Actual source code: mpibaij.c
petsc-3.5.3 2015-01-31
2: #include <../src/mat/impls/baij/mpi/mpibaij.h> /*I "petscmat.h" I*/
3: #include <petscblaslapack.h>
4: #include <petscsf.h>
6: extern PetscErrorCode MatSetUpMultiply_MPIBAIJ(Mat);
7: extern PetscErrorCode MatDisAssemble_MPIBAIJ(Mat);
8: extern PetscErrorCode MatGetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt [],PetscScalar []);
9: extern PetscErrorCode MatSetValues_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt [],const PetscScalar [],InsertMode);
10: extern PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
11: extern PetscErrorCode MatGetRow_SeqBAIJ(Mat,PetscInt,PetscInt*,PetscInt*[],PetscScalar*[]);
12: extern PetscErrorCode MatRestoreRow_SeqBAIJ(Mat,PetscInt,PetscInt*,PetscInt*[],PetscScalar*[]);
13: extern PetscErrorCode MatZeroRows_SeqBAIJ(Mat,PetscInt,const PetscInt[],PetscScalar,Vec,Vec);
17: PetscErrorCode MatGetRowMaxAbs_MPIBAIJ(Mat A,Vec v,PetscInt idx[])
18: {
19: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
21: PetscInt i,*idxb = 0;
22: PetscScalar *va,*vb;
23: Vec vtmp;
26: MatGetRowMaxAbs(a->A,v,idx);
27: VecGetArray(v,&va);
28: if (idx) {
29: for (i=0; i<A->rmap->n; i++) {
30: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
31: }
32: }
34: VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
35: if (idx) {PetscMalloc1(A->rmap->n,&idxb);}
36: MatGetRowMaxAbs(a->B,vtmp,idxb);
37: VecGetArray(vtmp,&vb);
39: for (i=0; i<A->rmap->n; i++) {
40: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
41: va[i] = vb[i];
42: if (idx) idx[i] = A->cmap->bs*a->garray[idxb[i]/A->cmap->bs] + (idxb[i] % A->cmap->bs);
43: }
44: }
46: VecRestoreArray(v,&va);
47: VecRestoreArray(vtmp,&vb);
48: PetscFree(idxb);
49: VecDestroy(&vtmp);
50: return(0);
51: }
55: PetscErrorCode MatStoreValues_MPIBAIJ(Mat mat)
56: {
57: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ*)mat->data;
61: MatStoreValues(aij->A);
62: MatStoreValues(aij->B);
63: return(0);
64: }
68: PetscErrorCode MatRetrieveValues_MPIBAIJ(Mat mat)
69: {
70: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ*)mat->data;
74: MatRetrieveValues(aij->A);
75: MatRetrieveValues(aij->B);
76: return(0);
77: }
79: /*
80: Local utility routine that creates a mapping from the global column
81: number to the local number in the off-diagonal part of the local
82: storage of the matrix. This is done in a non scalable way since the
83: length of colmap equals the global matrix length.
84: */
87: PetscErrorCode MatCreateColmap_MPIBAIJ_Private(Mat mat)
88: {
89: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
90: Mat_SeqBAIJ *B = (Mat_SeqBAIJ*)baij->B->data;
92: PetscInt nbs = B->nbs,i,bs=mat->rmap->bs;
95: #if defined(PETSC_USE_CTABLE)
96: PetscTableCreate(baij->nbs,baij->Nbs+1,&baij->colmap);
97: for (i=0; i<nbs; i++) {
98: PetscTableAdd(baij->colmap,baij->garray[i]+1,i*bs+1,INSERT_VALUES);
99: }
100: #else
101: PetscMalloc1((baij->Nbs+1),&baij->colmap);
102: PetscLogObjectMemory((PetscObject)mat,baij->Nbs*sizeof(PetscInt));
103: PetscMemzero(baij->colmap,baij->Nbs*sizeof(PetscInt));
104: for (i=0; i<nbs; i++) baij->colmap[baij->garray[i]] = i*bs+1;
105: #endif
106: return(0);
107: }
109: #define MatSetValues_SeqBAIJ_A_Private(row,col,value,addv) \
110: { \
111: \
112: brow = row/bs; \
113: rp = aj + ai[brow]; ap = aa + bs2*ai[brow]; \
114: rmax = aimax[brow]; nrow = ailen[brow]; \
115: bcol = col/bs; \
116: ridx = row % bs; cidx = col % bs; \
117: low = 0; high = nrow; \
118: while (high-low > 3) { \
119: t = (low+high)/2; \
120: if (rp[t] > bcol) high = t; \
121: else low = t; \
122: } \
123: for (_i=low; _i<high; _i++) { \
124: if (rp[_i] > bcol) break; \
125: if (rp[_i] == bcol) { \
126: bap = ap + bs2*_i + bs*cidx + ridx; \
127: if (addv == ADD_VALUES) *bap += value; \
128: else *bap = value; \
129: goto a_noinsert; \
130: } \
131: } \
132: if (a->nonew == 1) goto a_noinsert; \
133: if (a->nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
134: MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,aimax,a->nonew,MatScalar); \
135: N = nrow++ - 1; \
136: /* shift up all the later entries in this row */ \
137: for (ii=N; ii>=_i; ii--) { \
138: rp[ii+1] = rp[ii]; \
139: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
140: } \
141: if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); } \
142: rp[_i] = bcol; \
143: ap[bs2*_i + bs*cidx + ridx] = value; \
144: a_noinsert:; \
145: ailen[brow] = nrow; \
146: }
148: #define MatSetValues_SeqBAIJ_B_Private(row,col,value,addv) \
149: { \
150: brow = row/bs; \
151: rp = bj + bi[brow]; ap = ba + bs2*bi[brow]; \
152: rmax = bimax[brow]; nrow = bilen[brow]; \
153: bcol = col/bs; \
154: ridx = row % bs; cidx = col % bs; \
155: low = 0; high = nrow; \
156: while (high-low > 3) { \
157: t = (low+high)/2; \
158: if (rp[t] > bcol) high = t; \
159: else low = t; \
160: } \
161: for (_i=low; _i<high; _i++) { \
162: if (rp[_i] > bcol) break; \
163: if (rp[_i] == bcol) { \
164: bap = ap + bs2*_i + bs*cidx + ridx; \
165: if (addv == ADD_VALUES) *bap += value; \
166: else *bap = value; \
167: goto b_noinsert; \
168: } \
169: } \
170: if (b->nonew == 1) goto b_noinsert; \
171: if (b->nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
172: MatSeqXAIJReallocateAIJ(B,b->mbs,bs2,nrow,brow,bcol,rmax,ba,bi,bj,rp,ap,bimax,b->nonew,MatScalar); \
173: N = nrow++ - 1; \
174: /* shift up all the later entries in this row */ \
175: for (ii=N; ii>=_i; ii--) { \
176: rp[ii+1] = rp[ii]; \
177: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar)); \
178: } \
179: if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));} \
180: rp[_i] = bcol; \
181: ap[bs2*_i + bs*cidx + ridx] = value; \
182: b_noinsert:; \
183: bilen[brow] = nrow; \
184: }
188: PetscErrorCode MatSetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
189: {
190: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
191: MatScalar value;
192: PetscBool roworiented = baij->roworiented;
194: PetscInt i,j,row,col;
195: PetscInt rstart_orig=mat->rmap->rstart;
196: PetscInt rend_orig =mat->rmap->rend,cstart_orig=mat->cmap->rstart;
197: PetscInt cend_orig =mat->cmap->rend,bs=mat->rmap->bs;
199: /* Some Variables required in the macro */
200: Mat A = baij->A;
201: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)(A)->data;
202: PetscInt *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
203: MatScalar *aa =a->a;
205: Mat B = baij->B;
206: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(B)->data;
207: PetscInt *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
208: MatScalar *ba =b->a;
210: PetscInt *rp,ii,nrow,_i,rmax,N,brow,bcol;
211: PetscInt low,high,t,ridx,cidx,bs2=a->bs2;
212: MatScalar *ap,*bap;
215: for (i=0; i<m; i++) {
216: if (im[i] < 0) continue;
217: #if defined(PETSC_USE_DEBUG)
218: 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);
219: #endif
220: if (im[i] >= rstart_orig && im[i] < rend_orig) {
221: row = im[i] - rstart_orig;
222: for (j=0; j<n; j++) {
223: if (in[j] >= cstart_orig && in[j] < cend_orig) {
224: col = in[j] - cstart_orig;
225: if (roworiented) value = v[i*n+j];
226: else value = v[i+j*m];
227: MatSetValues_SeqBAIJ_A_Private(row,col,value,addv);
228: /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
229: } else if (in[j] < 0) continue;
230: #if defined(PETSC_USE_DEBUG)
231: 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);
232: #endif
233: else {
234: if (mat->was_assembled) {
235: if (!baij->colmap) {
236: MatCreateColmap_MPIBAIJ_Private(mat);
237: }
238: #if defined(PETSC_USE_CTABLE)
239: PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
240: col = col - 1;
241: #else
242: col = baij->colmap[in[j]/bs] - 1;
243: #endif
244: if (col < 0 && !((Mat_SeqBAIJ*)(baij->B->data))->nonew) {
245: MatDisAssemble_MPIBAIJ(mat);
246: col = in[j];
247: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
248: B = baij->B;
249: b = (Mat_SeqBAIJ*)(B)->data;
250: bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
251: ba =b->a;
252: } else if (col < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", im[i], in[j]);
253: else col += in[j]%bs;
254: } else col = in[j];
255: if (roworiented) value = v[i*n+j];
256: else value = v[i+j*m];
257: MatSetValues_SeqBAIJ_B_Private(row,col,value,addv);
258: /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
259: }
260: }
261: } else {
262: 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]);
263: if (!baij->donotstash) {
264: mat->assembled = PETSC_FALSE;
265: if (roworiented) {
266: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,PETSC_FALSE);
267: } else {
268: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,PETSC_FALSE);
269: }
270: }
271: }
272: }
273: return(0);
274: }
278: PetscErrorCode MatSetValuesBlocked_MPIBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
279: {
280: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
281: const PetscScalar *value;
282: MatScalar *barray = baij->barray;
283: PetscBool roworiented = baij->roworiented;
284: PetscErrorCode ierr;
285: PetscInt i,j,ii,jj,row,col,rstart=baij->rstartbs;
286: PetscInt rend=baij->rendbs,cstart=baij->cstartbs,stepval;
287: PetscInt cend=baij->cendbs,bs=mat->rmap->bs,bs2=baij->bs2;
290: if (!barray) {
291: PetscMalloc1(bs2,&barray);
292: baij->barray = barray;
293: }
295: if (roworiented) stepval = (n-1)*bs;
296: else stepval = (m-1)*bs;
298: for (i=0; i<m; i++) {
299: if (im[i] < 0) continue;
300: #if defined(PETSC_USE_DEBUG)
301: 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);
302: #endif
303: if (im[i] >= rstart && im[i] < rend) {
304: row = im[i] - rstart;
305: for (j=0; j<n; j++) {
306: /* If NumCol = 1 then a copy is not required */
307: if ((roworiented) && (n == 1)) {
308: barray = (MatScalar*)v + i*bs2;
309: } else if ((!roworiented) && (m == 1)) {
310: barray = (MatScalar*)v + j*bs2;
311: } else { /* Here a copy is required */
312: if (roworiented) {
313: value = v + (i*(stepval+bs) + j)*bs;
314: } else {
315: value = v + (j*(stepval+bs) + i)*bs;
316: }
317: for (ii=0; ii<bs; ii++,value+=bs+stepval) {
318: for (jj=0; jj<bs; jj++) barray[jj] = value[jj];
319: barray += bs;
320: }
321: barray -= bs2;
322: }
324: if (in[j] >= cstart && in[j] < cend) {
325: col = in[j] - cstart;
326: MatSetValuesBlocked_SeqBAIJ(baij->A,1,&row,1,&col,barray,addv);
327: } else if (in[j] < 0) continue;
328: #if defined(PETSC_USE_DEBUG)
329: 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);
330: #endif
331: else {
332: if (mat->was_assembled) {
333: if (!baij->colmap) {
334: MatCreateColmap_MPIBAIJ_Private(mat);
335: }
337: #if defined(PETSC_USE_DEBUG)
338: #if defined(PETSC_USE_CTABLE)
339: { PetscInt data;
340: PetscTableFind(baij->colmap,in[j]+1,&data);
341: if ((data - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
342: }
343: #else
344: if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
345: #endif
346: #endif
347: #if defined(PETSC_USE_CTABLE)
348: PetscTableFind(baij->colmap,in[j]+1,&col);
349: col = (col - 1)/bs;
350: #else
351: col = (baij->colmap[in[j]] - 1)/bs;
352: #endif
353: if (col < 0 && !((Mat_SeqBAIJ*)(baij->B->data))->nonew) {
354: MatDisAssemble_MPIBAIJ(mat);
355: col = in[j];
356: } else if (col < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", bs*im[i], bs*in[j]);
357: } else col = in[j];
358: MatSetValuesBlocked_SeqBAIJ(baij->B,1,&row,1,&col,barray,addv);
359: }
360: }
361: } else {
362: 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]);
363: if (!baij->donotstash) {
364: if (roworiented) {
365: MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
366: } else {
367: MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
368: }
369: }
370: }
371: }
372: return(0);
373: }
375: #define HASH_KEY 0.6180339887
376: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(PetscInt)((size)*(tmp-(PetscInt)tmp)))
377: /* #define HASH(size,key) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
378: /* #define HASH(size,key,tmp) ((PetscInt)((size)*fmod(((key)*HASH_KEY),1))) */
381: PetscErrorCode MatSetValues_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
382: {
383: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
384: PetscBool roworiented = baij->roworiented;
386: PetscInt i,j,row,col;
387: PetscInt rstart_orig=mat->rmap->rstart;
388: PetscInt rend_orig =mat->rmap->rend,Nbs=baij->Nbs;
389: PetscInt h1,key,size=baij->ht_size,bs=mat->rmap->bs,*HT=baij->ht,idx;
390: PetscReal tmp;
391: MatScalar **HD = baij->hd,value;
392: #if defined(PETSC_USE_DEBUG)
393: PetscInt total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
394: #endif
397: for (i=0; i<m; i++) {
398: #if defined(PETSC_USE_DEBUG)
399: if (im[i] < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
400: 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);
401: #endif
402: row = im[i];
403: if (row >= rstart_orig && row < rend_orig) {
404: for (j=0; j<n; j++) {
405: col = in[j];
406: if (roworiented) value = v[i*n+j];
407: else value = v[i+j*m];
408: /* Look up PetscInto the Hash Table */
409: key = (row/bs)*Nbs+(col/bs)+1;
410: h1 = HASH(size,key,tmp);
413: idx = h1;
414: #if defined(PETSC_USE_DEBUG)
415: insert_ct++;
416: total_ct++;
417: if (HT[idx] != key) {
418: for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++) ;
419: if (idx == size) {
420: for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++) ;
421: if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
422: }
423: }
424: #else
425: if (HT[idx] != key) {
426: for (idx=h1; (idx<size) && (HT[idx]!=key); idx++) ;
427: if (idx == size) {
428: for (idx=0; (idx<h1) && (HT[idx]!=key); idx++) ;
429: if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
430: }
431: }
432: #endif
433: /* A HASH table entry is found, so insert the values at the correct address */
434: if (addv == ADD_VALUES) *(HD[idx]+ (col % bs)*bs + (row % bs)) += value;
435: else *(HD[idx]+ (col % bs)*bs + (row % bs)) = value;
436: }
437: } else if (!baij->donotstash) {
438: if (roworiented) {
439: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,PETSC_FALSE);
440: } else {
441: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,PETSC_FALSE);
442: }
443: }
444: }
445: #if defined(PETSC_USE_DEBUG)
446: baij->ht_total_ct = total_ct;
447: baij->ht_insert_ct = insert_ct;
448: #endif
449: return(0);
450: }
454: PetscErrorCode MatSetValuesBlocked_MPIBAIJ_HT(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
455: {
456: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
457: PetscBool roworiented = baij->roworiented;
458: PetscErrorCode ierr;
459: PetscInt i,j,ii,jj,row,col;
460: PetscInt rstart=baij->rstartbs;
461: PetscInt rend =mat->rmap->rend,stepval,bs=mat->rmap->bs,bs2=baij->bs2,nbs2=n*bs2;
462: PetscInt h1,key,size=baij->ht_size,idx,*HT=baij->ht,Nbs=baij->Nbs;
463: PetscReal tmp;
464: MatScalar **HD = baij->hd,*baij_a;
465: const PetscScalar *v_t,*value;
466: #if defined(PETSC_USE_DEBUG)
467: PetscInt total_ct=baij->ht_total_ct,insert_ct=baij->ht_insert_ct;
468: #endif
471: if (roworiented) stepval = (n-1)*bs;
472: else stepval = (m-1)*bs;
474: for (i=0; i<m; i++) {
475: #if defined(PETSC_USE_DEBUG)
476: if (im[i] < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",im[i]);
477: 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);
478: #endif
479: row = im[i];
480: v_t = v + i*nbs2;
481: if (row >= rstart && row < rend) {
482: for (j=0; j<n; j++) {
483: col = in[j];
485: /* Look up into the Hash Table */
486: key = row*Nbs+col+1;
487: h1 = HASH(size,key,tmp);
489: idx = h1;
490: #if defined(PETSC_USE_DEBUG)
491: total_ct++;
492: insert_ct++;
493: if (HT[idx] != key) {
494: for (idx=h1; (idx<size) && (HT[idx]!=key); idx++,total_ct++) ;
495: if (idx == size) {
496: for (idx=0; (idx<h1) && (HT[idx]!=key); idx++,total_ct++) ;
497: if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
498: }
499: }
500: #else
501: if (HT[idx] != key) {
502: for (idx=h1; (idx<size) && (HT[idx]!=key); idx++) ;
503: if (idx == size) {
504: for (idx=0; (idx<h1) && (HT[idx]!=key); idx++) ;
505: if (idx == h1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"(%D,%D) has no entry in the hash table", row, col);
506: }
507: }
508: #endif
509: baij_a = HD[idx];
510: if (roworiented) {
511: /*value = v + i*(stepval+bs)*bs + j*bs;*/
512: /* value = v + (i*(stepval+bs)+j)*bs; */
513: value = v_t;
514: v_t += bs;
515: if (addv == ADD_VALUES) {
516: for (ii=0; ii<bs; ii++,value+=stepval) {
517: for (jj=ii; jj<bs2; jj+=bs) {
518: baij_a[jj] += *value++;
519: }
520: }
521: } else {
522: for (ii=0; ii<bs; ii++,value+=stepval) {
523: for (jj=ii; jj<bs2; jj+=bs) {
524: baij_a[jj] = *value++;
525: }
526: }
527: }
528: } else {
529: value = v + j*(stepval+bs)*bs + i*bs;
530: if (addv == ADD_VALUES) {
531: for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
532: for (jj=0; jj<bs; jj++) {
533: baij_a[jj] += *value++;
534: }
535: }
536: } else {
537: for (ii=0; ii<bs; ii++,value+=stepval,baij_a+=bs) {
538: for (jj=0; jj<bs; jj++) {
539: baij_a[jj] = *value++;
540: }
541: }
542: }
543: }
544: }
545: } else {
546: if (!baij->donotstash) {
547: if (roworiented) {
548: MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
549: } else {
550: MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
551: }
552: }
553: }
554: }
555: #if defined(PETSC_USE_DEBUG)
556: baij->ht_total_ct = total_ct;
557: baij->ht_insert_ct = insert_ct;
558: #endif
559: return(0);
560: }
564: PetscErrorCode MatGetValues_MPIBAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
565: {
566: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
568: PetscInt bs = mat->rmap->bs,i,j,bsrstart = mat->rmap->rstart,bsrend = mat->rmap->rend;
569: PetscInt bscstart = mat->cmap->rstart,bscend = mat->cmap->rend,row,col,data;
572: for (i=0; i<m; i++) {
573: if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);*/
574: 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);
575: if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
576: row = idxm[i] - bsrstart;
577: for (j=0; j<n; j++) {
578: if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]); */
579: 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);
580: if (idxn[j] >= bscstart && idxn[j] < bscend) {
581: col = idxn[j] - bscstart;
582: MatGetValues_SeqBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
583: } else {
584: if (!baij->colmap) {
585: MatCreateColmap_MPIBAIJ_Private(mat);
586: }
587: #if defined(PETSC_USE_CTABLE)
588: PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
589: data--;
590: #else
591: data = baij->colmap[idxn[j]/bs]-1;
592: #endif
593: if ((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
594: else {
595: col = data + idxn[j]%bs;
596: MatGetValues_SeqBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
597: }
598: }
599: }
600: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
601: }
602: return(0);
603: }
607: PetscErrorCode MatNorm_MPIBAIJ(Mat mat,NormType type,PetscReal *nrm)
608: {
609: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
610: Mat_SeqBAIJ *amat = (Mat_SeqBAIJ*)baij->A->data,*bmat = (Mat_SeqBAIJ*)baij->B->data;
612: PetscInt i,j,bs2=baij->bs2,bs=baij->A->rmap->bs,nz,row,col;
613: PetscReal sum = 0.0;
614: MatScalar *v;
617: if (baij->size == 1) {
618: MatNorm(baij->A,type,nrm);
619: } else {
620: if (type == NORM_FROBENIUS) {
621: v = amat->a;
622: nz = amat->nz*bs2;
623: for (i=0; i<nz; i++) {
624: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
625: }
626: v = bmat->a;
627: nz = bmat->nz*bs2;
628: for (i=0; i<nz; i++) {
629: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
630: }
631: MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
632: *nrm = PetscSqrtReal(*nrm);
633: } else if (type == NORM_1) { /* max column sum */
634: PetscReal *tmp,*tmp2;
635: PetscInt *jj,*garray=baij->garray,cstart=baij->rstartbs;
636: PetscMalloc2(mat->cmap->N,&tmp,mat->cmap->N,&tmp2);
637: PetscMemzero(tmp,mat->cmap->N*sizeof(PetscReal));
638: v = amat->a; jj = amat->j;
639: for (i=0; i<amat->nz; i++) {
640: for (j=0; j<bs; j++) {
641: col = bs*(cstart + *jj) + j; /* column index */
642: for (row=0; row<bs; row++) {
643: tmp[col] += PetscAbsScalar(*v); v++;
644: }
645: }
646: jj++;
647: }
648: v = bmat->a; jj = bmat->j;
649: for (i=0; i<bmat->nz; i++) {
650: for (j=0; j<bs; j++) {
651: col = bs*garray[*jj] + j;
652: for (row=0; row<bs; row++) {
653: tmp[col] += PetscAbsScalar(*v); v++;
654: }
655: }
656: jj++;
657: }
658: MPI_Allreduce(tmp,tmp2,mat->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
659: *nrm = 0.0;
660: for (j=0; j<mat->cmap->N; j++) {
661: if (tmp2[j] > *nrm) *nrm = tmp2[j];
662: }
663: PetscFree2(tmp,tmp2);
664: } else if (type == NORM_INFINITY) { /* max row sum */
665: PetscReal *sums;
666: PetscMalloc1(bs,&sums);
667: sum = 0.0;
668: for (j=0; j<amat->mbs; j++) {
669: for (row=0; row<bs; row++) sums[row] = 0.0;
670: v = amat->a + bs2*amat->i[j];
671: nz = amat->i[j+1]-amat->i[j];
672: for (i=0; i<nz; i++) {
673: for (col=0; col<bs; col++) {
674: for (row=0; row<bs; row++) {
675: sums[row] += PetscAbsScalar(*v); v++;
676: }
677: }
678: }
679: v = bmat->a + bs2*bmat->i[j];
680: nz = bmat->i[j+1]-bmat->i[j];
681: for (i=0; i<nz; i++) {
682: for (col=0; col<bs; col++) {
683: for (row=0; row<bs; row++) {
684: sums[row] += PetscAbsScalar(*v); v++;
685: }
686: }
687: }
688: for (row=0; row<bs; row++) {
689: if (sums[row] > sum) sum = sums[row];
690: }
691: }
692: MPI_Allreduce(&sum,nrm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)mat));
693: PetscFree(sums);
694: } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No support for this norm yet");
695: }
696: return(0);
697: }
699: /*
700: Creates the hash table, and sets the table
701: This table is created only once.
702: If new entried need to be added to the matrix
703: then the hash table has to be destroyed and
704: recreated.
705: */
708: PetscErrorCode MatCreateHashTable_MPIBAIJ_Private(Mat mat,PetscReal factor)
709: {
710: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
711: Mat A = baij->A,B=baij->B;
712: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data,*b=(Mat_SeqBAIJ*)B->data;
713: PetscInt i,j,k,nz=a->nz+b->nz,h1,*ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
715: PetscInt ht_size,bs2=baij->bs2,rstart=baij->rstartbs;
716: PetscInt cstart=baij->cstartbs,*garray=baij->garray,row,col,Nbs=baij->Nbs;
717: PetscInt *HT,key;
718: MatScalar **HD;
719: PetscReal tmp;
720: #if defined(PETSC_USE_INFO)
721: PetscInt ct=0,max=0;
722: #endif
725: if (baij->ht) return(0);
727: baij->ht_size = (PetscInt)(factor*nz);
728: ht_size = baij->ht_size;
730: /* Allocate Memory for Hash Table */
731: PetscCalloc2(ht_size,&baij->hd,ht_size,&baij->ht);
732: HD = baij->hd;
733: HT = baij->ht;
735: /* Loop Over A */
736: for (i=0; i<a->mbs; i++) {
737: for (j=ai[i]; j<ai[i+1]; j++) {
738: row = i+rstart;
739: col = aj[j]+cstart;
741: key = row*Nbs + col + 1;
742: h1 = HASH(ht_size,key,tmp);
743: for (k=0; k<ht_size; k++) {
744: if (!HT[(h1+k)%ht_size]) {
745: HT[(h1+k)%ht_size] = key;
746: HD[(h1+k)%ht_size] = a->a + j*bs2;
747: break;
748: #if defined(PETSC_USE_INFO)
749: } else {
750: ct++;
751: #endif
752: }
753: }
754: #if defined(PETSC_USE_INFO)
755: if (k> max) max = k;
756: #endif
757: }
758: }
759: /* Loop Over B */
760: for (i=0; i<b->mbs; i++) {
761: for (j=bi[i]; j<bi[i+1]; j++) {
762: row = i+rstart;
763: col = garray[bj[j]];
764: key = row*Nbs + col + 1;
765: h1 = HASH(ht_size,key,tmp);
766: for (k=0; k<ht_size; k++) {
767: if (!HT[(h1+k)%ht_size]) {
768: HT[(h1+k)%ht_size] = key;
769: HD[(h1+k)%ht_size] = b->a + j*bs2;
770: break;
771: #if defined(PETSC_USE_INFO)
772: } else {
773: ct++;
774: #endif
775: }
776: }
777: #if defined(PETSC_USE_INFO)
778: if (k> max) max = k;
779: #endif
780: }
781: }
783: /* Print Summary */
784: #if defined(PETSC_USE_INFO)
785: for (i=0,j=0; i<ht_size; i++) {
786: if (HT[i]) j++;
787: }
788: PetscInfo2(mat,"Average Search = %5.2f,max search = %D\n",(!j)? 0.0:((PetscReal)(ct+j))/j,max);
789: #endif
790: return(0);
791: }
795: PetscErrorCode MatAssemblyBegin_MPIBAIJ(Mat mat,MatAssemblyType mode)
796: {
797: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
799: PetscInt nstash,reallocs;
800: InsertMode addv;
803: if (baij->donotstash || mat->nooffprocentries) return(0);
805: /* make sure all processors are either in INSERTMODE or ADDMODE */
806: MPI_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,PetscObjectComm((PetscObject)mat));
807: if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
808: mat->insertmode = addv; /* in case this processor had no cache */
810: MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
811: MatStashScatterBegin_Private(mat,&mat->bstash,baij->rangebs);
812: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
813: PetscInfo2(mat,"Stash has %D entries,uses %D mallocs.\n",nstash,reallocs);
814: MatStashGetInfo_Private(&mat->bstash,&nstash,&reallocs);
815: PetscInfo2(mat,"Block-Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
816: return(0);
817: }
821: PetscErrorCode MatAssemblyEnd_MPIBAIJ(Mat mat,MatAssemblyType mode)
822: {
823: Mat_MPIBAIJ *baij=(Mat_MPIBAIJ*)mat->data;
824: Mat_SeqBAIJ *a =(Mat_SeqBAIJ*)baij->A->data;
826: PetscInt i,j,rstart,ncols,flg,bs2=baij->bs2;
827: PetscInt *row,*col;
828: PetscBool r1,r2,r3,other_disassembled;
829: MatScalar *val;
830: InsertMode addv = mat->insertmode;
831: PetscMPIInt n;
834: /* do not use 'b=(Mat_SeqBAIJ*)baij->B->data' as B can be reset in disassembly */
835: if (!baij->donotstash && !mat->nooffprocentries) {
836: while (1) {
837: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
838: if (!flg) break;
840: for (i=0; i<n;) {
841: /* Now identify the consecutive vals belonging to the same row */
842: for (j=i,rstart=row[j]; j<n; j++) {
843: if (row[j] != rstart) break;
844: }
845: if (j < n) ncols = j-i;
846: else ncols = n-i;
847: /* Now assemble all these values with a single function call */
848: MatSetValues_MPIBAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
849: i = j;
850: }
851: }
852: MatStashScatterEnd_Private(&mat->stash);
853: /* Now process the block-stash. Since the values are stashed column-oriented,
854: set the roworiented flag to column oriented, and after MatSetValues()
855: restore the original flags */
856: r1 = baij->roworiented;
857: r2 = a->roworiented;
858: r3 = ((Mat_SeqBAIJ*)baij->B->data)->roworiented;
860: baij->roworiented = PETSC_FALSE;
861: a->roworiented = PETSC_FALSE;
863: (((Mat_SeqBAIJ*)baij->B->data))->roworiented = PETSC_FALSE; /* b->roworiented */
864: while (1) {
865: MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
866: if (!flg) break;
868: for (i=0; i<n;) {
869: /* Now identify the consecutive vals belonging to the same row */
870: for (j=i,rstart=row[j]; j<n; j++) {
871: if (row[j] != rstart) break;
872: }
873: if (j < n) ncols = j-i;
874: else ncols = n-i;
875: MatSetValuesBlocked_MPIBAIJ(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
876: i = j;
877: }
878: }
879: MatStashScatterEnd_Private(&mat->bstash);
881: baij->roworiented = r1;
882: a->roworiented = r2;
884: ((Mat_SeqBAIJ*)baij->B->data)->roworiented = r3; /* b->roworiented */
885: }
887: MatAssemblyBegin(baij->A,mode);
888: MatAssemblyEnd(baij->A,mode);
890: /* determine if any processor has disassembled, if so we must
891: also disassemble ourselfs, in order that we may reassemble. */
892: /*
893: if nonzero structure of submatrix B cannot change then we know that
894: no processor disassembled thus we can skip this stuff
895: */
896: if (!((Mat_SeqBAIJ*)baij->B->data)->nonew) {
897: MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPIU_BOOL,MPI_PROD,PetscObjectComm((PetscObject)mat));
898: if (mat->was_assembled && !other_disassembled) {
899: MatDisAssemble_MPIBAIJ(mat);
900: }
901: }
903: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
904: MatSetUpMultiply_MPIBAIJ(mat);
905: }
906: MatAssemblyBegin(baij->B,mode);
907: MatAssemblyEnd(baij->B,mode);
909: #if defined(PETSC_USE_INFO)
910: if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
911: PetscInfo1(mat,"Average Hash Table Search in MatSetValues = %5.2f\n",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
913: baij->ht_total_ct = 0;
914: baij->ht_insert_ct = 0;
915: }
916: #endif
917: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
918: MatCreateHashTable_MPIBAIJ_Private(mat,baij->ht_fact);
920: mat->ops->setvalues = MatSetValues_MPIBAIJ_HT;
921: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPIBAIJ_HT;
922: }
924: PetscFree2(baij->rowvalues,baij->rowindices);
926: baij->rowvalues = 0;
928: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
929: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
930: PetscObjectState state = baij->A->nonzerostate + baij->B->nonzerostate;
931: MPI_Allreduce(&state,&mat->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)mat));
932: }
933: return(0);
934: }
936: extern PetscErrorCode MatView_SeqBAIJ(Mat,PetscViewer);
937: #include <petscdraw.h>
940: static PetscErrorCode MatView_MPIBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
941: {
942: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
943: PetscErrorCode ierr;
944: PetscMPIInt rank = baij->rank;
945: PetscInt bs = mat->rmap->bs;
946: PetscBool iascii,isdraw;
947: PetscViewer sviewer;
948: PetscViewerFormat format;
951: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
952: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
953: if (iascii) {
954: PetscViewerGetFormat(viewer,&format);
955: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
956: MatInfo info;
957: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
958: MatGetInfo(mat,MAT_LOCAL,&info);
959: PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);
960: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D bs %D mem %D\n",
961: rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,mat->rmap->bs,(PetscInt)info.memory);
962: MatGetInfo(baij->A,MAT_LOCAL,&info);
963: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
964: MatGetInfo(baij->B,MAT_LOCAL,&info);
965: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
966: PetscViewerFlush(viewer);
967: PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);
968: PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
969: VecScatterView(baij->Mvctx,viewer);
970: return(0);
971: } else if (format == PETSC_VIEWER_ASCII_INFO) {
972: PetscViewerASCIIPrintf(viewer," block size is %D\n",bs);
973: return(0);
974: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
975: return(0);
976: }
977: }
979: if (isdraw) {
980: PetscDraw draw;
981: PetscBool isnull;
982: PetscViewerDrawGetDraw(viewer,0,&draw);
983: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
984: }
986: {
987: /* assemble the entire matrix onto first processor. */
988: Mat A;
989: Mat_SeqBAIJ *Aloc;
990: PetscInt M = mat->rmap->N,N = mat->cmap->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
991: MatScalar *a;
992: const char *matname;
994: /* Here we are creating a temporary matrix, so will assume MPIBAIJ is acceptable */
995: /* Perhaps this should be the type of mat? */
996: MatCreate(PetscObjectComm((PetscObject)mat),&A);
997: if (!rank) {
998: MatSetSizes(A,M,N,M,N);
999: } else {
1000: MatSetSizes(A,0,0,M,N);
1001: }
1002: MatSetType(A,MATMPIBAIJ);
1003: MatMPIBAIJSetPreallocation(A,mat->rmap->bs,0,NULL,0,NULL);
1004: MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);
1005: PetscLogObjectParent((PetscObject)mat,(PetscObject)A);
1007: /* copy over the A part */
1008: Aloc = (Mat_SeqBAIJ*)baij->A->data;
1009: ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1010: PetscMalloc1(bs,&rvals);
1012: for (i=0; i<mbs; i++) {
1013: rvals[0] = bs*(baij->rstartbs + i);
1014: for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
1015: for (j=ai[i]; j<ai[i+1]; j++) {
1016: col = (baij->cstartbs+aj[j])*bs;
1017: for (k=0; k<bs; k++) {
1018: MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
1019: col++; a += bs;
1020: }
1021: }
1022: }
1023: /* copy over the B part */
1024: Aloc = (Mat_SeqBAIJ*)baij->B->data;
1025: ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1026: for (i=0; i<mbs; i++) {
1027: rvals[0] = bs*(baij->rstartbs + i);
1028: for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
1029: for (j=ai[i]; j<ai[i+1]; j++) {
1030: col = baij->garray[aj[j]]*bs;
1031: for (k=0; k<bs; k++) {
1032: MatSetValues_MPIBAIJ(A,bs,rvals,1,&col,a,INSERT_VALUES);
1033: col++; a += bs;
1034: }
1035: }
1036: }
1037: PetscFree(rvals);
1038: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1039: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1040: /*
1041: Everyone has to call to draw the matrix since the graphics waits are
1042: synchronized across all processors that share the PetscDraw object
1043: */
1044: PetscViewerGetSingleton(viewer,&sviewer);
1045: PetscObjectGetName((PetscObject)mat,&matname);
1046: if (!rank) {
1047: PetscObjectSetName((PetscObject)((Mat_MPIBAIJ*)(A->data))->A,matname);
1048: MatView_SeqBAIJ(((Mat_MPIBAIJ*)(A->data))->A,sviewer);
1049: }
1050: PetscViewerRestoreSingleton(viewer,&sviewer);
1051: MatDestroy(&A);
1052: }
1053: return(0);
1054: }
1058: static PetscErrorCode MatView_MPIBAIJ_Binary(Mat mat,PetscViewer viewer)
1059: {
1060: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)mat->data;
1061: Mat_SeqBAIJ *A = (Mat_SeqBAIJ*)a->A->data;
1062: Mat_SeqBAIJ *B = (Mat_SeqBAIJ*)a->B->data;
1064: PetscInt i,*row_lens,*crow_lens,bs = mat->rmap->bs,j,k,bs2=a->bs2,header[4],nz,rlen;
1065: PetscInt *range=0,nzmax,*column_indices,cnt,col,*garray = a->garray,cstart = mat->cmap->rstart/bs,len,pcnt,l,ll;
1066: int fd;
1067: PetscScalar *column_values;
1068: FILE *file;
1069: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag;
1070: PetscInt message_count,flowcontrolcount;
1073: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
1074: MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
1075: nz = bs2*(A->nz + B->nz);
1076: rlen = mat->rmap->n;
1077: if (!rank) {
1078: header[0] = MAT_FILE_CLASSID;
1079: header[1] = mat->rmap->N;
1080: header[2] = mat->cmap->N;
1082: MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
1083: PetscViewerBinaryGetDescriptor(viewer,&fd);
1084: PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
1085: /* get largest number of rows any processor has */
1086: range = mat->rmap->range;
1087: for (i=1; i<size; i++) {
1088: rlen = PetscMax(rlen,range[i+1] - range[i]);
1089: }
1090: } else {
1091: MPI_Reduce(&nz,0,1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
1092: }
1094: PetscMalloc1((rlen/bs),&crow_lens);
1095: /* compute lengths of each row */
1096: for (i=0; i<a->mbs; i++) {
1097: crow_lens[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i];
1098: }
1099: /* store the row lengths to the file */
1100: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1101: if (!rank) {
1102: MPI_Status status;
1103: PetscMalloc1(rlen,&row_lens);
1104: rlen = (range[1] - range[0])/bs;
1105: for (i=0; i<rlen; i++) {
1106: for (j=0; j<bs; j++) {
1107: row_lens[i*bs+j] = bs*crow_lens[i];
1108: }
1109: }
1110: PetscBinaryWrite(fd,row_lens,bs*rlen,PETSC_INT,PETSC_TRUE);
1111: for (i=1; i<size; i++) {
1112: rlen = (range[i+1] - range[i])/bs;
1113: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1114: MPI_Recv(crow_lens,rlen,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
1115: for (k=0; k<rlen; k++) {
1116: for (j=0; j<bs; j++) {
1117: row_lens[k*bs+j] = bs*crow_lens[k];
1118: }
1119: }
1120: PetscBinaryWrite(fd,row_lens,bs*rlen,PETSC_INT,PETSC_TRUE);
1121: }
1122: PetscViewerFlowControlEndMaster(viewer,&message_count);
1123: PetscFree(row_lens);
1124: } else {
1125: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1126: MPI_Send(crow_lens,mat->rmap->n/bs,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1127: PetscViewerFlowControlEndWorker(viewer,&message_count);
1128: }
1129: PetscFree(crow_lens);
1131: /* load up the local column indices. Include for all rows not just one for each block row since process 0 does not have the
1132: information needed to make it for each row from a block row. This does require more communication but still not more than
1133: the communication needed for the nonzero values */
1134: nzmax = nz; /* space a largest processor needs */
1135: MPI_Reduce(&nz,&nzmax,1,MPIU_INT,MPI_MAX,0,PetscObjectComm((PetscObject)mat));
1136: PetscMalloc1(nzmax,&column_indices);
1137: cnt = 0;
1138: for (i=0; i<a->mbs; i++) {
1139: pcnt = cnt;
1140: for (j=B->i[i]; j<B->i[i+1]; j++) {
1141: if ((col = garray[B->j[j]]) > cstart) break;
1142: for (l=0; l<bs; l++) {
1143: column_indices[cnt++] = bs*col+l;
1144: }
1145: }
1146: for (k=A->i[i]; k<A->i[i+1]; k++) {
1147: for (l=0; l<bs; l++) {
1148: column_indices[cnt++] = bs*(A->j[k] + cstart)+l;
1149: }
1150: }
1151: for (; j<B->i[i+1]; j++) {
1152: for (l=0; l<bs; l++) {
1153: column_indices[cnt++] = bs*garray[B->j[j]]+l;
1154: }
1155: }
1156: len = cnt - pcnt;
1157: for (k=1; k<bs; k++) {
1158: PetscMemcpy(&column_indices[cnt],&column_indices[pcnt],len*sizeof(PetscInt));
1159: cnt += len;
1160: }
1161: }
1162: if (cnt != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Internal PETSc error: cnt = %D nz = %D",cnt,nz);
1164: /* store the columns to the file */
1165: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1166: if (!rank) {
1167: MPI_Status status;
1168: PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,PETSC_TRUE);
1169: for (i=1; i<size; i++) {
1170: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1171: MPI_Recv(&cnt,1,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
1172: MPI_Recv(column_indices,cnt,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
1173: PetscBinaryWrite(fd,column_indices,cnt,PETSC_INT,PETSC_TRUE);
1174: }
1175: PetscViewerFlowControlEndMaster(viewer,&message_count);
1176: } else {
1177: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1178: MPI_Send(&cnt,1,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1179: MPI_Send(column_indices,cnt,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1180: PetscViewerFlowControlEndWorker(viewer,&message_count);
1181: }
1182: PetscFree(column_indices);
1184: /* load up the numerical values */
1185: PetscMalloc1(nzmax,&column_values);
1186: cnt = 0;
1187: for (i=0; i<a->mbs; i++) {
1188: rlen = bs*(B->i[i+1] - B->i[i] + A->i[i+1] - A->i[i]);
1189: for (j=B->i[i]; j<B->i[i+1]; j++) {
1190: if (garray[B->j[j]] > cstart) break;
1191: for (l=0; l<bs; l++) {
1192: for (ll=0; ll<bs; ll++) {
1193: column_values[cnt + l*rlen + ll] = B->a[bs2*j+l+bs*ll];
1194: }
1195: }
1196: cnt += bs;
1197: }
1198: for (k=A->i[i]; k<A->i[i+1]; k++) {
1199: for (l=0; l<bs; l++) {
1200: for (ll=0; ll<bs; ll++) {
1201: column_values[cnt + l*rlen + ll] = A->a[bs2*k+l+bs*ll];
1202: }
1203: }
1204: cnt += bs;
1205: }
1206: for (; j<B->i[i+1]; j++) {
1207: for (l=0; l<bs; l++) {
1208: for (ll=0; ll<bs; ll++) {
1209: column_values[cnt + l*rlen + ll] = B->a[bs2*j+l+bs*ll];
1210: }
1211: }
1212: cnt += bs;
1213: }
1214: cnt += (bs-1)*rlen;
1215: }
1216: if (cnt != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Internal PETSc error: cnt = %D nz = %D",cnt,nz);
1218: /* store the column values to the file */
1219: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1220: if (!rank) {
1221: MPI_Status status;
1222: PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,PETSC_TRUE);
1223: for (i=1; i<size; i++) {
1224: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1225: MPI_Recv(&cnt,1,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
1226: MPI_Recv(column_values,cnt,MPIU_SCALAR,i,tag,PetscObjectComm((PetscObject)mat),&status);
1227: PetscBinaryWrite(fd,column_values,cnt,PETSC_SCALAR,PETSC_TRUE);
1228: }
1229: PetscViewerFlowControlEndMaster(viewer,&message_count);
1230: } else {
1231: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1232: MPI_Send(&nz,1,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1233: MPI_Send(column_values,nz,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));
1234: PetscViewerFlowControlEndWorker(viewer,&message_count);
1235: }
1236: PetscFree(column_values);
1238: PetscViewerBinaryGetInfoPointer(viewer,&file);
1239: if (file) {
1240: fprintf(file,"-matload_block_size %d\n",(int)mat->rmap->bs);
1241: }
1242: return(0);
1243: }
1247: PetscErrorCode MatView_MPIBAIJ(Mat mat,PetscViewer viewer)
1248: {
1250: PetscBool iascii,isdraw,issocket,isbinary;
1253: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1254: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1255: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
1256: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1257: if (iascii || isdraw || issocket) {
1258: MatView_MPIBAIJ_ASCIIorDraworSocket(mat,viewer);
1259: } else if (isbinary) {
1260: MatView_MPIBAIJ_Binary(mat,viewer);
1261: }
1262: return(0);
1263: }
1267: PetscErrorCode MatDestroy_MPIBAIJ(Mat mat)
1268: {
1269: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1273: #if defined(PETSC_USE_LOG)
1274: PetscLogObjectState((PetscObject)mat,"Rows=%D,Cols=%D",mat->rmap->N,mat->cmap->N);
1275: #endif
1276: MatStashDestroy_Private(&mat->stash);
1277: MatStashDestroy_Private(&mat->bstash);
1278: MatDestroy(&baij->A);
1279: MatDestroy(&baij->B);
1280: #if defined(PETSC_USE_CTABLE)
1281: PetscTableDestroy(&baij->colmap);
1282: #else
1283: PetscFree(baij->colmap);
1284: #endif
1285: PetscFree(baij->garray);
1286: VecDestroy(&baij->lvec);
1287: VecScatterDestroy(&baij->Mvctx);
1288: PetscFree2(baij->rowvalues,baij->rowindices);
1289: PetscFree(baij->barray);
1290: PetscFree2(baij->hd,baij->ht);
1291: PetscFree(baij->rangebs);
1292: PetscFree(mat->data);
1294: PetscObjectChangeTypeName((PetscObject)mat,0);
1295: PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C",NULL);
1296: PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C",NULL);
1297: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",NULL);
1298: PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocation_C",NULL);
1299: PetscObjectComposeFunction((PetscObject)mat,"MatMPIBAIJSetPreallocationCSR_C",NULL);
1300: PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",NULL);
1301: PetscObjectComposeFunction((PetscObject)mat,"MatSetHashTableFactor_C",NULL);
1302: PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_mpisbaij_C",NULL);
1303: PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpibaij_mpibstrm_C",NULL);
1304: return(0);
1305: }
1309: PetscErrorCode MatMult_MPIBAIJ(Mat A,Vec xx,Vec yy)
1310: {
1311: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1313: PetscInt nt;
1316: VecGetLocalSize(xx,&nt);
1317: if (nt != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
1318: VecGetLocalSize(yy,&nt);
1319: if (nt != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
1320: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1321: (*a->A->ops->mult)(a->A,xx,yy);
1322: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1323: (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
1324: return(0);
1325: }
1329: PetscErrorCode MatMultAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1330: {
1331: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1335: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1336: (*a->A->ops->multadd)(a->A,xx,yy,zz);
1337: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1338: (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1339: return(0);
1340: }
1344: PetscErrorCode MatMultTranspose_MPIBAIJ(Mat A,Vec xx,Vec yy)
1345: {
1346: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1348: PetscBool merged;
1351: VecScatterGetMerged(a->Mvctx,&merged);
1352: /* do nondiagonal part */
1353: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1354: if (!merged) {
1355: /* send it on its way */
1356: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1357: /* do local part */
1358: (*a->A->ops->multtranspose)(a->A,xx,yy);
1359: /* receive remote parts: note this assumes the values are not actually */
1360: /* inserted in yy until the next line */
1361: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1362: } else {
1363: /* do local part */
1364: (*a->A->ops->multtranspose)(a->A,xx,yy);
1365: /* send it on its way */
1366: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1367: /* values actually were received in the Begin() but we need to call this nop */
1368: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1369: }
1370: return(0);
1371: }
1375: PetscErrorCode MatMultTransposeAdd_MPIBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1376: {
1377: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1381: /* do nondiagonal part */
1382: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1383: /* send it on its way */
1384: VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1385: /* do local part */
1386: (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1387: /* receive remote parts: note this assumes the values are not actually */
1388: /* inserted in yy until the next line, which is true for my implementation*/
1389: /* but is not perhaps always true. */
1390: VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1391: return(0);
1392: }
1394: /*
1395: This only works correctly for square matrices where the subblock A->A is the
1396: diagonal block
1397: */
1400: PetscErrorCode MatGetDiagonal_MPIBAIJ(Mat A,Vec v)
1401: {
1402: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1406: if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1407: MatGetDiagonal(a->A,v);
1408: return(0);
1409: }
1413: PetscErrorCode MatScale_MPIBAIJ(Mat A,PetscScalar aa)
1414: {
1415: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1419: MatScale(a->A,aa);
1420: MatScale(a->B,aa);
1421: return(0);
1422: }
1426: PetscErrorCode MatGetRow_MPIBAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1427: {
1428: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)matin->data;
1429: PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
1431: PetscInt bs = matin->rmap->bs,bs2 = mat->bs2,i,*cworkA,*cworkB,**pcA,**pcB;
1432: PetscInt nztot,nzA,nzB,lrow,brstart = matin->rmap->rstart,brend = matin->rmap->rend;
1433: PetscInt *cmap,*idx_p,cstart = mat->cstartbs;
1436: if (row < brstart || row >= brend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local rows");
1437: if (mat->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Already active");
1438: mat->getrowactive = PETSC_TRUE;
1440: if (!mat->rowvalues && (idx || v)) {
1441: /*
1442: allocate enough space to hold information from the longest row.
1443: */
1444: Mat_SeqBAIJ *Aa = (Mat_SeqBAIJ*)mat->A->data,*Ba = (Mat_SeqBAIJ*)mat->B->data;
1445: PetscInt max = 1,mbs = mat->mbs,tmp;
1446: for (i=0; i<mbs; i++) {
1447: tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1448: if (max < tmp) max = tmp;
1449: }
1450: PetscMalloc2(max*bs2,&mat->rowvalues,max*bs2,&mat->rowindices);
1451: }
1452: lrow = row - brstart;
1454: pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1455: if (!v) {pvA = 0; pvB = 0;}
1456: if (!idx) {pcA = 0; if (!v) pcB = 0;}
1457: (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1458: (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1459: nztot = nzA + nzB;
1461: cmap = mat->garray;
1462: if (v || idx) {
1463: if (nztot) {
1464: /* Sort by increasing column numbers, assuming A and B already sorted */
1465: PetscInt imark = -1;
1466: if (v) {
1467: *v = v_p = mat->rowvalues;
1468: for (i=0; i<nzB; i++) {
1469: if (cmap[cworkB[i]/bs] < cstart) v_p[i] = vworkB[i];
1470: else break;
1471: }
1472: imark = i;
1473: for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i];
1474: for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i];
1475: }
1476: if (idx) {
1477: *idx = idx_p = mat->rowindices;
1478: if (imark > -1) {
1479: for (i=0; i<imark; i++) {
1480: idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1481: }
1482: } else {
1483: for (i=0; i<nzB; i++) {
1484: if (cmap[cworkB[i]/bs] < cstart) idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1485: else break;
1486: }
1487: imark = i;
1488: }
1489: for (i=0; i<nzA; i++) idx_p[imark+i] = cstart*bs + cworkA[i];
1490: for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1491: }
1492: } else {
1493: if (idx) *idx = 0;
1494: if (v) *v = 0;
1495: }
1496: }
1497: *nz = nztot;
1498: (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1499: (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1500: return(0);
1501: }
1505: PetscErrorCode MatRestoreRow_MPIBAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1506: {
1507: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1510: if (!baij->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1511: baij->getrowactive = PETSC_FALSE;
1512: return(0);
1513: }
1517: PetscErrorCode MatZeroEntries_MPIBAIJ(Mat A)
1518: {
1519: Mat_MPIBAIJ *l = (Mat_MPIBAIJ*)A->data;
1523: MatZeroEntries(l->A);
1524: MatZeroEntries(l->B);
1525: return(0);
1526: }
1530: PetscErrorCode MatGetInfo_MPIBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1531: {
1532: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)matin->data;
1533: Mat A = a->A,B = a->B;
1535: PetscReal isend[5],irecv[5];
1538: info->block_size = (PetscReal)matin->rmap->bs;
1540: MatGetInfo(A,MAT_LOCAL,info);
1542: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1543: isend[3] = info->memory; isend[4] = info->mallocs;
1545: MatGetInfo(B,MAT_LOCAL,info);
1547: isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1548: isend[3] += info->memory; isend[4] += info->mallocs;
1550: if (flag == MAT_LOCAL) {
1551: info->nz_used = isend[0];
1552: info->nz_allocated = isend[1];
1553: info->nz_unneeded = isend[2];
1554: info->memory = isend[3];
1555: info->mallocs = isend[4];
1556: } else if (flag == MAT_GLOBAL_MAX) {
1557: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)matin));
1559: info->nz_used = irecv[0];
1560: info->nz_allocated = irecv[1];
1561: info->nz_unneeded = irecv[2];
1562: info->memory = irecv[3];
1563: info->mallocs = irecv[4];
1564: } else if (flag == MAT_GLOBAL_SUM) {
1565: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)matin));
1567: info->nz_used = irecv[0];
1568: info->nz_allocated = irecv[1];
1569: info->nz_unneeded = irecv[2];
1570: info->memory = irecv[3];
1571: info->mallocs = irecv[4];
1572: } else SETERRQ1(PetscObjectComm((PetscObject)matin),PETSC_ERR_ARG_WRONG,"Unknown MatInfoType argument %d",(int)flag);
1573: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1574: info->fill_ratio_needed = 0;
1575: info->factor_mallocs = 0;
1576: return(0);
1577: }
1581: PetscErrorCode MatSetOption_MPIBAIJ(Mat A,MatOption op,PetscBool flg)
1582: {
1583: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1587: switch (op) {
1588: case MAT_NEW_NONZERO_LOCATIONS:
1589: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1590: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1591: case MAT_KEEP_NONZERO_PATTERN:
1592: case MAT_NEW_NONZERO_LOCATION_ERR:
1593: MatSetOption(a->A,op,flg);
1594: MatSetOption(a->B,op,flg);
1595: break;
1596: case MAT_ROW_ORIENTED:
1597: a->roworiented = flg;
1599: MatSetOption(a->A,op,flg);
1600: MatSetOption(a->B,op,flg);
1601: break;
1602: case MAT_NEW_DIAGONALS:
1603: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1604: break;
1605: case MAT_IGNORE_OFF_PROC_ENTRIES:
1606: a->donotstash = flg;
1607: break;
1608: case MAT_USE_HASH_TABLE:
1609: a->ht_flag = flg;
1610: break;
1611: case MAT_SYMMETRIC:
1612: case MAT_STRUCTURALLY_SYMMETRIC:
1613: case MAT_HERMITIAN:
1614: case MAT_SYMMETRY_ETERNAL:
1615: MatSetOption(a->A,op,flg);
1616: break;
1617: default:
1618: SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"unknown option %d",op);
1619: }
1620: return(0);
1621: }
1625: PetscErrorCode MatTranspose_MPIBAIJ(Mat A,MatReuse reuse,Mat *matout)
1626: {
1627: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)A->data;
1628: Mat_SeqBAIJ *Aloc;
1629: Mat B;
1631: PetscInt M =A->rmap->N,N=A->cmap->N,*ai,*aj,i,*rvals,j,k,col;
1632: PetscInt bs=A->rmap->bs,mbs=baij->mbs;
1633: MatScalar *a;
1636: if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1637: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1638: MatCreate(PetscObjectComm((PetscObject)A),&B);
1639: MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
1640: MatSetType(B,((PetscObject)A)->type_name);
1641: /* Do not know preallocation information, but must set block size */
1642: MatMPIBAIJSetPreallocation(B,A->rmap->bs,PETSC_DECIDE,NULL,PETSC_DECIDE,NULL);
1643: } else {
1644: B = *matout;
1645: }
1647: /* copy over the A part */
1648: Aloc = (Mat_SeqBAIJ*)baij->A->data;
1649: ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1650: PetscMalloc1(bs,&rvals);
1652: for (i=0; i<mbs; i++) {
1653: rvals[0] = bs*(baij->rstartbs + i);
1654: for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
1655: for (j=ai[i]; j<ai[i+1]; j++) {
1656: col = (baij->cstartbs+aj[j])*bs;
1657: for (k=0; k<bs; k++) {
1658: MatSetValues_MPIBAIJ(B,1,&col,bs,rvals,a,INSERT_VALUES);
1660: col++; a += bs;
1661: }
1662: }
1663: }
1664: /* copy over the B part */
1665: Aloc = (Mat_SeqBAIJ*)baij->B->data;
1666: ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1667: for (i=0; i<mbs; i++) {
1668: rvals[0] = bs*(baij->rstartbs + i);
1669: for (j=1; j<bs; j++) rvals[j] = rvals[j-1] + 1;
1670: for (j=ai[i]; j<ai[i+1]; j++) {
1671: col = baij->garray[aj[j]]*bs;
1672: for (k=0; k<bs; k++) {
1673: MatSetValues_MPIBAIJ(B,1,&col,bs,rvals,a,INSERT_VALUES);
1674: col++;
1675: a += bs;
1676: }
1677: }
1678: }
1679: PetscFree(rvals);
1680: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1681: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1683: if (reuse == MAT_INITIAL_MATRIX || *matout != A) *matout = B;
1684: else {
1685: MatHeaderMerge(A,B);
1686: }
1687: return(0);
1688: }
1692: PetscErrorCode MatDiagonalScale_MPIBAIJ(Mat mat,Vec ll,Vec rr)
1693: {
1694: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
1695: Mat a = baij->A,b = baij->B;
1697: PetscInt s1,s2,s3;
1700: MatGetLocalSize(mat,&s2,&s3);
1701: if (rr) {
1702: VecGetLocalSize(rr,&s1);
1703: if (s1!=s3) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1704: /* Overlap communication with computation. */
1705: VecScatterBegin(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1706: }
1707: if (ll) {
1708: VecGetLocalSize(ll,&s1);
1709: if (s1!=s2) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1710: (*b->ops->diagonalscale)(b,ll,NULL);
1711: }
1712: /* scale the diagonal block */
1713: (*a->ops->diagonalscale)(a,ll,rr);
1715: if (rr) {
1716: /* Do a scatter end and then right scale the off-diagonal block */
1717: VecScatterEnd(baij->Mvctx,rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1718: (*b->ops->diagonalscale)(b,NULL,baij->lvec);
1719: }
1720: return(0);
1721: }
1725: PetscErrorCode MatZeroRows_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1726: {
1727: Mat_MPIBAIJ *l = (Mat_MPIBAIJ *) A->data;
1728: PetscInt *owners = A->rmap->range;
1729: PetscInt n = A->rmap->n;
1730: PetscSF sf;
1731: PetscInt *lrows;
1732: PetscSFNode *rrows;
1733: PetscInt r, p = 0, len = 0;
1737: /* Create SF where leaves are input rows and roots are owned rows */
1738: PetscMalloc1(n, &lrows);
1739: for (r = 0; r < n; ++r) lrows[r] = -1;
1740: if (!A->nooffproczerorows) {PetscMalloc1(N, &rrows);}
1741: for (r = 0; r < N; ++r) {
1742: const PetscInt idx = rows[r];
1743: if (idx < 0 || A->rmap->N <= idx) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range [0,%D)",idx,A->rmap->N);
1744: if (idx < owners[p] || owners[p+1] <= idx) { /* short-circuit the search if the last p owns this row too */
1745: PetscLayoutFindOwner(A->rmap,idx,&p);
1746: }
1747: if (A->nooffproczerorows) {
1748: if (p != l->rank) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"MAT_NO_OFF_PROC_ZERO_ROWS set, but row %D is not owned by rank %d",idx,l->rank);
1749: lrows[len++] = idx - owners[p];
1750: } else {
1751: rrows[r].rank = p;
1752: rrows[r].index = rows[r] - owners[p];
1753: }
1754: }
1755: if (!A->nooffproczerorows) {
1756: PetscSFCreate(PetscObjectComm((PetscObject) A), &sf);
1757: PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER);
1758: /* Collect flags for rows to be zeroed */
1759: PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
1760: PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
1761: PetscSFDestroy(&sf);
1762: /* Compress and put in row numbers */
1763: for (r = 0; r < n; ++r) if (lrows[r] >= 0) lrows[len++] = r;
1764: }
1765: /* fix right hand side if needed */
1766: if (x && b) {
1767: const PetscScalar *xx;
1768: PetscScalar *bb;
1770: VecGetArrayRead(x,&xx);
1771: VecGetArray(b,&bb);
1772: for (r = 0; r < len; ++r) bb[lrows[r]] = diag*xx[lrows[r]];
1773: VecRestoreArrayRead(x,&xx);
1774: VecRestoreArray(b,&bb);
1775: }
1777: /* actually zap the local rows */
1778: /*
1779: Zero the required rows. If the "diagonal block" of the matrix
1780: is square and the user wishes to set the diagonal we use separate
1781: code so that MatSetValues() is not called for each diagonal allocating
1782: new memory, thus calling lots of mallocs and slowing things down.
1784: */
1785: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1786: MatZeroRows_SeqBAIJ(l->B,len,lrows,0.0,NULL,NULL);
1787: if ((diag != 0.0) && (l->A->rmap->N == l->A->cmap->N)) {
1788: MatZeroRows_SeqBAIJ(l->A,len,lrows,diag,NULL,NULL);
1789: } else if (diag != 0.0) {
1790: MatZeroRows_SeqBAIJ(l->A,len,lrows,0.0,0,0);
1791: if (((Mat_SeqBAIJ*)l->A->data)->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options \n\
1792: MAT_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1793: for (r = 0; r < len; ++r) {
1794: const PetscInt row = lrows[r] + A->rmap->rstart;
1795: MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
1796: }
1797: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1798: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1799: } else {
1800: MatZeroRows_SeqBAIJ(l->A,len,lrows,0.0,NULL,NULL);
1801: }
1802: PetscFree(lrows);
1804: /* only change matrix nonzero state if pattern was allowed to be changed */
1805: if (!((Mat_SeqBAIJ*)(l->A->data))->keepnonzeropattern) {
1806: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1807: MPI_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
1808: }
1809: return(0);
1810: }
1814: PetscErrorCode MatZeroRowsColumns_MPIBAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1815: {
1816: Mat_MPIBAIJ *l = (Mat_MPIBAIJ*)A->data;
1817: PetscErrorCode ierr;
1818: PetscMPIInt n = A->rmap->n;
1819: PetscInt i,j,k,r,p = 0,len = 0,row,col,count;
1820: PetscInt *lrows,*owners = A->rmap->range;
1821: PetscSFNode *rrows;
1822: PetscSF sf;
1823: const PetscScalar *xx;
1824: PetscScalar *bb,*mask;
1825: Vec xmask,lmask;
1826: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ*)l->B->data;
1827: PetscInt bs = A->rmap->bs, bs2 = baij->bs2;
1828: PetscScalar *aa;
1831: /* Create SF where leaves are input rows and roots are owned rows */
1832: PetscMalloc1(n, &lrows);
1833: for (r = 0; r < n; ++r) lrows[r] = -1;
1834: PetscMalloc1(N, &rrows);
1835: for (r = 0; r < N; ++r) {
1836: const PetscInt idx = rows[r];
1837: if (idx < 0 || A->rmap->N <= idx) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range [0,%D)",idx,A->rmap->N);
1838: if (idx < owners[p] || owners[p+1] <= idx) { /* short-circuit the search if the last p owns this row too */
1839: PetscLayoutFindOwner(A->rmap,idx,&p);
1840: }
1841: rrows[r].rank = p;
1842: rrows[r].index = rows[r] - owners[p];
1843: }
1844: PetscSFCreate(PetscObjectComm((PetscObject) A), &sf);
1845: PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER);
1846: /* Collect flags for rows to be zeroed */
1847: PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
1848: PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
1849: PetscSFDestroy(&sf);
1850: /* Compress and put in row numbers */
1851: for (r = 0; r < n; ++r) if (lrows[r] >= 0) lrows[len++] = r;
1852: /* zero diagonal part of matrix */
1853: MatZeroRowsColumns(l->A,len,lrows,diag,x,b);
1854: /* handle off diagonal part of matrix */
1855: MatGetVecs(A,&xmask,NULL);
1856: VecDuplicate(l->lvec,&lmask);
1857: VecGetArray(xmask,&bb);
1858: for (i=0; i<len; i++) bb[lrows[i]] = 1;
1859: VecRestoreArray(xmask,&bb);
1860: VecScatterBegin(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
1861: VecScatterEnd(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
1862: VecDestroy(&xmask);
1863: if (x) {
1864: VecScatterBegin(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
1865: VecScatterEnd(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
1866: VecGetArrayRead(l->lvec,&xx);
1867: VecGetArray(b,&bb);
1868: }
1869: VecGetArray(lmask,&mask);
1870: /* remove zeroed rows of off diagonal matrix */
1871: for (i = 0; i < len; ++i) {
1872: row = lrows[i];
1873: count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
1874: aa = ((MatScalar*)(baij->a)) + baij->i[row/bs]*bs2 + (row%bs);
1875: for (k = 0; k < count; ++k) {
1876: aa[0] = 0.0;
1877: aa += bs;
1878: }
1879: }
1880: /* loop over all elements of off process part of matrix zeroing removed columns*/
1881: for (i = 0; i < l->B->rmap->N; ++i) {
1882: row = i/bs;
1883: for (j = baij->i[row]; j < baij->i[row+1]; ++j) {
1884: for (k = 0; k < bs; ++k) {
1885: col = bs*baij->j[j] + k;
1886: if (PetscAbsScalar(mask[col])) {
1887: aa = ((MatScalar*)(baij->a)) + j*bs2 + (i%bs) + bs*k;
1888: if (b) bb[i] -= aa[0]*xx[col];
1889: aa[0] = 0.0;
1890: }
1891: }
1892: }
1893: }
1894: if (x) {
1895: VecRestoreArray(b,&bb);
1896: VecRestoreArrayRead(l->lvec,&xx);
1897: }
1898: VecRestoreArray(lmask,&mask);
1899: VecDestroy(&lmask);
1900: PetscFree(lrows);
1902: /* only change matrix nonzero state if pattern was allowed to be changed */
1903: if (!((Mat_SeqBAIJ*)(l->A->data))->keepnonzeropattern) {
1904: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
1905: MPI_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
1906: }
1907: return(0);
1908: }
1912: PetscErrorCode MatSetUnfactored_MPIBAIJ(Mat A)
1913: {
1914: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1918: MatSetUnfactored(a->A);
1919: return(0);
1920: }
1922: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat,MatDuplicateOption,Mat*);
1926: PetscErrorCode MatEqual_MPIBAIJ(Mat A,Mat B,PetscBool *flag)
1927: {
1928: Mat_MPIBAIJ *matB = (Mat_MPIBAIJ*)B->data,*matA = (Mat_MPIBAIJ*)A->data;
1929: Mat a,b,c,d;
1930: PetscBool flg;
1934: a = matA->A; b = matA->B;
1935: c = matB->A; d = matB->B;
1937: MatEqual(a,c,&flg);
1938: if (flg) {
1939: MatEqual(b,d,&flg);
1940: }
1941: MPI_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
1942: return(0);
1943: }
1947: PetscErrorCode MatCopy_MPIBAIJ(Mat A,Mat B,MatStructure str)
1948: {
1950: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
1951: Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)B->data;
1954: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1955: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1956: MatCopy_Basic(A,B,str);
1957: } else {
1958: MatCopy(a->A,b->A,str);
1959: MatCopy(a->B,b->B,str);
1960: }
1961: return(0);
1962: }
1966: PetscErrorCode MatSetUp_MPIBAIJ(Mat A)
1967: {
1971: MatMPIBAIJSetPreallocation(A,A->rmap->bs,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1972: return(0);
1973: }
1977: PetscErrorCode MatAXPYGetPreallocation_MPIBAIJ(Mat Y,const PetscInt *yltog,Mat X,const PetscInt *xltog,PetscInt *nnz)
1978: {
1980: PetscInt bs = Y->rmap->bs,m = Y->rmap->N/bs;
1981: Mat_SeqBAIJ *x = (Mat_SeqBAIJ*)X->data;
1982: Mat_SeqBAIJ *y = (Mat_SeqBAIJ*)Y->data;
1985: MatAXPYGetPreallocation_MPIX_private(m,x->i,x->j,xltog,y->i,y->j,yltog,nnz);
1986: return(0);
1987: }
1991: PetscErrorCode MatAXPY_MPIBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1992: {
1994: Mat_MPIBAIJ *xx=(Mat_MPIBAIJ*)X->data,*yy=(Mat_MPIBAIJ*)Y->data;
1995: PetscBLASInt bnz,one=1;
1996: Mat_SeqBAIJ *x,*y;
1999: if (str == SAME_NONZERO_PATTERN) {
2000: PetscScalar alpha = a;
2001: x = (Mat_SeqBAIJ*)xx->A->data;
2002: y = (Mat_SeqBAIJ*)yy->A->data;
2003: PetscBLASIntCast(x->nz,&bnz);
2004: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2005: x = (Mat_SeqBAIJ*)xx->B->data;
2006: y = (Mat_SeqBAIJ*)yy->B->data;
2007: PetscBLASIntCast(x->nz,&bnz);
2008: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2009: PetscObjectStateIncrease((PetscObject)Y);
2010: } else {
2011: Mat B;
2012: PetscInt *nnz_d,*nnz_o,bs=Y->rmap->bs;
2013: PetscMalloc1(yy->A->rmap->N,&nnz_d);
2014: PetscMalloc1(yy->B->rmap->N,&nnz_o);
2015: MatCreate(PetscObjectComm((PetscObject)Y),&B);
2016: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2017: MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2018: MatSetBlockSizesFromMats(B,Y,Y);
2019: MatSetType(B,MATMPIBAIJ);
2020: MatAXPYGetPreallocation_SeqBAIJ(yy->A,xx->A,nnz_d);
2021: MatAXPYGetPreallocation_MPIBAIJ(yy->B,yy->garray,xx->B,xx->garray,nnz_o);
2022: MatMPIBAIJSetPreallocation(B,bs,0,nnz_d,0,nnz_o);
2023: /* MatAXPY_BasicWithPreallocation() for BAIJ matrix is much slower than AIJ, even for bs=1 ! */
2024: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2025: MatHeaderReplace(Y,B);
2026: PetscFree(nnz_d);
2027: PetscFree(nnz_o);
2028: }
2029: return(0);
2030: }
2034: PetscErrorCode MatRealPart_MPIBAIJ(Mat A)
2035: {
2036: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
2040: MatRealPart(a->A);
2041: MatRealPart(a->B);
2042: return(0);
2043: }
2047: PetscErrorCode MatImaginaryPart_MPIBAIJ(Mat A)
2048: {
2049: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
2053: MatImaginaryPart(a->A);
2054: MatImaginaryPart(a->B);
2055: return(0);
2056: }
2060: PetscErrorCode MatGetSubMatrix_MPIBAIJ(Mat mat,IS isrow,IS iscol,MatReuse call,Mat *newmat)
2061: {
2063: IS iscol_local;
2064: PetscInt csize;
2067: ISGetLocalSize(iscol,&csize);
2068: if (call == MAT_REUSE_MATRIX) {
2069: PetscObjectQuery((PetscObject)*newmat,"ISAllGather",(PetscObject*)&iscol_local);
2070: if (!iscol_local) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
2071: } else {
2072: ISAllGather(iscol,&iscol_local);
2073: }
2074: MatGetSubMatrix_MPIBAIJ_Private(mat,isrow,iscol_local,csize,call,newmat);
2075: if (call == MAT_INITIAL_MATRIX) {
2076: PetscObjectCompose((PetscObject)*newmat,"ISAllGather",(PetscObject)iscol_local);
2077: ISDestroy(&iscol_local);
2078: }
2079: return(0);
2080: }
2081: extern PetscErrorCode MatGetSubMatrices_MPIBAIJ_local(Mat,PetscInt,const IS[],const IS[],MatReuse,PetscBool*,PetscBool*,Mat*);
2084: /*
2085: Not great since it makes two copies of the submatrix, first an SeqBAIJ
2086: in local and then by concatenating the local matrices the end result.
2087: Writing it directly would be much like MatGetSubMatrices_MPIBAIJ()
2088: */
2089: PetscErrorCode MatGetSubMatrix_MPIBAIJ_Private(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
2090: {
2092: PetscMPIInt rank,size;
2093: PetscInt i,m,n,rstart,row,rend,nz,*cwork,j,bs;
2094: PetscInt *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal,ncol,nrow;
2095: Mat M,Mreuse;
2096: MatScalar *vwork,*aa;
2097: MPI_Comm comm;
2098: IS isrow_new, iscol_new;
2099: PetscBool idflag,allrows, allcols;
2100: Mat_SeqBAIJ *aij;
2103: PetscObjectGetComm((PetscObject)mat,&comm);
2104: MPI_Comm_rank(comm,&rank);
2105: MPI_Comm_size(comm,&size);
2106: /* The compression and expansion should be avoided. Doesn't point
2107: out errors, might change the indices, hence buggey */
2108: ISCompressIndicesGeneral(mat->rmap->N,mat->rmap->n,mat->rmap->bs,1,&isrow,&isrow_new);
2109: ISCompressIndicesGeneral(mat->cmap->N,mat->cmap->n,mat->cmap->bs,1,&iscol,&iscol_new);
2111: /* Check for special case: each processor gets entire matrix columns */
2112: ISIdentity(iscol,&idflag);
2113: ISGetLocalSize(iscol,&ncol);
2114: if (idflag && ncol == mat->cmap->N) allcols = PETSC_TRUE;
2115: else allcols = PETSC_FALSE;
2117: ISIdentity(isrow,&idflag);
2118: ISGetLocalSize(isrow,&nrow);
2119: if (idflag && nrow == mat->rmap->N) allrows = PETSC_TRUE;
2120: else allrows = PETSC_FALSE;
2122: if (call == MAT_REUSE_MATRIX) {
2123: PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject*)&Mreuse);
2124: if (!Mreuse) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
2125: MatGetSubMatrices_MPIBAIJ_local(mat,1,&isrow_new,&iscol_new,MAT_REUSE_MATRIX,&allrows,&allcols,&Mreuse);
2126: } else {
2127: MatGetSubMatrices_MPIBAIJ_local(mat,1,&isrow_new,&iscol_new,MAT_INITIAL_MATRIX,&allrows,&allcols,&Mreuse);
2128: }
2129: ISDestroy(&isrow_new);
2130: ISDestroy(&iscol_new);
2131: /*
2132: m - number of local rows
2133: n - number of columns (same on all processors)
2134: rstart - first row in new global matrix generated
2135: */
2136: MatGetBlockSize(mat,&bs);
2137: MatGetSize(Mreuse,&m,&n);
2138: m = m/bs;
2139: n = n/bs;
2141: if (call == MAT_INITIAL_MATRIX) {
2142: aij = (Mat_SeqBAIJ*)(Mreuse)->data;
2143: ii = aij->i;
2144: jj = aij->j;
2146: /*
2147: Determine the number of non-zeros in the diagonal and off-diagonal
2148: portions of the matrix in order to do correct preallocation
2149: */
2151: /* first get start and end of "diagonal" columns */
2152: if (csize == PETSC_DECIDE) {
2153: ISGetSize(isrow,&mglobal);
2154: if (mglobal == n*bs) { /* square matrix */
2155: nlocal = m;
2156: } else {
2157: nlocal = n/size + ((n % size) > rank);
2158: }
2159: } else {
2160: nlocal = csize/bs;
2161: }
2162: MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
2163: rstart = rend - nlocal;
2164: if (rank == size - 1 && rend != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);
2166: /* next, compute all the lengths */
2167: PetscMalloc2(m+1,&dlens,m+1,&olens);
2168: for (i=0; i<m; i++) {
2169: jend = ii[i+1] - ii[i];
2170: olen = 0;
2171: dlen = 0;
2172: for (j=0; j<jend; j++) {
2173: if (*jj < rstart || *jj >= rend) olen++;
2174: else dlen++;
2175: jj++;
2176: }
2177: olens[i] = olen;
2178: dlens[i] = dlen;
2179: }
2180: MatCreate(comm,&M);
2181: MatSetSizes(M,bs*m,bs*nlocal,PETSC_DECIDE,bs*n);
2182: MatSetType(M,((PetscObject)mat)->type_name);
2183: MatMPIBAIJSetPreallocation(M,bs,0,dlens,0,olens);
2184: PetscFree2(dlens,olens);
2185: } else {
2186: PetscInt ml,nl;
2188: M = *newmat;
2189: MatGetLocalSize(M,&ml,&nl);
2190: if (ml != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
2191: MatZeroEntries(M);
2192: /*
2193: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2194: rather than the slower MatSetValues().
2195: */
2196: M->was_assembled = PETSC_TRUE;
2197: M->assembled = PETSC_FALSE;
2198: }
2199: MatSetOption(M,MAT_ROW_ORIENTED,PETSC_FALSE);
2200: MatGetOwnershipRange(M,&rstart,&rend);
2201: aij = (Mat_SeqBAIJ*)(Mreuse)->data;
2202: ii = aij->i;
2203: jj = aij->j;
2204: aa = aij->a;
2205: for (i=0; i<m; i++) {
2206: row = rstart/bs + i;
2207: nz = ii[i+1] - ii[i];
2208: cwork = jj; jj += nz;
2209: vwork = aa; aa += nz*bs*bs;
2210: MatSetValuesBlocked_MPIBAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
2211: }
2213: MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
2214: MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
2215: *newmat = M;
2217: /* save submatrix used in processor for next request */
2218: if (call == MAT_INITIAL_MATRIX) {
2219: PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
2220: PetscObjectDereference((PetscObject)Mreuse);
2221: }
2222: return(0);
2223: }
2227: PetscErrorCode MatPermute_MPIBAIJ(Mat A,IS rowp,IS colp,Mat *B)
2228: {
2229: MPI_Comm comm,pcomm;
2230: PetscInt clocal_size,nrows;
2231: const PetscInt *rows;
2232: PetscMPIInt size;
2233: IS crowp,lcolp;
2237: PetscObjectGetComm((PetscObject)A,&comm);
2238: /* make a collective version of 'rowp' */
2239: PetscObjectGetComm((PetscObject)rowp,&pcomm);
2240: if (pcomm==comm) {
2241: crowp = rowp;
2242: } else {
2243: ISGetSize(rowp,&nrows);
2244: ISGetIndices(rowp,&rows);
2245: ISCreateGeneral(comm,nrows,rows,PETSC_COPY_VALUES,&crowp);
2246: ISRestoreIndices(rowp,&rows);
2247: }
2248: ISSetPermutation(crowp);
2249: /* make a local version of 'colp' */
2250: PetscObjectGetComm((PetscObject)colp,&pcomm);
2251: MPI_Comm_size(pcomm,&size);
2252: if (size==1) {
2253: lcolp = colp;
2254: } else {
2255: ISAllGather(colp,&lcolp);
2256: }
2257: ISSetPermutation(lcolp);
2258: /* now we just get the submatrix */
2259: MatGetLocalSize(A,NULL,&clocal_size);
2260: MatGetSubMatrix_MPIBAIJ_Private(A,crowp,lcolp,clocal_size,MAT_INITIAL_MATRIX,B);
2261: /* clean up */
2262: if (pcomm!=comm) {
2263: ISDestroy(&crowp);
2264: }
2265: if (size>1) {
2266: ISDestroy(&lcolp);
2267: }
2268: return(0);
2269: }
2273: PetscErrorCode MatGetGhosts_MPIBAIJ(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[])
2274: {
2275: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*) mat->data;
2276: Mat_SeqBAIJ *B = (Mat_SeqBAIJ*)baij->B->data;
2279: if (nghosts) *nghosts = B->nbs;
2280: if (ghosts) *ghosts = baij->garray;
2281: return(0);
2282: }
2286: PetscErrorCode MatGetSeqNonzeroStructure_MPIBAIJ(Mat A,Mat *newmat)
2287: {
2288: Mat B;
2289: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
2290: Mat_SeqBAIJ *ad = (Mat_SeqBAIJ*)a->A->data,*bd = (Mat_SeqBAIJ*)a->B->data;
2291: Mat_SeqAIJ *b;
2293: PetscMPIInt size,rank,*recvcounts = 0,*displs = 0;
2294: PetscInt sendcount,i,*rstarts = A->rmap->range,n,cnt,j,bs = A->rmap->bs;
2295: PetscInt m,*garray = a->garray,*lens,*jsendbuf,*a_jsendbuf,*b_jsendbuf;
2298: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
2299: MPI_Comm_rank(PetscObjectComm((PetscObject)A),&rank);
2301: /* ----------------------------------------------------------------
2302: Tell every processor the number of nonzeros per row
2303: */
2304: PetscMalloc1((A->rmap->N/bs),&lens);
2305: for (i=A->rmap->rstart/bs; i<A->rmap->rend/bs; i++) {
2306: lens[i] = ad->i[i-A->rmap->rstart/bs+1] - ad->i[i-A->rmap->rstart/bs] + bd->i[i-A->rmap->rstart/bs+1] - bd->i[i-A->rmap->rstart/bs];
2307: }
2308: sendcount = A->rmap->rend/bs - A->rmap->rstart/bs;
2309: PetscMalloc1(2*size,&recvcounts);
2310: displs = recvcounts + size;
2311: for (i=0; i<size; i++) {
2312: recvcounts[i] = A->rmap->range[i+1]/bs - A->rmap->range[i]/bs;
2313: displs[i] = A->rmap->range[i]/bs;
2314: }
2315: #if defined(PETSC_HAVE_MPI_IN_PLACE)
2316: MPI_Allgatherv(MPI_IN_PLACE,0,MPI_DATATYPE_NULL,lens,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
2317: #else
2318: MPI_Allgatherv(lens+A->rmap->rstart/bs,sendcount,MPIU_INT,lens,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
2319: #endif
2320: /* ---------------------------------------------------------------
2321: Create the sequential matrix of the same type as the local block diagonal
2322: */
2323: MatCreate(PETSC_COMM_SELF,&B);
2324: MatSetSizes(B,A->rmap->N/bs,A->cmap->N/bs,PETSC_DETERMINE,PETSC_DETERMINE);
2325: MatSetType(B,MATSEQAIJ);
2326: MatSeqAIJSetPreallocation(B,0,lens);
2327: b = (Mat_SeqAIJ*)B->data;
2329: /*--------------------------------------------------------------------
2330: Copy my part of matrix column indices over
2331: */
2332: sendcount = ad->nz + bd->nz;
2333: jsendbuf = b->j + b->i[rstarts[rank]/bs];
2334: a_jsendbuf = ad->j;
2335: b_jsendbuf = bd->j;
2336: n = A->rmap->rend/bs - A->rmap->rstart/bs;
2337: cnt = 0;
2338: for (i=0; i<n; i++) {
2340: /* put in lower diagonal portion */
2341: m = bd->i[i+1] - bd->i[i];
2342: while (m > 0) {
2343: /* is it above diagonal (in bd (compressed) numbering) */
2344: if (garray[*b_jsendbuf] > A->rmap->rstart/bs + i) break;
2345: jsendbuf[cnt++] = garray[*b_jsendbuf++];
2346: m--;
2347: }
2349: /* put in diagonal portion */
2350: for (j=ad->i[i]; j<ad->i[i+1]; j++) {
2351: jsendbuf[cnt++] = A->rmap->rstart/bs + *a_jsendbuf++;
2352: }
2354: /* put in upper diagonal portion */
2355: while (m-- > 0) {
2356: jsendbuf[cnt++] = garray[*b_jsendbuf++];
2357: }
2358: }
2359: if (cnt != sendcount) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Corrupted PETSc matrix: nz given %D actual nz %D",sendcount,cnt);
2361: /*--------------------------------------------------------------------
2362: Gather all column indices to all processors
2363: */
2364: for (i=0; i<size; i++) {
2365: recvcounts[i] = 0;
2366: for (j=A->rmap->range[i]/bs; j<A->rmap->range[i+1]/bs; j++) {
2367: recvcounts[i] += lens[j];
2368: }
2369: }
2370: displs[0] = 0;
2371: for (i=1; i<size; i++) {
2372: displs[i] = displs[i-1] + recvcounts[i-1];
2373: }
2374: #if defined(PETSC_HAVE_MPI_IN_PLACE)
2375: MPI_Allgatherv(MPI_IN_PLACE,0,MPI_DATATYPE_NULL,b->j,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
2376: #else
2377: MPI_Allgatherv(jsendbuf,sendcount,MPIU_INT,b->j,recvcounts,displs,MPIU_INT,PetscObjectComm((PetscObject)A));
2378: #endif
2379: /*--------------------------------------------------------------------
2380: Assemble the matrix into useable form (note numerical values not yet set)
2381: */
2382: /* set the b->ilen (length of each row) values */
2383: PetscMemcpy(b->ilen,lens,(A->rmap->N/bs)*sizeof(PetscInt));
2384: /* set the b->i indices */
2385: b->i[0] = 0;
2386: for (i=1; i<=A->rmap->N/bs; i++) {
2387: b->i[i] = b->i[i-1] + lens[i-1];
2388: }
2389: PetscFree(lens);
2390: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2391: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2392: PetscFree(recvcounts);
2394: if (A->symmetric) {
2395: MatSetOption(B,MAT_SYMMETRIC,PETSC_TRUE);
2396: } else if (A->hermitian) {
2397: MatSetOption(B,MAT_HERMITIAN,PETSC_TRUE);
2398: } else if (A->structurally_symmetric) {
2399: MatSetOption(B,MAT_STRUCTURALLY_SYMMETRIC,PETSC_TRUE);
2400: }
2401: *newmat = B;
2402: return(0);
2403: }
2407: PetscErrorCode MatSOR_MPIBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
2408: {
2409: Mat_MPIBAIJ *mat = (Mat_MPIBAIJ*)matin->data;
2411: Vec bb1 = 0;
2414: if (flag == SOR_APPLY_UPPER) {
2415: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2416: return(0);
2417: }
2419: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS) {
2420: VecDuplicate(bb,&bb1);
2421: }
2423: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
2424: if (flag & SOR_ZERO_INITIAL_GUESS) {
2425: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2426: its--;
2427: }
2429: while (its--) {
2430: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2431: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2433: /* update rhs: bb1 = bb - B*x */
2434: VecScale(mat->lvec,-1.0);
2435: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
2437: /* local sweep */
2438: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,1,xx);
2439: }
2440: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
2441: if (flag & SOR_ZERO_INITIAL_GUESS) {
2442: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2443: its--;
2444: }
2445: while (its--) {
2446: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2447: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2449: /* update rhs: bb1 = bb - B*x */
2450: VecScale(mat->lvec,-1.0);
2451: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
2453: /* local sweep */
2454: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,1,xx);
2455: }
2456: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
2457: if (flag & SOR_ZERO_INITIAL_GUESS) {
2458: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
2459: its--;
2460: }
2461: while (its--) {
2462: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2463: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
2465: /* update rhs: bb1 = bb - B*x */
2466: VecScale(mat->lvec,-1.0);
2467: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
2469: /* local sweep */
2470: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,1,xx);
2471: }
2472: } else SETERRQ(PetscObjectComm((PetscObject)matin),PETSC_ERR_SUP,"Parallel version of SOR requested not supported");
2474: VecDestroy(&bb1);
2475: return(0);
2476: }
2480: PetscErrorCode MatGetColumnNorms_MPIBAIJ(Mat A,NormType type,PetscReal *norms)
2481: {
2483: Mat_MPIBAIJ *aij = (Mat_MPIBAIJ*)A->data;
2484: PetscInt N,i,*garray = aij->garray;
2485: PetscInt ib,jb,bs = A->rmap->bs;
2486: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ*) aij->A->data;
2487: MatScalar *a_val = a_aij->a;
2488: Mat_SeqBAIJ *b_aij = (Mat_SeqBAIJ*) aij->B->data;
2489: MatScalar *b_val = b_aij->a;
2490: PetscReal *work;
2493: MatGetSize(A,NULL,&N);
2494: PetscCalloc1(N,&work);
2495: if (type == NORM_2) {
2496: for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
2497: for (jb=0; jb<bs; jb++) {
2498: for (ib=0; ib<bs; ib++) {
2499: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
2500: a_val++;
2501: }
2502: }
2503: }
2504: for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
2505: for (jb=0; jb<bs; jb++) {
2506: for (ib=0; ib<bs; ib++) {
2507: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val * *b_val);
2508: b_val++;
2509: }
2510: }
2511: }
2512: } else if (type == NORM_1) {
2513: for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
2514: for (jb=0; jb<bs; jb++) {
2515: for (ib=0; ib<bs; ib++) {
2516: work[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
2517: a_val++;
2518: }
2519: }
2520: }
2521: for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
2522: for (jb=0; jb<bs; jb++) {
2523: for (ib=0; ib<bs; ib++) {
2524: work[garray[b_aij->j[i]] * bs + jb] += PetscAbsScalar(*b_val);
2525: b_val++;
2526: }
2527: }
2528: }
2529: } else if (type == NORM_INFINITY) {
2530: for (i=a_aij->i[0]; i<a_aij->i[aij->A->rmap->n/bs]; i++) {
2531: for (jb=0; jb<bs; jb++) {
2532: for (ib=0; ib<bs; ib++) {
2533: int col = A->cmap->rstart + a_aij->j[i] * bs + jb;
2534: work[col] = PetscMax(PetscAbsScalar(*a_val), work[col]);
2535: a_val++;
2536: }
2537: }
2538: }
2539: for (i=b_aij->i[0]; i<b_aij->i[aij->B->rmap->n/bs]; i++) {
2540: for (jb=0; jb<bs; jb++) {
2541: for (ib=0; ib<bs; ib++) {
2542: int col = garray[b_aij->j[i]] * bs + jb;
2543: work[col] = PetscMax(PetscAbsScalar(*b_val), work[col]);
2544: b_val++;
2545: }
2546: }
2547: }
2548: } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Unknown NormType");
2549: if (type == NORM_INFINITY) {
2550: MPI_Allreduce(work,norms,N,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
2551: } else {
2552: MPI_Allreduce(work,norms,N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
2553: }
2554: PetscFree(work);
2555: if (type == NORM_2) {
2556: for (i=0; i<N; i++) norms[i] = PetscSqrtReal(norms[i]);
2557: }
2558: return(0);
2559: }
2563: PetscErrorCode MatInvertBlockDiagonal_MPIBAIJ(Mat A,const PetscScalar **values)
2564: {
2565: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*) A->data;
2569: MatInvertBlockDiagonal(a->A,values);
2570: return(0);
2571: }
2574: /* -------------------------------------------------------------------*/
2575: static struct _MatOps MatOps_Values = {MatSetValues_MPIBAIJ,
2576: MatGetRow_MPIBAIJ,
2577: MatRestoreRow_MPIBAIJ,
2578: MatMult_MPIBAIJ,
2579: /* 4*/ MatMultAdd_MPIBAIJ,
2580: MatMultTranspose_MPIBAIJ,
2581: MatMultTransposeAdd_MPIBAIJ,
2582: 0,
2583: 0,
2584: 0,
2585: /*10*/ 0,
2586: 0,
2587: 0,
2588: MatSOR_MPIBAIJ,
2589: MatTranspose_MPIBAIJ,
2590: /*15*/ MatGetInfo_MPIBAIJ,
2591: MatEqual_MPIBAIJ,
2592: MatGetDiagonal_MPIBAIJ,
2593: MatDiagonalScale_MPIBAIJ,
2594: MatNorm_MPIBAIJ,
2595: /*20*/ MatAssemblyBegin_MPIBAIJ,
2596: MatAssemblyEnd_MPIBAIJ,
2597: MatSetOption_MPIBAIJ,
2598: MatZeroEntries_MPIBAIJ,
2599: /*24*/ MatZeroRows_MPIBAIJ,
2600: 0,
2601: 0,
2602: 0,
2603: 0,
2604: /*29*/ MatSetUp_MPIBAIJ,
2605: 0,
2606: 0,
2607: 0,
2608: 0,
2609: /*34*/ MatDuplicate_MPIBAIJ,
2610: 0,
2611: 0,
2612: 0,
2613: 0,
2614: /*39*/ MatAXPY_MPIBAIJ,
2615: MatGetSubMatrices_MPIBAIJ,
2616: MatIncreaseOverlap_MPIBAIJ,
2617: MatGetValues_MPIBAIJ,
2618: MatCopy_MPIBAIJ,
2619: /*44*/ 0,
2620: MatScale_MPIBAIJ,
2621: 0,
2622: 0,
2623: MatZeroRowsColumns_MPIBAIJ,
2624: /*49*/ 0,
2625: 0,
2626: 0,
2627: 0,
2628: 0,
2629: /*54*/ MatFDColoringCreate_MPIXAIJ,
2630: 0,
2631: MatSetUnfactored_MPIBAIJ,
2632: MatPermute_MPIBAIJ,
2633: MatSetValuesBlocked_MPIBAIJ,
2634: /*59*/ MatGetSubMatrix_MPIBAIJ,
2635: MatDestroy_MPIBAIJ,
2636: MatView_MPIBAIJ,
2637: 0,
2638: 0,
2639: /*64*/ 0,
2640: 0,
2641: 0,
2642: 0,
2643: 0,
2644: /*69*/ MatGetRowMaxAbs_MPIBAIJ,
2645: 0,
2646: 0,
2647: 0,
2648: 0,
2649: /*74*/ 0,
2650: MatFDColoringApply_BAIJ,
2651: 0,
2652: 0,
2653: 0,
2654: /*79*/ 0,
2655: 0,
2656: 0,
2657: 0,
2658: MatLoad_MPIBAIJ,
2659: /*84*/ 0,
2660: 0,
2661: 0,
2662: 0,
2663: 0,
2664: /*89*/ 0,
2665: 0,
2666: 0,
2667: 0,
2668: 0,
2669: /*94*/ 0,
2670: 0,
2671: 0,
2672: 0,
2673: 0,
2674: /*99*/ 0,
2675: 0,
2676: 0,
2677: 0,
2678: 0,
2679: /*104*/0,
2680: MatRealPart_MPIBAIJ,
2681: MatImaginaryPart_MPIBAIJ,
2682: 0,
2683: 0,
2684: /*109*/0,
2685: 0,
2686: 0,
2687: 0,
2688: 0,
2689: /*114*/MatGetSeqNonzeroStructure_MPIBAIJ,
2690: 0,
2691: MatGetGhosts_MPIBAIJ,
2692: 0,
2693: 0,
2694: /*119*/0,
2695: 0,
2696: 0,
2697: 0,
2698: MatGetMultiProcBlock_MPIBAIJ,
2699: /*124*/0,
2700: MatGetColumnNorms_MPIBAIJ,
2701: MatInvertBlockDiagonal_MPIBAIJ,
2702: 0,
2703: 0,
2704: /*129*/ 0,
2705: 0,
2706: 0,
2707: 0,
2708: 0,
2709: /*134*/ 0,
2710: 0,
2711: 0,
2712: 0,
2713: 0,
2714: /*139*/ 0,
2715: 0,
2716: 0,
2717: MatFDColoringSetUp_MPIXAIJ
2718: };
2722: PetscErrorCode MatGetDiagonalBlock_MPIBAIJ(Mat A,Mat *a)
2723: {
2725: *a = ((Mat_MPIBAIJ*)A->data)->A;
2726: return(0);
2727: }
2729: PETSC_EXTERN PetscErrorCode MatConvert_MPIBAIJ_MPISBAIJ(Mat, MatType,MatReuse,Mat*);
2733: PetscErrorCode MatMPIBAIJSetPreallocationCSR_MPIBAIJ(Mat B,PetscInt bs,const PetscInt ii[],const PetscInt jj[],const PetscScalar V[])
2734: {
2735: PetscInt m,rstart,cstart,cend;
2736: PetscInt i,j,d,nz,nz_max=0,*d_nnz=0,*o_nnz=0;
2737: const PetscInt *JJ =0;
2738: PetscScalar *values=0;
2739: PetscBool roworiented = ((Mat_MPIBAIJ*)B->data)->roworiented;
2743: PetscLayoutSetBlockSize(B->rmap,bs);
2744: PetscLayoutSetBlockSize(B->cmap,bs);
2745: PetscLayoutSetUp(B->rmap);
2746: PetscLayoutSetUp(B->cmap);
2747: PetscLayoutGetBlockSize(B->rmap,&bs);
2748: m = B->rmap->n/bs;
2749: rstart = B->rmap->rstart/bs;
2750: cstart = B->cmap->rstart/bs;
2751: cend = B->cmap->rend/bs;
2753: if (ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"ii[0] must be 0 but it is %D",ii[0]);
2754: PetscMalloc2(m,&d_nnz,m,&o_nnz);
2755: for (i=0; i<m; i++) {
2756: nz = ii[i+1] - ii[i];
2757: if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative number of columns %D",i,nz);
2758: nz_max = PetscMax(nz_max,nz);
2759: JJ = jj + ii[i];
2760: for (j=0; j<nz; j++) {
2761: if (*JJ >= cstart) break;
2762: JJ++;
2763: }
2764: d = 0;
2765: for (; j<nz; j++) {
2766: if (*JJ++ >= cend) break;
2767: d++;
2768: }
2769: d_nnz[i] = d;
2770: o_nnz[i] = nz - d;
2771: }
2772: MatMPIBAIJSetPreallocation(B,bs,0,d_nnz,0,o_nnz);
2773: PetscFree2(d_nnz,o_nnz);
2775: values = (PetscScalar*)V;
2776: if (!values) {
2777: PetscMalloc1(bs*bs*nz_max,&values);
2778: PetscMemzero(values,bs*bs*nz_max*sizeof(PetscScalar));
2779: }
2780: for (i=0; i<m; i++) {
2781: PetscInt row = i + rstart;
2782: PetscInt ncols = ii[i+1] - ii[i];
2783: const PetscInt *icols = jj + ii[i];
2784: if (!roworiented) { /* block ordering matches the non-nested layout of MatSetValues so we can insert entire rows */
2785: const PetscScalar *svals = values + (V ? (bs*bs*ii[i]) : 0);
2786: MatSetValuesBlocked_MPIBAIJ(B,1,&row,ncols,icols,svals,INSERT_VALUES);
2787: } else { /* block ordering does not match so we can only insert one block at a time. */
2788: PetscInt j;
2789: for (j=0; j<ncols; j++) {
2790: const PetscScalar *svals = values + (V ? (bs*bs*(ii[i]+j)) : 0);
2791: MatSetValuesBlocked_MPIBAIJ(B,1,&row,1,&icols[j],svals,INSERT_VALUES);
2792: }
2793: }
2794: }
2796: if (!V) { PetscFree(values); }
2797: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2798: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2799: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
2800: return(0);
2801: }
2805: /*@C
2806: MatMPIBAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in BAIJ format
2807: (the default parallel PETSc format).
2809: Collective on MPI_Comm
2811: Input Parameters:
2812: + B - the matrix
2813: . bs - the block size
2814: . i - the indices into j for the start of each local row (starts with zero)
2815: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2816: - v - optional values in the matrix
2818: Level: developer
2820: Notes: The order of the entries in values is specified by the MatOption MAT_ROW_ORIENTED. For example, C programs
2821: may want to use the default MAT_ROW_ORIENTED=PETSC_TRUE and use an array v[nnz][bs][bs] where the second index is
2822: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
2823: MAT_ROW_ORIENTED=PETSC_FALSE and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a
2824: block column and the second index is over columns within a block.
2826: .keywords: matrix, aij, compressed row, sparse, parallel
2828: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIBAIJSetPreallocation(), MatCreateAIJ(), MPIAIJ, MatCreateMPIBAIJWithArrays(), MPIBAIJ
2829: @*/
2830: PetscErrorCode MatMPIBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2831: {
2838: PetscTryMethod(B,"MatMPIBAIJSetPreallocationCSR_C",(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,bs,i,j,v));
2839: return(0);
2840: }
2844: PetscErrorCode MatMPIBAIJSetPreallocation_MPIBAIJ(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt *d_nnz,PetscInt o_nz,const PetscInt *o_nnz)
2845: {
2846: Mat_MPIBAIJ *b;
2848: PetscInt i;
2851: MatSetBlockSize(B,PetscAbs(bs));
2852: PetscLayoutSetUp(B->rmap);
2853: PetscLayoutSetUp(B->cmap);
2854: PetscLayoutGetBlockSize(B->rmap,&bs);
2856: if (d_nnz) {
2857: for (i=0; i<B->rmap->n/bs; i++) {
2858: if (d_nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than -1: local row %D value %D",i,d_nnz[i]);
2859: }
2860: }
2861: if (o_nnz) {
2862: for (i=0; i<B->rmap->n/bs; i++) {
2863: if (o_nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than -1: local row %D value %D",i,o_nnz[i]);
2864: }
2865: }
2867: b = (Mat_MPIBAIJ*)B->data;
2868: b->bs2 = bs*bs;
2869: b->mbs = B->rmap->n/bs;
2870: b->nbs = B->cmap->n/bs;
2871: b->Mbs = B->rmap->N/bs;
2872: b->Nbs = B->cmap->N/bs;
2874: for (i=0; i<=b->size; i++) {
2875: b->rangebs[i] = B->rmap->range[i]/bs;
2876: }
2877: b->rstartbs = B->rmap->rstart/bs;
2878: b->rendbs = B->rmap->rend/bs;
2879: b->cstartbs = B->cmap->rstart/bs;
2880: b->cendbs = B->cmap->rend/bs;
2882: if (!B->preallocated) {
2883: MatCreate(PETSC_COMM_SELF,&b->A);
2884: MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
2885: MatSetType(b->A,MATSEQBAIJ);
2886: PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
2887: MatCreate(PETSC_COMM_SELF,&b->B);
2888: MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
2889: MatSetType(b->B,MATSEQBAIJ);
2890: PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);
2891: MatStashCreate_Private(PetscObjectComm((PetscObject)B),bs,&B->bstash);
2892: }
2894: MatSeqBAIJSetPreallocation(b->A,bs,d_nz,d_nnz);
2895: MatSeqBAIJSetPreallocation(b->B,bs,o_nz,o_nnz);
2896: B->preallocated = PETSC_TRUE;
2897: return(0);
2898: }
2900: extern PetscErrorCode MatDiagonalScaleLocal_MPIBAIJ(Mat,Vec);
2901: extern PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat,PetscReal);
2905: PETSC_EXTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAdj(Mat B, MatType newtype,MatReuse reuse,Mat *adj)
2906: {
2907: Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)B->data;
2909: Mat_SeqBAIJ *d = (Mat_SeqBAIJ*) b->A->data,*o = (Mat_SeqBAIJ*) b->B->data;
2910: PetscInt M = B->rmap->n/B->rmap->bs,i,*ii,*jj,cnt,j,k,rstart = B->rmap->rstart/B->rmap->bs;
2911: const PetscInt *id = d->i, *jd = d->j, *io = o->i, *jo = o->j, *garray = b->garray;
2914: PetscMalloc1((M+1),&ii);
2915: ii[0] = 0;
2916: for (i=0; i<M; i++) {
2917: if ((id[i+1] - id[i]) < 0) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Indices wrong %D %D %D",i,id[i],id[i+1]);
2918: if ((io[i+1] - io[i]) < 0) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Indices wrong %D %D %D",i,io[i],io[i+1]);
2919: ii[i+1] = ii[i] + id[i+1] - id[i] + io[i+1] - io[i];
2920: /* remove one from count of matrix has diagonal */
2921: for (j=id[i]; j<id[i+1]; j++) {
2922: if (jd[j] == i) {ii[i+1]--;break;}
2923: }
2924: }
2925: PetscMalloc1(ii[M],&jj);
2926: cnt = 0;
2927: for (i=0; i<M; i++) {
2928: for (j=io[i]; j<io[i+1]; j++) {
2929: if (garray[jo[j]] > rstart) break;
2930: jj[cnt++] = garray[jo[j]];
2931: }
2932: for (k=id[i]; k<id[i+1]; k++) {
2933: if (jd[k] != i) {
2934: jj[cnt++] = rstart + jd[k];
2935: }
2936: }
2937: for (; j<io[i+1]; j++) {
2938: jj[cnt++] = garray[jo[j]];
2939: }
2940: }
2941: MatCreateMPIAdj(PetscObjectComm((PetscObject)B),M,B->cmap->N/B->rmap->bs,ii,jj,NULL,adj);
2942: return(0);
2943: }
2945: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2947: PETSC_EXTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat,MatType,MatReuse,Mat*);
2951: PETSC_EXTERN PetscErrorCode MatConvert_MPIBAIJ_MPIAIJ(Mat A,MatType newtype,MatReuse reuse,Mat *newmat)
2952: {
2954: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
2955: Mat B;
2956: Mat_MPIAIJ *b;
2959: if (!A->assembled) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Matrix must be assembled");
2961: MatCreate(PetscObjectComm((PetscObject)A),&B);
2962: MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
2963: MatSetType(B,MATMPIAIJ);
2964: MatSeqAIJSetPreallocation(B,0,NULL);
2965: MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);
2966: b = (Mat_MPIAIJ*) B->data;
2968: MatDestroy(&b->A);
2969: MatDestroy(&b->B);
2970: MatDisAssemble_MPIBAIJ(A);
2971: MatConvert_SeqBAIJ_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A);
2972: MatConvert_SeqBAIJ_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B);
2973: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2974: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2975: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2976: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2977: if (reuse == MAT_REUSE_MATRIX) {
2978: MatHeaderReplace(A,B);
2979: } else {
2980: *newmat = B;
2981: }
2982: return(0);
2983: }
2985: #if defined(PETSC_HAVE_MUMPS)
2986: PETSC_EXTERN PetscErrorCode MatGetFactor_baij_mumps(Mat,MatFactorType,Mat*);
2987: #endif
2989: /*MC
2990: MATMPIBAIJ - MATMPIBAIJ = "mpibaij" - A matrix type to be used for distributed block sparse matrices.
2992: Options Database Keys:
2993: + -mat_type mpibaij - sets the matrix type to "mpibaij" during a call to MatSetFromOptions()
2994: . -mat_block_size <bs> - set the blocksize used to store the matrix
2995: - -mat_use_hash_table <fact>
2997: Level: beginner
2999: .seealso: MatCreateMPIBAIJ
3000: M*/
3002: PETSC_EXTERN PetscErrorCode MatConvert_MPIBAIJ_MPIBSTRM(Mat,MatType,MatReuse,Mat*);
3006: PETSC_EXTERN PetscErrorCode MatCreate_MPIBAIJ(Mat B)
3007: {
3008: Mat_MPIBAIJ *b;
3010: PetscBool flg;
3013: PetscNewLog(B,&b);
3014: B->data = (void*)b;
3016: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3017: B->assembled = PETSC_FALSE;
3019: B->insertmode = NOT_SET_VALUES;
3020: MPI_Comm_rank(PetscObjectComm((PetscObject)B),&b->rank);
3021: MPI_Comm_size(PetscObjectComm((PetscObject)B),&b->size);
3023: /* build local table of row and column ownerships */
3024: PetscMalloc1((b->size+1),&b->rangebs);
3026: /* build cache for off array entries formed */
3027: MatStashCreate_Private(PetscObjectComm((PetscObject)B),1,&B->stash);
3029: b->donotstash = PETSC_FALSE;
3030: b->colmap = NULL;
3031: b->garray = NULL;
3032: b->roworiented = PETSC_TRUE;
3034: /* stuff used in block assembly */
3035: b->barray = 0;
3037: /* stuff used for matrix vector multiply */
3038: b->lvec = 0;
3039: b->Mvctx = 0;
3041: /* stuff for MatGetRow() */
3042: b->rowindices = 0;
3043: b->rowvalues = 0;
3044: b->getrowactive = PETSC_FALSE;
3046: /* hash table stuff */
3047: b->ht = 0;
3048: b->hd = 0;
3049: b->ht_size = 0;
3050: b->ht_flag = PETSC_FALSE;
3051: b->ht_fact = 0;
3052: b->ht_total_ct = 0;
3053: b->ht_insert_ct = 0;
3055: /* stuff for MatGetSubMatrices_MPIBAIJ_local() */
3056: b->ijonly = PETSC_FALSE;
3058: PetscOptionsBegin(PetscObjectComm((PetscObject)B),NULL,"Options for loading MPIBAIJ matrix 1","Mat");
3059: PetscOptionsBool("-mat_use_hash_table","Use hash table to save memory in constructing matrix","MatSetOption",PETSC_FALSE,&flg,NULL);
3060: if (flg) {
3061: PetscReal fact = 1.39;
3062: MatSetOption(B,MAT_USE_HASH_TABLE,PETSC_TRUE);
3063: PetscOptionsReal("-mat_use_hash_table","Use hash table factor","MatMPIBAIJSetHashTableFactor",fact,&fact,NULL);
3064: if (fact <= 1.0) fact = 1.39;
3065: MatMPIBAIJSetHashTableFactor(B,fact);
3066: PetscInfo1(B,"Hash table Factor used %5.2f\n",fact);
3067: }
3068: PetscOptionsEnd();
3070: #if defined(PETSC_HAVE_MUMPS)
3071: PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_mumps_C",MatGetFactor_baij_mumps);
3072: #endif
3073: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpiadj_C",MatConvert_MPIBAIJ_MPIAdj);
3074: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpiaij_C",MatConvert_MPIBAIJ_MPIAIJ);
3075: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpisbaij_C",MatConvert_MPIBAIJ_MPISBAIJ);
3076: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_MPIBAIJ);
3077: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_MPIBAIJ);
3078: PetscObjectComposeFunction((PetscObject)B,"MatGetDiagonalBlock_C",MatGetDiagonalBlock_MPIBAIJ);
3079: PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocation_C",MatMPIBAIJSetPreallocation_MPIBAIJ);
3080: PetscObjectComposeFunction((PetscObject)B,"MatMPIBAIJSetPreallocationCSR_C",MatMPIBAIJSetPreallocationCSR_MPIBAIJ);
3081: PetscObjectComposeFunction((PetscObject)B,"MatDiagonalScaleLocal_C",MatDiagonalScaleLocal_MPIBAIJ);
3082: PetscObjectComposeFunction((PetscObject)B,"MatSetHashTableFactor_C",MatSetHashTableFactor_MPIBAIJ);
3083: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpibaij_mpibstrm_C",MatConvert_MPIBAIJ_MPIBSTRM);
3084: PetscObjectChangeTypeName((PetscObject)B,MATMPIBAIJ);
3085: return(0);
3086: }
3088: /*MC
3089: MATBAIJ - MATBAIJ = "baij" - A matrix type to be used for block sparse matrices.
3091: This matrix type is identical to MATSEQBAIJ when constructed with a single process communicator,
3092: and MATMPIBAIJ otherwise.
3094: Options Database Keys:
3095: . -mat_type baij - sets the matrix type to "baij" during a call to MatSetFromOptions()
3097: Level: beginner
3099: .seealso: MatCreateBAIJ(),MATSEQBAIJ,MATMPIBAIJ, MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
3100: M*/
3104: /*@C
3105: MatMPIBAIJSetPreallocation - Allocates memory for a sparse parallel matrix in block AIJ format
3106: (block compressed row). For good matrix assembly performance
3107: the user should preallocate the matrix storage by setting the parameters
3108: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
3109: performance can be increased by more than a factor of 50.
3111: Collective on Mat
3113: Input Parameters:
3114: + B - the matrix
3115: . bs - size of block
3116: . d_nz - number of block nonzeros per block row in diagonal portion of local
3117: submatrix (same for all local rows)
3118: . d_nnz - array containing the number of block nonzeros in the various block rows
3119: of the in diagonal portion of the local (possibly different for each block
3120: row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry and
3121: set it even if it is zero.
3122: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
3123: submatrix (same for all local rows).
3124: - o_nnz - array containing the number of nonzeros in the various block rows of the
3125: off-diagonal portion of the local submatrix (possibly different for
3126: each block row) or NULL.
3128: If the *_nnz parameter is given then the *_nz parameter is ignored
3130: Options Database Keys:
3131: + -mat_block_size - size of the blocks to use
3132: - -mat_use_hash_table <fact>
3134: Notes:
3135: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor
3136: than it must be used on all processors that share the object for that argument.
3138: Storage Information:
3139: For a square global matrix we define each processor's diagonal portion
3140: to be its local rows and the corresponding columns (a square submatrix);
3141: each processor's off-diagonal portion encompasses the remainder of the
3142: local matrix (a rectangular submatrix).
3144: The user can specify preallocated storage for the diagonal part of
3145: the local submatrix with either d_nz or d_nnz (not both). Set
3146: d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
3147: memory allocation. Likewise, specify preallocated storage for the
3148: off-diagonal part of the local submatrix with o_nz or o_nnz (not both).
3150: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3151: the figure below we depict these three local rows and all columns (0-11).
3153: .vb
3154: 0 1 2 3 4 5 6 7 8 9 10 11
3155: --------------------------
3156: row 3 |o o o d d d o o o o o o
3157: row 4 |o o o d d d o o o o o o
3158: row 5 |o o o d d d o o o o o o
3159: --------------------------
3160: .ve
3162: Thus, any entries in the d locations are stored in the d (diagonal)
3163: submatrix, and any entries in the o locations are stored in the
3164: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3165: stored simply in the MATSEQBAIJ format for compressed row storage.
3167: Now d_nz should indicate the number of block nonzeros per row in the d matrix,
3168: and o_nz should indicate the number of block nonzeros per row in the o matrix.
3169: In general, for PDE problems in which most nonzeros are near the diagonal,
3170: one expects d_nz >> o_nz. For large problems you MUST preallocate memory
3171: or you will get TERRIBLE performance; see the users' manual chapter on
3172: matrices.
3174: You can call MatGetInfo() to get information on how effective the preallocation was;
3175: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3176: You can also run with the option -info and look for messages with the string
3177: malloc in them to see if additional memory allocation was needed.
3179: Level: intermediate
3181: .keywords: matrix, block, aij, compressed row, sparse, parallel
3183: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateBAIJ(), MatMPIBAIJSetPreallocationCSR(), PetscSplitOwnership()
3184: @*/
3185: PetscErrorCode MatMPIBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
3186: {
3193: PetscTryMethod(B,"MatMPIBAIJSetPreallocation_C",(Mat,PetscInt,PetscInt,const PetscInt[],PetscInt,const PetscInt[]),(B,bs,d_nz,d_nnz,o_nz,o_nnz));
3194: return(0);
3195: }
3199: /*@C
3200: MatCreateBAIJ - Creates a sparse parallel matrix in block AIJ format
3201: (block compressed row). For good matrix assembly performance
3202: the user should preallocate the matrix storage by setting the parameters
3203: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
3204: performance can be increased by more than a factor of 50.
3206: Collective on MPI_Comm
3208: Input Parameters:
3209: + comm - MPI communicator
3210: . bs - size of blockk
3211: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
3212: This value should be the same as the local size used in creating the
3213: y vector for the matrix-vector product y = Ax.
3214: . n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
3215: This value should be the same as the local size used in creating the
3216: x vector for the matrix-vector product y = Ax.
3217: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3218: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3219: . d_nz - number of nonzero blocks per block row in diagonal portion of local
3220: submatrix (same for all local rows)
3221: . d_nnz - array containing the number of nonzero blocks in the various block rows
3222: of the in diagonal portion of the local (possibly different for each block
3223: row) or NULL. If you plan to factor the matrix you must leave room for the diagonal entry
3224: and set it even if it is zero.
3225: . o_nz - number of nonzero blocks per block row in the off-diagonal portion of local
3226: submatrix (same for all local rows).
3227: - o_nnz - array containing the number of nonzero blocks in the various block rows of the
3228: off-diagonal portion of the local submatrix (possibly different for
3229: each block row) or NULL.
3231: Output Parameter:
3232: . A - the matrix
3234: Options Database Keys:
3235: + -mat_block_size - size of the blocks to use
3236: - -mat_use_hash_table <fact>
3238: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3239: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3240: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3242: Notes:
3243: If the *_nnz parameter is given then the *_nz parameter is ignored
3245: A nonzero block is any block that as 1 or more nonzeros in it
3247: The user MUST specify either the local or global matrix dimensions
3248: (possibly both).
3250: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor
3251: than it must be used on all processors that share the object for that argument.
3253: Storage Information:
3254: For a square global matrix we define each processor's diagonal portion
3255: to be its local rows and the corresponding columns (a square submatrix);
3256: each processor's off-diagonal portion encompasses the remainder of the
3257: local matrix (a rectangular submatrix).
3259: The user can specify preallocated storage for the diagonal part of
3260: the local submatrix with either d_nz or d_nnz (not both). Set
3261: d_nz=PETSC_DEFAULT and d_nnz=NULL for PETSc to control dynamic
3262: memory allocation. Likewise, specify preallocated storage for the
3263: off-diagonal part of the local submatrix with o_nz or o_nnz (not both).
3265: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
3266: the figure below we depict these three local rows and all columns (0-11).
3268: .vb
3269: 0 1 2 3 4 5 6 7 8 9 10 11
3270: --------------------------
3271: row 3 |o o o d d d o o o o o o
3272: row 4 |o o o d d d o o o o o o
3273: row 5 |o o o d d d o o o o o o
3274: --------------------------
3275: .ve
3277: Thus, any entries in the d locations are stored in the d (diagonal)
3278: submatrix, and any entries in the o locations are stored in the
3279: o (off-diagonal) submatrix. Note that the d and the o submatrices are
3280: stored simply in the MATSEQBAIJ format for compressed row storage.
3282: Now d_nz should indicate the number of block nonzeros per row in the d matrix,
3283: and o_nz should indicate the number of block nonzeros per row in the o matrix.
3284: In general, for PDE problems in which most nonzeros are near the diagonal,
3285: one expects d_nz >> o_nz. For large problems you MUST preallocate memory
3286: or you will get TERRIBLE performance; see the users' manual chapter on
3287: matrices.
3289: Level: intermediate
3291: .keywords: matrix, block, aij, compressed row, sparse, parallel
3293: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatCreateBAIJ(), MatMPIBAIJSetPreallocation(), MatMPIBAIJSetPreallocationCSR()
3294: @*/
3295: PetscErrorCode MatCreateBAIJ(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)
3296: {
3298: PetscMPIInt size;
3301: MatCreate(comm,A);
3302: MatSetSizes(*A,m,n,M,N);
3303: MPI_Comm_size(comm,&size);
3304: if (size > 1) {
3305: MatSetType(*A,MATMPIBAIJ);
3306: MatMPIBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
3307: } else {
3308: MatSetType(*A,MATSEQBAIJ);
3309: MatSeqBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
3310: }
3311: return(0);
3312: }
3316: static PetscErrorCode MatDuplicate_MPIBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
3317: {
3318: Mat mat;
3319: Mat_MPIBAIJ *a,*oldmat = (Mat_MPIBAIJ*)matin->data;
3321: PetscInt len=0;
3324: *newmat = 0;
3325: MatCreate(PetscObjectComm((PetscObject)matin),&mat);
3326: MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
3327: MatSetType(mat,((PetscObject)matin)->type_name);
3328: PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
3330: mat->factortype = matin->factortype;
3331: mat->preallocated = PETSC_TRUE;
3332: mat->assembled = PETSC_TRUE;
3333: mat->insertmode = NOT_SET_VALUES;
3335: a = (Mat_MPIBAIJ*)mat->data;
3336: mat->rmap->bs = matin->rmap->bs;
3337: a->bs2 = oldmat->bs2;
3338: a->mbs = oldmat->mbs;
3339: a->nbs = oldmat->nbs;
3340: a->Mbs = oldmat->Mbs;
3341: a->Nbs = oldmat->Nbs;
3343: PetscLayoutReference(matin->rmap,&mat->rmap);
3344: PetscLayoutReference(matin->cmap,&mat->cmap);
3346: a->size = oldmat->size;
3347: a->rank = oldmat->rank;
3348: a->donotstash = oldmat->donotstash;
3349: a->roworiented = oldmat->roworiented;
3350: a->rowindices = 0;
3351: a->rowvalues = 0;
3352: a->getrowactive = PETSC_FALSE;
3353: a->barray = 0;
3354: a->rstartbs = oldmat->rstartbs;
3355: a->rendbs = oldmat->rendbs;
3356: a->cstartbs = oldmat->cstartbs;
3357: a->cendbs = oldmat->cendbs;
3359: /* hash table stuff */
3360: a->ht = 0;
3361: a->hd = 0;
3362: a->ht_size = 0;
3363: a->ht_flag = oldmat->ht_flag;
3364: a->ht_fact = oldmat->ht_fact;
3365: a->ht_total_ct = 0;
3366: a->ht_insert_ct = 0;
3368: PetscMemcpy(a->rangebs,oldmat->rangebs,(a->size+1)*sizeof(PetscInt));
3369: if (oldmat->colmap) {
3370: #if defined(PETSC_USE_CTABLE)
3371: PetscTableCreateCopy(oldmat->colmap,&a->colmap);
3372: #else
3373: PetscMalloc1((a->Nbs),&a->colmap);
3374: PetscLogObjectMemory((PetscObject)mat,(a->Nbs)*sizeof(PetscInt));
3375: PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(PetscInt));
3376: #endif
3377: } else a->colmap = 0;
3379: if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
3380: PetscMalloc1(len,&a->garray);
3381: PetscLogObjectMemory((PetscObject)mat,len*sizeof(PetscInt));
3382: PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt));
3383: } else a->garray = 0;
3385: MatStashCreate_Private(PetscObjectComm((PetscObject)matin),matin->rmap->bs,&mat->bstash);
3386: VecDuplicate(oldmat->lvec,&a->lvec);
3387: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->lvec);
3388: VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
3389: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->Mvctx);
3391: MatDuplicate(oldmat->A,cpvalues,&a->A);
3392: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
3393: MatDuplicate(oldmat->B,cpvalues,&a->B);
3394: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->B);
3395: PetscFunctionListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
3396: *newmat = mat;
3397: return(0);
3398: }
3402: PetscErrorCode MatLoad_MPIBAIJ(Mat newmat,PetscViewer viewer)
3403: {
3405: int fd;
3406: PetscInt i,nz,j,rstart,rend;
3407: PetscScalar *vals,*buf;
3408: MPI_Comm comm;
3409: MPI_Status status;
3410: PetscMPIInt rank,size,maxnz;
3411: PetscInt header[4],*rowlengths = 0,M,N,m,*rowners,*cols;
3412: PetscInt *locrowlens = NULL,*procsnz = NULL,*browners = NULL;
3413: PetscInt jj,*mycols,*ibuf,bs = newmat->rmap->bs,Mbs,mbs,extra_rows,mmax;
3414: PetscMPIInt tag = ((PetscObject)viewer)->tag;
3415: PetscInt *dlens = NULL,*odlens = NULL,*mask = NULL,*masked1 = NULL,*masked2 = NULL,rowcount,odcount;
3416: PetscInt dcount,kmax,k,nzcount,tmp,mend,sizesset=1,grows,gcols;
3419: PetscObjectGetComm((PetscObject)viewer,&comm);
3420: PetscOptionsBegin(comm,NULL,"Options for loading MPIBAIJ matrix 2","Mat");
3421: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
3422: PetscOptionsEnd();
3423: if (bs < 0) bs = 1;
3425: MPI_Comm_size(comm,&size);
3426: MPI_Comm_rank(comm,&rank);
3427: if (!rank) {
3428: PetscViewerBinaryGetDescriptor(viewer,&fd);
3429: PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
3430: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
3431: }
3433: if (newmat->rmap->n < 0 && newmat->rmap->N < 0 && newmat->cmap->n < 0 && newmat->cmap->N < 0) sizesset = 0;
3435: MPI_Bcast(header+1,3,MPIU_INT,0,comm);
3436: M = header[1]; N = header[2];
3438: /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */
3439: if (sizesset && newmat->rmap->N < 0) newmat->rmap->N = M;
3440: if (sizesset && newmat->cmap->N < 0) newmat->cmap->N = N;
3442: /* If global sizes are set, check if they are consistent with that given in the file */
3443: if (sizesset) {
3444: MatGetSize(newmat,&grows,&gcols);
3445: }
3446: 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);
3447: 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);
3449: if (M != N) SETERRQ(PetscObjectComm((PetscObject)viewer),PETSC_ERR_SUP,"Can only do square matrices");
3451: /*
3452: This code adds extra rows to make sure the number of rows is
3453: divisible by the blocksize
3454: */
3455: Mbs = M/bs;
3456: extra_rows = bs - M + bs*Mbs;
3457: if (extra_rows == bs) extra_rows = 0;
3458: else Mbs++;
3459: if (extra_rows && !rank) {
3460: PetscInfo(viewer,"Padding loaded matrix to match blocksize\n");
3461: }
3463: /* determine ownership of all rows */
3464: if (newmat->rmap->n < 0) { /* PETSC_DECIDE */
3465: mbs = Mbs/size + ((Mbs % size) > rank);
3466: m = mbs*bs;
3467: } else { /* User set */
3468: m = newmat->rmap->n;
3469: mbs = m/bs;
3470: }
3471: PetscMalloc2(size+1,&rowners,size+1,&browners);
3472: MPI_Allgather(&mbs,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
3474: /* process 0 needs enough room for process with most rows */
3475: if (!rank) {
3476: mmax = rowners[1];
3477: for (i=2; i<=size; i++) {
3478: mmax = PetscMax(mmax,rowners[i]);
3479: }
3480: mmax*=bs;
3481: } else mmax = -1; /* unused, but compiler warns anyway */
3483: rowners[0] = 0;
3484: for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
3485: for (i=0; i<=size; i++) browners[i] = rowners[i]*bs;
3486: rstart = rowners[rank];
3487: rend = rowners[rank+1];
3489: /* distribute row lengths to all processors */
3490: PetscMalloc1(m,&locrowlens);
3491: if (!rank) {
3492: mend = m;
3493: if (size == 1) mend = mend - extra_rows;
3494: PetscBinaryRead(fd,locrowlens,mend,PETSC_INT);
3495: for (j=mend; j<m; j++) locrowlens[j] = 1;
3496: PetscMalloc1(mmax,&rowlengths);
3497: PetscCalloc1(size,&procsnz);
3498: for (j=0; j<m; j++) {
3499: procsnz[0] += locrowlens[j];
3500: }
3501: for (i=1; i<size; i++) {
3502: mend = browners[i+1] - browners[i];
3503: if (i == size-1) mend = mend - extra_rows;
3504: PetscBinaryRead(fd,rowlengths,mend,PETSC_INT);
3505: for (j=mend; j<browners[i+1] - browners[i]; j++) rowlengths[j] = 1;
3506: /* calculate the number of nonzeros on each processor */
3507: for (j=0; j<browners[i+1]-browners[i]; j++) {
3508: procsnz[i] += rowlengths[j];
3509: }
3510: MPI_Send(rowlengths,browners[i+1]-browners[i],MPIU_INT,i,tag,comm);
3511: }
3512: PetscFree(rowlengths);
3513: } else {
3514: MPI_Recv(locrowlens,m,MPIU_INT,0,tag,comm,&status);
3515: }
3517: if (!rank) {
3518: /* determine max buffer needed and allocate it */
3519: maxnz = procsnz[0];
3520: for (i=1; i<size; i++) {
3521: maxnz = PetscMax(maxnz,procsnz[i]);
3522: }
3523: PetscMalloc1(maxnz,&cols);
3525: /* read in my part of the matrix column indices */
3526: nz = procsnz[0];
3527: PetscMalloc1((nz+1),&ibuf);
3528: mycols = ibuf;
3529: if (size == 1) nz -= extra_rows;
3530: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
3531: if (size == 1) {
3532: for (i=0; i< extra_rows; i++) mycols[nz+i] = M+i;
3533: }
3535: /* read in every ones (except the last) and ship off */
3536: for (i=1; i<size-1; i++) {
3537: nz = procsnz[i];
3538: PetscBinaryRead(fd,cols,nz,PETSC_INT);
3539: MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
3540: }
3541: /* read in the stuff for the last proc */
3542: if (size != 1) {
3543: nz = procsnz[size-1] - extra_rows; /* the extra rows are not on the disk */
3544: PetscBinaryRead(fd,cols,nz,PETSC_INT);
3545: for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
3546: MPI_Send(cols,nz+extra_rows,MPIU_INT,size-1,tag,comm);
3547: }
3548: PetscFree(cols);
3549: } else {
3550: /* determine buffer space needed for message */
3551: nz = 0;
3552: for (i=0; i<m; i++) {
3553: nz += locrowlens[i];
3554: }
3555: PetscMalloc1((nz+1),&ibuf);
3556: mycols = ibuf;
3557: /* receive message of column indices*/
3558: MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
3559: MPI_Get_count(&status,MPIU_INT,&maxnz);
3560: if (maxnz != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
3561: }
3563: /* loop over local rows, determining number of off diagonal entries */
3564: PetscMalloc2(rend-rstart,&dlens,rend-rstart,&odlens);
3565: PetscCalloc3(Mbs,&mask,Mbs,&masked1,Mbs,&masked2);
3566: rowcount = 0; nzcount = 0;
3567: for (i=0; i<mbs; i++) {
3568: dcount = 0;
3569: odcount = 0;
3570: for (j=0; j<bs; j++) {
3571: kmax = locrowlens[rowcount];
3572: for (k=0; k<kmax; k++) {
3573: tmp = mycols[nzcount++]/bs;
3574: if (!mask[tmp]) {
3575: mask[tmp] = 1;
3576: if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp;
3577: else masked1[dcount++] = tmp;
3578: }
3579: }
3580: rowcount++;
3581: }
3583: dlens[i] = dcount;
3584: odlens[i] = odcount;
3586: /* zero out the mask elements we set */
3587: for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
3588: for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
3589: }
3592: if (!sizesset) {
3593: MatSetSizes(newmat,m,m,M+extra_rows,N+extra_rows);
3594: }
3595: MatMPIBAIJSetPreallocation(newmat,bs,0,dlens,0,odlens);
3597: if (!rank) {
3598: PetscMalloc1((maxnz+1),&buf);
3599: /* read in my part of the matrix numerical values */
3600: nz = procsnz[0];
3601: vals = buf;
3602: mycols = ibuf;
3603: if (size == 1) nz -= extra_rows;
3604: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
3605: if (size == 1) {
3606: for (i=0; i< extra_rows; i++) vals[nz+i] = 1.0;
3607: }
3609: /* insert into matrix */
3610: jj = rstart*bs;
3611: for (i=0; i<m; i++) {
3612: MatSetValues_MPIBAIJ(newmat,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
3613: mycols += locrowlens[i];
3614: vals += locrowlens[i];
3615: jj++;
3616: }
3617: /* read in other processors (except the last one) and ship out */
3618: for (i=1; i<size-1; i++) {
3619: nz = procsnz[i];
3620: vals = buf;
3621: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
3622: MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newmat)->tag,comm);
3623: }
3624: /* the last proc */
3625: if (size != 1) {
3626: nz = procsnz[i] - extra_rows;
3627: vals = buf;
3628: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
3629: for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
3630: MPIULong_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,((PetscObject)newmat)->tag,comm);
3631: }
3632: PetscFree(procsnz);
3633: } else {
3634: /* receive numeric values */
3635: PetscMalloc1((nz+1),&buf);
3637: /* receive message of values*/
3638: vals = buf;
3639: mycols = ibuf;
3640: MPIULong_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newmat)->tag,comm);
3642: /* insert into matrix */
3643: jj = rstart*bs;
3644: for (i=0; i<m; i++) {
3645: MatSetValues_MPIBAIJ(newmat,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
3646: mycols += locrowlens[i];
3647: vals += locrowlens[i];
3648: jj++;
3649: }
3650: }
3651: PetscFree(locrowlens);
3652: PetscFree(buf);
3653: PetscFree(ibuf);
3654: PetscFree2(rowners,browners);
3655: PetscFree2(dlens,odlens);
3656: PetscFree3(mask,masked1,masked2);
3657: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
3658: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
3659: return(0);
3660: }
3664: /*@
3665: MatMPIBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.
3667: Input Parameters:
3668: . mat - the matrix
3669: . fact - factor
3671: Not Collective, each process can use a different factor
3673: Level: advanced
3675: Notes:
3676: This can also be set by the command line option: -mat_use_hash_table <fact>
3678: .keywords: matrix, hashtable, factor, HT
3680: .seealso: MatSetOption()
3681: @*/
3682: PetscErrorCode MatMPIBAIJSetHashTableFactor(Mat mat,PetscReal fact)
3683: {
3687: PetscTryMethod(mat,"MatSetHashTableFactor_C",(Mat,PetscReal),(mat,fact));
3688: return(0);
3689: }
3693: PetscErrorCode MatSetHashTableFactor_MPIBAIJ(Mat mat,PetscReal fact)
3694: {
3695: Mat_MPIBAIJ *baij;
3698: baij = (Mat_MPIBAIJ*)mat->data;
3699: baij->ht_fact = fact;
3700: return(0);
3701: }
3705: PetscErrorCode MatMPIBAIJGetSeqBAIJ(Mat A,Mat *Ad,Mat *Ao,const PetscInt *colmap[])
3706: {
3707: Mat_MPIBAIJ *a = (Mat_MPIBAIJ*)A->data;
3710: if (Ad) *Ad = a->A;
3711: if (Ao) *Ao = a->B;
3712: if (colmap) *colmap = a->garray;
3713: return(0);
3714: }
3716: /*
3717: Special version for direct calls from Fortran (to eliminate two function call overheads
3718: */
3719: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3720: #define matmpibaijsetvaluesblocked_ MATMPIBAIJSETVALUESBLOCKED
3721: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3722: #define matmpibaijsetvaluesblocked_ matmpibaijsetvaluesblocked
3723: #endif
3727: /*@C
3728: MatMPIBAIJSetValuesBlocked - Direct Fortran call to replace call to MatSetValuesBlocked()
3730: Collective on Mat
3732: Input Parameters:
3733: + mat - the matrix
3734: . min - number of input rows
3735: . im - input rows
3736: . nin - number of input columns
3737: . in - input columns
3738: . v - numerical values input
3739: - addvin - INSERT_VALUES or ADD_VALUES
3741: Notes: This has a complete copy of MatSetValuesBlocked_MPIBAIJ() which is terrible code un-reuse.
3743: Level: advanced
3745: .seealso: MatSetValuesBlocked()
3746: @*/
3747: PetscErrorCode matmpibaijsetvaluesblocked_(Mat *matin,PetscInt *min,const PetscInt im[],PetscInt *nin,const PetscInt in[],const MatScalar v[],InsertMode *addvin)
3748: {
3749: /* convert input arguments to C version */
3750: Mat mat = *matin;
3751: PetscInt m = *min, n = *nin;
3752: InsertMode addv = *addvin;
3754: Mat_MPIBAIJ *baij = (Mat_MPIBAIJ*)mat->data;
3755: const MatScalar *value;
3756: MatScalar *barray = baij->barray;
3757: PetscBool roworiented = baij->roworiented;
3758: PetscErrorCode ierr;
3759: PetscInt i,j,ii,jj,row,col,rstart=baij->rstartbs;
3760: PetscInt rend=baij->rendbs,cstart=baij->cstartbs,stepval;
3761: PetscInt cend=baij->cendbs,bs=mat->rmap->bs,bs2=baij->bs2;
3764: /* tasks normally handled by MatSetValuesBlocked() */
3765: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
3766: #if defined(PETSC_USE_DEBUG)
3767: else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
3768: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3769: #endif
3770: if (mat->assembled) {
3771: mat->was_assembled = PETSC_TRUE;
3772: mat->assembled = PETSC_FALSE;
3773: }
3774: PetscLogEventBegin(MAT_SetValues,mat,0,0,0);
3777: if (!barray) {
3778: PetscMalloc1(bs2,&barray);
3779: baij->barray = barray;
3780: }
3782: if (roworiented) stepval = (n-1)*bs;
3783: else stepval = (m-1)*bs;
3785: for (i=0; i<m; i++) {
3786: if (im[i] < 0) continue;
3787: #if defined(PETSC_USE_DEBUG)
3788: 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);
3789: #endif
3790: if (im[i] >= rstart && im[i] < rend) {
3791: row = im[i] - rstart;
3792: for (j=0; j<n; j++) {
3793: /* If NumCol = 1 then a copy is not required */
3794: if ((roworiented) && (n == 1)) {
3795: barray = (MatScalar*)v + i*bs2;
3796: } else if ((!roworiented) && (m == 1)) {
3797: barray = (MatScalar*)v + j*bs2;
3798: } else { /* Here a copy is required */
3799: if (roworiented) {
3800: value = v + i*(stepval+bs)*bs + j*bs;
3801: } else {
3802: value = v + j*(stepval+bs)*bs + i*bs;
3803: }
3804: for (ii=0; ii<bs; ii++,value+=stepval) {
3805: for (jj=0; jj<bs; jj++) {
3806: *barray++ = *value++;
3807: }
3808: }
3809: barray -=bs2;
3810: }
3812: if (in[j] >= cstart && in[j] < cend) {
3813: col = in[j] - cstart;
3814: MatSetValuesBlocked_SeqBAIJ(baij->A,1,&row,1,&col,barray,addv);
3815: } else if (in[j] < 0) continue;
3816: #if defined(PETSC_USE_DEBUG)
3817: 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);
3818: #endif
3819: else {
3820: if (mat->was_assembled) {
3821: if (!baij->colmap) {
3822: MatCreateColmap_MPIBAIJ_Private(mat);
3823: }
3825: #if defined(PETSC_USE_DEBUG)
3826: #if defined(PETSC_USE_CTABLE)
3827: { PetscInt data;
3828: PetscTableFind(baij->colmap,in[j]+1,&data);
3829: if ((data - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
3830: }
3831: #else
3832: if ((baij->colmap[in[j]] - 1) % bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Incorrect colmap");
3833: #endif
3834: #endif
3835: #if defined(PETSC_USE_CTABLE)
3836: PetscTableFind(baij->colmap,in[j]+1,&col);
3837: col = (col - 1)/bs;
3838: #else
3839: col = (baij->colmap[in[j]] - 1)/bs;
3840: #endif
3841: if (col < 0 && !((Mat_SeqBAIJ*)(baij->A->data))->nonew) {
3842: MatDisAssemble_MPIBAIJ(mat);
3843: col = in[j];
3844: }
3845: } else col = in[j];
3846: MatSetValuesBlocked_SeqBAIJ(baij->B,1,&row,1,&col,barray,addv);
3847: }
3848: }
3849: } else {
3850: if (!baij->donotstash) {
3851: if (roworiented) {
3852: MatStashValuesRowBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
3853: } else {
3854: MatStashValuesColBlocked_Private(&mat->bstash,im[i],n,in,v,m,n,i);
3855: }
3856: }
3857: }
3858: }
3860: /* task normally handled by MatSetValuesBlocked() */
3861: PetscLogEventEnd(MAT_SetValues,mat,0,0,0);
3862: return(0);
3863: }
3867: /*@
3868: MatCreateMPIBAIJWithArrays - creates a MPI BAIJ matrix using arrays that contain in standard
3869: CSR format the local rows.
3871: Collective on MPI_Comm
3873: Input Parameters:
3874: + comm - MPI communicator
3875: . bs - the block size, only a block size of 1 is supported
3876: . m - number of local rows (Cannot be PETSC_DECIDE)
3877: . n - This value should be the same as the local size used in creating the
3878: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3879: calculated if N is given) For square matrices n is almost always m.
3880: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3881: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3882: . i - row indices
3883: . j - column indices
3884: - a - matrix values
3886: Output Parameter:
3887: . mat - the matrix
3889: Level: intermediate
3891: Notes:
3892: The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3893: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3894: called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.
3896: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3897: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3898: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3899: with column-major ordering within blocks.
3901: The i and j indices are 0 based, and i indices are indices corresponding to the local j array.
3903: .keywords: matrix, aij, compressed row, sparse, parallel
3905: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3906: MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithSplitArrays()
3907: @*/
3908: PetscErrorCode MatCreateMPIBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
3909: {
3913: if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3914: if (m < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
3915: MatCreate(comm,mat);
3916: MatSetSizes(*mat,m,n,M,N);
3917: MatSetType(*mat,MATMPISBAIJ);
3918: MatSetOption(*mat,MAT_ROW_ORIENTED,PETSC_FALSE);
3919: MatMPIBAIJSetPreallocationCSR(*mat,bs,i,j,a);
3920: MatSetOption(*mat,MAT_ROW_ORIENTED,PETSC_TRUE);
3921: return(0);
3922: }