sparse_matrix_petsc.cc
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sparse_matrix_petsc.cc
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	/**
	* @file sparse_matrix_petsc.cc
	*
	* @author Aurelia Isabel Cuba Ramos <aurelia.cubaramos@epfl.ch>
	* @author Nicolas Richart <nicolas.richart@epfl.ch>
	*
	* @date creation: Mon Dec 13 2010
	* @date last modification: Fri Jul 24 2020
	*
	* @brief Implementation of PETSc matrix class
	*
	*
	* @section LICENSE
	*
	* Copyright (©) 2010-2021 EPFL (Ecole Polytechnique Fédérale de Lausanne)
	* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
	*
	* Akantu is free software: you can redistribute it and/or modify it under the
	* terms of the GNU Lesser General Public License as published by the Free
	* Software Foundation, either version 3 of the License, or (at your option) any
	* later version.
	*
	* Akantu is distributed in the hope that it will be useful, but WITHOUT ANY
	* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
	* A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
	* details.
	*
	* You should have received a copy of the GNU Lesser General Public License
	* along with Akantu. If not, see <http://www.gnu.org/licenses/>.
	*
	*/

	/* -------------------------------------------------------------------------- */
	#include "sparse_matrix_petsc.hh"
	#include "dof_manager_petsc.hh"
	#include "mpi_communicator_data.hh"
	#include "solver_vector_petsc.hh"
	/* -------------------------------------------------------------------------- */

	namespace akantu {

	/* -------------------------------------------------------------------------- */
	SparseMatrixPETSc::SparseMatrixPETSc(DOFManagerPETSc & dof_manager,
	const MatrixType & matrix_type,
	const ID & id)
	: SparseMatrix(dof_manager, matrix_type, id), dof_manager(dof_manager) {
	AKANTU_DEBUG_IN();

	auto && mpi_comm = dof_manager.getMPIComm();

	PETSc_call(MatCreate, mpi_comm, &mat);
	detail::PETScSetName(mat, id);

	resize();

	PETSc_call(MatSetFromOptions, mat);

	PETSc_call(MatSetUp, mat);

	PETSc_call(MatSetOption, mat, MAT_ROW_ORIENTED, PETSC_TRUE);
	PETSc_call(MatSetOption, mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_TRUE);

	if (matrix_type == _symmetric) {
	PETSc_call(MatSetOption, mat, MAT_SYMMETRIC, PETSC_TRUE);
	}

	AKANTU_DEBUG_OUT();
	}

	/* -------------------------------------------------------------------------- */
	SparseMatrixPETSc::SparseMatrixPETSc(const SparseMatrixPETSc & matrix,
	const ID & id)
	: SparseMatrix(matrix, id), dof_manager(matrix.dof_manager) {
	PETSc_call(MatDuplicate, matrix.mat, MAT_COPY_VALUES, &mat);
	detail::PETScSetName(mat, id);
	}

	/* -------------------------------------------------------------------------- */
	SparseMatrixPETSc::~SparseMatrixPETSc() {
	AKANTU_DEBUG_IN();

	if (mat != nullptr) {
	PETSc_call(MatDestroy, &mat);
	}

	AKANTU_DEBUG_OUT();
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::resize() {
	auto local_size = dof_manager.getPureLocalSystemSize();
	PETSc_call(MatSetSizes, mat, local_size, local_size, size_, size_);

	auto & is_ltog_mapping = dof_manager.getISLocalToGlobalMapping();
	PETSc_call(MatSetLocalToGlobalMapping, mat, is_ltog_mapping, is_ltog_mapping);
	}

	/* -------------------------------------------------------------------------- */
	/**
	* Method to save the nonzero pattern and the values stored at each position
	* @param filename name of the file in which the information will be stored
	*/
	void SparseMatrixPETSc::saveMatrix(const std::string & filename) const {
	AKANTU_DEBUG_IN();

	auto && mpi_comm = dof_manager.getMPIComm();

	/// create Petsc viewer
	PetscViewer viewer;
	PETSc_call(PetscViewerASCIIOpen, mpi_comm, filename.c_str(), &viewer);
	PETSc_call(PetscViewerPushFormat, viewer, PETSC_VIEWER_ASCII_MATRIXMARKET);
	PETSc_call(MatView, mat, viewer);
	PETSc_call(PetscViewerPopFormat, viewer);
	PETSc_call(PetscViewerDestroy, &viewer);

	AKANTU_DEBUG_OUT();
	}

	/* -------------------------------------------------------------------------- */
	/// Equivalent of *gemv in blas
	void SparseMatrixPETSc::matVecMul(const SolverVector & _x, SolverVector & _y,
	Real alpha, Real beta) const {
	const auto & x = aka::as_type<SolverVectorPETSc>(_x);
	auto & y = aka::as_type<SolverVectorPETSc>(_y);

	// y = alpha A x + beta y
	SolverVectorPETSc w(x, this->id + ":tmp");

	// w = A x
	if (release == 0) {
	PETSc_call(VecZeroEntries, w);
	} else {
	PETSc_call(MatMult, mat, x, w);
	}

	if (alpha != 1.) {
	// w = alpha w
	PETSc_call(VecScale, w, alpha);
	}

	// y = w + beta y
	PETSc_call(VecAYPX, y, beta, w);
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::addMeToImpl(SparseMatrixPETSc & B, Real alpha) const {
	PETSc_call(MatAXPY, B.mat, alpha, mat, SAME_NONZERO_PATTERN);

	B.release++;
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::addMeTo(SparseMatrix & B, Real alpha) const {
	if (aka::is_of_type<SparseMatrixPETSc>(B)) {
	auto & B_petsc = aka::as_type<SparseMatrixPETSc>(B);
	this->addMeToImpl(B_petsc, alpha);
	} else {
	AKANTU_TO_IMPLEMENT();
	// this->addMeToTemplated<SparseMatrix>(*this, alpha);
	}
	}

	/* -------------------------------------------------------------------------- */
	/**
	* MatSetValues() generally caches the values. The matrix is ready to
	* use only after MatAssemblyBegin() and MatAssemblyEnd() have been
	* called. (http://www.mcs.anl.gov/petsc/)
	*/
	void SparseMatrixPETSc::applyModifications() {
	this->beginAssembly();
	this->endAssembly();
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::beginAssembly() {
	PETSc_call(MatAssemblyBegin, mat, MAT_FINAL_ASSEMBLY);
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::endAssembly() {
	PETSc_call(MatAssemblyEnd, mat, MAT_FINAL_ASSEMBLY);
	PETSc_call(MatSetOption, mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);

	this->release++;
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::copyProfile(const SparseMatrix & other) {
	const auto & A = aka::as_type<SparseMatrixPETSc>(other);

	MatDestroy(&mat);
	MatDuplicate(A.mat, MAT_DO_NOT_COPY_VALUES, &mat);
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::applyBoundary(Real block_val) {
	AKANTU_DEBUG_IN();

	const auto & blocked_dofs = this->dof_manager.getGlobalBlockedDOFs();
	// std::vector<PetscInt> rows;
	// for (auto && data : enumerate(blocked)) {
	// if (std::get<1>(data)) {
	// rows.push_back(std::get<0>(data));
	// }
	// }
	// applyModifications();

	static int c = 0;

	saveMatrix("before_blocked_" + std::to_string(c) + ".mtx");

	PETSc_call(MatZeroRowsColumnsLocal, mat, blocked_dofs.size(),
	blocked_dofs.storage(), block_val, nullptr, nullptr);

	saveMatrix("after_blocked_" + std::to_string(c) + ".mtx");
	++c;

	AKANTU_DEBUG_OUT();
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::mul(Real alpha) {
	PETSc_call(MatScale, mat, alpha);
	this->release++;
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::zero() {
	PETSc_call(MatZeroEntries, mat);
	this->release++;
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::clearProfile() {
	SparseMatrix::clearProfile();
	PETSc_call(MatResetPreallocation, mat);
	PETSc_call(MatSetOption, mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_TRUE);
	// PETSc_call(MatSetOption, MAT_KEEP_NONZERO_PATTERN, PETSC_TRUE);
	// PETSc_call(MatSetOption, MAT_NEW_NONZERO_ALLOCATIONS, PETSC_TRUE);
	// PETSc_call(MatSetOption, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE);

	this->zero();
	}

	/* -------------------------------------------------------------------------- */
	UInt SparseMatrixPETSc::add(UInt i, UInt j) {
	PETSc_call(MatSetValue, mat, i, j, 0, ADD_VALUES);
	return 0;
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::add(UInt i, UInt j, Real val) {
	PETSc_call(MatSetValue, mat, i, j, val, ADD_VALUES);
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::addLocal(UInt i, UInt j) {
	PETSc_call(MatSetValueLocal, mat, i, j, 0, ADD_VALUES);
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::addLocal(UInt i, UInt j, Real val) {
	PETSc_call(MatSetValueLocal, mat, i, j, val, ADD_VALUES);
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::addLocal(const Vector<Int> & rows,
	const Vector<Int> & cols,
	const Matrix<Real> & values) {
	PETSc_call(MatSetValuesLocal, mat, rows.size(), rows.storage(), cols.size(),
	cols.storage(), values.storage(), ADD_VALUES);
	}

	/* -------------------------------------------------------------------------- */
	void SparseMatrixPETSc::addValues(const Vector<Int> & rows,
	const Vector<Int> & cols,
	const Matrix<Real> & values,
	MatrixType values_type) {
	if (values_type == _unsymmetric and matrix_type == _symmetric) {
	PETSc_call(MatSetOption, mat, MAT_SYMMETRIC, PETSC_FALSE);
	PETSc_call(MatSetOption, mat, MAT_STRUCTURALLY_SYMMETRIC, PETSC_FALSE);
	}

	PETSc_call(MatSetValues, mat, rows.size(), rows.storage(), cols.size(),
	cols.storage(), values.storage(), ADD_VALUES);
	}

	/* -------------------------------------------------------------------------- */

	} // namespace akantu

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