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