ViennaCL - The Vienna Computing Library: armadillo-with-viennacl.cpp

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armadillo-with-viennacl.cpp

This tutorial shows how data can be directly transferred from the Armadillo Library to ViennaCL objects using the built-in convenience wrappers.

The first step is to include the necessary headers and activate the Armadillo convenience functions in ViennaCL:

// System headers
#include <iostream>
// Armadillo headers (disable BLAS and LAPACK to avoid linking issues)
#define ARMA_DONT_USE_BLAS
#define ARMA_DONT_USE_LAPACK
#include <armadillo>
// IMPORTANT: Must be set prior to any ViennaCL includes if you want to use ViennaCL algorithms on Armadillo objects
#define VIENNACL_WITH_ARMADILLO 1
// ViennaCL includes
#include "viennacl/vector.hpp"
#include "viennacl/matrix.hpp"
#include "viennacl/compressed_matrix.hpp"
#include "viennacl/linalg/prod.hpp"
// Helper functions for this tutorial:
#include "vector-io.hpp"

The following function contains the main code for this tutorial. It consists of the following steps:

- Creates Armadillo matrices and vectors
- Initializes them with data
- Create ViennaCL objects
- Copy them over to the respective ViennaCL objects
- Compute matrix-vector products in both Armadillo and ViennaCL and compare results.

template<typename NumericT>
void run_tutorial()
{
typedef arma::SpMat<NumericT> ArmaSparseMatrix;
typedef arma::Mat<NumericT> ArmaMatrix;
typedef arma::Col<NumericT> ArmaVector;

Create and fill dense matrices from the Armadillo library:

ArmaMatrix arma_densemat(6, 5);
ArmaMatrix arma_densemat2(6, 5);
arma_densemat(0,0) = 2.0; arma_densemat(0,1) = -1.0;
arma_densemat(1,0) = -1.0; arma_densemat(1,1) = 2.0; arma_densemat(1,2) = -1.0;
arma_densemat(2,1) = -1.0; arma_densemat(2,2) = -1.0; arma_densemat(2,3) = -1.0;
arma_densemat(3,2) = -1.0; arma_densemat(3,3) = 2.0; arma_densemat(3,4) = -1.0;
arma_densemat(5,4) = -1.0; arma_densemat(4,4) = -1.0;

Create and fill sparse matrices from the Armadillo library:

ArmaSparseMatrix arma_sparsemat(6, 5);
ArmaSparseMatrix arma_sparsemat2(6, 5);
arma_sparsemat(0,0) = 2.0; arma_sparsemat(0,1) = -1.0;
arma_sparsemat(1,1) = 2.0; arma_sparsemat(1,2) = -1.0;
arma_sparsemat(2,2) = -1.0; arma_sparsemat(2,3) = -1.0;
arma_sparsemat(3,3) = 2.0; arma_sparsemat(3,4) = -1.0;
arma_sparsemat(5,4) = -1.0;

Create and fill a few vectors from the Armadillo library:

ArmaVector arma_rhs(5);
ArmaVector arma_result(6);
ArmaVector arma_temp(6);
arma_rhs(0) = 10.0;
arma_rhs(1) = 11.0;
arma_rhs(2) = 12.0;
arma_rhs(3) = 13.0;
arma_rhs(4) = 14.0;

Create the corresponding ViennaCL objects:

viennacl::vector<NumericT> vcl_rhs(5);
viennacl::vector<NumericT> vcl_result(6);
viennacl::matrix<NumericT> vcl_densemat(6, 5);
viennacl::compressed_matrix<NumericT> vcl_sparsemat(6, 5);

Directly copy the Armadillo objects to ViennaCL objects

viennacl::copy(arma_rhs.memptr(), arma_rhs.memptr() + arma_rhs.n_elem, vcl_rhs.begin()); //method 1: via iterator interface (cf. std::copy())
viennacl::copy(arma_rhs, vcl_rhs); //method 2: via built-in wrappers (convenience layer)
viennacl::copy(arma_densemat, vcl_densemat);
viennacl::copy(arma_sparsemat, vcl_sparsemat);
std::cout << "VCL sparsematrix dimensions: " << vcl_sparsemat.size1() << ", " << vcl_sparsemat.size2() << std::endl;
// For completeness: Copy matrices from ViennaCL back to Eigen:
viennacl::copy(vcl_densemat, arma_densemat2);
viennacl::copy(vcl_sparsemat, arma_sparsemat2);

Run dense matrix-vector products and compare results:

arma_result = arma_densemat * arma_rhs;
vcl_result = viennacl::linalg::prod(vcl_densemat, vcl_rhs);
viennacl::copy(vcl_result, arma_temp);
std::cout << "Difference for dense matrix-vector product: " << norm(arma_result - arma_temp) << std::endl;
std::cout << "Difference for dense matrix-vector product (Armadillo -> ViennaCL -> Armadillo): "
<< norm(arma_densemat2 * arma_rhs - arma_temp) << std::endl;

Run sparse matrix-vector products and compare results:

arma_result = arma_sparsemat * arma_rhs;
vcl_result = viennacl::linalg::prod(vcl_sparsemat, vcl_rhs);
viennacl::copy(vcl_result, arma_temp);
std::cout << "Difference for sparse matrix-vector product: " << norm(arma_result - arma_temp) << std::endl;
std::cout << "Difference for sparse matrix-vector product (Armadillo -> ViennaCL -> Armadillo): "
<< norm(arma_sparsemat2 * arma_rhs - arma_temp) << std::endl;
}

In the main() routine we only call the worker function defined above with both single and double precision arithmetic.

int main(int, char *[])
{
std::cout << "----------------------------------------------" << std::endl;
std::cout << "## Single precision" << std::endl;
std::cout << "----------------------------------------------" << std::endl;
run_tutorial<float>();
#ifdef VIENNACL_HAVE_OPENCL
if ( viennacl::ocl::current_device().double_support() )
#endif
{
std::cout << "----------------------------------------------" << std::endl;
std::cout << "## Double precision" << std::endl;
std::cout << "----------------------------------------------" << std::endl;
run_tutorial<double>();
}

That's it. Print a success message and exit.

std::cout << std::endl;
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
std::cout << std::endl;
}

## Full Example Code

/* =========================================================================
Copyright (c) 2010-2016, Institute for Microelectronics,
Institute for Analysis and Scientific Computing,
TU Wien.
Portions of this software are copyright by UChicago Argonne, LLC.
-----------------
ViennaCL - The Vienna Computing Library
-----------------
Project Head: Karl Rupp rupp@iue.tuwien.ac.at
(A list of authors and contributors can be found in the PDF manual)
License: MIT (X11), see file LICENSE in the base directory
============================================================================= */
// System headers
#include <iostream>
// Armadillo headers (disable BLAS and LAPACK to avoid linking issues)
#define ARMA_DONT_USE_BLAS
#define ARMA_DONT_USE_LAPACK
#include <armadillo>
// IMPORTANT: Must be set prior to any ViennaCL includes if you want to use ViennaCL algorithms on Armadillo objects
#define VIENNACL_WITH_ARMADILLO 1
// ViennaCL includes
#include "viennacl/vector.hpp"
#include "viennacl/matrix.hpp"
#include "viennacl/compressed_matrix.hpp"
#include "viennacl/linalg/prod.hpp"
// Helper functions for this tutorial:
#include "vector-io.hpp"
template<typename NumericT>
void run_tutorial()
{
typedef arma::SpMat<NumericT> ArmaSparseMatrix;
typedef arma::Mat<NumericT> ArmaMatrix;
typedef arma::Col<NumericT> ArmaVector;
ArmaMatrix arma_densemat(6, 5);
ArmaMatrix arma_densemat2(6, 5);
arma_densemat(0,0) = 2.0; arma_densemat(0,1) = -1.0;
arma_densemat(1,0) = -1.0; arma_densemat(1,1) = 2.0; arma_densemat(1,2) = -1.0;
arma_densemat(2,1) = -1.0; arma_densemat(2,2) = -1.0; arma_densemat(2,3) = -1.0;
arma_densemat(3,2) = -1.0; arma_densemat(3,3) = 2.0; arma_densemat(3,4) = -1.0;
arma_densemat(5,4) = -1.0; arma_densemat(4,4) = -1.0;
ArmaSparseMatrix arma_sparsemat(6, 5);
ArmaSparseMatrix arma_sparsemat2(6, 5);
arma_sparsemat(0,0) = 2.0; arma_sparsemat(0,1) = -1.0;
arma_sparsemat(1,1) = 2.0; arma_sparsemat(1,2) = -1.0;
arma_sparsemat(2,2) = -1.0; arma_sparsemat(2,3) = -1.0;
arma_sparsemat(3,3) = 2.0; arma_sparsemat(3,4) = -1.0;
arma_sparsemat(5,4) = -1.0;
ArmaVector arma_rhs(5);
ArmaVector arma_result(6);
ArmaVector arma_temp(6);
arma_rhs(0) = 10.0;
arma_rhs(1) = 11.0;
arma_rhs(2) = 12.0;
arma_rhs(3) = 13.0;
arma_rhs(4) = 14.0;
viennacl::vector<NumericT> vcl_rhs(5);
viennacl::vector<NumericT> vcl_result(6);
viennacl::matrix<NumericT> vcl_densemat(6, 5);
viennacl::compressed_matrix<NumericT> vcl_sparsemat(6, 5);
viennacl::copy(arma_rhs.memptr(), arma_rhs.memptr() + arma_rhs.n_elem, vcl_rhs. begin()); //method 1: via iterator interface (cf. std::copy())
viennacl::copy(arma_rhs, vcl_rhs); //method 2: via built-in wrappers (convenience layer)
viennacl::copy(arma_densemat, vcl_densemat);
viennacl::copy(arma_sparsemat, vcl_sparsemat);
std::cout << "VCL sparsematrix dimensions: " << vcl_sparsemat. size1() << ", " << vcl_sparsemat. size2() << std::endl;
// For completeness: Copy matrices from ViennaCL back to Eigen:
viennacl::copy(vcl_densemat, arma_densemat2);
viennacl::copy(vcl_sparsemat, arma_sparsemat2);
arma_result = arma_densemat * arma_rhs;
vcl_result = viennacl::linalg::prod(vcl_densemat, vcl_rhs);
viennacl::copy(vcl_result, arma_temp);
std::cout << "Difference for dense matrix-vector product: " << norm(arma_result - arma_temp) << std::endl;
std::cout << "Difference for dense matrix-vector product (Armadillo -> ViennaCL -> Armadillo): "
<< norm(arma_densemat2 * arma_rhs - arma_temp) << std::endl;
arma_result = arma_sparsemat * arma_rhs;
vcl_result = viennacl::linalg::prod(vcl_sparsemat, vcl_rhs);
viennacl::copy(vcl_result, arma_temp);
std::cout << "Difference for sparse matrix-vector product: " << norm(arma_result - arma_temp) << std::endl;
std::cout << "Difference for sparse matrix-vector product (Armadillo -> ViennaCL -> Armadillo): "
<< norm(arma_sparsemat2 * arma_rhs - arma_temp) << std::endl;
}
int main(int, char *[])
{
std::cout << "----------------------------------------------" << std::endl;
std::cout << "## Single precision" << std::endl;
std::cout << "----------------------------------------------" << std::endl;
run_tutorial<float>();
#ifdef VIENNACL_HAVE_OPENCL
if ( viennacl::ocl::current_device().double_support() )
#endif
{
std::cout << "----------------------------------------------" << std::endl;
std::cout << "## Double precision" << std::endl;
std::cout << "----------------------------------------------" << std::endl;
run_tutorial<double>();
}
std::cout << std::endl;
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
std::cout << std::endl;
}