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 <../src/mat/utils/petscheap.h> 10 #include <petscbt.h> 11 #include <../src/mat/impls/dense/seq/dense.h> 12 13 static PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat,Mat,PetscReal,Mat*); 14 15 #undef __FUNCT__ 16 #define __FUNCT__ "MatMatMult_SeqAIJ_SeqAIJ" 17 PetscErrorCode MatMatMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 18 { 19 PetscErrorCode ierr; 20 const char *algTypes[6] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed"}; 21 PetscInt alg=0; /* set default algorithm */ 22 23 PetscFunctionBegin; 24 if (scall == MAT_INITIAL_MATRIX) { 25 ierr = PetscObjectOptionsBegin((PetscObject)A);CHKERRQ(ierr); 26 ierr = PetscOptionsEList("-matmatmult_via","Algorithmic approach","MatMatMult",algTypes,6,algTypes[0],&alg,NULL);CHKERRQ(ierr); 27 ierr = PetscOptionsEnd();CHKERRQ(ierr); 28 ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 29 switch (alg) { 30 case 1: 31 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(A,B,fill,C);CHKERRQ(ierr); 32 break; 33 case 2: 34 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(A,B,fill,C);CHKERRQ(ierr); 35 break; 36 case 3: 37 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(A,B,fill,C);CHKERRQ(ierr); 38 break; 39 case 4: 40 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(A,B,fill,C);CHKERRQ(ierr); 41 break; 42 case 5: 43 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(A,B,fill,C);CHKERRQ(ierr); 44 break; 45 default: 46 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 47 break; 48 } 49 ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 50 } 51 52 ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 53 ierr = (*(*C)->ops->matmultnumeric)(A,B,*C);CHKERRQ(ierr); 54 ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 55 PetscFunctionReturn(0); 56 } 57 58 #undef __FUNCT__ 59 #define __FUNCT__ "MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed" 60 static PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat A,Mat B,PetscReal fill,Mat *C) 61 { 62 PetscErrorCode ierr; 63 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 64 PetscInt *ai=a->i,*bi=b->i,*ci,*cj; 65 PetscInt am =A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 66 PetscReal afill; 67 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,nlnk_max,*lnk,ndouble=0; 68 PetscBT lnkbt; 69 PetscFreeSpaceList free_space=NULL,current_space=NULL; 70 71 PetscFunctionBegin; 72 /* Get ci and cj */ 73 /*---------------*/ 74 /* Allocate ci array, arrays for fill computation and */ 75 /* free space for accumulating nonzero column info */ 76 ierr = PetscMalloc1(((am+1)+1),&ci);CHKERRQ(ierr); 77 ci[0] = 0; 78 79 /* create and initialize a linked list */ 80 nlnk_max = a->rmax*b->rmax; 81 if (!nlnk_max || nlnk_max > bn) nlnk_max = bn; 82 ierr = PetscLLCondensedCreate(nlnk_max,bn,&lnk,&lnkbt);CHKERRQ(ierr); 83 84 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 85 ierr = PetscFreeSpaceGet((PetscInt)(fill*(ai[am]+bi[bm])),&free_space);CHKERRQ(ierr); 86 87 current_space = free_space; 88 89 /* Determine ci and cj */ 90 for (i=0; i<am; i++) { 91 anzi = ai[i+1] - ai[i]; 92 aj = a->j + ai[i]; 93 for (j=0; j<anzi; j++) { 94 brow = aj[j]; 95 bnzj = bi[brow+1] - bi[brow]; 96 bj = b->j + bi[brow]; 97 /* add non-zero cols of B into the sorted linked list lnk */ 98 ierr = PetscLLCondensedAddSorted(bnzj,bj,lnk,lnkbt);CHKERRQ(ierr); 99 } 100 cnzi = lnk[0]; 101 102 /* If free space is not available, make more free space */ 103 /* Double the amount of total space in the list */ 104 if (current_space->local_remaining<cnzi) { 105 ierr = PetscFreeSpaceGet(cnzi+current_space->total_array_size,¤t_space);CHKERRQ(ierr); 106 ndouble++; 107 } 108 109 /* Copy data into free space, then initialize lnk */ 110 ierr = PetscLLCondensedClean(bn,cnzi,current_space->array,lnk,lnkbt);CHKERRQ(ierr); 111 112 current_space->array += cnzi; 113 current_space->local_used += cnzi; 114 current_space->local_remaining -= cnzi; 115 116 ci[i+1] = ci[i] + cnzi; 117 } 118 119 /* Column indices are in the list of free space */ 120 /* Allocate space for cj, initialize cj, and */ 121 /* destroy list of free space and other temporary array(s) */ 122 ierr = PetscMalloc1((ci[am]+1),&cj);CHKERRQ(ierr); 123 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 124 ierr = PetscLLCondensedDestroy(lnk,lnkbt);CHKERRQ(ierr); 125 126 /* put together the new symbolic matrix */ 127 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);CHKERRQ(ierr); 128 129 (*C)->rmap->bs = A->rmap->bs; 130 (*C)->cmap->bs = B->cmap->bs; 131 132 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 133 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 134 c = (Mat_SeqAIJ*)((*C)->data); 135 c->free_a = PETSC_FALSE; 136 c->free_ij = PETSC_TRUE; 137 c->nonew = 0; 138 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; /* fast, needs non-scalable O(bn) array 'abdense' */ 139 140 /* set MatInfo */ 141 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 142 if (afill < 1.0) afill = 1.0; 143 c->maxnz = ci[am]; 144 c->nz = ci[am]; 145 (*C)->info.mallocs = ndouble; 146 (*C)->info.fill_ratio_given = fill; 147 (*C)->info.fill_ratio_needed = afill; 148 149 #if defined(PETSC_USE_INFO) 150 if (ci[am]) { 151 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 152 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 153 } else { 154 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 155 } 156 #endif 157 PetscFunctionReturn(0); 158 } 159 160 #undef __FUNCT__ 161 #define __FUNCT__ "MatMatMultNumeric_SeqAIJ_SeqAIJ" 162 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 163 { 164 PetscErrorCode ierr; 165 PetscLogDouble flops=0.0; 166 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 167 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 168 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 169 PetscInt *ai =a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j; 170 PetscInt am =A->rmap->n,cm=C->rmap->n; 171 PetscInt i,j,k,anzi,bnzi,cnzi,brow; 172 PetscScalar *aa=a->a,*ba=b->a,*baj,*ca,valtmp; 173 PetscScalar *ab_dense; 174 175 PetscFunctionBegin; 176 if (!c->a) { /* first call of MatMatMultNumeric_SeqAIJ_SeqAIJ, allocate ca and matmult_abdense */ 177 ierr = PetscMalloc1((ci[cm]+1),&ca);CHKERRQ(ierr); 178 c->a = ca; 179 c->free_a = PETSC_TRUE; 180 } else { 181 ca = c->a; 182 } 183 if (!c->matmult_abdense) { 184 ierr = PetscCalloc1(B->cmap->N,&ab_dense);CHKERRQ(ierr); 185 c->matmult_abdense = ab_dense; 186 } else { 187 ab_dense = c->matmult_abdense; 188 } 189 190 /* clean old values in C */ 191 ierr = PetscMemzero(ca,ci[cm]*sizeof(MatScalar));CHKERRQ(ierr); 192 /* Traverse A row-wise. */ 193 /* Build the ith row in C by summing over nonzero columns in A, */ 194 /* the rows of B corresponding to nonzeros of A. */ 195 for (i=0; i<am; i++) { 196 anzi = ai[i+1] - ai[i]; 197 for (j=0; j<anzi; j++) { 198 brow = aj[j]; 199 bnzi = bi[brow+1] - bi[brow]; 200 bjj = bj + bi[brow]; 201 baj = ba + bi[brow]; 202 /* perform dense axpy */ 203 valtmp = aa[j]; 204 for (k=0; k<bnzi; k++) { 205 ab_dense[bjj[k]] += valtmp*baj[k]; 206 } 207 flops += 2*bnzi; 208 } 209 aj += anzi; aa += anzi; 210 211 cnzi = ci[i+1] - ci[i]; 212 for (k=0; k<cnzi; k++) { 213 ca[k] += ab_dense[cj[k]]; 214 ab_dense[cj[k]] = 0.0; /* zero ab_dense */ 215 } 216 flops += cnzi; 217 cj += cnzi; ca += cnzi; 218 } 219 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 220 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 221 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 222 PetscFunctionReturn(0); 223 } 224 225 #undef __FUNCT__ 226 #define __FUNCT__ "MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable" 227 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,Mat C) 228 { 229 PetscErrorCode ierr; 230 PetscLogDouble flops=0.0; 231 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 232 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 233 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 234 PetscInt *ai = a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j; 235 PetscInt am = A->rmap->N,cm=C->rmap->N; 236 PetscInt i,j,k,anzi,bnzi,cnzi,brow; 237 PetscScalar *aa=a->a,*ba=b->a,*baj,*ca=c->a,valtmp; 238 PetscInt nextb; 239 240 PetscFunctionBegin; 241 /* clean old values in C */ 242 ierr = PetscMemzero(ca,ci[cm]*sizeof(MatScalar));CHKERRQ(ierr); 243 /* Traverse A row-wise. */ 244 /* Build the ith row in C by summing over nonzero columns in A, */ 245 /* the rows of B corresponding to nonzeros of A. */ 246 for (i=0; i<am; i++) { 247 anzi = ai[i+1] - ai[i]; 248 cnzi = ci[i+1] - ci[i]; 249 for (j=0; j<anzi; j++) { 250 brow = aj[j]; 251 bnzi = bi[brow+1] - bi[brow]; 252 bjj = bj + bi[brow]; 253 baj = ba + bi[brow]; 254 /* perform sparse axpy */ 255 valtmp = aa[j]; 256 nextb = 0; 257 for (k=0; nextb<bnzi; k++) { 258 if (cj[k] == bjj[nextb]) { /* ccol == bcol */ 259 ca[k] += valtmp*baj[nextb++]; 260 } 261 } 262 flops += 2*bnzi; 263 } 264 aj += anzi; aa += anzi; 265 cj += cnzi; ca += cnzi; 266 } 267 268 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 269 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 270 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 271 PetscFunctionReturn(0); 272 } 273 274 #undef __FUNCT__ 275 #define __FUNCT__ "MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast" 276 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat A,Mat B,PetscReal fill,Mat *C) 277 { 278 PetscErrorCode ierr; 279 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 280 PetscInt *ai = a->i,*bi=b->i,*ci,*cj; 281 PetscInt am = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 282 MatScalar *ca; 283 PetscReal afill; 284 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,nlnk_max,*lnk,ndouble=0; 285 PetscFreeSpaceList free_space=NULL,current_space=NULL; 286 287 PetscFunctionBegin; 288 /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_fast() */ 289 /*-----------------------------------------------------------------------------------------*/ 290 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 291 ierr = PetscMalloc1(((am+1)+1),&ci);CHKERRQ(ierr); 292 ci[0] = 0; 293 294 /* create and initialize a linked list */ 295 nlnk_max = a->rmax*b->rmax; 296 if (!nlnk_max || nlnk_max > bn) nlnk_max = bn; /* in case rmax is not defined for A or B */ 297 ierr = PetscLLCondensedCreate_fast(nlnk_max,&lnk);CHKERRQ(ierr); 298 299 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 300 ierr = PetscFreeSpaceGet((PetscInt)(fill*(ai[am]+bi[bm])),&free_space);CHKERRQ(ierr); 301 current_space = free_space; 302 303 /* Determine ci and cj */ 304 for (i=0; i<am; i++) { 305 anzi = ai[i+1] - ai[i]; 306 aj = a->j + ai[i]; 307 for (j=0; j<anzi; j++) { 308 brow = aj[j]; 309 bnzj = bi[brow+1] - bi[brow]; 310 bj = b->j + bi[brow]; 311 /* add non-zero cols of B into the sorted linked list lnk */ 312 ierr = PetscLLCondensedAddSorted_fast(bnzj,bj,lnk);CHKERRQ(ierr); 313 } 314 cnzi = lnk[1]; 315 316 /* If free space is not available, make more free space */ 317 /* Double the amount of total space in the list */ 318 if (current_space->local_remaining<cnzi) { 319 ierr = PetscFreeSpaceGet(cnzi+current_space->total_array_size,¤t_space);CHKERRQ(ierr); 320 ndouble++; 321 } 322 323 /* Copy data into free space, then initialize lnk */ 324 ierr = PetscLLCondensedClean_fast(cnzi,current_space->array,lnk);CHKERRQ(ierr); 325 326 current_space->array += cnzi; 327 current_space->local_used += cnzi; 328 current_space->local_remaining -= cnzi; 329 330 ci[i+1] = ci[i] + cnzi; 331 } 332 333 /* Column indices are in the list of free space */ 334 /* Allocate space for cj, initialize cj, and */ 335 /* destroy list of free space and other temporary array(s) */ 336 ierr = PetscMalloc1((ci[am]+1),&cj);CHKERRQ(ierr); 337 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 338 ierr = PetscLLCondensedDestroy_fast(lnk);CHKERRQ(ierr); 339 340 /* Allocate space for ca */ 341 ierr = PetscMalloc1((ci[am]+1),&ca);CHKERRQ(ierr); 342 ierr = PetscMemzero(ca,(ci[am]+1)*sizeof(MatScalar));CHKERRQ(ierr); 343 344 /* put together the new symbolic matrix */ 345 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,C);CHKERRQ(ierr); 346 347 (*C)->rmap->bs = A->rmap->bs; 348 (*C)->cmap->bs = B->cmap->bs; 349 350 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 351 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 352 c = (Mat_SeqAIJ*)((*C)->data); 353 c->free_a = PETSC_TRUE; 354 c->free_ij = PETSC_TRUE; 355 c->nonew = 0; 356 357 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable; /* slower, less memory */ 358 359 /* set MatInfo */ 360 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 361 if (afill < 1.0) afill = 1.0; 362 c->maxnz = ci[am]; 363 c->nz = ci[am]; 364 (*C)->info.mallocs = ndouble; 365 (*C)->info.fill_ratio_given = fill; 366 (*C)->info.fill_ratio_needed = afill; 367 368 #if defined(PETSC_USE_INFO) 369 if (ci[am]) { 370 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 371 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 372 } else { 373 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 374 } 375 #endif 376 PetscFunctionReturn(0); 377 } 378 379 380 #undef __FUNCT__ 381 #define __FUNCT__ "MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable" 382 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,PetscReal fill,Mat *C) 383 { 384 PetscErrorCode ierr; 385 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 386 PetscInt *ai = a->i,*bi=b->i,*ci,*cj; 387 PetscInt am = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 388 MatScalar *ca; 389 PetscReal afill; 390 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,nlnk_max,*lnk,ndouble=0; 391 PetscFreeSpaceList free_space=NULL,current_space=NULL; 392 393 PetscFunctionBegin; 394 /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_Scalalbe() */ 395 /*---------------------------------------------------------------------------------------------*/ 396 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 397 ierr = PetscMalloc1(((am+1)+1),&ci);CHKERRQ(ierr); 398 ci[0] = 0; 399 400 /* create and initialize a linked list */ 401 nlnk_max = a->rmax*b->rmax; 402 if (!nlnk_max || nlnk_max > bn) nlnk_max = bn; /* in case rmax is not defined for A or B */ 403 ierr = PetscLLCondensedCreate_Scalable(nlnk_max,&lnk);CHKERRQ(ierr); 404 405 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 406 ierr = PetscFreeSpaceGet((PetscInt)(fill*(ai[am]+bi[bm])),&free_space);CHKERRQ(ierr); 407 current_space = free_space; 408 409 /* Determine ci and cj */ 410 for (i=0; i<am; i++) { 411 anzi = ai[i+1] - ai[i]; 412 aj = a->j + ai[i]; 413 for (j=0; j<anzi; j++) { 414 brow = aj[j]; 415 bnzj = bi[brow+1] - bi[brow]; 416 bj = b->j + bi[brow]; 417 /* add non-zero cols of B into the sorted linked list lnk */ 418 ierr = PetscLLCondensedAddSorted_Scalable(bnzj,bj,lnk);CHKERRQ(ierr); 419 } 420 cnzi = lnk[0]; 421 422 /* If free space is not available, make more free space */ 423 /* Double the amount of total space in the list */ 424 if (current_space->local_remaining<cnzi) { 425 ierr = PetscFreeSpaceGet(cnzi+current_space->total_array_size,¤t_space);CHKERRQ(ierr); 426 ndouble++; 427 } 428 429 /* Copy data into free space, then initialize lnk */ 430 ierr = PetscLLCondensedClean_Scalable(cnzi,current_space->array,lnk);CHKERRQ(ierr); 431 432 current_space->array += cnzi; 433 current_space->local_used += cnzi; 434 current_space->local_remaining -= cnzi; 435 436 ci[i+1] = ci[i] + cnzi; 437 } 438 439 /* Column indices are in the list of free space */ 440 /* Allocate space for cj, initialize cj, and */ 441 /* destroy list of free space and other temporary array(s) */ 442 ierr = PetscMalloc1((ci[am]+1),&cj);CHKERRQ(ierr); 443 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 444 ierr = PetscLLCondensedDestroy_Scalable(lnk);CHKERRQ(ierr); 445 446 /* Allocate space for ca */ 447 /*-----------------------*/ 448 ierr = PetscMalloc1((ci[am]+1),&ca);CHKERRQ(ierr); 449 ierr = PetscMemzero(ca,(ci[am]+1)*sizeof(MatScalar));CHKERRQ(ierr); 450 451 /* put together the new symbolic matrix */ 452 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,C);CHKERRQ(ierr); 453 454 (*C)->rmap->bs = A->rmap->bs; 455 (*C)->cmap->bs = B->cmap->bs; 456 457 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 458 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 459 c = (Mat_SeqAIJ*)((*C)->data); 460 c->free_a = PETSC_TRUE; 461 c->free_ij = PETSC_TRUE; 462 c->nonew = 0; 463 464 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable; /* slower, less memory */ 465 466 /* set MatInfo */ 467 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 468 if (afill < 1.0) afill = 1.0; 469 c->maxnz = ci[am]; 470 c->nz = ci[am]; 471 (*C)->info.mallocs = ndouble; 472 (*C)->info.fill_ratio_given = fill; 473 (*C)->info.fill_ratio_needed = afill; 474 475 #if defined(PETSC_USE_INFO) 476 if (ci[am]) { 477 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 478 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 479 } else { 480 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 481 } 482 #endif 483 PetscFunctionReturn(0); 484 } 485 486 #undef __FUNCT__ 487 #define __FUNCT__ "MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap" 488 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat A,Mat B,PetscReal fill,Mat *C) 489 { 490 PetscErrorCode ierr; 491 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 492 const PetscInt *ai=a->i,*bi=b->i,*aj=a->j,*bj=b->j; 493 PetscInt *ci,*cj,*bb; 494 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 495 PetscReal afill; 496 PetscInt i,j,col,ndouble = 0; 497 PetscFreeSpaceList free_space=NULL,current_space=NULL; 498 PetscHeap h; 499 500 PetscFunctionBegin; 501 /* Get ci and cj - by merging sorted rows using a heap */ 502 /*---------------------------------------------------------------------------------------------*/ 503 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 504 ierr = PetscMalloc1(((am+1)+1),&ci);CHKERRQ(ierr); 505 ci[0] = 0; 506 507 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 508 ierr = PetscFreeSpaceGet((PetscInt)(fill*(ai[am]+bi[bm])),&free_space);CHKERRQ(ierr); 509 current_space = free_space; 510 511 ierr = PetscHeapCreate(a->rmax,&h);CHKERRQ(ierr); 512 ierr = PetscMalloc1(a->rmax,&bb);CHKERRQ(ierr); 513 514 /* Determine ci and cj */ 515 for (i=0; i<am; i++) { 516 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 */ 517 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 518 ci[i+1] = ci[i]; 519 /* Populate the min heap */ 520 for (j=0; j<anzi; j++) { 521 bb[j] = bi[acol[j]]; /* bb points at the start of the row */ 522 if (bb[j] < bi[acol[j]+1]) { /* Add if row is nonempty */ 523 ierr = PetscHeapAdd(h,j,bj[bb[j]++]);CHKERRQ(ierr); 524 } 525 } 526 /* Pick off the min element, adding it to free space */ 527 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 528 while (j >= 0) { 529 if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */ 530 ierr = PetscFreeSpaceGet(PetscMin(2*current_space->total_array_size,16 << 20),¤t_space);CHKERRQ(ierr); 531 ndouble++; 532 } 533 *(current_space->array++) = col; 534 current_space->local_used++; 535 current_space->local_remaining--; 536 ci[i+1]++; 537 538 /* stash if anything else remains in this row of B */ 539 if (bb[j] < bi[acol[j]+1]) {ierr = PetscHeapStash(h,j,bj[bb[j]++]);CHKERRQ(ierr);} 540 while (1) { /* pop and stash any other rows of B that also had an entry in this column */ 541 PetscInt j2,col2; 542 ierr = PetscHeapPeek(h,&j2,&col2);CHKERRQ(ierr); 543 if (col2 != col) break; 544 ierr = PetscHeapPop(h,&j2,&col2);CHKERRQ(ierr); 545 if (bb[j2] < bi[acol[j2]+1]) {ierr = PetscHeapStash(h,j2,bj[bb[j2]++]);CHKERRQ(ierr);} 546 } 547 /* Put any stashed elements back into the min heap */ 548 ierr = PetscHeapUnstash(h);CHKERRQ(ierr); 549 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 550 } 551 } 552 ierr = PetscFree(bb);CHKERRQ(ierr); 553 ierr = PetscHeapDestroy(&h);CHKERRQ(ierr); 554 555 /* Column indices are in the list of free space */ 556 /* Allocate space for cj, initialize cj, and */ 557 /* destroy list of free space and other temporary array(s) */ 558 ierr = PetscMalloc1(ci[am],&cj);CHKERRQ(ierr); 559 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 560 561 /* put together the new symbolic matrix */ 562 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);CHKERRQ(ierr); 563 564 (*C)->rmap->bs = A->rmap->bs; 565 (*C)->cmap->bs = B->cmap->bs; 566 567 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 568 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 569 c = (Mat_SeqAIJ*)((*C)->data); 570 c->free_a = PETSC_TRUE; 571 c->free_ij = PETSC_TRUE; 572 c->nonew = 0; 573 574 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; 575 576 /* set MatInfo */ 577 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 578 if (afill < 1.0) afill = 1.0; 579 c->maxnz = ci[am]; 580 c->nz = ci[am]; 581 (*C)->info.mallocs = ndouble; 582 (*C)->info.fill_ratio_given = fill; 583 (*C)->info.fill_ratio_needed = afill; 584 585 #if defined(PETSC_USE_INFO) 586 if (ci[am]) { 587 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 588 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 589 } else { 590 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 591 } 592 #endif 593 PetscFunctionReturn(0); 594 } 595 596 #undef __FUNCT__ 597 #define __FUNCT__ "MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap" 598 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat A,Mat B,PetscReal fill,Mat *C) 599 { 600 PetscErrorCode ierr; 601 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 602 const PetscInt *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j; 603 PetscInt *ci,*cj,*bb; 604 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 605 PetscReal afill; 606 PetscInt i,j,col,ndouble = 0; 607 PetscFreeSpaceList free_space=NULL,current_space=NULL; 608 PetscHeap h; 609 PetscBT bt; 610 611 PetscFunctionBegin; 612 /* Get ci and cj - using a heap for the sorted rows, but use BT so that each index is only added once */ 613 /*---------------------------------------------------------------------------------------------*/ 614 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 615 ierr = PetscMalloc1(((am+1)+1),&ci);CHKERRQ(ierr); 616 ci[0] = 0; 617 618 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 619 ierr = PetscFreeSpaceGet((PetscInt)(fill*(ai[am]+bi[bm])),&free_space);CHKERRQ(ierr); 620 621 current_space = free_space; 622 623 ierr = PetscHeapCreate(a->rmax,&h);CHKERRQ(ierr); 624 ierr = PetscMalloc1(a->rmax,&bb);CHKERRQ(ierr); 625 ierr = PetscBTCreate(bn,&bt);CHKERRQ(ierr); 626 627 /* Determine ci and cj */ 628 for (i=0; i<am; i++) { 629 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 */ 630 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 631 const PetscInt *fptr = current_space->array; /* Save beginning of the row so we can clear the BT later */ 632 ci[i+1] = ci[i]; 633 /* Populate the min heap */ 634 for (j=0; j<anzi; j++) { 635 PetscInt brow = acol[j]; 636 for (bb[j] = bi[brow]; bb[j] < bi[brow+1]; bb[j]++) { 637 PetscInt bcol = bj[bb[j]]; 638 if (!PetscBTLookupSet(bt,bcol)) { /* new entry */ 639 ierr = PetscHeapAdd(h,j,bcol);CHKERRQ(ierr); 640 bb[j]++; 641 break; 642 } 643 } 644 } 645 /* Pick off the min element, adding it to free space */ 646 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 647 while (j >= 0) { 648 if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */ 649 fptr = NULL; /* need PetscBTMemzero */ 650 ierr = PetscFreeSpaceGet(PetscMin(2*current_space->total_array_size,16 << 20),¤t_space);CHKERRQ(ierr); 651 ndouble++; 652 } 653 *(current_space->array++) = col; 654 current_space->local_used++; 655 current_space->local_remaining--; 656 ci[i+1]++; 657 658 /* stash if anything else remains in this row of B */ 659 for (; bb[j] < bi[acol[j]+1]; bb[j]++) { 660 PetscInt bcol = bj[bb[j]]; 661 if (!PetscBTLookupSet(bt,bcol)) { /* new entry */ 662 ierr = PetscHeapAdd(h,j,bcol);CHKERRQ(ierr); 663 bb[j]++; 664 break; 665 } 666 } 667 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 668 } 669 if (fptr) { /* Clear the bits for this row */ 670 for (; fptr<current_space->array; fptr++) {ierr = PetscBTClear(bt,*fptr);CHKERRQ(ierr);} 671 } else { /* We reallocated so we don't remember (easily) how to clear only the bits we changed */ 672 ierr = PetscBTMemzero(bn,bt);CHKERRQ(ierr); 673 } 674 } 675 ierr = PetscFree(bb);CHKERRQ(ierr); 676 ierr = PetscHeapDestroy(&h);CHKERRQ(ierr); 677 ierr = PetscBTDestroy(&bt);CHKERRQ(ierr); 678 679 /* Column indices are in the list of free space */ 680 /* Allocate space for cj, initialize cj, and */ 681 /* destroy list of free space and other temporary array(s) */ 682 ierr = PetscMalloc1(ci[am],&cj);CHKERRQ(ierr); 683 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 684 685 /* put together the new symbolic matrix */ 686 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);CHKERRQ(ierr); 687 688 (*C)->rmap->bs = A->rmap->bs; 689 (*C)->cmap->bs = B->cmap->bs; 690 691 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 692 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 693 c = (Mat_SeqAIJ*)((*C)->data); 694 c->free_a = PETSC_TRUE; 695 c->free_ij = PETSC_TRUE; 696 c->nonew = 0; 697 698 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; 699 700 /* set MatInfo */ 701 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 702 if (afill < 1.0) afill = 1.0; 703 c->maxnz = ci[am]; 704 c->nz = ci[am]; 705 (*C)->info.mallocs = ndouble; 706 (*C)->info.fill_ratio_given = fill; 707 (*C)->info.fill_ratio_needed = afill; 708 709 #if defined(PETSC_USE_INFO) 710 if (ci[am]) { 711 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 712 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 713 } else { 714 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 715 } 716 #endif 717 PetscFunctionReturn(0); 718 } 719 720 #undef __FUNCT__ 721 #define __FUNCT__ "MatMatMultSymbolic_SeqAIJ_SeqAIJ" 722 /* concatenate unique entries and then sort */ 723 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C) 724 { 725 PetscErrorCode ierr; 726 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 727 const PetscInt *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j; 728 PetscInt *ci,*cj; 729 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 730 PetscReal afill; 731 PetscInt i,j,ndouble = 0; 732 PetscSegBuffer seg,segrow; 733 char *seen; 734 735 PetscFunctionBegin; 736 ierr = PetscMalloc1((am+1),&ci);CHKERRQ(ierr); 737 ci[0] = 0; 738 739 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 740 ierr = PetscSegBufferCreate(sizeof(PetscInt),(PetscInt)(fill*(ai[am]+bi[bm])),&seg);CHKERRQ(ierr); 741 ierr = PetscSegBufferCreate(sizeof(PetscInt),100,&segrow);CHKERRQ(ierr); 742 ierr = PetscMalloc1(bn,&seen);CHKERRQ(ierr); 743 ierr = PetscMemzero(seen,bn*sizeof(char));CHKERRQ(ierr); 744 745 /* Determine ci and cj */ 746 for (i=0; i<am; i++) { 747 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 */ 748 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 749 PetscInt packlen = 0,*PETSC_RESTRICT crow; 750 /* Pack segrow */ 751 for (j=0; j<anzi; j++) { 752 PetscInt brow = acol[j],bjstart = bi[brow],bjend = bi[brow+1],k; 753 for (k=bjstart; k<bjend; k++) { 754 PetscInt bcol = bj[k]; 755 if (!seen[bcol]) { /* new entry */ 756 PetscInt *PETSC_RESTRICT slot; 757 ierr = PetscSegBufferGetInts(segrow,1,&slot);CHKERRQ(ierr); 758 *slot = bcol; 759 seen[bcol] = 1; 760 packlen++; 761 } 762 } 763 } 764 ierr = PetscSegBufferGetInts(seg,packlen,&crow);CHKERRQ(ierr); 765 ierr = PetscSegBufferExtractTo(segrow,crow);CHKERRQ(ierr); 766 ierr = PetscSortInt(packlen,crow);CHKERRQ(ierr); 767 ci[i+1] = ci[i] + packlen; 768 for (j=0; j<packlen; j++) seen[crow[j]] = 0; 769 } 770 ierr = PetscSegBufferDestroy(&segrow);CHKERRQ(ierr); 771 ierr = PetscFree(seen);CHKERRQ(ierr); 772 773 /* Column indices are in the segmented buffer */ 774 ierr = PetscSegBufferExtractAlloc(seg,&cj);CHKERRQ(ierr); 775 ierr = PetscSegBufferDestroy(&seg);CHKERRQ(ierr); 776 777 /* put together the new symbolic matrix */ 778 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);CHKERRQ(ierr); 779 780 (*C)->rmap->bs = A->rmap->bs; 781 (*C)->cmap->bs = B->cmap->bs; 782 783 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 784 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 785 c = (Mat_SeqAIJ*)((*C)->data); 786 c->free_a = PETSC_TRUE; 787 c->free_ij = PETSC_TRUE; 788 c->nonew = 0; 789 790 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; 791 792 /* set MatInfo */ 793 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 794 if (afill < 1.0) afill = 1.0; 795 c->maxnz = ci[am]; 796 c->nz = ci[am]; 797 (*C)->info.mallocs = ndouble; 798 (*C)->info.fill_ratio_given = fill; 799 (*C)->info.fill_ratio_needed = afill; 800 801 #if defined(PETSC_USE_INFO) 802 if (ci[am]) { 803 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 804 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 805 } else { 806 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 807 } 808 #endif 809 PetscFunctionReturn(0); 810 } 811 812 /* This routine is not used. Should be removed! */ 813 #undef __FUNCT__ 814 #define __FUNCT__ "MatMatTransposeMult_SeqAIJ_SeqAIJ" 815 PetscErrorCode MatMatTransposeMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 816 { 817 PetscErrorCode ierr; 818 819 PetscFunctionBegin; 820 if (scall == MAT_INITIAL_MATRIX) { 821 ierr = PetscLogEventBegin(MAT_MatTransposeMultSymbolic,A,B,0,0);CHKERRQ(ierr); 822 ierr = MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 823 ierr = PetscLogEventEnd(MAT_MatTransposeMultSymbolic,A,B,0,0);CHKERRQ(ierr); 824 } 825 ierr = PetscLogEventBegin(MAT_MatTransposeMultNumeric,A,B,0,0);CHKERRQ(ierr); 826 ierr = MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(A,B,*C);CHKERRQ(ierr); 827 ierr = PetscLogEventEnd(MAT_MatTransposeMultNumeric,A,B,0,0);CHKERRQ(ierr); 828 PetscFunctionReturn(0); 829 } 830 831 #undef __FUNCT__ 832 #define __FUNCT__ "MatDestroy_SeqAIJ_MatMatMultTrans" 833 PetscErrorCode MatDestroy_SeqAIJ_MatMatMultTrans(Mat A) 834 { 835 PetscErrorCode ierr; 836 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 837 Mat_MatMatTransMult *abt=a->abt; 838 839 PetscFunctionBegin; 840 ierr = (abt->destroy)(A);CHKERRQ(ierr); 841 ierr = MatTransposeColoringDestroy(&abt->matcoloring);CHKERRQ(ierr); 842 ierr = MatDestroy(&abt->Bt_den);CHKERRQ(ierr); 843 ierr = MatDestroy(&abt->ABt_den);CHKERRQ(ierr); 844 ierr = PetscFree(abt);CHKERRQ(ierr); 845 PetscFunctionReturn(0); 846 } 847 848 #undef __FUNCT__ 849 #define __FUNCT__ "MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ" 850 PetscErrorCode MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C) 851 { 852 PetscErrorCode ierr; 853 Mat Bt; 854 PetscInt *bti,*btj; 855 Mat_MatMatTransMult *abt; 856 Mat_SeqAIJ *c; 857 858 PetscFunctionBegin; 859 /* create symbolic Bt */ 860 ierr = MatGetSymbolicTranspose_SeqAIJ(B,&bti,&btj);CHKERRQ(ierr); 861 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,B->cmap->n,B->rmap->n,bti,btj,NULL,&Bt);CHKERRQ(ierr); 862 863 Bt->rmap->bs = A->cmap->bs; 864 Bt->cmap->bs = B->cmap->bs; 865 866 /* get symbolic C=A*Bt */ 867 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,Bt,fill,C);CHKERRQ(ierr); 868 869 /* create a supporting struct for reuse intermidiate dense matrices with matcoloring */ 870 ierr = PetscNew(&abt);CHKERRQ(ierr); 871 c = (Mat_SeqAIJ*)(*C)->data; 872 c->abt = abt; 873 874 abt->usecoloring = PETSC_FALSE; 875 abt->destroy = (*C)->ops->destroy; 876 (*C)->ops->destroy = MatDestroy_SeqAIJ_MatMatMultTrans; 877 878 ierr = PetscOptionsGetBool(NULL,"-matmattransmult_color",&abt->usecoloring,NULL);CHKERRQ(ierr); 879 if (abt->usecoloring) { 880 /* Create MatTransposeColoring from symbolic C=A*B^T */ 881 MatTransposeColoring matcoloring; 882 MatColoring coloring; 883 ISColoring iscoloring; 884 Mat Bt_dense,C_dense; 885 Mat_SeqAIJ *c=(Mat_SeqAIJ*)(*C)->data; 886 /* inode causes memory problem, don't know why */ 887 if (c->inode.use) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MAT_USE_INODES is not supported. Use '-mat_no_inode'"); 888 889 ierr = MatColoringCreate(*C,&coloring);CHKERRQ(ierr); 890 ierr = MatColoringSetDistance(coloring,2);CHKERRQ(ierr); 891 ierr = MatColoringSetType(coloring,MATCOLORINGSL);CHKERRQ(ierr); 892 ierr = MatColoringSetFromOptions(coloring);CHKERRQ(ierr); 893 ierr = MatColoringApply(coloring,&iscoloring);CHKERRQ(ierr); 894 ierr = MatColoringDestroy(&coloring);CHKERRQ(ierr); 895 ierr = MatTransposeColoringCreate(*C,iscoloring,&matcoloring);CHKERRQ(ierr); 896 897 abt->matcoloring = matcoloring; 898 899 ierr = ISColoringDestroy(&iscoloring);CHKERRQ(ierr); 900 901 /* Create Bt_dense and C_dense = A*Bt_dense */ 902 ierr = MatCreate(PETSC_COMM_SELF,&Bt_dense);CHKERRQ(ierr); 903 ierr = MatSetSizes(Bt_dense,A->cmap->n,matcoloring->ncolors,A->cmap->n,matcoloring->ncolors);CHKERRQ(ierr); 904 ierr = MatSetType(Bt_dense,MATSEQDENSE);CHKERRQ(ierr); 905 ierr = MatSeqDenseSetPreallocation(Bt_dense,NULL);CHKERRQ(ierr); 906 907 Bt_dense->assembled = PETSC_TRUE; 908 abt->Bt_den = Bt_dense; 909 910 ierr = MatCreate(PETSC_COMM_SELF,&C_dense);CHKERRQ(ierr); 911 ierr = MatSetSizes(C_dense,A->rmap->n,matcoloring->ncolors,A->rmap->n,matcoloring->ncolors);CHKERRQ(ierr); 912 ierr = MatSetType(C_dense,MATSEQDENSE);CHKERRQ(ierr); 913 ierr = MatSeqDenseSetPreallocation(C_dense,NULL);CHKERRQ(ierr); 914 915 Bt_dense->assembled = PETSC_TRUE; 916 abt->ABt_den = C_dense; 917 918 #if defined(PETSC_USE_INFO) 919 { 920 Mat_SeqAIJ *c = (Mat_SeqAIJ*)(*C)->data; 921 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); 922 } 923 #endif 924 } 925 /* clean up */ 926 ierr = MatDestroy(&Bt);CHKERRQ(ierr); 927 ierr = MatRestoreSymbolicTranspose_SeqAIJ(B,&bti,&btj);CHKERRQ(ierr); 928 PetscFunctionReturn(0); 929 } 930 931 #undef __FUNCT__ 932 #define __FUNCT__ "MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ" 933 PetscErrorCode MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 934 { 935 PetscErrorCode ierr; 936 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data; 937 PetscInt *ai =a->i,*aj=a->j,*bi=b->i,*bj=b->j,anzi,bnzj,nexta,nextb,*acol,*bcol,brow; 938 PetscInt cm =C->rmap->n,*ci=c->i,*cj=c->j,i,j,cnzi,*ccol; 939 PetscLogDouble flops=0.0; 940 MatScalar *aa =a->a,*aval,*ba=b->a,*bval,*ca,*cval; 941 Mat_MatMatTransMult *abt = c->abt; 942 943 PetscFunctionBegin; 944 /* clear old values in C */ 945 if (!c->a) { 946 ierr = PetscMalloc1((ci[cm]+1),&ca);CHKERRQ(ierr); 947 c->a = ca; 948 c->free_a = PETSC_TRUE; 949 } else { 950 ca = c->a; 951 } 952 ierr = PetscMemzero(ca,ci[cm]*sizeof(MatScalar));CHKERRQ(ierr); 953 954 if (abt->usecoloring) { 955 MatTransposeColoring matcoloring = abt->matcoloring; 956 Mat Bt_dense,C_dense = abt->ABt_den; 957 958 /* Get Bt_dense by Apply MatTransposeColoring to B */ 959 Bt_dense = abt->Bt_den; 960 ierr = MatTransColoringApplySpToDen(matcoloring,B,Bt_dense);CHKERRQ(ierr); 961 962 /* C_dense = A*Bt_dense */ 963 ierr = MatMatMultNumeric_SeqAIJ_SeqDense(A,Bt_dense,C_dense);CHKERRQ(ierr); 964 965 /* Recover C from C_dense */ 966 ierr = MatTransColoringApplyDenToSp(matcoloring,C_dense,C);CHKERRQ(ierr); 967 PetscFunctionReturn(0); 968 } 969 970 for (i=0; i<cm; i++) { 971 anzi = ai[i+1] - ai[i]; 972 acol = aj + ai[i]; 973 aval = aa + ai[i]; 974 cnzi = ci[i+1] - ci[i]; 975 ccol = cj + ci[i]; 976 cval = ca + ci[i]; 977 for (j=0; j<cnzi; j++) { 978 brow = ccol[j]; 979 bnzj = bi[brow+1] - bi[brow]; 980 bcol = bj + bi[brow]; 981 bval = ba + bi[brow]; 982 983 /* perform sparse inner-product c(i,j)=A[i,:]*B[j,:]^T */ 984 nexta = 0; nextb = 0; 985 while (nexta<anzi && nextb<bnzj) { 986 while (nexta < anzi && acol[nexta] < bcol[nextb]) nexta++; 987 if (nexta == anzi) break; 988 while (nextb < bnzj && acol[nexta] > bcol[nextb]) nextb++; 989 if (nextb == bnzj) break; 990 if (acol[nexta] == bcol[nextb]) { 991 cval[j] += aval[nexta]*bval[nextb]; 992 nexta++; nextb++; 993 flops += 2; 994 } 995 } 996 } 997 } 998 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 999 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1000 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 1001 PetscFunctionReturn(0); 1002 } 1003 1004 #undef __FUNCT__ 1005 #define __FUNCT__ "MatTransposeMatMult_SeqAIJ_SeqAIJ" 1006 PetscErrorCode MatTransposeMatMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 1007 { 1008 PetscErrorCode ierr; 1009 1010 PetscFunctionBegin; 1011 if (scall == MAT_INITIAL_MATRIX) { 1012 ierr = PetscLogEventBegin(MAT_TransposeMatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1013 ierr = MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 1014 ierr = PetscLogEventEnd(MAT_TransposeMatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1015 } 1016 ierr = PetscLogEventBegin(MAT_TransposeMatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1017 ierr = MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(A,B,*C);CHKERRQ(ierr); 1018 ierr = PetscLogEventEnd(MAT_TransposeMatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1019 PetscFunctionReturn(0); 1020 } 1021 1022 #undef __FUNCT__ 1023 #define __FUNCT__ "MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ" 1024 PetscErrorCode MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C) 1025 { 1026 PetscErrorCode ierr; 1027 Mat At; 1028 PetscInt *ati,*atj; 1029 1030 PetscFunctionBegin; 1031 /* create symbolic At */ 1032 ierr = MatGetSymbolicTranspose_SeqAIJ(A,&ati,&atj);CHKERRQ(ierr); 1033 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,A->cmap->n,A->rmap->n,ati,atj,NULL,&At);CHKERRQ(ierr); 1034 1035 At->rmap->bs = A->cmap->bs; 1036 At->cmap->bs = B->cmap->bs; 1037 1038 /* get symbolic C=At*B */ 1039 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(At,B,fill,C);CHKERRQ(ierr); 1040 1041 /* clean up */ 1042 ierr = MatDestroy(&At);CHKERRQ(ierr); 1043 ierr = MatRestoreSymbolicTranspose_SeqAIJ(A,&ati,&atj);CHKERRQ(ierr); 1044 PetscFunctionReturn(0); 1045 } 1046 1047 #undef __FUNCT__ 1048 #define __FUNCT__ "MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ" 1049 PetscErrorCode MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 1050 { 1051 PetscErrorCode ierr; 1052 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data; 1053 PetscInt am =A->rmap->n,anzi,*ai=a->i,*aj=a->j,*bi=b->i,*bj,bnzi,nextb; 1054 PetscInt cm =C->rmap->n,*ci=c->i,*cj=c->j,crow,*cjj,i,j,k; 1055 PetscLogDouble flops=0.0; 1056 MatScalar *aa =a->a,*ba,*ca,*caj; 1057 1058 PetscFunctionBegin; 1059 if (!c->a) { 1060 ierr = PetscMalloc1((ci[cm]+1),&ca);CHKERRQ(ierr); 1061 1062 c->a = ca; 1063 c->free_a = PETSC_TRUE; 1064 } else { 1065 ca = c->a; 1066 } 1067 /* clear old values in C */ 1068 ierr = PetscMemzero(ca,ci[cm]*sizeof(MatScalar));CHKERRQ(ierr); 1069 1070 /* compute A^T*B using outer product (A^T)[:,i]*B[i,:] */ 1071 for (i=0; i<am; i++) { 1072 bj = b->j + bi[i]; 1073 ba = b->a + bi[i]; 1074 bnzi = bi[i+1] - bi[i]; 1075 anzi = ai[i+1] - ai[i]; 1076 for (j=0; j<anzi; j++) { 1077 nextb = 0; 1078 crow = *aj++; 1079 cjj = cj + ci[crow]; 1080 caj = ca + ci[crow]; 1081 /* perform sparse axpy operation. Note cjj includes bj. */ 1082 for (k=0; nextb<bnzi; k++) { 1083 if (cjj[k] == *(bj+nextb)) { /* ccol == bcol */ 1084 caj[k] += (*aa)*(*(ba+nextb)); 1085 nextb++; 1086 } 1087 } 1088 flops += 2*bnzi; 1089 aa++; 1090 } 1091 } 1092 1093 /* Assemble the final matrix and clean up */ 1094 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1095 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1096 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 1097 PetscFunctionReturn(0); 1098 } 1099 1100 #undef __FUNCT__ 1101 #define __FUNCT__ "MatMatMult_SeqAIJ_SeqDense" 1102 PetscErrorCode MatMatMult_SeqAIJ_SeqDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 1103 { 1104 PetscErrorCode ierr; 1105 1106 PetscFunctionBegin; 1107 if (scall == MAT_INITIAL_MATRIX) { 1108 ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1109 ierr = MatMatMultSymbolic_SeqAIJ_SeqDense(A,B,fill,C);CHKERRQ(ierr); 1110 ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1111 } 1112 ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1113 ierr = MatMatMultNumeric_SeqAIJ_SeqDense(A,B,*C);CHKERRQ(ierr); 1114 ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1115 PetscFunctionReturn(0); 1116 } 1117 1118 #undef __FUNCT__ 1119 #define __FUNCT__ "MatMatMultSymbolic_SeqAIJ_SeqDense" 1120 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqDense(Mat A,Mat B,PetscReal fill,Mat *C) 1121 { 1122 PetscErrorCode ierr; 1123 1124 PetscFunctionBegin; 1125 ierr = MatMatMultSymbolic_SeqDense_SeqDense(A,B,0.0,C);CHKERRQ(ierr); 1126 1127 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqDense; 1128 PetscFunctionReturn(0); 1129 } 1130 1131 #undef __FUNCT__ 1132 #define __FUNCT__ "MatMatMultNumeric_SeqAIJ_SeqDense" 1133 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqDense(Mat A,Mat B,Mat C) 1134 { 1135 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 1136 PetscErrorCode ierr; 1137 PetscScalar *c,*b,r1,r2,r3,r4,*c1,*c2,*c3,*c4,aatmp; 1138 const PetscScalar *aa,*b1,*b2,*b3,*b4; 1139 const PetscInt *aj; 1140 PetscInt cm=C->rmap->n,cn=B->cmap->n,bm=B->rmap->n,am=A->rmap->n; 1141 PetscInt am4=4*am,bm4=4*bm,col,i,j,n,ajtmp; 1142 1143 PetscFunctionBegin; 1144 if (!cm || !cn) PetscFunctionReturn(0); 1145 if (bm != 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,bm); 1146 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); 1147 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); 1148 ierr = MatDenseGetArray(B,&b);CHKERRQ(ierr); 1149 ierr = MatDenseGetArray(C,&c);CHKERRQ(ierr); 1150 b1 = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm; 1151 c1 = c; c2 = c1 + am; c3 = c2 + am; c4 = c3 + am; 1152 for (col=0; col<cn-4; col += 4) { /* over columns of C */ 1153 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1154 r1 = r2 = r3 = r4 = 0.0; 1155 n = a->i[i+1] - a->i[i]; 1156 aj = a->j + a->i[i]; 1157 aa = a->a + a->i[i]; 1158 for (j=0; j<n; j++) { 1159 aatmp = aa[j]; ajtmp = aj[j]; 1160 r1 += aatmp*b1[ajtmp]; 1161 r2 += aatmp*b2[ajtmp]; 1162 r3 += aatmp*b3[ajtmp]; 1163 r4 += aatmp*b4[ajtmp]; 1164 } 1165 c1[i] = r1; 1166 c2[i] = r2; 1167 c3[i] = r3; 1168 c4[i] = r4; 1169 } 1170 b1 += bm4; b2 += bm4; b3 += bm4; b4 += bm4; 1171 c1 += am4; c2 += am4; c3 += am4; c4 += am4; 1172 } 1173 for (; col<cn; col++) { /* over extra columns of C */ 1174 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1175 r1 = 0.0; 1176 n = a->i[i+1] - a->i[i]; 1177 aj = a->j + a->i[i]; 1178 aa = a->a + a->i[i]; 1179 for (j=0; j<n; j++) { 1180 r1 += aa[j]*b1[aj[j]]; 1181 } 1182 c1[i] = r1; 1183 } 1184 b1 += bm; 1185 c1 += am; 1186 } 1187 ierr = PetscLogFlops(cn*(2.0*a->nz));CHKERRQ(ierr); 1188 ierr = MatDenseRestoreArray(B,&b);CHKERRQ(ierr); 1189 ierr = MatDenseRestoreArray(C,&c);CHKERRQ(ierr); 1190 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1191 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1192 PetscFunctionReturn(0); 1193 } 1194 1195 /* 1196 Note very similar to MatMult_SeqAIJ(), should generate both codes from same base 1197 */ 1198 #undef __FUNCT__ 1199 #define __FUNCT__ "MatMatMultNumericAdd_SeqAIJ_SeqDense" 1200 PetscErrorCode MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat A,Mat B,Mat C) 1201 { 1202 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 1203 PetscErrorCode ierr; 1204 PetscScalar *b,*c,r1,r2,r3,r4,*b1,*b2,*b3,*b4; 1205 MatScalar *aa; 1206 PetscInt cm = C->rmap->n, cn=B->cmap->n, bm=B->rmap->n, col, i,j,n,*aj, am = A->rmap->n,*ii,arm; 1207 PetscInt am2 = 2*am, am3 = 3*am, bm4 = 4*bm,colam,*ridx; 1208 1209 PetscFunctionBegin; 1210 if (!cm || !cn) PetscFunctionReturn(0); 1211 ierr = MatDenseGetArray(B,&b);CHKERRQ(ierr); 1212 ierr = MatDenseGetArray(C,&c);CHKERRQ(ierr); 1213 b1 = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm; 1214 1215 if (a->compressedrow.use) { /* use compressed row format */ 1216 for (col=0; col<cn-4; col += 4) { /* over columns of C */ 1217 colam = col*am; 1218 arm = a->compressedrow.nrows; 1219 ii = a->compressedrow.i; 1220 ridx = a->compressedrow.rindex; 1221 for (i=0; i<arm; i++) { /* over rows of C in those columns */ 1222 r1 = r2 = r3 = r4 = 0.0; 1223 n = ii[i+1] - ii[i]; 1224 aj = a->j + ii[i]; 1225 aa = a->a + ii[i]; 1226 for (j=0; j<n; j++) { 1227 r1 += (*aa)*b1[*aj]; 1228 r2 += (*aa)*b2[*aj]; 1229 r3 += (*aa)*b3[*aj]; 1230 r4 += (*aa++)*b4[*aj++]; 1231 } 1232 c[colam + ridx[i]] += r1; 1233 c[colam + am + ridx[i]] += r2; 1234 c[colam + am2 + ridx[i]] += r3; 1235 c[colam + am3 + ridx[i]] += r4; 1236 } 1237 b1 += bm4; 1238 b2 += bm4; 1239 b3 += bm4; 1240 b4 += bm4; 1241 } 1242 for (; col<cn; col++) { /* over extra columns of C */ 1243 colam = col*am; 1244 arm = a->compressedrow.nrows; 1245 ii = a->compressedrow.i; 1246 ridx = a->compressedrow.rindex; 1247 for (i=0; i<arm; i++) { /* over rows of C in those columns */ 1248 r1 = 0.0; 1249 n = ii[i+1] - ii[i]; 1250 aj = a->j + ii[i]; 1251 aa = a->a + ii[i]; 1252 1253 for (j=0; j<n; j++) { 1254 r1 += (*aa++)*b1[*aj++]; 1255 } 1256 c[colam + ridx[i]] += r1; 1257 } 1258 b1 += bm; 1259 } 1260 } else { 1261 for (col=0; col<cn-4; col += 4) { /* over columns of C */ 1262 colam = col*am; 1263 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1264 r1 = r2 = r3 = r4 = 0.0; 1265 n = a->i[i+1] - a->i[i]; 1266 aj = a->j + a->i[i]; 1267 aa = a->a + a->i[i]; 1268 for (j=0; j<n; j++) { 1269 r1 += (*aa)*b1[*aj]; 1270 r2 += (*aa)*b2[*aj]; 1271 r3 += (*aa)*b3[*aj]; 1272 r4 += (*aa++)*b4[*aj++]; 1273 } 1274 c[colam + i] += r1; 1275 c[colam + am + i] += r2; 1276 c[colam + am2 + i] += r3; 1277 c[colam + am3 + i] += r4; 1278 } 1279 b1 += bm4; 1280 b2 += bm4; 1281 b3 += bm4; 1282 b4 += bm4; 1283 } 1284 for (; col<cn; col++) { /* over extra columns of C */ 1285 colam = col*am; 1286 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1287 r1 = 0.0; 1288 n = a->i[i+1] - a->i[i]; 1289 aj = a->j + a->i[i]; 1290 aa = a->a + a->i[i]; 1291 1292 for (j=0; j<n; j++) { 1293 r1 += (*aa++)*b1[*aj++]; 1294 } 1295 c[colam + i] += r1; 1296 } 1297 b1 += bm; 1298 } 1299 } 1300 ierr = PetscLogFlops(cn*2.0*a->nz);CHKERRQ(ierr); 1301 ierr = MatDenseRestoreArray(B,&b);CHKERRQ(ierr); 1302 ierr = MatDenseRestoreArray(C,&c);CHKERRQ(ierr); 1303 PetscFunctionReturn(0); 1304 } 1305 1306 #undef __FUNCT__ 1307 #define __FUNCT__ "MatTransColoringApplySpToDen_SeqAIJ" 1308 PetscErrorCode MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring coloring,Mat B,Mat Btdense) 1309 { 1310 PetscErrorCode ierr; 1311 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 1312 Mat_SeqDense *btdense = (Mat_SeqDense*)Btdense->data; 1313 PetscInt *bi = b->i,*bj=b->j; 1314 PetscInt m = Btdense->rmap->n,n=Btdense->cmap->n,j,k,l,col,anz,*btcol,brow,ncolumns; 1315 MatScalar *btval,*btval_den,*ba=b->a; 1316 PetscInt *columns=coloring->columns,*colorforcol=coloring->colorforcol,ncolors=coloring->ncolors; 1317 1318 PetscFunctionBegin; 1319 btval_den=btdense->v; 1320 ierr = PetscMemzero(btval_den,(m*n)*sizeof(MatScalar));CHKERRQ(ierr); 1321 for (k=0; k<ncolors; k++) { 1322 ncolumns = coloring->ncolumns[k]; 1323 for (l=0; l<ncolumns; l++) { /* insert a row of B to a column of Btdense */ 1324 col = *(columns + colorforcol[k] + l); 1325 btcol = bj + bi[col]; 1326 btval = ba + bi[col]; 1327 anz = bi[col+1] - bi[col]; 1328 for (j=0; j<anz; j++) { 1329 brow = btcol[j]; 1330 btval_den[brow] = btval[j]; 1331 } 1332 } 1333 btval_den += m; 1334 } 1335 PetscFunctionReturn(0); 1336 } 1337 1338 #undef __FUNCT__ 1339 #define __FUNCT__ "MatTransColoringApplyDenToSp_SeqAIJ" 1340 PetscErrorCode MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring matcoloring,Mat Cden,Mat Csp) 1341 { 1342 PetscErrorCode ierr; 1343 Mat_SeqAIJ *csp = (Mat_SeqAIJ*)Csp->data; 1344 PetscScalar *ca_den,*ca_den_ptr,*ca=csp->a; 1345 PetscInt k,l,m=Cden->rmap->n,ncolors=matcoloring->ncolors; 1346 PetscInt brows=matcoloring->brows,*den2sp=matcoloring->den2sp; 1347 PetscInt nrows,*row,*idx; 1348 PetscInt *rows=matcoloring->rows,*colorforrow=matcoloring->colorforrow; 1349 1350 PetscFunctionBegin; 1351 ierr = MatDenseGetArray(Cden,&ca_den);CHKERRQ(ierr); 1352 1353 if (brows > 0) { 1354 PetscInt *lstart,row_end,row_start; 1355 lstart = matcoloring->lstart; 1356 ierr = PetscMemzero(lstart,ncolors*sizeof(PetscInt));CHKERRQ(ierr); 1357 1358 row_end = brows; 1359 if (row_end > m) row_end = m; 1360 for (row_start=0; row_start<m; row_start+=brows) { /* loop over row blocks of Csp */ 1361 ca_den_ptr = ca_den; 1362 for (k=0; k<ncolors; k++) { /* loop over colors (columns of Cden) */ 1363 nrows = matcoloring->nrows[k]; 1364 row = rows + colorforrow[k]; 1365 idx = den2sp + colorforrow[k]; 1366 for (l=lstart[k]; l<nrows; l++) { 1367 if (row[l] >= row_end) { 1368 lstart[k] = l; 1369 break; 1370 } else { 1371 ca[idx[l]] = ca_den_ptr[row[l]]; 1372 } 1373 } 1374 ca_den_ptr += m; 1375 } 1376 row_end += brows; 1377 if (row_end > m) row_end = m; 1378 } 1379 } else { /* non-blocked impl: loop over columns of Csp - slow if Csp is large */ 1380 ca_den_ptr = ca_den; 1381 for (k=0; k<ncolors; k++) { 1382 nrows = matcoloring->nrows[k]; 1383 row = rows + colorforrow[k]; 1384 idx = den2sp + colorforrow[k]; 1385 for (l=0; l<nrows; l++) { 1386 ca[idx[l]] = ca_den_ptr[row[l]]; 1387 } 1388 ca_den_ptr += m; 1389 } 1390 } 1391 1392 ierr = MatDenseRestoreArray(Cden,&ca_den);CHKERRQ(ierr); 1393 #if defined(PETSC_USE_INFO) 1394 if (matcoloring->brows > 0) { 1395 ierr = PetscInfo1(Csp,"Loop over %D row blocks for den2sp\n",brows);CHKERRQ(ierr); 1396 } else { 1397 ierr = PetscInfo(Csp,"Loop over colors/columns of Cden, inefficient for large sparse matrix product \n");CHKERRQ(ierr); 1398 } 1399 #endif 1400 PetscFunctionReturn(0); 1401 } 1402 1403 #undef __FUNCT__ 1404 #define __FUNCT__ "MatTransposeColoringCreate_SeqAIJ" 1405 PetscErrorCode MatTransposeColoringCreate_SeqAIJ(Mat mat,ISColoring iscoloring,MatTransposeColoring c) 1406 { 1407 PetscErrorCode ierr; 1408 PetscInt i,n,nrows,Nbs,j,k,m,ncols,col,cm; 1409 const PetscInt *is,*ci,*cj,*row_idx; 1410 PetscInt nis = iscoloring->n,*rowhit,bs = 1; 1411 IS *isa; 1412 Mat_SeqAIJ *csp = (Mat_SeqAIJ*)mat->data; 1413 PetscInt *colorforrow,*rows,*rows_i,*idxhit,*spidx,*den2sp,*den2sp_i; 1414 PetscInt *colorforcol,*columns,*columns_i,brows; 1415 PetscBool flg; 1416 1417 PetscFunctionBegin; 1418 ierr = ISColoringGetIS(iscoloring,PETSC_IGNORE,&isa);CHKERRQ(ierr); 1419 1420 /* bs >1 is not being tested yet! */ 1421 Nbs = mat->cmap->N/bs; 1422 c->M = mat->rmap->N/bs; /* set total rows, columns and local rows */ 1423 c->N = Nbs; 1424 c->m = c->M; 1425 c->rstart = 0; 1426 c->brows = 100; 1427 1428 c->ncolors = nis; 1429 ierr = PetscMalloc3(nis,&c->ncolumns,nis,&c->nrows,nis+1,&colorforrow);CHKERRQ(ierr); 1430 ierr = PetscMalloc1((csp->nz+1),&rows);CHKERRQ(ierr); 1431 ierr = PetscMalloc1((csp->nz+1),&den2sp);CHKERRQ(ierr); 1432 1433 brows = c->brows; 1434 ierr = PetscOptionsGetInt(NULL,"-matden2sp_brows",&brows,&flg);CHKERRQ(ierr); 1435 if (flg) c->brows = brows; 1436 if (brows > 0) { 1437 ierr = PetscMalloc1((nis+1),&c->lstart);CHKERRQ(ierr); 1438 } 1439 1440 colorforrow[0] = 0; 1441 rows_i = rows; 1442 den2sp_i = den2sp; 1443 1444 ierr = PetscMalloc1((nis+1),&colorforcol);CHKERRQ(ierr); 1445 ierr = PetscMalloc1((Nbs+1),&columns);CHKERRQ(ierr); 1446 1447 colorforcol[0] = 0; 1448 columns_i = columns; 1449 1450 /* get column-wise storage of mat */ 1451 ierr = MatGetColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);CHKERRQ(ierr); 1452 1453 cm = c->m; 1454 ierr = PetscMalloc1((cm+1),&rowhit);CHKERRQ(ierr); 1455 ierr = PetscMalloc1((cm+1),&idxhit);CHKERRQ(ierr); 1456 for (i=0; i<nis; i++) { /* loop over color */ 1457 ierr = ISGetLocalSize(isa[i],&n);CHKERRQ(ierr); 1458 ierr = ISGetIndices(isa[i],&is);CHKERRQ(ierr); 1459 1460 c->ncolumns[i] = n; 1461 if (n) { 1462 ierr = PetscMemcpy(columns_i,is,n*sizeof(PetscInt));CHKERRQ(ierr); 1463 } 1464 colorforcol[i+1] = colorforcol[i] + n; 1465 columns_i += n; 1466 1467 /* fast, crude version requires O(N*N) work */ 1468 ierr = PetscMemzero(rowhit,cm*sizeof(PetscInt));CHKERRQ(ierr); 1469 1470 for (j=0; j<n; j++) { /* loop over columns*/ 1471 col = is[j]; 1472 row_idx = cj + ci[col]; 1473 m = ci[col+1] - ci[col]; 1474 for (k=0; k<m; k++) { /* loop over columns marking them in rowhit */ 1475 idxhit[*row_idx] = spidx[ci[col] + k]; 1476 rowhit[*row_idx++] = col + 1; 1477 } 1478 } 1479 /* count the number of hits */ 1480 nrows = 0; 1481 for (j=0; j<cm; j++) { 1482 if (rowhit[j]) nrows++; 1483 } 1484 c->nrows[i] = nrows; 1485 colorforrow[i+1] = colorforrow[i] + nrows; 1486 1487 nrows = 0; 1488 for (j=0; j<cm; j++) { /* loop over rows */ 1489 if (rowhit[j]) { 1490 rows_i[nrows] = j; 1491 den2sp_i[nrows] = idxhit[j]; 1492 nrows++; 1493 } 1494 } 1495 den2sp_i += nrows; 1496 1497 ierr = ISRestoreIndices(isa[i],&is);CHKERRQ(ierr); 1498 rows_i += nrows; 1499 } 1500 ierr = MatRestoreColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);CHKERRQ(ierr); 1501 ierr = PetscFree(rowhit);CHKERRQ(ierr); 1502 ierr = ISColoringRestoreIS(iscoloring,&isa);CHKERRQ(ierr); 1503 if (csp->nz != colorforrow[nis]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"csp->nz %d != colorforrow[nis] %d",csp->nz,colorforrow[nis]); 1504 1505 c->colorforrow = colorforrow; 1506 c->rows = rows; 1507 c->den2sp = den2sp; 1508 c->colorforcol = colorforcol; 1509 c->columns = columns; 1510 1511 ierr = PetscFree(idxhit);CHKERRQ(ierr); 1512 PetscFunctionReturn(0); 1513 } 1514