1 #if defined(PETSC_HAVE_LIBMKL_INTEL_ILP64) 2 #define MKL_ILP64 3 #endif 4 5 #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/ 6 #include <../src/mat/impls/aij/mpi/mpiaij.h> 7 8 #include <stdio.h> 9 #include <stdlib.h> 10 #include <math.h> 11 #include <mkl.h> 12 #include <mkl_cluster_sparse_solver.h> 13 14 /* 15 * Possible mkl_cpardiso phases that controls the execution of the solver. 16 * For more information check mkl_cpardiso manual. 17 */ 18 #define JOB_ANALYSIS 11 19 #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION 12 20 #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 13 21 #define JOB_NUMERICAL_FACTORIZATION 22 22 #define JOB_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 23 23 #define JOB_SOLVE_ITERATIVE_REFINEMENT 33 24 #define JOB_SOLVE_FORWARD_SUBSTITUTION 331 25 #define JOB_SOLVE_DIAGONAL_SUBSTITUTION 332 26 #define JOB_SOLVE_BACKWARD_SUBSTITUTION 333 27 #define JOB_RELEASE_OF_LU_MEMORY 0 28 #define JOB_RELEASE_OF_ALL_MEMORY -1 29 30 #define IPARM_SIZE 64 31 #define INT_TYPE MKL_INT 32 33 static const char *Err_MSG_CPardiso(int errNo){ 34 switch (errNo) { 35 case -1: 36 return "input inconsistent"; break; 37 case -2: 38 return "not enough memory"; break; 39 case -3: 40 return "reordering problem"; break; 41 case -4: 42 return "zero pivot, numerical factorization or iterative refinement problem"; break; 43 case -5: 44 return "unclassified (internal) error"; break; 45 case -6: 46 return "preordering failed (matrix types 11, 13 only)"; break; 47 case -7: 48 return "diagonal matrix problem"; break; 49 case -8: 50 return "32-bit integer overflow problem"; break; 51 case -9: 52 return "not enough memory for OOC"; break; 53 case -10: 54 return "problems with opening OOC temporary files"; break; 55 case -11: 56 return "read/write problems with the OOC data file"; break; 57 default : 58 return "unknown error"; 59 } 60 } 61 62 /* 63 * Internal data structure. 64 * For more information check mkl_cpardiso manual. 65 */ 66 67 typedef struct { 68 69 /* Configuration vector */ 70 INT_TYPE iparm[IPARM_SIZE]; 71 72 /* 73 * Internal mkl_cpardiso memory location. 74 * After the first call to mkl_cpardiso do not modify pt, as that could cause a serious memory leak. 75 */ 76 void *pt[IPARM_SIZE]; 77 78 MPI_Comm comm_mkl_cpardiso; 79 80 /* Basic mkl_cpardiso info*/ 81 INT_TYPE phase, maxfct, mnum, mtype, n, nrhs, msglvl, err; 82 83 /* Matrix structure */ 84 PetscScalar *a; 85 86 INT_TYPE *ia, *ja; 87 88 /* Number of non-zero elements */ 89 INT_TYPE nz; 90 91 /* Row permutaton vector*/ 92 INT_TYPE *perm; 93 94 /* Define is matrix preserve sparce structure. */ 95 MatStructure matstruc; 96 97 PetscErrorCode (*ConvertToTriples)(Mat, MatReuse, PetscInt*, PetscInt**, PetscInt**, PetscScalar**); 98 99 /* True if mkl_cpardiso function have been used. */ 100 PetscBool CleanUp; 101 } Mat_MKL_CPARDISO; 102 103 /* 104 * Copy the elements of matrix A. 105 * Input: 106 * - Mat A: MATSEQAIJ matrix 107 * - int shift: matrix index. 108 * - 0 for c representation 109 * - 1 for fortran representation 110 * - MatReuse reuse: 111 * - MAT_INITIAL_MATRIX: Create a new aij representation 112 * - MAT_REUSE_MATRIX: Reuse all aij representation and just change values 113 * Output: 114 * - int *nnz: Number of nonzero-elements. 115 * - int **r pointer to i index 116 * - int **c pointer to j elements 117 * - MATRIXTYPE **v: Non-zero elements 118 */ 119 #undef __FUNCT__ 120 #define __FUNCT__ "MatCopy_seqaij_seqaij_MKL_CPARDISO" 121 PetscErrorCode MatCopy_seqaij_seqaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v) 122 { 123 Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data; 124 125 PetscFunctionBegin; 126 *v=aa->a; 127 if (reuse == MAT_INITIAL_MATRIX) { 128 *r = (INT_TYPE*)aa->i; 129 *c = (INT_TYPE*)aa->j; 130 *nnz = aa->nz; 131 } 132 PetscFunctionReturn(0); 133 } 134 135 #undef __FUNCT__ 136 #define __FUNCT__ "MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO" 137 PetscErrorCode MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v) 138 { 139 const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj; 140 PetscErrorCode ierr; 141 PetscInt rstart,nz,i,j,jj,irow,countA,countB; 142 PetscInt *row,*col; 143 const PetscScalar *av, *bv,*v1,*v2; 144 PetscScalar *val; 145 Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data; 146 Mat_SeqAIJ *aa = (Mat_SeqAIJ*)(mat->A)->data; 147 Mat_SeqAIJ *bb = (Mat_SeqAIJ*)(mat->B)->data; 148 PetscInt nn, colA_start,jB,jcol; 149 150 PetscFunctionBegin; 151 ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart; 152 av=aa->a; bv=bb->a; 153 154 garray = mat->garray; 155 156 if (reuse == MAT_INITIAL_MATRIX) { 157 nz = aa->nz + bb->nz; 158 *nnz = nz; 159 ierr = PetscMalloc((nz*(sizeof(PetscInt)+sizeof(PetscScalar)) + (m+1)*sizeof(PetscInt)), &row);CHKERRQ(ierr); 160 col = row + m + 1; 161 val = (PetscScalar*)(col + nz); 162 *r = row; *c = col; *v = val; 163 row[0] = 0; 164 } else { 165 row = *r; col = *c; val = *v; 166 } 167 168 nz = 0; 169 for (i=0; i<m; i++) { 170 row[i] = nz; 171 countA = ai[i+1] - ai[i]; 172 countB = bi[i+1] - bi[i]; 173 ajj = aj + ai[i]; /* ptr to the beginning of this row */ 174 bjj = bj + bi[i]; 175 176 /* B part, smaller col index */ 177 colA_start = rstart + ajj[0]; /* the smallest global col index of A */ 178 jB = 0; 179 for (j=0; j<countB; j++) { 180 jcol = garray[bjj[j]]; 181 if (jcol > colA_start) { 182 jB = j; 183 break; 184 } 185 col[nz] = jcol; 186 val[nz++] = *bv++; 187 if (j==countB-1) jB = countB; 188 } 189 190 /* A part */ 191 for (j=0; j<countA; j++) { 192 col[nz] = rstart + ajj[j]; 193 val[nz++] = *av++; 194 } 195 196 /* B part, larger col index */ 197 for (j=jB; j<countB; j++) { 198 col[nz] = garray[bjj[j]]; 199 val[nz++] = *bv++; 200 } 201 } 202 row[m] = nz; 203 204 PetscFunctionReturn(0); 205 } 206 207 /* 208 * Free memory for Mat_MKL_CPARDISO structure and pointers to objects. 209 */ 210 #undef __FUNCT__ 211 #define __FUNCT__ "MatDestroy_MKL_CPARDISO" 212 PetscErrorCode MatDestroy_MKL_CPARDISO(Mat A) 213 { 214 Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data; 215 PetscErrorCode ierr; 216 217 PetscFunctionBegin; 218 /* Terminate instance, deallocate memories */ 219 if (mat_mkl_cpardiso->CleanUp) { 220 mat_mkl_cpardiso->phase = JOB_RELEASE_OF_ALL_MEMORY; 221 222 cluster_sparse_solver ( 223 mat_mkl_cpardiso->pt, 224 &mat_mkl_cpardiso->maxfct, 225 &mat_mkl_cpardiso->mnum, 226 &mat_mkl_cpardiso->mtype, 227 &mat_mkl_cpardiso->phase, 228 &mat_mkl_cpardiso->n, 229 NULL, 230 NULL, 231 NULL, 232 mat_mkl_cpardiso->perm, 233 &mat_mkl_cpardiso->nrhs, 234 mat_mkl_cpardiso->iparm, 235 &mat_mkl_cpardiso->msglvl, 236 NULL, 237 NULL, 238 &mat_mkl_cpardiso->comm_mkl_cpardiso, 239 &mat_mkl_cpardiso->err); 240 } 241 242 ierr = PetscFree(mat_mkl_cpardiso->ia);CHKERRQ(ierr); 243 ierr = MPI_Comm_free(&(mat_mkl_cpardiso->comm_mkl_cpardiso));CHKERRQ(ierr); 244 ierr = PetscFree(A->data);CHKERRQ(ierr); 245 246 /* clear composed functions */ 247 ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverPackage_C",NULL);CHKERRQ(ierr); 248 ierr = PetscObjectComposeFunction((PetscObject)A,"MatMkl_CPardisoSetCntl_C",NULL);CHKERRQ(ierr); 249 PetscFunctionReturn(0); 250 } 251 252 /* 253 * Computes Ax = b 254 */ 255 #undef __FUNCT__ 256 #define __FUNCT__ "MatSolve_MKL_CPARDISO" 257 PetscErrorCode MatSolve_MKL_CPARDISO(Mat A,Vec b,Vec x) 258 { 259 Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(A)->data; 260 PetscErrorCode ierr; 261 PetscScalar *xarray; 262 const PetscScalar *barray; 263 264 PetscFunctionBegin; 265 mat_mkl_cpardiso->nrhs = 1; 266 ierr = VecGetArray(x,&xarray);CHKERRQ(ierr); 267 ierr = VecGetArrayRead(b,&barray);CHKERRQ(ierr); 268 269 /* solve phase */ 270 /*-------------*/ 271 mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT; 272 cluster_sparse_solver ( 273 mat_mkl_cpardiso->pt, 274 &mat_mkl_cpardiso->maxfct, 275 &mat_mkl_cpardiso->mnum, 276 &mat_mkl_cpardiso->mtype, 277 &mat_mkl_cpardiso->phase, 278 &mat_mkl_cpardiso->n, 279 mat_mkl_cpardiso->a, 280 mat_mkl_cpardiso->ia, 281 mat_mkl_cpardiso->ja, 282 mat_mkl_cpardiso->perm, 283 &mat_mkl_cpardiso->nrhs, 284 mat_mkl_cpardiso->iparm, 285 &mat_mkl_cpardiso->msglvl, 286 (void*)barray, 287 (void*)xarray, 288 &mat_mkl_cpardiso->comm_mkl_cpardiso, 289 &mat_mkl_cpardiso->err); 290 291 if (mat_mkl_cpardiso->err < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_CPARDISO: err=%d, msg = \"%s\". Please check manual\n",mat_mkl_cpardiso->err,Err_MSG_CPardiso(mat_mkl_cpardiso->err)); 292 293 ierr = VecRestoreArray(x,&xarray);CHKERRQ(ierr); 294 ierr = VecRestoreArrayRead(b,&barray);CHKERRQ(ierr); 295 mat_mkl_cpardiso->CleanUp = PETSC_TRUE; 296 PetscFunctionReturn(0); 297 } 298 299 #undef __FUNCT__ 300 #define __FUNCT__ "MatSolveTranspose_MKL_CPARDISO" 301 PetscErrorCode MatSolveTranspose_MKL_CPARDISO(Mat A,Vec b,Vec x) 302 { 303 Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data; 304 PetscErrorCode ierr; 305 306 PetscFunctionBegin; 307 #if defined(PETSC_USE_COMPLEX) 308 mat_mkl_cpardiso->iparm[12 - 1] = 1; 309 #else 310 mat_mkl_cpardiso->iparm[12 - 1] = 2; 311 #endif 312 ierr = MatSolve_MKL_CPARDISO(A,b,x);CHKERRQ(ierr); 313 mat_mkl_cpardiso->iparm[12 - 1] = 0; 314 PetscFunctionReturn(0); 315 } 316 317 #undef __FUNCT__ 318 #define __FUNCT__ "MatMatSolve_MKL_CPARDISO" 319 PetscErrorCode MatMatSolve_MKL_CPARDISO(Mat A,Mat B,Mat X) 320 { 321 Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(A)->data; 322 PetscErrorCode ierr; 323 PetscScalar *barray, *xarray; 324 PetscBool flg; 325 326 PetscFunctionBegin; 327 ierr = PetscObjectTypeCompare((PetscObject)B,MATSEQDENSE,&flg);CHKERRQ(ierr); 328 if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATSEQDENSE matrix"); 329 ierr = PetscObjectTypeCompare((PetscObject)X,MATSEQDENSE,&flg);CHKERRQ(ierr); 330 if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATSEQDENSE matrix"); 331 332 ierr = MatGetSize(B,NULL,(PetscInt*)&mat_mkl_cpardiso->nrhs);CHKERRQ(ierr); 333 334 if(mat_mkl_cpardiso->nrhs > 0){ 335 ierr = MatDenseGetArray(B,&barray); 336 ierr = MatDenseGetArray(X,&xarray); 337 338 /* solve phase */ 339 /*-------------*/ 340 mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT; 341 cluster_sparse_solver ( 342 mat_mkl_cpardiso->pt, 343 &mat_mkl_cpardiso->maxfct, 344 &mat_mkl_cpardiso->mnum, 345 &mat_mkl_cpardiso->mtype, 346 &mat_mkl_cpardiso->phase, 347 &mat_mkl_cpardiso->n, 348 mat_mkl_cpardiso->a, 349 mat_mkl_cpardiso->ia, 350 mat_mkl_cpardiso->ja, 351 mat_mkl_cpardiso->perm, 352 &mat_mkl_cpardiso->nrhs, 353 mat_mkl_cpardiso->iparm, 354 &mat_mkl_cpardiso->msglvl, 355 (void*)barray, 356 (void*)xarray, 357 &mat_mkl_cpardiso->comm_mkl_cpardiso, 358 &mat_mkl_cpardiso->err); 359 if (mat_mkl_cpardiso->err < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_CPARDISO: err=%d, msg = \"%s\". Please check manual\n",mat_mkl_cpardiso->err,Err_MSG_CPardiso(mat_mkl_cpardiso->err)); 360 } 361 mat_mkl_cpardiso->CleanUp = PETSC_TRUE; 362 PetscFunctionReturn(0); 363 364 } 365 366 /* 367 * LU Decomposition 368 */ 369 #undef __FUNCT__ 370 #define __FUNCT__ "MatFactorNumeric_MKL_CPARDISO" 371 PetscErrorCode MatFactorNumeric_MKL_CPARDISO(Mat F,Mat A,const MatFactorInfo *info) 372 { 373 Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(F)->data; 374 PetscErrorCode ierr; 375 376 PetscFunctionBegin; 377 mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN; 378 ierr = (*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_REUSE_MATRIX,&mat_mkl_cpardiso->nz,&mat_mkl_cpardiso->ia,&mat_mkl_cpardiso->ja,&mat_mkl_cpardiso->a);CHKERRQ(ierr); 379 380 mat_mkl_cpardiso->phase = JOB_NUMERICAL_FACTORIZATION; 381 cluster_sparse_solver ( 382 mat_mkl_cpardiso->pt, 383 &mat_mkl_cpardiso->maxfct, 384 &mat_mkl_cpardiso->mnum, 385 &mat_mkl_cpardiso->mtype, 386 &mat_mkl_cpardiso->phase, 387 &mat_mkl_cpardiso->n, 388 mat_mkl_cpardiso->a, 389 mat_mkl_cpardiso->ia, 390 mat_mkl_cpardiso->ja, 391 mat_mkl_cpardiso->perm, 392 &mat_mkl_cpardiso->nrhs, 393 mat_mkl_cpardiso->iparm, 394 &mat_mkl_cpardiso->msglvl, 395 NULL, 396 NULL, 397 &mat_mkl_cpardiso->comm_mkl_cpardiso, 398 &mat_mkl_cpardiso->err); 399 if (mat_mkl_cpardiso->err < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_CPARDISO: err=%d, msg = \"%s\". Please check manual\n",mat_mkl_cpardiso->err,Err_MSG_CPardiso(mat_mkl_cpardiso->err)); 400 401 mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN; 402 mat_mkl_cpardiso->CleanUp = PETSC_TRUE; 403 PetscFunctionReturn(0); 404 } 405 406 /* Sets mkl_cpardiso options from the options database */ 407 #undef __FUNCT__ 408 #define __FUNCT__ "PetscSetMKL_CPARDISOFromOptions" 409 PetscErrorCode PetscSetMKL_CPARDISOFromOptions(Mat F, Mat A) 410 { 411 Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO*)F->data; 412 PetscErrorCode ierr; 413 PetscInt icntl; 414 PetscBool flg; 415 int pt[IPARM_SIZE], threads; 416 417 PetscFunctionBegin; 418 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MKL_CPARDISO Options","Mat");CHKERRQ(ierr); 419 ierr = PetscOptionsInt("-mat_mkl_cpardiso_65","Number of threads to use","None",threads,&threads,&flg);CHKERRQ(ierr); 420 if (flg) mkl_set_num_threads(threads); 421 422 ierr = PetscOptionsInt("-mat_mkl_cpardiso_66","Maximum number of factors with identical sparsity structure that must be kept in memory at the same time","None",mat_mkl_cpardiso->maxfct,&icntl,&flg);CHKERRQ(ierr); 423 if (flg) mat_mkl_cpardiso->maxfct = icntl; 424 425 ierr = PetscOptionsInt("-mat_mkl_cpardiso_67","Indicates the actual matrix for the solution phase","None",mat_mkl_cpardiso->mnum,&icntl,&flg);CHKERRQ(ierr); 426 if (flg) mat_mkl_cpardiso->mnum = icntl; 427 428 ierr = PetscOptionsInt("-mat_mkl_cpardiso_68","Message level information","None",mat_mkl_cpardiso->msglvl,&icntl,&flg);CHKERRQ(ierr); 429 if (flg) mat_mkl_cpardiso->msglvl = icntl; 430 431 ierr = PetscOptionsInt("-mat_mkl_cpardiso_69","Defines the matrix type","None",mat_mkl_cpardiso->mtype,&icntl,&flg);CHKERRQ(ierr); 432 if(flg){ 433 mat_mkl_cpardiso->mtype = icntl; 434 #if defined(PETSC_USE_REAL_SINGLE) 435 mat_mkl_cpardiso->iparm[27] = 1; 436 #else 437 mat_mkl_cpardiso->iparm[27] = 0; 438 #endif 439 mat_mkl_cpardiso->iparm[34] = 1; 440 } 441 ierr = PetscOptionsInt("-mat_mkl_cpardiso_1","Use default values","None",mat_mkl_cpardiso->iparm[0],&icntl,&flg);CHKERRQ(ierr); 442 443 if(flg && icntl != 0){ 444 ierr = PetscOptionsInt("-mat_mkl_cpardiso_2","Fill-in reducing ordering for the input matrix","None",mat_mkl_cpardiso->iparm[1],&icntl,&flg);CHKERRQ(ierr); 445 if (flg) mat_mkl_cpardiso->iparm[1] = icntl; 446 447 ierr = PetscOptionsInt("-mat_mkl_cpardiso_4","Preconditioned CGS/CG","None",mat_mkl_cpardiso->iparm[3],&icntl,&flg);CHKERRQ(ierr); 448 if (flg) mat_mkl_cpardiso->iparm[3] = icntl; 449 450 ierr = PetscOptionsInt("-mat_mkl_cpardiso_5","User permutation","None",mat_mkl_cpardiso->iparm[4],&icntl,&flg);CHKERRQ(ierr); 451 if (flg) mat_mkl_cpardiso->iparm[4] = icntl; 452 453 ierr = PetscOptionsInt("-mat_mkl_cpardiso_6","Write solution on x","None",mat_mkl_cpardiso->iparm[5],&icntl,&flg);CHKERRQ(ierr); 454 if (flg) mat_mkl_cpardiso->iparm[5] = icntl; 455 456 ierr = PetscOptionsInt("-mat_mkl_cpardiso_8","Iterative refinement step","None",mat_mkl_cpardiso->iparm[7],&icntl,&flg);CHKERRQ(ierr); 457 if (flg) mat_mkl_cpardiso->iparm[7] = icntl; 458 459 ierr = PetscOptionsInt("-mat_mkl_cpardiso_10","Pivoting perturbation","None",mat_mkl_cpardiso->iparm[9],&icntl,&flg);CHKERRQ(ierr); 460 if (flg) mat_mkl_cpardiso->iparm[9] = icntl; 461 462 ierr = PetscOptionsInt("-mat_mkl_cpardiso_11","Scaling vectors","None",mat_mkl_cpardiso->iparm[10],&icntl,&flg);CHKERRQ(ierr); 463 if (flg) mat_mkl_cpardiso->iparm[10] = icntl; 464 465 ierr = PetscOptionsInt("-mat_mkl_cpardiso_12","Solve with transposed or conjugate transposed matrix A","None",mat_mkl_cpardiso->iparm[11],&icntl,&flg);CHKERRQ(ierr); 466 if (flg) mat_mkl_cpardiso->iparm[11] = icntl; 467 468 ierr = PetscOptionsInt("-mat_mkl_cpardiso_13","Improved accuracy using (non-) symmetric weighted matching","None",mat_mkl_cpardiso->iparm[12],&icntl, 469 &flg);CHKERRQ(ierr); 470 if (flg) mat_mkl_cpardiso->iparm[12] = icntl; 471 472 ierr = PetscOptionsInt("-mat_mkl_cpardiso_18","Numbers of non-zero elements","None",mat_mkl_cpardiso->iparm[17],&icntl, 473 &flg);CHKERRQ(ierr); 474 if (flg) mat_mkl_cpardiso->iparm[17] = icntl; 475 476 ierr = PetscOptionsInt("-mat_mkl_cpardiso_19","Report number of floating point operations","None",mat_mkl_cpardiso->iparm[18],&icntl,&flg);CHKERRQ(ierr); 477 if (flg) mat_mkl_cpardiso->iparm[18] = icntl; 478 479 ierr = PetscOptionsInt("-mat_mkl_cpardiso_21","Pivoting for symmetric indefinite matrices","None",mat_mkl_cpardiso->iparm[20],&icntl,&flg);CHKERRQ(ierr); 480 if (flg) mat_mkl_cpardiso->iparm[20] = icntl; 481 482 ierr = PetscOptionsInt("-mat_mkl_cpardiso_24","Parallel factorization control","None",mat_mkl_cpardiso->iparm[23],&icntl,&flg);CHKERRQ(ierr); 483 if (flg) mat_mkl_cpardiso->iparm[23] = icntl; 484 485 ierr = PetscOptionsInt("-mat_mkl_cpardiso_25","Parallel forward/backward solve control","None",mat_mkl_cpardiso->iparm[24],&icntl,&flg);CHKERRQ(ierr); 486 if (flg) mat_mkl_cpardiso->iparm[24] = icntl; 487 488 ierr = PetscOptionsInt("-mat_mkl_cpardiso_27","Matrix checker","None",mat_mkl_cpardiso->iparm[26],&icntl,&flg);CHKERRQ(ierr); 489 if (flg) mat_mkl_cpardiso->iparm[26] = icntl; 490 491 ierr = PetscOptionsInt("-mat_mkl_cpardiso_31","Partial solve and computing selected components of the solution vectors","None",mat_mkl_cpardiso->iparm[30],&icntl,&flg);CHKERRQ(ierr); 492 if (flg) mat_mkl_cpardiso->iparm[30] = icntl; 493 494 ierr = PetscOptionsInt("-mat_mkl_cpardiso_34","Optimal number of threads for conditional numerical reproducibility (CNR) mode","None",mat_mkl_cpardiso->iparm[33],&icntl,&flg);CHKERRQ(ierr); 495 if (flg) mat_mkl_cpardiso->iparm[33] = icntl; 496 497 ierr = PetscOptionsInt("-mat_mkl_cpardiso_60","Intel MKL_CPARDISO mode","None",mat_mkl_cpardiso->iparm[59],&icntl,&flg);CHKERRQ(ierr); 498 if (flg) mat_mkl_cpardiso->iparm[59] = icntl; 499 } 500 501 ierr = PetscOptionsEnd();CHKERRQ(ierr); 502 PetscFunctionReturn(0); 503 } 504 505 #undef __FUNCT__ 506 #define __FUNCT__ "PetscInitialize_MKL_CPARDISO" 507 PetscErrorCode PetscInitialize_MKL_CPARDISO(Mat A, Mat_MKL_CPARDISO *mat_mkl_cpardiso) 508 { 509 PetscErrorCode ierr; 510 PetscInt i; 511 PetscMPIInt size; 512 513 PetscFunctionBegin; 514 515 ierr = MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mat_mkl_cpardiso->comm_mkl_cpardiso));CHKERRQ(ierr); 516 ierr = MPI_Comm_size(mat_mkl_cpardiso->comm_mkl_cpardiso, &size);CHKERRQ(ierr); 517 518 mat_mkl_cpardiso->CleanUp = PETSC_FALSE; 519 mat_mkl_cpardiso->maxfct = 1; 520 mat_mkl_cpardiso->mnum = 1; 521 mat_mkl_cpardiso->n = A->rmap->N; 522 mat_mkl_cpardiso->msglvl = 0; 523 mat_mkl_cpardiso->nrhs = 1; 524 mat_mkl_cpardiso->err = 0; 525 mat_mkl_cpardiso->phase = -1; 526 #if defined(PETSC_USE_COMPLEX) 527 mat_mkl_cpardiso->mtype = 13; 528 #else 529 mat_mkl_cpardiso->mtype = 11; 530 #endif 531 532 #if defined(PETSC_USE_REAL_SINGLE) 533 mat_mkl_cpardiso->iparm[27] = 1; 534 #else 535 mat_mkl_cpardiso->iparm[27] = 0; 536 #endif 537 538 mat_mkl_cpardiso->iparm[34] = 1; /* C style */ 539 540 mat_mkl_cpardiso->iparm[ 0] = 1; /* Solver default parameters overriden with provided by iparm */ 541 mat_mkl_cpardiso->iparm[ 1] = 2; /* Use METIS for fill-in reordering */ 542 mat_mkl_cpardiso->iparm[ 5] = 0; /* Write solution into x */ 543 mat_mkl_cpardiso->iparm[ 7] = 2; /* Max number of iterative refinement steps */ 544 mat_mkl_cpardiso->iparm[ 9] = 13; /* Perturb the pivot elements with 1E-13 */ 545 mat_mkl_cpardiso->iparm[10] = 1; /* Use nonsymmetric permutation and scaling MPS */ 546 mat_mkl_cpardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */ 547 mat_mkl_cpardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */ 548 mat_mkl_cpardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */ 549 mat_mkl_cpardiso->iparm[26] = 1; /* Check input data for correctness */ 550 551 mat_mkl_cpardiso->iparm[39] = 0; 552 if (size > 1) { 553 mat_mkl_cpardiso->iparm[39] = 2; 554 mat_mkl_cpardiso->iparm[40] = A->rmap->rstart; 555 mat_mkl_cpardiso->iparm[41] = A->rmap->rend-1; 556 } 557 mat_mkl_cpardiso->perm = 0; 558 PetscFunctionReturn(0); 559 } 560 561 /* 562 * Symbolic decomposition. Mkl_Pardiso analysis phase. 563 */ 564 #undef __FUNCT__ 565 #define __FUNCT__ "MatLUFactorSymbolic_AIJMKL_CPARDISO" 566 PetscErrorCode MatLUFactorSymbolic_AIJMKL_CPARDISO(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 567 { 568 Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO*)F->data; 569 PetscErrorCode ierr; 570 571 PetscFunctionBegin; 572 mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN; 573 574 /* Set MKL_CPARDISO options from the options database */ 575 ierr = PetscSetMKL_CPARDISOFromOptions(F,A);CHKERRQ(ierr); 576 ierr = (*mat_mkl_cpardiso->ConvertToTriples)(A,MAT_INITIAL_MATRIX,&mat_mkl_cpardiso->nz,&mat_mkl_cpardiso->ia,&mat_mkl_cpardiso->ja,&mat_mkl_cpardiso->a);CHKERRQ(ierr); 577 578 mat_mkl_cpardiso->n = A->rmap->N; 579 580 /* analysis phase */ 581 /*----------------*/ 582 mat_mkl_cpardiso->phase = JOB_ANALYSIS; 583 584 cluster_sparse_solver ( 585 mat_mkl_cpardiso->pt, 586 &mat_mkl_cpardiso->maxfct, 587 &mat_mkl_cpardiso->mnum, 588 &mat_mkl_cpardiso->mtype, 589 &mat_mkl_cpardiso->phase, 590 &mat_mkl_cpardiso->n, 591 mat_mkl_cpardiso->a, 592 mat_mkl_cpardiso->ia, 593 mat_mkl_cpardiso->ja, 594 mat_mkl_cpardiso->perm, 595 &mat_mkl_cpardiso->nrhs, 596 mat_mkl_cpardiso->iparm, 597 &mat_mkl_cpardiso->msglvl, 598 NULL, 599 NULL, 600 &mat_mkl_cpardiso->comm_mkl_cpardiso, 601 &mat_mkl_cpardiso->err); 602 603 if (mat_mkl_cpardiso->err < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_CPARDISO: err=%d, msg = \"%s\".Check manual\n",mat_mkl_cpardiso->err,Err_MSG_CPardiso(mat_mkl_cpardiso->err)); 604 605 mat_mkl_cpardiso->CleanUp = PETSC_TRUE; 606 F->ops->lufactornumeric = MatFactorNumeric_MKL_CPARDISO; 607 F->ops->solve = MatSolve_MKL_CPARDISO; 608 F->ops->solvetranspose = MatSolveTranspose_MKL_CPARDISO; 609 F->ops->matsolve = MatMatSolve_MKL_CPARDISO; 610 PetscFunctionReturn(0); 611 } 612 613 #undef __FUNCT__ 614 #define __FUNCT__ "MatView_MKL_CPARDISO" 615 PetscErrorCode MatView_MKL_CPARDISO(Mat A, PetscViewer viewer) 616 { 617 PetscErrorCode ierr; 618 PetscBool iascii; 619 PetscViewerFormat format; 620 Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data; 621 PetscInt i; 622 623 PetscFunctionBegin; 624 /* check if matrix is mkl_cpardiso type */ 625 if (A->ops->solve != MatSolve_MKL_CPARDISO) PetscFunctionReturn(0); 626 627 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 628 if (iascii) { 629 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 630 if (format == PETSC_VIEWER_ASCII_INFO) { 631 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO run parameters:\n");CHKERRQ(ierr); 632 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO phase: %d \n",mat_mkl_cpardiso->phase);CHKERRQ(ierr); 633 for(i = 1; i <= 64; i++){ 634 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO iparm[%d]: %d \n",i, mat_mkl_cpardiso->iparm[i - 1]);CHKERRQ(ierr); 635 } 636 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO maxfct: %d \n", mat_mkl_cpardiso->maxfct);CHKERRQ(ierr); 637 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO mnum: %d \n", mat_mkl_cpardiso->mnum);CHKERRQ(ierr); 638 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO mtype: %d \n", mat_mkl_cpardiso->mtype);CHKERRQ(ierr); 639 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO n: %d \n", mat_mkl_cpardiso->n);CHKERRQ(ierr); 640 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO nrhs: %d \n", mat_mkl_cpardiso->nrhs);CHKERRQ(ierr); 641 ierr = PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO msglvl: %d \n", mat_mkl_cpardiso->msglvl);CHKERRQ(ierr); 642 } 643 } 644 PetscFunctionReturn(0); 645 } 646 647 #undef __FUNCT__ 648 #define __FUNCT__ "MatGetInfo_MKL_CPARDISO" 649 PetscErrorCode MatGetInfo_MKL_CPARDISO(Mat A, MatInfoType flag, MatInfo *info) 650 { 651 Mat_MKL_CPARDISO *mat_mkl_cpardiso =(Mat_MKL_CPARDISO*)A->data; 652 653 PetscFunctionBegin; 654 info->block_size = 1.0; 655 info->nz_allocated = mat_mkl_cpardiso->nz + 0.0; 656 info->nz_unneeded = 0.0; 657 info->assemblies = 0.0; 658 info->mallocs = 0.0; 659 info->memory = 0.0; 660 info->fill_ratio_given = 0; 661 info->fill_ratio_needed = 0; 662 info->factor_mallocs = 0; 663 PetscFunctionReturn(0); 664 } 665 666 #undef __FUNCT__ 667 #define __FUNCT__ "MatMkl_CPardisoSetCntl_MKL_CPARDISO" 668 PetscErrorCode MatMkl_CPardisoSetCntl_MKL_CPARDISO(Mat F,PetscInt icntl,PetscInt ival) 669 { 670 Mat_MKL_CPARDISO *mat_mkl_cpardiso =(Mat_MKL_CPARDISO*)F->data; 671 672 PetscFunctionBegin; 673 if(icntl <= 64){ 674 mat_mkl_cpardiso->iparm[icntl - 1] = ival; 675 } else { 676 if(icntl == 65) mkl_set_num_threads((int)ival); 677 else if(icntl == 66) mat_mkl_cpardiso->maxfct = ival; 678 else if(icntl == 67) mat_mkl_cpardiso->mnum = ival; 679 else if(icntl == 68) mat_mkl_cpardiso->msglvl = ival; 680 else if(icntl == 69){ 681 int pt[IPARM_SIZE]; 682 mat_mkl_cpardiso->mtype = ival; 683 #if defined(PETSC_USE_REAL_SINGLE) 684 mat_mkl_cpardiso->iparm[27] = 1; 685 #else 686 mat_mkl_cpardiso->iparm[27] = 0; 687 #endif 688 mat_mkl_cpardiso->iparm[34] = 1; 689 } 690 } 691 PetscFunctionReturn(0); 692 } 693 694 #undef __FUNCT__ 695 #define __FUNCT__ "MatMkl_CPardisoSetCntl" 696 /*@ 697 MatMkl_CPardisoSetCntl - Set Mkl_Pardiso parameters 698 699 Logically Collective on Mat 700 701 Input Parameters: 702 + F - the factored matrix obtained by calling MatGetFactor() 703 . icntl - index of Mkl_Pardiso parameter 704 - ival - value of Mkl_Pardiso parameter 705 706 Options Database: 707 . -mat_mkl_cpardiso_<icntl> <ival> 708 709 Level: beginner 710 711 References: 712 . Mkl_Pardiso Users' Guide 713 714 .seealso: MatGetFactor() 715 @*/ 716 PetscErrorCode MatMkl_CPardisoSetCntl(Mat F,PetscInt icntl,PetscInt ival) 717 { 718 PetscErrorCode ierr; 719 720 PetscFunctionBegin; 721 ierr = PetscTryMethod(F,"MatMkl_CPardisoSetCntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));CHKERRQ(ierr); 722 PetscFunctionReturn(0); 723 } 724 725 #undef __FUNCT__ 726 #define __FUNCT__ "MatFactorGetSolverPackage_mkl_cpardiso" 727 static PetscErrorCode MatFactorGetSolverPackage_mkl_cpardiso(Mat A, const MatSolverPackage *type) 728 { 729 PetscFunctionBegin; 730 *type = MATSOLVERMKL_CPARDISO; 731 PetscFunctionReturn(0); 732 } 733 734 /* MatGetFactor for MPI AIJ matrices */ 735 #undef __FUNCT__ 736 #define __FUNCT__ "MatGetFactor_mpiaij_mkl_cpardiso" 737 static PetscErrorCode MatGetFactor_mpiaij_mkl_cpardiso(Mat A,MatFactorType ftype,Mat *F) 738 { 739 Mat B; 740 PetscErrorCode ierr; 741 Mat_MKL_CPARDISO *mat_mkl_cpardiso; 742 PetscBool isSeqAIJ; 743 744 PetscFunctionBegin; 745 /* Create the factorization matrix */ 746 747 ierr = PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);CHKERRQ(ierr); 748 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 749 ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);CHKERRQ(ierr); 750 ierr = PetscStrallocpy("mkl_cpardiso",&((PetscObject)B)->type_name);CHKERRQ(ierr); 751 ierr = MatSetUp(B);CHKERRQ(ierr); 752 753 ierr = PetscNewLog(B,&mat_mkl_cpardiso);CHKERRQ(ierr); 754 755 if (isSeqAIJ) mat_mkl_cpardiso->ConvertToTriples = MatCopy_seqaij_seqaij_MKL_CPARDISO; 756 else mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO; 757 758 B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_CPARDISO; 759 B->ops->destroy = MatDestroy_MKL_CPARDISO; 760 761 B->ops->view = MatView_MKL_CPARDISO; 762 B->ops->getinfo = MatGetInfo_MKL_CPARDISO; 763 764 B->factortype = ftype; 765 B->assembled = PETSC_TRUE; /* required by -ksp_view */ 766 767 B->data = mat_mkl_cpardiso; 768 769 /* set solvertype */ 770 ierr = PetscFree(B->solvertype);CHKERRQ(ierr); 771 ierr = PetscStrallocpy(MATSOLVERMKL_CPARDISO,&B->solvertype);CHKERRQ(ierr); 772 773 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_mkl_cpardiso);CHKERRQ(ierr); 774 ierr = PetscObjectComposeFunction((PetscObject)B,"MatMkl_CPardisoSetCntl_C",MatMkl_CPardisoSetCntl_MKL_CPARDISO);CHKERRQ(ierr); 775 ierr = PetscInitialize_MKL_CPARDISO(A, mat_mkl_cpardiso);CHKERRQ(ierr); 776 777 *F = B; 778 PetscFunctionReturn(0); 779 } 780 781 #undef __FUNCT__ 782 #define __FUNCT__ "MatSolverPackageRegister_MKL_CPardiso" 783 PETSC_EXTERN PetscErrorCode MatSolverPackageRegister_MKL_CPardiso(void) 784 { 785 PetscErrorCode ierr; 786 787 PetscFunctionBegin; 788 ierr = MatSolverPackageRegister(MATSOLVERMKL_CPARDISO,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_mpiaij_mkl_cpardiso);CHKERRQ(ierr); 789 ierr = MatSolverPackageRegister(MATSOLVERMKL_CPARDISO,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_mpiaij_mkl_cpardiso);CHKERRQ(ierr); 790 PetscFunctionReturn(0); 791 } 792