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add a benchmark routine for all sparse linear solvers in Eigen

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Desire NUENTSA
2012-03-29 14:29:55 +02:00
parent caecaf9c9e
commit ada9e79145
6 changed files with 926 additions and 5 deletions
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65
bench/spbench/CMakeLists.txt Normal file
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set(BLAS_FOUND TRUE)
set(LAPACK_FOUND TRUE)
set(BLAS_LIBRARIES eigen_blas_static)
set(LAPACK_LIBRARIES eigen_lapack_static)
set(SPARSE_LIBS "")
# find_library(PARDISO_LIBRARIES pardiso412-GNU450-X86-64)
# if(PARDISO_LIBRARIES)
# add_definitions("-DEIGEN_PARDISO_SUPPORT")
# set(SPARSE_LIBS ${SPARSE_LIBS} ${PARDISO_LIBRARIES})
# endif(PARDISO_LIBRARIES)
find_package(Cholmod)
if(CHOLMOD_FOUND AND BLAS_FOUND AND LAPACK_FOUND)
add_definitions("-DEIGEN_CHOLMOD_SUPPORT")
include_directories(${CHOLMOD_INCLUDES})
set(SPARSE_LIBS ${SPARSE_LIBS} ${CHOLMOD_LIBRARIES} ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES})
set(CHOLMOD_ALL_LIBS ${CHOLMOD_LIBRARIES} ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES})
endif()
find_package(Umfpack)
if(UMFPACK_FOUND AND BLAS_FOUND)
add_definitions("-DEIGEN_UMFPACK_SUPPORT")
include_directories(${UMFPACK_INCLUDES})
set(SPARSE_LIBS ${SPARSE_LIBS} ${UMFPACK_LIBRARIES} ${BLAS_LIBRARIES})
set(UMFPACK_ALL_LIBS ${UMFPACK_LIBRARIES} ${BLAS_LIBRARIES})
endif()
find_package(SuperLU)
if(SUPERLU_FOUND AND BLAS_FOUND)
add_definitions("-DEIGEN_SUPERLU_SUPPORT")
include_directories(${SUPERLU_INCLUDES})
set(SPARSE_LIBS ${SPARSE_LIBS} ${SUPERLU_LIBRARIES} ${BLAS_LIBRARIES})
set(SUPERLU_ALL_LIBS ${SUPERLU_LIBRARIES} ${BLAS_LIBRARIES})
endif()
find_package(Pastix)
find_package(Scotch)
find_package(Metis)
if(PASTIX_FOUND AND BLAS_FOUND)
add_definitions("-DEIGEN_PASTIX_SUPPORT")
include_directories(${PASTIX_INCLUDES})
if(SCOTCH_FOUND)
include_directories(${SCOTCH_INCLUDES})
set(PASTIX_LIBRARIES ${PASTIX_LIBRARIES} ${SCOTCH_LIBRARIES})
elseif(METIS_FOUND)
include_directories(${METIS_INCLUDES})
set(PASTIX_LIBRARIES ${PASTIX_LIBRARIES} ${METIS_LIBRARIES})
endif(SCOTCH_FOUND)
set(SPARSE_LIBS ${SPARSE_LIBS} ${PASTIX_LIBRARIES} ${ORDERING_LIBRARIES} ${BLAS_LIBRARIES})
set(PASTIX_ALL_LIBS ${PASTIX_LIBRARIES} ${BLAS_LIBRARIES})
endif(PASTIX_FOUND AND BLAS_FOUND)
find_library(RT_LIBRARY rt)
if(RT_LIBRARY)
set(SPARSE_LIBS ${SPARSE_LIBS} ${RT_LIBRARY})
endif(RT_LIBRARY)
add_executable(spbenchsolver spbenchsolver.cpp)
target_link_libraries (spbenchsolver ${SPARSE_LIBS})

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bench/spbench/spbenchsolver.cpp Normal file
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#include <bench/spbench/spbenchsolver.h>
void bench_printhelp()
{
cout<< " \nbenchsolver : performs a benchmark of all the solvers available in Eigen \n\n";
cout<< " MATRIX FOLDER : \n";
cout<< " The matrices for the benchmark should be collected in a folder specified with an environment variable EIGEN_MATRIXDIR \n";
cout<< " This folder should contain the subfolders real/ and complex/ : \n";
cout<< " The matrices are stored using the matrix market coordinate format \n";
cout<< " The matrix and associated right-hand side (rhs) files are named respectively \n";
cout<< " as MatrixName.mtx and MatrixName_b.mtx. If the rhs does not exist, a random one is generated. \n";
cout<< " If a matrix is SPD, the matrix should be named as MatrixName_SPD.mtx \n";
cout<< " If a true solution exists, it should be named as MatrixName_x.mtx; \n" ;
cout<< " it will be used to compute the norm of the error relative to the computed solutions\n\n";
cout<< " OPTIONS : \n";
cout<< " -h or --help \n print this help and return\n\n";
cout<< " -d matrixdir \n Use matrixdir as the matrix folder instead of the one specified in the environment variable EIGEN_MATRIXDIR\n\n";
cout<< " -o outputfile.html \n Output the statistics to a html file \n\n";
}
int main(int argc, char ** args)
{
bool help = ( get_options(argc, args, "-h") || get_options(argc, args, "--help") );
if(help) {
bench_printhelp();
return 0;
}
// Get the location of the test matrices
string matrix_dir;
if (!get_options(argc, args, "-d", &matrix_dir))
{
if(getenv("EIGEN_MATRIXDIR") == NULL){
std::cerr << "Please, specify the location of the matrices with -d mat_folder or the environment variable EIGEN_MATRIXDIR \n";
std::cerr << " Run with --help to see the list of all the available options \n";
return -1;
}
matrix_dir = getenv("EIGEN_MATRIXDIR");
}
std::ofstream statbuf;
string statFile ;
// Get the file to write the statistics
bool statFileExists = get_options(argc, args, "-o", &statFile);
if(statFileExists)
{
statbuf.open(statFile.c_str(), std::ios::out);
if(statbuf.good()){
statFileExists = true;
printStatheader(statbuf);
statbuf.close();
}
else
std::cerr << "Unable to open the provided file for writting... \n";
}
string current_dir;
// Test the matrices in %EIGEN_MATRIXDIR/real
current_dir = matrix_dir + "/real";
Browse_Matrices<double>(current_dir, statFileExists, statFile);
// Test the matrices in %EIGEN_MATRIXDIR/complex
current_dir = matrix_dir + "/complex";
Browse_Matrices<std::complex<double> >(current_dir, statFileExists, statFile);
if(statFileExists)
{
statbuf.open(statFile.c_str(), std::ios::app);
statbuf << "</TABLE> \n";
cout << "\n Output written in " << statFile << " ...\n";
statbuf.close();
}
return 0;
}

539
bench/spbench/spbenchsolver.h Normal file
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
//
// Eigen 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.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen 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 or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#include <iostream>
#include <fstream>
#include "Eigen/SparseCore"
#include <bench/BenchTimer.h>
#include <cstdlib>
#include <string>
#include <Eigen/Cholesky>
#include <Eigen/Jacobi>
#include <Eigen/Householder>
#include <Eigen/IterativeLinearSolvers>
#include <Eigen/LU>
#include <unsupported/Eigen/SparseExtra>
#ifdef EIGEN_CHOLMOD_SUPPORT
#include <Eigen/CholmodSupport>
#endif
#ifdef EIGEN_UMFPACK_SUPPORT
#include <Eigen/UmfPackSupport>
#endif
#ifdef EIGEN_PARDISO_SUPPORT
#include <Eigen/PardisoSupport>
#endif
#ifdef EIGEN_SUPERLU_SUPPORT
#include <Eigen/SuperLUSupport>
#endif
#ifdef EIGEN_PASTIX_SUPPORT
#include <Eigen/PaStiXSupport>
#endif
// CONSTANTS
#define EIGEN_UMFPACK 0
#define EIGEN_SUPERLU 1
#define EIGEN_PASTIX 2
#define EIGEN_PARDISO 3
#define EIGEN_BICGSTAB 4
#define EIGEN_BICGSTAB_ILUT 5
#define EIGEN_GMRES 6
#define EIGEN_GMRES_ILUT 7
#define EIGEN_SIMPLICIAL_LDLT 8
#define EIGEN_CHOLMOD_LDLT 9
#define EIGEN_PASTIX_LDLT 10
#define EIGEN_PARDISO_LDLT 11
#define EIGEN_SIMPLICIAL_LLT 12
#define EIGEN_CHOLMOD_SUPERNODAL_LLT 13
#define EIGEN_CHOLMOD_SIMPLICIAL_LLT 14
#define EIGEN_PASTIX_LLT 15
#define EIGEN_PARDISO_LLT 16
#define EIGEN_CG 17
#define EIGEN_CG_PRECOND 18
#define EIGEN_ALL_SOLVERS 19
using namespace Eigen;
using namespace std;
struct Stats{
ComputationInfo info;
double total_time;
double compute_time;
double solve_time;
double rel_error;
int memory_used;
int iterations;
int isavail;
int isIterative;
};
template<typename T> inline typename NumTraits<T>::Real test_precision() { return NumTraits<T>::dummy_precision(); }
template<> inline float test_precision<float>() { return 1e-3f; }
template<> inline double test_precision<double>() { return 1e-6; }
template<> inline float test_precision<std::complex<float> >() { return test_precision<float>(); }
template<> inline double test_precision<std::complex<double> >() { return test_precision<double>(); }
void printStatheader(std::ofstream& out)
{
int LUcnt = 0;
string LUlist =" ", LLTlist = "<TH > LLT", LDLTlist = "<TH > LDLT ";
#ifdef EIGEN_UMFPACK_SUPPORT
LUlist += "<TH > UMFPACK "; LUcnt++;
#endif
#ifdef EIGEN_SUPERLU_SUPPORT
LUlist += "<TH > SUPERLU "; LUcnt++;
#endif
#ifdef EIGEN_CHOLMOD_SUPPORT
LLTlist += "<TH > CHOLMOD SP LLT<TH > CHOLMOD LLT";
LDLTlist += "<TH>CHOLMOD LDLT";
#endif
#ifdef EIGEN_PARDISO_SUPPORT
LUlist += "<TH > PARDISO LU"; LUcnt++;
LLTlist += "<TH > PARDISO LLT";
LDLTlist += "<TH > PARDISO LDLT";
#endif
#ifdef EIGEN_PASTIX_SUPPORT
LUlist += "<TH > PASTIX LU"; LUcnt++;
LLTlist += "<TH > PASTIX LLT";
LDLTlist += "<TH > PASTIX LDLT";
#endif
out << "<TABLE border=\"1\" >\n ";
out << "<TR><TH>Matrix <TH> N <TH> NNZ <TH> ";
if (LUcnt) out << LUlist;
out << " <TH >BiCGSTAB <TH >BiCGSTAB+ILUT"<< "<TH >GMRES+ILUT" <<LDLTlist << LLTlist << "<TH> CG "<< std::endl;
}
template<typename Solver, typename Scalar>
Stats call_solver(Solver &solver, const typename Solver::MatrixType& A, const Matrix<Scalar, Dynamic, 1>& b, const Matrix<Scalar, Dynamic, 1>& refX)
{
Stats stat;
Matrix<Scalar, Dynamic, 1> x;
BenchTimer timer;
timer.reset();
timer.start();
solver.compute(A);
if (solver.info() != Success)
{
stat.info = NumericalIssue;
std::cerr << "Solver failed ... \n";
return stat;
}
timer.stop();
stat.compute_time = timer.value();
timer.reset();
timer.start();
x = solver.solve(b);
if (solver.info() == NumericalIssue)
{
stat.info = NumericalIssue;
std::cerr << "Solver failed ... \n";
return stat;
}
timer.stop();
stat.solve_time = timer.value();
stat.total_time = stat.solve_time + stat.compute_time;
stat.memory_used = 0;
// Verify the relative error
if(refX.size() != 0)
stat.rel_error = (refX - x).norm()/refX.norm();
else
{
// Compute the relative residual norm
Matrix<Scalar, Dynamic, 1> temp;
temp = A * x;
stat.rel_error = (b-temp).norm()/b.norm();
}
if ( stat.rel_error > test_precision<Scalar>() )
{
stat.info = NoConvergence;
return stat;
}
else
{
stat.info = Success;
return stat;
}
}
template<typename Solver, typename Scalar>
Stats call_directsolver(Solver& solver, const typename Solver::MatrixType& A, const Matrix<Scalar, Dynamic, 1>& b, const Matrix<Scalar, Dynamic, 1>& refX)
{
Stats stat;
stat = call_solver(solver, A, b, refX);
return stat;
}
template<typename Solver, typename Scalar>
Stats call_itersolver(Solver &solver, const typename Solver::MatrixType& A, const Matrix<Scalar, Dynamic, 1>& b, const Matrix<Scalar, Dynamic, 1>& refX)
{
Stats stat;
solver.setTolerance(1e-10);
stat = call_solver(solver, A, b, refX);
stat.iterations = solver.iterations();
return stat;
}
inline void printStatItem(Stats *stat, int solver_id, int& best_time_id, double& best_time_val)
{
stat[solver_id].isavail = 1;
if (stat[solver_id].info == NumericalIssue)
{
cout << " SOLVER FAILED ... Probably a numerical issue \n";
return;
}
if (stat[solver_id].info == NoConvergence){
cout << "REL. ERROR " << stat[solver_id].rel_error;
if(stat[solver_id].isIterative == 1)
cout << " (" << stat[solver_id].iterations << ") \n";
return;
}
// Record the best CPU time
if (!best_time_val)
{
best_time_val = stat[solver_id].total_time;
best_time_id = solver_id;
}
else if (stat[solver_id].total_time < best_time_val)
{
best_time_val = stat[solver_id].total_time;
best_time_id = solver_id;
}
// Print statistics to standard output
if (stat[solver_id].info == Success){
cout<< "COMPUTE TIME : " << stat[solver_id].compute_time<< " \n";
cout<< "SOLVE TIME : " << stat[solver_id].solve_time<< " \n";
cout<< "TOTAL TIME : " << stat[solver_id].total_time<< " \n";
cout << "REL. ERROR : " << stat[solver_id].rel_error ;
if(stat[solver_id].isIterative == 1) {
cout << " (" << stat[solver_id].iterations << ") ";
}
cout << std::endl;
}
}
/* Print the results from all solvers corresponding to a particular matrix
* The best CPU time is printed in bold
*/
inline void printHtmlStatLine(Stats *stat, int best_time_id, string& statline)
{
string markup;
ostringstream compute,solve,total,error;
for (int i = 0; i < EIGEN_ALL_SOLVERS; i++)
{
if (stat[i].isavail == 0) continue;
if(i == best_time_id)
markup = "<TD style=\"background-color:red\">";
else
markup = "<TD>";
if (stat[i].info == Success){
compute << markup << stat[i].compute_time;
solve << markup << stat[i].solve_time;
total << markup << stat[i].total_time;
error << " <TD> " << stat[i].rel_error;
if(stat[i].isIterative == 1) {
error << " (" << stat[i].iterations << ") ";
}
}
else {
compute << " <TD> -" ;
solve << " <TD> -" ;
total << " <TD> -" ;
if(stat[i].info == NoConvergence){
error << " <TD> "<< stat[i].rel_error ;
if(stat[i].isIterative == 1)
error << " (" << stat[i].iterations << ") ";
}
else error << " <TD> - ";
}
}
statline = "<TH>Compute Time " + compute.str() + "\n"
+ "<TR><TH>Solve Time " + solve.str() + "\n"
+ "<TR><TH>Total Time " + total.str() + "\n"
+"<TR><TH>Error(Iter)" + error.str() + "\n";
}
template <typename Scalar>
int SelectSolvers(const SparseMatrix<Scalar>&A, unsigned int sym, Matrix<Scalar, Dynamic, 1>& b, const Matrix<Scalar, Dynamic, 1>& refX, Stats *stat)
{
typedef SparseMatrix<Scalar, ColMajor> SpMat;
// First, deal with Nonsymmetric and symmetric matrices
int best_time_id = 0;
double best_time_val = 0.0;
//UMFPACK
#ifdef EIGEN_UMFPACK_SUPPORT
{
cout << "Solving with UMFPACK LU ... \n";
UmfPackLU<SpMat> solver;
stat[EIGEN_UMFPACK] = call_directsolver(solver, A, b, refX);
printStatItem(stat, EIGEN_UMFPACK, best_time_id, best_time_val);
}
#endif
//SuperLU
#ifdef EIGEN_SUPERLU_SUPPORT
{
cout << "\nSolving with SUPERLU ... \n";
SuperLU<SpMat> solver;
stat[EIGEN_SUPERLU] = call_directsolver(solver, A, b, refX);
printStatItem(stat, EIGEN_SUPERLU, best_time_id, best_time_val);
}
#endif
// PaStix LU
#ifdef EIGEN_PASTIX_SUPPORT
{
cout << "\nSolving with PASTIX LU ... \n";
PastixLU<SpMat> solver;
stat[EIGEN_PASTIX] = call_directsolver(solver, A, b, refX) ;
printStatItem(stat, EIGEN_PASTIX, best_time_id, best_time_val);
}
#endif
//PARDISO LU
#ifdef EIGEN_PARDISO_SUPPORT
{
cout << "\nSolving with PARDISO LU ... \n";
PardisoLU<SpMat> solver;
stat[EIGEN_PARDISO] = call_directsolver(solver, A, b, refX);
printStatItem(stat, EIGEN_PARDISO, best_time_id, best_time_val);
}
#endif
//BiCGSTAB
{
cout << "\nSolving with BiCGSTAB ... \n";
BiCGSTAB<SpMat> solver;
stat[EIGEN_BICGSTAB] = call_itersolver(solver, A, b, refX);
stat[EIGEN_BICGSTAB].isIterative = 1;
printStatItem(stat, EIGEN_BICGSTAB, best_time_id, best_time_val);
}
//BiCGSTAB+ILUT
{
cout << "\nSolving with BiCGSTAB and ILUT ... \n";
BiCGSTAB<SpMat, IncompleteLUT<Scalar> > solver;
stat[EIGEN_BICGSTAB_ILUT] = call_itersolver(solver, A, b, refX);
stat[EIGEN_BICGSTAB_ILUT].isIterative = 1;
printStatItem(stat, EIGEN_BICGSTAB_ILUT, best_time_id, best_time_val);
}
//GMRES
// {
// cout << "\nSolving with GMRES ... \n";
// GMRES<SpMat> solver;
// stat[EIGEN_GMRES] = call_itersolver(solver, A, b, refX);
// stat[EIGEN_GMRES].isIterative = 1;
// printStatItem(stat, EIGEN_GMRES, best_time_id, best_time_val);
// }
//GMRES+ILUT
{
cout << "\nSolving with GMRES and ILUT ... \n";
GMRES<SpMat, IncompleteLUT<Scalar> > solver;
stat[EIGEN_GMRES_ILUT] = call_itersolver(solver, A, b, refX);
stat[EIGEN_GMRES_ILUT].isIterative = 1;
printStatItem(stat, EIGEN_GMRES_ILUT, best_time_id, best_time_val);
}
// Symmetric and not necessarily positive-definites
if ( (sym == Symmetric) || (sym == SPD) )
{
// Internal Cholesky
{
cout << "\nSolving with Simplicial LDLT ... \n";
SimplicialLDLT<SpMat, Lower> solver;
stat[EIGEN_SIMPLICIAL_LDLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat, EIGEN_SIMPLICIAL_LDLT, best_time_id, best_time_val);
}
// CHOLMOD
#ifdef EIGEN_CHOLMOD_SUPPORT
{
cout << "\nSolving with CHOLMOD LDLT ... \n";
CholmodDecomposition<SpMat, Lower> solver;
solver.setMode(CholmodLDLt);
stat[EIGEN_CHOLMOD_LDLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat,EIGEN_CHOLMOD_LDLT, best_time_id, best_time_val);
}
#endif
//PASTIX LLT
#ifdef EIGEN_PASTIX_SUPPORT
{
cout << "\nSolving with PASTIX LDLT ... \n";
PastixLDLT<SpMat, Lower> solver;
stat[EIGEN_PASTIX_LDLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat,EIGEN_PASTIX_LDLT, best_time_id, best_time_val);
}
#endif
//PARDISO LLT
#ifdef EIGEN_PARDISO_SUPPORT
{
cout << "\nSolving with PARDISO LDLT ... \n";
PardisoLDLT<SpMat, Lower> solver;
stat[EIGEN_PARDISO_LDLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat,EIGEN_PARDISO_LDLT, best_time_id, best_time_val);
}
#endif
}
// Now, symmetric POSITIVE DEFINITE matrices
if (sym == SPD)
{
//Internal Sparse Cholesky
{
cout << "\nSolving with SIMPLICIAL LLT ... \n";
SimplicialLLT<SpMat, Lower> solver;
stat[EIGEN_SIMPLICIAL_LLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat,EIGEN_SIMPLICIAL_LLT, best_time_id, best_time_val);
}
// CHOLMOD
#ifdef EIGEN_CHOLMOD_SUPPORT
{
// CholMOD SuperNodal LLT
cout << "\nSolving with CHOLMOD LLT (Supernodal)... \n";
CholmodDecomposition<SpMat, Lower> solver;
solver.setMode(CholmodSupernodalLLt);
stat[EIGEN_CHOLMOD_SUPERNODAL_LLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat,EIGEN_CHOLMOD_SUPERNODAL_LLT, best_time_id, best_time_val);
// CholMod Simplicial LLT
cout << "\nSolving with CHOLMOD LLT (Simplicial) ... \n";
solver.setMode(CholmodSimplicialLLt);
stat[EIGEN_CHOLMOD_SIMPLICIAL_LLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat,EIGEN_CHOLMOD_SIMPLICIAL_LLT, best_time_id, best_time_val);
}
#endif
//PASTIX LLT
#ifdef EIGEN_PASTIX_SUPPORT
{
cout << "\nSolving with PASTIX LLT ... \n";
PastixLLT<SpMat, Lower> solver;
stat[EIGEN_PASTIX_LLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat,EIGEN_PASTIX_LLT, best_time_id, best_time_val);
}
#endif
//PARDISO LLT
#ifdef EIGEN_PARDISO_SUPPORT
{
cout << "\nSolving with PARDISO LLT ... \n";
PardisoLLT<SpMat, Lower> solver;
stat[EIGEN_PARDISO_LLT] = call_directsolver(solver, A, b, refX);
printStatItem(stat,EIGEN_PARDISO_LLT, best_time_id, best_time_val);
}
#endif
// Internal CG
{
cout << "\nSolving with CG ... \n";
ConjugateGradient<SpMat, Lower> solver;
stat[EIGEN_CG] = call_itersolver(solver, A, b, refX);
stat[EIGEN_CG].isIterative = 1;
printStatItem(stat,EIGEN_CG, best_time_id, best_time_val);
}
//CG+IdentityPreconditioner
// {
// cout << "\nSolving with CG and IdentityPreconditioner ... \n";
// ConjugateGradient<SpMat, Lower, IdentityPreconditioner> solver;
// stat[EIGEN_CG_PRECOND] = call_itersolver(solver, A, b, refX);
// stat[EIGEN_CG_PRECOND].isIterative = 1;
// printStatItem(stat,EIGEN_CG_PRECOND, best_time_id, best_time_val);
// }
} // End SPD matrices
return best_time_id;
}
/* Browse all the matrices available in the specified folder
* and solve the associated linear system.
* The results of each solve are printed in the standard output
* and optionally in the provided html file
*/
template <typename Scalar>
void Browse_Matrices(const string folder, bool statFileExists, std::string& statFile)
{
MatrixMarketIterator<Scalar> it(folder);
Stats stat[EIGEN_ALL_SOLVERS];
for ( ; it; ++it)
{
for (int i = 0; i < EIGEN_ALL_SOLVERS; i++)
{
stat[i].isavail = 0;
stat[i].isIterative = 0;
}
int best_time_id;
cout<< "\n\n===================================================== \n";
cout<< " ====== SOLVING WITH MATRIX " << it.matname() << " ====\n";
cout<< " =================================================== \n\n";
Matrix<Scalar, Dynamic, 1> refX;
if(it.hasrefX()) refX = it.refX();
best_time_id = SelectSolvers<Scalar>(it.matrix(), it.sym(), it.rhs(), refX, &stat[0]);
if(statFileExists)
{
string statline;
printHtmlStatLine(&stat[0], best_time_id, statline);
std::ofstream statbuf(statFile.c_str(), std::ios::app);
statbuf << "<TR><TH rowspan=\"4\">" << it.matname() << " <TD rowspan=\"4\"> "
<< it.matrix().rows() << " <TD rowspan=\"4\"> " << it.matrix().nonZeros()<< " "<< statline ;
statbuf.close();
}
}
}
bool get_options(int argc, char **args, string option, string* value=0)
{
int idx = 1, found=false;
while (idx<argc && !found){
if (option.compare(args[idx]) == 0){
found = true;
if(value) *value = args[idx+1];
}
idx+=2;
}
return found;
}