HYPRE_Int
hypre_BoomerAMGSolveT( void *amg_vdata,
hypre_ParCSRMatrix *A,
hypre_ParVector *f,
hypre_ParVector *u )
{
MPI_Comm comm = hypre_ParCSRMatrixComm(A);
hypre_ParAMGData *amg_data = amg_vdata;
/* Data Structure variables */
HYPRE_Int amg_print_level;
HYPRE_Int amg_logging;
HYPRE_Real *num_coeffs;
HYPRE_Int *num_variables;
HYPRE_Real cycle_op_count;
HYPRE_Int num_levels;
/* HYPRE_Int num_unknowns; */
HYPRE_Real tol;
char *file_name;
hypre_ParCSRMatrix **A_array;
hypre_ParVector **F_array;
hypre_ParVector **U_array;
/* Local variables */
/*FILE *fp;*/
HYPRE_Int j;
HYPRE_Int Solve_err_flag;
HYPRE_Int min_iter;
HYPRE_Int max_iter;
HYPRE_Int cycle_count;
HYPRE_Real total_coeffs;
HYPRE_Int total_variables;
HYPRE_Int num_procs, my_id;
HYPRE_Real alpha = 1.0;
HYPRE_Real beta = -1.0;
HYPRE_Real cycle_cmplxty = 0.0;
HYPRE_Real operat_cmplxty;
HYPRE_Real grid_cmplxty;
HYPRE_Real conv_factor;
HYPRE_Real resid_nrm;
HYPRE_Real resid_nrm_init;
HYPRE_Real relative_resid;
HYPRE_Real rhs_norm;
HYPRE_Real old_resid;
hypre_ParVector *Vtemp;
hypre_ParVector *Residual;
hypre_MPI_Comm_size(comm, &num_procs);
hypre_MPI_Comm_rank(comm,&my_id);
amg_print_level = hypre_ParAMGDataPrintLevel(amg_data);
amg_logging = hypre_ParAMGDataLogging(amg_data);
if ( amg_logging>1 )
Residual = hypre_ParAMGDataResidual(amg_data);
file_name = hypre_ParAMGDataLogFileName(amg_data);
/* num_unknowns = hypre_ParAMGDataNumUnknowns(amg_data); */
num_levels = hypre_ParAMGDataNumLevels(amg_data);
A_array = hypre_ParAMGDataAArray(amg_data);
F_array = hypre_ParAMGDataFArray(amg_data);
U_array = hypre_ParAMGDataUArray(amg_data);
tol = hypre_ParAMGDataTol(amg_data);
min_iter = hypre_ParAMGDataMinIter(amg_data);
max_iter = hypre_ParAMGDataMaxIter(amg_data);
num_coeffs = hypre_CTAlloc(HYPRE_Real, num_levels);
num_variables = hypre_CTAlloc(HYPRE_Int, num_levels);
num_coeffs[0] = hypre_ParCSRMatrixDNumNonzeros(A_array[0]);
num_variables[0] = hypre_ParCSRMatrixGlobalNumRows(A_array[0]);
A_array[0] = A;
F_array[0] = f;
U_array[0] = u;
/* Vtemp = hypre_ParVectorCreate(hypre_ParCSRMatrixComm(A_array[0]),
hypre_ParCSRMatrixGlobalNumRows(A_array[0]),
hypre_ParCSRMatrixRowStarts(A_array[0]));
hypre_ParVectorInitialize(Vtemp);
hypre_ParVectorSetPartitioningOwner(Vtemp,0);
hypre_ParAMGDataVtemp(amg_data) = Vtemp;
*/
Vtemp = hypre_ParAMGDataVtemp(amg_data);
for (j = 1; j < num_levels; j++)
{
num_coeffs[j] = hypre_ParCSRMatrixDNumNonzeros(A_array[j]);
num_variables[j] = hypre_ParCSRMatrixGlobalNumRows(A_array[j]);
}
/*-----------------------------------------------------------------------
* Write the solver parameters
*-----------------------------------------------------------------------*/
if (my_id == 0 && amg_print_level > 1)
hypre_BoomerAMGWriteSolverParams(amg_data);
/*-----------------------------------------------------------------------
* Initialize the solver error flag and assorted bookkeeping variables
*-----------------------------------------------------------------------*/
Solve_err_flag = 0;
total_coeffs = 0;
total_variables = 0;
cycle_count = 0;
operat_cmplxty = 0;
grid_cmplxty = 0;
/*-----------------------------------------------------------------------
* open the log file and write some initial info
*-----------------------------------------------------------------------*/
if (my_id == 0 && amg_print_level > 1)
{
/*fp = fopen(file_name, "a");*/
hypre_printf("\n\nAMG SOLUTION INFO:\n");
}
/*-----------------------------------------------------------------------
* Compute initial fine-grid residual and print to logfile
*-----------------------------------------------------------------------*/
if ( amg_logging > 1 ) {
hypre_ParVectorCopy(F_array[0], Residual );
hypre_ParCSRMatrixMatvecT(alpha, A_array[0], U_array[0], beta, Residual );
resid_nrm = sqrt(hypre_ParVectorInnerProd( Residual, Residual ));
}
else {
hypre_ParVectorCopy(F_array[0], Vtemp);
hypre_ParCSRMatrixMatvecT(alpha, A_array[0], U_array[0], beta, Vtemp);
resid_nrm = sqrt(hypre_ParVectorInnerProd(Vtemp, Vtemp));
}
resid_nrm_init = resid_nrm;
rhs_norm = sqrt(hypre_ParVectorInnerProd(f, f));
relative_resid = 9999;
if (rhs_norm)
{
relative_resid = resid_nrm_init / rhs_norm;
}
if (my_id ==0 && (amg_print_level > 1))
{
hypre_printf(" relative\n");
hypre_printf(" residual factor residual\n");
hypre_printf(" -------- ------ --------\n");
hypre_printf(" Initial %e %e\n",resid_nrm_init,
relative_resid);
}
/*-----------------------------------------------------------------------
* Main V-cycle loop
*-----------------------------------------------------------------------*/
while ((relative_resid >= tol || cycle_count < min_iter)
&& cycle_count < max_iter
&& Solve_err_flag == 0)
{
hypre_ParAMGDataCycleOpCount(amg_data) = 0;
/* Op count only needed for one cycle */
Solve_err_flag = hypre_BoomerAMGCycleT(amg_data, F_array, U_array);
old_resid = resid_nrm;
/*---------------------------------------------------------------
* Compute fine-grid residual and residual norm
*----------------------------------------------------------------*/
if ( amg_logging > 1 ) {
hypre_ParVectorCopy(F_array[0], Residual );
hypre_ParCSRMatrixMatvecT(alpha, A_array[0], U_array[0], beta, Residual );
resid_nrm = sqrt(hypre_ParVectorInnerProd( Residual, Residual ));
}
else {
hypre_ParVectorCopy(F_array[0], Vtemp);
hypre_ParCSRMatrixMatvecT(alpha, A_array[0], U_array[0], beta, Vtemp);
resid_nrm = sqrt(hypre_ParVectorInnerProd(Vtemp, Vtemp));
}
conv_factor = resid_nrm / old_resid;
relative_resid = 9999;
if (rhs_norm)
{
relative_resid = resid_nrm / rhs_norm;
}
++cycle_count;
hypre_ParAMGDataRelativeResidualNorm(amg_data) = relative_resid;
hypre_ParAMGDataNumIterations(amg_data) = cycle_count;
if (my_id == 0 && (amg_print_level > 1))
{
hypre_printf(" Cycle %2d %e %f %e \n", cycle_count,
resid_nrm, conv_factor, relative_resid);
}
}
if (cycle_count == max_iter) Solve_err_flag = 1;
/*-----------------------------------------------------------------------
* Compute closing statistics
*-----------------------------------------------------------------------*/
conv_factor = pow((resid_nrm/resid_nrm_init),(1.0/((HYPRE_Real) cycle_count)));
for (j=0;j<hypre_ParAMGDataNumLevels(amg_data);j++)
{
total_coeffs += num_coeffs[j];
total_variables += num_variables[j];
}
cycle_op_count = hypre_ParAMGDataCycleOpCount(amg_data);
if (num_variables[0])
grid_cmplxty = ((HYPRE_Real) total_variables) / ((HYPRE_Real) num_variables[0]);
if (num_coeffs[0])
{
operat_cmplxty = total_coeffs / num_coeffs[0];
cycle_cmplxty = cycle_op_count / num_coeffs[0];
}
if (my_id == 0 && amg_print_level > 1)
{
if (Solve_err_flag == 1)
{
hypre_printf("\n\n==============================================");
hypre_printf("\n NOTE: Convergence tolerance was not achieved\n");
hypre_printf(" within the allowed %d V-cycles\n",max_iter);
hypre_printf("==============================================");
}
hypre_printf("\n\n Average Convergence Factor = %f",conv_factor);
hypre_printf("\n\n Complexity: grid = %f\n",grid_cmplxty);
hypre_printf(" operator = %f\n",operat_cmplxty);
hypre_printf(" cycle = %f\n\n",cycle_cmplxty);
}
/*----------------------------------------------------------
* Close the output file (if open)
*----------------------------------------------------------*/
/*if (my_id == 0 && amg_print_level >= 1)
{
fclose(fp);
}*/
hypre_TFree(num_coeffs);
hypre_TFree(num_variables);
return(Solve_err_flag);
}
int
hypre_BoomerAMGSetupStats( void *amg_vdata,
hypre_ParCSRMatrix *A )
{
MPI_Comm comm = hypre_ParCSRMatrixComm(A);
hypre_ParAMGData *amg_data = (hypre_ParAMGData*)amg_vdata;
/*hypre_SeqAMGData *seq_data = hypre_ParAMGDataSeqData(amg_data);*/
/* Data Structure variables */
hypre_ParCSRMatrix **A_array;
hypre_ParCSRMatrix **P_array;
hypre_CSRMatrix *A_diag;
double *A_diag_data;
int *A_diag_i;
hypre_CSRMatrix *A_offd;
double *A_offd_data;
int *A_offd_i;
hypre_CSRMatrix *P_diag;
double *P_diag_data;
int *P_diag_i;
hypre_CSRMatrix *P_offd;
double *P_offd_data;
int *P_offd_i;
int numrows;
HYPRE_BigInt *row_starts;
int num_levels;
int coarsen_type;
int interp_type;
int measure_type;
double global_nonzeros;
double *send_buff;
double *gather_buff;
/* Local variables */
int level;
int j;
HYPRE_BigInt fine_size;
int min_entries;
int max_entries;
int num_procs,my_id, num_threads;
double min_rowsum;
double max_rowsum;
double sparse;
int i;
HYPRE_BigInt coarse_size;
int entries;
double avg_entries;
double rowsum;
double min_weight;
double max_weight;
int global_min_e;
int global_max_e;
double global_min_rsum;
double global_max_rsum;
double global_min_wt;
double global_max_wt;
double *num_coeffs;
double *num_variables;
double total_variables;
double operat_cmplxty;
double grid_cmplxty;
/* amg solve params */
int max_iter;
int cycle_type;
int *num_grid_sweeps;
int *grid_relax_type;
int relax_order;
int **grid_relax_points;
double *relax_weight;
double *omega;
double tol;
int one = 1;
int minus_one = -1;
int zero = 0;
int smooth_type;
int smooth_num_levels;
int agg_num_levels;
/*int seq_cg = 0;*/
/*if (seq_data)
seq_cg = 1;*/
MPI_Comm_size(comm, &num_procs);
MPI_Comm_rank(comm,&my_id);
num_threads = hypre_NumThreads();
if (my_id == 0)
printf("\nNumber of MPI processes: %d , Number of OpenMP threads: %d\n", num_procs, num_threads);
A_array = hypre_ParAMGDataAArray(amg_data);
P_array = hypre_ParAMGDataPArray(amg_data);
num_levels = hypre_ParAMGDataNumLevels(amg_data);
coarsen_type = hypre_ParAMGDataCoarsenType(amg_data);
interp_type = hypre_ParAMGDataInterpType(amg_data);
measure_type = hypre_ParAMGDataMeasureType(amg_data);
smooth_type = hypre_ParAMGDataSmoothType(amg_data);
smooth_num_levels = hypre_ParAMGDataSmoothNumLevels(amg_data);
agg_num_levels = hypre_ParAMGDataAggNumLevels(amg_data);
/*----------------------------------------------------------
* Get the amg_data data
*----------------------------------------------------------*/
num_levels = hypre_ParAMGDataNumLevels(amg_data);
max_iter = hypre_ParAMGDataMaxIter(amg_data);
cycle_type = hypre_ParAMGDataCycleType(amg_data);
num_grid_sweeps = hypre_ParAMGDataNumGridSweeps(amg_data);
grid_relax_type = hypre_ParAMGDataGridRelaxType(amg_data);
grid_relax_points = hypre_ParAMGDataGridRelaxPoints(amg_data);
relax_weight = hypre_ParAMGDataRelaxWeight(amg_data);
relax_order = hypre_ParAMGDataRelaxOrder(amg_data);
omega = hypre_ParAMGDataOmega(amg_data);
tol = hypre_ParAMGDataTol(amg_data);
/*block_mode = hypre_ParAMGDataBlockMode(amg_data);*/
send_buff = hypre_CTAlloc(double, 6);
#ifdef HYPRE_NO_GLOBAL_PARTITION
gather_buff = hypre_CTAlloc(double,6);
#else
gather_buff = hypre_CTAlloc(double,6*num_procs);
#endif
if (my_id==0)
{
printf("\nBoomerAMG SETUP PARAMETERS:\n\n");
printf(" Max levels = %d\n",hypre_ParAMGDataMaxLevels(amg_data));
printf(" Num levels = %d\n\n",num_levels);
printf(" Strength Threshold = %f\n",
hypre_ParAMGDataStrongThreshold(amg_data));
printf(" Interpolation Truncation Factor = %f\n",
hypre_ParAMGDataTruncFactor(amg_data));
printf(" Maximum Row Sum Threshold for Dependency Weakening = %f\n\n",
hypre_ParAMGDataMaxRowSum(amg_data));
if (coarsen_type == 0)
{
printf(" Coarsening Type = Cleary-Luby-Jones-Plassman\n");
}
else if (abs(coarsen_type) == 1)
{
printf(" Coarsening Type = Ruge\n");
}
else if (abs(coarsen_type) == 2)
{
printf(" Coarsening Type = Ruge2B\n");
}
else if (abs(coarsen_type) == 3)
{
printf(" Coarsening Type = Ruge3\n");
}
else if (abs(coarsen_type) == 4)
{
printf(" Coarsening Type = Ruge 3c \n");
}
else if (abs(coarsen_type) == 5)
{
printf(" Coarsening Type = Ruge relax special points \n");
}
else if (abs(coarsen_type) == 6)
{
printf(" Coarsening Type = Falgout-CLJP \n");
}
else if (abs(coarsen_type) == 8)
{
printf(" Coarsening Type = PMIS \n");
}
else if (abs(coarsen_type) == 10)
{
printf(" Coarsening Type = HMIS \n");
}
else if (abs(coarsen_type) == 11)
{
printf(" Coarsening Type = Ruge 1st pass only \n");
}
else if (abs(coarsen_type) == 9)
{
printf(" Coarsening Type = PMIS fixed random \n");
}
else if (abs(coarsen_type) == 7)
{
printf(" Coarsening Type = CLJP, fixed random \n");
}
if (coarsen_type > 0)
{
printf(" Hybrid Coarsening (switch to CLJP when coarsening slows)\n");
}
if (coarsen_type)
printf(" measures are determined %s\n\n",
(measure_type ? "globally" : "locally"));
if (agg_num_levels)
printf(" no. of levels of aggressive coarsening: %d\n\n", agg_num_levels);
#ifdef HYPRE_NO_GLOBAL_PARTITION
printf( "\n No global partition option chosen.\n\n");
#endif
if (interp_type == 0)
{
printf(" Interpolation = modified classical interpolation\n");
}
else if (interp_type == 1)
{
printf(" Interpolation = LS interpolation \n");
}
else if (interp_type == 2)
{
printf(" Interpolation = modified classical interpolation for hyperbolic PDEs\n");
}
else if (interp_type == 3)
{
printf(" Interpolation = direct interpolation with separation of weights\n");
}
else if (interp_type == 4)
{
printf(" Interpolation = multipass interpolation\n");
}
else if (interp_type == 5)
{
printf(" Interpolation = multipass interpolation with separation of weights\n");
}
else if (interp_type == 6)
{
printf(" Interpolation = extended+i interpolation\n");
}
else if (interp_type == 7)
{
printf(" Interpolation = extended+i interpolation (only when needed)\n");
}
else if (interp_type == 8)
{
printf(" Interpolation = standard interpolation\n");
}
else if (interp_type == 9)
{
printf(" Interpolation = standard interpolation with separation of weights\n");
}
else if (interp_type == 12)
{
printf(" FF interpolation \n");
}
else if (interp_type == 13)
{
printf(" FF1 interpolation \n");
}
{
printf( "\nOperator Matrix Information:\n\n");
}
#if HYPRE_LONG_LONG
printf(" nonzero entries p");
printf("er row row sums\n");
printf("lev rows entries sparse min max ");
printf("avg min max\n");
printf("=======================================");
printf("==================================\n");
#else
printf(" nonzero entries p");
printf("er row row sums\n");
printf("lev rows entries sparse min max ");
printf("avg min max\n");
printf("=======================================");
printf("============================\n");
#endif
}
/*-----------------------------------------------------
* Enter Statistics Loop
*-----------------------------------------------------*/
num_coeffs = hypre_CTAlloc(double,num_levels);
num_variables = hypre_CTAlloc(double,num_levels);
for (level = 0; level < num_levels; level++)
{
{
A_diag = hypre_ParCSRMatrixDiag(A_array[level]);
A_diag_data = hypre_CSRMatrixData(A_diag);
A_diag_i = hypre_CSRMatrixI(A_diag);
A_offd = hypre_ParCSRMatrixOffd(A_array[level]);
A_offd_data = hypre_CSRMatrixData(A_offd);
A_offd_i = hypre_CSRMatrixI(A_offd);
row_starts = hypre_ParCSRMatrixRowStarts(A_array[level]);
fine_size = hypre_ParCSRMatrixGlobalNumRows(A_array[level]);
global_nonzeros = hypre_ParCSRMatrixDNumNonzeros(A_array[level]);
num_coeffs[level] = global_nonzeros;
num_variables[level] = (double) fine_size;
sparse = global_nonzeros /((double) fine_size * (double) fine_size);
min_entries = 0;
max_entries = 0;
min_rowsum = 0.0;
max_rowsum = 0.0;
if (hypre_CSRMatrixNumRows(A_diag))
{
min_entries = (A_diag_i[1]-A_diag_i[0])+(A_offd_i[1]-A_offd_i[0]);
for (j = A_diag_i[0]; j < A_diag_i[1]; j++)
min_rowsum += A_diag_data[j];
for (j = A_offd_i[0]; j < A_offd_i[1]; j++)
min_rowsum += A_offd_data[j];
max_rowsum = min_rowsum;
for (j = 0; j < hypre_CSRMatrixNumRows(A_diag); j++)
{
entries = (A_diag_i[j+1]-A_diag_i[j])+(A_offd_i[j+1]-A_offd_i[j]);
min_entries = hypre_min(entries, min_entries);
max_entries = hypre_max(entries, max_entries);
rowsum = 0.0;
for (i = A_diag_i[j]; i < A_diag_i[j+1]; i++)
rowsum += A_diag_data[i];
for (i = A_offd_i[j]; i < A_offd_i[j+1]; i++)
rowsum += A_offd_data[i];
min_rowsum = hypre_min(rowsum, min_rowsum);
max_rowsum = hypre_max(rowsum, max_rowsum);
}
}
avg_entries = global_nonzeros / ((double) fine_size);
}
#ifdef HYPRE_NO_GLOBAL_PARTITION
numrows = (int)(row_starts[1]-row_starts[0]);
if (!numrows) /* if we don't have any rows, then don't have this count toward
min row sum or min num entries */
{
min_entries = 1000000;
min_rowsum = 1.0e7;
}
send_buff[0] = - (double) min_entries;
send_buff[1] = (double) max_entries;
send_buff[2] = - min_rowsum;
send_buff[3] = max_rowsum;
MPI_Reduce(send_buff, gather_buff, 4, MPI_DOUBLE, MPI_MAX, 0, comm);
if (my_id ==0)
{
global_min_e = - gather_buff[0];
global_max_e = gather_buff[1];
global_min_rsum = - gather_buff[2];
global_max_rsum = gather_buff[3];
#ifdef HYPRE_LONG_LONG
printf( "%2d %12lld %8.0f %0.3f %4d %4d",
level, fine_size, global_nonzeros, sparse, global_min_e,
global_max_e);
#else
printf( "%2d %7d %8.0f %0.3f %4d %4d",
level, fine_size, global_nonzeros, sparse, global_min_e,
global_max_e);
#endif
printf(" %4.1f %10.3e %10.3e\n", avg_entries,
global_min_rsum, global_max_rsum);
}
#else
send_buff[0] = (double) min_entries;
send_buff[1] = (double) max_entries;
send_buff[2] = min_rowsum;
send_buff[3] = max_rowsum;
MPI_Gather(send_buff,4,MPI_DOUBLE,gather_buff,4,MPI_DOUBLE,0,comm);
if (my_id == 0)
{
global_min_e = 1000000;
global_max_e = 0;
global_min_rsum = 1.0e7;
global_max_rsum = 0.0;
for (j = 0; j < num_procs; j++)
{
numrows = row_starts[j+1]-row_starts[j];
if (numrows)
{
global_min_e = hypre_min(global_min_e, (int) gather_buff[j*4]);
global_min_rsum = hypre_min(global_min_rsum, gather_buff[j*4 +2]);
}
global_max_e = hypre_max(global_max_e, (int) gather_buff[j*4 +1]);
global_max_rsum = hypre_max(global_max_rsum, gather_buff[j*4 +3]);
}
#ifdef HYPRE_LONG_LONG
printf( "%2d %12lld %8.0f %0.3f %4d %4d",
level, fine_size, global_nonzeros, sparse, global_min_e,
global_max_e);
#else
printf( "%2d %7d %8.0f %0.3f %4d %4d",
level, fine_size, global_nonzeros, sparse, global_min_e,
global_max_e);
#endif
printf(" %4.1f %10.3e %10.3e\n", avg_entries,
global_min_rsum, global_max_rsum);
}
#endif
}
if (my_id == 0)
{
{
printf( "\n\nInterpolation Matrix Information:\n\n");
}
#if HYPRE_LONG_LONG
printf(" entries/row min max");
printf(" row sums\n");
printf("lev rows x cols min max ");
printf(" weight weight min max \n");
printf("=======================================");
printf("======================================\n");
#else
printf(" entries/row min max");
printf(" row sums\n");
printf("lev rows cols min max ");
printf(" weight weight min max \n");
printf("=======================================");
printf("==========================\n");
#endif
}
/*-----------------------------------------------------
* Enter Statistics Loop
*-----------------------------------------------------*/
for (level = 0; level < num_levels-1; level++)
{
{
P_diag = hypre_ParCSRMatrixDiag(P_array[level]);
P_diag_data = hypre_CSRMatrixData(P_diag);
P_diag_i = hypre_CSRMatrixI(P_diag);
P_offd = hypre_ParCSRMatrixOffd(P_array[level]);
P_offd_data = hypre_CSRMatrixData(P_offd);
P_offd_i = hypre_CSRMatrixI(P_offd);
row_starts = hypre_ParCSRMatrixRowStarts(P_array[level]);
fine_size = hypre_ParCSRMatrixGlobalNumRows(P_array[level]);
coarse_size = hypre_ParCSRMatrixGlobalNumCols(P_array[level]);
global_nonzeros = hypre_ParCSRMatrixNumNonzeros(P_array[level]);
min_weight = 1.0;
max_weight = 0.0;
max_rowsum = 0.0;
min_rowsum = 0.0;
min_entries = 0;
max_entries = 0;
if (hypre_CSRMatrixNumRows(P_diag))
{
if (hypre_CSRMatrixNumCols(P_diag)) min_weight = P_diag_data[0];
for (j = P_diag_i[0]; j < P_diag_i[1]; j++)
{
min_weight = hypre_min(min_weight, P_diag_data[j]);
if (P_diag_data[j] != 1.0)
max_weight = hypre_max(max_weight, P_diag_data[j]);
min_rowsum += P_diag_data[j];
}
for (j = P_offd_i[0]; j < P_offd_i[1]; j++)
{
min_weight = hypre_min(min_weight, P_offd_data[j]);
if (P_offd_data[j] != 1.0)
max_weight = hypre_max(max_weight, P_offd_data[j]);
min_rowsum += P_offd_data[j];
}
max_rowsum = min_rowsum;
min_entries = (P_diag_i[1]-P_diag_i[0])+(P_offd_i[1]-P_offd_i[0]);
max_entries = 0;
for (j = 0; j < hypre_CSRMatrixNumRows(P_diag); j++)
{
entries = (P_diag_i[j+1]-P_diag_i[j])+(P_offd_i[j+1]-P_offd_i[j]);
min_entries = hypre_min(entries, min_entries);
max_entries = hypre_max(entries, max_entries);
rowsum = 0.0;
for (i = P_diag_i[j]; i < P_diag_i[j+1]; i++)
{
min_weight = hypre_min(min_weight, P_diag_data[i]);
if (P_diag_data[i] != 1.0)
max_weight = hypre_max(max_weight, P_diag_data[i]);
rowsum += P_diag_data[i];
}
for (i = P_offd_i[j]; i < P_offd_i[j+1]; i++)
{
min_weight = hypre_min(min_weight, P_offd_data[i]);
if (P_offd_data[i] != 1.0)
max_weight = hypre_max(max_weight, P_offd_data[i]);
rowsum += P_offd_data[i];
}
min_rowsum = hypre_min(rowsum, min_rowsum);
max_rowsum = hypre_max(rowsum, max_rowsum);
}
}
avg_entries = ((double) global_nonzeros) / ((double) fine_size);
}
#ifdef HYPRE_NO_GLOBAL_PARTITION
numrows = (int)(row_starts[1]-row_starts[0]);
if (!numrows) /* if we don't have any rows, then don't have this count toward
min row sum or min num entries */
{
min_entries = 1000000;
min_rowsum = 1.0e7;
min_weight = 1.0e7;
}
send_buff[0] = - (double) min_entries;
send_buff[1] = (double) max_entries;
send_buff[2] = - min_rowsum;
send_buff[3] = max_rowsum;
send_buff[4] = - min_weight;
send_buff[5] = max_weight;
MPI_Reduce(send_buff, gather_buff, 6, MPI_DOUBLE, MPI_MAX, 0, comm);
if (my_id == 0)
{
global_min_e = - gather_buff[0];
global_max_e = gather_buff[1];
global_min_rsum = -gather_buff[2];
global_max_rsum = gather_buff[3];
global_min_wt = -gather_buff[4];
global_max_wt = gather_buff[5];
#ifdef HYPRE_LONG_LONG
printf( "%2d %12lld x %-12lld %3d %3d",
level, fine_size, coarse_size, global_min_e, global_max_e);
#else
printf( "%2d %5d x %-5d %3d %3d",
level, fine_size, coarse_size, global_min_e, global_max_e);
#endif
printf(" %10.3e %9.3e %9.3e %9.3e\n",
global_min_wt, global_max_wt,
global_min_rsum, global_max_rsum);
}
#else
send_buff[0] = (double) min_entries;
send_buff[1] = (double) max_entries;
send_buff[2] = min_rowsum;
send_buff[3] = max_rowsum;
send_buff[4] = min_weight;
send_buff[5] = max_weight;
MPI_Gather(send_buff,6,MPI_DOUBLE,gather_buff,6,MPI_DOUBLE,0,comm);
if (my_id == 0)
{
global_min_e = 1000000;
global_max_e = 0;
global_min_rsum = 1.0e7;
global_max_rsum = 0.0;
global_min_wt = 1.0e7;
global_max_wt = 0.0;
for (j = 0; j < num_procs; j++)
{
numrows = row_starts[j+1] - row_starts[j];
if (numrows)
{
global_min_e = hypre_min(global_min_e, (int) gather_buff[j*6]);
global_min_rsum = hypre_min(global_min_rsum, gather_buff[j*6+2]);
global_min_wt = hypre_min(global_min_wt, gather_buff[j*6+4]);
}
global_max_e = hypre_max(global_max_e, (int) gather_buff[j*6+1]);
global_max_rsum = hypre_max(global_max_rsum, gather_buff[j*6+3]);
global_max_wt = hypre_max(global_max_wt, gather_buff[j*6+5]);
}
#ifdef HYPRE_LONG_LONG
printf( "%2d %12lld x %-12lld %3d %3d",
level, fine_size, coarse_size, global_min_e, global_max_e);
#else
printf( "%2d %5d x %-5d %3d %3d",
level, fine_size, coarse_size, global_min_e, global_max_e);
#endif
printf(" %10.3e %9.3e %9.3e %9.3e\n",
global_min_wt, global_max_wt,
global_min_rsum, global_max_rsum);
}
#endif
}
total_variables = 0;
operat_cmplxty = 0;
for (j=0;j<hypre_ParAMGDataNumLevels(amg_data);j++)
{
operat_cmplxty += num_coeffs[j] / num_coeffs[0];
total_variables += num_variables[j];
}
if (num_variables[0] != 0)
grid_cmplxty = total_variables / num_variables[0];
if (my_id == 0 )
{
printf("\n\n Complexity: grid = %f\n",grid_cmplxty);
printf(" operator = %f\n",operat_cmplxty);
}
if (my_id == 0) printf("\n\n");
if (my_id == 0)
{
printf("\n\nBoomerAMG SOLVER PARAMETERS:\n\n");
printf( " Maximum number of cycles: %d \n",max_iter);
printf( " Stopping Tolerance: %e \n",tol);
printf( " Cycle type (1 = V, 2 = W, etc.): %d\n\n", cycle_type);
printf( " Relaxation Parameters:\n");
printf( " Visiting Grid: down up coarse\n");
printf( " Number of partial sweeps: %4d %2d %4d \n",
num_grid_sweeps[1],
num_grid_sweeps[2],num_grid_sweeps[3]);
printf( " Type 0=Jac, 3=hGS, 6=hSGS, 9=GE: %4d %2d %4d \n",
grid_relax_type[1],
grid_relax_type[2],grid_relax_type[3]);
#if 1 /* TO DO: may not want this to print if CG in the coarse grid */
printf( " Point types, partial sweeps (1=C, -1=F):\n");
if (grid_relax_points)
{
printf( " Pre-CG relaxation (down):");
for (j = 0; j < num_grid_sweeps[1]; j++)
printf(" %2d", grid_relax_points[1][j]);
printf( "\n");
printf( " Post-CG relaxation (up):");
for (j = 0; j < num_grid_sweeps[2]; j++)
printf(" %2d", grid_relax_points[2][j]);
printf( "\n");
printf( " Coarsest grid:");
for (j = 0; j < num_grid_sweeps[3]; j++)
printf(" %2d", grid_relax_points[3][j]);
printf( "\n\n");
}
else if (relax_order == 1)
{
printf( " Pre-CG relaxation (down):");
for (j = 0; j < num_grid_sweeps[1]; j++)
printf(" %2d %2d", one, minus_one);
printf( "\n");
printf( " Post-CG relaxation (up):");
for (j = 0; j < num_grid_sweeps[2]; j++)
printf(" %2d %2d", minus_one, one);
printf( "\n");
printf( " Coarsest grid:");
for (j = 0; j < num_grid_sweeps[3]; j++)
printf(" %2d", zero);
printf( "\n\n");
}
else
{
printf( " Pre-CG relaxation (down):");
for (j = 0; j < num_grid_sweeps[1]; j++)
printf(" %2d", zero);
printf( "\n");
printf( " Post-CG relaxation (up):");
for (j = 0; j < num_grid_sweeps[2]; j++)
printf(" %2d", zero);
printf( "\n");
printf( " Coarsest grid:");
for (j = 0; j < num_grid_sweeps[3]; j++)
printf(" %2d", zero);
printf( "\n\n");
}
#endif
if (smooth_type == 6)
for (j=0; j < smooth_num_levels; j++)
printf( " Schwarz Relaxation Weight %f level %d\n",
hypre_ParAMGDataSchwarzRlxWeight(amg_data),j);
for (j=0; j < num_levels; j++)
if (relax_weight[j] != 1)
printf( " Relaxation Weight %f level %d\n",relax_weight[j],j);
for (j=0; j < num_levels; j++)
if (omega[j] != 1)
printf( " Outer relaxation weight %f level %d\n",omega[j],j);
}
/*if (seq_cg)
{
hypre_seqAMGSetupStats(amg_data,num_coeffs[0],num_variables[0],
operat_cmplxty, grid_cmplxty );
}*/
hypre_TFree(num_coeffs);
hypre_TFree(num_variables);
hypre_TFree(send_buff);
hypre_TFree(gather_buff);
return(0);
}
int hypre_BoomerAMGWriteSolverParams(void* data)
{
hypre_ParAMGData *amg_data = (hypre_ParAMGData*)data;
/* amg solve params */
int num_levels;
int max_iter;
int cycle_type;
int *num_grid_sweeps;
int *grid_relax_type;
int **grid_relax_points;
int relax_order;
double *relax_weight;
double *omega;
double tol;
int smooth_type;
int smooth_num_levels;
/* amg output params */
int amg_print_level;
int j;
int one = 1;
int minus_one = -1;
int zero = 0;
/*----------------------------------------------------------
* Get the amg_data data
*----------------------------------------------------------*/
num_levels = hypre_ParAMGDataNumLevels(amg_data);
max_iter = hypre_ParAMGDataMaxIter(amg_data);
cycle_type = hypre_ParAMGDataCycleType(amg_data);
num_grid_sweeps = hypre_ParAMGDataNumGridSweeps(amg_data);
grid_relax_type = hypre_ParAMGDataGridRelaxType(amg_data);
grid_relax_points = hypre_ParAMGDataGridRelaxPoints(amg_data);
relax_order = hypre_ParAMGDataRelaxOrder(amg_data);
relax_weight = hypre_ParAMGDataRelaxWeight(amg_data);
omega = hypre_ParAMGDataOmega(amg_data);
smooth_type = hypre_ParAMGDataSmoothType(amg_data);
smooth_num_levels = hypre_ParAMGDataSmoothNumLevels(amg_data);
tol = hypre_ParAMGDataTol(amg_data);
amg_print_level = hypre_ParAMGDataPrintLevel(amg_data);
/*----------------------------------------------------------
* AMG info
*----------------------------------------------------------*/
if (amg_print_level == 1 || amg_print_level == 3)
{
printf("\n\nBoomerAMG SOLVER PARAMETERS:\n\n");
printf( " Maximum number of cycles: %d \n",max_iter);
printf( " Stopping Tolerance: %e \n",tol);
printf( " Cycle type (1 = V, 2 = W, etc.): %d\n\n", cycle_type);
printf( " Relaxation Parameters:\n");
printf( " Visiting Grid: down up coarse\n");
printf( " Visiting Grid: down up coarse\n");
printf( " Number of partial sweeps: %4d %2d %4d \n",
num_grid_sweeps[1],
num_grid_sweeps[2],num_grid_sweeps[3]);
printf( " Type 0=Jac, 3=hGS, 6=hSGS, 9=GE: %4d %2d %4d \n",
grid_relax_type[1],
grid_relax_type[2],grid_relax_type[3]);
printf( " Point types, partial sweeps (1=C, -1=F):\n");
if (grid_relax_points)
{
printf( " Pre-CG relaxation (down):");
for (j = 0; j < num_grid_sweeps[1]; j++)
printf(" %2d", grid_relax_points[1][j]);
printf( "\n");
printf( " Post-CG relaxation (up):");
for (j = 0; j < num_grid_sweeps[2]; j++)
printf(" %2d", grid_relax_points[2][j]);
printf( "\n");
printf( " Coarsest grid:");
for (j = 0; j < num_grid_sweeps[3]; j++)
printf(" %2d", grid_relax_points[3][j]);
printf( "\n\n");
}
else if (relax_order == 1)
{
printf( " Pre-CG relaxation (down):");
for (j = 0; j < num_grid_sweeps[1]; j++)
printf(" %2d %2d", one, minus_one);
printf( "\n");
printf( " Post-CG relaxation (up):");
for (j = 0; j < num_grid_sweeps[2]; j++)
printf(" %2d %2d", minus_one, one);
printf( "\n");
printf( " Coarsest grid:");
for (j = 0; j < num_grid_sweeps[3]; j++)
printf(" %2d", zero);
printf( "\n\n");
}
else
{
printf( " Pre-CG relaxation (down):");
for (j = 0; j < num_grid_sweeps[1]; j++)
printf(" %2d", zero);
printf( "\n");
printf( " Post-CG relaxation (up):");
for (j = 0; j < num_grid_sweeps[2]; j++)
printf(" %2d", zero);
printf( "\n");
printf( " Coarsest grid:");
for (j = 0; j < num_grid_sweeps[3]; j++)
printf(" %2d", zero);
printf( "\n\n");
}
if (smooth_type == 6)
for (j=0; j < smooth_num_levels; j++)
printf( " Schwarz Relaxation Weight %f level %d\n",
hypre_ParAMGDataSchwarzRlxWeight(amg_data),j);
for (j=0; j < num_levels; j++)
if (relax_weight[j] != 1)
printf( " Relaxation Weight %f level %d\n",relax_weight[j],j);
for (j=0; j < num_levels; j++)
if (omega[j] != 1)
printf( " Outer relaxation weight %f level %d\n",omega[j],j);
printf( " Output flag (print_level): %d \n", amg_print_level);
}
return 0;
}
HYPRE_Int
hypre_BoomerAMGSolve( void *amg_vdata,
hypre_ParCSRMatrix *A,
hypre_ParVector *f,
hypre_ParVector *u )
{
MPI_Comm comm = hypre_ParCSRMatrixComm(A);
hypre_ParAMGData *amg_data = amg_vdata;
/* Data Structure variables */
HYPRE_Int amg_print_level;
HYPRE_Int amg_logging;
HYPRE_Int cycle_count;
HYPRE_Int num_levels;
/* HYPRE_Int num_unknowns; */
HYPRE_Real tol;
HYPRE_Int block_mode;
hypre_ParCSRMatrix **A_array;
hypre_ParVector **F_array;
hypre_ParVector **U_array;
hypre_ParCSRBlockMatrix **A_block_array;
/* Local variables */
HYPRE_Int j;
HYPRE_Int Solve_err_flag;
HYPRE_Int min_iter;
HYPRE_Int max_iter;
HYPRE_Int num_procs, my_id;
HYPRE_Int additive;
HYPRE_Int mult_additive;
HYPRE_Int simple;
HYPRE_Real alpha = 1.0;
HYPRE_Real beta = -1.0;
HYPRE_Real cycle_op_count;
HYPRE_Real total_coeffs;
HYPRE_Real total_variables;
HYPRE_Real *num_coeffs;
HYPRE_Real *num_variables;
HYPRE_Real cycle_cmplxty = 0.0;
HYPRE_Real operat_cmplxty;
HYPRE_Real grid_cmplxty;
HYPRE_Real conv_factor = 0.0;
HYPRE_Real resid_nrm = 1.0;
HYPRE_Real resid_nrm_init = 0.0;
HYPRE_Real relative_resid;
HYPRE_Real rhs_norm = 0.0;
HYPRE_Real old_resid;
HYPRE_Real ieee_check = 0.;
hypre_ParVector *Vtemp;
hypre_ParVector *Residual;
hypre_MPI_Comm_size(comm, &num_procs);
hypre_MPI_Comm_rank(comm,&my_id);
amg_print_level = hypre_ParAMGDataPrintLevel(amg_data);
amg_logging = hypre_ParAMGDataLogging(amg_data);
if ( amg_logging > 1 )
Residual = hypre_ParAMGDataResidual(amg_data);
/* num_unknowns = hypre_ParAMGDataNumUnknowns(amg_data); */
num_levels = hypre_ParAMGDataNumLevels(amg_data);
A_array = hypre_ParAMGDataAArray(amg_data);
F_array = hypre_ParAMGDataFArray(amg_data);
U_array = hypre_ParAMGDataUArray(amg_data);
tol = hypre_ParAMGDataTol(amg_data);
min_iter = hypre_ParAMGDataMinIter(amg_data);
max_iter = hypre_ParAMGDataMaxIter(amg_data);
additive = hypre_ParAMGDataAdditive(amg_data);
simple = hypre_ParAMGDataSimple(amg_data);
mult_additive = hypre_ParAMGDataMultAdditive(amg_data);
A_array[0] = A;
F_array[0] = f;
U_array[0] = u;
block_mode = hypre_ParAMGDataBlockMode(amg_data);
A_block_array = hypre_ParAMGDataABlockArray(amg_data);
/* Vtemp = hypre_ParVectorCreate(hypre_ParCSRMatrixComm(A_array[0]),
hypre_ParCSRMatrixGlobalNumRows(A_array[0]),
hypre_ParCSRMatrixRowStarts(A_array[0]));
hypre_ParVectorInitialize(Vtemp);
hypre_ParVectorSetPartitioningOwner(Vtemp,0);
hypre_ParAMGDataVtemp(amg_data) = Vtemp;
*/
Vtemp = hypre_ParAMGDataVtemp(amg_data);
/*-----------------------------------------------------------------------
* Write the solver parameters
*-----------------------------------------------------------------------*/
if (my_id == 0 && amg_print_level > 1)
hypre_BoomerAMGWriteSolverParams(amg_data);
/*-----------------------------------------------------------------------
* Initialize the solver error flag and assorted bookkeeping variables
*-----------------------------------------------------------------------*/
Solve_err_flag = 0;
total_coeffs = 0;
total_variables = 0;
cycle_count = 0;
operat_cmplxty = 0;
grid_cmplxty = 0;
/*-----------------------------------------------------------------------
* write some initial info
*-----------------------------------------------------------------------*/
if (my_id == 0 && amg_print_level > 1 && tol > 0.)
hypre_printf("\n\nAMG SOLUTION INFO:\n");
/*-----------------------------------------------------------------------
* Compute initial fine-grid residual and print
*-----------------------------------------------------------------------*/
if (amg_print_level > 1 || amg_logging > 1)
{
if ( amg_logging > 1 ) {
hypre_ParVectorCopy(F_array[0], Residual );
if (tol > 0)
hypre_ParCSRMatrixMatvec(alpha, A_array[0], U_array[0], beta, Residual );
resid_nrm = sqrt(hypre_ParVectorInnerProd( Residual, Residual ));
}
else {
hypre_ParVectorCopy(F_array[0], Vtemp);
if (tol > 0)
hypre_ParCSRMatrixMatvec(alpha, A_array[0], U_array[0], beta, Vtemp);
resid_nrm = sqrt(hypre_ParVectorInnerProd(Vtemp, Vtemp));
}
/* Since it is does not diminish performance, attempt to return an error flag
and notify users when they supply bad input. */
if (resid_nrm != 0.) ieee_check = resid_nrm/resid_nrm; /* INF -> NaN conversion */
if (ieee_check != ieee_check)
{
/* ...INFs or NaNs in input can make ieee_check a NaN. This test
for ieee_check self-equality works on all IEEE-compliant compilers/
machines, c.f. page 8 of "Lecture Notes on the Status of IEEE 754"
by W. Kahan, May 31, 1996. Currently (July 2002) this paper may be
found at http://HTTP.CS.Berkeley.EDU/~wkahan/ieee754status/IEEE754.PDF */
if (amg_print_level > 0)
{
hypre_printf("\n\nERROR detected by Hypre ... BEGIN\n");
hypre_printf("ERROR -- hypre_BoomerAMGSolve: INFs and/or NaNs detected in input.\n");
hypre_printf("User probably placed non-numerics in supplied A, x_0, or b.\n");
hypre_printf("ERROR detected by Hypre ... END\n\n\n");
}
hypre_error(HYPRE_ERROR_GENERIC);
return hypre_error_flag;
}
resid_nrm_init = resid_nrm;
rhs_norm = sqrt(hypre_ParVectorInnerProd(f, f));
if (rhs_norm)
{
relative_resid = resid_nrm_init / rhs_norm;
}
else
{
relative_resid = resid_nrm_init;
}
}
else
{
relative_resid = 1.;
}
if (my_id == 0 && amg_print_level > 1)
{
hypre_printf(" relative\n");
hypre_printf(" residual factor residual\n");
hypre_printf(" -------- ------ --------\n");
hypre_printf(" Initial %e %e\n",resid_nrm_init,
relative_resid);
}
/*-----------------------------------------------------------------------
* Main V-cycle loop
*-----------------------------------------------------------------------*/
while ((relative_resid >= tol || cycle_count < min_iter)
&& cycle_count < max_iter)
{
hypre_ParAMGDataCycleOpCount(amg_data) = 0;
/* Op count only needed for one cycle */
if ((additive < 0 || additive >= num_levels)
&& (mult_additive < 0 || mult_additive >= num_levels)
&& (simple < 0 || simple >= num_levels) )
hypre_BoomerAMGCycle(amg_data, F_array, U_array);
else
hypre_BoomerAMGAdditiveCycle(amg_data);
/*---------------------------------------------------------------
* Compute fine-grid residual and residual norm
*----------------------------------------------------------------*/
if (amg_print_level > 1 || amg_logging > 1 || tol > 0.)
{
old_resid = resid_nrm;
if ( amg_logging > 1 ) {
hypre_ParCSRMatrixMatvecOutOfPlace(alpha, A_array[0], U_array[0], beta, F_array[0], Residual );
resid_nrm = sqrt(hypre_ParVectorInnerProd( Residual, Residual ));
}
else {
hypre_ParCSRMatrixMatvecOutOfPlace(alpha, A_array[0], U_array[0], beta, F_array[0], Vtemp);
resid_nrm = sqrt(hypre_ParVectorInnerProd(Vtemp, Vtemp));
}
if (old_resid) conv_factor = resid_nrm / old_resid;
else conv_factor = resid_nrm;
if (rhs_norm)
{
relative_resid = resid_nrm / rhs_norm;
}
else
{
relative_resid = resid_nrm;
}
hypre_ParAMGDataRelativeResidualNorm(amg_data) = relative_resid;
}
++cycle_count;
hypre_ParAMGDataNumIterations(amg_data) = cycle_count;
#ifdef CUMNUMIT
++hypre_ParAMGDataCumNumIterations(amg_data);
#endif
if (my_id == 0 && amg_print_level > 1)
{
hypre_printf(" Cycle %2d %e %f %e \n", cycle_count,
resid_nrm, conv_factor, relative_resid);
}
}
if (cycle_count == max_iter && tol > 0.)
{
Solve_err_flag = 1;
hypre_error(HYPRE_ERROR_CONV);
}
/*-----------------------------------------------------------------------
* Compute closing statistics
*-----------------------------------------------------------------------*/
if (cycle_count > 0 && resid_nrm_init)
conv_factor = pow((resid_nrm/resid_nrm_init),(1.0/(HYPRE_Real) cycle_count));
else
conv_factor = 1.;
if (amg_print_level > 1)
{
num_coeffs = hypre_CTAlloc(HYPRE_Real, num_levels);
num_variables = hypre_CTAlloc(HYPRE_Real, num_levels);
num_coeffs[0] = hypre_ParCSRMatrixDNumNonzeros(A);
num_variables[0] = hypre_ParCSRMatrixGlobalNumRows(A);
if (block_mode)
{
for (j = 1; j < num_levels; j++)
{
num_coeffs[j] = (HYPRE_Real) hypre_ParCSRBlockMatrixNumNonzeros(A_block_array[j]);
num_variables[j] = (HYPRE_Real) hypre_ParCSRBlockMatrixGlobalNumRows(A_block_array[j]);
}
num_coeffs[0] = hypre_ParCSRBlockMatrixDNumNonzeros(A_block_array[0]);
num_variables[0] = hypre_ParCSRBlockMatrixGlobalNumRows(A_block_array[0]);
}
else
{
for (j = 1; j < num_levels; j++)
{
num_coeffs[j] = (HYPRE_Real) hypre_ParCSRMatrixNumNonzeros(A_array[j]);
num_variables[j] = (HYPRE_Real) hypre_ParCSRMatrixGlobalNumRows(A_array[j]);
}
}
for (j=0;j<hypre_ParAMGDataNumLevels(amg_data);j++)
{
total_coeffs += num_coeffs[j];
total_variables += num_variables[j];
}
cycle_op_count = hypre_ParAMGDataCycleOpCount(amg_data);
if (num_variables[0])
grid_cmplxty = total_variables / num_variables[0];
if (num_coeffs[0])
{
operat_cmplxty = total_coeffs / num_coeffs[0];
cycle_cmplxty = cycle_op_count / num_coeffs[0];
}
if (my_id == 0)
{
if (Solve_err_flag == 1)
{
hypre_printf("\n\n==============================================");
hypre_printf("\n NOTE: Convergence tolerance was not achieved\n");
hypre_printf(" within the allowed %d V-cycles\n",max_iter);
hypre_printf("==============================================");
}
hypre_printf("\n\n Average Convergence Factor = %f",conv_factor);
hypre_printf("\n\n Complexity: grid = %f\n",grid_cmplxty);
hypre_printf(" operator = %f\n",operat_cmplxty);
hypre_printf(" cycle = %f\n\n\n\n",cycle_cmplxty);
}
hypre_TFree(num_coeffs);
hypre_TFree(num_variables);
}
return hypre_error_flag;
}
