Actual source code: fdaij.c
petsc-3.7.1 2016-05-15
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      2: #include <../src/mat/impls/aij/seq/aij.h>
      3: #include <../src/mat/impls/baij/seq/baij.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: */
     12: PetscErrorCode MatFDColoringCreate_SeqXAIJ(Mat mat,ISColoring iscoloring,MatFDColoring c)
     13: {
     15:   PetscInt       bs,nis=iscoloring->n,m=mat->rmap->n;
     16:   PetscBool      isBAIJ;

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

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

     33:     nz    = spA->nz;
     34:     mem   = nz*(sizeof(PetscScalar) + sizeof(PetscInt)) + 3*m*sizeof(PetscInt);
     35:     bcols = (PetscInt)(0.5*mem /(m*sizeof(PetscScalar)));
     36:     brows = 1000/bcols;
     37:     if (bcols > nis) bcols = nis;
     38:     if (brows == 0 || brows > m) brows = m;
     39:     c->brows = brows;
     40:     c->bcols = bcols;
     41:   }

     43:   c->M       = mat->rmap->N/bs;   /* set total rows, columns and local rows */
     44:   c->N       = mat->cmap->N/bs;
     45:   c->m       = mat->rmap->N/bs;
     46:   c->rstart  = 0;
     47:   c->ncolors = nis;
     48:   c->ctype   = IS_COLORING_GHOSTED;
     49:   return(0);
     50: }

     52: /*
     53:  Reorder Jentry such that blocked brows*bols of entries from dense matrix are inserted into Jacobian for improved cache performance
     54:    Input Parameters:
     55: +  mat - the matrix containing the nonzero structure of the Jacobian
     56: .  color - the coloring context
     57: -  nz - number of local non-zeros in mat
     58: */
     61: PetscErrorCode MatFDColoringSetUpBlocked_AIJ_Private(Mat mat,MatFDColoring c,PetscInt nz)
     62: {
     64:   PetscInt       i,j,nrows,nbcols,brows=c->brows,bcols=c->bcols,mbs=c->m,nis=c->ncolors;
     65:   PetscInt       *color_start,*row_start,*nrows_new,nz_new,row_end;

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

     74:   nz_new = 0;
     75:   nbcols = 0;
     76:   color_start[bcols] = 0;

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

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

     93:       row_end = brows;
     94:       if (row_end > mbs) row_end = mbs;

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

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

    135:       row_end = brows;
    136:       if (row_end > mbs) row_end = mbs;

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

    163:   PetscFree2(color_start,row_start);

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

    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;
    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:   if (isBAIJ) {
    190:     Mat_SeqBAIJ *spA = (Mat_SeqBAIJ*)mat->data;
    191:     A_val = spA->a;
    192:     nz    = spA->nz;
    193:   } else {
    194:     Mat_SeqAIJ  *spA = (Mat_SeqAIJ*)mat->data;
    195:     A_val = spA->a;
    196:     nz    = spA->nz;
    197:     bs    = 1; /* only bs=1 is supported for SeqAIJ matrix */
    198:   }

    200:   PetscMalloc1(nis,&c->ncolumns);
    201:   PetscMalloc1(nis,&c->columns);
    202:   PetscMalloc1(nis,&c->nrows);
    203:   PetscLogObjectMemory((PetscObject)c,3*nis*sizeof(PetscInt));

    205:   if (c->htype[0] == 'd') {
    206:     PetscMalloc1(nz,&Jentry);
    207:     PetscLogObjectMemory((PetscObject)c,nz*sizeof(MatEntry));
    208:     c->matentry = Jentry;
    209:   } else if (c->htype[0] == 'w') {
    210:     PetscMalloc1(nz,&Jentry2);
    211:     PetscLogObjectMemory((PetscObject)c,nz*sizeof(MatEntry2));
    212:     c->matentry2 = Jentry2;
    213:   } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"htype is not supported");

    215:   if (isBAIJ) {
    216:     MatGetColumnIJ_SeqBAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    217:   } else {
    218:     MatGetColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    219:   }

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

    224:   nz = 0;
    225:   for (i=0; i<nis; i++) { /* loop over colors */
    226:     ISGetLocalSize(isa[i],&n);
    227:     ISGetIndices(isa[i],&is);

    229:     c->ncolumns[i] = n;
    230:     if (n) {
    231:       PetscMalloc1(n,&c->columns[i]);
    232:       PetscLogObjectMemory((PetscObject)c,n*sizeof(PetscInt));
    233:       PetscMemcpy(c->columns[i],is,n*sizeof(PetscInt));
    234:     } else {
    235:       c->columns[i] = 0;
    236:     }

    238:     /* fast, crude version requires O(N*N) work */
    239:     bs2   = bs*bs;
    240:     nrows = 0;
    241:     for (j=0; j<n; j++) {  /* loop over columns */
    242:       col    = is[j];
    243:       row    = cj + ci[col];
    244:       m      = ci[col+1] - ci[col];
    245:       nrows += m;
    246:       for (k=0; k<m; k++) {  /* loop over columns marking them in rowhit */
    247:         rowhit[*row]       = col + 1;
    248:         valaddrhit[*row++] = &A_val[bs2*spidx[ci[col] + k]];
    249:       }
    250:     }
    251:     c->nrows[i] = nrows; /* total num of rows for this color */

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

    276:   if (c->bcols > 1) {  /* reorder Jentry for faster MatFDColoringApply() */
    277:     MatFDColoringSetUpBlocked_AIJ_Private(mat,c,nz);
    278:   }

    280:   if (isBAIJ) {
    281:     MatRestoreColumnIJ_SeqBAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    282:     PetscMalloc1(bs*mat->rmap->n,&c->dy);
    283:     PetscLogObjectMemory((PetscObject)c,bs*mat->rmap->n*sizeof(PetscScalar));
    284:   } else {
    285:     MatRestoreColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
    286:   }
    287:   PetscFree2(rowhit,valaddrhit);
    288:   ISColoringRestoreIS(iscoloring,&isa);

    290:   VecCreateGhost(PetscObjectComm((PetscObject)mat),mat->rmap->n,PETSC_DETERMINE,0,NULL,&c->vscale);
    291:   PetscInfo3(c,"ncolors %D, brows %D and bcols %D are used.\n",c->ncolors,c->brows,c->bcols);
    292:   return(0);
    293: }