Actual source code: superlu.c
1: #define PETSCMAT_DLL
3: /* --------------------------------------------------------------------
5: This file implements a subclass of the SeqAIJ matrix class that uses
6: the SuperLU 3.0 sparse solver. You can use this as a starting point for
7: implementing your own subclass of a PETSc matrix class.
9: This demonstrates a way to make an implementation inheritence of a PETSc
10: matrix type. This means constructing a new matrix type (SuperLU) by changing some
11: of the methods of the previous type (SeqAIJ), adding additional data, and possibly
12: additional method. (See any book on object oriented programming).
13: */
15: /*
16: Defines the data structure for the base matrix type (SeqAIJ)
17: */
18: #include src/mat/impls/aij/seq/aij.h
20: /*
21: SuperLU include files
22: */
24: #if defined(PETSC_USE_COMPLEX)
25: #include "slu_zdefs.h"
26: #else
27: #include "slu_ddefs.h"
28: #endif
29: #include "slu_util.h"
32: /*
33: This is the data we are "ADDING" to the SeqAIJ matrix type to get the SuperLU matrix type
34: */
35: typedef struct {
36: SuperMatrix A,L,U,B,X;
37: superlu_options_t options;
38: PetscInt *perm_c; /* column permutation vector */
39: PetscInt *perm_r; /* row permutations from partial pivoting */
40: PetscInt *etree;
41: PetscReal *R, *C;
42: char equed[1];
43: PetscInt lwork;
44: void *work;
45: PetscReal rpg, rcond;
46: mem_usage_t mem_usage;
47: MatStructure flg;
49: /*
50: This is where the methods for the superclass (SeqAIJ) are kept while we
51: reset the pointers in the function table to the new (SuperLU) versions
52: */
53: PetscErrorCode (*MatDuplicate)(Mat,MatDuplicateOption,Mat*);
54: PetscErrorCode (*MatView)(Mat,PetscViewer);
55: PetscErrorCode (*MatAssemblyEnd)(Mat,MatAssemblyType);
56: PetscErrorCode (*MatLUFactorSymbolic)(Mat,IS,IS,MatFactorInfo*,Mat*);
57: PetscErrorCode (*MatDestroy)(Mat);
59: /* Flag to clean up (non-global) SuperLU objects during Destroy */
60: PetscTruth CleanUpSuperLU;
61: } Mat_SuperLU;
73: /*
74: Takes a SuperLU matrix (that is a SeqAIJ matrix with the additional SuperLU data-structures
75: and methods) and converts it back to a regular SeqAIJ matrix.
76: */
80: PetscErrorCode MatConvert_SuperLU_SeqAIJ(Mat A,MatType type,MatReuse reuse,Mat *newmat)
81: {
83: Mat B=*newmat;
84: Mat_SuperLU *lu=(Mat_SuperLU *)A->spptr;
87: if (reuse == MAT_INITIAL_MATRIX) {
88: MatDuplicate(A,MAT_COPY_VALUES,&B);
89: }
90: /* Reset the original SeqAIJ function pointers */
91: B->ops->duplicate = lu->MatDuplicate;
92: B->ops->view = lu->MatView;
93: B->ops->assemblyend = lu->MatAssemblyEnd;
94: B->ops->lufactorsymbolic = lu->MatLUFactorSymbolic;
95: B->ops->destroy = lu->MatDestroy;
97: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_superlu_C","",PETSC_NULL);
98: PetscObjectComposeFunction((PetscObject)B,"MatConvert_superlu_seqaij_C","",PETSC_NULL);
100: /* change the type name back to its original value */
101: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
102: *newmat = B;
103: return(0);
104: }
110: PetscErrorCode MatConvert_SeqAIJ_SuperLU(Mat A,MatType type,MatReuse reuse,Mat *newmat)
111: {
113: Mat B=*newmat;
114: Mat_SuperLU *lu;
117: if (reuse == MAT_INITIAL_MATRIX) {
118: MatDuplicate(A,MAT_COPY_VALUES,&B);
119: }
121: PetscNew(Mat_SuperLU,&lu);
122: /* save the original SeqAIJ methods that we are changing */
123: lu->MatDuplicate = A->ops->duplicate;
124: lu->MatView = A->ops->view;
125: lu->MatAssemblyEnd = A->ops->assemblyend;
126: lu->MatLUFactorSymbolic = A->ops->lufactorsymbolic;
127: lu->MatDestroy = A->ops->destroy;
128: lu->CleanUpSuperLU = PETSC_FALSE;
130: /* add to the matrix the location for all the SuperLU data is to be stored */
131: B->spptr = (void*)lu;
133: /* set the methods in the function table to the SuperLU versions */
134: B->ops->duplicate = MatDuplicate_SuperLU;
135: B->ops->view = MatView_SuperLU;
136: B->ops->assemblyend = MatAssemblyEnd_SuperLU;
137: B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU;
138: B->ops->choleskyfactorsymbolic = 0;
139: B->ops->destroy = MatDestroy_SuperLU;
141: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_superlu_C",
142: "MatConvert_SeqAIJ_SuperLU",MatConvert_SeqAIJ_SuperLU);
143: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_superlu_seqaij_C",
144: "MatConvert_SuperLU_SeqAIJ",MatConvert_SuperLU_SeqAIJ);
145: PetscInfo(0,"Using SuperLU for SeqAIJ LU factorization and solves.\n");
146: PetscObjectChangeTypeName((PetscObject)B,MATSUPERLU);
147: *newmat = B;
148: return(0);
149: }
152: /*
153: Utility function
154: */
157: PetscErrorCode MatFactorInfo_SuperLU(Mat A,PetscViewer viewer)
158: {
159: Mat_SuperLU *lu= (Mat_SuperLU*)A->spptr;
160: PetscErrorCode ierr;
161: superlu_options_t options;
164: /* check if matrix is superlu_dist type */
165: if (A->ops->solve != MatSolve_SuperLU) return(0);
167: options = lu->options;
168: PetscViewerASCIIPrintf(viewer,"SuperLU run parameters:\n");
169: PetscViewerASCIIPrintf(viewer," Equil: %s\n",(options.Equil != NO) ? "YES": "NO");
170: PetscViewerASCIIPrintf(viewer," ColPerm: %D\n",options.ColPerm);
171: PetscViewerASCIIPrintf(viewer," IterRefine: %D\n",options.IterRefine);
172: PetscViewerASCIIPrintf(viewer," SymmetricMode: %s\n",(options.SymmetricMode != NO) ? "YES": "NO");
173: PetscViewerASCIIPrintf(viewer," DiagPivotThresh: %g\n",options.DiagPivotThresh);
174: PetscViewerASCIIPrintf(viewer," PivotGrowth: %s\n",(options.PivotGrowth != NO) ? "YES": "NO");
175: PetscViewerASCIIPrintf(viewer," ConditionNumber: %s\n",(options.ConditionNumber != NO) ? "YES": "NO");
176: PetscViewerASCIIPrintf(viewer," RowPerm: %D\n",options.RowPerm);
177: PetscViewerASCIIPrintf(viewer," ReplaceTinyPivot: %s\n",(options.ReplaceTinyPivot != NO) ? "YES": "NO");
178: PetscViewerASCIIPrintf(viewer," PrintStat: %s\n",(options.PrintStat != NO) ? "YES": "NO");
179: PetscViewerASCIIPrintf(viewer," lwork: %D\n",lu->lwork);
181: return(0);
182: }
184: /*
185: These are the methods provided to REPLACE the corresponding methods of the
186: SeqAIJ matrix class. Other methods of SeqAIJ are not replaced
187: */
190: PetscErrorCode MatLUFactorNumeric_SuperLU(Mat A,MatFactorInfo *info,Mat *F)
191: {
192: Mat_SeqAIJ *aa = (Mat_SeqAIJ*)(A)->data;
193: Mat_SuperLU *lu = (Mat_SuperLU*)(*F)->spptr;
195: PetscInt sinfo;
196: SuperLUStat_t stat;
197: PetscReal ferr, berr;
198: NCformat *Ustore;
199: SCformat *Lstore;
200:
202: if (lu->flg == SAME_NONZERO_PATTERN){ /* successing numerical factorization */
203: lu->options.Fact = SamePattern;
204: /* Ref: ~SuperLU_3.0/EXAMPLE/dlinsolx2.c */
205: Destroy_SuperMatrix_Store(&lu->A);
206: if ( lu->lwork >= 0 ) {
207: Destroy_SuperNode_Matrix(&lu->L);
208: Destroy_CompCol_Matrix(&lu->U);
209: lu->options.Fact = SamePattern;
210: }
211: }
213: /* Create the SuperMatrix for lu->A=A^T:
214: Since SuperLU likes column-oriented matrices,we pass it the transpose,
215: and then solve A^T X = B in MatSolve(). */
216: #if defined(PETSC_USE_COMPLEX)
217: zCreate_CompCol_Matrix(&lu->A,A->cmap.n,A->rmap.n,aa->nz,(doublecomplex*)aa->a,aa->j,aa->i,
218: SLU_NC,SLU_Z,SLU_GE);
219: #else
220: dCreate_CompCol_Matrix(&lu->A,A->cmap.n,A->rmap.n,aa->nz,aa->a,aa->j,aa->i,
221: SLU_NC,SLU_D,SLU_GE);
222: #endif
223:
224: /* Initialize the statistics variables. */
225: StatInit(&stat);
227: /* Numerical factorization */
228: lu->B.ncol = 0; /* Indicate not to solve the system */
229: #if defined(PETSC_USE_COMPLEX)
230: zgssvx(&lu->options, &lu->A, lu->perm_c, lu->perm_r, lu->etree, lu->equed, lu->R, lu->C,
231: &lu->L, &lu->U, lu->work, lu->lwork, &lu->B, &lu->X, &lu->rpg, &lu->rcond, &ferr, &berr,
232: &lu->mem_usage, &stat, &sinfo);
233: #else
234: dgssvx(&lu->options, &lu->A, lu->perm_c, lu->perm_r, lu->etree, lu->equed, lu->R, lu->C,
235: &lu->L, &lu->U, lu->work, lu->lwork, &lu->B, &lu->X, &lu->rpg, &lu->rcond, &ferr, &berr,
236: &lu->mem_usage, &stat, &sinfo);
237: #endif
238: if ( !sinfo || sinfo == lu->A.ncol+1 ) {
239: if ( lu->options.PivotGrowth )
240: PetscPrintf(PETSC_COMM_SELF," Recip. pivot growth = %e\n", lu->rpg);
241: if ( lu->options.ConditionNumber )
242: PetscPrintf(PETSC_COMM_SELF," Recip. condition number = %e\n", lu->rcond);
243: } else if ( sinfo > 0 ){
244: if ( lu->lwork == -1 ) {
245: PetscPrintf(PETSC_COMM_SELF," ** Estimated memory: %D bytes\n", sinfo - lu->A.ncol);
246: } else {
247: PetscPrintf(PETSC_COMM_SELF," Warning: gssvx() returns info %D\n",sinfo);
248: }
249: } else { /* sinfo < 0 */
250: SETERRQ2(PETSC_ERR_LIB, "info = %D, the %D-th argument in gssvx() had an illegal value", sinfo,-sinfo);
251: }
253: if ( lu->options.PrintStat ) {
254: PetscPrintf(PETSC_COMM_SELF,"MatLUFactorNumeric_SuperLU():\n");
255: StatPrint(&stat);
256: Lstore = (SCformat *) lu->L.Store;
257: Ustore = (NCformat *) lu->U.Store;
258: PetscPrintf(PETSC_COMM_SELF," No of nonzeros in factor L = %D\n", Lstore->nnz);
259: PetscPrintf(PETSC_COMM_SELF," No of nonzeros in factor U = %D\n", Ustore->nnz);
260: PetscPrintf(PETSC_COMM_SELF," No of nonzeros in L+U = %D\n", Lstore->nnz + Ustore->nnz - lu->A.ncol);
261: PetscPrintf(PETSC_COMM_SELF," L\\U MB %.3f\ttotal MB needed %.3f\texpansions %D\n",
262: lu->mem_usage.for_lu/1e6, lu->mem_usage.total_needed/1e6,
263: lu->mem_usage.expansions);
264: }
265: StatFree(&stat);
267: lu->flg = SAME_NONZERO_PATTERN;
268: return(0);
269: }
273: PetscErrorCode MatDestroy_SuperLU(Mat A)
274: {
276: Mat_SuperLU *lu=(Mat_SuperLU*)A->spptr;
279: if (lu->CleanUpSuperLU) { /* Free the SuperLU datastructures */
280: Destroy_SuperMatrix_Store(&lu->A);
281: Destroy_SuperMatrix_Store(&lu->B);
282: Destroy_SuperMatrix_Store(&lu->X);
284: PetscFree(lu->etree);
285: PetscFree(lu->perm_r);
286: PetscFree(lu->perm_c);
287: PetscFree(lu->R);
288: PetscFree(lu->C);
289: if ( lu->lwork >= 0 ) {
290: Destroy_SuperNode_Matrix(&lu->L);
291: Destroy_CompCol_Matrix(&lu->U);
292: }
293: }
294: MatConvert_SuperLU_SeqAIJ(A,MATSEQAIJ,MAT_REUSE_MATRIX,&A);
295: (*A->ops->destroy)(A);
296: return(0);
297: }
301: PetscErrorCode MatView_SuperLU(Mat A,PetscViewer viewer)
302: {
303: PetscErrorCode ierr;
304: PetscTruth iascii;
305: PetscViewerFormat format;
306: Mat_SuperLU *lu=(Mat_SuperLU*)(A->spptr);
309: (*lu->MatView)(A,viewer);
311: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
312: if (iascii) {
313: PetscViewerGetFormat(viewer,&format);
314: if (format == PETSC_VIEWER_ASCII_INFO) {
315: MatFactorInfo_SuperLU(A,viewer);
316: }
317: }
318: return(0);
319: }
323: PetscErrorCode MatAssemblyEnd_SuperLU(Mat A,MatAssemblyType mode) {
325: Mat_SuperLU *lu=(Mat_SuperLU*)(A->spptr);
328: (*lu->MatAssemblyEnd)(A,mode);
329: lu->MatLUFactorSymbolic = A->ops->lufactorsymbolic;
330: A->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU;
331: return(0);
332: }
337: PetscErrorCode MatSolve_SuperLU_Private(Mat A,Vec b,Vec x)
338: {
339: Mat_SuperLU *lu = (Mat_SuperLU*)A->spptr;
340: PetscScalar *barray,*xarray;
342: PetscInt info,i;
343: SuperLUStat_t stat;
344: PetscReal ferr,berr;
347: if ( lu->lwork == -1 ) {
348: return(0);
349: }
350: lu->B.ncol = 1; /* Set the number of right-hand side */
351: VecGetArray(b,&barray);
352: VecGetArray(x,&xarray);
354: #if defined(PETSC_USE_COMPLEX)
355: ((DNformat*)lu->B.Store)->nzval = (doublecomplex*)barray;
356: ((DNformat*)lu->X.Store)->nzval = (doublecomplex*)xarray;
357: #else
358: ((DNformat*)lu->B.Store)->nzval = barray;
359: ((DNformat*)lu->X.Store)->nzval = xarray;
360: #endif
362: /* Initialize the statistics variables. */
363: StatInit(&stat);
365: lu->options.Fact = FACTORED; /* Indicate the factored form of A is supplied. */
366: #if defined(PETSC_USE_COMPLEX)
367: zgssvx(&lu->options, &lu->A, lu->perm_c, lu->perm_r, lu->etree, lu->equed, lu->R, lu->C,
368: &lu->L, &lu->U, lu->work, lu->lwork, &lu->B, &lu->X, &lu->rpg, &lu->rcond, &ferr, &berr,
369: &lu->mem_usage, &stat, &info);
370: #else
371: dgssvx(&lu->options, &lu->A, lu->perm_c, lu->perm_r, lu->etree, lu->equed, lu->R, lu->C,
372: &lu->L, &lu->U, lu->work, lu->lwork, &lu->B, &lu->X, &lu->rpg, &lu->rcond, &ferr, &berr,
373: &lu->mem_usage, &stat, &info);
374: #endif
375: VecRestoreArray(b,&barray);
376: VecRestoreArray(x,&xarray);
378: if ( !info || info == lu->A.ncol+1 ) {
379: if ( lu->options.IterRefine ) {
380: PetscPrintf(PETSC_COMM_SELF,"Iterative Refinement:\n");
381: PetscPrintf(PETSC_COMM_SELF," %8s%8s%16s%16s\n", "rhs", "Steps", "FERR", "BERR");
382: for (i = 0; i < 1; ++i)
383: PetscPrintf(PETSC_COMM_SELF," %8d%8d%16e%16e\n", i+1, stat.RefineSteps, ferr, berr);
384: }
385: } else if ( info > 0 ){
386: if ( lu->lwork == -1 ) {
387: PetscPrintf(PETSC_COMM_SELF," ** Estimated memory: %D bytes\n", info - lu->A.ncol);
388: } else {
389: PetscPrintf(PETSC_COMM_SELF," Warning: gssvx() returns info %D\n",info);
390: }
391: } else if (info < 0){
392: SETERRQ2(PETSC_ERR_LIB, "info = %D, the %D-th argument in gssvx() had an illegal value", info,-info);
393: }
395: if ( lu->options.PrintStat ) {
396: PetscPrintf(PETSC_COMM_SELF,"MatSolve__SuperLU():\n");
397: StatPrint(&stat);
398: }
399: StatFree(&stat);
400: return(0);
401: }
405: PetscErrorCode MatSolve_SuperLU(Mat A,Vec b,Vec x)
406: {
407: Mat_SuperLU *lu = (Mat_SuperLU*)A->spptr;
411: lu->options.Trans = TRANS;
412: MatSolve_SuperLU_Private(A,b,x);
413: return(0);
414: }
418: PetscErrorCode MatSolveTranspose_SuperLU(Mat A,Vec b,Vec x)
419: {
420: Mat_SuperLU *lu = (Mat_SuperLU*)A->spptr;
424: lu->options.Trans = NOTRANS;
425: MatSolve_SuperLU_Private(A,b,x);
426: return(0);
427: }
430: /*
431: Note the r permutation is ignored
432: */
435: PetscErrorCode MatLUFactorSymbolic_SuperLU(Mat A,IS r,IS c,MatFactorInfo *info,Mat *F)
436: {
437: Mat B;
438: Mat_SuperLU *lu;
440: PetscInt m=A->rmap.n,n=A->cmap.n,indx;
441: PetscTruth flg;
442: const char *colperm[]={"NATURAL","MMD_ATA","MMD_AT_PLUS_A","COLAMD"}; /* MY_PERMC - not supported by the petsc interface yet */
443: const char *iterrefine[]={"NOREFINE", "SINGLE", "DOUBLE", "EXTRA"};
444: const char *rowperm[]={"NOROWPERM", "LargeDiag"}; /* MY_PERMC - not supported by the petsc interface yet */
447: MatCreate(A->comm,&B);
448: MatSetSizes(B,A->rmap.n,A->cmap.n,PETSC_DETERMINE,PETSC_DETERMINE);
449: MatSetType(B,A->type_name);
450: MatSeqAIJSetPreallocation(B,0,PETSC_NULL);
452: B->ops->lufactornumeric = MatLUFactorNumeric_SuperLU;
453: B->ops->solve = MatSolve_SuperLU;
454: B->ops->solvetranspose = MatSolveTranspose_SuperLU;
455: B->factor = FACTOR_LU;
456: B->assembled = PETSC_TRUE; /* required by -ksp_view */
457:
458: lu = (Mat_SuperLU*)(B->spptr);
460: /* Set SuperLU options */
461: /* the default values for options argument:
462: options.Fact = DOFACT;
463: options.Equil = YES;
464: options.ColPerm = COLAMD;
465: options.DiagPivotThresh = 1.0;
466: options.Trans = NOTRANS;
467: options.IterRefine = NOREFINE;
468: options.SymmetricMode = NO;
469: options.PivotGrowth = NO;
470: options.ConditionNumber = NO;
471: options.PrintStat = YES;
472: */
473: set_default_options(&lu->options);
474: /* equilibration causes error in solve(), thus not supported here. See dgssvx.c for possible reason. */
475: lu->options.Equil = NO;
476: lu->options.PrintStat = NO;
477: lu->lwork = 0; /* allocate space internally by system malloc */
479: PetscOptionsBegin(A->comm,A->prefix,"SuperLU Options","Mat");
480: PetscOptionsEList("-mat_superlu_colperm","ColPerm","None",colperm,4,colperm[3],&indx,&flg);
481: if (flg) {lu->options.ColPerm = (colperm_t)indx;}
482: PetscOptionsEList("-mat_superlu_iterrefine","IterRefine","None",iterrefine,4,iterrefine[0],&indx,&flg);
483: if (flg) { lu->options.IterRefine = (IterRefine_t)indx;}
484: PetscOptionsTruth("-mat_superlu_symmetricmode","SymmetricMode","None",PETSC_FALSE,&flg,0);
485: if (flg) lu->options.SymmetricMode = YES;
486: PetscOptionsReal("-mat_superlu_diagpivotthresh","DiagPivotThresh","None",lu->options.DiagPivotThresh,&lu->options.DiagPivotThresh,PETSC_NULL);
487: PetscOptionsTruth("-mat_superlu_pivotgrowth","PivotGrowth","None",PETSC_FALSE,&flg,0);
488: if (flg) lu->options.PivotGrowth = YES;
489: PetscOptionsTruth("-mat_superlu_conditionnumber","ConditionNumber","None",PETSC_FALSE,&flg,0);
490: if (flg) lu->options.ConditionNumber = YES;
491: PetscOptionsEList("-mat_superlu_rowperm","rowperm","None",rowperm,2,rowperm[0],&indx,&flg);
492: if (flg) {lu->options.RowPerm = (rowperm_t)indx;}
493: PetscOptionsTruth("-mat_superlu_replacetinypivot","ReplaceTinyPivot","None",PETSC_FALSE,&flg,0);
494: if (flg) lu->options.ReplaceTinyPivot = YES;
495: PetscOptionsTruth("-mat_superlu_printstat","PrintStat","None",PETSC_FALSE,&flg,0);
496: if (flg) lu->options.PrintStat = YES;
497: PetscOptionsInt("-mat_superlu_lwork","size of work array in bytes used by factorization","None",lu->lwork,&lu->lwork,PETSC_NULL);
498: if (lu->lwork > 0 ){
499: PetscMalloc(lu->lwork,&lu->work);
500: } else if (lu->lwork != 0 && lu->lwork != -1){
501: PetscPrintf(PETSC_COMM_SELF," Warning: lwork %D is not supported by SUPERLU. The default lwork=0 is used.\n",lu->lwork);
502: lu->lwork = 0;
503: }
504: PetscOptionsEnd();
506: #ifdef SUPERLU2
507: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatCreateNull","MatCreateNull_SuperLU",
508: (void(*)(void))MatCreateNull_SuperLU);
509: #endif
511: /* Allocate spaces (notice sizes are for the transpose) */
512: PetscMalloc(m*sizeof(PetscInt),&lu->etree);
513: PetscMalloc(n*sizeof(PetscInt),&lu->perm_r);
514: PetscMalloc(m*sizeof(PetscInt),&lu->perm_c);
515: PetscMalloc(n*sizeof(PetscInt),&lu->R);
516: PetscMalloc(m*sizeof(PetscInt),&lu->C);
517:
518: /* create rhs and solution x without allocate space for .Store */
519: #if defined(PETSC_USE_COMPLEX)
520: zCreate_Dense_Matrix(&lu->B, m, 1, PETSC_NULL, m, SLU_DN, SLU_Z, SLU_GE);
521: zCreate_Dense_Matrix(&lu->X, m, 1, PETSC_NULL, m, SLU_DN, SLU_Z, SLU_GE);
522: #else
523: dCreate_Dense_Matrix(&lu->B, m, 1, PETSC_NULL, m, SLU_DN, SLU_D, SLU_GE);
524: dCreate_Dense_Matrix(&lu->X, m, 1, PETSC_NULL, m, SLU_DN, SLU_D, SLU_GE);
525: #endif
527: lu->flg = DIFFERENT_NONZERO_PATTERN;
528: lu->CleanUpSuperLU = PETSC_TRUE;
530: *F = B;
531: PetscLogObjectMemory(B,(A->rmap.n+A->cmap.n)*sizeof(PetscInt)+sizeof(Mat_SuperLU));
532: return(0);
533: }
538: PetscErrorCode MatDuplicate_SuperLU(Mat A, MatDuplicateOption op, Mat *M) {
540: Mat_SuperLU *lu=(Mat_SuperLU *)A->spptr;
543: (*lu->MatDuplicate)(A,op,M);
544: PetscMemcpy((*M)->spptr,lu,sizeof(Mat_SuperLU));
545: return(0);
546: }
549: /*MC
550: MATSUPERLU - MATSUPERLU = "superlu" - A matrix type providing direct solvers (LU) for sequential matrices
551: via the external package SuperLU.
553: If SuperLU is installed (see the manual for
554: instructions on how to declare the existence of external packages),
555: a matrix type can be constructed which invokes SuperLU solvers.
556: After calling MatCreate(...,A), simply call MatSetType(A,MATSUPERLU).
558: This matrix inherits from MATSEQAIJ. As a result, MatSeqAIJSetPreallocation is
559: supported for this matrix type. One can also call MatConvert for an inplace conversion to or from
560: the MATSEQAIJ type without data copy.
562: Options Database Keys:
563: + -mat_type superlu - sets the matrix type to "superlu" during a call to MatSetFromOptions()
564: . -mat_superlu_ordering <0,1,2,3> - 0: natural ordering,
565: 1: MMD applied to A'*A,
566: 2: MMD applied to A'+A,
567: 3: COLAMD, approximate minimum degree column ordering
568: . -mat_superlu_iterrefine - have SuperLU do iterative refinement after the triangular solve
569: choices: NOREFINE, SINGLE, DOUBLE, EXTRA; default is NOREFINE
570: - -mat_superlu_printstat - print SuperLU statistics about the factorization
572: Level: beginner
574: .seealso: PCLU
575: M*/
577: /*
578: Constructor for the new derived matrix class. It simply creates the base
579: matrix class and then adds the additional information/methods needed by SuperLU.
580: */
584: PetscErrorCode MatCreate_SuperLU(Mat A)
585: {
589: MatSetType(A,MATSEQAIJ);
590: MatConvert_SeqAIJ_SuperLU(A,MATSUPERLU,MAT_REUSE_MATRIX,&A);
591: return(0);
592: }