// ===================================================
// Methods
// ===================================================
Int
PreconditionerML::buildPreconditioner ( operator_type& matrix )
{
//the Trilinos::MultiLevelPreconditioner unsafely access to the area of memory co-owned by M_operator.
//to avoid the risk of dandling pointers always deallocate M_preconditioner first and then M_operator
M_preconditioner.reset();
M_operator = matrix;
M_precType = M_list.get ( "prec type", "undefined??" );
M_precType += "_ML";
// <one-level-postsmoothing> / <two-level-additive>
// <two-level-hybrid> / <two-level-hybrid2>
M_preconditioner.reset ( new prec_raw_type ( * (M_operator->matrixPtr() ), this->parametersList(), true ) );
if ( M_analyze )
{
ML_Epetra::MultiLevelPreconditioner* prec;
prec = dynamic_cast<ML_Epetra::MultiLevelPreconditioner*> ( M_preconditioner.get() );
Int NumPreCycles = 5;
Int NumPostCycles = 1;
Int NumMLCycles = 10;
prec->AnalyzeHierarchy ( true, NumPreCycles, NumPostCycles, NumMLCycles );
}
this->M_preconditionerCreated = true;
return ( EXIT_SUCCESS );
}
void
TrilinosPreconditioner<T>::compute()
{
Ifpack_Preconditioner * ifpack = NULL;
#ifdef LIBMESH_HAVE_ML
ML_Epetra::MultiLevelPreconditioner * ml = NULL;
#endif
switch (this->_preconditioner_type)
{
// IFPACK preconditioners
case ILU_PRECOND:
case SOR_PRECOND:
ifpack = dynamic_cast<Ifpack_Preconditioner *>(_prec);
ifpack->Compute();
break;
#ifdef LIBMESH_HAVE_ML
// ML preconditioners
case AMG_PRECOND:
ml = dynamic_cast<ML_Epetra::MultiLevelPreconditioner *>(_prec);
ml->ComputePreconditioner();
break;
#endif
default:
// no nothing here
break;
}
}
void
TrilinosPreconditioner<T>::compute()
{
#ifdef LIBMESH_TRILINOS_HAVE_IFPACK
Ifpack_Preconditioner * ifpack = libmesh_nullptr;
#endif
#ifdef LIBMESH_TRILINOS_HAVE_ML
ML_Epetra::MultiLevelPreconditioner * ml = libmesh_nullptr;
#endif
switch (this->_preconditioner_type)
{
#ifdef LIBMESH_TRILINOS_HAVE_IFPACK
// IFPACK preconditioners
case ILU_PRECOND:
case SOR_PRECOND:
ifpack = dynamic_cast<Ifpack_Preconditioner *>(_prec);
ifpack->Compute();
break;
#endif
#ifdef LIBMESH_TRILINOS_HAVE_ML
// ML preconditioners
case AMG_PRECOND:
ml = dynamic_cast<ML_Epetra::MultiLevelPreconditioner *>(_prec);
ml->ComputePreconditioner();
break;
#endif
default:
// If we made it here, there were no TrilinosPreconditioners
// active, so that's probably an error.
libmesh_error_msg("ERROR: No valid TrilinosPreconditioners available!");
break;
}
}
PetscErrorCode ShellApplyML(PC pc,Vec x,Vec y)
{
PetscErrorCode ierr;
ML_Epetra::MultiLevelPreconditioner *mlp = 0;
void* ctx;
ierr = PCShellGetContext(pc,&ctx); CHKERRQ(ierr);
mlp = (ML_Epetra::MultiLevelPreconditioner*)ctx;
#endif
/* Wrap x and y as Epetra_Vectors. */
PetscScalar *xvals,*yvals;
ierr = VecGetArray(x,&xvals);CHKERRQ(ierr);
Epetra_Vector epx(View,mlp->OperatorDomainMap(),xvals);
ierr = VecGetArray(y,&yvals);CHKERRQ(ierr);
Epetra_Vector epy(View,mlp->OperatorRangeMap(),yvals);
/* Apply ML. */
mlp->ApplyInverse(epx,epy);
/* Clean up and return. */
ierr = VecRestoreArray(x,&xvals);CHKERRQ(ierr);
ierr = VecRestoreArray(y,&yvals);CHKERRQ(ierr);
return 0;
} /*ShellApplyML*/
int main(int argc, char *argv[])
{
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
// Creates the linear problem using the Galeri package.
// Several matrix examples are supported; please refer to the
// Galeri documentation for more details.
// Most of the examples using the ML_Epetra::MultiLevelPreconditioner
// class are based on Epetra_CrsMatrix. Example
// `ml_EpetraVbr.cpp' shows how to define a Epetra_VbrMatrix.
// `Laplace2D' is a symmetric matrix; an example of non-symmetric
// matrices is `Recirc2D' (advection-diffusion in a box, with
// recirculating flow). The grid has nx x ny nodes, divided into
// mx x my subdomains, each assigned to a different processor.
int nx;
if (argc > 1)
nx = (int) strtol(argv[1],NULL,10);
else
nx = 8;
int ny = nx * Comm.NumProc(); // each subdomain is a square
ParameterList GaleriList;
GaleriList.set("nx", nx);
GaleriList.set("ny", ny);
GaleriList.set("mx", 1);
GaleriList.set("my", Comm.NumProc());
Epetra_Map* Map = CreateMap("Cartesian2D", Comm, GaleriList);
Epetra_CrsMatrix* A = CreateCrsMatrix("Laplace2D", Map, GaleriList);
// Build a linear system with trivial solution, using a random vector
// as starting solution.
Epetra_Vector LHS(*Map); LHS.Random();
Epetra_Vector RHS(*Map); RHS.PutScalar(0.0);
Epetra_LinearProblem Problem(A, &LHS, &RHS);
// As we wish to use AztecOO, we need to construct a solver object
// for this problem
AztecOO solver(Problem);
// =========================== begin of ML part ===========================
// create a parameter list for ML options
ParameterList MLList;
// Sets default parameters for classic smoothed aggregation. After this
// call, MLList contains the default values for the ML parameters,
// as required by typical smoothed aggregation for symmetric systems.
// Other sets of parameters are available for non-symmetric systems
// ("DD" and "DD-ML"), and for the Maxwell equations ("maxwell").
ML_Epetra::SetDefaults("SA",MLList);
// overwrite some parameters. Please refer to the user's guide
// for more information
// some of the parameters do not differ from their default value,
// and they are here reported for the sake of clarity
// output level, 0 being silent and 10 verbose
MLList.set("ML output", 10);
// maximum number of levels
MLList.set("max levels",5);
// set finest level to 0
MLList.set("increasing or decreasing","increasing");
// use Uncoupled scheme to create the aggregate
MLList.set("aggregation: type", "Uncoupled");
// smoother is symmetric Gauss-Seidel. Example file
// `ml/examples/TwoLevelDD/ml_2level_DD.cpp' shows how to use
// AZTEC's preconditioners as smoothers
//MLList.set("smoother: type","symmetric Gauss-Seidel");
// use both pre and post smoothing
MLList.set("smoother: pre or post", "both");
MLList.set("smoother: type", "user-defined");
//The function pointer to the user-defined smoother.
MLList.set("smoother: user-defined function",userSmoother);
//(optional) The label for the user-defined smoother.
MLList.set("smoother: user-defined name","myJacobi");
//Use this smoother on the finest level, 0.
MLList.set("smoother: type (level 0)", "user-defined");
MLList.set("smoother: sweeps (level 0)", 1);
//Use symmetric Gauss-Seidel on all subsequent levels.
MLList.set("smoother: type (level 1)", "symmetric Gauss-Seidel");
//user-defined smoothing for a 1-level method only
//to use this, uncomment the next 3 lines and comment out the direct-solver
//settings further below
//MLList.set("coarse: type", "user-defined");
//MLList.set("coarse: user-defined function",userSmoother);
//MLList.set("coarse: user-defined name","myJacobi");
#ifdef HAVE_ML_AMESOS
// solve with serial direct solver KLU
MLList.set("coarse: type","Amesos-KLU");
#else
// this is for testing purposes only, you should have
// a direct solver for the coarse problem (either Amesos, or the SuperLU/
// SuperLU_DIST interface of ML)
MLList.set("aggregation: type", "MIS");
MLList.set("smoother: type","Jacobi");
MLList.set("coarse: type","Jacobi");
#endif
MLList.set("read XML", false); // skip XML file in this directory
// Creates the preconditioning object. We suggest to use `new' and
// `delete' because the destructor contains some calls to MPI (as
// required by ML and possibly Amesos). This is an issue only if the
// destructor is called **after** MPI_Finalize().
ML_Epetra::MultiLevelPreconditioner* MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*A, MLList);
// verify unused parameters on process 0 (put -1 to print on all
// processes)
MLPrec->PrintUnused(0);
// =========================== end of ML part =============================
// tell AztecOO to use the ML preconditioner, specify the solver
// and the output, then solve with 500 maximum iterations and 1e-12
// of tolerance (see AztecOO's user guide for more details)
solver.SetPrecOperator(MLPrec);
solver.SetAztecOption(AZ_solver, AZ_gmres);
solver.SetAztecOption(AZ_output, 32);
solver.Iterate(500, 1e-12);
// destroy the preconditioner
delete MLPrec;
// compute the real residual
double residual;
LHS.Norm2(&residual);
if( Comm.MyPID()==0 ) {
cout << "||b-Ax||_2 = " << residual << endl;
}
// for testing purposes
if (residual > 1e-5)
exit(EXIT_FAILURE);
delete A;
delete Map;
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return(EXIT_SUCCESS);
} //main
int main(int argc, char *argv[])
{
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
#define ML_SCALING
#ifdef ML_SCALING
const int ntimers=4;
enum {total, probBuild, precBuild, solve};
ml_DblLoc timeVec[ntimers], maxTime[ntimers], minTime[ntimers];
for (int i=0; i<ntimers; i++) timeVec[i].rank = Comm.MyPID();
timeVec[total].value = MPI_Wtime();
#endif
int nx;
if (argc > 1) nx = (int) strtol(argv[1],NULL,10);
else nx = 256;
if (nx < 1) nx = 256; // input a nonpositive integer if you want to specify
// the XML input file name.
nx = nx*(int)sqrt((double)Comm.NumProc());
int ny = nx;
printf("nx = %d\nny = %d\n",nx,ny);
fflush(stdout);
char xmlFile[80];
bool readXML=false;
if (argc > 2) {strcpy(xmlFile,argv[2]); readXML = true;}
else sprintf(xmlFile,"%s","params.xml");
ParameterList GaleriList;
GaleriList.set("nx", nx);
GaleriList.set("ny", ny);
#ifdef ML_SCALING
timeVec[probBuild].value = MPI_Wtime();
#endif
Epetra_Map* Map = CreateMap("Cartesian2D", Comm, GaleriList);
Epetra_CrsMatrix* A = CreateCrsMatrix("Laplace2D", Map, GaleriList);
if (!Comm.MyPID()) printf("nx = %d, ny = %d, mx = %d, my = %d\n",nx,ny,GaleriList.get("mx",-1),GaleriList.get("my",-1));
fflush(stdout);
//avoid potential overflow
double numMyRows = A->NumMyRows();
double numGlobalRows;
Comm.SumAll(&numMyRows,&numGlobalRows,1);
if (!Comm.MyPID()) printf("# global rows = %1.0f\n",numGlobalRows);
//printf("pid %d: #rows = %d\n",Comm.MyPID(),A->NumMyRows());
fflush(stdout);
Epetra_MultiVector *coords = CreateCartesianCoordinates("2D", Map,GaleriList);
double *x_coord=0,*y_coord=0,*z_coord=0;
double **ttt;
if (!coords->ExtractView(&ttt)) {
x_coord = ttt[0];
y_coord = ttt[1];
} else {
if (!Comm.MyPID()) printf("Error extracting coordinate vectors\n");
MPI_Finalize();
exit(EXIT_FAILURE);
}
Epetra_Vector LHS(*Map); LHS.Random();
Epetra_Vector RHS(*Map); RHS.PutScalar(0.0);
Epetra_LinearProblem Problem(A, &LHS, &RHS);
AztecOO solver(Problem);
#ifdef ML_SCALING
timeVec[probBuild].value = MPI_Wtime() - timeVec[probBuild].value;
#endif
// =========================== begin of ML part ===========================
#ifdef ML_SCALING
timeVec[precBuild].value = MPI_Wtime();
#endif
ParameterList MLList;
if (readXML) {
MLList.set("read XML",true);
MLList.set("XML input file",xmlFile);
}
else {
cout << "here" << endl;
ML_Epetra::SetDefaults("SA",MLList);
MLList.set("smoother: type","Chebyshev");
MLList.set("smoother: sweeps",3);
MLList.set("coarse: max size",1);
}
MLList.set("x-coordinates", x_coord);
MLList.set("y-coordinates", y_coord);
MLList.set("z-coordinates", z_coord);
/*
RCP<std::vector<int> >
m_smootherAztecOptions = rcp(new std::vector<int>(AZ_OPTIONS_SIZE));
RCP<std::vector<double> >
m_smootherAztecParams = rcp(new std::vector<double>(AZ_PARAMS_SIZE));
//int m_smootherAztecOptions[AZ_OPTIONS_SIZE];
//double m_smootherAztecParams[AZ_PARAMS_SIZE];
std::string smootherType("Aztec");
AZ_defaults(&(*m_smootherAztecOptions)[0],&(*m_smootherAztecParams)[0]);
(*m_smootherAztecOptions)[AZ_precond] = AZ_dom_decomp;
(*m_smootherAztecOptions)[AZ_subdomain_solve] = AZ_icc;
bool smootherAztecAsSolver = true;
*/
MLList.set("ML output",10);
MLList.set("repartition: enable",1);
MLList.set("repartition: max min ratio",1.3);
MLList.set("repartition: min per proc",200);
MLList.set("repartition: partitioner","Zoltan");
MLList.set("repartition: Zoltan dimensions",2);
MLList.set("repartition: put on single proc",1);
MLList.set("repartition: Zoltan type","hypergraph");
MLList.set("repartition: estimated iterations",13);
/*
MLList.set("smoother: Aztec options",m_smootherAztecOptions);
MLList.set("smoother: Aztec params",m_smootherAztecParams);
MLList.set("smoother: type",smootherType.c_str());
MLList.set("smoother: Aztec as solver", smootherAztecAsSolver);
MLList.set("ML print initial list", 0);
MLList.set("ML print final list", 0);
*/
ML_Epetra::MultiLevelPreconditioner* MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*A, MLList);
// verify unused parameters on process 0 (put -1 to print on all
// processes)
MLPrec->PrintUnused(0);
#ifdef ML_SCALING
timeVec[precBuild].value = MPI_Wtime() - timeVec[precBuild].value;
#endif
// =========================== end of ML part =============================
#ifdef ML_SCALING
timeVec[solve].value = MPI_Wtime();
#endif
solver.SetPrecOperator(MLPrec);
solver.SetAztecOption(AZ_solver, AZ_cg);
solver.SetAztecOption(AZ_output, 1);
solver.Iterate(500, 1e-12);
#ifdef ML_SCALING
timeVec[solve].value = MPI_Wtime() - timeVec[solve].value;
#endif
// destroy the preconditioner
delete MLPrec;
// compute the real residual
double residual;
LHS.Norm2(&residual);
if( Comm.MyPID()==0 ) {
cout << "||b-Ax||_2 = " << residual << endl;
}
// for testing purposes
if (residual > 1e-5)
exit(EXIT_FAILURE);
delete A;
delete Map;
delete coords;
#ifdef ML_SCALING
timeVec[total].value = MPI_Wtime() - timeVec[total].value;
//avg
double dupTime[ntimers],avgTime[ntimers];
for (int i=0; i<ntimers; i++) dupTime[i] = timeVec[i].value;
MPI_Reduce(dupTime,avgTime,ntimers,MPI_DOUBLE,MPI_SUM,0,MPI_COMM_WORLD);
for (int i=0; i<ntimers; i++) avgTime[i] = avgTime[i]/Comm.NumProc();
//min
MPI_Reduce(timeVec,minTime,ntimers,MPI_DOUBLE_INT,MPI_MINLOC,0,MPI_COMM_WORLD);
//max
MPI_Reduce(timeVec,maxTime,ntimers,MPI_DOUBLE_INT,MPI_MAXLOC,0,MPI_COMM_WORLD);
if (Comm.MyPID() == 0) {
printf("timing : max (pid) min (pid) avg\n");
printf("Problem build : %2.3e (%d) %2.3e (%d) %2.3e \n",
maxTime[probBuild].value,maxTime[probBuild].rank,
minTime[probBuild].value,minTime[probBuild].rank,
avgTime[probBuild]);
printf("Preconditioner build : %2.3e (%d) %2.3e (%d) %2.3e \n",
maxTime[precBuild].value,maxTime[precBuild].rank,
minTime[precBuild].value,minTime[precBuild].rank,
avgTime[precBuild]);
printf("Solve : %2.3e (%d) %2.3e (%d) %2.3e \n",
maxTime[solve].value,maxTime[solve].rank,
minTime[solve].value,minTime[solve].rank,
avgTime[solve]);
printf("Total : %2.3e (%d) %2.3e (%d) %2.3e \n",
maxTime[total].value,maxTime[total].rank,
minTime[total].value,minTime[total].rank,
avgTime[total]);
}
#endif
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return(EXIT_SUCCESS);
}
int main(int argc, char *argv[])
{
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
Teuchos::CommandLineProcessor clp(false);
clp.setDocString("This is the canonical ML scaling example");
//Problem
std::string optMatrixType = "Laplace2D"; clp.setOption("matrixType", &optMatrixType, "matrix type ('Laplace2D', 'Laplace3D')");
int optNx = 100; clp.setOption("nx", &optNx, "mesh size in x direction");
int optNy = -1; clp.setOption("ny", &optNy, "mesh size in y direction");
int optNz = -1; clp.setOption("nz", &optNz, "mesh size in z direction");
//Smoothers
//std::string optSmooType = "Chebshev"; clp.setOption("smooType", &optSmooType, "smoother type ('l1-sgs', 'sgs 'or 'cheby')");
int optSweeps = 3; clp.setOption("sweeps", &optSweeps, "Chebyshev degreee (or SGS sweeps)");
double optAlpha = 7; clp.setOption("alpha", &optAlpha, "Chebyshev eigenvalue ratio (recommend 7 in 2D, 20 in 3D)");
//Coarsening
int optMaxCoarseSize = 500; clp.setOption("maxcoarse", &optMaxCoarseSize, "Size of coarsest grid when coarsening should stop");
int optMaxLevels = 10; clp.setOption("maxlevels", &optMaxLevels, "Maximum number of levels");
//Krylov solver
double optTol = 1e-12; clp.setOption("tol", &optTol, "stopping tolerance for Krylov method");
int optMaxIts = 500; clp.setOption("maxits", &optMaxIts, "maximum iterations for Krylov method");
//XML file with additional options
std::string xmlFile = ""; clp.setOption("xml", &xmlFile, "XML file containing ML options. [OPTIONAL]");
//Debugging
int optWriteMatrices = -2; clp.setOption("write", &optWriteMatrices, "write matrices to file (-1 means all; i>=0 means level i)");
switch (clp.parse(argc, argv)) {
case Teuchos::CommandLineProcessor::PARSE_HELP_PRINTED: return EXIT_SUCCESS; break;
case Teuchos::CommandLineProcessor::PARSE_ERROR:
case Teuchos::CommandLineProcessor::PARSE_UNRECOGNIZED_OPTION: return EXIT_FAILURE; break;
case Teuchos::CommandLineProcessor::PARSE_SUCCESSFUL: break;
}
#ifdef ML_SCALING
const int ntimers=4;
enum {total, probBuild, precBuild, solve};
ml_DblLoc timeVec[ntimers], maxTime[ntimers], minTime[ntimers];
for (int i=0; i<ntimers; i++) timeVec[i].rank = Comm.MyPID();
timeVec[total].value = MPI_Wtime();
#endif
// Creates the linear problem using the Galeri package.
// Several matrix examples are supported; please refer to the
// Galeri documentation for more details.
// Most of the examples using the ML_Epetra::MultiLevelPreconditioner
// class are based on Epetra_CrsMatrix. Example
// `ml_EpetraVbr.cpp' shows how to define a Epetra_VbrMatrix.
// `Laplace2D' is a symmetric matrix; an example of non-symmetric
// matrices is `Recirc2D' (advection-diffusion in a box, with
// recirculating flow). The grid has optNx x optNy nodes, divided into
// mx x my subdomains, each assigned to a different processor.
if (optNy == -1) optNy = optNx;
if (optNz == -1) optNz = optNx;
ParameterList GaleriList;
GaleriList.set("nx", optNx);
GaleriList.set("ny", optNy);
GaleriList.set("nz", optNz);
//GaleriList.set("mx", 1);
//GaleriList.set("my", Comm.NumProc());
#ifdef ML_SCALING
timeVec[probBuild].value = MPI_Wtime();
#endif
Epetra_Map* Map;
Epetra_CrsMatrix* A;
Epetra_MultiVector* Coord;
if (optMatrixType == "Laplace2D") {
Map = CreateMap("Cartesian2D", Comm, GaleriList);
A = CreateCrsMatrix("Laplace2D", Map, GaleriList);
Coord = CreateCartesianCoordinates("2D", &(A->Map()), GaleriList);
} else if (optMatrixType == "Laplace3D") {
Map = CreateMap("Cartesian3D", Comm, GaleriList);
A = CreateCrsMatrix("Laplace3D", Map, GaleriList);
Coord = CreateCartesianCoordinates("3D", &(A->Map()), GaleriList);
} else {
throw(std::runtime_error("Bad matrix type"));
}
//EpetraExt::RowMatrixToMatlabFile("A.m",*A);
double *x_coord = (*Coord)[0];
double *y_coord = (*Coord)[1];
double* z_coord=NULL;
if (optMatrixType == "Laplace3D") z_coord = (*Coord)[2];
//EpetraExt::MultiVectorToMatrixMarketFile("mlcoords.m",*Coord);
if( Comm.MyPID()==0 ) {
std::cout << "========================================================" << std::endl;
std::cout << " Matrix type: " << optMatrixType << std::endl;
if (optMatrixType == "Laplace2D")
std::cout << " Problem size: " << optNx*optNy << " (" << optNx << "x" << optNy << ")" << std::endl;
else if (optMatrixType == "Laplace3D")
std::cout << " Problem size: " << optNx*optNy*optNz << " (" << optNx << "x" << optNy << "x" << optNz << ")" << std::endl;
int mx = GaleriList.get("mx", -1);
int my = GaleriList.get("my", -1);
int mz = GaleriList.get("my", -1);
std::cout << " Processor subdomains in x direction: " << mx << std::endl
<< " Processor subdomains in y direction: " << my << std::endl;
if (optMatrixType == "Laplace3D")
std::cout << " Processor subdomains in z direction: " << mz << std::endl;
std::cout << "========================================================" << std::endl;
}
// Build a linear system with trivial solution, using a random vector
// as starting solution.
Epetra_Vector LHS(*Map); LHS.Random();
Epetra_Vector RHS(*Map); RHS.PutScalar(0.0);
Epetra_LinearProblem Problem(A, &LHS, &RHS);
// As we wish to use AztecOO, we need to construct a solver object
// for this problem
AztecOO solver(Problem);
#ifdef ML_SCALING
timeVec[probBuild].value = MPI_Wtime() - timeVec[probBuild].value;
#endif
// =========================== begin of ML part ===========================
#ifdef ML_SCALING
timeVec[precBuild].value = MPI_Wtime();
#endif
// create a parameter list for ML options
ParameterList MLList;
// Sets default parameters for classic smoothed aggregation. After this
// call, MLList contains the default values for the ML parameters,
// as required by typical smoothed aggregation for symmetric systems.
// Other sets of parameters are available for non-symmetric systems
// ("DD" and "DD-ML"), and for the Maxwell equations ("maxwell").
ML_Epetra::SetDefaults("SA",MLList);
// overwrite some parameters. Please refer to the user's guide
// for more information
// some of the parameters do not differ from their default value,
// and they are here reported for the sake of clarity
// output level, 0 being silent and 10 verbose
MLList.set("ML output", 10);
// maximum number of levels
MLList.set("max levels",optMaxLevels);
// set finest level to 0
MLList.set("increasing or decreasing","increasing");
MLList.set("coarse: max size",optMaxCoarseSize);
// use Uncoupled scheme to create the aggregate
MLList.set("aggregation: type", "Uncoupled");
// smoother is Chebyshev. Example file
// `ml/examples/TwoLevelDD/ml_2level_DD.cpp' shows how to use
// AZTEC's preconditioners as smoothers
MLList.set("smoother: type","Chebyshev");
MLList.set("smoother: Chebyshev alpha",optAlpha);
MLList.set("smoother: sweeps",optSweeps);
// use both pre and post smoothing
MLList.set("smoother: pre or post", "both");
#ifdef HAVE_ML_AMESOS
// solve with serial direct solver KLU
MLList.set("coarse: type","Amesos-KLU");
#else
// this is for testing purposes only, you should have
// a direct solver for the coarse problem (either Amesos, or the SuperLU/
// SuperLU_DIST interface of ML)
MLList.set("coarse: type","Jacobi");
#endif
MLList.set("repartition: enable",1);
MLList.set("repartition: start level",1);
MLList.set("repartition: max min ratio",1.1);
MLList.set("repartition: min per proc",800);
MLList.set("repartition: partitioner","Zoltan");
MLList.set("repartition: put on single proc",1);
MLList.set("x-coordinates", x_coord);
MLList.set("y-coordinates", y_coord);
if (optMatrixType == "Laplace2D") {
MLList.set("repartition: Zoltan dimensions",2);
} else if (optMatrixType == "Laplace3D") {
MLList.set("repartition: Zoltan dimensions",3);
MLList.set("z-coordinates", z_coord);
}
MLList.set("print hierarchy",optWriteMatrices);
//MLList.set("aggregation: damping factor",0.);
// Read in XML options
if (xmlFile != "")
ML_Epetra::ReadXML(xmlFile,MLList,Comm);
// Creates the preconditioning object. We suggest to use `new' and
// `delete' because the destructor contains some calls to MPI (as
// required by ML and possibly Amesos). This is an issue only if the
// destructor is called **after** MPI_Finalize().
ML_Epetra::MultiLevelPreconditioner* MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*A, MLList);
// verify unused parameters on process 0 (put -1 to print on all
// processes)
MLPrec->PrintUnused(0);
#ifdef ML_SCALING
timeVec[precBuild].value = MPI_Wtime() - timeVec[precBuild].value;
#endif
// ML allows the user to cheaply recompute the preconditioner. You can
// simply uncomment the following line:
//
// MLPrec->ReComputePreconditioner();
//
// It is supposed that the linear system matrix has different values, but
// **exactly** the same structure and layout. The code re-built the
// hierarchy and re-setup the smoothers and the coarse solver using
// already available information on the hierarchy. A particular
// care is required to use ReComputePreconditioner() with nonzero
// threshold.
// =========================== end of ML part =============================
// tell AztecOO to use the ML preconditioner, specify the solver
// and the output, then solve with 500 maximum iterations and 1e-12
// of tolerance (see AztecOO's user guide for more details)
#ifdef ML_SCALING
timeVec[solve].value = MPI_Wtime();
#endif
solver.SetPrecOperator(MLPrec);
solver.SetAztecOption(AZ_solver, AZ_cg);
solver.SetAztecOption(AZ_output, 32);
solver.Iterate(optMaxIts, optTol);
#ifdef ML_SCALING
timeVec[solve].value = MPI_Wtime() - timeVec[solve].value;
#endif
// destroy the preconditioner
delete MLPrec;
// compute the real residual
double residual;
LHS.Norm2(&residual);
if( Comm.MyPID()==0 ) {
cout << "||b-Ax||_2 = " << residual << endl;
}
// for testing purposes
if (residual > 1e-5)
exit(EXIT_FAILURE);
delete A;
delete Map;
#ifdef ML_SCALING
timeVec[total].value = MPI_Wtime() - timeVec[total].value;
//avg
double dupTime[ntimers],avgTime[ntimers];
for (int i=0; i<ntimers; i++) dupTime[i] = timeVec[i].value;
MPI_Reduce(dupTime,avgTime,ntimers,MPI_DOUBLE,MPI_SUM,0,MPI_COMM_WORLD);
for (int i=0; i<ntimers; i++) avgTime[i] = avgTime[i]/Comm.NumProc();
//min
MPI_Reduce(timeVec,minTime,ntimers,MPI_DOUBLE_INT,MPI_MINLOC,0,MPI_COMM_WORLD);
//max
MPI_Reduce(timeVec,maxTime,ntimers,MPI_DOUBLE_INT,MPI_MAXLOC,0,MPI_COMM_WORLD);
if (Comm.MyPID() == 0) {
printf("timing : max (pid) min (pid) avg\n");
printf("Problem build : %2.3e (%d) %2.3e (%d) %2.3e \n",
maxTime[probBuild].value,maxTime[probBuild].rank,
minTime[probBuild].value,minTime[probBuild].rank,
avgTime[probBuild]);
printf("Preconditioner build : %2.3e (%d) %2.3e (%d) %2.3e \n",
maxTime[precBuild].value,maxTime[precBuild].rank,
minTime[precBuild].value,minTime[precBuild].rank,
avgTime[precBuild]);
printf("Solve : %2.3e (%d) %2.3e (%d) %2.3e \n",
maxTime[solve].value,maxTime[solve].rank,
minTime[solve].value,minTime[solve].rank,
avgTime[solve]);
printf("Total : %2.3e (%d) %2.3e (%d) %2.3e \n",
maxTime[total].value,maxTime[total].rank,
minTime[total].value,minTime[total].rank,
avgTime[total]);
}
#endif
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return(EXIT_SUCCESS);
}
int main(int argc, char *argv[])
{
#ifdef EPETRA_MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
Epetra_Time Time(Comm);
// initialize an Gallery object
CrsMatrixGallery Gallery("laplace_3d", Comm, false); // CJ TODO FIXME: change for Epetra64
Gallery.Set("problem_size", 1000);
// retrive pointers to matrix and linear problem
Epetra_RowMatrix * A = Gallery.GetMatrix();
Epetra_LinearProblem * Problem = Gallery.GetLinearProblem();
// Construct a solver object for this problem
AztecOO solver(*Problem);
// create the preconditioner object and compute hierarchy
ML_Epetra::MultiLevelPreconditioner * MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*A, true);
// tell AztecOO to use this preconditioner, then solve
solver.SetPrecOperator(MLPrec);
solver.SetAztecOption(AZ_solver, AZ_gmres_condnum);
solver.SetAztecOption(AZ_output, 32);
solver.SetAztecOption(AZ_kspace, 160);
int Niters = 500;
solver.Iterate(Niters, 1e-12);
// print out some information about the preconditioner
if( Comm.MyPID() == 0 ) cout << MLPrec->GetOutputList();
delete MLPrec;
// compute the real residual
double residual, diff;
Gallery.ComputeResidual(&residual);
Gallery.ComputeDiffBetweenStartingAndExactSolutions(&diff);
if( Comm.MyPID()==0 ) {
cout << "||b-Ax||_2 = " << residual << endl;
cout << "||x_exact - x||_2 = " << diff << endl;
cout << "Total Time = " << Time.ElapsedTime() << endl;
}
if (residual > 1e-5)
exit(EXIT_FAILURE);
#ifdef EPETRA_MPI
MPI_Finalize() ;
#endif
return(EXIT_SUCCESS);
}
int main(int argc, char *argv[])
{
#ifdef EPETRA_MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
Epetra_Time Time(Comm);
// Create the linear problem using the class `Trilinos_Util::CrsMatrixGallery.'
// Various matrix examples are supported; please refer to the
// Trilinos tutorial for more details.
// create Aztec stuff
int proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
#ifdef ML_MPI
/* get number of processors and the name of this processor */
AZ_set_proc_config(proc_config, MPI_COMM_WORLD);
int proc = proc_config[AZ_node];
int nprocs = proc_config[AZ_N_procs];
#else
AZ_set_proc_config(proc_config, AZ_NOT_MPI);
int proc = 0;
int nprocs = 1;
#endif
// read in the matrix size
FILE *fp = fopen("ExampleMatrices/cantilever2D/data_matrix.txt","r");
int leng;
fscanf(fp,"%d",&leng);
int num_PDE_eqns=2;
int N_grid_pts = leng/num_PDE_eqns;
// make a linear distribution of the matrix respecting the blocks size
int leng1 = leng/nprocs;
int leng2 = leng-leng1*nprocs;
if (proc >= leng2)
{
leng2 += (proc*leng1);
}
else
{
leng1++;
leng2 = proc*leng1;
}
int N_update = leng1;
int* update = new int[N_update+1];
int i;
double *val=NULL;
int *bindx=NULL;
for (i=0; i<N_update; i++) update[i] = i+leng2;
// create the Epetra_CrSMatrix
Epetra_Map* StandardMap = new Epetra_Map(leng,N_update,update,0,Comm);
Epetra_CrsMatrix* A = new Epetra_CrsMatrix(Copy,*StandardMap,1);
AZ_input_msr_matrix("ExampleMatrices/cantilever2D/data_matrix.txt",
update, &val, &bindx, N_update, proc_config);
for (i=0; i<leng; i++)
{
int row = update[i];
A->SumIntoGlobalValues(row,1,&(val[i]),&row);
A->SumIntoGlobalValues(row,bindx[i+1]-bindx[i],&(val[bindx[i]]),&(bindx[bindx[i]]));
}
A->TransformToLocal();
// create solution and right-hand side (MultiVectors are fine as well)
Epetra_Vector* LHS = new Epetra_Vector(A->OperatorDomainMap());
Epetra_Vector* RHS = new Epetra_Vector(A->OperatorRangeMap());
LHS->Random();
RHS->Random();
// build the epetra linear problem
Epetra_LinearProblem Problem(A, LHS, RHS);
// Construct a solver object for this problem
AztecOO solver(Problem);
// =========================== begin of ML part ===========================
// create a parameter list for ML options
ParameterList MLList;
// set defaults for classic smoothed aggregation
ML_Epetra::SetDefaults("SA",MLList);
MLList.set("aggregation: damping factor", 0.0);
// number of relaxation sweeps
MLList.set("adaptive: max sweeps", 10);
// number of additional null space vectors to compute
MLList.set("adaptive: num vectors",2);
#if 1
ML_Epetra::MultiLevelPreconditioner* MLPrec =
new ML_Epetra::MultiLevelPreconditioner(dynamic_cast<Epetra_RowMatrix&>(*A), MLList, false);
// need to allocate and fill the null space (also the
// default one, as in this case). This vector is no longer
// needed after a call to ComputeAdaptivePreconditioner().
int NullSpaceSize = 2;
vector<double> NullSpace((NullSpaceSize*A->NumMyRows()));
for (i = 0 ; i < A->NumMyRows() ; ++i)
{
NullSpace[i] = 1.0;
++i;
NullSpace[i] = 0.0;
}
for (i = A->NumMyRows() ; i < 2*A->NumMyRows() ; ++i)
{
NullSpace[i] = 0.0;
++i;
NullSpace[i] = 1.0;
}
MLPrec->ComputeAdaptivePreconditioner(NullSpaceSize,&NullSpace[0]);
#else
ML_Epetra::MultiLevelPreconditioner* MLPrec =
new ML_Epetra::MultiLevelPreconditioner(dynamic_cast<Epetra_RowMatrix&>(*A), MLList);
#endif
// tell AztecOO to use this preconditioner, then solve
solver.SetPrecOperator(MLPrec);
// =========================== end of ML part =============================
solver.SetAztecOption(AZ_solver, AZ_gmres);
solver.SetAztecOption(AZ_output, 32);
// solve with 500 iterations and 1e-12 tolerance
solver.Iterate(1550, 1e-5);
delete MLPrec;
// compute the real residual
double residual, diff;
if( Comm.MyPID()==0 ) {
cout << "||b-Ax||_2 = " << residual << endl;
cout << "||x_exact - x||_2 = " << diff << endl;
cout << "Total Time = " << Time.ElapsedTime() << endl;
}
#ifdef EPETRA_MPI
MPI_Finalize();
#endif
return(0);
}
int main(int argc, char *argv[])
{
#ifdef ML_MPI
MPI_Init(&argc,&argv);
// This next bit of code drops a middle rank out of the calculation. This tests
// that the Hiptmair smoother does not hang in its apply. Hiptmair creates
// two separate ML objects, one for edge and one for nodes. By default, the ML
// objects use MPI_COMM_WORLD, whereas the matrix that Hiptmair is being applied to
// may have an MPI subcommunicator.
int commWorldSize;
MPI_Comm_size(MPI_COMM_WORLD,&commWorldSize);
std::vector<int> splitKey;
int rankToDrop = 1;
for (int i=0; i<commWorldSize; ++i)
splitKey.push_back(0);
splitKey[rankToDrop] = MPI_UNDEFINED; //drop the last process from subcommunicator
int myrank;
MPI_Comm_rank(MPI_COMM_WORLD, &myrank);
MPI_Comm subcomm;
MPI_Comm_split(MPI_COMM_WORLD, splitKey[myrank], myrank, &subcomm);
if (myrank == rankToDrop) goto droppedRankLabel;
#endif
{ //scoping to avoid compiler error about goto jumping over initialization
#ifdef ML_MPI
Epetra_MpiComm Comm(subcomm);
#else
Epetra_SerialComm Comm;
#endif
ML_Comm_Create(&mlcomm);
char *datafile;
#ifdef CurlCurlAndMassAreSeparate
if (argc != 5 && argc != 7) {
if (Comm.MyPID() == 0) {
std::cout << "usage: ml_maxwell.exe <S> <M> <T> <Kn> [edge map] [node map]"
<< std::endl;
std::cout << " S = edge stiffness matrix file" << std::endl;
std::cout << " M = edge mass matrix file" << std::endl;
std::cout << " T = discrete gradient file" << std::endl;
std::cout << " Kn = auxiliary nodal FE matrix file" << std::endl;
std::cout << " edge map = edge distribution over processors" << std::endl;
std::cout << " node map = node distribution over processors" << std::endl;
std::cout << argc << std::endl;
}
#else //ifdef CurlCurlAndMassAreSeparate
if (argc != 4 && argc != 6) {
if (Comm.MyPID() == 0) {
std::cout << "usage: ml_maxwell.exe <A> <T> <Kn> [edge map] [node map]" <<std::endl;
std::cout << " A = edge element matrix file" << std::endl;
std::cout << " T = discrete gradient file" << std::endl;
std::cout << " Kn = auxiliary nodal FE matrix file" << std::endl;
std::cout << " edge map = edge distribution over processors" << std::endl;
std::cout << " node map = node distribution over processors" << std::endl;
std::cout << argc << std::endl;
}
#endif //ifdef CurlCurlAndMassAreSeparate
#ifdef ML_MPI
MPI_Finalize();
#endif
exit(1);
}
Epetra_Map *edgeMap, *nodeMap;
Epetra_CrsMatrix *CCplusM=NULL, *CurlCurl=NULL, *Mass=NULL, *T=NULL, *Kn=NULL;
// ================================================= //
// READ IN MAPS FROM FILE //
// ================================================= //
// every processor reads this in
#ifdef CurlCurlAndMassAreSeparate
if (argc > 5)
#else
if (argc > 4)
#endif
{
datafile = argv[5];
if (Comm.MyPID() == 0) {
printf("Reading in edge map from %s ...\n",datafile);
fflush(stdout);
}
EpetraExt::MatrixMarketFileToMap(datafile, Comm, edgeMap);
datafile = argv[6];
if (Comm.MyPID() == 0) {
printf("Reading in node map from %s ...\n",datafile);
fflush(stdout);
}
EpetraExt::MatrixMarketFileToMap(datafile, Comm, nodeMap);
}
else { // linear maps
// Read the T matrix to determine the map sizes
// and then construct linear maps
if (Comm.MyPID() == 0)
printf("Using linear edge and node maps ...\n");
const int lineLength = 1025;
char line[lineLength];
FILE *handle;
int M,N,NZ;
#ifdef CurlCurlAndMassAreSeparate
handle = fopen(argv[3],"r");
#else
handle = fopen(argv[2],"r");
#endif
if (handle == 0) EPETRA_CHK_ERR(-1); // file not found
// Strip off header lines (which start with "%")
do {
if(fgets(line, lineLength, handle)==0) {if (handle!=0) fclose(handle);}
} while (line[0] == '%');
// Get problem dimensions: M, N, NZ
if(sscanf(line,"%d %d %d", &M, &N, &NZ)==0) {if (handle!=0) fclose(handle);}
fclose(handle);
edgeMap = new Epetra_Map(M,0,Comm);
nodeMap = new Epetra_Map(N,0,Comm);
}
// ===================================================== //
// READ IN MATRICES FROM FILE //
// ===================================================== //
#ifdef CurlCurlAndMassAreSeparate
for (int i = 1; i <5; i++) {
datafile = argv[i];
if (Comm.MyPID() == 0) {
printf("reading %s ....\n",datafile); fflush(stdout);
}
switch (i) {
case 1: //Curl
MatrixMarketFileToCrsMatrix(datafile,*edgeMap,*edgeMap,*edgeMap,CurlCurl);
break;
case 2: //Mass
MatrixMarketFileToCrsMatrix(datafile, *edgeMap, *edgeMap, *edgeMap, Mass);
break;
case 3: //Gradient
MatrixMarketFileToCrsMatrix(datafile, *edgeMap, *edgeMap,*nodeMap, T);
break;
case 4: //Auxiliary nodal matrix
MatrixMarketFileToCrsMatrix(datafile, *nodeMap,*nodeMap, *nodeMap, Kn);
break;
} //switch
} //for (int i = 1; i <5; i++)
#else
for (int i = 1; i <4; i++) {
datafile = argv[i];
if (Comm.MyPID() == 0) {
printf("reading %s ....\n",datafile); fflush(stdout);
}
switch (i) {
case 1: //Edge element matrix
MatrixMarketFileToCrsMatrix(datafile,*edgeMap,*edgeMap,*edgeMap,CCplusM);
break;
case 2: //Gradient
MatrixMarketFileToCrsMatrix(datafile, *edgeMap, *edgeMap, *nodeMap, T);
break;
case 3: //Auxiliary nodal matrix
MatrixMarketFileToCrsMatrix(datafile, *nodeMap, *nodeMap, *nodeMap, Kn);
break;
} //switch
} //for (int i = 1; i <4; i++)
#endif //ifdef CurlCurlAndMassAreSeparate
// ==================================================== //
// S E T U P O F M L P R E C O N D I T I O N E R //
// ==================================================== //
Teuchos::ParameterList MLList;
int *options = new int[AZ_OPTIONS_SIZE];
double *params = new double[AZ_PARAMS_SIZE];
ML_Epetra::SetDefaults("maxwell", MLList, options, params);
MLList.set("ML output", 10);
MLList.set("aggregation: type", "Uncoupled");
MLList.set("coarse: max size", 15);
MLList.set("aggregation: threshold", 0.0);
//MLList.set("negative conductivity",true);
//MLList.set("smoother: type", "Jacobi");
MLList.set("subsmoother: type", "symmetric Gauss-Seidel");
//MLList.set("max levels", 2);
// coarse level solve
MLList.set("coarse: type", "Amesos-KLU");
//MLList.set("coarse: type", "Hiptmair");
//MLList.set("coarse: type", "Jacobi");
//MLList.set("dump matrix: enable", true);
#ifdef CurlCurlAndMassAreSeparate
//Create the matrix of interest.
CCplusM = Epetra_MatrixAdd(CurlCurl,Mass,1.0);
#endif
#ifdef CurlCurlAndMassAreSeparate
ML_Epetra::MultiLevelPreconditioner * MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*CurlCurl, *Mass, *T, *Kn, MLList);
// Comment out the line above and uncomment the next one if you have
// mass and curl separately but want to precondition as if they are added
// together.
/*
ML_Epetra::MultiLevelPreconditioner * MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*CCplusM, *T, *Kn, MLList);
*/
#else
ML_Epetra::MultiLevelPreconditioner * MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*CCplusM, *T, *Kn, MLList);
#endif //ifdef CurlCurlAndMassAreSeparate
MLPrec->PrintUnused(0);
// ========================================================= //
// D E F I N I T I O N O F A Z T E C O O P R O B L E M //
// ========================================================= //
// create left-hand side and right-hand side, and populate them with
// data from file. Both vectors are defined on the domain map of the
// edge matrix.
// Epetra_Vectors can be created in View mode, to accept pointers to
// double vectors.
if (Comm.MyPID() == 0)
std::cout << "Putting in a zero initial guess and random rhs (in the range of S+M)" << std::endl;
Epetra_Vector x(CCplusM->DomainMap());
x.Random();
Epetra_Vector rhs(CCplusM->DomainMap());
CCplusM->Multiply(false,x,rhs);
x.PutScalar(0.0);
double vecnorm;
rhs.Norm2(&vecnorm);
if (Comm.MyPID() == 0) std::cout << "||rhs|| = " << vecnorm << std::endl;
x.Norm2(&vecnorm);
if (Comm.MyPID() == 0) std::cout << "||x|| = " << vecnorm << std::endl;
// for AztecOO, we need an Epetra_LinearProblem
Epetra_LinearProblem Problem(CCplusM,&x,&rhs);
// AztecOO Linear problem
AztecOO solver(Problem);
// set MLPrec as precondititoning operator for AztecOO linear problem
//std::cout << "no ml preconditioner!!!" << std::endl;
solver.SetPrecOperator(MLPrec);
//solver.SetAztecOption(AZ_precond, AZ_Jacobi);
// a few options for AztecOO
solver.SetAztecOption(AZ_solver, AZ_cg);
solver.SetAztecOption(AZ_output, 1);
solver.Iterate(15, 1e-3);
// =============== //
// C L E A N U P //
// =============== //
delete MLPrec; // destroy phase prints out some information
delete CurlCurl;
delete CCplusM;
delete Mass;
delete T;
delete Kn;
delete nodeMap;
delete edgeMap;
delete [] params;
delete [] options;
ML_Comm_Destroy(&mlcomm);
} //avoids compiler error about jumping over initialization
droppedRankLabel:
#ifdef ML_MPI
MPI_Finalize();
#endif
return 0;
} //main
#else
#include <stdlib.h>
#include <stdio.h>
#ifdef HAVE_MPI
#include "mpi.h"
#endif
int main(int argc, char *argv[])
{
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
#endif
puts("Please configure ML with:");
#if !defined(HAVE_ML_EPETRA)
puts("--enable-epetra");
#endif
#if !defined(HAVE_ML_TEUCHOS)
puts("--enable-teuchos");
#endif
#if !defined(HAVE_ML_EPETRAEXT)
puts("--enable-epetraext");
#endif
#if !defined(HAVE_ML_AZTECOO)
puts("--enable-aztecoo");
#endif
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return 0;
}
int main(int argc, char *argv[])
{
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
// Create the linear problem using the Galeri package.
int NumPDEEqns = 5;
Teuchos::ParameterList GaleriList;
int nx = 32;
GaleriList.set("nx", nx);
GaleriList.set("ny", nx * Comm.NumProc());
GaleriList.set("mx", 1);
GaleriList.set("my", Comm.NumProc());
Epetra_Map* Map = CreateMap("Cartesian2D", Comm, GaleriList);
Epetra_CrsMatrix* CrsA = CreateCrsMatrix("Laplace2D", Map, GaleriList);
Epetra_VbrMatrix* A = CreateVbrMatrix(CrsA, NumPDEEqns);
Epetra_Vector LHS(A->Map()); LHS.Random();
Epetra_Vector RHS(A->Map()); RHS.PutScalar(0.0);
Epetra_LinearProblem Problem(A, &LHS, &RHS);
AztecOO solver(Problem);
// =========================== definition of coordinates =================
// use the following Galeri function to get the
// coordinates for a Cartesian grid. Note however that the
// visualization capabilites of Trilinos accept non-structured grid as
// well. Visualization and statistics occurs just after the ML
// preconditioner has been build.
Epetra_MultiVector* Coord = CreateCartesianCoordinates("2D", &(A->Map()),
GaleriList);
double* x_coord = (*Coord)[0];
double* y_coord = (*Coord)[1];
// =========================== begin of ML part ===========================
// create a parameter list for ML options
ParameterList MLList;
int *options = new int[AZ_OPTIONS_SIZE];
double *params = new double[AZ_PARAMS_SIZE];
// set defaults
ML_Epetra::SetDefaults("SA",MLList, options, params);
// overwrite some parameters. Please refer to the user's guide
// for more information
// some of the parameters do not differ from their default value,
// and they are here reported for the sake of clarity
// maximum number of levels
MLList.set("max levels",3);
MLList.set("increasing or decreasing","increasing");
MLList.set("smoother: type", "symmetric Gauss-Seidel");
// aggregation scheme set to Uncoupled. Note that the aggregates
// created by MIS can be visualized for serial runs only, while
// Uncoupled, METIS for both serial and parallel runs.
MLList.set("aggregation: type", "Uncoupled");
// ======================== //
// visualization parameters //
// ======================== //
//
// - set "viz: enable" to `false' to disable visualization and
// statistics.
// - set "x-coordinates" to the pointer of x-coor
// - set "viz: equation to plot" to the number of equation to
// be plotted (for vector problems only). Default is -1 (that is,
// plot all the equations)
// - set "viz: print starting solution" to print on file
// the starting solution vector, that was used for pre-
// and post-smoothing, and for the cycle. This may help to
// understand whether the smoothed solution is "smooth"
// or not.
//
// NOTE: visualization occurs *after* the creation of the ML preconditioner,
// by calling VisualizeAggregates(), VisualizeSmoothers(), and
// VisualizeCycle(). However, the user *must* enable visualization
// *before* creating the ML object. This is because ML must store some
// additional information about the aggregates.
//
// NOTE: the options above work only for "viz: output format" == "xyz"
// (default value) or "viz: output format" == "vtk".
// If "viz: output format" == "dx", the user
// can only plot the aggregates.
MLList.set("viz: output format", "vtk");
MLList.set("viz: enable", true);
MLList.set("x-coordinates", x_coord);
MLList.set("y-coordinates", y_coord);
MLList.set("z-coordinates", (double *)0);
MLList.set("viz: print starting solution", true);
// =============================== //
// end of visualization parameters //
// =============================== //
// create the preconditioner object and compute hierarchy
ML_Epetra::MultiLevelPreconditioner * MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*A, MLList);
// ============= //
// visualization //
// ============= //
// 1.- print out the shape of the aggregates, plus some
// statistics
// 2.- print out the effect of presmoother and postsmoother
// on a random vector. Input integer number represent
// the number of applications of presmoother and postmsoother,
// respectively
// 3.- print out the effect of the ML cycle on a random vector.
// The integer parameter represents the number of cycles.
// Below, `5' and `1' refers to the number of pre-smoother and
// post-smoother applications. `10' refers to the number of ML
// cycle applications. In both cases, smoothers and ML cycle are
// applied to a random vector.
MLPrec->VisualizeAggregates();
MLPrec->VisualizeSmoothers(5,1);
MLPrec->VisualizeCycle(10);
// ==================== //
// end of visualization //
// ==================== //
// destroy the preconditioner
delete MLPrec;
delete [] options;
delete [] params;
delete A;
delete Coord;
delete Map;
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return(EXIT_SUCCESS);
}
int main(int argc, char *argv[])
{
#ifdef EPETRA_MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
// `Laplace2D' is a symmetric matrix; an example of non-symmetric
// matrices is `Recirc2D' (advection-diffusion in a box, with
// recirculating flow). The grid has nx x ny nodes, divided into
// mx x my subdomains, each assigned to a different processor.
int nx = 8;
int ny = 8 * Comm.NumProc();
ParameterList GaleriList;
GaleriList.set("nx", nx);
GaleriList.set("ny", ny);
GaleriList.set("mx", 1);
GaleriList.set("my", Comm.NumProc());
Epetra_Map* Map = CreateMap("Cartesian2D", Comm, GaleriList);
Epetra_CrsMatrix* A = CreateCrsMatrix("Laplace2D", Map, GaleriList);
// use the following Galeri function to get the
// coordinates for a Cartesian grid.
Epetra_MultiVector* Coord = CreateCartesianCoordinates("2D", &(A->Map()),
GaleriList);
double* x_coord = (*Coord)[0];
double* y_coord = (*Coord)[1];
// Create the linear problem, with a zero solution
Epetra_Vector LHS(*Map); LHS.Random();
Epetra_Vector RHS(*Map); RHS.PutScalar(0.0);
Epetra_LinearProblem Problem(A, &LHS, &RHS);
// As we wish to use AztecOO, we need to construct a solver object for this problem
AztecOO solver(Problem);
// =========================== begin of ML part ===========================
// create a parameter list for ML options
ParameterList MLList;
// set defaults for classic smoothed aggregation.
ML_Epetra::SetDefaults("SA",MLList);
// use user's defined aggregation scheme to create the aggregates
// 1.- set "user" as aggregation scheme (for all levels, or for
// a specify level only)
MLList.set("aggregation: type", "user");
// 2.- set the label (for output)
ML_SetUserLabel(UserLabel);
// 3.- set the aggregation scheme (see function above)
ML_SetUserPartitions(UserPartitions);
// 4.- set the coordinates.
MLList.set("x-coordinates", x_coord);
MLList.set("y-coordinates", y_coord);
MLList.set("aggregation: dimensions", 2);
// also setup some variables to visualize the aggregates
// (more details are reported in example `ml_viz.cpp'.
MLList.set("viz: enable", true);
// now we create the preconditioner
ML_Epetra::MultiLevelPreconditioner * MLPrec =
new ML_Epetra::MultiLevelPreconditioner(*A, MLList);
MLPrec->VisualizeAggregates();
// tell AztecOO to use this preconditioner, then solve
solver.SetPrecOperator(MLPrec);
// =========================== end of ML part =============================
solver.SetAztecOption(AZ_solver, AZ_cg_condnum);
solver.SetAztecOption(AZ_output, 32);
// solve with 500 iterations and 1e-12 tolerance
solver.Iterate(500, 1e-12);
delete MLPrec;
// compute the real residual
double residual;
LHS.Norm2(&residual);
if (Comm.MyPID() == 0)
{
cout << "||b-Ax||_2 = " << residual << endl;
}
delete Coord;
delete A;
delete Map;
if (residual > 1e-3)
exit(EXIT_FAILURE);
#ifdef EPETRA_MPI
MPI_Finalize();
#endif
exit(EXIT_SUCCESS);
}
