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Fixes for PARDISO: warnings, and defaults to metis+ in-core mode.

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This commit is contained in:
Gael Guennebaud 2016-06-08 18:31:19 +02:00
parent 9fc8379328
commit df095cab10
1 changed files with 22 additions and 13 deletions
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35
Eigen/src/PardisoSupport/PardisoSupport.h
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@ -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.