Actual source code: mpisbaij.c
petsc-3.5.0 2014-06-30
2: #include <../src/mat/impls/baij/mpi/mpibaij.h> /*I "petscmat.h" I*/
3: #include <../src/mat/impls/sbaij/mpi/mpisbaij.h>
4: #include <../src/mat/impls/sbaij/seq/sbaij.h>
5: #include <petscblaslapack.h>
7: extern PetscErrorCode MatSetUpMultiply_MPISBAIJ(Mat);
8: extern PetscErrorCode MatSetUpMultiply_MPISBAIJ_2comm(Mat);
9: extern PetscErrorCode MatDisAssemble_MPISBAIJ(Mat);
10: extern PetscErrorCode MatIncreaseOverlap_MPISBAIJ(Mat,PetscInt,IS[],PetscInt);
11: extern PetscErrorCode MatGetValues_SeqSBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],PetscScalar []);
12: extern PetscErrorCode MatGetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],PetscScalar []);
13: extern PetscErrorCode MatSetValues_SeqSBAIJ(Mat,PetscInt,const PetscInt [],PetscInt,const PetscInt [],const PetscScalar [],InsertMode);
14: extern PetscErrorCode MatSetValuesBlocked_SeqSBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
15: extern PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
16: extern PetscErrorCode MatGetRow_SeqSBAIJ(Mat,PetscInt,PetscInt*,PetscInt**,PetscScalar**);
17: extern PetscErrorCode MatRestoreRow_SeqSBAIJ(Mat,PetscInt,PetscInt*,PetscInt**,PetscScalar**);
18: extern PetscErrorCode MatZeroRows_SeqSBAIJ(Mat,IS,PetscScalar*,Vec,Vec);
19: extern PetscErrorCode MatZeroRows_SeqBAIJ(Mat,IS,PetscScalar*,Vec,Vec);
20: extern PetscErrorCode MatGetRowMaxAbs_MPISBAIJ(Mat,Vec,PetscInt[]);
21: extern PetscErrorCode MatSOR_MPISBAIJ(Mat,Vec,PetscReal,MatSORType,PetscReal,PetscInt,PetscInt,Vec);
25: PetscErrorCode MatStoreValues_MPISBAIJ(Mat mat)
26: {
27: Mat_MPISBAIJ *aij = (Mat_MPISBAIJ*)mat->data;
31: MatStoreValues(aij->A);
32: MatStoreValues(aij->B);
33: return(0);
34: }
38: PetscErrorCode MatRetrieveValues_MPISBAIJ(Mat mat)
39: {
40: Mat_MPISBAIJ *aij = (Mat_MPISBAIJ*)mat->data;
44: MatRetrieveValues(aij->A);
45: MatRetrieveValues(aij->B);
46: return(0);
47: }
49: #define MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv) \
50: { \
51: \
52: brow = row/bs; \
53: rp = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
54: rmax = aimax[brow]; nrow = ailen[brow]; \
55: bcol = col/bs; \
56: ridx = row % bs; cidx = col % bs; \
57: low = 0; high = nrow; \
58: while (high-low > 3) { \
59: t = (low+high)/2; \
60: if (rp[t] > bcol) high = t; \
61: else low = t; \
62: } \
63: for (_i=low; _i<high; _i++) { \
64: if (rp[_i] > bcol) break; \
65: if (rp[_i] == bcol) { \
66: bap = ap + bs2*_i + bs*cidx + ridx; \
67: if (addv == ADD_VALUES) *bap += value; \
68: else *bap = value; \
69: goto a_noinsert; \
70: } \
71: } \
72: if (a->nonew == 1) goto a_noinsert; \
73: if (a->nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
74: MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,aimax,a->nonew,MatScalar); \
75: N = nrow++ - 1; \
76: /* shift up all the later entries in this row */ \
77: for (ii=N; ii>=_i; ii--) { \
78: rp[ii+1] = rp[ii]; \
79: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
80: } \
81: if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); } \
82: rp[_i] = bcol; \
83: ap[bs2*_i + bs*cidx + ridx] = value; \
84: A->nonzerostate++;\
85: a_noinsert:; \
86: ailen[brow] = nrow; \
87: }
89: #define MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv) \
90: { \
91: brow = row/bs; \
92: rp = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
93: rmax = bimax[brow]; nrow = bilen[brow]; \
94: bcol = col/bs; \
95: ridx = row % bs; cidx = col % bs; \
96: low = 0; high = nrow; \
97: while (high-low > 3) { \
98: t = (low+high)/2; \
99: if (rp[t] > bcol) high = t; \
100: else low = t; \
101: } \
102: for (_i=low; _i<high; _i++) { \
103: if (rp[_i] > bcol) break; \
104: if (rp[_i] == bcol) { \
105: bap = ap + bs2*_i + bs*cidx + ridx; \
106: if (addv == ADD_VALUES) *bap += value; \
107: else *bap = value; \
108: goto b_noinsert; \
109: } \
110: } \
111: if (b->nonew == 1) goto b_noinsert; \
112: if (b->nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
113: MatSeqXAIJReallocateAIJ(B,b->mbs,bs2,nrow,brow,bcol,rmax,ba,bi,bj,rp,ap,bimax,b->nonew,MatScalar); \
114: N = nrow++ - 1; \
115: /* shift up all the later entries in this row */ \
116: for (ii=N; ii>=_i; ii--) { \
117: rp[ii+1] = rp[ii]; \
118: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
119: } \
120: if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));} \
121: rp[_i] = bcol; \
122: ap[bs2*_i + bs*cidx + ridx] = value; \
123: B->nonzerostate++;\
124: b_noinsert:; \
125: bilen[brow] = nrow; \
126: }
128: /* Only add/insert a(i,j) with i<=j (blocks).
129: Any a(i,j) with i>j input by user is ingored.
130: */
133: PetscErrorCode MatSetValues_MPISBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
134: {
135: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
136: MatScalar value;
137: PetscBool roworiented = baij->roworiented;
139: PetscInt i,j,row,col;
140: PetscInt rstart_orig=mat->rmap->rstart;
141: PetscInt rend_orig =mat->rmap->rend,cstart_orig=mat->cmap->rstart;
142: PetscInt cend_orig =mat->cmap->rend,bs=mat->rmap->bs;
144: /* Some Variables required in the macro */
145: Mat A = baij->A;
146: Mat_SeqSBAIJ *a = (Mat_SeqSBAIJ*)(A)->data;
147: PetscInt *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
148: MatScalar *aa =a->a;
150: Mat B = baij->B;
151: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(B)->data;
152: PetscInt *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
153: MatScalar *ba =b->a;
155: PetscInt *rp,ii,nrow,_i,rmax,N,brow,bcol;
156: PetscInt low,high,t,ridx,cidx,bs2=a->bs2;
157: MatScalar *ap,*bap;
159: /* for stash */
160: PetscInt n_loc, *in_loc = NULL;
161: MatScalar *v_loc = NULL;
164: if (!baij->donotstash) {
165: if (n > baij->n_loc) {
166: PetscFree(baij->in_loc);
167: PetscFree(baij->v_loc);
168: PetscMalloc1(n,&baij->in_loc);
169: PetscMalloc1(n,&baij->v_loc);
171: baij->n_loc = n;
172: }
173: in_loc = baij->in_loc;
174: v_loc = baij->v_loc;
175: }
177: for (i=0; i<m; i++) {
178: if (im[i] < 0) continue;
179: #if defined(PETSC_USE_DEBUG)
180: if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
181: #endif
182: if (im[i] >= rstart_orig && im[i] < rend_orig) { /* this processor entry */
183: row = im[i] - rstart_orig; /* local row index */
184: for (j=0; j<n; j++) {
185: if (im[i]/bs > in[j]/bs) {
186: if (a->ignore_ltriangular) {
187: continue; /* ignore lower triangular blocks */
188: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"Lower triangular value cannot be set for sbaij format. Ignoring these values, run with -mat_ignore_lower_triangular or call MatSetOption(mat,MAT_IGNORE_LOWER_TRIANGULAR,PETSC_TRUE)");
189: }
190: if (in[j] >= cstart_orig && in[j] < cend_orig) { /* diag entry (A) */
191: col = in[j] - cstart_orig; /* local col index */
192: brow = row/bs; bcol = col/bs;
193: if (brow > bcol) continue; /* ignore lower triangular blocks of A */
194: if (roworiented) value = v[i*n+j];
195: else value = v[i+j*m];
196: MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv);
197: /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
198: } else if (in[j] < 0) continue;
199: #if defined(PETSC_USE_DEBUG)
200: else if (in[j] >= mat->cmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap->N-1);
201: #endif
202: else { /* off-diag entry (B) */
203: if (mat->was_assembled) {
204: if (!baij->colmap) {
205: MatCreateColmap_MPIBAIJ_Private(mat);
206: }
207: #if defined(PETSC_USE_CTABLE)
208: PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
209: col = col - 1;
210: #else
211: col = baij->colmap[in[j]/bs] - 1;
212: #endif
213: if (col < 0 && !((Mat_SeqSBAIJ*)(baij->A->data))->nonew) {
214: MatDisAssemble_MPISBAIJ(mat);
215: col = in[j];
216: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
217: B = baij->B;
218: b = (Mat_SeqBAIJ*)(B)->data;
219: bimax= b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
220: ba = b->a;
221: } else col += in[j]%bs;
222: } else col = in[j];
223: if (roworiented) value = v[i*n+j];
224: else value = v[i+j*m];
225: MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv);
226: /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
227: }
228: }
229: } else { /* off processor entry */
230: if (mat->nooffprocentries) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Setting off process row %D even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set",im[i]);
231: if (!baij->donotstash) {
232: mat->assembled = PETSC_FALSE;
233: n_loc = 0;
234: for (j=0; j<n; j++) {
235: if (im[i]/bs > in[j]/bs) continue; /* ignore lower triangular blocks */
236: in_loc[n_loc] = in[j];
237: if (roworiented) {
238: v_loc[n_loc] = v[i*n+j];
239: } else {
240: v_loc[n_loc] = v[j*m+i];
241: }
242: n_loc++;
243: }
244: MatStashValuesRow_Private(&mat->stash,im[i],n_loc,in_loc,v_loc,PETSC_FALSE);
245: }
246: }
247: }
248: return(0);
249: }
253: PetscErrorCode MatSetValuesBlocked_MPISBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode addv)
254: {
255: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
256: const MatScalar *value;
257: MatScalar *barray =baij->barray;
258: PetscBool roworiented = baij->roworiented,ignore_ltriangular = ((Mat_SeqSBAIJ*)baij->A->data)->ignore_ltriangular;
259: PetscErrorCode ierr;
260: PetscInt i,j,ii,jj,row,col,rstart=baij->rstartbs;
261: PetscInt rend=baij->rendbs,cstart=baij->rstartbs,stepval;
262: PetscInt cend=baij->rendbs,bs=mat->rmap->bs,bs2=baij->bs2;
265: if (!barray) {
266: PetscMalloc1(bs2,&barray);
267: baij->barray = barray;
268: }
270: if (roworiented) {
271: stepval = (n-1)*bs;
272: } else {
273: stepval = (m-1)*bs;
274: }
275: for (i=0; i<m; i++) {
276: if (im[i] < 0) continue;
277: #if defined(PETSC_USE_DEBUG)
278: if (im[i] >= baij->Mbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large, row %D max %D",im[i],baij->Mbs-1);
279: #endif
280: if (im[i] >= rstart && im[i] < rend) {
281: row = im[i] - rstart;
282: for (j=0; j<n; j++) {
283: if (im[i] > in[j]) {
284: if (ignore_ltriangular) continue; /* ignore lower triangular blocks */
285: else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_USER,"Lower triangular value cannot be set for sbaij format. Ignoring these values, run with -mat_ignore_lower_triangular or call MatSetOption(mat,MAT_IGNORE_LOWER_TRIANGULAR,PETSC_TRUE)");
286: }
287: /* If NumCol = 1 then a copy is not required */
288: if ((roworiented) && (n == 1)) {
289: barray = (MatScalar*) v + i*bs2;
290: } else if ((!roworiented) && (m == 1)) {
291: barray = (MatScalar*) v + j*bs2;
292: } else { /* Here a copy is required */
293: if (roworiented) {
294: value = v + i*(stepval+bs)*bs + j*bs;
295: } else {
296: value = v + j*(stepval+bs)*bs + i*bs;
297: }
298: for (ii=0; ii<bs; ii++,value+=stepval) {
299: for (jj=0; jj<bs; jj++) {
300: *barray++ = *value++;
301: }
302: }
303: barray -=bs2;
304: }
306: if (in[j] >= cstart && in[j] < cend) {
307: col = in[j] - cstart;
308: MatSetValuesBlocked_SeqSBAIJ(baij->A,1,&row,1,&col,barray,addv);
309: } else if (in[j] < 0) continue;
310: #if defined(PETSC_USE_DEBUG)
311: else if (in[j] >= baij->Nbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large, col %D max %D",in[j],baij->Nbs-1);
312: #endif
313: else {
314: if (mat->was_assembled) {
315: if (!baij->colmap) {
316: MatCreateColmap_MPIBAIJ_Private(mat);
317: }
319: #if defined(PETSC_USE_DEBUG)
320: #if defined(PETSC_USE_CTABLE)
321: { PetscInt data;
322: PetscTableFind(baij->colmap,in[j]+1,&data);
323: if ((data - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
324: }
325: #else
326: if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
327: #endif
328: #endif
329: #if defined(PETSC_USE_CTABLE)
330: PetscTableFind(baij->colmap,in[j]+1,&col);
331: col = (col - 1)/bs;
332: #else
333: col = (baij->colmap[in[j]] - 1)/bs;
334: #endif
335: if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
336: MatDisAssemble_MPISBAIJ(mat);
337: col = in[j];
338: }
339: } else col = in[j];
340: MatSetValuesBlocked_SeqBAIJ(baij->B,1,&row,1,&col,barray,addv);
341: }
342: }
343: } else {
344: if (mat->nooffprocentries) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Setting off process row %D even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set",im[i]);
345: if (!baij->donotstash) {
346: if (roworiented) {
347: MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
348: } else {
349: MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
350: }
351: }
352: }
353: }
354: return(0);
355: }
359: PetscErrorCode MatGetValues_MPISBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
360: {
361: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
363: PetscInt bs = mat->rmap->bs,i,j,bsrstart = mat->rmap->rstart,bsrend = mat->rmap->rend;
364: PetscInt bscstart = mat->cmap->rstart,bscend = mat->cmap->rend,row,col,data;
367: for (i=0; i<m; i++) {
368: if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]); */
369: if (idxm[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap->N-1);
370: if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
371: row = idxm[i] - bsrstart;
372: for (j=0; j<n; j++) {
373: if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column %D",idxn[j]); */
374: if (idxn[j] >= mat->cmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap->N-1);
375: if (idxn[j] >= bscstart && idxn[j] < bscend) {
376: col = idxn[j] - bscstart;
377: MatGetValues_SeqSBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
378: } else {
379: if (!baij->colmap) {
380: MatCreateColmap_MPIBAIJ_Private(mat);
381: }
382: #if defined(PETSC_USE_CTABLE)
383: PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
384: data--;
385: #else
386: data = baij->colmap[idxn[j]/bs]-1;
387: #endif
388: if ((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
389: else {
390: col = data + idxn[j]%bs;
391: MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
392: }
393: }
394: }
395: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
396: }
397: return(0);
398: }
402: PetscErrorCode MatNorm_MPISBAIJ(Mat mat,NormType type,PetscReal *norm)
403: {
404: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
406: PetscReal sum[2],*lnorm2;
409: if (baij->size == 1) {
410: MatNorm(baij->A,type,norm);
411: } else {
412: if (type == NORM_FROBENIUS) {
413: PetscMalloc1(2,&lnorm2);
414: MatNorm(baij->A,type,lnorm2);
415: *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2++; /* squar power of norm(A) */
416: MatNorm(baij->B,type,lnorm2);
417: *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2--; /* squar power of norm(B) */
418: MPI_Allreduce(lnorm2,sum,2,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
419: *norm = PetscSqrtReal(sum[0] + 2*sum[1]);
420: PetscFree(lnorm2);
421: } else if (type == NORM_INFINITY || type == NORM_1) { /* max row/column sum */
422: Mat_SeqSBAIJ *amat=(Mat_SeqSBAIJ*)baij->A->data;
423: Mat_SeqBAIJ *bmat=(Mat_SeqBAIJ*)baij->B->data;
424: PetscReal *rsum,*rsum2,vabs;
425: PetscInt *jj,*garray=baij->garray,rstart=baij->rstartbs,nz;
426: PetscInt brow,bcol,col,bs=baij->A->rmap->bs,row,grow,gcol,mbs=amat->mbs;
427: MatScalar *v;
429: PetscMalloc2(mat->cmap->N,&rsum,mat->cmap->N,&rsum2);
430: PetscMemzero(rsum,mat->cmap->N*sizeof(PetscReal));
431: /* Amat */
432: v = amat->a; jj = amat->j;
433: for (brow=0; brow<mbs; brow++) {
434: grow = bs*(rstart + brow);
435: nz = amat->i[brow+1] - amat->i[brow];
436: for (bcol=0; bcol<nz; bcol++) {
437: gcol = bs*(rstart + *jj); jj++;
438: for (col=0; col<bs; col++) {
439: for (row=0; row<bs; row++) {
440: vabs = PetscAbsScalar(*v); v++;
441: rsum[gcol+col] += vabs;
442: /* non-diagonal block */
443: if (bcol > 0 && vabs > 0.0) rsum[grow+row] += vabs;
444: }
445: }
446: }
447: }
448: /* Bmat */
449: v = bmat->a; jj = bmat->j;
450: for (brow=0; brow<mbs; brow++) {
451: grow = bs*(rstart + brow);
452: nz = bmat->i[brow+1] - bmat->i[brow];
453: for (bcol=0; bcol<nz; bcol++) {
454: gcol = bs*garray[*jj]; jj++;
455: for (col=0; col<bs; col++) {
456: for (row=0; row<bs; row++) {
457: vabs = PetscAbsScalar(*v); v++;
458: rsum[gcol+col] += vabs;
459: rsum[grow+row] += vabs;
460: }
461: }
462: }
463: }
464: MPI_Allreduce(rsum,rsum2,mat->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
465: *norm = 0.0;
466: for (col=0; col<mat->cmap->N; col++) {
467: if (rsum2[col] > *norm) *norm = rsum2[col];
468: }
469: PetscFree2(rsum,rsum2);
470: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for this norm yet");
471: }
472: return(0);
473: }
477: PetscErrorCode MatAssemblyBegin_MPISBAIJ(Mat mat,MatAssemblyType mode)
478: {
479: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
481: PetscInt nstash,reallocs;
482: InsertMode addv;
485: if (baij->donotstash || mat->nooffprocentries) return(0);
487: /* make sure all processors are either in INSERTMODE or ADDMODE */
488: MPI_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,PetscObjectComm((PetscObject)mat));
489: if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
490: mat->insertmode = addv; /* in case this processor had no cache */
492: MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
493: MatStashScatterBegin_Private(mat,&mat->bstash,baij->rangebs);
494: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
495: PetscInfo2(mat,"Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
496: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
497: PetscInfo2(mat,"Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
498: return(0);
499: }
503: PetscErrorCode MatAssemblyEnd_MPISBAIJ(Mat mat,MatAssemblyType mode)
504: {
505: Mat_MPISBAIJ *baij=(Mat_MPISBAIJ*)mat->data;
506: Mat_SeqSBAIJ *a =(Mat_SeqSBAIJ*)baij->A->data;
508: PetscInt i,j,rstart,ncols,flg,bs2=baij->bs2;
509: PetscInt *row,*col;
510: PetscBool other_disassembled;
511: PetscMPIInt n;
512: PetscBool r1,r2,r3;
513: MatScalar *val;
514: InsertMode addv = mat->insertmode;
516: /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
518: if (!baij->donotstash && !mat->nooffprocentries) {
519: while (1) {
520: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
521: if (!flg) break;
523: for (i=0; i<n;) {
524: /* Now identify the consecutive vals belonging to the same row */
525: for (j=i,rstart=row[j]; j<n; j++) {
526: if (row[j] != rstart) break;
527: }
528: if (j < n) ncols = j-i;
529: else ncols = n-i;
530: /* Now assemble all these values with a single function call */
531: MatSetValues_MPISBAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
532: i = j;
533: }
534: }
535: MatStashScatterEnd_Private(&mat->stash);
536: /* Now process the block-stash. Since the values are stashed column-oriented,
537: set the roworiented flag to column oriented, and after MatSetValues()
538: restore the original flags */
539: r1 = baij->roworiented;
540: r2 = a->roworiented;
541: r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;
543: baij->roworiented = PETSC_FALSE;
544: a->roworiented = PETSC_FALSE;
546: ((Mat_SeqBAIJ*)baij->B->data)->roworiented = PETSC_FALSE; /* b->roworinted */
547: while (1) {
548: MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
549: if (!flg) break;
551: for (i=0; i<n;) {
552: /* Now identify the consecutive vals belonging to the same row */
553: for (j=i,rstart=row[j]; j<n; j++) {
554: if (row[j] != rstart) break;
555: }
556: if (j < n) ncols = j-i;
557: else ncols = n-i;
558: MatSetValuesBlocked_MPISBAIJ(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
559: i = j;
560: }
561: }
562: MatStashScatterEnd_Private(&mat->bstash);
564: baij->roworiented = r1;
565: a->roworiented = r2;
567: ((Mat_SeqBAIJ*)baij->B->data)->roworiented = r3; /* b->roworinted */
568: }
570: MatAssemblyBegin(baij->A,mode);
571: MatAssemblyEnd(baij->A,mode);
573: /* determine if any processor has disassembled, if so we must
574: also disassemble ourselfs, in order that we may reassemble. */
575: /*
576: if nonzero structure of submatrix B cannot change then we know that
577: no processor disassembled thus we can skip this stuff
578: */
579: if (!((Mat_SeqBAIJ*)baij->B->data)->nonew) {
580: MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPIU_BOOL,MPI_PROD,PetscObjectComm((PetscObject)mat));
581: if (mat->was_assembled && !other_disassembled) {
582: MatDisAssemble_MPISBAIJ(mat);
583: }
584: }
586: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
587: MatSetUpMultiply_MPISBAIJ(mat); /* setup Mvctx and sMvctx */
588: }
589: MatAssemblyBegin(baij->B,mode);
590: MatAssemblyEnd(baij->B,mode);
592: PetscFree2(baij->rowvalues,baij->rowindices);
594: baij->rowvalues = 0;
596: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
597: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
598: PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
599: MPI_Allreduce(&state,&mat->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)mat));
600: }
601: return(0);
602: }
604: extern PetscErrorCode MatView_SeqSBAIJ_ASCII(Mat,PetscViewer);
605: extern PetscErrorCode MatSetValues_MPIBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
606: #include <petscdraw.h>
609: static PetscErrorCode MatView_MPISBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
610: {
611: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
612: PetscErrorCode ierr;
613: PetscInt bs = mat->rmap->bs;
614: PetscMPIInt rank = baij->rank;
615: PetscBool iascii,isdraw;
616: PetscViewer sviewer;
617: PetscViewerFormat format;
620: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
621: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
622: if (iascii) {
623: PetscViewerGetFormat(viewer,&format);
624: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
625: MatInfo info;
626: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
627: MatGetInfo(mat,MAT_LOCAL,&info);
628: PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);
629: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,mat->rmap->bs,(PetscInt)info.memory);
630: MatGetInfo(baij->A,MAT_LOCAL,&info);
631: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
632: MatGetInfo(baij->B,MAT_LOCAL,&info);
633: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
634: PetscViewerFlush(viewer);
635: PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);
636: PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
637: VecScatterView(baij->Mvctx,viewer);
638: return(0);
639: } else if (format == PETSC_VIEWER_ASCII_INFO) {
640: PetscViewerASCIIPrintf(viewer," block size is %D\n",bs);
641: return(0);
642: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
643: return(0);
644: }
645: }
647: if (isdraw) {
648: PetscDraw draw;
649: PetscBool isnull;
650: PetscViewerDrawGetDraw(viewer,0,&draw);
651: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
652: }
654: {
655: /* assemble the entire matrix onto first processor. */
656: Mat A;
657: Mat_SeqSBAIJ *Aloc;
658: Mat_SeqBAIJ *Bloc;
659: PetscInt M = mat->rmap->N,N = mat->cmap->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
660: MatScalar *a;
662: /* Should this be the same type as mat? */
663: MatCreate(PetscObjectComm((PetscObject)mat),&A);
664: if (!rank) {
665: MatSetSizes(A,M,N,M,N);
666: } else {
667: MatSetSizes(A,0,0,M,N);
668: }
669: MatSetType(A,MATMPISBAIJ);
670: MatMPISBAIJSetPreallocation(A,mat->rmap->bs,0,NULL,0,NULL);
671: MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);
672: PetscLogObjectParent((PetscObject)mat,(PetscObject)A);
674: /* copy over the A part */
675: Aloc = (Mat_SeqSBAIJ*)baij->A->data;
676: ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
677: PetscMalloc1(bs,&rvals);
679: for (i=0; i<mbs; i++) {
680: rvals[0] = bs*(baij->rstartbs + i);
681: for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
682: for (j=ai[i]; j<ai[i+1]; j++) {
683: col = (baij->cstartbs+aj[j])*bs;
684: for (k=0; k<bs; k++) {
685: MatSetValues_MPISBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
686: col++;
687: a += bs;
688: }
689: }
690: }
691: /* copy over the B part */
692: Bloc = (Mat_SeqBAIJ*)baij->B->data;
693: ai = Bloc->i; aj = Bloc->j; a = Bloc->a;
694: for (i=0; i<mbs; i++) {
696: rvals[0] = bs*(baij->rstartbs + i);
697: for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
698: for (j=ai[i]; j<ai[i+1]; j++) {
699: col = baij->garray[aj[j]]*bs;
700: for (k=0; k<bs; k++) {
701: MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
702: col++;
703: a += bs;
704: }
705: }
706: }
707: PetscFree(rvals);
708: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
709: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
710: /*
711: Everyone has to call to draw the matrix since the graphics waits are
712: synchronized across all processors that share the PetscDraw object
713: */
714: PetscViewerGetSingleton(viewer,&sviewer);
715: if (!rank) {
716: MatView_SeqSBAIJ_ASCII(((Mat_MPISBAIJ*)(A->data))->A,sviewer);
717: }
718: PetscViewerRestoreSingleton(viewer,&sviewer);
719: MatDestroy(&A);
720: }
721: return(0);
722: }
726: PetscErrorCode MatView_MPISBAIJ(Mat mat,PetscViewer viewer)
727: {
729: PetscBool iascii,isdraw,issocket,isbinary;
732: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
733: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
734: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
735: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
736: if (iascii || isdraw || issocket || isbinary) {
737: MatView_MPISBAIJ_ASCIIorDraworSocket(mat,viewer);
738: }
739: return(0);
740: }
744: PetscErrorCode MatDestroy_MPISBAIJ(Mat mat)
745: {
746: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
750: #if defined(PETSC_USE_LOG)
751: PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap->N,mat->cmap->N);
752: #endif
753: MatStashDestroy_Private(&mat->stash);
754: MatStashDestroy_Private(&mat->bstash);
755: MatDestroy(&baij->A);
756: MatDestroy(&baij->B);
757: #if defined(PETSC_USE_CTABLE)
758: PetscTableDestroy(&baij->colmap);
759: #else
760: PetscFree(baij->colmap);
761: #endif
762: PetscFree(baij->garray);
763: VecDestroy(&baij->lvec);
764: VecScatterDestroy(&baij->Mvctx);
765: VecDestroy(&baij->slvec0);
766: VecDestroy(&baij->slvec0b);
767: VecDestroy(&baij->slvec1);
768: VecDestroy(&baij->slvec1a);
769: VecDestroy(&baij->slvec1b);
770: VecScatterDestroy(&baij->sMvctx);
771: PetscFree2(baij->rowvalues,baij->rowindices);
772: PetscFree(baij->barray);
773: PetscFree(baij->hd);
774: VecDestroy(&baij->diag);
775: VecDestroy(&baij->bb1);
776: VecDestroy(&baij->xx1);
777: #if defined(PETSC_USE_REAL_MAT_SINGLE)
778: PetscFree(baij->setvaluescopy);
779: #endif
780: PetscFree(baij->in_loc);
781: PetscFree(baij->v_loc);
782: PetscFree(baij->rangebs);
783: PetscFree(mat->data);
785: PetscObjectChangeTypeName((PetscObject)mat,0);
786: PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C",NULL);
787: PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C",NULL);
788: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",NULL);
789: PetscObjectComposeFunction((PetscObject)mat,"MatMPISBAIJSetPreallocation_C",NULL);
790: PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpisbaij_mpisbstrm_C",NULL);
791: return(0);
792: }
796: PetscErrorCode MatMult_MPISBAIJ_Hermitian(Mat A,Vec xx,Vec yy)
797: {
798: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
800: PetscInt nt,mbs=a->mbs,bs=A->rmap->bs;
801: PetscScalar *x,*from;
804: VecGetLocalSize(xx,&nt);
805: if (nt != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
807: /* diagonal part */
808: (*a->A->ops->mult)(a->A,xx,a->slvec1a);
809: VecSet(a->slvec1b,0.0);
811: /* subdiagonal part */
812: (*a->B->ops->multhermitiantranspose)(a->B,xx,a->slvec0b);
814: /* copy x into the vec slvec0 */
815: VecGetArray(a->slvec0,&from);
816: VecGetArray(xx,&x);
818: PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
819: VecRestoreArray(a->slvec0,&from);
820: VecRestoreArray(xx,&x);
822: VecScatterBegin(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
823: VecScatterEnd(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
824: /* supperdiagonal part */
825: (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,yy);
826: return(0);
827: }
831: PetscErrorCode MatMult_MPISBAIJ(Mat A,Vec xx,Vec yy)
832: {
833: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
835: PetscInt nt,mbs=a->mbs,bs=A->rmap->bs;
836: PetscScalar *x,*from;
839: VecGetLocalSize(xx,&nt);
840: if (nt != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
842: /* diagonal part */
843: (*a->A->ops->mult)(a->A,xx,a->slvec1a);
844: VecSet(a->slvec1b,0.0);
846: /* subdiagonal part */
847: (*a->B->ops->multtranspose)(a->B,xx,a->slvec0b);
849: /* copy x into the vec slvec0 */
850: VecGetArray(a->slvec0,&from);
851: VecGetArray(xx,&x);
853: PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
854: VecRestoreArray(a->slvec0,&from);
855: VecRestoreArray(xx,&x);
857: VecScatterBegin(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
858: VecScatterEnd(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
859: /* supperdiagonal part */
860: (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,yy);
861: return(0);
862: }
866: PetscErrorCode MatMult_MPISBAIJ_2comm(Mat A,Vec xx,Vec yy)
867: {
868: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
870: PetscInt nt;
873: VecGetLocalSize(xx,&nt);
874: if (nt != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
876: VecGetLocalSize(yy,&nt);
877: if (nt != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
879: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
880: /* do diagonal part */
881: (*a->A->ops->mult)(a->A,xx,yy);
882: /* do supperdiagonal part */
883: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
884: (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
885: /* do subdiagonal part */
886: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
887: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
888: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
889: return(0);
890: }
894: PetscErrorCode MatMultAdd_MPISBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
895: {
896: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
898: PetscInt mbs=a->mbs,bs=A->rmap->bs;
899: PetscScalar *x,*from,zero=0.0;
902: /*
903: PetscSynchronizedPrintf(PetscObjectComm((PetscObject)A)," MatMultAdd is called ...\n");
904: PetscSynchronizedFlush(PetscObjectComm((PetscObject)A),PETSC_STDOUT);
905: */
906: /* diagonal part */
907: (*a->A->ops->multadd)(a->A,xx,yy,a->slvec1a);
908: VecSet(a->slvec1b,zero);
910: /* subdiagonal part */
911: (*a->B->ops->multtranspose)(a->B,xx,a->slvec0b);
913: /* copy x into the vec slvec0 */
914: VecGetArray(a->slvec0,&from);
915: VecGetArray(xx,&x);
916: PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
917: VecRestoreArray(a->slvec0,&from);
919: VecScatterBegin(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
920: VecRestoreArray(xx,&x);
921: VecScatterEnd(a->sMvctx,a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD);
923: /* supperdiagonal part */
924: (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,zz);
925: return(0);
926: }
930: PetscErrorCode MatMultAdd_MPISBAIJ_2comm(Mat A,Vec xx,Vec yy,Vec zz)
931: {
932: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
936: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
937: /* do diagonal part */
938: (*a->A->ops->multadd)(a->A,xx,yy,zz);
939: /* do supperdiagonal part */
940: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
941: (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
943: /* do subdiagonal part */
944: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
945: VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
946: VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
947: return(0);
948: }
950: /*
951: This only works correctly for square matrices where the subblock A->A is the
952: diagonal block
953: */
956: PetscErrorCode MatGetDiagonal_MPISBAIJ(Mat A,Vec v)
957: {
958: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
962: /* if (a->rmap->N != a->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block"); */
963: MatGetDiagonal(a->A,v);
964: return(0);
965: }
969: PetscErrorCode MatScale_MPISBAIJ(Mat A,PetscScalar aa)
970: {
971: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
975: MatScale(a->A,aa);
976: MatScale(a->B,aa);
977: return(0);
978: }
982: PetscErrorCode MatGetRow_MPISBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
983: {
984: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)matin->data;
985: PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
987: PetscInt bs = matin->rmap->bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
988: PetscInt nztot,nzA,nzB,lrow,brstart = matin->rmap->rstart,brend = matin->rmap->rend;
989: PetscInt *cmap,*idx_p,cstart = mat->rstartbs;
992: if (mat->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Already active");
993: mat->getrowactive = PETSC_TRUE;
995: if (!mat->rowvalues && (idx || v)) {
996: /*
997: allocate enough space to hold information from the longest row.
998: */
999: Mat_SeqSBAIJ *Aa = (Mat_SeqSBAIJ*)mat->A->data;
1000: Mat_SeqBAIJ *Ba = (Mat_SeqBAIJ*)mat->B->data;
1001: PetscInt max = 1,mbs = mat->mbs,tmp;
1002: for (i=0; i<mbs; i++) {
1003: tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i]; /* row length */
1004: if (max < tmp) max = tmp;
1005: }
1006: PetscMalloc2(max*bs2,&mat->rowvalues,max*bs2,&mat->rowindices);
1007: }
1009: if (row < brstart || row >= brend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local rows");
1010: lrow = row - brstart; /* local row index */
1012: pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1013: if (!v) {pvA = 0; pvB = 0;}
1014: if (!idx) {pcA = 0; if (!v) pcB = 0;}
1015: (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1016: (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1017: nztot = nzA + nzB;
1019: cmap = mat->garray;
1020: if (v || idx) {
1021: if (nztot) {
1022: /* Sort by increasing column numbers, assuming A and B already sorted */
1023: PetscInt imark = -1;
1024: if (v) {
1025: *v = v_p = mat->rowvalues;
1026: for (i=0; i<nzB; i++) {
1027: if (cmap[cworkB[i]/bs] < cstart) v_p[i] = vworkB[i];
1028: else break;
1029: }
1030: imark = i;
1031: for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i];
1032: for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i];
1033: }
1034: if (idx) {
1035: *idx = idx_p = mat->rowindices;
1036: if (imark > -1) {
1037: for (i=0; i<imark; i++) {
1038: idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1039: }
1040: } else {
1041: for (i=0; i<nzB; i++) {
1042: if (cmap[cworkB[i]/bs] < cstart) idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1043: else break;
1044: }
1045: imark = i;
1046: }
1047: for (i=0; i<nzA; i++) idx_p[imark+i] = cstart*bs + cworkA[i];
1048: for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1049: }
1050: } else {
1051: if (idx) *idx = 0;
1052: if (v) *v = 0;
1053: }
1054: }
1055: *nz = nztot;
1056: (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1057: (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1058: return(0);
1059: }
1063: PetscErrorCode MatRestoreRow_MPISBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1064: {
1065: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
1068: if (!baij->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1069: baij->getrowactive = PETSC_FALSE;
1070: return(0);
1071: }
1075: PetscErrorCode MatGetRowUpperTriangular_MPISBAIJ(Mat A)
1076: {
1077: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1078: Mat_SeqSBAIJ *aA = (Mat_SeqSBAIJ*)a->A->data;
1081: aA->getrow_utriangular = PETSC_TRUE;
1082: return(0);
1083: }
1086: PetscErrorCode MatRestoreRowUpperTriangular_MPISBAIJ(Mat A)
1087: {
1088: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1089: Mat_SeqSBAIJ *aA = (Mat_SeqSBAIJ*)a->A->data;
1092: aA->getrow_utriangular = PETSC_FALSE;
1093: return(0);
1094: }
1098: PetscErrorCode MatRealPart_MPISBAIJ(Mat A)
1099: {
1100: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1104: MatRealPart(a->A);
1105: MatRealPart(a->B);
1106: return(0);
1107: }
1111: PetscErrorCode MatImaginaryPart_MPISBAIJ(Mat A)
1112: {
1113: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1117: MatImaginaryPart(a->A);
1118: MatImaginaryPart(a->B);
1119: return(0);
1120: }
1124: PetscErrorCode MatZeroEntries_MPISBAIJ(Mat A)
1125: {
1126: Mat_MPISBAIJ *l = (Mat_MPISBAIJ*)A->data;
1130: MatZeroEntries(l->A);
1131: MatZeroEntries(l->B);
1132: return(0);
1133: }
1137: PetscErrorCode MatGetInfo_MPISBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1138: {
1139: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)matin->data;
1140: Mat A = a->A,B = a->B;
1142: PetscReal isend[5],irecv[5];
1145: info->block_size = (PetscReal)matin->rmap->bs;
1147: MatGetInfo(A,MAT_LOCAL,info);
1149: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1150: isend[3] = info->memory; isend[4] = info->mallocs;
1152: MatGetInfo(B,MAT_LOCAL,info);
1154: isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1155: isend[3] += info->memory; isend[4] += info->mallocs;
1156: if (flag == MAT_LOCAL) {
1157: info->nz_used = isend[0];
1158: info->nz_allocated = isend[1];
1159: info->nz_unneeded = isend[2];
1160: info->memory = isend[3];
1161: info->mallocs = isend[4];
1162: } else if (flag == MAT_GLOBAL_MAX) {
1163: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)matin));
1165: info->nz_used = irecv[0];
1166: info->nz_allocated = irecv[1];
1167: info->nz_unneeded = irecv[2];
1168: info->memory = irecv[3];
1169: info->mallocs = irecv[4];
1170: } else if (flag == MAT_GLOBAL_SUM) {
1171: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)matin));
1173: info->nz_used = irecv[0];
1174: info->nz_allocated = irecv[1];
1175: info->nz_unneeded = irecv[2];
1176: info->memory = irecv[3];
1177: info->mallocs = irecv[4];
1178: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1179: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1180: info->fill_ratio_needed = 0;
1181: info->factor_mallocs = 0;
1182: return(0);
1183: }
1187: PetscErrorCode MatSetOption_MPISBAIJ(Mat A,MatOption op,PetscBool flg)
1188: {
1189: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1190: Mat_SeqSBAIJ *aA = (Mat_SeqSBAIJ*)a->A->data;
1194: switch (op) {
1195: case MAT_NEW_NONZERO_LOCATIONS:
1196: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1197: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1198: case MAT_KEEP_NONZERO_PATTERN:
1199: case MAT_NEW_NONZERO_LOCATION_ERR:
1200: MatSetOption(a->A,op,flg);
1201: MatSetOption(a->B,op,flg);
1202: break;
1203: case MAT_ROW_ORIENTED:
1204: a->roworiented = flg;
1206: MatSetOption(a->A,op,flg);
1207: MatSetOption(a->B,op,flg);
1208: break;
1209: case MAT_NEW_DIAGONALS:
1210: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1211: break;
1212: case MAT_IGNORE_OFF_PROC_ENTRIES:
1213: a->donotstash = flg;
1214: break;
1215: case MAT_USE_HASH_TABLE:
1216: a->ht_flag = flg;
1217: break;
1218: case MAT_HERMITIAN:
1219: if (!A->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call MatAssemblyEnd() first");
1220: MatSetOption(a->A,op,flg);
1222: A->ops->mult = MatMult_MPISBAIJ_Hermitian;
1223: break;
1224: case MAT_SPD:
1225: A->spd_set = PETSC_TRUE;
1226: A->spd = flg;
1227: if (flg) {
1228: A->symmetric = PETSC_TRUE;
1229: A->structurally_symmetric = PETSC_TRUE;
1230: A->symmetric_set = PETSC_TRUE;
1231: A->structurally_symmetric_set = PETSC_TRUE;
1232: }
1233: break;
1234: case MAT_SYMMETRIC:
1235: MatSetOption(a->A,op,flg);
1236: break;
1237: case MAT_STRUCTURALLY_SYMMETRIC:
1238: MatSetOption(a->A,op,flg);
1239: break;
1240: case MAT_SYMMETRY_ETERNAL:
1241: if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Matrix must be symmetric");
1242: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1243: break;
1244: case MAT_IGNORE_LOWER_TRIANGULAR:
1245: aA->ignore_ltriangular = flg;
1246: break;
1247: case MAT_ERROR_LOWER_TRIANGULAR:
1248: aA->ignore_ltriangular = flg;
1249: break;
1250: case MAT_GETROW_UPPERTRIANGULAR:
1251: aA->getrow_utriangular = flg;
1252: break;
1253: default:
1254: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1255: }
1256: return(0);
1257: }
1261: PetscErrorCode MatTranspose_MPISBAIJ(Mat A,MatReuse reuse,Mat *B)
1262: {
1266: if (MAT_INITIAL_MATRIX || *B != A) {
1267: MatDuplicate(A,MAT_COPY_VALUES,B);
1268: }
1269: return(0);
1270: }
1274: PetscErrorCode MatDiagonalScale_MPISBAIJ(Mat mat,Vec ll,Vec rr)
1275: {
1276: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
1277: Mat a = baij->A, b=baij->B;
1279: PetscInt nv,m,n;
1280: PetscBool flg;
1283: if (ll != rr) {
1284: VecEqual(ll,rr,&flg);
1285: if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"For symmetric format, left and right scaling vectors must be same\n");
1286: }
1287: if (!ll) return(0);
1289: MatGetLocalSize(mat,&m,&n);
1290: if (m != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"For symmetric format, local size %d %d must be same",m,n);
1292: VecGetLocalSize(rr,&nv);
1293: if (nv!=n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left and right vector non-conforming local size");
1295: VecScatterBegin(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1297: /* left diagonalscale the off-diagonal part */
1298: (*b->ops->diagonalscale)(b,ll,NULL);
1300: /* scale the diagonal part */
1301: (*a->ops->diagonalscale)(a,ll,rr);
1303: /* right diagonalscale the off-diagonal part */
1304: VecScatterEnd(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1305: (*b->ops->diagonalscale)(b,NULL,baij->lvec);
1306: return(0);
1307: }
1311: PetscErrorCode MatSetUnfactored_MPISBAIJ(Mat A)
1312: {
1313: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1317: MatSetUnfactored(a->A);
1318: return(0);
1319: }
1321: static PetscErrorCode MatDuplicate_MPISBAIJ(Mat,MatDuplicateOption,Mat*);
1325: PetscErrorCode MatEqual_MPISBAIJ(Mat A,Mat B,PetscBool *flag)
1326: {
1327: Mat_MPISBAIJ *matB = (Mat_MPISBAIJ*)B->data,*matA = (Mat_MPISBAIJ*)A->data;
1328: Mat a,b,c,d;
1329: PetscBool flg;
1333: a = matA->A; b = matA->B;
1334: c = matB->A; d = matB->B;
1336: MatEqual(a,c,&flg);
1337: if (flg) {
1338: MatEqual(b,d,&flg);
1339: }
1340: MPI_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
1341: return(0);
1342: }
1346: PetscErrorCode MatCopy_MPISBAIJ(Mat A,Mat B,MatStructure str)
1347: {
1349: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1350: Mat_MPISBAIJ *b = (Mat_MPISBAIJ*)B->data;
1353: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1354: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1355: MatGetRowUpperTriangular(A);
1356: MatCopy_Basic(A,B,str);
1357: MatRestoreRowUpperTriangular(A);
1358: } else {
1359: MatCopy(a->A,b->A,str);
1360: MatCopy(a->B,b->B,str);
1361: }
1362: return(0);
1363: }
1367: PetscErrorCode MatSetUp_MPISBAIJ(Mat A)
1368: {
1372: MatMPISBAIJSetPreallocation(A,A->rmap->bs,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1373: return(0);
1374: }
1378: PetscErrorCode MatAXPY_MPISBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1379: {
1381: Mat_MPISBAIJ *xx=(Mat_MPISBAIJ*)X->data,*yy=(Mat_MPISBAIJ*)Y->data;
1382: PetscBLASInt bnz,one=1;
1383: Mat_SeqSBAIJ *xa,*ya;
1384: Mat_SeqBAIJ *xb,*yb;
1387: if (str == SAME_NONZERO_PATTERN) {
1388: PetscScalar alpha = a;
1389: xa = (Mat_SeqSBAIJ*)xx->A->data;
1390: ya = (Mat_SeqSBAIJ*)yy->A->data;
1391: PetscBLASIntCast(xa->nz,&bnz);
1392: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,xa->a,&one,ya->a,&one));
1393: xb = (Mat_SeqBAIJ*)xx->B->data;
1394: yb = (Mat_SeqBAIJ*)yy->B->data;
1395: PetscBLASIntCast(xb->nz,&bnz);
1396: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,xb->a,&one,yb->a,&one));
1397: PetscObjectStateIncrease((PetscObject)Y);
1398: } else {
1399: MatGetRowUpperTriangular(X);
1400: MatAXPY_Basic(Y,a,X,str);
1401: MatRestoreRowUpperTriangular(X);
1402: }
1403: return(0);
1404: }
1408: PetscErrorCode MatGetSubMatrices_MPISBAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1409: {
1411: PetscInt i;
1412: PetscBool flg;
1415: for (i=0; i<n; i++) {
1416: ISEqual(irow[i],icol[i],&flg);
1417: if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Can only get symmetric submatrix for MPISBAIJ matrices");
1418: }
1419: MatGetSubMatrices_MPIBAIJ(A,n,irow,icol,scall,B);
1420: return(0);
1421: }
1424: /* -------------------------------------------------------------------*/
1425: static struct _MatOps MatOps_Values = {MatSetValues_MPISBAIJ,
1426: MatGetRow_MPISBAIJ,
1427: MatRestoreRow_MPISBAIJ,
1428: MatMult_MPISBAIJ,
1429: /* 4*/ MatMultAdd_MPISBAIJ,
1430: MatMult_MPISBAIJ, /* transpose versions are same as non-transpose */
1431: MatMultAdd_MPISBAIJ,
1432: 0,
1433: 0,
1434: 0,
1435: /* 10*/ 0,
1436: 0,
1437: 0,
1438: MatSOR_MPISBAIJ,
1439: MatTranspose_MPISBAIJ,
1440: /* 15*/ MatGetInfo_MPISBAIJ,
1441: MatEqual_MPISBAIJ,
1442: MatGetDiagonal_MPISBAIJ,
1443: MatDiagonalScale_MPISBAIJ,
1444: MatNorm_MPISBAIJ,
1445: /* 20*/ MatAssemblyBegin_MPISBAIJ,
1446: MatAssemblyEnd_MPISBAIJ,
1447: MatSetOption_MPISBAIJ,
1448: MatZeroEntries_MPISBAIJ,
1449: /* 24*/ 0,
1450: 0,
1451: 0,
1452: 0,
1453: 0,
1454: /* 29*/ MatSetUp_MPISBAIJ,
1455: 0,
1456: 0,
1457: 0,
1458: 0,
1459: /* 34*/ MatDuplicate_MPISBAIJ,
1460: 0,
1461: 0,
1462: 0,
1463: 0,
1464: /* 39*/ MatAXPY_MPISBAIJ,
1465: MatGetSubMatrices_MPISBAIJ,
1466: MatIncreaseOverlap_MPISBAIJ,
1467: MatGetValues_MPISBAIJ,
1468: MatCopy_MPISBAIJ,
1469: /* 44*/ 0,
1470: MatScale_MPISBAIJ,
1471: 0,
1472: 0,
1473: 0,
1474: /* 49*/ 0,
1475: 0,
1476: 0,
1477: 0,
1478: 0,
1479: /* 54*/ 0,
1480: 0,
1481: MatSetUnfactored_MPISBAIJ,
1482: 0,
1483: MatSetValuesBlocked_MPISBAIJ,
1484: /* 59*/ 0,
1485: 0,
1486: 0,
1487: 0,
1488: 0,
1489: /* 64*/ 0,
1490: 0,
1491: 0,
1492: 0,
1493: 0,
1494: /* 69*/ MatGetRowMaxAbs_MPISBAIJ,
1495: 0,
1496: 0,
1497: 0,
1498: 0,
1499: /* 74*/ 0,
1500: 0,
1501: 0,
1502: 0,
1503: 0,
1504: /* 79*/ 0,
1505: 0,
1506: 0,
1507: 0,
1508: MatLoad_MPISBAIJ,
1509: /* 84*/ 0,
1510: 0,
1511: 0,
1512: 0,
1513: 0,
1514: /* 89*/ 0,
1515: 0,
1516: 0,
1517: 0,
1518: 0,
1519: /* 94*/ 0,
1520: 0,
1521: 0,
1522: 0,
1523: 0,
1524: /* 99*/ 0,
1525: 0,
1526: 0,
1527: 0,
1528: 0,
1529: /*104*/ 0,
1530: MatRealPart_MPISBAIJ,
1531: MatImaginaryPart_MPISBAIJ,
1532: MatGetRowUpperTriangular_MPISBAIJ,
1533: MatRestoreRowUpperTriangular_MPISBAIJ,
1534: /*109*/ 0,
1535: 0,
1536: 0,
1537: 0,
1538: 0,
1539: /*114*/ 0,
1540: 0,
1541: 0,
1542: 0,
1543: 0,
1544: /*119*/ 0,
1545: 0,
1546: 0,
1547: 0,
1548: 0,
1549: /*124*/ 0,
1550: 0,
1551: 0,
1552: 0,
1553: 0,
1554: /*129*/ 0,
1555: 0,
1556: 0,
1557: 0,
1558: 0,
1559: /*134*/ 0,
1560: 0,
1561: 0,
1562: 0,
1563: 0,
1564: /*139*/ 0,
1565: 0,
1566: 0
1567: };
1572: PetscErrorCode MatGetDiagonalBlock_MPISBAIJ(Mat A,Mat *a)
1573: {
1575: *a = ((Mat_MPISBAIJ*)A->data)->A;
1576: return(0);
1577: }
1581: PetscErrorCode MatMPISBAIJSetPreallocation_MPISBAIJ(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt *d_nnz,PetscInt o_nz,const PetscInt *o_nnz)
1582: {
1583: Mat_MPISBAIJ *b;
1585: PetscInt i,mbs,Mbs;
1588: MatSetBlockSize(B,PetscAbs(bs));
1589: PetscLayoutSetUp(B->rmap);
1590: PetscLayoutSetUp(B->cmap);
1591: PetscLayoutGetBlockSize(B->rmap,&bs);
1593: b = (Mat_MPISBAIJ*)B->data;
1594: mbs = B->rmap->n/bs;
1595: Mbs = B->rmap->N/bs;
1596: if (mbs*bs != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"No of local rows %D must be divisible by blocksize %D",B->rmap->N,bs);
1598: B->rmap->bs = bs;
1599: b->bs2 = bs*bs;
1600: b->mbs = mbs;
1601: b->nbs = mbs;
1602: b->Mbs = Mbs;
1603: b->Nbs = Mbs;
1605: for (i=0; i<=b->size; i++) {
1606: b->rangebs[i] = B->rmap->range[i]/bs;
1607: }
1608: b->rstartbs = B->rmap->rstart/bs;
1609: b->rendbs = B->rmap->rend/bs;
1611: b->cstartbs = B->cmap->rstart/bs;
1612: b->cendbs = B->cmap->rend/bs;
1614: if (!B->preallocated) {
1615: MatCreate(PETSC_COMM_SELF,&b->A);
1616: MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
1617: MatSetType(b->A,MATSEQSBAIJ);
1618: PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
1619: MatCreate(PETSC_COMM_SELF,&b->B);
1620: MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
1621: MatSetType(b->B,MATSEQBAIJ);
1622: PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);
1623: MatStashCreate_Private(PetscObjectComm((PetscObject)B),bs,&B->bstash);
1624: }
1626: MatSeqSBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
1627: MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
1629: B->preallocated = PETSC_TRUE;
1630: return(0);
1631: }
1635: PetscErrorCode MatMPISBAIJSetPreallocationCSR_MPISBAIJ(Mat B,PetscInt bs,const PetscInt ii[],const PetscInt jj[],const PetscScalar V[])
1636: {
1637: PetscInt m,rstart,cstart,cend;
1638: PetscInt i,j,d,nz,nz_max=0,*d_nnz=0,*o_nnz=0;
1639: const PetscInt *JJ =0;
1640: PetscScalar *values=0;
1644: if (bs < 1) SETERRQ1(PetscObjectComm((PetscObject)B),PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive but it is %D",bs);
1645: PetscLayoutSetBlockSize(B->rmap,bs);
1646: PetscLayoutSetBlockSize(B->cmap,bs);
1647: PetscLayoutSetUp(B->rmap);
1648: PetscLayoutSetUp(B->cmap);
1649: PetscLayoutGetBlockSize(B->rmap,&bs);
1650: m = B->rmap->n/bs;
1651: rstart = B->rmap->rstart/bs;
1652: cstart = B->cmap->rstart/bs;
1653: cend = B->cmap->rend/bs;
1655: if (ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"ii[0] must be 0 but it is %D",ii[0]);
1656: PetscMalloc2(m,&d_nnz,m,&o_nnz);
1657: for (i=0; i<m; i++) {
1658: nz = ii[i+1] - ii[i];
1659: if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative number of columns %D",i,nz);
1660: nz_max = PetscMax(nz_max,nz);
1661: JJ = jj + ii[i];
1662: for (j=0; j<nz; j++) {
1663: if (*JJ >= cstart) break;
1664: JJ++;
1665: }
1666: d = 0;
1667: for (; j<nz; j++) {
1668: if (*JJ++ >= cend) break;
1669: d++;
1670: }
1671: d_nnz[i] = d;
1672: o_nnz[i] = nz - d;
1673: }
1674: MatMPISBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
1675: PetscFree2(d_nnz,o_nnz);
1677: values = (PetscScalar*)V;
1678: if (!values) {
1679: PetscMalloc1(bs*bs*nz_max,&values);
1680: PetscMemzero(values,bs*bs*nz_max*sizeof(PetscScalar));
1681: }
1682: for (i=0; i<m; i++) {
1683: PetscInt row = i + rstart;
1684: PetscInt ncols = ii[i+1] - ii[i];
1685: const PetscInt *icols = jj + ii[i];
1686: const PetscScalar *svals = values + (V ? (bs*bs*ii[i]) : 0);
1687: MatSetValuesBlocked_MPISBAIJ(B,1,&row,ncols,icols,svals,INSERT_VALUES);
1688: }
1690: if (!V) { PetscFree(values); }
1691: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1692: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1693: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
1694: return(0);
1695: }
1697: #if defined(PETSC_HAVE_MUMPS)
1698: PETSC_EXTERN PetscErrorCode MatGetFactor_sbaij_mumps(Mat,MatFactorType,Mat*);
1699: #endif
1700: #if defined(PETSC_HAVE_PASTIX)
1701: PETSC_EXTERN PetscErrorCode MatGetFactor_mpisbaij_pastix(Mat,MatFactorType,Mat*);
1702: #endif
1704: /*MC
1705: MATMPISBAIJ - MATMPISBAIJ = "mpisbaij" - A matrix type to be used for distributed symmetric sparse block matrices,
1706: based on block compressed sparse row format. Only the upper triangular portion of the "diagonal" portion of
1707: the matrix is stored.
1709: For complex numbers by default this matrix is symmetric, NOT Hermitian symmetric. To make it Hermitian symmetric you
1710: can call MatSetOption(Mat, MAT_HERMITIAN);
1712: Options Database Keys:
1713: . -mat_type mpisbaij - sets the matrix type to "mpisbaij" during a call to MatSetFromOptions()
1715: Level: beginner
1717: .seealso: MatCreateMPISBAIJ
1718: M*/
1720: PETSC_EXTERN PetscErrorCode MatConvert_MPISBAIJ_MPISBSTRM(Mat,MatType,MatReuse,Mat*);
1724: PETSC_EXTERN PetscErrorCode MatCreate_MPISBAIJ(Mat B)
1725: {
1726: Mat_MPISBAIJ *b;
1728: PetscBool flg;
1731: PetscNewLog(B,&b);
1732: B->data = (void*)b;
1733: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
1735: B->ops->destroy = MatDestroy_MPISBAIJ;
1736: B->ops->view = MatView_MPISBAIJ;
1737: B->assembled = PETSC_FALSE;
1738: B->insertmode = NOT_SET_VALUES;
1740: MPI_Comm_rank(PetscObjectComm((PetscObject)B),&b->rank);
1741: MPI_Comm_size(PetscObjectComm((PetscObject)B),&b->size);
1743: /* build local table of row and column ownerships */
1744: PetscMalloc1((b->size+2),&b->rangebs);
1746: /* build cache for off array entries formed */
1747: MatStashCreate_Private(PetscObjectComm((PetscObject)B),1,&B->stash);
1749: b->donotstash = PETSC_FALSE;
1750: b->colmap = NULL;
1751: b->garray = NULL;
1752: b->roworiented = PETSC_TRUE;
1754: /* stuff used in block assembly */
1755: b->barray = 0;
1757: /* stuff used for matrix vector multiply */
1758: b->lvec = 0;
1759: b->Mvctx = 0;
1760: b->slvec0 = 0;
1761: b->slvec0b = 0;
1762: b->slvec1 = 0;
1763: b->slvec1a = 0;
1764: b->slvec1b = 0;
1765: b->sMvctx = 0;
1767: /* stuff for MatGetRow() */
1768: b->rowindices = 0;
1769: b->rowvalues = 0;
1770: b->getrowactive = PETSC_FALSE;
1772: /* hash table stuff */
1773: b->ht = 0;
1774: b->hd = 0;
1775: b->ht_size = 0;
1776: b->ht_flag = PETSC_FALSE;
1777: b->ht_fact = 0;
1778: b->ht_total_ct = 0;
1779: b->ht_insert_ct = 0;
1781: /* stuff for MatGetSubMatrices_MPIBAIJ_local() */
1782: b->ijonly = PETSC_FALSE;
1784: b->in_loc = 0;
1785: b->v_loc = 0;
1786: b->n_loc = 0;
1787: PetscOptionsBegin(PetscObjectComm((PetscObject)B),NULL,"Options for loading MPISBAIJ matrix 1","Mat");
1788: PetscOptionsBool("-mat_use_hash_table","Use hash table to save memory in constructing matrix","MatSetOption",PETSC_FALSE,&flg,NULL);
1789: if (flg) {
1790: PetscReal fact = 1.39;
1791: MatSetOption(B,MAT_USE_HASH_TABLE,PETSC_TRUE);
1792: PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,NULL);
1793: if (fact <= 1.0) fact = 1.39;
1794: MatMPIBAIJSetHashTableFactor(B,fact);
1795: PetscInfo1(B,"Hash table Factor used %5.2f\n",fact);
1796: }
1797: PetscOptionsEnd();
1799: #if defined(PETSC_HAVE_PASTIX)
1800: PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_pastix_C",MatGetFactor_mpisbaij_pastix);
1801: #endif
1802: #if defined(PETSC_HAVE_MUMPS)
1803: PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_mumps_C",MatGetFactor_sbaij_mumps);
1804: #endif
1805: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_MPISBAIJ);
1806: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_MPISBAIJ);
1807: PetscObjectComposeFunction((PetscObject)B,"MatGetDiagonalBlock_C",MatGetDiagonalBlock_MPISBAIJ);
1808: PetscObjectComposeFunction((PetscObject)B,"MatMPISBAIJSetPreallocation_C",MatMPISBAIJSetPreallocation_MPISBAIJ);
1809: PetscObjectComposeFunction((PetscObject)B,"MatMPISBAIJSetPreallocationCSR_C",MatMPISBAIJSetPreallocationCSR_MPISBAIJ);
1810: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpisbaij_mpisbstrm_C",MatConvert_MPISBAIJ_MPISBSTRM);
1812: B->symmetric = PETSC_TRUE;
1813: B->structurally_symmetric = PETSC_TRUE;
1814: B->symmetric_set = PETSC_TRUE;
1815: B->structurally_symmetric_set = PETSC_TRUE;
1817: PetscObjectChangeTypeName((PetscObject)B,MATMPISBAIJ);
1818: return(0);
1819: }
1821: /*MC
1822: MATSBAIJ - MATSBAIJ = "sbaij" - A matrix type to be used for symmetric block sparse matrices.
1824: This matrix type is identical to MATSEQSBAIJ when constructed with a single process communicator,
1825: and MATMPISBAIJ otherwise.
1827: Options Database Keys:
1828: . -mat_type sbaij - sets the matrix type to "sbaij" during a call to MatSetFromOptions()
1830: Level: beginner
1832: .seealso: MatCreateMPISBAIJ,MATSEQSBAIJ,MATMPISBAIJ
1833: M*/
1837: /*@C
1838: MatMPISBAIJSetPreallocation - For good matrix assembly performance
1839: the user should preallocate the matrix storage by setting the parameters
1840: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
1841: performance can be increased by more than a factor of 50.
1843: Collective on Mat
1845: Input Parameters:
1846: + A - the matrix
1847: . bs - size of blockk
1848: . d_nz - number of block nonzeros per block row in diagonal portion of local
1849: submatrix (same for all local rows)
1850: . d_nnz - array containing the number of block nonzeros in the various block rows
1851: in the upper triangular and diagonal part of the in diagonal portion of the local
1852: (possibly different for each block row) or NULL. If you plan to factor the matrix you must leave room
1853: for the diagonal entry and set a value even if it is zero.
1854: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
1855: submatrix (same for all local rows).
1856: - o_nnz - array containing the number of nonzeros in the various block rows of the
1857: off-diagonal portion of the local submatrix that is right of the diagonal
1858: (possibly different for each block row) or NULL.
1861: Options Database Keys:
1862: . -mat_no_unroll - uses code that does not unroll the loops in the
1863: block calculations (much slower)
1864: . -mat_block_size - size of the blocks to use
1866: Notes:
1868: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor
1869: than it must be used on all processors that share the object for that argument.
1871: If the *_nnz parameter is given then the *_nz parameter is ignored
1873: Storage Information:
1874: For a square global matrix we define each processor's diagonal portion
1875: to be its local rows and the corresponding columns (a square submatrix);
1876: each processor's off-diagonal portion encompasses the remainder of the
1877: local matrix (a rectangular submatrix).
1879: The user can specify preallocated storage for the diagonal part of
1880: the local submatrix with either d_nz or d_nnz (not both). Set
1881: d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
1882: memory allocation. Likewise, specify preallocated storage for the
1883: off-diagonal part of the local submatrix with o_nz or o_nnz (not both).
1885: You can call MatGetInfo() to get information on how effective the preallocation was;
1886: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
1887: You can also run with the option -info and look for messages with the string
1888: malloc in them to see if additional memory allocation was needed.
1890: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1891: the figure below we depict these three local rows and all columns (0-11).
1893: .vb
1894: 0 1 2 3 4 5 6 7 8 9 10 11
1895: --------------------------
1896: row 3 |. . . d d d o o o o o o
1897: row 4 |. . . d d d o o o o o o
1898: row 5 |. . . d d d o o o o o o
1899: --------------------------
1900: .ve
1902: Thus, any entries in the d locations are stored in the d (diagonal)
1903: submatrix, and any entries in the o locations are stored in the
1904: o (off-diagonal) submatrix. Note that the d matrix is stored in
1905: MatSeqSBAIJ format and the o submatrix in MATSEQBAIJ format.
1907: Now d_nz should indicate the number of block nonzeros per row in the upper triangular
1908: plus the diagonal part of the d matrix,
1909: and o_nz should indicate the number of block nonzeros per row in the o matrix
1911: In general, for PDE problems in which most nonzeros are near the diagonal,
1912: one expects d_nz >> o_nz. For large problems you MUST preallocate memory
1913: or you will get TERRIBLE performance; see the users' manual chapter on
1914: matrices.
1916: Level: intermediate
1918: .keywords: matrix, block, aij, compressed row, sparse, parallel
1920: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateBAIJ(), PetscSplitOwnership()
1921: @*/
1922: PetscErrorCode MatMPISBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
1923: {
1930: PetscTryMethod(B,"MatMPISBAIJSetPreallocation_C",(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]),(B,bs,d_nz,d_nnz,o_nz,o_nnz));
1931: return(0);
1932: }
1936: /*@C
1937: MatCreateSBAIJ - Creates a sparse parallel matrix in symmetric block AIJ format
1938: (block compressed row). For good matrix assembly performance
1939: the user should preallocate the matrix storage by setting the parameters
1940: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
1941: performance can be increased by more than a factor of 50.
1943: Collective on MPI_Comm
1945: Input Parameters:
1946: + comm - MPI communicator
1947: . bs - size of blockk
1948: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1949: This value should be the same as the local size used in creating the
1950: y vector for the matrix-vector product y = Ax.
1951: . n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1952: This value should be the same as the local size used in creating the
1953: x vector for the matrix-vector product y = Ax.
1954: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
1955: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
1956: . d_nz - number of block nonzeros per block row in diagonal portion of local
1957: submatrix (same for all local rows)
1958: . d_nnz - array containing the number of block nonzeros in the various block rows
1959: in the upper triangular portion of the in diagonal portion of the local
1960: (possibly different for each block block row) or NULL.
1961: If you plan to factor the matrix you must leave room for the diagonal entry and
1962: set its value even if it is zero.
1963: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
1964: submatrix (same for all local rows).
1965: - o_nnz - array containing the number of nonzeros in the various block rows of the
1966: off-diagonal portion of the local submatrix (possibly different for
1967: each block row) or NULL.
1969: Output Parameter:
1970: . A - the matrix
1972: Options Database Keys:
1973: . -mat_no_unroll - uses code that does not unroll the loops in the
1974: block calculations (much slower)
1975: . -mat_block_size - size of the blocks to use
1976: . -mat_mpi - use the parallel matrix data structures even on one processor
1977: (defaults to using SeqBAIJ format on one processor)
1979: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
1980: MatXXXXSetPreallocation() paradgm instead of this routine directly.
1981: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
1983: Notes:
1984: The number of rows and columns must be divisible by blocksize.
1985: This matrix type does not support complex Hermitian operation.
1987: The user MUST specify either the local or global matrix dimensions
1988: (possibly both).
1990: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor
1991: than it must be used on all processors that share the object for that argument.
1993: If the *_nnz parameter is given then the *_nz parameter is ignored
1995: Storage Information:
1996: For a square global matrix we define each processor's diagonal portion
1997: to be its local rows and the corresponding columns (a square submatrix);
1998: each processor's off-diagonal portion encompasses the remainder of the
1999: local matrix (a rectangular submatrix).
2001: The user can specify preallocated storage for the diagonal part of
2002: the local submatrix with either d_nz or d_nnz (not both). Set
2003: d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
2004: memory allocation. Likewise, specify preallocated storage for the
2005: off-diagonal part of the local submatrix with o_nz or o_nnz (not both).
2007: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
2008: the figure below we depict these three local rows and all columns (0-11).
2010: .vb
2011: 0 1 2 3 4 5 6 7 8 9 10 11
2012: --------------------------
2013: row 3 |. . . d d d o o o o o o
2014: row 4 |. . . d d d o o o o o o
2015: row 5 |. . . d d d o o o o o o
2016: --------------------------
2017: .ve
2019: Thus, any entries in the d locations are stored in the d (diagonal)
2020: submatrix, and any entries in the o locations are stored in the
2021: o (off-diagonal) submatrix. Note that the d matrix is stored in
2022: MatSeqSBAIJ format and the o submatrix in MATSEQBAIJ format.
2024: Now d_nz should indicate the number of block nonzeros per row in the upper triangular
2025: plus the diagonal part of the d matrix,
2026: and o_nz should indicate the number of block nonzeros per row in the o matrix.
2027: In general, for PDE problems in which most nonzeros are near the diagonal,
2028: one expects d_nz >> o_nz. For large problems you MUST preallocate memory
2029: or you will get TERRIBLE performance; see the users' manual chapter on
2030: matrices.
2032: Level: intermediate
2034: .keywords: matrix, block, aij, compressed row, sparse, parallel
2036: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateBAIJ()
2037: @*/
2039: PetscErrorCode MatCreateSBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
2040: {
2042: PetscMPIInt size;
2045: MatCreate(comm,A);
2046: MatSetSizes(*A,m,n,M,N);
2047: MPI_Comm_size(comm,&size);
2048: if (size > 1) {
2049: MatSetType(*A,MATMPISBAIJ);
2050: MatMPISBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
2051: } else {
2052: MatSetType(*A,MATSEQSBAIJ);
2053: MatSeqSBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
2054: }
2055: return(0);
2056: }
2061: static PetscErrorCode MatDuplicate_MPISBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2062: {
2063: Mat mat;
2064: Mat_MPISBAIJ *a,*oldmat = (Mat_MPISBAIJ*)matin->data;
2066: PetscInt len=0,nt,bs=matin->rmap->bs,mbs=oldmat->mbs;
2067: PetscScalar *array;
2070: *newmat = 0;
2072: MatCreate(PetscObjectComm((PetscObject)matin),&mat);
2073: MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
2074: MatSetType(mat,((PetscObject)matin)->type_name);
2075: PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
2076: PetscLayoutReference(matin->rmap,&mat->rmap);
2077: PetscLayoutReference(matin->cmap,&mat->cmap);
2079: mat->factortype = matin->factortype;
2080: mat->preallocated = PETSC_TRUE;
2081: mat->assembled = PETSC_TRUE;
2082: mat->insertmode = NOT_SET_VALUES;
2084: a = (Mat_MPISBAIJ*)mat->data;
2085: a->bs2 = oldmat->bs2;
2086: a->mbs = oldmat->mbs;
2087: a->nbs = oldmat->nbs;
2088: a->Mbs = oldmat->Mbs;
2089: a->Nbs = oldmat->Nbs;
2092: a->size = oldmat->size;
2093: a->rank = oldmat->rank;
2094: a->donotstash = oldmat->donotstash;
2095: a->roworiented = oldmat->roworiented;
2096: a->rowindices = 0;
2097: a->rowvalues = 0;
2098: a->getrowactive = PETSC_FALSE;
2099: a->barray = 0;
2100: a->rstartbs = oldmat->rstartbs;
2101: a->rendbs = oldmat->rendbs;
2102: a->cstartbs = oldmat->cstartbs;
2103: a->cendbs = oldmat->cendbs;
2105: /* hash table stuff */
2106: a->ht = 0;
2107: a->hd = 0;
2108: a->ht_size = 0;
2109: a->ht_flag = oldmat->ht_flag;
2110: a->ht_fact = oldmat->ht_fact;
2111: a->ht_total_ct = 0;
2112: a->ht_insert_ct = 0;
2114: PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+2)*sizeof(PetscInt));
2115: if (oldmat->colmap) {
2116: #if defined(PETSC_USE_CTABLE)
2117: PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2118: #else
2119: PetscMalloc1((a->Nbs),&a->colmap);
2120: PetscLogObjectMemory((PetscObject)mat,(a->Nbs)*sizeof(PetscInt));
2121: PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
2122: #endif
2123: } else a->colmap = 0;
2125: if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
2126: PetscMalloc1(len,&a->garray);
2127: PetscLogObjectMemory((PetscObject)mat,len*sizeof(PetscInt));
2128: PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
2129: } else a->garray = 0;
2131: MatStashCreate_Private(PetscObjectComm((PetscObject)matin),matin->rmap->bs,&mat->bstash);
2132: VecDuplicate(oldmat->lvec,&a->lvec);
2133: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->lvec);
2134: VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2135: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->Mvctx);
2137: VecDuplicate(oldmat->slvec0,&a->slvec0);
2138: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->slvec0);
2139: VecDuplicate(oldmat->slvec1,&a->slvec1);
2140: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->slvec1);
2142: VecGetLocalSize(a->slvec1,&nt);
2143: VecGetArray(a->slvec1,&array);
2144: VecCreateSeqWithArray(PETSC_COMM_SELF,1,bs*mbs,array,&a->slvec1a);
2145: VecCreateSeqWithArray(PETSC_COMM_SELF,1,nt-bs*mbs,array+bs*mbs,&a->slvec1b);
2146: VecRestoreArray(a->slvec1,&array);
2147: VecGetArray(a->slvec0,&array);
2148: VecCreateSeqWithArray(PETSC_COMM_SELF,1,nt-bs*mbs,array+bs*mbs,&a->slvec0b);
2149: VecRestoreArray(a->slvec0,&array);
2150: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->slvec0);
2151: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->slvec1);
2152: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->slvec0b);
2153: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->slvec1a);
2154: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->slvec1b);
2156: /* VecScatterCopy(oldmat->sMvctx,&a->sMvctx); - not written yet, replaced by the lazy trick: */
2157: PetscObjectReference((PetscObject)oldmat->sMvctx);
2158: a->sMvctx = oldmat->sMvctx;
2159: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->sMvctx);
2161: MatDuplicate(oldmat->A,cpvalues,&a->A);
2162: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
2163: MatDuplicate(oldmat->B,cpvalues,&a->B);
2164: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->B);
2165: PetscFunctionListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
2166: *newmat = mat;
2167: return(0);
2168: }
2172: PetscErrorCode MatLoad_MPISBAIJ(Mat newmat,PetscViewer viewer)
2173: {
2175: PetscInt i,nz,j,rstart,rend;
2176: PetscScalar *vals,*buf;
2177: MPI_Comm comm;
2178: MPI_Status status;
2179: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag,*sndcounts = 0,*browners,maxnz,*rowners,mmbs;
2180: PetscInt header[4],*rowlengths = 0,M,N,m,*cols,*locrowlens;
2181: PetscInt *procsnz = 0,jj,*mycols,*ibuf;
2182: PetscInt bs =1,Mbs,mbs,extra_rows;
2183: PetscInt *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
2184: PetscInt dcount,kmax,k,nzcount,tmp,sizesset=1,grows,gcols;
2185: int fd;
2188: PetscObjectGetComm((PetscObject)viewer,&comm);
2189: PetscOptionsBegin(comm,NULL,"Options for loading MPISBAIJ matrix 2","Mat");
2190: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
2191: PetscOptionsEnd();
2193: MPI_Comm_size(comm,&size);
2194: MPI_Comm_rank(comm,&rank);
2195: if (!rank) {
2196: PetscViewerBinaryGetDescriptor(viewer,&fd);
2197: PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
2198: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2199: if (header[3] < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as MPISBAIJ");
2200: }
2202: if (newmat->rmap->n < 0 && newmat->rmap->N < 0 && newmat->cmap->n < 0 && newmat->cmap->N < 0) sizesset = 0;
2204: MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2205: M = header[1];
2206: N = header[2];
2208: /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */
2209: if (sizesset && newmat->rmap->N < 0) newmat->rmap->N = M;
2210: if (sizesset && newmat->cmap->N < 0) newmat->cmap->N = N;
2212: /* If global sizes are set, check if they are consistent with that given in the file */
2213: if (sizesset) {
2214: MatGetSize(newmat,&grows,&gcols);
2215: }
2216: if (sizesset && newmat->rmap->N != grows) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows:Matrix in file has (%d) and input matrix has (%d)",M,grows);
2217: if (sizesset && newmat->cmap->N != gcols) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of cols:Matrix in file has (%d) and input matrix has (%d)",N,gcols);
2219: if (M != N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Can only do square matrices");
2221: /*
2222: This code adds extra rows to make sure the number of rows is
2223: divisible by the blocksize
2224: */
2225: Mbs = M/bs;
2226: extra_rows = bs - M + bs*(Mbs);
2227: if (extra_rows == bs) extra_rows = 0;
2228: else Mbs++;
2229: if (extra_rows &&!rank) {
2230: PetscInfo(viewer,"Padding loaded matrix to match blocksize\n");
2231: }
2233: /* determine ownership of all rows */
2234: if (newmat->rmap->n < 0) { /* PETSC_DECIDE */
2235: mbs = Mbs/size + ((Mbs % size) > rank);
2236: m = mbs*bs;
2237: } else { /* User Set */
2238: m = newmat->rmap->n;
2239: mbs = m/bs;
2240: }
2241: PetscMalloc2(size+1,&rowners,size+1,&browners);
2242: PetscMPIIntCast(mbs,&mmbs);
2243: MPI_Allgather(&mmbs,1,MPI_INT,rowners+1,1,MPI_INT,comm);
2244: rowners[0] = 0;
2245: for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
2246: for (i=0; i<=size; i++) browners[i] = rowners[i]*bs;
2247: rstart = rowners[rank];
2248: rend = rowners[rank+1];
2250: /* distribute row lengths to all processors */
2251: PetscMalloc1((rend-rstart)*bs,&locrowlens);
2252: if (!rank) {
2253: PetscMalloc1((M+extra_rows),&rowlengths);
2254: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2255: for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2256: PetscMalloc1(size,&sndcounts);
2257: for (i=0; i<size; i++) sndcounts[i] = browners[i+1] - browners[i];
2258: MPI_Scatterv(rowlengths,sndcounts,browners,MPIU_INT,locrowlens,(rend-rstart)*bs,MPIU_INT,0,comm);
2259: PetscFree(sndcounts);
2260: } else {
2261: MPI_Scatterv(0,0,0,MPIU_INT,locrowlens,(rend-rstart)*bs,MPIU_INT,0,comm);
2262: }
2264: if (!rank) { /* procs[0] */
2265: /* calculate the number of nonzeros on each processor */
2266: PetscMalloc1(size,&procsnz);
2267: PetscMemzero(procsnz,size*sizeof(PetscInt));
2268: for (i=0; i<size; i++) {
2269: for (j=rowners[i]*bs; j< rowners[i+1]*bs; j++) {
2270: procsnz[i] += rowlengths[j];
2271: }
2272: }
2273: PetscFree(rowlengths);
2275: /* determine max buffer needed and allocate it */
2276: maxnz = 0;
2277: for (i=0; i<size; i++) {
2278: maxnz = PetscMax(maxnz,procsnz[i]);
2279: }
2280: PetscMalloc1(maxnz,&cols);
2282: /* read in my part of the matrix column indices */
2283: nz = procsnz[0];
2284: PetscMalloc1(nz,&ibuf);
2285: mycols = ibuf;
2286: if (size == 1) nz -= extra_rows;
2287: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2288: if (size == 1) {
2289: for (i=0; i< extra_rows; i++) mycols[nz+i] = M+i;
2290: }
2292: /* read in every ones (except the last) and ship off */
2293: for (i=1; i<size-1; i++) {
2294: nz = procsnz[i];
2295: PetscBinaryRead(fd,cols,nz,PETSC_INT);
2296: MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2297: }
2298: /* read in the stuff for the last proc */
2299: if (size != 1) {
2300: nz = procsnz[size-1] - extra_rows; /* the extra rows are not on the disk */
2301: PetscBinaryRead(fd,cols,nz,PETSC_INT);
2302: for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2303: MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
2304: }
2305: PetscFree(cols);
2306: } else { /* procs[i], i>0 */
2307: /* determine buffer space needed for message */
2308: nz = 0;
2309: for (i=0; i<m; i++) nz += locrowlens[i];
2310: PetscMalloc1(nz,&ibuf);
2311: mycols = ibuf;
2312: /* receive message of column indices*/
2313: MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2314: MPI_Get_count(&status,MPIU_INT,&maxnz);
2315: if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2316: }
2318: /* loop over local rows, determining number of off diagonal entries */
2319: PetscMalloc2(rend-rstart,&dlens,rend-rstart,&odlens);
2320: PetscMalloc3(Mbs,&mask,Mbs,&masked1,Mbs,&masked2);
2321: PetscMemzero(mask,Mbs*sizeof(PetscInt));
2322: PetscMemzero(masked1,Mbs*sizeof(PetscInt));
2323: PetscMemzero(masked2,Mbs*sizeof(PetscInt));
2324: rowcount = 0;
2325: nzcount = 0;
2326: for (i=0; i<mbs; i++) {
2327: dcount = 0;
2328: odcount = 0;
2329: for (j=0; j<bs; j++) {
2330: kmax = locrowlens[rowcount];
2331: for (k=0; k<kmax; k++) {
2332: tmp = mycols[nzcount++]/bs; /* block col. index */
2333: if (!mask[tmp]) {
2334: mask[tmp] = 1;
2335: if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp; /* entry in off-diag portion */
2336: else masked1[dcount++] = tmp; /* entry in diag portion */
2337: }
2338: }
2339: rowcount++;
2340: }
2342: dlens[i] = dcount; /* d_nzz[i] */
2343: odlens[i] = odcount; /* o_nzz[i] */
2345: /* zero out the mask elements we set */
2346: for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2347: for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2348: }
2349: if (!sizesset) {
2350: MatSetSizes(newmat,m,m,M+extra_rows,N+extra_rows);
2351: }
2352: MatMPISBAIJSetPreallocation(newmat,bs,0,dlens,0,odlens);
2353: MatSetOption(newmat,MAT_IGNORE_LOWER_TRIANGULAR,PETSC_TRUE);
2355: if (!rank) {
2356: PetscMalloc1(maxnz,&buf);
2357: /* read in my part of the matrix numerical values */
2358: nz = procsnz[0];
2359: vals = buf;
2360: mycols = ibuf;
2361: if (size == 1) nz -= extra_rows;
2362: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2363: if (size == 1) {
2364: for (i=0; i< extra_rows; i++) vals[nz+i] = 1.0;
2365: }
2367: /* insert into matrix */
2368: jj = rstart*bs;
2369: for (i=0; i<m; i++) {
2370: MatSetValues(newmat,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2371: mycols += locrowlens[i];
2372: vals += locrowlens[i];
2373: jj++;
2374: }
2376: /* read in other processors (except the last one) and ship out */
2377: for (i=1; i<size-1; i++) {
2378: nz = procsnz[i];
2379: vals = buf;
2380: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2381: MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);
2382: }
2383: /* the last proc */
2384: if (size != 1) {
2385: nz = procsnz[i] - extra_rows;
2386: vals = buf;
2387: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2388: for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2389: MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,((PetscObject)newmat)->tag,comm);
2390: }
2391: PetscFree(procsnz);
2393: } else {
2394: /* receive numeric values */
2395: PetscMalloc1(nz,&buf);
2397: /* receive message of values*/
2398: vals = buf;
2399: mycols = ibuf;
2400: MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm,&status);
2401: MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2402: if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2404: /* insert into matrix */
2405: jj = rstart*bs;
2406: for (i=0; i<m; i++) {
2407: MatSetValues_MPISBAIJ(newmat,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2408: mycols += locrowlens[i];
2409: vals += locrowlens[i];
2410: jj++;
2411: }
2412: }
2414: PetscFree(locrowlens);
2415: PetscFree(buf);
2416: PetscFree(ibuf);
2417: PetscFree2(rowners,browners);
2418: PetscFree2(dlens,odlens);
2419: PetscFree3(mask,masked1,masked2);
2420: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
2421: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
2422: return(0);
2423: }
2427: /*XXXXX@
2428: MatMPISBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.
2430: Input Parameters:
2431: . mat - the matrix
2432: . fact - factor
2434: Not Collective on Mat, each process can have a different hash factor
2436: Level: advanced
2438: Notes:
2439: This can also be set by the command line option: -mat_use_hash_table fact
2441: .keywords: matrix, hashtable, factor, HT
2443: .seealso: MatSetOption()
2444: @XXXXX*/
2449: PetscErrorCode MatGetRowMaxAbs_MPISBAIJ(Mat A,Vec v,PetscInt idx[])
2450: {
2451: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
2452: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(a->B)->data;
2453: PetscReal atmp;
2454: PetscReal *work,*svalues,*rvalues;
2456: PetscInt i,bs,mbs,*bi,*bj,brow,j,ncols,krow,kcol,col,row,Mbs,bcol;
2457: PetscMPIInt rank,size;
2458: PetscInt *rowners_bs,dest,count,source;
2459: PetscScalar *va;
2460: MatScalar *ba;
2461: MPI_Status stat;
2464: if (idx) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Send email to petsc-maint@mcs.anl.gov");
2465: MatGetRowMaxAbs(a->A,v,NULL);
2466: VecGetArray(v,&va);
2468: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
2469: MPI_Comm_rank(PetscObjectComm((PetscObject)A),&rank);
2471: bs = A->rmap->bs;
2472: mbs = a->mbs;
2473: Mbs = a->Mbs;
2474: ba = b->a;
2475: bi = b->i;
2476: bj = b->j;
2478: /* find ownerships */
2479: rowners_bs = A->rmap->range;
2481: /* each proc creates an array to be distributed */
2482: PetscMalloc1(bs*Mbs,&work);
2483: PetscMemzero(work,bs*Mbs*sizeof(PetscReal));
2485: /* row_max for B */
2486: if (rank != size-1) {
2487: for (i=0; i<mbs; i++) {
2488: ncols = bi[1] - bi[0]; bi++;
2489: brow = bs*i;
2490: for (j=0; j<ncols; j++) {
2491: bcol = bs*(*bj);
2492: for (kcol=0; kcol<bs; kcol++) {
2493: col = bcol + kcol; /* local col index */
2494: col += rowners_bs[rank+1]; /* global col index */
2495: for (krow=0; krow<bs; krow++) {
2496: atmp = PetscAbsScalar(*ba); ba++;
2497: row = brow + krow; /* local row index */
2498: if (PetscRealPart(va[row]) < atmp) va[row] = atmp;
2499: if (work[col] < atmp) work[col] = atmp;
2500: }
2501: }
2502: bj++;
2503: }
2504: }
2506: /* send values to its owners */
2507: for (dest=rank+1; dest<size; dest++) {
2508: svalues = work + rowners_bs[dest];
2509: count = rowners_bs[dest+1]-rowners_bs[dest];
2510: MPI_Send(svalues,count,MPIU_REAL,dest,rank,PetscObjectComm((PetscObject)A));
2511: }
2512: }
2514: /* receive values */
2515: if (rank) {
2516: rvalues = work;
2517: count = rowners_bs[rank+1]-rowners_bs[rank];
2518: for (source=0; source<rank; source++) {
2519: MPI_Recv(rvalues,count,MPIU_REAL,MPI_ANY_SOURCE,MPI_ANY_TAG,PetscObjectComm((PetscObject)A),&stat);
2520: /* process values */
2521: for (i=0; i<count; i++) {
2522: if (PetscRealPart(va[i]) < rvalues[i]) va[i] = rvalues[i];
2523: }
2524: }
2525: }
2527: VecRestoreArray(v,&va);
2528: PetscFree(work);
2529: return(0);
2530: }
2534: PetscErrorCode MatSOR_MPISBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
2535: {
2536: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)matin->data;
2537: PetscErrorCode ierr;
2538: PetscInt mbs=mat->mbs,bs=matin->rmap->bs;
2539: PetscScalar *x,*ptr,*from;
2540: Vec bb1;
2541: const PetscScalar *b;
2544: if (its <= 0 || lits <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
2545: if (bs > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");
2547: if (flag == SOR_APPLY_UPPER) {
2548: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2549: return(0);
2550: }
2552: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2553: if (flag & SOR_ZERO_INITIAL_GUESS) {
2554: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
2555: its--;
2556: }
2558: VecDuplicate(bb,&bb1);
2559: while (its--) {
2561: /* lower triangular part: slvec0b = - B^T*xx */
2562: (*mat->B->ops->multtranspose)(mat->B,xx,mat->slvec0b);
2564: /* copy xx into slvec0a */
2565: VecGetArray(mat->slvec0,&ptr);
2566: VecGetArray(xx,&x);
2567: PetscMemcpy(ptr,x,bs*mbs*sizeof(MatScalar));
2568: VecRestoreArray(mat->slvec0,&ptr);
2570: VecScale(mat->slvec0,-1.0);
2572: /* copy bb into slvec1a */
2573: VecGetArray(mat->slvec1,&ptr);
2574: VecGetArrayRead(bb,&b);
2575: PetscMemcpy(ptr,b,bs*mbs*sizeof(MatScalar));
2576: VecRestoreArray(mat->slvec1,&ptr);
2578: /* set slvec1b = 0 */
2579: VecSet(mat->slvec1b,0.0);
2581: VecScatterBegin(mat->sMvctx,mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD);
2582: VecRestoreArray(xx,&x);
2583: VecRestoreArrayRead(bb,&b);
2584: VecScatterEnd(mat->sMvctx,mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD);
2586: /* upper triangular part: bb1 = bb1 - B*x */
2587: (*mat->B->ops->multadd)(mat->B,mat->slvec1b,mat->slvec1a,bb1);
2589: /* local diagonal sweep */
2590: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
2591: }
2592: VecDestroy(&bb1);
2593: } else if ((flag & SOR_LOCAL_FORWARD_SWEEP) && (its == 1) && (flag & SOR_ZERO_INITIAL_GUESS)) {
2594: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2595: } else if ((flag & SOR_LOCAL_BACKWARD_SWEEP) && (its == 1) && (flag & SOR_ZERO_INITIAL_GUESS)) {
2596: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2597: } else if (flag & SOR_EISENSTAT) {
2598: Vec xx1;
2599: PetscBool hasop;
2600: const PetscScalar *diag;
2601: PetscScalar *sl,scale = (omega - 2.0)/omega;
2602: PetscInt i,n;
2604: if (!mat->xx1) {
2605: VecDuplicate(bb,&mat->xx1);
2606: VecDuplicate(bb,&mat->bb1);
2607: }
2608: xx1 = mat->xx1;
2609: bb1 = mat->bb1;
2611: (*mat->A->ops->sor)(mat->A,bb,omega,(MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP),fshift,lits,1,xx);
2613: if (!mat->diag) {
2614: /* this is wrong for same matrix with new nonzero values */
2615: MatGetVecs(matin,&mat->diag,NULL);
2616: MatGetDiagonal(matin,mat->diag);
2617: }
2618: MatHasOperation(matin,MATOP_MULT_DIAGONAL_BLOCK,&hasop);
2620: if (hasop) {
2621: MatMultDiagonalBlock(matin,xx,bb1);
2622: VecAYPX(mat->slvec1a,scale,bb);
2623: } else {
2624: /*
2625: These two lines are replaced by code that may be a bit faster for a good compiler
2626: VecPointwiseMult(mat->slvec1a,mat->diag,xx);
2627: VecAYPX(mat->slvec1a,scale,bb);
2628: */
2629: VecGetArray(mat->slvec1a,&sl);
2630: VecGetArrayRead(mat->diag,&diag);
2631: VecGetArrayRead(bb,&b);
2632: VecGetArray(xx,&x);
2633: VecGetLocalSize(xx,&n);
2634: if (omega == 1.0) {
2635: for (i=0; i<n; i++) sl[i] = b[i] - diag[i]*x[i];
2636: PetscLogFlops(2.0*n);
2637: } else {
2638: for (i=0; i<n; i++) sl[i] = b[i] + scale*diag[i]*x[i];
2639: PetscLogFlops(3.0*n);
2640: }
2641: VecRestoreArray(mat->slvec1a,&sl);
2642: VecRestoreArrayRead(mat->diag,&diag);
2643: VecRestoreArrayRead(bb,&b);
2644: VecRestoreArray(xx,&x);
2645: }
2647: /* multiply off-diagonal portion of matrix */
2648: VecSet(mat->slvec1b,0.0);
2649: (*mat->B->ops->multtranspose)(mat->B,xx,mat->slvec0b);
2650: VecGetArray(mat->slvec0,&from);
2651: VecGetArray(xx,&x);
2652: PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
2653: VecRestoreArray(mat->slvec0,&from);
2654: VecRestoreArray(xx,&x);
2655: VecScatterBegin(mat->sMvctx,mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD);
2656: VecScatterEnd(mat->sMvctx,mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD);
2657: (*mat->B->ops->multadd)(mat->B,mat->slvec1b,mat->slvec1a,mat->slvec1a);
2659: /* local sweep */
2660: (*mat->A->ops->sor)(mat->A,mat->slvec1a,omega,(MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP),fshift,lits,1,xx1);
2661: VecAXPY(xx,1.0,xx1);
2662: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2663: return(0);
2664: }
2668: PetscErrorCode MatSOR_MPISBAIJ_2comm(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
2669: {
2670: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)matin->data;
2672: Vec lvec1,bb1;
2675: if (its <= 0 || lits <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
2676: if (matin->rmap->bs > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");
2678: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2679: if (flag & SOR_ZERO_INITIAL_GUESS) {
2680: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
2681: its--;
2682: }
2684: VecDuplicate(mat->lvec,&lvec1);
2685: VecDuplicate(bb,&bb1);
2686: while (its--) {
2687: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2689: /* lower diagonal part: bb1 = bb - B^T*xx */
2690: (*mat->B->ops->multtranspose)(mat->B,xx,lvec1);
2691: VecScale(lvec1,-1.0);
2693: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2694: VecCopy(bb,bb1);
2695: VecScatterBegin(mat->Mvctx,lvec1,bb1,ADD_VALUES,SCATTER_REVERSE);
2697: /* upper diagonal part: bb1 = bb1 - B*x */
2698: VecScale(mat->lvec,-1.0);
2699: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb1,bb1);
2701: VecScatterEnd(mat->Mvctx,lvec1,bb1,ADD_VALUES,SCATTER_REVERSE);
2703: /* diagonal sweep */
2704: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
2705: }
2706: VecDestroy(&lvec1);
2707: VecDestroy(&bb1);
2708: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2709: return(0);
2710: }
2714: /*@
2715: MatCreateMPISBAIJWithArrays - creates a MPI SBAIJ matrix using arrays that contain in standard
2716: CSR format the local rows.
2718: Collective on MPI_Comm
2720: Input Parameters:
2721: + comm - MPI communicator
2722: . bs - the block size, only a block size of 1 is supported
2723: . m - number of local rows (Cannot be PETSC_DECIDE)
2724: . n - This value should be the same as the local size used in creating the
2725: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
2726: calculated if N is given) For square matrices n is almost always m.
2727: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2728: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2729: . i - row indices
2730: . j - column indices
2731: - a - matrix values
2733: Output Parameter:
2734: . mat - the matrix
2736: Level: intermediate
2738: Notes:
2739: The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
2740: thus you CANNOT change the matrix entries by changing the values of a[] after you have
2741: called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.
2743: The i and j indices are 0 based, and i indices are indices corresponding to the local j array.
2745: .keywords: matrix, aij, compressed row, sparse, parallel
2747: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
2748: MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithSplitArrays()
2749: @*/
2750: PetscErrorCode MatCreateMPISBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
2751: {
2756: if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
2757: if (m < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
2758: MatCreate(comm,mat);
2759: MatSetSizes(*mat,m,n,M,N);
2760: MatSetType(*mat,MATMPISBAIJ);
2761: MatMPISBAIJSetPreallocationCSR(*mat,bs,i,j,a);
2762: return(0);
2763: }
2768: /*@C
2769: MatMPISBAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in BAIJ format
2770: (the default parallel PETSc format).
2772: Collective on MPI_Comm
2774: Input Parameters:
2775: + A - the matrix
2776: . bs - the block size
2777: . i - the indices into j for the start of each local row (starts with zero)
2778: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2779: - v - optional values in the matrix
2781: Level: developer
2783: .keywords: matrix, aij, compressed row, sparse, parallel
2785: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateAIJ(), MPIAIJ
2786: @*/
2787: PetscErrorCode MatMPISBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2788: {
2792: PetscTryMethod(B,"MatMPISBAIJSetPreallocationCSR_C",(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,bs,i,j,v));
2793: return(0);
2794: }