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 static PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat,Mat,PetscReal,Mat*); 14 15 #if defined(PETSC_HAVE_HYPRE) 16 PETSC_INTERN PetscErrorCode MatMatMultSymbolic_AIJ_AIJ_wHYPRE(Mat,Mat,PetscReal,Mat*); 17 #endif 18 19 PETSC_INTERN PetscErrorCode MatMatMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 20 { 21 PetscErrorCode ierr; 22 #if !defined(PETSC_HAVE_HYPRE) 23 const char *algTypes[6] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed"}; 24 PetscInt nalg = 6; 25 #else 26 const char *algTypes[7] = {"sorted","scalable","scalable_fast","heap","btheap","llcondensed","hypre"}; 27 PetscInt nalg = 7; 28 #endif 29 PetscInt alg = 0; /* set default algorithm */ 30 31 PetscFunctionBegin; 32 if (scall == MAT_INITIAL_MATRIX) { 33 ierr = PetscObjectOptionsBegin((PetscObject)A);CHKERRQ(ierr); 34 PetscOptionsObject->alreadyprinted = PETSC_FALSE; /* a hack to ensure the option shows in '-help' */ 35 ierr = PetscOptionsEList("-matmatmult_via","Algorithmic approach","MatMatMult",algTypes,nalg,algTypes[0],&alg,NULL);CHKERRQ(ierr); 36 ierr = PetscOptionsEnd();CHKERRQ(ierr); 37 ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 38 switch (alg) { 39 case 1: 40 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(A,B,fill,C);CHKERRQ(ierr); 41 break; 42 case 2: 43 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(A,B,fill,C);CHKERRQ(ierr); 44 break; 45 case 3: 46 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(A,B,fill,C);CHKERRQ(ierr); 47 break; 48 case 4: 49 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(A,B,fill,C);CHKERRQ(ierr); 50 break; 51 case 5: 52 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(A,B,fill,C);CHKERRQ(ierr); 53 break; 54 #if defined(PETSC_HAVE_HYPRE) 55 case 6: 56 ierr = MatMatMultSymbolic_AIJ_AIJ_wHYPRE(A,B,fill,C);CHKERRQ(ierr); 57 break; 58 #endif 59 default: 60 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 61 break; 62 } 63 ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 64 } 65 66 ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 67 ierr = (*(*C)->ops->matmultnumeric)(A,B,*C);CHKERRQ(ierr); 68 ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 69 PetscFunctionReturn(0); 70 } 71 72 static PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat A,Mat B,PetscReal fill,Mat *C) 73 { 74 PetscErrorCode ierr; 75 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 76 PetscInt *ai=a->i,*bi=b->i,*ci,*cj; 77 PetscInt am =A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 78 PetscReal afill; 79 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax; 80 PetscTable ta; 81 PetscBT lnkbt; 82 PetscFreeSpaceList free_space=NULL,current_space=NULL; 83 84 PetscFunctionBegin; 85 /* Get ci and cj */ 86 /*---------------*/ 87 /* Allocate ci array, arrays for fill computation and */ 88 /* free space for accumulating nonzero column info */ 89 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 90 ci[0] = 0; 91 92 /* create and initialize a linked list */ 93 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr); 94 MatRowMergeMax_SeqAIJ(b,bm,ta); 95 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr); 96 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr); 97 98 ierr = PetscLLCondensedCreate(Crmax,bn,&lnk,&lnkbt);CHKERRQ(ierr); 99 100 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 101 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 102 103 current_space = free_space; 104 105 /* Determine ci and cj */ 106 for (i=0; i<am; i++) { 107 anzi = ai[i+1] - ai[i]; 108 aj = a->j + ai[i]; 109 for (j=0; j<anzi; j++) { 110 brow = aj[j]; 111 bnzj = bi[brow+1] - bi[brow]; 112 bj = b->j + bi[brow]; 113 /* add non-zero cols of B into the sorted linked list lnk */ 114 ierr = PetscLLCondensedAddSorted(bnzj,bj,lnk,lnkbt);CHKERRQ(ierr); 115 } 116 cnzi = lnk[0]; 117 118 /* If free space is not available, make more free space */ 119 /* Double the amount of total space in the list */ 120 if (current_space->local_remaining<cnzi) { 121 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr); 122 ndouble++; 123 } 124 125 /* Copy data into free space, then initialize lnk */ 126 ierr = PetscLLCondensedClean(bn,cnzi,current_space->array,lnk,lnkbt);CHKERRQ(ierr); 127 128 current_space->array += cnzi; 129 current_space->local_used += cnzi; 130 current_space->local_remaining -= cnzi; 131 132 ci[i+1] = ci[i] + cnzi; 133 } 134 135 /* Column indices are in the list of free space */ 136 /* Allocate space for cj, initialize cj, and */ 137 /* destroy list of free space and other temporary array(s) */ 138 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr); 139 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 140 ierr = PetscLLCondensedDestroy(lnk,lnkbt);CHKERRQ(ierr); 141 142 /* put together the new symbolic matrix */ 143 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);CHKERRQ(ierr); 144 ierr = MatSetBlockSizesFromMats(*C,A,B);CHKERRQ(ierr); 145 146 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 147 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 148 c = (Mat_SeqAIJ*)((*C)->data); 149 c->free_a = PETSC_FALSE; 150 c->free_ij = PETSC_TRUE; 151 c->nonew = 0; 152 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; /* fast, needs non-scalable O(bn) array 'abdense' */ 153 154 /* set MatInfo */ 155 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 156 if (afill < 1.0) afill = 1.0; 157 c->maxnz = ci[am]; 158 c->nz = ci[am]; 159 (*C)->info.mallocs = ndouble; 160 (*C)->info.fill_ratio_given = fill; 161 (*C)->info.fill_ratio_needed = afill; 162 163 #if defined(PETSC_USE_INFO) 164 if (ci[am]) { 165 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 166 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 167 } else { 168 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 169 } 170 #endif 171 PetscFunctionReturn(0); 172 } 173 174 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 175 { 176 PetscErrorCode ierr; 177 PetscLogDouble flops=0.0; 178 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 179 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 180 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 181 PetscInt *ai =a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j; 182 PetscInt am =A->rmap->n,cm=C->rmap->n; 183 PetscInt i,j,k,anzi,bnzi,cnzi,brow; 184 PetscScalar *aa=a->a,*ba=b->a,*baj,*ca,valtmp; 185 PetscScalar *ab_dense; 186 187 PetscFunctionBegin; 188 if (!c->a) { /* first call of MatMatMultNumeric_SeqAIJ_SeqAIJ, allocate ca and matmult_abdense */ 189 ierr = PetscMalloc1(ci[cm]+1,&ca);CHKERRQ(ierr); 190 c->a = ca; 191 c->free_a = PETSC_TRUE; 192 } else { 193 ca = c->a; 194 } 195 if (!c->matmult_abdense) { 196 ierr = PetscCalloc1(B->cmap->N,&ab_dense);CHKERRQ(ierr); 197 c->matmult_abdense = ab_dense; 198 } else { 199 ab_dense = c->matmult_abdense; 200 } 201 202 /* clean old values in C */ 203 ierr = PetscMemzero(ca,ci[cm]*sizeof(MatScalar));CHKERRQ(ierr); 204 /* Traverse A row-wise. */ 205 /* Build the ith row in C by summing over nonzero columns in A, */ 206 /* the rows of B corresponding to nonzeros of A. */ 207 for (i=0; i<am; i++) { 208 anzi = ai[i+1] - ai[i]; 209 for (j=0; j<anzi; j++) { 210 brow = aj[j]; 211 bnzi = bi[brow+1] - bi[brow]; 212 bjj = bj + bi[brow]; 213 baj = ba + bi[brow]; 214 /* perform dense axpy */ 215 valtmp = aa[j]; 216 for (k=0; k<bnzi; k++) { 217 ab_dense[bjj[k]] += valtmp*baj[k]; 218 } 219 flops += 2*bnzi; 220 } 221 aj += anzi; aa += anzi; 222 223 cnzi = ci[i+1] - ci[i]; 224 for (k=0; k<cnzi; k++) { 225 ca[k] += ab_dense[cj[k]]; 226 ab_dense[cj[k]] = 0.0; /* zero ab_dense */ 227 } 228 flops += cnzi; 229 cj += cnzi; ca += cnzi; 230 } 231 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 232 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 233 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 234 PetscFunctionReturn(0); 235 } 236 237 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,Mat C) 238 { 239 PetscErrorCode ierr; 240 PetscLogDouble flops=0.0; 241 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 242 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 243 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 244 PetscInt *ai = a->i,*aj=a->j,*bi=b->i,*bj=b->j,*bjj,*ci=c->i,*cj=c->j; 245 PetscInt am = A->rmap->N,cm=C->rmap->N; 246 PetscInt i,j,k,anzi,bnzi,cnzi,brow; 247 PetscScalar *aa=a->a,*ba=b->a,*baj,*ca=c->a,valtmp; 248 PetscInt nextb; 249 250 PetscFunctionBegin; 251 if (!ca) { /* first call of MatMatMultNumeric_SeqAIJ_SeqAIJ, allocate ca and matmult_abdense */ 252 ierr = PetscMalloc1(ci[cm]+1,&ca);CHKERRQ(ierr); 253 c->a = ca; 254 c->free_a = PETSC_TRUE; 255 } 256 257 /* clean old values in C */ 258 ierr = PetscMemzero(ca,ci[cm]*sizeof(MatScalar));CHKERRQ(ierr); 259 /* Traverse A row-wise. */ 260 /* Build the ith row in C by summing over nonzero columns in A, */ 261 /* the rows of B corresponding to nonzeros of A. */ 262 for (i=0; i<am; i++) { 263 anzi = ai[i+1] - ai[i]; 264 cnzi = ci[i+1] - ci[i]; 265 for (j=0; j<anzi; j++) { 266 brow = aj[j]; 267 bnzi = bi[brow+1] - bi[brow]; 268 bjj = bj + bi[brow]; 269 baj = ba + bi[brow]; 270 /* perform sparse axpy */ 271 valtmp = aa[j]; 272 nextb = 0; 273 for (k=0; nextb<bnzi; k++) { 274 if (cj[k] == bjj[nextb]) { /* ccol == bcol */ 275 ca[k] += valtmp*baj[nextb++]; 276 } 277 } 278 flops += 2*bnzi; 279 } 280 aj += anzi; aa += anzi; 281 cj += cnzi; ca += cnzi; 282 } 283 284 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 285 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 286 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 287 PetscFunctionReturn(0); 288 } 289 290 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat A,Mat B,PetscReal fill,Mat *C) 291 { 292 PetscErrorCode ierr; 293 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 294 PetscInt *ai = a->i,*bi=b->i,*ci,*cj; 295 PetscInt am = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 296 MatScalar *ca; 297 PetscReal afill; 298 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax; 299 PetscTable ta; 300 PetscFreeSpaceList free_space=NULL,current_space=NULL; 301 302 PetscFunctionBegin; 303 /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_fast() */ 304 /*-----------------------------------------------------------------------------------------*/ 305 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 306 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 307 ci[0] = 0; 308 309 /* create and initialize a linked list */ 310 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr); 311 MatRowMergeMax_SeqAIJ(b,bm,ta); 312 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr); 313 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr); 314 315 ierr = PetscLLCondensedCreate_fast(Crmax,&lnk);CHKERRQ(ierr); 316 317 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 318 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 319 current_space = free_space; 320 321 /* Determine ci and cj */ 322 for (i=0; i<am; i++) { 323 anzi = ai[i+1] - ai[i]; 324 aj = a->j + ai[i]; 325 for (j=0; j<anzi; j++) { 326 brow = aj[j]; 327 bnzj = bi[brow+1] - bi[brow]; 328 bj = b->j + bi[brow]; 329 /* add non-zero cols of B into the sorted linked list lnk */ 330 ierr = PetscLLCondensedAddSorted_fast(bnzj,bj,lnk);CHKERRQ(ierr); 331 } 332 cnzi = lnk[1]; 333 334 /* If free space is not available, make more free space */ 335 /* Double the amount of total space in the list */ 336 if (current_space->local_remaining<cnzi) { 337 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr); 338 ndouble++; 339 } 340 341 /* Copy data into free space, then initialize lnk */ 342 ierr = PetscLLCondensedClean_fast(cnzi,current_space->array,lnk);CHKERRQ(ierr); 343 344 current_space->array += cnzi; 345 current_space->local_used += cnzi; 346 current_space->local_remaining -= cnzi; 347 348 ci[i+1] = ci[i] + cnzi; 349 } 350 351 /* Column indices are in the list of free space */ 352 /* Allocate space for cj, initialize cj, and */ 353 /* destroy list of free space and other temporary array(s) */ 354 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr); 355 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 356 ierr = PetscLLCondensedDestroy_fast(lnk);CHKERRQ(ierr); 357 358 /* Allocate space for ca */ 359 ierr = PetscMalloc1(ci[am]+1,&ca);CHKERRQ(ierr); 360 ierr = PetscMemzero(ca,(ci[am]+1)*sizeof(MatScalar));CHKERRQ(ierr); 361 362 /* put together the new symbolic matrix */ 363 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,C);CHKERRQ(ierr); 364 ierr = MatSetBlockSizesFromMats(*C,A,B);CHKERRQ(ierr); 365 366 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 367 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 368 c = (Mat_SeqAIJ*)((*C)->data); 369 c->free_a = PETSC_TRUE; 370 c->free_ij = PETSC_TRUE; 371 c->nonew = 0; 372 373 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable; /* slower, less memory */ 374 375 /* set MatInfo */ 376 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 377 if (afill < 1.0) afill = 1.0; 378 c->maxnz = ci[am]; 379 c->nz = ci[am]; 380 (*C)->info.mallocs = ndouble; 381 (*C)->info.fill_ratio_given = fill; 382 (*C)->info.fill_ratio_needed = afill; 383 384 #if defined(PETSC_USE_INFO) 385 if (ci[am]) { 386 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 387 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 388 } else { 389 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 390 } 391 #endif 392 PetscFunctionReturn(0); 393 } 394 395 396 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat A,Mat B,PetscReal fill,Mat *C) 397 { 398 PetscErrorCode ierr; 399 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 400 PetscInt *ai = a->i,*bi=b->i,*ci,*cj; 401 PetscInt am = A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 402 MatScalar *ca; 403 PetscReal afill; 404 PetscInt i,j,anzi,brow,bnzj,cnzi,*bj,*aj,*lnk,ndouble=0,Crmax; 405 PetscTable ta; 406 PetscFreeSpaceList free_space=NULL,current_space=NULL; 407 408 PetscFunctionBegin; 409 /* Get ci and cj - same as MatMatMultSymbolic_SeqAIJ_SeqAIJ except using PetscLLxxx_Scalalbe() */ 410 /*---------------------------------------------------------------------------------------------*/ 411 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 412 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 413 ci[0] = 0; 414 415 /* create and initialize a linked list */ 416 ierr = PetscTableCreate(bn,bn,&ta);CHKERRQ(ierr); 417 MatRowMergeMax_SeqAIJ(b,bm,ta); 418 ierr = PetscTableGetCount(ta,&Crmax);CHKERRQ(ierr); 419 ierr = PetscTableDestroy(&ta);CHKERRQ(ierr); 420 ierr = PetscLLCondensedCreate_Scalable(Crmax,&lnk);CHKERRQ(ierr); 421 422 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 423 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 424 current_space = free_space; 425 426 /* Determine ci and cj */ 427 for (i=0; i<am; i++) { 428 anzi = ai[i+1] - ai[i]; 429 aj = a->j + ai[i]; 430 for (j=0; j<anzi; j++) { 431 brow = aj[j]; 432 bnzj = bi[brow+1] - bi[brow]; 433 bj = b->j + bi[brow]; 434 /* add non-zero cols of B into the sorted linked list lnk */ 435 ierr = PetscLLCondensedAddSorted_Scalable(bnzj,bj,lnk);CHKERRQ(ierr); 436 } 437 cnzi = lnk[0]; 438 439 /* If free space is not available, make more free space */ 440 /* Double the amount of total space in the list */ 441 if (current_space->local_remaining<cnzi) { 442 ierr = PetscFreeSpaceGet(PetscIntSumTruncate(cnzi,current_space->total_array_size),¤t_space);CHKERRQ(ierr); 443 ndouble++; 444 } 445 446 /* Copy data into free space, then initialize lnk */ 447 ierr = PetscLLCondensedClean_Scalable(cnzi,current_space->array,lnk);CHKERRQ(ierr); 448 449 current_space->array += cnzi; 450 current_space->local_used += cnzi; 451 current_space->local_remaining -= cnzi; 452 453 ci[i+1] = ci[i] + cnzi; 454 } 455 456 /* Column indices are in the list of free space */ 457 /* Allocate space for cj, initialize cj, and */ 458 /* destroy list of free space and other temporary array(s) */ 459 ierr = PetscMalloc1(ci[am]+1,&cj);CHKERRQ(ierr); 460 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 461 ierr = PetscLLCondensedDestroy_Scalable(lnk);CHKERRQ(ierr); 462 463 /* Allocate space for ca */ 464 /*-----------------------*/ 465 ierr = PetscMalloc1(ci[am]+1,&ca);CHKERRQ(ierr); 466 ierr = PetscMemzero(ca,(ci[am]+1)*sizeof(MatScalar));CHKERRQ(ierr); 467 468 /* put together the new symbolic matrix */ 469 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,ca,C);CHKERRQ(ierr); 470 ierr = MatSetBlockSizesFromMats(*C,A,B);CHKERRQ(ierr); 471 472 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 473 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 474 c = (Mat_SeqAIJ*)((*C)->data); 475 c->free_a = PETSC_TRUE; 476 c->free_ij = PETSC_TRUE; 477 c->nonew = 0; 478 479 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable; /* slower, less memory */ 480 481 /* set MatInfo */ 482 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 483 if (afill < 1.0) afill = 1.0; 484 c->maxnz = ci[am]; 485 c->nz = ci[am]; 486 (*C)->info.mallocs = ndouble; 487 (*C)->info.fill_ratio_given = fill; 488 (*C)->info.fill_ratio_needed = afill; 489 490 #if defined(PETSC_USE_INFO) 491 if (ci[am]) { 492 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 493 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 494 } else { 495 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 496 } 497 #endif 498 PetscFunctionReturn(0); 499 } 500 501 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat A,Mat B,PetscReal fill,Mat *C) 502 { 503 PetscErrorCode ierr; 504 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 505 const PetscInt *ai=a->i,*bi=b->i,*aj=a->j,*bj=b->j; 506 PetscInt *ci,*cj,*bb; 507 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 508 PetscReal afill; 509 PetscInt i,j,col,ndouble = 0; 510 PetscFreeSpaceList free_space=NULL,current_space=NULL; 511 PetscHeap h; 512 513 PetscFunctionBegin; 514 /* Get ci and cj - by merging sorted rows using a heap */ 515 /*---------------------------------------------------------------------------------------------*/ 516 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 517 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 518 ci[0] = 0; 519 520 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 521 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 522 current_space = free_space; 523 524 ierr = PetscHeapCreate(a->rmax,&h);CHKERRQ(ierr); 525 ierr = PetscMalloc1(a->rmax,&bb);CHKERRQ(ierr); 526 527 /* Determine ci and cj */ 528 for (i=0; i<am; i++) { 529 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 */ 530 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 531 ci[i+1] = ci[i]; 532 /* Populate the min heap */ 533 for (j=0; j<anzi; j++) { 534 bb[j] = bi[acol[j]]; /* bb points at the start of the row */ 535 if (bb[j] < bi[acol[j]+1]) { /* Add if row is nonempty */ 536 ierr = PetscHeapAdd(h,j,bj[bb[j]++]);CHKERRQ(ierr); 537 } 538 } 539 /* Pick off the min element, adding it to free space */ 540 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 541 while (j >= 0) { 542 if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */ 543 ierr = PetscFreeSpaceGet(PetscMin(PetscIntMultTruncate(2,current_space->total_array_size),16 << 20),¤t_space);CHKERRQ(ierr); 544 ndouble++; 545 } 546 *(current_space->array++) = col; 547 current_space->local_used++; 548 current_space->local_remaining--; 549 ci[i+1]++; 550 551 /* stash if anything else remains in this row of B */ 552 if (bb[j] < bi[acol[j]+1]) {ierr = PetscHeapStash(h,j,bj[bb[j]++]);CHKERRQ(ierr);} 553 while (1) { /* pop and stash any other rows of B that also had an entry in this column */ 554 PetscInt j2,col2; 555 ierr = PetscHeapPeek(h,&j2,&col2);CHKERRQ(ierr); 556 if (col2 != col) break; 557 ierr = PetscHeapPop(h,&j2,&col2);CHKERRQ(ierr); 558 if (bb[j2] < bi[acol[j2]+1]) {ierr = PetscHeapStash(h,j2,bj[bb[j2]++]);CHKERRQ(ierr);} 559 } 560 /* Put any stashed elements back into the min heap */ 561 ierr = PetscHeapUnstash(h);CHKERRQ(ierr); 562 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 563 } 564 } 565 ierr = PetscFree(bb);CHKERRQ(ierr); 566 ierr = PetscHeapDestroy(&h);CHKERRQ(ierr); 567 568 /* Column indices are in the list of free space */ 569 /* Allocate space for cj, initialize cj, and */ 570 /* destroy list of free space and other temporary array(s) */ 571 ierr = PetscMalloc1(ci[am],&cj);CHKERRQ(ierr); 572 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 573 574 /* put together the new symbolic matrix */ 575 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);CHKERRQ(ierr); 576 ierr = MatSetBlockSizesFromMats(*C,A,B);CHKERRQ(ierr); 577 578 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 579 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 580 c = (Mat_SeqAIJ*)((*C)->data); 581 c->free_a = PETSC_TRUE; 582 c->free_ij = PETSC_TRUE; 583 c->nonew = 0; 584 585 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; 586 587 /* set MatInfo */ 588 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 589 if (afill < 1.0) afill = 1.0; 590 c->maxnz = ci[am]; 591 c->nz = ci[am]; 592 (*C)->info.mallocs = ndouble; 593 (*C)->info.fill_ratio_given = fill; 594 (*C)->info.fill_ratio_needed = afill; 595 596 #if defined(PETSC_USE_INFO) 597 if (ci[am]) { 598 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 599 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 600 } else { 601 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 602 } 603 #endif 604 PetscFunctionReturn(0); 605 } 606 607 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat A,Mat B,PetscReal fill,Mat *C) 608 { 609 PetscErrorCode ierr; 610 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 611 const PetscInt *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j; 612 PetscInt *ci,*cj,*bb; 613 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 614 PetscReal afill; 615 PetscInt i,j,col,ndouble = 0; 616 PetscFreeSpaceList free_space=NULL,current_space=NULL; 617 PetscHeap h; 618 PetscBT bt; 619 620 PetscFunctionBegin; 621 /* Get ci and cj - using a heap for the sorted rows, but use BT so that each index is only added once */ 622 /*---------------------------------------------------------------------------------------------*/ 623 /* Allocate arrays for fill computation and free space for accumulating nonzero column */ 624 ierr = PetscMalloc1(am+2,&ci);CHKERRQ(ierr); 625 ci[0] = 0; 626 627 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 628 ierr = PetscFreeSpaceGet(PetscRealIntMultTruncate(fill,PetscIntSumTruncate(ai[am],bi[bm])),&free_space);CHKERRQ(ierr); 629 630 current_space = free_space; 631 632 ierr = PetscHeapCreate(a->rmax,&h);CHKERRQ(ierr); 633 ierr = PetscMalloc1(a->rmax,&bb);CHKERRQ(ierr); 634 ierr = PetscBTCreate(bn,&bt);CHKERRQ(ierr); 635 636 /* Determine ci and cj */ 637 for (i=0; i<am; i++) { 638 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 */ 639 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 640 const PetscInt *fptr = current_space->array; /* Save beginning of the row so we can clear the BT later */ 641 ci[i+1] = ci[i]; 642 /* Populate the min heap */ 643 for (j=0; j<anzi; j++) { 644 PetscInt brow = acol[j]; 645 for (bb[j] = bi[brow]; bb[j] < bi[brow+1]; bb[j]++) { 646 PetscInt bcol = bj[bb[j]]; 647 if (!PetscBTLookupSet(bt,bcol)) { /* new entry */ 648 ierr = PetscHeapAdd(h,j,bcol);CHKERRQ(ierr); 649 bb[j]++; 650 break; 651 } 652 } 653 } 654 /* Pick off the min element, adding it to free space */ 655 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 656 while (j >= 0) { 657 if (current_space->local_remaining < 1) { /* double the size, but don't exceed 16 MiB */ 658 fptr = NULL; /* need PetscBTMemzero */ 659 ierr = PetscFreeSpaceGet(PetscMin(PetscIntMultTruncate(2,current_space->total_array_size),16 << 20),¤t_space);CHKERRQ(ierr); 660 ndouble++; 661 } 662 *(current_space->array++) = col; 663 current_space->local_used++; 664 current_space->local_remaining--; 665 ci[i+1]++; 666 667 /* stash if anything else remains in this row of B */ 668 for (; bb[j] < bi[acol[j]+1]; bb[j]++) { 669 PetscInt bcol = bj[bb[j]]; 670 if (!PetscBTLookupSet(bt,bcol)) { /* new entry */ 671 ierr = PetscHeapAdd(h,j,bcol);CHKERRQ(ierr); 672 bb[j]++; 673 break; 674 } 675 } 676 ierr = PetscHeapPop(h,&j,&col);CHKERRQ(ierr); 677 } 678 if (fptr) { /* Clear the bits for this row */ 679 for (; fptr<current_space->array; fptr++) {ierr = PetscBTClear(bt,*fptr);CHKERRQ(ierr);} 680 } else { /* We reallocated so we don't remember (easily) how to clear only the bits we changed */ 681 ierr = PetscBTMemzero(bn,bt);CHKERRQ(ierr); 682 } 683 } 684 ierr = PetscFree(bb);CHKERRQ(ierr); 685 ierr = PetscHeapDestroy(&h);CHKERRQ(ierr); 686 ierr = PetscBTDestroy(&bt);CHKERRQ(ierr); 687 688 /* Column indices are in the list of free space */ 689 /* Allocate space for cj, initialize cj, and */ 690 /* destroy list of free space and other temporary array(s) */ 691 ierr = PetscMalloc1(ci[am],&cj);CHKERRQ(ierr); 692 ierr = PetscFreeSpaceContiguous(&free_space,cj);CHKERRQ(ierr); 693 694 /* put together the new symbolic matrix */ 695 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);CHKERRQ(ierr); 696 ierr = MatSetBlockSizesFromMats(*C,A,B);CHKERRQ(ierr); 697 698 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 699 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 700 c = (Mat_SeqAIJ*)((*C)->data); 701 c->free_a = PETSC_TRUE; 702 c->free_ij = PETSC_TRUE; 703 c->nonew = 0; 704 705 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; 706 707 /* set MatInfo */ 708 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 709 if (afill < 1.0) afill = 1.0; 710 c->maxnz = ci[am]; 711 c->nz = ci[am]; 712 (*C)->info.mallocs = ndouble; 713 (*C)->info.fill_ratio_given = fill; 714 (*C)->info.fill_ratio_needed = afill; 715 716 #if defined(PETSC_USE_INFO) 717 if (ci[am]) { 718 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 719 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 720 } else { 721 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 722 } 723 #endif 724 PetscFunctionReturn(0); 725 } 726 727 /* concatenate unique entries and then sort */ 728 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C) 729 { 730 PetscErrorCode ierr; 731 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c; 732 const PetscInt *ai = a->i,*bi=b->i,*aj=a->j,*bj=b->j; 733 PetscInt *ci,*cj; 734 PetscInt am=A->rmap->N,bn=B->cmap->N,bm=B->rmap->N; 735 PetscReal afill; 736 PetscInt i,j,ndouble = 0; 737 PetscSegBuffer seg,segrow; 738 char *seen; 739 740 PetscFunctionBegin; 741 ierr = PetscMalloc1(am+1,&ci);CHKERRQ(ierr); 742 ci[0] = 0; 743 744 /* Initial FreeSpace size is fill*(nnz(A)+nnz(B)) */ 745 ierr = PetscSegBufferCreate(sizeof(PetscInt),(PetscInt)(fill*(ai[am]+bi[bm])),&seg);CHKERRQ(ierr); 746 ierr = PetscSegBufferCreate(sizeof(PetscInt),100,&segrow);CHKERRQ(ierr); 747 ierr = PetscMalloc1(bn,&seen);CHKERRQ(ierr); 748 ierr = PetscMemzero(seen,bn*sizeof(char));CHKERRQ(ierr); 749 750 /* Determine ci and cj */ 751 for (i=0; i<am; i++) { 752 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 */ 753 const PetscInt *acol = aj + ai[i]; /* column indices of nonzero entries in this row */ 754 PetscInt packlen = 0,*PETSC_RESTRICT crow; 755 /* Pack segrow */ 756 for (j=0; j<anzi; j++) { 757 PetscInt brow = acol[j],bjstart = bi[brow],bjend = bi[brow+1],k; 758 for (k=bjstart; k<bjend; k++) { 759 PetscInt bcol = bj[k]; 760 if (!seen[bcol]) { /* new entry */ 761 PetscInt *PETSC_RESTRICT slot; 762 ierr = PetscSegBufferGetInts(segrow,1,&slot);CHKERRQ(ierr); 763 *slot = bcol; 764 seen[bcol] = 1; 765 packlen++; 766 } 767 } 768 } 769 ierr = PetscSegBufferGetInts(seg,packlen,&crow);CHKERRQ(ierr); 770 ierr = PetscSegBufferExtractTo(segrow,crow);CHKERRQ(ierr); 771 ierr = PetscSortInt(packlen,crow);CHKERRQ(ierr); 772 ci[i+1] = ci[i] + packlen; 773 for (j=0; j<packlen; j++) seen[crow[j]] = 0; 774 } 775 ierr = PetscSegBufferDestroy(&segrow);CHKERRQ(ierr); 776 ierr = PetscFree(seen);CHKERRQ(ierr); 777 778 /* Column indices are in the segmented buffer */ 779 ierr = PetscSegBufferExtractAlloc(seg,&cj);CHKERRQ(ierr); 780 ierr = PetscSegBufferDestroy(&seg);CHKERRQ(ierr); 781 782 /* put together the new symbolic matrix */ 783 ierr = MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),am,bn,ci,cj,NULL,C);CHKERRQ(ierr); 784 ierr = MatSetBlockSizesFromMats(*C,A,B);CHKERRQ(ierr); 785 786 /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */ 787 /* These are PETSc arrays, so change flags so arrays can be deleted by PETSc */ 788 c = (Mat_SeqAIJ*)((*C)->data); 789 c->free_a = PETSC_TRUE; 790 c->free_ij = PETSC_TRUE; 791 c->nonew = 0; 792 793 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqAIJ; 794 795 /* set MatInfo */ 796 afill = (PetscReal)ci[am]/(ai[am]+bi[bm]) + 1.e-5; 797 if (afill < 1.0) afill = 1.0; 798 c->maxnz = ci[am]; 799 c->nz = ci[am]; 800 (*C)->info.mallocs = ndouble; 801 (*C)->info.fill_ratio_given = fill; 802 (*C)->info.fill_ratio_needed = afill; 803 804 #if defined(PETSC_USE_INFO) 805 if (ci[am]) { 806 ierr = PetscInfo3((*C),"Reallocs %D; Fill ratio: given %g needed %g.\n",ndouble,(double)fill,(double)afill);CHKERRQ(ierr); 807 ierr = PetscInfo1((*C),"Use MatMatMult(A,B,MatReuse,%g,&C) for best performance.;\n",(double)afill);CHKERRQ(ierr); 808 } else { 809 ierr = PetscInfo((*C),"Empty matrix product\n");CHKERRQ(ierr); 810 } 811 #endif 812 PetscFunctionReturn(0); 813 } 814 815 /* This routine is not used. Should be removed! */ 816 PetscErrorCode MatMatTransposeMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 817 { 818 PetscErrorCode ierr; 819 820 PetscFunctionBegin; 821 if (scall == MAT_INITIAL_MATRIX) { 822 ierr = PetscLogEventBegin(MAT_MatTransposeMultSymbolic,A,B,0,0);CHKERRQ(ierr); 823 ierr = MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 824 ierr = PetscLogEventEnd(MAT_MatTransposeMultSymbolic,A,B,0,0);CHKERRQ(ierr); 825 } 826 ierr = PetscLogEventBegin(MAT_MatTransposeMultNumeric,A,B,0,0);CHKERRQ(ierr); 827 ierr = MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(A,B,*C);CHKERRQ(ierr); 828 ierr = PetscLogEventEnd(MAT_MatTransposeMultNumeric,A,B,0,0);CHKERRQ(ierr); 829 PetscFunctionReturn(0); 830 } 831 832 PetscErrorCode MatDestroy_SeqAIJ_MatMatMultTrans(Mat A) 833 { 834 PetscErrorCode ierr; 835 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 836 Mat_MatMatTransMult *abt=a->abt; 837 838 PetscFunctionBegin; 839 ierr = (abt->destroy)(A);CHKERRQ(ierr); 840 ierr = MatTransposeColoringDestroy(&abt->matcoloring);CHKERRQ(ierr); 841 ierr = MatDestroy(&abt->Bt_den);CHKERRQ(ierr); 842 ierr = MatDestroy(&abt->ABt_den);CHKERRQ(ierr); 843 ierr = PetscFree(abt);CHKERRQ(ierr); 844 PetscFunctionReturn(0); 845 } 846 847 PetscErrorCode MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C) 848 { 849 PetscErrorCode ierr; 850 Mat Bt; 851 PetscInt *bti,*btj; 852 Mat_MatMatTransMult *abt; 853 Mat_SeqAIJ *c; 854 855 PetscFunctionBegin; 856 /* create symbolic Bt */ 857 ierr = MatGetSymbolicTranspose_SeqAIJ(B,&bti,&btj);CHKERRQ(ierr); 858 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,B->cmap->n,B->rmap->n,bti,btj,NULL,&Bt);CHKERRQ(ierr); 859 ierr = MatSetBlockSizes(Bt,PetscAbs(A->cmap->bs),PetscAbs(B->cmap->bs));CHKERRQ(ierr); 860 861 /* get symbolic C=A*Bt */ 862 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(A,Bt,fill,C);CHKERRQ(ierr); 863 864 /* create a supporting struct for reuse intermidiate dense matrices with matcoloring */ 865 ierr = PetscNew(&abt);CHKERRQ(ierr); 866 c = (Mat_SeqAIJ*)(*C)->data; 867 c->abt = abt; 868 869 abt->usecoloring = PETSC_FALSE; 870 abt->destroy = (*C)->ops->destroy; 871 (*C)->ops->destroy = MatDestroy_SeqAIJ_MatMatMultTrans; 872 873 ierr = PetscOptionsGetBool(((PetscObject)A)->options,NULL,"-matmattransmult_color",&abt->usecoloring,NULL);CHKERRQ(ierr); 874 if (abt->usecoloring) { 875 /* Create MatTransposeColoring from symbolic C=A*B^T */ 876 MatTransposeColoring matcoloring; 877 MatColoring coloring; 878 ISColoring iscoloring; 879 Mat Bt_dense,C_dense; 880 Mat_SeqAIJ *c=(Mat_SeqAIJ*)(*C)->data; 881 /* inode causes memory problem, don't know why */ 882 if (c->inode.use) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MAT_USE_INODES is not supported. Use '-mat_no_inode'"); 883 884 ierr = MatColoringCreate(*C,&coloring);CHKERRQ(ierr); 885 ierr = MatColoringSetDistance(coloring,2);CHKERRQ(ierr); 886 ierr = MatColoringSetType(coloring,MATCOLORINGSL);CHKERRQ(ierr); 887 ierr = MatColoringSetFromOptions(coloring);CHKERRQ(ierr); 888 ierr = MatColoringApply(coloring,&iscoloring);CHKERRQ(ierr); 889 ierr = MatColoringDestroy(&coloring);CHKERRQ(ierr); 890 ierr = MatTransposeColoringCreate(*C,iscoloring,&matcoloring);CHKERRQ(ierr); 891 892 abt->matcoloring = matcoloring; 893 894 ierr = ISColoringDestroy(&iscoloring);CHKERRQ(ierr); 895 896 /* Create Bt_dense and C_dense = A*Bt_dense */ 897 ierr = MatCreate(PETSC_COMM_SELF,&Bt_dense);CHKERRQ(ierr); 898 ierr = MatSetSizes(Bt_dense,A->cmap->n,matcoloring->ncolors,A->cmap->n,matcoloring->ncolors);CHKERRQ(ierr); 899 ierr = MatSetType(Bt_dense,MATSEQDENSE);CHKERRQ(ierr); 900 ierr = MatSeqDenseSetPreallocation(Bt_dense,NULL);CHKERRQ(ierr); 901 902 Bt_dense->assembled = PETSC_TRUE; 903 abt->Bt_den = Bt_dense; 904 905 ierr = MatCreate(PETSC_COMM_SELF,&C_dense);CHKERRQ(ierr); 906 ierr = MatSetSizes(C_dense,A->rmap->n,matcoloring->ncolors,A->rmap->n,matcoloring->ncolors);CHKERRQ(ierr); 907 ierr = MatSetType(C_dense,MATSEQDENSE);CHKERRQ(ierr); 908 ierr = MatSeqDenseSetPreallocation(C_dense,NULL);CHKERRQ(ierr); 909 910 Bt_dense->assembled = PETSC_TRUE; 911 abt->ABt_den = C_dense; 912 913 #if defined(PETSC_USE_INFO) 914 { 915 Mat_SeqAIJ *c = (Mat_SeqAIJ*)(*C)->data; 916 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); 917 } 918 #endif 919 } 920 /* clean up */ 921 ierr = MatDestroy(&Bt);CHKERRQ(ierr); 922 ierr = MatRestoreSymbolicTranspose_SeqAIJ(B,&bti,&btj);CHKERRQ(ierr); 923 PetscFunctionReturn(0); 924 } 925 926 PetscErrorCode MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 927 { 928 PetscErrorCode ierr; 929 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data; 930 PetscInt *ai =a->i,*aj=a->j,*bi=b->i,*bj=b->j,anzi,bnzj,nexta,nextb,*acol,*bcol,brow; 931 PetscInt cm =C->rmap->n,*ci=c->i,*cj=c->j,i,j,cnzi,*ccol; 932 PetscLogDouble flops=0.0; 933 MatScalar *aa =a->a,*aval,*ba=b->a,*bval,*ca,*cval; 934 Mat_MatMatTransMult *abt = c->abt; 935 936 PetscFunctionBegin; 937 /* clear old values in C */ 938 if (!c->a) { 939 ierr = PetscMalloc1(ci[cm]+1,&ca);CHKERRQ(ierr); 940 c->a = ca; 941 c->free_a = PETSC_TRUE; 942 } else { 943 ca = c->a; 944 } 945 ierr = PetscMemzero(ca,ci[cm]*sizeof(MatScalar));CHKERRQ(ierr); 946 947 if (abt->usecoloring) { 948 MatTransposeColoring matcoloring = abt->matcoloring; 949 Mat Bt_dense,C_dense = abt->ABt_den; 950 951 /* Get Bt_dense by Apply MatTransposeColoring to B */ 952 Bt_dense = abt->Bt_den; 953 ierr = MatTransColoringApplySpToDen(matcoloring,B,Bt_dense);CHKERRQ(ierr); 954 955 /* C_dense = A*Bt_dense */ 956 ierr = MatMatMultNumeric_SeqAIJ_SeqDense(A,Bt_dense,C_dense);CHKERRQ(ierr); 957 958 /* Recover C from C_dense */ 959 ierr = MatTransColoringApplyDenToSp(matcoloring,C_dense,C);CHKERRQ(ierr); 960 PetscFunctionReturn(0); 961 } 962 963 for (i=0; i<cm; i++) { 964 anzi = ai[i+1] - ai[i]; 965 acol = aj + ai[i]; 966 aval = aa + ai[i]; 967 cnzi = ci[i+1] - ci[i]; 968 ccol = cj + ci[i]; 969 cval = ca + ci[i]; 970 for (j=0; j<cnzi; j++) { 971 brow = ccol[j]; 972 bnzj = bi[brow+1] - bi[brow]; 973 bcol = bj + bi[brow]; 974 bval = ba + bi[brow]; 975 976 /* perform sparse inner-product c(i,j)=A[i,:]*B[j,:]^T */ 977 nexta = 0; nextb = 0; 978 while (nexta<anzi && nextb<bnzj) { 979 while (nexta < anzi && acol[nexta] < bcol[nextb]) nexta++; 980 if (nexta == anzi) break; 981 while (nextb < bnzj && acol[nexta] > bcol[nextb]) nextb++; 982 if (nextb == bnzj) break; 983 if (acol[nexta] == bcol[nextb]) { 984 cval[j] += aval[nexta]*bval[nextb]; 985 nexta++; nextb++; 986 flops += 2; 987 } 988 } 989 } 990 } 991 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 992 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 993 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 994 PetscFunctionReturn(0); 995 } 996 997 PetscErrorCode MatTransposeMatMult_SeqAIJ_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 998 { 999 PetscErrorCode ierr; 1000 1001 PetscFunctionBegin; 1002 if (scall == MAT_INITIAL_MATRIX) { 1003 ierr = PetscLogEventBegin(MAT_TransposeMatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1004 ierr = MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(A,B,fill,C);CHKERRQ(ierr); 1005 ierr = PetscLogEventEnd(MAT_TransposeMatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1006 } 1007 ierr = PetscLogEventBegin(MAT_TransposeMatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1008 ierr = MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(A,B,*C);CHKERRQ(ierr); 1009 ierr = PetscLogEventEnd(MAT_TransposeMatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1010 PetscFunctionReturn(0); 1011 } 1012 1013 PetscErrorCode MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C) 1014 { 1015 PetscErrorCode ierr; 1016 Mat At; 1017 PetscInt *ati,*atj; 1018 1019 PetscFunctionBegin; 1020 /* create symbolic At */ 1021 ierr = MatGetSymbolicTranspose_SeqAIJ(A,&ati,&atj);CHKERRQ(ierr); 1022 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,A->cmap->n,A->rmap->n,ati,atj,NULL,&At);CHKERRQ(ierr); 1023 ierr = MatSetBlockSizes(At,PetscAbs(A->cmap->bs),PetscAbs(B->cmap->bs));CHKERRQ(ierr); 1024 1025 /* get symbolic C=At*B */ 1026 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(At,B,fill,C);CHKERRQ(ierr); 1027 1028 /* clean up */ 1029 ierr = MatDestroy(&At);CHKERRQ(ierr); 1030 ierr = MatRestoreSymbolicTranspose_SeqAIJ(A,&ati,&atj);CHKERRQ(ierr); 1031 PetscFunctionReturn(0); 1032 } 1033 1034 PetscErrorCode MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat A,Mat B,Mat C) 1035 { 1036 PetscErrorCode ierr; 1037 Mat_SeqAIJ *a =(Mat_SeqAIJ*)A->data,*b=(Mat_SeqAIJ*)B->data,*c=(Mat_SeqAIJ*)C->data; 1038 PetscInt am =A->rmap->n,anzi,*ai=a->i,*aj=a->j,*bi=b->i,*bj,bnzi,nextb; 1039 PetscInt cm =C->rmap->n,*ci=c->i,*cj=c->j,crow,*cjj,i,j,k; 1040 PetscLogDouble flops=0.0; 1041 MatScalar *aa =a->a,*ba,*ca,*caj; 1042 1043 PetscFunctionBegin; 1044 if (!c->a) { 1045 ierr = PetscMalloc1(ci[cm]+1,&ca);CHKERRQ(ierr); 1046 1047 c->a = ca; 1048 c->free_a = PETSC_TRUE; 1049 } else { 1050 ca = c->a; 1051 } 1052 /* clear old values in C */ 1053 ierr = PetscMemzero(ca,ci[cm]*sizeof(MatScalar));CHKERRQ(ierr); 1054 1055 /* compute A^T*B using outer product (A^T)[:,i]*B[i,:] */ 1056 for (i=0; i<am; i++) { 1057 bj = b->j + bi[i]; 1058 ba = b->a + bi[i]; 1059 bnzi = bi[i+1] - bi[i]; 1060 anzi = ai[i+1] - ai[i]; 1061 for (j=0; j<anzi; j++) { 1062 nextb = 0; 1063 crow = *aj++; 1064 cjj = cj + ci[crow]; 1065 caj = ca + ci[crow]; 1066 /* perform sparse axpy operation. Note cjj includes bj. */ 1067 for (k=0; nextb<bnzi; k++) { 1068 if (cjj[k] == *(bj+nextb)) { /* ccol == bcol */ 1069 caj[k] += (*aa)*(*(ba+nextb)); 1070 nextb++; 1071 } 1072 } 1073 flops += 2*bnzi; 1074 aa++; 1075 } 1076 } 1077 1078 /* Assemble the final matrix and clean up */ 1079 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1080 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1081 ierr = PetscLogFlops(flops);CHKERRQ(ierr); 1082 PetscFunctionReturn(0); 1083 } 1084 1085 PETSC_INTERN PetscErrorCode MatMatMult_SeqAIJ_SeqDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 1086 { 1087 PetscErrorCode ierr; 1088 1089 PetscFunctionBegin; 1090 if (scall == MAT_INITIAL_MATRIX) { 1091 ierr = PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1092 ierr = MatMatMultSymbolic_SeqAIJ_SeqDense(A,B,fill,C);CHKERRQ(ierr); 1093 ierr = PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);CHKERRQ(ierr); 1094 } 1095 ierr = PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1096 ierr = MatMatMultNumeric_SeqAIJ_SeqDense(A,B,*C);CHKERRQ(ierr); 1097 ierr = PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);CHKERRQ(ierr); 1098 PetscFunctionReturn(0); 1099 } 1100 1101 PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqDense(Mat A,Mat B,PetscReal fill,Mat *C) 1102 { 1103 PetscErrorCode ierr; 1104 1105 PetscFunctionBegin; 1106 ierr = MatMatMultSymbolic_SeqDense_SeqDense(A,B,0.0,C);CHKERRQ(ierr); 1107 1108 (*C)->ops->matmultnumeric = MatMatMultNumeric_SeqAIJ_SeqDense; 1109 PetscFunctionReturn(0); 1110 } 1111 1112 PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqDense(Mat A,Mat B,Mat C) 1113 { 1114 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 1115 Mat_SeqDense *bd = (Mat_SeqDense*)B->data; 1116 PetscErrorCode ierr; 1117 PetscScalar *c,*b,r1,r2,r3,r4,*c1,*c2,*c3,*c4,aatmp; 1118 const PetscScalar *aa,*b1,*b2,*b3,*b4; 1119 const PetscInt *aj; 1120 PetscInt cm=C->rmap->n,cn=B->cmap->n,bm=bd->lda,am=A->rmap->n; 1121 PetscInt am4=4*am,bm4=4*bm,col,i,j,n,ajtmp; 1122 1123 PetscFunctionBegin; 1124 if (!cm || !cn) PetscFunctionReturn(0); 1125 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); 1126 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); 1127 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); 1128 b = bd->v; 1129 ierr = MatDenseGetArray(C,&c);CHKERRQ(ierr); 1130 b1 = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm; 1131 c1 = c; c2 = c1 + am; c3 = c2 + am; c4 = c3 + am; 1132 for (col=0; col<cn-4; col += 4) { /* over columns of C */ 1133 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1134 r1 = r2 = r3 = r4 = 0.0; 1135 n = a->i[i+1] - a->i[i]; 1136 aj = a->j + a->i[i]; 1137 aa = a->a + a->i[i]; 1138 for (j=0; j<n; j++) { 1139 aatmp = aa[j]; ajtmp = aj[j]; 1140 r1 += aatmp*b1[ajtmp]; 1141 r2 += aatmp*b2[ajtmp]; 1142 r3 += aatmp*b3[ajtmp]; 1143 r4 += aatmp*b4[ajtmp]; 1144 } 1145 c1[i] = r1; 1146 c2[i] = r2; 1147 c3[i] = r3; 1148 c4[i] = r4; 1149 } 1150 b1 += bm4; b2 += bm4; b3 += bm4; b4 += bm4; 1151 c1 += am4; c2 += am4; c3 += am4; c4 += am4; 1152 } 1153 for (; col<cn; col++) { /* over extra columns of C */ 1154 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1155 r1 = 0.0; 1156 n = a->i[i+1] - a->i[i]; 1157 aj = a->j + a->i[i]; 1158 aa = a->a + a->i[i]; 1159 for (j=0; j<n; j++) { 1160 r1 += aa[j]*b1[aj[j]]; 1161 } 1162 c1[i] = r1; 1163 } 1164 b1 += bm; 1165 c1 += am; 1166 } 1167 ierr = PetscLogFlops(cn*(2.0*a->nz));CHKERRQ(ierr); 1168 ierr = MatDenseRestoreArray(C,&c);CHKERRQ(ierr); 1169 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1170 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1171 PetscFunctionReturn(0); 1172 } 1173 1174 /* 1175 Note very similar to MatMult_SeqAIJ(), should generate both codes from same base 1176 */ 1177 PetscErrorCode MatMatMultNumericAdd_SeqAIJ_SeqDense(Mat A,Mat B,Mat C) 1178 { 1179 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data; 1180 Mat_SeqDense *bd = (Mat_SeqDense*)B->data; 1181 PetscErrorCode ierr; 1182 PetscScalar *b,*c,r1,r2,r3,r4,*b1,*b2,*b3,*b4; 1183 MatScalar *aa; 1184 PetscInt cm = C->rmap->n, cn=B->cmap->n, bm=bd->lda, col, i,j,n,*aj, am = A->rmap->n,*ii,arm; 1185 PetscInt am2 = 2*am, am3 = 3*am, bm4 = 4*bm,colam,*ridx; 1186 1187 PetscFunctionBegin; 1188 if (!cm || !cn) PetscFunctionReturn(0); 1189 b = bd->v; 1190 ierr = MatDenseGetArray(C,&c);CHKERRQ(ierr); 1191 b1 = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm; 1192 1193 if (a->compressedrow.use) { /* use compressed row format */ 1194 for (col=0; col<cn-4; col += 4) { /* over columns of C */ 1195 colam = col*am; 1196 arm = a->compressedrow.nrows; 1197 ii = a->compressedrow.i; 1198 ridx = a->compressedrow.rindex; 1199 for (i=0; i<arm; i++) { /* over rows of C in those columns */ 1200 r1 = r2 = r3 = r4 = 0.0; 1201 n = ii[i+1] - ii[i]; 1202 aj = a->j + ii[i]; 1203 aa = a->a + ii[i]; 1204 for (j=0; j<n; j++) { 1205 r1 += (*aa)*b1[*aj]; 1206 r2 += (*aa)*b2[*aj]; 1207 r3 += (*aa)*b3[*aj]; 1208 r4 += (*aa++)*b4[*aj++]; 1209 } 1210 c[colam + ridx[i]] += r1; 1211 c[colam + am + ridx[i]] += r2; 1212 c[colam + am2 + ridx[i]] += r3; 1213 c[colam + am3 + ridx[i]] += r4; 1214 } 1215 b1 += bm4; 1216 b2 += bm4; 1217 b3 += bm4; 1218 b4 += bm4; 1219 } 1220 for (; col<cn; col++) { /* over extra columns of C */ 1221 colam = col*am; 1222 arm = a->compressedrow.nrows; 1223 ii = a->compressedrow.i; 1224 ridx = a->compressedrow.rindex; 1225 for (i=0; i<arm; i++) { /* over rows of C in those columns */ 1226 r1 = 0.0; 1227 n = ii[i+1] - ii[i]; 1228 aj = a->j + ii[i]; 1229 aa = a->a + ii[i]; 1230 1231 for (j=0; j<n; j++) { 1232 r1 += (*aa++)*b1[*aj++]; 1233 } 1234 c[colam + ridx[i]] += r1; 1235 } 1236 b1 += bm; 1237 } 1238 } else { 1239 for (col=0; col<cn-4; col += 4) { /* over columns of C */ 1240 colam = col*am; 1241 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1242 r1 = r2 = r3 = r4 = 0.0; 1243 n = a->i[i+1] - a->i[i]; 1244 aj = a->j + a->i[i]; 1245 aa = a->a + a->i[i]; 1246 for (j=0; j<n; j++) { 1247 r1 += (*aa)*b1[*aj]; 1248 r2 += (*aa)*b2[*aj]; 1249 r3 += (*aa)*b3[*aj]; 1250 r4 += (*aa++)*b4[*aj++]; 1251 } 1252 c[colam + i] += r1; 1253 c[colam + am + i] += r2; 1254 c[colam + am2 + i] += r3; 1255 c[colam + am3 + i] += r4; 1256 } 1257 b1 += bm4; 1258 b2 += bm4; 1259 b3 += bm4; 1260 b4 += bm4; 1261 } 1262 for (; col<cn; col++) { /* over extra columns of C */ 1263 colam = col*am; 1264 for (i=0; i<am; i++) { /* over rows of C in those columns */ 1265 r1 = 0.0; 1266 n = a->i[i+1] - a->i[i]; 1267 aj = a->j + a->i[i]; 1268 aa = a->a + a->i[i]; 1269 1270 for (j=0; j<n; j++) { 1271 r1 += (*aa++)*b1[*aj++]; 1272 } 1273 c[colam + i] += r1; 1274 } 1275 b1 += bm; 1276 } 1277 } 1278 ierr = PetscLogFlops(cn*2.0*a->nz);CHKERRQ(ierr); 1279 ierr = MatDenseRestoreArray(C,&c);CHKERRQ(ierr); 1280 PetscFunctionReturn(0); 1281 } 1282 1283 PetscErrorCode MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring coloring,Mat B,Mat Btdense) 1284 { 1285 PetscErrorCode ierr; 1286 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 1287 Mat_SeqDense *btdense = (Mat_SeqDense*)Btdense->data; 1288 PetscInt *bi = b->i,*bj=b->j; 1289 PetscInt m = Btdense->rmap->n,n=Btdense->cmap->n,j,k,l,col,anz,*btcol,brow,ncolumns; 1290 MatScalar *btval,*btval_den,*ba=b->a; 1291 PetscInt *columns=coloring->columns,*colorforcol=coloring->colorforcol,ncolors=coloring->ncolors; 1292 1293 PetscFunctionBegin; 1294 btval_den=btdense->v; 1295 ierr = PetscMemzero(btval_den,(m*n)*sizeof(MatScalar));CHKERRQ(ierr); 1296 for (k=0; k<ncolors; k++) { 1297 ncolumns = coloring->ncolumns[k]; 1298 for (l=0; l<ncolumns; l++) { /* insert a row of B to a column of Btdense */ 1299 col = *(columns + colorforcol[k] + l); 1300 btcol = bj + bi[col]; 1301 btval = ba + bi[col]; 1302 anz = bi[col+1] - bi[col]; 1303 for (j=0; j<anz; j++) { 1304 brow = btcol[j]; 1305 btval_den[brow] = btval[j]; 1306 } 1307 } 1308 btval_den += m; 1309 } 1310 PetscFunctionReturn(0); 1311 } 1312 1313 PetscErrorCode MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring matcoloring,Mat Cden,Mat Csp) 1314 { 1315 PetscErrorCode ierr; 1316 Mat_SeqAIJ *csp = (Mat_SeqAIJ*)Csp->data; 1317 PetscScalar *ca_den,*ca_den_ptr,*ca=csp->a; 1318 PetscInt k,l,m=Cden->rmap->n,ncolors=matcoloring->ncolors; 1319 PetscInt brows=matcoloring->brows,*den2sp=matcoloring->den2sp; 1320 PetscInt nrows,*row,*idx; 1321 PetscInt *rows=matcoloring->rows,*colorforrow=matcoloring->colorforrow; 1322 1323 PetscFunctionBegin; 1324 ierr = MatDenseGetArray(Cden,&ca_den);CHKERRQ(ierr); 1325 1326 if (brows > 0) { 1327 PetscInt *lstart,row_end,row_start; 1328 lstart = matcoloring->lstart; 1329 ierr = PetscMemzero(lstart,ncolors*sizeof(PetscInt));CHKERRQ(ierr); 1330 1331 row_end = brows; 1332 if (row_end > m) row_end = m; 1333 for (row_start=0; row_start<m; row_start+=brows) { /* loop over row blocks of Csp */ 1334 ca_den_ptr = ca_den; 1335 for (k=0; k<ncolors; k++) { /* loop over colors (columns of Cden) */ 1336 nrows = matcoloring->nrows[k]; 1337 row = rows + colorforrow[k]; 1338 idx = den2sp + colorforrow[k]; 1339 for (l=lstart[k]; l<nrows; l++) { 1340 if (row[l] >= row_end) { 1341 lstart[k] = l; 1342 break; 1343 } else { 1344 ca[idx[l]] = ca_den_ptr[row[l]]; 1345 } 1346 } 1347 ca_den_ptr += m; 1348 } 1349 row_end += brows; 1350 if (row_end > m) row_end = m; 1351 } 1352 } else { /* non-blocked impl: loop over columns of Csp - slow if Csp is large */ 1353 ca_den_ptr = ca_den; 1354 for (k=0; k<ncolors; k++) { 1355 nrows = matcoloring->nrows[k]; 1356 row = rows + colorforrow[k]; 1357 idx = den2sp + colorforrow[k]; 1358 for (l=0; l<nrows; l++) { 1359 ca[idx[l]] = ca_den_ptr[row[l]]; 1360 } 1361 ca_den_ptr += m; 1362 } 1363 } 1364 1365 ierr = MatDenseRestoreArray(Cden,&ca_den);CHKERRQ(ierr); 1366 #if defined(PETSC_USE_INFO) 1367 if (matcoloring->brows > 0) { 1368 ierr = PetscInfo1(Csp,"Loop over %D row blocks for den2sp\n",brows);CHKERRQ(ierr); 1369 } else { 1370 ierr = PetscInfo(Csp,"Loop over colors/columns of Cden, inefficient for large sparse matrix product \n");CHKERRQ(ierr); 1371 } 1372 #endif 1373 PetscFunctionReturn(0); 1374 } 1375 1376 PetscErrorCode MatTransposeColoringCreate_SeqAIJ(Mat mat,ISColoring iscoloring,MatTransposeColoring c) 1377 { 1378 PetscErrorCode ierr; 1379 PetscInt i,n,nrows,Nbs,j,k,m,ncols,col,cm; 1380 const PetscInt *is,*ci,*cj,*row_idx; 1381 PetscInt nis = iscoloring->n,*rowhit,bs = 1; 1382 IS *isa; 1383 Mat_SeqAIJ *csp = (Mat_SeqAIJ*)mat->data; 1384 PetscInt *colorforrow,*rows,*rows_i,*idxhit,*spidx,*den2sp,*den2sp_i; 1385 PetscInt *colorforcol,*columns,*columns_i,brows; 1386 PetscBool flg; 1387 1388 PetscFunctionBegin; 1389 ierr = ISColoringGetIS(iscoloring,PETSC_IGNORE,&isa);CHKERRQ(ierr); 1390 1391 /* bs >1 is not being tested yet! */ 1392 Nbs = mat->cmap->N/bs; 1393 c->M = mat->rmap->N/bs; /* set total rows, columns and local rows */ 1394 c->N = Nbs; 1395 c->m = c->M; 1396 c->rstart = 0; 1397 c->brows = 100; 1398 1399 c->ncolors = nis; 1400 ierr = PetscMalloc3(nis,&c->ncolumns,nis,&c->nrows,nis+1,&colorforrow);CHKERRQ(ierr); 1401 ierr = PetscMalloc1(csp->nz+1,&rows);CHKERRQ(ierr); 1402 ierr = PetscMalloc1(csp->nz+1,&den2sp);CHKERRQ(ierr); 1403 1404 brows = c->brows; 1405 ierr = PetscOptionsGetInt(NULL,NULL,"-matden2sp_brows",&brows,&flg);CHKERRQ(ierr); 1406 if (flg) c->brows = brows; 1407 if (brows > 0) { 1408 ierr = PetscMalloc1(nis+1,&c->lstart);CHKERRQ(ierr); 1409 } 1410 1411 colorforrow[0] = 0; 1412 rows_i = rows; 1413 den2sp_i = den2sp; 1414 1415 ierr = PetscMalloc1(nis+1,&colorforcol);CHKERRQ(ierr); 1416 ierr = PetscMalloc1(Nbs+1,&columns);CHKERRQ(ierr); 1417 1418 colorforcol[0] = 0; 1419 columns_i = columns; 1420 1421 /* get column-wise storage of mat */ 1422 ierr = MatGetColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);CHKERRQ(ierr); 1423 1424 cm = c->m; 1425 ierr = PetscMalloc1(cm+1,&rowhit);CHKERRQ(ierr); 1426 ierr = PetscMalloc1(cm+1,&idxhit);CHKERRQ(ierr); 1427 for (i=0; i<nis; i++) { /* loop over color */ 1428 ierr = ISGetLocalSize(isa[i],&n);CHKERRQ(ierr); 1429 ierr = ISGetIndices(isa[i],&is);CHKERRQ(ierr); 1430 1431 c->ncolumns[i] = n; 1432 if (n) { 1433 ierr = PetscMemcpy(columns_i,is,n*sizeof(PetscInt));CHKERRQ(ierr); 1434 } 1435 colorforcol[i+1] = colorforcol[i] + n; 1436 columns_i += n; 1437 1438 /* fast, crude version requires O(N*N) work */ 1439 ierr = PetscMemzero(rowhit,cm*sizeof(PetscInt));CHKERRQ(ierr); 1440 1441 for (j=0; j<n; j++) { /* loop over columns*/ 1442 col = is[j]; 1443 row_idx = cj + ci[col]; 1444 m = ci[col+1] - ci[col]; 1445 for (k=0; k<m; k++) { /* loop over columns marking them in rowhit */ 1446 idxhit[*row_idx] = spidx[ci[col] + k]; 1447 rowhit[*row_idx++] = col + 1; 1448 } 1449 } 1450 /* count the number of hits */ 1451 nrows = 0; 1452 for (j=0; j<cm; j++) { 1453 if (rowhit[j]) nrows++; 1454 } 1455 c->nrows[i] = nrows; 1456 colorforrow[i+1] = colorforrow[i] + nrows; 1457 1458 nrows = 0; 1459 for (j=0; j<cm; j++) { /* loop over rows */ 1460 if (rowhit[j]) { 1461 rows_i[nrows] = j; 1462 den2sp_i[nrows] = idxhit[j]; 1463 nrows++; 1464 } 1465 } 1466 den2sp_i += nrows; 1467 1468 ierr = ISRestoreIndices(isa[i],&is);CHKERRQ(ierr); 1469 rows_i += nrows; 1470 } 1471 ierr = MatRestoreColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);CHKERRQ(ierr); 1472 ierr = PetscFree(rowhit);CHKERRQ(ierr); 1473 ierr = ISColoringRestoreIS(iscoloring,&isa);CHKERRQ(ierr); 1474 if (csp->nz != colorforrow[nis]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"csp->nz %d != colorforrow[nis] %d",csp->nz,colorforrow[nis]); 1475 1476 c->colorforrow = colorforrow; 1477 c->rows = rows; 1478 c->den2sp = den2sp; 1479 c->colorforcol = colorforcol; 1480 c->columns = columns; 1481 1482 ierr = PetscFree(idxhit);CHKERRQ(ierr); 1483 PetscFunctionReturn(0); 1484 } 1485