Actual source code: mpimatmatmult.c
petsc-3.13.0 2020-03-29
2: /*
3: Defines matrix-matrix product routines for pairs of MPIAIJ matrices
4: C = A * B
5: */
6: #include <../src/mat/impls/aij/seq/aij.h>
7: #include <../src/mat/utils/freespace.h>
8: #include <../src/mat/impls/aij/mpi/mpiaij.h>
9: #include <petscbt.h>
10: #include <../src/mat/impls/dense/mpi/mpidense.h>
11: #include <petsc/private/vecimpl.h>
12: #include <petsc/private/vecscatterimpl.h>
14: #if defined(PETSC_HAVE_HYPRE)
15: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_AIJ_AIJ_wHYPRE(Mat,Mat,PetscReal,Mat);
16: #endif
18: PETSC_INTERN PetscErrorCode MatProductSymbolic_AB_MPIAIJ_MPIAIJ(Mat C)
19: {
20: PetscErrorCode ierr;
21: Mat_Product *product = C->product;
22: Mat A=product->A,B=product->B;
23: MatProductAlgorithm alg=product->alg;
24: PetscReal fill=product->fill;
25: PetscBool flg;
28: /* scalable */
29: PetscStrcmp(alg,"scalable",&flg);
30: if (flg) {
31: MatMatMultSymbolic_MPIAIJ_MPIAIJ(A,B,fill,C);
32: goto next;
33: }
35: /* nonscalable */
36: PetscStrcmp(alg,"nonscalable",&flg);
37: if (flg) {
38: MatMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(A,B,fill,C);
39: goto next;
40: }
42: /* seqmpi */
43: PetscStrcmp(alg,"seqmpi",&flg);
44: if (flg) {
45: MatMatMultSymbolic_MPIAIJ_MPIAIJ_seqMPI(A,B,fill,C);
46: goto next;
47: }
49: #if defined(PETSC_HAVE_HYPRE)
50: PetscStrcmp(alg,"hypre",&flg);
51: if (flg) {
52: MatMatMultSymbolic_AIJ_AIJ_wHYPRE(A,B,fill,C);
53: return(0);
54: }
55: #endif
56: SETERRQ(PetscObjectComm((PetscObject)C),PETSC_ERR_SUP,"Mat Product Algorithm is not supported");
58: next:
59: {
60: Mat_MPIAIJ *c = (Mat_MPIAIJ*)C->data;
61: Mat_APMPI *ap = c->ap;
62: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatFreeIntermediateDataStructures","Mat");
63: ap->freestruct = PETSC_FALSE;
64: PetscOptionsBool("-mat_freeintermediatedatastructures","Free intermediate data structures", "MatFreeIntermediateDataStructures",ap->freestruct,&ap->freestruct, NULL);
65: PetscOptionsEnd();
66: }
67: return(0);
68: }
70: PetscErrorCode MatDestroy_MPIAIJ_MatMatMult(Mat A)
71: {
73: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
74: Mat_APMPI *ptap = a->ap;
77: PetscFree2(ptap->startsj_s,ptap->startsj_r);
78: PetscFree(ptap->bufa);
79: MatDestroy(&ptap->P_loc);
80: MatDestroy(&ptap->P_oth);
81: MatDestroy(&ptap->Pt);
82: PetscFree(ptap->api);
83: PetscFree(ptap->apj);
84: PetscFree(ptap->apa);
85: ptap->destroy(A);
86: PetscFree(ptap);
87: return(0);
88: }
90: PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable(Mat A,Mat P,Mat C)
91: {
93: Mat_MPIAIJ *a =(Mat_MPIAIJ*)A->data,*c=(Mat_MPIAIJ*)C->data;
94: Mat_SeqAIJ *ad =(Mat_SeqAIJ*)(a->A)->data,*ao=(Mat_SeqAIJ*)(a->B)->data;
95: Mat_SeqAIJ *cd =(Mat_SeqAIJ*)(c->A)->data,*co=(Mat_SeqAIJ*)(c->B)->data;
96: PetscScalar *cda=cd->a,*coa=co->a;
97: Mat_SeqAIJ *p_loc,*p_oth;
98: PetscScalar *apa,*ca;
99: PetscInt cm =C->rmap->n;
100: Mat_APMPI *ptap=c->ap;
101: PetscInt *api,*apj,*apJ,i,k;
102: PetscInt cstart=C->cmap->rstart;
103: PetscInt cdnz,conz,k0,k1;
104: MPI_Comm comm;
105: PetscMPIInt size;
108: PetscObjectGetComm((PetscObject)A,&comm);
109: MPI_Comm_size(comm,&size);
111: if (!ptap->P_oth && size>1) SETERRQ(comm,PETSC_ERR_ARG_WRONGSTATE,"AP cannot be reused. Do not call MatFreeIntermediateDataStructures() or use '-mat_freeintermediatedatastructures'");
113: /* 1) get P_oth = ptap->P_oth and P_loc = ptap->P_loc */
114: /*-----------------------------------------------------*/
115: /* update numerical values of P_oth and P_loc */
116: MatGetBrowsOfAoCols_MPIAIJ(A,P,MAT_REUSE_MATRIX,&ptap->startsj_s,&ptap->startsj_r,&ptap->bufa,&ptap->P_oth);
117: MatMPIAIJGetLocalMat(P,MAT_REUSE_MATRIX,&ptap->P_loc);
119: /* 2) compute numeric C_loc = A_loc*P = Ad*P_loc + Ao*P_oth */
120: /*----------------------------------------------------------*/
121: /* get data from symbolic products */
122: p_loc = (Mat_SeqAIJ*)(ptap->P_loc)->data;
123: p_oth = NULL;
124: if (size >1) {
125: p_oth = (Mat_SeqAIJ*)(ptap->P_oth)->data;
126: }
128: /* get apa for storing dense row A[i,:]*P */
129: apa = ptap->apa;
131: api = ptap->api;
132: apj = ptap->apj;
133: for (i=0; i<cm; i++) {
134: /* compute apa = A[i,:]*P */
135: AProw_nonscalable(i,ad,ao,p_loc,p_oth,apa);
137: /* set values in C */
138: apJ = apj + api[i];
139: cdnz = cd->i[i+1] - cd->i[i];
140: conz = co->i[i+1] - co->i[i];
142: /* 1st off-diagoanl part of C */
143: ca = coa + co->i[i];
144: k = 0;
145: for (k0=0; k0<conz; k0++) {
146: if (apJ[k] >= cstart) break;
147: ca[k0] = apa[apJ[k]];
148: apa[apJ[k++]] = 0.0;
149: }
151: /* diagonal part of C */
152: ca = cda + cd->i[i];
153: for (k1=0; k1<cdnz; k1++) {
154: ca[k1] = apa[apJ[k]];
155: apa[apJ[k++]] = 0.0;
156: }
158: /* 2nd off-diagoanl part of C */
159: ca = coa + co->i[i];
160: for (; k0<conz; k0++) {
161: ca[k0] = apa[apJ[k]];
162: apa[apJ[k++]] = 0.0;
163: }
164: }
165: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
166: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
168: if (ptap->freestruct) {
169: MatFreeIntermediateDataStructures(C);
170: }
171: return(0);
172: }
174: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(Mat A,Mat P,PetscReal fill,Mat C)
175: {
176: PetscErrorCode ierr;
177: MPI_Comm comm;
178: PetscMPIInt size;
179: Mat_APMPI *ptap;
180: PetscFreeSpaceList free_space=NULL,current_space=NULL;
181: Mat_MPIAIJ *a =(Mat_MPIAIJ*)A->data,*c;
182: Mat_SeqAIJ *ad =(Mat_SeqAIJ*)(a->A)->data,*ao=(Mat_SeqAIJ*)(a->B)->data,*p_loc,*p_oth;
183: PetscInt *pi_loc,*pj_loc,*pi_oth,*pj_oth,*dnz,*onz;
184: PetscInt *adi=ad->i,*adj=ad->j,*aoi=ao->i,*aoj=ao->j,rstart=A->rmap->rstart;
185: PetscInt *lnk,i,pnz,row,*api,*apj,*Jptr,apnz,nspacedouble=0,j,nzi;
186: PetscInt am=A->rmap->n,pN=P->cmap->N,pn=P->cmap->n,pm=P->rmap->n;
187: PetscBT lnkbt;
188: PetscReal afill;
189: MatType mtype;
192: PetscObjectGetComm((PetscObject)A,&comm);
193: MPI_Comm_size(comm,&size);
195: /* create struct Mat_APMPI and attached it to C later */
196: PetscNew(&ptap);
198: /* get P_oth by taking rows of P (= non-zero cols of local A) from other processors */
199: MatGetBrowsOfAoCols_MPIAIJ(A,P,MAT_INITIAL_MATRIX,&ptap->startsj_s,&ptap->startsj_r,&ptap->bufa,&ptap->P_oth);
201: /* get P_loc by taking all local rows of P */
202: MatMPIAIJGetLocalMat(P,MAT_INITIAL_MATRIX,&ptap->P_loc);
204: p_loc = (Mat_SeqAIJ*)(ptap->P_loc)->data;
205: pi_loc = p_loc->i; pj_loc = p_loc->j;
206: if (size > 1) {
207: p_oth = (Mat_SeqAIJ*)(ptap->P_oth)->data;
208: pi_oth = p_oth->i; pj_oth = p_oth->j;
209: } else {
210: p_oth = NULL;
211: pi_oth = NULL; pj_oth = NULL;
212: }
214: /* first, compute symbolic AP = A_loc*P = A_diag*P_loc + A_off*P_oth */
215: /*-------------------------------------------------------------------*/
216: PetscMalloc1(am+2,&api);
217: ptap->api = api;
218: api[0] = 0;
220: /* create and initialize a linked list */
221: PetscLLCondensedCreate(pN,pN,&lnk,&lnkbt);
223: /* Initial FreeSpace size is fill*(nnz(A)+nnz(P)) */
224: PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(adi[am],PetscIntSumTruncate(aoi[am],pi_loc[pm]))),&free_space);
225: current_space = free_space;
227: MatPreallocateInitialize(comm,am,pn,dnz,onz);
228: for (i=0; i<am; i++) {
229: /* diagonal portion of A */
230: nzi = adi[i+1] - adi[i];
231: for (j=0; j<nzi; j++) {
232: row = *adj++;
233: pnz = pi_loc[row+1] - pi_loc[row];
234: Jptr = pj_loc + pi_loc[row];
235: /* add non-zero cols of P into the sorted linked list lnk */
236: PetscLLCondensedAddSorted(pnz,Jptr,lnk,lnkbt);
237: }
238: /* off-diagonal portion of A */
239: nzi = aoi[i+1] - aoi[i];
240: for (j=0; j<nzi; j++) {
241: row = *aoj++;
242: pnz = pi_oth[row+1] - pi_oth[row];
243: Jptr = pj_oth + pi_oth[row];
244: PetscLLCondensedAddSorted(pnz,Jptr,lnk,lnkbt);
245: }
247: apnz = lnk[0];
248: api[i+1] = api[i] + apnz;
250: /* if free space is not available, double the total space in the list */
251: if (current_space->local_remaining<apnz) {
252: PetscFreeSpaceGet(PetscIntSumTruncate(apnz,current_space->total_array_size),¤t_space);
253: nspacedouble++;
254: }
256: /* Copy data into free space, then initialize lnk */
257: PetscLLCondensedClean(pN,apnz,current_space->array,lnk,lnkbt);
258: MatPreallocateSet(i+rstart,apnz,current_space->array,dnz,onz);
260: current_space->array += apnz;
261: current_space->local_used += apnz;
262: current_space->local_remaining -= apnz;
263: }
265: /* Allocate space for apj, initialize apj, and */
266: /* destroy list of free space and other temporary array(s) */
267: PetscMalloc1(api[am]+1,&ptap->apj);
268: apj = ptap->apj;
269: PetscFreeSpaceContiguous(&free_space,ptap->apj);
270: PetscLLDestroy(lnk,lnkbt);
272: /* malloc apa to store dense row A[i,:]*P */
273: PetscCalloc1(pN,&ptap->apa);
275: /* set and assemble symbolic parallel matrix C */
276: /*---------------------------------------------*/
277: MatSetSizes(C,am,pn,PETSC_DETERMINE,PETSC_DETERMINE);
278: MatSetBlockSizesFromMats(C,A,P);
280: MatGetType(A,&mtype);
281: MatSetType(C,mtype);
282: MatMPIAIJSetPreallocation(C,0,dnz,0,onz);
284: MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(C, apj, api);
285: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
286: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
287: MatPreallocateFinalize(dnz,onz);
289: ptap->destroy = C->ops->destroy;
290: ptap->duplicate = C->ops->duplicate;
291: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable;
292: C->ops->productnumeric = MatProductNumeric_AB;
293: C->ops->destroy = MatDestroy_MPIAIJ_MatMatMult;
294: C->ops->freeintermediatedatastructures = MatFreeIntermediateDataStructures_MPIAIJ_AP;
296: /* attach the supporting struct to C for reuse */
297: c = (Mat_MPIAIJ*)C->data;
298: c->ap = ptap;
300: /* set MatInfo */
301: afill = (PetscReal)api[am]/(adi[am]+aoi[am]+pi_loc[pm]+1) + 1.e-5;
302: if (afill < 1.0) afill = 1.0;
303: C->info.mallocs = nspacedouble;
304: C->info.fill_ratio_given = fill;
305: C->info.fill_ratio_needed = afill;
307: #if defined(PETSC_USE_INFO)
308: if (api[am]) {
309: PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",nspacedouble,(double)fill,(double)afill);
310: PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
311: } else {
312: PetscInfo(C,"Empty matrix product\n");
313: }
314: #endif
315: return(0);
316: }
318: /* ------------------------------------------------------- */
319: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_MPIDense_AB(Mat C)
320: {
321: Mat_Product *product = C->product;
322: Mat A = product->A,B=product->B;
325: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend)
326: 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);
328: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIAIJ_MPIDense;
329: C->ops->productsymbolic = MatProductSymbolic_AB;
330: C->ops->productnumeric = MatProductNumeric_AB;
331: return(0);
332: }
333: /* -------------------------------------------------------------------- */
334: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_MPIDense_AtB(Mat C)
335: {
336: Mat_Product *product = C->product;
337: Mat A = product->A,B=product->B;
340: if (A->rmap->rstart != B->rmap->rstart || A->rmap->rend != B->rmap->rend)
341: SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, (%D, %D) != (%D,%D)",A->rmap->rstart,A->rmap->rend,B->rmap->rstart,B->rmap->rend);
343: C->ops->transposematmultsymbolic = MatTransposeMatMultSymbolic_MPIAIJ_MPIDense;
344: C->ops->productsymbolic = MatProductSymbolic_AtB;
345: return(0);
346: }
348: /* --------------------------------------------------------------------- */
349: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIAIJ_MPIDense(Mat C)
350: {
352: Mat_Product *product = C->product;
355: switch (product->type) {
356: case MATPRODUCT_AB:
357: MatProductSetFromOptions_MPIAIJ_MPIDense_AB(C);
358: break;
359: case MATPRODUCT_AtB:
360: MatProductSetFromOptions_MPIAIJ_MPIDense_AtB(C);
361: break;
362: default:
363: /* Use MatProduct_Basic() if there is no specific implementation */
364: C->ops->productsymbolic = MatProductSymbolic_Basic;
365: }
366: return(0);
367: }
368: /* ------------------------------------------------------- */
370: typedef struct {
371: Mat workB,Bb,Cb,workB1,Bb1,Cb1;
372: MPI_Request *rwaits,*swaits;
373: PetscInt numBb; /* num of Bb matrices */
374: PetscInt nsends,nrecvs;
375: MPI_Datatype *stype,*rtype;
376: } MPIAIJ_MPIDense;
378: PetscErrorCode MatMPIAIJ_MPIDenseDestroy(void *ctx)
379: {
380: MPIAIJ_MPIDense *contents = (MPIAIJ_MPIDense*)ctx;
381: PetscErrorCode ierr;
382: PetscInt i;
385: MatDestroy(&contents->workB);
387: if (contents->numBb) {
388: MatDestroy(&contents->Bb);
389: MatDestroy(&contents->Cb);
391: MatDestroy(&contents->workB1);
392: MatDestroy(&contents->Bb1);
393: MatDestroy(&contents->Cb1);
394: }
395: for (i=0; i<contents->nsends; i++) {
396: MPI_Type_free(&contents->stype[i]);
397: }
398: for (i=0; i<contents->nrecvs; i++) {
399: MPI_Type_free(&contents->rtype[i]);
400: }
401: PetscFree4(contents->stype,contents->rtype,contents->rwaits,contents->swaits);
402: PetscFree(contents);
403: return(0);
404: }
406: /*
407: This is a "dummy function" that handles the case where matrix C was created as a dense matrix
408: directly by the user and passed to MatMatMult() with the MAT_REUSE_MATRIX option
410: It is the same as MatMatMultSymbolic_MPIAIJ_MPIDense() except does not create C
411: */
412: PETSC_INTERN PetscErrorCode MatMatMultNumeric_MPIDense(Mat A,Mat B,Mat C)
413: {
414: PetscBool flg;
418: PetscObjectTypeCompare((PetscObject)A,MATNEST,&flg);
419: if (flg) {
420: PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
421: MatMatMultSymbolic_Nest_Dense(A,B,PETSC_DEFAULT,&C);
422: PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
423: C->ops->matmultnumeric = MatMatMultNumeric_Nest_Dense;
424: } else {
425: MatMatMultSymbolic_MPIAIJ_MPIDense(A,B,PETSC_DEFAULT,C);
426: }
427: (*C->ops->matmultnumeric)(A,B,C);
428: return(0);
429: }
431: /*
432: Create Bb, Cb, Bb1 and Cb1 matrices to be used by MatMatMultSymbolic_MPIAIJ_MPIDense().
433: These matrices are used as wrappers for sub-columns of B and C, thus their own matrix operations are not used.
434: Modified from MatCreateDense().
435: */
436: PETSC_STATIC_INLINE PetscErrorCode MatCreateSubMPIDense_private(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt rbs,PetscInt cbs,PetscScalar *data,Mat *A)
437: {
441: MatCreate(comm,A);
442: MatSetSizes(*A,m,n,M,N);
443: MatSetBlockSizes(*A,rbs,cbs);
444: MatSetType(*A,MATMPIDENSE);
445: MatMPIDenseSetPreallocation(*A,data);
446: (*A)->assembled = PETSC_TRUE;
447: return(0);
448: }
450: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIDense(Mat A,Mat B,PetscReal fill,Mat C)
451: {
452: PetscErrorCode ierr;
453: Mat_MPIAIJ *aij=(Mat_MPIAIJ*)A->data;
454: Mat_MPIDense *b=(Mat_MPIDense*)B->data;
455: Mat_SeqDense *bseq=(Mat_SeqDense*)(b->A)->data;
456: PetscInt nz=aij->B->cmap->n,nsends,nrecvs,i,nrows_to,j,lda=bseq->lda;
457: PetscContainer container;
458: MPIAIJ_MPIDense *contents;
459: VecScatter ctx=aij->Mvctx;
460: PetscInt Am=A->rmap->n,Bm=B->rmap->n,BN=B->cmap->N,Bbn,Bbn1,bs,nrows_from;
461: MPI_Comm comm;
462: MPI_Datatype type1,*stype,*rtype;
463: const PetscInt *sindices,*sstarts,*rstarts;
464: PetscMPIInt *disp;
467: PetscObjectGetComm((PetscObject)A,&comm);
468: if (!C->preallocated) {
469: MatSetSizes(C,Am,B->cmap->n,A->rmap->N,BN);
470: MatSetBlockSizesFromMats(C,A,B);
471: MatSetType(C,MATMPIDENSE);
472: MatMPIDenseSetPreallocation(C,NULL);
473: }
475: PetscNew(&contents);
476: contents->numBb = 0;
478: VecScatterGetRemote_Private(ctx,PETSC_TRUE/*send*/,&nsends,&sstarts,&sindices,NULL,NULL);
479: VecScatterGetRemoteOrdered_Private(ctx,PETSC_FALSE/*recv*/,&nrecvs,&rstarts,NULL,NULL,NULL);
481: /* Create column block of B and C for memory scalability when BN is too large */
482: /* Estimate Bbn, column size of Bb */
483: if (nz) {
484: Bbn1 = 2*Am*BN/nz;
485: } else Bbn1 = BN;
487: bs = PetscAbs(B->cmap->bs);
488: Bbn1 = Bbn1/bs *bs; /* Bbn1 is a multiple of bs */
489: if (Bbn1 > BN) Bbn1 = BN;
490: MPI_Allreduce(&Bbn1,&Bbn,1,MPIU_INT,MPI_MAX,comm);
492: /* Enable runtime option for Bbn */
493: PetscOptionsBegin(comm,((PetscObject)C)->prefix,"MatMatMult","Mat");
494: PetscOptionsInt("-matmatmult_Bbn","Number of columns in Bb","MatMatMult",Bbn,&Bbn,NULL);
495: if (Bbn > BN) SETERRQ2(comm,PETSC_ERR_ARG_SIZ,"Bbn=%D cannot be larger than %D, column size of B",Bbn,BN);
496: PetscOptionsEnd();
498: if (Bbn < BN) {
499: contents->numBb = BN/Bbn;
500: Bbn1 = BN - contents->numBb*Bbn;
501: }
503: if (contents->numBb) {
504: PetscScalar data[1]; /* fake array for Bb and Cb */
505: PetscInfo3(C,"use Bb, BN=%D, Bbn=%D; numBb=%D\n",BN,Bbn,contents->numBb);
506: MatCreateSubMPIDense_private(comm,B->rmap->n,PETSC_DECIDE,A->rmap->N,Bbn,B->rmap->bs,B->cmap->bs,data,&contents->Bb);
507: MatCreateSubMPIDense_private(comm,Am,PETSC_DECIDE,A->rmap->N,Bbn,C->rmap->bs,C->cmap->bs,data,&contents->Cb);
509: if (Bbn1) { /* Create Bb1 and Cb1 for the remaining columns */
510: PetscInfo2(C,"use Bb1, BN=%D, Bbn1=%D\n",BN,Bbn1);
511: MatCreateSubMPIDense_private(comm,B->rmap->n,PETSC_DECIDE,A->rmap->N,Bbn1,B->rmap->bs,B->cmap->bs,data,&contents->Bb1);
512: MatCreateSubMPIDense_private(comm,Am,PETSC_DECIDE,A->rmap->N,Bbn1,C->rmap->bs,C->cmap->bs,data,&contents->Cb1);
514: /* Create work matrix used to store off processor rows of B needed for local product */
515: MatCreateSeqDense(PETSC_COMM_SELF,nz,Bbn1,NULL,&contents->workB1);
516: }
517: }
519: /* Create work matrix used to store off processor rows of B needed for local product */
520: MatCreateSeqDense(PETSC_COMM_SELF,nz,Bbn,NULL,&contents->workB);
522: /* Use MPI derived data type to reduce memory required by the send/recv buffers */
523: PetscMalloc4(nsends,&stype,nrecvs,&rtype,nrecvs,&contents->rwaits,nsends,&contents->swaits);
524: contents->stype = stype;
525: contents->nsends = nsends;
527: contents->rtype = rtype;
528: contents->nrecvs = nrecvs;
530: PetscMalloc1(Bm+1,&disp);
531: for (i=0; i<nsends; i++) {
532: nrows_to = sstarts[i+1]-sstarts[i];
533: for (j=0; j<nrows_to; j++){
534: disp[j] = sindices[sstarts[i]+j]; /* rowB to be sent */
535: }
536: MPI_Type_create_indexed_block(nrows_to,1,(const PetscMPIInt *)disp,MPIU_SCALAR,&type1);
538: MPI_Type_create_resized(type1,0,lda*sizeof(PetscScalar),&stype[i]);
539: MPI_Type_commit(&stype[i]);
540: MPI_Type_free(&type1);
541: }
543: for (i=0; i<nrecvs; i++) {
544: /* received values from a process form a (nrows_from x Bbn) row block in workB (column-wise) */
545: nrows_from = rstarts[i+1]-rstarts[i];
546: disp[0] = 0;
547: MPI_Type_create_indexed_block(1, nrows_from, (const PetscMPIInt *)disp, MPIU_SCALAR, &type1);
548: MPI_Type_create_resized(type1, 0, nz*sizeof(PetscScalar), &rtype[i]);
549: MPI_Type_commit(&rtype[i]);
550: MPI_Type_free(&type1);
551: }
553: PetscFree(disp);
554: VecScatterRestoreRemote_Private(ctx,PETSC_TRUE/*send*/,&nsends,&sstarts,&sindices,NULL,NULL);
555: VecScatterRestoreRemoteOrdered_Private(ctx,PETSC_FALSE/*recv*/,&nrecvs,&rstarts,NULL,NULL,NULL);
557: PetscContainerCreate(comm,&container);
558: PetscContainerSetPointer(container,contents);
559: PetscContainerSetUserDestroy(container,MatMPIAIJ_MPIDenseDestroy);
560: PetscObjectCompose((PetscObject)C,"workB",(PetscObject)container);
561: PetscContainerDestroy(&container);
562: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
563: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
564: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIDense;
565: return(0);
566: }
568: extern PetscErrorCode MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat,Mat,Mat);
569: /*
570: Performs an efficient scatter on the rows of B needed by this process; this is
571: a modification of the VecScatterBegin_() routines.
573: Input: Bbidx = 0: B = Bb
574: = 1: B = Bb1, see MatMatMultSymbolic_MPIAIJ_MPIDense()
575: */
576: PetscErrorCode MatMPIDenseScatter(Mat A,Mat B,PetscInt Bbidx,Mat C,Mat *outworkB)
577: {
578: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)A->data;
579: PetscErrorCode ierr;
580: const PetscScalar *b;
581: PetscScalar *rvalues;
582: VecScatter ctx = aij->Mvctx;
583: const PetscInt *sindices,*sstarts,*rstarts;
584: const PetscMPIInt *sprocs,*rprocs;
585: PetscInt i,nsends,nrecvs,nrecvs2;
586: MPI_Request *swaits,*rwaits;
587: MPI_Comm comm;
588: PetscMPIInt tag=((PetscObject)ctx)->tag,ncols=B->cmap->N,nrows=aij->B->cmap->n,imdex,nsends_mpi,nrecvs_mpi;
589: MPI_Status status;
590: MPIAIJ_MPIDense *contents;
591: PetscContainer container;
592: Mat workB;
593: MPI_Datatype *stype,*rtype;
596: PetscObjectGetComm((PetscObject)A,&comm);
597: PetscObjectQuery((PetscObject)C,"workB",(PetscObject*)&container);
598: if (!container) SETERRQ(comm,PETSC_ERR_PLIB,"Container does not exist");
599: PetscContainerGetPointer(container,(void**)&contents);
601: VecScatterGetRemote_Private(ctx,PETSC_TRUE/*send*/,&nsends,&sstarts,&sindices,&sprocs,NULL/*bs*/);
602: VecScatterGetRemoteOrdered_Private(ctx,PETSC_FALSE/*recv*/,&nrecvs,&rstarts,NULL,&rprocs,NULL/*bs*/);
603: PetscMPIIntCast(nsends,&nsends_mpi);
604: PetscMPIIntCast(nrecvs,&nrecvs_mpi);
605: if (Bbidx == 0) {
606: workB = *outworkB = contents->workB;
607: } else {
608: workB = *outworkB = contents->workB1;
609: }
610: if (nrows != workB->rmap->n) SETERRQ2(comm,PETSC_ERR_PLIB,"Number of rows of workB %D not equal to columns of aij->B %D",workB->cmap->n,nrows);
611: swaits = contents->swaits;
612: rwaits = contents->rwaits;
614: MatDenseGetArrayRead(B,&b);
615: MatDenseGetArray(workB,&rvalues);
617: /* Post recv, use MPI derived data type to save memory */
618: rtype = contents->rtype;
619: for (i=0; i<nrecvs; i++) {
620: MPI_Irecv(rvalues+(rstarts[i]-rstarts[0]),ncols,rtype[i],rprocs[i],tag,comm,rwaits+i);
621: }
623: stype = contents->stype;
624: for (i=0; i<nsends; i++) {
625: MPI_Isend(b,ncols,stype[i],sprocs[i],tag,comm,swaits+i);
626: }
628: nrecvs2 = nrecvs;
629: while (nrecvs2) {
630: MPI_Waitany(nrecvs_mpi,rwaits,&imdex,&status);
631: nrecvs2--;
632: }
633: if (nsends) {MPI_Waitall(nsends_mpi,swaits,MPI_STATUSES_IGNORE);}
635: VecScatterRestoreRemote_Private(ctx,PETSC_TRUE/*send*/,&nsends,&sstarts,&sindices,&sprocs,NULL);
636: VecScatterRestoreRemoteOrdered_Private(ctx,PETSC_FALSE/*recv*/,&nrecvs,&rstarts,NULL,&rprocs,NULL);
637: MatDenseRestoreArrayRead(B,&b);
638: MatDenseRestoreArray(workB,&rvalues);
639: MatAssemblyBegin(workB,MAT_FINAL_ASSEMBLY);
640: MatAssemblyEnd(workB,MAT_FINAL_ASSEMBLY);
641: return(0);
642: }
644: /*
645: Compute Cb = A*Bb
646: */
647: PETSC_STATIC_INLINE PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIDense_private(Mat A,Mat Bb,PetscInt Bbidx,PetscInt start,Mat C,const PetscScalar *barray,PetscScalar *carray,Mat Cb)
648: {
649: PetscErrorCode ierr;
650: PetscInt start1;
651: Mat workB;
652: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)A->data;
653: Mat_MPIDense *cbdense = (Mat_MPIDense*)Cb->data;
656: /* Place barray to Bb */
657: start1 = start*Bb->rmap->n;
658: MatDensePlaceArray(Bb,barray+start1);
660: /* get off processor parts of Bb needed to complete Cb=A*Bb */
661: MatMPIDenseScatter(A,Bb,Bbidx,C,&workB);
662: MatDenseResetArray(Bb);
664: /* off-diagonal block of A times nonlocal rows of Bb */
665: /* Place carray to Cb */
666: start1 = start*Cb->rmap->n;
667: MatDensePlaceArray(Cb,carray+start1);
668: MatMatMultNumericAdd_SeqAIJ_SeqDense(aij->B,workB,cbdense->A);
669: MatDenseResetArray(Cb);
670: return(0);
671: }
673: PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIDense(Mat A,Mat B,Mat C)
674: {
675: PetscErrorCode ierr;
676: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)A->data;
677: Mat_MPIDense *bdense = (Mat_MPIDense*)B->data;
678: Mat_MPIDense *cdense = (Mat_MPIDense*)C->data;
679: Mat workB;
680: MPIAIJ_MPIDense *contents;
681: PetscContainer container;
682: MPI_Comm comm;
683: PetscInt numBb;
686: /* diagonal block of A times all local rows of B*/
687: MatMatMultNumeric_SeqAIJ_SeqDense(aij->A,bdense->A,cdense->A);
689: PetscObjectGetComm((PetscObject)A,&comm);
690: PetscObjectQuery((PetscObject)C,"workB",(PetscObject*)&container);
691: if (!container) SETERRQ(comm,PETSC_ERR_PLIB,"Container does not exist");
692: PetscContainerGetPointer(container,(void**)&contents);
693: numBb = contents->numBb;
695: if (!numBb) {
696: /* get off processor parts of B needed to complete C=A*B */
697: MatMPIDenseScatter(A,B,0,C,&workB);
699: /* off-diagonal block of A times nonlocal rows of B */
700: MatMatMultNumericAdd_SeqAIJ_SeqDense(aij->B,workB,cdense->A);
702: } else {
703: const PetscScalar *barray;
704: PetscScalar *carray;
705: Mat Bb=contents->Bb,Cb=contents->Cb;
706: PetscInt BbN=Bb->cmap->N,start,i;
708: MatDenseGetArrayRead(B,&barray);
709: MatDenseGetArray(C,&carray);
710: for (i=0; i<numBb; i++) {
711: start = i*BbN;
712: MatMatMultNumeric_MPIAIJ_MPIDense_private(A,Bb,0,start,C,barray,carray,Cb);
713: }
715: if (contents->Bb1) {
716: Bb = contents->Bb1; Cb = contents->Cb1;
717: start = i*BbN;
718: MatMatMultNumeric_MPIAIJ_MPIDense_private(A,Bb,1,start,C,barray,carray,Cb);
719: }
720: MatDenseRestoreArrayRead(B,&barray);
721: MatDenseRestoreArray(C,&carray);
722: }
723: return(0);
724: }
726: PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIAIJ(Mat A,Mat P,Mat C)
727: {
729: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data,*c=(Mat_MPIAIJ*)C->data;
730: Mat_SeqAIJ *ad = (Mat_SeqAIJ*)(a->A)->data,*ao=(Mat_SeqAIJ*)(a->B)->data;
731: Mat_SeqAIJ *cd = (Mat_SeqAIJ*)(c->A)->data,*co=(Mat_SeqAIJ*)(c->B)->data;
732: PetscInt *adi = ad->i,*adj,*aoi=ao->i,*aoj;
733: PetscScalar *ada,*aoa,*cda=cd->a,*coa=co->a;
734: Mat_SeqAIJ *p_loc,*p_oth;
735: PetscInt *pi_loc,*pj_loc,*pi_oth,*pj_oth,*pj;
736: PetscScalar *pa_loc,*pa_oth,*pa,valtmp,*ca;
737: PetscInt cm = C->rmap->n,anz,pnz;
738: Mat_APMPI *ptap = c->ap;
739: PetscScalar *apa_sparse;
740: PetscInt *api,*apj,*apJ,i,j,k,row;
741: PetscInt cstart = C->cmap->rstart;
742: PetscInt cdnz,conz,k0,k1,nextp;
743: MPI_Comm comm;
744: PetscMPIInt size;
747: PetscObjectGetComm((PetscObject)C,&comm);
748: MPI_Comm_size(comm,&size);
750: if (!ptap->P_oth && size>1) {
751: SETERRQ(comm,PETSC_ERR_ARG_WRONGSTATE,"AP cannot be reused. Do not call MatFreeIntermediateDataStructures() or use '-mat_freeintermediatedatastructures'");
752: }
753: apa_sparse = ptap->apa;
755: /* 1) get P_oth = ptap->P_oth and P_loc = ptap->P_loc */
756: /*-----------------------------------------------------*/
757: /* update numerical values of P_oth and P_loc */
758: MatGetBrowsOfAoCols_MPIAIJ(A,P,MAT_REUSE_MATRIX,&ptap->startsj_s,&ptap->startsj_r,&ptap->bufa,&ptap->P_oth);
759: MatMPIAIJGetLocalMat(P,MAT_REUSE_MATRIX,&ptap->P_loc);
761: /* 2) compute numeric C_loc = A_loc*P = Ad*P_loc + Ao*P_oth */
762: /*----------------------------------------------------------*/
763: /* get data from symbolic products */
764: p_loc = (Mat_SeqAIJ*)(ptap->P_loc)->data;
765: pi_loc = p_loc->i; pj_loc = p_loc->j; pa_loc = p_loc->a;
766: if (size >1) {
767: p_oth = (Mat_SeqAIJ*)(ptap->P_oth)->data;
768: pi_oth = p_oth->i; pj_oth = p_oth->j; pa_oth = p_oth->a;
769: } else {
770: p_oth = NULL; pi_oth = NULL; pj_oth = NULL; pa_oth = NULL;
771: }
773: api = ptap->api;
774: apj = ptap->apj;
775: for (i=0; i<cm; i++) {
776: apJ = apj + api[i];
778: /* diagonal portion of A */
779: anz = adi[i+1] - adi[i];
780: adj = ad->j + adi[i];
781: ada = ad->a + adi[i];
782: for (j=0; j<anz; j++) {
783: row = adj[j];
784: pnz = pi_loc[row+1] - pi_loc[row];
785: pj = pj_loc + pi_loc[row];
786: pa = pa_loc + pi_loc[row];
787: /* perform sparse axpy */
788: valtmp = ada[j];
789: nextp = 0;
790: for (k=0; nextp<pnz; k++) {
791: if (apJ[k] == pj[nextp]) { /* column of AP == column of P */
792: apa_sparse[k] += valtmp*pa[nextp++];
793: }
794: }
795: PetscLogFlops(2.0*pnz);
796: }
798: /* off-diagonal portion of A */
799: anz = aoi[i+1] - aoi[i];
800: aoj = ao->j + aoi[i];
801: aoa = ao->a + aoi[i];
802: for (j=0; j<anz; j++) {
803: row = aoj[j];
804: pnz = pi_oth[row+1] - pi_oth[row];
805: pj = pj_oth + pi_oth[row];
806: pa = pa_oth + pi_oth[row];
807: /* perform sparse axpy */
808: valtmp = aoa[j];
809: nextp = 0;
810: for (k=0; nextp<pnz; k++) {
811: if (apJ[k] == pj[nextp]) { /* column of AP == column of P */
812: apa_sparse[k] += valtmp*pa[nextp++];
813: }
814: }
815: PetscLogFlops(2.0*pnz);
816: }
818: /* set values in C */
819: cdnz = cd->i[i+1] - cd->i[i];
820: conz = co->i[i+1] - co->i[i];
822: /* 1st off-diagoanl part of C */
823: ca = coa + co->i[i];
824: k = 0;
825: for (k0=0; k0<conz; k0++) {
826: if (apJ[k] >= cstart) break;
827: ca[k0] = apa_sparse[k];
828: apa_sparse[k] = 0.0;
829: k++;
830: }
832: /* diagonal part of C */
833: ca = cda + cd->i[i];
834: for (k1=0; k1<cdnz; k1++) {
835: ca[k1] = apa_sparse[k];
836: apa_sparse[k] = 0.0;
837: k++;
838: }
840: /* 2nd off-diagoanl part of C */
841: ca = coa + co->i[i];
842: for (; k0<conz; k0++) {
843: ca[k0] = apa_sparse[k];
844: apa_sparse[k] = 0.0;
845: k++;
846: }
847: }
848: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
849: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
851: if (ptap->freestruct) {
852: MatFreeIntermediateDataStructures(C);
853: }
854: return(0);
855: }
857: /* same as MatMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(), except using LLCondensed to avoid O(BN) memory requirement */
858: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIAIJ(Mat A,Mat P,PetscReal fill,Mat C)
859: {
860: PetscErrorCode ierr;
861: MPI_Comm comm;
862: PetscMPIInt size;
863: Mat_APMPI *ptap;
864: PetscFreeSpaceList free_space = NULL,current_space=NULL;
865: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data,*c;
866: Mat_SeqAIJ *ad = (Mat_SeqAIJ*)(a->A)->data,*ao=(Mat_SeqAIJ*)(a->B)->data,*p_loc,*p_oth;
867: PetscInt *pi_loc,*pj_loc,*pi_oth,*pj_oth,*dnz,*onz;
868: PetscInt *adi=ad->i,*adj=ad->j,*aoi=ao->i,*aoj=ao->j,rstart=A->rmap->rstart;
869: PetscInt i,pnz,row,*api,*apj,*Jptr,apnz,nspacedouble=0,j,nzi,*lnk,apnz_max=0;
870: PetscInt am=A->rmap->n,pn=P->cmap->n,pm=P->rmap->n,lsize=pn+20;
871: PetscReal afill;
872: MatType mtype;
875: PetscObjectGetComm((PetscObject)A,&comm);
876: MPI_Comm_size(comm,&size);
878: /* create struct Mat_APMPI and attached it to C later */
879: PetscNew(&ptap);
881: /* get P_oth by taking rows of P (= non-zero cols of local A) from other processors */
882: MatGetBrowsOfAoCols_MPIAIJ(A,P,MAT_INITIAL_MATRIX,&ptap->startsj_s,&ptap->startsj_r,&ptap->bufa,&ptap->P_oth);
884: /* get P_loc by taking all local rows of P */
885: MatMPIAIJGetLocalMat(P,MAT_INITIAL_MATRIX,&ptap->P_loc);
887: p_loc = (Mat_SeqAIJ*)(ptap->P_loc)->data;
888: pi_loc = p_loc->i; pj_loc = p_loc->j;
889: if (size > 1) {
890: p_oth = (Mat_SeqAIJ*)(ptap->P_oth)->data;
891: pi_oth = p_oth->i; pj_oth = p_oth->j;
892: } else {
893: p_oth = NULL;
894: pi_oth = NULL; pj_oth = NULL;
895: }
897: /* first, compute symbolic AP = A_loc*P = A_diag*P_loc + A_off*P_oth */
898: /*-------------------------------------------------------------------*/
899: PetscMalloc1(am+2,&api);
900: ptap->api = api;
901: api[0] = 0;
903: PetscLLCondensedCreate_Scalable(lsize,&lnk);
905: /* Initial FreeSpace size is fill*(nnz(A)+nnz(P)) */
906: PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(adi[am],PetscIntSumTruncate(aoi[am],pi_loc[pm]))),&free_space);
907: current_space = free_space;
908: MatPreallocateInitialize(comm,am,pn,dnz,onz);
909: for (i=0; i<am; i++) {
910: /* diagonal portion of A */
911: nzi = adi[i+1] - adi[i];
912: for (j=0; j<nzi; j++) {
913: row = *adj++;
914: pnz = pi_loc[row+1] - pi_loc[row];
915: Jptr = pj_loc + pi_loc[row];
916: /* Expand list if it is not long enough */
917: if (pnz+apnz_max > lsize) {
918: lsize = pnz+apnz_max;
919: PetscLLCondensedExpand_Scalable(lsize, &lnk);
920: }
921: /* add non-zero cols of P into the sorted linked list lnk */
922: PetscLLCondensedAddSorted_Scalable(pnz,Jptr,lnk);
923: apnz = *lnk; /* The first element in the list is the number of items in the list */
924: api[i+1] = api[i] + apnz;
925: if (apnz > apnz_max) apnz_max = apnz;
926: }
927: /* off-diagonal portion of A */
928: nzi = aoi[i+1] - aoi[i];
929: for (j=0; j<nzi; j++) {
930: row = *aoj++;
931: pnz = pi_oth[row+1] - pi_oth[row];
932: Jptr = pj_oth + pi_oth[row];
933: /* Expand list if it is not long enough */
934: if (pnz+apnz_max > lsize) {
935: lsize = pnz + apnz_max;
936: PetscLLCondensedExpand_Scalable(lsize, &lnk);
937: }
938: /* add non-zero cols of P into the sorted linked list lnk */
939: PetscLLCondensedAddSorted_Scalable(pnz,Jptr,lnk);
940: apnz = *lnk; /* The first element in the list is the number of items in the list */
941: api[i+1] = api[i] + apnz;
942: if (apnz > apnz_max) apnz_max = apnz;
943: }
944: apnz = *lnk;
945: api[i+1] = api[i] + apnz;
946: if (apnz > apnz_max) apnz_max = apnz;
948: /* if free space is not available, double the total space in the list */
949: if (current_space->local_remaining<apnz) {
950: PetscFreeSpaceGet(PetscIntSumTruncate(apnz,current_space->total_array_size),¤t_space);
951: nspacedouble++;
952: }
954: /* Copy data into free space, then initialize lnk */
955: PetscLLCondensedClean_Scalable(apnz,current_space->array,lnk);
956: MatPreallocateSet(i+rstart,apnz,current_space->array,dnz,onz);
958: current_space->array += apnz;
959: current_space->local_used += apnz;
960: current_space->local_remaining -= apnz;
961: }
963: /* Allocate space for apj, initialize apj, and */
964: /* destroy list of free space and other temporary array(s) */
965: PetscMalloc1(api[am]+1,&ptap->apj);
966: apj = ptap->apj;
967: PetscFreeSpaceContiguous(&free_space,ptap->apj);
968: PetscLLCondensedDestroy_Scalable(lnk);
970: /* create and assemble symbolic parallel matrix C */
971: /*----------------------------------------------------*/
972: MatSetSizes(C,am,pn,PETSC_DETERMINE,PETSC_DETERMINE);
973: MatSetBlockSizesFromMats(C,A,P);
974: MatGetType(A,&mtype);
975: MatSetType(C,mtype);
976: MatMPIAIJSetPreallocation(C,0,dnz,0,onz);
978: /* malloc apa for assembly C */
979: PetscCalloc1(apnz_max,&ptap->apa);
981: MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(C, apj, api);
982: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
983: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
984: MatPreallocateFinalize(dnz,onz);
986: ptap->destroy = C->ops->destroy;
987: ptap->duplicate = C->ops->duplicate;
988: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIAIJ;
989: C->ops->productnumeric = MatProductNumeric_AB;
990: C->ops->destroy = MatDestroy_MPIAIJ_MatMatMult;
991: C->ops->freeintermediatedatastructures = MatFreeIntermediateDataStructures_MPIAIJ_AP;
993: /* attach the supporting struct to C for reuse */
994: c = (Mat_MPIAIJ*)C->data;
995: c->ap = ptap;
997: /* set MatInfo */
998: afill = (PetscReal)api[am]/(adi[am]+aoi[am]+pi_loc[pm]+1) + 1.e-5;
999: if (afill < 1.0) afill = 1.0;
1000: C->info.mallocs = nspacedouble;
1001: C->info.fill_ratio_given = fill;
1002: C->info.fill_ratio_needed = afill;
1004: #if defined(PETSC_USE_INFO)
1005: if (api[am]) {
1006: PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",nspacedouble,(double)fill,(double)afill);
1007: PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
1008: } else {
1009: PetscInfo(C,"Empty matrix product\n");
1010: }
1011: #endif
1012: return(0);
1013: }
1015: /* This function is needed for the seqMPI matrix-matrix multiplication. */
1016: /* Three input arrays are merged to one output array. The size of the */
1017: /* output array is also output. Duplicate entries only show up once. */
1018: static void Merge3SortedArrays(PetscInt size1, PetscInt *in1,
1019: PetscInt size2, PetscInt *in2,
1020: PetscInt size3, PetscInt *in3,
1021: PetscInt *size4, PetscInt *out)
1022: {
1023: int i = 0, j = 0, k = 0, l = 0;
1025: /* Traverse all three arrays */
1026: while (i<size1 && j<size2 && k<size3) {
1027: if (in1[i] < in2[j] && in1[i] < in3[k]) {
1028: out[l++] = in1[i++];
1029: }
1030: else if(in2[j] < in1[i] && in2[j] < in3[k]) {
1031: out[l++] = in2[j++];
1032: }
1033: else if(in3[k] < in1[i] && in3[k] < in2[j]) {
1034: out[l++] = in3[k++];
1035: }
1036: else if(in1[i] == in2[j] && in1[i] < in3[k]) {
1037: out[l++] = in1[i];
1038: i++, j++;
1039: }
1040: else if(in1[i] == in3[k] && in1[i] < in2[j]) {
1041: out[l++] = in1[i];
1042: i++, k++;
1043: }
1044: else if(in3[k] == in2[j] && in2[j] < in1[i]) {
1045: out[l++] = in2[j];
1046: k++, j++;
1047: }
1048: else if(in1[i] == in2[j] && in1[i] == in3[k]) {
1049: out[l++] = in1[i];
1050: i++, j++, k++;
1051: }
1052: }
1054: /* Traverse two remaining arrays */
1055: while (i<size1 && j<size2) {
1056: if (in1[i] < in2[j]) {
1057: out[l++] = in1[i++];
1058: }
1059: else if(in1[i] > in2[j]) {
1060: out[l++] = in2[j++];
1061: }
1062: else {
1063: out[l++] = in1[i];
1064: i++, j++;
1065: }
1066: }
1068: while (i<size1 && k<size3) {
1069: if (in1[i] < in3[k]) {
1070: out[l++] = in1[i++];
1071: }
1072: else if(in1[i] > in3[k]) {
1073: out[l++] = in3[k++];
1074: }
1075: else {
1076: out[l++] = in1[i];
1077: i++, k++;
1078: }
1079: }
1081: while (k<size3 && j<size2) {
1082: if (in3[k] < in2[j]) {
1083: out[l++] = in3[k++];
1084: }
1085: else if(in3[k] > in2[j]) {
1086: out[l++] = in2[j++];
1087: }
1088: else {
1089: out[l++] = in3[k];
1090: k++, j++;
1091: }
1092: }
1094: /* Traverse one remaining array */
1095: while (i<size1) out[l++] = in1[i++];
1096: while (j<size2) out[l++] = in2[j++];
1097: while (k<size3) out[l++] = in3[k++];
1099: *size4 = l;
1100: }
1102: /* This matrix-matrix multiplication algorithm divides the multiplication into three multiplications and */
1103: /* adds up the products. Two of these three multiplications are performed with existing (sequential) */
1104: /* matrix-matrix multiplications. */
1105: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIAIJ_seqMPI(Mat A, Mat P, PetscReal fill, Mat C)
1106: {
1107: PetscErrorCode ierr;
1108: MPI_Comm comm;
1109: PetscMPIInt size;
1110: Mat_APMPI *ptap;
1111: PetscFreeSpaceList free_space_diag=NULL, current_space=NULL;
1112: Mat_MPIAIJ *a =(Mat_MPIAIJ*)A->data;
1113: Mat_SeqAIJ *ad =(Mat_SeqAIJ*)(a->A)->data,*ao=(Mat_SeqAIJ*)(a->B)->data,*p_loc;
1114: Mat_MPIAIJ *p =(Mat_MPIAIJ*)P->data;
1115: Mat_MPIAIJ *c;
1116: Mat_SeqAIJ *adpd_seq, *p_off, *aopoth_seq;
1117: PetscInt adponz, adpdnz;
1118: PetscInt *pi_loc,*dnz,*onz;
1119: PetscInt *adi=ad->i,*adj=ad->j,*aoi=ao->i,rstart=A->rmap->rstart;
1120: PetscInt *lnk,i, i1=0,pnz,row,*adpoi,*adpoj, *api, *adpoJ, *aopJ, *apJ,*Jptr, aopnz, nspacedouble=0,j,nzi,
1121: *apj,apnz, *adpdi, *adpdj, *adpdJ, *poff_i, *poff_j, *j_temp, *aopothi, *aopothj;
1122: PetscInt am=A->rmap->n,pN=P->cmap->N,pn=P->cmap->n,pm=P->rmap->n, p_colstart, p_colend;
1123: PetscBT lnkbt;
1124: PetscReal afill;
1125: PetscMPIInt rank;
1126: Mat adpd, aopoth;
1127: MatType mtype;
1128: const char *prefix;
1131: PetscObjectGetComm((PetscObject)A,&comm);
1132: MPI_Comm_size(comm,&size);
1133: MPI_Comm_rank(comm, &rank);
1134: MatGetOwnershipRangeColumn(P, &p_colstart, &p_colend);
1136: /* create struct Mat_APMPI and attached it to C later */
1137: PetscNew(&ptap);
1139: /* get P_oth by taking rows of P (= non-zero cols of local A) from other processors */
1140: MatGetBrowsOfAoCols_MPIAIJ(A,P,MAT_INITIAL_MATRIX,&ptap->startsj_s,&ptap->startsj_r,&ptap->bufa,&ptap->P_oth);
1142: /* get P_loc by taking all local rows of P */
1143: MatMPIAIJGetLocalMat(P,MAT_INITIAL_MATRIX,&ptap->P_loc);
1146: p_loc = (Mat_SeqAIJ*)(ptap->P_loc)->data;
1147: pi_loc = p_loc->i;
1149: /* Allocate memory for the i arrays of the matrices A*P, A_diag*P_off and A_offd * P */
1150: PetscMalloc1(am+2,&api);
1151: PetscMalloc1(am+2,&adpoi);
1153: adpoi[0] = 0;
1154: ptap->api = api;
1155: api[0] = 0;
1157: /* create and initialize a linked list, will be used for both A_diag * P_loc_off and A_offd * P_oth */
1158: PetscLLCondensedCreate(pN,pN,&lnk,&lnkbt);
1159: MatPreallocateInitialize(comm,am,pn,dnz,onz);
1161: /* Symbolic calc of A_loc_diag * P_loc_diag */
1162: MatGetOptionsPrefix(A,&prefix);
1163: MatProductCreate(a->A,p->A,NULL,&adpd);
1164: MatGetOptionsPrefix(A,&prefix);
1165: MatSetOptionsPrefix(adpd,prefix);
1166: MatAppendOptionsPrefix(adpd,"inner_diag_");
1168: MatProductSetType(adpd,MATPRODUCT_AB);
1169: MatProductSetAlgorithm(adpd,"sorted");
1170: MatProductSetFill(adpd,fill);
1171: MatProductSetFromOptions(adpd);
1172: MatProductSymbolic(adpd);
1174: adpd_seq = (Mat_SeqAIJ*)((adpd)->data);
1175: adpdi = adpd_seq->i; adpdj = adpd_seq->j;
1176: p_off = (Mat_SeqAIJ*)((p->B)->data);
1177: poff_i = p_off->i; poff_j = p_off->j;
1179: /* j_temp stores indices of a result row before they are added to the linked list */
1180: PetscMalloc1(pN+2,&j_temp);
1183: /* Symbolic calc of the A_diag * p_loc_off */
1184: /* Initial FreeSpace size is fill*(nnz(A)+nnz(P)) */
1185: PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(adi[am],PetscIntSumTruncate(aoi[am],pi_loc[pm]))),&free_space_diag);
1186: current_space = free_space_diag;
1188: for (i=0; i<am; i++) {
1189: /* A_diag * P_loc_off */
1190: nzi = adi[i+1] - adi[i];
1191: for (j=0; j<nzi; j++) {
1192: row = *adj++;
1193: pnz = poff_i[row+1] - poff_i[row];
1194: Jptr = poff_j + poff_i[row];
1195: for(i1 = 0; i1 < pnz; i1++) {
1196: j_temp[i1] = p->garray[Jptr[i1]];
1197: }
1198: /* add non-zero cols of P into the sorted linked list lnk */
1199: PetscLLCondensedAddSorted(pnz,j_temp,lnk,lnkbt);
1200: }
1202: adponz = lnk[0];
1203: adpoi[i+1] = adpoi[i] + adponz;
1205: /* if free space is not available, double the total space in the list */
1206: if (current_space->local_remaining<adponz) {
1207: PetscFreeSpaceGet(PetscIntSumTruncate(adponz,current_space->total_array_size),¤t_space);
1208: nspacedouble++;
1209: }
1211: /* Copy data into free space, then initialize lnk */
1212: PetscLLCondensedClean(pN,adponz,current_space->array,lnk,lnkbt);
1214: current_space->array += adponz;
1215: current_space->local_used += adponz;
1216: current_space->local_remaining -= adponz;
1217: }
1219: /* Symbolic calc of A_off * P_oth */
1220: MatSetOptionsPrefix(a->B,prefix);
1221: MatAppendOptionsPrefix(a->B,"inner_offdiag_");
1222: MatCreate(PETSC_COMM_SELF,&aopoth);
1223: MatMatMultSymbolic_SeqAIJ_SeqAIJ(a->B, ptap->P_oth, fill, aopoth);
1224: aopoth_seq = (Mat_SeqAIJ*)((aopoth)->data);
1225: aopothi = aopoth_seq->i; aopothj = aopoth_seq->j;
1227: /* Allocate space for apj, adpj, aopj, ... */
1228: /* destroy lists of free space and other temporary array(s) */
1230: PetscMalloc1(aopothi[am] + adpoi[am] + adpdi[am]+2, &ptap->apj);
1231: PetscMalloc1(adpoi[am]+2, &adpoj);
1233: /* Copy from linked list to j-array */
1234: PetscFreeSpaceContiguous(&free_space_diag,adpoj);
1235: PetscLLDestroy(lnk,lnkbt);
1237: adpoJ = adpoj;
1238: adpdJ = adpdj;
1239: aopJ = aopothj;
1240: apj = ptap->apj;
1241: apJ = apj; /* still empty */
1243: /* Merge j-arrays of A_off * P, A_diag * P_loc_off, and */
1244: /* A_diag * P_loc_diag to get A*P */
1245: for (i = 0; i < am; i++) {
1246: aopnz = aopothi[i+1] - aopothi[i];
1247: adponz = adpoi[i+1] - adpoi[i];
1248: adpdnz = adpdi[i+1] - adpdi[i];
1250: /* Correct indices from A_diag*P_diag */
1251: for(i1 = 0; i1 < adpdnz; i1++) {
1252: adpdJ[i1] += p_colstart;
1253: }
1254: /* Merge j-arrays of A_diag * P_loc_off and A_diag * P_loc_diag and A_off * P_oth */
1255: Merge3SortedArrays(adponz, adpoJ, adpdnz, adpdJ, aopnz, aopJ, &apnz, apJ);
1256: MatPreallocateSet(i+rstart, apnz, apJ, dnz, onz);
1258: aopJ += aopnz;
1259: adpoJ += adponz;
1260: adpdJ += adpdnz;
1261: apJ += apnz;
1262: api[i+1] = api[i] + apnz;
1263: }
1265: /* malloc apa to store dense row A[i,:]*P */
1266: PetscCalloc1(pN+2,&ptap->apa);
1268: /* create and assemble symbolic parallel matrix C */
1269: MatSetSizes(C,am,pn,PETSC_DETERMINE,PETSC_DETERMINE);
1270: MatSetBlockSizesFromMats(C,A,P);
1271: MatGetType(A,&mtype);
1272: MatSetType(C,mtype);
1273: MatMPIAIJSetPreallocation(C,0,dnz,0,onz);
1276: MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(C, apj, api);
1277: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1278: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1279: MatPreallocateFinalize(dnz,onz);
1282: ptap->destroy = C->ops->destroy;
1283: ptap->duplicate = C->ops->duplicate;
1284: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable;
1285: C->ops->productnumeric = MatProductNumeric_AB;
1286: C->ops->destroy = MatDestroy_MPIAIJ_MatMatMult;
1288: /* attach the supporting struct to C for reuse */
1289: c = (Mat_MPIAIJ*)C->data;
1290: c->ap = ptap;
1292: /* set MatInfo */
1293: afill = (PetscReal)api[am]/(adi[am]+aoi[am]+pi_loc[pm]+1) + 1.e-5;
1294: if (afill < 1.0) afill = 1.0;
1295: C->info.mallocs = nspacedouble;
1296: C->info.fill_ratio_given = fill;
1297: C->info.fill_ratio_needed = afill;
1299: #if defined(PETSC_USE_INFO)
1300: if (api[am]) {
1301: PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",nspacedouble,(double)fill,(double)afill);
1302: PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);
1303: } else {
1304: PetscInfo(C,"Empty matrix product\n");
1305: }
1306: #endif
1308: MatDestroy(&aopoth);
1309: MatDestroy(&adpd);
1310: PetscFree(j_temp);
1311: PetscFree(adpoj);
1312: PetscFree(adpoi);
1313: return(0);
1314: }
1316: /*-------------------------------------------------------------------------*/
1317: /* This routine only works when scall=MAT_REUSE_MATRIX! */
1318: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_matmatmult(Mat P,Mat A,Mat C)
1319: {
1321: Mat_MPIAIJ *c=(Mat_MPIAIJ*)C->data;
1322: Mat_APMPI *ptap= c->ap;
1323: Mat Pt;
1326: if (!ptap->Pt) {
1327: MPI_Comm comm;
1328: PetscObjectGetComm((PetscObject)C,&comm);
1329: SETERRQ(comm,PETSC_ERR_ARG_WRONGSTATE,"PtA cannot be reused. Do not call MatFreeIntermediateDataStructures() or use '-mat_freeintermediatedatastructures'");
1330: }
1332: Pt = ptap->Pt;
1333: MatTranspose(P,MAT_REUSE_MATRIX,&Pt);
1334: MatMatMultNumeric_MPIAIJ_MPIAIJ(Pt,A,C);
1336: /* supporting struct ptap consumes almost same amount of memory as C=PtAP, release it if C will not be updated by A and P */
1337: if (ptap->freestruct) {
1338: MatFreeIntermediateDataStructures(C);
1339: }
1340: return(0);
1341: }
1343: /* This routine is modified from MatPtAPSymbolic_MPIAIJ_MPIAIJ() */
1344: PetscErrorCode MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(Mat P,Mat A,PetscReal fill,Mat C)
1345: {
1346: PetscErrorCode ierr;
1347: Mat_APMPI *ptap;
1348: Mat_MPIAIJ *p=(Mat_MPIAIJ*)P->data,*c;
1349: MPI_Comm comm;
1350: PetscMPIInt size,rank;
1351: PetscFreeSpaceList free_space=NULL,current_space=NULL;
1352: PetscInt pn=P->cmap->n,aN=A->cmap->N,an=A->cmap->n;
1353: PetscInt *lnk,i,k,nsend;
1354: PetscBT lnkbt;
1355: PetscMPIInt tagi,tagj,*len_si,*len_s,*len_ri,icompleted=0,nrecv;
1356: PetscInt **buf_rj,**buf_ri,**buf_ri_k;
1357: PetscInt len,proc,*dnz,*onz,*owners,nzi;
1358: PetscInt nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextci;
1359: MPI_Request *swaits,*rwaits;
1360: MPI_Status *sstatus,rstatus;
1361: PetscLayout rowmap;
1362: PetscInt *owners_co,*coi,*coj; /* i and j array of (p->B)^T*A*P - used in the communication */
1363: PetscMPIInt *len_r,*id_r; /* array of length of comm->size, store send/recv matrix values */
1364: PetscInt *Jptr,*prmap=p->garray,con,j,Crmax;
1365: Mat_SeqAIJ *a_loc,*c_loc,*c_oth;
1366: PetscTable ta;
1367: MatType mtype;
1368: const char *prefix;
1371: PetscObjectGetComm((PetscObject)A,&comm);
1372: MPI_Comm_size(comm,&size);
1373: MPI_Comm_rank(comm,&rank);
1375: /* create symbolic parallel matrix C */
1376: MatGetType(A,&mtype);
1377: MatSetType(C,mtype);
1379: C->ops->transposematmultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable;
1381: /* create struct Mat_APMPI and attached it to C later */
1382: PetscNew(&ptap);
1383: ptap->reuse = MAT_INITIAL_MATRIX;
1385: /* (0) compute Rd = Pd^T, Ro = Po^T */
1386: /* --------------------------------- */
1387: MatTranspose_SeqAIJ(p->A,MAT_INITIAL_MATRIX,&ptap->Rd);
1388: MatTranspose_SeqAIJ(p->B,MAT_INITIAL_MATRIX,&ptap->Ro);
1390: /* (1) compute symbolic A_loc */
1391: /* ---------------------------*/
1392: MatMPIAIJGetLocalMat(A,MAT_INITIAL_MATRIX,&ptap->A_loc);
1394: /* (2-1) compute symbolic C_oth = Ro*A_loc */
1395: /* ------------------------------------ */
1396: MatGetOptionsPrefix(A,&prefix);
1397: MatSetOptionsPrefix(ptap->Ro,prefix);
1398: MatAppendOptionsPrefix(ptap->Ro,"inner_offdiag_");
1399: MatCreate(PETSC_COMM_SELF,&ptap->C_oth);
1400: MatMatMultSymbolic_SeqAIJ_SeqAIJ(ptap->Ro,ptap->A_loc,fill,ptap->C_oth);
1402: /* (3) send coj of C_oth to other processors */
1403: /* ------------------------------------------ */
1404: /* determine row ownership */
1405: PetscLayoutCreate(comm,&rowmap);
1406: rowmap->n = pn;
1407: rowmap->bs = 1;
1408: PetscLayoutSetUp(rowmap);
1409: owners = rowmap->range;
1411: /* determine the number of messages to send, their lengths */
1412: PetscMalloc4(size,&len_s,size,&len_si,size,&sstatus,size+2,&owners_co);
1413: PetscArrayzero(len_s,size);
1414: PetscArrayzero(len_si,size);
1416: c_oth = (Mat_SeqAIJ*)ptap->C_oth->data;
1417: coi = c_oth->i; coj = c_oth->j;
1418: con = ptap->C_oth->rmap->n;
1419: proc = 0;
1420: for (i=0; i<con; i++) {
1421: while (prmap[i] >= owners[proc+1]) proc++;
1422: len_si[proc]++; /* num of rows in Co(=Pt*A) to be sent to [proc] */
1423: len_s[proc] += coi[i+1] - coi[i]; /* num of nonzeros in Co to be sent to [proc] */
1424: }
1426: len = 0; /* max length of buf_si[], see (4) */
1427: owners_co[0] = 0;
1428: nsend = 0;
1429: for (proc=0; proc<size; proc++) {
1430: owners_co[proc+1] = owners_co[proc] + len_si[proc];
1431: if (len_s[proc]) {
1432: nsend++;
1433: len_si[proc] = 2*(len_si[proc] + 1); /* length of buf_si to be sent to [proc] */
1434: len += len_si[proc];
1435: }
1436: }
1438: /* determine the number and length of messages to receive for coi and coj */
1439: PetscGatherNumberOfMessages(comm,NULL,len_s,&nrecv);
1440: PetscGatherMessageLengths2(comm,nsend,nrecv,len_s,len_si,&id_r,&len_r,&len_ri);
1442: /* post the Irecv and Isend of coj */
1443: PetscCommGetNewTag(comm,&tagj);
1444: PetscPostIrecvInt(comm,tagj,nrecv,id_r,len_r,&buf_rj,&rwaits);
1445: PetscMalloc1(nsend+1,&swaits);
1446: for (proc=0, k=0; proc<size; proc++) {
1447: if (!len_s[proc]) continue;
1448: i = owners_co[proc];
1449: MPI_Isend(coj+coi[i],len_s[proc],MPIU_INT,proc,tagj,comm,swaits+k);
1450: k++;
1451: }
1453: /* (2-2) compute symbolic C_loc = Rd*A_loc */
1454: /* ---------------------------------------- */
1455: MatSetOptionsPrefix(ptap->Rd,prefix);
1456: MatAppendOptionsPrefix(ptap->Rd,"inner_diag_");
1457: MatCreate(PETSC_COMM_SELF,&ptap->C_loc);
1458: MatMatMultSymbolic_SeqAIJ_SeqAIJ(ptap->Rd,ptap->A_loc,fill,ptap->C_loc);
1459: c_loc = (Mat_SeqAIJ*)ptap->C_loc->data;
1461: /* receives coj are complete */
1462: for (i=0; i<nrecv; i++) {
1463: MPI_Waitany(nrecv,rwaits,&icompleted,&rstatus);
1464: }
1465: PetscFree(rwaits);
1466: if (nsend) {MPI_Waitall(nsend,swaits,sstatus);}
1468: /* add received column indices into ta to update Crmax */
1469: a_loc = (Mat_SeqAIJ*)(ptap->A_loc)->data;
1471: /* create and initialize a linked list */
1472: PetscTableCreate(an,aN,&ta); /* for compute Crmax */
1473: MatRowMergeMax_SeqAIJ(a_loc,ptap->A_loc->rmap->N,ta);
1475: for (k=0; k<nrecv; k++) {/* k-th received message */
1476: Jptr = buf_rj[k];
1477: for (j=0; j<len_r[k]; j++) {
1478: PetscTableAdd(ta,*(Jptr+j)+1,1,INSERT_VALUES);
1479: }
1480: }
1481: PetscTableGetCount(ta,&Crmax);
1482: PetscTableDestroy(&ta);
1484: /* (4) send and recv coi */
1485: /*-----------------------*/
1486: PetscCommGetNewTag(comm,&tagi);
1487: PetscPostIrecvInt(comm,tagi,nrecv,id_r,len_ri,&buf_ri,&rwaits);
1488: PetscMalloc1(len+1,&buf_s);
1489: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
1490: for (proc=0,k=0; proc<size; proc++) {
1491: if (!len_s[proc]) continue;
1492: /* form outgoing message for i-structure:
1493: buf_si[0]: nrows to be sent
1494: [1:nrows]: row index (global)
1495: [nrows+1:2*nrows+1]: i-structure index
1496: */
1497: /*-------------------------------------------*/
1498: nrows = len_si[proc]/2 - 1; /* num of rows in Co to be sent to [proc] */
1499: buf_si_i = buf_si + nrows+1;
1500: buf_si[0] = nrows;
1501: buf_si_i[0] = 0;
1502: nrows = 0;
1503: for (i=owners_co[proc]; i<owners_co[proc+1]; i++) {
1504: nzi = coi[i+1] - coi[i];
1505: buf_si_i[nrows+1] = buf_si_i[nrows] + nzi; /* i-structure */
1506: buf_si[nrows+1] = prmap[i] -owners[proc]; /* local row index */
1507: nrows++;
1508: }
1509: MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,swaits+k);
1510: k++;
1511: buf_si += len_si[proc];
1512: }
1513: for (i=0; i<nrecv; i++) {
1514: MPI_Waitany(nrecv,rwaits,&icompleted,&rstatus);
1515: }
1516: PetscFree(rwaits);
1517: if (nsend) {MPI_Waitall(nsend,swaits,sstatus);}
1519: PetscFree4(len_s,len_si,sstatus,owners_co);
1520: PetscFree(len_ri);
1521: PetscFree(swaits);
1522: PetscFree(buf_s);
1524: /* (5) compute the local portion of C */
1525: /* ------------------------------------------ */
1526: /* set initial free space to be Crmax, sufficient for holding nozeros in each row of C */
1527: PetscFreeSpaceGet(Crmax,&free_space);
1528: current_space = free_space;
1530: PetscMalloc3(nrecv,&buf_ri_k,nrecv,&nextrow,nrecv,&nextci);
1531: for (k=0; k<nrecv; k++) {
1532: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
1533: nrows = *buf_ri_k[k];
1534: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
1535: nextci[k] = buf_ri_k[k] + (nrows + 1); /* poins to the next i-structure of k-th recved i-structure */
1536: }
1538: MatPreallocateInitialize(comm,pn,an,dnz,onz);
1539: PetscLLCondensedCreate(Crmax,aN,&lnk,&lnkbt);
1540: for (i=0; i<pn; i++) {
1541: /* add C_loc into C */
1542: nzi = c_loc->i[i+1] - c_loc->i[i];
1543: Jptr = c_loc->j + c_loc->i[i];
1544: PetscLLCondensedAddSorted(nzi,Jptr,lnk,lnkbt);
1546: /* add received col data into lnk */
1547: for (k=0; k<nrecv; k++) { /* k-th received message */
1548: if (i == *nextrow[k]) { /* i-th row */
1549: nzi = *(nextci[k]+1) - *nextci[k];
1550: Jptr = buf_rj[k] + *nextci[k];
1551: PetscLLCondensedAddSorted(nzi,Jptr,lnk,lnkbt);
1552: nextrow[k]++; nextci[k]++;
1553: }
1554: }
1555: nzi = lnk[0];
1557: /* copy data into free space, then initialize lnk */
1558: PetscLLCondensedClean(aN,nzi,current_space->array,lnk,lnkbt);
1559: MatPreallocateSet(i+owners[rank],nzi,current_space->array,dnz,onz);
1560: }
1561: PetscFree3(buf_ri_k,nextrow,nextci);
1562: PetscLLDestroy(lnk,lnkbt);
1563: PetscFreeSpaceDestroy(free_space);
1565: /* local sizes and preallocation */
1566: MatSetSizes(C,pn,an,PETSC_DETERMINE,PETSC_DETERMINE);
1567: if (P->cmap->bs > 0) {PetscLayoutSetBlockSize(C->rmap,P->cmap->bs);}
1568: if (A->cmap->bs > 0) {PetscLayoutSetBlockSize(C->cmap,A->cmap->bs);}
1569: MatMPIAIJSetPreallocation(C,0,dnz,0,onz);
1570: MatPreallocateFinalize(dnz,onz);
1572: /* members in merge */
1573: PetscFree(id_r);
1574: PetscFree(len_r);
1575: PetscFree(buf_ri[0]);
1576: PetscFree(buf_ri);
1577: PetscFree(buf_rj[0]);
1578: PetscFree(buf_rj);
1579: PetscLayoutDestroy(&rowmap);
1581: /* attach the supporting struct to C for reuse */
1582: c = (Mat_MPIAIJ*)C->data;
1583: c->ap = ptap;
1584: ptap->destroy = C->ops->destroy;
1586: /* C is not ready for use - assembly will be done by MatPtAPNumeric() */
1587: C->assembled = PETSC_FALSE;
1588: C->ops->destroy = MatDestroy_MPIAIJ_PtAP;
1589: C->ops->freeintermediatedatastructures = MatFreeIntermediateDataStructures_MPIAIJ_AP;
1590: return(0);
1591: }
1593: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable(Mat P,Mat A,Mat C)
1594: {
1595: PetscErrorCode ierr;
1596: Mat_MPIAIJ *p=(Mat_MPIAIJ*)P->data,*c=(Mat_MPIAIJ*)C->data;
1597: Mat_SeqAIJ *c_seq;
1598: Mat_APMPI *ptap = c->ap;
1599: Mat A_loc,C_loc,C_oth;
1600: PetscInt i,rstart,rend,cm,ncols,row;
1601: const PetscInt *cols;
1602: const PetscScalar *vals;
1605: if (!ptap->A_loc) {
1606: MPI_Comm comm;
1607: PetscObjectGetComm((PetscObject)C,&comm);
1608: SETERRQ(comm,PETSC_ERR_ARG_WRONGSTATE,"PtA cannot be reused. Do not call MatFreeIntermediateDataStructures() or use '-mat_freeintermediatedatastructures'");
1609: }
1611: MatZeroEntries(C);
1613: if (ptap->reuse == MAT_REUSE_MATRIX) {
1614: /* These matrices are obtained in MatTransposeMatMultSymbolic() */
1615: /* 1) get R = Pd^T, Ro = Po^T */
1616: /*----------------------------*/
1617: MatTranspose_SeqAIJ(p->A,MAT_REUSE_MATRIX,&ptap->Rd);
1618: MatTranspose_SeqAIJ(p->B,MAT_REUSE_MATRIX,&ptap->Ro);
1620: /* 2) compute numeric A_loc */
1621: /*--------------------------*/
1622: MatMPIAIJGetLocalMat(A,MAT_REUSE_MATRIX,&ptap->A_loc);
1623: }
1625: /* 3) C_loc = Rd*A_loc, C_oth = Ro*A_loc */
1626: A_loc = ptap->A_loc;
1627: ((ptap->C_loc)->ops->matmultnumeric)(ptap->Rd,A_loc,ptap->C_loc);
1628: ((ptap->C_oth)->ops->matmultnumeric)(ptap->Ro,A_loc,ptap->C_oth);
1629: C_loc = ptap->C_loc;
1630: C_oth = ptap->C_oth;
1632: /* add C_loc and Co to to C */
1633: MatGetOwnershipRange(C,&rstart,&rend);
1635: /* C_loc -> C */
1636: cm = C_loc->rmap->N;
1637: c_seq = (Mat_SeqAIJ*)C_loc->data;
1638: cols = c_seq->j;
1639: vals = c_seq->a;
1640: for (i=0; i<cm; i++) {
1641: ncols = c_seq->i[i+1] - c_seq->i[i];
1642: row = rstart + i;
1643: MatSetValues(C,1,&row,ncols,cols,vals,ADD_VALUES);
1644: cols += ncols; vals += ncols;
1645: }
1647: /* Co -> C, off-processor part */
1648: cm = C_oth->rmap->N;
1649: c_seq = (Mat_SeqAIJ*)C_oth->data;
1650: cols = c_seq->j;
1651: vals = c_seq->a;
1652: for (i=0; i<cm; i++) {
1653: ncols = c_seq->i[i+1] - c_seq->i[i];
1654: row = p->garray[i];
1655: MatSetValues(C,1,&row,ncols,cols,vals,ADD_VALUES);
1656: cols += ncols; vals += ncols;
1657: }
1658: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1659: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1661: ptap->reuse = MAT_REUSE_MATRIX;
1663: /* supporting struct ptap consumes almost same amount of memory as C=PtAP, release it if C will not be updated by A and P */
1664: if (ptap->freestruct) {
1665: MatFreeIntermediateDataStructures(C);
1666: }
1667: return(0);
1668: }
1670: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ(Mat P,Mat A,Mat C)
1671: {
1672: PetscErrorCode ierr;
1673: Mat_Merge_SeqsToMPI *merge;
1674: Mat_MPIAIJ *p =(Mat_MPIAIJ*)P->data,*c=(Mat_MPIAIJ*)C->data;
1675: Mat_SeqAIJ *pd=(Mat_SeqAIJ*)(p->A)->data,*po=(Mat_SeqAIJ*)(p->B)->data;
1676: Mat_APMPI *ptap;
1677: PetscInt *adj;
1678: PetscInt i,j,k,anz,pnz,row,*cj,nexta;
1679: MatScalar *ada,*ca,valtmp;
1680: PetscInt am =A->rmap->n,cm=C->rmap->n,pon=(p->B)->cmap->n;
1681: MPI_Comm comm;
1682: PetscMPIInt size,rank,taga,*len_s;
1683: PetscInt *owners,proc,nrows,**buf_ri_k,**nextrow,**nextci;
1684: PetscInt **buf_ri,**buf_rj;
1685: PetscInt cnz=0,*bj_i,*bi,*bj,bnz,nextcj; /* bi,bj,ba: local array of C(mpi mat) */
1686: MPI_Request *s_waits,*r_waits;
1687: MPI_Status *status;
1688: MatScalar **abuf_r,*ba_i,*pA,*coa,*ba;
1689: PetscInt *ai,*aj,*coi,*coj,*poJ,*pdJ;
1690: Mat A_loc;
1691: Mat_SeqAIJ *a_loc;
1694: PetscObjectGetComm((PetscObject)C,&comm);
1695: MPI_Comm_size(comm,&size);
1696: MPI_Comm_rank(comm,&rank);
1698: ptap = c->ap;
1699: if (!ptap->A_loc) SETERRQ(comm,PETSC_ERR_ARG_WRONGSTATE,"PtA cannot be reused. Do not call MatFreeIntermediateDataStructures() or use '-mat_freeintermediatedatastructures'");
1700: merge = ptap->merge;
1702: /* 2) compute numeric C_seq = P_loc^T*A_loc */
1703: /*------------------------------------------*/
1704: /* get data from symbolic products */
1705: coi = merge->coi; coj = merge->coj;
1706: PetscCalloc1(coi[pon]+1,&coa);
1707: bi = merge->bi; bj = merge->bj;
1708: owners = merge->rowmap->range;
1709: PetscCalloc1(bi[cm]+1,&ba);
1711: /* get A_loc by taking all local rows of A */
1712: A_loc = ptap->A_loc;
1713: MatMPIAIJGetLocalMat(A,MAT_REUSE_MATRIX,&A_loc);
1714: a_loc = (Mat_SeqAIJ*)(A_loc)->data;
1715: ai = a_loc->i;
1716: aj = a_loc->j;
1718: for (i=0; i<am; i++) {
1719: anz = ai[i+1] - ai[i];
1720: adj = aj + ai[i];
1721: ada = a_loc->a + ai[i];
1723: /* 2-b) Compute Cseq = P_loc[i,:]^T*A[i,:] using outer product */
1724: /*-------------------------------------------------------------*/
1725: /* put the value into Co=(p->B)^T*A (off-diagonal part, send to others) */
1726: pnz = po->i[i+1] - po->i[i];
1727: poJ = po->j + po->i[i];
1728: pA = po->a + po->i[i];
1729: for (j=0; j<pnz; j++) {
1730: row = poJ[j];
1731: cj = coj + coi[row];
1732: ca = coa + coi[row];
1733: /* perform sparse axpy */
1734: nexta = 0;
1735: valtmp = pA[j];
1736: for (k=0; nexta<anz; k++) {
1737: if (cj[k] == adj[nexta]) {
1738: ca[k] += valtmp*ada[nexta];
1739: nexta++;
1740: }
1741: }
1742: PetscLogFlops(2.0*anz);
1743: }
1745: /* put the value into Cd (diagonal part) */
1746: pnz = pd->i[i+1] - pd->i[i];
1747: pdJ = pd->j + pd->i[i];
1748: pA = pd->a + pd->i[i];
1749: for (j=0; j<pnz; j++) {
1750: row = pdJ[j];
1751: cj = bj + bi[row];
1752: ca = ba + bi[row];
1753: /* perform sparse axpy */
1754: nexta = 0;
1755: valtmp = pA[j];
1756: for (k=0; nexta<anz; k++) {
1757: if (cj[k] == adj[nexta]) {
1758: ca[k] += valtmp*ada[nexta];
1759: nexta++;
1760: }
1761: }
1762: PetscLogFlops(2.0*anz);
1763: }
1764: }
1766: /* 3) send and recv matrix values coa */
1767: /*------------------------------------*/
1768: buf_ri = merge->buf_ri;
1769: buf_rj = merge->buf_rj;
1770: len_s = merge->len_s;
1771: PetscCommGetNewTag(comm,&taga);
1772: PetscPostIrecvScalar(comm,taga,merge->nrecv,merge->id_r,merge->len_r,&abuf_r,&r_waits);
1774: PetscMalloc2(merge->nsend+1,&s_waits,size,&status);
1775: for (proc=0,k=0; proc<size; proc++) {
1776: if (!len_s[proc]) continue;
1777: i = merge->owners_co[proc];
1778: MPI_Isend(coa+coi[i],len_s[proc],MPIU_MATSCALAR,proc,taga,comm,s_waits+k);
1779: k++;
1780: }
1781: if (merge->nrecv) {MPI_Waitall(merge->nrecv,r_waits,status);}
1782: if (merge->nsend) {MPI_Waitall(merge->nsend,s_waits,status);}
1784: PetscFree2(s_waits,status);
1785: PetscFree(r_waits);
1786: PetscFree(coa);
1788: /* 4) insert local Cseq and received values into Cmpi */
1789: /*----------------------------------------------------*/
1790: PetscMalloc3(merge->nrecv,&buf_ri_k,merge->nrecv,&nextrow,merge->nrecv,&nextci);
1791: for (k=0; k<merge->nrecv; k++) {
1792: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
1793: nrows = *(buf_ri_k[k]);
1794: nextrow[k] = buf_ri_k[k]+1; /* next row number of k-th recved i-structure */
1795: nextci[k] = buf_ri_k[k] + (nrows + 1); /* poins to the next i-structure of k-th recved i-structure */
1796: }
1798: for (i=0; i<cm; i++) {
1799: row = owners[rank] + i; /* global row index of C_seq */
1800: bj_i = bj + bi[i]; /* col indices of the i-th row of C */
1801: ba_i = ba + bi[i];
1802: bnz = bi[i+1] - bi[i];
1803: /* add received vals into ba */
1804: for (k=0; k<merge->nrecv; k++) { /* k-th received message */
1805: /* i-th row */
1806: if (i == *nextrow[k]) {
1807: cnz = *(nextci[k]+1) - *nextci[k];
1808: cj = buf_rj[k] + *(nextci[k]);
1809: ca = abuf_r[k] + *(nextci[k]);
1810: nextcj = 0;
1811: for (j=0; nextcj<cnz; j++) {
1812: if (bj_i[j] == cj[nextcj]) { /* bcol == ccol */
1813: ba_i[j] += ca[nextcj++];
1814: }
1815: }
1816: nextrow[k]++; nextci[k]++;
1817: PetscLogFlops(2.0*cnz);
1818: }
1819: }
1820: MatSetValues(C,1,&row,bnz,bj_i,ba_i,INSERT_VALUES);
1821: }
1822: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1823: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1825: PetscFree(ba);
1826: PetscFree(abuf_r[0]);
1827: PetscFree(abuf_r);
1828: PetscFree3(buf_ri_k,nextrow,nextci);
1830: if (ptap->freestruct) {
1831: MatFreeIntermediateDataStructures(C);
1832: }
1833: return(0);
1834: }
1836: PetscErrorCode MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ(Mat P,Mat A,PetscReal fill,Mat C)
1837: {
1838: PetscErrorCode ierr;
1839: Mat A_loc,POt,PDt;
1840: Mat_APMPI *ptap;
1841: PetscFreeSpaceList free_space=NULL,current_space=NULL;
1842: Mat_MPIAIJ *p=(Mat_MPIAIJ*)P->data,*a=(Mat_MPIAIJ*)A->data,*c;
1843: PetscInt *pdti,*pdtj,*poti,*potj,*ptJ;
1844: PetscInt nnz;
1845: PetscInt *lnk,*owners_co,*coi,*coj,i,k,pnz,row;
1846: PetscInt am =A->rmap->n,pn=P->cmap->n;
1847: MPI_Comm comm;
1848: PetscMPIInt size,rank,tagi,tagj,*len_si,*len_s,*len_ri;
1849: PetscInt **buf_rj,**buf_ri,**buf_ri_k;
1850: PetscInt len,proc,*dnz,*onz,*owners;
1851: PetscInt nzi,*bi,*bj;
1852: PetscInt nrows,*buf_s,*buf_si,*buf_si_i,**nextrow,**nextci;
1853: MPI_Request *swaits,*rwaits;
1854: MPI_Status *sstatus,rstatus;
1855: Mat_Merge_SeqsToMPI *merge;
1856: PetscInt *ai,*aj,*Jptr,anz,*prmap=p->garray,pon,nspacedouble=0,j;
1857: PetscReal afill =1.0,afill_tmp;
1858: PetscInt rstart = P->cmap->rstart,rmax,aN=A->cmap->N,Armax;
1859: Mat_SeqAIJ *a_loc,*pdt,*pot;
1860: PetscTable ta;
1861: MatType mtype;
1864: PetscObjectGetComm((PetscObject)A,&comm);
1865: /* check if matrix local sizes are compatible */
1866: if (A->rmap->rstart != P->rmap->rstart || A->rmap->rend != P->rmap->rend) SETERRQ4(comm,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A (%D, %D) != P (%D,%D)",A->rmap->rstart,A->rmap->rend,P->rmap->rstart,P->rmap->rend);
1868: MPI_Comm_size(comm,&size);
1869: MPI_Comm_rank(comm,&rank);
1871: /* create struct Mat_APMPI and attached it to C later */
1872: PetscNew(&ptap);
1874: /* get A_loc by taking all local rows of A */
1875: MatMPIAIJGetLocalMat(A,MAT_INITIAL_MATRIX,&A_loc);
1877: ptap->A_loc = A_loc;
1878: a_loc = (Mat_SeqAIJ*)(A_loc)->data;
1879: ai = a_loc->i;
1880: aj = a_loc->j;
1882: /* determine symbolic Co=(p->B)^T*A - send to others */
1883: /*----------------------------------------------------*/
1884: MatTransposeSymbolic_SeqAIJ(p->A,&PDt);
1885: pdt = (Mat_SeqAIJ*)PDt->data;
1886: pdti = pdt->i; pdtj = pdt->j;
1888: MatTransposeSymbolic_SeqAIJ(p->B,&POt);
1889: pot = (Mat_SeqAIJ*)POt->data;
1890: poti = pot->i; potj = pot->j;
1892: /* then, compute symbolic Co = (p->B)^T*A */
1893: pon = (p->B)->cmap->n; /* total num of rows to be sent to other processors
1894: >= (num of nonzero rows of C_seq) - pn */
1895: PetscMalloc1(pon+1,&coi);
1896: coi[0] = 0;
1898: /* set initial free space to be fill*(nnz(p->B) + nnz(A)) */
1899: nnz = PetscRealIntMultTruncate(fill,PetscIntSumTruncate(poti[pon],ai[am]));
1900: PetscFreeSpaceGet(nnz,&free_space);
1901: current_space = free_space;
1903: /* create and initialize a linked list */
1904: PetscTableCreate(A->cmap->n + a->B->cmap->N,aN,&ta);
1905: MatRowMergeMax_SeqAIJ(a_loc,am,ta);
1906: PetscTableGetCount(ta,&Armax);
1908: PetscLLCondensedCreate_Scalable(Armax,&lnk);
1910: for (i=0; i<pon; i++) {
1911: pnz = poti[i+1] - poti[i];
1912: ptJ = potj + poti[i];
1913: for (j=0; j<pnz; j++) {
1914: row = ptJ[j]; /* row of A_loc == col of Pot */
1915: anz = ai[row+1] - ai[row];
1916: Jptr = aj + ai[row];
1917: /* add non-zero cols of AP into the sorted linked list lnk */
1918: PetscLLCondensedAddSorted_Scalable(anz,Jptr,lnk);
1919: }
1920: nnz = lnk[0];
1922: /* If free space is not available, double the total space in the list */
1923: if (current_space->local_remaining<nnz) {
1924: PetscFreeSpaceGet(PetscIntSumTruncate(nnz,current_space->total_array_size),¤t_space);
1925: nspacedouble++;
1926: }
1928: /* Copy data into free space, and zero out denserows */
1929: PetscLLCondensedClean_Scalable(nnz,current_space->array,lnk);
1931: current_space->array += nnz;
1932: current_space->local_used += nnz;
1933: current_space->local_remaining -= nnz;
1935: coi[i+1] = coi[i] + nnz;
1936: }
1938: PetscMalloc1(coi[pon]+1,&coj);
1939: PetscFreeSpaceContiguous(&free_space,coj);
1940: PetscLLCondensedDestroy_Scalable(lnk); /* must destroy to get a new one for C */
1942: afill_tmp = (PetscReal)coi[pon]/(poti[pon] + ai[am]+1);
1943: if (afill_tmp > afill) afill = afill_tmp;
1945: /* send j-array (coj) of Co to other processors */
1946: /*----------------------------------------------*/
1947: /* determine row ownership */
1948: PetscNew(&merge);
1949: PetscLayoutCreate(comm,&merge->rowmap);
1951: merge->rowmap->n = pn;
1952: merge->rowmap->bs = 1;
1954: PetscLayoutSetUp(merge->rowmap);
1955: owners = merge->rowmap->range;
1957: /* determine the number of messages to send, their lengths */
1958: PetscCalloc1(size,&len_si);
1959: PetscCalloc1(size,&merge->len_s);
1961: len_s = merge->len_s;
1962: merge->nsend = 0;
1964: PetscMalloc1(size+2,&owners_co);
1966: proc = 0;
1967: for (i=0; i<pon; i++) {
1968: while (prmap[i] >= owners[proc+1]) proc++;
1969: len_si[proc]++; /* num of rows in Co to be sent to [proc] */
1970: len_s[proc] += coi[i+1] - coi[i];
1971: }
1973: len = 0; /* max length of buf_si[] */
1974: owners_co[0] = 0;
1975: for (proc=0; proc<size; proc++) {
1976: owners_co[proc+1] = owners_co[proc] + len_si[proc];
1977: if (len_si[proc]) {
1978: merge->nsend++;
1979: len_si[proc] = 2*(len_si[proc] + 1);
1980: len += len_si[proc];
1981: }
1982: }
1984: /* determine the number and length of messages to receive for coi and coj */
1985: PetscGatherNumberOfMessages(comm,NULL,len_s,&merge->nrecv);
1986: PetscGatherMessageLengths2(comm,merge->nsend,merge->nrecv,len_s,len_si,&merge->id_r,&merge->len_r,&len_ri);
1988: /* post the Irecv and Isend of coj */
1989: PetscCommGetNewTag(comm,&tagj);
1990: PetscPostIrecvInt(comm,tagj,merge->nrecv,merge->id_r,merge->len_r,&buf_rj,&rwaits);
1991: PetscMalloc1(merge->nsend+1,&swaits);
1992: for (proc=0, k=0; proc<size; proc++) {
1993: if (!len_s[proc]) continue;
1994: i = owners_co[proc];
1995: MPI_Isend(coj+coi[i],len_s[proc],MPIU_INT,proc,tagj,comm,swaits+k);
1996: k++;
1997: }
1999: /* receives and sends of coj are complete */
2000: PetscMalloc1(size,&sstatus);
2001: for (i=0; i<merge->nrecv; i++) {
2002: PetscMPIInt icompleted;
2003: MPI_Waitany(merge->nrecv,rwaits,&icompleted,&rstatus);
2004: }
2005: PetscFree(rwaits);
2006: if (merge->nsend) {MPI_Waitall(merge->nsend,swaits,sstatus);}
2008: /* add received column indices into table to update Armax */
2009: /* Armax can be as large as aN if a P[row,:] is dense, see src/ksp/ksp/tutorials/ex56.c! */
2010: for (k=0; k<merge->nrecv; k++) {/* k-th received message */
2011: Jptr = buf_rj[k];
2012: for (j=0; j<merge->len_r[k]; j++) {
2013: PetscTableAdd(ta,*(Jptr+j)+1,1,INSERT_VALUES);
2014: }
2015: }
2016: PetscTableGetCount(ta,&Armax);
2017: /* printf("Armax %d, an %d + Bn %d = %d, aN %d\n",Armax,A->cmap->n,a->B->cmap->N,A->cmap->n+a->B->cmap->N,aN); */
2019: /* send and recv coi */
2020: /*-------------------*/
2021: PetscCommGetNewTag(comm,&tagi);
2022: PetscPostIrecvInt(comm,tagi,merge->nrecv,merge->id_r,len_ri,&buf_ri,&rwaits);
2023: PetscMalloc1(len+1,&buf_s);
2024: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
2025: for (proc=0,k=0; proc<size; proc++) {
2026: if (!len_s[proc]) continue;
2027: /* form outgoing message for i-structure:
2028: buf_si[0]: nrows to be sent
2029: [1:nrows]: row index (global)
2030: [nrows+1:2*nrows+1]: i-structure index
2031: */
2032: /*-------------------------------------------*/
2033: nrows = len_si[proc]/2 - 1;
2034: buf_si_i = buf_si + nrows+1;
2035: buf_si[0] = nrows;
2036: buf_si_i[0] = 0;
2037: nrows = 0;
2038: for (i=owners_co[proc]; i<owners_co[proc+1]; i++) {
2039: nzi = coi[i+1] - coi[i];
2040: buf_si_i[nrows+1] = buf_si_i[nrows] + nzi; /* i-structure */
2041: buf_si[nrows+1] = prmap[i] -owners[proc]; /* local row index */
2042: nrows++;
2043: }
2044: MPI_Isend(buf_si,len_si[proc],MPIU_INT,proc,tagi,comm,swaits+k);
2045: k++;
2046: buf_si += len_si[proc];
2047: }
2048: i = merge->nrecv;
2049: while (i--) {
2050: PetscMPIInt icompleted;
2051: MPI_Waitany(merge->nrecv,rwaits,&icompleted,&rstatus);
2052: }
2053: PetscFree(rwaits);
2054: if (merge->nsend) {MPI_Waitall(merge->nsend,swaits,sstatus);}
2055: PetscFree(len_si);
2056: PetscFree(len_ri);
2057: PetscFree(swaits);
2058: PetscFree(sstatus);
2059: PetscFree(buf_s);
2061: /* compute the local portion of C (mpi mat) */
2062: /*------------------------------------------*/
2063: /* allocate bi array and free space for accumulating nonzero column info */
2064: PetscMalloc1(pn+1,&bi);
2065: bi[0] = 0;
2067: /* set initial free space to be fill*(nnz(P) + nnz(AP)) */
2068: nnz = PetscRealIntMultTruncate(fill,PetscIntSumTruncate(pdti[pn],PetscIntSumTruncate(poti[pon],ai[am])));
2069: PetscFreeSpaceGet(nnz,&free_space);
2070: current_space = free_space;
2072: PetscMalloc3(merge->nrecv,&buf_ri_k,merge->nrecv,&nextrow,merge->nrecv,&nextci);
2073: for (k=0; k<merge->nrecv; k++) {
2074: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
2075: nrows = *buf_ri_k[k];
2076: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
2077: nextci[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recieved i-structure */
2078: }
2080: PetscLLCondensedCreate_Scalable(Armax,&lnk);
2081: MatPreallocateInitialize(comm,pn,A->cmap->n,dnz,onz);
2082: rmax = 0;
2083: for (i=0; i<pn; i++) {
2084: /* add pdt[i,:]*AP into lnk */
2085: pnz = pdti[i+1] - pdti[i];
2086: ptJ = pdtj + pdti[i];
2087: for (j=0; j<pnz; j++) {
2088: row = ptJ[j]; /* row of AP == col of Pt */
2089: anz = ai[row+1] - ai[row];
2090: Jptr = aj + ai[row];
2091: /* add non-zero cols of AP into the sorted linked list lnk */
2092: PetscLLCondensedAddSorted_Scalable(anz,Jptr,lnk);
2093: }
2095: /* add received col data into lnk */
2096: for (k=0; k<merge->nrecv; k++) { /* k-th received message */
2097: if (i == *nextrow[k]) { /* i-th row */
2098: nzi = *(nextci[k]+1) - *nextci[k];
2099: Jptr = buf_rj[k] + *nextci[k];
2100: PetscLLCondensedAddSorted_Scalable(nzi,Jptr,lnk);
2101: nextrow[k]++; nextci[k]++;
2102: }
2103: }
2104: nnz = lnk[0];
2106: /* if free space is not available, make more free space */
2107: if (current_space->local_remaining<nnz) {
2108: PetscFreeSpaceGet(PetscIntSumTruncate(nnz,current_space->total_array_size),¤t_space);
2109: nspacedouble++;
2110: }
2111: /* copy data into free space, then initialize lnk */
2112: PetscLLCondensedClean_Scalable(nnz,current_space->array,lnk);
2113: MatPreallocateSet(i+owners[rank],nnz,current_space->array,dnz,onz);
2115: current_space->array += nnz;
2116: current_space->local_used += nnz;
2117: current_space->local_remaining -= nnz;
2119: bi[i+1] = bi[i] + nnz;
2120: if (nnz > rmax) rmax = nnz;
2121: }
2122: PetscFree3(buf_ri_k,nextrow,nextci);
2124: PetscMalloc1(bi[pn]+1,&bj);
2125: PetscFreeSpaceContiguous(&free_space,bj);
2126: afill_tmp = (PetscReal)bi[pn]/(pdti[pn] + poti[pon] + ai[am]+1);
2127: if (afill_tmp > afill) afill = afill_tmp;
2128: PetscLLCondensedDestroy_Scalable(lnk);
2129: PetscTableDestroy(&ta);
2131: MatDestroy(&POt);
2132: MatDestroy(&PDt);
2134: /* create symbolic parallel matrix C - why cannot be assembled in Numeric part */
2135: /*-------------------------------------------------------------------------------*/
2136: MatSetSizes(C,pn,A->cmap->n,PETSC_DETERMINE,PETSC_DETERMINE);
2137: MatSetBlockSizes(C,PetscAbs(P->cmap->bs),PetscAbs(A->cmap->bs));
2138: MatGetType(A,&mtype);
2139: MatSetType(C,mtype);
2140: MatMPIAIJSetPreallocation(C,0,dnz,0,onz);
2141: MatPreallocateFinalize(dnz,onz);
2142: MatSetBlockSize(C,1);
2143: for (i=0; i<pn; i++) {
2144: row = i + rstart;
2145: nnz = bi[i+1] - bi[i];
2146: Jptr = bj + bi[i];
2147: MatSetValues(C,1,&row,nnz,Jptr,NULL,INSERT_VALUES);
2148: }
2149: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2150: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2151: merge->bi = bi;
2152: merge->bj = bj;
2153: merge->coi = coi;
2154: merge->coj = coj;
2155: merge->buf_ri = buf_ri;
2156: merge->buf_rj = buf_rj;
2157: merge->owners_co = owners_co;
2159: /* attach the supporting struct to C for reuse */
2160: c = (Mat_MPIAIJ*)C->data;
2162: c->ap = ptap;
2163: ptap->api = NULL;
2164: ptap->apj = NULL;
2165: ptap->merge = merge;
2166: ptap->apa = NULL;
2167: ptap->destroy = C->ops->destroy;
2168: ptap->duplicate = C->ops->duplicate;
2170: C->ops->mattransposemultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ;
2171: C->ops->destroy = MatDestroy_MPIAIJ_PtAP;
2172: C->ops->freeintermediatedatastructures = MatFreeIntermediateDataStructures_MPIAIJ_AP;
2174: #if defined(PETSC_USE_INFO)
2175: if (bi[pn] != 0) {
2176: PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",nspacedouble,(double)fill,(double)afill);
2177: PetscInfo1(C,"Use MatTransposeMatMult(A,B,MatReuse,%g,&C) for best performance.\n",(double)afill);
2178: } else {
2179: PetscInfo(C,"Empty matrix product\n");
2180: }
2181: #endif
2182: return(0);
2183: }
2185: /* ---------------------------------------------------------------- */
2186: static PetscErrorCode MatProductSymbolic_AtB_MPIAIJ_MPIAIJ(Mat C)
2187: {
2189: Mat_Product *product = C->product;
2190: Mat A=product->A,B=product->B;
2191: PetscReal fill=product->fill;
2192: PetscBool flg;
2195: /* scalable */
2196: PetscStrcmp(product->alg,"scalable",&flg);
2197: if (flg) {
2198: MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ(A,B,fill,C);
2199: goto next;
2200: }
2202: /* nonscalable */
2203: PetscStrcmp(product->alg,"nonscalable",&flg);
2204: if (flg) {
2205: MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(A,B,fill,C);
2206: goto next;
2207: }
2209: /* matmatmult */
2210: PetscStrcmp(product->alg,"at*b",&flg);
2211: if (flg) {
2212: Mat At;
2213: Mat_APMPI *ptap;
2214: Mat_MPIAIJ *c;
2215: MatTranspose(A,MAT_INITIAL_MATRIX,&At);
2217: MatMatMultSymbolic_MPIAIJ_MPIAIJ(At,B,fill,C);
2218: c = (Mat_MPIAIJ*)C->data;
2219: ptap = c->ap;
2220: if (ptap) {
2221: ptap->Pt = At;
2222: C->ops->freeintermediatedatastructures = MatFreeIntermediateDataStructures_MPIAIJ_AP;
2223: }
2224: C->ops->transposematmultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_matmatmult;
2225: goto next;
2226: }
2228: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatProduct type is not supported");
2230: next:
2231: C->ops->productnumeric = MatProductNumeric_AtB;
2233: {
2234: Mat_MPIAIJ *c = (Mat_MPIAIJ*)C->data;
2235: Mat_APMPI *ap = c->ap;
2236: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatFreeIntermediateDataStructures","Mat");
2237: ap->freestruct = PETSC_FALSE;
2238: PetscOptionsBool("-mat_freeintermediatedatastructures","Free intermediate data structures", "MatFreeIntermediateDataStructures",ap->freestruct,&ap->freestruct, NULL);
2239: PetscOptionsEnd();
2240: }
2241: return(0);
2242: }
2244: /* ---------------------------------------------------------------- */
2245: /* Set options for MatMatMultxxx_MPIAIJ_MPIAIJ */
2246: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_AB(Mat C)
2247: {
2249: Mat_Product *product = C->product;
2250: Mat A=product->A,B=product->B;
2251: #if defined(PETSC_HAVE_HYPRE)
2252: const char *algTypes[4] = {"scalable","nonscalable","seqmpi","hypre"};
2253: PetscInt nalg = 4;
2254: #else
2255: const char *algTypes[3] = {"scalable","nonscalable","seqmpi"};
2256: PetscInt nalg = 3;
2257: #endif
2258: PetscInt alg = 1; /* set nonscalable algorithm as default */
2259: PetscBool flg;
2260: MPI_Comm comm;
2263: /* Check matrix local sizes */
2264: PetscObjectGetComm((PetscObject)C,&comm);
2265: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend) 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);
2267: /* Set "nonscalable" as default algorithm */
2268: PetscStrcmp(C->product->alg,"default",&flg);
2269: if (flg) {
2270: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2272: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2273: if (B->cmap->N > 100000) { /* may switch to scalable algorithm as default */
2274: MatInfo Ainfo,Binfo;
2275: PetscInt nz_local;
2276: PetscBool alg_scalable_loc=PETSC_FALSE,alg_scalable;
2278: MatGetInfo(A,MAT_LOCAL,&Ainfo);
2279: MatGetInfo(B,MAT_LOCAL,&Binfo);
2280: nz_local = (PetscInt)(Ainfo.nz_allocated + Binfo.nz_allocated);
2282: if (B->cmap->N > product->fill*nz_local) alg_scalable_loc = PETSC_TRUE;
2283: MPIU_Allreduce(&alg_scalable_loc,&alg_scalable,1,MPIU_BOOL,MPI_LOR,comm);
2285: if (alg_scalable) {
2286: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2287: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2288: PetscInfo2(B,"Use scalable algorithm, BN %D, fill*nz_allocated %g\n",B->cmap->N,product->fill*nz_local);
2289: }
2290: }
2291: }
2293: /* Get runtime option */
2294: if (product->api_user) {
2295: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatMatMult","Mat");
2296: PetscOptionsEList("-matmatmult_via","Algorithmic approach","MatMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);
2297: PetscOptionsEnd();
2298: } else {
2299: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_AB","Mat");
2300: PetscOptionsEList("-matproduct_ab_via","Algorithmic approach","MatMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);
2301: PetscOptionsEnd();
2302: }
2303: if (flg) {
2304: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2305: }
2307: C->ops->productsymbolic = MatProductSymbolic_AB_MPIAIJ_MPIAIJ;
2308: return(0);
2309: }
2311: /* Set options for MatTransposeMatMultXXX_MPIAIJ_MPIAIJ */
2312: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_AtB(Mat C)
2313: {
2315: Mat_Product *product = C->product;
2316: Mat A=product->A,B=product->B;
2317: const char *algTypes[3] = {"scalable","nonscalable","at*b"};
2318: PetscInt nalg = 3;
2319: PetscInt alg = 1; /* set default algorithm */
2320: PetscBool flg;
2321: MPI_Comm comm;
2324: /* Check matrix local sizes */
2325: PetscObjectGetComm((PetscObject)C,&comm);
2326: if (A->rmap->rstart != B->rmap->rstart || A->rmap->rend != B->rmap->rend) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A (%D, %D) != B (%D,%D)",A->rmap->rstart,A->rmap->rend,B->rmap->rstart,B->rmap->rend);
2328: /* Set default algorithm */
2329: PetscStrcmp(C->product->alg,"default",&flg);
2330: if (flg) {
2331: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2332: }
2334: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2335: if (alg && B->cmap->N > 100000) { /* may switch to scalable algorithm as default */
2336: MatInfo Ainfo,Binfo;
2337: PetscInt nz_local;
2338: PetscBool alg_scalable_loc=PETSC_FALSE,alg_scalable;
2340: MatGetInfo(A,MAT_LOCAL,&Ainfo);
2341: MatGetInfo(B,MAT_LOCAL,&Binfo);
2342: nz_local = (PetscInt)(Ainfo.nz_allocated + Binfo.nz_allocated);
2344: if (B->cmap->N > product->fill*nz_local) alg_scalable_loc = PETSC_TRUE;
2345: MPIU_Allreduce(&alg_scalable_loc,&alg_scalable,1,MPIU_BOOL,MPI_LOR,comm);
2347: if (alg_scalable) {
2348: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2349: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2350: PetscInfo2(B,"Use scalable algorithm, BN %D, fill*nz_allocated %g\n",B->cmap->N,product->fill*nz_local);
2351: }
2352: }
2354: /* Get runtime option */
2355: if (product->api_user) {
2356: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatTransposeMatMult","Mat");
2357: PetscOptionsEList("-mattransposematmult_via","Algorithmic approach","MatTransposeMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);
2358: PetscOptionsEnd();
2359: } else {
2360: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_AtB","Mat");
2361: PetscOptionsEList("-matproduct_atb_via","Algorithmic approach","MatTransposeMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);
2362: PetscOptionsEnd();
2363: }
2364: if (flg) {
2365: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2366: }
2368: C->ops->productsymbolic = MatProductSymbolic_AtB_MPIAIJ_MPIAIJ;
2369: return(0);
2370: }
2372: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_PtAP(Mat C)
2373: {
2375: Mat_Product *product = C->product;
2376: Mat A=product->A,P=product->B;
2377: MPI_Comm comm;
2378: PetscBool flg;
2379: PetscInt alg=1; /* set default algorithm */
2380: #if !defined(PETSC_HAVE_HYPRE)
2381: const char *algTypes[4] = {"scalable","nonscalable","allatonce","allatonce_merged"};
2382: PetscInt nalg=4;
2383: #else
2384: const char *algTypes[5] = {"scalable","nonscalable","allatonce","allatonce_merged","hypre"};
2385: PetscInt nalg=5;
2386: #endif
2387: PetscInt pN=P->cmap->N;
2390: /* Check matrix local sizes */
2391: PetscObjectGetComm((PetscObject)C,&comm);
2392: if (A->rmap->rstart != P->rmap->rstart || A->rmap->rend != P->rmap->rend) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, Arow (%D, %D) != Prow (%D,%D)",A->rmap->rstart,A->rmap->rend,P->rmap->rstart,P->rmap->rend);
2393: if (A->cmap->rstart != P->rmap->rstart || A->cmap->rend != P->rmap->rend) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, Acol (%D, %D) != Prow (%D,%D)",A->cmap->rstart,A->cmap->rend,P->rmap->rstart,P->rmap->rend);
2395: /* Set "nonscalable" as default algorithm */
2396: PetscStrcmp(C->product->alg,"default",&flg);
2397: if (flg) {
2398: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2400: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2401: if (pN > 100000) {
2402: MatInfo Ainfo,Pinfo;
2403: PetscInt nz_local;
2404: PetscBool alg_scalable_loc=PETSC_FALSE,alg_scalable;
2406: MatGetInfo(A,MAT_LOCAL,&Ainfo);
2407: MatGetInfo(P,MAT_LOCAL,&Pinfo);
2408: nz_local = (PetscInt)(Ainfo.nz_allocated + Pinfo.nz_allocated);
2410: if (pN > product->fill*nz_local) alg_scalable_loc = PETSC_TRUE;
2411: MPIU_Allreduce(&alg_scalable_loc,&alg_scalable,1,MPIU_BOOL,MPI_LOR,comm);
2413: if (alg_scalable) {
2414: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2415: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2416: }
2417: }
2418: }
2420: /* Get runtime option */
2421: if (product->api_user) {
2422: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatPtAP","Mat");
2423: PetscOptionsEList("-matptap_via","Algorithmic approach","MatPtAP",algTypes,nalg,algTypes[alg],&alg,&flg);
2424: PetscOptionsEnd();
2425: } else {
2426: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_PtAP","Mat");
2427: PetscOptionsEList("-matproduct_ptap_via","Algorithmic approach","MatPtAP",algTypes,nalg,algTypes[alg],&alg,&flg);
2428: PetscOptionsEnd();
2429: }
2430: if (flg) {
2431: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2432: }
2434: C->ops->productsymbolic = MatProductSymbolic_PtAP_MPIAIJ_MPIAIJ;
2435: return(0);
2436: }
2438: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_RARt(Mat C)
2439: {
2440: Mat_Product *product = C->product;
2441: Mat A = product->A,R=product->B;
2444: /* Check matrix local sizes */
2445: if (A->cmap->n != R->cmap->n || A->rmap->n != R->cmap->n) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Matrix local dimensions are incompatible, A local (%D, %D), R local (%D,%D)",A->rmap->n,A->rmap->n,R->rmap->n,R->cmap->n);
2447: C->ops->productsymbolic = MatProductSymbolic_RARt_MPIAIJ_MPIAIJ;
2448: return(0);
2449: }
2451: /*
2452: Set options for ABC = A*B*C = A*(B*C); ABC's algorithm must be chosen from AB's algorithm
2453: */
2454: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_ABC(Mat C)
2455: {
2457: Mat_Product *product = C->product;
2458: PetscBool flg = PETSC_FALSE;
2459: PetscInt alg = 1; /* default algorithm */
2460: const char *algTypes[3] = {"scalable","nonscalable","seqmpi"};
2461: PetscInt nalg = 3;
2464: /* Set default algorithm */
2465: PetscStrcmp(C->product->alg,"default",&flg);
2466: if (flg) {
2467: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2468: }
2470: /* Get runtime option */
2471: if (product->api_user) {
2472: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatMatMatMult","Mat");
2473: PetscOptionsEList("-matmatmatmult_via","Algorithmic approach","MatMatMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);
2474: PetscOptionsEnd();
2475: } else {
2476: PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_ABC","Mat");
2477: PetscOptionsEList("-matproduct_abc_via","Algorithmic approach","MatProduct_ABC",algTypes,nalg,algTypes[alg],&alg,&flg);
2478: PetscOptionsEnd();
2479: }
2480: if (flg) {
2481: MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);
2482: }
2484: C->ops->matmatmultsymbolic = MatMatMatMultSymbolic_MPIAIJ_MPIAIJ_MPIAIJ;
2485: C->ops->productsymbolic = MatProductSymbolic_ABC;
2486: return(0);
2487: }
2489: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIAIJ(Mat C)
2490: {
2492: Mat_Product *product = C->product;
2495: switch (product->type) {
2496: case MATPRODUCT_AB:
2497: MatProductSetFromOptions_MPIAIJ_AB(C);
2498: break;
2499: case MATPRODUCT_AtB:
2500: MatProductSetFromOptions_MPIAIJ_AtB(C);
2501: break;
2502: case MATPRODUCT_PtAP:
2503: MatProductSetFromOptions_MPIAIJ_PtAP(C);
2504: break;
2505: case MATPRODUCT_RARt:
2506: MatProductSetFromOptions_MPIAIJ_RARt(C);
2507: break;
2508: case MATPRODUCT_ABC:
2509: MatProductSetFromOptions_MPIAIJ_ABC(C);
2510: break;
2511: default: SETERRQ(PetscObjectComm((PetscObject)C),PETSC_ERR_SUP,"MatProduct type is not supported");
2512: }
2513: return(0);
2514: }