points in the
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// unit square [0,1)x[0,1) and then removes all
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// points where x^2 + y^2 > 1
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//
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// The x and y coordinates are stored in separate arrays
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// and a zip_iterator is used to combine them together
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template <typename T>
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struct is_outside_circle
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{
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template <typename Tuple>
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inline __host__ __device__ bool operator()(const Tuple& tuple) const
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{
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// unpack the tuple into x and y coordinates
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const T x = cuda::std::get<0>(tuple);
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const T y = cuda::std::get<1>(tuple);
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if (x * x + y * y > 1)
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{
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return true;
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}
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else
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{
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return false;
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}
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}
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};
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int main()
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{
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const size_t N = 20;
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// generate random points in the unit square on the host
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thrust::default_random_engine rng;
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thrust::uniform_real_distribution<float> u01(0.0f, 1.0f);
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thrust::host_vector<float> x(N);
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thrust::host_vector<float> y(N);
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for (size_t i = 0; i < N; i++)
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{
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x[i] = u01(rng);
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y[i] = u01(rng);
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}
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// print the initial points
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std::cout << std::fixed;
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std::cout << "Generated " << N << " points" << '\n';
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for (size_t i = 0; i < x.size(); i++)
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{
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std::cout << "(" << x[i] << "," << y[i] << ")" << '\n';
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}
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std::cout << '\n';
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// remove points where x^2 + y^2 > 1 and determine new array sizes
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size_t new_size =
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thrust::remove_if(thrust::make_zip_iterator(x.begin(), y.begin()),
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thrust::make_zip_iterator(x.end(), y.end()),
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is_outside_circle<float>())
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- thrust::make_zip_iterator(x.begin(), y.begin());
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// resize the vectors (note: this does not free any memory)
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x.resize(new_size);
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y.resize(new_size);
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// print the filtered points
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std::cout << "After stream compaction, " << new_size << " points remain" << '\n';
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for (size_t i = 0; i < x.size(); i++)
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{
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std::cout << "(" << x[i] << "," << y[i] << ")" << '\n';
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}
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return 0;
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}
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