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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.
105 lines
3.2 KiB
C++
105 lines
3.2 KiB
C++
//---------------------------------------------------------------------------//
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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 <iostream>
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#include <vector>
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#include <boost/compute/lambda.hpp>
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#include <boost/compute/system.hpp>
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#include <boost/compute/algorithm/copy.hpp>
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#include <boost/compute/algorithm/transform.hpp>
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#include <boost/compute/container/vector.hpp>
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#include "perf.hpp"
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float rand_float()
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{
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return (float(rand()) / float(RAND_MAX)) * 1000.f;
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}
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// y <- alpha * x + y
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void serial_saxpy(size_t n, float alpha, const float *x, float *y)
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{
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for(size_t i = 0; i < n; i++){
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y[i] = alpha * x[i] + y[i];
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}
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}
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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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using boost::compute::lambda::_1;
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using boost::compute::lambda::_2;
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std::cout << "size: " << PERF_N << std::endl;
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float alpha = 2.5f;
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// setup context and queue for the default device
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boost::compute::device device = boost::compute::system::default_device();
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boost::compute::context context(device);
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boost::compute::command_queue queue(context, device);
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std::cout << "device: " << device.name() << std::endl;
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// create vector of random numbers on the host
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std::vector<float> host_x(PERF_N);
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std::vector<float> host_y(PERF_N);
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std::generate(host_x.begin(), host_x.end(), rand_float);
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std::generate(host_y.begin(), host_y.end(), rand_float);
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// create vector on the device and copy the data
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boost::compute::vector<float> device_x(host_x.begin(), host_x.end(), queue);
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boost::compute::vector<float> device_y(host_y.begin(), host_y.end(), queue);
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perf_timer t;
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for(size_t trial = 0; trial < PERF_TRIALS; trial++){
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boost::compute::copy(host_x.begin(), host_x.end(), device_x.begin(), queue);
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boost::compute::copy(host_y.begin(), host_y.end(), device_y.begin(), queue);
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t.start();
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boost::compute::transform(
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device_x.begin(),
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device_x.end(),
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device_y.begin(),
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device_y.begin(),
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alpha * _1 + _2,
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queue
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);
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queue.finish();
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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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// perform saxpy on host
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serial_saxpy(PERF_N, alpha, &host_x[0], &host_y[0]);
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// copy device_y to host_x
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boost::compute::copy(device_y.begin(), device_y.end(), host_x.begin(), queue);
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for(size_t i = 0; i < PERF_N; i++){
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float host_value = host_y[i];
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float device_value = host_x[i];
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if(std::abs(device_value - host_value) > 1e-3){
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std::cout << "ERROR: "
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<< "value at " << i << " "
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<< "device_value (" << device_value << ") "
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<< "!= "
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<< "host_value (" << host_value << ")"
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<< std::endl;
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return -1;
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}
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}
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return 0;
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}
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