Actual source code: aij.c

      1: /*
      2:     Defines the basic matrix operations for the AIJ (compressed row)
      3:   matrix storage format.
      4: */

      7: #include <../src/mat/impls/aij/seq/aij.h>
      8: #include <petscblaslapack.h>
      9: #include <petscbt.h>
     10: #include <petsc/private/kernels/blocktranspose.h>

     12: PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
     13: {
     14:   PetscErrorCode       ierr;
     15:   PetscBool            flg;
     16:   char                 type[256];

     19:   PetscObjectOptionsBegin((PetscObject)A);
     20:   PetscOptionsFList("-mat_seqaij_type","Matrix SeqAIJ type","MatSeqAIJSetType",MatSeqAIJList,"seqaij",type,256,&flg);
     21:   if (flg) {
     22:     MatSeqAIJSetType(A,type);
     23:   }
     24:   PetscOptionsEnd();
     25:   return(0);
     26: }

     28: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
     29: {
     31:   PetscInt       i,m,n;
     32:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

     35:   MatGetSize(A,&m,&n);
     36:   PetscArrayzero(norms,n);
     37:   if (type == NORM_2) {
     38:     for (i=0; i<aij->i[m]; i++) {
     39:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
     40:     }
     41:   } else if (type == NORM_1) {
     42:     for (i=0; i<aij->i[m]; i++) {
     43:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
     44:     }
     45:   } else if (type == NORM_INFINITY) {
     46:     for (i=0; i<aij->i[m]; i++) {
     47:       norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
     48:     }
     49:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");

     51:   if (type == NORM_2) {
     52:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
     53:   }
     54:   return(0);
     55: }

     57: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
     58: {
     59:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
     60:   PetscInt        i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
     61:   const PetscInt  *jj = a->j,*ii = a->i;
     62:   PetscInt        *rows;
     63:   PetscErrorCode  ierr;

     66:   for (i=0; i<m; i++) {
     67:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
     68:       cnt++;
     69:     }
     70:   }
     71:   PetscMalloc1(cnt,&rows);
     72:   cnt  = 0;
     73:   for (i=0; i<m; i++) {
     74:     if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
     75:       rows[cnt] = i;
     76:       cnt++;
     77:     }
     78:   }
     79:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
     80:   return(0);
     81: }

     83: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
     84: {
     85:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
     86:   const MatScalar *aa = a->a;
     87:   PetscInt        i,m=A->rmap->n,cnt = 0;
     88:   const PetscInt  *ii = a->i,*jj = a->j,*diag;
     89:   PetscInt        *rows;
     90:   PetscErrorCode  ierr;

     93:   MatMarkDiagonal_SeqAIJ(A);
     94:   diag = a->diag;
     95:   for (i=0; i<m; i++) {
     96:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
     97:       cnt++;
     98:     }
     99:   }
    100:   PetscMalloc1(cnt,&rows);
    101:   cnt  = 0;
    102:   for (i=0; i<m; i++) {
    103:     if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
    104:       rows[cnt++] = i;
    105:     }
    106:   }
    107:   *nrows = cnt;
    108:   *zrows = rows;
    109:   return(0);
    110: }

    112: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
    113: {
    114:   PetscInt       nrows,*rows;

    118:   *zrows = NULL;
    119:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
    120:   ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
    121:   return(0);
    122: }

    124: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
    125: {
    126:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    127:   const MatScalar *aa;
    128:   PetscInt        m=A->rmap->n,cnt = 0;
    129:   const PetscInt  *ii;
    130:   PetscInt        n,i,j,*rows;
    131:   PetscErrorCode  ierr;

    134:   MatSeqAIJGetArrayRead(A,&aa);
    135:   *keptrows = NULL;
    136:   ii        = a->i;
    137:   for (i=0; i<m; i++) {
    138:     n = ii[i+1] - ii[i];
    139:     if (!n) {
    140:       cnt++;
    141:       goto ok1;
    142:     }
    143:     for (j=ii[i]; j<ii[i+1]; j++) {
    144:       if (aa[j] != 0.0) goto ok1;
    145:     }
    146:     cnt++;
    147: ok1:;
    148:   }
    149:   if (!cnt) {
    150:     MatSeqAIJRestoreArrayRead(A,&aa);
    151:     return(0);
    152:   }
    153:   PetscMalloc1(A->rmap->n-cnt,&rows);
    154:   cnt  = 0;
    155:   for (i=0; i<m; i++) {
    156:     n = ii[i+1] - ii[i];
    157:     if (!n) continue;
    158:     for (j=ii[i]; j<ii[i+1]; j++) {
    159:       if (aa[j] != 0.0) {
    160:         rows[cnt++] = i;
    161:         break;
    162:       }
    163:     }
    164:   }
    165:   MatSeqAIJRestoreArrayRead(A,&aa);
    166:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
    167:   return(0);
    168: }

    170: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
    171: {
    172:   PetscErrorCode    ierr;
    173:   Mat_SeqAIJ        *aij = (Mat_SeqAIJ*) Y->data;
    174:   PetscInt          i,m = Y->rmap->n;
    175:   const PetscInt    *diag;
    176:   MatScalar         *aa;
    177:   const PetscScalar *v;
    178:   PetscBool         missing;
    179: #if defined(PETSC_HAVE_DEVICE)
    180:   PetscBool         inserted = PETSC_FALSE;
    181: #endif

    184:   if (Y->assembled) {
    185:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
    186:     if (!missing) {
    187:       diag = aij->diag;
    188:       VecGetArrayRead(D,&v);
    189:       MatSeqAIJGetArray(Y,&aa);
    190:       if (is == INSERT_VALUES) {
    191: #if defined(PETSC_HAVE_DEVICE)
    192:         inserted = PETSC_TRUE;
    193: #endif
    194:         for (i=0; i<m; i++) {
    195:           aa[diag[i]] = v[i];
    196:         }
    197:       } else {
    198:         for (i=0; i<m; i++) {
    199: #if defined(PETSC_HAVE_DEVICE)
    200:           if (v[i] != 0.0) inserted = PETSC_TRUE;
    201: #endif
    202:           aa[diag[i]] += v[i];
    203:         }
    204:       }
    205:       MatSeqAIJRestoreArray(Y,&aa);
    206: #if defined(PETSC_HAVE_DEVICE)
    207:       if (inserted) Y->offloadmask = PETSC_OFFLOAD_CPU;
    208: #endif
    209:       VecRestoreArrayRead(D,&v);
    210:       return(0);
    211:     }
    212:     MatSeqAIJInvalidateDiagonal(Y);
    213:   }
    214:   MatDiagonalSet_Default(Y,D,is);
    215:   return(0);
    216: }

    218: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    219: {
    220:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    222:   PetscInt       i,ishift;

    225:   *m = A->rmap->n;
    226:   if (!ia) return(0);
    227:   ishift = 0;
    228:   if (symmetric && !A->structurally_symmetric) {
    229:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
    230:   } else if (oshift == 1) {
    231:     PetscInt *tia;
    232:     PetscInt nz = a->i[A->rmap->n];
    233:     /* malloc space and  add 1 to i and j indices */
    234:     PetscMalloc1(A->rmap->n+1,&tia);
    235:     for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
    236:     *ia = tia;
    237:     if (ja) {
    238:       PetscInt *tja;
    239:       PetscMalloc1(nz+1,&tja);
    240:       for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
    241:       *ja = tja;
    242:     }
    243:   } else {
    244:     *ia = a->i;
    245:     if (ja) *ja = a->j;
    246:   }
    247:   return(0);
    248: }

    250: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    251: {

    255:   if (!ia) return(0);
    256:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
    257:     PetscFree(*ia);
    258:     if (ja) {PetscFree(*ja);}
    259:   }
    260:   return(0);
    261: }

    263: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    264: {
    265:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    267:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
    268:   PetscInt       nz = a->i[m],row,*jj,mr,col;

    271:   *nn = n;
    272:   if (!ia) return(0);
    273:   if (symmetric) {
    274:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
    275:   } else {
    276:     PetscCalloc1(n,&collengths);
    277:     PetscMalloc1(n+1,&cia);
    278:     PetscMalloc1(nz,&cja);
    279:     jj   = a->j;
    280:     for (i=0; i<nz; i++) {
    281:       collengths[jj[i]]++;
    282:     }
    283:     cia[0] = oshift;
    284:     for (i=0; i<n; i++) {
    285:       cia[i+1] = cia[i] + collengths[i];
    286:     }
    287:     PetscArrayzero(collengths,n);
    288:     jj   = a->j;
    289:     for (row=0; row<m; row++) {
    290:       mr = a->i[row+1] - a->i[row];
    291:       for (i=0; i<mr; i++) {
    292:         col = *jj++;

    294:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
    295:       }
    296:     }
    297:     PetscFree(collengths);
    298:     *ia  = cia; *ja = cja;
    299:   }
    300:   return(0);
    301: }

    303: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    304: {

    308:   if (!ia) return(0);

    310:   PetscFree(*ia);
    311:   PetscFree(*ja);
    312:   return(0);
    313: }

    315: /*
    316:  MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
    317:  MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
    318:  spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
    319: */
    320: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
    321: {
    322:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    324:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
    325:   PetscInt       nz = a->i[m],row,mr,col,tmp;
    326:   PetscInt       *cspidx;
    327:   const PetscInt *jj;

    330:   *nn = n;
    331:   if (!ia) return(0);

    333:   PetscCalloc1(n,&collengths);
    334:   PetscMalloc1(n+1,&cia);
    335:   PetscMalloc1(nz,&cja);
    336:   PetscMalloc1(nz,&cspidx);
    337:   jj   = a->j;
    338:   for (i=0; i<nz; i++) {
    339:     collengths[jj[i]]++;
    340:   }
    341:   cia[0] = oshift;
    342:   for (i=0; i<n; i++) {
    343:     cia[i+1] = cia[i] + collengths[i];
    344:   }
    345:   PetscArrayzero(collengths,n);
    346:   jj   = a->j;
    347:   for (row=0; row<m; row++) {
    348:     mr = a->i[row+1] - a->i[row];
    349:     for (i=0; i<mr; i++) {
    350:       col         = *jj++;
    351:       tmp         = cia[col] + collengths[col]++ - oshift;
    352:       cspidx[tmp] = a->i[row] + i; /* index of a->j */
    353:       cja[tmp]    = row + oshift;
    354:     }
    355:   }
    356:   PetscFree(collengths);
    357:   *ia    = cia;
    358:   *ja    = cja;
    359:   *spidx = cspidx;
    360:   return(0);
    361: }

    363: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool  *done)
    364: {

    368:   MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
    369:   PetscFree(*spidx);
    370:   return(0);
    371: }

    373: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
    374: {
    375:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    376:   PetscInt       *ai = a->i;

    380:   PetscArraycpy(a->a+ai[row],v,ai[row+1]-ai[row]);
    381: #if defined(PETSC_HAVE_DEVICE)
    382:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && ai[row+1]-ai[row]) A->offloadmask = PETSC_OFFLOAD_CPU;
    383: #endif
    384:   return(0);
    385: }

    387: /*
    388:     MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions

    390:       -   a single row of values is set with each call
    391:       -   no row or column indices are negative or (in error) larger than the number of rows or columns
    392:       -   the values are always added to the matrix, not set
    393:       -   no new locations are introduced in the nonzero structure of the matrix

    395:      This does NOT assume the global column indices are sorted

    397: */

    399: #include <petsc/private/isimpl.h>
    400: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    401: {
    402:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    403:   PetscInt       low,high,t,row,nrow,i,col,l;
    404:   const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
    405:   PetscInt       lastcol = -1;
    406:   MatScalar      *ap,value,*aa = a->a;
    407:   const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;

    409:   row  = ridx[im[0]];
    410:   rp   = aj + ai[row];
    411:   ap   = aa + ai[row];
    412:   nrow = ailen[row];
    413:   low  = 0;
    414:   high = nrow;
    415:   for (l=0; l<n; l++) { /* loop over added columns */
    416:     col = cidx[in[l]];
    417:     value = v[l];

    419:     if (col <= lastcol) low = 0;
    420:     else high = nrow;
    421:     lastcol = col;
    422:     while (high-low > 5) {
    423:       t = (low+high)/2;
    424:       if (rp[t] > col) high = t;
    425:       else low = t;
    426:     }
    427:     for (i=low; i<high; i++) {
    428:       if (rp[i] == col) {
    429:         ap[i] += value;
    430:         low = i + 1;
    431:         break;
    432:       }
    433:     }
    434:   }
    435: #if defined(PETSC_HAVE_DEVICE)
    436:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
    437: #endif
    438:   return 0;
    439: }

    441: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    442: {
    443:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    444:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
    445:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
    447:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
    448:   MatScalar      *ap=NULL,value=0.0,*aa;
    449:   PetscBool      ignorezeroentries = a->ignorezeroentries;
    450:   PetscBool      roworiented       = a->roworiented;
    451: #if defined(PETSC_HAVE_DEVICE)
    452:   PetscBool      inserted          = PETSC_FALSE;
    453: #endif

    456: #if defined(PETSC_HAVE_DEVICE)
    457:   if (A->offloadmask == PETSC_OFFLOAD_GPU) {
    458:     const PetscScalar *dummy;
    459:     MatSeqAIJGetArrayRead(A,&dummy);
    460:     MatSeqAIJRestoreArrayRead(A,&dummy);
    461:   }
    462: #endif
    463:   aa = a->a;
    464:   for (k=0; k<m; k++) { /* loop over added rows */
    465:     row = im[k];
    466:     if (row < 0) continue;
    467:     if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
    468:     rp   = aj + ai[row];
    469:     if (!A->structure_only) ap = aa + ai[row];
    470:     rmax = imax[row]; nrow = ailen[row];
    471:     low  = 0;
    472:     high = nrow;
    473:     for (l=0; l<n; l++) { /* loop over added columns */
    474:       if (in[l] < 0) continue;
    475:       if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
    476:       col = in[l];
    477:       if (v && !A->structure_only) value = roworiented ? v[l + k*n] : v[k + l*m];
    478:       if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;

    480:       if (col <= lastcol) low = 0;
    481:       else high = nrow;
    482:       lastcol = col;
    483:       while (high-low > 5) {
    484:         t = (low+high)/2;
    485:         if (rp[t] > col) high = t;
    486:         else low = t;
    487:       }
    488:       for (i=low; i<high; i++) {
    489:         if (rp[i] > col) break;
    490:         if (rp[i] == col) {
    491:           if (!A->structure_only) {
    492:             if (is == ADD_VALUES) {
    493:               ap[i] += value;
    494:               (void)PetscLogFlops(1.0);
    495:             }
    496:             else ap[i] = value;
    497: #if defined(PETSC_HAVE_DEVICE)
    498:             inserted = PETSC_TRUE;
    499: #endif
    500:           }
    501:           low = i + 1;
    502:           goto noinsert;
    503:         }
    504:       }
    505:       if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
    506:       if (nonew == 1) goto noinsert;
    507:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
    508:       if (A->structure_only) {
    509:         MatSeqXAIJReallocateAIJ_structure_only(A,A->rmap->n,1,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
    510:       } else {
    511:         MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    512:       }
    513:       N = nrow++ - 1; a->nz++; high++;
    514:       /* shift up all the later entries in this row */
    515:       PetscArraymove(rp+i+1,rp+i,N-i+1);
    516:       rp[i] = col;
    517:       if (!A->structure_only){
    518:         PetscArraymove(ap+i+1,ap+i,N-i+1);
    519:         ap[i] = value;
    520:       }
    521:       low = i + 1;
    522:       A->nonzerostate++;
    523: #if defined(PETSC_HAVE_DEVICE)
    524:       inserted = PETSC_TRUE;
    525: #endif
    526: noinsert:;
    527:     }
    528:     ailen[row] = nrow;
    529:   }
    530: #if defined(PETSC_HAVE_DEVICE)
    531:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && inserted) A->offloadmask = PETSC_OFFLOAD_CPU;
    532: #endif
    533:   return(0);
    534: }

    537: PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    538: {
    539:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    540:   PetscInt       *rp,k,row;
    541:   PetscInt       *ai = a->i;
    543:   PetscInt       *aj = a->j;
    544:   MatScalar      *aa = a->a,*ap;

    547:   if (A->was_assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot call on assembled matrix.");
    548:   if (m*n+a->nz > a->maxnz) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Number of entries in matrix will be larger than maximum nonzeros allocated for %D in MatSeqAIJSetTotalPreallocation()",a->maxnz);
    549:   for (k=0; k<m; k++) { /* loop over added rows */
    550:     row  = im[k];
    551:     rp   = aj + ai[row];
    552:     ap   = aa + ai[row];

    554:     PetscMemcpy(rp,in,n*sizeof(PetscInt));
    555:     if (!A->structure_only) {
    556:       if (v) {
    557:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
    558:         v   += n;
    559:       } else {
    560:         PetscMemzero(ap,n*sizeof(PetscScalar));
    561:       }
    562:     }
    563:     a->ilen[row] = n;
    564:     a->imax[row] = n;
    565:     a->i[row+1]  = a->i[row]+n;
    566:     a->nz       += n;
    567:   }
    568: #if defined(PETSC_HAVE_DEVICE)
    569:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
    570: #endif
    571:   return(0);
    572: }

    574: /*@
    575:     MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.

    577:   Input Parameters:
    578: +  A - the SeqAIJ matrix
    579: -  nztotal - bound on the number of nonzeros

    581:   Level: advanced

    583:   Notes:
    584:     This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
    585:     Simply call MatSetValues() after this call to provide the matrix entries in the usual manner. This matrix may be used
    586:     as always with multiple matrix assemblies.

    588: .seealso: MatSetOption(), MAT_SORTED_FULL, MatSetValues(), MatSeqAIJSetPreallocation()
    589: @*/

    591: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A,PetscInt nztotal)
    592: {
    594:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    597:   PetscLayoutSetUp(A->rmap);
    598:   PetscLayoutSetUp(A->cmap);
    599:   a->maxnz  = nztotal;
    600:   if (!a->imax) {
    601:     PetscMalloc1(A->rmap->n,&a->imax);
    602:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
    603:   }
    604:   if (!a->ilen) {
    605:     PetscMalloc1(A->rmap->n,&a->ilen);
    606:     PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
    607:   } else {
    608:     PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
    609:   }

    611:   /* allocate the matrix space */
    612:   if (A->structure_only) {
    613:     PetscMalloc1(nztotal,&a->j);
    614:     PetscMalloc1(A->rmap->n+1,&a->i);
    615:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*sizeof(PetscInt));
    616:   } else {
    617:     PetscMalloc3(nztotal,&a->a,nztotal,&a->j,A->rmap->n+1,&a->i);
    618:     PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*(sizeof(PetscScalar)+sizeof(PetscInt)));
    619:   }
    620:   a->i[0] = 0;
    621:   if (A->structure_only) {
    622:     a->singlemalloc = PETSC_FALSE;
    623:     a->free_a       = PETSC_FALSE;
    624:   } else {
    625:     a->singlemalloc = PETSC_TRUE;
    626:     a->free_a       = PETSC_TRUE;
    627:   }
    628:   a->free_ij         = PETSC_TRUE;
    629:   A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
    630:   A->preallocated   = PETSC_TRUE;
    631:   return(0);
    632: }

    634: PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    635: {
    636:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    637:   PetscInt       *rp,k,row;
    638:   PetscInt       *ai = a->i,*ailen = a->ilen;
    640:   PetscInt       *aj = a->j;
    641:   MatScalar      *aa = a->a,*ap;

    644:   for (k=0; k<m; k++) { /* loop over added rows */
    645:     row  = im[k];
    646:     if (PetscUnlikelyDebug(n > a->imax[row])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Preallocation for row %D does not match number of columns provided",n);
    647:     rp   = aj + ai[row];
    648:     ap   = aa + ai[row];
    649:     if (!A->was_assembled) {
    650:       PetscMemcpy(rp,in,n*sizeof(PetscInt));
    651:     }
    652:     if (!A->structure_only) {
    653:       if (v) {
    654:         PetscMemcpy(ap,v,n*sizeof(PetscScalar));
    655:         v   += n;
    656:       } else {
    657:         PetscMemzero(ap,n*sizeof(PetscScalar));
    658:       }
    659:     }
    660:     ailen[row] = n;
    661:     a->nz      += n;
    662:   }
    663: #if defined(PETSC_HAVE_DEVICE)
    664:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
    665: #endif
    666:   return(0);
    667: }

    670: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
    671: {
    672:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    673:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
    674:   PetscInt   *ai = a->i,*ailen = a->ilen;
    675:   MatScalar  *ap,*aa = a->a;

    678:   for (k=0; k<m; k++) { /* loop over rows */
    679:     row = im[k];
    680:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
    681:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
    682:     rp   = aj + ai[row]; ap = aa + ai[row];
    683:     nrow = ailen[row];
    684:     for (l=0; l<n; l++) { /* loop over columns */
    685:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
    686:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
    687:       col  = in[l];
    688:       high = nrow; low = 0; /* assume unsorted */
    689:       while (high-low > 5) {
    690:         t = (low+high)/2;
    691:         if (rp[t] > col) high = t;
    692:         else low = t;
    693:       }
    694:       for (i=low; i<high; i++) {
    695:         if (rp[i] > col) break;
    696:         if (rp[i] == col) {
    697:           *v++ = ap[i];
    698:           goto finished;
    699:         }
    700:       }
    701:       *v++ = 0.0;
    702: finished:;
    703:     }
    704:   }
    705:   return(0);
    706: }

    708: PetscErrorCode MatView_SeqAIJ_Binary(Mat mat,PetscViewer viewer)
    709: {
    710:   Mat_SeqAIJ        *A = (Mat_SeqAIJ*)mat->data;
    711:   const PetscScalar *av;
    712:   PetscInt          header[4],M,N,m,nz,i;
    713:   PetscInt          *rowlens;
    714:   PetscErrorCode    ierr;

    717:   PetscViewerSetUp(viewer);

    719:   M  = mat->rmap->N;
    720:   N  = mat->cmap->N;
    721:   m  = mat->rmap->n;
    722:   nz = A->nz;

    724:   /* write matrix header */
    725:   header[0] = MAT_FILE_CLASSID;
    726:   header[1] = M; header[2] = N; header[3] = nz;
    727:   PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);

    729:   /* fill in and store row lengths */
    730:   PetscMalloc1(m,&rowlens);
    731:   for (i=0; i<m; i++) rowlens[i] = A->i[i+1] - A->i[i];
    732:   PetscViewerBinaryWrite(viewer,rowlens,m,PETSC_INT);
    733:   PetscFree(rowlens);
    734:   /* store column indices */
    735:   PetscViewerBinaryWrite(viewer,A->j,nz,PETSC_INT);
    736:   /* store nonzero values */
    737:   MatSeqAIJGetArrayRead(mat,&av);
    738:   PetscViewerBinaryWrite(viewer,av,nz,PETSC_SCALAR);
    739:   MatSeqAIJRestoreArrayRead(mat,&av);

    741:   /* write block size option to the viewer's .info file */
    742:   MatView_Binary_BlockSizes(mat,viewer);
    743:   return(0);
    744: }

    746: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
    747: {
    749:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    750:   PetscInt       i,k,m=A->rmap->N;

    753:   PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    754:   for (i=0; i<m; i++) {
    755:     PetscViewerASCIIPrintf(viewer,"row %D:",i);
    756:     for (k=a->i[i]; k<a->i[i+1]; k++) {
    757:       PetscViewerASCIIPrintf(viewer," (%D) ",a->j[k]);
    758:     }
    759:     PetscViewerASCIIPrintf(viewer,"\n");
    760:   }
    761:   PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    762:   return(0);
    763: }

    765: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

    767: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
    768: {
    769:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    770:   const PetscScalar *av;
    771:   PetscErrorCode    ierr;
    772:   PetscInt          i,j,m = A->rmap->n;
    773:   const char        *name;
    774:   PetscViewerFormat format;

    777:   if (A->structure_only) {
    778:     MatView_SeqAIJ_ASCII_structonly(A,viewer);
    779:     return(0);
    780:   }

    782:   PetscViewerGetFormat(viewer,&format);
    783:   if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) return(0);

    785:   /* trigger copy to CPU if needed */
    786:   MatSeqAIJGetArrayRead(A,&av);
    787:   MatSeqAIJRestoreArrayRead(A,&av);
    788:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
    789:     PetscInt nofinalvalue = 0;
    790:     if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
    791:       /* Need a dummy value to ensure the dimension of the matrix. */
    792:       nofinalvalue = 1;
    793:     }
    794:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    795:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
    796:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
    797: #if defined(PETSC_USE_COMPLEX)
    798:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
    799: #else
    800:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
    801: #endif
    802:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

    804:     for (i=0; i<m; i++) {
    805:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    806: #if defined(PETSC_USE_COMPLEX)
    807:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",i+1,a->j[j]+1,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    808: #else
    809:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
    810: #endif
    811:       }
    812:     }
    813:     if (nofinalvalue) {
    814: #if defined(PETSC_USE_COMPLEX)
    815:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
    816: #else
    817:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
    818: #endif
    819:     }
    820:     PetscObjectGetName((PetscObject)A,&name);
    821:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
    822:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    823:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
    824:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    825:     for (i=0; i<m; i++) {
    826:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
    827:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    828: #if defined(PETSC_USE_COMPLEX)
    829:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    830:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    831:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    832:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
    833:         } else if (PetscRealPart(a->a[j]) != 0.0) {
    834:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    835:         }
    836: #else
    837:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
    838: #endif
    839:       }
    840:       PetscViewerASCIIPrintf(viewer,"\n");
    841:     }
    842:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    843:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
    844:     PetscInt nzd=0,fshift=1,*sptr;
    845:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    846:     PetscMalloc1(m+1,&sptr);
    847:     for (i=0; i<m; i++) {
    848:       sptr[i] = nzd+1;
    849:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    850:         if (a->j[j] >= i) {
    851: #if defined(PETSC_USE_COMPLEX)
    852:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
    853: #else
    854:           if (a->a[j] != 0.0) nzd++;
    855: #endif
    856:         }
    857:       }
    858:     }
    859:     sptr[m] = nzd+1;
    860:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
    861:     for (i=0; i<m+1; i+=6) {
    862:       if (i+4<m) {
    863:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);
    864:       } else if (i+3<m) {
    865:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
    866:       } else if (i+2<m) {
    867:         PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
    868:       } else if (i+1<m) {
    869:         PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
    870:       } else if (i<m) {
    871:         PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
    872:       } else {
    873:         PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
    874:       }
    875:     }
    876:     PetscViewerASCIIPrintf(viewer,"\n");
    877:     PetscFree(sptr);
    878:     for (i=0; i<m; i++) {
    879:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    880:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
    881:       }
    882:       PetscViewerASCIIPrintf(viewer,"\n");
    883:     }
    884:     PetscViewerASCIIPrintf(viewer,"\n");
    885:     for (i=0; i<m; i++) {
    886:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    887:         if (a->j[j] >= i) {
    888: #if defined(PETSC_USE_COMPLEX)
    889:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
    890:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    891:           }
    892: #else
    893:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
    894: #endif
    895:         }
    896:       }
    897:       PetscViewerASCIIPrintf(viewer,"\n");
    898:     }
    899:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    900:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
    901:     PetscInt    cnt = 0,jcnt;
    902:     PetscScalar value;
    903: #if defined(PETSC_USE_COMPLEX)
    904:     PetscBool   realonly = PETSC_TRUE;

    906:     for (i=0; i<a->i[m]; i++) {
    907:       if (PetscImaginaryPart(a->a[i]) != 0.0) {
    908:         realonly = PETSC_FALSE;
    909:         break;
    910:       }
    911:     }
    912: #endif

    914:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    915:     for (i=0; i<m; i++) {
    916:       jcnt = 0;
    917:       for (j=0; j<A->cmap->n; j++) {
    918:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
    919:           value = a->a[cnt++];
    920:           jcnt++;
    921:         } else {
    922:           value = 0.0;
    923:         }
    924: #if defined(PETSC_USE_COMPLEX)
    925:         if (realonly) {
    926:           PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
    927:         } else {
    928:           PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
    929:         }
    930: #else
    931:         PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
    932: #endif
    933:       }
    934:       PetscViewerASCIIPrintf(viewer,"\n");
    935:     }
    936:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    937:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
    938:     PetscInt fshift=1;
    939:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    940: #if defined(PETSC_USE_COMPLEX)
    941:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
    942: #else
    943:     PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
    944: #endif
    945:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
    946:     for (i=0; i<m; i++) {
    947:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    948: #if defined(PETSC_USE_COMPLEX)
    949:         PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    950: #else
    951:         PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
    952: #endif
    953:       }
    954:     }
    955:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    956:   } else {
    957:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    958:     if (A->factortype) {
    959:       for (i=0; i<m; i++) {
    960:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
    961:         /* L part */
    962:         for (j=a->i[i]; j<a->i[i+1]; j++) {
    963: #if defined(PETSC_USE_COMPLEX)
    964:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    965:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    966:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    967:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
    968:           } else {
    969:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    970:           }
    971: #else
    972:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
    973: #endif
    974:         }
    975:         /* diagonal */
    976:         j = a->diag[i];
    977: #if defined(PETSC_USE_COMPLEX)
    978:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
    979:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
    980:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    981:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
    982:         } else {
    983:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
    984:         }
    985: #else
    986:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
    987: #endif

    989:         /* U part */
    990:         for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
    991: #if defined(PETSC_USE_COMPLEX)
    992:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    993:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    994:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    995:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
    996:           } else {
    997:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    998:           }
    999: #else
    1000:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
    1001: #endif
    1002:         }
    1003:         PetscViewerASCIIPrintf(viewer,"\n");
    1004:       }
    1005:     } else {
    1006:       for (i=0; i<m; i++) {
    1007:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
    1008:         for (j=a->i[i]; j<a->i[i+1]; j++) {
    1009: #if defined(PETSC_USE_COMPLEX)
    1010:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    1011:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
    1012:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    1013:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
    1014:           } else {
    1015:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
    1016:           }
    1017: #else
    1018:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
    1019: #endif
    1020:         }
    1021:         PetscViewerASCIIPrintf(viewer,"\n");
    1022:       }
    1023:     }
    1024:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    1025:   }
    1026:   PetscViewerFlush(viewer);
    1027:   return(0);
    1028: }

    1030: #include <petscdraw.h>
    1031: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
    1032: {
    1033:   Mat               A  = (Mat) Aa;
    1034:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1035:   PetscErrorCode    ierr;
    1036:   PetscInt          i,j,m = A->rmap->n;
    1037:   int               color;
    1038:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r;
    1039:   PetscViewer       viewer;
    1040:   PetscViewerFormat format;

    1043:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
    1044:   PetscViewerGetFormat(viewer,&format);
    1045:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

    1047:   /* loop over matrix elements drawing boxes */

    1049:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
    1050:     PetscDrawCollectiveBegin(draw);
    1051:     /* Blue for negative, Cyan for zero and  Red for positive */
    1052:     color = PETSC_DRAW_BLUE;
    1053:     for (i=0; i<m; i++) {
    1054:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    1055:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1056:         x_l = a->j[j]; x_r = x_l + 1.0;
    1057:         if (PetscRealPart(a->a[j]) >=  0.) continue;
    1058:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1059:       }
    1060:     }
    1061:     color = PETSC_DRAW_CYAN;
    1062:     for (i=0; i<m; i++) {
    1063:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    1064:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1065:         x_l = a->j[j]; x_r = x_l + 1.0;
    1066:         if (a->a[j] !=  0.) continue;
    1067:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1068:       }
    1069:     }
    1070:     color = PETSC_DRAW_RED;
    1071:     for (i=0; i<m; i++) {
    1072:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    1073:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1074:         x_l = a->j[j]; x_r = x_l + 1.0;
    1075:         if (PetscRealPart(a->a[j]) <=  0.) continue;
    1076:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1077:       }
    1078:     }
    1079:     PetscDrawCollectiveEnd(draw);
    1080:   } else {
    1081:     /* use contour shading to indicate magnitude of values */
    1082:     /* first determine max of all nonzero values */
    1083:     PetscReal minv = 0.0, maxv = 0.0;
    1084:     PetscInt  nz = a->nz, count = 0;
    1085:     PetscDraw popup;

    1087:     for (i=0; i<nz; i++) {
    1088:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
    1089:     }
    1090:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
    1091:     PetscDrawGetPopup(draw,&popup);
    1092:     PetscDrawScalePopup(popup,minv,maxv);

    1094:     PetscDrawCollectiveBegin(draw);
    1095:     for (i=0; i<m; i++) {
    1096:       y_l = m - i - 1.0;
    1097:       y_r = y_l + 1.0;
    1098:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1099:         x_l = a->j[j];
    1100:         x_r = x_l + 1.0;
    1101:         color = PetscDrawRealToColor(PetscAbsScalar(a->a[count]),minv,maxv);
    1102:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    1103:         count++;
    1104:       }
    1105:     }
    1106:     PetscDrawCollectiveEnd(draw);
    1107:   }
    1108:   return(0);
    1109: }

    1111: #include <petscdraw.h>
    1112: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
    1113: {
    1115:   PetscDraw      draw;
    1116:   PetscReal      xr,yr,xl,yl,h,w;
    1117:   PetscBool      isnull;

    1120:   PetscViewerDrawGetDraw(viewer,0,&draw);
    1121:   PetscDrawIsNull(draw,&isnull);
    1122:   if (isnull) return(0);

    1124:   xr   = A->cmap->n; yr  = A->rmap->n; h = yr/10.0; w = xr/10.0;
    1125:   xr  += w;          yr += h;         xl = -w;     yl = -h;
    1126:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
    1127:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
    1128:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
    1129:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
    1130:   PetscDrawSave(draw);
    1131:   return(0);
    1132: }

    1134: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
    1135: {
    1137:   PetscBool      iascii,isbinary,isdraw;

    1140:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    1141:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    1142:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    1143:   if (iascii) {
    1144:     MatView_SeqAIJ_ASCII(A,viewer);
    1145:   } else if (isbinary) {
    1146:     MatView_SeqAIJ_Binary(A,viewer);
    1147:   } else if (isdraw) {
    1148:     MatView_SeqAIJ_Draw(A,viewer);
    1149:   }
    1150:   MatView_SeqAIJ_Inode(A,viewer);
    1151:   return(0);
    1152: }

    1154: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
    1155: {
    1156:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1158:   PetscInt       fshift = 0,i,*ai = a->i,*aj = a->j,*imax = a->imax;
    1159:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
    1160:   MatScalar      *aa    = a->a,*ap;
    1161:   PetscReal      ratio  = 0.6;

    1164:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);
    1165:   MatSeqAIJInvalidateDiagonal(A);
    1166:   if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
    1167:     /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
    1168:     MatAssemblyEnd_SeqAIJ_Inode(A,mode);
    1169:     return(0);
    1170:   }

    1172:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
    1173:   for (i=1; i<m; i++) {
    1174:     /* move each row back by the amount of empty slots (fshift) before it*/
    1175:     fshift += imax[i-1] - ailen[i-1];
    1176:     rmax    = PetscMax(rmax,ailen[i]);
    1177:     if (fshift) {
    1178:       ip = aj + ai[i];
    1179:       ap = aa + ai[i];
    1180:       N  = ailen[i];
    1181:       PetscArraymove(ip-fshift,ip,N);
    1182:       if (!A->structure_only) {
    1183:         PetscArraymove(ap-fshift,ap,N);
    1184:       }
    1185:     }
    1186:     ai[i] = ai[i-1] + ailen[i-1];
    1187:   }
    1188:   if (m) {
    1189:     fshift += imax[m-1] - ailen[m-1];
    1190:     ai[m]   = ai[m-1] + ailen[m-1];
    1191:   }

    1193:   /* reset ilen and imax for each row */
    1194:   a->nonzerorowcnt = 0;
    1195:   if (A->structure_only) {
    1196:     PetscFree(a->imax);
    1197:     PetscFree(a->ilen);
    1198:   } else { /* !A->structure_only */
    1199:     for (i=0; i<m; i++) {
    1200:       ailen[i] = imax[i] = ai[i+1] - ai[i];
    1201:       a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
    1202:     }
    1203:   }
    1204:   a->nz = ai[m];
    1205:   if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);

    1207:   MatMarkDiagonal_SeqAIJ(A);
    1208:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
    1209:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
    1210:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);

    1212:   A->info.mallocs    += a->reallocs;
    1213:   a->reallocs         = 0;
    1214:   A->info.nz_unneeded = (PetscReal)fshift;
    1215:   a->rmax             = rmax;

    1217:   if (!A->structure_only) {
    1218:     MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
    1219:   }
    1220:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);
    1221:   return(0);
    1222: }

    1224: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
    1225: {
    1226:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1227:   PetscInt       i,nz = a->nz;
    1228:   MatScalar      *aa;

    1232:   MatSeqAIJGetArray(A,&aa);
    1233:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
    1234:   MatSeqAIJRestoreArray(A,&aa);
    1235:   MatSeqAIJInvalidateDiagonal(A);
    1236: #if defined(PETSC_HAVE_DEVICE)
    1237:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    1238: #endif
    1239:   return(0);
    1240: }

    1242: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
    1243: {
    1244:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1245:   PetscInt       i,nz = a->nz;
    1246:   MatScalar      *aa;

    1250:   MatSeqAIJGetArray(A,&aa);
    1251:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
    1252:   MatSeqAIJRestoreArray(A,&aa);
    1253:   MatSeqAIJInvalidateDiagonal(A);
    1254: #if defined(PETSC_HAVE_DEVICE)
    1255:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    1256: #endif
    1257:   return(0);
    1258: }

    1260: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
    1261: {
    1262:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1266:   PetscArrayzero(a->a,a->i[A->rmap->n]);
    1267:   MatSeqAIJInvalidateDiagonal(A);
    1268: #if defined(PETSC_HAVE_DEVICE)
    1269:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    1270: #endif
    1271:   return(0);
    1272: }

    1274: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
    1275: {
    1276:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1280: #if defined(PETSC_USE_LOG)
    1281:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
    1282: #endif
    1283:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
    1284:   ISDestroy(&a->row);
    1285:   ISDestroy(&a->col);
    1286:   PetscFree(a->diag);
    1287:   PetscFree(a->ibdiag);
    1288:   PetscFree(a->imax);
    1289:   PetscFree(a->ilen);
    1290:   PetscFree(a->ipre);
    1291:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
    1292:   PetscFree(a->solve_work);
    1293:   ISDestroy(&a->icol);
    1294:   PetscFree(a->saved_values);
    1295:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);

    1297:   MatDestroy_SeqAIJ_Inode(A);
    1298:   PetscFree(A->data);

    1300:   /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
    1301:      That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
    1302:      that is hard to properly add this data to the MatProduct data. We free it here to avoid
    1303:      users reusing the matrix object with different data to incur in obscure segmentation faults
    1304:      due to different matrix sizes */
    1305:   PetscObjectCompose((PetscObject)A,"__PETSc__ab_dense",NULL);

    1307:   PetscObjectChangeTypeName((PetscObject)A,NULL);
    1308:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
    1309:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
    1310:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
    1311:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
    1312:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
    1313:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
    1314: #if defined(PETSC_HAVE_CUDA)
    1315:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcusparse_C",NULL);
    1316:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",NULL);
    1317:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",NULL);
    1318: #endif
    1319: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    1320:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijkokkos_C",NULL);
    1321: #endif
    1322:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcrl_C",NULL);
    1323: #if defined(PETSC_HAVE_ELEMENTAL)
    1324:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
    1325: #endif
    1326: #if defined(PETSC_HAVE_SCALAPACK)
    1327:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_scalapack_C",NULL);
    1328: #endif
    1329: #if defined(PETSC_HAVE_HYPRE)
    1330:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
    1331:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",NULL);
    1332: #endif
    1333:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
    1334:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsell_C",NULL);
    1335:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_is_C",NULL);
    1336:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
    1337:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
    1338:   PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
    1339:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
    1340:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
    1341:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_is_seqaij_C",NULL);
    1342:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqdense_seqaij_C",NULL);
    1343:   PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaij_C",NULL);
    1344:   return(0);
    1345: }

    1347: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
    1348: {
    1349:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1353:   switch (op) {
    1354:   case MAT_ROW_ORIENTED:
    1355:     a->roworiented = flg;
    1356:     break;
    1357:   case MAT_KEEP_NONZERO_PATTERN:
    1358:     a->keepnonzeropattern = flg;
    1359:     break;
    1360:   case MAT_NEW_NONZERO_LOCATIONS:
    1361:     a->nonew = (flg ? 0 : 1);
    1362:     break;
    1363:   case MAT_NEW_NONZERO_LOCATION_ERR:
    1364:     a->nonew = (flg ? -1 : 0);
    1365:     break;
    1366:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
    1367:     a->nonew = (flg ? -2 : 0);
    1368:     break;
    1369:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
    1370:     a->nounused = (flg ? -1 : 0);
    1371:     break;
    1372:   case MAT_IGNORE_ZERO_ENTRIES:
    1373:     a->ignorezeroentries = flg;
    1374:     break;
    1375:   case MAT_SPD:
    1376:   case MAT_SYMMETRIC:
    1377:   case MAT_STRUCTURALLY_SYMMETRIC:
    1378:   case MAT_HERMITIAN:
    1379:   case MAT_SYMMETRY_ETERNAL:
    1380:   case MAT_STRUCTURE_ONLY:
    1381:     /* These options are handled directly by MatSetOption() */
    1382:     break;
    1383:   case MAT_FORCE_DIAGONAL_ENTRIES:
    1384:   case MAT_IGNORE_OFF_PROC_ENTRIES:
    1385:   case MAT_USE_HASH_TABLE:
    1386:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
    1387:     break;
    1388:   case MAT_USE_INODES:
    1389:     MatSetOption_SeqAIJ_Inode(A,MAT_USE_INODES,flg);
    1390:     break;
    1391:   case MAT_SUBMAT_SINGLEIS:
    1392:     A->submat_singleis = flg;
    1393:     break;
    1394:   case MAT_SORTED_FULL:
    1395:     if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
    1396:     else     A->ops->setvalues = MatSetValues_SeqAIJ;
    1397:     break;
    1398:   case MAT_FORM_EXPLICIT_TRANSPOSE:
    1399:     A->form_explicit_transpose = flg;
    1400:     break;
    1401:   default:
    1402:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
    1403:   }
    1404:   return(0);
    1405: }

    1407: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
    1408: {
    1409:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1410:   PetscErrorCode    ierr;
    1411:   PetscInt          i,j,n,*ai=a->i,*aj=a->j;
    1412:   PetscScalar       *x;
    1413:   const PetscScalar *aa;

    1416:   VecGetLocalSize(v,&n);
    1417:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    1418:   MatSeqAIJGetArrayRead(A,&aa);
    1419:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
    1420:     PetscInt *diag=a->diag;
    1421:     VecGetArrayWrite(v,&x);
    1422:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
    1423:     VecRestoreArrayWrite(v,&x);
    1424:     MatSeqAIJRestoreArrayRead(A,&aa);
    1425:     return(0);
    1426:   }

    1428:   VecGetArrayWrite(v,&x);
    1429:   for (i=0; i<n; i++) {
    1430:     x[i] = 0.0;
    1431:     for (j=ai[i]; j<ai[i+1]; j++) {
    1432:       if (aj[j] == i) {
    1433:         x[i] = aa[j];
    1434:         break;
    1435:       }
    1436:     }
    1437:   }
    1438:   VecRestoreArrayWrite(v,&x);
    1439:   MatSeqAIJRestoreArrayRead(A,&aa);
    1440:   return(0);
    1441: }

    1443: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
    1444: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
    1445: {
    1446:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1447:   PetscScalar       *y;
    1448:   const PetscScalar *x;
    1449:   PetscErrorCode    ierr;
    1450:   PetscInt          m = A->rmap->n;
    1451: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1452:   const MatScalar   *v;
    1453:   PetscScalar       alpha;
    1454:   PetscInt          n,i,j;
    1455:   const PetscInt    *idx,*ii,*ridx=NULL;
    1456:   Mat_CompressedRow cprow    = a->compressedrow;
    1457:   PetscBool         usecprow = cprow.use;
    1458: #endif

    1461:   if (zz != yy) {VecCopy(zz,yy);}
    1462:   VecGetArrayRead(xx,&x);
    1463:   VecGetArray(yy,&y);

    1465: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1466:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
    1467: #else
    1468:   if (usecprow) {
    1469:     m    = cprow.nrows;
    1470:     ii   = cprow.i;
    1471:     ridx = cprow.rindex;
    1472:   } else {
    1473:     ii = a->i;
    1474:   }
    1475:   for (i=0; i<m; i++) {
    1476:     idx = a->j + ii[i];
    1477:     v   = a->a + ii[i];
    1478:     n   = ii[i+1] - ii[i];
    1479:     if (usecprow) {
    1480:       alpha = x[ridx[i]];
    1481:     } else {
    1482:       alpha = x[i];
    1483:     }
    1484:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
    1485:   }
    1486: #endif
    1487:   PetscLogFlops(2.0*a->nz);
    1488:   VecRestoreArrayRead(xx,&x);
    1489:   VecRestoreArray(yy,&y);
    1490:   return(0);
    1491: }

    1493: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
    1494: {

    1498:   VecSet(yy,0.0);
    1499:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
    1500:   return(0);
    1501: }

    1503: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>

    1505: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
    1506: {
    1507:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1508:   PetscScalar       *y;
    1509:   const PetscScalar *x;
    1510:   const MatScalar   *aa;
    1511:   PetscErrorCode    ierr;
    1512:   PetscInt          m=A->rmap->n;
    1513:   const PetscInt    *aj,*ii,*ridx=NULL;
    1514:   PetscInt          n,i;
    1515:   PetscScalar       sum;
    1516:   PetscBool         usecprow=a->compressedrow.use;

    1518: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1519: #pragma disjoint(*x,*y,*aa)
    1520: #endif

    1523:   if (a->inode.use && a->inode.checked) {
    1524:     MatMult_SeqAIJ_Inode(A,xx,yy);
    1525:     return(0);
    1526:   }
    1527:   VecGetArrayRead(xx,&x);
    1528:   VecGetArray(yy,&y);
    1529:   ii   = a->i;
    1530:   if (usecprow) { /* use compressed row format */
    1531:     PetscArrayzero(y,m);
    1532:     m    = a->compressedrow.nrows;
    1533:     ii   = a->compressedrow.i;
    1534:     ridx = a->compressedrow.rindex;
    1535:     for (i=0; i<m; i++) {
    1536:       n           = ii[i+1] - ii[i];
    1537:       aj          = a->j + ii[i];
    1538:       aa          = a->a + ii[i];
    1539:       sum         = 0.0;
    1540:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1541:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1542:       y[*ridx++] = sum;
    1543:     }
    1544:   } else { /* do not use compressed row format */
    1545: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
    1546:     aj   = a->j;
    1547:     aa   = a->a;
    1548:     fortranmultaij_(&m,x,ii,aj,aa,y);
    1549: #else
    1550:     for (i=0; i<m; i++) {
    1551:       n           = ii[i+1] - ii[i];
    1552:       aj          = a->j + ii[i];
    1553:       aa          = a->a + ii[i];
    1554:       sum         = 0.0;
    1555:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1556:       y[i] = sum;
    1557:     }
    1558: #endif
    1559:   }
    1560:   PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
    1561:   VecRestoreArrayRead(xx,&x);
    1562:   VecRestoreArray(yy,&y);
    1563:   return(0);
    1564: }

    1566: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
    1567: {
    1568:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1569:   PetscScalar       *y;
    1570:   const PetscScalar *x;
    1571:   const MatScalar   *aa;
    1572:   PetscErrorCode    ierr;
    1573:   PetscInt          m=A->rmap->n;
    1574:   const PetscInt    *aj,*ii,*ridx=NULL;
    1575:   PetscInt          n,i,nonzerorow=0;
    1576:   PetscScalar       sum;
    1577:   PetscBool         usecprow=a->compressedrow.use;

    1579: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1580: #pragma disjoint(*x,*y,*aa)
    1581: #endif

    1584:   VecGetArrayRead(xx,&x);
    1585:   VecGetArray(yy,&y);
    1586:   if (usecprow) { /* use compressed row format */
    1587:     m    = a->compressedrow.nrows;
    1588:     ii   = a->compressedrow.i;
    1589:     ridx = a->compressedrow.rindex;
    1590:     for (i=0; i<m; i++) {
    1591:       n           = ii[i+1] - ii[i];
    1592:       aj          = a->j + ii[i];
    1593:       aa          = a->a + ii[i];
    1594:       sum         = 0.0;
    1595:       nonzerorow += (n>0);
    1596:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1597:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1598:       y[*ridx++] = sum;
    1599:     }
    1600:   } else { /* do not use compressed row format */
    1601:     ii = a->i;
    1602:     for (i=0; i<m; i++) {
    1603:       n           = ii[i+1] - ii[i];
    1604:       aj          = a->j + ii[i];
    1605:       aa          = a->a + ii[i];
    1606:       sum         = 0.0;
    1607:       nonzerorow += (n>0);
    1608:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1609:       y[i] = sum;
    1610:     }
    1611:   }
    1612:   PetscLogFlops(2.0*a->nz - nonzerorow);
    1613:   VecRestoreArrayRead(xx,&x);
    1614:   VecRestoreArray(yy,&y);
    1615:   return(0);
    1616: }

    1618: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1619: {
    1620:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1621:   PetscScalar       *y,*z;
    1622:   const PetscScalar *x;
    1623:   const MatScalar   *aa;
    1624:   PetscErrorCode    ierr;
    1625:   PetscInt          m = A->rmap->n,*aj,*ii;
    1626:   PetscInt          n,i,*ridx=NULL;
    1627:   PetscScalar       sum;
    1628:   PetscBool         usecprow=a->compressedrow.use;

    1631:   VecGetArrayRead(xx,&x);
    1632:   VecGetArrayPair(yy,zz,&y,&z);
    1633:   if (usecprow) { /* use compressed row format */
    1634:     if (zz != yy) {
    1635:       PetscArraycpy(z,y,m);
    1636:     }
    1637:     m    = a->compressedrow.nrows;
    1638:     ii   = a->compressedrow.i;
    1639:     ridx = a->compressedrow.rindex;
    1640:     for (i=0; i<m; i++) {
    1641:       n   = ii[i+1] - ii[i];
    1642:       aj  = a->j + ii[i];
    1643:       aa  = a->a + ii[i];
    1644:       sum = y[*ridx];
    1645:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1646:       z[*ridx++] = sum;
    1647:     }
    1648:   } else { /* do not use compressed row format */
    1649:     ii = a->i;
    1650:     for (i=0; i<m; i++) {
    1651:       n   = ii[i+1] - ii[i];
    1652:       aj  = a->j + ii[i];
    1653:       aa  = a->a + ii[i];
    1654:       sum = y[i];
    1655:       PetscSparseDenseMaxDot(sum,x,aa,aj,n);
    1656:       z[i] = sum;
    1657:     }
    1658:   }
    1659:   PetscLogFlops(2.0*a->nz);
    1660:   VecRestoreArrayRead(xx,&x);
    1661:   VecRestoreArrayPair(yy,zz,&y,&z);
    1662:   return(0);
    1663: }

    1665: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
    1666: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1667: {
    1668:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1669:   PetscScalar       *y,*z;
    1670:   const PetscScalar *x;
    1671:   const MatScalar   *aa;
    1672:   PetscErrorCode    ierr;
    1673:   const PetscInt    *aj,*ii,*ridx=NULL;
    1674:   PetscInt          m = A->rmap->n,n,i;
    1675:   PetscScalar       sum;
    1676:   PetscBool         usecprow=a->compressedrow.use;

    1679:   if (a->inode.use && a->inode.checked) {
    1680:     MatMultAdd_SeqAIJ_Inode(A,xx,yy,zz);
    1681:     return(0);
    1682:   }
    1683:   VecGetArrayRead(xx,&x);
    1684:   VecGetArrayPair(yy,zz,&y,&z);
    1685:   if (usecprow) { /* use compressed row format */
    1686:     if (zz != yy) {
    1687:       PetscArraycpy(z,y,m);
    1688:     }
    1689:     m    = a->compressedrow.nrows;
    1690:     ii   = a->compressedrow.i;
    1691:     ridx = a->compressedrow.rindex;
    1692:     for (i=0; i<m; i++) {
    1693:       n   = ii[i+1] - ii[i];
    1694:       aj  = a->j + ii[i];
    1695:       aa  = a->a + ii[i];
    1696:       sum = y[*ridx];
    1697:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1698:       z[*ridx++] = sum;
    1699:     }
    1700:   } else { /* do not use compressed row format */
    1701:     ii = a->i;
    1702: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
    1703:     aj = a->j;
    1704:     aa = a->a;
    1705:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
    1706: #else
    1707:     for (i=0; i<m; i++) {
    1708:       n   = ii[i+1] - ii[i];
    1709:       aj  = a->j + ii[i];
    1710:       aa  = a->a + ii[i];
    1711:       sum = y[i];
    1712:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1713:       z[i] = sum;
    1714:     }
    1715: #endif
    1716:   }
    1717:   PetscLogFlops(2.0*a->nz);
    1718:   VecRestoreArrayRead(xx,&x);
    1719:   VecRestoreArrayPair(yy,zz,&y,&z);
    1720:   return(0);
    1721: }

    1723: /*
    1724:      Adds diagonal pointers to sparse matrix structure.
    1725: */
    1726: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
    1727: {
    1728:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1730:   PetscInt       i,j,m = A->rmap->n;

    1733:   if (!a->diag) {
    1734:     PetscMalloc1(m,&a->diag);
    1735:     PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
    1736:   }
    1737:   for (i=0; i<A->rmap->n; i++) {
    1738:     a->diag[i] = a->i[i+1];
    1739:     for (j=a->i[i]; j<a->i[i+1]; j++) {
    1740:       if (a->j[j] == i) {
    1741:         a->diag[i] = j;
    1742:         break;
    1743:       }
    1744:     }
    1745:   }
    1746:   return(0);
    1747: }

    1749: PetscErrorCode MatShift_SeqAIJ(Mat A,PetscScalar v)
    1750: {
    1751:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1752:   const PetscInt    *diag = (const PetscInt*)a->diag;
    1753:   const PetscInt    *ii = (const PetscInt*) a->i;
    1754:   PetscInt          i,*mdiag = NULL;
    1755:   PetscErrorCode    ierr;
    1756:   PetscInt          cnt = 0; /* how many diagonals are missing */

    1759:   if (!A->preallocated || !a->nz) {
    1760:     MatSeqAIJSetPreallocation(A,1,NULL);
    1761:     MatShift_Basic(A,v);
    1762:     return(0);
    1763:   }

    1765:   if (a->diagonaldense) {
    1766:     cnt = 0;
    1767:   } else {
    1768:     PetscCalloc1(A->rmap->n,&mdiag);
    1769:     for (i=0; i<A->rmap->n; i++) {
    1770:       if (diag[i] >= ii[i+1]) {
    1771:         cnt++;
    1772:         mdiag[i] = 1;
    1773:       }
    1774:     }
    1775:   }
    1776:   if (!cnt) {
    1777:     MatShift_Basic(A,v);
    1778:   } else {
    1779:     PetscScalar *olda = a->a;  /* preserve pointers to current matrix nonzeros structure and values */
    1780:     PetscInt    *oldj = a->j, *oldi = a->i;
    1781:     PetscBool   singlemalloc = a->singlemalloc,free_a = a->free_a,free_ij = a->free_ij;

    1783:     a->a = NULL;
    1784:     a->j = NULL;
    1785:     a->i = NULL;
    1786:     /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
    1787:     for (i=0; i<A->rmap->n; i++) {
    1788:       a->imax[i] += mdiag[i];
    1789:       a->imax[i] = PetscMin(a->imax[i],A->cmap->n);
    1790:     }
    1791:     MatSeqAIJSetPreallocation_SeqAIJ(A,0,a->imax);

    1793:     /* copy old values into new matrix data structure */
    1794:     for (i=0; i<A->rmap->n; i++) {
    1795:       MatSetValues(A,1,&i,a->imax[i] - mdiag[i],&oldj[oldi[i]],&olda[oldi[i]],ADD_VALUES);
    1796:       if (i < A->cmap->n) {
    1797:         MatSetValue(A,i,i,v,ADD_VALUES);
    1798:       }
    1799:     }
    1800:     MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    1801:     MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    1802:     if (singlemalloc) {
    1803:       PetscFree3(olda,oldj,oldi);
    1804:     } else {
    1805:       if (free_a)  {PetscFree(olda);}
    1806:       if (free_ij) {PetscFree(oldj);}
    1807:       if (free_ij) {PetscFree(oldi);}
    1808:     }
    1809:   }
    1810:   PetscFree(mdiag);
    1811:   a->diagonaldense = PETSC_TRUE;
    1812:   return(0);
    1813: }

    1815: /*
    1816:      Checks for missing diagonals
    1817: */
    1818: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
    1819: {
    1820:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1821:   PetscInt       *diag,*ii = a->i,i;

    1825:   *missing = PETSC_FALSE;
    1826:   if (A->rmap->n > 0 && !ii) {
    1827:     *missing = PETSC_TRUE;
    1828:     if (d) *d = 0;
    1829:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
    1830:   } else {
    1831:     PetscInt n;
    1832:     n = PetscMin(A->rmap->n, A->cmap->n);
    1833:     diag = a->diag;
    1834:     for (i=0; i<n; i++) {
    1835:       if (diag[i] >= ii[i+1]) {
    1836:         *missing = PETSC_TRUE;
    1837:         if (d) *d = i;
    1838:         PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
    1839:         break;
    1840:       }
    1841:     }
    1842:   }
    1843:   return(0);
    1844: }

    1846: #include <petscblaslapack.h>
    1847: #include <petsc/private/kernels/blockinvert.h>

    1849: /*
    1850:     Note that values is allocated externally by the PC and then passed into this routine
    1851: */
    1852: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
    1853: {
    1854:   PetscErrorCode  ierr;
    1855:   PetscInt        n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
    1856:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;
    1857:   const PetscReal shift = 0.0;
    1858:   PetscInt        ipvt[5];
    1859:   PetscScalar     work[25],*v_work;

    1862:   allowzeropivot = PetscNot(A->erroriffailure);
    1863:   for (i=0; i<nblocks; i++) ncnt += bsizes[i];
    1864:   if (ncnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Total blocksizes %D doesn't match number matrix rows %D",ncnt,n);
    1865:   for (i=0; i<nblocks; i++) {
    1866:     bsizemax = PetscMax(bsizemax,bsizes[i]);
    1867:   }
    1868:   PetscMalloc1(bsizemax,&indx);
    1869:   if (bsizemax > 7) {
    1870:     PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
    1871:   }
    1872:   ncnt = 0;
    1873:   for (i=0; i<nblocks; i++) {
    1874:     for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
    1875:     MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
    1876:     switch (bsizes[i]) {
    1877:     case 1:
    1878:       *diag = 1.0/(*diag);
    1879:       break;
    1880:     case 2:
    1881:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
    1882:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1883:       PetscKernel_A_gets_transpose_A_2(diag);
    1884:       break;
    1885:     case 3:
    1886:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
    1887:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1888:       PetscKernel_A_gets_transpose_A_3(diag);
    1889:       break;
    1890:     case 4:
    1891:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
    1892:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1893:       PetscKernel_A_gets_transpose_A_4(diag);
    1894:       break;
    1895:     case 5:
    1896:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
    1897:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1898:       PetscKernel_A_gets_transpose_A_5(diag);
    1899:       break;
    1900:     case 6:
    1901:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
    1902:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1903:       PetscKernel_A_gets_transpose_A_6(diag);
    1904:       break;
    1905:     case 7:
    1906:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
    1907:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1908:       PetscKernel_A_gets_transpose_A_7(diag);
    1909:       break;
    1910:     default:
    1911:       PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
    1912:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1913:       PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
    1914:     }
    1915:     ncnt   += bsizes[i];
    1916:     diag += bsizes[i]*bsizes[i];
    1917:   }
    1918:   if (bsizemax > 7) {
    1919:     PetscFree2(v_work,v_pivots);
    1920:   }
    1921:   PetscFree(indx);
    1922:   return(0);
    1923: }

    1925: /*
    1926:    Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
    1927: */
    1928: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
    1929: {
    1930:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
    1931:   PetscErrorCode  ierr;
    1932:   PetscInt        i,*diag,m = A->rmap->n;
    1933:   const MatScalar *v;
    1934:   PetscScalar     *idiag,*mdiag;

    1937:   if (a->idiagvalid) return(0);
    1938:   MatMarkDiagonal_SeqAIJ(A);
    1939:   diag = a->diag;
    1940:   if (!a->idiag) {
    1941:     PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
    1942:     PetscLogObjectMemory((PetscObject)A,3*m*sizeof(PetscScalar));
    1943:   }

    1945:   mdiag = a->mdiag;
    1946:   idiag = a->idiag;
    1947:   MatSeqAIJGetArrayRead(A,&v);
    1948:   if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
    1949:     for (i=0; i<m; i++) {
    1950:       mdiag[i] = v[diag[i]];
    1951:       if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
    1952:         if (PetscRealPart(fshift)) {
    1953:           PetscInfo1(A,"Zero diagonal on row %D\n",i);
    1954:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    1955:           A->factorerror_zeropivot_value = 0.0;
    1956:           A->factorerror_zeropivot_row   = i;
    1957:         } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
    1958:       }
    1959:       idiag[i] = 1.0/v[diag[i]];
    1960:     }
    1961:     PetscLogFlops(m);
    1962:   } else {
    1963:     for (i=0; i<m; i++) {
    1964:       mdiag[i] = v[diag[i]];
    1965:       idiag[i] = omega/(fshift + v[diag[i]]);
    1966:     }
    1967:     PetscLogFlops(2.0*m);
    1968:   }
    1969:   a->idiagvalid = PETSC_TRUE;
    1970:   MatSeqAIJRestoreArrayRead(A,&v);
    1971:   return(0);
    1972: }

    1974: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
    1975: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
    1976: {
    1977:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1978:   PetscScalar       *x,d,sum,*t,scale;
    1979:   const MatScalar   *v,*idiag=NULL,*mdiag,*aa;
    1980:   const PetscScalar *b, *bs,*xb, *ts;
    1981:   PetscErrorCode    ierr;
    1982:   PetscInt          n,m = A->rmap->n,i;
    1983:   const PetscInt    *idx,*diag;

    1986:   if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
    1987:     MatSOR_SeqAIJ_Inode(A,bb,omega,flag,fshift,its,lits,xx);
    1988:     return(0);
    1989:   }
    1990:   its = its*lits;

    1992:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
    1993:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
    1994:   a->fshift = fshift;
    1995:   a->omega  = omega;

    1997:   diag  = a->diag;
    1998:   t     = a->ssor_work;
    1999:   idiag = a->idiag;
    2000:   mdiag = a->mdiag;

    2002:   MatSeqAIJGetArrayRead(A,&aa);
    2003:   VecGetArray(xx,&x);
    2004:   VecGetArrayRead(bb,&b);
    2005:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
    2006:   if (flag == SOR_APPLY_UPPER) {
    2007:     /* apply (U + D/omega) to the vector */
    2008:     bs = b;
    2009:     for (i=0; i<m; i++) {
    2010:       d   = fshift + mdiag[i];
    2011:       n   = a->i[i+1] - diag[i] - 1;
    2012:       idx = a->j + diag[i] + 1;
    2013:       v   = aa + diag[i] + 1;
    2014:       sum = b[i]*d/omega;
    2015:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
    2016:       x[i] = sum;
    2017:     }
    2018:     VecRestoreArray(xx,&x);
    2019:     VecRestoreArrayRead(bb,&b);
    2020:     MatSeqAIJRestoreArrayRead(A,&aa);
    2021:     PetscLogFlops(a->nz);
    2022:     return(0);
    2023:   }

    2025:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
    2026:   else if (flag & SOR_EISENSTAT) {
    2027:     /* Let  A = L + U + D; where L is lower triangular,
    2028:     U is upper triangular, E = D/omega; This routine applies

    2030:             (L + E)^{-1} A (U + E)^{-1}

    2032:     to a vector efficiently using Eisenstat's trick.
    2033:     */
    2034:     scale = (2.0/omega) - 1.0;

    2036:     /*  x = (E + U)^{-1} b */
    2037:     for (i=m-1; i>=0; i--) {
    2038:       n   = a->i[i+1] - diag[i] - 1;
    2039:       idx = a->j + diag[i] + 1;
    2040:       v   = aa + diag[i] + 1;
    2041:       sum = b[i];
    2042:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2043:       x[i] = sum*idiag[i];
    2044:     }

    2046:     /*  t = b - (2*E - D)x */
    2047:     v = aa;
    2048:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

    2050:     /*  t = (E + L)^{-1}t */
    2051:     ts   = t;
    2052:     diag = a->diag;
    2053:     for (i=0; i<m; i++) {
    2054:       n   = diag[i] - a->i[i];
    2055:       idx = a->j + a->i[i];
    2056:       v   = aa + a->i[i];
    2057:       sum = t[i];
    2058:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
    2059:       t[i] = sum*idiag[i];
    2060:       /*  x = x + t */
    2061:       x[i] += t[i];
    2062:     }

    2064:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
    2065:     VecRestoreArray(xx,&x);
    2066:     VecRestoreArrayRead(bb,&b);
    2067:     return(0);
    2068:   }
    2069:   if (flag & SOR_ZERO_INITIAL_GUESS) {
    2070:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    2071:       for (i=0; i<m; i++) {
    2072:         n   = diag[i] - a->i[i];
    2073:         idx = a->j + a->i[i];
    2074:         v   = aa + a->i[i];
    2075:         sum = b[i];
    2076:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2077:         t[i] = sum;
    2078:         x[i] = sum*idiag[i];
    2079:       }
    2080:       xb   = t;
    2081:       PetscLogFlops(a->nz);
    2082:     } else xb = b;
    2083:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2084:       for (i=m-1; i>=0; i--) {
    2085:         n   = a->i[i+1] - diag[i] - 1;
    2086:         idx = a->j + diag[i] + 1;
    2087:         v   = aa + diag[i] + 1;
    2088:         sum = xb[i];
    2089:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2090:         if (xb == b) {
    2091:           x[i] = sum*idiag[i];
    2092:         } else {
    2093:           x[i] = (1-omega)*x[i] + sum*idiag[i];  /* omega in idiag */
    2094:         }
    2095:       }
    2096:       PetscLogFlops(a->nz); /* assumes 1/2 in upper */
    2097:     }
    2098:     its--;
    2099:   }
    2100:   while (its--) {
    2101:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    2102:       for (i=0; i<m; i++) {
    2103:         /* lower */
    2104:         n   = diag[i] - a->i[i];
    2105:         idx = a->j + a->i[i];
    2106:         v   = aa + a->i[i];
    2107:         sum = b[i];
    2108:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2109:         t[i] = sum;             /* save application of the lower-triangular part */
    2110:         /* upper */
    2111:         n   = a->i[i+1] - diag[i] - 1;
    2112:         idx = a->j + diag[i] + 1;
    2113:         v   = aa + diag[i] + 1;
    2114:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2115:         x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
    2116:       }
    2117:       xb   = t;
    2118:       PetscLogFlops(2.0*a->nz);
    2119:     } else xb = b;
    2120:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    2121:       for (i=m-1; i>=0; i--) {
    2122:         sum = xb[i];
    2123:         if (xb == b) {
    2124:           /* whole matrix (no checkpointing available) */
    2125:           n   = a->i[i+1] - a->i[i];
    2126:           idx = a->j + a->i[i];
    2127:           v   = aa + a->i[i];
    2128:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2129:           x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
    2130:         } else { /* lower-triangular part has been saved, so only apply upper-triangular */
    2131:           n   = a->i[i+1] - diag[i] - 1;
    2132:           idx = a->j + diag[i] + 1;
    2133:           v   = aa + diag[i] + 1;
    2134:           PetscSparseDenseMinusDot(sum,x,v,idx,n);
    2135:           x[i] = (1. - omega)*x[i] + sum*idiag[i];  /* omega in idiag */
    2136:         }
    2137:       }
    2138:       if (xb == b) {
    2139:         PetscLogFlops(2.0*a->nz);
    2140:       } else {
    2141:         PetscLogFlops(a->nz); /* assumes 1/2 in upper */
    2142:       }
    2143:     }
    2144:   }
    2145:   MatSeqAIJRestoreArrayRead(A,&aa);
    2146:   VecRestoreArray(xx,&x);
    2147:   VecRestoreArrayRead(bb,&b);
    2148:   return(0);
    2149: }

    2152: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
    2153: {
    2154:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    2157:   info->block_size   = 1.0;
    2158:   info->nz_allocated = a->maxnz;
    2159:   info->nz_used      = a->nz;
    2160:   info->nz_unneeded  = (a->maxnz - a->nz);
    2161:   info->assemblies   = A->num_ass;
    2162:   info->mallocs      = A->info.mallocs;
    2163:   info->memory       = ((PetscObject)A)->mem;
    2164:   if (A->factortype) {
    2165:     info->fill_ratio_given  = A->info.fill_ratio_given;
    2166:     info->fill_ratio_needed = A->info.fill_ratio_needed;
    2167:     info->factor_mallocs    = A->info.factor_mallocs;
    2168:   } else {
    2169:     info->fill_ratio_given  = 0;
    2170:     info->fill_ratio_needed = 0;
    2171:     info->factor_mallocs    = 0;
    2172:   }
    2173:   return(0);
    2174: }

    2176: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    2177: {
    2178:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2179:   PetscInt          i,m = A->rmap->n - 1;
    2180:   PetscErrorCode    ierr;
    2181:   const PetscScalar *xx;
    2182:   PetscScalar       *bb,*aa;
    2183:   PetscInt          d = 0;

    2186:   if (x && b) {
    2187:     VecGetArrayRead(x,&xx);
    2188:     VecGetArray(b,&bb);
    2189:     for (i=0; i<N; i++) {
    2190:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2191:       if (rows[i] >= A->cmap->n) continue;
    2192:       bb[rows[i]] = diag*xx[rows[i]];
    2193:     }
    2194:     VecRestoreArrayRead(x,&xx);
    2195:     VecRestoreArray(b,&bb);
    2196:   }

    2198:   MatSeqAIJGetArray(A,&aa);
    2199:   if (a->keepnonzeropattern) {
    2200:     for (i=0; i<N; i++) {
    2201:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2202:       PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);
    2203:     }
    2204:     if (diag != 0.0) {
    2205:       for (i=0; i<N; i++) {
    2206:         d = rows[i];
    2207:         if (rows[i] >= A->cmap->n) continue;
    2208:         if (a->diag[d] >= a->i[d+1]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in the zeroed row %D",d);
    2209:       }
    2210:       for (i=0; i<N; i++) {
    2211:         if (rows[i] >= A->cmap->n) continue;
    2212:         aa[a->diag[rows[i]]] = diag;
    2213:       }
    2214:     }
    2215:   } else {
    2216:     if (diag != 0.0) {
    2217:       for (i=0; i<N; i++) {
    2218:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2219:         if (a->ilen[rows[i]] > 0) {
    2220:           if (rows[i] >= A->cmap->n) {
    2221:             a->ilen[rows[i]] = 0;
    2222:           } else {
    2223:             a->ilen[rows[i]]    = 1;
    2224:             aa[a->i[rows[i]]]   = diag;
    2225:             a->j[a->i[rows[i]]] = rows[i];
    2226:           }
    2227:         } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
    2228:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
    2229:         }
    2230:       }
    2231:     } else {
    2232:       for (i=0; i<N; i++) {
    2233:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2234:         a->ilen[rows[i]] = 0;
    2235:       }
    2236:     }
    2237:     A->nonzerostate++;
    2238:   }
    2239:   MatSeqAIJRestoreArray(A,&aa);
    2240: #if defined(PETSC_HAVE_DEVICE)
    2241:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    2242: #endif
    2243:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
    2244:   return(0);
    2245: }

    2247: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    2248: {
    2249:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2250:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
    2251:   PetscErrorCode    ierr;
    2252:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
    2253:   const PetscScalar *xx;
    2254:   PetscScalar       *bb,*aa;

    2257:   if (!N) return(0);
    2258:   MatSeqAIJGetArray(A,&aa);
    2259:   if (x && b) {
    2260:     VecGetArrayRead(x,&xx);
    2261:     VecGetArray(b,&bb);
    2262:     vecs = PETSC_TRUE;
    2263:   }
    2264:   PetscCalloc1(A->rmap->n,&zeroed);
    2265:   for (i=0; i<N; i++) {
    2266:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    2267:     PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);

    2269:     zeroed[rows[i]] = PETSC_TRUE;
    2270:   }
    2271:   for (i=0; i<A->rmap->n; i++) {
    2272:     if (!zeroed[i]) {
    2273:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    2274:         if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
    2275:           if (vecs) bb[i] -= aa[j]*xx[a->j[j]];
    2276:           aa[j] = 0.0;
    2277:         }
    2278:       }
    2279:     } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
    2280:   }
    2281:   if (x && b) {
    2282:     VecRestoreArrayRead(x,&xx);
    2283:     VecRestoreArray(b,&bb);
    2284:   }
    2285:   PetscFree(zeroed);
    2286:   if (diag != 0.0) {
    2287:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
    2288:     if (missing) {
    2289:       for (i=0; i<N; i++) {
    2290:         if (rows[i] >= A->cmap->N) continue;
    2291:         if (a->nonew && rows[i] >= d) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D (%D)",d,rows[i]);
    2292:         MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
    2293:       }
    2294:     } else {
    2295:       for (i=0; i<N; i++) {
    2296:         aa[a->diag[rows[i]]] = diag;
    2297:       }
    2298:     }
    2299:   }
    2300:   MatSeqAIJRestoreArray(A,&aa);
    2301: #if defined(PETSC_HAVE_DEVICE)
    2302:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    2303: #endif
    2304:   (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
    2305:   return(0);
    2306: }

    2308: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    2309: {
    2310:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2311:   const PetscScalar *aa = a->a;
    2312:   PetscInt          *itmp;
    2313: #if defined(PETSC_HAVE_DEVICE)
    2314:   PetscErrorCode    ierr;
    2315:   PetscBool         rest = PETSC_FALSE;
    2316: #endif

    2319: #if defined(PETSC_HAVE_DEVICE)
    2320:   if (v && A->offloadmask == PETSC_OFFLOAD_GPU) {
    2321:     /* triggers copy to CPU */
    2322:     rest = PETSC_TRUE;
    2323:     MatSeqAIJGetArrayRead(A,&aa);
    2324:   } else aa = a->a;
    2325: #endif
    2326:   *nz = a->i[row+1] - a->i[row];
    2327:   if (v) *v = (PetscScalar*)(aa + a->i[row]);
    2328:   if (idx) {
    2329:     itmp = a->j + a->i[row];
    2330:     if (*nz) *idx = itmp;
    2331:     else *idx = NULL;
    2332:   }
    2333: #if defined(PETSC_HAVE_DEVICE)
    2334:   if (rest) {
    2335:     MatSeqAIJRestoreArrayRead(A,&aa);
    2336:   }
    2337: #endif
    2338:   return(0);
    2339: }

    2341: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    2342: {
    2344:   if (nz)  *nz = 0;
    2345:   if (idx) *idx = NULL;
    2346:   if (v)   *v = NULL;
    2347:   return(0);
    2348: }

    2350: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
    2351: {
    2352:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
    2353:   const MatScalar *v;
    2354:   PetscReal       sum = 0.0;
    2355:   PetscErrorCode  ierr;
    2356:   PetscInt        i,j;

    2359:   MatSeqAIJGetArrayRead(A,&v);
    2360:   if (type == NORM_FROBENIUS) {
    2361: #if defined(PETSC_USE_REAL___FP16)
    2362:     PetscBLASInt one = 1,nz = a->nz;
    2363:     PetscStackCallBLAS("BLASnrm2",*nrm = BLASnrm2_(&nz,v,&one));
    2364: #else
    2365:     for (i=0; i<a->nz; i++) {
    2366:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    2367:     }
    2368:     *nrm = PetscSqrtReal(sum);
    2369: #endif
    2370:     PetscLogFlops(2.0*a->nz);
    2371:   } else if (type == NORM_1) {
    2372:     PetscReal *tmp;
    2373:     PetscInt  *jj = a->j;
    2374:     PetscCalloc1(A->cmap->n+1,&tmp);
    2375:     *nrm = 0.0;
    2376:     for (j=0; j<a->nz; j++) {
    2377:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
    2378:     }
    2379:     for (j=0; j<A->cmap->n; j++) {
    2380:       if (tmp[j] > *nrm) *nrm = tmp[j];
    2381:     }
    2382:     PetscFree(tmp);
    2383:     PetscLogFlops(PetscMax(a->nz-1,0));
    2384:   } else if (type == NORM_INFINITY) {
    2385:     *nrm = 0.0;
    2386:     for (j=0; j<A->rmap->n; j++) {
    2387:       const PetscScalar *v2 = v + a->i[j];
    2388:       sum = 0.0;
    2389:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
    2390:         sum += PetscAbsScalar(*v2); v2++;
    2391:       }
    2392:       if (sum > *nrm) *nrm = sum;
    2393:     }
    2394:     PetscLogFlops(PetscMax(a->nz-1,0));
    2395:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
    2396:   MatSeqAIJRestoreArrayRead(A,&v);
    2397:   return(0);
    2398: }

    2400: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
    2401: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
    2402: {
    2404:   PetscInt       i,j,anzj;
    2405:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
    2406:   PetscInt       an=A->cmap->N,am=A->rmap->N;
    2407:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

    2410:   /* Allocate space for symbolic transpose info and work array */
    2411:   PetscCalloc1(an+1,&ati);
    2412:   PetscMalloc1(ai[am],&atj);
    2413:   PetscMalloc1(an,&atfill);

    2415:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
    2416:   /* Note: offset by 1 for fast conversion into csr format. */
    2417:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
    2418:   /* Form ati for csr format of A^T. */
    2419:   for (i=0;i<an;i++) ati[i+1] += ati[i];

    2421:   /* Copy ati into atfill so we have locations of the next free space in atj */
    2422:   PetscArraycpy(atfill,ati,an);

    2424:   /* Walk through A row-wise and mark nonzero entries of A^T. */
    2425:   for (i=0;i<am;i++) {
    2426:     anzj = ai[i+1] - ai[i];
    2427:     for (j=0;j<anzj;j++) {
    2428:       atj[atfill[*aj]] = i;
    2429:       atfill[*aj++]   += 1;
    2430:     }
    2431:   }

    2433:   /* Clean up temporary space and complete requests. */
    2434:   PetscFree(atfill);
    2435:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
    2436:   MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
    2437:   MatSetType(*B,((PetscObject)A)->type_name);

    2439:   b          = (Mat_SeqAIJ*)((*B)->data);
    2440:   b->free_a  = PETSC_FALSE;
    2441:   b->free_ij = PETSC_TRUE;
    2442:   b->nonew   = 0;
    2443:   return(0);
    2444: }

    2446: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
    2447: {
    2448:   Mat_SeqAIJ      *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
    2449:   PetscInt        *adx,*bdx,*aii,*bii,*aptr,*bptr;
    2450:   const MatScalar *va,*vb;
    2451:   PetscErrorCode  ierr;
    2452:   PetscInt        ma,na,mb,nb, i;

    2455:   MatGetSize(A,&ma,&na);
    2456:   MatGetSize(B,&mb,&nb);
    2457:   if (ma!=nb || na!=mb) {
    2458:     *f = PETSC_FALSE;
    2459:     return(0);
    2460:   }
    2461:   MatSeqAIJGetArrayRead(A,&va);
    2462:   MatSeqAIJGetArrayRead(B,&vb);
    2463:   aii  = aij->i; bii = bij->i;
    2464:   adx  = aij->j; bdx = bij->j;
    2465:   PetscMalloc1(ma,&aptr);
    2466:   PetscMalloc1(mb,&bptr);
    2467:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    2468:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    2470:   *f = PETSC_TRUE;
    2471:   for (i=0; i<ma; i++) {
    2472:     while (aptr[i]<aii[i+1]) {
    2473:       PetscInt    idc,idr;
    2474:       PetscScalar vc,vr;
    2475:       /* column/row index/value */
    2476:       idc = adx[aptr[i]];
    2477:       idr = bdx[bptr[idc]];
    2478:       vc  = va[aptr[i]];
    2479:       vr  = vb[bptr[idc]];
    2480:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
    2481:         *f = PETSC_FALSE;
    2482:         goto done;
    2483:       } else {
    2484:         aptr[i]++;
    2485:         if (B || i!=idc) bptr[idc]++;
    2486:       }
    2487:     }
    2488:   }
    2489: done:
    2490:   PetscFree(aptr);
    2491:   PetscFree(bptr);
    2492:   MatSeqAIJRestoreArrayRead(A,&va);
    2493:   MatSeqAIJRestoreArrayRead(B,&vb);
    2494:   return(0);
    2495: }

    2497: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
    2498: {
    2499:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
    2500:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
    2501:   MatScalar      *va,*vb;
    2503:   PetscInt       ma,na,mb,nb, i;

    2506:   MatGetSize(A,&ma,&na);
    2507:   MatGetSize(B,&mb,&nb);
    2508:   if (ma!=nb || na!=mb) {
    2509:     *f = PETSC_FALSE;
    2510:     return(0);
    2511:   }
    2512:   aii  = aij->i; bii = bij->i;
    2513:   adx  = aij->j; bdx = bij->j;
    2514:   va   = aij->a; vb = bij->a;
    2515:   PetscMalloc1(ma,&aptr);
    2516:   PetscMalloc1(mb,&bptr);
    2517:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    2518:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    2520:   *f = PETSC_TRUE;
    2521:   for (i=0; i<ma; i++) {
    2522:     while (aptr[i]<aii[i+1]) {
    2523:       PetscInt    idc,idr;
    2524:       PetscScalar vc,vr;
    2525:       /* column/row index/value */
    2526:       idc = adx[aptr[i]];
    2527:       idr = bdx[bptr[idc]];
    2528:       vc  = va[aptr[i]];
    2529:       vr  = vb[bptr[idc]];
    2530:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
    2531:         *f = PETSC_FALSE;
    2532:         goto done;
    2533:       } else {
    2534:         aptr[i]++;
    2535:         if (B || i!=idc) bptr[idc]++;
    2536:       }
    2537:     }
    2538:   }
    2539: done:
    2540:   PetscFree(aptr);
    2541:   PetscFree(bptr);
    2542:   return(0);
    2543: }

    2545: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
    2546: {

    2550:   MatIsTranspose_SeqAIJ(A,A,tol,f);
    2551:   return(0);
    2552: }

    2554: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
    2555: {

    2559:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
    2560:   return(0);
    2561: }

    2563: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
    2564: {
    2565:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    2566:   const PetscScalar *l,*r;
    2567:   PetscScalar       x;
    2568:   MatScalar         *v;
    2569:   PetscErrorCode    ierr;
    2570:   PetscInt          i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
    2571:   const PetscInt    *jj;

    2574:   if (ll) {
    2575:     /* The local size is used so that VecMPI can be passed to this routine
    2576:        by MatDiagonalScale_MPIAIJ */
    2577:     VecGetLocalSize(ll,&m);
    2578:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
    2579:     VecGetArrayRead(ll,&l);
    2580:     MatSeqAIJGetArray(A,&v);
    2581:     for (i=0; i<m; i++) {
    2582:       x = l[i];
    2583:       M = a->i[i+1] - a->i[i];
    2584:       for (j=0; j<M; j++) (*v++) *= x;
    2585:     }
    2586:     VecRestoreArrayRead(ll,&l);
    2587:     PetscLogFlops(nz);
    2588:     MatSeqAIJRestoreArray(A,&v);
    2589:   }
    2590:   if (rr) {
    2591:     VecGetLocalSize(rr,&n);
    2592:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
    2593:     VecGetArrayRead(rr,&r);
    2594:     MatSeqAIJGetArray(A,&v);
    2595:     jj = a->j;
    2596:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
    2597:     MatSeqAIJRestoreArray(A,&v);
    2598:     VecRestoreArrayRead(rr,&r);
    2599:     PetscLogFlops(nz);
    2600:   }
    2601:   MatSeqAIJInvalidateDiagonal(A);
    2602: #if defined(PETSC_HAVE_DEVICE)
    2603:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    2604: #endif
    2605:   return(0);
    2606: }

    2608: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
    2609: {
    2610:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data,*c;
    2611:   PetscErrorCode    ierr;
    2612:   PetscInt          *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
    2613:   PetscInt          row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
    2614:   const PetscInt    *irow,*icol;
    2615:   const PetscScalar *aa;
    2616:   PetscInt          nrows,ncols;
    2617:   PetscInt          *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
    2618:   MatScalar         *a_new,*mat_a;
    2619:   Mat               C;
    2620:   PetscBool         stride;

    2623:   ISGetIndices(isrow,&irow);
    2624:   ISGetLocalSize(isrow,&nrows);
    2625:   ISGetLocalSize(iscol,&ncols);

    2627:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
    2628:   if (stride) {
    2629:     ISStrideGetInfo(iscol,&first,&step);
    2630:   } else {
    2631:     first = 0;
    2632:     step  = 0;
    2633:   }
    2634:   if (stride && step == 1) {
    2635:     /* special case of contiguous rows */
    2636:     PetscMalloc2(nrows,&lens,nrows,&starts);
    2637:     /* loop over new rows determining lens and starting points */
    2638:     for (i=0; i<nrows; i++) {
    2639:       kstart = ai[irow[i]];
    2640:       kend   = kstart + ailen[irow[i]];
    2641:       starts[i] = kstart;
    2642:       for (k=kstart; k<kend; k++) {
    2643:         if (aj[k] >= first) {
    2644:           starts[i] = k;
    2645:           break;
    2646:         }
    2647:       }
    2648:       sum = 0;
    2649:       while (k < kend) {
    2650:         if (aj[k++] >= first+ncols) break;
    2651:         sum++;
    2652:       }
    2653:       lens[i] = sum;
    2654:     }
    2655:     /* create submatrix */
    2656:     if (scall == MAT_REUSE_MATRIX) {
    2657:       PetscInt n_cols,n_rows;
    2658:       MatGetSize(*B,&n_rows,&n_cols);
    2659:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
    2660:       MatZeroEntries(*B);
    2661:       C    = *B;
    2662:     } else {
    2663:       PetscInt rbs,cbs;
    2664:       MatCreate(PetscObjectComm((PetscObject)A),&C);
    2665:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    2666:       ISGetBlockSize(isrow,&rbs);
    2667:       ISGetBlockSize(iscol,&cbs);
    2668:       MatSetBlockSizes(C,rbs,cbs);
    2669:       MatSetType(C,((PetscObject)A)->type_name);
    2670:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    2671:     }
    2672:     c = (Mat_SeqAIJ*)C->data;

    2674:     /* loop over rows inserting into submatrix */
    2675:     a_new = c->a;
    2676:     j_new = c->j;
    2677:     i_new = c->i;
    2678:     MatSeqAIJGetArrayRead(A,&aa);
    2679:     for (i=0; i<nrows; i++) {
    2680:       ii    = starts[i];
    2681:       lensi = lens[i];
    2682:       for (k=0; k<lensi; k++) {
    2683:         *j_new++ = aj[ii+k] - first;
    2684:       }
    2685:       PetscArraycpy(a_new,aa + starts[i],lensi);
    2686:       a_new     += lensi;
    2687:       i_new[i+1] = i_new[i] + lensi;
    2688:       c->ilen[i] = lensi;
    2689:     }
    2690:     MatSeqAIJRestoreArrayRead(A,&aa);
    2691:     PetscFree2(lens,starts);
    2692:   } else {
    2693:     ISGetIndices(iscol,&icol);
    2694:     PetscCalloc1(oldcols,&smap);
    2695:     PetscMalloc1(1+nrows,&lens);
    2696:     for (i=0; i<ncols; i++) {
    2697:       if (PetscUnlikelyDebug(icol[i] >= oldcols)) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D >= A->cmap->n %D",i,icol[i],oldcols);
    2698:       smap[icol[i]] = i+1;
    2699:     }

    2701:     /* determine lens of each row */
    2702:     for (i=0; i<nrows; i++) {
    2703:       kstart  = ai[irow[i]];
    2704:       kend    = kstart + a->ilen[irow[i]];
    2705:       lens[i] = 0;
    2706:       for (k=kstart; k<kend; k++) {
    2707:         if (smap[aj[k]]) {
    2708:           lens[i]++;
    2709:         }
    2710:       }
    2711:     }
    2712:     /* Create and fill new matrix */
    2713:     if (scall == MAT_REUSE_MATRIX) {
    2714:       PetscBool equal;

    2716:       c = (Mat_SeqAIJ*)((*B)->data);
    2717:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
    2718:       PetscArraycmp(c->ilen,lens,(*B)->rmap->n,&equal);
    2719:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
    2720:       PetscArrayzero(c->ilen,(*B)->rmap->n);
    2721:       C    = *B;
    2722:     } else {
    2723:       PetscInt rbs,cbs;
    2724:       MatCreate(PetscObjectComm((PetscObject)A),&C);
    2725:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    2726:       ISGetBlockSize(isrow,&rbs);
    2727:       ISGetBlockSize(iscol,&cbs);
    2728:       MatSetBlockSizes(C,rbs,cbs);
    2729:       MatSetType(C,((PetscObject)A)->type_name);
    2730:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    2731:     }
    2732:     MatSeqAIJGetArrayRead(A,&aa);
    2733:     c = (Mat_SeqAIJ*)(C->data);
    2734:     for (i=0; i<nrows; i++) {
    2735:       row      = irow[i];
    2736:       kstart   = ai[row];
    2737:       kend     = kstart + a->ilen[row];
    2738:       mat_i    = c->i[i];
    2739:       mat_j    = c->j + mat_i;
    2740:       mat_a    = c->a + mat_i;
    2741:       mat_ilen = c->ilen + i;
    2742:       for (k=kstart; k<kend; k++) {
    2743:         if ((tcol=smap[a->j[k]])) {
    2744:           *mat_j++ = tcol - 1;
    2745:           *mat_a++ = aa[k];
    2746:           (*mat_ilen)++;

    2748:         }
    2749:       }
    2750:     }
    2751:     MatSeqAIJRestoreArrayRead(A,&aa);
    2752:     /* Free work space */
    2753:     ISRestoreIndices(iscol,&icol);
    2754:     PetscFree(smap);
    2755:     PetscFree(lens);
    2756:     /* sort */
    2757:     for (i = 0; i < nrows; i++) {
    2758:       PetscInt ilen;

    2760:       mat_i = c->i[i];
    2761:       mat_j = c->j + mat_i;
    2762:       mat_a = c->a + mat_i;
    2763:       ilen  = c->ilen[i];
    2764:       PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
    2765:     }
    2766:   }
    2767: #if defined(PETSC_HAVE_DEVICE)
    2768:   MatBindToCPU(C,A->boundtocpu);
    2769: #endif
    2770:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    2771:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

    2773:   ISRestoreIndices(isrow,&irow);
    2774:   *B   = C;
    2775:   return(0);
    2776: }

    2778: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
    2779: {
    2781:   Mat            B;

    2784:   if (scall == MAT_INITIAL_MATRIX) {
    2785:     MatCreate(subComm,&B);
    2786:     MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
    2787:     MatSetBlockSizesFromMats(B,mat,mat);
    2788:     MatSetType(B,MATSEQAIJ);
    2789:     MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
    2790:     *subMat = B;
    2791:   } else {
    2792:     MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
    2793:   }
    2794:   return(0);
    2795: }

    2797: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
    2798: {
    2799:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
    2801:   Mat            outA;
    2802:   PetscBool      row_identity,col_identity;

    2805:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

    2807:   ISIdentity(row,&row_identity);
    2808:   ISIdentity(col,&col_identity);

    2810:   outA             = inA;
    2811:   outA->factortype = MAT_FACTOR_LU;
    2812:   PetscFree(inA->solvertype);
    2813:   PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);

    2815:   PetscObjectReference((PetscObject)row);
    2816:   ISDestroy(&a->row);

    2818:   a->row = row;

    2820:   PetscObjectReference((PetscObject)col);
    2821:   ISDestroy(&a->col);

    2823:   a->col = col;

    2825:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
    2826:   ISDestroy(&a->icol);
    2827:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
    2828:   PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);

    2830:   if (!a->solve_work) { /* this matrix may have been factored before */
    2831:     PetscMalloc1(inA->rmap->n+1,&a->solve_work);
    2832:     PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
    2833:   }

    2835:   MatMarkDiagonal_SeqAIJ(inA);
    2836:   if (row_identity && col_identity) {
    2837:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
    2838:   } else {
    2839:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
    2840:   }
    2841:   return(0);
    2842: }

    2844: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
    2845: {
    2846:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
    2847:   PetscScalar    *v;
    2849:   PetscBLASInt   one = 1,bnz;

    2852:   MatSeqAIJGetArray(inA,&v);
    2853:   PetscBLASIntCast(a->nz,&bnz);
    2854:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&alpha,v,&one));
    2855:   PetscLogFlops(a->nz);
    2856:   MatSeqAIJRestoreArray(inA,&v);
    2857:   MatSeqAIJInvalidateDiagonal(inA);
    2858:   return(0);
    2859: }

    2861: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
    2862: {
    2864:   PetscInt       i;

    2867:   if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
    2868:     PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);

    2870:     for (i=0; i<submatj->nrqr; ++i) {
    2871:       PetscFree(submatj->sbuf2[i]);
    2872:     }
    2873:     PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);

    2875:     if (submatj->rbuf1) {
    2876:       PetscFree(submatj->rbuf1[0]);
    2877:       PetscFree(submatj->rbuf1);
    2878:     }

    2880:     for (i=0; i<submatj->nrqs; ++i) {
    2881:       PetscFree(submatj->rbuf3[i]);
    2882:     }
    2883:     PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
    2884:     PetscFree(submatj->pa);
    2885:   }

    2887: #if defined(PETSC_USE_CTABLE)
    2888:   PetscTableDestroy((PetscTable*)&submatj->rmap);
    2889:   if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
    2890:   PetscFree(submatj->rmap_loc);
    2891: #else
    2892:   PetscFree(submatj->rmap);
    2893: #endif

    2895:   if (!submatj->allcolumns) {
    2896: #if defined(PETSC_USE_CTABLE)
    2897:     PetscTableDestroy((PetscTable*)&submatj->cmap);
    2898: #else
    2899:     PetscFree(submatj->cmap);
    2900: #endif
    2901:   }
    2902:   PetscFree(submatj->row2proc);

    2904:   PetscFree(submatj);
    2905:   return(0);
    2906: }

    2908: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
    2909: {
    2911:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data;
    2912:   Mat_SubSppt    *submatj = c->submatis1;

    2915:   (*submatj->destroy)(C);
    2916:   MatDestroySubMatrix_Private(submatj);
    2917:   return(0);
    2918: }

    2920: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
    2921: {
    2923:   PetscInt       i;
    2924:   Mat            C;
    2925:   Mat_SeqAIJ     *c;
    2926:   Mat_SubSppt    *submatj;

    2929:   for (i=0; i<n; i++) {
    2930:     C       = (*mat)[i];
    2931:     c       = (Mat_SeqAIJ*)C->data;
    2932:     submatj = c->submatis1;
    2933:     if (submatj) {
    2934:       if (--((PetscObject)C)->refct <= 0) {
    2935:         (*submatj->destroy)(C);
    2936:         MatDestroySubMatrix_Private(submatj);
    2937:         PetscFree(C->defaultvectype);
    2938:         PetscLayoutDestroy(&C->rmap);
    2939:         PetscLayoutDestroy(&C->cmap);
    2940:         PetscHeaderDestroy(&C);
    2941:       }
    2942:     } else {
    2943:       MatDestroy(&C);
    2944:     }
    2945:   }

    2947:   /* Destroy Dummy submatrices created for reuse */
    2948:   MatDestroySubMatrices_Dummy(n,mat);

    2950:   PetscFree(*mat);
    2951:   return(0);
    2952: }

    2954: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
    2955: {
    2957:   PetscInt       i;

    2960:   if (scall == MAT_INITIAL_MATRIX) {
    2961:     PetscCalloc1(n+1,B);
    2962:   }

    2964:   for (i=0; i<n; i++) {
    2965:     MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
    2966:   }
    2967:   return(0);
    2968: }

    2970: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
    2971: {
    2972:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2974:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
    2975:   const PetscInt *idx;
    2976:   PetscInt       start,end,*ai,*aj;
    2977:   PetscBT        table;

    2980:   m  = A->rmap->n;
    2981:   ai = a->i;
    2982:   aj = a->j;

    2984:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

    2986:   PetscMalloc1(m+1,&nidx);
    2987:   PetscBTCreate(m,&table);

    2989:   for (i=0; i<is_max; i++) {
    2990:     /* Initialize the two local arrays */
    2991:     isz  = 0;
    2992:     PetscBTMemzero(m,table);

    2994:     /* Extract the indices, assume there can be duplicate entries */
    2995:     ISGetIndices(is[i],&idx);
    2996:     ISGetLocalSize(is[i],&n);

    2998:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
    2999:     for (j=0; j<n; ++j) {
    3000:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
    3001:     }
    3002:     ISRestoreIndices(is[i],&idx);
    3003:     ISDestroy(&is[i]);

    3005:     k = 0;
    3006:     for (j=0; j<ov; j++) { /* for each overlap */
    3007:       n = isz;
    3008:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
    3009:         row   = nidx[k];
    3010:         start = ai[row];
    3011:         end   = ai[row+1];
    3012:         for (l = start; l<end; l++) {
    3013:           val = aj[l];
    3014:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
    3015:         }
    3016:       }
    3017:     }
    3018:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
    3019:   }
    3020:   PetscBTDestroy(&table);
    3021:   PetscFree(nidx);
    3022:   return(0);
    3023: }

    3025: /* -------------------------------------------------------------- */
    3026: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
    3027: {
    3028:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    3030:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
    3031:   const PetscInt *row,*col;
    3032:   PetscInt       *cnew,j,*lens;
    3033:   IS             icolp,irowp;
    3034:   PetscInt       *cwork = NULL;
    3035:   PetscScalar    *vwork = NULL;

    3038:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
    3039:   ISGetIndices(irowp,&row);
    3040:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
    3041:   ISGetIndices(icolp,&col);

    3043:   /* determine lengths of permuted rows */
    3044:   PetscMalloc1(m+1,&lens);
    3045:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
    3046:   MatCreate(PetscObjectComm((PetscObject)A),B);
    3047:   MatSetSizes(*B,m,n,m,n);
    3048:   MatSetBlockSizesFromMats(*B,A,A);
    3049:   MatSetType(*B,((PetscObject)A)->type_name);
    3050:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
    3051:   PetscFree(lens);

    3053:   PetscMalloc1(n,&cnew);
    3054:   for (i=0; i<m; i++) {
    3055:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    3056:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
    3057:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
    3058:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    3059:   }
    3060:   PetscFree(cnew);

    3062:   (*B)->assembled = PETSC_FALSE;

    3064: #if defined(PETSC_HAVE_DEVICE)
    3065:   MatBindToCPU(*B,A->boundtocpu);
    3066: #endif
    3067:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
    3068:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
    3069:   ISRestoreIndices(irowp,&row);
    3070:   ISRestoreIndices(icolp,&col);
    3071:   ISDestroy(&irowp);
    3072:   ISDestroy(&icolp);
    3073:   if (rowp == colp) {
    3074:     MatPropagateSymmetryOptions(A,*B);
    3075:   }
    3076:   return(0);
    3077: }

    3079: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
    3080: {

    3084:   /* If the two matrices have the same copy implementation, use fast copy. */
    3085:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
    3086:     Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    3087:     Mat_SeqAIJ        *b = (Mat_SeqAIJ*)B->data;
    3088:     const PetscScalar *aa;

    3090:     MatSeqAIJGetArrayRead(A,&aa);
    3091:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different %D != %D",a->i[A->rmap->n],b->i[B->rmap->n]);
    3092:     PetscArraycpy(b->a,aa,a->i[A->rmap->n]);
    3093:     PetscObjectStateIncrease((PetscObject)B);
    3094:     MatSeqAIJRestoreArrayRead(A,&aa);
    3095:   } else {
    3096:     MatCopy_Basic(A,B,str);
    3097:   }
    3098:   return(0);
    3099: }

    3101: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
    3102: {

    3106:   MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,NULL);
    3107:   return(0);
    3108: }

    3110: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
    3111: {
    3112:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    3115:   *array = a->a;
    3116:   return(0);
    3117: }

    3119: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
    3120: {
    3122:   *array = NULL;
    3123:   return(0);
    3124: }

    3126: /*
    3127:    Computes the number of nonzeros per row needed for preallocation when X and Y
    3128:    have different nonzero structure.
    3129: */
    3130: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
    3131: {
    3132:   PetscInt       i,j,k,nzx,nzy;

    3135:   /* Set the number of nonzeros in the new matrix */
    3136:   for (i=0; i<m; i++) {
    3137:     const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
    3138:     nzx = xi[i+1] - xi[i];
    3139:     nzy = yi[i+1] - yi[i];
    3140:     nnz[i] = 0;
    3141:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
    3142:       for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
    3143:       if (k<nzy && yjj[k]==xjj[j]) k++;             /* Skip duplicate */
    3144:       nnz[i]++;
    3145:     }
    3146:     for (; k<nzy; k++) nnz[i]++;
    3147:   }
    3148:   return(0);
    3149: }

    3151: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
    3152: {
    3153:   PetscInt       m = Y->rmap->N;
    3154:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data;
    3155:   Mat_SeqAIJ     *y = (Mat_SeqAIJ*)Y->data;

    3159:   /* Set the number of nonzeros in the new matrix */
    3160:   MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
    3161:   return(0);
    3162: }

    3164: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
    3165: {
    3167:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;

    3170:   if (str == UNKNOWN_NONZERO_PATTERN && x->nz == y->nz) {
    3171:     PetscBool e;
    3172:     PetscArraycmp(x->i,y->i,Y->rmap->n+1,&e);
    3173:     if (e) {
    3174:       PetscArraycmp(x->j,y->j,y->nz,&e);
    3175:       if (e) {
    3176:         str = SAME_NONZERO_PATTERN;
    3177:       }
    3178:     }
    3179:   }
    3180:   if (str == SAME_NONZERO_PATTERN) {
    3181:     const PetscScalar *xa;
    3182:     PetscScalar       *ya,alpha = a;
    3183:     PetscBLASInt      one = 1,bnz;

    3185:     PetscBLASIntCast(x->nz,&bnz);
    3186:     MatSeqAIJGetArray(Y,&ya);
    3187:     MatSeqAIJGetArrayRead(X,&xa);
    3188:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,xa,&one,ya,&one));
    3189:     MatSeqAIJRestoreArrayRead(X,&xa);
    3190:     MatSeqAIJRestoreArray(Y,&ya);
    3191:     PetscLogFlops(2.0*bnz);
    3192:     MatSeqAIJInvalidateDiagonal(Y);
    3193:     PetscObjectStateIncrease((PetscObject)Y);
    3194:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
    3195:     MatAXPY_Basic(Y,a,X,str);
    3196:   } else {
    3197:     Mat      B;
    3198:     PetscInt *nnz;
    3199:     PetscMalloc1(Y->rmap->N,&nnz);
    3200:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
    3201:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
    3202:     MatSetLayouts(B,Y->rmap,Y->cmap);
    3203:     MatSetType(B,((PetscObject)Y)->type_name);
    3204:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
    3205:     MatSeqAIJSetPreallocation(B,0,nnz);
    3206:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
    3207:     MatHeaderReplace(Y,&B);
    3208:     PetscFree(nnz);
    3209:   }
    3210:   return(0);
    3211: }

    3213: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
    3214: {
    3215: #if defined(PETSC_USE_COMPLEX)
    3216:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3217:   PetscInt       i,nz;
    3218:   PetscScalar    *a;

    3222:   nz = aij->nz;
    3223:   MatSeqAIJGetArray(mat,&a);
    3224:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
    3225:   MatSeqAIJRestoreArray(mat,&a);
    3226: #else
    3228: #endif
    3229:   return(0);
    3230: }

    3232: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3233: {
    3234:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3235:   PetscErrorCode  ierr;
    3236:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3237:   PetscReal       atmp;
    3238:   PetscScalar     *x;
    3239:   const MatScalar *aa,*av;

    3242:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3243:   MatSeqAIJGetArrayRead(A,&av);
    3244:   aa = av;
    3245:   ai = a->i;
    3246:   aj = a->j;

    3248:   VecSet(v,0.0);
    3249:   VecGetArrayWrite(v,&x);
    3250:   VecGetLocalSize(v,&n);
    3251:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    3252:   for (i=0; i<m; i++) {
    3253:     ncols = ai[1] - ai[0]; ai++;
    3254:     for (j=0; j<ncols; j++) {
    3255:       atmp = PetscAbsScalar(*aa);
    3256:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
    3257:       aa++; aj++;
    3258:     }
    3259:   }
    3260:   VecRestoreArrayWrite(v,&x);
    3261:   MatSeqAIJRestoreArrayRead(A,&av);
    3262:   return(0);
    3263: }

    3265: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3266: {
    3267:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3268:   PetscErrorCode  ierr;
    3269:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3270:   PetscScalar     *x;
    3271:   const MatScalar *aa,*av;

    3274:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3275:   MatSeqAIJGetArrayRead(A,&av);
    3276:   aa = av;
    3277:   ai = a->i;
    3278:   aj = a->j;

    3280:   VecSet(v,0.0);
    3281:   VecGetArrayWrite(v,&x);
    3282:   VecGetLocalSize(v,&n);
    3283:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    3284:   for (i=0; i<m; i++) {
    3285:     ncols = ai[1] - ai[0]; ai++;
    3286:     if (ncols == A->cmap->n) { /* row is dense */
    3287:       x[i] = *aa; if (idx) idx[i] = 0;
    3288:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
    3289:       x[i] = 0.0;
    3290:       if (idx) {
    3291:         for (j=0; j<ncols; j++) { /* find first implicit 0.0 in the row */
    3292:           if (aj[j] > j) {
    3293:             idx[i] = j;
    3294:             break;
    3295:           }
    3296:         }
    3297:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3298:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3299:       }
    3300:     }
    3301:     for (j=0; j<ncols; j++) {
    3302:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3303:       aa++; aj++;
    3304:     }
    3305:   }
    3306:   VecRestoreArrayWrite(v,&x);
    3307:   MatSeqAIJRestoreArrayRead(A,&av);
    3308:   return(0);
    3309: }

    3311: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3312: {
    3313:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3314:   PetscErrorCode  ierr;
    3315:   PetscInt        i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    3316:   PetscScalar     *x;
    3317:   const MatScalar *aa,*av;

    3320:   MatSeqAIJGetArrayRead(A,&av);
    3321:   aa = av;
    3322:   ai = a->i;
    3323:   aj = a->j;

    3325:   VecSet(v,0.0);
    3326:   VecGetArrayWrite(v,&x);
    3327:   VecGetLocalSize(v,&n);
    3328:   if (n != m) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", m, n);
    3329:   for (i=0; i<m; i++) {
    3330:     ncols = ai[1] - ai[0]; ai++;
    3331:     if (ncols == A->cmap->n) { /* row is dense */
    3332:       x[i] = *aa; if (idx) idx[i] = 0;
    3333:     } else {  /* row is sparse so already KNOW minimum is 0.0 or higher */
    3334:       x[i] = 0.0;
    3335:       if (idx) {   /* find first implicit 0.0 in the row */
    3336:         for (j=0; j<ncols; j++) {
    3337:           if (aj[j] > j) {
    3338:             idx[i] = j;
    3339:             break;
    3340:           }
    3341:         }
    3342:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3343:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3344:       }
    3345:     }
    3346:     for (j=0; j<ncols; j++) {
    3347:       if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3348:       aa++; aj++;
    3349:     }
    3350:   }
    3351:   VecRestoreArrayWrite(v,&x);
    3352:   MatSeqAIJRestoreArrayRead(A,&av);
    3353:   return(0);
    3354: }

    3356: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    3357: {
    3358:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3359:   PetscErrorCode  ierr;
    3360:   PetscInt        i,j,m = A->rmap->n,ncols,n;
    3361:   const PetscInt  *ai,*aj;
    3362:   PetscScalar     *x;
    3363:   const MatScalar *aa,*av;

    3366:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3367:   MatSeqAIJGetArrayRead(A,&av);
    3368:   aa = av;
    3369:   ai = a->i;
    3370:   aj = a->j;

    3372:   VecSet(v,0.0);
    3373:   VecGetArrayWrite(v,&x);
    3374:   VecGetLocalSize(v,&n);
    3375:   if (n != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    3376:   for (i=0; i<m; i++) {
    3377:     ncols = ai[1] - ai[0]; ai++;
    3378:     if (ncols == A->cmap->n) { /* row is dense */
    3379:       x[i] = *aa; if (idx) idx[i] = 0;
    3380:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
    3381:       x[i] = 0.0;
    3382:       if (idx) {   /* find first implicit 0.0 in the row */
    3383:         for (j=0; j<ncols; j++) {
    3384:           if (aj[j] > j) {
    3385:             idx[i] = j;
    3386:             break;
    3387:           }
    3388:         }
    3389:         /* in case first implicit 0.0 in the row occurs at ncols-th column */
    3390:         if (j==ncols && j < A->cmap->n) idx[i] = j;
    3391:       }
    3392:     }
    3393:     for (j=0; j<ncols; j++) {
    3394:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    3395:       aa++; aj++;
    3396:     }
    3397:   }
    3398:   VecRestoreArrayWrite(v,&x);
    3399:   MatSeqAIJRestoreArrayRead(A,&av);
    3400:   return(0);
    3401: }

    3403: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
    3404: {
    3405:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*) A->data;
    3406:   PetscErrorCode  ierr;
    3407:   PetscInt        i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
    3408:   MatScalar       *diag,work[25],*v_work;
    3409:   const PetscReal shift = 0.0;
    3410:   PetscBool       allowzeropivot,zeropivotdetected=PETSC_FALSE;

    3413:   allowzeropivot = PetscNot(A->erroriffailure);
    3414:   if (a->ibdiagvalid) {
    3415:     if (values) *values = a->ibdiag;
    3416:     return(0);
    3417:   }
    3418:   MatMarkDiagonal_SeqAIJ(A);
    3419:   if (!a->ibdiag) {
    3420:     PetscMalloc1(bs2*mbs,&a->ibdiag);
    3421:     PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
    3422:   }
    3423:   diag = a->ibdiag;
    3424:   if (values) *values = a->ibdiag;
    3425:   /* factor and invert each block */
    3426:   switch (bs) {
    3427:   case 1:
    3428:     for (i=0; i<mbs; i++) {
    3429:       MatGetValues(A,1,&i,1,&i,diag+i);
    3430:       if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
    3431:         if (allowzeropivot) {
    3432:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3433:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
    3434:           A->factorerror_zeropivot_row   = i;
    3435:           PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
    3436:         } else SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot, row %D pivot %g tolerance %g",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
    3437:       }
    3438:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
    3439:     }
    3440:     break;
    3441:   case 2:
    3442:     for (i=0; i<mbs; i++) {
    3443:       ij[0] = 2*i; ij[1] = 2*i + 1;
    3444:       MatGetValues(A,2,ij,2,ij,diag);
    3445:       PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
    3446:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3447:       PetscKernel_A_gets_transpose_A_2(diag);
    3448:       diag += 4;
    3449:     }
    3450:     break;
    3451:   case 3:
    3452:     for (i=0; i<mbs; i++) {
    3453:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
    3454:       MatGetValues(A,3,ij,3,ij,diag);
    3455:       PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
    3456:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3457:       PetscKernel_A_gets_transpose_A_3(diag);
    3458:       diag += 9;
    3459:     }
    3460:     break;
    3461:   case 4:
    3462:     for (i=0; i<mbs; i++) {
    3463:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
    3464:       MatGetValues(A,4,ij,4,ij,diag);
    3465:       PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
    3466:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3467:       PetscKernel_A_gets_transpose_A_4(diag);
    3468:       diag += 16;
    3469:     }
    3470:     break;
    3471:   case 5:
    3472:     for (i=0; i<mbs; i++) {
    3473:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
    3474:       MatGetValues(A,5,ij,5,ij,diag);
    3475:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
    3476:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3477:       PetscKernel_A_gets_transpose_A_5(diag);
    3478:       diag += 25;
    3479:     }
    3480:     break;
    3481:   case 6:
    3482:     for (i=0; i<mbs; i++) {
    3483:       ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5;
    3484:       MatGetValues(A,6,ij,6,ij,diag);
    3485:       PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
    3486:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3487:       PetscKernel_A_gets_transpose_A_6(diag);
    3488:       diag += 36;
    3489:     }
    3490:     break;
    3491:   case 7:
    3492:     for (i=0; i<mbs; i++) {
    3493:       ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6;
    3494:       MatGetValues(A,7,ij,7,ij,diag);
    3495:       PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
    3496:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3497:       PetscKernel_A_gets_transpose_A_7(diag);
    3498:       diag += 49;
    3499:     }
    3500:     break;
    3501:   default:
    3502:     PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
    3503:     for (i=0; i<mbs; i++) {
    3504:       for (j=0; j<bs; j++) {
    3505:         IJ[j] = bs*i + j;
    3506:       }
    3507:       MatGetValues(A,bs,IJ,bs,IJ,diag);
    3508:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
    3509:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    3510:       PetscKernel_A_gets_transpose_A_N(diag,bs);
    3511:       diag += bs2;
    3512:     }
    3513:     PetscFree3(v_work,v_pivots,IJ);
    3514:   }
    3515:   a->ibdiagvalid = PETSC_TRUE;
    3516:   return(0);
    3517: }

    3519: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
    3520: {
    3522:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
    3523:   PetscScalar    a;
    3524:   PetscInt       m,n,i,j,col;

    3527:   if (!x->assembled) {
    3528:     MatGetSize(x,&m,&n);
    3529:     for (i=0; i<m; i++) {
    3530:       for (j=0; j<aij->imax[i]; j++) {
    3531:         PetscRandomGetValue(rctx,&a);
    3532:         col  = (PetscInt)(n*PetscRealPart(a));
    3533:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
    3534:       }
    3535:     }
    3536:   } else {
    3537:     for (i=0; i<aij->nz; i++) {PetscRandomGetValue(rctx,aij->a+i);}
    3538:   }
    3539: #if defined(PETSC_HAVE_DEVICE)
    3540:   if (x->offloadmask != PETSC_OFFLOAD_UNALLOCATED) x->offloadmask = PETSC_OFFLOAD_CPU;
    3541: #endif
    3542:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
    3543:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
    3544:   return(0);
    3545: }

    3547: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
    3548: PetscErrorCode  MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
    3549: {
    3551:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
    3552:   PetscScalar    a;
    3553:   PetscInt       m,n,i,j,col,nskip;

    3556:   nskip = high - low;
    3557:   MatGetSize(x,&m,&n);
    3558:   n    -= nskip; /* shrink number of columns where nonzeros can be set */
    3559:   for (i=0; i<m; i++) {
    3560:     for (j=0; j<aij->imax[i]; j++) {
    3561:       PetscRandomGetValue(rctx,&a);
    3562:       col  = (PetscInt)(n*PetscRealPart(a));
    3563:       if (col >= low) col += nskip; /* shift col rightward to skip the hole */
    3564:       MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
    3565:     }
    3566:   }
    3567:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
    3568:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
    3569:   return(0);
    3570: }

    3573: /* -------------------------------------------------------------------*/
    3574: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
    3575:                                         MatGetRow_SeqAIJ,
    3576:                                         MatRestoreRow_SeqAIJ,
    3577:                                         MatMult_SeqAIJ,
    3578:                                 /*  4*/ MatMultAdd_SeqAIJ,
    3579:                                         MatMultTranspose_SeqAIJ,
    3580:                                         MatMultTransposeAdd_SeqAIJ,
    3581:                                         NULL,
    3582:                                         NULL,
    3583:                                         NULL,
    3584:                                 /* 10*/ NULL,
    3585:                                         MatLUFactor_SeqAIJ,
    3586:                                         NULL,
    3587:                                         MatSOR_SeqAIJ,
    3588:                                         MatTranspose_SeqAIJ,
    3589:                                 /*1 5*/ MatGetInfo_SeqAIJ,
    3590:                                         MatEqual_SeqAIJ,
    3591:                                         MatGetDiagonal_SeqAIJ,
    3592:                                         MatDiagonalScale_SeqAIJ,
    3593:                                         MatNorm_SeqAIJ,
    3594:                                 /* 20*/ NULL,
    3595:                                         MatAssemblyEnd_SeqAIJ,
    3596:                                         MatSetOption_SeqAIJ,
    3597:                                         MatZeroEntries_SeqAIJ,
    3598:                                 /* 24*/ MatZeroRows_SeqAIJ,
    3599:                                         NULL,
    3600:                                         NULL,
    3601:                                         NULL,
    3602:                                         NULL,
    3603:                                 /* 29*/ MatSetUp_SeqAIJ,
    3604:                                         NULL,
    3605:                                         NULL,
    3606:                                         NULL,
    3607:                                         NULL,
    3608:                                 /* 34*/ MatDuplicate_SeqAIJ,
    3609:                                         NULL,
    3610:                                         NULL,
    3611:                                         MatILUFactor_SeqAIJ,
    3612:                                         NULL,
    3613:                                 /* 39*/ MatAXPY_SeqAIJ,
    3614:                                         MatCreateSubMatrices_SeqAIJ,
    3615:                                         MatIncreaseOverlap_SeqAIJ,
    3616:                                         MatGetValues_SeqAIJ,
    3617:                                         MatCopy_SeqAIJ,
    3618:                                 /* 44*/ MatGetRowMax_SeqAIJ,
    3619:                                         MatScale_SeqAIJ,
    3620:                                         MatShift_SeqAIJ,
    3621:                                         MatDiagonalSet_SeqAIJ,
    3622:                                         MatZeroRowsColumns_SeqAIJ,
    3623:                                 /* 49*/ MatSetRandom_SeqAIJ,
    3624:                                         MatGetRowIJ_SeqAIJ,
    3625:                                         MatRestoreRowIJ_SeqAIJ,
    3626:                                         MatGetColumnIJ_SeqAIJ,
    3627:                                         MatRestoreColumnIJ_SeqAIJ,
    3628:                                 /* 54*/ MatFDColoringCreate_SeqXAIJ,
    3629:                                         NULL,
    3630:                                         NULL,
    3631:                                         MatPermute_SeqAIJ,
    3632:                                         NULL,
    3633:                                 /* 59*/ NULL,
    3634:                                         MatDestroy_SeqAIJ,
    3635:                                         MatView_SeqAIJ,
    3636:                                         NULL,
    3637:                                         NULL,
    3638:                                 /* 64*/ NULL,
    3639:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
    3640:                                         NULL,
    3641:                                         NULL,
    3642:                                         NULL,
    3643:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
    3644:                                         MatGetRowMinAbs_SeqAIJ,
    3645:                                         NULL,
    3646:                                         NULL,
    3647:                                         NULL,
    3648:                                 /* 74*/ NULL,
    3649:                                         MatFDColoringApply_AIJ,
    3650:                                         NULL,
    3651:                                         NULL,
    3652:                                         NULL,
    3653:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
    3654:                                         NULL,
    3655:                                         NULL,
    3656:                                         NULL,
    3657:                                         MatLoad_SeqAIJ,
    3658:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
    3659:                                         MatIsHermitian_SeqAIJ,
    3660:                                         NULL,
    3661:                                         NULL,
    3662:                                         NULL,
    3663:                                 /* 89*/ NULL,
    3664:                                         NULL,
    3665:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
    3666:                                         NULL,
    3667:                                         NULL,
    3668:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
    3669:                                         NULL,
    3670:                                         NULL,
    3671:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
    3672:                                         NULL,
    3673:                                 /* 99*/ MatProductSetFromOptions_SeqAIJ,
    3674:                                         NULL,
    3675:                                         NULL,
    3676:                                         MatConjugate_SeqAIJ,
    3677:                                         NULL,
    3678:                                 /*104*/ MatSetValuesRow_SeqAIJ,
    3679:                                         MatRealPart_SeqAIJ,
    3680:                                         MatImaginaryPart_SeqAIJ,
    3681:                                         NULL,
    3682:                                         NULL,
    3683:                                 /*109*/ MatMatSolve_SeqAIJ,
    3684:                                         NULL,
    3685:                                         MatGetRowMin_SeqAIJ,
    3686:                                         NULL,
    3687:                                         MatMissingDiagonal_SeqAIJ,
    3688:                                 /*114*/ NULL,
    3689:                                         NULL,
    3690:                                         NULL,
    3691:                                         NULL,
    3692:                                         NULL,
    3693:                                 /*119*/ NULL,
    3694:                                         NULL,
    3695:                                         NULL,
    3696:                                         NULL,
    3697:                                         MatGetMultiProcBlock_SeqAIJ,
    3698:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
    3699:                                         MatGetColumnNorms_SeqAIJ,
    3700:                                         MatInvertBlockDiagonal_SeqAIJ,
    3701:                                         MatInvertVariableBlockDiagonal_SeqAIJ,
    3702:                                         NULL,
    3703:                                 /*129*/ NULL,
    3704:                                         NULL,
    3705:                                         NULL,
    3706:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
    3707:                                         MatTransposeColoringCreate_SeqAIJ,
    3708:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
    3709:                                         MatTransColoringApplyDenToSp_SeqAIJ,
    3710:                                         NULL,
    3711:                                         NULL,
    3712:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
    3713:                                  /*139*/NULL,
    3714:                                         NULL,
    3715:                                         NULL,
    3716:                                         MatFDColoringSetUp_SeqXAIJ,
    3717:                                         MatFindOffBlockDiagonalEntries_SeqAIJ,
    3718:                                         MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
    3719:                                  /*145*/MatDestroySubMatrices_SeqAIJ,
    3720:                                         NULL,
    3721:                                         NULL
    3722: };

    3724: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
    3725: {
    3726:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
    3727:   PetscInt   i,nz,n;

    3730:   nz = aij->maxnz;
    3731:   n  = mat->rmap->n;
    3732:   for (i=0; i<nz; i++) {
    3733:     aij->j[i] = indices[i];
    3734:   }
    3735:   aij->nz = nz;
    3736:   for (i=0; i<n; i++) {
    3737:     aij->ilen[i] = aij->imax[i];
    3738:   }
    3739:   return(0);
    3740: }

    3742: /*
    3743:  * When a sparse matrix has many zero columns, we should compact them out to save the space
    3744:  * This happens in MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
    3745:  * */
    3746: PetscErrorCode  MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
    3747: {
    3748:   Mat_SeqAIJ         *aij = (Mat_SeqAIJ*)mat->data;
    3749:   PetscTable         gid1_lid1;
    3750:   PetscTablePosition tpos;
    3751:   PetscInt           gid,lid,i,ec,nz = aij->nz;
    3752:   PetscInt           *garray,*jj = aij->j;
    3753:   PetscErrorCode     ierr;

    3758:   /* use a table */
    3759:   PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
    3760:   ec = 0;
    3761:   for (i=0; i<nz; i++) {
    3762:     PetscInt data,gid1 = jj[i] + 1;
    3763:     PetscTableFind(gid1_lid1,gid1,&data);
    3764:     if (!data) {
    3765:       /* one based table */
    3766:       PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
    3767:     }
    3768:   }
    3769:   /* form array of columns we need */
    3770:   PetscMalloc1(ec+1,&garray);
    3771:   PetscTableGetHeadPosition(gid1_lid1,&tpos);
    3772:   while (tpos) {
    3773:     PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
    3774:     gid--;
    3775:     lid--;
    3776:     garray[lid] = gid;
    3777:   }
    3778:   PetscSortInt(ec,garray); /* sort, and rebuild */
    3779:   PetscTableRemoveAll(gid1_lid1);
    3780:   for (i=0; i<ec; i++) {
    3781:     PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
    3782:   }
    3783:   /* compact out the extra columns in B */
    3784:   for (i=0; i<nz; i++) {
    3785:     PetscInt gid1 = jj[i] + 1;
    3786:     PetscTableFind(gid1_lid1,gid1,&lid);
    3787:     lid--;
    3788:     jj[i] = lid;
    3789:   }
    3790:   PetscLayoutDestroy(&mat->cmap);
    3791:   PetscTableDestroy(&gid1_lid1);
    3792:   PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat),ec,ec,1,&mat->cmap);
    3793:   ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
    3794:   ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
    3795:   return(0);
    3796: }

    3798: /*@
    3799:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
    3800:        in the matrix.

    3802:   Input Parameters:
    3803: +  mat - the SeqAIJ matrix
    3804: -  indices - the column indices

    3806:   Level: advanced

    3808:   Notes:
    3809:     This can be called if you have precomputed the nonzero structure of the
    3810:   matrix and want to provide it to the matrix object to improve the performance
    3811:   of the MatSetValues() operation.

    3813:     You MUST have set the correct numbers of nonzeros per row in the call to
    3814:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

    3816:     MUST be called before any calls to MatSetValues();

    3818:     The indices should start with zero, not one.

    3820: @*/
    3821: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
    3822: {

    3828:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
    3829:   return(0);
    3830: }

    3832: /* ----------------------------------------------------------------------------------------*/

    3834: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
    3835: {
    3836:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3838:   size_t         nz = aij->i[mat->rmap->n];

    3841:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

    3843:   /* allocate space for values if not already there */
    3844:   if (!aij->saved_values) {
    3845:     PetscMalloc1(nz+1,&aij->saved_values);
    3846:     PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
    3847:   }

    3849:   /* copy values over */
    3850:   PetscArraycpy(aij->saved_values,aij->a,nz);
    3851:   return(0);
    3852: }

    3854: /*@
    3855:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
    3856:        example, reuse of the linear part of a Jacobian, while recomputing the
    3857:        nonlinear portion.

    3859:    Collect on Mat

    3861:   Input Parameters:
    3862: .  mat - the matrix (currently only AIJ matrices support this option)

    3864:   Level: advanced

    3866:   Common Usage, with SNESSolve():
    3867: $    Create Jacobian matrix
    3868: $    Set linear terms into matrix
    3869: $    Apply boundary conditions to matrix, at this time matrix must have
    3870: $      final nonzero structure (i.e. setting the nonlinear terms and applying
    3871: $      boundary conditions again will not change the nonzero structure
    3872: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
    3873: $    MatStoreValues(mat);
    3874: $    Call SNESSetJacobian() with matrix
    3875: $    In your Jacobian routine
    3876: $      MatRetrieveValues(mat);
    3877: $      Set nonlinear terms in matrix

    3879:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
    3880: $    // build linear portion of Jacobian
    3881: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
    3882: $    MatStoreValues(mat);
    3883: $    loop over nonlinear iterations
    3884: $       MatRetrieveValues(mat);
    3885: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
    3886: $       // call MatAssemblyBegin/End() on matrix
    3887: $       Solve linear system with Jacobian
    3888: $    endloop

    3890:   Notes:
    3891:     Matrix must already be assemblied before calling this routine
    3892:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
    3893:     calling this routine.

    3895:     When this is called multiple times it overwrites the previous set of stored values
    3896:     and does not allocated additional space.

    3898: .seealso: MatRetrieveValues()

    3900: @*/
    3901: PetscErrorCode  MatStoreValues(Mat mat)
    3902: {

    3907:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    3908:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3909:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
    3910:   return(0);
    3911: }

    3913: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
    3914: {
    3915:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3917:   PetscInt       nz = aij->i[mat->rmap->n];

    3920:   if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
    3921:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
    3922:   /* copy values over */
    3923:   PetscArraycpy(aij->a,aij->saved_values,nz);
    3924:   return(0);
    3925: }

    3927: /*@
    3928:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
    3929:        example, reuse of the linear part of a Jacobian, while recomputing the
    3930:        nonlinear portion.

    3932:    Collect on Mat

    3934:   Input Parameters:
    3935: .  mat - the matrix (currently only AIJ matrices support this option)

    3937:   Level: advanced

    3939: .seealso: MatStoreValues()

    3941: @*/
    3942: PetscErrorCode  MatRetrieveValues(Mat mat)
    3943: {

    3948:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    3949:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3950:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
    3951:   return(0);
    3952: }

    3955: /* --------------------------------------------------------------------------------*/
    3956: /*@C
    3957:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
    3958:    (the default parallel PETSc format).  For good matrix assembly performance
    3959:    the user should preallocate the matrix storage by setting the parameter nz
    3960:    (or the array nnz).  By setting these parameters accurately, performance
    3961:    during matrix assembly can be increased by more than a factor of 50.

    3963:    Collective

    3965:    Input Parameters:
    3966: +  comm - MPI communicator, set to PETSC_COMM_SELF
    3967: .  m - number of rows
    3968: .  n - number of columns
    3969: .  nz - number of nonzeros per row (same for all rows)
    3970: -  nnz - array containing the number of nonzeros in the various rows
    3971:          (possibly different for each row) or NULL

    3973:    Output Parameter:
    3974: .  A - the matrix

    3976:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
    3977:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
    3978:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

    3980:    Notes:
    3981:    If nnz is given then nz is ignored

    3983:    The AIJ format (also called the Yale sparse matrix format or
    3984:    compressed row storage), is fully compatible with standard Fortran 77
    3985:    storage.  That is, the stored row and column indices can begin at
    3986:    either one (as in Fortran) or zero.  See the users' manual for details.

    3988:    Specify the preallocated storage with either nz or nnz (not both).
    3989:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    3990:    allocation.  For large problems you MUST preallocate memory or you
    3991:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    3993:    By default, this format uses inodes (identical nodes) when possible, to
    3994:    improve numerical efficiency of matrix-vector products and solves. We
    3995:    search for consecutive rows with the same nonzero structure, thereby
    3996:    reusing matrix information to achieve increased efficiency.

    3998:    Options Database Keys:
    3999: +  -mat_no_inode  - Do not use inodes
    4000: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    4002:    Level: intermediate

    4004: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

    4006: @*/
    4007: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
    4008: {

    4012:   MatCreate(comm,A);
    4013:   MatSetSizes(*A,m,n,m,n);
    4014:   MatSetType(*A,MATSEQAIJ);
    4015:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
    4016:   return(0);
    4017: }

    4019: /*@C
    4020:    MatSeqAIJSetPreallocation - For good matrix assembly performance
    4021:    the user should preallocate the matrix storage by setting the parameter nz
    4022:    (or the array nnz).  By setting these parameters accurately, performance
    4023:    during matrix assembly can be increased by more than a factor of 50.

    4025:    Collective

    4027:    Input Parameters:
    4028: +  B - The matrix
    4029: .  nz - number of nonzeros per row (same for all rows)
    4030: -  nnz - array containing the number of nonzeros in the various rows
    4031:          (possibly different for each row) or NULL

    4033:    Notes:
    4034:      If nnz is given then nz is ignored

    4036:     The AIJ format (also called the Yale sparse matrix format or
    4037:    compressed row storage), is fully compatible with standard Fortran 77
    4038:    storage.  That is, the stored row and column indices can begin at
    4039:    either one (as in Fortran) or zero.  See the users' manual for details.

    4041:    Specify the preallocated storage with either nz or nnz (not both).
    4042:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    4043:    allocation.  For large problems you MUST preallocate memory or you
    4044:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    4046:    You can call MatGetInfo() to get information on how effective the preallocation was;
    4047:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
    4048:    You can also run with the option -info and look for messages with the string
    4049:    malloc in them to see if additional memory allocation was needed.

    4051:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
    4052:    entries or columns indices

    4054:    By default, this format uses inodes (identical nodes) when possible, to
    4055:    improve numerical efficiency of matrix-vector products and solves. We
    4056:    search for consecutive rows with the same nonzero structure, thereby
    4057:    reusing matrix information to achieve increased efficiency.

    4059:    Options Database Keys:
    4060: +  -mat_no_inode  - Do not use inodes
    4061: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    4063:    Level: intermediate

    4065: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo(),
    4066:           MatSeqAIJSetTotalPreallocation()

    4068: @*/
    4069: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
    4070: {

    4076:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
    4077:   return(0);
    4078: }

    4080: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
    4081: {
    4082:   Mat_SeqAIJ     *b;
    4083:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
    4085:   PetscInt       i;

    4088:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
    4089:   if (nz == MAT_SKIP_ALLOCATION) {
    4090:     skipallocation = PETSC_TRUE;
    4091:     nz             = 0;
    4092:   }
    4093:   PetscLayoutSetUp(B->rmap);
    4094:   PetscLayoutSetUp(B->cmap);

    4096:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
    4097:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
    4098:   if (PetscUnlikelyDebug(nnz)) {
    4099:     for (i=0; i<B->rmap->n; i++) {
    4100:       if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
    4101:       if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %D value %d rowlength %D",i,nnz[i],B->cmap->n);
    4102:     }
    4103:   }

    4105:   B->preallocated = PETSC_TRUE;

    4107:   b = (Mat_SeqAIJ*)B->data;

    4109:   if (!skipallocation) {
    4110:     if (!b->imax) {
    4111:       PetscMalloc1(B->rmap->n,&b->imax);
    4112:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    4113:     }
    4114:     if (!b->ilen) {
    4115:       /* b->ilen will count nonzeros in each row so far. */
    4116:       PetscCalloc1(B->rmap->n,&b->ilen);
    4117:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    4118:     } else {
    4119:       PetscMemzero(b->ilen,B->rmap->n*sizeof(PetscInt));
    4120:     }
    4121:     if (!b->ipre) {
    4122:       PetscMalloc1(B->rmap->n,&b->ipre);
    4123:       PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
    4124:     }
    4125:     if (!nnz) {
    4126:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
    4127:       else if (nz < 0) nz = 1;
    4128:       nz = PetscMin(nz,B->cmap->n);
    4129:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
    4130:       nz = nz*B->rmap->n;
    4131:     } else {
    4132:       PetscInt64 nz64 = 0;
    4133:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz64 += nnz[i];}
    4134:       PetscIntCast(nz64,&nz);
    4135:     }

    4137:     /* allocate the matrix space */
    4138:     /* FIXME: should B's old memory be unlogged? */
    4139:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
    4140:     if (B->structure_only) {
    4141:       PetscMalloc1(nz,&b->j);
    4142:       PetscMalloc1(B->rmap->n+1,&b->i);
    4143:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
    4144:     } else {
    4145:       PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
    4146:       PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
    4147:     }
    4148:     b->i[0] = 0;
    4149:     for (i=1; i<B->rmap->n+1; i++) {
    4150:       b->i[i] = b->i[i-1] + b->imax[i-1];
    4151:     }
    4152:     if (B->structure_only) {
    4153:       b->singlemalloc = PETSC_FALSE;
    4154:       b->free_a       = PETSC_FALSE;
    4155:     } else {
    4156:       b->singlemalloc = PETSC_TRUE;
    4157:       b->free_a       = PETSC_TRUE;
    4158:     }
    4159:     b->free_ij      = PETSC_TRUE;
    4160:   } else {
    4161:     b->free_a  = PETSC_FALSE;
    4162:     b->free_ij = PETSC_FALSE;
    4163:   }

    4165:   if (b->ipre && nnz != b->ipre  && b->imax) {
    4166:     /* reserve user-requested sparsity */
    4167:     PetscArraycpy(b->ipre,b->imax,B->rmap->n);
    4168:   }

    4171:   b->nz               = 0;
    4172:   b->maxnz            = nz;
    4173:   B->info.nz_unneeded = (double)b->maxnz;
    4174:   if (realalloc) {
    4175:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
    4176:   }
    4177:   B->was_assembled = PETSC_FALSE;
    4178:   B->assembled     = PETSC_FALSE;
    4179:   return(0);
    4180: }

    4183: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
    4184: {
    4185:   Mat_SeqAIJ     *a;
    4186:   PetscInt       i;

    4192:   /* Check local size. If zero, then return */
    4193:   if (!A->rmap->n) return(0);

    4195:   a = (Mat_SeqAIJ*)A->data;
    4196:   /* if no saved info, we error out */
    4197:   if (!a->ipre) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");

    4199:   if (!a->i || !a->j || !a->a || !a->imax || !a->ilen) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"Memory info is incomplete, and can not reset preallocation \n");

    4201:   PetscArraycpy(a->imax,a->ipre,A->rmap->n);
    4202:   PetscArrayzero(a->ilen,A->rmap->n);
    4203:   a->i[0] = 0;
    4204:   for (i=1; i<A->rmap->n+1; i++) {
    4205:     a->i[i] = a->i[i-1] + a->imax[i-1];
    4206:   }
    4207:   A->preallocated     = PETSC_TRUE;
    4208:   a->nz               = 0;
    4209:   a->maxnz            = a->i[A->rmap->n];
    4210:   A->info.nz_unneeded = (double)a->maxnz;
    4211:   A->was_assembled    = PETSC_FALSE;
    4212:   A->assembled        = PETSC_FALSE;
    4213:   return(0);
    4214: }

    4216: /*@
    4217:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

    4219:    Input Parameters:
    4220: +  B - the matrix
    4221: .  i - the indices into j for the start of each row (starts with zero)
    4222: .  j - the column indices for each row (starts with zero) these must be sorted for each row
    4223: -  v - optional values in the matrix

    4225:    Level: developer

    4227:    Notes:
    4228:       The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

    4230:       This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
    4231:       structure will be the union of all the previous nonzero structures.

    4233:     Developer Notes:
    4234:       An optimization could be added to the implementation where it checks if the i, and j are identical to the current i and j and
    4235:       then just copies the v values directly with PetscMemcpy().

    4237:       This routine could also take a PetscCopyMode argument to allow sharing the values instead of always copying them.

    4239: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ, MatResetPreallocation()
    4240: @*/
    4241: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
    4242: {

    4248:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
    4249:   return(0);
    4250: }

    4252: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
    4253: {
    4254:   PetscInt       i;
    4255:   PetscInt       m,n;
    4256:   PetscInt       nz;
    4257:   PetscInt       *nnz;

    4261:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

    4263:   PetscLayoutSetUp(B->rmap);
    4264:   PetscLayoutSetUp(B->cmap);

    4266:   MatGetSize(B, &m, &n);
    4267:   PetscMalloc1(m+1, &nnz);
    4268:   for (i = 0; i < m; i++) {
    4269:     nz     = Ii[i+1]- Ii[i];
    4270:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
    4271:     nnz[i] = nz;
    4272:   }
    4273:   MatSeqAIJSetPreallocation(B, 0, nnz);
    4274:   PetscFree(nnz);

    4276:   for (i = 0; i < m; i++) {
    4277:     MatSetValues_SeqAIJ(B, 1, &i, Ii[i+1] - Ii[i], J+Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES);
    4278:   }

    4280:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    4281:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

    4283:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
    4284:   return(0);
    4285: }

    4287: #include <../src/mat/impls/dense/seq/dense.h>
    4288: #include <petsc/private/kernels/petscaxpy.h>

    4290: /*
    4291:     Computes (B'*A')' since computing B*A directly is untenable

    4293:                n                       p                          p
    4294:         [             ]       [             ]         [                 ]
    4295:       m [      A      ]  *  n [       B     ]   =   m [         C       ]
    4296:         [             ]       [             ]         [                 ]

    4298: */
    4299: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
    4300: {
    4301:   PetscErrorCode    ierr;
    4302:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
    4303:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
    4304:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
    4305:   PetscInt          i,j,n,m,q,p;
    4306:   const PetscInt    *ii,*idx;
    4307:   const PetscScalar *b,*a,*a_q;
    4308:   PetscScalar       *c,*c_q;
    4309:   PetscInt          clda = sub_c->lda;
    4310:   PetscInt          alda = sub_a->lda;

    4313:   m    = A->rmap->n;
    4314:   n    = A->cmap->n;
    4315:   p    = B->cmap->n;
    4316:   a    = sub_a->v;
    4317:   b    = sub_b->a;
    4318:   c    = sub_c->v;
    4319:   if (clda == m) {
    4320:     PetscArrayzero(c,m*p);
    4321:   } else {
    4322:     for (j=0;j<p;j++)
    4323:       for (i=0;i<m;i++)
    4324:         c[j*clda + i] = 0.0;
    4325:   }
    4326:   ii  = sub_b->i;
    4327:   idx = sub_b->j;
    4328:   for (i=0; i<n; i++) {
    4329:     q = ii[i+1] - ii[i];
    4330:     while (q-->0) {
    4331:       c_q = c + clda*(*idx);
    4332:       a_q = a + alda*i;
    4333:       PetscKernelAXPY(c_q,*b,a_q,m);
    4334:       idx++;
    4335:       b++;
    4336:     }
    4337:   }
    4338:   return(0);
    4339: }

    4341: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
    4342: {
    4344:   PetscInt       m=A->rmap->n,n=B->cmap->n;
    4345:   PetscBool      cisdense;

    4348:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %D != B->rmap->n %D\n",A->cmap->n,B->rmap->n);
    4349:   MatSetSizes(C,m,n,m,n);
    4350:   MatSetBlockSizesFromMats(C,A,B);
    4351:   PetscObjectTypeCompareAny((PetscObject)C,&cisdense,MATSEQDENSE,MATSEQDENSECUDA,"");
    4352:   if (!cisdense) {
    4353:     MatSetType(C,MATDENSE);
    4354:   }
    4355:   MatSetUp(C);

    4357:   C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
    4358:   return(0);
    4359: }

    4361: /* ----------------------------------------------------------------*/
    4362: /*MC
    4363:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
    4364:    based on compressed sparse row format.

    4366:    Options Database Keys:
    4367: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

    4369:    Level: beginner

    4371:    Notes:
    4372:     MatSetValues() may be called for this matrix type with a NULL argument for the numerical values,
    4373:     in this case the values associated with the rows and columns one passes in are set to zero
    4374:     in the matrix

    4376:     MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
    4377:     space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored

    4379:   Developer Notes:
    4380:     It would be nice if all matrix formats supported passing NULL in for the numerical values

    4382: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
    4383: M*/

    4385: /*MC
    4386:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

    4388:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
    4389:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
    4390:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation() is supported
    4391:   for communicators controlling multiple processes.  It is recommended that you call both of
    4392:   the above preallocation routines for simplicity.

    4394:    Options Database Keys:
    4395: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

    4397:   Developer Notes:
    4398:     Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
    4399:    enough exist.

    4401:   Level: beginner

    4403: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
    4404: M*/

    4406: /*MC
    4407:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

    4409:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
    4410:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
    4411:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
    4412:   for communicators controlling multiple processes.  It is recommended that you call both of
    4413:   the above preallocation routines for simplicity.

    4415:    Options Database Keys:
    4416: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

    4418:   Level: beginner

    4420: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
    4421: M*/

    4423: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
    4424: #if defined(PETSC_HAVE_ELEMENTAL)
    4425: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
    4426: #endif
    4427: #if defined(PETSC_HAVE_SCALAPACK)
    4428: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat,MatType,MatReuse,Mat*);
    4429: #endif
    4430: #if defined(PETSC_HAVE_HYPRE)
    4431: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
    4432: #endif

    4434: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
    4435: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
    4436: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);

    4438: /*@C
    4439:    MatSeqAIJGetArray - gives read/write access to the array where the data for a MATSEQAIJ matrix is stored

    4441:    Not Collective

    4443:    Input Parameter:
    4444: .  mat - a MATSEQAIJ matrix

    4446:    Output Parameter:
    4447: .   array - pointer to the data

    4449:    Level: intermediate

    4451: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
    4452: @*/
    4453: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
    4454: {

    4458:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
    4459: #if defined(PETSC_HAVE_DEVICE)
    4460:   if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
    4461: #endif
    4462:   return(0);
    4463: }

    4465: /*@C
    4466:    MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a MATSEQAIJ matrix is stored

    4468:    Not Collective

    4470:    Input Parameter:
    4471: .  mat - a MATSEQAIJ matrix

    4473:    Output Parameter:
    4474: .   array - pointer to the data

    4476:    Level: intermediate

    4478: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayRead()
    4479: @*/
    4480: PetscErrorCode  MatSeqAIJGetArrayRead(Mat A,const PetscScalar **array)
    4481: {
    4482: #if defined(PETSC_HAVE_DEVICE)
    4483:   PetscOffloadMask oval;
    4484: #endif

    4488: #if defined(PETSC_HAVE_DEVICE)
    4489:   oval = A->offloadmask;
    4490: #endif
    4491:   MatSeqAIJGetArray(A,(PetscScalar**)array);
    4492: #if defined(PETSC_HAVE_DEVICE)
    4493:   if (oval == PETSC_OFFLOAD_GPU || oval == PETSC_OFFLOAD_BOTH) A->offloadmask = PETSC_OFFLOAD_BOTH;
    4494: #endif
    4495:   return(0);
    4496: }

    4498: /*@C
    4499:    MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from MatSeqAIJGetArrayRead

    4501:    Not Collective

    4503:    Input Parameter:
    4504: .  mat - a MATSEQAIJ matrix

    4506:    Output Parameter:
    4507: .   array - pointer to the data

    4509:    Level: intermediate

    4511: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
    4512: @*/
    4513: PetscErrorCode  MatSeqAIJRestoreArrayRead(Mat A,const PetscScalar **array)
    4514: {
    4515: #if defined(PETSC_HAVE_DEVICE)
    4516:   PetscOffloadMask oval;
    4517: #endif

    4521: #if defined(PETSC_HAVE_DEVICE)
    4522:   oval = A->offloadmask;
    4523: #endif
    4524:   MatSeqAIJRestoreArray(A,(PetscScalar**)array);
    4525: #if defined(PETSC_HAVE_DEVICE)
    4526:   A->offloadmask = oval;
    4527: #endif
    4528:   return(0);
    4529: }

    4531: /*@C
    4532:    MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row

    4534:    Not Collective

    4536:    Input Parameter:
    4537: .  mat - a MATSEQAIJ matrix

    4539:    Output Parameter:
    4540: .   nz - the maximum number of nonzeros in any row

    4542:    Level: intermediate

    4544: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
    4545: @*/
    4546: PetscErrorCode  MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
    4547: {
    4548:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

    4551:   *nz = aij->rmax;
    4552:   return(0);
    4553: }

    4555: /*@C
    4556:    MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()

    4558:    Not Collective

    4560:    Input Parameters:
    4561: +  mat - a MATSEQAIJ matrix
    4562: -  array - pointer to the data

    4564:    Level: intermediate

    4566: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
    4567: @*/
    4568: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
    4569: {

    4573:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
    4574:   return(0);
    4575: }

    4577: #if defined(PETSC_HAVE_CUDA)
    4578: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat);
    4579: #endif
    4580: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    4581: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat);
    4582: #endif

    4584: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
    4585: {
    4586:   Mat_SeqAIJ     *b;
    4588:   PetscMPIInt    size;

    4591:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
    4592:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

    4594:   PetscNewLog(B,&b);

    4596:   B->data = (void*)b;

    4598:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
    4599:   if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;

    4601:   b->row                = NULL;
    4602:   b->col                = NULL;
    4603:   b->icol               = NULL;
    4604:   b->reallocs           = 0;
    4605:   b->ignorezeroentries  = PETSC_FALSE;
    4606:   b->roworiented        = PETSC_TRUE;
    4607:   b->nonew              = 0;
    4608:   b->diag               = NULL;
    4609:   b->solve_work         = NULL;
    4610:   B->spptr              = NULL;
    4611:   b->saved_values       = NULL;
    4612:   b->idiag              = NULL;
    4613:   b->mdiag              = NULL;
    4614:   b->ssor_work          = NULL;
    4615:   b->omega              = 1.0;
    4616:   b->fshift             = 0.0;
    4617:   b->idiagvalid         = PETSC_FALSE;
    4618:   b->ibdiagvalid        = PETSC_FALSE;
    4619:   b->keepnonzeropattern = PETSC_FALSE;

    4621:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
    4622:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
    4623:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

    4625: #if defined(PETSC_HAVE_MATLAB_ENGINE)
    4626:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
    4627:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
    4628: #endif

    4630:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
    4631:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
    4632:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
    4633:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
    4634:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
    4635:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
    4636:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
    4637: #if defined(PETSC_HAVE_MKL_SPARSE)
    4638:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
    4639: #endif
    4640: #if defined(PETSC_HAVE_CUDA)
    4641:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
    4642:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",MatProductSetFromOptions_SeqAIJ);
    4643:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",MatProductSetFromOptions_SeqAIJ);
    4644: #endif
    4645: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
    4646:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijkokkos_C",MatConvert_SeqAIJ_SeqAIJKokkos);
    4647: #endif
    4648:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
    4649: #if defined(PETSC_HAVE_ELEMENTAL)
    4650:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
    4651: #endif
    4652: #if defined(PETSC_HAVE_SCALAPACK)
    4653:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_scalapack_C",MatConvert_AIJ_ScaLAPACK);
    4654: #endif
    4655: #if defined(PETSC_HAVE_HYPRE)
    4656:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
    4657:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",MatProductSetFromOptions_Transpose_AIJ_AIJ);
    4658: #endif
    4659:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
    4660:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
    4661:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
    4662:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
    4663:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
    4664:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
    4665:   PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
    4666:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
    4667:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
    4668:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_is_seqaij_C",MatProductSetFromOptions_IS_XAIJ);
    4669:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqdense_seqaij_C",MatProductSetFromOptions_SeqDense_SeqAIJ);
    4670:   PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaij_C",MatProductSetFromOptions_SeqAIJ);
    4671:   MatCreate_SeqAIJ_Inode(B);
    4672:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
    4673:   MatSeqAIJSetTypeFromOptions(B);  /* this allows changing the matrix subtype to say MATSEQAIJPERM */
    4674:   return(0);
    4675: }

    4677: /*
    4678:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
    4679: */
    4680: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
    4681: {
    4682:   Mat_SeqAIJ     *c = (Mat_SeqAIJ*)C->data,*a = (Mat_SeqAIJ*)A->data;
    4684:   PetscInt       m = A->rmap->n,i;

    4687:   if (!A->assembled && cpvalues!=MAT_DO_NOT_COPY_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot duplicate unassembled matrix");

    4689:   C->factortype = A->factortype;
    4690:   c->row        = NULL;
    4691:   c->col        = NULL;
    4692:   c->icol       = NULL;
    4693:   c->reallocs   = 0;

    4695:   C->assembled = PETSC_TRUE;

    4697:   PetscLayoutReference(A->rmap,&C->rmap);
    4698:   PetscLayoutReference(A->cmap,&C->cmap);

    4700:   PetscMalloc1(m,&c->imax);
    4701:   PetscMemcpy(c->imax,a->imax,m*sizeof(PetscInt));
    4702:   PetscMalloc1(m,&c->ilen);
    4703:   PetscMemcpy(c->ilen,a->ilen,m*sizeof(PetscInt));
    4704:   PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));

    4706:   /* allocate the matrix space */
    4707:   if (mallocmatspace) {
    4708:     PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
    4709:     PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

    4711:     c->singlemalloc = PETSC_TRUE;

    4713:     PetscArraycpy(c->i,a->i,m+1);
    4714:     if (m > 0) {
    4715:       PetscArraycpy(c->j,a->j,a->i[m]);
    4716:       if (cpvalues == MAT_COPY_VALUES) {
    4717:         const PetscScalar *aa;

    4719:         MatSeqAIJGetArrayRead(A,&aa);
    4720:         PetscArraycpy(c->a,aa,a->i[m]);
    4721:         MatSeqAIJGetArrayRead(A,&aa);
    4722:       } else {
    4723:         PetscArrayzero(c->a,a->i[m]);
    4724:       }
    4725:     }
    4726:   }

    4728:   c->ignorezeroentries = a->ignorezeroentries;
    4729:   c->roworiented       = a->roworiented;
    4730:   c->nonew             = a->nonew;
    4731:   if (a->diag) {
    4732:     PetscMalloc1(m+1,&c->diag);
    4733:     PetscMemcpy(c->diag,a->diag,m*sizeof(PetscInt));
    4734:     PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
    4735:   } else c->diag = NULL;

    4737:   c->solve_work         = NULL;
    4738:   c->saved_values       = NULL;
    4739:   c->idiag              = NULL;
    4740:   c->ssor_work          = NULL;
    4741:   c->keepnonzeropattern = a->keepnonzeropattern;
    4742:   c->free_a             = PETSC_TRUE;
    4743:   c->free_ij            = PETSC_TRUE;

    4745:   c->rmax         = a->rmax;
    4746:   c->nz           = a->nz;
    4747:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
    4748:   C->preallocated = PETSC_TRUE;

    4750:   c->compressedrow.use   = a->compressedrow.use;
    4751:   c->compressedrow.nrows = a->compressedrow.nrows;
    4752:   if (a->compressedrow.use) {
    4753:     i    = a->compressedrow.nrows;
    4754:     PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
    4755:     PetscArraycpy(c->compressedrow.i,a->compressedrow.i,i+1);
    4756:     PetscArraycpy(c->compressedrow.rindex,a->compressedrow.rindex,i);
    4757:   } else {
    4758:     c->compressedrow.use    = PETSC_FALSE;
    4759:     c->compressedrow.i      = NULL;
    4760:     c->compressedrow.rindex = NULL;
    4761:   }
    4762:   c->nonzerorowcnt = a->nonzerorowcnt;
    4763:   C->nonzerostate  = A->nonzerostate;

    4765:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
    4766:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
    4767:   return(0);
    4768: }

    4770: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
    4771: {

    4775:   MatCreate(PetscObjectComm((PetscObject)A),B);
    4776:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
    4777:   if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
    4778:     MatSetBlockSizesFromMats(*B,A,A);
    4779:   }
    4780:   MatSetType(*B,((PetscObject)A)->type_name);
    4781:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
    4782:   return(0);
    4783: }

    4785: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
    4786: {
    4787:   PetscBool      isbinary, ishdf5;

    4793:   /* force binary viewer to load .info file if it has not yet done so */
    4794:   PetscViewerSetUp(viewer);
    4795:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    4796:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5,  &ishdf5);
    4797:   if (isbinary) {
    4798:     MatLoad_SeqAIJ_Binary(newMat,viewer);
    4799:   } else if (ishdf5) {
    4800: #if defined(PETSC_HAVE_HDF5)
    4801:     MatLoad_AIJ_HDF5(newMat,viewer);
    4802: #else
    4803:     SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
    4804: #endif
    4805:   } else {
    4806:     SETERRQ2(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"Viewer type %s not yet supported for reading %s matrices",((PetscObject)viewer)->type_name,((PetscObject)newMat)->type_name);
    4807:   }
    4808:   return(0);
    4809: }

    4811: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
    4812: {
    4813:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)mat->data;
    4815:   PetscInt       header[4],*rowlens,M,N,nz,sum,rows,cols,i;

    4818:   PetscViewerSetUp(viewer);

    4820:   /* read in matrix header */
    4821:   PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
    4822:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Not a matrix object in file");
    4823:   M = header[1]; N = header[2]; nz = header[3];
    4824:   if (M < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix row size (%D) in file is negative",M);
    4825:   if (N < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix column size (%D) in file is negative",N);
    4826:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk, cannot load as SeqAIJ");

    4828:   /* set block sizes from the viewer's .info file */
    4829:   MatLoad_Binary_BlockSizes(mat,viewer);
    4830:   /* set local and global sizes if not set already */
    4831:   if (mat->rmap->n < 0) mat->rmap->n = M;
    4832:   if (mat->cmap->n < 0) mat->cmap->n = N;
    4833:   if (mat->rmap->N < 0) mat->rmap->N = M;
    4834:   if (mat->cmap->N < 0) mat->cmap->N = N;
    4835:   PetscLayoutSetUp(mat->rmap);
    4836:   PetscLayoutSetUp(mat->cmap);

    4838:   /* check if the matrix sizes are correct */
    4839:   MatGetSize(mat,&rows,&cols);
    4840:   if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);

    4842:   /* read in row lengths */
    4843:   PetscMalloc1(M,&rowlens);
    4844:   PetscViewerBinaryRead(viewer,rowlens,M,NULL,PETSC_INT);
    4845:   /* check if sum(rowlens) is same as nz */
    4846:   sum = 0; for (i=0; i<M; i++) sum += rowlens[i];
    4847:   if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Inconsistent matrix data in file: nonzeros = %D, sum-row-lengths = %D\n",nz,sum);
    4848:   /* preallocate and check sizes */
    4849:   MatSeqAIJSetPreallocation_SeqAIJ(mat,0,rowlens);
    4850:   MatGetSize(mat,&rows,&cols);
    4851:   if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);
    4852:   /* store row lengths */
    4853:   PetscArraycpy(a->ilen,rowlens,M);
    4854:   PetscFree(rowlens);

    4856:   /* fill in "i" row pointers */
    4857:   a->i[0] = 0; for (i=0; i<M; i++) a->i[i+1] = a->i[i] + a->ilen[i];
    4858:   /* read in "j" column indices */
    4859:   PetscViewerBinaryRead(viewer,a->j,nz,NULL,PETSC_INT);
    4860:   /* read in "a" nonzero values */
    4861:   PetscViewerBinaryRead(viewer,a->a,nz,NULL,PETSC_SCALAR);

    4863:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
    4864:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
    4865:   return(0);
    4866: }

    4868: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
    4869: {
    4870:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
    4872: #if defined(PETSC_USE_COMPLEX)
    4873:   PetscInt k;
    4874: #endif

    4877:   /* If the  matrix dimensions are not equal,or no of nonzeros */
    4878:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
    4879:     *flg = PETSC_FALSE;
    4880:     return(0);
    4881:   }

    4883:   /* if the a->i are the same */
    4884:   PetscArraycmp(a->i,b->i,A->rmap->n+1,flg);
    4885:   if (!*flg) return(0);

    4887:   /* if a->j are the same */
    4888:   PetscArraycmp(a->j,b->j,a->nz,flg);
    4889:   if (!*flg) return(0);

    4891:   /* if a->a are the same */
    4892: #if defined(PETSC_USE_COMPLEX)
    4893:   for (k=0; k<a->nz; k++) {
    4894:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
    4895:       *flg = PETSC_FALSE;
    4896:       return(0);
    4897:     }
    4898:   }
    4899: #else
    4900:   PetscArraycmp(a->a,b->a,a->nz,flg);
    4901: #endif
    4902:   return(0);
    4903: }

    4905: /*@
    4906:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
    4907:               provided by the user.

    4909:       Collective

    4911:    Input Parameters:
    4912: +   comm - must be an MPI communicator of size 1
    4913: .   m - number of rows
    4914: .   n - number of columns
    4915: .   i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
    4916: .   j - column indices
    4917: -   a - matrix values

    4919:    Output Parameter:
    4920: .   mat - the matrix

    4922:    Level: intermediate

    4924:    Notes:
    4925:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
    4926:     once the matrix is destroyed and not before

    4928:        You cannot set new nonzero locations into this matrix, that will generate an error.

    4930:        The i and j indices are 0 based

    4932:        The format which is used for the sparse matrix input, is equivalent to a
    4933:     row-major ordering.. i.e for the following matrix, the input data expected is
    4934:     as shown

    4936: $        1 0 0
    4937: $        2 0 3
    4938: $        4 5 6
    4939: $
    4940: $        i =  {0,1,3,6}  [size = nrow+1  = 3+1]
    4941: $        j =  {0,0,2,0,1,2}  [size = 6]; values must be sorted for each row
    4942: $        v =  {1,2,3,4,5,6}  [size = 6]

    4945: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

    4947: @*/
    4948: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
    4949: {
    4951:   PetscInt       ii;
    4952:   Mat_SeqAIJ     *aij;
    4953:   PetscInt jj;

    4956:   if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
    4957:   MatCreate(comm,mat);
    4958:   MatSetSizes(*mat,m,n,m,n);
    4959:   /* MatSetBlockSizes(*mat,,); */
    4960:   MatSetType(*mat,MATSEQAIJ);
    4961:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,NULL);
    4962:   aij  = (Mat_SeqAIJ*)(*mat)->data;
    4963:   PetscMalloc1(m,&aij->imax);
    4964:   PetscMalloc1(m,&aij->ilen);

    4966:   aij->i            = i;
    4967:   aij->j            = j;
    4968:   aij->a            = a;
    4969:   aij->singlemalloc = PETSC_FALSE;
    4970:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
    4971:   aij->free_a       = PETSC_FALSE;
    4972:   aij->free_ij      = PETSC_FALSE;

    4974:   for (ii=0; ii<m; ii++) {
    4975:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
    4976:     if (PetscDefined(USE_DEBUG)) {
    4977:       if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %D length = %D",ii,i[ii+1] - i[ii]);
    4978:       for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
    4979:         if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
    4980:         if (j[jj] == j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
    4981:       }
    4982:     }
    4983:   }
    4984:   if (PetscDefined(USE_DEBUG)) {
    4985:     for (ii=0; ii<aij->i[m]; ii++) {
    4986:       if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
    4987:       if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %D index = %D",ii,j[ii]);
    4988:     }
    4989:   }

    4991:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    4992:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    4993:   return(0);
    4994: }
    4995: /*@C
    4996:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
    4997:               provided by the user.

    4999:       Collective

    5001:    Input Parameters:
    5002: +   comm - must be an MPI communicator of size 1
    5003: .   m   - number of rows
    5004: .   n   - number of columns
    5005: .   i   - row indices
    5006: .   j   - column indices
    5007: .   a   - matrix values
    5008: .   nz  - number of nonzeros
    5009: -   idx - 0 or 1 based

    5011:    Output Parameter:
    5012: .   mat - the matrix

    5014:    Level: intermediate

    5016:    Notes:
    5017:        The i and j indices are 0 based

    5019:        The format which is used for the sparse matrix input, is equivalent to a
    5020:     row-major ordering.. i.e for the following matrix, the input data expected is
    5021:     as shown:

    5023:         1 0 0
    5024:         2 0 3
    5025:         4 5 6

    5027:         i =  {0,1,1,2,2,2}
    5028:         j =  {0,0,2,0,1,2}
    5029:         v =  {1,2,3,4,5,6}

    5032: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

    5034: @*/
    5035: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
    5036: {
    5038:   PetscInt       ii, *nnz, one = 1,row,col;

    5042:   PetscCalloc1(m,&nnz);
    5043:   for (ii = 0; ii < nz; ii++) {
    5044:     nnz[i[ii] - !!idx] += 1;
    5045:   }
    5046:   MatCreate(comm,mat);
    5047:   MatSetSizes(*mat,m,n,m,n);
    5048:   MatSetType(*mat,MATSEQAIJ);
    5049:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
    5050:   for (ii = 0; ii < nz; ii++) {
    5051:     if (idx) {
    5052:       row = i[ii] - 1;
    5053:       col = j[ii] - 1;
    5054:     } else {
    5055:       row = i[ii];
    5056:       col = j[ii];
    5057:     }
    5058:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
    5059:   }
    5060:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    5061:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    5062:   PetscFree(nnz);
    5063:   return(0);
    5064: }

    5066: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
    5067: {
    5068:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

    5072:   a->idiagvalid  = PETSC_FALSE;
    5073:   a->ibdiagvalid = PETSC_FALSE;

    5075:   MatSeqAIJInvalidateDiagonal_Inode(A);
    5076:   return(0);
    5077: }

    5079: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
    5080: {
    5082:   PetscMPIInt    size;

    5085:   MPI_Comm_size(comm,&size);
    5086:   if (size == 1) {
    5087:     if (scall == MAT_INITIAL_MATRIX) {
    5088:       MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
    5089:     } else {
    5090:       MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
    5091:     }
    5092:   } else {
    5093:     MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
    5094:   }
    5095:   return(0);
    5096: }

    5098: /*
    5099:  Permute A into C's *local* index space using rowemb,colemb.
    5100:  The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
    5101:  of [0,m), colemb is in [0,n).
    5102:  If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
    5103:  */
    5104: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
    5105: {
    5106:   /* If making this function public, change the error returned in this function away from _PLIB. */
    5108:   Mat_SeqAIJ     *Baij;
    5109:   PetscBool      seqaij;
    5110:   PetscInt       m,n,*nz,i,j,count;
    5111:   PetscScalar    v;
    5112:   const PetscInt *rowindices,*colindices;

    5115:   if (!B) return(0);
    5116:   /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
    5117:   PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
    5118:   if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
    5119:   if (rowemb) {
    5120:     ISGetLocalSize(rowemb,&m);
    5121:     if (m != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Row IS of size %D is incompatible with matrix row size %D",m,B->rmap->n);
    5122:   } else {
    5123:     if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
    5124:   }
    5125:   if (colemb) {
    5126:     ISGetLocalSize(colemb,&n);
    5127:     if (n != B->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Diag col IS of size %D is incompatible with input matrix col size %D",n,B->cmap->n);
    5128:   } else {
    5129:     if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
    5130:   }

    5132:   Baij = (Mat_SeqAIJ*)(B->data);
    5133:   if (pattern == DIFFERENT_NONZERO_PATTERN) {
    5134:     PetscMalloc1(B->rmap->n,&nz);
    5135:     for (i=0; i<B->rmap->n; i++) {
    5136:       nz[i] = Baij->i[i+1] - Baij->i[i];
    5137:     }
    5138:     MatSeqAIJSetPreallocation(C,0,nz);
    5139:     PetscFree(nz);
    5140:   }
    5141:   if (pattern == SUBSET_NONZERO_PATTERN) {
    5142:     MatZeroEntries(C);
    5143:   }
    5144:   count = 0;
    5145:   rowindices = NULL;
    5146:   colindices = NULL;
    5147:   if (rowemb) {
    5148:     ISGetIndices(rowemb,&rowindices);
    5149:   }
    5150:   if (colemb) {
    5151:     ISGetIndices(colemb,&colindices);
    5152:   }
    5153:   for (i=0; i<B->rmap->n; i++) {
    5154:     PetscInt row;
    5155:     row = i;
    5156:     if (rowindices) row = rowindices[i];
    5157:     for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
    5158:       PetscInt col;
    5159:       col  = Baij->j[count];
    5160:       if (colindices) col = colindices[col];
    5161:       v    = Baij->a[count];
    5162:       MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
    5163:       ++count;
    5164:     }
    5165:   }
    5166:   /* FIXME: set C's nonzerostate correctly. */
    5167:   /* Assembly for C is necessary. */
    5168:   C->preallocated = PETSC_TRUE;
    5169:   C->assembled     = PETSC_TRUE;
    5170:   C->was_assembled = PETSC_FALSE;
    5171:   return(0);
    5172: }

    5174: PetscFunctionList MatSeqAIJList = NULL;

    5176: /*@C
    5177:    MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype

    5179:    Collective on Mat

    5181:    Input Parameters:
    5182: +  mat      - the matrix object
    5183: -  matype   - matrix type

    5185:    Options Database Key:
    5186: .  -mat_seqai_type  <method> - for example seqaijcrl

    5189:   Level: intermediate

    5191: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
    5192: @*/
    5193: PetscErrorCode  MatSeqAIJSetType(Mat mat, MatType matype)
    5194: {
    5195:   PetscErrorCode ierr,(*r)(Mat,MatType,MatReuse,Mat*);
    5196:   PetscBool      sametype;

    5200:   PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
    5201:   if (sametype) return(0);

    5203:    PetscFunctionListFind(MatSeqAIJList,matype,&r);
    5204:   if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
    5205:   (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
    5206:   return(0);
    5207: }

    5210: /*@C
    5211:   MatSeqAIJRegister -  - Adds a new sub-matrix type for sequential AIJ matrices

    5213:    Not Collective

    5215:    Input Parameters:
    5216: +  name - name of a new user-defined matrix type, for example MATSEQAIJCRL
    5217: -  function - routine to convert to subtype

    5219:    Notes:
    5220:    MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.

    5223:    Then, your matrix can be chosen with the procedural interface at runtime via the option
    5224: $     -mat_seqaij_type my_mat

    5226:    Level: advanced

    5228: .seealso: MatSeqAIJRegisterAll()

    5231:   Level: advanced
    5232: @*/
    5233: PetscErrorCode  MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
    5234: {

    5238:   MatInitializePackage();
    5239:   PetscFunctionListAdd(&MatSeqAIJList,sname,function);
    5240:   return(0);
    5241: }

    5243: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;

    5245: /*@C
    5246:   MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ

    5248:   Not Collective

    5250:   Level: advanced

    5252:   Developers Note: CUSPARSE does not yet support the MatConvert_SeqAIJ..() paradigm and thus cannot be registered here

    5254: .seealso:  MatRegisterAll(), MatSeqAIJRegister()
    5255: @*/
    5256: PetscErrorCode  MatSeqAIJRegisterAll(void)
    5257: {

    5261:   if (MatSeqAIJRegisterAllCalled) return(0);
    5262:   MatSeqAIJRegisterAllCalled = PETSC_TRUE;

    5264:   MatSeqAIJRegister(MATSEQAIJCRL,      MatConvert_SeqAIJ_SeqAIJCRL);
    5265:   MatSeqAIJRegister(MATSEQAIJPERM,     MatConvert_SeqAIJ_SeqAIJPERM);
    5266:   MatSeqAIJRegister(MATSEQAIJSELL,     MatConvert_SeqAIJ_SeqAIJSELL);
    5267: #if defined(PETSC_HAVE_MKL_SPARSE)
    5268:   MatSeqAIJRegister(MATSEQAIJMKL,      MatConvert_SeqAIJ_SeqAIJMKL);
    5269: #endif
    5270: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
    5271:   MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
    5272: #endif
    5273:   return(0);
    5274: }

    5276: /*
    5277:     Special version for direct calls from Fortran
    5278: */
    5279: #include <petsc/private/fortranimpl.h>
    5280: #if defined(PETSC_HAVE_FORTRAN_CAPS)
    5281: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
    5282: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
    5283: #define matsetvaluesseqaij_ matsetvaluesseqaij
    5284: #endif

    5286: /* Change these macros so can be used in void function */
    5287: #undef CHKERRQ
    5288: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
    5289: #undef SETERRQ2
    5290: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
    5291: #undef SETERRQ3
    5292: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

    5294: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
    5295: {
    5296:   Mat            A  = *AA;
    5297:   PetscInt       m  = *mm, n = *nn;
    5298:   InsertMode     is = *isis;
    5299:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    5300:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
    5301:   PetscInt       *imax,*ai,*ailen;
    5303:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
    5304:   MatScalar      *ap,value,*aa;
    5305:   PetscBool      ignorezeroentries = a->ignorezeroentries;
    5306:   PetscBool      roworiented       = a->roworiented;

    5309:   MatCheckPreallocated(A,1);
    5310:   imax  = a->imax;
    5311:   ai    = a->i;
    5312:   ailen = a->ilen;
    5313:   aj    = a->j;
    5314:   aa    = a->a;

    5316:   for (k=0; k<m; k++) { /* loop over added rows */
    5317:     row = im[k];
    5318:     if (row < 0) continue;
    5319:     if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
    5320:     rp   = aj + ai[row]; ap = aa + ai[row];
    5321:     rmax = imax[row]; nrow = ailen[row];
    5322:     low  = 0;
    5323:     high = nrow;
    5324:     for (l=0; l<n; l++) { /* loop over added columns */
    5325:       if (in[l] < 0) continue;
    5326:       if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
    5327:       col = in[l];
    5328:       if (roworiented) value = v[l + k*n];
    5329:       else value = v[k + l*m];

    5331:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

    5333:       if (col <= lastcol) low = 0;
    5334:       else high = nrow;
    5335:       lastcol = col;
    5336:       while (high-low > 5) {
    5337:         t = (low+high)/2;
    5338:         if (rp[t] > col) high = t;
    5339:         else             low  = t;
    5340:       }
    5341:       for (i=low; i<high; i++) {
    5342:         if (rp[i] > col) break;
    5343:         if (rp[i] == col) {
    5344:           if (is == ADD_VALUES) ap[i] += value;
    5345:           else                  ap[i] = value;
    5346:           goto noinsert;
    5347:         }
    5348:       }
    5349:       if (value == 0.0 && ignorezeroentries) goto noinsert;
    5350:       if (nonew == 1) goto noinsert;
    5351:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
    5352:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    5353:       N = nrow++ - 1; a->nz++; high++;
    5354:       /* shift up all the later entries in this row */
    5355:       for (ii=N; ii>=i; ii--) {
    5356:         rp[ii+1] = rp[ii];
    5357:         ap[ii+1] = ap[ii];
    5358:       }
    5359:       rp[i] = col;
    5360:       ap[i] = value;
    5361:       A->nonzerostate++;
    5362: noinsert:;
    5363:       low = i + 1;
    5364:     }
    5365:     ailen[row] = nrow;
    5366:   }
    5367:   PetscFunctionReturnVoid();
    5368: }