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

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

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

143:   if (flag & SOR_ZERO_INITIAL_GUESS) {
144:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
145:       x[0] = b[0]*idiag[0] + b[1]*idiag[2];
146:       x[1] = b[0]*idiag[1] + b[1]*idiag[3];
147:       i2     = 2;
148:       idiag += 4;
149:       for (i=1; i<m; i++) {
150:         v     = aa + 4*ai[i];
151:         vi    = aj + ai[i];
152:         nz    = diag[i] - ai[i];
153:         s1    = b[i2]; s2 = b[i2+1];
154:         while (nz--) {
155:           idx  = 2*(*vi++);
156:           x1   = x[idx]; x2 = x[1+idx];
157:           s1  -= v[0]*x1 + v[2]*x2;
158:           s2  -= v[1]*x1 + v[3]*x2;
159:           v   += 4;
160:         }
161:         x[i2]   = idiag[0]*s1 + idiag[2]*s2;
162:         x[i2+1] = idiag[1]*s1 + idiag[3]*s2;
163:         idiag   += 4;
164:         i2      += 2;
165:       }
166:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
167:       PetscLogFlops(4*(a->nz));
168:     }
169:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
170:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
171:       i2    = 0;
172:       mdiag = a->idiag+4*a->mbs;
173:       for (i=0; i<m; i++) {
174:         x1      = x[i2]; x2 = x[i2+1];
175:         x[i2]   = mdiag[0]*x1 + mdiag[2]*x2;
176:         x[i2+1] = mdiag[1]*x1 + mdiag[3]*x2;
177:         mdiag  += 4;
178:         i2     += 2;
179:       }
180:       PetscLogFlops(6*m);
181:     } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
182:       PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
183:     }
184:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
185:       idiag   = a->idiag+4*a->mbs - 4;
186:       i2      = 2*m - 2;
187:       x1      = x[i2]; x2 = x[i2+1];
188:       x[i2]   = idiag[0]*x1 + idiag[2]*x2;
189:       x[i2+1] = idiag[1]*x1 + idiag[3]*x2;
190:       idiag -= 4;
191:       i2    -= 2;
192:       for (i=m-2; i>=0; i--) {
193:         v     = aa + 4*(diag[i]+1);
194:         vi    = aj + diag[i] + 1;
195:         nz    = ai[i+1] - diag[i] - 1;
196:         s1    = x[i2]; s2 = x[i2+1];
197:         while (nz--) {
198:           idx  = 2*(*vi++);
199:           x1   = x[idx]; x2 = x[1+idx];
200:           s1  -= v[0]*x1 + v[2]*x2;
201:           s2  -= v[1]*x1 + v[3]*x2;
202:           v   += 4;
203:         }
204:         x[i2]   = idiag[0]*s1 + idiag[2]*s2;
205:         x[i2+1] = idiag[1]*s1 + idiag[3]*s2;
206:         idiag   -= 4;
207:         i2      -= 2;
208:       }
209:       PetscLogFlops(4*(a->nz));
210:     }
211:   } else {
212:     SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
213:   }
214:   VecRestoreArray(xx,&x);
215:   VecRestoreArray(bb,(PetscScalar**)&b);
216:   return(0);
217: }

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

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

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

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

245:   if (flag & SOR_ZERO_INITIAL_GUESS) {
246:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
247:       x[0] = b[0]*idiag[0] + b[1]*idiag[3] + b[2]*idiag[6];
248:       x[1] = b[0]*idiag[1] + b[1]*idiag[4] + b[2]*idiag[7];
249:       x[2] = b[0]*idiag[2] + b[1]*idiag[5] + b[2]*idiag[8];
250:       i2     = 3;
251:       idiag += 9;
252:       for (i=1; i<m; i++) {
253:         v     = aa + 9*ai[i];
254:         vi    = aj + ai[i];
255:         nz    = diag[i] - ai[i];
256:         s1    = b[i2]; s2 = b[i2+1]; s3 = b[i2+2];
257:         while (nz--) {
258:           idx  = 3*(*vi++);
259:           x1   = x[idx]; x2 = x[1+idx];x3 = x[2+idx];
260:           s1  -= v[0]*x1 + v[3]*x2 + v[6]*x3;
261:           s2  -= v[1]*x1 + v[4]*x2 + v[7]*x3;
262:           s3  -= v[2]*x1 + v[5]*x2 + v[8]*x3;
263:           v   += 9;
264:         }
265:         x[i2]   = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
266:         x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
267:         x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;
268:         idiag   += 9;
269:         i2      += 3;
270:       }
271:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
272:       PetscLogFlops(9*(a->nz));
273:     }
274:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
275:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
276:       i2    = 0;
277:       mdiag = a->idiag+9*a->mbs;
278:       for (i=0; i<m; i++) {
279:         x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
280:         x[i2]   = mdiag[0]*x1 + mdiag[3]*x2 + mdiag[6]*x3;
281:         x[i2+1] = mdiag[1]*x1 + mdiag[4]*x2 + mdiag[7]*x3;
282:         x[i2+2] = mdiag[2]*x1 + mdiag[5]*x2 + mdiag[8]*x3;
283:         mdiag  += 9;
284:         i2     += 3;
285:       }
286:       PetscLogFlops(15*m);
287:     } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
288:       PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
289:     }
290:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
291:       idiag   = a->idiag+9*a->mbs - 9;
292:       i2      = 3*m - 3;
293:       x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
294:       x[i2]   = idiag[0]*x1 + idiag[3]*x2 + idiag[6]*x3;
295:       x[i2+1] = idiag[1]*x1 + idiag[4]*x2 + idiag[7]*x3;
296:       x[i2+2] = idiag[2]*x1 + idiag[5]*x2 + idiag[8]*x3;
297:       idiag -= 9;
298:       i2    -= 3;
299:       for (i=m-2; i>=0; i--) {
300:         v     = aa + 9*(diag[i]+1);
301:         vi    = aj + diag[i] + 1;
302:         nz    = ai[i+1] - diag[i] - 1;
303:         s1    = x[i2]; s2 = x[i2+1]; s3 = x[i2+2];
304:         while (nz--) {
305:           idx  = 3*(*vi++);
306:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx];
307:           s1  -= v[0]*x1 + v[3]*x2 + v[6]*x3;
308:           s2  -= v[1]*x1 + v[4]*x2 + v[7]*x3;
309:           s3  -= v[2]*x1 + v[5]*x2 + v[8]*x3;
310:           v   += 9;
311:         }
312:         x[i2]   = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
313:         x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
314:         x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;
315:         idiag   -= 9;
316:         i2      -= 3;
317:       }
318:       PetscLogFlops(9*(a->nz));
319:     }
320:   } else {
321:     SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
322:   }
323:   VecRestoreArray(xx,&x);
324:   VecRestoreArray(bb,(PetscScalar**)&b);
325:   return(0);
326: }

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

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

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

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

354:   if (flag & SOR_ZERO_INITIAL_GUESS) {
355:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
356:       x[0] = b[0]*idiag[0] + b[1]*idiag[4] + b[2]*idiag[8]  + b[3]*idiag[12];
357:       x[1] = b[0]*idiag[1] + b[1]*idiag[5] + b[2]*idiag[9]  + b[3]*idiag[13];
358:       x[2] = b[0]*idiag[2] + b[1]*idiag[6] + b[2]*idiag[10] + b[3]*idiag[14];
359:       x[3] = b[0]*idiag[3] + b[1]*idiag[7] + b[2]*idiag[11] + b[3]*idiag[15];
360:       i2     = 4;
361:       idiag += 16;
362:       for (i=1; i<m; i++) {
363:         v     = aa + 16*ai[i];
364:         vi    = aj + ai[i];
365:         nz    = diag[i] - ai[i];
366:         s1    = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3];
367:         while (nz--) {
368:           idx  = 4*(*vi++);
369:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx];
370:           s1  -= v[0]*x1 + v[4]*x2 + v[8]*x3  + v[12]*x4;
371:           s2  -= v[1]*x1 + v[5]*x2 + v[9]*x3  + v[13]*x4;
372:           s3  -= v[2]*x1 + v[6]*x2 + v[10]*x3 + v[14]*x4;
373:           s4  -= v[3]*x1 + v[7]*x2 + v[11]*x3 + v[15]*x4;
374:           v   += 16;
375:         }
376:         x[i2]   = idiag[0]*s1 + idiag[4]*s2 + idiag[8]*s3  + idiag[12]*s4;
377:         x[i2+1] = idiag[1]*s1 + idiag[5]*s2 + idiag[9]*s3  + idiag[13]*s4;
378:         x[i2+2] = idiag[2]*s1 + idiag[6]*s2 + idiag[10]*s3 + idiag[14]*s4;
379:         x[i2+3] = idiag[3]*s1 + idiag[7]*s2 + idiag[11]*s3 + idiag[15]*s4;
380:         idiag   += 16;
381:         i2      += 4;
382:       }
383:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
384:       PetscLogFlops(16*(a->nz));
385:     }
386:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
387:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
388:       i2    = 0;
389:       mdiag = a->idiag+16*a->mbs;
390:       for (i=0; i<m; i++) {
391:         x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3];
392:         x[i2]   = mdiag[0]*x1 + mdiag[4]*x2 + mdiag[8]*x3  + mdiag[12]*x4;
393:         x[i2+1] = mdiag[1]*x1 + mdiag[5]*x2 + mdiag[9]*x3  + mdiag[13]*x4;
394:         x[i2+2] = mdiag[2]*x1 + mdiag[6]*x2 + mdiag[10]*x3 + mdiag[14]*x4;
395:         x[i2+3] = mdiag[3]*x1 + mdiag[7]*x2 + mdiag[11]*x3 + mdiag[15]*x4;
396:         mdiag  += 16;
397:         i2     += 4;
398:       }
399:       PetscLogFlops(28*m);
400:     } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
401:       PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
402:     }
403:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
404:       idiag   = a->idiag+16*a->mbs - 16;
405:       i2      = 4*m - 4;
406:       x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3];
407:       x[i2]   = idiag[0]*x1 + idiag[4]*x2 + idiag[8]*x3  + idiag[12]*x4;
408:       x[i2+1] = idiag[1]*x1 + idiag[5]*x2 + idiag[9]*x3  + idiag[13]*x4;
409:       x[i2+2] = idiag[2]*x1 + idiag[6]*x2 + idiag[10]*x3 + idiag[14]*x4;
410:       x[i2+3] = idiag[3]*x1 + idiag[7]*x2 + idiag[11]*x3 + idiag[15]*x4;
411:       idiag -= 16;
412:       i2    -= 4;
413:       for (i=m-2; i>=0; i--) {
414:         v     = aa + 16*(diag[i]+1);
415:         vi    = aj + diag[i] + 1;
416:         nz    = ai[i+1] - diag[i] - 1;
417:         s1    = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3];
418:         while (nz--) {
419:           idx  = 4*(*vi++);
420:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx];
421:           s1  -= v[0]*x1 + v[4]*x2 + v[8]*x3  + v[12]*x4;
422:           s2  -= v[1]*x1 + v[5]*x2 + v[9]*x3  + v[13]*x4;
423:           s3  -= v[2]*x1 + v[6]*x2 + v[10]*x3 + v[14]*x4;
424:           s4  -= v[3]*x1 + v[7]*x2 + v[11]*x3 + v[15]*x4;
425:           v   += 16;
426:         }
427:         x[i2]   = idiag[0]*s1 + idiag[4]*s2 + idiag[8]*s3  + idiag[12]*s4;
428:         x[i2+1] = idiag[1]*s1 + idiag[5]*s2 + idiag[9]*s3  + idiag[13]*s4;
429:         x[i2+2] = idiag[2]*s1 + idiag[6]*s2 + idiag[10]*s3 + idiag[14]*s4;
430:         x[i2+3] = idiag[3]*s1 + idiag[7]*s2 + idiag[11]*s3 + idiag[15]*s4;
431:         idiag   -= 16;
432:         i2      -= 4;
433:       }
434:       PetscLogFlops(16*(a->nz));
435:     }
436:   } else {
437:     SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
438:   }
439:   VecRestoreArray(xx,&x);
440:   VecRestoreArray(bb,(PetscScalar**)&b);
441:   return(0);
442: }

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

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

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

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

470:   if (flag & SOR_ZERO_INITIAL_GUESS) {
471:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
472:       x[0] = b[0]*idiag[0] + b[1]*idiag[5] + b[2]*idiag[10] + b[3]*idiag[15] + b[4]*idiag[20];
473:       x[1] = b[0]*idiag[1] + b[1]*idiag[6] + b[2]*idiag[11] + b[3]*idiag[16] + b[4]*idiag[21];
474:       x[2] = b[0]*idiag[2] + b[1]*idiag[7] + b[2]*idiag[12] + b[3]*idiag[17] + b[4]*idiag[22];
475:       x[3] = b[0]*idiag[3] + b[1]*idiag[8] + b[2]*idiag[13] + b[3]*idiag[18] + b[4]*idiag[23];
476:       x[4] = b[0]*idiag[4] + b[1]*idiag[9] + b[2]*idiag[14] + b[3]*idiag[19] + b[4]*idiag[24];
477:       i2     = 5;
478:       idiag += 25;
479:       for (i=1; i<m; i++) {
480:         v     = aa + 25*ai[i];
481:         vi    = aj + ai[i];
482:         nz    = diag[i] - ai[i];
483:         s1    = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3]; s5 = b[i2+4];
484:         while (nz--) {
485:           idx  = 5*(*vi++);
486:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx];
487:           s1  -= v[0]*x1 + v[5]*x2 + v[10]*x3 + v[15]*x4 + v[20]*x5;
488:           s2  -= v[1]*x1 + v[6]*x2 + v[11]*x3 + v[16]*x4 + v[21]*x5;
489:           s3  -= v[2]*x1 + v[7]*x2 + v[12]*x3 + v[17]*x4 + v[22]*x5;
490:           s4  -= v[3]*x1 + v[8]*x2 + v[13]*x3 + v[18]*x4 + v[23]*x5;
491:           s5  -= v[4]*x1 + v[9]*x2 + v[14]*x3 + v[19]*x4 + v[24]*x5;
492:           v   += 25;
493:         }
494:         x[i2]   = idiag[0]*s1 + idiag[5]*s2 + idiag[10]*s3 + idiag[15]*s4 + idiag[20]*s5;
495:         x[i2+1] = idiag[1]*s1 + idiag[6]*s2 + idiag[11]*s3 + idiag[16]*s4 + idiag[21]*s5;
496:         x[i2+2] = idiag[2]*s1 + idiag[7]*s2 + idiag[12]*s3 + idiag[17]*s4 + idiag[22]*s5;
497:         x[i2+3] = idiag[3]*s1 + idiag[8]*s2 + idiag[13]*s3 + idiag[18]*s4 + idiag[23]*s5;
498:         x[i2+4] = idiag[4]*s1 + idiag[9]*s2 + idiag[14]*s3 + idiag[19]*s4 + idiag[24]*s5;
499:         idiag   += 25;
500:         i2      += 5;
501:       }
502:       /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
503:       PetscLogFlops(25*(a->nz));
504:     }
505:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
506:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
507:       i2    = 0;
508:       mdiag = a->idiag+25*a->mbs;
509:       for (i=0; i<m; i++) {
510:         x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4];
511:         x[i2]   = mdiag[0]*x1 + mdiag[5]*x2 + mdiag[10]*x3 + mdiag[15]*x4 + mdiag[20]*x5;
512:         x[i2+1] = mdiag[1]*x1 + mdiag[6]*x2 + mdiag[11]*x3 + mdiag[16]*x4 + mdiag[21]*x5;
513:         x[i2+2] = mdiag[2]*x1 + mdiag[7]*x2 + mdiag[12]*x3 + mdiag[17]*x4 + mdiag[22]*x5;
514:         x[i2+3] = mdiag[3]*x1 + mdiag[8]*x2 + mdiag[13]*x3 + mdiag[18]*x4 + mdiag[23]*x5;
515:         x[i2+4] = mdiag[4]*x1 + mdiag[9]*x2 + mdiag[14]*x3 + mdiag[19]*x4 + mdiag[24]*x5;
516:         mdiag  += 25;
517:         i2     += 5;
518:       }
519:       PetscLogFlops(45*m);
520:     } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
521:       PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
522:     }
523:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
524:       idiag   = a->idiag+25*a->mbs - 25;
525:       i2      = 5*m - 5;
526:       x1      = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4];
527:       x[i2]   = idiag[0]*x1 + idiag[5]*x2 + idiag[10]*x3 + idiag[15]*x4 + idiag[20]*x5;
528:       x[i2+1] = idiag[1]*x1 + idiag[6]*x2 + idiag[11]*x3 + idiag[16]*x4 + idiag[21]*x5;
529:       x[i2+2] = idiag[2]*x1 + idiag[7]*x2 + idiag[12]*x3 + idiag[17]*x4 + idiag[22]*x5;
530:       x[i2+3] = idiag[3]*x1 + idiag[8]*x2 + idiag[13]*x3 + idiag[18]*x4 + idiag[23]*x5;
531:       x[i2+4] = idiag[4]*x1 + idiag[9]*x2 + idiag[14]*x3 + idiag[19]*x4 + idiag[24]*x5;
532:       idiag -= 25;
533:       i2    -= 5;
534:       for (i=m-2; i>=0; i--) {
535:         v     = aa + 25*(diag[i]+1);
536:         vi    = aj + diag[i] + 1;
537:         nz    = ai[i+1] - diag[i] - 1;
538:         s1    = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3]; s5 = x[i2+4];
539:         while (nz--) {
540:           idx  = 5*(*vi++);
541:           x1   = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx];
542:           s1  -= v[0]*x1 + v[5]*x2 + v[10]*x3 + v[15]*x4 + v[20]*x5;
543:           s2  -= v[1]*x1 + v[6]*x2 + v[11]*x3 + v[16]*x4 + v[21]*x5;
544:           s3  -= v[2]*x1 + v[7]*x2 + v[12]*x3 + v[17]*x4 + v[22]*x5;
545:           s4  -= v[3]*x1 + v[8]*x2 + v[13]*x3 + v[18]*x4 + v[23]*x5;
546:           s5  -= v[4]*x1 + v[9]*x2 + v[14]*x3 + v[19]*x4 + v[24]*x5;
547:           v   += 25;
548:         }
549:         x[i2]   = idiag[0]*s1 + idiag[5]*s2 + idiag[10]*s3 + idiag[15]*s4 + idiag[20]*s5;
550:         x[i2+1] = idiag[1]*s1 + idiag[6]*s2 + idiag[11]*s3 + idiag[16]*s4 + idiag[21]*s5;
551:         x[i2+2] = idiag[2]*s1 + idiag[7]*s2 + idiag[12]*s3 + idiag[17]*s4 + idiag[22]*s5;
552:         x[i2+3] = idiag[3]*s1 + idiag[8]*s2 + idiag[13]*s3 + idiag[18]*s4 + idiag[23]*s5;
553:         x[i2+4] = idiag[4]*s1 + idiag[9]*s2 + idiag[14]*s3 + idiag[19]*s4 + idiag[24]*s5;
554:         idiag   -= 25;
555:         i2      -= 5;
556:       }
557:       PetscLogFlops(25*(a->nz));
558:     }
559:   } else {
560:     SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
561:   }
562:   VecRestoreArray(xx,&x);
563:   VecRestoreArray(bb,(PetscScalar**)&b);
564:   return(0);
565: }

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

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

590:   if (A->rmap.bs != 4) SETERRABORT(A->comm,PETSC_ERR_ARG_WRONG,"Can only be called with a block size of 4");
591:   stepval = (n-1)*4;
592:   for (k=0; k<m; k++) { /* loop over added rows */
593:     row  = im[k];
594:     rp   = aj + ai[row];
595:     ap   = aa + 16*ai[row];
596:     nrow = ailen[row];
597:     low  = 0;
598:     high = nrow;
599:     for (l=0; l<n; l++) { /* loop over added columns */
600:       col = in[l];
601:       if (col <= lastcol) low = 0; else high = nrow;
602:       lastcol = col;
603:       value = v + k*(stepval+4 + l)*4;
604:       while (high-low > 7) {
605:         t = (low+high)/2;
606:         if (rp[t] > col) high = t;
607:         else             low  = t;
608:       }
609:       for (i=low; i<high; i++) {
610:         if (rp[i] > col) break;
611:         if (rp[i] == col) {
612:           bap  = ap +  16*i;
613:           for (ii=0; ii<4; ii++,value+=stepval) {
614:             for (jj=ii; jj<16; jj+=4) {
615:               bap[jj] += *value++;
616:             }
617:           }
618:           goto noinsert2;
619:         }
620:       }
621:       N = nrow++ - 1;
622:       high++; /* added new column index thus must search to one higher than before */
623:       /* shift up all the later entries in this row */
624:       for (ii=N; ii>=i; ii--) {
625:         rp[ii+1] = rp[ii];
626:         PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
627:       }
628:       if (N >= i) {
629:         PetscMemzero(ap+16*i,16*sizeof(MatScalar));
630:       }
631:       rp[i] = col;
632:       bap   = ap +  16*i;
633:       for (ii=0; ii<4; ii++,value+=stepval) {
634:         for (jj=ii; jj<16; jj+=4) {
635:           bap[jj] = *value++;
636:         }
637:       }
638:       noinsert2:;
639:       low = i;
640:     }
641:     ailen[row] = nrow;
642:   }
643:   PetscFunctionReturnVoid();
644: }

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

656: void  matsetvalues4_(Mat *AA,PetscInt *mm,PetscInt *im,PetscInt *nn,PetscInt *in,PetscScalar *v)
657: {
658:   Mat         A = *AA;
659:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
660:   PetscInt    *rp,k,low,high,t,ii,row,nrow,i,col,l,N,n = *nn,m = *mm;
661:   PetscInt    *ai=a->i,*ailen=a->ilen;
662:   PetscInt    *aj=a->j,brow,bcol;
663:   PetscInt    ridx,cidx,lastcol = -1;
664:   MatScalar   *ap,value,*aa=a->a,*bap;
665: 
667:   for (k=0; k<m; k++) { /* loop over added rows */
668:     row  = im[k]; brow = row/4;
669:     rp   = aj + ai[brow];
670:     ap   = aa + 16*ai[brow];
671:     nrow = ailen[brow];
672:     low  = 0;
673:     high = nrow;
674:     for (l=0; l<n; l++) { /* loop over added columns */
675:       col = in[l]; bcol = col/4;
676:       ridx = row % 4; cidx = col % 4;
677:       value = v[l + k*n];
678:       if (col <= lastcol) low = 0; else high = nrow;
679:       lastcol = col;
680:       while (high-low > 7) {
681:         t = (low+high)/2;
682:         if (rp[t] > bcol) high = t;
683:         else              low  = t;
684:       }
685:       for (i=low; i<high; i++) {
686:         if (rp[i] > bcol) break;
687:         if (rp[i] == bcol) {
688:           bap  = ap +  16*i + 4*cidx + ridx;
689:           *bap += value;
690:           goto noinsert1;
691:         }
692:       }
693:       N = nrow++ - 1;
694:       high++; /* added new column thus must search to one higher than before */
695:       /* shift up all the later entries in this row */
696:       for (ii=N; ii>=i; ii--) {
697:         rp[ii+1] = rp[ii];
698:         PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
699:       }
700:       if (N>=i) {
701:         PetscMemzero(ap+16*i,16*sizeof(MatScalar));
702:       }
703:       rp[i]                    = bcol;
704:       ap[16*i + 4*cidx + ridx] = value;
705:       noinsert1:;
706:       low = i;
707:     }
708:     ailen[brow] = nrow;
709:   }
710:   PetscFunctionReturnVoid();
711: }

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

727: #define CHUNKSIZE  10

729: /*
730:      Checks for missing diagonals
731: */
734: PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A)
735: {
736:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
738:   PetscInt       *diag,*jj = a->j,i;

741:   MatMarkDiagonal_SeqBAIJ(A);
742:   diag = a->diag;
743:   for (i=0; i<a->mbs; i++) {
744:     if (jj[diag[i]] != i) {
745:       SETERRQ1(PETSC_ERR_PLIB,"Matrix is missing diagonal number %D",i);
746:     }
747:   }
748:   return(0);
749: }

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

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


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

780: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
781: {
782:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
784:   PetscInt     i,j,n = a->mbs,nz = a->i[n],bs = A->rmap.bs;
785:   PetscInt     *tia, *tja;

788:   *nn = n;
789:   if (!ia) return(0);
790:   if (symmetric) {
791:     MatToSymmetricIJ_SeqAIJ(n,a->i,a->j,0,0,&tia,&tja);
792:   } else {
793:     tia = a->i; tja = a->j;
794:   }
795: 
796:   if (!blockcompressed) {
797:     /* malloc & create the natural set of indices */
798:     PetscMalloc2((n+1)*bs,PetscInt,ia,nz*bs,PetscInt,ja);
799:     for (i=0; i<n+1; i++) {
800:       for (j=0; j<bs; j++) {
801:         *ia[i*bs+j] = tia[i]*bs+j;
802:       }
803:     }
804:     for (i=0; i<nz; i++) {
805:       for (j=0; j<bs; j++) {
806:         *ja[i*bs+j] = tia[i]*bs+j;
807:       }
808:     }
809:     if (symmetric) { /* deallocate memory allocated in MatToSymmetricIJ_SeqAIJ() */
810:       PetscFree(tia);
811:       PetscFree(tja);
812:     }
813:   } else {
814:     *ia = tia;
815:     *ja = tja;
816:   }
817:   if (oshift == 1) {
818:     for (i=0; i<nz; i++) (*ja)[i]++;
819:     for (i=0; i<n+1; i++) (*ia)[i]++;
820:   }
821:   return(0);
822: }

826: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
827: {
828:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
830:   PetscInt       i,n = a->mbs,nz = a->i[n];

833:   if (!ia) return(0);
834:   if (!blockcompressed) {
835:     PetscFree2(*ia,*ja);
836:   }else if (symmetric) {
837:     PetscFree(*ia);
838:     PetscFree(*ja);
839:   } else if (oshift == 1) { /* blockcompressed */
840:     for (i=0; i<nz; i++) a->j[i]--;
841:     for (i=0; i<n+1; i++) a->i[i]--;
842:   }
843:   return(0);
844: }

848: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
849: {
850:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;

854: #if defined(PETSC_USE_LOG)
855:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap.N,A->cmap.n,a->nz);
856: #endif
857:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
858:   if (a->row) {
859:     ISDestroy(a->row);
860:   }
861:   if (a->col) {
862:     ISDestroy(a->col);
863:   }
864:   PetscFree(a->diag);
865:   PetscFree(a->idiag);
866:   PetscFree2(a->imax,a->ilen);
867:   PetscFree(a->solve_work);
868:   PetscFree(a->mult_work);
869:   if (a->icol) {ISDestroy(a->icol);}
870:   PetscFree(a->saved_values);
871: #if defined(PETSC_USE_MAT_SINGLE)
872:   PetscFree(a->setvaluescopy);
873: #endif
874:   PetscFree(a->xtoy);
875:   if (a->compressedrow.use){PetscFree(a->compressedrow.i);}

877:   PetscFree(a);

879:   PetscObjectChangeTypeName((PetscObject)A,0);
880:   PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJInvertBlockDiagonal_C","",PETSC_NULL);
881:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
882:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
883:   PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetColumnIndices_C","",PETSC_NULL);
884:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqaij_C","",PETSC_NULL);
885:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqsbaij_C","",PETSC_NULL);
886:   PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetPreallocation_C","",PETSC_NULL);
887:   return(0);
888: }

892: PetscErrorCode MatSetOption_SeqBAIJ(Mat A,MatOption op)
893: {
894:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;

898:   switch (op) {
899:   case MAT_ROW_ORIENTED:
900:     a->roworiented    = PETSC_TRUE;
901:     break;
902:   case MAT_COLUMN_ORIENTED:
903:     a->roworiented    = PETSC_FALSE;
904:     break;
905:   case MAT_COLUMNS_SORTED:
906:     a->sorted         = PETSC_TRUE;
907:     break;
908:   case MAT_COLUMNS_UNSORTED:
909:     a->sorted         = PETSC_FALSE;
910:     break;
911:   case MAT_KEEP_ZEROED_ROWS:
912:     a->keepzeroedrows = PETSC_TRUE;
913:     break;
914:   case MAT_NO_NEW_NONZERO_LOCATIONS:
915:     a->nonew          = 1;
916:     break;
917:   case MAT_NEW_NONZERO_LOCATION_ERR:
918:     a->nonew          = -1;
919:     break;
920:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
921:     a->nonew          = -2;
922:     break;
923:   case MAT_YES_NEW_NONZERO_LOCATIONS:
924:     a->nonew          = 0;
925:     break;
926:   case MAT_ROWS_SORTED:
927:   case MAT_ROWS_UNSORTED:
928:   case MAT_YES_NEW_DIAGONALS:
929:   case MAT_IGNORE_OFF_PROC_ENTRIES:
930:   case MAT_USE_HASH_TABLE:
931:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
932:     break;
933:   case MAT_NO_NEW_DIAGONALS:
934:     SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
935:   case MAT_SYMMETRIC:
936:   case MAT_STRUCTURALLY_SYMMETRIC:
937:   case MAT_NOT_SYMMETRIC:
938:   case MAT_NOT_STRUCTURALLY_SYMMETRIC:
939:   case MAT_HERMITIAN:
940:   case MAT_NOT_HERMITIAN:
941:   case MAT_SYMMETRY_ETERNAL:
942:   case MAT_NOT_SYMMETRY_ETERNAL:
943:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
944:     break;
945:   default:
946:     SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
947:   }
948:   return(0);
949: }

953: PetscErrorCode MatGetRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
954: {
955:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
957:   PetscInt       itmp,i,j,k,M,*ai,*aj,bs,bn,bp,*idx_i,bs2;
958:   MatScalar      *aa,*aa_i;
959:   PetscScalar    *v_i;

962:   bs  = A->rmap.bs;
963:   ai  = a->i;
964:   aj  = a->j;
965:   aa  = a->a;
966:   bs2 = a->bs2;
967: 
968:   if (row < 0 || row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range", row);
969: 
970:   bn  = row/bs;   /* Block number */
971:   bp  = row % bs; /* Block Position */
972:   M   = ai[bn+1] - ai[bn];
973:   *nz = bs*M;
974: 
975:   if (v) {
976:     *v = 0;
977:     if (*nz) {
978:       PetscMalloc((*nz)*sizeof(PetscScalar),v);
979:       for (i=0; i<M; i++) { /* for each block in the block row */
980:         v_i  = *v + i*bs;
981:         aa_i = aa + bs2*(ai[bn] + i);
982:         for (j=bp,k=0; j<bs2; j+=bs,k++) {v_i[k] = aa_i[j];}
983:       }
984:     }
985:   }

987:   if (idx) {
988:     *idx = 0;
989:     if (*nz) {
990:       PetscMalloc((*nz)*sizeof(PetscInt),idx);
991:       for (i=0; i<M; i++) { /* for each block in the block row */
992:         idx_i = *idx + i*bs;
993:         itmp  = bs*aj[ai[bn] + i];
994:         for (j=0; j<bs; j++) {idx_i[j] = itmp++;}
995:       }
996:     }
997:   }
998:   return(0);
999: }

1003: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1004: {

1008:   if (idx) {PetscFree(*idx);}
1009:   if (v)   {PetscFree(*v);}
1010:   return(0);
1011: }

1015: PetscErrorCode MatTranspose_SeqBAIJ(Mat A,Mat *B)
1016: {
1017:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ *)A->data;
1018:   Mat            C;
1020:   PetscInt       i,j,k,*aj=a->j,*ai=a->i,bs=A->rmap.bs,mbs=a->mbs,nbs=a->nbs,len,*col;
1021:   PetscInt       *rows,*cols,bs2=a->bs2;
1022:   PetscScalar    *array;

1025:   if (!B && mbs!=nbs) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Square matrix only for in-place");
1026:   PetscMalloc((1+nbs)*sizeof(PetscInt),&col);
1027:   PetscMemzero(col,(1+nbs)*sizeof(PetscInt));

1029: #if defined(PETSC_USE_MAT_SINGLE)
1030:   PetscMalloc(a->bs2*a->nz*sizeof(PetscScalar),&array);
1031:   for (i=0; i<a->bs2*a->nz; i++) array[i] = (PetscScalar)a->a[i];
1032: #else
1033:   array = a->a;
1034: #endif

1036:   for (i=0; i<ai[mbs]; i++) col[aj[i]] += 1;
1037:   MatCreate(A->comm,&C);
1038:   MatSetSizes(C,A->cmap.n,A->rmap.N,A->cmap.n,A->rmap.N);
1039:   MatSetType(C,A->type_name);
1040:   MatSeqBAIJSetPreallocation_SeqBAIJ(C,bs,PETSC_NULL,col);
1041:   PetscFree(col);
1042:   PetscMalloc(2*bs*sizeof(PetscInt),&rows);
1043:   cols = rows + bs;
1044:   for (i=0; i<mbs; i++) {
1045:     cols[0] = i*bs;
1046:     for (k=1; k<bs; k++) cols[k] = cols[k-1] + 1;
1047:     len = ai[i+1] - ai[i];
1048:     for (j=0; j<len; j++) {
1049:       rows[0] = (*aj++)*bs;
1050:       for (k=1; k<bs; k++) rows[k] = rows[k-1] + 1;
1051:       MatSetValues(C,bs,rows,bs,cols,array,INSERT_VALUES);
1052:       array += bs2;
1053:     }
1054:   }
1055:   PetscFree(rows);
1056: #if defined(PETSC_USE_MAT_SINGLE)
1057:   PetscFree(array);
1058: #endif
1059: 
1060:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1061:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1062: 
1063:   if (B) {
1064:     *B = C;
1065:   } else {
1066:     MatHeaderCopy(A,C);
1067:   }
1068:   return(0);
1069: }

1073: static PetscErrorCode MatView_SeqBAIJ_Binary(Mat A,PetscViewer viewer)
1074: {
1075:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
1077:   PetscInt       i,*col_lens,bs = A->rmap.bs,count,*jj,j,k,l,bs2=a->bs2;
1078:   int            fd;
1079:   PetscScalar    *aa;
1080:   FILE           *file;

1083:   PetscViewerBinaryGetDescriptor(viewer,&fd);
1084:   PetscMalloc((4+A->rmap.N)*sizeof(PetscInt),&col_lens);
1085:   col_lens[0] = MAT_FILE_COOKIE;

1087:   col_lens[1] = A->rmap.N;
1088:   col_lens[2] = A->cmap.n;
1089:   col_lens[3] = a->nz*bs2;

1091:   /* store lengths of each row and write (including header) to file */
1092:   count = 0;
1093:   for (i=0; i<a->mbs; i++) {
1094:     for (j=0; j<bs; j++) {
1095:       col_lens[4+count++] = bs*(a->i[i+1] - a->i[i]);
1096:     }
1097:   }
1098:   PetscBinaryWrite(fd,col_lens,4+A->rmap.N,PETSC_INT,PETSC_TRUE);
1099:   PetscFree(col_lens);

1101:   /* store column indices (zero start index) */
1102:   PetscMalloc((a->nz+1)*bs2*sizeof(PetscInt),&jj);
1103:   count = 0;
1104:   for (i=0; i<a->mbs; i++) {
1105:     for (j=0; j<bs; j++) {
1106:       for (k=a->i[i]; k<a->i[i+1]; k++) {
1107:         for (l=0; l<bs; l++) {
1108:           jj[count++] = bs*a->j[k] + l;
1109:         }
1110:       }
1111:     }
1112:   }
1113:   PetscBinaryWrite(fd,jj,bs2*a->nz,PETSC_INT,PETSC_FALSE);
1114:   PetscFree(jj);

1116:   /* store nonzero values */
1117:   PetscMalloc((a->nz+1)*bs2*sizeof(PetscScalar),&aa);
1118:   count = 0;
1119:   for (i=0; i<a->mbs; i++) {
1120:     for (j=0; j<bs; j++) {
1121:       for (k=a->i[i]; k<a->i[i+1]; k++) {
1122:         for (l=0; l<bs; l++) {
1123:           aa[count++] = a->a[bs2*k + l*bs + j];
1124:         }
1125:       }
1126:     }
1127:   }
1128:   PetscBinaryWrite(fd,aa,bs2*a->nz,PETSC_SCALAR,PETSC_FALSE);
1129:   PetscFree(aa);

1131:   PetscViewerBinaryGetInfoPointer(viewer,&file);
1132:   if (file) {
1133:     fprintf(file,"-matload_block_size %d\n",(int)A->rmap.bs);
1134:   }
1135:   return(0);
1136: }

1140: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A,PetscViewer viewer)
1141: {
1142:   Mat_SeqBAIJ       *a = (Mat_SeqBAIJ*)A->data;
1143:   PetscErrorCode    ierr;
1144:   PetscInt          i,j,bs = A->rmap.bs,k,l,bs2=a->bs2;
1145:   PetscViewerFormat format;

1148:   PetscViewerGetFormat(viewer,&format);
1149:   if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1150:     PetscViewerASCIIPrintf(viewer,"  block size is %D\n",bs);
1151:   } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1152:     Mat aij;
1153:     MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&aij);
1154:     MatView(aij,viewer);
1155:     MatDestroy(aij);
1156:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1157:      return(0);
1158:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1159:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
1160:     for (i=0; i<a->mbs; i++) {
1161:       for (j=0; j<bs; j++) {
1162:         PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1163:         for (k=a->i[i]; k<a->i[i+1]; k++) {
1164:           for (l=0; l<bs; l++) {
1165: #if defined(PETSC_USE_COMPLEX)
1166:             if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1167:               PetscViewerASCIIPrintf(viewer," (%D, %G + %Gi) ",bs*a->j[k]+l,
1168:                       PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1169:             } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1170:               PetscViewerASCIIPrintf(viewer," (%D, %G - %Gi) ",bs*a->j[k]+l,
1171:                       PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1172:             } else if (PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1173:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1174:             }
1175: #else
1176:             if (a->a[bs2*k + l*bs + j] != 0.0) {
1177:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1178:             }
1179: #endif
1180:           }
1181:         }
1182:         PetscViewerASCIIPrintf(viewer,"\n");
1183:       }
1184:     }
1185:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
1186:   } else {
1187:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
1188:     for (i=0; i<a->mbs; i++) {
1189:       for (j=0; j<bs; j++) {
1190:         PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1191:         for (k=a->i[i]; k<a->i[i+1]; k++) {
1192:           for (l=0; l<bs; l++) {
1193: #if defined(PETSC_USE_COMPLEX)
1194:             if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0) {
1195:               PetscViewerASCIIPrintf(viewer," (%D, %G + %G i) ",bs*a->j[k]+l,
1196:                 PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1197:             } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0) {
1198:               PetscViewerASCIIPrintf(viewer," (%D, %G - %G i) ",bs*a->j[k]+l,
1199:                 PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1200:             } else {
1201:               PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1202:             }
1203: #else
1204:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1205: #endif
1206:           }
1207:         }
1208:         PetscViewerASCIIPrintf(viewer,"\n");
1209:       }
1210:     }
1211:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
1212:   }
1213:   PetscViewerFlush(viewer);
1214:   return(0);
1215: }

1219: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1220: {
1221:   Mat            A = (Mat) Aa;
1222:   Mat_SeqBAIJ    *a=(Mat_SeqBAIJ*)A->data;
1224:   PetscInt       row,i,j,k,l,mbs=a->mbs,color,bs=A->rmap.bs,bs2=a->bs2;
1225:   PetscReal      xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1226:   MatScalar      *aa;
1227:   PetscViewer    viewer;


1231:   /* still need to add support for contour plot of nonzeros; see MatView_SeqAIJ_Draw_Zoom()*/
1232:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);

1234:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);

1236:   /* loop over matrix elements drawing boxes */
1237:   color = PETSC_DRAW_BLUE;
1238:   for (i=0,row=0; i<mbs; i++,row+=bs) {
1239:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1240:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1241:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
1242:       aa = a->a + j*bs2;
1243:       for (k=0; k<bs; k++) {
1244:         for (l=0; l<bs; l++) {
1245:           if (PetscRealPart(*aa++) >=  0.) continue;
1246:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1247:         }
1248:       }
1249:     }
1250:   }
1251:   color = PETSC_DRAW_CYAN;
1252:   for (i=0,row=0; i<mbs; i++,row+=bs) {
1253:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1254:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1255:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
1256:       aa = a->a + j*bs2;
1257:       for (k=0; k<bs; k++) {
1258:         for (l=0; l<bs; l++) {
1259:           if (PetscRealPart(*aa++) != 0.) continue;
1260:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1261:         }
1262:       }
1263:     }
1264:   }

1266:   color = PETSC_DRAW_RED;
1267:   for (i=0,row=0; i<mbs; i++,row+=bs) {
1268:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1269:       y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1270:       x_l = a->j[j]*bs; x_r = x_l + 1.0;
1271:       aa = a->a + j*bs2;
1272:       for (k=0; k<bs; k++) {
1273:         for (l=0; l<bs; l++) {
1274:           if (PetscRealPart(*aa++) <= 0.) continue;
1275:           PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1276:         }
1277:       }
1278:     }
1279:   }
1280:   return(0);
1281: }

1285: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A,PetscViewer viewer)
1286: {
1288:   PetscReal      xl,yl,xr,yr,w,h;
1289:   PetscDraw      draw;
1290:   PetscTruth     isnull;


1294:   PetscViewerDrawGetDraw(viewer,0,&draw);
1295:   PetscDrawIsNull(draw,&isnull); if (isnull) return(0);

1297:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1298:   xr  = A->cmap.n; yr = A->rmap.N; h = yr/10.0; w = xr/10.0;
1299:   xr += w;    yr += h;  xl = -w;     yl = -h;
1300:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1301:   PetscDrawZoom(draw,MatView_SeqBAIJ_Draw_Zoom,A);
1302:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
1303:   return(0);
1304: }

1308: PetscErrorCode MatView_SeqBAIJ(Mat A,PetscViewer viewer)
1309: {
1311:   PetscTruth     iascii,isbinary,isdraw;

1314:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1315:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1316:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1317:   if (iascii){
1318:     MatView_SeqBAIJ_ASCII(A,viewer);
1319:   } else if (isbinary) {
1320:     MatView_SeqBAIJ_Binary(A,viewer);
1321:   } else if (isdraw) {
1322:     MatView_SeqBAIJ_Draw(A,viewer);
1323:   } else {
1324:     Mat B;
1325:     MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&B);
1326:     MatView(B,viewer);
1327:     MatDestroy(B);
1328:   }
1329:   return(0);
1330: }


1335: PetscErrorCode MatGetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
1336: {
1337:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1338:   PetscInt    *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
1339:   PetscInt    *ai = a->i,*ailen = a->ilen;
1340:   PetscInt    brow,bcol,ridx,cidx,bs=A->rmap.bs,bs2=a->bs2;
1341:   MatScalar   *ap,*aa = a->a,zero = 0.0;

1344:   for (k=0; k<m; k++) { /* loop over rows */
1345:     row  = im[k]; brow = row/bs;
1346:     if (row < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
1347:     if (row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D too large", row);
1348:     rp   = aj + ai[brow] ; ap = aa + bs2*ai[brow] ;
1349:     nrow = ailen[brow];
1350:     for (l=0; l<n; l++) { /* loop over columns */
1351:       if (in[l] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column");
1352:       if (in[l] >= A->cmap.n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Column %D too large", in[l]);
1353:       col  = in[l] ;
1354:       bcol = col/bs;
1355:       cidx = col%bs;
1356:       ridx = row%bs;
1357:       high = nrow;
1358:       low  = 0; /* assume unsorted */
1359:       while (high-low > 5) {
1360:         t = (low+high)/2;
1361:         if (rp[t] > bcol) high = t;
1362:         else             low  = t;
1363:       }
1364:       for (i=low; i<high; i++) {
1365:         if (rp[i] > bcol) break;
1366:         if (rp[i] == bcol) {
1367:           *v++ = ap[bs2*i+bs*cidx+ridx];
1368:           goto finished;
1369:         }
1370:       }
1371:       *v++ = zero;
1372:       finished:;
1373:     }
1374:   }
1375:   return(0);
1376: }

1378: #if defined(PETSC_USE_MAT_SINGLE)
1381: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
1382: {
1383:   Mat_SeqBAIJ    *b = (Mat_SeqBAIJ*)mat->data;
1385:   PetscInt       i,N = m*n*b->bs2;
1386:   MatScalar      *vsingle;

1389:   if (N > b->setvalueslen) {
1390:     PetscFree(b->setvaluescopy);
1391:     PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
1392:     b->setvalueslen  = N;
1393:   }
1394:   vsingle = b->setvaluescopy;
1395:   for (i=0; i<N; i++) {
1396:     vsingle[i] = v[i];
1397:   }
1398:   MatSetValuesBlocked_SeqBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
1399:   return(0);
1400: }
1401: #endif


1406: PetscErrorCode MatSetValuesBlocked_SeqBAIJ_MatScalar(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode is)
1407: {
1408:   Mat_SeqBAIJ     *a = (Mat_SeqBAIJ*)A->data;
1409:   PetscInt        *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,rmax,N,lastcol = -1;
1410:   PetscInt        *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1411:   PetscErrorCode  ierr;
1412:   PetscInt        *aj=a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap.bs,stepval;
1413:   PetscTruth      roworiented=a->roworiented;
1414:   const MatScalar *value = v;
1415:   MatScalar       *ap,*aa = a->a,*bap;

1418:   if (roworiented) {
1419:     stepval = (n-1)*bs;
1420:   } else {
1421:     stepval = (m-1)*bs;
1422:   }
1423:   for (k=0; k<m; k++) { /* loop over added rows */
1424:     row  = im[k];
1425:     if (row < 0) continue;
1426: #if defined(PETSC_USE_DEBUG)  
1427:     if (row >= a->mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,a->mbs-1);
1428: #endif
1429:     rp   = aj + ai[row];
1430:     ap   = aa + bs2*ai[row];
1431:     rmax = imax[row];
1432:     nrow = ailen[row];
1433:     low  = 0;
1434:     high = nrow;
1435:     for (l=0; l<n; l++) { /* loop over added columns */
1436:       if (in[l] < 0) continue;
1437: #if defined(PETSC_USE_DEBUG)  
1438:       if (in[l] >= a->nbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],a->nbs-1);
1439: #endif
1440:       col = in[l];
1441:       if (roworiented) {
1442:         value = v + k*(stepval+bs)*bs + l*bs;
1443:       } else {
1444:         value = v + l*(stepval+bs)*bs + k*bs;
1445:       }
1446:       if (col <= lastcol) low = 0; else high = nrow;
1447:       lastcol = col;
1448:       while (high-low > 7) {
1449:         t = (low+high)/2;
1450:         if (rp[t] > col) high = t;
1451:         else             low  = t;
1452:       }
1453:       for (i=low; i<high; i++) {
1454:         if (rp[i] > col) break;
1455:         if (rp[i] == col) {
1456:           bap  = ap +  bs2*i;
1457:           if (roworiented) {
1458:             if (is == ADD_VALUES) {
1459:               for (ii=0; ii<bs; ii++,value+=stepval) {
1460:                 for (jj=ii; jj<bs2; jj+=bs) {
1461:                   bap[jj] += *value++;
1462:                 }
1463:               }
1464:             } else {
1465:               for (ii=0; ii<bs; ii++,value+=stepval) {
1466:                 for (jj=ii; jj<bs2; jj+=bs) {
1467:                   bap[jj] = *value++;
1468:                 }
1469:               }
1470:             }
1471:           } else {
1472:             if (is == ADD_VALUES) {
1473:               for (ii=0; ii<bs; ii++,value+=stepval) {
1474:                 for (jj=0; jj<bs; jj++) {
1475:                   *bap++ += *value++;
1476:                 }
1477:               }
1478:             } else {
1479:               for (ii=0; ii<bs; ii++,value+=stepval) {
1480:                 for (jj=0; jj<bs; jj++) {
1481:                   *bap++  = *value++;
1482:                 }
1483:               }
1484:             }
1485:           }
1486:           goto noinsert2;
1487:         }
1488:       }
1489:       if (nonew == 1) goto noinsert2;
1490:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1491:       MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
1492:       N = nrow++ - 1; high++;
1493:       /* shift up all the later entries in this row */
1494:       for (ii=N; ii>=i; ii--) {
1495:         rp[ii+1] = rp[ii];
1496:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1497:       }
1498:       if (N >= i) {
1499:         PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1500:       }
1501:       rp[i] = col;
1502:       bap   = ap +  bs2*i;
1503:       if (roworiented) {
1504:         for (ii=0; ii<bs; ii++,value+=stepval) {
1505:           for (jj=ii; jj<bs2; jj+=bs) {
1506:             bap[jj] = *value++;
1507:           }
1508:         }
1509:       } else {
1510:         for (ii=0; ii<bs; ii++,value+=stepval) {
1511:           for (jj=0; jj<bs; jj++) {
1512:             *bap++  = *value++;
1513:           }
1514:         }
1515:       }
1516:       noinsert2:;
1517:       low = i;
1518:     }
1519:     ailen[row] = nrow;
1520:   }
1521:   return(0);
1522: }

1526: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A,MatAssemblyType mode)
1527: {
1528:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
1529:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
1530:   PetscInt       m = A->rmap.N,*ip,N,*ailen = a->ilen;
1532:   PetscInt       mbs = a->mbs,bs2 = a->bs2,rmax = 0;
1533:   MatScalar      *aa = a->a,*ap;
1534:   PetscReal      ratio=0.6;

1537:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);

1539:   if (m) rmax = ailen[0];
1540:   for (i=1; i<mbs; i++) {
1541:     /* move each row back by the amount of empty slots (fshift) before it*/
1542:     fshift += imax[i-1] - ailen[i-1];
1543:     rmax   = PetscMax(rmax,ailen[i]);
1544:     if (fshift) {
1545:       ip = aj + ai[i]; ap = aa + bs2*ai[i];
1546:       N = ailen[i];
1547:       for (j=0; j<N; j++) {
1548:         ip[j-fshift] = ip[j];
1549:         PetscMemcpy(ap+(j-fshift)*bs2,ap+j*bs2,bs2*sizeof(MatScalar));
1550:       }
1551:     }
1552:     ai[i] = ai[i-1] + ailen[i-1];
1553:   }
1554:   if (mbs) {
1555:     fshift += imax[mbs-1] - ailen[mbs-1];
1556:     ai[mbs] = ai[mbs-1] + ailen[mbs-1];
1557:   }
1558:   /* reset ilen and imax for each row */
1559:   for (i=0; i<mbs; i++) {
1560:     ailen[i] = imax[i] = ai[i+1] - ai[i];
1561:   }
1562:   a->nz = ai[mbs];

1564:   /* diagonals may have moved, so kill the diagonal pointers */
1565:   a->idiagvalid = PETSC_FALSE;
1566:   if (fshift && a->diag) {
1567:     PetscFree(a->diag);
1568:     PetscLogObjectMemory(A,-(mbs+1)*sizeof(PetscInt));
1569:     a->diag = 0;
1570:   }
1571:   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);
1572:   PetscInfo1(A,"Number of mallocs during MatSetValues is %D\n",a->reallocs);
1573:   PetscInfo1(A,"Most nonzeros blocks in any row is %D\n",rmax);
1574:   a->reallocs          = 0;
1575:   A->info.nz_unneeded  = (PetscReal)fshift*bs2;

1577:   /* check for zero rows. If found a large number of zero rows, use CompressedRow functions */
1578:   if (a->compressedrow.use){
1579:     Mat_CheckCompressedRow(A,&a->compressedrow,a->i,mbs,ratio);
1580:   }

1582:   A->same_nonzero = PETSC_TRUE;
1583:   return(0);
1584: }

1586: /* 
1587:    This function returns an array of flags which indicate the locations of contiguous
1588:    blocks that should be zeroed. for eg: if bs = 3  and is = [0,1,2,3,5,6,7,8,9]
1589:    then the resulting sizes = [3,1,1,3,1] correspondig to sets [(0,1,2),(3),(5),(6,7,8),(9)]
1590:    Assume: sizes should be long enough to hold all the values.
1591: */
1594: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[],PetscInt n,PetscInt bs,PetscInt sizes[], PetscInt *bs_max)
1595: {
1596:   PetscInt   i,j,k,row;
1597:   PetscTruth flg;

1600:   for (i=0,j=0; i<n; j++) {
1601:     row = idx[i];
1602:     if (row%bs!=0) { /* Not the begining of a block */
1603:       sizes[j] = 1;
1604:       i++;
1605:     } else if (i+bs > n) { /* complete block doesn't exist (at idx end) */
1606:       sizes[j] = 1;         /* Also makes sure atleast 'bs' values exist for next else */
1607:       i++;
1608:     } else { /* Begining of the block, so check if the complete block exists */
1609:       flg = PETSC_TRUE;
1610:       for (k=1; k<bs; k++) {
1611:         if (row+k != idx[i+k]) { /* break in the block */
1612:           flg = PETSC_FALSE;
1613:           break;
1614:         }
1615:       }
1616:       if (flg) { /* No break in the bs */
1617:         sizes[j] = bs;
1618:         i+= bs;
1619:       } else {
1620:         sizes[j] = 1;
1621:         i++;
1622:       }
1623:     }
1624:   }
1625:   *bs_max = j;
1626:   return(0);
1627: }
1628: 
1631: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A,PetscInt is_n,const PetscInt is_idx[],PetscScalar diag)
1632: {
1633:   Mat_SeqBAIJ    *baij=(Mat_SeqBAIJ*)A->data;
1635:   PetscInt       i,j,k,count,*rows;
1636:   PetscInt       bs=A->rmap.bs,bs2=baij->bs2,*sizes,row,bs_max;
1637:   PetscScalar    zero = 0.0;
1638:   MatScalar      *aa;

1641:   /* Make a copy of the IS and  sort it */
1642:   /* allocate memory for rows,sizes */
1643:   PetscMalloc((3*is_n+1)*sizeof(PetscInt),&rows);
1644:   sizes = rows + is_n;

1646:   /* copy IS values to rows, and sort them */
1647:   for (i=0; i<is_n; i++) { rows[i] = is_idx[i]; }
1648:   PetscSortInt(is_n,rows);
1649:   if (baij->keepzeroedrows) {
1650:     for (i=0; i<is_n; i++) { sizes[i] = 1; }
1651:     bs_max = is_n;
1652:     A->same_nonzero = PETSC_TRUE;
1653:   } else {
1654:     MatZeroRows_SeqBAIJ_Check_Blocks(rows,is_n,bs,sizes,&bs_max);
1655:     A->same_nonzero = PETSC_FALSE;
1656:   }

1658:   for (i=0,j=0; i<bs_max; j+=sizes[i],i++) {
1659:     row   = rows[j];
1660:     if (row < 0 || row > A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range",row);
1661:     count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
1662:     aa    = baij->a + baij->i[row/bs]*bs2 + (row%bs);
1663:     if (sizes[i] == bs && !baij->keepzeroedrows) {
1664:       if (diag != 0.0) {
1665:         if (baij->ilen[row/bs] > 0) {
1666:           baij->ilen[row/bs]       = 1;
1667:           baij->j[baij->i[row/bs]] = row/bs;
1668:           PetscMemzero(aa,count*bs*sizeof(MatScalar));
1669:         }
1670:         /* Now insert all the diagonal values for this bs */
1671:         for (k=0; k<bs; k++) {
1672:           (*A->ops->setvalues)(A,1,rows+j+k,1,rows+j+k,&diag,INSERT_VALUES);
1673:         }
1674:       } else { /* (diag == 0.0) */
1675:         baij->ilen[row/bs] = 0;
1676:       } /* end (diag == 0.0) */
1677:     } else { /* (sizes[i] != bs) */
1678: #if defined (PETSC_USE_DEBUG)
1679:       if (sizes[i] != 1) SETERRQ(PETSC_ERR_PLIB,"Internal Error. Value should be 1");
1680: #endif
1681:       for (k=0; k<count; k++) {
1682:         aa[0] =  zero;
1683:         aa    += bs;
1684:       }
1685:       if (diag != 0.0) {
1686:         (*A->ops->setvalues)(A,1,rows+j,1,rows+j,&diag,INSERT_VALUES);
1687:       }
1688:     }
1689:   }

1691:   PetscFree(rows);
1692:   MatAssemblyEnd_SeqBAIJ(A,MAT_FINAL_ASSEMBLY);
1693:   return(0);
1694: }

1698: PetscErrorCode MatSetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
1699: {
1700:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
1701:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,lastcol = -1;
1702:   PetscInt       *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1703:   PetscInt       *aj=a->j,nonew=a->nonew,bs=A->rmap.bs,brow,bcol;
1705:   PetscInt       ridx,cidx,bs2=a->bs2;
1706:   PetscTruth     roworiented=a->roworiented;
1707:   MatScalar      *ap,value,*aa=a->a,*bap;

1710:   for (k=0; k<m; k++) { /* loop over added rows */
1711:     row  = im[k];
1712:     brow = row/bs;
1713:     if (row < 0) continue;
1714: #if defined(PETSC_USE_DEBUG)  
1715:     if (row >= A->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.N-1);
1716: #endif
1717:     rp   = aj + ai[brow];
1718:     ap   = aa + bs2*ai[brow];
1719:     rmax = imax[brow];
1720:     nrow = ailen[brow];
1721:     low  = 0;
1722:     high = nrow;
1723:     for (l=0; l<n; l++) { /* loop over added columns */
1724:       if (in[l] < 0) continue;
1725: #if defined(PETSC_USE_DEBUG)  
1726:       if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
1727: #endif
1728:       col = in[l]; bcol = col/bs;
1729:       ridx = row % bs; cidx = col % bs;
1730:       if (roworiented) {
1731:         value = v[l + k*n];
1732:       } else {
1733:         value = v[k + l*m];
1734:       }
1735:       if (col <= lastcol) low = 0; else high = nrow;
1736:       lastcol = col;
1737:       while (high-low > 7) {
1738:         t = (low+high)/2;
1739:         if (rp[t] > bcol) high = t;
1740:         else              low  = t;
1741:       }
1742:       for (i=low; i<high; i++) {
1743:         if (rp[i] > bcol) break;
1744:         if (rp[i] == bcol) {
1745:           bap  = ap +  bs2*i + bs*cidx + ridx;
1746:           if (is == ADD_VALUES) *bap += value;
1747:           else                  *bap  = value;
1748:           goto noinsert1;
1749:         }
1750:       }
1751:       if (nonew == 1) goto noinsert1;
1752:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1753:       MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
1754:       N = nrow++ - 1; high++;
1755:       /* shift up all the later entries in this row */
1756:       for (ii=N; ii>=i; ii--) {
1757:         rp[ii+1] = rp[ii];
1758:         PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1759:       }
1760:       if (N>=i) {
1761:         PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1762:       }
1763:       rp[i]                      = bcol;
1764:       ap[bs2*i + bs*cidx + ridx] = value;
1765:       a->nz++;
1766:       noinsert1:;
1767:       low = i;
1768:     }
1769:     ailen[brow] = nrow;
1770:   }
1771:   A->same_nonzero = PETSC_FALSE;
1772:   return(0);
1773: }


1778: PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1779: {
1780:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)inA->data;
1781:   Mat            outA;
1783:   PetscTruth     row_identity,col_identity;

1786:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels = 0 supported for in-place ILU");
1787:   ISIdentity(row,&row_identity);
1788:   ISIdentity(col,&col_identity);
1789:   if (!row_identity || !col_identity) {
1790:     SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU");
1791:   }

1793:   outA          = inA;
1794:   inA->factor   = FACTOR_LU;

1796:   MatMarkDiagonal_SeqBAIJ(inA);

1798:   PetscObjectReference((PetscObject)row);
1799:   if (a->row) { ISDestroy(a->row); }
1800:   a->row = row;
1801:   PetscObjectReference((PetscObject)col);
1802:   if (a->col) { ISDestroy(a->col); }
1803:   a->col = col;
1804: 
1805:   /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
1806:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1807:   PetscLogObjectParent(inA,a->icol);
1808: 
1809:   /*
1810:       Blocksize 2, 3, 4, 5, 6 and 7 have a special faster factorization/solver 
1811:       for ILU(0) factorization with natural ordering
1812:   */
1813:   if (inA->rmap.bs < 8) {
1814:     MatSeqBAIJ_UpdateFactorNumeric_NaturalOrdering(inA);
1815:   } else {
1816:     if (!a->solve_work) {
1817:       PetscMalloc((inA->rmap.N+inA->rmap.bs)*sizeof(PetscScalar),&a->solve_work);
1818:       PetscLogObjectMemory(inA,(inA->rmap.N+inA->rmap.bs)*sizeof(PetscScalar));
1819:     }
1820:   }

1822:   MatLUFactorNumeric(inA,info,&outA);

1824:   return(0);
1825: }

1830: PetscErrorCode  MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat,PetscInt *indices)
1831: {
1832:   Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data;
1833:   PetscInt    i,nz,nbs;

1836:   nz  = baij->maxnz/baij->bs2;
1837:   nbs = baij->nbs;
1838:   for (i=0; i<nz; i++) {
1839:     baij->j[i] = indices[i];
1840:   }
1841:   baij->nz = nz;
1842:   for (i=0; i<nbs; i++) {
1843:     baij->ilen[i] = baij->imax[i];
1844:   }

1846:   return(0);
1847: }

1852: /*@
1853:     MatSeqBAIJSetColumnIndices - Set the column indices for all the rows
1854:        in the matrix.

1856:   Input Parameters:
1857: +  mat - the SeqBAIJ matrix
1858: -  indices - the column indices

1860:   Level: advanced

1862:   Notes:
1863:     This can be called if you have precomputed the nonzero structure of the 
1864:   matrix and want to provide it to the matrix object to improve the performance
1865:   of the MatSetValues() operation.

1867:     You MUST have set the correct numbers of nonzeros per row in the call to 
1868:   MatCreateSeqBAIJ(), and the columns indices MUST be sorted.

1870:     MUST be called before any calls to MatSetValues();

1872: @*/
1873: PetscErrorCode  MatSeqBAIJSetColumnIndices(Mat mat,PetscInt *indices)
1874: {
1875:   PetscErrorCode ierr,(*f)(Mat,PetscInt *);

1880:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqBAIJSetColumnIndices_C",(void (**)(void))&f);
1881:   if (f) {
1882:     (*f)(mat,indices);
1883:   } else {
1884:     SETERRQ(PETSC_ERR_ARG_WRONG,"Wrong type of matrix to set column indices");
1885:   }
1886:   return(0);
1887: }

1891: PetscErrorCode MatGetRowMaxAbs_SeqBAIJ(Mat A,Vec v,PetscInt idx[])
1892: {
1893:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
1895:   PetscInt       i,j,n,row,bs,*ai,*aj,mbs;
1896:   PetscReal      atmp;
1897:   PetscScalar    *x,zero = 0.0;
1898:   MatScalar      *aa;
1899:   PetscInt       ncols,brow,krow,kcol;

1902:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1903:   bs   = A->rmap.bs;
1904:   aa   = a->a;
1905:   ai   = a->i;
1906:   aj   = a->j;
1907:   mbs  = a->mbs;

1909:   VecSet(v,zero);
1910:   if (idx) {
1911:     for (i=0; i<A->rmap.n;i++) idx[i] = 0;
1912:   }
1913:   VecGetArray(v,&x);
1914:   VecGetLocalSize(v,&n);
1915:   if (n != A->rmap.N) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1916:   for (i=0; i<mbs; i++) {
1917:     ncols = ai[1] - ai[0]; ai++;
1918:     brow  = bs*i;
1919:     for (j=0; j<ncols; j++){
1920:       for (kcol=0; kcol<bs; kcol++){
1921:         for (krow=0; krow<bs; krow++){
1922:           atmp = PetscAbsScalar(*aa);aa++;
1923:           row = brow + krow;    /* row index */
1924:           /* printf("val[%d,%d]: %G\n",row,bcol+kcol,atmp); */
1925:           if (PetscAbsScalar(x[row]) < atmp) {x[row] = atmp; if (idx) idx[row] = bs*(*aj) + kcol;}
1926:         }
1927:       }
1928:       aj++;
1929:     }
1930:   }
1931:   VecRestoreArray(v,&x);
1932:   return(0);
1933: }

1937: PetscErrorCode MatCopy_SeqBAIJ(Mat A,Mat B,MatStructure str)
1938: {

1942:   /* If the two matrices have the same copy implementation, use fast copy. */
1943:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1944:     Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1945:     Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)B->data;

1947:     if (a->i[A->rmap.N] != b->i[B->rmap.N]) {
1948:       SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1949:     }
1950:     PetscMemcpy(b->a,a->a,(a->i[A->rmap.N])*sizeof(PetscScalar));
1951:   } else {
1952:     MatCopy_Basic(A,B,str);
1953:   }
1954:   return(0);
1955: }

1959: PetscErrorCode MatSetUpPreallocation_SeqBAIJ(Mat A)
1960: {

1964:    MatSeqBAIJSetPreallocation_SeqBAIJ(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0);
1965:   return(0);
1966: }

1970: PetscErrorCode MatGetArray_SeqBAIJ(Mat A,PetscScalar *array[])
1971: {
1972:   Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1974:   *array = a->a;
1975:   return(0);
1976: }

1980: PetscErrorCode MatRestoreArray_SeqBAIJ(Mat A,PetscScalar *array[])
1981: {
1983:   return(0);
1984: }

1986:  #include petscblaslapack.h
1989: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1990: {
1991:   Mat_SeqBAIJ    *x  = (Mat_SeqBAIJ *)X->data,*y = (Mat_SeqBAIJ *)Y->data;
1993:   PetscInt       i,bs=Y->rmap.bs,j,bs2;
1994:   PetscBLASInt   one=1,bnz = (PetscBLASInt)x->nz;

1997:   if (str == SAME_NONZERO_PATTERN) {
1998:     PetscScalar alpha = a;
1999:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2000:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2001:     if (y->xtoy && y->XtoY != X) {
2002:       PetscFree(y->xtoy);
2003:       MatDestroy(y->XtoY);
2004:     }
2005:     if (!y->xtoy) { /* get xtoy */
2006:       MatAXPYGetxtoy_Private(x->mbs,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2007:       y->XtoY = X;
2008:     }
2009:     bs2 = bs*bs;
2010:     for (i=0; i<x->nz; i++) {
2011:       j = 0;
2012:       while (j < bs2){
2013:         y->a[bs2*y->xtoy[i]+j] += a*(x->a[bs2*i+j]);
2014:         j++;
2015:       }
2016:     }
2017:     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));
2018:   } else {
2019:     MatAXPY_Basic(Y,a,X,str);
2020:   }
2021:   return(0);
2022: }

2026: PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
2027: {
2028:   Mat_SeqBAIJ     *a = (Mat_SeqBAIJ*)A->data;
2029:   PetscInt       i,nz = a->bs2*a->i[a->mbs];
2030:   PetscScalar    *aa = a->a;

2033:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
2034:   return(0);
2035: }

2039: PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2040: {
2041:   Mat_SeqBAIJ    *a = (Mat_SeqBAIJ*)A->data;
2042:   PetscInt       i,nz = a->bs2*a->i[a->mbs];
2043:   PetscScalar    *aa = a->a;

2046:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2047:   return(0);
2048: }


2051: /* -------------------------------------------------------------------*/
2052: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
2053:        MatGetRow_SeqBAIJ,
2054:        MatRestoreRow_SeqBAIJ,
2055:        MatMult_SeqBAIJ_N,
2056: /* 4*/ MatMultAdd_SeqBAIJ_N,
2057:        MatMultTranspose_SeqBAIJ,
2058:        MatMultTransposeAdd_SeqBAIJ,
2059:        MatSolve_SeqBAIJ_N,
2060:        0,
2061:        0,
2062: /*10*/ 0,
2063:        MatLUFactor_SeqBAIJ,
2064:        0,
2065:        0,
2066:        MatTranspose_SeqBAIJ,
2067: /*15*/ MatGetInfo_SeqBAIJ,
2068:        MatEqual_SeqBAIJ,
2069:        MatGetDiagonal_SeqBAIJ,
2070:        MatDiagonalScale_SeqBAIJ,
2071:        MatNorm_SeqBAIJ,
2072: /*20*/ 0,
2073:        MatAssemblyEnd_SeqBAIJ,
2074:        0,
2075:        MatSetOption_SeqBAIJ,
2076:        MatZeroEntries_SeqBAIJ,
2077: /*25*/ MatZeroRows_SeqBAIJ,
2078:        MatLUFactorSymbolic_SeqBAIJ,
2079:        MatLUFactorNumeric_SeqBAIJ_N,
2080:        MatCholeskyFactorSymbolic_SeqBAIJ,
2081:        MatCholeskyFactorNumeric_SeqBAIJ_N,
2082: /*30*/ MatSetUpPreallocation_SeqBAIJ,
2083:        MatILUFactorSymbolic_SeqBAIJ,
2084:        MatICCFactorSymbolic_SeqBAIJ,
2085:        MatGetArray_SeqBAIJ,
2086:        MatRestoreArray_SeqBAIJ,
2087: /*35*/ MatDuplicate_SeqBAIJ,
2088:        0,
2089:        0,
2090:        MatILUFactor_SeqBAIJ,
2091:        0,
2092: /*40*/ MatAXPY_SeqBAIJ,
2093:        MatGetSubMatrices_SeqBAIJ,
2094:        MatIncreaseOverlap_SeqBAIJ,
2095:        MatGetValues_SeqBAIJ,
2096:        MatCopy_SeqBAIJ,
2097: /*45*/ 0,
2098:        MatScale_SeqBAIJ,
2099:        0,
2100:        0,
2101:        0,
2102: /*50*/ 0,
2103:        MatGetRowIJ_SeqBAIJ,
2104:        MatRestoreRowIJ_SeqBAIJ,
2105:        0,
2106:        0,
2107: /*55*/ 0,
2108:        0,
2109:        0,
2110:        0,
2111:        MatSetValuesBlocked_SeqBAIJ,
2112: /*60*/ MatGetSubMatrix_SeqBAIJ,
2113:        MatDestroy_SeqBAIJ,
2114:        MatView_SeqBAIJ,
2115:        0,
2116:        0,
2117: /*65*/ 0,
2118:        0,
2119:        0,
2120:        0,
2121:        0,
2122: /*70*/ MatGetRowMaxAbs_SeqBAIJ,
2123:        MatConvert_Basic,
2124:        0,
2125:        0,
2126:        0,
2127: /*75*/ 0,
2128:        0,
2129:        0,
2130:        0,
2131:        0,
2132: /*80*/ 0,
2133:        0,
2134:        0,
2135:        0,
2136:        MatLoad_SeqBAIJ,
2137: /*85*/ 0,
2138:        0,
2139:        0,
2140:        0,
2141:        0,
2142: /*90*/ 0,
2143:        0,
2144:        0,
2145:        0,
2146:        0,
2147: /*95*/ 0,
2148:        0,
2149:        0,
2150:        0,
2151:        0,
2152: /*100*/0,
2153:        0,
2154:        0,
2155:        0,
2156:        0,
2157: /*105*/0,
2158:        MatRealPart_SeqBAIJ,
2159:        MatImaginaryPart_SeqBAIJ
2160: };

2165: PetscErrorCode  MatStoreValues_SeqBAIJ(Mat mat)
2166: {
2167:   Mat_SeqBAIJ    *aij = (Mat_SeqBAIJ *)mat->data;
2168:   PetscInt       nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;

2172:   if (aij->nonew != 1) {
2173:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2174:   }

2176:   /* allocate space for values if not already there */
2177:   if (!aij->saved_values) {
2178:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2179:   }

2181:   /* copy values over */
2182:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2183:   return(0);
2184: }

2190: PetscErrorCode  MatRetrieveValues_SeqBAIJ(Mat mat)
2191: {
2192:   Mat_SeqBAIJ    *aij = (Mat_SeqBAIJ *)mat->data;
2194:   PetscInt       nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;

2197:   if (aij->nonew != 1) {
2198:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2199:   }
2200:   if (!aij->saved_values) {
2201:     SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2202:   }

2204:   /* copy values over */
2205:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2206:   return(0);
2207: }


2218: PetscErrorCode  MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B,PetscInt bs,PetscInt nz,PetscInt *nnz)
2219: {
2220:   Mat_SeqBAIJ    *b;
2222:   PetscInt       i,mbs,nbs,bs2,newbs = bs;
2223:   PetscTruth     flg,skipallocation = PETSC_FALSE;


2227:   if (nz == MAT_SKIP_ALLOCATION) {
2228:     skipallocation = PETSC_TRUE;
2229:     nz             = 0;
2230:   }

2232:   PetscOptionsBegin(B->comm,B->prefix,"Block options for SEQBAIJ matrix 1","Mat");
2233:     PetscOptionsInt("-mat_block_size","Set the blocksize used to store the matrix","MatSeqBAIJSetPreallocation",bs,&newbs,PETSC_NULL);
2234:   PetscOptionsEnd();

2236:   if (nnz && newbs != bs) {
2237:     SETERRQ(PETSC_ERR_ARG_WRONG,"Cannot change blocksize from command line if setting nnz");
2238:   }
2239:   bs   = newbs;

2241:   B->rmap.bs = B->cmap.bs = bs;
2242:   PetscMapSetUp(&B->rmap);
2243:   PetscMapSetUp(&B->cmap);

2245:   B->preallocated = PETSC_TRUE;

2247:   mbs  = B->rmap.n/bs;
2248:   nbs  = B->cmap.n/bs;
2249:   bs2  = bs*bs;

2251:   if (mbs*bs!=B->rmap.n || nbs*bs!=B->cmap.n) {
2252:     SETERRQ3(PETSC_ERR_ARG_SIZ,"Number rows %D, cols %D must be divisible by blocksize %D",B->rmap.N,B->cmap.n,bs);
2253:   }

2255:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2256:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
2257:   if (nnz) {
2258:     for (i=0; i<mbs; i++) {
2259:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
2260:       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);
2261:     }
2262:   }

2264:   b       = (Mat_SeqBAIJ*)B->data;
2265:   PetscOptionsBegin(B->comm,PETSC_NULL,"Optimize options for SEQBAIJ matrix 2 ","Mat");
2266:     PetscOptionsTruth("-mat_no_unroll","Do not optimize for block size (slow)",PETSC_NULL,PETSC_FALSE,&flg,PETSC_NULL);
2267:   PetscOptionsEnd();

2269:   B->ops->solve               = MatSolve_SeqBAIJ_Update;
2270:   B->ops->solvetranspose      = MatSolveTranspose_SeqBAIJ_Update;
2271:   if (!flg) {
2272:     switch (bs) {
2273:     case 1:
2274:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_1;
2275:       B->ops->mult            = MatMult_SeqBAIJ_1;
2276:       B->ops->multadd         = MatMultAdd_SeqBAIJ_1;
2277:       break;
2278:     case 2:
2279:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_2;
2280:       B->ops->mult            = MatMult_SeqBAIJ_2;
2281:       B->ops->multadd         = MatMultAdd_SeqBAIJ_2;
2282:       B->ops->pbrelax         = MatPBRelax_SeqBAIJ_2;
2283:       break;
2284:     case 3:
2285:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_3;
2286:       B->ops->mult            = MatMult_SeqBAIJ_3;
2287:       B->ops->multadd         = MatMultAdd_SeqBAIJ_3;
2288:       B->ops->pbrelax         = MatPBRelax_SeqBAIJ_3;
2289:       break;
2290:     case 4:
2291:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_4;
2292:       B->ops->mult            = MatMult_SeqBAIJ_4;
2293:       B->ops->multadd         = MatMultAdd_SeqBAIJ_4;
2294:       B->ops->pbrelax         = MatPBRelax_SeqBAIJ_4;
2295:       break;
2296:     case 5:
2297:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_5;
2298:       B->ops->mult            = MatMult_SeqBAIJ_5;
2299:       B->ops->multadd         = MatMultAdd_SeqBAIJ_5;
2300:       B->ops->pbrelax         = MatPBRelax_SeqBAIJ_5;
2301:       break;
2302:     case 6:
2303:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_6;
2304:       B->ops->mult            = MatMult_SeqBAIJ_6;
2305:       B->ops->multadd         = MatMultAdd_SeqBAIJ_6;
2306:       break;
2307:     case 7:
2308:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_7;
2309:       B->ops->mult            = MatMult_SeqBAIJ_7;
2310:       B->ops->multadd         = MatMultAdd_SeqBAIJ_7;
2311:       break;
2312:     default:
2313:       B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_N;
2314:       B->ops->mult            = MatMult_SeqBAIJ_N;
2315:       B->ops->multadd         = MatMultAdd_SeqBAIJ_N;
2316:       break;
2317:     }
2318:   }
2319:   B->rmap.bs      = bs;
2320:   b->mbs     = mbs;
2321:   b->nbs     = nbs;
2322:   if (!skipallocation) {
2323:     PetscMalloc2(mbs,PetscInt,&b->imax,mbs,PetscInt,&b->ilen);
2324:     /* b->ilen will count nonzeros in each block row so far. */
2325:     for (i=0; i<mbs; i++) { b->ilen[i] = 0;}
2326:     if (!nnz) {
2327:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2328:       else if (nz <= 0)        nz = 1;
2329:       for (i=0; i<mbs; i++) b->imax[i] = nz;
2330:       nz = nz*mbs;
2331:     } else {
2332:       nz = 0;
2333:       for (i=0; i<mbs; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2334:     }

2336:     /* allocate the matrix space */
2337:     PetscMalloc3(bs2*nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap.N+1,PetscInt,&b->i);
2338:     PetscMemzero(b->a,nz*bs2*sizeof(MatScalar));
2339:     PetscMemzero(b->j,nz*sizeof(PetscInt));
2340:     b->singlemalloc = PETSC_TRUE;

2342:     b->i[0] = 0;
2343:     for (i=1; i<mbs+1; i++) {
2344:       b->i[i] = b->i[i-1] + b->imax[i-1];
2345:     }
2346:     b->free_a     = PETSC_TRUE;
2347:     b->free_ij    = PETSC_TRUE;
2348:   } else {
2349:     b->free_a     = PETSC_FALSE;
2350:     b->free_ij    = PETSC_FALSE;
2351:   }

2353:   B->rmap.bs          = bs;
2354:   b->bs2              = bs2;
2355:   b->mbs              = mbs;
2356:   b->nz               = 0;
2357:   b->maxnz            = nz*bs2;
2358:   B->info.nz_unneeded = (PetscReal)b->maxnz;
2359:   return(0);
2360: }

2363: /*MC
2364:    MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on 
2365:    block sparse compressed row format.

2367:    Options Database Keys:
2368: . -mat_type seqbaij - sets the matrix type to "seqbaij" during a call to MatSetFromOptions()

2370:   Level: beginner

2372: .seealso: MatCreateSeqBAIJ()
2373: M*/

2378: PetscErrorCode  MatCreate_SeqBAIJ(Mat B)
2379: {
2381:   PetscMPIInt    size;
2382:   Mat_SeqBAIJ    *b;

2385:   MPI_Comm_size(B->comm,&size);
2386:   if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"Comm must be of size 1");

2388:   PetscNew(Mat_SeqBAIJ,&b);
2389:   B->data = (void*)b;
2390:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2391:   B->factor           = 0;
2392:   B->mapping          = 0;
2393:   b->row              = 0;
2394:   b->col              = 0;
2395:   b->icol             = 0;
2396:   b->reallocs         = 0;
2397:   b->saved_values     = 0;
2398: #if defined(PETSC_USE_MAT_SINGLE)
2399:   b->setvalueslen     = 0;
2400:   b->setvaluescopy    = PETSC_NULL;
2401: #endif

2403:   b->sorted           = PETSC_FALSE;
2404:   b->roworiented      = PETSC_TRUE;
2405:   b->nonew            = 0;
2406:   b->diag             = 0;
2407:   b->solve_work       = 0;
2408:   b->mult_work        = 0;
2409:   B->spptr            = 0;
2410:   B->info.nz_unneeded = (PetscReal)b->maxnz;
2411:   b->keepzeroedrows   = PETSC_FALSE;
2412:   b->xtoy              = 0;
2413:   b->XtoY              = 0;
2414:   b->compressedrow.use     = PETSC_FALSE;
2415:   b->compressedrow.nrows   = 0;
2416:   b->compressedrow.i       = PETSC_NULL;
2417:   b->compressedrow.rindex  = PETSC_NULL;
2418:   b->compressedrow.checked = PETSC_FALSE;
2419:   B->same_nonzero          = PETSC_FALSE;

2421:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJInvertBlockDiagonal_C",
2422:                                      "MatInvertBlockDiagonal_SeqBAIJ",
2423:                                       MatInvertBlockDiagonal_SeqBAIJ);
2424:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2425:                                      "MatStoreValues_SeqBAIJ",
2426:                                       MatStoreValues_SeqBAIJ);
2427:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2428:                                      "MatRetrieveValues_SeqBAIJ",
2429:                                       MatRetrieveValues_SeqBAIJ);
2430:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetColumnIndices_C",
2431:                                      "MatSeqBAIJSetColumnIndices_SeqBAIJ",
2432:                                       MatSeqBAIJSetColumnIndices_SeqBAIJ);
2433:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqaij_C",
2434:                                      "MatConvert_SeqBAIJ_SeqAIJ",
2435:                                       MatConvert_SeqBAIJ_SeqAIJ);
2436:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqsbaij_C",
2437:                                      "MatConvert_SeqBAIJ_SeqSBAIJ",
2438:                                       MatConvert_SeqBAIJ_SeqSBAIJ);
2439:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetPreallocation_C",
2440:                                      "MatSeqBAIJSetPreallocation_SeqBAIJ",
2441:                                       MatSeqBAIJSetPreallocation_SeqBAIJ);
2442:   PetscObjectChangeTypeName((PetscObject)B,MATSEQBAIJ);
2443:   return(0);
2444: }

2449: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2450: {
2451:   Mat            C;
2452:   Mat_SeqBAIJ    *c,*a = (Mat_SeqBAIJ*)A->data;
2454:   PetscInt       i,mbs = a->mbs,nz = a->nz,bs2 = a->bs2;

2457:   if (a->i[mbs] != nz) SETERRQ(PETSC_ERR_PLIB,"Corrupt matrix");

2459:   *B = 0;
2460:   MatCreate(A->comm,&C);
2461:   MatSetSizes(C,A->rmap.N,A->cmap.n,A->rmap.N,A->cmap.n);
2462:   MatSetType(C,A->type_name);
2463:   PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
2464:   c    = (Mat_SeqBAIJ*)C->data;

2466:   C->rmap.N   = A->rmap.N;
2467:   C->cmap.N   = A->cmap.N;
2468:   C->rmap.bs  = A->rmap.bs;
2469:   c->bs2 = a->bs2;
2470:   c->mbs = a->mbs;
2471:   c->nbs = a->nbs;

2473:   PetscMalloc2(mbs,PetscInt,&c->imax,mbs,PetscInt,&c->ilen);
2474:   for (i=0; i<mbs; i++) {
2475:     c->imax[i] = a->imax[i];
2476:     c->ilen[i] = a->ilen[i];
2477:   }

2479:   /* allocate the matrix space */
2480:   PetscMalloc3(bs2*nz,PetscScalar,&c->a,nz,PetscInt,&c->j,mbs+1,PetscInt,&c->i);
2481:   c->singlemalloc = PETSC_TRUE;
2482:   PetscMemcpy(c->i,a->i,(mbs+1)*sizeof(PetscInt));
2483:   if (mbs > 0) {
2484:     PetscMemcpy(c->j,a->j,nz*sizeof(PetscInt));
2485:     if (cpvalues == MAT_COPY_VALUES) {
2486:       PetscMemcpy(c->a,a->a,bs2*nz*sizeof(MatScalar));
2487:     } else {
2488:       PetscMemzero(c->a,bs2*nz*sizeof(MatScalar));
2489:     }
2490:   }
2491:   c->sorted      = a->sorted;
2492:   c->roworiented = a->roworiented;
2493:   c->nonew       = a->nonew;

2495:   if (a->diag) {
2496:     PetscMalloc((mbs+1)*sizeof(PetscInt),&c->diag);
2497:     PetscLogObjectMemory(C,(mbs+1)*sizeof(PetscInt));
2498:     for (i=0; i<mbs; i++) {
2499:       c->diag[i] = a->diag[i];
2500:     }
2501:   } else c->diag        = 0;
2502:   c->nz                 = a->nz;
2503:   c->maxnz              = a->maxnz;
2504:   c->solve_work         = 0;
2505:   c->mult_work          = 0;
2506:   c->free_a             = PETSC_TRUE;
2507:   c->free_ij            = PETSC_TRUE;
2508:   C->preallocated       = PETSC_TRUE;
2509:   C->assembled          = PETSC_TRUE;

2511:   c->compressedrow.use     = a->compressedrow.use;
2512:   c->compressedrow.nrows   = a->compressedrow.nrows;
2513:   c->compressedrow.checked = a->compressedrow.checked;
2514:   if ( a->compressedrow.checked && a->compressedrow.use){
2515:     i = a->compressedrow.nrows;
2516:     PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
2517:     c->compressedrow.rindex = c->compressedrow.i + i + 1;
2518:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
2519:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
2520:   } else {
2521:     c->compressedrow.use    = PETSC_FALSE;
2522:     c->compressedrow.i      = PETSC_NULL;
2523:     c->compressedrow.rindex = PETSC_NULL;
2524:   }
2525:   C->same_nonzero = A->same_nonzero;
2526:   *B = C;
2527:   PetscFListDuplicate(A->qlist,&C->qlist);
2528:   return(0);
2529: }

2533: PetscErrorCode MatLoad_SeqBAIJ(PetscViewer viewer, MatType type,Mat *A)
2534: {
2535:   Mat_SeqBAIJ    *a;
2536:   Mat            B;
2538:   PetscInt       i,nz,header[4],*rowlengths=0,M,N,bs=1;
2539:   PetscInt       *mask,mbs,*jj,j,rowcount,nzcount,k,*browlengths,maskcount;
2540:   PetscInt       kmax,jcount,block,idx,point,nzcountb,extra_rows;
2541:   PetscInt       *masked,nmask,tmp,bs2,ishift;
2542:   PetscMPIInt    size;
2543:   int            fd;
2544:   PetscScalar    *aa;
2545:   MPI_Comm       comm = ((PetscObject)viewer)->comm;

2548:   PetscOptionsBegin(comm,PETSC_NULL,"Options for loading SEQBAIJ matrix","Mat");
2549:     PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
2550:   PetscOptionsEnd();
2551:   bs2  = bs*bs;

2553:   MPI_Comm_size(comm,&size);
2554:   if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"view must have one processor");
2555:   PetscViewerBinaryGetDescriptor(viewer,&fd);
2556:   PetscBinaryRead(fd,header,4,PETSC_INT);
2557:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not Mat object");
2558:   M = header[1]; N = header[2]; nz = header[3];

2560:   if (header[3] < 0) {
2561:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as SeqBAIJ");
2562:   }

2564:   if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");

2566:   /* 
2567:      This code adds extra rows to make sure the number of rows is 
2568:     divisible by the blocksize
2569:   */
2570:   mbs        = M/bs;
2571:   extra_rows = bs - M + bs*(mbs);
2572:   if (extra_rows == bs) extra_rows = 0;
2573:   else                  mbs++;
2574:   if (extra_rows) {
2575:     PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2576:   }

2578:   /* read in row lengths */
2579:   PetscMalloc((M+extra_rows)*sizeof(PetscInt),&rowlengths);
2580:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2581:   for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;

2583:   /* read in column indices */
2584:   PetscMalloc((nz+extra_rows)*sizeof(PetscInt),&jj);
2585:   PetscBinaryRead(fd,jj,nz,PETSC_INT);
2586:   for (i=0; i<extra_rows; i++) jj[nz+i] = M+i;

2588:   /* loop over row lengths determining block row lengths */
2589:   PetscMalloc(mbs*sizeof(PetscInt),&browlengths);
2590:   PetscMemzero(browlengths,mbs*sizeof(PetscInt));
2591:   PetscMalloc(2*mbs*sizeof(PetscInt),&mask);
2592:   PetscMemzero(mask,mbs*sizeof(PetscInt));
2593:   masked   = mask + mbs;
2594:   rowcount = 0; nzcount = 0;
2595:   for (i=0; i<mbs; i++) {
2596:     nmask = 0;
2597:     for (j=0; j<bs; j++) {
2598:       kmax = rowlengths[rowcount];
2599:       for (k=0; k<kmax; k++) {
2600:         tmp = jj[nzcount++]/bs;
2601:         if (!mask[tmp]) {masked[nmask++] = tmp; mask[tmp] = 1;}
2602:       }
2603:       rowcount++;
2604:     }
2605:     browlengths[i] += nmask;
2606:     /* zero out the mask elements we set */
2607:     for (j=0; j<nmask; j++) mask[masked[j]] = 0;
2608:   }

2610:   /* create our matrix */
2611:   MatCreate(comm,&B);
2612:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M+extra_rows,N+extra_rows);
2613:   MatSetType(B,type);
2614:   MatSeqBAIJSetPreallocation_SeqBAIJ(B,bs,0,browlengths);
2615:   a = (Mat_SeqBAIJ*)B->data;

2617:   /* set matrix "i" values */
2618:   a->i[0] = 0;
2619:   for (i=1; i<= mbs; i++) {
2620:     a->i[i]      = a->i[i-1] + browlengths[i-1];
2621:     a->ilen[i-1] = browlengths[i-1];
2622:   }
2623:   a->nz         = 0;
2624:   for (i=0; i<mbs; i++) a->nz += browlengths[i];

2626:   /* read in nonzero values */
2627:   PetscMalloc((nz+extra_rows)*sizeof(PetscScalar),&aa);
2628:   PetscBinaryRead(fd,aa,nz,PETSC_SCALAR);
2629:   for (i=0; i<extra_rows; i++) aa[nz+i] = 1.0;

2631:   /* set "a" and "j" values into matrix */
2632:   nzcount = 0; jcount = 0;
2633:   for (i=0; i<mbs; i++) {
2634:     nzcountb = nzcount;
2635:     nmask    = 0;
2636:     for (j=0; j<bs; j++) {
2637:       kmax = rowlengths[i*bs+j];
2638:       for (k=0; k<kmax; k++) {
2639:         tmp = jj[nzcount++]/bs;
2640:         if (!mask[tmp]) { masked[nmask++] = tmp; mask[tmp] = 1;}
2641:       }
2642:     }
2643:     /* sort the masked values */
2644:     PetscSortInt(nmask,masked);

2646:     /* set "j" values into matrix */
2647:     maskcount = 1;
2648:     for (j=0; j<nmask; j++) {
2649:       a->j[jcount++]  = masked[j];
2650:       mask[masked[j]] = maskcount++;
2651:     }
2652:     /* set "a" values into matrix */
2653:     ishift = bs2*a->i[i];
2654:     for (j=0; j<bs; j++) {
2655:       kmax = rowlengths[i*bs+j];
2656:       for (k=0; k<kmax; k++) {
2657:         tmp       = jj[nzcountb]/bs ;
2658:         block     = mask[tmp] - 1;
2659:         point     = jj[nzcountb] - bs*tmp;
2660:         idx       = ishift + bs2*block + j + bs*point;
2661:         a->a[idx] = (MatScalar)aa[nzcountb++];
2662:       }
2663:     }
2664:     /* zero out the mask elements we set */
2665:     for (j=0; j<nmask; j++) mask[masked[j]] = 0;
2666:   }
2667:   if (jcount != a->nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Bad binary matrix");

2669:   PetscFree(rowlengths);
2670:   PetscFree(browlengths);
2671:   PetscFree(aa);
2672:   PetscFree(jj);
2673:   PetscFree(mask);

2675:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2676:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2677:   MatView_Private(B);

2679:   *A = B;
2680:   return(0);
2681: }

2685: /*@C
2686:    MatCreateSeqBAIJ - Creates a sparse matrix in block AIJ (block
2687:    compressed row) format.  For good matrix assembly performance the
2688:    user should preallocate the matrix storage by setting the parameter nz
2689:    (or the array nnz).  By setting these parameters accurately, performance
2690:    during matrix assembly can be increased by more than a factor of 50.

2692:    Collective on MPI_Comm

2694:    Input Parameters:
2695: +  comm - MPI communicator, set to PETSC_COMM_SELF
2696: .  bs - size of block
2697: .  m - number of rows
2698: .  n - number of columns
2699: .  nz - number of nonzero blocks  per block row (same for all rows)
2700: -  nnz - array containing the number of nonzero blocks in the various block rows 
2701:          (possibly different for each block row) or PETSC_NULL

2703:    Output Parameter:
2704: .  A - the matrix 

2706:    Options Database Keys:
2707: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2708:                      block calculations (much slower)
2709: .    -mat_block_size - size of the blocks to use

2711:    Level: intermediate

2713:    Notes:
2714:    The number of rows and columns must be divisible by blocksize.

2716:    If the nnz parameter is given then the nz parameter is ignored

2718:    A nonzero block is any block that as 1 or more nonzeros in it

2720:    The block AIJ format is fully compatible with standard Fortran 77
2721:    storage.  That is, the stored row and column indices can begin at
2722:    either one (as in Fortran) or zero.  See the users' manual for details.

2724:    Specify the preallocated storage with either nz or nnz (not both).
2725:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2726:    allocation.  For additional details, see the users manual chapter on
2727:    matrices.

2729: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2730: @*/
2731: PetscErrorCode  MatCreateSeqBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2732: {
2734: 
2736:   MatCreate(comm,A);
2737:   MatSetSizes(*A,m,n,m,n);
2738:   MatSetType(*A,MATSEQBAIJ);
2739:   MatSeqBAIJSetPreallocation_SeqBAIJ(*A,bs,nz,(PetscInt*)nnz);
2740:   return(0);
2741: }

2745: /*@C
2746:    MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
2747:    per row in the matrix. For good matrix assembly performance the
2748:    user should preallocate the matrix storage by setting the parameter nz
2749:    (or the array nnz).  By setting these parameters accurately, performance
2750:    during matrix assembly can be increased by more than a factor of 50.

2752:    Collective on MPI_Comm

2754:    Input Parameters:
2755: +  A - the matrix
2756: .  bs - size of block
2757: .  nz - number of block nonzeros per block row (same for all rows)
2758: -  nnz - array containing the number of block nonzeros in the various block rows 
2759:          (possibly different for each block row) or PETSC_NULL

2761:    Options Database Keys:
2762: .   -mat_no_unroll - uses code that does not unroll the loops in the 
2763:                      block calculations (much slower)
2764: .    -mat_block_size - size of the blocks to use

2766:    Level: intermediate

2768:    Notes:
2769:    If the nnz parameter is given then the nz parameter is ignored

2771:    The block AIJ format is fully compatible with standard Fortran 77
2772:    storage.  That is, the stored row and column indices can begin at
2773:    either one (as in Fortran) or zero.  See the users' manual for details.

2775:    Specify the preallocated storage with either nz or nnz (not both).
2776:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2777:    allocation.  For additional details, see the users manual chapter on
2778:    matrices.

2780: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2781: @*/
2782: PetscErrorCode  MatSeqBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt nz,const PetscInt nnz[])
2783: {
2784:   PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[]);

2787:   PetscObjectQueryFunction((PetscObject)B,"MatSeqBAIJSetPreallocation_C",(void (**)(void))&f);
2788:   if (f) {
2789:     (*f)(B,bs,nz,nnz);
2790:   }
2791:   return(0);
2792: }

2796: /*@
2797:      MatCreateSeqBAIJWithArrays - Creates an sequential BAIJ matrix using matrix elements 
2798:               (upper triangular entries in CSR format) provided by the user.

2800:      Collective on MPI_Comm

2802:    Input Parameters:
2803: +  comm - must be an MPI communicator of size 1
2804: .  bs - size of block
2805: .  m - number of rows
2806: .  n - number of columns
2807: .  i - row indices
2808: .  j - column indices
2809: -  a - matrix values

2811:    Output Parameter:
2812: .  mat - the matrix

2814:    Level: intermediate

2816:    Notes:
2817:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
2818:     once the matrix is destroyed

2820:        You cannot set new nonzero locations into this matrix, that will generate an error.

2822:        The i and j indices are 0 based

2824: .seealso: MatCreate(), MatCreateMPIBAIJ(), MatCreateSeqBAIJ()

2826: @*/
2827: PetscErrorCode  MatCreateSeqBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
2828: {
2830:   PetscInt       ii;
2831:   Mat_SeqBAIJ    *baij;

2834:   if (bs != 1) SETERRQ1(PETSC_ERR_SUP,"block size %D > 1 is not supported yet",bs);
2835:   if (i[0]) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
2836: 
2837:   MatCreate(comm,mat);
2838:   MatSetSizes(*mat,m,n,m,n);
2839:   MatSetType(*mat,MATSEQBAIJ);
2840:   MatSeqBAIJSetPreallocation_SeqBAIJ(*mat,bs,MAT_SKIP_ALLOCATION,0);
2841:   baij = (Mat_SeqBAIJ*)(*mat)->data;
2842:   PetscMalloc2(m,PetscInt,&baij->imax,m,PetscInt,&baij->ilen);

2844:   baij->i = i;
2845:   baij->j = j;
2846:   baij->a = a;
2847:   baij->singlemalloc = PETSC_FALSE;
2848:   baij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
2849:   baij->free_a       = PETSC_FALSE;
2850:   baij->free_ij       = PETSC_FALSE;

2852:   for (ii=0; ii<m; ii++) {
2853:     baij->ilen[ii] = baij->imax[ii] = i[ii+1] - i[ii];
2854: #if defined(PETSC_USE_DEBUG)
2855:     if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
2856: #endif    
2857:   }
2858: #if defined(PETSC_USE_DEBUG)
2859:   for (ii=0; ii<baij->i[m]; ii++) {
2860:     if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
2861:     if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
2862:   }
2863: #endif    

2865:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
2866:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
2867:   return(0);
2868: }