compute/example/batched_determinant.cpp
2015-05-17 20:32:09 -07:00

97 lines
3.4 KiB
C++

//---------------------------------------------------------------------------//
// 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 <Eigen/Core>
#include <Eigen/LU>
#include <boost/compute/function.hpp>
#include <boost/compute/system.hpp>
#include <boost/compute/algorithm/transform.hpp>
#include <boost/compute/container/vector.hpp>
#include <boost/compute/types/fundamental.hpp>
namespace compute = boost::compute;
// this example shows how to compute the determinant of many 4x4 matrices
// using a determinant function and the transform() algorithm. in OpenCL the
// float16 type can be used to store a 4x4 matrix and the components are laid
// out in the following order:
//
// M = [ s0 s4 s8 sc ]
// [ s1 s5 s9 sd ]
// [ s2 s6 sa se ]
// [ s3 s7 sb sf ]
//
// the input matrices are created using eigen's random matrix and then
// used again at the end to verify the results of the determinant function.
int main()
{
// get default device and setup context
compute::device gpu = compute::system::default_device();
compute::context context(gpu);
compute::command_queue queue(context, gpu);
std::cout << "device: " << gpu.name() << std::endl;
size_t n = 1000;
// create random 4x4 matrices on the host
std::vector<Eigen::Matrix4f> matrices(n);
for(size_t i = 0; i < n; i++){
matrices[i] = Eigen::Matrix4f::Random();
}
// copy matrices to the device
using compute::float16_;
compute::vector<float16_> input(n, context);
compute::copy(
matrices.begin(), matrices.end(), input.begin(), queue
);
// function returning the determinant of a 4x4 matrix.
BOOST_COMPUTE_FUNCTION(float, determinant4x4, (const float16_ m),
{
return m.s0*m.s5*m.sa*m.sf + m.s0*m.s6*m.sb*m.sd + m.s0*m.s7*m.s9*m.se +
m.s1*m.s4*m.sb*m.se + m.s1*m.s6*m.s8*m.sf + m.s1*m.s7*m.sa*m.sc +
m.s2*m.s4*m.s9*m.sf + m.s2*m.s5*m.sb*m.sc + m.s2*m.s7*m.s8*m.sd +
m.s3*m.s4*m.sa*m.sd + m.s3*m.s5*m.s8*m.se + m.s3*m.s6*m.s9*m.sc -
m.s0*m.s5*m.sb*m.se - m.s0*m.s6*m.s9*m.sf - m.s0*m.s7*m.sa*m.sd -
m.s1*m.s4*m.sa*m.sf - m.s1*m.s6*m.sb*m.sc - m.s1*m.s7*m.s8*m.se -
m.s2*m.s4*m.sb*m.sd - m.s2*m.s5*m.s8*m.sf - m.s2*m.s7*m.s9*m.sc -
m.s3*m.s4*m.s9*m.se - m.s3*m.s5*m.sa*m.sc - m.s3*m.s6*m.s8*m.sd;
});
// calculate determinants on the gpu
compute::vector<float> determinants(n, context);
compute::transform(
input.begin(), input.end(), determinants.begin(), determinant4x4, queue
);
// check determinants
std::vector<float> host_determinants(n);
compute::copy(
determinants.begin(), determinants.end(), host_determinants.begin(), queue
);
for(size_t i = 0; i < n; i++){
float det = matrices[i].determinant();
if(std::abs(det - host_determinants[i]) > 1e-6){
std::cerr << "error: wrong determinant at " << i << " ("
<< host_determinants[i] << " != " << det << ")"
<< std::endl;
return -1;
}
}
return 0;
}