SetRowsConst.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Khaled Nasr
 * All rights reserved.
 *
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 * modification, are permitted provided that the following conditions are met:
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 *    list of conditions and the following disclaimer.
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 *    this list of conditions and the following disclaimer in the documentation
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#ifndef SET_ROWS_CONST_IMPL_H_
#define SET_ROWS_CONST_IMPL_H_

#include <shogun/lib/config.h>
#include <shogun/lib/SGMatrix.h>
#include <shogun/lib/SGVector.h>

#ifdef HAVE_EIGEN3
#include <shogun/mathematics/eigen3.h>
#endif // HAVE_EIGEN3

#ifdef HAVE_VIENNACL
#include <shogun/lib/GPUMatrix.h>
#include <shogun/lib/GPUVector.h>
#include <shogun/mathematics/linalg/internal/opencl_util.h>
#endif // HAVE_VIENNACL

namespace shogun
{

namespace linalg
{

namespace implementation
{

template <enum Backend, class Matrix, class Vector>
struct set_rows_const
{
    typedef typename Matrix::Scalar T;

    static void compute(Matrix A, Vector v);
};

#ifdef HAVE_EIGEN3

template <class Matrix, class Vector>
struct set_rows_const<Backend::EIGEN3, Matrix, Vector>
{
    typedef typename Matrix::Scalar T;

    typedef Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic> MatrixXt;

    typedef Eigen::Matrix<T,Eigen::Dynamic,1> VectorXt;

    static void compute(SGMatrix<T> A, SGVector<T> v)
    {
        Eigen::Map<MatrixXt> A_eig = A;
        Eigen::Map<VectorXt> v_eig = v;

        A_eig.colwise() = v_eig;
    }
};
#endif // HAVE_EIGEN3

#ifdef HAVE_VIENNACL

template <class Matrix, class Vector>
struct set_rows_const<Backend::VIENNACL, Matrix, Vector>
{
    typedef typename Matrix::Scalar T;

    template <class T>
    static viennacl::ocl::kernel& generate_kernel()
    {
        std::string kernel_name = "set_rows_const_" + ocl::get_type_string<T>();

        if (ocl::kernel_exists(kernel_name))
            return ocl::get_kernel(kernel_name);

        std::string source = ocl::generate_kernel_preamble<T>(kernel_name);

        source.append(
            R"(
                __kernel void KERNEL_NAME(
                    __global DATATYPE* mat, int nrows, int ncols, int offset,
                    __global DATATYPE* vec, int vec_offset)
                {
                    int i = get_global_id(0);
                    int j = get_global_id(1);

                    if (i>=nrows || j>=ncols)
                        return;

                    mat[offset + i+j*nrows] = vec[i+offset];
                }
            )"
        );

        viennacl::ocl::kernel& kernel = ocl::compile_kernel(kernel_name, source);

        kernel.local_work_size(0, OCL_WORK_GROUP_SIZE_2D);
        kernel.local_work_size(1, OCL_WORK_GROUP_SIZE_2D);

        return kernel;
    }

    static void compute(CGPUMatrix<T> A, CGPUVector<T> v)
    {
        viennacl::ocl::kernel& kernel = generate_kernel<T>();
        kernel.global_work_size(0, ocl::align_to_multiple_2d(A.num_rows));
        kernel.global_work_size(1, ocl::align_to_multiple_2d(A.num_cols));

        viennacl::ocl::enqueue(kernel(A.vcl_matrix(),
            cl_int(A.num_rows), cl_int(A.num_cols), cl_int(A.offset),
            v.vcl_vector(), cl_int(v.offset)));
    }
};

#endif // HAVE_VIENNACL

}

}

}
#endif // SET_ROWS_CONST_IMPL_H_