Actual source code: mpimatmatmult.c
1: /*
2: Defines matrix-matrix product routines for pairs of MPIAIJ matrices
3: C = A * B
4: */
5: #include <../src/mat/impls/aij/seq/aij.h>
6: #include <../src/mat/utils/freespace.h>
7: #include <../src/mat/impls/aij/mpi/mpiaij.h>
8: #include <petscbt.h>
9: #include <../src/mat/impls/dense/mpi/mpidense.h>
10: #include <petsc/private/vecimpl.h>
11: #include <petsc/private/sfimpl.h>
13: #if defined(PETSC_HAVE_HYPRE)
14: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_AIJ_AIJ_wHYPRE(Mat, Mat, PetscReal, Mat);
15: #endif
17: PETSC_INTERN PetscErrorCode MatProductSymbolic_ABt_MPIAIJ_MPIAIJ(Mat C)
18: {
19: Mat_Product *product = C->product;
20: Mat B = product->B;
22: PetscFunctionBegin;
23: PetscCall(MatTranspose(B, MAT_INITIAL_MATRIX, &product->B));
24: PetscCall(MatDestroy(&B));
25: PetscCall(MatProductSymbolic_AB_MPIAIJ_MPIAIJ(C));
26: PetscFunctionReturn(PETSC_SUCCESS);
27: }
29: PETSC_INTERN PetscErrorCode MatProductSymbolic_AB_MPIAIJ_MPIAIJ(Mat C)
30: {
31: Mat_Product *product = C->product;
32: Mat A = product->A, B = product->B;
33: MatProductAlgorithm alg = product->alg;
34: PetscReal fill = product->fill;
35: PetscBool flg;
37: PetscFunctionBegin;
38: /* scalable */
39: PetscCall(PetscStrcmp(alg, "scalable", &flg));
40: if (flg) {
41: PetscCall(MatMatMultSymbolic_MPIAIJ_MPIAIJ(A, B, fill, C));
42: PetscFunctionReturn(PETSC_SUCCESS);
43: }
45: /* nonscalable */
46: PetscCall(PetscStrcmp(alg, "nonscalable", &flg));
47: if (flg) {
48: PetscCall(MatMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(A, B, fill, C));
49: PetscFunctionReturn(PETSC_SUCCESS);
50: }
52: /* seqmpi */
53: PetscCall(PetscStrcmp(alg, "seqmpi", &flg));
54: if (flg) {
55: PetscCall(MatMatMultSymbolic_MPIAIJ_MPIAIJ_seqMPI(A, B, fill, C));
56: PetscFunctionReturn(PETSC_SUCCESS);
57: }
59: /* backend general code */
60: PetscCall(PetscStrcmp(alg, "backend", &flg));
61: if (flg) {
62: PetscCall(MatProductSymbolic_MPIAIJBACKEND(C));
63: PetscFunctionReturn(PETSC_SUCCESS);
64: }
66: #if defined(PETSC_HAVE_HYPRE)
67: PetscCall(PetscStrcmp(alg, "hypre", &flg));
68: if (flg) {
69: PetscCall(MatMatMultSymbolic_AIJ_AIJ_wHYPRE(A, B, fill, C));
70: PetscFunctionReturn(PETSC_SUCCESS);
71: }
72: #endif
73: SETERRQ(PetscObjectComm((PetscObject)C), PETSC_ERR_SUP, "Mat Product Algorithm is not supported");
74: }
76: PetscErrorCode MatProductCtxDestroy_MPIAIJ_MatMatMult(void **data)
77: {
78: MatProductCtx_APMPI *ptap = *(MatProductCtx_APMPI **)data;
80: PetscFunctionBegin;
81: PetscCall(PetscFree2(ptap->startsj_s, ptap->startsj_r));
82: PetscCall(PetscFree(ptap->bufa));
83: PetscCall(MatDestroy(&ptap->P_loc));
84: PetscCall(MatDestroy(&ptap->P_oth));
85: PetscCall(MatDestroy(&ptap->Pt));
86: PetscCall(PetscFree(ptap->api));
87: PetscCall(PetscFree(ptap->apj));
88: PetscCall(PetscFree(ptap->apa));
89: PetscCall(PetscFree(ptap));
90: PetscFunctionReturn(PETSC_SUCCESS);
91: }
93: PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable(Mat A, Mat P, Mat C)
94: {
95: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *c = (Mat_MPIAIJ *)C->data;
96: Mat_SeqAIJ *ad = (Mat_SeqAIJ *)a->A->data, *ao = (Mat_SeqAIJ *)a->B->data;
97: Mat_SeqAIJ *cd = (Mat_SeqAIJ *)c->A->data, *co = (Mat_SeqAIJ *)c->B->data;
98: PetscScalar *cda, *coa;
99: Mat_SeqAIJ *p_loc, *p_oth;
100: PetscScalar *apa, *ca;
101: PetscInt cm = C->rmap->n;
102: MatProductCtx_APMPI *ptap;
103: PetscInt *api, *apj, *apJ, i, k;
104: PetscInt cstart = C->cmap->rstart;
105: PetscInt cdnz, conz, k0, k1;
106: const PetscScalar *dummy1, *dummy2, *dummy3, *dummy4;
107: MPI_Comm comm;
108: PetscMPIInt size;
110: PetscFunctionBegin;
111: MatCheckProduct(C, 3);
112: ptap = (MatProductCtx_APMPI *)C->product->data;
113: PetscCheck(ptap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtAP cannot be computed. Missing data");
114: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
115: PetscCallMPI(MPI_Comm_size(comm, &size));
116: PetscCheck(ptap->P_oth || size <= 1, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "AP cannot be reused. Do not call MatProductClear()");
118: /* flag CPU mask for C */
119: #if defined(PETSC_HAVE_DEVICE)
120: if (C->offloadmask != PETSC_OFFLOAD_UNALLOCATED) C->offloadmask = PETSC_OFFLOAD_CPU;
121: if (c->A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) c->A->offloadmask = PETSC_OFFLOAD_CPU;
122: if (c->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) c->B->offloadmask = PETSC_OFFLOAD_CPU;
123: #endif
125: /* 1) get P_oth = ptap->P_oth and P_loc = ptap->P_loc */
126: /* update numerical values of P_oth and P_loc */
127: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_REUSE_MATRIX, &ptap->startsj_s, &ptap->startsj_r, &ptap->bufa, &ptap->P_oth));
128: PetscCall(MatMPIAIJGetLocalMat(P, MAT_REUSE_MATRIX, &ptap->P_loc));
130: /* 2) compute numeric C_loc = A_loc*P = Ad*P_loc + Ao*P_oth */
131: /* get data from symbolic products */
132: p_loc = (Mat_SeqAIJ *)ptap->P_loc->data;
133: p_oth = NULL;
134: if (size > 1) p_oth = (Mat_SeqAIJ *)ptap->P_oth->data;
136: /* get apa for storing dense row A[i,:]*P */
137: apa = ptap->apa;
139: api = ptap->api;
140: apj = ptap->apj;
141: /* trigger copy to CPU */
142: PetscCall(MatSeqAIJGetArrayRead(a->A, &dummy1));
143: PetscCall(MatSeqAIJGetArrayRead(a->B, &dummy2));
144: PetscCall(MatSeqAIJGetArrayRead(ptap->P_loc, &dummy3));
145: if (ptap->P_oth) PetscCall(MatSeqAIJGetArrayRead(ptap->P_oth, &dummy4));
146: PetscCall(MatSeqAIJGetArrayWrite(c->A, &cda));
147: PetscCall(MatSeqAIJGetArrayWrite(c->B, &coa));
148: for (i = 0; i < cm; i++) {
149: /* compute apa = A[i,:]*P */
150: AProw_nonscalable(i, ad, ao, p_loc, p_oth, apa);
152: /* set values in C */
153: apJ = PetscSafePointerPlusOffset(apj, api[i]);
154: cdnz = cd->i[i + 1] - cd->i[i];
155: conz = co->i[i + 1] - co->i[i];
157: /* 1st off-diagonal part of C */
158: ca = PetscSafePointerPlusOffset(coa, co->i[i]);
159: k = 0;
160: for (k0 = 0; k0 < conz; k0++) {
161: if (apJ[k] >= cstart) break;
162: ca[k0] = apa[apJ[k]];
163: apa[apJ[k++]] = 0.0;
164: }
166: /* diagonal part of C */
167: ca = PetscSafePointerPlusOffset(cda, cd->i[i]);
168: for (k1 = 0; k1 < cdnz; k1++) {
169: ca[k1] = apa[apJ[k]];
170: apa[apJ[k++]] = 0.0;
171: }
173: /* 2nd off-diagonal part of C */
174: ca = PetscSafePointerPlusOffset(coa, co->i[i]);
175: for (; k0 < conz; k0++) {
176: ca[k0] = apa[apJ[k]];
177: apa[apJ[k++]] = 0.0;
178: }
179: }
180: PetscCall(MatSeqAIJRestoreArrayRead(a->A, &dummy1));
181: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &dummy2));
182: PetscCall(MatSeqAIJRestoreArrayRead(ptap->P_loc, &dummy3));
183: if (ptap->P_oth) PetscCall(MatSeqAIJRestoreArrayRead(ptap->P_oth, &dummy4));
184: PetscCall(MatSeqAIJRestoreArrayWrite(c->A, &cda));
185: PetscCall(MatSeqAIJRestoreArrayWrite(c->B, &coa));
187: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
188: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
189: PetscFunctionReturn(PETSC_SUCCESS);
190: }
192: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(Mat A, Mat P, PetscReal fill, Mat C)
193: {
194: MPI_Comm comm;
195: PetscMPIInt size;
196: MatProductCtx_APMPI *ptap;
197: PetscFreeSpaceList free_space = NULL, current_space = NULL;
198: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
199: Mat_SeqAIJ *ad = (Mat_SeqAIJ *)a->A->data, *ao = (Mat_SeqAIJ *)a->B->data, *p_loc, *p_oth;
200: PetscInt *pi_loc, *pj_loc, *pi_oth, *pj_oth, *dnz, *onz;
201: PetscInt *adi = ad->i, *adj = ad->j, *aoi = ao->i, *aoj = ao->j, rstart = A->rmap->rstart;
202: PetscInt *lnk, i, pnz, row, *api, *apj, *Jptr, apnz, nspacedouble = 0, j, nzi;
203: PetscInt am = A->rmap->n, pN = P->cmap->N, pn = P->cmap->n, pm = P->rmap->n;
204: PetscBT lnkbt;
205: PetscReal afill;
206: MatType mtype;
208: PetscFunctionBegin;
209: MatCheckProduct(C, 4);
210: PetscCheck(!C->product->data, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Extra product struct not empty");
211: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
212: PetscCallMPI(MPI_Comm_size(comm, &size));
214: /* create struct MatProductCtx_APMPI and attached it to C later */
215: PetscCall(PetscNew(&ptap));
217: /* get P_oth by taking rows of P (= non-zero cols of local A) from other processors */
218: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &ptap->startsj_s, &ptap->startsj_r, &ptap->bufa, &ptap->P_oth));
220: /* get P_loc by taking all local rows of P */
221: PetscCall(MatMPIAIJGetLocalMat(P, MAT_INITIAL_MATRIX, &ptap->P_loc));
223: p_loc = (Mat_SeqAIJ *)ptap->P_loc->data;
224: pi_loc = p_loc->i;
225: pj_loc = p_loc->j;
226: if (size > 1) {
227: p_oth = (Mat_SeqAIJ *)ptap->P_oth->data;
228: pi_oth = p_oth->i;
229: pj_oth = p_oth->j;
230: } else {
231: p_oth = NULL;
232: pi_oth = NULL;
233: pj_oth = NULL;
234: }
236: /* first, compute symbolic AP = A_loc*P = A_diag*P_loc + A_off*P_oth */
237: PetscCall(PetscMalloc1(am + 1, &api));
238: ptap->api = api;
239: api[0] = 0;
241: /* create and initialize a linked list */
242: PetscCall(PetscLLCondensedCreate(pN, pN, &lnk, &lnkbt));
244: /* Initial FreeSpace size is fill*(nnz(A)+nnz(P)) */
245: PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(fill, PetscIntSumTruncate(adi[am], PetscIntSumTruncate(aoi[am], pi_loc[pm]))), &free_space));
246: current_space = free_space;
248: MatPreallocateBegin(comm, am, pn, dnz, onz);
249: for (i = 0; i < am; i++) {
250: /* diagonal portion of A */
251: nzi = adi[i + 1] - adi[i];
252: for (j = 0; j < nzi; j++) {
253: row = *adj++;
254: pnz = pi_loc[row + 1] - pi_loc[row];
255: Jptr = pj_loc + pi_loc[row];
256: /* add non-zero cols of P into the sorted linked list lnk */
257: PetscCall(PetscLLCondensedAddSorted(pnz, Jptr, lnk, lnkbt));
258: }
259: /* off-diagonal portion of A */
260: nzi = aoi[i + 1] - aoi[i];
261: for (j = 0; j < nzi; j++) {
262: row = *aoj++;
263: pnz = pi_oth[row + 1] - pi_oth[row];
264: Jptr = pj_oth + pi_oth[row];
265: PetscCall(PetscLLCondensedAddSorted(pnz, Jptr, lnk, lnkbt));
266: }
267: /* add possible missing diagonal entry */
268: if (C->force_diagonals) {
269: j = i + rstart; /* column index */
270: PetscCall(PetscLLCondensedAddSorted(1, &j, lnk, lnkbt));
271: }
273: apnz = lnk[0];
274: api[i + 1] = api[i] + apnz;
276: /* if free space is not available, double the total space in the list */
277: if (current_space->local_remaining < apnz) {
278: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(apnz, current_space->total_array_size), ¤t_space));
279: nspacedouble++;
280: }
282: /* Copy data into free space, then initialize lnk */
283: PetscCall(PetscLLCondensedClean(pN, apnz, current_space->array, lnk, lnkbt));
284: PetscCall(MatPreallocateSet(i + rstart, apnz, current_space->array, dnz, onz));
286: current_space->array += apnz;
287: current_space->local_used += apnz;
288: current_space->local_remaining -= apnz;
289: }
291: /* Allocate space for apj, initialize apj, and */
292: /* destroy list of free space and other temporary array(s) */
293: PetscCall(PetscMalloc1(api[am], &ptap->apj));
294: apj = ptap->apj;
295: PetscCall(PetscFreeSpaceContiguous(&free_space, ptap->apj));
296: PetscCall(PetscLLDestroy(lnk, lnkbt));
298: /* malloc apa to store dense row A[i,:]*P */
299: PetscCall(PetscCalloc1(pN, &ptap->apa));
301: /* set and assemble symbolic parallel matrix C */
302: PetscCall(MatSetSizes(C, am, pn, PETSC_DETERMINE, PETSC_DETERMINE));
303: PetscCall(MatSetBlockSizesFromMats(C, A, P));
305: PetscCall(MatGetType(A, &mtype));
306: PetscCall(MatSetType(C, mtype));
307: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
308: MatPreallocateEnd(dnz, onz);
310: PetscCall(MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(C, apj, api));
311: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
312: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
313: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
314: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
316: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable;
317: C->ops->productnumeric = MatProductNumeric_AB;
319: /* attach the supporting struct to C for reuse */
320: C->product->data = ptap;
321: C->product->destroy = MatProductCtxDestroy_MPIAIJ_MatMatMult;
323: /* set MatInfo */
324: afill = (PetscReal)api[am] / (adi[am] + aoi[am] + pi_loc[pm] + 1) + 1.e-5;
325: if (afill < 1.0) afill = 1.0;
326: C->info.mallocs = nspacedouble;
327: C->info.fill_ratio_given = fill;
328: C->info.fill_ratio_needed = afill;
330: #if defined(PETSC_USE_INFO)
331: if (api[am]) {
332: PetscCall(PetscInfo(C, "Reallocs %" PetscInt_FMT "; Fill ratio: given %g needed %g.\n", nspacedouble, (double)fill, (double)afill));
333: PetscCall(PetscInfo(C, "Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n", (double)afill));
334: } else {
335: PetscCall(PetscInfo(C, "Empty matrix product\n"));
336: }
337: #endif
338: PetscFunctionReturn(PETSC_SUCCESS);
339: }
341: static PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIDense(Mat, Mat, PetscReal, Mat);
342: static PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIDense(Mat, Mat, Mat);
344: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_MPIDense_AB(Mat C)
345: {
346: Mat_Product *product = C->product;
347: Mat A = product->A, B = product->B;
349: PetscFunctionBegin;
350: if (A->cmap->rstart != B->rmap->rstart || A->cmap->rend != B->rmap->rend)
351: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, (%" PetscInt_FMT ", %" PetscInt_FMT ") != (%" PetscInt_FMT ",%" PetscInt_FMT ")", A->cmap->rstart, A->cmap->rend, B->rmap->rstart, B->rmap->rend);
353: C->ops->matmultsymbolic = MatMatMultSymbolic_MPIAIJ_MPIDense;
354: C->ops->productsymbolic = MatProductSymbolic_AB;
355: PetscFunctionReturn(PETSC_SUCCESS);
356: }
358: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_MPIDense_AtB(Mat C)
359: {
360: Mat_Product *product = C->product;
361: Mat A = product->A, B = product->B;
363: PetscFunctionBegin;
364: if (A->rmap->rstart != B->rmap->rstart || A->rmap->rend != B->rmap->rend)
365: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, (%" PetscInt_FMT ", %" PetscInt_FMT ") != (%" PetscInt_FMT ",%" PetscInt_FMT ")", A->rmap->rstart, A->rmap->rend, B->rmap->rstart, B->rmap->rend);
367: C->ops->transposematmultsymbolic = MatTransposeMatMultSymbolic_MPIAIJ_MPIDense;
368: C->ops->productsymbolic = MatProductSymbolic_AtB;
369: PetscFunctionReturn(PETSC_SUCCESS);
370: }
372: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIAIJ_MPIDense(Mat C)
373: {
374: Mat_Product *product = C->product;
376: PetscFunctionBegin;
377: switch (product->type) {
378: case MATPRODUCT_AB:
379: PetscCall(MatProductSetFromOptions_MPIAIJ_MPIDense_AB(C));
380: break;
381: case MATPRODUCT_AtB:
382: PetscCall(MatProductSetFromOptions_MPIAIJ_MPIDense_AtB(C));
383: break;
384: default:
385: break;
386: }
387: PetscFunctionReturn(PETSC_SUCCESS);
388: }
390: typedef struct {
391: Mat workB, workB1;
392: MPI_Request *rwaits, *swaits;
393: PetscInt nsends, nrecvs;
394: MPI_Datatype *stype, *rtype;
395: PetscInt blda;
396: } MPIAIJ_MPIDense;
398: static PetscErrorCode MatMPIAIJ_MPIDenseDestroy(void **ctx)
399: {
400: MPIAIJ_MPIDense *contents = *(MPIAIJ_MPIDense **)ctx;
401: PetscInt i;
403: PetscFunctionBegin;
404: PetscCall(MatDestroy(&contents->workB));
405: PetscCall(MatDestroy(&contents->workB1));
406: for (i = 0; i < contents->nsends; i++) PetscCallMPI(MPI_Type_free(&contents->stype[i]));
407: for (i = 0; i < contents->nrecvs; i++) PetscCallMPI(MPI_Type_free(&contents->rtype[i]));
408: PetscCall(PetscFree4(contents->stype, contents->rtype, contents->rwaits, contents->swaits));
409: PetscCall(PetscFree(contents));
410: PetscFunctionReturn(PETSC_SUCCESS);
411: }
413: static PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIDense(Mat A, Mat B, PetscReal fill, Mat C)
414: {
415: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
416: PetscInt nz = aij->B->cmap->n, blda, m, M, n, N;
417: MPIAIJ_MPIDense *contents;
418: VecScatter ctx = aij->Mvctx;
419: PetscInt Am = A->rmap->n, Bm = B->rmap->n, BN = B->cmap->N, Bbn, Bbn1, bs, numBb;
420: MPI_Comm comm;
421: MPI_Datatype type1, *stype, *rtype;
422: const PetscInt *sindices, *sstarts, *rstarts;
423: PetscMPIInt *disp, nsends, nrecvs, nrows_to, nrows_from;
424: PetscBool cisdense;
426: PetscFunctionBegin;
427: MatCheckProduct(C, 4);
428: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
429: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
430: PetscCall(PetscObjectBaseTypeCompare((PetscObject)C, MATMPIDENSE, &cisdense));
431: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)B)->type_name));
432: PetscCall(MatGetLocalSize(C, &m, &n));
433: PetscCall(MatGetSize(C, &M, &N));
434: if (m == PETSC_DECIDE || n == PETSC_DECIDE || M == PETSC_DECIDE || N == PETSC_DECIDE) PetscCall(MatSetSizes(C, Am, B->cmap->n, A->rmap->N, BN));
435: PetscCall(MatSetBlockSizesFromMats(C, A, B));
436: PetscCall(MatSetUp(C));
437: PetscCall(MatDenseGetLDA(B, &blda));
438: PetscCall(PetscNew(&contents));
440: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &sindices, NULL, NULL));
441: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL, NULL, NULL));
443: /* Create column block of B and C for memory scalability when BN is too large */
444: /* Estimate Bbn, column size of Bb */
445: if (nz) {
446: Bbn1 = 2 * Am * BN / nz;
447: if (!Bbn1) Bbn1 = 1;
448: } else Bbn1 = BN;
450: bs = B->cmap->bs;
451: Bbn1 = Bbn1 / bs * bs; /* Bbn1 is a multiple of bs */
452: if (Bbn1 > BN) Bbn1 = BN;
453: PetscCallMPI(MPIU_Allreduce(&Bbn1, &Bbn, 1, MPIU_INT, MPI_MAX, comm));
455: /* Enable runtime option for Bbn */
456: PetscOptionsBegin(comm, ((PetscObject)C)->prefix, "MatMatMult", "Mat");
457: PetscCall(PetscOptionsInt("-matmatmult_Bbn", "Number of columns in Bb", "MatMatMult", Bbn, &Bbn, NULL));
458: PetscOptionsEnd();
459: Bbn = PetscMin(Bbn, BN);
461: if (Bbn > 0 && Bbn < BN) {
462: numBb = BN / Bbn;
463: Bbn1 = BN - numBb * Bbn;
464: } else numBb = 0;
466: if (numBb) {
467: PetscCall(PetscInfo(C, "use Bb, BN=%" PetscInt_FMT ", Bbn=%" PetscInt_FMT "; numBb=%" PetscInt_FMT "\n", BN, Bbn, numBb));
468: if (Bbn1) { /* Create workB1 for the remaining columns */
469: PetscCall(PetscInfo(C, "use Bb1, BN=%" PetscInt_FMT ", Bbn1=%" PetscInt_FMT "\n", BN, Bbn1));
470: /* Create work matrix used to store off processor rows of B needed for local product */
471: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, nz, Bbn1, NULL, &contents->workB1));
472: } else contents->workB1 = NULL;
473: }
475: /* Create work matrix used to store off processor rows of B needed for local product */
476: PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, nz, Bbn, NULL, &contents->workB));
478: /* Use MPI derived data type to reduce memory required by the send/recv buffers */
479: PetscCall(PetscMalloc4(nsends, &stype, nrecvs, &rtype, nrecvs, &contents->rwaits, nsends, &contents->swaits));
480: contents->stype = stype;
481: contents->nsends = nsends;
483: contents->rtype = rtype;
484: contents->nrecvs = nrecvs;
485: contents->blda = blda;
487: PetscCall(PetscMalloc1(Bm + 1, &disp));
488: for (PetscMPIInt i = 0; i < nsends; i++) {
489: PetscCall(PetscMPIIntCast(sstarts[i + 1] - sstarts[i], &nrows_to));
490: for (PetscInt j = 0; j < nrows_to; j++) PetscCall(PetscMPIIntCast(sindices[sstarts[i] + j], &disp[j])); /* rowB to be sent */
491: PetscCallMPI(MPI_Type_create_indexed_block(nrows_to, 1, disp, MPIU_SCALAR, &type1));
492: PetscCallMPI(MPI_Type_create_resized(type1, 0, blda * sizeof(PetscScalar), &stype[i]));
493: PetscCallMPI(MPI_Type_commit(&stype[i]));
494: PetscCallMPI(MPI_Type_free(&type1));
495: }
497: for (PetscMPIInt i = 0; i < nrecvs; i++) {
498: /* received values from a process form a (nrows_from x Bbn) row block in workB (column-wise) */
499: PetscCall(PetscMPIIntCast(rstarts[i + 1] - rstarts[i], &nrows_from));
500: disp[0] = 0;
501: PetscCallMPI(MPI_Type_create_indexed_block(1, nrows_from, disp, MPIU_SCALAR, &type1));
502: PetscCallMPI(MPI_Type_create_resized(type1, 0, nz * sizeof(PetscScalar), &rtype[i]));
503: PetscCallMPI(MPI_Type_commit(&rtype[i]));
504: PetscCallMPI(MPI_Type_free(&type1));
505: }
507: PetscCall(PetscFree(disp));
508: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &sindices, NULL, NULL));
509: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL, NULL, NULL));
510: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
511: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
512: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
514: C->product->data = contents;
515: C->product->destroy = MatMPIAIJ_MPIDenseDestroy;
516: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIDense;
517: PetscFunctionReturn(PETSC_SUCCESS);
518: }
520: PETSC_INTERN PetscErrorCode MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat, Mat, Mat, const PetscBool);
522: /*
523: Performs an efficient scatter on the rows of B needed by this process; this is
524: a modification of the VecScatterBegin_() routines.
526: Input: If Bbidx = 0, uses B = Bb, else B = Bb1, see MatMatMultSymbolic_MPIAIJ_MPIDense()
527: */
529: static PetscErrorCode MatMPIDenseScatter(Mat A, Mat B, PetscInt Bbidx, Mat C, Mat *outworkB)
530: {
531: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
532: const PetscScalar *b;
533: PetscScalar *rvalues;
534: VecScatter ctx = aij->Mvctx;
535: const PetscInt *sindices, *sstarts, *rstarts;
536: const PetscMPIInt *sprocs, *rprocs;
537: PetscMPIInt nsends, nrecvs;
538: MPI_Request *swaits, *rwaits;
539: MPI_Comm comm;
540: PetscMPIInt tag = ((PetscObject)ctx)->tag, ncols, nrows, nsends_mpi, nrecvs_mpi;
541: MPIAIJ_MPIDense *contents;
542: Mat workB;
543: MPI_Datatype *stype, *rtype;
544: PetscInt blda;
546: PetscFunctionBegin;
547: MatCheckProduct(C, 4);
548: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
549: PetscCall(PetscMPIIntCast(B->cmap->N, &ncols));
550: PetscCall(PetscMPIIntCast(aij->B->cmap->n, &nrows));
551: contents = (MPIAIJ_MPIDense *)C->product->data;
552: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &sindices, &sprocs, NULL /*bs*/));
553: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL, &rprocs, NULL /*bs*/));
554: PetscCall(PetscMPIIntCast(nsends, &nsends_mpi));
555: PetscCall(PetscMPIIntCast(nrecvs, &nrecvs_mpi));
556: if (Bbidx == 0) workB = *outworkB = contents->workB;
557: else workB = *outworkB = contents->workB1;
558: PetscCheck(nrows == workB->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Number of rows of workB %" PetscInt_FMT " not equal to columns of aij->B %d", workB->cmap->n, nrows);
559: swaits = contents->swaits;
560: rwaits = contents->rwaits;
562: PetscCall(MatDenseGetArrayRead(B, &b));
563: PetscCall(MatDenseGetLDA(B, &blda));
564: PetscCheck(blda == contents->blda, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Cannot reuse an input matrix with lda %" PetscInt_FMT " != %" PetscInt_FMT, blda, contents->blda);
565: PetscCall(MatDenseGetArray(workB, &rvalues));
567: /* Post recv, use MPI derived data type to save memory */
568: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
569: rtype = contents->rtype;
570: for (PetscMPIInt i = 0; i < nrecvs; i++) PetscCallMPI(MPIU_Irecv(rvalues + (rstarts[i] - rstarts[0]), ncols, rtype[i], rprocs[i], tag, comm, rwaits + i));
572: stype = contents->stype;
573: for (PetscMPIInt i = 0; i < nsends; i++) PetscCallMPI(MPIU_Isend(b, ncols, stype[i], sprocs[i], tag, comm, swaits + i));
575: if (nrecvs) PetscCallMPI(MPI_Waitall(nrecvs_mpi, rwaits, MPI_STATUSES_IGNORE));
576: if (nsends) PetscCallMPI(MPI_Waitall(nsends_mpi, swaits, MPI_STATUSES_IGNORE));
578: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &sindices, &sprocs, NULL));
579: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL, &rprocs, NULL));
580: PetscCall(MatDenseRestoreArrayRead(B, &b));
581: PetscCall(MatDenseRestoreArray(workB, &rvalues));
582: PetscFunctionReturn(PETSC_SUCCESS);
583: }
585: static PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIDense(Mat A, Mat B, Mat C)
586: {
587: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
588: Mat_MPIDense *bdense = (Mat_MPIDense *)B->data;
589: Mat_MPIDense *cdense = (Mat_MPIDense *)C->data;
590: Mat workB;
591: MPIAIJ_MPIDense *contents;
593: PetscFunctionBegin;
594: MatCheckProduct(C, 3);
595: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
596: contents = (MPIAIJ_MPIDense *)C->product->data;
597: /* diagonal block of A times all local rows of B */
598: /* TODO: this calls a symbolic multiplication every time, which could be avoided */
599: PetscCall(MatMatMult(aij->A, bdense->A, MAT_REUSE_MATRIX, PETSC_CURRENT, &cdense->A));
600: if (contents->workB->cmap->n == B->cmap->N) {
601: /* get off processor parts of B needed to complete C=A*B */
602: PetscCall(MatMPIDenseScatter(A, B, 0, C, &workB));
604: /* off-diagonal block of A times nonlocal rows of B */
605: PetscCall(MatMatMultNumericAdd_SeqAIJ_SeqDense(aij->B, workB, cdense->A, PETSC_TRUE));
606: } else {
607: Mat Bb, Cb;
608: PetscInt BN = B->cmap->N, n = contents->workB->cmap->n;
609: PetscBool ccpu;
611: PetscCheck(n > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Column block size %" PetscInt_FMT " must be positive", n);
612: /* Prevent from unneeded copies back and forth from the GPU
613: when getting and restoring the submatrix
614: We need a proper GPU code for AIJ * dense in parallel */
615: PetscCall(MatBoundToCPU(C, &ccpu));
616: PetscCall(MatBindToCPU(C, PETSC_TRUE));
617: for (PetscInt i = 0; i < BN; i += n) {
618: PetscCall(MatDenseGetSubMatrix(B, PETSC_DECIDE, PETSC_DECIDE, i, PetscMin(i + n, BN), &Bb));
619: PetscCall(MatDenseGetSubMatrix(C, PETSC_DECIDE, PETSC_DECIDE, i, PetscMin(i + n, BN), &Cb));
621: /* get off processor parts of B needed to complete C=A*B */
622: PetscCall(MatMPIDenseScatter(A, Bb, (i + n) > BN, C, &workB));
624: /* off-diagonal block of A times nonlocal rows of B */
625: cdense = (Mat_MPIDense *)Cb->data;
626: PetscCall(MatMatMultNumericAdd_SeqAIJ_SeqDense(aij->B, workB, cdense->A, PETSC_TRUE));
627: PetscCall(MatDenseRestoreSubMatrix(B, &Bb));
628: PetscCall(MatDenseRestoreSubMatrix(C, &Cb));
629: }
630: PetscCall(MatBindToCPU(C, ccpu));
631: }
632: PetscFunctionReturn(PETSC_SUCCESS);
633: }
635: PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIAIJ(Mat A, Mat P, Mat C)
636: {
637: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *c = (Mat_MPIAIJ *)C->data;
638: Mat_SeqAIJ *ad = (Mat_SeqAIJ *)a->A->data, *ao = (Mat_SeqAIJ *)a->B->data;
639: Mat_SeqAIJ *cd = (Mat_SeqAIJ *)c->A->data, *co = (Mat_SeqAIJ *)c->B->data;
640: PetscInt *adi = ad->i, *adj, *aoi = ao->i, *aoj;
641: PetscScalar *ada, *aoa, *cda = cd->a, *coa = co->a;
642: Mat_SeqAIJ *p_loc, *p_oth;
643: PetscInt *pi_loc, *pj_loc, *pi_oth, *pj_oth, *pj;
644: PetscScalar *pa_loc, *pa_oth, *pa, valtmp, *ca;
645: PetscInt cm = C->rmap->n, anz, pnz;
646: MatProductCtx_APMPI *ptap;
647: PetscScalar *apa_sparse;
648: const PetscScalar *dummy;
649: PetscInt *api, *apj, *apJ, i, j, k, row;
650: PetscInt cstart = C->cmap->rstart;
651: PetscInt cdnz, conz, k0, k1, nextp;
652: MPI_Comm comm;
653: PetscMPIInt size;
655: PetscFunctionBegin;
656: MatCheckProduct(C, 3);
657: ptap = (MatProductCtx_APMPI *)C->product->data;
658: PetscCheck(ptap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtAP cannot be computed. Missing data");
659: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
660: PetscCallMPI(MPI_Comm_size(comm, &size));
661: PetscCheck(ptap->P_oth || size <= 1, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "AP cannot be reused. Do not call MatProductClear()");
663: /* flag CPU mask for C */
664: #if defined(PETSC_HAVE_DEVICE)
665: if (C->offloadmask != PETSC_OFFLOAD_UNALLOCATED) C->offloadmask = PETSC_OFFLOAD_CPU;
666: if (c->A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) c->A->offloadmask = PETSC_OFFLOAD_CPU;
667: if (c->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) c->B->offloadmask = PETSC_OFFLOAD_CPU;
668: #endif
669: apa_sparse = ptap->apa;
671: /* 1) get P_oth = ptap->P_oth and P_loc = ptap->P_loc */
672: /* update numerical values of P_oth and P_loc */
673: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_REUSE_MATRIX, &ptap->startsj_s, &ptap->startsj_r, &ptap->bufa, &ptap->P_oth));
674: PetscCall(MatMPIAIJGetLocalMat(P, MAT_REUSE_MATRIX, &ptap->P_loc));
676: /* 2) compute numeric C_loc = A_loc*P = Ad*P_loc + Ao*P_oth */
677: /* get data from symbolic products */
678: p_loc = (Mat_SeqAIJ *)ptap->P_loc->data;
679: pi_loc = p_loc->i;
680: pj_loc = p_loc->j;
681: pa_loc = p_loc->a;
682: if (size > 1) {
683: p_oth = (Mat_SeqAIJ *)ptap->P_oth->data;
684: pi_oth = p_oth->i;
685: pj_oth = p_oth->j;
686: pa_oth = p_oth->a;
687: } else {
688: p_oth = NULL;
689: pi_oth = NULL;
690: pj_oth = NULL;
691: pa_oth = NULL;
692: }
694: /* trigger copy to CPU */
695: PetscCall(MatSeqAIJGetArrayRead(a->A, &dummy));
696: PetscCall(MatSeqAIJRestoreArrayRead(a->A, &dummy));
697: PetscCall(MatSeqAIJGetArrayRead(a->B, &dummy));
698: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &dummy));
699: api = ptap->api;
700: apj = ptap->apj;
701: for (i = 0; i < cm; i++) {
702: apJ = apj + api[i];
704: /* diagonal portion of A */
705: anz = adi[i + 1] - adi[i];
706: adj = ad->j + adi[i];
707: ada = ad->a + adi[i];
708: for (j = 0; j < anz; j++) {
709: row = adj[j];
710: pnz = pi_loc[row + 1] - pi_loc[row];
711: pj = pj_loc + pi_loc[row];
712: pa = pa_loc + pi_loc[row];
713: /* perform sparse axpy */
714: valtmp = ada[j];
715: nextp = 0;
716: for (k = 0; nextp < pnz; k++) {
717: if (apJ[k] == pj[nextp]) { /* column of AP == column of P */
718: apa_sparse[k] += valtmp * pa[nextp++];
719: }
720: }
721: PetscCall(PetscLogFlops(2.0 * pnz));
722: }
724: /* off-diagonal portion of A */
725: anz = aoi[i + 1] - aoi[i];
726: aoj = PetscSafePointerPlusOffset(ao->j, aoi[i]);
727: aoa = PetscSafePointerPlusOffset(ao->a, aoi[i]);
728: for (j = 0; j < anz; j++) {
729: row = aoj[j];
730: pnz = pi_oth[row + 1] - pi_oth[row];
731: pj = pj_oth + pi_oth[row];
732: pa = pa_oth + pi_oth[row];
733: /* perform sparse axpy */
734: valtmp = aoa[j];
735: nextp = 0;
736: for (k = 0; nextp < pnz; k++) {
737: if (apJ[k] == pj[nextp]) { /* column of AP == column of P */
738: apa_sparse[k] += valtmp * pa[nextp++];
739: }
740: }
741: PetscCall(PetscLogFlops(2.0 * pnz));
742: }
744: /* set values in C */
745: cdnz = cd->i[i + 1] - cd->i[i];
746: conz = co->i[i + 1] - co->i[i];
748: /* 1st off-diagonal part of C */
749: ca = PetscSafePointerPlusOffset(coa, co->i[i]);
750: k = 0;
751: for (k0 = 0; k0 < conz; k0++) {
752: if (apJ[k] >= cstart) break;
753: ca[k0] = apa_sparse[k];
754: apa_sparse[k] = 0.0;
755: k++;
756: }
758: /* diagonal part of C */
759: ca = cda + cd->i[i];
760: for (k1 = 0; k1 < cdnz; k1++) {
761: ca[k1] = apa_sparse[k];
762: apa_sparse[k] = 0.0;
763: k++;
764: }
766: /* 2nd off-diagonal part of C */
767: ca = PetscSafePointerPlusOffset(coa, co->i[i]);
768: for (; k0 < conz; k0++) {
769: ca[k0] = apa_sparse[k];
770: apa_sparse[k] = 0.0;
771: k++;
772: }
773: }
774: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
775: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
776: PetscFunctionReturn(PETSC_SUCCESS);
777: }
779: /* same as MatMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(), except using LLCondensed to avoid O(BN) memory requirement */
780: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIAIJ(Mat A, Mat P, PetscReal fill, Mat C)
781: {
782: MPI_Comm comm;
783: PetscMPIInt size;
784: MatProductCtx_APMPI *ptap;
785: PetscFreeSpaceList free_space = NULL, current_space = NULL;
786: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
787: Mat_SeqAIJ *ad = (Mat_SeqAIJ *)a->A->data, *ao = (Mat_SeqAIJ *)a->B->data, *p_loc, *p_oth;
788: PetscInt *pi_loc, *pj_loc, *pi_oth, *pj_oth, *dnz, *onz;
789: PetscInt *adi = ad->i, *adj = ad->j, *aoi = ao->i, *aoj = ao->j, rstart = A->rmap->rstart;
790: PetscInt i, pnz, row, *api, *apj, *Jptr, apnz, nspacedouble = 0, j, nzi, *lnk, apnz_max = 1;
791: PetscInt am = A->rmap->n, pn = P->cmap->n, pm = P->rmap->n, lsize = pn + 20;
792: PetscReal afill;
793: MatType mtype;
795: PetscFunctionBegin;
796: MatCheckProduct(C, 4);
797: PetscCheck(!C->product->data, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Extra product struct not empty");
798: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
799: PetscCallMPI(MPI_Comm_size(comm, &size));
801: /* create struct MatProductCtx_APMPI and attached it to C later */
802: PetscCall(PetscNew(&ptap));
804: /* get P_oth by taking rows of P (= non-zero cols of local A) from other processors */
805: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &ptap->startsj_s, &ptap->startsj_r, &ptap->bufa, &ptap->P_oth));
807: /* get P_loc by taking all local rows of P */
808: PetscCall(MatMPIAIJGetLocalMat(P, MAT_INITIAL_MATRIX, &ptap->P_loc));
810: p_loc = (Mat_SeqAIJ *)ptap->P_loc->data;
811: pi_loc = p_loc->i;
812: pj_loc = p_loc->j;
813: if (size > 1) {
814: p_oth = (Mat_SeqAIJ *)ptap->P_oth->data;
815: pi_oth = p_oth->i;
816: pj_oth = p_oth->j;
817: } else {
818: p_oth = NULL;
819: pi_oth = NULL;
820: pj_oth = NULL;
821: }
823: /* first, compute symbolic AP = A_loc*P = A_diag*P_loc + A_off*P_oth */
824: PetscCall(PetscMalloc1(am + 1, &api));
825: ptap->api = api;
826: api[0] = 0;
828: PetscCall(PetscLLCondensedCreate_Scalable(lsize, &lnk));
830: /* Initial FreeSpace size is fill*(nnz(A)+nnz(P)) */
831: PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(fill, PetscIntSumTruncate(adi[am], PetscIntSumTruncate(aoi[am], pi_loc[pm]))), &free_space));
832: current_space = free_space;
833: MatPreallocateBegin(comm, am, pn, dnz, onz);
834: for (i = 0; i < am; i++) {
835: /* diagonal portion of A */
836: nzi = adi[i + 1] - adi[i];
837: for (j = 0; j < nzi; j++) {
838: row = *adj++;
839: pnz = pi_loc[row + 1] - pi_loc[row];
840: Jptr = pj_loc + pi_loc[row];
841: /* Expand list if it is not long enough */
842: if (pnz + apnz_max > lsize) {
843: lsize = pnz + apnz_max;
844: PetscCall(PetscLLCondensedExpand_Scalable(lsize, &lnk));
845: }
846: /* add non-zero cols of P into the sorted linked list lnk */
847: PetscCall(PetscLLCondensedAddSorted_Scalable(pnz, Jptr, lnk));
848: apnz = *lnk; /* The first element in the list is the number of items in the list */
849: api[i + 1] = api[i] + apnz;
850: if (apnz > apnz_max) apnz_max = apnz + 1; /* '1' for diagonal entry */
851: }
852: /* off-diagonal portion of A */
853: nzi = aoi[i + 1] - aoi[i];
854: for (j = 0; j < nzi; j++) {
855: row = *aoj++;
856: pnz = pi_oth[row + 1] - pi_oth[row];
857: Jptr = pj_oth + pi_oth[row];
858: /* Expand list if it is not long enough */
859: if (pnz + apnz_max > lsize) {
860: lsize = pnz + apnz_max;
861: PetscCall(PetscLLCondensedExpand_Scalable(lsize, &lnk));
862: }
863: /* add non-zero cols of P into the sorted linked list lnk */
864: PetscCall(PetscLLCondensedAddSorted_Scalable(pnz, Jptr, lnk));
865: apnz = *lnk; /* The first element in the list is the number of items in the list */
866: api[i + 1] = api[i] + apnz;
867: if (apnz > apnz_max) apnz_max = apnz + 1; /* '1' for diagonal entry */
868: }
870: /* add missing diagonal entry */
871: if (C->force_diagonals) {
872: j = i + rstart; /* column index */
873: PetscCall(PetscLLCondensedAddSorted_Scalable(1, &j, lnk));
874: }
876: apnz = *lnk;
877: api[i + 1] = api[i] + apnz;
878: if (apnz > apnz_max) apnz_max = apnz;
880: /* if free space is not available, double the total space in the list */
881: if (current_space->local_remaining < apnz) {
882: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(apnz, current_space->total_array_size), ¤t_space));
883: nspacedouble++;
884: }
886: /* Copy data into free space, then initialize lnk */
887: PetscCall(PetscLLCondensedClean_Scalable(apnz, current_space->array, lnk));
888: PetscCall(MatPreallocateSet(i + rstart, apnz, current_space->array, dnz, onz));
890: current_space->array += apnz;
891: current_space->local_used += apnz;
892: current_space->local_remaining -= apnz;
893: }
895: /* Allocate space for apj, initialize apj, and */
896: /* destroy list of free space and other temporary array(s) */
897: PetscCall(PetscMalloc1(api[am], &ptap->apj));
898: apj = ptap->apj;
899: PetscCall(PetscFreeSpaceContiguous(&free_space, ptap->apj));
900: PetscCall(PetscLLCondensedDestroy_Scalable(lnk));
902: /* create and assemble symbolic parallel matrix C */
903: PetscCall(MatSetSizes(C, am, pn, PETSC_DETERMINE, PETSC_DETERMINE));
904: PetscCall(MatSetBlockSizesFromMats(C, A, P));
905: PetscCall(MatGetType(A, &mtype));
906: PetscCall(MatSetType(C, mtype));
907: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
908: MatPreallocateEnd(dnz, onz);
910: /* malloc apa for assembly C */
911: PetscCall(PetscCalloc1(apnz_max, &ptap->apa));
913: PetscCall(MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(C, apj, api));
914: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
915: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
916: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
917: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
919: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIAIJ;
920: C->ops->productnumeric = MatProductNumeric_AB;
922: /* attach the supporting struct to C for reuse */
923: C->product->data = ptap;
924: C->product->destroy = MatProductCtxDestroy_MPIAIJ_MatMatMult;
926: /* set MatInfo */
927: afill = (PetscReal)api[am] / (adi[am] + aoi[am] + pi_loc[pm] + 1) + 1.e-5;
928: if (afill < 1.0) afill = 1.0;
929: C->info.mallocs = nspacedouble;
930: C->info.fill_ratio_given = fill;
931: C->info.fill_ratio_needed = afill;
933: #if defined(PETSC_USE_INFO)
934: if (api[am]) {
935: PetscCall(PetscInfo(C, "Reallocs %" PetscInt_FMT "; Fill ratio: given %g needed %g.\n", nspacedouble, (double)fill, (double)afill));
936: PetscCall(PetscInfo(C, "Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n", (double)afill));
937: } else {
938: PetscCall(PetscInfo(C, "Empty matrix product\n"));
939: }
940: #endif
941: PetscFunctionReturn(PETSC_SUCCESS);
942: }
944: /* This function is needed for the seqMPI matrix-matrix multiplication. */
945: /* Three input arrays are merged to one output array. The size of the */
946: /* output array is also output. Duplicate entries only show up once. */
947: static void Merge3SortedArrays(PetscInt size1, PetscInt *in1, PetscInt size2, PetscInt *in2, PetscInt size3, PetscInt *in3, PetscInt *size4, PetscInt *out)
948: {
949: int i = 0, j = 0, k = 0, l = 0;
951: /* Traverse all three arrays */
952: while (i < size1 && j < size2 && k < size3) {
953: if (in1[i] < in2[j] && in1[i] < in3[k]) {
954: out[l++] = in1[i++];
955: } else if (in2[j] < in1[i] && in2[j] < in3[k]) {
956: out[l++] = in2[j++];
957: } else if (in3[k] < in1[i] && in3[k] < in2[j]) {
958: out[l++] = in3[k++];
959: } else if (in1[i] == in2[j] && in1[i] < in3[k]) {
960: out[l++] = in1[i];
961: i++, j++;
962: } else if (in1[i] == in3[k] && in1[i] < in2[j]) {
963: out[l++] = in1[i];
964: i++, k++;
965: } else if (in3[k] == in2[j] && in2[j] < in1[i]) {
966: out[l++] = in2[j];
967: k++, j++;
968: } else if (in1[i] == in2[j] && in1[i] == in3[k]) {
969: out[l++] = in1[i];
970: i++, j++, k++;
971: }
972: }
974: /* Traverse two remaining arrays */
975: while (i < size1 && j < size2) {
976: if (in1[i] < in2[j]) {
977: out[l++] = in1[i++];
978: } else if (in1[i] > in2[j]) {
979: out[l++] = in2[j++];
980: } else {
981: out[l++] = in1[i];
982: i++, j++;
983: }
984: }
986: while (i < size1 && k < size3) {
987: if (in1[i] < in3[k]) {
988: out[l++] = in1[i++];
989: } else if (in1[i] > in3[k]) {
990: out[l++] = in3[k++];
991: } else {
992: out[l++] = in1[i];
993: i++, k++;
994: }
995: }
997: while (k < size3 && j < size2) {
998: if (in3[k] < in2[j]) {
999: out[l++] = in3[k++];
1000: } else if (in3[k] > in2[j]) {
1001: out[l++] = in2[j++];
1002: } else {
1003: out[l++] = in3[k];
1004: k++, j++;
1005: }
1006: }
1008: /* Traverse one remaining array */
1009: while (i < size1) out[l++] = in1[i++];
1010: while (j < size2) out[l++] = in2[j++];
1011: while (k < size3) out[l++] = in3[k++];
1013: *size4 = l;
1014: }
1016: /* This matrix-matrix multiplication algorithm divides the multiplication into three multiplications and */
1017: /* adds up the products. Two of these three multiplications are performed with existing (sequential) */
1018: /* matrix-matrix multiplications. */
1019: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIAIJ_seqMPI(Mat A, Mat P, PetscReal fill, Mat C)
1020: {
1021: MPI_Comm comm;
1022: PetscMPIInt size;
1023: MatProductCtx_APMPI *ptap;
1024: PetscFreeSpaceList free_space_diag = NULL, current_space = NULL;
1025: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1026: Mat_SeqAIJ *ad = (Mat_SeqAIJ *)a->A->data, *ao = (Mat_SeqAIJ *)a->B->data, *p_loc;
1027: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
1028: Mat_SeqAIJ *adpd_seq, *p_off, *aopoth_seq;
1029: PetscInt adponz, adpdnz;
1030: PetscInt *pi_loc, *dnz, *onz;
1031: PetscInt *adi = ad->i, *adj = ad->j, *aoi = ao->i, rstart = A->rmap->rstart;
1032: PetscInt *lnk, i, i1 = 0, pnz, row, *adpoi, *adpoj, *api, *adpoJ, *aopJ, *apJ, *Jptr, aopnz, nspacedouble = 0, j, nzi, *apj, apnz, *adpdi, *adpdj, *adpdJ, *poff_i, *poff_j, *j_temp, *aopothi, *aopothj;
1033: PetscInt am = A->rmap->n, pN = P->cmap->N, pn = P->cmap->n, pm = P->rmap->n, p_colstart, p_colend;
1034: PetscBT lnkbt;
1035: PetscReal afill;
1036: PetscMPIInt rank;
1037: Mat adpd, aopoth;
1038: MatType mtype;
1039: const char *prefix;
1041: PetscFunctionBegin;
1042: MatCheckProduct(C, 4);
1043: PetscCheck(!C->product->data, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Extra product struct not empty");
1044: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
1045: PetscCallMPI(MPI_Comm_size(comm, &size));
1046: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1047: PetscCall(MatGetOwnershipRangeColumn(P, &p_colstart, &p_colend));
1049: /* create struct MatProductCtx_APMPI and attached it to C later */
1050: PetscCall(PetscNew(&ptap));
1052: /* get P_oth by taking rows of P (= non-zero cols of local A) from other processors */
1053: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &ptap->startsj_s, &ptap->startsj_r, &ptap->bufa, &ptap->P_oth));
1055: /* get P_loc by taking all local rows of P */
1056: PetscCall(MatMPIAIJGetLocalMat(P, MAT_INITIAL_MATRIX, &ptap->P_loc));
1058: p_loc = (Mat_SeqAIJ *)ptap->P_loc->data;
1059: pi_loc = p_loc->i;
1061: /* Allocate memory for the i arrays of the matrices A*P, A_diag*P_off and A_offd * P */
1062: PetscCall(PetscMalloc1(am + 1, &api));
1063: PetscCall(PetscMalloc1(am + 1, &adpoi));
1065: adpoi[0] = 0;
1066: ptap->api = api;
1067: api[0] = 0;
1069: /* create and initialize a linked list, will be used for both A_diag * P_loc_off and A_offd * P_oth */
1070: PetscCall(PetscLLCondensedCreate(pN, pN, &lnk, &lnkbt));
1071: MatPreallocateBegin(comm, am, pn, dnz, onz);
1073: /* Symbolic calc of A_loc_diag * P_loc_diag */
1074: PetscCall(MatGetOptionsPrefix(A, &prefix));
1075: PetscCall(MatProductCreate(a->A, p->A, NULL, &adpd));
1076: PetscCall(MatGetOptionsPrefix(A, &prefix));
1077: PetscCall(MatSetOptionsPrefix(adpd, prefix));
1078: PetscCall(MatAppendOptionsPrefix(adpd, "inner_diag_"));
1080: PetscCall(MatProductSetType(adpd, MATPRODUCT_AB));
1081: PetscCall(MatProductSetAlgorithm(adpd, "sorted"));
1082: PetscCall(MatProductSetFill(adpd, fill));
1083: PetscCall(MatProductSetFromOptions(adpd));
1085: adpd->force_diagonals = C->force_diagonals;
1086: PetscCall(MatProductSymbolic(adpd));
1088: adpd_seq = (Mat_SeqAIJ *)((adpd)->data);
1089: adpdi = adpd_seq->i;
1090: adpdj = adpd_seq->j;
1091: p_off = (Mat_SeqAIJ *)p->B->data;
1092: poff_i = p_off->i;
1093: poff_j = p_off->j;
1095: /* j_temp stores indices of a result row before they are added to the linked list */
1096: PetscCall(PetscMalloc1(pN, &j_temp));
1098: /* Symbolic calc of the A_diag * p_loc_off */
1099: /* Initial FreeSpace size is fill*(nnz(A)+nnz(P)) */
1100: PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(fill, PetscIntSumTruncate(adi[am], PetscIntSumTruncate(aoi[am], pi_loc[pm]))), &free_space_diag));
1101: current_space = free_space_diag;
1103: for (i = 0; i < am; i++) {
1104: /* A_diag * P_loc_off */
1105: nzi = adi[i + 1] - adi[i];
1106: for (j = 0; j < nzi; j++) {
1107: row = *adj++;
1108: pnz = poff_i[row + 1] - poff_i[row];
1109: Jptr = poff_j + poff_i[row];
1110: for (i1 = 0; i1 < pnz; i1++) j_temp[i1] = p->garray[Jptr[i1]];
1111: /* add non-zero cols of P into the sorted linked list lnk */
1112: PetscCall(PetscLLCondensedAddSorted(pnz, j_temp, lnk, lnkbt));
1113: }
1115: adponz = lnk[0];
1116: adpoi[i + 1] = adpoi[i] + adponz;
1118: /* if free space is not available, double the total space in the list */
1119: if (current_space->local_remaining < adponz) {
1120: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(adponz, current_space->total_array_size), ¤t_space));
1121: nspacedouble++;
1122: }
1124: /* Copy data into free space, then initialize lnk */
1125: PetscCall(PetscLLCondensedClean(pN, adponz, current_space->array, lnk, lnkbt));
1127: current_space->array += adponz;
1128: current_space->local_used += adponz;
1129: current_space->local_remaining -= adponz;
1130: }
1132: /* Symbolic calc of A_off * P_oth */
1133: PetscCall(MatSetOptionsPrefix(a->B, prefix));
1134: PetscCall(MatAppendOptionsPrefix(a->B, "inner_offdiag_"));
1135: PetscCall(MatCreate(PETSC_COMM_SELF, &aopoth));
1136: PetscCall(MatMatMultSymbolic_SeqAIJ_SeqAIJ(a->B, ptap->P_oth, fill, aopoth));
1137: aopoth_seq = (Mat_SeqAIJ *)((aopoth)->data);
1138: aopothi = aopoth_seq->i;
1139: aopothj = aopoth_seq->j;
1141: /* Allocate space for apj, adpj, aopj, ... */
1142: /* destroy lists of free space and other temporary array(s) */
1144: PetscCall(PetscMalloc1(aopothi[am] + adpoi[am] + adpdi[am], &ptap->apj));
1145: PetscCall(PetscMalloc1(adpoi[am], &adpoj));
1147: /* Copy from linked list to j-array */
1148: PetscCall(PetscFreeSpaceContiguous(&free_space_diag, adpoj));
1149: PetscCall(PetscLLDestroy(lnk, lnkbt));
1151: adpoJ = adpoj;
1152: adpdJ = adpdj;
1153: aopJ = aopothj;
1154: apj = ptap->apj;
1155: apJ = apj; /* still empty */
1157: /* Merge j-arrays of A_off * P, A_diag * P_loc_off, and */
1158: /* A_diag * P_loc_diag to get A*P */
1159: for (i = 0; i < am; i++) {
1160: aopnz = aopothi[i + 1] - aopothi[i];
1161: adponz = adpoi[i + 1] - adpoi[i];
1162: adpdnz = adpdi[i + 1] - adpdi[i];
1164: /* Correct indices from A_diag*P_diag */
1165: for (i1 = 0; i1 < adpdnz; i1++) adpdJ[i1] += p_colstart;
1166: /* Merge j-arrays of A_diag * P_loc_off and A_diag * P_loc_diag and A_off * P_oth */
1167: Merge3SortedArrays(adponz, adpoJ, adpdnz, adpdJ, aopnz, aopJ, &apnz, apJ);
1168: PetscCall(MatPreallocateSet(i + rstart, apnz, apJ, dnz, onz));
1170: aopJ += aopnz;
1171: adpoJ += adponz;
1172: adpdJ += adpdnz;
1173: apJ += apnz;
1174: api[i + 1] = api[i] + apnz;
1175: }
1177: /* malloc apa to store dense row A[i,:]*P */
1178: PetscCall(PetscCalloc1(pN, &ptap->apa));
1180: /* create and assemble symbolic parallel matrix C */
1181: PetscCall(MatSetSizes(C, am, pn, PETSC_DETERMINE, PETSC_DETERMINE));
1182: PetscCall(MatSetBlockSizesFromMats(C, A, P));
1183: PetscCall(MatGetType(A, &mtype));
1184: PetscCall(MatSetType(C, mtype));
1185: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
1186: MatPreallocateEnd(dnz, onz);
1188: PetscCall(MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(C, apj, api));
1189: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
1190: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1191: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1192: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
1194: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable;
1195: C->ops->productnumeric = MatProductNumeric_AB;
1197: /* attach the supporting struct to C for reuse */
1198: C->product->data = ptap;
1199: C->product->destroy = MatProductCtxDestroy_MPIAIJ_MatMatMult;
1201: /* set MatInfo */
1202: afill = (PetscReal)api[am] / (adi[am] + aoi[am] + pi_loc[pm] + 1) + 1.e-5;
1203: if (afill < 1.0) afill = 1.0;
1204: C->info.mallocs = nspacedouble;
1205: C->info.fill_ratio_given = fill;
1206: C->info.fill_ratio_needed = afill;
1208: #if defined(PETSC_USE_INFO)
1209: if (api[am]) {
1210: PetscCall(PetscInfo(C, "Reallocs %" PetscInt_FMT "; Fill ratio: given %g needed %g.\n", nspacedouble, (double)fill, (double)afill));
1211: PetscCall(PetscInfo(C, "Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n", (double)afill));
1212: } else {
1213: PetscCall(PetscInfo(C, "Empty matrix product\n"));
1214: }
1215: #endif
1217: PetscCall(MatDestroy(&aopoth));
1218: PetscCall(MatDestroy(&adpd));
1219: PetscCall(PetscFree(j_temp));
1220: PetscCall(PetscFree(adpoj));
1221: PetscCall(PetscFree(adpoi));
1222: PetscFunctionReturn(PETSC_SUCCESS);
1223: }
1225: /* This routine only works when scall=MAT_REUSE_MATRIX! */
1226: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_matmatmult(Mat P, Mat A, Mat C)
1227: {
1228: MatProductCtx_APMPI *ptap;
1229: Mat Pt;
1231: PetscFunctionBegin;
1232: MatCheckProduct(C, 3);
1233: ptap = (MatProductCtx_APMPI *)C->product->data;
1234: PetscCheck(ptap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtAP cannot be computed. Missing data");
1235: PetscCheck(ptap->Pt, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtA cannot be reused. Do not call MatProductClear()");
1237: Pt = ptap->Pt;
1238: PetscCall(MatTransposeSetPrecursor(P, Pt));
1239: PetscCall(MatTranspose(P, MAT_REUSE_MATRIX, &Pt));
1240: PetscCall(MatMatMultNumeric_MPIAIJ_MPIAIJ(Pt, A, C));
1241: PetscFunctionReturn(PETSC_SUCCESS);
1242: }
1244: /* This routine is modified from MatPtAPSymbolic_MPIAIJ_MPIAIJ() */
1245: PetscErrorCode MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(Mat P, Mat A, PetscReal fill, Mat C)
1246: {
1247: MatProductCtx_APMPI *ptap;
1248: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
1249: MPI_Comm comm;
1250: PetscMPIInt size, rank;
1251: PetscFreeSpaceList free_space = NULL, current_space = NULL;
1252: PetscInt pn = P->cmap->n, aN = A->cmap->N, an = A->cmap->n;
1253: PetscInt *lnk, i, k, rstart;
1254: PetscBT lnkbt;
1255: PetscMPIInt tagi, tagj, *len_si, *len_s, *len_ri, nrecv, proc, nsend;
1256: PETSC_UNUSED PetscMPIInt icompleted = 0;
1257: PetscInt **buf_rj, **buf_ri, **buf_ri_k, row, ncols, *cols;
1258: PetscInt len, *dnz, *onz, *owners, nzi;
1259: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextci;
1260: MPI_Request *swaits, *rwaits;
1261: MPI_Status *sstatus, rstatus;
1262: PetscLayout rowmap;
1263: PetscInt *owners_co, *coi, *coj; /* i and j array of (p->B)^T*A*P - used in the communication */
1264: PetscMPIInt *len_r, *id_r; /* array of length of comm->size, store send/recv matrix values */
1265: PetscInt *Jptr, *prmap = p->garray, con, j, Crmax;
1266: Mat_SeqAIJ *a_loc, *c_loc, *c_oth;
1267: PetscHMapI ta;
1268: MatType mtype;
1269: const char *prefix;
1271: PetscFunctionBegin;
1272: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
1273: PetscCallMPI(MPI_Comm_size(comm, &size));
1274: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1276: /* create symbolic parallel matrix C */
1277: PetscCall(MatGetType(A, &mtype));
1278: PetscCall(MatSetType(C, mtype));
1280: C->ops->transposematmultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable;
1282: /* create struct MatProductCtx_APMPI and attached it to C later */
1283: PetscCall(PetscNew(&ptap));
1285: /* (0) compute Rd = Pd^T, Ro = Po^T */
1286: PetscCall(MatTranspose(p->A, MAT_INITIAL_MATRIX, &ptap->Rd));
1287: PetscCall(MatTranspose(p->B, MAT_INITIAL_MATRIX, &ptap->Ro));
1289: /* (1) compute symbolic A_loc */
1290: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &ptap->A_loc));
1292: /* (2-1) compute symbolic C_oth = Ro*A_loc */
1293: PetscCall(MatGetOptionsPrefix(A, &prefix));
1294: PetscCall(MatSetOptionsPrefix(ptap->Ro, prefix));
1295: PetscCall(MatAppendOptionsPrefix(ptap->Ro, "inner_offdiag_"));
1296: PetscCall(MatCreate(PETSC_COMM_SELF, &ptap->C_oth));
1297: PetscCall(MatMatMultSymbolic_SeqAIJ_SeqAIJ(ptap->Ro, ptap->A_loc, fill, ptap->C_oth));
1299: /* (3) send coj of C_oth to other processors */
1300: /* determine row ownership */
1301: PetscCall(PetscLayoutCreate(comm, &rowmap));
1302: rowmap->n = pn;
1303: rowmap->bs = 1;
1304: PetscCall(PetscLayoutSetUp(rowmap));
1305: owners = rowmap->range;
1307: /* determine the number of messages to send, their lengths */
1308: PetscCall(PetscMalloc4(size, &len_s, size, &len_si, size, &sstatus, size + 1, &owners_co));
1309: PetscCall(PetscArrayzero(len_s, size));
1310: PetscCall(PetscArrayzero(len_si, size));
1312: c_oth = (Mat_SeqAIJ *)ptap->C_oth->data;
1313: coi = c_oth->i;
1314: coj = c_oth->j;
1315: con = ptap->C_oth->rmap->n;
1316: proc = 0;
1317: for (i = 0; i < con; i++) {
1318: while (prmap[i] >= owners[proc + 1]) proc++;
1319: len_si[proc]++; /* num of rows in Co(=Pt*A) to be sent to [proc] */
1320: len_s[proc] += coi[i + 1] - coi[i]; /* num of nonzeros in Co to be sent to [proc] */
1321: }
1323: len = 0; /* max length of buf_si[], see (4) */
1324: owners_co[0] = 0;
1325: nsend = 0;
1326: for (proc = 0; proc < size; proc++) {
1327: owners_co[proc + 1] = owners_co[proc] + len_si[proc];
1328: if (len_s[proc]) {
1329: nsend++;
1330: len_si[proc] = 2 * (len_si[proc] + 1); /* length of buf_si to be sent to [proc] */
1331: len += len_si[proc];
1332: }
1333: }
1335: /* determine the number and length of messages to receive for coi and coj */
1336: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &nrecv));
1337: PetscCall(PetscGatherMessageLengths2(comm, nsend, nrecv, len_s, len_si, &id_r, &len_r, &len_ri));
1339: /* post the Irecv and Isend of coj */
1340: PetscCall(PetscCommGetNewTag(comm, &tagj));
1341: PetscCall(PetscPostIrecvInt(comm, tagj, nrecv, id_r, len_r, &buf_rj, &rwaits));
1342: PetscCall(PetscMalloc1(nsend, &swaits));
1343: for (proc = 0, k = 0; proc < size; proc++) {
1344: if (!len_s[proc]) continue;
1345: i = owners_co[proc];
1346: PetscCallMPI(MPIU_Isend(coj + coi[i], len_s[proc], MPIU_INT, proc, tagj, comm, swaits + k));
1347: k++;
1348: }
1350: /* (2-2) compute symbolic C_loc = Rd*A_loc */
1351: PetscCall(MatSetOptionsPrefix(ptap->Rd, prefix));
1352: PetscCall(MatAppendOptionsPrefix(ptap->Rd, "inner_diag_"));
1353: PetscCall(MatCreate(PETSC_COMM_SELF, &ptap->C_loc));
1354: PetscCall(MatMatMultSymbolic_SeqAIJ_SeqAIJ(ptap->Rd, ptap->A_loc, fill, ptap->C_loc));
1355: c_loc = (Mat_SeqAIJ *)ptap->C_loc->data;
1357: /* receives coj are complete */
1358: for (i = 0; i < nrecv; i++) PetscCallMPI(MPI_Waitany(nrecv, rwaits, &icompleted, &rstatus));
1359: PetscCall(PetscFree(rwaits));
1360: if (nsend) PetscCallMPI(MPI_Waitall(nsend, swaits, sstatus));
1362: /* add received column indices into ta to update Crmax */
1363: a_loc = (Mat_SeqAIJ *)ptap->A_loc->data;
1365: /* create and initialize a linked list */
1366: PetscCall(PetscHMapICreateWithSize(an, &ta)); /* for compute Crmax */
1367: MatRowMergeMax_SeqAIJ(a_loc, ptap->A_loc->rmap->N, ta);
1369: for (k = 0; k < nrecv; k++) { /* k-th received message */
1370: Jptr = buf_rj[k];
1371: for (j = 0; j < len_r[k]; j++) PetscCall(PetscHMapISet(ta, *(Jptr + j) + 1, 1));
1372: }
1373: PetscCall(PetscHMapIGetSize(ta, &Crmax));
1374: PetscCall(PetscHMapIDestroy(&ta));
1376: /* (4) send and recv coi */
1377: PetscCall(PetscCommGetNewTag(comm, &tagi));
1378: PetscCall(PetscPostIrecvInt(comm, tagi, nrecv, id_r, len_ri, &buf_ri, &rwaits));
1379: PetscCall(PetscMalloc1(len, &buf_s));
1380: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
1381: for (proc = 0, k = 0; proc < size; proc++) {
1382: if (!len_s[proc]) continue;
1383: /* form outgoing message for i-structure:
1384: buf_si[0]: nrows to be sent
1385: [1:nrows]: row index (global)
1386: [nrows+1:2*nrows+1]: i-structure index
1387: */
1388: nrows = len_si[proc] / 2 - 1; /* num of rows in Co to be sent to [proc] */
1389: buf_si_i = buf_si + nrows + 1;
1390: buf_si[0] = nrows;
1391: buf_si_i[0] = 0;
1392: nrows = 0;
1393: for (i = owners_co[proc]; i < owners_co[proc + 1]; i++) {
1394: nzi = coi[i + 1] - coi[i];
1395: buf_si_i[nrows + 1] = buf_si_i[nrows] + nzi; /* i-structure */
1396: buf_si[nrows + 1] = prmap[i] - owners[proc]; /* local row index */
1397: nrows++;
1398: }
1399: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, swaits + k));
1400: k++;
1401: buf_si += len_si[proc];
1402: }
1403: for (i = 0; i < nrecv; i++) PetscCallMPI(MPI_Waitany(nrecv, rwaits, &icompleted, &rstatus));
1404: PetscCall(PetscFree(rwaits));
1405: if (nsend) PetscCallMPI(MPI_Waitall(nsend, swaits, sstatus));
1407: PetscCall(PetscFree4(len_s, len_si, sstatus, owners_co));
1408: PetscCall(PetscFree(len_ri));
1409: PetscCall(PetscFree(swaits));
1410: PetscCall(PetscFree(buf_s));
1412: /* (5) compute the local portion of C */
1413: /* set initial free space to be Crmax, sufficient for holding nonzeros in each row of C */
1414: PetscCall(PetscFreeSpaceGet(Crmax, &free_space));
1415: current_space = free_space;
1417: PetscCall(PetscMalloc3(nrecv, &buf_ri_k, nrecv, &nextrow, nrecv, &nextci));
1418: for (k = 0; k < nrecv; k++) {
1419: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
1420: nrows = *buf_ri_k[k];
1421: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
1422: nextci[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
1423: }
1425: MatPreallocateBegin(comm, pn, an, dnz, onz);
1426: PetscCall(PetscLLCondensedCreate(Crmax, aN, &lnk, &lnkbt));
1427: for (i = 0; i < pn; i++) { /* for each local row of C */
1428: /* add C_loc into C */
1429: nzi = c_loc->i[i + 1] - c_loc->i[i];
1430: Jptr = c_loc->j + c_loc->i[i];
1431: PetscCall(PetscLLCondensedAddSorted(nzi, Jptr, lnk, lnkbt));
1433: /* add received col data into lnk */
1434: for (k = 0; k < nrecv; k++) { /* k-th received message */
1435: if (i == *nextrow[k]) { /* i-th row */
1436: nzi = *(nextci[k] + 1) - *nextci[k];
1437: Jptr = buf_rj[k] + *nextci[k];
1438: PetscCall(PetscLLCondensedAddSorted(nzi, Jptr, lnk, lnkbt));
1439: nextrow[k]++;
1440: nextci[k]++;
1441: }
1442: }
1444: /* add missing diagonal entry */
1445: if (C->force_diagonals) {
1446: k = i + owners[rank]; /* column index */
1447: PetscCall(PetscLLCondensedAddSorted(1, &k, lnk, lnkbt));
1448: }
1450: nzi = lnk[0];
1452: /* copy data into free space, then initialize lnk */
1453: PetscCall(PetscLLCondensedClean(aN, nzi, current_space->array, lnk, lnkbt));
1454: PetscCall(MatPreallocateSet(i + owners[rank], nzi, current_space->array, dnz, onz));
1455: }
1456: PetscCall(PetscFree3(buf_ri_k, nextrow, nextci));
1457: PetscCall(PetscLLDestroy(lnk, lnkbt));
1458: PetscCall(PetscFreeSpaceDestroy(free_space));
1460: /* local sizes and preallocation */
1461: PetscCall(MatSetSizes(C, pn, an, PETSC_DETERMINE, PETSC_DETERMINE));
1462: PetscCall(PetscLayoutSetBlockSize(C->rmap, P->cmap->bs));
1463: PetscCall(PetscLayoutSetBlockSize(C->cmap, A->cmap->bs));
1464: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
1465: MatPreallocateEnd(dnz, onz);
1467: /* add C_loc and C_oth to C */
1468: PetscCall(MatGetOwnershipRange(C, &rstart, NULL));
1469: for (i = 0; i < pn; i++) {
1470: ncols = c_loc->i[i + 1] - c_loc->i[i];
1471: cols = c_loc->j + c_loc->i[i];
1472: row = rstart + i;
1473: PetscCall(MatSetValues(C, 1, (const PetscInt *)&row, ncols, (const PetscInt *)cols, NULL, INSERT_VALUES));
1475: if (C->force_diagonals) PetscCall(MatSetValues(C, 1, (const PetscInt *)&row, 1, (const PetscInt *)&row, NULL, INSERT_VALUES));
1476: }
1477: for (i = 0; i < con; i++) {
1478: ncols = c_oth->i[i + 1] - c_oth->i[i];
1479: cols = c_oth->j + c_oth->i[i];
1480: row = prmap[i];
1481: PetscCall(MatSetValues(C, 1, (const PetscInt *)&row, ncols, (const PetscInt *)cols, NULL, INSERT_VALUES));
1482: }
1483: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1484: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1485: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
1487: /* members in merge */
1488: PetscCall(PetscFree(id_r));
1489: PetscCall(PetscFree(len_r));
1490: PetscCall(PetscFree(buf_ri[0]));
1491: PetscCall(PetscFree(buf_ri));
1492: PetscCall(PetscFree(buf_rj[0]));
1493: PetscCall(PetscFree(buf_rj));
1494: PetscCall(PetscLayoutDestroy(&rowmap));
1496: /* attach the supporting struct to C for reuse */
1497: C->product->data = ptap;
1498: C->product->destroy = MatProductCtxDestroy_MPIAIJ_PtAP;
1499: PetscFunctionReturn(PETSC_SUCCESS);
1500: }
1502: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable(Mat P, Mat A, Mat C)
1503: {
1504: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
1505: Mat_SeqAIJ *c_seq;
1506: MatProductCtx_APMPI *ptap;
1507: Mat A_loc, C_loc, C_oth;
1508: PetscInt i, rstart, rend, cm, ncols, row;
1509: const PetscInt *cols;
1510: const PetscScalar *vals;
1512: PetscFunctionBegin;
1513: MatCheckProduct(C, 3);
1514: ptap = (MatProductCtx_APMPI *)C->product->data;
1515: PetscCheck(ptap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtAP cannot be computed. Missing data");
1516: PetscCheck(ptap->A_loc, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtA cannot be reused. Do not call MatProductClear()");
1517: PetscCall(MatZeroEntries(C));
1519: /* These matrices are obtained in MatTransposeMatMultSymbolic() */
1520: /* 1) get R = Pd^T, Ro = Po^T */
1521: PetscCall(MatTransposeSetPrecursor(p->A, ptap->Rd));
1522: PetscCall(MatTranspose(p->A, MAT_REUSE_MATRIX, &ptap->Rd));
1523: PetscCall(MatTransposeSetPrecursor(p->B, ptap->Ro));
1524: PetscCall(MatTranspose(p->B, MAT_REUSE_MATRIX, &ptap->Ro));
1526: /* 2) compute numeric A_loc */
1527: PetscCall(MatMPIAIJGetLocalMat(A, MAT_REUSE_MATRIX, &ptap->A_loc));
1529: /* 3) C_loc = Rd*A_loc, C_oth = Ro*A_loc */
1530: A_loc = ptap->A_loc;
1531: PetscCall(ptap->C_loc->ops->matmultnumeric(ptap->Rd, A_loc, ptap->C_loc));
1532: PetscCall(ptap->C_oth->ops->matmultnumeric(ptap->Ro, A_loc, ptap->C_oth));
1533: C_loc = ptap->C_loc;
1534: C_oth = ptap->C_oth;
1536: /* add C_loc and C_oth to C */
1537: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
1539: /* C_loc -> C */
1540: cm = C_loc->rmap->N;
1541: c_seq = (Mat_SeqAIJ *)C_loc->data;
1542: cols = c_seq->j;
1543: vals = c_seq->a;
1544: for (i = 0; i < cm; i++) {
1545: ncols = c_seq->i[i + 1] - c_seq->i[i];
1546: row = rstart + i;
1547: PetscCall(MatSetValues(C, 1, &row, ncols, cols, vals, ADD_VALUES));
1548: cols += ncols;
1549: vals += ncols;
1550: }
1552: /* Co -> C, off-processor part */
1553: cm = C_oth->rmap->N;
1554: c_seq = (Mat_SeqAIJ *)C_oth->data;
1555: cols = c_seq->j;
1556: vals = c_seq->a;
1557: for (i = 0; i < cm; i++) {
1558: ncols = c_seq->i[i + 1] - c_seq->i[i];
1559: row = p->garray[i];
1560: PetscCall(MatSetValues(C, 1, &row, ncols, cols, vals, ADD_VALUES));
1561: cols += ncols;
1562: vals += ncols;
1563: }
1564: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1565: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1566: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
1567: PetscFunctionReturn(PETSC_SUCCESS);
1568: }
1570: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ(Mat P, Mat A, Mat C)
1571: {
1572: MatMergeSeqsToMPI *merge;
1573: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
1574: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data;
1575: MatProductCtx_APMPI *ap;
1576: PetscInt *adj;
1577: PetscInt i, j, k, anz, pnz, row, *cj, nexta;
1578: MatScalar *ada, *ca, valtmp;
1579: PetscInt am = A->rmap->n, cm = C->rmap->n, pon = (p->B)->cmap->n;
1580: MPI_Comm comm;
1581: PetscMPIInt size, rank, taga, *len_s, proc;
1582: PetscInt *owners, nrows, **buf_ri_k, **nextrow, **nextci;
1583: PetscInt **buf_ri, **buf_rj;
1584: PetscInt cnz = 0, *bj_i, *bi, *bj, bnz, nextcj; /* bi,bj,ba: local array of C(mpi mat) */
1585: MPI_Request *s_waits, *r_waits;
1586: MPI_Status *status;
1587: MatScalar **abuf_r, *ba_i, *pA, *coa, *ba;
1588: const PetscScalar *dummy;
1589: PetscInt *ai, *aj, *coi, *coj, *poJ, *pdJ;
1590: Mat A_loc;
1591: Mat_SeqAIJ *a_loc;
1593: PetscFunctionBegin;
1594: MatCheckProduct(C, 3);
1595: ap = (MatProductCtx_APMPI *)C->product->data;
1596: PetscCheck(ap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtA cannot be computed. Missing data");
1597: PetscCheck(ap->A_loc, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtA cannot be reused. Do not call MatProductClear()");
1598: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
1599: PetscCallMPI(MPI_Comm_size(comm, &size));
1600: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1602: merge = ap->merge;
1604: /* 2) compute numeric C_seq = P_loc^T*A_loc */
1605: /* get data from symbolic products */
1606: coi = merge->coi;
1607: coj = merge->coj;
1608: PetscCall(PetscCalloc1(coi[pon], &coa));
1609: bi = merge->bi;
1610: bj = merge->bj;
1611: owners = merge->rowmap->range;
1612: PetscCall(PetscCalloc1(bi[cm], &ba));
1614: /* get A_loc by taking all local rows of A */
1615: A_loc = ap->A_loc;
1616: PetscCall(MatMPIAIJGetLocalMat(A, MAT_REUSE_MATRIX, &A_loc));
1617: a_loc = (Mat_SeqAIJ *)A_loc->data;
1618: ai = a_loc->i;
1619: aj = a_loc->j;
1621: /* trigger copy to CPU */
1622: PetscCall(MatSeqAIJGetArrayRead(p->A, &dummy));
1623: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &dummy));
1624: PetscCall(MatSeqAIJGetArrayRead(p->B, &dummy));
1625: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &dummy));
1626: for (i = 0; i < am; i++) {
1627: anz = ai[i + 1] - ai[i];
1628: adj = aj + ai[i];
1629: ada = a_loc->a + ai[i];
1631: /* 2-b) Compute Cseq = P_loc[i,:]^T*A[i,:] using outer product */
1632: /* put the value into Co=(p->B)^T*A (off-diagonal part, send to others) */
1633: pnz = po->i[i + 1] - po->i[i];
1634: poJ = po->j + po->i[i];
1635: pA = po->a + po->i[i];
1636: for (j = 0; j < pnz; j++) {
1637: row = poJ[j];
1638: cj = coj + coi[row];
1639: ca = coa + coi[row];
1640: /* perform sparse axpy */
1641: nexta = 0;
1642: valtmp = pA[j];
1643: for (k = 0; nexta < anz; k++) {
1644: if (cj[k] == adj[nexta]) {
1645: ca[k] += valtmp * ada[nexta];
1646: nexta++;
1647: }
1648: }
1649: PetscCall(PetscLogFlops(2.0 * anz));
1650: }
1652: /* put the value into Cd (diagonal part) */
1653: pnz = pd->i[i + 1] - pd->i[i];
1654: pdJ = pd->j + pd->i[i];
1655: pA = pd->a + pd->i[i];
1656: for (j = 0; j < pnz; j++) {
1657: row = pdJ[j];
1658: cj = bj + bi[row];
1659: ca = ba + bi[row];
1660: /* perform sparse axpy */
1661: nexta = 0;
1662: valtmp = pA[j];
1663: for (k = 0; nexta < anz; k++) {
1664: if (cj[k] == adj[nexta]) {
1665: ca[k] += valtmp * ada[nexta];
1666: nexta++;
1667: }
1668: }
1669: PetscCall(PetscLogFlops(2.0 * anz));
1670: }
1671: }
1673: /* 3) send and recv matrix values coa */
1674: buf_ri = merge->buf_ri;
1675: buf_rj = merge->buf_rj;
1676: len_s = merge->len_s;
1677: PetscCall(PetscCommGetNewTag(comm, &taga));
1678: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
1680: PetscCall(PetscMalloc2(merge->nsend, &s_waits, size, &status));
1681: for (proc = 0, k = 0; proc < size; proc++) {
1682: if (!len_s[proc]) continue;
1683: i = merge->owners_co[proc];
1684: PetscCallMPI(MPIU_Isend(coa + coi[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
1685: k++;
1686: }
1687: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
1688: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
1690: PetscCall(PetscFree2(s_waits, status));
1691: PetscCall(PetscFree(r_waits));
1692: PetscCall(PetscFree(coa));
1694: /* 4) insert local Cseq and received values into Cmpi */
1695: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextci));
1696: for (k = 0; k < merge->nrecv; k++) {
1697: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
1698: nrows = *buf_ri_k[k];
1699: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
1700: nextci[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
1701: }
1703: for (i = 0; i < cm; i++) {
1704: row = owners[rank] + i; /* global row index of C_seq */
1705: bj_i = bj + bi[i]; /* col indices of the i-th row of C */
1706: ba_i = ba + bi[i];
1707: bnz = bi[i + 1] - bi[i];
1708: /* add received vals into ba */
1709: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
1710: /* i-th row */
1711: if (i == *nextrow[k]) {
1712: cnz = *(nextci[k] + 1) - *nextci[k];
1713: cj = buf_rj[k] + *nextci[k];
1714: ca = abuf_r[k] + *nextci[k];
1715: nextcj = 0;
1716: for (j = 0; nextcj < cnz; j++) {
1717: if (bj_i[j] == cj[nextcj]) { /* bcol == ccol */
1718: ba_i[j] += ca[nextcj++];
1719: }
1720: }
1721: nextrow[k]++;
1722: nextci[k]++;
1723: PetscCall(PetscLogFlops(2.0 * cnz));
1724: }
1725: }
1726: PetscCall(MatSetValues(C, 1, &row, bnz, bj_i, ba_i, INSERT_VALUES));
1727: }
1728: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1729: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1731: PetscCall(PetscFree(ba));
1732: PetscCall(PetscFree(abuf_r[0]));
1733: PetscCall(PetscFree(abuf_r));
1734: PetscCall(PetscFree3(buf_ri_k, nextrow, nextci));
1735: PetscFunctionReturn(PETSC_SUCCESS);
1736: }
1738: PetscErrorCode MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ(Mat P, Mat A, PetscReal fill, Mat C)
1739: {
1740: Mat A_loc;
1741: MatProductCtx_APMPI *ap;
1742: PetscFreeSpaceList free_space = NULL, current_space = NULL;
1743: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data, *a = (Mat_MPIAIJ *)A->data;
1744: PetscInt *pdti, *pdtj, *poti, *potj, *ptJ;
1745: PetscInt nnz;
1746: PetscInt *lnk, *owners_co, *coi, *coj, i, k, pnz, row;
1747: PetscInt am = A->rmap->n, pn = P->cmap->n;
1748: MPI_Comm comm;
1749: PetscMPIInt size, rank, tagi, tagj, *len_si, *len_s, *len_ri, proc;
1750: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
1751: PetscInt len, *dnz, *onz, *owners;
1752: PetscInt nzi, *bi, *bj;
1753: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextci;
1754: MPI_Request *swaits, *rwaits;
1755: MPI_Status *sstatus, rstatus;
1756: MatMergeSeqsToMPI *merge;
1757: PetscInt *ai, *aj, *Jptr, anz, *prmap = p->garray, pon, nspacedouble = 0, j;
1758: PetscReal afill = 1.0, afill_tmp;
1759: PetscInt rstart = P->cmap->rstart, rmax, Armax;
1760: Mat_SeqAIJ *a_loc;
1761: PetscHMapI ta;
1762: MatType mtype;
1764: PetscFunctionBegin;
1765: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
1766: /* check if matrix local sizes are compatible */
1767: PetscCheck(A->rmap->rstart == P->rmap->rstart && A->rmap->rend == P->rmap->rend, comm, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, A (%" PetscInt_FMT ", %" PetscInt_FMT ") != P (%" PetscInt_FMT ",%" PetscInt_FMT ")", A->rmap->rstart,
1768: A->rmap->rend, P->rmap->rstart, P->rmap->rend);
1770: PetscCallMPI(MPI_Comm_size(comm, &size));
1771: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1773: /* create struct MatProductCtx_APMPI and attached it to C later */
1774: PetscCall(PetscNew(&ap));
1776: /* get A_loc by taking all local rows of A */
1777: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &A_loc));
1779: ap->A_loc = A_loc;
1780: a_loc = (Mat_SeqAIJ *)A_loc->data;
1781: ai = a_loc->i;
1782: aj = a_loc->j;
1784: /* determine symbolic Co=(p->B)^T*A - send to others */
1785: PetscCall(MatGetSymbolicTranspose_SeqAIJ(p->A, &pdti, &pdtj));
1786: PetscCall(MatGetSymbolicTranspose_SeqAIJ(p->B, &poti, &potj));
1787: pon = (p->B)->cmap->n; /* total num of rows to be sent to other processors
1788: >= (num of nonzero rows of C_seq) - pn */
1789: PetscCall(PetscMalloc1(pon + 1, &coi));
1790: coi[0] = 0;
1792: /* set initial free space to be fill*(nnz(p->B) + nnz(A)) */
1793: nnz = PetscRealIntMultTruncate(fill, PetscIntSumTruncate(poti[pon], ai[am]));
1794: PetscCall(PetscFreeSpaceGet(nnz, &free_space));
1795: current_space = free_space;
1797: /* create and initialize a linked list */
1798: PetscCall(PetscHMapICreateWithSize(A->cmap->n + a->B->cmap->N, &ta));
1799: MatRowMergeMax_SeqAIJ(a_loc, am, ta);
1800: PetscCall(PetscHMapIGetSize(ta, &Armax));
1802: PetscCall(PetscLLCondensedCreate_Scalable(Armax, &lnk));
1804: for (i = 0; i < pon; i++) {
1805: pnz = poti[i + 1] - poti[i];
1806: ptJ = potj + poti[i];
1807: for (j = 0; j < pnz; j++) {
1808: row = ptJ[j]; /* row of A_loc == col of Pot */
1809: anz = ai[row + 1] - ai[row];
1810: Jptr = aj + ai[row];
1811: /* add non-zero cols of AP into the sorted linked list lnk */
1812: PetscCall(PetscLLCondensedAddSorted_Scalable(anz, Jptr, lnk));
1813: }
1814: nnz = lnk[0];
1816: /* If free space is not available, double the total space in the list */
1817: if (current_space->local_remaining < nnz) {
1818: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(nnz, current_space->total_array_size), ¤t_space));
1819: nspacedouble++;
1820: }
1822: /* Copy data into free space, and zero out denserows */
1823: PetscCall(PetscLLCondensedClean_Scalable(nnz, current_space->array, lnk));
1825: current_space->array += nnz;
1826: current_space->local_used += nnz;
1827: current_space->local_remaining -= nnz;
1829: coi[i + 1] = coi[i] + nnz;
1830: }
1832: PetscCall(PetscMalloc1(coi[pon], &coj));
1833: PetscCall(PetscFreeSpaceContiguous(&free_space, coj));
1834: PetscCall(PetscLLCondensedDestroy_Scalable(lnk)); /* must destroy to get a new one for C */
1836: afill_tmp = (PetscReal)coi[pon] / (poti[pon] + ai[am] + 1);
1837: if (afill_tmp > afill) afill = afill_tmp;
1839: /* send j-array (coj) of Co to other processors */
1840: /* determine row ownership */
1841: PetscCall(PetscNew(&merge));
1842: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
1844: merge->rowmap->n = pn;
1845: merge->rowmap->bs = 1;
1847: PetscCall(PetscLayoutSetUp(merge->rowmap));
1848: owners = merge->rowmap->range;
1850: /* determine the number of messages to send, their lengths */
1851: PetscCall(PetscCalloc1(size, &len_si));
1852: PetscCall(PetscCalloc1(size, &merge->len_s));
1854: len_s = merge->len_s;
1855: merge->nsend = 0;
1857: PetscCall(PetscMalloc1(size + 1, &owners_co));
1859: proc = 0;
1860: for (i = 0; i < pon; i++) {
1861: while (prmap[i] >= owners[proc + 1]) proc++;
1862: len_si[proc]++; /* num of rows in Co to be sent to [proc] */
1863: len_s[proc] += coi[i + 1] - coi[i];
1864: }
1866: len = 0; /* max length of buf_si[] */
1867: owners_co[0] = 0;
1868: for (proc = 0; proc < size; proc++) {
1869: owners_co[proc + 1] = owners_co[proc] + len_si[proc];
1870: if (len_s[proc]) {
1871: merge->nsend++;
1872: len_si[proc] = 2 * (len_si[proc] + 1);
1873: len += len_si[proc];
1874: }
1875: }
1877: /* determine the number and length of messages to receive for coi and coj */
1878: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
1879: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
1881: /* post the Irecv and Isend of coj */
1882: PetscCall(PetscCommGetNewTag(comm, &tagj));
1883: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rwaits));
1884: PetscCall(PetscMalloc1(merge->nsend, &swaits));
1885: for (proc = 0, k = 0; proc < size; proc++) {
1886: if (!len_s[proc]) continue;
1887: i = owners_co[proc];
1888: PetscCallMPI(MPIU_Isend(coj + coi[i], len_s[proc], MPIU_INT, proc, tagj, comm, swaits + k));
1889: k++;
1890: }
1892: /* receives and sends of coj are complete */
1893: PetscCall(PetscMalloc1(size, &sstatus));
1894: for (i = 0; i < merge->nrecv; i++) {
1895: PETSC_UNUSED PetscMPIInt icompleted;
1896: PetscCallMPI(MPI_Waitany(merge->nrecv, rwaits, &icompleted, &rstatus));
1897: }
1898: PetscCall(PetscFree(rwaits));
1899: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, swaits, sstatus));
1901: /* add received column indices into table to update Armax */
1902: /* Armax can be as large as aN if a P[row,:] is dense, see src/ksp/ksp/tutorials/ex56.c! */
1903: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
1904: Jptr = buf_rj[k];
1905: for (j = 0; j < merge->len_r[k]; j++) PetscCall(PetscHMapISet(ta, *(Jptr + j) + 1, 1));
1906: }
1907: PetscCall(PetscHMapIGetSize(ta, &Armax));
1909: /* send and recv coi */
1910: PetscCall(PetscCommGetNewTag(comm, &tagi));
1911: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &rwaits));
1912: PetscCall(PetscMalloc1(len, &buf_s));
1913: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
1914: for (proc = 0, k = 0; proc < size; proc++) {
1915: if (!len_s[proc]) continue;
1916: /* form outgoing message for i-structure:
1917: buf_si[0]: nrows to be sent
1918: [1:nrows]: row index (global)
1919: [nrows+1:2*nrows+1]: i-structure index
1920: */
1921: nrows = len_si[proc] / 2 - 1;
1922: buf_si_i = buf_si + nrows + 1;
1923: buf_si[0] = nrows;
1924: buf_si_i[0] = 0;
1925: nrows = 0;
1926: for (i = owners_co[proc]; i < owners_co[proc + 1]; i++) {
1927: nzi = coi[i + 1] - coi[i];
1928: buf_si_i[nrows + 1] = buf_si_i[nrows] + nzi; /* i-structure */
1929: buf_si[nrows + 1] = prmap[i] - owners[proc]; /* local row index */
1930: nrows++;
1931: }
1932: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, swaits + k));
1933: k++;
1934: buf_si += len_si[proc];
1935: }
1936: i = merge->nrecv;
1937: while (i--) {
1938: PETSC_UNUSED PetscMPIInt icompleted;
1939: PetscCallMPI(MPI_Waitany(merge->nrecv, rwaits, &icompleted, &rstatus));
1940: }
1941: PetscCall(PetscFree(rwaits));
1942: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, swaits, sstatus));
1943: PetscCall(PetscFree(len_si));
1944: PetscCall(PetscFree(len_ri));
1945: PetscCall(PetscFree(swaits));
1946: PetscCall(PetscFree(sstatus));
1947: PetscCall(PetscFree(buf_s));
1949: /* compute the local portion of C (mpi mat) */
1950: /* allocate bi array and free space for accumulating nonzero column info */
1951: PetscCall(PetscMalloc1(pn + 1, &bi));
1952: bi[0] = 0;
1954: /* set initial free space to be fill*(nnz(P) + nnz(AP)) */
1955: nnz = PetscRealIntMultTruncate(fill, PetscIntSumTruncate(pdti[pn], PetscIntSumTruncate(poti[pon], ai[am])));
1956: PetscCall(PetscFreeSpaceGet(nnz, &free_space));
1957: current_space = free_space;
1959: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextci));
1960: for (k = 0; k < merge->nrecv; k++) {
1961: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
1962: nrows = *buf_ri_k[k];
1963: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
1964: nextci[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th received i-structure */
1965: }
1967: PetscCall(PetscLLCondensedCreate_Scalable(Armax, &lnk));
1968: MatPreallocateBegin(comm, pn, A->cmap->n, dnz, onz);
1969: rmax = 0;
1970: for (i = 0; i < pn; i++) {
1971: /* add pdt[i,:]*AP into lnk */
1972: pnz = pdti[i + 1] - pdti[i];
1973: ptJ = pdtj + pdti[i];
1974: for (j = 0; j < pnz; j++) {
1975: row = ptJ[j]; /* row of AP == col of Pt */
1976: anz = ai[row + 1] - ai[row];
1977: Jptr = aj + ai[row];
1978: /* add non-zero cols of AP into the sorted linked list lnk */
1979: PetscCall(PetscLLCondensedAddSorted_Scalable(anz, Jptr, lnk));
1980: }
1982: /* add received col data into lnk */
1983: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
1984: if (i == *nextrow[k]) { /* i-th row */
1985: nzi = *(nextci[k] + 1) - *nextci[k];
1986: Jptr = buf_rj[k] + *nextci[k];
1987: PetscCall(PetscLLCondensedAddSorted_Scalable(nzi, Jptr, lnk));
1988: nextrow[k]++;
1989: nextci[k]++;
1990: }
1991: }
1993: /* add missing diagonal entry */
1994: if (C->force_diagonals) {
1995: k = i + owners[rank]; /* column index */
1996: PetscCall(PetscLLCondensedAddSorted_Scalable(1, &k, lnk));
1997: }
1999: nnz = lnk[0];
2001: /* if free space is not available, make more free space */
2002: if (current_space->local_remaining < nnz) {
2003: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(nnz, current_space->total_array_size), ¤t_space));
2004: nspacedouble++;
2005: }
2006: /* copy data into free space, then initialize lnk */
2007: PetscCall(PetscLLCondensedClean_Scalable(nnz, current_space->array, lnk));
2008: PetscCall(MatPreallocateSet(i + owners[rank], nnz, current_space->array, dnz, onz));
2010: current_space->array += nnz;
2011: current_space->local_used += nnz;
2012: current_space->local_remaining -= nnz;
2014: bi[i + 1] = bi[i] + nnz;
2015: if (nnz > rmax) rmax = nnz;
2016: }
2017: PetscCall(PetscFree3(buf_ri_k, nextrow, nextci));
2019: PetscCall(PetscMalloc1(bi[pn], &bj));
2020: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
2021: afill_tmp = (PetscReal)bi[pn] / (pdti[pn] + poti[pon] + ai[am] + 1);
2022: if (afill_tmp > afill) afill = afill_tmp;
2023: PetscCall(PetscLLCondensedDestroy_Scalable(lnk));
2024: PetscCall(PetscHMapIDestroy(&ta));
2025: PetscCall(MatRestoreSymbolicTranspose_SeqAIJ(p->A, &pdti, &pdtj));
2026: PetscCall(MatRestoreSymbolicTranspose_SeqAIJ(p->B, &poti, &potj));
2028: /* create symbolic parallel matrix C - why cannot be assembled in Numeric part */
2029: PetscCall(MatSetSizes(C, pn, A->cmap->n, PETSC_DETERMINE, PETSC_DETERMINE));
2030: PetscCall(MatSetBlockSizes(C, P->cmap->bs, A->cmap->bs));
2031: PetscCall(MatGetType(A, &mtype));
2032: PetscCall(MatSetType(C, mtype));
2033: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
2034: MatPreallocateEnd(dnz, onz);
2035: PetscCall(MatSetBlockSize(C, 1));
2036: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
2037: for (i = 0; i < pn; i++) {
2038: row = i + rstart;
2039: nnz = bi[i + 1] - bi[i];
2040: Jptr = bj + bi[i];
2041: PetscCall(MatSetValues(C, 1, &row, nnz, Jptr, NULL, INSERT_VALUES));
2042: }
2043: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2044: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2045: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
2046: merge->bi = bi;
2047: merge->bj = bj;
2048: merge->coi = coi;
2049: merge->coj = coj;
2050: merge->buf_ri = buf_ri;
2051: merge->buf_rj = buf_rj;
2052: merge->owners_co = owners_co;
2054: /* attach the supporting struct to C for reuse */
2055: C->product->data = ap;
2056: C->product->destroy = MatProductCtxDestroy_MPIAIJ_PtAP;
2057: ap->merge = merge;
2059: C->ops->mattransposemultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ;
2061: #if defined(PETSC_USE_INFO)
2062: if (bi[pn] != 0) {
2063: PetscCall(PetscInfo(C, "Reallocs %" PetscInt_FMT "; Fill ratio: given %g needed %g.\n", nspacedouble, (double)fill, (double)afill));
2064: PetscCall(PetscInfo(C, "Use MatTransposeMatMult(A,B,MatReuse,%g,&C) for best performance.\n", (double)afill));
2065: } else {
2066: PetscCall(PetscInfo(C, "Empty matrix product\n"));
2067: }
2068: #endif
2069: PetscFunctionReturn(PETSC_SUCCESS);
2070: }
2072: static PetscErrorCode MatProductSymbolic_AtB_MPIAIJ_MPIAIJ(Mat C)
2073: {
2074: Mat_Product *product = C->product;
2075: Mat A = product->A, B = product->B;
2076: PetscReal fill = product->fill;
2077: PetscBool flg;
2079: PetscFunctionBegin;
2080: /* scalable */
2081: PetscCall(PetscStrcmp(product->alg, "scalable", &flg));
2082: if (flg) {
2083: PetscCall(MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ(A, B, fill, C));
2084: goto next;
2085: }
2087: /* nonscalable */
2088: PetscCall(PetscStrcmp(product->alg, "nonscalable", &flg));
2089: if (flg) {
2090: PetscCall(MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(A, B, fill, C));
2091: goto next;
2092: }
2094: /* matmatmult */
2095: PetscCall(PetscStrcmp(product->alg, "at*b", &flg));
2096: if (flg) {
2097: Mat At;
2098: MatProductCtx_APMPI *ptap;
2100: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
2101: PetscCall(MatMatMultSymbolic_MPIAIJ_MPIAIJ(At, B, fill, C));
2102: ptap = (MatProductCtx_APMPI *)C->product->data;
2103: if (ptap) {
2104: ptap->Pt = At;
2105: C->product->destroy = MatProductCtxDestroy_MPIAIJ_PtAP;
2106: }
2107: C->ops->transposematmultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_matmatmult;
2108: goto next;
2109: }
2111: /* backend general code */
2112: PetscCall(PetscStrcmp(product->alg, "backend", &flg));
2113: if (flg) {
2114: PetscCall(MatProductSymbolic_MPIAIJBACKEND(C));
2115: PetscFunctionReturn(PETSC_SUCCESS);
2116: }
2118: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MatProduct type is not supported");
2120: next:
2121: C->ops->productnumeric = MatProductNumeric_AtB;
2122: PetscFunctionReturn(PETSC_SUCCESS);
2123: }
2125: /* Set options for MatMatMultxxx_MPIAIJ_MPIAIJ */
2126: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_AB(Mat C)
2127: {
2128: Mat_Product *product = C->product;
2129: Mat A = product->A, B = product->B;
2130: #if defined(PETSC_HAVE_HYPRE)
2131: const char *algTypes[5] = {"scalable", "nonscalable", "seqmpi", "backend", "hypre"};
2132: PetscInt nalg = 5;
2133: #else
2134: const char *algTypes[4] = {
2135: "scalable",
2136: "nonscalable",
2137: "seqmpi",
2138: "backend",
2139: };
2140: PetscInt nalg = 4;
2141: #endif
2142: PetscInt alg = 1; /* set nonscalable algorithm as default */
2143: PetscBool flg;
2144: MPI_Comm comm;
2146: PetscFunctionBegin;
2147: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
2149: /* Set "nonscalable" as default algorithm */
2150: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
2151: if (flg) {
2152: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2154: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2155: if (B->cmap->N > 100000) { /* may switch to scalable algorithm as default */
2156: MatInfo Ainfo, Binfo;
2157: PetscInt nz_local;
2158: PetscBool alg_scalable_loc = PETSC_FALSE, alg_scalable;
2160: PetscCall(MatGetInfo(A, MAT_LOCAL, &Ainfo));
2161: PetscCall(MatGetInfo(B, MAT_LOCAL, &Binfo));
2162: nz_local = (PetscInt)(Ainfo.nz_allocated + Binfo.nz_allocated);
2164: if (B->cmap->N > product->fill * nz_local) alg_scalable_loc = PETSC_TRUE;
2165: PetscCallMPI(MPIU_Allreduce(&alg_scalable_loc, &alg_scalable, 1, MPI_C_BOOL, MPI_LOR, comm));
2167: if (alg_scalable) {
2168: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2169: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2170: PetscCall(PetscInfo(B, "Use scalable algorithm, BN %" PetscInt_FMT ", fill*nz_allocated %g\n", B->cmap->N, (double)(product->fill * nz_local)));
2171: }
2172: }
2173: }
2175: /* Get runtime option */
2176: if (product->api_user) {
2177: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
2178: PetscCall(PetscOptionsEList("-matmatmult_via", "Algorithmic approach", "MatMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2179: PetscOptionsEnd();
2180: } else {
2181: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
2182: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2183: PetscOptionsEnd();
2184: }
2185: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2187: C->ops->productsymbolic = MatProductSymbolic_AB_MPIAIJ_MPIAIJ;
2188: PetscFunctionReturn(PETSC_SUCCESS);
2189: }
2191: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_ABt(Mat C)
2192: {
2193: PetscFunctionBegin;
2194: PetscCall(MatProductSetFromOptions_MPIAIJ_AB(C));
2195: C->ops->productsymbolic = MatProductSymbolic_ABt_MPIAIJ_MPIAIJ;
2196: PetscFunctionReturn(PETSC_SUCCESS);
2197: }
2199: /* Set options for MatTransposeMatMultXXX_MPIAIJ_MPIAIJ */
2200: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_AtB(Mat C)
2201: {
2202: Mat_Product *product = C->product;
2203: Mat A = product->A, B = product->B;
2204: const char *algTypes[4] = {"scalable", "nonscalable", "at*b", "backend"};
2205: PetscInt nalg = 4;
2206: PetscInt alg = 1; /* set default algorithm */
2207: PetscBool flg;
2208: MPI_Comm comm;
2210: PetscFunctionBegin;
2211: /* Check matrix local sizes */
2212: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
2213: PetscCheck(A->rmap->rstart == B->rmap->rstart && A->rmap->rend == B->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, A (%" PetscInt_FMT ", %" PetscInt_FMT ") != B (%" PetscInt_FMT ",%" PetscInt_FMT ")",
2214: A->rmap->rstart, A->rmap->rend, B->rmap->rstart, B->rmap->rend);
2216: /* Set default algorithm */
2217: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
2218: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2220: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2221: if (alg && B->cmap->N > 100000) { /* may switch to scalable algorithm as default */
2222: MatInfo Ainfo, Binfo;
2223: PetscInt nz_local;
2224: PetscBool alg_scalable_loc = PETSC_FALSE, alg_scalable;
2226: PetscCall(MatGetInfo(A, MAT_LOCAL, &Ainfo));
2227: PetscCall(MatGetInfo(B, MAT_LOCAL, &Binfo));
2228: nz_local = (PetscInt)(Ainfo.nz_allocated + Binfo.nz_allocated);
2230: if (B->cmap->N > product->fill * nz_local) alg_scalable_loc = PETSC_TRUE;
2231: PetscCallMPI(MPIU_Allreduce(&alg_scalable_loc, &alg_scalable, 1, MPI_C_BOOL, MPI_LOR, comm));
2233: if (alg_scalable) {
2234: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2235: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2236: PetscCall(PetscInfo(B, "Use scalable algorithm, BN %" PetscInt_FMT ", fill*nz_allocated %g\n", B->cmap->N, (double)(product->fill * nz_local)));
2237: }
2238: }
2240: /* Get runtime option */
2241: if (product->api_user) {
2242: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatTransposeMatMult", "Mat");
2243: PetscCall(PetscOptionsEList("-mattransposematmult_via", "Algorithmic approach", "MatTransposeMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2244: PetscOptionsEnd();
2245: } else {
2246: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AtB", "Mat");
2247: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatTransposeMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2248: PetscOptionsEnd();
2249: }
2250: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2252: C->ops->productsymbolic = MatProductSymbolic_AtB_MPIAIJ_MPIAIJ;
2253: PetscFunctionReturn(PETSC_SUCCESS);
2254: }
2256: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_PtAP(Mat C)
2257: {
2258: Mat_Product *product = C->product;
2259: Mat A = product->A, P = product->B;
2260: MPI_Comm comm;
2261: PetscBool flg;
2262: PetscInt alg = 1; /* set default algorithm */
2263: #if !defined(PETSC_HAVE_HYPRE)
2264: const char *algTypes[5] = {"scalable", "nonscalable", "allatonce", "allatonce_merged", "backend"};
2265: PetscInt nalg = 5;
2266: #else
2267: const char *algTypes[6] = {"scalable", "nonscalable", "allatonce", "allatonce_merged", "backend", "hypre"};
2268: PetscInt nalg = 6;
2269: #endif
2270: PetscInt pN = P->cmap->N;
2272: PetscFunctionBegin;
2273: /* Check matrix local sizes */
2274: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
2275: PetscCheck(A->rmap->rstart == P->rmap->rstart && A->rmap->rend == P->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, Arow (%" PetscInt_FMT ", %" PetscInt_FMT ") != Prow (%" PetscInt_FMT ",%" PetscInt_FMT ")",
2276: A->rmap->rstart, A->rmap->rend, P->rmap->rstart, P->rmap->rend);
2277: PetscCheck(A->cmap->rstart == P->rmap->rstart && A->cmap->rend == P->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, Acol (%" PetscInt_FMT ", %" PetscInt_FMT ") != Prow (%" PetscInt_FMT ",%" PetscInt_FMT ")",
2278: A->cmap->rstart, A->cmap->rend, P->rmap->rstart, P->rmap->rend);
2280: /* Set "nonscalable" as default algorithm */
2281: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
2282: if (flg) {
2283: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2285: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2286: if (pN > 100000) {
2287: MatInfo Ainfo, Pinfo;
2288: PetscInt nz_local;
2289: PetscBool alg_scalable_loc = PETSC_FALSE, alg_scalable;
2291: PetscCall(MatGetInfo(A, MAT_LOCAL, &Ainfo));
2292: PetscCall(MatGetInfo(P, MAT_LOCAL, &Pinfo));
2293: nz_local = (PetscInt)(Ainfo.nz_allocated + Pinfo.nz_allocated);
2295: if (pN > product->fill * nz_local) alg_scalable_loc = PETSC_TRUE;
2296: PetscCallMPI(MPIU_Allreduce(&alg_scalable_loc, &alg_scalable, 1, MPI_C_BOOL, MPI_LOR, comm));
2298: if (alg_scalable) {
2299: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2300: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2301: }
2302: }
2303: }
2305: /* Get runtime option */
2306: if (product->api_user) {
2307: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
2308: PetscCall(PetscOptionsEList("-matptap_via", "Algorithmic approach", "MatPtAP", algTypes, nalg, algTypes[alg], &alg, &flg));
2309: PetscOptionsEnd();
2310: } else {
2311: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
2312: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatPtAP", algTypes, nalg, algTypes[alg], &alg, &flg));
2313: PetscOptionsEnd();
2314: }
2315: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2317: C->ops->productsymbolic = MatProductSymbolic_PtAP_MPIAIJ_MPIAIJ;
2318: PetscFunctionReturn(PETSC_SUCCESS);
2319: }
2321: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_RARt(Mat C)
2322: {
2323: Mat_Product *product = C->product;
2324: Mat A = product->A, R = product->B;
2326: PetscFunctionBegin;
2327: /* Check matrix local sizes */
2328: PetscCheck(A->cmap->n == R->cmap->n && A->rmap->n == R->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, A local (%" PetscInt_FMT ", %" PetscInt_FMT "), R local (%" PetscInt_FMT ",%" PetscInt_FMT ")", A->rmap->n,
2329: A->rmap->n, R->rmap->n, R->cmap->n);
2331: C->ops->productsymbolic = MatProductSymbolic_RARt_MPIAIJ_MPIAIJ;
2332: PetscFunctionReturn(PETSC_SUCCESS);
2333: }
2335: /*
2336: Set options for ABC = A*B*C = A*(B*C); ABC's algorithm must be chosen from AB's algorithm
2337: */
2338: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_ABC(Mat C)
2339: {
2340: Mat_Product *product = C->product;
2341: PetscBool flg = PETSC_FALSE;
2342: PetscInt alg = 1; /* default algorithm */
2343: const char *algTypes[3] = {"scalable", "nonscalable", "seqmpi"};
2344: PetscInt nalg = 3;
2346: PetscFunctionBegin;
2347: /* Set default algorithm */
2348: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
2349: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2351: /* Get runtime option */
2352: if (product->api_user) {
2353: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMatMult", "Mat");
2354: PetscCall(PetscOptionsEList("-matmatmatmult_via", "Algorithmic approach", "MatMatMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2355: PetscOptionsEnd();
2356: } else {
2357: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_ABC", "Mat");
2358: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatProduct_ABC", algTypes, nalg, algTypes[alg], &alg, &flg));
2359: PetscOptionsEnd();
2360: }
2361: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2363: C->ops->matmatmultsymbolic = MatMatMatMultSymbolic_MPIAIJ_MPIAIJ_MPIAIJ;
2364: C->ops->productsymbolic = MatProductSymbolic_ABC;
2365: PetscFunctionReturn(PETSC_SUCCESS);
2366: }
2368: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIAIJ(Mat C)
2369: {
2370: Mat_Product *product = C->product;
2372: PetscFunctionBegin;
2373: switch (product->type) {
2374: case MATPRODUCT_AB:
2375: PetscCall(MatProductSetFromOptions_MPIAIJ_AB(C));
2376: break;
2377: case MATPRODUCT_ABt:
2378: PetscCall(MatProductSetFromOptions_MPIAIJ_ABt(C));
2379: break;
2380: case MATPRODUCT_AtB:
2381: PetscCall(MatProductSetFromOptions_MPIAIJ_AtB(C));
2382: break;
2383: case MATPRODUCT_PtAP:
2384: PetscCall(MatProductSetFromOptions_MPIAIJ_PtAP(C));
2385: break;
2386: case MATPRODUCT_RARt:
2387: PetscCall(MatProductSetFromOptions_MPIAIJ_RARt(C));
2388: break;
2389: case MATPRODUCT_ABC:
2390: PetscCall(MatProductSetFromOptions_MPIAIJ_ABC(C));
2391: break;
2392: default:
2393: break;
2394: }
2395: PetscFunctionReturn(PETSC_SUCCESS);
2396: }