http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/gmres.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/gmres.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/gmres.hpp deleted file mode 100644 index fb89742..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/gmres.hpp +++ /dev/null @@ -1,738 +0,0 @@ -#ifndef VIENNACL_GMRES_HPP_ -#define VIENNACL_GMRES_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/gmres.hpp - @brief Implementations of the generalized minimum residual method are in this file. -*/ - -#include <vector> -#include <cmath> -#include <limits> -#include "viennacl/forwards.h" -#include "viennacl/tools/tools.hpp" -#include "viennacl/linalg/norm_2.hpp" -#include "viennacl/linalg/prod.hpp" -#include "viennacl/linalg/inner_prod.hpp" -#include "viennacl/traits/clear.hpp" -#include "viennacl/traits/size.hpp" -#include "viennacl/traits/context.hpp" -#include "viennacl/meta/result_of.hpp" - -#include "viennacl/linalg/iterative_operations.hpp" -#include "viennacl/vector_proxy.hpp" - - -namespace viennacl -{ -namespace linalg -{ - -/** @brief A tag for the solver GMRES. Used for supplying solver parameters and for dispatching the solve() function -*/ -class gmres_tag //generalized minimum residual -{ -public: - /** @brief The constructor - * - * @param tol Relative tolerance for the residual (solver quits if ||r|| < tol * ||r_initial||) - * @param max_iterations The maximum number of iterations (including restarts - * @param krylov_dim The maximum dimension of the Krylov space before restart (number of restarts is found by max_iterations / krylov_dim) - */ - gmres_tag(double tol = 1e-10, unsigned int max_iterations = 300, unsigned int krylov_dim = 20) - : tol_(tol), abs_tol_(0), iterations_(max_iterations), krylov_dim_(krylov_dim), iters_taken_(0) {} - - /** @brief Returns the relative tolerance */ - double tolerance() const { return tol_; } - - /** @brief Returns the absolute tolerance */ - double abs_tolerance() const { return abs_tol_; } - /** @brief Sets the absolute tolerance */ - void abs_tolerance(double new_tol) { if (new_tol >= 0) abs_tol_ = new_tol; } - - /** @brief Returns the maximum number of iterations */ - unsigned int max_iterations() const { return iterations_; } - /** @brief Returns the maximum dimension of the Krylov space before restart */ - unsigned int krylov_dim() const { return krylov_dim_; } - /** @brief Returns the maximum number of GMRES restarts */ - unsigned int max_restarts() const - { - unsigned int ret = iterations_ / krylov_dim_; - if (ret > 0 && (ret * krylov_dim_ == iterations_) ) - return ret - 1; - return ret; - } - - /** @brief Return the number of solver iterations: */ - unsigned int iters() const { return iters_taken_; } - /** @brief Set the number of solver iterations (should only be modified by the solver) */ - void iters(unsigned int i) const { iters_taken_ = i; } - - /** @brief Returns the estimated relative error at the end of the solver run */ - double error() const { return last_error_; } - /** @brief Sets the estimated relative error at the end of the solver run */ - void error(double e) const { last_error_ = e; } - -private: - double tol_; - double abs_tol_; - unsigned int iterations_; - unsigned int krylov_dim_; - - //return values from solver - mutable unsigned int iters_taken_; - mutable double last_error_; -}; - -namespace detail -{ - - template<typename SrcVectorT, typename DestVectorT> - void gmres_copy_helper(SrcVectorT const & src, DestVectorT & dest, vcl_size_t len, vcl_size_t start = 0) - { - for (vcl_size_t i=0; i<len; ++i) - dest[start+i] = src[start+i]; - } - - template<typename NumericT, typename DestVectorT> - void gmres_copy_helper(viennacl::vector<NumericT> const & src, DestVectorT & dest, vcl_size_t len, vcl_size_t start = 0) - { - typedef typename viennacl::vector<NumericT>::difference_type difference_type; - viennacl::copy( src.begin() + static_cast<difference_type>(start), - src.begin() + static_cast<difference_type>(start + len), - dest.begin() + static_cast<difference_type>(start)); - } - - /** @brief Computes the householder vector 'hh_vec' which rotates 'input_vec' such that all entries below the j-th entry of 'v' become zero. - * - * @param input_vec The input vector - * @param hh_vec The householder vector defining the relection (I - beta * hh_vec * hh_vec^T) - * @param beta The coefficient beta in (I - beta * hh_vec * hh_vec^T) - * @param mu The norm of the input vector part relevant for the reflection: norm_2(input_vec[j:size]) - * @param j Index of the last nonzero index in 'input_vec' after applying the reflection - */ - template<typename VectorT, typename NumericT> - void gmres_setup_householder_vector(VectorT const & input_vec, VectorT & hh_vec, NumericT & beta, NumericT & mu, vcl_size_t j) - { - NumericT input_j = input_vec(j); - - // copy entries from input vector to householder vector: - detail::gmres_copy_helper(input_vec, hh_vec, viennacl::traits::size(hh_vec) - (j+1), j+1); - - NumericT sigma = viennacl::linalg::norm_2(hh_vec); - sigma *= sigma; - - if (sigma <= 0) - { - beta = 0; - mu = input_j; - } - else - { - mu = std::sqrt(sigma + input_j*input_j); - - NumericT hh_vec_0 = (input_j <= 0) ? (input_j - mu) : (-sigma / (input_j + mu)); - - beta = NumericT(2) * hh_vec_0 * hh_vec_0 / (sigma + hh_vec_0 * hh_vec_0); - - //divide hh_vec by its diagonal element hh_vec_0 - hh_vec /= hh_vec_0; - hh_vec[j] = NumericT(1); - } - } - - // Apply (I - beta h h^T) to x (Householder reflection with Householder vector h) - template<typename VectorT, typename NumericT> - void gmres_householder_reflect(VectorT & x, VectorT const & h, NumericT beta) - { - NumericT hT_in_x = viennacl::linalg::inner_prod(h, x); - x -= (beta * hT_in_x) * h; - } - - - /** @brief Implementation of a pipelined GMRES solver without preconditioner - * - * Following algorithm 2.1 proposed by Walker in "A Simpler GMRES", but uses classical Gram-Schmidt instead of modified Gram-Schmidt for better parallelization. - * Uses some pipelining techniques for minimizing host-device transfer - * - * @param A The system matrix - * @param rhs The load vector - * @param tag Solver configuration tag - * @param monitor A callback routine which is called at each GMRES restart - * @param monitor_data Data pointer to be passed to the callback routine to pass on user-specific data - * @return The result vector - */ - template <typename MatrixType, typename ScalarType> - viennacl::vector<ScalarType> pipelined_solve(MatrixType const & A, - viennacl::vector<ScalarType> const & rhs, - gmres_tag const & tag, - viennacl::linalg::no_precond, - bool (*monitor)(viennacl::vector<ScalarType> const &, ScalarType, void*) = NULL, - void *monitor_data = NULL) - { - viennacl::vector<ScalarType> residual(rhs); - viennacl::vector<ScalarType> result = viennacl::zero_vector<ScalarType>(rhs.size(), viennacl::traits::context(rhs)); - - viennacl::vector<ScalarType> device_krylov_basis(rhs.internal_size() * tag.krylov_dim(), viennacl::traits::context(rhs)); // not using viennacl::matrix here because of spurious padding in column number - viennacl::vector<ScalarType> device_buffer_R(tag.krylov_dim()*tag.krylov_dim(), viennacl::traits::context(rhs)); - std::vector<ScalarType> host_buffer_R(device_buffer_R.size()); - - vcl_size_t buffer_size_per_vector = 128; - vcl_size_t num_buffer_chunks = 3; - viennacl::vector<ScalarType> device_inner_prod_buffer = viennacl::zero_vector<ScalarType>(num_buffer_chunks*buffer_size_per_vector, viennacl::traits::context(rhs)); // temporary buffer - viennacl::vector<ScalarType> device_r_dot_vk_buffer = viennacl::zero_vector<ScalarType>(buffer_size_per_vector * tag.krylov_dim(), viennacl::traits::context(rhs)); // holds result of first reduction stage for <r, v_k> on device - viennacl::vector<ScalarType> device_vi_in_vk_buffer = viennacl::zero_vector<ScalarType>(buffer_size_per_vector * tag.krylov_dim(), viennacl::traits::context(rhs)); // holds <v_i, v_k> for i=0..k-1 on device - viennacl::vector<ScalarType> device_values_xi_k = viennacl::zero_vector<ScalarType>(tag.krylov_dim(), viennacl::traits::context(rhs)); // holds values \xi_k = <r, v_k> on device - std::vector<ScalarType> host_r_dot_vk_buffer(device_r_dot_vk_buffer.size()); - std::vector<ScalarType> host_values_xi_k(tag.krylov_dim()); - std::vector<ScalarType> host_values_eta_k_buffer(tag.krylov_dim()); - std::vector<ScalarType> host_update_coefficients(tag.krylov_dim()); - - ScalarType norm_rhs = viennacl::linalg::norm_2(residual); - ScalarType rho_0 = norm_rhs; - ScalarType rho = ScalarType(1); - - tag.iters(0); - - for (unsigned int restart_count = 0; restart_count <= tag.max_restarts(); ++restart_count) - { - // - // prepare restart: - // - if (restart_count > 0) - { - // compute new residual without introducing a temporary for A*x: - residual = viennacl::linalg::prod(A, result); - residual = rhs - residual; - - rho_0 = viennacl::linalg::norm_2(residual); - } - - if (rho_0 <= ScalarType(tag.abs_tolerance())) // trivial right hand side? - break; - - residual /= rho_0; - rho = ScalarType(1); - - // check for convergence: - if (rho_0 / norm_rhs < tag.tolerance() || rho_0 < tag.abs_tolerance()) - break; - - // - // minimize in Krylov basis: - // - vcl_size_t k = 0; - for (k = 0; k < static_cast<vcl_size_t>(tag.krylov_dim()); ++k) - { - if (k == 0) - { - // compute v0 = A*r and perform first reduction stage for ||v0|| - viennacl::vector_range<viennacl::vector<ScalarType> > v0(device_krylov_basis, viennacl::range(0, rhs.size())); - viennacl::linalg::pipelined_gmres_prod(A, residual, v0, device_inner_prod_buffer); - - // Normalize v_1 and compute first reduction stage for <r, v_0> in device_r_dot_vk_buffer: - viennacl::linalg::pipelined_gmres_normalize_vk(v0, residual, - device_buffer_R, k*tag.krylov_dim() + k, - device_inner_prod_buffer, device_r_dot_vk_buffer, - buffer_size_per_vector, k*buffer_size_per_vector); - } - else - { - // compute v0 = A*r and perform first reduction stage for ||v0|| - viennacl::vector_range<viennacl::vector<ScalarType> > vk (device_krylov_basis, viennacl::range( k *rhs.internal_size(), k *rhs.internal_size() + rhs.size())); - viennacl::vector_range<viennacl::vector<ScalarType> > vk_minus_1(device_krylov_basis, viennacl::range((k-1)*rhs.internal_size(), (k-1)*rhs.internal_size() + rhs.size())); - viennacl::linalg::pipelined_gmres_prod(A, vk_minus_1, vk, device_inner_prod_buffer); - - // - // Gram-Schmidt, stage 1: compute first reduction stage of <v_i, v_k> - // - viennacl::linalg::pipelined_gmres_gram_schmidt_stage1(device_krylov_basis, rhs.size(), rhs.internal_size(), k, device_vi_in_vk_buffer, buffer_size_per_vector); - - // - // Gram-Schmidt, stage 2: compute second reduction stage of <v_i, v_k> and use that to compute v_k -= sum_i <v_i, v_k> v_i. - // Store <v_i, v_k> in R-matrix and compute first reduction stage for ||v_k|| - // - viennacl::linalg::pipelined_gmres_gram_schmidt_stage2(device_krylov_basis, rhs.size(), rhs.internal_size(), k, - device_vi_in_vk_buffer, - device_buffer_R, tag.krylov_dim(), - device_inner_prod_buffer, buffer_size_per_vector); - - // - // Normalize v_k and compute first reduction stage for <r, v_k> in device_r_dot_vk_buffer: - // - viennacl::linalg::pipelined_gmres_normalize_vk(vk, residual, - device_buffer_R, k*tag.krylov_dim() + k, - device_inner_prod_buffer, device_r_dot_vk_buffer, - buffer_size_per_vector, k*buffer_size_per_vector); - } - } - - // - // Run reduction to obtain the values \xi_k = <r, v_k>. - // Note that unlike Algorithm 2.1 in Walker: "A Simpler GMRES", we do not update the residual - // - viennacl::fast_copy(device_r_dot_vk_buffer.begin(), device_r_dot_vk_buffer.end(), host_r_dot_vk_buffer.begin()); - for (std::size_t i=0; i<k; ++i) - { - host_values_xi_k[i] = ScalarType(0); - for (std::size_t j=0; j<buffer_size_per_vector; ++j) - host_values_xi_k[i] += host_r_dot_vk_buffer[i*buffer_size_per_vector + j]; - } - - // - // Bring values in R back to host: - // - viennacl::fast_copy(device_buffer_R.begin(), device_buffer_R.end(), host_buffer_R.begin()); - - // - // Check for premature convergence: If the diagonal element drops too far below the first norm, we're done and restrict the Krylov size accordingly. - // - vcl_size_t full_krylov_dim = k; //needed for proper access to R - for (std::size_t i=0; i<k; ++i) - { - if (std::fabs(host_buffer_R[i + i*k]) < tag.tolerance() * host_buffer_R[0]) - { - k = i; - break; - } - } - - - // Compute error estimator: - for (std::size_t i=0; i<k; ++i) - { - tag.iters( tag.iters() + 1 ); //increase iteration counter - - // check for accumulation of round-off errors for poorly conditioned systems - if (host_values_xi_k[i] >= rho || host_values_xi_k[i] <= -rho) - { - k = i; - break; // restrict Krylov space at this point. No gain from using additional basis vectors, since orthogonality is lost. - } - - // update error estimator - rho *= std::sin( std::acos(host_values_xi_k[i] / rho) ); - } - - // - // Solve minimization problem: - // - host_values_eta_k_buffer = host_values_xi_k; - - for (int i2=static_cast<int>(k)-1; i2>-1; --i2) - { - vcl_size_t i = static_cast<vcl_size_t>(i2); - for (vcl_size_t j=static_cast<vcl_size_t>(i)+1; j<k; ++j) - host_values_eta_k_buffer[i] -= host_buffer_R[i + j*full_krylov_dim] * host_values_eta_k_buffer[j]; - - host_values_eta_k_buffer[i] /= host_buffer_R[i + i*full_krylov_dim]; - } - - // - // Update x += rho * z with z = \eta_0 * residual + sum_{i=0}^{k-1} \eta_{i+1} v_i - // Note that we have not updated the residual yet, hence this slightly modified as compared to the form given in Algorithm 2.1 in Walker: "A Simpler GMRES" - // - for (vcl_size_t i=0; i<k; ++i) - host_update_coefficients[i] = rho_0 * host_values_eta_k_buffer[i]; - - viennacl::fast_copy(host_update_coefficients.begin(), host_update_coefficients.end(), device_values_xi_k.begin()); //reuse device_values_xi_k_buffer here for simplicity - - viennacl::linalg::pipelined_gmres_update_result(result, residual, - device_krylov_basis, rhs.size(), rhs.internal_size(), - device_values_xi_k, k); - - tag.error( std::fabs(rho*rho_0 / norm_rhs) ); - - if (monitor && monitor(result, std::fabs(rho*rho_0 / norm_rhs), monitor_data)) - break; - } - - return result; - } - - /** @brief Overload for the pipelined CG implementation for the ViennaCL sparse matrix types */ - template<typename NumericT> - viennacl::vector<NumericT> solve_impl(viennacl::compressed_matrix<NumericT> const & A, - viennacl::vector<NumericT> const & rhs, - gmres_tag const & tag, - viennacl::linalg::no_precond, - bool (*monitor)(viennacl::vector<NumericT> const &, NumericT, void*) = NULL, - void *monitor_data = NULL) - { - return pipelined_solve(A, rhs, tag, viennacl::linalg::no_precond(), monitor, monitor_data); - } - - - /** @brief Overload for the pipelined CG implementation for the ViennaCL sparse matrix types */ - template<typename NumericT> - viennacl::vector<NumericT> solve_impl(viennacl::coordinate_matrix<NumericT> const & A, - viennacl::vector<NumericT> const & rhs, - gmres_tag const & tag, - viennacl::linalg::no_precond, - bool (*monitor)(viennacl::vector<NumericT> const &, NumericT, void*) = NULL, - void *monitor_data = NULL) - { - return detail::pipelined_solve(A, rhs, tag, viennacl::linalg::no_precond(), monitor, monitor_data); - } - - - - /** @brief Overload for the pipelined CG implementation for the ViennaCL sparse matrix types */ - template<typename NumericT> - viennacl::vector<NumericT> solve_impl(viennacl::ell_matrix<NumericT> const & A, - viennacl::vector<NumericT> const & rhs, - gmres_tag const & tag, - viennacl::linalg::no_precond, - bool (*monitor)(viennacl::vector<NumericT> const &, NumericT, void*) = NULL, - void *monitor_data = NULL) - { - return detail::pipelined_solve(A, rhs, tag, viennacl::linalg::no_precond(), monitor, monitor_data); - } - - - - /** @brief Overload for the pipelined CG implementation for the ViennaCL sparse matrix types */ - template<typename NumericT> - viennacl::vector<NumericT> solve_impl(viennacl::sliced_ell_matrix<NumericT> const & A, - viennacl::vector<NumericT> const & rhs, - gmres_tag const & tag, - viennacl::linalg::no_precond, - bool (*monitor)(viennacl::vector<NumericT> const &, NumericT, void*) = NULL, - void *monitor_data = NULL) - { - return detail::pipelined_solve(A, rhs, tag, viennacl::linalg::no_precond(), monitor, monitor_data); - } - - - /** @brief Overload for the pipelined CG implementation for the ViennaCL sparse matrix types */ - template<typename NumericT> - viennacl::vector<NumericT> solve_impl(viennacl::hyb_matrix<NumericT> const & A, - viennacl::vector<NumericT> const & rhs, - gmres_tag const & tag, - viennacl::linalg::no_precond, - bool (*monitor)(viennacl::vector<NumericT> const &, NumericT, void*) = NULL, - void *monitor_data = NULL) - { - return detail::pipelined_solve(A, rhs, tag, viennacl::linalg::no_precond(), monitor, monitor_data); - } - - - /** @brief Implementation of the GMRES solver. - * - * Following the algorithm proposed by Walker in "A Simpler GMRES" - * - * @param matrix The system matrix - * @param rhs The load vector - * @param tag Solver configuration tag - * @param precond A preconditioner. Precondition operation is done via member function apply() - * @param monitor A callback routine which is called at each GMRES restart - * @param monitor_data Data pointer to be passed to the callback routine to pass on user-specific data - * - * @return The result vector - */ - template<typename MatrixT, typename VectorT, typename PreconditionerT> - VectorT solve_impl(MatrixT const & matrix, - VectorT const & rhs, - gmres_tag const & tag, - PreconditionerT const & precond, - bool (*monitor)(VectorT const &, typename viennacl::result_of::cpu_value_type<typename viennacl::result_of::value_type<VectorT>::type>::type, void*) = NULL, - void *monitor_data = NULL) - { - typedef typename viennacl::result_of::value_type<VectorT>::type NumericType; - typedef typename viennacl::result_of::cpu_value_type<NumericType>::type CPU_NumericType; - - unsigned int problem_size = static_cast<unsigned int>(viennacl::traits::size(rhs)); - VectorT result = rhs; - viennacl::traits::clear(result); - - vcl_size_t krylov_dim = static_cast<vcl_size_t>(tag.krylov_dim()); - if (problem_size < krylov_dim) - krylov_dim = problem_size; //A Krylov space larger than the matrix would lead to seg-faults (mathematically, error is certain to be zero already) - - VectorT res = rhs; - VectorT v_k_tilde = rhs; - VectorT v_k_tilde_temp = rhs; - - std::vector< std::vector<CPU_NumericType> > R(krylov_dim, std::vector<CPU_NumericType>(tag.krylov_dim())); - std::vector<CPU_NumericType> projection_rhs(krylov_dim); - - std::vector<VectorT> householder_reflectors(krylov_dim, rhs); - std::vector<CPU_NumericType> betas(krylov_dim); - - CPU_NumericType norm_rhs = viennacl::linalg::norm_2(rhs); - - if (norm_rhs <= tag.abs_tolerance()) //solution is zero if RHS norm is zero - return result; - - tag.iters(0); - - for (unsigned int it = 0; it <= tag.max_restarts(); ++it) - { - // - // (Re-)Initialize residual: r = b - A*x (without temporary for the result of A*x) - // - res = viennacl::linalg::prod(matrix, result); //initial guess zero - res = rhs - res; - precond.apply(res); - - CPU_NumericType rho_0 = viennacl::linalg::norm_2(res); - - // - // Check for premature convergence - // - if (rho_0 / norm_rhs < tag.tolerance() || rho_0 < tag.abs_tolerance()) // norm_rhs is known to be nonzero here - { - tag.error(rho_0 / norm_rhs); - return result; - } - - // - // Normalize residual and set 'rho' to 1 as requested in 'A Simpler GMRES' by Walker and Zhou. - // - res /= rho_0; - CPU_NumericType rho = static_cast<CPU_NumericType>(1.0); - - - // - // Iterate up until maximal Krylove space dimension is reached: - // - vcl_size_t k = 0; - for (k = 0; k < krylov_dim; ++k) - { - tag.iters( tag.iters() + 1 ); //increase iteration counter - - // prepare storage: - viennacl::traits::clear(R[k]); - viennacl::traits::clear(householder_reflectors[k]); - - //compute v_k = A * v_{k-1} via Householder matrices - if (k == 0) - { - v_k_tilde = viennacl::linalg::prod(matrix, res); - precond.apply(v_k_tilde); - } - else - { - viennacl::traits::clear(v_k_tilde); - v_k_tilde[k-1] = CPU_NumericType(1); - - //Householder rotations, part 1: Compute P_1 * P_2 * ... * P_{k-1} * e_{k-1} - for (int i = static_cast<int>(k)-1; i > -1; --i) - detail::gmres_householder_reflect(v_k_tilde, householder_reflectors[vcl_size_t(i)], betas[vcl_size_t(i)]); - - v_k_tilde_temp = viennacl::linalg::prod(matrix, v_k_tilde); - precond.apply(v_k_tilde_temp); - v_k_tilde = v_k_tilde_temp; - - //Householder rotations, part 2: Compute P_{k-1} * ... * P_{1} * v_k_tilde - for (vcl_size_t i = 0; i < k; ++i) - detail::gmres_householder_reflect(v_k_tilde, householder_reflectors[i], betas[i]); - } - - // - // Compute Householder reflection for v_k_tilde such that all entries below k-th entry are zero: - // - CPU_NumericType rho_k_k = 0; - detail::gmres_setup_householder_vector(v_k_tilde, householder_reflectors[k], betas[k], rho_k_k, k); - - // - // copy first k entries from v_k_tilde to R[k] in order to fill k-th column with result of - // P_k * v_k_tilde = (v[0], ... , v[k-1], norm(v), 0, 0, ...) =: (rho_{1,k}, rho_{2,k}, ..., rho_{k,k}, 0, ..., 0); - // - detail::gmres_copy_helper(v_k_tilde, R[k], k); - R[k][k] = rho_k_k; - - // - // Update residual: r = P_k r - // Set zeta_k = r[k] including machine precision considerations: mathematically we have |r[k]| <= rho - // Set rho *= sin(acos(r[k] / rho)) - // - detail::gmres_householder_reflect(res, householder_reflectors[k], betas[k]); - - if (res[k] > rho) //machine precision reached - res[k] = rho; - if (res[k] < -rho) //machine precision reached - res[k] = -rho; - projection_rhs[k] = res[k]; - - rho *= std::sin( std::acos(projection_rhs[k] / rho) ); - - if (std::fabs(rho * rho_0 / norm_rhs) < tag.tolerance()) // Residual is sufficiently reduced, stop here - { - tag.error( std::fabs(rho*rho_0 / norm_rhs) ); - ++k; - break; - } - } // for k - - // - // Triangular solver stage: - // - - for (int i2=static_cast<int>(k)-1; i2>-1; --i2) - { - vcl_size_t i = static_cast<vcl_size_t>(i2); - for (vcl_size_t j=i+1; j<k; ++j) - projection_rhs[i] -= R[j][i] * projection_rhs[j]; //R is transposed - - projection_rhs[i] /= R[i][i]; - } - - // - // Note: 'projection_rhs' now holds the solution (eta_1, ..., eta_k) - // - - res *= projection_rhs[0]; - - if (k > 0) - { - for (unsigned int i = 0; i < k-1; ++i) - res[i] += projection_rhs[i+1]; - } - - // - // Form z inplace in 'res' by applying P_1 * ... * P_{k} - // - for (int i=static_cast<int>(k)-1; i>=0; --i) - detail::gmres_householder_reflect(res, householder_reflectors[vcl_size_t(i)], betas[vcl_size_t(i)]); - - res *= rho_0; - result += res; // x += rho_0 * z in the paper - - // - // Check for convergence: - // - tag.error(std::fabs(rho*rho_0 / norm_rhs)); - - if (monitor && monitor(result, std::fabs(rho*rho_0 / norm_rhs), monitor_data)) - break; - - if ( tag.error() < tag.tolerance() ) - return result; - } - - return result; - } - -} - -template<typename MatrixT, typename VectorT, typename PreconditionerT> -VectorT solve(MatrixT const & matrix, VectorT const & rhs, gmres_tag const & tag, PreconditionerT const & precond) -{ - return detail::solve_impl(matrix, rhs, tag, precond); -} - -/** @brief Convenience overload for calling the preconditioned BiCGStab solver using types from the C++ STL. - * - * A std::vector<std::map<T, U> > matrix is convenient for e.g. finite element assembly. - * It is not the fastest option for setting up a system, but often it is fast enough - particularly for just trying things out. - */ -template<typename IndexT, typename NumericT, typename PreconditionerT> -std::vector<NumericT> solve(std::vector< std::map<IndexT, NumericT> > const & A, std::vector<NumericT> const & rhs, gmres_tag const & tag, PreconditionerT const & precond) -{ - viennacl::compressed_matrix<NumericT> vcl_A; - viennacl::copy(A, vcl_A); - - viennacl::vector<NumericT> vcl_rhs(rhs.size()); - viennacl::copy(rhs, vcl_rhs); - - viennacl::vector<NumericT> vcl_result = solve(vcl_A, vcl_rhs, tag, precond); - - std::vector<NumericT> result(vcl_result.size()); - viennacl::copy(vcl_result, result); - return result; -} - -/** @brief Entry point for the unpreconditioned GMRES method. - * - * @param A The system matrix - * @param rhs Right hand side vector (load vector) - * @param tag A BiCGStab tag providing relative tolerances, etc. - */ - -template<typename MatrixT, typename VectorT> -VectorT solve(MatrixT const & A, VectorT const & rhs, gmres_tag const & tag) -{ - return solve(A, rhs, tag, no_precond()); -} - - - -template<typename VectorT> -class gmres_solver -{ -public: - typedef typename viennacl::result_of::cpu_value_type<VectorT>::type numeric_type; - - gmres_solver(gmres_tag const & tag) : tag_(tag), monitor_callback_(NULL), user_data_(NULL) {} - - template<typename MatrixT, typename PreconditionerT> - VectorT operator()(MatrixT const & A, VectorT const & b, PreconditionerT const & precond) const - { - if (viennacl::traits::size(init_guess_) > 0) // take initial guess into account - { - VectorT mod_rhs = viennacl::linalg::prod(A, init_guess_); - mod_rhs = b - mod_rhs; - VectorT y = detail::solve_impl(A, mod_rhs, tag_, precond, monitor_callback_, user_data_); - return init_guess_ + y; - } - return detail::solve_impl(A, b, tag_, precond, monitor_callback_, user_data_); - } - - - template<typename MatrixT> - VectorT operator()(MatrixT const & A, VectorT const & b) const - { - return operator()(A, b, viennacl::linalg::no_precond()); - } - - /** @brief Specifies an initial guess for the iterative solver. - * - * An iterative solver for Ax = b with initial guess x_0 is equivalent to an iterative solver for Ay = b' := b - Ax_0, where x = x_0 + y. - */ - void set_initial_guess(VectorT const & x) { init_guess_ = x; } - - /** @brief Sets a monitor function pointer to be called in each iteration. Set to NULL to run without monitor. - * - * The monitor function is called with the current guess for the result as first argument and the current relative residual estimate as second argument. - * The third argument is a pointer to user-defined data, through which additional information can be passed. - * This pointer needs to be set with set_monitor_data. If not set, NULL is passed. - * If the montior function returns true, the solver terminates (either convergence or divergence). - */ - void set_monitor(bool (*monitor_fun)(VectorT const &, numeric_type, void *), void *user_data) - { - monitor_callback_ = monitor_fun; - user_data_ = user_data; - } - - /** @brief Returns the solver tag containing basic configuration such as tolerances, etc. */ - gmres_tag const & tag() const { return tag_; } - -private: - gmres_tag tag_; - VectorT init_guess_; - bool (*monitor_callback_)(VectorT const &, numeric_type, void *); - void *user_data_; -}; - - -} -} - -#endif
http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/hankel_matrix_operations.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/hankel_matrix_operations.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/hankel_matrix_operations.hpp deleted file mode 100644 index 43ca928..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/hankel_matrix_operations.hpp +++ /dev/null @@ -1,66 +0,0 @@ -#ifndef VIENNACL_LINALG_HANKEL_MATRIX_OPERATIONS_HPP_ -#define VIENNACL_LINALG_HANKEL_MATRIX_OPERATIONS_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/hankel_matrix_operations.hpp - @brief Implementations of operations using hankel_matrix. Experimental. -*/ - -#include "viennacl/forwards.h" -#include "viennacl/ocl/backend.hpp" -#include "viennacl/scalar.hpp" -#include "viennacl/vector.hpp" -#include "viennacl/tools/tools.hpp" -#include "viennacl/fft.hpp" -#include "viennacl/linalg/toeplitz_matrix_operations.hpp" - -namespace viennacl -{ -namespace linalg -{ - -// A * x - -/** @brief Carries out matrix-vector multiplication with a hankel_matrix -* -* Implementation of the convenience expression result = prod(mat, vec); -* -* @param A The matrix -* @param vec The vector -* @param result The result vector -*/ -template<typename NumericT, unsigned int AlignmentV> -void prod_impl(viennacl::hankel_matrix<NumericT, AlignmentV> const & A, - viennacl::vector_base<NumericT> const & vec, - viennacl::vector_base<NumericT> & result) -{ - assert(A.size1() == result.size() && bool("Dimension mismatch")); - assert(A.size2() == vec.size() && bool("Dimension mismatch")); - - prod_impl(A.elements(), vec, result); - viennacl::linalg::reverse(result); -} - -} //namespace linalg - - -} //namespace viennacl - - -#endif http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/host_based/amg_operations.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/host_based/amg_operations.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/host_based/amg_operations.hpp deleted file mode 100644 index 78bd150..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/host_based/amg_operations.hpp +++ /dev/null @@ -1,1123 +0,0 @@ -#ifndef VIENNACL_LINALG_HOST_BASED_AMG_OPERATIONS_HPP -#define VIENNACL_LINALG_HOST_BASED_AMG_OPERATIONS_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 PDF manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file host_based/amg_operations.hpp - @brief Implementations of routines for AMG using the CPU on the host (with OpenMP if enabled). -*/ - -#include <cstdlib> -#include <cmath> -#include "viennacl/linalg/detail/amg/amg_base.hpp" - -#include <map> -#include <set> -#include <functional> -#ifdef VIENNACL_WITH_OPENMP -#include <omp.h> -#endif - -namespace viennacl -{ -namespace linalg -{ -namespace host_based -{ -namespace amg -{ - - -/////////////////////////////////////////// - -/** @brief Routine for taking all connections in the matrix as strong */ -template<typename NumericT> -void amg_influence_trivial(compressed_matrix<NumericT> const & A, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - (void)tag; - - unsigned int const * A_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle1()); - unsigned int const * A_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle2()); - - unsigned int *influences_row_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_jumper_.handle()); - unsigned int *influences_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_ids_.handle()); - unsigned int *influences_values_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_values_.handle()); - -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - vcl_size_t i = vcl_size_t(i2); - influences_row_ptr[i] = A_row_buffer[i]; - influences_values_ptr[i] = A_row_buffer[i+1] - A_row_buffer[i]; - } - influences_row_ptr[A.size1()] = A_row_buffer[A.size1()]; - -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i=0; i<long(A.nnz()); ++i) - influences_id_ptr[i] = A_col_buffer[i]; -} - - -/** @brief Routine for extracting strongly connected points considering a user-provided threshold value */ -template<typename NumericT> -void amg_influence_advanced(compressed_matrix<NumericT> const & A, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - NumericT const * A_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(A.handle()); - unsigned int const * A_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle1()); - unsigned int const * A_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle2()); - - unsigned int *influences_row_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_jumper_.handle()); - unsigned int *influences_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_ids_.handle()); - - // - // Step 1: Scan influences in order to allocate the necessary memory - // -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - vcl_size_t i = vcl_size_t(i2); - unsigned int row_start = A_row_buffer[i]; - unsigned int row_stop = A_row_buffer[i+1]; - NumericT diag = 0; - NumericT largest_positive = 0; - NumericT largest_negative = 0; - unsigned int num_influences = 0; - - // obtain diagonal element as well as maximum positive and negative off-diagonal entries - for (unsigned int nnz_index = row_start; nnz_index < row_stop; ++nnz_index) - { - unsigned int col = A_col_buffer[nnz_index]; - NumericT value = A_elements[nnz_index]; - - if (col == i) - diag = value; - else if (value > largest_positive) - largest_positive = value; - else if (value < largest_negative) - largest_negative = value; - } - - if (largest_positive <= 0 && largest_negative >= 0) // no offdiagonal entries - { - influences_row_ptr[i] = 0; - continue; - } - - // Find all points that strongly influence current point (Yang, p.5) - //std::cout << "Looking for strongly influencing points for point " << i << std::endl; - for (unsigned int nnz_index = row_start; nnz_index < row_stop; ++nnz_index) - { - unsigned int col = A_col_buffer[nnz_index]; - - if (i == col) - continue; - - NumericT value = A_elements[nnz_index]; - - if ( (diag > 0 && diag * value <= tag.get_strong_connection_threshold() * diag * largest_negative) - || (diag < 0 && diag * value <= tag.get_strong_connection_threshold() * diag * largest_positive)) - { - ++num_influences; - } - } - - influences_row_ptr[i] = num_influences; - } - - // - // Step 2: Exclusive scan on number of influences to obtain CSR-like datastructure - // - unsigned int current_entry = 0; - for (std::size_t i=0; i<A.size1(); ++i) - { - unsigned int tmp = influences_row_ptr[i]; - influences_row_ptr[i] = current_entry; - current_entry += tmp; - } - influences_row_ptr[A.size1()] = current_entry; - - - // - // Step 3: Write actual influences - // -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - unsigned int i = static_cast<unsigned int>(i2); - unsigned int row_start = A_row_buffer[i]; - unsigned int row_stop = A_row_buffer[i+1]; - NumericT diag = 0; - NumericT largest_positive = 0; - NumericT largest_negative = 0; - - // obtain diagonal element as well as maximum positive and negative off-diagonal entries - for (unsigned int nnz_index = row_start; nnz_index < row_stop; ++nnz_index) - { - unsigned int col = A_col_buffer[nnz_index]; - NumericT value = A_elements[nnz_index]; - - if (col == i) - diag = value; - else if (value > largest_positive) - largest_positive = value; - else if (value < largest_negative) - largest_negative = value; - } - - if (largest_positive <= 0 && largest_negative >= 0) // no offdiagonal entries - continue; - - // Find all points that strongly influence current point (Yang, p.5) - //std::cout << "Looking for strongly influencing points for point " << i << std::endl; - unsigned int *influences_id_write_ptr = influences_id_ptr + influences_row_ptr[i]; - for (unsigned int nnz_index = row_start; nnz_index < row_stop; ++nnz_index) - { - unsigned int col = A_col_buffer[nnz_index]; - - if (i == col) - continue; - - NumericT value = A_elements[nnz_index]; - - if ( (diag > 0 && diag * value <= tag.get_strong_connection_threshold() * diag * largest_negative) - || (diag < 0 && diag * value <= tag.get_strong_connection_threshold() * diag * largest_positive)) - { - //std::cout << " - Adding influence from point " << col << std::endl; - *influences_id_write_ptr = col; - ++influences_id_write_ptr; - } - } - } - -} - - -/** @brief Dispatcher for influence processing */ -template<typename NumericT> -void amg_influence(compressed_matrix<NumericT> const & A, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - // TODO: dispatch based on influence tolerance provided - amg_influence_trivial(A, amg_context, tag); -} - - - -/** @brief Assign IDs to coarse points */ -inline void enumerate_coarse_points(viennacl::linalg::detail::amg::amg_level_context & amg_context) -{ - unsigned int *point_types_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.point_types_.handle()); - unsigned int *coarse_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.coarse_id_.handle()); - - unsigned int coarse_id = 0; - for (vcl_size_t i=0; i<amg_context.coarse_id_.size(); ++i) - { - //assert(point_types_ptr[i] != viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED && bool("Logic error in enumerate_coarse_points(): Undecided points detected!")); - - if (point_types_ptr[i] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE) - coarse_id_ptr[i] = coarse_id++; - } - - //std::cout << "Coarse nodes after enumerate_coarse_points(): " << coarse_id << std::endl; - amg_context.num_coarse_ = coarse_id; -} - - - - -////////////////////////////////////// - - -/** @brief Helper struct for sequential classical one-pass coarsening */ -struct amg_id_influence -{ - amg_id_influence(std::size_t id2, std::size_t influences2) : id(static_cast<unsigned int>(id2)), influences(static_cast<unsigned int>(influences2)) {} - - unsigned int id; - unsigned int influences; -}; - -inline bool operator>(amg_id_influence const & a, amg_id_influence const & b) -{ - if (a.influences > b.influences) - return true; - if (a.influences == b.influences) - return a.id > b.id; - return false; -} - -/** @brief Classical (RS) one-pass coarsening. Single-Threaded! (VIENNACL_AMG_COARSE_CLASSIC_ONEPASS) -* -* @param A Operator matrix for the respective level -* @param amg_context AMG datastructure object for the grid hierarchy -* @param tag AMG preconditioner tag -*/ -template<typename NumericT> -void amg_coarse_classic_onepass(compressed_matrix<NumericT> const & A, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - unsigned int *point_types_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.point_types_.handle()); - unsigned int *influences_row_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_jumper_.handle()); - unsigned int *influences_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_ids_.handle()); - unsigned int *influences_values_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_values_.handle()); - - std::set<amg_id_influence, std::greater<amg_id_influence> > points_by_influences; - - amg_influence_advanced(A, amg_context, tag); - - for (std::size_t i=0; i<A.size1(); ++i) - points_by_influences.insert(amg_id_influence(i, influences_values_ptr[i])); - - //std::cout << "Starting coarsening process..." << std::endl; - - while (!points_by_influences.empty()) - { - amg_id_influence point = *(points_by_influences.begin()); - - // remove point from queue: - points_by_influences.erase(points_by_influences.begin()); - - //std::cout << "Working on point " << point.id << std::endl; - - // point is already coarse or fine point, continue; - if (point_types_ptr[point.id] != viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED) - continue; - - //std::cout << " Setting point " << point.id << " to a coarse point." << std::endl; - // make this a coarse point: - point_types_ptr[point.id] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE; - - // Set strongly influenced points to fine points: - unsigned int j_stop = influences_row_ptr[point.id + 1]; - for (unsigned int j = influences_row_ptr[point.id]; j < j_stop; ++j) - { - unsigned int influenced_point_id = influences_id_ptr[j]; - - //std::cout << "Checking point " << influenced_point_id << std::endl; - if (point_types_ptr[influenced_point_id] != viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED) - continue; - - //std::cout << " Setting point " << influenced_point_id << " to a fine point." << std::endl; - point_types_ptr[influenced_point_id] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_FINE; - - // add one to influence measure for all undecided points strongly influencing this fine point. - unsigned int k_stop = influences_row_ptr[influenced_point_id + 1]; - for (unsigned int k = influences_row_ptr[influenced_point_id]; k < k_stop; ++k) - { - unsigned int influenced_influenced_point_id = influences_id_ptr[k]; - if (point_types_ptr[influenced_influenced_point_id] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED) - { - // grab and remove from set, increase influence counter, store back: - amg_id_influence point_to_find(influenced_influenced_point_id, influences_values_ptr[influenced_influenced_point_id]); - points_by_influences.erase(point_to_find); - - point_to_find.influences += 1; - influences_values_ptr[influenced_influenced_point_id] += 1; // for consistency - - points_by_influences.insert(point_to_find); - } - } //for - } // for - - } // while - - viennacl::linalg::host_based::amg::enumerate_coarse_points(amg_context); -} - - -////////////////////////// - - -/** @brief AG (aggregation based) coarsening, single-threaded version of stage 1 -* -* @param A Operator matrix for the respective level -* @param amg_context AMG datastructure object for the grid hierarchy -* @param tag AMG preconditioner tag -*/ -template<typename NumericT> -void amg_coarse_ag_stage1_sequential(compressed_matrix<NumericT> const & A, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - (void)tag; - unsigned int *point_types_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.point_types_.handle()); - unsigned int *influences_row_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_jumper_.handle()); - unsigned int *influences_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_ids_.handle()); - - for (unsigned int i=0; i<static_cast<unsigned int>(A.size1()); ++i) - { - // check if node has no aggregates next to it (MIS-2) - bool is_new_coarse_node = true; - - // Set strongly influenced points to fine points: - unsigned int j_stop = influences_row_ptr[i + 1]; - for (unsigned int j = influences_row_ptr[i]; j < j_stop; ++j) - { - unsigned int influenced_point_id = influences_id_ptr[j]; - if (point_types_ptr[influenced_point_id] != viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED) // either coarse or fine point - { - is_new_coarse_node = false; - break; - } - } - - if (is_new_coarse_node) - { - // make all strongly influenced neighbors fine points: - for (unsigned int j = influences_row_ptr[i]; j < j_stop; ++j) - { - unsigned int influenced_point_id = influences_id_ptr[j]; - point_types_ptr[influenced_point_id] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_FINE; - } - - //std::cout << "Setting new coarse node: " << i << std::endl; - // Note: influences may include diagonal element, so it's important to *first* set fine points above before setting the coarse information here - point_types_ptr[i] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE; - } - } -} - - - -/** @brief AG (aggregation based) coarsening, multi-threaded version of stage 1 using parallel maximum independent sets -* -* @param A Operator matrix for the respective level -* @param amg_context AMG datastructure object for the grid hierarchy -* @param tag AMG preconditioner tag -*/ -template<typename NumericT> -void amg_coarse_ag_stage1_mis2(compressed_matrix<NumericT> const & A, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - (void)tag; - unsigned int *point_types_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.point_types_.handle()); - unsigned int *influences_row_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_jumper_.handle()); - unsigned int *influences_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_ids_.handle()); - - std::vector<unsigned int> random_weights(A.size1()); - for (std::size_t i=0; i<random_weights.size(); ++i) - random_weights[i] = static_cast<unsigned int>(rand()) % static_cast<unsigned int>(A.size1()); - - std::size_t num_threads = 1; -#ifdef VIENNACL_WITH_OPENMP - num_threads = omp_get_max_threads(); -#endif - - viennacl::vector<unsigned int> work_state(A.size1(), viennacl::traits::context(A)); - viennacl::vector<unsigned int> work_random(A.size1(), viennacl::traits::context(A)); - viennacl::vector<unsigned int> work_index(A.size1(), viennacl::traits::context(A)); - - unsigned int *work_state_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(work_state.handle()); - unsigned int *work_random_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(work_random.handle()); - unsigned int *work_index_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(work_index.handle()); - - viennacl::vector<unsigned int> work_state2(A.size1(), viennacl::traits::context(A)); - viennacl::vector<unsigned int> work_random2(A.size1(), viennacl::traits::context(A)); - viennacl::vector<unsigned int> work_index2(A.size1(), viennacl::traits::context(A)); - - unsigned int *work_state2_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(work_state2.handle()); - unsigned int *work_random2_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(work_random2.handle()); - unsigned int *work_index2_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(work_index2.handle()); - - - unsigned int num_undecided = static_cast<unsigned int>(A.size1()); - unsigned int pmis_iters = 0; - while (num_undecided > 0) - { - ++pmis_iters; - - // - // init temporary work data: - // -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - unsigned int i = static_cast<unsigned int>(i2); - switch (point_types_ptr[i]) - { - case viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED: work_state_ptr[i] = 1; break; - case viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_FINE: work_state_ptr[i] = 0; break; - case viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE: work_state_ptr[i] = 2; break; - default: - throw std::runtime_error("Unexpected state encountered in MIS2 setup for AMG."); - } - - work_random_ptr[i] = random_weights[i]; - work_index_ptr[i] = i; - } - - - // - // Propagate maximum tuple twice - // - for (unsigned int r = 0; r < 2; ++r) - { - // max operation -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - unsigned int i = static_cast<unsigned int>(i2); - // load - unsigned int state = work_state_ptr[i]; - unsigned int random = work_random_ptr[i]; - unsigned int index = work_index_ptr[i]; - - // max - unsigned int j_stop = influences_row_ptr[i + 1]; - for (unsigned int j = influences_row_ptr[i]; j < j_stop; ++j) - { - unsigned int influenced_point_id = influences_id_ptr[j]; - - // lexigraphical triple-max (not particularly pretty, but does the job): - if (state < work_state_ptr[influenced_point_id]) - { - state = work_state_ptr[influenced_point_id]; - random = work_random_ptr[influenced_point_id]; - index = work_index_ptr[influenced_point_id]; - } - else if (state == work_state_ptr[influenced_point_id]) - { - if (random < work_random_ptr[influenced_point_id]) - { - state = work_state_ptr[influenced_point_id]; - random = work_random_ptr[influenced_point_id]; - index = work_index_ptr[influenced_point_id]; - } - else if (random == work_random_ptr[influenced_point_id]) - { - if (index < work_index_ptr[influenced_point_id]) - { - state = work_state_ptr[influenced_point_id]; - random = work_random_ptr[influenced_point_id]; - index = work_index_ptr[influenced_point_id]; - } - } // max(random) - } // max(state) - } // for - - // store - work_state2_ptr[i] = state; - work_random2_ptr[i] = random; - work_index2_ptr[i] = index; - } - - // copy work array -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - unsigned int i = static_cast<unsigned int>(i2); - work_state_ptr[i] = work_state2_ptr[i]; - work_random_ptr[i] = work_random2_ptr[i]; - work_index_ptr[i] = work_index2_ptr[i]; - } - } - - // - // mark MIS and non-MIS nodes: - // - std::vector<unsigned int> thread_buffer(num_threads); - -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - unsigned int i = static_cast<unsigned int>(i2); - unsigned int max_state = work_state_ptr[i]; - unsigned int max_index = work_index_ptr[i]; - - if (point_types_ptr[i] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED) - { - if (i == max_index) // make this a MIS node - point_types_ptr[i] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE; - else if (max_state == 2) // mind the mapping of viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE above! - point_types_ptr[i] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_FINE; - else -#ifdef VIENNACL_WITH_OPENMP - thread_buffer[omp_get_thread_num()] += 1; -#else - thread_buffer[0] += 1; -#endif - } - } - - num_undecided = 0; - for (std::size_t i=0; i<thread_buffer.size(); ++i) - num_undecided += thread_buffer[i]; - } // while - - // consistency with sequential MIS: reset state for non-coarse points, so that coarse indices are correctly picked up later -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i=0; i<static_cast<long>(A.size1()); ++i) - if (point_types_ptr[i] != viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE) - point_types_ptr[i] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED; - -} - - - -/** @brief AG (aggregation based) coarsening. Partially single-threaded version (VIENNACL_AMG_COARSE_AG) -* -* @param A Operator matrix for the respective level -* @param amg_context AMG datastructure object for the grid hierarchy -* @param tag AMG preconditioner tag -*/ -template<typename NumericT> -void amg_coarse_ag(compressed_matrix<NumericT> const & A, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - unsigned int *point_types_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.point_types_.handle()); - unsigned int *influences_row_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_jumper_.handle()); - unsigned int *influences_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_ids_.handle()); - unsigned int *coarse_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.coarse_id_.handle()); - - amg_influence_trivial(A, amg_context, tag); - - // - // Stage 1: Build aggregates: - // - if (tag.get_coarsening_method() == viennacl::linalg::AMG_COARSENING_METHOD_AGGREGATION) amg_coarse_ag_stage1_sequential(A, amg_context, tag); - if (tag.get_coarsening_method() == viennacl::linalg::AMG_COARSENING_METHOD_MIS2_AGGREGATION) amg_coarse_ag_stage1_mis2(A, amg_context, tag); - - viennacl::linalg::host_based::amg::enumerate_coarse_points(amg_context); - - // - // Stage 2: Propagate coarse aggregate indices to neighbors: - // -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - unsigned int i = static_cast<unsigned int>(i2); - if (point_types_ptr[i] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE) - { - unsigned int coarse_index = coarse_id_ptr[i]; - - unsigned int j_stop = influences_row_ptr[i + 1]; - for (unsigned int j = influences_row_ptr[i]; j < j_stop; ++j) - { - unsigned int influenced_point_id = influences_id_ptr[j]; - coarse_id_ptr[influenced_point_id] = coarse_index; // Set aggregate index for fine point - - if (influenced_point_id != i) // Note: Any write races between threads are harmless here - point_types_ptr[influenced_point_id] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_FINE; - } - } - } - - - // - // Stage 3: Merge remaining undecided points (merging to first aggregate found when cycling over the hierarchy - // -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i2=0; i2<static_cast<long>(A.size1()); ++i2) - { - unsigned int i = static_cast<unsigned int>(i2); - if (point_types_ptr[i] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED) - { - unsigned int j_stop = influences_row_ptr[i + 1]; - for (unsigned int j = influences_row_ptr[i]; j < j_stop; ++j) - { - unsigned int influenced_point_id = influences_id_ptr[j]; - if (point_types_ptr[influenced_point_id] != viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED) // either coarse or fine point - { - //std::cout << "Setting fine node " << i << " to be aggregated with node " << *influence_iter << "/" << pointvector.get_coarse_index(*influence_iter) << std::endl; - coarse_id_ptr[i] = coarse_id_ptr[influenced_point_id]; - break; - } - } - } - } - - // - // Stage 4: Set undecided points to fine points (coarse ID already set in Stage 3) - // Note: Stage 3 and Stage 4 were initially fused, but are now split in order to avoid race conditions (or a fallback to sequential execution). - // -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long i=0; i<static_cast<long>(A.size1()); ++i) - if (point_types_ptr[i] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_UNDECIDED) - point_types_ptr[i] = viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_FINE; - -} - - - - -/** @brief Entry point and dispatcher for coarsening procedures -* -* @param A Operator matrix for the respective level -* @param amg_context AMG datastructure object for the grid hierarchy -* @param tag AMG preconditioner tag -*/ -template<typename MatrixT> -void amg_coarse(MatrixT & A, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - switch (tag.get_coarsening_method()) - { - case viennacl::linalg::AMG_COARSENING_METHOD_ONEPASS: amg_coarse_classic_onepass(A, amg_context, tag); break; - case viennacl::linalg::AMG_COARSENING_METHOD_AGGREGATION: - case viennacl::linalg::AMG_COARSENING_METHOD_MIS2_AGGREGATION: amg_coarse_ag(A, amg_context, tag); break; - //default: throw std::runtime_error("not implemented yet"); - } -} - - - - -////////////////////////////////////// Interpolation ///////////////////////////// - - -/** @brief Direct interpolation. Multi-threaded! (VIENNACL_AMG_INTERPOL_DIRECT) - * - * @param A Operator matrix - * @param P Prolongation matrix - * @param amg_context AMG hierarchy datastructures - * @param tag AMG preconditioner tag -*/ -template<typename NumericT> -void amg_interpol_direct(compressed_matrix<NumericT> const & A, - compressed_matrix<NumericT> & P, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - NumericT const * A_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(A.handle()); - unsigned int const * A_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle1()); - unsigned int const * A_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle2()); - - unsigned int *point_types_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.point_types_.handle()); - unsigned int *influences_row_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_jumper_.handle()); - unsigned int *influences_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.influence_ids_.handle()); - unsigned int *coarse_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.coarse_id_.handle()); - - P.resize(A.size1(), amg_context.num_coarse_, false); - - std::vector<std::map<unsigned int, NumericT> > P_setup(A.size1()); - - // Iterate over all points to build the interpolation matrix row-by-row - // Interpolation for coarse points is immediate using '1'. - // Interpolation for fine points is set up via corresponding row weights (cf. Yang paper, p. 14) -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long row2=0; row2<static_cast<long>(A.size1()); ++row2) - { - unsigned int row = static_cast<unsigned int>(row2); - std::map<unsigned int, NumericT> & P_setup_row = P_setup[row]; - //std::cout << "Row " << row << ": " << std::endl; - if (point_types_ptr[row] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE) - { - //std::cout << " Setting value 1.0 at " << coarse_id_ptr[row] << std::endl; - P_setup_row[coarse_id_ptr[row]] = NumericT(1); - } - else if (point_types_ptr[row] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_FINE) - { - //std::cout << "Building interpolant for fine point " << row << std::endl; - - NumericT row_sum = 0; - NumericT row_coarse_sum = 0; - NumericT diag = 0; - - // Row sum of coefficients (without diagonal) and sum of influencing coarse point coefficients has to be computed - unsigned int row_A_start = A_row_buffer[row]; - unsigned int row_A_end = A_row_buffer[row + 1]; - unsigned int const *influence_iter = influences_id_ptr + influences_row_ptr[row]; - unsigned int const *influence_end = influences_id_ptr + influences_row_ptr[row + 1]; - for (unsigned int index = row_A_start; index < row_A_end; ++index) - { - unsigned int col = A_col_buffer[index]; - NumericT value = A_elements[index]; - - if (col == row) - { - diag = value; - continue; - } - else if (point_types_ptr[col] == viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE) - { - // Note: One increment is sufficient, because influence_iter traverses an ordered subset of the column indices in this row - while (influence_iter != influence_end && *influence_iter < col) - ++influence_iter; - - if (influence_iter != influence_end && *influence_iter == col) - row_coarse_sum += value; - } - - row_sum += value; - } - - NumericT temp_res = -row_sum/(row_coarse_sum*diag); - //std::cout << "row_sum: " << row_sum << ", row_coarse_sum: " << row_coarse_sum << ", diag: " << diag << std::endl; - - if (std::fabs(temp_res) > 1e-2 * std::fabs(diag)) - { - // Iterate over all strongly influencing points to build the interpolant - influence_iter = influences_id_ptr + influences_row_ptr[row]; - for (unsigned int index = row_A_start; index < row_A_end; ++index) - { - unsigned int col = A_col_buffer[index]; - if (point_types_ptr[col] != viennacl::linalg::detail::amg::amg_level_context::POINT_TYPE_COARSE) - continue; - NumericT value = A_elements[index]; - - // Advance to correct influence metric: - while (influence_iter != influence_end && *influence_iter < col) - ++influence_iter; - - if (influence_iter != influence_end && *influence_iter == col) - { - //std::cout << " Setting entry " << temp_res * value << " at " << coarse_id_ptr[col] << " for point " << col << std::endl; - P_setup_row[coarse_id_ptr[col]] = temp_res * value; - } - } - } - - // TODO truncate interpolation if specified by the user. - (void)tag; - } - else - throw std::runtime_error("Logic error in direct interpolation: Point is neither coarse-point nor fine-point!"); - } - - // TODO: P_setup can be avoided without sacrificing parallelism. - viennacl::tools::sparse_matrix_adapter<NumericT> P_adapter(P_setup, P.size1(), P.size2()); - viennacl::copy(P_adapter, P); -} - - -/** @brief AG (aggregation based) interpolation. Multi-Threaded! (VIENNACL_INTERPOL_AG) - * - * @param A Operator matrix - * @param P Prolongation matrix - * @param amg_context AMG hierarchy datastructures - * @param tag AMG configuration tag -*/ -template<typename NumericT> -void amg_interpol_ag(compressed_matrix<NumericT> const & A, - compressed_matrix<NumericT> & P, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - (void)tag; - P = compressed_matrix<NumericT>(A.size1(), amg_context.num_coarse_, A.size1(), viennacl::traits::context(A)); - - NumericT * P_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(P.handle()); - unsigned int * P_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(P.handle1()); - unsigned int * P_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(P.handle2()); - - unsigned int *coarse_id_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(amg_context.coarse_id_.handle()); - - // Build interpolation matrix: -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long row2 = 0; row2 < long(A.size1()); ++row2) - { - unsigned int row = static_cast<unsigned int>(row2); - P_elements[row] = NumericT(1); - P_row_buffer[row] = row; - P_col_buffer[row] = coarse_id_ptr[row]; - } - P_row_buffer[A.size1()] = static_cast<unsigned int>(A.size1()); // don't forget finalizer - - P.generate_row_block_information(); -} - - -/** @brief Smoothed aggregation interpolation. Multi-Threaded! (VIENNACL_INTERPOL_SA) - * - * @param A Operator matrix - * @param P Prolongation matrix - * @param amg_context AMG hierarchy datastructures - * @param tag AMG configuration tag -*/ -template<typename NumericT> -void amg_interpol_sa(compressed_matrix<NumericT> const & A, - compressed_matrix<NumericT> & P, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - (void)tag; - viennacl::compressed_matrix<NumericT> P_tentative(A.size1(), amg_context.num_coarse_, A.size1(), viennacl::traits::context(A)); - - // form tentative operator: - amg_interpol_ag(A, P_tentative, amg_context, tag); - - unsigned int const * A_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle1()); - unsigned int const * A_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle2()); - NumericT const * A_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(A.handle()); - - viennacl::compressed_matrix<NumericT> Jacobi(A.size1(), A.size1(), A.nnz(), viennacl::traits::context(A)); - unsigned int * Jacobi_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(Jacobi.handle1()); - unsigned int * Jacobi_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(Jacobi.handle2()); - NumericT * Jacobi_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(Jacobi.handle()); - - - // Build Jacobi matrix: -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long row2=0; row2<static_cast<long>(A.size1()); ++row2) - { - unsigned int row = static_cast<unsigned int>(row2); - unsigned int row_begin = A_row_buffer[row]; - unsigned int row_end = A_row_buffer[row+1]; - - Jacobi_row_buffer[row] = row_begin; - - // Step 1: Extract diagonal: - NumericT diag = 0; - for (unsigned int j = row_begin; j < row_end; ++j) - { - if (A_col_buffer[j] == row) - { - diag = A_elements[j]; - break; - } - } - - // Step 2: Write entries: - for (unsigned int j = row_begin; j < row_end; ++j) - { - unsigned int col_index = A_col_buffer[j]; - Jacobi_col_buffer[j] = col_index; - - if (col_index == row) - Jacobi_elements[j] = NumericT(1) - NumericT(tag.get_jacobi_weight()); - else - Jacobi_elements[j] = - NumericT(tag.get_jacobi_weight()) * A_elements[j] / diag; - } - } - Jacobi_row_buffer[A.size1()] = static_cast<unsigned int>(Jacobi.nnz()); // don't forget finalizer - - P = viennacl::linalg::prod(Jacobi, P_tentative); - - P.generate_row_block_information(); -} - - -/** @brief Dispatcher for building the interpolation matrix - * - * @param A Operator matrix - * @param P Prolongation matrix - * @param amg_context AMG hierarchy datastructures - * @param tag AMG configuration tag -*/ -template<typename MatrixT> -void amg_interpol(MatrixT const & A, - MatrixT & P, - viennacl::linalg::detail::amg::amg_level_context & amg_context, - viennacl::linalg::amg_tag & tag) -{ - switch (tag.get_interpolation_method()) - { - case viennacl::linalg::AMG_INTERPOLATION_METHOD_DIRECT: amg_interpol_direct (A, P, amg_context, tag); break; - case viennacl::linalg::AMG_INTERPOLATION_METHOD_AGGREGATION: amg_interpol_ag (A, P, amg_context, tag); break; - case viennacl::linalg::AMG_INTERPOLATION_METHOD_SMOOTHED_AGGREGATION: amg_interpol_sa (A, P, amg_context, tag); break; - default: throw std::runtime_error("Not implemented yet!"); - } -} - - -/** @brief Computes B = trans(A). - * - * To be replaced by native functionality in ViennaCL. - */ -template<typename NumericT> -void amg_transpose(compressed_matrix<NumericT> const & A, - compressed_matrix<NumericT> & B) -{ - NumericT const * A_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(A.handle()); - unsigned int const * A_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle1()); - unsigned int const * A_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle2()); - - // initialize datastructures for B: - B = compressed_matrix<NumericT>(A.size2(), A.size1(), A.nnz(), viennacl::traits::context(A)); - - NumericT * B_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(B.handle()); - unsigned int * B_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(B.handle1()); - unsigned int * B_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(B.handle2()); - - // prepare uninitialized B_row_buffer: - for (std::size_t i = 0; i < B.size1(); ++i) - B_row_buffer[i] = 0; - - // - // Stage 1: Compute pattern for B - // - for (std::size_t row = 0; row < A.size1(); ++row) - { - unsigned int row_start = A_row_buffer[row]; - unsigned int row_stop = A_row_buffer[row+1]; - - for (unsigned int nnz_index = row_start; nnz_index < row_stop; ++nnz_index) - B_row_buffer[A_col_buffer[nnz_index]] += 1; - } - - // Bring row-start array in place using inclusive-scan: - unsigned int offset = B_row_buffer[0]; - B_row_buffer[0] = 0; - for (std::size_t row = 1; row < B.size1(); ++row) - { - unsigned int tmp = B_row_buffer[row]; - B_row_buffer[row] = offset; - offset += tmp; - } - B_row_buffer[B.size1()] = offset; - - // - // Stage 2: Fill with data - // - - std::vector<unsigned int> B_row_offsets(B.size1()); //number of elements already written per row - - for (std::size_t row = 0; row < A.size1(); ++row) - { - //std::cout << "Row " << row << ": "; - unsigned int row_start = A_row_buffer[row]; - unsigned int row_stop = A_row_buffer[row+1]; - - for (unsigned int nnz_index = row_start; nnz_index < row_stop; ++nnz_index) - { - unsigned int col_in_A = A_col_buffer[nnz_index]; - unsigned int B_nnz_index = B_row_buffer[col_in_A] + B_row_offsets[col_in_A]; - B_col_buffer[B_nnz_index] = static_cast<unsigned int>(row); - B_elements[B_nnz_index] = A_elements[nnz_index]; - ++B_row_offsets[col_in_A]; - //B_temp.at(A_col_buffer[nnz_index])[row] = A_elements[nnz_index]; - } - } - - // Step 3: Make datastructure consistent (row blocks!) - B.generate_row_block_information(); -} - -/** Assign sparse matrix A to dense matrix B */ -template<typename NumericT, unsigned int AlignmentV> -void assign_to_dense(viennacl::compressed_matrix<NumericT, AlignmentV> const & A, - viennacl::matrix_base<NumericT> & B) -{ - NumericT const * A_elements = detail::extract_raw_pointer<NumericT>(A.handle()); - unsigned int const * A_row_buffer = detail::extract_raw_pointer<unsigned int>(A.handle1()); - unsigned int const * A_col_buffer = detail::extract_raw_pointer<unsigned int>(A.handle2()); - - NumericT * B_elements = detail::extract_raw_pointer<NumericT>(B.handle()); - -#ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for -#endif - for (long row = 0; row < static_cast<long>(A.size1()); ++row) - { - unsigned int row_stop = A_row_buffer[row+1]; - - for (unsigned int nnz_index = A_row_buffer[row]; nnz_index < row_stop; ++nnz_index) - B_elements[static_cast<unsigned int>(row) * static_cast<unsigned int>(B.internal_size2()) + A_col_buffer[nnz_index]] = A_elements[nnz_index]; - } - -} - -/** @brief Damped Jacobi Smoother (CUDA version) -* -* @param iterations Number of smoother iterations -* @param A Operator matrix for the smoothing -* @param x The vector smoothing is applied to -* @param x_backup (Different) Vector holding the same values as x -* @param rhs_smooth The right hand side of the equation for the smoother -* @param weight Damping factor. 0: No effect of smoother. 1: Undamped Jacobi iteration -*/ -template<typename NumericT> -void smooth_jacobi(unsigned int iterations, - compressed_matrix<NumericT> const & A, - vector<NumericT> & x, - vector<NumericT> & x_backup, - vector<NumericT> const & rhs_smooth, - NumericT weight) -{ - - NumericT const * A_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(A.handle()); - unsigned int const * A_row_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle1()); - unsigned int const * A_col_buffer = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(A.handle2()); - NumericT const * rhs_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(rhs_smooth.handle()); - - NumericT * x_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(x.handle()); - NumericT const * x_old_elements = viennacl::linalg::host_based::detail::extract_raw_pointer<NumericT>(x_backup.handle()); - - for (unsigned int i=0; i<iterations; ++i) - { - x_backup = x; - - #ifdef VIENNACL_WITH_OPENMP - #pragma omp parallel for - #endif - for (long row2 = 0; row2 < static_cast<long>(A.size1()); ++row2) - { - unsigned int row = static_cast<unsigned int>(row2); - unsigned int col_end = A_row_buffer[row+1]; - - NumericT sum = NumericT(0); - NumericT diag = NumericT(1); - for (unsigned int index = A_row_buffer[row]; index != col_end; ++index) - { - unsigned int col = A_col_buffer[index]; - if (col == row) - diag = A_elements[index]; - else - sum += A_elements[index] * x_old_elements[col]; - } - - x_elements[row] = weight * (rhs_elements[row] - sum) / diag + (NumericT(1) - weight) * x_old_elements[row]; - } - } -} - -} //namespace amg -} //namespace host_based -} //namespace linalg -} //namespace viennacl - -#endif http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/host_based/common.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/host_based/common.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/host_based/common.hpp deleted file mode 100644 index 8ddb4c1..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/host_based/common.hpp +++ /dev/null @@ -1,149 +0,0 @@ -#ifndef VIENNACL_LINALG_HOST_BASED_COMMON_HPP_ -#define VIENNACL_LINALG_HOST_BASED_COMMON_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/host_based/common.hpp - @brief Common routines for single-threaded or OpenMP-enabled execution on CPU -*/ - -#include "viennacl/traits/handle.hpp" - -namespace viennacl -{ -namespace linalg -{ -namespace host_based -{ -namespace detail -{ - -template<typename ResultT, typename VectorT> -ResultT * extract_raw_pointer(VectorT & vec) -{ - return reinterpret_cast<ResultT *>(viennacl::traits::ram_handle(vec).get()); -} - -template<typename ResultT, typename VectorT> -ResultT const * extract_raw_pointer(VectorT const & vec) -{ - return reinterpret_cast<ResultT const *>(viennacl::traits::ram_handle(vec).get()); -} - -/** @brief Helper class for accessing a strided subvector of a larger vector. */ -template<typename NumericT> -class vector_array_wrapper -{ -public: - typedef NumericT value_type; - - vector_array_wrapper(value_type * A, - vcl_size_t start, - vcl_size_t inc) - : A_(A), - start_(start), - inc_(inc) {} - - value_type & operator()(vcl_size_t i) { return A_[i * inc_ + start_]; } - -private: - value_type * A_; - vcl_size_t start_; - vcl_size_t inc_; -}; - - -/** @brief Helper array for accessing a strided submatrix embedded in a larger matrix. */ -template<typename NumericT, typename LayoutT, bool is_transposed> -class matrix_array_wrapper -{ - public: - typedef NumericT value_type; - - matrix_array_wrapper(value_type * A, - vcl_size_t start1, vcl_size_t start2, - vcl_size_t inc1, vcl_size_t inc2, - vcl_size_t internal_size1, vcl_size_t internal_size2) - : A_(A), - start1_(start1), start2_(start2), - inc1_(inc1), inc2_(inc2), - internal_size1_(internal_size1), internal_size2_(internal_size2) {} - - value_type & operator()(vcl_size_t i, vcl_size_t j) - { - return A_[LayoutT::mem_index(i * inc1_ + start1_, - j * inc2_ + start2_, - internal_size1_, internal_size2_)]; - } - - // convenience overloads to address signed index types for OpenMP: - value_type & operator()(vcl_size_t i, long j) { return operator()(i, static_cast<vcl_size_t>(j)); } - value_type & operator()(long i, vcl_size_t j) { return operator()(static_cast<vcl_size_t>(i), j); } - value_type & operator()(long i, long j) { return operator()(static_cast<vcl_size_t>(i), static_cast<vcl_size_t>(j)); } - - private: - value_type * A_; - vcl_size_t start1_, start2_; - vcl_size_t inc1_, inc2_; - vcl_size_t internal_size1_, internal_size2_; -}; - -/** \cond */ -template<typename NumericT, typename LayoutT> -class matrix_array_wrapper<NumericT, LayoutT, true> -{ -public: - typedef NumericT value_type; - - matrix_array_wrapper(value_type * A, - vcl_size_t start1, vcl_size_t start2, - vcl_size_t inc1, vcl_size_t inc2, - vcl_size_t internal_size1, vcl_size_t internal_size2) - : A_(A), - start1_(start1), start2_(start2), - inc1_(inc1), inc2_(inc2), - internal_size1_(internal_size1), internal_size2_(internal_size2) {} - - value_type & operator()(vcl_size_t i, vcl_size_t j) - { - //swapping row and column indices here - return A_[LayoutT::mem_index(j * inc1_ + start1_, - i * inc2_ + start2_, - internal_size1_, internal_size2_)]; - } - - // convenience overloads to address signed index types for OpenMP: - value_type & operator()(vcl_size_t i, long j) { return operator()(i, static_cast<vcl_size_t>(j)); } - value_type & operator()(long i, vcl_size_t j) { return operator()(static_cast<vcl_size_t>(i), j); } - value_type & operator()(long i, long j) { return operator()(static_cast<vcl_size_t>(i), static_cast<vcl_size_t>(j)); } - -private: - value_type * A_; - vcl_size_t start1_, start2_; - vcl_size_t inc1_, inc2_; - vcl_size_t internal_size1_, internal_size2_; -}; -/** \endcond */ - -} //namespace detail -} //namespace host_based -} //namespace linalg -} //namespace viennacl - - -#endif
