1 2 /* 3 Provides an interface to the KLUv1.2 sparse solver 4 5 When build with PETSC_USE_64BIT_INDICES this will use SuiteSparse_long as the 6 integer type in KLU, otherwise it will use int. This means 7 all integers in this file are simply declared as PetscInt. Also it means 8 that KLU SuiteSparse_long version MUST be built with 64 bit integers when used. 9 10 */ 11 #include <../src/mat/impls/aij/seq/aij.h> 12 13 #if defined(PETSC_USE_64BIT_INDICES) 14 #define klu_K_defaults klu_l_defaults 15 #define klu_K_analyze klu_l_analyze 16 #define klu_K_analyze_given klu_l_analyze_given 17 #define klu_K_free_symbolic klu_l_free_symbolic 18 #define klu_K_free_numeric klu_l_free_numeric 19 #define klu_K_common klu_l_common 20 #define klu_K_symbolic klu_l_symbolic 21 #define klu_K_numeric klu_l_numeric 22 #if defined(PETSC_USE_COMPLEX) 23 #define klu_K_factor klu_zl_factor 24 #define klu_K_solve klu_zl_solve 25 #define klu_K_tsolve klu_zl_tsolve 26 #define klu_K_refactor klu_zl_refactor 27 #define klu_K_sort klu_zl_sort 28 #define klu_K_flops klu_zl_flops 29 #define klu_K_rgrowth klu_zl_rgrowth 30 #define klu_K_condest klu_zl_condest 31 #define klu_K_rcond klu_zl_rcond 32 #define klu_K_scale klu_zl_scale 33 #else 34 #define klu_K_factor klu_l_factor 35 #define klu_K_solve klu_l_solve 36 #define klu_K_tsolve klu_l_tsolve 37 #define klu_K_refactor klu_l_refactor 38 #define klu_K_sort klu_l_sort 39 #define klu_K_flops klu_l_flops 40 #define klu_K_rgrowth klu_l_rgrowth 41 #define klu_K_condest klu_l_condest 42 #define klu_K_rcond klu_l_rcond 43 #define klu_K_scale klu_l_scale 44 #endif 45 #else 46 #define klu_K_defaults klu_defaults 47 #define klu_K_analyze klu_analyze 48 #define klu_K_analyze_given klu_analyze_given 49 #define klu_K_free_symbolic klu_free_symbolic 50 #define klu_K_free_numeric klu_free_numeric 51 #define klu_K_common klu_common 52 #define klu_K_symbolic klu_symbolic 53 #define klu_K_numeric klu_numeric 54 #if defined(PETSC_USE_COMPLEX) 55 #define klu_K_factor klu_z_factor 56 #define klu_K_solve klu_z_solve 57 #define klu_K_tsolve klu_z_tsolve 58 #define klu_K_refactor klu_z_refactor 59 #define klu_K_sort klu_z_sort 60 #define klu_K_flops klu_z_flops 61 #define klu_K_rgrowth klu_z_rgrowth 62 #define klu_K_condest klu_z_condest 63 #define klu_K_rcond klu_z_rcond 64 #define klu_K_scale klu_z_scale 65 #else 66 #define klu_K_factor klu_factor 67 #define klu_K_solve klu_solve 68 #define klu_K_tsolve klu_tsolve 69 #define klu_K_refactor klu_refactor 70 #define klu_K_sort klu_sort 71 #define klu_K_flops klu_flops 72 #define klu_K_rgrowth klu_rgrowth 73 #define klu_K_condest klu_condest 74 #define klu_K_rcond klu_rcond 75 #define klu_K_scale klu_scale 76 #endif 77 #endif 78 79 80 #define SuiteSparse_long long long 81 #define SuiteSparse_long_max LONG_LONG_MAX 82 #define SuiteSparse_long_id "%lld" 83 84 EXTERN_C_BEGIN 85 #include <klu.h> 86 EXTERN_C_END 87 88 static const char *KluOrderingTypes[] = {"AMD","COLAMD","PETSC"}; 89 static const char *scale[] ={"NONE","SUM","MAX"}; 90 91 typedef struct { 92 klu_K_common Common; 93 klu_K_symbolic *Symbolic; 94 klu_K_numeric *Numeric; 95 PetscInt *perm_c,*perm_r; 96 MatStructure flg; 97 PetscBool PetscMatOrdering; 98 99 /* Flag to clean up KLU objects during Destroy */ 100 PetscBool CleanUpKLU; 101 } Mat_KLU; 102 103 #undef __FUNCT__ 104 #define __FUNCT__ "MatDestroy_KLU" 105 static PetscErrorCode MatDestroy_KLU(Mat A) 106 { 107 PetscErrorCode ierr; 108 Mat_KLU *lu=(Mat_KLU*)A->spptr; 109 110 PetscFunctionBegin; 111 if (lu && lu->CleanUpKLU) { 112 klu_K_free_symbolic(&lu->Symbolic,&lu->Common); 113 klu_K_free_numeric(&lu->Numeric,&lu->Common); 114 ierr = PetscFree2(lu->perm_r,lu->perm_c);CHKERRQ(ierr); 115 } 116 ierr = PetscFree(A->spptr);CHKERRQ(ierr); 117 ierr = MatDestroy_SeqAIJ(A);CHKERRQ(ierr); 118 PetscFunctionReturn(0); 119 } 120 121 #undef __FUNCT__ 122 #define __FUNCT__ "MatSolveTranspose_KLU" 123 static PetscErrorCode MatSolveTranspose_KLU(Mat A,Vec b,Vec x) 124 { 125 Mat_KLU *lu = (Mat_KLU*)A->spptr; 126 PetscScalar *xa; 127 PetscErrorCode ierr; 128 PetscInt status; 129 130 PetscFunctionBegin; 131 /* KLU uses a column major format, solve Ax = b by klu_*_solve */ 132 /* ----------------------------------*/ 133 ierr = VecCopy(b,x); /* klu_solve stores the solution in rhs */ 134 ierr = VecGetArray(x,&xa); 135 status = klu_K_solve(lu->Symbolic,lu->Numeric,A->rmap->n,1,(PetscReal*)xa,&lu->Common); 136 if (status != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Solve failed"); 137 ierr = VecRestoreArray(x,&xa);CHKERRQ(ierr); 138 PetscFunctionReturn(0); 139 } 140 141 #undef __FUNCT__ 142 #define __FUNCT__ "MatSolve_KLU" 143 static PetscErrorCode MatSolve_KLU(Mat A,Vec b,Vec x) 144 { 145 Mat_KLU *lu = (Mat_KLU*)A->spptr; 146 PetscScalar *xa; 147 PetscErrorCode ierr; 148 PetscInt status; 149 150 PetscFunctionBegin; 151 /* KLU uses a column major format, solve A^Tx = b by klu_*_tsolve */ 152 /* ----------------------------------*/ 153 ierr = VecCopy(b,x); /* klu_solve stores the solution in rhs */ 154 ierr = VecGetArray(x,&xa); 155 #if defined(PETSC_USE_COMPLEX) 156 PetscInt conj_solve=1; 157 status = klu_K_tsolve(lu->Symbolic,lu->Numeric,A->rmap->n,1,(PetscReal*)xa,conj_solve,&lu->Common); /* conjugate solve */ 158 #else 159 status = klu_K_tsolve(lu->Symbolic,lu->Numeric,A->rmap->n,1,xa,&lu->Common); 160 #endif 161 if (status != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Solve failed"); 162 ierr = VecRestoreArray(x,&xa);CHKERRQ(ierr); 163 PetscFunctionReturn(0); 164 } 165 166 #undef __FUNCT__ 167 #define __FUNCT__ "MatLUFactorNumeric_KLU" 168 static PetscErrorCode MatLUFactorNumeric_KLU(Mat F,Mat A,const MatFactorInfo *info) 169 { 170 Mat_KLU *lu = (Mat_KLU*)(F)->spptr; 171 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 172 PetscInt *ai = a->i,*aj=a->j; 173 PetscScalar *av = a->a; 174 175 PetscFunctionBegin; 176 /* numeric factorization of A' */ 177 /* ----------------------------*/ 178 179 if (lu->flg == SAME_NONZERO_PATTERN && lu->Numeric) { 180 klu_K_free_numeric(&lu->Numeric,&lu->Common); 181 } 182 lu->Numeric = klu_K_factor(ai,aj,(PetscReal*)av,lu->Symbolic,&lu->Common); 183 if(!lu->Numeric) { 184 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Numeric factorization failed"); 185 } 186 187 lu->flg = SAME_NONZERO_PATTERN; 188 lu->CleanUpKLU = PETSC_TRUE; 189 F->ops->solve = MatSolve_KLU; 190 F->ops->solvetranspose = MatSolveTranspose_KLU; 191 PetscFunctionReturn(0); 192 } 193 194 #undef __FUNCT__ 195 #define __FUNCT__ "MatLUFactorSymbolic_KLU" 196 static PetscErrorCode MatLUFactorSymbolic_KLU(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info) 197 { 198 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 199 Mat_KLU *lu = (Mat_KLU*)(F->spptr); 200 PetscErrorCode ierr; 201 PetscInt i,*ai = a->i,*aj = a->j,m=A->rmap->n,n=A->cmap->n; 202 const PetscInt *ra,*ca; 203 204 PetscFunctionBegin; 205 if (lu->PetscMatOrdering) { 206 ierr = ISGetIndices(r,&ra);CHKERRQ(ierr); 207 ierr = ISGetIndices(c,&ca);CHKERRQ(ierr); 208 ierr = PetscMalloc2(m,&lu->perm_r,n,&lu->perm_c);CHKERRQ(ierr); 209 /* we cannot simply memcpy on 64 bit archs */ 210 for (i = 0; i < m; i++) lu->perm_r[i] = ra[i]; 211 for (i = 0; i < n; i++) lu->perm_c[i] = ca[i]; 212 ierr = ISRestoreIndices(r,&ra);CHKERRQ(ierr); 213 ierr = ISRestoreIndices(c,&ca);CHKERRQ(ierr); 214 } 215 216 /* symbolic factorization of A' */ 217 /* ---------------------------------------------------------------------- */ 218 if (lu->PetscMatOrdering) { /* use Petsc ordering */ 219 lu->Symbolic = klu_K_analyze_given(n,ai,aj,lu->perm_c,lu->perm_r,&lu->Common); 220 } else { /* use klu internal ordering */ 221 lu->Symbolic = klu_K_analyze(n,ai,aj,&lu->Common); 222 } 223 if (!lu->Symbolic) { 224 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Symbolic Factorization failed"); 225 } 226 227 lu->flg = DIFFERENT_NONZERO_PATTERN; 228 lu->CleanUpKLU = PETSC_TRUE; 229 (F)->ops->lufactornumeric = MatLUFactorNumeric_KLU; 230 PetscFunctionReturn(0); 231 } 232 233 #undef __FUNCT__ 234 #define __FUNCT__ "MatFactorInfo_KLU" 235 static PetscErrorCode MatFactorInfo_KLU(Mat A,PetscViewer viewer) 236 { 237 Mat_KLU *lu= (Mat_KLU*)A->spptr; 238 klu_K_numeric *Numeric=(klu_K_numeric*)lu->Numeric; 239 PetscErrorCode ierr; 240 241 PetscFunctionBegin; 242 /* check if matrix is KLU type */ 243 if (A->ops->solve != MatSolve_KLU) PetscFunctionReturn(0); 244 245 ierr = PetscViewerASCIIPrintf(viewer,"KLU stats:\n");CHKERRQ(ierr); 246 ierr = PetscViewerASCIIPrintf(viewer," Number of diagonal blocks: %d\n",Numeric->nblocks); 247 ierr = PetscViewerASCIIPrintf(viewer," Total nonzeros=%d\n",Numeric->lnz+Numeric->unz);CHKERRQ(ierr); 248 249 ierr = PetscViewerASCIIPrintf(viewer,"KLU runtime parameters:\n");CHKERRQ(ierr); 250 251 /* Control parameters used by numeric factorization */ 252 ierr = PetscViewerASCIIPrintf(viewer," Partial pivoting tolerance: %g\n",lu->Common.tol);CHKERRQ(ierr); 253 /* BTF preordering */ 254 ierr = PetscViewerASCIIPrintf(viewer," BTF preordering enabled: %d\n",lu->Common.btf);CHKERRQ(ierr); 255 /* mat ordering */ 256 if (!lu->PetscMatOrdering) { 257 ierr = PetscViewerASCIIPrintf(viewer," Ordering: %s (not using the PETSc ordering)\n",KluOrderingTypes[(int)lu->Common.ordering]);CHKERRQ(ierr); 258 } else { 259 ierr = PetscViewerASCIIPrintf(viewer," Using PETSc ordering\n");CHKERRQ(ierr); 260 } 261 /* matrix row scaling */ 262 ierr = PetscViewerASCIIPrintf(viewer, " Matrix row scaling: %s\n",scale[(int)lu->Common.scale]);CHKERRQ(ierr); 263 PetscFunctionReturn(0); 264 } 265 266 #undef __FUNCT__ 267 #define __FUNCT__ "MatView_KLU" 268 static PetscErrorCode MatView_KLU(Mat A,PetscViewer viewer) 269 { 270 PetscErrorCode ierr; 271 PetscBool iascii; 272 PetscViewerFormat format; 273 274 PetscFunctionBegin; 275 ierr = MatView_SeqAIJ(A,viewer);CHKERRQ(ierr); 276 277 ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr); 278 if (iascii) { 279 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 280 if (format == PETSC_VIEWER_ASCII_INFO) { 281 ierr = MatFactorInfo_KLU(A,viewer);CHKERRQ(ierr); 282 } 283 } 284 PetscFunctionReturn(0); 285 } 286 287 #undef __FUNCT__ 288 #define __FUNCT__ "MatFactorGetSolverPackage_seqaij_klu" 289 PetscErrorCode MatFactorGetSolverPackage_seqaij_klu(Mat A,const MatSolverPackage *type) 290 { 291 PetscFunctionBegin; 292 *type = MATSOLVERKLU; 293 PetscFunctionReturn(0); 294 } 295 296 297 /*MC 298 MATSOLVERKLU = "klu" - A matrix type providing direct solvers (LU) for sequential matrices 299 via the external package KLU. 300 301 ./configure --download-suitesparse to install PETSc to use KLU 302 303 Use -pc_type lu -pc_factor_mat_solver_package klu to us this direct solver 304 305 Consult KLU documentation for more information on the options database keys below. 306 307 Options Database Keys: 308 + -mat_klu_pivot_tol <0.001> - Partial pivoting tolerance 309 . -mat_klu_use_btf <1> - Use BTF preordering 310 . -mat_klu_ordering <AMD> - KLU reordering scheme to reduce fill-in (choose one of) AMD COLAMD PETSC 311 - -mat_klu_row_scale <NONE> - Matrix row scaling (choose one of) NONE SUM MAX 312 313 Note: KLU is part of SuiteSparse http://faculty.cse.tamu.edu/davis/suitesparse.html 314 315 Level: beginner 316 317 .seealso: PCLU, MATSOLVERUMFPACK, MATSOLVERCHOLMOD, PCFactorSetMatSolverPackage(), MatSolverPackage 318 M*/ 319 320 #undef __FUNCT__ 321 #define __FUNCT__ "MatGetFactor_seqaij_klu" 322 PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_klu(Mat A,MatFactorType ftype,Mat *F) 323 { 324 Mat B; 325 Mat_KLU *lu; 326 PetscErrorCode ierr; 327 PetscInt m=A->rmap->n,n=A->cmap->n,idx,status; 328 PetscBool flg; 329 330 PetscFunctionBegin; 331 /* Create the factorization matrix F */ 332 ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr); 333 ierr = MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,m,n);CHKERRQ(ierr); 334 ierr = MatSetType(B,((PetscObject)A)->type_name);CHKERRQ(ierr); 335 ierr = MatSeqAIJSetPreallocation(B,0,NULL);CHKERRQ(ierr); 336 ierr = PetscNewLog(B,&lu);CHKERRQ(ierr); 337 338 B->spptr = lu; 339 B->ops->lufactorsymbolic = MatLUFactorSymbolic_KLU; 340 B->ops->destroy = MatDestroy_KLU; 341 B->ops->view = MatView_KLU; 342 343 ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_seqaij_klu);CHKERRQ(ierr); 344 345 B->factortype = MAT_FACTOR_LU; 346 B->assembled = PETSC_TRUE; /* required by -ksp_view */ 347 B->preallocated = PETSC_TRUE; 348 349 /* initializations */ 350 /* ------------------------------------------------*/ 351 /* get the default control parameters */ 352 status = klu_K_defaults(&lu->Common); 353 if(status <= 0) { 354 SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"KLU Initialization failed"); 355 } 356 lu->Common.scale = 0; /* No row scaling */ 357 358 ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"KLU Options","Mat");CHKERRQ(ierr); 359 /* Partial pivoting tolerance */ 360 ierr = PetscOptionsReal("-mat_klu_pivot_tol","Partial pivoting tolerance","None",lu->Common.tol,&lu->Common.tol,NULL);CHKERRQ(ierr); 361 /* BTF pre-ordering */ 362 ierr = PetscOptionsInt("-mat_klu_use_btf","Enable BTF preordering","None",lu->Common.btf,&lu->Common.btf,NULL);CHKERRQ(ierr); 363 /* Matrix reordering */ 364 ierr = PetscOptionsEList("-mat_klu_ordering","Internal ordering method","None",KluOrderingTypes,sizeof(KluOrderingTypes)/sizeof(KluOrderingTypes[0]),KluOrderingTypes[0],&idx,&flg);CHKERRQ(ierr); 365 if (flg) { 366 if ((int)idx == 2) lu->PetscMatOrdering = PETSC_TRUE; /* use Petsc mat ordering (note: size is for the transpose, and PETSc r = Klu perm_c) */ 367 else lu->Common.ordering = (int)idx; 368 } 369 /* Matrix row scaling */ 370 ierr = PetscOptionsEList("-mat_klu_row_scale","Matrix row scaling","None",scale,3,scale[0],&idx,&flg);CHKERRQ(ierr); 371 PetscOptionsEnd(); 372 *F = B; 373 PetscFunctionReturn(0); 374 } 375