Actual source code: mpiaij.c
1: #define PETSCMAT_DLL
3: #include src/mat/impls/aij/mpi/mpiaij.h
4: #include src/inline/spops.h
6: /*
7: Local utility routine that creates a mapping from the global column
8: number to the local number in the off-diagonal part of the local
9: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
10: a slightly higher hash table cost; without it it is not scalable (each processor
11: has an order N integer array but is fast to acess.
12: */
15: PetscErrorCode CreateColmap_MPIAIJ_Private(Mat mat)
16: {
17: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
19: PetscInt n = aij->B->cmap.n,i;
22: #if defined (PETSC_USE_CTABLE)
23: PetscTableCreate(n,&aij->colmap);
24: for (i=0; i<n; i++){
25: PetscTableAdd(aij->colmap,aij->garray[i]+1,i+1);
26: }
27: #else
28: PetscMalloc((mat->cmap.N+1)*sizeof(PetscInt),&aij->colmap);
29: PetscLogObjectMemory(mat,mat->cmap.N*sizeof(PetscInt));
30: PetscMemzero(aij->colmap,mat->cmap.N*sizeof(PetscInt));
31: for (i=0; i<n; i++) aij->colmap[aij->garray[i]] = i+1;
32: #endif
33: return(0);
34: }
37: #define CHUNKSIZE 15
38: #define MatSetValues_SeqAIJ_A_Private(row,col,value,addv) \
39: { \
40: if (col <= lastcol1) low1 = 0; else high1 = nrow1; \
41: lastcol1 = col;\
42: while (high1-low1 > 5) { \
43: t = (low1+high1)/2; \
44: if (rp1[t] > col) high1 = t; \
45: else low1 = t; \
46: } \
47: for (_i=low1; _i<high1; _i++) { \
48: if (rp1[_i] > col) break; \
49: if (rp1[_i] == col) { \
50: if (addv == ADD_VALUES) ap1[_i] += value; \
51: else ap1[_i] = value; \
52: goto a_noinsert; \
53: } \
54: } \
55: if (value == 0.0 && ignorezeroentries) goto a_noinsert; \
56: if (nonew == 1) goto a_noinsert; \
57: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
58: MatSeqXAIJReallocateAIJ(a,1,nrow1,row,col,rmax1,aa,ai,aj,am,rp1,ap1,aimax,nonew); \
59: N = nrow1++ - 1; a->nz++; high1++; \
60: /* shift up all the later entries in this row */ \
61: for (ii=N; ii>=_i; ii--) { \
62: rp1[ii+1] = rp1[ii]; \
63: ap1[ii+1] = ap1[ii]; \
64: } \
65: rp1[_i] = col; \
66: ap1[_i] = value; \
67: a_noinsert: ; \
68: ailen[row] = nrow1; \
69: }
72: #define MatSetValues_SeqAIJ_B_Private(row,col,value,addv) \
73: { \
74: if (col <= lastcol2) low2 = 0; else high2 = nrow2; \
75: lastcol2 = col;\
76: while (high2-low2 > 5) { \
77: t = (low2+high2)/2; \
78: if (rp2[t] > col) high2 = t; \
79: else low2 = t; \
80: } \
81: for (_i=low2; _i<high2; _i++) { \
82: if (rp2[_i] > col) break; \
83: if (rp2[_i] == col) { \
84: if (addv == ADD_VALUES) ap2[_i] += value; \
85: else ap2[_i] = value; \
86: goto b_noinsert; \
87: } \
88: } \
89: if (value == 0.0 && ignorezeroentries) goto b_noinsert; \
90: if (nonew == 1) goto b_noinsert; \
91: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
92: MatSeqXAIJReallocateAIJ(b,1,nrow2,row,col,rmax2,ba,bi,bj,bm,rp2,ap2,bimax,nonew); \
93: N = nrow2++ - 1; b->nz++; high2++;\
94: /* shift up all the later entries in this row */ \
95: for (ii=N; ii>=_i; ii--) { \
96: rp2[ii+1] = rp2[ii]; \
97: ap2[ii+1] = ap2[ii]; \
98: } \
99: rp2[_i] = col; \
100: ap2[_i] = value; \
101: b_noinsert: ; \
102: bilen[row] = nrow2; \
103: }
107: PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A,PetscInt row,const PetscScalar v[])
108: {
109: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
110: Mat_SeqAIJ *a = (Mat_SeqAIJ*)mat->A->data,*b = (Mat_SeqAIJ*)mat->B->data;
112: PetscInt l,*garray = mat->garray,diag;
115: /* code only works for square matrices A */
117: /* find size of row to the left of the diagonal part */
118: MatGetOwnershipRange(A,&diag,0);
119: row = row - diag;
120: for (l=0; l<b->i[row+1]-b->i[row]; l++) {
121: if (garray[b->j[b->i[row]+l]] > diag) break;
122: }
123: PetscMemcpy(b->a+b->i[row],v,l*sizeof(PetscScalar));
125: /* diagonal part */
126: PetscMemcpy(a->a+a->i[row],v+l,(a->i[row+1]-a->i[row])*sizeof(PetscScalar));
128: /* right of diagonal part */
129: PetscMemcpy(b->a+b->i[row]+l,v+l+a->i[row+1]-a->i[row],(b->i[row+1]-b->i[row]-l)*sizeof(PetscScalar));
130: return(0);
131: }
135: PetscErrorCode MatSetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
136: {
137: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
138: PetscScalar value;
140: PetscInt i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend;
141: PetscInt cstart = mat->cmap.rstart,cend = mat->cmap.rend,row,col;
142: PetscTruth roworiented = aij->roworiented;
144: /* Some Variables required in the macro */
145: Mat A = aij->A;
146: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
147: PetscInt *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
148: PetscScalar *aa = a->a;
149: PetscTruth ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES))?PETSC_TRUE:PETSC_FALSE);
150: Mat B = aij->B;
151: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
152: PetscInt *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap.n,am = aij->A->rmap.n;
153: PetscScalar *ba = b->a;
155: PetscInt *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
156: PetscInt nonew = a->nonew;
157: PetscScalar *ap1,*ap2;
160: for (i=0; i<m; i++) {
161: if (im[i] < 0) continue;
162: #if defined(PETSC_USE_DEBUG)
163: if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
164: #endif
165: if (im[i] >= rstart && im[i] < rend) {
166: row = im[i] - rstart;
167: lastcol1 = -1;
168: rp1 = aj + ai[row];
169: ap1 = aa + ai[row];
170: rmax1 = aimax[row];
171: nrow1 = ailen[row];
172: low1 = 0;
173: high1 = nrow1;
174: lastcol2 = -1;
175: rp2 = bj + bi[row];
176: ap2 = ba + bi[row];
177: rmax2 = bimax[row];
178: nrow2 = bilen[row];
179: low2 = 0;
180: high2 = nrow2;
182: for (j=0; j<n; j++) {
183: if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
184: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
185: if (in[j] >= cstart && in[j] < cend){
186: col = in[j] - cstart;
187: MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
188: } else if (in[j] < 0) continue;
189: #if defined(PETSC_USE_DEBUG)
190: else if (in[j] >= mat->cmap.N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap.N-1);}
191: #endif
192: else {
193: if (mat->was_assembled) {
194: if (!aij->colmap) {
195: CreateColmap_MPIAIJ_Private(mat);
196: }
197: #if defined (PETSC_USE_CTABLE)
198: PetscTableFind(aij->colmap,in[j]+1,&col);
199: col--;
200: #else
201: col = aij->colmap[in[j]] - 1;
202: #endif
203: if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
204: DisAssemble_MPIAIJ(mat);
205: col = in[j];
206: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
207: B = aij->B;
208: b = (Mat_SeqAIJ*)B->data;
209: bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
210: rp2 = bj + bi[row];
211: ap2 = ba + bi[row];
212: rmax2 = bimax[row];
213: nrow2 = bilen[row];
214: low2 = 0;
215: high2 = nrow2;
216: bm = aij->B->rmap.n;
217: ba = b->a;
218: }
219: } else col = in[j];
220: MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
221: }
222: }
223: } else {
224: if (!aij->donotstash) {
225: if (roworiented) {
226: if (ignorezeroentries && v[i*n] == 0.0) continue;
227: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
228: } else {
229: if (ignorezeroentries && v[i] == 0.0) continue;
230: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
231: }
232: }
233: }
234: }
235: return(0);
236: }
241: PetscErrorCode MatGetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
242: {
243: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
245: PetscInt i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend;
246: PetscInt cstart = mat->cmap.rstart,cend = mat->cmap.rend,row,col;
249: for (i=0; i<m; i++) {
250: if (idxm[i] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);
251: if (idxm[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap.N-1);
252: if (idxm[i] >= rstart && idxm[i] < rend) {
253: row = idxm[i] - rstart;
254: for (j=0; j<n; j++) {
255: if (idxn[j] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]);
256: if (idxn[j] >= mat->cmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap.N-1);
257: if (idxn[j] >= cstart && idxn[j] < cend){
258: col = idxn[j] - cstart;
259: MatGetValues(aij->A,1,&row,1,&col,v+i*n+j);
260: } else {
261: if (!aij->colmap) {
262: CreateColmap_MPIAIJ_Private(mat);
263: }
264: #if defined (PETSC_USE_CTABLE)
265: PetscTableFind(aij->colmap,idxn[j]+1,&col);
266: col --;
267: #else
268: col = aij->colmap[idxn[j]] - 1;
269: #endif
270: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v+i*n+j) = 0.0;
271: else {
272: MatGetValues(aij->B,1,&row,1,&col,v+i*n+j);
273: }
274: }
275: }
276: } else {
277: SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
278: }
279: }
280: return(0);
281: }
285: PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat,MatAssemblyType mode)
286: {
287: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
289: PetscInt nstash,reallocs;
290: InsertMode addv;
293: if (aij->donotstash) {
294: return(0);
295: }
297: /* make sure all processors are either in INSERTMODE or ADDMODE */
298: MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,mat->comm);
299: if (addv == (ADD_VALUES|INSERT_VALUES)) {
300: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
301: }
302: mat->insertmode = addv; /* in case this processor had no cache */
304: MatStashScatterBegin_Private(&mat->stash,mat->rmap.range);
305: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
306: PetscInfo2(aij->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
307: return(0);
308: }
312: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat,MatAssemblyType mode)
313: {
314: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
315: Mat_SeqAIJ *a=(Mat_SeqAIJ *)aij->A->data;
317: PetscMPIInt n;
318: PetscInt i,j,rstart,ncols,flg;
319: PetscInt *row,*col,other_disassembled;
320: PetscScalar *val;
321: InsertMode addv = mat->insertmode;
323: /* do not use 'b = (Mat_SeqAIJ *)aij->B->data' as B can be reset in disassembly */
325: if (!aij->donotstash) {
326: while (1) {
327: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
328: if (!flg) break;
330: for (i=0; i<n;) {
331: /* Now identify the consecutive vals belonging to the same row */
332: for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
333: if (j < n) ncols = j-i;
334: else ncols = n-i;
335: /* Now assemble all these values with a single function call */
336: MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
337: i = j;
338: }
339: }
340: MatStashScatterEnd_Private(&mat->stash);
341: }
342: a->compressedrow.use = PETSC_FALSE;
343: MatAssemblyBegin(aij->A,mode);
344: MatAssemblyEnd(aij->A,mode);
346: /* determine if any processor has disassembled, if so we must
347: also disassemble ourselfs, in order that we may reassemble. */
348: /*
349: if nonzero structure of submatrix B cannot change then we know that
350: no processor disassembled thus we can skip this stuff
351: */
352: if (!((Mat_SeqAIJ*)aij->B->data)->nonew) {
353: MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,mat->comm);
354: if (mat->was_assembled && !other_disassembled) {
355: DisAssemble_MPIAIJ(mat);
356: }
357: }
358: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
359: MatSetUpMultiply_MPIAIJ(mat);
360: }
361: MatSetOption(aij->B,MAT_DO_NOT_USE_INODES);
362: ((Mat_SeqAIJ *)aij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
363: MatAssemblyBegin(aij->B,mode);
364: MatAssemblyEnd(aij->B,mode);
366: PetscFree(aij->rowvalues);
367: aij->rowvalues = 0;
369: /* used by MatAXPY() */
370: a->xtoy = 0; ((Mat_SeqAIJ *)aij->B->data)->xtoy = 0; /* b->xtoy = 0 */
371: a->XtoY = 0; ((Mat_SeqAIJ *)aij->B->data)->XtoY = 0; /* b->XtoY = 0 */
373: return(0);
374: }
378: PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
379: {
380: Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data;
384: MatZeroEntries(l->A);
385: MatZeroEntries(l->B);
386: return(0);
387: }
391: PetscErrorCode MatZeroRows_MPIAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
392: {
393: Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data;
395: PetscMPIInt size = l->size,imdex,n,rank = l->rank,tag = A->tag,lastidx = -1;
396: PetscInt i,*owners = A->rmap.range;
397: PetscInt *nprocs,j,idx,nsends,row;
398: PetscInt nmax,*svalues,*starts,*owner,nrecvs;
399: PetscInt *rvalues,count,base,slen,*source;
400: PetscInt *lens,*lrows,*values,rstart=A->rmap.rstart;
401: MPI_Comm comm = A->comm;
402: MPI_Request *send_waits,*recv_waits;
403: MPI_Status recv_status,*send_status;
404: #if defined(PETSC_DEBUG)
405: PetscTruth found = PETSC_FALSE;
406: #endif
409: /* first count number of contributors to each processor */
410: PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
411: PetscMemzero(nprocs,2*size*sizeof(PetscInt));
412: PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
413: j = 0;
414: for (i=0; i<N; i++) {
415: if (lastidx > (idx = rows[i])) j = 0;
416: lastidx = idx;
417: for (; j<size; j++) {
418: if (idx >= owners[j] && idx < owners[j+1]) {
419: nprocs[2*j]++;
420: nprocs[2*j+1] = 1;
421: owner[i] = j;
422: #if defined(PETSC_DEBUG)
423: found = PETSC_TRUE;
424: #endif
425: break;
426: }
427: }
428: #if defined(PETSC_DEBUG)
429: if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
430: found = PETSC_FALSE;
431: #endif
432: }
433: nsends = 0; for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
435: /* inform other processors of number of messages and max length*/
436: PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
438: /* post receives: */
439: PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
440: PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
441: for (i=0; i<nrecvs; i++) {
442: MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
443: }
445: /* do sends:
446: 1) starts[i] gives the starting index in svalues for stuff going to
447: the ith processor
448: */
449: PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
450: PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
451: PetscMalloc((size+1)*sizeof(PetscInt),&starts);
452: starts[0] = 0;
453: for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
454: for (i=0; i<N; i++) {
455: svalues[starts[owner[i]]++] = rows[i];
456: }
458: starts[0] = 0;
459: for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
460: count = 0;
461: for (i=0; i<size; i++) {
462: if (nprocs[2*i+1]) {
463: MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
464: }
465: }
466: PetscFree(starts);
468: base = owners[rank];
470: /* wait on receives */
471: PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
472: source = lens + nrecvs;
473: count = nrecvs; slen = 0;
474: while (count) {
475: MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
476: /* unpack receives into our local space */
477: MPI_Get_count(&recv_status,MPIU_INT,&n);
478: source[imdex] = recv_status.MPI_SOURCE;
479: lens[imdex] = n;
480: slen += n;
481: count--;
482: }
483: PetscFree(recv_waits);
484:
485: /* move the data into the send scatter */
486: PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
487: count = 0;
488: for (i=0; i<nrecvs; i++) {
489: values = rvalues + i*nmax;
490: for (j=0; j<lens[i]; j++) {
491: lrows[count++] = values[j] - base;
492: }
493: }
494: PetscFree(rvalues);
495: PetscFree(lens);
496: PetscFree(owner);
497: PetscFree(nprocs);
498:
499: /* actually zap the local rows */
500: /*
501: Zero the required rows. If the "diagonal block" of the matrix
502: is square and the user wishes to set the diagonal we use separate
503: code so that MatSetValues() is not called for each diagonal allocating
504: new memory, thus calling lots of mallocs and slowing things down.
506: Contributed by: Matthew Knepley
507: */
508: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
509: MatZeroRows(l->B,slen,lrows,0.0);
510: if ((diag != 0.0) && (l->A->rmap.N == l->A->cmap.N)) {
511: MatZeroRows(l->A,slen,lrows,diag);
512: } else if (diag != 0.0) {
513: MatZeroRows(l->A,slen,lrows,0.0);
514: if (((Mat_SeqAIJ*)l->A->data)->nonew) {
515: SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options\n\
516: MAT_NO_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
517: }
518: for (i = 0; i < slen; i++) {
519: row = lrows[i] + rstart;
520: MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
521: }
522: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
523: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
524: } else {
525: MatZeroRows(l->A,slen,lrows,0.0);
526: }
527: PetscFree(lrows);
529: /* wait on sends */
530: if (nsends) {
531: PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
532: MPI_Waitall(nsends,send_waits,send_status);
533: PetscFree(send_status);
534: }
535: PetscFree(send_waits);
536: PetscFree(svalues);
538: return(0);
539: }
543: PetscErrorCode MatMult_MPIAIJ(Mat A,Vec xx,Vec yy)
544: {
545: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
547: PetscInt nt;
550: VecGetLocalSize(xx,&nt);
551: if (nt != A->cmap.n) {
552: SETERRQ2(PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%D) and xx (%D)",A->cmap.n,nt);
553: }
554: VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
555: (*a->A->ops->mult)(a->A,xx,yy);
556: VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
557: (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
558: return(0);
559: }
563: PetscErrorCode MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
564: {
565: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
569: VecScatterBegin(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
570: (*a->A->ops->multadd)(a->A,xx,yy,zz);
571: VecScatterEnd(xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD,a->Mvctx);
572: (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
573: return(0);
574: }
578: PetscErrorCode MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy)
579: {
580: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
582: PetscTruth merged;
585: VecScatterGetMerged(a->Mvctx,&merged);
586: /* do nondiagonal part */
587: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
588: if (!merged) {
589: /* send it on its way */
590: VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
591: /* do local part */
592: (*a->A->ops->multtranspose)(a->A,xx,yy);
593: /* receive remote parts: note this assumes the values are not actually */
594: /* added in yy until the next line, */
595: VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
596: } else {
597: /* do local part */
598: (*a->A->ops->multtranspose)(a->A,xx,yy);
599: /* send it on its way */
600: VecScatterBegin(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
601: /* values actually were received in the Begin() but we need to call this nop */
602: VecScatterEnd(a->lvec,yy,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
603: }
604: return(0);
605: }
610: PetscErrorCode PETSCMAT_DLLEXPORT MatIsTranspose_MPIAIJ(Mat Amat,Mat Bmat,PetscReal tol,PetscTruth *f)
611: {
612: MPI_Comm comm;
613: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *) Amat->data, *Bij;
614: Mat Adia = Aij->A, Bdia, Aoff,Boff,*Aoffs,*Boffs;
615: IS Me,Notme;
617: PetscInt M,N,first,last,*notme,i;
618: PetscMPIInt size;
622: /* Easy test: symmetric diagonal block */
623: Bij = (Mat_MPIAIJ *) Bmat->data; Bdia = Bij->A;
624: MatIsTranspose(Adia,Bdia,tol,f);
625: if (!*f) return(0);
626: PetscObjectGetComm((PetscObject)Amat,&comm);
627: MPI_Comm_size(comm,&size);
628: if (size == 1) return(0);
630: /* Hard test: off-diagonal block. This takes a MatGetSubMatrix. */
631: MatGetSize(Amat,&M,&N);
632: MatGetOwnershipRange(Amat,&first,&last);
633: PetscMalloc((N-last+first)*sizeof(PetscInt),¬me);
634: for (i=0; i<first; i++) notme[i] = i;
635: for (i=last; i<M; i++) notme[i-last+first] = i;
636: ISCreateGeneral(MPI_COMM_SELF,N-last+first,notme,&Notme);
637: ISCreateStride(MPI_COMM_SELF,last-first,first,1,&Me);
638: MatGetSubMatrices(Amat,1,&Me,&Notme,MAT_INITIAL_MATRIX,&Aoffs);
639: Aoff = Aoffs[0];
640: MatGetSubMatrices(Bmat,1,&Notme,&Me,MAT_INITIAL_MATRIX,&Boffs);
641: Boff = Boffs[0];
642: MatIsTranspose(Aoff,Boff,tol,f);
643: MatDestroyMatrices(1,&Aoffs);
644: MatDestroyMatrices(1,&Boffs);
645: ISDestroy(Me);
646: ISDestroy(Notme);
648: return(0);
649: }
654: PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
655: {
656: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
660: /* do nondiagonal part */
661: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
662: /* send it on its way */
663: VecScatterBegin(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
664: /* do local part */
665: (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
666: /* receive remote parts */
667: VecScatterEnd(a->lvec,zz,ADD_VALUES,SCATTER_REVERSE,a->Mvctx);
668: return(0);
669: }
671: /*
672: This only works correctly for square matrices where the subblock A->A is the
673: diagonal block
674: */
677: PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A,Vec v)
678: {
680: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
683: if (A->rmap.N != A->cmap.N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
684: if (A->rmap.rstart != A->cmap.rstart || A->rmap.rend != A->cmap.rend) {
685: SETERRQ(PETSC_ERR_ARG_SIZ,"row partition must equal col partition");
686: }
687: MatGetDiagonal(a->A,v);
688: return(0);
689: }
693: PetscErrorCode MatScale_MPIAIJ(Mat A,PetscScalar aa)
694: {
695: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
699: MatScale(a->A,aa);
700: MatScale(a->B,aa);
701: return(0);
702: }
706: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
707: {
708: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
712: #if defined(PETSC_USE_LOG)
713: PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap.N,mat->cmap.N);
714: #endif
715: MatStashDestroy_Private(&mat->stash);
716: MatDestroy(aij->A);
717: MatDestroy(aij->B);
718: #if defined (PETSC_USE_CTABLE)
719: if (aij->colmap) {PetscTableDelete(aij->colmap);}
720: #else
721: PetscFree(aij->colmap);
722: #endif
723: PetscFree(aij->garray);
724: if (aij->lvec) {VecDestroy(aij->lvec);}
725: if (aij->Mvctx) {VecScatterDestroy(aij->Mvctx);}
726: PetscFree(aij->rowvalues);
727: PetscFree(aij);
729: PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
730: PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
731: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
732: PetscObjectComposeFunction((PetscObject)mat,"MatIsTranspose_C","",PETSC_NULL);
733: PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocation_C","",PETSC_NULL);
734: PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocationCSR_C","",PETSC_NULL);
735: PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
736: return(0);
737: }
741: PetscErrorCode MatView_MPIAIJ_Binary(Mat mat,PetscViewer viewer)
742: {
743: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
744: Mat_SeqAIJ* A = (Mat_SeqAIJ*)aij->A->data;
745: Mat_SeqAIJ* B = (Mat_SeqAIJ*)aij->B->data;
746: PetscErrorCode ierr;
747: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag;
748: int fd;
749: PetscInt nz,header[4],*row_lengths,*range=0,rlen,i;
750: PetscInt nzmax,*column_indices,j,k,col,*garray = aij->garray,cnt,cstart = mat->cmap.rstart,rnz;
751: PetscScalar *column_values;
754: MPI_Comm_rank(mat->comm,&rank);
755: MPI_Comm_size(mat->comm,&size);
756: nz = A->nz + B->nz;
757: if (!rank) {
758: header[0] = MAT_FILE_COOKIE;
759: header[1] = mat->rmap.N;
760: header[2] = mat->cmap.N;
761: MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,mat->comm);
762: PetscViewerBinaryGetDescriptor(viewer,&fd);
763: PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
764: /* get largest number of rows any processor has */
765: rlen = mat->rmap.n;
766: range = mat->rmap.range;
767: for (i=1; i<size; i++) {
768: rlen = PetscMax(rlen,range[i+1] - range[i]);
769: }
770: } else {
771: MPI_Reduce(&nz,0,1,MPIU_INT,MPI_SUM,0,mat->comm);
772: rlen = mat->rmap.n;
773: }
775: /* load up the local row counts */
776: PetscMalloc((rlen+1)*sizeof(PetscInt),&row_lengths);
777: for (i=0; i<mat->rmap.n; i++) {
778: row_lengths[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i];
779: }
781: /* store the row lengths to the file */
782: if (!rank) {
783: MPI_Status status;
784: PetscBinaryWrite(fd,row_lengths,mat->rmap.n,PETSC_INT,PETSC_TRUE);
785: for (i=1; i<size; i++) {
786: rlen = range[i+1] - range[i];
787: MPI_Recv(row_lengths,rlen,MPIU_INT,i,tag,mat->comm,&status);
788: PetscBinaryWrite(fd,row_lengths,rlen,PETSC_INT,PETSC_TRUE);
789: }
790: } else {
791: MPI_Send(row_lengths,mat->rmap.n,MPIU_INT,0,tag,mat->comm);
792: }
793: PetscFree(row_lengths);
795: /* load up the local column indices */
796: nzmax = nz; /* )th processor needs space a largest processor needs */
797: MPI_Reduce(&nz,&nzmax,1,MPIU_INT,MPI_MAX,0,mat->comm);
798: PetscMalloc((nzmax+1)*sizeof(PetscInt),&column_indices);
799: cnt = 0;
800: for (i=0; i<mat->rmap.n; i++) {
801: for (j=B->i[i]; j<B->i[i+1]; j++) {
802: if ( (col = garray[B->j[j]]) > cstart) break;
803: column_indices[cnt++] = col;
804: }
805: for (k=A->i[i]; k<A->i[i+1]; k++) {
806: column_indices[cnt++] = A->j[k] + cstart;
807: }
808: for (; j<B->i[i+1]; j++) {
809: column_indices[cnt++] = garray[B->j[j]];
810: }
811: }
812: if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);
814: /* store the column indices to the file */
815: if (!rank) {
816: MPI_Status status;
817: PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,PETSC_TRUE);
818: for (i=1; i<size; i++) {
819: MPI_Recv(&rnz,1,MPIU_INT,i,tag,mat->comm,&status);
820: if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
821: MPI_Recv(column_indices,rnz,MPIU_INT,i,tag,mat->comm,&status);
822: PetscBinaryWrite(fd,column_indices,rnz,PETSC_INT,PETSC_TRUE);
823: }
824: } else {
825: MPI_Send(&nz,1,MPIU_INT,0,tag,mat->comm);
826: MPI_Send(column_indices,nz,MPIU_INT,0,tag,mat->comm);
827: }
828: PetscFree(column_indices);
830: /* load up the local column values */
831: PetscMalloc((nzmax+1)*sizeof(PetscScalar),&column_values);
832: cnt = 0;
833: for (i=0; i<mat->rmap.n; i++) {
834: for (j=B->i[i]; j<B->i[i+1]; j++) {
835: if ( garray[B->j[j]] > cstart) break;
836: column_values[cnt++] = B->a[j];
837: }
838: for (k=A->i[i]; k<A->i[i+1]; k++) {
839: column_values[cnt++] = A->a[k];
840: }
841: for (; j<B->i[i+1]; j++) {
842: column_values[cnt++] = B->a[j];
843: }
844: }
845: if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_PLIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);
847: /* store the column values to the file */
848: if (!rank) {
849: MPI_Status status;
850: PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,PETSC_TRUE);
851: for (i=1; i<size; i++) {
852: MPI_Recv(&rnz,1,MPIU_INT,i,tag,mat->comm,&status);
853: if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
854: MPI_Recv(column_values,rnz,MPIU_SCALAR,i,tag,mat->comm,&status);
855: PetscBinaryWrite(fd,column_values,rnz,PETSC_SCALAR,PETSC_TRUE);
856: }
857: } else {
858: MPI_Send(&nz,1,MPIU_INT,0,tag,mat->comm);
859: MPI_Send(column_values,nz,MPIU_SCALAR,0,tag,mat->comm);
860: }
861: PetscFree(column_values);
862: return(0);
863: }
867: PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
868: {
869: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
870: PetscErrorCode ierr;
871: PetscMPIInt rank = aij->rank,size = aij->size;
872: PetscTruth isdraw,iascii,isbinary;
873: PetscViewer sviewer;
874: PetscViewerFormat format;
877: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
878: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
879: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
880: if (iascii) {
881: PetscViewerGetFormat(viewer,&format);
882: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
883: MatInfo info;
884: PetscTruth inodes;
886: MPI_Comm_rank(mat->comm,&rank);
887: MatGetInfo(mat,MAT_LOCAL,&info);
888: MatInodeGetInodeSizes(aij->A,PETSC_NULL,(PetscInt **)&inodes,PETSC_NULL);
889: if (!inodes) {
890: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, not using I-node routines\n",
891: rank,mat->rmap.n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
892: } else {
893: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, using I-node routines\n",
894: rank,mat->rmap.n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
895: }
896: MatGetInfo(aij->A,MAT_LOCAL,&info);
897: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
898: MatGetInfo(aij->B,MAT_LOCAL,&info);
899: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
900: PetscViewerFlush(viewer);
901: VecScatterView(aij->Mvctx,viewer);
902: return(0);
903: } else if (format == PETSC_VIEWER_ASCII_INFO) {
904: PetscInt inodecount,inodelimit,*inodes;
905: MatInodeGetInodeSizes(aij->A,&inodecount,&inodes,&inodelimit);
906: if (inodes) {
907: PetscViewerASCIIPrintf(viewer,"using I-node (on process 0) routines: found %D nodes, limit used is %D\n",inodecount,inodelimit);
908: } else {
909: PetscViewerASCIIPrintf(viewer,"not using I-node (on process 0) routines\n");
910: }
911: return(0);
912: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
913: return(0);
914: }
915: } else if (isbinary) {
916: if (size == 1) {
917: PetscObjectSetName((PetscObject)aij->A,mat->name);
918: MatView(aij->A,viewer);
919: } else {
920: MatView_MPIAIJ_Binary(mat,viewer);
921: }
922: return(0);
923: } else if (isdraw) {
924: PetscDraw draw;
925: PetscTruth isnull;
926: PetscViewerDrawGetDraw(viewer,0,&draw);
927: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
928: }
930: if (size == 1) {
931: PetscObjectSetName((PetscObject)aij->A,mat->name);
932: MatView(aij->A,viewer);
933: } else {
934: /* assemble the entire matrix onto first processor. */
935: Mat A;
936: Mat_SeqAIJ *Aloc;
937: PetscInt M = mat->rmap.N,N = mat->cmap.N,m,*ai,*aj,row,*cols,i,*ct;
938: PetscScalar *a;
940: MatCreate(mat->comm,&A);
941: if (!rank) {
942: MatSetSizes(A,M,N,M,N);
943: } else {
944: MatSetSizes(A,0,0,M,N);
945: }
946: /* This is just a temporary matrix, so explicitly using MATMPIAIJ is probably best */
947: MatSetType(A,MATMPIAIJ);
948: MatMPIAIJSetPreallocation(A,0,PETSC_NULL,0,PETSC_NULL);
949: PetscLogObjectParent(mat,A);
951: /* copy over the A part */
952: Aloc = (Mat_SeqAIJ*)aij->A->data;
953: m = aij->A->rmap.n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
954: row = mat->rmap.rstart;
955: for (i=0; i<ai[m]; i++) {aj[i] += mat->cmap.rstart ;}
956: for (i=0; i<m; i++) {
957: MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);
958: row++; a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i];
959: }
960: aj = Aloc->j;
961: for (i=0; i<ai[m]; i++) {aj[i] -= mat->cmap.rstart;}
963: /* copy over the B part */
964: Aloc = (Mat_SeqAIJ*)aij->B->data;
965: m = aij->B->rmap.n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
966: row = mat->rmap.rstart;
967: PetscMalloc((ai[m]+1)*sizeof(PetscInt),&cols);
968: ct = cols;
969: for (i=0; i<ai[m]; i++) {cols[i] = aij->garray[aj[i]];}
970: for (i=0; i<m; i++) {
971: MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);
972: row++; a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
973: }
974: PetscFree(ct);
975: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
976: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
977: /*
978: Everyone has to call to draw the matrix since the graphics waits are
979: synchronized across all processors that share the PetscDraw object
980: */
981: PetscViewerGetSingleton(viewer,&sviewer);
982: if (!rank) {
983: PetscObjectSetName((PetscObject)((Mat_MPIAIJ*)(A->data))->A,mat->name);
984: MatView(((Mat_MPIAIJ*)(A->data))->A,sviewer);
985: }
986: PetscViewerRestoreSingleton(viewer,&sviewer);
987: MatDestroy(A);
988: }
989: return(0);
990: }
994: PetscErrorCode MatView_MPIAIJ(Mat mat,PetscViewer viewer)
995: {
997: PetscTruth iascii,isdraw,issocket,isbinary;
998:
1000: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1001: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1002: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1003: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1004: if (iascii || isdraw || isbinary || issocket) {
1005: MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);
1006: } else {
1007: SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIAIJ matrices",((PetscObject)viewer)->type_name);
1008: }
1009: return(0);
1010: }
1016: PetscErrorCode MatRelax_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1017: {
1018: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1020: Vec bb1;
1023: if (its <= 0 || lits <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
1025: VecDuplicate(bb,&bb1);
1027: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
1028: if (flag & SOR_ZERO_INITIAL_GUESS) {
1029: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
1030: its--;
1031: }
1032:
1033: while (its--) {
1034: VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1035: VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1037: /* update rhs: bb1 = bb - B*x */
1038: VecScale(mat->lvec,-1.0);
1039: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1041: /* local sweep */
1042: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
1043:
1044: }
1045: } else if (flag & SOR_LOCAL_FORWARD_SWEEP){
1046: if (flag & SOR_ZERO_INITIAL_GUESS) {
1047: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1048: its--;
1049: }
1050: while (its--) {
1051: VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1052: VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1054: /* update rhs: bb1 = bb - B*x */
1055: VecScale(mat->lvec,-1.0);
1056: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1058: /* local sweep */
1059: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1060:
1061: }
1062: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP){
1063: if (flag & SOR_ZERO_INITIAL_GUESS) {
1064: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1065: its--;
1066: }
1067: while (its--) {
1068: VecScatterBegin(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1069: VecScatterEnd(xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD,mat->Mvctx);
1071: /* update rhs: bb1 = bb - B*x */
1072: VecScale(mat->lvec,-1.0);
1073: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1075: /* local sweep */
1076: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1077:
1078: }
1079: } else {
1080: SETERRQ(PETSC_ERR_SUP,"Parallel SOR not supported");
1081: }
1083: VecDestroy(bb1);
1084: return(0);
1085: }
1089: PetscErrorCode MatPermute_MPIAIJ(Mat A,IS rowp,IS colp,Mat *B)
1090: {
1091: MPI_Comm comm,pcomm;
1092: PetscInt first,local_size,nrows,*rows;
1093: int ntids;
1094: IS crowp,growp,irowp,lrowp,lcolp,icolp;
1098: PetscObjectGetComm((PetscObject)A,&comm);
1099: /* make a collective version of 'rowp' */
1100: PetscObjectGetComm((PetscObject)rowp,&pcomm);
1101: if (pcomm==comm) {
1102: crowp = rowp;
1103: } else {
1104: ISGetSize(rowp,&nrows);
1105: ISGetIndices(rowp,&rows);
1106: ISCreateGeneral(comm,nrows,rows,&crowp);
1107: ISRestoreIndices(rowp,&rows);
1108: }
1109: /* collect the global row permutation and invert it */
1110: ISAllGather(crowp,&growp);
1111: ISSetPermutation(growp);
1112: if (pcomm!=comm) {
1113: ISDestroy(crowp);
1114: }
1115: ISInvertPermutation(growp,PETSC_DECIDE,&irowp);
1116: /* get the local target indices */
1117: MatGetOwnershipRange(A,&first,PETSC_NULL);
1118: MatGetLocalSize(A,&local_size,PETSC_NULL);
1119: ISGetIndices(irowp,&rows);
1120: ISCreateGeneral(MPI_COMM_SELF,local_size,rows+first,&lrowp);
1121: ISRestoreIndices(irowp,&rows);
1122: ISDestroy(irowp);
1123: /* the column permutation is so much easier;
1124: make a local version of 'colp' and invert it */
1125: PetscObjectGetComm((PetscObject)colp,&pcomm);
1126: MPI_Comm_size(pcomm,&ntids);
1127: if (ntids==1) {
1128: lcolp = colp;
1129: } else {
1130: ISGetSize(colp,&nrows);
1131: ISGetIndices(colp,&rows);
1132: ISCreateGeneral(MPI_COMM_SELF,nrows,rows,&lcolp);
1133: }
1134: ISInvertPermutation(lcolp,PETSC_DECIDE,&icolp);
1135: ISSetPermutation(lcolp);
1136: if (ntids>1) {
1137: ISRestoreIndices(colp,&rows);
1138: ISDestroy(lcolp);
1139: }
1140: /* now we just get the submatrix */
1141: MatGetSubMatrix(A,lrowp,icolp,local_size,MAT_INITIAL_MATRIX,B);
1142: /* clean up */
1143: ISDestroy(lrowp);
1144: ISDestroy(icolp);
1145: return(0);
1146: }
1150: PetscErrorCode MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1151: {
1152: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1153: Mat A = mat->A,B = mat->B;
1155: PetscReal isend[5],irecv[5];
1158: info->block_size = 1.0;
1159: MatGetInfo(A,MAT_LOCAL,info);
1160: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1161: isend[3] = info->memory; isend[4] = info->mallocs;
1162: MatGetInfo(B,MAT_LOCAL,info);
1163: isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1164: isend[3] += info->memory; isend[4] += info->mallocs;
1165: if (flag == MAT_LOCAL) {
1166: info->nz_used = isend[0];
1167: info->nz_allocated = isend[1];
1168: info->nz_unneeded = isend[2];
1169: info->memory = isend[3];
1170: info->mallocs = isend[4];
1171: } else if (flag == MAT_GLOBAL_MAX) {
1172: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,matin->comm);
1173: info->nz_used = irecv[0];
1174: info->nz_allocated = irecv[1];
1175: info->nz_unneeded = irecv[2];
1176: info->memory = irecv[3];
1177: info->mallocs = irecv[4];
1178: } else if (flag == MAT_GLOBAL_SUM) {
1179: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,matin->comm);
1180: info->nz_used = irecv[0];
1181: info->nz_allocated = irecv[1];
1182: info->nz_unneeded = irecv[2];
1183: info->memory = irecv[3];
1184: info->mallocs = irecv[4];
1185: }
1186: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1187: info->fill_ratio_needed = 0;
1188: info->factor_mallocs = 0;
1189: info->rows_global = (double)matin->rmap.N;
1190: info->columns_global = (double)matin->cmap.N;
1191: info->rows_local = (double)matin->rmap.n;
1192: info->columns_local = (double)matin->cmap.N;
1194: return(0);
1195: }
1199: PetscErrorCode MatSetOption_MPIAIJ(Mat A,MatOption op)
1200: {
1201: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1205: switch (op) {
1206: case MAT_NO_NEW_NONZERO_LOCATIONS:
1207: case MAT_YES_NEW_NONZERO_LOCATIONS:
1208: case MAT_COLUMNS_UNSORTED:
1209: case MAT_COLUMNS_SORTED:
1210: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1211: case MAT_KEEP_ZEROED_ROWS:
1212: case MAT_NEW_NONZERO_LOCATION_ERR:
1213: case MAT_USE_INODES:
1214: case MAT_DO_NOT_USE_INODES:
1215: case MAT_IGNORE_ZERO_ENTRIES:
1216: MatSetOption(a->A,op);
1217: MatSetOption(a->B,op);
1218: break;
1219: case MAT_ROW_ORIENTED:
1220: a->roworiented = PETSC_TRUE;
1221: MatSetOption(a->A,op);
1222: MatSetOption(a->B,op);
1223: break;
1224: case MAT_ROWS_SORTED:
1225: case MAT_ROWS_UNSORTED:
1226: case MAT_YES_NEW_DIAGONALS:
1227: PetscInfo(A,"Option ignored\n");
1228: break;
1229: case MAT_COLUMN_ORIENTED:
1230: a->roworiented = PETSC_FALSE;
1231: MatSetOption(a->A,op);
1232: MatSetOption(a->B,op);
1233: break;
1234: case MAT_IGNORE_OFF_PROC_ENTRIES:
1235: a->donotstash = PETSC_TRUE;
1236: break;
1237: case MAT_NO_NEW_DIAGONALS:
1238: SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
1239: case MAT_SYMMETRIC:
1240: case MAT_STRUCTURALLY_SYMMETRIC:
1241: case MAT_HERMITIAN:
1242: case MAT_SYMMETRY_ETERNAL:
1243: MatSetOption(a->A,op);
1244: break;
1245: case MAT_NOT_SYMMETRIC:
1246: case MAT_NOT_STRUCTURALLY_SYMMETRIC:
1247: case MAT_NOT_HERMITIAN:
1248: case MAT_NOT_SYMMETRY_ETERNAL:
1249: break;
1250: default:
1251: SETERRQ(PETSC_ERR_SUP,"unknown option");
1252: }
1253: return(0);
1254: }
1258: PetscErrorCode MatGetRow_MPIAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1259: {
1260: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1261: PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
1263: PetscInt i,*cworkA,*cworkB,**pcA,**pcB,cstart = matin->cmap.rstart;
1264: PetscInt nztot,nzA,nzB,lrow,rstart = matin->rmap.rstart,rend = matin->rmap.rend;
1265: PetscInt *cmap,*idx_p;
1268: if (mat->getrowactive) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1269: mat->getrowactive = PETSC_TRUE;
1271: if (!mat->rowvalues && (idx || v)) {
1272: /*
1273: allocate enough space to hold information from the longest row.
1274: */
1275: Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data;
1276: PetscInt max = 1,tmp;
1277: for (i=0; i<matin->rmap.n; i++) {
1278: tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1279: if (max < tmp) { max = tmp; }
1280: }
1281: PetscMalloc(max*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1282: mat->rowindices = (PetscInt*)(mat->rowvalues + max);
1283: }
1285: if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Only local rows")
1286: lrow = row - rstart;
1288: pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1289: if (!v) {pvA = 0; pvB = 0;}
1290: if (!idx) {pcA = 0; if (!v) pcB = 0;}
1291: (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1292: (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1293: nztot = nzA + nzB;
1295: cmap = mat->garray;
1296: if (v || idx) {
1297: if (nztot) {
1298: /* Sort by increasing column numbers, assuming A and B already sorted */
1299: PetscInt imark = -1;
1300: if (v) {
1301: *v = v_p = mat->rowvalues;
1302: for (i=0; i<nzB; i++) {
1303: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1304: else break;
1305: }
1306: imark = i;
1307: for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i];
1308: for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i];
1309: }
1310: if (idx) {
1311: *idx = idx_p = mat->rowindices;
1312: if (imark > -1) {
1313: for (i=0; i<imark; i++) {
1314: idx_p[i] = cmap[cworkB[i]];
1315: }
1316: } else {
1317: for (i=0; i<nzB; i++) {
1318: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1319: else break;
1320: }
1321: imark = i;
1322: }
1323: for (i=0; i<nzA; i++) idx_p[imark+i] = cstart + cworkA[i];
1324: for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]];
1325: }
1326: } else {
1327: if (idx) *idx = 0;
1328: if (v) *v = 0;
1329: }
1330: }
1331: *nz = nztot;
1332: (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1333: (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1334: return(0);
1335: }
1339: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1340: {
1341: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1344: if (!aij->getrowactive) {
1345: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1346: }
1347: aij->getrowactive = PETSC_FALSE;
1348: return(0);
1349: }
1353: PetscErrorCode MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm)
1354: {
1355: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1356: Mat_SeqAIJ *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data;
1358: PetscInt i,j,cstart = mat->cmap.rstart;
1359: PetscReal sum = 0.0;
1360: PetscScalar *v;
1363: if (aij->size == 1) {
1364: MatNorm(aij->A,type,norm);
1365: } else {
1366: if (type == NORM_FROBENIUS) {
1367: v = amat->a;
1368: for (i=0; i<amat->nz; i++) {
1369: #if defined(PETSC_USE_COMPLEX)
1370: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1371: #else
1372: sum += (*v)*(*v); v++;
1373: #endif
1374: }
1375: v = bmat->a;
1376: for (i=0; i<bmat->nz; i++) {
1377: #if defined(PETSC_USE_COMPLEX)
1378: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1379: #else
1380: sum += (*v)*(*v); v++;
1381: #endif
1382: }
1383: MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPI_SUM,mat->comm);
1384: *norm = sqrt(*norm);
1385: } else if (type == NORM_1) { /* max column norm */
1386: PetscReal *tmp,*tmp2;
1387: PetscInt *jj,*garray = aij->garray;
1388: PetscMalloc((mat->cmap.N+1)*sizeof(PetscReal),&tmp);
1389: PetscMalloc((mat->cmap.N+1)*sizeof(PetscReal),&tmp2);
1390: PetscMemzero(tmp,mat->cmap.N*sizeof(PetscReal));
1391: *norm = 0.0;
1392: v = amat->a; jj = amat->j;
1393: for (j=0; j<amat->nz; j++) {
1394: tmp[cstart + *jj++ ] += PetscAbsScalar(*v); v++;
1395: }
1396: v = bmat->a; jj = bmat->j;
1397: for (j=0; j<bmat->nz; j++) {
1398: tmp[garray[*jj++]] += PetscAbsScalar(*v); v++;
1399: }
1400: MPI_Allreduce(tmp,tmp2,mat->cmap.N,MPIU_REAL,MPI_SUM,mat->comm);
1401: for (j=0; j<mat->cmap.N; j++) {
1402: if (tmp2[j] > *norm) *norm = tmp2[j];
1403: }
1404: PetscFree(tmp);
1405: PetscFree(tmp2);
1406: } else if (type == NORM_INFINITY) { /* max row norm */
1407: PetscReal ntemp = 0.0;
1408: for (j=0; j<aij->A->rmap.n; j++) {
1409: v = amat->a + amat->i[j];
1410: sum = 0.0;
1411: for (i=0; i<amat->i[j+1]-amat->i[j]; i++) {
1412: sum += PetscAbsScalar(*v); v++;
1413: }
1414: v = bmat->a + bmat->i[j];
1415: for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) {
1416: sum += PetscAbsScalar(*v); v++;
1417: }
1418: if (sum > ntemp) ntemp = sum;
1419: }
1420: MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPI_MAX,mat->comm);
1421: } else {
1422: SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1423: }
1424: }
1425: return(0);
1426: }
1430: PetscErrorCode MatTranspose_MPIAIJ(Mat A,Mat *matout)
1431: {
1432: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1433: Mat_SeqAIJ *Aloc = (Mat_SeqAIJ*)a->A->data;
1435: PetscInt M = A->rmap.N,N = A->cmap.N,m,*ai,*aj,row,*cols,i,*ct;
1436: Mat B;
1437: PetscScalar *array;
1440: if (!matout && M != N) {
1441: SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1442: }
1444: MatCreate(A->comm,&B);
1445: MatSetSizes(B,A->cmap.n,A->rmap.n,N,M);
1446: MatSetType(B,A->type_name);
1447: MatMPIAIJSetPreallocation(B,0,PETSC_NULL,0,PETSC_NULL);
1449: /* copy over the A part */
1450: Aloc = (Mat_SeqAIJ*)a->A->data;
1451: m = a->A->rmap.n; ai = Aloc->i; aj = Aloc->j; array = Aloc->a;
1452: row = A->rmap.rstart;
1453: for (i=0; i<ai[m]; i++) {aj[i] += A->cmap.rstart ;}
1454: for (i=0; i<m; i++) {
1455: MatSetValues(B,ai[i+1]-ai[i],aj,1,&row,array,INSERT_VALUES);
1456: row++; array += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i];
1457: }
1458: aj = Aloc->j;
1459: for (i=0; i<ai[m]; i++) {aj[i] -= A->cmap.rstart ;}
1461: /* copy over the B part */
1462: Aloc = (Mat_SeqAIJ*)a->B->data;
1463: m = a->B->rmap.n; ai = Aloc->i; aj = Aloc->j; array = Aloc->a;
1464: row = A->rmap.rstart;
1465: PetscMalloc((1+ai[m])*sizeof(PetscInt),&cols);
1466: ct = cols;
1467: for (i=0; i<ai[m]; i++) {cols[i] = a->garray[aj[i]];}
1468: for (i=0; i<m; i++) {
1469: MatSetValues(B,ai[i+1]-ai[i],cols,1,&row,array,INSERT_VALUES);
1470: row++; array += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
1471: }
1472: PetscFree(ct);
1473: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1474: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1475: if (matout) {
1476: *matout = B;
1477: } else {
1478: MatHeaderCopy(A,B);
1479: }
1480: return(0);
1481: }
1485: PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr)
1486: {
1487: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1488: Mat a = aij->A,b = aij->B;
1490: PetscInt s1,s2,s3;
1493: MatGetLocalSize(mat,&s2,&s3);
1494: if (rr) {
1495: VecGetLocalSize(rr,&s1);
1496: if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1497: /* Overlap communication with computation. */
1498: VecScatterBegin(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);
1499: }
1500: if (ll) {
1501: VecGetLocalSize(ll,&s1);
1502: if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1503: (*b->ops->diagonalscale)(b,ll,0);
1504: }
1505: /* scale the diagonal block */
1506: (*a->ops->diagonalscale)(a,ll,rr);
1508: if (rr) {
1509: /* Do a scatter end and then right scale the off-diagonal block */
1510: VecScatterEnd(rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD,aij->Mvctx);
1511: (*b->ops->diagonalscale)(b,0,aij->lvec);
1512: }
1513:
1514: return(0);
1515: }
1520: PetscErrorCode MatPrintHelp_MPIAIJ(Mat A)
1521: {
1522: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1526: if (!a->rank) {
1527: MatPrintHelp_SeqAIJ(a->A);
1528: }
1529: return(0);
1530: }
1534: PetscErrorCode MatSetBlockSize_MPIAIJ(Mat A,PetscInt bs)
1535: {
1536: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1540: MatSetBlockSize(a->A,bs);
1541: MatSetBlockSize(a->B,bs);
1542: return(0);
1543: }
1546: PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
1547: {
1548: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1552: MatSetUnfactored(a->A);
1553: return(0);
1554: }
1558: PetscErrorCode MatEqual_MPIAIJ(Mat A,Mat B,PetscTruth *flag)
1559: {
1560: Mat_MPIAIJ *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data;
1561: Mat a,b,c,d;
1562: PetscTruth flg;
1566: a = matA->A; b = matA->B;
1567: c = matB->A; d = matB->B;
1569: MatEqual(a,c,&flg);
1570: if (flg) {
1571: MatEqual(b,d,&flg);
1572: }
1573: MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,A->comm);
1574: return(0);
1575: }
1579: PetscErrorCode MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str)
1580: {
1582: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1583: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
1586: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1587: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1588: /* because of the column compression in the off-processor part of the matrix a->B,
1589: the number of columns in a->B and b->B may be different, hence we cannot call
1590: the MatCopy() directly on the two parts. If need be, we can provide a more
1591: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
1592: then copying the submatrices */
1593: MatCopy_Basic(A,B,str);
1594: } else {
1595: MatCopy(a->A,b->A,str);
1596: MatCopy(a->B,b->B,str);
1597: }
1598: return(0);
1599: }
1603: PetscErrorCode MatSetUpPreallocation_MPIAIJ(Mat A)
1604: {
1608: MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1609: return(0);
1610: }
1612: #include petscblaslapack.h
1615: PetscErrorCode MatAXPY_MPIAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1616: {
1618: PetscInt i;
1619: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data,*yy = (Mat_MPIAIJ *)Y->data;
1620: PetscBLASInt bnz,one=1;
1621: Mat_SeqAIJ *x,*y;
1624: if (str == SAME_NONZERO_PATTERN) {
1625: PetscScalar alpha = a;
1626: x = (Mat_SeqAIJ *)xx->A->data;
1627: y = (Mat_SeqAIJ *)yy->A->data;
1628: bnz = (PetscBLASInt)x->nz;
1629: BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1630: x = (Mat_SeqAIJ *)xx->B->data;
1631: y = (Mat_SeqAIJ *)yy->B->data;
1632: bnz = (PetscBLASInt)x->nz;
1633: BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1634: } else if (str == SUBSET_NONZERO_PATTERN) {
1635: MatAXPY_SeqAIJ(yy->A,a,xx->A,str);
1637: x = (Mat_SeqAIJ *)xx->B->data;
1638: y = (Mat_SeqAIJ *)yy->B->data;
1639: if (y->xtoy && y->XtoY != xx->B) {
1640: PetscFree(y->xtoy);
1641: MatDestroy(y->XtoY);
1642: }
1643: if (!y->xtoy) { /* get xtoy */
1644: MatAXPYGetxtoy_Private(xx->B->rmap.n,x->i,x->j,xx->garray,y->i,y->j,yy->garray,&y->xtoy);
1645: y->XtoY = xx->B;
1646: }
1647: for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
1648: } else {
1649: MatAXPY_Basic(Y,a,X,str);
1650: }
1651: return(0);
1652: }
1654: EXTERN PetscErrorCode PETSCMAT_DLLEXPORT MatConjugate_SeqAIJ(Mat);
1658: PetscErrorCode PETSCMAT_DLLEXPORT MatConjugate_MPIAIJ(Mat mat)
1659: {
1660: #if defined(PETSC_USE_COMPLEX)
1662: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1665: MatConjugate_SeqAIJ(aij->A);
1666: MatConjugate_SeqAIJ(aij->B);
1667: #else
1669: #endif
1670: return(0);
1671: }
1675: PetscErrorCode MatRealPart_MPIAIJ(Mat A)
1676: {
1677: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1681: MatRealPart(a->A);
1682: MatRealPart(a->B);
1683: return(0);
1684: }
1688: PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
1689: {
1690: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1694: MatImaginaryPart(a->A);
1695: MatImaginaryPart(a->B);
1696: return(0);
1697: }
1699: /* -------------------------------------------------------------------*/
1700: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
1701: MatGetRow_MPIAIJ,
1702: MatRestoreRow_MPIAIJ,
1703: MatMult_MPIAIJ,
1704: /* 4*/ MatMultAdd_MPIAIJ,
1705: MatMultTranspose_MPIAIJ,
1706: MatMultTransposeAdd_MPIAIJ,
1707: 0,
1708: 0,
1709: 0,
1710: /*10*/ 0,
1711: 0,
1712: 0,
1713: MatRelax_MPIAIJ,
1714: MatTranspose_MPIAIJ,
1715: /*15*/ MatGetInfo_MPIAIJ,
1716: MatEqual_MPIAIJ,
1717: MatGetDiagonal_MPIAIJ,
1718: MatDiagonalScale_MPIAIJ,
1719: MatNorm_MPIAIJ,
1720: /*20*/ MatAssemblyBegin_MPIAIJ,
1721: MatAssemblyEnd_MPIAIJ,
1722: 0,
1723: MatSetOption_MPIAIJ,
1724: MatZeroEntries_MPIAIJ,
1725: /*25*/ MatZeroRows_MPIAIJ,
1726: 0,
1727: 0,
1728: 0,
1729: 0,
1730: /*30*/ MatSetUpPreallocation_MPIAIJ,
1731: 0,
1732: 0,
1733: 0,
1734: 0,
1735: /*35*/ MatDuplicate_MPIAIJ,
1736: 0,
1737: 0,
1738: 0,
1739: 0,
1740: /*40*/ MatAXPY_MPIAIJ,
1741: MatGetSubMatrices_MPIAIJ,
1742: MatIncreaseOverlap_MPIAIJ,
1743: MatGetValues_MPIAIJ,
1744: MatCopy_MPIAIJ,
1745: /*45*/ MatPrintHelp_MPIAIJ,
1746: MatScale_MPIAIJ,
1747: 0,
1748: 0,
1749: 0,
1750: /*50*/ MatSetBlockSize_MPIAIJ,
1751: 0,
1752: 0,
1753: 0,
1754: 0,
1755: /*55*/ MatFDColoringCreate_MPIAIJ,
1756: 0,
1757: MatSetUnfactored_MPIAIJ,
1758: MatPermute_MPIAIJ,
1759: 0,
1760: /*60*/ MatGetSubMatrix_MPIAIJ,
1761: MatDestroy_MPIAIJ,
1762: MatView_MPIAIJ,
1763: 0,
1764: 0,
1765: /*65*/ 0,
1766: 0,
1767: 0,
1768: 0,
1769: 0,
1770: /*70*/ 0,
1771: 0,
1772: MatSetColoring_MPIAIJ,
1773: #if defined(PETSC_HAVE_ADIC)
1774: MatSetValuesAdic_MPIAIJ,
1775: #else
1776: 0,
1777: #endif
1778: MatSetValuesAdifor_MPIAIJ,
1779: /*75*/ 0,
1780: 0,
1781: 0,
1782: 0,
1783: 0,
1784: /*80*/ 0,
1785: 0,
1786: 0,
1787: 0,
1788: /*84*/ MatLoad_MPIAIJ,
1789: 0,
1790: 0,
1791: 0,
1792: 0,
1793: 0,
1794: /*90*/ MatMatMult_MPIAIJ_MPIAIJ,
1795: MatMatMultSymbolic_MPIAIJ_MPIAIJ,
1796: MatMatMultNumeric_MPIAIJ_MPIAIJ,
1797: MatPtAP_Basic,
1798: MatPtAPSymbolic_MPIAIJ,
1799: /*95*/ MatPtAPNumeric_MPIAIJ,
1800: 0,
1801: 0,
1802: 0,
1803: 0,
1804: /*100*/0,
1805: MatPtAPSymbolic_MPIAIJ_MPIAIJ,
1806: MatPtAPNumeric_MPIAIJ_MPIAIJ,
1807: MatConjugate_MPIAIJ,
1808: 0,
1809: /*105*/MatSetValuesRow_MPIAIJ,
1810: MatRealPart_MPIAIJ,
1811: MatImaginaryPart_MPIAIJ};
1813: /* ----------------------------------------------------------------------------------------*/
1818: PetscErrorCode PETSCMAT_DLLEXPORT MatStoreValues_MPIAIJ(Mat mat)
1819: {
1820: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1824: MatStoreValues(aij->A);
1825: MatStoreValues(aij->B);
1826: return(0);
1827: }
1833: PetscErrorCode PETSCMAT_DLLEXPORT MatRetrieveValues_MPIAIJ(Mat mat)
1834: {
1835: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1839: MatRetrieveValues(aij->A);
1840: MatRetrieveValues(aij->B);
1841: return(0);
1842: }
1845: #include petscpc.h
1849: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIAIJSetPreallocation_MPIAIJ(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
1850: {
1851: Mat_MPIAIJ *b;
1853: PetscInt i;
1856: B->preallocated = PETSC_TRUE;
1857: if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
1858: if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
1859: if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
1860: if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);
1862: B->rmap.bs = B->cmap.bs = 1;
1863: PetscMapInitialize(B->comm,&B->rmap);
1864: PetscMapInitialize(B->comm,&B->cmap);
1865: if (d_nnz) {
1866: for (i=0; i<B->rmap.n; i++) {
1867: if (d_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than 0: local row %D value %D",i,d_nnz[i]);
1868: }
1869: }
1870: if (o_nnz) {
1871: for (i=0; i<B->rmap.n; i++) {
1872: if (o_nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than 0: local row %D value %D",i,o_nnz[i]);
1873: }
1874: }
1875: b = (Mat_MPIAIJ*)B->data;
1877: /* Explicitly create 2 MATSEQAIJ matrices. */
1878: MatCreate(PETSC_COMM_SELF,&b->A);
1879: MatSetSizes(b->A,B->rmap.n,B->cmap.n,B->rmap.n,B->cmap.n);
1880: MatSetType(b->A,MATSEQAIJ);
1881: PetscLogObjectParent(B,b->A);
1882: MatCreate(PETSC_COMM_SELF,&b->B);
1883: MatSetSizes(b->B,B->rmap.n,B->cmap.N,B->rmap.n,B->cmap.N);
1884: MatSetType(b->B,MATSEQAIJ);
1885: PetscLogObjectParent(B,b->B);
1887: MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
1888: MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
1890: return(0);
1891: }
1896: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
1897: {
1898: Mat mat;
1899: Mat_MPIAIJ *a,*oldmat = (Mat_MPIAIJ*)matin->data;
1903: *newmat = 0;
1904: MatCreate(matin->comm,&mat);
1905: MatSetSizes(mat,matin->rmap.n,matin->cmap.n,matin->rmap.N,matin->cmap.N);
1906: MatSetType(mat,matin->type_name);
1907: PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
1908: a = (Mat_MPIAIJ*)mat->data;
1909:
1910: mat->factor = matin->factor;
1911: mat->rmap.bs = matin->rmap.bs;
1912: mat->assembled = PETSC_TRUE;
1913: mat->insertmode = NOT_SET_VALUES;
1914: mat->preallocated = PETSC_TRUE;
1916: a->size = oldmat->size;
1917: a->rank = oldmat->rank;
1918: a->donotstash = oldmat->donotstash;
1919: a->roworiented = oldmat->roworiented;
1920: a->rowindices = 0;
1921: a->rowvalues = 0;
1922: a->getrowactive = PETSC_FALSE;
1924: PetscMapCopy(mat->comm,&matin->rmap,&mat->rmap);
1925: PetscMapCopy(mat->comm,&matin->cmap,&mat->cmap);
1927: MatStashCreate_Private(matin->comm,1,&mat->stash);
1928: if (oldmat->colmap) {
1929: #if defined (PETSC_USE_CTABLE)
1930: PetscTableCreateCopy(oldmat->colmap,&a->colmap);
1931: #else
1932: PetscMalloc((mat->cmap.N)*sizeof(PetscInt),&a->colmap);
1933: PetscLogObjectMemory(mat,(mat->cmap.N)*sizeof(PetscInt));
1934: PetscMemcpy(a->colmap,oldmat->colmap,(mat->cmap.N)*sizeof(PetscInt));
1935: #endif
1936: } else a->colmap = 0;
1937: if (oldmat->garray) {
1938: PetscInt len;
1939: len = oldmat->B->cmap.n;
1940: PetscMalloc((len+1)*sizeof(PetscInt),&a->garray);
1941: PetscLogObjectMemory(mat,len*sizeof(PetscInt));
1942: if (len) { PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt)); }
1943: } else a->garray = 0;
1944:
1945: VecDuplicate(oldmat->lvec,&a->lvec);
1946: PetscLogObjectParent(mat,a->lvec);
1947: VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
1948: PetscLogObjectParent(mat,a->Mvctx);
1949: MatDuplicate(oldmat->A,cpvalues,&a->A);
1950: PetscLogObjectParent(mat,a->A);
1951: MatDuplicate(oldmat->B,cpvalues,&a->B);
1952: PetscLogObjectParent(mat,a->B);
1953: PetscFListDuplicate(matin->qlist,&mat->qlist);
1954: *newmat = mat;
1955: return(0);
1956: }
1958: #include petscsys.h
1962: PetscErrorCode MatLoad_MPIAIJ(PetscViewer viewer, MatType type,Mat *newmat)
1963: {
1964: Mat A;
1965: PetscScalar *vals,*svals;
1966: MPI_Comm comm = ((PetscObject)viewer)->comm;
1967: MPI_Status status;
1969: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag,maxnz;
1970: PetscInt i,nz,j,rstart,rend,mmax;
1971: PetscInt header[4],*rowlengths = 0,M,N,m,*cols;
1972: PetscInt *ourlens,*procsnz = 0,*offlens,jj,*mycols,*smycols;
1973: PetscInt cend,cstart,n,*rowners;
1974: int fd;
1977: MPI_Comm_size(comm,&size);
1978: MPI_Comm_rank(comm,&rank);
1979: if (!rank) {
1980: PetscViewerBinaryGetDescriptor(viewer,&fd);
1981: PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
1982: if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
1983: }
1985: MPI_Bcast(header+1,3,MPIU_INT,0,comm);
1986: M = header[1]; N = header[2];
1987: /* determine ownership of all rows */
1988: m = M/size + ((M % size) > rank);
1989: PetscMalloc((size+1)*sizeof(PetscInt),&rowners);
1990: MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
1992: /* First process needs enough room for process with most rows */
1993: if (!rank) {
1994: mmax = rowners[1];
1995: for (i=2; i<size; i++) {
1996: mmax = PetscMax(mmax,rowners[i]);
1997: }
1998: } else mmax = m;
2000: rowners[0] = 0;
2001: for (i=2; i<=size; i++) {
2002: mmax = PetscMax(mmax,rowners[i]);
2003: rowners[i] += rowners[i-1];
2004: }
2005: rstart = rowners[rank];
2006: rend = rowners[rank+1];
2008: /* distribute row lengths to all processors */
2009: PetscMalloc2(mmax,PetscInt,&ourlens,mmax,PetscInt,&offlens);
2010: if (!rank) {
2011: PetscBinaryRead(fd,ourlens,m,PETSC_INT);
2012: PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2013: PetscMalloc(size*sizeof(PetscInt),&procsnz);
2014: PetscMemzero(procsnz,size*sizeof(PetscInt));
2015: for (j=0; j<m; j++) {
2016: procsnz[0] += ourlens[j];
2017: }
2018: for (i=1; i<size; i++) {
2019: PetscBinaryRead(fd,rowlengths,rowners[i+1]-rowners[i],PETSC_INT);
2020: /* calculate the number of nonzeros on each processor */
2021: for (j=0; j<rowners[i+1]-rowners[i]; j++) {
2022: procsnz[i] += rowlengths[j];
2023: }
2024: MPI_Send(rowlengths,rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
2025: }
2026: PetscFree(rowlengths);
2027: } else {
2028: MPI_Recv(ourlens,m,MPIU_INT,0,tag,comm,&status);
2029: }
2031: if (!rank) {
2032: /* determine max buffer needed and allocate it */
2033: maxnz = 0;
2034: for (i=0; i<size; i++) {
2035: maxnz = PetscMax(maxnz,procsnz[i]);
2036: }
2037: PetscMalloc(maxnz*sizeof(PetscInt),&cols);
2039: /* read in my part of the matrix column indices */
2040: nz = procsnz[0];
2041: PetscMalloc(nz*sizeof(PetscInt),&mycols);
2042: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2044: /* read in every one elses and ship off */
2045: for (i=1; i<size; i++) {
2046: nz = procsnz[i];
2047: PetscBinaryRead(fd,cols,nz,PETSC_INT);
2048: MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2049: }
2050: PetscFree(cols);
2051: } else {
2052: /* determine buffer space needed for message */
2053: nz = 0;
2054: for (i=0; i<m; i++) {
2055: nz += ourlens[i];
2056: }
2057: PetscMalloc(nz*sizeof(PetscInt),&mycols);
2059: /* receive message of column indices*/
2060: MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2061: MPI_Get_count(&status,MPIU_INT,&maxnz);
2062: if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2063: }
2065: /* determine column ownership if matrix is not square */
2066: if (N != M) {
2067: n = N/size + ((N % size) > rank);
2068: MPI_Scan(&n,&cend,1,MPIU_INT,MPI_SUM,comm);
2069: cstart = cend - n;
2070: } else {
2071: cstart = rstart;
2072: cend = rend;
2073: n = cend - cstart;
2074: }
2076: /* loop over local rows, determining number of off diagonal entries */
2077: PetscMemzero(offlens,m*sizeof(PetscInt));
2078: jj = 0;
2079: for (i=0; i<m; i++) {
2080: for (j=0; j<ourlens[i]; j++) {
2081: if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++;
2082: jj++;
2083: }
2084: }
2086: /* create our matrix */
2087: for (i=0; i<m; i++) {
2088: ourlens[i] -= offlens[i];
2089: }
2090: MatCreate(comm,&A);
2091: MatSetSizes(A,m,n,M,N);
2092: MatSetType(A,type);
2093: MatMPIAIJSetPreallocation(A,0,ourlens,0,offlens);
2095: MatSetOption(A,MAT_COLUMNS_SORTED);
2096: for (i=0; i<m; i++) {
2097: ourlens[i] += offlens[i];
2098: }
2100: if (!rank) {
2101: PetscMalloc((maxnz+1)*sizeof(PetscScalar),&vals);
2103: /* read in my part of the matrix numerical values */
2104: nz = procsnz[0];
2105: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2106:
2107: /* insert into matrix */
2108: jj = rstart;
2109: smycols = mycols;
2110: svals = vals;
2111: for (i=0; i<m; i++) {
2112: MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2113: smycols += ourlens[i];
2114: svals += ourlens[i];
2115: jj++;
2116: }
2118: /* read in other processors and ship out */
2119: for (i=1; i<size; i++) {
2120: nz = procsnz[i];
2121: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2122: MPI_Send(vals,nz,MPIU_SCALAR,i,A->tag,comm);
2123: }
2124: PetscFree(procsnz);
2125: } else {
2126: /* receive numeric values */
2127: PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);
2129: /* receive message of values*/
2130: MPI_Recv(vals,nz,MPIU_SCALAR,0,A->tag,comm,&status);
2131: MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2132: if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2134: /* insert into matrix */
2135: jj = rstart;
2136: smycols = mycols;
2137: svals = vals;
2138: for (i=0; i<m; i++) {
2139: MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2140: smycols += ourlens[i];
2141: svals += ourlens[i];
2142: jj++;
2143: }
2144: }
2145: PetscFree2(ourlens,offlens);
2146: PetscFree(vals);
2147: PetscFree(mycols);
2148: PetscFree(rowners);
2150: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2151: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2152: *newmat = A;
2153: return(0);
2154: }
2158: /*
2159: Not great since it makes two copies of the submatrix, first an SeqAIJ
2160: in local and then by concatenating the local matrices the end result.
2161: Writing it directly would be much like MatGetSubMatrices_MPIAIJ()
2162: */
2163: PetscErrorCode MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
2164: {
2166: PetscMPIInt rank,size;
2167: PetscInt i,m,n,rstart,row,rend,nz,*cwork,j;
2168: PetscInt *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal;
2169: Mat *local,M,Mreuse;
2170: PetscScalar *vwork,*aa;
2171: MPI_Comm comm = mat->comm;
2172: Mat_SeqAIJ *aij;
2176: MPI_Comm_rank(comm,&rank);
2177: MPI_Comm_size(comm,&size);
2179: if (call == MAT_REUSE_MATRIX) {
2180: PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject *)&Mreuse);
2181: if (!Mreuse) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
2182: local = &Mreuse;
2183: MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&local);
2184: } else {
2185: MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);
2186: Mreuse = *local;
2187: PetscFree(local);
2188: }
2190: /*
2191: m - number of local rows
2192: n - number of columns (same on all processors)
2193: rstart - first row in new global matrix generated
2194: */
2195: MatGetSize(Mreuse,&m,&n);
2196: if (call == MAT_INITIAL_MATRIX) {
2197: aij = (Mat_SeqAIJ*)(Mreuse)->data;
2198: ii = aij->i;
2199: jj = aij->j;
2201: /*
2202: Determine the number of non-zeros in the diagonal and off-diagonal
2203: portions of the matrix in order to do correct preallocation
2204: */
2206: /* first get start and end of "diagonal" columns */
2207: if (csize == PETSC_DECIDE) {
2208: ISGetSize(isrow,&mglobal);
2209: if (mglobal == n) { /* square matrix */
2210: nlocal = m;
2211: } else {
2212: nlocal = n/size + ((n % size) > rank);
2213: }
2214: } else {
2215: nlocal = csize;
2216: }
2217: MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
2218: rstart = rend - nlocal;
2219: if (rank == size - 1 && rend != n) {
2220: SETERRQ2(PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);
2221: }
2223: /* next, compute all the lengths */
2224: PetscMalloc((2*m+1)*sizeof(PetscInt),&dlens);
2225: olens = dlens + m;
2226: for (i=0; i<m; i++) {
2227: jend = ii[i+1] - ii[i];
2228: olen = 0;
2229: dlen = 0;
2230: for (j=0; j<jend; j++) {
2231: if (*jj < rstart || *jj >= rend) olen++;
2232: else dlen++;
2233: jj++;
2234: }
2235: olens[i] = olen;
2236: dlens[i] = dlen;
2237: }
2238: MatCreate(comm,&M);
2239: MatSetSizes(M,m,nlocal,PETSC_DECIDE,n);
2240: MatSetType(M,mat->type_name);
2241: MatMPIAIJSetPreallocation(M,0,dlens,0,olens);
2242: PetscFree(dlens);
2243: } else {
2244: PetscInt ml,nl;
2246: M = *newmat;
2247: MatGetLocalSize(M,&ml,&nl);
2248: if (ml != m) SETERRQ(PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
2249: MatZeroEntries(M);
2250: /*
2251: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
2252: rather than the slower MatSetValues().
2253: */
2254: M->was_assembled = PETSC_TRUE;
2255: M->assembled = PETSC_FALSE;
2256: }
2257: MatGetOwnershipRange(M,&rstart,&rend);
2258: aij = (Mat_SeqAIJ*)(Mreuse)->data;
2259: ii = aij->i;
2260: jj = aij->j;
2261: aa = aij->a;
2262: for (i=0; i<m; i++) {
2263: row = rstart + i;
2264: nz = ii[i+1] - ii[i];
2265: cwork = jj; jj += nz;
2266: vwork = aa; aa += nz;
2267: MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
2268: }
2270: MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
2271: MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
2272: *newmat = M;
2274: /* save submatrix used in processor for next request */
2275: if (call == MAT_INITIAL_MATRIX) {
2276: PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
2277: PetscObjectDereference((PetscObject)Mreuse);
2278: }
2280: return(0);
2281: }
2286: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B,const PetscInt I[],const PetscInt J[],const PetscScalar v[])
2287: {
2288: PetscInt m,cstart, cend,j,nnz,i,d;
2289: PetscInt *d_nnz,*o_nnz,nnz_max = 0,rstart,ii;
2290: const PetscInt *JJ;
2291: PetscScalar *values;
2295: if (I[0]) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"I[0] must be 0 it is %D",I[0]);
2297: B->rmap.bs = B->cmap.bs = 1;
2298: PetscMapInitialize(B->comm,&B->rmap);
2299: PetscMapInitialize(B->comm,&B->cmap);
2300: m = B->rmap.n;
2301: cstart = B->cmap.rstart;
2302: cend = B->cmap.rend;
2303: rstart = B->rmap.rstart;
2305: PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
2306: o_nnz = d_nnz + m;
2308: for (i=0; i<m; i++) {
2309: nnz = I[i+1]- I[i];
2310: JJ = J + I[i];
2311: nnz_max = PetscMax(nnz_max,nnz);
2312: if (nnz < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative %D number of columns",i,nnz);
2313: for (j=0; j<nnz; j++) {
2314: if (*JJ >= cstart) break;
2315: JJ++;
2316: }
2317: d = 0;
2318: for (; j<nnz; j++) {
2319: if (*JJ++ >= cend) break;
2320: d++;
2321: }
2322: d_nnz[i] = d;
2323: o_nnz[i] = nnz - d;
2324: }
2325: MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
2326: PetscFree(d_nnz);
2328: if (v) values = (PetscScalar*)v;
2329: else {
2330: PetscMalloc((nnz_max+1)*sizeof(PetscScalar),&values);
2331: PetscMemzero(values,nnz_max*sizeof(PetscScalar));
2332: }
2334: MatSetOption(B,MAT_COLUMNS_SORTED);
2335: for (i=0; i<m; i++) {
2336: ii = i + rstart;
2337: nnz = I[i+1]- I[i];
2338: MatSetValues_MPIAIJ(B,1,&ii,nnz,J+I[i],values+(v ? I[i] : 0),INSERT_VALUES);
2339: }
2340: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2341: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2342: MatSetOption(B,MAT_COLUMNS_UNSORTED);
2344: if (!v) {
2345: PetscFree(values);
2346: }
2347: return(0);
2348: }
2353: /*@
2354: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
2355: (the default parallel PETSc format).
2357: Collective on MPI_Comm
2359: Input Parameters:
2360: + B - the matrix
2361: . i - the indices into j for the start of each local row (starts with zero)
2362: . j - the column indices for each local row (starts with zero) these must be sorted for each row
2363: - v - optional values in the matrix
2365: Level: developer
2367: .keywords: matrix, aij, compressed row, sparse, parallel
2369: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ
2370: @*/
2371: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
2372: {
2373: PetscErrorCode ierr,(*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);
2376: PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",(void (**)(void))&f);
2377: if (f) {
2378: (*f)(B,i,j,v);
2379: }
2380: return(0);
2381: }
2385: /*@C
2386: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in AIJ format
2387: (the default parallel PETSc format). For good matrix assembly performance
2388: the user should preallocate the matrix storage by setting the parameters
2389: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
2390: performance can be increased by more than a factor of 50.
2392: Collective on MPI_Comm
2394: Input Parameters:
2395: + A - the matrix
2396: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
2397: (same value is used for all local rows)
2398: . d_nnz - array containing the number of nonzeros in the various rows of the
2399: DIAGONAL portion of the local submatrix (possibly different for each row)
2400: or PETSC_NULL, if d_nz is used to specify the nonzero structure.
2401: The size of this array is equal to the number of local rows, i.e 'm'.
2402: You must leave room for the diagonal entry even if it is zero.
2403: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
2404: submatrix (same value is used for all local rows).
2405: - o_nnz - array containing the number of nonzeros in the various rows of the
2406: OFF-DIAGONAL portion of the local submatrix (possibly different for
2407: each row) or PETSC_NULL, if o_nz is used to specify the nonzero
2408: structure. The size of this array is equal to the number
2409: of local rows, i.e 'm'.
2411: If the *_nnz parameter is given then the *_nz parameter is ignored
2413: The AIJ format (also called the Yale sparse matrix format or
2414: compressed row storage (CSR)), is fully compatible with standard Fortran 77
2415: storage. The stored row and column indices begin with zero. See the users manual for details.
2417: The parallel matrix is partitioned such that the first m0 rows belong to
2418: process 0, the next m1 rows belong to process 1, the next m2 rows belong
2419: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
2421: The DIAGONAL portion of the local submatrix of a processor can be defined
2422: as the submatrix which is obtained by extraction the part corresponding
2423: to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the
2424: first row that belongs to the processor, and r2 is the last row belonging
2425: to the this processor. This is a square mxm matrix. The remaining portion
2426: of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.
2428: If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.
2430: Example usage:
2431:
2432: Consider the following 8x8 matrix with 34 non-zero values, that is
2433: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
2434: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
2435: as follows:
2437: .vb
2438: 1 2 0 | 0 3 0 | 0 4
2439: Proc0 0 5 6 | 7 0 0 | 8 0
2440: 9 0 10 | 11 0 0 | 12 0
2441: -------------------------------------
2442: 13 0 14 | 15 16 17 | 0 0
2443: Proc1 0 18 0 | 19 20 21 | 0 0
2444: 0 0 0 | 22 23 0 | 24 0
2445: -------------------------------------
2446: Proc2 25 26 27 | 0 0 28 | 29 0
2447: 30 0 0 | 31 32 33 | 0 34
2448: .ve
2450: This can be represented as a collection of submatrices as:
2452: .vb
2453: A B C
2454: D E F
2455: G H I
2456: .ve
2458: Where the submatrices A,B,C are owned by proc0, D,E,F are
2459: owned by proc1, G,H,I are owned by proc2.
2461: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2462: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2463: The 'M','N' parameters are 8,8, and have the same values on all procs.
2465: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
2466: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
2467: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
2468: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
2469: part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
2470: matrix, ans [DF] as another SeqAIJ matrix.
2472: When d_nz, o_nz parameters are specified, d_nz storage elements are
2473: allocated for every row of the local diagonal submatrix, and o_nz
2474: storage locations are allocated for every row of the OFF-DIAGONAL submat.
2475: One way to choose d_nz and o_nz is to use the max nonzerors per local
2476: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
2477: In this case, the values of d_nz,o_nz are:
2478: .vb
2479: proc0 : dnz = 2, o_nz = 2
2480: proc1 : dnz = 3, o_nz = 2
2481: proc2 : dnz = 1, o_nz = 4
2482: .ve
2483: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
2484: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
2485: for proc3. i.e we are using 12+15+10=37 storage locations to store
2486: 34 values.
2488: When d_nnz, o_nnz parameters are specified, the storage is specified
2489: for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
2490: In the above case the values for d_nnz,o_nnz are:
2491: .vb
2492: proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
2493: proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
2494: proc2: d_nnz = [1,1] and o_nnz = [4,4]
2495: .ve
2496: Here the space allocated is sum of all the above values i.e 34, and
2497: hence pre-allocation is perfect.
2499: Level: intermediate
2501: .keywords: matrix, aij, compressed row, sparse, parallel
2503: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIAIJ(), MatMPIAIJSetPreallocationCSR(),
2504: MPIAIJ
2505: @*/
2506: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIAIJSetPreallocation(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2507: {
2508: PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);
2511: PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocation_C",(void (**)(void))&f);
2512: if (f) {
2513: (*f)(B,d_nz,d_nnz,o_nz,o_nnz);
2514: }
2515: return(0);
2516: }
2520: /*@C
2521: MatCreateMPIAIJ - Creates a sparse parallel matrix in AIJ format
2522: (the default parallel PETSc format). For good matrix assembly performance
2523: the user should preallocate the matrix storage by setting the parameters
2524: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
2525: performance can be increased by more than a factor of 50.
2527: Collective on MPI_Comm
2529: Input Parameters:
2530: + comm - MPI communicator
2531: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
2532: This value should be the same as the local size used in creating the
2533: y vector for the matrix-vector product y = Ax.
2534: . n - This value should be the same as the local size used in creating the
2535: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
2536: calculated if N is given) For square matrices n is almost always m.
2537: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
2538: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
2539: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
2540: (same value is used for all local rows)
2541: . d_nnz - array containing the number of nonzeros in the various rows of the
2542: DIAGONAL portion of the local submatrix (possibly different for each row)
2543: or PETSC_NULL, if d_nz is used to specify the nonzero structure.
2544: The size of this array is equal to the number of local rows, i.e 'm'.
2545: You must leave room for the diagonal entry even if it is zero.
2546: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
2547: submatrix (same value is used for all local rows).
2548: - o_nnz - array containing the number of nonzeros in the various rows of the
2549: OFF-DIAGONAL portion of the local submatrix (possibly different for
2550: each row) or PETSC_NULL, if o_nz is used to specify the nonzero
2551: structure. The size of this array is equal to the number
2552: of local rows, i.e 'm'.
2554: Output Parameter:
2555: . A - the matrix
2557: Notes:
2558: If the *_nnz parameter is given then the *_nz parameter is ignored
2560: m,n,M,N parameters specify the size of the matrix, and its partitioning across
2561: processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate
2562: storage requirements for this matrix.
2564: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one
2565: processor than it must be used on all processors that share the object for
2566: that argument.
2568: The user MUST specify either the local or global matrix dimensions
2569: (possibly both).
2571: The parallel matrix is partitioned such that the first m0 rows belong to
2572: process 0, the next m1 rows belong to process 1, the next m2 rows belong
2573: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
2575: The DIAGONAL portion of the local submatrix of a processor can be defined
2576: as the submatrix which is obtained by extraction the part corresponding
2577: to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the
2578: first row that belongs to the processor, and r2 is the last row belonging
2579: to the this processor. This is a square mxm matrix. The remaining portion
2580: of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.
2582: If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.
2584: When calling this routine with a single process communicator, a matrix of
2585: type SEQAIJ is returned. If a matrix of type MPIAIJ is desired for this
2586: type of communicator, use the construction mechanism:
2587: MatCreate(...,&A); MatSetType(A,MPIAIJ); MatMPIAIJSetPreallocation(A,...);
2589: By default, this format uses inodes (identical nodes) when possible.
2590: We search for consecutive rows with the same nonzero structure, thereby
2591: reusing matrix information to achieve increased efficiency.
2593: Options Database Keys:
2594: + -mat_no_inode - Do not use inodes
2595: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2596: - -mat_aij_oneindex - Internally use indexing starting at 1
2597: rather than 0. Note that when calling MatSetValues(),
2598: the user still MUST index entries starting at 0!
2601: Example usage:
2602:
2603: Consider the following 8x8 matrix with 34 non-zero values, that is
2604: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
2605: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
2606: as follows:
2608: .vb
2609: 1 2 0 | 0 3 0 | 0 4
2610: Proc0 0 5 6 | 7 0 0 | 8 0
2611: 9 0 10 | 11 0 0 | 12 0
2612: -------------------------------------
2613: 13 0 14 | 15 16 17 | 0 0
2614: Proc1 0 18 0 | 19 20 21 | 0 0
2615: 0 0 0 | 22 23 0 | 24 0
2616: -------------------------------------
2617: Proc2 25 26 27 | 0 0 28 | 29 0
2618: 30 0 0 | 31 32 33 | 0 34
2619: .ve
2621: This can be represented as a collection of submatrices as:
2623: .vb
2624: A B C
2625: D E F
2626: G H I
2627: .ve
2629: Where the submatrices A,B,C are owned by proc0, D,E,F are
2630: owned by proc1, G,H,I are owned by proc2.
2632: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2633: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
2634: The 'M','N' parameters are 8,8, and have the same values on all procs.
2636: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
2637: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
2638: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
2639: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
2640: part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
2641: matrix, ans [DF] as another SeqAIJ matrix.
2643: When d_nz, o_nz parameters are specified, d_nz storage elements are
2644: allocated for every row of the local diagonal submatrix, and o_nz
2645: storage locations are allocated for every row of the OFF-DIAGONAL submat.
2646: One way to choose d_nz and o_nz is to use the max nonzerors per local
2647: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
2648: In this case, the values of d_nz,o_nz are:
2649: .vb
2650: proc0 : dnz = 2, o_nz = 2
2651: proc1 : dnz = 3, o_nz = 2
2652: proc2 : dnz = 1, o_nz = 4
2653: .ve
2654: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
2655: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
2656: for proc3. i.e we are using 12+15+10=37 storage locations to store
2657: 34 values.
2659: When d_nnz, o_nnz parameters are specified, the storage is specified
2660: for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
2661: In the above case the values for d_nnz,o_nnz are:
2662: .vb
2663: proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
2664: proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
2665: proc2: d_nnz = [1,1] and o_nnz = [4,4]
2666: .ve
2667: Here the space allocated is sum of all the above values i.e 34, and
2668: hence pre-allocation is perfect.
2670: Level: intermediate
2672: .keywords: matrix, aij, compressed row, sparse, parallel
2674: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
2675: MPIAIJ
2676: @*/
2677: PetscErrorCode PETSCMAT_DLLEXPORT MatCreateMPIAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
2678: {
2680: PetscMPIInt size;
2683: MatCreate(comm,A);
2684: MatSetSizes(*A,m,n,M,N);
2685: MPI_Comm_size(comm,&size);
2686: if (size > 1) {
2687: MatSetType(*A,MATMPIAIJ);
2688: MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
2689: } else {
2690: MatSetType(*A,MATSEQAIJ);
2691: MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
2692: }
2693: return(0);
2694: }
2698: PetscErrorCode PETSCMAT_DLLEXPORT MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
2699: {
2700: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
2703: *Ad = a->A;
2704: *Ao = a->B;
2705: *colmap = a->garray;
2706: return(0);
2707: }
2711: PetscErrorCode MatSetColoring_MPIAIJ(Mat A,ISColoring coloring)
2712: {
2714: PetscInt i;
2715: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2718: if (coloring->ctype == IS_COLORING_LOCAL) {
2719: ISColoringValue *allcolors,*colors;
2720: ISColoring ocoloring;
2722: /* set coloring for diagonal portion */
2723: MatSetColoring_SeqAIJ(a->A,coloring);
2725: /* set coloring for off-diagonal portion */
2726: ISAllGatherColors(A->comm,coloring->n,coloring->colors,PETSC_NULL,&allcolors);
2727: PetscMalloc((a->B->cmap.n+1)*sizeof(ISColoringValue),&colors);
2728: for (i=0; i<a->B->cmap.n; i++) {
2729: colors[i] = allcolors[a->garray[i]];
2730: }
2731: PetscFree(allcolors);
2732: ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap.n,colors,&ocoloring);
2733: MatSetColoring_SeqAIJ(a->B,ocoloring);
2734: ISColoringDestroy(ocoloring);
2735: } else if (coloring->ctype == IS_COLORING_GHOSTED) {
2736: ISColoringValue *colors;
2737: PetscInt *larray;
2738: ISColoring ocoloring;
2740: /* set coloring for diagonal portion */
2741: PetscMalloc((a->A->cmap.n+1)*sizeof(PetscInt),&larray);
2742: for (i=0; i<a->A->cmap.n; i++) {
2743: larray[i] = i + A->cmap.rstart;
2744: }
2745: ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->A->cmap.n,larray,PETSC_NULL,larray);
2746: PetscMalloc((a->A->cmap.n+1)*sizeof(ISColoringValue),&colors);
2747: for (i=0; i<a->A->cmap.n; i++) {
2748: colors[i] = coloring->colors[larray[i]];
2749: }
2750: PetscFree(larray);
2751: ISColoringCreate(PETSC_COMM_SELF,coloring->n,a->A->cmap.n,colors,&ocoloring);
2752: MatSetColoring_SeqAIJ(a->A,ocoloring);
2753: ISColoringDestroy(ocoloring);
2755: /* set coloring for off-diagonal portion */
2756: PetscMalloc((a->B->cmap.n+1)*sizeof(PetscInt),&larray);
2757: ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->B->cmap.n,a->garray,PETSC_NULL,larray);
2758: PetscMalloc((a->B->cmap.n+1)*sizeof(ISColoringValue),&colors);
2759: for (i=0; i<a->B->cmap.n; i++) {
2760: colors[i] = coloring->colors[larray[i]];
2761: }
2762: PetscFree(larray);
2763: ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap.n,colors,&ocoloring);
2764: MatSetColoring_SeqAIJ(a->B,ocoloring);
2765: ISColoringDestroy(ocoloring);
2766: } else {
2767: SETERRQ1(PETSC_ERR_SUP,"No support ISColoringType %d",(int)coloring->ctype);
2768: }
2770: return(0);
2771: }
2773: #if defined(PETSC_HAVE_ADIC)
2776: PetscErrorCode MatSetValuesAdic_MPIAIJ(Mat A,void *advalues)
2777: {
2778: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2782: MatSetValuesAdic_SeqAIJ(a->A,advalues);
2783: MatSetValuesAdic_SeqAIJ(a->B,advalues);
2784: return(0);
2785: }
2786: #endif
2790: PetscErrorCode MatSetValuesAdifor_MPIAIJ(Mat A,PetscInt nl,void *advalues)
2791: {
2792: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2796: MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);
2797: MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);
2798: return(0);
2799: }
2803: /*@C
2804: MatMerge - Creates a single large PETSc matrix by concatinating sequential
2805: matrices from each processor
2807: Collective on MPI_Comm
2809: Input Parameters:
2810: + comm - the communicators the parallel matrix will live on
2811: . inmat - the input sequential matrices
2812: . n - number of local columns (or PETSC_DECIDE)
2813: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
2815: Output Parameter:
2816: . outmat - the parallel matrix generated
2818: Level: advanced
2820: Notes: The number of columns of the matrix in EACH processor MUST be the same.
2822: @*/
2823: PetscErrorCode PETSCMAT_DLLEXPORT MatMerge(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
2824: {
2826: PetscInt m,N,i,rstart,nnz,I,*dnz,*onz;
2827: PetscInt *indx;
2828: PetscScalar *values;
2831: MatGetSize(inmat,&m,&N);
2832: if (scall == MAT_INITIAL_MATRIX){
2833: /* count nonzeros in each row, for diagonal and off diagonal portion of matrix */
2834: if (n == PETSC_DECIDE){
2835: PetscSplitOwnership(comm,&n,&N);
2836: }
2837: MPI_Scan(&m, &rstart,1,MPIU_INT,MPI_SUM,comm);
2838: rstart -= m;
2840: MatPreallocateInitialize(comm,m,n,dnz,onz);
2841: for (i=0;i<m;i++) {
2842: MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
2843: MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);
2844: MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
2845: }
2846: /* This routine will ONLY return MPIAIJ type matrix */
2847: MatCreate(comm,outmat);
2848: MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
2849: MatSetType(*outmat,MATMPIAIJ);
2850: MatMPIAIJSetPreallocation(*outmat,0,dnz,0,onz);
2851: MatPreallocateFinalize(dnz,onz);
2852:
2853: } else if (scall == MAT_REUSE_MATRIX){
2854: MatGetOwnershipRange(*outmat,&rstart,PETSC_NULL);
2855: } else {
2856: SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
2857: }
2859: for (i=0;i<m;i++) {
2860: MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
2861: I = i + rstart;
2862: MatSetValues(*outmat,1,&I,nnz,indx,values,INSERT_VALUES);
2863: MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
2864: }
2865: MatDestroy(inmat);
2866: MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
2867: MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);
2869: return(0);
2870: }
2874: PetscErrorCode MatFileSplit(Mat A,char *outfile)
2875: {
2876: PetscErrorCode ierr;
2877: PetscMPIInt rank;
2878: PetscInt m,N,i,rstart,nnz;
2879: size_t len;
2880: const PetscInt *indx;
2881: PetscViewer out;
2882: char *name;
2883: Mat B;
2884: const PetscScalar *values;
2887: MatGetLocalSize(A,&m,0);
2888: MatGetSize(A,0,&N);
2889: /* Should this be the type of the diagonal block of A? */
2890: MatCreate(PETSC_COMM_SELF,&B);
2891: MatSetSizes(B,m,N,m,N);
2892: MatSetType(B,MATSEQAIJ);
2893: MatSeqAIJSetPreallocation(B,0,PETSC_NULL);
2894: MatGetOwnershipRange(A,&rstart,0);
2895: for (i=0;i<m;i++) {
2896: MatGetRow(A,i+rstart,&nnz,&indx,&values);
2897: MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);
2898: MatRestoreRow(A,i+rstart,&nnz,&indx,&values);
2899: }
2900: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2901: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2903: MPI_Comm_rank(A->comm,&rank);
2904: PetscStrlen(outfile,&len);
2905: PetscMalloc((len+5)*sizeof(char),&name);
2906: sprintf(name,"%s.%d",outfile,rank);
2907: PetscViewerBinaryOpen(PETSC_COMM_SELF,name,FILE_MODE_APPEND,&out);
2908: PetscFree(name);
2909: MatView(B,out);
2910: PetscViewerDestroy(out);
2911: MatDestroy(B);
2912: return(0);
2913: }
2915: EXTERN PetscErrorCode MatDestroy_MPIAIJ(Mat);
2918: PetscErrorCode PETSCMAT_DLLEXPORT MatDestroy_MPIAIJ_SeqsToMPI(Mat A)
2919: {
2920: PetscErrorCode ierr;
2921: Mat_Merge_SeqsToMPI *merge;
2922: PetscObjectContainer container;
2925: PetscObjectQuery((PetscObject)A,"MatMergeSeqsToMPI",(PetscObject *)&container);
2926: if (container) {
2927: PetscObjectContainerGetPointer(container,(void **)&merge);
2928: PetscFree(merge->id_r);
2929: PetscFree(merge->len_s);
2930: PetscFree(merge->len_r);
2931: PetscFree(merge->bi);
2932: PetscFree(merge->bj);
2933: PetscFree(merge->buf_ri);
2934: PetscFree(merge->buf_rj);
2935: PetscFree(merge->coi);
2936: PetscFree(merge->coj);
2937: PetscFree(merge->owners_co);
2938: PetscFree(merge->rowmap.range);
2939:
2940: PetscObjectContainerDestroy(container);
2941: PetscObjectCompose((PetscObject)A,"MatMergeSeqsToMPI",0);
2942: }
2943: PetscFree(merge);
2945: MatDestroy_MPIAIJ(A);
2946: return(0);
2947: }
2949: #include src/mat/utils/freespace.h
2950: #include petscbt.h
2951: static PetscEvent logkey_seqstompinum = 0;
2954: /*@C
2955: MatMerge_SeqsToMPI - Creates a MPIAIJ matrix by adding sequential
2956: matrices from each processor
2958: Collective on MPI_Comm
2960: Input Parameters:
2961: + comm - the communicators the parallel matrix will live on
2962: . seqmat - the input sequential matrices
2963: . m - number of local rows (or PETSC_DECIDE)
2964: . n - number of local columns (or PETSC_DECIDE)
2965: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
2967: Output Parameter:
2968: . mpimat - the parallel matrix generated
2970: Level: advanced
2972: Notes:
2973: The dimensions of the sequential matrix in each processor MUST be the same.
2974: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
2975: destroyed when mpimat is destroyed. Call PetscObjectQuery() to access seqmat.
2976: @*/
2977: PetscErrorCode PETSCMAT_DLLEXPORT MatMerge_SeqsToMPINumeric(Mat seqmat,Mat mpimat)
2978: {
2979: PetscErrorCode ierr;
2980: MPI_Comm comm=mpimat->comm;
2981: Mat_SeqAIJ *a=(Mat_SeqAIJ*)seqmat->data;
2982: PetscMPIInt size,rank,taga,*len_s;
2983: PetscInt N=mpimat->cmap.N,i,j,*owners,*ai=a->i,*aj=a->j;
2984: PetscInt proc,m;
2985: PetscInt **buf_ri,**buf_rj;
2986: PetscInt k,anzi,*bj_i,*bi,*bj,arow,bnzi,nextaj;
2987: PetscInt nrows,**buf_ri_k,**nextrow,**nextai;
2988: MPI_Request *s_waits,*r_waits;
2989: MPI_Status *status;
2990: MatScalar *aa=a->a,**abuf_r,*ba_i;
2991: Mat_Merge_SeqsToMPI *merge;
2992: PetscObjectContainer container;
2993:
2995: if (!logkey_seqstompinum) {
2996: PetscLogEventRegister(&logkey_seqstompinum,"MatMerge_SeqsToMPINumeric",MAT_COOKIE);
2997: }
2998: PetscLogEventBegin(logkey_seqstompinum,seqmat,0,0,0);
3000: MPI_Comm_size(comm,&size);
3001: MPI_Comm_rank(comm,&rank);
3003: PetscObjectQuery((PetscObject)mpimat,"MatMergeSeqsToMPI",(PetscObject *)&container);
3004: if (container) {
3005: PetscObjectContainerGetPointer(container,(void **)&merge);
3006: }
3007: bi = merge->bi;
3008: bj = merge->bj;
3009: buf_ri = merge->buf_ri;
3010: buf_rj = merge->buf_rj;
3012: PetscMalloc(size*sizeof(MPI_Status),&status);
3013: owners = merge->rowmap.range;
3014: len_s = merge->len_s;
3016: /* send and recv matrix values */
3017: /*-----------------------------*/
3018: PetscObjectGetNewTag((PetscObject)mpimat,&taga);
3019: PetscPostIrecvScalar(comm,taga,merge->nrecv,merge->id_r,merge->len_r,&abuf_r,&r_waits);
3021: PetscMalloc((merge->nsend+1)*sizeof(MPI_Request),&s_waits);
3022: for (proc=0,k=0; proc<size; proc++){
3023: if (!len_s[proc]) continue;
3024: i = owners[proc];
3025: MPI_Isend(aa+ai[i],len_s[proc],MPIU_MATSCALAR,proc,taga,comm,s_waits+k);
3026: k++;
3027: }
3029: if (merge->nrecv) {MPI_Waitall(merge->nrecv,r_waits,status);}
3030: if (merge->nsend) {MPI_Waitall(merge->nsend,s_waits,status);}
3031: PetscFree(status);
3033: PetscFree(s_waits);
3034: PetscFree(r_waits);
3036: /* insert mat values of mpimat */
3037: /*----------------------------*/
3038: PetscMalloc(N*sizeof(MatScalar),&ba_i);
3039: PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
3040: nextrow = buf_ri_k + merge->nrecv;
3041: nextai = nextrow + merge->nrecv;
3043: for (k=0; k<merge->nrecv; k++){
3044: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
3045: nrows = *(buf_ri_k[k]);
3046: nextrow[k] = buf_ri_k[k]+1; /* next row number of k-th recved i-structure */
3047: nextai[k] = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure */
3048: }
3050: /* set values of ba */
3051: m = merge->rowmap.n;
3052: for (i=0; i<m; i++) {
3053: arow = owners[rank] + i;
3054: bj_i = bj+bi[i]; /* col indices of the i-th row of mpimat */
3055: bnzi = bi[i+1] - bi[i];
3056: PetscMemzero(ba_i,bnzi*sizeof(MatScalar));
3058: /* add local non-zero vals of this proc's seqmat into ba */
3059: anzi = ai[arow+1] - ai[arow];
3060: aj = a->j + ai[arow];
3061: aa = a->a + ai[arow];
3062: nextaj = 0;
3063: for (j=0; nextaj<anzi; j++){
3064: if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
3065: ba_i[j] += aa[nextaj++];
3066: }
3067: }
3069: /* add received vals into ba */
3070: for (k=0; k<merge->nrecv; k++){ /* k-th received message */
3071: /* i-th row */
3072: if (i == *nextrow[k]) {
3073: anzi = *(nextai[k]+1) - *nextai[k];
3074: aj = buf_rj[k] + *(nextai[k]);
3075: aa = abuf_r[k] + *(nextai[k]);
3076: nextaj = 0;
3077: for (j=0; nextaj<anzi; j++){
3078: if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
3079: ba_i[j] += aa[nextaj++];
3080: }
3081: }
3082: nextrow[k]++; nextai[k]++;
3083: }
3084: }
3085: MatSetValues(mpimat,1,&arow,bnzi,bj_i,ba_i,INSERT_VALUES);
3086: }
3087: MatAssemblyBegin(mpimat,MAT_FINAL_ASSEMBLY);
3088: MatAssemblyEnd(mpimat,MAT_FINAL_ASSEMBLY);
3090: PetscFree(abuf_r);
3091: PetscFree(ba_i);
3092: PetscFree(buf_ri_k);
3093: PetscLogEventEnd(logkey_seqstompinum,seqmat,0,0,0);
3094: return(0);
3095: }
3097: static PetscEvent logkey_seqstompisym = 0;
3100: PetscErrorCode PETSCMAT_DLLEXPORT MatMerge_SeqsToMPISymbolic(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,Mat *mpimat)
3101: {
3102: PetscErrorCode ierr;
3103: Mat B_mpi;
3104: Mat_SeqAIJ *a=(Mat_SeqAIJ*)seqmat->data;
3105: PetscMPIInt size,rank,tagi,tagj,*len_s,*len_si,*len_ri;
3106: PetscInt **buf_rj,**buf_ri,**buf_ri_k;
3107: PetscInt M=seqmat->rmap.n,N=seqmat->cmap.n,i,*owners,*ai=a->i,*aj=a->j;
3108: PetscInt len,proc,*dnz,*onz;
3109: PetscInt k,anzi,*bi,*bj,*lnk,nlnk,arow,bnzi,nspacedouble=0;
3110: PetscInt nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextai;
3111: MPI_Request *si_waits,*sj_waits,*ri_waits,*rj_waits;
3112: MPI_Status *status;
3113: PetscFreeSpaceList free_space=PETSC_NULL,current_space=PETSC_NULL;
3114: PetscBT lnkbt;
3115: Mat_Merge_SeqsToMPI *merge;
3116: PetscObjectContainer container;
3119: if (!logkey_seqstompisym) {
3120: PetscLogEventRegister(&logkey_seqstompisym,"MatMerge_SeqsToMPISymbolic",MAT_COOKIE);
3121: }
3122: PetscLogEventBegin(logkey_seqstompisym,seqmat,0,0,0);
3124: /* make sure it is a PETSc comm */
3125: PetscCommDuplicate(comm,&comm,PETSC_NULL);
3126: MPI_Comm_size(comm,&size);
3127: MPI_Comm_rank(comm,&rank);
3128:
3129: PetscNew(Mat_Merge_SeqsToMPI,&merge);
3130: PetscMalloc(size*sizeof(MPI_Status),&status);
3132: /* determine row ownership */
3133: /*---------------------------------------------------------*/
3134: merge->rowmap.n = m;
3135: merge->rowmap.N = M;
3136: merge->rowmap.bs = 1;
3137: PetscMapInitialize(comm,&merge->rowmap);
3138: PetscMalloc(size*sizeof(PetscMPIInt),&len_si);
3139: PetscMalloc(size*sizeof(PetscMPIInt),&merge->len_s);
3140:
3141: m = merge->rowmap.n;
3142: M = merge->rowmap.N;
3143: owners = merge->rowmap.range;
3145: /* determine the number of messages to send, their lengths */
3146: /*---------------------------------------------------------*/
3147: len_s = merge->len_s;
3149: len = 0; /* length of buf_si[] */
3150: merge->nsend = 0;
3151: for (proc=0; proc<size; proc++){
3152: len_si[proc] = 0;
3153: if (proc == rank){
3154: len_s[proc] = 0;
3155: } else {
3156: len_si[proc] = owners[proc+1] - owners[proc] + 1;
3157: len_s[proc] = ai[owners[proc+1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
3158: }
3159: if (len_s[proc]) {
3160: merge->nsend++;
3161: nrows = 0;
3162: for (i=owners[proc]; i<owners[proc+1]; i++){
3163: if (ai[i+1] > ai[i]) nrows++;
3164: }
3165: len_si[proc] = 2*(nrows+1);
3166: len += len_si[proc];
3167: }
3168: }
3170: /* determine the number and length of messages to receive for ij-structure */
3171: /*-------------------------------------------------------------------------*/
3172: PetscGatherNumberOfMessages(comm,PETSC_NULL,len_s,&merge->nrecv);
3173: PetscGatherMessageLengths2(comm,merge->nsend,merge->nrecv,len_s,len_si,&merge->id_r,&merge->len_r,&len_ri);
3175: /* post the Irecv of j-structure */
3176: /*-------------------------------*/
3177: PetscCommGetNewTag(comm,&tagj);
3178: PetscPostIrecvInt(comm,tagj,merge->nrecv,merge->id_r,merge->len_r,&buf_rj,&rj_waits);
3180: /* post the Isend of j-structure */
3181: /*--------------------------------*/
3182: PetscMalloc((2*merge->nsend+1)*sizeof(MPI_Request),&si_waits);
3183: sj_waits = si_waits + merge->nsend;
3185: for (proc=0, k=0; proc<size; proc++){
3186: if (!len_s[proc]) continue;
3187: i = owners[proc];
3188: MPI_Isend(aj+ai[i],len_s[proc],MPIU_INT,proc,tagj,comm,sj_waits+k);
3189: k++;
3190: }
3192: /* receives and sends of j-structure are complete */
3193: /*------------------------------------------------*/
3194: if (merge->nrecv) {MPI_Waitall(merge->nrecv,rj_waits,status);}
3195: if (merge->nsend) {MPI_Waitall(merge->nsend,sj_waits,status);}
3196:
3197: /* send and recv i-structure */
3198: /*---------------------------*/
3199: PetscCommGetNewTag(comm,&tagi);
3200: PetscPostIrecvInt(comm,tagi,merge->nrecv,merge->id_r,len_ri,&buf_ri,&ri_waits);
3201:
3202: PetscMalloc((len+1)*sizeof(PetscInt),&buf_s);
3203: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
3204: for (proc=0,k=0; proc<size; proc++){
3205: if (!len_s[proc]) continue;
3206: /* form outgoing message for i-structure:
3207: buf_si[0]: nrows to be sent
3208: [1:nrows]: row index (global)
3209: [nrows+1:2*nrows+1]: i-structure index
3210: */
3211: /*-------------------------------------------*/
3212: nrows = len_si[proc]/2 - 1;
3213: buf_si_i = buf_si + nrows+1;
3214: buf_si[0] = nrows;
3215: buf_si_i[0] = 0;
3216: nrows = 0;
3217: for (i=owners[proc]; i<owners[proc+1]; i++){
3218: anzi = ai[i+1] - ai[i];
3219: if (anzi) {
3220: buf_si_i[nrows+1] = buf_si_i[nrows] + anzi; /* i-structure */
3221: buf_si[nrows+1] = i-owners[proc]; /* local row index */
3222: nrows++;
3223: }
3224: }
3225: MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,si_waits+k);
3226: k++;
3227: buf_si += len_si[proc];
3228: }
3230: if (merge->nrecv) {MPI_Waitall(merge->nrecv,ri_waits,status);}
3231: if (merge->nsend) {MPI_Waitall(merge->nsend,si_waits,status);}
3233: PetscInfo2(seqmat,"nsend: %D, nrecv: %D\n",merge->nsend,merge->nrecv);
3234: for (i=0; i<merge->nrecv; i++){
3235: PetscInfo3(seqmat,"recv len_ri=%D, len_rj=%D from [%D]\n",len_ri[i],merge->len_r[i],merge->id_r[i]);
3236: }
3238: PetscFree(len_si);
3239: PetscFree(len_ri);
3240: PetscFree(rj_waits);
3241: PetscFree(si_waits);
3242: PetscFree(ri_waits);
3243: PetscFree(buf_s);
3244: PetscFree(status);
3246: /* compute a local seq matrix in each processor */
3247: /*----------------------------------------------*/
3248: /* allocate bi array and free space for accumulating nonzero column info */
3249: PetscMalloc((m+1)*sizeof(PetscInt),&bi);
3250: bi[0] = 0;
3252: /* create and initialize a linked list */
3253: nlnk = N+1;
3254: PetscLLCreate(N,N,nlnk,lnk,lnkbt);
3255:
3256: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
3257: len = 0;
3258: len = ai[owners[rank+1]] - ai[owners[rank]];
3259: PetscFreeSpaceGet((PetscInt)(2*len+1),&free_space);
3260: current_space = free_space;
3262: /* determine symbolic info for each local row */
3263: PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
3264: nextrow = buf_ri_k + merge->nrecv;
3265: nextai = nextrow + merge->nrecv;
3266: for (k=0; k<merge->nrecv; k++){
3267: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
3268: nrows = *buf_ri_k[k];
3269: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
3270: nextai[k] = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure */
3271: }
3273: MatPreallocateInitialize(comm,m,n,dnz,onz);
3274: len = 0;
3275: for (i=0;i<m;i++) {
3276: bnzi = 0;
3277: /* add local non-zero cols of this proc's seqmat into lnk */
3278: arow = owners[rank] + i;
3279: anzi = ai[arow+1] - ai[arow];
3280: aj = a->j + ai[arow];
3281: PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
3282: bnzi += nlnk;
3283: /* add received col data into lnk */
3284: for (k=0; k<merge->nrecv; k++){ /* k-th received message */
3285: if (i == *nextrow[k]) { /* i-th row */
3286: anzi = *(nextai[k]+1) - *nextai[k];
3287: aj = buf_rj[k] + *nextai[k];
3288: PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
3289: bnzi += nlnk;
3290: nextrow[k]++; nextai[k]++;
3291: }
3292: }
3293: if (len < bnzi) len = bnzi; /* =max(bnzi) */
3295: /* if free space is not available, make more free space */
3296: if (current_space->local_remaining<bnzi) {
3297: PetscFreeSpaceGet(current_space->total_array_size,¤t_space);
3298: nspacedouble++;
3299: }
3300: /* copy data into free space, then initialize lnk */
3301: PetscLLClean(N,N,bnzi,lnk,current_space->array,lnkbt);
3302: MatPreallocateSet(i+owners[rank],bnzi,current_space->array,dnz,onz);
3304: current_space->array += bnzi;
3305: current_space->local_used += bnzi;
3306: current_space->local_remaining -= bnzi;
3307:
3308: bi[i+1] = bi[i] + bnzi;
3309: }
3310:
3311: PetscFree(buf_ri_k);
3313: PetscMalloc((bi[m]+1)*sizeof(PetscInt),&bj);
3314: PetscFreeSpaceContiguous(&free_space,bj);
3315: PetscLLDestroy(lnk,lnkbt);
3317: /* create symbolic parallel matrix B_mpi */
3318: /*---------------------------------------*/
3319: MatCreate(comm,&B_mpi);
3320: if (n==PETSC_DECIDE) {
3321: MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,N);
3322: } else {
3323: MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3324: }
3325: MatSetType(B_mpi,MATMPIAIJ);
3326: MatMPIAIJSetPreallocation(B_mpi,0,dnz,0,onz);
3327: MatPreallocateFinalize(dnz,onz);
3329: /* B_mpi is not ready for use - assembly will be done by MatMerge_SeqsToMPINumeric() */
3330: B_mpi->assembled = PETSC_FALSE;
3331: B_mpi->ops->destroy = MatDestroy_MPIAIJ_SeqsToMPI;
3332: merge->bi = bi;
3333: merge->bj = bj;
3334: merge->buf_ri = buf_ri;
3335: merge->buf_rj = buf_rj;
3336: merge->coi = PETSC_NULL;
3337: merge->coj = PETSC_NULL;
3338: merge->owners_co = PETSC_NULL;
3340: /* attach the supporting struct to B_mpi for reuse */
3341: PetscObjectContainerCreate(PETSC_COMM_SELF,&container);
3342: PetscObjectContainerSetPointer(container,merge);
3343: PetscObjectCompose((PetscObject)B_mpi,"MatMergeSeqsToMPI",(PetscObject)container);
3344: *mpimat = B_mpi;
3346: PetscCommDestroy(&comm);
3347: PetscLogEventEnd(logkey_seqstompisym,seqmat,0,0,0);
3348: return(0);
3349: }
3351: static PetscEvent logkey_seqstompi = 0;
3354: PetscErrorCode PETSCMAT_DLLEXPORT MatMerge_SeqsToMPI(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,MatReuse scall,Mat *mpimat)
3355: {
3356: PetscErrorCode ierr;
3359: if (!logkey_seqstompi) {
3360: PetscLogEventRegister(&logkey_seqstompi,"MatMerge_SeqsToMPI",MAT_COOKIE);
3361: }
3362: PetscLogEventBegin(logkey_seqstompi,seqmat,0,0,0);
3363: if (scall == MAT_INITIAL_MATRIX){
3364: MatMerge_SeqsToMPISymbolic(comm,seqmat,m,n,mpimat);
3365: }
3366: MatMerge_SeqsToMPINumeric(seqmat,*mpimat);
3367: PetscLogEventEnd(logkey_seqstompi,seqmat,0,0,0);
3368: return(0);
3369: }
3370: static PetscEvent logkey_getlocalmat = 0;
3373: /*@C
3374: MatGetLocalMat - Creates a SeqAIJ matrix by taking all its local rows
3376: Not Collective
3378: Input Parameters:
3379: + A - the matrix
3380: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3382: Output Parameter:
3383: . A_loc - the local sequential matrix generated
3385: Level: developer
3387: @*/
3388: PetscErrorCode PETSCMAT_DLLEXPORT MatGetLocalMat(Mat A,MatReuse scall,Mat *A_loc)
3389: {
3390: PetscErrorCode ierr;
3391: Mat_MPIAIJ *mpimat=(Mat_MPIAIJ*)A->data;
3392: Mat_SeqAIJ *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
3393: PetscInt *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,*cmap=mpimat->garray;
3394: PetscScalar *aa=a->a,*ba=b->a,*ca;
3395: PetscInt am=A->rmap.n,i,j,k,cstart=A->cmap.rstart;
3396: PetscInt *ci,*cj,col,ncols_d,ncols_o,jo;
3399: if (!logkey_getlocalmat) {
3400: PetscLogEventRegister(&logkey_getlocalmat,"MatGetLocalMat",MAT_COOKIE);
3401: }
3402: PetscLogEventBegin(logkey_getlocalmat,A,0,0,0);
3403: if (scall == MAT_INITIAL_MATRIX){
3404: PetscMalloc((1+am)*sizeof(PetscInt),&ci);
3405: ci[0] = 0;
3406: for (i=0; i<am; i++){
3407: ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
3408: }
3409: PetscMalloc((1+ci[am])*sizeof(PetscInt),&cj);
3410: PetscMalloc((1+ci[am])*sizeof(PetscScalar),&ca);
3411: k = 0;
3412: for (i=0; i<am; i++) {
3413: ncols_o = bi[i+1] - bi[i];
3414: ncols_d = ai[i+1] - ai[i];
3415: /* off-diagonal portion of A */
3416: for (jo=0; jo<ncols_o; jo++) {
3417: col = cmap[*bj];
3418: if (col >= cstart) break;
3419: cj[k] = col; bj++;
3420: ca[k++] = *ba++;
3421: }
3422: /* diagonal portion of A */
3423: for (j=0; j<ncols_d; j++) {
3424: cj[k] = cstart + *aj++;
3425: ca[k++] = *aa++;
3426: }
3427: /* off-diagonal portion of A */
3428: for (j=jo; j<ncols_o; j++) {
3429: cj[k] = cmap[*bj++];
3430: ca[k++] = *ba++;
3431: }
3432: }
3433: /* put together the new matrix */
3434: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,A->cmap.N,ci,cj,ca,A_loc);
3435: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
3436: /* Since these are PETSc arrays, change flags to free them as necessary. */
3437: mat = (Mat_SeqAIJ*)(*A_loc)->data;
3438: mat->freedata = PETSC_TRUE;
3439: mat->nonew = 0;
3440: } else if (scall == MAT_REUSE_MATRIX){
3441: mat=(Mat_SeqAIJ*)(*A_loc)->data;
3442: ci = mat->i; cj = mat->j; ca = mat->a;
3443: for (i=0; i<am; i++) {
3444: /* off-diagonal portion of A */
3445: ncols_o = bi[i+1] - bi[i];
3446: for (jo=0; jo<ncols_o; jo++) {
3447: col = cmap[*bj];
3448: if (col >= cstart) break;
3449: *ca++ = *ba++; bj++;
3450: }
3451: /* diagonal portion of A */
3452: ncols_d = ai[i+1] - ai[i];
3453: for (j=0; j<ncols_d; j++) *ca++ = *aa++;
3454: /* off-diagonal portion of A */
3455: for (j=jo; j<ncols_o; j++) {
3456: *ca++ = *ba++; bj++;
3457: }
3458: }
3459: } else {
3460: SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
3461: }
3463: PetscLogEventEnd(logkey_getlocalmat,A,0,0,0);
3464: return(0);
3465: }
3467: static PetscEvent logkey_getlocalmatcondensed = 0;
3470: /*@C
3471: MatGetLocalMatCondensed - Creates a SeqAIJ matrix by taking all its local rows and NON-ZERO columns
3473: Not Collective
3475: Input Parameters:
3476: + A - the matrix
3477: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3478: - row, col - index sets of rows and columns to extract (or PETSC_NULL)
3480: Output Parameter:
3481: . A_loc - the local sequential matrix generated
3483: Level: developer
3485: @*/
3486: PetscErrorCode PETSCMAT_DLLEXPORT MatGetLocalMatCondensed(Mat A,MatReuse scall,IS *row,IS *col,Mat *A_loc)
3487: {
3488: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
3489: PetscErrorCode ierr;
3490: PetscInt i,start,end,ncols,nzA,nzB,*cmap,imark,*idx;
3491: IS isrowa,iscola;
3492: Mat *aloc;
3495: if (!logkey_getlocalmatcondensed) {
3496: PetscLogEventRegister(&logkey_getlocalmatcondensed,"MatGetLocalMatCondensed",MAT_COOKIE);
3497: }
3498: PetscLogEventBegin(logkey_getlocalmatcondensed,A,0,0,0);
3499: if (!row){
3500: start = A->rmap.rstart; end = A->rmap.rend;
3501: ISCreateStride(PETSC_COMM_SELF,end-start,start,1,&isrowa);
3502: } else {
3503: isrowa = *row;
3504: }
3505: if (!col){
3506: start = A->cmap.rstart;
3507: cmap = a->garray;
3508: nzA = a->A->cmap.n;
3509: nzB = a->B->cmap.n;
3510: PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
3511: ncols = 0;
3512: for (i=0; i<nzB; i++) {
3513: if (cmap[i] < start) idx[ncols++] = cmap[i];
3514: else break;
3515: }
3516: imark = i;
3517: for (i=0; i<nzA; i++) idx[ncols++] = start + i;
3518: for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i];
3519: ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&iscola);
3520: PetscFree(idx);
3521: } else {
3522: iscola = *col;
3523: }
3524: if (scall != MAT_INITIAL_MATRIX){
3525: PetscMalloc(sizeof(Mat),&aloc);
3526: aloc[0] = *A_loc;
3527: }
3528: MatGetSubMatrices(A,1,&isrowa,&iscola,scall,&aloc);
3529: *A_loc = aloc[0];
3530: PetscFree(aloc);
3531: if (!row){
3532: ISDestroy(isrowa);
3533: }
3534: if (!col){
3535: ISDestroy(iscola);
3536: }
3537: PetscLogEventEnd(logkey_getlocalmatcondensed,A,0,0,0);
3538: return(0);
3539: }
3541: static PetscEvent logkey_GetBrowsOfAcols = 0;
3544: /*@C
3545: MatGetBrowsOfAcols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
3547: Collective on Mat
3549: Input Parameters:
3550: + A,B - the matrices in mpiaij format
3551: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3552: - rowb, colb - index sets of rows and columns of B to extract (or PETSC_NULL)
3554: Output Parameter:
3555: + rowb, colb - index sets of rows and columns of B to extract
3556: . brstart - row index of B_seq from which next B->rmap.n rows are taken from B's local rows
3557: - B_seq - the sequential matrix generated
3559: Level: developer
3561: @*/
3562: PetscErrorCode PETSCMAT_DLLEXPORT MatGetBrowsOfAcols(Mat A,Mat B,MatReuse scall,IS *rowb,IS *colb,PetscInt *brstart,Mat *B_seq)
3563: {
3564: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
3565: PetscErrorCode ierr;
3566: PetscInt *idx,i,start,ncols,nzA,nzB,*cmap,imark;
3567: IS isrowb,iscolb;
3568: Mat *bseq;
3569:
3571: if (A->cmap.rstart != B->rmap.rstart || A->cmap.rend != B->rmap.rend){
3572: SETERRQ4(PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, (%D, %D) != (%D,%D)",A->cmap.rstart,A->cmap.rend,B->rmap.rstart,B->rmap.rend);
3573: }
3574: if (!logkey_GetBrowsOfAcols) {
3575: PetscLogEventRegister(&logkey_GetBrowsOfAcols,"MatGetBrowsOfAcols",MAT_COOKIE);
3576: }
3577: PetscLogEventBegin(logkey_GetBrowsOfAcols,A,B,0,0);
3578:
3579: if (scall == MAT_INITIAL_MATRIX){
3580: start = A->cmap.rstart;
3581: cmap = a->garray;
3582: nzA = a->A->cmap.n;
3583: nzB = a->B->cmap.n;
3584: PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
3585: ncols = 0;
3586: for (i=0; i<nzB; i++) { /* row < local row index */
3587: if (cmap[i] < start) idx[ncols++] = cmap[i];
3588: else break;
3589: }
3590: imark = i;
3591: for (i=0; i<nzA; i++) idx[ncols++] = start + i; /* local rows */
3592: for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
3593: ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&isrowb);
3594: PetscFree(idx);
3595: *brstart = imark;
3596: ISCreateStride(PETSC_COMM_SELF,B->cmap.N,0,1,&iscolb);
3597: } else {
3598: if (!rowb || !colb) SETERRQ(PETSC_ERR_SUP,"IS rowb and colb must be provided for MAT_REUSE_MATRIX");
3599: isrowb = *rowb; iscolb = *colb;
3600: PetscMalloc(sizeof(Mat),&bseq);
3601: bseq[0] = *B_seq;
3602: }
3603: MatGetSubMatrices(B,1,&isrowb,&iscolb,scall,&bseq);
3604: *B_seq = bseq[0];
3605: PetscFree(bseq);
3606: if (!rowb){
3607: ISDestroy(isrowb);
3608: } else {
3609: *rowb = isrowb;
3610: }
3611: if (!colb){
3612: ISDestroy(iscolb);
3613: } else {
3614: *colb = iscolb;
3615: }
3616: PetscLogEventEnd(logkey_GetBrowsOfAcols,A,B,0,0);
3617: return(0);
3618: }
3620: static PetscEvent logkey_GetBrowsOfAocols = 0;
3623: /*@C
3624: MatGetBrowsOfAoCols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns
3625: of the OFF-DIAGONAL portion of local A
3627: Collective on Mat
3629: Input Parameters:
3630: + A,B - the matrices in mpiaij format
3631: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3632: . startsj - starting point in B's sending and receiving j-arrays, saved for MAT_REUSE (or PETSC_NULL)
3633: - bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or PETSC_NULL)
3635: Output Parameter:
3636: + B_oth - the sequential matrix generated
3638: Level: developer
3640: @*/
3641: PetscErrorCode PETSCMAT_DLLEXPORT MatGetBrowsOfAoCols(Mat A,Mat B,MatReuse scall,PetscInt **startsj,PetscScalar **bufa_ptr,Mat *B_oth)
3642: {
3643: VecScatter_MPI_General *gen_to,*gen_from;
3644: PetscErrorCode ierr;
3645: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
3646: Mat_SeqAIJ *b_oth;
3647: VecScatter ctx=a->Mvctx;
3648: MPI_Comm comm=ctx->comm;
3649: PetscMPIInt *rprocs,*sprocs,tag=ctx->tag,rank;
3650: PetscInt *rowlen,*bufj,*bufJ,ncols,aBn=a->B->cmap.n,row,*b_othi,*b_othj;
3651: PetscScalar *rvalues,*svalues,*b_otha,*bufa,*bufA;
3652: PetscInt i,k,l,nrecvs,nsends,nrows,*srow,*rstarts,*rstartsj = 0,*sstarts,*sstartsj,len;
3653: MPI_Request *rwaits,*swaits;
3654: MPI_Status *sstatus,rstatus;
3655: PetscInt *cols;
3656: PetscScalar *vals;
3657: PetscMPIInt j;
3658:
3660: if (A->cmap.rstart != B->rmap.rstart || A->cmap.rend != B->rmap.rend){
3661: SETERRQ4(PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, (%d, %d) != (%d,%d)",A->cmap.rstart,A->cmap.rend,B->rmap.rstart,B->rmap.rend);
3662: }
3663: if (!logkey_GetBrowsOfAocols) {
3664: PetscLogEventRegister(&logkey_GetBrowsOfAocols,"MatGetBrAoCol",MAT_COOKIE);
3665: }
3666: PetscLogEventBegin(logkey_GetBrowsOfAocols,A,B,0,0);
3667: MPI_Comm_rank(comm,&rank);
3669: gen_to = (VecScatter_MPI_General*)ctx->todata;
3670: gen_from = (VecScatter_MPI_General*)ctx->fromdata;
3671: rvalues = gen_from->values; /* holds the length of sending row */
3672: svalues = gen_to->values; /* holds the length of receiving row */
3673: nrecvs = gen_from->n;
3674: nsends = gen_to->n;
3675: rwaits = gen_from->requests;
3676: swaits = gen_to->requests;
3677: srow = gen_to->indices; /* local row index to be sent */
3678: rstarts = gen_from->starts;
3679: sstarts = gen_to->starts;
3680: rprocs = gen_from->procs;
3681: sprocs = gen_to->procs;
3682: sstatus = gen_to->sstatus;
3684: if (!startsj || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
3685: if (scall == MAT_INITIAL_MATRIX){
3686: /* i-array */
3687: /*---------*/
3688: /* post receives */
3689: for (i=0; i<nrecvs; i++){
3690: rowlen = (PetscInt*)rvalues + rstarts[i];
3691: nrows = rstarts[i+1]-rstarts[i];
3692: MPI_Irecv(rowlen,nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
3693: }
3695: /* pack the outgoing message */
3696: PetscMalloc((nsends+nrecvs+3)*sizeof(PetscInt),&sstartsj);
3697: rstartsj = sstartsj + nsends +1;
3698: sstartsj[0] = 0; rstartsj[0] = 0;
3699: len = 0; /* total length of j or a array to be sent */
3700: k = 0;
3701: for (i=0; i<nsends; i++){
3702: rowlen = (PetscInt*)svalues + sstarts[i];
3703: nrows = sstarts[i+1]-sstarts[i]; /* num of rows */
3704: for (j=0; j<nrows; j++) {
3705: row = srow[k] + B->rmap.range[rank]; /* global row idx */
3706: MatGetRow_MPIAIJ(B,row,&rowlen[j],PETSC_NULL,PETSC_NULL); /* rowlength */
3707: len += rowlen[j];
3708: MatRestoreRow_MPIAIJ(B,row,&ncols,PETSC_NULL,PETSC_NULL);
3709: k++;
3710: }
3711: MPI_Isend(rowlen,nrows,MPIU_INT,sprocs[i],tag,comm,swaits+i);
3712: sstartsj[i+1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
3713: }
3714: /* recvs and sends of i-array are completed */
3715: i = nrecvs;
3716: while (i--) {
3717: MPI_Waitany(nrecvs,rwaits,&j,&rstatus);
3718: }
3719: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
3720: /* allocate buffers for sending j and a arrays */
3721: PetscMalloc((len+1)*sizeof(PetscInt),&bufj);
3722: PetscMalloc((len+1)*sizeof(PetscScalar),&bufa);
3724: /* create i-array of B_oth */
3725: PetscMalloc((aBn+2)*sizeof(PetscInt),&b_othi);
3726: b_othi[0] = 0;
3727: len = 0; /* total length of j or a array to be received */
3728: k = 0;
3729: for (i=0; i<nrecvs; i++){
3730: rowlen = (PetscInt*)rvalues + rstarts[i];
3731: nrows = rstarts[i+1]-rstarts[i];
3732: for (j=0; j<nrows; j++) {
3733: b_othi[k+1] = b_othi[k] + rowlen[j];
3734: len += rowlen[j]; k++;
3735: }
3736: rstartsj[i+1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
3737: }
3739: /* allocate space for j and a arrrays of B_oth */
3740: PetscMalloc((b_othi[aBn]+1)*sizeof(PetscInt),&b_othj);
3741: PetscMalloc((b_othi[aBn]+1)*sizeof(PetscScalar),&b_otha);
3743: /* j-array */
3744: /*---------*/
3745: /* post receives of j-array */
3746: for (i=0; i<nrecvs; i++){
3747: nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
3748: MPI_Irecv(b_othj+rstartsj[i],nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
3749: }
3750: k = 0;
3751: for (i=0; i<nsends; i++){
3752: nrows = sstarts[i+1]-sstarts[i]; /* num of rows */
3753: bufJ = bufj+sstartsj[i];
3754: for (j=0; j<nrows; j++) {
3755: row = srow[k++] + B->rmap.range[rank]; /* global row idx */
3756: MatGetRow_MPIAIJ(B,row,&ncols,&cols,PETSC_NULL);
3757: for (l=0; l<ncols; l++){
3758: *bufJ++ = cols[l];
3759: }
3760: MatRestoreRow_MPIAIJ(B,row,&ncols,&cols,PETSC_NULL);
3761: }
3762: MPI_Isend(bufj+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_INT,sprocs[i],tag,comm,swaits+i);
3763: }
3765: /* recvs and sends of j-array are completed */
3766: i = nrecvs;
3767: while (i--) {
3768: MPI_Waitany(nrecvs,rwaits,&j,&rstatus);
3769: }
3770: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
3771: } else if (scall == MAT_REUSE_MATRIX){
3772: sstartsj = *startsj;
3773: rstartsj = sstartsj + nsends +1;
3774: bufa = *bufa_ptr;
3775: b_oth = (Mat_SeqAIJ*)(*B_oth)->data;
3776: b_otha = b_oth->a;
3777: } else {
3778: SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not posses an object container");
3779: }
3781: /* a-array */
3782: /*---------*/
3783: /* post receives of a-array */
3784: for (i=0; i<nrecvs; i++){
3785: nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
3786: MPI_Irecv(b_otha+rstartsj[i],nrows,MPIU_SCALAR,rprocs[i],tag,comm,rwaits+i);
3787: }
3788: k = 0;
3789: for (i=0; i<nsends; i++){
3790: nrows = sstarts[i+1]-sstarts[i];
3791: bufA = bufa+sstartsj[i];
3792: for (j=0; j<nrows; j++) {
3793: row = srow[k++] + B->rmap.range[rank]; /* global row idx */
3794: MatGetRow_MPIAIJ(B,row,&ncols,PETSC_NULL,&vals);
3795: for (l=0; l<ncols; l++){
3796: *bufA++ = vals[l];
3797: }
3798: MatRestoreRow_MPIAIJ(B,row,&ncols,PETSC_NULL,&vals);
3800: }
3801: MPI_Isend(bufa+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_SCALAR,sprocs[i],tag,comm,swaits+i);
3802: }
3803: /* recvs and sends of a-array are completed */
3804: i = nrecvs;
3805: while (i--) {
3806: MPI_Waitany(nrecvs,rwaits,&j,&rstatus);
3807: }
3808: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
3809:
3810: if (scall == MAT_INITIAL_MATRIX){
3811: /* put together the new matrix */
3812: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,aBn,B->cmap.N,b_othi,b_othj,b_otha,B_oth);
3814: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
3815: /* Since these are PETSc arrays, change flags to free them as necessary. */
3816: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
3817: b_oth->freedata = PETSC_TRUE;
3818: b_oth->nonew = 0;
3820: PetscFree(bufj);
3821: if (!startsj || !bufa_ptr){
3822: PetscFree(sstartsj);
3823: PetscFree(bufa_ptr);
3824: } else {
3825: *startsj = sstartsj;
3826: *bufa_ptr = bufa;
3827: }
3828: }
3829: PetscLogEventEnd(logkey_GetBrowsOfAocols,A,B,0,0);
3830:
3831: return(0);
3832: }
3836: /*@C
3837: MatGetCommunicationStructs - Provides access to the communication structures used in matrix-vector multiplication.
3839: Not Collective
3841: Input Parameters:
3842: . A - The matrix in mpiaij format
3844: Output Parameter:
3845: + lvec - The local vector holding off-process values from the argument to a matrix-vector product
3846: . colmap - A map from global column index to local index into lvec
3847: - multScatter - A scatter from the argument of a matrix-vector product to lvec
3849: Level: developer
3851: @*/
3852: #if defined (PETSC_USE_CTABLE)
3853: PetscErrorCode PETSCMAT_DLLEXPORT MatGetCommunicationStructs(Mat A, Vec *lvec, PetscTable *colmap, VecScatter *multScatter)
3854: #else
3855: PetscErrorCode PETSCMAT_DLLEXPORT MatGetCommunicationStructs(Mat A, Vec *lvec, PetscInt *colmap[], VecScatter *multScatter)
3856: #endif
3857: {
3858: Mat_MPIAIJ *a;
3865: a = (Mat_MPIAIJ *) A->data;
3866: if (lvec) *lvec = a->lvec;
3867: if (colmap) *colmap = a->colmap;
3868: if (multScatter) *multScatter = a->Mvctx;
3869: return(0);
3870: }
3872: /*MC
3873: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
3875: Options Database Keys:
3876: . -mat_type mpiaij - sets the matrix type to "mpiaij" during a call to MatSetFromOptions()
3878: Level: beginner
3880: .seealso: MatCreateMPIAIJ
3881: M*/
3886: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_MPIAIJ(Mat B)
3887: {
3888: Mat_MPIAIJ *b;
3890: PetscMPIInt size;
3893: MPI_Comm_size(B->comm,&size);
3895: PetscNew(Mat_MPIAIJ,&b);
3896: B->data = (void*)b;
3897: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3898: B->factor = 0;
3899: B->rmap.bs = 1;
3900: B->assembled = PETSC_FALSE;
3901: B->mapping = 0;
3903: B->insertmode = NOT_SET_VALUES;
3904: b->size = size;
3905: MPI_Comm_rank(B->comm,&b->rank);
3907: /* build cache for off array entries formed */
3908: MatStashCreate_Private(B->comm,1,&B->stash);
3909: b->donotstash = PETSC_FALSE;
3910: b->colmap = 0;
3911: b->garray = 0;
3912: b->roworiented = PETSC_TRUE;
3914: /* stuff used for matrix vector multiply */
3915: b->lvec = PETSC_NULL;
3916: b->Mvctx = PETSC_NULL;
3918: /* stuff for MatGetRow() */
3919: b->rowindices = 0;
3920: b->rowvalues = 0;
3921: b->getrowactive = PETSC_FALSE;
3924: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
3925: "MatStoreValues_MPIAIJ",
3926: MatStoreValues_MPIAIJ);
3927: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
3928: "MatRetrieveValues_MPIAIJ",
3929: MatRetrieveValues_MPIAIJ);
3930: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
3931: "MatGetDiagonalBlock_MPIAIJ",
3932: MatGetDiagonalBlock_MPIAIJ);
3933: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
3934: "MatIsTranspose_MPIAIJ",
3935: MatIsTranspose_MPIAIJ);
3936: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocation_C",
3937: "MatMPIAIJSetPreallocation_MPIAIJ",
3938: MatMPIAIJSetPreallocation_MPIAIJ);
3939: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",
3940: "MatMPIAIJSetPreallocationCSR_MPIAIJ",
3941: MatMPIAIJSetPreallocationCSR_MPIAIJ);
3942: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
3943: "MatDiagonalScaleLocal_MPIAIJ",
3944: MatDiagonalScaleLocal_MPIAIJ);
3945: return(0);
3946: }
3949: /*
3950: Special version for direct calls from Fortran
3951: */
3952: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3953: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
3954: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3955: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
3956: #endif
3958: /* Change these macros so can be used in void function */
3959: #undef CHKERRQ
3960: #define CHKERRQ(ierr) CHKERRABORT(mat->comm,ierr)
3961: #undef SETERRQ2
3962: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(mat->comm,ierr)
3963: #undef SETERRQ
3964: #define SETERRQ(ierr,b) CHKERRABORT(mat->comm,ierr)
3969: void matsetvaluesmpiaij_(Mat *mmat,PetscInt *mm,const PetscInt im[],PetscInt *mn,const PetscInt in[],const PetscScalar v[],InsertMode *maddv)
3970: {
3971: Mat mat = *mmat;
3972: PetscInt m = *mm, n = *mn;
3973: InsertMode addv = *maddv;
3974: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
3975: PetscScalar value;
3978: MatPreallocated(mat);
3979: if (mat->insertmode == NOT_SET_VALUES) {
3980: mat->insertmode = addv;
3981: }
3982: #if defined(PETSC_USE_DEBUG)
3983: else if (mat->insertmode != addv) {
3984: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
3985: }
3986: #endif
3987: {
3988: PetscInt i,j,rstart = mat->rmap.rstart,rend = mat->rmap.rend;
3989: PetscInt cstart = mat->cmap.rstart,cend = mat->cmap.rend,row,col;
3990: PetscTruth roworiented = aij->roworiented;
3992: /* Some Variables required in the macro */
3993: Mat A = aij->A;
3994: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3995: PetscInt *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
3996: PetscScalar *aa = a->a;
3997: PetscTruth ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES))?PETSC_TRUE:PETSC_FALSE);
3998: Mat B = aij->B;
3999: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
4000: PetscInt *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap.n,am = aij->A->rmap.n;
4001: PetscScalar *ba = b->a;
4003: PetscInt *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
4004: PetscInt nonew = a->nonew;
4005: PetscScalar *ap1,*ap2;
4008: for (i=0; i<m; i++) {
4009: if (im[i] < 0) continue;
4010: #if defined(PETSC_USE_DEBUG)
4011: if (im[i] >= mat->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap.N-1);
4012: #endif
4013: if (im[i] >= rstart && im[i] < rend) {
4014: row = im[i] - rstart;
4015: lastcol1 = -1;
4016: rp1 = aj + ai[row];
4017: ap1 = aa + ai[row];
4018: rmax1 = aimax[row];
4019: nrow1 = ailen[row];
4020: low1 = 0;
4021: high1 = nrow1;
4022: lastcol2 = -1;
4023: rp2 = bj + bi[row];
4024: ap2 = ba + bi[row];
4025: rmax2 = bimax[row];
4026: nrow2 = bilen[row];
4027: low2 = 0;
4028: high2 = nrow2;
4030: for (j=0; j<n; j++) {
4031: if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
4032: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
4033: if (in[j] >= cstart && in[j] < cend){
4034: col = in[j] - cstart;
4035: MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
4036: } else if (in[j] < 0) continue;
4037: #if defined(PETSC_USE_DEBUG)
4038: else if (in[j] >= mat->cmap.N) {SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap.N-1);}
4039: #endif
4040: else {
4041: if (mat->was_assembled) {
4042: if (!aij->colmap) {
4043: CreateColmap_MPIAIJ_Private(mat);
4044: }
4045: #if defined (PETSC_USE_CTABLE)
4046: PetscTableFind(aij->colmap,in[j]+1,&col);
4047: col--;
4048: #else
4049: col = aij->colmap[in[j]] - 1;
4050: #endif
4051: if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
4052: DisAssemble_MPIAIJ(mat);
4053: col = in[j];
4054: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
4055: B = aij->B;
4056: b = (Mat_SeqAIJ*)B->data;
4057: bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
4058: rp2 = bj + bi[row];
4059: ap2 = ba + bi[row];
4060: rmax2 = bimax[row];
4061: nrow2 = bilen[row];
4062: low2 = 0;
4063: high2 = nrow2;
4064: bm = aij->B->rmap.n;
4065: ba = b->a;
4066: }
4067: } else col = in[j];
4068: MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
4069: }
4070: }
4071: } else {
4072: if (!aij->donotstash) {
4073: if (roworiented) {
4074: if (ignorezeroentries && v[i*n] == 0.0) continue;
4075: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
4076: } else {
4077: if (ignorezeroentries && v[i] == 0.0) continue;
4078: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
4079: }
4080: }
4081: }
4082: }}
4083: PetscFunctionReturnVoid();
4084: }