Actual source code: mpiaij.c
petsc-3.6.2 2015-10-02
2: #include <../src/mat/impls/aij/mpi/mpiaij.h> /*I "petscmat.h" I*/
3: #include <petsc/private/vecimpl.h>
4: #include <petsc/private/isimpl.h>
5: #include <petscblaslapack.h>
6: #include <petscsf.h>
8: /*MC
9: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
11: This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
12: and MATMPIAIJ otherwise. As a result, for single process communicators,
13: MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
14: for communicators controlling multiple processes. It is recommended that you call both of
15: the above preallocation routines for simplicity.
17: Options Database Keys:
18: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()
20: Developer Notes: Subclasses include MATAIJCUSP, MATAIJCUSPARSE, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when
21: enough exist.
23: Level: beginner
25: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
26: M*/
28: /*MC
29: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
31: This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
32: and MATMPIAIJCRL otherwise. As a result, for single process communicators,
33: MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
34: for communicators controlling multiple processes. It is recommended that you call both of
35: the above preallocation routines for simplicity.
37: Options Database Keys:
38: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()
40: Level: beginner
42: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
43: M*/
47: PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M,IS *keptrows)
48: {
49: PetscErrorCode ierr;
50: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)M->data;
51: Mat_SeqAIJ *a = (Mat_SeqAIJ*)mat->A->data;
52: Mat_SeqAIJ *b = (Mat_SeqAIJ*)mat->B->data;
53: const PetscInt *ia,*ib;
54: const MatScalar *aa,*bb;
55: PetscInt na,nb,i,j,*rows,cnt=0,n0rows;
56: PetscInt m = M->rmap->n,rstart = M->rmap->rstart;
59: *keptrows = 0;
60: ia = a->i;
61: ib = b->i;
62: for (i=0; i<m; i++) {
63: na = ia[i+1] - ia[i];
64: nb = ib[i+1] - ib[i];
65: if (!na && !nb) {
66: cnt++;
67: goto ok1;
68: }
69: aa = a->a + ia[i];
70: for (j=0; j<na; j++) {
71: if (aa[j] != 0.0) goto ok1;
72: }
73: bb = b->a + ib[i];
74: for (j=0; j <nb; j++) {
75: if (bb[j] != 0.0) goto ok1;
76: }
77: cnt++;
78: ok1:;
79: }
80: MPI_Allreduce(&cnt,&n0rows,1,MPIU_INT,MPI_SUM,PetscObjectComm((PetscObject)M));
81: if (!n0rows) return(0);
82: PetscMalloc1(M->rmap->n-cnt,&rows);
83: cnt = 0;
84: for (i=0; i<m; i++) {
85: na = ia[i+1] - ia[i];
86: nb = ib[i+1] - ib[i];
87: if (!na && !nb) continue;
88: aa = a->a + ia[i];
89: for (j=0; j<na;j++) {
90: if (aa[j] != 0.0) {
91: rows[cnt++] = rstart + i;
92: goto ok2;
93: }
94: }
95: bb = b->a + ib[i];
96: for (j=0; j<nb; j++) {
97: if (bb[j] != 0.0) {
98: rows[cnt++] = rstart + i;
99: goto ok2;
100: }
101: }
102: ok2:;
103: }
104: ISCreateGeneral(PetscObjectComm((PetscObject)M),cnt,rows,PETSC_OWN_POINTER,keptrows);
105: return(0);
106: }
110: PetscErrorCode MatDiagonalSet_MPIAIJ(Mat Y,Vec D,InsertMode is)
111: {
112: PetscErrorCode ierr;
113: Mat_MPIAIJ *aij = (Mat_MPIAIJ*) Y->data;
116: if (Y->assembled && Y->rmap->rstart == Y->cmap->rstart && Y->rmap->rend == Y->cmap->rend) {
117: MatDiagonalSet(aij->A,D,is);
118: } else {
119: MatDiagonalSet_Default(Y,D,is);
120: }
121: return(0);
122: }
127: PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M,IS *zrows)
128: {
129: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)M->data;
131: PetscInt i,rstart,nrows,*rows;
134: *zrows = NULL;
135: MatFindZeroDiagonals_SeqAIJ_Private(aij->A,&nrows,&rows);
136: MatGetOwnershipRange(M,&rstart,NULL);
137: for (i=0; i<nrows; i++) rows[i] += rstart;
138: ISCreateGeneral(PetscObjectComm((PetscObject)M),nrows,rows,PETSC_OWN_POINTER,zrows);
139: return(0);
140: }
144: PetscErrorCode MatGetColumnNorms_MPIAIJ(Mat A,NormType type,PetscReal *norms)
145: {
147: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)A->data;
148: PetscInt i,n,*garray = aij->garray;
149: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ*) aij->A->data;
150: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ*) aij->B->data;
151: PetscReal *work;
154: MatGetSize(A,NULL,&n);
155: PetscCalloc1(n,&work);
156: if (type == NORM_2) {
157: for (i=0; i<a_aij->i[aij->A->rmap->n]; i++) {
158: work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]*a_aij->a[i]);
159: }
160: for (i=0; i<b_aij->i[aij->B->rmap->n]; i++) {
161: work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]*b_aij->a[i]);
162: }
163: } else if (type == NORM_1) {
164: for (i=0; i<a_aij->i[aij->A->rmap->n]; i++) {
165: work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
166: }
167: for (i=0; i<b_aij->i[aij->B->rmap->n]; i++) {
168: work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
169: }
170: } else if (type == NORM_INFINITY) {
171: for (i=0; i<a_aij->i[aij->A->rmap->n]; i++) {
172: work[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), work[A->cmap->rstart + a_aij->j[i]]);
173: }
174: for (i=0; i<b_aij->i[aij->B->rmap->n]; i++) {
175: work[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]),work[garray[b_aij->j[i]]]);
176: }
178: } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Unknown NormType");
179: if (type == NORM_INFINITY) {
180: MPI_Allreduce(work,norms,n,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
181: } else {
182: MPI_Allreduce(work,norms,n,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
183: }
184: PetscFree(work);
185: if (type == NORM_2) {
186: for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
187: }
188: return(0);
189: }
193: PetscErrorCode MatFindOffBlockDiagonalEntries_MPIAIJ(Mat A,IS *is)
194: {
195: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
196: IS sis,gis;
197: PetscErrorCode ierr;
198: const PetscInt *isis,*igis;
199: PetscInt n,*iis,nsis,ngis,rstart,i;
202: MatFindOffBlockDiagonalEntries(a->A,&sis);
203: MatFindNonzeroRows(a->B,&gis);
204: ISGetSize(gis,&ngis);
205: ISGetSize(sis,&nsis);
206: ISGetIndices(sis,&isis);
207: ISGetIndices(gis,&igis);
209: PetscMalloc1(ngis+nsis,&iis);
210: PetscMemcpy(iis,igis,ngis*sizeof(PetscInt));
211: PetscMemcpy(iis+ngis,isis,nsis*sizeof(PetscInt));
212: n = ngis + nsis;
213: PetscSortRemoveDupsInt(&n,iis);
214: MatGetOwnershipRange(A,&rstart,NULL);
215: for (i=0; i<n; i++) iis[i] += rstart;
216: ISCreateGeneral(PetscObjectComm((PetscObject)A),n,iis,PETSC_OWN_POINTER,is);
218: ISRestoreIndices(sis,&isis);
219: ISRestoreIndices(gis,&igis);
220: ISDestroy(&sis);
221: ISDestroy(&gis);
222: return(0);
223: }
227: /*
228: Distributes a SeqAIJ matrix across a set of processes. Code stolen from
229: MatLoad_MPIAIJ(). Horrible lack of reuse. Should be a routine for each matrix type.
231: Only for square matrices
233: Used by a preconditioner, hence PETSC_EXTERN
234: */
235: PETSC_EXTERN PetscErrorCode MatDistribute_MPIAIJ(MPI_Comm comm,Mat gmat,PetscInt m,MatReuse reuse,Mat *inmat)
236: {
237: PetscMPIInt rank,size;
238: PetscInt *rowners,*dlens,*olens,i,rstart,rend,j,jj,nz = 0,*gmataj,cnt,row,*ld,bses[2];
240: Mat mat;
241: Mat_SeqAIJ *gmata;
242: PetscMPIInt tag;
243: MPI_Status status;
244: PetscBool aij;
245: MatScalar *gmataa,*ao,*ad,*gmataarestore=0;
248: MPI_Comm_rank(comm,&rank);
249: MPI_Comm_size(comm,&size);
250: if (!rank) {
251: PetscObjectTypeCompare((PetscObject)gmat,MATSEQAIJ,&aij);
252: if (!aij) SETERRQ1(PetscObjectComm((PetscObject)gmat),PETSC_ERR_SUP,"Currently no support for input matrix of type %s\n",((PetscObject)gmat)->type_name);
253: }
254: if (reuse == MAT_INITIAL_MATRIX) {
255: MatCreate(comm,&mat);
256: MatSetSizes(mat,m,m,PETSC_DETERMINE,PETSC_DETERMINE);
257: MatGetBlockSizes(gmat,&bses[0],&bses[1]);
258: MPI_Bcast(bses,2,MPIU_INT,0,comm);
259: MatSetBlockSizes(mat,bses[0],bses[1]);
260: MatSetType(mat,MATAIJ);
261: PetscMalloc1(size+1,&rowners);
262: PetscMalloc2(m,&dlens,m,&olens);
263: MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
265: rowners[0] = 0;
266: for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
267: rstart = rowners[rank];
268: rend = rowners[rank+1];
269: PetscObjectGetNewTag((PetscObject)mat,&tag);
270: if (!rank) {
271: gmata = (Mat_SeqAIJ*) gmat->data;
272: /* send row lengths to all processors */
273: for (i=0; i<m; i++) dlens[i] = gmata->ilen[i];
274: for (i=1; i<size; i++) {
275: MPI_Send(gmata->ilen + rowners[i],rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
276: }
277: /* determine number diagonal and off-diagonal counts */
278: PetscMemzero(olens,m*sizeof(PetscInt));
279: PetscCalloc1(m,&ld);
280: jj = 0;
281: for (i=0; i<m; i++) {
282: for (j=0; j<dlens[i]; j++) {
283: if (gmata->j[jj] < rstart) ld[i]++;
284: if (gmata->j[jj] < rstart || gmata->j[jj] >= rend) olens[i]++;
285: jj++;
286: }
287: }
288: /* send column indices to other processes */
289: for (i=1; i<size; i++) {
290: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
291: MPI_Send(&nz,1,MPIU_INT,i,tag,comm);
292: MPI_Send(gmata->j + gmata->i[rowners[i]],nz,MPIU_INT,i,tag,comm);
293: }
295: /* send numerical values to other processes */
296: for (i=1; i<size; i++) {
297: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
298: MPI_Send(gmata->a + gmata->i[rowners[i]],nz,MPIU_SCALAR,i,tag,comm);
299: }
300: gmataa = gmata->a;
301: gmataj = gmata->j;
303: } else {
304: /* receive row lengths */
305: MPI_Recv(dlens,m,MPIU_INT,0,tag,comm,&status);
306: /* receive column indices */
307: MPI_Recv(&nz,1,MPIU_INT,0,tag,comm,&status);
308: PetscMalloc2(nz,&gmataa,nz,&gmataj);
309: MPI_Recv(gmataj,nz,MPIU_INT,0,tag,comm,&status);
310: /* determine number diagonal and off-diagonal counts */
311: PetscMemzero(olens,m*sizeof(PetscInt));
312: PetscCalloc1(m,&ld);
313: jj = 0;
314: for (i=0; i<m; i++) {
315: for (j=0; j<dlens[i]; j++) {
316: if (gmataj[jj] < rstart) ld[i]++;
317: if (gmataj[jj] < rstart || gmataj[jj] >= rend) olens[i]++;
318: jj++;
319: }
320: }
321: /* receive numerical values */
322: PetscMemzero(gmataa,nz*sizeof(PetscScalar));
323: MPI_Recv(gmataa,nz,MPIU_SCALAR,0,tag,comm,&status);
324: }
325: /* set preallocation */
326: for (i=0; i<m; i++) {
327: dlens[i] -= olens[i];
328: }
329: MatSeqAIJSetPreallocation(mat,0,dlens);
330: MatMPIAIJSetPreallocation(mat,0,dlens,0,olens);
332: for (i=0; i<m; i++) {
333: dlens[i] += olens[i];
334: }
335: cnt = 0;
336: for (i=0; i<m; i++) {
337: row = rstart + i;
338: MatSetValues(mat,1,&row,dlens[i],gmataj+cnt,gmataa+cnt,INSERT_VALUES);
339: cnt += dlens[i];
340: }
341: if (rank) {
342: PetscFree2(gmataa,gmataj);
343: }
344: PetscFree2(dlens,olens);
345: PetscFree(rowners);
347: ((Mat_MPIAIJ*)(mat->data))->ld = ld;
349: *inmat = mat;
350: } else { /* column indices are already set; only need to move over numerical values from process 0 */
351: Mat_SeqAIJ *Ad = (Mat_SeqAIJ*)((Mat_MPIAIJ*)((*inmat)->data))->A->data;
352: Mat_SeqAIJ *Ao = (Mat_SeqAIJ*)((Mat_MPIAIJ*)((*inmat)->data))->B->data;
353: mat = *inmat;
354: PetscObjectGetNewTag((PetscObject)mat,&tag);
355: if (!rank) {
356: /* send numerical values to other processes */
357: gmata = (Mat_SeqAIJ*) gmat->data;
358: MatGetOwnershipRanges(mat,(const PetscInt**)&rowners);
359: gmataa = gmata->a;
360: for (i=1; i<size; i++) {
361: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
362: MPI_Send(gmataa + gmata->i[rowners[i]],nz,MPIU_SCALAR,i,tag,comm);
363: }
364: nz = gmata->i[rowners[1]]-gmata->i[rowners[0]];
365: } else {
366: /* receive numerical values from process 0*/
367: nz = Ad->nz + Ao->nz;
368: PetscMalloc1(nz,&gmataa); gmataarestore = gmataa;
369: MPI_Recv(gmataa,nz,MPIU_SCALAR,0,tag,comm,&status);
370: }
371: /* transfer numerical values into the diagonal A and off diagonal B parts of mat */
372: ld = ((Mat_MPIAIJ*)(mat->data))->ld;
373: ad = Ad->a;
374: ao = Ao->a;
375: if (mat->rmap->n) {
376: i = 0;
377: nz = ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
378: nz = Ad->i[i+1] - Ad->i[i]; PetscMemcpy(ad,gmataa,nz*sizeof(PetscScalar)); ad += nz; gmataa += nz;
379: }
380: for (i=1; i<mat->rmap->n; i++) {
381: nz = Ao->i[i] - Ao->i[i-1] - ld[i-1] + ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
382: nz = Ad->i[i+1] - Ad->i[i]; PetscMemcpy(ad,gmataa,nz*sizeof(PetscScalar)); ad += nz; gmataa += nz;
383: }
384: i--;
385: if (mat->rmap->n) {
386: nz = Ao->i[i+1] - Ao->i[i] - ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar));
387: }
388: if (rank) {
389: PetscFree(gmataarestore);
390: }
391: }
392: MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
393: MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
394: return(0);
395: }
397: /*
398: Local utility routine that creates a mapping from the global column
399: number to the local number in the off-diagonal part of the local
400: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
401: a slightly higher hash table cost; without it it is not scalable (each processor
402: has an order N integer array but is fast to acess.
403: */
406: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
407: {
408: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
410: PetscInt n = aij->B->cmap->n,i;
413: if (!aij->garray) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"MPIAIJ Matrix was assembled but is missing garray");
414: #if defined(PETSC_USE_CTABLE)
415: PetscTableCreate(n,mat->cmap->N+1,&aij->colmap);
416: for (i=0; i<n; i++) {
417: PetscTableAdd(aij->colmap,aij->garray[i]+1,i+1,INSERT_VALUES);
418: }
419: #else
420: PetscCalloc1(mat->cmap->N+1,&aij->colmap);
421: PetscLogObjectMemory((PetscObject)mat,(mat->cmap->N+1)*sizeof(PetscInt));
422: for (i=0; i<n; i++) aij->colmap[aij->garray[i]] = i+1;
423: #endif
424: return(0);
425: }
427: #define MatSetValues_SeqAIJ_A_Private(row,col,value,addv,orow,ocol) \
428: { \
429: if (col <= lastcol1) low1 = 0; \
430: else high1 = nrow1; \
431: lastcol1 = col;\
432: while (high1-low1 > 5) { \
433: t = (low1+high1)/2; \
434: if (rp1[t] > col) high1 = t; \
435: else low1 = t; \
436: } \
437: for (_i=low1; _i<high1; _i++) { \
438: if (rp1[_i] > col) break; \
439: if (rp1[_i] == col) { \
440: if (addv == ADD_VALUES) ap1[_i] += value; \
441: else ap1[_i] = value; \
442: goto a_noinsert; \
443: } \
444: } \
445: if (value == 0.0 && ignorezeroentries) {low1 = 0; high1 = nrow1;goto a_noinsert;} \
446: if (nonew == 1) {low1 = 0; high1 = nrow1; goto a_noinsert;} \
447: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at global row/column (%D, %D) into matrix", orow, ocol); \
448: MatSeqXAIJReallocateAIJ(A,am,1,nrow1,row,col,rmax1,aa,ai,aj,rp1,ap1,aimax,nonew,MatScalar); \
449: N = nrow1++ - 1; a->nz++; high1++; \
450: /* shift up all the later entries in this row */ \
451: for (ii=N; ii>=_i; ii--) { \
452: rp1[ii+1] = rp1[ii]; \
453: ap1[ii+1] = ap1[ii]; \
454: } \
455: rp1[_i] = col; \
456: ap1[_i] = value; \
457: A->nonzerostate++;\
458: a_noinsert: ; \
459: ailen[row] = nrow1; \
460: }
463: #define MatSetValues_SeqAIJ_B_Private(row,col,value,addv,orow,ocol) \
464: { \
465: if (col <= lastcol2) low2 = 0; \
466: else high2 = nrow2; \
467: lastcol2 = col; \
468: while (high2-low2 > 5) { \
469: t = (low2+high2)/2; \
470: if (rp2[t] > col) high2 = t; \
471: else low2 = t; \
472: } \
473: for (_i=low2; _i<high2; _i++) { \
474: if (rp2[_i] > col) break; \
475: if (rp2[_i] == col) { \
476: if (addv == ADD_VALUES) ap2[_i] += value; \
477: else ap2[_i] = value; \
478: goto b_noinsert; \
479: } \
480: } \
481: if (value == 0.0 && ignorezeroentries) {low2 = 0; high2 = nrow2; goto b_noinsert;} \
482: if (nonew == 1) {low2 = 0; high2 = nrow2; goto b_noinsert;} \
483: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at global row/column (%D, %D) into matrix", orow, ocol); \
484: MatSeqXAIJReallocateAIJ(B,bm,1,nrow2,row,col,rmax2,ba,bi,bj,rp2,ap2,bimax,nonew,MatScalar); \
485: N = nrow2++ - 1; b->nz++; high2++; \
486: /* shift up all the later entries in this row */ \
487: for (ii=N; ii>=_i; ii--) { \
488: rp2[ii+1] = rp2[ii]; \
489: ap2[ii+1] = ap2[ii]; \
490: } \
491: rp2[_i] = col; \
492: ap2[_i] = value; \
493: B->nonzerostate++; \
494: b_noinsert: ; \
495: bilen[row] = nrow2; \
496: }
500: PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A,PetscInt row,const PetscScalar v[])
501: {
502: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
503: Mat_SeqAIJ *a = (Mat_SeqAIJ*)mat->A->data,*b = (Mat_SeqAIJ*)mat->B->data;
505: PetscInt l,*garray = mat->garray,diag;
508: /* code only works for square matrices A */
510: /* find size of row to the left of the diagonal part */
511: MatGetOwnershipRange(A,&diag,0);
512: row = row - diag;
513: for (l=0; l<b->i[row+1]-b->i[row]; l++) {
514: if (garray[b->j[b->i[row]+l]] > diag) break;
515: }
516: PetscMemcpy(b->a+b->i[row],v,l*sizeof(PetscScalar));
518: /* diagonal part */
519: PetscMemcpy(a->a+a->i[row],v+l,(a->i[row+1]-a->i[row])*sizeof(PetscScalar));
521: /* right of diagonal part */
522: 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));
523: return(0);
524: }
528: PetscErrorCode MatSetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
529: {
530: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
531: PetscScalar value;
533: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
534: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
535: PetscBool roworiented = aij->roworiented;
537: /* Some Variables required in the macro */
538: Mat A = aij->A;
539: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
540: PetscInt *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
541: MatScalar *aa = a->a;
542: PetscBool ignorezeroentries = a->ignorezeroentries;
543: Mat B = aij->B;
544: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
545: PetscInt *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap->n,am = aij->A->rmap->n;
546: MatScalar *ba = b->a;
548: PetscInt *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
549: PetscInt nonew;
550: MatScalar *ap1,*ap2;
553: for (i=0; i<m; i++) {
554: if (im[i] < 0) continue;
555: #if defined(PETSC_USE_DEBUG)
556: if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
557: #endif
558: if (im[i] >= rstart && im[i] < rend) {
559: row = im[i] - rstart;
560: lastcol1 = -1;
561: rp1 = aj + ai[row];
562: ap1 = aa + ai[row];
563: rmax1 = aimax[row];
564: nrow1 = ailen[row];
565: low1 = 0;
566: high1 = nrow1;
567: lastcol2 = -1;
568: rp2 = bj + bi[row];
569: ap2 = ba + bi[row];
570: rmax2 = bimax[row];
571: nrow2 = bilen[row];
572: low2 = 0;
573: high2 = nrow2;
575: for (j=0; j<n; j++) {
576: if (roworiented) value = v[i*n+j];
577: else value = v[i+j*m];
578: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
579: if (in[j] >= cstart && in[j] < cend) {
580: col = in[j] - cstart;
581: nonew = a->nonew;
582: MatSetValues_SeqAIJ_A_Private(row,col,value,addv,im[i],in[j]);
583: } else if (in[j] < 0) continue;
584: #if defined(PETSC_USE_DEBUG)
585: else if (in[j] >= mat->cmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap->N-1);
586: #endif
587: else {
588: if (mat->was_assembled) {
589: if (!aij->colmap) {
590: MatCreateColmap_MPIAIJ_Private(mat);
591: }
592: #if defined(PETSC_USE_CTABLE)
593: PetscTableFind(aij->colmap,in[j]+1,&col);
594: col--;
595: #else
596: col = aij->colmap[in[j]] - 1;
597: #endif
598: if (col < 0 && !((Mat_SeqAIJ*)(aij->B->data))->nonew) {
599: MatDisAssemble_MPIAIJ(mat);
600: col = in[j];
601: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
602: B = aij->B;
603: b = (Mat_SeqAIJ*)B->data;
604: bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j; ba = b->a;
605: rp2 = bj + bi[row];
606: ap2 = ba + bi[row];
607: rmax2 = bimax[row];
608: nrow2 = bilen[row];
609: low2 = 0;
610: high2 = nrow2;
611: bm = aij->B->rmap->n;
612: ba = b->a;
613: } else if (col < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at global row/column (%D, %D) into matrix", im[i], in[j]);
614: } else col = in[j];
615: nonew = b->nonew;
616: MatSetValues_SeqAIJ_B_Private(row,col,value,addv,im[i],in[j]);
617: }
618: }
619: } else {
620: if (mat->nooffprocentries) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Setting off process row %D even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set",im[i]);
621: if (!aij->donotstash) {
622: mat->assembled = PETSC_FALSE;
623: if (roworiented) {
624: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,(PetscBool)(ignorezeroentries && (addv == ADD_VALUES)));
625: } else {
626: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,(PetscBool)(ignorezeroentries && (addv == ADD_VALUES)));
627: }
628: }
629: }
630: }
631: return(0);
632: }
636: PetscErrorCode MatGetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
637: {
638: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
640: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
641: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
644: for (i=0; i<m; i++) {
645: if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);*/
646: if (idxm[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",idxm[i],mat->rmap->N-1);
647: if (idxm[i] >= rstart && idxm[i] < rend) {
648: row = idxm[i] - rstart;
649: for (j=0; j<n; j++) {
650: if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]); */
651: if (idxn[j] >= mat->cmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",idxn[j],mat->cmap->N-1);
652: if (idxn[j] >= cstart && idxn[j] < cend) {
653: col = idxn[j] - cstart;
654: MatGetValues(aij->A,1,&row,1,&col,v+i*n+j);
655: } else {
656: if (!aij->colmap) {
657: MatCreateColmap_MPIAIJ_Private(mat);
658: }
659: #if defined(PETSC_USE_CTABLE)
660: PetscTableFind(aij->colmap,idxn[j]+1,&col);
661: col--;
662: #else
663: col = aij->colmap[idxn[j]] - 1;
664: #endif
665: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v+i*n+j) = 0.0;
666: else {
667: MatGetValues(aij->B,1,&row,1,&col,v+i*n+j);
668: }
669: }
670: }
671: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
672: }
673: return(0);
674: }
676: extern PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat,Vec,Vec);
680: PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat,MatAssemblyType mode)
681: {
682: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
684: PetscInt nstash,reallocs;
685: InsertMode addv;
688: if (aij->donotstash || mat->nooffprocentries) return(0);
690: /* make sure all processors are either in INSERTMODE or ADDMODE */
691: MPI_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,PetscObjectComm((PetscObject)mat));
692: if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
693: mat->insertmode = addv; /* in case this processor had no cache */
695: MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
696: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
697: PetscInfo2(aij->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
698: return(0);
699: }
703: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat,MatAssemblyType mode)
704: {
705: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
706: Mat_SeqAIJ *a = (Mat_SeqAIJ*)aij->A->data;
708: PetscMPIInt n;
709: PetscInt i,j,rstart,ncols,flg;
710: PetscInt *row,*col;
711: PetscBool other_disassembled;
712: PetscScalar *val;
713: InsertMode addv = mat->insertmode;
715: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
718: if (!aij->donotstash && !mat->nooffprocentries) {
719: while (1) {
720: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
721: if (!flg) break;
723: for (i=0; i<n; ) {
724: /* Now identify the consecutive vals belonging to the same row */
725: for (j=i,rstart=row[j]; j<n; j++) {
726: if (row[j] != rstart) break;
727: }
728: if (j < n) ncols = j-i;
729: else ncols = n-i;
730: /* Now assemble all these values with a single function call */
731: MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
733: i = j;
734: }
735: }
736: MatStashScatterEnd_Private(&mat->stash);
737: }
738: MatAssemblyBegin(aij->A,mode);
739: MatAssemblyEnd(aij->A,mode);
741: /* determine if any processor has disassembled, if so we must
742: also disassemble ourselfs, in order that we may reassemble. */
743: /*
744: if nonzero structure of submatrix B cannot change then we know that
745: no processor disassembled thus we can skip this stuff
746: */
747: if (!((Mat_SeqAIJ*)aij->B->data)->nonew) {
748: MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPIU_BOOL,MPI_PROD,PetscObjectComm((PetscObject)mat));
749: if (mat->was_assembled && !other_disassembled) {
750: MatDisAssemble_MPIAIJ(mat);
751: }
752: }
753: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
754: MatSetUpMultiply_MPIAIJ(mat);
755: }
756: MatSetOption(aij->B,MAT_USE_INODES,PETSC_FALSE);
757: MatAssemblyBegin(aij->B,mode);
758: MatAssemblyEnd(aij->B,mode);
760: PetscFree2(aij->rowvalues,aij->rowindices);
762: aij->rowvalues = 0;
764: VecDestroy(&aij->diag);
765: if (a->inode.size) mat->ops->multdiagonalblock = MatMultDiagonalBlock_MPIAIJ;
767: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
768: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
769: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
770: MPI_Allreduce(&state,&mat->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)mat));
771: }
772: return(0);
773: }
777: PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
778: {
779: Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data;
783: MatZeroEntries(l->A);
784: MatZeroEntries(l->B);
785: return(0);
786: }
790: PetscErrorCode MatZeroRows_MPIAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
791: {
792: Mat_MPIAIJ *mat = (Mat_MPIAIJ *) A->data;
793: PetscInt *owners = A->rmap->range;
794: PetscInt n = A->rmap->n;
795: PetscSF sf;
796: PetscInt *lrows;
797: PetscSFNode *rrows;
798: PetscInt r, p = 0, len = 0;
802: /* Create SF where leaves are input rows and roots are owned rows */
803: PetscMalloc1(n, &lrows);
804: for (r = 0; r < n; ++r) lrows[r] = -1;
805: if (!A->nooffproczerorows) {PetscMalloc1(N, &rrows);}
806: for (r = 0; r < N; ++r) {
807: const PetscInt idx = rows[r];
808: if (idx < 0 || A->rmap->N <= idx) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range [0,%D)",idx,A->rmap->N);
809: if (idx < owners[p] || owners[p+1] <= idx) { /* short-circuit the search if the last p owns this row too */
810: PetscLayoutFindOwner(A->rmap,idx,&p);
811: }
812: if (A->nooffproczerorows) {
813: if (p != mat->rank) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"MAT_NO_OFF_PROC_ZERO_ROWS set, but row %D is not owned by rank %d",idx,mat->rank);
814: lrows[len++] = idx - owners[p];
815: } else {
816: rrows[r].rank = p;
817: rrows[r].index = rows[r] - owners[p];
818: }
819: }
820: if (!A->nooffproczerorows) {
821: PetscSFCreate(PetscObjectComm((PetscObject) A), &sf);
822: PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER);
823: /* Collect flags for rows to be zeroed */
824: PetscSFReduceBegin(sf, MPIU_INT, (PetscInt*)rows, lrows, MPI_LOR);
825: PetscSFReduceEnd(sf, MPIU_INT, (PetscInt*)rows, lrows, MPI_LOR);
826: PetscSFDestroy(&sf);
827: /* Compress and put in row numbers */
828: for (r = 0; r < n; ++r) if (lrows[r] >= 0) lrows[len++] = r;
829: }
830: /* fix right hand side if needed */
831: if (x && b) {
832: const PetscScalar *xx;
833: PetscScalar *bb;
835: VecGetArrayRead(x, &xx);
836: VecGetArray(b, &bb);
837: for (r = 0; r < len; ++r) bb[lrows[r]] = diag*xx[lrows[r]];
838: VecRestoreArrayRead(x, &xx);
839: VecRestoreArray(b, &bb);
840: }
841: /* Must zero l->B before l->A because the (diag) case below may put values into l->B*/
842: MatZeroRows(mat->B, len, lrows, 0.0, NULL, NULL);
843: if ((diag != 0.0) && (mat->A->rmap->N == mat->A->cmap->N)) {
844: MatZeroRows(mat->A, len, lrows, diag, NULL, NULL);
845: } else if (diag != 0.0) {
846: MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL);
847: if (((Mat_SeqAIJ *) mat->A->data)->nonew) SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MatZeroRows() on rectangular matrices cannot be used with the Mat options\nMAT_NEW_NONZERO_LOCATIONS,MAT_NEW_NONZERO_LOCATION_ERR,MAT_NEW_NONZERO_ALLOCATION_ERR");
848: for (r = 0; r < len; ++r) {
849: const PetscInt row = lrows[r] + A->rmap->rstart;
850: MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES);
851: }
852: MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);
853: MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);
854: } else {
855: MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL);
856: }
857: PetscFree(lrows);
859: /* only change matrix nonzero state if pattern was allowed to be changed */
860: if (!((Mat_SeqAIJ*)(mat->A->data))->keepnonzeropattern) {
861: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
862: MPI_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
863: }
864: return(0);
865: }
869: PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
870: {
871: Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data;
872: PetscErrorCode ierr;
873: PetscMPIInt n = A->rmap->n;
874: PetscInt i,j,r,m,p = 0,len = 0;
875: PetscInt *lrows,*owners = A->rmap->range;
876: PetscSFNode *rrows;
877: PetscSF sf;
878: const PetscScalar *xx;
879: PetscScalar *bb,*mask;
880: Vec xmask,lmask;
881: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)l->B->data;
882: const PetscInt *aj, *ii,*ridx;
883: PetscScalar *aa;
886: /* Create SF where leaves are input rows and roots are owned rows */
887: PetscMalloc1(n, &lrows);
888: for (r = 0; r < n; ++r) lrows[r] = -1;
889: PetscMalloc1(N, &rrows);
890: for (r = 0; r < N; ++r) {
891: const PetscInt idx = rows[r];
892: if (idx < 0 || A->rmap->N <= idx) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range [0,%D)",idx,A->rmap->N);
893: if (idx < owners[p] || owners[p+1] <= idx) { /* short-circuit the search if the last p owns this row too */
894: PetscLayoutFindOwner(A->rmap,idx,&p);
895: }
896: rrows[r].rank = p;
897: rrows[r].index = rows[r] - owners[p];
898: }
899: PetscSFCreate(PetscObjectComm((PetscObject) A), &sf);
900: PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER);
901: /* Collect flags for rows to be zeroed */
902: PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
903: PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
904: PetscSFDestroy(&sf);
905: /* Compress and put in row numbers */
906: for (r = 0; r < n; ++r) if (lrows[r] >= 0) lrows[len++] = r;
907: /* zero diagonal part of matrix */
908: MatZeroRowsColumns(l->A,len,lrows,diag,x,b);
909: /* handle off diagonal part of matrix */
910: MatCreateVecs(A,&xmask,NULL);
911: VecDuplicate(l->lvec,&lmask);
912: VecGetArray(xmask,&bb);
913: for (i=0; i<len; i++) bb[lrows[i]] = 1;
914: VecRestoreArray(xmask,&bb);
915: VecScatterBegin(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
916: VecScatterEnd(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
917: VecDestroy(&xmask);
918: if (x) {
919: VecScatterBegin(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
920: VecScatterEnd(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
921: VecGetArrayRead(l->lvec,&xx);
922: VecGetArray(b,&bb);
923: }
924: VecGetArray(lmask,&mask);
925: /* remove zeroed rows of off diagonal matrix */
926: ii = aij->i;
927: for (i=0; i<len; i++) {
928: PetscMemzero(aij->a + ii[lrows[i]],(ii[lrows[i]+1] - ii[lrows[i]])*sizeof(PetscScalar));
929: }
930: /* loop over all elements of off process part of matrix zeroing removed columns*/
931: if (aij->compressedrow.use) {
932: m = aij->compressedrow.nrows;
933: ii = aij->compressedrow.i;
934: ridx = aij->compressedrow.rindex;
935: for (i=0; i<m; i++) {
936: n = ii[i+1] - ii[i];
937: aj = aij->j + ii[i];
938: aa = aij->a + ii[i];
940: for (j=0; j<n; j++) {
941: if (PetscAbsScalar(mask[*aj])) {
942: if (b) bb[*ridx] -= *aa*xx[*aj];
943: *aa = 0.0;
944: }
945: aa++;
946: aj++;
947: }
948: ridx++;
949: }
950: } else { /* do not use compressed row format */
951: m = l->B->rmap->n;
952: for (i=0; i<m; i++) {
953: n = ii[i+1] - ii[i];
954: aj = aij->j + ii[i];
955: aa = aij->a + ii[i];
956: for (j=0; j<n; j++) {
957: if (PetscAbsScalar(mask[*aj])) {
958: if (b) bb[i] -= *aa*xx[*aj];
959: *aa = 0.0;
960: }
961: aa++;
962: aj++;
963: }
964: }
965: }
966: if (x) {
967: VecRestoreArray(b,&bb);
968: VecRestoreArrayRead(l->lvec,&xx);
969: }
970: VecRestoreArray(lmask,&mask);
971: VecDestroy(&lmask);
972: PetscFree(lrows);
974: /* only change matrix nonzero state if pattern was allowed to be changed */
975: if (!((Mat_SeqAIJ*)(l->A->data))->keepnonzeropattern) {
976: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
977: MPI_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
978: }
979: return(0);
980: }
984: PetscErrorCode MatMult_MPIAIJ(Mat A,Vec xx,Vec yy)
985: {
986: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
988: PetscInt nt;
991: VecGetLocalSize(xx,&nt);
992: if (nt != A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%D) and xx (%D)",A->cmap->n,nt);
993: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
994: (*a->A->ops->mult)(a->A,xx,yy);
995: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
996: (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
997: return(0);
998: }
1002: PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A,Vec bb,Vec xx)
1003: {
1004: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1008: MatMultDiagonalBlock(a->A,bb,xx);
1009: return(0);
1010: }
1014: PetscErrorCode MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1015: {
1016: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1020: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1021: (*a->A->ops->multadd)(a->A,xx,yy,zz);
1022: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
1023: (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
1024: return(0);
1025: }
1029: PetscErrorCode MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy)
1030: {
1031: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1033: PetscBool merged;
1036: VecScatterGetMerged(a->Mvctx,&merged);
1037: /* do nondiagonal part */
1038: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1039: if (!merged) {
1040: /* send it on its way */
1041: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1042: /* do local part */
1043: (*a->A->ops->multtranspose)(a->A,xx,yy);
1044: /* receive remote parts: note this assumes the values are not actually */
1045: /* added in yy until the next line, */
1046: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1047: } else {
1048: /* do local part */
1049: (*a->A->ops->multtranspose)(a->A,xx,yy);
1050: /* send it on its way */
1051: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1052: /* values actually were received in the Begin() but we need to call this nop */
1053: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1054: }
1055: return(0);
1056: }
1060: PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat,Mat Bmat,PetscReal tol,PetscBool *f)
1061: {
1062: MPI_Comm comm;
1063: Mat_MPIAIJ *Aij = (Mat_MPIAIJ*) Amat->data, *Bij;
1064: Mat Adia = Aij->A, Bdia, Aoff,Boff,*Aoffs,*Boffs;
1065: IS Me,Notme;
1067: PetscInt M,N,first,last,*notme,i;
1068: PetscMPIInt size;
1071: /* Easy test: symmetric diagonal block */
1072: Bij = (Mat_MPIAIJ*) Bmat->data; Bdia = Bij->A;
1073: MatIsTranspose(Adia,Bdia,tol,f);
1074: if (!*f) return(0);
1075: PetscObjectGetComm((PetscObject)Amat,&comm);
1076: MPI_Comm_size(comm,&size);
1077: if (size == 1) return(0);
1079: /* Hard test: off-diagonal block. This takes a MatGetSubMatrix. */
1080: MatGetSize(Amat,&M,&N);
1081: MatGetOwnershipRange(Amat,&first,&last);
1082: PetscMalloc1(N-last+first,¬me);
1083: for (i=0; i<first; i++) notme[i] = i;
1084: for (i=last; i<M; i++) notme[i-last+first] = i;
1085: ISCreateGeneral(MPI_COMM_SELF,N-last+first,notme,PETSC_COPY_VALUES,&Notme);
1086: ISCreateStride(MPI_COMM_SELF,last-first,first,1,&Me);
1087: MatGetSubMatrices(Amat,1,&Me,&Notme,MAT_INITIAL_MATRIX,&Aoffs);
1088: Aoff = Aoffs[0];
1089: MatGetSubMatrices(Bmat,1,&Notme,&Me,MAT_INITIAL_MATRIX,&Boffs);
1090: Boff = Boffs[0];
1091: MatIsTranspose(Aoff,Boff,tol,f);
1092: MatDestroyMatrices(1,&Aoffs);
1093: MatDestroyMatrices(1,&Boffs);
1094: ISDestroy(&Me);
1095: ISDestroy(&Notme);
1096: PetscFree(notme);
1097: return(0);
1098: }
1102: PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1103: {
1104: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1108: /* do nondiagonal part */
1109: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1110: /* send it on its way */
1111: VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1112: /* do local part */
1113: (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1114: /* receive remote parts */
1115: VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1116: return(0);
1117: }
1119: /*
1120: This only works correctly for square matrices where the subblock A->A is the
1121: diagonal block
1122: */
1125: PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A,Vec v)
1126: {
1128: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1131: if (A->rmap->N != A->cmap->N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1132: if (A->rmap->rstart != A->cmap->rstart || A->rmap->rend != A->cmap->rend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"row partition must equal col partition");
1133: MatGetDiagonal(a->A,v);
1134: return(0);
1135: }
1139: PetscErrorCode MatScale_MPIAIJ(Mat A,PetscScalar aa)
1140: {
1141: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1145: MatScale(a->A,aa);
1146: MatScale(a->B,aa);
1147: return(0);
1148: }
1152: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
1153: {
1154: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1158: #if defined(PETSC_USE_LOG)
1159: PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N);
1160: #endif
1161: MatStashDestroy_Private(&mat->stash);
1162: VecDestroy(&aij->diag);
1163: MatDestroy(&aij->A);
1164: MatDestroy(&aij->B);
1165: #if defined(PETSC_USE_CTABLE)
1166: PetscTableDestroy(&aij->colmap);
1167: #else
1168: PetscFree(aij->colmap);
1169: #endif
1170: PetscFree(aij->garray);
1171: VecDestroy(&aij->lvec);
1172: VecScatterDestroy(&aij->Mvctx);
1173: PetscFree2(aij->rowvalues,aij->rowindices);
1174: PetscFree(aij->ld);
1175: PetscFree(mat->data);
1177: PetscObjectChangeTypeName((PetscObject)mat,0);
1178: PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C",NULL);
1179: PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C",NULL);
1180: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",NULL);
1181: PetscObjectComposeFunction((PetscObject)mat,"MatIsTranspose_C",NULL);
1182: PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocation_C",NULL);
1183: PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocationCSR_C",NULL);
1184: PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",NULL);
1185: PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpiaij_mpisbaij_C",NULL);
1186: #if defined(PETSC_HAVE_ELEMENTAL)
1187: PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpiaij_elemental_C",NULL);
1188: #endif
1189: return(0);
1190: }
1194: PetscErrorCode MatView_MPIAIJ_Binary(Mat mat,PetscViewer viewer)
1195: {
1196: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1197: Mat_SeqAIJ *A = (Mat_SeqAIJ*)aij->A->data;
1198: Mat_SeqAIJ *B = (Mat_SeqAIJ*)aij->B->data;
1200: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag;
1201: int fd;
1202: PetscInt nz,header[4],*row_lengths,*range=0,rlen,i;
1203: PetscInt nzmax,*column_indices,j,k,col,*garray = aij->garray,cnt,cstart = mat->cmap->rstart,rnz = 0;
1204: PetscScalar *column_values;
1205: PetscInt message_count,flowcontrolcount;
1206: FILE *file;
1209: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
1210: MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
1211: nz = A->nz + B->nz;
1212: PetscViewerBinaryGetDescriptor(viewer,&fd);
1213: if (!rank) {
1214: header[0] = MAT_FILE_CLASSID;
1215: header[1] = mat->rmap->N;
1216: header[2] = mat->cmap->N;
1218: MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
1219: PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
1220: /* get largest number of rows any processor has */
1221: rlen = mat->rmap->n;
1222: range = mat->rmap->range;
1223: for (i=1; i<size; i++) rlen = PetscMax(rlen,range[i+1] - range[i]);
1224: } else {
1225: MPI_Reduce(&nz,0,1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
1226: rlen = mat->rmap->n;
1227: }
1229: /* load up the local row counts */
1230: PetscMalloc1(rlen+1,&row_lengths);
1231: for (i=0; i<mat->rmap->n; i++) row_lengths[i] = A->i[i+1] - A->i[i] + B->i[i+1] - B->i[i];
1233: /* store the row lengths to the file */
1234: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1235: if (!rank) {
1236: PetscBinaryWrite(fd,row_lengths,mat->rmap->n,PETSC_INT,PETSC_TRUE);
1237: for (i=1; i<size; i++) {
1238: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1239: rlen = range[i+1] - range[i];
1240: MPIULong_Recv(row_lengths,rlen,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat));
1241: PetscBinaryWrite(fd,row_lengths,rlen,PETSC_INT,PETSC_TRUE);
1242: }
1243: PetscViewerFlowControlEndMaster(viewer,&message_count);
1244: } else {
1245: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1246: MPIULong_Send(row_lengths,mat->rmap->n,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1247: PetscViewerFlowControlEndWorker(viewer,&message_count);
1248: }
1249: PetscFree(row_lengths);
1251: /* load up the local column indices */
1252: nzmax = nz; /* th processor needs space a largest processor needs */
1253: MPI_Reduce(&nz,&nzmax,1,MPIU_INT,MPI_MAX,0,PetscObjectComm((PetscObject)mat));
1254: PetscMalloc1(nzmax+1,&column_indices);
1255: cnt = 0;
1256: for (i=0; i<mat->rmap->n; i++) {
1257: for (j=B->i[i]; j<B->i[i+1]; j++) {
1258: if ((col = garray[B->j[j]]) > cstart) break;
1259: column_indices[cnt++] = col;
1260: }
1261: for (k=A->i[i]; k<A->i[i+1]; k++) column_indices[cnt++] = A->j[k] + cstart;
1262: for (; j<B->i[i+1]; j++) column_indices[cnt++] = garray[B->j[j]];
1263: }
1264: if (cnt != A->nz + B->nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);
1266: /* store the column indices to the file */
1267: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1268: if (!rank) {
1269: MPI_Status status;
1270: PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,PETSC_TRUE);
1271: for (i=1; i<size; i++) {
1272: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1273: MPI_Recv(&rnz,1,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
1274: if (rnz > nzmax) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
1275: MPIULong_Recv(column_indices,rnz,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat));
1276: PetscBinaryWrite(fd,column_indices,rnz,PETSC_INT,PETSC_TRUE);
1277: }
1278: PetscViewerFlowControlEndMaster(viewer,&message_count);
1279: } else {
1280: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1281: MPI_Send(&nz,1,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1282: MPIULong_Send(column_indices,nz,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1283: PetscViewerFlowControlEndWorker(viewer,&message_count);
1284: }
1285: PetscFree(column_indices);
1287: /* load up the local column values */
1288: PetscMalloc1(nzmax+1,&column_values);
1289: cnt = 0;
1290: for (i=0; i<mat->rmap->n; i++) {
1291: for (j=B->i[i]; j<B->i[i+1]; j++) {
1292: if (garray[B->j[j]] > cstart) break;
1293: column_values[cnt++] = B->a[j];
1294: }
1295: for (k=A->i[i]; k<A->i[i+1]; k++) column_values[cnt++] = A->a[k];
1296: for (; j<B->i[i+1]; j++) column_values[cnt++] = B->a[j];
1297: }
1298: if (cnt != A->nz + B->nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Internal PETSc error: cnt = %D nz = %D",cnt,A->nz+B->nz);
1300: /* store the column values to the file */
1301: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1302: if (!rank) {
1303: MPI_Status status;
1304: PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,PETSC_TRUE);
1305: for (i=1; i<size; i++) {
1306: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1307: MPI_Recv(&rnz,1,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
1308: if (rnz > nzmax) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
1309: MPIULong_Recv(column_values,rnz,MPIU_SCALAR,i,tag,PetscObjectComm((PetscObject)mat));
1310: PetscBinaryWrite(fd,column_values,rnz,PETSC_SCALAR,PETSC_TRUE);
1311: }
1312: PetscViewerFlowControlEndMaster(viewer,&message_count);
1313: } else {
1314: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1315: MPI_Send(&nz,1,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1316: MPIULong_Send(column_values,nz,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));
1317: PetscViewerFlowControlEndWorker(viewer,&message_count);
1318: }
1319: PetscFree(column_values);
1321: PetscViewerBinaryGetInfoPointer(viewer,&file);
1322: if (file) fprintf(file,"-matload_block_size %d\n",(int)PetscAbs(mat->rmap->bs));
1323: return(0);
1324: }
1326: #include <petscdraw.h>
1329: PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1330: {
1331: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1332: PetscErrorCode ierr;
1333: PetscMPIInt rank = aij->rank,size = aij->size;
1334: PetscBool isdraw,iascii,isbinary;
1335: PetscViewer sviewer;
1336: PetscViewerFormat format;
1339: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1340: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1341: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1342: if (iascii) {
1343: PetscViewerGetFormat(viewer,&format);
1344: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1345: MatInfo info;
1346: PetscBool inodes;
1348: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
1349: MatGetInfo(mat,MAT_LOCAL,&info);
1350: MatInodeGetInodeSizes(aij->A,NULL,(PetscInt**)&inodes,NULL);
1351: PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);
1352: if (!inodes) {
1353: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, not using I-node routines\n",
1354: rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
1355: } else {
1356: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, using I-node routines\n",
1357: rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
1358: }
1359: MatGetInfo(aij->A,MAT_LOCAL,&info);
1360: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1361: MatGetInfo(aij->B,MAT_LOCAL,&info);
1362: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1363: PetscViewerFlush(viewer);
1364: PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);
1365: PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
1366: VecScatterView(aij->Mvctx,viewer);
1367: return(0);
1368: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1369: PetscInt inodecount,inodelimit,*inodes;
1370: MatInodeGetInodeSizes(aij->A,&inodecount,&inodes,&inodelimit);
1371: if (inodes) {
1372: PetscViewerASCIIPrintf(viewer,"using I-node (on process 0) routines: found %D nodes, limit used is %D\n",inodecount,inodelimit);
1373: } else {
1374: PetscViewerASCIIPrintf(viewer,"not using I-node (on process 0) routines\n");
1375: }
1376: return(0);
1377: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1378: return(0);
1379: }
1380: } else if (isbinary) {
1381: if (size == 1) {
1382: PetscObjectSetName((PetscObject)aij->A,((PetscObject)mat)->name);
1383: MatView(aij->A,viewer);
1384: } else {
1385: MatView_MPIAIJ_Binary(mat,viewer);
1386: }
1387: return(0);
1388: } else if (isdraw) {
1389: PetscDraw draw;
1390: PetscBool isnull;
1391: PetscViewerDrawGetDraw(viewer,0,&draw);
1392: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1393: }
1395: {
1396: /* assemble the entire matrix onto first processor. */
1397: Mat A;
1398: Mat_SeqAIJ *Aloc;
1399: PetscInt M = mat->rmap->N,N = mat->cmap->N,m,*ai,*aj,row,*cols,i,*ct;
1400: MatScalar *a;
1402: MatCreate(PetscObjectComm((PetscObject)mat),&A);
1403: if (!rank) {
1404: MatSetSizes(A,M,N,M,N);
1405: } else {
1406: MatSetSizes(A,0,0,M,N);
1407: }
1408: /* This is just a temporary matrix, so explicitly using MATMPIAIJ is probably best */
1409: MatSetType(A,MATMPIAIJ);
1410: MatMPIAIJSetPreallocation(A,0,NULL,0,NULL);
1411: MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);
1412: PetscLogObjectParent((PetscObject)mat,(PetscObject)A);
1414: /* copy over the A part */
1415: Aloc = (Mat_SeqAIJ*)aij->A->data;
1416: m = aij->A->rmap->n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1417: row = mat->rmap->rstart;
1418: for (i=0; i<ai[m]; i++) aj[i] += mat->cmap->rstart;
1419: for (i=0; i<m; i++) {
1420: MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);
1421: row++;
1422: a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i];
1423: }
1424: aj = Aloc->j;
1425: for (i=0; i<ai[m]; i++) aj[i] -= mat->cmap->rstart;
1427: /* copy over the B part */
1428: Aloc = (Mat_SeqAIJ*)aij->B->data;
1429: m = aij->B->rmap->n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1430: row = mat->rmap->rstart;
1431: PetscMalloc1(ai[m]+1,&cols);
1432: ct = cols;
1433: for (i=0; i<ai[m]; i++) cols[i] = aij->garray[aj[i]];
1434: for (i=0; i<m; i++) {
1435: MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);
1436: row++;
1437: a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
1438: }
1439: PetscFree(ct);
1440: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1441: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1442: /*
1443: Everyone has to call to draw the matrix since the graphics waits are
1444: synchronized across all processors that share the PetscDraw object
1445: */
1446: PetscViewerGetSingleton(viewer,&sviewer);
1447: if (!rank) {
1448: PetscObjectSetName((PetscObject)((Mat_MPIAIJ*)(A->data))->A,((PetscObject)mat)->name);
1449: MatView_SeqAIJ(((Mat_MPIAIJ*)(A->data))->A,sviewer);
1450: }
1451: PetscViewerRestoreSingleton(viewer,&sviewer);
1452: MatDestroy(&A);
1453: }
1454: return(0);
1455: }
1459: PetscErrorCode MatView_MPIAIJ(Mat mat,PetscViewer viewer)
1460: {
1462: PetscBool iascii,isdraw,issocket,isbinary;
1465: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1466: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1467: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1468: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
1469: if (iascii || isdraw || isbinary || issocket) {
1470: MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);
1471: }
1472: return(0);
1473: }
1477: PetscErrorCode MatSOR_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1478: {
1479: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1481: Vec bb1 = 0;
1482: PetscBool hasop;
1485: if (flag == SOR_APPLY_UPPER) {
1486: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
1487: return(0);
1488: }
1490: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) {
1491: VecDuplicate(bb,&bb1);
1492: }
1494: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1495: if (flag & SOR_ZERO_INITIAL_GUESS) {
1496: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
1497: its--;
1498: }
1500: while (its--) {
1501: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1502: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1504: /* update rhs: bb1 = bb - B*x */
1505: VecScale(mat->lvec,-1.0);
1506: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1508: /* local sweep */
1509: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,1,xx);
1510: }
1511: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1512: if (flag & SOR_ZERO_INITIAL_GUESS) {
1513: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
1514: its--;
1515: }
1516: while (its--) {
1517: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1518: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1520: /* update rhs: bb1 = bb - B*x */
1521: VecScale(mat->lvec,-1.0);
1522: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1524: /* local sweep */
1525: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,1,xx);
1526: }
1527: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1528: if (flag & SOR_ZERO_INITIAL_GUESS) {
1529: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
1530: its--;
1531: }
1532: while (its--) {
1533: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1534: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1536: /* update rhs: bb1 = bb - B*x */
1537: VecScale(mat->lvec,-1.0);
1538: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1540: /* local sweep */
1541: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,1,xx);
1542: }
1543: } else if (flag & SOR_EISENSTAT) {
1544: Vec xx1;
1546: VecDuplicate(bb,&xx1);
1547: (*mat->A->ops->sor)(mat->A,bb,omega,(MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP),fshift,lits,1,xx);
1549: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1550: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1551: if (!mat->diag) {
1552: MatCreateVecs(matin,&mat->diag,NULL);
1553: MatGetDiagonal(matin,mat->diag);
1554: }
1555: MatHasOperation(matin,MATOP_MULT_DIAGONAL_BLOCK,&hasop);
1556: if (hasop) {
1557: MatMultDiagonalBlock(matin,xx,bb1);
1558: } else {
1559: VecPointwiseMult(bb1,mat->diag,xx);
1560: }
1561: VecAYPX(bb1,(omega-2.0)/omega,bb);
1563: MatMultAdd(mat->B,mat->lvec,bb1,bb1);
1565: /* local sweep */
1566: (*mat->A->ops->sor)(mat->A,bb1,omega,(MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP),fshift,lits,1,xx1);
1567: VecAXPY(xx,1.0,xx1);
1568: VecDestroy(&xx1);
1569: } else SETERRQ(PetscObjectComm((PetscObject)matin),PETSC_ERR_SUP,"Parallel SOR not supported");
1571: VecDestroy(&bb1);
1572: return(0);
1573: }
1577: PetscErrorCode MatPermute_MPIAIJ(Mat A,IS rowp,IS colp,Mat *B)
1578: {
1579: Mat aA,aB,Aperm;
1580: const PetscInt *rwant,*cwant,*gcols,*ai,*bi,*aj,*bj;
1581: PetscScalar *aa,*ba;
1582: PetscInt i,j,m,n,ng,anz,bnz,*dnnz,*onnz,*tdnnz,*tonnz,*rdest,*cdest,*work,*gcdest;
1583: PetscSF rowsf,sf;
1584: IS parcolp = NULL;
1585: PetscBool done;
1589: MatGetLocalSize(A,&m,&n);
1590: ISGetIndices(rowp,&rwant);
1591: ISGetIndices(colp,&cwant);
1592: PetscMalloc3(PetscMax(m,n),&work,m,&rdest,n,&cdest);
1594: /* Invert row permutation to find out where my rows should go */
1595: PetscSFCreate(PetscObjectComm((PetscObject)A),&rowsf);
1596: PetscSFSetGraphLayout(rowsf,A->rmap,A->rmap->n,NULL,PETSC_OWN_POINTER,rwant);
1597: PetscSFSetFromOptions(rowsf);
1598: for (i=0; i<m; i++) work[i] = A->rmap->rstart + i;
1599: PetscSFReduceBegin(rowsf,MPIU_INT,work,rdest,MPIU_REPLACE);
1600: PetscSFReduceEnd(rowsf,MPIU_INT,work,rdest,MPIU_REPLACE);
1602: /* Invert column permutation to find out where my columns should go */
1603: PetscSFCreate(PetscObjectComm((PetscObject)A),&sf);
1604: PetscSFSetGraphLayout(sf,A->cmap,A->cmap->n,NULL,PETSC_OWN_POINTER,cwant);
1605: PetscSFSetFromOptions(sf);
1606: for (i=0; i<n; i++) work[i] = A->cmap->rstart + i;
1607: PetscSFReduceBegin(sf,MPIU_INT,work,cdest,MPIU_REPLACE);
1608: PetscSFReduceEnd(sf,MPIU_INT,work,cdest,MPIU_REPLACE);
1609: PetscSFDestroy(&sf);
1611: ISRestoreIndices(rowp,&rwant);
1612: ISRestoreIndices(colp,&cwant);
1613: MatMPIAIJGetSeqAIJ(A,&aA,&aB,&gcols);
1615: /* Find out where my gcols should go */
1616: MatGetSize(aB,NULL,&ng);
1617: PetscMalloc1(ng,&gcdest);
1618: PetscSFCreate(PetscObjectComm((PetscObject)A),&sf);
1619: PetscSFSetGraphLayout(sf,A->cmap,ng,NULL,PETSC_OWN_POINTER,gcols);
1620: PetscSFSetFromOptions(sf);
1621: PetscSFBcastBegin(sf,MPIU_INT,cdest,gcdest);
1622: PetscSFBcastEnd(sf,MPIU_INT,cdest,gcdest);
1623: PetscSFDestroy(&sf);
1625: PetscCalloc4(m,&dnnz,m,&onnz,m,&tdnnz,m,&tonnz);
1626: MatGetRowIJ(aA,0,PETSC_FALSE,PETSC_FALSE,&anz,&ai,&aj,&done);
1627: MatGetRowIJ(aB,0,PETSC_FALSE,PETSC_FALSE,&bnz,&bi,&bj,&done);
1628: for (i=0; i<m; i++) {
1629: PetscInt row = rdest[i],rowner;
1630: PetscLayoutFindOwner(A->rmap,row,&rowner);
1631: for (j=ai[i]; j<ai[i+1]; j++) {
1632: PetscInt cowner,col = cdest[aj[j]];
1633: PetscLayoutFindOwner(A->cmap,col,&cowner); /* Could build an index for the columns to eliminate this search */
1634: if (rowner == cowner) dnnz[i]++;
1635: else onnz[i]++;
1636: }
1637: for (j=bi[i]; j<bi[i+1]; j++) {
1638: PetscInt cowner,col = gcdest[bj[j]];
1639: PetscLayoutFindOwner(A->cmap,col,&cowner);
1640: if (rowner == cowner) dnnz[i]++;
1641: else onnz[i]++;
1642: }
1643: }
1644: PetscSFBcastBegin(rowsf,MPIU_INT,dnnz,tdnnz);
1645: PetscSFBcastEnd(rowsf,MPIU_INT,dnnz,tdnnz);
1646: PetscSFBcastBegin(rowsf,MPIU_INT,onnz,tonnz);
1647: PetscSFBcastEnd(rowsf,MPIU_INT,onnz,tonnz);
1648: PetscSFDestroy(&rowsf);
1650: MatCreateAIJ(PetscObjectComm((PetscObject)A),A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N,0,tdnnz,0,tonnz,&Aperm);
1651: MatSeqAIJGetArray(aA,&aa);
1652: MatSeqAIJGetArray(aB,&ba);
1653: for (i=0; i<m; i++) {
1654: PetscInt *acols = dnnz,*bcols = onnz; /* Repurpose now-unneeded arrays */
1655: PetscInt j0,rowlen;
1656: rowlen = ai[i+1] - ai[i];
1657: for (j0=j=0; j<rowlen; j0=j) { /* rowlen could be larger than number of rows m, so sum in batches */
1658: for ( ; j<PetscMin(rowlen,j0+m); j++) acols[j-j0] = cdest[aj[ai[i]+j]];
1659: MatSetValues(Aperm,1,&rdest[i],j-j0,acols,aa+ai[i]+j0,INSERT_VALUES);
1660: }
1661: rowlen = bi[i+1] - bi[i];
1662: for (j0=j=0; j<rowlen; j0=j) {
1663: for ( ; j<PetscMin(rowlen,j0+m); j++) bcols[j-j0] = gcdest[bj[bi[i]+j]];
1664: MatSetValues(Aperm,1,&rdest[i],j-j0,bcols,ba+bi[i]+j0,INSERT_VALUES);
1665: }
1666: }
1667: MatAssemblyBegin(Aperm,MAT_FINAL_ASSEMBLY);
1668: MatAssemblyEnd(Aperm,MAT_FINAL_ASSEMBLY);
1669: MatRestoreRowIJ(aA,0,PETSC_FALSE,PETSC_FALSE,&anz,&ai,&aj,&done);
1670: MatRestoreRowIJ(aB,0,PETSC_FALSE,PETSC_FALSE,&bnz,&bi,&bj,&done);
1671: MatSeqAIJRestoreArray(aA,&aa);
1672: MatSeqAIJRestoreArray(aB,&ba);
1673: PetscFree4(dnnz,onnz,tdnnz,tonnz);
1674: PetscFree3(work,rdest,cdest);
1675: PetscFree(gcdest);
1676: if (parcolp) {ISDestroy(&colp);}
1677: *B = Aperm;
1678: return(0);
1679: }
1683: PetscErrorCode MatGetGhosts_MPIAIJ(Mat mat,PetscInt *nghosts,const PetscInt *ghosts[])
1684: {
1685: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1689: MatGetSize(aij->B,NULL,nghosts);
1690: if (ghosts) *ghosts = aij->garray;
1691: return(0);
1692: }
1696: PetscErrorCode MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1697: {
1698: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1699: Mat A = mat->A,B = mat->B;
1701: PetscReal isend[5],irecv[5];
1704: info->block_size = 1.0;
1705: MatGetInfo(A,MAT_LOCAL,info);
1707: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1708: isend[3] = info->memory; isend[4] = info->mallocs;
1710: MatGetInfo(B,MAT_LOCAL,info);
1712: isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1713: isend[3] += info->memory; isend[4] += info->mallocs;
1714: if (flag == MAT_LOCAL) {
1715: info->nz_used = isend[0];
1716: info->nz_allocated = isend[1];
1717: info->nz_unneeded = isend[2];
1718: info->memory = isend[3];
1719: info->mallocs = isend[4];
1720: } else if (flag == MAT_GLOBAL_MAX) {
1721: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)matin));
1723: info->nz_used = irecv[0];
1724: info->nz_allocated = irecv[1];
1725: info->nz_unneeded = irecv[2];
1726: info->memory = irecv[3];
1727: info->mallocs = irecv[4];
1728: } else if (flag == MAT_GLOBAL_SUM) {
1729: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)matin));
1731: info->nz_used = irecv[0];
1732: info->nz_allocated = irecv[1];
1733: info->nz_unneeded = irecv[2];
1734: info->memory = irecv[3];
1735: info->mallocs = irecv[4];
1736: }
1737: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1738: info->fill_ratio_needed = 0;
1739: info->factor_mallocs = 0;
1740: return(0);
1741: }
1745: PetscErrorCode MatSetOption_MPIAIJ(Mat A,MatOption op,PetscBool flg)
1746: {
1747: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1751: switch (op) {
1752: case MAT_NEW_NONZERO_LOCATIONS:
1753: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1754: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1755: case MAT_KEEP_NONZERO_PATTERN:
1756: case MAT_NEW_NONZERO_LOCATION_ERR:
1757: case MAT_USE_INODES:
1758: case MAT_IGNORE_ZERO_ENTRIES:
1759: MatCheckPreallocated(A,1);
1760: MatSetOption(a->A,op,flg);
1761: MatSetOption(a->B,op,flg);
1762: break;
1763: case MAT_ROW_ORIENTED:
1764: a->roworiented = flg;
1766: MatSetOption(a->A,op,flg);
1767: MatSetOption(a->B,op,flg);
1768: break;
1769: case MAT_NEW_DIAGONALS:
1770: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1771: break;
1772: case MAT_IGNORE_OFF_PROC_ENTRIES:
1773: a->donotstash = flg;
1774: break;
1775: case MAT_SPD:
1776: A->spd_set = PETSC_TRUE;
1777: A->spd = flg;
1778: if (flg) {
1779: A->symmetric = PETSC_TRUE;
1780: A->structurally_symmetric = PETSC_TRUE;
1781: A->symmetric_set = PETSC_TRUE;
1782: A->structurally_symmetric_set = PETSC_TRUE;
1783: }
1784: break;
1785: case MAT_SYMMETRIC:
1786: MatSetOption(a->A,op,flg);
1787: break;
1788: case MAT_STRUCTURALLY_SYMMETRIC:
1789: MatSetOption(a->A,op,flg);
1790: break;
1791: case MAT_HERMITIAN:
1792: MatSetOption(a->A,op,flg);
1793: break;
1794: case MAT_SYMMETRY_ETERNAL:
1795: MatSetOption(a->A,op,flg);
1796: break;
1797: default:
1798: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1799: }
1800: return(0);
1801: }
1805: PetscErrorCode MatGetRow_MPIAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1806: {
1807: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1808: PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
1810: PetscInt i,*cworkA,*cworkB,**pcA,**pcB,cstart = matin->cmap->rstart;
1811: PetscInt nztot,nzA,nzB,lrow,rstart = matin->rmap->rstart,rend = matin->rmap->rend;
1812: PetscInt *cmap,*idx_p;
1815: if (mat->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Already active");
1816: mat->getrowactive = PETSC_TRUE;
1818: if (!mat->rowvalues && (idx || v)) {
1819: /*
1820: allocate enough space to hold information from the longest row.
1821: */
1822: Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data;
1823: PetscInt max = 1,tmp;
1824: for (i=0; i<matin->rmap->n; i++) {
1825: tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1826: if (max < tmp) max = tmp;
1827: }
1828: PetscMalloc2(max,&mat->rowvalues,max,&mat->rowindices);
1829: }
1831: if (row < rstart || row >= rend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Only local rows");
1832: lrow = row - rstart;
1834: pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1835: if (!v) {pvA = 0; pvB = 0;}
1836: if (!idx) {pcA = 0; if (!v) pcB = 0;}
1837: (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1838: (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1839: nztot = nzA + nzB;
1841: cmap = mat->garray;
1842: if (v || idx) {
1843: if (nztot) {
1844: /* Sort by increasing column numbers, assuming A and B already sorted */
1845: PetscInt imark = -1;
1846: if (v) {
1847: *v = v_p = mat->rowvalues;
1848: for (i=0; i<nzB; i++) {
1849: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1850: else break;
1851: }
1852: imark = i;
1853: for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i];
1854: for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i];
1855: }
1856: if (idx) {
1857: *idx = idx_p = mat->rowindices;
1858: if (imark > -1) {
1859: for (i=0; i<imark; i++) {
1860: idx_p[i] = cmap[cworkB[i]];
1861: }
1862: } else {
1863: for (i=0; i<nzB; i++) {
1864: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1865: else break;
1866: }
1867: imark = i;
1868: }
1869: for (i=0; i<nzA; i++) idx_p[imark+i] = cstart + cworkA[i];
1870: for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]];
1871: }
1872: } else {
1873: if (idx) *idx = 0;
1874: if (v) *v = 0;
1875: }
1876: }
1877: *nz = nztot;
1878: (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1879: (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1880: return(0);
1881: }
1885: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1886: {
1887: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1890: if (!aij->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1891: aij->getrowactive = PETSC_FALSE;
1892: return(0);
1893: }
1897: PetscErrorCode MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm)
1898: {
1899: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1900: Mat_SeqAIJ *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data;
1902: PetscInt i,j,cstart = mat->cmap->rstart;
1903: PetscReal sum = 0.0;
1904: MatScalar *v;
1907: if (aij->size == 1) {
1908: MatNorm(aij->A,type,norm);
1909: } else {
1910: if (type == NORM_FROBENIUS) {
1911: v = amat->a;
1912: for (i=0; i<amat->nz; i++) {
1913: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1914: }
1915: v = bmat->a;
1916: for (i=0; i<bmat->nz; i++) {
1917: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1918: }
1919: MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
1920: *norm = PetscSqrtReal(*norm);
1921: } else if (type == NORM_1) { /* max column norm */
1922: PetscReal *tmp,*tmp2;
1923: PetscInt *jj,*garray = aij->garray;
1924: PetscCalloc1(mat->cmap->N+1,&tmp);
1925: PetscMalloc1(mat->cmap->N+1,&tmp2);
1926: *norm = 0.0;
1927: v = amat->a; jj = amat->j;
1928: for (j=0; j<amat->nz; j++) {
1929: tmp[cstart + *jj++] += PetscAbsScalar(*v); v++;
1930: }
1931: v = bmat->a; jj = bmat->j;
1932: for (j=0; j<bmat->nz; j++) {
1933: tmp[garray[*jj++]] += PetscAbsScalar(*v); v++;
1934: }
1935: MPI_Allreduce(tmp,tmp2,mat->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
1936: for (j=0; j<mat->cmap->N; j++) {
1937: if (tmp2[j] > *norm) *norm = tmp2[j];
1938: }
1939: PetscFree(tmp);
1940: PetscFree(tmp2);
1941: } else if (type == NORM_INFINITY) { /* max row norm */
1942: PetscReal ntemp = 0.0;
1943: for (j=0; j<aij->A->rmap->n; j++) {
1944: v = amat->a + amat->i[j];
1945: sum = 0.0;
1946: for (i=0; i<amat->i[j+1]-amat->i[j]; i++) {
1947: sum += PetscAbsScalar(*v); v++;
1948: }
1949: v = bmat->a + bmat->i[j];
1950: for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) {
1951: sum += PetscAbsScalar(*v); v++;
1952: }
1953: if (sum > ntemp) ntemp = sum;
1954: }
1955: MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)mat));
1956: } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No support for two norm");
1957: }
1958: return(0);
1959: }
1963: PetscErrorCode MatTranspose_MPIAIJ(Mat A,MatReuse reuse,Mat *matout)
1964: {
1965: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1966: Mat_SeqAIJ *Aloc=(Mat_SeqAIJ*)a->A->data,*Bloc=(Mat_SeqAIJ*)a->B->data;
1968: PetscInt M = A->rmap->N,N = A->cmap->N,ma,na,mb,nb,*ai,*aj,*bi,*bj,row,*cols,*cols_tmp,i;
1969: PetscInt cstart = A->cmap->rstart,ncol;
1970: Mat B;
1971: MatScalar *array;
1974: if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1976: ma = A->rmap->n; na = A->cmap->n; mb = a->B->rmap->n; nb = a->B->cmap->n;
1977: ai = Aloc->i; aj = Aloc->j;
1978: bi = Bloc->i; bj = Bloc->j;
1979: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1980: PetscInt *d_nnz,*g_nnz,*o_nnz;
1981: PetscSFNode *oloc;
1982: PETSC_UNUSED PetscSF sf;
1984: PetscMalloc4(na,&d_nnz,na,&o_nnz,nb,&g_nnz,nb,&oloc);
1985: /* compute d_nnz for preallocation */
1986: PetscMemzero(d_nnz,na*sizeof(PetscInt));
1987: for (i=0; i<ai[ma]; i++) {
1988: d_nnz[aj[i]]++;
1989: aj[i] += cstart; /* global col index to be used by MatSetValues() */
1990: }
1991: /* compute local off-diagonal contributions */
1992: PetscMemzero(g_nnz,nb*sizeof(PetscInt));
1993: for (i=0; i<bi[ma]; i++) g_nnz[bj[i]]++;
1994: /* map those to global */
1995: PetscSFCreate(PetscObjectComm((PetscObject)A),&sf);
1996: PetscSFSetGraphLayout(sf,A->cmap,nb,NULL,PETSC_USE_POINTER,a->garray);
1997: PetscSFSetFromOptions(sf);
1998: PetscMemzero(o_nnz,na*sizeof(PetscInt));
1999: PetscSFReduceBegin(sf,MPIU_INT,g_nnz,o_nnz,MPIU_SUM);
2000: PetscSFReduceEnd(sf,MPIU_INT,g_nnz,o_nnz,MPIU_SUM);
2001: PetscSFDestroy(&sf);
2003: MatCreate(PetscObjectComm((PetscObject)A),&B);
2004: MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
2005: MatSetBlockSizes(B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2006: MatSetType(B,((PetscObject)A)->type_name);
2007: MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
2008: PetscFree4(d_nnz,o_nnz,g_nnz,oloc);
2009: } else {
2010: B = *matout;
2011: MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
2012: for (i=0; i<ai[ma]; i++) aj[i] += cstart; /* global col index to be used by MatSetValues() */
2013: }
2015: /* copy over the A part */
2016: array = Aloc->a;
2017: row = A->rmap->rstart;
2018: for (i=0; i<ma; i++) {
2019: ncol = ai[i+1]-ai[i];
2020: MatSetValues(B,ncol,aj,1,&row,array,INSERT_VALUES);
2021: row++;
2022: array += ncol; aj += ncol;
2023: }
2024: aj = Aloc->j;
2025: for (i=0; i<ai[ma]; i++) aj[i] -= cstart; /* resume local col index */
2027: /* copy over the B part */
2028: PetscCalloc1(bi[mb],&cols);
2029: array = Bloc->a;
2030: row = A->rmap->rstart;
2031: for (i=0; i<bi[mb]; i++) cols[i] = a->garray[bj[i]];
2032: cols_tmp = cols;
2033: for (i=0; i<mb; i++) {
2034: ncol = bi[i+1]-bi[i];
2035: MatSetValues(B,ncol,cols_tmp,1,&row,array,INSERT_VALUES);
2036: row++;
2037: array += ncol; cols_tmp += ncol;
2038: }
2039: PetscFree(cols);
2041: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2042: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2043: if (reuse == MAT_INITIAL_MATRIX || *matout != A) {
2044: *matout = B;
2045: } else {
2046: MatHeaderMerge(A,B);
2047: }
2048: return(0);
2049: }
2053: PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr)
2054: {
2055: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
2056: Mat a = aij->A,b = aij->B;
2058: PetscInt s1,s2,s3;
2061: MatGetLocalSize(mat,&s2,&s3);
2062: if (rr) {
2063: VecGetLocalSize(rr,&s1);
2064: if (s1!=s3) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
2065: /* Overlap communication with computation. */
2066: VecScatterBegin(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
2067: }
2068: if (ll) {
2069: VecGetLocalSize(ll,&s1);
2070: if (s1!=s2) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
2071: (*b->ops->diagonalscale)(b,ll,0);
2072: }
2073: /* scale the diagonal block */
2074: (*a->ops->diagonalscale)(a,ll,rr);
2076: if (rr) {
2077: /* Do a scatter end and then right scale the off-diagonal block */
2078: VecScatterEnd(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
2079: (*b->ops->diagonalscale)(b,0,aij->lvec);
2080: }
2081: return(0);
2082: }
2086: PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2087: {
2088: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2092: MatSetUnfactored(a->A);
2093: return(0);
2094: }
2098: PetscErrorCode MatEqual_MPIAIJ(Mat A,Mat B,PetscBool *flag)
2099: {
2100: Mat_MPIAIJ *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data;
2101: Mat a,b,c,d;
2102: PetscBool flg;
2106: a = matA->A; b = matA->B;
2107: c = matB->A; d = matB->B;
2109: MatEqual(a,c,&flg);
2110: if (flg) {
2111: MatEqual(b,d,&flg);
2112: }
2113: MPI_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
2114: return(0);
2115: }
2119: PetscErrorCode MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str)
2120: {
2122: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2123: Mat_MPIAIJ *b = (Mat_MPIAIJ*)B->data;
2126: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2127: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2128: /* because of the column compression in the off-processor part of the matrix a->B,
2129: the number of columns in a->B and b->B may be different, hence we cannot call
2130: the MatCopy() directly on the two parts. If need be, we can provide a more
2131: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2132: then copying the submatrices */
2133: MatCopy_Basic(A,B,str);
2134: } else {
2135: MatCopy(a->A,b->A,str);
2136: MatCopy(a->B,b->B,str);
2137: }
2138: return(0);
2139: }
2143: PetscErrorCode MatSetUp_MPIAIJ(Mat A)
2144: {
2148: MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
2149: return(0);
2150: }
2152: /*
2153: Computes the number of nonzeros per row needed for preallocation when X and Y
2154: have different nonzero structure.
2155: */
2158: PetscErrorCode MatAXPYGetPreallocation_MPIX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *xltog,const PetscInt *yi,const PetscInt *yj,const PetscInt *yltog,PetscInt *nnz)
2159: {
2160: PetscInt i,j,k,nzx,nzy;
2163: /* Set the number of nonzeros in the new matrix */
2164: for (i=0; i<m; i++) {
2165: const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
2166: nzx = xi[i+1] - xi[i];
2167: nzy = yi[i+1] - yi[i];
2168: nnz[i] = 0;
2169: for (j=0,k=0; j<nzx; j++) { /* Point in X */
2170: for (; k<nzy && yltog[yjj[k]]<xltog[xjj[j]]; k++) nnz[i]++; /* Catch up to X */
2171: if (k<nzy && yltog[yjj[k]]==xltog[xjj[j]]) k++; /* Skip duplicate */
2172: nnz[i]++;
2173: }
2174: for (; k<nzy; k++) nnz[i]++;
2175: }
2176: return(0);
2177: }
2179: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2182: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y,const PetscInt *yltog,Mat X,const PetscInt *xltog,PetscInt *nnz)
2183: {
2185: PetscInt m = Y->rmap->N;
2186: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data;
2187: Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data;
2190: MatAXPYGetPreallocation_MPIX_private(m,x->i,x->j,xltog,y->i,y->j,yltog,nnz);
2191: return(0);
2192: }
2196: PetscErrorCode MatAXPY_MPIAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2197: {
2199: Mat_MPIAIJ *xx = (Mat_MPIAIJ*)X->data,*yy = (Mat_MPIAIJ*)Y->data;
2200: PetscBLASInt bnz,one=1;
2201: Mat_SeqAIJ *x,*y;
2204: if (str == SAME_NONZERO_PATTERN) {
2205: PetscScalar alpha = a;
2206: x = (Mat_SeqAIJ*)xx->A->data;
2207: PetscBLASIntCast(x->nz,&bnz);
2208: y = (Mat_SeqAIJ*)yy->A->data;
2209: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2210: x = (Mat_SeqAIJ*)xx->B->data;
2211: y = (Mat_SeqAIJ*)yy->B->data;
2212: PetscBLASIntCast(x->nz,&bnz);
2213: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2214: PetscObjectStateIncrease((PetscObject)Y);
2215: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2216: MatAXPY_Basic(Y,a,X,str);
2217: } else {
2218: Mat B;
2219: PetscInt *nnz_d,*nnz_o;
2220: PetscMalloc1(yy->A->rmap->N,&nnz_d);
2221: PetscMalloc1(yy->B->rmap->N,&nnz_o);
2222: MatCreate(PetscObjectComm((PetscObject)Y),&B);
2223: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2224: MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2225: MatSetBlockSizesFromMats(B,Y,Y);
2226: MatSetType(B,MATMPIAIJ);
2227: MatAXPYGetPreallocation_SeqAIJ(yy->A,xx->A,nnz_d);
2228: MatAXPYGetPreallocation_MPIAIJ(yy->B,yy->garray,xx->B,xx->garray,nnz_o);
2229: MatMPIAIJSetPreallocation(B,0,nnz_d,0,nnz_o);
2230: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2231: MatHeaderReplace(Y,B);
2232: PetscFree(nnz_d);
2233: PetscFree(nnz_o);
2234: }
2235: return(0);
2236: }
2238: extern PetscErrorCode MatConjugate_SeqAIJ(Mat);
2242: PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2243: {
2244: #if defined(PETSC_USE_COMPLEX)
2246: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
2249: MatConjugate_SeqAIJ(aij->A);
2250: MatConjugate_SeqAIJ(aij->B);
2251: #else
2253: #endif
2254: return(0);
2255: }
2259: PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2260: {
2261: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2265: MatRealPart(a->A);
2266: MatRealPart(a->B);
2267: return(0);
2268: }
2272: PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2273: {
2274: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2278: MatImaginaryPart(a->A);
2279: MatImaginaryPart(a->B);
2280: return(0);
2281: }
2283: #if defined(PETSC_HAVE_PBGL)
2285: #include <boost/parallel/mpi/bsp_process_group.hpp>
2286: #include <boost/graph/distributed/ilu_default_graph.hpp>
2287: #include <boost/graph/distributed/ilu_0_block.hpp>
2288: #include <boost/graph/distributed/ilu_preconditioner.hpp>
2289: #include <boost/graph/distributed/petsc/interface.hpp>
2290: #include <boost/multi_array.hpp>
2291: #include <boost/parallel/distributed_property_map.hpp>
2295: /*
2296: This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
2297: */
2298: PetscErrorCode MatILUFactorSymbolic_MPIAIJ(Mat fact,Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2299: {
2300: namespace petsc = boost::distributed::petsc;
2302: namespace graph_dist = boost::graph::distributed;
2303: using boost::graph::distributed::ilu_default::process_group_type;
2304: using boost::graph::ilu_permuted;
2306: PetscBool row_identity, col_identity;
2307: PetscContainer c;
2308: PetscInt m, n, M, N;
2312: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels = 0 supported for parallel ilu");
2313: ISIdentity(isrow, &row_identity);
2314: ISIdentity(iscol, &col_identity);
2315: if (!row_identity || !col_identity) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for parallel ILU");
2317: process_group_type pg;
2318: typedef graph_dist::ilu_default::ilu_level_graph_type lgraph_type;
2319: lgraph_type *lgraph_p = new lgraph_type(petsc::num_global_vertices(A), pg, petsc::matrix_distribution(A, pg));
2320: lgraph_type& level_graph = *lgraph_p;
2321: graph_dist::ilu_default::graph_type& graph(level_graph.graph);
2323: petsc::read_matrix(A, graph, get(boost::edge_weight, graph));
2324: ilu_permuted(level_graph);
2326: /* put together the new matrix */
2327: MatCreate(PetscObjectComm((PetscObject)A), fact);
2328: MatGetLocalSize(A, &m, &n);
2329: MatGetSize(A, &M, &N);
2330: MatSetSizes(fact, m, n, M, N);
2331: MatSetBlockSizesFromMats(fact,A,A);
2332: MatSetType(fact, ((PetscObject)A)->type_name);
2333: MatAssemblyBegin(fact, MAT_FINAL_ASSEMBLY);
2334: MatAssemblyEnd(fact, MAT_FINAL_ASSEMBLY);
2336: PetscContainerCreate(PetscObjectComm((PetscObject)A), &c);
2337: PetscContainerSetPointer(c, lgraph_p);
2338: PetscObjectCompose((PetscObject) (fact), "graph", (PetscObject) c);
2339: PetscContainerDestroy(&c);
2340: return(0);
2341: }
2345: PetscErrorCode MatLUFactorNumeric_MPIAIJ(Mat B,Mat A, const MatFactorInfo *info)
2346: {
2348: return(0);
2349: }
2353: /*
2354: This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
2355: */
2356: PetscErrorCode MatSolve_MPIAIJ(Mat A, Vec b, Vec x)
2357: {
2358: namespace graph_dist = boost::graph::distributed;
2360: typedef graph_dist::ilu_default::ilu_level_graph_type lgraph_type;
2361: lgraph_type *lgraph_p;
2362: PetscContainer c;
2366: PetscObjectQuery((PetscObject) A, "graph", (PetscObject*) &c);
2367: PetscContainerGetPointer(c, (void**) &lgraph_p);
2368: VecCopy(b, x);
2370: PetscScalar *array_x;
2371: VecGetArray(x, &array_x);
2372: PetscInt sx;
2373: VecGetSize(x, &sx);
2375: PetscScalar *array_b;
2376: VecGetArray(b, &array_b);
2377: PetscInt sb;
2378: VecGetSize(b, &sb);
2380: lgraph_type& level_graph = *lgraph_p;
2381: graph_dist::ilu_default::graph_type& graph(level_graph.graph);
2383: typedef boost::multi_array_ref<PetscScalar, 1> array_ref_type;
2384: array_ref_type ref_b(array_b, boost::extents[num_vertices(graph)]);
2385: array_ref_type ref_x(array_x, boost::extents[num_vertices(graph)]);
2387: typedef boost::iterator_property_map<array_ref_type::iterator,
2388: boost::property_map<graph_dist::ilu_default::graph_type, boost::vertex_index_t>::type> gvector_type;
2389: gvector_type vector_b(ref_b.begin(), get(boost::vertex_index, graph));
2390: gvector_type vector_x(ref_x.begin(), get(boost::vertex_index, graph));
2392: ilu_set_solve(*lgraph_p, vector_b, vector_x);
2393: return(0);
2394: }
2395: #endif
2399: PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2400: {
2401: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2403: PetscInt i,*idxb = 0;
2404: PetscScalar *va,*vb;
2405: Vec vtmp;
2408: MatGetRowMaxAbs(a->A,v,idx);
2409: VecGetArray(v,&va);
2410: if (idx) {
2411: for (i=0; i<A->rmap->n; i++) {
2412: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2413: }
2414: }
2416: VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
2417: if (idx) {
2418: PetscMalloc1(A->rmap->n,&idxb);
2419: }
2420: MatGetRowMaxAbs(a->B,vtmp,idxb);
2421: VecGetArray(vtmp,&vb);
2423: for (i=0; i<A->rmap->n; i++) {
2424: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2425: va[i] = vb[i];
2426: if (idx) idx[i] = a->garray[idxb[i]];
2427: }
2428: }
2430: VecRestoreArray(v,&va);
2431: VecRestoreArray(vtmp,&vb);
2432: PetscFree(idxb);
2433: VecDestroy(&vtmp);
2434: return(0);
2435: }
2439: PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2440: {
2441: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2443: PetscInt i,*idxb = 0;
2444: PetscScalar *va,*vb;
2445: Vec vtmp;
2448: MatGetRowMinAbs(a->A,v,idx);
2449: VecGetArray(v,&va);
2450: if (idx) {
2451: for (i=0; i<A->cmap->n; i++) {
2452: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2453: }
2454: }
2456: VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
2457: if (idx) {
2458: PetscMalloc1(A->rmap->n,&idxb);
2459: }
2460: MatGetRowMinAbs(a->B,vtmp,idxb);
2461: VecGetArray(vtmp,&vb);
2463: for (i=0; i<A->rmap->n; i++) {
2464: if (PetscAbsScalar(va[i]) > PetscAbsScalar(vb[i])) {
2465: va[i] = vb[i];
2466: if (idx) idx[i] = a->garray[idxb[i]];
2467: }
2468: }
2470: VecRestoreArray(v,&va);
2471: VecRestoreArray(vtmp,&vb);
2472: PetscFree(idxb);
2473: VecDestroy(&vtmp);
2474: return(0);
2475: }
2479: PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2480: {
2481: Mat_MPIAIJ *mat = (Mat_MPIAIJ*) A->data;
2482: PetscInt n = A->rmap->n;
2483: PetscInt cstart = A->cmap->rstart;
2484: PetscInt *cmap = mat->garray;
2485: PetscInt *diagIdx, *offdiagIdx;
2486: Vec diagV, offdiagV;
2487: PetscScalar *a, *diagA, *offdiagA;
2488: PetscInt r;
2492: PetscMalloc2(n,&diagIdx,n,&offdiagIdx);
2493: VecCreateSeq(PetscObjectComm((PetscObject)A), n, &diagV);
2494: VecCreateSeq(PetscObjectComm((PetscObject)A), n, &offdiagV);
2495: MatGetRowMin(mat->A, diagV, diagIdx);
2496: MatGetRowMin(mat->B, offdiagV, offdiagIdx);
2497: VecGetArray(v, &a);
2498: VecGetArray(diagV, &diagA);
2499: VecGetArray(offdiagV, &offdiagA);
2500: for (r = 0; r < n; ++r) {
2501: if (PetscAbsScalar(diagA[r]) <= PetscAbsScalar(offdiagA[r])) {
2502: a[r] = diagA[r];
2503: idx[r] = cstart + diagIdx[r];
2504: } else {
2505: a[r] = offdiagA[r];
2506: idx[r] = cmap[offdiagIdx[r]];
2507: }
2508: }
2509: VecRestoreArray(v, &a);
2510: VecRestoreArray(diagV, &diagA);
2511: VecRestoreArray(offdiagV, &offdiagA);
2512: VecDestroy(&diagV);
2513: VecDestroy(&offdiagV);
2514: PetscFree2(diagIdx, offdiagIdx);
2515: return(0);
2516: }
2520: PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2521: {
2522: Mat_MPIAIJ *mat = (Mat_MPIAIJ*) A->data;
2523: PetscInt n = A->rmap->n;
2524: PetscInt cstart = A->cmap->rstart;
2525: PetscInt *cmap = mat->garray;
2526: PetscInt *diagIdx, *offdiagIdx;
2527: Vec diagV, offdiagV;
2528: PetscScalar *a, *diagA, *offdiagA;
2529: PetscInt r;
2533: PetscMalloc2(n,&diagIdx,n,&offdiagIdx);
2534: VecCreateSeq(PETSC_COMM_SELF, n, &diagV);
2535: VecCreateSeq(PETSC_COMM_SELF, n, &offdiagV);
2536: MatGetRowMax(mat->A, diagV, diagIdx);
2537: MatGetRowMax(mat->B, offdiagV, offdiagIdx);
2538: VecGetArray(v, &a);
2539: VecGetArray(diagV, &diagA);
2540: VecGetArray(offdiagV, &offdiagA);
2541: for (r = 0; r < n; ++r) {
2542: if (PetscAbsScalar(diagA[r]) >= PetscAbsScalar(offdiagA[r])) {
2543: a[r] = diagA[r];
2544: idx[r] = cstart + diagIdx[r];
2545: } else {
2546: a[r] = offdiagA[r];
2547: idx[r] = cmap[offdiagIdx[r]];
2548: }
2549: }
2550: VecRestoreArray(v, &a);
2551: VecRestoreArray(diagV, &diagA);
2552: VecRestoreArray(offdiagV, &offdiagA);
2553: VecDestroy(&diagV);
2554: VecDestroy(&offdiagV);
2555: PetscFree2(diagIdx, offdiagIdx);
2556: return(0);
2557: }
2561: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat,Mat *newmat)
2562: {
2564: Mat *dummy;
2567: MatGetSubMatrix_MPIAIJ_All(mat,MAT_DO_NOT_GET_VALUES,MAT_INITIAL_MATRIX,&dummy);
2568: *newmat = *dummy;
2569: PetscFree(dummy);
2570: return(0);
2571: }
2575: PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A,const PetscScalar **values)
2576: {
2577: Mat_MPIAIJ *a = (Mat_MPIAIJ*) A->data;
2581: MatInvertBlockDiagonal(a->A,values);
2582: return(0);
2583: }
2587: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x,PetscRandom rctx)
2588: {
2590: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)x->data;
2593: MatSetRandom(aij->A,rctx);
2594: MatSetRandom(aij->B,rctx);
2595: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
2596: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
2597: return(0);
2598: }
2602: PetscErrorCode MatShift_MPIAIJ(Mat Y,PetscScalar a)
2603: {
2605: Mat_MPIAIJ *maij = (Mat_MPIAIJ*)Y->data;
2606: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)maij->A->data;
2609: if (!Y->preallocated) {
2610: MatMPIAIJSetPreallocation(Y,1,NULL,0,NULL);
2611: } else if (!aij->nz) {
2612: MatSeqAIJSetPreallocation(maij->A,1,NULL);
2613: }
2614: MatShift_Basic(Y,a);
2615: return(0);
2616: }
2618: /* -------------------------------------------------------------------*/
2619: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
2620: MatGetRow_MPIAIJ,
2621: MatRestoreRow_MPIAIJ,
2622: MatMult_MPIAIJ,
2623: /* 4*/ MatMultAdd_MPIAIJ,
2624: MatMultTranspose_MPIAIJ,
2625: MatMultTransposeAdd_MPIAIJ,
2626: #if defined(PETSC_HAVE_PBGL)
2627: MatSolve_MPIAIJ,
2628: #else
2629: 0,
2630: #endif
2631: 0,
2632: 0,
2633: /*10*/ 0,
2634: 0,
2635: 0,
2636: MatSOR_MPIAIJ,
2637: MatTranspose_MPIAIJ,
2638: /*15*/ MatGetInfo_MPIAIJ,
2639: MatEqual_MPIAIJ,
2640: MatGetDiagonal_MPIAIJ,
2641: MatDiagonalScale_MPIAIJ,
2642: MatNorm_MPIAIJ,
2643: /*20*/ MatAssemblyBegin_MPIAIJ,
2644: MatAssemblyEnd_MPIAIJ,
2645: MatSetOption_MPIAIJ,
2646: MatZeroEntries_MPIAIJ,
2647: /*24*/ MatZeroRows_MPIAIJ,
2648: 0,
2649: #if defined(PETSC_HAVE_PBGL)
2650: 0,
2651: #else
2652: 0,
2653: #endif
2654: 0,
2655: 0,
2656: /*29*/ MatSetUp_MPIAIJ,
2657: #if defined(PETSC_HAVE_PBGL)
2658: 0,
2659: #else
2660: 0,
2661: #endif
2662: 0,
2663: 0,
2664: 0,
2665: /*34*/ MatDuplicate_MPIAIJ,
2666: 0,
2667: 0,
2668: 0,
2669: 0,
2670: /*39*/ MatAXPY_MPIAIJ,
2671: MatGetSubMatrices_MPIAIJ,
2672: MatIncreaseOverlap_MPIAIJ,
2673: MatGetValues_MPIAIJ,
2674: MatCopy_MPIAIJ,
2675: /*44*/ MatGetRowMax_MPIAIJ,
2676: MatScale_MPIAIJ,
2677: MatShift_MPIAIJ,
2678: MatDiagonalSet_MPIAIJ,
2679: MatZeroRowsColumns_MPIAIJ,
2680: /*49*/ MatSetRandom_MPIAIJ,
2681: 0,
2682: 0,
2683: 0,
2684: 0,
2685: /*54*/ MatFDColoringCreate_MPIXAIJ,
2686: 0,
2687: MatSetUnfactored_MPIAIJ,
2688: MatPermute_MPIAIJ,
2689: 0,
2690: /*59*/ MatGetSubMatrix_MPIAIJ,
2691: MatDestroy_MPIAIJ,
2692: MatView_MPIAIJ,
2693: 0,
2694: MatMatMatMult_MPIAIJ_MPIAIJ_MPIAIJ,
2695: /*64*/ MatMatMatMultSymbolic_MPIAIJ_MPIAIJ_MPIAIJ,
2696: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
2697: 0,
2698: 0,
2699: 0,
2700: /*69*/ MatGetRowMaxAbs_MPIAIJ,
2701: MatGetRowMinAbs_MPIAIJ,
2702: 0,
2703: MatSetColoring_MPIAIJ,
2704: 0,
2705: MatSetValuesAdifor_MPIAIJ,
2706: /*75*/ MatFDColoringApply_AIJ,
2707: 0,
2708: 0,
2709: 0,
2710: MatFindZeroDiagonals_MPIAIJ,
2711: /*80*/ 0,
2712: 0,
2713: 0,
2714: /*83*/ MatLoad_MPIAIJ,
2715: 0,
2716: 0,
2717: 0,
2718: 0,
2719: 0,
2720: /*89*/ MatMatMult_MPIAIJ_MPIAIJ,
2721: MatMatMultSymbolic_MPIAIJ_MPIAIJ,
2722: MatMatMultNumeric_MPIAIJ_MPIAIJ,
2723: MatPtAP_MPIAIJ_MPIAIJ,
2724: MatPtAPSymbolic_MPIAIJ_MPIAIJ,
2725: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
2726: 0,
2727: 0,
2728: 0,
2729: 0,
2730: /*99*/ 0,
2731: 0,
2732: 0,
2733: MatConjugate_MPIAIJ,
2734: 0,
2735: /*104*/MatSetValuesRow_MPIAIJ,
2736: MatRealPart_MPIAIJ,
2737: MatImaginaryPart_MPIAIJ,
2738: 0,
2739: 0,
2740: /*109*/0,
2741: 0,
2742: MatGetRowMin_MPIAIJ,
2743: 0,
2744: 0,
2745: /*114*/MatGetSeqNonzeroStructure_MPIAIJ,
2746: 0,
2747: MatGetGhosts_MPIAIJ,
2748: 0,
2749: 0,
2750: /*119*/0,
2751: 0,
2752: 0,
2753: 0,
2754: MatGetMultiProcBlock_MPIAIJ,
2755: /*124*/MatFindNonzeroRows_MPIAIJ,
2756: MatGetColumnNorms_MPIAIJ,
2757: MatInvertBlockDiagonal_MPIAIJ,
2758: 0,
2759: MatGetSubMatricesMPI_MPIAIJ,
2760: /*129*/0,
2761: MatTransposeMatMult_MPIAIJ_MPIAIJ,
2762: MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ,
2763: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
2764: 0,
2765: /*134*/0,
2766: 0,
2767: 0,
2768: 0,
2769: 0,
2770: /*139*/0,
2771: 0,
2772: 0,
2773: MatFDColoringSetUp_MPIXAIJ,
2774: MatFindOffBlockDiagonalEntries_MPIAIJ,
2775: /*144*/MatCreateMPIMatConcatenateSeqMat_MPIAIJ
2776: };
2778: /* ----------------------------------------------------------------------------------------*/
2782: PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
2783: {
2784: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
2788: MatStoreValues(aij->A);
2789: MatStoreValues(aij->B);
2790: return(0);
2791: }
2795: PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
2796: {
2797: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
2801: MatRetrieveValues(aij->A);
2802: MatRetrieveValues(aij->B);
2803: return(0);
2804: }
2808: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
2809: {
2810: Mat_MPIAIJ *b;
2814: PetscLayoutSetUp(B->rmap);
2815: PetscLayoutSetUp(B->cmap);
2816: b = (Mat_MPIAIJ*)B->data;
2818: if (!B->preallocated) {
2819: /* Explicitly create 2 MATSEQAIJ matrices. */
2820: MatCreate(PETSC_COMM_SELF,&b->A);
2821: MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
2822: MatSetBlockSizesFromMats(b->A,B,B);
2823: MatSetType(b->A,MATSEQAIJ);
2824: PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
2825: MatCreate(PETSC_COMM_SELF,&b->B);
2826: MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
2827: MatSetBlockSizesFromMats(b->B,B,B);
2828: MatSetType(b->B,MATSEQAIJ);
2829: PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);
2830: }
2832: MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
2833: MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
2834: B->preallocated = PETSC_TRUE;
2835: return(0);
2836: }
2840: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
2841: {
2842: Mat mat;
2843: Mat_MPIAIJ *a,*oldmat = (Mat_MPIAIJ*)matin->data;
2847: *newmat = 0;
2848: MatCreate(PetscObjectComm((PetscObject)matin),&mat);
2849: MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
2850: MatSetBlockSizesFromMats(mat,matin,matin);
2851: MatSetType(mat,((PetscObject)matin)->type_name);
2852: PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
2853: a = (Mat_MPIAIJ*)mat->data;
2855: mat->factortype = matin->factortype;
2856: mat->assembled = PETSC_TRUE;
2857: mat->insertmode = NOT_SET_VALUES;
2858: mat->preallocated = PETSC_TRUE;
2860: a->size = oldmat->size;
2861: a->rank = oldmat->rank;
2862: a->donotstash = oldmat->donotstash;
2863: a->roworiented = oldmat->roworiented;
2864: a->rowindices = 0;
2865: a->rowvalues = 0;
2866: a->getrowactive = PETSC_FALSE;
2868: PetscLayoutReference(matin->rmap,&mat->rmap);
2869: PetscLayoutReference(matin->cmap,&mat->cmap);
2871: if (oldmat->colmap) {
2872: #if defined(PETSC_USE_CTABLE)
2873: PetscTableCreateCopy(oldmat->colmap,&a->colmap);
2874: #else
2875: PetscMalloc1(mat->cmap->N,&a->colmap);
2876: PetscLogObjectMemory((PetscObject)mat,(mat->cmap->N)*sizeof(PetscInt));
2877: PetscMemcpy(a->colmap,oldmat->colmap,(mat->cmap->N)*sizeof(PetscInt));
2878: #endif
2879: } else a->colmap = 0;
2880: if (oldmat->garray) {
2881: PetscInt len;
2882: len = oldmat->B->cmap->n;
2883: PetscMalloc1(len+1,&a->garray);
2884: PetscLogObjectMemory((PetscObject)mat,len*sizeof(PetscInt));
2885: if (len) { PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt)); }
2886: } else a->garray = 0;
2888: VecDuplicate(oldmat->lvec,&a->lvec);
2889: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->lvec);
2890: VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
2891: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->Mvctx);
2892: MatDuplicate(oldmat->A,cpvalues,&a->A);
2893: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
2894: MatDuplicate(oldmat->B,cpvalues,&a->B);
2895: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->B);
2896: PetscFunctionListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
2897: *newmat = mat;
2898: return(0);
2899: }
2905: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
2906: {
2907: PetscScalar *vals,*svals;
2908: MPI_Comm comm;
2910: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag;
2911: PetscInt i,nz,j,rstart,rend,mmax,maxnz = 0;
2912: PetscInt header[4],*rowlengths = 0,M,N,m,*cols;
2913: PetscInt *ourlens = NULL,*procsnz = NULL,*offlens = NULL,jj,*mycols,*smycols;
2914: PetscInt cend,cstart,n,*rowners;
2915: int fd;
2916: PetscInt bs = newMat->rmap->bs;
2919: /* force binary viewer to load .info file if it has not yet done so */
2920: PetscViewerSetUp(viewer);
2921: PetscObjectGetComm((PetscObject)viewer,&comm);
2922: MPI_Comm_size(comm,&size);
2923: MPI_Comm_rank(comm,&rank);
2924: PetscViewerBinaryGetDescriptor(viewer,&fd);
2925: if (!rank) {
2926: PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
2927: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
2928: }
2930: PetscOptionsBegin(comm,NULL,"Options for loading MPIAIJ matrix","Mat");
2931: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
2932: PetscOptionsEnd();
2933: if (bs < 0) bs = 1;
2935: MPI_Bcast(header+1,3,MPIU_INT,0,comm);
2936: M = header[1]; N = header[2];
2938: /* If global sizes are set, check if they are consistent with that given in the file */
2939: if (newMat->rmap->N >= 0 && newMat->rmap->N != M) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Inconsistent # of rows:Matrix in file has (%D) and input matrix has (%D)",newMat->rmap->N,M);
2940: if (newMat->cmap->N >=0 && newMat->cmap->N != N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Inconsistent # of cols:Matrix in file has (%D) and input matrix has (%D)",newMat->cmap->N,N);
2942: /* determine ownership of all (block) rows */
2943: if (M%bs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows (%d) and block size (%d)",M,bs);
2944: if (newMat->rmap->n < 0) m = bs*((M/bs)/size + (((M/bs) % size) > rank)); /* PETSC_DECIDE */
2945: else m = newMat->rmap->n; /* Set by user */
2947: PetscMalloc1(size+1,&rowners);
2948: MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
2950: /* First process needs enough room for process with most rows */
2951: if (!rank) {
2952: mmax = rowners[1];
2953: for (i=2; i<=size; i++) {
2954: mmax = PetscMax(mmax, rowners[i]);
2955: }
2956: } else mmax = -1; /* unused, but compilers complain */
2958: rowners[0] = 0;
2959: for (i=2; i<=size; i++) {
2960: rowners[i] += rowners[i-1];
2961: }
2962: rstart = rowners[rank];
2963: rend = rowners[rank+1];
2965: /* distribute row lengths to all processors */
2966: PetscMalloc2(m,&ourlens,m,&offlens);
2967: if (!rank) {
2968: PetscBinaryRead(fd,ourlens,m,PETSC_INT);
2969: PetscMalloc1(mmax,&rowlengths);
2970: PetscCalloc1(size,&procsnz);
2971: for (j=0; j<m; j++) {
2972: procsnz[0] += ourlens[j];
2973: }
2974: for (i=1; i<size; i++) {
2975: PetscBinaryRead(fd,rowlengths,rowners[i+1]-rowners[i],PETSC_INT);
2976: /* calculate the number of nonzeros on each processor */
2977: for (j=0; j<rowners[i+1]-rowners[i]; j++) {
2978: procsnz[i] += rowlengths[j];
2979: }
2980: MPIULong_Send(rowlengths,rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
2981: }
2982: PetscFree(rowlengths);
2983: } else {
2984: MPIULong_Recv(ourlens,m,MPIU_INT,0,tag,comm);
2985: }
2987: if (!rank) {
2988: /* determine max buffer needed and allocate it */
2989: maxnz = 0;
2990: for (i=0; i<size; i++) {
2991: maxnz = PetscMax(maxnz,procsnz[i]);
2992: }
2993: PetscMalloc1(maxnz,&cols);
2995: /* read in my part of the matrix column indices */
2996: nz = procsnz[0];
2997: PetscMalloc1(nz,&mycols);
2998: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
3000: /* read in every one elses and ship off */
3001: for (i=1; i<size; i++) {
3002: nz = procsnz[i];
3003: PetscBinaryRead(fd,cols,nz,PETSC_INT);
3004: MPIULong_Send(cols,nz,MPIU_INT,i,tag,comm);
3005: }
3006: PetscFree(cols);
3007: } else {
3008: /* determine buffer space needed for message */
3009: nz = 0;
3010: for (i=0; i<m; i++) {
3011: nz += ourlens[i];
3012: }
3013: PetscMalloc1(nz,&mycols);
3015: /* receive message of column indices*/
3016: MPIULong_Recv(mycols,nz,MPIU_INT,0,tag,comm);
3017: }
3019: /* determine column ownership if matrix is not square */
3020: if (N != M) {
3021: if (newMat->cmap->n < 0) n = N/size + ((N % size) > rank);
3022: else n = newMat->cmap->n;
3023: MPI_Scan(&n,&cend,1,MPIU_INT,MPI_SUM,comm);
3024: cstart = cend - n;
3025: } else {
3026: cstart = rstart;
3027: cend = rend;
3028: n = cend - cstart;
3029: }
3031: /* loop over local rows, determining number of off diagonal entries */
3032: PetscMemzero(offlens,m*sizeof(PetscInt));
3033: jj = 0;
3034: for (i=0; i<m; i++) {
3035: for (j=0; j<ourlens[i]; j++) {
3036: if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++;
3037: jj++;
3038: }
3039: }
3041: for (i=0; i<m; i++) {
3042: ourlens[i] -= offlens[i];
3043: }
3044: MatSetSizes(newMat,m,n,M,N);
3046: if (bs > 1) {MatSetBlockSize(newMat,bs);}
3048: MatMPIAIJSetPreallocation(newMat,0,ourlens,0,offlens);
3050: for (i=0; i<m; i++) {
3051: ourlens[i] += offlens[i];
3052: }
3054: if (!rank) {
3055: PetscMalloc1(maxnz+1,&vals);
3057: /* read in my part of the matrix numerical values */
3058: nz = procsnz[0];
3059: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
3061: /* insert into matrix */
3062: jj = rstart;
3063: smycols = mycols;
3064: svals = vals;
3065: for (i=0; i<m; i++) {
3066: MatSetValues_MPIAIJ(newMat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
3067: smycols += ourlens[i];
3068: svals += ourlens[i];
3069: jj++;
3070: }
3072: /* read in other processors and ship out */
3073: for (i=1; i<size; i++) {
3074: nz = procsnz[i];
3075: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
3076: MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newMat)->tag,comm);
3077: }
3078: PetscFree(procsnz);
3079: } else {
3080: /* receive numeric values */
3081: PetscMalloc1(nz+1,&vals);
3083: /* receive message of values*/
3084: MPIULong_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newMat)->tag,comm);
3086: /* insert into matrix */
3087: jj = rstart;
3088: smycols = mycols;
3089: svals = vals;
3090: for (i=0; i<m; i++) {
3091: MatSetValues_MPIAIJ(newMat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
3092: smycols += ourlens[i];
3093: svals += ourlens[i];
3094: jj++;
3095: }
3096: }
3097: PetscFree2(ourlens,offlens);
3098: PetscFree(vals);
3099: PetscFree(mycols);
3100: PetscFree(rowners);
3101: MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
3102: MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
3103: return(0);
3104: }
3108: /* TODO: Not scalable because of ISAllGather(). */
3109: PetscErrorCode MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,MatReuse call,Mat *newmat)
3110: {
3112: IS iscol_local;
3113: PetscInt csize;
3116: ISGetLocalSize(iscol,&csize);
3117: if (call == MAT_REUSE_MATRIX) {
3118: PetscObjectQuery((PetscObject)*newmat,"ISAllGather",(PetscObject*)&iscol_local);
3119: if (!iscol_local) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
3120: } else {
3121: PetscInt cbs;
3122: ISGetBlockSize(iscol,&cbs);
3123: ISAllGather(iscol,&iscol_local);
3124: ISSetBlockSize(iscol_local,cbs);
3125: }
3126: MatGetSubMatrix_MPIAIJ_Private(mat,isrow,iscol_local,csize,call,newmat);
3127: if (call == MAT_INITIAL_MATRIX) {
3128: PetscObjectCompose((PetscObject)*newmat,"ISAllGather",(PetscObject)iscol_local);
3129: ISDestroy(&iscol_local);
3130: }
3131: return(0);
3132: }
3134: extern PetscErrorCode MatGetSubMatrices_MPIAIJ_Local(Mat,PetscInt,const IS[],const IS[],MatReuse,PetscBool*,Mat*);
3137: /*
3138: Not great since it makes two copies of the submatrix, first an SeqAIJ
3139: in local and then by concatenating the local matrices the end result.
3140: Writing it directly would be much like MatGetSubMatrices_MPIAIJ()
3142: Note: This requires a sequential iscol with all indices.
3143: */
3144: PetscErrorCode MatGetSubMatrix_MPIAIJ_Private(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
3145: {
3147: PetscMPIInt rank,size;
3148: PetscInt i,m,n,rstart,row,rend,nz,*cwork,j,bs,cbs;
3149: PetscInt *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal,ncol;
3150: PetscBool allcolumns, colflag;
3151: Mat M,Mreuse;
3152: MatScalar *vwork,*aa;
3153: MPI_Comm comm;
3154: Mat_SeqAIJ *aij;
3157: PetscObjectGetComm((PetscObject)mat,&comm);
3158: MPI_Comm_rank(comm,&rank);
3159: MPI_Comm_size(comm,&size);
3161: ISIdentity(iscol,&colflag);
3162: ISGetLocalSize(iscol,&ncol);
3163: if (colflag && ncol == mat->cmap->N) {
3164: allcolumns = PETSC_TRUE;
3165: } else {
3166: allcolumns = PETSC_FALSE;
3167: }
3168: if (call == MAT_REUSE_MATRIX) {
3169: PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject*)&Mreuse);
3170: if (!Mreuse) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
3171: MatGetSubMatrices_MPIAIJ_Local(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&allcolumns,&Mreuse);
3172: } else {
3173: MatGetSubMatrices_MPIAIJ_Local(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&allcolumns,&Mreuse);
3174: }
3176: /*
3177: m - number of local rows
3178: n - number of columns (same on all processors)
3179: rstart - first row in new global matrix generated
3180: */
3181: MatGetSize(Mreuse,&m,&n);
3182: MatGetBlockSizes(Mreuse,&bs,&cbs);
3183: if (call == MAT_INITIAL_MATRIX) {
3184: aij = (Mat_SeqAIJ*)(Mreuse)->data;
3185: ii = aij->i;
3186: jj = aij->j;
3188: /*
3189: Determine the number of non-zeros in the diagonal and off-diagonal
3190: portions of the matrix in order to do correct preallocation
3191: */
3193: /* first get start and end of "diagonal" columns */
3194: if (csize == PETSC_DECIDE) {
3195: ISGetSize(isrow,&mglobal);
3196: if (mglobal == n) { /* square matrix */
3197: nlocal = m;
3198: } else {
3199: nlocal = n/size + ((n % size) > rank);
3200: }
3201: } else {
3202: nlocal = csize;
3203: }
3204: MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
3205: rstart = rend - nlocal;
3206: if (rank == size - 1 && rend != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Local column sizes %D do not add up to total number of columns %D",rend,n);
3208: /* next, compute all the lengths */
3209: PetscMalloc1(2*m+1,&dlens);
3210: olens = dlens + m;
3211: for (i=0; i<m; i++) {
3212: jend = ii[i+1] - ii[i];
3213: olen = 0;
3214: dlen = 0;
3215: for (j=0; j<jend; j++) {
3216: if (*jj < rstart || *jj >= rend) olen++;
3217: else dlen++;
3218: jj++;
3219: }
3220: olens[i] = olen;
3221: dlens[i] = dlen;
3222: }
3223: MatCreate(comm,&M);
3224: MatSetSizes(M,m,nlocal,PETSC_DECIDE,n);
3225: MatSetBlockSizes(M,bs,cbs);
3226: MatSetType(M,((PetscObject)mat)->type_name);
3227: MatMPIAIJSetPreallocation(M,0,dlens,0,olens);
3228: PetscFree(dlens);
3229: } else {
3230: PetscInt ml,nl;
3232: M = *newmat;
3233: MatGetLocalSize(M,&ml,&nl);
3234: if (ml != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
3235: MatZeroEntries(M);
3236: /*
3237: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3238: rather than the slower MatSetValues().
3239: */
3240: M->was_assembled = PETSC_TRUE;
3241: M->assembled = PETSC_FALSE;
3242: }
3243: MatGetOwnershipRange(M,&rstart,&rend);
3244: aij = (Mat_SeqAIJ*)(Mreuse)->data;
3245: ii = aij->i;
3246: jj = aij->j;
3247: aa = aij->a;
3248: for (i=0; i<m; i++) {
3249: row = rstart + i;
3250: nz = ii[i+1] - ii[i];
3251: cwork = jj; jj += nz;
3252: vwork = aa; aa += nz;
3253: MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
3254: }
3256: MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
3257: MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
3258: *newmat = M;
3260: /* save submatrix used in processor for next request */
3261: if (call == MAT_INITIAL_MATRIX) {
3262: PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
3263: MatDestroy(&Mreuse);
3264: }
3265: return(0);
3266: }
3270: PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3271: {
3272: PetscInt m,cstart, cend,j,nnz,i,d;
3273: PetscInt *d_nnz,*o_nnz,nnz_max = 0,rstart,ii;
3274: const PetscInt *JJ;
3275: PetscScalar *values;
3279: if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Ii[0] must be 0 it is %D",Ii[0]);
3281: PetscLayoutSetUp(B->rmap);
3282: PetscLayoutSetUp(B->cmap);
3283: m = B->rmap->n;
3284: cstart = B->cmap->rstart;
3285: cend = B->cmap->rend;
3286: rstart = B->rmap->rstart;
3288: PetscMalloc2(m,&d_nnz,m,&o_nnz);
3290: #if defined(PETSC_USE_DEBUGGING)
3291: for (i=0; i<m; i++) {
3292: nnz = Ii[i+1]- Ii[i];
3293: JJ = J + Ii[i];
3294: if (nnz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative %D number of columns",i,nnz);
3295: if (nnz && (JJ[0] < 0)) SETERRRQ1(PETSC_ERR_ARG_WRONGSTATE,"Row %D starts with negative column index",i,j);
3296: 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);
3297: }
3298: #endif
3300: for (i=0; i<m; i++) {
3301: nnz = Ii[i+1]- Ii[i];
3302: JJ = J + Ii[i];
3303: nnz_max = PetscMax(nnz_max,nnz);
3304: d = 0;
3305: for (j=0; j<nnz; j++) {
3306: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3307: }
3308: d_nnz[i] = d;
3309: o_nnz[i] = nnz - d;
3310: }
3311: MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
3312: PetscFree2(d_nnz,o_nnz);
3314: if (v) values = (PetscScalar*)v;
3315: else {
3316: PetscCalloc1(nnz_max+1,&values);
3317: }
3319: for (i=0; i<m; i++) {
3320: ii = i + rstart;
3321: nnz = Ii[i+1]- Ii[i];
3322: MatSetValues_MPIAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
3323: }
3324: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3325: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3327: if (!v) {
3328: PetscFree(values);
3329: }
3330: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3331: return(0);
3332: }
3336: /*@
3337: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
3338: (the default parallel PETSc format).
3340: Collective on MPI_Comm
3342: Input Parameters:
3343: + B - the matrix
3344: . i - the indices into j for the start of each local row (starts with zero)
3345: . j - the column indices for each local row (starts with zero)
3346: - v - optional values in the matrix
3348: Level: developer
3350: Notes:
3351: The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3352: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3353: called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.
3355: The i and j indices are 0 based, and i indices are indices corresponding to the local j array.
3357: The format which is used for the sparse matrix input, is equivalent to a
3358: row-major ordering.. i.e for the following matrix, the input data expected is
3359: as shown:
3361: 1 0 0
3362: 2 0 3 P0
3363: -------
3364: 4 5 6 P1
3366: Process0 [P0]: rows_owned=[0,1]
3367: i = {0,1,3} [size = nrow+1 = 2+1]
3368: j = {0,0,2} [size = nz = 6]
3369: v = {1,2,3} [size = nz = 6]
3371: Process1 [P1]: rows_owned=[2]
3372: i = {0,3} [size = nrow+1 = 1+1]
3373: j = {0,1,2} [size = nz = 6]
3374: v = {4,5,6} [size = nz = 6]
3376: .keywords: matrix, aij, compressed row, sparse, parallel
3378: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatCreateAIJ(), MPIAIJ,
3379: MatCreateSeqAIJWithArrays(), MatCreateMPIAIJWithSplitArrays()
3380: @*/
3381: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
3382: {
3386: PetscTryMethod(B,"MatMPIAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3387: return(0);
3388: }
3392: /*@C
3393: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in AIJ format
3394: (the default parallel PETSc format). For good matrix assembly performance
3395: the user should preallocate the matrix storage by setting the parameters
3396: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
3397: performance can be increased by more than a factor of 50.
3399: Collective on MPI_Comm
3401: Input Parameters:
3402: + B - the matrix
3403: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
3404: (same value is used for all local rows)
3405: . d_nnz - array containing the number of nonzeros in the various rows of the
3406: DIAGONAL portion of the local submatrix (possibly different for each row)
3407: or NULL (PETSC_NULL_INTEGER in Fortran), if d_nz is used to specify the nonzero structure.
3408: The size of this array is equal to the number of local rows, i.e 'm'.
3409: For matrices that will be factored, you must leave room for (and set)
3410: the diagonal entry even if it is zero.
3411: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
3412: submatrix (same value is used for all local rows).
3413: - o_nnz - array containing the number of nonzeros in the various rows of the
3414: OFF-DIAGONAL portion of the local submatrix (possibly different for
3415: each row) or NULL (PETSC_NULL_INTEGER in Fortran), if o_nz is used to specify the nonzero
3416: structure. The size of this array is equal to the number
3417: of local rows, i.e 'm'.
3419: If the *_nnz parameter is given then the *_nz parameter is ignored
3421: The AIJ format (also called the Yale sparse matrix format or
3422: compressed row storage (CSR)), is fully compatible with standard Fortran 77
3423: storage. The stored row and column indices begin with zero.
3424: See Users-Manual: ch_mat for details.
3426: The parallel matrix is partitioned such that the first m0 rows belong to
3427: process 0, the next m1 rows belong to process 1, the next m2 rows belong
3428: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
3430: The DIAGONAL portion of the local submatrix of a processor can be defined
3431: as the submatrix which is obtained by extraction the part corresponding to
3432: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
3433: first row that belongs to the processor, r2 is the last row belonging to
3434: the this processor, and c1-c2 is range of indices of the local part of a
3435: vector suitable for applying the matrix to. This is an mxn matrix. In the
3436: common case of a square matrix, the row and column ranges are the same and
3437: the DIAGONAL part is also square. The remaining portion of the local
3438: submatrix (mxN) constitute the OFF-DIAGONAL portion.
3440: If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.
3442: You can call MatGetInfo() to get information on how effective the preallocation was;
3443: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3444: You can also run with the option -info and look for messages with the string
3445: malloc in them to see if additional memory allocation was needed.
3447: Example usage:
3449: Consider the following 8x8 matrix with 34 non-zero values, that is
3450: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
3451: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
3452: as follows:
3454: .vb
3455: 1 2 0 | 0 3 0 | 0 4
3456: Proc0 0 5 6 | 7 0 0 | 8 0
3457: 9 0 10 | 11 0 0 | 12 0
3458: -------------------------------------
3459: 13 0 14 | 15 16 17 | 0 0
3460: Proc1 0 18 0 | 19 20 21 | 0 0
3461: 0 0 0 | 22 23 0 | 24 0
3462: -------------------------------------
3463: Proc2 25 26 27 | 0 0 28 | 29 0
3464: 30 0 0 | 31 32 33 | 0 34
3465: .ve
3467: This can be represented as a collection of submatrices as:
3469: .vb
3470: A B C
3471: D E F
3472: G H I
3473: .ve
3475: Where the submatrices A,B,C are owned by proc0, D,E,F are
3476: owned by proc1, G,H,I are owned by proc2.
3478: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3479: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3480: The 'M','N' parameters are 8,8, and have the same values on all procs.
3482: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
3483: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
3484: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
3485: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
3486: part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
3487: matrix, ans [DF] as another SeqAIJ matrix.
3489: When d_nz, o_nz parameters are specified, d_nz storage elements are
3490: allocated for every row of the local diagonal submatrix, and o_nz
3491: storage locations are allocated for every row of the OFF-DIAGONAL submat.
3492: One way to choose d_nz and o_nz is to use the max nonzerors per local
3493: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
3494: In this case, the values of d_nz,o_nz are:
3495: .vb
3496: proc0 : dnz = 2, o_nz = 2
3497: proc1 : dnz = 3, o_nz = 2
3498: proc2 : dnz = 1, o_nz = 4
3499: .ve
3500: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
3501: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
3502: for proc3. i.e we are using 12+15+10=37 storage locations to store
3503: 34 values.
3505: When d_nnz, o_nnz parameters are specified, the storage is specified
3506: for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
3507: In the above case the values for d_nnz,o_nnz are:
3508: .vb
3509: proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
3510: proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
3511: proc2: d_nnz = [1,1] and o_nnz = [4,4]
3512: .ve
3513: Here the space allocated is sum of all the above values i.e 34, and
3514: hence pre-allocation is perfect.
3516: Level: intermediate
3518: .keywords: matrix, aij, compressed row, sparse, parallel
3520: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateAIJ(), MatMPIAIJSetPreallocationCSR(),
3521: MPIAIJ, MatGetInfo(), PetscSplitOwnership()
3522: @*/
3523: PetscErrorCode MatMPIAIJSetPreallocation(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
3524: {
3530: PetscTryMethod(B,"MatMPIAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[]),(B,d_nz,d_nnz,o_nz,o_nnz));
3531: return(0);
3532: }
3536: /*@
3537: MatCreateMPIAIJWithArrays - creates a MPI AIJ matrix using arrays that contain in standard
3538: CSR format the local rows.
3540: Collective on MPI_Comm
3542: Input Parameters:
3543: + comm - MPI communicator
3544: . m - number of local rows (Cannot be PETSC_DECIDE)
3545: . n - This value should be the same as the local size used in creating the
3546: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3547: calculated if N is given) For square matrices n is almost always m.
3548: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3549: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3550: . i - row indices
3551: . j - column indices
3552: - a - matrix values
3554: Output Parameter:
3555: . mat - the matrix
3557: Level: intermediate
3559: Notes:
3560: The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3561: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3562: called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.
3564: The i and j indices are 0 based, and i indices are indices corresponding to the local j array.
3566: The format which is used for the sparse matrix input, is equivalent to a
3567: row-major ordering.. i.e for the following matrix, the input data expected is
3568: as shown:
3570: 1 0 0
3571: 2 0 3 P0
3572: -------
3573: 4 5 6 P1
3575: Process0 [P0]: rows_owned=[0,1]
3576: i = {0,1,3} [size = nrow+1 = 2+1]
3577: j = {0,0,2} [size = nz = 6]
3578: v = {1,2,3} [size = nz = 6]
3580: Process1 [P1]: rows_owned=[2]
3581: i = {0,3} [size = nrow+1 = 1+1]
3582: j = {0,1,2} [size = nz = 6]
3583: v = {4,5,6} [size = nz = 6]
3585: .keywords: matrix, aij, compressed row, sparse, parallel
3587: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3588: MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithSplitArrays()
3589: @*/
3590: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
3591: {
3595: if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3596: if (m < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
3597: MatCreate(comm,mat);
3598: MatSetSizes(*mat,m,n,M,N);
3599: /* MatSetBlockSizes(M,bs,cbs); */
3600: MatSetType(*mat,MATMPIAIJ);
3601: MatMPIAIJSetPreallocationCSR(*mat,i,j,a);
3602: return(0);
3603: }
3607: /*@C
3608: MatCreateAIJ - Creates a sparse parallel matrix in AIJ format
3609: (the default parallel PETSc format). For good matrix assembly performance
3610: the user should preallocate the matrix storage by setting the parameters
3611: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
3612: performance can be increased by more than a factor of 50.
3614: Collective on MPI_Comm
3616: Input Parameters:
3617: + comm - MPI communicator
3618: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
3619: This value should be the same as the local size used in creating the
3620: y vector for the matrix-vector product y = Ax.
3621: . n - This value should be the same as the local size used in creating the
3622: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3623: calculated if N is given) For square matrices n is almost always m.
3624: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3625: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3626: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
3627: (same value is used for all local rows)
3628: . d_nnz - array containing the number of nonzeros in the various rows of the
3629: DIAGONAL portion of the local submatrix (possibly different for each row)
3630: or NULL, if d_nz is used to specify the nonzero structure.
3631: The size of this array is equal to the number of local rows, i.e 'm'.
3632: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
3633: submatrix (same value is used for all local rows).
3634: - o_nnz - array containing the number of nonzeros in the various rows of the
3635: OFF-DIAGONAL portion of the local submatrix (possibly different for
3636: each row) or NULL, if o_nz is used to specify the nonzero
3637: structure. The size of this array is equal to the number
3638: of local rows, i.e 'm'.
3640: Output Parameter:
3641: . A - the matrix
3643: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3644: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3645: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3647: Notes:
3648: If the *_nnz parameter is given then the *_nz parameter is ignored
3650: m,n,M,N parameters specify the size of the matrix, and its partitioning across
3651: processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate
3652: storage requirements for this matrix.
3654: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one
3655: processor than it must be used on all processors that share the object for
3656: that argument.
3658: The user MUST specify either the local or global matrix dimensions
3659: (possibly both).
3661: The parallel matrix is partitioned across processors such that the
3662: first m0 rows belong to process 0, the next m1 rows belong to
3663: process 1, the next m2 rows belong to process 2 etc.. where
3664: m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
3665: values corresponding to [m x N] submatrix.
3667: The columns are logically partitioned with the n0 columns belonging
3668: to 0th partition, the next n1 columns belonging to the next
3669: partition etc.. where n0,n1,n2... are the input parameter 'n'.
3671: The DIAGONAL portion of the local submatrix on any given processor
3672: is the submatrix corresponding to the rows and columns m,n
3673: corresponding to the given processor. i.e diagonal matrix on
3674: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
3675: etc. The remaining portion of the local submatrix [m x (N-n)]
3676: constitute the OFF-DIAGONAL portion. The example below better
3677: illustrates this concept.
3679: For a square global matrix we define each processor's diagonal portion
3680: to be its local rows and the corresponding columns (a square submatrix);
3681: each processor's off-diagonal portion encompasses the remainder of the
3682: local matrix (a rectangular submatrix).
3684: If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.
3686: When calling this routine with a single process communicator, a matrix of
3687: type SEQAIJ is returned. If a matrix of type MPIAIJ is desired for this
3688: type of communicator, use the construction mechanism:
3689: MatCreate(...,&A); MatSetType(A,MATMPIAIJ); MatSetSizes(A, m,n,M,N); MatMPIAIJSetPreallocation(A,...);
3691: By default, this format uses inodes (identical nodes) when possible.
3692: We search for consecutive rows with the same nonzero structure, thereby
3693: reusing matrix information to achieve increased efficiency.
3695: Options Database Keys:
3696: + -mat_no_inode - Do not use inodes
3697: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3698: - -mat_aij_oneindex - Internally use indexing starting at 1
3699: rather than 0. Note that when calling MatSetValues(),
3700: the user still MUST index entries starting at 0!
3703: Example usage:
3705: Consider the following 8x8 matrix with 34 non-zero values, that is
3706: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
3707: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
3708: as follows:
3710: .vb
3711: 1 2 0 | 0 3 0 | 0 4
3712: Proc0 0 5 6 | 7 0 0 | 8 0
3713: 9 0 10 | 11 0 0 | 12 0
3714: -------------------------------------
3715: 13 0 14 | 15 16 17 | 0 0
3716: Proc1 0 18 0 | 19 20 21 | 0 0
3717: 0 0 0 | 22 23 0 | 24 0
3718: -------------------------------------
3719: Proc2 25 26 27 | 0 0 28 | 29 0
3720: 30 0 0 | 31 32 33 | 0 34
3721: .ve
3723: This can be represented as a collection of submatrices as:
3725: .vb
3726: A B C
3727: D E F
3728: G H I
3729: .ve
3731: Where the submatrices A,B,C are owned by proc0, D,E,F are
3732: owned by proc1, G,H,I are owned by proc2.
3734: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3735: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3736: The 'M','N' parameters are 8,8, and have the same values on all procs.
3738: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
3739: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
3740: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
3741: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
3742: part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
3743: matrix, ans [DF] as another SeqAIJ matrix.
3745: When d_nz, o_nz parameters are specified, d_nz storage elements are
3746: allocated for every row of the local diagonal submatrix, and o_nz
3747: storage locations are allocated for every row of the OFF-DIAGONAL submat.
3748: One way to choose d_nz and o_nz is to use the max nonzerors per local
3749: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
3750: In this case, the values of d_nz,o_nz are:
3751: .vb
3752: proc0 : dnz = 2, o_nz = 2
3753: proc1 : dnz = 3, o_nz = 2
3754: proc2 : dnz = 1, o_nz = 4
3755: .ve
3756: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
3757: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
3758: for proc3. i.e we are using 12+15+10=37 storage locations to store
3759: 34 values.
3761: When d_nnz, o_nnz parameters are specified, the storage is specified
3762: for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
3763: In the above case the values for d_nnz,o_nnz are:
3764: .vb
3765: proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
3766: proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
3767: proc2: d_nnz = [1,1] and o_nnz = [4,4]
3768: .ve
3769: Here the space allocated is sum of all the above values i.e 34, and
3770: hence pre-allocation is perfect.
3772: Level: intermediate
3774: .keywords: matrix, aij, compressed row, sparse, parallel
3776: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
3777: MPIAIJ, MatCreateMPIAIJWithArrays()
3778: @*/
3779: PetscErrorCode MatCreateAIJ(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)
3780: {
3782: PetscMPIInt size;
3785: MatCreate(comm,A);
3786: MatSetSizes(*A,m,n,M,N);
3787: MPI_Comm_size(comm,&size);
3788: if (size > 1) {
3789: MatSetType(*A,MATMPIAIJ);
3790: MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
3791: } else {
3792: MatSetType(*A,MATSEQAIJ);
3793: MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
3794: }
3795: return(0);
3796: }
3800: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,const PetscInt *colmap[])
3801: {
3802: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
3805: if (Ad) *Ad = a->A;
3806: if (Ao) *Ao = a->B;
3807: if (colmap) *colmap = a->garray;
3808: return(0);
3809: }
3813: PetscErrorCode MatSetColoring_MPIAIJ(Mat A,ISColoring coloring)
3814: {
3816: PetscInt i;
3817: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
3820: if (coloring->ctype == IS_COLORING_GLOBAL) {
3821: ISColoringValue *allcolors,*colors;
3822: ISColoring ocoloring;
3824: /* set coloring for diagonal portion */
3825: MatSetColoring_SeqAIJ(a->A,coloring);
3827: /* set coloring for off-diagonal portion */
3828: ISAllGatherColors(PetscObjectComm((PetscObject)A),coloring->n,coloring->colors,NULL,&allcolors);
3829: PetscMalloc1(a->B->cmap->n+1,&colors);
3830: for (i=0; i<a->B->cmap->n; i++) {
3831: colors[i] = allcolors[a->garray[i]];
3832: }
3833: PetscFree(allcolors);
3834: ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap->n,colors,PETSC_OWN_POINTER,&ocoloring);
3835: MatSetColoring_SeqAIJ(a->B,ocoloring);
3836: ISColoringDestroy(&ocoloring);
3837: } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3838: ISColoringValue *colors;
3839: PetscInt *larray;
3840: ISColoring ocoloring;
3842: /* set coloring for diagonal portion */
3843: PetscMalloc1(a->A->cmap->n+1,&larray);
3844: for (i=0; i<a->A->cmap->n; i++) {
3845: larray[i] = i + A->cmap->rstart;
3846: }
3847: ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,a->A->cmap->n,larray,NULL,larray);
3848: PetscMalloc1(a->A->cmap->n+1,&colors);
3849: for (i=0; i<a->A->cmap->n; i++) {
3850: colors[i] = coloring->colors[larray[i]];
3851: }
3852: PetscFree(larray);
3853: ISColoringCreate(PETSC_COMM_SELF,coloring->n,a->A->cmap->n,colors,PETSC_OWN_POINTER,&ocoloring);
3854: MatSetColoring_SeqAIJ(a->A,ocoloring);
3855: ISColoringDestroy(&ocoloring);
3857: /* set coloring for off-diagonal portion */
3858: PetscMalloc1(a->B->cmap->n+1,&larray);
3859: ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,a->B->cmap->n,a->garray,NULL,larray);
3860: PetscMalloc1(a->B->cmap->n+1,&colors);
3861: for (i=0; i<a->B->cmap->n; i++) {
3862: colors[i] = coloring->colors[larray[i]];
3863: }
3864: PetscFree(larray);
3865: ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap->n,colors,PETSC_OWN_POINTER,&ocoloring);
3866: MatSetColoring_SeqAIJ(a->B,ocoloring);
3867: ISColoringDestroy(&ocoloring);
3868: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support ISColoringType %d",(int)coloring->ctype);
3869: return(0);
3870: }
3874: PetscErrorCode MatSetValuesAdifor_MPIAIJ(Mat A,PetscInt nl,void *advalues)
3875: {
3876: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
3880: MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);
3881: MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);
3882: return(0);
3883: }
3887: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
3888: {
3890: PetscInt m,N,i,rstart,nnz,Ii;
3891: PetscInt *indx;
3892: PetscScalar *values;
3895: MatGetSize(inmat,&m,&N);
3896: if (scall == MAT_INITIAL_MATRIX) { /* symbolic phase */
3897: PetscInt *dnz,*onz,sum,bs,cbs;
3899: if (n == PETSC_DECIDE) {
3900: PetscSplitOwnership(comm,&n,&N);
3901: }
3902: /* Check sum(n) = N */
3903: MPI_Allreduce(&n,&sum,1,MPIU_INT,MPI_SUM,comm);
3904: if (sum != N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Sum of local columns != global columns %d",N);
3906: MPI_Scan(&m, &rstart,1,MPIU_INT,MPI_SUM,comm);
3907: rstart -= m;
3909: MatPreallocateInitialize(comm,m,n,dnz,onz);
3910: for (i=0; i<m; i++) {
3911: MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,NULL);
3912: MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);
3913: MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,NULL);
3914: }
3916: MatCreate(comm,outmat);
3917: MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
3918: MatGetBlockSizes(inmat,&bs,&cbs);
3919: MatSetBlockSizes(*outmat,bs,cbs);
3920: MatSetType(*outmat,MATMPIAIJ);
3921: MatMPIAIJSetPreallocation(*outmat,0,dnz,0,onz);
3922: MatPreallocateFinalize(dnz,onz);
3923: }
3925: /* numeric phase */
3926: MatGetOwnershipRange(*outmat,&rstart,NULL);
3927: for (i=0; i<m; i++) {
3928: MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3929: Ii = i + rstart;
3930: MatSetValues(*outmat,1,&Ii,nnz,indx,values,INSERT_VALUES);
3931: MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
3932: }
3933: MatAssemblyBegin(*outmat,MAT_FINAL_ASSEMBLY);
3934: MatAssemblyEnd(*outmat,MAT_FINAL_ASSEMBLY);
3935: return(0);
3936: }
3940: PetscErrorCode MatFileSplit(Mat A,char *outfile)
3941: {
3942: PetscErrorCode ierr;
3943: PetscMPIInt rank;
3944: PetscInt m,N,i,rstart,nnz;
3945: size_t len;
3946: const PetscInt *indx;
3947: PetscViewer out;
3948: char *name;
3949: Mat B;
3950: const PetscScalar *values;
3953: MatGetLocalSize(A,&m,0);
3954: MatGetSize(A,0,&N);
3955: /* Should this be the type of the diagonal block of A? */
3956: MatCreate(PETSC_COMM_SELF,&B);
3957: MatSetSizes(B,m,N,m,N);
3958: MatSetBlockSizesFromMats(B,A,A);
3959: MatSetType(B,MATSEQAIJ);
3960: MatSeqAIJSetPreallocation(B,0,NULL);
3961: MatGetOwnershipRange(A,&rstart,0);
3962: for (i=0; i<m; i++) {
3963: MatGetRow(A,i+rstart,&nnz,&indx,&values);
3964: MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);
3965: MatRestoreRow(A,i+rstart,&nnz,&indx,&values);
3966: }
3967: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3968: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3970: MPI_Comm_rank(PetscObjectComm((PetscObject)A),&rank);
3971: PetscStrlen(outfile,&len);
3972: PetscMalloc1(len+5,&name);
3973: sprintf(name,"%s.%d",outfile,rank);
3974: PetscViewerBinaryOpen(PETSC_COMM_SELF,name,FILE_MODE_APPEND,&out);
3975: PetscFree(name);
3976: MatView(B,out);
3977: PetscViewerDestroy(&out);
3978: MatDestroy(&B);
3979: return(0);
3980: }
3982: extern PetscErrorCode MatDestroy_MPIAIJ(Mat);
3985: PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(Mat A)
3986: {
3987: PetscErrorCode ierr;
3988: Mat_Merge_SeqsToMPI *merge;
3989: PetscContainer container;
3992: PetscObjectQuery((PetscObject)A,"MatMergeSeqsToMPI",(PetscObject*)&container);
3993: if (container) {
3994: PetscContainerGetPointer(container,(void**)&merge);
3995: PetscFree(merge->id_r);
3996: PetscFree(merge->len_s);
3997: PetscFree(merge->len_r);
3998: PetscFree(merge->bi);
3999: PetscFree(merge->bj);
4000: PetscFree(merge->buf_ri[0]);
4001: PetscFree(merge->buf_ri);
4002: PetscFree(merge->buf_rj[0]);
4003: PetscFree(merge->buf_rj);
4004: PetscFree(merge->coi);
4005: PetscFree(merge->coj);
4006: PetscFree(merge->owners_co);
4007: PetscLayoutDestroy(&merge->rowmap);
4008: PetscFree(merge);
4009: PetscObjectCompose((PetscObject)A,"MatMergeSeqsToMPI",0);
4010: }
4011: MatDestroy_MPIAIJ(A);
4012: return(0);
4013: }
4015: #include <../src/mat/utils/freespace.h>
4016: #include <petscbt.h>
4020: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat,Mat mpimat)
4021: {
4022: PetscErrorCode ierr;
4023: MPI_Comm comm;
4024: Mat_SeqAIJ *a =(Mat_SeqAIJ*)seqmat->data;
4025: PetscMPIInt size,rank,taga,*len_s;
4026: PetscInt N=mpimat->cmap->N,i,j,*owners,*ai=a->i,*aj;
4027: PetscInt proc,m;
4028: PetscInt **buf_ri,**buf_rj;
4029: PetscInt k,anzi,*bj_i,*bi,*bj,arow,bnzi,nextaj;
4030: PetscInt nrows,**buf_ri_k,**nextrow,**nextai;
4031: MPI_Request *s_waits,*r_waits;
4032: MPI_Status *status;
4033: MatScalar *aa=a->a;
4034: MatScalar **abuf_r,*ba_i;
4035: Mat_Merge_SeqsToMPI *merge;
4036: PetscContainer container;
4039: PetscObjectGetComm((PetscObject)mpimat,&comm);
4040: PetscLogEventBegin(MAT_Seqstompinum,seqmat,0,0,0);
4042: MPI_Comm_size(comm,&size);
4043: MPI_Comm_rank(comm,&rank);
4045: PetscObjectQuery((PetscObject)mpimat,"MatMergeSeqsToMPI",(PetscObject*)&container);
4046: PetscContainerGetPointer(container,(void**)&merge);
4048: bi = merge->bi;
4049: bj = merge->bj;
4050: buf_ri = merge->buf_ri;
4051: buf_rj = merge->buf_rj;
4053: PetscMalloc1(size,&status);
4054: owners = merge->rowmap->range;
4055: len_s = merge->len_s;
4057: /* send and recv matrix values */
4058: /*-----------------------------*/
4059: PetscObjectGetNewTag((PetscObject)mpimat,&taga);
4060: PetscPostIrecvScalar(comm,taga,merge->nrecv,merge->id_r,merge->len_r,&abuf_r,&r_waits);
4062: PetscMalloc1(merge->nsend+1,&s_waits);
4063: for (proc=0,k=0; proc<size; proc++) {
4064: if (!len_s[proc]) continue;
4065: i = owners[proc];
4066: MPI_Isend(aa+ai[i],len_s[proc],MPIU_MATSCALAR,proc,taga,comm,s_waits+k);
4067: k++;
4068: }
4070: if (merge->nrecv) {MPI_Waitall(merge->nrecv,r_waits,status);}
4071: if (merge->nsend) {MPI_Waitall(merge->nsend,s_waits,status);}
4072: PetscFree(status);
4074: PetscFree(s_waits);
4075: PetscFree(r_waits);
4077: /* insert mat values of mpimat */
4078: /*----------------------------*/
4079: PetscMalloc1(N,&ba_i);
4080: PetscMalloc3(merge->nrecv,&buf_ri_k,merge->nrecv,&nextrow,merge->nrecv,&nextai);
4082: for (k=0; k<merge->nrecv; k++) {
4083: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4084: nrows = *(buf_ri_k[k]);
4085: nextrow[k] = buf_ri_k[k]+1; /* next row number of k-th recved i-structure */
4086: nextai[k] = buf_ri_k[k] + (nrows + 1); /* poins to the next i-structure of k-th recved i-structure */
4087: }
4089: /* set values of ba */
4090: m = merge->rowmap->n;
4091: for (i=0; i<m; i++) {
4092: arow = owners[rank] + i;
4093: bj_i = bj+bi[i]; /* col indices of the i-th row of mpimat */
4094: bnzi = bi[i+1] - bi[i];
4095: PetscMemzero(ba_i,bnzi*sizeof(PetscScalar));
4097: /* add local non-zero vals of this proc's seqmat into ba */
4098: anzi = ai[arow+1] - ai[arow];
4099: aj = a->j + ai[arow];
4100: aa = a->a + ai[arow];
4101: nextaj = 0;
4102: for (j=0; nextaj<anzi; j++) {
4103: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4104: ba_i[j] += aa[nextaj++];
4105: }
4106: }
4108: /* add received vals into ba */
4109: for (k=0; k<merge->nrecv; k++) { /* k-th received message */
4110: /* i-th row */
4111: if (i == *nextrow[k]) {
4112: anzi = *(nextai[k]+1) - *nextai[k];
4113: aj = buf_rj[k] + *(nextai[k]);
4114: aa = abuf_r[k] + *(nextai[k]);
4115: nextaj = 0;
4116: for (j=0; nextaj<anzi; j++) {
4117: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4118: ba_i[j] += aa[nextaj++];
4119: }
4120: }
4121: nextrow[k]++; nextai[k]++;
4122: }
4123: }
4124: MatSetValues(mpimat,1,&arow,bnzi,bj_i,ba_i,INSERT_VALUES);
4125: }
4126: MatAssemblyBegin(mpimat,MAT_FINAL_ASSEMBLY);
4127: MatAssemblyEnd(mpimat,MAT_FINAL_ASSEMBLY);
4129: PetscFree(abuf_r[0]);
4130: PetscFree(abuf_r);
4131: PetscFree(ba_i);
4132: PetscFree3(buf_ri_k,nextrow,nextai);
4133: PetscLogEventEnd(MAT_Seqstompinum,seqmat,0,0,0);
4134: return(0);
4135: }
4137: extern PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(Mat);
4141: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,Mat *mpimat)
4142: {
4143: PetscErrorCode ierr;
4144: Mat B_mpi;
4145: Mat_SeqAIJ *a=(Mat_SeqAIJ*)seqmat->data;
4146: PetscMPIInt size,rank,tagi,tagj,*len_s,*len_si,*len_ri;
4147: PetscInt **buf_rj,**buf_ri,**buf_ri_k;
4148: PetscInt M=seqmat->rmap->n,N=seqmat->cmap->n,i,*owners,*ai=a->i,*aj=a->j;
4149: PetscInt len,proc,*dnz,*onz,bs,cbs;
4150: PetscInt k,anzi,*bi,*bj,*lnk,nlnk,arow,bnzi,nspacedouble=0;
4151: PetscInt nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextai;
4152: MPI_Request *si_waits,*sj_waits,*ri_waits,*rj_waits;
4153: MPI_Status *status;
4154: PetscFreeSpaceList free_space=NULL,current_space=NULL;
4155: PetscBT lnkbt;
4156: Mat_Merge_SeqsToMPI *merge;
4157: PetscContainer container;
4160: PetscLogEventBegin(MAT_Seqstompisym,seqmat,0,0,0);
4162: /* make sure it is a PETSc comm */
4163: PetscCommDuplicate(comm,&comm,NULL);
4164: MPI_Comm_size(comm,&size);
4165: MPI_Comm_rank(comm,&rank);
4167: PetscNew(&merge);
4168: PetscMalloc1(size,&status);
4170: /* determine row ownership */
4171: /*---------------------------------------------------------*/
4172: PetscLayoutCreate(comm,&merge->rowmap);
4173: PetscLayoutSetLocalSize(merge->rowmap,m);
4174: PetscLayoutSetSize(merge->rowmap,M);
4175: PetscLayoutSetBlockSize(merge->rowmap,1);
4176: PetscLayoutSetUp(merge->rowmap);
4177: PetscMalloc1(size,&len_si);
4178: PetscMalloc1(size,&merge->len_s);
4180: m = merge->rowmap->n;
4181: owners = merge->rowmap->range;
4183: /* determine the number of messages to send, their lengths */
4184: /*---------------------------------------------------------*/
4185: len_s = merge->len_s;
4187: len = 0; /* length of buf_si[] */
4188: merge->nsend = 0;
4189: for (proc=0; proc<size; proc++) {
4190: len_si[proc] = 0;
4191: if (proc == rank) {
4192: len_s[proc] = 0;
4193: } else {
4194: len_si[proc] = owners[proc+1] - owners[proc] + 1;
4195: len_s[proc] = ai[owners[proc+1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4196: }
4197: if (len_s[proc]) {
4198: merge->nsend++;
4199: nrows = 0;
4200: for (i=owners[proc]; i<owners[proc+1]; i++) {
4201: if (ai[i+1] > ai[i]) nrows++;
4202: }
4203: len_si[proc] = 2*(nrows+1);
4204: len += len_si[proc];
4205: }
4206: }
4208: /* determine the number and length of messages to receive for ij-structure */
4209: /*-------------------------------------------------------------------------*/
4210: PetscGatherNumberOfMessages(comm,NULL,len_s,&merge->nrecv);
4211: PetscGatherMessageLengths2(comm,merge->nsend,merge->nrecv,len_s,len_si,&merge->id_r,&merge->len_r,&len_ri);
4213: /* post the Irecv of j-structure */
4214: /*-------------------------------*/
4215: PetscCommGetNewTag(comm,&tagj);
4216: PetscPostIrecvInt(comm,tagj,merge->nrecv,merge->id_r,merge->len_r,&buf_rj,&rj_waits);
4218: /* post the Isend of j-structure */
4219: /*--------------------------------*/
4220: PetscMalloc2(merge->nsend,&si_waits,merge->nsend,&sj_waits);
4222: for (proc=0, k=0; proc<size; proc++) {
4223: if (!len_s[proc]) continue;
4224: i = owners[proc];
4225: MPI_Isend(aj+ai[i],len_s[proc],MPIU_INT,proc,tagj,comm,sj_waits+k);
4226: k++;
4227: }
4229: /* receives and sends of j-structure are complete */
4230: /*------------------------------------------------*/
4231: if (merge->nrecv) {MPI_Waitall(merge->nrecv,rj_waits,status);}
4232: if (merge->nsend) {MPI_Waitall(merge->nsend,sj_waits,status);}
4234: /* send and recv i-structure */
4235: /*---------------------------*/
4236: PetscCommGetNewTag(comm,&tagi);
4237: PetscPostIrecvInt(comm,tagi,merge->nrecv,merge->id_r,len_ri,&buf_ri,&ri_waits);
4239: PetscMalloc1(len+1,&buf_s);
4240: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4241: for (proc=0,k=0; proc<size; proc++) {
4242: if (!len_s[proc]) continue;
4243: /* form outgoing message for i-structure:
4244: buf_si[0]: nrows to be sent
4245: [1:nrows]: row index (global)
4246: [nrows+1:2*nrows+1]: i-structure index
4247: */
4248: /*-------------------------------------------*/
4249: nrows = len_si[proc]/2 - 1;
4250: buf_si_i = buf_si + nrows+1;
4251: buf_si[0] = nrows;
4252: buf_si_i[0] = 0;
4253: nrows = 0;
4254: for (i=owners[proc]; i<owners[proc+1]; i++) {
4255: anzi = ai[i+1] - ai[i];
4256: if (anzi) {
4257: buf_si_i[nrows+1] = buf_si_i[nrows] + anzi; /* i-structure */
4258: buf_si[nrows+1] = i-owners[proc]; /* local row index */
4259: nrows++;
4260: }
4261: }
4262: MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,si_waits+k);
4263: k++;
4264: buf_si += len_si[proc];
4265: }
4267: if (merge->nrecv) {MPI_Waitall(merge->nrecv,ri_waits,status);}
4268: if (merge->nsend) {MPI_Waitall(merge->nsend,si_waits,status);}
4270: PetscInfo2(seqmat,"nsend: %D, nrecv: %D\n",merge->nsend,merge->nrecv);
4271: for (i=0; i<merge->nrecv; i++) {
4272: PetscInfo3(seqmat,"recv len_ri=%D, len_rj=%D from [%D]\n",len_ri[i],merge->len_r[i],merge->id_r[i]);
4273: }
4275: PetscFree(len_si);
4276: PetscFree(len_ri);
4277: PetscFree(rj_waits);
4278: PetscFree2(si_waits,sj_waits);
4279: PetscFree(ri_waits);
4280: PetscFree(buf_s);
4281: PetscFree(status);
4283: /* compute a local seq matrix in each processor */
4284: /*----------------------------------------------*/
4285: /* allocate bi array and free space for accumulating nonzero column info */
4286: PetscMalloc1(m+1,&bi);
4287: bi[0] = 0;
4289: /* create and initialize a linked list */
4290: nlnk = N+1;
4291: PetscLLCreate(N,N,nlnk,lnk,lnkbt);
4293: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4294: len = ai[owners[rank+1]] - ai[owners[rank]];
4295: PetscFreeSpaceGet((PetscInt)(2*len+1),&free_space);
4297: current_space = free_space;
4299: /* determine symbolic info for each local row */
4300: PetscMalloc3(merge->nrecv,&buf_ri_k,merge->nrecv,&nextrow,merge->nrecv,&nextai);
4302: for (k=0; k<merge->nrecv; k++) {
4303: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4304: nrows = *buf_ri_k[k];
4305: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4306: nextai[k] = buf_ri_k[k] + (nrows + 1); /* poins to the next i-structure of k-th recved i-structure */
4307: }
4309: MatPreallocateInitialize(comm,m,n,dnz,onz);
4310: len = 0;
4311: for (i=0; i<m; i++) {
4312: bnzi = 0;
4313: /* add local non-zero cols of this proc's seqmat into lnk */
4314: arow = owners[rank] + i;
4315: anzi = ai[arow+1] - ai[arow];
4316: aj = a->j + ai[arow];
4317: PetscLLAddSorted(anzi,aj,N,nlnk,lnk,lnkbt);
4318: bnzi += nlnk;
4319: /* add received col data into lnk */
4320: for (k=0; k<merge->nrecv; k++) { /* k-th received message */
4321: if (i == *nextrow[k]) { /* i-th row */
4322: anzi = *(nextai[k]+1) - *nextai[k];
4323: aj = buf_rj[k] + *nextai[k];
4324: PetscLLAddSorted(anzi,aj,N,nlnk,lnk,lnkbt);
4325: bnzi += nlnk;
4326: nextrow[k]++; nextai[k]++;
4327: }
4328: }
4329: if (len < bnzi) len = bnzi; /* =max(bnzi) */
4331: /* if free space is not available, make more free space */
4332: if (current_space->local_remaining<bnzi) {
4333: PetscFreeSpaceGet(bnzi+current_space->total_array_size,¤t_space);
4334: nspacedouble++;
4335: }
4336: /* copy data into free space, then initialize lnk */
4337: PetscLLClean(N,N,bnzi,lnk,current_space->array,lnkbt);
4338: MatPreallocateSet(i+owners[rank],bnzi,current_space->array,dnz,onz);
4340: current_space->array += bnzi;
4341: current_space->local_used += bnzi;
4342: current_space->local_remaining -= bnzi;
4344: bi[i+1] = bi[i] + bnzi;
4345: }
4347: PetscFree3(buf_ri_k,nextrow,nextai);
4349: PetscMalloc1(bi[m]+1,&bj);
4350: PetscFreeSpaceContiguous(&free_space,bj);
4351: PetscLLDestroy(lnk,lnkbt);
4353: /* create symbolic parallel matrix B_mpi */
4354: /*---------------------------------------*/
4355: MatGetBlockSizes(seqmat,&bs,&cbs);
4356: MatCreate(comm,&B_mpi);
4357: if (n==PETSC_DECIDE) {
4358: MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,N);
4359: } else {
4360: MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
4361: }
4362: MatSetBlockSizes(B_mpi,bs,cbs);
4363: MatSetType(B_mpi,MATMPIAIJ);
4364: MatMPIAIJSetPreallocation(B_mpi,0,dnz,0,onz);
4365: MatPreallocateFinalize(dnz,onz);
4366: MatSetOption(B_mpi,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_FALSE);
4368: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
4369: B_mpi->assembled = PETSC_FALSE;
4370: B_mpi->ops->destroy = MatDestroy_MPIAIJ_SeqsToMPI;
4371: merge->bi = bi;
4372: merge->bj = bj;
4373: merge->buf_ri = buf_ri;
4374: merge->buf_rj = buf_rj;
4375: merge->coi = NULL;
4376: merge->coj = NULL;
4377: merge->owners_co = NULL;
4379: PetscCommDestroy(&comm);
4381: /* attach the supporting struct to B_mpi for reuse */
4382: PetscContainerCreate(PETSC_COMM_SELF,&container);
4383: PetscContainerSetPointer(container,merge);
4384: PetscObjectCompose((PetscObject)B_mpi,"MatMergeSeqsToMPI",(PetscObject)container);
4385: PetscContainerDestroy(&container);
4386: *mpimat = B_mpi;
4388: PetscLogEventEnd(MAT_Seqstompisym,seqmat,0,0,0);
4389: return(0);
4390: }
4394: /*@C
4395: MatCreateMPIAIJSumSeqAIJ - Creates a MPIAIJ matrix by adding sequential
4396: matrices from each processor
4398: Collective on MPI_Comm
4400: Input Parameters:
4401: + comm - the communicators the parallel matrix will live on
4402: . seqmat - the input sequential matrices
4403: . m - number of local rows (or PETSC_DECIDE)
4404: . n - number of local columns (or PETSC_DECIDE)
4405: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4407: Output Parameter:
4408: . mpimat - the parallel matrix generated
4410: Level: advanced
4412: Notes:
4413: The dimensions of the sequential matrix in each processor MUST be the same.
4414: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
4415: destroyed when mpimat is destroyed. Call PetscObjectQuery() to access seqmat.
4416: @*/
4417: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,MatReuse scall,Mat *mpimat)
4418: {
4420: PetscMPIInt size;
4423: MPI_Comm_size(comm,&size);
4424: if (size == 1) {
4425: PetscLogEventBegin(MAT_Seqstompi,seqmat,0,0,0);
4426: if (scall == MAT_INITIAL_MATRIX) {
4427: MatDuplicate(seqmat,MAT_COPY_VALUES,mpimat);
4428: } else {
4429: MatCopy(seqmat,*mpimat,SAME_NONZERO_PATTERN);
4430: }
4431: PetscLogEventEnd(MAT_Seqstompi,seqmat,0,0,0);
4432: return(0);
4433: }
4434: PetscLogEventBegin(MAT_Seqstompi,seqmat,0,0,0);
4435: if (scall == MAT_INITIAL_MATRIX) {
4436: MatCreateMPIAIJSumSeqAIJSymbolic(comm,seqmat,m,n,mpimat);
4437: }
4438: MatCreateMPIAIJSumSeqAIJNumeric(seqmat,*mpimat);
4439: PetscLogEventEnd(MAT_Seqstompi,seqmat,0,0,0);
4440: return(0);
4441: }
4445: /*@
4446: MatMPIAIJGetLocalMat - Creates a SeqAIJ from a MPIAIJ matrix by taking all its local rows and putting them into a sequential vector with
4447: mlocal rows and n columns. Where mlocal is the row count obtained with MatGetLocalSize() and n is the global column count obtained
4448: with MatGetSize()
4450: Not Collective
4452: Input Parameters:
4453: + A - the matrix
4454: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4456: Output Parameter:
4457: . A_loc - the local sequential matrix generated
4459: Level: developer
4461: .seealso: MatGetOwnerShipRange(), MatMPIAIJGetLocalMatCondensed()
4463: @*/
4464: PetscErrorCode MatMPIAIJGetLocalMat(Mat A,MatReuse scall,Mat *A_loc)
4465: {
4467: Mat_MPIAIJ *mpimat=(Mat_MPIAIJ*)A->data;
4468: Mat_SeqAIJ *mat,*a,*b;
4469: PetscInt *ai,*aj,*bi,*bj,*cmap=mpimat->garray;
4470: MatScalar *aa,*ba,*cam;
4471: PetscScalar *ca;
4472: PetscInt am=A->rmap->n,i,j,k,cstart=A->cmap->rstart;
4473: PetscInt *ci,*cj,col,ncols_d,ncols_o,jo;
4474: PetscBool match;
4475: MPI_Comm comm;
4476: PetscMPIInt size;
4479: PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&match);
4480: if (!match) SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP,"Requires MPIAIJ matrix as input");
4481: PetscObjectGetComm((PetscObject)A,&comm);
4482: MPI_Comm_size(comm,&size);
4483: if (size == 1 && scall == MAT_REUSE_MATRIX) return(0);
4485: PetscLogEventBegin(MAT_Getlocalmat,A,0,0,0);
4486: a = (Mat_SeqAIJ*)(mpimat->A)->data;
4487: b = (Mat_SeqAIJ*)(mpimat->B)->data;
4488: ai = a->i; aj = a->j; bi = b->i; bj = b->j;
4489: aa = a->a; ba = b->a;
4490: if (scall == MAT_INITIAL_MATRIX) {
4491: if (size == 1) {
4492: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,A->cmap->N,ai,aj,aa,A_loc);
4493: return(0);
4494: }
4496: PetscMalloc1(1+am,&ci);
4497: ci[0] = 0;
4498: for (i=0; i<am; i++) {
4499: ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
4500: }
4501: PetscMalloc1(1+ci[am],&cj);
4502: PetscMalloc1(1+ci[am],&ca);
4503: k = 0;
4504: for (i=0; i<am; i++) {
4505: ncols_o = bi[i+1] - bi[i];
4506: ncols_d = ai[i+1] - ai[i];
4507: /* off-diagonal portion of A */
4508: for (jo=0; jo<ncols_o; jo++) {
4509: col = cmap[*bj];
4510: if (col >= cstart) break;
4511: cj[k] = col; bj++;
4512: ca[k++] = *ba++;
4513: }
4514: /* diagonal portion of A */
4515: for (j=0; j<ncols_d; j++) {
4516: cj[k] = cstart + *aj++;
4517: ca[k++] = *aa++;
4518: }
4519: /* off-diagonal portion of A */
4520: for (j=jo; j<ncols_o; j++) {
4521: cj[k] = cmap[*bj++];
4522: ca[k++] = *ba++;
4523: }
4524: }
4525: /* put together the new matrix */
4526: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,A->cmap->N,ci,cj,ca,A_loc);
4527: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4528: /* Since these are PETSc arrays, change flags to free them as necessary. */
4529: mat = (Mat_SeqAIJ*)(*A_loc)->data;
4530: mat->free_a = PETSC_TRUE;
4531: mat->free_ij = PETSC_TRUE;
4532: mat->nonew = 0;
4533: } else if (scall == MAT_REUSE_MATRIX) {
4534: mat=(Mat_SeqAIJ*)(*A_loc)->data;
4535: ci = mat->i; cj = mat->j; cam = mat->a;
4536: for (i=0; i<am; i++) {
4537: /* off-diagonal portion of A */
4538: ncols_o = bi[i+1] - bi[i];
4539: for (jo=0; jo<ncols_o; jo++) {
4540: col = cmap[*bj];
4541: if (col >= cstart) break;
4542: *cam++ = *ba++; bj++;
4543: }
4544: /* diagonal portion of A */
4545: ncols_d = ai[i+1] - ai[i];
4546: for (j=0; j<ncols_d; j++) *cam++ = *aa++;
4547: /* off-diagonal portion of A */
4548: for (j=jo; j<ncols_o; j++) {
4549: *cam++ = *ba++; bj++;
4550: }
4551: }
4552: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
4553: PetscLogEventEnd(MAT_Getlocalmat,A,0,0,0);
4554: return(0);
4555: }
4559: /*@C
4560: MatMPIAIJGetLocalMatCondensed - Creates a SeqAIJ matrix from an MPIAIJ matrix by taking all its local rows and NON-ZERO columns
4562: Not Collective
4564: Input Parameters:
4565: + A - the matrix
4566: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4567: - row, col - index sets of rows and columns to extract (or NULL)
4569: Output Parameter:
4570: . A_loc - the local sequential matrix generated
4572: Level: developer
4574: .seealso: MatGetOwnershipRange(), MatMPIAIJGetLocalMat()
4576: @*/
4577: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A,MatReuse scall,IS *row,IS *col,Mat *A_loc)
4578: {
4579: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
4581: PetscInt i,start,end,ncols,nzA,nzB,*cmap,imark,*idx;
4582: IS isrowa,iscola;
4583: Mat *aloc;
4584: PetscBool match;
4587: PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&match);
4588: if (!match) SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP,"Requires MPIAIJ matrix as input");
4589: PetscLogEventBegin(MAT_Getlocalmatcondensed,A,0,0,0);
4590: if (!row) {
4591: start = A->rmap->rstart; end = A->rmap->rend;
4592: ISCreateStride(PETSC_COMM_SELF,end-start,start,1,&isrowa);
4593: } else {
4594: isrowa = *row;
4595: }
4596: if (!col) {
4597: start = A->cmap->rstart;
4598: cmap = a->garray;
4599: nzA = a->A->cmap->n;
4600: nzB = a->B->cmap->n;
4601: PetscMalloc1(nzA+nzB, &idx);
4602: ncols = 0;
4603: for (i=0; i<nzB; i++) {
4604: if (cmap[i] < start) idx[ncols++] = cmap[i];
4605: else break;
4606: }
4607: imark = i;
4608: for (i=0; i<nzA; i++) idx[ncols++] = start + i;
4609: for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i];
4610: ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,PETSC_OWN_POINTER,&iscola);
4611: } else {
4612: iscola = *col;
4613: }
4614: if (scall != MAT_INITIAL_MATRIX) {
4615: PetscMalloc1(1,&aloc);
4616: aloc[0] = *A_loc;
4617: }
4618: MatGetSubMatrices(A,1,&isrowa,&iscola,scall,&aloc);
4619: *A_loc = aloc[0];
4620: PetscFree(aloc);
4621: if (!row) {
4622: ISDestroy(&isrowa);
4623: }
4624: if (!col) {
4625: ISDestroy(&iscola);
4626: }
4627: PetscLogEventEnd(MAT_Getlocalmatcondensed,A,0,0,0);
4628: return(0);
4629: }
4633: /*@C
4634: MatGetBrowsOfAcols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
4636: Collective on Mat
4638: Input Parameters:
4639: + A,B - the matrices in mpiaij format
4640: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4641: - rowb, colb - index sets of rows and columns of B to extract (or NULL)
4643: Output Parameter:
4644: + rowb, colb - index sets of rows and columns of B to extract
4645: - B_seq - the sequential matrix generated
4647: Level: developer
4649: @*/
4650: PetscErrorCode MatGetBrowsOfAcols(Mat A,Mat B,MatReuse scall,IS *rowb,IS *colb,Mat *B_seq)
4651: {
4652: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
4654: PetscInt *idx,i,start,ncols,nzA,nzB,*cmap,imark;
4655: IS isrowb,iscolb;
4656: Mat *bseq=NULL;
4659: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) {
4660: SETERRQ4(PETSC_COMM_SELF,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);
4661: }
4662: PetscLogEventBegin(MAT_GetBrowsOfAcols,A,B,0,0);
4664: if (scall == MAT_INITIAL_MATRIX) {
4665: start = A->cmap->rstart;
4666: cmap = a->garray;
4667: nzA = a->A->cmap->n;
4668: nzB = a->B->cmap->n;
4669: PetscMalloc1(nzA+nzB, &idx);
4670: ncols = 0;
4671: for (i=0; i<nzB; i++) { /* row < local row index */
4672: if (cmap[i] < start) idx[ncols++] = cmap[i];
4673: else break;
4674: }
4675: imark = i;
4676: for (i=0; i<nzA; i++) idx[ncols++] = start + i; /* local rows */
4677: for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
4678: ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,PETSC_OWN_POINTER,&isrowb);
4679: ISCreateStride(PETSC_COMM_SELF,B->cmap->N,0,1,&iscolb);
4680: } else {
4681: if (!rowb || !colb) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"IS rowb and colb must be provided for MAT_REUSE_MATRIX");
4682: isrowb = *rowb; iscolb = *colb;
4683: PetscMalloc1(1,&bseq);
4684: bseq[0] = *B_seq;
4685: }
4686: MatGetSubMatrices(B,1,&isrowb,&iscolb,scall,&bseq);
4687: *B_seq = bseq[0];
4688: PetscFree(bseq);
4689: if (!rowb) {
4690: ISDestroy(&isrowb);
4691: } else {
4692: *rowb = isrowb;
4693: }
4694: if (!colb) {
4695: ISDestroy(&iscolb);
4696: } else {
4697: *colb = iscolb;
4698: }
4699: PetscLogEventEnd(MAT_GetBrowsOfAcols,A,B,0,0);
4700: return(0);
4701: }
4705: /*
4706: MatGetBrowsOfAoCols_MPIAIJ - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns
4707: of the OFF-DIAGONAL portion of local A
4709: Collective on Mat
4711: Input Parameters:
4712: + A,B - the matrices in mpiaij format
4713: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4715: Output Parameter:
4716: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
4717: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
4718: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
4719: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
4721: Level: developer
4723: */
4724: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A,Mat B,MatReuse scall,PetscInt **startsj_s,PetscInt **startsj_r,MatScalar **bufa_ptr,Mat *B_oth)
4725: {
4726: VecScatter_MPI_General *gen_to,*gen_from;
4727: PetscErrorCode ierr;
4728: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
4729: Mat_SeqAIJ *b_oth;
4730: VecScatter ctx =a->Mvctx;
4731: MPI_Comm comm;
4732: PetscMPIInt *rprocs,*sprocs,tag=((PetscObject)ctx)->tag,rank;
4733: PetscInt *rowlen,*bufj,*bufJ,ncols,aBn=a->B->cmap->n,row,*b_othi,*b_othj;
4734: PetscScalar *rvalues,*svalues;
4735: MatScalar *b_otha,*bufa,*bufA;
4736: PetscInt i,j,k,l,ll,nrecvs,nsends,nrows,*srow,*rstarts,*rstartsj = 0,*sstarts,*sstartsj,len;
4737: MPI_Request *rwaits = NULL,*swaits = NULL;
4738: MPI_Status *sstatus,rstatus;
4739: PetscMPIInt jj,size;
4740: PetscInt *cols,sbs,rbs;
4741: PetscScalar *vals;
4744: PetscObjectGetComm((PetscObject)A,&comm);
4745: MPI_Comm_size(comm,&size);
4747: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) {
4748: SETERRQ4(PETSC_COMM_SELF,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);
4749: }
4750: PetscLogEventBegin(MAT_GetBrowsOfAocols,A,B,0,0);
4751: MPI_Comm_rank(comm,&rank);
4753: gen_to = (VecScatter_MPI_General*)ctx->todata;
4754: gen_from = (VecScatter_MPI_General*)ctx->fromdata;
4755: rvalues = gen_from->values; /* holds the length of receiving row */
4756: svalues = gen_to->values; /* holds the length of sending row */
4757: nrecvs = gen_from->n;
4758: nsends = gen_to->n;
4760: PetscMalloc2(nrecvs,&rwaits,nsends,&swaits);
4761: srow = gen_to->indices; /* local row index to be sent */
4762: sstarts = gen_to->starts;
4763: sprocs = gen_to->procs;
4764: sstatus = gen_to->sstatus;
4765: sbs = gen_to->bs;
4766: rstarts = gen_from->starts;
4767: rprocs = gen_from->procs;
4768: rbs = gen_from->bs;
4770: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
4771: if (scall == MAT_INITIAL_MATRIX) {
4772: /* i-array */
4773: /*---------*/
4774: /* post receives */
4775: for (i=0; i<nrecvs; i++) {
4776: rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
4777: nrows = (rstarts[i+1]-rstarts[i])*rbs; /* num of indices to be received */
4778: MPI_Irecv(rowlen,nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
4779: }
4781: /* pack the outgoing message */
4782: PetscMalloc2(nsends+1,&sstartsj,nrecvs+1,&rstartsj);
4784: sstartsj[0] = 0;
4785: rstartsj[0] = 0;
4786: len = 0; /* total length of j or a array to be sent */
4787: k = 0;
4788: for (i=0; i<nsends; i++) {
4789: rowlen = (PetscInt*)svalues + sstarts[i]*sbs;
4790: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4791: for (j=0; j<nrows; j++) {
4792: row = srow[k] + B->rmap->range[rank]; /* global row idx */
4793: for (l=0; l<sbs; l++) {
4794: MatGetRow_MPIAIJ(B,row+l,&ncols,NULL,NULL); /* rowlength */
4796: rowlen[j*sbs+l] = ncols;
4798: len += ncols;
4799: MatRestoreRow_MPIAIJ(B,row+l,&ncols,NULL,NULL);
4800: }
4801: k++;
4802: }
4803: MPI_Isend(rowlen,nrows*sbs,MPIU_INT,sprocs[i],tag,comm,swaits+i);
4805: sstartsj[i+1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
4806: }
4807: /* recvs and sends of i-array are completed */
4808: i = nrecvs;
4809: while (i--) {
4810: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4811: }
4812: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4814: /* allocate buffers for sending j and a arrays */
4815: PetscMalloc1(len+1,&bufj);
4816: PetscMalloc1(len+1,&bufa);
4818: /* create i-array of B_oth */
4819: PetscMalloc1(aBn+2,&b_othi);
4821: b_othi[0] = 0;
4822: len = 0; /* total length of j or a array to be received */
4823: k = 0;
4824: for (i=0; i<nrecvs; i++) {
4825: rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
4826: nrows = rbs*(rstarts[i+1]-rstarts[i]); /* num of rows to be recieved */
4827: for (j=0; j<nrows; j++) {
4828: b_othi[k+1] = b_othi[k] + rowlen[j];
4829: len += rowlen[j]; k++;
4830: }
4831: rstartsj[i+1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
4832: }
4834: /* allocate space for j and a arrrays of B_oth */
4835: PetscMalloc1(b_othi[aBn]+1,&b_othj);
4836: PetscMalloc1(b_othi[aBn]+1,&b_otha);
4838: /* j-array */
4839: /*---------*/
4840: /* post receives of j-array */
4841: for (i=0; i<nrecvs; i++) {
4842: nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
4843: MPI_Irecv(b_othj+rstartsj[i],nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
4844: }
4846: /* pack the outgoing message j-array */
4847: k = 0;
4848: for (i=0; i<nsends; i++) {
4849: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4850: bufJ = bufj+sstartsj[i];
4851: for (j=0; j<nrows; j++) {
4852: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
4853: for (ll=0; ll<sbs; ll++) {
4854: MatGetRow_MPIAIJ(B,row+ll,&ncols,&cols,NULL);
4855: for (l=0; l<ncols; l++) {
4856: *bufJ++ = cols[l];
4857: }
4858: MatRestoreRow_MPIAIJ(B,row+ll,&ncols,&cols,NULL);
4859: }
4860: }
4861: MPI_Isend(bufj+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_INT,sprocs[i],tag,comm,swaits+i);
4862: }
4864: /* recvs and sends of j-array are completed */
4865: i = nrecvs;
4866: while (i--) {
4867: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4868: }
4869: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4870: } else if (scall == MAT_REUSE_MATRIX) {
4871: sstartsj = *startsj_s;
4872: rstartsj = *startsj_r;
4873: bufa = *bufa_ptr;
4874: b_oth = (Mat_SeqAIJ*)(*B_oth)->data;
4875: b_otha = b_oth->a;
4876: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not posses an object container");
4878: /* a-array */
4879: /*---------*/
4880: /* post receives of a-array */
4881: for (i=0; i<nrecvs; i++) {
4882: nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
4883: MPI_Irecv(b_otha+rstartsj[i],nrows,MPIU_SCALAR,rprocs[i],tag,comm,rwaits+i);
4884: }
4886: /* pack the outgoing message a-array */
4887: k = 0;
4888: for (i=0; i<nsends; i++) {
4889: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
4890: bufA = bufa+sstartsj[i];
4891: for (j=0; j<nrows; j++) {
4892: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
4893: for (ll=0; ll<sbs; ll++) {
4894: MatGetRow_MPIAIJ(B,row+ll,&ncols,NULL,&vals);
4895: for (l=0; l<ncols; l++) {
4896: *bufA++ = vals[l];
4897: }
4898: MatRestoreRow_MPIAIJ(B,row+ll,&ncols,NULL,&vals);
4899: }
4900: }
4901: MPI_Isend(bufa+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_SCALAR,sprocs[i],tag,comm,swaits+i);
4902: }
4903: /* recvs and sends of a-array are completed */
4904: i = nrecvs;
4905: while (i--) {
4906: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
4907: }
4908: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
4909: PetscFree2(rwaits,swaits);
4911: if (scall == MAT_INITIAL_MATRIX) {
4912: /* put together the new matrix */
4913: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,aBn,B->cmap->N,b_othi,b_othj,b_otha,B_oth);
4915: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4916: /* Since these are PETSc arrays, change flags to free them as necessary. */
4917: b_oth = (Mat_SeqAIJ*)(*B_oth)->data;
4918: b_oth->free_a = PETSC_TRUE;
4919: b_oth->free_ij = PETSC_TRUE;
4920: b_oth->nonew = 0;
4922: PetscFree(bufj);
4923: if (!startsj_s || !bufa_ptr) {
4924: PetscFree2(sstartsj,rstartsj);
4925: PetscFree(bufa_ptr);
4926: } else {
4927: *startsj_s = sstartsj;
4928: *startsj_r = rstartsj;
4929: *bufa_ptr = bufa;
4930: }
4931: }
4932: PetscLogEventEnd(MAT_GetBrowsOfAocols,A,B,0,0);
4933: return(0);
4934: }
4938: /*@C
4939: MatGetCommunicationStructs - Provides access to the communication structures used in matrix-vector multiplication.
4941: Not Collective
4943: Input Parameters:
4944: . A - The matrix in mpiaij format
4946: Output Parameter:
4947: + lvec - The local vector holding off-process values from the argument to a matrix-vector product
4948: . colmap - A map from global column index to local index into lvec
4949: - multScatter - A scatter from the argument of a matrix-vector product to lvec
4951: Level: developer
4953: @*/
4954: #if defined(PETSC_USE_CTABLE)
4955: PetscErrorCode MatGetCommunicationStructs(Mat A, Vec *lvec, PetscTable *colmap, VecScatter *multScatter)
4956: #else
4957: PetscErrorCode MatGetCommunicationStructs(Mat A, Vec *lvec, PetscInt *colmap[], VecScatter *multScatter)
4958: #endif
4959: {
4960: Mat_MPIAIJ *a;
4967: a = (Mat_MPIAIJ*) A->data;
4968: if (lvec) *lvec = a->lvec;
4969: if (colmap) *colmap = a->colmap;
4970: if (multScatter) *multScatter = a->Mvctx;
4971: return(0);
4972: }
4974: PETSC_EXTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat,MatType,MatReuse,Mat*);
4975: PETSC_EXTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat,MatType,MatReuse,Mat*);
4976: PETSC_EXTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat,MatType,MatReuse,Mat*);
4977: #if defined(PETSC_HAVE_ELEMENTAL)
4978: PETSC_EXTERN PetscErrorCode MatConvert_MPIAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
4979: #endif
4983: /*
4984: Computes (B'*A')' since computing B*A directly is untenable
4986: n p p
4987: ( ) ( ) ( )
4988: m ( A ) * n ( B ) = m ( C )
4989: ( ) ( ) ( )
4991: */
4992: PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A,Mat B,Mat C)
4993: {
4995: Mat At,Bt,Ct;
4998: MatTranspose(A,MAT_INITIAL_MATRIX,&At);
4999: MatTranspose(B,MAT_INITIAL_MATRIX,&Bt);
5000: MatMatMult(Bt,At,MAT_INITIAL_MATRIX,1.0,&Ct);
5001: MatDestroy(&At);
5002: MatDestroy(&Bt);
5003: MatTranspose(Ct,MAT_REUSE_MATRIX,&C);
5004: MatDestroy(&Ct);
5005: return(0);
5006: }
5010: PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
5011: {
5013: PetscInt m=A->rmap->n,n=B->cmap->n;
5014: Mat Cmat;
5017: if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %d != B->rmap->n %d\n",A->cmap->n,B->rmap->n);
5018: MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
5019: MatSetSizes(Cmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
5020: MatSetBlockSizesFromMats(Cmat,A,B);
5021: MatSetType(Cmat,MATMPIDENSE);
5022: MatMPIDenseSetPreallocation(Cmat,NULL);
5023: MatAssemblyBegin(Cmat,MAT_FINAL_ASSEMBLY);
5024: MatAssemblyEnd(Cmat,MAT_FINAL_ASSEMBLY);
5026: Cmat->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
5028: *C = Cmat;
5029: return(0);
5030: }
5032: /* ----------------------------------------------------------------*/
5035: PetscErrorCode MatMatMult_MPIDense_MPIAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
5036: {
5040: if (scall == MAT_INITIAL_MATRIX) {
5041: PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
5042: MatMatMultSymbolic_MPIDense_MPIAIJ(A,B,fill,C);
5043: PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
5044: }
5045: PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
5046: MatMatMultNumeric_MPIDense_MPIAIJ(A,B,*C);
5047: PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
5048: return(0);
5049: }
5051: /*MC
5052: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
5054: Options Database Keys:
5055: . -mat_type mpiaij - sets the matrix type to "mpiaij" during a call to MatSetFromOptions()
5057: Level: beginner
5059: .seealso: MatCreateAIJ()
5060: M*/
5064: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
5065: {
5066: Mat_MPIAIJ *b;
5068: PetscMPIInt size;
5071: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
5073: PetscNewLog(B,&b);
5074: B->data = (void*)b;
5075: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
5076: B->assembled = PETSC_FALSE;
5077: B->insertmode = NOT_SET_VALUES;
5078: b->size = size;
5080: MPI_Comm_rank(PetscObjectComm((PetscObject)B),&b->rank);
5082: /* build cache for off array entries formed */
5083: MatStashCreate_Private(PetscObjectComm((PetscObject)B),1,&B->stash);
5085: b->donotstash = PETSC_FALSE;
5086: b->colmap = 0;
5087: b->garray = 0;
5088: b->roworiented = PETSC_TRUE;
5090: /* stuff used for matrix vector multiply */
5091: b->lvec = NULL;
5092: b->Mvctx = NULL;
5094: /* stuff for MatGetRow() */
5095: b->rowindices = 0;
5096: b->rowvalues = 0;
5097: b->getrowactive = PETSC_FALSE;
5099: /* flexible pointer used in CUSP/CUSPARSE classes */
5100: b->spptr = NULL;
5102: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_MPIAIJ);
5103: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_MPIAIJ);
5104: PetscObjectComposeFunction((PetscObject)B,"MatGetDiagonalBlock_C",MatGetDiagonalBlock_MPIAIJ);
5105: PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_MPIAIJ);
5106: PetscObjectComposeFunction((PetscObject)B,"MatMPIAIJSetPreallocation_C",MatMPIAIJSetPreallocation_MPIAIJ);
5107: PetscObjectComposeFunction((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",MatMPIAIJSetPreallocationCSR_MPIAIJ);
5108: PetscObjectComposeFunction((PetscObject)B,"MatDiagonalScaleLocal_C",MatDiagonalScaleLocal_MPIAIJ);
5109: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_mpiaijperm_C",MatConvert_MPIAIJ_MPIAIJPERM);
5110: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_mpiaijcrl_C",MatConvert_MPIAIJ_MPIAIJCRL);
5111: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_mpisbaij_C",MatConvert_MPIAIJ_MPISBAIJ);
5112: #if defined(PETSC_HAVE_ELEMENTAL)
5113: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_elemental_C",MatConvert_MPIAIJ_Elemental);
5114: #endif
5115: PetscObjectComposeFunction((PetscObject)B,"MatMatMult_mpidense_mpiaij_C",MatMatMult_MPIDense_MPIAIJ);
5116: PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_mpidense_mpiaij_C",MatMatMultSymbolic_MPIDense_MPIAIJ);
5117: PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_mpidense_mpiaij_C",MatMatMultNumeric_MPIDense_MPIAIJ);
5118: PetscObjectChangeTypeName((PetscObject)B,MATMPIAIJ);
5119: return(0);
5120: }
5124: /*@C
5125: MatCreateMPIAIJWithSplitArrays - creates a MPI AIJ matrix using arrays that contain the "diagonal"
5126: and "off-diagonal" part of the matrix in CSR format.
5128: Collective on MPI_Comm
5130: Input Parameters:
5131: + comm - MPI communicator
5132: . m - number of local rows (Cannot be PETSC_DECIDE)
5133: . n - This value should be the same as the local size used in creating the
5134: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
5135: calculated if N is given) For square matrices n is almost always m.
5136: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
5137: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
5138: . i - row indices for "diagonal" portion of matrix
5139: . j - column indices
5140: . a - matrix values
5141: . oi - row indices for "off-diagonal" portion of matrix
5142: . oj - column indices
5143: - oa - matrix values
5145: Output Parameter:
5146: . mat - the matrix
5148: Level: advanced
5150: Notes:
5151: The i, j, and a arrays ARE NOT copied by this routine into the internal format used by PETSc. The user
5152: must free the arrays once the matrix has been destroyed and not before.
5154: The i and j indices are 0 based
5156: See MatCreateAIJ() for the definition of "diagonal" and "off-diagonal" portion of the matrix
5158: This sets local rows and cannot be used to set off-processor values.
5160: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
5161: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
5162: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
5163: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
5164: keep track of the underlying array. Use MatSetOption(A,MAT_IGNORE_OFF_PROC_ENTRIES,PETSC_TRUE) to disable all
5165: communication if it is known that only local entries will be set.
5167: .keywords: matrix, aij, compressed row, sparse, parallel
5169: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
5170: MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithArrays()
5171: @*/
5172: PetscErrorCode MatCreateMPIAIJWithSplitArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt i[],PetscInt j[],PetscScalar a[],PetscInt oi[], PetscInt oj[],PetscScalar oa[],Mat *mat)
5173: {
5175: Mat_MPIAIJ *maij;
5178: if (m < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
5179: if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
5180: if (oi[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"oi (row indices) must start with 0");
5181: MatCreate(comm,mat);
5182: MatSetSizes(*mat,m,n,M,N);
5183: MatSetType(*mat,MATMPIAIJ);
5184: maij = (Mat_MPIAIJ*) (*mat)->data;
5186: (*mat)->preallocated = PETSC_TRUE;
5188: PetscLayoutSetUp((*mat)->rmap);
5189: PetscLayoutSetUp((*mat)->cmap);
5191: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,i,j,a,&maij->A);
5192: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,(*mat)->cmap->N,oi,oj,oa,&maij->B);
5194: MatAssemblyBegin(maij->A,MAT_FINAL_ASSEMBLY);
5195: MatAssemblyEnd(maij->A,MAT_FINAL_ASSEMBLY);
5196: MatAssemblyBegin(maij->B,MAT_FINAL_ASSEMBLY);
5197: MatAssemblyEnd(maij->B,MAT_FINAL_ASSEMBLY);
5199: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
5200: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
5201: MatSetOption(*mat,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
5202: return(0);
5203: }
5205: /*
5206: Special version for direct calls from Fortran
5207: */
5208: #include <petsc/private/fortranimpl.h>
5210: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5211: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
5212: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5213: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
5214: #endif
5216: /* Change these macros so can be used in void function */
5217: #undef CHKERRQ
5218: #define CHKERRQ(ierr) CHKERRABORT(PETSC_COMM_WORLD,ierr)
5219: #undef SETERRQ2
5220: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
5221: #undef SETERRQ3
5222: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)
5223: #undef SETERRQ
5224: #define SETERRQ(c,ierr,b) CHKERRABORT(c,ierr)
5228: PETSC_EXTERN void PETSC_STDCALL matsetvaluesmpiaij_(Mat *mmat,PetscInt *mm,const PetscInt im[],PetscInt *mn,const PetscInt in[],const PetscScalar v[],InsertMode *maddv,PetscErrorCode *_ierr)
5229: {
5230: Mat mat = *mmat;
5231: PetscInt m = *mm, n = *mn;
5232: InsertMode addv = *maddv;
5233: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
5234: PetscScalar value;
5237: MatCheckPreallocated(mat,1);
5238: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
5240: #if defined(PETSC_USE_DEBUG)
5241: else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
5242: #endif
5243: {
5244: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
5245: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
5246: PetscBool roworiented = aij->roworiented;
5248: /* Some Variables required in the macro */
5249: Mat A = aij->A;
5250: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
5251: PetscInt *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
5252: MatScalar *aa = a->a;
5253: PetscBool ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
5254: Mat B = aij->B;
5255: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
5256: PetscInt *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap->n,am = aij->A->rmap->n;
5257: MatScalar *ba = b->a;
5259: PetscInt *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
5260: PetscInt nonew = a->nonew;
5261: MatScalar *ap1,*ap2;
5264: for (i=0; i<m; i++) {
5265: if (im[i] < 0) continue;
5266: #if defined(PETSC_USE_DEBUG)
5267: if (im[i] >= mat->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",im[i],mat->rmap->N-1);
5268: #endif
5269: if (im[i] >= rstart && im[i] < rend) {
5270: row = im[i] - rstart;
5271: lastcol1 = -1;
5272: rp1 = aj + ai[row];
5273: ap1 = aa + ai[row];
5274: rmax1 = aimax[row];
5275: nrow1 = ailen[row];
5276: low1 = 0;
5277: high1 = nrow1;
5278: lastcol2 = -1;
5279: rp2 = bj + bi[row];
5280: ap2 = ba + bi[row];
5281: rmax2 = bimax[row];
5282: nrow2 = bilen[row];
5283: low2 = 0;
5284: high2 = nrow2;
5286: for (j=0; j<n; j++) {
5287: if (roworiented) value = v[i*n+j];
5288: else value = v[i+j*m];
5289: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
5290: if (in[j] >= cstart && in[j] < cend) {
5291: col = in[j] - cstart;
5292: MatSetValues_SeqAIJ_A_Private(row,col,value,addv,im[i],in[j]);
5293: } else if (in[j] < 0) continue;
5294: #if defined(PETSC_USE_DEBUG)
5295: else if (in[j] >= mat->cmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[j],mat->cmap->N-1);
5296: #endif
5297: else {
5298: if (mat->was_assembled) {
5299: if (!aij->colmap) {
5300: MatCreateColmap_MPIAIJ_Private(mat);
5301: }
5302: #if defined(PETSC_USE_CTABLE)
5303: PetscTableFind(aij->colmap,in[j]+1,&col);
5304: col--;
5305: #else
5306: col = aij->colmap[in[j]] - 1;
5307: #endif
5308: if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
5309: MatDisAssemble_MPIAIJ(mat);
5310: col = in[j];
5311: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
5312: B = aij->B;
5313: b = (Mat_SeqAIJ*)B->data;
5314: bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
5315: rp2 = bj + bi[row];
5316: ap2 = ba + bi[row];
5317: rmax2 = bimax[row];
5318: nrow2 = bilen[row];
5319: low2 = 0;
5320: high2 = nrow2;
5321: bm = aij->B->rmap->n;
5322: ba = b->a;
5323: }
5324: } else col = in[j];
5325: MatSetValues_SeqAIJ_B_Private(row,col,value,addv,im[i],in[j]);
5326: }
5327: }
5328: } else if (!aij->donotstash) {
5329: if (roworiented) {
5330: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,(PetscBool)(ignorezeroentries && (addv == ADD_VALUES)));
5331: } else {
5332: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,(PetscBool)(ignorezeroentries && (addv == ADD_VALUES)));
5333: }
5334: }
5335: }
5336: }
5337: PetscFunctionReturnVoid();
5338: }