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" /*I "petscmat.h" I*/
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 DisAssemble_MPISBAIJ(Mat);
10: extern int MatIncreaseOverlap_MPISBAIJ(Mat,int,IS *,int);
11: extern int MatGetSubMatrices_MPISBAIJ(Mat,int,IS *,IS *,MatReuse,Mat **);
12: extern int MatGetValues_SeqSBAIJ(Mat,int,int *,int,int *,PetscScalar *);
13: extern int MatSetValues_SeqSBAIJ(Mat,int,int *,int,int *,PetscScalar *,InsertMode);
14: extern int MatSetValuesBlocked_SeqSBAIJ(Mat,int,int*,int,int*,PetscScalar*,InsertMode);
15: extern int MatGetRow_SeqSBAIJ(Mat,int,int*,int**,PetscScalar**);
16: extern int MatRestoreRow_SeqSBAIJ(Mat,int,int*,int**,PetscScalar**);
17: extern int MatPrintHelp_SeqSBAIJ(Mat);
18: extern int MatZeroRows_SeqSBAIJ(Mat,IS,PetscScalar*);
19: extern int MatZeroRows_SeqBAIJ(Mat,IS,PetscScalar *);
20: extern int MatGetRowMax_MPISBAIJ(Mat,Vec);
21: extern int MatRelax_MPISBAIJ(Mat,Vec,PetscReal,MatSORType,PetscReal,int,int,Vec);
23: /* UGLY, ugly, ugly
24: When MatScalar == PetscScalar the function MatSetValuesBlocked_MPIBAIJ_MatScalar() does
25: not exist. Otherwise ..._MatScalar() takes matrix elements in single precision and
26: inserts them into the single precision data structure. The function MatSetValuesBlocked_MPIBAIJ()
27: converts the entries into single precision and then calls ..._MatScalar() to put them
28: into the single precision data structures.
29: */
30: #if defined(PETSC_USE_MAT_SINGLE)
31: extern int MatSetValuesBlocked_SeqSBAIJ_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
32: extern int MatSetValues_MPISBAIJ_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
33: extern int MatSetValuesBlocked_MPISBAIJ_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
34: extern int MatSetValues_MPISBAIJ_HT_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
35: extern int MatSetValuesBlocked_MPISBAIJ_HT_MatScalar(Mat,int,int*,int,int*,MatScalar*,InsertMode);
36: #else
37: #define MatSetValuesBlocked_SeqSBAIJ_MatScalar MatSetValuesBlocked_SeqSBAIJ
38: #define MatSetValues_MPISBAIJ_MatScalar MatSetValues_MPISBAIJ
39: #define MatSetValuesBlocked_MPISBAIJ_MatScalar MatSetValuesBlocked_MPISBAIJ
40: #define MatSetValues_MPISBAIJ_HT_MatScalar MatSetValues_MPISBAIJ_HT
41: #define MatSetValuesBlocked_MPISBAIJ_HT_MatScalar MatSetValuesBlocked_MPISBAIJ_HT
42: #endif
44: EXTERN_C_BEGIN
45: int MatStoreValues_MPISBAIJ(Mat mat)
46: {
47: Mat_MPISBAIJ *aij = (Mat_MPISBAIJ *)mat->data;
48: int ierr;
51: MatStoreValues(aij->A);
52: MatStoreValues(aij->B);
53: return(0);
54: }
55: EXTERN_C_END
57: EXTERN_C_BEGIN
58: int MatRetrieveValues_MPISBAIJ(Mat mat)
59: {
60: Mat_MPISBAIJ *aij = (Mat_MPISBAIJ *)mat->data;
61: int ierr;
64: MatRetrieveValues(aij->A);
65: MatRetrieveValues(aij->B);
66: return(0);
67: }
68: EXTERN_C_END
70: /*
71: Local utility routine that creates a mapping from the global column
72: number to the local number in the off-diagonal part of the local
73: storage of the matrix. This is done in a non scable way since the
74: length of colmap equals the global matrix length.
75: */
76: static int CreateColmap_MPISBAIJ_Private(Mat mat)
77: {
79: SETERRQ(1,"Function not yet written for SBAIJ format");
80: /* return(0); */
81: }
83: #define CHUNKSIZE 10
85: #define MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv)
86: {
87:
88: brow = row/bs;
89: rp = aj + ai[brow]; ap = aa + bs2*ai[brow];
90: rmax = aimax[brow]; nrow = ailen[brow];
91: bcol = col/bs;
92: ridx = row % bs; cidx = col % bs;
93: low = 0; high = nrow;
94: while (high-low > 3) {
95: t = (low+high)/2;
96: if (rp[t] > bcol) high = t;
97: else low = t;
98: }
99: for (_i=low; _i<high; _i++) {
100: if (rp[_i] > bcol) break;
101: if (rp[_i] == bcol) {
102: bap = ap + bs2*_i + bs*cidx + ridx;
103: if (addv == ADD_VALUES) *bap += value;
104: else *bap = value;
105: goto a_noinsert;
106: }
107: }
108: if (a->nonew == 1) goto a_noinsert;
109: else if (a->nonew == -1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero into matrix");
110: if (nrow >= rmax) {
111: /* there is no extra room in row, therefore enlarge */
112: int new_nz = ai[a->mbs] + CHUNKSIZE,len,*new_i,*new_j;
113: MatScalar *new_a;
114:
115: if (a->nonew == -2) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
116:
117: /* malloc new storage space */
118: len = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(a->mbs+1)*sizeof(int);
119: ierr = PetscMalloc(len,&new_a);
120: new_j = (int*)(new_a + bs2*new_nz);
121: new_i = new_j + new_nz;
122:
123: /* copy over old data into new slots */
124: for (ii=0; ii<brow+1; ii++) {new_i[ii] = ai[ii];}
125: for (ii=brow+1; ii<a->mbs+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;}
126: PetscMemcpy(new_j,aj,(ai[brow]+nrow)*sizeof(int));
127: len = (new_nz - CHUNKSIZE - ai[brow] - nrow);
128: PetscMemcpy(new_j+ai[brow]+nrow+CHUNKSIZE,aj+ai[brow]+nrow,len*sizeof(int));
129: PetscMemcpy(new_a,aa,(ai[brow]+nrow)*bs2*sizeof(MatScalar));
130: PetscMemzero(new_a+bs2*(ai[brow]+nrow),bs2*CHUNKSIZE*sizeof(PetscScalar));
131: PetscMemcpy(new_a+bs2*(ai[brow]+nrow+CHUNKSIZE),
132: aa+bs2*(ai[brow]+nrow),bs2*len*sizeof(MatScalar));
133: /* free up old matrix storage */
134: PetscFree(a->a);
135: if (!a->singlemalloc) {
136: PetscFree(a->i);
137: PetscFree(a->j);
138: }
139: aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;
140: a->singlemalloc = PETSC_TRUE;
141:
142: rp = aj + ai[brow]; ap = aa + bs2*ai[brow];
143: rmax = aimax[brow] = aimax[brow] + CHUNKSIZE;
144: PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar)));
145: a->s_maxnz += bs2*CHUNKSIZE;
146: a->reallocs++;
147: a->s_nz++;
148: }
149: N = nrow++ - 1;
150: /* shift up all the later entries in this row */
151: for (ii=N; ii>=_i; ii--) {
152: rp[ii+1] = rp[ii];
153: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
154: }
155: if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar)); }
156: rp[_i] = bcol;
157: ap[bs2*_i + bs*cidx + ridx] = value;
158: a_noinsert:;
159: ailen[brow] = nrow;
160: }
161: #ifndef MatSetValues_SeqBAIJ_B_Private
162: #define MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv)
163: {
164: brow = row/bs;
165: rp = bj + bi[brow]; ap = ba + bs2*bi[brow];
166: rmax = bimax[brow]; nrow = bilen[brow];
167: bcol = col/bs;
168: ridx = row % bs; cidx = col % bs;
169: low = 0; high = nrow;
170: while (high-low > 3) {
171: t = (low+high)/2;
172: if (rp[t] > bcol) high = t;
173: else low = t;
174: }
175: for (_i=low; _i<high; _i++) {
176: if (rp[_i] > bcol) break;
177: if (rp[_i] == bcol) {
178: bap = ap + bs2*_i + bs*cidx + ridx;
179: if (addv == ADD_VALUES) *bap += value;
180: else *bap = value;
181: goto b_noinsert;
182: }
183: }
184: if (b->nonew == 1) goto b_noinsert;
185: else if (b->nonew == -1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero into matrix");
186: if (nrow >= rmax) {
187: /* there is no extra room in row, therefore enlarge */
188: int new_nz = bi[b->mbs] + CHUNKSIZE,len,*new_i,*new_j;
189: MatScalar *new_a;
190:
191: if (b->nonew == -2) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
192:
193: /* malloc new storage space */
194: len = new_nz*(sizeof(int)+bs2*sizeof(MatScalar))+(b->mbs+1)*sizeof(int);
195: ierr = PetscMalloc(len,&new_a);
196: new_j = (int*)(new_a + bs2*new_nz);
197: new_i = new_j + new_nz;
198:
199: /* copy over old data into new slots */
200: for (ii=0; ii<brow+1; ii++) {new_i[ii] = bi[ii];}
201: for (ii=brow+1; ii<b->mbs+1; ii++) {new_i[ii] = bi[ii]+CHUNKSIZE;}
202: PetscMemcpy(new_j,bj,(bi[brow]+nrow)*sizeof(int));
203: len = (new_nz - CHUNKSIZE - bi[brow] - nrow);
204: PetscMemcpy(new_j+bi[brow]+nrow+CHUNKSIZE,bj+bi[brow]+nrow,len*sizeof(int));
205: PetscMemcpy(new_a,ba,(bi[brow]+nrow)*bs2*sizeof(MatScalar));
206: PetscMemzero(new_a+bs2*(bi[brow]+nrow),bs2*CHUNKSIZE*sizeof(MatScalar));
207: PetscMemcpy(new_a+bs2*(bi[brow]+nrow+CHUNKSIZE),
208: ba+bs2*(bi[brow]+nrow),bs2*len*sizeof(MatScalar));
209: /* free up old matrix storage */
210: PetscFree(b->a);
211: if (!b->singlemalloc) {
212: PetscFree(b->i);
213: PetscFree(b->j);
214: }
215: ba = b->a = new_a; bi = b->i = new_i; bj = b->j = new_j;
216: b->singlemalloc = PETSC_TRUE;
217:
218: rp = bj + bi[brow]; ap = ba + bs2*bi[brow];
219: rmax = bimax[brow] = bimax[brow] + CHUNKSIZE;
220: PetscLogObjectMemory(B,CHUNKSIZE*(sizeof(int) + bs2*sizeof(MatScalar)));
221: b->maxnz += bs2*CHUNKSIZE;
222: b->reallocs++;
223: b->nz++;
224: }
225: N = nrow++ - 1;
226: /* shift up all the later entries in this row */
227: for (ii=N; ii>=_i; ii--) {
228: rp[ii+1] = rp[ii];
229: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
230: }
231: if (N>=_i) { PetscMemzero(ap+bs2*_i,bs2*sizeof(MatScalar));}
232: rp[_i] = bcol;
233: ap[bs2*_i + bs*cidx + ridx] = value;
234: b_noinsert:;
235: bilen[brow] = nrow;
236: }
237: #endif
239: #if defined(PETSC_USE_MAT_SINGLE)
240: int MatSetValues_MPISBAIJ(Mat mat,int m,int *im,int n,int *in,PetscScalar *v,InsertMode addv)
241: {
242: Mat_MPISBAIJ *b = (Mat_MPISBAIJ*)mat->data;
243: int ierr,i,N = m*n;
244: MatScalar *vsingle;
247: if (N > b->setvalueslen) {
248: if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
249: PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
250: b->setvalueslen = N;
251: }
252: vsingle = b->setvaluescopy;
254: for (i=0; i<N; i++) {
255: vsingle[i] = v[i];
256: }
257: MatSetValues_MPISBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
258: return(0);
259: }
261: int MatSetValuesBlocked_MPISBAIJ(Mat mat,int m,int *im,int n,int *in,PetscScalar *v,InsertMode addv)
262: {
263: Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
264: int ierr,i,N = m*n*b->bs2;
265: MatScalar *vsingle;
268: if (N > b->setvalueslen) {
269: if (b->setvaluescopy) {PetscFree(b->setvaluescopy);}
270: PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
271: b->setvalueslen = N;
272: }
273: vsingle = b->setvaluescopy;
274: for (i=0; i<N; i++) {
275: vsingle[i] = v[i];
276: }
277: MatSetValuesBlocked_MPISBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
278: return(0);
279: }
281: int MatSetValues_MPISBAIJ_HT(Mat mat,int m,int *im,int n,int *in,PetscScalar *v,InsertMode addv)
282: {
283: Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
284: int ierr,i,N = m*n;
285: MatScalar *vsingle;
288: SETERRQ(1,"Function not yet written for SBAIJ format");
289: /* return(0); */
290: }
292: int MatSetValuesBlocked_MPISBAIJ_HT(Mat mat,int m,int *im,int n,int *in,PetscScalar *v,InsertMode addv)
293: {
294: Mat_MPIBAIJ *b = (Mat_MPIBAIJ*)mat->data;
295: int ierr,i,N = m*n*b->bs2;
296: MatScalar *vsingle;
299: SETERRQ(1,"Function not yet written for SBAIJ format");
300: /* return(0); */
301: }
302: #endif
304: /* Only add/insert a(i,j) with i<=j (blocks).
305: Any a(i,j) with i>j input by user is ingored.
306: */
307: int MatSetValues_MPISBAIJ_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
308: {
309: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
310: MatScalar value;
311: PetscTruth roworiented = baij->roworiented;
312: int ierr,i,j,row,col;
313: int rstart_orig=baij->rstart_bs;
314: int rend_orig=baij->rend_bs,cstart_orig=baij->cstart_bs;
315: int cend_orig=baij->cend_bs,bs=baij->bs;
317: /* Some Variables required in the macro */
318: Mat A = baij->A;
319: Mat_SeqSBAIJ *a = (Mat_SeqSBAIJ*)(A)->data;
320: int *aimax=a->imax,*ai=a->i,*ailen=a->ilen,*aj=a->j;
321: MatScalar *aa=a->a;
323: Mat B = baij->B;
324: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(B)->data;
325: int *bimax=b->imax,*bi=b->i,*bilen=b->ilen,*bj=b->j;
326: MatScalar *ba=b->a;
328: int *rp,ii,nrow,_i,rmax,N,brow,bcol;
329: int low,high,t,ridx,cidx,bs2=a->bs2;
330: MatScalar *ap,*bap;
332: /* for stash */
333: int n_loc, *in_loc=0;
334: MatScalar *v_loc=0;
338: if(!baij->donotstash){
339: PetscMalloc(n*sizeof(int),&in_loc);
340: PetscMalloc(n*sizeof(MatScalar),&v_loc);
341: }
343: for (i=0; i<m; i++) {
344: if (im[i] < 0) continue;
345: #if defined(PETSC_USE_BOPT_g)
346: if (im[i] >= mat->M) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
347: #endif
348: if (im[i] >= rstart_orig && im[i] < rend_orig) { /* this processor entry */
349: row = im[i] - rstart_orig; /* local row index */
350: for (j=0; j<n; j++) {
351: if (im[i]/bs > in[j]/bs) continue; /* ignore lower triangular blocks */
352: if (in[j] >= cstart_orig && in[j] < cend_orig){ /* diag entry (A) */
353: col = in[j] - cstart_orig; /* local col index */
354: brow = row/bs; bcol = col/bs;
355: if (brow > bcol) continue; /* ignore lower triangular blocks of A */
356: if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
357: MatSetValues_SeqSBAIJ_A_Private(row,col,value,addv);
358: /* MatSetValues_SeqBAIJ(baij->A,1,&row,1,&col,&value,addv); */
359: } else if (in[j] < 0) continue;
360: #if defined(PETSC_USE_BOPT_g)
361: else if (in[j] >= mat->N) {SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Col too large");}
362: #endif
363: else { /* off-diag entry (B) */
364: if (mat->was_assembled) {
365: if (!baij->colmap) {
366: CreateColmap_MPISBAIJ_Private(mat);
367: }
368: #if defined (PETSC_USE_CTABLE)
369: PetscTableFind(baij->colmap,in[j]/bs + 1,&col);
370: col = col - 1;
371: #else
372: col = baij->colmap[in[j]/bs] - 1;
373: #endif
374: if (col < 0 && !((Mat_SeqSBAIJ*)(baij->A->data))->nonew) {
375: DisAssemble_MPISBAIJ(mat);
376: col = in[j];
377: /* Reinitialize the variables required by MatSetValues_SeqBAIJ_B_Private() */
378: B = baij->B;
379: b = (Mat_SeqBAIJ*)(B)->data;
380: bimax=b->imax;bi=b->i;bilen=b->ilen;bj=b->j;
381: ba=b->a;
382: } else col += in[j]%bs;
383: } else col = in[j];
384: if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
385: MatSetValues_SeqSBAIJ_B_Private(row,col,value,addv);
386: /* MatSetValues_SeqBAIJ(baij->B,1,&row,1,&col,&value,addv); */
387: }
388: }
389: } else { /* off processor entry */
390: if (!baij->donotstash) {
391: n_loc = 0;
392: for (j=0; j<n; j++){
393: if (im[i]/bs > in[j]/bs) continue; /* ignore lower triangular blocks */
394: in_loc[n_loc] = in[j];
395: if (roworiented) {
396: v_loc[n_loc] = v[i*n+j];
397: } else {
398: v_loc[n_loc] = v[j*m+i];
399: }
400: n_loc++;
401: }
402: MatStashValuesRow_Private(&mat->stash,im[i],n_loc,in_loc,v_loc);
403: }
404: }
405: }
407: if(!baij->donotstash){
408: PetscFree(in_loc);
409: PetscFree(v_loc);
410: }
411: return(0);
412: }
414: int MatSetValuesBlocked_MPISBAIJ_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
415: {
417: SETERRQ(1,"Function not yet written for SBAIJ format");
418: /* return(0); */
419: }
421: #define HASH_KEY 0.6180339887
422: #define HASH(size,key,tmp) (tmp = (key)*HASH_KEY,(int)((size)*(tmp-(int)tmp)))
423: /* #define HASH(size,key) ((int)((size)*fmod(((key)*HASH_KEY),1))) */
424: /* #define HASH(size,key,tmp) ((int)((size)*fmod(((key)*HASH_KEY),1))) */
425: int MatSetValues_MPISBAIJ_HT_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
426: {
428: SETERRQ(1,"Function not yet written for SBAIJ format");
429: /* return(0); */
430: }
432: int MatSetValuesBlocked_MPISBAIJ_HT_MatScalar(Mat mat,int m,int *im,int n,int *in,MatScalar *v,InsertMode addv)
433: {
435: SETERRQ(1,"Function not yet written for SBAIJ format");
436: /* return(0); */
437: }
439: int MatGetValues_MPISBAIJ(Mat mat,int m,int *idxm,int n,int *idxn,PetscScalar *v)
440: {
441: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
442: int bs=baij->bs,ierr,i,j,bsrstart = baij->rstart*bs,bsrend = baij->rend*bs;
443: int bscstart = baij->cstart*bs,bscend = baij->cend*bs,row,col,data;
446: for (i=0; i<m; i++) {
447: if (idxm[i] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
448: if (idxm[i] >= mat->M) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
449: if (idxm[i] >= bsrstart && idxm[i] < bsrend) {
450: row = idxm[i] - bsrstart;
451: for (j=0; j<n; j++) {
452: if (idxn[j] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column");
453: if (idxn[j] >= mat->N) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
454: if (idxn[j] >= bscstart && idxn[j] < bscend){
455: col = idxn[j] - bscstart;
456: MatGetValues_SeqSBAIJ(baij->A,1,&row,1,&col,v+i*n+j);
457: } else {
458: if (!baij->colmap) {
459: CreateColmap_MPISBAIJ_Private(mat);
460: }
461: #if defined (PETSC_USE_CTABLE)
462: PetscTableFind(baij->colmap,idxn[j]/bs+1,&data);
463: data --;
464: #else
465: data = baij->colmap[idxn[j]/bs]-1;
466: #endif
467: if((data < 0) || (baij->garray[data/bs] != idxn[j]/bs)) *(v+i*n+j) = 0.0;
468: else {
469: col = data + idxn[j]%bs;
470: MatGetValues_SeqSBAIJ(baij->B,1,&row,1,&col,v+i*n+j);
471: }
472: }
473: }
474: } else {
475: SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
476: }
477: }
478: return(0);
479: }
481: int MatNorm_MPISBAIJ(Mat mat,NormType type,PetscReal *norm)
482: {
483: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
484: /* Mat_SeqSBAIJ *amat = (Mat_SeqSBAIJ*)baij->A->data; */
485: /* Mat_SeqBAIJ *bmat = (Mat_SeqBAIJ*)baij->B->data; */
486: int ierr;
487: PetscReal sum[2],*lnorm2;
490: if (baij->size == 1) {
491: MatNorm(baij->A,type,norm);
492: } else {
493: if (type == NORM_FROBENIUS) {
494: PetscMalloc(2*sizeof(PetscReal),&lnorm2);
495: MatNorm(baij->A,type,lnorm2);
496: *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2++; /* squar power of norm(A) */
497: MatNorm(baij->B,type,lnorm2);
498: *lnorm2 = (*lnorm2)*(*lnorm2); lnorm2--; /* squar power of norm(B) */
499: /*
500: MPI_Comm_rank(PETSC_COMM_WORLD,&rank);
501: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d], lnorm2=%g, %gn",rank,lnorm2[0],lnorm2[1]);
502: */
503: MPI_Allreduce(lnorm2,&sum,2,MPIU_REAL,MPI_SUM,mat->comm);
504: /*
505: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d], sum=%g, %gn",rank,sum[0],sum[1]);
506: PetscSynchronizedFlush(PETSC_COMM_WORLD); */
507:
508: *norm = sqrt(sum[0] + 2*sum[1]);
509: PetscFree(lnorm2);
510: } else {
511: SETERRQ(PETSC_ERR_SUP,"No support for this norm yet");
512: }
513: }
514: return(0);
515: }
517: /*
518: Creates the hash table, and sets the table
519: This table is created only once.
520: If new entried need to be added to the matrix
521: then the hash table has to be destroyed and
522: recreated.
523: */
524: int MatCreateHashTable_MPISBAIJ_Private(Mat mat,PetscReal factor)
525: {
527: SETERRQ(1,"Function not yet written for SBAIJ format");
528: /* return(0); */
529: }
531: int MatAssemblyBegin_MPISBAIJ(Mat mat,MatAssemblyType mode)
532: {
533: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
534: int ierr,nstash,reallocs;
535: InsertMode addv;
538: if (baij->donotstash) {
539: return(0);
540: }
542: /* make sure all processors are either in INSERTMODE or ADDMODE */
543: MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
544: if (addv == (ADD_VALUES|INSERT_VALUES)) {
545: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
546: }
547: mat->insertmode = addv; /* in case this processor had no cache */
549: MatStashScatterBegin_Private(&mat->stash,baij->rowners_bs);
550: MatStashScatterBegin_Private(&mat->bstash,baij->rowners);
551: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
552: PetscLogInfo(0,"MatAssemblyBegin_MPISBAIJ:Stash has %d entries,uses %d mallocs.n",nstash,reallocs);
553: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
554: PetscLogInfo(0,"MatAssemblyBegin_MPISBAIJ:Block-Stash has %d entries, uses %d mallocs.n",nstash,reallocs);
555: return(0);
556: }
558: int MatAssemblyEnd_MPISBAIJ(Mat mat,MatAssemblyType mode)
559: {
560: Mat_MPISBAIJ *baij=(Mat_MPISBAIJ*)mat->data;
561: Mat_SeqSBAIJ *a=(Mat_SeqSBAIJ*)baij->A->data;
562: Mat_SeqBAIJ *b=(Mat_SeqBAIJ*)baij->B->data;
563: int i,j,rstart,ncols,n,ierr,flg,bs2=baij->bs2;
564: int *row,*col,other_disassembled;
565: PetscTruth r1,r2,r3;
566: MatScalar *val;
567: InsertMode addv = mat->insertmode;
568: /* int rank;*/
571: /* remove 2 line below later */
572: /*MPI_Comm_rank(PETSC_COMM_WORLD, &rank); */
574: if (!baij->donotstash) {
575: while (1) {
576: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
577: /*
578: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d]: in AssemblyEnd, stash, flg=%dn",rank,flg);
579: PetscSynchronizedFlush(PETSC_COMM_WORLD);
580: */
581: if (!flg) break;
583: for (i=0; i<n;) {
584: /* Now identify the consecutive vals belonging to the same row */
585: for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
586: if (j < n) ncols = j-i;
587: else ncols = n-i;
588: /* Now assemble all these values with a single function call */
589: MatSetValues_MPISBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i,addv);
590: i = j;
591: }
592: }
593: MatStashScatterEnd_Private(&mat->stash);
594: /* Now process the block-stash. Since the values are stashed column-oriented,
595: set the roworiented flag to column oriented, and after MatSetValues()
596: restore the original flags */
597: r1 = baij->roworiented;
598: r2 = a->roworiented;
599: r3 = b->roworiented;
600: baij->roworiented = PETSC_FALSE;
601: a->roworiented = PETSC_FALSE;
602: b->roworiented = PETSC_FALSE;
603: while (1) {
604: MatStashScatterGetMesg_Private(&mat->bstash,&n,&row,&col,&val,&flg);
605: if (!flg) break;
606:
607: for (i=0; i<n;) {
608: /* Now identify the consecutive vals belonging to the same row */
609: for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
610: if (j < n) ncols = j-i;
611: else ncols = n-i;
612: MatSetValuesBlocked_MPISBAIJ_MatScalar(mat,1,row+i,ncols,col+i,val+i*bs2,addv);
613: i = j;
614: }
615: }
616: MatStashScatterEnd_Private(&mat->bstash);
617: baij->roworiented = r1;
618: a->roworiented = r2;
619: b->roworiented = r3;
620: }
622: MatAssemblyBegin(baij->A,mode);
623: MatAssemblyEnd(baij->A,mode);
625: /* determine if any processor has disassembled, if so we must
626: also disassemble ourselfs, in order that we may reassemble. */
627: /*
628: if nonzero structure of submatrix B cannot change then we know that
629: no processor disassembled thus we can skip this stuff
630: */
631: if (!((Mat_SeqBAIJ*)baij->B->data)->nonew) {
632: MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
633: if (mat->was_assembled && !other_disassembled) {
634: DisAssemble_MPISBAIJ(mat);
635: }
636: }
638: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
639: MatSetUpMultiply_MPISBAIJ(mat);
640: }
641: MatAssemblyBegin(baij->B,mode);
642: MatAssemblyEnd(baij->B,mode);
643:
644: #if defined(PETSC_USE_BOPT_g)
645: if (baij->ht && mode== MAT_FINAL_ASSEMBLY) {
646: PetscLogInfo(0,"MatAssemblyEnd_MPISBAIJ:Average Hash Table Search in MatSetValues = %5.2fn",((PetscReal)baij->ht_total_ct)/baij->ht_insert_ct);
647: baij->ht_total_ct = 0;
648: baij->ht_insert_ct = 0;
649: }
650: #endif
651: if (baij->ht_flag && !baij->ht && mode == MAT_FINAL_ASSEMBLY) {
652: MatCreateHashTable_MPISBAIJ_Private(mat,baij->ht_fact);
653: mat->ops->setvalues = MatSetValues_MPISBAIJ_HT;
654: mat->ops->setvaluesblocked = MatSetValuesBlocked_MPISBAIJ_HT;
655: }
657: if (baij->rowvalues) {
658: PetscFree(baij->rowvalues);
659: baij->rowvalues = 0;
660: }
661: return(0);
662: }
664: static int MatView_MPISBAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
665: {
666: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
667: int ierr,bs = baij->bs,size = baij->size,rank = baij->rank;
668: PetscTruth isascii,isdraw;
669: PetscViewer sviewer;
670: PetscViewerFormat format;
673: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
674: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
675: if (isascii) {
676: PetscViewerGetFormat(viewer,&format);
677: if (format == PETSC_VIEWER_ASCII_INFO_LONG) {
678: MatInfo info;
679: MPI_Comm_rank(mat->comm,&rank);
680: MatGetInfo(mat,MAT_LOCAL,&info);
681: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %d nz %d nz alloced %d bs %d mem %dn",
682: rank,mat->m,(int)info.nz_used*bs,(int)info.nz_allocated*bs,
683: baij->bs,(int)info.memory);
684: MatGetInfo(baij->A,MAT_LOCAL,&info);
685: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %d n",rank,(int)info.nz_used*bs);
686: MatGetInfo(baij->B,MAT_LOCAL,&info);
687: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %d n",rank,(int)info.nz_used*bs);
688: PetscViewerFlush(viewer);
689: VecScatterView(baij->Mvctx,viewer);
690: return(0);
691: } else if (format == PETSC_VIEWER_ASCII_INFO) {
692: PetscViewerASCIIPrintf(viewer," block size is %dn",bs);
693: return(0);
694: }
695: }
697: if (isdraw) {
698: PetscDraw draw;
699: PetscTruth isnull;
700: PetscViewerDrawGetDraw(viewer,0,&draw);
701: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
702: }
704: if (size == 1) {
705: PetscObjectSetName((PetscObject)baij->A,mat->name);
706: MatView(baij->A,viewer);
707: } else {
708: /* assemble the entire matrix onto first processor. */
709: Mat A;
710: Mat_SeqSBAIJ *Aloc;
711: Mat_SeqBAIJ *Bloc;
712: int M = mat->M,N = mat->N,*ai,*aj,col,i,j,k,*rvals,mbs = baij->mbs;
713: MatScalar *a;
715: if (!rank) {
716: MatCreateMPISBAIJ(mat->comm,baij->bs,M,N,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);
717: } else {
718: MatCreateMPISBAIJ(mat->comm,baij->bs,0,0,M,N,0,PETSC_NULL,0,PETSC_NULL,&A);
719: }
720: PetscLogObjectParent(mat,A);
722: /* copy over the A part */
723: Aloc = (Mat_SeqSBAIJ*)baij->A->data;
724: ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
725: ierr = PetscMalloc(bs*sizeof(int),&rvals);
727: for (i=0; i<mbs; i++) {
728: rvals[0] = bs*(baij->rstart + i);
729: for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
730: for (j=ai[i]; j<ai[i+1]; j++) {
731: col = (baij->cstart+aj[j])*bs;
732: for (k=0; k<bs; k++) {
733: MatSetValues_MPISBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
734: col++; a += bs;
735: }
736: }
737: }
738: /* copy over the B part */
739: Bloc = (Mat_SeqBAIJ*)baij->B->data;
740: ai = Bloc->i; aj = Bloc->j; a = Bloc->a;
741: for (i=0; i<mbs; i++) {
742: rvals[0] = bs*(baij->rstart + i);
743: for (j=1; j<bs; j++) { rvals[j] = rvals[j-1] + 1; }
744: for (j=ai[i]; j<ai[i+1]; j++) {
745: col = baij->garray[aj[j]]*bs;
746: for (k=0; k<bs; k++) {
747: MatSetValues_MPISBAIJ_MatScalar(A,bs,rvals,1,&col,a,INSERT_VALUES);
748: col++; a += bs;
749: }
750: }
751: }
752: PetscFree(rvals);
753: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
754: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
755: /*
756: Everyone has to call to draw the matrix since the graphics waits are
757: synchronized across all processors that share the PetscDraw object
758: */
759: PetscViewerGetSingleton(viewer,&sviewer);
760: if (!rank) {
761: PetscObjectSetName((PetscObject)((Mat_MPISBAIJ*)(A->data))->A,mat->name);
762: MatView(((Mat_MPISBAIJ*)(A->data))->A,sviewer);
763: }
764: PetscViewerRestoreSingleton(viewer,&sviewer);
765: MatDestroy(A);
766: }
767: return(0);
768: }
770: int MatView_MPISBAIJ(Mat mat,PetscViewer viewer)
771: {
772: int ierr;
773: PetscTruth isascii,isdraw,issocket,isbinary;
776: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
777: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
778: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
779: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
780: if (isascii || isdraw || issocket || isbinary) {
781: MatView_MPISBAIJ_ASCIIorDraworSocket(mat,viewer);
782: } else {
783: SETERRQ1(1,"Viewer type %s not supported by MPISBAIJ matrices",((PetscObject)viewer)->type_name);
784: }
785: return(0);
786: }
788: int MatDestroy_MPISBAIJ(Mat mat)
789: {
790: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
791: int ierr;
794: #if defined(PETSC_USE_LOG)
795: PetscLogObjectState((PetscObject)mat,"Rows=%d,Cols=%d",mat->M,mat->N);
796: #endif
797: MatStashDestroy_Private(&mat->stash);
798: MatStashDestroy_Private(&mat->bstash);
799: PetscFree(baij->rowners);
800: MatDestroy(baij->A);
801: MatDestroy(baij->B);
802: #if defined (PETSC_USE_CTABLE)
803: if (baij->colmap) {PetscTableDelete(baij->colmap);}
804: #else
805: if (baij->colmap) {PetscFree(baij->colmap);}
806: #endif
807: if (baij->garray) {PetscFree(baij->garray);}
808: if (baij->lvec) {VecDestroy(baij->lvec);}
809: if (baij->Mvctx) {VecScatterDestroy(baij->Mvctx);}
810: /* if (baij->slvec0) {VecDestroy(baij->slvec0);}
811: if (baij->slvec1) {VecDestroy(baij->slvec1);}
812: if (baij->sMvctx) {VecScatterDestroy(baij->sMvctx);} */
813: if (baij->rowvalues) {PetscFree(baij->rowvalues);}
814: if (baij->barray) {PetscFree(baij->barray);}
815: if (baij->hd) {PetscFree(baij->hd);}
816: #if defined(PETSC_USE_MAT_SINGLE)
817: if (baij->setvaluescopy) {PetscFree(baij->setvaluescopy);}
818: #endif
819: PetscFree(baij);
820: return(0);
821: }
823: int MatMult_MPISBAIJ(Mat A,Vec xx,Vec yy)
824: {
825: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
826: int ierr,nt;
829: VecGetLocalSize(xx,&nt);
830: if (nt != A->n) {
831: SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible partition of A and xx");
832: }
833: VecGetLocalSize(yy,&nt);
834: if (nt != A->m) {
835: SETERRQ(PETSC_ERR_ARG_SIZ,"Incompatible parition of A and yy");
836: }
838: VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
839: /* do diagonal part */
840: (*a->A->ops->mult)(a->A,xx,yy);
841: /* do supperdiagonal part */
842: VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
843: (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
844: /* do subdiagonal part */
845: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
846: VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
847: VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
849: return(0);
850: }
852: int MatMultAdd_MPISBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
853: {
854: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
855: int ierr;
858: VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
859: /* do diagonal part */
860: (*a->A->ops->multadd)(a->A,xx,yy,zz);
861: /* do supperdiagonal part */
862: VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
863: (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
865: /* do subdiagonal part */
866: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
867: VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
868: VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
870: return(0);
871: }
873: int MatMultTranspose_MPISBAIJ(Mat A,Vec xx,Vec yy)
874: {
876: SETERRQ(1,"Matrix is symmetric. Call MatMult().");
877: /* return(0); */
878: }
880: int MatMultTransposeAdd_MPISBAIJ(Mat A,Vec xx,Vec yy,Vec zz)
881: {
883: SETERRQ(1,"Matrix is symmetric. Call MatMultAdd().");
884: /* return(0); */
885: }
887: /*
888: This only works correctly for square matrices where the subblock A->A is the
889: diagonal block
890: */
891: int MatGetDiagonal_MPISBAIJ(Mat A,Vec v)
892: {
893: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
894: int ierr;
897: /* if (a->M != a->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block"); */
898: MatGetDiagonal(a->A,v);
899: return(0);
900: }
902: int MatScale_MPISBAIJ(PetscScalar *aa,Mat A)
903: {
904: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
905: int ierr;
908: MatScale(aa,a->A);
909: MatScale(aa,a->B);
910: return(0);
911: }
913: int MatGetRow_MPISBAIJ(Mat matin,int row,int *nz,int **idx,PetscScalar **v)
914: {
915: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)matin->data;
916: PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
917: int bs = mat->bs,bs2 = mat->bs2,i,ierr,*cworkA,*cworkB,**pcA,**pcB;
918: int nztot,nzA,nzB,lrow,brstart = mat->rstart*bs,brend = mat->rend*bs;
919: int *cmap,*idx_p,cstart = mat->cstart;
922: if (mat->getrowactive == PETSC_TRUE) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
923: mat->getrowactive = PETSC_TRUE;
925: if (!mat->rowvalues && (idx || v)) {
926: /*
927: allocate enough space to hold information from the longest row.
928: */
929: Mat_SeqSBAIJ *Aa = (Mat_SeqSBAIJ*)mat->A->data;
930: Mat_SeqBAIJ *Ba = (Mat_SeqBAIJ*)mat->B->data;
931: int max = 1,mbs = mat->mbs,tmp;
932: for (i=0; i<mbs; i++) {
933: tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i]; /* row length */
934: if (max < tmp) { max = tmp; }
935: }
936: PetscMalloc(max*bs2*(sizeof(int)+sizeof(PetscScalar)),&mat->rowvalues);
937: mat->rowindices = (int*)(mat->rowvalues + max*bs2);
938: }
939:
940: if (row < brstart || row >= brend) SETERRQ(PETSC_ERR_SUP,"Only local rows")
941: lrow = row - brstart; /* local row index */
943: pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
944: if (!v) {pvA = 0; pvB = 0;}
945: if (!idx) {pcA = 0; if (!v) pcB = 0;}
946: (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
947: (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
948: nztot = nzA + nzB;
950: cmap = mat->garray;
951: if (v || idx) {
952: if (nztot) {
953: /* Sort by increasing column numbers, assuming A and B already sorted */
954: int imark = -1;
955: if (v) {
956: *v = v_p = mat->rowvalues;
957: for (i=0; i<nzB; i++) {
958: if (cmap[cworkB[i]/bs] < cstart) v_p[i] = vworkB[i];
959: else break;
960: }
961: imark = i;
962: for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i];
963: for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i];
964: }
965: if (idx) {
966: *idx = idx_p = mat->rowindices;
967: if (imark > -1) {
968: for (i=0; i<imark; i++) {
969: idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs;
970: }
971: } else {
972: for (i=0; i<nzB; i++) {
973: if (cmap[cworkB[i]/bs] < cstart)
974: idx_p[i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
975: else break;
976: }
977: imark = i;
978: }
979: for (i=0; i<nzA; i++) idx_p[imark+i] = cstart*bs + cworkA[i];
980: for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]/bs]*bs + cworkB[i]%bs ;
981: }
982: } else {
983: if (idx) *idx = 0;
984: if (v) *v = 0;
985: }
986: }
987: *nz = nztot;
988: (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
989: (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
990: return(0);
991: }
993: int MatRestoreRow_MPISBAIJ(Mat mat,int row,int *nz,int **idx,PetscScalar **v)
994: {
995: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
998: if (baij->getrowactive == PETSC_FALSE) {
999: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow not called");
1000: }
1001: baij->getrowactive = PETSC_FALSE;
1002: return(0);
1003: }
1005: int MatGetBlockSize_MPISBAIJ(Mat mat,int *bs)
1006: {
1007: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
1010: *bs = baij->bs;
1011: return(0);
1012: }
1014: int MatZeroEntries_MPISBAIJ(Mat A)
1015: {
1016: Mat_MPISBAIJ *l = (Mat_MPISBAIJ*)A->data;
1017: int ierr;
1020: MatZeroEntries(l->A);
1021: MatZeroEntries(l->B);
1022: return(0);
1023: }
1025: int MatGetInfo_MPISBAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1026: {
1027: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)matin->data;
1028: Mat A = a->A,B = a->B;
1029: int ierr;
1030: PetscReal isend[5],irecv[5];
1033: info->block_size = (PetscReal)a->bs;
1034: MatGetInfo(A,MAT_LOCAL,info);
1035: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1036: isend[3] = info->memory; isend[4] = info->mallocs;
1037: MatGetInfo(B,MAT_LOCAL,info);
1038: isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1039: isend[3] += info->memory; isend[4] += info->mallocs;
1040: if (flag == MAT_LOCAL) {
1041: info->nz_used = isend[0];
1042: info->nz_allocated = isend[1];
1043: info->nz_unneeded = isend[2];
1044: info->memory = isend[3];
1045: info->mallocs = isend[4];
1046: } else if (flag == MAT_GLOBAL_MAX) {
1047: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1048: info->nz_used = irecv[0];
1049: info->nz_allocated = irecv[1];
1050: info->nz_unneeded = irecv[2];
1051: info->memory = irecv[3];
1052: info->mallocs = irecv[4];
1053: } else if (flag == MAT_GLOBAL_SUM) {
1054: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1055: info->nz_used = irecv[0];
1056: info->nz_allocated = irecv[1];
1057: info->nz_unneeded = irecv[2];
1058: info->memory = irecv[3];
1059: info->mallocs = irecv[4];
1060: } else {
1061: SETERRQ1(1,"Unknown MatInfoType argument %d",flag);
1062: }
1063: info->rows_global = (PetscReal)A->M;
1064: info->columns_global = (PetscReal)A->N;
1065: info->rows_local = (PetscReal)A->m;
1066: info->columns_local = (PetscReal)A->N;
1067: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1068: info->fill_ratio_needed = 0;
1069: info->factor_mallocs = 0;
1070: return(0);
1071: }
1073: int MatSetOption_MPISBAIJ(Mat A,MatOption op)
1074: {
1075: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1076: int ierr;
1079: switch (op) {
1080: case MAT_NO_NEW_NONZERO_LOCATIONS:
1081: case MAT_YES_NEW_NONZERO_LOCATIONS:
1082: case MAT_COLUMNS_UNSORTED:
1083: case MAT_COLUMNS_SORTED:
1084: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1085: case MAT_KEEP_ZEROED_ROWS:
1086: case MAT_NEW_NONZERO_LOCATION_ERR:
1087: MatSetOption(a->A,op);
1088: MatSetOption(a->B,op);
1089: break;
1090: case MAT_ROW_ORIENTED:
1091: a->roworiented = PETSC_TRUE;
1092: MatSetOption(a->A,op);
1093: MatSetOption(a->B,op);
1094: break;
1095: case MAT_ROWS_SORTED:
1096: case MAT_ROWS_UNSORTED:
1097: case MAT_YES_NEW_DIAGONALS:
1098: case MAT_USE_SINGLE_PRECISION_SOLVES:
1099: PetscLogInfo(A,"Info:MatSetOption_MPIBAIJ:Option ignoredn");
1100: break;
1101: case MAT_COLUMN_ORIENTED:
1102: a->roworiented = PETSC_FALSE;
1103: MatSetOption(a->A,op);
1104: MatSetOption(a->B,op);
1105: break;
1106: case MAT_IGNORE_OFF_PROC_ENTRIES:
1107: a->donotstash = PETSC_TRUE;
1108: break;
1109: case MAT_NO_NEW_DIAGONALS:
1110: SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1111: case MAT_USE_HASH_TABLE:
1112: a->ht_flag = PETSC_TRUE;
1113: break;
1114: default:
1115: SETERRQ(PETSC_ERR_SUP,"unknown option");
1116: }
1117: return(0);
1118: }
1120: int MatTranspose_MPISBAIJ(Mat A,Mat *matout)
1121: {
1123: SETERRQ(1,"Matrix is symmetric. MatTranspose() should not be called");
1124: /* return(0); */
1125: }
1127: int MatDiagonalScale_MPISBAIJ(Mat mat,Vec ll,Vec rr)
1128: {
1129: Mat_MPISBAIJ *baij = (Mat_MPISBAIJ*)mat->data;
1130: Mat a = baij->A,b = baij->B;
1131: int ierr,s1,s2,s3;
1134: if (ll != rr) {
1135: SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"For symmetric format, left and right scaling vectors must be samen");
1136: }
1137: MatGetLocalSize(mat,&s2,&s3);
1138: if (rr) {
1139: VecGetLocalSize(rr,&s1);
1140: if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1141: /* Overlap communication with computation. */
1142: VecScatterBegin(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1143: /*} if (ll) { */
1144: VecGetLocalSize(ll,&s1);
1145: if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1146: (*b->ops->diagonalscale)(b,ll,PETSC_NULL);
1147: /* } */
1148: /* scale the diagonal block */
1149: (*a->ops->diagonalscale)(a,ll,rr);
1151: /* if (rr) { */
1152: /* Do a scatter end and then right scale the off-diagonal block */
1153: VecScatterEnd(rr,baij->lvec,INSERT_VALUES,SCATTER_FORWARD,baij->Mvctx);
1154: (*b->ops->diagonalscale)(b,PETSC_NULL,baij->lvec);
1155: }
1156:
1157: return(0);
1158: }
1160: int MatZeroRows_MPISBAIJ(Mat A,IS is,PetscScalar *diag)
1161: {
1162: Mat_MPISBAIJ *l = (Mat_MPISBAIJ*)A->data;
1163: int i,ierr,N,*rows,*owners = l->rowners,size = l->size;
1164: int *procs,*nprocs,j,idx,nsends,*work,row;
1165: int nmax,*svalues,*starts,*owner,nrecvs,rank = l->rank;
1166: int *rvalues,tag = A->tag,count,base,slen,n,*source;
1167: int *lens,imdex,*lrows,*values,bs=l->bs,rstart_bs=l->rstart_bs;
1168: MPI_Comm comm = A->comm;
1169: MPI_Request *send_waits,*recv_waits;
1170: MPI_Status recv_status,*send_status;
1171: IS istmp;
1172: PetscTruth found;
1175: ISGetSize(is,&N);
1176: ISGetIndices(is,&rows);
1177:
1178: /* first count number of contributors to each processor */
1179: ierr = PetscMalloc(2*size*sizeof(int),&nprocs);
1180: ierr = PetscMemzero(nprocs,2*size*sizeof(int));
1181: procs = nprocs + size;
1182: ierr = PetscMalloc((N+1)*sizeof(int),&owner); /* see note*/
1183: for (i=0; i<N; i++) {
1184: idx = rows[i];
1185: found = PETSC_FALSE;
1186: for (j=0; j<size; j++) {
1187: if (idx >= owners[j]*bs && idx < owners[j+1]*bs) {
1188: nprocs[j]++; procs[j] = 1; owner[i] = j; found = PETSC_TRUE; break;
1189: }
1190: }
1191: if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
1192: }
1193: nsends = 0; for (i=0; i<size; i++) { nsends += procs[i];}
1194:
1195: /* inform other processors of number of messages and max length*/
1196: ierr = PetscMalloc(2*size*sizeof(int),&work);
1197: ierr = MPI_Allreduce(nprocs,work,2*size,MPI_INT,PetscMaxSum_Op,comm);
1198: nmax = work[rank];
1199: nrecvs = work[size+rank];
1200: ierr = PetscFree(work);
1201:
1202: /* post receives: */
1203: PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(int),&rvalues);
1204: PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
1205: for (i=0; i<nrecvs; i++) {
1206: MPI_Irecv(rvalues+nmax*i,nmax,MPI_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
1207: }
1208:
1209: /* do sends:
1210: 1) starts[i] gives the starting index in svalues for stuff going to
1211: the ith processor
1212: */
1213: PetscMalloc((N+1)*sizeof(int),&svalues);
1214: PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
1215: PetscMalloc((size+1)*sizeof(int),&starts);
1216: starts[0] = 0;
1217: for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[i-1];}
1218: for (i=0; i<N; i++) {
1219: svalues[starts[owner[i]]++] = rows[i];
1220: }
1221: ISRestoreIndices(is,&rows);
1222:
1223: starts[0] = 0;
1224: for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[i-1];}
1225: count = 0;
1226: for (i=0; i<size; i++) {
1227: if (procs[i]) {
1228: MPI_Isend(svalues+starts[i],nprocs[i],MPI_INT,i,tag,comm,send_waits+count++);
1229: }
1230: }
1231: PetscFree(starts);
1233: base = owners[rank]*bs;
1234:
1235: /* wait on receives */
1236: ierr = PetscMalloc(2*(nrecvs+1)*sizeof(int),&lens);
1237: source = lens + nrecvs;
1238: count = nrecvs; slen = 0;
1239: while (count) {
1240: MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
1241: /* unpack receives into our local space */
1242: MPI_Get_count(&recv_status,MPI_INT,&n);
1243: source[imdex] = recv_status.MPI_SOURCE;
1244: lens[imdex] = n;
1245: slen += n;
1246: count--;
1247: }
1248: PetscFree(recv_waits);
1249:
1250: /* move the data into the send scatter */
1251: PetscMalloc((slen+1)*sizeof(int),&lrows);
1252: count = 0;
1253: for (i=0; i<nrecvs; i++) {
1254: values = rvalues + i*nmax;
1255: for (j=0; j<lens[i]; j++) {
1256: lrows[count++] = values[j] - base;
1257: }
1258: }
1259: PetscFree(rvalues);
1260: PetscFree(lens);
1261: PetscFree(owner);
1262: PetscFree(nprocs);
1263:
1264: /* actually zap the local rows */
1265: ISCreateGeneral(PETSC_COMM_SELF,slen,lrows,&istmp);
1266: PetscLogObjectParent(A,istmp);
1268: /*
1269: Zero the required rows. If the "diagonal block" of the matrix
1270: is square and the user wishes to set the diagonal we use seperate
1271: code so that MatSetValues() is not called for each diagonal allocating
1272: new memory, thus calling lots of mallocs and slowing things down.
1274: Contributed by: Mathew Knepley
1275: */
1276: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
1277: MatZeroRows_SeqBAIJ(l->B,istmp,0);
1278: if (diag && (l->A->M == l->A->N)) {
1279: MatZeroRows_SeqSBAIJ(l->A,istmp,diag);
1280: } else if (diag) {
1281: MatZeroRows_SeqSBAIJ(l->A,istmp,0);
1282: if (((Mat_SeqSBAIJ*)l->A->data)->nonew) {
1283: SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options n
1284: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
1285: }
1286: for (i=0; i<slen; i++) {
1287: row = lrows[i] + rstart_bs;
1288: MatSetValues(A,1,&row,1,&row,diag,INSERT_VALUES);
1289: }
1290: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1291: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1292: } else {
1293: MatZeroRows_SeqSBAIJ(l->A,istmp,0);
1294: }
1296: ISDestroy(istmp);
1297: PetscFree(lrows);
1299: /* wait on sends */
1300: if (nsends) {
1301: PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
1302: ierr = MPI_Waitall(nsends,send_waits,send_status);
1303: ierr = PetscFree(send_status);
1304: }
1305: PetscFree(send_waits);
1306: PetscFree(svalues);
1308: return(0);
1309: }
1311: int MatPrintHelp_MPISBAIJ(Mat A)
1312: {
1313: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1314: MPI_Comm comm = A->comm;
1315: static int called = 0;
1316: int ierr;
1319: if (!a->rank) {
1320: MatPrintHelp_SeqSBAIJ(a->A);
1321: }
1322: if (called) {return(0);} else called = 1;
1323: (*PetscHelpPrintf)(comm," Options for MATMPISBAIJ matrix format (the defaults):n");
1324: (*PetscHelpPrintf)(comm," -mat_use_hash_table <factor>: Use hashtable for efficient matrix assemblyn");
1325: return(0);
1326: }
1328: int MatSetUnfactored_MPISBAIJ(Mat A)
1329: {
1330: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
1331: int ierr;
1334: MatSetUnfactored(a->A);
1335: return(0);
1336: }
1338: static int MatDuplicate_MPISBAIJ(Mat,MatDuplicateOption,Mat *);
1340: int MatEqual_MPISBAIJ(Mat A,Mat B,PetscTruth *flag)
1341: {
1342: Mat_MPISBAIJ *matB = (Mat_MPISBAIJ*)B->data,*matA = (Mat_MPISBAIJ*)A->data;
1343: Mat a,b,c,d;
1344: PetscTruth flg;
1345: int ierr;
1348: PetscTypeCompare((PetscObject)B,MATMPISBAIJ,&flg);
1349: if (!flg) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type");
1350: a = matA->A; b = matA->B;
1351: c = matB->A; d = matB->B;
1353: MatEqual(a,c,&flg);
1354: if (flg == PETSC_TRUE) {
1355: MatEqual(b,d,&flg);
1356: }
1357: MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1358: return(0);
1359: }
1361: int MatSetUpPreallocation_MPISBAIJ(Mat A)
1362: {
1363: int ierr;
1366: MatMPISBAIJSetPreallocation(A,1,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1367: return(0);
1368: }
1369: /* -------------------------------------------------------------------*/
1370: static struct _MatOps MatOps_Values = {
1371: MatSetValues_MPISBAIJ,
1372: MatGetRow_MPISBAIJ,
1373: MatRestoreRow_MPISBAIJ,
1374: MatMult_MPISBAIJ,
1375: MatMultAdd_MPISBAIJ,
1376: MatMultTranspose_MPISBAIJ,
1377: MatMultTransposeAdd_MPISBAIJ,
1378: 0,
1379: 0,
1380: 0,
1381: 0,
1382: 0,
1383: 0,
1384: MatRelax_MPISBAIJ,
1385: MatTranspose_MPISBAIJ,
1386: MatGetInfo_MPISBAIJ,
1387: MatEqual_MPISBAIJ,
1388: MatGetDiagonal_MPISBAIJ,
1389: MatDiagonalScale_MPISBAIJ,
1390: MatNorm_MPISBAIJ,
1391: MatAssemblyBegin_MPISBAIJ,
1392: MatAssemblyEnd_MPISBAIJ,
1393: 0,
1394: MatSetOption_MPISBAIJ,
1395: MatZeroEntries_MPISBAIJ,
1396: MatZeroRows_MPISBAIJ,
1397: 0,
1398: 0,
1399: 0,
1400: 0,
1401: MatSetUpPreallocation_MPISBAIJ,
1402: 0,
1403: 0,
1404: 0,
1405: 0,
1406: MatDuplicate_MPISBAIJ,
1407: 0,
1408: 0,
1409: 0,
1410: 0,
1411: 0,
1412: MatGetSubMatrices_MPISBAIJ,
1413: MatIncreaseOverlap_MPISBAIJ,
1414: MatGetValues_MPISBAIJ,
1415: 0,
1416: MatPrintHelp_MPISBAIJ,
1417: MatScale_MPISBAIJ,
1418: 0,
1419: 0,
1420: 0,
1421: MatGetBlockSize_MPISBAIJ,
1422: 0,
1423: 0,
1424: 0,
1425: 0,
1426: 0,
1427: 0,
1428: MatSetUnfactored_MPISBAIJ,
1429: 0,
1430: MatSetValuesBlocked_MPISBAIJ,
1431: 0,
1432: 0,
1433: 0,
1434: MatGetPetscMaps_Petsc,
1435: 0,
1436: 0,
1437: 0,
1438: 0,
1439: 0,
1440: 0,
1441: MatGetRowMax_MPISBAIJ};
1444: EXTERN_C_BEGIN
1445: int MatGetDiagonalBlock_MPISBAIJ(Mat A,PetscTruth *iscopy,MatReuse reuse,Mat *a)
1446: {
1448: *a = ((Mat_MPISBAIJ *)A->data)->A;
1449: *iscopy = PETSC_FALSE;
1450: return(0);
1451: }
1452: EXTERN_C_END
1454: EXTERN_C_BEGIN
1455: int MatCreate_MPISBAIJ(Mat B)
1456: {
1457: Mat_MPISBAIJ *b;
1458: int ierr;
1459: PetscTruth flg;
1463: ierr = PetscNew(Mat_MPISBAIJ,&b);
1464: B->data = (void*)b;
1465: ierr = PetscMemzero(b,sizeof(Mat_MPISBAIJ));
1466: ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
1468: B->ops->destroy = MatDestroy_MPISBAIJ;
1469: B->ops->view = MatView_MPISBAIJ;
1470: B->mapping = 0;
1471: B->factor = 0;
1472: B->assembled = PETSC_FALSE;
1474: B->insertmode = NOT_SET_VALUES;
1475: MPI_Comm_rank(B->comm,&b->rank);
1476: MPI_Comm_size(B->comm,&b->size);
1478: /* build local table of row and column ownerships */
1479: ierr = PetscMalloc(3*(b->size+2)*sizeof(int),&b->rowners);
1480: b->cowners = b->rowners + b->size + 2;
1481: b->rowners_bs = b->cowners + b->size + 2;
1482: PetscLogObjectMemory(B,3*(b->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPISBAIJ));
1484: /* build cache for off array entries formed */
1485: MatStashCreate_Private(B->comm,1,&B->stash);
1486: b->donotstash = PETSC_FALSE;
1487: b->colmap = PETSC_NULL;
1488: b->garray = PETSC_NULL;
1489: b->roworiented = PETSC_TRUE;
1491: #if defined(PETSC_USE_MAT_SINGLE)
1492: /* stuff for MatSetValues_XXX in single precision */
1493: b->setvalueslen = 0;
1494: b->setvaluescopy = PETSC_NULL;
1495: #endif
1497: /* stuff used in block assembly */
1498: b->barray = 0;
1500: /* stuff used for matrix vector multiply */
1501: b->lvec = 0;
1502: b->Mvctx = 0;
1504: /* stuff for MatGetRow() */
1505: b->rowindices = 0;
1506: b->rowvalues = 0;
1507: b->getrowactive = PETSC_FALSE;
1509: /* hash table stuff */
1510: b->ht = 0;
1511: b->hd = 0;
1512: b->ht_size = 0;
1513: b->ht_flag = PETSC_FALSE;
1514: b->ht_fact = 0;
1515: b->ht_total_ct = 0;
1516: b->ht_insert_ct = 0;
1518: PetscOptionsHasName(PETSC_NULL,"-mat_use_hash_table",&flg);
1519: if (flg) {
1520: PetscReal fact = 1.39;
1521: MatSetOption(B,MAT_USE_HASH_TABLE);
1522: PetscOptionsGetReal(PETSC_NULL,"-mat_use_hash_table",&fact,PETSC_NULL);
1523: if (fact <= 1.0) fact = 1.39;
1524: MatMPIBAIJSetHashTableFactor(B,fact);
1525: PetscLogInfo(0,"MatCreateMPISBAIJ:Hash table Factor used %5.2fn",fact);
1526: }
1527: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
1528: "MatStoreValues_MPISBAIJ",
1529: MatStoreValues_MPISBAIJ);
1530: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
1531: "MatRetrieveValues_MPISBAIJ",
1532: MatRetrieveValues_MPISBAIJ);
1533: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
1534: "MatGetDiagonalBlock_MPISBAIJ",
1535: MatGetDiagonalBlock_MPISBAIJ);
1536: return(0);
1537: }
1538: EXTERN_C_END
1540: /*@C
1541: MatMPISBAIJSetPreallocation - For good matrix assembly performance
1542: the user should preallocate the matrix storage by setting the parameters
1543: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
1544: performance can be increased by more than a factor of 50.
1546: Collective on Mat
1548: Input Parameters:
1549: + A - the matrix
1550: . bs - size of blockk
1551: . d_nz - number of block nonzeros per block row in diagonal portion of local
1552: submatrix (same for all local rows)
1553: . d_nnz - array containing the number of block nonzeros in the various block rows
1554: of the in diagonal portion of the local (possibly different for each block
1555: row) or PETSC_NULL. You must leave room for the diagonal entry even if it is zero.
1556: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
1557: submatrix (same for all local rows).
1558: - o_nnz - array containing the number of nonzeros in the various block rows of the
1559: off-diagonal portion of the local submatrix (possibly different for
1560: each block row) or PETSC_NULL.
1563: Options Database Keys:
1564: . -mat_no_unroll - uses code that does not unroll the loops in the
1565: block calculations (much slower)
1566: . -mat_block_size - size of the blocks to use
1568: Notes:
1570: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor
1571: than it must be used on all processors that share the object for that argument.
1573: Storage Information:
1574: For a square global matrix we define each processor's diagonal portion
1575: to be its local rows and the corresponding columns (a square submatrix);
1576: each processor's off-diagonal portion encompasses the remainder of the
1577: local matrix (a rectangular submatrix).
1579: The user can specify preallocated storage for the diagonal part of
1580: the local submatrix with either d_nz or d_nnz (not both). Set
1581: d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
1582: memory allocation. Likewise, specify preallocated storage for the
1583: off-diagonal part of the local submatrix with o_nz or o_nnz (not both).
1585: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1586: the figure below we depict these three local rows and all columns (0-11).
1588: .vb
1589: 0 1 2 3 4 5 6 7 8 9 10 11
1590: -------------------
1591: row 3 | o o o d d d o o o o o o
1592: row 4 | o o o d d d o o o o o o
1593: row 5 | o o o d d d o o o o o o
1594: -------------------
1595: .ve
1596:
1597: Thus, any entries in the d locations are stored in the d (diagonal)
1598: submatrix, and any entries in the o locations are stored in the
1599: o (off-diagonal) submatrix. Note that the d and the o submatrices are
1600: stored simply in the MATSEQBAIJ format for compressed row storage.
1602: Now d_nz should indicate the number of block nonzeros per row in the d matrix,
1603: and o_nz should indicate the number of block nonzeros per row in the o matrix.
1604: In general, for PDE problems in which most nonzeros are near the diagonal,
1605: one expects d_nz >> o_nz. For large problems you MUST preallocate memory
1606: or you will get TERRIBLE performance; see the users' manual chapter on
1607: matrices.
1609: Level: intermediate
1611: .keywords: matrix, block, aij, compressed row, sparse, parallel
1613: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
1614: @*/
1616: int MatMPISBAIJSetPreallocation(Mat B,int bs,int d_nz,int *d_nnz,int o_nz,int *o_nnz)
1617: {
1618: Mat_MPISBAIJ *b;
1619: int ierr,i,mbs,Mbs;
1620: PetscTruth flg2;
1623: PetscTypeCompare((PetscObject)B,MATMPISBAIJ,&flg2);
1624: if (!flg2) return(0);
1626: PetscOptionsGetInt(PETSC_NULL,"-mat_block_size",&bs,PETSC_NULL);
1628: if (bs < 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive");
1629: if (d_nz == PETSC_DECIDE || d_nz == PETSC_DEFAULT) d_nz = 3;
1630: if (o_nz == PETSC_DECIDE || o_nz == PETSC_DEFAULT) o_nz = 1;
1631: if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %d",d_nz);
1632: if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %d",o_nz);
1633: if (d_nnz) {
1634: for (i=0; i<B->m/bs; i++) {
1635: 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]);
1636: }
1637: }
1638: if (o_nnz) {
1639: for (i=0; i<B->m/bs; i++) {
1640: 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]);
1641: }
1642: }
1643: B->preallocated = PETSC_TRUE;
1644: PetscSplitOwnershipBlock(B->comm,bs,&B->m,&B->M);
1645: PetscSplitOwnershipBlock(B->comm,bs,&B->n,&B->N);
1646: PetscMapCreateMPI(B->comm,B->m,B->M,&B->rmap);
1647: PetscMapCreateMPI(B->comm,B->m,B->M,&B->cmap);
1649: b = (Mat_MPISBAIJ*)B->data;
1650: mbs = B->m/bs;
1651: Mbs = B->M/bs;
1652: if (mbs*bs != B->m) {
1653: SETERRQ2(PETSC_ERR_ARG_SIZ,"No of local rows %d must be divisible by blocksize %d",B->m,bs);
1654: }
1656: b->bs = bs;
1657: b->bs2 = bs*bs;
1658: b->mbs = mbs;
1659: b->nbs = mbs;
1660: b->Mbs = Mbs;
1661: b->Nbs = Mbs;
1663: MPI_Allgather(&b->mbs,1,MPI_INT,b->rowners+1,1,MPI_INT,B->comm);
1664: b->rowners[0] = 0;
1665: for (i=2; i<=b->size; i++) {
1666: b->rowners[i] += b->rowners[i-1];
1667: }
1668: b->rstart = b->rowners[b->rank];
1669: b->rend = b->rowners[b->rank+1];
1670: b->cstart = b->rstart;
1671: b->cend = b->rend;
1672: for (i=0; i<=b->size; i++) {
1673: b->rowners_bs[i] = b->rowners[i]*bs;
1674: }
1675: b->rstart_bs = b-> rstart*bs;
1676: b->rend_bs = b->rend*bs;
1677:
1678: b->cstart_bs = b->cstart*bs;
1679: b->cend_bs = b->cend*bs;
1680:
1682: MatCreateSeqSBAIJ(PETSC_COMM_SELF,bs,B->m,B->m,d_nz,d_nnz,&b->A);
1683: PetscLogObjectParent(B,b->A);
1684: MatCreateSeqBAIJ(PETSC_COMM_SELF,bs,B->m,B->M,o_nz,o_nnz,&b->B);
1685: PetscLogObjectParent(B,b->B);
1687: /* build cache for off array entries formed */
1688: MatStashCreate_Private(B->comm,bs,&B->bstash);
1690: return(0);
1691: }
1693: /*@C
1694: MatCreateMPISBAIJ - Creates a sparse parallel matrix in symmetric block AIJ format
1695: (block compressed row). For good matrix assembly performance
1696: the user should preallocate the matrix storage by setting the parameters
1697: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
1698: performance can be increased by more than a factor of 50.
1700: Collective on MPI_Comm
1702: Input Parameters:
1703: + comm - MPI communicator
1704: . bs - size of blockk
1705: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
1706: This value should be the same as the local size used in creating the
1707: y vector for the matrix-vector product y = Ax.
1708: . n - number of local columns (or PETSC_DECIDE to have calculated if N is given)
1709: This value should be the same as the local size used in creating the
1710: x vector for the matrix-vector product y = Ax.
1711: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
1712: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
1713: . d_nz - number of block nonzeros per block row in diagonal portion of local
1714: submatrix (same for all local rows)
1715: . d_nnz - array containing the number of block nonzeros in the various block rows
1716: of the in diagonal portion of the local (possibly different for each block
1717: row) or PETSC_NULL. You must leave room for the diagonal entry even if it is zero.
1718: . o_nz - number of block nonzeros per block row in the off-diagonal portion of local
1719: submatrix (same for all local rows).
1720: - o_nnz - array containing the number of nonzeros in the various block rows of the
1721: off-diagonal portion of the local submatrix (possibly different for
1722: each block row) or PETSC_NULL.
1724: Output Parameter:
1725: . A - the matrix
1727: Options Database Keys:
1728: . -mat_no_unroll - uses code that does not unroll the loops in the
1729: block calculations (much slower)
1730: . -mat_block_size - size of the blocks to use
1731: . -mat_mpi - use the parallel matrix data structures even on one processor
1732: (defaults to using SeqBAIJ format on one processor)
1734: Notes:
1735: The user MUST specify either the local or global matrix dimensions
1736: (possibly both).
1738: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one processor
1739: than it must be used on all processors that share the object for that argument.
1741: Storage Information:
1742: For a square global matrix we define each processor's diagonal portion
1743: to be its local rows and the corresponding columns (a square submatrix);
1744: each processor's off-diagonal portion encompasses the remainder of the
1745: local matrix (a rectangular submatrix).
1747: The user can specify preallocated storage for the diagonal part of
1748: the local submatrix with either d_nz or d_nnz (not both). Set
1749: d_nz=PETSC_DEFAULT and d_nnz=PETSC_NULL for PETSc to control dynamic
1750: memory allocation. Likewise, specify preallocated storage for the
1751: off-diagonal part of the local submatrix with o_nz or o_nnz (not both).
1753: Consider a processor that owns rows 3, 4 and 5 of a parallel matrix. In
1754: the figure below we depict these three local rows and all columns (0-11).
1756: .vb
1757: 0 1 2 3 4 5 6 7 8 9 10 11
1758: -------------------
1759: row 3 | o o o d d d o o o o o o
1760: row 4 | o o o d d d o o o o o o
1761: row 5 | o o o d d d o o o o o o
1762: -------------------
1763: .ve
1764:
1765: Thus, any entries in the d locations are stored in the d (diagonal)
1766: submatrix, and any entries in the o locations are stored in the
1767: o (off-diagonal) submatrix. Note that the d and the o submatrices are
1768: stored simply in the MATSEQBAIJ format for compressed row storage.
1770: Now d_nz should indicate the number of block nonzeros per row in the d matrix,
1771: and o_nz should indicate the number of block nonzeros per row in the o matrix.
1772: In general, for PDE problems in which most nonzeros are near the diagonal,
1773: one expects d_nz >> o_nz. For large problems you MUST preallocate memory
1774: or you will get TERRIBLE performance; see the users' manual chapter on
1775: matrices.
1777: Level: intermediate
1779: .keywords: matrix, block, aij, compressed row, sparse, parallel
1781: .seealso: MatCreate(), MatCreateSeqSBAIJ(), MatSetValues(), MatCreateMPIBAIJ()
1782: @*/
1784: 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)
1785: {
1786: int ierr,size;
1789: MatCreate(comm,m,n,M,N,A);
1790: MPI_Comm_size(comm,&size);
1791: if (size > 1) {
1792: MatSetType(*A,MATMPISBAIJ);
1793: MatMPISBAIJSetPreallocation(*A,bs,d_nz,d_nnz,o_nz,o_nnz);
1794: } else {
1795: MatSetType(*A,MATSEQSBAIJ);
1796: MatSeqSBAIJSetPreallocation(*A,bs,d_nz,d_nnz);
1797: }
1798: return(0);
1799: }
1802: static int MatDuplicate_MPISBAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
1803: {
1804: Mat mat;
1805: Mat_MPISBAIJ *a,*oldmat = (Mat_MPISBAIJ*)matin->data;
1806: int ierr,len=0;
1809: *newmat = 0;
1810: MatCreate(matin->comm,matin->m,matin->n,matin->M,matin->N,&mat);
1811: MatSetType(mat,MATMPISBAIJ);
1812: mat->preallocated = PETSC_TRUE;
1813: a = (Mat_MPISBAIJ*)mat->data;
1814: a->bs = oldmat->bs;
1815: a->bs2 = oldmat->bs2;
1816: a->mbs = oldmat->mbs;
1817: a->nbs = oldmat->nbs;
1818: a->Mbs = oldmat->Mbs;
1819: a->Nbs = oldmat->Nbs;
1820:
1821: a->rstart = oldmat->rstart;
1822: a->rend = oldmat->rend;
1823: a->cstart = oldmat->cstart;
1824: a->cend = oldmat->cend;
1825: a->size = oldmat->size;
1826: a->rank = oldmat->rank;
1827: a->donotstash = oldmat->donotstash;
1828: a->roworiented = oldmat->roworiented;
1829: a->rowindices = 0;
1830: a->rowvalues = 0;
1831: a->getrowactive = PETSC_FALSE;
1832: a->barray = 0;
1833: a->rstart_bs = oldmat->rstart_bs;
1834: a->rend_bs = oldmat->rend_bs;
1835: a->cstart_bs = oldmat->cstart_bs;
1836: a->cend_bs = oldmat->cend_bs;
1838: /* hash table stuff */
1839: a->ht = 0;
1840: a->hd = 0;
1841: a->ht_size = 0;
1842: a->ht_flag = oldmat->ht_flag;
1843: a->ht_fact = oldmat->ht_fact;
1844: a->ht_total_ct = 0;
1845: a->ht_insert_ct = 0;
1847: PetscMalloc(3*(a->size+2)*sizeof(int),&a->rowners);
1848: PetscLogObjectMemory(mat,3*(a->size+2)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_MPISBAIJ));
1849: a->cowners = a->rowners + a->size + 2;
1850: a->rowners_bs = a->cowners + a->size + 2;
1851: PetscMemcpy(a->rowners,oldmat->rowners,3*(a->size+2)*sizeof(int));
1852: MatStashCreate_Private(matin->comm,1,&mat->stash);
1853: MatStashCreate_Private(matin->comm,oldmat->bs,&mat->bstash);
1854: if (oldmat->colmap) {
1855: #if defined (PETSC_USE_CTABLE)
1856: PetscTableCreateCopy(oldmat->colmap,&a->colmap);
1857: #else
1858: PetscMalloc((a->Nbs)*sizeof(int),&a->colmap);
1859: PetscLogObjectMemory(mat,(a->Nbs)*sizeof(int));
1860: PetscMemcpy(a->colmap,oldmat->colmap,(a->Nbs)*sizeof(int));
1861: #endif
1862: } else a->colmap = 0;
1863: if (oldmat->garray && (len = ((Mat_SeqBAIJ*)(oldmat->B->data))->nbs)) {
1864: PetscMalloc(len*sizeof(int),&a->garray);
1865: PetscLogObjectMemory(mat,len*sizeof(int));
1866: PetscMemcpy(a->garray,oldmat->garray,len*sizeof(int));
1867: } else a->garray = 0;
1868:
1869: VecDuplicate(oldmat->lvec,&a->lvec);
1870: PetscLogObjectParent(mat,a->lvec);
1871: VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
1873: PetscLogObjectParent(mat,a->Mvctx);
1874: MatDuplicate(oldmat->A,cpvalues,&a->A);
1875: PetscLogObjectParent(mat,a->A);
1876: MatDuplicate(oldmat->B,cpvalues,&a->B);
1877: PetscLogObjectParent(mat,a->B);
1878: PetscFListDuplicate(mat->qlist,&matin->qlist);
1879: *newmat = mat;
1880: return(0);
1881: }
1883: #include "petscsys.h"
1885: EXTERN_C_BEGIN
1886: int MatLoad_MPISBAIJ(PetscViewer viewer,MatType type,Mat *newmat)
1887: {
1888: Mat A;
1889: int i,nz,ierr,j,rstart,rend,fd;
1890: PetscScalar *vals,*buf;
1891: MPI_Comm comm = ((PetscObject)viewer)->comm;
1892: MPI_Status status;
1893: int header[4],rank,size,*rowlengths = 0,M,N,m,*rowners,*browners,maxnz,*cols;
1894: int *locrowlens,*sndcounts = 0,*procsnz = 0,jj,*mycols,*ibuf;
1895: int tag = ((PetscObject)viewer)->tag,bs=1,Mbs,mbs,extra_rows;
1896: int *dlens,*odlens,*mask,*masked1,*masked2,rowcount,odcount;
1897: int dcount,kmax,k,nzcount,tmp;
1898:
1900: PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);
1902: MPI_Comm_size(comm,&size);
1903: MPI_Comm_rank(comm,&rank);
1904: if (!rank) {
1905: PetscViewerBinaryGetDescriptor(viewer,&fd);
1906: PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
1907: if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
1908: if (header[3] < 0) {
1909: SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as MPISBAIJ");
1910: }
1911: }
1913: MPI_Bcast(header+1,3,MPI_INT,0,comm);
1914: M = header[1]; N = header[2];
1916: if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");
1918: /*
1919: This code adds extra rows to make sure the number of rows is
1920: divisible by the blocksize
1921: */
1922: Mbs = M/bs;
1923: extra_rows = bs - M + bs*(Mbs);
1924: if (extra_rows == bs) extra_rows = 0;
1925: else Mbs++;
1926: if (extra_rows &&!rank) {
1927: PetscLogInfo(0,"MatLoad_MPISBAIJ:Padding loaded matrix to match blocksizen");
1928: }
1930: /* determine ownership of all rows */
1931: mbs = Mbs/size + ((Mbs % size) > rank);
1932: m = mbs*bs;
1933: ierr = PetscMalloc(2*(size+2)*sizeof(int),&rowners);
1934: browners = rowners + size + 1;
1935: ierr = MPI_Allgather(&mbs,1,MPI_INT,rowners+1,1,MPI_INT,comm);
1936: rowners[0] = 0;
1937: for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
1938: for (i=0; i<=size; i++) browners[i] = rowners[i]*bs;
1939: rstart = rowners[rank];
1940: rend = rowners[rank+1];
1941:
1942: /* distribute row lengths to all processors */
1943: PetscMalloc((rend-rstart)*bs*sizeof(int),&locrowlens);
1944: if (!rank) {
1945: PetscMalloc((M+extra_rows)*sizeof(int),&rowlengths);
1946: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
1947: for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
1948: PetscMalloc(size*sizeof(int),&sndcounts);
1949: for (i=0; i<size; i++) sndcounts[i] = browners[i+1] - browners[i];
1950: MPI_Scatterv(rowlengths,sndcounts,browners,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
1951: PetscFree(sndcounts);
1952: } else {
1953: MPI_Scatterv(0,0,0,MPI_INT,locrowlens,(rend-rstart)*bs,MPI_INT,0,comm);
1954: }
1955:
1956: if (!rank) { /* procs[0] */
1957: /* calculate the number of nonzeros on each processor */
1958: PetscMalloc(size*sizeof(int),&procsnz);
1959: PetscMemzero(procsnz,size*sizeof(int));
1960: for (i=0; i<size; i++) {
1961: for (j=rowners[i]*bs; j< rowners[i+1]*bs; j++) {
1962: procsnz[i] += rowlengths[j];
1963: }
1964: }
1965: PetscFree(rowlengths);
1966:
1967: /* determine max buffer needed and allocate it */
1968: maxnz = 0;
1969: for (i=0; i<size; i++) {
1970: maxnz = PetscMax(maxnz,procsnz[i]);
1971: }
1972: PetscMalloc(maxnz*sizeof(int),&cols);
1974: /* read in my part of the matrix column indices */
1975: nz = procsnz[0];
1976: ierr = PetscMalloc(nz*sizeof(int),&ibuf);
1977: mycols = ibuf;
1978: if (size == 1) nz -= extra_rows;
1979: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
1980: if (size == 1) for (i=0; i< extra_rows; i++) { mycols[nz+i] = M+i; }
1982: /* read in every ones (except the last) and ship off */
1983: for (i=1; i<size-1; i++) {
1984: nz = procsnz[i];
1985: PetscBinaryRead(fd,cols,nz,PETSC_INT);
1986: MPI_Send(cols,nz,MPI_INT,i,tag,comm);
1987: }
1988: /* read in the stuff for the last proc */
1989: if (size != 1) {
1990: nz = procsnz[size-1] - extra_rows; /* the extra rows are not on the disk */
1991: PetscBinaryRead(fd,cols,nz,PETSC_INT);
1992: for (i=0; i<extra_rows; i++) cols[nz+i] = M+i;
1993: MPI_Send(cols,nz+extra_rows,MPI_INT,size-1,tag,comm);
1994: }
1995: PetscFree(cols);
1996: } else { /* procs[i], i>0 */
1997: /* determine buffer space needed for message */
1998: nz = 0;
1999: for (i=0; i<m; i++) {
2000: nz += locrowlens[i];
2001: }
2002: ierr = PetscMalloc(nz*sizeof(int),&ibuf);
2003: mycols = ibuf;
2004: /* receive message of column indices*/
2005: MPI_Recv(mycols,nz,MPI_INT,0,tag,comm,&status);
2006: MPI_Get_count(&status,MPI_INT,&maxnz);
2007: if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2008: }
2010: /* loop over local rows, determining number of off diagonal entries */
2011: ierr = PetscMalloc(2*(rend-rstart+1)*sizeof(int),&dlens);
2012: odlens = dlens + (rend-rstart);
2013: ierr = PetscMalloc(3*Mbs*sizeof(int),&mask);
2014: ierr = PetscMemzero(mask,3*Mbs*sizeof(int));
2015: masked1 = mask + Mbs;
2016: masked2 = masked1 + Mbs;
2017: rowcount = 0; nzcount = 0;
2018: for (i=0; i<mbs; i++) {
2019: dcount = 0;
2020: odcount = 0;
2021: for (j=0; j<bs; j++) {
2022: kmax = locrowlens[rowcount];
2023: for (k=0; k<kmax; k++) {
2024: tmp = mycols[nzcount++]/bs; /* block col. index */
2025: if (!mask[tmp]) {
2026: mask[tmp] = 1;
2027: if (tmp < rstart || tmp >= rend) masked2[odcount++] = tmp; /* entry in off-diag portion */
2028: else masked1[dcount++] = tmp; /* entry in diag portion */
2029: }
2030: }
2031: rowcount++;
2032: }
2033:
2034: dlens[i] = dcount; /* d_nzz[i] */
2035: odlens[i] = odcount; /* o_nzz[i] */
2037: /* zero out the mask elements we set */
2038: for (j=0; j<dcount; j++) mask[masked1[j]] = 0;
2039: for (j=0; j<odcount; j++) mask[masked2[j]] = 0;
2040: }
2041:
2042: /* create our matrix */
2043: MatCreateMPISBAIJ(comm,bs,m,m,PETSC_DETERMINE,PETSC_DETERMINE,0,dlens,0,odlens,newmat);
2044:
2045: A = *newmat;
2046: MatSetOption(A,MAT_COLUMNS_SORTED);
2047:
2048: if (!rank) {
2049: PetscMalloc(maxnz*sizeof(PetscScalar),&buf);
2050: /* read in my part of the matrix numerical values */
2051: nz = procsnz[0];
2052: vals = buf;
2053: mycols = ibuf;
2054: if (size == 1) nz -= extra_rows;
2055: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2056: if (size == 1) for (i=0; i< extra_rows; i++) { vals[nz+i] = 1.0; }
2058: /* insert into matrix */
2059: jj = rstart*bs;
2060: for (i=0; i<m; i++) {
2061: MatSetValues(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2062: mycols += locrowlens[i];
2063: vals += locrowlens[i];
2064: jj++;
2065: }
2067: /* read in other processors (except the last one) and ship out */
2068: for (i=1; i<size-1; i++) {
2069: nz = procsnz[i];
2070: vals = buf;
2071: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2072: MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2073: }
2074: /* the last proc */
2075: if (size != 1){
2076: nz = procsnz[i] - extra_rows;
2077: vals = buf;
2078: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2079: for (i=0; i<extra_rows; i++) vals[nz+i] = 1.0;
2080: MPI_Send(vals,nz+extra_rows,MPIU_SCALAR,size-1,A->tag,comm);
2081: }
2082: PetscFree(procsnz);
2084: } else {
2085: /* receive numeric values */
2086: PetscMalloc(nz*sizeof(PetscScalar),&buf);
2088: /* receive message of values*/
2089: vals = buf;
2090: mycols = ibuf;
2091: ierr = MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2092: ierr = MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2093: if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2095: /* insert into matrix */
2096: jj = rstart*bs;
2097: for (i=0; i<m; i++) {
2098: ierr = MatSetValues_MPISBAIJ(A,1,&jj,locrowlens[i],mycols,vals,INSERT_VALUES);
2099: mycols += locrowlens[i];
2100: vals += locrowlens[i];
2101: jj++;
2102: }
2103: }
2105: PetscFree(locrowlens);
2106: PetscFree(buf);
2107: PetscFree(ibuf);
2108: PetscFree(rowners);
2109: PetscFree(dlens);
2110: PetscFree(mask);
2111: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2112: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2113: return(0);
2114: }
2115: EXTERN_C_END
2117: /*@
2118: MatMPISBAIJSetHashTableFactor - Sets the factor required to compute the size of the HashTable.
2120: Input Parameters:
2121: . mat - the matrix
2122: . fact - factor
2124: Collective on Mat
2126: Level: advanced
2128: Notes:
2129: This can also be set by the command line option: -mat_use_hash_table fact
2131: .keywords: matrix, hashtable, factor, HT
2133: .seealso: MatSetOption()
2134: @*/
2135: int MatMPISBAIJSetHashTableFactor(Mat mat,PetscReal fact)
2136: {
2138: SETERRQ(1,"Function not yet written for SBAIJ format");
2139: /* return(0); */
2140: }
2142: int MatGetRowMax_MPISBAIJ(Mat A,Vec v)
2143: {
2144: Mat_MPISBAIJ *a = (Mat_MPISBAIJ*)A->data;
2145: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)(a->B)->data;
2146: PetscReal atmp;
2147: PetscReal *work,*svalues,*rvalues;
2148: int ierr,i,bs,mbs,*bi,*bj,brow,j,ncols,krow,kcol,col,row,Mbs,bcol;
2149: int rank,size,*rowners_bs,dest,count,source;
2150: PetscScalar *va;
2151: MatScalar *ba;
2152: MPI_Status stat;
2155: MatGetRowMax(a->A,v);
2156: VecGetArray(v,&va);
2158: MPI_Comm_size(PETSC_COMM_WORLD,&size);
2159: MPI_Comm_rank(PETSC_COMM_WORLD,&rank);
2161: bs = a->bs;
2162: mbs = a->mbs;
2163: Mbs = a->Mbs;
2164: ba = b->a;
2165: bi = b->i;
2166: bj = b->j;
2167: /*
2168: PetscSynchronizedPrintf(PETSC_COMM_WORLD,"[%d] M: %d, bs: %d, mbs: %d n",rank,bs*Mbs,bs,mbs);
2169: PetscSynchronizedFlush(PETSC_COMM_WORLD);
2170: */
2172: /* find ownerships */
2173: rowners_bs = a->rowners_bs;
2174: /*
2175: if (!rank){
2176: for (i=0; i<size+1; i++) PetscPrintf(PETSC_COMM_SELF," rowners_bs[%d]: %dn",i,rowners_bs[i]);
2177: }
2178: */
2180: /* each proc creates an array to be distributed */
2181: PetscMalloc(bs*Mbs*sizeof(PetscReal),&work);
2182: PetscMemzero(work,bs*Mbs*sizeof(PetscReal));
2184: /* row_max for B */
2185: if (rank != size-1){
2186: for (i=0; i<mbs; i++) {
2187: ncols = bi[1] - bi[0]; bi++;
2188: brow = bs*i;
2189: for (j=0; j<ncols; j++){
2190: bcol = bs*(*bj);
2191: for (kcol=0; kcol<bs; kcol++){
2192: col = bcol + kcol; /* local col index */
2193: col += rowners_bs[rank+1]; /* global col index */
2194: /* PetscPrintf(PETSC_COMM_SELF,"[%d], col: %dn",rank,col); */
2195: for (krow=0; krow<bs; krow++){
2196: atmp = PetscAbsScalar(*ba); ba++;
2197: row = brow + krow; /* local row index */
2198: /* printf("val[%d,%d]: %gn",row,col,atmp); */
2199: if (PetscRealPart(va[row]) < atmp) va[row] = atmp;
2200: if (work[col] < atmp) work[col] = atmp;
2201: }
2202: }
2203: bj++;
2204: }
2205: }
2206: /*
2207: PetscPrintf(PETSC_COMM_SELF,"[%d], work: ",rank);
2208: for (i=0; i<bs*Mbs; i++) PetscPrintf(PETSC_COMM_SELF,"%g ",work[i]);
2209: PetscPrintf(PETSC_COMM_SELF,"[%d]: n");
2210: */
2212: /* send values to its owners */
2213: for (dest=rank+1; dest<size; dest++){
2214: svalues = work + rowners_bs[dest];
2215: count = rowners_bs[dest+1]-rowners_bs[dest];
2216: ierr = MPI_Send(svalues,count,MPIU_REAL,dest,rank,PETSC_COMM_WORLD);
2217: /*
2218: 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]);
2219: PetscSynchronizedFlush(PETSC_COMM_WORLD);
2220: */
2221: }
2222: }
2223:
2224: /* receive values */
2225: if (rank){
2226: rvalues = work;
2227: count = rowners_bs[rank+1]-rowners_bs[rank];
2228: for (source=0; source<rank; source++){
2229: MPI_Recv(rvalues,count,MPIU_REAL,MPI_ANY_SOURCE,MPI_ANY_TAG,PETSC_COMM_WORLD,&stat);
2230: /* process values */
2231: for (i=0; i<count; i++){
2232: if (PetscRealPart(va[i]) < rvalues[i]) va[i] = rvalues[i];
2233: }
2234: /*
2235: 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]);
2236: PetscSynchronizedFlush(PETSC_COMM_WORLD);
2237: */
2238: }
2239: }
2241: VecRestoreArray(v,&va);
2242: PetscFree(work);
2243: return(0);
2244: }
2246: int MatRelax_MPISBAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx)
2247: {
2248: Mat_MPISBAIJ *mat = (Mat_MPISBAIJ*)matin->data;
2249: int ierr;
2250: PetscScalar mone=-1.0;
2251: Vec lvec1,bb1;
2252:
2254: if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits);
2255: if (mat->bs > 1)
2256: SETERRQ(PETSC_ERR_SUP,"SSOR for block size > 1 is not yet implemented");
2258: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
2259: if ( flag & SOR_ZERO_INITIAL_GUESS ) {
2260: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
2261: its--;
2262: }
2264: VecDuplicate(mat->lvec,&lvec1);
2265: VecDuplicate(bb,&bb1);
2266: while (its--){
2267: VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
2268:
2269: /* lower diagonal part: bb1 = bb - B^T*xx */
2270: (*mat->B->ops->multtranspose)(mat->B,xx,lvec1);
2271: VecScale(&mone,lvec1);
2273: VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
2274: VecCopy(bb,bb1);
2275: VecScatterBegin(lvec1,bb1,ADD_VALUES,SCATTER_REVERSE,mat->Mvctx);
2277: /* upper diagonal part: bb1 = bb1 - B*x */
2278: VecScale(&mone,mat->lvec);
2279: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb1,bb1);
2281: VecScatterEnd(lvec1,bb1,ADD_VALUES,SCATTER_REVERSE,mat->Mvctx);
2282:
2283: /* diagonal sweep */
2284: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,PETSC_NULL,xx);
2285: }
2286: VecDestroy(lvec1);
2287: VecDestroy(bb1);
2288: } else {
2289: SETERRQ(PETSC_ERR_SUP,"MatSORType is not supported for SBAIJ matrix format");
2290: }
2291: return(0);
2292: }