Actual source code: mpiaij.c
1: #define PETSCMAT_DLL
3: #include ../src/mat/impls/aij/mpi/mpiaij.h
4: #include ../src/inline/spops.h
8: /*
9: Distributes a SeqAIJ matrix across a set of processes. Code stolen from
10: MatLoad_MPIAIJ(). Horrible lack of reuse. Should be a routine for each matrix type.
12: Only for square matrices
13: */
14: PetscErrorCode MatDistribute_MPIAIJ(MPI_Comm comm,Mat gmat,PetscInt m,MatReuse reuse,Mat *inmat)
15: {
16: PetscMPIInt rank,size;
17: PetscInt *rowners,*dlens,*olens,i,rstart,rend,j,jj,nz,*gmataj,cnt,row,*ld;
19: Mat mat;
20: Mat_SeqAIJ *gmata;
21: PetscMPIInt tag;
22: MPI_Status status;
23: PetscTruth aij;
24: MatScalar *gmataa,*ao,*ad,*gmataarestore=0;
27: CHKMEMQ;
28: MPI_Comm_rank(comm,&rank);
29: MPI_Comm_size(comm,&size);
30: if (!rank) {
31: PetscTypeCompare((PetscObject)gmat,MATSEQAIJ,&aij);
32: if (!aij) SETERRQ1(PETSC_ERR_SUP,"Currently no support for input matrix of type %s\n",((PetscObject)gmat)->type_name);
33: }
34: if (reuse == MAT_INITIAL_MATRIX) {
35: MatCreate(comm,&mat);
36: MatSetSizes(mat,m,m,PETSC_DETERMINE,PETSC_DETERMINE);
37: MatSetType(mat,MATAIJ);
38: PetscMalloc((size+1)*sizeof(PetscInt),&rowners);
39: PetscMalloc2(m,PetscInt,&dlens,m,PetscInt,&olens);
40: MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
41: rowners[0] = 0;
42: for (i=2; i<=size; i++) {
43: rowners[i] += rowners[i-1];
44: }
45: rstart = rowners[rank];
46: rend = rowners[rank+1];
47: PetscObjectGetNewTag((PetscObject)mat,&tag);
48: if (!rank) {
49: gmata = (Mat_SeqAIJ*) gmat->data;
50: /* send row lengths to all processors */
51: for (i=0; i<m; i++) dlens[i] = gmata->ilen[i];
52: for (i=1; i<size; i++) {
53: MPI_Send(gmata->ilen + rowners[i],rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
54: }
55: /* determine number diagonal and off-diagonal counts */
56: PetscMemzero(olens,m*sizeof(PetscInt));
57: PetscMalloc(m*sizeof(PetscInt),&ld);
58: PetscMemzero(ld,m*sizeof(PetscInt));
59: jj = 0;
60: for (i=0; i<m; i++) {
61: for (j=0; j<dlens[i]; j++) {
62: if (gmata->j[jj] < rstart) ld[i]++;
63: if (gmata->j[jj] < rstart || gmata->j[jj] >= rend) olens[i]++;
64: jj++;
65: }
66: }
67: /* send column indices to other processes */
68: for (i=1; i<size; i++) {
69: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
70: MPI_Send(&nz,1,MPIU_INT,i,tag,comm);
71: MPI_Send(gmata->j + gmata->i[rowners[i]],nz,MPIU_INT,i,tag,comm);
72: }
74: /* send numerical values to other processes */
75: for (i=1; i<size; i++) {
76: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
77: MPI_Send(gmata->a + gmata->i[rowners[i]],nz,MPIU_SCALAR,i,tag,comm);
78: }
79: gmataa = gmata->a;
80: gmataj = gmata->j;
82: } else {
83: /* receive row lengths */
84: MPI_Recv(dlens,m,MPIU_INT,0,tag,comm,&status);
85: /* receive column indices */
86: MPI_Recv(&nz,1,MPIU_INT,0,tag,comm,&status);
87: PetscMalloc2(nz,PetscScalar,&gmataa,nz,PetscInt,&gmataj);
88: MPI_Recv(gmataj,nz,MPIU_INT,0,tag,comm,&status);
89: /* determine number diagonal and off-diagonal counts */
90: PetscMemzero(olens,m*sizeof(PetscInt));
91: PetscMalloc(m*sizeof(PetscInt),&ld);
92: PetscMemzero(ld,m*sizeof(PetscInt));
93: jj = 0;
94: for (i=0; i<m; i++) {
95: for (j=0; j<dlens[i]; j++) {
96: if (gmataj[jj] < rstart) ld[i]++;
97: if (gmataj[jj] < rstart || gmataj[jj] >= rend) olens[i]++;
98: jj++;
99: }
100: }
101: /* receive numerical values */
102: PetscMemzero(gmataa,nz*sizeof(PetscScalar));
103: MPI_Recv(gmataa,nz,MPIU_SCALAR,0,tag,comm,&status);
104: }
105: /* set preallocation */
106: for (i=0; i<m; i++) {
107: dlens[i] -= olens[i];
108: }
109: MatSeqAIJSetPreallocation(mat,0,dlens);
110: MatMPIAIJSetPreallocation(mat,0,dlens,0,olens);
111:
112: for (i=0; i<m; i++) {
113: dlens[i] += olens[i];
114: }
115: cnt = 0;
116: for (i=0; i<m; i++) {
117: row = rstart + i;
118: MatSetValues(mat,1,&row,dlens[i],gmataj+cnt,gmataa+cnt,INSERT_VALUES);
119: cnt += dlens[i];
120: }
121: if (rank) {
122: PetscFree2(gmataa,gmataj);
123: }
124: PetscFree2(dlens,olens);
125: PetscFree(rowners);
126: ((Mat_MPIAIJ*)(mat->data))->ld = ld;
127: *inmat = mat;
128: } else { /* column indices are already set; only need to move over numerical values from process 0 */
129: Mat_SeqAIJ *Ad = (Mat_SeqAIJ*)((Mat_MPIAIJ*)((*inmat)->data))->A->data;
130: Mat_SeqAIJ *Ao = (Mat_SeqAIJ*)((Mat_MPIAIJ*)((*inmat)->data))->B->data;
131: mat = *inmat;
132: PetscObjectGetNewTag((PetscObject)mat,&tag);
133: if (!rank) {
134: /* send numerical values to other processes */
135: gmata = (Mat_SeqAIJ*) gmat->data;
136: MatGetOwnershipRanges(mat,(const PetscInt**)&rowners);
137: gmataa = gmata->a;
138: for (i=1; i<size; i++) {
139: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
140: MPI_Send(gmataa + gmata->i[rowners[i]],nz,MPIU_SCALAR,i,tag,comm);
141: }
142: nz = gmata->i[rowners[1]]-gmata->i[rowners[0]];
143: } else {
144: /* receive numerical values from process 0*/
145: nz = Ad->nz + Ao->nz;
146: PetscMalloc(nz*sizeof(PetscScalar),&gmataa); gmataarestore = gmataa;
147: MPI_Recv(gmataa,nz,MPIU_SCALAR,0,tag,comm,&status);
148: }
149: /* transfer numerical values into the diagonal A and off diagonal B parts of mat */
150: ld = ((Mat_MPIAIJ*)(mat->data))->ld;
151: ad = Ad->a;
152: ao = Ao->a;
153: if (mat->rmap->n) {
154: i = 0;
155: nz = ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
156: nz = Ad->i[i+1] - Ad->i[i]; PetscMemcpy(ad,gmataa,nz*sizeof(PetscScalar)); ad += nz; gmataa += nz;
157: }
158: for (i=1; i<mat->rmap->n; i++) {
159: nz = Ao->i[i] - Ao->i[i-1] - ld[i-1] + ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
160: nz = Ad->i[i+1] - Ad->i[i]; PetscMemcpy(ad,gmataa,nz*sizeof(PetscScalar)); ad += nz; gmataa += nz;
161: }
162: i--;
163: if (mat->rmap->n) {
164: nz = Ao->i[i+1] - Ao->i[i] - ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
165: }
166: if (rank) {
167: PetscFree(gmataarestore);
168: }
169: }
170: MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
171: MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
172: CHKMEMQ;
173: return(0);
174: }
176: /*
177: Local utility routine that creates a mapping from the global column
178: number to the local number in the off-diagonal part of the local
179: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
180: a slightly higher hash table cost; without it it is not scalable (each processor
181: has an order N integer array but is fast to acess.
182: */
185: PetscErrorCode CreateColmap_MPIAIJ_Private(Mat mat)
186: {
187: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
189: PetscInt n = aij->B->cmap->n,i;
192: #if defined (PETSC_USE_CTABLE)
193: PetscTableCreate(n,&aij->colmap);
194: for (i=0; i<n; i++){
195: PetscTableAdd(aij->colmap,aij->garray[i]+1,i+1);
196: }
197: #else
198: PetscMalloc((mat->cmap->N+1)*sizeof(PetscInt),&aij->colmap);
199: PetscLogObjectMemory(mat,mat->cmap->N*sizeof(PetscInt));
200: PetscMemzero(aij->colmap,mat->cmap->N*sizeof(PetscInt));
201: for (i=0; i<n; i++) aij->colmap[aij->garray[i]] = i+1;
202: #endif
203: return(0);
204: }
207: #define CHUNKSIZE 15
208: #define MatSetValues_SeqAIJ_A_Private(row,col,value,addv) \
209: { \
210: if (col <= lastcol1) low1 = 0; else high1 = nrow1; \
211: lastcol1 = col;\
212: while (high1-low1 > 5) { \
213: t = (low1+high1)/2; \
214: if (rp1[t] > col) high1 = t; \
215: else low1 = t; \
216: } \
217: for (_i=low1; _i<high1; _i++) { \
218: if (rp1[_i] > col) break; \
219: if (rp1[_i] == col) { \
220: if (addv == ADD_VALUES) ap1[_i] += value; \
221: else ap1[_i] = value; \
222: goto a_noinsert; \
223: } \
224: } \
225: if (value == 0.0 && ignorezeroentries) {low1 = 0; high1 = nrow1;goto a_noinsert;} \
226: if (nonew == 1) {low1 = 0; high1 = nrow1; goto a_noinsert;} \
227: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
228: MatSeqXAIJReallocateAIJ(A,am,1,nrow1,row,col,rmax1,aa,ai,aj,rp1,ap1,aimax,nonew,MatScalar); \
229: N = nrow1++ - 1; a->nz++; high1++; \
230: /* shift up all the later entries in this row */ \
231: for (ii=N; ii>=_i; ii--) { \
232: rp1[ii+1] = rp1[ii]; \
233: ap1[ii+1] = ap1[ii]; \
234: } \
235: rp1[_i] = col; \
236: ap1[_i] = value; \
237: a_noinsert: ; \
238: ailen[row] = nrow1; \
239: }
242: #define MatSetValues_SeqAIJ_B_Private(row,col,value,addv) \
243: { \
244: if (col <= lastcol2) low2 = 0; else high2 = nrow2; \
245: lastcol2 = col;\
246: while (high2-low2 > 5) { \
247: t = (low2+high2)/2; \
248: if (rp2[t] > col) high2 = t; \
249: else low2 = t; \
250: } \
251: for (_i=low2; _i<high2; _i++) { \
252: if (rp2[_i] > col) break; \
253: if (rp2[_i] == col) { \
254: if (addv == ADD_VALUES) ap2[_i] += value; \
255: else ap2[_i] = value; \
256: goto b_noinsert; \
257: } \
258: } \
259: if (value == 0.0 && ignorezeroentries) {low2 = 0; high2 = nrow2; goto b_noinsert;} \
260: if (nonew == 1) {low2 = 0; high2 = nrow2; goto b_noinsert;} \
261: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
262: MatSeqXAIJReallocateAIJ(B,bm,1,nrow2,row,col,rmax2,ba,bi,bj,rp2,ap2,bimax,nonew,MatScalar); \
263: N = nrow2++ - 1; b->nz++; high2++; \
264: /* shift up all the later entries in this row */ \
265: for (ii=N; ii>=_i; ii--) { \
266: rp2[ii+1] = rp2[ii]; \
267: ap2[ii+1] = ap2[ii]; \
268: } \
269: rp2[_i] = col; \
270: ap2[_i] = value; \
271: b_noinsert: ; \
272: bilen[row] = nrow2; \
273: }
277: PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A,PetscInt row,const PetscScalar v[])
278: {
279: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
280: Mat_SeqAIJ *a = (Mat_SeqAIJ*)mat->A->data,*b = (Mat_SeqAIJ*)mat->B->data;
282: PetscInt l,*garray = mat->garray,diag;
285: /* code only works for square matrices A */
287: /* find size of row to the left of the diagonal part */
288: MatGetOwnershipRange(A,&diag,0);
289: row = row - diag;
290: for (l=0; l<b->i[row+1]-b->i[row]; l++) {
291: if (garray[b->j[b->i[row]+l]] > diag) break;
292: }
293: PetscMemcpy(b->a+b->i[row],v,l*sizeof(PetscScalar));
295: /* diagonal part */
296: PetscMemcpy(a->a+a->i[row],v+l,(a->i[row+1]-a->i[row])*sizeof(PetscScalar));
298: /* right of diagonal part */
299: 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));
300: return(0);
301: }
305: PetscErrorCode MatSetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
306: {
307: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
308: PetscScalar value;
310: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
311: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
312: PetscTruth roworiented = aij->roworiented;
314: /* Some Variables required in the macro */
315: Mat A = aij->A;
316: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
317: PetscInt *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
318: MatScalar *aa = a->a;
319: PetscTruth ignorezeroentries = a->ignorezeroentries;
320: Mat B = aij->B;
321: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
322: PetscInt *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap->n,am = aij->A->rmap->n;
323: MatScalar *ba = b->a;
325: PetscInt *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
326: PetscInt nonew = a->nonew;
327: MatScalar *ap1,*ap2;
330: for (i=0; i<m; i++) {
331: if (im[i] < 0) continue;
332: #if defined(PETSC_USE_DEBUG)
333: if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
334: #endif
335: if (im[i] >= rstart && im[i] < rend) {
336: row = im[i] - rstart;
337: lastcol1 = -1;
338: rp1 = aj + ai[row];
339: ap1 = aa + ai[row];
340: rmax1 = aimax[row];
341: nrow1 = ailen[row];
342: low1 = 0;
343: high1 = nrow1;
344: lastcol2 = -1;
345: rp2 = bj + bi[row];
346: ap2 = ba + bi[row];
347: rmax2 = bimax[row];
348: nrow2 = bilen[row];
349: low2 = 0;
350: high2 = nrow2;
352: for (j=0; j<n; j++) {
353: if (v) {if (roworiented) value = v[i*n+j]; else value = v[i+j*m];} else value = 0.0;
354: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
355: if (in[j] >= cstart && in[j] < cend){
356: col = in[j] - cstart;
357: MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
358: } else if (in[j] < 0) continue;
359: #if defined(PETSC_USE_DEBUG)
360: 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);}
361: #endif
362: else {
363: if (mat->was_assembled) {
364: if (!aij->colmap) {
365: CreateColmap_MPIAIJ_Private(mat);
366: }
367: #if defined (PETSC_USE_CTABLE)
368: PetscTableFind(aij->colmap,in[j]+1,&col);
369: col--;
370: #else
371: col = aij->colmap[in[j]] - 1;
372: #endif
373: if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
374: DisAssemble_MPIAIJ(mat);
375: col = in[j];
376: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
377: B = aij->B;
378: b = (Mat_SeqAIJ*)B->data;
379: bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j; ba = b->a;
380: rp2 = bj + bi[row];
381: ap2 = ba + bi[row];
382: rmax2 = bimax[row];
383: nrow2 = bilen[row];
384: low2 = 0;
385: high2 = nrow2;
386: bm = aij->B->rmap->n;
387: ba = b->a;
388: }
389: } else col = in[j];
390: MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
391: }
392: }
393: } else {
394: if (!aij->donotstash) {
395: if (roworiented) {
396: if (ignorezeroentries && v[i*n] == 0.0) continue;
397: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
398: } else {
399: if (ignorezeroentries && v[i] == 0.0) continue;
400: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
401: }
402: }
403: }
404: }
405: return(0);
406: }
410: PetscErrorCode MatGetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
411: {
412: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
414: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
415: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
418: for (i=0; i<m; i++) {
419: if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);*/
420: if (idxm[i] >= mat->rmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap->N-1);
421: if (idxm[i] >= rstart && idxm[i] < rend) {
422: row = idxm[i] - rstart;
423: for (j=0; j<n; j++) {
424: if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]); */
425: if (idxn[j] >= mat->cmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap->N-1);
426: if (idxn[j] >= cstart && idxn[j] < cend){
427: col = idxn[j] - cstart;
428: MatGetValues(aij->A,1,&row,1,&col,v+i*n+j);
429: } else {
430: if (!aij->colmap) {
431: CreateColmap_MPIAIJ_Private(mat);
432: }
433: #if defined (PETSC_USE_CTABLE)
434: PetscTableFind(aij->colmap,idxn[j]+1,&col);
435: col --;
436: #else
437: col = aij->colmap[idxn[j]] - 1;
438: #endif
439: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v+i*n+j) = 0.0;
440: else {
441: MatGetValues(aij->B,1,&row,1,&col,v+i*n+j);
442: }
443: }
444: }
445: } else {
446: SETERRQ(PETSC_ERR_SUP,"Only local values currently supported");
447: }
448: }
449: return(0);
450: }
454: PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat,MatAssemblyType mode)
455: {
456: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
458: PetscInt nstash,reallocs;
459: InsertMode addv;
462: if (aij->donotstash) {
463: return(0);
464: }
466: /* make sure all processors are either in INSERTMODE or ADDMODE */
467: MPI_Allreduce(&mat->insertmode,&addv,1,MPI_INT,MPI_BOR,((PetscObject)mat)->comm);
468: if (addv == (ADD_VALUES|INSERT_VALUES)) {
469: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
470: }
471: mat->insertmode = addv; /* in case this processor had no cache */
473: MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
474: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
475: PetscInfo2(aij->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
476: return(0);
477: }
481: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat,MatAssemblyType mode)
482: {
483: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
484: Mat_SeqAIJ *a=(Mat_SeqAIJ *)aij->A->data;
486: PetscMPIInt n;
487: PetscInt i,j,rstart,ncols,flg;
488: PetscInt *row,*col;
489: PetscTruth other_disassembled;
490: PetscScalar *val;
491: InsertMode addv = mat->insertmode;
493: /* do not use 'b = (Mat_SeqAIJ *)aij->B->data' as B can be reset in disassembly */
495: if (!aij->donotstash) {
496: while (1) {
497: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
498: if (!flg) break;
500: for (i=0; i<n;) {
501: /* Now identify the consecutive vals belonging to the same row */
502: for (j=i,rstart=row[j]; j<n; j++) { if (row[j] != rstart) break; }
503: if (j < n) ncols = j-i;
504: else ncols = n-i;
505: /* Now assemble all these values with a single function call */
506: MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
507: i = j;
508: }
509: }
510: MatStashScatterEnd_Private(&mat->stash);
511: }
512: a->compressedrow.use = PETSC_FALSE;
513: MatAssemblyBegin(aij->A,mode);
514: MatAssemblyEnd(aij->A,mode);
516: /* determine if any processor has disassembled, if so we must
517: also disassemble ourselfs, in order that we may reassemble. */
518: /*
519: if nonzero structure of submatrix B cannot change then we know that
520: no processor disassembled thus we can skip this stuff
521: */
522: if (!((Mat_SeqAIJ*)aij->B->data)->nonew) {
523: MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPI_INT,MPI_PROD,((PetscObject)mat)->comm);
524: if (mat->was_assembled && !other_disassembled) {
525: DisAssemble_MPIAIJ(mat);
526: }
527: }
528: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
529: MatSetUpMultiply_MPIAIJ(mat);
530: }
531: MatSetOption(aij->B,MAT_USE_INODES,PETSC_FALSE);
532: ((Mat_SeqAIJ *)aij->B->data)->compressedrow.use = PETSC_TRUE; /* b->compressedrow.use */
533: MatAssemblyBegin(aij->B,mode);
534: MatAssemblyEnd(aij->B,mode);
536: PetscFree(aij->rowvalues);
537: aij->rowvalues = 0;
539: /* used by MatAXPY() */
540: a->xtoy = 0; ((Mat_SeqAIJ *)aij->B->data)->xtoy = 0; /* b->xtoy = 0 */
541: a->XtoY = 0; ((Mat_SeqAIJ *)aij->B->data)->XtoY = 0; /* b->XtoY = 0 */
543: return(0);
544: }
548: PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
549: {
550: Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data;
554: MatZeroEntries(l->A);
555: MatZeroEntries(l->B);
556: return(0);
557: }
561: PetscErrorCode MatZeroRows_MPIAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
562: {
563: Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data;
565: PetscMPIInt size = l->size,imdex,n,rank = l->rank,tag = ((PetscObject)A)->tag,lastidx = -1;
566: PetscInt i,*owners = A->rmap->range;
567: PetscInt *nprocs,j,idx,nsends,row;
568: PetscInt nmax,*svalues,*starts,*owner,nrecvs;
569: PetscInt *rvalues,count,base,slen,*source;
570: PetscInt *lens,*lrows,*values,rstart=A->rmap->rstart;
571: MPI_Comm comm = ((PetscObject)A)->comm;
572: MPI_Request *send_waits,*recv_waits;
573: MPI_Status recv_status,*send_status;
574: #if defined(PETSC_DEBUG)
575: PetscTruth found = PETSC_FALSE;
576: #endif
579: /* first count number of contributors to each processor */
580: PetscMalloc(2*size*sizeof(PetscInt),&nprocs);
581: PetscMemzero(nprocs,2*size*sizeof(PetscInt));
582: PetscMalloc((N+1)*sizeof(PetscInt),&owner); /* see note*/
583: j = 0;
584: for (i=0; i<N; i++) {
585: if (lastidx > (idx = rows[i])) j = 0;
586: lastidx = idx;
587: for (; j<size; j++) {
588: if (idx >= owners[j] && idx < owners[j+1]) {
589: nprocs[2*j]++;
590: nprocs[2*j+1] = 1;
591: owner[i] = j;
592: #if defined(PETSC_DEBUG)
593: found = PETSC_TRUE;
594: #endif
595: break;
596: }
597: }
598: #if defined(PETSC_DEBUG)
599: if (!found) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Index out of range");
600: found = PETSC_FALSE;
601: #endif
602: }
603: nsends = 0; for (i=0; i<size; i++) { nsends += nprocs[2*i+1];}
605: /* inform other processors of number of messages and max length*/
606: PetscMaxSum(comm,nprocs,&nmax,&nrecvs);
608: /* post receives: */
609: PetscMalloc((nrecvs+1)*(nmax+1)*sizeof(PetscInt),&rvalues);
610: PetscMalloc((nrecvs+1)*sizeof(MPI_Request),&recv_waits);
611: for (i=0; i<nrecvs; i++) {
612: MPI_Irecv(rvalues+nmax*i,nmax,MPIU_INT,MPI_ANY_SOURCE,tag,comm,recv_waits+i);
613: }
615: /* do sends:
616: 1) starts[i] gives the starting index in svalues for stuff going to
617: the ith processor
618: */
619: PetscMalloc((N+1)*sizeof(PetscInt),&svalues);
620: PetscMalloc((nsends+1)*sizeof(MPI_Request),&send_waits);
621: PetscMalloc((size+1)*sizeof(PetscInt),&starts);
622: starts[0] = 0;
623: for (i=1; i<size; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
624: for (i=0; i<N; i++) {
625: svalues[starts[owner[i]]++] = rows[i];
626: }
628: starts[0] = 0;
629: for (i=1; i<size+1; i++) { starts[i] = starts[i-1] + nprocs[2*i-2];}
630: count = 0;
631: for (i=0; i<size; i++) {
632: if (nprocs[2*i+1]) {
633: MPI_Isend(svalues+starts[i],nprocs[2*i],MPIU_INT,i,tag,comm,send_waits+count++);
634: }
635: }
636: PetscFree(starts);
638: base = owners[rank];
640: /* wait on receives */
641: PetscMalloc(2*(nrecvs+1)*sizeof(PetscInt),&lens);
642: source = lens + nrecvs;
643: count = nrecvs; slen = 0;
644: while (count) {
645: MPI_Waitany(nrecvs,recv_waits,&imdex,&recv_status);
646: /* unpack receives into our local space */
647: MPI_Get_count(&recv_status,MPIU_INT,&n);
648: source[imdex] = recv_status.MPI_SOURCE;
649: lens[imdex] = n;
650: slen += n;
651: count--;
652: }
653: PetscFree(recv_waits);
654:
655: /* move the data into the send scatter */
656: PetscMalloc((slen+1)*sizeof(PetscInt),&lrows);
657: count = 0;
658: for (i=0; i<nrecvs; i++) {
659: values = rvalues + i*nmax;
660: for (j=0; j<lens[i]; j++) {
661: lrows[count++] = values[j] - base;
662: }
663: }
664: PetscFree(rvalues);
665: PetscFree(lens);
666: PetscFree(owner);
667: PetscFree(nprocs);
668:
669: /* actually zap the local rows */
670: /*
671: Zero the required rows. If the "diagonal block" of the matrix
672: is square and the user wishes to set the diagonal we use separate
673: code so that MatSetValues() is not called for each diagonal allocating
674: new memory, thus calling lots of mallocs and slowing things down.
676: Contributed by: Matthew Knepley
677: */
678: /* must zero l->B before l->A because the (diag) case below may put values into l->B*/
679: MatZeroRows(l->B,slen,lrows,0.0);
680: if ((diag != 0.0) && (l->A->rmap->N == l->A->cmap->N)) {
681: MatZeroRows(l->A,slen,lrows,diag);
682: } else if (diag != 0.0) {
683: MatZeroRows(l->A,slen,lrows,0.0);
684: if (((Mat_SeqAIJ*)l->A->data)->nonew) {
685: SETERRQ(PETSC_ERR_SUP,"MatZeroRows() on rectangular matrices cannot be used with the Mat options\n\
686: MAT_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
687: }
688: for (i = 0; i < slen; i++) {
689: row = lrows[i] + rstart;
690: MatSetValues(A,1,&row,1,&row,&diag,INSERT_VALUES);
691: }
692: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
693: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
694: } else {
695: MatZeroRows(l->A,slen,lrows,0.0);
696: }
697: PetscFree(lrows);
699: /* wait on sends */
700: if (nsends) {
701: PetscMalloc(nsends*sizeof(MPI_Status),&send_status);
702: MPI_Waitall(nsends,send_waits,send_status);
703: PetscFree(send_status);
704: }
705: PetscFree(send_waits);
706: PetscFree(svalues);
708: return(0);
709: }
713: PetscErrorCode MatMult_MPIAIJ(Mat A,Vec xx,Vec yy)
714: {
715: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
717: PetscInt nt;
720: VecGetLocalSize(xx,&nt);
721: if (nt != A->cmap->n) {
722: SETERRQ2(PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%D) and xx (%D)",A->cmap->n,nt);
723: }
724: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
725: (*a->A->ops->mult)(a->A,xx,yy);
726: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
727: (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
728: return(0);
729: }
733: PetscErrorCode MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
734: {
735: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
739: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
740: (*a->A->ops->multadd)(a->A,xx,yy,zz);
741: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
742: (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
743: return(0);
744: }
748: PetscErrorCode MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy)
749: {
750: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
752: PetscTruth merged;
755: VecScatterGetMerged(a->Mvctx,&merged);
756: /* do nondiagonal part */
757: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
758: if (!merged) {
759: /* send it on its way */
760: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
761: /* do local part */
762: (*a->A->ops->multtranspose)(a->A,xx,yy);
763: /* receive remote parts: note this assumes the values are not actually */
764: /* added in yy until the next line, */
765: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
766: } else {
767: /* do local part */
768: (*a->A->ops->multtranspose)(a->A,xx,yy);
769: /* send it on its way */
770: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
771: /* values actually were received in the Begin() but we need to call this nop */
772: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
773: }
774: return(0);
775: }
780: PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat,Mat Bmat,PetscReal tol,PetscTruth *f)
781: {
782: MPI_Comm comm;
783: Mat_MPIAIJ *Aij = (Mat_MPIAIJ *) Amat->data, *Bij;
784: Mat Adia = Aij->A, Bdia, Aoff,Boff,*Aoffs,*Boffs;
785: IS Me,Notme;
787: PetscInt M,N,first,last,*notme,i;
788: PetscMPIInt size;
792: /* Easy test: symmetric diagonal block */
793: Bij = (Mat_MPIAIJ *) Bmat->data; Bdia = Bij->A;
794: MatIsTranspose(Adia,Bdia,tol,f);
795: if (!*f) return(0);
796: PetscObjectGetComm((PetscObject)Amat,&comm);
797: MPI_Comm_size(comm,&size);
798: if (size == 1) return(0);
800: /* Hard test: off-diagonal block. This takes a MatGetSubMatrix. */
801: MatGetSize(Amat,&M,&N);
802: MatGetOwnershipRange(Amat,&first,&last);
803: PetscMalloc((N-last+first)*sizeof(PetscInt),¬me);
804: for (i=0; i<first; i++) notme[i] = i;
805: for (i=last; i<M; i++) notme[i-last+first] = i;
806: ISCreateGeneral(MPI_COMM_SELF,N-last+first,notme,&Notme);
807: ISCreateStride(MPI_COMM_SELF,last-first,first,1,&Me);
808: MatGetSubMatrices(Amat,1,&Me,&Notme,MAT_INITIAL_MATRIX,&Aoffs);
809: Aoff = Aoffs[0];
810: MatGetSubMatrices(Bmat,1,&Notme,&Me,MAT_INITIAL_MATRIX,&Boffs);
811: Boff = Boffs[0];
812: MatIsTranspose(Aoff,Boff,tol,f);
813: MatDestroyMatrices(1,&Aoffs);
814: MatDestroyMatrices(1,&Boffs);
815: ISDestroy(Me);
816: ISDestroy(Notme);
818: return(0);
819: }
824: PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
825: {
826: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
830: /* do nondiagonal part */
831: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
832: /* send it on its way */
833: VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
834: /* do local part */
835: (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
836: /* receive remote parts */
837: VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
838: return(0);
839: }
841: /*
842: This only works correctly for square matrices where the subblock A->A is the
843: diagonal block
844: */
847: PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A,Vec v)
848: {
850: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
853: if (A->rmap->N != A->cmap->N) SETERRQ(PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
854: if (A->rmap->rstart != A->cmap->rstart || A->rmap->rend != A->cmap->rend) {
855: SETERRQ(PETSC_ERR_ARG_SIZ,"row partition must equal col partition");
856: }
857: MatGetDiagonal(a->A,v);
858: return(0);
859: }
863: PetscErrorCode MatScale_MPIAIJ(Mat A,PetscScalar aa)
864: {
865: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
869: MatScale(a->A,aa);
870: MatScale(a->B,aa);
871: return(0);
872: }
876: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
877: {
878: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
882: #if defined(PETSC_USE_LOG)
883: PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N);
884: #endif
885: MatStashDestroy_Private(&mat->stash);
886: MatDestroy(aij->A);
887: MatDestroy(aij->B);
888: #if defined (PETSC_USE_CTABLE)
889: if (aij->colmap) {PetscTableDestroy(aij->colmap);}
890: #else
891: PetscFree(aij->colmap);
892: #endif
893: PetscFree(aij->garray);
894: if (aij->lvec) {VecDestroy(aij->lvec);}
895: if (aij->Mvctx) {VecScatterDestroy(aij->Mvctx);}
896: PetscFree(aij->rowvalues);
897: PetscFree(aij->ld);
898: PetscFree(aij);
900: PetscObjectChangeTypeName((PetscObject)mat,0);
901: PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C","",PETSC_NULL);
902: PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C","",PETSC_NULL);
903: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C","",PETSC_NULL);
904: PetscObjectComposeFunction((PetscObject)mat,"MatIsTranspose_C","",PETSC_NULL);
905: PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocation_C","",PETSC_NULL);
906: PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocationCSR_C","",PETSC_NULL);
907: PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C","",PETSC_NULL);
908: return(0);
909: }
913: PetscErrorCode MatView_MPIAIJ_Binary(Mat mat,PetscViewer viewer)
914: {
915: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
916: Mat_SeqAIJ* A = (Mat_SeqAIJ*)aij->A->data;
917: Mat_SeqAIJ* B = (Mat_SeqAIJ*)aij->B->data;
918: PetscErrorCode ierr;
919: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag;
920: int fd;
921: PetscInt nz,header[4],*row_lengths,*range=0,rlen,i;
922: PetscInt nzmax,*column_indices,j,k,col,*garray = aij->garray,cnt,cstart = mat->cmap->rstart,rnz;
923: PetscScalar *column_values;
926: MPI_Comm_rank(((PetscObject)mat)->comm,&rank);
927: MPI_Comm_size(((PetscObject)mat)->comm,&size);
928: nz = A->nz + B->nz;
929: if (!rank) {
930: header[0] = MAT_FILE_COOKIE;
931: header[1] = mat->rmap->N;
932: header[2] = mat->cmap->N;
933: MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,((PetscObject)mat)->comm);
934: PetscViewerBinaryGetDescriptor(viewer,&fd);
935: PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
936: /* get largest number of rows any processor has */
937: rlen = mat->rmap->n;
938: range = mat->rmap->range;
939: for (i=1; i<size; i++) {
940: rlen = PetscMax(rlen,range[i+1] - range[i]);
941: }
942: } else {
943: MPI_Reduce(&nz,0,1,MPIU_INT,MPI_SUM,0,((PetscObject)mat)->comm);
944: rlen = mat->rmap->n;
945: }
947: /* load up the local row counts */
948: PetscMalloc((rlen+1)*sizeof(PetscInt),&row_lengths);
949: for (i=0; i<mat->rmap->n; i++) {
950: row_lengths[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i];
951: }
953: /* store the row lengths to the file */
954: if (!rank) {
955: MPI_Status status;
956: PetscBinaryWrite(fd,row_lengths,mat->rmap->n,PETSC_INT,PETSC_TRUE);
957: for (i=1; i<size; i++) {
958: rlen = range[i+1] - range[i];
959: MPI_Recv(row_lengths,rlen,MPIU_INT,i,tag,((PetscObject)mat)->comm,&status);
960: PetscBinaryWrite(fd,row_lengths,rlen,PETSC_INT,PETSC_TRUE);
961: }
962: } else {
963: MPI_Send(row_lengths,mat->rmap->n,MPIU_INT,0,tag,((PetscObject)mat)->comm);
964: }
965: PetscFree(row_lengths);
967: /* load up the local column indices */
968: nzmax = nz; /* )th processor needs space a largest processor needs */
969: MPI_Reduce(&nz,&nzmax,1,MPIU_INT,MPI_MAX,0,((PetscObject)mat)->comm);
970: PetscMalloc((nzmax+1)*sizeof(PetscInt),&column_indices);
971: cnt = 0;
972: for (i=0; i<mat->rmap->n; i++) {
973: for (j=B->i[i]; j<B->i[i+1]; j++) {
974: if ( (col = garray[B->j[j]]) > cstart) break;
975: column_indices[cnt++] = col;
976: }
977: for (k=A->i[i]; k<A->i[i+1]; k++) {
978: column_indices[cnt++] = A->j[k] + cstart;
979: }
980: for (; j<B->i[i+1]; j++) {
981: column_indices[cnt++] = garray[B->j[j]];
982: }
983: }
984: if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);
986: /* store the column indices to the file */
987: if (!rank) {
988: MPI_Status status;
989: PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,PETSC_TRUE);
990: for (i=1; i<size; i++) {
991: MPI_Recv(&rnz,1,MPIU_INT,i,tag,((PetscObject)mat)->comm,&status);
992: if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
993: MPI_Recv(column_indices,rnz,MPIU_INT,i,tag,((PetscObject)mat)->comm,&status);
994: PetscBinaryWrite(fd,column_indices,rnz,PETSC_INT,PETSC_TRUE);
995: }
996: } else {
997: MPI_Send(&nz,1,MPIU_INT,0,tag,((PetscObject)mat)->comm);
998: MPI_Send(column_indices,nz,MPIU_INT,0,tag,((PetscObject)mat)->comm);
999: }
1000: PetscFree(column_indices);
1002: /* load up the local column values */
1003: PetscMalloc((nzmax+1)*sizeof(PetscScalar),&column_values);
1004: cnt = 0;
1005: for (i=0; i<mat->rmap->n; i++) {
1006: for (j=B->i[i]; j<B->i[i+1]; j++) {
1007: if ( garray[B->j[j]] > cstart) break;
1008: column_values[cnt++] = B->a[j];
1009: }
1010: for (k=A->i[i]; k<A->i[i+1]; k++) {
1011: column_values[cnt++] = A->a[k];
1012: }
1013: for (; j<B->i[i+1]; j++) {
1014: column_values[cnt++] = B->a[j];
1015: }
1016: }
1017: if (cnt != A->nz + B->nz) SETERRQ2(PETSC_ERR_PLIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);
1019: /* store the column values to the file */
1020: if (!rank) {
1021: MPI_Status status;
1022: PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,PETSC_TRUE);
1023: for (i=1; i<size; i++) {
1024: MPI_Recv(&rnz,1,MPIU_INT,i,tag,((PetscObject)mat)->comm,&status);
1025: if (rnz > nzmax) SETERRQ2(PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
1026: MPI_Recv(column_values,rnz,MPIU_SCALAR,i,tag,((PetscObject)mat)->comm,&status);
1027: PetscBinaryWrite(fd,column_values,rnz,PETSC_SCALAR,PETSC_TRUE);
1028: }
1029: } else {
1030: MPI_Send(&nz,1,MPIU_INT,0,tag,((PetscObject)mat)->comm);
1031: MPI_Send(column_values,nz,MPIU_SCALAR,0,tag,((PetscObject)mat)->comm);
1032: }
1033: PetscFree(column_values);
1034: return(0);
1035: }
1039: PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1040: {
1041: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1042: PetscErrorCode ierr;
1043: PetscMPIInt rank = aij->rank,size = aij->size;
1044: PetscTruth isdraw,iascii,isbinary;
1045: PetscViewer sviewer;
1046: PetscViewerFormat format;
1049: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1050: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1051: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1052: if (iascii) {
1053: PetscViewerGetFormat(viewer,&format);
1054: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1055: MatInfo info;
1056: PetscTruth inodes;
1058: MPI_Comm_rank(((PetscObject)mat)->comm,&rank);
1059: MatGetInfo(mat,MAT_LOCAL,&info);
1060: MatInodeGetInodeSizes(aij->A,PETSC_NULL,(PetscInt **)&inodes,PETSC_NULL);
1061: if (!inodes) {
1062: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, not using I-node routines\n",
1063: rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
1064: } else {
1065: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, using I-node routines\n",
1066: rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
1067: }
1068: MatGetInfo(aij->A,MAT_LOCAL,&info);
1069: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1070: MatGetInfo(aij->B,MAT_LOCAL,&info);
1071: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1072: PetscViewerFlush(viewer);
1073: PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
1074: VecScatterView(aij->Mvctx,viewer);
1075: return(0);
1076: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1077: PetscInt inodecount,inodelimit,*inodes;
1078: MatInodeGetInodeSizes(aij->A,&inodecount,&inodes,&inodelimit);
1079: if (inodes) {
1080: PetscViewerASCIIPrintf(viewer,"using I-node (on process 0) routines: found %D nodes, limit used is %D\n",inodecount,inodelimit);
1081: } else {
1082: PetscViewerASCIIPrintf(viewer,"not using I-node (on process 0) routines\n");
1083: }
1084: return(0);
1085: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1086: return(0);
1087: }
1088: } else if (isbinary) {
1089: if (size == 1) {
1090: PetscObjectSetName((PetscObject)aij->A,((PetscObject)mat)->name);
1091: MatView(aij->A,viewer);
1092: } else {
1093: MatView_MPIAIJ_Binary(mat,viewer);
1094: }
1095: return(0);
1096: } else if (isdraw) {
1097: PetscDraw draw;
1098: PetscTruth isnull;
1099: PetscViewerDrawGetDraw(viewer,0,&draw);
1100: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1101: }
1103: if (size == 1) {
1104: PetscObjectSetName((PetscObject)aij->A,((PetscObject)mat)->name);
1105: MatView(aij->A,viewer);
1106: } else {
1107: /* assemble the entire matrix onto first processor. */
1108: Mat A;
1109: Mat_SeqAIJ *Aloc;
1110: PetscInt M = mat->rmap->N,N = mat->cmap->N,m,*ai,*aj,row,*cols,i,*ct;
1111: MatScalar *a;
1113: if (mat->rmap->N > 1024) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"ASCII matrix output not allowed for matrices with more than 512 rows, use binary format instead");
1115: MatCreate(((PetscObject)mat)->comm,&A);
1116: if (!rank) {
1117: MatSetSizes(A,M,N,M,N);
1118: } else {
1119: MatSetSizes(A,0,0,M,N);
1120: }
1121: /* This is just a temporary matrix, so explicitly using MATMPIAIJ is probably best */
1122: MatSetType(A,MATMPIAIJ);
1123: MatMPIAIJSetPreallocation(A,0,PETSC_NULL,0,PETSC_NULL);
1124: PetscLogObjectParent(mat,A);
1126: /* copy over the A part */
1127: Aloc = (Mat_SeqAIJ*)aij->A->data;
1128: m = aij->A->rmap->n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1129: row = mat->rmap->rstart;
1130: for (i=0; i<ai[m]; i++) {aj[i] += mat->cmap->rstart ;}
1131: for (i=0; i<m; i++) {
1132: MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);
1133: row++; a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i];
1134: }
1135: aj = Aloc->j;
1136: for (i=0; i<ai[m]; i++) {aj[i] -= mat->cmap->rstart;}
1138: /* copy over the B part */
1139: Aloc = (Mat_SeqAIJ*)aij->B->data;
1140: m = aij->B->rmap->n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1141: row = mat->rmap->rstart;
1142: PetscMalloc((ai[m]+1)*sizeof(PetscInt),&cols);
1143: ct = cols;
1144: for (i=0; i<ai[m]; i++) {cols[i] = aij->garray[aj[i]];}
1145: for (i=0; i<m; i++) {
1146: MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);
1147: row++; a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
1148: }
1149: PetscFree(ct);
1150: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1151: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1152: /*
1153: Everyone has to call to draw the matrix since the graphics waits are
1154: synchronized across all processors that share the PetscDraw object
1155: */
1156: PetscViewerGetSingleton(viewer,&sviewer);
1157: if (!rank) {
1158: PetscObjectSetName((PetscObject)((Mat_MPIAIJ*)(A->data))->A,((PetscObject)mat)->name);
1159: MatView(((Mat_MPIAIJ*)(A->data))->A,sviewer);
1160: }
1161: PetscViewerRestoreSingleton(viewer,&sviewer);
1162: MatDestroy(A);
1163: }
1164: return(0);
1165: }
1169: PetscErrorCode MatView_MPIAIJ(Mat mat,PetscViewer viewer)
1170: {
1172: PetscTruth iascii,isdraw,issocket,isbinary;
1173:
1175: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1176: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1177: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1178: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
1179: if (iascii || isdraw || isbinary || issocket) {
1180: MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);
1181: } else {
1182: SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by MPIAIJ matrices",((PetscObject)viewer)->type_name);
1183: }
1184: return(0);
1185: }
1189: PetscErrorCode MatRelax_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1190: {
1191: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1193: Vec bb1;
1196: VecDuplicate(bb,&bb1);
1198: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP){
1199: if (flag & SOR_ZERO_INITIAL_GUESS) {
1200: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,lits,xx);
1201: its--;
1202: }
1203:
1204: while (its--) {
1205: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1206: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1208: /* update rhs: bb1 = bb - B*x */
1209: VecScale(mat->lvec,-1.0);
1210: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1212: /* local sweep */
1213: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,lits,xx);
1214: }
1215: } else if (flag & SOR_LOCAL_FORWARD_SWEEP){
1216: if (flag & SOR_ZERO_INITIAL_GUESS) {
1217: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1218: its--;
1219: }
1220: while (its--) {
1221: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1222: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1224: /* update rhs: bb1 = bb - B*x */
1225: VecScale(mat->lvec,-1.0);
1226: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1228: /* local sweep */
1229: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1230: }
1231: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP){
1232: if (flag & SOR_ZERO_INITIAL_GUESS) {
1233: (*mat->A->ops->relax)(mat->A,bb,omega,flag,fshift,lits,PETSC_NULL,xx);
1234: its--;
1235: }
1236: while (its--) {
1237: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1238: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1240: /* update rhs: bb1 = bb - B*x */
1241: VecScale(mat->lvec,-1.0);
1242: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1244: /* local sweep */
1245: (*mat->A->ops->relax)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,PETSC_NULL,xx);
1246: }
1247: } else {
1248: SETERRQ(PETSC_ERR_SUP,"Parallel SOR not supported");
1249: }
1251: VecDestroy(bb1);
1252: return(0);
1253: }
1257: PetscErrorCode MatPermute_MPIAIJ(Mat A,IS rowp,IS colp,Mat *B)
1258: {
1259: MPI_Comm comm,pcomm;
1260: PetscInt first,local_size,nrows;
1261: const PetscInt *rows;
1262: int ntids;
1263: IS crowp,growp,irowp,lrowp,lcolp,icolp;
1267: PetscObjectGetComm((PetscObject)A,&comm);
1268: /* make a collective version of 'rowp' */
1269: PetscObjectGetComm((PetscObject)rowp,&pcomm);
1270: if (pcomm==comm) {
1271: crowp = rowp;
1272: } else {
1273: ISGetSize(rowp,&nrows);
1274: ISGetIndices(rowp,&rows);
1275: ISCreateGeneral(comm,nrows,rows,&crowp);
1276: ISRestoreIndices(rowp,&rows);
1277: }
1278: /* collect the global row permutation and invert it */
1279: ISAllGather(crowp,&growp);
1280: ISSetPermutation(growp);
1281: if (pcomm!=comm) {
1282: ISDestroy(crowp);
1283: }
1284: ISInvertPermutation(growp,PETSC_DECIDE,&irowp);
1285: /* get the local target indices */
1286: MatGetOwnershipRange(A,&first,PETSC_NULL);
1287: MatGetLocalSize(A,&local_size,PETSC_NULL);
1288: ISGetIndices(irowp,&rows);
1289: ISCreateGeneral(MPI_COMM_SELF,local_size,rows+first,&lrowp);
1290: ISRestoreIndices(irowp,&rows);
1291: ISDestroy(irowp);
1292: /* the column permutation is so much easier;
1293: make a local version of 'colp' and invert it */
1294: PetscObjectGetComm((PetscObject)colp,&pcomm);
1295: MPI_Comm_size(pcomm,&ntids);
1296: if (ntids==1) {
1297: lcolp = colp;
1298: } else {
1299: ISGetSize(colp,&nrows);
1300: ISGetIndices(colp,&rows);
1301: ISCreateGeneral(MPI_COMM_SELF,nrows,rows,&lcolp);
1302: }
1303: ISInvertPermutation(lcolp,PETSC_DECIDE,&icolp);
1304: ISSetPermutation(lcolp);
1305: if (ntids>1) {
1306: ISRestoreIndices(colp,&rows);
1307: ISDestroy(lcolp);
1308: }
1309: /* now we just get the submatrix */
1310: MatGetSubMatrix(A,lrowp,icolp,local_size,MAT_INITIAL_MATRIX,B);
1311: /* clean up */
1312: ISDestroy(lrowp);
1313: ISDestroy(icolp);
1314: return(0);
1315: }
1319: PetscErrorCode MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1320: {
1321: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1322: Mat A = mat->A,B = mat->B;
1324: PetscReal isend[5],irecv[5];
1327: info->block_size = 1.0;
1328: MatGetInfo(A,MAT_LOCAL,info);
1329: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1330: isend[3] = info->memory; isend[4] = info->mallocs;
1331: MatGetInfo(B,MAT_LOCAL,info);
1332: isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1333: isend[3] += info->memory; isend[4] += info->mallocs;
1334: if (flag == MAT_LOCAL) {
1335: info->nz_used = isend[0];
1336: info->nz_allocated = isend[1];
1337: info->nz_unneeded = isend[2];
1338: info->memory = isend[3];
1339: info->mallocs = isend[4];
1340: } else if (flag == MAT_GLOBAL_MAX) {
1341: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_MAX,((PetscObject)matin)->comm);
1342: info->nz_used = irecv[0];
1343: info->nz_allocated = irecv[1];
1344: info->nz_unneeded = irecv[2];
1345: info->memory = irecv[3];
1346: info->mallocs = irecv[4];
1347: } else if (flag == MAT_GLOBAL_SUM) {
1348: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPI_SUM,((PetscObject)matin)->comm);
1349: info->nz_used = irecv[0];
1350: info->nz_allocated = irecv[1];
1351: info->nz_unneeded = irecv[2];
1352: info->memory = irecv[3];
1353: info->mallocs = irecv[4];
1354: }
1355: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1356: info->fill_ratio_needed = 0;
1357: info->factor_mallocs = 0;
1359: return(0);
1360: }
1364: PetscErrorCode MatSetOption_MPIAIJ(Mat A,MatOption op,PetscTruth flg)
1365: {
1366: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1370: switch (op) {
1371: case MAT_NEW_NONZERO_LOCATIONS:
1372: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1373: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1374: case MAT_KEEP_ZEROED_ROWS:
1375: case MAT_NEW_NONZERO_LOCATION_ERR:
1376: case MAT_USE_INODES:
1377: case MAT_IGNORE_ZERO_ENTRIES:
1378: MatSetOption(a->A,op,flg);
1379: MatSetOption(a->B,op,flg);
1380: break;
1381: case MAT_ROW_ORIENTED:
1382: a->roworiented = flg;
1383: MatSetOption(a->A,op,flg);
1384: MatSetOption(a->B,op,flg);
1385: break;
1386: case MAT_NEW_DIAGONALS:
1387: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1388: break;
1389: case MAT_IGNORE_OFF_PROC_ENTRIES:
1390: a->donotstash = PETSC_TRUE;
1391: break;
1392: case MAT_SYMMETRIC:
1393: MatSetOption(a->A,op,flg);
1394: break;
1395: case MAT_STRUCTURALLY_SYMMETRIC:
1396: case MAT_HERMITIAN:
1397: case MAT_SYMMETRY_ETERNAL:
1398: MatSetOption(a->A,op,flg);
1399: break;
1400: default:
1401: SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
1402: }
1403: return(0);
1404: }
1408: PetscErrorCode MatGetRow_MPIAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1409: {
1410: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1411: PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
1413: PetscInt i,*cworkA,*cworkB,**pcA,**pcB,cstart = matin->cmap->rstart;
1414: PetscInt nztot,nzA,nzB,lrow,rstart = matin->rmap->rstart,rend = matin->rmap->rend;
1415: PetscInt *cmap,*idx_p;
1418: if (mat->getrowactive) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Already active");
1419: mat->getrowactive = PETSC_TRUE;
1421: if (!mat->rowvalues && (idx || v)) {
1422: /*
1423: allocate enough space to hold information from the longest row.
1424: */
1425: Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data;
1426: PetscInt max = 1,tmp;
1427: for (i=0; i<matin->rmap->n; i++) {
1428: tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1429: if (max < tmp) { max = tmp; }
1430: }
1431: PetscMalloc(max*(sizeof(PetscInt)+sizeof(PetscScalar)),&mat->rowvalues);
1432: mat->rowindices = (PetscInt*)(mat->rowvalues + max);
1433: }
1435: if (row < rstart || row >= rend) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Only local rows")
1436: lrow = row - rstart;
1438: pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1439: if (!v) {pvA = 0; pvB = 0;}
1440: if (!idx) {pcA = 0; if (!v) pcB = 0;}
1441: (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1442: (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1443: nztot = nzA + nzB;
1445: cmap = mat->garray;
1446: if (v || idx) {
1447: if (nztot) {
1448: /* Sort by increasing column numbers, assuming A and B already sorted */
1449: PetscInt imark = -1;
1450: if (v) {
1451: *v = v_p = mat->rowvalues;
1452: for (i=0; i<nzB; i++) {
1453: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1454: else break;
1455: }
1456: imark = i;
1457: for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i];
1458: for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i];
1459: }
1460: if (idx) {
1461: *idx = idx_p = mat->rowindices;
1462: if (imark > -1) {
1463: for (i=0; i<imark; i++) {
1464: idx_p[i] = cmap[cworkB[i]];
1465: }
1466: } else {
1467: for (i=0; i<nzB; i++) {
1468: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1469: else break;
1470: }
1471: imark = i;
1472: }
1473: for (i=0; i<nzA; i++) idx_p[imark+i] = cstart + cworkA[i];
1474: for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]];
1475: }
1476: } else {
1477: if (idx) *idx = 0;
1478: if (v) *v = 0;
1479: }
1480: }
1481: *nz = nztot;
1482: (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1483: (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1484: return(0);
1485: }
1489: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1490: {
1491: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1494: if (!aij->getrowactive) {
1495: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1496: }
1497: aij->getrowactive = PETSC_FALSE;
1498: return(0);
1499: }
1503: PetscErrorCode MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm)
1504: {
1505: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1506: Mat_SeqAIJ *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data;
1508: PetscInt i,j,cstart = mat->cmap->rstart;
1509: PetscReal sum = 0.0;
1510: MatScalar *v;
1513: if (aij->size == 1) {
1514: MatNorm(aij->A,type,norm);
1515: } else {
1516: if (type == NORM_FROBENIUS) {
1517: v = amat->a;
1518: for (i=0; i<amat->nz; i++) {
1519: #if defined(PETSC_USE_COMPLEX)
1520: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1521: #else
1522: sum += (*v)*(*v); v++;
1523: #endif
1524: }
1525: v = bmat->a;
1526: for (i=0; i<bmat->nz; i++) {
1527: #if defined(PETSC_USE_COMPLEX)
1528: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1529: #else
1530: sum += (*v)*(*v); v++;
1531: #endif
1532: }
1533: MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPI_SUM,((PetscObject)mat)->comm);
1534: *norm = sqrt(*norm);
1535: } else if (type == NORM_1) { /* max column norm */
1536: PetscReal *tmp,*tmp2;
1537: PetscInt *jj,*garray = aij->garray;
1538: PetscMalloc((mat->cmap->N+1)*sizeof(PetscReal),&tmp);
1539: PetscMalloc((mat->cmap->N+1)*sizeof(PetscReal),&tmp2);
1540: PetscMemzero(tmp,mat->cmap->N*sizeof(PetscReal));
1541: *norm = 0.0;
1542: v = amat->a; jj = amat->j;
1543: for (j=0; j<amat->nz; j++) {
1544: tmp[cstart + *jj++ ] += PetscAbsScalar(*v); v++;
1545: }
1546: v = bmat->a; jj = bmat->j;
1547: for (j=0; j<bmat->nz; j++) {
1548: tmp[garray[*jj++]] += PetscAbsScalar(*v); v++;
1549: }
1550: MPI_Allreduce(tmp,tmp2,mat->cmap->N,MPIU_REAL,MPI_SUM,((PetscObject)mat)->comm);
1551: for (j=0; j<mat->cmap->N; j++) {
1552: if (tmp2[j] > *norm) *norm = tmp2[j];
1553: }
1554: PetscFree(tmp);
1555: PetscFree(tmp2);
1556: } else if (type == NORM_INFINITY) { /* max row norm */
1557: PetscReal ntemp = 0.0;
1558: for (j=0; j<aij->A->rmap->n; j++) {
1559: v = amat->a + amat->i[j];
1560: sum = 0.0;
1561: for (i=0; i<amat->i[j+1]-amat->i[j]; i++) {
1562: sum += PetscAbsScalar(*v); v++;
1563: }
1564: v = bmat->a + bmat->i[j];
1565: for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) {
1566: sum += PetscAbsScalar(*v); v++;
1567: }
1568: if (sum > ntemp) ntemp = sum;
1569: }
1570: MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPI_MAX,((PetscObject)mat)->comm);
1571: } else {
1572: SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1573: }
1574: }
1575: return(0);
1576: }
1580: PetscErrorCode MatTranspose_MPIAIJ(Mat A,MatReuse reuse,Mat *matout)
1581: {
1582: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1583: Mat_SeqAIJ *Aloc=(Mat_SeqAIJ*)a->A->data,*Bloc=(Mat_SeqAIJ*)a->B->data;
1585: PetscInt M = A->rmap->N,N = A->cmap->N,ma,na,mb,*ai,*aj,*bi,*bj,row,*cols,*cols_tmp,i,*d_nnz;
1586: PetscInt cstart=A->cmap->rstart,ncol;
1587: Mat B;
1588: MatScalar *array;
1591: if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1593: ma = A->rmap->n; na = A->cmap->n; mb = a->B->rmap->n;
1594: ai = Aloc->i; aj = Aloc->j;
1595: bi = Bloc->i; bj = Bloc->j;
1596: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1597: /* compute d_nnz for preallocation; o_nnz is approximated by d_nnz to avoid communication */
1598: PetscMalloc((1+na)*sizeof(PetscInt),&d_nnz);
1599: PetscMemzero(d_nnz,(1+na)*sizeof(PetscInt));
1600: for (i=0; i<ai[ma]; i++){
1601: d_nnz[aj[i]] ++;
1602: aj[i] += cstart; /* global col index to be used by MatSetValues() */
1603: }
1605: MatCreate(((PetscObject)A)->comm,&B);
1606: MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
1607: MatSetType(B,((PetscObject)A)->type_name);
1608: MatMPIAIJSetPreallocation(B,0,d_nnz,0,d_nnz);
1609: PetscFree(d_nnz);
1610: } else {
1611: B = *matout;
1612: }
1614: /* copy over the A part */
1615: array = Aloc->a;
1616: row = A->rmap->rstart;
1617: for (i=0; i<ma; i++) {
1618: ncol = ai[i+1]-ai[i];
1619: MatSetValues(B,ncol,aj,1,&row,array,INSERT_VALUES);
1620: row++; array += ncol; aj += ncol;
1621: }
1622: aj = Aloc->j;
1623: for (i=0; i<ai[ma]; i++) aj[i] -= cstart; /* resume local col index */
1625: /* copy over the B part */
1626: PetscMalloc(bi[mb]*sizeof(PetscInt),&cols);
1627: PetscMemzero(cols,bi[mb]*sizeof(PetscInt));
1628: array = Bloc->a;
1629: row = A->rmap->rstart;
1630: for (i=0; i<bi[mb]; i++) {cols[i] = a->garray[bj[i]];}
1631: cols_tmp = cols;
1632: for (i=0; i<mb; i++) {
1633: ncol = bi[i+1]-bi[i];
1634: MatSetValues(B,ncol,cols_tmp,1,&row,array,INSERT_VALUES);
1635: row++; array += ncol; cols_tmp += ncol;
1636: }
1637: PetscFree(cols);
1638:
1639: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
1640: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
1641: if (reuse == MAT_INITIAL_MATRIX || *matout != A) {
1642: *matout = B;
1643: } else {
1644: MatHeaderCopy(A,B);
1645: }
1646: return(0);
1647: }
1651: PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr)
1652: {
1653: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1654: Mat a = aij->A,b = aij->B;
1656: PetscInt s1,s2,s3;
1659: MatGetLocalSize(mat,&s2,&s3);
1660: if (rr) {
1661: VecGetLocalSize(rr,&s1);
1662: if (s1!=s3) SETERRQ(PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
1663: /* Overlap communication with computation. */
1664: VecScatterBegin(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1665: }
1666: if (ll) {
1667: VecGetLocalSize(ll,&s1);
1668: if (s1!=s2) SETERRQ(PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
1669: (*b->ops->diagonalscale)(b,ll,0);
1670: }
1671: /* scale the diagonal block */
1672: (*a->ops->diagonalscale)(a,ll,rr);
1674: if (rr) {
1675: /* Do a scatter end and then right scale the off-diagonal block */
1676: VecScatterEnd(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
1677: (*b->ops->diagonalscale)(b,0,aij->lvec);
1678: }
1679:
1680: return(0);
1681: }
1685: PetscErrorCode MatSetBlockSize_MPIAIJ(Mat A,PetscInt bs)
1686: {
1687: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1691: MatSetBlockSize(a->A,bs);
1692: MatSetBlockSize(a->B,bs);
1693: return(0);
1694: }
1697: PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
1698: {
1699: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1703: MatSetUnfactored(a->A);
1704: return(0);
1705: }
1709: PetscErrorCode MatEqual_MPIAIJ(Mat A,Mat B,PetscTruth *flag)
1710: {
1711: Mat_MPIAIJ *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data;
1712: Mat a,b,c,d;
1713: PetscTruth flg;
1717: a = matA->A; b = matA->B;
1718: c = matB->A; d = matB->B;
1720: MatEqual(a,c,&flg);
1721: if (flg) {
1722: MatEqual(b,d,&flg);
1723: }
1724: MPI_Allreduce(&flg,flag,1,MPI_INT,MPI_LAND,((PetscObject)A)->comm);
1725: return(0);
1726: }
1730: PetscErrorCode MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str)
1731: {
1733: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1734: Mat_MPIAIJ *b = (Mat_MPIAIJ *)B->data;
1737: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
1738: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
1739: /* because of the column compression in the off-processor part of the matrix a->B,
1740: the number of columns in a->B and b->B may be different, hence we cannot call
1741: the MatCopy() directly on the two parts. If need be, we can provide a more
1742: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
1743: then copying the submatrices */
1744: MatCopy_Basic(A,B,str);
1745: } else {
1746: MatCopy(a->A,b->A,str);
1747: MatCopy(a->B,b->B,str);
1748: }
1749: return(0);
1750: }
1754: PetscErrorCode MatSetUpPreallocation_MPIAIJ(Mat A)
1755: {
1759: MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
1760: return(0);
1761: }
1763: #include petscblaslapack.h
1766: PetscErrorCode MatAXPY_MPIAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1767: {
1769: PetscInt i;
1770: Mat_MPIAIJ *xx = (Mat_MPIAIJ *)X->data,*yy = (Mat_MPIAIJ *)Y->data;
1771: PetscBLASInt bnz,one=1;
1772: Mat_SeqAIJ *x,*y;
1775: if (str == SAME_NONZERO_PATTERN) {
1776: PetscScalar alpha = a;
1777: x = (Mat_SeqAIJ *)xx->A->data;
1778: y = (Mat_SeqAIJ *)yy->A->data;
1779: bnz = PetscBLASIntCast(x->nz);
1780: BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1781: x = (Mat_SeqAIJ *)xx->B->data;
1782: y = (Mat_SeqAIJ *)yy->B->data;
1783: bnz = PetscBLASIntCast(x->nz);
1784: BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1785: } else if (str == SUBSET_NONZERO_PATTERN) {
1786: MatAXPY_SeqAIJ(yy->A,a,xx->A,str);
1788: x = (Mat_SeqAIJ *)xx->B->data;
1789: y = (Mat_SeqAIJ *)yy->B->data;
1790: if (y->xtoy && y->XtoY != xx->B) {
1791: PetscFree(y->xtoy);
1792: MatDestroy(y->XtoY);
1793: }
1794: if (!y->xtoy) { /* get xtoy */
1795: MatAXPYGetxtoy_Private(xx->B->rmap->n,x->i,x->j,xx->garray,y->i,y->j,yy->garray,&y->xtoy);
1796: y->XtoY = xx->B;
1797: PetscObjectReference((PetscObject)xx->B);
1798: }
1799: for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
1800: } else {
1801: MatAXPY_Basic(Y,a,X,str);
1802: }
1803: return(0);
1804: }
1806: EXTERN PetscErrorCode MatConjugate_SeqAIJ(Mat);
1810: PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
1811: {
1812: #if defined(PETSC_USE_COMPLEX)
1814: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
1817: MatConjugate_SeqAIJ(aij->A);
1818: MatConjugate_SeqAIJ(aij->B);
1819: #else
1821: #endif
1822: return(0);
1823: }
1827: PetscErrorCode MatRealPart_MPIAIJ(Mat A)
1828: {
1829: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1833: MatRealPart(a->A);
1834: MatRealPart(a->B);
1835: return(0);
1836: }
1840: PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
1841: {
1842: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1846: MatImaginaryPart(a->A);
1847: MatImaginaryPart(a->B);
1848: return(0);
1849: }
1851: #ifdef PETSC_HAVE_PBGL
1853: #include <boost/parallel/mpi/bsp_process_group.hpp>
1854: #include <boost/graph/distributed/ilu_default_graph.hpp>
1855: #include <boost/graph/distributed/ilu_0_block.hpp>
1856: #include <boost/graph/distributed/ilu_preconditioner.hpp>
1857: #include <boost/graph/distributed/petsc/interface.hpp>
1858: #include <boost/multi_array.hpp>
1859: #include <boost/parallel/distributed_property_map->hpp>
1863: /*
1864: This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
1865: */
1866: PetscErrorCode MatILUFactorSymbolic_MPIAIJ(Mat fact,Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
1867: {
1868: namespace petsc = boost::distributed::petsc;
1869:
1870: namespace graph_dist = boost::graph::distributed;
1871: using boost::graph::distributed::ilu_default::process_group_type;
1872: using boost::graph::ilu_permuted;
1874: PetscTruth row_identity, col_identity;
1875: PetscContainer c;
1876: PetscInt m, n, M, N;
1877: PetscErrorCode ierr;
1880: if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels = 0 supported for parallel ilu");
1881: ISIdentity(isrow, &row_identity);
1882: ISIdentity(iscol, &col_identity);
1883: if (!row_identity || !col_identity) {
1884: SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for parallel ILU");
1885: }
1887: process_group_type pg;
1888: typedef graph_dist::ilu_default::ilu_level_graph_type lgraph_type;
1889: lgraph_type* lgraph_p = new lgraph_type(petsc::num_global_vertices(A), pg, petsc::matrix_distribution(A, pg));
1890: lgraph_type& level_graph = *lgraph_p;
1891: graph_dist::ilu_default::graph_type& graph(level_graph.graph);
1893: petsc::read_matrix(A, graph, get(boost::edge_weight, graph));
1894: ilu_permuted(level_graph);
1896: /* put together the new matrix */
1897: MatCreate(((PetscObject)A)->comm, fact);
1898: MatGetLocalSize(A, &m, &n);
1899: MatGetSize(A, &M, &N);
1900: MatSetSizes(fact, m, n, M, N);
1901: MatSetType(fact, ((PetscObject)A)->type_name);
1902: MatAssemblyBegin(fact, MAT_FINAL_ASSEMBLY);
1903: MatAssemblyEnd(fact, MAT_FINAL_ASSEMBLY);
1905: PetscContainerCreate(((PetscObject)A)->comm, &c);
1906: PetscContainerSetPointer(c, lgraph_p);
1907: PetscObjectCompose((PetscObject) (fact), "graph", (PetscObject) c);
1908: return(0);
1909: }
1913: PetscErrorCode MatLUFactorNumeric_MPIAIJ(Mat B,Mat A, const MatFactorInfo *info)
1914: {
1916: return(0);
1917: }
1921: /*
1922: This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
1923: */
1924: PetscErrorCode MatSolve_MPIAIJ(Mat A, Vec b, Vec x)
1925: {
1926: namespace graph_dist = boost::graph::distributed;
1928: typedef graph_dist::ilu_default::ilu_level_graph_type lgraph_type;
1929: lgraph_type* lgraph_p;
1930: PetscContainer c;
1934: PetscObjectQuery((PetscObject) A, "graph", (PetscObject *) &c);
1935: PetscContainerGetPointer(c, (void **) &lgraph_p);
1936: VecCopy(b, x);
1938: PetscScalar* array_x;
1939: VecGetArray(x, &array_x);
1940: PetscInt sx;
1941: VecGetSize(x, &sx);
1942:
1943: PetscScalar* array_b;
1944: VecGetArray(b, &array_b);
1945: PetscInt sb;
1946: VecGetSize(b, &sb);
1948: lgraph_type& level_graph = *lgraph_p;
1949: graph_dist::ilu_default::graph_type& graph(level_graph.graph);
1951: typedef boost::multi_array_ref<PetscScalar, 1> array_ref_type;
1952: array_ref_type ref_b(array_b, boost::extents[num_vertices(graph)]),
1953: ref_x(array_x, boost::extents[num_vertices(graph)]);
1955: typedef boost::iterator_property_map<array_ref_type::iterator,
1956: boost::property_map<graph_dist::ilu_default::graph_type, boost::vertex_index_t>::type> gvector_type;
1957: gvector_type vector_b(ref_b.begin(), get(boost::vertex_index, graph)),
1958: vector_x(ref_x.begin(), get(boost::vertex_index, graph));
1959:
1960: ilu_set_solve(*lgraph_p, vector_b, vector_x);
1962: return(0);
1963: }
1964: #endif
1966: typedef struct { /* used by MatGetRedundantMatrix() for reusing matredundant */
1967: PetscInt nzlocal,nsends,nrecvs;
1968: PetscMPIInt *send_rank;
1969: PetscInt *sbuf_nz,*sbuf_j,**rbuf_j;
1970: PetscScalar *sbuf_a,**rbuf_a;
1971: PetscErrorCode (*MatDestroy)(Mat);
1972: } Mat_Redundant;
1976: PetscErrorCode PetscContainerDestroy_MatRedundant(void *ptr)
1977: {
1978: PetscErrorCode ierr;
1979: Mat_Redundant *redund=(Mat_Redundant*)ptr;
1980: PetscInt i;
1983: PetscFree(redund->send_rank);
1984: PetscFree(redund->sbuf_j);
1985: PetscFree(redund->sbuf_a);
1986: for (i=0; i<redund->nrecvs; i++){
1987: PetscFree(redund->rbuf_j[i]);
1988: PetscFree(redund->rbuf_a[i]);
1989: }
1990: PetscFree3(redund->sbuf_nz,redund->rbuf_j,redund->rbuf_a);
1991: PetscFree(redund);
1992: return(0);
1993: }
1997: PetscErrorCode MatDestroy_MatRedundant(Mat A)
1998: {
1999: PetscErrorCode ierr;
2000: PetscContainer container;
2001: Mat_Redundant *redund=PETSC_NULL;
2004: PetscObjectQuery((PetscObject)A,"Mat_Redundant",(PetscObject *)&container);
2005: if (container) {
2006: PetscContainerGetPointer(container,(void **)&redund);
2007: } else {
2008: SETERRQ(PETSC_ERR_PLIB,"Container does not exit");
2009: }
2010: A->ops->destroy = redund->MatDestroy;
2011: PetscObjectCompose((PetscObject)A,"Mat_Redundant",0);
2012: (*A->ops->destroy)(A);
2013: PetscContainerDestroy(container);
2014: return(0);
2015: }
2019: PetscErrorCode MatGetRedundantMatrix_MPIAIJ(Mat mat,PetscInt nsubcomm,MPI_Comm subcomm,PetscInt mlocal_sub,MatReuse reuse,Mat *matredundant)
2020: {
2021: PetscMPIInt rank,size;
2022: MPI_Comm comm=((PetscObject)mat)->comm;
2024: PetscInt nsends=0,nrecvs=0,i,rownz_max=0;
2025: PetscMPIInt *send_rank=PETSC_NULL,*recv_rank=PETSC_NULL;
2026: PetscInt *rowrange=mat->rmap->range;
2027: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
2028: Mat A=aij->A,B=aij->B,C=*matredundant;
2029: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data;
2030: PetscScalar *sbuf_a;
2031: PetscInt nzlocal=a->nz+b->nz;
2032: PetscInt j,cstart=mat->cmap->rstart,cend=mat->cmap->rend,row,nzA,nzB,ncols,*cworkA,*cworkB;
2033: PetscInt rstart=mat->rmap->rstart,rend=mat->rmap->rend,*bmap=aij->garray,M,N;
2034: PetscInt *cols,ctmp,lwrite,*rptr,l,*sbuf_j;
2035: MatScalar *aworkA,*aworkB;
2036: PetscScalar *vals;
2037: PetscMPIInt tag1,tag2,tag3,imdex;
2038: MPI_Request *s_waits1=PETSC_NULL,*s_waits2=PETSC_NULL,*s_waits3=PETSC_NULL,
2039: *r_waits1=PETSC_NULL,*r_waits2=PETSC_NULL,*r_waits3=PETSC_NULL;
2040: MPI_Status recv_status,*send_status;
2041: PetscInt *sbuf_nz=PETSC_NULL,*rbuf_nz=PETSC_NULL,count;
2042: PetscInt **rbuf_j=PETSC_NULL;
2043: PetscScalar **rbuf_a=PETSC_NULL;
2044: Mat_Redundant *redund=PETSC_NULL;
2045: PetscContainer container;
2048: MPI_Comm_rank(comm,&rank);
2049: MPI_Comm_size(comm,&size);
2051: if (reuse == MAT_REUSE_MATRIX) {
2052: MatGetSize(C,&M,&N);
2053: if (M != N || M != mat->rmap->N) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong global size");
2054: MatGetLocalSize(C,&M,&N);
2055: if (M != N || M != mlocal_sub) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong local size");
2056: PetscObjectQuery((PetscObject)C,"Mat_Redundant",(PetscObject *)&container);
2057: if (container) {
2058: PetscContainerGetPointer(container,(void **)&redund);
2059: } else {
2060: SETERRQ(PETSC_ERR_PLIB,"Container does not exit");
2061: }
2062: if (nzlocal != redund->nzlocal) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong nzlocal");
2064: nsends = redund->nsends;
2065: nrecvs = redund->nrecvs;
2066: send_rank = redund->send_rank; recv_rank = send_rank + size;
2067: sbuf_nz = redund->sbuf_nz; rbuf_nz = sbuf_nz + nsends;
2068: sbuf_j = redund->sbuf_j;
2069: sbuf_a = redund->sbuf_a;
2070: rbuf_j = redund->rbuf_j;
2071: rbuf_a = redund->rbuf_a;
2072: }
2074: if (reuse == MAT_INITIAL_MATRIX){
2075: PetscMPIInt subrank,subsize;
2076: PetscInt nleftover,np_subcomm;
2077: /* get the destination processors' id send_rank, nsends and nrecvs */
2078: MPI_Comm_rank(subcomm,&subrank);
2079: MPI_Comm_size(subcomm,&subsize);
2080: PetscMalloc((2*size+1)*sizeof(PetscMPIInt),&send_rank);
2081: recv_rank = send_rank + size;
2082: np_subcomm = size/nsubcomm;
2083: nleftover = size - nsubcomm*np_subcomm;
2084: nsends = 0; nrecvs = 0;
2085: for (i=0; i<size; i++){ /* i=rank*/
2086: if (subrank == i/nsubcomm && rank != i){ /* my_subrank == other's subrank */
2087: send_rank[nsends] = i; nsends++;
2088: recv_rank[nrecvs++] = i;
2089: }
2090: }
2091: if (rank >= size - nleftover){/* this proc is a leftover processor */
2092: i = size-nleftover-1;
2093: j = 0;
2094: while (j < nsubcomm - nleftover){
2095: send_rank[nsends++] = i;
2096: i--; j++;
2097: }
2098: }
2100: if (nleftover && subsize == size/nsubcomm && subrank==subsize-1){ /* this proc recvs from leftover processors */
2101: for (i=0; i<nleftover; i++){
2102: recv_rank[nrecvs++] = size-nleftover+i;
2103: }
2104: }
2106: /* allocate sbuf_j, sbuf_a */
2107: i = nzlocal + rowrange[rank+1] - rowrange[rank] + 2;
2108: PetscMalloc(i*sizeof(PetscInt),&sbuf_j);
2109: PetscMalloc((nzlocal+1)*sizeof(PetscScalar),&sbuf_a);
2110: } /* endof if (reuse == MAT_INITIAL_MATRIX) */
2111:
2112: /* copy mat's local entries into the buffers */
2113: if (reuse == MAT_INITIAL_MATRIX){
2114: rownz_max = 0;
2115: rptr = sbuf_j;
2116: cols = sbuf_j + rend-rstart + 1;
2117: vals = sbuf_a;
2118: rptr[0] = 0;
2119: for (i=0; i<rend-rstart; i++){
2120: row = i + rstart;
2121: nzA = a->i[i+1] - a->i[i]; nzB = b->i[i+1] - b->i[i];
2122: ncols = nzA + nzB;
2123: cworkA = a->j + a->i[i]; cworkB = b->j + b->i[i];
2124: aworkA = a->a + a->i[i]; aworkB = b->a + b->i[i];
2125: /* load the column indices for this row into cols */
2126: lwrite = 0;
2127: for (l=0; l<nzB; l++) {
2128: if ((ctmp = bmap[cworkB[l]]) < cstart){
2129: vals[lwrite] = aworkB[l];
2130: cols[lwrite++] = ctmp;
2131: }
2132: }
2133: for (l=0; l<nzA; l++){
2134: vals[lwrite] = aworkA[l];
2135: cols[lwrite++] = cstart + cworkA[l];
2136: }
2137: for (l=0; l<nzB; l++) {
2138: if ((ctmp = bmap[cworkB[l]]) >= cend){
2139: vals[lwrite] = aworkB[l];
2140: cols[lwrite++] = ctmp;
2141: }
2142: }
2143: vals += ncols;
2144: cols += ncols;
2145: rptr[i+1] = rptr[i] + ncols;
2146: if (rownz_max < ncols) rownz_max = ncols;
2147: }
2148: if (rptr[rend-rstart] != a->nz + b->nz) SETERRQ4(1, "rptr[%d] %d != %d + %d",rend-rstart,rptr[rend-rstart+1],a->nz,b->nz);
2149: } else { /* only copy matrix values into sbuf_a */
2150: rptr = sbuf_j;
2151: vals = sbuf_a;
2152: rptr[0] = 0;
2153: for (i=0; i<rend-rstart; i++){
2154: row = i + rstart;
2155: nzA = a->i[i+1] - a->i[i]; nzB = b->i[i+1] - b->i[i];
2156: ncols = nzA + nzB;
2157: cworkA = a->j + a->i[i]; cworkB = b->j + b->i[i];
2158: aworkA = a->a + a->i[i]; aworkB = b->a + b->i[i];
2159: lwrite = 0;
2160: for (l=0; l<nzB; l++) {
2161: if ((ctmp = bmap[cworkB[l]]) < cstart) vals[lwrite++] = aworkB[l];
2162: }
2163: for (l=0; l<nzA; l++) vals[lwrite++] = aworkA[l];
2164: for (l=0; l<nzB; l++) {
2165: if ((ctmp = bmap[cworkB[l]]) >= cend) vals[lwrite++] = aworkB[l];
2166: }
2167: vals += ncols;
2168: rptr[i+1] = rptr[i] + ncols;
2169: }
2170: } /* endof if (reuse == MAT_INITIAL_MATRIX) */
2172: /* send nzlocal to others, and recv other's nzlocal */
2173: /*--------------------------------------------------*/
2174: if (reuse == MAT_INITIAL_MATRIX){
2175: PetscMalloc2(3*(nsends + nrecvs)+1,MPI_Request,&s_waits3,nsends+1,MPI_Status,&send_status);
2176: s_waits2 = s_waits3 + nsends;
2177: s_waits1 = s_waits2 + nsends;
2178: r_waits1 = s_waits1 + nsends;
2179: r_waits2 = r_waits1 + nrecvs;
2180: r_waits3 = r_waits2 + nrecvs;
2181: } else {
2182: PetscMalloc2(nsends + nrecvs +1,MPI_Request,&s_waits3,nsends+1,MPI_Status,&send_status);
2183: r_waits3 = s_waits3 + nsends;
2184: }
2186: PetscObjectGetNewTag((PetscObject)mat,&tag3);
2187: if (reuse == MAT_INITIAL_MATRIX){
2188: /* get new tags to keep the communication clean */
2189: PetscObjectGetNewTag((PetscObject)mat,&tag1);
2190: PetscObjectGetNewTag((PetscObject)mat,&tag2);
2191: PetscMalloc3(nsends+nrecvs+1,PetscInt,&sbuf_nz,nrecvs,PetscInt*,&rbuf_j,nrecvs,PetscScalar*,&rbuf_a);
2192: rbuf_nz = sbuf_nz + nsends;
2193:
2194: /* post receives of other's nzlocal */
2195: for (i=0; i<nrecvs; i++){
2196: MPI_Irecv(rbuf_nz+i,1,MPIU_INT,MPI_ANY_SOURCE,tag1,comm,r_waits1+i);
2197: }
2198: /* send nzlocal to others */
2199: for (i=0; i<nsends; i++){
2200: sbuf_nz[i] = nzlocal;
2201: MPI_Isend(sbuf_nz+i,1,MPIU_INT,send_rank[i],tag1,comm,s_waits1+i);
2202: }
2203: /* wait on receives of nzlocal; allocate space for rbuf_j, rbuf_a */
2204: count = nrecvs;
2205: while (count) {
2206: MPI_Waitany(nrecvs,r_waits1,&imdex,&recv_status);
2207: recv_rank[imdex] = recv_status.MPI_SOURCE;
2208: /* allocate rbuf_a and rbuf_j; then post receives of rbuf_j */
2209: PetscMalloc((rbuf_nz[imdex]+1)*sizeof(PetscScalar),&rbuf_a[imdex]);
2211: i = rowrange[recv_status.MPI_SOURCE+1] - rowrange[recv_status.MPI_SOURCE]; /* number of expected mat->i */
2212: rbuf_nz[imdex] += i + 2;
2213: PetscMalloc(rbuf_nz[imdex]*sizeof(PetscInt),&rbuf_j[imdex]);
2214: MPI_Irecv(rbuf_j[imdex],rbuf_nz[imdex],MPIU_INT,recv_status.MPI_SOURCE,tag2,comm,r_waits2+imdex);
2215: count--;
2216: }
2217: /* wait on sends of nzlocal */
2218: if (nsends) {MPI_Waitall(nsends,s_waits1,send_status);}
2219: /* send mat->i,j to others, and recv from other's */
2220: /*------------------------------------------------*/
2221: for (i=0; i<nsends; i++){
2222: j = nzlocal + rowrange[rank+1] - rowrange[rank] + 1;
2223: MPI_Isend(sbuf_j,j,MPIU_INT,send_rank[i],tag2,comm,s_waits2+i);
2224: }
2225: /* wait on receives of mat->i,j */
2226: /*------------------------------*/
2227: count = nrecvs;
2228: while (count) {
2229: MPI_Waitany(nrecvs,r_waits2,&imdex,&recv_status);
2230: if (recv_rank[imdex] != recv_status.MPI_SOURCE) SETERRQ2(1, "recv_rank %d != MPI_SOURCE %d",recv_rank[imdex],recv_status.MPI_SOURCE);
2231: count--;
2232: }
2233: /* wait on sends of mat->i,j */
2234: /*---------------------------*/
2235: if (nsends) {
2236: MPI_Waitall(nsends,s_waits2,send_status);
2237: }
2238: } /* endof if (reuse == MAT_INITIAL_MATRIX) */
2240: /* post receives, send and receive mat->a */
2241: /*----------------------------------------*/
2242: for (imdex=0; imdex<nrecvs; imdex++) {
2243: MPI_Irecv(rbuf_a[imdex],rbuf_nz[imdex],MPIU_SCALAR,recv_rank[imdex],tag3,comm,r_waits3+imdex);
2244: }
2245: for (i=0; i<nsends; i++){
2246: MPI_Isend(sbuf_a,nzlocal,MPIU_SCALAR,send_rank[i],tag3,comm,s_waits3+i);
2247: }
2248: count = nrecvs;
2249: while (count) {
2250: MPI_Waitany(nrecvs,r_waits3,&imdex,&recv_status);
2251: if (recv_rank[imdex] != recv_status.MPI_SOURCE) SETERRQ2(1, "recv_rank %d != MPI_SOURCE %d",recv_rank[imdex],recv_status.MPI_SOURCE);
2252: count--;
2253: }
2254: if (nsends) {
2255: MPI_Waitall(nsends,s_waits3,send_status);
2256: }
2258: PetscFree2(s_waits3,send_status);
2259:
2260: /* create redundant matrix */
2261: /*-------------------------*/
2262: if (reuse == MAT_INITIAL_MATRIX){
2263: /* compute rownz_max for preallocation */
2264: for (imdex=0; imdex<nrecvs; imdex++){
2265: j = rowrange[recv_rank[imdex]+1] - rowrange[recv_rank[imdex]];
2266: rptr = rbuf_j[imdex];
2267: for (i=0; i<j; i++){
2268: ncols = rptr[i+1] - rptr[i];
2269: if (rownz_max < ncols) rownz_max = ncols;
2270: }
2271: }
2272:
2273: MatCreate(subcomm,&C);
2274: MatSetSizes(C,mlocal_sub,mlocal_sub,PETSC_DECIDE,PETSC_DECIDE);
2275: MatSetFromOptions(C);
2276: MatSeqAIJSetPreallocation(C,rownz_max,PETSC_NULL);
2277: MatMPIAIJSetPreallocation(C,rownz_max,PETSC_NULL,rownz_max,PETSC_NULL);
2278: } else {
2279: C = *matredundant;
2280: }
2282: /* insert local matrix entries */
2283: rptr = sbuf_j;
2284: cols = sbuf_j + rend-rstart + 1;
2285: vals = sbuf_a;
2286: for (i=0; i<rend-rstart; i++){
2287: row = i + rstart;
2288: ncols = rptr[i+1] - rptr[i];
2289: MatSetValues(C,1,&row,ncols,cols,vals,INSERT_VALUES);
2290: vals += ncols;
2291: cols += ncols;
2292: }
2293: /* insert received matrix entries */
2294: for (imdex=0; imdex<nrecvs; imdex++){
2295: rstart = rowrange[recv_rank[imdex]];
2296: rend = rowrange[recv_rank[imdex]+1];
2297: rptr = rbuf_j[imdex];
2298: cols = rbuf_j[imdex] + rend-rstart + 1;
2299: vals = rbuf_a[imdex];
2300: for (i=0; i<rend-rstart; i++){
2301: row = i + rstart;
2302: ncols = rptr[i+1] - rptr[i];
2303: MatSetValues(C,1,&row,ncols,cols,vals,INSERT_VALUES);
2304: vals += ncols;
2305: cols += ncols;
2306: }
2307: }
2308: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2309: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2310: MatGetSize(C,&M,&N);
2311: if (M != mat->rmap->N || N != mat->cmap->N) SETERRQ2(PETSC_ERR_ARG_INCOMP,"redundant mat size %d != input mat size %d",M,mat->rmap->N);
2312: if (reuse == MAT_INITIAL_MATRIX){
2313: PetscContainer container;
2314: *matredundant = C;
2315: /* create a supporting struct and attach it to C for reuse */
2316: PetscNewLog(C,Mat_Redundant,&redund);
2317: PetscContainerCreate(PETSC_COMM_SELF,&container);
2318: PetscContainerSetPointer(container,redund);
2319: PetscObjectCompose((PetscObject)C,"Mat_Redundant",(PetscObject)container);
2320: PetscContainerSetUserDestroy(container,PetscContainerDestroy_MatRedundant);
2321:
2322: redund->nzlocal = nzlocal;
2323: redund->nsends = nsends;
2324: redund->nrecvs = nrecvs;
2325: redund->send_rank = send_rank;
2326: redund->sbuf_nz = sbuf_nz;
2327: redund->sbuf_j = sbuf_j;
2328: redund->sbuf_a = sbuf_a;
2329: redund->rbuf_j = rbuf_j;
2330: redund->rbuf_a = rbuf_a;
2332: redund->MatDestroy = C->ops->destroy;
2333: C->ops->destroy = MatDestroy_MatRedundant;
2334: }
2335: return(0);
2336: }
2340: PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2341: {
2342: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2344: PetscInt i,*idxb = 0;
2345: PetscScalar *va,*vb;
2346: Vec vtmp;
2349: MatGetRowMaxAbs(a->A,v,idx);
2350: VecGetArray(v,&va);
2351: if (idx) {
2352: for (i=0; i<A->rmap->n; i++) {
2353: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2354: }
2355: }
2357: VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
2358: if (idx) {
2359: PetscMalloc(A->rmap->n*sizeof(PetscInt),&idxb);
2360: }
2361: MatGetRowMaxAbs(a->B,vtmp,idxb);
2362: VecGetArray(vtmp,&vb);
2364: for (i=0; i<A->rmap->n; i++){
2365: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2366: va[i] = vb[i];
2367: if (idx) idx[i] = a->garray[idxb[i]];
2368: }
2369: }
2371: VecRestoreArray(v,&va);
2372: VecRestoreArray(vtmp,&vb);
2373: if (idxb) {
2374: PetscFree(idxb);
2375: }
2376: VecDestroy(vtmp);
2377: return(0);
2378: }
2382: PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2383: {
2384: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2386: PetscInt i,*idxb = 0;
2387: PetscScalar *va,*vb;
2388: Vec vtmp;
2391: MatGetRowMinAbs(a->A,v,idx);
2392: VecGetArray(v,&va);
2393: if (idx) {
2394: for (i=0; i<A->cmap->n; i++) {
2395: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2396: }
2397: }
2399: VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
2400: if (idx) {
2401: PetscMalloc(A->rmap->n*sizeof(PetscInt),&idxb);
2402: }
2403: MatGetRowMinAbs(a->B,vtmp,idxb);
2404: VecGetArray(vtmp,&vb);
2406: for (i=0; i<A->rmap->n; i++){
2407: if (PetscAbsScalar(va[i]) > PetscAbsScalar(vb[i])) {
2408: va[i] = vb[i];
2409: if (idx) idx[i] = a->garray[idxb[i]];
2410: }
2411: }
2413: VecRestoreArray(v,&va);
2414: VecRestoreArray(vtmp,&vb);
2415: if (idxb) {
2416: PetscFree(idxb);
2417: }
2418: VecDestroy(vtmp);
2419: return(0);
2420: }
2424: PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2425: {
2426: Mat_MPIAIJ *mat = (Mat_MPIAIJ *) A->data;
2427: PetscInt n = A->rmap->n;
2428: PetscInt cstart = A->cmap->rstart;
2429: PetscInt *cmap = mat->garray;
2430: PetscInt *diagIdx, *offdiagIdx;
2431: Vec diagV, offdiagV;
2432: PetscScalar *a, *diagA, *offdiagA;
2433: PetscInt r;
2437: PetscMalloc2(n,PetscInt,&diagIdx,n,PetscInt,&offdiagIdx);
2438: VecCreateSeq(((PetscObject)A)->comm, n, &diagV);
2439: VecCreateSeq(((PetscObject)A)->comm, n, &offdiagV);
2440: MatGetRowMin(mat->A, diagV, diagIdx);
2441: MatGetRowMin(mat->B, offdiagV, offdiagIdx);
2442: VecGetArray(v, &a);
2443: VecGetArray(diagV, &diagA);
2444: VecGetArray(offdiagV, &offdiagA);
2445: for(r = 0; r < n; ++r) {
2446: if (PetscAbsScalar(diagA[r]) <= PetscAbsScalar(offdiagA[r])) {
2447: a[r] = diagA[r];
2448: idx[r] = cstart + diagIdx[r];
2449: } else {
2450: a[r] = offdiagA[r];
2451: idx[r] = cmap[offdiagIdx[r]];
2452: }
2453: }
2454: VecRestoreArray(v, &a);
2455: VecRestoreArray(diagV, &diagA);
2456: VecRestoreArray(offdiagV, &offdiagA);
2457: VecDestroy(diagV);
2458: VecDestroy(offdiagV);
2459: PetscFree2(diagIdx, offdiagIdx);
2460: return(0);
2461: }
2465: PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2466: {
2467: Mat_MPIAIJ *mat = (Mat_MPIAIJ *) A->data;
2468: PetscInt n = A->rmap->n;
2469: PetscInt cstart = A->cmap->rstart;
2470: PetscInt *cmap = mat->garray;
2471: PetscInt *diagIdx, *offdiagIdx;
2472: Vec diagV, offdiagV;
2473: PetscScalar *a, *diagA, *offdiagA;
2474: PetscInt r;
2478: PetscMalloc2(n,PetscInt,&diagIdx,n,PetscInt,&offdiagIdx);
2479: VecCreateSeq(((PetscObject)A)->comm, n, &diagV);
2480: VecCreateSeq(((PetscObject)A)->comm, n, &offdiagV);
2481: MatGetRowMax(mat->A, diagV, diagIdx);
2482: MatGetRowMax(mat->B, offdiagV, offdiagIdx);
2483: VecGetArray(v, &a);
2484: VecGetArray(diagV, &diagA);
2485: VecGetArray(offdiagV, &offdiagA);
2486: for(r = 0; r < n; ++r) {
2487: if (PetscAbsScalar(diagA[r]) >= PetscAbsScalar(offdiagA[r])) {
2488: a[r] = diagA[r];
2489: idx[r] = cstart + diagIdx[r];
2490: } else {
2491: a[r] = offdiagA[r];
2492: idx[r] = cmap[offdiagIdx[r]];
2493: }
2494: }
2495: VecRestoreArray(v, &a);
2496: VecRestoreArray(diagV, &diagA);
2497: VecRestoreArray(offdiagV, &offdiagA);
2498: VecDestroy(diagV);
2499: VecDestroy(offdiagV);
2500: PetscFree2(diagIdx, offdiagIdx);
2501: return(0);
2502: }
2506: PetscErrorCode MatGetSeqNonzerostructure_MPIAIJ(Mat mat,Mat *newmat[])
2507: {
2511: MatGetSubMatrix_MPIAIJ_All(mat,MAT_DO_NOT_GET_VALUES,MAT_INITIAL_MATRIX,newmat);
2512: return(0);
2513: }
2515: /* -------------------------------------------------------------------*/
2516: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2517: MatGetRow_MPIAIJ,
2518: MatRestoreRow_MPIAIJ,
2519: MatMult_MPIAIJ,
2520: /* 4*/ MatMultAdd_MPIAIJ,
2521: MatMultTranspose_MPIAIJ,
2522: MatMultTransposeAdd_MPIAIJ,
2523: #ifdef PETSC_HAVE_PBGL
2524: MatSolve_MPIAIJ,
2525: #else
2526: 0,
2527: #endif
2528: 0,
2529: 0,
2530: /*10*/ 0,
2531: 0,
2532: 0,
2533: MatRelax_MPIAIJ,
2534: MatTranspose_MPIAIJ,
2535: /*15*/ MatGetInfo_MPIAIJ,
2536: MatEqual_MPIAIJ,
2537: MatGetDiagonal_MPIAIJ,
2538: MatDiagonalScale_MPIAIJ,
2539: MatNorm_MPIAIJ,
2540: /*20*/ MatAssemblyBegin_MPIAIJ,
2541: MatAssemblyEnd_MPIAIJ,
2542: 0,
2543: MatSetOption_MPIAIJ,
2544: MatZeroEntries_MPIAIJ,
2545: /*25*/ MatZeroRows_MPIAIJ,
2546: 0,
2547: #ifdef PETSC_HAVE_PBGL
2548: 0,
2549: #else
2550: 0,
2551: #endif
2552: 0,
2553: 0,
2554: /*30*/ MatSetUpPreallocation_MPIAIJ,
2555: #ifdef PETSC_HAVE_PBGL
2556: 0,
2557: #else
2558: 0,
2559: #endif
2560: 0,
2561: 0,
2562: 0,
2563: /*35*/ MatDuplicate_MPIAIJ,
2564: 0,
2565: 0,
2566: 0,
2567: 0,
2568: /*40*/ MatAXPY_MPIAIJ,
2569: MatGetSubMatrices_MPIAIJ,
2570: MatIncreaseOverlap_MPIAIJ,
2571: MatGetValues_MPIAIJ,
2572: MatCopy_MPIAIJ,
2573: /*45*/ MatGetRowMax_MPIAIJ,
2574: MatScale_MPIAIJ,
2575: 0,
2576: 0,
2577: 0,
2578: /*50*/ MatSetBlockSize_MPIAIJ,
2579: 0,
2580: 0,
2581: 0,
2582: 0,
2583: /*55*/ MatFDColoringCreate_MPIAIJ,
2584: 0,
2585: MatSetUnfactored_MPIAIJ,
2586: MatPermute_MPIAIJ,
2587: 0,
2588: /*60*/ MatGetSubMatrix_MPIAIJ,
2589: MatDestroy_MPIAIJ,
2590: MatView_MPIAIJ,
2591: 0,
2592: 0,
2593: /*65*/ 0,
2594: 0,
2595: 0,
2596: 0,
2597: 0,
2598: /*70*/ MatGetRowMaxAbs_MPIAIJ,
2599: MatGetRowMinAbs_MPIAIJ,
2600: 0,
2601: MatSetColoring_MPIAIJ,
2602: #if defined(PETSC_HAVE_ADIC)
2603: MatSetValuesAdic_MPIAIJ,
2604: #else
2605: 0,
2606: #endif
2607: MatSetValuesAdifor_MPIAIJ,
2608: /*75*/ 0,
2609: 0,
2610: 0,
2611: 0,
2612: 0,
2613: /*80*/ 0,
2614: 0,
2615: 0,
2616: /*84*/ MatLoad_MPIAIJ,
2617: 0,
2618: 0,
2619: 0,
2620: 0,
2621: 0,
2622: /*90*/ MatMatMult_MPIAIJ_MPIAIJ,
2623: MatMatMultSymbolic_MPIAIJ_MPIAIJ,
2624: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2625: MatPtAP_Basic,
2626: MatPtAPSymbolic_MPIAIJ,
2627: /*95*/ MatPtAPNumeric_MPIAIJ,
2628: 0,
2629: 0,
2630: 0,
2631: 0,
2632: /*100*/0,
2633: MatPtAPSymbolic_MPIAIJ_MPIAIJ,
2634: MatPtAPNumeric_MPIAIJ_MPIAIJ,
2635: MatConjugate_MPIAIJ,
2636: 0,
2637: /*105*/MatSetValuesRow_MPIAIJ,
2638: MatRealPart_MPIAIJ,
2639: MatImaginaryPart_MPIAIJ,
2640: 0,
2641: 0,
2642: /*110*/0,
2643: MatGetRedundantMatrix_MPIAIJ,
2644: MatGetRowMin_MPIAIJ,
2645: 0,
2646: 0,
2647: /*115*/MatGetSeqNonzerostructure_MPIAIJ};
2649: /* ----------------------------------------------------------------------------------------*/
2654: PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2655: {
2656: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2660: MatStoreValues(aij->A);
2661: MatStoreValues(aij->B);
2662: return(0);
2663: }
2669: PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2670: {
2671: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)mat->data;
2675: MatRetrieveValues(aij->A);
2676: MatRetrieveValues(aij->B);
2677: return(0);
2678: }
2681: #include petscpc.h
2685: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2686: {
2687: Mat_MPIAIJ *b;
2689: PetscInt i;
2692: B->preallocated = PETSC_TRUE;
2693: if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
2694: if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
2695: if (d_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
2696: if (o_nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);
2698: PetscMapSetBlockSize(B->rmap,1);
2699: PetscMapSetBlockSize(B->cmap,1);
2700: PetscMapSetUp(B->rmap);
2701: PetscMapSetUp(B->cmap);
2702: if (d_nnz) {
2703: for (i=0; i<B->rmap->n; i++) {
2704: 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]);
2705: }
2706: }
2707: if (o_nnz) {
2708: for (i=0; i<B->rmap->n; i++) {
2709: 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]);
2710: }
2711: }
2712: b = (Mat_MPIAIJ*)B->data;
2714: /* Explicitly create 2 MATSEQAIJ matrices. */
2715: MatCreate(PETSC_COMM_SELF,&b->A);
2716: MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
2717: MatSetType(b->A,MATSEQAIJ);
2718: PetscLogObjectParent(B,b->A);
2719: MatCreate(PETSC_COMM_SELF,&b->B);
2720: MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
2721: MatSetType(b->B,MATSEQAIJ);
2722: PetscLogObjectParent(B,b->B);
2724: MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
2725: MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
2727: return(0);
2728: }
2733: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2734: {
2735: Mat mat;
2736: Mat_MPIAIJ *a,*oldmat = (Mat_MPIAIJ*)matin->data;
2740: *newmat = 0;
2741: MatCreate(((PetscObject)matin)->comm,&mat);
2742: MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
2743: MatSetType(mat,((PetscObject)matin)->type_name);
2744: PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
2745: a = (Mat_MPIAIJ*)mat->data;
2746:
2747: mat->factor = matin->factor;
2748: mat->rmap->bs = matin->rmap->bs;
2749: mat->assembled = PETSC_TRUE;
2750: mat->insertmode = NOT_SET_VALUES;
2751: mat->preallocated = PETSC_TRUE;
2753: a->size = oldmat->size;
2754: a->rank = oldmat->rank;
2755: a->donotstash = oldmat->donotstash;
2756: a->roworiented = oldmat->roworiented;
2757: a->rowindices = 0;
2758: a->rowvalues = 0;
2759: a->getrowactive = PETSC_FALSE;
2761: PetscMapCopy(((PetscObject)mat)->comm,matin->rmap,mat->rmap);
2762: PetscMapCopy(((PetscObject)mat)->comm,matin->cmap,mat->cmap);
2764: MatStashCreate_Private(((PetscObject)matin)->comm,1,&mat->stash);
2765: if (oldmat->colmap) {
2766: #if defined (PETSC_USE_CTABLE)
2767: PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2768: #else
2769: PetscMalloc((mat->cmap->N)*sizeof(PetscInt),&a->colmap);
2770: PetscLogObjectMemory(mat,(mat->cmap->N)*sizeof(PetscInt));
2771: PetscMemcpy(a->colmap,oldmat->colmap,(mat->cmap->N)*sizeof(PetscInt));
2772: #endif
2773: } else a->colmap = 0;
2774: if (oldmat->garray) {
2775: PetscInt len;
2776: len = oldmat->B->cmap->n;
2777: PetscMalloc((len+1)*sizeof(PetscInt),&a->garray);
2778: PetscLogObjectMemory(mat,len*sizeof(PetscInt));
2779: if (len) { PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt)); }
2780: } else a->garray = 0;
2781:
2782: VecDuplicate(oldmat->lvec,&a->lvec);
2783: PetscLogObjectParent(mat,a->lvec);
2784: VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2785: PetscLogObjectParent(mat,a->Mvctx);
2786: MatDuplicate(oldmat->A,cpvalues,&a->A);
2787: PetscLogObjectParent(mat,a->A);
2788: MatDuplicate(oldmat->B,cpvalues,&a->B);
2789: PetscLogObjectParent(mat,a->B);
2790: PetscFListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
2791: *newmat = mat;
2792: return(0);
2793: }
2795: #include petscsys.h
2799: PetscErrorCode MatLoad_MPIAIJ(PetscViewer viewer, const MatType type,Mat *newmat)
2800: {
2801: Mat A;
2802: PetscScalar *vals,*svals;
2803: MPI_Comm comm = ((PetscObject)viewer)->comm;
2804: MPI_Status status;
2806: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag,maxnz;
2807: PetscInt i,nz,j,rstart,rend,mmax;
2808: PetscInt header[4],*rowlengths = 0,M,N,m,*cols;
2809: PetscInt *ourlens = PETSC_NULL,*procsnz = PETSC_NULL,*offlens = PETSC_NULL,jj,*mycols,*smycols;
2810: PetscInt cend,cstart,n,*rowners;
2811: int fd;
2814: MPI_Comm_size(comm,&size);
2815: MPI_Comm_rank(comm,&rank);
2816: if (!rank) {
2817: PetscViewerBinaryGetDescriptor(viewer,&fd);
2818: PetscBinaryRead(fd,(char *)header,4,PETSC_INT);
2819: if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2820: }
2822: MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2823: M = header[1]; N = header[2];
2824: /* determine ownership of all rows */
2825: m = M/size + ((M % size) > rank);
2826: PetscMalloc((size+1)*sizeof(PetscInt),&rowners);
2827: MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
2829: /* First process needs enough room for process with most rows */
2830: if (!rank) {
2831: mmax = rowners[1];
2832: for (i=2; i<size; i++) {
2833: mmax = PetscMax(mmax,rowners[i]);
2834: }
2835: } else mmax = m;
2837: rowners[0] = 0;
2838: for (i=2; i<=size; i++) {
2839: rowners[i] += rowners[i-1];
2840: }
2841: rstart = rowners[rank];
2842: rend = rowners[rank+1];
2844: /* distribute row lengths to all processors */
2845: PetscMalloc2(mmax,PetscInt,&ourlens,mmax,PetscInt,&offlens);
2846: if (!rank) {
2847: PetscBinaryRead(fd,ourlens,m,PETSC_INT);
2848: PetscMalloc(m*sizeof(PetscInt),&rowlengths);
2849: PetscMalloc(size*sizeof(PetscInt),&procsnz);
2850: PetscMemzero(procsnz,size*sizeof(PetscInt));
2851: for (j=0; j<m; j++) {
2852: procsnz[0] += ourlens[j];
2853: }
2854: for (i=1; i<size; i++) {
2855: PetscBinaryRead(fd,rowlengths,rowners[i+1]-rowners[i],PETSC_INT);
2856: /* calculate the number of nonzeros on each processor */
2857: for (j=0; j<rowners[i+1]-rowners[i]; j++) {
2858: procsnz[i] += rowlengths[j];
2859: }
2860: MPI_Send(rowlengths,rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
2861: }
2862: PetscFree(rowlengths);
2863: } else {
2864: MPI_Recv(ourlens,m,MPIU_INT,0,tag,comm,&status);
2865: }
2867: if (!rank) {
2868: /* determine max buffer needed and allocate it */
2869: maxnz = 0;
2870: for (i=0; i<size; i++) {
2871: maxnz = PetscMax(maxnz,procsnz[i]);
2872: }
2873: PetscMalloc(maxnz*sizeof(PetscInt),&cols);
2875: /* read in my part of the matrix column indices */
2876: nz = procsnz[0];
2877: PetscMalloc(nz*sizeof(PetscInt),&mycols);
2878: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
2880: /* read in every one elses and ship off */
2881: for (i=1; i<size; i++) {
2882: nz = procsnz[i];
2883: PetscBinaryRead(fd,cols,nz,PETSC_INT);
2884: MPI_Send(cols,nz,MPIU_INT,i,tag,comm);
2885: }
2886: PetscFree(cols);
2887: } else {
2888: /* determine buffer space needed for message */
2889: nz = 0;
2890: for (i=0; i<m; i++) {
2891: nz += ourlens[i];
2892: }
2893: PetscMalloc(nz*sizeof(PetscInt),&mycols);
2895: /* receive message of column indices*/
2896: MPI_Recv(mycols,nz,MPIU_INT,0,tag,comm,&status);
2897: MPI_Get_count(&status,MPIU_INT,&maxnz);
2898: if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2899: }
2901: /* determine column ownership if matrix is not square */
2902: if (N != M) {
2903: n = N/size + ((N % size) > rank);
2904: MPI_Scan(&n,&cend,1,MPIU_INT,MPI_SUM,comm);
2905: cstart = cend - n;
2906: } else {
2907: cstart = rstart;
2908: cend = rend;
2909: n = cend - cstart;
2910: }
2912: /* loop over local rows, determining number of off diagonal entries */
2913: PetscMemzero(offlens,m*sizeof(PetscInt));
2914: jj = 0;
2915: for (i=0; i<m; i++) {
2916: for (j=0; j<ourlens[i]; j++) {
2917: if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++;
2918: jj++;
2919: }
2920: }
2922: /* create our matrix */
2923: for (i=0; i<m; i++) {
2924: ourlens[i] -= offlens[i];
2925: }
2926: MatCreate(comm,&A);
2927: MatSetSizes(A,m,n,M,N);
2928: MatSetType(A,type);
2929: MatMPIAIJSetPreallocation(A,0,ourlens,0,offlens);
2931: for (i=0; i<m; i++) {
2932: ourlens[i] += offlens[i];
2933: }
2935: if (!rank) {
2936: PetscMalloc((maxnz+1)*sizeof(PetscScalar),&vals);
2938: /* read in my part of the matrix numerical values */
2939: nz = procsnz[0];
2940: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2941:
2942: /* insert into matrix */
2943: jj = rstart;
2944: smycols = mycols;
2945: svals = vals;
2946: for (i=0; i<m; i++) {
2947: MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2948: smycols += ourlens[i];
2949: svals += ourlens[i];
2950: jj++;
2951: }
2953: /* read in other processors and ship out */
2954: for (i=1; i<size; i++) {
2955: nz = procsnz[i];
2956: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
2957: MPI_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)A)->tag,comm);
2958: }
2959: PetscFree(procsnz);
2960: } else {
2961: /* receive numeric values */
2962: PetscMalloc((nz+1)*sizeof(PetscScalar),&vals);
2964: /* receive message of values*/
2965: MPI_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)A)->tag,comm,&status);
2966: MPI_Get_count(&status,MPIU_SCALAR,&maxnz);
2967: if (maxnz != nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"something is wrong with file");
2969: /* insert into matrix */
2970: jj = rstart;
2971: smycols = mycols;
2972: svals = vals;
2973: for (i=0; i<m; i++) {
2974: MatSetValues_MPIAIJ(A,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
2975: smycols += ourlens[i];
2976: svals += ourlens[i];
2977: jj++;
2978: }
2979: }
2980: PetscFree2(ourlens,offlens);
2981: PetscFree(vals);
2982: PetscFree(mycols);
2983: PetscFree(rowners);
2985: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
2986: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
2987: *newmat = A;
2988: return(0);
2989: }
2993: /*
2994: Not great since it makes two copies of the submatrix, first an SeqAIJ
2995: in local and then by concatenating the local matrices the end result.
2996: Writing it directly would be much like MatGetSubMatrices_MPIAIJ()
2997: */
2998: PetscErrorCode MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
2999: {
3001: PetscMPIInt rank,size;
3002: PetscInt i,m,n,rstart,row,rend,nz,*cwork,j;
3003: PetscInt *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal;
3004: Mat *local,M,Mreuse;
3005: MatScalar *vwork,*aa;
3006: MPI_Comm comm = ((PetscObject)mat)->comm;
3007: Mat_SeqAIJ *aij;
3011: MPI_Comm_rank(comm,&rank);
3012: MPI_Comm_size(comm,&size);
3014: if (call == MAT_REUSE_MATRIX) {
3015: PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject *)&Mreuse);
3016: if (!Mreuse) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
3017: local = &Mreuse;
3018: MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&local);
3019: } else {
3020: MatGetSubMatrices(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&local);
3021: Mreuse = *local;
3022: PetscFree(local);
3023: }
3025: /*
3026: m - number of local rows
3027: n - number of columns (same on all processors)
3028: rstart - first row in new global matrix generated
3029: */
3030: MatGetSize(Mreuse,&m,&n);
3031: if (call == MAT_INITIAL_MATRIX) {
3032: aij = (Mat_SeqAIJ*)(Mreuse)->data;
3033: ii = aij->i;
3034: jj = aij->j;
3036: /*
3037: Determine the number of non-zeros in the diagonal and off-diagonal
3038: portions of the matrix in order to do correct preallocation
3039: */
3041: /* first get start and end of "diagonal" columns */
3042: if (csize == PETSC_DECIDE) {
3043: ISGetSize(isrow,&mglobal);
3044: if (mglobal == n) { /* square matrix */
3045: nlocal = m;
3046: } else {
3047: nlocal = n/size + ((n % size) > rank);
3048: }
3049: } else {
3050: nlocal = csize;
3051: }
3052: MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
3053: rstart = rend - nlocal;
3054: if (rank == size - 1 && rend != n) {
3055: SETERRQ2(PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);
3056: }
3058: /* next, compute all the lengths */
3059: PetscMalloc((2*m+1)*sizeof(PetscInt),&dlens);
3060: olens = dlens + m;
3061: for (i=0; i<m; i++) {
3062: jend = ii[i+1] - ii[i];
3063: olen = 0;
3064: dlen = 0;
3065: for (j=0; j<jend; j++) {
3066: if (*jj < rstart || *jj >= rend) olen++;
3067: else dlen++;
3068: jj++;
3069: }
3070: olens[i] = olen;
3071: dlens[i] = dlen;
3072: }
3073: MatCreate(comm,&M);
3074: MatSetSizes(M,m,nlocal,PETSC_DECIDE,n);
3075: MatSetType(M,((PetscObject)mat)->type_name);
3076: MatMPIAIJSetPreallocation(M,0,dlens,0,olens);
3077: PetscFree(dlens);
3078: } else {
3079: PetscInt ml,nl;
3081: M = *newmat;
3082: MatGetLocalSize(M,&ml,&nl);
3083: if (ml != m) SETERRQ(PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
3084: MatZeroEntries(M);
3085: /*
3086: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3087: rather than the slower MatSetValues().
3088: */
3089: M->was_assembled = PETSC_TRUE;
3090: M->assembled = PETSC_FALSE;
3091: }
3092: MatGetOwnershipRange(M,&rstart,&rend);
3093: aij = (Mat_SeqAIJ*)(Mreuse)->data;
3094: ii = aij->i;
3095: jj = aij->j;
3096: aa = aij->a;
3097: for (i=0; i<m; i++) {
3098: row = rstart + i;
3099: nz = ii[i+1] - ii[i];
3100: cwork = jj; jj += nz;
3101: vwork = aa; aa += nz;
3102: MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
3103: }
3105: MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
3106: MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
3107: *newmat = M;
3109: /* save submatrix used in processor for next request */
3110: if (call == MAT_INITIAL_MATRIX) {
3111: PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
3112: PetscObjectDereference((PetscObject)Mreuse);
3113: }
3115: return(0);
3116: }
3121: PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3122: {
3123: PetscInt m,cstart, cend,j,nnz,i,d;
3124: PetscInt *d_nnz,*o_nnz,nnz_max = 0,rstart,ii;
3125: const PetscInt *JJ;
3126: PetscScalar *values;
3130: if (Ii[0]) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Ii[0] must be 0 it is %D",Ii[0]);
3132: PetscMapSetBlockSize(B->rmap,1);
3133: PetscMapSetBlockSize(B->cmap,1);
3134: PetscMapSetUp(B->rmap);
3135: PetscMapSetUp(B->cmap);
3136: m = B->rmap->n;
3137: cstart = B->cmap->rstart;
3138: cend = B->cmap->rend;
3139: rstart = B->rmap->rstart;
3141: PetscMalloc((2*m+1)*sizeof(PetscInt),&d_nnz);
3142: o_nnz = d_nnz + m;
3144: #if defined(PETSC_USE_DEBUGGING)
3145: for (i=0; i<m; i++) {
3146: nnz = Ii[i+1]- Ii[i];
3147: JJ = J + Ii[i];
3148: if (nnz < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative %D number of columns",i,nnz);
3149: if (nnz && (JJ[0] < 0)) SETERRRQ1(PETSC_ERR_ARG_WRONGSTATE,"Row %D starts with negative column index",i,j);
3150: if (nnz && (JJ[nnz-1] >= B->cmap->N) SETERRRQ3(PETSC_ERR_ARG_WRONGSTATE,"Row %D ends with too large a column index %D (max allowed %D)",i,JJ[nnz-1],B->cmap->N);
3151: for (j=1; j<nnz; j++) {
3152: if (JJ[i] <= JJ[i-1]) SETERRRQ(PETSC_ERR_ARG_WRONGSTATE,"Row %D has unsorted column index at %D location in column indices",i,j);
3153: }
3154: }
3155: #endif
3157: for (i=0; i<m; i++) {
3158: nnz = Ii[i+1]- Ii[i];
3159: JJ = J + Ii[i];
3160: nnz_max = PetscMax(nnz_max,nnz);
3161: for (j=0; j<nnz; j++) {
3162: if (*JJ >= cstart) break;
3163: JJ++;
3164: }
3165: d = 0;
3166: for (; j<nnz; j++) {
3167: if (*JJ++ >= cend) break;
3168: d++;
3169: }
3170: d_nnz[i] = d;
3171: o_nnz[i] = nnz - d;
3172: }
3173: MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
3174: PetscFree(d_nnz);
3176: if (v) values = (PetscScalar*)v;
3177: else {
3178: PetscMalloc((nnz_max+1)*sizeof(PetscScalar),&values);
3179: PetscMemzero(values,nnz_max*sizeof(PetscScalar));
3180: }
3182: for (i=0; i<m; i++) {
3183: ii = i + rstart;
3184: nnz = Ii[i+1]- Ii[i];
3185: MatSetValues_MPIAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
3186: }
3187: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3188: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3190: if (!v) {
3191: PetscFree(values);
3192: }
3193: return(0);
3194: }
3199: /*@
3200: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
3201: (the default parallel PETSc format).
3203: Collective on MPI_Comm
3205: Input Parameters:
3206: + B - the matrix
3207: . i - the indices into j for the start of each local row (starts with zero)
3208: . j - the column indices for each local row (starts with zero) these must be sorted for each row
3209: - v - optional values in the matrix
3211: Level: developer
3213: Notes:
3214: The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3215: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3216: called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.
3218: The i and j indices are 0 based, and i indices are indices corresponding to the local j array.
3220: The format which is used for the sparse matrix input, is equivalent to a
3221: row-major ordering.. i.e for the following matrix, the input data expected is
3222: as shown:
3224: 1 0 0
3225: 2 0 3 P0
3226: -------
3227: 4 5 6 P1
3229: Process0 [P0]: rows_owned=[0,1]
3230: i = {0,1,3} [size = nrow+1 = 2+1]
3231: j = {0,0,2} [size = nz = 6]
3232: v = {1,2,3} [size = nz = 6]
3234: Process1 [P1]: rows_owned=[2]
3235: i = {0,3} [size = nrow+1 = 1+1]
3236: j = {0,1,2} [size = nz = 6]
3237: v = {4,5,6} [size = nz = 6]
3239: The column indices for each row MUST be sorted.
3241: .keywords: matrix, aij, compressed row, sparse, parallel
3243: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatCreateMPIAIJ(), MPIAIJ,
3244: MatCreateSeqAIJWithArrays(), MatCreateMPIAIJWithSplitArrays()
3245: @*/
3246: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
3247: {
3248: PetscErrorCode ierr,(*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);
3251: PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",(void (**)(void))&f);
3252: if (f) {
3253: (*f)(B,i,j,v);
3254: }
3255: return(0);
3256: }
3260: /*@C
3261: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in AIJ format
3262: (the default parallel PETSc format). For good matrix assembly performance
3263: the user should preallocate the matrix storage by setting the parameters
3264: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
3265: performance can be increased by more than a factor of 50.
3267: Collective on MPI_Comm
3269: Input Parameters:
3270: + A - the matrix
3271: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
3272: (same value is used for all local rows)
3273: . d_nnz - array containing the number of nonzeros in the various rows of the
3274: DIAGONAL portion of the local submatrix (possibly different for each row)
3275: or PETSC_NULL, if d_nz is used to specify the nonzero structure.
3276: The size of this array is equal to the number of local rows, i.e 'm'.
3277: You must leave room for the diagonal entry even if it is zero.
3278: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
3279: submatrix (same value is used for all local rows).
3280: - o_nnz - array containing the number of nonzeros in the various rows of the
3281: OFF-DIAGONAL portion of the local submatrix (possibly different for
3282: each row) or PETSC_NULL, if o_nz is used to specify the nonzero
3283: structure. The size of this array is equal to the number
3284: of local rows, i.e 'm'.
3286: If the *_nnz parameter is given then the *_nz parameter is ignored
3288: The AIJ format (also called the Yale sparse matrix format or
3289: compressed row storage (CSR)), is fully compatible with standard Fortran 77
3290: storage. The stored row and column indices begin with zero. See the users manual for details.
3292: The parallel matrix is partitioned such that the first m0 rows belong to
3293: process 0, the next m1 rows belong to process 1, the next m2 rows belong
3294: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
3296: The DIAGONAL portion of the local submatrix of a processor can be defined
3297: as the submatrix which is obtained by extraction the part corresponding
3298: to the rows r1-r2 and columns r1-r2 of the global matrix, where r1 is the
3299: first row that belongs to the processor, and r2 is the last row belonging
3300: to the this processor. This is a square mxm matrix. The remaining portion
3301: of the local submatrix (mxN) constitute the OFF-DIAGONAL portion.
3303: If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.
3305: You can call MatGetInfo() to get information on how effective the preallocation was;
3306: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3307: You can also run with the option -info and look for messages with the string
3308: malloc in them to see if additional memory allocation was needed.
3310: Example usage:
3311:
3312: Consider the following 8x8 matrix with 34 non-zero values, that is
3313: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
3314: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
3315: as follows:
3317: .vb
3318: 1 2 0 | 0 3 0 | 0 4
3319: Proc0 0 5 6 | 7 0 0 | 8 0
3320: 9 0 10 | 11 0 0 | 12 0
3321: -------------------------------------
3322: 13 0 14 | 15 16 17 | 0 0
3323: Proc1 0 18 0 | 19 20 21 | 0 0
3324: 0 0 0 | 22 23 0 | 24 0
3325: -------------------------------------
3326: Proc2 25 26 27 | 0 0 28 | 29 0
3327: 30 0 0 | 31 32 33 | 0 34
3328: .ve
3330: This can be represented as a collection of submatrices as:
3332: .vb
3333: A B C
3334: D E F
3335: G H I
3336: .ve
3338: Where the submatrices A,B,C are owned by proc0, D,E,F are
3339: owned by proc1, G,H,I are owned by proc2.
3341: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3342: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3343: The 'M','N' parameters are 8,8, and have the same values on all procs.
3345: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
3346: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
3347: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
3348: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
3349: part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
3350: matrix, ans [DF] as another SeqAIJ matrix.
3352: When d_nz, o_nz parameters are specified, d_nz storage elements are
3353: allocated for every row of the local diagonal submatrix, and o_nz
3354: storage locations are allocated for every row of the OFF-DIAGONAL submat.
3355: One way to choose d_nz and o_nz is to use the max nonzerors per local
3356: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
3357: In this case, the values of d_nz,o_nz are:
3358: .vb
3359: proc0 : dnz = 2, o_nz = 2
3360: proc1 : dnz = 3, o_nz = 2
3361: proc2 : dnz = 1, o_nz = 4
3362: .ve
3363: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
3364: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
3365: for proc3. i.e we are using 12+15+10=37 storage locations to store
3366: 34 values.
3368: When d_nnz, o_nnz parameters are specified, the storage is specified
3369: for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
3370: In the above case the values for d_nnz,o_nnz are:
3371: .vb
3372: proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
3373: proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
3374: proc2: d_nnz = [1,1] and o_nnz = [4,4]
3375: .ve
3376: Here the space allocated is sum of all the above values i.e 34, and
3377: hence pre-allocation is perfect.
3379: Level: intermediate
3381: .keywords: matrix, aij, compressed row, sparse, parallel
3383: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIAIJ(), MatMPIAIJSetPreallocationCSR(),
3384: MPIAIJ, MatGetInfo()
3385: @*/
3386: PetscErrorCode MatMPIAIJSetPreallocation(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
3387: {
3388: PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[]);
3391: PetscObjectQueryFunction((PetscObject)B,"MatMPIAIJSetPreallocation_C",(void (**)(void))&f);
3392: if (f) {
3393: (*f)(B,d_nz,d_nnz,o_nz,o_nnz);
3394: }
3395: return(0);
3396: }
3400: /*@
3401: MatCreateMPIAIJWithArrays - creates a MPI AIJ matrix using arrays that contain in standard
3402: CSR format the local rows.
3404: Collective on MPI_Comm
3406: Input Parameters:
3407: + comm - MPI communicator
3408: . m - number of local rows (Cannot be PETSC_DECIDE)
3409: . n - This value should be the same as the local size used in creating the
3410: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3411: calculated if N is given) For square matrices n is almost always m.
3412: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3413: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3414: . i - row indices
3415: . j - column indices
3416: - a - matrix values
3418: Output Parameter:
3419: . mat - the matrix
3421: Level: intermediate
3423: Notes:
3424: The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3425: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3426: called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.
3428: The i and j indices are 0 based, and i indices are indices corresponding to the local j array.
3430: The format which is used for the sparse matrix input, is equivalent to a
3431: row-major ordering.. i.e for the following matrix, the input data expected is
3432: as shown:
3434: 1 0 0
3435: 2 0 3 P0
3436: -------
3437: 4 5 6 P1
3439: Process0 [P0]: rows_owned=[0,1]
3440: i = {0,1,3} [size = nrow+1 = 2+1]
3441: j = {0,0,2} [size = nz = 6]
3442: v = {1,2,3} [size = nz = 6]
3444: Process1 [P1]: rows_owned=[2]
3445: i = {0,3} [size = nrow+1 = 1+1]
3446: j = {0,1,2} [size = nz = 6]
3447: v = {4,5,6} [size = nz = 6]
3449: .keywords: matrix, aij, compressed row, sparse, parallel
3451: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3452: MPIAIJ, MatCreateMPIAIJ(), MatCreateMPIAIJWithSplitArrays()
3453: @*/
3454: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
3455: {
3459: if (i[0]) {
3460: SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3461: }
3462: if (m < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
3463: MatCreate(comm,mat);
3464: MatSetSizes(*mat,m,n,M,N);
3465: MatSetType(*mat,MATMPIAIJ);
3466: MatMPIAIJSetPreallocationCSR(*mat,i,j,a);
3467: return(0);
3468: }
3472: /*@C
3473: MatCreateMPIAIJ - Creates a sparse parallel matrix in AIJ format
3474: (the default parallel PETSc format). For good matrix assembly performance
3475: the user should preallocate the matrix storage by setting the parameters
3476: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
3477: performance can be increased by more than a factor of 50.
3479: Collective on MPI_Comm
3481: Input Parameters:
3482: + comm - MPI communicator
3483: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
3484: This value should be the same as the local size used in creating the
3485: y vector for the matrix-vector product y = Ax.
3486: . n - This value should be the same as the local size used in creating the
3487: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3488: calculated if N is given) For square matrices n is almost always m.
3489: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3490: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3491: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
3492: (same value is used for all local rows)
3493: . d_nnz - array containing the number of nonzeros in the various rows of the
3494: DIAGONAL portion of the local submatrix (possibly different for each row)
3495: or PETSC_NULL, if d_nz is used to specify the nonzero structure.
3496: The size of this array is equal to the number of local rows, i.e 'm'.
3497: You must leave room for the diagonal entry even if it is zero.
3498: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
3499: submatrix (same value is used for all local rows).
3500: - o_nnz - array containing the number of nonzeros in the various rows of the
3501: OFF-DIAGONAL portion of the local submatrix (possibly different for
3502: each row) or PETSC_NULL, if o_nz is used to specify the nonzero
3503: structure. The size of this array is equal to the number
3504: of local rows, i.e 'm'.
3506: Output Parameter:
3507: . A - the matrix
3509: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3510: MatXXXXSetPreallocation() paradgm instead of this routine directly. This is definitely
3511: true if you plan to use the external direct solvers such as SuperLU, MUMPS or Spooles.
3512: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3514: Notes:
3515: If the *_nnz parameter is given then the *_nz parameter is ignored
3517: m,n,M,N parameters specify the size of the matrix, and its partitioning across
3518: processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate
3519: storage requirements for this matrix.
3521: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one
3522: processor than it must be used on all processors that share the object for
3523: that argument.
3525: The user MUST specify either the local or global matrix dimensions
3526: (possibly both).
3528: The parallel matrix is partitioned across processors such that the
3529: first m0 rows belong to process 0, the next m1 rows belong to
3530: process 1, the next m2 rows belong to process 2 etc.. where
3531: m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
3532: values corresponding to [m x N] submatrix.
3534: The columns are logically partitioned with the n0 columns belonging
3535: to 0th partition, the next n1 columns belonging to the next
3536: partition etc.. where n0,n1,n2... are the the input parameter 'n'.
3538: The DIAGONAL portion of the local submatrix on any given processor
3539: is the submatrix corresponding to the rows and columns m,n
3540: corresponding to the given processor. i.e diagonal matrix on
3541: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
3542: etc. The remaining portion of the local submatrix [m x (N-n)]
3543: constitute the OFF-DIAGONAL portion. The example below better
3544: illustrates this concept.
3546: For a square global matrix we define each processor's diagonal portion
3547: to be its local rows and the corresponding columns (a square submatrix);
3548: each processor's off-diagonal portion encompasses the remainder of the
3549: local matrix (a rectangular submatrix).
3551: If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.
3553: When calling this routine with a single process communicator, a matrix of
3554: type SEQAIJ is returned. If a matrix of type MPIAIJ is desired for this
3555: type of communicator, use the construction mechanism:
3556: MatCreate(...,&A); MatSetType(A,MPIAIJ); MatMPIAIJSetPreallocation(A,...);
3558: By default, this format uses inodes (identical nodes) when possible.
3559: We search for consecutive rows with the same nonzero structure, thereby
3560: reusing matrix information to achieve increased efficiency.
3562: Options Database Keys:
3563: + -mat_no_inode - Do not use inodes
3564: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3565: - -mat_aij_oneindex - Internally use indexing starting at 1
3566: rather than 0. Note that when calling MatSetValues(),
3567: the user still MUST index entries starting at 0!
3570: Example usage:
3571:
3572: Consider the following 8x8 matrix with 34 non-zero values, that is
3573: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
3574: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
3575: as follows:
3577: .vb
3578: 1 2 0 | 0 3 0 | 0 4
3579: Proc0 0 5 6 | 7 0 0 | 8 0
3580: 9 0 10 | 11 0 0 | 12 0
3581: -------------------------------------
3582: 13 0 14 | 15 16 17 | 0 0
3583: Proc1 0 18 0 | 19 20 21 | 0 0
3584: 0 0 0 | 22 23 0 | 24 0
3585: -------------------------------------
3586: Proc2 25 26 27 | 0 0 28 | 29 0
3587: 30 0 0 | 31 32 33 | 0 34
3588: .ve
3590: This can be represented as a collection of submatrices as:
3592: .vb
3593: A B C
3594: D E F
3595: G H I
3596: .ve
3598: Where the submatrices A,B,C are owned by proc0, D,E,F are
3599: owned by proc1, G,H,I are owned by proc2.
3601: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3602: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3603: The 'M','N' parameters are 8,8, and have the same values on all procs.
3605: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
3606: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
3607: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
3608: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
3609: part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
3610: matrix, ans [DF] as another SeqAIJ matrix.
3612: When d_nz, o_nz parameters are specified, d_nz storage elements are
3613: allocated for every row of the local diagonal submatrix, and o_nz
3614: storage locations are allocated for every row of the OFF-DIAGONAL submat.
3615: One way to choose d_nz and o_nz is to use the max nonzerors per local
3616: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
3617: In this case, the values of d_nz,o_nz are:
3618: .vb
3619: proc0 : dnz = 2, o_nz = 2
3620: proc1 : dnz = 3, o_nz = 2
3621: proc2 : dnz = 1, o_nz = 4
3622: .ve
3623: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
3624: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
3625: for proc3. i.e we are using 12+15+10=37 storage locations to store
3626: 34 values.
3628: When d_nnz, o_nnz parameters are specified, the storage is specified
3629: for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
3630: In the above case the values for d_nnz,o_nnz are:
3631: .vb
3632: proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
3633: proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
3634: proc2: d_nnz = [1,1] and o_nnz = [4,4]
3635: .ve
3636: Here the space allocated is sum of all the above values i.e 34, and
3637: hence pre-allocation is perfect.
3639: Level: intermediate
3641: .keywords: matrix, aij, compressed row, sparse, parallel
3643: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3644: MPIAIJ, MatCreateMPIAIJWithArrays()
3645: @*/
3646: PetscErrorCode 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)
3647: {
3649: PetscMPIInt size;
3652: MatCreate(comm,A);
3653: MatSetSizes(*A,m,n,M,N);
3654: MPI_Comm_size(comm,&size);
3655: if (size > 1) {
3656: MatSetType(*A,MATMPIAIJ);
3657: MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
3658: } else {
3659: MatSetType(*A,MATSEQAIJ);
3660: MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
3661: }
3662: return(0);
3663: }
3667: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,PetscInt *colmap[])
3668: {
3669: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
3672: *Ad = a->A;
3673: *Ao = a->B;
3674: *colmap = a->garray;
3675: return(0);
3676: }
3680: PetscErrorCode MatSetColoring_MPIAIJ(Mat A,ISColoring coloring)
3681: {
3683: PetscInt i;
3684: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
3687: if (coloring->ctype == IS_COLORING_GLOBAL) {
3688: ISColoringValue *allcolors,*colors;
3689: ISColoring ocoloring;
3691: /* set coloring for diagonal portion */
3692: MatSetColoring_SeqAIJ(a->A,coloring);
3694: /* set coloring for off-diagonal portion */
3695: ISAllGatherColors(((PetscObject)A)->comm,coloring->n,coloring->colors,PETSC_NULL,&allcolors);
3696: PetscMalloc((a->B->cmap->n+1)*sizeof(ISColoringValue),&colors);
3697: for (i=0; i<a->B->cmap->n; i++) {
3698: colors[i] = allcolors[a->garray[i]];
3699: }
3700: PetscFree(allcolors);
3701: ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap->n,colors,&ocoloring);
3702: MatSetColoring_SeqAIJ(a->B,ocoloring);
3703: ISColoringDestroy(ocoloring);
3704: } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3705: ISColoringValue *colors;
3706: PetscInt *larray;
3707: ISColoring ocoloring;
3709: /* set coloring for diagonal portion */
3710: PetscMalloc((a->A->cmap->n+1)*sizeof(PetscInt),&larray);
3711: for (i=0; i<a->A->cmap->n; i++) {
3712: larray[i] = i + A->cmap->rstart;
3713: }
3714: ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->A->cmap->n,larray,PETSC_NULL,larray);
3715: PetscMalloc((a->A->cmap->n+1)*sizeof(ISColoringValue),&colors);
3716: for (i=0; i<a->A->cmap->n; i++) {
3717: colors[i] = coloring->colors[larray[i]];
3718: }
3719: PetscFree(larray);
3720: ISColoringCreate(PETSC_COMM_SELF,coloring->n,a->A->cmap->n,colors,&ocoloring);
3721: MatSetColoring_SeqAIJ(a->A,ocoloring);
3722: ISColoringDestroy(ocoloring);
3724: /* set coloring for off-diagonal portion */
3725: PetscMalloc((a->B->cmap->n+1)*sizeof(PetscInt),&larray);
3726: ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,a->B->cmap->n,a->garray,PETSC_NULL,larray);
3727: PetscMalloc((a->B->cmap->n+1)*sizeof(ISColoringValue),&colors);
3728: for (i=0; i<a->B->cmap->n; i++) {
3729: colors[i] = coloring->colors[larray[i]];
3730: }
3731: PetscFree(larray);
3732: ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap->n,colors,&ocoloring);
3733: MatSetColoring_SeqAIJ(a->B,ocoloring);
3734: ISColoringDestroy(ocoloring);
3735: } else {
3736: SETERRQ1(PETSC_ERR_SUP,"No support ISColoringType %d",(int)coloring->ctype);
3737: }
3739: return(0);
3740: }
3742: #if defined(PETSC_HAVE_ADIC)
3745: PetscErrorCode MatSetValuesAdic_MPIAIJ(Mat A,void *advalues)
3746: {
3747: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
3751: MatSetValuesAdic_SeqAIJ(a->A,advalues);
3752: MatSetValuesAdic_SeqAIJ(a->B,advalues);
3753: return(0);
3754: }
3755: #endif
3759: PetscErrorCode MatSetValuesAdifor_MPIAIJ(Mat A,PetscInt nl,void *advalues)
3760: {
3761: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
3765: MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);
3766: MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);
3767: return(0);
3768: }
3772: /*@
3773: MatMerge - Creates a single large PETSc matrix by concatinating sequential
3774: matrices from each processor
3776: Collective on MPI_Comm
3778: Input Parameters:
3779: + comm - the communicators the parallel matrix will live on
3780: . inmat - the input sequential matrices
3781: . n - number of local columns (or PETSC_DECIDE)
3782: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3784: Output Parameter:
3785: . outmat - the parallel matrix generated
3787: Level: advanced
3789: Notes: The number of columns of the matrix in EACH processor MUST be the same.
3791: @*/
3792: PetscErrorCode MatMerge(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
3793: {
3795: PetscInt m,N,i,rstart,nnz,Ii,*dnz,*onz;
3796: PetscInt *indx;
3797: PetscScalar *values;
3800: MatGetSize(inmat,&m,&N);
3801: if (scall == MAT_INITIAL_MATRIX){
3802: /* count nonzeros in each row, for diagonal and off diagonal portion of matrix */
3803: if (n == PETSC_DECIDE){
3804: PetscSplitOwnership(comm,&n,&N);
3805: }
3806: MPI_Scan(&m, &rstart,1,MPIU_INT,MPI_SUM,comm);
3807: rstart -= m;
3809: MatPreallocateInitialize(comm,m,n,dnz,onz);
3810: for (i=0;i<m;i++) {
3811: MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
3812: MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);
3813: MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,PETSC_NULL);
3814: }
3815: /* This routine will ONLY return MPIAIJ type matrix */
3816: MatCreate(comm,outmat);
3817: MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3818: MatSetType(*outmat,MATMPIAIJ);
3819: MatMPIAIJSetPreallocation(*outmat,0,dnz,0,onz);
3820: MatPreallocateFinalize(dnz,onz);
3821:
3822: } else if (scall == MAT_REUSE_MATRIX){
3823: MatGetOwnershipRange(*outmat,&rstart,PETSC_NULL);
3824: } else {
3825: SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
3826: }
3828: for (i=0;i<m;i++) {
3829: MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3830: Ii = i + rstart;
3831: MatSetValues(*outmat,1,&Ii,nnz,indx,values,INSERT_VALUES);
3832: MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3833: }
3834: MatDestroy(inmat);
3835: MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
3836: MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);
3838: return(0);
3839: }
3843: PetscErrorCode MatFileSplit(Mat A,char *outfile)
3844: {
3845: PetscErrorCode ierr;
3846: PetscMPIInt rank;
3847: PetscInt m,N,i,rstart,nnz;
3848: size_t len;
3849: const PetscInt *indx;
3850: PetscViewer out;
3851: char *name;
3852: Mat B;
3853: const PetscScalar *values;
3856: MatGetLocalSize(A,&m,0);
3857: MatGetSize(A,0,&N);
3858: /* Should this be the type of the diagonal block of A? */
3859: MatCreate(PETSC_COMM_SELF,&B);
3860: MatSetSizes(B,m,N,m,N);
3861: MatSetType(B,MATSEQAIJ);
3862: MatSeqAIJSetPreallocation(B,0,PETSC_NULL);
3863: MatGetOwnershipRange(A,&rstart,0);
3864: for (i=0;i<m;i++) {
3865: MatGetRow(A,i+rstart,&nnz,&indx,&values);
3866: MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);
3867: MatRestoreRow(A,i+rstart,&nnz,&indx,&values);
3868: }
3869: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3870: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3872: MPI_Comm_rank(((PetscObject)A)->comm,&rank);
3873: PetscStrlen(outfile,&len);
3874: PetscMalloc((len+5)*sizeof(char),&name);
3875: sprintf(name,"%s.%d",outfile,rank);
3876: PetscViewerBinaryOpen(PETSC_COMM_SELF,name,FILE_MODE_APPEND,&out);
3877: PetscFree(name);
3878: MatView(B,out);
3879: PetscViewerDestroy(out);
3880: MatDestroy(B);
3881: return(0);
3882: }
3884: EXTERN PetscErrorCode MatDestroy_MPIAIJ(Mat);
3887: PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(Mat A)
3888: {
3889: PetscErrorCode ierr;
3890: Mat_Merge_SeqsToMPI *merge;
3891: PetscContainer container;
3894: PetscObjectQuery((PetscObject)A,"MatMergeSeqsToMPI",(PetscObject *)&container);
3895: if (container) {
3896: PetscContainerGetPointer(container,(void **)&merge);
3897: PetscFree(merge->id_r);
3898: PetscFree(merge->len_s);
3899: PetscFree(merge->len_r);
3900: PetscFree(merge->bi);
3901: PetscFree(merge->bj);
3902: PetscFree(merge->buf_ri);
3903: PetscFree(merge->buf_rj);
3904: PetscFree(merge->coi);
3905: PetscFree(merge->coj);
3906: PetscFree(merge->owners_co);
3907: PetscFree(merge->rowmap.range);
3908:
3909: PetscContainerDestroy(container);
3910: PetscObjectCompose((PetscObject)A,"MatMergeSeqsToMPI",0);
3911: }
3912: PetscFree(merge);
3914: MatDestroy_MPIAIJ(A);
3915: return(0);
3916: }
3918: #include ../src/mat/utils/freespace.h
3919: #include petscbt.h
3923: /*@C
3924: MatMerge_SeqsToMPI - Creates a MPIAIJ matrix by adding sequential
3925: matrices from each processor
3927: Collective on MPI_Comm
3929: Input Parameters:
3930: + comm - the communicators the parallel matrix will live on
3931: . seqmat - the input sequential matrices
3932: . m - number of local rows (or PETSC_DECIDE)
3933: . n - number of local columns (or PETSC_DECIDE)
3934: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
3936: Output Parameter:
3937: . mpimat - the parallel matrix generated
3939: Level: advanced
3941: Notes:
3942: The dimensions of the sequential matrix in each processor MUST be the same.
3943: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
3944: destroyed when mpimat is destroyed. Call PetscObjectQuery() to access seqmat.
3945: @*/
3946: PetscErrorCode MatMerge_SeqsToMPINumeric(Mat seqmat,Mat mpimat)
3947: {
3948: PetscErrorCode ierr;
3949: MPI_Comm comm=((PetscObject)mpimat)->comm;
3950: Mat_SeqAIJ *a=(Mat_SeqAIJ*)seqmat->data;
3951: PetscMPIInt size,rank,taga,*len_s;
3952: PetscInt N=mpimat->cmap->N,i,j,*owners,*ai=a->i,*aj=a->j;
3953: PetscInt proc,m;
3954: PetscInt **buf_ri,**buf_rj;
3955: PetscInt k,anzi,*bj_i,*bi,*bj,arow,bnzi,nextaj;
3956: PetscInt nrows,**buf_ri_k,**nextrow,**nextai;
3957: MPI_Request *s_waits,*r_waits;
3958: MPI_Status *status;
3959: MatScalar *aa=a->a;
3960: MatScalar **abuf_r,*ba_i;
3961: Mat_Merge_SeqsToMPI *merge;
3962: PetscContainer container;
3963:
3965: PetscLogEventBegin(MAT_Seqstompinum,seqmat,0,0,0);
3967: MPI_Comm_size(comm,&size);
3968: MPI_Comm_rank(comm,&rank);
3970: PetscObjectQuery((PetscObject)mpimat,"MatMergeSeqsToMPI",(PetscObject *)&container);
3971: if (container) {
3972: PetscContainerGetPointer(container,(void **)&merge);
3973: }
3974: bi = merge->bi;
3975: bj = merge->bj;
3976: buf_ri = merge->buf_ri;
3977: buf_rj = merge->buf_rj;
3979: PetscMalloc(size*sizeof(MPI_Status),&status);
3980: owners = merge->rowmap.range;
3981: len_s = merge->len_s;
3983: /* send and recv matrix values */
3984: /*-----------------------------*/
3985: PetscObjectGetNewTag((PetscObject)mpimat,&taga);
3986: PetscPostIrecvScalar(comm,taga,merge->nrecv,merge->id_r,merge->len_r,&abuf_r,&r_waits);
3988: PetscMalloc((merge->nsend+1)*sizeof(MPI_Request),&s_waits);
3989: for (proc=0,k=0; proc<size; proc++){
3990: if (!len_s[proc]) continue;
3991: i = owners[proc];
3992: MPI_Isend(aa+ai[i],len_s[proc],MPIU_MATSCALAR,proc,taga,comm,s_waits+k);
3993: k++;
3994: }
3996: if (merge->nrecv) {MPI_Waitall(merge->nrecv,r_waits,status);}
3997: if (merge->nsend) {MPI_Waitall(merge->nsend,s_waits,status);}
3998: PetscFree(status);
4000: PetscFree(s_waits);
4001: PetscFree(r_waits);
4003: /* insert mat values of mpimat */
4004: /*----------------------------*/
4005: PetscMalloc(N*sizeof(PetscScalar),&ba_i);
4006: PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
4007: nextrow = buf_ri_k + merge->nrecv;
4008: nextai = nextrow + merge->nrecv;
4010: for (k=0; k<merge->nrecv; k++){
4011: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4012: nrows = *(buf_ri_k[k]);
4013: nextrow[k] = buf_ri_k[k]+1; /* next row number of k-th recved i-structure */
4014: nextai[k] = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure */
4015: }
4017: /* set values of ba */
4018: m = merge->rowmap.n;
4019: for (i=0; i<m; i++) {
4020: arow = owners[rank] + i;
4021: bj_i = bj+bi[i]; /* col indices of the i-th row of mpimat */
4022: bnzi = bi[i+1] - bi[i];
4023: PetscMemzero(ba_i,bnzi*sizeof(PetscScalar));
4025: /* add local non-zero vals of this proc's seqmat into ba */
4026: anzi = ai[arow+1] - ai[arow];
4027: aj = a->j + ai[arow];
4028: aa = a->a + ai[arow];
4029: nextaj = 0;
4030: for (j=0; nextaj<anzi; j++){
4031: if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
4032: ba_i[j] += aa[nextaj++];
4033: }
4034: }
4036: /* add received vals into ba */
4037: for (k=0; k<merge->nrecv; k++){ /* k-th received message */
4038: /* i-th row */
4039: if (i == *nextrow[k]) {
4040: anzi = *(nextai[k]+1) - *nextai[k];
4041: aj = buf_rj[k] + *(nextai[k]);
4042: aa = abuf_r[k] + *(nextai[k]);
4043: nextaj = 0;
4044: for (j=0; nextaj<anzi; j++){
4045: if (*(bj_i + j) == aj[nextaj]){ /* bcol == acol */
4046: ba_i[j] += aa[nextaj++];
4047: }
4048: }
4049: nextrow[k]++; nextai[k]++;
4050: }
4051: }
4052: MatSetValues(mpimat,1,&arow,bnzi,bj_i,ba_i,INSERT_VALUES);
4053: }
4054: MatAssemblyBegin(mpimat,MAT_FINAL_ASSEMBLY);
4055: MatAssemblyEnd(mpimat,MAT_FINAL_ASSEMBLY);
4057: PetscFree(abuf_r);
4058: PetscFree(ba_i);
4059: PetscFree(buf_ri_k);
4060: PetscLogEventEnd(MAT_Seqstompinum,seqmat,0,0,0);
4061: return(0);
4062: }
4066: PetscErrorCode MatMerge_SeqsToMPISymbolic(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,Mat *mpimat)
4067: {
4068: PetscErrorCode ierr;
4069: Mat B_mpi;
4070: Mat_SeqAIJ *a=(Mat_SeqAIJ*)seqmat->data;
4071: PetscMPIInt size,rank,tagi,tagj,*len_s,*len_si,*len_ri;
4072: PetscInt **buf_rj,**buf_ri,**buf_ri_k;
4073: PetscInt M=seqmat->rmap->n,N=seqmat->cmap->n,i,*owners,*ai=a->i,*aj=a->j;
4074: PetscInt len,proc,*dnz,*onz;
4075: PetscInt k,anzi,*bi,*bj,*lnk,nlnk,arow,bnzi,nspacedouble=0;
4076: PetscInt nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextai;
4077: MPI_Request *si_waits,*sj_waits,*ri_waits,*rj_waits;
4078: MPI_Status *status;
4079: PetscFreeSpaceList free_space=PETSC_NULL,current_space=PETSC_NULL;
4080: PetscBT lnkbt;
4081: Mat_Merge_SeqsToMPI *merge;
4082: PetscContainer container;
4085: PetscLogEventBegin(MAT_Seqstompisym,seqmat,0,0,0);
4087: /* make sure it is a PETSc comm */
4088: PetscCommDuplicate(comm,&comm,PETSC_NULL);
4089: MPI_Comm_size(comm,&size);
4090: MPI_Comm_rank(comm,&rank);
4091:
4092: PetscNew(Mat_Merge_SeqsToMPI,&merge);
4093: PetscMalloc(size*sizeof(MPI_Status),&status);
4095: /* determine row ownership */
4096: /*---------------------------------------------------------*/
4097: PetscMapInitialize(comm,&merge->rowmap);
4098: merge->rowmap.n = m;
4099: merge->rowmap.N = M;
4100: merge->rowmap.bs = 1;
4101: PetscMapSetUp(&merge->rowmap);
4102: PetscMalloc(size*sizeof(PetscMPIInt),&len_si);
4103: PetscMalloc(size*sizeof(PetscMPIInt),&merge->len_s);
4104:
4105: m = merge->rowmap.n;
4106: M = merge->rowmap.N;
4107: owners = merge->rowmap.range;
4109: /* determine the number of messages to send, their lengths */
4110: /*---------------------------------------------------------*/
4111: len_s = merge->len_s;
4113: len = 0; /* length of buf_si[] */
4114: merge->nsend = 0;
4115: for (proc=0; proc<size; proc++){
4116: len_si[proc] = 0;
4117: if (proc == rank){
4118: len_s[proc] = 0;
4119: } else {
4120: len_si[proc] = owners[proc+1] - owners[proc] + 1;
4121: len_s[proc] = ai[owners[proc+1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4122: }
4123: if (len_s[proc]) {
4124: merge->nsend++;
4125: nrows = 0;
4126: for (i=owners[proc]; i<owners[proc+1]; i++){
4127: if (ai[i+1] > ai[i]) nrows++;
4128: }
4129: len_si[proc] = 2*(nrows+1);
4130: len += len_si[proc];
4131: }
4132: }
4134: /* determine the number and length of messages to receive for ij-structure */
4135: /*-------------------------------------------------------------------------*/
4136: PetscGatherNumberOfMessages(comm,PETSC_NULL,len_s,&merge->nrecv);
4137: PetscGatherMessageLengths2(comm,merge->nsend,merge->nrecv,len_s,len_si,&merge->id_r,&merge->len_r,&len_ri);
4139: /* post the Irecv of j-structure */
4140: /*-------------------------------*/
4141: PetscCommGetNewTag(comm,&tagj);
4142: PetscPostIrecvInt(comm,tagj,merge->nrecv,merge->id_r,merge->len_r,&buf_rj,&rj_waits);
4144: /* post the Isend of j-structure */
4145: /*--------------------------------*/
4146: PetscMalloc((2*merge->nsend+1)*sizeof(MPI_Request),&si_waits);
4147: sj_waits = si_waits + merge->nsend;
4149: for (proc=0, k=0; proc<size; proc++){
4150: if (!len_s[proc]) continue;
4151: i = owners[proc];
4152: MPI_Isend(aj+ai[i],len_s[proc],MPIU_INT,proc,tagj,comm,sj_waits+k);
4153: k++;
4154: }
4156: /* receives and sends of j-structure are complete */
4157: /*------------------------------------------------*/
4158: if (merge->nrecv) {MPI_Waitall(merge->nrecv,rj_waits,status);}
4159: if (merge->nsend) {MPI_Waitall(merge->nsend,sj_waits,status);}
4160:
4161: /* send and recv i-structure */
4162: /*---------------------------*/
4163: PetscCommGetNewTag(comm,&tagi);
4164: PetscPostIrecvInt(comm,tagi,merge->nrecv,merge->id_r,len_ri,&buf_ri,&ri_waits);
4165:
4166: PetscMalloc((len+1)*sizeof(PetscInt),&buf_s);
4167: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4168: for (proc=0,k=0; proc<size; proc++){
4169: if (!len_s[proc]) continue;
4170: /* form outgoing message for i-structure:
4171: buf_si[0]: nrows to be sent
4172: [1:nrows]: row index (global)
4173: [nrows+1:2*nrows+1]: i-structure index
4174: */
4175: /*-------------------------------------------*/
4176: nrows = len_si[proc]/2 - 1;
4177: buf_si_i = buf_si + nrows+1;
4178: buf_si[0] = nrows;
4179: buf_si_i[0] = 0;
4180: nrows = 0;
4181: for (i=owners[proc]; i<owners[proc+1]; i++){
4182: anzi = ai[i+1] - ai[i];
4183: if (anzi) {
4184: buf_si_i[nrows+1] = buf_si_i[nrows] + anzi; /* i-structure */
4185: buf_si[nrows+1] = i-owners[proc]; /* local row index */
4186: nrows++;
4187: }
4188: }
4189: MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,si_waits+k);
4190: k++;
4191: buf_si += len_si[proc];
4192: }
4194: if (merge->nrecv) {MPI_Waitall(merge->nrecv,ri_waits,status);}
4195: if (merge->nsend) {MPI_Waitall(merge->nsend,si_waits,status);}
4197: PetscInfo2(seqmat,"nsend: %D, nrecv: %D\n",merge->nsend,merge->nrecv);
4198: for (i=0; i<merge->nrecv; i++){
4199: PetscInfo3(seqmat,"recv len_ri=%D, len_rj=%D from [%D]\n",len_ri[i],merge->len_r[i],merge->id_r[i]);
4200: }
4202: PetscFree(len_si);
4203: PetscFree(len_ri);
4204: PetscFree(rj_waits);
4205: PetscFree(si_waits);
4206: PetscFree(ri_waits);
4207: PetscFree(buf_s);
4208: PetscFree(status);
4210: /* compute a local seq matrix in each processor */
4211: /*----------------------------------------------*/
4212: /* allocate bi array and free space for accumulating nonzero column info */
4213: PetscMalloc((m+1)*sizeof(PetscInt),&bi);
4214: bi[0] = 0;
4216: /* create and initialize a linked list */
4217: nlnk = N+1;
4218: PetscLLCreate(N,N,nlnk,lnk,lnkbt);
4219:
4220: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4221: len = 0;
4222: len = ai[owners[rank+1]] - ai[owners[rank]];
4223: PetscFreeSpaceGet((PetscInt)(2*len+1),&free_space);
4224: current_space = free_space;
4226: /* determine symbolic info for each local row */
4227: PetscMalloc((3*merge->nrecv+1)*sizeof(PetscInt**),&buf_ri_k);
4228: nextrow = buf_ri_k + merge->nrecv;
4229: nextai = nextrow + merge->nrecv;
4230: for (k=0; k<merge->nrecv; k++){
4231: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4232: nrows = *buf_ri_k[k];
4233: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4234: nextai[k] = buf_ri_k[k] + (nrows + 1);/* poins to the next i-structure of k-th recved i-structure */
4235: }
4237: MatPreallocateInitialize(comm,m,n,dnz,onz);
4238: len = 0;
4239: for (i=0;i<m;i++) {
4240: bnzi = 0;
4241: /* add local non-zero cols of this proc's seqmat into lnk */
4242: arow = owners[rank] + i;
4243: anzi = ai[arow+1] - ai[arow];
4244: aj = a->j + ai[arow];
4245: PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
4246: bnzi += nlnk;
4247: /* add received col data into lnk */
4248: for (k=0; k<merge->nrecv; k++){ /* k-th received message */
4249: if (i == *nextrow[k]) { /* i-th row */
4250: anzi = *(nextai[k]+1) - *nextai[k];
4251: aj = buf_rj[k] + *nextai[k];
4252: PetscLLAdd(anzi,aj,N,nlnk,lnk,lnkbt);
4253: bnzi += nlnk;
4254: nextrow[k]++; nextai[k]++;
4255: }
4256: }
4257: if (len < bnzi) len = bnzi; /* =max(bnzi) */
4259: /* if free space is not available, make more free space */
4260: if (current_space->local_remaining<bnzi) {
4261: PetscFreeSpaceGet(bnzi+current_space->total_array_size,¤t_space);
4262: nspacedouble++;
4263: }
4264: /* copy data into free space, then initialize lnk */
4265: PetscLLClean(N,N,bnzi,lnk,current_space->array,lnkbt);
4266: MatPreallocateSet(i+owners[rank],bnzi,current_space->array,dnz,onz);
4268: current_space->array += bnzi;
4269: current_space->local_used += bnzi;
4270: current_space->local_remaining -= bnzi;
4271:
4272: bi[i+1] = bi[i] + bnzi;
4273: }
4274:
4275: PetscFree(buf_ri_k);
4277: PetscMalloc((bi[m]+1)*sizeof(PetscInt),&bj);
4278: PetscFreeSpaceContiguous(&free_space,bj);
4279: PetscLLDestroy(lnk,lnkbt);
4281: /* create symbolic parallel matrix B_mpi */
4282: /*---------------------------------------*/
4283: MatCreate(comm,&B_mpi);
4284: if (n==PETSC_DECIDE) {
4285: MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,N);
4286: } else {
4287: MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
4288: }
4289: MatSetType(B_mpi,MATMPIAIJ);
4290: MatMPIAIJSetPreallocation(B_mpi,0,dnz,0,onz);
4291: MatPreallocateFinalize(dnz,onz);
4293: /* B_mpi is not ready for use - assembly will be done by MatMerge_SeqsToMPINumeric() */
4294: B_mpi->assembled = PETSC_FALSE;
4295: B_mpi->ops->destroy = MatDestroy_MPIAIJ_SeqsToMPI;
4296: merge->bi = bi;
4297: merge->bj = bj;
4298: merge->buf_ri = buf_ri;
4299: merge->buf_rj = buf_rj;
4300: merge->coi = PETSC_NULL;
4301: merge->coj = PETSC_NULL;
4302: merge->owners_co = PETSC_NULL;
4304: /* attach the supporting struct to B_mpi for reuse */
4305: PetscContainerCreate(PETSC_COMM_SELF,&container);
4306: PetscContainerSetPointer(container,merge);
4307: PetscObjectCompose((PetscObject)B_mpi,"MatMergeSeqsToMPI",(PetscObject)container);
4308: *mpimat = B_mpi;
4310: PetscCommDestroy(&comm);
4311: PetscLogEventEnd(MAT_Seqstompisym,seqmat,0,0,0);
4312: return(0);
4313: }
4317: PetscErrorCode MatMerge_SeqsToMPI(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,MatReuse scall,Mat *mpimat)
4318: {
4319: PetscErrorCode ierr;
4322: PetscLogEventBegin(MAT_Seqstompi,seqmat,0,0,0);
4323: if (scall == MAT_INITIAL_MATRIX){
4324: MatMerge_SeqsToMPISymbolic(comm,seqmat,m,n,mpimat);
4325: }
4326: MatMerge_SeqsToMPINumeric(seqmat,*mpimat);
4327: PetscLogEventEnd(MAT_Seqstompi,seqmat,0,0,0);
4328: return(0);
4329: }
4333: /*@
4334: MatGetLocalMat - Creates a SeqAIJ matrix by taking all its local rows
4336: Not Collective
4338: Input Parameters:
4339: + A - the matrix
4340: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4342: Output Parameter:
4343: . A_loc - the local sequential matrix generated
4345: Level: developer
4347: @*/
4348: PetscErrorCode MatGetLocalMat(Mat A,MatReuse scall,Mat *A_loc)
4349: {
4350: PetscErrorCode ierr;
4351: Mat_MPIAIJ *mpimat=(Mat_MPIAIJ*)A->data;
4352: Mat_SeqAIJ *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
4353: PetscInt *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j,*cmap=mpimat->garray;
4354: MatScalar *aa=a->a,*ba=b->a,*cam;
4355: PetscScalar *ca;
4356: PetscInt am=A->rmap->n,i,j,k,cstart=A->cmap->rstart;
4357: PetscInt *ci,*cj,col,ncols_d,ncols_o,jo;
4360: PetscLogEventBegin(MAT_Getlocalmat,A,0,0,0);
4361: if (scall == MAT_INITIAL_MATRIX){
4362: PetscMalloc((1+am)*sizeof(PetscInt),&ci);
4363: ci[0] = 0;
4364: for (i=0; i<am; i++){
4365: ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
4366: }
4367: PetscMalloc((1+ci[am])*sizeof(PetscInt),&cj);
4368: PetscMalloc((1+ci[am])*sizeof(PetscScalar),&ca);
4369: k = 0;
4370: for (i=0; i<am; i++) {
4371: ncols_o = bi[i+1] - bi[i];
4372: ncols_d = ai[i+1] - ai[i];
4373: /* off-diagonal portion of A */
4374: for (jo=0; jo<ncols_o; jo++) {
4375: col = cmap[*bj];
4376: if (col >= cstart) break;
4377: cj[k] = col; bj++;
4378: ca[k++] = *ba++;
4379: }
4380: /* diagonal portion of A */
4381: for (j=0; j<ncols_d; j++) {
4382: cj[k] = cstart + *aj++;
4383: ca[k++] = *aa++;
4384: }
4385: /* off-diagonal portion of A */
4386: for (j=jo; j<ncols_o; j++) {
4387: cj[k] = cmap[*bj++];
4388: ca[k++] = *ba++;
4389: }
4390: }
4391: /* put together the new matrix */
4392: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,A->cmap->N,ci,cj,ca,A_loc);
4393: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4394: /* Since these are PETSc arrays, change flags to free them as necessary. */
4395: mat = (Mat_SeqAIJ*)(*A_loc)->data;
4396: mat->free_a = PETSC_TRUE;
4397: mat->free_ij = PETSC_TRUE;
4398: mat->nonew = 0;
4399: } else if (scall == MAT_REUSE_MATRIX){
4400: mat=(Mat_SeqAIJ*)(*A_loc)->data;
4401: ci = mat->i; cj = mat->j; cam = mat->a;
4402: for (i=0; i<am; i++) {
4403: /* off-diagonal portion of A */
4404: ncols_o = bi[i+1] - bi[i];
4405: for (jo=0; jo<ncols_o; jo++) {
4406: col = cmap[*bj];
4407: if (col >= cstart) break;
4408: *cam++ = *ba++; bj++;
4409: }
4410: /* diagonal portion of A */
4411: ncols_d = ai[i+1] - ai[i];
4412: for (j=0; j<ncols_d; j++) *cam++ = *aa++;
4413: /* off-diagonal portion of A */
4414: for (j=jo; j<ncols_o; j++) {
4415: *cam++ = *ba++; bj++;
4416: }
4417: }
4418: } else {
4419: SETERRQ1(PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
4420: }
4422: PetscLogEventEnd(MAT_Getlocalmat,A,0,0,0);
4423: return(0);
4424: }
4428: /*@C
4429: MatGetLocalMatCondensed - Creates a SeqAIJ matrix by taking all its local rows and NON-ZERO columns
4431: Not Collective
4433: Input Parameters:
4434: + A - the matrix
4435: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4436: - row, col - index sets of rows and columns to extract (or PETSC_NULL)
4438: Output Parameter:
4439: . A_loc - the local sequential matrix generated
4441: Level: developer
4443: @*/
4444: PetscErrorCode MatGetLocalMatCondensed(Mat A,MatReuse scall,IS *row,IS *col,Mat *A_loc)
4445: {
4446: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
4447: PetscErrorCode ierr;
4448: PetscInt i,start,end,ncols,nzA,nzB,*cmap,imark,*idx;
4449: IS isrowa,iscola;
4450: Mat *aloc;
4453: PetscLogEventBegin(MAT_Getlocalmatcondensed,A,0,0,0);
4454: if (!row){
4455: start = A->rmap->rstart; end = A->rmap->rend;
4456: ISCreateStride(PETSC_COMM_SELF,end-start,start,1,&isrowa);
4457: } else {
4458: isrowa = *row;
4459: }
4460: if (!col){
4461: start = A->cmap->rstart;
4462: cmap = a->garray;
4463: nzA = a->A->cmap->n;
4464: nzB = a->B->cmap->n;
4465: PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
4466: ncols = 0;
4467: for (i=0; i<nzB; i++) {
4468: if (cmap[i] < start) idx[ncols++] = cmap[i];
4469: else break;
4470: }
4471: imark = i;
4472: for (i=0; i<nzA; i++) idx[ncols++] = start + i;
4473: for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i];
4474: ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&iscola);
4475: PetscFree(idx);
4476: } else {
4477: iscola = *col;
4478: }
4479: if (scall != MAT_INITIAL_MATRIX){
4480: PetscMalloc(sizeof(Mat),&aloc);
4481: aloc[0] = *A_loc;
4482: }
4483: MatGetSubMatrices(A,1,&isrowa,&iscola,scall,&aloc);
4484: *A_loc = aloc[0];
4485: PetscFree(aloc);
4486: if (!row){
4487: ISDestroy(isrowa);
4488: }
4489: if (!col){
4490: ISDestroy(iscola);
4491: }
4492: PetscLogEventEnd(MAT_Getlocalmatcondensed,A,0,0,0);
4493: return(0);
4494: }
4498: /*@C
4499: MatGetBrowsOfAcols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
4501: Collective on Mat
4503: Input Parameters:
4504: + A,B - the matrices in mpiaij format
4505: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4506: - rowb, colb - index sets of rows and columns of B to extract (or PETSC_NULL)
4508: Output Parameter:
4509: + rowb, colb - index sets of rows and columns of B to extract
4510: . brstart - row index of B_seq from which next B->rmap->n rows are taken from B's local rows
4511: - B_seq - the sequential matrix generated
4513: Level: developer
4515: @*/
4516: PetscErrorCode MatGetBrowsOfAcols(Mat A,Mat B,MatReuse scall,IS *rowb,IS *colb,PetscInt *brstart,Mat *B_seq)
4517: {
4518: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
4519: PetscErrorCode ierr;
4520: PetscInt *idx,i,start,ncols,nzA,nzB,*cmap,imark;
4521: IS isrowb,iscolb;
4522: Mat *bseq;
4523:
4525: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend){
4526: 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);
4527: }
4528: PetscLogEventBegin(MAT_GetBrowsOfAcols,A,B,0,0);
4529:
4530: if (scall == MAT_INITIAL_MATRIX){
4531: start = A->cmap->rstart;
4532: cmap = a->garray;
4533: nzA = a->A->cmap->n;
4534: nzB = a->B->cmap->n;
4535: PetscMalloc((nzA+nzB)*sizeof(PetscInt), &idx);
4536: ncols = 0;
4537: for (i=0; i<nzB; i++) { /* row < local row index */
4538: if (cmap[i] < start) idx[ncols++] = cmap[i];
4539: else break;
4540: }
4541: imark = i;
4542: for (i=0; i<nzA; i++) idx[ncols++] = start + i; /* local rows */
4543: for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
4544: ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,&isrowb);
4545: PetscFree(idx);
4546: *brstart = imark;
4547: ISCreateStride(PETSC_COMM_SELF,B->cmap->N,0,1,&iscolb);
4548: } else {
4549: if (!rowb || !colb) SETERRQ(PETSC_ERR_SUP,"IS rowb and colb must be provided for MAT_REUSE_MATRIX");
4550: isrowb = *rowb; iscolb = *colb;
4551: PetscMalloc(sizeof(Mat),&bseq);
4552: bseq[0] = *B_seq;
4553: }
4554: MatGetSubMatrices(B,1,&isrowb,&iscolb,scall,&bseq);
4555: *B_seq = bseq[0];
4556: PetscFree(bseq);
4557: if (!rowb){
4558: ISDestroy(isrowb);
4559: } else {
4560: *rowb = isrowb;
4561: }
4562: if (!colb){
4563: ISDestroy(iscolb);
4564: } else {
4565: *colb = iscolb;
4566: }
4567: PetscLogEventEnd(MAT_GetBrowsOfAcols,A,B,0,0);
4568: return(0);
4569: }
4573: /*@C
4574: MatGetBrowsOfAoCols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns
4575: of the OFF-DIAGONAL portion of local A
4577: Collective on Mat
4579: Input Parameters:
4580: + A,B - the matrices in mpiaij format
4581: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4582: . startsj - starting point in B's sending and receiving j-arrays, saved for MAT_REUSE (or PETSC_NULL)
4583: - bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or PETSC_NULL)
4585: Output Parameter:
4586: + B_oth - the sequential matrix generated
4588: Level: developer
4590: @*/
4591: PetscErrorCode MatGetBrowsOfAoCols(Mat A,Mat B,MatReuse scall,PetscInt **startsj,MatScalar **bufa_ptr,Mat *B_oth)
4592: {
4593: VecScatter_MPI_General *gen_to,*gen_from;
4594: PetscErrorCode ierr;
4595: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
4596: Mat_SeqAIJ *b_oth;
4597: VecScatter ctx=a->Mvctx;
4598: MPI_Comm comm=((PetscObject)ctx)->comm;
4599: PetscMPIInt *rprocs,*sprocs,tag=((PetscObject)ctx)->tag,rank;
4600: PetscInt *rowlen,*bufj,*bufJ,ncols,aBn=a->B->cmap->n,row,*b_othi,*b_othj;
4601: PetscScalar *rvalues,*svalues;
4602: MatScalar *b_otha,*bufa,*bufA;
4603: PetscInt i,j,k,l,ll,nrecvs,nsends,nrows,*srow,*rstarts,*rstartsj = 0,*sstarts,*sstartsj,len;
4604: MPI_Request *rwaits = PETSC_NULL,*swaits = PETSC_NULL;
4605: MPI_Status *sstatus,rstatus;
4606: PetscMPIInt jj;
4607: PetscInt *cols,sbs,rbs;
4608: PetscScalar *vals;
4611: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend){
4612: 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);
4613: }
4614: PetscLogEventBegin(MAT_GetBrowsOfAocols,A,B,0,0);
4615: MPI_Comm_rank(comm,&rank);
4617: gen_to = (VecScatter_MPI_General*)ctx->todata;
4618: gen_from = (VecScatter_MPI_General*)ctx->fromdata;
4619: rvalues = gen_from->values; /* holds the length of receiving row */
4620: svalues = gen_to->values; /* holds the length of sending row */
4621: nrecvs = gen_from->n;
4622: nsends = gen_to->n;
4624: PetscMalloc2(nrecvs,MPI_Request,&rwaits,nsends,MPI_Request,&swaits);
4625: srow = gen_to->indices; /* local row index to be sent */
4626: sstarts = gen_to->starts;
4627: sprocs = gen_to->procs;
4628: sstatus = gen_to->sstatus;
4629: sbs = gen_to->bs;
4630: rstarts = gen_from->starts;
4631: rprocs = gen_from->procs;
4632: rbs = gen_from->bs;
4634: if (!startsj || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
4635: if (scall == MAT_INITIAL_MATRIX){
4636: /* i-array */
4637: /*---------*/
4638: /* post receives */
4639: for (i=0; i<nrecvs; i++){
4640: rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
4641: nrows = (rstarts[i+1]-rstarts[i])*rbs; /* num of indices to be received */
4642: MPI_Irecv(rowlen,nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
4643: }
4645: /* pack the outgoing message */
4646: PetscMalloc((nsends+nrecvs+3)*sizeof(PetscInt),&sstartsj);
4647: rstartsj = sstartsj + nsends +1;
4648: sstartsj[0] = 0; rstartsj[0] = 0;
4649: len = 0; /* total length of j or a array to be sent */
4650: k = 0;
4651: for (i=0; i<nsends; i++){
4652: rowlen = (PetscInt*)svalues + sstarts[i]*sbs;
4653: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4654: for (j=0; j<nrows; j++) {
4655: row = srow[k] + B->rmap->range[rank]; /* global row idx */
4656: for (l=0; l<sbs; l++){
4657: MatGetRow_MPIAIJ(B,row+l,&ncols,PETSC_NULL,PETSC_NULL); /* rowlength */
4658: rowlen[j*sbs+l] = ncols;
4659: len += ncols;
4660: MatRestoreRow_MPIAIJ(B,row+l,&ncols,PETSC_NULL,PETSC_NULL);
4661: }
4662: k++;
4663: }
4664: MPI_Isend(rowlen,nrows*sbs,MPIU_INT,sprocs[i],tag,comm,swaits+i);
4665: sstartsj[i+1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
4666: }
4667: /* recvs and sends of i-array are completed */
4668: i = nrecvs;
4669: while (i--) {
4670: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4671: }
4672: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4674: /* allocate buffers for sending j and a arrays */
4675: PetscMalloc((len+1)*sizeof(PetscInt),&bufj);
4676: PetscMalloc((len+1)*sizeof(PetscScalar),&bufa);
4678: /* create i-array of B_oth */
4679: PetscMalloc((aBn+2)*sizeof(PetscInt),&b_othi);
4680: b_othi[0] = 0;
4681: len = 0; /* total length of j or a array to be received */
4682: k = 0;
4683: for (i=0; i<nrecvs; i++){
4684: rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
4685: nrows = rbs*(rstarts[i+1]-rstarts[i]); /* num of rows to be recieved */
4686: for (j=0; j<nrows; j++) {
4687: b_othi[k+1] = b_othi[k] + rowlen[j];
4688: len += rowlen[j]; k++;
4689: }
4690: rstartsj[i+1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
4691: }
4693: /* allocate space for j and a arrrays of B_oth */
4694: PetscMalloc((b_othi[aBn]+1)*sizeof(PetscInt),&b_othj);
4695: PetscMalloc((b_othi[aBn]+1)*sizeof(MatScalar),&b_otha);
4697: /* j-array */
4698: /*---------*/
4699: /* post receives of j-array */
4700: for (i=0; i<nrecvs; i++){
4701: nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
4702: MPI_Irecv(b_othj+rstartsj[i],nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
4703: }
4705: /* pack the outgoing message j-array */
4706: k = 0;
4707: for (i=0; i<nsends; i++){
4708: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4709: bufJ = bufj+sstartsj[i];
4710: for (j=0; j<nrows; j++) {
4711: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
4712: for (ll=0; ll<sbs; ll++){
4713: MatGetRow_MPIAIJ(B,row+ll,&ncols,&cols,PETSC_NULL);
4714: for (l=0; l<ncols; l++){
4715: *bufJ++ = cols[l];
4716: }
4717: MatRestoreRow_MPIAIJ(B,row+ll,&ncols,&cols,PETSC_NULL);
4718: }
4719: }
4720: MPI_Isend(bufj+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_INT,sprocs[i],tag,comm,swaits+i);
4721: }
4723: /* recvs and sends of j-array are completed */
4724: i = nrecvs;
4725: while (i--) {
4726: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4727: }
4728: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4729: } else if (scall == MAT_REUSE_MATRIX){
4730: sstartsj = *startsj;
4731: rstartsj = sstartsj + nsends +1;
4732: bufa = *bufa_ptr;
4733: b_oth = (Mat_SeqAIJ*)(*B_oth)->data;
4734: b_otha = b_oth->a;
4735: } else {
4736: SETERRQ(PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not posses an object container");
4737: }
4739: /* a-array */
4740: /*---------*/
4741: /* post receives of a-array */
4742: for (i=0; i<nrecvs; i++){
4743: nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
4744: MPI_Irecv(b_otha+rstartsj[i],nrows,MPIU_SCALAR,rprocs[i],tag,comm,rwaits+i);
4745: }
4747: /* pack the outgoing message a-array */
4748: k = 0;
4749: for (i=0; i<nsends; i++){
4750: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4751: bufA = bufa+sstartsj[i];
4752: for (j=0; j<nrows; j++) {
4753: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
4754: for (ll=0; ll<sbs; ll++){
4755: MatGetRow_MPIAIJ(B,row+ll,&ncols,PETSC_NULL,&vals);
4756: for (l=0; l<ncols; l++){
4757: *bufA++ = vals[l];
4758: }
4759: MatRestoreRow_MPIAIJ(B,row+ll,&ncols,PETSC_NULL,&vals);
4760: }
4761: }
4762: MPI_Isend(bufa+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_SCALAR,sprocs[i],tag,comm,swaits+i);
4763: }
4764: /* recvs and sends of a-array are completed */
4765: i = nrecvs;
4766: while (i--) {
4767: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4768: }
4769: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4770: PetscFree2(rwaits,swaits);
4772: if (scall == MAT_INITIAL_MATRIX){
4773: /* put together the new matrix */
4774: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,aBn,B->cmap->N,b_othi,b_othj,b_otha,B_oth);
4776: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4777: /* Since these are PETSc arrays, change flags to free them as necessary. */
4778: b_oth = (Mat_SeqAIJ *)(*B_oth)->data;
4779: b_oth->free_a = PETSC_TRUE;
4780: b_oth->free_ij = PETSC_TRUE;
4781: b_oth->nonew = 0;
4783: PetscFree(bufj);
4784: if (!startsj || !bufa_ptr){
4785: PetscFree(sstartsj);
4786: PetscFree(bufa_ptr);
4787: } else {
4788: *startsj = sstartsj;
4789: *bufa_ptr = bufa;
4790: }
4791: }
4792: PetscLogEventEnd(MAT_GetBrowsOfAocols,A,B,0,0);
4793: return(0);
4794: }
4798: /*@C
4799: MatGetCommunicationStructs - Provides access to the communication structures used in matrix-vector multiplication.
4801: Not Collective
4803: Input Parameters:
4804: . A - The matrix in mpiaij format
4806: Output Parameter:
4807: + lvec - The local vector holding off-process values from the argument to a matrix-vector product
4808: . colmap - A map from global column index to local index into lvec
4809: - multScatter - A scatter from the argument of a matrix-vector product to lvec
4811: Level: developer
4813: @*/
4814: #if defined (PETSC_USE_CTABLE)
4815: PetscErrorCode MatGetCommunicationStructs(Mat A, Vec *lvec, PetscTable *colmap, VecScatter *multScatter)
4816: #else
4817: PetscErrorCode MatGetCommunicationStructs(Mat A, Vec *lvec, PetscInt *colmap[], VecScatter *multScatter)
4818: #endif
4819: {
4820: Mat_MPIAIJ *a;
4827: a = (Mat_MPIAIJ *) A->data;
4828: if (lvec) *lvec = a->lvec;
4829: if (colmap) *colmap = a->colmap;
4830: if (multScatter) *multScatter = a->Mvctx;
4831: return(0);
4832: }
4839: #include ../src/mat/impls/dense/mpi/mpidense.h
4843: /*
4844: Computes (B'*A')' since computing B*A directly is untenable
4846: n p p
4847: ( ) ( ) ( )
4848: m ( A ) * n ( B ) = m ( C )
4849: ( ) ( ) ( )
4851: */
4852: PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A,Mat B,Mat C)
4853: {
4854: PetscErrorCode ierr;
4855: Mat At,Bt,Ct;
4858: MatTranspose(A,MAT_INITIAL_MATRIX,&At);
4859: MatTranspose(B,MAT_INITIAL_MATRIX,&Bt);
4860: MatMatMult(Bt,At,MAT_INITIAL_MATRIX,1.0,&Ct);
4861: MatDestroy(At);
4862: MatDestroy(Bt);
4863: MatTranspose(Ct,MAT_REUSE_MATRIX,&C);
4864: MatDestroy(Ct);
4865: return(0);
4866: }
4870: PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
4871: {
4873: PetscInt m=A->rmap->n,n=B->cmap->n;
4874: Mat Cmat;
4877: if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"A->cmap->n %d != B->rmap->n %d\n",A->cmap->n,B->rmap->n);
4878: MatCreate(((PetscObject)A)->comm,&Cmat);
4879: MatSetSizes(Cmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
4880: MatSetType(Cmat,MATMPIDENSE);
4881: MatMPIDenseSetPreallocation(Cmat,PETSC_NULL);
4882: MatAssemblyBegin(Cmat,MAT_FINAL_ASSEMBLY);
4883: MatAssemblyEnd(Cmat,MAT_FINAL_ASSEMBLY);
4884: *C = Cmat;
4885: return(0);
4886: }
4888: /* ----------------------------------------------------------------*/
4891: PetscErrorCode MatMatMult_MPIDense_MPIAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
4892: {
4896: if (scall == MAT_INITIAL_MATRIX){
4897: MatMatMultSymbolic_MPIDense_MPIAIJ(A,B,fill,C);
4898: }
4899: MatMatMultNumeric_MPIDense_MPIAIJ(A,B,*C);
4900: return(0);
4901: }
4904: #if defined(PETSC_HAVE_MUMPS)
4906: #endif
4907: #if defined(PETSC_HAVE_PASTIX)
4909: #endif
4910: #if defined(PETSC_HAVE_SUPERLU_DIST)
4912: #endif
4913: #if defined(PETSC_HAVE_SPOOLES)
4915: #endif
4918: /*MC
4919: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
4921: Options Database Keys:
4922: . -mat_type mpiaij - sets the matrix type to "mpiaij" during a call to MatSetFromOptions()
4924: Level: beginner
4926: .seealso: MatCreateMPIAIJ()
4927: M*/
4932: PetscErrorCode MatCreate_MPIAIJ(Mat B)
4933: {
4934: Mat_MPIAIJ *b;
4936: PetscMPIInt size;
4939: MPI_Comm_size(((PetscObject)B)->comm,&size);
4941: PetscNewLog(B,Mat_MPIAIJ,&b);
4942: B->data = (void*)b;
4943: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4944: B->rmap->bs = 1;
4945: B->assembled = PETSC_FALSE;
4946: B->mapping = 0;
4948: B->insertmode = NOT_SET_VALUES;
4949: b->size = size;
4950: MPI_Comm_rank(((PetscObject)B)->comm,&b->rank);
4952: /* build cache for off array entries formed */
4953: MatStashCreate_Private(((PetscObject)B)->comm,1,&B->stash);
4954: b->donotstash = PETSC_FALSE;
4955: b->colmap = 0;
4956: b->garray = 0;
4957: b->roworiented = PETSC_TRUE;
4959: /* stuff used for matrix vector multiply */
4960: b->lvec = PETSC_NULL;
4961: b->Mvctx = PETSC_NULL;
4963: /* stuff for MatGetRow() */
4964: b->rowindices = 0;
4965: b->rowvalues = 0;
4966: b->getrowactive = PETSC_FALSE;
4968: #if defined(PETSC_HAVE_SPOOLES)
4969: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpiaij_spooles_C",
4970: "MatGetFactor_mpiaij_spooles",
4971: MatGetFactor_mpiaij_spooles);
4972: #endif
4973: #if defined(PETSC_HAVE_MUMPS)
4974: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpiaij_mumps_C",
4975: "MatGetFactor_mpiaij_mumps",
4976: MatGetFactor_mpiaij_mumps);
4977: #endif
4978: #if defined(PETSC_HAVE_PASTIX)
4979: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpiaij_pastix_C",
4980: "MatGetFactor_mpiaij_pastix",
4981: MatGetFactor_mpiaij_pastix);
4982: #endif
4983: #if defined(PETSC_HAVE_SUPERLU_DIST)
4984: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mpiaij_superlu_dist_C",
4985: "MatGetFactor_mpiaij_superlu_dist",
4986: MatGetFactor_mpiaij_superlu_dist);
4987: #endif
4988: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
4989: "MatStoreValues_MPIAIJ",
4990: MatStoreValues_MPIAIJ);
4991: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
4992: "MatRetrieveValues_MPIAIJ",
4993: MatRetrieveValues_MPIAIJ);
4994: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetDiagonalBlock_C",
4995: "MatGetDiagonalBlock_MPIAIJ",
4996: MatGetDiagonalBlock_MPIAIJ);
4997: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
4998: "MatIsTranspose_MPIAIJ",
4999: MatIsTranspose_MPIAIJ);
5000: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocation_C",
5001: "MatMPIAIJSetPreallocation_MPIAIJ",
5002: MatMPIAIJSetPreallocation_MPIAIJ);
5003: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",
5004: "MatMPIAIJSetPreallocationCSR_MPIAIJ",
5005: MatMPIAIJSetPreallocationCSR_MPIAIJ);
5006: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatDiagonalScaleLocal_C",
5007: "MatDiagonalScaleLocal_MPIAIJ",
5008: MatDiagonalScaleLocal_MPIAIJ);
5009: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpicsrperm_C",
5010: "MatConvert_MPIAIJ_MPICSRPERM",
5011: MatConvert_MPIAIJ_MPICSRPERM);
5012: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_mpicrl_C",
5013: "MatConvert_MPIAIJ_MPICRL",
5014: MatConvert_MPIAIJ_MPICRL);
5015: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMult_mpidense_mpiaij_C",
5016: "MatMatMult_MPIDense_MPIAIJ",
5017: MatMatMult_MPIDense_MPIAIJ);
5018: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultSymbolic_mpidense_mpiaij_C",
5019: "MatMatMultSymbolic_MPIDense_MPIAIJ",
5020: MatMatMultSymbolic_MPIDense_MPIAIJ);
5021: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultNumeric_mpidense_mpiaij_C",
5022: "MatMatMultNumeric_MPIDense_MPIAIJ",
5023: MatMatMultNumeric_MPIDense_MPIAIJ);
5024: PetscObjectChangeTypeName((PetscObject)B,MATMPIAIJ);
5025: return(0);
5026: }
5031: /*@
5032: MatCreateMPIAIJWithSplitArrays - creates a MPI AIJ matrix using arrays that contain the "diagonal"
5033: and "off-diagonal" part of the matrix in CSR format.
5035: Collective on MPI_Comm
5037: Input Parameters:
5038: + comm - MPI communicator
5039: . m - number of local rows (Cannot be PETSC_DECIDE)
5040: . n - This value should be the same as the local size used in creating the
5041: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
5042: calculated if N is given) For square matrices n is almost always m.
5043: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
5044: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
5045: . i - row indices for "diagonal" portion of matrix
5046: . j - column indices
5047: . a - matrix values
5048: . oi - row indices for "off-diagonal" portion of matrix
5049: . oj - column indices
5050: - oa - matrix values
5052: Output Parameter:
5053: . mat - the matrix
5055: Level: advanced
5057: Notes:
5058: The i, j, and a arrays ARE NOT copied by this routine into the internal format used by PETSc.
5060: The i and j indices are 0 based
5061:
5062: See MatCreateMPIAIJ() for the definition of "diagonal" and "off-diagonal" portion of the matrix
5065: .keywords: matrix, aij, compressed row, sparse, parallel
5067: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
5068: MPIAIJ, MatCreateMPIAIJ(), MatCreateMPIAIJWithArrays()
5069: @*/
5070: PetscErrorCode MatCreateMPIAIJWithSplitArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt i[],PetscInt j[],PetscScalar a[],
5071: PetscInt oi[], PetscInt oj[],PetscScalar oa[],Mat *mat)
5072: {
5074: Mat_MPIAIJ *maij;
5077: if (m < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
5078: if (i[0]) {
5079: SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
5080: }
5081: if (oi[0]) {
5082: SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"oi (row indices) must start with 0");
5083: }
5084: MatCreate(comm,mat);
5085: MatSetSizes(*mat,m,n,M,N);
5086: MatSetType(*mat,MATMPIAIJ);
5087: maij = (Mat_MPIAIJ*) (*mat)->data;
5088: maij->donotstash = PETSC_TRUE;
5089: (*mat)->preallocated = PETSC_TRUE;
5091: PetscMapSetBlockSize((*mat)->rmap,1);
5092: PetscMapSetBlockSize((*mat)->cmap,1);
5093: PetscMapSetUp((*mat)->rmap);
5094: PetscMapSetUp((*mat)->cmap);
5096: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,i,j,a,&maij->A);
5097: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,(*mat)->cmap->N,oi,oj,oa,&maij->B);
5099: MatAssemblyBegin(maij->A,MAT_FINAL_ASSEMBLY);
5100: MatAssemblyEnd(maij->A,MAT_FINAL_ASSEMBLY);
5101: MatAssemblyBegin(maij->B,MAT_FINAL_ASSEMBLY);
5102: MatAssemblyEnd(maij->B,MAT_FINAL_ASSEMBLY);
5104: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
5105: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
5106: return(0);
5107: }
5109: /*
5110: Special version for direct calls from Fortran
5111: */
5112: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5113: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
5114: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5115: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
5116: #endif
5118: /* Change these macros so can be used in void function */
5119: #undef CHKERRQ
5120: #define CHKERRQ(ierr) CHKERRABORT(((PetscObject)mat)->comm,ierr)
5121: #undef SETERRQ2
5122: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(((PetscObject)mat)->comm,ierr)
5123: #undef SETERRQ
5124: #define SETERRQ(ierr,b) CHKERRABORT(((PetscObject)mat)->comm,ierr)
5129: void PETSC_STDCALL matsetvaluesmpiaij_(Mat *mmat,PetscInt *mm,const PetscInt im[],PetscInt *mn,const PetscInt in[],const PetscScalar v[],InsertMode *maddv,PetscErrorCode *_ierr)
5130: {
5131: Mat mat = *mmat;
5132: PetscInt m = *mm, n = *mn;
5133: InsertMode addv = *maddv;
5134: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
5135: PetscScalar value;
5136: PetscErrorCode ierr;
5138: MatPreallocated(mat);
5139: if (mat->insertmode == NOT_SET_VALUES) {
5140: mat->insertmode = addv;
5141: }
5142: #if defined(PETSC_USE_DEBUG)
5143: else if (mat->insertmode != addv) {
5144: SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
5145: }
5146: #endif
5147: {
5148: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
5149: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
5150: PetscTruth roworiented = aij->roworiented;
5152: /* Some Variables required in the macro */
5153: Mat A = aij->A;
5154: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
5155: PetscInt *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
5156: MatScalar *aa = a->a;
5157: PetscTruth ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES))?PETSC_TRUE:PETSC_FALSE);
5158: Mat B = aij->B;
5159: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
5160: PetscInt *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap->n,am = aij->A->rmap->n;
5161: MatScalar *ba = b->a;
5163: PetscInt *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
5164: PetscInt nonew = a->nonew;
5165: MatScalar *ap1,*ap2;
5168: for (i=0; i<m; i++) {
5169: if (im[i] < 0) continue;
5170: #if defined(PETSC_USE_DEBUG)
5171: if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
5172: #endif
5173: if (im[i] >= rstart && im[i] < rend) {
5174: row = im[i] - rstart;
5175: lastcol1 = -1;
5176: rp1 = aj + ai[row];
5177: ap1 = aa + ai[row];
5178: rmax1 = aimax[row];
5179: nrow1 = ailen[row];
5180: low1 = 0;
5181: high1 = nrow1;
5182: lastcol2 = -1;
5183: rp2 = bj + bi[row];
5184: ap2 = ba + bi[row];
5185: rmax2 = bimax[row];
5186: nrow2 = bilen[row];
5187: low2 = 0;
5188: high2 = nrow2;
5190: for (j=0; j<n; j++) {
5191: if (roworiented) value = v[i*n+j]; else value = v[i+j*m];
5192: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
5193: if (in[j] >= cstart && in[j] < cend){
5194: col = in[j] - cstart;
5195: MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
5196: } else if (in[j] < 0) continue;
5197: #if defined(PETSC_USE_DEBUG)
5198: 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);}
5199: #endif
5200: else {
5201: if (mat->was_assembled) {
5202: if (!aij->colmap) {
5203: CreateColmap_MPIAIJ_Private(mat);
5204: }
5205: #if defined (PETSC_USE_CTABLE)
5206: PetscTableFind(aij->colmap,in[j]+1,&col);
5207: col--;
5208: #else
5209: col = aij->colmap[in[j]] - 1;
5210: #endif
5211: if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
5212: DisAssemble_MPIAIJ(mat);
5213: col = in[j];
5214: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
5215: B = aij->B;
5216: b = (Mat_SeqAIJ*)B->data;
5217: bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
5218: rp2 = bj + bi[row];
5219: ap2 = ba + bi[row];
5220: rmax2 = bimax[row];
5221: nrow2 = bilen[row];
5222: low2 = 0;
5223: high2 = nrow2;
5224: bm = aij->B->rmap->n;
5225: ba = b->a;
5226: }
5227: } else col = in[j];
5228: MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
5229: }
5230: }
5231: } else {
5232: if (!aij->donotstash) {
5233: if (roworiented) {
5234: if (ignorezeroentries && v[i*n] == 0.0) continue;
5235: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n);
5236: } else {
5237: if (ignorezeroentries && v[i] == 0.0) continue;
5238: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m);
5239: }
5240: }
5241: }
5242: }}
5243: PetscFunctionReturnVoid();
5244: }