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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 <iostream>
#include <boost/compute/command_queue.hpp>
#include <boost/compute/kernel.hpp>
#include <boost/compute/program.hpp>
#include <boost/compute/system.hpp>
#include <boost/compute/algorithm/copy.hpp>
#include <boost/compute/container/vector.hpp>
#include <boost/compute/utility/source.hpp>
namespace compute = boost::compute;
// this example shows how to use the static c++ kernel language
// extension (currently only supported by AMD) to compile and
// execute a templated c++ kernel.
// Using platform vendor info to decide if this is AMD platform
int main()
{
// get default device and setup context
compute::device device = compute::system::default_device();
compute::context context(device);
compute::command_queue queue(context, device);
// check the platform vendor string
if(device.platform().vendor() != "Advanced Micro Devices, Inc."){
std::cerr << "error: static C++ kernel language is only "
<< "supported on AMD devices."
<< std::endl;
return 0;
}
// create input int values and copy them to the device
int int_data[] = { 1, 2, 3, 4};
compute::vector<int> int_vector(int_data, int_data + 4, queue);
// create input float values and copy them to the device
float float_data[] = { 2.0f, 4.0f, 6.0f, 8.0f };
compute::vector<float> float_vector(float_data, float_data + 4, queue);
// create kernel source with a templated function and templated kernel
const char source[] = BOOST_COMPUTE_STRINGIZE_SOURCE(
// define our templated function which returns the square of its input
template<typename T>
inline T square(const T x)
{
return x * x;
}
// define our templated kernel which calls square on each value in data
template<typename T>
__kernel void square_kernel(__global T *data)
{
const uint i = get_global_id(0);
data[i] = square(data[i]);
}
// explicitly instantiate the square kernel for int's. this allows
// for it to be called from the host with the given mangled name.
template __attribute__((mangled_name(square_kernel_int)))
__kernel void square_kernel(__global int *data);
// also instantiate the square kernel for float's.
template __attribute__((mangled_name(square_kernel_float)))
__kernel void square_kernel(__global float *data);
);
// build the program. must enable the c++ static kernel language
// by passing the "-x clc++" compile option.
compute::program square_program =
compute::program::build_with_source(source, context, "-x clc++");
// create the square kernel for int's by using its mangled name declared
// in the explicit template instantiation.
compute::kernel square_int_kernel(square_program, "square_kernel_int");
square_int_kernel.set_arg(0, int_vector);
// execute the square int kernel
queue.enqueue_1d_range_kernel(square_int_kernel, 0, int_vector.size(), 4);
// print out the squared int values
std::cout << "int's: ";
compute::copy(
int_vector.begin(), int_vector.end(),
std::ostream_iterator<int>(std::cout, " "),
queue
);
std::cout << std::endl;
// now create the square kernel for float's
compute::kernel square_float_kernel(square_program, "square_kernel_float");
square_float_kernel.set_arg(0, float_vector);
// execute the square int kernel
queue.enqueue_1d_range_kernel(square_float_kernel, 0, float_vector.size(), 4);
// print out the squared float values
std::cout << "float's: ";
compute::copy(
float_vector.begin(), float_vector.end(),
std::ostream_iterator<float>(std::cout, " "),
queue
);
std::cout << std::endl;
return 0;
}
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