Actual source code: mpiaij.c
petsc-3.5.1 2014-07-24
2: #include <../src/mat/impls/aij/mpi/mpiaij.h> /*I "petscmat.h" I*/
3: #include <petsc-private/vecimpl.h>
4: #include <petscblaslapack.h>
5: #include <petscsf.h>
7: /*MC
8: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
10: This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
11: and MATMPIAIJ otherwise. As a result, for single process communicators,
12: MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
13: for communicators controlling multiple processes. It is recommended that you call both of
14: the above preallocation routines for simplicity.
16: Options Database Keys:
17: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()
19: Developer Notes: Subclasses include MATAIJCUSP, MATAIJCUSPARSE, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when
20: enough exist.
22: Level: beginner
24: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
25: M*/
27: /*MC
28: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
30: This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
31: and MATMPIAIJCRL otherwise. As a result, for single process communicators,
32: MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
33: for communicators controlling multiple processes. It is recommended that you call both of
34: the above preallocation routines for simplicity.
36: Options Database Keys:
37: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()
39: Level: beginner
41: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
42: M*/
46: PetscErrorCode MatFindNonzeroRows_MPIAIJ(Mat M,IS *keptrows)
47: {
48: PetscErrorCode ierr;
49: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)M->data;
50: Mat_SeqAIJ *a = (Mat_SeqAIJ*)mat->A->data;
51: Mat_SeqAIJ *b = (Mat_SeqAIJ*)mat->B->data;
52: const PetscInt *ia,*ib;
53: const MatScalar *aa,*bb;
54: PetscInt na,nb,i,j,*rows,cnt=0,n0rows;
55: PetscInt m = M->rmap->n,rstart = M->rmap->rstart;
58: *keptrows = 0;
59: ia = a->i;
60: ib = b->i;
61: for (i=0; i<m; i++) {
62: na = ia[i+1] - ia[i];
63: nb = ib[i+1] - ib[i];
64: if (!na && !nb) {
65: cnt++;
66: goto ok1;
67: }
68: aa = a->a + ia[i];
69: for (j=0; j<na; j++) {
70: if (aa[j] != 0.0) goto ok1;
71: }
72: bb = b->a + ib[i];
73: for (j=0; j <nb; j++) {
74: if (bb[j] != 0.0) goto ok1;
75: }
76: cnt++;
77: ok1:;
78: }
79: MPI_Allreduce(&cnt,&n0rows,1,MPIU_INT,MPIU_SUM,PetscObjectComm((PetscObject)M));
80: if (!n0rows) return(0);
81: PetscMalloc1((M->rmap->n-cnt),&rows);
82: cnt = 0;
83: for (i=0; i<m; i++) {
84: na = ia[i+1] - ia[i];
85: nb = ib[i+1] - ib[i];
86: if (!na && !nb) continue;
87: aa = a->a + ia[i];
88: for (j=0; j<na;j++) {
89: if (aa[j] != 0.0) {
90: rows[cnt++] = rstart + i;
91: goto ok2;
92: }
93: }
94: bb = b->a + ib[i];
95: for (j=0; j<nb; j++) {
96: if (bb[j] != 0.0) {
97: rows[cnt++] = rstart + i;
98: goto ok2;
99: }
100: }
101: ok2:;
102: }
103: ISCreateGeneral(PetscObjectComm((PetscObject)M),cnt,rows,PETSC_OWN_POINTER,keptrows);
104: return(0);
105: }
109: PetscErrorCode MatFindZeroDiagonals_MPIAIJ(Mat M,IS *zrows)
110: {
111: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)M->data;
113: PetscInt i,rstart,nrows,*rows;
116: *zrows = NULL;
117: MatFindZeroDiagonals_SeqAIJ_Private(aij->A,&nrows,&rows);
118: MatGetOwnershipRange(M,&rstart,NULL);
119: for (i=0; i<nrows; i++) rows[i] += rstart;
120: ISCreateGeneral(PetscObjectComm((PetscObject)M),nrows,rows,PETSC_OWN_POINTER,zrows);
121: return(0);
122: }
126: PetscErrorCode MatGetColumnNorms_MPIAIJ(Mat A,NormType type,PetscReal *norms)
127: {
129: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)A->data;
130: PetscInt i,n,*garray = aij->garray;
131: Mat_SeqAIJ *a_aij = (Mat_SeqAIJ*) aij->A->data;
132: Mat_SeqAIJ *b_aij = (Mat_SeqAIJ*) aij->B->data;
133: PetscReal *work;
136: MatGetSize(A,NULL,&n);
137: PetscCalloc1(n,&work);
138: if (type == NORM_2) {
139: for (i=0; i<a_aij->i[aij->A->rmap->n]; i++) {
140: work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]*a_aij->a[i]);
141: }
142: for (i=0; i<b_aij->i[aij->B->rmap->n]; i++) {
143: work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]*b_aij->a[i]);
144: }
145: } else if (type == NORM_1) {
146: for (i=0; i<a_aij->i[aij->A->rmap->n]; i++) {
147: work[A->cmap->rstart + a_aij->j[i]] += PetscAbsScalar(a_aij->a[i]);
148: }
149: for (i=0; i<b_aij->i[aij->B->rmap->n]; i++) {
150: work[garray[b_aij->j[i]]] += PetscAbsScalar(b_aij->a[i]);
151: }
152: } else if (type == NORM_INFINITY) {
153: for (i=0; i<a_aij->i[aij->A->rmap->n]; i++) {
154: work[A->cmap->rstart + a_aij->j[i]] = PetscMax(PetscAbsScalar(a_aij->a[i]), work[A->cmap->rstart + a_aij->j[i]]);
155: }
156: for (i=0; i<b_aij->i[aij->B->rmap->n]; i++) {
157: work[garray[b_aij->j[i]]] = PetscMax(PetscAbsScalar(b_aij->a[i]),work[garray[b_aij->j[i]]]);
158: }
160: } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Unknown NormType");
161: if (type == NORM_INFINITY) {
162: MPI_Allreduce(work,norms,n,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)A));
163: } else {
164: MPI_Allreduce(work,norms,n,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)A));
165: }
166: PetscFree(work);
167: if (type == NORM_2) {
168: for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
169: }
170: return(0);
171: }
175: /*
176: Distributes a SeqAIJ matrix across a set of processes. Code stolen from
177: MatLoad_MPIAIJ(). Horrible lack of reuse. Should be a routine for each matrix type.
179: Only for square matrices
181: Used by a preconditioner, hence PETSC_EXTERN
182: */
183: PETSC_EXTERN PetscErrorCode MatDistribute_MPIAIJ(MPI_Comm comm,Mat gmat,PetscInt m,MatReuse reuse,Mat *inmat)
184: {
185: PetscMPIInt rank,size;
186: PetscInt *rowners,*dlens,*olens,i,rstart,rend,j,jj,nz = 0,*gmataj,cnt,row,*ld,bses[2];
188: Mat mat;
189: Mat_SeqAIJ *gmata;
190: PetscMPIInt tag;
191: MPI_Status status;
192: PetscBool aij;
193: MatScalar *gmataa,*ao,*ad,*gmataarestore=0;
196: MPI_Comm_rank(comm,&rank);
197: MPI_Comm_size(comm,&size);
198: if (!rank) {
199: PetscObjectTypeCompare((PetscObject)gmat,MATSEQAIJ,&aij);
200: if (!aij) SETERRQ1(PetscObjectComm((PetscObject)gmat),PETSC_ERR_SUP,"Currently no support for input matrix of type %s\n",((PetscObject)gmat)->type_name);
201: }
202: if (reuse == MAT_INITIAL_MATRIX) {
203: MatCreate(comm,&mat);
204: MatSetSizes(mat,m,m,PETSC_DETERMINE,PETSC_DETERMINE);
205: MatGetBlockSizes(gmat,&bses[0],&bses[1]);
206: MPI_Bcast(bses,2,MPIU_INT,0,comm);
207: MatSetBlockSizes(mat,bses[0],bses[1]);
208: MatSetType(mat,MATAIJ);
209: PetscMalloc1((size+1),&rowners);
210: PetscMalloc2(m,&dlens,m,&olens);
211: MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
213: rowners[0] = 0;
214: for (i=2; i<=size; i++) rowners[i] += rowners[i-1];
215: rstart = rowners[rank];
216: rend = rowners[rank+1];
217: PetscObjectGetNewTag((PetscObject)mat,&tag);
218: if (!rank) {
219: gmata = (Mat_SeqAIJ*) gmat->data;
220: /* send row lengths to all processors */
221: for (i=0; i<m; i++) dlens[i] = gmata->ilen[i];
222: for (i=1; i<size; i++) {
223: MPI_Send(gmata->ilen + rowners[i],rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
224: }
225: /* determine number diagonal and off-diagonal counts */
226: PetscMemzero(olens,m*sizeof(PetscInt));
227: PetscCalloc1(m,&ld);
228: jj = 0;
229: for (i=0; i<m; i++) {
230: for (j=0; j<dlens[i]; j++) {
231: if (gmata->j[jj] < rstart) ld[i]++;
232: if (gmata->j[jj] < rstart || gmata->j[jj] >= rend) olens[i]++;
233: jj++;
234: }
235: }
236: /* send column indices to other processes */
237: for (i=1; i<size; i++) {
238: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
239: MPI_Send(&nz,1,MPIU_INT,i,tag,comm);
240: MPI_Send(gmata->j + gmata->i[rowners[i]],nz,MPIU_INT,i,tag,comm);
241: }
243: /* send numerical values to other processes */
244: for (i=1; i<size; i++) {
245: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
246: MPI_Send(gmata->a + gmata->i[rowners[i]],nz,MPIU_SCALAR,i,tag,comm);
247: }
248: gmataa = gmata->a;
249: gmataj = gmata->j;
251: } else {
252: /* receive row lengths */
253: MPI_Recv(dlens,m,MPIU_INT,0,tag,comm,&status);
254: /* receive column indices */
255: MPI_Recv(&nz,1,MPIU_INT,0,tag,comm,&status);
256: PetscMalloc2(nz,&gmataa,nz,&gmataj);
257: MPI_Recv(gmataj,nz,MPIU_INT,0,tag,comm,&status);
258: /* determine number diagonal and off-diagonal counts */
259: PetscMemzero(olens,m*sizeof(PetscInt));
260: PetscCalloc1(m,&ld);
261: jj = 0;
262: for (i=0; i<m; i++) {
263: for (j=0; j<dlens[i]; j++) {
264: if (gmataj[jj] < rstart) ld[i]++;
265: if (gmataj[jj] < rstart || gmataj[jj] >= rend) olens[i]++;
266: jj++;
267: }
268: }
269: /* receive numerical values */
270: PetscMemzero(gmataa,nz*sizeof(PetscScalar));
271: MPI_Recv(gmataa,nz,MPIU_SCALAR,0,tag,comm,&status);
272: }
273: /* set preallocation */
274: for (i=0; i<m; i++) {
275: dlens[i] -= olens[i];
276: }
277: MatSeqAIJSetPreallocation(mat,0,dlens);
278: MatMPIAIJSetPreallocation(mat,0,dlens,0,olens);
280: for (i=0; i<m; i++) {
281: dlens[i] += olens[i];
282: }
283: cnt = 0;
284: for (i=0; i<m; i++) {
285: row = rstart + i;
286: MatSetValues(mat,1,&row,dlens[i],gmataj+cnt,gmataa+cnt,INSERT_VALUES);
287: cnt += dlens[i];
288: }
289: if (rank) {
290: PetscFree2(gmataa,gmataj);
291: }
292: PetscFree2(dlens,olens);
293: PetscFree(rowners);
295: ((Mat_MPIAIJ*)(mat->data))->ld = ld;
297: *inmat = mat;
298: } else { /* column indices are already set; only need to move over numerical values from process 0 */
299: Mat_SeqAIJ *Ad = (Mat_SeqAIJ*)((Mat_MPIAIJ*)((*inmat)->data))->A->data;
300: Mat_SeqAIJ *Ao = (Mat_SeqAIJ*)((Mat_MPIAIJ*)((*inmat)->data))->B->data;
301: mat = *inmat;
302: PetscObjectGetNewTag((PetscObject)mat,&tag);
303: if (!rank) {
304: /* send numerical values to other processes */
305: gmata = (Mat_SeqAIJ*) gmat->data;
306: MatGetOwnershipRanges(mat,(const PetscInt**)&rowners);
307: gmataa = gmata->a;
308: for (i=1; i<size; i++) {
309: nz = gmata->i[rowners[i+1]]-gmata->i[rowners[i]];
310: MPI_Send(gmataa + gmata->i[rowners[i]],nz,MPIU_SCALAR,i,tag,comm);
311: }
312: nz = gmata->i[rowners[1]]-gmata->i[rowners[0]];
313: } else {
314: /* receive numerical values from process 0*/
315: nz = Ad->nz + Ao->nz;
316: PetscMalloc1(nz,&gmataa); gmataarestore = gmataa;
317: MPI_Recv(gmataa,nz,MPIU_SCALAR,0,tag,comm,&status);
318: }
319: /* transfer numerical values into the diagonal A and off diagonal B parts of mat */
320: ld = ((Mat_MPIAIJ*)(mat->data))->ld;
321: ad = Ad->a;
322: ao = Ao->a;
323: if (mat->rmap->n) {
324: i = 0;
325: nz = ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
326: nz = Ad->i[i+1] - Ad->i[i]; PetscMemcpy(ad,gmataa,nz*sizeof(PetscScalar)); ad += nz; gmataa += nz;
327: }
328: for (i=1; i<mat->rmap->n; i++) {
329: nz = Ao->i[i] - Ao->i[i-1] - ld[i-1] + ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar)); ao += nz; gmataa += nz;
330: nz = Ad->i[i+1] - Ad->i[i]; PetscMemcpy(ad,gmataa,nz*sizeof(PetscScalar)); ad += nz; gmataa += nz;
331: }
332: i--;
333: if (mat->rmap->n) {
334: nz = Ao->i[i+1] - Ao->i[i] - ld[i]; PetscMemcpy(ao,gmataa,nz*sizeof(PetscScalar));
335: }
336: if (rank) {
337: PetscFree(gmataarestore);
338: }
339: }
340: MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
341: MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
342: return(0);
343: }
345: /*
346: Local utility routine that creates a mapping from the global column
347: number to the local number in the off-diagonal part of the local
348: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
349: a slightly higher hash table cost; without it it is not scalable (each processor
350: has an order N integer array but is fast to acess.
351: */
354: PetscErrorCode MatCreateColmap_MPIAIJ_Private(Mat mat)
355: {
356: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
358: PetscInt n = aij->B->cmap->n,i;
361: if (!aij->garray) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"MPIAIJ Matrix was assembled but is missing garray");
362: #if defined(PETSC_USE_CTABLE)
363: PetscTableCreate(n,mat->cmap->N+1,&aij->colmap);
364: for (i=0; i<n; i++) {
365: PetscTableAdd(aij->colmap,aij->garray[i]+1,i+1,INSERT_VALUES);
366: }
367: #else
368: PetscCalloc1((mat->cmap->N+1),&aij->colmap);
369: PetscLogObjectMemory((PetscObject)mat,(mat->cmap->N+1)*sizeof(PetscInt));
370: for (i=0; i<n; i++) aij->colmap[aij->garray[i]] = i+1;
371: #endif
372: return(0);
373: }
375: #define MatSetValues_SeqAIJ_A_Private(row,col,value,addv) \
376: { \
377: if (col <= lastcol1) low1 = 0; \
378: else high1 = nrow1; \
379: lastcol1 = col;\
380: while (high1-low1 > 5) { \
381: t = (low1+high1)/2; \
382: if (rp1[t] > col) high1 = t; \
383: else low1 = t; \
384: } \
385: for (_i=low1; _i<high1; _i++) { \
386: if (rp1[_i] > col) break; \
387: if (rp1[_i] == col) { \
388: if (addv == ADD_VALUES) ap1[_i] += value; \
389: else ap1[_i] = value; \
390: goto a_noinsert; \
391: } \
392: } \
393: if (value == 0.0 && ignorezeroentries) {low1 = 0; high1 = nrow1;goto a_noinsert;} \
394: if (nonew == 1) {low1 = 0; high1 = nrow1; goto a_noinsert;} \
395: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
396: MatSeqXAIJReallocateAIJ(A,am,1,nrow1,row,col,rmax1,aa,ai,aj,rp1,ap1,aimax,nonew,MatScalar); \
397: N = nrow1++ - 1; a->nz++; high1++; \
398: /* shift up all the later entries in this row */ \
399: for (ii=N; ii>=_i; ii--) { \
400: rp1[ii+1] = rp1[ii]; \
401: ap1[ii+1] = ap1[ii]; \
402: } \
403: rp1[_i] = col; \
404: ap1[_i] = value; \
405: A->nonzerostate++;\
406: a_noinsert: ; \
407: ailen[row] = nrow1; \
408: }
411: #define MatSetValues_SeqAIJ_B_Private(row,col,value,addv) \
412: { \
413: if (col <= lastcol2) low2 = 0; \
414: else high2 = nrow2; \
415: lastcol2 = col; \
416: while (high2-low2 > 5) { \
417: t = (low2+high2)/2; \
418: if (rp2[t] > col) high2 = t; \
419: else low2 = t; \
420: } \
421: for (_i=low2; _i<high2; _i++) { \
422: if (rp2[_i] > col) break; \
423: if (rp2[_i] == col) { \
424: if (addv == ADD_VALUES) ap2[_i] += value; \
425: else ap2[_i] = value; \
426: goto b_noinsert; \
427: } \
428: } \
429: if (value == 0.0 && ignorezeroentries) {low2 = 0; high2 = nrow2; goto b_noinsert;} \
430: if (nonew == 1) {low2 = 0; high2 = nrow2; goto b_noinsert;} \
431: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", row, col); \
432: MatSeqXAIJReallocateAIJ(B,bm,1,nrow2,row,col,rmax2,ba,bi,bj,rp2,ap2,bimax,nonew,MatScalar); \
433: N = nrow2++ - 1; b->nz++; high2++; \
434: /* shift up all the later entries in this row */ \
435: for (ii=N; ii>=_i; ii--) { \
436: rp2[ii+1] = rp2[ii]; \
437: ap2[ii+1] = ap2[ii]; \
438: } \
439: rp2[_i] = col; \
440: ap2[_i] = value; \
441: B->nonzerostate++; \
442: b_noinsert: ; \
443: bilen[row] = nrow2; \
444: }
448: PetscErrorCode MatSetValuesRow_MPIAIJ(Mat A,PetscInt row,const PetscScalar v[])
449: {
450: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
451: Mat_SeqAIJ *a = (Mat_SeqAIJ*)mat->A->data,*b = (Mat_SeqAIJ*)mat->B->data;
453: PetscInt l,*garray = mat->garray,diag;
456: /* code only works for square matrices A */
458: /* find size of row to the left of the diagonal part */
459: MatGetOwnershipRange(A,&diag,0);
460: row = row - diag;
461: for (l=0; l<b->i[row+1]-b->i[row]; l++) {
462: if (garray[b->j[b->i[row]+l]] > diag) break;
463: }
464: PetscMemcpy(b->a+b->i[row],v,l*sizeof(PetscScalar));
466: /* diagonal part */
467: PetscMemcpy(a->a+a->i[row],v+l,(a->i[row+1]-a->i[row])*sizeof(PetscScalar));
469: /* right of diagonal part */
470: 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));
471: return(0);
472: }
476: PetscErrorCode MatSetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
477: {
478: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
479: PetscScalar value;
481: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
482: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
483: PetscBool roworiented = aij->roworiented;
485: /* Some Variables required in the macro */
486: Mat A = aij->A;
487: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
488: PetscInt *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
489: MatScalar *aa = a->a;
490: PetscBool ignorezeroentries = a->ignorezeroentries;
491: Mat B = aij->B;
492: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
493: PetscInt *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap->n,am = aij->A->rmap->n;
494: MatScalar *ba = b->a;
496: PetscInt *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
497: PetscInt nonew;
498: MatScalar *ap1,*ap2;
501: for (i=0; i<m; i++) {
502: if (im[i] < 0) continue;
503: #if defined(PETSC_USE_DEBUG)
504: 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);
505: #endif
506: if (im[i] >= rstart && im[i] < rend) {
507: row = im[i] - rstart;
508: lastcol1 = -1;
509: rp1 = aj + ai[row];
510: ap1 = aa + ai[row];
511: rmax1 = aimax[row];
512: nrow1 = ailen[row];
513: low1 = 0;
514: high1 = nrow1;
515: lastcol2 = -1;
516: rp2 = bj + bi[row];
517: ap2 = ba + bi[row];
518: rmax2 = bimax[row];
519: nrow2 = bilen[row];
520: low2 = 0;
521: high2 = nrow2;
523: for (j=0; j<n; j++) {
524: if (roworiented) value = v[i*n+j];
525: else value = v[i+j*m];
526: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
527: if (in[j] >= cstart && in[j] < cend) {
528: col = in[j] - cstart;
529: nonew = a->nonew;
530: MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
531: } else if (in[j] < 0) continue;
532: #if defined(PETSC_USE_DEBUG)
533: 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);
534: #endif
535: else {
536: if (mat->was_assembled) {
537: if (!aij->colmap) {
538: MatCreateColmap_MPIAIJ_Private(mat);
539: }
540: #if defined(PETSC_USE_CTABLE)
541: PetscTableFind(aij->colmap,in[j]+1,&col);
542: col--;
543: #else
544: col = aij->colmap[in[j]] - 1;
545: #endif
546: if (col < 0 && !((Mat_SeqAIJ*)(aij->B->data))->nonew) {
547: MatDisAssemble_MPIAIJ(mat);
548: col = in[j];
549: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
550: B = aij->B;
551: b = (Mat_SeqAIJ*)B->data;
552: bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j; ba = b->a;
553: rp2 = bj + bi[row];
554: ap2 = ba + bi[row];
555: rmax2 = bimax[row];
556: nrow2 = bilen[row];
557: low2 = 0;
558: high2 = nrow2;
559: bm = aij->B->rmap->n;
560: ba = b->a;
561: } else if (col < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) into matrix", im[i], in[j]);
562: } else col = in[j];
563: nonew = b->nonew;
564: MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
565: }
566: }
567: } else {
568: 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]);
569: if (!aij->donotstash) {
570: mat->assembled = PETSC_FALSE;
571: if (roworiented) {
572: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,(PetscBool)(ignorezeroentries && (addv == ADD_VALUES)));
573: } else {
574: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,(PetscBool)(ignorezeroentries && (addv == ADD_VALUES)));
575: }
576: }
577: }
578: }
579: return(0);
580: }
584: PetscErrorCode MatGetValues_MPIAIJ(Mat mat,PetscInt m,const PetscInt idxm[],PetscInt n,const PetscInt idxn[],PetscScalar v[])
585: {
586: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
588: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
589: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
592: for (i=0; i<m; i++) {
593: if (idxm[i] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",idxm[i]);*/
594: 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);
595: if (idxm[i] >= rstart && idxm[i] < rend) {
596: row = idxm[i] - rstart;
597: for (j=0; j<n; j++) {
598: if (idxn[j] < 0) continue; /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",idxn[j]); */
599: 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);
600: if (idxn[j] >= cstart && idxn[j] < cend) {
601: col = idxn[j] - cstart;
602: MatGetValues(aij->A,1,&row,1,&col,v+i*n+j);
603: } else {
604: if (!aij->colmap) {
605: MatCreateColmap_MPIAIJ_Private(mat);
606: }
607: #if defined(PETSC_USE_CTABLE)
608: PetscTableFind(aij->colmap,idxn[j]+1,&col);
609: col--;
610: #else
611: col = aij->colmap[idxn[j]] - 1;
612: #endif
613: if ((col < 0) || (aij->garray[col] != idxn[j])) *(v+i*n+j) = 0.0;
614: else {
615: MatGetValues(aij->B,1,&row,1,&col,v+i*n+j);
616: }
617: }
618: }
619: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only local values currently supported");
620: }
621: return(0);
622: }
624: extern PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat,Vec,Vec);
628: PetscErrorCode MatAssemblyBegin_MPIAIJ(Mat mat,MatAssemblyType mode)
629: {
630: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
632: PetscInt nstash,reallocs;
633: InsertMode addv;
636: if (aij->donotstash || mat->nooffprocentries) return(0);
638: /* make sure all processors are either in INSERTMODE or ADDMODE */
639: MPI_Allreduce((PetscEnum*)&mat->insertmode,(PetscEnum*)&addv,1,MPIU_ENUM,MPI_BOR,PetscObjectComm((PetscObject)mat));
640: if (addv == (ADD_VALUES|INSERT_VALUES)) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_WRONGSTATE,"Some processors inserted others added");
641: mat->insertmode = addv; /* in case this processor had no cache */
643: MatStashScatterBegin_Private(mat,&mat->stash,mat->rmap->range);
644: MatStashGetInfo_Private(&mat->stash,&nstash,&reallocs);
645: PetscInfo2(aij->A,"Stash has %D entries, uses %D mallocs.\n",nstash,reallocs);
646: return(0);
647: }
651: PetscErrorCode MatAssemblyEnd_MPIAIJ(Mat mat,MatAssemblyType mode)
652: {
653: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
654: Mat_SeqAIJ *a = (Mat_SeqAIJ*)aij->A->data;
656: PetscMPIInt n;
657: PetscInt i,j,rstart,ncols,flg;
658: PetscInt *row,*col;
659: PetscBool other_disassembled;
660: PetscScalar *val;
661: InsertMode addv = mat->insertmode;
663: /* do not use 'b = (Mat_SeqAIJ*)aij->B->data' as B can be reset in disassembly */
666: if (!aij->donotstash && !mat->nooffprocentries) {
667: while (1) {
668: MatStashScatterGetMesg_Private(&mat->stash,&n,&row,&col,&val,&flg);
669: if (!flg) break;
671: for (i=0; i<n; ) {
672: /* Now identify the consecutive vals belonging to the same row */
673: for (j=i,rstart=row[j]; j<n; j++) {
674: if (row[j] != rstart) break;
675: }
676: if (j < n) ncols = j-i;
677: else ncols = n-i;
678: /* Now assemble all these values with a single function call */
679: MatSetValues_MPIAIJ(mat,1,row+i,ncols,col+i,val+i,addv);
681: i = j;
682: }
683: }
684: MatStashScatterEnd_Private(&mat->stash);
685: }
686: MatAssemblyBegin(aij->A,mode);
687: MatAssemblyEnd(aij->A,mode);
689: /* determine if any processor has disassembled, if so we must
690: also disassemble ourselfs, in order that we may reassemble. */
691: /*
692: if nonzero structure of submatrix B cannot change then we know that
693: no processor disassembled thus we can skip this stuff
694: */
695: if (!((Mat_SeqAIJ*)aij->B->data)->nonew) {
696: MPI_Allreduce(&mat->was_assembled,&other_disassembled,1,MPIU_BOOL,MPI_PROD,PetscObjectComm((PetscObject)mat));
697: if (mat->was_assembled && !other_disassembled) {
698: MatDisAssemble_MPIAIJ(mat);
699: }
700: }
701: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) {
702: MatSetUpMultiply_MPIAIJ(mat);
703: }
704: MatSetOption(aij->B,MAT_USE_INODES,PETSC_FALSE);
705: MatAssemblyBegin(aij->B,mode);
706: MatAssemblyEnd(aij->B,mode);
708: PetscFree2(aij->rowvalues,aij->rowindices);
710: aij->rowvalues = 0;
712: /* used by MatAXPY() */
713: a->xtoy = 0; ((Mat_SeqAIJ*)aij->B->data)->xtoy = 0; /* b->xtoy = 0 */
714: a->XtoY = 0; ((Mat_SeqAIJ*)aij->B->data)->XtoY = 0; /* b->XtoY = 0 */
716: VecDestroy(&aij->diag);
717: if (a->inode.size) mat->ops->multdiagonalblock = MatMultDiagonalBlock_MPIAIJ;
719: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
720: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
721: PetscObjectState state = aij->A->nonzerostate + aij->B->nonzerostate;
722: MPI_Allreduce(&state,&mat->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)mat));
723: }
724: return(0);
725: }
729: PetscErrorCode MatZeroEntries_MPIAIJ(Mat A)
730: {
731: Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data;
735: MatZeroEntries(l->A);
736: MatZeroEntries(l->B);
737: return(0);
738: }
742: PetscErrorCode MatZeroRows_MPIAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
743: {
744: Mat_MPIAIJ *mat = (Mat_MPIAIJ *) A->data;
745: PetscInt *owners = A->rmap->range;
746: PetscInt n = A->rmap->n;
747: PetscMPIInt size = mat->size;
748: PetscSF sf;
749: PetscInt *lrows;
750: PetscSFNode *rrows;
751: PetscInt lastidx = -1, r, p = 0, len = 0;
755: /* Create SF where leaves are input rows and roots are owned rows */
756: PetscMalloc1(n, &lrows);
757: for (r = 0; r < n; ++r) lrows[r] = -1;
758: PetscMalloc1(N, &rrows);
759: for (r = 0; r < N; ++r) {
760: const PetscInt idx = rows[r];
761: PetscBool found = PETSC_FALSE;
762: /* Trick for efficient searching for sorted rows */
763: if (lastidx > idx) p = 0;
764: lastidx = idx;
765: for (; p < size; ++p) {
766: if (idx >= owners[p] && idx < owners[p+1]) {
767: rrows[r].rank = p;
768: rrows[r].index = rows[r] - owners[p];
769: found = PETSC_TRUE;
770: break;
771: }
772: }
773: if (!found) SETERRQ1(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %d not found in matrix distribution", idx);
774: }
775: PetscSFCreate(PetscObjectComm((PetscObject) A), &sf);
776: PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER);
777: /* Collect flags for rows to be zeroed */
778: PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
779: PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
780: PetscSFDestroy(&sf);
781: /* Compress and put in row numbers */
782: for (r = 0; r < n; ++r) if (lrows[r] >= 0) lrows[len++] = r;
783: /* fix right hand side if needed */
784: if (x && b) {
785: const PetscScalar *xx;
786: PetscScalar *bb;
788: VecGetArrayRead(x, &xx);
789: VecGetArray(b, &bb);
790: for (r = 0; r < len; ++r) bb[lrows[r]] = diag*xx[lrows[r]];
791: VecRestoreArrayRead(x, &xx);
792: VecRestoreArray(b, &bb);
793: }
794: /* Must zero l->B before l->A because the (diag) case below may put values into l->B*/
795: MatZeroRows(mat->B, len, lrows, 0.0, 0,0);
796: if ((diag != 0.0) && (mat->A->rmap->N == mat->A->cmap->N)) {
797: MatZeroRows(mat->A, len, lrows, diag, NULL, NULL);
798: } else if (diag != 0.0) {
799: MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL);
800: 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");
801: for (r = 0; r < len; ++r) {
802: const PetscInt row = lrows[r] + A->rmap->rstart;
803: MatSetValues(A, 1, &row, 1, &row, &diag, INSERT_VALUES);
804: }
805: MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);
806: MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);
807: } else {
808: MatZeroRows(mat->A, len, lrows, 0.0, NULL, NULL);
809: }
810: PetscFree(lrows);
812: /* only change matrix nonzero state if pattern was allowed to be changed */
813: if (!((Mat_SeqAIJ*)(mat->A->data))->keepnonzeropattern) {
814: PetscObjectState state = mat->A->nonzerostate + mat->B->nonzerostate;
815: MPI_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
816: }
817: return(0);
818: }
822: PetscErrorCode MatZeroRowsColumns_MPIAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
823: {
824: Mat_MPIAIJ *l = (Mat_MPIAIJ*)A->data;
825: PetscErrorCode ierr;
826: PetscMPIInt size = l->size,n = A->rmap->n,lastidx = -1;
827: PetscInt i,j,r,m,p = 0,len = 0;
828: PetscInt *lrows,*owners = A->rmap->range;
829: PetscSFNode *rrows;
830: PetscSF sf;
831: const PetscScalar *xx;
832: PetscScalar *bb,*mask;
833: Vec xmask,lmask;
834: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)l->B->data;
835: const PetscInt *aj, *ii,*ridx;
836: PetscScalar *aa;
837: #if defined(PETSC_DEBUG)
838: PetscBool found = PETSC_FALSE;
839: #endif
842: /* Create SF where leaves are input rows and roots are owned rows */
843: PetscMalloc1(n, &lrows);
844: for (r = 0; r < n; ++r) lrows[r] = -1;
845: PetscMalloc1(N, &rrows);
846: for (r = 0; r < N; ++r) {
847: const PetscInt idx = rows[r];
848: PetscBool found = PETSC_FALSE;
849: /* Trick for efficient searching for sorted rows */
850: if (lastidx > idx) p = 0;
851: lastidx = idx;
852: for (; p < size; ++p) {
853: if (idx >= owners[p] && idx < owners[p+1]) {
854: rrows[r].rank = p;
855: rrows[r].index = rows[r] - owners[p];
856: found = PETSC_TRUE;
857: break;
858: }
859: }
860: if (!found) SETERRQ1(PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %d not found in matrix distribution", idx);
861: }
862: PetscSFCreate(PetscObjectComm((PetscObject) A), &sf);
863: PetscSFSetGraph(sf, n, N, NULL, PETSC_OWN_POINTER, rrows, PETSC_OWN_POINTER);
864: /* Collect flags for rows to be zeroed */
865: PetscSFReduceBegin(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
866: PetscSFReduceEnd(sf, MPIU_INT, (PetscInt *) rows, lrows, MPI_LOR);
867: PetscSFDestroy(&sf);
868: /* Compress and put in row numbers */
869: for (r = 0; r < n; ++r) if (lrows[r] >= 0) lrows[len++] = r;
870: /* zero diagonal part of matrix */
871: MatZeroRowsColumns(l->A,len,lrows,diag,x,b);
872: /* handle off diagonal part of matrix */
873: MatGetVecs(A,&xmask,NULL);
874: VecDuplicate(l->lvec,&lmask);
875: VecGetArray(xmask,&bb);
876: for (i=0; i<len; i++) bb[lrows[i]] = 1;
877: VecRestoreArray(xmask,&bb);
878: VecScatterBegin(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
879: VecScatterEnd(l->Mvctx,xmask,lmask,ADD_VALUES,SCATTER_FORWARD);
880: VecDestroy(&xmask);
881: if (x) {
882: VecScatterBegin(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
883: VecScatterEnd(l->Mvctx,x,l->lvec,INSERT_VALUES,SCATTER_FORWARD);
884: VecGetArrayRead(l->lvec,&xx);
885: VecGetArray(b,&bb);
886: }
887: VecGetArray(lmask,&mask);
888: /* remove zeroed rows of off diagonal matrix */
889: ii = aij->i;
890: for (i=0; i<len; i++) {
891: PetscMemzero(aij->a + ii[lrows[i]],(ii[lrows[i]+1] - ii[lrows[i]])*sizeof(PetscScalar));
892: }
893: /* loop over all elements of off process part of matrix zeroing removed columns*/
894: if (aij->compressedrow.use) {
895: m = aij->compressedrow.nrows;
896: ii = aij->compressedrow.i;
897: ridx = aij->compressedrow.rindex;
898: for (i=0; i<m; i++) {
899: n = ii[i+1] - ii[i];
900: aj = aij->j + ii[i];
901: aa = aij->a + ii[i];
903: for (j=0; j<n; j++) {
904: if (PetscAbsScalar(mask[*aj])) {
905: if (b) bb[*ridx] -= *aa*xx[*aj];
906: *aa = 0.0;
907: }
908: aa++;
909: aj++;
910: }
911: ridx++;
912: }
913: } else { /* do not use compressed row format */
914: m = l->B->rmap->n;
915: for (i=0; i<m; i++) {
916: n = ii[i+1] - ii[i];
917: aj = aij->j + ii[i];
918: aa = aij->a + ii[i];
919: for (j=0; j<n; j++) {
920: if (PetscAbsScalar(mask[*aj])) {
921: if (b) bb[i] -= *aa*xx[*aj];
922: *aa = 0.0;
923: }
924: aa++;
925: aj++;
926: }
927: }
928: }
929: if (x) {
930: VecRestoreArray(b,&bb);
931: VecRestoreArrayRead(l->lvec,&xx);
932: }
933: VecRestoreArray(lmask,&mask);
934: VecDestroy(&lmask);
935: PetscFree(lrows);
937: /* only change matrix nonzero state if pattern was allowed to be changed */
938: if (!((Mat_SeqAIJ*)(l->A->data))->keepnonzeropattern) {
939: PetscObjectState state = l->A->nonzerostate + l->B->nonzerostate;
940: MPI_Allreduce(&state,&A->nonzerostate,1,MPIU_INT64,MPI_SUM,PetscObjectComm((PetscObject)A));
941: }
942: return(0);
943: }
947: PetscErrorCode MatMult_MPIAIJ(Mat A,Vec xx,Vec yy)
948: {
949: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
951: PetscInt nt;
954: VecGetLocalSize(xx,&nt);
955: 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);
956: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
957: (*a->A->ops->mult)(a->A,xx,yy);
958: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
959: (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
960: return(0);
961: }
965: PetscErrorCode MatMultDiagonalBlock_MPIAIJ(Mat A,Vec bb,Vec xx)
966: {
967: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
971: MatMultDiagonalBlock(a->A,bb,xx);
972: return(0);
973: }
977: PetscErrorCode MatMultAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
978: {
979: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
983: VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
984: (*a->A->ops->multadd)(a->A,xx,yy,zz);
985: VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
986: (*a->B->ops->multadd)(a->B,a->lvec,zz,zz);
987: return(0);
988: }
992: PetscErrorCode MatMultTranspose_MPIAIJ(Mat A,Vec xx,Vec yy)
993: {
994: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
996: PetscBool merged;
999: VecScatterGetMerged(a->Mvctx,&merged);
1000: /* do nondiagonal part */
1001: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1002: if (!merged) {
1003: /* send it on its way */
1004: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1005: /* do local part */
1006: (*a->A->ops->multtranspose)(a->A,xx,yy);
1007: /* receive remote parts: note this assumes the values are not actually */
1008: /* added in yy until the next line, */
1009: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1010: } else {
1011: /* do local part */
1012: (*a->A->ops->multtranspose)(a->A,xx,yy);
1013: /* send it on its way */
1014: VecScatterBegin(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1015: /* values actually were received in the Begin() but we need to call this nop */
1016: VecScatterEnd(a->Mvctx,a->lvec,yy,ADD_VALUES,SCATTER_REVERSE);
1017: }
1018: return(0);
1019: }
1023: PetscErrorCode MatIsTranspose_MPIAIJ(Mat Amat,Mat Bmat,PetscReal tol,PetscBool *f)
1024: {
1025: MPI_Comm comm;
1026: Mat_MPIAIJ *Aij = (Mat_MPIAIJ*) Amat->data, *Bij;
1027: Mat Adia = Aij->A, Bdia, Aoff,Boff,*Aoffs,*Boffs;
1028: IS Me,Notme;
1030: PetscInt M,N,first,last,*notme,i;
1031: PetscMPIInt size;
1034: /* Easy test: symmetric diagonal block */
1035: Bij = (Mat_MPIAIJ*) Bmat->data; Bdia = Bij->A;
1036: MatIsTranspose(Adia,Bdia,tol,f);
1037: if (!*f) return(0);
1038: PetscObjectGetComm((PetscObject)Amat,&comm);
1039: MPI_Comm_size(comm,&size);
1040: if (size == 1) return(0);
1042: /* Hard test: off-diagonal block. This takes a MatGetSubMatrix. */
1043: MatGetSize(Amat,&M,&N);
1044: MatGetOwnershipRange(Amat,&first,&last);
1045: PetscMalloc1((N-last+first),¬me);
1046: for (i=0; i<first; i++) notme[i] = i;
1047: for (i=last; i<M; i++) notme[i-last+first] = i;
1048: ISCreateGeneral(MPI_COMM_SELF,N-last+first,notme,PETSC_COPY_VALUES,&Notme);
1049: ISCreateStride(MPI_COMM_SELF,last-first,first,1,&Me);
1050: MatGetSubMatrices(Amat,1,&Me,&Notme,MAT_INITIAL_MATRIX,&Aoffs);
1051: Aoff = Aoffs[0];
1052: MatGetSubMatrices(Bmat,1,&Notme,&Me,MAT_INITIAL_MATRIX,&Boffs);
1053: Boff = Boffs[0];
1054: MatIsTranspose(Aoff,Boff,tol,f);
1055: MatDestroyMatrices(1,&Aoffs);
1056: MatDestroyMatrices(1,&Boffs);
1057: ISDestroy(&Me);
1058: ISDestroy(&Notme);
1059: PetscFree(notme);
1060: return(0);
1061: }
1065: PetscErrorCode MatMultTransposeAdd_MPIAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1066: {
1067: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1071: /* do nondiagonal part */
1072: (*a->B->ops->multtranspose)(a->B,xx,a->lvec);
1073: /* send it on its way */
1074: VecScatterBegin(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1075: /* do local part */
1076: (*a->A->ops->multtransposeadd)(a->A,xx,yy,zz);
1077: /* receive remote parts */
1078: VecScatterEnd(a->Mvctx,a->lvec,zz,ADD_VALUES,SCATTER_REVERSE);
1079: return(0);
1080: }
1082: /*
1083: This only works correctly for square matrices where the subblock A->A is the
1084: diagonal block
1085: */
1088: PetscErrorCode MatGetDiagonal_MPIAIJ(Mat A,Vec v)
1089: {
1091: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1094: if (A->rmap->N != A->cmap->N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Supports only square matrix where A->A is diag block");
1095: 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");
1096: MatGetDiagonal(a->A,v);
1097: return(0);
1098: }
1102: PetscErrorCode MatScale_MPIAIJ(Mat A,PetscScalar aa)
1103: {
1104: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1108: MatScale(a->A,aa);
1109: MatScale(a->B,aa);
1110: return(0);
1111: }
1115: PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1116: {
1118: Mat_Redundant *redund = *redundant;
1119: PetscInt i;
1122: *redundant = NULL;
1123: if (redund){
1124: if (redund->matseq) { /* via MatGetSubMatrices() */
1125: ISDestroy(&redund->isrow);
1126: ISDestroy(&redund->iscol);
1127: MatDestroy(&redund->matseq[0]);
1128: PetscFree(redund->matseq);
1129: } else {
1130: PetscFree2(redund->send_rank,redund->recv_rank);
1131: PetscFree(redund->sbuf_j);
1132: PetscFree(redund->sbuf_a);
1133: for (i=0; i<redund->nrecvs; i++) {
1134: PetscFree(redund->rbuf_j[i]);
1135: PetscFree(redund->rbuf_a[i]);
1136: }
1137: PetscFree4(redund->sbuf_nz,redund->rbuf_nz,redund->rbuf_j,redund->rbuf_a);
1138: }
1140: if (redund->psubcomm) {
1141: PetscSubcommDestroy(&redund->psubcomm);
1142: }
1143: PetscFree(redund);
1144: }
1145: return(0);
1146: }
1150: PetscErrorCode MatDestroy_MPIAIJ(Mat mat)
1151: {
1152: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1156: #if defined(PETSC_USE_LOG)
1157: PetscLogObjectState((PetscObject)mat,"Rows=%D, Cols=%D",mat->rmap->N,mat->cmap->N);
1158: #endif
1159: MatDestroy_Redundant(&aij->redundant);
1160: MatStashDestroy_Private(&mat->stash);
1161: VecDestroy(&aij->diag);
1162: MatDestroy(&aij->A);
1163: MatDestroy(&aij->B);
1164: #if defined(PETSC_USE_CTABLE)
1165: PetscTableDestroy(&aij->colmap);
1166: #else
1167: PetscFree(aij->colmap);
1168: #endif
1169: PetscFree(aij->garray);
1170: VecDestroy(&aij->lvec);
1171: VecScatterDestroy(&aij->Mvctx);
1172: PetscFree2(aij->rowvalues,aij->rowindices);
1173: PetscFree(aij->ld);
1174: PetscFree(mat->data);
1176: PetscObjectChangeTypeName((PetscObject)mat,0);
1177: PetscObjectComposeFunction((PetscObject)mat,"MatStoreValues_C",NULL);
1178: PetscObjectComposeFunction((PetscObject)mat,"MatRetrieveValues_C",NULL);
1179: PetscObjectComposeFunction((PetscObject)mat,"MatGetDiagonalBlock_C",NULL);
1180: PetscObjectComposeFunction((PetscObject)mat,"MatIsTranspose_C",NULL);
1181: PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocation_C",NULL);
1182: PetscObjectComposeFunction((PetscObject)mat,"MatMPIAIJSetPreallocationCSR_C",NULL);
1183: PetscObjectComposeFunction((PetscObject)mat,"MatDiagonalScaleLocal_C",NULL);
1184: PetscObjectComposeFunction((PetscObject)mat,"MatConvert_mpiaij_mpisbaij_C",NULL);
1185: return(0);
1186: }
1190: PetscErrorCode MatView_MPIAIJ_Binary(Mat mat,PetscViewer viewer)
1191: {
1192: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1193: Mat_SeqAIJ *A = (Mat_SeqAIJ*)aij->A->data;
1194: Mat_SeqAIJ *B = (Mat_SeqAIJ*)aij->B->data;
1196: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag;
1197: int fd;
1198: PetscInt nz,header[4],*row_lengths,*range=0,rlen,i;
1199: PetscInt nzmax,*column_indices,j,k,col,*garray = aij->garray,cnt,cstart = mat->cmap->rstart,rnz = 0;
1200: PetscScalar *column_values;
1201: PetscInt message_count,flowcontrolcount;
1202: FILE *file;
1205: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
1206: MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
1207: nz = A->nz + B->nz;
1208: if (!rank) {
1209: header[0] = MAT_FILE_CLASSID;
1210: header[1] = mat->rmap->N;
1211: header[2] = mat->cmap->N;
1213: MPI_Reduce(&nz,&header[3],1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
1214: PetscViewerBinaryGetDescriptor(viewer,&fd);
1215: PetscBinaryWrite(fd,header,4,PETSC_INT,PETSC_TRUE);
1216: /* get largest number of rows any processor has */
1217: rlen = mat->rmap->n;
1218: range = mat->rmap->range;
1219: for (i=1; i<size; i++) rlen = PetscMax(rlen,range[i+1] - range[i]);
1220: } else {
1221: MPI_Reduce(&nz,0,1,MPIU_INT,MPI_SUM,0,PetscObjectComm((PetscObject)mat));
1222: rlen = mat->rmap->n;
1223: }
1225: /* load up the local row counts */
1226: PetscMalloc1((rlen+1),&row_lengths);
1227: 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];
1229: /* store the row lengths to the file */
1230: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1231: if (!rank) {
1232: PetscBinaryWrite(fd,row_lengths,mat->rmap->n,PETSC_INT,PETSC_TRUE);
1233: for (i=1; i<size; i++) {
1234: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1235: rlen = range[i+1] - range[i];
1236: MPIULong_Recv(row_lengths,rlen,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat));
1237: PetscBinaryWrite(fd,row_lengths,rlen,PETSC_INT,PETSC_TRUE);
1238: }
1239: PetscViewerFlowControlEndMaster(viewer,&message_count);
1240: } else {
1241: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1242: MPIULong_Send(row_lengths,mat->rmap->n,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1243: PetscViewerFlowControlEndWorker(viewer,&message_count);
1244: }
1245: PetscFree(row_lengths);
1247: /* load up the local column indices */
1248: nzmax = nz; /* th processor needs space a largest processor needs */
1249: MPI_Reduce(&nz,&nzmax,1,MPIU_INT,MPI_MAX,0,PetscObjectComm((PetscObject)mat));
1250: PetscMalloc1((nzmax+1),&column_indices);
1251: cnt = 0;
1252: for (i=0; i<mat->rmap->n; i++) {
1253: for (j=B->i[i]; j<B->i[i+1]; j++) {
1254: if ((col = garray[B->j[j]]) > cstart) break;
1255: column_indices[cnt++] = col;
1256: }
1257: for (k=A->i[i]; k<A->i[i+1]; k++) column_indices[cnt++] = A->j[k] + cstart;
1258: for (; j<B->i[i+1]; j++) column_indices[cnt++] = garray[B->j[j]];
1259: }
1260: 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);
1262: /* store the column indices to the file */
1263: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1264: if (!rank) {
1265: MPI_Status status;
1266: PetscBinaryWrite(fd,column_indices,nz,PETSC_INT,PETSC_TRUE);
1267: for (i=1; i<size; i++) {
1268: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1269: MPI_Recv(&rnz,1,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
1270: if (rnz > nzmax) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
1271: MPIULong_Recv(column_indices,rnz,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat));
1272: PetscBinaryWrite(fd,column_indices,rnz,PETSC_INT,PETSC_TRUE);
1273: }
1274: PetscViewerFlowControlEndMaster(viewer,&message_count);
1275: } else {
1276: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1277: MPI_Send(&nz,1,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1278: MPIULong_Send(column_indices,nz,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1279: PetscViewerFlowControlEndWorker(viewer,&message_count);
1280: }
1281: PetscFree(column_indices);
1283: /* load up the local column values */
1284: PetscMalloc1((nzmax+1),&column_values);
1285: cnt = 0;
1286: for (i=0; i<mat->rmap->n; i++) {
1287: for (j=B->i[i]; j<B->i[i+1]; j++) {
1288: if (garray[B->j[j]] > cstart) break;
1289: column_values[cnt++] = B->a[j];
1290: }
1291: for (k=A->i[i]; k<A->i[i+1]; k++) column_values[cnt++] = A->a[k];
1292: for (; j<B->i[i+1]; j++) column_values[cnt++] = B->a[j];
1293: }
1294: 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);
1296: /* store the column values to the file */
1297: PetscViewerFlowControlStart(viewer,&message_count,&flowcontrolcount);
1298: if (!rank) {
1299: MPI_Status status;
1300: PetscBinaryWrite(fd,column_values,nz,PETSC_SCALAR,PETSC_TRUE);
1301: for (i=1; i<size; i++) {
1302: PetscViewerFlowControlStepMaster(viewer,i,&message_count,flowcontrolcount);
1303: MPI_Recv(&rnz,1,MPIU_INT,i,tag,PetscObjectComm((PetscObject)mat),&status);
1304: if (rnz > nzmax) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Internal PETSc error: nz = %D nzmax = %D",nz,nzmax);
1305: MPIULong_Recv(column_values,rnz,MPIU_SCALAR,i,tag,PetscObjectComm((PetscObject)mat));
1306: PetscBinaryWrite(fd,column_values,rnz,PETSC_SCALAR,PETSC_TRUE);
1307: }
1308: PetscViewerFlowControlEndMaster(viewer,&message_count);
1309: } else {
1310: PetscViewerFlowControlStepWorker(viewer,rank,&message_count);
1311: MPI_Send(&nz,1,MPIU_INT,0,tag,PetscObjectComm((PetscObject)mat));
1312: MPIULong_Send(column_values,nz,MPIU_SCALAR,0,tag,PetscObjectComm((PetscObject)mat));
1313: PetscViewerFlowControlEndWorker(viewer,&message_count);
1314: }
1315: PetscFree(column_values);
1317: PetscViewerBinaryGetInfoPointer(viewer,&file);
1318: if (file) fprintf(file,"-matload_block_size %d\n",(int)PetscAbs(mat->rmap->bs));
1319: return(0);
1320: }
1322: #include <petscdraw.h>
1325: PetscErrorCode MatView_MPIAIJ_ASCIIorDraworSocket(Mat mat,PetscViewer viewer)
1326: {
1327: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1328: PetscErrorCode ierr;
1329: PetscMPIInt rank = aij->rank,size = aij->size;
1330: PetscBool isdraw,iascii,isbinary;
1331: PetscViewer sviewer;
1332: PetscViewerFormat format;
1335: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1336: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1337: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1338: if (iascii) {
1339: PetscViewerGetFormat(viewer,&format);
1340: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1341: MatInfo info;
1342: PetscBool inodes;
1344: MPI_Comm_rank(PetscObjectComm((PetscObject)mat),&rank);
1345: MatGetInfo(mat,MAT_LOCAL,&info);
1346: MatInodeGetInodeSizes(aij->A,NULL,(PetscInt**)&inodes,NULL);
1347: PetscViewerASCIISynchronizedAllow(viewer,PETSC_TRUE);
1348: if (!inodes) {
1349: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, not using I-node routines\n",
1350: rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
1351: } else {
1352: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] Local rows %D nz %D nz alloced %D mem %D, using I-node routines\n",
1353: rank,mat->rmap->n,(PetscInt)info.nz_used,(PetscInt)info.nz_allocated,(PetscInt)info.memory);
1354: }
1355: MatGetInfo(aij->A,MAT_LOCAL,&info);
1356: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] on-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1357: MatGetInfo(aij->B,MAT_LOCAL,&info);
1358: PetscViewerASCIISynchronizedPrintf(viewer,"[%d] off-diagonal part: nz %D \n",rank,(PetscInt)info.nz_used);
1359: PetscViewerFlush(viewer);
1360: PetscViewerASCIISynchronizedAllow(viewer,PETSC_FALSE);
1361: PetscViewerASCIIPrintf(viewer,"Information on VecScatter used in matrix-vector product: \n");
1362: VecScatterView(aij->Mvctx,viewer);
1363: return(0);
1364: } else if (format == PETSC_VIEWER_ASCII_INFO) {
1365: PetscInt inodecount,inodelimit,*inodes;
1366: MatInodeGetInodeSizes(aij->A,&inodecount,&inodes,&inodelimit);
1367: if (inodes) {
1368: PetscViewerASCIIPrintf(viewer,"using I-node (on process 0) routines: found %D nodes, limit used is %D\n",inodecount,inodelimit);
1369: } else {
1370: PetscViewerASCIIPrintf(viewer,"not using I-node (on process 0) routines\n");
1371: }
1372: return(0);
1373: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1374: return(0);
1375: }
1376: } else if (isbinary) {
1377: if (size == 1) {
1378: PetscObjectSetName((PetscObject)aij->A,((PetscObject)mat)->name);
1379: MatView(aij->A,viewer);
1380: } else {
1381: MatView_MPIAIJ_Binary(mat,viewer);
1382: }
1383: return(0);
1384: } else if (isdraw) {
1385: PetscDraw draw;
1386: PetscBool isnull;
1387: PetscViewerDrawGetDraw(viewer,0,&draw);
1388: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1389: }
1391: {
1392: /* assemble the entire matrix onto first processor. */
1393: Mat A;
1394: Mat_SeqAIJ *Aloc;
1395: PetscInt M = mat->rmap->N,N = mat->cmap->N,m,*ai,*aj,row,*cols,i,*ct;
1396: MatScalar *a;
1398: MatCreate(PetscObjectComm((PetscObject)mat),&A);
1399: if (!rank) {
1400: MatSetSizes(A,M,N,M,N);
1401: } else {
1402: MatSetSizes(A,0,0,M,N);
1403: }
1404: /* This is just a temporary matrix, so explicitly using MATMPIAIJ is probably best */
1405: MatSetType(A,MATMPIAIJ);
1406: MatMPIAIJSetPreallocation(A,0,NULL,0,NULL);
1407: MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_FALSE);
1408: PetscLogObjectParent((PetscObject)mat,(PetscObject)A);
1410: /* copy over the A part */
1411: Aloc = (Mat_SeqAIJ*)aij->A->data;
1412: m = aij->A->rmap->n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1413: row = mat->rmap->rstart;
1414: for (i=0; i<ai[m]; i++) aj[i] += mat->cmap->rstart;
1415: for (i=0; i<m; i++) {
1416: MatSetValues(A,1,&row,ai[i+1]-ai[i],aj,a,INSERT_VALUES);
1417: row++;
1418: a += ai[i+1]-ai[i]; aj += ai[i+1]-ai[i];
1419: }
1420: aj = Aloc->j;
1421: for (i=0; i<ai[m]; i++) aj[i] -= mat->cmap->rstart;
1423: /* copy over the B part */
1424: Aloc = (Mat_SeqAIJ*)aij->B->data;
1425: m = aij->B->rmap->n; ai = Aloc->i; aj = Aloc->j; a = Aloc->a;
1426: row = mat->rmap->rstart;
1427: PetscMalloc1((ai[m]+1),&cols);
1428: ct = cols;
1429: for (i=0; i<ai[m]; i++) cols[i] = aij->garray[aj[i]];
1430: for (i=0; i<m; i++) {
1431: MatSetValues(A,1,&row,ai[i+1]-ai[i],cols,a,INSERT_VALUES);
1432: row++;
1433: a += ai[i+1]-ai[i]; cols += ai[i+1]-ai[i];
1434: }
1435: PetscFree(ct);
1436: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1437: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1438: /*
1439: Everyone has to call to draw the matrix since the graphics waits are
1440: synchronized across all processors that share the PetscDraw object
1441: */
1442: PetscViewerGetSingleton(viewer,&sviewer);
1443: if (!rank) {
1444: MatView_SeqAIJ(((Mat_MPIAIJ*)(A->data))->A,sviewer);
1445: }
1446: PetscViewerRestoreSingleton(viewer,&sviewer);
1447: MatDestroy(&A);
1448: }
1449: return(0);
1450: }
1454: PetscErrorCode MatView_MPIAIJ(Mat mat,PetscViewer viewer)
1455: {
1457: PetscBool iascii,isdraw,issocket,isbinary;
1460: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1461: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1462: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1463: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSOCKET,&issocket);
1464: if (iascii || isdraw || isbinary || issocket) {
1465: MatView_MPIAIJ_ASCIIorDraworSocket(mat,viewer);
1466: }
1467: return(0);
1468: }
1472: PetscErrorCode MatSOR_MPIAIJ(Mat matin,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1473: {
1474: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1476: Vec bb1 = 0;
1477: PetscBool hasop;
1480: if (flag == SOR_APPLY_UPPER) {
1481: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
1482: return(0);
1483: }
1485: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) {
1486: VecDuplicate(bb,&bb1);
1487: }
1489: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1490: if (flag & SOR_ZERO_INITIAL_GUESS) {
1491: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
1492: its--;
1493: }
1495: while (its--) {
1496: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1497: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1499: /* update rhs: bb1 = bb - B*x */
1500: VecScale(mat->lvec,-1.0);
1501: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1503: /* local sweep */
1504: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_SYMMETRIC_SWEEP,fshift,lits,1,xx);
1505: }
1506: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1507: if (flag & SOR_ZERO_INITIAL_GUESS) {
1508: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
1509: its--;
1510: }
1511: while (its--) {
1512: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1513: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1515: /* update rhs: bb1 = bb - B*x */
1516: VecScale(mat->lvec,-1.0);
1517: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1519: /* local sweep */
1520: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_FORWARD_SWEEP,fshift,lits,1,xx);
1521: }
1522: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1523: if (flag & SOR_ZERO_INITIAL_GUESS) {
1524: (*mat->A->ops->sor)(mat->A,bb,omega,flag,fshift,lits,1,xx);
1525: its--;
1526: }
1527: while (its--) {
1528: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1529: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1531: /* update rhs: bb1 = bb - B*x */
1532: VecScale(mat->lvec,-1.0);
1533: (*mat->B->ops->multadd)(mat->B,mat->lvec,bb,bb1);
1535: /* local sweep */
1536: (*mat->A->ops->sor)(mat->A,bb1,omega,SOR_BACKWARD_SWEEP,fshift,lits,1,xx);
1537: }
1538: } else if (flag & SOR_EISENSTAT) {
1539: Vec xx1;
1541: VecDuplicate(bb,&xx1);
1542: (*mat->A->ops->sor)(mat->A,bb,omega,(MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_BACKWARD_SWEEP),fshift,lits,1,xx);
1544: VecScatterBegin(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1545: VecScatterEnd(mat->Mvctx,xx,mat->lvec,INSERT_VALUES,SCATTER_FORWARD);
1546: if (!mat->diag) {
1547: MatGetVecs(matin,&mat->diag,NULL);
1548: MatGetDiagonal(matin,mat->diag);
1549: }
1550: MatHasOperation(matin,MATOP_MULT_DIAGONAL_BLOCK,&hasop);
1551: if (hasop) {
1552: MatMultDiagonalBlock(matin,xx,bb1);
1553: } else {
1554: VecPointwiseMult(bb1,mat->diag,xx);
1555: }
1556: VecAYPX(bb1,(omega-2.0)/omega,bb);
1558: MatMultAdd(mat->B,mat->lvec,bb1,bb1);
1560: /* local sweep */
1561: (*mat->A->ops->sor)(mat->A,bb1,omega,(MatSORType)(SOR_ZERO_INITIAL_GUESS | SOR_LOCAL_FORWARD_SWEEP),fshift,lits,1,xx1);
1562: VecAXPY(xx,1.0,xx1);
1563: VecDestroy(&xx1);
1564: } else SETERRQ(PetscObjectComm((PetscObject)matin),PETSC_ERR_SUP,"Parallel SOR not supported");
1566: VecDestroy(&bb1);
1567: return(0);
1568: }
1572: PetscErrorCode MatPermute_MPIAIJ(Mat A,IS rowp,IS colp,Mat *B)
1573: {
1574: Mat aA,aB,Aperm;
1575: const PetscInt *rwant,*cwant,*gcols,*ai,*bi,*aj,*bj;
1576: PetscScalar *aa,*ba;
1577: PetscInt i,j,m,n,ng,anz,bnz,*dnnz,*onnz,*tdnnz,*tonnz,*rdest,*cdest,*work,*gcdest;
1578: PetscSF rowsf,sf;
1579: IS parcolp = NULL;
1580: PetscBool done;
1584: MatGetLocalSize(A,&m,&n);
1585: ISGetIndices(rowp,&rwant);
1586: ISGetIndices(colp,&cwant);
1587: PetscMalloc3(PetscMax(m,n),&work,m,&rdest,n,&cdest);
1589: /* Invert row permutation to find out where my rows should go */
1590: PetscSFCreate(PetscObjectComm((PetscObject)A),&rowsf);
1591: PetscSFSetGraphLayout(rowsf,A->rmap,A->rmap->n,NULL,PETSC_OWN_POINTER,rwant);
1592: PetscSFSetFromOptions(rowsf);
1593: for (i=0; i<m; i++) work[i] = A->rmap->rstart + i;
1594: PetscSFReduceBegin(rowsf,MPIU_INT,work,rdest,MPIU_REPLACE);
1595: PetscSFReduceEnd(rowsf,MPIU_INT,work,rdest,MPIU_REPLACE);
1597: /* Invert column permutation to find out where my columns should go */
1598: PetscSFCreate(PetscObjectComm((PetscObject)A),&sf);
1599: PetscSFSetGraphLayout(sf,A->cmap,A->cmap->n,NULL,PETSC_OWN_POINTER,cwant);
1600: PetscSFSetFromOptions(sf);
1601: for (i=0; i<n; i++) work[i] = A->cmap->rstart + i;
1602: PetscSFReduceBegin(sf,MPIU_INT,work,cdest,MPIU_REPLACE);
1603: PetscSFReduceEnd(sf,MPIU_INT,work,cdest,MPIU_REPLACE);
1604: PetscSFDestroy(&sf);
1606: ISRestoreIndices(rowp,&rwant);
1607: ISRestoreIndices(colp,&cwant);
1608: MatMPIAIJGetSeqAIJ(A,&aA,&aB,&gcols);
1610: /* Find out where my gcols should go */
1611: MatGetSize(aB,NULL,&ng);
1612: PetscMalloc1(ng,&gcdest);
1613: PetscSFCreate(PetscObjectComm((PetscObject)A),&sf);
1614: PetscSFSetGraphLayout(sf,A->cmap,ng,NULL,PETSC_OWN_POINTER,gcols);
1615: PetscSFSetFromOptions(sf);
1616: PetscSFBcastBegin(sf,MPIU_INT,cdest,gcdest);
1617: PetscSFBcastEnd(sf,MPIU_INT,cdest,gcdest);
1618: PetscSFDestroy(&sf);
1620: PetscCalloc4(m,&dnnz,m,&onnz,m,&tdnnz,m,&tonnz);
1621: MatGetRowIJ(aA,0,PETSC_FALSE,PETSC_FALSE,&anz,&ai,&aj,&done);
1622: MatGetRowIJ(aB,0,PETSC_FALSE,PETSC_FALSE,&bnz,&bi,&bj,&done);
1623: for (i=0; i<m; i++) {
1624: PetscInt row = rdest[i],rowner;
1625: PetscLayoutFindOwner(A->rmap,row,&rowner);
1626: for (j=ai[i]; j<ai[i+1]; j++) {
1627: PetscInt cowner,col = cdest[aj[j]];
1628: PetscLayoutFindOwner(A->cmap,col,&cowner); /* Could build an index for the columns to eliminate this search */
1629: if (rowner == cowner) dnnz[i]++;
1630: else onnz[i]++;
1631: }
1632: for (j=bi[i]; j<bi[i+1]; j++) {
1633: PetscInt cowner,col = gcdest[bj[j]];
1634: PetscLayoutFindOwner(A->cmap,col,&cowner);
1635: if (rowner == cowner) dnnz[i]++;
1636: else onnz[i]++;
1637: }
1638: }
1639: PetscSFBcastBegin(rowsf,MPIU_INT,dnnz,tdnnz);
1640: PetscSFBcastEnd(rowsf,MPIU_INT,dnnz,tdnnz);
1641: PetscSFBcastBegin(rowsf,MPIU_INT,onnz,tonnz);
1642: PetscSFBcastEnd(rowsf,MPIU_INT,onnz,tonnz);
1643: PetscSFDestroy(&rowsf);
1645: MatCreateAIJ(PetscObjectComm((PetscObject)A),A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N,0,tdnnz,0,tonnz,&Aperm);
1646: MatSeqAIJGetArray(aA,&aa);
1647: MatSeqAIJGetArray(aB,&ba);
1648: for (i=0; i<m; i++) {
1649: PetscInt *acols = dnnz,*bcols = onnz; /* Repurpose now-unneeded arrays */
1650: PetscInt j0,rowlen;
1651: rowlen = ai[i+1] - ai[i];
1652: for (j0=j=0; j<rowlen; j0=j) { /* rowlen could be larger than number of rows m, so sum in batches */
1653: for ( ; j<PetscMin(rowlen,j0+m); j++) acols[j-j0] = cdest[aj[ai[i]+j]];
1654: MatSetValues(Aperm,1,&rdest[i],j-j0,acols,aa+ai[i]+j0,INSERT_VALUES);
1655: }
1656: rowlen = bi[i+1] - bi[i];
1657: for (j0=j=0; j<rowlen; j0=j) {
1658: for ( ; j<PetscMin(rowlen,j0+m); j++) bcols[j-j0] = gcdest[bj[bi[i]+j]];
1659: MatSetValues(Aperm,1,&rdest[i],j-j0,bcols,ba+bi[i]+j0,INSERT_VALUES);
1660: }
1661: }
1662: MatAssemblyBegin(Aperm,MAT_FINAL_ASSEMBLY);
1663: MatAssemblyEnd(Aperm,MAT_FINAL_ASSEMBLY);
1664: MatRestoreRowIJ(aA,0,PETSC_FALSE,PETSC_FALSE,&anz,&ai,&aj,&done);
1665: MatRestoreRowIJ(aB,0,PETSC_FALSE,PETSC_FALSE,&bnz,&bi,&bj,&done);
1666: MatSeqAIJRestoreArray(aA,&aa);
1667: MatSeqAIJRestoreArray(aB,&ba);
1668: PetscFree4(dnnz,onnz,tdnnz,tonnz);
1669: PetscFree3(work,rdest,cdest);
1670: PetscFree(gcdest);
1671: if (parcolp) {ISDestroy(&colp);}
1672: *B = Aperm;
1673: return(0);
1674: }
1678: PetscErrorCode MatGetInfo_MPIAIJ(Mat matin,MatInfoType flag,MatInfo *info)
1679: {
1680: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1681: Mat A = mat->A,B = mat->B;
1683: PetscReal isend[5],irecv[5];
1686: info->block_size = 1.0;
1687: MatGetInfo(A,MAT_LOCAL,info);
1689: isend[0] = info->nz_used; isend[1] = info->nz_allocated; isend[2] = info->nz_unneeded;
1690: isend[3] = info->memory; isend[4] = info->mallocs;
1692: MatGetInfo(B,MAT_LOCAL,info);
1694: isend[0] += info->nz_used; isend[1] += info->nz_allocated; isend[2] += info->nz_unneeded;
1695: isend[3] += info->memory; isend[4] += info->mallocs;
1696: if (flag == MAT_LOCAL) {
1697: info->nz_used = isend[0];
1698: info->nz_allocated = isend[1];
1699: info->nz_unneeded = isend[2];
1700: info->memory = isend[3];
1701: info->mallocs = isend[4];
1702: } else if (flag == MAT_GLOBAL_MAX) {
1703: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)matin));
1705: info->nz_used = irecv[0];
1706: info->nz_allocated = irecv[1];
1707: info->nz_unneeded = irecv[2];
1708: info->memory = irecv[3];
1709: info->mallocs = irecv[4];
1710: } else if (flag == MAT_GLOBAL_SUM) {
1711: MPI_Allreduce(isend,irecv,5,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)matin));
1713: info->nz_used = irecv[0];
1714: info->nz_allocated = irecv[1];
1715: info->nz_unneeded = irecv[2];
1716: info->memory = irecv[3];
1717: info->mallocs = irecv[4];
1718: }
1719: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
1720: info->fill_ratio_needed = 0;
1721: info->factor_mallocs = 0;
1722: return(0);
1723: }
1727: PetscErrorCode MatSetOption_MPIAIJ(Mat A,MatOption op,PetscBool flg)
1728: {
1729: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1733: switch (op) {
1734: case MAT_NEW_NONZERO_LOCATIONS:
1735: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1736: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1737: case MAT_KEEP_NONZERO_PATTERN:
1738: case MAT_NEW_NONZERO_LOCATION_ERR:
1739: case MAT_USE_INODES:
1740: case MAT_IGNORE_ZERO_ENTRIES:
1741: MatCheckPreallocated(A,1);
1742: MatSetOption(a->A,op,flg);
1743: MatSetOption(a->B,op,flg);
1744: break;
1745: case MAT_ROW_ORIENTED:
1746: a->roworiented = flg;
1748: MatSetOption(a->A,op,flg);
1749: MatSetOption(a->B,op,flg);
1750: break;
1751: case MAT_NEW_DIAGONALS:
1752: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1753: break;
1754: case MAT_IGNORE_OFF_PROC_ENTRIES:
1755: a->donotstash = flg;
1756: break;
1757: case MAT_SPD:
1758: A->spd_set = PETSC_TRUE;
1759: A->spd = flg;
1760: if (flg) {
1761: A->symmetric = PETSC_TRUE;
1762: A->structurally_symmetric = PETSC_TRUE;
1763: A->symmetric_set = PETSC_TRUE;
1764: A->structurally_symmetric_set = PETSC_TRUE;
1765: }
1766: break;
1767: case MAT_SYMMETRIC:
1768: MatSetOption(a->A,op,flg);
1769: break;
1770: case MAT_STRUCTURALLY_SYMMETRIC:
1771: MatSetOption(a->A,op,flg);
1772: break;
1773: case MAT_HERMITIAN:
1774: MatSetOption(a->A,op,flg);
1775: break;
1776: case MAT_SYMMETRY_ETERNAL:
1777: MatSetOption(a->A,op,flg);
1778: break;
1779: default:
1780: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1781: }
1782: return(0);
1783: }
1787: PetscErrorCode MatGetRow_MPIAIJ(Mat matin,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1788: {
1789: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)matin->data;
1790: PetscScalar *vworkA,*vworkB,**pvA,**pvB,*v_p;
1792: PetscInt i,*cworkA,*cworkB,**pcA,**pcB,cstart = matin->cmap->rstart;
1793: PetscInt nztot,nzA,nzB,lrow,rstart = matin->rmap->rstart,rend = matin->rmap->rend;
1794: PetscInt *cmap,*idx_p;
1797: if (mat->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Already active");
1798: mat->getrowactive = PETSC_TRUE;
1800: if (!mat->rowvalues && (idx || v)) {
1801: /*
1802: allocate enough space to hold information from the longest row.
1803: */
1804: Mat_SeqAIJ *Aa = (Mat_SeqAIJ*)mat->A->data,*Ba = (Mat_SeqAIJ*)mat->B->data;
1805: PetscInt max = 1,tmp;
1806: for (i=0; i<matin->rmap->n; i++) {
1807: tmp = Aa->i[i+1] - Aa->i[i] + Ba->i[i+1] - Ba->i[i];
1808: if (max < tmp) max = tmp;
1809: }
1810: PetscMalloc2(max,&mat->rowvalues,max,&mat->rowindices);
1811: }
1813: if (row < rstart || row >= rend) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Only local rows");
1814: lrow = row - rstart;
1816: pvA = &vworkA; pcA = &cworkA; pvB = &vworkB; pcB = &cworkB;
1817: if (!v) {pvA = 0; pvB = 0;}
1818: if (!idx) {pcA = 0; if (!v) pcB = 0;}
1819: (*mat->A->ops->getrow)(mat->A,lrow,&nzA,pcA,pvA);
1820: (*mat->B->ops->getrow)(mat->B,lrow,&nzB,pcB,pvB);
1821: nztot = nzA + nzB;
1823: cmap = mat->garray;
1824: if (v || idx) {
1825: if (nztot) {
1826: /* Sort by increasing column numbers, assuming A and B already sorted */
1827: PetscInt imark = -1;
1828: if (v) {
1829: *v = v_p = mat->rowvalues;
1830: for (i=0; i<nzB; i++) {
1831: if (cmap[cworkB[i]] < cstart) v_p[i] = vworkB[i];
1832: else break;
1833: }
1834: imark = i;
1835: for (i=0; i<nzA; i++) v_p[imark+i] = vworkA[i];
1836: for (i=imark; i<nzB; i++) v_p[nzA+i] = vworkB[i];
1837: }
1838: if (idx) {
1839: *idx = idx_p = mat->rowindices;
1840: if (imark > -1) {
1841: for (i=0; i<imark; i++) {
1842: idx_p[i] = cmap[cworkB[i]];
1843: }
1844: } else {
1845: for (i=0; i<nzB; i++) {
1846: if (cmap[cworkB[i]] < cstart) idx_p[i] = cmap[cworkB[i]];
1847: else break;
1848: }
1849: imark = i;
1850: }
1851: for (i=0; i<nzA; i++) idx_p[imark+i] = cstart + cworkA[i];
1852: for (i=imark; i<nzB; i++) idx_p[nzA+i] = cmap[cworkB[i]];
1853: }
1854: } else {
1855: if (idx) *idx = 0;
1856: if (v) *v = 0;
1857: }
1858: }
1859: *nz = nztot;
1860: (*mat->A->ops->restorerow)(mat->A,lrow,&nzA,pcA,pvA);
1861: (*mat->B->ops->restorerow)(mat->B,lrow,&nzB,pcB,pvB);
1862: return(0);
1863: }
1867: PetscErrorCode MatRestoreRow_MPIAIJ(Mat mat,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1868: {
1869: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1872: if (!aij->getrowactive) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"MatGetRow() must be called first");
1873: aij->getrowactive = PETSC_FALSE;
1874: return(0);
1875: }
1879: PetscErrorCode MatNorm_MPIAIJ(Mat mat,NormType type,PetscReal *norm)
1880: {
1881: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
1882: Mat_SeqAIJ *amat = (Mat_SeqAIJ*)aij->A->data,*bmat = (Mat_SeqAIJ*)aij->B->data;
1884: PetscInt i,j,cstart = mat->cmap->rstart;
1885: PetscReal sum = 0.0;
1886: MatScalar *v;
1889: if (aij->size == 1) {
1890: MatNorm(aij->A,type,norm);
1891: } else {
1892: if (type == NORM_FROBENIUS) {
1893: v = amat->a;
1894: for (i=0; i<amat->nz; i++) {
1895: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1896: }
1897: v = bmat->a;
1898: for (i=0; i<bmat->nz; i++) {
1899: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1900: }
1901: MPI_Allreduce(&sum,norm,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
1902: *norm = PetscSqrtReal(*norm);
1903: } else if (type == NORM_1) { /* max column norm */
1904: PetscReal *tmp,*tmp2;
1905: PetscInt *jj,*garray = aij->garray;
1906: PetscCalloc1((mat->cmap->N+1),&tmp);
1907: PetscMalloc1((mat->cmap->N+1),&tmp2);
1908: *norm = 0.0;
1909: v = amat->a; jj = amat->j;
1910: for (j=0; j<amat->nz; j++) {
1911: tmp[cstart + *jj++] += PetscAbsScalar(*v); v++;
1912: }
1913: v = bmat->a; jj = bmat->j;
1914: for (j=0; j<bmat->nz; j++) {
1915: tmp[garray[*jj++]] += PetscAbsScalar(*v); v++;
1916: }
1917: MPI_Allreduce(tmp,tmp2,mat->cmap->N,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)mat));
1918: for (j=0; j<mat->cmap->N; j++) {
1919: if (tmp2[j] > *norm) *norm = tmp2[j];
1920: }
1921: PetscFree(tmp);
1922: PetscFree(tmp2);
1923: } else if (type == NORM_INFINITY) { /* max row norm */
1924: PetscReal ntemp = 0.0;
1925: for (j=0; j<aij->A->rmap->n; j++) {
1926: v = amat->a + amat->i[j];
1927: sum = 0.0;
1928: for (i=0; i<amat->i[j+1]-amat->i[j]; i++) {
1929: sum += PetscAbsScalar(*v); v++;
1930: }
1931: v = bmat->a + bmat->i[j];
1932: for (i=0; i<bmat->i[j+1]-bmat->i[j]; i++) {
1933: sum += PetscAbsScalar(*v); v++;
1934: }
1935: if (sum > ntemp) ntemp = sum;
1936: }
1937: MPI_Allreduce(&ntemp,norm,1,MPIU_REAL,MPIU_MAX,PetscObjectComm((PetscObject)mat));
1938: } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"No support for two norm");
1939: }
1940: return(0);
1941: }
1945: PetscErrorCode MatTranspose_MPIAIJ(Mat A,MatReuse reuse,Mat *matout)
1946: {
1947: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
1948: Mat_SeqAIJ *Aloc=(Mat_SeqAIJ*)a->A->data,*Bloc=(Mat_SeqAIJ*)a->B->data;
1950: PetscInt M = A->rmap->N,N = A->cmap->N,ma,na,mb,nb,*ai,*aj,*bi,*bj,row,*cols,*cols_tmp,i;
1951: PetscInt cstart = A->cmap->rstart,ncol;
1952: Mat B;
1953: MatScalar *array;
1956: if (reuse == MAT_REUSE_MATRIX && A == *matout && M != N) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1958: ma = A->rmap->n; na = A->cmap->n; mb = a->B->rmap->n; nb = a->B->cmap->n;
1959: ai = Aloc->i; aj = Aloc->j;
1960: bi = Bloc->i; bj = Bloc->j;
1961: if (reuse == MAT_INITIAL_MATRIX || *matout == A) {
1962: PetscInt *d_nnz,*g_nnz,*o_nnz;
1963: PetscSFNode *oloc;
1964: PETSC_UNUSED PetscSF sf;
1966: PetscMalloc4(na,&d_nnz,na,&o_nnz,nb,&g_nnz,nb,&oloc);
1967: /* compute d_nnz for preallocation */
1968: PetscMemzero(d_nnz,na*sizeof(PetscInt));
1969: for (i=0; i<ai[ma]; i++) {
1970: d_nnz[aj[i]]++;
1971: aj[i] += cstart; /* global col index to be used by MatSetValues() */
1972: }
1973: /* compute local off-diagonal contributions */
1974: PetscMemzero(g_nnz,nb*sizeof(PetscInt));
1975: for (i=0; i<bi[ma]; i++) g_nnz[bj[i]]++;
1976: /* map those to global */
1977: PetscSFCreate(PetscObjectComm((PetscObject)A),&sf);
1978: PetscSFSetGraphLayout(sf,A->cmap,nb,NULL,PETSC_USE_POINTER,a->garray);
1979: PetscSFSetFromOptions(sf);
1980: PetscMemzero(o_nnz,na*sizeof(PetscInt));
1981: PetscSFReduceBegin(sf,MPIU_INT,g_nnz,o_nnz,MPIU_SUM);
1982: PetscSFReduceEnd(sf,MPIU_INT,g_nnz,o_nnz,MPIU_SUM);
1983: PetscSFDestroy(&sf);
1985: MatCreate(PetscObjectComm((PetscObject)A),&B);
1986: MatSetSizes(B,A->cmap->n,A->rmap->n,N,M);
1987: MatSetBlockSizes(B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
1988: MatSetType(B,((PetscObject)A)->type_name);
1989: MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
1990: PetscFree4(d_nnz,o_nnz,g_nnz,oloc);
1991: } else {
1992: B = *matout;
1993: MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
1994: for (i=0; i<ai[ma]; i++) aj[i] += cstart; /* global col index to be used by MatSetValues() */
1995: }
1997: /* copy over the A part */
1998: array = Aloc->a;
1999: row = A->rmap->rstart;
2000: for (i=0; i<ma; i++) {
2001: ncol = ai[i+1]-ai[i];
2002: MatSetValues(B,ncol,aj,1,&row,array,INSERT_VALUES);
2003: row++;
2004: array += ncol; aj += ncol;
2005: }
2006: aj = Aloc->j;
2007: for (i=0; i<ai[ma]; i++) aj[i] -= cstart; /* resume local col index */
2009: /* copy over the B part */
2010: PetscCalloc1(bi[mb],&cols);
2011: array = Bloc->a;
2012: row = A->rmap->rstart;
2013: for (i=0; i<bi[mb]; i++) cols[i] = a->garray[bj[i]];
2014: cols_tmp = cols;
2015: for (i=0; i<mb; i++) {
2016: ncol = bi[i+1]-bi[i];
2017: MatSetValues(B,ncol,cols_tmp,1,&row,array,INSERT_VALUES);
2018: row++;
2019: array += ncol; cols_tmp += ncol;
2020: }
2021: PetscFree(cols);
2023: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2024: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2025: if (reuse == MAT_INITIAL_MATRIX || *matout != A) {
2026: *matout = B;
2027: } else {
2028: MatHeaderMerge(A,B);
2029: }
2030: return(0);
2031: }
2035: PetscErrorCode MatDiagonalScale_MPIAIJ(Mat mat,Vec ll,Vec rr)
2036: {
2037: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
2038: Mat a = aij->A,b = aij->B;
2040: PetscInt s1,s2,s3;
2043: MatGetLocalSize(mat,&s2,&s3);
2044: if (rr) {
2045: VecGetLocalSize(rr,&s1);
2046: if (s1!=s3) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"right vector non-conforming local size");
2047: /* Overlap communication with computation. */
2048: VecScatterBegin(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
2049: }
2050: if (ll) {
2051: VecGetLocalSize(ll,&s1);
2052: if (s1!=s2) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"left vector non-conforming local size");
2053: (*b->ops->diagonalscale)(b,ll,0);
2054: }
2055: /* scale the diagonal block */
2056: (*a->ops->diagonalscale)(a,ll,rr);
2058: if (rr) {
2059: /* Do a scatter end and then right scale the off-diagonal block */
2060: VecScatterEnd(aij->Mvctx,rr,aij->lvec,INSERT_VALUES,SCATTER_FORWARD);
2061: (*b->ops->diagonalscale)(b,0,aij->lvec);
2062: }
2063: return(0);
2064: }
2068: PetscErrorCode MatSetUnfactored_MPIAIJ(Mat A)
2069: {
2070: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2074: MatSetUnfactored(a->A);
2075: return(0);
2076: }
2080: PetscErrorCode MatEqual_MPIAIJ(Mat A,Mat B,PetscBool *flag)
2081: {
2082: Mat_MPIAIJ *matB = (Mat_MPIAIJ*)B->data,*matA = (Mat_MPIAIJ*)A->data;
2083: Mat a,b,c,d;
2084: PetscBool flg;
2088: a = matA->A; b = matA->B;
2089: c = matB->A; d = matB->B;
2091: MatEqual(a,c,&flg);
2092: if (flg) {
2093: MatEqual(b,d,&flg);
2094: }
2095: MPI_Allreduce(&flg,flag,1,MPIU_BOOL,MPI_LAND,PetscObjectComm((PetscObject)A));
2096: return(0);
2097: }
2101: PetscErrorCode MatCopy_MPIAIJ(Mat A,Mat B,MatStructure str)
2102: {
2104: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2105: Mat_MPIAIJ *b = (Mat_MPIAIJ*)B->data;
2108: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
2109: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
2110: /* because of the column compression in the off-processor part of the matrix a->B,
2111: the number of columns in a->B and b->B may be different, hence we cannot call
2112: the MatCopy() directly on the two parts. If need be, we can provide a more
2113: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
2114: then copying the submatrices */
2115: MatCopy_Basic(A,B,str);
2116: } else {
2117: MatCopy(a->A,b->A,str);
2118: MatCopy(a->B,b->B,str);
2119: }
2120: return(0);
2121: }
2125: PetscErrorCode MatSetUp_MPIAIJ(Mat A)
2126: {
2130: MatMPIAIJSetPreallocation(A,PETSC_DEFAULT,0,PETSC_DEFAULT,0);
2131: return(0);
2132: }
2136: /* This is the same as MatAXPYGetPreallocation_SeqAIJ, except that the local-to-global map is provided */
2137: static PetscErrorCode MatAXPYGetPreallocation_MPIAIJ(Mat Y,const PetscInt *yltog,Mat X,const PetscInt *xltog,PetscInt *nnz)
2138: {
2139: PetscInt i,m=Y->rmap->N;
2140: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data;
2141: Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data;
2142: const PetscInt *xi = x->i,*yi = y->i;
2145: /* Set the number of nonzeros in the new matrix */
2146: for (i=0; i<m; i++) {
2147: PetscInt j,k,nzx = xi[i+1] - xi[i],nzy = yi[i+1] - yi[i];
2148: const PetscInt *xj = x->j+xi[i],*yj = y->j+yi[i];
2149: nnz[i] = 0;
2150: for (j=0,k=0; j<nzx; j++) { /* Point in X */
2151: for (; k<nzy && yltog[yj[k]]<xltog[xj[j]]; k++) nnz[i]++; /* Catch up to X */
2152: if (k<nzy && yltog[yj[k]]==xltog[xj[j]]) k++; /* Skip duplicate */
2153: nnz[i]++;
2154: }
2155: for (; k<nzy; k++) nnz[i]++;
2156: }
2157: return(0);
2158: }
2162: PetscErrorCode MatAXPY_MPIAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2163: {
2165: PetscInt i;
2166: Mat_MPIAIJ *xx = (Mat_MPIAIJ*)X->data,*yy = (Mat_MPIAIJ*)Y->data;
2167: PetscBLASInt bnz,one=1;
2168: Mat_SeqAIJ *x,*y;
2171: if (str == SAME_NONZERO_PATTERN) {
2172: PetscScalar alpha = a;
2173: x = (Mat_SeqAIJ*)xx->A->data;
2174: PetscBLASIntCast(x->nz,&bnz);
2175: y = (Mat_SeqAIJ*)yy->A->data;
2176: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2177: x = (Mat_SeqAIJ*)xx->B->data;
2178: y = (Mat_SeqAIJ*)yy->B->data;
2179: PetscBLASIntCast(x->nz,&bnz);
2180: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2181: PetscObjectStateIncrease((PetscObject)Y);
2182: } else if (str == SUBSET_NONZERO_PATTERN) {
2183: MatAXPY_SeqAIJ(yy->A,a,xx->A,str);
2185: x = (Mat_SeqAIJ*)xx->B->data;
2186: y = (Mat_SeqAIJ*)yy->B->data;
2187: if (y->xtoy && y->XtoY != xx->B) {
2188: PetscFree(y->xtoy);
2189: MatDestroy(&y->XtoY);
2190: }
2191: if (!y->xtoy) { /* get xtoy */
2192: MatAXPYGetxtoy_Private(xx->B->rmap->n,x->i,x->j,xx->garray,y->i,y->j,yy->garray,&y->xtoy);
2193: y->XtoY = xx->B;
2194: PetscObjectReference((PetscObject)xx->B);
2195: }
2196: for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2197: PetscObjectStateIncrease((PetscObject)Y);
2198: } else {
2199: Mat B;
2200: PetscInt *nnz_d,*nnz_o;
2201: PetscMalloc1(yy->A->rmap->N,&nnz_d);
2202: PetscMalloc1(yy->B->rmap->N,&nnz_o);
2203: MatCreate(PetscObjectComm((PetscObject)Y),&B);
2204: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2205: MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2206: MatSetBlockSizesFromMats(B,Y,Y);
2207: MatSetType(B,MATMPIAIJ);
2208: MatAXPYGetPreallocation_SeqAIJ(yy->A,xx->A,nnz_d);
2209: MatAXPYGetPreallocation_MPIAIJ(yy->B,yy->garray,xx->B,xx->garray,nnz_o);
2210: MatMPIAIJSetPreallocation(B,0,nnz_d,0,nnz_o);
2211: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2212: MatHeaderReplace(Y,B);
2213: PetscFree(nnz_d);
2214: PetscFree(nnz_o);
2215: }
2216: return(0);
2217: }
2219: extern PetscErrorCode MatConjugate_SeqAIJ(Mat);
2223: PetscErrorCode MatConjugate_MPIAIJ(Mat mat)
2224: {
2225: #if defined(PETSC_USE_COMPLEX)
2227: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
2230: MatConjugate_SeqAIJ(aij->A);
2231: MatConjugate_SeqAIJ(aij->B);
2232: #else
2234: #endif
2235: return(0);
2236: }
2240: PetscErrorCode MatRealPart_MPIAIJ(Mat A)
2241: {
2242: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2246: MatRealPart(a->A);
2247: MatRealPart(a->B);
2248: return(0);
2249: }
2253: PetscErrorCode MatImaginaryPart_MPIAIJ(Mat A)
2254: {
2255: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2259: MatImaginaryPart(a->A);
2260: MatImaginaryPart(a->B);
2261: return(0);
2262: }
2264: #if defined(PETSC_HAVE_PBGL)
2266: #include <boost/parallel/mpi/bsp_process_group.hpp>
2267: #include <boost/graph/distributed/ilu_default_graph.hpp>
2268: #include <boost/graph/distributed/ilu_0_block.hpp>
2269: #include <boost/graph/distributed/ilu_preconditioner.hpp>
2270: #include <boost/graph/distributed/petsc/interface.hpp>
2271: #include <boost/multi_array.hpp>
2272: #include <boost/parallel/distributed_property_map->hpp>
2276: /*
2277: This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
2278: */
2279: PetscErrorCode MatILUFactorSymbolic_MPIAIJ(Mat fact,Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
2280: {
2281: namespace petsc = boost::distributed::petsc;
2283: namespace graph_dist = boost::graph::distributed;
2284: using boost::graph::distributed::ilu_default::process_group_type;
2285: using boost::graph::ilu_permuted;
2287: PetscBool row_identity, col_identity;
2288: PetscContainer c;
2289: PetscInt m, n, M, N;
2293: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels = 0 supported for parallel ilu");
2294: ISIdentity(isrow, &row_identity);
2295: ISIdentity(iscol, &col_identity);
2296: if (!row_identity || !col_identity) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for parallel ILU");
2298: process_group_type pg;
2299: typedef graph_dist::ilu_default::ilu_level_graph_type lgraph_type;
2300: lgraph_type *lgraph_p = new lgraph_type(petsc::num_global_vertices(A), pg, petsc::matrix_distribution(A, pg));
2301: lgraph_type& level_graph = *lgraph_p;
2302: graph_dist::ilu_default::graph_type& graph(level_graph.graph);
2304: petsc::read_matrix(A, graph, get(boost::edge_weight, graph));
2305: ilu_permuted(level_graph);
2307: /* put together the new matrix */
2308: MatCreate(PetscObjectComm((PetscObject)A), fact);
2309: MatGetLocalSize(A, &m, &n);
2310: MatGetSize(A, &M, &N);
2311: MatSetSizes(fact, m, n, M, N);
2312: MatSetBlockSizesFromMats(fact,A,A);
2313: MatSetType(fact, ((PetscObject)A)->type_name);
2314: MatAssemblyBegin(fact, MAT_FINAL_ASSEMBLY);
2315: MatAssemblyEnd(fact, MAT_FINAL_ASSEMBLY);
2317: PetscContainerCreate(PetscObjectComm((PetscObject)A), &c);
2318: PetscContainerSetPointer(c, lgraph_p);
2319: PetscObjectCompose((PetscObject) (fact), "graph", (PetscObject) c);
2320: PetscContainerDestroy(&c);
2321: return(0);
2322: }
2326: PetscErrorCode MatLUFactorNumeric_MPIAIJ(Mat B,Mat A, const MatFactorInfo *info)
2327: {
2329: return(0);
2330: }
2334: /*
2335: This uses the parallel ILU factorization of Peter Gottschling <pgottsch@osl.iu.edu>
2336: */
2337: PetscErrorCode MatSolve_MPIAIJ(Mat A, Vec b, Vec x)
2338: {
2339: namespace graph_dist = boost::graph::distributed;
2341: typedef graph_dist::ilu_default::ilu_level_graph_type lgraph_type;
2342: lgraph_type *lgraph_p;
2343: PetscContainer c;
2347: PetscObjectQuery((PetscObject) A, "graph", (PetscObject*) &c);
2348: PetscContainerGetPointer(c, (void**) &lgraph_p);
2349: VecCopy(b, x);
2351: PetscScalar *array_x;
2352: VecGetArray(x, &array_x);
2353: PetscInt sx;
2354: VecGetSize(x, &sx);
2356: PetscScalar *array_b;
2357: VecGetArray(b, &array_b);
2358: PetscInt sb;
2359: VecGetSize(b, &sb);
2361: lgraph_type& level_graph = *lgraph_p;
2362: graph_dist::ilu_default::graph_type& graph(level_graph.graph);
2364: typedef boost::multi_array_ref<PetscScalar, 1> array_ref_type;
2365: array_ref_type ref_b(array_b, boost::extents[num_vertices(graph)]);
2366: array_ref_type ref_x(array_x, boost::extents[num_vertices(graph)]);
2368: typedef boost::iterator_property_map<array_ref_type::iterator,
2369: boost::property_map<graph_dist::ilu_default::graph_type, boost::vertex_index_t>::type> gvector_type;
2370: gvector_type vector_b(ref_b.begin(), get(boost::vertex_index, graph));
2371: gvector_type vector_x(ref_x.begin(), get(boost::vertex_index, graph));
2373: ilu_set_solve(*lgraph_p, vector_b, vector_x);
2374: return(0);
2375: }
2376: #endif
2381: PetscErrorCode MatGetRedundantMatrix_MPIAIJ_interlaced(Mat mat,PetscInt nsubcomm,MPI_Comm subcomm,MatReuse reuse,Mat *matredundant)
2382: {
2383: PetscMPIInt rank,size;
2384: MPI_Comm comm;
2386: PetscInt nsends=0,nrecvs=0,i,rownz_max=0,M=mat->rmap->N,N=mat->cmap->N;
2387: PetscMPIInt *send_rank= NULL,*recv_rank=NULL,subrank,subsize;
2388: PetscInt *rowrange = mat->rmap->range;
2389: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
2390: Mat A = aij->A,B=aij->B,C=*matredundant;
2391: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data;
2392: PetscScalar *sbuf_a;
2393: PetscInt nzlocal=a->nz+b->nz;
2394: PetscInt j,cstart=mat->cmap->rstart,cend=mat->cmap->rend,row,nzA,nzB,ncols,*cworkA,*cworkB;
2395: PetscInt rstart=mat->rmap->rstart,rend=mat->rmap->rend,*bmap=aij->garray;
2396: PetscInt *cols,ctmp,lwrite,*rptr,l,*sbuf_j;
2397: MatScalar *aworkA,*aworkB;
2398: PetscScalar *vals;
2399: PetscMPIInt tag1,tag2,tag3,imdex;
2400: MPI_Request *s_waits1=NULL,*s_waits2=NULL,*s_waits3=NULL;
2401: MPI_Request *r_waits1=NULL,*r_waits2=NULL,*r_waits3=NULL;
2402: MPI_Status recv_status,*send_status;
2403: PetscInt *sbuf_nz=NULL,*rbuf_nz=NULL,count;
2404: PetscInt **rbuf_j=NULL;
2405: PetscScalar **rbuf_a=NULL;
2406: Mat_Redundant *redund =NULL;
2407:
2409: PetscObjectGetComm((PetscObject)mat,&comm);
2410: MPI_Comm_rank(comm,&rank);
2411: MPI_Comm_size(comm,&size);
2412: MPI_Comm_rank(subcomm,&subrank);
2413: MPI_Comm_size(subcomm,&subsize);
2415: if (reuse == MAT_REUSE_MATRIX) {
2416: if (M != mat->rmap->N || N != mat->cmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong global size");
2417: if (subsize == 1) {
2418: Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
2419: redund = c->redundant;
2420: } else {
2421: Mat_MPIAIJ *c = (Mat_MPIAIJ*)C->data;
2422: redund = c->redundant;
2423: }
2424: if (nzlocal != redund->nzlocal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. Wrong nzlocal");
2426: nsends = redund->nsends;
2427: nrecvs = redund->nrecvs;
2428: send_rank = redund->send_rank;
2429: recv_rank = redund->recv_rank;
2430: sbuf_nz = redund->sbuf_nz;
2431: rbuf_nz = redund->rbuf_nz;
2432: sbuf_j = redund->sbuf_j;
2433: sbuf_a = redund->sbuf_a;
2434: rbuf_j = redund->rbuf_j;
2435: rbuf_a = redund->rbuf_a;
2436: }
2438: if (reuse == MAT_INITIAL_MATRIX) {
2439: PetscInt nleftover,np_subcomm;
2441: /* get the destination processors' id send_rank, nsends and nrecvs */
2442: PetscMalloc2(size,&send_rank,size,&recv_rank);
2444: np_subcomm = size/nsubcomm;
2445: nleftover = size - nsubcomm*np_subcomm;
2447: /* block of codes below is specific for INTERLACED */
2448: /* ------------------------------------------------*/
2449: nsends = 0; nrecvs = 0;
2450: for (i=0; i<size; i++) {
2451: if (subrank == i/nsubcomm && i != rank) { /* my_subrank == other's subrank */
2452: send_rank[nsends++] = i;
2453: recv_rank[nrecvs++] = i;
2454: }
2455: }
2456: if (rank >= size - nleftover) { /* this proc is a leftover processor */
2457: i = size-nleftover-1;
2458: j = 0;
2459: while (j < nsubcomm - nleftover) {
2460: send_rank[nsends++] = i;
2461: i--; j++;
2462: }
2463: }
2465: if (nleftover && subsize == size/nsubcomm && subrank==subsize-1) { /* this proc recvs from leftover processors */
2466: for (i=0; i<nleftover; i++) {
2467: recv_rank[nrecvs++] = size-nleftover+i;
2468: }
2469: }
2470: /*----------------------------------------------*/
2472: /* allocate sbuf_j, sbuf_a */
2473: i = nzlocal + rowrange[rank+1] - rowrange[rank] + 2;
2474: PetscMalloc1(i,&sbuf_j);
2475: PetscMalloc1((nzlocal+1),&sbuf_a);
2476: /*
2477: PetscSynchronizedPrintf(comm,"[%d] nsends %d, nrecvs %d\n",rank,nsends,nrecvs);
2478: PetscSynchronizedFlush(comm,PETSC_STDOUT);
2479: */
2480: } /* endof if (reuse == MAT_INITIAL_MATRIX) */
2482: /* copy mat's local entries into the buffers */
2483: if (reuse == MAT_INITIAL_MATRIX) {
2484: rownz_max = 0;
2485: rptr = sbuf_j;
2486: cols = sbuf_j + rend-rstart + 1;
2487: vals = sbuf_a;
2488: rptr[0] = 0;
2489: for (i=0; i<rend-rstart; i++) {
2490: row = i + rstart;
2491: nzA = a->i[i+1] - a->i[i]; nzB = b->i[i+1] - b->i[i];
2492: ncols = nzA + nzB;
2493: cworkA = a->j + a->i[i]; cworkB = b->j + b->i[i];
2494: aworkA = a->a + a->i[i]; aworkB = b->a + b->i[i];
2495: /* load the column indices for this row into cols */
2496: lwrite = 0;
2497: for (l=0; l<nzB; l++) {
2498: if ((ctmp = bmap[cworkB[l]]) < cstart) {
2499: vals[lwrite] = aworkB[l];
2500: cols[lwrite++] = ctmp;
2501: }
2502: }
2503: for (l=0; l<nzA; l++) {
2504: vals[lwrite] = aworkA[l];
2505: cols[lwrite++] = cstart + cworkA[l];
2506: }
2507: for (l=0; l<nzB; l++) {
2508: if ((ctmp = bmap[cworkB[l]]) >= cend) {
2509: vals[lwrite] = aworkB[l];
2510: cols[lwrite++] = ctmp;
2511: }
2512: }
2513: vals += ncols;
2514: cols += ncols;
2515: rptr[i+1] = rptr[i] + ncols;
2516: if (rownz_max < ncols) rownz_max = ncols;
2517: }
2518: if (rptr[rend-rstart] != a->nz + b->nz) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_PLIB, "rptr[%d] %d != %d + %d",rend-rstart,rptr[rend-rstart+1],a->nz,b->nz);
2519: } else { /* only copy matrix values into sbuf_a */
2520: rptr = sbuf_j;
2521: vals = sbuf_a;
2522: rptr[0] = 0;
2523: for (i=0; i<rend-rstart; i++) {
2524: row = i + rstart;
2525: nzA = a->i[i+1] - a->i[i]; nzB = b->i[i+1] - b->i[i];
2526: ncols = nzA + nzB;
2527: cworkB = b->j + b->i[i];
2528: aworkA = a->a + a->i[i];
2529: aworkB = b->a + b->i[i];
2530: lwrite = 0;
2531: for (l=0; l<nzB; l++) {
2532: if ((ctmp = bmap[cworkB[l]]) < cstart) vals[lwrite++] = aworkB[l];
2533: }
2534: for (l=0; l<nzA; l++) vals[lwrite++] = aworkA[l];
2535: for (l=0; l<nzB; l++) {
2536: if ((ctmp = bmap[cworkB[l]]) >= cend) vals[lwrite++] = aworkB[l];
2537: }
2538: vals += ncols;
2539: rptr[i+1] = rptr[i] + ncols;
2540: }
2541: } /* endof if (reuse == MAT_INITIAL_MATRIX) */
2543: /* send nzlocal to others, and recv other's nzlocal */
2544: /*--------------------------------------------------*/
2545: if (reuse == MAT_INITIAL_MATRIX) {
2546: PetscMalloc2(3*(nsends + nrecvs)+1,&s_waits3,nsends+1,&send_status);
2548: s_waits2 = s_waits3 + nsends;
2549: s_waits1 = s_waits2 + nsends;
2550: r_waits1 = s_waits1 + nsends;
2551: r_waits2 = r_waits1 + nrecvs;
2552: r_waits3 = r_waits2 + nrecvs;
2553: } else {
2554: PetscMalloc2(nsends + nrecvs +1,&s_waits3,nsends+1,&send_status);
2556: r_waits3 = s_waits3 + nsends;
2557: }
2559: PetscObjectGetNewTag((PetscObject)mat,&tag3);
2560: if (reuse == MAT_INITIAL_MATRIX) {
2561: /* get new tags to keep the communication clean */
2562: PetscObjectGetNewTag((PetscObject)mat,&tag1);
2563: PetscObjectGetNewTag((PetscObject)mat,&tag2);
2564: PetscMalloc4(nsends,&sbuf_nz,nrecvs,&rbuf_nz,nrecvs,&rbuf_j,nrecvs,&rbuf_a);
2566: /* post receives of other's nzlocal */
2567: for (i=0; i<nrecvs; i++) {
2568: MPI_Irecv(rbuf_nz+i,1,MPIU_INT,MPI_ANY_SOURCE,tag1,comm,r_waits1+i);
2569: }
2570: /* send nzlocal to others */
2571: for (i=0; i<nsends; i++) {
2572: sbuf_nz[i] = nzlocal;
2573: MPI_Isend(sbuf_nz+i,1,MPIU_INT,send_rank[i],tag1,comm,s_waits1+i);
2574: }
2575: /* wait on receives of nzlocal; allocate space for rbuf_j, rbuf_a */
2576: count = nrecvs;
2577: while (count) {
2578: MPI_Waitany(nrecvs,r_waits1,&imdex,&recv_status);
2580: recv_rank[imdex] = recv_status.MPI_SOURCE;
2581: /* allocate rbuf_a and rbuf_j; then post receives of rbuf_j */
2582: PetscMalloc1((rbuf_nz[imdex]+1),&rbuf_a[imdex]);
2584: i = rowrange[recv_status.MPI_SOURCE+1] - rowrange[recv_status.MPI_SOURCE]; /* number of expected mat->i */
2586: rbuf_nz[imdex] += i + 2;
2588: PetscMalloc1(rbuf_nz[imdex],&rbuf_j[imdex]);
2589: MPI_Irecv(rbuf_j[imdex],rbuf_nz[imdex],MPIU_INT,recv_status.MPI_SOURCE,tag2,comm,r_waits2+imdex);
2590: count--;
2591: }
2592: /* wait on sends of nzlocal */
2593: if (nsends) {MPI_Waitall(nsends,s_waits1,send_status);}
2594: /* send mat->i,j to others, and recv from other's */
2595: /*------------------------------------------------*/
2596: for (i=0; i<nsends; i++) {
2597: j = nzlocal + rowrange[rank+1] - rowrange[rank] + 1;
2598: MPI_Isend(sbuf_j,j,MPIU_INT,send_rank[i],tag2,comm,s_waits2+i);
2599: }
2600: /* wait on receives of mat->i,j */
2601: /*------------------------------*/
2602: count = nrecvs;
2603: while (count) {
2604: MPI_Waitany(nrecvs,r_waits2,&imdex,&recv_status);
2605: if (recv_rank[imdex] != recv_status.MPI_SOURCE) SETERRQ2(PETSC_COMM_SELF,1, "recv_rank %d != MPI_SOURCE %d",recv_rank[imdex],recv_status.MPI_SOURCE);
2606: count--;
2607: }
2608: /* wait on sends of mat->i,j */
2609: /*---------------------------*/
2610: if (nsends) {
2611: MPI_Waitall(nsends,s_waits2,send_status);
2612: }
2613: } /* endof if (reuse == MAT_INITIAL_MATRIX) */
2615: /* post receives, send and receive mat->a */
2616: /*----------------------------------------*/
2617: for (imdex=0; imdex<nrecvs; imdex++) {
2618: MPI_Irecv(rbuf_a[imdex],rbuf_nz[imdex],MPIU_SCALAR,recv_rank[imdex],tag3,comm,r_waits3+imdex);
2619: }
2620: for (i=0; i<nsends; i++) {
2621: MPI_Isend(sbuf_a,nzlocal,MPIU_SCALAR,send_rank[i],tag3,comm,s_waits3+i);
2622: }
2623: count = nrecvs;
2624: while (count) {
2625: MPI_Waitany(nrecvs,r_waits3,&imdex,&recv_status);
2626: if (recv_rank[imdex] != recv_status.MPI_SOURCE) SETERRQ2(PETSC_COMM_SELF,1, "recv_rank %d != MPI_SOURCE %d",recv_rank[imdex],recv_status.MPI_SOURCE);
2627: count--;
2628: }
2629: if (nsends) {
2630: MPI_Waitall(nsends,s_waits3,send_status);
2631: }
2633: PetscFree2(s_waits3,send_status);
2635: /* create redundant matrix */
2636: /*-------------------------*/
2637: if (reuse == MAT_INITIAL_MATRIX) {
2638: const PetscInt *range;
2639: PetscInt rstart_sub,rend_sub,mloc_sub;
2641: /* compute rownz_max for preallocation */
2642: for (imdex=0; imdex<nrecvs; imdex++) {
2643: j = rowrange[recv_rank[imdex]+1] - rowrange[recv_rank[imdex]];
2644: rptr = rbuf_j[imdex];
2645: for (i=0; i<j; i++) {
2646: ncols = rptr[i+1] - rptr[i];
2647: if (rownz_max < ncols) rownz_max = ncols;
2648: }
2649: }
2651: MatCreate(subcomm,&C);
2653: /* get local size of redundant matrix
2654: - mloc_sub is chosen for PETSC_SUBCOMM_INTERLACED, works for other types, but may not efficient! */
2655: MatGetOwnershipRanges(mat,&range);
2656: rstart_sub = range[nsubcomm*subrank];
2657: if (subrank+1 < subsize) { /* not the last proc in subcomm */
2658: rend_sub = range[nsubcomm*(subrank+1)];
2659: } else {
2660: rend_sub = mat->rmap->N;
2661: }
2662: mloc_sub = rend_sub - rstart_sub;
2664: if (M == N) {
2665: MatSetSizes(C,mloc_sub,mloc_sub,PETSC_DECIDE,PETSC_DECIDE);
2666: } else { /* non-square matrix */
2667: MatSetSizes(C,mloc_sub,PETSC_DECIDE,PETSC_DECIDE,mat->cmap->N);
2668: }
2669: MatSetBlockSizesFromMats(C,mat,mat);
2670: MatSetFromOptions(C);
2671: MatSeqAIJSetPreallocation(C,rownz_max,NULL);
2672: MatMPIAIJSetPreallocation(C,rownz_max,NULL,rownz_max,NULL);
2673: } else {
2674: C = *matredundant;
2675: }
2677: /* insert local matrix entries */
2678: rptr = sbuf_j;
2679: cols = sbuf_j + rend-rstart + 1;
2680: vals = sbuf_a;
2681: for (i=0; i<rend-rstart; i++) {
2682: row = i + rstart;
2683: ncols = rptr[i+1] - rptr[i];
2684: MatSetValues(C,1,&row,ncols,cols,vals,INSERT_VALUES);
2685: vals += ncols;
2686: cols += ncols;
2687: }
2688: /* insert received matrix entries */
2689: for (imdex=0; imdex<nrecvs; imdex++) {
2690: rstart = rowrange[recv_rank[imdex]];
2691: rend = rowrange[recv_rank[imdex]+1];
2692: /* printf("[%d] insert rows %d - %d\n",rank,rstart,rend-1); */
2693: rptr = rbuf_j[imdex];
2694: cols = rbuf_j[imdex] + rend-rstart + 1;
2695: vals = rbuf_a[imdex];
2696: for (i=0; i<rend-rstart; i++) {
2697: row = i + rstart;
2698: ncols = rptr[i+1] - rptr[i];
2699: MatSetValues(C,1,&row,ncols,cols,vals,INSERT_VALUES);
2700: vals += ncols;
2701: cols += ncols;
2702: }
2703: }
2704: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2705: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2707: if (reuse == MAT_INITIAL_MATRIX) {
2708: *matredundant = C;
2710: /* create a supporting struct and attach it to C for reuse */
2711: PetscNewLog(C,&redund);
2712: if (subsize == 1) {
2713: Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
2714: c->redundant = redund;
2715: } else {
2716: Mat_MPIAIJ *c = (Mat_MPIAIJ*)C->data;
2717: c->redundant = redund;
2718: }
2720: redund->nzlocal = nzlocal;
2721: redund->nsends = nsends;
2722: redund->nrecvs = nrecvs;
2723: redund->send_rank = send_rank;
2724: redund->recv_rank = recv_rank;
2725: redund->sbuf_nz = sbuf_nz;
2726: redund->rbuf_nz = rbuf_nz;
2727: redund->sbuf_j = sbuf_j;
2728: redund->sbuf_a = sbuf_a;
2729: redund->rbuf_j = rbuf_j;
2730: redund->rbuf_a = rbuf_a;
2731: redund->psubcomm = NULL;
2732: }
2733: return(0);
2734: }
2738: PetscErrorCode MatGetRedundantMatrix_MPIAIJ(Mat mat,PetscInt nsubcomm,MPI_Comm subcomm,MatReuse reuse,Mat *matredundant)
2739: {
2741: MPI_Comm comm;
2742: PetscMPIInt size,subsize;
2743: PetscInt mloc_sub,rstart,rend,M=mat->rmap->N,N=mat->cmap->N;
2744: Mat_Redundant *redund=NULL;
2745: PetscSubcomm psubcomm=NULL;
2746: MPI_Comm subcomm_in=subcomm;
2747: Mat *matseq;
2748: IS isrow,iscol;
2751: if (subcomm_in == MPI_COMM_NULL) { /* user does not provide subcomm */
2752: if (reuse == MAT_INITIAL_MATRIX) {
2753: /* create psubcomm, then get subcomm */
2754: PetscObjectGetComm((PetscObject)mat,&comm);
2755: MPI_Comm_size(comm,&size);
2756: if (nsubcomm < 1 || nsubcomm > size) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"nsubcomm must between 1 and %D",size);
2758: PetscSubcommCreate(comm,&psubcomm);
2759: PetscSubcommSetNumber(psubcomm,nsubcomm);
2760: PetscSubcommSetType(psubcomm,PETSC_SUBCOMM_CONTIGUOUS);
2761: PetscSubcommSetFromOptions(psubcomm);
2762: subcomm = psubcomm->comm;
2763: } else { /* retrieve psubcomm and subcomm */
2764: PetscObjectGetComm((PetscObject)(*matredundant),&subcomm);
2765: MPI_Comm_size(subcomm,&subsize);
2766: if (subsize == 1) {
2767: Mat_SeqAIJ *c = (Mat_SeqAIJ*)(*matredundant)->data;
2768: redund = c->redundant;
2769: } else {
2770: Mat_MPIAIJ *c = (Mat_MPIAIJ*)(*matredundant)->data;
2771: redund = c->redundant;
2772: }
2773: psubcomm = redund->psubcomm;
2774: }
2775: if (psubcomm->type == PETSC_SUBCOMM_INTERLACED) {
2776: MatGetRedundantMatrix_MPIAIJ_interlaced(mat,nsubcomm,subcomm,reuse,matredundant);
2777: if (reuse == MAT_INITIAL_MATRIX) { /* psubcomm is created in this routine, free it in MatDestroy_Redundant() */
2778: MPI_Comm_size(psubcomm->comm,&subsize);
2779: if (subsize == 1) {
2780: Mat_SeqAIJ *c = (Mat_SeqAIJ*)(*matredundant)->data;
2781: c->redundant->psubcomm = psubcomm;
2782: } else {
2783: Mat_MPIAIJ *c = (Mat_MPIAIJ*)(*matredundant)->data;
2784: c->redundant->psubcomm = psubcomm ;
2785: }
2786: }
2787: return(0);
2788: }
2789: }
2791: /* use MPI subcomm via MatGetSubMatrices(); use subcomm_in or psubcomm->comm (psubcomm->type != INTERLACED) */
2792: MPI_Comm_size(subcomm,&subsize);
2793: if (reuse == MAT_INITIAL_MATRIX) {
2794: /* create a local sequential matrix matseq[0] */
2795: mloc_sub = PETSC_DECIDE;
2796: PetscSplitOwnership(subcomm,&mloc_sub,&M);
2797: MPI_Scan(&mloc_sub,&rend,1,MPIU_INT,MPI_SUM,subcomm);
2798: rstart = rend - mloc_sub;
2799: ISCreateStride(PETSC_COMM_SELF,mloc_sub,rstart,1,&isrow);
2800: ISCreateStride(PETSC_COMM_SELF,N,0,1,&iscol);
2801: } else { /* reuse == MAT_REUSE_MATRIX */
2802: if (subsize == 1) {
2803: Mat_SeqAIJ *c = (Mat_SeqAIJ*)(*matredundant)->data;
2804: redund = c->redundant;
2805: } else {
2806: Mat_MPIAIJ *c = (Mat_MPIAIJ*)(*matredundant)->data;
2807: redund = c->redundant;
2808: }
2810: isrow = redund->isrow;
2811: iscol = redund->iscol;
2812: matseq = redund->matseq;
2813: }
2814: MatGetSubMatrices(mat,1,&isrow,&iscol,reuse,&matseq);
2815: MatCreateMPIAIJConcatenateSeqAIJ(subcomm,matseq[0],PETSC_DECIDE,reuse,matredundant);
2817: if (reuse == MAT_INITIAL_MATRIX) {
2818: /* create a supporting struct and attach it to C for reuse */
2819: PetscNewLog(*matredundant,&redund);
2820: if (subsize == 1) {
2821: Mat_SeqAIJ *c = (Mat_SeqAIJ*)(*matredundant)->data;
2822: c->redundant = redund;
2823: } else {
2824: Mat_MPIAIJ *c = (Mat_MPIAIJ*)(*matredundant)->data;
2825: c->redundant = redund;
2826: }
2827: redund->isrow = isrow;
2828: redund->iscol = iscol;
2829: redund->matseq = matseq;
2830: redund->psubcomm = psubcomm;
2831: }
2832: return(0);
2833: }
2837: PetscErrorCode MatGetRowMaxAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2838: {
2839: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2841: PetscInt i,*idxb = 0;
2842: PetscScalar *va,*vb;
2843: Vec vtmp;
2846: MatGetRowMaxAbs(a->A,v,idx);
2847: VecGetArray(v,&va);
2848: if (idx) {
2849: for (i=0; i<A->rmap->n; i++) {
2850: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2851: }
2852: }
2854: VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
2855: if (idx) {
2856: PetscMalloc1(A->rmap->n,&idxb);
2857: }
2858: MatGetRowMaxAbs(a->B,vtmp,idxb);
2859: VecGetArray(vtmp,&vb);
2861: for (i=0; i<A->rmap->n; i++) {
2862: if (PetscAbsScalar(va[i]) < PetscAbsScalar(vb[i])) {
2863: va[i] = vb[i];
2864: if (idx) idx[i] = a->garray[idxb[i]];
2865: }
2866: }
2868: VecRestoreArray(v,&va);
2869: VecRestoreArray(vtmp,&vb);
2870: PetscFree(idxb);
2871: VecDestroy(&vtmp);
2872: return(0);
2873: }
2877: PetscErrorCode MatGetRowMinAbs_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2878: {
2879: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
2881: PetscInt i,*idxb = 0;
2882: PetscScalar *va,*vb;
2883: Vec vtmp;
2886: MatGetRowMinAbs(a->A,v,idx);
2887: VecGetArray(v,&va);
2888: if (idx) {
2889: for (i=0; i<A->cmap->n; i++) {
2890: if (PetscAbsScalar(va[i])) idx[i] += A->cmap->rstart;
2891: }
2892: }
2894: VecCreateSeq(PETSC_COMM_SELF,A->rmap->n,&vtmp);
2895: if (idx) {
2896: PetscMalloc1(A->rmap->n,&idxb);
2897: }
2898: MatGetRowMinAbs(a->B,vtmp,idxb);
2899: VecGetArray(vtmp,&vb);
2901: for (i=0; i<A->rmap->n; i++) {
2902: if (PetscAbsScalar(va[i]) > PetscAbsScalar(vb[i])) {
2903: va[i] = vb[i];
2904: if (idx) idx[i] = a->garray[idxb[i]];
2905: }
2906: }
2908: VecRestoreArray(v,&va);
2909: VecRestoreArray(vtmp,&vb);
2910: PetscFree(idxb);
2911: VecDestroy(&vtmp);
2912: return(0);
2913: }
2917: PetscErrorCode MatGetRowMin_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2918: {
2919: Mat_MPIAIJ *mat = (Mat_MPIAIJ*) A->data;
2920: PetscInt n = A->rmap->n;
2921: PetscInt cstart = A->cmap->rstart;
2922: PetscInt *cmap = mat->garray;
2923: PetscInt *diagIdx, *offdiagIdx;
2924: Vec diagV, offdiagV;
2925: PetscScalar *a, *diagA, *offdiagA;
2926: PetscInt r;
2930: PetscMalloc2(n,&diagIdx,n,&offdiagIdx);
2931: VecCreateSeq(PetscObjectComm((PetscObject)A), n, &diagV);
2932: VecCreateSeq(PetscObjectComm((PetscObject)A), n, &offdiagV);
2933: MatGetRowMin(mat->A, diagV, diagIdx);
2934: MatGetRowMin(mat->B, offdiagV, offdiagIdx);
2935: VecGetArray(v, &a);
2936: VecGetArray(diagV, &diagA);
2937: VecGetArray(offdiagV, &offdiagA);
2938: for (r = 0; r < n; ++r) {
2939: if (PetscAbsScalar(diagA[r]) <= PetscAbsScalar(offdiagA[r])) {
2940: a[r] = diagA[r];
2941: idx[r] = cstart + diagIdx[r];
2942: } else {
2943: a[r] = offdiagA[r];
2944: idx[r] = cmap[offdiagIdx[r]];
2945: }
2946: }
2947: VecRestoreArray(v, &a);
2948: VecRestoreArray(diagV, &diagA);
2949: VecRestoreArray(offdiagV, &offdiagA);
2950: VecDestroy(&diagV);
2951: VecDestroy(&offdiagV);
2952: PetscFree2(diagIdx, offdiagIdx);
2953: return(0);
2954: }
2958: PetscErrorCode MatGetRowMax_MPIAIJ(Mat A, Vec v, PetscInt idx[])
2959: {
2960: Mat_MPIAIJ *mat = (Mat_MPIAIJ*) A->data;
2961: PetscInt n = A->rmap->n;
2962: PetscInt cstart = A->cmap->rstart;
2963: PetscInt *cmap = mat->garray;
2964: PetscInt *diagIdx, *offdiagIdx;
2965: Vec diagV, offdiagV;
2966: PetscScalar *a, *diagA, *offdiagA;
2967: PetscInt r;
2971: PetscMalloc2(n,&diagIdx,n,&offdiagIdx);
2972: VecCreateSeq(PETSC_COMM_SELF, n, &diagV);
2973: VecCreateSeq(PETSC_COMM_SELF, n, &offdiagV);
2974: MatGetRowMax(mat->A, diagV, diagIdx);
2975: MatGetRowMax(mat->B, offdiagV, offdiagIdx);
2976: VecGetArray(v, &a);
2977: VecGetArray(diagV, &diagA);
2978: VecGetArray(offdiagV, &offdiagA);
2979: for (r = 0; r < n; ++r) {
2980: if (PetscAbsScalar(diagA[r]) >= PetscAbsScalar(offdiagA[r])) {
2981: a[r] = diagA[r];
2982: idx[r] = cstart + diagIdx[r];
2983: } else {
2984: a[r] = offdiagA[r];
2985: idx[r] = cmap[offdiagIdx[r]];
2986: }
2987: }
2988: VecRestoreArray(v, &a);
2989: VecRestoreArray(diagV, &diagA);
2990: VecRestoreArray(offdiagV, &offdiagA);
2991: VecDestroy(&diagV);
2992: VecDestroy(&offdiagV);
2993: PetscFree2(diagIdx, offdiagIdx);
2994: return(0);
2995: }
2999: PetscErrorCode MatGetSeqNonzeroStructure_MPIAIJ(Mat mat,Mat *newmat)
3000: {
3002: Mat *dummy;
3005: MatGetSubMatrix_MPIAIJ_All(mat,MAT_DO_NOT_GET_VALUES,MAT_INITIAL_MATRIX,&dummy);
3006: *newmat = *dummy;
3007: PetscFree(dummy);
3008: return(0);
3009: }
3013: PetscErrorCode MatInvertBlockDiagonal_MPIAIJ(Mat A,const PetscScalar **values)
3014: {
3015: Mat_MPIAIJ *a = (Mat_MPIAIJ*) A->data;
3019: MatInvertBlockDiagonal(a->A,values);
3020: return(0);
3021: }
3025: static PetscErrorCode MatSetRandom_MPIAIJ(Mat x,PetscRandom rctx)
3026: {
3028: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)x->data;
3031: MatSetRandom(aij->A,rctx);
3032: MatSetRandom(aij->B,rctx);
3033: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3034: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3035: return(0);
3036: }
3038: /* -------------------------------------------------------------------*/
3039: static struct _MatOps MatOps_Values = {MatSetValues_MPIAIJ,
3040: MatGetRow_MPIAIJ,
3041: MatRestoreRow_MPIAIJ,
3042: MatMult_MPIAIJ,
3043: /* 4*/ MatMultAdd_MPIAIJ,
3044: MatMultTranspose_MPIAIJ,
3045: MatMultTransposeAdd_MPIAIJ,
3046: #if defined(PETSC_HAVE_PBGL)
3047: MatSolve_MPIAIJ,
3048: #else
3049: 0,
3050: #endif
3051: 0,
3052: 0,
3053: /*10*/ 0,
3054: 0,
3055: 0,
3056: MatSOR_MPIAIJ,
3057: MatTranspose_MPIAIJ,
3058: /*15*/ MatGetInfo_MPIAIJ,
3059: MatEqual_MPIAIJ,
3060: MatGetDiagonal_MPIAIJ,
3061: MatDiagonalScale_MPIAIJ,
3062: MatNorm_MPIAIJ,
3063: /*20*/ MatAssemblyBegin_MPIAIJ,
3064: MatAssemblyEnd_MPIAIJ,
3065: MatSetOption_MPIAIJ,
3066: MatZeroEntries_MPIAIJ,
3067: /*24*/ MatZeroRows_MPIAIJ,
3068: 0,
3069: #if defined(PETSC_HAVE_PBGL)
3070: 0,
3071: #else
3072: 0,
3073: #endif
3074: 0,
3075: 0,
3076: /*29*/ MatSetUp_MPIAIJ,
3077: #if defined(PETSC_HAVE_PBGL)
3078: 0,
3079: #else
3080: 0,
3081: #endif
3082: 0,
3083: 0,
3084: 0,
3085: /*34*/ MatDuplicate_MPIAIJ,
3086: 0,
3087: 0,
3088: 0,
3089: 0,
3090: /*39*/ MatAXPY_MPIAIJ,
3091: MatGetSubMatrices_MPIAIJ,
3092: MatIncreaseOverlap_MPIAIJ,
3093: MatGetValues_MPIAIJ,
3094: MatCopy_MPIAIJ,
3095: /*44*/ MatGetRowMax_MPIAIJ,
3096: MatScale_MPIAIJ,
3097: 0,
3098: 0,
3099: MatZeroRowsColumns_MPIAIJ,
3100: /*49*/ MatSetRandom_MPIAIJ,
3101: 0,
3102: 0,
3103: 0,
3104: 0,
3105: /*54*/ MatFDColoringCreate_MPIXAIJ,
3106: 0,
3107: MatSetUnfactored_MPIAIJ,
3108: MatPermute_MPIAIJ,
3109: 0,
3110: /*59*/ MatGetSubMatrix_MPIAIJ,
3111: MatDestroy_MPIAIJ,
3112: MatView_MPIAIJ,
3113: 0,
3114: MatMatMatMult_MPIAIJ_MPIAIJ_MPIAIJ,
3115: /*64*/ MatMatMatMultSymbolic_MPIAIJ_MPIAIJ_MPIAIJ,
3116: MatMatMatMultNumeric_MPIAIJ_MPIAIJ_MPIAIJ,
3117: 0,
3118: 0,
3119: 0,
3120: /*69*/ MatGetRowMaxAbs_MPIAIJ,
3121: MatGetRowMinAbs_MPIAIJ,
3122: 0,
3123: MatSetColoring_MPIAIJ,
3124: 0,
3125: MatSetValuesAdifor_MPIAIJ,
3126: /*75*/ MatFDColoringApply_AIJ,
3127: 0,
3128: 0,
3129: 0,
3130: MatFindZeroDiagonals_MPIAIJ,
3131: /*80*/ 0,
3132: 0,
3133: 0,
3134: /*83*/ MatLoad_MPIAIJ,
3135: 0,
3136: 0,
3137: 0,
3138: 0,
3139: 0,
3140: /*89*/ MatMatMult_MPIAIJ_MPIAIJ,
3141: MatMatMultSymbolic_MPIAIJ_MPIAIJ,
3142: MatMatMultNumeric_MPIAIJ_MPIAIJ,
3143: MatPtAP_MPIAIJ_MPIAIJ,
3144: MatPtAPSymbolic_MPIAIJ_MPIAIJ,
3145: /*94*/ MatPtAPNumeric_MPIAIJ_MPIAIJ,
3146: 0,
3147: 0,
3148: 0,
3149: 0,
3150: /*99*/ 0,
3151: 0,
3152: 0,
3153: MatConjugate_MPIAIJ,
3154: 0,
3155: /*104*/MatSetValuesRow_MPIAIJ,
3156: MatRealPart_MPIAIJ,
3157: MatImaginaryPart_MPIAIJ,
3158: 0,
3159: 0,
3160: /*109*/0,
3161: MatGetRedundantMatrix_MPIAIJ,
3162: MatGetRowMin_MPIAIJ,
3163: 0,
3164: 0,
3165: /*114*/MatGetSeqNonzeroStructure_MPIAIJ,
3166: 0,
3167: 0,
3168: 0,
3169: 0,
3170: /*119*/0,
3171: 0,
3172: 0,
3173: 0,
3174: MatGetMultiProcBlock_MPIAIJ,
3175: /*124*/MatFindNonzeroRows_MPIAIJ,
3176: MatGetColumnNorms_MPIAIJ,
3177: MatInvertBlockDiagonal_MPIAIJ,
3178: 0,
3179: MatGetSubMatricesParallel_MPIAIJ,
3180: /*129*/0,
3181: MatTransposeMatMult_MPIAIJ_MPIAIJ,
3182: MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ,
3183: MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ,
3184: 0,
3185: /*134*/0,
3186: 0,
3187: 0,
3188: 0,
3189: 0,
3190: /*139*/0,
3191: 0,
3192: 0,
3193: MatFDColoringSetUp_MPIXAIJ
3194: };
3196: /* ----------------------------------------------------------------------------------------*/
3200: PetscErrorCode MatStoreValues_MPIAIJ(Mat mat)
3201: {
3202: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
3206: MatStoreValues(aij->A);
3207: MatStoreValues(aij->B);
3208: return(0);
3209: }
3213: PetscErrorCode MatRetrieveValues_MPIAIJ(Mat mat)
3214: {
3215: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
3219: MatRetrieveValues(aij->A);
3220: MatRetrieveValues(aij->B);
3221: return(0);
3222: }
3226: PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJ(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
3227: {
3228: Mat_MPIAIJ *b;
3232: PetscLayoutSetUp(B->rmap);
3233: PetscLayoutSetUp(B->cmap);
3234: b = (Mat_MPIAIJ*)B->data;
3236: if (!B->preallocated) {
3237: /* Explicitly create 2 MATSEQAIJ matrices. */
3238: MatCreate(PETSC_COMM_SELF,&b->A);
3239: MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
3240: MatSetBlockSizesFromMats(b->A,B,B);
3241: MatSetType(b->A,MATSEQAIJ);
3242: PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
3243: MatCreate(PETSC_COMM_SELF,&b->B);
3244: MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
3245: MatSetBlockSizesFromMats(b->B,B,B);
3246: MatSetType(b->B,MATSEQAIJ);
3247: PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);
3248: }
3250: MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
3251: MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
3252: B->preallocated = PETSC_TRUE;
3253: return(0);
3254: }
3258: PetscErrorCode MatDuplicate_MPIAIJ(Mat matin,MatDuplicateOption cpvalues,Mat *newmat)
3259: {
3260: Mat mat;
3261: Mat_MPIAIJ *a,*oldmat = (Mat_MPIAIJ*)matin->data;
3265: *newmat = 0;
3266: MatCreate(PetscObjectComm((PetscObject)matin),&mat);
3267: MatSetSizes(mat,matin->rmap->n,matin->cmap->n,matin->rmap->N,matin->cmap->N);
3268: MatSetBlockSizesFromMats(mat,matin,matin);
3269: MatSetType(mat,((PetscObject)matin)->type_name);
3270: PetscMemcpy(mat->ops,matin->ops,sizeof(struct _MatOps));
3271: a = (Mat_MPIAIJ*)mat->data;
3273: mat->factortype = matin->factortype;
3274: mat->assembled = PETSC_TRUE;
3275: mat->insertmode = NOT_SET_VALUES;
3276: mat->preallocated = PETSC_TRUE;
3278: a->size = oldmat->size;
3279: a->rank = oldmat->rank;
3280: a->donotstash = oldmat->donotstash;
3281: a->roworiented = oldmat->roworiented;
3282: a->rowindices = 0;
3283: a->rowvalues = 0;
3284: a->getrowactive = PETSC_FALSE;
3286: PetscLayoutReference(matin->rmap,&mat->rmap);
3287: PetscLayoutReference(matin->cmap,&mat->cmap);
3289: if (oldmat->colmap) {
3290: #if defined(PETSC_USE_CTABLE)
3291: PetscTableCreateCopy(oldmat->colmap,&a->colmap);
3292: #else
3293: PetscMalloc1((mat->cmap->N),&a->colmap);
3294: PetscLogObjectMemory((PetscObject)mat,(mat->cmap->N)*sizeof(PetscInt));
3295: PetscMemcpy(a->colmap,oldmat->colmap,(mat->cmap->N)*sizeof(PetscInt));
3296: #endif
3297: } else a->colmap = 0;
3298: if (oldmat->garray) {
3299: PetscInt len;
3300: len = oldmat->B->cmap->n;
3301: PetscMalloc1((len+1),&a->garray);
3302: PetscLogObjectMemory((PetscObject)mat,len*sizeof(PetscInt));
3303: if (len) { PetscMemcpy(a->garray,oldmat->garray,len*sizeof(PetscInt)); }
3304: } else a->garray = 0;
3306: VecDuplicate(oldmat->lvec,&a->lvec);
3307: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->lvec);
3308: VecScatterCopy(oldmat->Mvctx,&a->Mvctx);
3309: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->Mvctx);
3310: MatDuplicate(oldmat->A,cpvalues,&a->A);
3311: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->A);
3312: MatDuplicate(oldmat->B,cpvalues,&a->B);
3313: PetscLogObjectParent((PetscObject)mat,(PetscObject)a->B);
3314: PetscFunctionListDuplicate(((PetscObject)matin)->qlist,&((PetscObject)mat)->qlist);
3315: *newmat = mat;
3316: return(0);
3317: }
3323: PetscErrorCode MatLoad_MPIAIJ(Mat newMat, PetscViewer viewer)
3324: {
3325: PetscScalar *vals,*svals;
3326: MPI_Comm comm;
3328: PetscMPIInt rank,size,tag = ((PetscObject)viewer)->tag;
3329: PetscInt i,nz,j,rstart,rend,mmax,maxnz = 0,grows,gcols;
3330: PetscInt header[4],*rowlengths = 0,M,N,m,*cols;
3331: PetscInt *ourlens = NULL,*procsnz = NULL,*offlens = NULL,jj,*mycols,*smycols;
3332: PetscInt cend,cstart,n,*rowners,sizesset=1;
3333: int fd;
3334: PetscInt bs = 1;
3337: PetscObjectGetComm((PetscObject)viewer,&comm);
3338: MPI_Comm_size(comm,&size);
3339: MPI_Comm_rank(comm,&rank);
3340: if (!rank) {
3341: PetscViewerBinaryGetDescriptor(viewer,&fd);
3342: PetscBinaryRead(fd,(char*)header,4,PETSC_INT);
3343: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object");
3344: }
3346: PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
3347: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
3348: PetscOptionsEnd();
3350: if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) sizesset = 0;
3352: MPI_Bcast(header+1,3,MPIU_INT,0,comm);
3353: M = header[1]; N = header[2];
3354: /* If global rows/cols are set to PETSC_DECIDE, set it to the sizes given in the file */
3355: if (sizesset && newMat->rmap->N < 0) newMat->rmap->N = M;
3356: if (sizesset && newMat->cmap->N < 0) newMat->cmap->N = N;
3358: /* If global sizes are set, check if they are consistent with that given in the file */
3359: if (sizesset) {
3360: MatGetSize(newMat,&grows,&gcols);
3361: }
3362: if (sizesset && newMat->rmap->N != grows) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows:Matrix in file has (%d) and input matrix has (%d)",M,grows);
3363: if (sizesset && newMat->cmap->N != gcols) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of cols:Matrix in file has (%d) and input matrix has (%d)",N,gcols);
3365: /* determine ownership of all (block) rows */
3366: if (M%bs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Inconsistent # of rows (%d) and block size (%d)",M,bs);
3367: if (newMat->rmap->n < 0) m = bs*((M/bs)/size + (((M/bs) % size) > rank)); /* PETSC_DECIDE */
3368: else m = newMat->rmap->n; /* Set by user */
3370: PetscMalloc1((size+1),&rowners);
3371: MPI_Allgather(&m,1,MPIU_INT,rowners+1,1,MPIU_INT,comm);
3373: /* First process needs enough room for process with most rows */
3374: if (!rank) {
3375: mmax = rowners[1];
3376: for (i=2; i<=size; i++) {
3377: mmax = PetscMax(mmax, rowners[i]);
3378: }
3379: } else mmax = -1; /* unused, but compilers complain */
3381: rowners[0] = 0;
3382: for (i=2; i<=size; i++) {
3383: rowners[i] += rowners[i-1];
3384: }
3385: rstart = rowners[rank];
3386: rend = rowners[rank+1];
3388: /* distribute row lengths to all processors */
3389: PetscMalloc2(m,&ourlens,m,&offlens);
3390: if (!rank) {
3391: PetscBinaryRead(fd,ourlens,m,PETSC_INT);
3392: PetscMalloc1(mmax,&rowlengths);
3393: PetscCalloc1(size,&procsnz);
3394: for (j=0; j<m; j++) {
3395: procsnz[0] += ourlens[j];
3396: }
3397: for (i=1; i<size; i++) {
3398: PetscBinaryRead(fd,rowlengths,rowners[i+1]-rowners[i],PETSC_INT);
3399: /* calculate the number of nonzeros on each processor */
3400: for (j=0; j<rowners[i+1]-rowners[i]; j++) {
3401: procsnz[i] += rowlengths[j];
3402: }
3403: MPIULong_Send(rowlengths,rowners[i+1]-rowners[i],MPIU_INT,i,tag,comm);
3404: }
3405: PetscFree(rowlengths);
3406: } else {
3407: MPIULong_Recv(ourlens,m,MPIU_INT,0,tag,comm);
3408: }
3410: if (!rank) {
3411: /* determine max buffer needed and allocate it */
3412: maxnz = 0;
3413: for (i=0; i<size; i++) {
3414: maxnz = PetscMax(maxnz,procsnz[i]);
3415: }
3416: PetscMalloc1(maxnz,&cols);
3418: /* read in my part of the matrix column indices */
3419: nz = procsnz[0];
3420: PetscMalloc1(nz,&mycols);
3421: PetscBinaryRead(fd,mycols,nz,PETSC_INT);
3423: /* read in every one elses and ship off */
3424: for (i=1; i<size; i++) {
3425: nz = procsnz[i];
3426: PetscBinaryRead(fd,cols,nz,PETSC_INT);
3427: MPIULong_Send(cols,nz,MPIU_INT,i,tag,comm);
3428: }
3429: PetscFree(cols);
3430: } else {
3431: /* determine buffer space needed for message */
3432: nz = 0;
3433: for (i=0; i<m; i++) {
3434: nz += ourlens[i];
3435: }
3436: PetscMalloc1(nz,&mycols);
3438: /* receive message of column indices*/
3439: MPIULong_Recv(mycols,nz,MPIU_INT,0,tag,comm);
3440: }
3442: /* determine column ownership if matrix is not square */
3443: if (N != M) {
3444: if (newMat->cmap->n < 0) n = N/size + ((N % size) > rank);
3445: else n = newMat->cmap->n;
3446: MPI_Scan(&n,&cend,1,MPIU_INT,MPI_SUM,comm);
3447: cstart = cend - n;
3448: } else {
3449: cstart = rstart;
3450: cend = rend;
3451: n = cend - cstart;
3452: }
3454: /* loop over local rows, determining number of off diagonal entries */
3455: PetscMemzero(offlens,m*sizeof(PetscInt));
3456: jj = 0;
3457: for (i=0; i<m; i++) {
3458: for (j=0; j<ourlens[i]; j++) {
3459: if (mycols[jj] < cstart || mycols[jj] >= cend) offlens[i]++;
3460: jj++;
3461: }
3462: }
3464: for (i=0; i<m; i++) {
3465: ourlens[i] -= offlens[i];
3466: }
3467: if (!sizesset) {
3468: MatSetSizes(newMat,m,n,M,N);
3469: }
3471: if (bs > 1) {MatSetBlockSize(newMat,bs);}
3473: MatMPIAIJSetPreallocation(newMat,0,ourlens,0,offlens);
3475: for (i=0; i<m; i++) {
3476: ourlens[i] += offlens[i];
3477: }
3479: if (!rank) {
3480: PetscMalloc1((maxnz+1),&vals);
3482: /* read in my part of the matrix numerical values */
3483: nz = procsnz[0];
3484: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
3486: /* insert into matrix */
3487: jj = rstart;
3488: smycols = mycols;
3489: svals = vals;
3490: for (i=0; i<m; i++) {
3491: MatSetValues_MPIAIJ(newMat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
3492: smycols += ourlens[i];
3493: svals += ourlens[i];
3494: jj++;
3495: }
3497: /* read in other processors and ship out */
3498: for (i=1; i<size; i++) {
3499: nz = procsnz[i];
3500: PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);
3501: MPIULong_Send(vals,nz,MPIU_SCALAR,i,((PetscObject)newMat)->tag,comm);
3502: }
3503: PetscFree(procsnz);
3504: } else {
3505: /* receive numeric values */
3506: PetscMalloc1((nz+1),&vals);
3508: /* receive message of values*/
3509: MPIULong_Recv(vals,nz,MPIU_SCALAR,0,((PetscObject)newMat)->tag,comm);
3511: /* insert into matrix */
3512: jj = rstart;
3513: smycols = mycols;
3514: svals = vals;
3515: for (i=0; i<m; i++) {
3516: MatSetValues_MPIAIJ(newMat,1,&jj,ourlens[i],smycols,svals,INSERT_VALUES);
3517: smycols += ourlens[i];
3518: svals += ourlens[i];
3519: jj++;
3520: }
3521: }
3522: PetscFree2(ourlens,offlens);
3523: PetscFree(vals);
3524: PetscFree(mycols);
3525: PetscFree(rowners);
3526: MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
3527: MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
3528: return(0);
3529: }
3533: PetscErrorCode MatGetSubMatrix_MPIAIJ(Mat mat,IS isrow,IS iscol,MatReuse call,Mat *newmat)
3534: {
3536: IS iscol_local;
3537: PetscInt csize;
3540: ISGetLocalSize(iscol,&csize);
3541: if (call == MAT_REUSE_MATRIX) {
3542: PetscObjectQuery((PetscObject)*newmat,"ISAllGather",(PetscObject*)&iscol_local);
3543: if (!iscol_local) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
3544: } else {
3545: PetscInt cbs;
3546: ISGetBlockSize(iscol,&cbs);
3547: ISAllGather(iscol,&iscol_local);
3548: ISSetBlockSize(iscol_local,cbs);
3549: }
3550: MatGetSubMatrix_MPIAIJ_Private(mat,isrow,iscol_local,csize,call,newmat);
3551: if (call == MAT_INITIAL_MATRIX) {
3552: PetscObjectCompose((PetscObject)*newmat,"ISAllGather",(PetscObject)iscol_local);
3553: ISDestroy(&iscol_local);
3554: }
3555: return(0);
3556: }
3558: extern PetscErrorCode MatGetSubMatrices_MPIAIJ_Local(Mat,PetscInt,const IS[],const IS[],MatReuse,PetscBool*,Mat*);
3561: /*
3562: Not great since it makes two copies of the submatrix, first an SeqAIJ
3563: in local and then by concatenating the local matrices the end result.
3564: Writing it directly would be much like MatGetSubMatrices_MPIAIJ()
3566: Note: This requires a sequential iscol with all indices.
3567: */
3568: PetscErrorCode MatGetSubMatrix_MPIAIJ_Private(Mat mat,IS isrow,IS iscol,PetscInt csize,MatReuse call,Mat *newmat)
3569: {
3571: PetscMPIInt rank,size;
3572: PetscInt i,m,n,rstart,row,rend,nz,*cwork,j,bs,cbs;
3573: PetscInt *ii,*jj,nlocal,*dlens,*olens,dlen,olen,jend,mglobal,ncol;
3574: PetscBool allcolumns, colflag;
3575: Mat M,Mreuse;
3576: MatScalar *vwork,*aa;
3577: MPI_Comm comm;
3578: Mat_SeqAIJ *aij;
3581: PetscObjectGetComm((PetscObject)mat,&comm);
3582: MPI_Comm_rank(comm,&rank);
3583: MPI_Comm_size(comm,&size);
3585: ISIdentity(iscol,&colflag);
3586: ISGetLocalSize(iscol,&ncol);
3587: if (colflag && ncol == mat->cmap->N) {
3588: allcolumns = PETSC_TRUE;
3589: } else {
3590: allcolumns = PETSC_FALSE;
3591: }
3592: if (call == MAT_REUSE_MATRIX) {
3593: PetscObjectQuery((PetscObject)*newmat,"SubMatrix",(PetscObject*)&Mreuse);
3594: if (!Mreuse) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Submatrix passed in was not used before, cannot reuse");
3595: MatGetSubMatrices_MPIAIJ_Local(mat,1,&isrow,&iscol,MAT_REUSE_MATRIX,&allcolumns,&Mreuse);
3596: } else {
3597: MatGetSubMatrices_MPIAIJ_Local(mat,1,&isrow,&iscol,MAT_INITIAL_MATRIX,&allcolumns,&Mreuse);
3598: }
3600: /*
3601: m - number of local rows
3602: n - number of columns (same on all processors)
3603: rstart - first row in new global matrix generated
3604: */
3605: MatGetSize(Mreuse,&m,&n);
3606: MatGetBlockSizes(Mreuse,&bs,&cbs);
3607: if (call == MAT_INITIAL_MATRIX) {
3608: aij = (Mat_SeqAIJ*)(Mreuse)->data;
3609: ii = aij->i;
3610: jj = aij->j;
3612: /*
3613: Determine the number of non-zeros in the diagonal and off-diagonal
3614: portions of the matrix in order to do correct preallocation
3615: */
3617: /* first get start and end of "diagonal" columns */
3618: if (csize == PETSC_DECIDE) {
3619: ISGetSize(isrow,&mglobal);
3620: if (mglobal == n) { /* square matrix */
3621: nlocal = m;
3622: } else {
3623: nlocal = n/size + ((n % size) > rank);
3624: }
3625: } else {
3626: nlocal = csize;
3627: }
3628: MPI_Scan(&nlocal,&rend,1,MPIU_INT,MPI_SUM,comm);
3629: rstart = rend - nlocal;
3630: 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);
3632: /* next, compute all the lengths */
3633: PetscMalloc1((2*m+1),&dlens);
3634: olens = dlens + m;
3635: for (i=0; i<m; i++) {
3636: jend = ii[i+1] - ii[i];
3637: olen = 0;
3638: dlen = 0;
3639: for (j=0; j<jend; j++) {
3640: if (*jj < rstart || *jj >= rend) olen++;
3641: else dlen++;
3642: jj++;
3643: }
3644: olens[i] = olen;
3645: dlens[i] = dlen;
3646: }
3647: MatCreate(comm,&M);
3648: MatSetSizes(M,m,nlocal,PETSC_DECIDE,n);
3649: MatSetBlockSizes(M,bs,cbs);
3650: MatSetType(M,((PetscObject)mat)->type_name);
3651: MatMPIAIJSetPreallocation(M,0,dlens,0,olens);
3652: PetscFree(dlens);
3653: } else {
3654: PetscInt ml,nl;
3656: M = *newmat;
3657: MatGetLocalSize(M,&ml,&nl);
3658: if (ml != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Previous matrix must be same size/layout as request");
3659: MatZeroEntries(M);
3660: /*
3661: The next two lines are needed so we may call MatSetValues_MPIAIJ() below directly,
3662: rather than the slower MatSetValues().
3663: */
3664: M->was_assembled = PETSC_TRUE;
3665: M->assembled = PETSC_FALSE;
3666: }
3667: MatGetOwnershipRange(M,&rstart,&rend);
3668: aij = (Mat_SeqAIJ*)(Mreuse)->data;
3669: ii = aij->i;
3670: jj = aij->j;
3671: aa = aij->a;
3672: for (i=0; i<m; i++) {
3673: row = rstart + i;
3674: nz = ii[i+1] - ii[i];
3675: cwork = jj; jj += nz;
3676: vwork = aa; aa += nz;
3677: MatSetValues_MPIAIJ(M,1,&row,nz,cwork,vwork,INSERT_VALUES);
3678: }
3680: MatAssemblyBegin(M,MAT_FINAL_ASSEMBLY);
3681: MatAssemblyEnd(M,MAT_FINAL_ASSEMBLY);
3682: *newmat = M;
3684: /* save submatrix used in processor for next request */
3685: if (call == MAT_INITIAL_MATRIX) {
3686: PetscObjectCompose((PetscObject)M,"SubMatrix",(PetscObject)Mreuse);
3687: MatDestroy(&Mreuse);
3688: }
3689: return(0);
3690: }
3694: PetscErrorCode MatMPIAIJSetPreallocationCSR_MPIAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3695: {
3696: PetscInt m,cstart, cend,j,nnz,i,d;
3697: PetscInt *d_nnz,*o_nnz,nnz_max = 0,rstart,ii;
3698: const PetscInt *JJ;
3699: PetscScalar *values;
3703: if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Ii[0] must be 0 it is %D",Ii[0]);
3705: PetscLayoutSetUp(B->rmap);
3706: PetscLayoutSetUp(B->cmap);
3707: m = B->rmap->n;
3708: cstart = B->cmap->rstart;
3709: cend = B->cmap->rend;
3710: rstart = B->rmap->rstart;
3712: PetscMalloc2(m,&d_nnz,m,&o_nnz);
3714: #if defined(PETSC_USE_DEBUGGING)
3715: for (i=0; i<m; i++) {
3716: nnz = Ii[i+1]- Ii[i];
3717: JJ = J + Ii[i];
3718: if (nnz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Local row %D has a negative %D number of columns",i,nnz);
3719: if (nnz && (JJ[0] < 0)) SETERRRQ1(PETSC_ERR_ARG_WRONGSTATE,"Row %D starts with negative column index",i,j);
3720: 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);
3721: }
3722: #endif
3724: for (i=0; i<m; i++) {
3725: nnz = Ii[i+1]- Ii[i];
3726: JJ = J + Ii[i];
3727: nnz_max = PetscMax(nnz_max,nnz);
3728: d = 0;
3729: for (j=0; j<nnz; j++) {
3730: if (cstart <= JJ[j] && JJ[j] < cend) d++;
3731: }
3732: d_nnz[i] = d;
3733: o_nnz[i] = nnz - d;
3734: }
3735: MatMPIAIJSetPreallocation(B,0,d_nnz,0,o_nnz);
3736: PetscFree2(d_nnz,o_nnz);
3738: if (v) values = (PetscScalar*)v;
3739: else {
3740: PetscCalloc1((nnz_max+1),&values);
3741: }
3743: for (i=0; i<m; i++) {
3744: ii = i + rstart;
3745: nnz = Ii[i+1]- Ii[i];
3746: MatSetValues_MPIAIJ(B,1,&ii,nnz,J+Ii[i],values+(v ? Ii[i] : 0),INSERT_VALUES);
3747: }
3748: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3749: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3751: if (!v) {
3752: PetscFree(values);
3753: }
3754: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3755: return(0);
3756: }
3760: /*@
3761: MatMPIAIJSetPreallocationCSR - Allocates memory for a sparse parallel matrix in AIJ format
3762: (the default parallel PETSc format).
3764: Collective on MPI_Comm
3766: Input Parameters:
3767: + B - the matrix
3768: . i - the indices into j for the start of each local row (starts with zero)
3769: . j - the column indices for each local row (starts with zero)
3770: - v - optional values in the matrix
3772: Level: developer
3774: Notes:
3775: The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3776: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3777: called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.
3779: The i and j indices are 0 based, and i indices are indices corresponding to the local j array.
3781: The format which is used for the sparse matrix input, is equivalent to a
3782: row-major ordering.. i.e for the following matrix, the input data expected is
3783: as shown:
3785: 1 0 0
3786: 2 0 3 P0
3787: -------
3788: 4 5 6 P1
3790: Process0 [P0]: rows_owned=[0,1]
3791: i = {0,1,3} [size = nrow+1 = 2+1]
3792: j = {0,0,2} [size = nz = 6]
3793: v = {1,2,3} [size = nz = 6]
3795: Process1 [P1]: rows_owned=[2]
3796: i = {0,3} [size = nrow+1 = 1+1]
3797: j = {0,1,2} [size = nz = 6]
3798: v = {4,5,6} [size = nz = 6]
3800: .keywords: matrix, aij, compressed row, sparse, parallel
3802: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatCreateAIJ(), MPIAIJ,
3803: MatCreateSeqAIJWithArrays(), MatCreateMPIAIJWithSplitArrays()
3804: @*/
3805: PetscErrorCode MatMPIAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
3806: {
3810: PetscTryMethod(B,"MatMPIAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3811: return(0);
3812: }
3816: /*@C
3817: MatMPIAIJSetPreallocation - Preallocates memory for a sparse parallel matrix in AIJ format
3818: (the default parallel PETSc format). For good matrix assembly performance
3819: the user should preallocate the matrix storage by setting the parameters
3820: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
3821: performance can be increased by more than a factor of 50.
3823: Collective on MPI_Comm
3825: Input Parameters:
3826: + B - the matrix
3827: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
3828: (same value is used for all local rows)
3829: . d_nnz - array containing the number of nonzeros in the various rows of the
3830: DIAGONAL portion of the local submatrix (possibly different for each row)
3831: or NULL, if d_nz is used to specify the nonzero structure.
3832: The size of this array is equal to the number of local rows, i.e 'm'.
3833: For matrices that will be factored, you must leave room for (and set)
3834: the diagonal entry even if it is zero.
3835: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
3836: submatrix (same value is used for all local rows).
3837: - o_nnz - array containing the number of nonzeros in the various rows of the
3838: OFF-DIAGONAL portion of the local submatrix (possibly different for
3839: each row) or NULL, if o_nz is used to specify the nonzero
3840: structure. The size of this array is equal to the number
3841: of local rows, i.e 'm'.
3843: If the *_nnz parameter is given then the *_nz parameter is ignored
3845: The AIJ format (also called the Yale sparse matrix format or
3846: compressed row storage (CSR)), is fully compatible with standard Fortran 77
3847: storage. The stored row and column indices begin with zero.
3848: See Users-Manual: ch_mat for details.
3850: The parallel matrix is partitioned such that the first m0 rows belong to
3851: process 0, the next m1 rows belong to process 1, the next m2 rows belong
3852: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
3854: The DIAGONAL portion of the local submatrix of a processor can be defined
3855: as the submatrix which is obtained by extraction the part corresponding to
3856: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
3857: first row that belongs to the processor, r2 is the last row belonging to
3858: the this processor, and c1-c2 is range of indices of the local part of a
3859: vector suitable for applying the matrix to. This is an mxn matrix. In the
3860: common case of a square matrix, the row and column ranges are the same and
3861: the DIAGONAL part is also square. The remaining portion of the local
3862: submatrix (mxN) constitute the OFF-DIAGONAL portion.
3864: If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.
3866: You can call MatGetInfo() to get information on how effective the preallocation was;
3867: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3868: You can also run with the option -info and look for messages with the string
3869: malloc in them to see if additional memory allocation was needed.
3871: Example usage:
3873: Consider the following 8x8 matrix with 34 non-zero values, that is
3874: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
3875: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
3876: as follows:
3878: .vb
3879: 1 2 0 | 0 3 0 | 0 4
3880: Proc0 0 5 6 | 7 0 0 | 8 0
3881: 9 0 10 | 11 0 0 | 12 0
3882: -------------------------------------
3883: 13 0 14 | 15 16 17 | 0 0
3884: Proc1 0 18 0 | 19 20 21 | 0 0
3885: 0 0 0 | 22 23 0 | 24 0
3886: -------------------------------------
3887: Proc2 25 26 27 | 0 0 28 | 29 0
3888: 30 0 0 | 31 32 33 | 0 34
3889: .ve
3891: This can be represented as a collection of submatrices as:
3893: .vb
3894: A B C
3895: D E F
3896: G H I
3897: .ve
3899: Where the submatrices A,B,C are owned by proc0, D,E,F are
3900: owned by proc1, G,H,I are owned by proc2.
3902: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3903: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
3904: The 'M','N' parameters are 8,8, and have the same values on all procs.
3906: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
3907: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
3908: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
3909: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
3910: part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
3911: matrix, ans [DF] as another SeqAIJ matrix.
3913: When d_nz, o_nz parameters are specified, d_nz storage elements are
3914: allocated for every row of the local diagonal submatrix, and o_nz
3915: storage locations are allocated for every row of the OFF-DIAGONAL submat.
3916: One way to choose d_nz and o_nz is to use the max nonzerors per local
3917: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
3918: In this case, the values of d_nz,o_nz are:
3919: .vb
3920: proc0 : dnz = 2, o_nz = 2
3921: proc1 : dnz = 3, o_nz = 2
3922: proc2 : dnz = 1, o_nz = 4
3923: .ve
3924: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
3925: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
3926: for proc3. i.e we are using 12+15+10=37 storage locations to store
3927: 34 values.
3929: When d_nnz, o_nnz parameters are specified, the storage is specified
3930: for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
3931: In the above case the values for d_nnz,o_nnz are:
3932: .vb
3933: proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
3934: proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
3935: proc2: d_nnz = [1,1] and o_nnz = [4,4]
3936: .ve
3937: Here the space allocated is sum of all the above values i.e 34, and
3938: hence pre-allocation is perfect.
3940: Level: intermediate
3942: .keywords: matrix, aij, compressed row, sparse, parallel
3944: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateAIJ(), MatMPIAIJSetPreallocationCSR(),
3945: MPIAIJ, MatGetInfo(), PetscSplitOwnership()
3946: @*/
3947: PetscErrorCode MatMPIAIJSetPreallocation(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
3948: {
3954: PetscTryMethod(B,"MatMPIAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[]),(B,d_nz,d_nnz,o_nz,o_nnz));
3955: return(0);
3956: }
3960: /*@
3961: MatCreateMPIAIJWithArrays - creates a MPI AIJ matrix using arrays that contain in standard
3962: CSR format the local rows.
3964: Collective on MPI_Comm
3966: Input Parameters:
3967: + comm - MPI communicator
3968: . m - number of local rows (Cannot be PETSC_DECIDE)
3969: . n - This value should be the same as the local size used in creating the
3970: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
3971: calculated if N is given) For square matrices n is almost always m.
3972: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
3973: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
3974: . i - row indices
3975: . j - column indices
3976: - a - matrix values
3978: Output Parameter:
3979: . mat - the matrix
3981: Level: intermediate
3983: Notes:
3984: The i, j, and a arrays ARE copied by this routine into the internal format used by PETSc;
3985: thus you CANNOT change the matrix entries by changing the values of a[] after you have
3986: called this routine. Use MatCreateMPIAIJWithSplitArrays() to avoid needing to copy the arrays.
3988: The i and j indices are 0 based, and i indices are indices corresponding to the local j array.
3990: The format which is used for the sparse matrix input, is equivalent to a
3991: row-major ordering.. i.e for the following matrix, the input data expected is
3992: as shown:
3994: 1 0 0
3995: 2 0 3 P0
3996: -------
3997: 4 5 6 P1
3999: Process0 [P0]: rows_owned=[0,1]
4000: i = {0,1,3} [size = nrow+1 = 2+1]
4001: j = {0,0,2} [size = nz = 6]
4002: v = {1,2,3} [size = nz = 6]
4004: Process1 [P1]: rows_owned=[2]
4005: i = {0,3} [size = nrow+1 = 1+1]
4006: j = {0,1,2} [size = nz = 6]
4007: v = {4,5,6} [size = nz = 6]
4009: .keywords: matrix, aij, compressed row, sparse, parallel
4011: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
4012: MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithSplitArrays()
4013: @*/
4014: PetscErrorCode MatCreateMPIAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,const PetscInt i[],const PetscInt j[],const PetscScalar a[],Mat *mat)
4015: {
4019: if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4020: if (m < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
4021: MatCreate(comm,mat);
4022: MatSetSizes(*mat,m,n,M,N);
4023: /* MatSetBlockSizes(M,bs,cbs); */
4024: MatSetType(*mat,MATMPIAIJ);
4025: MatMPIAIJSetPreallocationCSR(*mat,i,j,a);
4026: return(0);
4027: }
4031: /*@C
4032: MatCreateAIJ - Creates a sparse parallel matrix in AIJ format
4033: (the default parallel PETSc format). For good matrix assembly performance
4034: the user should preallocate the matrix storage by setting the parameters
4035: d_nz (or d_nnz) and o_nz (or o_nnz). By setting these parameters accurately,
4036: performance can be increased by more than a factor of 50.
4038: Collective on MPI_Comm
4040: Input Parameters:
4041: + comm - MPI communicator
4042: . m - number of local rows (or PETSC_DECIDE to have calculated if M is given)
4043: This value should be the same as the local size used in creating the
4044: y vector for the matrix-vector product y = Ax.
4045: . n - This value should be the same as the local size used in creating the
4046: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
4047: calculated if N is given) For square matrices n is almost always m.
4048: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
4049: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
4050: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
4051: (same value is used for all local rows)
4052: . d_nnz - array containing the number of nonzeros in the various rows of the
4053: DIAGONAL portion of the local submatrix (possibly different for each row)
4054: or NULL, if d_nz is used to specify the nonzero structure.
4055: The size of this array is equal to the number of local rows, i.e 'm'.
4056: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
4057: submatrix (same value is used for all local rows).
4058: - o_nnz - array containing the number of nonzeros in the various rows of the
4059: OFF-DIAGONAL portion of the local submatrix (possibly different for
4060: each row) or NULL, if o_nz is used to specify the nonzero
4061: structure. The size of this array is equal to the number
4062: of local rows, i.e 'm'.
4064: Output Parameter:
4065: . A - the matrix
4067: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
4068: MatXXXXSetPreallocation() paradgm instead of this routine directly.
4069: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
4071: Notes:
4072: If the *_nnz parameter is given then the *_nz parameter is ignored
4074: m,n,M,N parameters specify the size of the matrix, and its partitioning across
4075: processors, while d_nz,d_nnz,o_nz,o_nnz parameters specify the approximate
4076: storage requirements for this matrix.
4078: If PETSC_DECIDE or PETSC_DETERMINE is used for a particular argument on one
4079: processor than it must be used on all processors that share the object for
4080: that argument.
4082: The user MUST specify either the local or global matrix dimensions
4083: (possibly both).
4085: The parallel matrix is partitioned across processors such that the
4086: first m0 rows belong to process 0, the next m1 rows belong to
4087: process 1, the next m2 rows belong to process 2 etc.. where
4088: m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
4089: values corresponding to [m x N] submatrix.
4091: The columns are logically partitioned with the n0 columns belonging
4092: to 0th partition, the next n1 columns belonging to the next
4093: partition etc.. where n0,n1,n2... are the the input parameter 'n'.
4095: The DIAGONAL portion of the local submatrix on any given processor
4096: is the submatrix corresponding to the rows and columns m,n
4097: corresponding to the given processor. i.e diagonal matrix on
4098: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
4099: etc. The remaining portion of the local submatrix [m x (N-n)]
4100: constitute the OFF-DIAGONAL portion. The example below better
4101: illustrates this concept.
4103: For a square global matrix we define each processor's diagonal portion
4104: to be its local rows and the corresponding columns (a square submatrix);
4105: each processor's off-diagonal portion encompasses the remainder of the
4106: local matrix (a rectangular submatrix).
4108: If o_nnz, d_nnz are specified, then o_nz, and d_nz are ignored.
4110: When calling this routine with a single process communicator, a matrix of
4111: type SEQAIJ is returned. If a matrix of type MPIAIJ is desired for this
4112: type of communicator, use the construction mechanism:
4113: MatCreate(...,&A); MatSetType(A,MATMPIAIJ); MatSetSizes(A, m,n,M,N); MatMPIAIJSetPreallocation(A,...);
4115: By default, this format uses inodes (identical nodes) when possible.
4116: We search for consecutive rows with the same nonzero structure, thereby
4117: reusing matrix information to achieve increased efficiency.
4119: Options Database Keys:
4120: + -mat_no_inode - Do not use inodes
4121: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4122: - -mat_aij_oneindex - Internally use indexing starting at 1
4123: rather than 0. Note that when calling MatSetValues(),
4124: the user still MUST index entries starting at 0!
4127: Example usage:
4129: Consider the following 8x8 matrix with 34 non-zero values, that is
4130: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
4131: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
4132: as follows:
4134: .vb
4135: 1 2 0 | 0 3 0 | 0 4
4136: Proc0 0 5 6 | 7 0 0 | 8 0
4137: 9 0 10 | 11 0 0 | 12 0
4138: -------------------------------------
4139: 13 0 14 | 15 16 17 | 0 0
4140: Proc1 0 18 0 | 19 20 21 | 0 0
4141: 0 0 0 | 22 23 0 | 24 0
4142: -------------------------------------
4143: Proc2 25 26 27 | 0 0 28 | 29 0
4144: 30 0 0 | 31 32 33 | 0 34
4145: .ve
4147: This can be represented as a collection of submatrices as:
4149: .vb
4150: A B C
4151: D E F
4152: G H I
4153: .ve
4155: Where the submatrices A,B,C are owned by proc0, D,E,F are
4156: owned by proc1, G,H,I are owned by proc2.
4158: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4159: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
4160: The 'M','N' parameters are 8,8, and have the same values on all procs.
4162: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
4163: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
4164: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
4165: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
4166: part as SeqAIJ matrices. for eg: proc1 will store [E] as a SeqAIJ
4167: matrix, ans [DF] as another SeqAIJ matrix.
4169: When d_nz, o_nz parameters are specified, d_nz storage elements are
4170: allocated for every row of the local diagonal submatrix, and o_nz
4171: storage locations are allocated for every row of the OFF-DIAGONAL submat.
4172: One way to choose d_nz and o_nz is to use the max nonzerors per local
4173: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
4174: In this case, the values of d_nz,o_nz are:
4175: .vb
4176: proc0 : dnz = 2, o_nz = 2
4177: proc1 : dnz = 3, o_nz = 2
4178: proc2 : dnz = 1, o_nz = 4
4179: .ve
4180: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
4181: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
4182: for proc3. i.e we are using 12+15+10=37 storage locations to store
4183: 34 values.
4185: When d_nnz, o_nnz parameters are specified, the storage is specified
4186: for every row, coresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
4187: In the above case the values for d_nnz,o_nnz are:
4188: .vb
4189: proc0: d_nnz = [2,2,2] and o_nnz = [2,2,2]
4190: proc1: d_nnz = [3,3,2] and o_nnz = [2,1,1]
4191: proc2: d_nnz = [1,1] and o_nnz = [4,4]
4192: .ve
4193: Here the space allocated is sum of all the above values i.e 34, and
4194: hence pre-allocation is perfect.
4196: Level: intermediate
4198: .keywords: matrix, aij, compressed row, sparse, parallel
4200: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
4201: MPIAIJ, MatCreateMPIAIJWithArrays()
4202: @*/
4203: 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)
4204: {
4206: PetscMPIInt size;
4209: MatCreate(comm,A);
4210: MatSetSizes(*A,m,n,M,N);
4211: MPI_Comm_size(comm,&size);
4212: if (size > 1) {
4213: MatSetType(*A,MATMPIAIJ);
4214: MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
4215: } else {
4216: MatSetType(*A,MATSEQAIJ);
4217: MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
4218: }
4219: return(0);
4220: }
4224: PetscErrorCode MatMPIAIJGetSeqAIJ(Mat A,Mat *Ad,Mat *Ao,const PetscInt *colmap[])
4225: {
4226: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
4229: if (Ad) *Ad = a->A;
4230: if (Ao) *Ao = a->B;
4231: if (colmap) *colmap = a->garray;
4232: return(0);
4233: }
4237: PetscErrorCode MatSetColoring_MPIAIJ(Mat A,ISColoring coloring)
4238: {
4240: PetscInt i;
4241: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
4244: if (coloring->ctype == IS_COLORING_GLOBAL) {
4245: ISColoringValue *allcolors,*colors;
4246: ISColoring ocoloring;
4248: /* set coloring for diagonal portion */
4249: MatSetColoring_SeqAIJ(a->A,coloring);
4251: /* set coloring for off-diagonal portion */
4252: ISAllGatherColors(PetscObjectComm((PetscObject)A),coloring->n,coloring->colors,NULL,&allcolors);
4253: PetscMalloc1((a->B->cmap->n+1),&colors);
4254: for (i=0; i<a->B->cmap->n; i++) {
4255: colors[i] = allcolors[a->garray[i]];
4256: }
4257: PetscFree(allcolors);
4258: ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap->n,colors,&ocoloring);
4259: MatSetColoring_SeqAIJ(a->B,ocoloring);
4260: ISColoringDestroy(&ocoloring);
4261: } else if (coloring->ctype == IS_COLORING_GHOSTED) {
4262: ISColoringValue *colors;
4263: PetscInt *larray;
4264: ISColoring ocoloring;
4266: /* set coloring for diagonal portion */
4267: PetscMalloc1((a->A->cmap->n+1),&larray);
4268: for (i=0; i<a->A->cmap->n; i++) {
4269: larray[i] = i + A->cmap->rstart;
4270: }
4271: ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,a->A->cmap->n,larray,NULL,larray);
4272: PetscMalloc1((a->A->cmap->n+1),&colors);
4273: for (i=0; i<a->A->cmap->n; i++) {
4274: colors[i] = coloring->colors[larray[i]];
4275: }
4276: PetscFree(larray);
4277: ISColoringCreate(PETSC_COMM_SELF,coloring->n,a->A->cmap->n,colors,&ocoloring);
4278: MatSetColoring_SeqAIJ(a->A,ocoloring);
4279: ISColoringDestroy(&ocoloring);
4281: /* set coloring for off-diagonal portion */
4282: PetscMalloc1((a->B->cmap->n+1),&larray);
4283: ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,a->B->cmap->n,a->garray,NULL,larray);
4284: PetscMalloc1((a->B->cmap->n+1),&colors);
4285: for (i=0; i<a->B->cmap->n; i++) {
4286: colors[i] = coloring->colors[larray[i]];
4287: }
4288: PetscFree(larray);
4289: ISColoringCreate(MPI_COMM_SELF,coloring->n,a->B->cmap->n,colors,&ocoloring);
4290: MatSetColoring_SeqAIJ(a->B,ocoloring);
4291: ISColoringDestroy(&ocoloring);
4292: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support ISColoringType %d",(int)coloring->ctype);
4293: return(0);
4294: }
4298: PetscErrorCode MatSetValuesAdifor_MPIAIJ(Mat A,PetscInt nl,void *advalues)
4299: {
4300: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
4304: MatSetValuesAdifor_SeqAIJ(a->A,nl,advalues);
4305: MatSetValuesAdifor_SeqAIJ(a->B,nl,advalues);
4306: return(0);
4307: }
4311: PetscErrorCode MatCreateMPIAIJConcatenateSeqAIJSymbolic(MPI_Comm comm,Mat inmat,PetscInt n,Mat *outmat)
4312: {
4314: PetscInt m,N,i,rstart,nnz,*dnz,*onz,sum,bs,cbs;
4315: PetscInt *indx;
4318: /* This routine will ONLY return MPIAIJ type matrix */
4319: MatGetSize(inmat,&m,&N);
4320: MatGetBlockSizes(inmat,&bs,&cbs);
4321: if (n == PETSC_DECIDE) {
4322: PetscSplitOwnership(comm,&n,&N);
4323: }
4324: /* Check sum(n) = N */
4325: MPI_Allreduce(&n,&sum,1,MPIU_INT,MPI_SUM,comm);
4326: if (sum != N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Sum of local columns != global columns %d",N);
4328: MPI_Scan(&m, &rstart,1,MPIU_INT,MPI_SUM,comm);
4329: rstart -= m;
4331: MatPreallocateInitialize(comm,m,n,dnz,onz);
4332: for (i=0; i<m; i++) {
4333: MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,NULL);
4334: MatPreallocateSet(i+rstart,nnz,indx,dnz,onz);
4335: MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,NULL);
4336: }
4338: MatCreate(comm,outmat);
4339: MatSetSizes(*outmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
4340: MatSetBlockSizes(*outmat,bs,cbs);
4341: MatSetType(*outmat,MATMPIAIJ);
4342: MatMPIAIJSetPreallocation(*outmat,0,dnz,0,onz);
4343: MatPreallocateFinalize(dnz,onz);
4344: return(0);
4345: }
4349: PetscErrorCode MatCreateMPIAIJConcatenateSeqAIJNumeric(MPI_Comm comm,Mat inmat,PetscInt n,Mat outmat)
4350: {
4352: PetscInt m,N,i,rstart,nnz,Ii;
4353: PetscInt *indx;
4354: PetscScalar *values;
4357: MatGetSize(inmat,&m,&N);
4358: MatGetOwnershipRange(outmat,&rstart,NULL);
4359: for (i=0; i<m; i++) {
4360: MatGetRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
4361: Ii = i + rstart;
4362: MatSetValues(outmat,1,&Ii,nnz,indx,values,INSERT_VALUES);
4363: MatRestoreRow_SeqAIJ(inmat,i,&nnz,&indx,&values);
4364: }
4365: MatAssemblyBegin(outmat,MAT_FINAL_ASSEMBLY);
4366: MatAssemblyEnd(outmat,MAT_FINAL_ASSEMBLY);
4367: return(0);
4368: }
4372: /*@
4373: MatCreateMPIAIJConcatenateSeqAIJ - Creates a single large PETSc matrix by concatenating sequential
4374: matrices from each processor
4376: Collective on MPI_Comm
4378: Input Parameters:
4379: + comm - the communicators the parallel matrix will live on
4380: . inmat - the input sequential matrices
4381: . n - number of local columns (or PETSC_DECIDE)
4382: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4384: Output Parameter:
4385: . outmat - the parallel matrix generated
4387: Level: advanced
4389: Notes: The number of columns of the matrix in EACH processor MUST be the same.
4391: @*/
4392: PetscErrorCode MatCreateMPIAIJConcatenateSeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4393: {
4395: PetscMPIInt size;
4398: MPI_Comm_size(comm,&size);
4399: PetscLogEventBegin(MAT_Merge,inmat,0,0,0);
4400: if (size == 1) {
4401: if (scall == MAT_INITIAL_MATRIX) {
4402: MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
4403: } else {
4404: MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
4405: }
4406: } else {
4407: if (scall == MAT_INITIAL_MATRIX) {
4408: MatCreateMPIAIJConcatenateSeqAIJSymbolic(comm,inmat,n,outmat);
4409: }
4410: MatCreateMPIAIJConcatenateSeqAIJNumeric(comm,inmat,n,*outmat);
4411: }
4412: PetscLogEventEnd(MAT_Merge,inmat,0,0,0);
4413: return(0);
4414: }
4418: PetscErrorCode MatFileSplit(Mat A,char *outfile)
4419: {
4420: PetscErrorCode ierr;
4421: PetscMPIInt rank;
4422: PetscInt m,N,i,rstart,nnz;
4423: size_t len;
4424: const PetscInt *indx;
4425: PetscViewer out;
4426: char *name;
4427: Mat B;
4428: const PetscScalar *values;
4431: MatGetLocalSize(A,&m,0);
4432: MatGetSize(A,0,&N);
4433: /* Should this be the type of the diagonal block of A? */
4434: MatCreate(PETSC_COMM_SELF,&B);
4435: MatSetSizes(B,m,N,m,N);
4436: MatSetBlockSizesFromMats(B,A,A);
4437: MatSetType(B,MATSEQAIJ);
4438: MatSeqAIJSetPreallocation(B,0,NULL);
4439: MatGetOwnershipRange(A,&rstart,0);
4440: for (i=0; i<m; i++) {
4441: MatGetRow(A,i+rstart,&nnz,&indx,&values);
4442: MatSetValues(B,1,&i,nnz,indx,values,INSERT_VALUES);
4443: MatRestoreRow(A,i+rstart,&nnz,&indx,&values);
4444: }
4445: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
4446: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
4448: MPI_Comm_rank(PetscObjectComm((PetscObject)A),&rank);
4449: PetscStrlen(outfile,&len);
4450: PetscMalloc1((len+5),&name);
4451: sprintf(name,"%s.%d",outfile,rank);
4452: PetscViewerBinaryOpen(PETSC_COMM_SELF,name,FILE_MODE_APPEND,&out);
4453: PetscFree(name);
4454: MatView(B,out);
4455: PetscViewerDestroy(&out);
4456: MatDestroy(&B);
4457: return(0);
4458: }
4460: extern PetscErrorCode MatDestroy_MPIAIJ(Mat);
4463: PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(Mat A)
4464: {
4465: PetscErrorCode ierr;
4466: Mat_Merge_SeqsToMPI *merge;
4467: PetscContainer container;
4470: PetscObjectQuery((PetscObject)A,"MatMergeSeqsToMPI",(PetscObject*)&container);
4471: if (container) {
4472: PetscContainerGetPointer(container,(void**)&merge);
4473: PetscFree(merge->id_r);
4474: PetscFree(merge->len_s);
4475: PetscFree(merge->len_r);
4476: PetscFree(merge->bi);
4477: PetscFree(merge->bj);
4478: PetscFree(merge->buf_ri[0]);
4479: PetscFree(merge->buf_ri);
4480: PetscFree(merge->buf_rj[0]);
4481: PetscFree(merge->buf_rj);
4482: PetscFree(merge->coi);
4483: PetscFree(merge->coj);
4484: PetscFree(merge->owners_co);
4485: PetscLayoutDestroy(&merge->rowmap);
4486: PetscFree(merge);
4487: PetscObjectCompose((PetscObject)A,"MatMergeSeqsToMPI",0);
4488: }
4489: MatDestroy_MPIAIJ(A);
4490: return(0);
4491: }
4493: #include <../src/mat/utils/freespace.h>
4494: #include <petscbt.h>
4498: PetscErrorCode MatCreateMPIAIJSumSeqAIJNumeric(Mat seqmat,Mat mpimat)
4499: {
4500: PetscErrorCode ierr;
4501: MPI_Comm comm;
4502: Mat_SeqAIJ *a =(Mat_SeqAIJ*)seqmat->data;
4503: PetscMPIInt size,rank,taga,*len_s;
4504: PetscInt N=mpimat->cmap->N,i,j,*owners,*ai=a->i,*aj;
4505: PetscInt proc,m;
4506: PetscInt **buf_ri,**buf_rj;
4507: PetscInt k,anzi,*bj_i,*bi,*bj,arow,bnzi,nextaj;
4508: PetscInt nrows,**buf_ri_k,**nextrow,**nextai;
4509: MPI_Request *s_waits,*r_waits;
4510: MPI_Status *status;
4511: MatScalar *aa=a->a;
4512: MatScalar **abuf_r,*ba_i;
4513: Mat_Merge_SeqsToMPI *merge;
4514: PetscContainer container;
4517: PetscObjectGetComm((PetscObject)mpimat,&comm);
4518: PetscLogEventBegin(MAT_Seqstompinum,seqmat,0,0,0);
4520: MPI_Comm_size(comm,&size);
4521: MPI_Comm_rank(comm,&rank);
4523: PetscObjectQuery((PetscObject)mpimat,"MatMergeSeqsToMPI",(PetscObject*)&container);
4524: PetscContainerGetPointer(container,(void**)&merge);
4526: bi = merge->bi;
4527: bj = merge->bj;
4528: buf_ri = merge->buf_ri;
4529: buf_rj = merge->buf_rj;
4531: PetscMalloc1(size,&status);
4532: owners = merge->rowmap->range;
4533: len_s = merge->len_s;
4535: /* send and recv matrix values */
4536: /*-----------------------------*/
4537: PetscObjectGetNewTag((PetscObject)mpimat,&taga);
4538: PetscPostIrecvScalar(comm,taga,merge->nrecv,merge->id_r,merge->len_r,&abuf_r,&r_waits);
4540: PetscMalloc1((merge->nsend+1),&s_waits);
4541: for (proc=0,k=0; proc<size; proc++) {
4542: if (!len_s[proc]) continue;
4543: i = owners[proc];
4544: MPI_Isend(aa+ai[i],len_s[proc],MPIU_MATSCALAR,proc,taga,comm,s_waits+k);
4545: k++;
4546: }
4548: if (merge->nrecv) {MPI_Waitall(merge->nrecv,r_waits,status);}
4549: if (merge->nsend) {MPI_Waitall(merge->nsend,s_waits,status);}
4550: PetscFree(status);
4552: PetscFree(s_waits);
4553: PetscFree(r_waits);
4555: /* insert mat values of mpimat */
4556: /*----------------------------*/
4557: PetscMalloc1(N,&ba_i);
4558: PetscMalloc3(merge->nrecv,&buf_ri_k,merge->nrecv,&nextrow,merge->nrecv,&nextai);
4560: for (k=0; k<merge->nrecv; k++) {
4561: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4562: nrows = *(buf_ri_k[k]);
4563: nextrow[k] = buf_ri_k[k]+1; /* next row number of k-th recved i-structure */
4564: nextai[k] = buf_ri_k[k] + (nrows + 1); /* poins to the next i-structure of k-th recved i-structure */
4565: }
4567: /* set values of ba */
4568: m = merge->rowmap->n;
4569: for (i=0; i<m; i++) {
4570: arow = owners[rank] + i;
4571: bj_i = bj+bi[i]; /* col indices of the i-th row of mpimat */
4572: bnzi = bi[i+1] - bi[i];
4573: PetscMemzero(ba_i,bnzi*sizeof(PetscScalar));
4575: /* add local non-zero vals of this proc's seqmat into ba */
4576: anzi = ai[arow+1] - ai[arow];
4577: aj = a->j + ai[arow];
4578: aa = a->a + ai[arow];
4579: nextaj = 0;
4580: for (j=0; nextaj<anzi; j++) {
4581: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4582: ba_i[j] += aa[nextaj++];
4583: }
4584: }
4586: /* add received vals into ba */
4587: for (k=0; k<merge->nrecv; k++) { /* k-th received message */
4588: /* i-th row */
4589: if (i == *nextrow[k]) {
4590: anzi = *(nextai[k]+1) - *nextai[k];
4591: aj = buf_rj[k] + *(nextai[k]);
4592: aa = abuf_r[k] + *(nextai[k]);
4593: nextaj = 0;
4594: for (j=0; nextaj<anzi; j++) {
4595: if (*(bj_i + j) == aj[nextaj]) { /* bcol == acol */
4596: ba_i[j] += aa[nextaj++];
4597: }
4598: }
4599: nextrow[k]++; nextai[k]++;
4600: }
4601: }
4602: MatSetValues(mpimat,1,&arow,bnzi,bj_i,ba_i,INSERT_VALUES);
4603: }
4604: MatAssemblyBegin(mpimat,MAT_FINAL_ASSEMBLY);
4605: MatAssemblyEnd(mpimat,MAT_FINAL_ASSEMBLY);
4607: PetscFree(abuf_r[0]);
4608: PetscFree(abuf_r);
4609: PetscFree(ba_i);
4610: PetscFree3(buf_ri_k,nextrow,nextai);
4611: PetscLogEventEnd(MAT_Seqstompinum,seqmat,0,0,0);
4612: return(0);
4613: }
4615: extern PetscErrorCode MatDestroy_MPIAIJ_SeqsToMPI(Mat);
4619: PetscErrorCode MatCreateMPIAIJSumSeqAIJSymbolic(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,Mat *mpimat)
4620: {
4621: PetscErrorCode ierr;
4622: Mat B_mpi;
4623: Mat_SeqAIJ *a=(Mat_SeqAIJ*)seqmat->data;
4624: PetscMPIInt size,rank,tagi,tagj,*len_s,*len_si,*len_ri;
4625: PetscInt **buf_rj,**buf_ri,**buf_ri_k;
4626: PetscInt M=seqmat->rmap->n,N=seqmat->cmap->n,i,*owners,*ai=a->i,*aj=a->j;
4627: PetscInt len,proc,*dnz,*onz,bs,cbs;
4628: PetscInt k,anzi,*bi,*bj,*lnk,nlnk,arow,bnzi,nspacedouble=0;
4629: PetscInt nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextai;
4630: MPI_Request *si_waits,*sj_waits,*ri_waits,*rj_waits;
4631: MPI_Status *status;
4632: PetscFreeSpaceList free_space=NULL,current_space=NULL;
4633: PetscBT lnkbt;
4634: Mat_Merge_SeqsToMPI *merge;
4635: PetscContainer container;
4638: PetscLogEventBegin(MAT_Seqstompisym,seqmat,0,0,0);
4640: /* make sure it is a PETSc comm */
4641: PetscCommDuplicate(comm,&comm,NULL);
4642: MPI_Comm_size(comm,&size);
4643: MPI_Comm_rank(comm,&rank);
4645: PetscNew(&merge);
4646: PetscMalloc1(size,&status);
4648: /* determine row ownership */
4649: /*---------------------------------------------------------*/
4650: PetscLayoutCreate(comm,&merge->rowmap);
4651: PetscLayoutSetLocalSize(merge->rowmap,m);
4652: PetscLayoutSetSize(merge->rowmap,M);
4653: PetscLayoutSetBlockSize(merge->rowmap,1);
4654: PetscLayoutSetUp(merge->rowmap);
4655: PetscMalloc1(size,&len_si);
4656: PetscMalloc1(size,&merge->len_s);
4658: m = merge->rowmap->n;
4659: owners = merge->rowmap->range;
4661: /* determine the number of messages to send, their lengths */
4662: /*---------------------------------------------------------*/
4663: len_s = merge->len_s;
4665: len = 0; /* length of buf_si[] */
4666: merge->nsend = 0;
4667: for (proc=0; proc<size; proc++) {
4668: len_si[proc] = 0;
4669: if (proc == rank) {
4670: len_s[proc] = 0;
4671: } else {
4672: len_si[proc] = owners[proc+1] - owners[proc] + 1;
4673: len_s[proc] = ai[owners[proc+1]] - ai[owners[proc]]; /* num of rows to be sent to [proc] */
4674: }
4675: if (len_s[proc]) {
4676: merge->nsend++;
4677: nrows = 0;
4678: for (i=owners[proc]; i<owners[proc+1]; i++) {
4679: if (ai[i+1] > ai[i]) nrows++;
4680: }
4681: len_si[proc] = 2*(nrows+1);
4682: len += len_si[proc];
4683: }
4684: }
4686: /* determine the number and length of messages to receive for ij-structure */
4687: /*-------------------------------------------------------------------------*/
4688: PetscGatherNumberOfMessages(comm,NULL,len_s,&merge->nrecv);
4689: PetscGatherMessageLengths2(comm,merge->nsend,merge->nrecv,len_s,len_si,&merge->id_r,&merge->len_r,&len_ri);
4691: /* post the Irecv of j-structure */
4692: /*-------------------------------*/
4693: PetscCommGetNewTag(comm,&tagj);
4694: PetscPostIrecvInt(comm,tagj,merge->nrecv,merge->id_r,merge->len_r,&buf_rj,&rj_waits);
4696: /* post the Isend of j-structure */
4697: /*--------------------------------*/
4698: PetscMalloc2(merge->nsend,&si_waits,merge->nsend,&sj_waits);
4700: for (proc=0, k=0; proc<size; proc++) {
4701: if (!len_s[proc]) continue;
4702: i = owners[proc];
4703: MPI_Isend(aj+ai[i],len_s[proc],MPIU_INT,proc,tagj,comm,sj_waits+k);
4704: k++;
4705: }
4707: /* receives and sends of j-structure are complete */
4708: /*------------------------------------------------*/
4709: if (merge->nrecv) {MPI_Waitall(merge->nrecv,rj_waits,status);}
4710: if (merge->nsend) {MPI_Waitall(merge->nsend,sj_waits,status);}
4712: /* send and recv i-structure */
4713: /*---------------------------*/
4714: PetscCommGetNewTag(comm,&tagi);
4715: PetscPostIrecvInt(comm,tagi,merge->nrecv,merge->id_r,len_ri,&buf_ri,&ri_waits);
4717: PetscMalloc1((len+1),&buf_s);
4718: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
4719: for (proc=0,k=0; proc<size; proc++) {
4720: if (!len_s[proc]) continue;
4721: /* form outgoing message for i-structure:
4722: buf_si[0]: nrows to be sent
4723: [1:nrows]: row index (global)
4724: [nrows+1:2*nrows+1]: i-structure index
4725: */
4726: /*-------------------------------------------*/
4727: nrows = len_si[proc]/2 - 1;
4728: buf_si_i = buf_si + nrows+1;
4729: buf_si[0] = nrows;
4730: buf_si_i[0] = 0;
4731: nrows = 0;
4732: for (i=owners[proc]; i<owners[proc+1]; i++) {
4733: anzi = ai[i+1] - ai[i];
4734: if (anzi) {
4735: buf_si_i[nrows+1] = buf_si_i[nrows] + anzi; /* i-structure */
4736: buf_si[nrows+1] = i-owners[proc]; /* local row index */
4737: nrows++;
4738: }
4739: }
4740: MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,si_waits+k);
4741: k++;
4742: buf_si += len_si[proc];
4743: }
4745: if (merge->nrecv) {MPI_Waitall(merge->nrecv,ri_waits,status);}
4746: if (merge->nsend) {MPI_Waitall(merge->nsend,si_waits,status);}
4748: PetscInfo2(seqmat,"nsend: %D, nrecv: %D\n",merge->nsend,merge->nrecv);
4749: for (i=0; i<merge->nrecv; i++) {
4750: PetscInfo3(seqmat,"recv len_ri=%D, len_rj=%D from [%D]\n",len_ri[i],merge->len_r[i],merge->id_r[i]);
4751: }
4753: PetscFree(len_si);
4754: PetscFree(len_ri);
4755: PetscFree(rj_waits);
4756: PetscFree2(si_waits,sj_waits);
4757: PetscFree(ri_waits);
4758: PetscFree(buf_s);
4759: PetscFree(status);
4761: /* compute a local seq matrix in each processor */
4762: /*----------------------------------------------*/
4763: /* allocate bi array and free space for accumulating nonzero column info */
4764: PetscMalloc1((m+1),&bi);
4765: bi[0] = 0;
4767: /* create and initialize a linked list */
4768: nlnk = N+1;
4769: PetscLLCreate(N,N,nlnk,lnk,lnkbt);
4771: /* initial FreeSpace size is 2*(num of local nnz(seqmat)) */
4772: len = ai[owners[rank+1]] - ai[owners[rank]];
4773: PetscFreeSpaceGet((PetscInt)(2*len+1),&free_space);
4775: current_space = free_space;
4777: /* determine symbolic info for each local row */
4778: PetscMalloc3(merge->nrecv,&buf_ri_k,merge->nrecv,&nextrow,merge->nrecv,&nextai);
4780: for (k=0; k<merge->nrecv; k++) {
4781: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
4782: nrows = *buf_ri_k[k];
4783: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
4784: nextai[k] = buf_ri_k[k] + (nrows + 1); /* poins to the next i-structure of k-th recved i-structure */
4785: }
4787: MatPreallocateInitialize(comm,m,n,dnz,onz);
4788: len = 0;
4789: for (i=0; i<m; i++) {
4790: bnzi = 0;
4791: /* add local non-zero cols of this proc's seqmat into lnk */
4792: arow = owners[rank] + i;
4793: anzi = ai[arow+1] - ai[arow];
4794: aj = a->j + ai[arow];
4795: PetscLLAddSorted(anzi,aj,N,nlnk,lnk,lnkbt);
4796: bnzi += nlnk;
4797: /* add received col data into lnk */
4798: for (k=0; k<merge->nrecv; k++) { /* k-th received message */
4799: if (i == *nextrow[k]) { /* i-th row */
4800: anzi = *(nextai[k]+1) - *nextai[k];
4801: aj = buf_rj[k] + *nextai[k];
4802: PetscLLAddSorted(anzi,aj,N,nlnk,lnk,lnkbt);
4803: bnzi += nlnk;
4804: nextrow[k]++; nextai[k]++;
4805: }
4806: }
4807: if (len < bnzi) len = bnzi; /* =max(bnzi) */
4809: /* if free space is not available, make more free space */
4810: if (current_space->local_remaining<bnzi) {
4811: PetscFreeSpaceGet(bnzi+current_space->total_array_size,¤t_space);
4812: nspacedouble++;
4813: }
4814: /* copy data into free space, then initialize lnk */
4815: PetscLLClean(N,N,bnzi,lnk,current_space->array,lnkbt);
4816: MatPreallocateSet(i+owners[rank],bnzi,current_space->array,dnz,onz);
4818: current_space->array += bnzi;
4819: current_space->local_used += bnzi;
4820: current_space->local_remaining -= bnzi;
4822: bi[i+1] = bi[i] + bnzi;
4823: }
4825: PetscFree3(buf_ri_k,nextrow,nextai);
4827: PetscMalloc1((bi[m]+1),&bj);
4828: PetscFreeSpaceContiguous(&free_space,bj);
4829: PetscLLDestroy(lnk,lnkbt);
4831: /* create symbolic parallel matrix B_mpi */
4832: /*---------------------------------------*/
4833: MatGetBlockSizes(seqmat,&bs,&cbs);
4834: MatCreate(comm,&B_mpi);
4835: if (n==PETSC_DECIDE) {
4836: MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,N);
4837: } else {
4838: MatSetSizes(B_mpi,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
4839: }
4840: MatSetBlockSizes(B_mpi,bs,cbs);
4841: MatSetType(B_mpi,MATMPIAIJ);
4842: MatMPIAIJSetPreallocation(B_mpi,0,dnz,0,onz);
4843: MatPreallocateFinalize(dnz,onz);
4844: MatSetOption(B_mpi,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_FALSE);
4846: /* B_mpi is not ready for use - assembly will be done by MatCreateMPIAIJSumSeqAIJNumeric() */
4847: B_mpi->assembled = PETSC_FALSE;
4848: B_mpi->ops->destroy = MatDestroy_MPIAIJ_SeqsToMPI;
4849: merge->bi = bi;
4850: merge->bj = bj;
4851: merge->buf_ri = buf_ri;
4852: merge->buf_rj = buf_rj;
4853: merge->coi = NULL;
4854: merge->coj = NULL;
4855: merge->owners_co = NULL;
4857: PetscCommDestroy(&comm);
4859: /* attach the supporting struct to B_mpi for reuse */
4860: PetscContainerCreate(PETSC_COMM_SELF,&container);
4861: PetscContainerSetPointer(container,merge);
4862: PetscObjectCompose((PetscObject)B_mpi,"MatMergeSeqsToMPI",(PetscObject)container);
4863: PetscContainerDestroy(&container);
4864: *mpimat = B_mpi;
4866: PetscLogEventEnd(MAT_Seqstompisym,seqmat,0,0,0);
4867: return(0);
4868: }
4872: /*@C
4873: MatCreateMPIAIJSumSeqAIJ - Creates a MPIAIJ matrix by adding sequential
4874: matrices from each processor
4876: Collective on MPI_Comm
4878: Input Parameters:
4879: + comm - the communicators the parallel matrix will live on
4880: . seqmat - the input sequential matrices
4881: . m - number of local rows (or PETSC_DECIDE)
4882: . n - number of local columns (or PETSC_DECIDE)
4883: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4885: Output Parameter:
4886: . mpimat - the parallel matrix generated
4888: Level: advanced
4890: Notes:
4891: The dimensions of the sequential matrix in each processor MUST be the same.
4892: The input seqmat is included into the container "Mat_Merge_SeqsToMPI", and will be
4893: destroyed when mpimat is destroyed. Call PetscObjectQuery() to access seqmat.
4894: @*/
4895: PetscErrorCode MatCreateMPIAIJSumSeqAIJ(MPI_Comm comm,Mat seqmat,PetscInt m,PetscInt n,MatReuse scall,Mat *mpimat)
4896: {
4898: PetscMPIInt size;
4901: MPI_Comm_size(comm,&size);
4902: if (size == 1) {
4903: PetscLogEventBegin(MAT_Seqstompi,seqmat,0,0,0);
4904: if (scall == MAT_INITIAL_MATRIX) {
4905: MatDuplicate(seqmat,MAT_COPY_VALUES,mpimat);
4906: } else {
4907: MatCopy(seqmat,*mpimat,SAME_NONZERO_PATTERN);
4908: }
4909: PetscLogEventEnd(MAT_Seqstompi,seqmat,0,0,0);
4910: return(0);
4911: }
4912: PetscLogEventBegin(MAT_Seqstompi,seqmat,0,0,0);
4913: if (scall == MAT_INITIAL_MATRIX) {
4914: MatCreateMPIAIJSumSeqAIJSymbolic(comm,seqmat,m,n,mpimat);
4915: }
4916: MatCreateMPIAIJSumSeqAIJNumeric(seqmat,*mpimat);
4917: PetscLogEventEnd(MAT_Seqstompi,seqmat,0,0,0);
4918: return(0);
4919: }
4923: /*@
4924: MatMPIAIJGetLocalMat - Creates a SeqAIJ from a MPIAIJ matrix by taking all its local rows and putting them into a sequential vector with
4925: mlocal rows and n columns. Where mlocal is the row count obtained with MatGetLocalSize() and n is the global column count obtained
4926: with MatGetSize()
4928: Not Collective
4930: Input Parameters:
4931: + A - the matrix
4932: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
4934: Output Parameter:
4935: . A_loc - the local sequential matrix generated
4937: Level: developer
4939: .seealso: MatGetOwnerShipRange(), MatMPIAIJGetLocalMatCondensed()
4941: @*/
4942: PetscErrorCode MatMPIAIJGetLocalMat(Mat A,MatReuse scall,Mat *A_loc)
4943: {
4945: Mat_MPIAIJ *mpimat=(Mat_MPIAIJ*)A->data;
4946: Mat_SeqAIJ *mat,*a,*b;
4947: PetscInt *ai,*aj,*bi,*bj,*cmap=mpimat->garray;
4948: MatScalar *aa,*ba,*cam;
4949: PetscScalar *ca;
4950: PetscInt am=A->rmap->n,i,j,k,cstart=A->cmap->rstart;
4951: PetscInt *ci,*cj,col,ncols_d,ncols_o,jo;
4952: PetscBool match;
4955: PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&match);
4956: if (!match) SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP,"Requires MPIAIJ matrix as input");
4957: PetscLogEventBegin(MAT_Getlocalmat,A,0,0,0);
4958: a = (Mat_SeqAIJ*)(mpimat->A)->data;
4959: b = (Mat_SeqAIJ*)(mpimat->B)->data;
4960: ai = a->i; aj = a->j; bi = b->i; bj = b->j;
4961: aa = a->a; ba = b->a;
4962: if (scall == MAT_INITIAL_MATRIX) {
4963: PetscMalloc1((1+am),&ci);
4964: ci[0] = 0;
4965: for (i=0; i<am; i++) {
4966: ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
4967: }
4968: PetscMalloc1((1+ci[am]),&cj);
4969: PetscMalloc1((1+ci[am]),&ca);
4970: k = 0;
4971: for (i=0; i<am; i++) {
4972: ncols_o = bi[i+1] - bi[i];
4973: ncols_d = ai[i+1] - ai[i];
4974: /* off-diagonal portion of A */
4975: for (jo=0; jo<ncols_o; jo++) {
4976: col = cmap[*bj];
4977: if (col >= cstart) break;
4978: cj[k] = col; bj++;
4979: ca[k++] = *ba++;
4980: }
4981: /* diagonal portion of A */
4982: for (j=0; j<ncols_d; j++) {
4983: cj[k] = cstart + *aj++;
4984: ca[k++] = *aa++;
4985: }
4986: /* off-diagonal portion of A */
4987: for (j=jo; j<ncols_o; j++) {
4988: cj[k] = cmap[*bj++];
4989: ca[k++] = *ba++;
4990: }
4991: }
4992: /* put together the new matrix */
4993: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,A->cmap->N,ci,cj,ca,A_loc);
4994: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
4995: /* Since these are PETSc arrays, change flags to free them as necessary. */
4996: mat = (Mat_SeqAIJ*)(*A_loc)->data;
4997: mat->free_a = PETSC_TRUE;
4998: mat->free_ij = PETSC_TRUE;
4999: mat->nonew = 0;
5000: } else if (scall == MAT_REUSE_MATRIX) {
5001: mat=(Mat_SeqAIJ*)(*A_loc)->data;
5002: ci = mat->i; cj = mat->j; cam = mat->a;
5003: for (i=0; i<am; i++) {
5004: /* off-diagonal portion of A */
5005: ncols_o = bi[i+1] - bi[i];
5006: for (jo=0; jo<ncols_o; jo++) {
5007: col = cmap[*bj];
5008: if (col >= cstart) break;
5009: *cam++ = *ba++; bj++;
5010: }
5011: /* diagonal portion of A */
5012: ncols_d = ai[i+1] - ai[i];
5013: for (j=0; j<ncols_d; j++) *cam++ = *aa++;
5014: /* off-diagonal portion of A */
5015: for (j=jo; j<ncols_o; j++) {
5016: *cam++ = *ba++; bj++;
5017: }
5018: }
5019: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
5020: PetscLogEventEnd(MAT_Getlocalmat,A,0,0,0);
5021: return(0);
5022: }
5026: /*@C
5027: MatMPIAIJGetLocalMatCondensed - Creates a SeqAIJ matrix from an MPIAIJ matrix by taking all its local rows and NON-ZERO columns
5029: Not Collective
5031: Input Parameters:
5032: + A - the matrix
5033: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
5034: - row, col - index sets of rows and columns to extract (or NULL)
5036: Output Parameter:
5037: . A_loc - the local sequential matrix generated
5039: Level: developer
5041: .seealso: MatGetOwnershipRange(), MatMPIAIJGetLocalMat()
5043: @*/
5044: PetscErrorCode MatMPIAIJGetLocalMatCondensed(Mat A,MatReuse scall,IS *row,IS *col,Mat *A_loc)
5045: {
5046: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
5048: PetscInt i,start,end,ncols,nzA,nzB,*cmap,imark,*idx;
5049: IS isrowa,iscola;
5050: Mat *aloc;
5051: PetscBool match;
5054: PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&match);
5055: if (!match) SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP,"Requires MPIAIJ matrix as input");
5056: PetscLogEventBegin(MAT_Getlocalmatcondensed,A,0,0,0);
5057: if (!row) {
5058: start = A->rmap->rstart; end = A->rmap->rend;
5059: ISCreateStride(PETSC_COMM_SELF,end-start,start,1,&isrowa);
5060: } else {
5061: isrowa = *row;
5062: }
5063: if (!col) {
5064: start = A->cmap->rstart;
5065: cmap = a->garray;
5066: nzA = a->A->cmap->n;
5067: nzB = a->B->cmap->n;
5068: PetscMalloc1((nzA+nzB), &idx);
5069: ncols = 0;
5070: for (i=0; i<nzB; i++) {
5071: if (cmap[i] < start) idx[ncols++] = cmap[i];
5072: else break;
5073: }
5074: imark = i;
5075: for (i=0; i<nzA; i++) idx[ncols++] = start + i;
5076: for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i];
5077: ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,PETSC_OWN_POINTER,&iscola);
5078: } else {
5079: iscola = *col;
5080: }
5081: if (scall != MAT_INITIAL_MATRIX) {
5082: PetscMalloc(sizeof(Mat),&aloc);
5083: aloc[0] = *A_loc;
5084: }
5085: MatGetSubMatrices(A,1,&isrowa,&iscola,scall,&aloc);
5086: *A_loc = aloc[0];
5087: PetscFree(aloc);
5088: if (!row) {
5089: ISDestroy(&isrowa);
5090: }
5091: if (!col) {
5092: ISDestroy(&iscola);
5093: }
5094: PetscLogEventEnd(MAT_Getlocalmatcondensed,A,0,0,0);
5095: return(0);
5096: }
5100: /*@C
5101: MatGetBrowsOfAcols - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns of local A
5103: Collective on Mat
5105: Input Parameters:
5106: + A,B - the matrices in mpiaij format
5107: . scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
5108: - rowb, colb - index sets of rows and columns of B to extract (or NULL)
5110: Output Parameter:
5111: + rowb, colb - index sets of rows and columns of B to extract
5112: - B_seq - the sequential matrix generated
5114: Level: developer
5116: @*/
5117: PetscErrorCode MatGetBrowsOfAcols(Mat A,Mat B,MatReuse scall,IS *rowb,IS *colb,Mat *B_seq)
5118: {
5119: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
5121: PetscInt *idx,i,start,ncols,nzA,nzB,*cmap,imark;
5122: IS isrowb,iscolb;
5123: Mat *bseq=NULL;
5126: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) {
5127: 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);
5128: }
5129: PetscLogEventBegin(MAT_GetBrowsOfAcols,A,B,0,0);
5131: if (scall == MAT_INITIAL_MATRIX) {
5132: start = A->cmap->rstart;
5133: cmap = a->garray;
5134: nzA = a->A->cmap->n;
5135: nzB = a->B->cmap->n;
5136: PetscMalloc1((nzA+nzB), &idx);
5137: ncols = 0;
5138: for (i=0; i<nzB; i++) { /* row < local row index */
5139: if (cmap[i] < start) idx[ncols++] = cmap[i];
5140: else break;
5141: }
5142: imark = i;
5143: for (i=0; i<nzA; i++) idx[ncols++] = start + i; /* local rows */
5144: for (i=imark; i<nzB; i++) idx[ncols++] = cmap[i]; /* row > local row index */
5145: ISCreateGeneral(PETSC_COMM_SELF,ncols,idx,PETSC_OWN_POINTER,&isrowb);
5146: ISCreateStride(PETSC_COMM_SELF,B->cmap->N,0,1,&iscolb);
5147: } else {
5148: if (!rowb || !colb) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"IS rowb and colb must be provided for MAT_REUSE_MATRIX");
5149: isrowb = *rowb; iscolb = *colb;
5150: PetscMalloc(sizeof(Mat),&bseq);
5151: bseq[0] = *B_seq;
5152: }
5153: MatGetSubMatrices(B,1,&isrowb,&iscolb,scall,&bseq);
5154: *B_seq = bseq[0];
5155: PetscFree(bseq);
5156: if (!rowb) {
5157: ISDestroy(&isrowb);
5158: } else {
5159: *rowb = isrowb;
5160: }
5161: if (!colb) {
5162: ISDestroy(&iscolb);
5163: } else {
5164: *colb = iscolb;
5165: }
5166: PetscLogEventEnd(MAT_GetBrowsOfAcols,A,B,0,0);
5167: return(0);
5168: }
5172: /*
5173: MatGetBrowsOfAoCols_MPIAIJ - Creates a SeqAIJ matrix by taking rows of B that equal to nonzero columns
5174: of the OFF-DIAGONAL portion of local A
5176: Collective on Mat
5178: Input Parameters:
5179: + A,B - the matrices in mpiaij format
5180: - scall - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
5182: Output Parameter:
5183: + startsj_s - starting point in B's sending j-arrays, saved for MAT_REUSE (or NULL)
5184: . startsj_r - starting point in B's receiving j-arrays, saved for MAT_REUSE (or NULL)
5185: . bufa_ptr - array for sending matrix values, saved for MAT_REUSE (or NULL)
5186: - B_oth - the sequential matrix generated with size aBn=a->B->cmap->n by B->cmap->N
5188: Level: developer
5190: */
5191: PetscErrorCode MatGetBrowsOfAoCols_MPIAIJ(Mat A,Mat B,MatReuse scall,PetscInt **startsj_s,PetscInt **startsj_r,MatScalar **bufa_ptr,Mat *B_oth)
5192: {
5193: VecScatter_MPI_General *gen_to,*gen_from;
5194: PetscErrorCode ierr;
5195: Mat_MPIAIJ *a=(Mat_MPIAIJ*)A->data;
5196: Mat_SeqAIJ *b_oth;
5197: VecScatter ctx =a->Mvctx;
5198: MPI_Comm comm;
5199: PetscMPIInt *rprocs,*sprocs,tag=((PetscObject)ctx)->tag,rank;
5200: PetscInt *rowlen,*bufj,*bufJ,ncols,aBn=a->B->cmap->n,row,*b_othi,*b_othj;
5201: PetscScalar *rvalues,*svalues;
5202: MatScalar *b_otha,*bufa,*bufA;
5203: PetscInt i,j,k,l,ll,nrecvs,nsends,nrows,*srow,*rstarts,*rstartsj = 0,*sstarts,*sstartsj,len;
5204: MPI_Request *rwaits = NULL,*swaits = NULL;
5205: MPI_Status *sstatus,rstatus;
5206: PetscMPIInt jj;
5207: PetscInt *cols,sbs,rbs;
5208: PetscScalar *vals;
5211: PetscObjectGetComm((PetscObject)A,&comm);
5212: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) {
5213: 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);
5214: }
5215: PetscLogEventBegin(MAT_GetBrowsOfAocols,A,B,0,0);
5216: MPI_Comm_rank(comm,&rank);
5218: gen_to = (VecScatter_MPI_General*)ctx->todata;
5219: gen_from = (VecScatter_MPI_General*)ctx->fromdata;
5220: rvalues = gen_from->values; /* holds the length of receiving row */
5221: svalues = gen_to->values; /* holds the length of sending row */
5222: nrecvs = gen_from->n;
5223: nsends = gen_to->n;
5225: PetscMalloc2(nrecvs,&rwaits,nsends,&swaits);
5226: srow = gen_to->indices; /* local row index to be sent */
5227: sstarts = gen_to->starts;
5228: sprocs = gen_to->procs;
5229: sstatus = gen_to->sstatus;
5230: sbs = gen_to->bs;
5231: rstarts = gen_from->starts;
5232: rprocs = gen_from->procs;
5233: rbs = gen_from->bs;
5235: if (!startsj_s || !bufa_ptr) scall = MAT_INITIAL_MATRIX;
5236: if (scall == MAT_INITIAL_MATRIX) {
5237: /* i-array */
5238: /*---------*/
5239: /* post receives */
5240: for (i=0; i<nrecvs; i++) {
5241: rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
5242: nrows = (rstarts[i+1]-rstarts[i])*rbs; /* num of indices to be received */
5243: MPI_Irecv(rowlen,nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
5244: }
5246: /* pack the outgoing message */
5247: PetscMalloc2(nsends+1,&sstartsj,nrecvs+1,&rstartsj);
5249: sstartsj[0] = 0;
5250: rstartsj[0] = 0;
5251: len = 0; /* total length of j or a array to be sent */
5252: k = 0;
5253: for (i=0; i<nsends; i++) {
5254: rowlen = (PetscInt*)svalues + sstarts[i]*sbs;
5255: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
5256: for (j=0; j<nrows; j++) {
5257: row = srow[k] + B->rmap->range[rank]; /* global row idx */
5258: for (l=0; l<sbs; l++) {
5259: MatGetRow_MPIAIJ(B,row+l,&ncols,NULL,NULL); /* rowlength */
5261: rowlen[j*sbs+l] = ncols;
5263: len += ncols;
5264: MatRestoreRow_MPIAIJ(B,row+l,&ncols,NULL,NULL);
5265: }
5266: k++;
5267: }
5268: MPI_Isend(rowlen,nrows*sbs,MPIU_INT,sprocs[i],tag,comm,swaits+i);
5270: sstartsj[i+1] = len; /* starting point of (i+1)-th outgoing msg in bufj and bufa */
5271: }
5272: /* recvs and sends of i-array are completed */
5273: i = nrecvs;
5274: while (i--) {
5275: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
5276: }
5277: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
5279: /* allocate buffers for sending j and a arrays */
5280: PetscMalloc1((len+1),&bufj);
5281: PetscMalloc1((len+1),&bufa);
5283: /* create i-array of B_oth */
5284: PetscMalloc1((aBn+2),&b_othi);
5286: b_othi[0] = 0;
5287: len = 0; /* total length of j or a array to be received */
5288: k = 0;
5289: for (i=0; i<nrecvs; i++) {
5290: rowlen = (PetscInt*)rvalues + rstarts[i]*rbs;
5291: nrows = rbs*(rstarts[i+1]-rstarts[i]); /* num of rows to be recieved */
5292: for (j=0; j<nrows; j++) {
5293: b_othi[k+1] = b_othi[k] + rowlen[j];
5294: len += rowlen[j]; k++;
5295: }
5296: rstartsj[i+1] = len; /* starting point of (i+1)-th incoming msg in bufj and bufa */
5297: }
5299: /* allocate space for j and a arrrays of B_oth */
5300: PetscMalloc1((b_othi[aBn]+1),&b_othj);
5301: PetscMalloc1((b_othi[aBn]+1),&b_otha);
5303: /* j-array */
5304: /*---------*/
5305: /* post receives of j-array */
5306: for (i=0; i<nrecvs; i++) {
5307: nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
5308: MPI_Irecv(b_othj+rstartsj[i],nrows,MPIU_INT,rprocs[i],tag,comm,rwaits+i);
5309: }
5311: /* pack the outgoing message j-array */
5312: k = 0;
5313: for (i=0; i<nsends; i++) {
5314: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
5315: bufJ = bufj+sstartsj[i];
5316: for (j=0; j<nrows; j++) {
5317: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5318: for (ll=0; ll<sbs; ll++) {
5319: MatGetRow_MPIAIJ(B,row+ll,&ncols,&cols,NULL);
5320: for (l=0; l<ncols; l++) {
5321: *bufJ++ = cols[l];
5322: }
5323: MatRestoreRow_MPIAIJ(B,row+ll,&ncols,&cols,NULL);
5324: }
5325: }
5326: MPI_Isend(bufj+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_INT,sprocs[i],tag,comm,swaits+i);
5327: }
5329: /* recvs and sends of j-array are completed */
5330: i = nrecvs;
5331: while (i--) {
5332: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
5333: }
5334: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
5335: } else if (scall == MAT_REUSE_MATRIX) {
5336: sstartsj = *startsj_s;
5337: rstartsj = *startsj_r;
5338: bufa = *bufa_ptr;
5339: b_oth = (Mat_SeqAIJ*)(*B_oth)->data;
5340: b_otha = b_oth->a;
5341: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE, "Matrix P does not posses an object container");
5343: /* a-array */
5344: /*---------*/
5345: /* post receives of a-array */
5346: for (i=0; i<nrecvs; i++) {
5347: nrows = rstartsj[i+1]-rstartsj[i]; /* length of the msg received */
5348: MPI_Irecv(b_otha+rstartsj[i],nrows,MPIU_SCALAR,rprocs[i],tag,comm,rwaits+i);
5349: }
5351: /* pack the outgoing message a-array */
5352: k = 0;
5353: for (i=0; i<nsends; i++) {
5354: nrows = sstarts[i+1]-sstarts[i]; /* num of block rows */
5355: bufA = bufa+sstartsj[i];
5356: for (j=0; j<nrows; j++) {
5357: row = srow[k++] + B->rmap->range[rank]; /* global row idx */
5358: for (ll=0; ll<sbs; ll++) {
5359: MatGetRow_MPIAIJ(B,row+ll,&ncols,NULL,&vals);
5360: for (l=0; l<ncols; l++) {
5361: *bufA++ = vals[l];
5362: }
5363: MatRestoreRow_MPIAIJ(B,row+ll,&ncols,NULL,&vals);
5364: }
5365: }
5366: MPI_Isend(bufa+sstartsj[i],sstartsj[i+1]-sstartsj[i],MPIU_SCALAR,sprocs[i],tag,comm,swaits+i);
5367: }
5368: /* recvs and sends of a-array are completed */
5369: i = nrecvs;
5370: while (i--) {
5371: MPI_Waitany(nrecvs,rwaits,&jj,&rstatus);
5372: }
5373: if (nsends) {MPI_Waitall(nsends,swaits,sstatus);}
5374: PetscFree2(rwaits,swaits);
5376: if (scall == MAT_INITIAL_MATRIX) {
5377: /* put together the new matrix */
5378: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,aBn,B->cmap->N,b_othi,b_othj,b_otha,B_oth);
5380: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
5381: /* Since these are PETSc arrays, change flags to free them as necessary. */
5382: b_oth = (Mat_SeqAIJ*)(*B_oth)->data;
5383: b_oth->free_a = PETSC_TRUE;
5384: b_oth->free_ij = PETSC_TRUE;
5385: b_oth->nonew = 0;
5387: PetscFree(bufj);
5388: if (!startsj_s || !bufa_ptr) {
5389: PetscFree2(sstartsj,rstartsj);
5390: PetscFree(bufa_ptr);
5391: } else {
5392: *startsj_s = sstartsj;
5393: *startsj_r = rstartsj;
5394: *bufa_ptr = bufa;
5395: }
5396: }
5397: PetscLogEventEnd(MAT_GetBrowsOfAocols,A,B,0,0);
5398: return(0);
5399: }
5403: /*@C
5404: MatGetCommunicationStructs - Provides access to the communication structures used in matrix-vector multiplication.
5406: Not Collective
5408: Input Parameters:
5409: . A - The matrix in mpiaij format
5411: Output Parameter:
5412: + lvec - The local vector holding off-process values from the argument to a matrix-vector product
5413: . colmap - A map from global column index to local index into lvec
5414: - multScatter - A scatter from the argument of a matrix-vector product to lvec
5416: Level: developer
5418: @*/
5419: #if defined(PETSC_USE_CTABLE)
5420: PetscErrorCode MatGetCommunicationStructs(Mat A, Vec *lvec, PetscTable *colmap, VecScatter *multScatter)
5421: #else
5422: PetscErrorCode MatGetCommunicationStructs(Mat A, Vec *lvec, PetscInt *colmap[], VecScatter *multScatter)
5423: #endif
5424: {
5425: Mat_MPIAIJ *a;
5432: a = (Mat_MPIAIJ*) A->data;
5433: if (lvec) *lvec = a->lvec;
5434: if (colmap) *colmap = a->colmap;
5435: if (multScatter) *multScatter = a->Mvctx;
5436: return(0);
5437: }
5439: PETSC_EXTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJCRL(Mat,MatType,MatReuse,Mat*);
5440: PETSC_EXTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJPERM(Mat,MatType,MatReuse,Mat*);
5441: PETSC_EXTERN PetscErrorCode MatConvert_MPIAIJ_MPISBAIJ(Mat,MatType,MatReuse,Mat*);
5445: /*
5446: Computes (B'*A')' since computing B*A directly is untenable
5448: n p p
5449: ( ) ( ) ( )
5450: m ( A ) * n ( B ) = m ( C )
5451: ( ) ( ) ( )
5453: */
5454: PetscErrorCode MatMatMultNumeric_MPIDense_MPIAIJ(Mat A,Mat B,Mat C)
5455: {
5457: Mat At,Bt,Ct;
5460: MatTranspose(A,MAT_INITIAL_MATRIX,&At);
5461: MatTranspose(B,MAT_INITIAL_MATRIX,&Bt);
5462: MatMatMult(Bt,At,MAT_INITIAL_MATRIX,1.0,&Ct);
5463: MatDestroy(&At);
5464: MatDestroy(&Bt);
5465: MatTranspose(Ct,MAT_REUSE_MATRIX,&C);
5466: MatDestroy(&Ct);
5467: return(0);
5468: }
5472: PetscErrorCode MatMatMultSymbolic_MPIDense_MPIAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
5473: {
5475: PetscInt m=A->rmap->n,n=B->cmap->n;
5476: Mat Cmat;
5479: 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);
5480: MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
5481: MatSetSizes(Cmat,m,n,PETSC_DETERMINE,PETSC_DETERMINE);
5482: MatSetBlockSizesFromMats(Cmat,A,B);
5483: MatSetType(Cmat,MATMPIDENSE);
5484: MatMPIDenseSetPreallocation(Cmat,NULL);
5485: MatAssemblyBegin(Cmat,MAT_FINAL_ASSEMBLY);
5486: MatAssemblyEnd(Cmat,MAT_FINAL_ASSEMBLY);
5488: Cmat->ops->matmultnumeric = MatMatMultNumeric_MPIDense_MPIAIJ;
5490: *C = Cmat;
5491: return(0);
5492: }
5494: /* ----------------------------------------------------------------*/
5497: PetscErrorCode MatMatMult_MPIDense_MPIAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
5498: {
5502: if (scall == MAT_INITIAL_MATRIX) {
5503: PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
5504: MatMatMultSymbolic_MPIDense_MPIAIJ(A,B,fill,C);
5505: PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
5506: }
5507: PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
5508: MatMatMultNumeric_MPIDense_MPIAIJ(A,B,*C);
5509: PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
5510: return(0);
5511: }
5513: #if defined(PETSC_HAVE_MUMPS)
5514: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mumps(Mat,MatFactorType,Mat*);
5515: #endif
5516: #if defined(PETSC_HAVE_PASTIX)
5517: PETSC_EXTERN PetscErrorCode MatGetFactor_mpiaij_pastix(Mat,MatFactorType,Mat*);
5518: #endif
5519: #if defined(PETSC_HAVE_SUPERLU_DIST)
5520: PETSC_EXTERN PetscErrorCode MatGetFactor_mpiaij_superlu_dist(Mat,MatFactorType,Mat*);
5521: #endif
5522: #if defined(PETSC_HAVE_CLIQUE)
5523: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_clique(Mat,MatFactorType,Mat*);
5524: #endif
5526: /*MC
5527: MATMPIAIJ - MATMPIAIJ = "mpiaij" - A matrix type to be used for parallel sparse matrices.
5529: Options Database Keys:
5530: . -mat_type mpiaij - sets the matrix type to "mpiaij" during a call to MatSetFromOptions()
5532: Level: beginner
5534: .seealso: MatCreateAIJ()
5535: M*/
5539: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJ(Mat B)
5540: {
5541: Mat_MPIAIJ *b;
5543: PetscMPIInt size;
5546: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
5548: PetscNewLog(B,&b);
5549: B->data = (void*)b;
5550: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
5551: B->assembled = PETSC_FALSE;
5552: B->insertmode = NOT_SET_VALUES;
5553: b->size = size;
5555: MPI_Comm_rank(PetscObjectComm((PetscObject)B),&b->rank);
5557: /* build cache for off array entries formed */
5558: MatStashCreate_Private(PetscObjectComm((PetscObject)B),1,&B->stash);
5560: b->donotstash = PETSC_FALSE;
5561: b->colmap = 0;
5562: b->garray = 0;
5563: b->roworiented = PETSC_TRUE;
5565: /* stuff used for matrix vector multiply */
5566: b->lvec = NULL;
5567: b->Mvctx = NULL;
5569: /* stuff for MatGetRow() */
5570: b->rowindices = 0;
5571: b->rowvalues = 0;
5572: b->getrowactive = PETSC_FALSE;
5574: /* flexible pointer used in CUSP/CUSPARSE classes */
5575: b->spptr = NULL;
5577: #if defined(PETSC_HAVE_MUMPS)
5578: PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_mumps_C",MatGetFactor_aij_mumps);
5579: #endif
5580: #if defined(PETSC_HAVE_PASTIX)
5581: PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_pastix_C",MatGetFactor_mpiaij_pastix);
5582: #endif
5583: #if defined(PETSC_HAVE_SUPERLU_DIST)
5584: PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_superlu_dist_C",MatGetFactor_mpiaij_superlu_dist);
5585: #endif
5586: #if defined(PETSC_HAVE_CLIQUE)
5587: PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_clique_C",MatGetFactor_aij_clique);
5588: #endif
5589: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_MPIAIJ);
5590: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_MPIAIJ);
5591: PetscObjectComposeFunction((PetscObject)B,"MatGetDiagonalBlock_C",MatGetDiagonalBlock_MPIAIJ);
5592: PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_MPIAIJ);
5593: PetscObjectComposeFunction((PetscObject)B,"MatMPIAIJSetPreallocation_C",MatMPIAIJSetPreallocation_MPIAIJ);
5594: PetscObjectComposeFunction((PetscObject)B,"MatMPIAIJSetPreallocationCSR_C",MatMPIAIJSetPreallocationCSR_MPIAIJ);
5595: PetscObjectComposeFunction((PetscObject)B,"MatDiagonalScaleLocal_C",MatDiagonalScaleLocal_MPIAIJ);
5596: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_mpiaijperm_C",MatConvert_MPIAIJ_MPIAIJPERM);
5597: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_mpiaijcrl_C",MatConvert_MPIAIJ_MPIAIJCRL);
5598: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_mpisbaij_C",MatConvert_MPIAIJ_MPISBAIJ);
5599: PetscObjectComposeFunction((PetscObject)B,"MatMatMult_mpidense_mpiaij_C",MatMatMult_MPIDense_MPIAIJ);
5600: PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_mpidense_mpiaij_C",MatMatMultSymbolic_MPIDense_MPIAIJ);
5601: PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_mpidense_mpiaij_C",MatMatMultNumeric_MPIDense_MPIAIJ);
5602: PetscObjectChangeTypeName((PetscObject)B,MATMPIAIJ);
5603: return(0);
5604: }
5608: /*@
5609: MatCreateMPIAIJWithSplitArrays - creates a MPI AIJ matrix using arrays that contain the "diagonal"
5610: and "off-diagonal" part of the matrix in CSR format.
5612: Collective on MPI_Comm
5614: Input Parameters:
5615: + comm - MPI communicator
5616: . m - number of local rows (Cannot be PETSC_DECIDE)
5617: . n - This value should be the same as the local size used in creating the
5618: x vector for the matrix-vector product y = Ax. (or PETSC_DECIDE to have
5619: calculated if N is given) For square matrices n is almost always m.
5620: . M - number of global rows (or PETSC_DETERMINE to have calculated if m is given)
5621: . N - number of global columns (or PETSC_DETERMINE to have calculated if n is given)
5622: . i - row indices for "diagonal" portion of matrix
5623: . j - column indices
5624: . a - matrix values
5625: . oi - row indices for "off-diagonal" portion of matrix
5626: . oj - column indices
5627: - oa - matrix values
5629: Output Parameter:
5630: . mat - the matrix
5632: Level: advanced
5634: Notes:
5635: The i, j, and a arrays ARE NOT copied by this routine into the internal format used by PETSc. The user
5636: must free the arrays once the matrix has been destroyed and not before.
5638: The i and j indices are 0 based
5640: See MatCreateAIJ() for the definition of "diagonal" and "off-diagonal" portion of the matrix
5642: This sets local rows and cannot be used to set off-processor values.
5644: Use of this routine is discouraged because it is inflexible and cumbersome to use. It is extremely rare that a
5645: legacy application natively assembles into exactly this split format. The code to do so is nontrivial and does
5646: not easily support in-place reassembly. It is recommended to use MatSetValues() (or a variant thereof) because
5647: the resulting assembly is easier to implement, will work with any matrix format, and the user does not have to
5648: keep track of the underlying array. Use MatSetOption(A,MAT_IGNORE_OFF_PROC_ENTRIES,PETSC_TRUE) to disable all
5649: communication if it is known that only local entries will be set.
5651: .keywords: matrix, aij, compressed row, sparse, parallel
5653: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatMPIAIJSetPreallocation(), MatMPIAIJSetPreallocationCSR(),
5654: MPIAIJ, MatCreateAIJ(), MatCreateMPIAIJWithArrays()
5655: @*/
5656: 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)
5657: {
5659: Mat_MPIAIJ *maij;
5662: if (m < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"local number of rows (m) cannot be PETSC_DECIDE, or negative");
5663: if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
5664: if (oi[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"oi (row indices) must start with 0");
5665: MatCreate(comm,mat);
5666: MatSetSizes(*mat,m,n,M,N);
5667: MatSetType(*mat,MATMPIAIJ);
5668: maij = (Mat_MPIAIJ*) (*mat)->data;
5670: (*mat)->preallocated = PETSC_TRUE;
5672: PetscLayoutSetUp((*mat)->rmap);
5673: PetscLayoutSetUp((*mat)->cmap);
5675: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,i,j,a,&maij->A);
5676: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,(*mat)->cmap->N,oi,oj,oa,&maij->B);
5678: MatAssemblyBegin(maij->A,MAT_FINAL_ASSEMBLY);
5679: MatAssemblyEnd(maij->A,MAT_FINAL_ASSEMBLY);
5680: MatAssemblyBegin(maij->B,MAT_FINAL_ASSEMBLY);
5681: MatAssemblyEnd(maij->B,MAT_FINAL_ASSEMBLY);
5683: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
5684: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
5685: MatSetOption(*mat,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
5686: return(0);
5687: }
5689: /*
5690: Special version for direct calls from Fortran
5691: */
5692: #include <petsc-private/fortranimpl.h>
5694: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5695: #define matsetvaluesmpiaij_ MATSETVALUESMPIAIJ
5696: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5697: #define matsetvaluesmpiaij_ matsetvaluesmpiaij
5698: #endif
5700: /* Change these macros so can be used in void function */
5701: #undef CHKERRQ
5702: #define CHKERRQ(ierr) CHKERRABORT(PETSC_COMM_WORLD,ierr)
5703: #undef SETERRQ2
5704: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
5705: #undef SETERRQ3
5706: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)
5707: #undef SETERRQ
5708: #define SETERRQ(c,ierr,b) CHKERRABORT(c,ierr)
5712: 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)
5713: {
5714: Mat mat = *mmat;
5715: PetscInt m = *mm, n = *mn;
5716: InsertMode addv = *maddv;
5717: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)mat->data;
5718: PetscScalar value;
5721: MatCheckPreallocated(mat,1);
5722: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
5724: #if defined(PETSC_USE_DEBUG)
5725: else if (mat->insertmode != addv) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot mix add values and insert values");
5726: #endif
5727: {
5728: PetscInt i,j,rstart = mat->rmap->rstart,rend = mat->rmap->rend;
5729: PetscInt cstart = mat->cmap->rstart,cend = mat->cmap->rend,row,col;
5730: PetscBool roworiented = aij->roworiented;
5732: /* Some Variables required in the macro */
5733: Mat A = aij->A;
5734: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
5735: PetscInt *aimax = a->imax,*ai = a->i,*ailen = a->ilen,*aj = a->j;
5736: MatScalar *aa = a->a;
5737: PetscBool ignorezeroentries = (((a->ignorezeroentries)&&(addv==ADD_VALUES)) ? PETSC_TRUE : PETSC_FALSE);
5738: Mat B = aij->B;
5739: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
5740: PetscInt *bimax = b->imax,*bi = b->i,*bilen = b->ilen,*bj = b->j,bm = aij->B->rmap->n,am = aij->A->rmap->n;
5741: MatScalar *ba = b->a;
5743: PetscInt *rp1,*rp2,ii,nrow1,nrow2,_i,rmax1,rmax2,N,low1,high1,low2,high2,t,lastcol1,lastcol2;
5744: PetscInt nonew = a->nonew;
5745: MatScalar *ap1,*ap2;
5748: for (i=0; i<m; i++) {
5749: if (im[i] < 0) continue;
5750: #if defined(PETSC_USE_DEBUG)
5751: 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);
5752: #endif
5753: if (im[i] >= rstart && im[i] < rend) {
5754: row = im[i] - rstart;
5755: lastcol1 = -1;
5756: rp1 = aj + ai[row];
5757: ap1 = aa + ai[row];
5758: rmax1 = aimax[row];
5759: nrow1 = ailen[row];
5760: low1 = 0;
5761: high1 = nrow1;
5762: lastcol2 = -1;
5763: rp2 = bj + bi[row];
5764: ap2 = ba + bi[row];
5765: rmax2 = bimax[row];
5766: nrow2 = bilen[row];
5767: low2 = 0;
5768: high2 = nrow2;
5770: for (j=0; j<n; j++) {
5771: if (roworiented) value = v[i*n+j];
5772: else value = v[i+j*m];
5773: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
5774: if (in[j] >= cstart && in[j] < cend) {
5775: col = in[j] - cstart;
5776: MatSetValues_SeqAIJ_A_Private(row,col,value,addv);
5777: } else if (in[j] < 0) continue;
5778: #if defined(PETSC_USE_DEBUG)
5779: 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);
5780: #endif
5781: else {
5782: if (mat->was_assembled) {
5783: if (!aij->colmap) {
5784: MatCreateColmap_MPIAIJ_Private(mat);
5785: }
5786: #if defined(PETSC_USE_CTABLE)
5787: PetscTableFind(aij->colmap,in[j]+1,&col);
5788: col--;
5789: #else
5790: col = aij->colmap[in[j]] - 1;
5791: #endif
5792: if (col < 0 && !((Mat_SeqAIJ*)(aij->A->data))->nonew) {
5793: MatDisAssemble_MPIAIJ(mat);
5794: col = in[j];
5795: /* Reinitialize the variables required by MatSetValues_SeqAIJ_B_Private() */
5796: B = aij->B;
5797: b = (Mat_SeqAIJ*)B->data;
5798: bimax = b->imax; bi = b->i; bilen = b->ilen; bj = b->j;
5799: rp2 = bj + bi[row];
5800: ap2 = ba + bi[row];
5801: rmax2 = bimax[row];
5802: nrow2 = bilen[row];
5803: low2 = 0;
5804: high2 = nrow2;
5805: bm = aij->B->rmap->n;
5806: ba = b->a;
5807: }
5808: } else col = in[j];
5809: MatSetValues_SeqAIJ_B_Private(row,col,value,addv);
5810: }
5811: }
5812: } else if (!aij->donotstash) {
5813: if (roworiented) {
5814: MatStashValuesRow_Private(&mat->stash,im[i],n,in,v+i*n,(PetscBool)(ignorezeroentries && (addv == ADD_VALUES)));
5815: } else {
5816: MatStashValuesCol_Private(&mat->stash,im[i],n,in,v+i,m,(PetscBool)(ignorezeroentries && (addv == ADD_VALUES)));
5817: }
5818: }
5819: }
5820: }
5821: PetscFunctionReturnVoid();
5822: }