Date: 2022-03-04T17:38:52+00:00

Actual source code: ml.c

      2: /*
      3:    Provides an interface to the ML smoothed Aggregation
      4:    Note: Something non-obvious breaks -pc_mg_type ADDITIVE for parallel runs
      5:                                     Jed Brown, see [PETSC #18321, #18449].
      6: */
      7: #include <petsc/private/pcimpl.h>
      8: #include <petsc/private/pcmgimpl.h>
      9: #include <../src/mat/impls/aij/seq/aij.h>
     10: #include <../src/mat/impls/aij/mpi/mpiaij.h>
     11: #include <petscdm.h>

     13: EXTERN_C_BEGIN
     14: /* HAVE_CONFIG_H flag is required by ML include files */
     15: #if !defined(HAVE_CONFIG_H)
     16: #define HAVE_CONFIG_H
     17: #endif
     18: #include <ml_include.h>
     19: #include <ml_viz_stats.h>
     20: EXTERN_C_END

     22: typedef enum {PCML_NULLSPACE_AUTO,PCML_NULLSPACE_USER,PCML_NULLSPACE_BLOCK,PCML_NULLSPACE_SCALAR} PCMLNullSpaceType;
     23: static const char *const PCMLNullSpaceTypes[] = {"AUTO","USER","BLOCK","SCALAR","PCMLNullSpaceType","PCML_NULLSPACE_",0};

     25: /* The context (data structure) at each grid level */
     26: typedef struct {
     27:   Vec x,b,r;                  /* global vectors */
     28:   Mat A,P,R;
     29:   KSP ksp;
     30:   Vec coords;                 /* projected by ML, if PCSetCoordinates is called; values packed by node */
     31: } GridCtx;

     33: /* The context used to input PETSc matrix into ML at fine grid */
     34: typedef struct {
     35:   Mat         A;       /* Petsc matrix in aij format */
     36:   Mat         Aloc;    /* local portion of A to be used by ML */
     37:   Vec         x,y;
     38:   ML_Operator *mlmat;
     39:   PetscScalar *pwork;  /* tmp array used by PetscML_comm() */
     40: } FineGridCtx;

     42: /* The context associates a ML matrix with a PETSc shell matrix */
     43: typedef struct {
     44:   Mat         A;        /* PETSc shell matrix associated with mlmat */
     45:   ML_Operator *mlmat;   /* ML matrix assorciated with A */
     46: } Mat_MLShell;

     48: /* Private context for the ML preconditioner */
     49: typedef struct {
     50:   ML                *ml_object;
     51:   ML_Aggregate      *agg_object;
     52:   GridCtx           *gridctx;
     53:   FineGridCtx       *PetscMLdata;
     54:   PetscInt          Nlevels,MaxNlevels,MaxCoarseSize,CoarsenScheme,EnergyMinimization,MinPerProc,PutOnSingleProc,RepartitionType,ZoltanScheme;
     55:   PetscReal         Threshold,DampingFactor,EnergyMinimizationDropTol,MaxMinRatio,AuxThreshold;
     56:   PetscBool         SpectralNormScheme_Anorm,BlockScaling,EnergyMinimizationCheap,Symmetrize,OldHierarchy,KeepAggInfo,Reusable,Repartition,Aux;
     57:   PetscBool         reuse_interpolation;
     58:   PCMLNullSpaceType nulltype;
     59:   PetscMPIInt       size; /* size of communicator for pc->pmat */
     60:   PetscInt          dim;  /* data from PCSetCoordinates(_ML) */
     61:   PetscInt          nloc;
     62:   PetscReal         *coords; /* ML has a grid object for each level: the finest grid will point into coords */
     63: } PC_ML;

     65: static int PetscML_getrow(ML_Operator *ML_data, int N_requested_rows, int requested_rows[],int allocated_space, int columns[], double values[], int row_lengths[])
     66: {
     68:   PetscInt       m,i,j,k=0,row,*aj;
     69:   PetscScalar    *aa;
     70:   FineGridCtx    *ml=(FineGridCtx*)ML_Get_MyGetrowData(ML_data);
     71:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)ml->Aloc->data;

     73:   MatGetSize(ml->Aloc,&m,NULL); if (ierr) return(0);
     74:   for (i = 0; i<N_requested_rows; i++) {
     75:     row            = requested_rows[i];
     76:     row_lengths[i] = a->ilen[row];
     77:     if (allocated_space < k+row_lengths[i]) return(0);
     78:     if ((row >= 0) || (row <= (m-1))) {
     79:       aj = a->j + a->i[row];
     80:       aa = a->a + a->i[row];
     81:       for (j=0; j<row_lengths[i]; j++) {
     82:         columns[k]  = aj[j];
     83:         values[k++] = aa[j];
     84:       }
     85:     }
     86:   }
     87:   return(1);
     88: }

     90: static PetscErrorCode PetscML_comm(double p[],void *ML_data)
     91: {
     92:   PetscErrorCode    ierr;
     93:   FineGridCtx       *ml = (FineGridCtx*)ML_data;
     94:   Mat               A   = ml->A;
     95:   Mat_MPIAIJ        *a  = (Mat_MPIAIJ*)A->data;
     96:   PetscMPIInt       size;
     97:   PetscInt          i,in_length=A->rmap->n,out_length=ml->Aloc->cmap->n;
     98:   const PetscScalar *array;

    101:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
    102:   if (size == 1) return(0);

    104:   VecPlaceArray(ml->y,p);
    105:   VecScatterBegin(a->Mvctx,ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    106:   VecScatterEnd(a->Mvctx,ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    107:   VecResetArray(ml->y);
    108:   VecGetArrayRead(a->lvec,&array);
    109:   for (i=in_length; i<out_length; i++) p[i] = array[i-in_length];
    110:   VecRestoreArrayRead(a->lvec,&array);
    111:   return(0);
    112: }

    114: static int PetscML_matvec(ML_Operator *ML_data,int in_length,double p[],int out_length,double ap[])
    115: {
    117:   FineGridCtx    *ml = (FineGridCtx*)ML_Get_MyMatvecData(ML_data);
    118:   Mat            A   = ml->A, Aloc=ml->Aloc;
    119:   PetscMPIInt    size;
    120:   PetscScalar    *pwork=ml->pwork;
    121:   PetscInt       i;

    124:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
    125:   if (size == 1) {
    126:     VecPlaceArray(ml->x,p);
    127:   } else {
    128:     for (i=0; i<in_length; i++) pwork[i] = p[i];
    129:     PetscML_comm(pwork,ml);
    130:     VecPlaceArray(ml->x,pwork);
    131:   }
    132:   VecPlaceArray(ml->y,ap);
    133:   MatMult(Aloc,ml->x,ml->y);
    134:   VecResetArray(ml->x);
    135:   VecResetArray(ml->y);
    136:   return(0);
    137: }

    139: static PetscErrorCode MatMult_ML(Mat A,Vec x,Vec y)
    140: {
    141:   PetscErrorCode    ierr;
    142:   Mat_MLShell       *shell;
    143:   PetscScalar       *yarray;
    144:   const PetscScalar *xarray;
    145:   PetscInt          x_length,y_length;

    148:   MatShellGetContext(A,&shell);
    149:   VecGetArrayRead(x,&xarray);
    150:   VecGetArray(y,&yarray);
    151:   x_length = shell->mlmat->invec_leng;
    152:   y_length = shell->mlmat->outvec_leng;
    153:   PetscStackCall("ML_Operator_Apply",ML_Operator_Apply(shell->mlmat,x_length,(PetscScalar*)xarray,y_length,yarray));
    154:   VecRestoreArrayRead(x,&xarray);
    155:   VecRestoreArray(y,&yarray);
    156:   return(0);
    157: }

    159: /* newtype is ignored since only handles one case */
    160: static PetscErrorCode MatConvert_MPIAIJ_ML(Mat A,MatType newtype,MatReuse scall,Mat *Aloc)
    161: {
    163:   Mat_MPIAIJ     *mpimat=(Mat_MPIAIJ*)A->data;
    164:   Mat_SeqAIJ     *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
    165:   PetscInt       *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
    166:   PetscScalar    *aa,*ba,*ca;
    167:   PetscInt       am =A->rmap->n,an=A->cmap->n,i,j,k;
    168:   PetscInt       *ci,*cj,ncols;

    171:   if (am != an) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"A must have a square diagonal portion, am: %d != an: %d",am,an);
    172:   MatSeqAIJGetArrayRead(mpimat->A,(const PetscScalar**)&aa);
    173:   MatSeqAIJGetArrayRead(mpimat->B,(const PetscScalar**)&ba);
    174:   if (scall == MAT_INITIAL_MATRIX) {
    175:     PetscMalloc1(1+am,&ci);
    176:     ci[0] = 0;
    177:     for (i=0; i<am; i++) ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
    178:     PetscMalloc1(1+ci[am],&cj);
    179:     PetscMalloc1(1+ci[am],&ca);

    181:     k = 0;
    182:     for (i=0; i<am; i++) {
    183:       /* diagonal portion of A */
    184:       ncols = ai[i+1] - ai[i];
    185:       for (j=0; j<ncols; j++) {
    186:         cj[k]   = *aj++;
    187:         ca[k++] = *aa++;
    188:       }
    189:       /* off-diagonal portion of A */
    190:       ncols = bi[i+1] - bi[i];
    191:       for (j=0; j<ncols; j++) {
    192:         cj[k]   = an + (*bj); bj++;
    193:         ca[k++] = *ba++;
    194:       }
    195:     }
    196:     if (k != ci[am]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"k: %d != ci[am]: %d",k,ci[am]);

    198:     /* put together the new matrix */
    199:     an   = mpimat->A->cmap->n+mpimat->B->cmap->n;
    200:     MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,an,ci,cj,ca,Aloc);

    202:     /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
    203:     /* Since these are PETSc arrays, change flags to free them as necessary. */
    204:     mat          = (Mat_SeqAIJ*)(*Aloc)->data;
    205:     mat->free_a  = PETSC_TRUE;
    206:     mat->free_ij = PETSC_TRUE;

    208:     mat->nonew = 0;
    209:   } else if (scall == MAT_REUSE_MATRIX) {
    210:     mat=(Mat_SeqAIJ*)(*Aloc)->data;
    211:     ci = mat->i; cj = mat->j; ca = mat->a;
    212:     for (i=0; i<am; i++) {
    213:       /* diagonal portion of A */
    214:       ncols = ai[i+1] - ai[i];
    215:       for (j=0; j<ncols; j++) *ca++ = *aa++;
    216:       /* off-diagonal portion of A */
    217:       ncols = bi[i+1] - bi[i];
    218:       for (j=0; j<ncols; j++) *ca++ = *ba++;
    219:     }
    220:   } else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
    221:   MatSeqAIJRestoreArrayRead(mpimat->A,(const PetscScalar**)&aa);
    222:   MatSeqAIJRestoreArrayRead(mpimat->B,(const PetscScalar**)&ba);
    223:   return(0);
    224: }

    226: static PetscErrorCode MatDestroy_ML(Mat A)
    227: {
    229:   Mat_MLShell    *shell;

    232:   MatShellGetContext(A,&shell);
    233:   PetscFree(shell);
    234:   return(0);
    235: }

    237: static PetscErrorCode MatWrapML_SeqAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
    238: {
    239:   struct ML_CSR_MSRdata *matdata = (struct ML_CSR_MSRdata*)mlmat->data;
    240:   PetscErrorCode        ierr;
    241:   PetscInt              m       =mlmat->outvec_leng,n=mlmat->invec_leng,*nnz = NULL,nz_max;
    242:   PetscInt              *ml_cols=matdata->columns,*ml_rowptr=matdata->rowptr,*aj,i;
    243:   PetscScalar           *ml_vals=matdata->values,*aa;

    246:   if (!mlmat->getrow) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
    247:   if (m != n) { /* ML Pmat and Rmat are in CSR format. Pass array pointers into SeqAIJ matrix */
    248:     if (reuse) {
    249:       Mat_SeqAIJ *aij= (Mat_SeqAIJ*)(*newmat)->data;
    250:       aij->i = ml_rowptr;
    251:       aij->j = ml_cols;
    252:       aij->a = ml_vals;
    253:     } else {
    254:       /* sort ml_cols and ml_vals */
    255:       PetscMalloc1(m+1,&nnz);
    256:       for (i=0; i<m; i++) nnz[i] = ml_rowptr[i+1] - ml_rowptr[i];
    257:       aj = ml_cols; aa = ml_vals;
    258:       for (i=0; i<m; i++) {
    259:         PetscSortIntWithScalarArray(nnz[i],aj,aa);
    260:         aj  += nnz[i]; aa += nnz[i];
    261:       }
    262:       MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,ml_rowptr,ml_cols,ml_vals,newmat);
    263:       PetscFree(nnz);
    264:     }
    265:     MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
    266:     MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);
    267:     return(0);
    268:   }

    270:   nz_max = PetscMax(1,mlmat->max_nz_per_row);
    271:   PetscMalloc2(nz_max,&aa,nz_max,&aj);
    272:   if (!reuse) {
    273:     MatCreate(PETSC_COMM_SELF,newmat);
    274:     MatSetSizes(*newmat,m,n,PETSC_DECIDE,PETSC_DECIDE);
    275:     MatSetType(*newmat,MATSEQAIJ);
    276:     /* keep track of block size for A matrices */
    277:     MatSetBlockSize (*newmat, mlmat->num_PDEs);

    279:     PetscMalloc1(m,&nnz);
    280:     for (i=0; i<m; i++) {
    281:       PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&nnz[i]));
    282:     }
    283:     MatSeqAIJSetPreallocation(*newmat,0,nnz);
    284:   }
    285:   for (i=0; i<m; i++) {
    286:     PetscInt ncols;

    288:     PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&ncols));
    289:     MatSetValues(*newmat,1,&i,ncols,aj,aa,INSERT_VALUES);
    290:   }
    291:   MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
    292:   MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);

    294:   PetscFree2(aa,aj);
    295:   PetscFree(nnz);
    296:   return(0);
    297: }

    299: static PetscErrorCode MatWrapML_SHELL(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
    300: {
    302:   PetscInt       m,n;
    303:   ML_Comm        *MLcomm;
    304:   Mat_MLShell    *shellctx;

    307:   m = mlmat->outvec_leng;
    308:   n = mlmat->invec_leng;

    310:   if (reuse) {
    311:     MatShellGetContext(*newmat,&shellctx);
    312:     shellctx->mlmat = mlmat;
    313:     return(0);
    314:   }

    316:   MLcomm = mlmat->comm;

    318:   PetscNew(&shellctx);
    319:   MatCreateShell(MLcomm->USR_comm,m,n,PETSC_DETERMINE,PETSC_DETERMINE,shellctx,newmat);
    320:   MatShellSetOperation(*newmat,MATOP_MULT,(void(*)(void))MatMult_ML);
    321:   MatShellSetOperation(*newmat,MATOP_DESTROY,(void(*)(void))MatDestroy_ML);

    323:   shellctx->A         = *newmat;
    324:   shellctx->mlmat     = mlmat;
    325:   return(0);
    326: }

    328: static PetscErrorCode MatWrapML_MPIAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
    329: {
    330:   PetscInt       *aj;
    331:   PetscScalar    *aa;
    333:   PetscInt       i,j,*gordering;
    334:   PetscInt       m=mlmat->outvec_leng,n,nz_max,row;
    335:   Mat            A;

    338:   if (!mlmat->getrow) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
    339:   n = mlmat->invec_leng;
    340:   if (m != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"m %d must equal to n %d",m,n);

    342:   /* create global row numbering for a ML_Operator */
    343:   PetscStackCall("ML_build_global_numbering",ML_build_global_numbering(mlmat,&gordering,"rows"));

    345:   nz_max = PetscMax(1,mlmat->max_nz_per_row) + 1;
    346:   PetscMalloc2(nz_max,&aa,nz_max,&aj);
    347:   if (reuse) {
    348:     A = *newmat;
    349:   } else {
    350:     PetscInt *nnzA,*nnzB,*nnz;
    351:     PetscInt rstart;
    352:     MatCreate(mlmat->comm->USR_comm,&A);
    353:     MatSetSizes(A,m,n,PETSC_DECIDE,PETSC_DECIDE);
    354:     MatSetType(A,MATMPIAIJ);
    355:     /* keep track of block size for A matrices */
    356:     MatSetBlockSize (A,mlmat->num_PDEs);
    357:     PetscMalloc3(m,&nnzA,m,&nnzB,m,&nnz);
    358:     MPI_Scan(&m,&rstart,1,MPIU_INT,MPI_SUM,mlmat->comm->USR_comm);
    359:     rstart -= m;

    361:     for (i=0; i<m; i++) {
    362:       row = gordering[i] - rstart;
    363:       PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&nnz[i]));
    364:       nnzA[row] = 0;
    365:       for (j=0; j<nnz[i]; j++) {
    366:         if (aj[j] < m) nnzA[row]++;
    367:       }
    368:       nnzB[row] = nnz[i] - nnzA[row];
    369:     }
    370:     MatMPIAIJSetPreallocation(A,0,nnzA,0,nnzB);
    371:     PetscFree3(nnzA,nnzB,nnz);
    372:   }
    373:   for (i=0; i<m; i++) {
    374:     PetscInt ncols;
    375:     row = gordering[i];

    377:     PetscStackCall(",ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&ncols));
    378:     for (j = 0; j < ncols; j++) aj[j] = gordering[aj[j]];
    379:     MatSetValues(A,1,&row,ncols,aj,aa,INSERT_VALUES);
    380:   }
    381:   PetscStackCall("ML_free",ML_free(gordering));
    382:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    383:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    384:   *newmat = A;

    386:   PetscFree2(aa,aj);
    387:   return(0);
    388: }

    390: /* -------------------------------------------------------------------------- */
    391: /*
    392:    PCSetCoordinates_ML

    394:    Input Parameter:
    395:    .  pc - the preconditioner context
    396: */
    397: static PetscErrorCode PCSetCoordinates_ML(PC pc, PetscInt ndm, PetscInt a_nloc, PetscReal *coords)
    398: {
    399:   PC_MG          *mg    = (PC_MG*)pc->data;
    400:   PC_ML          *pc_ml = (PC_ML*)mg->innerctx;
    402:   PetscInt       arrsz,oldarrsz,bs,my0,kk,ii,nloc,Iend,aloc;
    403:   Mat            Amat = pc->pmat;

    405:   /* this function copied and modified from PCSetCoordinates_GEO -TGI */
    408:   MatGetBlockSize(Amat, &bs);

    410:   MatGetOwnershipRange(Amat, &my0, &Iend);
    411:   aloc = (Iend-my0);
    412:   nloc = (Iend-my0)/bs;

    414:   if (nloc!=a_nloc && aloc != a_nloc) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Number of local blocks %D must be %D or %D.",a_nloc,nloc,aloc);

    416:   oldarrsz    = pc_ml->dim * pc_ml->nloc;
    417:   pc_ml->dim  = ndm;
    418:   pc_ml->nloc = nloc;
    419:   arrsz       = ndm * nloc;

    421:   /* create data - syntactic sugar that should be refactored at some point */
    422:   if (pc_ml->coords==0 || (oldarrsz != arrsz)) {
    423:     PetscFree(pc_ml->coords);
    424:     PetscMalloc1(arrsz, &pc_ml->coords);
    425:   }
    426:   for (kk=0; kk<arrsz; kk++) pc_ml->coords[kk] = -999.;
    427:   /* copy data in - column oriented */
    428:   if (nloc == a_nloc) {
    429:     for (kk = 0; kk < nloc; kk++) {
    430:       for (ii = 0; ii < ndm; ii++) {
    431:         pc_ml->coords[ii*nloc + kk] =  coords[kk*ndm + ii];
    432:       }
    433:     }
    434:   } else { /* assumes the coordinates are blocked */
    435:     for (kk = 0; kk < nloc; kk++) {
    436:       for (ii = 0; ii < ndm; ii++) {
    437:         pc_ml->coords[ii*nloc + kk] =  coords[bs*kk*ndm + ii];
    438:       }
    439:     }
    440:   }
    441:   return(0);
    442: }

    444: /* -----------------------------------------------------------------------------*/
    445: extern PetscErrorCode PCReset_MG(PC);
    446: PetscErrorCode PCReset_ML(PC pc)
    447: {
    449:   PC_MG          *mg    = (PC_MG*)pc->data;
    450:   PC_ML          *pc_ml = (PC_ML*)mg->innerctx;
    451:   PetscInt       level,fine_level=pc_ml->Nlevels-1,dim=pc_ml->dim;

    454:   if (dim) {
    455:     for (level=0; level<=fine_level; level++) {
    456:       VecDestroy(&pc_ml->gridctx[level].coords);
    457:     }
    458:     if (pc_ml->ml_object && pc_ml->ml_object->Grid) {
    459:       ML_Aggregate_Viz_Stats * grid_info = (ML_Aggregate_Viz_Stats*) pc_ml->ml_object->Grid[0].Grid;
    460:       grid_info->x = 0; /* do this so ML doesn't try to free coordinates */
    461:       grid_info->y = 0;
    462:       grid_info->z = 0;
    463:       PetscStackCall("ML_Operator_Getrow",ML_Aggregate_VizAndStats_Clean(pc_ml->ml_object));
    464:     }
    465:   }
    466:   PetscStackCall("ML_Aggregate_Destroy",ML_Aggregate_Destroy(&pc_ml->agg_object));
    467:   PetscStackCall("ML_Aggregate_Destroy",ML_Destroy(&pc_ml->ml_object));

    469:   if (pc_ml->PetscMLdata) {
    470:     PetscFree(pc_ml->PetscMLdata->pwork);
    471:     MatDestroy(&pc_ml->PetscMLdata->Aloc);
    472:     VecDestroy(&pc_ml->PetscMLdata->x);
    473:     VecDestroy(&pc_ml->PetscMLdata->y);
    474:   }
    475:   PetscFree(pc_ml->PetscMLdata);

    477:   if (pc_ml->gridctx) {
    478:     for (level=0; level<fine_level; level++) {
    479:       if (pc_ml->gridctx[level].A) {MatDestroy(&pc_ml->gridctx[level].A);}
    480:       if (pc_ml->gridctx[level].P) {MatDestroy(&pc_ml->gridctx[level].P);}
    481:       if (pc_ml->gridctx[level].R) {MatDestroy(&pc_ml->gridctx[level].R);}
    482:       if (pc_ml->gridctx[level].x) {VecDestroy(&pc_ml->gridctx[level].x);}
    483:       if (pc_ml->gridctx[level].b) {VecDestroy(&pc_ml->gridctx[level].b);}
    484:       if (pc_ml->gridctx[level+1].r) {VecDestroy(&pc_ml->gridctx[level+1].r);}
    485:     }
    486:   }
    487:   PetscFree(pc_ml->gridctx);
    488:   PetscFree(pc_ml->coords);

    490:   pc_ml->dim  = 0;
    491:   pc_ml->nloc = 0;
    492:   PCReset_MG(pc);
    493:   return(0);
    494: }
    495: /* -------------------------------------------------------------------------- */
    496: /*
    497:    PCSetUp_ML - Prepares for the use of the ML preconditioner
    498:                     by setting data structures and options.

    500:    Input Parameter:
    501: .  pc - the preconditioner context

    503:    Application Interface Routine: PCSetUp()

    505:    Notes:
    506:    The interface routine PCSetUp() is not usually called directly by
    507:    the user, but instead is called by PCApply() if necessary.
    508: */
    509: extern PetscErrorCode PCSetFromOptions_MG(PetscOptionItems *PetscOptionsObject,PC);
    510: extern PetscErrorCode PCReset_MG(PC);

    512: PetscErrorCode PCSetUp_ML(PC pc)
    513: {
    514:   PetscErrorCode   ierr;
    515:   PetscMPIInt      size;
    516:   FineGridCtx      *PetscMLdata;
    517:   ML               *ml_object;
    518:   ML_Aggregate     *agg_object;
    519:   ML_Operator      *mlmat;
    520:   PetscInt         nlocal_allcols,Nlevels,mllevel,level,level1,m,fine_level,bs;
    521:   Mat              A,Aloc;
    522:   GridCtx          *gridctx;
    523:   PC_MG            *mg    = (PC_MG*)pc->data;
    524:   PC_ML            *pc_ml = (PC_ML*)mg->innerctx;
    525:   PetscBool        isSeq, isMPI;
    526:   KSP              smoother;
    527:   PC               subpc;
    528:   PetscInt         mesh_level, old_mesh_level;
    529:   MatInfo          info;
    530:   static PetscBool cite = PETSC_FALSE;

    533:   PetscCitationsRegister("@TechReport{ml_users_guide,\n  author = {M. Sala and J.J. Hu and R.S. Tuminaro},\n  title = {{ML}3.1 {S}moothed {A}ggregation {U}ser's {G}uide},\n  institution =  {Sandia National Laboratories},\n  number = {SAND2004-4821},\n  year = 2004\n}\n",&cite);
    534:   A    = pc->pmat;
    535:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);

    537:   if (pc->setupcalled) {
    538:     if (pc->flag == SAME_NONZERO_PATTERN && pc_ml->reuse_interpolation) {
    539:       /*
    540:        Reuse interpolaton instead of recomputing aggregates and updating the whole hierarchy. This is less expensive for
    541:        multiple solves in which the matrix is not changing too quickly.
    542:        */
    543:       ml_object             = pc_ml->ml_object;
    544:       gridctx               = pc_ml->gridctx;
    545:       Nlevels               = pc_ml->Nlevels;
    546:       fine_level            = Nlevels - 1;
    547:       gridctx[fine_level].A = A;

    549:       PetscObjectBaseTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);
    550:       PetscObjectBaseTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);
    551:       if (isMPI) {
    552:         MatConvert_MPIAIJ_ML(A,NULL,MAT_INITIAL_MATRIX,&Aloc);
    553:       } else if (isSeq) {
    554:         Aloc = A;
    555:         PetscObjectReference((PetscObject)Aloc);
    556:       } else SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONG, "Matrix type '%s' cannot be used with ML. ML can only handle AIJ matrices.",((PetscObject)A)->type_name);

    558:       MatGetSize(Aloc,&m,&nlocal_allcols);
    559:       PetscMLdata       = pc_ml->PetscMLdata;
    560:       MatDestroy(&PetscMLdata->Aloc);
    561:       PetscMLdata->A    = A;
    562:       PetscMLdata->Aloc = Aloc;
    563:       PetscStackCall("ML_Aggregate_Destroy",ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata));
    564:       PetscStackCall("ML_Set_Amatrix_Matvec",ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec));

    566:       mesh_level = ml_object->ML_finest_level;
    567:       while (ml_object->SingleLevel[mesh_level].Rmat->to) {
    568:         old_mesh_level = mesh_level;
    569:         mesh_level     = ml_object->SingleLevel[mesh_level].Rmat->to->levelnum;

    571:         /* clean and regenerate A */
    572:         mlmat = &(ml_object->Amat[mesh_level]);
    573:         PetscStackCall("ML_Operator_Clean",ML_Operator_Clean(mlmat));
    574:         PetscStackCall("ML_Operator_Init",ML_Operator_Init(mlmat,ml_object->comm));
    575:         PetscStackCall("ML_Gen_AmatrixRAP",ML_Gen_AmatrixRAP(ml_object, old_mesh_level, mesh_level));
    576:       }

    578:       level = fine_level - 1;
    579:       if (size == 1) { /* convert ML P, R and A into seqaij format */
    580:         for (mllevel=1; mllevel<Nlevels; mllevel++) {
    581:           mlmat = &(ml_object->Amat[mllevel]);
    582:           MatWrapML_SeqAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);
    583:           level--;
    584:         }
    585:       } else { /* convert ML P and R into shell format, ML A into mpiaij format */
    586:         for (mllevel=1; mllevel<Nlevels; mllevel++) {
    587:           mlmat  = &(ml_object->Amat[mllevel]);
    588:           MatWrapML_MPIAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);
    589:           level--;
    590:         }
    591:       }

    593:       for (level=0; level<fine_level; level++) {
    594:         if (level > 0) {
    595:           PCMGSetResidual(pc,level,PCMGResidualDefault,gridctx[level].A);
    596:         }
    597:         KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A);
    598:       }
    599:       PCMGSetResidual(pc,fine_level,PCMGResidualDefault,gridctx[fine_level].A);
    600:       KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A);

    602:       PCSetUp_MG(pc);
    603:       return(0);
    604:     } else {
    605:       /* since ML can change the size of vectors/matrices at any level we must destroy everything */
    606:       PCReset_ML(pc);
    607:     }
    608:   }

    610:   /* setup special features of PCML */
    611:   /*--------------------------------*/
    612:   /* covert A to Aloc to be used by ML at fine grid */
    613:   pc_ml->size = size;
    614:   PetscObjectBaseTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);
    615:   PetscObjectBaseTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);
    616:   if (isMPI) {
    617:     MatConvert_MPIAIJ_ML(A,NULL,MAT_INITIAL_MATRIX,&Aloc);
    618:   } else if (isSeq) {
    619:     Aloc = A;
    620:     PetscObjectReference((PetscObject)Aloc);
    621:   } else SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONG, "Matrix type '%s' cannot be used with ML. ML can only handle AIJ matrices.",((PetscObject)A)->type_name);

    623:   /* create and initialize struct 'PetscMLdata' */
    624:   PetscNewLog(pc,&PetscMLdata);
    625:   pc_ml->PetscMLdata = PetscMLdata;
    626:   PetscMalloc1(Aloc->cmap->n+1,&PetscMLdata->pwork);

    628:   MatCreateVecs(Aloc,&PetscMLdata->x,&PetscMLdata->y);

    630:   PetscMLdata->A    = A;
    631:   PetscMLdata->Aloc = Aloc;
    632:   if (pc_ml->dim) { /* create vecs around the coordinate data given */
    633:     PetscInt  i,j,dim=pc_ml->dim;
    634:     PetscInt  nloc = pc_ml->nloc,nlocghost;
    635:     PetscReal *ghostedcoords;

    637:     MatGetBlockSize(A,&bs);
    638:     nlocghost = Aloc->cmap->n / bs;
    639:     PetscMalloc1(dim*nlocghost,&ghostedcoords);
    640:     for (i = 0; i < dim; i++) {
    641:       /* copy coordinate values into first component of pwork */
    642:       for (j = 0; j < nloc; j++) {
    643:         PetscMLdata->pwork[bs * j] = pc_ml->coords[nloc * i + j];
    644:       }
    645:       /* get the ghost values */
    646:       PetscML_comm(PetscMLdata->pwork,PetscMLdata);
    647:       /* write into the vector */
    648:       for (j = 0; j < nlocghost; j++) {
    649:         ghostedcoords[i * nlocghost + j] = PetscMLdata->pwork[bs * j];
    650:       }
    651:     }
    652:     /* replace the original coords with the ghosted coords, because these are
    653:      * what ML needs */
    654:     PetscFree(pc_ml->coords);
    655:     pc_ml->coords = ghostedcoords;
    656:   }

    658:   /* create ML discretization matrix at fine grid */
    659:   /* ML requires input of fine-grid matrix. It determines nlevels. */
    660:   MatGetSize(Aloc,&m,&nlocal_allcols);
    661:   MatGetBlockSize(A,&bs);
    662:   PetscStackCall("ML_Create",ML_Create(&ml_object,pc_ml->MaxNlevels));
    663:   PetscStackCall("ML_Comm_Set_UsrComm",ML_Comm_Set_UsrComm(ml_object->comm,PetscObjectComm((PetscObject)A)));
    664:   pc_ml->ml_object = ml_object;
    665:   PetscStackCall("ML_Init_Amatrix",ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata));
    666:   PetscStackCall("ML_Set_Amatrix_Getrow",ML_Set_Amatrix_Getrow(ml_object,0,PetscML_getrow,PetscML_comm,nlocal_allcols));
    667:   PetscStackCall("ML_Set_Amatrix_Matvec",ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec));

    669:   PetscStackCall("ML_Set_Symmetrize",ML_Set_Symmetrize(ml_object,pc_ml->Symmetrize ? ML_YES : ML_NO));

    671:   /* aggregation */
    672:   PetscStackCall("ML_Aggregate_Create",ML_Aggregate_Create(&agg_object));
    673:   pc_ml->agg_object = agg_object;

    675:   {
    676:     MatNullSpace mnull;
    677:     MatGetNearNullSpace(A,&mnull);
    678:     if (pc_ml->nulltype == PCML_NULLSPACE_AUTO) {
    679:       if (mnull) pc_ml->nulltype = PCML_NULLSPACE_USER;
    680:       else if (bs > 1) pc_ml->nulltype = PCML_NULLSPACE_BLOCK;
    681:       else pc_ml->nulltype = PCML_NULLSPACE_SCALAR;
    682:     }
    683:     switch (pc_ml->nulltype) {
    684:     case PCML_NULLSPACE_USER: {
    685:       PetscScalar       *nullvec;
    686:       const PetscScalar *v;
    687:       PetscBool         has_const;
    688:       PetscInt          i,j,mlocal,nvec,M;
    689:       const Vec         *vecs;

    691:       if (!mnull) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"Must provide explicit null space using MatSetNearNullSpace() to use user-specified null space");
    692:       MatGetSize(A,&M,NULL);
    693:       MatGetLocalSize(Aloc,&mlocal,NULL);
    694:       MatNullSpaceGetVecs(mnull,&has_const,&nvec,&vecs);
    695:       PetscMalloc1((nvec+!!has_const)*mlocal,&nullvec);
    696:       if (has_const) for (i=0; i<mlocal; i++) nullvec[i] = 1.0/M;
    697:       for (i=0; i<nvec; i++) {
    698:         VecGetArrayRead(vecs[i],&v);
    699:         for (j=0; j<mlocal; j++) nullvec[(i+!!has_const)*mlocal + j] = v[j];
    700:         VecRestoreArrayRead(vecs[i],&v);
    701:       }
    702:       PetscStackCall("ML_Aggregate_Create",ML_Aggregate_Set_NullSpace(agg_object,bs,nvec+!!has_const,nullvec,mlocal);CHKERRQ(ierr));
    703:       PetscFree(nullvec);
    704:     } break;
    705:     case PCML_NULLSPACE_BLOCK:
    706:       PetscStackCall("ML_Aggregate_Set_NullSpace",ML_Aggregate_Set_NullSpace(agg_object,bs,bs,0,0);CHKERRQ(ierr));
    707:       break;
    708:     case PCML_NULLSPACE_SCALAR:
    709:       break;
    710:     default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_SUP,"Unknown null space type");
    711:     }
    712:   }
    713:   PetscStackCall("ML_Aggregate_Set_MaxCoarseSize",ML_Aggregate_Set_MaxCoarseSize(agg_object,pc_ml->MaxCoarseSize));
    714:   /* set options */
    715:   switch (pc_ml->CoarsenScheme) {
    716:   case 1:
    717:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_Coupled",ML_Aggregate_Set_CoarsenScheme_Coupled(agg_object));break;
    718:   case 2:
    719:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_MIS",ML_Aggregate_Set_CoarsenScheme_MIS(agg_object));break;
    720:   case 3:
    721:     PetscStackCall("ML_Aggregate_Set_CoarsenScheme_METIS",ML_Aggregate_Set_CoarsenScheme_METIS(agg_object));break;
    722:   }
    723:   PetscStackCall("ML_Aggregate_Set_Threshold",ML_Aggregate_Set_Threshold(agg_object,pc_ml->Threshold));
    724:   PetscStackCall("ML_Aggregate_Set_DampingFactor",ML_Aggregate_Set_DampingFactor(agg_object,pc_ml->DampingFactor));
    725:   if (pc_ml->SpectralNormScheme_Anorm) {
    726:     PetscStackCall("ML_Set_SpectralNormScheme_Anorm",ML_Set_SpectralNormScheme_Anorm(ml_object));
    727:   }
    728:   agg_object->keep_agg_information      = (int)pc_ml->KeepAggInfo;
    729:   agg_object->keep_P_tentative          = (int)pc_ml->Reusable;
    730:   agg_object->block_scaled_SA           = (int)pc_ml->BlockScaling;
    731:   agg_object->minimizing_energy         = (int)pc_ml->EnergyMinimization;
    732:   agg_object->minimizing_energy_droptol = (double)pc_ml->EnergyMinimizationDropTol;
    733:   agg_object->cheap_minimizing_energy   = (int)pc_ml->EnergyMinimizationCheap;

    735:   if (pc_ml->Aux) {
    736:     if (!pc_ml->dim) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"Auxiliary matrix requires coordinates");
    737:     ml_object->Amat[0].aux_data->threshold = pc_ml->AuxThreshold;
    738:     ml_object->Amat[0].aux_data->enable    = 1;
    739:     ml_object->Amat[0].aux_data->max_level = 10;
    740:     ml_object->Amat[0].num_PDEs            = bs;
    741:   }

    743:   MatGetInfo(A,MAT_LOCAL,&info);
    744:   ml_object->Amat[0].N_nonzeros = (int) info.nz_used;

    746:   if (pc_ml->dim) {
    747:     PetscInt               i,dim = pc_ml->dim;
    748:     ML_Aggregate_Viz_Stats *grid_info;
    749:     PetscInt               nlocghost;

    751:     MatGetBlockSize(A,&bs);
    752:     nlocghost = Aloc->cmap->n / bs;

    754:     PetscStackCall("ML_Aggregate_VizAndStats_Setup(",ML_Aggregate_VizAndStats_Setup(ml_object)); /* create ml info for coords */
    755:     grid_info = (ML_Aggregate_Viz_Stats*) ml_object->Grid[0].Grid;
    756:     for (i = 0; i < dim; i++) {
    757:       /* set the finest level coordinates to point to the column-order array
    758:        * in pc_ml */
    759:       /* NOTE: must point away before VizAndStats_Clean so ML doesn't free */
    760:       switch (i) {
    761:       case 0: grid_info->x = pc_ml->coords + nlocghost * i; break;
    762:       case 1: grid_info->y = pc_ml->coords + nlocghost * i; break;
    763:       case 2: grid_info->z = pc_ml->coords + nlocghost * i; break;
    764:       default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_SIZ,"PCML coordinate dimension must be <= 3");
    765:       }
    766:     }
    767:     grid_info->Ndim = dim;
    768:   }

    770:   /* repartitioning */
    771:   if (pc_ml->Repartition) {
    772:     PetscStackCall("ML_Repartition_Activate",ML_Repartition_Activate(ml_object));
    773:     PetscStackCall("ML_Repartition_Set_LargestMinMaxRatio",ML_Repartition_Set_LargestMinMaxRatio(ml_object,pc_ml->MaxMinRatio));
    774:     PetscStackCall("ML_Repartition_Set_MinPerProc",ML_Repartition_Set_MinPerProc(ml_object,pc_ml->MinPerProc));
    775:     PetscStackCall("ML_Repartition_Set_PutOnSingleProc",ML_Repartition_Set_PutOnSingleProc(ml_object,pc_ml->PutOnSingleProc));
    776: #if 0                           /* Function not yet defined in ml-6.2 */
    777:     /* I'm not sure what compatibility issues might crop up if we partitioned
    778:      * on the finest level, so to be safe repartition starts on the next
    779:      * finest level (reflection default behavior in
    780:      * ml_MultiLevelPreconditioner) */
    781:     PetscStackCall("ML_Repartition_Set_StartLevel",ML_Repartition_Set_StartLevel(ml_object,1));
    782: #endif

    784:     if (!pc_ml->RepartitionType) {
    785:       PetscInt i;

    787:       if (!pc_ml->dim) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"ML Zoltan repartitioning requires coordinates");
    788:       PetscStackCall("ML_Repartition_Set_Partitioner",ML_Repartition_Set_Partitioner(ml_object,ML_USEZOLTAN));
    789:       PetscStackCall("ML_Aggregate_Set_Dimensions",ML_Aggregate_Set_Dimensions(agg_object, pc_ml->dim));

    791:       for (i = 0; i < ml_object->ML_num_levels; i++) {
    792:         ML_Aggregate_Viz_Stats *grid_info = (ML_Aggregate_Viz_Stats*)ml_object->Grid[i].Grid;
    793:         grid_info->zoltan_type = pc_ml->ZoltanScheme + 1; /* ml numbers options 1, 2, 3 */
    794:         /* defaults from ml_agg_info.c */
    795:         grid_info->zoltan_estimated_its = 40; /* only relevant to hypergraph / fast hypergraph */
    796:         grid_info->zoltan_timers        = 0;
    797:         grid_info->smoothing_steps      = 4;  /* only relevant to hypergraph / fast hypergraph */
    798:       }
    799:     } else {
    800:       PetscStackCall("ML_Repartition_Set_Partitioner",ML_Repartition_Set_Partitioner(ml_object,ML_USEPARMETIS));
    801:     }
    802:   }

    804:   if (pc_ml->OldHierarchy) {
    805:     PetscStackCall("ML_Gen_MGHierarchy_UsingAggregation",Nlevels = ML_Gen_MGHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object));
    806:   } else {
    807:     PetscStackCall("ML_Gen_MultiLevelHierarchy_UsingAggregation",Nlevels = ML_Gen_MultiLevelHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object));
    808:   }
    809:   if (Nlevels<=0) SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_OUTOFRANGE,"Nlevels %d must > 0",Nlevels);
    810:   pc_ml->Nlevels = Nlevels;
    811:   fine_level     = Nlevels - 1;

    813:   PCMGSetLevels(pc,Nlevels,NULL);
    814:   /* set default smoothers */
    815:   for (level=1; level<=fine_level; level++) {
    816:     PCMGGetSmoother(pc,level,&smoother);
    817:     KSPSetType(smoother,KSPRICHARDSON);
    818:     KSPGetPC(smoother,&subpc);
    819:     PCSetType(subpc,PCSOR);
    820:   }
    821:   PetscObjectOptionsBegin((PetscObject)pc);
    822:   PCSetFromOptions_MG(PetscOptionsObject,pc); /* should be called in PCSetFromOptions_ML(), but cannot be called prior to PCMGSetLevels() */
    823:   PetscOptionsEnd();

    825:   PetscMalloc1(Nlevels,&gridctx);

    827:   pc_ml->gridctx = gridctx;

    829:   /* wrap ML matrices by PETSc shell matrices at coarsened grids.
    830:      Level 0 is the finest grid for ML, but coarsest for PETSc! */
    831:   gridctx[fine_level].A = A;

    833:   level = fine_level - 1;
    834:   /* TODO: support for GPUs */
    835:   if (size == 1) { /* convert ML P, R and A into seqaij format */
    836:     for (mllevel=1; mllevel<Nlevels; mllevel++) {
    837:       mlmat = &(ml_object->Pmat[mllevel]);
    838:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);
    839:       mlmat = &(ml_object->Rmat[mllevel-1]);
    840:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);

    842:       mlmat = &(ml_object->Amat[mllevel]);
    843:       MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
    844:       level--;
    845:     }
    846:   } else { /* convert ML P and R into shell format, ML A into mpiaij format */
    847:     for (mllevel=1; mllevel<Nlevels; mllevel++) {
    848:       mlmat  = &(ml_object->Pmat[mllevel]);
    849:       MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);
    850:       mlmat  = &(ml_object->Rmat[mllevel-1]);
    851:       MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);

    853:       mlmat  = &(ml_object->Amat[mllevel]);
    854:       MatWrapML_MPIAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
    855:       level--;
    856:     }
    857:   }

    859:   /* create vectors and ksp at all levels */
    860:   for (level=0; level<fine_level; level++) {
    861:     level1 = level + 1;

    863:     MatCreateVecs(gridctx[level].A,&gridctx[level].x,&gridctx[level].b);
    864:     MatCreateVecs(gridctx[level1].A,NULL,&gridctx[level1].r);
    865:     PCMGSetX(pc,level,gridctx[level].x);
    866:     PCMGSetRhs(pc,level,gridctx[level].b);
    867:     PCMGSetR(pc,level1,gridctx[level1].r);

    869:     if (level == 0) {
    870:       PCMGGetCoarseSolve(pc,&gridctx[level].ksp);
    871:     } else {
    872:       PCMGGetSmoother(pc,level,&gridctx[level].ksp);
    873:     }
    874:   }
    875:   PCMGGetSmoother(pc,fine_level,&gridctx[fine_level].ksp);

    877:   /* create coarse level and the interpolation between the levels */
    878:   for (level=0; level<fine_level; level++) {
    879:     level1 = level + 1;

    881:     PCMGSetInterpolation(pc,level1,gridctx[level].P);
    882:     PCMGSetRestriction(pc,level1,gridctx[level].R);
    883:     if (level > 0) {
    884:       PCMGSetResidual(pc,level,PCMGResidualDefault,gridctx[level].A);
    885:     }
    886:     KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A);
    887:   }
    888:   PCMGSetResidual(pc,fine_level,PCMGResidualDefault,gridctx[fine_level].A);
    889:   KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A);

    891:   /* put coordinate info in levels */
    892:   if (pc_ml->dim) {
    893:     PetscInt  i,j,dim = pc_ml->dim;
    894:     PetscInt  bs, nloc;
    895:     PC        subpc;
    896:     PetscReal *array;

    898:     level = fine_level;
    899:     for (mllevel = 0; mllevel < Nlevels; mllevel++) {
    900:       ML_Aggregate_Viz_Stats *grid_info = (ML_Aggregate_Viz_Stats*)ml_object->Amat[mllevel].to->Grid->Grid;
    901:       MPI_Comm               comm       = ((PetscObject)gridctx[level].A)->comm;

    903:       MatGetBlockSize (gridctx[level].A, &bs);
    904:       MatGetLocalSize (gridctx[level].A, NULL, &nloc);
    905:       nloc /= bs; /* number of local nodes */

    907:       VecCreate(comm,&gridctx[level].coords);
    908:       VecSetSizes(gridctx[level].coords,dim * nloc,PETSC_DECIDE);
    909:       VecSetType(gridctx[level].coords,VECMPI);
    910:       VecGetArray(gridctx[level].coords,&array);
    911:       for (j = 0; j < nloc; j++) {
    912:         for (i = 0; i < dim; i++) {
    913:           switch (i) {
    914:           case 0: array[dim * j + i] = grid_info->x[j]; break;
    915:           case 1: array[dim * j + i] = grid_info->y[j]; break;
    916:           case 2: array[dim * j + i] = grid_info->z[j]; break;
    917:           default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_SIZ,"PCML coordinate dimension must be <= 3");
    918:           }
    919:         }
    920:       }

    922:       /* passing coordinates to smoothers/coarse solver, should they need them */
    923:       KSPGetPC(gridctx[level].ksp,&subpc);
    924:       PCSetCoordinates(subpc,dim,nloc,array);
    925:       VecRestoreArray(gridctx[level].coords,&array);
    926:       level--;
    927:     }
    928:   }

    930:   /* setupcalled is set to 0 so that MG is setup from scratch */
    931:   pc->setupcalled = 0;
    932:   PCSetUp_MG(pc);
    933:   return(0);
    934: }

    936: /* -------------------------------------------------------------------------- */
    937: /*
    938:    PCDestroy_ML - Destroys the private context for the ML preconditioner
    939:    that was created with PCCreate_ML().

    941:    Input Parameter:
    942: .  pc - the preconditioner context

    944:    Application Interface Routine: PCDestroy()
    945: */
    946: PetscErrorCode PCDestroy_ML(PC pc)
    947: {
    949:   PC_MG          *mg   = (PC_MG*)pc->data;
    950:   PC_ML          *pc_ml= (PC_ML*)mg->innerctx;

    953:   PCReset_ML(pc);
    954:   PetscFree(pc_ml);
    955:   PCDestroy_MG(pc);
    956:   PetscObjectComposeFunction((PetscObject)pc,"PCSetCoordinates_C",NULL);
    957:   return(0);
    958: }

    960: PetscErrorCode PCSetFromOptions_ML(PetscOptionItems *PetscOptionsObject,PC pc)
    961: {
    963:   PetscInt       indx,PrintLevel,partindx;
    964:   const char     *scheme[] = {"Uncoupled","Coupled","MIS","METIS"};
    965:   const char     *part[]   = {"Zoltan","ParMETIS"};
    966: #if defined(HAVE_ML_ZOLTAN)
    967:   const char *zscheme[] = {"RCB","hypergraph","fast_hypergraph"};
    968: #endif
    969:   PC_MG       *mg    = (PC_MG*)pc->data;
    970:   PC_ML       *pc_ml = (PC_ML*)mg->innerctx;
    971:   PetscMPIInt size;
    972:   MPI_Comm    comm;

    975:   PetscObjectGetComm((PetscObject)pc,&comm);
    976:   MPI_Comm_size(comm,&size);
    977:   PetscOptionsHead(PetscOptionsObject,"ML options");

    979:   PrintLevel = 0;
    980:   indx       = 0;
    981:   partindx   = 0;

    983:   PetscOptionsInt("-pc_ml_PrintLevel","Print level","ML_Set_PrintLevel",PrintLevel,&PrintLevel,NULL);
    984:   PetscStackCall("ML_Set_PrintLevel",ML_Set_PrintLevel(PrintLevel));
    985:   PetscOptionsInt("-pc_ml_maxNlevels","Maximum number of levels","None",pc_ml->MaxNlevels,&pc_ml->MaxNlevels,NULL);
    986:   PetscOptionsInt("-pc_ml_maxCoarseSize","Maximum coarsest mesh size","ML_Aggregate_Set_MaxCoarseSize",pc_ml->MaxCoarseSize,&pc_ml->MaxCoarseSize,NULL);
    987:   PetscOptionsEList("-pc_ml_CoarsenScheme","Aggregate Coarsen Scheme","ML_Aggregate_Set_CoarsenScheme_*",scheme,4,scheme[0],&indx,NULL);

    989:   pc_ml->CoarsenScheme = indx;

    991:   PetscOptionsReal("-pc_ml_DampingFactor","P damping factor","ML_Aggregate_Set_DampingFactor",pc_ml->DampingFactor,&pc_ml->DampingFactor,NULL);
    992:   PetscOptionsReal("-pc_ml_Threshold","Smoother drop tol","ML_Aggregate_Set_Threshold",pc_ml->Threshold,&pc_ml->Threshold,NULL);
    993:   PetscOptionsBool("-pc_ml_SpectralNormScheme_Anorm","Method used for estimating spectral radius","ML_Set_SpectralNormScheme_Anorm",pc_ml->SpectralNormScheme_Anorm,&pc_ml->SpectralNormScheme_Anorm,NULL);
    994:   PetscOptionsBool("-pc_ml_Symmetrize","Symmetrize aggregation","ML_Set_Symmetrize",pc_ml->Symmetrize,&pc_ml->Symmetrize,NULL);
    995:   PetscOptionsBool("-pc_ml_BlockScaling","Scale all dofs at each node together","None",pc_ml->BlockScaling,&pc_ml->BlockScaling,NULL);
    996:   PetscOptionsEnum("-pc_ml_nullspace","Which type of null space information to use","None",PCMLNullSpaceTypes,(PetscEnum)pc_ml->nulltype,(PetscEnum*)&pc_ml->nulltype,NULL);
    997:   PetscOptionsInt("-pc_ml_EnergyMinimization","Energy minimization norm type (0=no minimization; see ML manual for 1,2,3; -1 and 4 undocumented)","None",pc_ml->EnergyMinimization,&pc_ml->EnergyMinimization,NULL);
    998:   PetscOptionsBool("-pc_ml_reuse_interpolation","Reuse the interpolation operators when possible (cheaper, weaker when matrix entries change a lot)","None",pc_ml->reuse_interpolation,&pc_ml->reuse_interpolation,NULL);
    999:   /*
    1000:     The following checks a number of conditions.  If we let this stuff slip by, then ML's error handling will take over.
    1001:     This is suboptimal because it amounts to calling exit(1) so we check for the most common conditions.

    1003:     We also try to set some sane defaults when energy minimization is activated, otherwise it's hard to find a working
    1004:     combination of options and ML's exit(1) explanations don't help matters.
    1005:   */
    1006:   if (pc_ml->EnergyMinimization < -1 || pc_ml->EnergyMinimization > 4) SETERRQ(comm,PETSC_ERR_ARG_OUTOFRANGE,"EnergyMinimization must be in range -1..4");
    1007:   if (pc_ml->EnergyMinimization == 4 && size > 1) SETERRQ(comm,PETSC_ERR_SUP,"Energy minimization type 4 does not work in parallel");
    1008:   if (pc_ml->EnergyMinimization == 4) {PetscInfo(pc,"Mandel's energy minimization scheme is experimental and broken in ML-6.2\n");}
    1009:   if (pc_ml->EnergyMinimization) {
    1010:     PetscOptionsReal("-pc_ml_EnergyMinimizationDropTol","Energy minimization drop tolerance","None",pc_ml->EnergyMinimizationDropTol,&pc_ml->EnergyMinimizationDropTol,NULL);
    1011:   }
    1012:   if (pc_ml->EnergyMinimization == 2) {
    1013:     /* According to ml_MultiLevelPreconditioner.cpp, this option is only meaningful for norm type (2) */
    1014:     PetscOptionsBool("-pc_ml_EnergyMinimizationCheap","Use cheaper variant of norm type 2","None",pc_ml->EnergyMinimizationCheap,&pc_ml->EnergyMinimizationCheap,NULL);
    1015:   }
    1016:   /* energy minimization sometimes breaks if this is turned off, the more classical stuff should be okay without it */
    1017:   if (pc_ml->EnergyMinimization) pc_ml->KeepAggInfo = PETSC_TRUE;
    1018:   PetscOptionsBool("-pc_ml_KeepAggInfo","Allows the preconditioner to be reused, or auxilliary matrices to be generated","None",pc_ml->KeepAggInfo,&pc_ml->KeepAggInfo,NULL);
    1019:   /* Option (-1) doesn't work at all (calls exit(1)) if the tentative restriction operator isn't stored. */
    1020:   if (pc_ml->EnergyMinimization == -1) pc_ml->Reusable = PETSC_TRUE;
    1021:   PetscOptionsBool("-pc_ml_Reusable","Store intermedaiate data structures so that the multilevel hierarchy is reusable","None",pc_ml->Reusable,&pc_ml->Reusable,NULL);
    1022:   /*
    1023:     ML's C API is severely underdocumented and lacks significant functionality.  The C++ API calls
    1024:     ML_Gen_MultiLevelHierarchy_UsingAggregation() which is a modified copy (!?) of the documented function
    1025:     ML_Gen_MGHierarchy_UsingAggregation().  This modification, however, does not provide a strict superset of the
    1026:     functionality in the old function, so some users may still want to use it.  Note that many options are ignored in
    1027:     this context, but ML doesn't provide a way to find out which ones.
    1028:    */
    1029:   PetscOptionsBool("-pc_ml_OldHierarchy","Use old routine to generate hierarchy","None",pc_ml->OldHierarchy,&pc_ml->OldHierarchy,NULL);
    1030:   PetscOptionsBool("-pc_ml_repartition", "Allow ML to repartition levels of the hierarchy","ML_Repartition_Activate",pc_ml->Repartition,&pc_ml->Repartition,NULL);
    1031:   if (pc_ml->Repartition) {
    1032:     PetscOptionsReal("-pc_ml_repartitionMaxMinRatio", "Acceptable ratio of repartitioned sizes","ML_Repartition_Set_LargestMinMaxRatio",pc_ml->MaxMinRatio,&pc_ml->MaxMinRatio,NULL);
    1033:     PetscOptionsInt("-pc_ml_repartitionMinPerProc", "Smallest repartitioned size","ML_Repartition_Set_MinPerProc",pc_ml->MinPerProc,&pc_ml->MinPerProc,NULL);
    1034:     PetscOptionsInt("-pc_ml_repartitionPutOnSingleProc", "Problem size automatically repartitioned to one processor","ML_Repartition_Set_PutOnSingleProc",pc_ml->PutOnSingleProc,&pc_ml->PutOnSingleProc,NULL);
    1035: #if defined(HAVE_ML_ZOLTAN)
    1036:     partindx = 0;
    1037:     PetscOptionsEList("-pc_ml_repartitionType", "Repartitioning library to use","ML_Repartition_Set_Partitioner",part,2,part[0],&partindx,NULL);

    1039:     pc_ml->RepartitionType = partindx;
    1040:     if (!partindx) {
    1041:       PetscInt zindx = 0;

    1043:       PetscOptionsEList("-pc_ml_repartitionZoltanScheme", "Repartitioning scheme to use","None",zscheme,3,zscheme[0],&zindx,NULL);

    1045:       pc_ml->ZoltanScheme = zindx;
    1046:     }
    1047: #else
    1048:     partindx = 1;
    1049:     PetscOptionsEList("-pc_ml_repartitionType", "Repartitioning library to use","ML_Repartition_Set_Partitioner",part,2,part[1],&partindx,NULL);
    1050:     pc_ml->RepartitionType = partindx;
    1051:     if (!partindx) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_SUP_SYS,"ML not compiled with Zoltan");
    1052: #endif
    1053:     PetscOptionsBool("-pc_ml_Aux","Aggregate using auxiliary coordinate-based laplacian","None",pc_ml->Aux,&pc_ml->Aux,NULL);
    1054:     PetscOptionsReal("-pc_ml_AuxThreshold","Auxiliary smoother drop tol","None",pc_ml->AuxThreshold,&pc_ml->AuxThreshold,NULL);
    1055:   }
    1056:   PetscOptionsTail();
    1057:   return(0);
    1058: }

    1060: /* -------------------------------------------------------------------------- */
    1061: /*
    1062:    PCCreate_ML - Creates a ML preconditioner context, PC_ML,
    1063:    and sets this as the private data within the generic preconditioning
    1064:    context, PC, that was created within PCCreate().

    1066:    Input Parameter:
    1067: .  pc - the preconditioner context

    1069:    Application Interface Routine: PCCreate()
    1070: */

    1072: /*MC
    1073:      PCML - Use algebraic multigrid preconditioning. This preconditioner requires you provide
    1074:        fine grid discretization matrix. The coarser grid matrices and restriction/interpolation
    1075:        operators are computed by ML, with the matrices coverted to PETSc matrices in aij format
    1076:        and the restriction/interpolation operators wrapped as PETSc shell matrices.

    1078:    Options Database Key:
    1079:    Multigrid options(inherited):
    1080: +  -pc_mg_cycles <1> - 1 for V cycle, 2 for W-cycle (MGSetCycles)
    1081: .  -pc_mg_distinct_smoothup - Should one configure the up and down smoothers separately (PCMGSetDistinctSmoothUp)
    1082: -  -pc_mg_type <multiplicative> - (one of) additive multiplicative full kascade

    1084:    ML options:
    1085: +  -pc_ml_PrintLevel <0> - Print level (ML_Set_PrintLevel)
    1086: .  -pc_ml_maxNlevels <10> - Maximum number of levels (None)
    1087: .  -pc_ml_maxCoarseSize <1> - Maximum coarsest mesh size (ML_Aggregate_Set_MaxCoarseSize)
    1088: .  -pc_ml_CoarsenScheme <Uncoupled> - (one of) Uncoupled Coupled MIS METIS
    1089: .  -pc_ml_DampingFactor <1.33333> - P damping factor (ML_Aggregate_Set_DampingFactor)
    1090: .  -pc_ml_Threshold <0> - Smoother drop tol (ML_Aggregate_Set_Threshold)
    1091: .  -pc_ml_SpectralNormScheme_Anorm <false> - Method used for estimating spectral radius (ML_Set_SpectralNormScheme_Anorm)
    1092: .  -pc_ml_repartition <false> - Allow ML to repartition levels of the hierarchy (ML_Repartition_Activate)
    1093: .  -pc_ml_repartitionMaxMinRatio <1.3> - Acceptable ratio of repartitioned sizes (ML_Repartition_Set_LargestMinMaxRatio)
    1094: .  -pc_ml_repartitionMinPerProc <512>: Smallest repartitioned size (ML_Repartition_Set_MinPerProc)
    1095: .  -pc_ml_repartitionPutOnSingleProc <5000> - Problem size automatically repartitioned to one processor (ML_Repartition_Set_PutOnSingleProc)
    1096: .  -pc_ml_repartitionType <Zoltan> - Repartitioning library to use (ML_Repartition_Set_Partitioner)
    1097: .  -pc_ml_repartitionZoltanScheme <RCB> - Repartitioning scheme to use (None)
    1098: .  -pc_ml_Aux <false> - Aggregate using auxiliary coordinate-based laplacian (None)
    1099: -  -pc_ml_AuxThreshold <0.0> - Auxiliary smoother drop tol (None)

    1101:    Level: intermediate

    1103: .seealso:  PCCreate(), PCSetType(), PCType (for list of available types), PC, PCMGType,
    1104:            PCMGSetLevels(), PCMGGetLevels(), PCMGSetType(), MPSetCycles(), PCMGSetDistinctSmoothUp(),
    1105:            PCMGGetCoarseSolve(), PCMGSetResidual(), PCMGSetInterpolation(),
    1106:            PCMGSetRestriction(), PCMGGetSmoother(), PCMGGetSmootherUp(), PCMGGetSmootherDown(),
    1107:            PCMGSetCycleTypeOnLevel(), PCMGSetRhs(), PCMGSetX(), PCMGSetR()
    1108: M*/

    1110: PETSC_EXTERN PetscErrorCode PCCreate_ML(PC pc)
    1111: {
    1113:   PC_ML          *pc_ml;
    1114:   PC_MG          *mg;

    1117:   /* PCML is an inherited class of PCMG. Initialize pc as PCMG */
    1118:   PCSetType(pc,PCMG); /* calls PCCreate_MG() and MGCreate_Private() */
    1119:   PetscObjectChangeTypeName((PetscObject)pc,PCML);
    1120:   /* Since PCMG tries to use DM assocated with PC must delete it */
    1121:   DMDestroy(&pc->dm);
    1122:   PCMGSetGalerkin(pc,PC_MG_GALERKIN_EXTERNAL);
    1123:   mg   = (PC_MG*)pc->data;

    1125:   /* create a supporting struct and attach it to pc */
    1126:   PetscNewLog(pc,&pc_ml);
    1127:   mg->innerctx = pc_ml;

    1129:   pc_ml->ml_object                = 0;
    1130:   pc_ml->agg_object               = 0;
    1131:   pc_ml->gridctx                  = 0;
    1132:   pc_ml->PetscMLdata              = 0;
    1133:   pc_ml->Nlevels                  = -1;
    1134:   pc_ml->MaxNlevels               = 10;
    1135:   pc_ml->MaxCoarseSize            = 1;
    1136:   pc_ml->CoarsenScheme            = 1;
    1137:   pc_ml->Threshold                = 0.0;
    1138:   pc_ml->DampingFactor            = 4.0/3.0;
    1139:   pc_ml->SpectralNormScheme_Anorm = PETSC_FALSE;
    1140:   pc_ml->size                     = 0;
    1141:   pc_ml->dim                      = 0;
    1142:   pc_ml->nloc                     = 0;
    1143:   pc_ml->coords                   = 0;
    1144:   pc_ml->Repartition              = PETSC_FALSE;
    1145:   pc_ml->MaxMinRatio              = 1.3;
    1146:   pc_ml->MinPerProc               = 512;
    1147:   pc_ml->PutOnSingleProc          = 5000;
    1148:   pc_ml->RepartitionType          = 0;
    1149:   pc_ml->ZoltanScheme             = 0;
    1150:   pc_ml->Aux                      = PETSC_FALSE;
    1151:   pc_ml->AuxThreshold             = 0.0;

    1153:   /* allow for coordinates to be passed */
    1154:   PetscObjectComposeFunction((PetscObject)pc,"PCSetCoordinates_C",PCSetCoordinates_ML);

    1156:   /* overwrite the pointers of PCMG by the functions of PCML */
    1157:   pc->ops->setfromoptions = PCSetFromOptions_ML;
    1158:   pc->ops->setup          = PCSetUp_ML;
    1159:   pc->ops->reset          = PCReset_ML;
    1160:   pc->ops->destroy        = PCDestroy_ML;
    1161:   return(0);
    1162: }