void sample1(struct data *Afine_data, struct data *Acoarse_data,
struct data *Rmat_data, struct data *Pmat_data,
double *sol, double *rhs )
{
ML *my_ml;
int i;
int fine_grid, output_level = 10, N_grids = 2, grid0 = 0, grid1 = 1;
int Nfine, Ncoarse;
double *diagonal;
Nfine = Rmat_data->from_size;
Ncoarse = Rmat_data->to_size;
diagonal = (double *) malloc(Nfine*sizeof(double));
for (i = 0; i < Nfine; i++) diagonal[i] = 2.;
fine_grid = grid1;
ML_Create (&my_ml, N_grids);
ML_Set_OutputLevel( my_ml, output_level);
ML_Init_Amatrix (my_ml, grid1, Nfine, Nfine,(void *) Afine_data);
ML_Set_Amatrix_Getrow(my_ml, grid1, myAgetrow, my_comm, Nfine+1);
ML_Set_Amatrix_Matvec(my_ml, grid1, mymatvec);
ML_Set_Amatrix_Diag (my_ml, grid1, Nfine, diagonal);
ML_Gen_Smoother_Jacobi(my_ml, grid1, ML_PRESMOOTHER, 2, ML_DEFAULT);
ML_Init_Prolongator(my_ml, grid0, grid1, Ncoarse,Nfine,(void *)Pmat_data);
ML_Set_Prolongator_Getrow(my_ml, grid0, myPgetrow, my_comm, Ncoarse+1);
ML_Set_Prolongator_Matvec(my_ml, grid0, myinterp);
ML_Init_Restrictor(my_ml, grid1, grid0, Nfine, Ncoarse,(void *)Rmat_data);
ML_Set_Restrictor_Getrow(my_ml, grid1, myRgetrow, my_comm, Nfine+1);
ML_Set_Restrictor_Matvec(my_ml, grid1, myrestrict);
ML_Gen_AmatrixRAP(my_ml,grid1, grid0);
#ifdef SUPERLU
ML_Gen_CoarseSolverSuperLU(my_ml, grid0);
#else
ML_Gen_Smoother_Jacobi(my_ml, grid0, ML_PRESMOOTHER, 100, ML_DEFAULT);
#endif
/* ML_Gen_Smoother_Jacobi(my_ml, grid0, ML_PRESMOOTHER, 200, ML_DEFAULT); */
/* ML_Gen_Smoother_GaussSeidel(my_ml, grid0, ML_PRESMOOTHER, 200, 1.); */
ML_Gen_Solver (my_ml, 0, fine_grid, grid0);
ML_Iterate(my_ml, sol, rhs);
ML_Destroy(&my_ml);
ML_free(diagonal);
}
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;
}
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;
}
HYPRE_Int HYPRE_ParCSRMLSetup( HYPRE_Solver solver, HYPRE_ParCSRMatrix A,
HYPRE_ParVector b, HYPRE_ParVector x )
{
HYPRE_Int i, my_id, nprocs, coarsest_level, level, sweeps, nlevels;
HYPRE_Int *row_partition, localEqns, length;
HYPRE_Int Nblocks, *blockList;
double wght;
MH_Context *context;
MH_Matrix *mh_mat;
/* -------------------------------------------------------- */
/* fetch the ML pointer */
/* -------------------------------------------------------- */
MH_Link *link = (MH_Link *) solver;
ML *ml = link->ml_ptr;
nlevels = link->nlevels;
/* -------------------------------------------------------- */
/* set up the parallel environment */
/* -------------------------------------------------------- */
hypre_MPI_Comm_rank(link->comm, &my_id);
hypre_MPI_Comm_size(link->comm, &nprocs);
/* -------------------------------------------------------- */
/* fetch the matrix row partition information and put it */
/* into the matrix data object (for matvec and getrow) */
/* -------------------------------------------------------- */
HYPRE_ParCSRMatrixGetRowPartitioning( A, &row_partition );
localEqns = row_partition[my_id+1] - row_partition[my_id];
context = (MH_Context *) malloc(sizeof(MH_Context));
link->contxt = context;
context->comm = link->comm;
context->globalEqns = row_partition[nprocs];
context->partition = (HYPRE_Int *) malloc(sizeof(HYPRE_Int)*(nprocs+1));
for (i=0; i<=nprocs; i++) context->partition[i] = row_partition[i];
hypre_TFree( row_partition );
mh_mat = ( MH_Matrix * ) malloc( sizeof( MH_Matrix) );
context->Amat = mh_mat;
HYPRE_ParCSRMLConstructMHMatrix(A,mh_mat,link->comm,
context->partition,context);
/* -------------------------------------------------------- */
/* set up the ML communicator information */
/* -------------------------------------------------------- */
ML_Set_Comm_Communicator(ml, link->comm);
ML_Set_Comm_MyRank(ml, my_id);
ML_Set_Comm_Nprocs(ml, nprocs);
ML_Set_Comm_Send(ml, MH_Send);
ML_Set_Comm_Recv(ml, MH_Irecv);
ML_Set_Comm_Wait(ml, MH_Wait);
/* -------------------------------------------------------- */
/* set up the ML matrix information */
/* -------------------------------------------------------- */
ML_Init_Amatrix(ml, nlevels-1, localEqns, localEqns, (void *) context);
ML_Set_Amatrix_Matvec(ml, nlevels-1, MH_MatVec);
length = localEqns;
for (i=0; i<mh_mat->recvProcCnt; i++ ) length += mh_mat->recvLeng[i];
ML_Set_Amatrix_Getrow(ml, nlevels-1, MH_GetRow, MH_ExchBdry, length);
/* -------------------------------------------------------- */
/* create an aggregate context */
/* -------------------------------------------------------- */
ML_Aggregate_Create(&(link->ml_ag));
link->ml_ag->max_levels = link->nlevels;
ML_Aggregate_Set_Threshold( link->ml_ag, link->ag_threshold );
/* -------------------------------------------------------- */
/* perform aggregation */
/* -------------------------------------------------------- */
coarsest_level = ML_Gen_MGHierarchy_UsingAggregation(ml, nlevels-1,
ML_DECREASING, link->ml_ag);
if ( my_id == 0 )
hypre_printf("ML : number of levels = %d\n", coarsest_level);
coarsest_level = nlevels - coarsest_level;
/* -------------------------------------------------------- */
/* set up smoother and coarse solver */
/* -------------------------------------------------------- */
for (level = nlevels-1; level > coarsest_level; level--)
{
sweeps = link->pre_sweeps;
wght = link->jacobi_wt;
switch ( link->pre )
{
case 0 :
ML_Gen_SmootherJacobi(ml, level, ML_PRESMOOTHER, sweeps, wght);
break;
case 1 :
ML_Gen_SmootherGaussSeidel(ml, level, ML_PRESMOOTHER, sweeps);
break;
case 2 :
ML_Gen_SmootherSymGaussSeidel(ml,level,ML_PRESMOOTHER,sweeps,1.0);
break;
case 3 :
Nblocks = ML_Aggregate_Get_AggrCount( link->ml_ag, level );
ML_Aggregate_Get_AggrMap( link->ml_ag, level, &blockList );
ML_Gen_SmootherVBlockGaussSeidel(ml,level,ML_PRESMOOTHER,
sweeps, Nblocks, blockList);
break;
case 4 :
Nblocks = ML_Aggregate_Get_AggrCount( link->ml_ag, level );
ML_Aggregate_Get_AggrMap( link->ml_ag, level, &blockList );
ML_Gen_SmootherVBlockJacobi(ml,level,ML_PRESMOOTHER,
sweeps, wght, Nblocks, blockList);
break;
}
sweeps = link->post_sweeps;
switch ( link->post )
{
case 0 :
ML_Gen_SmootherJacobi(ml, level, ML_POSTSMOOTHER, sweeps, wght);
break;
case 1 :
ML_Gen_SmootherGaussSeidel(ml, level, ML_POSTSMOOTHER, sweeps);
break;
case 2 :
ML_Gen_SmootherSymGaussSeidel(ml,level,ML_POSTSMOOTHER,sweeps,1.0);
break;
case 3 :
Nblocks = ML_Aggregate_Get_AggrCount( link->ml_ag, level );
ML_Aggregate_Get_AggrMap( link->ml_ag, level, &blockList );
ML_Gen_SmootherVBlockGaussSeidel(ml,level,ML_POSTSMOOTHER,
sweeps, Nblocks, blockList);
break;
case 4 :
Nblocks = ML_Aggregate_Get_AggrCount( link->ml_ag, level );
ML_Aggregate_Get_AggrMap( link->ml_ag, level, &blockList );
ML_Gen_SmootherVBlockJacobi(ml,level,ML_POSTSMOOTHER,
sweeps, wght, Nblocks, blockList);
break;
}
}
ML_Gen_CoarseSolverSuperLU(ml, coarsest_level);
//ML_Gen_SmootherGaussSeidel(ml, coarsest_level, ML_PRESMOOTHER, 100);
ML_Gen_Solver(ml, ML_MGV, nlevels-1, coarsest_level);
return 0;
}
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;
}
