Actual source code: mpisbaij.c
1: /*$Id: mpisbaij.c,v 1.61 2001/08/10 03:31:37 bsmith Exp $*/
3: #include src/mat/impls/baij/mpi/mpibaij.h
4: #include src/vec/vecimpl.h
5: #include mpisbaij.h
6: #include src/mat/impls/sbaij/seq/sbaij.h
8: extern int MatSetUpMultiply_MPISBAIJ(Mat);
9: extern int MatSetUpMultiply_MPISBAIJ_2comm(Mat);
10: extern int DisAssemble_MPISBAIJ(Mat);
11: extern int MatIncreaseOverlap_MPISBAIJ(Mat,int,IS *,int);
12: extern int MatGetSubMatrices_MPISBAIJ(Mat,int,IS *,IS *,MatReuse,Mat **);
13: extern int MatGetValues_SeqSBAIJ(Mat,int,int *,int,int *,PetscScalar *);
14: extern int MatSetValues_SeqSBAIJ(Mat,int,int *,int,int *,PetscScalar *,InsertMode);
15: extern int MatSetValuesBlocked_SeqSBAIJ(Mat,int,int*,int,int*,PetscScalar*,InsertMode);
16: extern int MatGetRow_SeqSBAIJ(Mat,int,int*,int**,PetscScalar**);
17: extern int MatRestoreRow_SeqSBAIJ(Mat,int,int*,int**,PetscScalar**);
18: extern int MatPrintHelp_SeqSBAIJ(Mat);
19: extern int MatZeroRows_SeqSBAIJ(Mat,IS,PetscScalar*);
20: extern int MatZeroRows_SeqBAIJ(Mat,IS,PetscScalar *);
21: extern int MatGetRowMax_MPISBAIJ(Mat,Vec);
22: extern int MatRelax_MPISBAIJ(Mat,Vec,PetscReal,MatSORType,PetscReal,int,int,Vec);
23: extern int MatUseSpooles_MPISBAIJ(Mat);
25: /* UGLY, ugly, ugly
26: When MatScalar == PetscScalar the function MatSetValuesBlocked_MPIBAIJ_MatScalar() does
27: not exist. Otherwise ..._MatScalar() takes matrix elements in single precision and
28: inserts them into the single precision data structure. The function MatSetValuesBlocked_MPIBAIJ()
29: converts the entries into single precision and then calls ..._MatScalar() to put them
30: into the single precision data structures.
31: */
32: #if defined(PETSC_USE_MAT_SINGLE)
33: extern int MatSetValuesBlocked_SeqSBAIJ_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
34: extern int MatSetValues_MPISBAIJ_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
35: extern int MatSetValuesBlocked_MPISBAIJ_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
36: extern int MatSetValues_MPISBAIJ_HT_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
37: extern int MatSetValuesBlocked_MPISBAIJ_HT_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
38: #else
39: #define MatSetValuesBlocked_SeqSBAIJ_MatScalar MatSetValuesBlocked_SeqSBAIJ
40: #define MatSetValues_MPISBAIJ_MatScalar MatSetValues_MPISBAIJ
41: #define MatSetValuesBlocked_MPISBAIJ_MatScalar MatSetValuesBlocked_MPISBAIJ
42: #define MatSetValues_MPISBAIJ_HT_MatScalar MatSetValues_MPISBAIJ_HT
43: #define MatSetValuesBlocked_MPISBAIJ_HT_MatScalar MatSetValuesBlocked_MPISBAIJ_HT
44: #endif
46: EXTERN_C_BEGIN
47: int MatStoreValues_MPISBAIJ(Mat mat)
48: {
49: Mat_MPISBAIJ *aij = (Mat_MPISBAIJ *)mat->data;
50: int ierr;
53: MatStoreValues(aij->A);
54: MatStoreValues(aij->B);
55: return(0);
56: }
57: EXTERN_C_END
59: EXTERN_C_BEGIN
60: int MatRetrieveValues_MPISBAIJ(Mat mat)
61: {
62: Mat_MPISBAIJ *aij = (Mat_MPISBAIJ *)mat->data;
63: int ierr;
66: MatRetrieveValues(aij->A);
67: MatRetrieveValues(aij->B);
68: return(0);
69: }
70: EXTERN_C_END
72: /*
73: Local utility routine that creates a mapping from the global column
74: number to the local number in the off-diagonal part of the local
75: storage of the matrix. This is done in a non scable way since the
76: length of colmap equals the global matrix length.
77: */
78: static int CreateColmap_MPISBAIJ_Private(Mat mat)
79: {
81: SETERRQ(1,"Function not yet written for SBAIJ format");
82: /* return(0); */
83: }
85: #define CHUNKSIZE 10
87: #define MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv)
88: {
89:
90: brow = row/bs;
91: rp = aj + ai[brow]; ap = aa + bs2*ai[brow];
92: rmax = aimax[brow]; nrow = ailen[brow];
93: bcol = col/bs;
94: ridx = row % bs; cidx = col % bs;
95: low = 0; high = nrow;
96: while (high-low > 3) {
97: t = (low+high)/2;
98: if (rp[t] > bcol) high = t;
99: else low = t;
100: }
101: for (_i=low; _i<high; _i++) {
102: if (rp[_i] > bcol) break;
103: if (rp[_i] == bcol) {
104: bap = ap + bs2*_i + bs*cidx + ridx;
105: if (addv == ADD_VALUES) *bap += value;
106: else *bap = value;
107: goto a_noinsert;
108: }
109: }
110: if (a->nonew == 1) goto a_noinsert;
111: else if (a->nonew == -1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero into matrix");
112: if (nrow >= rmax) {
113: /* there is no extra room in row, therefore enlarge */
114: int new_nz = ai[a->mbs] + CHUNKSIZE,len,*new_i,*new_j;
115: MatScalar *new_a;
116:
117: if (a->nonew == -2) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
118:
119: /* malloc new storage space */
120: len = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(a->mbs+1)*sizeof(int);
121: ierr = PetscMalloc(len,&new_a);
122: new_j = (int*)(new_a + bs2*new_nz);
123: new_i = new_j + new_nz;
124:
125: /* copy over old data into new slots */
126: for (ii=0; ii<brow+1; ii++) {new_i[ii] = ai[ii];}
127: for (ii=brow+1; ii<a->mbs+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;}
128: PetscMemcpy(new_j,aj,(ai[brow]+nrow)*sizeof(int));
129: len = (new_nz - CHUNKSIZE - ai[brow] - nrow);
130: PetscMemcpy(new_j+ai[brow]+nrow+CHUNKSIZE,aj+ai[brow]+nrow,len*sizeof(int));
131: PetscMemcpy(new_a,aa,(ai[brow]+nrow)*bs2*sizeof(MatScalar));
132: PetscMemzero(new_a+bs2*(ai[brow]+nrow),bs2*CHUNKSIZE*sizeof(PetscScalar));
133: PetscMemcpy(new_a+bs2*(ai[brow]+nrow+CHUNKSIZE),
134: aa+bs2*(ai[brow]+nrow),bs2*len*sizeof(MatScalar));
135: /* free up old matrix storage */
136: PetscFree(a->a);
137: if (!a->singlemalloc) {
138: PetscFree(a->i);
139: PetscFree(a->j);
140: }
141: aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;
142: a->singlemalloc = PETSC_TRUE;
143:
144: rp = aj + ai[brow]; ap = aa + bs2*ai[brow];
145: rmax = aimax[brow] = aimax[brow] + CHUNKSIZE;
146: PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar)));
147: a->s_maxnz += bs2*CHUNKSIZE;
148: a->reallocs++;
149: a->s_nz++;
150: }
151: N = nrow++ - 1;
152: /* shift up all the later entries in this row */
153: for (ii=N; ii>=_i; ii--) {
154: rp[ii+1] = rp[ii];
155: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
156: }
157: if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }
158: rp[_i] = bcol;
159: ap[bs2*_i + bs*cidx + ridx] = value;
160: a_noinsert:;
161: ailen[brow] = nrow;
162: }
163: #ifndef MatSetValues_SeqBAIJ_B_Private
164: #define MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv)
165: {
166: brow = row/bs;
167: rp = bj + bi[brow]; ap = ba + bs2*bi[brow];
168: rmax = bimax[brow]; nrow = bilen[brow];
169: bcol = col/bs;
170: ridx = row % bs; cidx = col % bs;
171: low = 0; high = nrow;
172: while (high-low > 3) {
173: t = (low+high)/2;
174: if (rp[t] > bcol) high = t;
175: else low = t;
176: }
177: for (_i=low; _i<high; _i++) {
178: if (rp[_i] > bcol) break;
179: if (rp[_i] == bcol) {
180: bap = ap + bs2*_i + bs*cidx + ridx;
181: if (addv == ADD_VALUES) *bap += value;
182: else *bap = value;
183: goto b_noinsert;
184: }
185: }
186: if (b->nonew == 1) goto b_noinsert;
187: else if (b->nonew == -1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero into matrix");
188: if (nrow >= rmax) {
189: /* there is no extra room in row, therefore enlarge */
190: int new_nz = bi[b->mbs] + CHUNKSIZE,len,*new_i,*new_j;
191: MatScalar *new_a;
192:
193: if (b->nonew == -2) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
194:
195: /* malloc new storage space */
196: len = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(b->mbs+1)*sizeof(int);
197: ierr = PetscMalloc(len,&new_a);
198: new_j = (int*)(new_a + bs2*new_nz);
199: new_i = new_j + new_nz;
200:
201: /* copy over old data into new slots */
202: for (ii=0; ii<brow+1; ii++) {new_i[ii] = bi[ii];}
203: for (ii=brow+1; ii<b->mbs+1; ii++) {new_i[ii] = bi[ii]+CHUNKSIZE;}
204: PetscMemcpy(new_j,bj,(bi[brow]+nrow)*sizeof(int));
205: len = (new_nz - CHUNKSIZE - bi[brow] - nrow);
206: PetscMemcpy(new_j+bi[brow]+nrow+CHUNKSIZE,bj+bi[brow]+nrow,len*sizeof(int));
207: PetscMemcpy(new_a,ba,(bi[brow]+nrow)*bs2*sizeof(MatScalar));
208: PetscMemzero(new_a+bs2*(bi[brow]+nrow),bs2*CHUNKSIZE*sizeof(MatScalar));
209: PetscMemcpy(new_a+bs2*(bi[brow]+nrow+CHUNKSIZE),
210: ba+bs2*(bi[brow]+nrow),bs2*len*sizeof(MatScalar));
211: /* free up old matrix storage */
212: PetscFree(b->a);
213: if (!b->singlemalloc) {
214: PetscFree(b->i);
215: PetscFree(b->j);
216: }
217: ba = b->a = new_a; bi = b->i = new_i; bj = b->j = new_j;
218: b->singlemalloc = PETSC_TRUE;
219:
220: rp = bj + bi[brow]; ap = ba + bs2*bi[brow];
221: rmax = bimax[brow] = bimax[brow] + CHUNKSIZE;
222: PetscLogObjectMemory(B,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar)));
223: b->maxnz += bs2*CHUNKSIZE;
224: b->reallocs++;
225: b->nz++;
226: }
227: N = nrow++ - 1;
228: /* shift up all the later entries in this row */
229: for (ii=N; ii>=_i; ii--) {
230: rp[ii+1] = rp[ii];
231: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
232: }
233: if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}
234: rp[_i] = bcol;
235: ap[bs2*_i + bs*cidx + ridx] = value;
236: b_noinsert:;
237: bilen[brow] = nrow;
238: }
239: #endif
241: #if defined(PETSC_USE_MAT_SINGLE)
242: int MatSetValues_MPISBAIJ(Mat mat,int m,int *im,int n,int *in,PetscScalar *v,InsertMode addv)
243: {
244: Mat_MPISBAIJ *b = (Mat_MPISBAIJ*)mat->data;
245: int ierr,i,N = m*n;
246: MatScalar *vsingle;
249: if (N > b->setvalueslen) {
250: if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
251: PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
252: b->setvalueslen = N;
253: }
254: vsingle = b->setvaluescopy;
256: for (i=0; i<N; i++) {
257: vsingle[i] = v[i];
258: }
259: MatSetValues_MPISBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
260: return(0);
261: }
263: int MatSetValuesBlocked_MPISBAIJ(Mat mat,int m,int *im,int n,int *in,PetscScalar *v,InsertMode addv)
264: {
265: Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
266: int ierr,i,N = m*n*b->bs2;
267: MatScalar *vsingle;
270: if (N > b->setvalueslen) {
271: if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
272: PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
273: b->setvalueslen = N;
274: }
275: vsingle = b->setvaluescopy;
276: for (i=0; i<N; i++) {
277: vsingle[i] = v[i];
278: }
279: MatSetValuesBlocked_MPISBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
280: return(0);
281: }
283: int MatSetValues_MPISBAIJ_HT(Mat mat,int m,int *im,int n,int *in,PetscScalar *v,InsertMode addv)
284: {
285: Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
286: int ierr,i,N = m*n;
287: MatScalar *vsingle;
290: SETERRQ(1,"Function not yet written for SBAIJ format");
291: /* return(0); */
292: }
294: int MatSetValuesBlocked_MPISBAIJ_HT(Mat mat,int m,int *im,int n,int *in,PetscScalar *v,InsertMode addv)
295: {
296: Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
297: int ierr,i,N = m*n*b->bs2;
298: MatScalar *vsingle;
301: SETERRQ(1,"Function not yet written for SBAIJ format");
302: /* return(0); */
303: }
304: #endif
306: /* Only add/insert a(i,j) with i<=j (blocks).
307: Any a(i,j) with i>j input by user is ingored.
308: */
309: int MatSetValues_MPISBAIJ_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
310: {
311: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
312: MatScalar value;
313: PetscTruth roworiented = baij->roworiented;
314: int ierr,i,j,row,col;
315: int rstart_orig=baij->rstart_bs;
316: int rend_orig=baij->rend_bs,cstart_orig=baij->cstart_bs;
317: int cend_orig=baij->cend_bs,bs=baij->bs;
319: /* Some Variables required in the macro */
320: Mat A = baij->A;
321: Mat_SeqSBAIJ *a = (Mat_SeqSBAIJ*)(A)->data;
322: int *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
323: MatScalar *aa=a->a;
325: Mat B = baij->B;
326: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(B)->data;
327: int *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
328: MatScalar *ba=b->a;
330: int *rp,ii,nrow,_i,rmax,N,brow,bcol;
331: int low,high,t,ridx,cidx,bs2=a->bs2;
332: MatScalar *ap,*bap;
334: /* for stash */
335: int n_loc, *in_loc=0;
336: MatScalar *v_loc=0;
340: if(!baij->donotstash){
341: PetscMalloc(n*sizeof(int),&in_loc);
342: PetscMalloc(n*sizeof(MatScalar),&v_loc);
343: }
345: for (i=0; i<m; i++) {
346: if (im[i] < 0) continue;
347: #if defined(PETSC_USE_BOPT_g)
348: if (im[i] >= mat->M) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
349: #endif
350: if (im[i] >= rstart_orig && im[i] < rend_orig) { /* this processor entry */
351: row = im[i] - rstart_orig; /* local row index */
352: for (j=0; j<n; j++) {
353: if (im[i]/bs > in[j]/bs) continue; /* ignore lower triangular blocks */
354: if (in[j] >= cstart_orig && in[j] < cend_orig){ /* diag entry (A) */
355: col = in[j] - cstart_orig; /* local col index */
356: brow = row/bs; bcol = col/bs;
357: if (brow > bcol) continue; /* ignore lower triangular blocks of A */
358: if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
359: MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv);
360: /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
361: } else if (in[j] < 0) continue;
362: #if defined(PETSC_USE_BOPT_g)
363: else if (in[j] >= mat->N) {SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Col too large");}
364: #endif
365: else { /* off-diag entry (B) */
366: if (mat->was_assembled) {
367: if (!baij->colmap) {
368: CreateColmap_MPISBAIJ_Private(mat);
369: }
370: #if defined (PETSC_USE_CTABLE)
371: PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
372: col = col - 1;
373: #else
374: col = baij->colmap[in[j]/bs] - 1;
375: #endif
376: if (col < 0 && !((Mat_SeqSBAIJ*)(baij->A->data))->nonew) {
377: DisAssemble_MPISBAIJ(mat);
378: col = in[j];
379: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
380: B = baij->B;
381: b = (Mat_SeqBAIJ*)(B)->data;
382: bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
383: ba=b->a;
384: } else col += in[j]%bs;
385: } else col = in[j];
386: if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
387: MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv);
388: /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
389: }
390: }
391: } else { /* off processor entry */
392: if (!baij->donotstash) {
393: n_loc = 0;
394: for (j=0; j<n; j++){
395: if (im[i]/bs > in[j]/bs) continue; /* ignore lower triangular blocks */
396: in_loc[n_loc] = in[j];
397: if (roworiented) {
398: v_loc[n_loc] = v[i*n+j];
399: } else {
400: v_loc[n_loc] = v[j*m+i];
401: }
402: n_loc++;
403: }
404: MatStashValuesRow_Private(&mat->stash,im[i],n_loc,in_loc,v_loc);
405: }
406: }
407: }
409: if(!baij->donotstash){
410: PetscFree(in_loc);
411: PetscFree(v_loc);
412: }
413: return(0);
414: }
416: int MatSetValuesBlocked_MPISBAIJ_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
417: {
419: SETERRQ(1,"Function not yet written for SBAIJ format");
420: /* return(0); */
421: }
423: #define HASH_KEY 0.6180339887
424: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(int)((size)*(tmp-(int)tmp)))
425: /* #define HASH(size,key) ((int)((size)*fmod(((key)*HASH_KEY),1))) */
426: /* #define HASH(size,key,tmp) ((int)((size)*fmod(((key)*HASH_KEY),1))) */
427: int MatSetValues_MPISBAIJ_HT_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
428: {
430: SETERRQ(1,"Function not yet written for SBAIJ format");
431: /* return(0); */
432: }
434: int MatSetValuesBlocked_MPISBAIJ_HT_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
435: {
437: SETERRQ(1,"Function not yet written for SBAIJ format");
438: /* return(0); */
439: }
441: int MatGetValues_MPISBAIJ(Mat mat,int m,int *idxm,int n,int *idxn,PetscScalar *v)
442: {
443: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
444: int bs=baij->bs,ierr,i,j,bsrstart = baij->rstart*bs,bsrend = baij->rend*bs;
445: int bscstart = baij->cstart*bs,bscend = baij->cend*bs,row,col,data;
448: for (i=0; i<m; i++) {
449: if (idxm[i] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
450: if (idxm[i] >= mat->M) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
451: if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
452: row = idxm[i] - bsrstart;
453: for (j=0; j<n; j++) {
454: if (idxn[j] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column");
455: if (idxn[j] >= mat->N) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
456: if (idxn[j] >= bscstart && idxn[j] < bscend){
457: col = idxn[j] - bscstart;
458: MatGetValues_SeqSBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
459: } else {
460: if (!baij->colmap) {
461: CreateColmap_MPISBAIJ_Private(mat);
462: }
463: #if defined (PETSC_USE_CTABLE)
464: PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
465: data --;
466: #else
467: data = baij->colmap[idxn[j]/bs]-1;
468: #endif
469: if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
470: else {
471: col = data + idxn[j]%bs;
472: MatGetValues_SeqSBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
473: }
474: }
475: }
476: } else {
477: SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
478: }
479: }
480: return(0);
481: }
483: int MatNorm_MPISBAIJ(Mat mat,NormType type,PetscReal *norm)
484: {
485: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
486: /* Mat_SeqSBAIJ *amat = (Mat_SeqSBAIJ*)baij->A->data; */
487: /* Mat_SeqBAIJ *bmat = (Mat_SeqBAIJ*)baij->B->data; */
488: int ierr;
489: PetscReal sum[2],*lnorm2;
492: if (baij->size == 1) {
493: MatNorm(baij->A,type,norm);
494: } else {
495: if (type == NORM_FROBENIUS) {
496: PetscMalloc(2*sizeof(PetscReal),&lnorm2);
497: MatNorm(baij->A,type,lnorm2);
498: *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2++; /* squar power of norm(A) */
499: MatNorm(baij->B,type,lnorm2);
500: *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2--; /* squar power of norm(B) */
501: /*
502: MPI_Comm_rank(PETSC_COMM_WORLD,&rank);
503: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d], lnorm2=%g, %gn",rank,lnorm2[0],lnorm2[1]);
504: */
505: MPI_Allreduce(lnorm2,&sum,2,MPIU_REAL,MPI_SUM,mat->comm);
506: /*
507: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d], sum=%g, %gn",rank,sum[0],sum[1]);
508: PetscSynchronizedFlush(PETSC_COMM_WORLD); */
509:
510: *norm = sqrt(sum[0] + 2*sum[1]);
511: PetscFree(lnorm2);
512: } else {
513: SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
514: }
515: }
516: return(0);
517: }
519: /*
520: Creates the hash table, and sets the table
521: This table is created only once.
522: If new entried need to be added to the matrix
523: then the hash table has to be destroyed and
524: recreated.
525: */
526: int MatCreateHashTable_MPISBAIJ_Private(Mat mat,PetscReal factor)
527: {
529: SETERRQ(1,"Function not yet written for SBAIJ format");
530: /* return(0); */
531: }
533: int MatAssemblyBegin_MPISBAIJ(Mat mat,MatAssemblyType mode)
534: {
535: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
536: int ierr,nstash,reallocs;
537: InsertMode addv;
540: if (baij->donotstash) {
541: return(0);
542: }
544: /* make sure all processors are either in INSERTMODE or ADDMODE */
545: MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
546: if (addv == (ADD_VALUES|INSERT_VALUES)) {
547: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
548: }
549: mat->insertmode = addv; /* in case this processor had no cache */
551: MatStashScatterBegin_Private(&mat->stash,baij->rowners_bs);
552: MatStashScatterBegin_Private(&mat->bstash,baij->rowners);
553: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
554: PetscLogInfo(0,"MatAssemblyBegin_MPISBAIJ:Stash has %d entries,uses %d mallocs.n",nstash,reallocs);
555: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
556: PetscLogInfo(0,"MatAssemblyBegin_MPISBAIJ:Block-Stash has %d entries, uses %d mallocs.n",nstash,reallocs);
557: return(0);
558: }
560: int MatAssemblyEnd_MPISBAIJ(Mat mat,MatAssemblyType mode)
561: {
562: Mat_MPISBAIJ *baij=(Mat_MPISBAIJ*)mat->data;
563: Mat_SeqSBAIJ *a=(Mat_SeqSBAIJ*)baij->A->data;
564: Mat_SeqBAIJ *b=(Mat_SeqBAIJ*)baij->B->data;
565: int i,j,rstart,ncols,n,ierr,flg,bs2=baij->bs2;
566: int *row,*col,other_disassembled;
567: PetscTruth r1,r2,r3;
568: MatScalar *val;
569: InsertMode addv = mat->insertmode;
570: #if defined(PETSC_HAVE_SPOOLES)
571: PetscTruth flag;
572: #endif
576: if (!baij->donotstash) {
577: while (1) {
578: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
579: /*
580: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d]: in AssemblyEnd, stash, flg=%dn",rank,flg);
581: PetscSynchronizedFlush(PETSC_COMM_WORLD);
582: */
583: if (!flg) break;
585: for (i=0; i<n;) {
586: /* Now identify the consecutive vals belonging to the same row */
587: for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
588: if (j < n) ncols = j-i;
589: else ncols = n-i;
590: /* Now assemble all these values with a single function call */
591: MatSetValues_MPISBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i,addv);
592: i = j;
593: }
594: }
595: MatStashScatterEnd_Private(&mat->stash);
596: /* Now process the block-stash. Since the values are stashed column-oriented,
597: set the roworiented flag to column oriented, and after MatSetValues()
598: restore the original flags */
599: r1 = baij->roworiented;
600: r2 = a->roworiented;
601: r3 = b->roworiented;
602: baij->roworiented = PETSC_FALSE;
603: a->roworiented = PETSC_FALSE;
604: b->roworiented = PETSC_FALSE;
605: while (1) {
606: MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
607: if (!flg) break;
608:
609: for (i=0; i<n;) {
610: /* Now identify the consecutive vals belonging to the same row */
611: for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
612: if (j < n) ncols = j-i;
613: else ncols = n-i;
614: MatSetValuesBlocked_MPISBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
615: i = j;
616: }
617: }
618: MatStashScatterEnd_Private(&mat->bstash);
619: baij->roworiented = r1;
620: a->roworiented = r2;
621: b->roworiented = r3;
622: }
624: MatAssemblyBegin(baij->A,mode);
625: MatAssemblyEnd(baij->A,mode);
627: /* determine if any processor has disassembled, if so we must
628: also disassemble ourselfs, in order that we may reassemble. */
629: /*
630: if nonzero structure of submatrix B cannot change then we know that
631: no processor disassembled thus we can skip this stuff
632: */
633: if (!((Mat_SeqBAIJ*)baij->B->data)->nonew) {
634: MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
635: if (mat->was_assembled && !other_disassembled) {
636: DisAssemble_MPISBAIJ(mat);
637: }
638: }
640: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
641: MatSetUpMultiply_MPISBAIJ(mat); /* setup Mvctx and sMvctx */
642: }
643: MatAssemblyBegin(baij->B,mode);
644: MatAssemblyEnd(baij->B,mode);
645:
646: #if defined(PETSC_USE_BOPT_g)
647: if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
648: PetscLogInfo(0,"MatAssemblyEnd_MPISBAIJ:Average Hash Table Search in MatSetValues = %5.2fn",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
649: baij->ht_total_ct = 0;
650: baij->ht_insert_ct = 0;
651: }
652: #endif
653: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
654: MatCreateHashTable_MPISBAIJ_Private(mat,baij->ht_fact);
655: mat->ops->setvalues = MatSetValues_MPISBAIJ_HT;
656: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPISBAIJ_HT;
657: }
659: if (baij->rowvalues) {
660: PetscFree(baij->rowvalues);
661: baij->rowvalues = 0;
662: }
664: #if defined(PETSC_HAVE_SPOOLES)
665: PetscOptionsHasName(PETSC_NULL,"-mat_sbaij_spooles",&flag);
666: if (flag) { MatUseSpooles_MPISBAIJ(mat); }
667: #endif
668: return(0);
669: }
671: static int MatView_MPISBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
672: {
673: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
674: int ierr,bs = baij->bs,size = baij->size,rank = baij->rank;
675: PetscTruth isascii,isdraw;
676: PetscViewer sviewer;
677: PetscViewerFormat format;
680: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
681: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
682: if (isascii) {
683: PetscViewerGetFormat(viewer,&format);
684: if (format == PETSC_VIEWER_ASCII_INFO_LONG) {
685: MatInfo info;
686: MPI_Comm_rank(mat->comm,&rank);
687: MatGetInfo(mat,MAT_LOCAL,&info);
688: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d bs %d mem %dn",
689: rank,mat->m,(int)info.nz_used*bs,(int)info.nz_allocated*bs,
690: baij->bs,(int)info.memory);
691: MatGetInfo(baij->A,MAT_LOCAL,&info);
692: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %d n",rank,(int)info.nz_used*bs);
693: MatGetInfo(baij->B,MAT_LOCAL,&info);
694: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %d n",rank,(int)info.nz_used*bs);
695: PetscViewerFlush(viewer);
696: VecScatterView(baij->Mvctx,viewer);
697: return(0);
698: } else if (format == PETSC_VIEWER_ASCII_INFO) {
699: PetscViewerASCIIPrintf(viewer," block size is %dn",bs);
700: return(0);
701: }
702: }
704: if (isdraw) {
705: PetscDraw draw;
706: PetscTruth isnull;
707: PetscViewerDrawGetDraw(viewer,0,&draw);
708: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
709: }
711: if (size == 1) {
712: PetscObjectSetName((PetscObject)baij->A,mat->name);
713: MatView(baij->A,viewer);
714: } else {
715: /* assemble the entire matrix onto first processor. */
716: Mat A;
717: Mat_SeqSBAIJ *Aloc;
718: Mat_SeqBAIJ *Bloc;
719: int M = mat->M,N = mat->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
720: MatScalar *a;
722: if (!rank) {
723: MatCreateMPISBAIJ(mat->comm,baij->bs,M,N,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);
724: } else {
725: MatCreateMPISBAIJ(mat->comm,baij->bs,0,0,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);
726: }
727: PetscLogObjectParent(mat,A);
729: /* copy over the A part */
730: Aloc = (Mat_SeqSBAIJ*)baij->A->data;
731: ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
732: ierr = PetscMalloc(bs*sizeof(int),&rvals);
734: for (i=0; i<mbs; i++) {
735: rvals[0] = bs*(baij->rstart + i);
736: for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
737: for (j=ai[i]; j<ai[i+1]; j++) {
738: col = (baij->cstart+aj[j])*bs;
739: for (k=0; k<bs; k++) {
740: MatSetValues_MPISBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
741: col++; a += bs;
742: }
743: }
744: }
745: /* copy over the B part */
746: Bloc = (Mat_SeqBAIJ*)baij->B->data;
747: ai = Bloc->i; aj = Bloc->j; a = Bloc->a;
748: for (i=0; i<mbs; i++) {
749: rvals[0] = bs*(baij->rstart + i);
750: for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
751: for (j=ai[i]; j<ai[i+1]; j++) {
752: col = baij->garray[aj[j]]*bs;
753: for (k=0; k<bs; k++) {
754: MatSetValues_MPISBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
755: col++; a += bs;
756: }
757: }
758: }
759: PetscFree(rvals);
760: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
761: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
762: /*
763: Everyone has to call to draw the matrix since the graphics waits are
764: synchronized across all processors that share the PetscDraw object
765: */
766: PetscViewerGetSingleton(viewer,&sviewer);
767: if (!rank) {
768: PetscObjectSetName((PetscObject)((Mat_MPISBAIJ*)(A->data))->A,mat->name);
769: MatView(((Mat_MPISBAIJ*)(A->data))->A,sviewer);
770: }
771: PetscViewerRestoreSingleton(viewer,&sviewer);
772: MatDestroy(A);
773: }
774: return(0);
775: }
777: int MatView_MPISBAIJ(Mat mat,PetscViewer viewer)
778: {
779: int ierr;
780: PetscTruth isascii,isdraw,issocket,isbinary;
783: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
784: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
785: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
786: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
787: if (isascii || isdraw || issocket || isbinary) {
788: MatView_MPISBAIJ_ASCIIorDraworSocket(mat,viewer);
789: } else {
790: SETERRQ1(1,"Viewer type %s not supported by MPISBAIJ matrices",((PetscObject)viewer)->type_name);
791: }
792: return(0);
793: }
795: int MatDestroy_MPISBAIJ(Mat mat)
796: {
797: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
798: int ierr;
801: #if defined(PETSC_USE_LOG)
802: PetscLogObjectState((PetscObject)mat,"Rows=%d,Cols=%d",mat->M,mat->N);
803: #endif
804: MatStashDestroy_Private(&mat->stash);
805: MatStashDestroy_Private(&mat->bstash);
806: PetscFree(baij->rowners);
807: MatDestroy(baij->A);
808: MatDestroy(baij->B);
809: #if defined (PETSC_USE_CTABLE)
810: if (baij->colmap) {PetscTableDelete(baij->colmap);}
811: #else
812: if (baij->colmap) {PetscFree(baij->colmap);}
813: #endif
814: if (baij->garray) {PetscFree(baij->garray);}
815: if (baij->lvec) {VecDestroy(baij->lvec);}
816: if (baij->Mvctx) {VecScatterDestroy(baij->Mvctx);}
817: if (baij->slvec0) {
818: VecDestroy(baij->slvec0);
819: VecDestroy(baij->slvec0b);
820: }
821: if (baij->slvec1) {
822: VecDestroy(baij->slvec1);
823: VecDestroy(baij->slvec1a);
824: VecDestroy(baij->slvec1b);
825: }
826: if (baij->sMvctx) {VecScatterDestroy(baij->sMvctx);}
827: if (baij->rowvalues) {PetscFree(baij->rowvalues);}
828: if (baij->barray) {PetscFree(baij->barray);}
829: if (baij->hd) {PetscFree(baij->hd);}
830: #if defined(PETSC_USE_MAT_SINGLE)
831: if (baij->setvaluescopy) {PetscFree(baij->setvaluescopy);}
832: #endif
833: PetscFree(baij);
834: return(0);
835: }
837: int MatMult_MPISBAIJ(Mat A,Vec xx,Vec yy)
838: {
839: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
840: int ierr,nt,mbs=a->mbs,bs=a->bs;
841: PetscScalar *x,*from,zero=0.0;
842:
844: /*
845: PetscSynchronizedPrintf(PETSC_COMM_WORLD," _1comm is called ...n");
846: PetscSynchronizedFlush(PETSC_COMM_WORLD);
847: */
848: VecGetLocalSize(xx,&nt);
849: if (nt != A->n) {
850: SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
851: }
852: VecGetLocalSize(yy,&nt);
853: if (nt != A->m) {
854: SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
855: }
857: /* diagonal part */
858: (*a->A->ops->mult)(a->A,xx,a->slvec1a);
859: VecSet(&zero,a->slvec1b);
861: /* subdiagonal part */
862: (*a->B->ops->multtranspose)(a->B,xx,a->slvec0b);
864: /* copy x into the vec slvec0 */
865: VecGetArray(a->slvec0,&from);
866: VecGetArray(xx,&x);
867: PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
868: VecRestoreArray(a->slvec0,&from);
869:
870: VecScatterBegin(a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD,a->sMvctx);
871: VecRestoreArray(xx,&x);
872: VecScatterEnd(a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD,a->sMvctx);
873:
874: /* supperdiagonal part */
875: (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,yy);
876:
877: return(0);
878: }
880: int MatMult_MPISBAIJ_2comm(Mat A,Vec xx,Vec yy)
881: {
882: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
883: int ierr,nt;
886: VecGetLocalSize(xx,&nt);
887: if (nt != A->n) {
888: SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
889: }
890: VecGetLocalSize(yy,&nt);
891: if (nt != A->m) {
892: SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
893: }
895: VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
896: /* do diagonal part */
897: (*a->A->ops->mult)(a->A,xx,yy);
898: /* do supperdiagonal part */
899: VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
900: (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
901: /* do subdiagonal part */
902: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
903: VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
904: VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
906: return(0);
907: }
909: int MatMultAdd_MPISBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
910: {
911: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
912: int ierr,mbs=a->mbs,bs=a->bs;
913: PetscScalar *x,*from,zero=0.0;
914:
916: /*
917: PetscSynchronizedPrintf(PETSC_COMM_WORLD," MatMultAdd is called ...n");
918: PetscSynchronizedFlush(PETSC_COMM_WORLD);
919: */
920: /* diagonal part */
921: (*a->A->ops->multadd)(a->A,xx,yy,a->slvec1a);
922: VecSet(&zero,a->slvec1b);
924: /* subdiagonal part */
925: (*a->B->ops->multtranspose)(a->B,xx,a->slvec0b);
927: /* copy x into the vec slvec0 */
928: VecGetArray(a->slvec0,&from);
929: VecGetArray(xx,&x);
930: PetscMemcpy(from,x,bs*mbs*sizeof(MatScalar));
931: VecRestoreArray(a->slvec0,&from);
932:
933: VecScatterBegin(a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD,a->sMvctx);
934: VecRestoreArray(xx,&x);
935: VecScatterEnd(a->slvec0,a->slvec1,ADD_VALUES,SCATTER_FORWARD,a->sMvctx);
936:
937: /* supperdiagonal part */
938: (*a->B->ops->multadd)(a->B,a->slvec1b,a->slvec1a,zz);
939:
940: return(0);
941: }
943: int MatMultAdd_MPISBAIJ_2comm(Mat A,Vec xx,Vec yy,Vec zz)
944: {
945: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
946: int ierr;
949: VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
950: /* do diagonal part */
951: (*a->A->ops->multadd)(a->A,xx,yy,zz);
952: /* do supperdiagonal part */
953: VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
954: (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
956: /* do subdiagonal part */
957: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
958: VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
959: VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
961: return(0);
962: }
964: int MatMultTranspose_MPISBAIJ(Mat A,Vec xx,Vec yy)
965: {
967: SETERRQ(1,"Matrix is symmetric. Call MatMult().");
968: /* return(0); */
969: }
971: int MatMultTransposeAdd_MPISBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
972: {
974: SETERRQ(1,"Matrix is symmetric. Call MatMultAdd().");
975: /* return(0); */
976: }
978: /*
979: This only works correctly for square matrices where the subblock A->A is the
980: diagonal block
981: */
982: int MatGetDiagonal_MPISBAIJ(Mat A,Vec v)
983: {
984: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
985: int ierr;
988: /* if (a->M != a->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block"); */
989: MatGetDiagonal(a->A,v);
990: return(0);
991: }
993: int MatScale_MPISBAIJ(PetscScalar *aa,Mat A)
994: {
995: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
996: int ierr;
999: MatScale(aa,a->A);
1000: MatScale(aa,a->B);
1001: return(0);
1002: }
1004: int MatGetRow_MPISBAIJ(Mat matin,int row,int *nz,int **idx,PetscScalar **v)
1005: {
1006: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)matin->data;
1007: PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
1008: int bs = mat->bs,bs2 = mat->bs2,i,ierr,*cworkA,*cworkB,**pcA,**pcB;
1009: int nztot,nzA,nzB,lrow,brstart = mat->rstart*bs,brend = mat->rend*bs;
1010: int *cmap,*idx_p,cstart = mat->cstart;
1013: if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1014: mat->getrowactive = PETSC_TRUE;
1016: if (!mat->rowvalues && (idx || v)) {
1017: /*
1018: allocate enough space to hold information from the longest row.
1019: */
1020: Mat_SeqSBAIJ *Aa = (Mat_SeqSBAIJ*)mat->A->data;
1021: Mat_SeqBAIJ *Ba = (Mat_SeqBAIJ*)mat->B->data;
1022: int max = 1,mbs = mat->mbs,tmp;
1023: for (i=0; i<mbs; i++) {
1024: tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i]; /* row length */
1025: if (max < tmp) { max = tmp; }
1026: }
1027: PetscMalloc(max*bs2*(sizeof(int)+sizeof(PetscScalar)),&mat->rowvalues);
1028: mat->rowindices = (int*)(mat->rowvalues + max*bs2);
1029: }
1030:
1031: if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
1032: lrow = row - brstart; /* local row index */
1034: pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1035: if (!v) {pvA = 0; pvB = 0;}
1036: if (!idx) {pcA = 0; if (!v) pcB = 0;}
1037: (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1038: (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1039: nztot = nzA + nzB;
1041: cmap = mat->garray;
1042: if (v || idx) {
1043: if (nztot) {
1044: /* Sort by increasing column numbers, assuming A and B already sorted */
1045: int imark = -1;
1046: if (v) {
1047: *v = v_p = mat->rowvalues;
1048: for (i=0; i<nzB; i++) {
1049: if (cmap[cworkB[i]/bs] < cstart) v_p[i] = vworkB[i];
1050: else break;
1051: }
1052: imark = i;
1053: for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i];
1054: for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i];
1055: }
1056: if (idx) {
1057: *idx = idx_p = mat->rowindices;
1058: if (imark > -1) {
1059: for (i=0; i<imark; i++) {
1060: idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
1061: }
1062: } else {
1063: for (i=0; i<nzB; i++) {
1064: if (cmap[cworkB[i]/bs] < cstart)
1065: idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1066: else break;
1067: }
1068: imark = i;
1069: }
1070: for (i=0; i<nzA; i++) idx_p[imark+i] = cstart*bs + cworkA[i];
1071: for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
1072: }
1073: } else {
1074: if (idx) *idx = 0;
1075: if (v) *v = 0;
1076: }
1077: }
1078: *nz = nztot;
1079: (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1080: (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1081: return(0);
1082: }
1084: int MatRestoreRow_MPISBAIJ(Mat mat,int row,int *nz,int **idx,PetscScalar **v)
1085: {
1086: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
1089: if (baij->getrowactive == PETSC_FALSE) {
1090: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1091: }
1092: baij->getrowactive = PETSC_FALSE;
1093: return(0);
1094: }
1096: int MatGetBlockSize_MPISBAIJ(Mat mat,int *bs)
1097: {
1098: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
1101: *bs = baij->bs;
1102: return(0);
1103: }
1105: int MatZeroEntries_MPISBAIJ(Mat A)
1106: {
1107: Mat_MPISBAIJ *l = (Mat_MPISBAIJ*)A->data;
1108: int ierr;
1111: MatZeroEntries(l->A);
1112: MatZeroEntries(l->B);
1113: return(0);
1114: }
1116: int MatGetInfo_MPISBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1117: {
1118: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)matin->data;
1119: Mat A = a->A,B = a->B;
1120: int ierr;
1121: PetscReal isend[5],irecv[5];
1124: info->block_size = (PetscReal)a->bs;
1125: MatGetInfo(A,MAT_LOCAL,info);
1126: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1127: isend[3] = info->memory; isend[4] = info->mallocs;
1128: MatGetInfo(B,MAT_LOCAL,info);
1129: isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1130: isend[3] += info->memory; isend[4] += info->mallocs;
1131: if (flag == MAT_LOCAL) {
1132: info->nz_used = isend[0];
1133: info->nz_allocated = isend[1];
1134: info->nz_unneeded = isend[2];
1135: info->memory = isend[3];
1136: info->mallocs = isend[4];
1137: } else if (flag == MAT_GLOBAL_MAX) {
1138: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1139: info->nz_used = irecv[0];
1140: info->nz_allocated = irecv[1];
1141: info->nz_unneeded = irecv[2];
1142: info->memory = irecv[3];
1143: info->mallocs = irecv[4];
1144: } else if (flag == MAT_GLOBAL_SUM) {
1145: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1146: info->nz_used = irecv[0];
1147: info->nz_allocated = irecv[1];
1148: info->nz_unneeded = irecv[2];
1149: info->memory = irecv[3];
1150: info->mallocs = irecv[4];
1151: } else {
1152: SETERRQ1(1,"Unknown MatInfoType argument %d",flag);
1153: }
1154: info->rows_global = (PetscReal)A->M;
1155: info->columns_global = (PetscReal)A->N;
1156: info->rows_local = (PetscReal)A->m;
1157: info->columns_local = (PetscReal)A->N;
1158: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1159: info->fill_ratio_needed = 0;
1160: info->factor_mallocs = 0;
1161: return(0);
1162: }
1164: int MatSetOption_MPISBAIJ(Mat A,MatOption op)
1165: {
1166: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1167: int ierr;
1170: switch (op) {
1171: case MAT_NO_NEW_NONZERO_LOCATIONS:
1172: case MAT_YES_NEW_NONZERO_LOCATIONS:
1173: case MAT_COLUMNS_UNSORTED:
1174: case MAT_COLUMNS_SORTED:
1175: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1176: case MAT_KEEP_ZEROED_ROWS:
1177: case MAT_NEW_NONZERO_LOCATION_ERR:
1178: MatSetOption(a->A,op);
1179: MatSetOption(a->B,op);
1180: break;
1181: case MAT_ROW_ORIENTED:
1182: a->roworiented = PETSC_TRUE;
1183: MatSetOption(a->A,op);
1184: MatSetOption(a->B,op);
1185: break;
1186: case MAT_ROWS_SORTED:
1187: case MAT_ROWS_UNSORTED:
1188: case MAT_YES_NEW_DIAGONALS:
1189: case MAT_USE_SINGLE_PRECISION_SOLVES:
1190: PetscLogInfo(A,"Info:MatSetOption_MPIBAIJ:Option ignoredn");
1191: break;
1192: case MAT_COLUMN_ORIENTED:
1193: a->roworiented = PETSC_FALSE;
1194: MatSetOption(a->A,op);
1195: MatSetOption(a->B,op);
1196: break;
1197: case MAT_IGNORE_OFF_PROC_ENTRIES:
1198: a->donotstash = PETSC_TRUE;
1199: break;
1200: case MAT_NO_NEW_DIAGONALS:
1201: SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1202: case MAT_USE_HASH_TABLE:
1203: a->ht_flag = PETSC_TRUE;
1204: break;
1205: default:
1206: SETERRQ(PETSC_ERR_SUP,"unknown option");
1207: }
1208: return(0);
1209: }
1211: int MatTranspose_MPISBAIJ(Mat A,Mat *matout)
1212: {
1214: SETERRQ(1,"Matrix is symmetric. MatTranspose() should not be called");
1215: /* return(0); */
1216: }
1218: int MatDiagonalScale_MPISBAIJ(Mat mat,Vec ll,Vec rr)
1219: {
1220: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
1221: Mat a = baij->A,b = baij->B;
1222: int ierr,s1,s2,s3;
1225: if (ll != rr) {
1226: SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"For symmetric format, left and right scaling vectors must be samen");
1227: }
1228: MatGetLocalSize(mat,&s2,&s3);
1229: if (rr) {
1230: VecGetLocalSize(rr,&s1);
1231: if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1232: /* Overlap communication with computation. */
1233: VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1234: /*} if (ll) { */
1235: VecGetLocalSize(ll,&s1);
1236: if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1237: (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1238: /* } */
1239: /* scale the diagonal block */
1240: (*a->ops->diagonalscale)(a,ll,rr);
1242: /* if (rr) { */
1243: /* Do a scatter end and then right scale the off-diagonal block */
1244: VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1245: (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1246: }
1247:
1248: return(0);
1249: }
1251: int MatZeroRows_MPISBAIJ(Mat A,IS is,PetscScalar *diag)
1252: {
1253: Mat_MPISBAIJ *l = (Mat_MPISBAIJ*)A->data;
1254: int i,ierr,N,*rows,*owners = l->rowners,size = l->size;
1255: int *procs,*nprocs,j,idx,nsends,*work,row;
1256: int nmax,*svalues,*starts,*owner,nrecvs,rank = l->rank;
1257: int *rvalues,tag = A->tag,count,base,slen,n,*source;
1258: int *lens,imdex,*lrows,*values,bs=l->bs,rstart_bs=l->rstart_bs;
1259: MPI_Comm comm = A->comm;
1260: MPI_Request *send_waits,*recv_waits;
1261: MPI_Status recv_status,*send_status;
1262: IS istmp;
1263: PetscTruth found;
1266: ISGetSize(is,&N);
1267: ISGetIndices(is,&rows);
1268:
1269: /* first count number of contributors to each processor */
1270: ierr = PetscMalloc(2*size*sizeof(int),&nprocs);
1271: ierr = PetscMemzero(nprocs,2*size*sizeof(int));
1272: procs = nprocs + size;
1273: ierr = PetscMalloc((N+1)*sizeof(int),&owner); /* see note*/
1274: for (i=0; i<N; i++) {
1275: idx = rows[i];
1276: found = PETSC_FALSE;
1277: for (j=0; j<size; j++) {
1278: if (idx >= owners[j]*bs && idx < owners[j+1]*bs) {
1279: nprocs[j]++; procs[j] = 1; owner[i] = j; found = PETSC_TRUE; break;
1280: }
1281: }
1282: if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1283: }
1284: nsends = 0; for (i=0; i<size; i++) { nsends += procs[i];}
1285:
1286: /* inform other processors of number of messages and max length*/
1287: ierr = PetscMalloc(2*size*sizeof(int),&work);
1288: ierr = MPI_Allreduce(nprocs,work,2*size,MPI_INT,PetscMaxSum_Op,comm);
1289: nmax = work[rank];
1290: nrecvs = work[size+rank];
1291: ierr = PetscFree(work);
1292:
1293: /* post receives: */
1294: PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(int),&rvalues);
1295: PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1296: for (i=0; i<nrecvs; i++) {
1297: MPI_Irecv(rvalues+nmax*i,nmax,MPI_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1298: }
1299:
1300: /* do sends:
1301: 1) starts[i] gives the starting index in svalues for stuff going to
1302: the ith processor
1303: */
1304: PetscMalloc((N+1)*sizeof(int),&svalues);
1305: PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1306: PetscMalloc((size+1)*sizeof(int),&starts);
1307: starts[0] = 0;
1308: for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[i-1];}
1309: for (i=0; i<N; i++) {
1310: svalues[starts[owner[i]]++] = rows[i];
1311: }
1312: ISRestoreIndices(is,&rows);
1313:
1314: starts[0] = 0;
1315: for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[i-1];}
1316: count = 0;
1317: for (i=0; i<size; i++) {
1318: if (procs[i]) {
1319: MPI_Isend(svalues+starts[i],nprocs[i],MPI_INT,i,tag,comm,send_waits+count++);
1320: }
1321: }
1322: PetscFree(starts);
1324: base = owners[rank]*bs;
1325:
1326: /* wait on receives */
1327: ierr = PetscMalloc(2*(nrecvs+1)*sizeof(int),&lens);
1328: source = lens + nrecvs;
1329: count = nrecvs; slen = 0;
1330: while (count) {
1331: MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1332: /* unpack receives into our local space */
1333: MPI_Get_count(&recv_status,MPI_INT,&n);
1334: source[imdex] = recv_status.MPI_SOURCE;
1335: lens[imdex] = n;
1336: slen += n;
1337: count--;
1338: }
1339: PetscFree(recv_waits);
1340:
1341: /* move the data into the send scatter */
1342: PetscMalloc((slen+1)*sizeof(int),&lrows);
1343: count = 0;
1344: for (i=0; i<nrecvs; i++) {
1345: values = rvalues + i*nmax;
1346: for (j=0; j<lens[i]; j++) {
1347: lrows[count++] = values[j] - base;
1348: }
1349: }
1350: PetscFree(rvalues);
1351: PetscFree(lens);
1352: PetscFree(owner);
1353: PetscFree(nprocs);
1354:
1355: /* actually zap the local rows */
1356: ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);
1357: PetscLogObjectParent(A,istmp);
1359: /*
1360: Zero the required rows. If the "diagonal block" of the matrix
1361: is square and the user wishes to set the diagonal we use seperate
1362: code so that MatSetValues() is not called for each diagonal allocating
1363: new memory, thus calling lots of mallocs and slowing things down.
1365: Contributed by: Mathew Knepley
1366: */
1367: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1368: MatZeroRows_SeqBAIJ(l->B,istmp,0);
1369: if (diag && (l->A->M == l->A->N)) {
1370: MatZeroRows_SeqSBAIJ(l->A,istmp,diag);
1371: } else if (diag) {
1372: MatZeroRows_SeqSBAIJ(l->A,istmp,0);
1373: if (((Mat_SeqSBAIJ*)l->A->data)->nonew) {
1374: SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options n
1375: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1376: }
1377: for (i=0; i<slen; i++) {
1378: row = lrows[i] + rstart_bs;
1379: MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);
1380: }
1381: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1382: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1383: } else {
1384: MatZeroRows_SeqSBAIJ(l->A,istmp,0);
1385: }
1387: ISDestroy(istmp);
1388: PetscFree(lrows);
1390: /* wait on sends */
1391: if (nsends) {
1392: PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1393: ierr = MPI_Waitall(nsends,send_waits,send_status);
1394: ierr = PetscFree(send_status);
1395: }
1396: PetscFree(send_waits);
1397: PetscFree(svalues);
1399: return(0);
1400: }
1402: int MatPrintHelp_MPISBAIJ(Mat A)
1403: {
1404: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1405: MPI_Comm comm = A->comm;
1406: static int called = 0;
1407: int ierr;
1410: if (!a->rank) {
1411: MatPrintHelp_SeqSBAIJ(a->A);
1412: }
1413: if (called) {return(0);} else called = 1;
1414: (*PetscHelpPrintf)(comm," Options for MATMPISBAIJ matrix format (the defaults):n");
1415: (*PetscHelpPrintf)(comm," -mat_use_hash_table <factor>: Use hashtable for efficient matrix assemblyn");
1416: return(0);
1417: }
1419: int MatSetUnfactored_MPISBAIJ(Mat A)
1420: {
1421: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1422: int ierr;
1425: MatSetUnfactored(a->A);
1426: return(0);
1427: }
1429: static int MatDuplicate_MPISBAIJ(Mat,MatDuplicateOption,Mat *);
1431: int MatEqual_MPISBAIJ(Mat A,Mat B,PetscTruth *flag)
1432: {
1433: Mat_MPISBAIJ *matB = (Mat_MPISBAIJ*)B->data,*matA = (Mat_MPISBAIJ*)A->data;
1434: Mat a,b,c,d;
1435: PetscTruth flg;
1436: int ierr;
1439: PetscTypeCompare((PetscObject)B,MATMPISBAIJ,&flg);
1440: if (!flg) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type");
1441: a = matA->A; b = matA->B;
1442: c = matB->A; d = matB->B;
1444: MatEqual(a,c,&flg);
1445: if (flg == PETSC_TRUE) {
1446: MatEqual(b,d,&flg);
1447: }
1448: MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1449: return(0);
1450: }
1452: int MatSetUpPreallocation_MPISBAIJ(Mat A)
1453: {
1454: int ierr;
1457: MatMPISBAIJSetPreallocation(A,1,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1458: return(0);
1459: }
1460: /* -------------------------------------------------------------------*/
1461: static struct _MatOps MatOps_Values = {
1462: MatSetValues_MPISBAIJ,
1463: MatGetRow_MPISBAIJ,
1464: MatRestoreRow_MPISBAIJ,
1465: MatMult_MPISBAIJ,
1466: MatMultAdd_MPISBAIJ,
1467: MatMultTranspose_MPISBAIJ,
1468: MatMultTransposeAdd_MPISBAIJ,
1469: 0,
1470: 0,
1471: 0,
1472: 0,
1473: 0,
1474: 0,
1475: MatRelax_MPISBAIJ,
1476: MatTranspose_MPISBAIJ,
1477: MatGetInfo_MPISBAIJ,
1478: MatEqual_MPISBAIJ,
1479: MatGetDiagonal_MPISBAIJ,
1480: MatDiagonalScale_MPISBAIJ,
1481: MatNorm_MPISBAIJ,
1482: MatAssemblyBegin_MPISBAIJ,
1483: MatAssemblyEnd_MPISBAIJ,
1484: 0,
1485: MatSetOption_MPISBAIJ,
1486: MatZeroEntries_MPISBAIJ,
1487: MatZeroRows_MPISBAIJ,
1488: 0,
1489: 0,
1490: 0,
1491: 0,
1492: MatSetUpPreallocation_MPISBAIJ,
1493: 0,
1494: 0,
1495: 0,
1496: 0,
1497: MatDuplicate_MPISBAIJ,
1498: 0,
1499: 0,
1500: 0,
1501: 0,
1502: 0,
1503: MatGetSubMatrices_MPISBAIJ,
1504: MatIncreaseOverlap_MPISBAIJ,
1505: MatGetValues_MPISBAIJ,
1506: 0,
1507: MatPrintHelp_MPISBAIJ,
1508: MatScale_MPISBAIJ,
1509: 0,
1510: 0,
1511: 0,
1512: MatGetBlockSize_MPISBAIJ,
1513: 0,
1514: 0,
1515: 0,
1516: 0,
1517: 0,
1518: 0,
1519: MatSetUnfactored_MPISBAIJ,
1520: 0,
1521: MatSetValuesBlocked_MPISBAIJ,
1522: 0,
1523: 0,
1524: 0,
1525: MatGetPetscMaps_Petsc,
1526: 0,
1527: 0,
1528: 0,
1529: 0,
1530: 0,
1531: 0,
1532: MatGetRowMax_MPISBAIJ};
1535: EXTERN_C_BEGIN
1536: int MatGetDiagonalBlock_MPISBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
1537: {
1539: *a = ((Mat_MPISBAIJ *)A->data)->A;
1540: *iscopy = PETSC_FALSE;
1541: return(0);
1542: }
1543: EXTERN_C_END
1545: EXTERN_C_BEGIN
1546: int MatCreate_MPISBAIJ(Mat B)
1547: {
1548: Mat_MPISBAIJ *b;
1549: int ierr;
1550: PetscTruth flg;
1554: ierr = PetscNew(Mat_MPISBAIJ,&b);
1555: B->data = (void*)b;
1556: ierr = PetscMemzero(b,sizeof(Mat_MPISBAIJ));
1557: ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
1559: B->ops->destroy = MatDestroy_MPISBAIJ;
1560: B->ops->view = MatView_MPISBAIJ;
1561: B->mapping = 0;
1562: B->factor = 0;
1563: B->assembled = PETSC_FALSE;
1565: B->insertmode = NOT_SET_VALUES;
1566: MPI_Comm_rank(B->comm,&b->rank);
1567: MPI_Comm_size(B->comm,&b->size);
1569: /* build local table of row and column ownerships */
1570: ierr = PetscMalloc(3*(b->size+2)*sizeof(int),&b->rowners);
1571: b->cowners = b->rowners + b->size + 2;
1572: b->rowners_bs = b->cowners + b->size + 2;
1573: PetscLogObjectMemory(B,3*(b->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPISBAIJ));
1575: /* build cache for off array entries formed */
1576: MatStashCreate_Private(B->comm,1,&B->stash);
1577: b->donotstash = PETSC_FALSE;
1578: b->colmap = PETSC_NULL;
1579: b->garray = PETSC_NULL;
1580: b->roworiented = PETSC_TRUE;
1582: #if defined(PETSC_USE_MAT_SINGLE)
1583: /* stuff for MatSetValues_XXX in single precision */
1584: b->setvalueslen = 0;
1585: b->setvaluescopy = PETSC_NULL;
1586: #endif
1588: /* stuff used in block assembly */
1589: b->barray = 0;
1591: /* stuff used for matrix vector multiply */
1592: b->lvec = 0;
1593: b->Mvctx = 0;
1594: b->slvec0 = 0;
1595: b->slvec0b = 0;
1596: b->slvec1 = 0;
1597: b->slvec1a = 0;
1598: b->slvec1b = 0;
1599: b->sMvctx = 0;
1601: /* stuff for MatGetRow() */
1602: b->rowindices = 0;
1603: b->rowvalues = 0;
1604: b->getrowactive = PETSC_FALSE;
1606: /* hash table stuff */
1607: b->ht = 0;
1608: b->hd = 0;
1609: b->ht_size = 0;
1610: b->ht_flag = PETSC_FALSE;
1611: b->ht_fact = 0;
1612: b->ht_total_ct = 0;
1613: b->ht_insert_ct = 0;
1615: PetscOptionsHasName(PETSC_NULL,"-mat_use_hash_table",&flg);
1616: if (flg) {
1617: PetscReal fact = 1.39;
1618: MatSetOption(B,MAT_USE_HASH_TABLE);
1619: PetscOptionsGetReal(PETSC_NULL,"-mat_use_hash_table",&fact,PETSC_NULL);
1620: if (fact <= 1.0) fact = 1.39;
1621: MatMPIBAIJSetHashTableFactor(B,fact);
1622: PetscLogInfo(0,"MatCreateMPISBAIJ:Hash table Factor used %5.2fn",fact);
1623: }
1624: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
1625: "MatStoreValues_MPISBAIJ",
1626: MatStoreValues_MPISBAIJ);
1627: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
1628: "MatRetrieveValues_MPISBAIJ",
1629: MatRetrieveValues_MPISBAIJ);
1630: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
1631: "MatGetDiagonalBlock_MPISBAIJ",
1632: MatGetDiagonalBlock_MPISBAIJ);
1633: return(0);
1634: }
1635: EXTERN_C_END
1637: /*@C
1638: MatMPISBAIJSetPreallocation - For good matrix assembly performance
1639: the user should preallocate the matrix storage by setting the parameters
1640: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
1641: performance can be increased by more than a factor of 50.
1643: Collective on Mat
1645: Input Parameters:
1646: + A - the matrix
1647: . bs - size of blockk
1648: . d_nz - number of block nonzeros per block row in diagonal portion of local
1649: submatrix (same for all local rows)
1650: . d_nnz - array containing the number of block nonzeros in the various block rows
1651: of the in diagonal portion of the local (possibly different for each block
1652: row) or PETSC_NULL. You must leave room for the diagonal entry even if it is zero.
1653: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
1654: submatrix (same for all local rows).
1655: - o_nnz - array containing the number of nonzeros in the various block rows of the
1656: off-diagonal portion of the local submatrix (possibly different for
1657: each block row) or PETSC_NULL.
1660: Options Database Keys:
1661: . -mat_no_unroll - uses code that does not unroll the loops in the
1662: block calculations (much slower)
1663: . -mat_block_size - size of the blocks to use
1665: Notes:
1667: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor
1668: than it must be used on all processors that share the object for that argument.
1670: Storage Information:
1671: For a square global matrix we define each processor's diagonal portion
1672: to be its local rows and the corresponding columns (a square submatrix);
1673: each processor's off-diagonal portion encompasses the remainder of the
1674: local matrix (a rectangular submatrix).
1676: The user can specify preallocated storage for the diagonal part of
1677: the local submatrix with either d_nz or d_nnz (not both). Set
1678: d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
1679: memory allocation. Likewise, specify preallocated storage for the
1680: off-diagonal part of the local submatrix with o_nz or o_nnz (not both).
1682: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1683: the figure below we depict these three local rows and all columns (0-11).
1685: .vb
1686: 0 1 2 3 4 5 6 7 8 9 10 11
1687: -------------------
1688: row 3 | o o o d d d o o o o o o
1689: row 4 | o o o d d d o o o o o o
1690: row 5 | o o o d d d o o o o o o
1691: -------------------
1692: .ve
1693:
1694: Thus, any entries in the d locations are stored in the d (diagonal)
1695: submatrix, and any entries in the o locations are stored in the
1696: o (off-diagonal) submatrix. Note that the d and the o submatrices are
1697: stored simply in the MATSEQBAIJ format for compressed row storage.
1699: Now d_nz should indicate the number of block nonzeros per row in the d matrix,
1700: and o_nz should indicate the number of block nonzeros per row in the o matrix.
1701: In general, for PDE problems in which most nonzeros are near the diagonal,
1702: one expects d_nz >> o_nz. For large problems you MUST preallocate memory
1703: or you will get TERRIBLE performance; see the users' manual chapter on
1704: matrices.
1706: Level: intermediate
1708: .keywords: matrix, block, aij, compressed row, sparse, parallel
1710: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
1711: @*/
1713: int MatMPISBAIJSetPreallocation(Mat B,int bs,int d_nz,int *d_nnz,int o_nz,int *o_nnz)
1714: {
1715: Mat_MPISBAIJ *b;
1716: int ierr,i,mbs,Mbs;
1717: PetscTruth flg2;
1720: PetscTypeCompare((PetscObject)B,MATMPISBAIJ,&flg2);
1721: if (!flg2) return(0);
1723: PetscOptionsGetInt(PETSC_NULL,"-mat_block_size",&bs,PETSC_NULL);
1725: if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
1726: if (d_nz == PETSC_DECIDE || d_nz == PETSC_DEFAULT) d_nz = 3;
1727: if (o_nz == PETSC_DECIDE || o_nz == PETSC_DEFAULT) o_nz = 1;
1728: if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %d",d_nz);
1729: if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %d",o_nz);
1730: if (d_nnz) {
1731: for (i=0; i<B->m/bs; i++) {
1732: if (d_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than -1: local row %d value %d",i,d_nnz[i]);
1733: }
1734: }
1735: if (o_nnz) {
1736: for (i=0; i<B->m/bs; i++) {
1737: if (o_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than -1: local row %d value %d",i,o_nnz[i]);
1738: }
1739: }
1740: B->preallocated = PETSC_TRUE;
1741: PetscSplitOwnershipBlock(B->comm,bs,&B->m,&B->M);
1742: PetscSplitOwnershipBlock(B->comm,bs,&B->n,&B->N);
1743: PetscMapCreateMPI(B->comm,B->m,B->M,&B->rmap);
1744: PetscMapCreateMPI(B->comm,B->m,B->M,&B->cmap);
1746: b = (Mat_MPISBAIJ*)B->data;
1747: mbs = B->m/bs;
1748: Mbs = B->M/bs;
1749: if (mbs*bs != B->m) {
1750: SETERRQ2(PETSC_ERR_ARG_SIZ,"No of local rows %d must be divisible by blocksize %d",B->m,bs);
1751: }
1753: b->bs = bs;
1754: b->bs2 = bs*bs;
1755: b->mbs = mbs;
1756: b->nbs = mbs;
1757: b->Mbs = Mbs;
1758: b->Nbs = Mbs;
1760: MPI_Allgather(&b->mbs,1,MPI_INT,b->rowners+1,1,MPI_INT,B->comm);
1761: b->rowners[0] = 0;
1762: for (i=2; i<=b->size; i++) {
1763: b->rowners[i] += b->rowners[i-1];
1764: }
1765: b->rstart = b->rowners[b->rank];
1766: b->rend = b->rowners[b->rank+1];
1767: b->cstart = b->rstart;
1768: b->cend = b->rend;
1769: for (i=0; i<=b->size; i++) {
1770: b->rowners_bs[i] = b->rowners[i]*bs;
1771: }
1772: b->rstart_bs = b-> rstart*bs;
1773: b->rend_bs = b->rend*bs;
1774:
1775: b->cstart_bs = b->cstart*bs;
1776: b->cend_bs = b->cend*bs;
1777:
1779: MatCreateSeqSBAIJ(PETSC_COMM_SELF,bs,B->m,B->m,d_nz,d_nnz,&b->A);
1780: PetscLogObjectParent(B,b->A);
1781: MatCreateSeqBAIJ(PETSC_COMM_SELF,bs,B->m,B->M,o_nz,o_nnz,&b->B);
1782: PetscLogObjectParent(B,b->B);
1784: /* build cache for off array entries formed */
1785: MatStashCreate_Private(B->comm,bs,&B->bstash);
1787: return(0);
1788: }
1790: /*@C
1791: MatCreateMPISBAIJ - Creates a sparse parallel matrix in symmetric block AIJ format
1792: (block compressed row). For good matrix assembly performance
1793: the user should preallocate the matrix storage by setting the parameters
1794: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
1795: performance can be increased by more than a factor of 50.
1797: Collective on MPI_Comm
1799: Input Parameters:
1800: + comm - MPI communicator
1801: . bs - size of blockk
1802: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1803: This value should be the same as the local size used in creating the
1804: y vector for the matrix-vector product y = Ax.
1805: . n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1806: This value should be the same as the local size used in creating the
1807: x vector for the matrix-vector product y = Ax.
1808: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
1809: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
1810: . d_nz - number of block nonzeros per block row in diagonal portion of local
1811: submatrix (same for all local rows)
1812: . d_nnz - array containing the number of block nonzeros in the various block rows
1813: of the in diagonal portion of the local (possibly different for each block
1814: row) or PETSC_NULL. You must leave room for the diagonal entry even if it is zero.
1815: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
1816: submatrix (same for all local rows).
1817: - o_nnz - array containing the number of nonzeros in the various block rows of the
1818: off-diagonal portion of the local submatrix (possibly different for
1819: each block row) or PETSC_NULL.
1821: Output Parameter:
1822: . A - the matrix
1824: Options Database Keys:
1825: . -mat_no_unroll - uses code that does not unroll the loops in the
1826: block calculations (much slower)
1827: . -mat_block_size - size of the blocks to use
1828: . -mat_mpi - use the parallel matrix data structures even on one processor
1829: (defaults to using SeqBAIJ format on one processor)
1831: Notes:
1832: The user MUST specify either the local or global matrix dimensions
1833: (possibly both).
1835: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor
1836: than it must be used on all processors that share the object for that argument.
1838: Storage Information:
1839: For a square global matrix we define each processor's diagonal portion
1840: to be its local rows and the corresponding columns (a square submatrix);
1841: each processor's off-diagonal portion encompasses the remainder of the
1842: local matrix (a rectangular submatrix).
1844: The user can specify preallocated storage for the diagonal part of
1845: the local submatrix with either d_nz or d_nnz (not both). Set
1846: d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
1847: memory allocation. Likewise, specify preallocated storage for the
1848: off-diagonal part of the local submatrix with o_nz or o_nnz (not both).
1850: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1851: the figure below we depict these three local rows and all columns (0-11).
1853: .vb
1854: 0 1 2 3 4 5 6 7 8 9 10 11
1855: -------------------
1856: row 3 | o o o d d d o o o o o o
1857: row 4 | o o o d d d o o o o o o
1858: row 5 | o o o d d d o o o o o o
1859: -------------------
1860: .ve
1861:
1862: Thus, any entries in the d locations are stored in the d (diagonal)
1863: submatrix, and any entries in the o locations are stored in the
1864: o (off-diagonal) submatrix. Note that the d and the o submatrices are
1865: stored simply in the MATSEQBAIJ format for compressed row storage.
1867: Now d_nz should indicate the number of block nonzeros per row in the d matrix,
1868: and o_nz should indicate the number of block nonzeros per row in the o matrix.
1869: In general, for PDE problems in which most nonzeros are near the diagonal,
1870: one expects d_nz >> o_nz. For large problems you MUST preallocate memory
1871: or you will get TERRIBLE performance; see the users' manual chapter on
1872: matrices.
1874: Level: intermediate
1876: .keywords: matrix, block, aij, compressed row, sparse, parallel
1878: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
1879: @*/
1881: int MatCreateMPISBAIJ(MPI_Comm comm,int bs,int m,int n,int M,int N,int d_nz,int *d_nnz,int o_nz,int *o_nnz,Mat *A)
1882: {
1883: int ierr,size;
1886: MatCreate(comm,m,n,M,N,A);
1887: MPI_Comm_size(comm,&size);
1888: if (size > 1) {
1889: MatSetType(*A,MATMPISBAIJ);
1890: MatMPISBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
1891: } else {
1892: MatSetType(*A,MATSEQSBAIJ);
1893: MatSeqSBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
1894: }
1895: return(0);
1896: }
1899: static int MatDuplicate_MPISBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
1900: {
1901: Mat mat;
1902: Mat_MPISBAIJ *a,*oldmat = (Mat_MPISBAIJ*)matin->data;
1903: int ierr,len=0;
1906: *newmat = 0;
1907: MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);
1908: MatSetType(mat,MATMPISBAIJ);
1909: mat->preallocated = PETSC_TRUE;
1910: a = (Mat_MPISBAIJ*)mat->data;
1911: a->bs = oldmat->bs;
1912: a->bs2 = oldmat->bs2;
1913: a->mbs = oldmat->mbs;
1914: a->nbs = oldmat->nbs;
1915: a->Mbs = oldmat->Mbs;
1916: a->Nbs = oldmat->Nbs;
1917:
1918: a->rstart = oldmat->rstart;
1919: a->rend = oldmat->rend;
1920: a->cstart = oldmat->cstart;
1921: a->cend = oldmat->cend;
1922: a->size = oldmat->size;
1923: a->rank = oldmat->rank;
1924: a->donotstash = oldmat->donotstash;
1925: a->roworiented = oldmat->roworiented;
1926: a->rowindices = 0;
1927: a->rowvalues = 0;
1928: a->getrowactive = PETSC_FALSE;
1929: a->barray = 0;
1930: a->rstart_bs = oldmat->rstart_bs;
1931: a->rend_bs = oldmat->rend_bs;
1932: a->cstart_bs = oldmat->cstart_bs;
1933: a->cend_bs = oldmat->cend_bs;
1935: /* hash table stuff */
1936: a->ht = 0;
1937: a->hd = 0;
1938: a->ht_size = 0;
1939: a->ht_flag = oldmat->ht_flag;
1940: a->ht_fact = oldmat->ht_fact;
1941: a->ht_total_ct = 0;
1942: a->ht_insert_ct = 0;
1944: PetscMalloc(3*(a->size+2)*sizeof(int),&a->rowners);
1945: PetscLogObjectMemory(mat,3*(a->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPISBAIJ));
1946: a->cowners = a->rowners + a->size + 2;
1947: a->rowners_bs = a->cowners + a->size + 2;
1948: PetscMemcpy(a->rowners,oldmat->rowners,3*(a->size+2)*sizeof(int));
1949: MatStashCreate_Private(matin->comm,1,&mat->stash);
1950: MatStashCreate_Private(matin->comm,oldmat->bs,&mat->bstash);
1951: if (oldmat->colmap) {
1952: #if defined (PETSC_USE_CTABLE)
1953: PetscTableCreateCopy(oldmat->colmap,&a->colmap);
1954: #else
1955: PetscMalloc((a->Nbs)*sizeof(int),&a->colmap);
1956: PetscLogObjectMemory(mat,(a->Nbs)*sizeof(int));
1957: PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(int));
1958: #endif
1959: } else a->colmap = 0;
1960: if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
1961: PetscMalloc(len*sizeof(int),&a->garray);
1962: PetscLogObjectMemory(mat,len*sizeof(int));
1963: PetscMemcpy(a->garray,oldmat->garray,len*sizeof(int));
1964: } else a->garray = 0;
1965:
1966: VecDuplicate(oldmat->lvec,&a->lvec);
1967: PetscLogObjectParent(mat,a->lvec);
1968: VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
1970: PetscLogObjectParent(mat,a->Mvctx);
1971: MatDuplicate(oldmat->A,cpvalues,&a->A);
1972: PetscLogObjectParent(mat,a->A);
1973: MatDuplicate(oldmat->B,cpvalues,&a->B);
1974: PetscLogObjectParent(mat,a->B);
1975: PetscFListDuplicate(mat->qlist,&matin->qlist);
1976: *newmat = mat;
1977: return(0);
1978: }
1980: #include petscsys.h
1982: EXTERN_C_BEGIN
1983: int MatLoad_MPISBAIJ(PetscViewer viewer,MatType type,Mat *newmat)
1984: {
1985: Mat A;
1986: int i,nz,ierr,j,rstart,rend,fd;
1987: PetscScalar *vals,*buf;
1988: MPI_Comm comm = ((PetscObject)viewer)->comm;
1989: MPI_Status status;
1990: int header[4],rank,size,*rowlengths = 0,M,N,m,*rowners,*browners,maxnz,*cols;
1991: int *locrowlens,*sndcounts = 0,*procsnz = 0,jj,*mycols,*ibuf;
1992: int tag = ((PetscObject)viewer)->tag,bs=1,Mbs,mbs,extra_rows;
1993: int *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
1994: int dcount,kmax,k,nzcount,tmp;
1995:
1997: PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);
1999: MPI_Comm_size(comm,&size);
2000: MPI_Comm_rank(comm,&rank);
2001: if (!rank) {
2002: PetscViewerBinaryGetDescriptor(viewer,&fd);
2003: PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2004: if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2005: if (header[3] < 0) {
2006: SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as MPISBAIJ");
2007: }
2008: }
2010: MPI_Bcast(header+1,3,MPI_INT,0,comm);
2011: M = header[1]; N = header[2];
2013: if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");
2015: /*
2016: This code adds extra rows to make sure the number of rows is
2017: divisible by the blocksize
2018: */
2019: Mbs = M/bs;
2020: extra_rows = bs - M + bs*(Mbs);
2021: if (extra_rows == bs) extra_rows = 0;
2022: else Mbs++;
2023: if (extra_rows &&!rank) {
2024: PetscLogInfo(0,"MatLoad_MPISBAIJ:Padding loaded matrix to match blocksizen");
2025: }
2027: /* determine ownership of all rows */
2028: mbs = Mbs/size + ((Mbs % size) > rank);
2029: m = mbs*bs;
2030: ierr = PetscMalloc(2*(size+2)*sizeof(int),&rowners);
2031: browners = rowners + size + 1;
2032: ierr = MPI_Allgather(&mbs,1,MPI_INT,rowners+1,1,MPI_INT,comm);
2033: rowners[0] = 0;
2034: for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
2035: for (i=0; i<=size; i++) browners[i] = rowners[i]*bs;
2036: rstart = rowners[rank];
2037: rend = rowners[rank+1];
2038:
2039: /* distribute row lengths to all processors */
2040: PetscMalloc((rend-rstart)*bs*sizeof(int),&locrowlens);
2041: if (!rank) {
2042: PetscMalloc((M+extra_rows)*sizeof(int),&rowlengths);
2043: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2044: for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2045: PetscMalloc(size*sizeof(int),&sndcounts);
2046: for (i=0; i<size; i++) sndcounts[i] = browners[i+1] - browners[i];
2047: MPI_Scatterv(rowlengths,sndcounts,browners,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
2048: PetscFree(sndcounts);
2049: } else {
2050: MPI_Scatterv(0,0,0,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
2051: }
2052:
2053: if (!rank) { /* procs[0] */
2054: /* calculate the number of nonzeros on each processor */
2055: PetscMalloc(size*sizeof(int),&procsnz);
2056: PetscMemzero(procsnz,size*sizeof(int));
2057: for (i=0; i<size; i++) {
2058: for (j=rowners[i]*bs; j< rowners[i+1]*bs; j++) {
2059: procsnz[i] += rowlengths[j];
2060: }
2061: }
2062: PetscFree(rowlengths);
2063:
2064: /* determine max buffer needed and allocate it */
2065: maxnz = 0;
2066: for (i=0; i<size; i++) {
2067: maxnz = PetscMax(maxnz,procsnz[i]);
2068: }
2069: PetscMalloc(maxnz*sizeof(int),&cols);
2071: /* read in my part of the matrix column indices */
2072: nz = procsnz[0];
2073: ierr = PetscMalloc(nz*sizeof(int),&ibuf);
2074: mycols = ibuf;
2075: if (size == 1) nz -= extra_rows;
2076: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2077: if (size == 1) for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }
2079: /* read in every ones (except the last) and ship off */
2080: for (i=1; i<size-1; i++) {
2081: nz = procsnz[i];
2082: PetscBinaryRead(fd,cols,nz,PETSC_INT);
2083: MPI_Send(cols,nz,MPI_INT,i,tag,comm);
2084: }
2085: /* read in the stuff for the last proc */
2086: if (size != 1) {
2087: nz = procsnz[size-1] - extra_rows; /* the extra rows are not on the disk */
2088: PetscBinaryRead(fd,cols,nz,PETSC_INT);
2089: for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
2090: MPI_Send(cols,nz+extra_rows,MPI_INT,size-1,tag,comm);
2091: }
2092: PetscFree(cols);
2093: } else { /* procs[i], i>0 */
2094: /* determine buffer space needed for message */
2095: nz = 0;
2096: for (i=0; i<m; i++) {
2097: nz += locrowlens[i];
2098: }
2099: ierr = PetscMalloc(nz*sizeof(int),&ibuf);
2100: mycols = ibuf;
2101: /* receive message of column indices*/
2102: MPI_Recv(mycols,nz,MPI_INT,0,tag,comm,&status);
2103: MPI_Get_count(&status,MPI_INT,&maxnz);
2104: if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2105: }
2107: /* loop over local rows, determining number of off diagonal entries */
2108: ierr = PetscMalloc(2*(rend-rstart+1)*sizeof(int),&dlens);
2109: odlens = dlens + (rend-rstart);
2110: ierr = PetscMalloc(3*Mbs*sizeof(int),&mask);
2111: ierr = PetscMemzero(mask,3*Mbs*sizeof(int));
2112: masked1 = mask + Mbs;
2113: masked2 = masked1 + Mbs;
2114: rowcount = 0; nzcount = 0;
2115: for (i=0; i<mbs; i++) {
2116: dcount = 0;
2117: odcount = 0;
2118: for (j=0; j<bs; j++) {
2119: kmax = locrowlens[rowcount];
2120: for (k=0; k<kmax; k++) {
2121: tmp = mycols[nzcount++]/bs; /* block col. index */
2122: if (!mask[tmp]) {
2123: mask[tmp] = 1;
2124: if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp; /* entry in off-diag portion */
2125: else masked1[dcount++] = tmp; /* entry in diag portion */
2126: }
2127: }
2128: rowcount++;
2129: }
2130:
2131: dlens[i] = dcount; /* d_nzz[i] */
2132: odlens[i] = odcount; /* o_nzz[i] */
2134: /* zero out the mask elements we set */
2135: for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2136: for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2137: }
2138:
2139: /* create our matrix */
2140: MatCreateMPISBAIJ(comm,bs,m,m,PETSC_DETERMINE,PETSC_DETERMINE,0,dlens,0,odlens,newmat);
2141:
2142: A = *newmat;
2143: MatSetOption(A,MAT_COLUMNS_SORTED);
2144:
2145: if (!rank) {
2146: PetscMalloc(maxnz*sizeof(PetscScalar),&buf);
2147: /* read in my part of the matrix numerical values */
2148: nz = procsnz[0];
2149: vals = buf;
2150: mycols = ibuf;
2151: if (size == 1) nz -= extra_rows;
2152: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2153: if (size == 1) for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }
2155: /* insert into matrix */
2156: jj = rstart*bs;
2157: for (i=0; i<m; i++) {
2158: MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2159: mycols += locrowlens[i];
2160: vals += locrowlens[i];
2161: jj++;
2162: }
2164: /* read in other processors (except the last one) and ship out */
2165: for (i=1; i<size-1; i++) {
2166: nz = procsnz[i];
2167: vals = buf;
2168: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2169: MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2170: }
2171: /* the last proc */
2172: if (size != 1){
2173: nz = procsnz[i] - extra_rows;
2174: vals = buf;
2175: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2176: for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2177: MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2178: }
2179: PetscFree(procsnz);
2181: } else {
2182: /* receive numeric values */
2183: PetscMalloc(nz*sizeof(PetscScalar),&buf);
2185: /* receive message of values*/
2186: vals = buf;
2187: mycols = ibuf;
2188: ierr = MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2189: ierr = MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2190: if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2192: /* insert into matrix */
2193: jj = rstart*bs;
2194: for (i=0; i<m; i++) {
2195: ierr = MatSetValues_MPISBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2196: mycols += locrowlens[i];
2197: vals += locrowlens[i];
2198: jj++;
2199: }
2200: }
2202: PetscFree(locrowlens);
2203: PetscFree(buf);
2204: PetscFree(ibuf);
2205: PetscFree(rowners);
2206: PetscFree(dlens);
2207: PetscFree(mask);
2208: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2209: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2210: return(0);
2211: }
2212: EXTERN_C_END
2214: /*@
2215: MatMPISBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.
2217: Input Parameters:
2218: . mat - the matrix
2219: . fact - factor
2221: Collective on Mat
2223: Level: advanced
2225: Notes:
2226: This can also be set by the command line option: -mat_use_hash_table fact
2228: .keywords: matrix, hashtable, factor, HT
2230: .seealso: MatSetOption()
2231: @*/
2232: int MatMPISBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2233: {
2235: SETERRQ(1,"Function not yet written for SBAIJ format");
2236: /* return(0); */
2237: }
2239: int MatGetRowMax_MPISBAIJ(Mat A,Vec v)
2240: {
2241: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
2242: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(a->B)->data;
2243: PetscReal atmp;
2244: PetscReal *work,*svalues,*rvalues;
2245: int ierr,i,bs,mbs,*bi,*bj,brow,j,ncols,krow,kcol,col,row,Mbs,bcol;
2246: int rank,size,*rowners_bs,dest,count,source;
2247: PetscScalar *va;
2248: MatScalar *ba;
2249: MPI_Status stat;
2252: MatGetRowMax(a->A,v);
2253: VecGetArray(v,&va);
2255: MPI_Comm_size(PETSC_COMM_WORLD,&size);
2256: MPI_Comm_rank(PETSC_COMM_WORLD,&rank);
2258: bs = a->bs;
2259: mbs = a->mbs;
2260: Mbs = a->Mbs;
2261: ba = b->a;
2262: bi = b->i;
2263: bj = b->j;
2264: /*
2265: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d] M: %d, bs: %d, mbs: %d n",rank,bs*Mbs,bs,mbs);
2266: PetscSynchronizedFlush(PETSC_COMM_WORLD);
2267: */
2269: /* find ownerships */
2270: rowners_bs = a->rowners_bs;
2271: /*
2272: if (!rank){
2273: for (i=0; i<size+1; i++) PetscPrintf(PETSC_COMM_SELF," rowners_bs[%d]: %dn",i,rowners_bs[i]);
2274: }
2275: */
2277: /* each proc creates an array to be distributed */
2278: PetscMalloc(bs*Mbs*sizeof(PetscReal),&work);
2279: PetscMemzero(work,bs*Mbs*sizeof(PetscReal));
2281: /* row_max for B */
2282: if (rank != size-1){
2283: for (i=0; i<mbs; i++) {
2284: ncols = bi[1] - bi[0]; bi++;
2285: brow = bs*i;
2286: for (j=0; j<ncols; j++){
2287: bcol = bs*(*bj);
2288: for (kcol=0; kcol<bs; kcol++){
2289: col = bcol + kcol; /* local col index */
2290: col += rowners_bs[rank+1]; /* global col index */
2291: /* PetscPrintf(PETSC_COMM_SELF,"[%d], col: %dn",rank,col); */
2292: for (krow=0; krow<bs; krow++){
2293: atmp = PetscAbsScalar(*ba); ba++;
2294: row = brow + krow; /* local row index */
2295: /* printf("val[%d,%d]: %gn",row,col,atmp); */
2296: if (PetscRealPart(va[row]) < atmp) va[row] = atmp;
2297: if (work[col] < atmp) work[col] = atmp;
2298: }
2299: }
2300: bj++;
2301: }
2302: }
2303: /*
2304: PetscPrintf(PETSC_COMM_SELF,"[%d], work: ",rank);
2305: for (i=0; i<bs*Mbs; i++) PetscPrintf(PETSC_COMM_SELF,"%g ",work[i]);
2306: PetscPrintf(PETSC_COMM_SELF,"[%d]: n");
2307: */
2309: /* send values to its owners */
2310: for (dest=rank+1; dest<size; dest++){
2311: svalues = work + rowners_bs[dest];
2312: count = rowners_bs[dest+1]-rowners_bs[dest];
2313: ierr = MPI_Send(svalues,count,MPIU_REAL,dest,rank,PETSC_COMM_WORLD);
2314: /*
2315: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d] sends %d values to [%d]: %g, %g, %g, %gn",rank,count,dest,svalues[0],svalues[1],svalues[2],svalues[3]);
2316: PetscSynchronizedFlush(PETSC_COMM_WORLD);
2317: */
2318: }
2319: }
2320:
2321: /* receive values */
2322: if (rank){
2323: rvalues = work;
2324: count = rowners_bs[rank+1]-rowners_bs[rank];
2325: for (source=0; source<rank; source++){
2326: MPI_Recv(rvalues,count,MPIU_REAL,MPI_ANY_SOURCE,MPI_ANY_TAG,PETSC_COMM_WORLD,&stat);
2327: /* process values */
2328: for (i=0; i<count; i++){
2329: if (PetscRealPart(va[i]) < rvalues[i]) va[i] = rvalues[i];
2330: }
2331: /*
2332: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d] received %d values from [%d]: %g, %g, %g, %g n",rank,count,stat.MPI_SOURCE,rvalues[0],rvalues[1],rvalues[2],rvalues[3]);
2333: PetscSynchronizedFlush(PETSC_COMM_WORLD);
2334: */
2335: }
2336: }
2338: VecRestoreArray(v,&va);
2339: PetscFree(work);
2340: return(0);
2341: }
2343: int MatRelax_MPISBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx)
2344: {
2345: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)matin->data;
2346: int ierr,mbs=mat->mbs,bs=mat->bs;
2347: PetscScalar mone=-1.0,*x,*b,*ptr,zero=0.0;
2348: Vec bb1;
2349:
2351: if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits);
2352: if (bs > 1)
2353: SETERRQ(PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");
2355: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
2356: if ( flag & SOR_ZERO_INITIAL_GUESS ) {
2357: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
2358: its--;
2359: }
2361: VecDuplicate(bb,&bb1);
2362: while (its--){
2363:
2364: /* lower triangular part: slvec0b = - B^T*xx */
2365: (*mat->B->ops->multtranspose)(mat->B,xx,mat->slvec0b);
2366:
2367: /* copy xx into slvec0a */
2368: VecGetArray(mat->slvec0,&ptr);
2369: VecGetArray(xx,&x);
2370: PetscMemcpy(ptr,x,bs*mbs*sizeof(MatScalar));
2371: VecRestoreArray(mat->slvec0,&ptr);
2373: VecScale(&mone,mat->slvec0);
2375: /* copy bb into slvec1a */
2376: VecGetArray(mat->slvec1,&ptr);
2377: VecGetArray(bb,&b);
2378: PetscMemcpy(ptr,b,bs*mbs*sizeof(MatScalar));
2379: VecRestoreArray(mat->slvec1,&ptr);
2381: /* set slvec1b = 0 */
2382: VecSet(&zero,mat->slvec1b);
2384: VecScatterBegin(mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD,mat->sMvctx);
2385: VecRestoreArray(xx,&x);
2386: VecRestoreArray(bb,&b);
2387: VecScatterEnd(mat->slvec0,mat->slvec1,ADD_VALUES,SCATTER_FORWARD,mat->sMvctx);
2389: /* upper triangular part: bb1 = bb1 - B*x */
2390: (*mat->B->ops->multadd)(mat->B,mat->slvec1b,mat->slvec1a,bb1);
2391:
2392: /* local diagonal sweep */
2393: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
2394: }
2395: VecDestroy(bb1);
2396: } else {
2397: SETERRQ(PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2398: }
2399: return(0);
2400: }
2402: int MatRelax_MPISBAIJ_2comm(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx)
2403: {
2404: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)matin->data;
2405: int ierr;
2406: PetscScalar mone=-1.0;
2407: Vec lvec1,bb1;
2408:
2410: if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits);
2411: if (mat->bs > 1)
2412: SETERRQ(PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");
2414: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
2415: if ( flag & SOR_ZERO_INITIAL_GUESS ) {
2416: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
2417: its--;
2418: }
2420: VecDuplicate(mat->lvec,&lvec1);
2421: VecDuplicate(bb,&bb1);
2422: while (its--){
2423: VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
2424:
2425: /* lower diagonal part: bb1 = bb - B^T*xx */
2426: (*mat->B->ops->multtranspose)(mat->B,xx,lvec1);
2427: VecScale(&mone,lvec1);
2429: VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
2430: VecCopy(bb,bb1);
2431: VecScatterBegin(lvec1,bb1,ADD_VALUES,SCATTER_REVERSE,mat->Mvctx);
2433: /* upper diagonal part: bb1 = bb1 - B*x */
2434: VecScale(&mone,mat->lvec);
2435: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb1,bb1);
2437: VecScatterEnd(lvec1,bb1,ADD_VALUES,SCATTER_REVERSE,mat->Mvctx);
2438:
2439: /* diagonal sweep */
2440: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
2441: }
2442: VecDestroy(lvec1);
2443: VecDestroy(bb1);
2444: } else {
2445: SETERRQ(PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2446: }
2447: return(0);
2448: }