Actual source code: baij.c


  2: /*
  3:     Defines the basic matrix operations for the BAIJ (compressed row)
  4:   matrix storage format.
  5: */
  6: #include <../src/mat/impls/baij/seq/baij.h>
  7: #include <petscblaslapack.h>
  8: #include <petsc/private/kernels/blockinvert.h>
  9: #include <petsc/private/kernels/blockmatmult.h>

 11: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
 12: #define TYPE BAIJ
 13: #define TYPE_BS
 14: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
 15: #undef TYPE_BS
 16: #define TYPE_BS _BS
 17: #define TYPE_BS_ON
 18: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
 19: #undef TYPE_BS
 20: #include "../src/mat/impls/aij/seq/seqhashmat.h"
 21: #undef TYPE
 22: #undef TYPE_BS_ON

 24: #if defined(PETSC_HAVE_HYPRE)
 25: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
 26: #endif

 28: #if defined(PETSC_HAVE_MKL_SPARSE_OPTIMIZE)
 29: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat, MatType, MatReuse, Mat *);
 30: #endif
 31: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);

 33: static PetscErrorCode MatGetColumnReductions_SeqBAIJ(Mat A, PetscInt type, PetscReal *reductions)
 34: {
 35:   Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)A->data;
 36:   PetscInt     m, n, ib, jb, bs = A->rmap->bs;
 37:   MatScalar   *a_val = a_aij->a;

 39:   PetscFunctionBegin;
 40:   PetscCall(MatGetSize(A, &m, &n));
 41:   PetscCall(PetscArrayzero(reductions, n));
 42:   if (type == NORM_2) {
 43:     for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
 44:       for (jb = 0; jb < bs; jb++) {
 45:         for (ib = 0; ib < bs; ib++) {
 46:           reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
 47:           a_val++;
 48:         }
 49:       }
 50:     }
 51:   } else if (type == NORM_1) {
 52:     for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
 53:       for (jb = 0; jb < bs; jb++) {
 54:         for (ib = 0; ib < bs; ib++) {
 55:           reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
 56:           a_val++;
 57:         }
 58:       }
 59:     }
 60:   } else if (type == NORM_INFINITY) {
 61:     for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
 62:       for (jb = 0; jb < bs; jb++) {
 63:         for (ib = 0; ib < bs; ib++) {
 64:           int col         = A->cmap->rstart + a_aij->j[i] * bs + jb;
 65:           reductions[col] = PetscMax(PetscAbsScalar(*a_val), reductions[col]);
 66:           a_val++;
 67:         }
 68:       }
 69:     }
 70:   } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
 71:     for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
 72:       for (jb = 0; jb < bs; jb++) {
 73:         for (ib = 0; ib < bs; ib++) {
 74:           reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
 75:           a_val++;
 76:         }
 77:       }
 78:     }
 79:   } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
 80:     for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
 81:       for (jb = 0; jb < bs; jb++) {
 82:         for (ib = 0; ib < bs; ib++) {
 83:           reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
 84:           a_val++;
 85:         }
 86:       }
 87:     }
 88:   } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
 89:   if (type == NORM_2) {
 90:     for (PetscInt i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
 91:   } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
 92:     for (PetscInt i = 0; i < n; i++) reductions[i] /= m;
 93:   }
 94:   PetscFunctionReturn(PETSC_SUCCESS);
 95: }

 97: PetscErrorCode MatInvertBlockDiagonal_SeqBAIJ(Mat A, const PetscScalar **values)
 98: {
 99:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
100:   PetscInt    *diag_offset, i, bs = A->rmap->bs, mbs = a->mbs, ipvt[5], bs2 = bs * bs, *v_pivots;
101:   MatScalar   *v     = a->a, *odiag, *diag, work[25], *v_work;
102:   PetscReal    shift = 0.0;
103:   PetscBool    allowzeropivot, zeropivotdetected = PETSC_FALSE;

105:   PetscFunctionBegin;
106:   allowzeropivot = PetscNot(A->erroriffailure);

108:   if (a->idiagvalid) {
109:     if (values) *values = a->idiag;
110:     PetscFunctionReturn(PETSC_SUCCESS);
111:   }
112:   PetscCall(MatMarkDiagonal_SeqBAIJ(A));
113:   diag_offset = a->diag;
114:   if (!a->idiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->idiag)); }
115:   diag = a->idiag;
116:   if (values) *values = a->idiag;
117:   /* factor and invert each block */
118:   switch (bs) {
119:   case 1:
120:     for (i = 0; i < mbs; i++) {
121:       odiag   = v + 1 * diag_offset[i];
122:       diag[0] = odiag[0];

124:       if (PetscAbsScalar(diag[0] + shift) < PETSC_MACHINE_EPSILON) {
125:         if (allowzeropivot) {
126:           A->factorerrortype             = MAT_FACTOR_NUMERIC_ZEROPIVOT;
127:           A->factorerror_zeropivot_value = PetscAbsScalar(diag[0]);
128:           A->factorerror_zeropivot_row   = i;
129:           PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT "\n", i));
130:         } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot value %g tolerance %g", i, (double)PetscAbsScalar(diag[0]), (double)PETSC_MACHINE_EPSILON);
131:       }

133:       diag[0] = (PetscScalar)1.0 / (diag[0] + shift);
134:       diag += 1;
135:     }
136:     break;
137:   case 2:
138:     for (i = 0; i < mbs; i++) {
139:       odiag   = v + 4 * diag_offset[i];
140:       diag[0] = odiag[0];
141:       diag[1] = odiag[1];
142:       diag[2] = odiag[2];
143:       diag[3] = odiag[3];
144:       PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
145:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
146:       diag += 4;
147:     }
148:     break;
149:   case 3:
150:     for (i = 0; i < mbs; i++) {
151:       odiag   = v + 9 * diag_offset[i];
152:       diag[0] = odiag[0];
153:       diag[1] = odiag[1];
154:       diag[2] = odiag[2];
155:       diag[3] = odiag[3];
156:       diag[4] = odiag[4];
157:       diag[5] = odiag[5];
158:       diag[6] = odiag[6];
159:       diag[7] = odiag[7];
160:       diag[8] = odiag[8];
161:       PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
162:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
163:       diag += 9;
164:     }
165:     break;
166:   case 4:
167:     for (i = 0; i < mbs; i++) {
168:       odiag = v + 16 * diag_offset[i];
169:       PetscCall(PetscArraycpy(diag, odiag, 16));
170:       PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
171:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
172:       diag += 16;
173:     }
174:     break;
175:   case 5:
176:     for (i = 0; i < mbs; i++) {
177:       odiag = v + 25 * diag_offset[i];
178:       PetscCall(PetscArraycpy(diag, odiag, 25));
179:       PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
180:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
181:       diag += 25;
182:     }
183:     break;
184:   case 6:
185:     for (i = 0; i < mbs; i++) {
186:       odiag = v + 36 * diag_offset[i];
187:       PetscCall(PetscArraycpy(diag, odiag, 36));
188:       PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
189:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
190:       diag += 36;
191:     }
192:     break;
193:   case 7:
194:     for (i = 0; i < mbs; i++) {
195:       odiag = v + 49 * diag_offset[i];
196:       PetscCall(PetscArraycpy(diag, odiag, 49));
197:       PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
198:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
199:       diag += 49;
200:     }
201:     break;
202:   default:
203:     PetscCall(PetscMalloc2(bs, &v_work, bs, &v_pivots));
204:     for (i = 0; i < mbs; i++) {
205:       odiag = v + bs2 * diag_offset[i];
206:       PetscCall(PetscArraycpy(diag, odiag, bs2));
207:       PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
208:       if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
209:       diag += bs2;
210:     }
211:     PetscCall(PetscFree2(v_work, v_pivots));
212:   }
213:   a->idiagvalid = PETSC_TRUE;
214:   PetscFunctionReturn(PETSC_SUCCESS);
215: }

217: PetscErrorCode MatSOR_SeqBAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
218: {
219:   Mat_SeqBAIJ       *a = (Mat_SeqBAIJ *)A->data;
220:   PetscScalar       *x, *work, *w, *workt, *t;
221:   const MatScalar   *v, *aa = a->a, *idiag;
222:   const PetscScalar *b, *xb;
223:   PetscScalar        s[7], xw[7] = {0}; /* avoid some compilers thinking xw is uninitialized */
224:   PetscInt           m = a->mbs, i, i2, nz, bs = A->rmap->bs, bs2 = bs * bs, k, j, idx, it;
225:   const PetscInt    *diag, *ai = a->i, *aj = a->j, *vi;

227:   PetscFunctionBegin;
228:   its = its * lits;
229:   PetscCheck(!(flag & SOR_EISENSTAT), PETSC_COMM_SELF, PETSC_ERR_SUP, "No support yet for Eisenstat");
230:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " and local its %" PetscInt_FMT " both positive", its, lits);
231:   PetscCheck(!fshift, PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for diagonal shift");
232:   PetscCheck(omega == 1.0, PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for non-trivial relaxation factor");
233:   PetscCheck(!(flag & SOR_APPLY_UPPER) && !(flag & SOR_APPLY_LOWER), PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for applying upper or lower triangular parts");

235:   if (!a->idiagvalid) PetscCall(MatInvertBlockDiagonal(A, NULL));

237:   if (!m) PetscFunctionReturn(PETSC_SUCCESS);
238:   diag  = a->diag;
239:   idiag = a->idiag;
240:   k     = PetscMax(A->rmap->n, A->cmap->n);
241:   if (!a->mult_work) PetscCall(PetscMalloc1(k + 1, &a->mult_work));
242:   if (!a->sor_workt) PetscCall(PetscMalloc1(k, &a->sor_workt));
243:   if (!a->sor_work) PetscCall(PetscMalloc1(bs, &a->sor_work));
244:   work = a->mult_work;
245:   t    = a->sor_workt;
246:   w    = a->sor_work;

248:   PetscCall(VecGetArray(xx, &x));
249:   PetscCall(VecGetArrayRead(bb, &b));

251:   if (flag & SOR_ZERO_INITIAL_GUESS) {
252:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
253:       switch (bs) {
254:       case 1:
255:         PetscKernel_v_gets_A_times_w_1(x, idiag, b);
256:         t[0] = b[0];
257:         i2   = 1;
258:         idiag += 1;
259:         for (i = 1; i < m; i++) {
260:           v    = aa + ai[i];
261:           vi   = aj + ai[i];
262:           nz   = diag[i] - ai[i];
263:           s[0] = b[i2];
264:           for (j = 0; j < nz; j++) {
265:             xw[0] = x[vi[j]];
266:             PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
267:           }
268:           t[i2] = s[0];
269:           PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
270:           x[i2] = xw[0];
271:           idiag += 1;
272:           i2 += 1;
273:         }
274:         break;
275:       case 2:
276:         PetscKernel_v_gets_A_times_w_2(x, idiag, b);
277:         t[0] = b[0];
278:         t[1] = b[1];
279:         i2   = 2;
280:         idiag += 4;
281:         for (i = 1; i < m; i++) {
282:           v    = aa + 4 * ai[i];
283:           vi   = aj + ai[i];
284:           nz   = diag[i] - ai[i];
285:           s[0] = b[i2];
286:           s[1] = b[i2 + 1];
287:           for (j = 0; j < nz; j++) {
288:             idx   = 2 * vi[j];
289:             it    = 4 * j;
290:             xw[0] = x[idx];
291:             xw[1] = x[1 + idx];
292:             PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
293:           }
294:           t[i2]     = s[0];
295:           t[i2 + 1] = s[1];
296:           PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
297:           x[i2]     = xw[0];
298:           x[i2 + 1] = xw[1];
299:           idiag += 4;
300:           i2 += 2;
301:         }
302:         break;
303:       case 3:
304:         PetscKernel_v_gets_A_times_w_3(x, idiag, b);
305:         t[0] = b[0];
306:         t[1] = b[1];
307:         t[2] = b[2];
308:         i2   = 3;
309:         idiag += 9;
310:         for (i = 1; i < m; i++) {
311:           v    = aa + 9 * ai[i];
312:           vi   = aj + ai[i];
313:           nz   = diag[i] - ai[i];
314:           s[0] = b[i2];
315:           s[1] = b[i2 + 1];
316:           s[2] = b[i2 + 2];
317:           while (nz--) {
318:             idx   = 3 * (*vi++);
319:             xw[0] = x[idx];
320:             xw[1] = x[1 + idx];
321:             xw[2] = x[2 + idx];
322:             PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
323:             v += 9;
324:           }
325:           t[i2]     = s[0];
326:           t[i2 + 1] = s[1];
327:           t[i2 + 2] = s[2];
328:           PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
329:           x[i2]     = xw[0];
330:           x[i2 + 1] = xw[1];
331:           x[i2 + 2] = xw[2];
332:           idiag += 9;
333:           i2 += 3;
334:         }
335:         break;
336:       case 4:
337:         PetscKernel_v_gets_A_times_w_4(x, idiag, b);
338:         t[0] = b[0];
339:         t[1] = b[1];
340:         t[2] = b[2];
341:         t[3] = b[3];
342:         i2   = 4;
343:         idiag += 16;
344:         for (i = 1; i < m; i++) {
345:           v    = aa + 16 * ai[i];
346:           vi   = aj + ai[i];
347:           nz   = diag[i] - ai[i];
348:           s[0] = b[i2];
349:           s[1] = b[i2 + 1];
350:           s[2] = b[i2 + 2];
351:           s[3] = b[i2 + 3];
352:           while (nz--) {
353:             idx   = 4 * (*vi++);
354:             xw[0] = x[idx];
355:             xw[1] = x[1 + idx];
356:             xw[2] = x[2 + idx];
357:             xw[3] = x[3 + idx];
358:             PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
359:             v += 16;
360:           }
361:           t[i2]     = s[0];
362:           t[i2 + 1] = s[1];
363:           t[i2 + 2] = s[2];
364:           t[i2 + 3] = s[3];
365:           PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
366:           x[i2]     = xw[0];
367:           x[i2 + 1] = xw[1];
368:           x[i2 + 2] = xw[2];
369:           x[i2 + 3] = xw[3];
370:           idiag += 16;
371:           i2 += 4;
372:         }
373:         break;
374:       case 5:
375:         PetscKernel_v_gets_A_times_w_5(x, idiag, b);
376:         t[0] = b[0];
377:         t[1] = b[1];
378:         t[2] = b[2];
379:         t[3] = b[3];
380:         t[4] = b[4];
381:         i2   = 5;
382:         idiag += 25;
383:         for (i = 1; i < m; i++) {
384:           v    = aa + 25 * ai[i];
385:           vi   = aj + ai[i];
386:           nz   = diag[i] - ai[i];
387:           s[0] = b[i2];
388:           s[1] = b[i2 + 1];
389:           s[2] = b[i2 + 2];
390:           s[3] = b[i2 + 3];
391:           s[4] = b[i2 + 4];
392:           while (nz--) {
393:             idx   = 5 * (*vi++);
394:             xw[0] = x[idx];
395:             xw[1] = x[1 + idx];
396:             xw[2] = x[2 + idx];
397:             xw[3] = x[3 + idx];
398:             xw[4] = x[4 + idx];
399:             PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
400:             v += 25;
401:           }
402:           t[i2]     = s[0];
403:           t[i2 + 1] = s[1];
404:           t[i2 + 2] = s[2];
405:           t[i2 + 3] = s[3];
406:           t[i2 + 4] = s[4];
407:           PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
408:           x[i2]     = xw[0];
409:           x[i2 + 1] = xw[1];
410:           x[i2 + 2] = xw[2];
411:           x[i2 + 3] = xw[3];
412:           x[i2 + 4] = xw[4];
413:           idiag += 25;
414:           i2 += 5;
415:         }
416:         break;
417:       case 6:
418:         PetscKernel_v_gets_A_times_w_6(x, idiag, b);
419:         t[0] = b[0];
420:         t[1] = b[1];
421:         t[2] = b[2];
422:         t[3] = b[3];
423:         t[4] = b[4];
424:         t[5] = b[5];
425:         i2   = 6;
426:         idiag += 36;
427:         for (i = 1; i < m; i++) {
428:           v    = aa + 36 * ai[i];
429:           vi   = aj + ai[i];
430:           nz   = diag[i] - ai[i];
431:           s[0] = b[i2];
432:           s[1] = b[i2 + 1];
433:           s[2] = b[i2 + 2];
434:           s[3] = b[i2 + 3];
435:           s[4] = b[i2 + 4];
436:           s[5] = b[i2 + 5];
437:           while (nz--) {
438:             idx   = 6 * (*vi++);
439:             xw[0] = x[idx];
440:             xw[1] = x[1 + idx];
441:             xw[2] = x[2 + idx];
442:             xw[3] = x[3 + idx];
443:             xw[4] = x[4 + idx];
444:             xw[5] = x[5 + idx];
445:             PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
446:             v += 36;
447:           }
448:           t[i2]     = s[0];
449:           t[i2 + 1] = s[1];
450:           t[i2 + 2] = s[2];
451:           t[i2 + 3] = s[3];
452:           t[i2 + 4] = s[4];
453:           t[i2 + 5] = s[5];
454:           PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
455:           x[i2]     = xw[0];
456:           x[i2 + 1] = xw[1];
457:           x[i2 + 2] = xw[2];
458:           x[i2 + 3] = xw[3];
459:           x[i2 + 4] = xw[4];
460:           x[i2 + 5] = xw[5];
461:           idiag += 36;
462:           i2 += 6;
463:         }
464:         break;
465:       case 7:
466:         PetscKernel_v_gets_A_times_w_7(x, idiag, b);
467:         t[0] = b[0];
468:         t[1] = b[1];
469:         t[2] = b[2];
470:         t[3] = b[3];
471:         t[4] = b[4];
472:         t[5] = b[5];
473:         t[6] = b[6];
474:         i2   = 7;
475:         idiag += 49;
476:         for (i = 1; i < m; i++) {
477:           v    = aa + 49 * ai[i];
478:           vi   = aj + ai[i];
479:           nz   = diag[i] - ai[i];
480:           s[0] = b[i2];
481:           s[1] = b[i2 + 1];
482:           s[2] = b[i2 + 2];
483:           s[3] = b[i2 + 3];
484:           s[4] = b[i2 + 4];
485:           s[5] = b[i2 + 5];
486:           s[6] = b[i2 + 6];
487:           while (nz--) {
488:             idx   = 7 * (*vi++);
489:             xw[0] = x[idx];
490:             xw[1] = x[1 + idx];
491:             xw[2] = x[2 + idx];
492:             xw[3] = x[3 + idx];
493:             xw[4] = x[4 + idx];
494:             xw[5] = x[5 + idx];
495:             xw[6] = x[6 + idx];
496:             PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
497:             v += 49;
498:           }
499:           t[i2]     = s[0];
500:           t[i2 + 1] = s[1];
501:           t[i2 + 2] = s[2];
502:           t[i2 + 3] = s[3];
503:           t[i2 + 4] = s[4];
504:           t[i2 + 5] = s[5];
505:           t[i2 + 6] = s[6];
506:           PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
507:           x[i2]     = xw[0];
508:           x[i2 + 1] = xw[1];
509:           x[i2 + 2] = xw[2];
510:           x[i2 + 3] = xw[3];
511:           x[i2 + 4] = xw[4];
512:           x[i2 + 5] = xw[5];
513:           x[i2 + 6] = xw[6];
514:           idiag += 49;
515:           i2 += 7;
516:         }
517:         break;
518:       default:
519:         PetscKernel_w_gets_Ar_times_v(bs, bs, b, idiag, x);
520:         PetscCall(PetscArraycpy(t, b, bs));
521:         i2 = bs;
522:         idiag += bs2;
523:         for (i = 1; i < m; i++) {
524:           v  = aa + bs2 * ai[i];
525:           vi = aj + ai[i];
526:           nz = diag[i] - ai[i];

528:           PetscCall(PetscArraycpy(w, b + i2, bs));
529:           /* copy all rows of x that are needed into contiguous space */
530:           workt = work;
531:           for (j = 0; j < nz; j++) {
532:             PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
533:             workt += bs;
534:           }
535:           PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
536:           PetscCall(PetscArraycpy(t + i2, w, bs));
537:           PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);

539:           idiag += bs2;
540:           i2 += bs;
541:         }
542:         break;
543:       }
544:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
545:       PetscCall(PetscLogFlops(1.0 * bs2 * a->nz));
546:       xb = t;
547:     } else xb = b;
548:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
549:       idiag = a->idiag + bs2 * (a->mbs - 1);
550:       i2    = bs * (m - 1);
551:       switch (bs) {
552:       case 1:
553:         s[0] = xb[i2];
554:         PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
555:         x[i2] = xw[0];
556:         i2 -= 1;
557:         for (i = m - 2; i >= 0; i--) {
558:           v    = aa + (diag[i] + 1);
559:           vi   = aj + diag[i] + 1;
560:           nz   = ai[i + 1] - diag[i] - 1;
561:           s[0] = xb[i2];
562:           for (j = 0; j < nz; j++) {
563:             xw[0] = x[vi[j]];
564:             PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
565:           }
566:           PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
567:           x[i2] = xw[0];
568:           idiag -= 1;
569:           i2 -= 1;
570:         }
571:         break;
572:       case 2:
573:         s[0] = xb[i2];
574:         s[1] = xb[i2 + 1];
575:         PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
576:         x[i2]     = xw[0];
577:         x[i2 + 1] = xw[1];
578:         i2 -= 2;
579:         idiag -= 4;
580:         for (i = m - 2; i >= 0; i--) {
581:           v    = aa + 4 * (diag[i] + 1);
582:           vi   = aj + diag[i] + 1;
583:           nz   = ai[i + 1] - diag[i] - 1;
584:           s[0] = xb[i2];
585:           s[1] = xb[i2 + 1];
586:           for (j = 0; j < nz; j++) {
587:             idx   = 2 * vi[j];
588:             it    = 4 * j;
589:             xw[0] = x[idx];
590:             xw[1] = x[1 + idx];
591:             PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
592:           }
593:           PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
594:           x[i2]     = xw[0];
595:           x[i2 + 1] = xw[1];
596:           idiag -= 4;
597:           i2 -= 2;
598:         }
599:         break;
600:       case 3:
601:         s[0] = xb[i2];
602:         s[1] = xb[i2 + 1];
603:         s[2] = xb[i2 + 2];
604:         PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
605:         x[i2]     = xw[0];
606:         x[i2 + 1] = xw[1];
607:         x[i2 + 2] = xw[2];
608:         i2 -= 3;
609:         idiag -= 9;
610:         for (i = m - 2; i >= 0; i--) {
611:           v    = aa + 9 * (diag[i] + 1);
612:           vi   = aj + diag[i] + 1;
613:           nz   = ai[i + 1] - diag[i] - 1;
614:           s[0] = xb[i2];
615:           s[1] = xb[i2 + 1];
616:           s[2] = xb[i2 + 2];
617:           while (nz--) {
618:             idx   = 3 * (*vi++);
619:             xw[0] = x[idx];
620:             xw[1] = x[1 + idx];
621:             xw[2] = x[2 + idx];
622:             PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
623:             v += 9;
624:           }
625:           PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
626:           x[i2]     = xw[0];
627:           x[i2 + 1] = xw[1];
628:           x[i2 + 2] = xw[2];
629:           idiag -= 9;
630:           i2 -= 3;
631:         }
632:         break;
633:       case 4:
634:         s[0] = xb[i2];
635:         s[1] = xb[i2 + 1];
636:         s[2] = xb[i2 + 2];
637:         s[3] = xb[i2 + 3];
638:         PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
639:         x[i2]     = xw[0];
640:         x[i2 + 1] = xw[1];
641:         x[i2 + 2] = xw[2];
642:         x[i2 + 3] = xw[3];
643:         i2 -= 4;
644:         idiag -= 16;
645:         for (i = m - 2; i >= 0; i--) {
646:           v    = aa + 16 * (diag[i] + 1);
647:           vi   = aj + diag[i] + 1;
648:           nz   = ai[i + 1] - diag[i] - 1;
649:           s[0] = xb[i2];
650:           s[1] = xb[i2 + 1];
651:           s[2] = xb[i2 + 2];
652:           s[3] = xb[i2 + 3];
653:           while (nz--) {
654:             idx   = 4 * (*vi++);
655:             xw[0] = x[idx];
656:             xw[1] = x[1 + idx];
657:             xw[2] = x[2 + idx];
658:             xw[3] = x[3 + idx];
659:             PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
660:             v += 16;
661:           }
662:           PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
663:           x[i2]     = xw[0];
664:           x[i2 + 1] = xw[1];
665:           x[i2 + 2] = xw[2];
666:           x[i2 + 3] = xw[3];
667:           idiag -= 16;
668:           i2 -= 4;
669:         }
670:         break;
671:       case 5:
672:         s[0] = xb[i2];
673:         s[1] = xb[i2 + 1];
674:         s[2] = xb[i2 + 2];
675:         s[3] = xb[i2 + 3];
676:         s[4] = xb[i2 + 4];
677:         PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
678:         x[i2]     = xw[0];
679:         x[i2 + 1] = xw[1];
680:         x[i2 + 2] = xw[2];
681:         x[i2 + 3] = xw[3];
682:         x[i2 + 4] = xw[4];
683:         i2 -= 5;
684:         idiag -= 25;
685:         for (i = m - 2; i >= 0; i--) {
686:           v    = aa + 25 * (diag[i] + 1);
687:           vi   = aj + diag[i] + 1;
688:           nz   = ai[i + 1] - diag[i] - 1;
689:           s[0] = xb[i2];
690:           s[1] = xb[i2 + 1];
691:           s[2] = xb[i2 + 2];
692:           s[3] = xb[i2 + 3];
693:           s[4] = xb[i2 + 4];
694:           while (nz--) {
695:             idx   = 5 * (*vi++);
696:             xw[0] = x[idx];
697:             xw[1] = x[1 + idx];
698:             xw[2] = x[2 + idx];
699:             xw[3] = x[3 + idx];
700:             xw[4] = x[4 + idx];
701:             PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
702:             v += 25;
703:           }
704:           PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
705:           x[i2]     = xw[0];
706:           x[i2 + 1] = xw[1];
707:           x[i2 + 2] = xw[2];
708:           x[i2 + 3] = xw[3];
709:           x[i2 + 4] = xw[4];
710:           idiag -= 25;
711:           i2 -= 5;
712:         }
713:         break;
714:       case 6:
715:         s[0] = xb[i2];
716:         s[1] = xb[i2 + 1];
717:         s[2] = xb[i2 + 2];
718:         s[3] = xb[i2 + 3];
719:         s[4] = xb[i2 + 4];
720:         s[5] = xb[i2 + 5];
721:         PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
722:         x[i2]     = xw[0];
723:         x[i2 + 1] = xw[1];
724:         x[i2 + 2] = xw[2];
725:         x[i2 + 3] = xw[3];
726:         x[i2 + 4] = xw[4];
727:         x[i2 + 5] = xw[5];
728:         i2 -= 6;
729:         idiag -= 36;
730:         for (i = m - 2; i >= 0; i--) {
731:           v    = aa + 36 * (diag[i] + 1);
732:           vi   = aj + diag[i] + 1;
733:           nz   = ai[i + 1] - diag[i] - 1;
734:           s[0] = xb[i2];
735:           s[1] = xb[i2 + 1];
736:           s[2] = xb[i2 + 2];
737:           s[3] = xb[i2 + 3];
738:           s[4] = xb[i2 + 4];
739:           s[5] = xb[i2 + 5];
740:           while (nz--) {
741:             idx   = 6 * (*vi++);
742:             xw[0] = x[idx];
743:             xw[1] = x[1 + idx];
744:             xw[2] = x[2 + idx];
745:             xw[3] = x[3 + idx];
746:             xw[4] = x[4 + idx];
747:             xw[5] = x[5 + idx];
748:             PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
749:             v += 36;
750:           }
751:           PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
752:           x[i2]     = xw[0];
753:           x[i2 + 1] = xw[1];
754:           x[i2 + 2] = xw[2];
755:           x[i2 + 3] = xw[3];
756:           x[i2 + 4] = xw[4];
757:           x[i2 + 5] = xw[5];
758:           idiag -= 36;
759:           i2 -= 6;
760:         }
761:         break;
762:       case 7:
763:         s[0] = xb[i2];
764:         s[1] = xb[i2 + 1];
765:         s[2] = xb[i2 + 2];
766:         s[3] = xb[i2 + 3];
767:         s[4] = xb[i2 + 4];
768:         s[5] = xb[i2 + 5];
769:         s[6] = xb[i2 + 6];
770:         PetscKernel_v_gets_A_times_w_7(x, idiag, b);
771:         x[i2]     = xw[0];
772:         x[i2 + 1] = xw[1];
773:         x[i2 + 2] = xw[2];
774:         x[i2 + 3] = xw[3];
775:         x[i2 + 4] = xw[4];
776:         x[i2 + 5] = xw[5];
777:         x[i2 + 6] = xw[6];
778:         i2 -= 7;
779:         idiag -= 49;
780:         for (i = m - 2; i >= 0; i--) {
781:           v    = aa + 49 * (diag[i] + 1);
782:           vi   = aj + diag[i] + 1;
783:           nz   = ai[i + 1] - diag[i] - 1;
784:           s[0] = xb[i2];
785:           s[1] = xb[i2 + 1];
786:           s[2] = xb[i2 + 2];
787:           s[3] = xb[i2 + 3];
788:           s[4] = xb[i2 + 4];
789:           s[5] = xb[i2 + 5];
790:           s[6] = xb[i2 + 6];
791:           while (nz--) {
792:             idx   = 7 * (*vi++);
793:             xw[0] = x[idx];
794:             xw[1] = x[1 + idx];
795:             xw[2] = x[2 + idx];
796:             xw[3] = x[3 + idx];
797:             xw[4] = x[4 + idx];
798:             xw[5] = x[5 + idx];
799:             xw[6] = x[6 + idx];
800:             PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
801:             v += 49;
802:           }
803:           PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
804:           x[i2]     = xw[0];
805:           x[i2 + 1] = xw[1];
806:           x[i2 + 2] = xw[2];
807:           x[i2 + 3] = xw[3];
808:           x[i2 + 4] = xw[4];
809:           x[i2 + 5] = xw[5];
810:           x[i2 + 6] = xw[6];
811:           idiag -= 49;
812:           i2 -= 7;
813:         }
814:         break;
815:       default:
816:         PetscCall(PetscArraycpy(w, xb + i2, bs));
817:         PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);
818:         i2 -= bs;
819:         idiag -= bs2;
820:         for (i = m - 2; i >= 0; i--) {
821:           v  = aa + bs2 * (diag[i] + 1);
822:           vi = aj + diag[i] + 1;
823:           nz = ai[i + 1] - diag[i] - 1;

825:           PetscCall(PetscArraycpy(w, xb + i2, bs));
826:           /* copy all rows of x that are needed into contiguous space */
827:           workt = work;
828:           for (j = 0; j < nz; j++) {
829:             PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
830:             workt += bs;
831:           }
832:           PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
833:           PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);

835:           idiag -= bs2;
836:           i2 -= bs;
837:         }
838:         break;
839:       }
840:       PetscCall(PetscLogFlops(1.0 * bs2 * (a->nz)));
841:     }
842:     its--;
843:   }
844:   while (its--) {
845:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
846:       idiag = a->idiag;
847:       i2    = 0;
848:       switch (bs) {
849:       case 1:
850:         for (i = 0; i < m; i++) {
851:           v    = aa + ai[i];
852:           vi   = aj + ai[i];
853:           nz   = ai[i + 1] - ai[i];
854:           s[0] = b[i2];
855:           for (j = 0; j < nz; j++) {
856:             xw[0] = x[vi[j]];
857:             PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
858:           }
859:           PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
860:           x[i2] += xw[0];
861:           idiag += 1;
862:           i2 += 1;
863:         }
864:         break;
865:       case 2:
866:         for (i = 0; i < m; i++) {
867:           v    = aa + 4 * ai[i];
868:           vi   = aj + ai[i];
869:           nz   = ai[i + 1] - ai[i];
870:           s[0] = b[i2];
871:           s[1] = b[i2 + 1];
872:           for (j = 0; j < nz; j++) {
873:             idx   = 2 * vi[j];
874:             it    = 4 * j;
875:             xw[0] = x[idx];
876:             xw[1] = x[1 + idx];
877:             PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
878:           }
879:           PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
880:           x[i2] += xw[0];
881:           x[i2 + 1] += xw[1];
882:           idiag += 4;
883:           i2 += 2;
884:         }
885:         break;
886:       case 3:
887:         for (i = 0; i < m; i++) {
888:           v    = aa + 9 * ai[i];
889:           vi   = aj + ai[i];
890:           nz   = ai[i + 1] - ai[i];
891:           s[0] = b[i2];
892:           s[1] = b[i2 + 1];
893:           s[2] = b[i2 + 2];
894:           while (nz--) {
895:             idx   = 3 * (*vi++);
896:             xw[0] = x[idx];
897:             xw[1] = x[1 + idx];
898:             xw[2] = x[2 + idx];
899:             PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
900:             v += 9;
901:           }
902:           PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
903:           x[i2] += xw[0];
904:           x[i2 + 1] += xw[1];
905:           x[i2 + 2] += xw[2];
906:           idiag += 9;
907:           i2 += 3;
908:         }
909:         break;
910:       case 4:
911:         for (i = 0; i < m; i++) {
912:           v    = aa + 16 * ai[i];
913:           vi   = aj + ai[i];
914:           nz   = ai[i + 1] - ai[i];
915:           s[0] = b[i2];
916:           s[1] = b[i2 + 1];
917:           s[2] = b[i2 + 2];
918:           s[3] = b[i2 + 3];
919:           while (nz--) {
920:             idx   = 4 * (*vi++);
921:             xw[0] = x[idx];
922:             xw[1] = x[1 + idx];
923:             xw[2] = x[2 + idx];
924:             xw[3] = x[3 + idx];
925:             PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
926:             v += 16;
927:           }
928:           PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
929:           x[i2] += xw[0];
930:           x[i2 + 1] += xw[1];
931:           x[i2 + 2] += xw[2];
932:           x[i2 + 3] += xw[3];
933:           idiag += 16;
934:           i2 += 4;
935:         }
936:         break;
937:       case 5:
938:         for (i = 0; i < m; i++) {
939:           v    = aa + 25 * ai[i];
940:           vi   = aj + ai[i];
941:           nz   = ai[i + 1] - ai[i];
942:           s[0] = b[i2];
943:           s[1] = b[i2 + 1];
944:           s[2] = b[i2 + 2];
945:           s[3] = b[i2 + 3];
946:           s[4] = b[i2 + 4];
947:           while (nz--) {
948:             idx   = 5 * (*vi++);
949:             xw[0] = x[idx];
950:             xw[1] = x[1 + idx];
951:             xw[2] = x[2 + idx];
952:             xw[3] = x[3 + idx];
953:             xw[4] = x[4 + idx];
954:             PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
955:             v += 25;
956:           }
957:           PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
958:           x[i2] += xw[0];
959:           x[i2 + 1] += xw[1];
960:           x[i2 + 2] += xw[2];
961:           x[i2 + 3] += xw[3];
962:           x[i2 + 4] += xw[4];
963:           idiag += 25;
964:           i2 += 5;
965:         }
966:         break;
967:       case 6:
968:         for (i = 0; i < m; i++) {
969:           v    = aa + 36 * ai[i];
970:           vi   = aj + ai[i];
971:           nz   = ai[i + 1] - ai[i];
972:           s[0] = b[i2];
973:           s[1] = b[i2 + 1];
974:           s[2] = b[i2 + 2];
975:           s[3] = b[i2 + 3];
976:           s[4] = b[i2 + 4];
977:           s[5] = b[i2 + 5];
978:           while (nz--) {
979:             idx   = 6 * (*vi++);
980:             xw[0] = x[idx];
981:             xw[1] = x[1 + idx];
982:             xw[2] = x[2 + idx];
983:             xw[3] = x[3 + idx];
984:             xw[4] = x[4 + idx];
985:             xw[5] = x[5 + idx];
986:             PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
987:             v += 36;
988:           }
989:           PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
990:           x[i2] += xw[0];
991:           x[i2 + 1] += xw[1];
992:           x[i2 + 2] += xw[2];
993:           x[i2 + 3] += xw[3];
994:           x[i2 + 4] += xw[4];
995:           x[i2 + 5] += xw[5];
996:           idiag += 36;
997:           i2 += 6;
998:         }
999:         break;
1000:       case 7:
1001:         for (i = 0; i < m; i++) {
1002:           v    = aa + 49 * ai[i];
1003:           vi   = aj + ai[i];
1004:           nz   = ai[i + 1] - ai[i];
1005:           s[0] = b[i2];
1006:           s[1] = b[i2 + 1];
1007:           s[2] = b[i2 + 2];
1008:           s[3] = b[i2 + 3];
1009:           s[4] = b[i2 + 4];
1010:           s[5] = b[i2 + 5];
1011:           s[6] = b[i2 + 6];
1012:           while (nz--) {
1013:             idx   = 7 * (*vi++);
1014:             xw[0] = x[idx];
1015:             xw[1] = x[1 + idx];
1016:             xw[2] = x[2 + idx];
1017:             xw[3] = x[3 + idx];
1018:             xw[4] = x[4 + idx];
1019:             xw[5] = x[5 + idx];
1020:             xw[6] = x[6 + idx];
1021:             PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
1022:             v += 49;
1023:           }
1024:           PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
1025:           x[i2] += xw[0];
1026:           x[i2 + 1] += xw[1];
1027:           x[i2 + 2] += xw[2];
1028:           x[i2 + 3] += xw[3];
1029:           x[i2 + 4] += xw[4];
1030:           x[i2 + 5] += xw[5];
1031:           x[i2 + 6] += xw[6];
1032:           idiag += 49;
1033:           i2 += 7;
1034:         }
1035:         break;
1036:       default:
1037:         for (i = 0; i < m; i++) {
1038:           v  = aa + bs2 * ai[i];
1039:           vi = aj + ai[i];
1040:           nz = ai[i + 1] - ai[i];

1042:           PetscCall(PetscArraycpy(w, b + i2, bs));
1043:           /* copy all rows of x that are needed into contiguous space */
1044:           workt = work;
1045:           for (j = 0; j < nz; j++) {
1046:             PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
1047:             workt += bs;
1048:           }
1049:           PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
1050:           PetscKernel_w_gets_w_plus_Ar_times_v(bs, bs, w, idiag, x + i2);

1052:           idiag += bs2;
1053:           i2 += bs;
1054:         }
1055:         break;
1056:       }
1057:       PetscCall(PetscLogFlops(2.0 * bs2 * a->nz));
1058:     }
1059:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1060:       idiag = a->idiag + bs2 * (a->mbs - 1);
1061:       i2    = bs * (m - 1);
1062:       switch (bs) {
1063:       case 1:
1064:         for (i = m - 1; i >= 0; i--) {
1065:           v    = aa + ai[i];
1066:           vi   = aj + ai[i];
1067:           nz   = ai[i + 1] - ai[i];
1068:           s[0] = b[i2];
1069:           for (j = 0; j < nz; j++) {
1070:             xw[0] = x[vi[j]];
1071:             PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
1072:           }
1073:           PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
1074:           x[i2] += xw[0];
1075:           idiag -= 1;
1076:           i2 -= 1;
1077:         }
1078:         break;
1079:       case 2:
1080:         for (i = m - 1; i >= 0; i--) {
1081:           v    = aa + 4 * ai[i];
1082:           vi   = aj + ai[i];
1083:           nz   = ai[i + 1] - ai[i];
1084:           s[0] = b[i2];
1085:           s[1] = b[i2 + 1];
1086:           for (j = 0; j < nz; j++) {
1087:             idx   = 2 * vi[j];
1088:             it    = 4 * j;
1089:             xw[0] = x[idx];
1090:             xw[1] = x[1 + idx];
1091:             PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
1092:           }
1093:           PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
1094:           x[i2] += xw[0];
1095:           x[i2 + 1] += xw[1];
1096:           idiag -= 4;
1097:           i2 -= 2;
1098:         }
1099:         break;
1100:       case 3:
1101:         for (i = m - 1; i >= 0; i--) {
1102:           v    = aa + 9 * ai[i];
1103:           vi   = aj + ai[i];
1104:           nz   = ai[i + 1] - ai[i];
1105:           s[0] = b[i2];
1106:           s[1] = b[i2 + 1];
1107:           s[2] = b[i2 + 2];
1108:           while (nz--) {
1109:             idx   = 3 * (*vi++);
1110:             xw[0] = x[idx];
1111:             xw[1] = x[1 + idx];
1112:             xw[2] = x[2 + idx];
1113:             PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
1114:             v += 9;
1115:           }
1116:           PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
1117:           x[i2] += xw[0];
1118:           x[i2 + 1] += xw[1];
1119:           x[i2 + 2] += xw[2];
1120:           idiag -= 9;
1121:           i2 -= 3;
1122:         }
1123:         break;
1124:       case 4:
1125:         for (i = m - 1; i >= 0; i--) {
1126:           v    = aa + 16 * ai[i];
1127:           vi   = aj + ai[i];
1128:           nz   = ai[i + 1] - ai[i];
1129:           s[0] = b[i2];
1130:           s[1] = b[i2 + 1];
1131:           s[2] = b[i2 + 2];
1132:           s[3] = b[i2 + 3];
1133:           while (nz--) {
1134:             idx   = 4 * (*vi++);
1135:             xw[0] = x[idx];
1136:             xw[1] = x[1 + idx];
1137:             xw[2] = x[2 + idx];
1138:             xw[3] = x[3 + idx];
1139:             PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
1140:             v += 16;
1141:           }
1142:           PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
1143:           x[i2] += xw[0];
1144:           x[i2 + 1] += xw[1];
1145:           x[i2 + 2] += xw[2];
1146:           x[i2 + 3] += xw[3];
1147:           idiag -= 16;
1148:           i2 -= 4;
1149:         }
1150:         break;
1151:       case 5:
1152:         for (i = m - 1; i >= 0; i--) {
1153:           v    = aa + 25 * ai[i];
1154:           vi   = aj + ai[i];
1155:           nz   = ai[i + 1] - ai[i];
1156:           s[0] = b[i2];
1157:           s[1] = b[i2 + 1];
1158:           s[2] = b[i2 + 2];
1159:           s[3] = b[i2 + 3];
1160:           s[4] = b[i2 + 4];
1161:           while (nz--) {
1162:             idx   = 5 * (*vi++);
1163:             xw[0] = x[idx];
1164:             xw[1] = x[1 + idx];
1165:             xw[2] = x[2 + idx];
1166:             xw[3] = x[3 + idx];
1167:             xw[4] = x[4 + idx];
1168:             PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
1169:             v += 25;
1170:           }
1171:           PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
1172:           x[i2] += xw[0];
1173:           x[i2 + 1] += xw[1];
1174:           x[i2 + 2] += xw[2];
1175:           x[i2 + 3] += xw[3];
1176:           x[i2 + 4] += xw[4];
1177:           idiag -= 25;
1178:           i2 -= 5;
1179:         }
1180:         break;
1181:       case 6:
1182:         for (i = m - 1; i >= 0; i--) {
1183:           v    = aa + 36 * ai[i];
1184:           vi   = aj + ai[i];
1185:           nz   = ai[i + 1] - ai[i];
1186:           s[0] = b[i2];
1187:           s[1] = b[i2 + 1];
1188:           s[2] = b[i2 + 2];
1189:           s[3] = b[i2 + 3];
1190:           s[4] = b[i2 + 4];
1191:           s[5] = b[i2 + 5];
1192:           while (nz--) {
1193:             idx   = 6 * (*vi++);
1194:             xw[0] = x[idx];
1195:             xw[1] = x[1 + idx];
1196:             xw[2] = x[2 + idx];
1197:             xw[3] = x[3 + idx];
1198:             xw[4] = x[4 + idx];
1199:             xw[5] = x[5 + idx];
1200:             PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
1201:             v += 36;
1202:           }
1203:           PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
1204:           x[i2] += xw[0];
1205:           x[i2 + 1] += xw[1];
1206:           x[i2 + 2] += xw[2];
1207:           x[i2 + 3] += xw[3];
1208:           x[i2 + 4] += xw[4];
1209:           x[i2 + 5] += xw[5];
1210:           idiag -= 36;
1211:           i2 -= 6;
1212:         }
1213:         break;
1214:       case 7:
1215:         for (i = m - 1; i >= 0; i--) {
1216:           v    = aa + 49 * ai[i];
1217:           vi   = aj + ai[i];
1218:           nz   = ai[i + 1] - ai[i];
1219:           s[0] = b[i2];
1220:           s[1] = b[i2 + 1];
1221:           s[2] = b[i2 + 2];
1222:           s[3] = b[i2 + 3];
1223:           s[4] = b[i2 + 4];
1224:           s[5] = b[i2 + 5];
1225:           s[6] = b[i2 + 6];
1226:           while (nz--) {
1227:             idx   = 7 * (*vi++);
1228:             xw[0] = x[idx];
1229:             xw[1] = x[1 + idx];
1230:             xw[2] = x[2 + idx];
1231:             xw[3] = x[3 + idx];
1232:             xw[4] = x[4 + idx];
1233:             xw[5] = x[5 + idx];
1234:             xw[6] = x[6 + idx];
1235:             PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
1236:             v += 49;
1237:           }
1238:           PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
1239:           x[i2] += xw[0];
1240:           x[i2 + 1] += xw[1];
1241:           x[i2 + 2] += xw[2];
1242:           x[i2 + 3] += xw[3];
1243:           x[i2 + 4] += xw[4];
1244:           x[i2 + 5] += xw[5];
1245:           x[i2 + 6] += xw[6];
1246:           idiag -= 49;
1247:           i2 -= 7;
1248:         }
1249:         break;
1250:       default:
1251:         for (i = m - 1; i >= 0; i--) {
1252:           v  = aa + bs2 * ai[i];
1253:           vi = aj + ai[i];
1254:           nz = ai[i + 1] - ai[i];

1256:           PetscCall(PetscArraycpy(w, b + i2, bs));
1257:           /* copy all rows of x that are needed into contiguous space */
1258:           workt = work;
1259:           for (j = 0; j < nz; j++) {
1260:             PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
1261:             workt += bs;
1262:           }
1263:           PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
1264:           PetscKernel_w_gets_w_plus_Ar_times_v(bs, bs, w, idiag, x + i2);

1266:           idiag -= bs2;
1267:           i2 -= bs;
1268:         }
1269:         break;
1270:       }
1271:       PetscCall(PetscLogFlops(2.0 * bs2 * (a->nz)));
1272:     }
1273:   }
1274:   PetscCall(VecRestoreArray(xx, &x));
1275:   PetscCall(VecRestoreArrayRead(bb, &b));
1276:   PetscFunctionReturn(PETSC_SUCCESS);
1277: }

1279: /*
1280:     Special version for direct calls from Fortran (Used in PETSc-fun3d)
1281: */
1282: #if defined(PETSC_HAVE_FORTRAN_CAPS)
1283:   #define matsetvaluesblocked4_ MATSETVALUESBLOCKED4
1284: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
1285:   #define matsetvaluesblocked4_ matsetvaluesblocked4
1286: #endif

1288: PETSC_EXTERN void matsetvaluesblocked4_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[])
1289: {
1290:   Mat                A = *AA;
1291:   Mat_SeqBAIJ       *a = (Mat_SeqBAIJ *)A->data;
1292:   PetscInt          *rp, k, low, high, t, ii, jj, row, nrow, i, col, l, N, m = *mm, n = *nn;
1293:   PetscInt          *ai = a->i, *ailen = a->ilen;
1294:   PetscInt          *aj = a->j, stepval, lastcol = -1;
1295:   const PetscScalar *value = v;
1296:   MatScalar         *ap, *aa = a->a, *bap;

1298:   PetscFunctionBegin;
1299:   if (A->rmap->bs != 4) SETERRABORT(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Can only be called with a block size of 4");
1300:   stepval = (n - 1) * 4;
1301:   for (k = 0; k < m; k++) { /* loop over added rows */
1302:     row  = im[k];
1303:     rp   = aj + ai[row];
1304:     ap   = aa + 16 * ai[row];
1305:     nrow = ailen[row];
1306:     low  = 0;
1307:     high = nrow;
1308:     for (l = 0; l < n; l++) { /* loop over added columns */
1309:       col = in[l];
1310:       if (col <= lastcol) low = 0;
1311:       else high = nrow;
1312:       lastcol = col;
1313:       value   = v + k * (stepval + 4 + l) * 4;
1314:       while (high - low > 7) {
1315:         t = (low + high) / 2;
1316:         if (rp[t] > col) high = t;
1317:         else low = t;
1318:       }
1319:       for (i = low; i < high; i++) {
1320:         if (rp[i] > col) break;
1321:         if (rp[i] == col) {
1322:           bap = ap + 16 * i;
1323:           for (ii = 0; ii < 4; ii++, value += stepval) {
1324:             for (jj = ii; jj < 16; jj += 4) bap[jj] += *value++;
1325:           }
1326:           goto noinsert2;
1327:         }
1328:       }
1329:       N = nrow++ - 1;
1330:       high++; /* added new column index thus must search to one higher than before */
1331:       /* shift up all the later entries in this row */
1332:       for (ii = N; ii >= i; ii--) {
1333:         rp[ii + 1] = rp[ii];
1334:         PetscCallVoid(PetscArraycpy(ap + 16 * (ii + 1), ap + 16 * (ii), 16));
1335:       }
1336:       if (N >= i) PetscCallVoid(PetscArrayzero(ap + 16 * i, 16));
1337:       rp[i] = col;
1338:       bap   = ap + 16 * i;
1339:       for (ii = 0; ii < 4; ii++, value += stepval) {
1340:         for (jj = ii; jj < 16; jj += 4) bap[jj] = *value++;
1341:       }
1342:     noinsert2:;
1343:       low = i;
1344:     }
1345:     ailen[row] = nrow;
1346:   }
1347:   PetscFunctionReturnVoid();
1348: }

1350: #if defined(PETSC_HAVE_FORTRAN_CAPS)
1351:   #define matsetvalues4_ MATSETVALUES4
1352: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
1353:   #define matsetvalues4_ matsetvalues4
1354: #endif

1356: PETSC_EXTERN void matsetvalues4_(Mat *AA, PetscInt *mm, PetscInt *im, PetscInt *nn, PetscInt *in, PetscScalar *v)
1357: {
1358:   Mat          A = *AA;
1359:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1360:   PetscInt    *rp, k, low, high, t, row, nrow, i, col, l, N, n = *nn, m = *mm;
1361:   PetscInt    *ai = a->i, *ailen = a->ilen;
1362:   PetscInt    *aj = a->j, brow, bcol;
1363:   PetscInt     ridx, cidx, lastcol = -1;
1364:   MatScalar   *ap, value, *aa      = a->a, *bap;

1366:   PetscFunctionBegin;
1367:   for (k = 0; k < m; k++) { /* loop over added rows */
1368:     row  = im[k];
1369:     brow = row / 4;
1370:     rp   = aj + ai[brow];
1371:     ap   = aa + 16 * ai[brow];
1372:     nrow = ailen[brow];
1373:     low  = 0;
1374:     high = nrow;
1375:     for (l = 0; l < n; l++) { /* loop over added columns */
1376:       col   = in[l];
1377:       bcol  = col / 4;
1378:       ridx  = row % 4;
1379:       cidx  = col % 4;
1380:       value = v[l + k * n];
1381:       if (col <= lastcol) low = 0;
1382:       else high = nrow;
1383:       lastcol = col;
1384:       while (high - low > 7) {
1385:         t = (low + high) / 2;
1386:         if (rp[t] > bcol) high = t;
1387:         else low = t;
1388:       }
1389:       for (i = low; i < high; i++) {
1390:         if (rp[i] > bcol) break;
1391:         if (rp[i] == bcol) {
1392:           bap = ap + 16 * i + 4 * cidx + ridx;
1393:           *bap += value;
1394:           goto noinsert1;
1395:         }
1396:       }
1397:       N = nrow++ - 1;
1398:       high++; /* added new column thus must search to one higher than before */
1399:       /* shift up all the later entries in this row */
1400:       PetscCallVoid(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
1401:       PetscCallVoid(PetscArraymove(ap + 16 * i + 16, ap + 16 * i, 16 * (N - i + 1)));
1402:       PetscCallVoid(PetscArrayzero(ap + 16 * i, 16));
1403:       rp[i]                        = bcol;
1404:       ap[16 * i + 4 * cidx + ridx] = value;
1405:     noinsert1:;
1406:       low = i;
1407:     }
1408:     ailen[brow] = nrow;
1409:   }
1410:   PetscFunctionReturnVoid();
1411: }

1413: /*
1414:      Checks for missing diagonals
1415: */
1416: PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A, PetscBool *missing, PetscInt *d)
1417: {
1418:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1419:   PetscInt    *diag, *ii = a->i, i;

1421:   PetscFunctionBegin;
1422:   PetscCall(MatMarkDiagonal_SeqBAIJ(A));
1423:   *missing = PETSC_FALSE;
1424:   if (A->rmap->n > 0 && !ii) {
1425:     *missing = PETSC_TRUE;
1426:     if (d) *d = 0;
1427:     PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1428:   } else {
1429:     PetscInt n;
1430:     n    = PetscMin(a->mbs, a->nbs);
1431:     diag = a->diag;
1432:     for (i = 0; i < n; i++) {
1433:       if (diag[i] >= ii[i + 1]) {
1434:         *missing = PETSC_TRUE;
1435:         if (d) *d = i;
1436:         PetscCall(PetscInfo(A, "Matrix is missing block diagonal number %" PetscInt_FMT "\n", i));
1437:         break;
1438:       }
1439:     }
1440:   }
1441:   PetscFunctionReturn(PETSC_SUCCESS);
1442: }

1444: PetscErrorCode MatMarkDiagonal_SeqBAIJ(Mat A)
1445: {
1446:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1447:   PetscInt     i, j, m = a->mbs;

1449:   PetscFunctionBegin;
1450:   if (!a->diag) {
1451:     PetscCall(PetscMalloc1(m, &a->diag));
1452:     a->free_diag = PETSC_TRUE;
1453:   }
1454:   for (i = 0; i < m; i++) {
1455:     a->diag[i] = a->i[i + 1];
1456:     for (j = a->i[i]; j < a->i[i + 1]; j++) {
1457:       if (a->j[j] == i) {
1458:         a->diag[i] = j;
1459:         break;
1460:       }
1461:     }
1462:   }
1463:   PetscFunctionReturn(PETSC_SUCCESS);
1464: }

1466: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool blockcompressed, PetscInt *nn, const PetscInt *inia[], const PetscInt *inja[], PetscBool *done)
1467: {
1468:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1469:   PetscInt     i, j, n = a->mbs, nz = a->i[n], *tia, *tja, bs = A->rmap->bs, k, l, cnt;
1470:   PetscInt   **ia = (PetscInt **)inia, **ja = (PetscInt **)inja;

1472:   PetscFunctionBegin;
1473:   *nn = n;
1474:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
1475:   if (symmetric) {
1476:     PetscCall(MatToSymmetricIJ_SeqAIJ(n, a->i, a->j, PETSC_TRUE, 0, 0, &tia, &tja));
1477:     nz = tia[n];
1478:   } else {
1479:     tia = a->i;
1480:     tja = a->j;
1481:   }

1483:   if (!blockcompressed && bs > 1) {
1484:     (*nn) *= bs;
1485:     /* malloc & create the natural set of indices */
1486:     PetscCall(PetscMalloc1((n + 1) * bs, ia));
1487:     if (n) {
1488:       (*ia)[0] = oshift;
1489:       for (j = 1; j < bs; j++) (*ia)[j] = (tia[1] - tia[0]) * bs + (*ia)[j - 1];
1490:     }

1492:     for (i = 1; i < n; i++) {
1493:       (*ia)[i * bs] = (tia[i] - tia[i - 1]) * bs + (*ia)[i * bs - 1];
1494:       for (j = 1; j < bs; j++) (*ia)[i * bs + j] = (tia[i + 1] - tia[i]) * bs + (*ia)[i * bs + j - 1];
1495:     }
1496:     if (n) (*ia)[n * bs] = (tia[n] - tia[n - 1]) * bs + (*ia)[n * bs - 1];

1498:     if (inja) {
1499:       PetscCall(PetscMalloc1(nz * bs * bs, ja));
1500:       cnt = 0;
1501:       for (i = 0; i < n; i++) {
1502:         for (j = 0; j < bs; j++) {
1503:           for (k = tia[i]; k < tia[i + 1]; k++) {
1504:             for (l = 0; l < bs; l++) (*ja)[cnt++] = bs * tja[k] + l;
1505:           }
1506:         }
1507:       }
1508:     }

1510:     if (symmetric) { /* deallocate memory allocated in MatToSymmetricIJ_SeqAIJ() */
1511:       PetscCall(PetscFree(tia));
1512:       PetscCall(PetscFree(tja));
1513:     }
1514:   } else if (oshift == 1) {
1515:     if (symmetric) {
1516:       nz = tia[A->rmap->n / bs];
1517:       /*  add 1 to i and j indices */
1518:       for (i = 0; i < A->rmap->n / bs + 1; i++) tia[i] = tia[i] + 1;
1519:       *ia = tia;
1520:       if (ja) {
1521:         for (i = 0; i < nz; i++) tja[i] = tja[i] + 1;
1522:         *ja = tja;
1523:       }
1524:     } else {
1525:       nz = a->i[A->rmap->n / bs];
1526:       /* malloc space and  add 1 to i and j indices */
1527:       PetscCall(PetscMalloc1(A->rmap->n / bs + 1, ia));
1528:       for (i = 0; i < A->rmap->n / bs + 1; i++) (*ia)[i] = a->i[i] + 1;
1529:       if (ja) {
1530:         PetscCall(PetscMalloc1(nz, ja));
1531:         for (i = 0; i < nz; i++) (*ja)[i] = a->j[i] + 1;
1532:       }
1533:     }
1534:   } else {
1535:     *ia = tia;
1536:     if (ja) *ja = tja;
1537:   }
1538:   PetscFunctionReturn(PETSC_SUCCESS);
1539: }

1541: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool blockcompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
1542: {
1543:   PetscFunctionBegin;
1544:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
1545:   if ((!blockcompressed && A->rmap->bs > 1) || (symmetric || oshift == 1)) {
1546:     PetscCall(PetscFree(*ia));
1547:     if (ja) PetscCall(PetscFree(*ja));
1548:   }
1549:   PetscFunctionReturn(PETSC_SUCCESS);
1550: }

1552: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
1553: {
1554:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;

1556:   PetscFunctionBegin;
1557: #if defined(PETSC_USE_LOG)
1558:   PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->N, A->cmap->n, a->nz));
1559: #endif
1560:   PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1561:   PetscCall(ISDestroy(&a->row));
1562:   PetscCall(ISDestroy(&a->col));
1563:   if (a->free_diag) PetscCall(PetscFree(a->diag));
1564:   PetscCall(PetscFree(a->idiag));
1565:   if (a->free_imax_ilen) PetscCall(PetscFree2(a->imax, a->ilen));
1566:   PetscCall(PetscFree(a->solve_work));
1567:   PetscCall(PetscFree(a->mult_work));
1568:   PetscCall(PetscFree(a->sor_workt));
1569:   PetscCall(PetscFree(a->sor_work));
1570:   PetscCall(ISDestroy(&a->icol));
1571:   PetscCall(PetscFree(a->saved_values));
1572:   PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));

1574:   PetscCall(MatDestroy(&a->sbaijMat));
1575:   PetscCall(MatDestroy(&a->parent));
1576:   PetscCall(PetscFree(A->data));

1578:   PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1579:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJGetArray_C", NULL));
1580:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJRestoreArray_C", NULL));
1581:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1582:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1583:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetColumnIndices_C", NULL));
1584:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqaij_C", NULL));
1585:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqsbaij_C", NULL));
1586:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetPreallocation_C", NULL));
1587:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetPreallocationCSR_C", NULL));
1588:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqbstrm_C", NULL));
1589:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1590: #if defined(PETSC_HAVE_HYPRE)
1591:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_hypre_C", NULL));
1592: #endif
1593:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_is_C", NULL));
1594:   PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1595:   PetscFunctionReturn(PETSC_SUCCESS);
1596: }

1598: PetscErrorCode MatSetOption_SeqBAIJ(Mat A, MatOption op, PetscBool flg)
1599: {
1600:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;

1602:   PetscFunctionBegin;
1603:   switch (op) {
1604:   case MAT_ROW_ORIENTED:
1605:     a->roworiented = flg;
1606:     break;
1607:   case MAT_KEEP_NONZERO_PATTERN:
1608:     a->keepnonzeropattern = flg;
1609:     break;
1610:   case MAT_NEW_NONZERO_LOCATIONS:
1611:     a->nonew = (flg ? 0 : 1);
1612:     break;
1613:   case MAT_NEW_NONZERO_LOCATION_ERR:
1614:     a->nonew = (flg ? -1 : 0);
1615:     break;
1616:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1617:     a->nonew = (flg ? -2 : 0);
1618:     break;
1619:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1620:     a->nounused = (flg ? -1 : 0);
1621:     break;
1622:   case MAT_FORCE_DIAGONAL_ENTRIES:
1623:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1624:   case MAT_USE_HASH_TABLE:
1625:   case MAT_SORTED_FULL:
1626:     PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1627:     break;
1628:   case MAT_SPD:
1629:   case MAT_SYMMETRIC:
1630:   case MAT_STRUCTURALLY_SYMMETRIC:
1631:   case MAT_HERMITIAN:
1632:   case MAT_SYMMETRY_ETERNAL:
1633:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1634:   case MAT_SUBMAT_SINGLEIS:
1635:   case MAT_STRUCTURE_ONLY:
1636:   case MAT_SPD_ETERNAL:
1637:     /* if the diagonal matrix is square it inherits some of the properties above */
1638:     break;
1639:   default:
1640:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1641:   }
1642:   PetscFunctionReturn(PETSC_SUCCESS);
1643: }

1645: /* used for both SeqBAIJ and SeqSBAIJ matrices */
1646: PetscErrorCode MatGetRow_SeqBAIJ_private(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v, PetscInt *ai, PetscInt *aj, PetscScalar *aa)
1647: {
1648:   PetscInt     itmp, i, j, k, M, bn, bp, *idx_i, bs, bs2;
1649:   MatScalar   *aa_i;
1650:   PetscScalar *v_i;

1652:   PetscFunctionBegin;
1653:   bs  = A->rmap->bs;
1654:   bs2 = bs * bs;
1655:   PetscCheck(row >= 0 && row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " out of range", row);

1657:   bn  = row / bs; /* Block number */
1658:   bp  = row % bs; /* Block Position */
1659:   M   = ai[bn + 1] - ai[bn];
1660:   *nz = bs * M;

1662:   if (v) {
1663:     *v = NULL;
1664:     if (*nz) {
1665:       PetscCall(PetscMalloc1(*nz, v));
1666:       for (i = 0; i < M; i++) { /* for each block in the block row */
1667:         v_i  = *v + i * bs;
1668:         aa_i = aa + bs2 * (ai[bn] + i);
1669:         for (j = bp, k = 0; j < bs2; j += bs, k++) v_i[k] = aa_i[j];
1670:       }
1671:     }
1672:   }

1674:   if (idx) {
1675:     *idx = NULL;
1676:     if (*nz) {
1677:       PetscCall(PetscMalloc1(*nz, idx));
1678:       for (i = 0; i < M; i++) { /* for each block in the block row */
1679:         idx_i = *idx + i * bs;
1680:         itmp  = bs * aj[ai[bn] + i];
1681:         for (j = 0; j < bs; j++) idx_i[j] = itmp++;
1682:       }
1683:     }
1684:   }
1685:   PetscFunctionReturn(PETSC_SUCCESS);
1686: }

1688: PetscErrorCode MatGetRow_SeqBAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1689: {
1690:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;

1692:   PetscFunctionBegin;
1693:   PetscCall(MatGetRow_SeqBAIJ_private(A, row, nz, idx, v, a->i, a->j, a->a));
1694:   PetscFunctionReturn(PETSC_SUCCESS);
1695: }

1697: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1698: {
1699:   PetscFunctionBegin;
1700:   if (nz) *nz = 0;
1701:   if (idx) PetscCall(PetscFree(*idx));
1702:   if (v) PetscCall(PetscFree(*v));
1703:   PetscFunctionReturn(PETSC_SUCCESS);
1704: }

1706: PetscErrorCode MatTranspose_SeqBAIJ(Mat A, MatReuse reuse, Mat *B)
1707: {
1708:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *at;
1709:   Mat          C;
1710:   PetscInt     i, j, k, *aj = a->j, *ai = a->i, bs = A->rmap->bs, mbs = a->mbs, nbs = a->nbs, *atfill;
1711:   PetscInt     bs2 = a->bs2, *ati, *atj, anzj, kr;
1712:   MatScalar   *ata, *aa = a->a;

1714:   PetscFunctionBegin;
1715:   if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *B));
1716:   PetscCall(PetscCalloc1(1 + nbs, &atfill));
1717:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1718:     for (i = 0; i < ai[mbs]; i++) atfill[aj[i]] += 1; /* count num of non-zeros in row aj[i] */

1720:     PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
1721:     PetscCall(MatSetSizes(C, A->cmap->n, A->rmap->N, A->cmap->n, A->rmap->N));
1722:     PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
1723:     PetscCall(MatSeqBAIJSetPreallocation(C, bs, 0, atfill));

1725:     at  = (Mat_SeqBAIJ *)C->data;
1726:     ati = at->i;
1727:     for (i = 0; i < nbs; i++) at->ilen[i] = at->imax[i] = ati[i + 1] - ati[i];
1728:   } else {
1729:     C   = *B;
1730:     at  = (Mat_SeqBAIJ *)C->data;
1731:     ati = at->i;
1732:   }

1734:   atj = at->j;
1735:   ata = at->a;

1737:   /* Copy ati into atfill so we have locations of the next free space in atj */
1738:   PetscCall(PetscArraycpy(atfill, ati, nbs));

1740:   /* Walk through A row-wise and mark nonzero entries of A^T. */
1741:   for (i = 0; i < mbs; i++) {
1742:     anzj = ai[i + 1] - ai[i];
1743:     for (j = 0; j < anzj; j++) {
1744:       atj[atfill[*aj]] = i;
1745:       for (kr = 0; kr < bs; kr++) {
1746:         for (k = 0; k < bs; k++) ata[bs2 * atfill[*aj] + k * bs + kr] = *aa++;
1747:       }
1748:       atfill[*aj++] += 1;
1749:     }
1750:   }
1751:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1752:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));

1754:   /* Clean up temporary space and complete requests. */
1755:   PetscCall(PetscFree(atfill));

1757:   if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1758:     PetscCall(MatSetBlockSizes(C, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1759:     *B = C;
1760:   } else {
1761:     PetscCall(MatHeaderMerge(A, &C));
1762:   }
1763:   PetscFunctionReturn(PETSC_SUCCESS);
1764: }

1766: static PetscErrorCode MatIsTranspose_SeqBAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
1767: {
1768:   Mat Btrans;

1770:   PetscFunctionBegin;
1771:   *f = PETSC_FALSE;
1772:   PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &Btrans));
1773:   PetscCall(MatEqual_SeqBAIJ(B, Btrans, f));
1774:   PetscCall(MatDestroy(&Btrans));
1775:   PetscFunctionReturn(PETSC_SUCCESS);
1776: }

1778: /* Used for both SeqBAIJ and SeqSBAIJ matrices */
1779: PetscErrorCode MatView_SeqBAIJ_Binary(Mat mat, PetscViewer viewer)
1780: {
1781:   Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)mat->data;
1782:   PetscInt     header[4], M, N, m, bs, nz, cnt, i, j, k, l;
1783:   PetscInt    *rowlens, *colidxs;
1784:   PetscScalar *matvals;

1786:   PetscFunctionBegin;
1787:   PetscCall(PetscViewerSetUp(viewer));

1789:   M  = mat->rmap->N;
1790:   N  = mat->cmap->N;
1791:   m  = mat->rmap->n;
1792:   bs = mat->rmap->bs;
1793:   nz = bs * bs * A->nz;

1795:   /* write matrix header */
1796:   header[0] = MAT_FILE_CLASSID;
1797:   header[1] = M;
1798:   header[2] = N;
1799:   header[3] = nz;
1800:   PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));

1802:   /* store row lengths */
1803:   PetscCall(PetscMalloc1(m, &rowlens));
1804:   for (cnt = 0, i = 0; i < A->mbs; i++)
1805:     for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i]);
1806:   PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
1807:   PetscCall(PetscFree(rowlens));

1809:   /* store column indices  */
1810:   PetscCall(PetscMalloc1(nz, &colidxs));
1811:   for (cnt = 0, i = 0; i < A->mbs; i++)
1812:     for (k = 0; k < bs; k++)
1813:       for (j = A->i[i]; j < A->i[i + 1]; j++)
1814:         for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[j] + l;
1815:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz);
1816:   PetscCall(PetscViewerBinaryWrite(viewer, colidxs, nz, PETSC_INT));
1817:   PetscCall(PetscFree(colidxs));

1819:   /* store nonzero values */
1820:   PetscCall(PetscMalloc1(nz, &matvals));
1821:   for (cnt = 0, i = 0; i < A->mbs; i++)
1822:     for (k = 0; k < bs; k++)
1823:       for (j = A->i[i]; j < A->i[i + 1]; j++)
1824:         for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * j + l) + k];
1825:   PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz);
1826:   PetscCall(PetscViewerBinaryWrite(viewer, matvals, nz, PETSC_SCALAR));
1827:   PetscCall(PetscFree(matvals));

1829:   /* write block size option to the viewer's .info file */
1830:   PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1831:   PetscFunctionReturn(PETSC_SUCCESS);
1832: }

1834: static PetscErrorCode MatView_SeqBAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
1835: {
1836:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1837:   PetscInt     i, bs = A->rmap->bs, k;

1839:   PetscFunctionBegin;
1840:   PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1841:   for (i = 0; i < a->mbs; i++) {
1842:     PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT "-%" PetscInt_FMT ":", i * bs, i * bs + bs - 1));
1843:     for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT "-%" PetscInt_FMT ") ", bs * a->j[k], bs * a->j[k] + bs - 1));
1844:     PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1845:   }
1846:   PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1847:   PetscFunctionReturn(PETSC_SUCCESS);
1848: }

1850: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A, PetscViewer viewer)
1851: {
1852:   Mat_SeqBAIJ      *a = (Mat_SeqBAIJ *)A->data;
1853:   PetscInt          i, j, bs = A->rmap->bs, k, l, bs2 = a->bs2;
1854:   PetscViewerFormat format;

1856:   PetscFunctionBegin;
1857:   if (A->structure_only) {
1858:     PetscCall(MatView_SeqBAIJ_ASCII_structonly(A, viewer));
1859:     PetscFunctionReturn(PETSC_SUCCESS);
1860:   }

1862:   PetscCall(PetscViewerGetFormat(viewer, &format));
1863:   if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1864:     PetscCall(PetscViewerASCIIPrintf(viewer, "  block size is %" PetscInt_FMT "\n", bs));
1865:   } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1866:     const char *matname;
1867:     Mat         aij;
1868:     PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &aij));
1869:     PetscCall(PetscObjectGetName((PetscObject)A, &matname));
1870:     PetscCall(PetscObjectSetName((PetscObject)aij, matname));
1871:     PetscCall(MatView(aij, viewer));
1872:     PetscCall(MatDestroy(&aij));
1873:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1874:     PetscFunctionReturn(PETSC_SUCCESS);
1875:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1876:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1877:     for (i = 0; i < a->mbs; i++) {
1878:       for (j = 0; j < bs; j++) {
1879:         PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i * bs + j));
1880:         for (k = a->i[i]; k < a->i[i + 1]; k++) {
1881:           for (l = 0; l < bs; l++) {
1882: #if defined(PETSC_USE_COMPLEX)
1883:             if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) > 0.0 && PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1884:               PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %gi) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), (double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1885:             } else if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) < 0.0 && PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1886:               PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %gi) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), -(double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1887:             } else if (PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1888:               PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j])));
1889:             }
1890: #else
1891:             if (a->a[bs2 * k + l * bs + j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)a->a[bs2 * k + l * bs + j]));
1892: #endif
1893:           }
1894:         }
1895:         PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1896:       }
1897:     }
1898:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1899:   } else {
1900:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1901:     for (i = 0; i < a->mbs; i++) {
1902:       for (j = 0; j < bs; j++) {
1903:         PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i * bs + j));
1904:         for (k = a->i[i]; k < a->i[i + 1]; k++) {
1905:           for (l = 0; l < bs; l++) {
1906: #if defined(PETSC_USE_COMPLEX)
1907:             if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) > 0.0) {
1908:               PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), (double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1909:             } else if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) < 0.0) {
1910:               PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), -(double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1911:             } else {
1912:               PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j])));
1913:             }
1914: #else
1915:             PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)a->a[bs2 * k + l * bs + j]));
1916: #endif
1917:           }
1918:         }
1919:         PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1920:       }
1921:     }
1922:     PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1923:   }
1924:   PetscCall(PetscViewerFlush(viewer));
1925:   PetscFunctionReturn(PETSC_SUCCESS);
1926: }

1928: #include <petscdraw.h>
1929: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
1930: {
1931:   Mat               A = (Mat)Aa;
1932:   Mat_SeqBAIJ      *a = (Mat_SeqBAIJ *)A->data;
1933:   PetscInt          row, i, j, k, l, mbs = a->mbs, color, bs = A->rmap->bs, bs2 = a->bs2;
1934:   PetscReal         xl, yl, xr, yr, x_l, x_r, y_l, y_r;
1935:   MatScalar        *aa;
1936:   PetscViewer       viewer;
1937:   PetscViewerFormat format;

1939:   PetscFunctionBegin;
1940:   PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
1941:   PetscCall(PetscViewerGetFormat(viewer, &format));
1942:   PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));

1944:   /* loop over matrix elements drawing boxes */

1946:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1947:     PetscDrawCollectiveBegin(draw);
1948:     /* Blue for negative, Cyan for zero and  Red for positive */
1949:     color = PETSC_DRAW_BLUE;
1950:     for (i = 0, row = 0; i < mbs; i++, row += bs) {
1951:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
1952:         y_l = A->rmap->N - row - 1.0;
1953:         y_r = y_l + 1.0;
1954:         x_l = a->j[j] * bs;
1955:         x_r = x_l + 1.0;
1956:         aa  = a->a + j * bs2;
1957:         for (k = 0; k < bs; k++) {
1958:           for (l = 0; l < bs; l++) {
1959:             if (PetscRealPart(*aa++) >= 0.) continue;
1960:             PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
1961:           }
1962:         }
1963:       }
1964:     }
1965:     color = PETSC_DRAW_CYAN;
1966:     for (i = 0, row = 0; i < mbs; i++, row += bs) {
1967:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
1968:         y_l = A->rmap->N - row - 1.0;
1969:         y_r = y_l + 1.0;
1970:         x_l = a->j[j] * bs;
1971:         x_r = x_l + 1.0;
1972:         aa  = a->a + j * bs2;
1973:         for (k = 0; k < bs; k++) {
1974:           for (l = 0; l < bs; l++) {
1975:             if (PetscRealPart(*aa++) != 0.) continue;
1976:             PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
1977:           }
1978:         }
1979:       }
1980:     }
1981:     color = PETSC_DRAW_RED;
1982:     for (i = 0, row = 0; i < mbs; i++, row += bs) {
1983:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
1984:         y_l = A->rmap->N - row - 1.0;
1985:         y_r = y_l + 1.0;
1986:         x_l = a->j[j] * bs;
1987:         x_r = x_l + 1.0;
1988:         aa  = a->a + j * bs2;
1989:         for (k = 0; k < bs; k++) {
1990:           for (l = 0; l < bs; l++) {
1991:             if (PetscRealPart(*aa++) <= 0.) continue;
1992:             PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
1993:           }
1994:         }
1995:       }
1996:     }
1997:     PetscDrawCollectiveEnd(draw);
1998:   } else {
1999:     /* use contour shading to indicate magnitude of values */
2000:     /* first determine max of all nonzero values */
2001:     PetscReal minv = 0.0, maxv = 0.0;
2002:     PetscDraw popup;

2004:     for (i = 0; i < a->nz * a->bs2; i++) {
2005:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
2006:     }
2007:     if (minv >= maxv) maxv = minv + PETSC_SMALL;
2008:     PetscCall(PetscDrawGetPopup(draw, &popup));
2009:     PetscCall(PetscDrawScalePopup(popup, 0.0, maxv));

2011:     PetscDrawCollectiveBegin(draw);
2012:     for (i = 0, row = 0; i < mbs; i++, row += bs) {
2013:       for (j = a->i[i]; j < a->i[i + 1]; j++) {
2014:         y_l = A->rmap->N - row - 1.0;
2015:         y_r = y_l + 1.0;
2016:         x_l = a->j[j] * bs;
2017:         x_r = x_l + 1.0;
2018:         aa  = a->a + j * bs2;
2019:         for (k = 0; k < bs; k++) {
2020:           for (l = 0; l < bs; l++) {
2021:             MatScalar v = *aa++;
2022:             color       = PetscDrawRealToColor(PetscAbsScalar(v), minv, maxv);
2023:             PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
2024:           }
2025:         }
2026:       }
2027:     }
2028:     PetscDrawCollectiveEnd(draw);
2029:   }
2030:   PetscFunctionReturn(PETSC_SUCCESS);
2031: }

2033: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A, PetscViewer viewer)
2034: {
2035:   PetscReal xl, yl, xr, yr, w, h;
2036:   PetscDraw draw;
2037:   PetscBool isnull;

2039:   PetscFunctionBegin;
2040:   PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
2041:   PetscCall(PetscDrawIsNull(draw, &isnull));
2042:   if (isnull) PetscFunctionReturn(PETSC_SUCCESS);

2044:   xr = A->cmap->n;
2045:   yr = A->rmap->N;
2046:   h  = yr / 10.0;
2047:   w  = xr / 10.0;
2048:   xr += w;
2049:   yr += h;
2050:   xl = -w;
2051:   yl = -h;
2052:   PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
2053:   PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
2054:   PetscCall(PetscDrawZoom(draw, MatView_SeqBAIJ_Draw_Zoom, A));
2055:   PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
2056:   PetscCall(PetscDrawSave(draw));
2057:   PetscFunctionReturn(PETSC_SUCCESS);
2058: }

2060: PetscErrorCode MatView_SeqBAIJ(Mat A, PetscViewer viewer)
2061: {
2062:   PetscBool iascii, isbinary, isdraw;

2064:   PetscFunctionBegin;
2065:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2066:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
2067:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
2068:   if (iascii) {
2069:     PetscCall(MatView_SeqBAIJ_ASCII(A, viewer));
2070:   } else if (isbinary) {
2071:     PetscCall(MatView_SeqBAIJ_Binary(A, viewer));
2072:   } else if (isdraw) {
2073:     PetscCall(MatView_SeqBAIJ_Draw(A, viewer));
2074:   } else {
2075:     Mat B;
2076:     PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &B));
2077:     PetscCall(MatView(B, viewer));
2078:     PetscCall(MatDestroy(&B));
2079:   }
2080:   PetscFunctionReturn(PETSC_SUCCESS);
2081: }

2083: PetscErrorCode MatGetValues_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
2084: {
2085:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2086:   PetscInt    *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
2087:   PetscInt    *ai = a->i, *ailen = a->ilen;
2088:   PetscInt     brow, bcol, ridx, cidx, bs = A->rmap->bs, bs2 = a->bs2;
2089:   MatScalar   *ap, *aa = a->a;

2091:   PetscFunctionBegin;
2092:   for (k = 0; k < m; k++) { /* loop over rows */
2093:     row  = im[k];
2094:     brow = row / bs;
2095:     if (row < 0) {
2096:       v += n;
2097:       continue;
2098:     } /* negative row */
2099:     PetscCheck(row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " too large", row);
2100:     rp   = aj ? aj + ai[brow] : NULL;       /* mustn't add to NULL, that is UB */
2101:     ap   = aa ? aa + bs2 * ai[brow] : NULL; /* mustn't add to NULL, that is UB */
2102:     nrow = ailen[brow];
2103:     for (l = 0; l < n; l++) { /* loop over columns */
2104:       if (in[l] < 0) {
2105:         v++;
2106:         continue;
2107:       } /* negative column */
2108:       PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column %" PetscInt_FMT " too large", in[l]);
2109:       col  = in[l];
2110:       bcol = col / bs;
2111:       cidx = col % bs;
2112:       ridx = row % bs;
2113:       high = nrow;
2114:       low  = 0; /* assume unsorted */
2115:       while (high - low > 5) {
2116:         t = (low + high) / 2;
2117:         if (rp[t] > bcol) high = t;
2118:         else low = t;
2119:       }
2120:       for (i = low; i < high; i++) {
2121:         if (rp[i] > bcol) break;
2122:         if (rp[i] == bcol) {
2123:           *v++ = ap[bs2 * i + bs * cidx + ridx];
2124:           goto finished;
2125:         }
2126:       }
2127:       *v++ = 0.0;
2128:     finished:;
2129:     }
2130:   }
2131:   PetscFunctionReturn(PETSC_SUCCESS);
2132: }

2134: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
2135: {
2136:   Mat_SeqBAIJ       *a = (Mat_SeqBAIJ *)A->data;
2137:   PetscInt          *rp, k, low, high, t, ii, jj, row, nrow, i, col, l, rmax, N, lastcol = -1;
2138:   PetscInt          *imax = a->imax, *ai = a->i, *ailen = a->ilen;
2139:   PetscInt          *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs, stepval;
2140:   PetscBool          roworiented = a->roworiented;
2141:   const PetscScalar *value       = v;
2142:   MatScalar         *ap = NULL, *aa = a->a, *bap;

2144:   PetscFunctionBegin;
2145:   if (roworiented) {
2146:     stepval = (n - 1) * bs;
2147:   } else {
2148:     stepval = (m - 1) * bs;
2149:   }
2150:   for (k = 0; k < m; k++) { /* loop over added rows */
2151:     row = im[k];
2152:     if (row < 0) continue;
2153:     PetscCheck(row < a->mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block row index too large %" PetscInt_FMT " max %" PetscInt_FMT, row, a->mbs - 1);
2154:     rp = aj + ai[row];
2155:     if (!A->structure_only) ap = aa + bs2 * ai[row];
2156:     rmax = imax[row];
2157:     nrow = ailen[row];
2158:     low  = 0;
2159:     high = nrow;
2160:     for (l = 0; l < n; l++) { /* loop over added columns */
2161:       if (in[l] < 0) continue;
2162:       PetscCheck(in[l] < a->nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block column index too large %" PetscInt_FMT " max %" PetscInt_FMT, in[l], a->nbs - 1);
2163:       col = in[l];
2164:       if (!A->structure_only) {
2165:         if (roworiented) {
2166:           value = v + (k * (stepval + bs) + l) * bs;
2167:         } else {
2168:           value = v + (l * (stepval + bs) + k) * bs;
2169:         }
2170:       }
2171:       if (col <= lastcol) low = 0;
2172:       else high = nrow;
2173:       lastcol = col;
2174:       while (high - low > 7) {
2175:         t = (low + high) / 2;
2176:         if (rp[t] > col) high = t;
2177:         else low = t;
2178:       }
2179:       for (i = low; i < high; i++) {
2180:         if (rp[i] > col) break;
2181:         if (rp[i] == col) {
2182:           if (A->structure_only) goto noinsert2;
2183:           bap = ap + bs2 * i;
2184:           if (roworiented) {
2185:             if (is == ADD_VALUES) {
2186:               for (ii = 0; ii < bs; ii++, value += stepval) {
2187:                 for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++;
2188:               }
2189:             } else {
2190:               for (ii = 0; ii < bs; ii++, value += stepval) {
2191:                 for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
2192:               }
2193:             }
2194:           } else {
2195:             if (is == ADD_VALUES) {
2196:               for (ii = 0; ii < bs; ii++, value += bs + stepval) {
2197:                 for (jj = 0; jj < bs; jj++) bap[jj] += value[jj];
2198:                 bap += bs;
2199:               }
2200:             } else {
2201:               for (ii = 0; ii < bs; ii++, value += bs + stepval) {
2202:                 for (jj = 0; jj < bs; jj++) bap[jj] = value[jj];
2203:                 bap += bs;
2204:               }
2205:             }
2206:           }
2207:           goto noinsert2;
2208:         }
2209:       }
2210:       if (nonew == 1) goto noinsert2;
2211:       PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new blocked index new nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", row, col);
2212:       if (A->structure_only) {
2213:         MatSeqXAIJReallocateAIJ_structure_only(A, a->mbs, bs2, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar);
2214:       } else {
2215:         MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
2216:       }
2217:       N = nrow++ - 1;
2218:       high++;
2219:       /* shift up all the later entries in this row */
2220:       PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
2221:       rp[i] = col;
2222:       if (!A->structure_only) {
2223:         PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
2224:         bap = ap + bs2 * i;
2225:         if (roworiented) {
2226:           for (ii = 0; ii < bs; ii++, value += stepval) {
2227:             for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
2228:           }
2229:         } else {
2230:           for (ii = 0; ii < bs; ii++, value += stepval) {
2231:             for (jj = 0; jj < bs; jj++) *bap++ = *value++;
2232:           }
2233:         }
2234:       }
2235:     noinsert2:;
2236:       low = i;
2237:     }
2238:     ailen[row] = nrow;
2239:   }
2240:   PetscFunctionReturn(PETSC_SUCCESS);
2241: }

2243: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A, MatAssemblyType mode)
2244: {
2245:   Mat_SeqBAIJ *a      = (Mat_SeqBAIJ *)A->data;
2246:   PetscInt     fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
2247:   PetscInt     m = A->rmap->N, *ip, N, *ailen = a->ilen;
2248:   PetscInt     mbs = a->mbs, bs2 = a->bs2, rmax = 0;
2249:   MatScalar   *aa    = a->a, *ap;
2250:   PetscReal    ratio = 0.6;

2252:   PetscFunctionBegin;
2253:   if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);

2255:   if (m) rmax = ailen[0];
2256:   for (i = 1; i < mbs; i++) {
2257:     /* move each row back by the amount of empty slots (fshift) before it*/
2258:     fshift += imax[i - 1] - ailen[i - 1];
2259:     rmax = PetscMax(rmax, ailen[i]);
2260:     if (fshift) {
2261:       ip = aj + ai[i];
2262:       ap = aa + bs2 * ai[i];
2263:       N  = ailen[i];
2264:       PetscCall(PetscArraymove(ip - fshift, ip, N));
2265:       if (!A->structure_only) PetscCall(PetscArraymove(ap - bs2 * fshift, ap, bs2 * N));
2266:     }
2267:     ai[i] = ai[i - 1] + ailen[i - 1];
2268:   }
2269:   if (mbs) {
2270:     fshift += imax[mbs - 1] - ailen[mbs - 1];
2271:     ai[mbs] = ai[mbs - 1] + ailen[mbs - 1];
2272:   }

2274:   /* reset ilen and imax for each row */
2275:   a->nonzerorowcnt = 0;
2276:   if (A->structure_only) {
2277:     PetscCall(PetscFree2(a->imax, a->ilen));
2278:   } else { /* !A->structure_only */
2279:     for (i = 0; i < mbs; i++) {
2280:       ailen[i] = imax[i] = ai[i + 1] - ai[i];
2281:       a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
2282:     }
2283:   }
2284:   a->nz = ai[mbs];

2286:   /* diagonals may have moved, so kill the diagonal pointers */
2287:   a->idiagvalid = PETSC_FALSE;
2288:   if (fshift && a->diag) PetscCall(PetscFree(a->diag));
2289:   if (fshift) PetscCheck(a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT " block size %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, A->rmap->bs, fshift * bs2);
2290:   PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT ", block size %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded, %" PetscInt_FMT " used\n", m, A->cmap->n, A->rmap->bs, fshift * bs2, a->nz * bs2));
2291:   PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues is %" PetscInt_FMT "\n", a->reallocs));
2292:   PetscCall(PetscInfo(A, "Most nonzeros blocks in any row is %" PetscInt_FMT "\n", rmax));

2294:   A->info.mallocs += a->reallocs;
2295:   a->reallocs         = 0;
2296:   A->info.nz_unneeded = (PetscReal)fshift * bs2;
2297:   a->rmax             = rmax;

2299:   if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, mbs, ratio));
2300:   PetscFunctionReturn(PETSC_SUCCESS);
2301: }

2303: /*
2304:    This function returns an array of flags which indicate the locations of contiguous
2305:    blocks that should be zeroed. for eg: if bs = 3  and is = [0,1,2,3,5,6,7,8,9]
2306:    then the resulting sizes = [3,1,1,3,1] corresponding to sets [(0,1,2),(3),(5),(6,7,8),(9)]
2307:    Assume: sizes should be long enough to hold all the values.
2308: */
2309: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[], PetscInt n, PetscInt bs, PetscInt sizes[], PetscInt *bs_max)
2310: {
2311:   PetscInt j = 0;

2313:   PetscFunctionBegin;
2314:   for (PetscInt i = 0; i < n; j++) {
2315:     PetscInt row = idx[i];
2316:     if (row % bs != 0) { /* Not the beginning of a block */
2317:       sizes[j] = 1;
2318:       i++;
2319:     } else if (i + bs > n) { /* complete block doesn't exist (at idx end) */
2320:       sizes[j] = 1;          /* Also makes sure at least 'bs' values exist for next else */
2321:       i++;
2322:     } else { /* Beginning of the block, so check if the complete block exists */
2323:       PetscBool flg = PETSC_TRUE;
2324:       for (PetscInt k = 1; k < bs; k++) {
2325:         if (row + k != idx[i + k]) { /* break in the block */
2326:           flg = PETSC_FALSE;
2327:           break;
2328:         }
2329:       }
2330:       if (flg) { /* No break in the bs */
2331:         sizes[j] = bs;
2332:         i += bs;
2333:       } else {
2334:         sizes[j] = 1;
2335:         i++;
2336:       }
2337:     }
2338:   }
2339:   *bs_max = j;
2340:   PetscFunctionReturn(PETSC_SUCCESS);
2341: }

2343: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A, PetscInt is_n, const PetscInt is_idx[], PetscScalar diag, Vec x, Vec b)
2344: {
2345:   Mat_SeqBAIJ       *baij = (Mat_SeqBAIJ *)A->data;
2346:   PetscInt           i, j, k, count, *rows;
2347:   PetscInt           bs = A->rmap->bs, bs2 = baij->bs2, *sizes, row, bs_max;
2348:   PetscScalar        zero = 0.0;
2349:   MatScalar         *aa;
2350:   const PetscScalar *xx;
2351:   PetscScalar       *bb;

2353:   PetscFunctionBegin;
2354:   /* fix right hand side if needed */
2355:   if (x && b) {
2356:     PetscCall(VecGetArrayRead(x, &xx));
2357:     PetscCall(VecGetArray(b, &bb));
2358:     for (i = 0; i < is_n; i++) bb[is_idx[i]] = diag * xx[is_idx[i]];
2359:     PetscCall(VecRestoreArrayRead(x, &xx));
2360:     PetscCall(VecRestoreArray(b, &bb));
2361:   }

2363:   /* Make a copy of the IS and  sort it */
2364:   /* allocate memory for rows,sizes */
2365:   PetscCall(PetscMalloc2(is_n, &rows, 2 * is_n, &sizes));

2367:   /* copy IS values to rows, and sort them */
2368:   for (i = 0; i < is_n; i++) rows[i] = is_idx[i];
2369:   PetscCall(PetscSortInt(is_n, rows));

2371:   if (baij->keepnonzeropattern) {
2372:     for (i = 0; i < is_n; i++) sizes[i] = 1;
2373:     bs_max = is_n;
2374:   } else {
2375:     PetscCall(MatZeroRows_SeqBAIJ_Check_Blocks(rows, is_n, bs, sizes, &bs_max));
2376:     A->nonzerostate++;
2377:   }

2379:   for (i = 0, j = 0; i < bs_max; j += sizes[i], i++) {
2380:     row = rows[j];
2381:     PetscCheck(row >= 0 && row <= A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", row);
2382:     count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
2383:     aa    = ((MatScalar *)(baij->a)) + baij->i[row / bs] * bs2 + (row % bs);
2384:     if (sizes[i] == bs && !baij->keepnonzeropattern) {
2385:       if (diag != (PetscScalar)0.0) {
2386:         if (baij->ilen[row / bs] > 0) {
2387:           baij->ilen[row / bs]       = 1;
2388:           baij->j[baij->i[row / bs]] = row / bs;

2390:           PetscCall(PetscArrayzero(aa, count * bs));
2391:         }
2392:         /* Now insert all the diagonal values for this bs */
2393:         for (k = 0; k < bs; k++) PetscCall((*A->ops->setvalues)(A, 1, rows + j + k, 1, rows + j + k, &diag, INSERT_VALUES));
2394:       } else { /* (diag == 0.0) */
2395:         baij->ilen[row / bs] = 0;
2396:       }      /* end (diag == 0.0) */
2397:     } else { /* (sizes[i] != bs) */
2398:       PetscAssert(sizes[i] == 1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal Error. Value should be 1");
2399:       for (k = 0; k < count; k++) {
2400:         aa[0] = zero;
2401:         aa += bs;
2402:       }
2403:       if (diag != (PetscScalar)0.0) PetscCall((*A->ops->setvalues)(A, 1, rows + j, 1, rows + j, &diag, INSERT_VALUES));
2404:     }
2405:   }

2407:   PetscCall(PetscFree2(rows, sizes));
2408:   PetscCall(MatAssemblyEnd_SeqBAIJ(A, MAT_FINAL_ASSEMBLY));
2409:   PetscFunctionReturn(PETSC_SUCCESS);
2410: }

2412: static PetscErrorCode MatZeroRowsColumns_SeqBAIJ(Mat A, PetscInt is_n, const PetscInt is_idx[], PetscScalar diag, Vec x, Vec b)
2413: {
2414:   Mat_SeqBAIJ       *baij = (Mat_SeqBAIJ *)A->data;
2415:   PetscInt           i, j, k, count;
2416:   PetscInt           bs = A->rmap->bs, bs2 = baij->bs2, row, col;
2417:   PetscScalar        zero = 0.0;
2418:   MatScalar         *aa;
2419:   const PetscScalar *xx;
2420:   PetscScalar       *bb;
2421:   PetscBool         *zeroed, vecs = PETSC_FALSE;

2423:   PetscFunctionBegin;
2424:   /* fix right hand side if needed */
2425:   if (x && b) {
2426:     PetscCall(VecGetArrayRead(x, &xx));
2427:     PetscCall(VecGetArray(b, &bb));
2428:     vecs = PETSC_TRUE;
2429:   }

2431:   /* zero the columns */
2432:   PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2433:   for (i = 0; i < is_n; i++) {
2434:     PetscCheck(is_idx[i] >= 0 && is_idx[i] < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", is_idx[i]);
2435:     zeroed[is_idx[i]] = PETSC_TRUE;
2436:   }
2437:   for (i = 0; i < A->rmap->N; i++) {
2438:     if (!zeroed[i]) {
2439:       row = i / bs;
2440:       for (j = baij->i[row]; j < baij->i[row + 1]; j++) {
2441:         for (k = 0; k < bs; k++) {
2442:           col = bs * baij->j[j] + k;
2443:           if (zeroed[col]) {
2444:             aa = ((MatScalar *)(baij->a)) + j * bs2 + (i % bs) + bs * k;
2445:             if (vecs) bb[i] -= aa[0] * xx[col];
2446:             aa[0] = 0.0;
2447:           }
2448:         }
2449:       }
2450:     } else if (vecs) bb[i] = diag * xx[i];
2451:   }
2452:   PetscCall(PetscFree(zeroed));
2453:   if (vecs) {
2454:     PetscCall(VecRestoreArrayRead(x, &xx));
2455:     PetscCall(VecRestoreArray(b, &bb));
2456:   }

2458:   /* zero the rows */
2459:   for (i = 0; i < is_n; i++) {
2460:     row   = is_idx[i];
2461:     count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
2462:     aa    = ((MatScalar *)(baij->a)) + baij->i[row / bs] * bs2 + (row % bs);
2463:     for (k = 0; k < count; k++) {
2464:       aa[0] = zero;
2465:       aa += bs;
2466:     }
2467:     if (diag != (PetscScalar)0.0) PetscUseTypeMethod(A, setvalues, 1, &row, 1, &row, &diag, INSERT_VALUES);
2468:   }
2469:   PetscCall(MatAssemblyEnd_SeqBAIJ(A, MAT_FINAL_ASSEMBLY));
2470:   PetscFunctionReturn(PETSC_SUCCESS);
2471: }

2473: PetscErrorCode MatSetValues_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
2474: {
2475:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2476:   PetscInt    *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N, lastcol = -1;
2477:   PetscInt    *imax = a->imax, *ai = a->i, *ailen = a->ilen;
2478:   PetscInt    *aj = a->j, nonew = a->nonew, bs = A->rmap->bs, brow, bcol;
2479:   PetscInt     ridx, cidx, bs2                 = a->bs2;
2480:   PetscBool    roworiented = a->roworiented;
2481:   MatScalar   *ap = NULL, value = 0.0, *aa = a->a, *bap;

2483:   PetscFunctionBegin;
2484:   for (k = 0; k < m; k++) { /* loop over added rows */
2485:     row  = im[k];
2486:     brow = row / bs;
2487:     if (row < 0) continue;
2488:     PetscCheck(row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->N - 1);
2489:     rp = aj + ai[brow];
2490:     if (!A->structure_only) ap = aa + bs2 * ai[brow];
2491:     rmax = imax[brow];
2492:     nrow = ailen[brow];
2493:     low  = 0;
2494:     high = nrow;
2495:     for (l = 0; l < n; l++) { /* loop over added columns */
2496:       if (in[l] < 0) continue;
2497:       PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
2498:       col  = in[l];
2499:       bcol = col / bs;
2500:       ridx = row % bs;
2501:       cidx = col % bs;
2502:       if (!A->structure_only) {
2503:         if (roworiented) {
2504:           value = v[l + k * n];
2505:         } else {
2506:           value = v[k + l * m];
2507:         }
2508:       }
2509:       if (col <= lastcol) low = 0;
2510:       else high = nrow;
2511:       lastcol = col;
2512:       while (high - low > 7) {
2513:         t = (low + high) / 2;
2514:         if (rp[t] > bcol) high = t;
2515:         else low = t;
2516:       }
2517:       for (i = low; i < high; i++) {
2518:         if (rp[i] > bcol) break;
2519:         if (rp[i] == bcol) {
2520:           bap = ap + bs2 * i + bs * cidx + ridx;
2521:           if (!A->structure_only) {
2522:             if (is == ADD_VALUES) *bap += value;
2523:             else *bap = value;
2524:           }
2525:           goto noinsert1;
2526:         }
2527:       }
2528:       if (nonew == 1) goto noinsert1;
2529:       PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", row, col);
2530:       if (A->structure_only) {
2531:         MatSeqXAIJReallocateAIJ_structure_only(A, a->mbs, bs2, nrow, brow, bcol, rmax, ai, aj, rp, imax, nonew, MatScalar);
2532:       } else {
2533:         MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
2534:       }
2535:       N = nrow++ - 1;
2536:       high++;
2537:       /* shift up all the later entries in this row */
2538:       PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
2539:       rp[i] = bcol;
2540:       if (!A->structure_only) {
2541:         PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
2542:         PetscCall(PetscArrayzero(ap + bs2 * i, bs2));
2543:         ap[bs2 * i + bs * cidx + ridx] = value;
2544:       }
2545:       a->nz++;
2546:       A->nonzerostate++;
2547:     noinsert1:;
2548:       low = i;
2549:     }
2550:     ailen[brow] = nrow;
2551:   }
2552:   PetscFunctionReturn(PETSC_SUCCESS);
2553: }

2555: static PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2556: {
2557:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inA->data;
2558:   Mat          outA;
2559:   PetscBool    row_identity, col_identity;

2561:   PetscFunctionBegin;
2562:   PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels = 0 supported for in-place ILU");
2563:   PetscCall(ISIdentity(row, &row_identity));
2564:   PetscCall(ISIdentity(col, &col_identity));
2565:   PetscCheck(row_identity && col_identity, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Row and column permutations must be identity for in-place ILU");

2567:   outA            = inA;
2568:   inA->factortype = MAT_FACTOR_LU;
2569:   PetscCall(PetscFree(inA->solvertype));
2570:   PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));

2572:   PetscCall(MatMarkDiagonal_SeqBAIJ(inA));

2574:   PetscCall(PetscObjectReference((PetscObject)row));
2575:   PetscCall(ISDestroy(&a->row));
2576:   a->row = row;
2577:   PetscCall(PetscObjectReference((PetscObject)col));
2578:   PetscCall(ISDestroy(&a->col));
2579:   a->col = col;

2581:   /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
2582:   PetscCall(ISDestroy(&a->icol));
2583:   PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));

2585:   PetscCall(MatSeqBAIJSetNumericFactorization_inplace(inA, (PetscBool)(row_identity && col_identity)));
2586:   if (!a->solve_work) PetscCall(PetscMalloc1(inA->rmap->N + inA->rmap->bs, &a->solve_work));
2587:   PetscCall(MatLUFactorNumeric(outA, inA, info));
2588:   PetscFunctionReturn(PETSC_SUCCESS);
2589: }

2591: static PetscErrorCode MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat, const PetscInt *indices)
2592: {
2593:   Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data;

2595:   PetscFunctionBegin;
2596:   baij->nz = baij->maxnz;
2597:   PetscCall(PetscArraycpy(baij->j, indices, baij->nz));
2598:   PetscCall(PetscArraycpy(baij->ilen, baij->imax, baij->mbs));
2599:   PetscFunctionReturn(PETSC_SUCCESS);
2600: }

2602: /*@
2603:     MatSeqBAIJSetColumnIndices - Set the column indices for all the rows in the matrix.

2605:   Input Parameters:
2606: +  mat - the `MATSEQBAIJ` matrix
2607: -  indices - the column indices

2609:   Level: advanced

2611:   Notes:
2612:     This can be called if you have precomputed the nonzero structure of the
2613:   matrix and want to provide it to the matrix object to improve the performance
2614:   of the `MatSetValues()` operation.

2616:     You MUST have set the correct numbers of nonzeros per row in the call to
2617:   `MatCreateSeqBAIJ()`, and the columns indices MUST be sorted.

2619:     MUST be called before any calls to `MatSetValues()`

2621: .seealso: [](chapter_matrices), `Mat`, `MATSEQBAIJ`, `MatSetValues()`
2622: @*/
2623: PetscErrorCode MatSeqBAIJSetColumnIndices(Mat mat, PetscInt *indices)
2624: {
2625:   PetscFunctionBegin;
2628:   PetscUseMethod(mat, "MatSeqBAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
2629:   PetscFunctionReturn(PETSC_SUCCESS);
2630: }

2632: PetscErrorCode MatGetRowMaxAbs_SeqBAIJ(Mat A, Vec v, PetscInt idx[])
2633: {
2634:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2635:   PetscInt     i, j, n, row, bs, *ai, *aj, mbs;
2636:   PetscReal    atmp;
2637:   PetscScalar *x, zero = 0.0;
2638:   MatScalar   *aa;
2639:   PetscInt     ncols, brow, krow, kcol;

2641:   PetscFunctionBegin;
2642:   /* why is this not a macro???????????????????????????????????????????????????????????????? */
2643:   PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2644:   bs  = A->rmap->bs;
2645:   aa  = a->a;
2646:   ai  = a->i;
2647:   aj  = a->j;
2648:   mbs = a->mbs;

2650:   PetscCall(VecSet(v, zero));
2651:   PetscCall(VecGetArray(v, &x));
2652:   PetscCall(VecGetLocalSize(v, &n));
2653:   PetscCheck(n == A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
2654:   for (i = 0; i < mbs; i++) {
2655:     ncols = ai[1] - ai[0];
2656:     ai++;
2657:     brow = bs * i;
2658:     for (j = 0; j < ncols; j++) {
2659:       for (kcol = 0; kcol < bs; kcol++) {
2660:         for (krow = 0; krow < bs; krow++) {
2661:           atmp = PetscAbsScalar(*aa);
2662:           aa++;
2663:           row = brow + krow; /* row index */
2664:           if (PetscAbsScalar(x[row]) < atmp) {
2665:             x[row] = atmp;
2666:             if (idx) idx[row] = bs * (*aj) + kcol;
2667:           }
2668:         }
2669:       }
2670:       aj++;
2671:     }
2672:   }
2673:   PetscCall(VecRestoreArray(v, &x));
2674:   PetscFunctionReturn(PETSC_SUCCESS);
2675: }

2677: PetscErrorCode MatCopy_SeqBAIJ(Mat A, Mat B, MatStructure str)
2678: {
2679:   PetscFunctionBegin;
2680:   /* If the two matrices have the same copy implementation, use fast copy. */
2681:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2682:     Mat_SeqBAIJ *a    = (Mat_SeqBAIJ *)A->data;
2683:     Mat_SeqBAIJ *b    = (Mat_SeqBAIJ *)B->data;
2684:     PetscInt     ambs = a->mbs, bmbs = b->mbs, abs = A->rmap->bs, bbs = B->rmap->bs, bs2 = abs * abs;

2686:     PetscCheck(a->i[ambs] == b->i[bmbs], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzero blocks in matrices A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", a->i[ambs], b->i[bmbs]);
2687:     PetscCheck(abs == bbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Block size A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", abs, bbs);
2688:     PetscCall(PetscArraycpy(b->a, a->a, bs2 * a->i[ambs]));
2689:     PetscCall(PetscObjectStateIncrease((PetscObject)B));
2690:   } else {
2691:     PetscCall(MatCopy_Basic(A, B, str));
2692:   }
2693:   PetscFunctionReturn(PETSC_SUCCESS);
2694: }

2696: static PetscErrorCode MatSeqBAIJGetArray_SeqBAIJ(Mat A, PetscScalar *array[])
2697: {
2698:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;

2700:   PetscFunctionBegin;
2701:   *array = a->a;
2702:   PetscFunctionReturn(PETSC_SUCCESS);
2703: }

2705: static PetscErrorCode MatSeqBAIJRestoreArray_SeqBAIJ(Mat A, PetscScalar *array[])
2706: {
2707:   PetscFunctionBegin;
2708:   *array = NULL;
2709:   PetscFunctionReturn(PETSC_SUCCESS);
2710: }

2712: PetscErrorCode MatAXPYGetPreallocation_SeqBAIJ(Mat Y, Mat X, PetscInt *nnz)
2713: {
2714:   PetscInt     bs = Y->rmap->bs, mbs = Y->rmap->N / bs;
2715:   Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
2716:   Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;

2718:   PetscFunctionBegin;
2719:   /* Set the number of nonzeros in the new matrix */
2720:   PetscCall(MatAXPYGetPreallocation_SeqX_private(mbs, x->i, x->j, y->i, y->j, nnz));
2721:   PetscFunctionReturn(PETSC_SUCCESS);
2722: }

2724: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2725: {
2726:   Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data, *y = (Mat_SeqBAIJ *)Y->data;
2727:   PetscInt     bs = Y->rmap->bs, bs2 = bs * bs;
2728:   PetscBLASInt one = 1;

2730:   PetscFunctionBegin;
2731:   if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
2732:     PetscBool e = x->nz == y->nz && x->mbs == y->mbs && bs == X->rmap->bs ? PETSC_TRUE : PETSC_FALSE;
2733:     if (e) {
2734:       PetscCall(PetscArraycmp(x->i, y->i, x->mbs + 1, &e));
2735:       if (e) {
2736:         PetscCall(PetscArraycmp(x->j, y->j, x->i[x->mbs], &e));
2737:         if (e) str = SAME_NONZERO_PATTERN;
2738:       }
2739:     }
2740:     if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
2741:   }
2742:   if (str == SAME_NONZERO_PATTERN) {
2743:     PetscScalar  alpha = a;
2744:     PetscBLASInt bnz;
2745:     PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
2746:     PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
2747:     PetscCall(PetscObjectStateIncrease((PetscObject)Y));
2748:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2749:     PetscCall(MatAXPY_Basic(Y, a, X, str));
2750:   } else {
2751:     Mat       B;
2752:     PetscInt *nnz;
2753:     PetscCheck(bs == X->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrices must have same block size");
2754:     PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
2755:     PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2756:     PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2757:     PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
2758:     PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
2759:     PetscCall(MatSetType(B, (MatType)((PetscObject)Y)->type_name));
2760:     PetscCall(MatAXPYGetPreallocation_SeqBAIJ(Y, X, nnz));
2761:     PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz));
2762:     PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2763:     PetscCall(MatHeaderMerge(Y, &B));
2764:     PetscCall(PetscFree(nnz));
2765:   }
2766:   PetscFunctionReturn(PETSC_SUCCESS);
2767: }

2769: PETSC_INTERN PetscErrorCode MatConjugate_SeqBAIJ(Mat A)
2770: {
2771: #if PetscDefined(USE_COMPLEX)
2772:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2773:   PetscInt     i, nz = a->bs2 * a->i[a->mbs];
2774:   MatScalar   *aa = a->a;

2776:   PetscFunctionBegin;
2777:   for (i = 0; i < nz; i++) aa[i] = PetscConj(aa[i]);
2778:   PetscFunctionReturn(PETSC_SUCCESS);
2779: #else
2780:   (void)A;
2781:   return PETSC_SUCCESS;
2782: #endif
2783: }

2785: static PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
2786: {
2787: #if PetscDefined(USE_COMPLEX)
2788:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2789:   PetscInt     i, nz = a->bs2 * a->i[a->mbs];
2790:   MatScalar   *aa = a->a;

2792:   PetscFunctionBegin;
2793:   for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
2794:   PetscFunctionReturn(PETSC_SUCCESS);
2795: #else
2796:   (void)A;
2797:   return PETSC_SUCCESS;
2798: #endif
2799: }

2801: static PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2802: {
2803: #if PetscDefined(USE_COMPLEX)
2804:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2805:   PetscInt     i, nz = a->bs2 * a->i[a->mbs];
2806:   MatScalar   *aa = a->a;

2808:   PetscFunctionBegin;
2809:   for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2810:   PetscFunctionReturn(PETSC_SUCCESS);
2811: #else
2812:   (void)A;
2813:   return PETSC_SUCCESS;
2814: #endif
2815: }

2817: /*
2818:     Code almost identical to MatGetColumnIJ_SeqAIJ() should share common code
2819: */
2820: static PetscErrorCode MatGetColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
2821: {
2822:   Mat_SeqBAIJ *a  = (Mat_SeqBAIJ *)A->data;
2823:   PetscInt     bs = A->rmap->bs, i, *collengths, *cia, *cja, n = A->cmap->n / bs, m = A->rmap->n / bs;
2824:   PetscInt     nz = a->i[m], row, *jj, mr, col;

2826:   PetscFunctionBegin;
2827:   *nn = n;
2828:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
2829:   PetscCheck(!symmetric, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not for BAIJ matrices");
2830:   PetscCall(PetscCalloc1(n, &collengths));
2831:   PetscCall(PetscMalloc1(n + 1, &cia));
2832:   PetscCall(PetscMalloc1(nz, &cja));
2833:   jj = a->j;
2834:   for (i = 0; i < nz; i++) collengths[jj[i]]++;
2835:   cia[0] = oshift;
2836:   for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
2837:   PetscCall(PetscArrayzero(collengths, n));
2838:   jj = a->j;
2839:   for (row = 0; row < m; row++) {
2840:     mr = a->i[row + 1] - a->i[row];
2841:     for (i = 0; i < mr; i++) {
2842:       col = *jj++;

2844:       cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
2845:     }
2846:   }
2847:   PetscCall(PetscFree(collengths));
2848:   *ia = cia;
2849:   *ja = cja;
2850:   PetscFunctionReturn(PETSC_SUCCESS);
2851: }

2853: static PetscErrorCode MatRestoreColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
2854: {
2855:   PetscFunctionBegin;
2856:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
2857:   PetscCall(PetscFree(*ia));
2858:   PetscCall(PetscFree(*ja));
2859:   PetscFunctionReturn(PETSC_SUCCESS);
2860: }

2862: /*
2863:  MatGetColumnIJ_SeqBAIJ_Color() and MatRestoreColumnIJ_SeqBAIJ_Color() are customized from
2864:  MatGetColumnIJ_SeqBAIJ() and MatRestoreColumnIJ_SeqBAIJ() by adding an output
2865:  spidx[], index of a->a, to be used in MatTransposeColoringCreate() and MatFDColoringCreate()
2866:  */
2867: PetscErrorCode MatGetColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
2868: {
2869:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2870:   PetscInt     i, *collengths, *cia, *cja, n = a->nbs, m = a->mbs;
2871:   PetscInt     nz = a->i[m], row, *jj, mr, col;
2872:   PetscInt    *cspidx;

2874:   PetscFunctionBegin;
2875:   *nn = n;
2876:   if (!ia) PetscFunctionReturn(PETSC_SUCCESS);

2878:   PetscCall(PetscCalloc1(n, &collengths));
2879:   PetscCall(PetscMalloc1(n + 1, &cia));
2880:   PetscCall(PetscMalloc1(nz, &cja));
2881:   PetscCall(PetscMalloc1(nz, &cspidx));
2882:   jj = a->j;
2883:   for (i = 0; i < nz; i++) collengths[jj[i]]++;
2884:   cia[0] = oshift;
2885:   for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
2886:   PetscCall(PetscArrayzero(collengths, n));
2887:   jj = a->j;
2888:   for (row = 0; row < m; row++) {
2889:     mr = a->i[row + 1] - a->i[row];
2890:     for (i = 0; i < mr; i++) {
2891:       col                                         = *jj++;
2892:       cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */
2893:       cja[cia[col] + collengths[col]++ - oshift]  = row + oshift;
2894:     }
2895:   }
2896:   PetscCall(PetscFree(collengths));
2897:   *ia    = cia;
2898:   *ja    = cja;
2899:   *spidx = cspidx;
2900:   PetscFunctionReturn(PETSC_SUCCESS);
2901: }

2903: PetscErrorCode MatRestoreColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
2904: {
2905:   PetscFunctionBegin;
2906:   PetscCall(MatRestoreColumnIJ_SeqBAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
2907:   PetscCall(PetscFree(*spidx));
2908:   PetscFunctionReturn(PETSC_SUCCESS);
2909: }

2911: PetscErrorCode MatShift_SeqBAIJ(Mat Y, PetscScalar a)
2912: {
2913:   Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)Y->data;

2915:   PetscFunctionBegin;
2916:   if (!Y->preallocated || !aij->nz) PetscCall(MatSeqBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL));
2917:   PetscCall(MatShift_Basic(Y, a));
2918:   PetscFunctionReturn(PETSC_SUCCESS);
2919: }

2921: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
2922:                                        MatGetRow_SeqBAIJ,
2923:                                        MatRestoreRow_SeqBAIJ,
2924:                                        MatMult_SeqBAIJ_N,
2925:                                        /* 4*/ MatMultAdd_SeqBAIJ_N,
2926:                                        MatMultTranspose_SeqBAIJ,
2927:                                        MatMultTransposeAdd_SeqBAIJ,
2928:                                        NULL,
2929:                                        NULL,
2930:                                        NULL,
2931:                                        /* 10*/ NULL,
2932:                                        MatLUFactor_SeqBAIJ,
2933:                                        NULL,
2934:                                        NULL,
2935:                                        MatTranspose_SeqBAIJ,
2936:                                        /* 15*/ MatGetInfo_SeqBAIJ,
2937:                                        MatEqual_SeqBAIJ,
2938:                                        MatGetDiagonal_SeqBAIJ,
2939:                                        MatDiagonalScale_SeqBAIJ,
2940:                                        MatNorm_SeqBAIJ,
2941:                                        /* 20*/ NULL,
2942:                                        MatAssemblyEnd_SeqBAIJ,
2943:                                        MatSetOption_SeqBAIJ,
2944:                                        MatZeroEntries_SeqBAIJ,
2945:                                        /* 24*/ MatZeroRows_SeqBAIJ,
2946:                                        NULL,
2947:                                        NULL,
2948:                                        NULL,
2949:                                        NULL,
2950:                                        /* 29*/ MatSetUp_Seq_Hash,
2951:                                        NULL,
2952:                                        NULL,
2953:                                        NULL,
2954:                                        NULL,
2955:                                        /* 34*/ MatDuplicate_SeqBAIJ,
2956:                                        NULL,
2957:                                        NULL,
2958:                                        MatILUFactor_SeqBAIJ,
2959:                                        NULL,
2960:                                        /* 39*/ MatAXPY_SeqBAIJ,
2961:                                        MatCreateSubMatrices_SeqBAIJ,
2962:                                        MatIncreaseOverlap_SeqBAIJ,
2963:                                        MatGetValues_SeqBAIJ,
2964:                                        MatCopy_SeqBAIJ,
2965:                                        /* 44*/ NULL,
2966:                                        MatScale_SeqBAIJ,
2967:                                        MatShift_SeqBAIJ,
2968:                                        NULL,
2969:                                        MatZeroRowsColumns_SeqBAIJ,
2970:                                        /* 49*/ NULL,
2971:                                        MatGetRowIJ_SeqBAIJ,
2972:                                        MatRestoreRowIJ_SeqBAIJ,
2973:                                        MatGetColumnIJ_SeqBAIJ,
2974:                                        MatRestoreColumnIJ_SeqBAIJ,
2975:                                        /* 54*/ MatFDColoringCreate_SeqXAIJ,
2976:                                        NULL,
2977:                                        NULL,
2978:                                        NULL,
2979:                                        MatSetValuesBlocked_SeqBAIJ,
2980:                                        /* 59*/ MatCreateSubMatrix_SeqBAIJ,
2981:                                        MatDestroy_SeqBAIJ,
2982:                                        MatView_SeqBAIJ,
2983:                                        NULL,
2984:                                        NULL,
2985:                                        /* 64*/ NULL,
2986:                                        NULL,
2987:                                        NULL,
2988:                                        NULL,
2989:                                        NULL,
2990:                                        /* 69*/ MatGetRowMaxAbs_SeqBAIJ,
2991:                                        NULL,
2992:                                        MatConvert_Basic,
2993:                                        NULL,
2994:                                        NULL,
2995:                                        /* 74*/ NULL,
2996:                                        MatFDColoringApply_BAIJ,
2997:                                        NULL,
2998:                                        NULL,
2999:                                        NULL,
3000:                                        /* 79*/ NULL,
3001:                                        NULL,
3002:                                        NULL,
3003:                                        NULL,
3004:                                        MatLoad_SeqBAIJ,
3005:                                        /* 84*/ NULL,
3006:                                        NULL,
3007:                                        NULL,
3008:                                        NULL,
3009:                                        NULL,
3010:                                        /* 89*/ NULL,
3011:                                        NULL,
3012:                                        NULL,
3013:                                        NULL,
3014:                                        NULL,
3015:                                        /* 94*/ NULL,
3016:                                        NULL,
3017:                                        NULL,
3018:                                        NULL,
3019:                                        NULL,
3020:                                        /* 99*/ NULL,
3021:                                        NULL,
3022:                                        NULL,
3023:                                        MatConjugate_SeqBAIJ,
3024:                                        NULL,
3025:                                        /*104*/ NULL,
3026:                                        MatRealPart_SeqBAIJ,
3027:                                        MatImaginaryPart_SeqBAIJ,
3028:                                        NULL,
3029:                                        NULL,
3030:                                        /*109*/ NULL,
3031:                                        NULL,
3032:                                        NULL,
3033:                                        NULL,
3034:                                        MatMissingDiagonal_SeqBAIJ,
3035:                                        /*114*/ NULL,
3036:                                        NULL,
3037:                                        NULL,
3038:                                        NULL,
3039:                                        NULL,
3040:                                        /*119*/ NULL,
3041:                                        NULL,
3042:                                        MatMultHermitianTranspose_SeqBAIJ,
3043:                                        MatMultHermitianTransposeAdd_SeqBAIJ,
3044:                                        NULL,
3045:                                        /*124*/ NULL,
3046:                                        MatGetColumnReductions_SeqBAIJ,
3047:                                        MatInvertBlockDiagonal_SeqBAIJ,
3048:                                        NULL,
3049:                                        NULL,
3050:                                        /*129*/ NULL,
3051:                                        NULL,
3052:                                        NULL,
3053:                                        NULL,
3054:                                        NULL,
3055:                                        /*134*/ NULL,
3056:                                        NULL,
3057:                                        NULL,
3058:                                        NULL,
3059:                                        NULL,
3060:                                        /*139*/ MatSetBlockSizes_Default,
3061:                                        NULL,
3062:                                        NULL,
3063:                                        MatFDColoringSetUp_SeqXAIJ,
3064:                                        NULL,
3065:                                        /*144*/ MatCreateMPIMatConcatenateSeqMat_SeqBAIJ,
3066:                                        MatDestroySubMatrices_SeqBAIJ,
3067:                                        NULL,
3068:                                        NULL,
3069:                                        NULL,
3070:                                        NULL,
3071:                                        /*150*/ NULL,
3072:                                        NULL};

3074: static PetscErrorCode MatStoreValues_SeqBAIJ(Mat mat)
3075: {
3076:   Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
3077:   PetscInt     nz  = aij->i[aij->mbs] * aij->bs2;

3079:   PetscFunctionBegin;
3080:   PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3082:   /* allocate space for values if not already there */
3083:   if (!aij->saved_values) PetscCall(PetscMalloc1(nz + 1, &aij->saved_values));

3085:   /* copy values over */
3086:   PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3087:   PetscFunctionReturn(PETSC_SUCCESS);
3088: }

3090: static PetscErrorCode MatRetrieveValues_SeqBAIJ(Mat mat)
3091: {
3092:   Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
3093:   PetscInt     nz  = aij->i[aij->mbs] * aij->bs2;

3095:   PetscFunctionBegin;
3096:   PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3097:   PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");

3099:   /* copy values over */
3100:   PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3101:   PetscFunctionReturn(PETSC_SUCCESS);
3102: }

3104: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
3105: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqSBAIJ(Mat, MatType, MatReuse, Mat *);

3107: PetscErrorCode MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B, PetscInt bs, PetscInt nz, PetscInt *nnz)
3108: {
3109:   Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data;
3110:   PetscInt     i, mbs, nbs, bs2;
3111:   PetscBool    flg = PETSC_FALSE, skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;

3113:   PetscFunctionBegin;
3114:   if (B->hash_active) {
3115:     PetscInt bs;
3116:     PetscCall(PetscMemcpy(&B->ops, &b->cops, sizeof(*(B->ops))));
3117:     PetscCall(PetscHMapIJVDestroy(&b->ht));
3118:     PetscCall(MatGetBlockSize(B, &bs));
3119:     if (bs > 1) PetscCall(PetscHSetIJDestroy(&b->bht));
3120:     PetscCall(PetscFree(b->dnz));
3121:     PetscCall(PetscFree(b->bdnz));
3122:     B->hash_active = PETSC_FALSE;
3123:   }
3124:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3125:   if (nz == MAT_SKIP_ALLOCATION) {
3126:     skipallocation = PETSC_TRUE;
3127:     nz             = 0;
3128:   }

3130:   PetscCall(MatSetBlockSize(B, PetscAbs(bs)));
3131:   PetscCall(PetscLayoutSetUp(B->rmap));
3132:   PetscCall(PetscLayoutSetUp(B->cmap));
3133:   PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));

3135:   B->preallocated = PETSC_TRUE;

3137:   mbs = B->rmap->n / bs;
3138:   nbs = B->cmap->n / bs;
3139:   bs2 = bs * bs;

3141:   PetscCheck(mbs * bs == B->rmap->n && nbs * bs == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number rows %" PetscInt_FMT ", cols %" PetscInt_FMT " must be divisible by blocksize %" PetscInt_FMT, B->rmap->N, B->cmap->n, bs);

3143:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3144:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3145:   if (nnz) {
3146:     for (i = 0; i < mbs; i++) {
3147:       PetscCheck(nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, nnz[i]);
3148:       PetscCheck(nnz[i] <= nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than block row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], nbs);
3149:     }
3150:   }

3152:   PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Optimize options for SEQBAIJ matrix 2 ", "Mat");
3153:   PetscCall(PetscOptionsBool("-mat_no_unroll", "Do not optimize for block size (slow)", NULL, flg, &flg, NULL));
3154:   PetscOptionsEnd();

3156:   if (!flg) {
3157:     switch (bs) {
3158:     case 1:
3159:       B->ops->mult    = MatMult_SeqBAIJ_1;
3160:       B->ops->multadd = MatMultAdd_SeqBAIJ_1;
3161:       break;
3162:     case 2:
3163:       B->ops->mult    = MatMult_SeqBAIJ_2;
3164:       B->ops->multadd = MatMultAdd_SeqBAIJ_2;
3165:       break;
3166:     case 3:
3167:       B->ops->mult    = MatMult_SeqBAIJ_3;
3168:       B->ops->multadd = MatMultAdd_SeqBAIJ_3;
3169:       break;
3170:     case 4:
3171:       B->ops->mult    = MatMult_SeqBAIJ_4;
3172:       B->ops->multadd = MatMultAdd_SeqBAIJ_4;
3173:       break;
3174:     case 5:
3175:       B->ops->mult    = MatMult_SeqBAIJ_5;
3176:       B->ops->multadd = MatMultAdd_SeqBAIJ_5;
3177:       break;
3178:     case 6:
3179:       B->ops->mult    = MatMult_SeqBAIJ_6;
3180:       B->ops->multadd = MatMultAdd_SeqBAIJ_6;
3181:       break;
3182:     case 7:
3183:       B->ops->mult    = MatMult_SeqBAIJ_7;
3184:       B->ops->multadd = MatMultAdd_SeqBAIJ_7;
3185:       break;
3186:     case 9: {
3187:       PetscInt version = 1;
3188:       PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3189:       switch (version) {
3190: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
3191:       case 1:
3192:         B->ops->mult    = MatMult_SeqBAIJ_9_AVX2;
3193:         B->ops->multadd = MatMultAdd_SeqBAIJ_9_AVX2;
3194:         PetscCall(PetscInfo((PetscObject)B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3195:         break;
3196: #endif
3197:       default:
3198:         B->ops->mult    = MatMult_SeqBAIJ_N;
3199:         B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3200:         PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3201:         break;
3202:       }
3203:       break;
3204:     }
3205:     case 11:
3206:       B->ops->mult    = MatMult_SeqBAIJ_11;
3207:       B->ops->multadd = MatMultAdd_SeqBAIJ_11;
3208:       break;
3209:     case 12: {
3210:       PetscInt version = 1;
3211:       PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3212:       switch (version) {
3213:       case 1:
3214:         B->ops->mult    = MatMult_SeqBAIJ_12_ver1;
3215:         B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1;
3216:         PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3217:         break;
3218:       case 2:
3219:         B->ops->mult    = MatMult_SeqBAIJ_12_ver2;
3220:         B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver2;
3221:         PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3222:         break;
3223: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
3224:       case 3:
3225:         B->ops->mult    = MatMult_SeqBAIJ_12_AVX2;
3226:         B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1;
3227:         PetscCall(PetscInfo((PetscObject)B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3228:         break;
3229: #endif
3230:       default:
3231:         B->ops->mult    = MatMult_SeqBAIJ_N;
3232:         B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3233:         PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3234:         break;
3235:       }
3236:       break;
3237:     }
3238:     case 15: {
3239:       PetscInt version = 1;
3240:       PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3241:       switch (version) {
3242:       case 1:
3243:         B->ops->mult = MatMult_SeqBAIJ_15_ver1;
3244:         PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3245:         break;
3246:       case 2:
3247:         B->ops->mult = MatMult_SeqBAIJ_15_ver2;
3248:         PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3249:         break;
3250:       case 3:
3251:         B->ops->mult = MatMult_SeqBAIJ_15_ver3;
3252:         PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3253:         break;
3254:       case 4:
3255:         B->ops->mult = MatMult_SeqBAIJ_15_ver4;
3256:         PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3257:         break;
3258:       default:
3259:         B->ops->mult = MatMult_SeqBAIJ_N;
3260:         PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3261:         break;
3262:       }
3263:       B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3264:       break;
3265:     }
3266:     default:
3267:       B->ops->mult    = MatMult_SeqBAIJ_N;
3268:       B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3269:       PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3270:       break;
3271:     }
3272:   }
3273:   B->ops->sor = MatSOR_SeqBAIJ;
3274:   b->mbs      = mbs;
3275:   b->nbs      = nbs;
3276:   if (!skipallocation) {
3277:     if (!b->imax) {
3278:       PetscCall(PetscMalloc2(mbs, &b->imax, mbs, &b->ilen));

3280:       b->free_imax_ilen = PETSC_TRUE;
3281:     }
3282:     /* b->ilen will count nonzeros in each block row so far. */
3283:     for (i = 0; i < mbs; i++) b->ilen[i] = 0;
3284:     if (!nnz) {
3285:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3286:       else if (nz < 0) nz = 1;
3287:       nz = PetscMin(nz, nbs);
3288:       for (i = 0; i < mbs; i++) b->imax[i] = nz;
3289:       PetscCall(PetscIntMultError(nz, mbs, &nz));
3290:     } else {
3291:       PetscInt64 nz64 = 0;
3292:       for (i = 0; i < mbs; i++) {
3293:         b->imax[i] = nnz[i];
3294:         nz64 += nnz[i];
3295:       }
3296:       PetscCall(PetscIntCast(nz64, &nz));
3297:     }

3299:     /* allocate the matrix space */
3300:     PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
3301:     if (B->structure_only) {
3302:       PetscCall(PetscMalloc1(nz, &b->j));
3303:       PetscCall(PetscMalloc1(B->rmap->N + 1, &b->i));
3304:     } else {
3305:       PetscInt nzbs2 = 0;
3306:       PetscCall(PetscIntMultError(nz, bs2, &nzbs2));
3307:       PetscCall(PetscMalloc3(nzbs2, &b->a, nz, &b->j, B->rmap->N + 1, &b->i));
3308:       PetscCall(PetscArrayzero(b->a, nz * bs2));
3309:     }
3310:     PetscCall(PetscArrayzero(b->j, nz));

3312:     if (B->structure_only) {
3313:       b->singlemalloc = PETSC_FALSE;
3314:       b->free_a       = PETSC_FALSE;
3315:     } else {
3316:       b->singlemalloc = PETSC_TRUE;
3317:       b->free_a       = PETSC_TRUE;
3318:     }
3319:     b->free_ij = PETSC_TRUE;

3321:     b->i[0] = 0;
3322:     for (i = 1; i < mbs + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];

3324:   } else {
3325:     b->free_a  = PETSC_FALSE;
3326:     b->free_ij = PETSC_FALSE;
3327:   }

3329:   b->bs2              = bs2;
3330:   b->mbs              = mbs;
3331:   b->nz               = 0;
3332:   b->maxnz            = nz;
3333:   B->info.nz_unneeded = (PetscReal)b->maxnz * bs2;
3334:   B->was_assembled    = PETSC_FALSE;
3335:   B->assembled        = PETSC_FALSE;
3336:   if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
3337:   PetscFunctionReturn(PETSC_SUCCESS);
3338: }

3340: PetscErrorCode MatSeqBAIJSetPreallocationCSR_SeqBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
3341: {
3342:   PetscInt     i, m, nz, nz_max = 0, *nnz;
3343:   PetscScalar *values      = NULL;
3344:   PetscBool    roworiented = ((Mat_SeqBAIJ *)B->data)->roworiented;

3346:   PetscFunctionBegin;
3347:   PetscCheck(bs >= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Invalid block size specified, must be positive but it is %" PetscInt_FMT, bs);
3348:   PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
3349:   PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
3350:   PetscCall(PetscLayoutSetUp(B->rmap));
3351:   PetscCall(PetscLayoutSetUp(B->cmap));
3352:   PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
3353:   m = B->rmap->n / bs;

3355:   PetscCheck(ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
3356:   PetscCall(PetscMalloc1(m + 1, &nnz));
3357:   for (i = 0; i < m; i++) {
3358:     nz = ii[i + 1] - ii[i];
3359:     PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
3360:     nz_max = PetscMax(nz_max, nz);
3361:     nnz[i] = nz;
3362:   }
3363:   PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz));
3364:   PetscCall(PetscFree(nnz));

3366:   values = (PetscScalar *)V;
3367:   if (!values) PetscCall(PetscCalloc1(bs * bs * (nz_max + 1), &values));
3368:   for (i = 0; i < m; i++) {
3369:     PetscInt        ncols = ii[i + 1] - ii[i];
3370:     const PetscInt *icols = jj + ii[i];
3371:     if (bs == 1 || !roworiented) {
3372:       const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
3373:       PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, ncols, icols, svals, INSERT_VALUES));
3374:     } else {
3375:       PetscInt j;
3376:       for (j = 0; j < ncols; j++) {
3377:         const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
3378:         PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, 1, &icols[j], svals, INSERT_VALUES));
3379:       }
3380:     }
3381:   }
3382:   if (!V) PetscCall(PetscFree(values));
3383:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3384:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3385:   PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3386:   PetscFunctionReturn(PETSC_SUCCESS);
3387: }

3389: /*@C
3390:    MatSeqBAIJGetArray - gives read/write access to the array where the data for a `MATSEQBAIJ` matrix is stored

3392:    Not Collective

3394:    Input Parameter:
3395: .  mat - a `MATSEQBAIJ` matrix

3397:    Output Parameter:
3398: .   array - pointer to the data

3400:    Level: intermediate

3402: .seealso: [](chapter_matrices), `Mat`, `MATSEQBAIJ`, `MatSeqBAIJRestoreArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`
3403: @*/
3404: PetscErrorCode MatSeqBAIJGetArray(Mat A, PetscScalar **array)
3405: {
3406:   PetscFunctionBegin;
3407:   PetscUseMethod(A, "MatSeqBAIJGetArray_C", (Mat, PetscScalar **), (A, array));
3408:   PetscFunctionReturn(PETSC_SUCCESS);
3409: }

3411: /*@C
3412:    MatSeqBAIJRestoreArray - returns access to the array where the data for a `MATSEQBAIJ` matrix is stored obtained by `MatSeqBAIJGetArray()`

3414:    Not Collective

3416:    Input Parameters:
3417: +  mat - a `MATSEQBAIJ` matrix
3418: -  array - pointer to the data

3420:    Level: intermediate

3422: .seealso: [](chapter_matrices), `Mat`, `MatSeqBAIJGetArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`
3423: @*/
3424: PetscErrorCode MatSeqBAIJRestoreArray(Mat A, PetscScalar **array)
3425: {
3426:   PetscFunctionBegin;
3427:   PetscUseMethod(A, "MatSeqBAIJRestoreArray_C", (Mat, PetscScalar **), (A, array));
3428:   PetscFunctionReturn(PETSC_SUCCESS);
3429: }

3431: /*MC
3432:    MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on
3433:    block sparse compressed row format.

3435:    Options Database Keys:
3436: + -mat_type seqbaij - sets the matrix type to "seqbaij" during a call to MatSetFromOptions()
3437: - -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)

3439:    Level: beginner

3441:    Notes:
3442:     `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
3443:     space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored

3445:    Run with `-info` to see what version of the matrix-vector product is being used

3447: .seealso: [](chapter_matrices), `Mat`, `MatCreateSeqBAIJ()`
3448: M*/

3450: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBSTRM(Mat, MatType, MatReuse, Mat *);

3452: PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJ(Mat B)
3453: {
3454:   PetscMPIInt  size;
3455:   Mat_SeqBAIJ *b;

3457:   PetscFunctionBegin;
3458:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
3459:   PetscCheck(size == 1, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Comm must be of size 1");

3461:   PetscCall(PetscNew(&b));
3462:   B->data = (void *)b;
3463:   PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps)));

3465:   b->row          = NULL;
3466:   b->col          = NULL;
3467:   b->icol         = NULL;
3468:   b->reallocs     = 0;
3469:   b->saved_values = NULL;

3471:   b->roworiented        = PETSC_TRUE;
3472:   b->nonew              = 0;
3473:   b->diag               = NULL;
3474:   B->spptr              = NULL;
3475:   B->info.nz_unneeded   = (PetscReal)b->maxnz * b->bs2;
3476:   b->keepnonzeropattern = PETSC_FALSE;

3478:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJGetArray_C", MatSeqBAIJGetArray_SeqBAIJ));
3479:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJRestoreArray_C", MatSeqBAIJRestoreArray_SeqBAIJ));
3480:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqBAIJ));
3481:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqBAIJ));
3482:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetColumnIndices_C", MatSeqBAIJSetColumnIndices_SeqBAIJ));
3483:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqaij_C", MatConvert_SeqBAIJ_SeqAIJ));
3484:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqsbaij_C", MatConvert_SeqBAIJ_SeqSBAIJ));
3485:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocation_C", MatSeqBAIJSetPreallocation_SeqBAIJ));
3486:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocationCSR_C", MatSeqBAIJSetPreallocationCSR_SeqBAIJ));
3487:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqBAIJ));
3488: #if defined(PETSC_HAVE_HYPRE)
3489:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_hypre_C", MatConvert_AIJ_HYPRE));
3490: #endif
3491:   PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_is_C", MatConvert_XAIJ_IS));
3492:   PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJ));
3493:   PetscFunctionReturn(PETSC_SUCCESS);
3494: }

3496: PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
3497: {
3498:   Mat_SeqBAIJ *c = (Mat_SeqBAIJ *)C->data, *a = (Mat_SeqBAIJ *)A->data;
3499:   PetscInt     i, mbs = a->mbs, nz = a->nz, bs2 = a->bs2;

3501:   PetscFunctionBegin;
3502:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
3503:   PetscCheck(a->i[mbs] == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupt matrix");

3505:   if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3506:     c->imax           = a->imax;
3507:     c->ilen           = a->ilen;
3508:     c->free_imax_ilen = PETSC_FALSE;
3509:   } else {
3510:     PetscCall(PetscMalloc2(mbs, &c->imax, mbs, &c->ilen));
3511:     for (i = 0; i < mbs; i++) {
3512:       c->imax[i] = a->imax[i];
3513:       c->ilen[i] = a->ilen[i];
3514:     }
3515:     c->free_imax_ilen = PETSC_TRUE;
3516:   }

3518:   /* allocate the matrix space */
3519:   if (mallocmatspace) {
3520:     if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3521:       PetscCall(PetscCalloc1(bs2 * nz, &c->a));

3523:       c->i            = a->i;
3524:       c->j            = a->j;
3525:       c->singlemalloc = PETSC_FALSE;
3526:       c->free_a       = PETSC_TRUE;
3527:       c->free_ij      = PETSC_FALSE;
3528:       c->parent       = A;
3529:       C->preallocated = PETSC_TRUE;
3530:       C->assembled    = PETSC_TRUE;

3532:       PetscCall(PetscObjectReference((PetscObject)A));
3533:       PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3534:       PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3535:     } else {
3536:       PetscCall(PetscMalloc3(bs2 * nz, &c->a, nz, &c->j, mbs + 1, &c->i));

3538:       c->singlemalloc = PETSC_TRUE;
3539:       c->free_a       = PETSC_TRUE;
3540:       c->free_ij      = PETSC_TRUE;

3542:       PetscCall(PetscArraycpy(c->i, a->i, mbs + 1));
3543:       if (mbs > 0) {
3544:         PetscCall(PetscArraycpy(c->j, a->j, nz));
3545:         if (cpvalues == MAT_COPY_VALUES) {
3546:           PetscCall(PetscArraycpy(c->a, a->a, bs2 * nz));
3547:         } else {
3548:           PetscCall(PetscArrayzero(c->a, bs2 * nz));
3549:         }
3550:       }
3551:       C->preallocated = PETSC_TRUE;
3552:       C->assembled    = PETSC_TRUE;
3553:     }
3554:   }

3556:   c->roworiented = a->roworiented;
3557:   c->nonew       = a->nonew;

3559:   PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
3560:   PetscCall(PetscLayoutReference(A->cmap, &C->cmap));

3562:   c->bs2 = a->bs2;
3563:   c->mbs = a->mbs;
3564:   c->nbs = a->nbs;

3566:   if (a->diag) {
3567:     if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3568:       c->diag      = a->diag;
3569:       c->free_diag = PETSC_FALSE;
3570:     } else {
3571:       PetscCall(PetscMalloc1(mbs + 1, &c->diag));
3572:       for (i = 0; i < mbs; i++) c->diag[i] = a->diag[i];
3573:       c->free_diag = PETSC_TRUE;
3574:     }
3575:   } else c->diag = NULL;

3577:   c->nz         = a->nz;
3578:   c->maxnz      = a->nz; /* Since we allocate exactly the right amount */
3579:   c->solve_work = NULL;
3580:   c->mult_work  = NULL;
3581:   c->sor_workt  = NULL;
3582:   c->sor_work   = NULL;

3584:   c->compressedrow.use   = a->compressedrow.use;
3585:   c->compressedrow.nrows = a->compressedrow.nrows;
3586:   if (a->compressedrow.use) {
3587:     i = a->compressedrow.nrows;
3588:     PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i + 1, &c->compressedrow.rindex));
3589:     PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
3590:     PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
3591:   } else {
3592:     c->compressedrow.use    = PETSC_FALSE;
3593:     c->compressedrow.i      = NULL;
3594:     c->compressedrow.rindex = NULL;
3595:   }
3596:   C->nonzerostate = A->nonzerostate;

3598:   PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
3599:   PetscFunctionReturn(PETSC_SUCCESS);
3600: }

3602: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
3603: {
3604:   PetscFunctionBegin;
3605:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
3606:   PetscCall(MatSetSizes(*B, A->rmap->N, A->cmap->n, A->rmap->N, A->cmap->n));
3607:   PetscCall(MatSetType(*B, MATSEQBAIJ));
3608:   PetscCall(MatDuplicateNoCreate_SeqBAIJ(*B, A, cpvalues, PETSC_TRUE));
3609:   PetscFunctionReturn(PETSC_SUCCESS);
3610: }

3612: /* Used for both SeqBAIJ and SeqSBAIJ matrices */
3613: PetscErrorCode MatLoad_SeqBAIJ_Binary(Mat mat, PetscViewer viewer)
3614: {
3615:   PetscInt     header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3616:   PetscInt    *rowidxs, *colidxs;
3617:   PetscScalar *matvals;

3619:   PetscFunctionBegin;
3620:   PetscCall(PetscViewerSetUp(viewer));

3622:   /* read matrix header */
3623:   PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3624:   PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3625:   M  = header[1];
3626:   N  = header[2];
3627:   nz = header[3];
3628:   PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3629:   PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3630:   PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqBAIJ");

3632:   /* set block sizes from the viewer's .info file */
3633:   PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3634:   /* set local and global sizes if not set already */
3635:   if (mat->rmap->n < 0) mat->rmap->n = M;
3636:   if (mat->cmap->n < 0) mat->cmap->n = N;
3637:   if (mat->rmap->N < 0) mat->rmap->N = M;
3638:   if (mat->cmap->N < 0) mat->cmap->N = N;
3639:   PetscCall(PetscLayoutSetUp(mat->rmap));
3640:   PetscCall(PetscLayoutSetUp(mat->cmap));

3642:   /* check if the matrix sizes are correct */
3643:   PetscCall(MatGetSize(mat, &rows, &cols));
3644:   PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
3645:   PetscCall(MatGetBlockSize(mat, &bs));
3646:   PetscCall(MatGetLocalSize(mat, &m, &n));
3647:   mbs = m / bs;
3648:   nbs = n / bs;

3650:   /* read in row lengths, column indices and nonzero values */
3651:   PetscCall(PetscMalloc1(m + 1, &rowidxs));
3652:   PetscCall(PetscViewerBinaryRead(viewer, rowidxs + 1, m, NULL, PETSC_INT));
3653:   rowidxs[0] = 0;
3654:   for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3655:   sum = rowidxs[m];
3656:   PetscCheck(sum == nz, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);

3658:   /* read in column indices and nonzero values */
3659:   PetscCall(PetscMalloc2(rowidxs[m], &colidxs, nz, &matvals));
3660:   PetscCall(PetscViewerBinaryRead(viewer, colidxs, rowidxs[m], NULL, PETSC_INT));
3661:   PetscCall(PetscViewerBinaryRead(viewer, matvals, rowidxs[m], NULL, PETSC_SCALAR));

3663:   {               /* preallocate matrix storage */
3664:     PetscBT   bt; /* helper bit set to count nonzeros */
3665:     PetscInt *nnz;
3666:     PetscBool sbaij;

3668:     PetscCall(PetscBTCreate(nbs, &bt));
3669:     PetscCall(PetscCalloc1(mbs, &nnz));
3670:     PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATSEQSBAIJ, &sbaij));
3671:     for (i = 0; i < mbs; i++) {
3672:       PetscCall(PetscBTMemzero(nbs, bt));
3673:       for (k = 0; k < bs; k++) {
3674:         PetscInt row = bs * i + k;
3675:         for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3676:           PetscInt col = colidxs[j];
3677:           if (!sbaij || col >= row)
3678:             if (!PetscBTLookupSet(bt, col / bs)) nnz[i]++;
3679:         }
3680:       }
3681:     }
3682:     PetscCall(PetscBTDestroy(&bt));
3683:     PetscCall(MatSeqBAIJSetPreallocation(mat, bs, 0, nnz));
3684:     PetscCall(MatSeqSBAIJSetPreallocation(mat, bs, 0, nnz));
3685:     PetscCall(PetscFree(nnz));
3686:   }

3688:   /* store matrix values */
3689:   for (i = 0; i < m; i++) {
3690:     PetscInt row = i, s = rowidxs[i], e = rowidxs[i + 1];
3691:     PetscCall((*mat->ops->setvalues)(mat, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES));
3692:   }

3694:   PetscCall(PetscFree(rowidxs));
3695:   PetscCall(PetscFree2(colidxs, matvals));
3696:   PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3697:   PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3698:   PetscFunctionReturn(PETSC_SUCCESS);
3699: }

3701: PetscErrorCode MatLoad_SeqBAIJ(Mat mat, PetscViewer viewer)
3702: {
3703:   PetscBool isbinary;

3705:   PetscFunctionBegin;
3706:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3707:   PetscCheck(isbinary, PetscObjectComm((PetscObject)viewer), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)mat)->type_name);
3708:   PetscCall(MatLoad_SeqBAIJ_Binary(mat, viewer));
3709:   PetscFunctionReturn(PETSC_SUCCESS);
3710: }

3712: /*@C
3713:    MatCreateSeqBAIJ - Creates a sparse matrix in `MATSEQAIJ` (block
3714:    compressed row) format.  For good matrix assembly performance the
3715:    user should preallocate the matrix storage by setting the parameter nz
3716:    (or the array nnz).  By setting these parameters accurately, performance
3717:    during matrix assembly can be increased by more than a factor of 50.

3719:    Collective

3721:    Input Parameters:
3722: +  comm - MPI communicator, set to `PETSC_COMM_SELF`
3723: .  bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3724:           blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3725: .  m - number of rows
3726: .  n - number of columns
3727: .  nz - number of nonzero blocks  per block row (same for all rows)
3728: -  nnz - array containing the number of nonzero blocks in the various block rows
3729:          (possibly different for each block row) or NULL

3731:    Output Parameter:
3732: .  A - the matrix

3734:    Options Database Keys:
3735: +   -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower)
3736: -   -mat_block_size - size of the blocks to use

3738:    Level: intermediate

3740:    Notes:
3741:    It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3742:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
3743:    [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]

3745:    The number of rows and columns must be divisible by blocksize.

3747:    If the `nnz` parameter is given then the `nz` parameter is ignored

3749:    A nonzero block is any block that as 1 or more nonzeros in it

3751:    The `MATSEQBAIJ` format is fully compatible with standard Fortran
3752:    storage.  That is, the stored row and column indices can begin at
3753:    either one (as in Fortran) or zero.  See the users' manual for details.

3755:    Specify the preallocated storage with either `nz` or `nnz` (not both).
3756:    Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3757:    allocation.  See [Sparse Matrices](sec_matsparse) for details.
3758:    matrices.

3760: .seealso: [](chapter_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`
3761: @*/
3762: PetscErrorCode MatCreateSeqBAIJ(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3763: {
3764:   PetscFunctionBegin;
3765:   PetscCall(MatCreate(comm, A));
3766:   PetscCall(MatSetSizes(*A, m, n, m, n));
3767:   PetscCall(MatSetType(*A, MATSEQBAIJ));
3768:   PetscCall(MatSeqBAIJSetPreallocation(*A, bs, nz, (PetscInt *)nnz));
3769:   PetscFunctionReturn(PETSC_SUCCESS);
3770: }

3772: /*@C
3773:    MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
3774:    per row in the matrix. For good matrix assembly performance the
3775:    user should preallocate the matrix storage by setting the parameter nz
3776:    (or the array nnz).  By setting these parameters accurately, performance
3777:    during matrix assembly can be increased by more than a factor of 50.

3779:    Collective

3781:    Input Parameters:
3782: +  B - the matrix
3783: .  bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3784:           blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3785: .  nz - number of block nonzeros per block row (same for all rows)
3786: -  nnz - array containing the number of block nonzeros in the various block rows
3787:          (possibly different for each block row) or `NULL`

3789:    Options Database Keys:
3790: +   -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower)
3791: -   -mat_block_size - size of the blocks to use

3793:    Level: intermediate

3795:    Notes:
3796:    If the `nnz` parameter is given then the `nz` parameter is ignored

3798:    You can call `MatGetInfo()` to get information on how effective the preallocation was;
3799:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3800:    You can also run with the option -info and look for messages with the string
3801:    malloc in them to see if additional memory allocation was needed.

3803:    The `MATSEQBAIJ` format is fully compatible with standard Fortran
3804:    storage.  That is, the stored row and column indices can begin at
3805:    either one (as in Fortran) or zero.  See the users' manual for details.

3807:    Specify the preallocated storage with either nz or nnz (not both).
3808:    Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3809:    allocation.  See [Sparse Matrices](sec_matsparse) for details.

3811: .seealso: [](chapter_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatGetInfo()`
3812: @*/
3813: PetscErrorCode MatSeqBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt nz, const PetscInt nnz[])
3814: {
3815:   PetscFunctionBegin;
3819:   PetscTryMethod(B, "MatSeqBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[]), (B, bs, nz, nnz));
3820:   PetscFunctionReturn(PETSC_SUCCESS);
3821: }

3823: /*@C
3824:    MatSeqBAIJSetPreallocationCSR - Creates a sparse sequential matrix in `MATSEQBAIJ` format using the given nonzero structure and (optional) numerical values

3826:    Collective

3828:    Input Parameters:
3829: +  B - the matrix
3830: .  i - the indices into `j` for the start of each local row (starts with zero)
3831: .  j - the column indices for each local row (starts with zero) these must be sorted for each row
3832: -  v - optional values in the matrix

3834:    Level: advanced

3836:    Notes:
3837:    The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`.  For example, C programs
3838:    may want to use the default `MAT_ROW_ORIENTED` of `PETSC_TRUE` and use an array v[nnz][bs][bs] where the second index is
3839:    over rows within a block and the last index is over columns within a block row.  Fortran programs will likely set
3840:    `MAT_ROW_ORIENTED` of `PETSC_FALSE` and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a
3841:    block column and the second index is over columns within a block.

3843:    Though this routine has Preallocation() in the name it also sets the exact nonzero locations of the matrix entries and usually the numerical values as well

3845: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatSeqBAIJSetPreallocation()`, `MATSEQBAIJ`
3846: @*/
3847: PetscErrorCode MatSeqBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
3848: {
3849:   PetscFunctionBegin;
3853:   PetscTryMethod(B, "MatSeqBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
3854:   PetscFunctionReturn(PETSC_SUCCESS);
3855: }

3857: /*@
3858:      MatCreateSeqBAIJWithArrays - Creates a `MATSEQBAIJ` matrix using matrix elements provided by the user.

3860:      Collective

3862:    Input Parameters:
3863: +  comm - must be an MPI communicator of size 1
3864: .  bs - size of block
3865: .  m - number of rows
3866: .  n - number of columns
3867: .  i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row block row of the matrix
3868: .  j - column indices
3869: -  a - matrix values

3871:    Output Parameter:
3872: .  mat - the matrix

3874:    Level: advanced

3876:    Notes:
3877:        The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
3878:     once the matrix is destroyed

3880:        You cannot set new nonzero locations into this matrix, that will generate an error.

3882:        The `i` and `j` indices are 0 based

3884:        When block size is greater than 1 the matrix values must be stored using the `MATSEQBAIJ` storage format

3886:       The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3887:       the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3888:       block, followed by the second column of the first block etc etc.  That is, the blocks are contiguous in memory
3889:       with column-major ordering within blocks.

3891: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateBAIJ()`, `MatCreateSeqBAIJ()`
3892: @*/
3893: PetscErrorCode MatCreateSeqBAIJWithArrays(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
3894: {
3895:   Mat_SeqBAIJ *baij;

3897:   PetscFunctionBegin;
3898:   PetscCheck(bs == 1, PETSC_COMM_SELF, PETSC_ERR_SUP, "block size %" PetscInt_FMT " > 1 is not supported yet", bs);
3899:   if (m > 0) PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");

3901:   PetscCall(MatCreate(comm, mat));
3902:   PetscCall(MatSetSizes(*mat, m, n, m, n));
3903:   PetscCall(MatSetType(*mat, MATSEQBAIJ));
3904:   PetscCall(MatSeqBAIJSetPreallocation(*mat, bs, MAT_SKIP_ALLOCATION, NULL));
3905:   baij = (Mat_SeqBAIJ *)(*mat)->data;
3906:   PetscCall(PetscMalloc2(m, &baij->imax, m, &baij->ilen));

3908:   baij->i = i;
3909:   baij->j = j;
3910:   baij->a = a;

3912:   baij->singlemalloc   = PETSC_FALSE;
3913:   baij->nonew          = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3914:   baij->free_a         = PETSC_FALSE;
3915:   baij->free_ij        = PETSC_FALSE;
3916:   baij->free_imax_ilen = PETSC_TRUE;

3918:   for (PetscInt ii = 0; ii < m; ii++) {
3919:     const PetscInt row_len = i[ii + 1] - i[ii];

3921:     baij->ilen[ii] = baij->imax[ii] = row_len;
3922:     PetscCheck(row_len >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, row_len);
3923:   }
3924:   if (PetscDefined(USE_DEBUG)) {
3925:     for (PetscInt ii = 0; ii < baij->i[m]; ii++) {
3926:       PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
3927:       PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
3928:     }
3929:   }

3931:   PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3932:   PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3933:   PetscFunctionReturn(PETSC_SUCCESS);
3934: }

3936: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
3937: {
3938:   PetscFunctionBegin;
3939:   PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(comm, inmat, n, scall, outmat));
3940:   PetscFunctionReturn(PETSC_SUCCESS);
3941: }