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 13 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 14 { 15 PetscErrorCode ierr; 16 17 PetscFunctionBegin; 18 if (C->ops->matmultnumeric) { 19 ierr = (*C->ops->matmultnumeric)(A,B,C);CHKERRQ(ierr); 20 } else { 21 ierr = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted(A,B,C);CHKERRQ(ierr); 22 } 23 PetscFunctionReturn(0); 24 } 25 26 /* Modified from MatCreateSeqAIJWithArrays() */ 27 PETSC_INTERN PetscErrorCode MatSetSeqAIJWithArrays_private(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],MatType mtype,Mat mat) 28 { 29 PetscErrorCode ierr; 30 PetscInt ii; 31 Mat_SeqAIJ *aij; 32 PetscBool isseqaij; 33 34 PetscFunctionBegin; 35 if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0"); 36 ierr = MatSetSizes(mat,m,n,m,n);CHKERRQ(ierr); 37 38 if (!mtype) { 39 ierr = PetscObjectBaseTypeCompare((PetscObject)mat,MATSEQAIJ,&isseqaij);CHKERRQ(ierr); 40 if (!isseqaij) { ierr = MatSetType(mat,MATSEQAIJ);CHKERRQ(ierr); } 41 } else { 42 ierr = MatSetType(mat,mtype);CHKERRQ(ierr); 43 } 44 ierr = MatSeqAIJSetPreallocation_SeqAIJ(mat,MAT_SKIP_ALLOCATION,0);CHKERRQ(ierr); 45 aij = (Mat_SeqAIJ*)(mat)->data; 46 ierr = PetscMalloc1(m,&aij->imax);CHKERRQ(ierr); 47 ierr = PetscMalloc1(m,&aij->ilen);CHKERRQ(ierr); 48 49 aij->i = i; 50 aij->j = j; 51 aij->a = a; 52 aij->singlemalloc = PETSC_FALSE; 53 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 54 aij->free_a = PETSC_FALSE; 55 aij->free_ij = PETSC_FALSE; 56 57 for (ii=0; ii<m; ii++) { 58 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii]; 59 } 60 61 PetscFunctionReturn(0); 62 } 63 64 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C) 65 { 66 PetscErrorCode ierr; 67 Mat_Product *product = C->product; 68 MatProductAlgorithm alg; 69 PetscBool flg; 70 71 PetscFunctionBegin; 72 if (product) { 73 alg = product->alg; 74 } else { 75 alg = "sorted"; 76 } 77 /* sorted */ 78 ierr = PetscStrcmp(alg,"sorted",&flg);CHKERRQ(ierr); 79 if (flg) { 80 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Sorted(A,B,fill,C);CHKERRQ(ierr); 81 PetscFunctionReturn(0); 82 } 83 84 /* scalable */ 85 ierr = PetscStrcmp(alg,"scalable",&flg);CHKERRQ(ierr); 86 if (flg) { 87 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(A,B,fill,C);CHKERRQ(ierr); 88 PetscFunctionReturn(0); 89 } 90 91 /* scalable_fast */ 92 ierr = PetscStrcmp(alg,"scalable_fast",&flg);CHKERRQ(ierr); 93 if (flg) { 94 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(A,B,fill,C);CHKERRQ(ierr); 95 PetscFunctionReturn(0); 96 } 97 98 /* heap */ 99 ierr = PetscStrcmp(alg,"heap",&flg);CHKERRQ(ierr); 100 if (flg) { 101 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(A,B,fill,C);CHKERRQ(ierr); 102 PetscFunctionReturn(0); 103 } 104 105 /* btheap */ 106 ierr = PetscStrcmp(alg,"btheap",&flg);CHKERRQ(ierr); 107 if (flg) { 108 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(A,B,fill,C);CHKERRQ(ierr); 109 PetscFunctionReturn(0); 110 } 111 112 /* llcondensed */ 113 ierr = PetscStrcmp(alg,"llcondensed",&flg);CHKERRQ(ierr); 114 if (flg) { 115 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(A,B,fill,C);CHKERRQ(ierr); 116 PetscFunctionReturn(0); 117 } 118 119 /* rowmerge */ 120 ierr = PetscStrcmp(alg,"rowmerge",&flg);CHKERRQ(ierr); 121 if (flg) { 122 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_RowMerge(A,B,fill,C);CHKERRQ(ierr); 123 PetscFunctionReturn(0); 124 } 125 126 #if defined(PETSC_HAVE_HYPRE) 127 ierr = PetscStrcmp(alg,"hypre",&flg);CHKERRQ(ierr); 128 if (flg) { 129 ierr = MatMatMultSymbolic_AIJ_AIJ_wHYPRE(A,B,fill,C);CHKERRQ(ierr); 130 PetscFunctionReturn(0); 131 } 132 #endif 133 134 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat Product Algorithm is not supported"); 135 PetscFunctionReturn(0); 136 } 137 138 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat A,Mat B,PetscReal fill,Mat C) 139 { 140 PetscErrorCode ierr; 141 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 142 PetscInt *ai=a->i,*bi=b->i,*ci,*cj; 143 PetscInt am =A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 144 PetscReal afill; 145 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax; 146 PetscTable ta; 147 PetscBT lnkbt; 148 PetscFreeSpaceList free_space=NULL,current_space=NULL; 149 150 PetscFunctionBegin; 151 /* Get ci and cj */ 152 /*---------------*/ 153 /* Allocate ci array, arrays for fill computation and */ 154 /* free space for accumulating nonzero column info */ 155 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 156 ci[0] = 0; 157 158 /* create and initialize a linked list */ 159 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr); 160 MatRowMergeMax_SeqAIJ(b,bm,ta); 161 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr); 162 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr); 163 164 ierr = PetscLLCondensedCreate(Crmax,bn,&lnk,&lnkbt);CHKERRQ(ierr); 165 166 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 167 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 168 169 current_space = free_space; 170 171 /* Determine ci and cj */ 172 for (i=0; i<am; i++) { 173 anzi = ai[i+1] - ai[i]; 174 aj = a->j + ai[i]; 175 for (j=0; j<anzi; j++) { 176 brow = aj[j]; 177 bnzj = bi[brow+1] - bi[brow]; 178 bj = b->j + bi[brow]; 179 /* add non-zero cols of B into the sorted linked list lnk */ 180 ierr = PetscLLCondensedAddSorted(bnzj,bj,lnk,lnkbt);CHKERRQ(ierr); 181 } 182 cnzi = lnk[0]; 183 184 /* If free space is not available, make more free space */ 185 /* Double the amount of total space in the list */ 186 if (current_space->local_remaining<cnzi) { 187 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr); 188 ndouble++; 189 } 190 191 /* Copy data into free space, then initialize lnk */ 192 ierr = PetscLLCondensedClean(bn,cnzi,current_space->array,lnk,lnkbt);CHKERRQ(ierr); 193 194 current_space->array += cnzi; 195 current_space->local_used += cnzi; 196 current_space->local_remaining -= cnzi; 197 198 ci[i+1] = ci[i] + cnzi; 199 } 200 201 /* Column indices are in the list of free space */ 202 /* Allocate space for cj, initialize cj, and */ 203 /* destroy list of free space and other temporary array(s) */ 204 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr); 205 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 206 ierr = PetscLLCondensedDestroy(lnk,lnkbt);CHKERRQ(ierr); 207 208 /* put together the new symbolic matrix */ 209 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr); 210 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr); 211 212 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 213 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 214 c = (Mat_SeqAIJ*)(C->data); 215 c->free_a = PETSC_FALSE; 216 c->free_ij = PETSC_TRUE; 217 c->nonew = 0; 218 219 /* fast, needs non-scalable O(bn) array 'abdense' */ 220 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted; 221 222 /* set MatInfo */ 223 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 224 if (afill < 1.0) afill = 1.0; 225 c->maxnz = ci[am]; 226 c->nz = ci[am]; 227 C->info.mallocs = ndouble; 228 C->info.fill_ratio_given = fill; 229 C->info.fill_ratio_needed = afill; 230 231 #if defined(PETSC_USE_INFO) 232 if (ci[am]) { 233 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 234 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 235 } else { 236 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr); 237 } 238 #endif 239 PetscFunctionReturn(0); 240 } 241 242 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted(Mat A,Mat B,Mat C) 243 { 244 PetscErrorCode ierr; 245 PetscLogDouble flops=0.0; 246 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 247 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 248 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 249 PetscInt *ai =a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j; 250 PetscInt am =A->rmap->n,cm=C->rmap->n; 251 PetscInt i,j,k,anzi,bnzi,cnzi,brow; 252 PetscScalar *aa=a->a,*ba=b->a,*baj,*ca,valtmp; 253 PetscScalar *ab_dense; 254 255 PetscFunctionBegin; 256 if (!c->a) { /* first call of MatMatMultNumeric_SeqAIJ_SeqAIJ, allocate ca and matmult_abdense */ 257 ierr = PetscMalloc1(ci[cm]+1,&ca);CHKERRQ(ierr); 258 c->a = ca; 259 c->free_a = PETSC_TRUE; 260 } else { 261 ca = c->a; 262 } 263 if (!c->matmult_abdense) { 264 ierr = PetscCalloc1(B->cmap->N,&ab_dense);CHKERRQ(ierr); 265 c->matmult_abdense = ab_dense; 266 } else { 267 ab_dense = c->matmult_abdense; 268 } 269 270 /* clean old values in C */ 271 ierr = PetscArrayzero(ca,ci[cm]);CHKERRQ(ierr); 272 /* Traverse A row-wise. */ 273 /* Build the ith row in C by summing over nonzero columns in A, */ 274 /* the rows of B corresponding to nonzeros of A. */ 275 for (i=0; i<am; i++) { 276 anzi = ai[i+1] - ai[i]; 277 for (j=0; j<anzi; j++) { 278 brow = aj[j]; 279 bnzi = bi[brow+1] - bi[brow]; 280 bjj = bj + bi[brow]; 281 baj = ba + bi[brow]; 282 /* perform dense axpy */ 283 valtmp = aa[j]; 284 for (k=0; k<bnzi; k++) { 285 ab_dense[bjj[k]] += valtmp*baj[k]; 286 } 287 flops += 2*bnzi; 288 } 289 aj += anzi; aa += anzi; 290 291 cnzi = ci[i+1] - ci[i]; 292 for (k=0; k<cnzi; k++) { 293 ca[k] += ab_dense[cj[k]]; 294 ab_dense[cj[k]] = 0.0; /* zero ab_dense */ 295 } 296 flops += cnzi; 297 cj += cnzi; ca += cnzi; 298 } 299 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 300 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 301 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 302 PetscFunctionReturn(0); 303 } 304 305 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,Mat C) 306 { 307 PetscErrorCode ierr; 308 PetscLogDouble flops=0.0; 309 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 310 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 311 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 312 PetscInt *ai = a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j; 313 PetscInt am = A->rmap->N,cm=C->rmap->N; 314 PetscInt i,j,k,anzi,bnzi,cnzi,brow; 315 PetscScalar *aa=a->a,*ba=b->a,*baj,*ca=c->a,valtmp; 316 PetscInt nextb; 317 318 PetscFunctionBegin; 319 if (!ca) { /* first call of MatMatMultNumeric_SeqAIJ_SeqAIJ, allocate ca and matmult_abdense */ 320 ierr = PetscMalloc1(ci[cm]+1,&ca);CHKERRQ(ierr); 321 c->a = ca; 322 c->free_a = PETSC_TRUE; 323 } 324 325 /* clean old values in C */ 326 ierr = PetscArrayzero(ca,ci[cm]);CHKERRQ(ierr); 327 /* Traverse A row-wise. */ 328 /* Build the ith row in C by summing over nonzero columns in A, */ 329 /* the rows of B corresponding to nonzeros of A. */ 330 for (i=0; i<am; i++) { 331 anzi = ai[i+1] - ai[i]; 332 cnzi = ci[i+1] - ci[i]; 333 for (j=0; j<anzi; j++) { 334 brow = aj[j]; 335 bnzi = bi[brow+1] - bi[brow]; 336 bjj = bj + bi[brow]; 337 baj = ba + bi[brow]; 338 /* perform sparse axpy */ 339 valtmp = aa[j]; 340 nextb = 0; 341 for (k=0; nextb<bnzi; k++) { 342 if (cj[k] == bjj[nextb]) { /* ccol == bcol */ 343 ca[k] += valtmp*baj[nextb++]; 344 } 345 } 346 flops += 2*bnzi; 347 } 348 aj += anzi; aa += anzi; 349 cj += cnzi; ca += cnzi; 350 } 351 352 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 353 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 354 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 355 PetscFunctionReturn(0); 356 } 357 358 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat A,Mat B,PetscReal fill,Mat C) 359 { 360 PetscErrorCode ierr; 361 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 362 PetscInt *ai = a->i,*bi=b->i,*ci,*cj; 363 PetscInt am = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 364 MatScalar *ca; 365 PetscReal afill; 366 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax; 367 PetscTable ta; 368 PetscFreeSpaceList free_space=NULL,current_space=NULL; 369 370 PetscFunctionBegin; 371 /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_fast() */ 372 /*-----------------------------------------------------------------------------------------*/ 373 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 374 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 375 ci[0] = 0; 376 377 /* create and initialize a linked list */ 378 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr); 379 MatRowMergeMax_SeqAIJ(b,bm,ta); 380 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr); 381 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr); 382 383 ierr = PetscLLCondensedCreate_fast(Crmax,&lnk);CHKERRQ(ierr); 384 385 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 386 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 387 current_space = free_space; 388 389 /* Determine ci and cj */ 390 for (i=0; i<am; i++) { 391 anzi = ai[i+1] - ai[i]; 392 aj = a->j + ai[i]; 393 for (j=0; j<anzi; j++) { 394 brow = aj[j]; 395 bnzj = bi[brow+1] - bi[brow]; 396 bj = b->j + bi[brow]; 397 /* add non-zero cols of B into the sorted linked list lnk */ 398 ierr = PetscLLCondensedAddSorted_fast(bnzj,bj,lnk);CHKERRQ(ierr); 399 } 400 cnzi = lnk[1]; 401 402 /* If free space is not available, make more free space */ 403 /* Double the amount of total space in the list */ 404 if (current_space->local_remaining<cnzi) { 405 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr); 406 ndouble++; 407 } 408 409 /* Copy data into free space, then initialize lnk */ 410 ierr = PetscLLCondensedClean_fast(cnzi,current_space->array,lnk);CHKERRQ(ierr); 411 412 current_space->array += cnzi; 413 current_space->local_used += cnzi; 414 current_space->local_remaining -= cnzi; 415 416 ci[i+1] = ci[i] + cnzi; 417 } 418 419 /* Column indices are in the list of free space */ 420 /* Allocate space for cj, initialize cj, and */ 421 /* destroy list of free space and other temporary array(s) */ 422 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr); 423 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 424 ierr = PetscLLCondensedDestroy_fast(lnk);CHKERRQ(ierr); 425 426 /* Allocate space for ca */ 427 ierr = PetscCalloc1(ci[am]+1,&ca);CHKERRQ(ierr); 428 429 /* put together the new symbolic matrix */ 430 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,((PetscObject)A)->type_name,C);CHKERRQ(ierr); 431 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr); 432 433 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 434 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 435 c = (Mat_SeqAIJ*)(C->data); 436 c->free_a = PETSC_TRUE; 437 c->free_ij = PETSC_TRUE; 438 c->nonew = 0; 439 440 /* slower, less memory */ 441 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable; 442 443 /* set MatInfo */ 444 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 445 if (afill < 1.0) afill = 1.0; 446 c->maxnz = ci[am]; 447 c->nz = ci[am]; 448 C->info.mallocs = ndouble; 449 C->info.fill_ratio_given = fill; 450 C->info.fill_ratio_needed = afill; 451 452 #if defined(PETSC_USE_INFO) 453 if (ci[am]) { 454 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 455 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 456 } else { 457 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr); 458 } 459 #endif 460 PetscFunctionReturn(0); 461 } 462 463 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,PetscReal fill,Mat C) 464 { 465 PetscErrorCode ierr; 466 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 467 PetscInt *ai = a->i,*bi=b->i,*ci,*cj; 468 PetscInt am = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 469 MatScalar *ca; 470 PetscReal afill; 471 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax; 472 PetscTable ta; 473 PetscFreeSpaceList free_space=NULL,current_space=NULL; 474 475 PetscFunctionBegin; 476 /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_Scalalbe() */ 477 /*---------------------------------------------------------------------------------------------*/ 478 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 479 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 480 ci[0] = 0; 481 482 /* create and initialize a linked list */ 483 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr); 484 MatRowMergeMax_SeqAIJ(b,bm,ta); 485 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr); 486 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr); 487 ierr = PetscLLCondensedCreate_Scalable(Crmax,&lnk);CHKERRQ(ierr); 488 489 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 490 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 491 current_space = free_space; 492 493 /* Determine ci and cj */ 494 for (i=0; i<am; i++) { 495 anzi = ai[i+1] - ai[i]; 496 aj = a->j + ai[i]; 497 for (j=0; j<anzi; j++) { 498 brow = aj[j]; 499 bnzj = bi[brow+1] - bi[brow]; 500 bj = b->j + bi[brow]; 501 /* add non-zero cols of B into the sorted linked list lnk */ 502 ierr = PetscLLCondensedAddSorted_Scalable(bnzj,bj,lnk);CHKERRQ(ierr); 503 } 504 cnzi = lnk[0]; 505 506 /* If free space is not available, make more free space */ 507 /* Double the amount of total space in the list */ 508 if (current_space->local_remaining<cnzi) { 509 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr); 510 ndouble++; 511 } 512 513 /* Copy data into free space, then initialize lnk */ 514 ierr = PetscLLCondensedClean_Scalable(cnzi,current_space->array,lnk);CHKERRQ(ierr); 515 516 current_space->array += cnzi; 517 current_space->local_used += cnzi; 518 current_space->local_remaining -= cnzi; 519 520 ci[i+1] = ci[i] + cnzi; 521 } 522 523 /* Column indices are in the list of free space */ 524 /* Allocate space for cj, initialize cj, and */ 525 /* destroy list of free space and other temporary array(s) */ 526 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr); 527 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 528 ierr = PetscLLCondensedDestroy_Scalable(lnk);CHKERRQ(ierr); 529 530 /* Allocate space for ca */ 531 /*-----------------------*/ 532 ierr = PetscCalloc1(ci[am]+1,&ca);CHKERRQ(ierr); 533 534 /* put together the new symbolic matrix */ 535 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,((PetscObject)A)->type_name,C);CHKERRQ(ierr); 536 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr); 537 538 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 539 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 540 c = (Mat_SeqAIJ*)(C->data); 541 c->free_a = PETSC_TRUE; 542 c->free_ij = PETSC_TRUE; 543 c->nonew = 0; 544 545 /* slower, less memory */ 546 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable; 547 548 /* set MatInfo */ 549 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 550 if (afill < 1.0) afill = 1.0; 551 c->maxnz = ci[am]; 552 c->nz = ci[am]; 553 C->info.mallocs = ndouble; 554 C->info.fill_ratio_given = fill; 555 C->info.fill_ratio_needed = afill; 556 557 #if defined(PETSC_USE_INFO) 558 if (ci[am]) { 559 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 560 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 561 } else { 562 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr); 563 } 564 #endif 565 PetscFunctionReturn(0); 566 } 567 568 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat A,Mat B,PetscReal fill,Mat C) 569 { 570 PetscErrorCode ierr; 571 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 572 const PetscInt *ai=a->i,*bi=b->i,*aj=a->j,*bj=b->j; 573 PetscInt *ci,*cj,*bb; 574 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 575 PetscReal afill; 576 PetscInt i,j,col,ndouble = 0; 577 PetscFreeSpaceList free_space=NULL,current_space=NULL; 578 PetscHeap h; 579 580 PetscFunctionBegin; 581 /* Get ci and cj - by merging sorted rows using a heap */ 582 /*---------------------------------------------------------------------------------------------*/ 583 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 584 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 585 ci[0] = 0; 586 587 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 588 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 589 current_space = free_space; 590 591 ierr = PetscHeapCreate(a->rmax,&h);CHKERRQ(ierr); 592 ierr = PetscMalloc1(a->rmax,&bb);CHKERRQ(ierr); 593 594 /* Determine ci and cj */ 595 for (i=0; i<am; i++) { 596 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 */ 597 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 598 ci[i+1] = ci[i]; 599 /* Populate the min heap */ 600 for (j=0; j<anzi; j++) { 601 bb[j] = bi[acol[j]]; /* bb points at the start of the row */ 602 if (bb[j] < bi[acol[j]+1]) { /* Add if row is nonempty */ 603 ierr = PetscHeapAdd(h,j,bj[bb[j]++]);CHKERRQ(ierr); 604 } 605 } 606 /* Pick off the min element, adding it to free space */ 607 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 608 while (j >= 0) { 609 if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */ 610 ierr = PetscFreeSpaceGet(PetscMin(PetscIntMultTruncate(2,current_space->total_array_size),16 << 20),¤t_space);CHKERRQ(ierr); 611 ndouble++; 612 } 613 *(current_space->array++) = col; 614 current_space->local_used++; 615 current_space->local_remaining--; 616 ci[i+1]++; 617 618 /* stash if anything else remains in this row of B */ 619 if (bb[j] < bi[acol[j]+1]) {ierr = PetscHeapStash(h,j,bj[bb[j]++]);CHKERRQ(ierr);} 620 while (1) { /* pop and stash any other rows of B that also had an entry in this column */ 621 PetscInt j2,col2; 622 ierr = PetscHeapPeek(h,&j2,&col2);CHKERRQ(ierr); 623 if (col2 != col) break; 624 ierr = PetscHeapPop(h,&j2,&col2);CHKERRQ(ierr); 625 if (bb[j2] < bi[acol[j2]+1]) {ierr = PetscHeapStash(h,j2,bj[bb[j2]++]);CHKERRQ(ierr);} 626 } 627 /* Put any stashed elements back into the min heap */ 628 ierr = PetscHeapUnstash(h);CHKERRQ(ierr); 629 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 630 } 631 } 632 ierr = PetscFree(bb);CHKERRQ(ierr); 633 ierr = PetscHeapDestroy(&h);CHKERRQ(ierr); 634 635 /* Column indices are in the list of free space */ 636 /* Allocate space for cj, initialize cj, and */ 637 /* destroy list of free space and other temporary array(s) */ 638 ierr = PetscMalloc1(ci[am],&cj);CHKERRQ(ierr); 639 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 640 641 /* put together the new symbolic matrix */ 642 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr); 643 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr); 644 645 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 646 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 647 c = (Mat_SeqAIJ*)(C->data); 648 c->free_a = PETSC_TRUE; 649 c->free_ij = PETSC_TRUE; 650 c->nonew = 0; 651 652 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted; 653 654 /* set MatInfo */ 655 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 656 if (afill < 1.0) afill = 1.0; 657 c->maxnz = ci[am]; 658 c->nz = ci[am]; 659 C->info.mallocs = ndouble; 660 C->info.fill_ratio_given = fill; 661 C->info.fill_ratio_needed = afill; 662 663 #if defined(PETSC_USE_INFO) 664 if (ci[am]) { 665 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 666 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 667 } else { 668 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr); 669 } 670 #endif 671 PetscFunctionReturn(0); 672 } 673 674 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat A,Mat B,PetscReal fill,Mat C) 675 { 676 PetscErrorCode ierr; 677 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 678 const PetscInt *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j; 679 PetscInt *ci,*cj,*bb; 680 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 681 PetscReal afill; 682 PetscInt i,j,col,ndouble = 0; 683 PetscFreeSpaceList free_space=NULL,current_space=NULL; 684 PetscHeap h; 685 PetscBT bt; 686 687 PetscFunctionBegin; 688 /* Get ci and cj - using a heap for the sorted rows, but use BT so that each index is only added once */ 689 /*---------------------------------------------------------------------------------------------*/ 690 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 691 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 692 ci[0] = 0; 693 694 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 695 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 696 697 current_space = free_space; 698 699 ierr = PetscHeapCreate(a->rmax,&h);CHKERRQ(ierr); 700 ierr = PetscMalloc1(a->rmax,&bb);CHKERRQ(ierr); 701 ierr = PetscBTCreate(bn,&bt);CHKERRQ(ierr); 702 703 /* Determine ci and cj */ 704 for (i=0; i<am; i++) { 705 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 */ 706 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 707 const PetscInt *fptr = current_space->array; /* Save beginning of the row so we can clear the BT later */ 708 ci[i+1] = ci[i]; 709 /* Populate the min heap */ 710 for (j=0; j<anzi; j++) { 711 PetscInt brow = acol[j]; 712 for (bb[j] = bi[brow]; bb[j] < bi[brow+1]; bb[j]++) { 713 PetscInt bcol = bj[bb[j]]; 714 if (!PetscBTLookupSet(bt,bcol)) { /* new entry */ 715 ierr = PetscHeapAdd(h,j,bcol);CHKERRQ(ierr); 716 bb[j]++; 717 break; 718 } 719 } 720 } 721 /* Pick off the min element, adding it to free space */ 722 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 723 while (j >= 0) { 724 if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */ 725 fptr = NULL; /* need PetscBTMemzero */ 726 ierr = PetscFreeSpaceGet(PetscMin(PetscIntMultTruncate(2,current_space->total_array_size),16 << 20),¤t_space);CHKERRQ(ierr); 727 ndouble++; 728 } 729 *(current_space->array++) = col; 730 current_space->local_used++; 731 current_space->local_remaining--; 732 ci[i+1]++; 733 734 /* stash if anything else remains in this row of B */ 735 for (; bb[j] < bi[acol[j]+1]; bb[j]++) { 736 PetscInt bcol = bj[bb[j]]; 737 if (!PetscBTLookupSet(bt,bcol)) { /* new entry */ 738 ierr = PetscHeapAdd(h,j,bcol);CHKERRQ(ierr); 739 bb[j]++; 740 break; 741 } 742 } 743 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 744 } 745 if (fptr) { /* Clear the bits for this row */ 746 for (; fptr<current_space->array; fptr++) {ierr = PetscBTClear(bt,*fptr);CHKERRQ(ierr);} 747 } else { /* We reallocated so we don't remember (easily) how to clear only the bits we changed */ 748 ierr = PetscBTMemzero(bn,bt);CHKERRQ(ierr); 749 } 750 } 751 ierr = PetscFree(bb);CHKERRQ(ierr); 752 ierr = PetscHeapDestroy(&h);CHKERRQ(ierr); 753 ierr = PetscBTDestroy(&bt);CHKERRQ(ierr); 754 755 /* Column indices are in the list of free space */ 756 /* Allocate space for cj, initialize cj, and */ 757 /* destroy list of free space and other temporary array(s) */ 758 ierr = PetscMalloc1(ci[am],&cj);CHKERRQ(ierr); 759 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 760 761 /* put together the new symbolic matrix */ 762 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr); 763 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr); 764 765 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 766 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 767 c = (Mat_SeqAIJ*)(C->data); 768 c->free_a = PETSC_TRUE; 769 c->free_ij = PETSC_TRUE; 770 c->nonew = 0; 771 772 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted; 773 774 /* set MatInfo */ 775 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 776 if (afill < 1.0) afill = 1.0; 777 c->maxnz = ci[am]; 778 c->nz = ci[am]; 779 C->info.mallocs = ndouble; 780 C->info.fill_ratio_given = fill; 781 C->info.fill_ratio_needed = afill; 782 783 #if defined(PETSC_USE_INFO) 784 if (ci[am]) { 785 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 786 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 787 } else { 788 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr); 789 } 790 #endif 791 PetscFunctionReturn(0); 792 } 793 794 795 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_RowMerge(Mat A,Mat B,PetscReal fill,Mat C) 796 { 797 PetscErrorCode ierr; 798 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 799 const PetscInt *ai=a->i,*bi=b->i,*aj=a->j,*bj=b->j,*inputi,*inputj,*inputcol,*inputcol_L1; 800 PetscInt *ci,*cj,*outputj,worki_L1[9],worki_L2[9]; 801 PetscInt c_maxmem,a_maxrownnz=0,a_rownnz; 802 const PetscInt workcol[8]={0,1,2,3,4,5,6,7}; 803 const PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 804 const PetscInt *brow_ptr[8],*brow_end[8]; 805 PetscInt window[8]; 806 PetscInt window_min,old_window_min,ci_nnz,outputi_nnz=0,L1_nrows,L2_nrows; 807 PetscInt i,k,ndouble=0,L1_rowsleft,rowsleft; 808 PetscReal afill; 809 PetscInt *workj_L1,*workj_L2,*workj_L3; 810 PetscInt L1_nnz,L2_nnz; 811 812 /* Step 1: Get upper bound on memory required for allocation. 813 Because of the way virtual memory works, 814 only the memory pages that are actually needed will be physically allocated. */ 815 PetscFunctionBegin; 816 ierr = PetscMalloc1(am+1,&ci);CHKERRQ(ierr); 817 for (i=0; i<am; i++) { 818 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 */ 819 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 820 a_rownnz = 0; 821 for (k=0; k<anzi; ++k) { 822 a_rownnz += bi[acol[k]+1] - bi[acol[k]]; 823 if (a_rownnz > bn) { 824 a_rownnz = bn; 825 break; 826 } 827 } 828 a_maxrownnz = PetscMax(a_maxrownnz, a_rownnz); 829 } 830 /* temporary work areas for merging rows */ 831 ierr = PetscMalloc1(a_maxrownnz*8,&workj_L1);CHKERRQ(ierr); 832 ierr = PetscMalloc1(a_maxrownnz*8,&workj_L2);CHKERRQ(ierr); 833 ierr = PetscMalloc1(a_maxrownnz,&workj_L3);CHKERRQ(ierr); 834 835 /* This should be enough for almost all matrices. If not, memory is reallocated later. */ 836 c_maxmem = 8*(ai[am]+bi[bm]); 837 /* Step 2: Populate pattern for C */ 838 ierr = PetscMalloc1(c_maxmem,&cj);CHKERRQ(ierr); 839 840 ci_nnz = 0; 841 ci[0] = 0; 842 worki_L1[0] = 0; 843 worki_L2[0] = 0; 844 for (i=0; i<am; i++) { 845 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 */ 846 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 847 rowsleft = anzi; 848 inputcol_L1 = acol; 849 L2_nnz = 0; 850 L2_nrows = 1; /* Number of rows to be merged on Level 3. output of L3 already exists -> initial value 1 */ 851 worki_L2[1] = 0; 852 outputi_nnz = 0; 853 854 /* If the number of indices in C so far + the max number of columns in the next row > c_maxmem -> allocate more memory */ 855 while (ci_nnz+a_maxrownnz > c_maxmem) { 856 c_maxmem *= 2; 857 ndouble++; 858 ierr = PetscRealloc(sizeof(PetscInt)*c_maxmem,&cj);CHKERRQ(ierr); 859 } 860 861 while (rowsleft) { 862 L1_rowsleft = PetscMin(64, rowsleft); /* In the inner loop max 64 rows of B can be merged */ 863 L1_nrows = 0; 864 L1_nnz = 0; 865 inputcol = inputcol_L1; 866 inputi = bi; 867 inputj = bj; 868 869 /* The following macro is used to specialize for small rows in A. 870 This helps with compiler unrolling, improving performance substantially. 871 Input: inputj inputi inputcol bn 872 Output: outputj outputi_nnz */ 873 #define MatMatMultSymbolic_RowMergeMacro(ANNZ) \ 874 window_min = bn; \ 875 outputi_nnz = 0; \ 876 for (k=0; k<ANNZ; ++k) { \ 877 brow_ptr[k] = inputj + inputi[inputcol[k]]; \ 878 brow_end[k] = inputj + inputi[inputcol[k]+1]; \ 879 window[k] = (brow_ptr[k] != brow_end[k]) ? *brow_ptr[k] : bn; \ 880 window_min = PetscMin(window[k], window_min); \ 881 } \ 882 while (window_min < bn) { \ 883 outputj[outputi_nnz++] = window_min; \ 884 /* advance front and compute new minimum */ \ 885 old_window_min = window_min; \ 886 window_min = bn; \ 887 for (k=0; k<ANNZ; ++k) { \ 888 if (window[k] == old_window_min) { \ 889 brow_ptr[k]++; \ 890 window[k] = (brow_ptr[k] != brow_end[k]) ? *brow_ptr[k] : bn; \ 891 } \ 892 window_min = PetscMin(window[k], window_min); \ 893 } \ 894 } 895 896 /************** L E V E L 1 ***************/ 897 /* Merge up to 8 rows of B to L1 work array*/ 898 while (L1_rowsleft) { 899 outputi_nnz = 0; 900 if (anzi > 8) outputj = workj_L1 + L1_nnz; /* Level 1 rowmerge*/ 901 else outputj = cj + ci_nnz; /* Merge directly to C */ 902 903 switch (L1_rowsleft) { 904 case 1: brow_ptr[0] = inputj + inputi[inputcol[0]]; 905 brow_end[0] = inputj + inputi[inputcol[0]+1]; 906 for (; brow_ptr[0] != brow_end[0]; ++brow_ptr[0]) outputj[outputi_nnz++] = *brow_ptr[0]; /* copy row in b over */ 907 inputcol += L1_rowsleft; 908 rowsleft -= L1_rowsleft; 909 L1_rowsleft = 0; 910 break; 911 case 2: MatMatMultSymbolic_RowMergeMacro(2); 912 inputcol += L1_rowsleft; 913 rowsleft -= L1_rowsleft; 914 L1_rowsleft = 0; 915 break; 916 case 3: MatMatMultSymbolic_RowMergeMacro(3); 917 inputcol += L1_rowsleft; 918 rowsleft -= L1_rowsleft; 919 L1_rowsleft = 0; 920 break; 921 case 4: MatMatMultSymbolic_RowMergeMacro(4); 922 inputcol += L1_rowsleft; 923 rowsleft -= L1_rowsleft; 924 L1_rowsleft = 0; 925 break; 926 case 5: MatMatMultSymbolic_RowMergeMacro(5); 927 inputcol += L1_rowsleft; 928 rowsleft -= L1_rowsleft; 929 L1_rowsleft = 0; 930 break; 931 case 6: MatMatMultSymbolic_RowMergeMacro(6); 932 inputcol += L1_rowsleft; 933 rowsleft -= L1_rowsleft; 934 L1_rowsleft = 0; 935 break; 936 case 7: MatMatMultSymbolic_RowMergeMacro(7); 937 inputcol += L1_rowsleft; 938 rowsleft -= L1_rowsleft; 939 L1_rowsleft = 0; 940 break; 941 default: MatMatMultSymbolic_RowMergeMacro(8); 942 inputcol += 8; 943 rowsleft -= 8; 944 L1_rowsleft -= 8; 945 break; 946 } 947 inputcol_L1 = inputcol; 948 L1_nnz += outputi_nnz; 949 worki_L1[++L1_nrows] = L1_nnz; 950 } 951 952 /********************** L E V E L 2 ************************/ 953 /* Merge from L1 work array to either C or to L2 work array */ 954 if (anzi > 8) { 955 inputi = worki_L1; 956 inputj = workj_L1; 957 inputcol = workcol; 958 outputi_nnz = 0; 959 960 if (anzi <= 64) outputj = cj + ci_nnz; /* Merge from L1 work array to C */ 961 else outputj = workj_L2 + L2_nnz; /* Merge from L1 work array to L2 work array */ 962 963 switch (L1_nrows) { 964 case 1: brow_ptr[0] = inputj + inputi[inputcol[0]]; 965 brow_end[0] = inputj + inputi[inputcol[0]+1]; 966 for (; brow_ptr[0] != brow_end[0]; ++brow_ptr[0]) outputj[outputi_nnz++] = *brow_ptr[0]; /* copy row in b over */ 967 break; 968 case 2: MatMatMultSymbolic_RowMergeMacro(2); break; 969 case 3: MatMatMultSymbolic_RowMergeMacro(3); break; 970 case 4: MatMatMultSymbolic_RowMergeMacro(4); break; 971 case 5: MatMatMultSymbolic_RowMergeMacro(5); break; 972 case 6: MatMatMultSymbolic_RowMergeMacro(6); break; 973 case 7: MatMatMultSymbolic_RowMergeMacro(7); break; 974 case 8: MatMatMultSymbolic_RowMergeMacro(8); break; 975 default: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatMatMult logic error: Not merging 1-8 rows from L1 work array!"); 976 } 977 L2_nnz += outputi_nnz; 978 worki_L2[++L2_nrows] = L2_nnz; 979 980 /************************ L E V E L 3 **********************/ 981 /* Merge from L2 work array to either C or to L2 work array */ 982 if (anzi > 64 && (L2_nrows == 8 || rowsleft == 0)) { 983 inputi = worki_L2; 984 inputj = workj_L2; 985 inputcol = workcol; 986 outputi_nnz = 0; 987 if (rowsleft) outputj = workj_L3; 988 else outputj = cj + ci_nnz; 989 switch (L2_nrows) { 990 case 1: brow_ptr[0] = inputj + inputi[inputcol[0]]; 991 brow_end[0] = inputj + inputi[inputcol[0]+1]; 992 for (; brow_ptr[0] != brow_end[0]; ++brow_ptr[0]) outputj[outputi_nnz++] = *brow_ptr[0]; /* copy row in b over */ 993 break; 994 case 2: MatMatMultSymbolic_RowMergeMacro(2); break; 995 case 3: MatMatMultSymbolic_RowMergeMacro(3); break; 996 case 4: MatMatMultSymbolic_RowMergeMacro(4); break; 997 case 5: MatMatMultSymbolic_RowMergeMacro(5); break; 998 case 6: MatMatMultSymbolic_RowMergeMacro(6); break; 999 case 7: MatMatMultSymbolic_RowMergeMacro(7); break; 1000 case 8: MatMatMultSymbolic_RowMergeMacro(8); break; 1001 default: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatMatMult logic error: Not merging 1-8 rows from L2 work array!"); 1002 } 1003 L2_nrows = 1; 1004 L2_nnz = outputi_nnz; 1005 worki_L2[1] = outputi_nnz; 1006 /* Copy to workj_L2 */ 1007 if (rowsleft) { 1008 for (k=0; k<outputi_nnz; ++k) workj_L2[k] = outputj[k]; 1009 } 1010 } 1011 } 1012 } /* while (rowsleft) */ 1013 #undef MatMatMultSymbolic_RowMergeMacro 1014 1015 /* terminate current row */ 1016 ci_nnz += outputi_nnz; 1017 ci[i+1] = ci_nnz; 1018 } 1019 1020 /* Step 3: Create the new symbolic matrix */ 1021 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr); 1022 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr); 1023 1024 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 1025 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 1026 c = (Mat_SeqAIJ*)(C->data); 1027 c->free_a = PETSC_TRUE; 1028 c->free_ij = PETSC_TRUE; 1029 c->nonew = 0; 1030 1031 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted; 1032 1033 /* set MatInfo */ 1034 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 1035 if (afill < 1.0) afill = 1.0; 1036 c->maxnz = ci[am]; 1037 c->nz = ci[am]; 1038 C->info.mallocs = ndouble; 1039 C->info.fill_ratio_given = fill; 1040 C->info.fill_ratio_needed = afill; 1041 1042 #if defined(PETSC_USE_INFO) 1043 if (ci[am]) { 1044 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 1045 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 1046 } else { 1047 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr); 1048 } 1049 #endif 1050 1051 /* Step 4: Free temporary work areas */ 1052 ierr = PetscFree(workj_L1);CHKERRQ(ierr); 1053 ierr = PetscFree(workj_L2);CHKERRQ(ierr); 1054 ierr = PetscFree(workj_L3);CHKERRQ(ierr); 1055 PetscFunctionReturn(0); 1056 } 1057 1058 /* concatenate unique entries and then sort */ 1059 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Sorted(Mat A,Mat B,PetscReal fill,Mat C) 1060 { 1061 PetscErrorCode ierr; 1062 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 1063 const PetscInt *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j; 1064 PetscInt *ci,*cj; 1065 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 1066 PetscReal afill; 1067 PetscInt i,j,ndouble = 0; 1068 PetscSegBuffer seg,segrow; 1069 char *seen; 1070 1071 PetscFunctionBegin; 1072 ierr = PetscMalloc1(am+1,&ci);CHKERRQ(ierr); 1073 ci[0] = 0; 1074 1075 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 1076 ierr = PetscSegBufferCreate(sizeof(PetscInt),(PetscInt)(fill*(ai[am]+bi[bm])),&seg);CHKERRQ(ierr); 1077 ierr = PetscSegBufferCreate(sizeof(PetscInt),100,&segrow);CHKERRQ(ierr); 1078 ierr = PetscCalloc1(bn,&seen);CHKERRQ(ierr); 1079 1080 /* Determine ci and cj */ 1081 for (i=0; i<am; i++) { 1082 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 */ 1083 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 1084 PetscInt packlen = 0,*PETSC_RESTRICT crow; 1085 /* Pack segrow */ 1086 for (j=0; j<anzi; j++) { 1087 PetscInt brow = acol[j],bjstart = bi[brow],bjend = bi[brow+1],k; 1088 for (k=bjstart; k<bjend; k++) { 1089 PetscInt bcol = bj[k]; 1090 if (!seen[bcol]) { /* new entry */ 1091 PetscInt *PETSC_RESTRICT slot; 1092 ierr = PetscSegBufferGetInts(segrow,1,&slot);CHKERRQ(ierr); 1093 *slot = bcol; 1094 seen[bcol] = 1; 1095 packlen++; 1096 } 1097 } 1098 } 1099 ierr = PetscSegBufferGetInts(seg,packlen,&crow);CHKERRQ(ierr); 1100 ierr = PetscSegBufferExtractTo(segrow,crow);CHKERRQ(ierr); 1101 ierr = PetscSortInt(packlen,crow);CHKERRQ(ierr); 1102 ci[i+1] = ci[i] + packlen; 1103 for (j=0; j<packlen; j++) seen[crow[j]] = 0; 1104 } 1105 ierr = PetscSegBufferDestroy(&segrow);CHKERRQ(ierr); 1106 ierr = PetscFree(seen);CHKERRQ(ierr); 1107 1108 /* Column indices are in the segmented buffer */ 1109 ierr = PetscSegBufferExtractAlloc(seg,&cj);CHKERRQ(ierr); 1110 ierr = PetscSegBufferDestroy(&seg);CHKERRQ(ierr); 1111 1112 /* put together the new symbolic matrix */ 1113 ierr = MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,((PetscObject)A)->type_name,C);CHKERRQ(ierr); 1114 ierr = MatSetBlockSizesFromMats(C,A,B);CHKERRQ(ierr); 1115 1116 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 1117 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 1118 c = (Mat_SeqAIJ*)(C->data); 1119 c->free_a = PETSC_TRUE; 1120 c->free_ij = PETSC_TRUE; 1121 c->nonew = 0; 1122 1123 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted; 1124 1125 /* set MatInfo */ 1126 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 1127 if (afill < 1.0) afill = 1.0; 1128 c->maxnz = ci[am]; 1129 c->nz = ci[am]; 1130 C->info.mallocs = ndouble; 1131 C->info.fill_ratio_given = fill; 1132 C->info.fill_ratio_needed = afill; 1133 1134 #if defined(PETSC_USE_INFO) 1135 if (ci[am]) { 1136 ierr = PetscInfo3(C,"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 1137 ierr = PetscInfo1(C,"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 1138 } else { 1139 ierr = PetscInfo(C,"Empty matrix product\n");CHKERRQ(ierr); 1140 } 1141 #endif 1142 PetscFunctionReturn(0); 1143 } 1144 1145 PetscErrorCode MatDestroy_SeqAIJ_MatMatMultTrans(Mat A) 1146 { 1147 PetscErrorCode ierr; 1148 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 1149 Mat_MatMatTransMult *abt=a->abt; 1150 1151 PetscFunctionBegin; 1152 ierr = (abt->destroy)(A);CHKERRQ(ierr); 1153 ierr = MatTransposeColoringDestroy(&abt->matcoloring);CHKERRQ(ierr); 1154 ierr = MatDestroy(&abt->Bt_den);CHKERRQ(ierr); 1155 ierr = MatDestroy(&abt->ABt_den);CHKERRQ(ierr); 1156 ierr = PetscFree(abt);CHKERRQ(ierr); 1157 PetscFunctionReturn(0); 1158 } 1159 1160 PetscErrorCode MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C) 1161 { 1162 PetscErrorCode ierr; 1163 Mat Bt; 1164 PetscInt *bti,*btj; 1165 Mat_MatMatTransMult *abt; 1166 Mat_SeqAIJ *c; 1167 Mat_Product *product = C->product; 1168 MatProductAlgorithm alg = product->alg; 1169 1170 PetscFunctionBegin; 1171 /* create symbolic Bt */ 1172 ierr = MatGetSymbolicTranspose_SeqAIJ(B,&bti,&btj);CHKERRQ(ierr); 1173 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,B->cmap->n,B->rmap->n,bti,btj,NULL,&Bt);CHKERRQ(ierr); 1174 ierr = MatSetBlockSizes(Bt,PetscAbs(A->cmap->bs),PetscAbs(B->cmap->bs));CHKERRQ(ierr); 1175 ierr = MatSetType(Bt,((PetscObject)A)->type_name);CHKERRQ(ierr); 1176 1177 /* get symbolic C=A*Bt */ 1178 ierr = MatProductSetAlgorithm(C,"sorted");CHKERRQ(ierr); /* set algorithm for C = A*Bt */ 1179 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,Bt,fill,C);CHKERRQ(ierr); 1180 ierr = MatProductSetAlgorithm(C,alg);CHKERRQ(ierr); /* resume original algorithm for ABt product */ 1181 1182 /* create a supporting struct for reuse intermidiate dense matrices with matcoloring */ 1183 ierr = PetscNew(&abt);CHKERRQ(ierr); 1184 c = (Mat_SeqAIJ*)C->data; 1185 c->abt = abt; 1186 1187 abt->usecoloring = PETSC_FALSE; 1188 abt->destroy = C->ops->destroy; 1189 C->ops->destroy = MatDestroy_SeqAIJ_MatMatMultTrans; 1190 C->ops->mattransposemultnumeric = MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ; 1191 1192 abt->usecoloring = PETSC_FALSE; 1193 ierr = PetscStrcmp(product->alg,"color",&abt->usecoloring);CHKERRQ(ierr); 1194 if (abt->usecoloring) { 1195 /* Create MatTransposeColoring from symbolic C=A*B^T */ 1196 MatTransposeColoring matcoloring; 1197 MatColoring coloring; 1198 ISColoring iscoloring; 1199 Mat Bt_dense,C_dense; 1200 1201 /* inode causes memory problem */ 1202 ierr = MatSetOption(C,MAT_USE_INODES,PETSC_FALSE);CHKERRQ(ierr); 1203 1204 ierr = MatColoringCreate(C,&coloring);CHKERRQ(ierr); 1205 ierr = MatColoringSetDistance(coloring,2);CHKERRQ(ierr); 1206 ierr = MatColoringSetType(coloring,MATCOLORINGSL);CHKERRQ(ierr); 1207 ierr = MatColoringSetFromOptions(coloring);CHKERRQ(ierr); 1208 ierr = MatColoringApply(coloring,&iscoloring);CHKERRQ(ierr); 1209 ierr = MatColoringDestroy(&coloring);CHKERRQ(ierr); 1210 ierr = MatTransposeColoringCreate(C,iscoloring,&matcoloring);CHKERRQ(ierr); 1211 1212 abt->matcoloring = matcoloring; 1213 1214 ierr = ISColoringDestroy(&iscoloring);CHKERRQ(ierr); 1215 1216 /* Create Bt_dense and C_dense = A*Bt_dense */ 1217 ierr = MatCreate(PETSC_COMM_SELF,&Bt_dense);CHKERRQ(ierr); 1218 ierr = MatSetSizes(Bt_dense,A->cmap->n,matcoloring->ncolors,A->cmap->n,matcoloring->ncolors);CHKERRQ(ierr); 1219 ierr = MatSetType(Bt_dense,MATSEQDENSE);CHKERRQ(ierr); 1220 ierr = MatSeqDenseSetPreallocation(Bt_dense,NULL);CHKERRQ(ierr); 1221 1222 Bt_dense->assembled = PETSC_TRUE; 1223 abt->Bt_den = Bt_dense; 1224 1225 ierr = MatCreate(PETSC_COMM_SELF,&C_dense);CHKERRQ(ierr); 1226 ierr = MatSetSizes(C_dense,A->rmap->n,matcoloring->ncolors,A->rmap->n,matcoloring->ncolors);CHKERRQ(ierr); 1227 ierr = MatSetType(C_dense,MATSEQDENSE);CHKERRQ(ierr); 1228 ierr = MatSeqDenseSetPreallocation(C_dense,NULL);CHKERRQ(ierr); 1229 1230 Bt_dense->assembled = PETSC_TRUE; 1231 abt->ABt_den = C_dense; 1232 1233 #if defined(PETSC_USE_INFO) 1234 { 1235 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 1236 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); 1237 } 1238 #endif 1239 } 1240 /* clean up */ 1241 ierr = MatDestroy(&Bt);CHKERRQ(ierr); 1242 ierr = MatRestoreSymbolicTranspose_SeqAIJ(B,&bti,&btj);CHKERRQ(ierr); 1243 PetscFunctionReturn(0); 1244 } 1245 1246 PetscErrorCode MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 1247 { 1248 PetscErrorCode ierr; 1249 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data; 1250 PetscInt *ai =a->i,*aj=a->j,*bi=b->i,*bj=b->j,anzi,bnzj,nexta,nextb,*acol,*bcol,brow; 1251 PetscInt cm =C->rmap->n,*ci=c->i,*cj=c->j,i,j,cnzi,*ccol; 1252 PetscLogDouble flops=0.0; 1253 MatScalar *aa =a->a,*aval,*ba=b->a,*bval,*ca,*cval; 1254 Mat_MatMatTransMult *abt = c->abt; 1255 1256 PetscFunctionBegin; 1257 /* clear old values in C */ 1258 if (!c->a) { 1259 ierr = PetscCalloc1(ci[cm]+1,&ca);CHKERRQ(ierr); 1260 c->a = ca; 1261 c->free_a = PETSC_TRUE; 1262 } else { 1263 ca = c->a; 1264 ierr = PetscArrayzero(ca,ci[cm]+1);CHKERRQ(ierr); 1265 } 1266 1267 if (abt->usecoloring) { 1268 MatTransposeColoring matcoloring = abt->matcoloring; 1269 Mat Bt_dense,C_dense = abt->ABt_den; 1270 1271 /* Get Bt_dense by Apply MatTransposeColoring to B */ 1272 Bt_dense = abt->Bt_den; 1273 ierr = MatTransColoringApplySpToDen(matcoloring,B,Bt_dense);CHKERRQ(ierr); 1274 1275 /* C_dense = A*Bt_dense */ 1276 ierr = MatMatMultNumeric_SeqAIJ_SeqDense(A,Bt_dense,C_dense);CHKERRQ(ierr); 1277 1278 /* Recover C from C_dense */ 1279 ierr = MatTransColoringApplyDenToSp(matcoloring,C_dense,C);CHKERRQ(ierr); 1280 PetscFunctionReturn(0); 1281 } 1282 1283 for (i=0; i<cm; i++) { 1284 anzi = ai[i+1] - ai[i]; 1285 acol = aj + ai[i]; 1286 aval = aa + ai[i]; 1287 cnzi = ci[i+1] - ci[i]; 1288 ccol = cj + ci[i]; 1289 cval = ca + ci[i]; 1290 for (j=0; j<cnzi; j++) { 1291 brow = ccol[j]; 1292 bnzj = bi[brow+1] - bi[brow]; 1293 bcol = bj + bi[brow]; 1294 bval = ba + bi[brow]; 1295 1296 /* perform sparse inner-product c(i,j)=A[i,:]*B[j,:]^T */ 1297 nexta = 0; nextb = 0; 1298 while (nexta<anzi && nextb<bnzj) { 1299 while (nexta < anzi && acol[nexta] < bcol[nextb]) nexta++; 1300 if (nexta == anzi) break; 1301 while (nextb < bnzj && acol[nexta] > bcol[nextb]) nextb++; 1302 if (nextb == bnzj) break; 1303 if (acol[nexta] == bcol[nextb]) { 1304 cval[j] += aval[nexta]*bval[nextb]; 1305 nexta++; nextb++; 1306 flops += 2; 1307 } 1308 } 1309 } 1310 } 1311 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1312 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1313 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 1314 PetscFunctionReturn(0); 1315 } 1316 1317 PetscErrorCode MatDestroy_SeqAIJ_MatTransMatMult(Mat A) 1318 { 1319 PetscErrorCode ierr; 1320 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1321 Mat_MatTransMatMult *atb = a->atb; 1322 1323 PetscFunctionBegin; 1324 if (atb) { 1325 ierr = MatDestroy(&atb->At);CHKERRQ(ierr); 1326 ierr = (*atb->destroy)(A);CHKERRQ(ierr); 1327 } 1328 ierr = PetscFree(atb);CHKERRQ(ierr); 1329 PetscFunctionReturn(0); 1330 } 1331 1332 PetscErrorCode MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C) 1333 { 1334 PetscErrorCode ierr; 1335 Mat At; 1336 PetscInt *ati,*atj; 1337 Mat_Product *product = C->product; 1338 MatProductAlgorithm alg; 1339 PetscBool flg; 1340 1341 PetscFunctionBegin; 1342 if (product) { 1343 alg = product->alg; 1344 } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"!product, not supported yet"); 1345 1346 /* outerproduct */ 1347 ierr = PetscStrcmp(alg,"outerproduct",&flg);CHKERRQ(ierr); 1348 if (flg) { 1349 /* create symbolic At */ 1350 ierr = MatGetSymbolicTranspose_SeqAIJ(A,&ati,&atj);CHKERRQ(ierr); 1351 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,A->cmap->n,A->rmap->n,ati,atj,NULL,&At);CHKERRQ(ierr); 1352 ierr = MatSetBlockSizes(At,PetscAbs(A->cmap->bs),PetscAbs(B->cmap->bs));CHKERRQ(ierr); 1353 ierr = MatSetType(At,((PetscObject)A)->type_name);CHKERRQ(ierr); 1354 1355 /* get symbolic C=At*B */ 1356 product->alg = "sorted"; 1357 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(At,B,fill,C);CHKERRQ(ierr); 1358 1359 /* clean up */ 1360 ierr = MatDestroy(&At);CHKERRQ(ierr); 1361 ierr = MatRestoreSymbolicTranspose_SeqAIJ(A,&ati,&atj);CHKERRQ(ierr); 1362 1363 C->ops->mattransposemultnumeric = MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ; /* outerproduct */ 1364 PetscFunctionReturn(0); 1365 } 1366 1367 /* matmatmult */ 1368 ierr = PetscStrcmp(alg,"at*b",&flg);CHKERRQ(ierr); 1369 if (flg) { 1370 Mat_MatTransMatMult *atb; 1371 Mat_SeqAIJ *c; 1372 1373 ierr = PetscNew(&atb);CHKERRQ(ierr); 1374 ierr = MatTranspose_SeqAIJ(A,MAT_INITIAL_MATRIX,&At);CHKERRQ(ierr); 1375 product->alg = "sorted"; 1376 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(At,B,fill,C);CHKERRQ(ierr); 1377 1378 c = (Mat_SeqAIJ*)C->data; 1379 c->atb = atb; 1380 atb->At = At; 1381 atb->destroy = C->ops->destroy; 1382 atb->updateAt = PETSC_FALSE; /* because At is computed here */ 1383 C->ops->destroy = MatDestroy_SeqAIJ_MatTransMatMult; 1384 1385 C->ops->mattransposemultnumeric = NULL; /* see MatProductNumeric_AtB_SeqAIJ_SeqAIJ */ 1386 PetscFunctionReturn(0); 1387 } 1388 1389 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Mat Product Algorithm is not supported"); 1390 PetscFunctionReturn(0); 1391 } 1392 1393 PetscErrorCode MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 1394 { 1395 PetscErrorCode ierr; 1396 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data; 1397 PetscInt am =A->rmap->n,anzi,*ai=a->i,*aj=a->j,*bi=b->i,*bj,bnzi,nextb; 1398 PetscInt cm =C->rmap->n,*ci=c->i,*cj=c->j,crow,*cjj,i,j,k; 1399 PetscLogDouble flops=0.0; 1400 MatScalar *aa =a->a,*ba,*ca,*caj; 1401 1402 PetscFunctionBegin; 1403 if (!c->a) { 1404 ierr = PetscCalloc1(ci[cm]+1,&ca);CHKERRQ(ierr); 1405 1406 c->a = ca; 1407 c->free_a = PETSC_TRUE; 1408 } else { 1409 ca = c->a; 1410 ierr = PetscArrayzero(ca,ci[cm]);CHKERRQ(ierr); 1411 } 1412 1413 /* compute A^T*B using outer product (A^T)[:,i]*B[i,:] */ 1414 for (i=0; i<am; i++) { 1415 bj = b->j + bi[i]; 1416 ba = b->a + bi[i]; 1417 bnzi = bi[i+1] - bi[i]; 1418 anzi = ai[i+1] - ai[i]; 1419 for (j=0; j<anzi; j++) { 1420 nextb = 0; 1421 crow = *aj++; 1422 cjj = cj + ci[crow]; 1423 caj = ca + ci[crow]; 1424 /* perform sparse axpy operation. Note cjj includes bj. */ 1425 for (k=0; nextb<bnzi; k++) { 1426 if (cjj[k] == *(bj+nextb)) { /* ccol == bcol */ 1427 caj[k] += (*aa)*(*(ba+nextb)); 1428 nextb++; 1429 } 1430 } 1431 flops += 2*bnzi; 1432 aa++; 1433 } 1434 } 1435 1436 /* Assemble the final matrix and clean up */ 1437 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1438 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1439 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 1440 PetscFunctionReturn(0); 1441 } 1442 1443 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqDense(Mat A,Mat B,PetscReal fill,Mat C) 1444 { 1445 PetscErrorCode ierr; 1446 1447 PetscFunctionBegin; 1448 ierr = MatMatMultSymbolic_SeqDense_SeqDense(A,B,0.0,C);CHKERRQ(ierr); 1449 1450 C->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqDense; 1451 PetscFunctionReturn(0); 1452 } 1453 1454 PetscErrorCode MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat A,Mat B,Mat C) 1455 { 1456 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 1457 Mat_SeqDense *bd=(Mat_SeqDense*)B->data; 1458 Mat_SeqDense *cd=(Mat_SeqDense*)C->data; 1459 PetscErrorCode ierr; 1460 PetscScalar *c,r1,r2,r3,r4,*c1,*c2,*c3,*c4; 1461 const PetscScalar *aa,*b,*b1,*b2,*b3,*b4,*av; 1462 const PetscInt *aj; 1463 PetscInt cm=C->rmap->n,cn=B->cmap->n,bm=bd->lda,am=A->rmap->n; 1464 PetscInt clda=cd->lda; 1465 PetscInt am4=4*clda,bm4=4*bm,col,i,j,n; 1466 1467 PetscFunctionBegin; 1468 if (!cm || !cn) PetscFunctionReturn(0); 1469 ierr = MatSeqAIJGetArrayRead(A,&av);CHKERRQ(ierr); 1470 ierr = MatDenseGetArray(C,&c);CHKERRQ(ierr); 1471 ierr = MatDenseGetArrayRead(B,&b);CHKERRQ(ierr); 1472 b1 = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm; 1473 c1 = c; c2 = c1 + clda; c3 = c2 + clda; c4 = c3 + clda; 1474 for (col=0; col<(cn/4)*4; col += 4) { /* over columns of C */ 1475 for (i=0; i<am; i++) { /* over rows of A in those columns */ 1476 r1 = r2 = r3 = r4 = 0.0; 1477 n = a->i[i+1] - a->i[i]; 1478 aj = a->j + a->i[i]; 1479 aa = av + a->i[i]; 1480 for (j=0; j<n; j++) { 1481 const PetscScalar aatmp = aa[j]; 1482 const PetscInt ajtmp = aj[j]; 1483 r1 += aatmp*b1[ajtmp]; 1484 r2 += aatmp*b2[ajtmp]; 1485 r3 += aatmp*b3[ajtmp]; 1486 r4 += aatmp*b4[ajtmp]; 1487 } 1488 c1[i] += r1; 1489 c2[i] += r2; 1490 c3[i] += r3; 1491 c4[i] += r4; 1492 } 1493 b1 += bm4; b2 += bm4; b3 += bm4; b4 += bm4; 1494 c1 += am4; c2 += am4; c3 += am4; c4 += am4; 1495 } 1496 for (; col<cn; col++) { /* over extra columns of C */ 1497 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1498 r1 = 0.0; 1499 n = a->i[i+1] - a->i[i]; 1500 aj = a->j + a->i[i]; 1501 aa = av + a->i[i]; 1502 for (j=0; j<n; j++) { 1503 r1 += aa[j]*b1[aj[j]]; 1504 } 1505 c1[i] += r1; 1506 } 1507 b1 += bm; 1508 c1 += clda; 1509 } 1510 ierr = PetscLogFlops(cn*(2.0*a->nz));CHKERRQ(ierr); 1511 ierr = MatDenseRestoreArray(C,&c);CHKERRQ(ierr); 1512 ierr = MatDenseRestoreArrayRead(B,&b);CHKERRQ(ierr); 1513 ierr = MatSeqAIJRestoreArrayRead(A,&av);CHKERRQ(ierr); 1514 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1515 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1516 PetscFunctionReturn(0); 1517 } 1518 1519 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqDense(Mat A,Mat B,Mat C) 1520 { 1521 PetscErrorCode ierr; 1522 1523 PetscFunctionBegin; 1524 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); 1525 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); 1526 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); 1527 1528 ierr = MatZeroEntries(C);CHKERRQ(ierr); 1529 ierr = MatMatMultNumericAdd_SeqAIJ_SeqDense(A,B,C);CHKERRQ(ierr); 1530 PetscFunctionReturn(0); 1531 } 1532 1533 /* ------------------------------------------------------- */ 1534 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense_AB(Mat C) 1535 { 1536 PetscFunctionBegin; 1537 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqAIJ_SeqDense; 1538 C->ops->productsymbolic = MatProductSymbolic_AB; 1539 /* dense mat may not call MatProductSymbolic(), thus set C->ops->productnumeric here */ 1540 C->ops->productnumeric = MatProductNumeric_AB; 1541 PetscFunctionReturn(0); 1542 } 1543 1544 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense_AtB(Mat C) 1545 { 1546 PetscFunctionBegin; 1547 C->ops->transposematmultsymbolic = MatTransposeMatMultSymbolic_SeqAIJ_SeqDense; 1548 C->ops->productsymbolic = MatProductSymbolic_AtB; 1549 C->ops->productnumeric = MatProductNumeric_AtB; 1550 PetscFunctionReturn(0); 1551 } 1552 1553 PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqDense(Mat C) 1554 { 1555 PetscErrorCode ierr; 1556 Mat_Product *product = C->product; 1557 1558 PetscFunctionBegin; 1559 switch (product->type) { 1560 case MATPRODUCT_AB: 1561 ierr = MatProductSetFromOptions_SeqAIJ_SeqDense_AB(C);CHKERRQ(ierr); 1562 break; 1563 case MATPRODUCT_AtB: 1564 ierr = MatProductSetFromOptions_SeqAIJ_SeqDense_AtB(C);CHKERRQ(ierr); 1565 break; 1566 case MATPRODUCT_PtAP: 1567 ierr = MatProductSetFromOptions_SeqDense(C);CHKERRQ(ierr); 1568 break; 1569 default: 1570 /* Use MatProduct_Basic() if there is no specific implementation */ 1571 C->ops->productsymbolic = MatProductSymbolic_Basic; 1572 } 1573 PetscFunctionReturn(0); 1574 } 1575 /* ------------------------------------------------------- */ 1576 static PetscErrorCode MatProductSetFromOptions_SeqXBAIJ_SeqDense_AB(Mat C) 1577 { 1578 PetscErrorCode ierr; 1579 Mat_Product *product = C->product; 1580 Mat A = product->A; 1581 PetscBool baij; 1582 1583 PetscFunctionBegin; 1584 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQBAIJ,&baij);CHKERRQ(ierr); 1585 if (!baij) { /* A is seqsbaij */ 1586 PetscBool sbaij; 1587 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQSBAIJ,&sbaij);CHKERRQ(ierr); 1588 if (!sbaij) SETERRQ(PetscObjectComm((PetscObject)C),PETSC_ERR_ARG_WRONGSTATE,"Mat must be either seqbaij or seqsbaij format"); 1589 1590 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqSBAIJ_SeqDense; 1591 } else { /* A is seqbaij */ 1592 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqBAIJ_SeqDense; 1593 } 1594 1595 C->ops->productsymbolic = MatProductSymbolic_AB; 1596 C->ops->productnumeric = MatProductNumeric_AB; 1597 PetscFunctionReturn(0); 1598 } 1599 1600 PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqXBAIJ_SeqDense(Mat C) 1601 { 1602 PetscErrorCode ierr; 1603 Mat_Product *product = C->product; 1604 1605 PetscFunctionBegin; 1606 if (product->type == MATPRODUCT_AB) { 1607 ierr = MatProductSetFromOptions_SeqXBAIJ_SeqDense_AB(C);CHKERRQ(ierr); 1608 } else SETERRQ1(PetscObjectComm((PetscObject)C),PETSC_ERR_SUP,"MatProduct type %s is not supported for SeqXBAIJ and SeqDense matrices",MatProductTypes[product->type]); 1609 PetscFunctionReturn(0); 1610 } 1611 /* ------------------------------------------------------- */ 1612 static PetscErrorCode MatProductSetFromOptions_SeqDense_SeqAIJ_AB(Mat C) 1613 { 1614 PetscFunctionBegin; 1615 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqDense_SeqAIJ; 1616 C->ops->productsymbolic = MatProductSymbolic_AB; 1617 C->ops->productnumeric = MatProductNumeric_AB; 1618 PetscFunctionReturn(0); 1619 } 1620 1621 PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqDense_SeqAIJ(Mat C) 1622 { 1623 PetscErrorCode ierr; 1624 Mat_Product *product = C->product; 1625 1626 PetscFunctionBegin; 1627 if (product->type == MATPRODUCT_AB) { 1628 ierr = MatProductSetFromOptions_SeqDense_SeqAIJ_AB(C);CHKERRQ(ierr); 1629 } else SETERRQ1(PetscObjectComm((PetscObject)C),PETSC_ERR_SUP,"MatProduct type %s is not supported for SeqDense and SeqAIJ matrices",MatProductTypes[product->type]); 1630 PetscFunctionReturn(0); 1631 } 1632 /* ------------------------------------------------------- */ 1633 1634 PetscErrorCode MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring coloring,Mat B,Mat Btdense) 1635 { 1636 PetscErrorCode ierr; 1637 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 1638 Mat_SeqDense *btdense = (Mat_SeqDense*)Btdense->data; 1639 PetscInt *bi = b->i,*bj=b->j; 1640 PetscInt m = Btdense->rmap->n,n=Btdense->cmap->n,j,k,l,col,anz,*btcol,brow,ncolumns; 1641 MatScalar *btval,*btval_den,*ba=b->a; 1642 PetscInt *columns=coloring->columns,*colorforcol=coloring->colorforcol,ncolors=coloring->ncolors; 1643 1644 PetscFunctionBegin; 1645 btval_den=btdense->v; 1646 ierr = PetscArrayzero(btval_den,m*n);CHKERRQ(ierr); 1647 for (k=0; k<ncolors; k++) { 1648 ncolumns = coloring->ncolumns[k]; 1649 for (l=0; l<ncolumns; l++) { /* insert a row of B to a column of Btdense */ 1650 col = *(columns + colorforcol[k] + l); 1651 btcol = bj + bi[col]; 1652 btval = ba + bi[col]; 1653 anz = bi[col+1] - bi[col]; 1654 for (j=0; j<anz; j++) { 1655 brow = btcol[j]; 1656 btval_den[brow] = btval[j]; 1657 } 1658 } 1659 btval_den += m; 1660 } 1661 PetscFunctionReturn(0); 1662 } 1663 1664 PetscErrorCode MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring matcoloring,Mat Cden,Mat Csp) 1665 { 1666 PetscErrorCode ierr; 1667 Mat_SeqAIJ *csp = (Mat_SeqAIJ*)Csp->data; 1668 const PetscScalar *ca_den,*ca_den_ptr; 1669 PetscScalar *ca=csp->a; 1670 PetscInt k,l,m=Cden->rmap->n,ncolors=matcoloring->ncolors; 1671 PetscInt brows=matcoloring->brows,*den2sp=matcoloring->den2sp; 1672 PetscInt nrows,*row,*idx; 1673 PetscInt *rows=matcoloring->rows,*colorforrow=matcoloring->colorforrow; 1674 1675 PetscFunctionBegin; 1676 ierr = MatDenseGetArrayRead(Cden,&ca_den);CHKERRQ(ierr); 1677 1678 if (brows > 0) { 1679 PetscInt *lstart,row_end,row_start; 1680 lstart = matcoloring->lstart; 1681 ierr = PetscArrayzero(lstart,ncolors);CHKERRQ(ierr); 1682 1683 row_end = brows; 1684 if (row_end > m) row_end = m; 1685 for (row_start=0; row_start<m; row_start+=brows) { /* loop over row blocks of Csp */ 1686 ca_den_ptr = ca_den; 1687 for (k=0; k<ncolors; k++) { /* loop over colors (columns of Cden) */ 1688 nrows = matcoloring->nrows[k]; 1689 row = rows + colorforrow[k]; 1690 idx = den2sp + colorforrow[k]; 1691 for (l=lstart[k]; l<nrows; l++) { 1692 if (row[l] >= row_end) { 1693 lstart[k] = l; 1694 break; 1695 } else { 1696 ca[idx[l]] = ca_den_ptr[row[l]]; 1697 } 1698 } 1699 ca_den_ptr += m; 1700 } 1701 row_end += brows; 1702 if (row_end > m) row_end = m; 1703 } 1704 } else { /* non-blocked impl: loop over columns of Csp - slow if Csp is large */ 1705 ca_den_ptr = ca_den; 1706 for (k=0; k<ncolors; k++) { 1707 nrows = matcoloring->nrows[k]; 1708 row = rows + colorforrow[k]; 1709 idx = den2sp + colorforrow[k]; 1710 for (l=0; l<nrows; l++) { 1711 ca[idx[l]] = ca_den_ptr[row[l]]; 1712 } 1713 ca_den_ptr += m; 1714 } 1715 } 1716 1717 ierr = MatDenseRestoreArrayRead(Cden,&ca_den);CHKERRQ(ierr); 1718 #if defined(PETSC_USE_INFO) 1719 if (matcoloring->brows > 0) { 1720 ierr = PetscInfo1(Csp,"Loop over %D row blocks for den2sp\n",brows);CHKERRQ(ierr); 1721 } else { 1722 ierr = PetscInfo(Csp,"Loop over colors/columns of Cden, inefficient for large sparse matrix product \n");CHKERRQ(ierr); 1723 } 1724 #endif 1725 PetscFunctionReturn(0); 1726 } 1727 1728 PetscErrorCode MatTransposeColoringCreate_SeqAIJ(Mat mat,ISColoring iscoloring,MatTransposeColoring c) 1729 { 1730 PetscErrorCode ierr; 1731 PetscInt i,n,nrows,Nbs,j,k,m,ncols,col,cm; 1732 const PetscInt *is,*ci,*cj,*row_idx; 1733 PetscInt nis = iscoloring->n,*rowhit,bs = 1; 1734 IS *isa; 1735 Mat_SeqAIJ *csp = (Mat_SeqAIJ*)mat->data; 1736 PetscInt *colorforrow,*rows,*rows_i,*idxhit,*spidx,*den2sp,*den2sp_i; 1737 PetscInt *colorforcol,*columns,*columns_i,brows; 1738 PetscBool flg; 1739 1740 PetscFunctionBegin; 1741 ierr = ISColoringGetIS(iscoloring,PETSC_USE_POINTER,PETSC_IGNORE,&isa);CHKERRQ(ierr); 1742 1743 /* bs >1 is not being tested yet! */ 1744 Nbs = mat->cmap->N/bs; 1745 c->M = mat->rmap->N/bs; /* set total rows, columns and local rows */ 1746 c->N = Nbs; 1747 c->m = c->M; 1748 c->rstart = 0; 1749 c->brows = 100; 1750 1751 c->ncolors = nis; 1752 ierr = PetscMalloc3(nis,&c->ncolumns,nis,&c->nrows,nis+1,&colorforrow);CHKERRQ(ierr); 1753 ierr = PetscMalloc1(csp->nz+1,&rows);CHKERRQ(ierr); 1754 ierr = PetscMalloc1(csp->nz+1,&den2sp);CHKERRQ(ierr); 1755 1756 brows = c->brows; 1757 ierr = PetscOptionsGetInt(NULL,NULL,"-matden2sp_brows",&brows,&flg);CHKERRQ(ierr); 1758 if (flg) c->brows = brows; 1759 if (brows > 0) { 1760 ierr = PetscMalloc1(nis+1,&c->lstart);CHKERRQ(ierr); 1761 } 1762 1763 colorforrow[0] = 0; 1764 rows_i = rows; 1765 den2sp_i = den2sp; 1766 1767 ierr = PetscMalloc1(nis+1,&colorforcol);CHKERRQ(ierr); 1768 ierr = PetscMalloc1(Nbs+1,&columns);CHKERRQ(ierr); 1769 1770 colorforcol[0] = 0; 1771 columns_i = columns; 1772 1773 /* get column-wise storage of mat */ 1774 ierr = MatGetColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);CHKERRQ(ierr); 1775 1776 cm = c->m; 1777 ierr = PetscMalloc1(cm+1,&rowhit);CHKERRQ(ierr); 1778 ierr = PetscMalloc1(cm+1,&idxhit);CHKERRQ(ierr); 1779 for (i=0; i<nis; i++) { /* loop over color */ 1780 ierr = ISGetLocalSize(isa[i],&n);CHKERRQ(ierr); 1781 ierr = ISGetIndices(isa[i],&is);CHKERRQ(ierr); 1782 1783 c->ncolumns[i] = n; 1784 if (n) { 1785 ierr = PetscArraycpy(columns_i,is,n);CHKERRQ(ierr); 1786 } 1787 colorforcol[i+1] = colorforcol[i] + n; 1788 columns_i += n; 1789 1790 /* fast, crude version requires O(N*N) work */ 1791 ierr = PetscArrayzero(rowhit,cm);CHKERRQ(ierr); 1792 1793 for (j=0; j<n; j++) { /* loop over columns*/ 1794 col = is[j]; 1795 row_idx = cj + ci[col]; 1796 m = ci[col+1] - ci[col]; 1797 for (k=0; k<m; k++) { /* loop over columns marking them in rowhit */ 1798 idxhit[*row_idx] = spidx[ci[col] + k]; 1799 rowhit[*row_idx++] = col + 1; 1800 } 1801 } 1802 /* count the number of hits */ 1803 nrows = 0; 1804 for (j=0; j<cm; j++) { 1805 if (rowhit[j]) nrows++; 1806 } 1807 c->nrows[i] = nrows; 1808 colorforrow[i+1] = colorforrow[i] + nrows; 1809 1810 nrows = 0; 1811 for (j=0; j<cm; j++) { /* loop over rows */ 1812 if (rowhit[j]) { 1813 rows_i[nrows] = j; 1814 den2sp_i[nrows] = idxhit[j]; 1815 nrows++; 1816 } 1817 } 1818 den2sp_i += nrows; 1819 1820 ierr = ISRestoreIndices(isa[i],&is);CHKERRQ(ierr); 1821 rows_i += nrows; 1822 } 1823 ierr = MatRestoreColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);CHKERRQ(ierr); 1824 ierr = PetscFree(rowhit);CHKERRQ(ierr); 1825 ierr = ISColoringRestoreIS(iscoloring,PETSC_USE_POINTER,&isa);CHKERRQ(ierr); 1826 if (csp->nz != colorforrow[nis]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"csp->nz %d != colorforrow[nis] %d",csp->nz,colorforrow[nis]); 1827 1828 c->colorforrow = colorforrow; 1829 c->rows = rows; 1830 c->den2sp = den2sp; 1831 c->colorforcol = colorforcol; 1832 c->columns = columns; 1833 1834 ierr = PetscFree(idxhit);CHKERRQ(ierr); 1835 PetscFunctionReturn(0); 1836 } 1837 1838 /* --------------------------------------------------------------- */ 1839 static PetscErrorCode MatProductNumeric_AtB_SeqAIJ_SeqAIJ(Mat C) 1840 { 1841 PetscErrorCode ierr; 1842 Mat_Product *product = C->product; 1843 Mat A=product->A,B=product->B; 1844 1845 PetscFunctionBegin; 1846 if (C->ops->mattransposemultnumeric) { 1847 /* Alg: "outerproduct" */ 1848 ierr = (C->ops->mattransposemultnumeric)(A,B,C);CHKERRQ(ierr); 1849 } else { 1850 /* Alg: "matmatmult" -- C = At*B */ 1851 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 1852 Mat_MatTransMatMult *atb = c->atb; 1853 Mat At = atb->At; 1854 1855 if (atb->updateAt) { /* At is computed in MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ() */ 1856 ierr = MatTranspose_SeqAIJ(A,MAT_REUSE_MATRIX,&At);CHKERRQ(ierr); 1857 } 1858 ierr = MatMatMultNumeric_SeqAIJ_SeqAIJ(At,B,C);CHKERRQ(ierr); 1859 atb->updateAt = PETSC_TRUE; 1860 } 1861 PetscFunctionReturn(0); 1862 } 1863 1864 static PetscErrorCode MatProductSymbolic_AtB_SeqAIJ_SeqAIJ(Mat C) 1865 { 1866 PetscErrorCode ierr; 1867 Mat_Product *product = C->product; 1868 Mat A=product->A,B=product->B; 1869 PetscReal fill=product->fill; 1870 1871 PetscFunctionBegin; 1872 ierr = MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 1873 1874 C->ops->productnumeric = MatProductNumeric_AtB_SeqAIJ_SeqAIJ; 1875 PetscFunctionReturn(0); 1876 } 1877 1878 /* --------------------------------------------------------------- */ 1879 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_AB(Mat C) 1880 { 1881 PetscErrorCode ierr; 1882 Mat_Product *product = C->product; 1883 PetscInt alg = 0; /* default algorithm */ 1884 PetscBool flg = PETSC_FALSE; 1885 #if !defined(PETSC_HAVE_HYPRE) 1886 const char *algTypes[7] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed","rowmerge"}; 1887 PetscInt nalg = 7; 1888 #else 1889 const char *algTypes[8] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed","rowmerge","hypre"}; 1890 PetscInt nalg = 8; 1891 #endif 1892 1893 PetscFunctionBegin; 1894 /* Set default algorithm */ 1895 ierr = PetscStrcmp(C->product->alg,"default",&flg);CHKERRQ(ierr); 1896 if (flg) { 1897 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 1898 } 1899 1900 /* Get runtime option */ 1901 if (product->api_user) { 1902 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatMatMult","Mat");CHKERRQ(ierr); 1903 ierr = PetscOptionsEList("-matmatmult_via","Algorithmic approach","MatMatMult",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr); 1904 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1905 } else { 1906 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_AB","Mat");CHKERRQ(ierr); 1907 ierr = PetscOptionsEList("-matproduct_ab_via","Algorithmic approach","MatProduct_AB",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr); 1908 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1909 } 1910 if (flg) { 1911 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 1912 } 1913 1914 C->ops->productsymbolic = MatProductSymbolic_AB; 1915 C->ops->matmultsymbolic = MatMatMultSymbolic_SeqAIJ_SeqAIJ; 1916 PetscFunctionReturn(0); 1917 } 1918 1919 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_AtB(Mat C) 1920 { 1921 PetscErrorCode ierr; 1922 Mat_Product *product = C->product; 1923 PetscInt alg = 0; /* default algorithm */ 1924 PetscBool flg = PETSC_FALSE; 1925 const char *algTypes[2] = {"at*b","outerproduct"}; 1926 PetscInt nalg = 2; 1927 1928 PetscFunctionBegin; 1929 /* Set default algorithm */ 1930 ierr = PetscStrcmp(product->alg,"default",&flg);CHKERRQ(ierr); 1931 if (flg) { 1932 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 1933 } 1934 1935 /* Get runtime option */ 1936 if (product->api_user) { 1937 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatTransposeMatMult","Mat");CHKERRQ(ierr); 1938 ierr = PetscOptionsEList("-mattransposematmult_via","Algorithmic approach","MatTransposeMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr); 1939 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1940 } else { 1941 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_AtB","Mat");CHKERRQ(ierr); 1942 ierr = PetscOptionsEList("-matproduct_atb_via","Algorithmic approach","MatProduct_AtB",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr); 1943 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1944 } 1945 if (flg) { 1946 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 1947 } 1948 1949 C->ops->productsymbolic = MatProductSymbolic_AtB_SeqAIJ_SeqAIJ; 1950 PetscFunctionReturn(0); 1951 } 1952 1953 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_ABt(Mat C) 1954 { 1955 PetscErrorCode ierr; 1956 Mat_Product *product = C->product; 1957 PetscInt alg = 0; /* default algorithm */ 1958 PetscBool flg = PETSC_FALSE; 1959 const char *algTypes[2] = {"default","color"}; 1960 PetscInt nalg = 2; 1961 1962 PetscFunctionBegin; 1963 /* Set default algorithm */ 1964 ierr = PetscStrcmp(C->product->alg,"default",&flg);CHKERRQ(ierr); 1965 if (!flg) { 1966 alg = 1; 1967 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 1968 } 1969 1970 /* Get runtime option */ 1971 if (product->api_user) { 1972 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatMatTransposeMult","Mat");CHKERRQ(ierr); 1973 ierr = PetscOptionsEList("-matmattransmult_via","Algorithmic approach","MatMatTransposeMult",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr); 1974 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1975 } else { 1976 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_ABt","Mat");CHKERRQ(ierr); 1977 ierr = PetscOptionsEList("-matproduct_abt_via","Algorithmic approach","MatProduct_ABt",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr); 1978 ierr = PetscOptionsEnd();CHKERRQ(ierr); 1979 } 1980 if (flg) { 1981 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 1982 } 1983 1984 C->ops->mattransposemultsymbolic = MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ; 1985 C->ops->productsymbolic = MatProductSymbolic_ABt; 1986 PetscFunctionReturn(0); 1987 } 1988 1989 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_PtAP(Mat C) 1990 { 1991 PetscErrorCode ierr; 1992 Mat_Product *product = C->product; 1993 PetscBool flg = PETSC_FALSE; 1994 PetscInt alg = 0; /* default algorithm -- alg=1 should be default!!! */ 1995 #if !defined(PETSC_HAVE_HYPRE) 1996 const char *algTypes[2] = {"scalable","rap"}; 1997 PetscInt nalg = 2; 1998 #else 1999 const char *algTypes[3] = {"scalable","rap","hypre"}; 2000 PetscInt nalg = 3; 2001 #endif 2002 2003 PetscFunctionBegin; 2004 /* Set default algorithm */ 2005 ierr = PetscStrcmp(product->alg,"default",&flg);CHKERRQ(ierr); 2006 if (flg) { 2007 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 2008 } 2009 2010 /* Get runtime option */ 2011 if (product->api_user) { 2012 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatPtAP","Mat");CHKERRQ(ierr); 2013 ierr = PetscOptionsEList("-matptap_via","Algorithmic approach","MatPtAP",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr); 2014 ierr = PetscOptionsEnd();CHKERRQ(ierr); 2015 } else { 2016 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_PtAP","Mat");CHKERRQ(ierr); 2017 ierr = PetscOptionsEList("-matproduct_ptap_via","Algorithmic approach","MatProduct_PtAP",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr); 2018 ierr = PetscOptionsEnd();CHKERRQ(ierr); 2019 } 2020 if (flg) { 2021 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 2022 } 2023 2024 C->ops->productsymbolic = MatProductSymbolic_PtAP_SeqAIJ_SeqAIJ; 2025 PetscFunctionReturn(0); 2026 } 2027 2028 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_RARt(Mat C) 2029 { 2030 PetscErrorCode ierr; 2031 Mat_Product *product = C->product; 2032 PetscBool flg = PETSC_FALSE; 2033 PetscInt alg = 0; /* default algorithm */ 2034 const char *algTypes[3] = {"r*a*rt","r*art","coloring_rart"}; 2035 PetscInt nalg = 3; 2036 2037 PetscFunctionBegin; 2038 /* Set default algorithm */ 2039 ierr = PetscStrcmp(product->alg,"default",&flg);CHKERRQ(ierr); 2040 if (flg) { 2041 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 2042 } 2043 2044 /* Get runtime option */ 2045 if (product->api_user) { 2046 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatRARt","Mat");CHKERRQ(ierr); 2047 ierr = PetscOptionsEList("-matrart_via","Algorithmic approach","MatRARt",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr); 2048 ierr = PetscOptionsEnd();CHKERRQ(ierr); 2049 } else { 2050 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_RARt","Mat");CHKERRQ(ierr); 2051 ierr = PetscOptionsEList("-matproduct_rart_via","Algorithmic approach","MatProduct_RARt",algTypes,nalg,algTypes[0],&alg,&flg);CHKERRQ(ierr); 2052 ierr = PetscOptionsEnd();CHKERRQ(ierr); 2053 } 2054 if (flg) { 2055 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 2056 } 2057 2058 C->ops->productsymbolic = MatProductSymbolic_RARt_SeqAIJ_SeqAIJ; 2059 PetscFunctionReturn(0); 2060 } 2061 2062 /* ABC = A*B*C = A*(B*C); ABC's algorithm must be chosen from AB's algorithm */ 2063 static PetscErrorCode MatProductSetFromOptions_SeqAIJ_ABC(Mat C) 2064 { 2065 PetscErrorCode ierr; 2066 Mat_Product *product = C->product; 2067 PetscInt alg = 0; /* default algorithm */ 2068 PetscBool flg = PETSC_FALSE; 2069 const char *algTypes[7] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed","rowmerge"}; 2070 PetscInt nalg = 7; 2071 2072 PetscFunctionBegin; 2073 /* Set default algorithm */ 2074 ierr = PetscStrcmp(product->alg,"default",&flg);CHKERRQ(ierr); 2075 if (flg) { 2076 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 2077 } 2078 2079 /* Get runtime option */ 2080 if (product->api_user) { 2081 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatMatMatMult","Mat");CHKERRQ(ierr); 2082 ierr = PetscOptionsEList("-matmatmatmult_via","Algorithmic approach","MatMatMatMult",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr); 2083 ierr = PetscOptionsEnd();CHKERRQ(ierr); 2084 } else { 2085 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)C),((PetscObject)C)->prefix,"MatProduct_ABC","Mat");CHKERRQ(ierr); 2086 ierr = PetscOptionsEList("-matproduct_abc_via","Algorithmic approach","MatProduct_ABC",algTypes,nalg,algTypes[alg],&alg,&flg);CHKERRQ(ierr); 2087 ierr = PetscOptionsEnd();CHKERRQ(ierr); 2088 } 2089 if (flg) { 2090 ierr = MatProductSetAlgorithm(C,(MatProductAlgorithm)algTypes[alg]);CHKERRQ(ierr); 2091 } 2092 2093 C->ops->matmatmultsymbolic = MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ; 2094 C->ops->productsymbolic = MatProductSymbolic_ABC; 2095 PetscFunctionReturn(0); 2096 } 2097 2098 PetscErrorCode MatProductSetFromOptions_SeqAIJ(Mat C) 2099 { 2100 PetscErrorCode ierr; 2101 Mat_Product *product = C->product; 2102 2103 PetscFunctionBegin; 2104 switch (product->type) { 2105 case MATPRODUCT_AB: 2106 ierr = MatProductSetFromOptions_SeqAIJ_AB(C);CHKERRQ(ierr); 2107 break; 2108 case MATPRODUCT_AtB: 2109 ierr = MatProductSetFromOptions_SeqAIJ_AtB(C);CHKERRQ(ierr); 2110 break; 2111 case MATPRODUCT_ABt: 2112 ierr = MatProductSetFromOptions_SeqAIJ_ABt(C);CHKERRQ(ierr); 2113 break; 2114 case MATPRODUCT_PtAP: 2115 ierr = MatProductSetFromOptions_SeqAIJ_PtAP(C);CHKERRQ(ierr); 2116 break; 2117 case MATPRODUCT_RARt: 2118 ierr = MatProductSetFromOptions_SeqAIJ_RARt(C);CHKERRQ(ierr); 2119 break; 2120 case MATPRODUCT_ABC: 2121 ierr = MatProductSetFromOptions_SeqAIJ_ABC(C);CHKERRQ(ierr); 2122 break; 2123 default: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatProduct type %s is not supported for SeqAIJ and SeqAIJ matrices",MatProductTypes[product->type]); 2124 } 2125 PetscFunctionReturn(0); 2126 } 2127