/* Delete any matrix entry C(i,j) for which the corresponding entry P(i,j) doesn't exist -
but only for "fine" rows C(i)<0
This is done as a purely local computation - C and P must have the same data distribution
(among processors).
*/
void hypre_ParCSRMatrixDropEntries( hypre_ParCSRMatrix * C,
hypre_ParCSRMatrix * P, HYPRE_Int * CF_marker )
{
hypre_CSRMatrix *C_diag = hypre_ParCSRMatrixDiag(C);
hypre_CSRMatrix *C_offd = hypre_ParCSRMatrixOffd(C);
double *C_diag_data = hypre_CSRMatrixData(C_diag);
HYPRE_Int *C_diag_i = hypre_CSRMatrixI(C_diag);
HYPRE_Int *C_diag_j = hypre_CSRMatrixJ(C_diag);
double *C_offd_data = hypre_CSRMatrixData(C_offd);
HYPRE_Int *C_offd_i = hypre_CSRMatrixI(C_offd);
HYPRE_Int *C_offd_j = hypre_CSRMatrixJ(C_offd);
hypre_CSRMatrix *P_diag = hypre_ParCSRMatrixDiag(P);
hypre_CSRMatrix *P_offd = hypre_ParCSRMatrixOffd(P);
HYPRE_Int *P_diag_i = hypre_CSRMatrixI(P_diag);
HYPRE_Int *P_diag_j = hypre_CSRMatrixJ(P_diag);
HYPRE_Int *P_offd_i = hypre_CSRMatrixI(P_offd);
HYPRE_Int *P_offd_j = hypre_CSRMatrixJ(P_offd);
HYPRE_Int *new_C_diag_i;
HYPRE_Int *new_C_offd_i;
HYPRE_Int num_rows_diag_C = hypre_CSRMatrixNumRows(C_diag);
HYPRE_Int num_rows_offd_C = hypre_CSRMatrixNumCols(C_offd);
HYPRE_Int num_nonzeros_diag = hypre_CSRMatrixNumNonzeros(C_diag);
HYPRE_Int num_nonzeros_offd = hypre_CSRMatrixNumNonzeros(C_offd);
double vmax = 0.0;
double vmin = 0.0;
double v, old_sum, new_sum, scale;
HYPRE_Int i1, m, m1d, m1o, jC, mP, keep;
/* Repack the i,j,and data arrays of C so as to discard those elements for which
there is no corresponding element in P.
Elements of Coarse rows (CF_marker>=0) are always kept.
The arrays are not re-allocated, so there will generally be unused space
at the ends of the arrays. */
new_C_diag_i = hypre_CTAlloc( HYPRE_Int, num_rows_diag_C+1 );
new_C_offd_i = hypre_CTAlloc( HYPRE_Int, num_rows_offd_C+1 );
m1d = C_diag_i[0];
m1o = C_offd_i[0];
for ( i1 = 0; i1 < num_rows_diag_C; i1++ )
{
old_sum = 0;
new_sum = 0;
for ( m=C_diag_i[i1]; m<C_diag_i[i1+1]; ++m )
{
v = C_diag_data[m];
jC = C_diag_j[m];
old_sum += v;
/* Do we know anything about the order of P_diag_j? It would be better
not to search through it all here. If we know nothing, some ordering or
index scheme will be needed for efficiency (worth doing iff this function
gets called at all ) (may2006: this function is no longer called) */
keep=0;
for ( mP=P_diag_i[i1]; mP<P_diag_i[i1+1]; ++mP )
{
if ( jC==P_diag_j[m] )
{
keep=1;
break;
}
}
if ( CF_marker[i1]>=0 || keep==1 )
{ /* keep v in C */
new_sum += v;
C_diag_j[m1d] = C_diag_j[m];
C_diag_data[m1d] = C_diag_data[m];
++m1d;
}
else
{ /* discard v */
--num_nonzeros_diag;
}
}
for ( m=C_offd_i[i1]; m<C_offd_i[i1+1]; ++m )
{
v = C_offd_data[m];
jC = C_diag_j[m];
old_sum += v;
keep=0;
for ( mP=P_offd_i[i1]; mP<P_offd_i[i1+1]; ++mP )
{
if ( jC==P_offd_j[m] )
{
keep=1;
break;
}
}
if ( CF_marker[i1]>=0 || v>=vmax || v<=vmin )
{ /* keep v in C */
new_sum += v;
C_offd_j[m1o] = C_offd_j[m];
C_offd_data[m1o] = C_offd_data[m];
++m1o;
}
else
{ /* discard v */
--num_nonzeros_offd;
}
}
new_C_diag_i[i1+1] = m1d;
if ( i1<num_rows_offd_C ) new_C_offd_i[i1+1] = m1o;
/* rescale to keep row sum the same */
if (new_sum!=0) scale = old_sum/new_sum; else scale = 1.0;
for ( m=new_C_diag_i[i1]; m<new_C_diag_i[i1+1]; ++m )
C_diag_data[m] *= scale;
if ( i1<num_rows_offd_C ) /* this test fails when there is no offd block */
for ( m=new_C_offd_i[i1]; m<new_C_offd_i[i1+1]; ++m )
C_offd_data[m] *= scale;
}
for ( i1 = 1; i1 <= num_rows_diag_C; i1++ )
{
C_diag_i[i1] = new_C_diag_i[i1];
if ( i1<num_rows_offd_C ) C_offd_i[i1] = new_C_offd_i[i1];
}
hypre_TFree( new_C_diag_i );
if ( num_rows_offd_C>0 ) hypre_TFree( new_C_offd_i );
hypre_CSRMatrixNumNonzeros(C_diag) = num_nonzeros_diag;
hypre_CSRMatrixNumNonzeros(C_offd) = num_nonzeros_offd;
/* SetNumNonzeros, SetDNumNonzeros are global, need hypre_MPI_Allreduce.
I suspect, but don't know, that other parts of hypre do not assume that
the correct values have been set.
hypre_ParCSRMatrixSetNumNonzeros( C );
hypre_ParCSRMatrixSetDNumNonzeros( C );*/
hypre_ParCSRMatrixNumNonzeros( C ) = 0;
hypre_ParCSRMatrixDNumNonzeros( C ) = 0.0;
}
HYPRE_Int
hypre_BoomerAMGCycleT( void *amg_vdata,
hypre_ParVector **F_array,
hypre_ParVector **U_array )
{
hypre_ParAMGData *amg_data = amg_vdata;
/* Data Structure variables */
hypre_ParCSRMatrix **A_array;
hypre_ParCSRMatrix **P_array;
hypre_ParCSRMatrix **R_array;
hypre_ParVector *Vtemp;
HYPRE_Int **CF_marker_array;
/* HYPRE_Int **unknown_map_array; */
/* HYPRE_Int **point_map_array; */
/* HYPRE_Int **v_at_point_array; */
HYPRE_Real cycle_op_count;
HYPRE_Int cycle_type;
HYPRE_Int num_levels;
HYPRE_Int max_levels;
HYPRE_Real *num_coeffs;
HYPRE_Int *num_grid_sweeps;
HYPRE_Int *grid_relax_type;
HYPRE_Int **grid_relax_points;
/* Local variables */
HYPRE_Int *lev_counter;
HYPRE_Int Solve_err_flag;
HYPRE_Int k;
HYPRE_Int j;
HYPRE_Int level;
HYPRE_Int cycle_param;
HYPRE_Int coarse_grid;
HYPRE_Int fine_grid;
HYPRE_Int Not_Finished;
HYPRE_Int num_sweep;
HYPRE_Int relax_type;
HYPRE_Int relax_points;
HYPRE_Real *relax_weight;
HYPRE_Int relax_local;
HYPRE_Int relax_order;
HYPRE_Int old_version = 0;
HYPRE_Real alpha;
HYPRE_Real beta;
#if 0
HYPRE_Real *D_mat;
HYPRE_Real *S_vec;
#endif
/* Acquire data and allocate storage */
A_array = hypre_ParAMGDataAArray(amg_data);
P_array = hypre_ParAMGDataPArray(amg_data);
R_array = hypre_ParAMGDataRArray(amg_data);
CF_marker_array = hypre_ParAMGDataCFMarkerArray(amg_data);
/* unknown_map_array = hypre_ParAMGDataUnknownMapArray(amg_data); */
/* point_map_array = hypre_ParAMGDataPointMapArray(amg_data); */
/* v_at_point_array = hypre_ParAMGDataVatPointArray(amg_data); */
Vtemp = hypre_ParAMGDataVtemp(amg_data);
num_levels = hypre_ParAMGDataNumLevels(amg_data);
max_levels = hypre_ParAMGDataMaxLevels(amg_data);
cycle_type = hypre_ParAMGDataCycleType(amg_data);
/* num_unknowns = hypre_ParCSRMatrixNumRows(A_array[0]); */
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);
cycle_op_count = hypre_ParAMGDataCycleOpCount(amg_data);
lev_counter = hypre_CTAlloc(HYPRE_Int, num_levels);
/* Initialize */
Solve_err_flag = 0;
if (grid_relax_points) old_version = 1;
num_coeffs = hypre_CTAlloc(HYPRE_Real, num_levels);
num_coeffs[0] = hypre_ParCSRMatrixDNumNonzeros(A_array[0]);
for (j = 1; j < num_levels; j++)
num_coeffs[j] = hypre_ParCSRMatrixDNumNonzeros(A_array[j]);
/*---------------------------------------------------------------------
* Initialize cycling control counter
*
* Cycling is controlled using a level counter: lev_counter[k]
*
* Each time relaxation is performed on level k, the
* counter is decremented by 1. If the counter is then
* negative, we go to the next finer level. If non-
* negative, we go to the next coarser level. The
* following actions control cycling:
*
* a. lev_counter[0] is initialized to 1.
* b. lev_counter[k] is initialized to cycle_type for k>0.
*
* c. During cycling, when going down to level k, lev_counter[k]
* is set to the max of (lev_counter[k],cycle_type)
*---------------------------------------------------------------------*/
Not_Finished = 1;
lev_counter[0] = 1;
for (k = 1; k < num_levels; ++k)
{
lev_counter[k] = cycle_type;
}
level = 0;
cycle_param = 0;
/*---------------------------------------------------------------------
* Main loop of cycling
*--------------------------------------------------------------------*/
while (Not_Finished)
{
num_sweep = num_grid_sweeps[cycle_param];
relax_type = grid_relax_type[cycle_param];
if (relax_type != 7 && relax_type != 9) relax_type = 7;
/*------------------------------------------------------------------
* Do the relaxation num_sweep times
*-----------------------------------------------------------------*/
for (j = 0; j < num_sweep; j++)
{
if (num_levels == 1 && max_levels > 1)
{
relax_points = 0;
relax_local = 0;
}
else
{
if (old_version)
relax_points = grid_relax_points[cycle_param][j];
relax_local = relax_order;
}
/*-----------------------------------------------
* VERY sloppy approximation to cycle complexity
*-----------------------------------------------*/
if (old_version && level < num_levels -1)
{
switch (relax_points)
{
case 1:
cycle_op_count += num_coeffs[level+1];
break;
case -1:
cycle_op_count += (num_coeffs[level]-num_coeffs[level+1]);
break;
}
}
else
{
cycle_op_count += num_coeffs[level];
}
/* note: this does not use relax_points, so it doesn't matter if
its the "old version" */
Solve_err_flag = hypre_BoomerAMGRelaxT(A_array[level],
F_array[level],
CF_marker_array[level],
relax_type,
relax_points,
relax_weight[level],
U_array[level],
Vtemp);
if (Solve_err_flag != 0)
{
hypre_TFree(lev_counter);
hypre_TFree(num_coeffs);
return(Solve_err_flag);
}
}
/*------------------------------------------------------------------
* Decrement the control counter and determine which grid to visit next
*-----------------------------------------------------------------*/
--lev_counter[level];
if (lev_counter[level] >= 0 && level != num_levels-1)
{
/*---------------------------------------------------------------
* Visit coarser level next. Compute residual using hypre_ParCSRMatrixMatvec.
* Use interpolation (since transpose i.e. P^TATR instead of
* RAP) using hypre_ParCSRMatrixMatvecT.
* Reset counters and cycling parameters for coarse level
*--------------------------------------------------------------*/
fine_grid = level;
coarse_grid = level + 1;
hypre_ParVectorSetConstantValues(U_array[coarse_grid], 0.0);
hypre_ParVectorCopy(F_array[fine_grid],Vtemp);
alpha = -1.0;
beta = 1.0;
hypre_ParCSRMatrixMatvecT(alpha, A_array[fine_grid], U_array[fine_grid],
beta, Vtemp);
alpha = 1.0;
beta = 0.0;
hypre_ParCSRMatrixMatvecT(alpha,P_array[fine_grid],Vtemp,
beta,F_array[coarse_grid]);
++level;
lev_counter[level] = hypre_max(lev_counter[level],cycle_type);
cycle_param = 1;
if (level == num_levels-1) cycle_param = 3;
}
else if (level != 0)
{
/*---------------------------------------------------------------
* Visit finer level next.
* Use restriction (since transpose i.e. P^TA^TR instead of RAP)
* and add correction using hypre_ParCSRMatrixMatvec.
* Reset counters and cycling parameters for finer level.
*--------------------------------------------------------------*/
fine_grid = level - 1;
coarse_grid = level;
alpha = 1.0;
beta = 1.0;
hypre_ParCSRMatrixMatvec(alpha, R_array[fine_grid], U_array[coarse_grid],
beta, U_array[fine_grid]);
--level;
cycle_param = 2;
if (level == 0) cycle_param = 0;
}
else
{
Not_Finished = 0;
}
}
hypre_ParAMGDataCycleOpCount(amg_data) = cycle_op_count;
hypre_TFree(lev_counter);
hypre_TFree(num_coeffs);
return(Solve_err_flag);
}
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);
}
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;
}
HYPRE_Int
hypre_BoomerAMGCycle( void *amg_vdata,
hypre_ParVector **F_array,
hypre_ParVector **U_array )
{
hypre_ParAMGData *amg_data = amg_vdata;
HYPRE_Solver *smoother;
/* Data Structure variables */
hypre_ParCSRMatrix **A_array;
hypre_ParCSRMatrix **P_array;
hypre_ParCSRMatrix **R_array;
hypre_ParVector *Utemp;
hypre_ParVector *Vtemp;
hypre_ParVector *Rtemp;
hypre_ParVector *Ptemp;
hypre_ParVector *Ztemp;
hypre_ParVector *Aux_U;
hypre_ParVector *Aux_F;
hypre_ParCSRBlockMatrix **A_block_array;
hypre_ParCSRBlockMatrix **P_block_array;
hypre_ParCSRBlockMatrix **R_block_array;
HYPRE_Real *Ztemp_data;
HYPRE_Real *Ptemp_data;
HYPRE_Int **CF_marker_array;
/* HYPRE_Int **unknown_map_array;
HYPRE_Int **point_map_array;
HYPRE_Int **v_at_point_array; */
HYPRE_Real cycle_op_count;
HYPRE_Int cycle_type;
HYPRE_Int num_levels;
HYPRE_Int max_levels;
HYPRE_Real *num_coeffs;
HYPRE_Int *num_grid_sweeps;
HYPRE_Int *grid_relax_type;
HYPRE_Int **grid_relax_points;
HYPRE_Int block_mode;
HYPRE_Real *max_eig_est;
HYPRE_Real *min_eig_est;
HYPRE_Int cheby_order;
HYPRE_Real cheby_fraction;
/* Local variables */
HYPRE_Int *lev_counter;
HYPRE_Int Solve_err_flag;
HYPRE_Int k;
HYPRE_Int i, j, jj;
HYPRE_Int level;
HYPRE_Int cycle_param;
HYPRE_Int coarse_grid;
HYPRE_Int fine_grid;
HYPRE_Int Not_Finished;
HYPRE_Int num_sweep;
HYPRE_Int cg_num_sweep = 1;
HYPRE_Int relax_type;
HYPRE_Int relax_points;
HYPRE_Int relax_order;
HYPRE_Int relax_local;
HYPRE_Int old_version = 0;
HYPRE_Real *relax_weight;
HYPRE_Real *omega;
HYPRE_Real alfa, beta, gammaold;
HYPRE_Real gamma = 1.0;
HYPRE_Int local_size;
/* HYPRE_Int *smooth_option; */
HYPRE_Int smooth_type;
HYPRE_Int smooth_num_levels;
HYPRE_Int num_threads, my_id;
HYPRE_Real alpha;
HYPRE_Real **l1_norms = NULL;
HYPRE_Real *l1_norms_level;
HYPRE_Int seq_cg = 0;
MPI_Comm comm;
#if 0
HYPRE_Real *D_mat;
HYPRE_Real *S_vec;
#endif
/* Acquire data and allocate storage */
num_threads = hypre_NumThreads();
A_array = hypre_ParAMGDataAArray(amg_data);
P_array = hypre_ParAMGDataPArray(amg_data);
R_array = hypre_ParAMGDataRArray(amg_data);
CF_marker_array = hypre_ParAMGDataCFMarkerArray(amg_data);
Vtemp = hypre_ParAMGDataVtemp(amg_data);
Rtemp = hypre_ParAMGDataRtemp(amg_data);
Ptemp = hypre_ParAMGDataPtemp(amg_data);
Ztemp = hypre_ParAMGDataZtemp(amg_data);
num_levels = hypre_ParAMGDataNumLevels(amg_data);
max_levels = hypre_ParAMGDataMaxLevels(amg_data);
cycle_type = hypre_ParAMGDataCycleType(amg_data);
A_block_array = hypre_ParAMGDataABlockArray(amg_data);
P_block_array = hypre_ParAMGDataPBlockArray(amg_data);
R_block_array = hypre_ParAMGDataRBlockArray(amg_data);
block_mode = hypre_ParAMGDataBlockMode(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);
l1_norms = hypre_ParAMGDataL1Norms(amg_data);
/* smooth_option = hypre_ParAMGDataSmoothOption(amg_data); */
max_eig_est = hypre_ParAMGDataMaxEigEst(amg_data);
min_eig_est = hypre_ParAMGDataMinEigEst(amg_data);
cheby_order = hypre_ParAMGDataChebyOrder(amg_data);
cheby_fraction = hypre_ParAMGDataChebyFraction(amg_data);
cycle_op_count = hypre_ParAMGDataCycleOpCount(amg_data);
lev_counter = hypre_CTAlloc(HYPRE_Int, num_levels);
if (hypre_ParAMGDataParticipate(amg_data)) seq_cg = 1;
/* Initialize */
Solve_err_flag = 0;
if (grid_relax_points) old_version = 1;
num_coeffs = hypre_CTAlloc(HYPRE_Real, num_levels);
num_coeffs[0] = hypre_ParCSRMatrixDNumNonzeros(A_array[0]);
comm = hypre_ParCSRMatrixComm(A_array[0]);
hypre_MPI_Comm_rank(comm,&my_id);
if (block_mode)
{
for (j = 1; j < num_levels; j++)
num_coeffs[j] = hypre_ParCSRBlockMatrixNumNonzeros(A_block_array[j]);
}
else
{
for (j = 1; j < num_levels; j++)
num_coeffs[j] = hypre_ParCSRMatrixDNumNonzeros(A_array[j]);
}
/*---------------------------------------------------------------------
* Initialize cycling control counter
*
* Cycling is controlled using a level counter: lev_counter[k]
*
* Each time relaxation is performed on level k, the
* counter is decremented by 1. If the counter is then
* negative, we go to the next finer level. If non-
* negative, we go to the next coarser level. The
* following actions control cycling:
*
* a. lev_counter[0] is initialized to 1.
* b. lev_counter[k] is initialized to cycle_type for k>0.
*
* c. During cycling, when going down to level k, lev_counter[k]
* is set to the max of (lev_counter[k],cycle_type)
*---------------------------------------------------------------------*/
Not_Finished = 1;
lev_counter[0] = 1;
for (k = 1; k < num_levels; ++k)
{
lev_counter[k] = cycle_type;
}
level = 0;
cycle_param = 1;
smoother = hypre_ParAMGDataSmoother(amg_data);
if (smooth_num_levels > 0)
{
if (smooth_type == 7 || smooth_type == 8
|| smooth_type == 17 || smooth_type == 18
|| smooth_type == 9 || smooth_type == 19)
{
HYPRE_Int actual_local_size = hypre_ParVectorActualLocalSize(Vtemp);
Utemp = hypre_ParVectorCreate(comm,hypre_ParVectorGlobalSize(Vtemp),
hypre_ParVectorPartitioning(Vtemp));
hypre_ParVectorOwnsPartitioning(Utemp) = 0;
local_size
= hypre_VectorSize(hypre_ParVectorLocalVector(Vtemp));
if (local_size < actual_local_size)
{
hypre_VectorData(hypre_ParVectorLocalVector(Utemp)) =
hypre_CTAlloc(HYPRE_Complex, actual_local_size);
hypre_ParVectorActualLocalSize(Utemp) = actual_local_size;
}
else
hypre_ParVectorInitialize(Utemp);
}
}
/*---------------------------------------------------------------------
* Main loop of cycling
*--------------------------------------------------------------------*/
while (Not_Finished)
{
if (num_levels > 1)
{
local_size
= hypre_VectorSize(hypre_ParVectorLocalVector(F_array[level]));
hypre_VectorSize(hypre_ParVectorLocalVector(Vtemp)) = local_size;
if (smooth_num_levels <= level)
{
cg_num_sweep = 1;
num_sweep = num_grid_sweeps[cycle_param];
Aux_U = U_array[level];
Aux_F = F_array[level];
}
else if (smooth_type > 9)
{
hypre_VectorSize(hypre_ParVectorLocalVector(Ztemp)) = local_size;
hypre_VectorSize(hypre_ParVectorLocalVector(Rtemp)) = local_size;
hypre_VectorSize(hypre_ParVectorLocalVector(Ptemp)) = local_size;
Ztemp_data = hypre_VectorData(hypre_ParVectorLocalVector(Ztemp));
Ptemp_data = hypre_VectorData(hypre_ParVectorLocalVector(Ptemp));
hypre_ParVectorSetConstantValues(Ztemp,0);
alpha = -1.0;
beta = 1.0;
hypre_ParCSRMatrixMatvecOutOfPlace(alpha, A_array[level],
U_array[level], beta, F_array[level], Rtemp);
cg_num_sweep = hypre_ParAMGDataSmoothNumSweeps(amg_data);
num_sweep = num_grid_sweeps[cycle_param];
Aux_U = Ztemp;
Aux_F = Rtemp;
}
else
{
cg_num_sweep = 1;
num_sweep = hypre_ParAMGDataSmoothNumSweeps(amg_data);
Aux_U = U_array[level];
Aux_F = F_array[level];
}
relax_type = grid_relax_type[cycle_param];
}
else /* AB: 4/08: removed the max_levels > 1 check - should do this when max-levels = 1 also */
{
/* If no coarsening occurred, apply a simple smoother once */
Aux_U = U_array[level];
Aux_F = F_array[level];
num_sweep = 1;
/* TK: Use the user relax type (instead of 0) to allow for setting a
convergent smoother (e.g. in the solution of singular problems). */
relax_type = hypre_ParAMGDataUserRelaxType(amg_data);
}
if (l1_norms != NULL)
l1_norms_level = l1_norms[level];
else
l1_norms_level = NULL;
if (cycle_param == 3 && seq_cg)
{
hypre_seqAMGCycle(amg_data, level, F_array, U_array);
}
else
{
/*------------------------------------------------------------------
* Do the relaxation num_sweep times
*-----------------------------------------------------------------*/
for (jj = 0; jj < cg_num_sweep; jj++)
{
if (smooth_num_levels > level && smooth_type > 9)
hypre_ParVectorSetConstantValues(Aux_U,0);
for (j = 0; j < num_sweep; j++)
{
if (num_levels == 1 && max_levels > 1)
{
relax_points = 0;
relax_local = 0;
}
else
{
if (old_version)
relax_points = grid_relax_points[cycle_param][j];
relax_local = relax_order;
}
/*-----------------------------------------------
* VERY sloppy approximation to cycle complexity
*-----------------------------------------------*/
if (old_version && level < num_levels -1)
{
switch (relax_points)
{
case 1:
cycle_op_count += num_coeffs[level+1];
break;
case -1:
cycle_op_count += (num_coeffs[level]-num_coeffs[level+1]);
break;
}
}
else
{
cycle_op_count += num_coeffs[level];
}
/*-----------------------------------------------
Choose Smoother
-----------------------------------------------*/
if (smooth_num_levels > level &&
(smooth_type == 7 || smooth_type == 8 ||
smooth_type == 9 || smooth_type == 19 ||
smooth_type == 17 || smooth_type == 18))
{
hypre_VectorSize(hypre_ParVectorLocalVector(Utemp)) = local_size;
alpha = -1.0;
beta = 1.0;
hypre_ParCSRMatrixMatvecOutOfPlace(alpha, A_array[level],
U_array[level], beta, Aux_F, Vtemp);
if (smooth_type == 8 || smooth_type == 18)
HYPRE_ParCSRParaSailsSolve(smoother[level],
(HYPRE_ParCSRMatrix) A_array[level],
(HYPRE_ParVector) Vtemp,
(HYPRE_ParVector) Utemp);
else if (smooth_type == 7 || smooth_type == 17)
HYPRE_ParCSRPilutSolve(smoother[level],
(HYPRE_ParCSRMatrix) A_array[level],
(HYPRE_ParVector) Vtemp,
(HYPRE_ParVector) Utemp);
else if (smooth_type == 9 || smooth_type == 19)
HYPRE_EuclidSolve(smoother[level],
(HYPRE_ParCSRMatrix) A_array[level],
(HYPRE_ParVector) Vtemp,
(HYPRE_ParVector) Utemp);
hypre_ParVectorAxpy(relax_weight[level],Utemp,Aux_U);
}
else if (smooth_num_levels > level &&
(smooth_type == 6 || smooth_type == 16))
{
HYPRE_SchwarzSolve(smoother[level],
(HYPRE_ParCSRMatrix) A_array[level],
(HYPRE_ParVector) Aux_F,
(HYPRE_ParVector) Aux_U);
}
/*else if (relax_type == 99)*/
else if (relax_type == 9 || relax_type == 99)
{ /* Gaussian elimination */
hypre_GaussElimSolve(amg_data, level, relax_type);
}
else if (relax_type == 18)
{ /* L1 - Jacobi*/
if (relax_order == 1 && cycle_param < 3)
{
/* need to do CF - so can't use the AMS one */
HYPRE_Int i;
HYPRE_Int loc_relax_points[2];
if (cycle_type < 2)
{
loc_relax_points[0] = 1;
loc_relax_points[1] = -1;
}
else
{
loc_relax_points[0] = -1;
loc_relax_points[1] = 1;
}
for (i=0; i < 2; i++)
hypre_ParCSRRelax_L1_Jacobi(A_array[level],
Aux_F,
CF_marker_array[level],
loc_relax_points[i],
relax_weight[level],
l1_norms[level],
Aux_U,
Vtemp);
}
else /* not CF - so use through AMS */
{
if (num_threads == 1)
hypre_ParCSRRelax(A_array[level],
Aux_F,
1,
1,
l1_norms_level,
relax_weight[level],
omega[level],0,0,0,0,
Aux_U,
Vtemp,
Ztemp);
else
hypre_ParCSRRelaxThreads(A_array[level],
Aux_F,
1,
1,
l1_norms_level,
relax_weight[level],
omega[level],
Aux_U,
Vtemp,
Ztemp);
}
}
else if (relax_type == 15)
{ /* CG */
if (j ==0) /* do num sweep iterations of CG */
hypre_ParCSRRelax_CG( smoother[level],
A_array[level],
Aux_F,
Aux_U,
num_sweep);
}
else if (relax_type == 16)
{ /* scaled Chebyshev */
HYPRE_Int scale = 1;
HYPRE_Int variant = 0;
hypre_ParCSRRelax_Cheby(A_array[level],
Aux_F,
max_eig_est[level],
min_eig_est[level],
cheby_fraction, cheby_order, scale,
variant, Aux_U, Vtemp, Ztemp );
}
else if (relax_type ==17)
{
hypre_BoomerAMGRelax_FCFJacobi(A_array[level],
Aux_F,
CF_marker_array[level],
relax_weight[level],
Aux_U,
Vtemp);
}
else if (old_version)
{
Solve_err_flag = hypre_BoomerAMGRelax(A_array[level],
Aux_F,
CF_marker_array[level],
relax_type, relax_points,
relax_weight[level],
omega[level],
l1_norms_level,
Aux_U,
Vtemp,
Ztemp);
}
else
{
/* smoother than can have CF ordering */
if (block_mode)
{
Solve_err_flag = hypre_BoomerAMGBlockRelaxIF(A_block_array[level],
Aux_F,
CF_marker_array[level],
relax_type,
relax_local,
cycle_param,
relax_weight[level],
omega[level],
Aux_U,
Vtemp);
}
else
{
Solve_err_flag = hypre_BoomerAMGRelaxIF(A_array[level],
Aux_F,
CF_marker_array[level],
relax_type,
relax_local,
cycle_param,
relax_weight[level],
omega[level],
l1_norms_level,
Aux_U,
Vtemp,
Ztemp);
}
}
if (Solve_err_flag != 0)
return(Solve_err_flag);
}
if (smooth_num_levels > level && smooth_type > 9)
{
gammaold = gamma;
gamma = hypre_ParVectorInnerProd(Rtemp,Ztemp);
if (jj == 0)
hypre_ParVectorCopy(Ztemp,Ptemp);
else
{
beta = gamma/gammaold;
for (i=0; i < local_size; i++)
Ptemp_data[i] = Ztemp_data[i] + beta*Ptemp_data[i];
}
hypre_ParCSRMatrixMatvec(1.0,A_array[level],Ptemp,0.0,Vtemp);
alfa = gamma /hypre_ParVectorInnerProd(Ptemp,Vtemp);
hypre_ParVectorAxpy(alfa,Ptemp,U_array[level]);
hypre_ParVectorAxpy(-alfa,Vtemp,Rtemp);
}
}
}
/*------------------------------------------------------------------
* Decrement the control counter and determine which grid to visit next
*-----------------------------------------------------------------*/
--lev_counter[level];
if (lev_counter[level] >= 0 && level != num_levels-1)
{
/*---------------------------------------------------------------
* Visit coarser level next.
* Compute residual using hypre_ParCSRMatrixMatvec.
* Perform restriction using hypre_ParCSRMatrixMatvecT.
* Reset counters and cycling parameters for coarse level
*--------------------------------------------------------------*/
fine_grid = level;
coarse_grid = level + 1;
hypre_ParVectorSetConstantValues(U_array[coarse_grid], 0.0);
alpha = -1.0;
beta = 1.0;
if (block_mode)
{
hypre_ParVectorCopy(F_array[fine_grid],Vtemp);
hypre_ParCSRBlockMatrixMatvec(alpha, A_block_array[fine_grid], U_array[fine_grid],
beta, Vtemp);
}
else
{
// JSP: avoid unnecessary copy using out-of-place version of SpMV
hypre_ParCSRMatrixMatvecOutOfPlace(alpha, A_array[fine_grid], U_array[fine_grid],
beta, F_array[fine_grid], Vtemp);
}
alpha = 1.0;
beta = 0.0;
if (block_mode)
{
hypre_ParCSRBlockMatrixMatvecT(alpha,R_block_array[fine_grid],Vtemp,
beta,F_array[coarse_grid]);
}
else
{
hypre_ParCSRMatrixMatvecT(alpha,R_array[fine_grid],Vtemp,
beta,F_array[coarse_grid]);
}
++level;
lev_counter[level] = hypre_max(lev_counter[level],cycle_type);
cycle_param = 1;
if (level == num_levels-1) cycle_param = 3;
}
else if (level != 0)
{
/*---------------------------------------------------------------
* Visit finer level next.
* Interpolate and add correction using hypre_ParCSRMatrixMatvec.
* Reset counters and cycling parameters for finer level.
*--------------------------------------------------------------*/
fine_grid = level - 1;
coarse_grid = level;
alpha = 1.0;
beta = 1.0;
if (block_mode)
{
hypre_ParCSRBlockMatrixMatvec(alpha, P_block_array[fine_grid],
U_array[coarse_grid],
beta, U_array[fine_grid]);
}
else
{
hypre_ParCSRMatrixMatvec(alpha, P_array[fine_grid],
U_array[coarse_grid],
beta, U_array[fine_grid]);
}
--level;
cycle_param = 2;
}
else
{
Not_Finished = 0;
}
}
hypre_ParAMGDataCycleOpCount(amg_data) = cycle_op_count;
hypre_TFree(lev_counter);
hypre_TFree(num_coeffs);
if (smooth_num_levels > 0)
{
if (smooth_type == 7 || smooth_type == 8 || smooth_type == 9 ||
smooth_type == 17 || smooth_type == 18 || smooth_type == 19 )
hypre_ParVectorDestroy(Utemp);
}
return(Solve_err_flag);
}