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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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