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- //---------------------------------------------------------------------------//
- // Copyright (c) 2013-2014 Kyle Lutz <kyle.r.lutz@gmail.com>
- //
- // Distributed under the Boost Software License, Version 1.0
- // See accompanying file LICENSE_1_0.txt or copy at
- // http://www.boost.org/LICENSE_1_0.txt
- //
- // See http://boostorg.github.com/compute for more information.
- //---------------------------------------------------------------------------//
- #include <algorithm>
- #include <iostream>
- #include <vector>
- #include <boost/program_options.hpp>
- #include <boost/compute/lambda.hpp>
- #include <boost/compute/system.hpp>
- #include <boost/compute/algorithm/copy.hpp>
- #include <boost/compute/algorithm/transform.hpp>
- #include <boost/compute/container/vector.hpp>
- #include "perf.hpp"
- namespace po = boost::program_options;
- namespace compute = boost::compute;
- float rand_float()
- {
- return (float(rand()) / float(RAND_MAX)) * 1000.f;
- }
- template<class T>
- double perf_saxpy(const compute::vector<T>& x,
- const compute::vector<T>& y,
- const T alpha,
- const size_t trials,
- compute::command_queue& queue)
- {
- // create vector on the device to store the result
- compute::vector<T> result(x.size(), queue.get_context());
- perf_timer t;
- for(size_t trial = 0; trial < trials; trial++){
- compute::fill(result.begin(), result.end(), T(0), queue);
- queue.finish();
- t.start();
- using compute::lambda::_1;
- using compute::lambda::_2;
- compute::transform(
- x.begin(), x.end(), y.begin(), result.begin(), alpha * _1 + _2, queue
- );
- queue.finish();
- t.stop();
- }
- return t.min_time();
- }
- template<class T>
- void tune_saxpy(const compute::vector<T>& x,
- const compute::vector<T>& y,
- const T alpha,
- const size_t trials,
- compute::command_queue& queue)
- {
- boost::shared_ptr<compute::detail::parameter_cache>
- params = compute::detail::parameter_cache::get_global_cache(queue.get_device());
- const std::string cache_key =
- std::string("__boost_copy_kernel_") + boost::lexical_cast<std::string>(sizeof(T));
- const compute::uint_ tpbs[] = { 4, 8, 16, 32, 64, 128, 256, 512, 1024 };
- const compute::uint_ vpts[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 };
- double min_time = (std::numeric_limits<double>::max)();
- compute::uint_ best_tpb = 0;
- compute::uint_ best_vpt = 0;
- for(size_t i = 0; i < sizeof(tpbs) / sizeof(*tpbs); i++){
- params->set(cache_key, "tpb", tpbs[i]);
- for(size_t j = 0; j < sizeof(vpts) / sizeof(*vpts); j++){
- params->set(cache_key, "vpt", vpts[j]);
- try {
- const double t = perf_saxpy(x, y, alpha, trials, queue);
- if(t < min_time){
- best_tpb = tpbs[i];
- best_vpt = vpts[j];
- min_time = t;
- }
- }
- catch(compute::opencl_error&){
- // invalid parameters for this device, skip
- }
- }
- }
- // store optimal parameters
- params->set(cache_key, "tpb", best_tpb);
- params->set(cache_key, "vpt", best_vpt);
- }
- int main(int argc, char *argv[])
- {
- // setup command line arguments
- po::options_description options("options");
- options.add_options()
- ("help", "show usage instructions")
- ("size", po::value<size_t>()->default_value(8192), "input size")
- ("trials", po::value<size_t>()->default_value(3), "number of trials to run")
- ("tune", "run tuning procedure")
- ("alpha", po::value<double>()->default_value(2.5), "saxpy alpha value")
- ;
- po::positional_options_description positional_options;
- positional_options.add("size", 1);
- // parse command line
- po::variables_map vm;
- po::store(
- po::command_line_parser(argc, argv)
- .options(options).positional(positional_options).run(),
- vm
- );
- po::notify(vm);
- const size_t size = vm["size"].as<size_t>();
- const size_t trials = vm["trials"].as<size_t>();
- const float alpha = vm["alpha"].as<double>();
- std::cout << "size: " << size << std::endl;
- // setup context and queue for the default device
- compute::device device = boost::compute::system::default_device();
- compute::context context(device);
- compute::command_queue queue(context, device);
- std::cout << "device: " << device.name() << std::endl;
- // create vector of random numbers on the host
- std::vector<float> host_x(size);
- std::vector<float> host_y(size);
- std::generate(host_x.begin(), host_x.end(), rand_float);
- std::generate(host_y.begin(), host_y.end(), rand_float);
- // create vector on the device and copy the data
- compute::vector<float> x(host_x.begin(), host_x.end(), queue);
- compute::vector<float> y(host_y.begin(), host_y.end(), queue);
- // run tuning proceure (if requested)
- if(vm.count("tune")){
- tune_saxpy(x, y, alpha, trials, queue);
- }
- // run benchmark
- double t = perf_saxpy(x, y, alpha, trials, queue);
- std::cout << "time: " << t / 1e6 << " ms" << std::endl;
- return 0;
- }
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