Actual source code: baijfact2.c

petsc-3.12.2 2019-11-22
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  2: /*
  3:     Factorization code for BAIJ format.
  4: */

  6:  #include <../src/mat/impls/baij/seq/baij.h>
  7:  #include <petsc/private/kernels/blockinvert.h>
  8:  #include <petscbt.h>
  9:  #include <../src/mat/utils/freespace.h>

 11: /* ----------------------------------------------------------------*/
 12: extern PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat,Mat,MatDuplicateOption,PetscBool);

 14: /*
 15:    This is not much faster than MatLUFactorNumeric_SeqBAIJ_N() but the solve is faster at least sometimes
 16: */
 17: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_15_NaturalOrdering(Mat B,Mat A,const MatFactorInfo *info)
 18: {
 19:   Mat             C =B;
 20:   Mat_SeqBAIJ     *a=(Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)C->data;
 21:   PetscErrorCode  ierr;
 22:   PetscInt        i,j,k,ipvt[15];
 23:   const PetscInt  n=a->mbs,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,*ajtmp,*bjtmp,*bdiag=b->diag,*pj;
 24:   PetscInt        nz,nzL,row;
 25:   MatScalar       *rtmp,*pc,*mwork,*pv,*vv,work[225];
 26:   const MatScalar *v,*aa=a->a;
 27:   PetscInt        bs2 = a->bs2,bs=A->rmap->bs,flg;
 28:   PetscInt        sol_ver;
 29:   PetscBool       allowzeropivot,zeropivotdetected;

 32:   allowzeropivot = PetscNot(A->erroriffailure);
 33:   PetscOptionsGetInt(NULL,((PetscObject)A)->prefix,"-sol_ver",&sol_ver,NULL);

 35:   /* generate work space needed by the factorization */
 36:   PetscMalloc2(bs2*n,&rtmp,bs2,&mwork);
 37:   PetscArrayzero(rtmp,bs2*n);

 39:   for (i=0; i<n; i++) {
 40:     /* zero rtmp */
 41:     /* L part */
 42:     nz    = bi[i+1] - bi[i];
 43:     bjtmp = bj + bi[i];
 44:     for  (j=0; j<nz; j++) {
 45:       PetscArrayzero(rtmp+bs2*bjtmp[j],bs2);
 46:     }

 48:     /* U part */
 49:     nz    = bdiag[i] - bdiag[i+1];
 50:     bjtmp = bj + bdiag[i+1]+1;
 51:     for  (j=0; j<nz; j++) {
 52:       PetscArrayzero(rtmp+bs2*bjtmp[j],bs2);
 53:     }

 55:     /* load in initial (unfactored row) */
 56:     nz    = ai[i+1] - ai[i];
 57:     ajtmp = aj + ai[i];
 58:     v     = aa + bs2*ai[i];
 59:     for (j=0; j<nz; j++) {
 60:       PetscArraycpy(rtmp+bs2*ajtmp[j],v+bs2*j,bs2);
 61:     }

 63:     /* elimination */
 64:     bjtmp = bj + bi[i];
 65:     nzL   = bi[i+1] - bi[i];
 66:     for (k=0; k < nzL; k++) {
 67:       row = bjtmp[k];
 68:       pc  = rtmp + bs2*row;
 69:       for (flg=0,j=0; j<bs2; j++) {
 70:         if (pc[j]!=0.0) {
 71:           flg = 1;
 72:           break;
 73:         }
 74:       }
 75:       if (flg) {
 76:         pv = b->a + bs2*bdiag[row];
 77:         PetscKernel_A_gets_A_times_B(bs,pc,pv,mwork);
 78:         /*PetscKernel_A_gets_A_times_B_15(pc,pv,mwork);*/
 79:         pj = b->j + bdiag[row+1]+1; /* begining of U(row,:) */
 80:         pv = b->a + bs2*(bdiag[row+1]+1);
 81:         nz = bdiag[row] - bdiag[row+1] - 1; /* num of entries inU(row,:), excluding diag */
 82:         for (j=0; j<nz; j++) {
 83:           vv = rtmp + bs2*pj[j];
 84:           PetscKernel_A_gets_A_minus_B_times_C(bs,vv,pc,pv);
 85:           /* PetscKernel_A_gets_A_minus_B_times_C_15(vv,pc,pv); */
 86:           pv += bs2;
 87:         }
 88:         PetscLogFlops(2*bs2*bs*(nz+1)-bs2); /* flops = 2*bs^3*nz + 2*bs^3 - bs2) */
 89:       }
 90:     }

 92:     /* finished row so stick it into b->a */
 93:     /* L part */
 94:     pv = b->a + bs2*bi[i];
 95:     pj = b->j + bi[i];
 96:     nz = bi[i+1] - bi[i];
 97:     for (j=0; j<nz; j++) {
 98:       PetscArraycpy(pv+bs2*j,rtmp+bs2*pj[j],bs2);
 99:     }

101:     /* Mark diagonal and invert diagonal for simplier triangular solves */
102:     pv   = b->a + bs2*bdiag[i];
103:     pj   = b->j + bdiag[i];
104:     PetscArraycpy(pv,rtmp+bs2*pj[0],bs2);
105:     PetscKernel_A_gets_inverse_A_15(pv,ipvt,work,info->shiftamount,allowzeropivot,&zeropivotdetected);
106:     if (zeropivotdetected) C->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;

108:     /* U part */
109:     pv = b->a + bs2*(bdiag[i+1]+1);
110:     pj = b->j + bdiag[i+1]+1;
111:     nz = bdiag[i] - bdiag[i+1] - 1;
112:     for (j=0; j<nz; j++) {
113:       PetscArraycpy(pv+bs2*j,rtmp+bs2*pj[j],bs2);
114:     }
115:   }

117:   PetscFree2(rtmp,mwork);

119:   C->ops->solve          = MatSolve_SeqBAIJ_15_NaturalOrdering_ver1;
120:   C->ops->solvetranspose = MatSolve_SeqBAIJ_N_NaturalOrdering;
121:   C->assembled           = PETSC_TRUE;

123:   PetscLogFlops(1.333333333333*bs*bs2*b->mbs); /* from inverting diagonal blocks */
124:   return(0);
125: }

127: PetscErrorCode MatLUFactorNumeric_SeqBAIJ_N(Mat B,Mat A,const MatFactorInfo *info)
128: {
129:   Mat            C     =B;
130:   Mat_SeqBAIJ    *a    =(Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)C->data;
131:   IS             isrow = b->row,isicol = b->icol;
133:   const PetscInt *r,*ic;
134:   PetscInt       i,j,k,n=a->mbs,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
135:   PetscInt       *ajtmp,*bjtmp,nz,nzL,row,*bdiag=b->diag,*pj;
136:   MatScalar      *rtmp,*pc,*mwork,*v,*pv,*aa=a->a;
137:   PetscInt       bs=A->rmap->bs,bs2 = a->bs2,*v_pivots,flg;
138:   MatScalar      *v_work;
139:   PetscBool      col_identity,row_identity,both_identity;
140:   PetscBool      allowzeropivot,zeropivotdetected;

143:   ISGetIndices(isrow,&r);
144:   ISGetIndices(isicol,&ic);
145:   allowzeropivot = PetscNot(A->erroriffailure);

147:   PetscCalloc1(bs2*n,&rtmp);

149:   /* generate work space needed by dense LU factorization */
150:   PetscMalloc3(bs,&v_work,bs2,&mwork,bs,&v_pivots);

152:   for (i=0; i<n; i++) {
153:     /* zero rtmp */
154:     /* L part */
155:     nz    = bi[i+1] - bi[i];
156:     bjtmp = bj + bi[i];
157:     for  (j=0; j<nz; j++) {
158:       PetscArrayzero(rtmp+bs2*bjtmp[j],bs2);
159:     }

161:     /* U part */
162:     nz    = bdiag[i] - bdiag[i+1];
163:     bjtmp = bj + bdiag[i+1]+1;
164:     for  (j=0; j<nz; j++) {
165:       PetscArrayzero(rtmp+bs2*bjtmp[j],bs2);
166:     }

168:     /* load in initial (unfactored row) */
169:     nz    = ai[r[i]+1] - ai[r[i]];
170:     ajtmp = aj + ai[r[i]];
171:     v     = aa + bs2*ai[r[i]];
172:     for (j=0; j<nz; j++) {
173:       PetscArraycpy(rtmp+bs2*ic[ajtmp[j]],v+bs2*j,bs2);
174:     }

176:     /* elimination */
177:     bjtmp = bj + bi[i];
178:     nzL   = bi[i+1] - bi[i];
179:     for (k=0; k < nzL; k++) {
180:       row = bjtmp[k];
181:       pc  = rtmp + bs2*row;
182:       for (flg=0,j=0; j<bs2; j++) {
183:         if (pc[j]!=0.0) {
184:           flg = 1;
185:           break;
186:         }
187:       }
188:       if (flg) {
189:         pv = b->a + bs2*bdiag[row];
190:         PetscKernel_A_gets_A_times_B(bs,pc,pv,mwork); /* *pc = *pc * (*pv); */
191:         pj = b->j + bdiag[row+1]+1;         /* begining of U(row,:) */
192:         pv = b->a + bs2*(bdiag[row+1]+1);
193:         nz = bdiag[row] - bdiag[row+1] - 1;         /* num of entries inU(row,:), excluding diag */
194:         for (j=0; j<nz; j++) {
195:           PetscKernel_A_gets_A_minus_B_times_C(bs,rtmp+bs2*pj[j],pc,pv+bs2*j);
196:         }
197:         PetscLogFlops(2*bs2*bs*(nz+1)-bs2); /* flops = 2*bs^3*nz + 2*bs^3 - bs2) */
198:       }
199:     }

201:     /* finished row so stick it into b->a */
202:     /* L part */
203:     pv = b->a + bs2*bi[i];
204:     pj = b->j + bi[i];
205:     nz = bi[i+1] - bi[i];
206:     for (j=0; j<nz; j++) {
207:       PetscArraycpy(pv+bs2*j,rtmp+bs2*pj[j],bs2);
208:     }

210:     /* Mark diagonal and invert diagonal for simplier triangular solves */
211:     pv = b->a + bs2*bdiag[i];
212:     pj = b->j + bdiag[i];
213:     PetscArraycpy(pv,rtmp+bs2*pj[0],bs2);

215:     PetscKernel_A_gets_inverse_A(bs,pv,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
216:     if (zeropivotdetected) B->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;

218:     /* U part */
219:     pv = b->a + bs2*(bdiag[i+1]+1);
220:     pj = b->j + bdiag[i+1]+1;
221:     nz = bdiag[i] - bdiag[i+1] - 1;
222:     for (j=0; j<nz; j++) {
223:       PetscArraycpy(pv+bs2*j,rtmp+bs2*pj[j],bs2);
224:     }
225:   }

227:   PetscFree(rtmp);
228:   PetscFree3(v_work,mwork,v_pivots);
229:   ISRestoreIndices(isicol,&ic);
230:   ISRestoreIndices(isrow,&r);

232:   ISIdentity(isrow,&row_identity);
233:   ISIdentity(isicol,&col_identity);

235:   both_identity = (PetscBool) (row_identity && col_identity);
236:   if (both_identity) {
237:     switch (bs) {
238:     case  9:
239: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
240:       C->ops->solve = MatSolve_SeqBAIJ_9_NaturalOrdering;
241: #else
242:       C->ops->solve = MatSolve_SeqBAIJ_N_NaturalOrdering;
243: #endif
244:       break;
245:     case 11:
246:       C->ops->solve = MatSolve_SeqBAIJ_11_NaturalOrdering;
247:       break;
248:     case 12:
249:       C->ops->solve = MatSolve_SeqBAIJ_12_NaturalOrdering;
250:       break;
251:     case 13:
252:       C->ops->solve = MatSolve_SeqBAIJ_13_NaturalOrdering;
253:       break;
254:     case 14:
255:       C->ops->solve = MatSolve_SeqBAIJ_14_NaturalOrdering;
256:       break;
257:     default:
258:       C->ops->solve = MatSolve_SeqBAIJ_N_NaturalOrdering;
259:       break;
260:     }
261:   } else {
262:     C->ops->solve = MatSolve_SeqBAIJ_N;
263:   }
264:   C->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_N;

266:   C->assembled = PETSC_TRUE;

268:   PetscLogFlops(1.333333333333*bs*bs2*b->mbs); /* from inverting diagonal blocks */
269:   return(0);
270: }

272: /*
273:    ilu(0) with natural ordering under new data structure.
274:    See MatILUFactorSymbolic_SeqAIJ_ilu0() for detailed description
275:    because this code is almost identical to MatILUFactorSymbolic_SeqAIJ_ilu0_inplace().
276: */

278: PetscErrorCode MatILUFactorSymbolic_SeqBAIJ_ilu0(Mat fact,Mat A,IS isrow,IS iscol,const MatFactorInfo *info)
279: {

281:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data,*b;
283:   PetscInt       n=a->mbs,*ai=a->i,*aj,*adiag=a->diag,bs2 = a->bs2;
284:   PetscInt       i,j,nz,*bi,*bj,*bdiag,bi_temp;

287:   MatDuplicateNoCreate_SeqBAIJ(fact,A,MAT_DO_NOT_COPY_VALUES,PETSC_FALSE);
288:   b    = (Mat_SeqBAIJ*)(fact)->data;

290:   /* allocate matrix arrays for new data structure */
291:   PetscMalloc3(bs2*ai[n]+1,&b->a,ai[n]+1,&b->j,n+1,&b->i);
292:   PetscLogObjectMemory((PetscObject)fact,ai[n]*(bs2*sizeof(PetscScalar)+sizeof(PetscInt))+(n+1)*sizeof(PetscInt));

294:   b->singlemalloc    = PETSC_TRUE;
295:   b->free_a          = PETSC_TRUE;
296:   b->free_ij         = PETSC_TRUE;
297:   fact->preallocated = PETSC_TRUE;
298:   fact->assembled    = PETSC_TRUE;
299:   if (!b->diag) {
300:     PetscMalloc1(n+1,&b->diag);
301:     PetscLogObjectMemory((PetscObject)fact,(n+1)*sizeof(PetscInt));
302:   }
303:   bdiag = b->diag;

305:   if (n > 0) {
306:     PetscArrayzero(b->a,bs2*ai[n]);
307:   }

309:   /* set bi and bj with new data structure */
310:   bi = b->i;
311:   bj = b->j;

313:   /* L part */
314:   bi[0] = 0;
315:   for (i=0; i<n; i++) {
316:     nz      = adiag[i] - ai[i];
317:     bi[i+1] = bi[i] + nz;
318:     aj      = a->j + ai[i];
319:     for (j=0; j<nz; j++) {
320:       *bj = aj[j]; bj++;
321:     }
322:   }

324:   /* U part */
325:   bi_temp  = bi[n];
326:   bdiag[n] = bi[n]-1;
327:   for (i=n-1; i>=0; i--) {
328:     nz      = ai[i+1] - adiag[i] - 1;
329:     bi_temp = bi_temp + nz + 1;
330:     aj      = a->j + adiag[i] + 1;
331:     for (j=0; j<nz; j++) {
332:       *bj = aj[j]; bj++;
333:     }
334:     /* diag[i] */
335:     *bj      = i; bj++;
336:     bdiag[i] = bi_temp - 1;
337:   }
338:   return(0);
339: }

341: PetscErrorCode MatILUFactorSymbolic_SeqBAIJ(Mat fact,Mat A,IS isrow,IS iscol,const MatFactorInfo *info)
342: {
343:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data,*b;
344:   IS                 isicol;
345:   PetscErrorCode     ierr;
346:   const PetscInt     *r,*ic;
347:   PetscInt           n=a->mbs,*ai=a->i,*aj=a->j,d;
348:   PetscInt           *bi,*cols,nnz,*cols_lvl;
349:   PetscInt           *bdiag,prow,fm,nzbd,reallocs=0,dcount=0;
350:   PetscInt           i,levels,diagonal_fill;
351:   PetscBool          col_identity,row_identity,both_identity;
352:   PetscReal          f;
353:   PetscInt           nlnk,*lnk,*lnk_lvl=NULL;
354:   PetscBT            lnkbt;
355:   PetscInt           nzi,*bj,**bj_ptr,**bjlvl_ptr;
356:   PetscFreeSpaceList free_space    =NULL,current_space=NULL;
357:   PetscFreeSpaceList free_space_lvl=NULL,current_space_lvl=NULL;
358:   PetscBool          missing;
359:   PetscInt           bs=A->rmap->bs,bs2=a->bs2;

362:   if (A->rmap->n != A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Must be square matrix, rows %D columns %D",A->rmap->n,A->cmap->n);
363:   if (bs>1) {  /* check shifttype */
364:     if (info->shifttype == MAT_SHIFT_NONZERO || info->shifttype == MAT_SHIFT_POSITIVE_DEFINITE) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only MAT_SHIFT_NONE and MAT_SHIFT_INBLOCKS are supported for BAIJ matrix");
365:   }

367:   MatMissingDiagonal(A,&missing,&d);
368:   if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry %D",d);

370:   f             = info->fill;
371:   levels        = (PetscInt)info->levels;
372:   diagonal_fill = (PetscInt)info->diagonal_fill;

374:   ISInvertPermutation(iscol,PETSC_DECIDE,&isicol);

376:   ISIdentity(isrow,&row_identity);
377:   ISIdentity(iscol,&col_identity);

379:   both_identity = (PetscBool) (row_identity && col_identity);

381:   if (!levels && both_identity) {
382:     /* special case: ilu(0) with natural ordering */
383:     MatILUFactorSymbolic_SeqBAIJ_ilu0(fact,A,isrow,iscol,info);
384:     MatSeqBAIJSetNumericFactorization(fact,both_identity);

386:     fact->factortype               = MAT_FACTOR_ILU;
387:     (fact)->info.factor_mallocs    = 0;
388:     (fact)->info.fill_ratio_given  = info->fill;
389:     (fact)->info.fill_ratio_needed = 1.0;

391:     b                = (Mat_SeqBAIJ*)(fact)->data;
392:     b->row           = isrow;
393:     b->col           = iscol;
394:     b->icol          = isicol;
395:     PetscObjectReference((PetscObject)isrow);
396:     PetscObjectReference((PetscObject)iscol);
397:     b->pivotinblocks = (info->pivotinblocks) ? PETSC_TRUE : PETSC_FALSE;

399:     PetscMalloc1((n+1)*bs,&b->solve_work);
400:     return(0);
401:   }

403:   ISGetIndices(isrow,&r);
404:   ISGetIndices(isicol,&ic);

406:   /* get new row pointers */
407:   PetscMalloc1(n+1,&bi);
408:   bi[0] = 0;
409:   /* bdiag is location of diagonal in factor */
410:   PetscMalloc1(n+1,&bdiag);
411:   bdiag[0] = 0;

413:   PetscMalloc2(n,&bj_ptr,n,&bjlvl_ptr);

415:   /* create a linked list for storing column indices of the active row */
416:   nlnk = n + 1;
417:   PetscIncompleteLLCreate(n,n,nlnk,lnk,lnk_lvl,lnkbt);

419:   /* initial FreeSpace size is f*(ai[n]+1) */
420:   PetscFreeSpaceGet(PetscRealIntMultTruncate(f,ai[n]+1),&free_space);
421:   current_space     = free_space;
422:   PetscFreeSpaceGet(PetscRealIntMultTruncate(f,ai[n]+1),&free_space_lvl);
423:   current_space_lvl = free_space_lvl;

425:   for (i=0; i<n; i++) {
426:     nzi = 0;
427:     /* copy current row into linked list */
428:     nnz = ai[r[i]+1] - ai[r[i]];
429:     if (!nnz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Empty row in matrix: row in original ordering %D in permuted ordering %D",r[i],i);
430:     cols   = aj + ai[r[i]];
431:     lnk[i] = -1; /* marker to indicate if diagonal exists */
432:     PetscIncompleteLLInit(nnz,cols,n,ic,nlnk,lnk,lnk_lvl,lnkbt);
433:     nzi   += nlnk;

435:     /* make sure diagonal entry is included */
436:     if (diagonal_fill && lnk[i] == -1) {
437:       fm = n;
438:       while (lnk[fm] < i) fm = lnk[fm];
439:       lnk[i]     = lnk[fm]; /* insert diagonal into linked list */
440:       lnk[fm]    = i;
441:       lnk_lvl[i] = 0;
442:       nzi++; dcount++;
443:     }

445:     /* add pivot rows into the active row */
446:     nzbd = 0;
447:     prow = lnk[n];
448:     while (prow < i) {
449:       nnz      = bdiag[prow];
450:       cols     = bj_ptr[prow] + nnz + 1;
451:       cols_lvl = bjlvl_ptr[prow] + nnz + 1;
452:       nnz      = bi[prow+1] - bi[prow] - nnz - 1;

454:       PetscILULLAddSorted(nnz,cols,levels,cols_lvl,prow,nlnk,lnk,lnk_lvl,lnkbt,prow);
455:       nzi += nlnk;
456:       prow = lnk[prow];
457:       nzbd++;
458:     }
459:     bdiag[i] = nzbd;
460:     bi[i+1]  = bi[i] + nzi;

462:     /* if free space is not available, make more free space */
463:     if (current_space->local_remaining<nzi) {
464:       nnz  = PetscIntMultTruncate(2,PetscIntMultTruncate(nzi,(n - i))); /* estimated and max additional space needed */
465:       PetscFreeSpaceGet(nnz,&current_space);
466:       PetscFreeSpaceGet(nnz,&current_space_lvl);
467:       reallocs++;
468:     }

470:     /* copy data into free_space and free_space_lvl, then initialize lnk */
471:     PetscIncompleteLLClean(n,n,nzi,lnk,lnk_lvl,current_space->array,current_space_lvl->array,lnkbt);

473:     bj_ptr[i]    = current_space->array;
474:     bjlvl_ptr[i] = current_space_lvl->array;

476:     /* make sure the active row i has diagonal entry */
477:     if (*(bj_ptr[i]+bdiag[i]) != i) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Row %D has missing diagonal in factored matrix\ntry running with -pc_factor_nonzeros_along_diagonal or -pc_factor_diagonal_fill",i);

479:     current_space->array           += nzi;
480:     current_space->local_used      += nzi;
481:     current_space->local_remaining -= nzi;

483:     current_space_lvl->array           += nzi;
484:     current_space_lvl->local_used      += nzi;
485:     current_space_lvl->local_remaining -= nzi;
486:   }

488:   ISRestoreIndices(isrow,&r);
489:   ISRestoreIndices(isicol,&ic);

491:   /* copy free_space into bj and free free_space; set bi, bj, bdiag in new datastructure; */
492:   PetscMalloc1(bi[n]+1,&bj);
493:   PetscFreeSpaceContiguous_LU(&free_space,bj,n,bi,bdiag);

495:   PetscIncompleteLLDestroy(lnk,lnkbt);
496:   PetscFreeSpaceDestroy(free_space_lvl);
497:   PetscFree2(bj_ptr,bjlvl_ptr);

499: #if defined(PETSC_USE_INFO)
500:   {
501:     PetscReal af = ((PetscReal)(bdiag[0]+1))/((PetscReal)ai[n]);
502:     PetscInfo3(A,"Reallocs %D Fill ratio:given %g needed %g\n",reallocs,(double)f,(double)af);
503:     PetscInfo1(A,"Run with -[sub_]pc_factor_fill %g or use \n",(double)af);
504:     PetscInfo1(A,"PCFactorSetFill([sub]pc,%g);\n",(double)af);
505:     PetscInfo(A,"for best performance.\n");
506:     if (diagonal_fill) {
507:       PetscInfo1(A,"Detected and replaced %D missing diagonals\n",dcount);
508:     }
509:   }
510: #endif

512:   /* put together the new matrix */
513:   MatSeqBAIJSetPreallocation(fact,bs,MAT_SKIP_ALLOCATION,NULL);
514:   PetscLogObjectParent((PetscObject)fact,(PetscObject)isicol);

516:   b               = (Mat_SeqBAIJ*)(fact)->data;
517:   b->free_a       = PETSC_TRUE;
518:   b->free_ij      = PETSC_TRUE;
519:   b->singlemalloc = PETSC_FALSE;

521:   PetscMalloc1(bs2*(bdiag[0]+1),&b->a);

523:   b->j          = bj;
524:   b->i          = bi;
525:   b->diag       = bdiag;
526:   b->free_diag  = PETSC_TRUE;
527:   b->ilen       = 0;
528:   b->imax       = 0;
529:   b->row        = isrow;
530:   b->col        = iscol;
531:   PetscObjectReference((PetscObject)isrow);
532:   PetscObjectReference((PetscObject)iscol);
533:   b->icol       = isicol;

535:   PetscMalloc1(bs*n+bs,&b->solve_work);
536:   /* In b structure:  Free imax, ilen, old a, old j.
537:      Allocate bdiag, solve_work, new a, new j */
538:   PetscLogObjectMemory((PetscObject)fact,(bdiag[0]+1) * (sizeof(PetscInt)+bs2*sizeof(PetscScalar)));
539:   b->maxnz = b->nz = bdiag[0]+1;

541:   fact->info.factor_mallocs    = reallocs;
542:   fact->info.fill_ratio_given  = f;
543:   fact->info.fill_ratio_needed = ((PetscReal)(bdiag[0]+1))/((PetscReal)ai[n]);

545:   MatSeqBAIJSetNumericFactorization(fact,both_identity);
546:   return(0);
547: }

549: /*
550:      This code is virtually identical to MatILUFactorSymbolic_SeqAIJ
551:    except that the data structure of Mat_SeqAIJ is slightly different.
552:    Not a good example of code reuse.
553: */
554: PetscErrorCode MatILUFactorSymbolic_SeqBAIJ_inplace(Mat fact,Mat A,IS isrow,IS iscol,const MatFactorInfo *info)
555: {
556:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data,*b;
557:   IS             isicol;
559:   const PetscInt *r,*ic,*ai = a->i,*aj = a->j,*xi;
560:   PetscInt       prow,n = a->mbs,*ainew,*ajnew,jmax,*fill,nz,*im,*ajfill,*flev,*xitmp;
561:   PetscInt       *dloc,idx,row,m,fm,nzf,nzi,reallocate = 0,dcount = 0;
562:   PetscInt       incrlev,nnz,i,bs = A->rmap->bs,bs2 = a->bs2,levels,diagonal_fill,dd;
563:   PetscBool      col_identity,row_identity,both_identity,flg;
564:   PetscReal      f;

567:   MatMissingDiagonal_SeqBAIJ(A,&flg,&dd);
568:   if (flg) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix A is missing diagonal entry in row %D",dd);

570:   f             = info->fill;
571:   levels        = (PetscInt)info->levels;
572:   diagonal_fill = (PetscInt)info->diagonal_fill;

574:   ISInvertPermutation(iscol,PETSC_DECIDE,&isicol);

576:   ISIdentity(isrow,&row_identity);
577:   ISIdentity(iscol,&col_identity);
578:   both_identity = (PetscBool) (row_identity && col_identity);

580:   if (!levels && both_identity) {  /* special case copy the nonzero structure */
581:     MatDuplicateNoCreate_SeqBAIJ(fact,A,MAT_DO_NOT_COPY_VALUES,PETSC_TRUE);
582:     MatSeqBAIJSetNumericFactorization_inplace(fact,both_identity);

584:     fact->factortype = MAT_FACTOR_ILU;
585:     b                = (Mat_SeqBAIJ*)fact->data;
586:     b->row           = isrow;
587:     b->col           = iscol;
588:     PetscObjectReference((PetscObject)isrow);
589:     PetscObjectReference((PetscObject)iscol);
590:     b->icol          = isicol;
591:     b->pivotinblocks = (info->pivotinblocks) ? PETSC_TRUE : PETSC_FALSE;

593:     PetscMalloc1((n+1)*bs,&b->solve_work);
594:     return(0);
595:   }

597:   /* general case perform the symbolic factorization */
598:   ISGetIndices(isrow,&r);
599:   ISGetIndices(isicol,&ic);

601:   /* get new row pointers */
602:   PetscMalloc1(n+1,&ainew);
603:   ainew[0] = 0;
604:   /* don't know how many column pointers are needed so estimate */
605:   jmax = (PetscInt)(f*ai[n] + 1);
606:   PetscMalloc1(jmax,&ajnew);
607:   /* ajfill is level of fill for each fill entry */
608:   PetscMalloc1(jmax,&ajfill);
609:   /* fill is a linked list of nonzeros in active row */
610:   PetscMalloc1(n+1,&fill);
611:   /* im is level for each filled value */
612:   PetscMalloc1(n+1,&im);
613:   /* dloc is location of diagonal in factor */
614:   PetscMalloc1(n+1,&dloc);
615:   dloc[0] = 0;
616:   for (prow=0; prow<n; prow++) {

618:     /* copy prow into linked list */
619:     nzf = nz = ai[r[prow]+1] - ai[r[prow]];
620:     if (!nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Empty row in matrix: row in original ordering %D in permuted ordering %D",r[prow],prow);
621:     xi         = aj + ai[r[prow]];
622:     fill[n]    = n;
623:     fill[prow] = -1;   /* marker for diagonal entry */
624:     while (nz--) {
625:       fm  = n;
626:       idx = ic[*xi++];
627:       do {
628:         m  = fm;
629:         fm = fill[m];
630:       } while (fm < idx);
631:       fill[m]   = idx;
632:       fill[idx] = fm;
633:       im[idx]   = 0;
634:     }

636:     /* make sure diagonal entry is included */
637:     if (diagonal_fill && fill[prow] == -1) {
638:       fm = n;
639:       while (fill[fm] < prow) fm = fill[fm];
640:       fill[prow] = fill[fm];    /* insert diagonal into linked list */
641:       fill[fm]   = prow;
642:       im[prow]   = 0;
643:       nzf++;
644:       dcount++;
645:     }

647:     nzi = 0;
648:     row = fill[n];
649:     while (row < prow) {
650:       incrlev = im[row] + 1;
651:       nz      = dloc[row];
652:       xi      = ajnew  + ainew[row] + nz + 1;
653:       flev    = ajfill + ainew[row] + nz + 1;
654:       nnz     = ainew[row+1] - ainew[row] - nz - 1;
655:       fm      = row;
656:       while (nnz-- > 0) {
657:         idx = *xi++;
658:         if (*flev + incrlev > levels) {
659:           flev++;
660:           continue;
661:         }
662:         do {
663:           m  = fm;
664:           fm = fill[m];
665:         } while (fm < idx);
666:         if (fm != idx) {
667:           im[idx]   = *flev + incrlev;
668:           fill[m]   = idx;
669:           fill[idx] = fm;
670:           fm        = idx;
671:           nzf++;
672:         } else if (im[idx] > *flev + incrlev) im[idx] = *flev+incrlev;
673:         flev++;
674:       }
675:       row = fill[row];
676:       nzi++;
677:     }
678:     /* copy new filled row into permanent storage */
679:     ainew[prow+1] = ainew[prow] + nzf;
680:     if (ainew[prow+1] > jmax) {

682:       /* estimate how much additional space we will need */
683:       /* use the strategy suggested by David Hysom <hysom@perch-t.icase.edu> */
684:       /* just double the memory each time */
685:       PetscInt maxadd = jmax;
686:       /* maxadd = (int)(((f*ai[n]+1)*(n-prow+5))/n); */
687:       if (maxadd < nzf) maxadd = (n-prow)*(nzf+1);
688:       jmax += maxadd;

690:       /* allocate a longer ajnew and ajfill */
691:       PetscMalloc1(jmax,&xitmp);
692:       PetscArraycpy(xitmp,ajnew,ainew[prow]);
693:       PetscFree(ajnew);
694:       ajnew  = xitmp;
695:       PetscMalloc1(jmax,&xitmp);
696:       PetscArraycpy(xitmp,ajfill,ainew[prow]);
697:       PetscFree(ajfill);
698:       ajfill = xitmp;
699:       reallocate++;   /* count how many reallocations are needed */
700:     }
701:     xitmp      = ajnew + ainew[prow];
702:     flev       = ajfill + ainew[prow];
703:     dloc[prow] = nzi;
704:     fm         = fill[n];
705:     while (nzf--) {
706:       *xitmp++ = fm;
707:       *flev++  = im[fm];
708:       fm       = fill[fm];
709:     }
710:     /* make sure row has diagonal entry */
711:     if (ajnew[ainew[prow]+dloc[prow]] != prow) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Row %D has missing diagonal in factored matrix\n\
712:                                                         try running with -pc_factor_nonzeros_along_diagonal or -pc_factor_diagonal_fill",prow);
713:   }
714:   PetscFree(ajfill);
715:   ISRestoreIndices(isrow,&r);
716:   ISRestoreIndices(isicol,&ic);
717:   PetscFree(fill);
718:   PetscFree(im);

720: #if defined(PETSC_USE_INFO)
721:   {
722:     PetscReal af = ((PetscReal)ainew[n])/((PetscReal)ai[n]);
723:     PetscInfo3(A,"Reallocs %D Fill ratio:given %g needed %g\n",reallocate,(double)f,(double)af);
724:     PetscInfo1(A,"Run with -pc_factor_fill %g or use \n",(double)af);
725:     PetscInfo1(A,"PCFactorSetFill(pc,%g);\n",(double)af);
726:     PetscInfo(A,"for best performance.\n");
727:     if (diagonal_fill) {
728:       PetscInfo1(A,"Detected and replaced %D missing diagonals\n",dcount);
729:     }
730:   }
731: #endif

733:   /* put together the new matrix */
734:   MatSeqBAIJSetPreallocation(fact,bs,MAT_SKIP_ALLOCATION,NULL);
735:   PetscLogObjectParent((PetscObject)fact,(PetscObject)isicol);
736:   b    = (Mat_SeqBAIJ*)fact->data;

738:   b->free_a       = PETSC_TRUE;
739:   b->free_ij      = PETSC_TRUE;
740:   b->singlemalloc = PETSC_FALSE;

742:   PetscMalloc1(bs2*ainew[n],&b->a);

744:   b->j          = ajnew;
745:   b->i          = ainew;
746:   for (i=0; i<n; i++) dloc[i] += ainew[i];
747:   b->diag          = dloc;
748:   b->free_diag     = PETSC_TRUE;
749:   b->ilen          = 0;
750:   b->imax          = 0;
751:   b->row           = isrow;
752:   b->col           = iscol;
753:   b->pivotinblocks = (info->pivotinblocks) ? PETSC_TRUE : PETSC_FALSE;

755:   PetscObjectReference((PetscObject)isrow);
756:   PetscObjectReference((PetscObject)iscol);
757:   b->icol = isicol;
758:   PetscMalloc1(bs*n+bs,&b->solve_work);
759:   /* In b structure:  Free imax, ilen, old a, old j.
760:      Allocate dloc, solve_work, new a, new j */
761:   PetscLogObjectMemory((PetscObject)fact,(ainew[n]-n)*(sizeof(PetscInt))+bs2*ainew[n]*sizeof(PetscScalar));
762:   b->maxnz = b->nz = ainew[n];

764:   fact->info.factor_mallocs    = reallocate;
765:   fact->info.fill_ratio_given  = f;
766:   fact->info.fill_ratio_needed = ((PetscReal)ainew[n])/((PetscReal)ai[prow]);

768:   MatSeqBAIJSetNumericFactorization_inplace(fact,both_identity);
769:   return(0);
770: }

772: PetscErrorCode MatSetUnfactored_SeqBAIJ_4_NaturalOrdering_SSE(Mat A)
773: {
774:   /* Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data; */
775:   /* int i,*AJ=a->j,nz=a->nz; */

778:   /* Undo Column scaling */
779:   /*    while (nz--) { */
780:   /*      AJ[i] = AJ[i]/4; */
781:   /*    } */
782:   /* This should really invoke a push/pop logic, but we don't have that yet. */
783:   A->ops->setunfactored = NULL;
784:   return(0);
785: }

787: PetscErrorCode MatSetUnfactored_SeqBAIJ_4_NaturalOrdering_SSE_usj(Mat A)
788: {
789:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
790:   PetscInt       *AJ=a->j,nz=a->nz;
791:   unsigned short *aj=(unsigned short*)AJ;

794:   /* Is this really necessary? */
795:   while (nz--) {
796:     AJ[nz] = (int)((unsigned int)aj[nz]); /* First extend, then convert to signed. */
797:   }
798:   A->ops->setunfactored = NULL;
799:   return(0);
800: }