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