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(PetscCtxRt 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(PetscCtxRt ctx)
399: {
400: MPIAIJ_MPIDense *contents = *(MPIAIJ_MPIDense **)ctx;
402: PetscFunctionBegin;
403: PetscCall(MatDestroy(&contents->workB));
404: PetscCall(MatDestroy(&contents->workB1));
405: for (PetscInt i = 0; i < contents->nsends; i++) PetscCallMPI(MPI_Type_free(&contents->stype[i]));
406: for (PetscInt i = 0; i < contents->nrecvs; i++) PetscCallMPI(MPI_Type_free(&contents->rtype[i]));
407: PetscCall(PetscFree4(contents->stype, contents->rtype, contents->rwaits, contents->swaits));
408: PetscCall(PetscFree(contents));
409: PetscFunctionReturn(PETSC_SUCCESS);
410: }
412: static PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIDense(Mat A, Mat B, PetscReal fill, Mat C)
413: {
414: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
415: PetscInt nz = aij->B->cmap->n, blda, m, M, n, N;
416: MPIAIJ_MPIDense *contents;
417: VecScatter ctx = aij->Mvctx;
418: PetscInt Am = A->rmap->n, Bm = B->rmap->n, BN = B->cmap->N, Bbn, Bbn1, bs, numBb;
419: MPI_Comm comm;
420: MPI_Datatype type1, *stype, *rtype;
421: const PetscInt *sindices, *sstarts, *rstarts;
422: PetscMPIInt *disp, nsends, nrecvs, nrows_to, nrows_from;
423: PetscBool cisdense;
425: PetscFunctionBegin;
426: MatCheckProduct(C, 4);
427: PetscCheck(!C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data not empty");
428: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
429: PetscCall(PetscObjectBaseTypeCompare((PetscObject)C, MATMPIDENSE, &cisdense));
430: if (!cisdense) PetscCall(MatSetType(C, ((PetscObject)B)->type_name));
431: PetscCall(MatGetLocalSize(C, &m, &n));
432: PetscCall(MatGetSize(C, &M, &N));
433: if (m == PETSC_DECIDE || n == PETSC_DECIDE || M == PETSC_DECIDE || N == PETSC_DECIDE) PetscCall(MatSetSizes(C, Am, B->cmap->n, A->rmap->N, BN));
434: PetscCall(MatSetBlockSizesFromMats(C, A, B));
435: PetscCall(MatSetUp(C));
436: PetscCall(MatDenseGetLDA(B, &blda));
437: PetscCall(PetscNew(&contents));
439: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &sindices, NULL, NULL));
440: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL, NULL, NULL));
442: /* Create column block of B and C for memory scalability when BN is too large */
443: /* Estimate Bbn, column size of Bb */
444: if (nz) {
445: Bbn1 = 2 * Am * BN / nz;
446: if (!Bbn1) Bbn1 = 1;
447: } else Bbn1 = BN;
449: bs = B->cmap->bs;
450: Bbn1 = Bbn1 / bs * bs; /* Bbn1 is a multiple of bs */
451: if (Bbn1 > BN) Bbn1 = BN;
452: PetscCallMPI(MPIU_Allreduce(&Bbn1, &Bbn, 1, MPIU_INT, MPI_MAX, comm));
454: /* Enable runtime option for Bbn */
455: PetscOptionsBegin(comm, ((PetscObject)C)->prefix, "MatProduct", "Mat");
456: PetscCall(PetscOptionsDeprecated("-matmatmult_Bbn", "-matproduct_batch_size", "3.25", NULL));
457: PetscCall(PetscOptionsInt("-matproduct_batch_size", "Number of columns in Bb", "MatProduct", 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, &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));
513: PetscCall(MatProductClear(aij->A));
514: PetscCall(MatProductClear(((Mat_MPIDense *)B->data)->A));
515: PetscCall(MatProductClear(((Mat_MPIDense *)C->data)->A));
516: PetscCall(MatProductCreateWithMat(aij->A, ((Mat_MPIDense *)B->data)->A, NULL, ((Mat_MPIDense *)C->data)->A));
517: PetscCall(MatProductSetType(((Mat_MPIDense *)C->data)->A, MATPRODUCT_AB));
518: PetscCall(MatProductSetFromOptions(((Mat_MPIDense *)C->data)->A));
519: PetscCall(MatProductSymbolic(((Mat_MPIDense *)C->data)->A));
520: C->product->data = contents;
521: C->product->destroy = MatMPIAIJ_MPIDenseDestroy;
522: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIDense;
523: PetscFunctionReturn(PETSC_SUCCESS);
524: }
526: PETSC_INTERN PetscErrorCode MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat, Mat, Mat, const PetscBool);
528: /*
529: Performs an efficient scatter on the rows of B needed by this process; this is
530: a modification of the VecScatterBegin_() routines.
532: Input: If Bbidx = 0, uses B = Bb, else B = Bb1, see MatMatMultSymbolic_MPIAIJ_MPIDense()
533: */
535: static PetscErrorCode MatMPIDenseScatter(Mat A, Mat B, PetscInt Bbidx, Mat C, Mat *outworkB)
536: {
537: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
538: const PetscScalar *b;
539: PetscScalar *rvalues;
540: VecScatter ctx = aij->Mvctx;
541: const PetscInt *sindices, *sstarts, *rstarts;
542: const PetscMPIInt *sprocs, *rprocs;
543: PetscMPIInt nsends, nrecvs;
544: MPI_Request *swaits, *rwaits;
545: MPI_Comm comm;
546: PetscMPIInt tag = ((PetscObject)ctx)->tag, ncols, nrows, nsends_mpi, nrecvs_mpi;
547: MPIAIJ_MPIDense *contents;
548: Mat workB;
549: MPI_Datatype *stype, *rtype;
550: PetscInt blda;
552: PetscFunctionBegin;
553: MatCheckProduct(C, 4);
554: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
555: PetscCall(PetscMPIIntCast(B->cmap->N, &ncols));
556: PetscCall(PetscMPIIntCast(aij->B->cmap->n, &nrows));
557: contents = (MPIAIJ_MPIDense *)C->product->data;
558: PetscCall(VecScatterGetRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &sindices, &sprocs, NULL /*bs*/));
559: PetscCall(VecScatterGetRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL, &rprocs, NULL /*bs*/));
560: PetscCall(PetscMPIIntCast(nsends, &nsends_mpi));
561: PetscCall(PetscMPIIntCast(nrecvs, &nrecvs_mpi));
562: if (Bbidx == 0) workB = *outworkB = contents->workB;
563: else workB = *outworkB = contents->workB1;
564: 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);
565: swaits = contents->swaits;
566: rwaits = contents->rwaits;
568: PetscCall(MatDenseGetArrayRead(B, &b));
569: PetscCall(MatDenseGetLDA(B, &blda));
570: 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);
571: PetscCall(MatDenseGetArray(workB, &rvalues));
573: /* Post recv, use MPI derived data type to save memory */
574: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
575: rtype = contents->rtype;
576: for (PetscMPIInt i = 0; i < nrecvs; i++) PetscCallMPI(MPIU_Irecv(rvalues + (rstarts[i] - rstarts[0]), ncols, rtype[i], rprocs[i], tag, comm, rwaits + i));
578: stype = contents->stype;
579: for (PetscMPIInt i = 0; i < nsends; i++) PetscCallMPI(MPIU_Isend(b, ncols, stype[i], sprocs[i], tag, comm, swaits + i));
581: if (nrecvs) PetscCallMPI(MPI_Waitall(nrecvs_mpi, rwaits, MPI_STATUSES_IGNORE));
582: if (nsends) PetscCallMPI(MPI_Waitall(nsends_mpi, swaits, MPI_STATUSES_IGNORE));
584: PetscCall(VecScatterRestoreRemote_Private(ctx, PETSC_TRUE /*send*/, &nsends, &sstarts, &sindices, &sprocs, NULL));
585: PetscCall(VecScatterRestoreRemoteOrdered_Private(ctx, PETSC_FALSE /*recv*/, &nrecvs, &rstarts, NULL, &rprocs, NULL));
586: PetscCall(MatDenseRestoreArrayRead(B, &b));
587: PetscCall(MatDenseRestoreArray(workB, &rvalues));
588: PetscFunctionReturn(PETSC_SUCCESS);
589: }
591: static PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIDense(Mat A, Mat B, Mat C)
592: {
593: Mat_MPIAIJ *aij = (Mat_MPIAIJ *)A->data;
594: Mat_MPIDense *bdense = (Mat_MPIDense *)B->data;
595: Mat_MPIDense *cdense = (Mat_MPIDense *)C->data;
596: Mat workB;
597: MPIAIJ_MPIDense *contents;
599: PetscFunctionBegin;
600: MatCheckProduct(C, 3);
601: PetscCheck(C->product->data, PetscObjectComm((PetscObject)C), PETSC_ERR_PLIB, "Product data empty");
602: contents = (MPIAIJ_MPIDense *)C->product->data;
603: /* diagonal block of A times all local rows of B, first make sure that everything is up-to-date */
604: if (!cdense->A->product) {
605: PetscCall(MatProductCreateWithMat(aij->A, bdense->A, NULL, cdense->A));
606: PetscCall(MatProductSetType(cdense->A, MATPRODUCT_AB));
607: PetscCall(MatProductSetFromOptions(cdense->A));
608: PetscCall(MatProductSymbolic(cdense->A));
609: } else PetscCall(MatProductReplaceMats(aij->A, bdense->A, NULL, cdense->A));
610: if (PetscDefined(HAVE_CUPM) && !cdense->A->product->clear) {
611: PetscBool flg;
613: PetscCall(PetscObjectTypeCompare((PetscObject)C, MATMPIDENSE, &flg));
614: if (flg) PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIAIJ, &flg));
615: if (!flg) cdense->A->product->clear = PETSC_TRUE; /* if either A or C is a device Mat, make sure MatProductClear() is called */
616: }
617: PetscCall(MatProductNumeric(cdense->A));
618: if (contents->workB->cmap->n == B->cmap->N) {
619: /* get off processor parts of B needed to complete C=A*B */
620: PetscCall(MatMPIDenseScatter(A, B, 0, C, &workB));
622: /* off-diagonal block of A times nonlocal rows of B */
623: PetscCall(MatMatMultNumericAdd_SeqAIJ_SeqDense(aij->B, workB, cdense->A, PETSC_TRUE));
624: } else {
625: Mat Bb, Cb;
626: PetscInt BN = B->cmap->N, n = contents->workB->cmap->n;
627: PetscBool ccpu;
629: PetscCheck(n > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Column block size %" PetscInt_FMT " must be positive", n);
630: /* Prevent from unneeded copies back and forth from the GPU
631: when getting and restoring the submatrix
632: We need a proper GPU code for AIJ * dense in parallel */
633: PetscCall(MatBoundToCPU(C, &ccpu));
634: PetscCall(MatBindToCPU(C, PETSC_TRUE));
635: for (PetscInt i = 0; i < BN; i += n) {
636: PetscCall(MatDenseGetSubMatrix(B, PETSC_DECIDE, PETSC_DECIDE, i, PetscMin(i + n, BN), &Bb));
637: PetscCall(MatDenseGetSubMatrix(C, PETSC_DECIDE, PETSC_DECIDE, i, PetscMin(i + n, BN), &Cb));
639: /* get off processor parts of B needed to complete C=A*B */
640: PetscCall(MatMPIDenseScatter(A, Bb, (i + n) > BN, C, &workB));
642: /* off-diagonal block of A times nonlocal rows of B */
643: cdense = (Mat_MPIDense *)Cb->data;
644: PetscCall(MatMatMultNumericAdd_SeqAIJ_SeqDense(aij->B, workB, cdense->A, PETSC_TRUE));
645: PetscCall(MatDenseRestoreSubMatrix(B, &Bb));
646: PetscCall(MatDenseRestoreSubMatrix(C, &Cb));
647: }
648: PetscCall(MatBindToCPU(C, ccpu));
649: }
650: PetscFunctionReturn(PETSC_SUCCESS);
651: }
653: PetscErrorCode MatMatMultNumeric_MPIAIJ_MPIAIJ(Mat A, Mat P, Mat C)
654: {
655: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data, *c = (Mat_MPIAIJ *)C->data;
656: Mat_SeqAIJ *ad = (Mat_SeqAIJ *)a->A->data, *ao = (Mat_SeqAIJ *)a->B->data;
657: Mat_SeqAIJ *cd = (Mat_SeqAIJ *)c->A->data, *co = (Mat_SeqAIJ *)c->B->data;
658: PetscInt *adi = ad->i, *adj, *aoi = ao->i, *aoj;
659: PetscScalar *ada, *aoa, *cda = cd->a, *coa = co->a;
660: Mat_SeqAIJ *p_loc, *p_oth;
661: PetscInt *pi_loc, *pj_loc, *pi_oth, *pj_oth, *pj;
662: PetscScalar *pa_loc, *pa_oth, *pa, valtmp, *ca;
663: PetscInt cm = C->rmap->n, anz, pnz;
664: MatProductCtx_APMPI *ptap;
665: PetscScalar *apa_sparse;
666: const PetscScalar *dummy;
667: PetscInt *api, *apj, *apJ, i, j, k, row;
668: PetscInt cstart = C->cmap->rstart;
669: PetscInt cdnz, conz, k0, k1, nextp;
670: MPI_Comm comm;
671: PetscMPIInt size;
673: PetscFunctionBegin;
674: MatCheckProduct(C, 3);
675: ptap = (MatProductCtx_APMPI *)C->product->data;
676: PetscCheck(ptap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtAP cannot be computed. Missing data");
677: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
678: PetscCallMPI(MPI_Comm_size(comm, &size));
679: PetscCheck(ptap->P_oth || size <= 1, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "AP cannot be reused. Do not call MatProductClear()");
681: /* flag CPU mask for C */
682: #if defined(PETSC_HAVE_DEVICE)
683: if (C->offloadmask != PETSC_OFFLOAD_UNALLOCATED) C->offloadmask = PETSC_OFFLOAD_CPU;
684: if (c->A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) c->A->offloadmask = PETSC_OFFLOAD_CPU;
685: if (c->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) c->B->offloadmask = PETSC_OFFLOAD_CPU;
686: #endif
687: apa_sparse = ptap->apa;
689: /* 1) get P_oth = ptap->P_oth and P_loc = ptap->P_loc */
690: /* update numerical values of P_oth and P_loc */
691: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_REUSE_MATRIX, &ptap->startsj_s, &ptap->startsj_r, &ptap->bufa, &ptap->P_oth));
692: PetscCall(MatMPIAIJGetLocalMat(P, MAT_REUSE_MATRIX, &ptap->P_loc));
694: /* 2) compute numeric C_loc = A_loc*P = Ad*P_loc + Ao*P_oth */
695: /* get data from symbolic products */
696: p_loc = (Mat_SeqAIJ *)ptap->P_loc->data;
697: pi_loc = p_loc->i;
698: pj_loc = p_loc->j;
699: pa_loc = p_loc->a;
700: if (size > 1) {
701: p_oth = (Mat_SeqAIJ *)ptap->P_oth->data;
702: pi_oth = p_oth->i;
703: pj_oth = p_oth->j;
704: pa_oth = p_oth->a;
705: } else {
706: p_oth = NULL;
707: pi_oth = NULL;
708: pj_oth = NULL;
709: pa_oth = NULL;
710: }
712: /* trigger copy to CPU */
713: PetscCall(MatSeqAIJGetArrayRead(a->A, &dummy));
714: PetscCall(MatSeqAIJRestoreArrayRead(a->A, &dummy));
715: PetscCall(MatSeqAIJGetArrayRead(a->B, &dummy));
716: PetscCall(MatSeqAIJRestoreArrayRead(a->B, &dummy));
717: api = ptap->api;
718: apj = ptap->apj;
719: for (i = 0; i < cm; i++) {
720: apJ = apj + api[i];
722: /* diagonal portion of A */
723: anz = adi[i + 1] - adi[i];
724: adj = ad->j + adi[i];
725: ada = ad->a + adi[i];
726: for (j = 0; j < anz; j++) {
727: row = adj[j];
728: pnz = pi_loc[row + 1] - pi_loc[row];
729: pj = pj_loc + pi_loc[row];
730: pa = pa_loc + pi_loc[row];
731: /* perform sparse axpy */
732: valtmp = ada[j];
733: nextp = 0;
734: for (k = 0; nextp < pnz; k++) {
735: if (apJ[k] == pj[nextp]) { /* column of AP == column of P */
736: apa_sparse[k] += valtmp * pa[nextp++];
737: }
738: }
739: PetscCall(PetscLogFlops(2.0 * pnz));
740: }
742: /* off-diagonal portion of A */
743: anz = aoi[i + 1] - aoi[i];
744: aoj = PetscSafePointerPlusOffset(ao->j, aoi[i]);
745: aoa = PetscSafePointerPlusOffset(ao->a, aoi[i]);
746: for (j = 0; j < anz; j++) {
747: row = aoj[j];
748: pnz = pi_oth[row + 1] - pi_oth[row];
749: pj = pj_oth + pi_oth[row];
750: pa = pa_oth + pi_oth[row];
751: /* perform sparse axpy */
752: valtmp = aoa[j];
753: nextp = 0;
754: for (k = 0; nextp < pnz; k++) {
755: if (apJ[k] == pj[nextp]) { /* column of AP == column of P */
756: apa_sparse[k] += valtmp * pa[nextp++];
757: }
758: }
759: PetscCall(PetscLogFlops(2.0 * pnz));
760: }
762: /* set values in C */
763: cdnz = cd->i[i + 1] - cd->i[i];
764: conz = co->i[i + 1] - co->i[i];
766: /* 1st off-diagonal part of C */
767: ca = PetscSafePointerPlusOffset(coa, co->i[i]);
768: k = 0;
769: for (k0 = 0; k0 < conz; k0++) {
770: if (apJ[k] >= cstart) break;
771: ca[k0] = apa_sparse[k];
772: apa_sparse[k] = 0.0;
773: k++;
774: }
776: /* diagonal part of C */
777: ca = cda + cd->i[i];
778: for (k1 = 0; k1 < cdnz; k1++) {
779: ca[k1] = apa_sparse[k];
780: apa_sparse[k] = 0.0;
781: k++;
782: }
784: /* 2nd off-diagonal part of C */
785: ca = PetscSafePointerPlusOffset(coa, co->i[i]);
786: for (; k0 < conz; k0++) {
787: ca[k0] = apa_sparse[k];
788: apa_sparse[k] = 0.0;
789: k++;
790: }
791: }
792: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
793: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
794: PetscFunctionReturn(PETSC_SUCCESS);
795: }
797: /* same as MatMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(), except using LLCondensed to avoid O(BN) memory requirement */
798: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIAIJ(Mat A, Mat P, PetscReal fill, Mat C)
799: {
800: MPI_Comm comm;
801: PetscMPIInt size;
802: MatProductCtx_APMPI *ptap;
803: PetscFreeSpaceList free_space = NULL, current_space = NULL;
804: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
805: Mat_SeqAIJ *ad = (Mat_SeqAIJ *)a->A->data, *ao = (Mat_SeqAIJ *)a->B->data, *p_loc, *p_oth;
806: PetscInt *pi_loc, *pj_loc, *pi_oth, *pj_oth, *dnz, *onz;
807: PetscInt *adi = ad->i, *adj = ad->j, *aoi = ao->i, *aoj = ao->j, rstart = A->rmap->rstart;
808: PetscInt i, pnz, row, *api, *apj, *Jptr, apnz, nspacedouble = 0, j, nzi, *lnk, apnz_max = 1;
809: PetscInt am = A->rmap->n, pn = P->cmap->n, pm = P->rmap->n, lsize = pn + 20;
810: PetscReal afill;
811: MatType mtype;
813: PetscFunctionBegin;
814: MatCheckProduct(C, 4);
815: PetscCheck(!C->product->data, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Extra product struct not empty");
816: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
817: PetscCallMPI(MPI_Comm_size(comm, &size));
819: /* create struct MatProductCtx_APMPI and attached it to C later */
820: PetscCall(PetscNew(&ptap));
822: /* get P_oth by taking rows of P (= non-zero cols of local A) from other processors */
823: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &ptap->startsj_s, &ptap->startsj_r, &ptap->bufa, &ptap->P_oth));
825: /* get P_loc by taking all local rows of P */
826: PetscCall(MatMPIAIJGetLocalMat(P, MAT_INITIAL_MATRIX, &ptap->P_loc));
828: p_loc = (Mat_SeqAIJ *)ptap->P_loc->data;
829: pi_loc = p_loc->i;
830: pj_loc = p_loc->j;
831: if (size > 1) {
832: p_oth = (Mat_SeqAIJ *)ptap->P_oth->data;
833: pi_oth = p_oth->i;
834: pj_oth = p_oth->j;
835: } else {
836: p_oth = NULL;
837: pi_oth = NULL;
838: pj_oth = NULL;
839: }
841: /* first, compute symbolic AP = A_loc*P = A_diag*P_loc + A_off*P_oth */
842: PetscCall(PetscMalloc1(am + 1, &api));
843: ptap->api = api;
844: api[0] = 0;
846: PetscCall(PetscLLCondensedCreate_Scalable(lsize, &lnk));
848: /* Initial FreeSpace size is fill*(nnz(A)+nnz(P)) */
849: PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(fill, PetscIntSumTruncate(adi[am], PetscIntSumTruncate(aoi[am], pi_loc[pm]))), &free_space));
850: current_space = free_space;
851: MatPreallocateBegin(comm, am, pn, dnz, onz);
852: for (i = 0; i < am; i++) {
853: /* diagonal portion of A */
854: nzi = adi[i + 1] - adi[i];
855: for (j = 0; j < nzi; j++) {
856: row = *adj++;
857: pnz = pi_loc[row + 1] - pi_loc[row];
858: Jptr = pj_loc + pi_loc[row];
859: /* Expand list if it is not long enough */
860: if (pnz + apnz_max > lsize) {
861: lsize = pnz + apnz_max;
862: PetscCall(PetscLLCondensedExpand_Scalable(lsize, &lnk));
863: }
864: /* add non-zero cols of P into the sorted linked list lnk */
865: PetscCall(PetscLLCondensedAddSorted_Scalable(pnz, Jptr, lnk));
866: apnz = *lnk; /* The first element in the list is the number of items in the list */
867: api[i + 1] = api[i] + apnz;
868: if (apnz > apnz_max) apnz_max = apnz + 1; /* '1' for diagonal entry */
869: }
870: /* off-diagonal portion of A */
871: nzi = aoi[i + 1] - aoi[i];
872: for (j = 0; j < nzi; j++) {
873: row = *aoj++;
874: pnz = pi_oth[row + 1] - pi_oth[row];
875: Jptr = pj_oth + pi_oth[row];
876: /* Expand list if it is not long enough */
877: if (pnz + apnz_max > lsize) {
878: lsize = pnz + apnz_max;
879: PetscCall(PetscLLCondensedExpand_Scalable(lsize, &lnk));
880: }
881: /* add non-zero cols of P into the sorted linked list lnk */
882: PetscCall(PetscLLCondensedAddSorted_Scalable(pnz, Jptr, lnk));
883: apnz = *lnk; /* The first element in the list is the number of items in the list */
884: api[i + 1] = api[i] + apnz;
885: if (apnz > apnz_max) apnz_max = apnz + 1; /* '1' for diagonal entry */
886: }
888: /* add missing diagonal entry */
889: if (C->force_diagonals) {
890: j = i + rstart; /* column index */
891: PetscCall(PetscLLCondensedAddSorted_Scalable(1, &j, lnk));
892: }
894: apnz = *lnk;
895: api[i + 1] = api[i] + apnz;
896: if (apnz > apnz_max) apnz_max = apnz;
898: /* if free space is not available, double the total space in the list */
899: if (current_space->local_remaining < apnz) {
900: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(apnz, current_space->total_array_size), ¤t_space));
901: nspacedouble++;
902: }
904: /* Copy data into free space, then initialize lnk */
905: PetscCall(PetscLLCondensedClean_Scalable(apnz, current_space->array, lnk));
906: PetscCall(MatPreallocateSet(i + rstart, apnz, current_space->array, dnz, onz));
908: current_space->array += apnz;
909: current_space->local_used += apnz;
910: current_space->local_remaining -= apnz;
911: }
913: /* Allocate space for apj, initialize apj, and */
914: /* destroy list of free space and other temporary array(s) */
915: PetscCall(PetscMalloc1(api[am], &ptap->apj));
916: apj = ptap->apj;
917: PetscCall(PetscFreeSpaceContiguous(&free_space, ptap->apj));
918: PetscCall(PetscLLCondensedDestroy_Scalable(lnk));
920: /* create and assemble symbolic parallel matrix C */
921: PetscCall(MatSetSizes(C, am, pn, PETSC_DETERMINE, PETSC_DETERMINE));
922: PetscCall(MatSetBlockSizesFromMats(C, A, P));
923: PetscCall(MatGetType(A, &mtype));
924: PetscCall(MatSetType(C, mtype));
925: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
926: MatPreallocateEnd(dnz, onz);
928: /* malloc apa for assembly C */
929: PetscCall(PetscCalloc1(apnz_max, &ptap->apa));
931: PetscCall(MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(C, apj, api));
932: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
933: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
934: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
935: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
937: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIAIJ;
938: C->ops->productnumeric = MatProductNumeric_AB;
940: /* attach the supporting struct to C for reuse */
941: C->product->data = ptap;
942: C->product->destroy = MatProductCtxDestroy_MPIAIJ_MatMatMult;
944: /* set MatInfo */
945: afill = (PetscReal)api[am] / (adi[am] + aoi[am] + pi_loc[pm] + 1) + 1.e-5;
946: if (afill < 1.0) afill = 1.0;
947: C->info.mallocs = nspacedouble;
948: C->info.fill_ratio_given = fill;
949: C->info.fill_ratio_needed = afill;
951: #if defined(PETSC_USE_INFO)
952: if (api[am]) {
953: PetscCall(PetscInfo(C, "Reallocs %" PetscInt_FMT "; Fill ratio: given %g needed %g.\n", nspacedouble, (double)fill, (double)afill));
954: PetscCall(PetscInfo(C, "Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n", (double)afill));
955: } else {
956: PetscCall(PetscInfo(C, "Empty matrix product\n"));
957: }
958: #endif
959: PetscFunctionReturn(PETSC_SUCCESS);
960: }
962: /* This function is needed for the seqMPI matrix-matrix multiplication. */
963: /* Three input arrays are merged to one output array. The size of the */
964: /* output array is also output. Duplicate entries only show up once. */
965: static void Merge3SortedArrays(PetscInt size1, PetscInt *in1, PetscInt size2, PetscInt *in2, PetscInt size3, PetscInt *in3, PetscInt *size4, PetscInt *out)
966: {
967: int i = 0, j = 0, k = 0, l = 0;
969: /* Traverse all three arrays */
970: while (i < size1 && j < size2 && k < size3) {
971: if (in1[i] < in2[j] && in1[i] < in3[k]) {
972: out[l++] = in1[i++];
973: } else if (in2[j] < in1[i] && in2[j] < in3[k]) {
974: out[l++] = in2[j++];
975: } else if (in3[k] < in1[i] && in3[k] < in2[j]) {
976: out[l++] = in3[k++];
977: } else if (in1[i] == in2[j] && in1[i] < in3[k]) {
978: out[l++] = in1[i];
979: i++, j++;
980: } else if (in1[i] == in3[k] && in1[i] < in2[j]) {
981: out[l++] = in1[i];
982: i++, k++;
983: } else if (in3[k] == in2[j] && in2[j] < in1[i]) {
984: out[l++] = in2[j];
985: k++, j++;
986: } else if (in1[i] == in2[j] && in1[i] == in3[k]) {
987: out[l++] = in1[i];
988: i++, j++, k++;
989: }
990: }
992: /* Traverse two remaining arrays */
993: while (i < size1 && j < size2) {
994: if (in1[i] < in2[j]) {
995: out[l++] = in1[i++];
996: } else if (in1[i] > in2[j]) {
997: out[l++] = in2[j++];
998: } else {
999: out[l++] = in1[i];
1000: i++, j++;
1001: }
1002: }
1004: while (i < size1 && k < size3) {
1005: if (in1[i] < in3[k]) {
1006: out[l++] = in1[i++];
1007: } else if (in1[i] > in3[k]) {
1008: out[l++] = in3[k++];
1009: } else {
1010: out[l++] = in1[i];
1011: i++, k++;
1012: }
1013: }
1015: while (k < size3 && j < size2) {
1016: if (in3[k] < in2[j]) {
1017: out[l++] = in3[k++];
1018: } else if (in3[k] > in2[j]) {
1019: out[l++] = in2[j++];
1020: } else {
1021: out[l++] = in3[k];
1022: k++, j++;
1023: }
1024: }
1026: /* Traverse one remaining array */
1027: while (i < size1) out[l++] = in1[i++];
1028: while (j < size2) out[l++] = in2[j++];
1029: while (k < size3) out[l++] = in3[k++];
1031: *size4 = l;
1032: }
1034: /* This matrix-matrix multiplication algorithm divides the multiplication into three multiplications and */
1035: /* adds up the products. Two of these three multiplications are performed with existing (sequential) */
1036: /* matrix-matrix multiplications. */
1037: PetscErrorCode MatMatMultSymbolic_MPIAIJ_MPIAIJ_seqMPI(Mat A, Mat P, PetscReal fill, Mat C)
1038: {
1039: MPI_Comm comm;
1040: PetscMPIInt size;
1041: MatProductCtx_APMPI *ptap;
1042: PetscFreeSpaceList free_space_diag = NULL, current_space = NULL;
1043: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1044: Mat_SeqAIJ *ad = (Mat_SeqAIJ *)a->A->data, *ao = (Mat_SeqAIJ *)a->B->data, *p_loc;
1045: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
1046: Mat_SeqAIJ *adpd_seq, *p_off, *aopoth_seq;
1047: PetscInt adponz, adpdnz;
1048: PetscInt *pi_loc, *dnz, *onz;
1049: PetscInt *adi = ad->i, *adj = ad->j, *aoi = ao->i, rstart = A->rmap->rstart;
1050: 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;
1051: PetscInt am = A->rmap->n, pN = P->cmap->N, pn = P->cmap->n, pm = P->rmap->n, p_colstart, p_colend;
1052: PetscBT lnkbt;
1053: PetscReal afill;
1054: PetscMPIInt rank;
1055: Mat adpd, aopoth;
1056: MatType mtype;
1057: const char *prefix;
1059: PetscFunctionBegin;
1060: MatCheckProduct(C, 4);
1061: PetscCheck(!C->product->data, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Extra product struct not empty");
1062: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
1063: PetscCallMPI(MPI_Comm_size(comm, &size));
1064: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1065: PetscCall(MatGetOwnershipRangeColumn(P, &p_colstart, &p_colend));
1067: /* create struct MatProductCtx_APMPI and attached it to C later */
1068: PetscCall(PetscNew(&ptap));
1070: /* get P_oth by taking rows of P (= non-zero cols of local A) from other processors */
1071: PetscCall(MatGetBrowsOfAoCols_MPIAIJ(A, P, MAT_INITIAL_MATRIX, &ptap->startsj_s, &ptap->startsj_r, &ptap->bufa, &ptap->P_oth));
1073: /* get P_loc by taking all local rows of P */
1074: PetscCall(MatMPIAIJGetLocalMat(P, MAT_INITIAL_MATRIX, &ptap->P_loc));
1076: p_loc = (Mat_SeqAIJ *)ptap->P_loc->data;
1077: pi_loc = p_loc->i;
1079: /* Allocate memory for the i arrays of the matrices A*P, A_diag*P_off and A_offd * P */
1080: PetscCall(PetscMalloc1(am + 1, &api));
1081: PetscCall(PetscMalloc1(am + 1, &adpoi));
1083: adpoi[0] = 0;
1084: ptap->api = api;
1085: api[0] = 0;
1087: /* create and initialize a linked list, will be used for both A_diag * P_loc_off and A_offd * P_oth */
1088: PetscCall(PetscLLCondensedCreate(pN, pN, &lnk, &lnkbt));
1089: MatPreallocateBegin(comm, am, pn, dnz, onz);
1091: /* Symbolic calc of A_loc_diag * P_loc_diag */
1092: PetscCall(MatGetOptionsPrefix(A, &prefix));
1093: PetscCall(MatProductCreate(a->A, p->A, NULL, &adpd));
1094: PetscCall(MatGetOptionsPrefix(A, &prefix));
1095: PetscCall(MatSetOptionsPrefix(adpd, prefix));
1096: PetscCall(MatAppendOptionsPrefix(adpd, "inner_diag_"));
1098: PetscCall(MatProductSetType(adpd, MATPRODUCT_AB));
1099: PetscCall(MatProductSetAlgorithm(adpd, "sorted"));
1100: PetscCall(MatProductSetFill(adpd, fill));
1101: PetscCall(MatProductSetFromOptions(adpd));
1103: adpd->force_diagonals = C->force_diagonals;
1104: PetscCall(MatProductSymbolic(adpd));
1106: adpd_seq = (Mat_SeqAIJ *)((adpd)->data);
1107: adpdi = adpd_seq->i;
1108: adpdj = adpd_seq->j;
1109: p_off = (Mat_SeqAIJ *)p->B->data;
1110: poff_i = p_off->i;
1111: poff_j = p_off->j;
1113: /* j_temp stores indices of a result row before they are added to the linked list */
1114: PetscCall(PetscMalloc1(pN, &j_temp));
1116: /* Symbolic calc of the A_diag * p_loc_off */
1117: /* Initial FreeSpace size is fill*(nnz(A)+nnz(P)) */
1118: PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(fill, PetscIntSumTruncate(adi[am], PetscIntSumTruncate(aoi[am], pi_loc[pm]))), &free_space_diag));
1119: current_space = free_space_diag;
1121: for (i = 0; i < am; i++) {
1122: /* A_diag * P_loc_off */
1123: nzi = adi[i + 1] - adi[i];
1124: for (j = 0; j < nzi; j++) {
1125: row = *adj++;
1126: pnz = poff_i[row + 1] - poff_i[row];
1127: Jptr = poff_j + poff_i[row];
1128: for (i1 = 0; i1 < pnz; i1++) j_temp[i1] = p->garray[Jptr[i1]];
1129: /* add non-zero cols of P into the sorted linked list lnk */
1130: PetscCall(PetscLLCondensedAddSorted(pnz, j_temp, lnk, lnkbt));
1131: }
1133: adponz = lnk[0];
1134: adpoi[i + 1] = adpoi[i] + adponz;
1136: /* if free space is not available, double the total space in the list */
1137: if (current_space->local_remaining < adponz) {
1138: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(adponz, current_space->total_array_size), ¤t_space));
1139: nspacedouble++;
1140: }
1142: /* Copy data into free space, then initialize lnk */
1143: PetscCall(PetscLLCondensedClean(pN, adponz, current_space->array, lnk, lnkbt));
1145: current_space->array += adponz;
1146: current_space->local_used += adponz;
1147: current_space->local_remaining -= adponz;
1148: }
1150: /* Symbolic calc of A_off * P_oth */
1151: PetscCall(MatSetOptionsPrefix(a->B, prefix));
1152: PetscCall(MatAppendOptionsPrefix(a->B, "inner_offdiag_"));
1153: PetscCall(MatCreate(PETSC_COMM_SELF, &aopoth));
1154: PetscCall(MatMatMultSymbolic_SeqAIJ_SeqAIJ(a->B, ptap->P_oth, fill, aopoth));
1155: aopoth_seq = (Mat_SeqAIJ *)((aopoth)->data);
1156: aopothi = aopoth_seq->i;
1157: aopothj = aopoth_seq->j;
1159: /* Allocate space for apj, adpj, aopj, ... */
1160: /* destroy lists of free space and other temporary array(s) */
1162: PetscCall(PetscMalloc1(aopothi[am] + adpoi[am] + adpdi[am], &ptap->apj));
1163: PetscCall(PetscMalloc1(adpoi[am], &adpoj));
1165: /* Copy from linked list to j-array */
1166: PetscCall(PetscFreeSpaceContiguous(&free_space_diag, adpoj));
1167: PetscCall(PetscLLDestroy(lnk, lnkbt));
1169: adpoJ = adpoj;
1170: adpdJ = adpdj;
1171: aopJ = aopothj;
1172: apj = ptap->apj;
1173: apJ = apj; /* still empty */
1175: /* Merge j-arrays of A_off * P, A_diag * P_loc_off, and */
1176: /* A_diag * P_loc_diag to get A*P */
1177: for (i = 0; i < am; i++) {
1178: aopnz = aopothi[i + 1] - aopothi[i];
1179: adponz = adpoi[i + 1] - adpoi[i];
1180: adpdnz = adpdi[i + 1] - adpdi[i];
1182: /* Correct indices from A_diag*P_diag */
1183: for (i1 = 0; i1 < adpdnz; i1++) adpdJ[i1] += p_colstart;
1184: /* Merge j-arrays of A_diag * P_loc_off and A_diag * P_loc_diag and A_off * P_oth */
1185: Merge3SortedArrays(adponz, adpoJ, adpdnz, adpdJ, aopnz, aopJ, &apnz, apJ);
1186: PetscCall(MatPreallocateSet(i + rstart, apnz, apJ, dnz, onz));
1188: aopJ += aopnz;
1189: adpoJ += adponz;
1190: adpdJ += adpdnz;
1191: apJ += apnz;
1192: api[i + 1] = api[i] + apnz;
1193: }
1195: /* malloc apa to store dense row A[i,:]*P */
1196: PetscCall(PetscCalloc1(pN, &ptap->apa));
1198: /* create and assemble symbolic parallel matrix C */
1199: PetscCall(MatSetSizes(C, am, pn, PETSC_DETERMINE, PETSC_DETERMINE));
1200: PetscCall(MatSetBlockSizesFromMats(C, A, P));
1201: PetscCall(MatGetType(A, &mtype));
1202: PetscCall(MatSetType(C, mtype));
1203: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
1204: MatPreallocateEnd(dnz, onz);
1206: PetscCall(MatSetValues_MPIAIJ_CopyFromCSRFormat_Symbolic(C, apj, api));
1207: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
1208: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1209: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1210: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
1212: C->ops->matmultnumeric = MatMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable;
1213: C->ops->productnumeric = MatProductNumeric_AB;
1215: /* attach the supporting struct to C for reuse */
1216: C->product->data = ptap;
1217: C->product->destroy = MatProductCtxDestroy_MPIAIJ_MatMatMult;
1219: /* set MatInfo */
1220: afill = (PetscReal)api[am] / (adi[am] + aoi[am] + pi_loc[pm] + 1) + 1.e-5;
1221: if (afill < 1.0) afill = 1.0;
1222: C->info.mallocs = nspacedouble;
1223: C->info.fill_ratio_given = fill;
1224: C->info.fill_ratio_needed = afill;
1226: #if defined(PETSC_USE_INFO)
1227: if (api[am]) {
1228: PetscCall(PetscInfo(C, "Reallocs %" PetscInt_FMT "; Fill ratio: given %g needed %g.\n", nspacedouble, (double)fill, (double)afill));
1229: PetscCall(PetscInfo(C, "Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n", (double)afill));
1230: } else {
1231: PetscCall(PetscInfo(C, "Empty matrix product\n"));
1232: }
1233: #endif
1235: PetscCall(MatDestroy(&aopoth));
1236: PetscCall(MatDestroy(&adpd));
1237: PetscCall(PetscFree(j_temp));
1238: PetscCall(PetscFree(adpoj));
1239: PetscCall(PetscFree(adpoi));
1240: PetscFunctionReturn(PETSC_SUCCESS);
1241: }
1243: /* This routine only works when scall=MAT_REUSE_MATRIX! */
1244: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_matmatmult(Mat P, Mat A, Mat C)
1245: {
1246: MatProductCtx_APMPI *ptap;
1247: Mat Pt;
1249: PetscFunctionBegin;
1250: MatCheckProduct(C, 3);
1251: ptap = (MatProductCtx_APMPI *)C->product->data;
1252: PetscCheck(ptap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtAP cannot be computed. Missing data");
1253: PetscCheck(ptap->Pt, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtA cannot be reused. Do not call MatProductClear()");
1255: Pt = ptap->Pt;
1256: PetscCall(MatTransposeSetPrecursor(P, Pt));
1257: PetscCall(MatTranspose(P, MAT_REUSE_MATRIX, &Pt));
1258: PetscCall(MatMatMultNumeric_MPIAIJ_MPIAIJ(Pt, A, C));
1259: PetscFunctionReturn(PETSC_SUCCESS);
1260: }
1262: /* This routine is modified from MatPtAPSymbolic_MPIAIJ_MPIAIJ() */
1263: PetscErrorCode MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(Mat P, Mat A, PetscReal fill, Mat C)
1264: {
1265: MatProductCtx_APMPI *ptap;
1266: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
1267: MPI_Comm comm;
1268: PetscMPIInt size, rank;
1269: PetscFreeSpaceList free_space = NULL, current_space = NULL;
1270: PetscInt pn = P->cmap->n, aN = A->cmap->N, an = A->cmap->n;
1271: PetscInt *lnk, i, k, rstart;
1272: PetscBT lnkbt;
1273: PetscMPIInt tagi, tagj, *len_si, *len_s, *len_ri, nrecv, proc, nsend;
1274: PETSC_UNUSED PetscMPIInt icompleted = 0;
1275: PetscInt **buf_rj, **buf_ri, **buf_ri_k, row, ncols, *cols;
1276: PetscInt len, *dnz, *onz, *owners, nzi;
1277: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextci;
1278: MPI_Request *swaits, *rwaits;
1279: MPI_Status *sstatus, rstatus;
1280: PetscLayout rowmap;
1281: PetscInt *owners_co, *coi, *coj; /* i and j array of (p->B)^T*A*P - used in the communication */
1282: PetscMPIInt *len_r, *id_r; /* array of length of comm->size, store send/recv matrix values */
1283: PetscInt *Jptr, *prmap = p->garray, con, j, Crmax;
1284: Mat_SeqAIJ *a_loc, *c_loc, *c_oth;
1285: PetscHMapI ta;
1286: MatType mtype;
1287: const char *prefix;
1289: PetscFunctionBegin;
1290: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
1291: PetscCallMPI(MPI_Comm_size(comm, &size));
1292: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1294: /* create symbolic parallel matrix C */
1295: PetscCall(MatGetType(A, &mtype));
1296: PetscCall(MatSetType(C, mtype));
1298: C->ops->transposematmultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable;
1300: /* create struct MatProductCtx_APMPI and attached it to C later */
1301: PetscCall(PetscNew(&ptap));
1303: /* (0) compute Rd = Pd^T, Ro = Po^T */
1304: PetscCall(MatTranspose(p->A, MAT_INITIAL_MATRIX, &ptap->Rd));
1305: PetscCall(MatTranspose(p->B, MAT_INITIAL_MATRIX, &ptap->Ro));
1307: /* (1) compute symbolic A_loc */
1308: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &ptap->A_loc));
1310: /* (2-1) compute symbolic C_oth = Ro*A_loc */
1311: PetscCall(MatGetOptionsPrefix(A, &prefix));
1312: PetscCall(MatSetOptionsPrefix(ptap->Ro, prefix));
1313: PetscCall(MatAppendOptionsPrefix(ptap->Ro, "inner_offdiag_"));
1314: PetscCall(MatCreate(PETSC_COMM_SELF, &ptap->C_oth));
1315: PetscCall(MatMatMultSymbolic_SeqAIJ_SeqAIJ(ptap->Ro, ptap->A_loc, fill, ptap->C_oth));
1317: /* (3) send coj of C_oth to other processors */
1318: /* determine row ownership */
1319: PetscCall(PetscLayoutCreate(comm, &rowmap));
1320: rowmap->n = pn;
1321: rowmap->bs = 1;
1322: PetscCall(PetscLayoutSetUp(rowmap));
1323: owners = rowmap->range;
1325: /* determine the number of messages to send, their lengths */
1326: PetscCall(PetscMalloc4(size, &len_s, size, &len_si, size, &sstatus, size + 1, &owners_co));
1327: PetscCall(PetscArrayzero(len_s, size));
1328: PetscCall(PetscArrayzero(len_si, size));
1330: c_oth = (Mat_SeqAIJ *)ptap->C_oth->data;
1331: coi = c_oth->i;
1332: coj = c_oth->j;
1333: con = ptap->C_oth->rmap->n;
1334: proc = 0;
1335: for (i = 0; i < con; i++) {
1336: while (prmap[i] >= owners[proc + 1]) proc++;
1337: len_si[proc]++; /* num of rows in Co(=Pt*A) to be sent to [proc] */
1338: len_s[proc] += coi[i + 1] - coi[i]; /* num of nonzeros in Co to be sent to [proc] */
1339: }
1341: len = 0; /* max length of buf_si[], see (4) */
1342: owners_co[0] = 0;
1343: nsend = 0;
1344: for (proc = 0; proc < size; proc++) {
1345: owners_co[proc + 1] = owners_co[proc] + len_si[proc];
1346: if (len_s[proc]) {
1347: nsend++;
1348: len_si[proc] = 2 * (len_si[proc] + 1); /* length of buf_si to be sent to [proc] */
1349: len += len_si[proc];
1350: }
1351: }
1353: /* determine the number and length of messages to receive for coi and coj */
1354: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &nrecv));
1355: PetscCall(PetscGatherMessageLengths2(comm, nsend, nrecv, len_s, len_si, &id_r, &len_r, &len_ri));
1357: /* post the Irecv and Isend of coj */
1358: PetscCall(PetscCommGetNewTag(comm, &tagj));
1359: PetscCall(PetscPostIrecvInt(comm, tagj, nrecv, id_r, len_r, &buf_rj, &rwaits));
1360: PetscCall(PetscMalloc1(nsend, &swaits));
1361: for (proc = 0, k = 0; proc < size; proc++) {
1362: if (!len_s[proc]) continue;
1363: i = owners_co[proc];
1364: PetscCallMPI(MPIU_Isend(coj + coi[i], len_s[proc], MPIU_INT, proc, tagj, comm, swaits + k));
1365: k++;
1366: }
1368: /* (2-2) compute symbolic C_loc = Rd*A_loc */
1369: PetscCall(MatSetOptionsPrefix(ptap->Rd, prefix));
1370: PetscCall(MatAppendOptionsPrefix(ptap->Rd, "inner_diag_"));
1371: PetscCall(MatCreate(PETSC_COMM_SELF, &ptap->C_loc));
1372: PetscCall(MatMatMultSymbolic_SeqAIJ_SeqAIJ(ptap->Rd, ptap->A_loc, fill, ptap->C_loc));
1373: c_loc = (Mat_SeqAIJ *)ptap->C_loc->data;
1375: /* receives coj are complete */
1376: for (i = 0; i < nrecv; i++) PetscCallMPI(MPI_Waitany(nrecv, rwaits, &icompleted, &rstatus));
1377: PetscCall(PetscFree(rwaits));
1378: if (nsend) PetscCallMPI(MPI_Waitall(nsend, swaits, sstatus));
1380: /* add received column indices into ta to update Crmax */
1381: a_loc = (Mat_SeqAIJ *)ptap->A_loc->data;
1383: /* create and initialize a linked list */
1384: PetscCall(PetscHMapICreateWithSize(an, &ta)); /* for compute Crmax */
1385: MatRowMergeMax_SeqAIJ(a_loc, ptap->A_loc->rmap->N, ta);
1387: for (k = 0; k < nrecv; k++) { /* k-th received message */
1388: Jptr = buf_rj[k];
1389: for (j = 0; j < len_r[k]; j++) PetscCall(PetscHMapISet(ta, *(Jptr + j) + 1, 1));
1390: }
1391: PetscCall(PetscHMapIGetSize(ta, &Crmax));
1392: PetscCall(PetscHMapIDestroy(&ta));
1394: /* (4) send and recv coi */
1395: PetscCall(PetscCommGetNewTag(comm, &tagi));
1396: PetscCall(PetscPostIrecvInt(comm, tagi, nrecv, id_r, len_ri, &buf_ri, &rwaits));
1397: PetscCall(PetscMalloc1(len, &buf_s));
1398: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
1399: for (proc = 0, k = 0; proc < size; proc++) {
1400: if (!len_s[proc]) continue;
1401: /* form outgoing message for i-structure:
1402: buf_si[0]: nrows to be sent
1403: [1:nrows]: row index (global)
1404: [nrows+1:2*nrows+1]: i-structure index
1405: */
1406: nrows = len_si[proc] / 2 - 1; /* num of rows in Co to be sent to [proc] */
1407: buf_si_i = buf_si + nrows + 1;
1408: buf_si[0] = nrows;
1409: buf_si_i[0] = 0;
1410: nrows = 0;
1411: for (i = owners_co[proc]; i < owners_co[proc + 1]; i++) {
1412: nzi = coi[i + 1] - coi[i];
1413: buf_si_i[nrows + 1] = buf_si_i[nrows] + nzi; /* i-structure */
1414: buf_si[nrows + 1] = prmap[i] - owners[proc]; /* local row index */
1415: nrows++;
1416: }
1417: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, swaits + k));
1418: k++;
1419: buf_si += len_si[proc];
1420: }
1421: for (i = 0; i < nrecv; i++) PetscCallMPI(MPI_Waitany(nrecv, rwaits, &icompleted, &rstatus));
1422: PetscCall(PetscFree(rwaits));
1423: if (nsend) PetscCallMPI(MPI_Waitall(nsend, swaits, sstatus));
1425: PetscCall(PetscFree4(len_s, len_si, sstatus, owners_co));
1426: PetscCall(PetscFree(len_ri));
1427: PetscCall(PetscFree(swaits));
1428: PetscCall(PetscFree(buf_s));
1430: /* (5) compute the local portion of C */
1431: /* set initial free space to be Crmax, sufficient for holding nonzeros in each row of C */
1432: PetscCall(PetscFreeSpaceGet(Crmax, &free_space));
1433: current_space = free_space;
1435: PetscCall(PetscMalloc3(nrecv, &buf_ri_k, nrecv, &nextrow, nrecv, &nextci));
1436: for (k = 0; k < nrecv; k++) {
1437: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
1438: nrows = *buf_ri_k[k];
1439: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
1440: nextci[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
1441: }
1443: MatPreallocateBegin(comm, pn, an, dnz, onz);
1444: PetscCall(PetscLLCondensedCreate(Crmax, aN, &lnk, &lnkbt));
1445: for (i = 0; i < pn; i++) { /* for each local row of C */
1446: /* add C_loc into C */
1447: nzi = c_loc->i[i + 1] - c_loc->i[i];
1448: Jptr = c_loc->j + c_loc->i[i];
1449: PetscCall(PetscLLCondensedAddSorted(nzi, Jptr, lnk, lnkbt));
1451: /* add received col data into lnk */
1452: for (k = 0; k < nrecv; k++) { /* k-th received message */
1453: if (i == *nextrow[k]) { /* i-th row */
1454: nzi = *(nextci[k] + 1) - *nextci[k];
1455: Jptr = buf_rj[k] + *nextci[k];
1456: PetscCall(PetscLLCondensedAddSorted(nzi, Jptr, lnk, lnkbt));
1457: nextrow[k]++;
1458: nextci[k]++;
1459: }
1460: }
1462: /* add missing diagonal entry */
1463: if (C->force_diagonals) {
1464: k = i + owners[rank]; /* column index */
1465: PetscCall(PetscLLCondensedAddSorted(1, &k, lnk, lnkbt));
1466: }
1468: nzi = lnk[0];
1470: /* copy data into free space, then initialize lnk */
1471: PetscCall(PetscLLCondensedClean(aN, nzi, current_space->array, lnk, lnkbt));
1472: PetscCall(MatPreallocateSet(i + owners[rank], nzi, current_space->array, dnz, onz));
1473: }
1474: PetscCall(PetscFree3(buf_ri_k, nextrow, nextci));
1475: PetscCall(PetscLLDestroy(lnk, lnkbt));
1476: PetscCall(PetscFreeSpaceDestroy(free_space));
1478: /* local sizes and preallocation */
1479: PetscCall(MatSetSizes(C, pn, an, PETSC_DETERMINE, PETSC_DETERMINE));
1480: PetscCall(PetscLayoutSetBlockSize(C->rmap, P->cmap->bs));
1481: PetscCall(PetscLayoutSetBlockSize(C->cmap, A->cmap->bs));
1482: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
1483: MatPreallocateEnd(dnz, onz);
1485: /* add C_loc and C_oth to C */
1486: PetscCall(MatGetOwnershipRange(C, &rstart, NULL));
1487: for (i = 0; i < pn; i++) {
1488: ncols = c_loc->i[i + 1] - c_loc->i[i];
1489: cols = c_loc->j + c_loc->i[i];
1490: row = rstart + i;
1491: PetscCall(MatSetValues(C, 1, (const PetscInt *)&row, ncols, (const PetscInt *)cols, NULL, INSERT_VALUES));
1493: if (C->force_diagonals) PetscCall(MatSetValues(C, 1, (const PetscInt *)&row, 1, (const PetscInt *)&row, NULL, INSERT_VALUES));
1494: }
1495: for (i = 0; i < con; i++) {
1496: ncols = c_oth->i[i + 1] - c_oth->i[i];
1497: cols = c_oth->j + c_oth->i[i];
1498: row = prmap[i];
1499: PetscCall(MatSetValues(C, 1, (const PetscInt *)&row, ncols, (const PetscInt *)cols, NULL, INSERT_VALUES));
1500: }
1501: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1502: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1503: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
1505: /* members in merge */
1506: PetscCall(PetscFree(id_r));
1507: PetscCall(PetscFree(len_r));
1508: PetscCall(PetscFree(buf_ri[0]));
1509: PetscCall(PetscFree(buf_ri));
1510: PetscCall(PetscFree(buf_rj[0]));
1511: PetscCall(PetscFree(buf_rj));
1512: PetscCall(PetscLayoutDestroy(&rowmap));
1514: /* attach the supporting struct to C for reuse */
1515: C->product->data = ptap;
1516: C->product->destroy = MatProductCtxDestroy_MPIAIJ_PtAP;
1517: PetscFunctionReturn(PETSC_SUCCESS);
1518: }
1520: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_nonscalable(Mat P, Mat A, Mat C)
1521: {
1522: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
1523: Mat_SeqAIJ *c_seq;
1524: MatProductCtx_APMPI *ptap;
1525: Mat A_loc, C_loc, C_oth;
1526: PetscInt i, rstart, rend, cm, ncols, row;
1527: const PetscInt *cols;
1528: const PetscScalar *vals;
1530: PetscFunctionBegin;
1531: MatCheckProduct(C, 3);
1532: ptap = (MatProductCtx_APMPI *)C->product->data;
1533: PetscCheck(ptap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtAP cannot be computed. Missing data");
1534: PetscCheck(ptap->A_loc, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtA cannot be reused. Do not call MatProductClear()");
1535: PetscCall(MatZeroEntries(C));
1537: /* These matrices are obtained in MatTransposeMatMultSymbolic() */
1538: /* 1) get R = Pd^T, Ro = Po^T */
1539: PetscCall(MatTransposeSetPrecursor(p->A, ptap->Rd));
1540: PetscCall(MatTranspose(p->A, MAT_REUSE_MATRIX, &ptap->Rd));
1541: PetscCall(MatTransposeSetPrecursor(p->B, ptap->Ro));
1542: PetscCall(MatTranspose(p->B, MAT_REUSE_MATRIX, &ptap->Ro));
1544: /* 2) compute numeric A_loc */
1545: PetscCall(MatMPIAIJGetLocalMat(A, MAT_REUSE_MATRIX, &ptap->A_loc));
1547: /* 3) C_loc = Rd*A_loc, C_oth = Ro*A_loc */
1548: A_loc = ptap->A_loc;
1549: PetscCall(ptap->C_loc->ops->matmultnumeric(ptap->Rd, A_loc, ptap->C_loc));
1550: PetscCall(ptap->C_oth->ops->matmultnumeric(ptap->Ro, A_loc, ptap->C_oth));
1551: C_loc = ptap->C_loc;
1552: C_oth = ptap->C_oth;
1554: /* add C_loc and C_oth to C */
1555: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
1557: /* C_loc -> C */
1558: cm = C_loc->rmap->N;
1559: c_seq = (Mat_SeqAIJ *)C_loc->data;
1560: cols = c_seq->j;
1561: vals = c_seq->a;
1562: for (i = 0; i < cm; i++) {
1563: ncols = c_seq->i[i + 1] - c_seq->i[i];
1564: row = rstart + i;
1565: PetscCall(MatSetValues(C, 1, &row, ncols, cols, vals, ADD_VALUES));
1566: cols += ncols;
1567: vals += ncols;
1568: }
1570: /* Co -> C, off-processor part */
1571: cm = C_oth->rmap->N;
1572: c_seq = (Mat_SeqAIJ *)C_oth->data;
1573: cols = c_seq->j;
1574: vals = c_seq->a;
1575: for (i = 0; i < cm; i++) {
1576: ncols = c_seq->i[i + 1] - c_seq->i[i];
1577: row = p->garray[i];
1578: PetscCall(MatSetValues(C, 1, &row, ncols, cols, vals, ADD_VALUES));
1579: cols += ncols;
1580: vals += ncols;
1581: }
1582: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1583: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1584: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
1585: PetscFunctionReturn(PETSC_SUCCESS);
1586: }
1588: PetscErrorCode MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ(Mat P, Mat A, Mat C)
1589: {
1590: MatMergeSeqsToMPI *merge;
1591: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data;
1592: Mat_SeqAIJ *pd = (Mat_SeqAIJ *)p->A->data, *po = (Mat_SeqAIJ *)p->B->data;
1593: MatProductCtx_APMPI *ap;
1594: PetscInt *adj;
1595: PetscInt i, j, k, anz, pnz, row, *cj, nexta;
1596: MatScalar *ada, *ca, valtmp;
1597: PetscInt am = A->rmap->n, cm = C->rmap->n, pon = (p->B)->cmap->n;
1598: MPI_Comm comm;
1599: PetscMPIInt size, rank, taga, *len_s, proc;
1600: PetscInt *owners, nrows, **buf_ri_k, **nextrow, **nextci;
1601: PetscInt **buf_ri, **buf_rj;
1602: PetscInt cnz = 0, *bj_i, *bi, *bj, bnz, nextcj; /* bi,bj,ba: local array of C(mpi mat) */
1603: MPI_Request *s_waits, *r_waits;
1604: MPI_Status *status;
1605: MatScalar **abuf_r, *ba_i, *pA, *coa, *ba;
1606: const PetscScalar *dummy;
1607: PetscInt *ai, *aj, *coi, *coj, *poJ, *pdJ;
1608: Mat A_loc;
1609: Mat_SeqAIJ *a_loc;
1611: PetscFunctionBegin;
1612: MatCheckProduct(C, 3);
1613: ap = (MatProductCtx_APMPI *)C->product->data;
1614: PetscCheck(ap, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtA cannot be computed. Missing data");
1615: PetscCheck(ap->A_loc, PetscObjectComm((PetscObject)C), PETSC_ERR_ARG_WRONGSTATE, "PtA cannot be reused. Do not call MatProductClear()");
1616: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
1617: PetscCallMPI(MPI_Comm_size(comm, &size));
1618: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1620: merge = ap->merge;
1622: /* 2) compute numeric C_seq = P_loc^T*A_loc */
1623: /* get data from symbolic products */
1624: coi = merge->coi;
1625: coj = merge->coj;
1626: PetscCall(PetscCalloc1(coi[pon], &coa));
1627: bi = merge->bi;
1628: bj = merge->bj;
1629: owners = merge->rowmap->range;
1630: PetscCall(PetscCalloc1(bi[cm], &ba));
1632: /* get A_loc by taking all local rows of A */
1633: A_loc = ap->A_loc;
1634: PetscCall(MatMPIAIJGetLocalMat(A, MAT_REUSE_MATRIX, &A_loc));
1635: a_loc = (Mat_SeqAIJ *)A_loc->data;
1636: ai = a_loc->i;
1637: aj = a_loc->j;
1639: /* trigger copy to CPU */
1640: PetscCall(MatSeqAIJGetArrayRead(p->A, &dummy));
1641: PetscCall(MatSeqAIJRestoreArrayRead(p->A, &dummy));
1642: PetscCall(MatSeqAIJGetArrayRead(p->B, &dummy));
1643: PetscCall(MatSeqAIJRestoreArrayRead(p->B, &dummy));
1644: for (i = 0; i < am; i++) {
1645: anz = ai[i + 1] - ai[i];
1646: adj = aj + ai[i];
1647: ada = a_loc->a + ai[i];
1649: /* 2-b) Compute Cseq = P_loc[i,:]^T*A[i,:] using outer product */
1650: /* put the value into Co=(p->B)^T*A (off-diagonal part, send to others) */
1651: pnz = po->i[i + 1] - po->i[i];
1652: poJ = po->j + po->i[i];
1653: pA = po->a + po->i[i];
1654: for (j = 0; j < pnz; j++) {
1655: row = poJ[j];
1656: cj = coj + coi[row];
1657: ca = coa + coi[row];
1658: /* perform sparse axpy */
1659: nexta = 0;
1660: valtmp = pA[j];
1661: for (k = 0; nexta < anz; k++) {
1662: if (cj[k] == adj[nexta]) {
1663: ca[k] += valtmp * ada[nexta];
1664: nexta++;
1665: }
1666: }
1667: PetscCall(PetscLogFlops(2.0 * anz));
1668: }
1670: /* put the value into Cd (diagonal part) */
1671: pnz = pd->i[i + 1] - pd->i[i];
1672: pdJ = pd->j + pd->i[i];
1673: pA = pd->a + pd->i[i];
1674: for (j = 0; j < pnz; j++) {
1675: row = pdJ[j];
1676: cj = bj + bi[row];
1677: ca = ba + bi[row];
1678: /* perform sparse axpy */
1679: nexta = 0;
1680: valtmp = pA[j];
1681: for (k = 0; nexta < anz; k++) {
1682: if (cj[k] == adj[nexta]) {
1683: ca[k] += valtmp * ada[nexta];
1684: nexta++;
1685: }
1686: }
1687: PetscCall(PetscLogFlops(2.0 * anz));
1688: }
1689: }
1691: /* 3) send and recv matrix values coa */
1692: buf_ri = merge->buf_ri;
1693: buf_rj = merge->buf_rj;
1694: len_s = merge->len_s;
1695: PetscCall(PetscCommGetNewTag(comm, &taga));
1696: PetscCall(PetscPostIrecvScalar(comm, taga, merge->nrecv, merge->id_r, merge->len_r, &abuf_r, &r_waits));
1698: PetscCall(PetscMalloc2(merge->nsend, &s_waits, size, &status));
1699: for (proc = 0, k = 0; proc < size; proc++) {
1700: if (!len_s[proc]) continue;
1701: i = merge->owners_co[proc];
1702: PetscCallMPI(MPIU_Isend(coa + coi[i], len_s[proc], MPIU_MATSCALAR, proc, taga, comm, s_waits + k));
1703: k++;
1704: }
1705: if (merge->nrecv) PetscCallMPI(MPI_Waitall(merge->nrecv, r_waits, status));
1706: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, s_waits, status));
1708: PetscCall(PetscFree2(s_waits, status));
1709: PetscCall(PetscFree(r_waits));
1710: PetscCall(PetscFree(coa));
1712: /* 4) insert local Cseq and received values into Cmpi */
1713: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextci));
1714: for (k = 0; k < merge->nrecv; k++) {
1715: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
1716: nrows = *buf_ri_k[k];
1717: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
1718: nextci[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th recved i-structure */
1719: }
1721: for (i = 0; i < cm; i++) {
1722: row = owners[rank] + i; /* global row index of C_seq */
1723: bj_i = bj + bi[i]; /* col indices of the i-th row of C */
1724: ba_i = ba + bi[i];
1725: bnz = bi[i + 1] - bi[i];
1726: /* add received vals into ba */
1727: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
1728: /* i-th row */
1729: if (i == *nextrow[k]) {
1730: cnz = *(nextci[k] + 1) - *nextci[k];
1731: cj = buf_rj[k] + *nextci[k];
1732: ca = abuf_r[k] + *nextci[k];
1733: nextcj = 0;
1734: for (j = 0; nextcj < cnz; j++) {
1735: if (bj_i[j] == cj[nextcj]) { /* bcol == ccol */
1736: ba_i[j] += ca[nextcj++];
1737: }
1738: }
1739: nextrow[k]++;
1740: nextci[k]++;
1741: PetscCall(PetscLogFlops(2.0 * cnz));
1742: }
1743: }
1744: PetscCall(MatSetValues(C, 1, &row, bnz, bj_i, ba_i, INSERT_VALUES));
1745: }
1746: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1747: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1749: PetscCall(PetscFree(ba));
1750: PetscCall(PetscFree(abuf_r[0]));
1751: PetscCall(PetscFree(abuf_r));
1752: PetscCall(PetscFree3(buf_ri_k, nextrow, nextci));
1753: PetscFunctionReturn(PETSC_SUCCESS);
1754: }
1756: PetscErrorCode MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ(Mat P, Mat A, PetscReal fill, Mat C)
1757: {
1758: Mat A_loc;
1759: MatProductCtx_APMPI *ap;
1760: PetscFreeSpaceList free_space = NULL, current_space = NULL;
1761: Mat_MPIAIJ *p = (Mat_MPIAIJ *)P->data, *a = (Mat_MPIAIJ *)A->data;
1762: PetscInt *pdti, *pdtj, *poti, *potj, *ptJ;
1763: PetscInt nnz;
1764: PetscInt *lnk, *owners_co, *coi, *coj, i, k, pnz, row;
1765: PetscInt am = A->rmap->n, pn = P->cmap->n;
1766: MPI_Comm comm;
1767: PetscMPIInt size, rank, tagi, tagj, *len_si, *len_s, *len_ri, proc;
1768: PetscInt **buf_rj, **buf_ri, **buf_ri_k;
1769: PetscInt len, *dnz, *onz, *owners;
1770: PetscInt nzi, *bi, *bj;
1771: PetscInt nrows, *buf_s, *buf_si, *buf_si_i, **nextrow, **nextci;
1772: MPI_Request *swaits, *rwaits;
1773: MPI_Status *sstatus, rstatus;
1774: MatMergeSeqsToMPI *merge;
1775: PetscInt *ai, *aj, *Jptr, anz, *prmap = p->garray, pon, nspacedouble = 0, j;
1776: PetscReal afill = 1.0, afill_tmp;
1777: PetscInt rstart = P->cmap->rstart, rmax, Armax;
1778: Mat_SeqAIJ *a_loc;
1779: PetscHMapI ta;
1780: MatType mtype;
1782: PetscFunctionBegin;
1783: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
1784: /* check if matrix local sizes are compatible */
1785: 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,
1786: A->rmap->rend, P->rmap->rstart, P->rmap->rend);
1788: PetscCallMPI(MPI_Comm_size(comm, &size));
1789: PetscCallMPI(MPI_Comm_rank(comm, &rank));
1791: /* create struct MatProductCtx_APMPI and attached it to C later */
1792: PetscCall(PetscNew(&ap));
1794: /* get A_loc by taking all local rows of A */
1795: PetscCall(MatMPIAIJGetLocalMat(A, MAT_INITIAL_MATRIX, &A_loc));
1797: ap->A_loc = A_loc;
1798: a_loc = (Mat_SeqAIJ *)A_loc->data;
1799: ai = a_loc->i;
1800: aj = a_loc->j;
1802: /* determine symbolic Co=(p->B)^T*A - send to others */
1803: PetscCall(MatGetSymbolicTranspose_SeqAIJ(p->A, &pdti, &pdtj));
1804: PetscCall(MatGetSymbolicTranspose_SeqAIJ(p->B, &poti, &potj));
1805: pon = (p->B)->cmap->n; /* total num of rows to be sent to other processors
1806: >= (num of nonzero rows of C_seq) - pn */
1807: PetscCall(PetscMalloc1(pon + 1, &coi));
1808: coi[0] = 0;
1810: /* set initial free space to be fill*(nnz(p->B) + nnz(A)) */
1811: nnz = PetscRealIntMultTruncate(fill, PetscIntSumTruncate(poti[pon], ai[am]));
1812: PetscCall(PetscFreeSpaceGet(nnz, &free_space));
1813: current_space = free_space;
1815: /* create and initialize a linked list */
1816: PetscCall(PetscHMapICreateWithSize(A->cmap->n + a->B->cmap->N, &ta));
1817: MatRowMergeMax_SeqAIJ(a_loc, am, ta);
1818: PetscCall(PetscHMapIGetSize(ta, &Armax));
1820: PetscCall(PetscLLCondensedCreate_Scalable(Armax, &lnk));
1822: for (i = 0; i < pon; i++) {
1823: pnz = poti[i + 1] - poti[i];
1824: ptJ = potj + poti[i];
1825: for (j = 0; j < pnz; j++) {
1826: row = ptJ[j]; /* row of A_loc == col of Pot */
1827: anz = ai[row + 1] - ai[row];
1828: Jptr = aj + ai[row];
1829: /* add non-zero cols of AP into the sorted linked list lnk */
1830: PetscCall(PetscLLCondensedAddSorted_Scalable(anz, Jptr, lnk));
1831: }
1832: nnz = lnk[0];
1834: /* If free space is not available, double the total space in the list */
1835: if (current_space->local_remaining < nnz) {
1836: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(nnz, current_space->total_array_size), ¤t_space));
1837: nspacedouble++;
1838: }
1840: /* Copy data into free space, and zero out denserows */
1841: PetscCall(PetscLLCondensedClean_Scalable(nnz, current_space->array, lnk));
1843: current_space->array += nnz;
1844: current_space->local_used += nnz;
1845: current_space->local_remaining -= nnz;
1847: coi[i + 1] = coi[i] + nnz;
1848: }
1850: PetscCall(PetscMalloc1(coi[pon], &coj));
1851: PetscCall(PetscFreeSpaceContiguous(&free_space, coj));
1852: PetscCall(PetscLLCondensedDestroy_Scalable(lnk)); /* must destroy to get a new one for C */
1854: afill_tmp = (PetscReal)coi[pon] / (poti[pon] + ai[am] + 1);
1855: if (afill_tmp > afill) afill = afill_tmp;
1857: /* send j-array (coj) of Co to other processors */
1858: /* determine row ownership */
1859: PetscCall(PetscNew(&merge));
1860: PetscCall(PetscLayoutCreate(comm, &merge->rowmap));
1862: merge->rowmap->n = pn;
1863: merge->rowmap->bs = 1;
1865: PetscCall(PetscLayoutSetUp(merge->rowmap));
1866: owners = merge->rowmap->range;
1868: /* determine the number of messages to send, their lengths */
1869: PetscCall(PetscCalloc1(size, &len_si));
1870: PetscCall(PetscCalloc1(size, &merge->len_s));
1872: len_s = merge->len_s;
1873: merge->nsend = 0;
1875: PetscCall(PetscMalloc1(size + 1, &owners_co));
1877: proc = 0;
1878: for (i = 0; i < pon; i++) {
1879: while (prmap[i] >= owners[proc + 1]) proc++;
1880: len_si[proc]++; /* num of rows in Co to be sent to [proc] */
1881: len_s[proc] += coi[i + 1] - coi[i];
1882: }
1884: len = 0; /* max length of buf_si[] */
1885: owners_co[0] = 0;
1886: for (proc = 0; proc < size; proc++) {
1887: owners_co[proc + 1] = owners_co[proc] + len_si[proc];
1888: if (len_s[proc]) {
1889: merge->nsend++;
1890: len_si[proc] = 2 * (len_si[proc] + 1);
1891: len += len_si[proc];
1892: }
1893: }
1895: /* determine the number and length of messages to receive for coi and coj */
1896: PetscCall(PetscGatherNumberOfMessages(comm, NULL, len_s, &merge->nrecv));
1897: PetscCall(PetscGatherMessageLengths2(comm, merge->nsend, merge->nrecv, len_s, len_si, &merge->id_r, &merge->len_r, &len_ri));
1899: /* post the Irecv and Isend of coj */
1900: PetscCall(PetscCommGetNewTag(comm, &tagj));
1901: PetscCall(PetscPostIrecvInt(comm, tagj, merge->nrecv, merge->id_r, merge->len_r, &buf_rj, &rwaits));
1902: PetscCall(PetscMalloc1(merge->nsend, &swaits));
1903: for (proc = 0, k = 0; proc < size; proc++) {
1904: if (!len_s[proc]) continue;
1905: i = owners_co[proc];
1906: PetscCallMPI(MPIU_Isend(coj + coi[i], len_s[proc], MPIU_INT, proc, tagj, comm, swaits + k));
1907: k++;
1908: }
1910: /* receives and sends of coj are complete */
1911: PetscCall(PetscMalloc1(size, &sstatus));
1912: for (i = 0; i < merge->nrecv; i++) {
1913: PETSC_UNUSED PetscMPIInt icompleted;
1914: PetscCallMPI(MPI_Waitany(merge->nrecv, rwaits, &icompleted, &rstatus));
1915: }
1916: PetscCall(PetscFree(rwaits));
1917: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, swaits, sstatus));
1919: /* add received column indices into table to update Armax */
1920: /* Armax can be as large as aN if a P[row,:] is dense, see src/ksp/ksp/tutorials/ex56.c! */
1921: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
1922: Jptr = buf_rj[k];
1923: for (j = 0; j < merge->len_r[k]; j++) PetscCall(PetscHMapISet(ta, *(Jptr + j) + 1, 1));
1924: }
1925: PetscCall(PetscHMapIGetSize(ta, &Armax));
1927: /* send and recv coi */
1928: PetscCall(PetscCommGetNewTag(comm, &tagi));
1929: PetscCall(PetscPostIrecvInt(comm, tagi, merge->nrecv, merge->id_r, len_ri, &buf_ri, &rwaits));
1930: PetscCall(PetscMalloc1(len, &buf_s));
1931: buf_si = buf_s; /* points to the beginning of k-th msg to be sent */
1932: for (proc = 0, k = 0; proc < size; proc++) {
1933: if (!len_s[proc]) continue;
1934: /* form outgoing message for i-structure:
1935: buf_si[0]: nrows to be sent
1936: [1:nrows]: row index (global)
1937: [nrows+1:2*nrows+1]: i-structure index
1938: */
1939: nrows = len_si[proc] / 2 - 1;
1940: buf_si_i = buf_si + nrows + 1;
1941: buf_si[0] = nrows;
1942: buf_si_i[0] = 0;
1943: nrows = 0;
1944: for (i = owners_co[proc]; i < owners_co[proc + 1]; i++) {
1945: nzi = coi[i + 1] - coi[i];
1946: buf_si_i[nrows + 1] = buf_si_i[nrows] + nzi; /* i-structure */
1947: buf_si[nrows + 1] = prmap[i] - owners[proc]; /* local row index */
1948: nrows++;
1949: }
1950: PetscCallMPI(MPIU_Isend(buf_si, len_si[proc], MPIU_INT, proc, tagi, comm, swaits + k));
1951: k++;
1952: buf_si += len_si[proc];
1953: }
1954: i = merge->nrecv;
1955: while (i--) {
1956: PETSC_UNUSED PetscMPIInt icompleted;
1957: PetscCallMPI(MPI_Waitany(merge->nrecv, rwaits, &icompleted, &rstatus));
1958: }
1959: PetscCall(PetscFree(rwaits));
1960: if (merge->nsend) PetscCallMPI(MPI_Waitall(merge->nsend, swaits, sstatus));
1961: PetscCall(PetscFree(len_si));
1962: PetscCall(PetscFree(len_ri));
1963: PetscCall(PetscFree(swaits));
1964: PetscCall(PetscFree(sstatus));
1965: PetscCall(PetscFree(buf_s));
1967: /* compute the local portion of C (mpi mat) */
1968: /* allocate bi array and free space for accumulating nonzero column info */
1969: PetscCall(PetscMalloc1(pn + 1, &bi));
1970: bi[0] = 0;
1972: /* set initial free space to be fill*(nnz(P) + nnz(AP)) */
1973: nnz = PetscRealIntMultTruncate(fill, PetscIntSumTruncate(pdti[pn], PetscIntSumTruncate(poti[pon], ai[am])));
1974: PetscCall(PetscFreeSpaceGet(nnz, &free_space));
1975: current_space = free_space;
1977: PetscCall(PetscMalloc3(merge->nrecv, &buf_ri_k, merge->nrecv, &nextrow, merge->nrecv, &nextci));
1978: for (k = 0; k < merge->nrecv; k++) {
1979: buf_ri_k[k] = buf_ri[k]; /* beginning of k-th recved i-structure */
1980: nrows = *buf_ri_k[k];
1981: nextrow[k] = buf_ri_k[k] + 1; /* next row number of k-th recved i-structure */
1982: nextci[k] = buf_ri_k[k] + (nrows + 1); /* points to the next i-structure of k-th received i-structure */
1983: }
1985: PetscCall(PetscLLCondensedCreate_Scalable(Armax, &lnk));
1986: MatPreallocateBegin(comm, pn, A->cmap->n, dnz, onz);
1987: rmax = 0;
1988: for (i = 0; i < pn; i++) {
1989: /* add pdt[i,:]*AP into lnk */
1990: pnz = pdti[i + 1] - pdti[i];
1991: ptJ = pdtj + pdti[i];
1992: for (j = 0; j < pnz; j++) {
1993: row = ptJ[j]; /* row of AP == col of Pt */
1994: anz = ai[row + 1] - ai[row];
1995: Jptr = aj + ai[row];
1996: /* add non-zero cols of AP into the sorted linked list lnk */
1997: PetscCall(PetscLLCondensedAddSorted_Scalable(anz, Jptr, lnk));
1998: }
2000: /* add received col data into lnk */
2001: for (k = 0; k < merge->nrecv; k++) { /* k-th received message */
2002: if (i == *nextrow[k]) { /* i-th row */
2003: nzi = *(nextci[k] + 1) - *nextci[k];
2004: Jptr = buf_rj[k] + *nextci[k];
2005: PetscCall(PetscLLCondensedAddSorted_Scalable(nzi, Jptr, lnk));
2006: nextrow[k]++;
2007: nextci[k]++;
2008: }
2009: }
2011: /* add missing diagonal entry */
2012: if (C->force_diagonals) {
2013: k = i + owners[rank]; /* column index */
2014: PetscCall(PetscLLCondensedAddSorted_Scalable(1, &k, lnk));
2015: }
2017: nnz = lnk[0];
2019: /* if free space is not available, make more free space */
2020: if (current_space->local_remaining < nnz) {
2021: PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(nnz, current_space->total_array_size), ¤t_space));
2022: nspacedouble++;
2023: }
2024: /* copy data into free space, then initialize lnk */
2025: PetscCall(PetscLLCondensedClean_Scalable(nnz, current_space->array, lnk));
2026: PetscCall(MatPreallocateSet(i + owners[rank], nnz, current_space->array, dnz, onz));
2028: current_space->array += nnz;
2029: current_space->local_used += nnz;
2030: current_space->local_remaining -= nnz;
2032: bi[i + 1] = bi[i] + nnz;
2033: if (nnz > rmax) rmax = nnz;
2034: }
2035: PetscCall(PetscFree3(buf_ri_k, nextrow, nextci));
2037: PetscCall(PetscMalloc1(bi[pn], &bj));
2038: PetscCall(PetscFreeSpaceContiguous(&free_space, bj));
2039: afill_tmp = (PetscReal)bi[pn] / (pdti[pn] + poti[pon] + ai[am] + 1);
2040: if (afill_tmp > afill) afill = afill_tmp;
2041: PetscCall(PetscLLCondensedDestroy_Scalable(lnk));
2042: PetscCall(PetscHMapIDestroy(&ta));
2043: PetscCall(MatRestoreSymbolicTranspose_SeqAIJ(p->A, &pdti, &pdtj));
2044: PetscCall(MatRestoreSymbolicTranspose_SeqAIJ(p->B, &poti, &potj));
2046: /* create symbolic parallel matrix C - why cannot be assembled in Numeric part */
2047: PetscCall(MatSetSizes(C, pn, A->cmap->n, PETSC_DETERMINE, PETSC_DETERMINE));
2048: PetscCall(MatSetBlockSizes(C, P->cmap->bs, A->cmap->bs));
2049: PetscCall(MatGetType(A, &mtype));
2050: PetscCall(MatSetType(C, mtype));
2051: PetscCall(MatMPIAIJSetPreallocation(C, 0, dnz, 0, onz));
2052: MatPreallocateEnd(dnz, onz);
2053: PetscCall(MatSetBlockSize(C, 1));
2054: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
2055: for (i = 0; i < pn; i++) {
2056: row = i + rstart;
2057: nnz = bi[i + 1] - bi[i];
2058: Jptr = bj + bi[i];
2059: PetscCall(MatSetValues(C, 1, &row, nnz, Jptr, NULL, INSERT_VALUES));
2060: }
2061: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2062: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2063: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
2064: merge->bi = bi;
2065: merge->bj = bj;
2066: merge->coi = coi;
2067: merge->coj = coj;
2068: merge->buf_ri = buf_ri;
2069: merge->buf_rj = buf_rj;
2070: merge->owners_co = owners_co;
2072: /* attach the supporting struct to C for reuse */
2073: C->product->data = ap;
2074: C->product->destroy = MatProductCtxDestroy_MPIAIJ_PtAP;
2075: ap->merge = merge;
2077: C->ops->mattransposemultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ;
2079: #if defined(PETSC_USE_INFO)
2080: if (bi[pn] != 0) {
2081: PetscCall(PetscInfo(C, "Reallocs %" PetscInt_FMT "; Fill ratio: given %g needed %g.\n", nspacedouble, (double)fill, (double)afill));
2082: PetscCall(PetscInfo(C, "Use MatTransposeMatMult(A,B,MatReuse,%g,&C) for best performance.\n", (double)afill));
2083: } else {
2084: PetscCall(PetscInfo(C, "Empty matrix product\n"));
2085: }
2086: #endif
2087: PetscFunctionReturn(PETSC_SUCCESS);
2088: }
2090: static PetscErrorCode MatProductSymbolic_AtB_MPIAIJ_MPIAIJ(Mat C)
2091: {
2092: Mat_Product *product = C->product;
2093: Mat A = product->A, B = product->B;
2094: PetscReal fill = product->fill;
2095: PetscBool flg;
2097: PetscFunctionBegin;
2098: /* scalable */
2099: PetscCall(PetscStrcmp(product->alg, "scalable", &flg));
2100: if (flg) {
2101: PetscCall(MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ(A, B, fill, C));
2102: goto next;
2103: }
2105: /* nonscalable */
2106: PetscCall(PetscStrcmp(product->alg, "nonscalable", &flg));
2107: if (flg) {
2108: PetscCall(MatTransposeMatMultSymbolic_MPIAIJ_MPIAIJ_nonscalable(A, B, fill, C));
2109: goto next;
2110: }
2112: /* matmatmult */
2113: PetscCall(PetscStrcmp(product->alg, "at*b", &flg));
2114: if (flg) {
2115: Mat At;
2116: MatProductCtx_APMPI *ptap;
2118: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &At));
2119: PetscCall(MatMatMultSymbolic_MPIAIJ_MPIAIJ(At, B, fill, C));
2120: ptap = (MatProductCtx_APMPI *)C->product->data;
2121: if (ptap) {
2122: ptap->Pt = At;
2123: C->product->destroy = MatProductCtxDestroy_MPIAIJ_PtAP;
2124: }
2125: C->ops->transposematmultnumeric = MatTransposeMatMultNumeric_MPIAIJ_MPIAIJ_matmatmult;
2126: goto next;
2127: }
2129: /* backend general code */
2130: PetscCall(PetscStrcmp(product->alg, "backend", &flg));
2131: if (flg) {
2132: PetscCall(MatProductSymbolic_MPIAIJBACKEND(C));
2133: PetscFunctionReturn(PETSC_SUCCESS);
2134: }
2136: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "MatProduct type is not supported");
2138: next:
2139: C->ops->productnumeric = MatProductNumeric_AtB;
2140: PetscFunctionReturn(PETSC_SUCCESS);
2141: }
2143: /* Set options for MatMatMultxxx_MPIAIJ_MPIAIJ */
2144: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_AB(Mat C)
2145: {
2146: Mat_Product *product = C->product;
2147: Mat A = product->A, B = product->B;
2148: #if defined(PETSC_HAVE_HYPRE)
2149: const char *algTypes[5] = {"scalable", "nonscalable", "seqmpi", "backend", "hypre"};
2150: PetscInt nalg = 5;
2151: #else
2152: const char *algTypes[4] = {
2153: "scalable",
2154: "nonscalable",
2155: "seqmpi",
2156: "backend",
2157: };
2158: PetscInt nalg = 4;
2159: #endif
2160: PetscInt alg = 1; /* set nonscalable algorithm as default */
2161: PetscBool flg;
2162: MPI_Comm comm;
2164: PetscFunctionBegin;
2165: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
2167: /* Set "nonscalable" as default algorithm */
2168: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
2169: if (flg) {
2170: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2172: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2173: if (B->cmap->N > 100000) { /* may switch to scalable algorithm as default */
2174: MatInfo Ainfo, Binfo;
2175: PetscInt nz_local;
2176: PetscBool alg_scalable_loc = PETSC_FALSE, alg_scalable;
2178: PetscCall(MatGetInfo(A, MAT_LOCAL, &Ainfo));
2179: PetscCall(MatGetInfo(B, MAT_LOCAL, &Binfo));
2180: nz_local = (PetscInt)(Ainfo.nz_allocated + Binfo.nz_allocated);
2182: if (B->cmap->N > product->fill * nz_local) alg_scalable_loc = PETSC_TRUE;
2183: PetscCallMPI(MPIU_Allreduce(&alg_scalable_loc, &alg_scalable, 1, MPI_C_BOOL, MPI_LOR, comm));
2185: if (alg_scalable) {
2186: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2187: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2188: PetscCall(PetscInfo(B, "Use scalable algorithm, BN %" PetscInt_FMT ", fill*nz_allocated %g\n", B->cmap->N, (double)(product->fill * nz_local)));
2189: }
2190: }
2191: }
2193: /* Get runtime option */
2194: if (product->api_user) {
2195: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMult", "Mat");
2196: PetscCall(PetscOptionsEList("-matmatmult_via", "Algorithmic approach", "MatMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2197: PetscOptionsEnd();
2198: } else {
2199: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AB", "Mat");
2200: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2201: PetscOptionsEnd();
2202: }
2203: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2205: C->ops->productsymbolic = MatProductSymbolic_AB_MPIAIJ_MPIAIJ;
2206: PetscFunctionReturn(PETSC_SUCCESS);
2207: }
2209: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_ABt(Mat C)
2210: {
2211: PetscFunctionBegin;
2212: PetscCall(MatProductSetFromOptions_MPIAIJ_AB(C));
2213: C->ops->productsymbolic = MatProductSymbolic_ABt_MPIAIJ_MPIAIJ;
2214: PetscFunctionReturn(PETSC_SUCCESS);
2215: }
2217: /* Set options for MatTransposeMatMultXXX_MPIAIJ_MPIAIJ */
2218: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_AtB(Mat C)
2219: {
2220: Mat_Product *product = C->product;
2221: Mat A = product->A, B = product->B;
2222: const char *algTypes[4] = {"scalable", "nonscalable", "at*b", "backend"};
2223: PetscInt nalg = 4;
2224: PetscInt alg = 1; /* set default algorithm */
2225: PetscBool flg;
2226: MPI_Comm comm;
2228: PetscFunctionBegin;
2229: /* Check matrix local sizes */
2230: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
2231: 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 ")",
2232: A->rmap->rstart, A->rmap->rend, B->rmap->rstart, B->rmap->rend);
2234: /* Set default algorithm */
2235: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
2236: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2238: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2239: if (alg && B->cmap->N > 100000) { /* may switch to scalable algorithm as default */
2240: MatInfo Ainfo, Binfo;
2241: PetscInt nz_local;
2242: PetscBool alg_scalable_loc = PETSC_FALSE, alg_scalable;
2244: PetscCall(MatGetInfo(A, MAT_LOCAL, &Ainfo));
2245: PetscCall(MatGetInfo(B, MAT_LOCAL, &Binfo));
2246: nz_local = (PetscInt)(Ainfo.nz_allocated + Binfo.nz_allocated);
2248: if (B->cmap->N > product->fill * nz_local) alg_scalable_loc = PETSC_TRUE;
2249: PetscCallMPI(MPIU_Allreduce(&alg_scalable_loc, &alg_scalable, 1, MPI_C_BOOL, MPI_LOR, comm));
2251: if (alg_scalable) {
2252: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2253: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2254: PetscCall(PetscInfo(B, "Use scalable algorithm, BN %" PetscInt_FMT ", fill*nz_allocated %g\n", B->cmap->N, (double)(product->fill * nz_local)));
2255: }
2256: }
2258: /* Get runtime option */
2259: if (product->api_user) {
2260: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatTransposeMatMult", "Mat");
2261: PetscCall(PetscOptionsEList("-mattransposematmult_via", "Algorithmic approach", "MatTransposeMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2262: PetscOptionsEnd();
2263: } else {
2264: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_AtB", "Mat");
2265: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatTransposeMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2266: PetscOptionsEnd();
2267: }
2268: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2270: C->ops->productsymbolic = MatProductSymbolic_AtB_MPIAIJ_MPIAIJ;
2271: PetscFunctionReturn(PETSC_SUCCESS);
2272: }
2274: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_PtAP(Mat C)
2275: {
2276: Mat_Product *product = C->product;
2277: Mat A = product->A, P = product->B;
2278: MPI_Comm comm;
2279: PetscBool flg;
2280: PetscInt alg = 1; /* set default algorithm */
2281: #if !defined(PETSC_HAVE_HYPRE)
2282: const char *algTypes[5] = {"scalable", "nonscalable", "allatonce", "allatonce_merged", "backend"};
2283: PetscInt nalg = 5;
2284: #else
2285: const char *algTypes[6] = {"scalable", "nonscalable", "allatonce", "allatonce_merged", "backend", "hypre"};
2286: PetscInt nalg = 6;
2287: #endif
2288: PetscInt pN = P->cmap->N;
2290: PetscFunctionBegin;
2291: /* Check matrix local sizes */
2292: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
2293: 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 ")",
2294: A->rmap->rstart, A->rmap->rend, P->rmap->rstart, P->rmap->rend);
2295: 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 ")",
2296: A->cmap->rstart, A->cmap->rend, P->rmap->rstart, P->rmap->rend);
2298: /* Set "nonscalable" as default algorithm */
2299: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
2300: if (flg) {
2301: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2303: /* Set "scalable" as default if BN and local nonzeros of A and B are large */
2304: if (pN > 100000) {
2305: MatInfo Ainfo, Pinfo;
2306: PetscInt nz_local;
2307: PetscBool alg_scalable_loc = PETSC_FALSE, alg_scalable;
2309: PetscCall(MatGetInfo(A, MAT_LOCAL, &Ainfo));
2310: PetscCall(MatGetInfo(P, MAT_LOCAL, &Pinfo));
2311: nz_local = (PetscInt)(Ainfo.nz_allocated + Pinfo.nz_allocated);
2313: if (pN > product->fill * nz_local) alg_scalable_loc = PETSC_TRUE;
2314: PetscCallMPI(MPIU_Allreduce(&alg_scalable_loc, &alg_scalable, 1, MPI_C_BOOL, MPI_LOR, comm));
2316: if (alg_scalable) {
2317: alg = 0; /* scalable algorithm would 50% slower than nonscalable algorithm */
2318: PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2319: }
2320: }
2321: }
2323: /* Get runtime option */
2324: if (product->api_user) {
2325: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatPtAP", "Mat");
2326: PetscCall(PetscOptionsEList("-matptap_via", "Algorithmic approach", "MatPtAP", algTypes, nalg, algTypes[alg], &alg, &flg));
2327: PetscOptionsEnd();
2328: } else {
2329: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
2330: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatPtAP", algTypes, nalg, algTypes[alg], &alg, &flg));
2331: PetscOptionsEnd();
2332: }
2333: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2335: C->ops->productsymbolic = MatProductSymbolic_PtAP_MPIAIJ_MPIAIJ;
2336: PetscFunctionReturn(PETSC_SUCCESS);
2337: }
2339: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_RARt(Mat C)
2340: {
2341: Mat_Product *product = C->product;
2342: Mat A = product->A, R = product->B;
2344: PetscFunctionBegin;
2345: /* Check matrix local sizes */
2346: 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,
2347: A->rmap->n, R->rmap->n, R->cmap->n);
2349: C->ops->productsymbolic = MatProductSymbolic_RARt_MPIAIJ_MPIAIJ;
2350: PetscFunctionReturn(PETSC_SUCCESS);
2351: }
2353: /*
2354: Set options for ABC = A*B*C = A*(B*C); ABC's algorithm must be chosen from AB's algorithm
2355: */
2356: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_ABC(Mat C)
2357: {
2358: Mat_Product *product = C->product;
2359: PetscBool flg = PETSC_FALSE;
2360: PetscInt alg = 1; /* default algorithm */
2361: const char *algTypes[3] = {"scalable", "nonscalable", "seqmpi"};
2362: PetscInt nalg = 3;
2364: PetscFunctionBegin;
2365: /* Set default algorithm */
2366: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
2367: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2369: /* Get runtime option */
2370: if (product->api_user) {
2371: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatMatMatMult", "Mat");
2372: PetscCall(PetscOptionsEList("-matmatmatmult_via", "Algorithmic approach", "MatMatMatMult", algTypes, nalg, algTypes[alg], &alg, &flg));
2373: PetscOptionsEnd();
2374: } else {
2375: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_ABC", "Mat");
2376: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatProduct_ABC", algTypes, nalg, algTypes[alg], &alg, &flg));
2377: PetscOptionsEnd();
2378: }
2379: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
2381: C->ops->matmatmultsymbolic = MatMatMatMultSymbolic_MPIAIJ_MPIAIJ_MPIAIJ;
2382: C->ops->productsymbolic = MatProductSymbolic_ABC;
2383: PetscFunctionReturn(PETSC_SUCCESS);
2384: }
2386: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIAIJ(Mat C)
2387: {
2388: Mat_Product *product = C->product;
2390: PetscFunctionBegin;
2391: switch (product->type) {
2392: case MATPRODUCT_AB:
2393: PetscCall(MatProductSetFromOptions_MPIAIJ_AB(C));
2394: break;
2395: case MATPRODUCT_ABt:
2396: PetscCall(MatProductSetFromOptions_MPIAIJ_ABt(C));
2397: break;
2398: case MATPRODUCT_AtB:
2399: PetscCall(MatProductSetFromOptions_MPIAIJ_AtB(C));
2400: break;
2401: case MATPRODUCT_PtAP:
2402: PetscCall(MatProductSetFromOptions_MPIAIJ_PtAP(C));
2403: break;
2404: case MATPRODUCT_RARt:
2405: PetscCall(MatProductSetFromOptions_MPIAIJ_RARt(C));
2406: break;
2407: case MATPRODUCT_ABC:
2408: PetscCall(MatProductSetFromOptions_MPIAIJ_ABC(C));
2409: break;
2410: default:
2411: break;
2412: }
2413: PetscFunctionReturn(PETSC_SUCCESS);
2414: }