Actual source code: baij.c

  1: #define PETSCMAT_DLL

  3: /*
  4:     Defines the basic matrix operations for the BAIJ (compressed row)
  5:   matrix storage format.
  6: */
 7:  #include src/mat/impls/baij/seq/baij.h
 8:  #include src/inline/spops.h
 9:  #include petscsys.h

 11:  #include src/inline/ilu.h

 15: /*@C
 16:   MatSeqBAIJInvertBlockDiagonal - Inverts the block diagonal entries.

 18:   Collective on Mat

 20:   Input Parameters:
 21: . mat - the matrix

 23:   Level: advanced
 24: @*/
 25: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJInvertBlockDiagonal(Mat mat)
 26: {
 27:   PetscErrorCode ierr,(*f)(Mat);

 31:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
 32:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

 34:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqBAIJInvertBlockDiagonal_C",(void (**)(void))&f);
 35:   if (f) {
 36:     (*f)(mat);
 37:   } else {
 38:     SETERRQ(PETSC_ERR_SUP,"Currently only implemented for SeqBAIJ.");
 39:   }
 40:   return(0);
 41: }

 46: PetscErrorCode PETSCMAT_DLLEXPORT MatInvertBlockDiagonal_SeqBAIJ(Mat A)
 47: {
 48:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*) A->data;
 50:   PetscInt       *diag_offset,i,bs = A->rmap.bs,mbs = a->mbs;
 51:   PetscScalar    *v = a->a,*odiag,*diag,*mdiag;

 54:   if (a->idiagvalid) return(0);
 55:   MatMarkDiagonal_SeqBAIJ(A);
 56:   diag_offset = a->diag;
 57:   if (!a->idiag) {
 58:     PetscMalloc(2*bs*bs*mbs*sizeof(PetscScalar),&a->idiag);
 59:   }
 60:   diag  = a->idiag;
 61:   mdiag = a->idiag+bs*bs*mbs;
 62:   /* factor and invert each block */
 63:   switch (bs){
 64:     case 2:
 65:       for (i=0; i<mbs; i++) {
 66:         odiag   = v + 4*diag_offset[i];
 67:         diag[0]  = odiag[0]; diag[1] = odiag[1]; diag[2] = odiag[2]; diag[3] = odiag[3];
 68:         mdiag[0] = odiag[0]; mdiag[1] = odiag[1]; mdiag[2] = odiag[2]; mdiag[3] = odiag[3];
 69:         Kernel_A_gets_inverse_A_2(diag);
 70:         diag    += 4;
 71:         mdiag   += 4;
 72:       }
 73:       break;
 74:     case 3:
 75:       for (i=0; i<mbs; i++) {
 76:         odiag    = v + 9*diag_offset[i];
 77:         diag[0]  = odiag[0]; diag[1] = odiag[1]; diag[2] = odiag[2]; diag[3] = odiag[3];
 78:         diag[4]  = odiag[4]; diag[5] = odiag[5]; diag[6] = odiag[6]; diag[7] = odiag[7];
 79:         diag[8]  = odiag[8];
 80:         mdiag[0] = odiag[0]; mdiag[1] = odiag[1]; mdiag[2] = odiag[2]; mdiag[3] = odiag[3];
 81:         mdiag[4] = odiag[4]; mdiag[5] = odiag[5]; mdiag[6] = odiag[6]; mdiag[7] = odiag[7];
 82:         mdiag[8] = odiag[8];
 83:         Kernel_A_gets_inverse_A_3(diag);
 84:         diag    += 9;
 85:         mdiag   += 9;
 86:       }
 87:       break;
 88:     case 4:
 89:       for (i=0; i<mbs; i++) {
 90:         odiag  = v + 16*diag_offset[i];
 91:         PetscMemcpy(diag,odiag,16*sizeof(PetscScalar));
 92:         PetscMemcpy(mdiag,odiag,16*sizeof(PetscScalar));
 93:         Kernel_A_gets_inverse_A_4(diag);
 94:         diag  += 16;
 95:         mdiag += 16;
 96:       }
 97:       break;
 98:     case 5:
 99:       for (i=0; i<mbs; i++) {
100:         odiag = v + 25*diag_offset[i];
101:         PetscMemcpy(diag,odiag,25*sizeof(PetscScalar));
102:         PetscMemcpy(mdiag,odiag,25*sizeof(PetscScalar));
103:         Kernel_A_gets_inverse_A_5(diag);
104:         diag  += 25;
105:         mdiag += 25;
106:       }
107:       break;
108:     default:
109:       SETERRQ1(PETSC_ERR_SUP,"not supported for block size %D",bs);
110:   }
111:   a->idiagvalid = PETSC_TRUE;
112:   return(0);
113: }

118: PetscErrorCode MatPBRelax_SeqBAIJ_2(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
119: {
120:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data;
121:   PetscScalar        *x,x1,x2,s1,s2;
122:   const PetscScalar  *v,*aa = a->a, *b, *idiag,*mdiag;
123:   PetscErrorCode     ierr;
124:   PetscInt           m = a->mbs,i,i2,nz,idx;
125:   const PetscInt     *diag,*ai = a->i,*aj = a->j,*vi;

128:   its = its*lits;
129:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
130:   if (fshift) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
131:   if (omega != 1.0) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
132:   if ((flag & SOR_EISENSTAT) ||(flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER) ) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for Eisenstat trick");
133:   if (its > 1) SETERRQ(PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");

135:   if (!a->idiagvalid){MatInvertBlockDiagonal_SeqBAIJ(A);}

137:   diag  = a->diag;
138:   idiag = a->idiag;
139:   VecGetArray(xx,&x);
140:   VecGetArray(bb,(PetscScalar**)&b);

142:   if (flag & SOR_ZERO_INITIAL_GUESS) {
143:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
144:       x[0] = b[0]*idiag[0] + b[1]*idiag[2];
145:       x[1] = b[0]*idiag[1] + b[1]*idiag[3];
146:       i2     = 2;
147:       idiag += 4;
148:       for (i=1; i<m; i++) {
149:         v     = aa + 4*ai[i];
150:         vi    = aj + ai[i];
151:         nz    = diag[i] - ai[i];
152:         s1    = b[i2]; s2 = b[i2+1];
153:         while (nz--) {
154:           idx  = 2*(*vi++);
155:           x1   = x[idx]; x2 = x[1+idx];
156:           s1  -= v[0]*x1 + v[2]*x2;
157:           s2  -= v[1]*x1 + v[3]*x2;
158:           v   += 4;
159:         }
160:         x[i2]   = idiag[0]*s1 + idiag[2]*s2;
161:         x[i2+1] = idiag[1]*s1 + idiag[3]*s2;
162:         idiag   += 4;
163:         i2      += 2;
164:       }
165:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
166:       PetscLogFlops(4*(a->nz));
167:     }
168:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
169:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
170:       i2    = 0;
171:       mdiag = a->idiag+4*a->mbs;
172:       for (i=0; i<m; i++) {
173:         x1      = x[i2]; x2 = x[i2+1];
174:         x[i2]   = mdiag[0]*x1 + mdiag[2]*x2;
175:         x[i2+1] = mdiag[1]*x1 + mdiag[3]*x2;
176:         mdiag  += 4;
177:         i2     += 2;
178:       }
179:       PetscLogFlops(6*m);
180:     } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
181:       PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
182:     }
183:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
184:       idiag   = a->idiag+4*a->mbs - 4;
185:       i2      = 2*m - 2;
186:       x1      = x[i2]; x2 = x[i2+1];
187:       x[i2]   = idiag[0]*x1 + idiag[2]*x2;
188:       x[i2+1] = idiag[1]*x1 + idiag[3]*x2;
189:       idiag -= 4;
190:       i2    -= 2;
191:       for (i=m-2; i>=0; i--) {
192:         v     = aa + 4*(diag[i]+1);
193:         vi    = aj + diag[i] + 1;
194:         nz    = ai[i+1] - diag[i] - 1;
195:         s1    = x[i2]; s2 = x[i2+1];
196:         while (nz--) {
197:           idx  = 2*(*vi++);
198:           x1   = x[idx]; x2 = x[1+idx];
199:           s1  -= v[0]*x1 + v[2]*x2;
200:           s2  -= v[1]*x1 + v[3]*x2;
201:           v   += 4;
202:         }
203:         x[i2]   = idiag[0]*s1 + idiag[2]*s2;
204:         x[i2+1] = idiag[1]*s1 + idiag[3]*s2;
205:         idiag   -= 4;
206:         i2      -= 2;
207:       }
208:       PetscLogFlops(4*(a->nz));
209:     }
210:   } else {
211:     SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
212:   }
213:   VecRestoreArray(xx,&x);
214:   VecRestoreArray(bb,(PetscScalar**)&b);
215:   return(0);
216: }

220: PetscErrorCode MatPBRelax_SeqBAIJ_3(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
221: {
222:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data;
223:   PetscScalar        *x,x1,x2,x3,s1,s2,s3;
224:   const PetscScalar  *v,*aa = a->a, *b, *idiag,*mdiag;
225:   PetscErrorCode     ierr;
226:   PetscInt           m = a->mbs,i,i2,nz,idx;
227:   const PetscInt     *diag,*ai = a->i,*aj = a->j,*vi;

230:   its = its*lits;
231:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
232:   if (fshift) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
233:   if (omega != 1.0) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
234:   if ((flag & SOR_EISENSTAT) ||(flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER) ) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for Eisenstat trick");
235:   if (its > 1) SETERRQ(PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");

237:   if (!a->idiagvalid){MatInvertBlockDiagonal_SeqBAIJ(A);}

239:   diag  = a->diag;
240:   idiag = a->idiag;
241:   VecGetArray(xx,&x);
242:   VecGetArray(bb,(PetscScalar**)&b);

244:   if (flag & SOR_ZERO_INITIAL_GUESS) {
245:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
246:       x[0] = b[0]*idiag[0] + b[1]*idiag[3] + b[2]*idiag[6];
247:       x[1] = b[0]*idiag[1] + b[1]*idiag[4] + b[2]*idiag[7];
248:       x[2] = b[0]*idiag[2] + b[1]*idiag[5] + b[2]*idiag[8];
249:       i2     = 3;
250:       idiag += 9;
251:       for (i=1; i<m; i++) {
252:         v     = aa + 9*ai[i];
253:         vi    = aj + ai[i];
254:         nz    = diag[i] - ai[i];
255:         s1    = b[i2]; s2 = b[i2+1]; s3 = b[i2+2];
256:         while (nz--) {
257:           idx  = 3*(*vi++);
258:           x1   = x[idx]; x2 = x[1+idx];x3 = x[2+idx];
259:           s1  -= v[0]*x1 + v[3]*x2 + v[6]*x3;
260:           s2  -= v[1]*x1 + v[4]*x2 + v[7]*x3;
261:           s3  -= v[2]*x1 + v[5]*x2 + v[8]*x3;
262:           v   += 9;
263:         }
264:         x[i2]   = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
265:         x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
266:         x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;
267:         idiag   += 9;
268:         i2      += 3;
269:       }
270:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
271:       PetscLogFlops(9*(a->nz));
272:     }
273:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
274:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
275:       i2    = 0;
276:       mdiag = a->idiag+9*a->mbs;
277:       for (i=0; i<m; i++) {
278:         x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
279:         x[i2]   = mdiag[0]*x1 + mdiag[3]*x2 + mdiag[6]*x3;
280:         x[i2+1] = mdiag[1]*x1 + mdiag[4]*x2 + mdiag[7]*x3;
281:         x[i2+2] = mdiag[2]*x1 + mdiag[5]*x2 + mdiag[8]*x3;
282:         mdiag  += 9;
283:         i2     += 3;
284:       }
285:       PetscLogFlops(15*m);
286:     } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
287:       PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
288:     }
289:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
290:       idiag   = a->idiag+9*a->mbs - 9;
291:       i2      = 3*m - 3;
292:       x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
293:       x[i2]   = idiag[0]*x1 + idiag[3]*x2 + idiag[6]*x3;
294:       x[i2+1] = idiag[1]*x1 + idiag[4]*x2 + idiag[7]*x3;
295:       x[i2+2] = idiag[2]*x1 + idiag[5]*x2 + idiag[8]*x3;
296:       idiag -= 9;
297:       i2    -= 3;
298:       for (i=m-2; i>=0; i--) {
299:         v     = aa + 9*(diag[i]+1);
300:         vi    = aj + diag[i] + 1;
301:         nz    = ai[i+1] - diag[i] - 1;
302:         s1    = x[i2]; s2 = x[i2+1]; s3 = x[i2+2];
303:         while (nz--) {
304:           idx  = 3*(*vi++);
305:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx];
306:           s1  -= v[0]*x1 + v[3]*x2 + v[6]*x3;
307:           s2  -= v[1]*x1 + v[4]*x2 + v[7]*x3;
308:           s3  -= v[2]*x1 + v[5]*x2 + v[8]*x3;
309:           v   += 9;
310:         }
311:         x[i2]   = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
312:         x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
313:         x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;
314:         idiag   -= 9;
315:         i2      -= 3;
316:       }
317:       PetscLogFlops(9*(a->nz));
318:     }
319:   } else {
320:     SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
321:   }
322:   VecRestoreArray(xx,&x);
323:   VecRestoreArray(bb,(PetscScalar**)&b);
324:   return(0);
325: }

329: PetscErrorCode MatPBRelax_SeqBAIJ_4(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
330: {
331:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data;
332:   PetscScalar        *x,x1,x2,x3,x4,s1,s2,s3,s4;
333:   const PetscScalar  *v,*aa = a->a, *b, *idiag,*mdiag;
334:   PetscErrorCode     ierr;
335:   PetscInt           m = a->mbs,i,i2,nz,idx;
336:   const PetscInt     *diag,*ai = a->i,*aj = a->j,*vi;

339:   its = its*lits;
340:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
341:   if (fshift) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
342:   if (omega != 1.0) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
343:   if ((flag & SOR_EISENSTAT) ||(flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER) ) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for Eisenstat trick");
344:   if (its > 1) SETERRQ(PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");

346:   if (!a->idiagvalid){MatInvertBlockDiagonal_SeqBAIJ(A);}

348:   diag  = a->diag;
349:   idiag = a->idiag;
350:   VecGetArray(xx,&x);
351:   VecGetArray(bb,(PetscScalar**)&b);

353:   if (flag & SOR_ZERO_INITIAL_GUESS) {
354:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
355:       x[0] = b[0]*idiag[0] + b[1]*idiag[4] + b[2]*idiag[8]  + b[3]*idiag[12];
356:       x[1] = b[0]*idiag[1] + b[1]*idiag[5] + b[2]*idiag[9]  + b[3]*idiag[13];
357:       x[2] = b[0]*idiag[2] + b[1]*idiag[6] + b[2]*idiag[10] + b[3]*idiag[14];
358:       x[3] = b[0]*idiag[3] + b[1]*idiag[7] + b[2]*idiag[11] + b[3]*idiag[15];
359:       i2     = 4;
360:       idiag += 16;
361:       for (i=1; i<m; i++) {
362:         v     = aa + 16*ai[i];
363:         vi    = aj + ai[i];
364:         nz    = diag[i] - ai[i];
365:         s1    = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3];
366:         while (nz--) {
367:           idx  = 4*(*vi++);
368:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx];
369:           s1  -= v[0]*x1 + v[4]*x2 + v[8]*x3  + v[12]*x4;
370:           s2  -= v[1]*x1 + v[5]*x2 + v[9]*x3  + v[13]*x4;
371:           s3  -= v[2]*x1 + v[6]*x2 + v[10]*x3 + v[14]*x4;
372:           s4  -= v[3]*x1 + v[7]*x2 + v[11]*x3 + v[15]*x4;
373:           v   += 16;
374:         }
375:         x[i2]   = idiag[0]*s1 + idiag[4]*s2 + idiag[8]*s3  + idiag[12]*s4;
376:         x[i2+1] = idiag[1]*s1 + idiag[5]*s2 + idiag[9]*s3  + idiag[13]*s4;
377:         x[i2+2] = idiag[2]*s1 + idiag[6]*s2 + idiag[10]*s3 + idiag[14]*s4;
378:         x[i2+3] = idiag[3]*s1 + idiag[7]*s2 + idiag[11]*s3 + idiag[15]*s4;
379:         idiag   += 16;
380:         i2      += 4;
381:       }
382:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
383:       PetscLogFlops(16*(a->nz));
384:     }
385:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
386:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
387:       i2    = 0;
388:       mdiag = a->idiag+16*a->mbs;
389:       for (i=0; i<m; i++) {
390:         x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3];
391:         x[i2]   = mdiag[0]*x1 + mdiag[4]*x2 + mdiag[8]*x3  + mdiag[12]*x4;
392:         x[i2+1] = mdiag[1]*x1 + mdiag[5]*x2 + mdiag[9]*x3  + mdiag[13]*x4;
393:         x[i2+2] = mdiag[2]*x1 + mdiag[6]*x2 + mdiag[10]*x3 + mdiag[14]*x4;
394:         x[i2+3] = mdiag[3]*x1 + mdiag[7]*x2 + mdiag[11]*x3 + mdiag[15]*x4;
395:         mdiag  += 16;
396:         i2     += 4;
397:       }
398:       PetscLogFlops(28*m);
399:     } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
400:       PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
401:     }
402:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
403:       idiag   = a->idiag+16*a->mbs - 16;
404:       i2      = 4*m - 4;
405:       x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3];
406:       x[i2]   = idiag[0]*x1 + idiag[4]*x2 + idiag[8]*x3  + idiag[12]*x4;
407:       x[i2+1] = idiag[1]*x1 + idiag[5]*x2 + idiag[9]*x3  + idiag[13]*x4;
408:       x[i2+2] = idiag[2]*x1 + idiag[6]*x2 + idiag[10]*x3 + idiag[14]*x4;
409:       x[i2+3] = idiag[3]*x1 + idiag[7]*x2 + idiag[11]*x3 + idiag[15]*x4;
410:       idiag -= 16;
411:       i2    -= 4;
412:       for (i=m-2; i>=0; i--) {
413:         v     = aa + 16*(diag[i]+1);
414:         vi    = aj + diag[i] + 1;
415:         nz    = ai[i+1] - diag[i] - 1;
416:         s1    = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3];
417:         while (nz--) {
418:           idx  = 4*(*vi++);
419:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx];
420:           s1  -= v[0]*x1 + v[4]*x2 + v[8]*x3  + v[12]*x4;
421:           s2  -= v[1]*x1 + v[5]*x2 + v[9]*x3  + v[13]*x4;
422:           s3  -= v[2]*x1 + v[6]*x2 + v[10]*x3 + v[14]*x4;
423:           s4  -= v[3]*x1 + v[7]*x2 + v[11]*x3 + v[15]*x4;
424:           v   += 16;
425:         }
426:         x[i2]   = idiag[0]*s1 + idiag[4]*s2 + idiag[8]*s3  + idiag[12]*s4;
427:         x[i2+1] = idiag[1]*s1 + idiag[5]*s2 + idiag[9]*s3  + idiag[13]*s4;
428:         x[i2+2] = idiag[2]*s1 + idiag[6]*s2 + idiag[10]*s3 + idiag[14]*s4;
429:         x[i2+3] = idiag[3]*s1 + idiag[7]*s2 + idiag[11]*s3 + idiag[15]*s4;
430:         idiag   -= 16;
431:         i2      -= 4;
432:       }
433:       PetscLogFlops(16*(a->nz));
434:     }
435:   } else {
436:     SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
437:   }
438:   VecRestoreArray(xx,&x);
439:   VecRestoreArray(bb,(PetscScalar**)&b);
440:   return(0);
441: }

445: PetscErrorCode MatPBRelax_SeqBAIJ_5(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
446: {
447:   Mat_SeqBAIJ        *a = (Mat_SeqBAIJ*)A->data;
448:   PetscScalar        *x,x1,x2,x3,x4,x5,s1,s2,s3,s4,s5;
449:   const PetscScalar  *v,*aa = a->a, *b, *idiag,*mdiag;
450:   PetscErrorCode     ierr;
451:   PetscInt           m = a->mbs,i,i2,nz,idx;
452:   const PetscInt     *diag,*ai = a->i,*aj = a->j,*vi;

455:   its = its*lits;
456:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
457:   if (fshift) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
458:   if (omega != 1.0) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
459:   if ((flag & SOR_EISENSTAT) ||(flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER) ) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for Eisenstat trick");
460:   if (its > 1) SETERRQ(PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");

462:   if (!a->idiagvalid){MatInvertBlockDiagonal_SeqBAIJ(A);}

464:   diag  = a->diag;
465:   idiag = a->idiag;
466:   VecGetArray(xx,&x);
467:   VecGetArray(bb,(PetscScalar**)&b);

469:   if (flag & SOR_ZERO_INITIAL_GUESS) {
470:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
471:       x[0] = b[0]*idiag[0] + b[1]*idiag[5] + b[2]*idiag[10] + b[3]*idiag[15] + b[4]*idiag[20];
472:       x[1] = b[0]*idiag[1] + b[1]*idiag[6] + b[2]*idiag[11] + b[3]*idiag[16] + b[4]*idiag[21];
473:       x[2] = b[0]*idiag[2] + b[1]*idiag[7] + b[2]*idiag[12] + b[3]*idiag[17] + b[4]*idiag[22];
474:       x[3] = b[0]*idiag[3] + b[1]*idiag[8] + b[2]*idiag[13] + b[3]*idiag[18] + b[4]*idiag[23];
475:       x[4] = b[0]*idiag[4] + b[1]*idiag[9] + b[2]*idiag[14] + b[3]*idiag[19] + b[4]*idiag[24];
476:       i2     = 5;
477:       idiag += 25;
478:       for (i=1; i<m; i++) {
479:         v     = aa + 25*ai[i];
480:         vi    = aj + ai[i];
481:         nz    = diag[i] - ai[i];
482:         s1    = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3]; s5 = b[i2+4];
483:         while (nz--) {
484:           idx  = 5*(*vi++);
485:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx];
486:           s1  -= v[0]*x1 + v[5]*x2 + v[10]*x3 + v[15]*x4 + v[20]*x5;
487:           s2  -= v[1]*x1 + v[6]*x2 + v[11]*x3 + v[16]*x4 + v[21]*x5;
488:           s3  -= v[2]*x1 + v[7]*x2 + v[12]*x3 + v[17]*x4 + v[22]*x5;
489:           s4  -= v[3]*x1 + v[8]*x2 + v[13]*x3 + v[18]*x4 + v[23]*x5;
490:           s5  -= v[4]*x1 + v[9]*x2 + v[14]*x3 + v[19]*x4 + v[24]*x5;
491:           v   += 25;
492:         }
493:         x[i2]   = idiag[0]*s1 + idiag[5]*s2 + idiag[10]*s3 + idiag[15]*s4 + idiag[20]*s5;
494:         x[i2+1] = idiag[1]*s1 + idiag[6]*s2 + idiag[11]*s3 + idiag[16]*s4 + idiag[21]*s5;
495:         x[i2+2] = idiag[2]*s1 + idiag[7]*s2 + idiag[12]*s3 + idiag[17]*s4 + idiag[22]*s5;
496:         x[i2+3] = idiag[3]*s1 + idiag[8]*s2 + idiag[13]*s3 + idiag[18]*s4 + idiag[23]*s5;
497:         x[i2+4] = idiag[4]*s1 + idiag[9]*s2 + idiag[14]*s3 + idiag[19]*s4 + idiag[24]*s5;
498:         idiag   += 25;
499:         i2      += 5;
500:       }
501:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
502:       PetscLogFlops(25*(a->nz));
503:     }
504:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
505:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
506:       i2    = 0;
507:       mdiag = a->idiag+25*a->mbs;
508:       for (i=0; i<m; i++) {
509:         x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4];
510:         x[i2]   = mdiag[0]*x1 + mdiag[5]*x2 + mdiag[10]*x3 + mdiag[15]*x4 + mdiag[20]*x5;
511:         x[i2+1] = mdiag[1]*x1 + mdiag[6]*x2 + mdiag[11]*x3 + mdiag[16]*x4 + mdiag[21]*x5;
512:         x[i2+2] = mdiag[2]*x1 + mdiag[7]*x2 + mdiag[12]*x3 + mdiag[17]*x4 + mdiag[22]*x5;
513:         x[i2+3] = mdiag[3]*x1 + mdiag[8]*x2 + mdiag[13]*x3 + mdiag[18]*x4 + mdiag[23]*x5;
514:         x[i2+4] = mdiag[4]*x1 + mdiag[9]*x2 + mdiag[14]*x3 + mdiag[19]*x4 + mdiag[24]*x5;
515:         mdiag  += 25;
516:         i2     += 5;
517:       }
518:       PetscLogFlops(45*m);
519:     } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
520:       PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
521:     }
522:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
523:       idiag   = a->idiag+25*a->mbs - 25;
524:       i2      = 5*m - 5;
525:       x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4];
526:       x[i2]   = idiag[0]*x1 + idiag[5]*x2 + idiag[10]*x3 + idiag[15]*x4 + idiag[20]*x5;
527:       x[i2+1] = idiag[1]*x1 + idiag[6]*x2 + idiag[11]*x3 + idiag[16]*x4 + idiag[21]*x5;
528:       x[i2+2] = idiag[2]*x1 + idiag[7]*x2 + idiag[12]*x3 + idiag[17]*x4 + idiag[22]*x5;
529:       x[i2+3] = idiag[3]*x1 + idiag[8]*x2 + idiag[13]*x3 + idiag[18]*x4 + idiag[23]*x5;
530:       x[i2+4] = idiag[4]*x1 + idiag[9]*x2 + idiag[14]*x3 + idiag[19]*x4 + idiag[24]*x5;
531:       idiag -= 25;
532:       i2    -= 5;
533:       for (i=m-2; i>=0; i--) {
534:         v     = aa + 25*(diag[i]+1);
535:         vi    = aj + diag[i] + 1;
536:         nz    = ai[i+1] - diag[i] - 1;
537:         s1    = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3]; s5 = x[i2+4];
538:         while (nz--) {
539:           idx  = 5*(*vi++);
540:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx];
541:           s1  -= v[0]*x1 + v[5]*x2 + v[10]*x3 + v[15]*x4 + v[20]*x5;
542:           s2  -= v[1]*x1 + v[6]*x2 + v[11]*x3 + v[16]*x4 + v[21]*x5;
543:           s3  -= v[2]*x1 + v[7]*x2 + v[12]*x3 + v[17]*x4 + v[22]*x5;
544:           s4  -= v[3]*x1 + v[8]*x2 + v[13]*x3 + v[18]*x4 + v[23]*x5;
545:           s5  -= v[4]*x1 + v[9]*x2 + v[14]*x3 + v[19]*x4 + v[24]*x5;
546:           v   += 25;
547:         }
548:         x[i2]   = idiag[0]*s1 + idiag[5]*s2 + idiag[10]*s3 + idiag[15]*s4 + idiag[20]*s5;
549:         x[i2+1] = idiag[1]*s1 + idiag[6]*s2 + idiag[11]*s3 + idiag[16]*s4 + idiag[21]*s5;
550:         x[i2+2] = idiag[2]*s1 + idiag[7]*s2 + idiag[12]*s3 + idiag[17]*s4 + idiag[22]*s5;
551:         x[i2+3] = idiag[3]*s1 + idiag[8]*s2 + idiag[13]*s3 + idiag[18]*s4 + idiag[23]*s5;
552:         x[i2+4] = idiag[4]*s1 + idiag[9]*s2 + idiag[14]*s3 + idiag[19]*s4 + idiag[24]*s5;
553:         idiag   -= 25;
554:         i2      -= 5;
555:       }
556:       PetscLogFlops(25*(a->nz));
557:     }
558:   } else {
559:     SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
560:   }
561:   VecRestoreArray(xx,&x);
562:   VecRestoreArray(bb,(PetscScalar**)&b);
563:   return(0);
564: }

566: /*
567:     Special version for direct calls from Fortran (Used in PETSc-fun3d)
568: */
569: #if defined(PETSC_HAVE_FORTRAN_CAPS)
570: #define matsetvaluesblocked4_ MATSETVALUESBLOCKED4
571: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
572: #define matsetvaluesblocked4_ matsetvaluesblocked4
573: #endif

578: void PETSCMAT_DLLEXPORT matsetvaluesblocked4_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[])
579: {
580:   Mat               A = *AA;
581:   Mat_SeqBAIJ       *a = (Mat_SeqBAIJ*)A->data;
582:   PetscInt          *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,N,m = *mm,n = *nn;
583:   PetscInt          *ai=a->i,*ailen=a->ilen;
584:   PetscInt          *aj=a->j,stepval,lastcol = -1;
585:   const PetscScalar *value = v;
586:   MatScalar         *ap,*aa = a->a,*bap;

589:   if (A->rmap.bs != 4) SETERRABORT(A->comm,PETSC_ERR_ARG_WRONG,"Can only be called with a block size of 4");
590:   stepval = (n-1)*4;
591:   for (k=0; k<m; k++) { /* loop over added rows */
592:     row  = im[k];
593:     rp   = aj + ai[row];
594:     ap   = aa + 16*ai[row];
595:     nrow = ailen[row];
596:     low  = 0;
597:     high = nrow;
598:     for (l=0; l<n; l++) { /* loop over added columns */
599:       col = in[l];
600:       if (col <= lastcol) low = 0; else high = nrow;
601:       lastcol = col;
602:       value = v + k*(stepval+4)*4 + l*4;
603:       while (high-low > 7) {
604:         t = (low+high)/2;
605:         if (rp[t] > col) high = t;
606:         else             low  = t;
607:       }
608:       for (i=low; i<high; i++) {
609:         if (rp[i] > col) break;
610:         if (rp[i] == col) {
611:           bap  = ap +  16*i;
612:           for (ii=0; ii<4; ii++,value+=stepval) {
613:             for (jj=ii; jj<16; jj+=4) {
614:               bap[jj] += *value++;
615:             }
616:           }
617:           goto noinsert2;
618:         }
619:       }
620:       N = nrow++ - 1;
621:       high++; /* added new column index thus must search to one higher than before */
622:       /* shift up all the later entries in this row */
623:       for (ii=N; ii>=i; ii--) {
624:         rp[ii+1] = rp[ii];
625:         PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
626:       }
627:       if (N >= i) {
628:         PetscMemzero(ap+16*i,16*sizeof(MatScalar));
629:       }
630:       rp[i] = col;
631:       bap   = ap +  16*i;
632:       for (ii=0; ii<4; ii++,value+=stepval) {
633:         for (jj=ii; jj<16; jj+=4) {
634:           bap[jj] = *value++;
635:         }
636:       }
637:       noinsert2:;
638:       low = i;
639:     }
640:     ailen[row] = nrow;
641:   }
642:   PetscFunctionReturnVoid();
643: }

646: #if defined(PETSC_HAVE_FORTRAN_CAPS)
647: #define matsetvalues4_ MATSETVALUES4
648: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
649: #define matsetvalues4_ matsetvalues4
650: #endif

655: void PETSCMAT_DLLEXPORT matsetvalues4_(Mat *AA,PetscInt *mm,PetscInt *im,PetscInt *nn,PetscInt *in,PetscScalar *v)
656: {
657:   Mat         A = *AA;
658:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
659:   PetscInt    *rp,k,low,high,t,ii,row,nrow,i,col,l,N,n = *nn,m = *mm;
660:   PetscInt    *ai=a->i,*ailen=a->ilen;
661:   PetscInt    *aj=a->j,brow,bcol;
662:   PetscInt    ridx,cidx,lastcol = -1;
663:   MatScalar   *ap,value,*aa=a->a,*bap;
664: 
666:   for (k=0; k<m; k++) { /* loop over added rows */
667:     row  = im[k]; brow = row/4;
668:     rp   = aj + ai[brow];
669:     ap   = aa + 16*ai[brow];
670:     nrow = ailen[brow];
671:     low  = 0;
672:     high = nrow;
673:     for (l=0; l<n; l++) { /* loop over added columns */
674:       col = in[l]; bcol = col/4;
675:       ridx = row % 4; cidx = col % 4;
676:       value = v[l + k*n];
677:       if (col <= lastcol) low = 0; else high = nrow;
678:       lastcol = col;
679:       while (high-low > 7) {
680:         t = (low+high)/2;
681:         if (rp[t] > bcol) high = t;
682:         else              low  = t;
683:       }
684:       for (i=low; i<high; i++) {
685:         if (rp[i] > bcol) break;
686:         if (rp[i] == bcol) {
687:           bap  = ap +  16*i + 4*cidx + ridx;
688:           *bap += value;
689:           goto noinsert1;
690:         }
691:       }
692:       N = nrow++ - 1;
693:       high++; /* added new column thus must search to one higher than before */
694:       /* shift up all the later entries in this row */
695:       for (ii=N; ii>=i; ii--) {
696:         rp[ii+1] = rp[ii];
697:         PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
698:       }
699:       if (N>=i) {
700:         PetscMemzero(ap+16*i,16*sizeof(MatScalar));
701:       }
702:       rp[i]                    = bcol;
703:       ap[16*i + 4*cidx + ridx] = value;
704:       noinsert1:;
705:       low = i;
706:     }
707:     ailen[brow] = nrow;
708:   }
709:   PetscFunctionReturnVoid();
710: }

713: /*  UGLY, ugly, ugly
714:    When MatScalar == PetscScalar the function MatSetValuesBlocked_SeqBAIJ_MatScalar() does 
715:    not exist. Otherwise ..._MatScalar() takes matrix dlements in single precision and 
716:    inserts them into the single precision data structure. The function MatSetValuesBlocked_SeqBAIJ()
717:    converts the entries into single precision and then calls ..._MatScalar() to put them
718:    into the single precision data structures.
719: */
720: #if defined(PETSC_USE_MAT_SINGLE)
721: EXTERN PetscErrorCode MatSetValuesBlocked_SeqBAIJ_MatScalar(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const MatScalar[],InsertMode);
722: #else
723: #define MatSetValuesBlocked_SeqBAIJ_MatScalar MatSetValuesBlocked_SeqBAIJ
724: #endif

726: #define CHUNKSIZE  10

728: /*
729:      Checks for missing diagonals
730: */
733: PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A)
734: {
735:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
737:   PetscInt       *diag,*jj = a->j,i;

740:   MatMarkDiagonal_SeqBAIJ(A);
741:   diag = a->diag;
742:   for (i=0; i<a->mbs; i++) {
743:     if (jj[diag[i]] != i) {
744:       SETERRQ1(PETSC_ERR_PLIB,"Matrix is missing diagonal number %D",i);
745:     }
746:   }
747:   return(0);
748: }

752: PetscErrorCode MatMarkDiagonal_SeqBAIJ(Mat A)
753: {
754:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
756:   PetscInt       i,j,*diag,m = a->mbs;

759:   if (a->diag) return(0);

761:   PetscMalloc((m+1)*sizeof(PetscInt),&diag);
762:   PetscLogObjectMemory(A,(m+1)*sizeof(PetscInt));
763:   for (i=0; i<m; i++) {
764:     diag[i] = a->i[i+1];
765:     for (j=a->i[i]; j<a->i[i+1]; j++) {
766:       if (a->j[j] == i) {
767:         diag[i] = j;
768:         break;
769:       }
770:     }
771:   }
772:   a->diag = diag;
773:   return(0);
774: }


777: EXTERN PetscErrorCode MatToSymmetricIJ_SeqAIJ(PetscInt,PetscInt*,PetscInt*,PetscInt,PetscInt,PetscInt**,PetscInt**);

781: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
782: {
783:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
785:   PetscInt       n = a->mbs,i;

788:   *nn = n;
789:   if (!ia) return(0);
790:   if (symmetric) {
791:     MatToSymmetricIJ_SeqAIJ(n,a->i,a->j,0,oshift,ia,ja);
792:   } else if (oshift == 1) {
793:     /* temporarily add 1 to i and j indices */
794:     PetscInt nz = a->i[n];
795:     for (i=0; i<nz; i++) a->j[i]++;
796:     for (i=0; i<n+1; i++) a->i[i]++;
797:     *ia = a->i; *ja = a->j;
798:   } else {
799:     *ia = a->i; *ja = a->j;
800:   }

802:   return(0);
803: }

807: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
808: {
809:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
811:   PetscInt       i,n = a->mbs;

814:   if (!ia) return(0);
815:   if (symmetric) {
816:     PetscFree(*ia);
817:     PetscFree(*ja);
818:   } else if (oshift == 1) {
819:     PetscInt nz = a->i[n]-1;
820:     for (i=0; i<nz; i++) a->j[i]--;
821:     for (i=0; i<n+1; i++) a->i[i]--;
822:   }
823:   return(0);
824: }

828: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
829: {
830:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;

834: #if defined(PETSC_USE_LOG)
835:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap.N,A->cmap.n,a->nz);
836: #endif
837:   MatSeqXAIJFreeAIJ(a->singlemalloc,&a->a,&a->j,&a->i);
838:   if (a->row) {
839:     ISDestroy(a->row);
840:   }
841:   if (a->col) {
842:     ISDestroy(a->col);
843:   }
844:   PetscFree(a->diag);
845:   PetscFree(a->idiag);
846:   PetscFree2(a->imax,a->ilen);
847:   PetscFree(a->solve_work);
848:   PetscFree(a->mult_work);
849:   if (a->icol) {ISDestroy(a->icol);}
850:   PetscFree(a->saved_values);
851: #if defined(PETSC_USE_MAT_SINGLE)
852:   PetscFree(a->setvaluescopy);
853: #endif
854:   PetscFree(a->xtoy);
855:   if (a->compressedrow.use){PetscFree(a->compressedrow.i);}

857:   PetscFree(a);

859:   PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJInvertBlockDiagonal_C","",PETSC_NULL);
860:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
861:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
862:   PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetColumnIndices_C","",PETSC_NULL);
863:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqaij_C","",PETSC_NULL);
864:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqsbaij_C","",PETSC_NULL);
865:   PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetPreallocation_C","",PETSC_NULL);
866:   return(0);
867: }

871: PetscErrorCode MatSetOption_SeqBAIJ(Mat A,MatOption op)
872: {
873:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;

877:   switch (op) {
878:   case MAT_ROW_ORIENTED:
879:     a->roworiented    = PETSC_TRUE;
880:     break;
881:   case MAT_COLUMN_ORIENTED:
882:     a->roworiented    = PETSC_FALSE;
883:     break;
884:   case MAT_COLUMNS_SORTED:
885:     a->sorted         = PETSC_TRUE;
886:     break;
887:   case MAT_COLUMNS_UNSORTED:
888:     a->sorted         = PETSC_FALSE;
889:     break;
890:   case MAT_KEEP_ZEROED_ROWS:
891:     a->keepzeroedrows = PETSC_TRUE;
892:     break;
893:   case MAT_NO_NEW_NONZERO_LOCATIONS:
894:     a->nonew          = 1;
895:     break;
896:   case MAT_NEW_NONZERO_LOCATION_ERR:
897:     a->nonew          = -1;
898:     break;
899:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
900:     a->nonew          = -2;
901:     break;
902:   case MAT_YES_NEW_NONZERO_LOCATIONS:
903:     a->nonew          = 0;
904:     break;
905:   case MAT_ROWS_SORTED:
906:   case MAT_ROWS_UNSORTED:
907:   case MAT_YES_NEW_DIAGONALS:
908:   case MAT_IGNORE_OFF_PROC_ENTRIES:
909:   case MAT_USE_HASH_TABLE:
910:     PetscInfo(A,"Option ignored\n");
911:     break;
912:   case MAT_NO_NEW_DIAGONALS:
913:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
914:   case MAT_SYMMETRIC:
915:   case MAT_STRUCTURALLY_SYMMETRIC:
916:   case MAT_NOT_SYMMETRIC:
917:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
918:   case MAT_HERMITIAN:
919:   case MAT_NOT_HERMITIAN:
920:   case MAT_SYMMETRY_ETERNAL:
921:   case MAT_NOT_SYMMETRY_ETERNAL:
922:     break;
923:   default:
924:     SETERRQ(PETSC_ERR_SUP,"unknown option");
925:   }
926:   return(0);
927: }

931: PetscErrorCode MatGetRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
932: {
933:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
935:   PetscInt       itmp,i,j,k,M,*ai,*aj,bs,bn,bp,*idx_i,bs2;
936:   MatScalar      *aa,*aa_i;
937:   PetscScalar    *v_i;

940:   bs  = A->rmap.bs;
941:   ai  = a->i;
942:   aj  = a->j;
943:   aa  = a->a;
944:   bs2 = a->bs2;
945: 
946:   if (row < 0 || row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range", row);
947: 
948:   bn  = row/bs;   /* Block number */
949:   bp  = row % bs; /* Block Position */
950:   M   = ai[bn+1] - ai[bn];
951:   *nz = bs*M;
952: 
953:   if (v) {
954:     *v = 0;
955:     if (*nz) {
956:       PetscMalloc((*nz)*sizeof(PetscScalar),v);
957:       for (i=0; i<M; i++) { /* for each block in the block row */
958:         v_i  = *v + i*bs;
959:         aa_i = aa + bs2*(ai[bn] + i);
960:         for (j=bp,k=0; j<bs2; j+=bs,k++) {v_i[k] = aa_i[j];}
961:       }
962:     }
963:   }

965:   if (idx) {
966:     *idx = 0;
967:     if (*nz) {
968:       PetscMalloc((*nz)*sizeof(PetscInt),idx);
969:       for (i=0; i<M; i++) { /* for each block in the block row */
970:         idx_i = *idx + i*bs;
971:         itmp  = bs*aj[ai[bn] + i];
972:         for (j=0; j<bs; j++) {idx_i[j] = itmp++;}
973:       }
974:     }
975:   }
976:   return(0);
977: }

981: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
982: {

986:   if (idx) {PetscFree(*idx);}
987:   if (v)   {PetscFree(*v);}
988:   return(0);
989: }

993: PetscErrorCode MatTranspose_SeqBAIJ(Mat A,Mat *B)
994: {
995:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ *)A->data;
996:   Mat            C;
998:   PetscInt       i,j,k,*aj=a->j,*ai=a->i,bs=A->rmap.bs,mbs=a->mbs,nbs=a->nbs,len,*col;
999:   PetscInt       *rows,*cols,bs2=a->bs2;
1000:   PetscScalar    *array;

1003:   if (!B && mbs!=nbs) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Square matrix only for in-place");
1004:   PetscMalloc((1+nbs)*sizeof(PetscInt),&col);
1005:   PetscMemzero(col,(1+nbs)*sizeof(PetscInt));

1007: #if defined(PETSC_USE_MAT_SINGLE)
1008:   PetscMalloc(a->bs2*a->nz*sizeof(PetscScalar),&array);
1009:   for (i=0; i<a->bs2*a->nz; i++) array[i] = (PetscScalar)a->a[i];
1010: #else
1011:   array = a->a;
1012: #endif

1014:   for (i=0; i<ai[mbs]; i++) col[aj[i]] += 1;
1015:   MatCreate(A->comm,&C);
1016:   MatSetSizes(C,A->cmap.n,A->rmap.N,A->cmap.n,A->rmap.N);
1017:   MatSetType(C,A->type_name);
1018:   MatSeqBAIJSetPreallocation_SeqBAIJ(C,bs,PETSC_NULL,col);
1019:   PetscFree(col);
1020:   PetscMalloc(2*bs*sizeof(PetscInt),&rows);
1021:   cols = rows + bs;
1022:   for (i=0; i<mbs; i++) {
1023:     cols[0] = i*bs;
1024:     for (k=1; k<bs; k++) cols[k] = cols[k-1] + 1;
1025:     len = ai[i+1] - ai[i];
1026:     for (j=0; j<len; j++) {
1027:       rows[0] = (*aj++)*bs;
1028:       for (k=1; k<bs; k++) rows[k] = rows[k-1] + 1;
1029:       MatSetValues(C,bs,rows,bs,cols,array,INSERT_VALUES);
1030:       array += bs2;
1031:     }
1032:   }
1033:   PetscFree(rows);
1034: #if defined(PETSC_USE_MAT_SINGLE)
1035:   PetscFree(array);
1036: #endif
1037: 
1038:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1039:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1040: 
1041:   if (B) {
1042:     *B = C;
1043:   } else {
1044:     MatHeaderCopy(A,C);
1045:   }
1046:   return(0);
1047: }

1051: static PetscErrorCode MatView_SeqBAIJ_Binary(Mat A,PetscViewer viewer)
1052: {
1053:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
1055:   PetscInt       i,*col_lens,bs = A->rmap.bs,count,*jj,j,k,l,bs2=a->bs2;
1056:   int            fd;
1057:   PetscScalar    *aa;
1058:   FILE           *file;

1061:   PetscViewerBinaryGetDescriptor(viewer,&fd);
1062:   PetscMalloc((4+A->rmap.N)*sizeof(PetscInt),&col_lens);
1063:   col_lens[0] = MAT_FILE_COOKIE;

1065:   col_lens[1] = A->rmap.N;
1066:   col_lens[2] = A->cmap.n;
1067:   col_lens[3] = a->nz*bs2;

1069:   /* store lengths of each row and write (including header) to file */
1070:   count = 0;
1071:   for (i=0; i<a->mbs; i++) {
1072:     for (j=0; j<bs; j++) {
1073:       col_lens[4+count++] = bs*(a->i[i+1] - a->i[i]);
1074:     }
1075:   }
1076:   PetscBinaryWrite(fd,col_lens,4+A->rmap.N,PETSC_INT,PETSC_TRUE);
1077:   PetscFree(col_lens);

1079:   /* store column indices (zero start index) */
1080:   PetscMalloc((a->nz+1)*bs2*sizeof(PetscInt),&jj);
1081:   count = 0;
1082:   for (i=0; i<a->mbs; i++) {
1083:     for (j=0; j<bs; j++) {
1084:       for (k=a->i[i]; k<a->i[i+1]; k++) {
1085:         for (l=0; l<bs; l++) {
1086:           jj[count++] = bs*a->j[k] + l;
1087:         }
1088:       }
1089:     }
1090:   }
1091:   PetscBinaryWrite(fd,jj,bs2*a->nz,PETSC_INT,PETSC_FALSE);
1092:   PetscFree(jj);

1094:   /* store nonzero values */
1095:   PetscMalloc((a->nz+1)*bs2*sizeof(PetscScalar),&aa);
1096:   count = 0;
1097:   for (i=0; i<a->mbs; i++) {
1098:     for (j=0; j<bs; j++) {
1099:       for (k=a->i[i]; k<a->i[i+1]; k++) {
1100:         for (l=0; l<bs; l++) {
1101:           aa[count++] = a->a[bs2*k + l*bs + j];
1102:         }
1103:       }
1104:     }
1105:   }
1106:   PetscBinaryWrite(fd,aa,bs2*a->nz,PETSC_SCALAR,PETSC_FALSE);
1107:   PetscFree(aa);

1109:   PetscViewerBinaryGetInfoPointer(viewer,&file);
1110:   if (file) {
1111:     fprintf(file,"-matload_block_size %d\n",(int)A->rmap.bs);
1112:   }
1113:   return(0);
1114: }

1118: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A,PetscViewer viewer)
1119: {
1120:   Mat_SeqBAIJ       *a = (Mat_SeqBAIJ*)A->data;
1121:   PetscErrorCode    ierr;
1122:   PetscInt          i,j,bs = A->rmap.bs,k,l,bs2=a->bs2;
1123:   PetscViewerFormat format;

1126:   PetscViewerGetFormat(viewer,&format);
1127:   if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1128:     PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
1129:   } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1130:     Mat aij;
1131:     MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&aij);
1132:     MatView(aij,viewer);
1133:     MatDestroy(aij);
1134:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1135:      return(0);
1136:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1137:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
1138:     for (i=0; i<a->mbs; i++) {
1139:       for (j=0; j<bs; j++) {
1140:         PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1141:         for (k=a->i[i]; k<a->i[i+1]; k++) {
1142:           for (l=0; l<bs; l++) {
1143: #if defined(PETSC_USE_COMPLEX)
1144:             if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1145:               PetscViewerASCIIPrintf(viewer," (%D, %G + %Gi) ",bs*a->j[k]+l,
1146:                       PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1147:             } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1148:               PetscViewerASCIIPrintf(viewer," (%D, %G - %Gi) ",bs*a->j[k]+l,
1149:                       PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1150:             } else if (PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1151:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1152:             }
1153: #else
1154:             if (a->a[bs2*k + l*bs + j] != 0.0) {
1155:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1156:             }
1157: #endif
1158:           }
1159:         }
1160:         PetscViewerASCIIPrintf(viewer,"\n");
1161:       }
1162:     }
1163:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
1164:   } else {
1165:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
1166:     for (i=0; i<a->mbs; i++) {
1167:       for (j=0; j<bs; j++) {
1168:         PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1169:         for (k=a->i[i]; k<a->i[i+1]; k++) {
1170:           for (l=0; l<bs; l++) {
1171: #if defined(PETSC_USE_COMPLEX)
1172:             if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0) {
1173:               PetscViewerASCIIPrintf(viewer," (%D, %G + %G i) ",bs*a->j[k]+l,
1174:                 PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1175:             } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0) {
1176:               PetscViewerASCIIPrintf(viewer," (%D, %G - %G i) ",bs*a->j[k]+l,
1177:                 PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1178:             } else {
1179:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1180:             }
1181: #else
1182:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1183: #endif
1184:           }
1185:         }
1186:         PetscViewerASCIIPrintf(viewer,"\n");
1187:       }
1188:     }
1189:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
1190:   }
1191:   PetscViewerFlush(viewer);
1192:   return(0);
1193: }

1197: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1198: {
1199:   Mat            A = (Mat) Aa;
1200:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ*)A->data;
1202:   PetscInt       row,i,j,k,l,mbs=a->mbs,color,bs=A->rmap.bs,bs2=a->bs2;
1203:   PetscReal      xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1204:   MatScalar      *aa;
1205:   PetscViewer    viewer;


1209:   /* still need to add support for contour plot of nonzeros; see MatView_SeqAIJ_Draw_Zoom()*/
1210:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);

1212:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

1214:   /* loop over matrix elements drawing boxes */
1215:   color = PETSC_DRAW_BLUE;
1216:   for (i=0,row=0; i<mbs; i++,row+=bs) {
1217:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1218:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1219:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
1220:       aa = a->a + j*bs2;
1221:       for (k=0; k<bs; k++) {
1222:         for (l=0; l<bs; l++) {
1223:           if (PetscRealPart(*aa++) >=  0.) continue;
1224:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1225:         }
1226:       }
1227:     }
1228:   }
1229:   color = PETSC_DRAW_CYAN;
1230:   for (i=0,row=0; i<mbs; i++,row+=bs) {
1231:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1232:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1233:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
1234:       aa = a->a + j*bs2;
1235:       for (k=0; k<bs; k++) {
1236:         for (l=0; l<bs; l++) {
1237:           if (PetscRealPart(*aa++) != 0.) continue;
1238:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1239:         }
1240:       }
1241:     }
1242:   }

1244:   color = PETSC_DRAW_RED;
1245:   for (i=0,row=0; i<mbs; i++,row+=bs) {
1246:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1247:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1248:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
1249:       aa = a->a + j*bs2;
1250:       for (k=0; k<bs; k++) {
1251:         for (l=0; l<bs; l++) {
1252:           if (PetscRealPart(*aa++) <= 0.) continue;
1253:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1254:         }
1255:       }
1256:     }
1257:   }
1258:   return(0);
1259: }

1263: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A,PetscViewer viewer)
1264: {
1266:   PetscReal      xl,yl,xr,yr,w,h;
1267:   PetscDraw      draw;
1268:   PetscTruth     isnull;


1272:   PetscViewerDrawGetDraw(viewer,0,&draw);
1273:   PetscDrawIsNull(draw,&isnull); if (isnull) return(0);

1275:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1276:   xr  = A->cmap.n; yr = A->rmap.N; h = yr/10.0; w = xr/10.0;
1277:   xr += w;    yr += h;  xl = -w;     yl = -h;
1278:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1279:   PetscDrawZoom(draw,MatView_SeqBAIJ_Draw_Zoom,A);
1280:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
1281:   return(0);
1282: }

1286: PetscErrorCode MatView_SeqBAIJ(Mat A,PetscViewer viewer)
1287: {
1289:   PetscTruth     iascii,isbinary,isdraw;

1292:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1293:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1294:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1295:   if (iascii){
1296:     MatView_SeqBAIJ_ASCII(A,viewer);
1297:   } else if (isbinary) {
1298:     MatView_SeqBAIJ_Binary(A,viewer);
1299:   } else if (isdraw) {
1300:     MatView_SeqBAIJ_Draw(A,viewer);
1301:   } else {
1302:     Mat B;
1303:     MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&B);
1304:     MatView(B,viewer);
1305:     MatDestroy(B);
1306:   }
1307:   return(0);
1308: }


1313: PetscErrorCode MatGetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
1314: {
1315:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1316:   PetscInt    *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
1317:   PetscInt    *ai = a->i,*ailen = a->ilen;
1318:   PetscInt    brow,bcol,ridx,cidx,bs=A->rmap.bs,bs2=a->bs2;
1319:   MatScalar   *ap,*aa = a->a,zero = 0.0;

1322:   for (k=0; k<m; k++) { /* loop over rows */
1323:     row  = im[k]; brow = row/bs;
1324:     if (row < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
1325:     if (row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D too large", row);
1326:     rp   = aj + ai[brow] ; ap = aa + bs2*ai[brow] ;
1327:     nrow = ailen[brow];
1328:     for (l=0; l<n; l++) { /* loop over columns */
1329:       if (in[l] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column");
1330:       if (in[l] >= A->cmap.n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Column %D too large", in[l]);
1331:       col  = in[l] ;
1332:       bcol = col/bs;
1333:       cidx = col%bs;
1334:       ridx = row%bs;
1335:       high = nrow;
1336:       low  = 0; /* assume unsorted */
1337:       while (high-low > 5) {
1338:         t = (low+high)/2;
1339:         if (rp[t] > bcol) high = t;
1340:         else             low  = t;
1341:       }
1342:       for (i=low; i<high; i++) {
1343:         if (rp[i] > bcol) break;
1344:         if (rp[i] == bcol) {
1345:           *v++ = ap[bs2*i+bs*cidx+ridx];
1346:           goto finished;
1347:         }
1348:       }
1349:       *v++ = zero;
1350:       finished:;
1351:     }
1352:   }
1353:   return(0);
1354: }

1356: #if defined(PETSC_USE_MAT_SINGLE)
1359: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
1360: {
1361:   Mat_SeqBAIJ    *b = (Mat_SeqBAIJ*)mat->data;
1363:   PetscInt       i,N = m*n*b->bs2;
1364:   MatScalar      *vsingle;

1367:   if (N > b->setvalueslen) {
1368:     PetscFree(b->setvaluescopy);
1369:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
1370:     b->setvalueslen  = N;
1371:   }
1372:   vsingle = b->setvaluescopy;
1373:   for (i=0; i<N; i++) {
1374:     vsingle[i] = v[i];
1375:   }
1376:   MatSetValuesBlocked_SeqBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
1377:   return(0);
1378: }
1379: #endif


1384: PetscErrorCode MatSetValuesBlocked_SeqBAIJ_MatScalar(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode is)
1385: {
1386:   Mat_SeqBAIJ     *a = (Mat_SeqBAIJ*)A->data;
1387:   PetscInt        *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,rmax,N,lastcol = -1;
1388:   PetscInt        *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1389:   PetscErrorCode  ierr;
1390:   PetscInt        *aj=a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap.bs,stepval;
1391:   PetscTruth      roworiented=a->roworiented;
1392:   const MatScalar *value = v;
1393:   MatScalar       *ap,*aa = a->a,*bap;

1396:   if (roworiented) {
1397:     stepval = (n-1)*bs;
1398:   } else {
1399:     stepval = (m-1)*bs;
1400:   }
1401:   for (k=0; k<m; k++) { /* loop over added rows */
1402:     row  = im[k];
1403:     if (row < 0) continue;
1404: #if defined(PETSC_USE_DEBUG)  
1405:     if (row >= a->mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,a->mbs-1);
1406: #endif
1407:     rp   = aj + ai[row];
1408:     ap   = aa + bs2*ai[row];
1409:     rmax = imax[row];
1410:     nrow = ailen[row];
1411:     low  = 0;
1412:     high = nrow;
1413:     for (l=0; l<n; l++) { /* loop over added columns */
1414:       if (in[l] < 0) continue;
1415: #if defined(PETSC_USE_DEBUG)  
1416:       if (in[l] >= a->nbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],a->nbs-1);
1417: #endif
1418:       col = in[l];
1419:       if (roworiented) {
1420:         value = v + k*(stepval+bs)*bs + l*bs;
1421:       } else {
1422:         value = v + l*(stepval+bs)*bs + k*bs;
1423:       }
1424:       if (col <= lastcol) low = 0; else high = nrow;
1425:       lastcol = col;
1426:       while (high-low > 7) {
1427:         t = (low+high)/2;
1428:         if (rp[t] > col) high = t;
1429:         else             low  = t;
1430:       }
1431:       for (i=low; i<high; i++) {
1432:         if (rp[i] > col) break;
1433:         if (rp[i] == col) {
1434:           bap  = ap +  bs2*i;
1435:           if (roworiented) {
1436:             if (is == ADD_VALUES) {
1437:               for (ii=0; ii<bs; ii++,value+=stepval) {
1438:                 for (jj=ii; jj<bs2; jj+=bs) {
1439:                   bap[jj] += *value++;
1440:                 }
1441:               }
1442:             } else {
1443:               for (ii=0; ii<bs; ii++,value+=stepval) {
1444:                 for (jj=ii; jj<bs2; jj+=bs) {
1445:                   bap[jj] = *value++;
1446:                 }
1447:               }
1448:             }
1449:           } else {
1450:             if (is == ADD_VALUES) {
1451:               for (ii=0; ii<bs; ii++,value+=stepval) {
1452:                 for (jj=0; jj<bs; jj++) {
1453:                   *bap++ += *value++;
1454:                 }
1455:               }
1456:             } else {
1457:               for (ii=0; ii<bs; ii++,value+=stepval) {
1458:                 for (jj=0; jj<bs; jj++) {
1459:                   *bap++  = *value++;
1460:                 }
1461:               }
1462:             }
1463:           }
1464:           goto noinsert2;
1465:         }
1466:       }
1467:       if (nonew == 1) goto noinsert2;
1468:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1469:       MatSeqXAIJReallocateAIJ(a,bs2,nrow,row,col,rmax,aa,ai,aj,a->mbs,rp,ap,imax,nonew);
1470:       N = nrow++ - 1; high++;
1471:       /* shift up all the later entries in this row */
1472:       for (ii=N; ii>=i; ii--) {
1473:         rp[ii+1] = rp[ii];
1474:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1475:       }
1476:       if (N >= i) {
1477:         PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1478:       }
1479:       rp[i] = col;
1480:       bap   = ap +  bs2*i;
1481:       if (roworiented) {
1482:         for (ii=0; ii<bs; ii++,value+=stepval) {
1483:           for (jj=ii; jj<bs2; jj+=bs) {
1484:             bap[jj] = *value++;
1485:           }
1486:         }
1487:       } else {
1488:         for (ii=0; ii<bs; ii++,value+=stepval) {
1489:           for (jj=0; jj<bs; jj++) {
1490:             *bap++  = *value++;
1491:           }
1492:         }
1493:       }
1494:       noinsert2:;
1495:       low = i;
1496:     }
1497:     ailen[row] = nrow;
1498:   }
1499:   return(0);
1500: }

1504: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A,MatAssemblyType mode)
1505: {
1506:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
1507:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
1508:   PetscInt       m = A->rmap.N,*ip,N,*ailen = a->ilen;
1510:   PetscInt       mbs = a->mbs,bs2 = a->bs2,rmax = 0;
1511:   MatScalar      *aa = a->a,*ap;
1512:   PetscReal      ratio=0.6;

1515:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);

1517:   if (m) rmax = ailen[0];
1518:   for (i=1; i<mbs; i++) {
1519:     /* move each row back by the amount of empty slots (fshift) before it*/
1520:     fshift += imax[i-1] - ailen[i-1];
1521:     rmax   = PetscMax(rmax,ailen[i]);
1522:     if (fshift) {
1523:       ip = aj + ai[i]; ap = aa + bs2*ai[i];
1524:       N = ailen[i];
1525:       for (j=0; j<N; j++) {
1526:         ip[j-fshift] = ip[j];
1527:         PetscMemcpy(ap+(j-fshift)*bs2,ap+j*bs2,bs2*sizeof(MatScalar));
1528:       }
1529:     }
1530:     ai[i] = ai[i-1] + ailen[i-1];
1531:   }
1532:   if (mbs) {
1533:     fshift += imax[mbs-1] - ailen[mbs-1];
1534:     ai[mbs] = ai[mbs-1] + ailen[mbs-1];
1535:   }
1536:   /* reset ilen and imax for each row */
1537:   for (i=0; i<mbs; i++) {
1538:     ailen[i] = imax[i] = ai[i+1] - ai[i];
1539:   }
1540:   a->nz = ai[mbs];

1542:   /* diagonals may have moved, so kill the diagonal pointers */
1543:   a->idiagvalid = PETSC_FALSE;
1544:   if (fshift && a->diag) {
1545:     PetscFree(a->diag);
1546:     PetscLogObjectMemory(A,-(mbs+1)*sizeof(PetscInt));
1547:     a->diag = 0;
1548:   }
1549:   PetscInfo5(A,"Matrix size: %D X %D, block size %D; storage space: %D unneeded, %D used\n",m,A->cmap.n,A->rmap.bs,fshift*bs2,a->nz*bs2);
1550:   PetscInfo1(A,"Number of mallocs during MatSetValues is %D\n",a->reallocs);
1551:   PetscInfo1(A,"Most nonzeros blocks in any row is %D\n",rmax);
1552:   a->reallocs          = 0;
1553:   A->info.nz_unneeded  = (PetscReal)fshift*bs2;

1555:   /* check for zero rows. If found a large number of zero rows, use CompressedRow functions */
1556:   if (a->compressedrow.use){
1557:     Mat_CheckCompressedRow(A,&a->compressedrow,a->i,mbs,ratio);
1558:   }

1560:   A->same_nonzero = PETSC_TRUE;
1561:   return(0);
1562: }

1564: /* 
1565:    This function returns an array of flags which indicate the locations of contiguous
1566:    blocks that should be zeroed. for eg: if bs = 3  and is = [0,1,2,3,5,6,7,8,9]
1567:    then the resulting sizes = [3,1,1,3,1] correspondig to sets [(0,1,2),(3),(5),(6,7,8),(9)]
1568:    Assume: sizes should be long enough to hold all the values.
1569: */
1572: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[],PetscInt n,PetscInt bs,PetscInt sizes[], PetscInt *bs_max)
1573: {
1574:   PetscInt   i,j,k,row;
1575:   PetscTruth flg;

1578:   for (i=0,j=0; i<n; j++) {
1579:     row = idx[i];
1580:     if (row%bs!=0) { /* Not the begining of a block */
1581:       sizes[j] = 1;
1582:       i++;
1583:     } else if (i+bs > n) { /* complete block doesn't exist (at idx end) */
1584:       sizes[j] = 1;         /* Also makes sure atleast 'bs' values exist for next else */
1585:       i++;
1586:     } else { /* Begining of the block, so check if the complete block exists */
1587:       flg = PETSC_TRUE;
1588:       for (k=1; k<bs; k++) {
1589:         if (row+k != idx[i+k]) { /* break in the block */
1590:           flg = PETSC_FALSE;
1591:           break;
1592:         }
1593:       }
1594:       if (flg) { /* No break in the bs */
1595:         sizes[j] = bs;
1596:         i+= bs;
1597:       } else {
1598:         sizes[j] = 1;
1599:         i++;
1600:       }
1601:     }
1602:   }
1603:   *bs_max = j;
1604:   return(0);
1605: }
1606: 
1609: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A,PetscInt is_n,const PetscInt is_idx[],PetscScalar diag)
1610: {
1611:   Mat_SeqBAIJ    *baij=(Mat_SeqBAIJ*)A->data;
1613:   PetscInt       i,j,k,count,*rows;
1614:   PetscInt       bs=A->rmap.bs,bs2=baij->bs2,*sizes,row,bs_max;
1615:   PetscScalar    zero = 0.0;
1616:   MatScalar      *aa;

1619:   /* Make a copy of the IS and  sort it */
1620:   /* allocate memory for rows,sizes */
1621:   PetscMalloc((3*is_n+1)*sizeof(PetscInt),&rows);
1622:   sizes = rows + is_n;

1624:   /* copy IS values to rows, and sort them */
1625:   for (i=0; i<is_n; i++) { rows[i] = is_idx[i]; }
1626:   PetscSortInt(is_n,rows);
1627:   if (baij->keepzeroedrows) {
1628:     for (i=0; i<is_n; i++) { sizes[i] = 1; }
1629:     bs_max = is_n;
1630:     A->same_nonzero = PETSC_TRUE;
1631:   } else {
1632:     MatZeroRows_SeqBAIJ_Check_Blocks(rows,is_n,bs,sizes,&bs_max);
1633:     A->same_nonzero = PETSC_FALSE;
1634:   }

1636:   for (i=0,j=0; i<bs_max; j+=sizes[i],i++) {
1637:     row   = rows[j];
1638:     if (row < 0 || row > A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range",row);
1639:     count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
1640:     aa    = baij->a + baij->i[row/bs]*bs2 + (row%bs);
1641:     if (sizes[i] == bs && !baij->keepzeroedrows) {
1642:       if (diag != 0.0) {
1643:         if (baij->ilen[row/bs] > 0) {
1644:           baij->ilen[row/bs]       = 1;
1645:           baij->j[baij->i[row/bs]] = row/bs;
1646:           PetscMemzero(aa,count*bs*sizeof(MatScalar));
1647:         }
1648:         /* Now insert all the diagonal values for this bs */
1649:         for (k=0; k<bs; k++) {
1650:           (*A->ops->setvalues)(A,1,rows+j+k,1,rows+j+k,&diag,INSERT_VALUES);
1651:         }
1652:       } else { /* (diag == 0.0) */
1653:         baij->ilen[row/bs] = 0;
1654:       } /* end (diag == 0.0) */
1655:     } else { /* (sizes[i] != bs) */
1656: #if defined (PETSC_USE_DEBUG)
1657:       if (sizes[i] != 1) SETERRQ(PETSC_ERR_PLIB,"Internal Error. Value should be 1");
1658: #endif
1659:       for (k=0; k<count; k++) {
1660:         aa[0] =  zero;
1661:         aa    += bs;
1662:       }
1663:       if (diag != 0.0) {
1664:         (*A->ops->setvalues)(A,1,rows+j,1,rows+j,&diag,INSERT_VALUES);
1665:       }
1666:     }
1667:   }

1669:   PetscFree(rows);
1670:   MatAssemblyEnd_SeqBAIJ(A,MAT_FINAL_ASSEMBLY);
1671:   return(0);
1672: }

1676: PetscErrorCode MatSetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
1677: {
1678:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
1679:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,lastcol = -1;
1680:   PetscInt       *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1681:   PetscInt       *aj=a->j,nonew=a->nonew,bs=A->rmap.bs,brow,bcol;
1683:   PetscInt       ridx,cidx,bs2=a->bs2;
1684:   PetscTruth     roworiented=a->roworiented;
1685:   MatScalar      *ap,value,*aa=a->a,*bap;

1688:   for (k=0; k<m; k++) { /* loop over added rows */
1689:     row  = im[k];
1690:     brow = row/bs;
1691:     if (row < 0) continue;
1692: #if defined(PETSC_USE_DEBUG)  
1693:     if (row >= A->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.N-1);
1694: #endif
1695:     rp   = aj + ai[brow];
1696:     ap   = aa + bs2*ai[brow];
1697:     rmax = imax[brow];
1698:     nrow = ailen[brow];
1699:     low  = 0;
1700:     high = nrow;
1701:     for (l=0; l<n; l++) { /* loop over added columns */
1702:       if (in[l] < 0) continue;
1703: #if defined(PETSC_USE_DEBUG)  
1704:       if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
1705: #endif
1706:       col = in[l]; bcol = col/bs;
1707:       ridx = row % bs; cidx = col % bs;
1708:       if (roworiented) {
1709:         value = v[l + k*n];
1710:       } else {
1711:         value = v[k + l*m];
1712:       }
1713:       if (col <= lastcol) low = 0; else high = nrow;
1714:       lastcol = col;
1715:       while (high-low > 7) {
1716:         t = (low+high)/2;
1717:         if (rp[t] > bcol) high = t;
1718:         else              low  = t;
1719:       }
1720:       for (i=low; i<high; i++) {
1721:         if (rp[i] > bcol) break;
1722:         if (rp[i] == bcol) {
1723:           bap  = ap +  bs2*i + bs*cidx + ridx;
1724:           if (is == ADD_VALUES) *bap += value;
1725:           else                  *bap  = value;
1726:           goto noinsert1;
1727:         }
1728:       }
1729:       if (nonew == 1) goto noinsert1;
1730:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1731:       MatSeqXAIJReallocateAIJ(a,bs2,nrow,brow,bcol,rmax,aa,ai,aj,a->mbs,rp,ap,imax,nonew);
1732:       N = nrow++ - 1; high++;
1733:       /* shift up all the later entries in this row */
1734:       for (ii=N; ii>=i; ii--) {
1735:         rp[ii+1] = rp[ii];
1736:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1737:       }
1738:       if (N>=i) {
1739:         PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1740:       }
1741:       rp[i]                      = bcol;
1742:       ap[bs2*i + bs*cidx + ridx] = value;
1743:       a->nz++;
1744:       noinsert1:;
1745:       low = i;
1746:     }
1747:     ailen[brow] = nrow;
1748:   }
1749:   A->same_nonzero = PETSC_FALSE;
1750:   return(0);
1751: }


1756: PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1757: {
1758:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)inA->data;
1759:   Mat            outA;
1761:   PetscTruth     row_identity,col_identity;

1764:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels = 0 supported for in-place ILU");
1765:   ISIdentity(row,&row_identity);
1766:   ISIdentity(col,&col_identity);
1767:   if (!row_identity || !col_identity) {
1768:     SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU");
1769:   }

1771:   outA          = inA;
1772:   inA->factor   = FACTOR_LU;

1774:   if (!a->diag) {
1775:     MatMarkDiagonal_SeqBAIJ(inA);
1776:   }

1778:   a->row        = row;
1779:   a->col        = col;
1780:   PetscObjectReference((PetscObject)row);
1781:   PetscObjectReference((PetscObject)col);
1782: 
1783:   /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
1784:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1785:   PetscLogObjectParent(inA,a->icol);
1786: 
1787:   /*
1788:       Blocksize 2, 3, 4, 5, 6 and 7 have a special faster factorization/solver 
1789:       for ILU(0) factorization with natural ordering
1790:   */
1791:   if (inA->rmap.bs < 8) {
1792:     MatSeqBAIJ_UpdateFactorNumeric_NaturalOrdering(inA);
1793:   } else {
1794:     if (!a->solve_work) {
1795:       PetscMalloc((inA->rmap.N+inA->rmap.bs)*sizeof(PetscScalar),&a->solve_work);
1796:       PetscLogObjectMemory(inA,(inA->rmap.N+inA->rmap.bs)*sizeof(PetscScalar));
1797:     }
1798:   }

1800:   MatLUFactorNumeric(inA,info,&outA);

1802:   return(0);
1803: }
1806: PetscErrorCode MatPrintHelp_SeqBAIJ(Mat A)
1807: {
1808:   static PetscTruth called = PETSC_FALSE;
1809:   MPI_Comm          comm = A->comm;
1810:   PetscErrorCode    ierr;

1813:   if (called) {return(0);} else called = PETSC_TRUE;
1814:   (*PetscHelpPrintf)(comm," Options for MATSEQBAIJ and MATMPIBAIJ matrix formats (the defaults):\n");
1815:   (*PetscHelpPrintf)(comm,"  -mat_block_size <block_size>\n");
1816:   return(0);
1817: }

1822: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat,PetscInt *indices)
1823: {
1824:   Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data;
1825:   PetscInt    i,nz,nbs;

1828:   nz  = baij->maxnz/baij->bs2;
1829:   nbs = baij->nbs;
1830:   for (i=0; i<nz; i++) {
1831:     baij->j[i] = indices[i];
1832:   }
1833:   baij->nz = nz;
1834:   for (i=0; i<nbs; i++) {
1835:     baij->ilen[i] = baij->imax[i];
1836:   }

1838:   return(0);
1839: }

1844: /*@
1845:     MatSeqBAIJSetColumnIndices - Set the column indices for all the rows
1846:        in the matrix.

1848:   Input Parameters:
1849: +  mat - the SeqBAIJ matrix
1850: -  indices - the column indices

1852:   Level: advanced

1854:   Notes:
1855:     This can be called if you have precomputed the nonzero structure of the 
1856:   matrix and want to provide it to the matrix object to improve the performance
1857:   of the MatSetValues() operation.

1859:     You MUST have set the correct numbers of nonzeros per row in the call to 
1860:   MatCreateSeqBAIJ(), and the columns indices MUST be sorted.

1862:     MUST be called before any calls to MatSetValues();

1864: @*/
1865: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJSetColumnIndices(Mat mat,PetscInt *indices)
1866: {
1867:   PetscErrorCode ierr,(*f)(Mat,PetscInt *);

1872:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqBAIJSetColumnIndices_C",(void (**)(void))&f);
1873:   if (f) {
1874:     (*f)(mat,indices);
1875:   } else {
1876:     SETERRQ(PETSC_ERR_ARG_WRONG,"Wrong type of matrix to set column indices");
1877:   }
1878:   return(0);
1879: }

1883: PetscErrorCode MatGetRowMax_SeqBAIJ(Mat A,Vec v)
1884: {
1885:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
1887:   PetscInt       i,j,n,row,bs,*ai,*aj,mbs;
1888:   PetscReal      atmp;
1889:   PetscScalar    *x,zero = 0.0;
1890:   MatScalar      *aa;
1891:   PetscInt       ncols,brow,krow,kcol;

1894:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1895:   bs   = A->rmap.bs;
1896:   aa   = a->a;
1897:   ai   = a->i;
1898:   aj   = a->j;
1899:   mbs = a->mbs;

1901:   VecSet(v,zero);
1902:   VecGetArray(v,&x);
1903:   VecGetLocalSize(v,&n);
1904:   if (n != A->rmap.N) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1905:   for (i=0; i<mbs; i++) {
1906:     ncols = ai[1] - ai[0]; ai++;
1907:     brow  = bs*i;
1908:     for (j=0; j<ncols; j++){
1909:       /* bcol = bs*(*aj); */
1910:       for (kcol=0; kcol<bs; kcol++){
1911:         for (krow=0; krow<bs; krow++){
1912:           atmp = PetscAbsScalar(*aa); aa++;
1913:           row = brow + krow;    /* row index */
1914:           /* printf("val[%d,%d]: %G\n",row,bcol+kcol,atmp); */
1915:           if (PetscAbsScalar(x[row]) < atmp) x[row] = atmp;
1916:         }
1917:       }
1918:       aj++;
1919:     }
1920:   }
1921:   VecRestoreArray(v,&x);
1922:   return(0);
1923: }

1927: PetscErrorCode MatCopy_SeqBAIJ(Mat A,Mat B,MatStructure str)
1928: {

1932:   /* If the two matrices have the same copy implementation, use fast copy. */
1933:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1934:     Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1935:     Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)B->data;

1937:     if (a->i[A->rmap.N] != b->i[B->rmap.N]) {
1938:       SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1939:     }
1940:     PetscMemcpy(b->a,a->a,(a->i[A->rmap.N])*sizeof(PetscScalar));
1941:   } else {
1942:     MatCopy_Basic(A,B,str);
1943:   }
1944:   return(0);
1945: }

1949: PetscErrorCode MatSetUpPreallocation_SeqBAIJ(Mat A)
1950: {

1954:    MatSeqBAIJSetPreallocation_SeqBAIJ(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0);
1955:   return(0);
1956: }

1960: PetscErrorCode MatGetArray_SeqBAIJ(Mat A,PetscScalar *array[])
1961: {
1962:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1964:   *array = a->a;
1965:   return(0);
1966: }

1970: PetscErrorCode MatRestoreArray_SeqBAIJ(Mat A,PetscScalar *array[])
1971: {
1973:   return(0);
1974: }

1976:  #include petscblaslapack.h
1979: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1980: {
1981:   Mat_SeqBAIJ    *x  = (Mat_SeqBAIJ *)X->data,*y = (Mat_SeqBAIJ *)Y->data;
1983:   PetscInt       i,bs=Y->rmap.bs,j,bs2;
1984:   PetscBLASInt   one=1,bnz = (PetscBLASInt)x->nz;

1987:   if (str == SAME_NONZERO_PATTERN) {
1988:     PetscScalar alpha = a;
1989:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1990:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1991:     if (y->xtoy && y->XtoY != X) {
1992:       PetscFree(y->xtoy);
1993:       MatDestroy(y->XtoY);
1994:     }
1995:     if (!y->xtoy) { /* get xtoy */
1996:       MatAXPYGetxtoy_Private(x->mbs,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
1997:       y->XtoY = X;
1998:     }
1999:     bs2 = bs*bs;
2000:     for (i=0; i<x->nz; i++) {
2001:       j = 0;
2002:       while (j < bs2){
2003:         y->a[bs2*y->xtoy[i]+j] += a*(x->a[bs2*i+j]);
2004:         j++;
2005:       }
2006:     }
2007:     PetscInfo3(0,"ratio of nnz(X)/nnz(Y): %D/%D = %G\n",bs2*x->nz,bs2*y->nz,(PetscReal)(bs2*x->nz)/(bs2*y->nz));
2008:   } else {
2009:     MatAXPY_Basic(Y,a,X,str);
2010:   }
2011:   return(0);
2012: }

2016: PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
2017: {
2018:   Mat_SeqBAIJ     *a = (Mat_SeqBAIJ*)A->data;
2019:   PetscInt       i,nz = a->bs2*a->i[a->mbs];
2020:   PetscScalar    *aa = a->a;

2023:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
2024:   return(0);
2025: }

2029: PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2030: {
2031:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
2032:   PetscInt       i,nz = a->bs2*a->i[a->mbs];
2033:   PetscScalar    *aa = a->a;

2036:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2037:   return(0);
2038: }


2041: /* -------------------------------------------------------------------*/
2042: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
2043:        MatGetRow_SeqBAIJ,
2044:        MatRestoreRow_SeqBAIJ,
2045:        MatMult_SeqBAIJ_N,
2046: /* 4*/ MatMultAdd_SeqBAIJ_N,
2047:        MatMultTranspose_SeqBAIJ,
2048:        MatMultTransposeAdd_SeqBAIJ,
2049:        MatSolve_SeqBAIJ_N,
2050:        0,
2051:        0,
2052: /*10*/ 0,
2053:        MatLUFactor_SeqBAIJ,
2054:        0,
2055:        0,
2056:        MatTranspose_SeqBAIJ,
2057: /*15*/ MatGetInfo_SeqBAIJ,
2058:        MatEqual_SeqBAIJ,
2059:        MatGetDiagonal_SeqBAIJ,
2060:        MatDiagonalScale_SeqBAIJ,
2061:        MatNorm_SeqBAIJ,
2062: /*20*/ 0,
2063:        MatAssemblyEnd_SeqBAIJ,
2064:        0,
2065:        MatSetOption_SeqBAIJ,
2066:        MatZeroEntries_SeqBAIJ,
2067: /*25*/ MatZeroRows_SeqBAIJ,
2068:        MatLUFactorSymbolic_SeqBAIJ,
2069:        MatLUFactorNumeric_SeqBAIJ_N,
2070:        MatCholeskyFactorSymbolic_SeqBAIJ,
2071:        MatCholeskyFactorNumeric_SeqBAIJ_N,
2072: /*30*/ MatSetUpPreallocation_SeqBAIJ,
2073:        MatILUFactorSymbolic_SeqBAIJ,
2074:        MatICCFactorSymbolic_SeqBAIJ,
2075:        MatGetArray_SeqBAIJ,
2076:        MatRestoreArray_SeqBAIJ,
2077: /*35*/ MatDuplicate_SeqBAIJ,
2078:        0,
2079:        0,
2080:        MatILUFactor_SeqBAIJ,
2081:        0,
2082: /*40*/ MatAXPY_SeqBAIJ,
2083:        MatGetSubMatrices_SeqBAIJ,
2084:        MatIncreaseOverlap_SeqBAIJ,
2085:        MatGetValues_SeqBAIJ,
2086:        MatCopy_SeqBAIJ,
2087: /*45*/ MatPrintHelp_SeqBAIJ,
2088:        MatScale_SeqBAIJ,
2089:        0,
2090:        0,
2091:        0,
2092: /*50*/ 0,
2093:        MatGetRowIJ_SeqBAIJ,
2094:        MatRestoreRowIJ_SeqBAIJ,
2095:        0,
2096:        0,
2097: /*55*/ 0,
2098:        0,
2099:        0,
2100:        0,
2101:        MatSetValuesBlocked_SeqBAIJ,
2102: /*60*/ MatGetSubMatrix_SeqBAIJ,
2103:        MatDestroy_SeqBAIJ,
2104:        MatView_SeqBAIJ,
2105:        0,
2106:        0,
2107: /*65*/ 0,
2108:        0,
2109:        0,
2110:        0,
2111:        0,
2112: /*70*/ MatGetRowMax_SeqBAIJ,
2113:        MatConvert_Basic,
2114:        0,
2115:        0,
2116:        0,
2117: /*75*/ 0,
2118:        0,
2119:        0,
2120:        0,
2121:        0,
2122: /*80*/ 0,
2123:        0,
2124:        0,
2125:        0,
2126:        MatLoad_SeqBAIJ,
2127: /*85*/ 0,
2128:        0,
2129:        0,
2130:        0,
2131:        0,
2132: /*90*/ 0,
2133:        0,
2134:        0,
2135:        0,
2136:        0,
2137: /*95*/ 0,
2138:        0,
2139:        0,
2140:        0,
2141:        0,
2142: /*100*/0,
2143:        0,
2144:        0,
2145:        0,
2146:        0,
2147: /*105*/0,
2148:        MatRealPart_SeqBAIJ,
2149:        MatImaginaryPart_SeqBAIJ
2150: };

2155: PetscErrorCode PETSCMAT_DLLEXPORT MatStoreValues_SeqBAIJ(Mat mat)
2156: {
2157:   Mat_SeqBAIJ    *aij = (Mat_SeqBAIJ *)mat->data;
2158:   PetscInt       nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;

2162:   if (aij->nonew != 1) {
2163:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2164:   }

2166:   /* allocate space for values if not already there */
2167:   if (!aij->saved_values) {
2168:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2169:   }

2171:   /* copy values over */
2172:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2173:   return(0);
2174: }

2180: PetscErrorCode PETSCMAT_DLLEXPORT MatRetrieveValues_SeqBAIJ(Mat mat)
2181: {
2182:   Mat_SeqBAIJ    *aij = (Mat_SeqBAIJ *)mat->data;
2184:   PetscInt       nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;

2187:   if (aij->nonew != 1) {
2188:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2189:   }
2190:   if (!aij->saved_values) {
2191:     SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2192:   }

2194:   /* copy values over */
2195:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2196:   return(0);
2197: }


2208: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B,PetscInt bs,PetscInt nz,PetscInt *nnz)
2209: {
2210:   Mat_SeqBAIJ    *b;
2212:   PetscInt       i,mbs,nbs,bs2,newbs = bs;
2213:   PetscTruth     flg,skipallocation = PETSC_FALSE;


2217:   if (nz == MAT_SKIP_ALLOCATION) {
2218:     skipallocation = PETSC_TRUE;
2219:     nz             = 0;
2220:   }
2221:   PetscOptionsGetInt(B->prefix,"-mat_block_size",&newbs,PETSC_NULL);
2222:   if (nnz && newbs != bs) {
2223:     SETERRQ(PETSC_ERR_ARG_WRONG,"Cannot change blocksize from command line if setting nnz");
2224:   }
2225:   bs   = newbs;

2227:   B->rmap.bs = B->cmap.bs = bs;
2228:   PetscMapInitialize(B->comm,&B->rmap);
2229:   PetscMapInitialize(B->comm,&B->cmap);

2231:   B->preallocated = PETSC_TRUE;

2233:   mbs  = B->rmap.n/bs;
2234:   nbs  = B->cmap.n/bs;
2235:   bs2  = bs*bs;

2237:   if (mbs*bs!=B->rmap.n || nbs*bs!=B->cmap.n) {
2238:     SETERRQ3(PETSC_ERR_ARG_SIZ,"Number rows %D, cols %D must be divisible by blocksize %D",B->rmap.N,B->cmap.n,bs);
2239:   }

2241:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2242:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
2243:   if (nnz) {
2244:     for (i=0; i<mbs; i++) {
2245:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
2246:       if (nnz[i] > nbs) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than block row length: local row %D value %D rowlength %D",i,nnz[i],nbs);
2247:     }
2248:   }

2250:   b       = (Mat_SeqBAIJ*)B->data;
2251:   PetscOptionsHasName(PETSC_NULL,"-mat_no_unroll",&flg);
2252:   B->ops->solve               = MatSolve_SeqBAIJ_Update;
2253:   B->ops->solvetranspose      = MatSolveTranspose_SeqBAIJ_Update;
2254:   if (!flg) {
2255:     switch (bs) {
2256:     case 1:
2257:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_1;
2258:       B->ops->mult            = MatMult_SeqBAIJ_1;
2259:       B->ops->multadd         = MatMultAdd_SeqBAIJ_1;
2260:       break;
2261:     case 2:
2262:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_2;
2263:       B->ops->mult            = MatMult_SeqBAIJ_2;
2264:       B->ops->multadd         = MatMultAdd_SeqBAIJ_2;
2265:       B->ops->pbrelax         = MatPBRelax_SeqBAIJ_2;
2266:       break;
2267:     case 3:
2268:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_3;
2269:       B->ops->mult            = MatMult_SeqBAIJ_3;
2270:       B->ops->multadd         = MatMultAdd_SeqBAIJ_3;
2271:       B->ops->pbrelax         = MatPBRelax_SeqBAIJ_3;
2272:       break;
2273:     case 4:
2274:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_4;
2275:       B->ops->mult            = MatMult_SeqBAIJ_4;
2276:       B->ops->multadd         = MatMultAdd_SeqBAIJ_4;
2277:       B->ops->pbrelax         = MatPBRelax_SeqBAIJ_4;
2278:       break;
2279:     case 5:
2280:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_5;
2281:       B->ops->mult            = MatMult_SeqBAIJ_5;
2282:       B->ops->multadd         = MatMultAdd_SeqBAIJ_5;
2283:       B->ops->pbrelax         = MatPBRelax_SeqBAIJ_5;
2284:       break;
2285:     case 6:
2286:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_6;
2287:       B->ops->mult            = MatMult_SeqBAIJ_6;
2288:       B->ops->multadd         = MatMultAdd_SeqBAIJ_6;
2289:       break;
2290:     case 7:
2291:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_7;
2292:       B->ops->mult            = MatMult_SeqBAIJ_7;
2293:       B->ops->multadd         = MatMultAdd_SeqBAIJ_7;
2294:       break;
2295:     default:
2296:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_N;
2297:       B->ops->mult            = MatMult_SeqBAIJ_N;
2298:       B->ops->multadd         = MatMultAdd_SeqBAIJ_N;
2299:       break;
2300:     }
2301:   }
2302:   B->rmap.bs      = bs;
2303:   b->mbs     = mbs;
2304:   b->nbs     = nbs;
2305:   if (!skipallocation) {
2306:     PetscMalloc2(mbs,PetscInt,&b->imax,mbs,PetscInt,&b->ilen);
2307:     /* b->ilen will count nonzeros in each block row so far. */
2308:     for (i=0; i<mbs; i++) { b->ilen[i] = 0;}
2309:     if (!nnz) {
2310:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2311:       else if (nz <= 0)        nz = 1;
2312:       for (i=0; i<mbs; i++) b->imax[i] = nz;
2313:       nz = nz*mbs;
2314:     } else {
2315:       nz = 0;
2316:       for (i=0; i<mbs; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2317:     }

2319:     /* allocate the matrix space */
2320:     PetscMalloc3(bs2*nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap.N+1,PetscInt,&b->i);
2321:     PetscMemzero(b->a,nz*bs2*sizeof(MatScalar));
2322:     PetscMemzero(b->j,nz*sizeof(PetscInt));
2323:     b->singlemalloc = PETSC_TRUE;

2325:     b->i[0] = 0;
2326:     for (i=1; i<mbs+1; i++) {
2327:       b->i[i] = b->i[i-1] + b->imax[i-1];
2328:     }
2329:   }

2331:   B->rmap.bs          = bs;
2332:   b->bs2              = bs2;
2333:   b->mbs              = mbs;
2334:   b->nz               = 0;
2335:   b->maxnz            = nz*bs2;
2336:   B->info.nz_unneeded = (PetscReal)b->maxnz;
2337:   return(0);
2338: }

2341: /*MC
2342:    MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on 
2343:    block sparse compressed row format.

2345:    Options Database Keys:
2346: . -mat_type seqbaij - sets the matrix type to "seqbaij" during a call to MatSetFromOptions()

2348:   Level: beginner

2350: .seealso: MatCreateSeqBAIJ()
2351: M*/

2356: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SeqBAIJ(Mat B)
2357: {
2359:   PetscMPIInt    size;
2360:   Mat_SeqBAIJ    *b;

2363:   MPI_Comm_size(B->comm,&size);
2364:   if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"Comm must be of size 1");

2366:   PetscNew(Mat_SeqBAIJ,&b);
2367:   B->data = (void*)b;
2368:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2369:   B->factor           = 0;
2370:   B->mapping          = 0;
2371:   b->row              = 0;
2372:   b->col              = 0;
2373:   b->icol             = 0;
2374:   b->reallocs         = 0;
2375:   b->saved_values     = 0;
2376: #if defined(PETSC_USE_MAT_SINGLE)
2377:   b->setvalueslen     = 0;
2378:   b->setvaluescopy    = PETSC_NULL;
2379: #endif

2381:   b->sorted           = PETSC_FALSE;
2382:   b->roworiented      = PETSC_TRUE;
2383:   b->nonew            = 0;
2384:   b->diag             = 0;
2385:   b->solve_work       = 0;
2386:   b->mult_work        = 0;
2387:   B->spptr            = 0;
2388:   B->info.nz_unneeded = (PetscReal)b->maxnz;
2389:   b->keepzeroedrows   = PETSC_FALSE;
2390:   b->xtoy              = 0;
2391:   b->XtoY              = 0;
2392:   b->compressedrow.use     = PETSC_FALSE;
2393:   b->compressedrow.nrows   = 0;
2394:   b->compressedrow.i       = PETSC_NULL;
2395:   b->compressedrow.rindex  = PETSC_NULL;
2396:   b->compressedrow.checked = PETSC_FALSE;
2397:   B->same_nonzero          = PETSC_FALSE;

2399:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJInvertBlockDiagonal_C",
2400:                                      "MatInvertBlockDiagonal_SeqBAIJ",
2401:                                       MatInvertBlockDiagonal_SeqBAIJ);
2402:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2403:                                      "MatStoreValues_SeqBAIJ",
2404:                                       MatStoreValues_SeqBAIJ);
2405:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2406:                                      "MatRetrieveValues_SeqBAIJ",
2407:                                       MatRetrieveValues_SeqBAIJ);
2408:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetColumnIndices_C",
2409:                                      "MatSeqBAIJSetColumnIndices_SeqBAIJ",
2410:                                       MatSeqBAIJSetColumnIndices_SeqBAIJ);
2411:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqaij_C",
2412:                                      "MatConvert_SeqBAIJ_SeqAIJ",
2413:                                       MatConvert_SeqBAIJ_SeqAIJ);
2414:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqsbaij_C",
2415:                                      "MatConvert_SeqBAIJ_SeqSBAIJ",
2416:                                       MatConvert_SeqBAIJ_SeqSBAIJ);
2417:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetPreallocation_C",
2418:                                      "MatSeqBAIJSetPreallocation_SeqBAIJ",
2419:                                       MatSeqBAIJSetPreallocation_SeqBAIJ);
2420:   return(0);
2421: }

2426: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2427: {
2428:   Mat            C;
2429:   Mat_SeqBAIJ    *c,*a = (Mat_SeqBAIJ*)A->data;
2431:   PetscInt       i,mbs = a->mbs,nz = a->nz,bs2 = a->bs2;

2434:   if (a->i[mbs] != nz) SETERRQ(PETSC_ERR_PLIB,"Corrupt matrix");

2436:   *B = 0;
2437:   MatCreate(A->comm,&C);
2438:   MatSetSizes(C,A->rmap.N,A->cmap.n,A->rmap.N,A->cmap.n);
2439:   MatSetType(C,A->type_name);
2440:   PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
2441:   c    = (Mat_SeqBAIJ*)C->data;

2443:   C->rmap.N   = A->rmap.N;
2444:   C->cmap.N   = A->cmap.N;
2445:   C->rmap.bs  = A->rmap.bs;
2446:   c->bs2 = a->bs2;
2447:   c->mbs = a->mbs;
2448:   c->nbs = a->nbs;

2450:   PetscMalloc2(mbs,PetscInt,&c->imax,mbs,PetscInt,&c->ilen);
2451:   for (i=0; i<mbs; i++) {
2452:     c->imax[i] = a->imax[i];
2453:     c->ilen[i] = a->ilen[i];
2454:   }

2456:   /* allocate the matrix space */
2457:   PetscMalloc3(bs2*nz,PetscScalar,&c->a,nz,PetscInt,&c->j,mbs+1,PetscInt,&c->i);
2458:   c->singlemalloc = PETSC_TRUE;
2459:   PetscMemcpy(c->i,a->i,(mbs+1)*sizeof(PetscInt));
2460:   if (mbs > 0) {
2461:     PetscMemcpy(c->j,a->j,nz*sizeof(PetscInt));
2462:     if (cpvalues == MAT_COPY_VALUES) {
2463:       PetscMemcpy(c->a,a->a,bs2*nz*sizeof(MatScalar));
2464:     } else {
2465:       PetscMemzero(c->a,bs2*nz*sizeof(MatScalar));
2466:     }
2467:   }
2468:   c->sorted      = a->sorted;
2469:   c->roworiented = a->roworiented;
2470:   c->nonew       = a->nonew;

2472:   if (a->diag) {
2473:     PetscMalloc((mbs+1)*sizeof(PetscInt),&c->diag);
2474:     PetscLogObjectMemory(C,(mbs+1)*sizeof(PetscInt));
2475:     for (i=0; i<mbs; i++) {
2476:       c->diag[i] = a->diag[i];
2477:     }
2478:   } else c->diag        = 0;
2479:   c->nz                 = a->nz;
2480:   c->maxnz              = a->maxnz;
2481:   c->solve_work         = 0;
2482:   c->mult_work          = 0;
2483:   C->preallocated       = PETSC_TRUE;
2484:   C->assembled          = PETSC_TRUE;

2486:   c->compressedrow.use     = a->compressedrow.use;
2487:   c->compressedrow.nrows   = a->compressedrow.nrows;
2488:   c->compressedrow.checked = a->compressedrow.checked;
2489:   if ( a->compressedrow.checked && a->compressedrow.use){
2490:     i = a->compressedrow.nrows;
2491:     PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
2492:     c->compressedrow.rindex = c->compressedrow.i + i + 1;
2493:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
2494:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
2495:   } else {
2496:     c->compressedrow.use    = PETSC_FALSE;
2497:     c->compressedrow.i      = PETSC_NULL;
2498:     c->compressedrow.rindex = PETSC_NULL;
2499:   }
2500:   C->same_nonzero = A->same_nonzero;
2501:   *B = C;
2502:   PetscFListDuplicate(A->qlist,&C->qlist);
2503:   return(0);
2504: }

2508: PetscErrorCode MatLoad_SeqBAIJ(PetscViewer viewer, MatType type,Mat *A)
2509: {
2510:   Mat_SeqBAIJ    *a;
2511:   Mat            B;
2513:   PetscInt       i,nz,header[4],*rowlengths=0,M,N,bs=1;
2514:   PetscInt       *mask,mbs,*jj,j,rowcount,nzcount,k,*browlengths,maskcount;
2515:   PetscInt       kmax,jcount,block,idx,point,nzcountb,extra_rows;
2516:   PetscInt       *masked,nmask,tmp,bs2,ishift;
2517:   PetscMPIInt    size;
2518:   int            fd;
2519:   PetscScalar    *aa;
2520:   MPI_Comm       comm = ((PetscObject)viewer)->comm;

2523:   PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);
2524:   bs2  = bs*bs;

2526:   MPI_Comm_size(comm,&size);
2527:   if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"view must have one processor");
2528:   PetscViewerBinaryGetDescriptor(viewer,&fd);
2529:   PetscBinaryRead(fd,header,4,PETSC_INT);
2530:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not Mat object");
2531:   M = header[1]; N = header[2]; nz = header[3];

2533:   if (header[3] < 0) {
2534:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as SeqBAIJ");
2535:   }

2537:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2539:   /* 
2540:      This code adds extra rows to make sure the number of rows is 
2541:     divisible by the blocksize
2542:   */
2543:   mbs        = M/bs;
2544:   extra_rows = bs - M + bs*(mbs);
2545:   if (extra_rows == bs) extra_rows = 0;
2546:   else                  mbs++;
2547:   if (extra_rows) {
2548:     PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2549:   }

2551:   /* read in row lengths */
2552:   PetscMalloc((M+extra_rows)*sizeof(PetscInt),&rowlengths);
2553:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2554:   for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;

2556:   /* read in column indices */
2557:   PetscMalloc((nz+extra_rows)*sizeof(PetscInt),&jj);
2558:   PetscBinaryRead(fd,jj,nz,PETSC_INT);
2559:   for (i=0; i<extra_rows; i++) jj[nz+i] = M+i;

2561:   /* loop over row lengths determining block row lengths */
2562:   PetscMalloc(mbs*sizeof(PetscInt),&browlengths);
2563:   PetscMemzero(browlengths,mbs*sizeof(PetscInt));
2564:   PetscMalloc(2*mbs*sizeof(PetscInt),&mask);
2565:   PetscMemzero(mask,mbs*sizeof(PetscInt));
2566:   masked   = mask + mbs;
2567:   rowcount = 0; nzcount = 0;
2568:   for (i=0; i<mbs; i++) {
2569:     nmask = 0;
2570:     for (j=0; j<bs; j++) {
2571:       kmax = rowlengths[rowcount];
2572:       for (k=0; k<kmax; k++) {
2573:         tmp = jj[nzcount++]/bs;
2574:         if (!mask[tmp]) {masked[nmask++] = tmp; mask[tmp] = 1;}
2575:       }
2576:       rowcount++;
2577:     }
2578:     browlengths[i] += nmask;
2579:     /* zero out the mask elements we set */
2580:     for (j=0; j<nmask; j++) mask[masked[j]] = 0;
2581:   }

2583:   /* create our matrix */
2584:   MatCreate(comm,&B);
2585:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M+extra_rows,N+extra_rows);
2586:   MatSetType(B,type);
2587:   MatSeqBAIJSetPreallocation_SeqBAIJ(B,bs,0,browlengths);
2588:   a = (Mat_SeqBAIJ*)B->data;

2590:   /* set matrix "i" values */
2591:   a->i[0] = 0;
2592:   for (i=1; i<= mbs; i++) {
2593:     a->i[i]      = a->i[i-1] + browlengths[i-1];
2594:     a->ilen[i-1] = browlengths[i-1];
2595:   }
2596:   a->nz         = 0;
2597:   for (i=0; i<mbs; i++) a->nz += browlengths[i];

2599:   /* read in nonzero values */
2600:   PetscMalloc((nz+extra_rows)*sizeof(PetscScalar),&aa);
2601:   PetscBinaryRead(fd,aa,nz,PETSC_SCALAR);
2602:   for (i=0; i<extra_rows; i++) aa[nz+i] = 1.0;

2604:   /* set "a" and "j" values into matrix */
2605:   nzcount = 0; jcount = 0;
2606:   for (i=0; i<mbs; i++) {
2607:     nzcountb = nzcount;
2608:     nmask    = 0;
2609:     for (j=0; j<bs; j++) {
2610:       kmax = rowlengths[i*bs+j];
2611:       for (k=0; k<kmax; k++) {
2612:         tmp = jj[nzcount++]/bs;
2613:         if (!mask[tmp]) { masked[nmask++] = tmp; mask[tmp] = 1;}
2614:       }
2615:     }
2616:     /* sort the masked values */
2617:     PetscSortInt(nmask,masked);

2619:     /* set "j" values into matrix */
2620:     maskcount = 1;
2621:     for (j=0; j<nmask; j++) {
2622:       a->j[jcount++]  = masked[j];
2623:       mask[masked[j]] = maskcount++;
2624:     }
2625:     /* set "a" values into matrix */
2626:     ishift = bs2*a->i[i];
2627:     for (j=0; j<bs; j++) {
2628:       kmax = rowlengths[i*bs+j];
2629:       for (k=0; k<kmax; k++) {
2630:         tmp       = jj[nzcountb]/bs ;
2631:         block     = mask[tmp] - 1;
2632:         point     = jj[nzcountb] - bs*tmp;
2633:         idx       = ishift + bs2*block + j + bs*point;
2634:         a->a[idx] = (MatScalar)aa[nzcountb++];
2635:       }
2636:     }
2637:     /* zero out the mask elements we set */
2638:     for (j=0; j<nmask; j++) mask[masked[j]] = 0;
2639:   }
2640:   if (jcount != a->nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Bad binary matrix");

2642:   PetscFree(rowlengths);
2643:   PetscFree(browlengths);
2644:   PetscFree(aa);
2645:   PetscFree(jj);
2646:   PetscFree(mask);

2648:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2649:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2650:   MatView_Private(B);

2652:   *A = B;
2653:   return(0);
2654: }

2658: /*@C
2659:    MatCreateSeqBAIJ - Creates a sparse matrix in block AIJ (block
2660:    compressed row) format.  For good matrix assembly performance the
2661:    user should preallocate the matrix storage by setting the parameter nz
2662:    (or the array nnz).  By setting these parameters accurately, performance
2663:    during matrix assembly can be increased by more than a factor of 50.

2665:    Collective on MPI_Comm

2667:    Input Parameters:
2668: +  comm - MPI communicator, set to PETSC_COMM_SELF
2669: .  bs - size of block
2670: .  m - number of rows
2671: .  n - number of columns
2672: .  nz - number of nonzero blocks  per block row (same for all rows)
2673: -  nnz - array containing the number of nonzero blocks in the various block rows 
2674:          (possibly different for each block row) or PETSC_NULL

2676:    Output Parameter:
2677: .  A - the matrix 

2679:    Options Database Keys:
2680: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2681:                      block calculations (much slower)
2682: .    -mat_block_size - size of the blocks to use

2684:    Level: intermediate

2686:    Notes:
2687:    The number of rows and columns must be divisible by blocksize.

2689:    If the nnz parameter is given then the nz parameter is ignored

2691:    A nonzero block is any block that as 1 or more nonzeros in it

2693:    The block AIJ format is fully compatible with standard Fortran 77
2694:    storage.  That is, the stored row and column indices can begin at
2695:    either one (as in Fortran) or zero.  See the users' manual for details.

2697:    Specify the preallocated storage with either nz or nnz (not both).
2698:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2699:    allocation.  For additional details, see the users manual chapter on
2700:    matrices.

2702: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2703: @*/
2704: PetscErrorCode PETSCMAT_DLLEXPORT MatCreateSeqBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2705: {
2707: 
2709:   MatCreate(comm,A);
2710:   MatSetSizes(*A,m,n,m,n);
2711:   MatSetType(*A,MATSEQBAIJ);
2712:   MatSeqBAIJSetPreallocation_SeqBAIJ(*A,bs,nz,(PetscInt*)nnz);
2713:   return(0);
2714: }

2718: /*@C
2719:    MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
2720:    per row in the matrix. For good matrix assembly performance the
2721:    user should preallocate the matrix storage by setting the parameter nz
2722:    (or the array nnz).  By setting these parameters accurately, performance
2723:    during matrix assembly can be increased by more than a factor of 50.

2725:    Collective on MPI_Comm

2727:    Input Parameters:
2728: +  A - the matrix
2729: .  bs - size of block
2730: .  nz - number of block nonzeros per block row (same for all rows)
2731: -  nnz - array containing the number of block nonzeros in the various block rows 
2732:          (possibly different for each block row) or PETSC_NULL

2734:    Options Database Keys:
2735: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2736:                      block calculations (much slower)
2737: .    -mat_block_size - size of the blocks to use

2739:    Level: intermediate

2741:    Notes:
2742:    If the nnz parameter is given then the nz parameter is ignored

2744:    The block AIJ format is fully compatible with standard Fortran 77
2745:    storage.  That is, the stored row and column indices can begin at
2746:    either one (as in Fortran) or zero.  See the users' manual for details.

2748:    Specify the preallocated storage with either nz or nnz (not both).
2749:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2750:    allocation.  For additional details, see the users manual chapter on
2751:    matrices.

2753: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2754: @*/
2755: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt nz,const PetscInt nnz[])
2756: {
2757:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[]);

2760:   PetscObjectQueryFunction((PetscObject)B,"MatSeqBAIJSetPreallocation_C",(void (**)(void))&f);
2761:   if (f) {
2762:     (*f)(B,bs,nz,nnz);
2763:   }
2764:   return(0);
2765: }