http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/qr.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/qr.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/qr.hpp deleted file mode 100644 index 907eb57..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/qr.hpp +++ /dev/null @@ -1,497 +0,0 @@ -#ifndef VIENNACL_LINALG_DETAIL_SPAI_QR_HPP -#define VIENNACL_LINALG_DETAIL_SPAI_QR_HPP - -/* ========================================================================= - 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 [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/detail/spai/qr.hpp - @brief Implementation of a simultaneous QR factorization of multiple matrices. Experimental. - - SPAI code contributed by Nikolay Lukash -*/ - -#include <utility> -#include <iostream> -#include <fstream> -#include <string> -#include <algorithm> -#include <vector> -#include <math.h> -#include <cmath> -#include <sstream> -#include "viennacl/ocl/backend.hpp" -#include "boost/numeric/ublas/vector.hpp" -#include "boost/numeric/ublas/matrix.hpp" -#include "boost/numeric/ublas/matrix_proxy.hpp" -#include "boost/numeric/ublas/storage.hpp" -#include "boost/numeric/ublas/io.hpp" -#include "boost/numeric/ublas/matrix_expression.hpp" -#include "boost/numeric/ublas/detail/matrix_assign.hpp" - -#include "viennacl/vector.hpp" -#include "viennacl/matrix.hpp" - -#include "viennacl/linalg/detail/spai/block_matrix.hpp" -#include "viennacl/linalg/detail/spai/block_vector.hpp" -#include "viennacl/linalg/opencl/kernels/spai.hpp" - -namespace viennacl -{ -namespace linalg -{ -namespace detail -{ -namespace spai -{ - -//********** DEBUG FUNCTIONS *****************// -template< typename T, typename InputIteratorT> -void Print(std::ostream & ostr, InputIteratorT it_begin, InputIteratorT it_end) -{ - //std::ostream_iterator<int> it_os(ostr, delimiter); - std::string delimiters = " "; - std::copy(it_begin, it_end, std::ostream_iterator<T>(ostr, delimiters.c_str())); - ostr << std::endl; -} - -template<typename VectorT, typename MatrixT> -void write_to_block(VectorT & con_A_I_J, - unsigned int start_ind, - std::vector<unsigned int> const & I, - std::vector<unsigned int> const & J, - MatrixT& m) -{ - m.resize(I.size(), J.size(), false); - for (vcl_size_t i = 0; i < J.size(); ++i) - for (vcl_size_t j = 0; j < I.size(); ++j) - m(j,i) = con_A_I_J[start_ind + i*I.size() + j]; -} - -template<typename VectorT> -void print_continious_matrix(VectorT & con_A_I_J, - std::vector<cl_uint> & blocks_ind, - std::vector<std::vector<unsigned int> > const & g_I, - std::vector<std::vector<unsigned int> > const & g_J) -{ - typedef typename VectorT::value_type NumericType; - - std::vector<boost::numeric::ublas::matrix<NumericType> > com_A_I_J(g_I.size()); - for (vcl_size_t i = 0; i < g_I.size(); ++i) - { - write_to_block(con_A_I_J, blocks_ind[i], g_I[i], g_J[i], com_A_I_J[i]); - std::cout << com_A_I_J[i] << std::endl; - } -} - -template<typename VectorT> -void print_continious_vector(VectorT & con_v, - std::vector<cl_uint> & block_ind, - std::vector<std::vector<unsigned int> > const & g_J) -{ - typedef typename VectorT::value_type NumericType; - - std::vector<boost::numeric::ublas::vector<NumericType> > com_v(g_J.size()); - //Print<ScalarType>(std::cout, con_v.begin(), con_v.end()); - for (vcl_size_t i = 0; i < g_J.size(); ++i) - { - com_v[i].resize(g_J[i].size()); - for (vcl_size_t j = 0; j < g_J[i].size(); ++j) - com_v[i](j) = con_v[block_ind[i] + j]; - std::cout << com_v[i] << std::endl; - } -} - - -///**************************************** BLOCK FUNCTIONS ************************************// - -/** @brief Computes size of elements, start indices and matrix dimensions for a certain block - * - * @param g_I container of row indices - * @param g_J container of column indices - * @param sz general size for all elements in a certain block - * @param blocks_ind start indices in a certain - * @param matrix_dims matrix dimensions for each block - */ -inline void compute_blocks_size(std::vector<std::vector<unsigned int> > const & g_I, - std::vector<std::vector<unsigned int> > const & g_J, - unsigned int& sz, - std::vector<cl_uint> & blocks_ind, - std::vector<cl_uint> & matrix_dims) -{ - sz = 0; - for (vcl_size_t i = 0; i < g_I.size(); ++i) - { - sz += static_cast<unsigned int>(g_I[i].size()*g_J[i].size()); - matrix_dims[2*i] = static_cast<cl_uint>(g_I[i].size()); - matrix_dims[2*i + 1] = static_cast<cl_uint>(g_J[i].size()); - blocks_ind[i+1] = blocks_ind[i] + static_cast<cl_uint>(g_I[i].size()*g_J[i].size()); - } -} - -/** @brief Computes size of particular container of index set - * - * @param inds container of index sets - * @param size output size - */ -template<typename SizeT> -void get_size(std::vector<std::vector<SizeT> > const & inds, - SizeT & size) -{ - size = 0; - for (vcl_size_t i = 0; i < inds.size(); ++i) - size += static_cast<unsigned int>(inds[i].size()); -} - -/** @brief Initializes start indices of particular index set - * - * @param inds container of index sets - * @param start_inds output index set - */ -template<typename SizeT> -void init_start_inds(std::vector<std::vector<SizeT> > const & inds, - std::vector<cl_uint>& start_inds) -{ - for (vcl_size_t i = 0; i < inds.size(); ++i) - start_inds[i+1] = start_inds[i] + static_cast<cl_uint>(inds[i].size()); -} - - -//************************************* QR FUNCTIONS ***************************************// - -/** @brief Dot prod of particular column of martix A with it's self starting at a certain index beg_ind - * - * @param A init matrix - * @param beg_ind starting index - * @param res result of dot product - */ -template<typename MatrixT, typename NumericT> -void dot_prod(MatrixT const & A, - unsigned int beg_ind, - NumericT & res) -{ - res = NumericT(0); - for (vcl_size_t i = beg_ind; i < A.size1(); ++i) - res += A(i, beg_ind-1)*A(i, beg_ind-1); -} - -/** @brief Dot prod of particular matrix column with arbitrary vector: A(:, col_ind) - * - * @param A init matrix - * @param v input vector - * @param col_ind starting column index - * @param start_ind starting index inside column - * @param res result of dot product - */ -template<typename MatrixT, typename VectorT, typename NumericT> -void custom_inner_prod(MatrixT const & A, - VectorT const & v, - unsigned int col_ind, - unsigned int start_ind, - NumericT & res) -{ - res = static_cast<NumericT>(0); - for (unsigned int i = start_ind; i < static_cast<unsigned int>(A.size1()); ++i) - res += A(i, col_ind)*v(i); -} - -/** @brief Copying part of matrix column - * - * @param A init matrix - * @param v output vector - * @param beg_ind start index for copying - */ -template<typename MatrixT, typename VectorT> -void copy_vector(MatrixT const & A, - VectorT & v, - unsigned int beg_ind) -{ - for (unsigned int i = beg_ind; i < static_cast<unsigned int>(A.size1()); ++i) - v(i) = A( i, beg_ind-1); -} - - -//householder reflection c.f. Gene H. Golub, Charles F. Van Loan "Matrix Computations" 3rd edition p.210 -/** @brief Computation of Householder vector, householder reflection c.f. Gene H. Golub, Charles F. Van Loan "Matrix Computations" 3rd edition p.210 - * - * @param A init matrix - * @param j start index for computations - * @param v output Householder vector - * @param b beta - */ -template<typename MatrixT, typename VectorT, typename NumericT> -void householder_vector(MatrixT const & A, - unsigned int j, - VectorT & v, - NumericT & b) -{ - NumericT sg; - - dot_prod(A, j+1, sg); - copy_vector(A, v, j+1); - NumericT mu; - v(j) = static_cast<NumericT>(1.0); - if (!sg) - b = 0; - else - { - mu = std::sqrt(A(j,j)*A(j, j) + sg); - if (A(j, j) <= 0) - v(j) = A(j, j) - mu; - else - v(j) = -sg/(A(j, j) + mu); - - b = 2*(v(j)*v(j))/(sg + v(j)*v(j)); - v = v/v(j); - } -} - - -/** @brief Inplace application of Householder vector to a matrix A - * - * @param A init matrix - * @param iter_cnt current iteration - * @param v Householder vector - * @param b beta - */ -template<typename MatrixT, typename VectorT, typename NumericT> -void apply_householder_reflection(MatrixT & A, - unsigned int iter_cnt, - VectorT & v, - NumericT b) -{ - //update every column of matrix A - NumericT in_prod_res; - - for (unsigned int i = iter_cnt; i < static_cast<unsigned int>(A.size2()); ++i) - { - //update each column in a fashion: ai = ai - b*v*(v'*ai) - custom_inner_prod(A, v, i, iter_cnt, in_prod_res); - for (unsigned int j = iter_cnt; j < static_cast<unsigned int>(A.size1()); ++j) - A(j, i) -= b*in_prod_res*v(j); - } -} - -/** @brief Storage of vector v in column(A, ind), starting from ind-1 index of a column - * - * @param A init matrix - * @param ind index of a column - * @param v vector that should be stored - */ -template<typename MatrixT, typename VectorT> -void store_householder_vector(MatrixT & A, - unsigned int ind, - VectorT & v) -{ - for (unsigned int i = ind; i < static_cast<unsigned int>(A.size1()); ++i) - A(i, ind-1) = v(i); -} - - -//QR algorithm -/** @brief Inplace QR factorization via Householder reflections c.f. Gene H. Golub, Charles F. Van Loan "Matrix Computations" 3rd edition p.224 - * - * @param R input matrix - * @param b_v vector of betas - */ -template<typename MatrixT, typename VectorT> -void single_qr(MatrixT & R, VectorT & b_v) -{ - typedef typename MatrixT::value_type NumericType; - - if ((R.size1() > 0) && (R.size2() > 0)) - { - VectorT v = static_cast<VectorT>(boost::numeric::ublas::zero_vector<NumericType>(R.size1())); - b_v = static_cast<VectorT>(boost::numeric::ublas::zero_vector<NumericType>(R.size2())); - - for (unsigned int i = 0; i < static_cast<unsigned int>(R.size2()); ++i) - { - householder_vector(R, i, v, b_v[i]); - apply_householder_reflection(R, i, v, b_v[i]); - if (i < R.size1()) - store_householder_vector(R, i+1, v); - } - } -} - -//********************** HELP FUNCTIONS FOR GPU-based QR factorization *************************// - -/** @brief Getting max size of rows/columns from container of index set - * - * @param inds container of index set - * @param max_size max size that corresponds to that container - */ -template<typename SizeT> -void get_max_block_size(std::vector<std::vector<SizeT> > const & inds, - SizeT & max_size) -{ - max_size = 0; - for (vcl_size_t i = 0; i < inds.size(); ++i) - if (inds[i].size() > max_size) - max_size = static_cast<SizeT>(inds[i].size()); -} - -/** @brief Dot_prod(column(A, ind), v) starting from index ind+1 - * - * @param A input matrix - * @param v input vector - * @param ind index - * @param res result value - */ -template<typename MatrixT, typename VectorT, typename NumericT> -void custom_dot_prod(MatrixT const & A, - VectorT const & v, - unsigned int ind, - NumericT & res) -{ - res = static_cast<NumericT>(0); - for (unsigned int j = ind; j < A.size1(); ++j) - { - if (j == ind) - res += v(j); - else - res += A(j, ind)*v(j); - } -} - -/** @brief Recovery Q from matrix R and vector of betas b_v - * - * @param R input matrix - * @param b_v vector of betas - * @param y output vector - */ -template<typename MatrixT, typename VectorT> -void apply_q_trans_vec(MatrixT const & R, - VectorT const & b_v, - VectorT & y) -{ - typedef typename MatrixT::value_type NumericT; - - NumericT inn_prod = NumericT(0); - for (vcl_size_t i = 0; i < R.size2(); ++i) - { - custom_dot_prod(R, y, static_cast<unsigned int>(i), inn_prod); - for (vcl_size_t j = i; j < R.size1(); ++j) - { - if (i == j) - y(j) -= b_v(i)*inn_prod; - else - y(j) -= b_v(i)*inn_prod*R(j,i); - } - } -} - -/** @brief Multiplication of Q'*A, where Q is in implicit for lower part of R and vector of betas - b_v - * - * @param R input matrix - * @param b_v vector of betas - * @param A output matrix - */ -template<typename MatrixT, typename VectorT> -void apply_q_trans_mat(MatrixT const & R, - VectorT const & b_v, - MatrixT & A) -{ - VectorT tmp_v; - for (vcl_size_t i = 0; i < A.size2(); ++i) - { - tmp_v = static_cast<VectorT>(column(A,i)); - apply_q_trans_vec(R, b_v, tmp_v); - column(A,i) = tmp_v; - } -} - -//parallel QR for GPU -/** @brief Inplace QR factorization via Householder reflections c.f. Gene H. Golub, Charles F. Van Loan "Matrix Computations" 3rd edition p.224 performed on GPU - * - * @param g_I container of row indices - * @param g_J container of column indices - * @param g_A_I_J_vcl contigious matrices, GPU memory is used - * @param g_bv_vcl contigiuos vectors beta, GPU memory is used - * @param g_is_update container of indicators that show active blocks - * @param ctx Optional context in which the auxiliary data is created (one out of multiple OpenCL contexts, CUDA, host) - */ -template<typename NumericT> -void block_qr(std::vector<std::vector<unsigned int> > & g_I, - std::vector<std::vector<unsigned int> > & g_J, - block_matrix & g_A_I_J_vcl, - block_vector & g_bv_vcl, - std::vector<cl_uint> & g_is_update, - viennacl::context ctx) -{ - viennacl::ocl::context & opencl_ctx = const_cast<viennacl::ocl::context &>(ctx.opencl_context()); - - //typedef typename MatrixType::value_type ScalarType; - unsigned int bv_size = 0; - unsigned int v_size = 0; - //set up arguments for GPU - //find maximum size of rows/columns - unsigned int local_r_n = 0; - unsigned int local_c_n = 0; - //find max size for blocks - get_max_block_size(g_I, local_r_n); - get_max_block_size(g_J, local_c_n); - //get size - get_size(g_J, bv_size); - get_size(g_I, v_size); - //get start indices - std::vector<cl_uint> start_bv_inds(g_I.size() + 1, 0); - std::vector<cl_uint> start_v_inds(g_I.size() + 1, 0); - init_start_inds(g_J, start_bv_inds); - init_start_inds(g_I, start_v_inds); - //init arrays - std::vector<NumericT> b_v(bv_size, NumericT(0)); - std::vector<NumericT> v(v_size, NumericT(0)); - //call qr program - block_vector v_vcl; - - g_bv_vcl.handle() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(NumericT)*bv_size), - &(b_v[0])); - - v_vcl.handle() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(NumericT)*v_size), - &(v[0])); - //the same as j_start_inds - g_bv_vcl.handle1() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*g_I.size()), - &(start_bv_inds[0])); - - v_vcl.handle1() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*g_I.size()), - &(start_v_inds[0])); - viennacl::ocl::handle<cl_mem> g_is_update_vcl = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*g_is_update.size()), - &(g_is_update[0])); - //local memory - //viennacl::ocl::enqueue(k(vcl_vec, size, viennacl::ocl::local_mem(sizeof(SCALARTYPE) * k.local_work_size()), temp)); - viennacl::linalg::opencl::kernels::spai<NumericT>::init(opencl_ctx); - viennacl::ocl::kernel & qr_kernel = opencl_ctx.get_kernel(viennacl::linalg::opencl::kernels::spai<NumericT>::program_name(), "block_qr"); - - qr_kernel.local_work_size(0, local_c_n); - qr_kernel.global_work_size(0, local_c_n*256); - viennacl::ocl::enqueue(qr_kernel(g_A_I_J_vcl.handle(), g_A_I_J_vcl.handle1(), g_bv_vcl.handle(), - v_vcl.handle(), g_A_I_J_vcl.handle2(), - g_bv_vcl.handle1(), v_vcl.handle1(), g_is_update_vcl, - viennacl::ocl::local_mem(static_cast<unsigned int>(sizeof(NumericT)*(local_r_n*local_c_n))), - static_cast<cl_uint>(g_I.size()))); - -} -} -} -} -} -#endif
http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/small_matrix.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/small_matrix.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/small_matrix.hpp deleted file mode 100644 index 3cfdbb3..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/small_matrix.hpp +++ /dev/null @@ -1,113 +0,0 @@ -#ifndef VIENNACL_LINALG_DETAIL_SPAI_SMALL_MATRIX_HPP -#define VIENNACL_LINALG_DETAIL_SPAI_SMALL_MATRIX_HPP - -/* ========================================================================= - 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 [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/detail/spai/small_matrix.hpp - @brief Implementation of a routines for small matrices (helper for SPAI). Experimental. - - SPAI code contributed by Nikolay Lukash -*/ - -#include <utility> -#include <iostream> -#include <fstream> -#include <string> -#include <algorithm> -#include <vector> -#include <math.h> -#include <map> -#include "boost/numeric/ublas/vector.hpp" -#include "boost/numeric/ublas/matrix.hpp" -#include "boost/numeric/ublas/matrix_proxy.hpp" -#include "boost/numeric/ublas/vector_proxy.hpp" -#include "boost/numeric/ublas/storage.hpp" -#include "boost/numeric/ublas/io.hpp" -#include "boost/numeric/ublas/lu.hpp" -#include "boost/numeric/ublas/triangular.hpp" -#include "boost/numeric/ublas/matrix_expression.hpp" -#include "boost/numeric/ublas/detail/matrix_assign.hpp" - -#include "viennacl/forwards.h" - -namespace viennacl -{ -namespace linalg -{ -namespace detail -{ -namespace spai -{ - -// -// Constructs an orthonormal sparse matrix M (with M^T M = Id). Is composed of elementary 2x2 rotation matrices with suitable renumbering. -// -template<typename MatrixT> -void make_rotation_matrix(MatrixT & mat, - vcl_size_t new_size, - vcl_size_t off_diagonal_distance = 4) -{ - mat.resize(new_size, new_size, false); - mat.clear(); - - double val = 1.0 / std::sqrt(2.0); - - for (vcl_size_t i=0; i<new_size; ++i) - mat(i,i) = val; - - for (vcl_size_t i=off_diagonal_distance; i<new_size; ++i) - { - mat(i-off_diagonal_distance, i) = val; - mat(i, i-off_diagonal_distance) = -val; - } - -} - - -//calcualtes matrix determinant -template<typename MatrixT> -double determinant(boost::numeric::ublas::matrix_expression<MatrixT> const & mat_r) -{ - double det = 1.0; - - MatrixT mLu(mat_r()); - boost::numeric::ublas::permutation_matrix<vcl_size_t> pivots(mat_r().size1()); - - int is_singular = static_cast<int>(lu_factorize(mLu, pivots)); - - if (!is_singular) - { - for (vcl_size_t i=0; i < pivots.size(); ++i) - { - if (pivots(i) != i) - det *= -1.0; - - det *= mLu(i,i); - } - } - else - det = 0.0; - - return det; -} - -} -} -} -} -#endif http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/spai-dynamic.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/spai-dynamic.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/spai-dynamic.hpp deleted file mode 100644 index bac0b9e..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/spai-dynamic.hpp +++ /dev/null @@ -1,687 +0,0 @@ -#ifndef VIENNACL_LINALG_DETAIL_SPAI_SPAI_DYNAMIC_HPP -#define VIENNACL_LINALG_DETAIL_SPAI_SPAI_DYNAMIC_HPP - -/* ========================================================================= - 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 [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/detail/spai/spai-dynamic.hpp - @brief Implementation of a dynamic SPAI. Provides the routines for automatic pattern updates Experimental. - - SPAI code contributed by Nikolay Lukash -*/ - -#include <utility> -#include <iostream> -#include <fstream> -#include <string> -#include <algorithm> -#include <vector> -#include <math.h> -#include <map> -//#include "block_matrix.hpp" -//#include "block_vector.hpp" -//#include "benchmark-utils.hpp" -#include "boost/numeric/ublas/vector.hpp" -#include "boost/numeric/ublas/matrix.hpp" -#include "boost/numeric/ublas/matrix_proxy.hpp" -#include "boost/numeric/ublas/vector_proxy.hpp" -#include "boost/numeric/ublas/storage.hpp" -#include "boost/numeric/ublas/io.hpp" -#include "boost/numeric/ublas/lu.hpp" -#include "boost/numeric/ublas/triangular.hpp" -#include "boost/numeric/ublas/matrix_expression.hpp" -// ViennaCL includes -#include "viennacl/linalg/prod.hpp" -#include "viennacl/matrix.hpp" -#include "viennacl/compressed_matrix.hpp" -#include "viennacl/linalg/sparse_matrix_operations.hpp" -#include "viennacl/linalg/matrix_operations.hpp" -#include "viennacl/scalar.hpp" -#include "viennacl/linalg/cg.hpp" -#include "viennacl/linalg/inner_prod.hpp" -#include "viennacl/linalg/ilu.hpp" -#include "viennacl/ocl/backend.hpp" - -#include "viennacl/linalg/detail/spai/block_matrix.hpp" -#include "viennacl/linalg/detail/spai/block_vector.hpp" -#include "viennacl/linalg/detail/spai/qr.hpp" -#include "viennacl/linalg/detail/spai/spai-static.hpp" -#include "viennacl/linalg/detail/spai/spai.hpp" -#include "viennacl/linalg/detail/spai/spai_tag.hpp" -#include "viennacl/linalg/opencl/kernels/spai.hpp" - -namespace viennacl -{ -namespace linalg -{ -namespace detail -{ -namespace spai -{ - -/** @brief Helper functor for comparing std::pair<> based on the second member. */ -struct CompareSecond -{ - template<typename T1, typename T2> - bool operator()(std::pair<T1, T2> const & left, std::pair<T1, T2> const & right) - { - return static_cast<double>(left.second) > static_cast<double>(right.second); - } -}; - - -/** @brief Composition of new matrix R, that is going to be used in Least Square problem solving - * - * @param A matrix Q'*A(I, \\tilde J), where \\tilde J - set of new column indices - * @param R_n matrix A_Iu_J_u after QR factorization - * @param R previously composed matrix R - */ -template<typename MatrixT> -void composeNewR(MatrixT const & A, - MatrixT const & R_n, - MatrixT & R) -{ - typedef typename MatrixT::value_type NumericType; - - vcl_size_t row_n = R_n.size1() - (A.size1() - R.size2()); - MatrixT C = boost::numeric::ublas::zero_matrix<NumericType>(R.size1() + row_n, R.size2() + A.size2()); - - //write original R to new Composite R - boost::numeric::ublas::project(C, boost::numeric::ublas::range(0,R.size1()), boost::numeric::ublas::range(0, R.size2())) += R; - //write upper part of Q'*A_I_\hatJ, all columns and number of rows that equals to R.size2() - boost::numeric::ublas::project(C, boost::numeric::ublas::range(0, R.size2()), boost::numeric::ublas::range(R.size2(), - R.size2() + A.size2())) += - boost::numeric::ublas::project(A, boost::numeric::ublas::range(0, R.size2()), boost::numeric::ublas::range(0, A.size2())); - - //adding decomposed(QR) block to Composite R - if (R_n.size1() > 0 && R_n.size2() > 0) - boost::numeric::ublas::project(C, - boost::numeric::ublas::range(R.size2(), R.size1() + row_n), - boost::numeric::ublas::range(R.size2(), R.size2() + A.size2())) += R_n; - R = C; -} - -/** @brief Composition of new vector of coefficients beta from QR factorizations(necessary for Q recovery) - * - * @param v_n new vector from last QR factorization - * @param v composition of previous vectors from QR factorizations - */ -template<typename VectorT> -void composeNewVector(VectorT const & v_n, - VectorT & v) -{ - typedef typename VectorT::value_type NumericType; - - VectorT w = boost::numeric::ublas::zero_vector<NumericType>(v.size() + v_n.size()); - boost::numeric::ublas::project(w, boost::numeric::ublas::range(0, v.size())) += v; - boost::numeric::ublas::project(w, boost::numeric::ublas::range(v.size(), v.size() + v_n.size())) += v_n; - v = w; -} - -/** @brief Computation of Euclidean norm for sparse vector - * - * @param v initial sparse vector - * @param norm scalar that represents Euclidean norm - */ -template<typename SparseVectorT, typename NumericT> -void sparse_norm_2(SparseVectorT const & v, - NumericT & norm) -{ - for (typename SparseVectorT::const_iterator vec_it = v.begin(); vec_it != v.end(); ++vec_it) - norm += (vec_it->second)*(vec_it->second); - - norm = std::sqrt(norm); -} - -/** @brief Dot product of two sparse vectors - * - * @param v1 initial sparse vector - * @param v2 initial sparse vector - * @param res_v scalar that represents dot product result - */ -template<typename SparseVectorT, typename NumericT> -void sparse_inner_prod(SparseVectorT const & v1, - SparseVectorT const & v2, - NumericT & res_v) -{ - typename SparseVectorT::const_iterator v_it1 = v1.begin(); - typename SparseVectorT::const_iterator v_it2 = v2.begin(); - - while ((v_it1 != v1.end())&&(v_it2 != v2.end())) - { - if (v_it1->first == v_it2->first) - { - res_v += (v_it1->second)*(v_it2->second); - ++v_it1; - ++v_it2; - } - else if (v_it1->first < v_it2->first) - ++v_it1; - else - ++v_it2; - } -} - -/** @brief Building a new set of column indices J_u, cf. Kallischko dissertation p.31 - * - * @param A_v_c vectorized column-wise initial matrix - * @param res residual vector - * @param J set of column indices - * @param J_u set of new column indices - * @param tag SPAI tag with parameters - */ -template<typename SparseVectorT, typename NumericT> -bool buildAugmentedIndexSet(std::vector<SparseVectorT> const & A_v_c, - SparseVectorT const & res, - std::vector<unsigned int> & J, - std::vector<unsigned int> & J_u, - spai_tag const & tag) -{ - std::vector<std::pair<unsigned int, NumericT> > p; - vcl_size_t cur_size = 0; - NumericT inprod, norm2; - - for (typename SparseVectorT::const_iterator res_it = res.begin(); res_it != res.end(); ++res_it) - { - if (!isInIndexSet(J, res_it->first) && (std::fabs(res_it->second) > tag.getResidualThreshold())) - { - inprod = norm2 = 0; - sparse_inner_prod(res, A_v_c[res_it->first], inprod); - sparse_norm_2(A_v_c[res_it->first], norm2); - p.push_back(std::pair<unsigned int, NumericT>(res_it->first, (inprod*inprod)/(norm2*norm2))); - } - } - - std::sort(p.begin(), p.end(), CompareSecond()); - while ((cur_size < J.size()) && (p.size() > 0)) - { - J_u.push_back(p[0].first); - p.erase(p.begin()); - cur_size++; - } - p.clear(); - return (cur_size > 0); -} - -/** @brief Building a new indices to current set of row indices I_n, cf. Kallischko dissertation p.32 - * - * @param A_v_c vectorized column-wise initial matrix - * @param I set of previous determined row indices - * @param J_n set of new column indices - * @param I_n set of new indices - */ -template<typename SparseVectorT> -void buildNewRowSet(std::vector<SparseVectorT> const & A_v_c, - std::vector<unsigned int> const & I, - std::vector<unsigned int> const & J_n, - std::vector<unsigned int> & I_n) -{ - for (vcl_size_t i = 0; i < J_n.size(); ++i) - { - for (typename SparseVectorT::const_iterator col_it = A_v_c[J_n[i]].begin(); col_it!=A_v_c[J_n[i]].end(); ++col_it) - { - if (!isInIndexSet(I, col_it->first) && !isInIndexSet(I_n, col_it->first)) - I_n.push_back(col_it->first); - } - } -} - -/** @brief Composition of new block for QR factorization cf. Kallischko dissertation p.82, figure 4.7 - * - * @param A_I_J previously composed block - * @param A_I_J_u matrix Q'*A(I, \\tilde J), where \\tilde J - set of new column indices - * @param A_I_u_J_u is composition of lower part A(I, \\tilde J) and A(\\tilde I, \\tilde J) - new block for QR decomposition - */ -template<typename MatrixT> -void QRBlockComposition(MatrixT const & A_I_J, - MatrixT const & A_I_J_u, - MatrixT & A_I_u_J_u) -{ - typedef typename MatrixT::value_type NumericType; - - vcl_size_t row_n1 = A_I_J_u.size1() - A_I_J.size2(); - vcl_size_t row_n2 = A_I_u_J_u.size1(); - vcl_size_t row_n = row_n1 + row_n2; - vcl_size_t col_n = A_I_J_u.size2(); - - MatrixT C = boost::numeric::ublas::zero_matrix<NumericType>(row_n, col_n); - boost::numeric::ublas::project(C, - boost::numeric::ublas::range(0, row_n1), - boost::numeric::ublas::range(0, col_n)) - += boost::numeric::ublas::project(A_I_J_u, - boost::numeric::ublas::range(A_I_J.size2(), A_I_J_u.size1()), - boost::numeric::ublas::range(0, col_n)); - - boost::numeric::ublas::project(C, - boost::numeric::ublas::range(row_n1, row_n1 + row_n2), - boost::numeric::ublas::range(0, col_n)) += A_I_u_J_u; - A_I_u_J_u = C; -} - -/** @brief CPU-based dynamic update for SPAI preconditioner - * - * @param A initial sparse matrix - * @param A_v_c vectorized column-wise initial matrix - * @param g_res container of residuals for all columns - * @param g_is_update container with identificators that shows which block should be modified - * @param g_I container of row index sets for all columns - * @param g_J container of column index sets for all columns - * @param g_b_v container of vectors of beta for Q recovery(cf. Golub Van Loan "Matrix Computations", 3rd edition p.211) - * @param g_A_I_J container of block matrices from previous update - * @param tag SPAI configuration tag - */ -template<typename SparseMatrixT, - typename SparseVectorT, - typename DenseMatrixT, - typename VectorT> -void block_update(SparseMatrixT const & A, - std::vector<SparseVectorT> const & A_v_c, - std::vector<SparseVectorT> & g_res, - std::vector<bool> & g_is_update, - std::vector<std::vector<unsigned int> >& g_I, - std::vector<std::vector<unsigned int> >& g_J, - std::vector<VectorT> & g_b_v, - std::vector<DenseMatrixT> & g_A_I_J, - spai_tag const & tag) -{ - typedef typename DenseMatrixT::value_type NumericType; - - - std::vector<std::vector<unsigned int> > g_J_u(g_J.size()); // set of new column indices - std::vector<std::vector<unsigned int> > g_I_u(g_J.size()); // set of new row indices - std::vector<DenseMatrixT> g_A_I_J_u(g_J.size()); // matrix A(I, \tilde J), cf. Kallischko p.31-32 - std::vector<DenseMatrixT> g_A_I_u_J_u(g_J.size()); // matrix A(\tilde I, \tilde J), cf. Kallischko - std::vector<VectorT> g_b_v_u(g_J.size()); // new vector of beta coefficients from QR factorization - -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i = 0; i < static_cast<long>(g_J.size()); ++i) - { - if (g_is_update[static_cast<vcl_size_t>(i)]) - { - if (buildAugmentedIndexSet<SparseVectorT, NumericType>(A_v_c, g_res[static_cast<vcl_size_t>(i)], g_J[static_cast<vcl_size_t>(i)], g_J_u[static_cast<vcl_size_t>(i)], tag)) - { - //initialize matrix A_I_\hatJ - initProjectSubMatrix(A, g_J_u[static_cast<vcl_size_t>(i)], g_I[static_cast<vcl_size_t>(i)], g_A_I_J_u[static_cast<vcl_size_t>(i)]); - //multiplication of Q'*A_I_\hatJ - apply_q_trans_mat(g_A_I_J[static_cast<vcl_size_t>(i)], g_b_v[static_cast<vcl_size_t>(i)], g_A_I_J_u[static_cast<vcl_size_t>(i)]); - //building new rows index set \hatI - buildNewRowSet(A_v_c, g_I[static_cast<vcl_size_t>(i)], g_J_u[static_cast<vcl_size_t>(i)], g_I_u[static_cast<vcl_size_t>(i)]); - initProjectSubMatrix(A, g_J_u[static_cast<vcl_size_t>(i)], g_I_u[static_cast<vcl_size_t>(i)], g_A_I_u_J_u[static_cast<vcl_size_t>(i)]); - //composition of block for new QR factorization - QRBlockComposition(g_A_I_J[static_cast<vcl_size_t>(i)], g_A_I_J_u[static_cast<vcl_size_t>(i)], g_A_I_u_J_u[static_cast<vcl_size_t>(i)]); - //QR factorization - single_qr(g_A_I_u_J_u[static_cast<vcl_size_t>(i)], g_b_v_u[static_cast<vcl_size_t>(i)]); - //composition of new R and new vector b_v - composeNewR(g_A_I_J_u[static_cast<vcl_size_t>(i)], g_A_I_u_J_u[static_cast<vcl_size_t>(i)], g_A_I_J[static_cast<vcl_size_t>(i)]); - composeNewVector(g_b_v_u[static_cast<vcl_size_t>(i)], g_b_v[static_cast<vcl_size_t>(i)]); - //composition of new sets: I and J - g_J[static_cast<vcl_size_t>(i)].insert(g_J[static_cast<vcl_size_t>(i)].end(), g_J_u[static_cast<vcl_size_t>(i)].begin(), g_J_u[static_cast<vcl_size_t>(i)].end()); - g_I[static_cast<vcl_size_t>(i)].insert(g_I[static_cast<vcl_size_t>(i)].end(), g_I_u[static_cast<vcl_size_t>(i)].begin(), g_I_u[static_cast<vcl_size_t>(i)].end()); - } - else - { - g_is_update[static_cast<vcl_size_t>(i)] = false; - } - } - } -} - - -/**************************************************** GPU SPAI Update ****************************************************************/ - - -//performs Q'*A(I, \tilde J) on GPU -/** @brief Performs multiplication Q'*A(I, \\tilde J) on GPU - * - * @param g_J_u container of sets of new column indices - * @param g_I container of row indices - * @param g_A_I_J_vcl block matrix composed from previous blocks, they are blocks of R - * @param g_bv_vcl block of beta vectors - * @param g_A_I_J_u_vcl block of matrices A(I, \\tilde J) - * @param g_is_update indicators, that show if a certain block should be processed - * @param ctx Optional context in which the auxiliary data is created (one out of multiple OpenCL contexts, CUDA, host) - */ -template<typename NumericT> -void block_q_multiplication(std::vector<std::vector<unsigned int> > const & g_J_u, - std::vector<std::vector<unsigned int> > const & g_I, - block_matrix & g_A_I_J_vcl, - block_vector & g_bv_vcl, - block_matrix & g_A_I_J_u_vcl, - std::vector<cl_uint> & g_is_update, - viennacl::context ctx) -{ - viennacl::ocl::context & opencl_ctx = const_cast<viennacl::ocl::context &>(ctx.opencl_context()); - unsigned int local_r_n = 0; - unsigned int local_c_n = 0; - unsigned int sz_blocks = 0; - - get_max_block_size(g_I, local_r_n); - get_max_block_size(g_J_u, local_c_n); - - //for debug - std::vector<cl_uint> matrix_dims(g_I.size()*2, static_cast<cl_uint>(0)); - std::vector<cl_uint> blocks_ind(g_I.size() + 1, static_cast<cl_uint>(0)); - compute_blocks_size(g_I, g_J_u, sz_blocks, blocks_ind, matrix_dims); - //std::vector<ScalarType> con_A_I_J(sz_blocks, static_cast<ScalarType>(0)); - - viennacl::ocl::handle<cl_mem> g_is_update_vcl = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*(g_is_update.size())), - &(g_is_update[0])); - viennacl::linalg::opencl::kernels::spai<NumericT>::init(opencl_ctx); - viennacl::ocl::kernel& block_q_kernel = opencl_ctx.get_kernel(viennacl::linalg::opencl::kernels::spai<NumericT>::program_name(), "block_q_mult"); - - block_q_kernel.local_work_size(0, local_c_n); - block_q_kernel.global_work_size(0, 128*local_c_n); - viennacl::ocl::enqueue(block_q_kernel(g_A_I_J_vcl.handle(), g_A_I_J_vcl.handle2(), g_A_I_J_u_vcl.handle(), g_A_I_J_u_vcl.handle2(), - g_bv_vcl.handle(), - g_bv_vcl.handle1(), g_A_I_J_vcl.handle1(), g_A_I_J_u_vcl.handle1(), g_is_update_vcl, - viennacl::ocl::local_mem(static_cast<unsigned int>(sizeof(NumericT)*(local_r_n*local_c_n))), - static_cast<cl_uint>(g_I.size()))); -} - -/** @brief Assembly of container of index row sets: I_q, row indices for new "QR block" - * - * @param g_I container of row indices - * @param g_J container of column indices - * @param g_I_u container of new row indices - * @param g_I_q container of row indices for new QR blocks - */ -template<typename SizeT> -void assemble_qr_row_inds(std::vector<std::vector<SizeT> > const & g_I, - std::vector<std::vector<SizeT> > const & g_J, - std::vector<std::vector<SizeT> > const & g_I_u, - std::vector<std::vector<SizeT> > & g_I_q) -{ -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i = 0; i < static_cast<long>(g_I.size()); ++i) - { - for (vcl_size_t j = g_J[static_cast<vcl_size_t>(i)].size(); j < g_I[static_cast<vcl_size_t>(i)].size(); ++j) - g_I_q[static_cast<vcl_size_t>(i)].push_back(g_I[static_cast<vcl_size_t>(i)][j]); - - for (vcl_size_t j = 0; j < g_I_u[static_cast<vcl_size_t>(i)].size(); ++j) - g_I_q[static_cast<vcl_size_t>(i)].push_back(g_I_u[static_cast<vcl_size_t>(i)][j]); - } -} - -/** @brief Performs assembly for new QR block - * - * @param g_J container of column indices - * @param g_I container of row indices - * @param g_J_u container of new column indices - * @param g_I_u container of new row indices - * @param g_I_q container of row indices for new QR blocks - * @param g_A_I_J_u_vcl blocks of Q'*A(I, \\tilde J) - * @param matrix_dimensions array with matrix dimensions for all blocks - * @param g_A_I_u_J_u_vcl blocks A(\\tilde I, \\tilde J) - * @param g_is_update container with update indicators - * @param is_empty_block indicator if all previous blocks A(\\tilde I, \\tilde J) - are empty, in case if they are empty kernel with smaller number of arguments is used - * @param ctx Optional context in which the matrix is created (one out of multiple OpenCL contexts, CUDA, host) -*/ -template<typename NumericT> -void assemble_qr_block(std::vector<std::vector<unsigned int> > const & g_J, - std::vector<std::vector<unsigned int> > const& g_I, - std::vector<std::vector<unsigned int> > const& g_J_u, - std::vector<std::vector<unsigned int> > const& g_I_u, - std::vector<std::vector<unsigned int> >& g_I_q, - block_matrix & g_A_I_J_u_vcl, - viennacl::ocl::handle<cl_mem> & matrix_dimensions, - block_matrix & g_A_I_u_J_u_vcl, - std::vector<cl_uint> & g_is_update, - bool is_empty_block, - viennacl::context ctx) -{ - viennacl::ocl::context & opencl_ctx = const_cast<viennacl::ocl::context &>(ctx.opencl_context()); - - //std::vector<std::vector<unsigned int> > g_I_q(g_I.size()); - assemble_qr_row_inds(g_I, g_J, g_I_u, g_I_q); - unsigned int sz_blocks; - std::vector<cl_uint> matrix_dims(g_I.size()*2, static_cast<cl_uint>(0)); - std::vector<cl_uint> blocks_ind(g_I.size() + 1, static_cast<cl_uint>(0)); - - compute_blocks_size(g_I_q, g_J_u, sz_blocks, blocks_ind, matrix_dims); - - std::vector<NumericT> con_A_I_J_q(sz_blocks, static_cast<NumericT>(0)); - - block_matrix g_A_I_J_q_vcl; - //need to allocate memory for QR block - g_A_I_J_q_vcl.handle() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(NumericT)*sz_blocks), - &(con_A_I_J_q[0])); - g_A_I_J_q_vcl.handle().context(opencl_ctx); - - g_A_I_J_q_vcl.handle1() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*2*static_cast<unsigned int>(g_I.size())), - &(matrix_dims[0])); - g_A_I_J_q_vcl.handle1().context(opencl_ctx); - - g_A_I_J_q_vcl.handle2() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*static_cast<unsigned int>(g_I.size() + 1)), - &(blocks_ind[0])); - g_A_I_J_q_vcl.handle2().context(opencl_ctx); - - viennacl::ocl::handle<cl_mem> g_is_update_vcl = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*(g_is_update.size())), - &(g_is_update[0])); - - viennacl::linalg::opencl::kernels::spai<NumericT>::init(opencl_ctx); - if (!is_empty_block) - { - viennacl::ocl::kernel& qr_assembly_kernel = opencl_ctx.get_kernel(viennacl::linalg::opencl::kernels::spai<NumericT>::program_name(), "block_qr_assembly"); - qr_assembly_kernel.local_work_size(0, 1); - qr_assembly_kernel.global_work_size(0, 256); - viennacl::ocl::enqueue(qr_assembly_kernel(matrix_dimensions, - g_A_I_J_u_vcl.handle(), - g_A_I_J_u_vcl.handle2(), - g_A_I_J_u_vcl.handle1(), - g_A_I_u_J_u_vcl.handle(), - g_A_I_u_J_u_vcl.handle2(), - g_A_I_u_J_u_vcl.handle1(), - g_A_I_J_q_vcl.handle(), - g_A_I_J_q_vcl.handle2(), - g_A_I_J_q_vcl.handle1(), - g_is_update_vcl, - static_cast<unsigned int>(g_I.size()))); - } - else - { - viennacl::ocl::kernel& qr_assembly_kernel = opencl_ctx.get_kernel(viennacl::linalg::opencl::kernels::spai<NumericT>::program_name(), "block_qr_assembly_1"); - qr_assembly_kernel.local_work_size(0, 1); - qr_assembly_kernel.global_work_size(0, 256); - viennacl::ocl::enqueue(qr_assembly_kernel(matrix_dimensions, g_A_I_J_u_vcl.handle(), g_A_I_J_u_vcl.handle2(), - g_A_I_J_u_vcl.handle1(), - g_A_I_J_q_vcl.handle(), - g_A_I_J_q_vcl.handle2(), g_A_I_J_q_vcl.handle1(), - g_is_update_vcl, - static_cast<unsigned int>(g_I.size()))); - } - g_A_I_u_J_u_vcl.handle() = g_A_I_J_q_vcl.handle(); - g_A_I_u_J_u_vcl.handle1() = g_A_I_J_q_vcl.handle1(); - g_A_I_u_J_u_vcl.handle2() = g_A_I_J_q_vcl.handle2(); -} - -/** @brief Performs assembly for new R matrix on GPU - * - * @param g_I container of row indices - * @param g_J container of column indices - * @param g_A_I_J_vcl container of block matrices from previous update - * @param g_A_I_J_u_vcl container of block matrices Q'*A(I, \\tilde J) - * @param g_A_I_u_J_u_vcl container of block matrices QR factored on current iteration - * @param g_bv_vcl block of beta vectors from previous iteration - * @param g_bv_vcl_u block of updated beta vectors got after recent QR factorization - * @param g_is_update container with identificators that shows which block should be modified - * @param ctx Optional context in which the auxiliary data is created (one out of multiple OpenCL contexts, CUDA, host) - */ -template<typename NumericT> -void assemble_r(std::vector<std::vector<unsigned int> > & g_I, - std::vector<std::vector<unsigned int> > & g_J, - block_matrix & g_A_I_J_vcl, - block_matrix & g_A_I_J_u_vcl, - block_matrix & g_A_I_u_J_u_vcl, - block_vector & g_bv_vcl, - block_vector & g_bv_vcl_u, - std::vector<cl_uint> & g_is_update, - viennacl::context ctx) -{ - viennacl::ocl::context & opencl_ctx = const_cast<viennacl::ocl::context &>(ctx.opencl_context()); - std::vector<cl_uint> matrix_dims(g_I.size()*2, static_cast<cl_uint>(0)); - std::vector<cl_uint> blocks_ind(g_I.size() + 1, static_cast<cl_uint>(0)); - std::vector<cl_uint> start_bv_r_inds(g_I.size() + 1, 0); - unsigned int sz_blocks, bv_size; - - compute_blocks_size(g_I, g_J, sz_blocks, blocks_ind, matrix_dims); - get_size(g_J, bv_size); - init_start_inds(g_J, start_bv_r_inds); - - std::vector<NumericT> con_A_I_J_r(sz_blocks, static_cast<NumericT>(0)); - std::vector<NumericT> b_v_r(bv_size, static_cast<NumericT>(0)); - - block_matrix g_A_I_J_r_vcl; - block_vector g_bv_r_vcl; - g_A_I_J_r_vcl.handle() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(NumericT)*sz_blocks), - &(con_A_I_J_r[0])); - g_A_I_J_r_vcl.handle().context(opencl_ctx); - - g_A_I_J_r_vcl.handle1() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*2*static_cast<unsigned int>(g_I.size())), - &(matrix_dims[0])); - g_A_I_J_r_vcl.handle1().context(opencl_ctx); - - g_A_I_J_r_vcl.handle2() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*static_cast<unsigned int>(g_I.size() + 1)), - &(blocks_ind[0])); - g_A_I_J_r_vcl.handle2().context(opencl_ctx); - - g_bv_r_vcl.handle() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(NumericT)*bv_size), - &(b_v_r[0])); - g_bv_r_vcl.handle().context(opencl_ctx); - - g_bv_r_vcl.handle1() = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*(g_I.size() + 1)), - &(start_bv_r_inds[0])); - g_bv_r_vcl.handle().context(opencl_ctx); - - viennacl::ocl::handle<cl_mem> g_is_update_vcl = opencl_ctx.create_memory(CL_MEM_READ_WRITE, - static_cast<unsigned int>(sizeof(cl_uint)*(g_is_update.size())), - &(g_is_update[0])); - viennacl::linalg::opencl::kernels::spai<NumericT>::init(opencl_ctx); - viennacl::ocl::kernel& r_assembly_kernel = opencl_ctx.get_kernel(viennacl::linalg::opencl::kernels::spai<NumericT>::program_name(), "block_r_assembly"); - r_assembly_kernel.local_work_size(0, 1); - r_assembly_kernel.global_work_size(0, 256); - - viennacl::ocl::enqueue(r_assembly_kernel(g_A_I_J_vcl.handle(), g_A_I_J_vcl.handle2(), g_A_I_J_vcl.handle1(), - g_A_I_J_u_vcl.handle(), g_A_I_J_u_vcl.handle2(), g_A_I_J_u_vcl.handle1(), - g_A_I_u_J_u_vcl.handle(), g_A_I_u_J_u_vcl.handle2(), g_A_I_u_J_u_vcl.handle1(), - g_A_I_J_r_vcl.handle(), g_A_I_J_r_vcl.handle2(), g_A_I_J_r_vcl.handle1(), - g_is_update_vcl, static_cast<cl_uint>(g_I.size()))); - - viennacl::ocl::kernel & bv_assembly_kernel = opencl_ctx.get_kernel(viennacl::linalg::opencl::kernels::spai<NumericT>::program_name(), "block_bv_assembly"); - bv_assembly_kernel.local_work_size(0, 1); - bv_assembly_kernel.global_work_size(0, 256); - viennacl::ocl::enqueue(bv_assembly_kernel(g_bv_vcl.handle(), g_bv_vcl.handle1(), g_A_I_J_vcl.handle1(), g_bv_vcl_u.handle(), - g_bv_vcl_u.handle1(), g_A_I_J_u_vcl.handle1(), - g_bv_r_vcl.handle(), g_bv_r_vcl.handle1(), g_A_I_J_r_vcl.handle1(), g_is_update_vcl, - static_cast<cl_uint>(g_I.size()))); - g_bv_vcl.handle() = g_bv_r_vcl.handle(); - g_bv_vcl.handle1() = g_bv_r_vcl.handle1(); - - g_A_I_J_vcl.handle() = g_A_I_J_r_vcl.handle(); - g_A_I_J_vcl.handle2() = g_A_I_J_r_vcl.handle2(); - g_A_I_J_vcl.handle1() = g_A_I_J_r_vcl.handle1(); -} - -/** @brief GPU-based block update - * - * @param A sparse matrix - * @param A_v_c vectorized column-wise initial matrix - * @param g_is_update container with identificators that shows which block should be modified - * @param g_res container of residuals for all columns - * @param g_J container of column index sets for all columns - * @param g_I container of row index sets for all columns - * @param g_A_I_J_vcl container of block matrices from previous update - * @param g_bv_vcl block of beta vectors from previous iteration - * @param tag SPAI configuration tag - */ -template<typename NumericT, unsigned int AlignmentV, typename SparseVectorT> -void block_update(viennacl::compressed_matrix<NumericT, AlignmentV> const & A, - std::vector<SparseVectorT> const & A_v_c, - std::vector<cl_uint> & g_is_update, - std::vector<SparseVectorT> & g_res, - std::vector<std::vector<unsigned int> > & g_J, - std::vector<std::vector<unsigned int> > & g_I, - block_matrix & g_A_I_J_vcl, - block_vector & g_bv_vcl, - spai_tag const & tag) -{ - viennacl::context ctx = viennacl::traits::context(A); - //updated index set for columns - std::vector<std::vector<unsigned int> > g_J_u(g_J.size()); - //updated index set for rows - std::vector<std::vector<unsigned int> > g_I_u(g_J.size()); - //mixed index set of old and updated indices for rows - std::vector<std::vector<unsigned int> > g_I_q(g_J.size()); - //GPU memory for A_I_\hatJ - block_matrix g_A_I_J_u_vcl; - //GPU memory for A_\hatI_\hatJ - block_matrix g_A_I_u_J_u_vcl; - bool is_empty_block; - //GPU memory for new b_v - block_vector g_bv_u_vcl; - -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i = 0; i < static_cast<long>(g_J.size()); ++i) - { - if (g_is_update[static_cast<vcl_size_t>(i)]) - { - if (buildAugmentedIndexSet<SparseVectorT, NumericT>(A_v_c, g_res[static_cast<vcl_size_t>(i)], g_J[static_cast<vcl_size_t>(i)], g_J_u[static_cast<vcl_size_t>(i)], tag)) - buildNewRowSet(A_v_c, g_I[static_cast<vcl_size_t>(i)], g_J_u[static_cast<vcl_size_t>(i)], g_I_u[static_cast<vcl_size_t>(i)]); - } - } - //assemble new A_I_J_u blocks on GPU and multiply them with Q' - block_assembly(A, g_J_u, g_I, g_A_I_J_u_vcl, g_is_update, is_empty_block); - //I have matrix A_I_J_u ready.. - block_q_multiplication<NumericT>(g_J_u, g_I, g_A_I_J_vcl, g_bv_vcl, g_A_I_J_u_vcl, g_is_update, ctx); - //assemble A_\hatI_\hatJ - block_assembly(A, g_J_u, g_I_u, g_A_I_u_J_u_vcl, g_is_update, is_empty_block); - assemble_qr_block<NumericT>(g_J, g_I, g_J_u, g_I_u, g_I_q, g_A_I_J_u_vcl, g_A_I_J_vcl.handle1(), - g_A_I_u_J_u_vcl, g_is_update, is_empty_block, ctx); - - block_qr<NumericT>(g_I_q, g_J_u, g_A_I_u_J_u_vcl, g_bv_u_vcl, g_is_update, ctx); - //concatanation of new and old indices -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i = 0; i < static_cast<long>(g_J.size()); ++i) - { - g_J[static_cast<vcl_size_t>(i)].insert(g_J[static_cast<vcl_size_t>(i)].end(), g_J_u[static_cast<vcl_size_t>(i)].begin(), g_J_u[static_cast<vcl_size_t>(i)].end()); - g_I[static_cast<vcl_size_t>(i)].insert(g_I[static_cast<vcl_size_t>(i)].end(), g_I_u[static_cast<vcl_size_t>(i)].begin(), g_I_u[static_cast<vcl_size_t>(i)].end()); - } - assemble_r<NumericT>(g_I, g_J, g_A_I_J_vcl, g_A_I_J_u_vcl, g_A_I_u_J_u_vcl, g_bv_vcl, g_bv_u_vcl, g_is_update, ctx); -} - -} -} -} -} -#endif http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/spai-static.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/spai-static.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/spai-static.hpp deleted file mode 100644 index 0fd11146..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/detail/spai/spai-static.hpp +++ /dev/null @@ -1,192 +0,0 @@ -#ifndef VIENNACL_LINALG_DETAIL_SPAI_SPAI_STATIC_HPP -#define VIENNACL_LINALG_DETAIL_SPAI_SPAI_STATIC_HPP - -/* ========================================================================= - 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 [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/detail/spai/spai-static.hpp - @brief Implementation of a static SPAI. Experimental. - - SPAI code contributed by Nikolay Lukash -*/ - -#include <utility> -#include <iostream> -#include <fstream> -#include <string> -#include <algorithm> -#include <vector> -#include <math.h> -#include <map> -//#include "spai-dynamic.hpp" -#include "boost/numeric/ublas/vector.hpp" -#include "boost/numeric/ublas/matrix.hpp" -#include "boost/numeric/ublas/matrix_proxy.hpp" -#include "boost/numeric/ublas/vector_proxy.hpp" -#include "boost/numeric/ublas/storage.hpp" -#include "boost/numeric/ublas/io.hpp" -#include "boost/numeric/ublas/lu.hpp" -#include "boost/numeric/ublas/triangular.hpp" -#include "boost/numeric/ublas/matrix_expression.hpp" -// ViennaCL includes -#include "viennacl/linalg/prod.hpp" -#include "viennacl/matrix.hpp" -#include "viennacl/compressed_matrix.hpp" -#include "viennacl/linalg/sparse_matrix_operations.hpp" -#include "viennacl/linalg/matrix_operations.hpp" -#include "viennacl/scalar.hpp" -#include "viennacl/linalg/cg.hpp" -#include "viennacl/linalg/inner_prod.hpp" - -//#include "boost/numeric/ublas/detail/matrix_assign.hpp" - -namespace viennacl -{ -namespace linalg -{ -namespace detail -{ -namespace spai -{ - -/** @brief Determines if element ind is in set {J} - * - * @param J current set - * @param ind current element - */ -template<typename SizeT> -bool isInIndexSet(std::vector<SizeT> const & J, SizeT ind) -{ - return (std::find(J.begin(), J.end(), ind) != J.end()); -} - - - -/********************************* STATIC SPAI FUNCTIONS******************************************/ - -/** @brief Projects solution of LS problem onto original column m - * - * @param m_in solution of LS - * @param J set of non-zero columns - * @param m original column of M - */ -template<typename VectorT, typename SparseVectorT> -void fanOutVector(VectorT const & m_in, std::vector<unsigned int> const & J, SparseVectorT & m) -{ - unsigned int cnt = 0; - for (vcl_size_t i = 0; i < J.size(); ++i) - m[J[i]] = m_in(cnt++); -} - -/** @brief Solution of linear:R*x=y system by backward substitution - * - * @param R uppertriangular matrix - * @param y right handside vector - * @param x solution vector - */ -template<typename MatrixT, typename VectorT> -void backwardSolve(MatrixT const & R, VectorT const & y, VectorT & x) -{ - for (long i2 = static_cast<long>(R.size2())-1; i2 >= 0; i2--) - { - vcl_size_t i = static_cast<vcl_size_t>(i2); - x(i) = y(i); - for (vcl_size_t j = static_cast<vcl_size_t>(i)+1; j < R.size2(); ++j) - x(i) -= R(i,j)*x(j); - - x(i) /= R(i,i); - } -} - -/** @brief Perform projection of set I on the unit-vector - * - * @param I set of non-zero rows - * @param y result vector - * @param ind index of unit vector - */ -template<typename VectorT, typename NumericT> -void projectI(std::vector<unsigned int> const & I, VectorT & y, unsigned int ind) -{ - for (vcl_size_t i = 0; i < I.size(); ++i) - { - //y.resize(y.size()+1); - if (I[i] == ind) - y(i) = NumericT(1.0); - else - y(i) = NumericT(0.0); - } -} - -/** @brief Builds index set of projected columns for current column of preconditioner - * - * @param v current column of preconditioner - * @param J output - index set of non-zero columns - */ -template<typename SparseVectorT> -void buildColumnIndexSet(SparseVectorT const & v, std::vector<unsigned int> & J) -{ - for (typename SparseVectorT::const_iterator vec_it = v.begin(); vec_it != v.end(); ++vec_it) - J.push_back(vec_it->first); - - std::sort(J.begin(), J.end()); -} - -/** @brief Initialize preconditioner with sparcity pattern = p(A) - * - * @param A input matrix - * @param M output matrix - initialized preconditioner - */ -template<typename SparseMatrixT> -void initPreconditioner(SparseMatrixT const & A, SparseMatrixT & M) -{ - typedef typename SparseMatrixT::value_type NumericType; - - M.resize(A.size1(), A.size2(), false); - for (typename SparseMatrixT::const_iterator1 row_it = A.begin1(); row_it!= A.end1(); ++row_it) - for (typename SparseMatrixT::const_iterator2 col_it = row_it.begin(); col_it != row_it.end(); ++col_it) - M(col_it.index1(),col_it.index2()) = NumericType(1); -} - -/** @brief Row projection for matrix A(:,J) -> A(I,J), building index set of non-zero rows - * - * @param A_v_c input matrix - * @param J set of non-zero rows - * @param I output matrix - */ -template<typename SparseVectorT> -void projectRows(std::vector<SparseVectorT> const & A_v_c, - std::vector<unsigned int> const & J, - std::vector<unsigned int> & I) -{ - for (vcl_size_t i = 0; i < J.size(); ++i) - { - for (typename SparseVectorT::const_iterator col_it = A_v_c[J[i]].begin(); col_it!=A_v_c[J[i]].end(); ++col_it) - { - if (!isInIndexSet(I, col_it->first)) - I.push_back(col_it->first); - } - } - std::sort(I.begin(), I.end()); -} - - -} //namespace spai -} //namespace detail -} //namespace linalg -} //namespace viennacl - -#endif
