141 lines
4.4 KiB
C++
141 lines
4.4 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://boostorg.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 <numeric>
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#include <vector>
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#include <boost/program_options.hpp>
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#include <boost/compute/system.hpp>
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#include <boost/compute/algorithm/accumulate.hpp>
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#include <boost/compute/container/vector.hpp>
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#include "perf.hpp"
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namespace po = boost::program_options;
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namespace compute = boost::compute;
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int rand_int()
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{
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return static_cast<int>((rand() / double(RAND_MAX)) * 25.0);
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}
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template<class T>
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double perf_accumulate(const compute::vector<T>& data,
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const size_t trials,
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compute::command_queue& queue)
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{
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perf_timer t;
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for(size_t trial = 0; trial < trials; trial++){
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t.start();
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compute::accumulate(data.begin(), data.end(), T(0), queue);
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queue.finish();
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t.stop();
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}
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return t.min_time();
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}
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template<class T>
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void tune_accumulate(const compute::vector<T>& data,
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const size_t trials,
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compute::command_queue& queue)
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{
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boost::shared_ptr<compute::detail::parameter_cache>
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params = compute::detail::parameter_cache::get_global_cache(queue.get_device());
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const std::string cache_key =
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std::string("__boost_reduce_on_gpu_") + compute::type_name<T>();
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const compute::uint_ tpbs[] = { 4, 8, 16, 32, 64, 128, 256, 512, 1024 };
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const compute::uint_ vpts[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 };
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double min_time = (std::numeric_limits<double>::max)();
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compute::uint_ best_tpb = 0;
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compute::uint_ best_vpt = 0;
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for(size_t i = 0; i < sizeof(tpbs) / sizeof(*tpbs); i++){
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params->set(cache_key, "tpb", tpbs[i]);
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for(size_t j = 0; j < sizeof(vpts) / sizeof(*vpts); j++){
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params->set(cache_key, "vpt", vpts[j]);
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try {
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const double t = perf_accumulate(data, trials, queue);
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if(t < min_time){
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best_tpb = tpbs[i];
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best_vpt = vpts[j];
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min_time = t;
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}
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}
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catch(compute::opencl_error&){
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// invalid parameters for this device, skip
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}
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}
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}
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// store optimal parameters
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params->set(cache_key, "tpb", best_tpb);
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params->set(cache_key, "vpt", best_vpt);
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}
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int main(int argc, char *argv[])
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{
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// setup command line arguments
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po::options_description options("options");
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options.add_options()
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("help", "show usage instructions")
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("size", po::value<size_t>()->default_value(8192), "input size")
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("trials", po::value<size_t>()->default_value(3), "number of trials to run")
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("tune", "run tuning procedure")
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;
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po::positional_options_description positional_options;
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positional_options.add("size", 1);
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// parse command line
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po::variables_map vm;
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po::store(
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po::command_line_parser(argc, argv)
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.options(options).positional(positional_options).run(),
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vm
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);
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po::notify(vm);
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const size_t size = vm["size"].as<size_t>();
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const size_t trials = vm["trials"].as<size_t>();
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std::cout << "size: " << size << std::endl;
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// setup context and queue for the default device
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compute::device device = compute::system::default_device();
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compute::context context(device);
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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<int> host_data(size);
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std::generate(host_data.begin(), host_data.end(), rand_int);
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// create vector on the device and copy the data
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compute::vector<int> device_data(
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host_data.begin(), host_data.end(), queue
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);
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// run tuning proceure (if requested)
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if(vm.count("tune")){
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tune_accumulate(device_data, trials, queue);
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}
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// run benchmark
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double t = perf_accumulate(device_data, trials, queue);
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std::cout << "time: " << t / 1e6 << " ms" << std::endl;
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return 0;
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}
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