int ML_strcmp(char *input, char *string)
{
/* Similar to 'C' strcmp except this one converts everything to lower case. */
int i;
char *input_copy, *string_copy;
input_copy = (char *) ML_allocate(sizeof(char)*(strlen(input)+1));
string_copy = (char *) ML_allocate(sizeof(char)*(strlen(string)+1));
strcpy(input_copy,input);
strcpy(string_copy,string);
i = 0;
while ((input_copy[i] != '\0') && (input_copy[i] != '\n')) {
if ((input_copy[i] >= 'A') && (input_copy[i] <= 'Z'))
input_copy[i] = 'a' + input_copy[i] - 'A';
i++;
}
i = 0;
while ((string_copy[i] != '\0') && (string_copy[i] != '\n')) {
if ((string_copy[i] >= 'A') && (string_copy[i] <= 'Z'))
string_copy[i] = 'a' + string_copy[i] - 'A';
i++;
}
i = strcmp(input_copy, string_copy);
ML_free(input_copy);
ML_free(string_copy);
return(i);
}
int ML_Reitzinger_Check_Hierarchy(ML *ml, ML_Operator **Tmat_array, int incr_or_decr)
{
int i,j;
int finest_level, coarsest_level;
ML_Operator *Amat, *Tmat;
double *randvec, *result, *result1;
double dnorm;
finest_level = ml->ML_finest_level;
coarsest_level = ml->ML_coarsest_level;
if (incr_or_decr == ML_INCREASING) {
if (ml->comm->ML_mypid == 0) {
printf("ML_Reitzinger_Check_Hierarchy: ML_INCREASING is not supported ");
printf(" at this time. Not checking hierarchy.\n");
}
return 1;
}
if ( ML_Get_PrintLevel() > 5 ) {
printf("ML_Reitzinger_Check_Hierarchy: Checking null space\n");
}
for (i=finest_level; i>coarsest_level; i--) {
Amat = ml->Amat+i;
Tmat = Tmat_array[i];
/* normalized random vector */
randvec = (double *) ML_allocate(Tmat->invec_leng * sizeof(double) );
ML_random_vec(randvec,Tmat->invec_leng, ml->comm);
dnorm = sqrt( ML_gdot(Tmat->invec_leng, randvec, randvec, ml->comm) );
for (j=0; j<Tmat->invec_leng; j++) randvec[j] /= dnorm;
result = (double *) ML_allocate(Amat->invec_leng * sizeof(double) );
result1 = (double *) ML_allocate(Amat->outvec_leng * sizeof(double) );
ML_Operator_Apply(Tmat, Tmat->invec_leng, randvec,
Tmat->outvec_leng, result);
ML_Operator_Apply(Amat, Amat->invec_leng, result,
Amat->outvec_leng, result1);
dnorm = sqrt( ML_gdot(Amat->outvec_leng, result1, result1, ml->comm) );
if ( (ML_Get_PrintLevel() > 5) && (ml->comm->ML_mypid == 0) ) {
printf("Level %d: for random v, ||S*T*v|| = %15.10e\n",i,dnorm);
}
ML_free(randvec);
ML_free(result);
ML_free(result1);
}
if ( (ML_Get_PrintLevel() > 5) && (ml->comm->ML_mypid == 0) ) printf("\n");
return 0;
}
int ML_qr_fix_Create(const int nCoarseNod)
{
int i, nBytes;
xCDeadNodDof = (ML_qr_fix*) ML_allocate(sizeof(ML_qr_fix));
xCDeadNodDof->level = 0;
xCDeadNodDof->numDeadNodDof = 0;
xCDeadNodDof->nDeadNodDof = nCoarseNod;
nBytes = (nCoarseNod+1)*sizeof(ML_QR_FIX_TYPE *);
xCDeadNodDof->xDeadNodDof = (ML_QR_FIX_TYPE *)ML_allocate(nBytes);
for (i=0; i < nCoarseNod; i++) (xCDeadNodDof->xDeadNodDof)[i] = 0;
return(0);
}
int ML_Smoother_Ifpack(ML_Smoother *sm,int inlen,double x[],int outlen,
double rhs[])
{
ML_Smoother *smooth_ptr = (ML_Smoother *) sm;
void *Ifpack_Handle = smooth_ptr->smoother->data;
double* x2 = NULL,* rhs2 = NULL;
/*int i;*/
int n, kk;
int one_int = 1;
double minus_one_double = -1.0;
if (sm->init_guess == ML_NONZERO)
{
n = sm->my_level->Amat->invec_leng;
assert (n == sm->my_level->Amat->outvec_leng);
rhs2 = (double*) ML_allocate(sizeof(double) * (n + 1));
x2 = (double*) ML_allocate(sizeof(double) * (n + 1));
ML_Operator_Apply(sm->my_level->Amat, n, x, n, rhs2);
DCOPY_F77(&n, x, &one_int, x2, &one_int);
DAXPY_F77(&n, &minus_one_double, rhs, &one_int, rhs2, &one_int);
ML_Ifpack_Solve(Ifpack_Handle, x2, rhs2);
DAXPY_F77(&n, &minus_one_double, x2, &one_int, x, &one_int);
ML_free(rhs2);
ML_free(x2);
}
else
ML_Ifpack_Solve(Ifpack_Handle, x, rhs);
for (kk = 1; kk < sm->ntimes; kk++) {
n = sm->my_level->Amat->invec_leng;
assert (n == sm->my_level->Amat->outvec_leng);
rhs2 = (double*) ML_allocate(sizeof(double) * (n + 1));
x2 = (double*) ML_allocate(sizeof(double) * (n + 1));
ML_Operator_Apply(sm->my_level->Amat, n, x, n, rhs2);
DCOPY_F77(&n, x, &one_int, x2, &one_int);
DAXPY_F77(&n, &minus_one_double, rhs, &one_int, rhs2, &one_int);
ML_Ifpack_Solve(Ifpack_Handle, x2, rhs2);
DAXPY_F77(&n, &minus_one_double, x2, &one_int, x, &one_int);
ML_free(rhs2);
ML_free(x2);
}
return 0;
} /* ML_Smoother_Ifpack */
void main(int argc, char *argv[])
{
int i, j, nrows, *mat_ia, *mat_ja, startRow, mypid, scheme, globalN;
int nprocs, nullDim, method;
double *mat_a, *sol, *rhs;
MLI_Solver *solver;
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &mypid);
MPI_Comm_size(MPI_COMM_WORLD, &nprocs);
if ( mypid == 0 )
{
printf("Test program : no. of processors = %d\n", nprocs);
printf("multigrid method (1-AMG,2-Smoothed Aggregation) : ");
scanf("%d", &method);
if ( method == 2 )
{
printf("Coarsen scheme (1-Uncoupled,2-Coupled) : ");
scanf("%d", &scheme);
} else scheme = 1;
}
MPI_Bcast(&method, 1, MPI_INT, 0, MPI_COMM_WORLD);
MPI_Bcast(&scheme, 1, MPI_INT, 0, MPI_COMM_WORLD);
if ( method != 1 && method != 2 ) method = 1;
if ( scheme < 1 || scheme > 2 ) scheme = 1;
solver = MLI_Solver_Create(MPI_COMM_WORLD);
/*
nrows = MLI_Solver_Get_IJAFromFile(solver,"matrix.data","rhs.data");
MLI_Solver_Get_NullSpaceFromFile(solver,"rbm.data");
*/
globalN = 128 * 128;
nrows = ML_PDE_GenMat(solver,globalN);
sol = (double *) ML_allocate( nrows * sizeof(double) );
for ( i = 0; i < nrows; i++ ) sol[i] = 0.0;
MLI_Solver_Set_MLNumLevels(solver, 7);
MLI_Solver_Set_KrylovMethod(solver, 0);
MLI_Solver_Set_MGMethod(solver,method);
MLI_Solver_Set_CoarsenScheme(solver,scheme);
if ( method == 1 ) MLI_Solver_Set_StrongThreshold(solver,0.25);
else MLI_Solver_Set_StrongThreshold(solver,0.08);
MLI_Solver_Set_NumPreSmoothings(solver,1);
MLI_Solver_Set_NumPostSmoothings(solver,1);
MLI_Solver_Set_PreSmoother(solver,2);
MLI_Solver_Set_PostSmoother(solver,2);
MLI_Solver_Set_DampingFactor(solver,1.0);
MLI_Solver_Setup(solver, sol);
MLI_Solver_Solve(solver);
MPI_Finalize();
}
// ======================================================================
MultiVector GetDiagonal(const Operator& A, const int offset)
{
// FIXME
if (A.GetDomainSpace() != A.GetRangeSpace())
ML_THROW("Currently only square matrices are supported", -1);
MultiVector D(A.GetDomainSpace());
D = 0.0;
ML_Operator* matrix = A.GetML_Operator();
if (matrix->getrow == NULL)
ML_THROW("getrow() not set!", -1);
int row_length;
int allocated = 128;
int* bindx = (int *) ML_allocate(allocated*sizeof(int ));
double* val = (double *) ML_allocate(allocated*sizeof(double));
for (int i = 0 ; i < matrix->getrow->Nrows; i++) {
int GlobalRow = A.GetGRID(i);
ML_get_matrix_row(matrix, 1, &i, &allocated, &bindx, &val,
&row_length, 0);
for (int j = 0; j < row_length; j++) {
D(i) = 0.0;
if (A.GetGCID(bindx[j]) == GlobalRow + offset) {
D(i) = val[j];
break;
}
}
}
ML_free(val);
ML_free(bindx);
return (D);
}
void ML_Reader_ReadInput(char *cmd_file_name, struct reader_context **context)
/* Read the input file for multigrid driver according to the tumi/shadi
* format (not written down anywhere) and set algorithm and problem dependent
* parameters accordingly.
*
* Authors: John N. Shadid, 1421
* Harry K. Moffat 1421
* Scott A. Hutchinson 1421
* Gary L. Hennigan 1421
*/
{
FILE *ifp;
/*********************** BEGIN EXECUTION ***********************************/
*context =(struct reader_context *) ML_allocate(sizeof(struct reader_context));
ML_Reader_InitContext(*context);
/* Open the command input file */
if ( (ifp = fopen(cmd_file_name, "r")) != NULL) {
;
/* (void) fprintf(stderr, "ML input mode\n"); */
} else {
(void) fprintf(stderr, "read_input_file: Can't open input file, %s,",
cmd_file_name);
(void) fprintf(stderr, " for reading\n");
exit(-1);
}
ML_Reader_GetGeneralSpecs(ifp, *context);
ML_Reader_GetSolutionSpecs(ifp, *context);
ML_Reader_GetAggregationSpecs(ifp, *context);
}
int main(int argc, char *argv[])
{
int Nnodes=16*16; /* Total number of nodes in the problem.*/
/* 'Nnodes' must be a perfect square. */
int MaxMgLevels=6; /* Maximum number of Multigrid Levels */
int Nits_per_presmooth=1; /* # of pre & post smoothings per level */
double tolerance = 1.0e-8; /* At convergence: */
/* ||r_k||_2 < tolerance ||r_0||_2 */
int smoothPe_flag = ML_YES; /* ML_YES: smooth tentative prolongator */
/* ML_NO: don't smooth prolongator */
/***************************************************************************/
/* Select Hiptmair relaxation subsmoothers for the nodal and edge problems */
/* Choices include */
/* 1) ML_Gen_Smoother_SymGaussSeidel: this corresponds to a processor */
/* local version of symmetric Gauss-Seidel/SOR. The number of sweeps */
/* can be set via either 'edge_its' or 'nodal_its'. The damping can */
/* be set via 'edge_omega' or 'nodal_omega'. When set to ML_DDEFAULT, */
/* the damping is set to '1' on one processor. On multiple processors */
/* a lower damping value is set. This is needed to converge processor */
/* local SOR. */
/* 2) ML_Gen_Smoother_Cheby: this corresponds to polynomial relaxation. */
/* The degree of the polynomial is set via 'edge_its' or 'nodal_its'. */
/* If the degree is '-1', Marian Brezina's MLS polynomial is chosen. */
/* Otherwise, a Chebyshev polynomial is used over high frequencies */
/* [ lambda_max/alpha , lambda_max]. Lambda_max is computed. 'alpha' */
/* is hardwired in this example to correspond to twice the ratio of */
/* unknowns in the fine and coarse meshes. */
/* */
/* Using 'hiptmair_type' (see comments below) it is also possible to choose*/
/* when edge and nodal problems are relaxed within the Hiptmair smoother. */
/***************************************************************************/
void *edge_smoother=(void *) /* Edge relaxation: */
ML_Gen_Smoother_Cheby; /* ML_Gen_Smoother_Cheby */
/* ML_Gen_Smoother_SymGaussSeidel */
void *nodal_smoother=(void *) /* Nodal relaxation */
ML_Gen_Smoother_Cheby;/* ML_Gen_Smoother_Cheby */
/* ML_Gen_Smoother_SymGaussSeidel */
int edge_its = 3; /* Iterations or polynomial degree for */
int nodal_its = 3; /* edge/nodal subsmoothers. */
double nodal_omega = ML_DDEFAULT, /* SOR damping parameter for noda/edge */
edge_omega = ML_DDEFAULT; /* subsmoothers (see comments above). */
int hiptmair_type=HALF_HIPTMAIR;/* FULL_HIPTMAIR: each invokation */
/* smoothes on edges, then nodes, */
/* and then once again on edges. */
/* HALF_HIPTMAIR: each pre-invokation */
/* smoothes on edges, then nodes. */
/* Each post-invokation smoothes */
/* on nodes then edges. . */
ML_Operator *Tmat, *Tmat_trans, **Tmat_array, **Tmat_trans_array;
ML *ml_edges, *ml_nodes;
ML_Aggregate *ag;
int Nfine_edge, Ncoarse_edge, Nfine_node, Ncoarse_node, Nlevels;
int level, coarsest_level, itmp;
double edge_coarsening_rate, node_coarsening_rate, *rhs, *xxx;
void **edge_args, **nodal_args;
struct user_partition Edge_Partition = {NULL, NULL,0,0},
Node_Partition = {NULL, NULL,0,0};
struct Tmat_data Tmat_data;
int i, Ntotal;
ML_Comm *comm;
/* See Aztec User's Guide for information on these variables */
#ifdef AZTEC
AZ_MATRIX *Ke_mat, *Kn_mat;
AZ_PRECOND *Pmat = NULL;
int proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
double params[AZ_PARAMS_SIZE], status[AZ_STATUS_SIZE];
#endif
/* get processor information (proc id & # of procs) and set ML's printlevel. */
#ifdef ML_MPI
MPI_Init(&argc,&argv);
#endif
#ifdef AZTEC
AZ_set_proc_config(proc_config, COMMUNICATOR);
#endif
ML_Set_PrintLevel(10); /* set ML's output level: 0 gives least output */
/* Set the # of global nodes/edges and partition both the edges and the */
/* nodes over the processors. NOTE: I believe we assume that if an edge */
/* is assigned to a processor at least one of its nodes must be also */
/* assigned to that processor. */
Node_Partition.Nglobal = Nnodes;
Edge_Partition.Nglobal = Node_Partition.Nglobal*2;
Node_Partition.type = NODE;
Edge_Partition.type = EDGE;
#define perxodic
#ifdef periodic
Node_Partition.Nglobal += 2;
#endif
partition_edges(&Edge_Partition);
partition_nodes(&Node_Partition);
xxx = (double *) ML_allocate((Edge_Partition.Nlocal+100)*sizeof(double));
rhs = (double *) ML_allocate((Edge_Partition.Nlocal+100)*sizeof(double));
for (i = 0; i < Edge_Partition.Nlocal + 100; i++) xxx[i] = -1.;
for (i = 0; i < Edge_Partition.Nlocal; i++) xxx[i] = (double)
Edge_Partition.my_global_ids[i];
update_ghost_edges(xxx, (void *) &Edge_Partition);
/* Create an empty multigrid hierarchy and set the 'MaxMGLevels-1'th */
/* level discretization within this hierarchy to the ML matrix */
/* representing Ke (Maxwell edge discretization). */
ML_Create(&ml_edges, MaxMgLevels);
#ifdef AZTEC
/* Build Ke as an Aztec matrix. Use built-in function AZ_ML_Set_Amat() */
/* to convert to an ML matrix and put in hierarchy. */
Ke_mat = user_Ke_build(&Edge_Partition);
AZ_ML_Set_Amat(ml_edges, MaxMgLevels-1, Edge_Partition.Nlocal,
Edge_Partition.Nlocal, Ke_mat, proc_config);
#else
/* Build Ke directly as an ML matrix. */
ML_Init_Amatrix (ml_edges, MaxMgLevels-1, Edge_Partition.Nlocal,
Edge_Partition.Nlocal, &Edge_Partition);
Ntotal = Edge_Partition.Nlocal;
if (Edge_Partition.nprocs == 2) Ntotal += Edge_Partition.Nghost;
ML_Set_Amatrix_Getrow(ml_edges, MaxMgLevels-1, Ke_getrow, update_ghost_edges, Ntotal);
ML_Set_Amatrix_Matvec(ml_edges, MaxMgLevels-1, Ke_matvec);
#endif
/* Build an Aztec matrix representing an auxiliary nodal PDE problem. */
/* This should be a variable coefficient Poisson problem (with unknowns*/
/* at the nodes). The coefficients should be chosen to reflect the */
/* conductivity of the original edge problems. */
/* Create an empty multigrid hierarchy. Convert the Aztec matrix to an */
/* ML matrix and put it in the 'MaxMGLevels-1' level of the hierarchy. */
/* Note it is possible to multiply T'*T for get this matrix though this*/
/* will not incorporate material properties. */
ML_Create(&ml_nodes, MaxMgLevels);
#ifdef AZTEC
Kn_mat = user_Kn_build( &Node_Partition);
AZ_ML_Set_Amat(ml_nodes, MaxMgLevels-1, Node_Partition.Nlocal,
Node_Partition.Nlocal, Kn_mat, proc_config);
#else
ML_Init_Amatrix (ml_nodes, MaxMgLevels-1 , Node_Partition.Nlocal,
Node_Partition.Nlocal, &Node_Partition);
Ntotal = Node_Partition.Nlocal;
if (Node_Partition.nprocs == 2) Ntotal += Node_Partition.Nghost;
ML_Set_Amatrix_Getrow(ml_nodes, MaxMgLevels-1, Kn_getrow, update_ghost_nodes, Ntotal);
#endif
/* Build an ML matrix representing the null space of the PDE problem. */
/* This should be a discrete gradient (nodes to edges). */
#ifdef AZTEC
Tmat = user_T_build (&Edge_Partition, &Node_Partition,
&(ml_nodes->Amat[MaxMgLevels-1]));
#else
Tmat = ML_Operator_Create(ml_nodes->comm);
Tmat_data.edge = &Edge_Partition;
Tmat_data.node = &Node_Partition;
Tmat_data.Kn = &(ml_nodes->Amat[MaxMgLevels-1]);
ML_Operator_Set_ApplyFuncData( Tmat, Node_Partition.Nlocal,
Edge_Partition.Nlocal, ML_EMPTY, (void *) &Tmat_data,
Edge_Partition.Nlocal, NULL, 0);
ML_Operator_Set_Getrow( Tmat, ML_INTERNAL, Edge_Partition.Nlocal,Tmat_getrow);
ML_Operator_Set_ApplyFunc(Tmat, ML_INTERNAL, Tmat_matvec);
ML_Comm_Create( &comm);
ML_CommInfoOP_Generate( &(Tmat->getrow->pre_comm), update_ghost_nodes,
&Node_Partition,comm, Tmat->invec_leng,
Node_Partition.Nghost);
#endif
/********************************************************************/
/* Set some ML parameters. */
/*------------------------------------------------------------------*/
ML_Set_ResidualOutputFrequency(ml_edges, 1);
ML_Set_Tolerance(ml_edges, 1.0e-8);
ML_Aggregate_Create( &ag );
ML_Aggregate_Set_CoarsenScheme_Uncoupled(ag);
ML_Aggregate_Set_DampingFactor(ag, 0.0); /* must use 0 for maxwell */
ML_Aggregate_Set_MaxCoarseSize(ag, 30);
ML_Aggregate_Set_Threshold(ag, 0.0);
/********************************************************************/
/* Set up Tmat_trans */
/*------------------------------------------------------------------*/
Tmat_trans = ML_Operator_Create(ml_edges->comm);
ML_Operator_Transpose_byrow(Tmat, Tmat_trans);
Nlevels=ML_Gen_MGHierarchy_UsingReitzinger(ml_edges, &ml_nodes,MaxMgLevels-1,
ML_DECREASING,ag,Tmat,Tmat_trans,
&Tmat_array,&Tmat_trans_array,
smoothPe_flag, 1.5);
/* Set the Hiptmair subsmoothers */
if (nodal_smoother == (void *) ML_Gen_Smoother_SymGaussSeidel) {
nodal_args = ML_Smoother_Arglist_Create(2);
ML_Smoother_Arglist_Set(nodal_args, 0, &nodal_its);
ML_Smoother_Arglist_Set(nodal_args, 1, &nodal_omega);
}
if (edge_smoother == (void *) ML_Gen_Smoother_SymGaussSeidel) {
edge_args = ML_Smoother_Arglist_Create(2);
ML_Smoother_Arglist_Set(edge_args, 0, &edge_its);
ML_Smoother_Arglist_Set(edge_args, 1, &edge_omega);
}
if (nodal_smoother == (void *) ML_Gen_Smoother_Cheby) {
nodal_args = ML_Smoother_Arglist_Create(2);
ML_Smoother_Arglist_Set(nodal_args, 0, &nodal_its);
Nfine_node = Tmat_array[MaxMgLevels-1]->invec_leng;
Nfine_node = ML_gsum_int(Nfine_node, ml_edges->comm);
}
if (edge_smoother == (void *) ML_Gen_Smoother_Cheby) {
edge_args = ML_Smoother_Arglist_Create(2);
ML_Smoother_Arglist_Set(edge_args, 0, &edge_its);
Nfine_edge = Tmat_array[MaxMgLevels-1]->outvec_leng;
Nfine_edge = ML_gsum_int(Nfine_edge, ml_edges->comm);
}
/****************************************************
* Set up smoothers for all levels but the coarsest. *
****************************************************/
coarsest_level = MaxMgLevels - Nlevels;
for (level = MaxMgLevels-1; level > coarsest_level; level--)
{
if (edge_smoother == (void *) ML_Gen_Smoother_Cheby) {
Ncoarse_edge = Tmat_array[level-1]->outvec_leng;
Ncoarse_edge = ML_gsum_int(Ncoarse_edge, ml_edges->comm);
edge_coarsening_rate = 2.*((double) Nfine_edge)/ ((double) Ncoarse_edge);
ML_Smoother_Arglist_Set(edge_args, 1, &edge_coarsening_rate);
Nfine_edge = Ncoarse_edge;
}
if (nodal_smoother == (void *) ML_Gen_Smoother_Cheby) {
Ncoarse_node = Tmat_array[level-1]->invec_leng;
Ncoarse_node = ML_gsum_int(Ncoarse_node, ml_edges->comm);
node_coarsening_rate = 2.*((double) Nfine_node)/ ((double) Ncoarse_node);
ML_Smoother_Arglist_Set(nodal_args, 1, &node_coarsening_rate);
Nfine_node = Ncoarse_node;
}
ML_Gen_Smoother_Hiptmair(ml_edges, level, ML_BOTH, Nits_per_presmooth,
Tmat_array, Tmat_trans_array, NULL, edge_smoother,
edge_args, nodal_smoother,nodal_args, hiptmair_type);
}
/*******************************************
* Set up coarsest level smoother
*******************************************/
if (edge_smoother == (void *) ML_Gen_Smoother_Cheby) {
edge_coarsening_rate = (double) Nfine_edge;
ML_Smoother_Arglist_Set(edge_args, 1, &edge_coarsening_rate);
}
if (nodal_smoother == (void *) ML_Gen_Smoother_Cheby) {
node_coarsening_rate = (double) Nfine_node;
ML_Smoother_Arglist_Set(nodal_args,1,&node_coarsening_rate);
}
ML_Gen_CoarseSolverSuperLU( ml_edges, coarsest_level);
/* Must be called before invoking the preconditioner */
ML_Gen_Solver(ml_edges, ML_MGV, MaxMgLevels-1, coarsest_level);
/* Set the initial guess and the right hand side. Invoke solver */
xxx = (double *) ML_allocate(Edge_Partition.Nlocal*sizeof(double));
ML_random_vec(xxx, Edge_Partition.Nlocal, ml_edges->comm);
rhs = (double *) ML_allocate(Edge_Partition.Nlocal*sizeof(double));
ML_random_vec(rhs, Edge_Partition.Nlocal, ml_edges->comm);
#ifdef AZTEC
/* Choose the Aztec solver and criteria. Also tell Aztec that */
/* ML will be supplying the preconditioner. */
AZ_defaults(options, params);
options[AZ_solver] = AZ_fixed_pt;
options[AZ_solver] = AZ_gmres;
options[AZ_kspace] = 80;
params[AZ_tol] = tolerance;
AZ_set_ML_preconditioner(&Pmat, Ke_mat, ml_edges, options);
options[AZ_conv] = AZ_noscaled;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Ke_mat, Pmat, NULL);
#else
ML_Iterate(ml_edges, xxx, rhs);
#endif
/* clean up. */
ML_Smoother_Arglist_Delete(&nodal_args);
ML_Smoother_Arglist_Delete(&edge_args);
ML_Aggregate_Destroy(&ag);
ML_Destroy(&ml_edges);
ML_Destroy(&ml_nodes);
#ifdef AZTEC
AZ_free((void *) Ke_mat->data_org);
AZ_free((void *) Ke_mat->val);
AZ_free((void *) Ke_mat->bindx);
if (Ke_mat != NULL) AZ_matrix_destroy(&Ke_mat);
if (Pmat != NULL) AZ_precond_destroy(&Pmat);
if (Kn_mat != NULL) AZ_matrix_destroy(&Kn_mat);
#endif
free(xxx);
free(rhs);
ML_Operator_Destroy(&Tmat);
ML_Operator_Destroy(&Tmat_trans);
ML_MGHierarchy_ReitzingerDestroy(MaxMgLevels-2, &Tmat_array, &Tmat_trans_array);
#ifdef ML_MPI
MPI_Finalize();
#endif
return 0;
}
ML_Operator *user_T_build(struct user_partition *Edge_Partition,
struct user_partition *Node_Partition,
ML_Operator *Kn_mat)
{
int nx, i, ii, jj, horv, Ncols, Nexterns;
int *Tmat_bindx;
double *Tmat_val;
ML_Operator *Tmat;
struct ML_CSR_MSRdata *csr_data;
ML_Comm *comm;
struct aztec_context *aztec_context;
int global_id;
int Nlocal_nodes, Nlocal_edges;
int nz_ptr;
Nlocal_nodes = Node_Partition->Nlocal;
Nlocal_edges = Edge_Partition->Nlocal;
nx = (int) sqrt( ((double) Node_Partition->Nglobal) + .00001);
#ifdef periodic
nx = (int) sqrt( ((double) Node_Partition->Nglobal - 2) + .00001);
#endif
Tmat_bindx = (int *) malloc((6*Nlocal_edges+5)*sizeof(int));
Tmat_val = (double *) malloc((6*Nlocal_edges+5)*sizeof(double));
Tmat_bindx[0] = Nlocal_edges + 1;
for (i = 0; i < Nlocal_edges; i++) {
global_id = (Edge_Partition->my_global_ids)[i];
Tmat_bindx[i+1] = Tmat_bindx[i] + 2;
#ifdef periodic
Tmat_bindx[i+1] += 1;
#endif
Tmat_val[i] = 0.0;
inv2dindex(global_id, &ii, &jj, nx, &horv);
nz_ptr = Tmat_bindx[i];
if (horv == HORIZONTAL) {
Tmat_bindx[nz_ptr] = southwest2d(ii,jj,nx); Tmat_val[nz_ptr++] = -1.;
Tmat_bindx[nz_ptr] = southeast2d(ii,jj,nx); Tmat_val[nz_ptr++] = 1.;
#ifdef periodic
Tmat_bindx[nz_ptr] = Nlocal_nodes-2; Tmat_val[nz_ptr++] = 1.;
#endif
}
else {
Tmat_bindx[nz_ptr] = northwest2d(ii,jj,nx); Tmat_val[nz_ptr++] = -1.;
Tmat_bindx[nz_ptr] = southwest2d(ii,jj,nx); Tmat_val[nz_ptr++] = 1.;
#ifdef periodic
Tmat_bindx[nz_ptr] = Nlocal_nodes - 1; Tmat_val[nz_ptr++] = 1.;
#endif
}
}
/* Convert the MSR matrix to a CSR matrix. Then use a modified Aztec */
/* routine to convert the global CSR matrix to a local ML matrix */
/* Since this routine does not compute the communication structure, */
/* we assume that it is identical to Kn's and just clone it. */
csr_data = (struct ML_CSR_MSRdata *) ML_allocate(sizeof(struct ML_CSR_MSRdata));
csr_data->columns = Tmat_bindx;
csr_data->values = Tmat_val;
ML_MSR2CSR(csr_data, Nlocal_edges, &Ncols);
aztec_context = (struct aztec_context *) Kn_mat->data;
Nexterns = (aztec_context->Amat->data_org)[AZ_N_external];
Nexterns = 0;
ML_Comm_Create( &comm);
AZ_Tmat_transform2ml(Nexterns, Node_Partition->needed_external_ids,
reordered_node_externs,
Tmat_bindx, Tmat_val, csr_data->rowptr, Nlocal_nodes,
Node_Partition->my_global_ids,
comm, Nlocal_edges, &Tmat);
ML_free(csr_data);
Tmat->data_destroy = ML_CSR_MSRdata_Destroy;
ML_CommInfoOP_Clone(&(Tmat->getrow->pre_comm), Kn_mat->getrow->pre_comm);
return(Tmat);
}
int ML_memory_alloc( void **memptr, unsigned int leng, char *name )
{
int i, *int_ptr, nchunks, ndouble=sizeof(double);
char *var_ptr;
double *dptr;
/* ----------------------------------------------------------------- */
/* if the first time, initialize the bookkeeping array */
/* ----------------------------------------------------------------- */
if (malloc_initialized == -1)
{
for (i=0; i<MAX_MALLOC_LOG; i++) malloc_leng_log[i] = -1;
malloc_leng_log[0] = 0;
malloc_initialized = 0;
}
/* ----------------------------------------------------------------- */
/* check for error before allocating any storage space */
/* ----------------------------------------------------------------- */
/*
TAKING THIS OUT TO HANDLE CASE WHEN THERE ARE NO POINTS ON PROC
else if (leng == 0)
{
printf("ML_malloc warning : %s - leng = 0 (%d).\n",name,leng);
(*memptr) = NULL;
return 0;
}
*/
{
/* -------------------------------------------------------------- */
/* allocate more space than requested for error checking */
/* -------------------------------------------------------------- */
nchunks = leng / ndouble;
if ((nchunks * ndouble) < (int) leng) nchunks = nchunks + 3;
else nchunks = nchunks + 2;
var_ptr = (char *) ML_allocate(nchunks*ndouble);
dptr = (double *) var_ptr;
for (i = 0; i < nchunks; i++) dptr[i] = 0.;
/* -------------------------------------------------------------- */
/* if an error is returned from malloc, terminate execution */
/* -------------------------------------------------------------- */
if (var_ptr == NULL)
{
int mypid=0;
#ifdef ML_MPI
MPI_Comm_rank(MPI_COMM_WORLD,&mypid);
#endif
pr_error("(%d) %s: unable to allocate %d bytes to %s.\n",mypid, ML_FUNCTION_NAME, leng, name );
}
/* -------------------------------------------------------------- */
/* check if there is any available slot in the bookkeeping array */
/* -------------------------------------------------------------- */
for ( i = 1; i < MAX_MALLOC_LOG; i++ )
if (malloc_leng_log[i] == -1) break;
/* -------------------------------------------------------------- */
/* if the reply is positive, register the allocation information */
/* -------------------------------------------------------------- */
if ( i < MAX_MALLOC_LOG )
{
int_ptr = (int *) var_ptr;
(*int_ptr) = i + 1;
int_ptr = (int*) ((ml_size_t) var_ptr + nchunks*ndouble - ndouble);
(*int_ptr) = i + 1;
malloc_addr_log[i] = (ml_size_t) memptr;
malloc_leng_log[i] = nchunks * ndouble;
malloc_name_log[i][0] = name[0];
malloc_name_log[i][1] = name[1];
malloc_name_log[i][2] = name[2];
var_ptr = (char*) ((ml_size_t) var_ptr + ndouble);
(*memptr) = (void *) var_ptr;
return i;
/* -------------------------------------------------------------- */
/* otherwise, signal the inability to track error for future */
/* investigation of this allocation */
/* -------------------------------------------------------------- */
}
else
{
/*
if (malloc_initialized != 1)
printf("ML_malloc message : unable to track any more %s.\n",
name);
*/
malloc_initialized = 1;
int_ptr = (int*) var_ptr;
(*int_ptr) = -1;
int_ptr = (int*) ((ml_size_t) var_ptr + nchunks*ndouble - ndouble);
(*int_ptr) = -1;
var_ptr = (char*) ((ml_size_t) var_ptr + ndouble);
(*memptr) = (void *) var_ptr;
return 0;
}
}
}
int ML_Aggregate_CoarsenUser(ML_Aggregate *ml_ag, ML_Operator *Amatrix,
ML_Operator **Pmatrix, ML_Comm *comm)
{
unsigned int nbytes, length;
int i, j, k, Nrows, exp_Nrows;
int diff_level;
int aggr_count, index, mypid, num_PDE_eqns;
int *aggr_index = NULL, nullspace_dim;
int Ncoarse, count;
int *new_ia = NULL, *new_ja = NULL, new_Nrows;
int exp_Ncoarse;
int *aggr_cnt_array = NULL;
int level, index3, max_agg_size;
int **rows_in_aggs = NULL, lwork, info;
double *new_val = NULL, epsilon;
double *nullspace_vect = NULL, *qr_tmp = NULL;
double *tmp_vect = NULL, *work = NULL, *new_null = NULL;
ML_SuperNode *aggr_head = NULL, *aggr_curr, *supernode;
struct ML_CSR_MSRdata *csr_data;
int total_nz = 0;
char str[80];
int * graph_decomposition = NULL;
ML_Aggregate_Viz_Stats * aggr_viz_and_stats;
ML_Aggregate_Viz_Stats * grid_info;
int Nprocs;
char * unamalg_bdry = NULL;
char* label;
int N_dimensions;
double* x_coord = NULL;
double* y_coord = NULL;
double* z_coord = NULL;
/* ------------------- execution begins --------------------------------- */
label = ML_GetUserLabel();
sprintf(str, "%s (level %d) :", label, ml_ag->cur_level);
/* ============================================================= */
/* get the machine information and matrix references */
/* ============================================================= */
mypid = comm->ML_mypid;
Nprocs = comm->ML_nprocs;
epsilon = ml_ag->threshold;
num_PDE_eqns = ml_ag->num_PDE_eqns;
nullspace_dim = ml_ag->nullspace_dim;
nullspace_vect = ml_ag->nullspace_vect;
Nrows = Amatrix->outvec_leng;
if (mypid == 0 && 5 < ML_Get_PrintLevel()) {
printf("%s num PDE eqns = %d\n",
str,
num_PDE_eqns);
}
/* ============================================================= */
/* check the system size versus null dimension size */
/* ============================================================= */
if ( Nrows % num_PDE_eqns != 0 )
{
printf("ML_Aggregate_CoarsenUser ERROR : Nrows must be multiples");
printf(" of num_PDE_eqns.\n");
exit(EXIT_FAILURE);
}
diff_level = ml_ag->max_levels - ml_ag->cur_level - 1;
if ( diff_level > 0 ) num_PDE_eqns = nullspace_dim; /* ## 12/20/99 */
/* ============================================================= */
/* set up the threshold for weight-based coarsening */
/* ============================================================= */
diff_level = ml_ag->begin_level - ml_ag->cur_level;
if (diff_level == 0)
ml_ag->curr_threshold = ml_ag->threshold;
epsilon = ml_ag->curr_threshold;
ml_ag->curr_threshold *= 0.5;
if (mypid == 0 && 7 < ML_Get_PrintLevel())
printf("%s current eps = %e\n", str, epsilon);
epsilon = epsilon * epsilon;
ML_Operator_AmalgamateAndDropWeak(Amatrix, num_PDE_eqns, epsilon);
Nrows /= num_PDE_eqns;
exp_Nrows = Nrows;
/* ********************************************************************** */
/* allocate memory for aggr_index, which will contain the decomposition */
/* ********************************************************************** */
nbytes = (Nrows*num_PDE_eqns) * sizeof(int);
if ( nbytes > 0 ) {
ML_memory_alloc((void**) &aggr_index, nbytes, "ACJ");
if( aggr_index == NULL ) {
fprintf( stderr,
"*ML*ERR* not enough memory for %d bytes\n"
"*ML*ERR* (file %s, line %d)\n",
nbytes,
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
}
else aggr_index = NULL;
for( i=0 ; i<Nrows*num_PDE_eqns ; i++ ) aggr_index[i] = -1;
unamalg_bdry = (char *) ML_allocate( sizeof(char) * (Nrows+1) );
if( unamalg_bdry == NULL ) {
fprintf( stderr,
"*ML*ERR* on proc %d, not enough space for %d bytes\n"
"*ML*ERR* (file %s, line %d)\n",
mypid,
(int)sizeof(char) * Nrows,
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
N_dimensions = ml_ag->N_dimensions;
grid_info = (ML_Aggregate_Viz_Stats*) Amatrix->to->Grid->Grid;
x_coord = grid_info->x;
if (N_dimensions > 1 && x_coord)
y_coord = grid_info->y;
else
y_coord = 0;
if (N_dimensions > 2 && x_coord)
z_coord = grid_info->z;
else
z_coord = 0;
aggr_count = ML_GetUserPartitions(Amatrix,unamalg_bdry,
epsilon,
x_coord,y_coord,z_coord,
aggr_index,&total_nz);
#ifdef ML_MPI
MPI_Allreduce( &Nrows, &i, 1, MPI_INT, MPI_SUM, Amatrix->comm->USR_comm );
MPI_Allreduce( &aggr_count, &j, 1, MPI_INT, MPI_SUM, Amatrix->comm->USR_comm );
#else
i = Nrows;
j = aggr_count;
#endif
if( mypid == 0 && 7 < ML_Get_PrintLevel() ) {
printf("%s Using %d (block) aggregates (globally)\n",
str,
j );
printf("%s # (block) aggre/ # (block) rows = %8.5f %% ( = %d / %d)\n",
str,
100.0*j/i,
j, i);
}
j = ML_gsum_int( aggr_count, comm );
if (mypid == 0 && 7 < ML_Get_PrintLevel()) {
printf("%s %d (block) aggregates (globally)\n",
str, j );
}
/* ********************************************************************** */
/* I allocate room to copy aggr_index and pass this value to the user, */
/* who will be able to analyze and visualize this after the construction */
/* of the levels. This way, the only price we have to pay for stats and */
/* viz is essentially a little bit of memory. */
/* this memory will be cleaned with the object ML_Aggregate ml_ag. */
/* I set the pointers using the ML_Aggregate_Info structure. This is */
/* allocated using ML_Aggregate_Info_Setup(ml,MaxNumLevels) */
/* ********************************************************************** */
if (Amatrix->to->Grid->Grid != NULL) {
graph_decomposition = (int *)ML_allocate(sizeof(int)*(Nrows+1));
if( graph_decomposition == NULL ) {
fprintf( stderr,
"*ML*ERR* Not enough memory for %d bytes\n"
"*ML*ERR* (file %s, line %d)\n",
(int)sizeof(int)*Nrows,
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
for( i=0 ; i<Nrows ; i++ ) graph_decomposition[i] = aggr_index[i];
aggr_viz_and_stats = (ML_Aggregate_Viz_Stats *) (Amatrix->to->Grid->Grid);
aggr_viz_and_stats->graph_decomposition = graph_decomposition;
aggr_viz_and_stats->Nlocal = Nrows;
aggr_viz_and_stats->Naggregates = aggr_count;
aggr_viz_and_stats->local_or_global = ML_LOCAL_INDICES;
aggr_viz_and_stats->is_filled = ML_YES;
aggr_viz_and_stats->Amatrix = Amatrix;
}
/* ********************************************************************** */
/* take the decomposition as created by METIS and form the aggregates */
/* ********************************************************************** */
total_nz = ML_Comm_GsumInt( comm, total_nz);
i = ML_Comm_GsumInt( comm, Nrows);
if ( mypid == 0 && 7 < ML_Get_PrintLevel())
printf("%s Total (block) nnz = %d ( = %5.2f/(block)row)\n",
str,
total_nz,1.0*total_nz/i);
if ( ml_ag->operator_complexity == 0.0 ) {
ml_ag->fine_complexity = total_nz;
ml_ag->operator_complexity = total_nz;
}
else ml_ag->operator_complexity += total_nz;
/* fix aggr_index for num_PDE_eqns > 1 */
for (i = Nrows - 1; i >= 0; i-- ) {
for (j = num_PDE_eqns-1; j >= 0; j--) {
aggr_index[i*num_PDE_eqns+j] = aggr_index[i];
}
}
if ( mypid == 0 && 8 < ML_Get_PrintLevel())
{
printf("Calling ML_Operator_UnAmalgamateAndDropWeak\n");
fflush(stdout);
}
ML_Operator_UnAmalgamateAndDropWeak(Amatrix, num_PDE_eqns, epsilon);
Nrows *= num_PDE_eqns;
exp_Nrows *= num_PDE_eqns;
/* count the size of each aggregate */
aggr_cnt_array = (int *) ML_allocate(sizeof(int)*(aggr_count+1));
for (i = 0; i < aggr_count ; i++) aggr_cnt_array[i] = 0;
for (i = 0; i < exp_Nrows; i++) {
if (aggr_index[i] >= 0) {
if( aggr_index[i] >= aggr_count ) {
fprintf( stderr,
"*ML*WRN* on process %d, something weird happened...\n"
"*ML*WRN* node %d belong to aggregate %d (#aggr = %d)\n"
"*ML*WRN* (file %s, line %d)\n",
comm->ML_mypid,
i,
aggr_index[i],
aggr_count,
__FILE__,
__LINE__ );
} else {
aggr_cnt_array[aggr_index[i]]++;
}
}
}
/* ============================================================= */
/* Form tentative prolongator */
/* ============================================================= */
Ncoarse = aggr_count;
/* ============================================================= */
/* check and copy aggr_index */
/* ------------------------------------------------------------- */
level = ml_ag->cur_level;
nbytes = (Nrows+1) * sizeof( int );
ML_memory_alloc((void**) &(ml_ag->aggr_info[level]), nbytes, "AGl");
count = aggr_count;
for ( i = 0; i < Nrows; i+=num_PDE_eqns )
{
if ( aggr_index[i] >= 0 )
{
for ( j = 0; j < num_PDE_eqns; j++ )
ml_ag->aggr_info[level][i+j] = aggr_index[i];
if (aggr_index[i] >= count) count = aggr_index[i] + 1;
}
/*else
*{
* printf("%d : CoarsenMIS error : aggr_index[%d] < 0\n",
* mypid,i);
* exit(1);
*}*/
}
ml_ag->aggr_count[level] = count; /* for relaxing boundary points */
/* ============================================================= */
/* set up the new operator */
/* ------------------------------------------------------------- */
new_Nrows = Nrows;
exp_Ncoarse = Nrows;
for ( i = 0; i < new_Nrows; i++ )
{
if ( aggr_index[i] >= exp_Ncoarse )
{
printf("*ML*WRN* index out of bound %d = %d(%d)\n",
i, aggr_index[i],
exp_Ncoarse);
}
}
nbytes = ( new_Nrows+1 ) * sizeof(int);
ML_memory_alloc((void**)&(new_ia), nbytes, "AIA");
nbytes = ( new_Nrows+1) * nullspace_dim * sizeof(int);
ML_memory_alloc((void**)&(new_ja), nbytes, "AJA");
nbytes = ( new_Nrows+1) * nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&(new_val), nbytes, "AVA");
for ( i = 0; i < new_Nrows*nullspace_dim; i++ ) new_val[i] = 0.0;
/* ------------------------------------------------------------- */
/* set up the space for storing the new null space */
/* ------------------------------------------------------------- */
nbytes = (Ncoarse+1) * nullspace_dim * nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&(new_null),nbytes,"AGr");
if( new_null == NULL ) {
fprintf( stderr,
"*ML*ERR* on process %d, not enough memory for %d bytes\n"
"*ML*ERR* (file %s, line %d)\n",
mypid,
nbytes,
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
for (i = 0; i < Ncoarse*nullspace_dim*nullspace_dim; i++)
new_null[i] = 0.0;
/* ------------------------------------------------------------- */
/* initialize the row pointer for the CSR prolongation operator */
/* (each row will have at most nullspace_dim nonzero entries) */
/* ------------------------------------------------------------- */
for (i = 0; i <= Nrows; i++) new_ia[i] = i * nullspace_dim;
/* trying this when a Dirichlet row is taken out */
j = 0;
new_ia[0] = 0;
for (i = 0; i < Nrows; i++) {
if (aggr_index[i] != -1) j += nullspace_dim;
new_ia[i+1] = j;
}
/* ------------------------------------------------------------- */
/* generate an array to store which aggregate has which rows.Then*/
/* loop through the rows of A checking which aggregate each row */
/* is in, and adding it to the appropriate spot in rows_in_aggs */
/* ------------------------------------------------------------- */
ML_memory_alloc((void**)&rows_in_aggs,aggr_count*sizeof(int*),"MLs");
for (i = 0; i < aggr_count; i++) {
nbytes = aggr_cnt_array[i]+1;
rows_in_aggs[i] = (int *) ML_allocate(nbytes*sizeof(int));
aggr_cnt_array[i] = 0;
if (rows_in_aggs[i] == NULL) {
printf("*ML*ERR* couldn't allocate memory in CoarsenMETIS\n");
exit(1);
}
}
for (i = 0; i < exp_Nrows; i+=num_PDE_eqns) {
if ( aggr_index[i] >= 0 && aggr_index[i] < aggr_count)
{
for (j = 0; j < num_PDE_eqns; j++)
{
index = aggr_cnt_array[aggr_index[i]]++;
rows_in_aggs[aggr_index[i]][index] = i + j;
}
}
}
/* ------------------------------------------------------------- */
/* allocate work arrays for QR factorization */
/* work and lwork are needed for lapack's QR routine. These */
/* settings seemed easiest since I don't quite understand */
/* what they do, but may want to do something better here later */
/* ------------------------------------------------------------- */
max_agg_size = 0;
for (i = 0; i < aggr_count; i++)
{
if (aggr_cnt_array[i] > max_agg_size) max_agg_size = aggr_cnt_array[i];
}
nbytes = max_agg_size * nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&qr_tmp, nbytes, "AGu");
nbytes = nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&tmp_vect, nbytes, "AGv");
lwork = nullspace_dim;
nbytes = nullspace_dim * sizeof(double);
ML_memory_alloc((void**)&work, nbytes, "AGw");
/* ------------------------------------------------------------- */
/* perform block QR decomposition */
/* ------------------------------------------------------------- */
for (i = 0; i < aggr_count; i++)
{
/* ---------------------------------------------------------- */
/* set up the matrix we want to decompose into Q and R: */
/* ---------------------------------------------------------- */
length = aggr_cnt_array[i];
if (nullspace_vect == NULL)
{
for (j = 0; j < (int) length; j++)
{
index = rows_in_aggs[i][j];
for (k = 0; k < nullspace_dim; k++)
{
if ( unamalg_bdry[index/num_PDE_eqns] == 'T')
qr_tmp[k*length+j] = 0.;
else
{
if (index % num_PDE_eqns == k) qr_tmp[k*length+j] = 1.0;
else qr_tmp[k*length+j] = 0.0;
}
}
}
}
else
{
for (k = 0; k < nullspace_dim; k++)
{
for (j = 0; j < (int) length; j++)
{
index = rows_in_aggs[i][j];
if ( unamalg_bdry[index/num_PDE_eqns] == 'T')
qr_tmp[k*length+j] = 0.;
else {
if (index < Nrows) {
qr_tmp[k*length+j] = nullspace_vect[k*Nrows+index];
}
else {
fprintf( stderr,
"*ML*ERR* in QR\n"
"*ML*ERR* (file %s, line %d)\n",
__FILE__,
__LINE__ );
exit( EXIT_FAILURE );
}
}
}
}
}
/* ---------------------------------------------------------- */
/* now calculate QR using an LAPACK routine */
/* ---------------------------------------------------------- */
if (aggr_cnt_array[i] >= nullspace_dim) {
DGEQRF_F77(&(aggr_cnt_array[i]), &nullspace_dim, qr_tmp,
&(aggr_cnt_array[i]), tmp_vect, work, &lwork, &info);
if (info != 0)
pr_error("ErrOr in CoarsenMIS : dgeqrf returned a non-zero %d %d\n",
aggr_cnt_array[i],i);
if (work[0] > lwork)
{
lwork=(int) work[0];
ML_memory_free((void**) &work);
ML_memory_alloc((void**) &work, sizeof(double)*lwork, "AGx");
}
else lwork=(int) work[0];
/* ---------------------------------------------------------- */
/* the upper triangle of qr_tmp is now R, so copy that into */
/* the new nullspace */
/* ---------------------------------------------------------- */
for (j = 0; j < nullspace_dim; j++)
for (k = j; k < nullspace_dim; k++)
new_null[i*nullspace_dim+j+k*Ncoarse*nullspace_dim] =
qr_tmp[j+aggr_cnt_array[i]*k];
/* ---------------------------------------------------------- */
/* to get this block of P, need to run qr_tmp through another */
/* LAPACK function: */
/* ---------------------------------------------------------- */
if ( aggr_cnt_array[i] < nullspace_dim ){
printf("Error in dorgqr on %d row (dims are %d, %d)\n",i,aggr_cnt_array[i],
nullspace_dim);
printf("ERROR : performing QR on a MxN matrix where M<N.\n");
}
DORGQR_F77(&(aggr_cnt_array[i]), &nullspace_dim, &nullspace_dim,
qr_tmp, &(aggr_cnt_array[i]), tmp_vect, work, &lwork, &info);
if (info != 0) {
printf("Error in dorgqr on %d row (dims are %d, %d)\n",i,aggr_cnt_array[i],
nullspace_dim);
pr_error("Error in CoarsenMIS: dorgqr returned a non-zero\n");
}
if (work[0] > lwork)
{
lwork=(int) work[0];
ML_memory_free((void**) &work);
ML_memory_alloc((void**) &work, sizeof(double)*lwork, "AGy");
}
else lwork=(int) work[0];
/* ---------------------------------------------------------- */
/* now copy Q over into the appropriate part of P: */
/* The rows of P get calculated out of order, so I assume the */
/* Q is totally dense and use what I know of how big each Q */
/* will be to determine where in ia, ja, etc each nonzero in */
/* Q belongs. If I did not assume this, I would have to keep */
/* all of P in memory in order to determine where each entry */
/* should go */
/* ---------------------------------------------------------- */
for (j = 0; j < aggr_cnt_array[i]; j++)
{
index = rows_in_aggs[i][j];
if ( index < Nrows )
{
index3 = new_ia[index];
for (k = 0; k < nullspace_dim; k++)
{
new_ja [index3+k] = i * nullspace_dim + k;
new_val[index3+k] = qr_tmp[ k*aggr_cnt_array[i]+j];
}
}
else
{
fprintf( stderr,
"*ML*ERR* in QR: index out of bounds (%d - %d)\n",
index,
Nrows );
}
}
}
else {
/* We have a small aggregate such that the QR factorization can not */
/* be performed. Instead let us copy the null space from the fine */
/* into the coarse grid nullspace and put the identity for the */
/* prolongator???? */
for (j = 0; j < nullspace_dim; j++)
for (k = 0; k < nullspace_dim; k++)
new_null[i*nullspace_dim+j+k*Ncoarse*nullspace_dim] =
qr_tmp[j+aggr_cnt_array[i]*k];
for (j = 0; j < aggr_cnt_array[i]; j++) {
index = rows_in_aggs[i][j];
index3 = new_ia[index];
for (k = 0; k < nullspace_dim; k++) {
new_ja [index3+k] = i * nullspace_dim + k;
if (k == j) new_val[index3+k] = 1.;
else new_val[index3+k] = 0.;
}
}
}
}
ML_Aggregate_Set_NullSpace(ml_ag, num_PDE_eqns, nullspace_dim,
new_null, Ncoarse*nullspace_dim);
ML_memory_free( (void **) &new_null);
/* ------------------------------------------------------------- */
/* set up the csr_data data structure */
/* ------------------------------------------------------------- */
ML_memory_alloc((void**) &csr_data, sizeof(struct ML_CSR_MSRdata),"CSR");
csr_data->rowptr = new_ia;
csr_data->columns = new_ja;
csr_data->values = new_val;
ML_Operator_Set_ApplyFuncData( *Pmatrix, nullspace_dim*Ncoarse, Nrows,
csr_data, Nrows, NULL, 0);
(*Pmatrix)->data_destroy = ML_CSR_MSR_ML_memorydata_Destroy;
(*Pmatrix)->getrow->pre_comm = ML_CommInfoOP_Create();
(*Pmatrix)->max_nz_per_row = 1;
ML_Operator_Set_Getrow((*Pmatrix), Nrows, CSR_getrow);
ML_Operator_Set_ApplyFunc((*Pmatrix), CSR_matvec);
(*Pmatrix)->max_nz_per_row = 1;
/* this must be set so that the hierarchy generation does not abort early
in adaptive SA */
(*Pmatrix)->num_PDEs = nullspace_dim;
/* ------------------------------------------------------------- */
/* clean up */
/* ------------------------------------------------------------- */
ML_free(unamalg_bdry);
ML_memory_free((void**)&aggr_index);
ML_free(aggr_cnt_array);
for (i = 0; i < aggr_count; i++) ML_free(rows_in_aggs[i]);
ML_memory_free((void**)&rows_in_aggs);
ML_memory_free((void**)&qr_tmp);
ML_memory_free((void**)&tmp_vect);
ML_memory_free((void**)&work);
aggr_curr = aggr_head;
while ( aggr_curr != NULL )
{
supernode = aggr_curr;
aggr_curr = aggr_curr->next;
if ( supernode->length > 0 ) ML_free( supernode->list );
ML_free( supernode );
}
return Ncoarse*nullspace_dim;
} /* ML_Aggregate_CoarsenUser */
// ================================================ ====== ==== ==== == =
int RefMaxwell_SetupCoordinates(ML_Operator* A, Teuchos::ParameterList &List_, double *&coordx, double *&coordy, double *&coordz)
// Coppied From int ML_Epetra::MultiLevelPreconditioner::SetupCoordinates()
{
double* in_x_coord = 0;
double* in_y_coord = 0;
double* in_z_coord = 0;
int NumPDEEqns_ =1; // Always use 1 because A is a nodal matrix.
// For node coordinates
in_x_coord = List_.get("x-coordinates", (double *)0);
in_y_coord = List_.get("y-coordinates", (double *)0);
in_z_coord = List_.get("z-coordinates", (double *)0);
if (!(in_x_coord == 0 && in_y_coord == 0 && in_z_coord == 0))
{
ML_Operator* AAA = A;
int n = AAA->invec_leng, Nghost = 0;
if (AAA->getrow->pre_comm)
{
if (AAA->getrow->pre_comm->total_rcv_length <= 0)
ML_CommInfoOP_Compute_TotalRcvLength(AAA->getrow->pre_comm);
Nghost = AAA->getrow->pre_comm->total_rcv_length;
}
std::vector<double> tmp(Nghost + n);
for (int i = 0 ; i < Nghost + n ; ++i)
tmp[i] = 0.0;
n /= NumPDEEqns_;
Nghost /= NumPDEEqns_;
if (in_x_coord)
{
double* x_coord = (double *) ML_allocate(sizeof(double) * (Nghost+n));
for (int i = 0 ; i < n ; ++i)
tmp[i * NumPDEEqns_] = in_x_coord[i];
ML_exchange_bdry(&tmp[0],AAA->getrow->pre_comm, NumPDEEqns_ * n,
AAA->comm, ML_OVERWRITE,NULL);
for (int i = 0 ; i < n + Nghost ; ++i)
x_coord[i] = tmp[i * NumPDEEqns_];
coordx = x_coord;
}
if (in_y_coord)
{
double* y_coord = (double *) ML_allocate(sizeof(double) * (Nghost+n));
for (int i = 0 ; i < n ; ++i)
tmp[i * NumPDEEqns_] = in_y_coord[i];
ML_exchange_bdry(&tmp[0],AAA->getrow->pre_comm, NumPDEEqns_ * n,
AAA->comm, ML_OVERWRITE,NULL);
for (int i = 0 ; i < n + Nghost ; ++i)
y_coord[i] = tmp[i * NumPDEEqns_];
coordy = y_coord;
}
if (in_z_coord)
{
double* z_coord = (double *) ML_allocate(sizeof(double) * (Nghost+n));
for (int i = 0 ; i < n ; ++i)
tmp[i * NumPDEEqns_] = in_z_coord[i];
ML_exchange_bdry(&tmp[0],AAA->getrow->pre_comm, NumPDEEqns_ * n,
AAA->comm, ML_OVERWRITE,NULL);
for (int i = 0 ; i < n + Nghost ; ++i)
z_coord[i] = tmp[i * NumPDEEqns_];
coordz = z_coord;
}
} // if (!(in_x_coord == 0 && in_y_coord == 0 && in_z_coord == 0))
return(0);
}
void ML_get_row_CSR_norow_map(ML_Operator *input_matrix, int N_requested_rows,
int requested_rows[], int *allocated_space, int **columns,
double **values, int row_lengths[], int index)
{
int i, *mapper, *t1, row;
ML_Operator *next;
double *t2;
struct ML_CSR_MSRdata *matrix;
int *rowptr, *bindx, *col_ptr, itemp, j;
double *val, *val_ptr;
#ifdef DEBUG2
if (N_requested_rows != 1) {
printf("ML_get_matrix_row is currently implemented for only 1 row");
printf(" at a time.\n");
exit(1);
}
#endif
row = requested_rows[0];
#ifdef DEBUG2
if ( (row >= input_matrix->getrow->Nrows) || (row < 0) ) {
row_lengths[0] = 0;
return;
}
#endif
next = input_matrix->sub_matrix;
while ( (next != NULL) && (row < next->getrow->Nrows) ) {
input_matrix = next;
next = next->sub_matrix;
}
if (next != NULL) row -= next->getrow->Nrows;
matrix = (struct ML_CSR_MSRdata *) input_matrix->data;
rowptr = matrix->rowptr;
itemp = rowptr[row];
bindx = &(matrix->columns[itemp]);
val = &(matrix->values[itemp]);
*row_lengths = rowptr[row+1] - itemp;
if (*row_lengths+index > *allocated_space) {
*allocated_space = 2*(*allocated_space) + 1;
if (*row_lengths+index > *allocated_space)
*allocated_space = *row_lengths + 5 + index;
t1 = (int *) ML_allocate(*allocated_space*sizeof(int ));
t2 = (double *) ML_allocate(*allocated_space*sizeof(double));
if (t2 == NULL) {
printf("Not enough space to get a matrix row. A row length of \n");
printf("%d was not sufficient\n",(*allocated_space-1)/2);
fflush(stdout);
ML_avoid_unused_param( (void *) &N_requested_rows);
exit(1);
}
for (i = 0; i < index; i++) t1[i] = (*columns)[i];
for (i = 0; i < index; i++) t2[i] = (*values)[i];
ML_free(*columns); ML_free(*values);
*columns = t1;
*values = t2;
}
col_ptr = &((*columns)[index]);
val_ptr = &((*values)[index]);
for (j = 0 ; j < *row_lengths; j++) {
*col_ptr++ = *bindx++;
}
for (j = 0 ; j < *row_lengths; j++) {
*val_ptr++ = *val++;
}
if ( (input_matrix->getrow->use_loc_glob_map == ML_YES)) {
mapper = input_matrix->getrow->loc_glob_map;
for (i = 0; i < row_lengths[0]; i++)
(*columns)[i+index] = mapper[(*columns)[index+i]];
}
}
void ML_get_matrix_row(ML_Operator *input_matrix, int N_requested_rows,
int requested_rows[], int *allocated_space, int **columns,
double **values, int row_lengths[], int index)
{
int i, *mapper, *t1, row;
ML_Operator *next;
double *t2;
void *data;
int (*getfunction)(void *,int,int*,int,int*,double*,int*);
#ifdef DEBUG2
if (N_requested_rows != 1) {
printf("ML_get_matrix_row is currently implemented for only 1 row");
printf(" at a time.\n");
exit(1);
}
#endif
row = requested_rows[0];
#ifdef DEBUG2
if ( (row >= input_matrix->getrow->Nrows) || (row < 0) ) {
row_lengths[0] = 0;
return;
}
#endif
if (input_matrix->getrow->row_map != NULL) {
if (input_matrix->getrow->row_map[row] != -1)
row = input_matrix->getrow->row_map[row];
else {
row_lengths[0] = 0;
ML_avoid_unused_param( (void *) &N_requested_rows);
return;}
}
next = input_matrix->sub_matrix;
while ( (next != NULL) && (row < next->getrow->Nrows) ) {
input_matrix = next;
next = next->sub_matrix;
}
if (next != NULL) row -= next->getrow->Nrows;
data = (void *) input_matrix;
getfunction = (int (*)(void *,int,int*,int,int*,double*,int*))
input_matrix->getrow->func_ptr;
while(getfunction(data,1,&row,*allocated_space-index,
&((*columns)[index]), &((*values)[index]), row_lengths) == 0) {
*allocated_space = 2*(*allocated_space) + 1;
t1 = (int *) ML_allocate(*allocated_space*sizeof(int ));
if (t1 == NULL) {
printf("Not enough space to get a matrix row. A row length of \n");
printf("%d Was not sufficient\n",(*allocated_space-1)/2);
fflush(stdout);
exit(1);
}
else {
for (i = 0; i < index; i++) t1[i] = (*columns)[i];
if (*columns != NULL) ML_free(*columns);
*columns = t1;
}
t2 = (double *) ML_allocate(*allocated_space*sizeof(double));
if (t2 == NULL) {
printf("Not enough space to get a matrix row. A row length of \n");
printf("%d Was not sufficient\n",(*allocated_space-1)/2);
fflush(stdout);
exit(1);
}
for (i = 0; i < index; i++) t2[i] = (*values)[i];
if (*values != NULL) ML_free(*values);
*values = t2;
}
if ( (input_matrix->getrow->use_loc_glob_map == ML_YES)) {
mapper = input_matrix->getrow->loc_glob_map;
for (i = 0; i < row_lengths[0]; i++)
(*columns)[i+index] = mapper[(*columns)[index+i]];
}
}
int main(int argc, char *argv[])
{
int num_PDE_eqns=3, N_levels=3, nsmooth=1;
int leng, level, N_grid_pts, coarsest_level;
/* See Aztec User's Guide for more information on the */
/* variables that follow. */
int proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
double params[AZ_PARAMS_SIZE], status[AZ_STATUS_SIZE];
/* data structure for matrix corresponding to the fine grid */
int *data_org = NULL, *update = NULL, *external = NULL;
int *update_index = NULL, *extern_index = NULL;
int *cpntr = NULL;
int *bindx = NULL, N_update, iii;
double *val = NULL;
double *xxx, *rhs;
AZ_MATRIX *Amat;
AZ_PRECOND *Pmat = NULL;
ML *ml;
FILE *fp;
int ch,i,j, Nrigid, *garbage;
struct AZ_SCALING *scaling;
double solve_time, setup_time, start_time, *mode, *rigid;
ML_Aggregate *ag;
int nblocks, *blocks;
char filename[80];
double alpha;
int one = 1;
#ifdef ML_MPI
MPI_Init(&argc,&argv);
/* get number of processors and the name of this processor */
AZ_set_proc_config(proc_config, MPI_COMM_WORLD);
#else
AZ_set_proc_config(proc_config, AZ_NOT_MPI);
#endif
leng = 0;
if (proc_config[AZ_node] == 0) {
#ifdef binary
fp=fopen(".data","rb");
#else
fp=fopen(".data","r");
#endif
if (fp==NULL)
{
printf("couldn't open file .data\n");
exit(1);
}
#ifdef binary
fread(&leng, sizeof(int), 1, fp);
#else
fscanf(fp,"%d",&leng);
#endif
fclose(fp);
}
leng = AZ_gsum_int(leng, proc_config);
N_grid_pts=leng/num_PDE_eqns;
/* initialize the list of global indices. NOTE: the list of global */
/* indices must be in ascending order so that subsequent calls to */
/* AZ_find_index() will function properly. */
AZ_read_update(&N_update, &update, proc_config, N_grid_pts, num_PDE_eqns,
AZ_linear);
AZ_read_msr_matrix(update, &val, &bindx, N_update, proc_config);
AZ_transform(proc_config, &external, bindx, val, update, &update_index,
&extern_index, &data_org, N_update, 0, 0, 0, &cpntr,
AZ_MSR_MATRIX);
Amat = AZ_matrix_create( leng );
AZ_set_MSR(Amat, bindx, val, data_org, 0, NULL, AZ_LOCAL);
Amat->matrix_type = data_org[AZ_matrix_type];
data_org[AZ_N_rows] = data_org[AZ_N_internal] + data_org[AZ_N_border];
start_time = AZ_second();
AZ_defaults(options, params);
/*
scaling = AZ_scaling_create();
xxx = (double *) calloc( leng,sizeof(double));
rhs=(double *)calloc(leng,sizeof(double));
options[AZ_scaling] = AZ_sym_diag;
options[AZ_precond] = AZ_none;
options[AZ_max_iter] = 30;
options[AZ_keep_info] = 1;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
don't forget vector rescaling ...
free(xxx);
free(rhs);
*/
options[AZ_scaling] = AZ_none;
ML_Create(&ml, N_levels);
/* set up discretization matrix and matrix vector function */
AZ_ML_Set_Amat(ml, N_levels-1, N_update, N_update, Amat, proc_config);
ML_Aggregate_Create( &ag );
Nrigid = 0;
if (proc_config[AZ_node] == 0) {
sprintf(filename,"rigid_body_mode%d",Nrigid+1);
while( (fp = fopen(filename,"r")) != NULL) {
fclose(fp);
Nrigid++;
sprintf(filename,"rigid_body_mode%d",Nrigid+1);
}
}
Nrigid = AZ_gsum_int(Nrigid,proc_config);
if (Nrigid != 0) {
rigid = (double *) ML_allocate( sizeof(double)*Nrigid*(N_update+1) );
if (rigid == NULL) {
printf("Error: Not enough space for rigid body modes\n");
}
}
rhs=(double *)malloc(leng*sizeof(double));
AZ_random_vector(rhs, data_org, proc_config);
for (i = 0; i < Nrigid; i++) {
sprintf(filename,"rigid_body_mode%d",i+1);
AZ_input_msr_matrix(filename, update, &mode, &garbage,
N_update, proc_config);
/*
AZ_sym_rescale_sl(mode, Amat->data_org, options, proc_config, scaling);
*/
/*
Amat->matvec(mode, rigid, Amat, proc_config);
for (j = 0; j < N_update; j++) printf("this is %d %e\n",j,rigid[j]);
*/
for (j = 0; j < i; j++) {
alpha = -AZ_gdot(N_update, mode, &(rigid[j*N_update]), proc_config)/AZ_gdot(N_update, &(rigid[j*N_update]), &(rigid[j*N_update]), proc_config);
daxpy_(&N_update, &alpha, &(rigid[j*N_update]), &one, mode, &one);
printf("alpha1 is %e\n",alpha);
}
alpha = -AZ_gdot(N_update, mode, rhs, proc_config)/AZ_gdot(N_update, mode, mode, proc_config);
printf("alpha2 is %e\n",alpha);
daxpy_(&N_update, &alpha, mode, &one, rhs, &one);
for (j = 0; j < N_update; j++) rigid[i*N_update+j] = mode[j];
free(mode);
free(garbage);
}
for (j = 0; j < Nrigid; j++) {
alpha = -AZ_gdot(N_update, rhs, &(rigid[j*N_update]), proc_config)/AZ_gdot(N_update, &(rigid[j*N_update]), &(rigid[j*N_update]), proc_config);
daxpy_(&N_update, &alpha, &(rigid[j*N_update]), &one, rhs, &one);
printf("alpha4 is %e\n",alpha);
}
for (i = 0; i < Nrigid; i++) {
alpha = -AZ_gdot(N_update, &(rigid[i*N_update]), rhs, proc_config);
printf("alpha is %e\n",alpha);
}
if (Nrigid != 0) {
ML_Aggregate_Set_NullSpace(ag, num_PDE_eqns, Nrigid, rigid, N_update);
/*
free(rigid);
*/
}
coarsest_level = ML_Gen_MGHierarchy_UsingAggregation(ml, N_levels-1, ML_DECREASING, ag);
coarsest_level = N_levels - coarsest_level;
/*
ML_Operator_Print(&(ml->Pmat[N_levels-2]), "Pmat");
exit(1);
*/
if ( proc_config[AZ_node] == 0 )
printf("Coarse level = %d \n", coarsest_level);
/* set up smoothers */
for (level = N_levels-1; level > coarsest_level; level--) {
j = 10;
if (level == N_levels-1) j = 10;
options[AZ_solver] = AZ_cg;
options[AZ_precond]=AZ_sym_GS; options[AZ_subdomain_solve]=AZ_icc;
/*
options[AZ_precond] = AZ_none;
*/
options[AZ_poly_ord] = 5;
ML_Gen_SmootherAztec(ml, level, options, params, proc_config, status,
j, ML_PRESMOOTHER,NULL);
ML_Gen_SmootherAztec(ml, level, options, params, proc_config, status,
j, ML_POSTSMOOTHER,NULL);
/*
ML_Gen_Smoother_SymGaussSeidel(ml , level, ML_PRESMOOTHER, nsmooth,1.0);
ML_Gen_Smoother_SymGaussSeidel(ml , level, ML_POSTSMOOTHER, nsmooth,1.0);
*/
/*
nblocks = ML_Aggregate_Get_AggrCount( ag, level );
ML_Aggregate_Get_AggrMap( ag, level, &blocks);
ML_Gen_Smoother_VBlockSymGaussSeidel( ml , level, ML_BOTH, nsmooth, 1.0,
nblocks, blocks);
ML_Gen_Smoother_VBlockSymGaussSeidel( ml , level, ML_POSTSMOOTHER, nsmooth, 1.0,
nblocks, blocks);
*/
/*
ML_Gen_Smoother_VBlockJacobi( ml , level, ML_PRESMOOTHER, nsmooth, .5,
nblocks, blocks);
ML_Gen_Smoother_VBlockJacobi( ml , level, ML_POSTSMOOTHER, nsmooth,.5,
nblocks, blocks);
*/
/*
ML_Gen_Smoother_GaussSeidel(ml , level, ML_PRESMOOTHER, nsmooth);
ML_Gen_Smoother_GaussSeidel(ml , level, ML_POSTSMOOTHER, nsmooth);
*/
/*
need to change this when num_pdes is different on different levels
*/
/*
if (level == N_levels-1) {
ML_Gen_Smoother_BlockGaussSeidel(ml , level, ML_PRESMOOTHER, nsmooth, 0.5, num_PDE_eqns);
ML_Gen_Smoother_BlockGaussSeidel(ml , level, ML_POSTSMOOTHER, nsmooth, 0.5, num_PDE_eqns);
}
else {
ML_Gen_Smoother_BlockGaussSeidel(ml , level, ML_PRESMOOTHER, nsmooth, 0.5, 2*num_PDE_eqns);
ML_Gen_Smoother_BlockGaussSeidel(ml , level, ML_POSTSMOOTHER, nsmooth, 0.5, 2*num_PDE_eqns);
}
*/
/*
*/
/*
ML_Gen_SmootherJacobi(ml , level, ML_PRESMOOTHER, nsmooth, .67);
ML_Gen_SmootherJacobi(ml , level, ML_POSTSMOOTHER, nsmooth, .67 );
*/
}
/*
ML_Gen_CoarseSolverSuperLU( ml, coarsest_level);
*/
/*
ML_Gen_SmootherSymGaussSeidel(ml , coarsest_level, ML_PRESMOOTHER, 2*nsmooth,1.);
*/
/*
ML_Gen_SmootherBlockGaussSeidel(ml , level, ML_PRESMOOTHER, 50*nsmooth, 1.0, 2*num_PDE_eqns);
*/
ML_Gen_Smoother_BlockGaussSeidel(ml , level, ML_PRESMOOTHER, 2*nsmooth, 1.0, num_PDE_eqns);
ML_Gen_Solver(ml, ML_MGV, N_levels-1, coarsest_level);
AZ_defaults(options, params);
options[AZ_solver] = AZ_GMRESR;
options[AZ_scaling] = AZ_none;
options[AZ_precond] = AZ_user_precond;
options[AZ_conv] = AZ_rhs;
options[AZ_output] = 1;
options[AZ_max_iter] = 1500;
options[AZ_poly_ord] = 5;
options[AZ_kspace] = 130;
params[AZ_tol] = 1.0e-8;
AZ_set_ML_preconditioner(&Pmat, Amat, ml, options);
setup_time = AZ_second() - start_time;
xxx = (double *) malloc( leng*sizeof(double));
/* Set rhs */
fp = fopen("AZ_capture_rhs.dat","r");
if (fp == NULL) {
if (proc_config[AZ_node] == 0) printf("taking random vector for rhs\n");
/*
AZ_random_vector(rhs, data_org, proc_config);
AZ_reorder_vec(rhs, data_org, update_index, NULL);
AZ_random_vector(xxx, data_org, proc_config);
AZ_reorder_vec(xxx, data_org, update_index, NULL);
Amat->matvec(xxx, rhs, Amat, proc_config);
*/
}
else {
ch = getc(fp);
if (ch == 'S') {
while ( (ch = getc(fp)) != '\n') ;
}
else ungetc(ch,fp);
for (i = 0; i < data_org[AZ_N_internal]+data_org[AZ_N_border]; i++)
fscanf(fp,"%lf",&(rhs[i]));
fclose(fp);
}
for (iii = 0; iii < leng; iii++) xxx[iii] = 0.0;
/* Set x */
fp = fopen("AZ_capture_init_guess.dat","r");
if (fp != NULL) {
ch = getc(fp);
if (ch == 'S') {
while ( (ch = getc(fp)) != '\n') ;
}
else ungetc(ch,fp);
for (i = 0; i < data_org[AZ_N_internal]+data_org[AZ_N_border]; i++)
fscanf(fp,"%lf",&(xxx[i]));
fclose(fp);
options[AZ_conv] = AZ_expected_values;
}
/* if Dirichlet BC ... put the answer in */
for (i = 0; i < data_org[AZ_N_internal]+data_org[AZ_N_border]; i++) {
if ( (val[i] > .99999999) && (val[i] < 1.0000001))
xxx[i] = rhs[i];
}
fp = fopen("AZ_no_multilevel.dat","r");
scaling = AZ_scaling_create();
start_time = AZ_second();
if (fp != NULL) {
fclose(fp);
options[AZ_precond] = AZ_none;
options[AZ_scaling] = AZ_sym_diag;
options[AZ_ignore_scaling] = AZ_TRUE;
options[AZ_keep_info] = 1;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
/*
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
*/
}
else {
options[AZ_keep_info] = 1;
/*
options[AZ_max_iter] = 40;
*/
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
for (j = 0; j < Nrigid; j++) {
alpha = -AZ_gdot(N_update, xxx, &(rigid[j*N_update]), proc_config)/AZ_gdot(N_update, &(rigid[j*N_update]), &(rigid[j*N_update]), proc_config);
daxpy_(&N_update, &alpha, &(rigid[j*N_update]), &one, xxx, &one);
printf("alpha5 is %e\n",alpha);
}
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
/*
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
*/
}
solve_time = AZ_second() - start_time;
if (proc_config[AZ_node] == 0)
printf("Solve time = %e, MG Setup time = %e\n", solve_time, setup_time);
ML_Aggregate_Destroy(&ag);
ML_Destroy(&ml);
AZ_free((void *) Amat->data_org);
AZ_free((void *) Amat->val);
AZ_free((void *) Amat->bindx);
AZ_free((void *) update);
AZ_free((void *) external);
AZ_free((void *) extern_index);
AZ_free((void *) update_index);
if (Amat != NULL) AZ_matrix_destroy(&Amat);
if (Pmat != NULL) AZ_precond_destroy(&Pmat);
free(xxx);
free(rhs);
#ifdef ML_MPI
MPI_Finalize();
#endif
return 0;
}
int main(int argc, char *argv[])
{
int num_PDE_eqns=6, N_levels=4, nsmooth=2;
int leng, level, N_grid_pts, coarsest_level;
/* See Aztec User's Guide for more information on the */
/* variables that follow. */
int proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
double params[AZ_PARAMS_SIZE], status[AZ_STATUS_SIZE];
/* data structure for matrix corresponding to the fine grid */
double *val = NULL, *xxx, *rhs, solve_time, setup_time, start_time;
AZ_MATRIX *Amat;
AZ_PRECOND *Pmat = NULL;
ML *ml;
FILE *fp;
int i, j, Nrigid, *garbage = NULL;
#ifdef ML_partition
int nblocks;
int *block_list = NULL;
int k;
#endif
struct AZ_SCALING *scaling;
ML_Aggregate *ag;
double *mode, *rigid;
char filename[80];
double alpha;
int allocated = 0;
int old_prec, old_sol;
double old_tol;
/*
double *Amode, beta, biggest;
int big_ind = -1, ii;
*/
ML_Operator *Amatrix;
int *rowi_col = NULL, rowi_N, count2, ccc;
double *rowi_val = NULL;
double max_diag, min_diag, max_sum, sum;
int nBlocks, *blockIndices, Ndof;
#ifdef ML_partition
FILE *fp2;
int count;
if (argc != 2) {
printf("Usage: ml_read_elas num_processors\n");
exit(1);
}
else sscanf(argv[1],"%d",&nblocks);
#endif
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
/* get number of processors and the name of this processor */
AZ_set_proc_config(proc_config, MPI_COMM_WORLD);
#else
AZ_set_proc_config(proc_config, AZ_NOT_MPI);
#endif
/* read in the number of matrix equations */
leng = 0;
if (proc_config[AZ_node] == 0) {
# ifdef binary
fp=fopen(".data","rb");
# else
fp=fopen(".data","r");
# endif
if (fp==NULL) {
printf("couldn't open file .data\n");
exit(1);
}
# ifdef binary
fread(&leng, sizeof(int), 1, fp);
# else
fscanf(fp,"%d",&leng);
# endif
fclose(fp);
}
leng = AZ_gsum_int(leng, proc_config);
N_grid_pts=leng/num_PDE_eqns;
/* initialize the list of global indices. NOTE: the list of global */
/* indices must be in ascending order so that subsequent calls to */
/* AZ_find_index() will function properly. */
if (proc_config[AZ_N_procs] == 1) i = AZ_linear;
else i = AZ_file;
AZ_read_update(&N_update, &update, proc_config, N_grid_pts, num_PDE_eqns,i);
AZ_read_msr_matrix(update, &val, &bindx, N_update, proc_config);
/* This code is to fix things up so that we are sure we have */
/* all block (including the ghost nodes the same size. */
AZ_block_MSR(&bindx, &val, N_update, num_PDE_eqns, update);
AZ_transform_norowreordering(proc_config, &external, bindx, val, update, &update_index,
&extern_index, &data_org, N_update, 0, 0, 0, &cpntr,
AZ_MSR_MATRIX);
Amat = AZ_matrix_create( leng );
AZ_set_MSR(Amat, bindx, val, data_org, 0, NULL, AZ_LOCAL);
Amat->matrix_type = data_org[AZ_matrix_type];
data_org[AZ_N_rows] = data_org[AZ_N_internal] + data_org[AZ_N_border];
#ifdef SCALE_ME
ML_MSR_sym_diagonal_scaling(Amat, proc_config, &scaling_vect);
#endif
start_time = AZ_second();
options[AZ_scaling] = AZ_none;
ML_Create(&ml, N_levels);
ML_Set_PrintLevel(10);
/* set up discretization matrix and matrix vector function */
AZ_ML_Set_Amat(ml, N_levels-1, N_update, N_update, Amat, proc_config);
#ifdef ML_partition
/* this code is meant to partition the matrices so that things can be */
/* run in parallel later. */
/* It is meant to be run on only one processor. */
#ifdef MB_MODIF
fp2 = fopen(".update","w");
#else
fp2 = fopen("partition_file","w");
#endif
ML_Operator_AmalgamateAndDropWeak(&(ml->Amat[N_levels-1]), num_PDE_eqns, 0.0);
ML_Gen_Blocks_Metis(ml, N_levels-1, &nblocks, &block_list);
for (i = 0; i < nblocks; i++) {
count = 0;
for (j = 0; j < ml->Amat[N_levels-1].outvec_leng; j++) {
if (block_list[j] == i) count++;
}
fprintf(fp2," %d\n",count*num_PDE_eqns);
for (j = 0; j < ml->Amat[N_levels-1].outvec_leng; j++) {
if (block_list[j] == i) {
for (k = 0; k < num_PDE_eqns; k++) fprintf(fp2,"%d\n",j*num_PDE_eqns+k);
}
}
}
fclose(fp2);
ML_Operator_UnAmalgamateAndDropWeak(&(ml->Amat[N_levels-1]),num_PDE_eqns,0.0);
#ifdef MB_MODIF
printf(" partition file dumped in .update\n");
#endif
exit(1);
#endif
ML_Aggregate_Create( &ag );
/*
ML_Aggregate_Set_CoarsenScheme_MIS(ag);
*/
#ifdef MB_MODIF
ML_Aggregate_Set_DampingFactor(ag,1.50);
#else
ML_Aggregate_Set_DampingFactor(ag,1.5);
#endif
ML_Aggregate_Set_CoarsenScheme_METIS(ag);
ML_Aggregate_Set_NodesPerAggr( ml, ag, -1, 35);
/*
ML_Aggregate_Set_Phase3AggregateCreationAggressiveness(ag, 10.001);
*/
ML_Aggregate_Set_Threshold(ag, 0.0);
ML_Aggregate_Set_MaxCoarseSize( ag, 300);
/* read in the rigid body modes */
Nrigid = 0;
/* to ensure compatibility with RBM dumping software */
if (proc_config[AZ_node] == 0) {
sprintf(filename,"rigid_body_mode%02d",Nrigid+1);
while( (fp = fopen(filename,"r")) != NULL) {
which_filename = 1;
fclose(fp);
Nrigid++;
sprintf(filename,"rigid_body_mode%02d",Nrigid+1);
}
sprintf(filename,"rigid_body_mode%d",Nrigid+1);
while( (fp = fopen(filename,"r")) != NULL) {
fclose(fp);
Nrigid++;
sprintf(filename,"rigid_body_mode%d",Nrigid+1);
}
}
Nrigid = AZ_gsum_int(Nrigid,proc_config);
if (Nrigid != 0) {
rigid = (double *) ML_allocate( sizeof(double)*Nrigid*(N_update+1) );
if (rigid == NULL) {
printf("Error: Not enough space for rigid body modes\n");
}
}
rhs = (double *) malloc(leng*sizeof(double));
xxx = (double *) malloc(leng*sizeof(double));
for (iii = 0; iii < leng; iii++) xxx[iii] = 0.0;
for (i = 0; i < Nrigid; i++) {
if (which_filename == 1) sprintf(filename,"rigid_body_mode%02d",i+1);
else sprintf(filename,"rigid_body_mode%d",i+1);
AZ_input_msr_matrix(filename,update,&mode,&garbage,N_update,proc_config);
AZ_reorder_vec(mode, data_org, update_index, NULL);
/* here is something to stick a rigid body mode as the initial */
/* The idea is to solve A x = 0 without smoothing with a two */
/* level method. If everything is done properly, we should */
/* converge in 2 iterations. */
/* Note: we must also zero out components of the rigid body */
/* mode that correspond to Dirichlet bcs. */
if (i == -4) {
for (iii = 0; iii < leng; iii++) xxx[iii] = mode[iii];
ccc = 0;
Amatrix = &(ml->Amat[N_levels-1]);
for (iii = 0; iii < Amatrix->outvec_leng; iii++) {
ML_get_matrix_row(Amatrix,1,&iii,&allocated,&rowi_col,&rowi_val,
&rowi_N, 0);
count2 = 0;
for (j = 0; j < rowi_N; j++) if (rowi_val[j] != 0.) count2++;
if (count2 <= 1) { xxx[iii] = 0.; ccc++; }
}
free(rowi_col); free(rowi_val);
allocated = 0; rowi_col = NULL; rowi_val = NULL;
}
/*
* Rescale matrix/rigid body modes and checking
*
AZ_sym_rescale_sl(mode, Amat->data_org, options, proc_config, scaling);
Amat->matvec(mode, rigid, Amat, proc_config);
for (j = 0; j < N_update; j++) printf("this is %d %e\n",j,rigid[j]);
*/
/* Here is some code to check that the rigid body modes are */
/* really rigid body modes. The idea is to multiply by A and */
/* then to zero out things that we "think" are boundaries. */
/* In this hardwired example, things near boundaries */
/* correspond to matrix rows that do not have 81 nonzeros. */
/*
Amode = (double *) malloc(leng*sizeof(double));
Amat->matvec(mode, Amode, Amat, proc_config);
j = 0;
biggest = 0.0;
for (ii = 0; ii < N_update; ii++) {
if ( Amat->bindx[ii+1] - Amat->bindx[ii] != 80) {
Amode[ii] = 0.; j++;
}
else {
if ( fabs(Amode[ii]) > biggest) {
biggest=fabs(Amode[ii]); big_ind = ii;
}
}
}
printf("%d entries zeroed out of %d elements\n",j,N_update);
alpha = AZ_gdot(N_update, Amode, Amode, proc_config);
beta = AZ_gdot(N_update, mode, mode, proc_config);
printf("||A r||^2 =%e, ||r||^2 = %e, ratio = %e\n",
alpha,beta,alpha/beta);
printf("the biggest is %e at row %d\n",biggest,big_ind);
free(Amode);
*/
/* orthogonalize mode with respect to previous modes. */
for (j = 0; j < i; j++) {
alpha = -AZ_gdot(N_update, mode, &(rigid[j*N_update]), proc_config)/
AZ_gdot(N_update, &(rigid[j*N_update]),
&(rigid[j*N_update]), proc_config);
/* daxpy_(&N_update,&alpha,&(rigid[j*N_update]), &one, mode, &one); */
}
#ifndef MB_MODIF
printf(" after mb %e %e %e\n",mode[0],mode[1],mode[2]);
#endif
for (j = 0; j < N_update; j++) rigid[i*N_update+j] = mode[j];
free(mode);
free(garbage); garbage = NULL;
}
if (Nrigid != 0) {
ML_Aggregate_Set_BlockDiagScaling(ag);
ML_Aggregate_Set_NullSpace(ag, num_PDE_eqns, Nrigid, rigid, N_update);
free(rigid);
}
#ifdef SCALE_ME
ML_Aggregate_Scale_NullSpace(ag, scaling_vect, N_update);
#endif
coarsest_level = ML_Gen_MGHierarchy_UsingAggregation(ml, N_levels-1,
ML_DECREASING, ag);
AZ_defaults(options, params);
coarsest_level = N_levels - coarsest_level;
if ( proc_config[AZ_node] == 0 )
printf("Coarse level = %d \n", coarsest_level);
/* set up smoothers */
for (level = N_levels-1; level > coarsest_level; level--) {
/*
ML_Gen_Smoother_BlockGaussSeidel(ml, level,ML_BOTH, 1, 1., num_PDE_eqns);
*/
/* Sparse approximate inverse smoother that acutally does both */
/* pre and post smoothing. */
/*
ML_Gen_Smoother_ParaSails(ml , level, ML_PRESMOOTHER, nsmooth,
parasails_sym, parasails_thresh,
parasails_nlevels, parasails_filter,
parasails_loadbal, parasails_factorized);
*/
/* This is the symmetric Gauss-Seidel smoothing that we usually use. */
/* In parallel, it is not a true Gauss-Seidel in that each processor */
/* does a Gauss-Seidel on its local submatrix independent of the */
/* other processors. */
/* ML_Gen_Smoother_Cheby(ml, level, ML_BOTH, 30., nsmooth); */
Ndof = ml->Amat[level].invec_leng;
ML_Gen_Blocks_Aggregates(ag, level, &nBlocks, &blockIndices);
ML_Gen_Smoother_BlockDiagScaledCheby(ml, level, ML_BOTH, 30.,nsmooth,
nBlocks, blockIndices);
/*
ML_Gen_Smoother_SymGaussSeidel(ml , level, ML_BOTH, nsmooth,1.);
*/
/* This is a true Gauss Seidel in parallel. This seems to work for */
/* elasticity problems. However, I don't believe that this is very */
/* efficient in parallel. */
/*
nblocks = ml->Amat[level].invec_leng/num_PDE_eqns;
blocks = (int *) ML_allocate(sizeof(int)*N_update);
for (i =0; i < ml->Amat[level].invec_leng; i++)
blocks[i] = i/num_PDE_eqns;
ML_Gen_Smoother_VBlockSymGaussSeidelSequential(ml , level, ML_PRESMOOTHER,
nsmooth, 1., nblocks, blocks);
ML_Gen_Smoother_VBlockSymGaussSeidelSequential(ml, level, ML_POSTSMOOTHER,
nsmooth, 1., nblocks, blocks);
free(blocks);
*/
/* Block Jacobi Smoothing */
/*
nblocks = ml->Amat[level].invec_leng/num_PDE_eqns;
blocks = (int *) ML_allocate(sizeof(int)*N_update);
for (i =0; i < ml->Amat[level].invec_leng; i++)
blocks[i] = i/num_PDE_eqns;
ML_Gen_Smoother_VBlockJacobi(ml , level, ML_BOTH, nsmooth,
ML_ONE_STEP_CG, nblocks, blocks);
free(blocks);
*/
/* Jacobi Smoothing */
/*
ML_Gen_Smoother_Jacobi(ml , level, ML_PRESMOOTHER, nsmooth, ML_ONE_STEP_CG);
ML_Gen_Smoother_Jacobi(ml , level, ML_POSTSMOOTHER, nsmooth,ML_ONE_STEP_CG);
*/
/* This does a block Gauss-Seidel (not true GS in parallel) */
/* where each processor has 'nblocks' blocks. */
/*
nblocks = 250;
ML_Gen_Blocks_Metis(ml, level, &nblocks, &blocks);
ML_Gen_Smoother_VBlockJacobi(ml , level, ML_BOTH, nsmooth,ML_ONE_STEP_CG,
nblocks, blocks);
free(blocks);
*/
num_PDE_eqns = 6;
}
/* Choose coarse grid solver: mls, superlu, symGS, or Aztec */
/*
ML_Gen_Smoother_Cheby(ml, coarsest_level, ML_BOTH, 30., nsmooth);
ML_Gen_CoarseSolverSuperLU( ml, coarsest_level);
*/
/*
ML_Gen_Smoother_SymGaussSeidel(ml , coarsest_level, ML_BOTH, nsmooth,1.);
*/
old_prec = options[AZ_precond];
old_sol = options[AZ_solver];
old_tol = params[AZ_tol];
params[AZ_tol] = 1.0e-9;
params[AZ_tol] = 1.0e-5;
options[AZ_precond] = AZ_Jacobi;
options[AZ_solver] = AZ_cg;
options[AZ_poly_ord] = 1;
options[AZ_conv] = AZ_r0;
options[AZ_orth_kvecs] = AZ_TRUE;
j = AZ_gsum_int(ml->Amat[coarsest_level].outvec_leng, proc_config);
options[AZ_keep_kvecs] = j - 6;
options[AZ_max_iter] = options[AZ_keep_kvecs];
ML_Gen_SmootherAztec(ml, coarsest_level, options, params,
proc_config, status, options[AZ_keep_kvecs], ML_PRESMOOTHER, NULL);
options[AZ_conv] = AZ_noscaled;
options[AZ_keep_kvecs] = 0;
options[AZ_orth_kvecs] = 0;
options[AZ_precond] = old_prec;
options[AZ_solver] = old_sol;
params[AZ_tol] = old_tol;
/* */
#ifdef RST_MODIF
ML_Gen_Solver(ml, ML_MGV, N_levels-1, coarsest_level);
#else
#ifdef MB_MODIF
ML_Gen_Solver(ml, ML_SAAMG, N_levels-1, coarsest_level);
#else
ML_Gen_Solver(ml, ML_MGFULLV, N_levels-1, coarsest_level);
#endif
#endif
options[AZ_solver] = AZ_GMRESR;
options[AZ_solver] = AZ_cg;
options[AZ_scaling] = AZ_none;
options[AZ_precond] = AZ_user_precond;
options[AZ_conv] = AZ_r0;
options[AZ_conv] = AZ_noscaled;
options[AZ_output] = 1;
options[AZ_max_iter] = 500;
options[AZ_poly_ord] = 5;
options[AZ_kspace] = 40;
params[AZ_tol] = 4.8e-6;
AZ_set_ML_preconditioner(&Pmat, Amat, ml, options);
setup_time = AZ_second() - start_time;
/* Set rhs */
fp = fopen("AZ_capture_rhs.dat","r");
if (fp == NULL) {
AZ_random_vector(rhs, data_org, proc_config);
if (proc_config[AZ_node] == 0) printf("taking random vector for rhs\n");
for (i = 0; i < -N_update; i++) {
rhs[i] = (double) update[i]; rhs[i] = 7.;
}
}
else {
if (proc_config[AZ_node]== 0) printf("reading rhs guess from file\n");
AZ_input_msr_matrix("AZ_capture_rhs.dat", update, &rhs, &garbage,
N_update, proc_config);
free(garbage);
}
AZ_reorder_vec(rhs, data_org, update_index, NULL);
printf("changing rhs by multiplying with A\n");
Amat->matvec(rhs, xxx, Amat, proc_config);
for (i = 0; i < N_update; i++) rhs[i] = xxx[i];
fp = fopen("AZ_capture_init_guess.dat","r");
if (fp != NULL) {
fclose(fp);
if (proc_config[AZ_node]== 0) printf("reading initial guess from file\n");
AZ_input_msr_matrix("AZ_capture_init_guess.dat", update, &xxx, &garbage,
N_update, proc_config);
free(garbage);
xxx = (double *) realloc(xxx, sizeof(double)*(
Amat->data_org[AZ_N_internal]+
Amat->data_org[AZ_N_border] +
Amat->data_org[AZ_N_external]));
}
AZ_reorder_vec(xxx, data_org, update_index, NULL);
/* if Dirichlet BC ... put the answer in */
/*
for (i = 0; i < data_org[AZ_N_internal]+data_org[AZ_N_border]; i++) {
if ( (val[i] > .99999999) && (val[i] < 1.0000001))
xxx[i] = rhs[i];
}
*/
fp = fopen("AZ_no_multilevel.dat","r");
scaling = AZ_scaling_create();
start_time = AZ_second();
if (fp != NULL) {
fclose(fp);
options[AZ_precond] = AZ_none;
options[AZ_scaling] = AZ_sym_diag;
options[AZ_ignore_scaling] = AZ_TRUE;
options[AZ_keep_info] = 1;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
/*
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
*/
}
else {
options[AZ_keep_info] = 1;
options[AZ_conv] = AZ_noscaled;
options[AZ_conv] = AZ_r0;
params[AZ_tol] = 1.0e-7;
/* ML_Iterate(ml, xxx, rhs); */
alpha = sqrt(AZ_gdot(N_update, xxx, xxx, proc_config));
printf("init guess = %e\n",alpha);
alpha = sqrt(AZ_gdot(N_update, rhs, rhs, proc_config));
printf("rhs = %e\n",alpha);
#ifdef SCALE_ME
ML_MSR_scalerhs(rhs, scaling_vect, data_org[AZ_N_internal] +
data_org[AZ_N_border]);
ML_MSR_scalesol(xxx, scaling_vect, data_org[AZ_N_internal] +
data_org[AZ_N_border]);
#endif
max_diag = 0.;
min_diag = 1.e30;
max_sum = 0.;
for (i = 0; i < N_update; i++) {
if (Amat->val[i] < 0.) printf("woops negative diagonal A(%d,%d) = %e\n",
i,i,Amat->val[i]);
if (Amat->val[i] > max_diag) max_diag = Amat->val[i];
if (Amat->val[i] < min_diag) min_diag = Amat->val[i];
sum = fabs(Amat->val[i]);
for (j = Amat->bindx[i]; j < Amat->bindx[i+1]; j++) {
sum += fabs(Amat->val[j]);
}
if (sum > max_sum) max_sum = sum;
}
printf("Largest diagonal = %e, min diag = %e large abs row sum = %e\n",
max_diag, min_diag, max_sum);
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
/*
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
*/
}
solve_time = AZ_second() - start_time;
if (proc_config[AZ_node] == 0)
printf("Solve time = %e, MG Setup time = %e\n", solve_time, setup_time);
if (proc_config[AZ_node] == 0)
printf("Printing out a few entries of the solution ...\n");
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 7) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 23) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 47) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 101) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 171) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
ML_Aggregate_Destroy(&ag);
ML_Destroy(&ml);
AZ_free((void *) Amat->data_org);
AZ_free((void *) Amat->val);
AZ_free((void *) Amat->bindx);
AZ_free((void *) update);
AZ_free((void *) external);
AZ_free((void *) extern_index);
AZ_free((void *) update_index);
AZ_scaling_destroy(&scaling);
if (Amat != NULL) AZ_matrix_destroy(&Amat);
if (Pmat != NULL) AZ_precond_destroy(&Pmat);
free(xxx);
free(rhs);
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return 0;
}
// ======================================================================
int ML_Operator_Add2(ML_Operator *A, ML_Operator *B, ML_Operator *C,
int matrix_type, double scalarA, double scalarB)
{
int A_allocated = 0, *A_bindx = NULL, B_allocated = 0, *B_bindx = NULL;
double *A_val = NULL, *B_val = NULL, *hashed_vals;
int i, A_length, B_length, *hashed_inds;
int max_nz_per_row = 0, min_nz_per_row=1e6, j;
int hash_val, index_length;
int *columns, *rowptr, nz_ptr, hash_used, global_col;
double *values;
struct ML_CSR_MSRdata *temp;
int *A_gids, *B_gids;
int max_per_proc;
#ifdef ML_WITH_EPETRA
int count;
#endif
if (A->getrow == NULL)
pr_error("ML_Operator_Add: A does not have a getrow function.\n");
if (B->getrow == NULL)
pr_error("ML_Operator_Add: B does not have a getrow function.\n");
if (A->getrow->Nrows != B->getrow->Nrows) {
printf("ML_Operator_Add: Can not add, two matrices do not have the same");
printf(" number of rows %d vs %d",A->getrow->Nrows,B->getrow->Nrows);
exit(1);
}
if (A->invec_leng != B->invec_leng) {
printf("ML_Operator_Add: Can not add, two matrices do not have the same");
printf(" number of columns %d vs %d",A->getrow->Nrows,B->getrow->Nrows);
exit(1);
}
/* let's just count some things */
index_length = A->invec_leng + 1;
if (A->getrow->pre_comm != NULL) {
ML_CommInfoOP_Compute_TotalRcvLength(A->getrow->pre_comm);
index_length += A->getrow->pre_comm->total_rcv_length;
}
if (B->getrow->pre_comm != NULL) {
ML_CommInfoOP_Compute_TotalRcvLength(B->getrow->pre_comm);
index_length += B->getrow->pre_comm->total_rcv_length;
}
ML_create_unique_col_id(A->invec_leng, &A_gids, A->getrow->pre_comm,
&max_per_proc,A->comm);
ML_create_unique_col_id(B->invec_leng, &B_gids, B->getrow->pre_comm,
&max_per_proc,B->comm);
hashed_inds = (int *) ML_allocate(sizeof(int)*index_length);
hashed_vals = (double *) ML_allocate(sizeof(double)*index_length);
for (i = 0; i < index_length; i++) hashed_inds[i] = -1;
for (i = 0; i < index_length; i++) hashed_vals[i] = 0.;
nz_ptr = 0;
for (i = 0 ; i < A->getrow->Nrows; i++) {
hash_used = 0;
ML_get_matrix_row(A, 1, &i, &A_allocated, &A_bindx, &A_val,
&A_length, 0);
for (j = 0; j < A_length; j++) {
global_col = A_gids[A_bindx[j]];
ML_hash_it(global_col, hashed_inds, index_length,&hash_used,&hash_val);
hashed_inds[hash_val] = global_col;
hashed_vals[hash_val] += scalarA * A_val[j];
A_bindx[j] = hash_val;
}
ML_get_matrix_row(B, 1, &i, &B_allocated, &B_bindx, &B_val,
&B_length, 0);
for (j = 0; j < B_length; j++) {
global_col = B_gids[B_bindx[j]];
ML_hash_it(global_col, hashed_inds, index_length,&hash_used, &hash_val);
hashed_inds[hash_val] = global_col;
hashed_vals[hash_val] += scalarB*B_val[j];
B_bindx[j] = hash_val;
}
for (j = 0; j < A_length; j++) {
nz_ptr++;
hashed_inds[A_bindx[j]] = -1;
hashed_vals[A_bindx[j]] = 0.;
}
for (j = 0; j < B_length; j++) {
if (hashed_inds[B_bindx[j]] != -1) {
nz_ptr++;
hashed_inds[B_bindx[j]] = -1;
hashed_vals[B_bindx[j]] = 0.;
}
}
}
nz_ptr++;
columns = 0;
values = 0;
rowptr = (int *) ML_allocate(sizeof(int)*(A->outvec_leng+1));
if (matrix_type == ML_CSR_MATRIX) {
columns= (int *) ML_allocate(sizeof(int)*nz_ptr);
values = (double *) ML_allocate(sizeof(double)*nz_ptr);
}
#ifdef ML_WITH_EPETRA
else if (matrix_type == ML_EpetraCRS_MATRIX) {
columns= (int *) ML_allocate(sizeof(int)*(index_length+1));
values = (double *) ML_allocate(sizeof(double)*(index_length+1));
}
#endif
else {
pr_error("ML_Operator_Add: Unknown matrix type\n");
}
nz_ptr = 0;
rowptr[0] = 0;
for (i = 0 ; i < A->getrow->Nrows; i++) {
hash_used = 0;
ML_get_matrix_row(A, 1, &i, &A_allocated, &A_bindx, &A_val,
&A_length, 0);
for (j = 0; j < A_length; j++) {
global_col = A_gids[A_bindx[j]];
ML_hash_it(global_col, hashed_inds, index_length,&hash_used, &hash_val);
hashed_inds[hash_val] = global_col;
hashed_vals[hash_val] += scalarA * A_val[j];
A_bindx[j] = hash_val;
}
ML_get_matrix_row(B, 1, &i, &B_allocated, &B_bindx, &B_val,
&B_length, 0);
for (j = 0; j < B_length; j++) {
global_col = B_gids[B_bindx[j]];
ML_hash_it(global_col, hashed_inds, index_length,&hash_used, &hash_val);
hashed_inds[hash_val] = global_col;
hashed_vals[hash_val] += scalarB*B_val[j];
B_bindx[j] = hash_val;
}
#ifdef ML_WITH_EPETRA
if (matrix_type == ML_EpetraCRS_MATRIX) {
for (j = 0; j < A_length; j++) {
columns[j] = hashed_inds[A_bindx[j]];
values[j] = hashed_vals[A_bindx[j]];
nz_ptr++;
hashed_inds[A_bindx[j]] = -1;
hashed_vals[A_bindx[j]] = 0.;
}
count = A_length;
for (j = 0; j < B_length; j++) {
if (hashed_inds[B_bindx[j]] != -1) {
columns[count] = hashed_inds[B_bindx[j]];
values[count++] = hashed_vals[B_bindx[j]];
nz_ptr++;
hashed_inds[B_bindx[j]] = -1;
hashed_vals[B_bindx[j]] = 0.;
}
}
ML_Epetra_CRSinsert(C,i,columns,values,count);
}
else {
#endif
for (j = 0; j < A_length; j++) {
columns[nz_ptr] = hashed_inds[A_bindx[j]];
values[nz_ptr] = hashed_vals[A_bindx[j]];
nz_ptr++;
hashed_inds[A_bindx[j]] = -1;
hashed_vals[A_bindx[j]] = 0.;
}
for (j = 0; j < B_length; j++) {
if (hashed_inds[B_bindx[j]] != -1) {
columns[nz_ptr] = hashed_inds[B_bindx[j]];
values[nz_ptr] = hashed_vals[B_bindx[j]];
nz_ptr++;
hashed_inds[B_bindx[j]] = -1;
hashed_vals[B_bindx[j]] = 0.;
}
}
#ifdef ML_WITH_EPETRA
}
#endif
rowptr[i+1] = nz_ptr;
j = rowptr[i+1] - rowptr[i];
if (j > max_nz_per_row)
max_nz_per_row = j;
if (j < min_nz_per_row && j>0)
min_nz_per_row = j;
}
if (matrix_type == ML_CSR_MATRIX) {
temp = (struct ML_CSR_MSRdata *) ML_allocate(sizeof(struct ML_CSR_MSRdata));
if (temp == NULL) pr_error("ML_Operator_Add: no space for temp\n");
temp->columns = columns;
temp->values = values;
temp->rowptr = rowptr;
ML_Operator_Set_ApplyFuncData(C, B->invec_leng, A->outvec_leng,
temp,A->outvec_leng, NULL,0);
ML_Operator_Set_Getrow(C, A->outvec_leng, CSR_getrow);
ML_Operator_Set_ApplyFunc (C, CSR_matvec);
ML_globalcsr2localcsr(C, max_per_proc);
C->data_destroy = ML_CSR_MSRdata_Destroy;
C->max_nz_per_row = max_nz_per_row;
C->min_nz_per_row = min_nz_per_row;
C->N_nonzeros = nz_ptr;
}
#ifdef ML_WITH_EPETRA
else {
ML_free(rowptr);
ML_free(columns);
ML_free(values);
}
#endif
ML_free(A_gids);
ML_free(B_gids);
ML_free(hashed_vals);
ML_free(hashed_inds);
ML_free(A_val);
ML_free(A_bindx);
ML_free(B_val);
ML_free(B_bindx);
return 1;
}
// ================================================ ====== ==== ==== == =
// Copied from ml_agg_genP.c
static void ML_Init_Aux(ML_Operator* A, Teuchos::ParameterList &List) {
int i, j, n, count, num_PDEs, BlockRow, BlockCol;
double threshold;
int* columns;
double* values;
int allocated, entries = 0;
int N_dimensions;
int DiagID;
double DiagValue;
int** filter;
double dist;
double *LaplacianDiag;
int Nghost;
// Boundary exchange the coords
double *x_coord=0, *y_coord=0, *z_coord=0;
RefMaxwell_SetupCoordinates(A,List,x_coord,y_coord,z_coord);
int dim=(x_coord!=0) + (y_coord!=0) + (z_coord!=0);
/* Sanity Checks */
if(dim == 0 || ((!x_coord && (y_coord || z_coord)) || (x_coord && !y_coord && z_coord))){
std::cerr<<"Error: Coordinates not defined. This is necessary for aux aggregation (found "<<dim<<" coordinates).\n";
exit(-1);
}
num_PDEs = A->num_PDEs;
N_dimensions = dim;
threshold = A->aux_data->threshold;
ML_Operator_AmalgamateAndDropWeak(A, num_PDEs, 0.0);
n = A->invec_leng;
Nghost = ML_CommInfoOP_Compute_TotalRcvLength(A->getrow->pre_comm);
LaplacianDiag = (double *) ML_allocate((A->getrow->Nrows+Nghost+1)*
sizeof(double));
filter = (int**) ML_allocate(sizeof(int*) * n);
allocated = 128;
columns = (int *) ML_allocate(allocated * sizeof(int));
values = (double *) ML_allocate(allocated * sizeof(double));
for (i = 0 ; i < n ; ++i) {
BlockRow = i;
DiagID = -1;
DiagValue = 0.0;
ML_get_matrix_row(A,1,&i,&allocated,&columns,&values, &entries,0);
for (j = 0; j < entries; j++) {
BlockCol = columns[j];
if (BlockRow != BlockCol) {
dist = 0.0;
switch (N_dimensions) {
case 3:
dist += (z_coord[BlockRow] - z_coord[BlockCol]) * (z_coord[BlockRow] - z_coord[BlockCol]);
case 2:
dist += (y_coord[BlockRow] - y_coord[BlockCol]) * (y_coord[BlockRow] - y_coord[BlockCol]);
case 1:
dist += (x_coord[BlockRow] - x_coord[BlockCol]) * (x_coord[BlockRow] - x_coord[BlockCol]);
}
if (dist == 0.0) {
printf("node %d = %e ", i, x_coord[BlockRow]);
if (N_dimensions > 1) printf(" %e ", y_coord[BlockRow]);
if (N_dimensions > 2) printf(" %e ", z_coord[BlockRow]);
printf("\n");
printf("node %d = %e ", j, x_coord[BlockCol]);
if (N_dimensions > 1) printf(" %e ", y_coord[BlockCol]);
if (N_dimensions > 2) printf(" %e ", z_coord[BlockCol]);
printf("\n");
printf("Operator has inlen = %d and outlen = %d\n",
A->invec_leng, A->outvec_leng);
}
dist = 1.0 / dist;
DiagValue += dist;
}
else if (columns[j] == i) {
DiagID = j;
}
}
if (DiagID == -1) {
fprintf(stderr, "ERROR: matrix has no diagonal!\n"
"ERROR: (file %s, line %d)\n",
__FILE__, __LINE__);
exit(EXIT_FAILURE);
}
LaplacianDiag[BlockRow] = DiagValue;
}
if ( A->getrow->pre_comm != NULL )
ML_exchange_bdry(LaplacianDiag,A->getrow->pre_comm,A->getrow->Nrows,
A->comm, ML_OVERWRITE,NULL);
for (i = 0 ; i < n ; ++i) {
BlockRow = i;
ML_get_matrix_row(A,1,&i,&allocated,&columns,&values, &entries,0);
for (j = 0; j < entries; j++) {
BlockCol = columns[j];
if (BlockRow != BlockCol) {
dist = 0.0;
switch (N_dimensions) {
case 3:
dist += (z_coord[BlockRow] - z_coord[BlockCol]) * (z_coord[BlockRow] - z_coord[BlockCol]);
case 2:
dist += (y_coord[BlockRow] - y_coord[BlockCol]) * (y_coord[BlockRow] - y_coord[BlockCol]);
case 1:
dist += (x_coord[BlockRow] - x_coord[BlockCol]) * (x_coord[BlockRow] - x_coord[BlockCol]);
}
dist = 1.0 / dist;
values[j] = dist;
}
}
count = 0;
for (j = 0 ; j < entries ; ++j) {
if ( (i != columns[j]) &&
(values[j]*values[j] <
LaplacianDiag[BlockRow]*LaplacianDiag[columns[j]]*threshold*threshold)){
columns[count++] = columns[j];
}
}
/* insert the rows */
filter[BlockRow] = (int*) ML_allocate(sizeof(int) * (count + 1));
filter[BlockRow][0] = count;
for (j = 0 ; j < count ; ++j)
filter[BlockRow][j + 1] = columns[j];
}
ML_free(columns);
ML_free(values);
ML_free(LaplacianDiag);
ML_Operator_UnAmalgamateAndDropWeak(A, num_PDEs, 0.0);
A->aux_data->aux_func_ptr = A->getrow->func_ptr;
A->getrow->func_ptr = ML_Aux_Getrow;
A->aux_data->filter = filter;
A->aux_data->filter_size = n;
// Cleanup
ML_free(x_coord);
ML_free(y_coord);
ML_free(z_coord);
}
void ML_rap_check(ML *ml, ML_Operator *RAP, ML_Operator *R,
ML_Operator *A, ML_Operator *P, int iNvec, int oNvec)
{
int i,j;
double *vec1, *vec2, *vec3, *vec4, *vec5;
double norm1, norm2;
#ifdef DEBUG
printf("ML_rap_check begins ...\n");
#endif
if (RAP->getrow->ML_id != ML_ID_MATRIX) {
if (ml->comm->ML_mypid == 0)
printf("ML_rap_check: RAP is the wrong object (=%d). \n",
RAP->getrow->ML_id);
exit(1);
}
if (R->getrow->ML_id != ML_ID_MATRIX) {
if (ml->comm->ML_mypid == 0)
printf("ML_rap_check: R is the wrong object (=%d). \n",
RAP->getrow->ML_id);
exit(1);
}
if (P->getrow->ML_id != ML_ID_MATRIX) {
if (ml->comm->ML_mypid == 0)
printf("ML_rap_check: P is the wrong object (=%d). \n",
RAP->getrow->ML_id);
exit(1);
}
if (A->getrow->ML_id != ML_ID_MATRIX) {
if (ml->comm->ML_mypid == 0)
printf("ML_rap_check: A is the wrong object (=%d). \n",
RAP->getrow->ML_id);
exit(1);
}
/* j is the number of external variables */
j = 0;
for (i = 0; i < RAP->getrow->pre_comm->N_neighbors; i++)
j += RAP->getrow->pre_comm->neighbors[i].N_rcv;
vec1 = (double *) ML_allocate((iNvec+ 1 + j)*sizeof(double));
vec2 = (double *) ML_allocate((P->getrow->Nrows+1)*sizeof(double));
vec3 = (double *) ML_allocate((A->getrow->Nrows+1)*sizeof(double));
vec4 = (double *) ML_allocate((oNvec + 1)*sizeof(double));
vec5 = (double *) ML_allocate((oNvec + 1)*sizeof(double));
for (i = 0; i < iNvec; i++)
vec1[i] = (double) (ml->comm->ML_mypid*2301 + i*7 + 1);
j = P->getrow->Nrows;
ML_getrow_matvec(P, vec1, iNvec, vec2,&j);
i = A->getrow->Nrows;
ML_getrow_matvec(A, vec2, j, vec3,&i);
ML_getrow_matvec(R, vec3, i, vec4,&oNvec);
/* j is the number of variables sent in communication */
j = 0;
for (i = 0; i < RAP->getrow->pre_comm->N_neighbors; i++)
j += RAP->getrow->pre_comm->neighbors[i].N_send;
ML_restricted_MSR_mult( RAP, oNvec, vec1, vec5, j);
norm1 = sqrt(ML_gdot(oNvec, vec5, vec5, ml->comm));
for (i = 0; i < oNvec; i++) vec5[i] -= vec4[i];
norm2 = sqrt(ML_gdot(oNvec, vec5, vec5, ml->comm));
if (norm2 > norm1*1e-10) {
norm2 = sqrt(ML_gdot(oNvec, vec4, vec4, ml->comm));
if (ml->comm->ML_mypid == 0) {
printf("***************************************\n");
printf("RAP seems inaccurate:\n");
printf(" || RAP v ||_2 = %e\n\n", norm1);
printf(" || R (A (P v)) ||_2 = %e\n",norm2);
printf("***************************************\n");
}
}
ML_free(vec5);
ML_free(vec4);
ML_free(vec3);
ML_free(vec2);
ML_free(vec1);
#ifdef DEBUG
printf("ML_rap_check ends ...\n");
#endif
}
int main(int argc, char *argv[])
{
int num_PDE_eqns=5, N_levels=3;
/* int nsmooth=1; */
int leng, level, N_grid_pts, coarsest_level;
/* See Aztec User's Guide for more information on the */
/* variables that follow. */
int proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
double params[AZ_PARAMS_SIZE], status[AZ_STATUS_SIZE];
/* data structure for matrix corresponding to the fine grid */
int *data_org = NULL, *update = NULL, *external = NULL;
int *update_index = NULL, *extern_index = NULL;
int *cpntr = NULL;
int *bindx = NULL, N_update, iii;
double *val = NULL;
double *xxx, *rhs;
AZ_MATRIX *Amat;
AZ_PRECOND *Pmat = NULL;
ML *ml;
FILE *fp;
int ch,i;
struct AZ_SCALING *scaling;
double solve_time, setup_time, start_time;
ML_Aggregate *ag;
int *ivec;
#ifdef VBR_VERSION
ML_Operator *B, *C, *D;
int *vbr_cnptr, *vbr_rnptr, *vbr_indx, *vbr_bindx, *vbr_bnptr, total_blk_rows;
int total_blk_cols, blk_space, nz_space;
double *vbr_val;
struct ML_CSR_MSRdata *csr_data;
#endif
#ifdef ML_MPI
MPI_Init(&argc,&argv);
/* get number of processors and the name of this processor */
AZ_set_proc_config(proc_config, MPI_COMM_WORLD);
#else
AZ_set_proc_config(proc_config, AZ_NOT_MPI);
#endif
#ifdef binary
fp=fopen(".data","rb");
#else
fp=fopen(".data","r");
#endif
if (fp==NULL)
{
printf("couldn't open file .data\n");
exit(1);
}
#ifdef binary
fread(&leng, sizeof(int), 1, fp);
#else
fscanf(fp,"%d",&leng);
#endif
fclose(fp);
N_grid_pts=leng/num_PDE_eqns;
/* initialize the list of global indices. NOTE: the list of global */
/* indices must be in ascending order so that subsequent calls to */
/* AZ_find_index() will function properly. */
AZ_read_update(&N_update, &update, proc_config, N_grid_pts, num_PDE_eqns,
AZ_linear);
AZ_read_msr_matrix(update, &val, &bindx, N_update, proc_config);
/* This code is to fix things up so that we are sure we have */
/* all block (including the ghost nodes the same size. */
AZ_block_MSR(&bindx, &val, N_update, num_PDE_eqns, update);
AZ_transform(proc_config, &external, bindx, val, update, &update_index,
&extern_index, &data_org, N_update, 0, 0, 0, &cpntr,
AZ_MSR_MATRIX);
Amat = AZ_matrix_create( leng );
#ifndef VBR_VERSION
AZ_set_MSR(Amat, bindx, val, data_org, 0, NULL, AZ_LOCAL);
Amat->matrix_type = data_org[AZ_matrix_type];
data_org[AZ_N_rows] = data_org[AZ_N_internal] + data_org[AZ_N_border];
#else
total_blk_rows = N_update/num_PDE_eqns;
total_blk_cols = total_blk_rows;
blk_space = total_blk_rows*20;
nz_space = blk_space*num_PDE_eqns*num_PDE_eqns;
vbr_cnptr = (int *) ML_allocate(sizeof(int )*(total_blk_cols+1));
vbr_rnptr = (int *) ML_allocate(sizeof(int )*(total_blk_cols+1));
vbr_bnptr = (int *) ML_allocate(sizeof(int )*(total_blk_cols+2));
vbr_indx = (int *) ML_allocate(sizeof(int )*(blk_space+1));
vbr_bindx = (int *) ML_allocate(sizeof(int )*(blk_space+1));
vbr_val = (double *) ML_allocate(sizeof(double)*(nz_space+1));
for (i = 0; i <= total_blk_cols; i++) vbr_cnptr[i] = num_PDE_eqns;
AZ_msr2vbr(vbr_val, vbr_indx, vbr_rnptr, vbr_cnptr, vbr_bnptr,
vbr_bindx, bindx, val,
total_blk_rows, total_blk_cols, blk_space,
nz_space, -1);
data_org[AZ_N_rows] = data_org[AZ_N_internal] + data_org[AZ_N_border];
data_org[AZ_N_int_blk] = data_org[AZ_N_internal]/num_PDE_eqns;
data_org[AZ_N_bord_blk] = data_org[AZ_N_bord_blk]/num_PDE_eqns;
data_org[AZ_N_ext_blk] = data_org[AZ_N_ext_blk]/num_PDE_eqns;
data_org[AZ_matrix_type] = AZ_VBR_MATRIX;
AZ_set_VBR(Amat, vbr_rnptr, vbr_cnptr, vbr_bnptr, vbr_indx, vbr_bindx,
vbr_val, data_org, 0, NULL, AZ_LOCAL);
Amat->matrix_type = data_org[AZ_matrix_type];
#endif
start_time = AZ_second();
ML_Create(&ml, N_levels);
ML_Set_PrintLevel(3);
/* set up discretization matrix and matrix vector function */
AZ_ML_Set_Amat(ml, N_levels-1, N_update, N_update, Amat, proc_config);
ML_Aggregate_Create( &ag );
ML_Aggregate_Set_Threshold(ag,0.0);
ML_Set_SpectralNormScheme_PowerMethod(ml);
/*
To run SA:
a) set damping factor to 1 and use power method
ML_Aggregate_Set_DampingFactor(ag, 4./3.);
To run NSA:
a) set damping factor to 0
ML_Aggregate_Set_DampingFactor(ag, 0.);
To run NSR
a) set damping factor to 1 and use power method
ML_Aggregate_Set_DampingFactor(ag, 1.);
ag->Restriction_smoothagg_transpose = ML_FALSE;
ag->keep_agg_information=1;
ag->keep_P_tentative=1;
b) hack code so it calls the energy minimizing restriction
line 2973 of ml_agg_genP.c
c) turn on the NSR flag in ml_agg_energy_min.cpp
To run Emin
a) set min_eneryg = 2 and keep_agg_info = 1;
ag->minimizing_energy=2;
ag->keep_agg_information=1;
ag->cheap_minimizing_energy = 0;
ag->block_scaled_SA = 1;
*/
ag->minimizing_energy=2;
ag->keep_agg_information=1;
ag->block_scaled_SA = 1;
ML_Aggregate_Set_NullSpace(ag, num_PDE_eqns, num_PDE_eqns, NULL, N_update);
ML_Aggregate_Set_MaxCoarseSize( ag, 20);
/*
ML_Aggregate_Set_RandomOrdering( ag );
ML_Aggregate_Set_DampingFactor(ag, .1);
ag->drop_tol_for_smoothing = 1.0e-3;
ML_Aggregate_Set_Threshold(ag, 1.0e-3);
ML_Aggregate_Set_MaxCoarseSize( ag, 300);
*/
coarsest_level = ML_Gen_MultiLevelHierarchy_UsingAggregation(ml, N_levels-1, ML_DECREASING, ag);
coarsest_level = N_levels - coarsest_level;
if ( proc_config[AZ_node] == 0 )
printf("Coarse level = %d \n", coarsest_level);
/* set up smoothers */
AZ_defaults(options, params);
for (level = N_levels-1; level > coarsest_level; level--) {
/* This is the Aztec domain decomp/ilu smoother that we */
/* usually use for this problem. */
/*
options[AZ_precond] = AZ_dom_decomp;
options[AZ_subdomain_solve] = AZ_ilut;
params[AZ_ilut_fill] = 1.0;
options[AZ_reorder] = 1;
ML_Gen_SmootherAztec(ml, level, options, params,
proc_config, status, AZ_ONLY_PRECONDITIONER,
ML_PRESMOOTHER,NULL);
*/
/* Sparse approximate inverse smoother that acutally does both */
/* pre and post smoothing. */
/*
ML_Gen_Smoother_ParaSails(ml , level, ML_PRESMOOTHER, nsmooth,
parasails_sym, parasails_thresh,
parasails_nlevels, parasails_filter,
parasails_loadbal, parasails_factorized);
parasails_thresh /= 4.;
*/
/* This is the symmetric Gauss-Seidel smoothing. In parallel, */
/* it is not a true Gauss-Seidel in that each processor */
/* does a Gauss-Seidel on its local submatrix independent of the */
/* other processors. */
/*
ML_Gen_Smoother_SymGaussSeidel(ml,level,ML_PRESMOOTHER, nsmooth,1.);
ML_Gen_Smoother_SymGaussSeidel(ml,level,ML_POSTSMOOTHER,nsmooth,1.);
*/
/* Block Gauss-Seidel with block size equal to #DOF per node. */
/* Not a true Gauss-Seidel in that each processor does a */
/* Gauss-Seidel on its local submatrix independent of the other */
/* processors. */
/*
ML_Gen_Smoother_BlockGaussSeidel(ml,level,ML_PRESMOOTHER,
nsmooth,0.67, num_PDE_eqns);
ML_Gen_Smoother_BlockGaussSeidel(ml,level,ML_POSTSMOOTHER,
nsmooth, 0.67, num_PDE_eqns);
*/
ML_Gen_Smoother_SymBlockGaussSeidel(ml,level,ML_POSTSMOOTHER,
1, 1.0, num_PDE_eqns);
}
ML_Gen_CoarseSolverSuperLU( ml, coarsest_level);
ML_Gen_Solver(ml, ML_MGW, N_levels-1, coarsest_level);
AZ_defaults(options, params);
options[AZ_solver] = AZ_gmres;
options[AZ_scaling] = AZ_none;
options[AZ_precond] = AZ_user_precond;
/*
options[AZ_conv] = AZ_r0;
*/
options[AZ_output] = 1;
options[AZ_max_iter] = 1500;
options[AZ_poly_ord] = 5;
options[AZ_kspace] = 130;
params[AZ_tol] = 1.0e-8;
/*
options[AZ_precond] = AZ_dom_decomp;
options[AZ_subdomain_solve] = AZ_ilut;
params[AZ_ilut_fill] = 2.0;
*/
AZ_set_ML_preconditioner(&Pmat, Amat, ml, options);
setup_time = AZ_second() - start_time;
xxx = (double *) malloc( leng*sizeof(double));
rhs=(double *)malloc(leng*sizeof(double));
for (iii = 0; iii < leng; iii++) xxx[iii] = 0.0;
/* Set rhs */
fp = fopen("AZ_capture_rhs.mat","r");
if (fp == NULL) {
if (proc_config[AZ_node] == 0) printf("taking random vector for rhs\n");
AZ_random_vector(rhs, data_org, proc_config);
AZ_reorder_vec(rhs, data_org, update_index, NULL);
}
else {
fclose(fp);
ivec =(int *)malloc((leng+1)*sizeof(int));
AZ_input_msr_matrix("AZ_capture_rhs.mat", update, &rhs, &ivec,
N_update, proc_config);
free(ivec);
AZ_reorder_vec(rhs, data_org, update_index, NULL);
}
/* Set x */
fp = fopen("AZ_capture_init_guess.mat","r");
if (fp != NULL) {
fclose(fp);
ivec =(int *)malloc((leng+1)*sizeof(int));
AZ_input_msr_matrix("AZ_capture_init_guess.mat",update, &xxx, &ivec,
N_update, proc_config);
free(ivec);
AZ_reorder_vec(xxx, data_org, update_index, NULL);
}
/* if Dirichlet BC ... put the answer in */
for (i = 0; i < data_org[AZ_N_internal]+data_org[AZ_N_border]; i++) {
if ( (val[i] > .99999999) && (val[i] < 1.0000001))
xxx[i] = rhs[i];
}
fp = fopen("AZ_no_multilevel.dat","r");
scaling = AZ_scaling_create();
start_time = AZ_second();
if (fp != NULL) {
fclose(fp);
options[AZ_precond] = AZ_none;
options[AZ_scaling] = AZ_sym_diag;
options[AZ_ignore_scaling] = AZ_TRUE;
options[AZ_keep_info] = 1;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
/*
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
*/
}
else {
options[AZ_keep_info] = 1;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
/*
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
*/
}
solve_time = AZ_second() - start_time;
if (proc_config[AZ_node] == 0)
printf("Solve time = %e, MG Setup time = %e\n", solve_time, setup_time);
ML_Aggregate_Destroy(&ag);
ML_Destroy(&ml);
AZ_free((void *) Amat->data_org);
AZ_free((void *) Amat->val);
AZ_free((void *) Amat->bindx);
AZ_free((void *) update);
AZ_free((void *) external);
AZ_free((void *) extern_index);
AZ_free((void *) update_index);
AZ_scaling_destroy(&scaling);
if (Amat != NULL) AZ_matrix_destroy(&Amat);
if (Pmat != NULL) AZ_precond_destroy(&Pmat);
free(xxx);
free(rhs);
#ifdef ML_MPI
MPI_Finalize();
#endif
return 0;
}
void ML_getrow_matvec(ML_Operator *matrix, double *vec, int Nvec,
double *ovec, int *Novec)
{
ML_Operator *temp, *temp2, *temp3, *temp4, *tptr;
int *cols, i;
int allocated, row_length;
if (matrix->getrow->func_ptr == NULL) {
printf("ML_getrow_matvec: empty object? \n");
exit(1);
}
temp = ML_Operator_Create(matrix->comm);
ML_Operator_Set_1Levels(temp, matrix->from, matrix->from);
ML_Operator_Set_ApplyFuncData(temp,1,Nvec,vec,Nvec,NULL,0);
ML_Operator_Set_Getrow(temp,Nvec, VECTOR_getrows);
temp->max_nz_per_row = 1;
temp->N_nonzeros = Nvec;
if (matrix->getrow->pre_comm != NULL) {
ML_exchange_rows(temp, &temp2, matrix->getrow->pre_comm);
}
else temp2 = temp;
ML_matmat_mult(matrix, temp2, &temp3);
if (matrix->getrow->post_comm != NULL)
ML_exchange_rows(temp3, &temp4, matrix->getrow->post_comm);
else temp4 = temp3;
allocated = temp4->getrow->Nrows + 1;
cols = (int *) ML_allocate(allocated*sizeof(int));
if (cols == NULL) {
printf("no space in ML_getrow_matvec()\n");
exit(1);
}
for (i = 0; i < temp4->getrow->Nrows; i++) {
ML_get_matrix_row(temp4, 1, &i, &allocated , &cols, &ovec,
&row_length, i);
if (allocated != temp4->getrow->Nrows + 1)
printf("memory problems ... we can't reallocate here\n");
}
ML_free(cols);
if ( *Novec != temp4->getrow->Nrows) {
printf("Warning: The length of ML's output vector does not agree with\n");
printf(" the user's length for the output vector (%d vs. %d).\n",
*Novec, temp4->getrow->Nrows);
printf(" indicate a problem.\n");
}
*Novec = temp4->getrow->Nrows;
if (matrix->getrow->pre_comm != NULL) {
tptr = temp2;
while ( (tptr!= NULL) && (tptr->sub_matrix != temp))
tptr = tptr->sub_matrix;
if (tptr != NULL) tptr->sub_matrix = NULL;
ML_RECUR_CSR_MSRdata_Destroy(temp2);
ML_Operator_Destroy(&temp2);
}
if (matrix->getrow->post_comm != NULL) {
tptr = temp4;
while ( (tptr!= NULL) && (tptr->sub_matrix != temp3))
tptr = tptr->sub_matrix;
if (tptr != NULL) tptr->sub_matrix = NULL;
ML_RECUR_CSR_MSRdata_Destroy(temp4);
ML_Operator_Destroy(&temp4);
}
ML_Operator_Destroy(&temp);
ML_RECUR_CSR_MSRdata_Destroy(temp3);
ML_Operator_Destroy(&temp3);
}
void ML_interp_check(ML *ml, int coarse_level, int fine_level)
{
int ii, jj, ncoarse, nfine;
double *c_data, *f_data, coords[3], dtemp, d2, dlargest;
ML_GridFunc *coarse_funs, *fine_funs;
void *coarse_data, *fine_data;
int nfine_eqn, ncoarse_eqn, stride = 1;
/* check an interpolated linear function */
coarse_data = ml->SingleLevel[coarse_level].Grid->Grid;
fine_data = ml->SingleLevel[ fine_level].Grid->Grid;
coarse_funs = ml->SingleLevel[coarse_level].Grid->gridfcn;
fine_funs = ml->SingleLevel[ fine_level].Grid->gridfcn;
if ( (coarse_data==NULL)||(fine_data==NULL)) {
printf("ML_interp_check: grid data not found?\n");
exit(1);
}
if ( (coarse_funs==NULL)||(fine_funs==NULL)) {
printf("ML_interp_check: grid functions not found?\n");
exit(1);
}
if ( (coarse_funs->USR_grid_get_nvertices == 0) ||
( fine_funs->USR_grid_get_nvertices == 0)) {
printf("ML_interp_check: USR_grid_get_nvertices not found?\n");
exit(1);
}
ncoarse = coarse_funs->USR_grid_get_nvertices(coarse_data);
nfine = fine_funs->USR_grid_get_nvertices( fine_data);
nfine_eqn = ml->SingleLevel[coarse_level].Pmat->outvec_leng;
ncoarse_eqn = ml->SingleLevel[coarse_level].Pmat->invec_leng;
c_data = (double *) ML_allocate(ncoarse_eqn*sizeof(double));
f_data = (double *) ML_allocate(nfine_eqn*sizeof(double));
for (ii = 0; ii < ncoarse_eqn; ii++) c_data[ii] = 0.;
for (ii = 0; ii < nfine_eqn; ii++) f_data[ii] = 0.;
/* ASSUMING that for each grid point on this processor there are a */
/* set of equations and that all points at a grid point are numbered */
/* consecutively !!!!!!!!!!!!!! */
stride = nfine_eqn/nfine;
for (ii = 0 ; ii < ncoarse ; ii++) {
coarse_funs->USR_grid_get_vertex_coordinate(coarse_data,ii,coords);
for (jj = 0; jj < stride; jj++) {
c_data[ii*stride + jj] = coords[0] + 3.*coords[1] + .5;
}
}
ML_Operator_Apply(ml->SingleLevel[coarse_level].Pmat,ncoarse_eqn,c_data,
nfine_eqn,f_data);
dlargest = 0.0;
for (ii = 0 ; ii < nfine; ii++) {
fine_funs->USR_grid_get_vertex_coordinate(fine_data , ii, coords);
dtemp = coords[0] + 3.*coords[1] + .5;
d2 = ML_dabs(dtemp - f_data[ii*stride])/(ML_dabs(dtemp)+1.e-9);
/* Ray debugging
if ( d2 > 1.e-8)
printf("%d: f_data[%d] = %e %e | %e %e\n",ml->comm->ML_mypid,
ii,f_data[ii*stride],dtemp,coords[0],coords[1]);
*/
if ( d2 > dlargest) {
dlargest = d2;
}
}
ML_free(f_data);
ML_free(c_data);
}
int main(int argc, char *argv[])
{
int num_PDE_eqns=1, N_levels=3, nsmooth=2;
int leng, level, N_grid_pts, coarsest_level;
int leng1,leng2;
/* See Aztec User's Guide for more information on the */
/* variables that follow. */
int proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
double params[AZ_PARAMS_SIZE], status[AZ_STATUS_SIZE];
/* data structure for matrix corresponding to the fine grid */
double *val = NULL, *xxx, *rhs, solve_time, setup_time, start_time;
AZ_MATRIX *Amat;
AZ_PRECOND *Pmat = NULL;
ML *ml;
FILE *fp;
int i, j, Nrigid, *garbage, nblocks=0, *blocks = NULL, *block_pde=NULL;
struct AZ_SCALING *scaling;
ML_Aggregate *ag;
double *mode, *rigid=NULL, alpha;
char filename[80];
int one = 1;
int proc,nprocs;
char pathfilename[100];
#ifdef ML_MPI
MPI_Init(&argc,&argv);
/* get number of processors and the name of this processor */
AZ_set_proc_config(proc_config, MPI_COMM_WORLD);
proc = proc_config[AZ_node];
nprocs = proc_config[AZ_N_procs];
#else
AZ_set_proc_config(proc_config, AZ_NOT_MPI);
proc = 0;
nprocs = 1;
#endif
if (proc_config[AZ_node] == 0) {
sprintf(pathfilename,"%s/inputfile",argv[1]);
ML_Reader_ReadInput(pathfilename, &context);
}
else context = (struct reader_context *) ML_allocate(sizeof(struct reader_context));
AZ_broadcast((char *) context, sizeof(struct reader_context), proc_config,
AZ_PACK);
AZ_broadcast((char *) NULL , 0 , proc_config, AZ_SEND);
N_levels = context->N_levels;
printf("N_levels %d\n",N_levels);
nsmooth = context->nsmooth;
num_PDE_eqns = context->N_dofPerNode;
printf("num_PDE_eqns %d\n",num_PDE_eqns);
ML_Set_PrintLevel(context->output_level);
/* read in the number of matrix equations */
leng = 0;
if (proc_config[AZ_node] == 0) {
sprintf(pathfilename,"%s/data_matrix.txt",argv[1]);
fp=fopen(pathfilename,"r");
if (fp==NULL) {
printf("**ERR** couldn't open file data_matrix.txt\n");
exit(1);
}
fscanf(fp,"%d",&leng);
fclose(fp);
}
leng = AZ_gsum_int(leng, proc_config);
N_grid_pts=leng/num_PDE_eqns;
/* initialize the list of global indices. NOTE: the list of global */
/* indices must be in ascending order so that subsequent calls to */
/* AZ_find_index() will function properly. */
#if 0
if (proc_config[AZ_N_procs] == 1) i = AZ_linear;
else i = AZ_file;
#endif
i = AZ_linear;
/* cannot use AZ_input_update for variable blocks (forgot why, but debugged through it)*/
/* make a linear distribution of the matrix */
/* if the linear distribution does not align with the blocks, */
/* this is corrected in ML_AZ_Reader_ReadVariableBlocks */
leng1 = leng/nprocs;
leng2 = leng-leng1*nprocs;
if (proc >= leng2)
{
leng2 += (proc*leng1);
}
else
{
leng1++;
leng2 = proc*leng1;
}
N_update = leng1;
update = (int*)AZ_allocate((N_update+1)*sizeof(int));
if (update==NULL)
{
(void) fprintf (stderr, "Not enough space to allocate 'update'\n");
fflush(stderr); exit(EXIT_FAILURE);
}
for (i=0; i<N_update; i++) update[i] = i+leng2;
#if 0 /* debug */
printf("proc %d N_update %d\n",proc_config[AZ_node],N_update);
fflush(stdout);
#endif
sprintf(pathfilename,"%s/data_vblocks.txt",argv[1]);
ML_AZ_Reader_ReadVariableBlocks(pathfilename,&nblocks,&blocks,&block_pde,
&N_update,&update,proc_config);
#if 0 /* debug */
printf("proc %d N_update %d\n",proc_config[AZ_node],N_update);
fflush(stdout);
#endif
sprintf(pathfilename,"%s/data_matrix.txt",argv[1]);
AZ_input_msr_matrix(pathfilename,update, &val, &bindx, N_update, proc_config);
/* This code is to fix things up so that we are sure we have */
/* all blocks (including the ghost nodes) the same size. */
/* not sure, whether this is a good idea with variable blocks */
/* the examples inpufiles (see top of this file) don't need it */
/* anyway */
/*
AZ_block_MSR(&bindx, &val, N_update, num_PDE_eqns, update);
*/
AZ_transform_norowreordering(proc_config, &external, bindx, val, update, &update_index,
&extern_index, &data_org, N_update, 0, 0, 0, &cpntr,
AZ_MSR_MATRIX);
Amat = AZ_matrix_create( leng );
AZ_set_MSR(Amat, bindx, val, data_org, 0, NULL, AZ_LOCAL);
Amat->matrix_type = data_org[AZ_matrix_type];
data_org[AZ_N_rows] = data_org[AZ_N_internal] + data_org[AZ_N_border];
start_time = AZ_second();
options[AZ_scaling] = AZ_none;
ML_Create(&ml, N_levels);
/* set up discretization matrix and matrix vector function */
AZ_ML_Set_Amat(ml, 0, N_update, N_update, Amat, proc_config);
ML_Set_ResidualOutputFrequency(ml, context->output);
ML_Set_Tolerance(ml, context->tol);
ML_Aggregate_Create( &ag );
if (ML_strcmp(context->agg_coarsen_scheme,"Mis") == 0) {
ML_Aggregate_Set_CoarsenScheme_MIS(ag);
}
else if (ML_strcmp(context->agg_coarsen_scheme,"Uncoupled") == 0) {
ML_Aggregate_Set_CoarsenScheme_Uncoupled(ag);
}
else if (ML_strcmp(context->agg_coarsen_scheme,"Coupled") == 0) {
ML_Aggregate_Set_CoarsenScheme_Coupled(ag);
}
else if (ML_strcmp(context->agg_coarsen_scheme,"Metis") == 0) {
ML_Aggregate_Set_CoarsenScheme_METIS(ag);
for (i=0; i<N_levels; i++)
ML_Aggregate_Set_NodesPerAggr(ml,ag,i,9);
}
else if (ML_strcmp(context->agg_coarsen_scheme,"VBMetis") == 0) {
/* when no blocks read, use standard metis assuming constant block sizes */
if (!blocks)
ML_Aggregate_Set_CoarsenScheme_METIS(ag);
else {
ML_Aggregate_Set_CoarsenScheme_VBMETIS(ag);
ML_Aggregate_Set_Vblocks_CoarsenScheme_VBMETIS(ag,0,N_levels,nblocks,
blocks,block_pde,N_update);
}
for (i=0; i<N_levels; i++)
ML_Aggregate_Set_NodesPerAggr(ml,ag,i,9);
}
else {
printf("**ERR** ML: Unknown aggregation scheme %s\n",context->agg_coarsen_scheme);
exit(-1);
}
ML_Aggregate_Set_DampingFactor(ag, context->agg_damping);
ML_Aggregate_Set_MaxCoarseSize( ag, context->maxcoarsesize);
ML_Aggregate_Set_Threshold(ag, context->agg_thresh);
if (ML_strcmp(context->agg_spectral_norm,"Calc") == 0) {
ML_Set_SpectralNormScheme_Calc(ml);
}
else if (ML_strcmp(context->agg_spectral_norm,"Anorm") == 0) {
ML_Set_SpectralNormScheme_Anorm(ml);
}
else {
printf("**WRN** ML: Unknown spectral norm scheme %s\n",context->agg_spectral_norm);
}
/* read in the rigid body modes */
Nrigid = 0;
if (proc_config[AZ_node] == 0) {
sprintf(filename,"data_nullsp%d.txt",Nrigid);
sprintf(pathfilename,"%s/%s",argv[1],filename);
while( (fp = fopen(pathfilename,"r")) != NULL) {
fclose(fp);
Nrigid++;
sprintf(filename,"data_nullsp%d.txt",Nrigid);
sprintf(pathfilename,"%s/%s",argv[1],filename);
}
}
Nrigid = AZ_gsum_int(Nrigid,proc_config);
if (Nrigid != 0) {
rigid = (double *) ML_allocate( sizeof(double)*Nrigid*(N_update+1) );
if (rigid == NULL) {
printf("Error: Not enough space for rigid body modes\n");
}
}
/* Set rhs */
sprintf(pathfilename,"%s/data_rhs.txt",argv[1]);
fp = fopen(pathfilename,"r");
if (fp == NULL) {
rhs=(double *)ML_allocate(leng*sizeof(double));
if (proc_config[AZ_node] == 0) printf("taking linear vector for rhs\n");
for (i = 0; i < N_update; i++) rhs[i] = (double) update[i];
}
else {
fclose(fp);
if (proc_config[AZ_node] == 0) printf("reading rhs from a file\n");
AZ_input_msr_matrix(pathfilename, update, &rhs, &garbage, N_update,
proc_config);
}
AZ_reorder_vec(rhs, data_org, update_index, NULL);
for (i = 0; i < Nrigid; i++) {
sprintf(filename,"data_nullsp%d.txt",i);
sprintf(pathfilename,"%s/%s",argv[1],filename);
AZ_input_msr_matrix(pathfilename, update, &mode, &garbage, N_update,
proc_config);
AZ_reorder_vec(mode, data_org, update_index, NULL);
#if 0 /* test the given rigid body mode, output-vector should be ~0 */
Amat->matvec(mode, rigid, Amat, proc_config);
for (j = 0; j < N_update; j++) printf("this is %d %e\n",j,rigid[j]);
#endif
for (j = 0; j < i; j++) {
alpha = -AZ_gdot(N_update, mode, &(rigid[j*N_update]), proc_config)/
AZ_gdot(N_update, &(rigid[j*N_update]), &(rigid[j*N_update]),
proc_config);
DAXPY_F77(&N_update, &alpha, &(rigid[j*N_update]), &one, mode, &one);
}
/* rhs orthogonalization */
alpha = -AZ_gdot(N_update, mode, rhs, proc_config)/
AZ_gdot(N_update, mode, mode, proc_config);
DAXPY_F77(&N_update, &alpha, mode, &one, rhs, &one);
for (j = 0; j < N_update; j++) rigid[i*N_update+j] = mode[j];
free(mode);
free(garbage);
}
for (j = 0; j < Nrigid; j++) {
alpha = -AZ_gdot(N_update, rhs, &(rigid[j*N_update]), proc_config)/
AZ_gdot(N_update, &(rigid[j*N_update]), &(rigid[j*N_update]),
proc_config);
DAXPY_F77(&N_update, &alpha, &(rigid[j*N_update]), &one, rhs, &one);
}
#if 0 /* for testing the default nullsp */
ML_Aggregate_Set_NullSpace(ag, num_PDE_eqns, 6, NULL, N_update);
#else
if (Nrigid != 0) {
ML_Aggregate_Set_NullSpace(ag, num_PDE_eqns, Nrigid, rigid, N_update);
}
#endif
if (rigid) ML_free(rigid);
ag->keep_agg_information = 1;
coarsest_level = ML_Gen_MGHierarchy_UsingAggregation(ml, 0,
ML_INCREASING, ag);
coarsest_level--;
if ( proc_config[AZ_node] == 0 )
printf("Coarse level = %d \n", coarsest_level);
#if 0
/* set up smoothers */
if (!blocks)
blocks = (int *) ML_allocate(sizeof(int)*N_update);
#endif
for (level = 0; level < coarsest_level; level++) {
num_PDE_eqns = ml->Amat[level].num_PDEs;
/* Sparse approximate inverse smoother that acutally does both */
/* pre and post smoothing. */
if (ML_strcmp(context->smoother,"Parasails") == 0) {
ML_Gen_Smoother_ParaSails(ml , level, ML_PRESMOOTHER, nsmooth,
parasails_sym, parasails_thresh,
parasails_nlevels, parasails_filter,
(int) parasails_loadbal, parasails_factorized);
}
/* This is the symmetric Gauss-Seidel smoothing that we usually use. */
/* In parallel, it is not a true Gauss-Seidel in that each processor */
/* does a Gauss-Seidel on its local submatrix independent of the */
/* other processors. */
else if (ML_strcmp(context->smoother,"GaussSeidel") == 0) {
ML_Gen_Smoother_GaussSeidel(ml , level, ML_BOTH, nsmooth,1.);
}
else if (ML_strcmp(context->smoother,"SymGaussSeidel") == 0) {
ML_Gen_Smoother_SymGaussSeidel(ml , level, ML_BOTH, nsmooth,1.);
}
else if (ML_strcmp(context->smoother,"Poly") == 0) {
ML_Gen_Smoother_Cheby(ml, level, ML_BOTH, 30., nsmooth);
}
else if (ML_strcmp(context->smoother,"BlockGaussSeidel") == 0) {
ML_Gen_Smoother_BlockGaussSeidel(ml , level, ML_BOTH, nsmooth,1.,
num_PDE_eqns);
}
else if (ML_strcmp(context->smoother,"VBSymGaussSeidel") == 0) {
if (blocks) ML_free(blocks);
if (block_pde) ML_free(block_pde);
blocks = NULL;
block_pde = NULL;
nblocks = 0;
ML_Aggregate_Get_Vblocks_CoarsenScheme_VBMETIS(ag,level,N_levels,&nblocks,
&blocks,&block_pde);
if (blocks==NULL) ML_Gen_Blocks_Aggregates(ag, level, &nblocks, &blocks);
ML_Gen_Smoother_VBlockSymGaussSeidel(ml , level, ML_BOTH, nsmooth,1.,
nblocks, blocks);
}
/* This is a true Gauss Seidel in parallel. This seems to work for */
/* elasticity problems. However, I don't believe that this is very */
/* efficient in parallel. */
/*
nblocks = ml->Amat[level].invec_leng;
for (i =0; i < nblocks; i++) blocks[i] = i;
ML_Gen_Smoother_VBlockSymGaussSeidelSequential(ml , level, ML_PRESMOOTHER,
nsmooth, 1., nblocks, blocks);
ML_Gen_Smoother_VBlockSymGaussSeidelSequential(ml, level, ML_POSTSMOOTHER,
nsmooth, 1., nblocks, blocks);
*/
/* Jacobi Smoothing */
else if (ML_strcmp(context->smoother,"Jacobi") == 0) {
ML_Gen_Smoother_Jacobi(ml , level, ML_PRESMOOTHER, nsmooth,.4);
ML_Gen_Smoother_Jacobi(ml , level, ML_POSTSMOOTHER, nsmooth,.4);
}
/* This does a block Gauss-Seidel (not true GS in parallel) */
/* where each processor has 'nblocks' blocks. */
/* */
else if (ML_strcmp(context->smoother,"Metis") == 0) {
if (blocks) ML_free(blocks);
if (block_pde) ML_free(block_pde);
nblocks = 250;
ML_Gen_Blocks_Metis(ml, level, &nblocks, &blocks);
ML_Gen_Smoother_VBlockSymGaussSeidel(ml , level, ML_BOTH, nsmooth,1.,
nblocks, blocks);
}
else {
printf("unknown smoother %s\n",context->smoother);
exit(1);
}
}
/* set coarse level solver */
nsmooth = context->coarse_its;
/* Sparse approximate inverse smoother that acutally does both */
/* pre and post smoothing. */
if (ML_strcmp(context->coarse_solve,"Parasails") == 0) {
ML_Gen_Smoother_ParaSails(ml , coarsest_level, ML_PRESMOOTHER, nsmooth,
parasails_sym, parasails_thresh,
parasails_nlevels, parasails_filter,
(int) parasails_loadbal, parasails_factorized);
}
else if (ML_strcmp(context->coarse_solve,"GaussSeidel") == 0) {
ML_Gen_Smoother_GaussSeidel(ml , coarsest_level, ML_BOTH, nsmooth,1.);
}
else if (ML_strcmp(context->coarse_solve,"Poly") == 0) {
ML_Gen_Smoother_Cheby(ml, coarsest_level, ML_BOTH, 30., nsmooth);
}
else if (ML_strcmp(context->coarse_solve,"SymGaussSeidel") == 0) {
ML_Gen_Smoother_SymGaussSeidel(ml , coarsest_level, ML_BOTH, nsmooth,1.);
}
else if (ML_strcmp(context->coarse_solve,"BlockGaussSeidel") == 0) {
ML_Gen_Smoother_BlockGaussSeidel(ml, coarsest_level, ML_BOTH, nsmooth,1.,
num_PDE_eqns);
}
else if (ML_strcmp(context->coarse_solve,"Aggregate") == 0) {
if (blocks) ML_free(blocks);
if (block_pde) ML_free(block_pde);
ML_Gen_Blocks_Aggregates(ag, coarsest_level, &nblocks, &blocks);
ML_Gen_Smoother_VBlockSymGaussSeidel(ml , coarsest_level, ML_BOTH,
nsmooth,1., nblocks, blocks);
}
else if (ML_strcmp(context->coarse_solve,"Jacobi") == 0) {
ML_Gen_Smoother_Jacobi(ml , coarsest_level, ML_BOTH, nsmooth,.5);
}
else if (ML_strcmp(context->coarse_solve,"Metis") == 0) {
if (blocks) ML_free(blocks);
if (block_pde) ML_free(block_pde);
nblocks = 250;
ML_Gen_Blocks_Metis(ml, coarsest_level, &nblocks, &blocks);
ML_Gen_Smoother_VBlockSymGaussSeidel(ml , coarsest_level, ML_BOTH,
nsmooth,1., nblocks, blocks);
}
else if (ML_strcmp(context->coarse_solve,"SuperLU") == 0) {
ML_Gen_CoarseSolverSuperLU( ml, coarsest_level);
}
else if (ML_strcmp(context->coarse_solve,"Amesos") == 0) {
ML_Gen_Smoother_Amesos(ml,coarsest_level,ML_AMESOS_KLU,-1, 0.0);
}
else {
printf("unknown coarse grid solver %s\n",context->coarse_solve);
exit(1);
}
ML_Gen_Solver(ml, ML_MGV, 0, coarsest_level);
AZ_defaults(options, params);
if (ML_strcmp(context->krylov,"Cg") == 0) {
options[AZ_solver] = AZ_cg;
}
else if (ML_strcmp(context->krylov,"Bicgstab") == 0) {
options[AZ_solver] = AZ_bicgstab;
}
else if (ML_strcmp(context->krylov,"Tfqmr") == 0) {
options[AZ_solver] = AZ_tfqmr;
}
else if (ML_strcmp(context->krylov,"Gmres") == 0) {
options[AZ_solver] = AZ_gmres;
}
else {
printf("unknown krylov method %s\n",context->krylov);
}
if (blocks) ML_free(blocks);
if (block_pde) ML_free(block_pde);
options[AZ_scaling] = AZ_none;
options[AZ_precond] = AZ_user_precond;
options[AZ_conv] = AZ_r0;
options[AZ_output] = 1;
options[AZ_max_iter] = context->max_outer_its;
options[AZ_poly_ord] = 5;
options[AZ_kspace] = 130;
params[AZ_tol] = context->tol;
options[AZ_output] = context->output;
ML_free(context);
AZ_set_ML_preconditioner(&Pmat, Amat, ml, options);
setup_time = AZ_second() - start_time;
xxx = (double *) malloc( leng*sizeof(double));
for (iii = 0; iii < leng; iii++) xxx[iii] = 0.0;
/* Set x */
/*
there is no initguess supplied with these examples for the moment....
*/
fp = fopen("initguessfile","r");
if (fp != NULL) {
fclose(fp);
if (proc_config[AZ_node]== 0) printf("reading initial guess from file\n");
AZ_input_msr_matrix("data_initguess.txt", update, &xxx, &garbage, N_update,
proc_config);
options[AZ_conv] = AZ_expected_values;
}
else if (proc_config[AZ_node]== 0) printf("taking 0 initial guess \n");
AZ_reorder_vec(xxx, data_org, update_index, NULL);
/* if Dirichlet BC ... put the answer in */
for (i = 0; i < data_org[AZ_N_internal]+data_org[AZ_N_border]; i++) {
if ( (val[i] > .99999999) && (val[i] < 1.0000001))
xxx[i] = rhs[i];
}
fp = fopen("AZ_no_multilevel.dat","r");
scaling = AZ_scaling_create();
start_time = AZ_second();
if (fp != NULL) {
fclose(fp);
options[AZ_precond] = AZ_none;
options[AZ_scaling] = AZ_sym_diag;
options[AZ_ignore_scaling] = AZ_TRUE;
options[AZ_keep_info] = 1;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
/*
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, NULL, scaling);
*/
}
else {
options[AZ_keep_info] = 1;
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
options[AZ_pre_calc] = AZ_reuse;
options[AZ_conv] = AZ_expected_values;
/*
if (proc_config[AZ_node] == 0)
printf("\n-------- Second solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
if (proc_config[AZ_node] == 0)
printf("\n-------- Third solve with improved convergence test -----\n");
AZ_iterate(xxx, rhs, options, params, status, proc_config, Amat, Pmat, scaling);
*/
}
solve_time = AZ_second() - start_time;
if (proc_config[AZ_node] == 0)
printf("Solve time = %e, MG Setup time = %e\n", solve_time, setup_time);
if (proc_config[AZ_node] == 0)
printf("Printing out a few entries of the solution ...\n");
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 7) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 23) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 47) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 101) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
j = AZ_gsum_int(7, proc_config); /* sync processors */
for (j=0;j<Amat->data_org[AZ_N_internal]+ Amat->data_org[AZ_N_border];j++)
if (update[j] == 171) {printf("solution(gid = %d) = %10.4e\n",
update[j],xxx[update_index[j]]); fflush(stdout);}
ML_Aggregate_Destroy(&ag);
ML_Destroy(&ml);
AZ_free((void *) Amat->data_org);
AZ_free((void *) Amat->val);
AZ_free((void *) Amat->bindx);
AZ_free((void *) update);
AZ_free((void *) external);
AZ_free((void *) extern_index);
AZ_free((void *) update_index);
AZ_scaling_destroy(&scaling);
if (Amat != NULL) AZ_matrix_destroy(&Amat);
if (Pmat != NULL) AZ_precond_destroy(&Pmat);
free(xxx);
free(rhs);
#ifdef ML_MPI
MPI_Finalize();
#endif
return 0;
}
char instring[100], jobz, jobz2;
FILE *fp;
#endif
Amat = curr->Amat;
Rmat = curr->Rmat;
pre = curr->pre_smoother;
post = curr->post_smoother;
csolve = curr->csolve;
lengf = Amat->outvec_leng;
/* ------------------------------------------------------------ */
/* first do the normalization */
/* ------------------------------------------------------------ */
rhss = (double *) ML_allocate( lengf * sizeof(double) );
ML_DVector_GetDataPtr(curr->Amat_Normalization, &normalscales) ;
for ( i = 0; i < lengf; i++ ) rhss[i] = rhs[i];
#ifdef ML_ANALYSIS
if ( comm->ML_nprocs == 1 && curr->Pmat->to == NULL && lengf < 1000 )
{
fp = fopen("mlmatlab.m", "w");
Nrows = lengf;
fprintf(fp, "A = sparse(%d,%d);\n", Nrows, Nrows);
allocated_space = 100;
cols = (int *) ML_allocate(allocated_space*sizeof(int ));
vals = (double *) ML_allocate(allocated_space*sizeof(double));
for (i = 0; i < lengf; i++) {
while(ML_Operator_Getrow(Amat,1,&i,allocated_space,cols,vals,&ncols)==0)
{
int ML_Epetra::MultiLevelPreconditioner::SetupCoordinates()
{
ML* ml_ptr = 0;
int NumDimensions;
double* in_x_coord = 0;
double* in_y_coord = 0;
double* in_z_coord = 0;
// Check first for node coordinates, then for edge coordinates
for (int ii=0; ii<2; ii++)
{
switch (ii) {
case 0:
ml_ptr = ml_;
//??else ml_ptr = ml_;
in_x_coord = List_.get("x-coordinates", (double *)0);
in_y_coord = List_.get("y-coordinates", (double *)0);
in_z_coord = List_.get("z-coordinates", (double *)0);
break;
case 1:
if (AMGSolver_ == ML_MAXWELL) {
ml_ptr = ml_nodes_;
in_x_coord = List_.get("node: x-coordinates", (double *)0);
in_y_coord = List_.get("node: y-coordinates", (double *)0);
in_z_coord = List_.get("node: z-coordinates", (double *)0);
}
else {
in_x_coord = NULL;
in_y_coord = NULL;
in_z_coord = NULL;
}
break;
}
NumDimensions = 0;
if (!(in_x_coord == 0 && in_y_coord == 0 && in_z_coord == 0))
{
ML_Aggregate_Viz_Stats *grid_info =
(ML_Aggregate_Viz_Stats *) ml_ptr->Grid[LevelID_[0]].Grid;
ML_Operator* AAA = &(ml_ptr->Amat[LevelID_[0]]);
int n = AAA->invec_leng, Nghost = 0;
if (AAA->getrow->pre_comm)
{
if (AAA->getrow->pre_comm->total_rcv_length <= 0)
ML_CommInfoOP_Compute_TotalRcvLength(AAA->getrow->pre_comm);
Nghost = AAA->getrow->pre_comm->total_rcv_length;
}
std::vector<double> tmp(Nghost + n);
for (int i = 0 ; i < Nghost + n ; ++i)
tmp[i] = 0.0;
n /= NumPDEEqns_;
Nghost /= NumPDEEqns_;
if (in_x_coord)
{
NumDimensions++;
double* x_coord = (double *) ML_allocate(sizeof(double) * (Nghost+n));
for (int i = 0 ; i < n ; ++i)
tmp[i * NumPDEEqns_] = in_x_coord[i];
ML_exchange_bdry(&tmp[0],AAA->getrow->pre_comm, NumPDEEqns_ * n,
AAA->comm, ML_OVERWRITE,NULL);
for (int i = 0 ; i < n + Nghost ; ++i)
x_coord[i] = tmp[i * NumPDEEqns_];
grid_info->x = x_coord;
}
if (in_y_coord)
{
NumDimensions++;
double* y_coord = (double *) ML_allocate(sizeof(double) * (Nghost+n));
for (int i = 0 ; i < n ; ++i)
tmp[i * NumPDEEqns_] = in_y_coord[i];
ML_exchange_bdry(&tmp[0],AAA->getrow->pre_comm, NumPDEEqns_ * n,
AAA->comm, ML_OVERWRITE,NULL);
for (int i = 0 ; i < n + Nghost ; ++i)
y_coord[i] = tmp[i * NumPDEEqns_];
grid_info->y = y_coord;
}
if (in_z_coord)
{
NumDimensions++;
double* z_coord = (double *) ML_allocate(sizeof(double) * (Nghost+n));
for (int i = 0 ; i < n ; ++i)
tmp[i * NumPDEEqns_] = in_z_coord[i];
ML_exchange_bdry(&tmp[0],AAA->getrow->pre_comm, NumPDEEqns_ * n,
AAA->comm, ML_OVERWRITE,NULL);
for (int i = 0 ; i < n + Nghost ; ++i)
z_coord[i] = tmp[i * NumPDEEqns_];
grid_info->z = z_coord;
}
grid_info->Ndim = NumDimensions;
} // if (!(in_x_coord == 0 && in_y_coord == 0 && in_z_coord == 0))
} //for (int ii=0; ii<2; ii++)
return(0);
}
ML_Operator *user_T_build(struct user_partition *Edge_Partition,
struct user_partition *Node_Partition,
ML_Operator *Kn_mat, ML_Comm *comm)
{
int nx, i, ii, jj, horv, Ncols, Nexterns;
int *Tmat_bindx;
double *Tmat_val;
ML_Operator *Tmat;
struct ML_CSR_MSRdata *csr_data;
struct aztec_context *aztec_context;
int global_id;
int Nlocal_nodes, Nlocal_edges;
int nz_ptr;
Nlocal_nodes = Node_Partition->Nlocal;
Nlocal_edges = Edge_Partition->Nlocal;
nx = (int) sqrt( ((double) Node_Partition->Nglobal) + .00001);
Tmat_bindx = (int *) malloc((3*Nlocal_edges+1)*sizeof(int));
Tmat_val = (double *) malloc((3*Nlocal_edges+1)*sizeof(double));
Tmat_bindx[0] = Nlocal_edges + 1;
for (i = 0; i < Nlocal_edges; i++) {
global_id = (Edge_Partition->my_global_ids)[i];
Tmat_val[i] = 0.0;
invindex(global_id, &ii, &jj, nx, &horv);
nz_ptr = Tmat_bindx[i];
ii--;
if (horv == HORIZONTAL) {
if(ii != -1) {
Tmat_bindx[nz_ptr] = southwest(ii,jj,nx); Tmat_val[nz_ptr++] = -1.;
}
Tmat_bindx[nz_ptr] = southeast(ii,jj,nx); Tmat_val[nz_ptr++] = 1.;
}
else {
if (ii == -1) ii = nx-1;
Tmat_bindx[nz_ptr] = northwest(ii,jj,nx); Tmat_val[nz_ptr++] = -1.;
if (jj != 0) {
Tmat_bindx[nz_ptr] = southwest(ii,jj,nx); Tmat_val[nz_ptr++] = 1.;}
}
Tmat_bindx[i+1] = nz_ptr;
}
csr_data = (struct ML_CSR_MSRdata *) ML_allocate(sizeof(struct ML_CSR_MSRdata));
csr_data->columns = Tmat_bindx;
csr_data->values = Tmat_val;
ML_MSR2CSR(csr_data, Nlocal_edges, &Ncols);
aztec_context = (struct aztec_context *) Kn_mat->data;
Nexterns = (aztec_context->Amat->data_org)[AZ_N_external];
AZ_Tmat_transform2ml(Nexterns, Node_Partition->needed_external_ids,
reordered_node_externs,
Tmat_bindx, Tmat_val, csr_data->rowptr, Nlocal_nodes,
Node_Partition->my_global_ids,
comm, Nlocal_edges, &Tmat);
ML_free(csr_data);
Tmat->data_destroy = ML_CSR_MSRdata_Destroy;
ML_CommInfoOP_Clone(&(Tmat->getrow->pre_comm), Kn_mat->getrow->pre_comm);
return(Tmat);
}