Actual source code: fdaij.c
petsc-3.10.5 2019-03-28
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      1:  #include <../src/mat/impls/aij/seq/aij.h>
      2:  #include <../src/mat/impls/baij/seq/baij.h>
      3:  #include <../src/mat/impls/sell/seq/sell.h>
      4:  #include <petsc/private/isimpl.h>

      6: /*
      7:     This routine is shared by SeqAIJ and SeqBAIJ matrices,
      8:     since it operators only on the nonzero structure of the elements or blocks.
      9: */
     10: PetscErrorCode MatFDColoringCreate_SeqXAIJ(Mat mat,ISColoring iscoloring,MatFDColoring c)
     11: {
     13:   PetscInt       bs,nis=iscoloring->n,m=mat->rmap->n;
     14:   PetscBool      isBAIJ,isSELL;

     17:   /* set default brows and bcols for speedup inserting the dense matrix into sparse Jacobian */
     18:   MatGetBlockSize(mat,&bs);
     19:   PetscObjectTypeCompare((PetscObject)mat,MATSEQBAIJ,&isBAIJ);
     20:   PetscObjectTypeCompare((PetscObject)mat,MATSEQSELL,&isSELL);
     21:   if (isBAIJ) {
     22:     c->brows = m;
     23:     c->bcols = 1;
     24:   } else { /* seqaij matrix */
     25:     /* bcols is chosen s.t. dy-array takes 50% of memory space as mat */
     26:     PetscReal  mem;
     27:     PetscInt   nz,brows,bcols;
     28:     if (isSELL) {
     29:       Mat_SeqSELL *spA = (Mat_SeqSELL*)mat->data;
     30:       nz = spA->nz;
     31:     } else {
     32:       Mat_SeqAIJ *spA = (Mat_SeqAIJ*)mat->data;
     33:       nz = spA->nz;
     34:     }

     36:     bs    = 1; /* only bs=1 is supported for SeqAIJ matrix */

     38:     mem   = nz*(sizeof(PetscScalar) + sizeof(PetscInt)) + 3*m*sizeof(PetscInt);
     39:     bcols = (PetscInt)(0.5*mem /(m*sizeof(PetscScalar)));
     40:     brows = 1000/bcols;
     41:     if (bcols > nis) bcols = nis;
     42:     if (brows == 0 || brows > m) brows = m;
     43:     c->brows = brows;
     44:     c->bcols = bcols;
     45:   }

     47:   c->M       = mat->rmap->N/bs;   /* set total rows, columns and local rows */
     48:   c->N       = mat->cmap->N/bs;
     49:   c->m       = mat->rmap->N/bs;
     50:   c->rstart  = 0;
     51:   c->ncolors = nis;
     52:   c->ctype   = iscoloring->ctype;
     53:   return(0);
     54: }

     56: /*
     57:  Reorder Jentry such that blocked brows*bols of entries from dense matrix are inserted into Jacobian for improved cache performance
     58:    Input Parameters:
     59: +  mat - the matrix containing the nonzero structure of the Jacobian
     60: .  color - the coloring context
     61: -  nz - number of local non-zeros in mat
     62: */
     63: PetscErrorCode MatFDColoringSetUpBlocked_AIJ_Private(Mat mat,MatFDColoring c,PetscInt nz)
     64: {
     66:   PetscInt       i,j,nrows,nbcols,brows=c->brows,bcols=c->bcols,mbs=c->m,nis=c->ncolors;
     67:   PetscInt       *color_start,*row_start,*nrows_new,nz_new,row_end;

     70:   if (brows < 1 || brows > mbs) brows = mbs;
     71:   PetscMalloc2(bcols+1,&color_start,bcols,&row_start);
     72:   PetscCalloc1(nis,&nrows_new);
     73:   PetscMalloc1(bcols*mat->rmap->n,&c->dy);
     74:   PetscLogObjectMemory((PetscObject)c,bcols*mat->rmap->n*sizeof(PetscScalar));

     76:   nz_new = 0;
     77:   nbcols = 0;
     78:   color_start[bcols] = 0;

     80:   if (c->htype[0] == 'd') { /* ----  c->htype == 'ds', use MatEntry --------*/
     81:     MatEntry       *Jentry_new,*Jentry=c->matentry;
     82:     PetscMalloc1(nz,&Jentry_new);
     83:     for (i=0; i<nis; i+=bcols) { /* loop over colors */
     84:       if (i + bcols > nis) {
     85:         color_start[nis - i] = color_start[bcols];
     86:         bcols                = nis - i;
     87:       }

     89:       color_start[0] = color_start[bcols];
     90:       for (j=0; j<bcols; j++) {
     91:         color_start[j+1] = c->nrows[i+j] + color_start[j];
     92:         row_start[j]     = 0;
     93:       }

     95:       row_end = brows;
     96:       if (row_end > mbs) row_end = mbs;

     98:       while (row_end <= mbs) {   /* loop over block rows */
     99:         for (j=0; j<bcols; j++) {       /* loop over block columns */
    100:           nrows = c->nrows[i+j];
    101:           nz    = color_start[j];
    102:           while (row_start[j] < nrows) {
    103:             if (Jentry[nz].row >= row_end) {
    104:               color_start[j] = nz;
    105:               break;
    106:             } else { /* copy Jentry[nz] to Jentry_new[nz_new] */
    107:               Jentry_new[nz_new].row     = Jentry[nz].row + j*mbs; /* index in dy-array */
    108:               Jentry_new[nz_new].col     = Jentry[nz].col;
    109:               Jentry_new[nz_new].valaddr = Jentry[nz].valaddr;
    110:               nz_new++; nz++; row_start[j]++;
    111:             }
    112:           }
    113:         }
    114:         if (row_end == mbs) break;
    115:         row_end += brows;
    116:         if (row_end > mbs) row_end = mbs;
    117:       }
    118:       nrows_new[nbcols++] = nz_new;
    119:     }
    120:     PetscFree(Jentry);
    121:     c->matentry = Jentry_new;
    122:   } else { /* ---------  c->htype == 'wp', use MatEntry2 ------------------*/
    123:     MatEntry2      *Jentry2_new,*Jentry2=c->matentry2;
    124:     PetscMalloc1(nz,&Jentry2_new);
    125:     for (i=0; i<nis; i+=bcols) { /* loop over colors */
    126:       if (i + bcols > nis) {
    127:         color_start[nis - i] = color_start[bcols];
    128:         bcols                = nis - i;
    129:       }

    131:       color_start[0] = color_start[bcols];
    132:       for (j=0; j<bcols; j++) {
    133:         color_start[j+1] = c->nrows[i+j] + color_start[j];
    134:         row_start[j]     = 0;
    135:       }

    137:       row_end = brows;
    138:       if (row_end > mbs) row_end = mbs;

    140:       while (row_end <= mbs) {   /* loop over block rows */
    141:         for (j=0; j<bcols; j++) {       /* loop over block columns */
    142:           nrows = c->nrows[i+j];
    143:           nz    = color_start[j];
    144:           while (row_start[j] < nrows) {
    145:             if (Jentry2[nz].row >= row_end) {
    146:               color_start[j] = nz;
    147:               break;
    148:             } else { /* copy Jentry2[nz] to Jentry2_new[nz_new] */
    149:               Jentry2_new[nz_new].row     = Jentry2[nz].row + j*mbs; /* index in dy-array */
    150:               Jentry2_new[nz_new].valaddr = Jentry2[nz].valaddr;
    151:               nz_new++; nz++; row_start[j]++;
    152:             }
    153:           }
    154:         }
    155:         if (row_end == mbs) break;
    156:         row_end += brows;
    157:         if (row_end > mbs) row_end = mbs;
    158:       }
    159:       nrows_new[nbcols++] = nz_new;
    160:     }
    161:     PetscFree(Jentry2);
    162:     c->matentry2 = Jentry2_new;
    163:   } /* ---------------------------------------------*/

    165:   PetscFree2(color_start,row_start);

    167:   for (i=nbcols-1; i>0; i--) nrows_new[i] -= nrows_new[i-1];
    168:   PetscFree(c->nrows);
    169:   c->nrows = nrows_new;
    170:   return(0);
    171: }

    173: PetscErrorCode MatFDColoringSetUp_SeqXAIJ(Mat mat,ISColoring iscoloring,MatFDColoring c)
    174: {
    176:   PetscInt       i,n,nrows,mbs=c->m,j,k,m,ncols,col,nis=iscoloring->n,*rowhit,bs,bs2,*spidx,nz;
    177:   const PetscInt *is,*row,*ci,*cj;
    178:   IS             *isa;
    179:   PetscBool      isBAIJ,isSELL;
    180:   PetscScalar    *A_val,**valaddrhit;
    181:   MatEntry       *Jentry;
    182:   MatEntry2      *Jentry2;

    185:   ISColoringGetIS(iscoloring,PETSC_IGNORE,&isa);

    187:   MatGetBlockSize(mat,&bs);
    188:   PetscObjectTypeCompare((PetscObject)mat,MATSEQBAIJ,&isBAIJ);
    189:   PetscObjectTypeCompare((PetscObject)mat,MATSEQSELL,&isSELL);
    190:   if (isBAIJ) {
    191:     Mat_SeqBAIJ *spA = (Mat_SeqBAIJ*)mat->data;
    192:     A_val = spA->a;
    193:     nz    = spA->nz;
    194:   } else if (isSELL) {
    195:     Mat_SeqSELL  *spA = (Mat_SeqSELL*)mat->data;
    196:     A_val = spA->val;
    197:     nz    = spA->nz;
    198:     bs    = 1; /* only bs=1 is supported for SeqSELL matrix */
    199:   } else {
    200:     Mat_SeqAIJ  *spA = (Mat_SeqAIJ*)mat->data;
    201:     A_val = spA->a;
    202:     nz    = spA->nz;
    203:     bs    = 1; /* only bs=1 is supported for SeqAIJ matrix */
    204:   }

    206:   PetscMalloc1(nis,&c->ncolumns);
    207:   PetscMalloc1(nis,&c->columns);
    208:   PetscMalloc1(nis,&c->nrows);
    209:   PetscLogObjectMemory((PetscObject)c,3*nis*sizeof(PetscInt));

    211:   if (c->htype[0] == 'd') {
    212:     PetscMalloc1(nz,&Jentry);
    213:     PetscLogObjectMemory((PetscObject)c,nz*sizeof(MatEntry));
    214:     c->matentry = Jentry;
    215:   } else if (c->htype[0] == 'w') {
    216:     PetscMalloc1(nz,&Jentry2);
    217:     PetscLogObjectMemory((PetscObject)c,nz*sizeof(MatEntry2));
    218:     c->matentry2 = Jentry2;
    219:   } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"htype is not supported");

    221:   if (isBAIJ) {
    222:     MatGetColumnIJ_SeqBAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    223:   } else if (isSELL) {
    224:     MatGetColumnIJ_SeqSELL_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    225:   } else {
    226:     MatGetColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    227:   }

    229:   PetscMalloc2(c->m,&rowhit,c->m,&valaddrhit);
    230:   PetscMemzero(rowhit,c->m*sizeof(PetscInt));

    232:   nz = 0;
    233:   for (i=0; i<nis; i++) { /* loop over colors */
    234:     ISGetLocalSize(isa[i],&n);
    235:     ISGetIndices(isa[i],&is);

    237:     c->ncolumns[i] = n;
    238:     if (n) {
    239:       PetscMalloc1(n,&c->columns[i]);
    240:       PetscLogObjectMemory((PetscObject)c,n*sizeof(PetscInt));
    241:       PetscMemcpy(c->columns[i],is,n*sizeof(PetscInt));
    242:     } else {
    243:       c->columns[i] = 0;
    244:     }

    246:     /* fast, crude version requires O(N*N) work */
    247:     bs2   = bs*bs;
    248:     nrows = 0;
    249:     for (j=0; j<n; j++) {  /* loop over columns */
    250:       col    = is[j];
    251:       row    = cj + ci[col];
    252:       m      = ci[col+1] - ci[col];
    253:       nrows += m;
    254:       for (k=0; k<m; k++) {  /* loop over columns marking them in rowhit */
    255:         rowhit[*row]       = col + 1;
    256:         valaddrhit[*row++] = &A_val[bs2*spidx[ci[col] + k]];
    257:       }
    258:     }
    259:     c->nrows[i] = nrows; /* total num of rows for this color */

    261:     if (c->htype[0] == 'd') {
    262:       for (j=0; j<mbs; j++) { /* loop over rows */
    263:         if (rowhit[j]) {
    264:           Jentry[nz].row     = j;              /* local row index */
    265:           Jentry[nz].col     = rowhit[j] - 1;  /* local column index */
    266:           Jentry[nz].valaddr = valaddrhit[j];  /* address of mat value for this entry */
    267:           nz++;
    268:           rowhit[j] = 0.0;                     /* zero rowhit for reuse */
    269:         }
    270:       }
    271:     }  else { /* c->htype == 'wp' */
    272:       for (j=0; j<mbs; j++) { /* loop over rows */
    273:         if (rowhit[j]) {
    274:           Jentry2[nz].row     = j;              /* local row index */
    275:           Jentry2[nz].valaddr = valaddrhit[j];  /* address of mat value for this entry */
    276:           nz++;
    277:           rowhit[j] = 0.0;                     /* zero rowhit for reuse */
    278:         }
    279:       }
    280:     }
    281:     ISRestoreIndices(isa[i],&is);
    282:   }

    284:   if (c->bcols > 1) {  /* reorder Jentry for faster MatFDColoringApply() */
    285:     MatFDColoringSetUpBlocked_AIJ_Private(mat,c,nz);
    286:   }

    288:   if (isBAIJ) {
    289:     MatRestoreColumnIJ_SeqBAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    290:     PetscMalloc1(bs*mat->rmap->n,&c->dy);
    291:     PetscLogObjectMemory((PetscObject)c,bs*mat->rmap->n*sizeof(PetscScalar));
    292:   } else if (isSELL) {
    293:     MatRestoreColumnIJ_SeqSELL_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    294:   } else {
    295:     MatRestoreColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    296:   }
    297:   PetscFree2(rowhit,valaddrhit);
    298:   ISColoringRestoreIS(iscoloring,&isa);

    300:   VecCreateGhost(PetscObjectComm((PetscObject)mat),mat->rmap->n,PETSC_DETERMINE,0,NULL,&c->vscale);
    301:   PetscInfo3(c,"ncolors %D, brows %D and bcols %D are used.\n",c->ncolors,c->brows,c->bcols);
    302:   return(0);
    303: }