diff options
Diffstat (limited to 'Eigen/src/PardisoSupport/PardisoSupport.h')
| -rw-r--r-- | Eigen/src/PardisoSupport/PardisoSupport.h | 35 |
1 files changed, 22 insertions, 13 deletions
diff --git a/Eigen/src/PardisoSupport/PardisoSupport.h b/Eigen/src/PardisoSupport/PardisoSupport.h index 80d914f25..091c3970e 100644 --- a/Eigen/src/PardisoSupport/PardisoSupport.h +++ b/Eigen/src/PardisoSupport/PardisoSupport.h @@ -183,7 +183,7 @@ class PardisoImpl : public SparseSolverBase<Derived> { if(m_isInitialized) // Factorization ran at least once { - internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, -1, m_size,0, 0, 0, m_perm.data(), 0, + internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, -1, internal::convert_index<StorageIndex>(m_size),0, 0, 0, m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); m_isInitialized = false; } @@ -194,11 +194,11 @@ class PardisoImpl : public SparseSolverBase<Derived> m_type = type; bool symmetric = std::abs(m_type) < 10; m_iparm[0] = 1; // No solver default - m_iparm[1] = 3; // use Metis for the ordering - m_iparm[2] = 1; // Numbers of processors, value of OMP_NUM_THREADS + m_iparm[1] = 2; // use Metis for the ordering + m_iparm[2] = 0; // Reserved. Set to zero. (??Numbers of processors, value of OMP_NUM_THREADS??) m_iparm[3] = 0; // No iterative-direct algorithm m_iparm[4] = 0; // No user fill-in reducing permutation - m_iparm[5] = 0; // Write solution into x + m_iparm[5] = 0; // Write solution into x, b is left unchanged m_iparm[6] = 0; // Not in use m_iparm[7] = 2; // Max numbers of iterative refinement steps m_iparm[8] = 0; // Not in use @@ -219,7 +219,8 @@ class PardisoImpl : public SparseSolverBase<Derived> m_iparm[26] = 0; // No matrix checker m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0; m_iparm[34] = 1; // C indexing - m_iparm[59] = 1; // Automatic switch between In-Core and Out-of-Core modes + m_iparm[36] = 0; // CSR + m_iparm[59] = 0; // 0 - In-Core ; 1 - Automatic switch between In-Core and Out-of-Core modes ; 2 - Out-of-Core memset(m_pt, 0, sizeof(m_pt)); } @@ -246,7 +247,7 @@ class PardisoImpl : public SparseSolverBase<Derived> mutable SparseMatrixType m_matrix; mutable ComputationInfo m_info; bool m_analysisIsOk, m_factorizationIsOk; - Index m_type, m_msglvl; + StorageIndex m_type, m_msglvl; mutable void *m_pt[64]; mutable ParameterType m_iparm; mutable IntColVectorType m_perm; @@ -265,10 +266,9 @@ Derived& PardisoImpl<Derived>::compute(const MatrixType& a) derived().getMatrix(a); Index error; - error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 12, m_size, + error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 12, internal::convert_index<StorageIndex>(m_size), m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); - manageErrorCode(error); m_analysisIsOk = true; m_factorizationIsOk = true; @@ -287,7 +287,7 @@ Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a) derived().getMatrix(a); Index error; - error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 11, m_size, + error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 11, internal::convert_index<StorageIndex>(m_size), m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); @@ -306,8 +306,8 @@ Derived& PardisoImpl<Derived>::factorize(const MatrixType& a) derived().getMatrix(a); - Index error; - error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 22, m_size, + Index error; + error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 22, internal::convert_index<StorageIndex>(m_size), m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); @@ -354,9 +354,9 @@ void PardisoImpl<Derived>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase } Index error; - error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 33, m_size, + error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 33, internal::convert_index<StorageIndex>(m_size), m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), - m_perm.data(), nrhs, m_iparm.data(), m_msglvl, + m_perm.data(), internal::convert_index<StorageIndex>(nrhs), m_iparm.data(), m_msglvl, rhs_ptr, x.derived().data()); manageErrorCode(error); @@ -371,6 +371,9 @@ void PardisoImpl<Derived>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible. * The vectors or matrices X and B can be either dense or sparse. * + * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set: + * \code solver.pardisoParameterArray()[59] = 1; \endcode + * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * * \implsparsesolverconcept @@ -421,6 +424,9 @@ class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> > * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite. * The vectors or matrices X and B can be either dense or sparse. * + * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set: + * \code solver.pardisoParameterArray()[59] = 1; \endcode + * * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used. * Upper|Lower can be used to tell both triangular parts can be used as input. @@ -480,6 +486,9 @@ class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> > * For complex matrices, A can also be symmetric only, see the \a Options template parameter. * The vectors or matrices X and B can be either dense or sparse. * + * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set: + * \code solver.pardisoParameterArray()[59] = 1; \endcode + * * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used. * Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix. |