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ec11d8cdc4
This adds third-party performance tests to use in comparing Boost.Compute with other parallel/GPGPU frameworks like Intel's TBB and NVIDIA's Thrust along with the C++ STL. Also refactors the timing and profiling infrastructure and adds a simple perf.py driver script for running performance tests.
45 lines
1.3 KiB
Plaintext
45 lines
1.3 KiB
Plaintext
//---------------------------------------------------------------------------//
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// Copyright (c) 2013-2014 Kyle Lutz <kyle.r.lutz@gmail.com>
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//
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// Distributed under the Boost Software License, Version 1.0
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// See accompanying file LICENSE_1_0.txt or copy at
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// http://www.boost.org/LICENSE_1_0.txt
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//
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// See http://kylelutz.github.com/compute for more information.
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//---------------------------------------------------------------------------//
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#include <algorithm>
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#include <cstdlib>
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#include <thrust/copy.h>
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#include <thrust/device_vector.h>
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#include <thrust/generate.h>
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#include <thrust/host_vector.h>
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#include <thrust/reduce.h>
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#include "perf.hpp"
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int main(int argc, char *argv[])
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{
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perf_parse_args(argc, argv);
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std::cout << "size: " << PERF_N << std::endl;
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thrust::host_vector<int> h_vec = generate_random_vector<int>(PERF_N);
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// transfer data to the device
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thrust::device_vector<int> d_vec = h_vec;
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int sum = 0;
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perf_timer t;
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for(size_t trial = 0; trial < PERF_TRIALS; trial++){
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t.start();
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sum = thrust::reduce(d_vec.begin(), d_vec.end());
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cudaDeviceSynchronize();
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t.stop();
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}
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std::cout << "time: " << t.min_time() / 1e6 << " ms" << std::endl;
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std::cout << "sum: " << sum << std::endl;
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return 0;
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}
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