GitHub-Proxy/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2025-06-04 18:54:00 +08:00
Code Issues Packages Projects Releases Wiki Activity

fix and clean Pardiso solver and s/PARDISOSupport/PardisoSupport

Browse Source
This commit is contained in:
Gael Guennebaud 2012-02-27 13:23:21 +01:00
parent b240a3fad9
commit 122f28626c
4 changed files with 201 additions and 113 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
Eigen/PARDISOSupport → Eigen/PardisoSupport
View File

@ -12,16 +12,16 @@
namespace Eigen {
/** \ingroup Support_modules
* \defgroup PARDISOSupport_Module PARDISOSupport module
* \defgroup PardisoSupport_Module PardisoSupport module
*
* This module brings support for the Intel(R) MKL PARDISO direct sparse solvers
*
* \code
* #include <Eigen/PARDISOSupport>
* #include <Eigen/PardisoSupport>
* \endcode
*/
#include "src/PARDISOSupport/PARDISOSupport.h"
#include "src/PardisoSupport/PardisoSupport.h"
} // namespace Eigen

0
Eigen/src/PARDISOSupport/CMakeLists.txt → Eigen/src/PardisoSupport/CMakeLists.txt
View File

294
Eigen/src/PARDISOSupport/PARDISOSupport.h → Eigen/src/PardisoSupport/PardisoSupport.h
View File

@ -73,8 +73,8 @@ namespace internal
typedef typename _MatrixType::Index Index;
};
template<typename _MatrixType>
struct pardiso_traits< PardisoLLT<_MatrixType> >
template<typename _MatrixType, int Options>
struct pardiso_traits< PardisoLLT<_MatrixType, Options> >
{
typedef _MatrixType MatrixType;
typedef typename _MatrixType::Scalar Scalar;
@ -82,13 +82,13 @@ namespace internal
typedef typename _MatrixType::Index Index;
};
template<typename _MatrixType>
struct pardiso_traits< PardisoLDLT<_MatrixType> >
template<typename _MatrixType, int Options>
struct pardiso_traits< PardisoLDLT<_MatrixType, Options> >
{
typedef _MatrixType MatrixType;
typedef typename _MatrixType::Scalar Scalar;
typedef typename _MatrixType::RealScalar RealScalar;
typedef typename _MatrixType::Index Index;
typedef typename _MatrixType::Index Index;
};
}
@ -96,11 +96,13 @@ namespace internal
template<class Derived>
class PardisoImpl
{
typedef internal::pardiso_traits<Derived> Traits;
public:
typedef typename internal::pardiso_traits<Derived>::MatrixType MatrixType;
typedef typename internal::pardiso_traits<Derived>::Scalar Scalar;
typedef typename internal::pardiso_traits<Derived>::RealScalar RealScalar;
typedef typename internal::pardiso_traits<Derived>::Index Index;
typedef typename Traits::MatrixType MatrixType;
typedef typename Traits::Scalar Scalar;
typedef typename Traits::RealScalar RealScalar;
typedef typename Traits::Index Index;
typedef SparseMatrix<Scalar,RowMajor,Index> SparseMatrixType;
typedef Matrix<Scalar,Dynamic,1> VectorType;
typedef Matrix<Index, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
typedef Matrix<Index, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
@ -112,7 +114,7 @@ class PardisoImpl
{
eigen_assert((sizeof(Index) >= sizeof(_INTEGER_t) && sizeof(Index) <= 8) && "Non-supported index type");
m_iparm.setZero();
m_msglvl = 0; /* No output */
m_msglvl = 0; // No output
m_initialized = false;
}
@ -121,8 +123,8 @@ class PardisoImpl
pardisoRelease();
}
inline Index cols() const { return m_matrix.cols(); }
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_size; }
inline Index rows() const { return m_size; }
/** \brief Reports whether previous computation was successful.
*
@ -142,8 +144,25 @@ class PardisoImpl
{
return m_param;
}
/** Performs a symbolic decomposition on the sparcity of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
*
* \sa factorize()
*/
Derived& analyzePattern(const MatrixType& matrix);
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
*
* \sa analyzePattern()
*/
Derived& factorize(const MatrixType& matrix);
Derived& compute(const MatrixType& matrix);
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
@ -188,19 +207,22 @@ class PardisoImpl
template<typename Rhs, typename DestScalar, int DestOptions, typename DestIndex>
void _solve_sparse(const Rhs& b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
{
eigen_assert(m_matrix.rows()==b.rows());
eigen_assert(m_size==b.rows());
// we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix.
static const int NbColsAtOnce = 4;
int rhsCols = b.cols();
int size = b.rows();
Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmp(size,rhsCols);
// Pardiso cannot solve in-place,
// so we need two temporaries
Eigen::Matrix<DestScalar,Dynamic,Dynamic,ColMajor> tmp_rhs(size,rhsCols);
Eigen::Matrix<DestScalar,Dynamic,Dynamic,ColMajor> tmp_res(size,rhsCols);
for(int k=0; k<rhsCols; k+=NbColsAtOnce)
{
int actualCols = std::min<int>(rhsCols-k, NbColsAtOnce);
tmp.leftCols(actualCols) = b.middleCols(k,actualCols);
tmp.leftCols(actualCols) = derived().solve(tmp.leftCols(actualCols));
dest.middleCols(k,actualCols) = tmp.leftCols(actualCols).sparseView();
tmp_rhs.leftCols(actualCols) = b.middleCols(k,actualCols);
tmp_res.leftCols(actualCols) = derived().solve(tmp_rhs.leftCols(actualCols));
dest.middleCols(k,actualCols) = tmp_res.leftCols(actualCols).sparseView();
}
}
@ -209,9 +231,8 @@ class PardisoImpl
{
if(m_initialized) // Factorization ran at least once
{
internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, -1, m_matrix.rows(), NULL, NULL, NULL, m_perm.data(), 0,
m_iparm.data(), m_msglvl, NULL, NULL);
m_iparm.setZero();
internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, -1, m_size, 0, 0, 0, m_perm.data(), 0,
m_iparm.data(), m_msglvl, 0, 0);
}
}
@ -219,106 +240,142 @@ class PardisoImpl
{
m_type = type;
bool symmetric = abs(m_type) < 10;
m_iparm[0] = 1; /* No solver default */
m_iparm[1] = 3; // use Metis for the ordering
/* Numbers of processors, value of OMP_NUM_THREADS */
m_iparm[2] = 1;
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[6] = 0; /* Not in use */
m_iparm[7] = 2; /* Max numbers of iterative refinement steps */
m_iparm[8] = 0; /* Not in use */
m_iparm[9] = 13; /* Perturb the pivot elements with 1E-13 */
m_iparm[10] = symmetric ? 0 : 1; /* Use nonsymmetric permutation and scaling MPS */
m_iparm[11] = 0; /* Not in use */
m_iparm[12] = symmetric ? 0 : 1; /* Maximum weighted matching algorithm is switched-off (default for symmetric). Try m_iparm[12] = 1 in case of inappropriate accuracy */
m_iparm[13] = 0; /* Output: Number of perturbed pivots */
m_iparm[14] = 0; /* Not in use */
m_iparm[15] = 0; /* Not in use */
m_iparm[16] = 0; /* Not in use */
m_iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
m_iparm[18] = -1; /* Output: Mflops for LU factorization */
m_iparm[19] = 0; /* Output: Numbers of CG Iterations */
m_iparm[20] = 0; /* 1x1 pivoting */
m_iparm[26] = 0; /* No matrix checker */
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[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[6] = 0; // Not in use
m_iparm[7] = 2; // Max numbers of iterative refinement steps
m_iparm[8] = 0; // Not in use
m_iparm[9] = 13; // Perturb the pivot elements with 1E-13
m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS
m_iparm[11] = 0; // Not in use
m_iparm[12] = symmetric ? 0 : 1; // Maximum weighted matching algorithm is switched-off (default for symmetric).
// Try m_iparm[12] = 1 in case of inappropriate accuracy
m_iparm[13] = 0; // Output: Number of perturbed pivots
m_iparm[14] = 0; // Not in use
m_iparm[15] = 0; // Not in use
m_iparm[16] = 0; // Not in use
m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU
m_iparm[18] = -1; // Output: Mflops for LU factorization
m_iparm[19] = 0; // Output: Numbers of CG Iterations
m_iparm[20] = 0; // 1x1 pivoting
m_iparm[26] = 0; // No matrix checker
m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
m_iparm[34] = 0; /* Fortran indexing */
m_iparm[59] = 1; /* Automatic switch between In-Core and Out-of-Core modes */
m_iparm[34] = 1; // C indexing
m_iparm[59] = 1; // Automatic switch between In-Core and Out-of-Core modes
}
protected:
// cached data to reduce reallocation, etc.
void manageErrorCode(Index error)
{
switch(error)
{
case 0:
m_info = Success;
break;
case -4:
case -7:
m_info = NumericalIssue;
break;
default:
m_info = InvalidInput;
}
}
mutable SparseMatrixType m_matrix;
ComputationInfo m_info;
bool m_initialized, m_succeeded;
bool m_initialized, m_analysisIsOk, m_factorizationIsOk;
Index m_type, m_msglvl;
mutable void *m_pt[64];
mutable Array<Index,64,1> m_iparm;
mutable SparseMatrix<Scalar, RowMajor> m_matrix;
mutable IntColVectorType m_perm;
Index m_size;
};
template<class Derived>
Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
{
Index n = a.rows(), i;
m_size = a.rows();
eigen_assert(a.rows() == a.cols());
pardisoRelease();
memset(m_pt, 0, sizeof(m_pt));
m_perm.setZero(m_size);
derived().getMatrix(a);
Index error;
error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 12, 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;
m_initialized = true;
return derived();
}
bool symmetric = abs(m_type) < 10;
m_iparm[10] = symmetric ? 0 : 1; /* Use nonsymmetric permutation and scaling MPS */
m_iparm[12] = symmetric ? 0 : 1; /* Maximum weighted matching algorithm is switched-off (default for symmetric). Try m_iparm[12] = 1 in case of inappropriate accuracy */
template<class Derived>
Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
{
m_size = a.rows();
eigen_assert(m_size == a.cols());
m_perm.resize(n);
m_matrix = a;
pardisoRelease();
memset(m_pt, 0, sizeof(m_pt));
m_perm.setZero(m_size);
derived().getMatrix(a);
Index error;
error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 11, 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 = false;
m_initialized = true;
return derived();
}
/* Convert to Fortran-style indexing */
for(i = 0; i <= m_matrix.rows(); ++i)
++m_matrix.outerIndexPtr()[i];
for(i = 0; i < m_matrix.nonZeros(); ++i)
++m_matrix.innerIndexPtr()[i];
template<class Derived>
Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
{
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
eigen_assert(m_size == a.rows() && m_size == a.cols());
derived().getMatrix(a);
Index error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 12, n,
m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
switch(error)
{
case 0:
m_succeeded = true;
m_info = Success;
return derived();
case -4:
case -7:
m_info = NumericalIssue;
break;
default:
m_info = InvalidInput;
}
m_succeeded = false;
Index error;
error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 22, 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_factorizationIsOk = true;
return derived();
}
template<class Base>
template<typename BDerived,typename XDerived>
bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b,
MatrixBase<XDerived>& x) const
bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const
{
if(m_iparm[0] == 0) // Factorization was not computed
return false;
Index n = m_matrix.rows();
//Index n = m_matrix.rows();
Index nrhs = b.cols();
eigen_assert(n==b.rows());
eigen_assert(m_size==b.rows());
eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported");
eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported");
eigen_assert(((nrhs == 1) || b.outerStride() == b.rows()));
//x.derived().resizeLike(b);
// switch (transposed) {
// case SvNoTrans : m_iparm[11] = 0 ; break;
@ -329,15 +386,27 @@ bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b,
// m_iparm[11] = 0;
// }
Index error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 33, n,
m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
m_perm.data(), nrhs, m_iparm.data(), m_msglvl, const_cast<Scalar*>(&b(0, 0)), &x(0, 0));
Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp;
// Pardiso cannot solve in-place
if(rhs_ptr == x.derived().data())
{
tmp = b;
rhs_ptr = tmp.data();
}
Index error;
error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 33, m_size,
m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
m_perm.data(), nrhs, m_iparm.data(), m_msglvl,
rhs_ptr, x.derived().data());
return error==0;
}
/** \ingroup PARDISOSupport_Module
/** \ingroup PardisoSupport_Module
* \class PardisoLU
* \brief A sparse direct LU factorization and solver based on the PARDISO library
*
@ -357,6 +426,8 @@ class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
typedef typename Base::Scalar Scalar;
typedef typename Base::RealScalar RealScalar;
using Base::pardisoInit;
using Base::m_matrix;
friend class PardisoImpl< PardisoLU<MatrixType> >;
public:
@ -375,9 +446,14 @@ class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
pardisoInit(Base::ScalarIsComplex ? 13 : 11);
compute(matrix);
}
protected:
void getMatrix(const MatrixType& matrix)
{
m_matrix = matrix;
}
};
/** \ingroup PARDISOSupport_Module
/** \ingroup PardisoSupport_Module
* \class PardisoLLT
* \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
*
@ -395,10 +471,13 @@ template<typename MatrixType, int _UpLo>
class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
{
protected:
typedef PardisoImpl< PardisoLLT<MatrixType> > Base;
typedef PardisoImpl< PardisoLLT<MatrixType,_UpLo> > Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::Index Index;
typedef typename Base::RealScalar RealScalar;
using Base::pardisoInit;
using Base::m_matrix;
friend class PardisoImpl< PardisoLLT<MatrixType,_UpLo> >;
public:
@ -418,11 +497,21 @@ class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
pardisoInit(Base::ScalarIsComplex ? 4 : 2);
compute(matrix);
}
protected:
void getMatrix(const MatrixType& matrix)
{
// PARDISO supports only upper, row-major matrices
PermutationMatrix<Dynamic,Dynamic,Index> p_null;
m_matrix.resize(matrix.rows(), matrix.cols());
m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
}
};
/** \ingroup PARDISOSupport_Module
/** \ingroup PardisoSupport_Module
* \class PardisoLDLT
* \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
* \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
*
* This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
* using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
@ -440,11 +529,13 @@ template<typename MatrixType, int Options>
class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
{
protected:
typedef PardisoImpl< PardisoLDLT<MatrixType> > Base;
typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::Index Index;
typedef typename Base::RealScalar RealScalar;
using Base::pardisoInit;
using Base::m_matrix;
friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >;
public:
@ -459,26 +550,19 @@ class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
}
PardisoLDLT(const MatrixType& matrix)
: Base(flags)
: Base()
{
pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
compute(matrix, hermitian);
compute(matrix);
}
void compute(const MatrixType& matrix)
void getMatrix(const MatrixType& matrix)
{
if(Options&Upper==0)
{
// PARDISO supports only upper, row-major matrices
PermutationMatrix<Dynamic,Dynamic,Index> P(0);
SparseMatrix<Scalar,RowMajor> tmp(matrix.rows(), matrix.cols());
tmp.template selfadjointView<Upper>() = matrix.template selfadjointView<Lower>().twistedBy(P);
Base::compute(tmp);
}
else
Base::compute(matrix);
// PARDISO supports only upper, row-major matrices
PermutationMatrix<Dynamic,Dynamic,Index> p_null;
m_matrix.resize(matrix.rows(), matrix.cols());
m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
}
};
namespace internal {

14
test/pardiso_support.cpp
View File

@ -3,16 +3,20 @@
*/
#include "sparse_solver.h"
#include <Eigen/PARDISOSupport>
#include <Eigen/PardisoSupport>
template<typename T> void test_pardiso_T()
{
PardisoLLT < SparseMatrix<T, RowMajor> > pardiso_llt;
PardisoLDLT< SparseMatrix<T, RowMajor> > pardiso_ldlt;
PardisoLLT < SparseMatrix<T, RowMajor>, Lower> pardiso_llt_lower;
PardisoLLT < SparseMatrix<T, RowMajor>, Upper> pardiso_llt_upper;
PardisoLDLT < SparseMatrix<T, RowMajor>, Lower> pardiso_ldlt_lower;
PardisoLDLT < SparseMatrix<T, RowMajor>, Upper> pardiso_ldlt_upper;
PardisoLU < SparseMatrix<T, RowMajor> > pardiso_lu;
check_sparse_spd_solving(pardiso_llt);
check_sparse_spd_solving(pardiso_ldlt);
check_sparse_spd_solving(pardiso_llt_lower);
check_sparse_spd_solving(pardiso_llt_upper);
check_sparse_spd_solving(pardiso_ldlt_lower);
check_sparse_spd_solving(pardiso_ldlt_upper);
check_sparse_square_solving(pardiso_lu);
}