1
2 /*
3 Defines matrix-matrix product routines for pairs of SeqAIJ matrices
4 C = A * B
5 */
6
7 #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/
8 #include <../src/mat/utils/freespace.h>
9 #include <petscbt.h>
10 #include <petsc/private/isimpl.h>
11 #include <../src/mat/impls/dense/seq/dense.h>
12
MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)13 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)
14 {
15 PetscErrorCode ierr;
16
17 PetscFunctionBegin;
18 if (C->ops->matmultnumeric) {
19 if (C->ops->matmultnumeric == MatMatMultNumeric_SeqAIJ_SeqAIJ) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Recursive call");
20 ierr = (*C->ops->matmultnumeric)(A,B,C);CHKERRQ(ierr);
21 } else {
22 ierr = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted(A,B,C);CHKERRQ(ierr);
23 }
24 PetscFunctionReturn(0);
25 }
26
27 /* Modified from MatCreateSeqAIJWithArrays() */
MatSetSeqAIJWithArrays_private(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],MatType mtype,Mat mat)28 PETSC_INTERN PetscErrorCode MatSetSeqAIJWithArrays_private(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],MatType mtype,Mat mat)
29 {
30 PetscErrorCode ierr;
31 PetscInt ii;
32 Mat_SeqAIJ *aij;
33 PetscBool isseqaij;
34
35 PetscFunctionBegin;
36 if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
37 ierr = MatSetSizes(mat,m,n,m,n);CHKERRQ(ierr);
38
39 if (!mtype) {
40 ierr = PetscObjectBaseTypeCompare((PetscObject)mat,MATSEQAIJ,&isseqaij);CHKERRQ(ierr);
41 if (!isseqaij) { ierr = MatSetType(mat,MATSEQAIJ);CHKERRQ(ierr); }
42 } else {
43 ierr = MatSetType(mat,mtype);CHKERRQ(ierr);
44 }
45 ierr = MatSeqAIJSetPreallocation_SeqAIJ(mat,MAT_SKIP_ALLOCATION,NULL);CHKERRQ(ierr);
46 aij = (Mat_SeqAIJ*)(mat)->data;
47 ierr = PetscMalloc1(m,&aij->imax);CHKERRQ(ierr);
48 ierr = PetscMalloc1(m,&aij->ilen);CHKERRQ(ierr);
49
50 aij->i = i;
51 aij->j = j;
52 aij->a = a;
53 aij->singlemalloc = PETSC_FALSE;
54 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
55 aij->free_a = PETSC_FALSE;
56 aij->free_ij = PETSC_FALSE;
57
58 for (ii=0; ii<m; ii++) {
59 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
60 }
61
62 PetscFunctionReturn(0);
63 }
64
MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)65 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
66 {
67 PetscErrorCode ierr;
68 Mat_Product *product = C->product;
69 MatProductAlgorithm alg;
70 PetscBool flg;
71
72 PetscFunctionBegin;
73 if (product) {
74 alg = product->alg;
75 } else {
76 alg = "sorted";
77 }
78 /* sorted */
79 ierr = PetscStrcmp(alg,"sorted",&flg);CHKERRQ(ierr);
80 if (flg) {
81 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Sorted(A,B,fill,C);CHKERRQ(ierr);
82 PetscFunctionReturn(0);
83 }
84
85 /* scalable */
86 ierr = PetscStrcmp(alg,"scalable",&flg);CHKERRQ(ierr);
87 if (flg) {
88 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(A,B,fill,C);CHKERRQ(ierr);
89 PetscFunctionReturn(0);
90 }
91
92 /* scalable_fast */
93 ierr = PetscStrcmp(alg,"scalable_fast",&flg);CHKERRQ(ierr);
94 if (flg) {
95 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(A,B,fill,C);CHKERRQ(ierr);
96 PetscFunctionReturn(0);
97 }
98
99 /* heap */
100 ierr = PetscStrcmp(alg,"heap",&flg);CHKERRQ(ierr);
101 if (flg) {
102 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(A,B,fill,C);CHKERRQ(ierr);
103 PetscFunctionReturn(0);
104 }
105
106 /* btheap */
107 ierr = PetscStrcmp(alg,"btheap",&flg);CHKERRQ(ierr);
108 if (flg) {
109 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(A,B,fill,C);CHKERRQ(ierr);
110 PetscFunctionReturn(0);
111 }
112
113 /* llcondensed */
114 ierr = PetscStrcmp(alg,"llcondensed",&flg);CHKERRQ(ierr);
115 if (flg) {
116 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(A,B,fill,C);CHKERRQ(ierr);
117 PetscFunctionReturn(0);
118 }
119
120 /* rowmerge */
121 ierr = PetscStrcmp(alg,"rowmerge",&flg);CHKERRQ(ierr);
122 if (flg) {
123 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_RowMerge(A,B,fill,C);CHKERRQ(ierr);
124 PetscFunctionReturn(0);
125 }
126
127 #if defined(PETSC_HAVE_HYPRE)
128 ierr = PetscStrcmp(alg,"hypre",&flg);CHKERRQ(ierr);
129 if (flg) {
130 ierr = MatMatMultSymbolic_AIJ_AIJ_wHYPRE(A,B,fill,C);CHKERRQ(ierr);
131 PetscFunctionReturn(0);
132 }
133 #endif
134
135 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat Product Algorithm is not supported");
136 PetscFunctionReturn(0);
137 }
138
MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat A,Mat B,PetscReal fill,Mat C)139 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat A,Mat B,PetscReal fill,Mat C)
140 {
141 PetscErrorCode ierr;
142 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
143 PetscInt *ai=a->i,*bi=b->i,*ci,*cj;
144 PetscInt am =A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
145 PetscReal afill;
146 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax;
147 PetscTable ta;
148 PetscBT lnkbt;
149 PetscFreeSpaceList free_space=NULL,current_space=NULL;
150
151 PetscFunctionBegin;
152 /* Get ci and cj */
153 /*---------------*/
154 /* Allocate ci array, arrays for fill computation and */
155 /* free space for accumulating nonzero column info */
156 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr);
157 ci[0] = 0;
158
159 /* create and initialize a linked list */
160 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr);
161 MatRowMergeMax_SeqAIJ(b,bm,ta);
162 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr);
163 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr);
164
165 ierr = PetscLLCondensedCreate(Crmax,bn,&lnk,&lnkbt);CHKERRQ(ierr);
166
167 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
168 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr);
169
170 current_space = free_space;
171
172 /* Determine ci and cj */
173 for (i=0; i<am; i++) {
174 anzi = ai[i+1] - ai[i];
175 aj = a->j + ai[i];
176 for (j=0; j<anzi; j++) {
177 brow = aj[j];
178 bnzj = bi[brow+1] - bi[brow];
179 bj = b->j + bi[brow];
180 /* add non-zero cols of B into the sorted linked list lnk */
181 ierr = PetscLLCondensedAddSorted(bnzj,bj,lnk,lnkbt);CHKERRQ(ierr);
182 }
183 cnzi = lnk[0];
184
185 /* If free space is not available, make more free space */
186 /* Double the amount of total space in the list */
187 if (current_space->local_remaining<cnzi) {
188 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr);
189 ndouble++;
190 }
191
192 /* Copy data into free space, then initialize lnk */
193 ierr = PetscLLCondensedClean(bn,cnzi,current_space->array,lnk,lnkbt);CHKERRQ(ierr);
194
195 current_space->array += cnzi;
196 current_space->local_used += cnzi;
197 current_space->local_remaining -= cnzi;
198
199 ci[i+1] = ci[i] + cnzi;
200 }
201
202 /* Column indices are in the list of free space */
203 /* Allocate space for cj, initialize cj, and */
204 /* destroy list of free space and other temporary array(s) */
205 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr);
206 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr);
207 ierr = PetscLLCondensedDestroy(lnk,lnkbt);CHKERRQ(ierr);
208
209 /* put together the new symbolic matrix */
210 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr);
211 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr);
212
213 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
214 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
215 c = (Mat_SeqAIJ*)(C->data);
216 c->free_a = PETSC_FALSE;
217 c->free_ij = PETSC_TRUE;
218 c->nonew = 0;
219
220 /* fast, needs non-scalable O(bn) array 'abdense' */
221 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted;
222
223 /* set MatInfo */
224 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
225 if (afill < 1.0) afill = 1.0;
226 c->maxnz = ci[am];
227 c->nz = ci[am];
228 C->info.mallocs = ndouble;
229 C->info.fill_ratio_given = fill;
230 C->info.fill_ratio_needed = afill;
231
232 #if defined(PETSC_USE_INFO)
233 if (ci[am]) {
234 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr);
235 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr);
236 } else {
237 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr);
238 }
239 #endif
240 PetscFunctionReturn(0);
241 }
242
MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted(Mat A,Mat B,Mat C)243 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted(Mat A,Mat B,Mat C)
244 {
245 PetscErrorCode ierr;
246 PetscLogDouble flops=0.0;
247 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
248 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
249 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
250 PetscInt *ai =a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j;
251 PetscInt am =A->rmap->n,cm=C->rmap->n;
252 PetscInt i,j,k,anzi,bnzi,cnzi,brow;
253 PetscScalar *aa=a->a,*ba=b->a,*baj,*ca,valtmp;
254 PetscScalar *ab_dense;
255 PetscContainer cab_dense;
256
257 PetscFunctionBegin;
258 if (!c->a) { /* first call of MatMatMultNumeric_SeqAIJ_SeqAIJ, allocate ca and matmult_abdense */
259 ierr = PetscMalloc1(ci[cm]+1,&ca);CHKERRQ(ierr);
260 c->a = ca;
261 c->free_a = PETSC_TRUE;
262 } else ca = c->a;
263
264 /* TODO this should be done in the symbolic phase */
265 /* However, this function is so heavily used (sometimes in an hidden way through multnumeric function pointers
266 that is hard to eradicate) */
267 ierr = PetscObjectQuery((PetscObject)C,"__PETSc__ab_dense",(PetscObject*)&cab_dense);CHKERRQ(ierr);
268 if (!cab_dense) {
269 ierr = PetscMalloc1(B->cmap->N,&ab_dense);CHKERRQ(ierr);
270 ierr = PetscContainerCreate(PETSC_COMM_SELF,&cab_dense);CHKERRQ(ierr);
271 ierr = PetscContainerSetPointer(cab_dense,ab_dense);CHKERRQ(ierr);
272 ierr = PetscContainerSetUserDestroy(cab_dense,PetscContainerUserDestroyDefault);CHKERRQ(ierr);
273 ierr = PetscObjectCompose((PetscObject)C,"__PETSc__ab_dense",(PetscObject)cab_dense);CHKERRQ(ierr);
274 ierr = PetscObjectDereference((PetscObject)cab_dense);CHKERRQ(ierr);
275 }
276 ierr = PetscContainerGetPointer(cab_dense,(void**)&ab_dense);CHKERRQ(ierr);
277 ierr = PetscArrayzero(ab_dense,B->cmap->N);CHKERRQ(ierr);
278
279 /* clean old values in C */
280 ierr = PetscArrayzero(ca,ci[cm]);CHKERRQ(ierr);
281 /* Traverse A row-wise. */
282 /* Build the ith row in C by summing over nonzero columns in A, */
283 /* the rows of B corresponding to nonzeros of A. */
284 for (i=0; i<am; i++) {
285 anzi = ai[i+1] - ai[i];
286 for (j=0; j<anzi; j++) {
287 brow = aj[j];
288 bnzi = bi[brow+1] - bi[brow];
289 bjj = bj + bi[brow];
290 baj = ba + bi[brow];
291 /* perform dense axpy */
292 valtmp = aa[j];
293 for (k=0; k<bnzi; k++) {
294 ab_dense[bjj[k]] += valtmp*baj[k];
295 }
296 flops += 2*bnzi;
297 }
298 aj += anzi; aa += anzi;
299
300 cnzi = ci[i+1] - ci[i];
301 for (k=0; k<cnzi; k++) {
302 ca[k] += ab_dense[cj[k]];
303 ab_dense[cj[k]] = 0.0; /* zero ab_dense */
304 }
305 flops += cnzi;
306 cj += cnzi; ca += cnzi;
307 }
308 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
309 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
310 ierr = PetscLogFlops(flops);CHKERRQ(ierr);
311 PetscFunctionReturn(0);
312 }
313
MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,Mat C)314 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,Mat C)
315 {
316 PetscErrorCode ierr;
317 PetscLogDouble flops=0.0;
318 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
319 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
320 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
321 PetscInt *ai = a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j;
322 PetscInt am = A->rmap->N,cm=C->rmap->N;
323 PetscInt i,j,k,anzi,bnzi,cnzi,brow;
324 PetscScalar *aa=a->a,*ba=b->a,*baj,*ca=c->a,valtmp;
325 PetscInt nextb;
326
327 PetscFunctionBegin;
328 if (!ca) { /* first call of MatMatMultNumeric_SeqAIJ_SeqAIJ, allocate ca and matmult_abdense */
329 ierr = PetscMalloc1(ci[cm]+1,&ca);CHKERRQ(ierr);
330 c->a = ca;
331 c->free_a = PETSC_TRUE;
332 }
333
334 /* clean old values in C */
335 ierr = PetscArrayzero(ca,ci[cm]);CHKERRQ(ierr);
336 /* Traverse A row-wise. */
337 /* Build the ith row in C by summing over nonzero columns in A, */
338 /* the rows of B corresponding to nonzeros of A. */
339 for (i=0; i<am; i++) {
340 anzi = ai[i+1] - ai[i];
341 cnzi = ci[i+1] - ci[i];
342 for (j=0; j<anzi; j++) {
343 brow = aj[j];
344 bnzi = bi[brow+1] - bi[brow];
345 bjj = bj + bi[brow];
346 baj = ba + bi[brow];
347 /* perform sparse axpy */
348 valtmp = aa[j];
349 nextb = 0;
350 for (k=0; nextb<bnzi; k++) {
351 if (cj[k] == bjj[nextb]) { /* ccol == bcol */
352 ca[k] += valtmp*baj[nextb++];
353 }
354 }
355 flops += 2*bnzi;
356 }
357 aj += anzi; aa += anzi;
358 cj += cnzi; ca += cnzi;
359 }
360 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
361 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
362 ierr = PetscLogFlops(flops);CHKERRQ(ierr);
363 PetscFunctionReturn(0);
364 }
365
MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat A,Mat B,PetscReal fill,Mat C)366 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat A,Mat B,PetscReal fill,Mat C)
367 {
368 PetscErrorCode ierr;
369 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
370 PetscInt *ai = a->i,*bi=b->i,*ci,*cj;
371 PetscInt am = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
372 MatScalar *ca;
373 PetscReal afill;
374 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax;
375 PetscTable ta;
376 PetscFreeSpaceList free_space=NULL,current_space=NULL;
377
378 PetscFunctionBegin;
379 /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_fast() */
380 /*-----------------------------------------------------------------------------------------*/
381 /* Allocate arrays for fill computation and free space for accumulating nonzero column */
382 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr);
383 ci[0] = 0;
384
385 /* create and initialize a linked list */
386 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr);
387 MatRowMergeMax_SeqAIJ(b,bm,ta);
388 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr);
389 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr);
390
391 ierr = PetscLLCondensedCreate_fast(Crmax,&lnk);CHKERRQ(ierr);
392
393 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
394 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr);
395 current_space = free_space;
396
397 /* Determine ci and cj */
398 for (i=0; i<am; i++) {
399 anzi = ai[i+1] - ai[i];
400 aj = a->j + ai[i];
401 for (j=0; j<anzi; j++) {
402 brow = aj[j];
403 bnzj = bi[brow+1] - bi[brow];
404 bj = b->j + bi[brow];
405 /* add non-zero cols of B into the sorted linked list lnk */
406 ierr = PetscLLCondensedAddSorted_fast(bnzj,bj,lnk);CHKERRQ(ierr);
407 }
408 cnzi = lnk[1];
409
410 /* If free space is not available, make more free space */
411 /* Double the amount of total space in the list */
412 if (current_space->local_remaining<cnzi) {
413 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr);
414 ndouble++;
415 }
416
417 /* Copy data into free space, then initialize lnk */
418 ierr = PetscLLCondensedClean_fast(cnzi,current_space->array,lnk);CHKERRQ(ierr);
419
420 current_space->array += cnzi;
421 current_space->local_used += cnzi;
422 current_space->local_remaining -= cnzi;
423
424 ci[i+1] = ci[i] + cnzi;
425 }
426
427 /* Column indices are in the list of free space */
428 /* Allocate space for cj, initialize cj, and */
429 /* destroy list of free space and other temporary array(s) */
430 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr);
431 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr);
432 ierr = PetscLLCondensedDestroy_fast(lnk);CHKERRQ(ierr);
433
434 /* Allocate space for ca */
435 ierr = PetscCalloc1(ci[am]+1,&ca);CHKERRQ(ierr);
436
437 /* put together the new symbolic matrix */
438 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,((PetscObject)A)->type_name,C);CHKERRQ(ierr);
439 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr);
440
441 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
442 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
443 c = (Mat_SeqAIJ*)(C->data);
444 c->free_a = PETSC_TRUE;
445 c->free_ij = PETSC_TRUE;
446 c->nonew = 0;
447
448 /* slower, less memory */
449 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable;
450
451 /* set MatInfo */
452 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
453 if (afill < 1.0) afill = 1.0;
454 c->maxnz = ci[am];
455 c->nz = ci[am];
456 C->info.mallocs = ndouble;
457 C->info.fill_ratio_given = fill;
458 C->info.fill_ratio_needed = afill;
459
460 #if defined(PETSC_USE_INFO)
461 if (ci[am]) {
462 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr);
463 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr);
464 } else {
465 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr);
466 }
467 #endif
468 PetscFunctionReturn(0);
469 }
470
MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,PetscReal fill,Mat C)471 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,PetscReal fill,Mat C)
472 {
473 PetscErrorCode ierr;
474 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
475 PetscInt *ai = a->i,*bi=b->i,*ci,*cj;
476 PetscInt am = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
477 MatScalar *ca;
478 PetscReal afill;
479 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax;
480 PetscTable ta;
481 PetscFreeSpaceList free_space=NULL,current_space=NULL;
482
483 PetscFunctionBegin;
484 /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_Scalalbe() */
485 /*---------------------------------------------------------------------------------------------*/
486 /* Allocate arrays for fill computation and free space for accumulating nonzero column */
487 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr);
488 ci[0] = 0;
489
490 /* create and initialize a linked list */
491 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr);
492 MatRowMergeMax_SeqAIJ(b,bm,ta);
493 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr);
494 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr);
495 ierr = PetscLLCondensedCreate_Scalable(Crmax,&lnk);CHKERRQ(ierr);
496
497 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
498 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr);
499 current_space = free_space;
500
501 /* Determine ci and cj */
502 for (i=0; i<am; i++) {
503 anzi = ai[i+1] - ai[i];
504 aj = a->j + ai[i];
505 for (j=0; j<anzi; j++) {
506 brow = aj[j];
507 bnzj = bi[brow+1] - bi[brow];
508 bj = b->j + bi[brow];
509 /* add non-zero cols of B into the sorted linked list lnk */
510 ierr = PetscLLCondensedAddSorted_Scalable(bnzj,bj,lnk);CHKERRQ(ierr);
511 }
512 cnzi = lnk[0];
513
514 /* If free space is not available, make more free space */
515 /* Double the amount of total space in the list */
516 if (current_space->local_remaining<cnzi) {
517 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr);
518 ndouble++;
519 }
520
521 /* Copy data into free space, then initialize lnk */
522 ierr = PetscLLCondensedClean_Scalable(cnzi,current_space->array,lnk);CHKERRQ(ierr);
523
524 current_space->array += cnzi;
525 current_space->local_used += cnzi;
526 current_space->local_remaining -= cnzi;
527
528 ci[i+1] = ci[i] + cnzi;
529 }
530
531 /* Column indices are in the list of free space */
532 /* Allocate space for cj, initialize cj, and */
533 /* destroy list of free space and other temporary array(s) */
534 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr);
535 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr);
536 ierr = PetscLLCondensedDestroy_Scalable(lnk);CHKERRQ(ierr);
537
538 /* Allocate space for ca */
539 /*-----------------------*/
540 ierr = PetscCalloc1(ci[am]+1,&ca);CHKERRQ(ierr);
541
542 /* put together the new symbolic matrix */
543 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,((PetscObject)A)->type_name,C);CHKERRQ(ierr);
544 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr);
545
546 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
547 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
548 c = (Mat_SeqAIJ*)(C->data);
549 c->free_a = PETSC_TRUE;
550 c->free_ij = PETSC_TRUE;
551 c->nonew = 0;
552
553 /* slower, less memory */
554 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable;
555
556 /* set MatInfo */
557 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
558 if (afill < 1.0) afill = 1.0;
559 c->maxnz = ci[am];
560 c->nz = ci[am];
561 C->info.mallocs = ndouble;
562 C->info.fill_ratio_given = fill;
563 C->info.fill_ratio_needed = afill;
564
565 #if defined(PETSC_USE_INFO)
566 if (ci[am]) {
567 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr);
568 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr);
569 } else {
570 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr);
571 }
572 #endif
573 PetscFunctionReturn(0);
574 }
575
MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat A,Mat B,PetscReal fill,Mat C)576 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat A,Mat B,PetscReal fill,Mat C)
577 {
578 PetscErrorCode ierr;
579 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
580 const PetscInt *ai=a->i,*bi=b->i,*aj=a->j,*bj=b->j;
581 PetscInt *ci,*cj,*bb;
582 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
583 PetscReal afill;
584 PetscInt i,j,col,ndouble = 0;
585 PetscFreeSpaceList free_space=NULL,current_space=NULL;
586 PetscHeap h;
587
588 PetscFunctionBegin;
589 /* Get ci and cj - by merging sorted rows using a heap */
590 /*---------------------------------------------------------------------------------------------*/
591 /* Allocate arrays for fill computation and free space for accumulating nonzero column */
592 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr);
593 ci[0] = 0;
594
595 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
596 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr);
597 current_space = free_space;
598
599 ierr = PetscHeapCreate(a->rmax,&h);CHKERRQ(ierr);
600 ierr = PetscMalloc1(a->rmax,&bb);CHKERRQ(ierr);
601
602 /* Determine ci and cj */
603 for (i=0; i<am; i++) {
604 const PetscInt anzi = ai[i+1] - ai[i]; /* number of nonzeros in this row of A, this is the number of rows of B that we merge */
605 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */
606 ci[i+1] = ci[i];
607 /* Populate the min heap */
608 for (j=0; j<anzi; j++) {
609 bb[j] = bi[acol[j]]; /* bb points at the start of the row */
610 if (bb[j] < bi[acol[j]+1]) { /* Add if row is nonempty */
611 ierr = PetscHeapAdd(h,j,bj[bb[j]++]);CHKERRQ(ierr);
612 }
613 }
614 /* Pick off the min element, adding it to free space */
615 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr);
616 while (j >= 0) {
617 if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */
618 ierr = PetscFreeSpaceGet(PetscMin(PetscIntMultTruncate(2,current_space->total_array_size),16 << 20),¤t_space);CHKERRQ(ierr);
619 ndouble++;
620 }
621 *(current_space->array++) = col;
622 current_space->local_used++;
623 current_space->local_remaining--;
624 ci[i+1]++;
625
626 /* stash if anything else remains in this row of B */
627 if (bb[j] < bi[acol[j]+1]) {ierr = PetscHeapStash(h,j,bj[bb[j]++]);CHKERRQ(ierr);}
628 while (1) { /* pop and stash any other rows of B that also had an entry in this column */
629 PetscInt j2,col2;
630 ierr = PetscHeapPeek(h,&j2,&col2);CHKERRQ(ierr);
631 if (col2 != col) break;
632 ierr = PetscHeapPop(h,&j2,&col2);CHKERRQ(ierr);
633 if (bb[j2] < bi[acol[j2]+1]) {ierr = PetscHeapStash(h,j2,bj[bb[j2]++]);CHKERRQ(ierr);}
634 }
635 /* Put any stashed elements back into the min heap */
636 ierr = PetscHeapUnstash(h);CHKERRQ(ierr);
637 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr);
638 }
639 }
640 ierr = PetscFree(bb);CHKERRQ(ierr);
641 ierr = PetscHeapDestroy(&h);CHKERRQ(ierr);
642
643 /* Column indices are in the list of free space */
644 /* Allocate space for cj, initialize cj, and */
645 /* destroy list of free space and other temporary array(s) */
646 ierr = PetscMalloc1(ci[am],&cj);CHKERRQ(ierr);
647 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr);
648
649 /* put together the new symbolic matrix */
650 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr);
651 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr);
652
653 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
654 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
655 c = (Mat_SeqAIJ*)(C->data);
656 c->free_a = PETSC_TRUE;
657 c->free_ij = PETSC_TRUE;
658 c->nonew = 0;
659
660 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted;
661
662 /* set MatInfo */
663 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
664 if (afill < 1.0) afill = 1.0;
665 c->maxnz = ci[am];
666 c->nz = ci[am];
667 C->info.mallocs = ndouble;
668 C->info.fill_ratio_given = fill;
669 C->info.fill_ratio_needed = afill;
670
671 #if defined(PETSC_USE_INFO)
672 if (ci[am]) {
673 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr);
674 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr);
675 } else {
676 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr);
677 }
678 #endif
679 PetscFunctionReturn(0);
680 }
681
MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat A,Mat B,PetscReal fill,Mat C)682 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat A,Mat B,PetscReal fill,Mat C)
683 {
684 PetscErrorCode ierr;
685 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
686 const PetscInt *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j;
687 PetscInt *ci,*cj,*bb;
688 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
689 PetscReal afill;
690 PetscInt i,j,col,ndouble = 0;
691 PetscFreeSpaceList free_space=NULL,current_space=NULL;
692 PetscHeap h;
693 PetscBT bt;
694
695 PetscFunctionBegin;
696 /* Get ci and cj - using a heap for the sorted rows, but use BT so that each index is only added once */
697 /*---------------------------------------------------------------------------------------------*/
698 /* Allocate arrays for fill computation and free space for accumulating nonzero column */
699 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr);
700 ci[0] = 0;
701
702 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
703 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr);
704
705 current_space = free_space;
706
707 ierr = PetscHeapCreate(a->rmax,&h);CHKERRQ(ierr);
708 ierr = PetscMalloc1(a->rmax,&bb);CHKERRQ(ierr);
709 ierr = PetscBTCreate(bn,&bt);CHKERRQ(ierr);
710
711 /* Determine ci and cj */
712 for (i=0; i<am; i++) {
713 const PetscInt anzi = ai[i+1] - ai[i]; /* number of nonzeros in this row of A, this is the number of rows of B that we merge */
714 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */
715 const PetscInt *fptr = current_space->array; /* Save beginning of the row so we can clear the BT later */
716 ci[i+1] = ci[i];
717 /* Populate the min heap */
718 for (j=0; j<anzi; j++) {
719 PetscInt brow = acol[j];
720 for (bb[j] = bi[brow]; bb[j] < bi[brow+1]; bb[j]++) {
721 PetscInt bcol = bj[bb[j]];
722 if (!PetscBTLookupSet(bt,bcol)) { /* new entry */
723 ierr = PetscHeapAdd(h,j,bcol);CHKERRQ(ierr);
724 bb[j]++;
725 break;
726 }
727 }
728 }
729 /* Pick off the min element, adding it to free space */
730 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr);
731 while (j >= 0) {
732 if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */
733 fptr = NULL; /* need PetscBTMemzero */
734 ierr = PetscFreeSpaceGet(PetscMin(PetscIntMultTruncate(2,current_space->total_array_size),16 << 20),¤t_space);CHKERRQ(ierr);
735 ndouble++;
736 }
737 *(current_space->array++) = col;
738 current_space->local_used++;
739 current_space->local_remaining--;
740 ci[i+1]++;
741
742 /* stash if anything else remains in this row of B */
743 for (; bb[j] < bi[acol[j]+1]; bb[j]++) {
744 PetscInt bcol = bj[bb[j]];
745 if (!PetscBTLookupSet(bt,bcol)) { /* new entry */
746 ierr = PetscHeapAdd(h,j,bcol);CHKERRQ(ierr);
747 bb[j]++;
748 break;
749 }
750 }
751 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr);
752 }
753 if (fptr) { /* Clear the bits for this row */
754 for (; fptr<current_space->array; fptr++) {ierr = PetscBTClear(bt,*fptr);CHKERRQ(ierr);}
755 } else { /* We reallocated so we don't remember (easily) how to clear only the bits we changed */
756 ierr = PetscBTMemzero(bn,bt);CHKERRQ(ierr);
757 }
758 }
759 ierr = PetscFree(bb);CHKERRQ(ierr);
760 ierr = PetscHeapDestroy(&h);CHKERRQ(ierr);
761 ierr = PetscBTDestroy(&bt);CHKERRQ(ierr);
762
763 /* Column indices are in the list of free space */
764 /* Allocate space for cj, initialize cj, and */
765 /* destroy list of free space and other temporary array(s) */
766 ierr = PetscMalloc1(ci[am],&cj);CHKERRQ(ierr);
767 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr);
768
769 /* put together the new symbolic matrix */
770 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr);
771 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr);
772
773 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
774 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
775 c = (Mat_SeqAIJ*)(C->data);
776 c->free_a = PETSC_TRUE;
777 c->free_ij = PETSC_TRUE;
778 c->nonew = 0;
779
780 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted;
781
782 /* set MatInfo */
783 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
784 if (afill < 1.0) afill = 1.0;
785 c->maxnz = ci[am];
786 c->nz = ci[am];
787 C->info.mallocs = ndouble;
788 C->info.fill_ratio_given = fill;
789 C->info.fill_ratio_needed = afill;
790
791 #if defined(PETSC_USE_INFO)
792 if (ci[am]) {
793 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr);
794 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr);
795 } else {
796 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr);
797 }
798 #endif
799 PetscFunctionReturn(0);
800 }
801
802
MatMatMultSymbolic_SeqAIJ_SeqAIJ_RowMerge(Mat A,Mat B,PetscReal fill,Mat C)803 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_RowMerge(Mat A,Mat B,PetscReal fill,Mat C)
804 {
805 PetscErrorCode ierr;
806 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
807 const PetscInt *ai=a->i,*bi=b->i,*aj=a->j,*bj=b->j,*inputi,*inputj,*inputcol,*inputcol_L1;
808 PetscInt *ci,*cj,*outputj,worki_L1[9],worki_L2[9];
809 PetscInt c_maxmem,a_maxrownnz=0,a_rownnz;
810 const PetscInt workcol[8]={0,1,2,3,4,5,6,7};
811 const PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
812 const PetscInt *brow_ptr[8],*brow_end[8];
813 PetscInt window[8];
814 PetscInt window_min,old_window_min,ci_nnz,outputi_nnz=0,L1_nrows,L2_nrows;
815 PetscInt i,k,ndouble=0,L1_rowsleft,rowsleft;
816 PetscReal afill;
817 PetscInt *workj_L1,*workj_L2,*workj_L3;
818 PetscInt L1_nnz,L2_nnz;
819
820 /* Step 1: Get upper bound on memory required for allocation.
821 Because of the way virtual memory works,
822 only the memory pages that are actually needed will be physically allocated. */
823 PetscFunctionBegin;
824 ierr = PetscMalloc1(am+1,&ci);CHKERRQ(ierr);
825 for (i=0; i<am; i++) {
826 const PetscInt anzi = ai[i+1] - ai[i]; /* number of nonzeros in this row of A, this is the number of rows of B that we merge */
827 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */
828 a_rownnz = 0;
829 for (k=0; k<anzi; ++k) {
830 a_rownnz += bi[acol[k]+1] - bi[acol[k]];
831 if (a_rownnz > bn) {
832 a_rownnz = bn;
833 break;
834 }
835 }
836 a_maxrownnz = PetscMax(a_maxrownnz, a_rownnz);
837 }
838 /* temporary work areas for merging rows */
839 ierr = PetscMalloc1(a_maxrownnz*8,&workj_L1);CHKERRQ(ierr);
840 ierr = PetscMalloc1(a_maxrownnz*8,&workj_L2);CHKERRQ(ierr);
841 ierr = PetscMalloc1(a_maxrownnz,&workj_L3);CHKERRQ(ierr);
842
843 /* This should be enough for almost all matrices. If not, memory is reallocated later. */
844 c_maxmem = 8*(ai[am]+bi[bm]);
845 /* Step 2: Populate pattern for C */
846 ierr = PetscMalloc1(c_maxmem,&cj);CHKERRQ(ierr);
847
848 ci_nnz = 0;
849 ci[0] = 0;
850 worki_L1[0] = 0;
851 worki_L2[0] = 0;
852 for (i=0; i<am; i++) {
853 const PetscInt anzi = ai[i+1] - ai[i]; /* number of nonzeros in this row of A, this is the number of rows of B that we merge */
854 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */
855 rowsleft = anzi;
856 inputcol_L1 = acol;
857 L2_nnz = 0;
858 L2_nrows = 1; /* Number of rows to be merged on Level 3. output of L3 already exists -> initial value 1 */
859 worki_L2[1] = 0;
860 outputi_nnz = 0;
861
862 /* If the number of indices in C so far + the max number of columns in the next row > c_maxmem -> allocate more memory */
863 while (ci_nnz+a_maxrownnz > c_maxmem) {
864 c_maxmem *= 2;
865 ndouble++;
866 ierr = PetscRealloc(sizeof(PetscInt)*c_maxmem,&cj);CHKERRQ(ierr);
867 }
868
869 while (rowsleft) {
870 L1_rowsleft = PetscMin(64, rowsleft); /* In the inner loop max 64 rows of B can be merged */
871 L1_nrows = 0;
872 L1_nnz = 0;
873 inputcol = inputcol_L1;
874 inputi = bi;
875 inputj = bj;
876
877 /* The following macro is used to specialize for small rows in A.
878 This helps with compiler unrolling, improving performance substantially.
879 Input: inputj inputi inputcol bn
880 Output: outputj outputi_nnz */
881 #define MatMatMultSymbolic_RowMergeMacro(ANNZ) \
882 window_min = bn; \
883 outputi_nnz = 0; \
884 for (k=0; k<ANNZ; ++k) { \
885 brow_ptr[k] = inputj + inputi[inputcol[k]]; \
886 brow_end[k] = inputj + inputi[inputcol[k]+1]; \
887 window[k] = (brow_ptr[k] != brow_end[k]) ? *brow_ptr[k] : bn; \
888 window_min = PetscMin(window[k], window_min); \
889 } \
890 while (window_min < bn) { \
891 outputj[outputi_nnz++] = window_min; \
892 /* advance front and compute new minimum */ \
893 old_window_min = window_min; \
894 window_min = bn; \
895 for (k=0; k<ANNZ; ++k) { \
896 if (window[k] == old_window_min) { \
897 brow_ptr[k]++; \
898 window[k] = (brow_ptr[k] != brow_end[k]) ? *brow_ptr[k] : bn; \
899 } \
900 window_min = PetscMin(window[k], window_min); \
901 } \
902 }
903
904 /************** L E V E L 1 ***************/
905 /* Merge up to 8 rows of B to L1 work array*/
906 while (L1_rowsleft) {
907 outputi_nnz = 0;
908 if (anzi > 8) outputj = workj_L1 + L1_nnz; /* Level 1 rowmerge*/
909 else outputj = cj + ci_nnz; /* Merge directly to C */
910
911 switch (L1_rowsleft) {
912 case 1: brow_ptr[0] = inputj + inputi[inputcol[0]];
913 brow_end[0] = inputj + inputi[inputcol[0]+1];
914 for (; brow_ptr[0] != brow_end[0]; ++brow_ptr[0]) outputj[outputi_nnz++] = *brow_ptr[0]; /* copy row in b over */
915 inputcol += L1_rowsleft;
916 rowsleft -= L1_rowsleft;
917 L1_rowsleft = 0;
918 break;
919 case 2: MatMatMultSymbolic_RowMergeMacro(2);
920 inputcol += L1_rowsleft;
921 rowsleft -= L1_rowsleft;
922 L1_rowsleft = 0;
923 break;
924 case 3: MatMatMultSymbolic_RowMergeMacro(3);
925 inputcol += L1_rowsleft;
926 rowsleft -= L1_rowsleft;
927 L1_rowsleft = 0;
928 break;
929 case 4: MatMatMultSymbolic_RowMergeMacro(4);
930 inputcol += L1_rowsleft;
931 rowsleft -= L1_rowsleft;
932 L1_rowsleft = 0;
933 break;
934 case 5: MatMatMultSymbolic_RowMergeMacro(5);
935 inputcol += L1_rowsleft;
936 rowsleft -= L1_rowsleft;
937 L1_rowsleft = 0;
938 break;
939 case 6: MatMatMultSymbolic_RowMergeMacro(6);
940 inputcol += L1_rowsleft;
941 rowsleft -= L1_rowsleft;
942 L1_rowsleft = 0;
943 break;
944 case 7: MatMatMultSymbolic_RowMergeMacro(7);
945 inputcol += L1_rowsleft;
946 rowsleft -= L1_rowsleft;
947 L1_rowsleft = 0;
948 break;
949 default: MatMatMultSymbolic_RowMergeMacro(8);
950 inputcol += 8;
951 rowsleft -= 8;
952 L1_rowsleft -= 8;
953 break;
954 }
955 inputcol_L1 = inputcol;
956 L1_nnz += outputi_nnz;
957 worki_L1[++L1_nrows] = L1_nnz;
958 }
959
960 /********************** L E V E L 2 ************************/
961 /* Merge from L1 work array to either C or to L2 work array */
962 if (anzi > 8) {
963 inputi = worki_L1;
964 inputj = workj_L1;
965 inputcol = workcol;
966 outputi_nnz = 0;
967
968 if (anzi <= 64) outputj = cj + ci_nnz; /* Merge from L1 work array to C */
969 else outputj = workj_L2 + L2_nnz; /* Merge from L1 work array to L2 work array */
970
971 switch (L1_nrows) {
972 case 1: brow_ptr[0] = inputj + inputi[inputcol[0]];
973 brow_end[0] = inputj + inputi[inputcol[0]+1];
974 for (; brow_ptr[0] != brow_end[0]; ++brow_ptr[0]) outputj[outputi_nnz++] = *brow_ptr[0]; /* copy row in b over */
975 break;
976 case 2: MatMatMultSymbolic_RowMergeMacro(2); break;
977 case 3: MatMatMultSymbolic_RowMergeMacro(3); break;
978 case 4: MatMatMultSymbolic_RowMergeMacro(4); break;
979 case 5: MatMatMultSymbolic_RowMergeMacro(5); break;
980 case 6: MatMatMultSymbolic_RowMergeMacro(6); break;
981 case 7: MatMatMultSymbolic_RowMergeMacro(7); break;
982 case 8: MatMatMultSymbolic_RowMergeMacro(8); break;
983 default: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatMatMult logic error: Not merging 1-8 rows from L1 work array!");
984 }
985 L2_nnz += outputi_nnz;
986 worki_L2[++L2_nrows] = L2_nnz;
987
988 /************************ L E V E L 3 **********************/
989 /* Merge from L2 work array to either C or to L2 work array */
990 if (anzi > 64 && (L2_nrows == 8 || rowsleft == 0)) {
991 inputi = worki_L2;
992 inputj = workj_L2;
993 inputcol = workcol;
994 outputi_nnz = 0;
995 if (rowsleft) outputj = workj_L3;
996 else outputj = cj + ci_nnz;
997 switch (L2_nrows) {
998 case 1: brow_ptr[0] = inputj + inputi[inputcol[0]];
999 brow_end[0] = inputj + inputi[inputcol[0]+1];
1000 for (; brow_ptr[0] != brow_end[0]; ++brow_ptr[0]) outputj[outputi_nnz++] = *brow_ptr[0]; /* copy row in b over */
1001 break;
1002 case 2: MatMatMultSymbolic_RowMergeMacro(2); break;
1003 case 3: MatMatMultSymbolic_RowMergeMacro(3); break;
1004 case 4: MatMatMultSymbolic_RowMergeMacro(4); break;
1005 case 5: MatMatMultSymbolic_RowMergeMacro(5); break;
1006 case 6: MatMatMultSymbolic_RowMergeMacro(6); break;
1007 case 7: MatMatMultSymbolic_RowMergeMacro(7); break;
1008 case 8: MatMatMultSymbolic_RowMergeMacro(8); break;
1009 default: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatMatMult logic error: Not merging 1-8 rows from L2 work array!");
1010 }
1011 L2_nrows = 1;
1012 L2_nnz = outputi_nnz;
1013 worki_L2[1] = outputi_nnz;
1014 /* Copy to workj_L2 */
1015 if (rowsleft) {
1016 for (k=0; k<outputi_nnz; ++k) workj_L2[k] = outputj[k];
1017 }
1018 }
1019 }
1020 } /* while (rowsleft) */
1021 #undef MatMatMultSymbolic_RowMergeMacro
1022
1023 /* terminate current row */
1024 ci_nnz += outputi_nnz;
1025 ci[i+1] = ci_nnz;
1026 }
1027
1028 /* Step 3: Create the new symbolic matrix */
1029 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr);
1030 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr);
1031
1032 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
1033 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
1034 c = (Mat_SeqAIJ*)(C->data);
1035 c->free_a = PETSC_TRUE;
1036 c->free_ij = PETSC_TRUE;
1037 c->nonew = 0;
1038
1039 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted;
1040
1041 /* set MatInfo */
1042 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5;
1043 if (afill < 1.0) afill = 1.0;
1044 c->maxnz = ci[am];
1045 c->nz = ci[am];
1046 C->info.mallocs = ndouble;
1047 C->info.fill_ratio_given = fill;
1048 C->info.fill_ratio_needed = afill;
1049
1050 #if defined(PETSC_USE_INFO)
1051 if (ci[am]) {
1052 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr);
1053 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr);
1054 } else {
1055 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr);
1056 }
1057 #endif
1058
1059 /* Step 4: Free temporary work areas */
1060 ierr = PetscFree(workj_L1);CHKERRQ(ierr);
1061 ierr = PetscFree(workj_L2);CHKERRQ(ierr);
1062 ierr = PetscFree(workj_L3);CHKERRQ(ierr);
1063 PetscFunctionReturn(0);
1064 }
1065
1066 /* concatenate unique entries and then sort */
MatMatMultSymbolic_SeqAIJ_SeqAIJ_Sorted(Mat A,Mat B,PetscReal fill,Mat C)1067 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Sorted(Mat A,Mat B,PetscReal fill,Mat C)
1068 {
1069 PetscErrorCode ierr;
1070 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c;
1071 const PetscInt *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j;
1072 PetscInt *ci,*cj;
1073 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N;
1074 PetscReal afill;
1075 PetscInt i,j,ndouble = 0;
1076 PetscSegBuffer seg,segrow;
1077 char *seen;
1078
1079 PetscFunctionBegin;
1080 ierr = PetscMalloc1(am+1,&ci);CHKERRQ(ierr);
1081 ci[0] = 0;
1082
1083 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */
1084 ierr = PetscSegBufferCreate(sizeof(PetscInt),(PetscInt)(fill*(ai[am]+bi[bm])),&seg);CHKERRQ(ierr);
1085 ierr = PetscSegBufferCreate(sizeof(PetscInt),100,&segrow);CHKERRQ(ierr);
1086 ierr = PetscCalloc1(bn,&seen);CHKERRQ(ierr);
1087
1088 /* Determine ci and cj */
1089 for (i=0; i<am; i++) {
1090 const PetscInt anzi = ai[i+1] - ai[i]; /* number of nonzeros in this row of A, this is the number of rows of B that we merge */
1091 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */
1092 PetscInt packlen = 0,*PETSC_RESTRICT crow;
1093 /* Pack segrow */
1094 for (j=0; j<anzi; j++) {
1095 PetscInt brow = acol[j],bjstart = bi[brow],bjend = bi[brow+1],k;
1096 for (k=bjstart; k<bjend; k++) {
1097 PetscInt bcol = bj[k];
1098 if (!seen[bcol]) { /* new entry */
1099 PetscInt *PETSC_RESTRICT slot;
1100 ierr = PetscSegBufferGetInts(segrow,1,&slot);CHKERRQ(ierr);
1101 *slot = bcol;
1102 seen[bcol] = 1;
1103 packlen++;
1104 }
1105 }
1106 }
1107 ierr = PetscSegBufferGetInts(seg,packlen,&crow);CHKERRQ(ierr);
1108 ierr = PetscSegBufferExtractTo(segrow,crow);CHKERRQ(ierr);
1109 ierr = PetscSortInt(packlen,crow);CHKERRQ(ierr);
1110 ci[i+1] = ci[i] + packlen;
1111 for (j=0; j<packlen; j++) seen[crow[j]] = 0;
1112 }
1113 ierr = PetscSegBufferDestroy(&segrow);CHKERRQ(ierr);
1114 ierr = PetscFree(seen);CHKERRQ(ierr);
1115
1116 /* Column indices are in the segmented buffer */
1117 ierr = PetscSegBufferExtractAlloc(seg,&cj);CHKERRQ(ierr);
1118 ierr = PetscSegBufferDestroy(&seg);CHKERRQ(ierr);
1119
1120 /* put together the new symbolic matrix */
1121 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr);
1122 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr);
1123
1124 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
1125 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */
1126 c = (Mat_SeqAIJ*)(C->data);
1127 c->free_a = PETSC_TRUE;
1128 c->free_ij = PETSC_TRUE;
1129 c->nonew = 0;
1130
1131 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted;
1132
1133 /* set MatInfo */
1134 afill = (PetscReal)ci[am]/PetscMax(ai[am]+bi[bm],1) + 1.e-5;
1135 if (afill < 1.0) afill = 1.0;
1136 c->maxnz = ci[am];
1137 c->nz = ci[am];
1138 C->info.mallocs = ndouble;
1139 C->info.fill_ratio_given = fill;
1140 C->info.fill_ratio_needed = afill;
1141
1142 #if defined(PETSC_USE_INFO)
1143 if (ci[am]) {
1144 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr);
1145 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr);
1146 } else {
1147 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr);
1148 }
1149 #endif
1150 PetscFunctionReturn(0);
1151 }
1152
MatDestroy_SeqAIJ_MatMatMultTrans(void * data)1153 PetscErrorCode MatDestroy_SeqAIJ_MatMatMultTrans(void *data)
1154 {
1155 PetscErrorCode ierr;
1156 Mat_MatMatTransMult *abt=(Mat_MatMatTransMult *)data;
1157
1158 PetscFunctionBegin;
1159 ierr = MatTransposeColoringDestroy(&abt->matcoloring);CHKERRQ(ierr);
1160 ierr = MatDestroy(&abt->Bt_den);CHKERRQ(ierr);
1161 ierr = MatDestroy(&abt->ABt_den);CHKERRQ(ierr);
1162 ierr = PetscFree(abt);CHKERRQ(ierr);
1163 PetscFunctionReturn(0);
1164 }
1165
MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)1166 PetscErrorCode MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
1167 {
1168 PetscErrorCode ierr;
1169 Mat Bt;
1170 PetscInt *bti,*btj;
1171 Mat_MatMatTransMult *abt;
1172 Mat_Product *product = C->product;
1173 char *alg;
1174
1175 PetscFunctionBegin;
1176 if (!product) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Missing product struct");
1177 if (product->data) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Extra product struct not empty");
1178
1179 /* create symbolic Bt */
1180 ierr = MatGetSymbolicTranspose_SeqAIJ(B,&bti,&btj);CHKERRQ(ierr);
1181 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,B->cmap->n,B->rmap->n,bti,btj,NULL,&Bt);CHKERRQ(ierr);
1182 ierr = MatSetBlockSizes(Bt,PetscAbs(A->cmap->bs),PetscAbs(B->cmap->bs));CHKERRQ(ierr);
1183 ierr = MatSetType(Bt,((PetscObject)A)->type_name);CHKERRQ(ierr);
1184
1185 /* get symbolic C=A*Bt */
1186 ierr = PetscStrallocpy(product->alg,&alg);CHKERRQ(ierr);
1187 ierr = MatProductSetAlgorithm(C,"sorted");CHKERRQ(ierr); /* set algorithm for C = A*Bt */
1188 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,Bt,fill,C);CHKERRQ(ierr);
1189 ierr = MatProductSetAlgorithm(C,alg);CHKERRQ(ierr); /* resume original algorithm for ABt product */
1190 ierr = PetscFree(alg);CHKERRQ(ierr);
1191
1192 /* create a supporting struct for reuse intermidiate dense matrices with matcoloring */
1193 ierr = PetscNew(&abt);CHKERRQ(ierr);
1194
1195 product->data = abt;
1196 product->destroy = MatDestroy_SeqAIJ_MatMatMultTrans;
1197
1198 C->ops->mattransposemultnumeric = MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ;
1199
1200 abt->usecoloring = PETSC_FALSE;
1201 ierr = PetscStrcmp(product->alg,"color",&abt->usecoloring);CHKERRQ(ierr);
1202 if (abt->usecoloring) {
1203 /* Create MatTransposeColoring from symbolic C=A*B^T */
1204 MatTransposeColoring matcoloring;
1205 MatColoring coloring;
1206 ISColoring iscoloring;
1207 Mat Bt_dense,C_dense;
1208
1209 /* inode causes memory problem */
1210 ierr = MatSetOption(C,MAT_USE_INODES,PETSC_FALSE);CHKERRQ(ierr);
1211
1212 ierr = MatColoringCreate(C,&coloring);CHKERRQ(ierr);
1213 ierr = MatColoringSetDistance(coloring,2);CHKERRQ(ierr);
1214 ierr = MatColoringSetType(coloring,MATCOLORINGSL);CHKERRQ(ierr);
1215 ierr = MatColoringSetFromOptions(coloring);CHKERRQ(ierr);
1216 ierr = MatColoringApply(coloring,&iscoloring);CHKERRQ(ierr);
1217 ierr = MatColoringDestroy(&coloring);CHKERRQ(ierr);
1218 ierr = MatTransposeColoringCreate(C,iscoloring,&matcoloring);CHKERRQ(ierr);
1219
1220 abt->matcoloring = matcoloring;
1221
1222 ierr = ISColoringDestroy(&iscoloring);CHKERRQ(ierr);
1223
1224 /* Create Bt_dense and C_dense = A*Bt_dense */
1225 ierr = MatCreate(PETSC_COMM_SELF,&Bt_dense);CHKERRQ(ierr);
1226 ierr = MatSetSizes(Bt_dense,A->cmap->n,matcoloring->ncolors,A->cmap->n,matcoloring->ncolors);CHKERRQ(ierr);
1227 ierr = MatSetType(Bt_dense,MATSEQDENSE);CHKERRQ(ierr);
1228 ierr = MatSeqDenseSetPreallocation(Bt_dense,NULL);CHKERRQ(ierr);
1229
1230 Bt_dense->assembled = PETSC_TRUE;
1231 abt->Bt_den = Bt_dense;
1232
1233 ierr = MatCreate(PETSC_COMM_SELF,&C_dense);CHKERRQ(ierr);
1234 ierr = MatSetSizes(C_dense,A->rmap->n,matcoloring->ncolors,A->rmap->n,matcoloring->ncolors);CHKERRQ(ierr);
1235 ierr = MatSetType(C_dense,MATSEQDENSE);CHKERRQ(ierr);
1236 ierr = MatSeqDenseSetPreallocation(C_dense,NULL);CHKERRQ(ierr);
1237
1238 Bt_dense->assembled = PETSC_TRUE;
1239 abt->ABt_den = C_dense;
1240
1241 #if defined(PETSC_USE_INFO)
1242 {
1243 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
1244 ierr = PetscInfo7(C,"Use coloring of C=A*B^T; B^T: %D %D, Bt_dense: %D,%D; Cnz %D / (cm*ncolors %D) = %g\n",B->cmap->n,B->rmap->n,Bt_dense->rmap->n,Bt_dense->cmap->n,c->nz,A->rmap->n*matcoloring->ncolors,(PetscReal)(c->nz)/(A->rmap->n*matcoloring->ncolors));CHKERRQ(ierr);
1245 }
1246 #endif
1247 }
1248 /* clean up */
1249 ierr = MatDestroy(&Bt);CHKERRQ(ierr);
1250 ierr = MatRestoreSymbolicTranspose_SeqAIJ(B,&bti,&btj);CHKERRQ(ierr);
1251 PetscFunctionReturn(0);
1252 }
1253
MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)1254 PetscErrorCode MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)
1255 {
1256 PetscErrorCode ierr;
1257 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data;
1258 PetscInt *ai =a->i,*aj=a->j,*bi=b->i,*bj=b->j,anzi,bnzj,nexta,nextb,*acol,*bcol,brow;
1259 PetscInt cm =C->rmap->n,*ci=c->i,*cj=c->j,i,j,cnzi,*ccol;
1260 PetscLogDouble flops=0.0;
1261 MatScalar *aa =a->a,*aval,*ba=b->a,*bval,*ca,*cval;
1262 Mat_MatMatTransMult *abt;
1263 Mat_Product *product = C->product;
1264
1265 PetscFunctionBegin;
1266 if (!product) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Missing product struct");
1267 abt = (Mat_MatMatTransMult *)product->data;
1268 if (!abt) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Missing product struct");
1269 /* clear old values in C */
1270 if (!c->a) {
1271 ierr = PetscCalloc1(ci[cm]+1,&ca);CHKERRQ(ierr);
1272 c->a = ca;
1273 c->free_a = PETSC_TRUE;
1274 } else {
1275 ca = c->a;
1276 ierr = PetscArrayzero(ca,ci[cm]+1);CHKERRQ(ierr);
1277 }
1278
1279 if (abt->usecoloring) {
1280 MatTransposeColoring matcoloring = abt->matcoloring;
1281 Mat Bt_dense,C_dense = abt->ABt_den;
1282
1283 /* Get Bt_dense by Apply MatTransposeColoring to B */
1284 Bt_dense = abt->Bt_den;
1285 ierr = MatTransColoringApplySpToDen(matcoloring,B,Bt_dense);CHKERRQ(ierr);
1286
1287 /* C_dense = A*Bt_dense */
1288 ierr = MatMatMultNumeric_SeqAIJ_SeqDense(A,Bt_dense,C_dense);CHKERRQ(ierr);
1289
1290 /* Recover C from C_dense */
1291 ierr = MatTransColoringApplyDenToSp(matcoloring,C_dense,C);CHKERRQ(ierr);
1292 PetscFunctionReturn(0);
1293 }
1294
1295 for (i=0; i<cm; i++) {
1296 anzi = ai[i+1] - ai[i];
1297 acol = aj + ai[i];
1298 aval = aa + ai[i];
1299 cnzi = ci[i+1] - ci[i];
1300 ccol = cj + ci[i];
1301 cval = ca + ci[i];
1302 for (j=0; j<cnzi; j++) {
1303 brow = ccol[j];
1304 bnzj = bi[brow+1] - bi[brow];
1305 bcol = bj + bi[brow];
1306 bval = ba + bi[brow];
1307
1308 /* perform sparse inner-product c(i,j)=A[i,:]*B[j,:]^T */
1309 nexta = 0; nextb = 0;
1310 while (nexta<anzi && nextb<bnzj) {
1311 while (nexta < anzi && acol[nexta] < bcol[nextb]) nexta++;
1312 if (nexta == anzi) break;
1313 while (nextb < bnzj && acol[nexta] > bcol[nextb]) nextb++;
1314 if (nextb == bnzj) break;
1315 if (acol[nexta] == bcol[nextb]) {
1316 cval[j] += aval[nexta]*bval[nextb];
1317 nexta++; nextb++;
1318 flops += 2;
1319 }
1320 }
1321 }
1322 }
1323 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
1324 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
1325 ierr = PetscLogFlops(flops);CHKERRQ(ierr);
1326 PetscFunctionReturn(0);
1327 }
1328
MatDestroy_SeqAIJ_MatTransMatMult(void * data)1329 PetscErrorCode MatDestroy_SeqAIJ_MatTransMatMult(void *data)
1330 {
1331 PetscErrorCode ierr;
1332 Mat_MatTransMatMult *atb = (Mat_MatTransMatMult*)data;
1333
1334 PetscFunctionBegin;
1335 ierr = MatDestroy(&atb->At);CHKERRQ(ierr);
1336 if (atb->destroy) {
1337 ierr = (*atb->destroy)(atb->data);CHKERRQ(ierr);
1338 }
1339 ierr = PetscFree(atb);CHKERRQ(ierr);
1340 PetscFunctionReturn(0);
1341 }
1342
MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)1343 PetscErrorCode MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
1344 {
1345 PetscErrorCode ierr;
1346 Mat At = NULL;
1347 PetscInt *ati,*atj;
1348 Mat_Product *product = C->product;
1349 PetscBool flg,def,square;
1350
1351 PetscFunctionBegin;
1352 MatCheckProduct(C,4);
1353 square = (PetscBool)(A == B && A->symmetric && A->symmetric_set);
1354 /* outerproduct */
1355 ierr = PetscStrcmp(product->alg,"outerproduct",&flg);CHKERRQ(ierr);
1356 if (flg) {
1357 /* create symbolic At */
1358 if (!square) {
1359 ierr = MatGetSymbolicTranspose_SeqAIJ(A,&ati,&atj);CHKERRQ(ierr);
1360 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,A->cmap->n,A->rmap->n,ati,atj,NULL,&At);CHKERRQ(ierr);
1361 ierr = MatSetBlockSizes(At,PetscAbs(A->cmap->bs),PetscAbs(B->cmap->bs));CHKERRQ(ierr);
1362 ierr = MatSetType(At,((PetscObject)A)->type_name);CHKERRQ(ierr);
1363 }
1364 /* get symbolic C=At*B */
1365 ierr = MatProductSetAlgorithm(C,"sorted");CHKERRQ(ierr);
1366 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(square ? A : At,B,fill,C);CHKERRQ(ierr);
1367
1368 /* clean up */
1369 if (!square) {
1370 ierr = MatDestroy(&At);CHKERRQ(ierr);
1371 ierr = MatRestoreSymbolicTranspose_SeqAIJ(A,&ati,&atj);CHKERRQ(ierr);
1372 }
1373
1374 C->ops->mattransposemultnumeric = MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ; /* outerproduct */
1375 ierr = MatProductSetAlgorithm(C,"outerproduct");CHKERRQ(ierr);
1376 PetscFunctionReturn(0);
1377 }
1378
1379 /* matmatmult */
1380 ierr = PetscStrcmp(product->alg,"default",&def);CHKERRQ(ierr);
1381 ierr = PetscStrcmp(product->alg,"at*b",&flg);CHKERRQ(ierr);
1382 if (flg || def) {
1383 Mat_MatTransMatMult *atb;
1384
1385 if (product->data) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Extra product struct not empty");
1386 ierr = PetscNew(&atb);CHKERRQ(ierr);
1387 if (!square) {
1388 ierr = MatTranspose_SeqAIJ(A,MAT_INITIAL_MATRIX,&At);CHKERRQ(ierr);
1389 }
1390 ierr = MatProductSetAlgorithm(C,"sorted");CHKERRQ(ierr);
1391 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(square ? A : At,B,fill,C);CHKERRQ(ierr);
1392 ierr = MatProductSetAlgorithm(C,"at*b");CHKERRQ(ierr);
1393 product->data = atb;
1394 product->destroy = MatDestroy_SeqAIJ_MatTransMatMult;
1395 atb->At = At;
1396 atb->updateAt = PETSC_FALSE; /* because At is computed here */
1397
1398 C->ops->mattransposemultnumeric = NULL; /* see MatProductNumeric_AtB_SeqAIJ_SeqAIJ */
1399 PetscFunctionReturn(0);
1400 }
1401
1402 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat Product Algorithm is not supported");
1403 PetscFunctionReturn(0);
1404 }
1405
MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)1406 PetscErrorCode MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C)
1407 {
1408 PetscErrorCode ierr;
1409 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data;
1410 PetscInt am=A->rmap->n,anzi,*ai=a->i,*aj=a->j,*bi=b->i,*bj,bnzi,nextb;
1411 PetscInt cm=C->rmap->n,*ci=c->i,*cj=c->j,crow,*cjj,i,j,k;
1412 PetscLogDouble flops=0.0;
1413 MatScalar *aa=a->a,*ba,*ca,*caj;
1414
1415 PetscFunctionBegin;
1416 if (!c->a) {
1417 ierr = PetscCalloc1(ci[cm]+1,&ca);CHKERRQ(ierr);
1418
1419 c->a = ca;
1420 c->free_a = PETSC_TRUE;
1421 } else {
1422 ca = c->a;
1423 ierr = PetscArrayzero(ca,ci[cm]);CHKERRQ(ierr);
1424 }
1425
1426 /* compute A^T*B using outer product (A^T)[:,i]*B[i,:] */
1427 for (i=0; i<am; i++) {
1428 bj = b->j + bi[i];
1429 ba = b->a + bi[i];
1430 bnzi = bi[i+1] - bi[i];
1431 anzi = ai[i+1] - ai[i];
1432 for (j=0; j<anzi; j++) {
1433 nextb = 0;
1434 crow = *aj++;
1435 cjj = cj + ci[crow];
1436 caj = ca + ci[crow];
1437 /* perform sparse axpy operation. Note cjj includes bj. */
1438 for (k=0; nextb<bnzi; k++) {
1439 if (cjj[k] == *(bj+nextb)) { /* ccol == bcol */
1440 caj[k] += (*aa)*(*(ba+nextb));
1441 nextb++;
1442 }
1443 }
1444 flops += 2*bnzi;
1445 aa++;
1446 }
1447 }
1448
1449 /* Assemble the final matrix and clean up */
1450 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
1451 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
1452 ierr = PetscLogFlops(flops);CHKERRQ(ierr);
1453 PetscFunctionReturn(0);
1454 }
1455
MatMatMultSymbolic_SeqAIJ_SeqDense(Mat A,Mat B,PetscReal fill,Mat C)1456 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqDense(Mat A,Mat B,PetscReal fill,Mat C)
1457 {
1458 PetscErrorCode ierr;
1459
1460 PetscFunctionBegin;
1461 ierr = MatMatMultSymbolic_SeqDense_SeqDense(A,B,0.0,C);CHKERRQ(ierr);
1462 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqDense;
1463 PetscFunctionReturn(0);
1464 }
1465
MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat A,Mat B,Mat C,const PetscBool add)1466 PETSC_INTERN PetscErrorCode MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat A,Mat B,Mat C,const PetscBool add)
1467 {
1468 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data;
1469 Mat_SeqDense *bd=(Mat_SeqDense*)B->data;
1470 Mat_SeqDense *cd=(Mat_SeqDense*)C->data;
1471 PetscErrorCode ierr;
1472 PetscScalar *c,r1,r2,r3,r4,*c1,*c2,*c3,*c4;
1473 const PetscScalar *aa,*b,*b1,*b2,*b3,*b4,*av;
1474 const PetscInt *aj;
1475 PetscInt cm=C->rmap->n,cn=B->cmap->n,bm=bd->lda,am=A->rmap->n;
1476 PetscInt clda=cd->lda;
1477 PetscInt am4=4*clda,bm4=4*bm,col,i,j,n;
1478
1479 PetscFunctionBegin;
1480 if (!cm || !cn) PetscFunctionReturn(0);
1481 ierr = MatSeqAIJGetArrayRead(A,&av);CHKERRQ(ierr);
1482 if (add) {
1483 ierr = MatDenseGetArray(C,&c);CHKERRQ(ierr);
1484 } else {
1485 ierr = MatDenseGetArrayWrite(C,&c);CHKERRQ(ierr);
1486 }
1487 ierr = MatDenseGetArrayRead(B,&b);CHKERRQ(ierr);
1488 b1 = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm;
1489 c1 = c; c2 = c1 + clda; c3 = c2 + clda; c4 = c3 + clda;
1490 for (col=0; col<(cn/4)*4; col += 4) { /* over columns of C */
1491 for (i=0; i<am; i++) { /* over rows of A in those columns */
1492 r1 = r2 = r3 = r4 = 0.0;
1493 n = a->i[i+1] - a->i[i];
1494 aj = a->j + a->i[i];
1495 aa = av + a->i[i];
1496 for (j=0; j<n; j++) {
1497 const PetscScalar aatmp = aa[j];
1498 const PetscInt ajtmp = aj[j];
1499 r1 += aatmp*b1[ajtmp];
1500 r2 += aatmp*b2[ajtmp];
1501 r3 += aatmp*b3[ajtmp];
1502 r4 += aatmp*b4[ajtmp];
1503 }
1504 if (add) {
1505 c1[i] += r1;
1506 c2[i] += r2;
1507 c3[i] += r3;
1508 c4[i] += r4;
1509 } else {
1510 c1[i] = r1;
1511 c2[i] = r2;
1512 c3[i] = r3;
1513 c4[i] = r4;
1514 }
1515 }
1516 b1 += bm4; b2 += bm4; b3 += bm4; b4 += bm4;
1517 c1 += am4; c2 += am4; c3 += am4; c4 += am4;
1518 }
1519 /* process remaining columns */
1520 if (col != cn) {
1521 PetscInt rc = cn-col;
1522
1523 if (rc == 1) {
1524 for (i=0; i<am; i++) {
1525 r1 = 0.0;
1526 n = a->i[i+1] - a->i[i];
1527 aj = a->j + a->i[i];
1528 aa = av + a->i[i];
1529 for (j=0; j<n; j++) r1 += aa[j]*b1[aj[j]];
1530 if (add) c1[i] += r1;
1531 else c1[i] = r1;
1532 }
1533 } else if (rc == 2) {
1534 for (i=0; i<am; i++) {
1535 r1 = r2 = 0.0;
1536 n = a->i[i+1] - a->i[i];
1537 aj = a->j + a->i[i];
1538 aa = av + a->i[i];
1539 for (j=0; j<n; j++) {
1540 const PetscScalar aatmp = aa[j];
1541 const PetscInt ajtmp = aj[j];
1542 r1 += aatmp*b1[ajtmp];
1543 r2 += aatmp*b2[ajtmp];
1544 }
1545 if (add) {
1546 c1[i] += r1;
1547 c2[i] += r2;
1548 } else {
1549 c1[i] = r1;
1550 c2[i] = r2;
1551 }
1552 }
1553 } else {
1554 for (i=0; i<am; i++) {
1555 r1 = r2 = r3 = 0.0;
1556 n = a->i[i+1] - a->i[i];
1557 aj = a->j + a->i[i];
1558 aa = av + a->i[i];
1559 for (j=0; j<n; j++) {
1560 const PetscScalar aatmp = aa[j];
1561 const PetscInt ajtmp = aj[j];
1562 r1 += aatmp*b1[ajtmp];
1563 r2 += aatmp*b2[ajtmp];
1564 r3 += aatmp*b3[ajtmp];
1565 }
1566 if (add) {
1567 c1[i] += r1;
1568 c2[i] += r2;
1569 c3[i] += r3;
1570 } else {
1571 c1[i] = r1;
1572 c2[i] = r2;
1573 c3[i] = r3;
1574 }
1575 }
1576 }
1577 }
1578 ierr = PetscLogFlops(cn*(2.0*a->nz));CHKERRQ(ierr);
1579 if (add) {
1580 ierr = MatDenseRestoreArray(C,&c);CHKERRQ(ierr);
1581 } else {
1582 ierr = MatDenseRestoreArrayWrite(C,&c);CHKERRQ(ierr);
1583 }
1584 ierr = MatDenseRestoreArrayRead(B,&b);CHKERRQ(ierr);
1585 ierr = MatSeqAIJRestoreArrayRead(A,&av);CHKERRQ(ierr);
1586 PetscFunctionReturn(0);
1587 }
1588
MatMatMultNumeric_SeqAIJ_SeqDense(Mat A,Mat B,Mat C)1589 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqDense(Mat A,Mat B,Mat C)
1590 {
1591 PetscErrorCode ierr;
1592
1593 PetscFunctionBegin;
1594 if (B->rmap->n != A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number columns in A %D not equal rows in B %D\n",A->cmap->n,B->rmap->n);
1595 if (A->rmap->n != C->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number rows in C %D not equal rows in A %D\n",C->rmap->n,A->rmap->n);
1596 if (B->cmap->n != C->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number columns in B %D not equal columns in C %D\n",B->cmap->n,C->cmap->n);
1597
1598 ierr = MatMatMultNumericAdd_SeqAIJ_SeqDense(A,B,C,PETSC_FALSE);CHKERRQ(ierr);
1599 PetscFunctionReturn(0);
1600 }
1601
1602 /* ------------------------------------------------------- */
MatProductSetFromOptions_SeqAIJ_SeqDense_AB(Mat C)1603 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense_AB(Mat C)
1604 {
1605 PetscFunctionBegin;
1606 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqAIJ_SeqDense;
1607 C->ops->productsymbolic = MatProductSymbolic_AB;
1608 PetscFunctionReturn(0);
1609 }
1610
1611 PETSC_INTERN PetscErrorCode MatTMatTMultSymbolic_SeqAIJ_SeqDense(Mat,Mat,PetscReal,Mat);
1612
MatProductSetFromOptions_SeqAIJ_SeqDense_AtB(Mat C)1613 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense_AtB(Mat C)
1614 {
1615 PetscFunctionBegin;
1616 C->ops->transposematmultsymbolic = MatTMatTMultSymbolic_SeqAIJ_SeqDense;
1617 C->ops->productsymbolic = MatProductSymbolic_AtB;
1618 PetscFunctionReturn(0);
1619 }
1620
MatProductSetFromOptions_SeqAIJ_SeqDense_ABt(Mat C)1621 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense_ABt(Mat C)
1622 {
1623 PetscFunctionBegin;
1624 C->ops->mattransposemultsymbolic = MatTMatTMultSymbolic_SeqAIJ_SeqDense;
1625 C->ops->productsymbolic = MatProductSymbolic_ABt;
1626 PetscFunctionReturn(0);
1627 }
1628
MatProductSetFromOptions_SeqAIJ_SeqDense(Mat C)1629 PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense(Mat C)
1630 {
1631 PetscErrorCode ierr;
1632 Mat_Product *product = C->product;
1633
1634 PetscFunctionBegin;
1635 switch (product->type) {
1636 case MATPRODUCT_AB:
1637 ierr = MatProductSetFromOptions_SeqAIJ_SeqDense_AB(C);CHKERRQ(ierr);
1638 break;
1639 case MATPRODUCT_AtB:
1640 ierr = MatProductSetFromOptions_SeqAIJ_SeqDense_AtB(C);CHKERRQ(ierr);
1641 break;
1642 case MATPRODUCT_ABt:
1643 ierr = MatProductSetFromOptions_SeqAIJ_SeqDense_ABt(C);CHKERRQ(ierr);
1644 break;
1645 default:
1646 break;
1647 }
1648 PetscFunctionReturn(0);
1649 }
1650 /* ------------------------------------------------------- */
MatProductSetFromOptions_SeqXBAIJ_SeqDense_AB(Mat C)1651 static PetscErrorCode MatProductSetFromOptions_SeqXBAIJ_SeqDense_AB(Mat C)
1652 {
1653 PetscErrorCode ierr;
1654 Mat_Product *product = C->product;
1655 Mat A = product->A;
1656 PetscBool baij;
1657
1658 PetscFunctionBegin;
1659 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&baij);CHKERRQ(ierr);
1660 if (!baij) { /* A is seqsbaij */
1661 PetscBool sbaij;
1662 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&sbaij);CHKERRQ(ierr);
1663 if (!sbaij) SETERRQ(PetscObjectComm((PetscObject)C),PETSC_ERR_ARG_WRONGSTATE,"Mat must be either seqbaij or seqsbaij format");
1664
1665 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqSBAIJ_SeqDense;
1666 } else { /* A is seqbaij */
1667 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqBAIJ_SeqDense;
1668 }
1669
1670 C->ops->productsymbolic = MatProductSymbolic_AB;
1671 PetscFunctionReturn(0);
1672 }
1673
MatProductSetFromOptions_SeqXBAIJ_SeqDense(Mat C)1674 PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqXBAIJ_SeqDense(Mat C)
1675 {
1676 PetscErrorCode ierr;
1677 Mat_Product *product = C->product;
1678
1679 PetscFunctionBegin;
1680 MatCheckProduct(C,1);
1681 if (!product->A) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Missing A");
1682 if (product->type == MATPRODUCT_AB || (product->type == MATPRODUCT_AtB && product->A->symmetric)) {
1683 ierr = MatProductSetFromOptions_SeqXBAIJ_SeqDense_AB(C);CHKERRQ(ierr);
1684 }
1685 PetscFunctionReturn(0);
1686 }
1687
1688 /* ------------------------------------------------------- */
MatProductSetFromOptions_SeqDense_SeqAIJ_AB(Mat C)1689 static PetscErrorCode MatProductSetFromOptions_SeqDense_SeqAIJ_AB(Mat C)
1690 {
1691 PetscFunctionBegin;
1692 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqDense_SeqAIJ;
1693 C->ops->productsymbolic = MatProductSymbolic_AB;
1694 PetscFunctionReturn(0);
1695 }
1696
MatProductSetFromOptions_SeqDense_SeqAIJ(Mat C)1697 PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqDense_SeqAIJ(Mat C)
1698 {
1699 PetscErrorCode ierr;
1700 Mat_Product *product = C->product;
1701
1702 PetscFunctionBegin;
1703 if (product->type == MATPRODUCT_AB) {
1704 ierr = MatProductSetFromOptions_SeqDense_SeqAIJ_AB(C);CHKERRQ(ierr);
1705 }
1706 PetscFunctionReturn(0);
1707 }
1708 /* ------------------------------------------------------- */
1709
MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring coloring,Mat B,Mat Btdense)1710 PetscErrorCode MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring coloring,Mat B,Mat Btdense)
1711 {
1712 PetscErrorCode ierr;
1713 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
1714 Mat_SeqDense *btdense = (Mat_SeqDense*)Btdense->data;
1715 PetscInt *bi = b->i,*bj=b->j;
1716 PetscInt m = Btdense->rmap->n,n=Btdense->cmap->n,j,k,l,col,anz,*btcol,brow,ncolumns;
1717 MatScalar *btval,*btval_den,*ba=b->a;
1718 PetscInt *columns=coloring->columns,*colorforcol=coloring->colorforcol,ncolors=coloring->ncolors;
1719
1720 PetscFunctionBegin;
1721 btval_den=btdense->v;
1722 ierr = PetscArrayzero(btval_den,m*n);CHKERRQ(ierr);
1723 for (k=0; k<ncolors; k++) {
1724 ncolumns = coloring->ncolumns[k];
1725 for (l=0; l<ncolumns; l++) { /* insert a row of B to a column of Btdense */
1726 col = *(columns + colorforcol[k] + l);
1727 btcol = bj + bi[col];
1728 btval = ba + bi[col];
1729 anz = bi[col+1] - bi[col];
1730 for (j=0; j<anz; j++) {
1731 brow = btcol[j];
1732 btval_den[brow] = btval[j];
1733 }
1734 }
1735 btval_den += m;
1736 }
1737 PetscFunctionReturn(0);
1738 }
1739
MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring matcoloring,Mat Cden,Mat Csp)1740 PetscErrorCode MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring matcoloring,Mat Cden,Mat Csp)
1741 {
1742 PetscErrorCode ierr;
1743 Mat_SeqAIJ *csp = (Mat_SeqAIJ*)Csp->data;
1744 const PetscScalar *ca_den,*ca_den_ptr;
1745 PetscScalar *ca=csp->a;
1746 PetscInt k,l,m=Cden->rmap->n,ncolors=matcoloring->ncolors;
1747 PetscInt brows=matcoloring->brows,*den2sp=matcoloring->den2sp;
1748 PetscInt nrows,*row,*idx;
1749 PetscInt *rows=matcoloring->rows,*colorforrow=matcoloring->colorforrow;
1750
1751 PetscFunctionBegin;
1752 ierr = MatDenseGetArrayRead(Cden,&ca_den);CHKERRQ(ierr);
1753
1754 if (brows > 0) {
1755 PetscInt *lstart,row_end,row_start;
1756 lstart = matcoloring->lstart;
1757 ierr = PetscArrayzero(lstart,ncolors);CHKERRQ(ierr);
1758
1759 row_end = brows;
1760 if (row_end > m) row_end = m;
1761 for (row_start=0; row_start<m; row_start+=brows) { /* loop over row blocks of Csp */
1762 ca_den_ptr = ca_den;
1763 for (k=0; k<ncolors; k++) { /* loop over colors (columns of Cden) */
1764 nrows = matcoloring->nrows[k];
1765 row = rows + colorforrow[k];
1766 idx = den2sp + colorforrow[k];
1767 for (l=lstart[k]; l<nrows; l++) {
1768 if (row[l] >= row_end) {
1769 lstart[k] = l;
1770 break;
1771 } else {
1772 ca[idx[l]] = ca_den_ptr[row[l]];
1773 }
1774 }
1775 ca_den_ptr += m;
1776 }
1777 row_end += brows;
1778 if (row_end > m) row_end = m;
1779 }
1780 } else { /* non-blocked impl: loop over columns of Csp - slow if Csp is large */
1781 ca_den_ptr = ca_den;
1782 for (k=0; k<ncolors; k++) {
1783 nrows = matcoloring->nrows[k];
1784 row = rows + colorforrow[k];
1785 idx = den2sp + colorforrow[k];
1786 for (l=0; l<nrows; l++) {
1787 ca[idx[l]] = ca_den_ptr[row[l]];
1788 }
1789 ca_den_ptr += m;
1790 }
1791 }
1792
1793 ierr = MatDenseRestoreArrayRead(Cden,&ca_den);CHKERRQ(ierr);
1794 #if defined(PETSC_USE_INFO)
1795 if (matcoloring->brows > 0) {
1796 ierr = PetscInfo1(Csp,"Loop over %D row blocks for den2sp\n",brows);CHKERRQ(ierr);
1797 } else {
1798 ierr = PetscInfo(Csp,"Loop over colors/columns of Cden, inefficient for large sparse matrix product \n");CHKERRQ(ierr);
1799 }
1800 #endif
1801 PetscFunctionReturn(0);
1802 }
1803
MatTransposeColoringCreate_SeqAIJ(Mat mat,ISColoring iscoloring,MatTransposeColoring c)1804 PetscErrorCode MatTransposeColoringCreate_SeqAIJ(Mat mat,ISColoring iscoloring,MatTransposeColoring c)
1805 {
1806 PetscErrorCode ierr;
1807 PetscInt i,n,nrows,Nbs,j,k,m,ncols,col,cm;
1808 const PetscInt *is,*ci,*cj,*row_idx;
1809 PetscInt nis = iscoloring->n,*rowhit,bs = 1;
1810 IS *isa;
1811 Mat_SeqAIJ *csp = (Mat_SeqAIJ*)mat->data;
1812 PetscInt *colorforrow,*rows,*rows_i,*idxhit,*spidx,*den2sp,*den2sp_i;
1813 PetscInt *colorforcol,*columns,*columns_i,brows;
1814 PetscBool flg;
1815
1816 PetscFunctionBegin;
1817 ierr = ISColoringGetIS(iscoloring,PETSC_USE_POINTER,PETSC_IGNORE,&isa);CHKERRQ(ierr);
1818
1819 /* bs >1 is not being tested yet! */
1820 Nbs = mat->cmap->N/bs;
1821 c->M = mat->rmap->N/bs; /* set total rows, columns and local rows */
1822 c->N = Nbs;
1823 c->m = c->M;
1824 c->rstart = 0;
1825 c->brows = 100;
1826
1827 c->ncolors = nis;
1828 ierr = PetscMalloc3(nis,&c->ncolumns,nis,&c->nrows,nis+1,&colorforrow);CHKERRQ(ierr);
1829 ierr = PetscMalloc1(csp->nz+1,&rows);CHKERRQ(ierr);
1830 ierr = PetscMalloc1(csp->nz+1,&den2sp);CHKERRQ(ierr);
1831
1832 brows = c->brows;
1833 ierr = PetscOptionsGetInt(NULL,NULL,"-matden2sp_brows",&brows,&flg);CHKERRQ(ierr);
1834 if (flg) c->brows = brows;
1835 if (brows > 0) {
1836 ierr = PetscMalloc1(nis+1,&c->lstart);CHKERRQ(ierr);
1837 }
1838
1839 colorforrow[0] = 0;
1840 rows_i = rows;
1841 den2sp_i = den2sp;
1842
1843 ierr = PetscMalloc1(nis+1,&colorforcol);CHKERRQ(ierr);
1844 ierr = PetscMalloc1(Nbs+1,&columns);CHKERRQ(ierr);
1845
1846 colorforcol[0] = 0;
1847 columns_i = columns;
1848
1849 /* get column-wise storage of mat */
1850 ierr = MatGetColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);CHKERRQ(ierr);
1851
1852 cm = c->m;
1853 ierr = PetscMalloc1(cm+1,&rowhit);CHKERRQ(ierr);
1854 ierr = PetscMalloc1(cm+1,&idxhit);CHKERRQ(ierr);
1855 for (i=0; i<nis; i++) { /* loop over color */
1856 ierr = ISGetLocalSize(isa[i],&n);CHKERRQ(ierr);
1857 ierr = ISGetIndices(isa[i],&is);CHKERRQ(ierr);
1858
1859 c->ncolumns[i] = n;
1860 if (n) {
1861 ierr = PetscArraycpy(columns_i,is,n);CHKERRQ(ierr);
1862 }
1863 colorforcol[i+1] = colorforcol[i] + n;
1864 columns_i += n;
1865
1866 /* fast, crude version requires O(N*N) work */
1867 ierr = PetscArrayzero(rowhit,cm);CHKERRQ(ierr);
1868
1869 for (j=0; j<n; j++) { /* loop over columns*/
1870 col = is[j];
1871 row_idx = cj + ci[col];
1872 m = ci[col+1] - ci[col];
1873 for (k=0; k<m; k++) { /* loop over columns marking them in rowhit */
1874 idxhit[*row_idx] = spidx[ci[col] + k];
1875 rowhit[*row_idx++] = col + 1;
1876 }
1877 }
1878 /* count the number of hits */
1879 nrows = 0;
1880 for (j=0; j<cm; j++) {
1881 if (rowhit[j]) nrows++;
1882 }
1883 c->nrows[i] = nrows;
1884 colorforrow[i+1] = colorforrow[i] + nrows;
1885
1886 nrows = 0;
1887 for (j=0; j<cm; j++) { /* loop over rows */
1888 if (rowhit[j]) {
1889 rows_i[nrows] = j;
1890 den2sp_i[nrows] = idxhit[j];
1891 nrows++;
1892 }
1893 }
1894 den2sp_i += nrows;
1895
1896 ierr = ISRestoreIndices(isa[i],&is);CHKERRQ(ierr);
1897 rows_i += nrows;
1898 }
1899 ierr = MatRestoreColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);CHKERRQ(ierr);
1900 ierr = PetscFree(rowhit);CHKERRQ(ierr);
1901 ierr = ISColoringRestoreIS(iscoloring,PETSC_USE_POINTER,&isa);CHKERRQ(ierr);
1902 if (csp->nz != colorforrow[nis]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"csp->nz %d != colorforrow[nis] %d",csp->nz,colorforrow[nis]);
1903
1904 c->colorforrow = colorforrow;
1905 c->rows = rows;
1906 c->den2sp = den2sp;
1907 c->colorforcol = colorforcol;
1908 c->columns = columns;
1909
1910 ierr = PetscFree(idxhit);CHKERRQ(ierr);
1911 PetscFunctionReturn(0);
1912 }
1913
1914 /* --------------------------------------------------------------- */
MatProductNumeric_AtB_SeqAIJ_SeqAIJ(Mat C)1915 static PetscErrorCode MatProductNumeric_AtB_SeqAIJ_SeqAIJ(Mat C)
1916 {
1917 PetscErrorCode ierr;
1918 Mat_Product *product = C->product;
1919 Mat A=product->A,B=product->B;
1920
1921 PetscFunctionBegin;
1922 if (C->ops->mattransposemultnumeric) {
1923 /* Alg: "outerproduct" */
1924 ierr = (*C->ops->mattransposemultnumeric)(A,B,C);CHKERRQ(ierr);
1925 } else {
1926 /* Alg: "matmatmult" -- C = At*B */
1927 Mat_MatTransMatMult *atb = (Mat_MatTransMatMult *)product->data;
1928 Mat At;
1929
1930 if (!atb) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Missing product struct");
1931 At = atb->At;
1932 if (atb->updateAt && At) { /* At is computed in MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ() */
1933 ierr = MatTranspose_SeqAIJ(A,MAT_REUSE_MATRIX,&At);CHKERRQ(ierr);
1934 }
1935 ierr = MatMatMultNumeric_SeqAIJ_SeqAIJ(At ? At : A,B,C);CHKERRQ(ierr);
1936 atb->updateAt = PETSC_TRUE;
1937 }
1938 PetscFunctionReturn(0);
1939 }
1940
MatProductSymbolic_AtB_SeqAIJ_SeqAIJ(Mat C)1941 static PetscErrorCode MatProductSymbolic_AtB_SeqAIJ_SeqAIJ(Mat C)
1942 {
1943 PetscErrorCode ierr;
1944 Mat_Product *product = C->product;
1945 Mat A=product->A,B=product->B;
1946 PetscReal fill=product->fill;
1947
1948 PetscFunctionBegin;
1949 ierr = MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);CHKERRQ(ierr);
1950
1951 C->ops->productnumeric = MatProductNumeric_AtB_SeqAIJ_SeqAIJ;
1952 PetscFunctionReturn(0);
1953 }
1954
1955 /* --------------------------------------------------------------- */
MatProductSetFromOptions_SeqAIJ_AB(Mat C)1956 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_AB(Mat C)
1957 {
1958 PetscErrorCode ierr;
1959 Mat_Product *product = C->product;
1960 PetscInt alg = 0; /* default algorithm */
1961 PetscBool flg = PETSC_FALSE;
1962 #if !defined(PETSC_HAVE_HYPRE)
1963 const char *algTypes[7] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed","rowmerge"};
1964 PetscInt nalg = 7;
1965 #else
1966 const char *algTypes[8] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed","rowmerge","hypre"};
1967 PetscInt nalg = 8;
1968 #endif
1969
1970 PetscFunctionBegin;
1971 /* Set default algorithm */
1972 ierr = PetscStrcmp(C->product->alg,"default",&flg);CHKERRQ(ierr);
1973 if (flg) {
1974 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
1975 }
1976
1977 /* Get runtime option */
1978 if (product->api_user) {
1979 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatMatMult","Mat");CHKERRQ(ierr);
1980 ierr = PetscOptionsEList("-matmatmult_via","Algorithmic approach","MatMatMult",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr);
1981 ierr = PetscOptionsEnd();CHKERRQ(ierr);
1982 } else {
1983 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_AB","Mat");CHKERRQ(ierr);
1984 ierr = PetscOptionsEList("-matproduct_ab_via","Algorithmic approach","MatProduct_AB",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr);
1985 ierr = PetscOptionsEnd();CHKERRQ(ierr);
1986 }
1987 if (flg) {
1988 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
1989 }
1990
1991 C->ops->productsymbolic = MatProductSymbolic_AB;
1992 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqAIJ_SeqAIJ;
1993 PetscFunctionReturn(0);
1994 }
1995
MatProductSetFromOptions_SeqAIJ_AtB(Mat C)1996 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_AtB(Mat C)
1997 {
1998 PetscErrorCode ierr;
1999 Mat_Product *product = C->product;
2000 PetscInt alg = 0; /* default algorithm */
2001 PetscBool flg = PETSC_FALSE;
2002 const char *algTypes[3] = {"default","at*b","outerproduct"};
2003 PetscInt nalg = 3;
2004
2005 PetscFunctionBegin;
2006 /* Get runtime option */
2007 if (product->api_user) {
2008 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatTransposeMatMult","Mat");CHKERRQ(ierr);
2009 ierr = PetscOptionsEList("-mattransposematmult_via","Algorithmic approach","MatTransposeMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr);
2010 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2011 } else {
2012 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_AtB","Mat");CHKERRQ(ierr);
2013 ierr = PetscOptionsEList("-matproduct_atb_via","Algorithmic approach","MatProduct_AtB",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr);
2014 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2015 }
2016 if (flg) {
2017 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2018 }
2019
2020 C->ops->productsymbolic = MatProductSymbolic_AtB_SeqAIJ_SeqAIJ;
2021 PetscFunctionReturn(0);
2022 }
2023
MatProductSetFromOptions_SeqAIJ_ABt(Mat C)2024 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_ABt(Mat C)
2025 {
2026 PetscErrorCode ierr;
2027 Mat_Product *product = C->product;
2028 PetscInt alg = 0; /* default algorithm */
2029 PetscBool flg = PETSC_FALSE;
2030 const char *algTypes[2] = {"default","color"};
2031 PetscInt nalg = 2;
2032
2033 PetscFunctionBegin;
2034 /* Set default algorithm */
2035 ierr = PetscStrcmp(C->product->alg,"default",&flg);CHKERRQ(ierr);
2036 if (!flg) {
2037 alg = 1;
2038 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2039 }
2040
2041 /* Get runtime option */
2042 if (product->api_user) {
2043 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatMatTransposeMult","Mat");CHKERRQ(ierr);
2044 ierr = PetscOptionsEList("-matmattransmult_via","Algorithmic approach","MatMatTransposeMult",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr);
2045 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2046 } else {
2047 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_ABt","Mat");CHKERRQ(ierr);
2048 ierr = PetscOptionsEList("-matproduct_abt_via","Algorithmic approach","MatProduct_ABt",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr);
2049 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2050 }
2051 if (flg) {
2052 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2053 }
2054
2055 C->ops->mattransposemultsymbolic = MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ;
2056 C->ops->productsymbolic = MatProductSymbolic_ABt;
2057 PetscFunctionReturn(0);
2058 }
2059
MatProductSetFromOptions_SeqAIJ_PtAP(Mat C)2060 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_PtAP(Mat C)
2061 {
2062 PetscErrorCode ierr;
2063 Mat_Product *product = C->product;
2064 PetscBool flg = PETSC_FALSE;
2065 PetscInt alg = 0; /* default algorithm -- alg=1 should be default!!! */
2066 #if !defined(PETSC_HAVE_HYPRE)
2067 const char *algTypes[2] = {"scalable","rap"};
2068 PetscInt nalg = 2;
2069 #else
2070 const char *algTypes[3] = {"scalable","rap","hypre"};
2071 PetscInt nalg = 3;
2072 #endif
2073
2074 PetscFunctionBegin;
2075 /* Set default algorithm */
2076 ierr = PetscStrcmp(product->alg,"default",&flg);CHKERRQ(ierr);
2077 if (flg) {
2078 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2079 }
2080
2081 /* Get runtime option */
2082 if (product->api_user) {
2083 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatPtAP","Mat");CHKERRQ(ierr);
2084 ierr = PetscOptionsEList("-matptap_via","Algorithmic approach","MatPtAP",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr);
2085 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2086 } else {
2087 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_PtAP","Mat");CHKERRQ(ierr);
2088 ierr = PetscOptionsEList("-matproduct_ptap_via","Algorithmic approach","MatProduct_PtAP",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr);
2089 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2090 }
2091 if (flg) {
2092 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2093 }
2094
2095 C->ops->productsymbolic = MatProductSymbolic_PtAP_SeqAIJ_SeqAIJ;
2096 PetscFunctionReturn(0);
2097 }
2098
MatProductSetFromOptions_SeqAIJ_RARt(Mat C)2099 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_RARt(Mat C)
2100 {
2101 PetscErrorCode ierr;
2102 Mat_Product *product = C->product;
2103 PetscBool flg = PETSC_FALSE;
2104 PetscInt alg = 0; /* default algorithm */
2105 const char *algTypes[3] = {"r*a*rt","r*art","coloring_rart"};
2106 PetscInt nalg = 3;
2107
2108 PetscFunctionBegin;
2109 /* Set default algorithm */
2110 ierr = PetscStrcmp(product->alg,"default",&flg);CHKERRQ(ierr);
2111 if (flg) {
2112 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2113 }
2114
2115 /* Get runtime option */
2116 if (product->api_user) {
2117 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatRARt","Mat");CHKERRQ(ierr);
2118 ierr = PetscOptionsEList("-matrart_via","Algorithmic approach","MatRARt",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr);
2119 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2120 } else {
2121 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_RARt","Mat");CHKERRQ(ierr);
2122 ierr = PetscOptionsEList("-matproduct_rart_via","Algorithmic approach","MatProduct_RARt",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr);
2123 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2124 }
2125 if (flg) {
2126 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2127 }
2128
2129 C->ops->productsymbolic = MatProductSymbolic_RARt_SeqAIJ_SeqAIJ;
2130 PetscFunctionReturn(0);
2131 }
2132
2133 /* ABC = A*B*C = A*(B*C); ABC's algorithm must be chosen from AB's algorithm */
MatProductSetFromOptions_SeqAIJ_ABC(Mat C)2134 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_ABC(Mat C)
2135 {
2136 PetscErrorCode ierr;
2137 Mat_Product *product = C->product;
2138 PetscInt alg = 0; /* default algorithm */
2139 PetscBool flg = PETSC_FALSE;
2140 const char *algTypes[7] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed","rowmerge"};
2141 PetscInt nalg = 7;
2142
2143 PetscFunctionBegin;
2144 /* Set default algorithm */
2145 ierr = PetscStrcmp(product->alg,"default",&flg);CHKERRQ(ierr);
2146 if (flg) {
2147 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2148 }
2149
2150 /* Get runtime option */
2151 if (product->api_user) {
2152 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatMatMatMult","Mat");CHKERRQ(ierr);
2153 ierr = PetscOptionsEList("-matmatmatmult_via","Algorithmic approach","MatMatMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr);
2154 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2155 } else {
2156 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_ABC","Mat");CHKERRQ(ierr);
2157 ierr = PetscOptionsEList("-matproduct_abc_via","Algorithmic approach","MatProduct_ABC",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr);
2158 ierr = PetscOptionsEnd();CHKERRQ(ierr);
2159 }
2160 if (flg) {
2161 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr);
2162 }
2163
2164 C->ops->matmatmultsymbolic = MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ;
2165 C->ops->productsymbolic = MatProductSymbolic_ABC;
2166 PetscFunctionReturn(0);
2167 }
2168
MatProductSetFromOptions_SeqAIJ(Mat C)2169 PetscErrorCode MatProductSetFromOptions_SeqAIJ(Mat C)
2170 {
2171 PetscErrorCode ierr;
2172 Mat_Product *product = C->product;
2173
2174 PetscFunctionBegin;
2175 switch (product->type) {
2176 case MATPRODUCT_AB:
2177 ierr = MatProductSetFromOptions_SeqAIJ_AB(C);CHKERRQ(ierr);
2178 break;
2179 case MATPRODUCT_AtB:
2180 ierr = MatProductSetFromOptions_SeqAIJ_AtB(C);CHKERRQ(ierr);
2181 break;
2182 case MATPRODUCT_ABt:
2183 ierr = MatProductSetFromOptions_SeqAIJ_ABt(C);CHKERRQ(ierr);
2184 break;
2185 case MATPRODUCT_PtAP:
2186 ierr = MatProductSetFromOptions_SeqAIJ_PtAP(C);CHKERRQ(ierr);
2187 break;
2188 case MATPRODUCT_RARt:
2189 ierr = MatProductSetFromOptions_SeqAIJ_RARt(C);CHKERRQ(ierr);
2190 break;
2191 case MATPRODUCT_ABC:
2192 ierr = MatProductSetFromOptions_SeqAIJ_ABC(C);CHKERRQ(ierr);
2193 break;
2194 default:
2195 break;
2196 }
2197 PetscFunctionReturn(0);
2198 }
2199