Actual source code: superlu_dist.c
1: #define PETSCMAT_DLL
3: /*
4: Provides an interface to the SuperLU_DIST_2.0 sparse solver
5: */
7: #include "src/mat/impls/aij/seq/aij.h"
8: #include "src/mat/impls/aij/mpi/mpiaij.h"
9: #if defined(PETSC_HAVE_STDLIB_H) /* This is to get arround weird problem with SuperLU on cray */
10: #include "stdlib.h"
11: #endif
14: #if defined(PETSC_USE_COMPLEX)
15: #include "superlu_zdefs.h"
16: #else
17: #include "superlu_ddefs.h"
18: #endif
21: typedef enum { GLOBAL,DISTRIBUTED
22: } SuperLU_MatInputMode;
24: typedef struct {
25: int_t nprow,npcol,*row,*col;
26: gridinfo_t grid;
27: superlu_options_t options;
28: SuperMatrix A_sup;
29: ScalePermstruct_t ScalePermstruct;
30: LUstruct_t LUstruct;
31: int StatPrint;
32: int MatInputMode;
33: SOLVEstruct_t SOLVEstruct;
34: MatStructure flg;
35: MPI_Comm comm_superlu;
36: #if defined(PETSC_USE_COMPLEX)
37: doublecomplex *val;
38: #else
39: double *val;
40: #endif
42: /* A few function pointers for inheritance */
43: PetscErrorCode (*MatDuplicate)(Mat,MatDuplicateOption,Mat*);
44: PetscErrorCode (*MatView)(Mat,PetscViewer);
45: PetscErrorCode (*MatAssemblyEnd)(Mat,MatAssemblyType);
46: PetscErrorCode (*MatLUFactorSymbolic)(Mat,IS,IS,MatFactorInfo*,Mat*);
47: PetscErrorCode (*MatDestroy)(Mat);
49: /* Flag to clean up (non-global) SuperLU objects during Destroy */
50: PetscTruth CleanUpSuperLU_Dist;
51: } Mat_SuperLU_DIST;
53: EXTERN PetscErrorCode MatDuplicate_SuperLU_DIST(Mat,MatDuplicateOption,Mat*);
58: PetscErrorCode PETSCMAT_DLLEXPORT MatConvert_SuperLU_DIST_Base(Mat A,MatType type,MatReuse reuse,Mat *newmat)
59: {
60: PetscErrorCode ierr;
61: Mat B=*newmat;
62: Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST *)A->spptr;
65: if (reuse == MAT_INITIAL_MATRIX) {
66: MatDuplicate(A,MAT_COPY_VALUES,&B);
67: }
68: /* Reset the original function pointers */
69: B->ops->duplicate = lu->MatDuplicate;
70: B->ops->view = lu->MatView;
71: B->ops->assemblyend = lu->MatAssemblyEnd;
72: B->ops->lufactorsymbolic = lu->MatLUFactorSymbolic;
73: B->ops->destroy = lu->MatDestroy;
75: PetscFree(lu);
77: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_superlu_dist_C","",PETSC_NULL);
78: PetscObjectComposeFunction((PetscObject)B,"MatConvert_superlu_dist_seqaij_C","",PETSC_NULL);
79: PetscObjectComposeFunction((PetscObject)B,"MatConvert_mpiaij_superlu_dist_C","",PETSC_NULL);
80: PetscObjectComposeFunction((PetscObject)B,"MatConvert_superlu_dist_mpiaij_C","",PETSC_NULL);
82: PetscObjectChangeTypeName((PetscObject)B,type);
83: *newmat = B;
84: return(0);
85: }
90: PetscErrorCode MatDestroy_SuperLU_DIST(Mat A)
91: {
92: PetscErrorCode ierr;
93: PetscMPIInt size;
94: Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
95:
97: if (lu->CleanUpSuperLU_Dist) {
98: /* Deallocate SuperLU_DIST storage */
99: if (lu->MatInputMode == GLOBAL) {
100: Destroy_CompCol_Matrix_dist(&lu->A_sup);
101: } else {
102: Destroy_CompRowLoc_Matrix_dist(&lu->A_sup);
103: if ( lu->options.SolveInitialized ) {
104: #if defined(PETSC_USE_COMPLEX)
105: zSolveFinalize(&lu->options, &lu->SOLVEstruct);
106: #else
107: dSolveFinalize(&lu->options, &lu->SOLVEstruct);
108: #endif
109: }
110: }
111: Destroy_LU(A->cmap.N, &lu->grid, &lu->LUstruct);
112: ScalePermstructFree(&lu->ScalePermstruct);
113: LUstructFree(&lu->LUstruct);
115: /* Release the SuperLU_DIST process grid. */
116: superlu_gridexit(&lu->grid);
117:
118: MPI_Comm_free(&(lu->comm_superlu));
119: }
121: MPI_Comm_size(A->comm,&size);
122: if (size == 1) {
123: MatConvert_SuperLU_DIST_Base(A,MATSEQAIJ,MAT_REUSE_MATRIX,&A);
124: } else {
125: MatConvert_SuperLU_DIST_Base(A,MATMPIAIJ,MAT_REUSE_MATRIX,&A);
126: }
127: (*A->ops->destroy)(A);
128: return(0);
129: }
133: PetscErrorCode MatSolve_SuperLU_DIST(Mat A,Vec b_mpi,Vec x)
134: {
135: Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
136: PetscErrorCode ierr;
137: PetscMPIInt size;
138: PetscInt m=A->rmap.N, N=A->cmap.N;
139: SuperLUStat_t stat;
140: double berr[1];
141: PetscScalar *bptr;
142: PetscInt info, nrhs=1;
143: Vec x_seq;
144: IS iden;
145: VecScatter scat;
146:
148: MPI_Comm_size(A->comm,&size);
149: if (size > 1) {
150: if (lu->MatInputMode == GLOBAL) { /* global mat input, convert b to x_seq */
151: VecCreateSeq(PETSC_COMM_SELF,N,&x_seq);
152: ISCreateStride(PETSC_COMM_SELF,N,0,1,&iden);
153: VecScatterCreate(b_mpi,iden,x_seq,iden,&scat);
154: ISDestroy(iden);
156: VecScatterBegin(b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD,scat);
157: VecScatterEnd(b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD,scat);
158: VecGetArray(x_seq,&bptr);
159: } else { /* distributed mat input */
160: VecCopy(b_mpi,x);
161: VecGetArray(x,&bptr);
162: }
163: } else { /* size == 1 */
164: VecCopy(b_mpi,x);
165: VecGetArray(x,&bptr);
166: }
167:
168: lu->options.Fact = FACTORED; /* The factored form of A is supplied. Local option used by this func. only.*/
170: PStatInit(&stat); /* Initialize the statistics variables. */
171: if (lu->MatInputMode == GLOBAL) {
172: #if defined(PETSC_USE_COMPLEX)
173: pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,(doublecomplex*)bptr, m, nrhs,
174: &lu->grid, &lu->LUstruct, berr, &stat, &info);
175: #else
176: pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr, m, nrhs,
177: &lu->grid, &lu->LUstruct, berr, &stat, &info);
178: #endif
179: } else { /* distributed mat input */
180: #if defined(PETSC_USE_COMPLEX)
181: pzgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, (doublecomplex*)bptr, A->rmap.N, nrhs, &lu->grid,
182: &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &info);
183: if (info) SETERRQ1(PETSC_ERR_LIB,"pzgssvx fails, info: %d\n",info);
184: #else
185: pdgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, bptr, A->rmap.N, nrhs, &lu->grid,
186: &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &info);
187: if (info) SETERRQ1(PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
188: #endif
189: }
190: if (lu->options.PrintStat) {
191: PStatPrint(&lu->options, &stat, &lu->grid); /* Print the statistics. */
192: }
193: PStatFree(&stat);
194:
195: if (size > 1) {
196: if (lu->MatInputMode == GLOBAL){ /* convert seq x to mpi x */
197: VecRestoreArray(x_seq,&bptr);
198: VecScatterBegin(x_seq,x,INSERT_VALUES,SCATTER_REVERSE,scat);
199: VecScatterEnd(x_seq,x,INSERT_VALUES,SCATTER_REVERSE,scat);
200: VecScatterDestroy(scat);
201: VecDestroy(x_seq);
202: } else {
203: VecRestoreArray(x,&bptr);
204: }
205: } else {
206: VecRestoreArray(x,&bptr);
207: }
208: return(0);
209: }
213: PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat A,MatFactorInfo *info,Mat *F)
214: {
215: Mat *tseq,A_seq = PETSC_NULL;
216: Mat_SeqAIJ *aa,*bb;
217: Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)(*F)->spptr;
218: PetscErrorCode ierr;
219: PetscInt M=A->rmap.N,N=A->cmap.N,sinfo,i,*ai,*aj,*bi,*bj,nz,rstart,*garray,
220: m=A->rmap.n, irow,colA_start,j,jcol,jB,countA,countB,*bjj,*ajj;
221: PetscMPIInt size,rank;
222: SuperLUStat_t stat;
223: double *berr=0;
224: IS isrow;
225: PetscLogDouble time0,time,time_min,time_max;
226: Mat F_diag=PETSC_NULL;
227: #if defined(PETSC_USE_COMPLEX)
228: doublecomplex *av, *bv;
229: #else
230: double *av, *bv;
231: #endif
234: MPI_Comm_size(A->comm,&size);
235: MPI_Comm_rank(A->comm,&rank);
236:
237: if (lu->options.PrintStat) { /* collect time for mat conversion */
238: MPI_Barrier(A->comm);
239: PetscGetTime(&time0);
240: }
242: if (lu->MatInputMode == GLOBAL) { /* global mat input */
243: if (size > 1) { /* convert mpi A to seq mat A */
244: ISCreateStride(PETSC_COMM_SELF,M,0,1,&isrow);
245: MatGetSubMatrices(A,1,&isrow,&isrow,MAT_INITIAL_MATRIX,&tseq);
246: ISDestroy(isrow);
247:
248: A_seq = *tseq;
249: PetscFree(tseq);
250: aa = (Mat_SeqAIJ*)A_seq->data;
251: } else {
252: aa = (Mat_SeqAIJ*)A->data;
253: }
255: /* Convert Petsc NR matrix to SuperLU_DIST NC.
256: Note: memories of lu->val, col and row are allocated by CompRow_to_CompCol_dist()! */
257: if (lu->flg == SAME_NONZERO_PATTERN) {/* successive numeric factorization, sparsity pattern is reused. */
258: Destroy_CompCol_Matrix_dist(&lu->A_sup);
259: Destroy_LU(N, &lu->grid, &lu->LUstruct);
260: lu->options.Fact = SamePattern;
261: }
262: #if defined(PETSC_USE_COMPLEX)
263: zCompRow_to_CompCol_dist(M,N,aa->nz,(doublecomplex*)aa->a,aa->j,aa->i,&lu->val,&lu->col, &lu->row);
264: #else
265: dCompRow_to_CompCol_dist(M,N,aa->nz,aa->a,aa->j,aa->i,&lu->val, &lu->col, &lu->row);
266: #endif
268: /* Create compressed column matrix A_sup. */
269: #if defined(PETSC_USE_COMPLEX)
270: zCreate_CompCol_Matrix_dist(&lu->A_sup, M, N, aa->nz, lu->val, lu->col, lu->row, SLU_NC, SLU_Z, SLU_GE);
271: #else
272: dCreate_CompCol_Matrix_dist(&lu->A_sup, M, N, aa->nz, lu->val, lu->col, lu->row, SLU_NC, SLU_D, SLU_GE);
273: #endif
274: } else { /* distributed mat input */
275: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
276: aa=(Mat_SeqAIJ*)(mat->A)->data;
277: bb=(Mat_SeqAIJ*)(mat->B)->data;
278: ai=aa->i; aj=aa->j;
279: bi=bb->i; bj=bb->j;
280: #if defined(PETSC_USE_COMPLEX)
281: av=(doublecomplex*)aa->a;
282: bv=(doublecomplex*)bb->a;
283: #else
284: av=aa->a;
285: bv=bb->a;
286: #endif
287: rstart = A->rmap.rstart;
288: nz = aa->nz + bb->nz;
289: garray = mat->garray;
291: if (lu->flg == DIFFERENT_NONZERO_PATTERN) {/* first numeric factorization */
292: #if defined(PETSC_USE_COMPLEX)
293: zallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row);
294: #else
295: dallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row);
296: #endif
297: } else { /* successive numeric factorization, sparsity pattern and perm_c are reused. */
298: /* Destroy_CompRowLoc_Matrix_dist(&lu->A_sup); */ /* crash! */
299: Destroy_LU(N, &lu->grid, &lu->LUstruct);
300: lu->options.Fact = SamePattern;
301: }
302: nz = 0; irow = rstart;
303: for ( i=0; i<m; i++ ) {
304: lu->row[i] = nz;
305: countA = ai[i+1] - ai[i];
306: countB = bi[i+1] - bi[i];
307: ajj = aj + ai[i]; /* ptr to the beginning of this row */
308: bjj = bj + bi[i];
310: /* B part, smaller col index */
311: colA_start = rstart + ajj[0]; /* the smallest global col index of A */
312: jB = 0;
313: for (j=0; j<countB; j++){
314: jcol = garray[bjj[j]];
315: if (jcol > colA_start) {
316: jB = j;
317: break;
318: }
319: lu->col[nz] = jcol;
320: lu->val[nz++] = *bv++;
321: if (j==countB-1) jB = countB;
322: }
324: /* A part */
325: for (j=0; j<countA; j++){
326: lu->col[nz] = rstart + ajj[j];
327: lu->val[nz++] = *av++;
328: }
330: /* B part, larger col index */
331: for (j=jB; j<countB; j++){
332: lu->col[nz] = garray[bjj[j]];
333: lu->val[nz++] = *bv++;
334: }
335: }
336: lu->row[m] = nz;
337: #if defined(PETSC_USE_COMPLEX)
338: zCreate_CompRowLoc_Matrix_dist(&lu->A_sup, M, N, nz, m, rstart,
339: lu->val, lu->col, lu->row, SLU_NR_loc, SLU_Z, SLU_GE);
340: #else
341: dCreate_CompRowLoc_Matrix_dist(&lu->A_sup, M, N, nz, m, rstart,
342: lu->val, lu->col, lu->row, SLU_NR_loc, SLU_D, SLU_GE);
343: #endif
344: }
345: if (lu->options.PrintStat) {
346: PetscGetTime(&time);
347: time0 = time - time0;
348: }
350: /* Factor the matrix. */
351: PStatInit(&stat); /* Initialize the statistics variables. */
353: if (lu->MatInputMode == GLOBAL) { /* global mat input */
354: #if defined(PETSC_USE_COMPLEX)
355: pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,
356: &lu->grid, &lu->LUstruct, berr, &stat, &sinfo);
357: #else
358: pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,
359: &lu->grid, &lu->LUstruct, berr, &stat, &sinfo);
360: #endif
361: } else { /* distributed mat input */
362: #if defined(PETSC_USE_COMPLEX)
363: pzgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0, &lu->grid,
364: &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo);
365: if (sinfo) SETERRQ1(PETSC_ERR_LIB,"pzgssvx fails, info: %d\n",sinfo);
366: #else
367: pdgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0, &lu->grid,
368: &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo);
369: if (sinfo) SETERRQ1(PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",sinfo);
370: #endif
371: }
373: if (lu->MatInputMode == GLOBAL && size > 1){
374: MatDestroy(A_seq);
375: }
377: if (lu->options.PrintStat) {
378: if (size > 1){
379: MPI_Reduce(&time0,&time_max,1,MPI_DOUBLE,MPI_MAX,0,A->comm);
380: MPI_Reduce(&time0,&time_min,1,MPI_DOUBLE,MPI_MIN,0,A->comm);
381: MPI_Reduce(&time0,&time,1,MPI_DOUBLE,MPI_SUM,0,A->comm);
382: time = time/size; /* average time */
383: if (!rank)
384: PetscPrintf(PETSC_COMM_SELF, " Mat conversion(PETSc->SuperLU_DIST) time (max/min/avg): \n \
385: %g / %g / %g\n",time_max,time_min,time);
386: } else {
387: PetscPrintf(PETSC_COMM_SELF, " Mat conversion(PETSc->SuperLU_DIST) time: \n \
388: %g\n",time0);
389: }
390:
391: PStatPrint(&lu->options, &stat, &lu->grid); /* Print the statistics. */
392: }
393: PStatFree(&stat);
394: if (size > 1){
395: F_diag = ((Mat_MPIAIJ *)(*F)->data)->A;
396: F_diag->assembled = PETSC_TRUE;
397: }
398: (*F)->assembled = PETSC_TRUE;
399: lu->flg = SAME_NONZERO_PATTERN;
400: return(0);
401: }
403: /* Note the Petsc r and c permutations are ignored */
406: PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat A,IS r,IS c,MatFactorInfo *info,Mat *F)
407: {
408: Mat B;
409: Mat_SuperLU_DIST *lu;
410: PetscErrorCode ierr;
411: PetscInt M=A->rmap.N,N=A->cmap.N,indx;
412: PetscMPIInt size;
413: superlu_options_t options;
414: PetscTruth flg;
415: const char *ptype[] = {"MMD_AT_PLUS_A","NATURAL","MMD_ATA"};
416: const char *prtype[] = {"LargeDiag","NATURAL"};
419: /* Create the factorization matrix */
420: MatCreate(A->comm,&B);
421: MatSetSizes(B,A->rmap.n,A->cmap.n,M,N);
422: MatSetType(B,A->type_name);
423: MatSeqAIJSetPreallocation(B,0,PETSC_NULL);
424: MatMPIAIJSetPreallocation(B,0,PETSC_NULL,0,PETSC_NULL);
426: B->ops->lufactornumeric = MatLUFactorNumeric_SuperLU_DIST;
427: B->ops->solve = MatSolve_SuperLU_DIST;
428: B->factor = FACTOR_LU;
430: lu = (Mat_SuperLU_DIST*)(B->spptr);
432: /* Set the input options */
433: set_default_options_dist(&options);
435: lu->MatInputMode = GLOBAL;
436: MPI_Comm_dup(A->comm,&(lu->comm_superlu));
438: MPI_Comm_size(A->comm,&size);
439: lu->nprow = size/2; /* Default process rows. */
440: if (!lu->nprow) lu->nprow = 1;
441: lu->npcol = size/lu->nprow; /* Default process columns. */
443: PetscOptionsBegin(A->comm,A->prefix,"SuperLU_Dist Options","Mat");
444:
445: PetscOptionsInt("-mat_superlu_dist_r","Number rows in processor partition","None",lu->nprow,&lu->nprow,PETSC_NULL);
446: PetscOptionsInt("-mat_superlu_dist_c","Number columns in processor partition","None",lu->npcol,&lu->npcol,PETSC_NULL);
447: if (size != lu->nprow * lu->npcol) SETERRQ(PETSC_ERR_ARG_SIZ,"Number of processes should be equal to nprow*npcol");
448:
449: PetscOptionsInt("-mat_superlu_dist_matinput","Matrix input mode (0: GLOBAL; 1: DISTRIBUTED)","None",lu->MatInputMode,&lu->MatInputMode,PETSC_NULL);
450: if(lu->MatInputMode == DISTRIBUTED && size == 1) lu->MatInputMode = GLOBAL;
452: PetscOptionsTruth("-mat_superlu_dist_equil","Equilibrate matrix","None",PETSC_TRUE,&flg,0);
453: if (!flg) {
454: options.Equil = NO;
455: }
457: PetscOptionsEList("-mat_superlu_dist_rowperm","Row permutation","None",prtype,2,prtype[0],&indx,&flg);
458: if (flg) {
459: switch (indx) {
460: case 0:
461: options.RowPerm = LargeDiag;
462: break;
463: case 1:
464: options.RowPerm = NOROWPERM;
465: break;
466: }
467: }
469: PetscOptionsEList("-mat_superlu_dist_colperm","Column permutation","None",ptype,4,ptype[0],&indx,&flg);
470: if (flg) {
471: switch (indx) {
472: case 0:
473: options.ColPerm = MMD_AT_PLUS_A;
474: break;
475: case 1:
476: options.ColPerm = NATURAL;
477: break;
478: case 2:
479: options.ColPerm = MMD_ATA;
480: break;
481: }
482: }
484: PetscOptionsTruth("-mat_superlu_dist_replacetinypivot","Replace tiny pivots","None",PETSC_TRUE,&flg,0);
485: if (!flg) {
486: options.ReplaceTinyPivot = NO;
487: }
489: options.IterRefine = NOREFINE;
490: PetscOptionsTruth("-mat_superlu_dist_iterrefine","Use iterative refinement","None",PETSC_FALSE,&flg,0);
491: if (flg) {
492: options.IterRefine = DOUBLE;
493: }
495: if (PetscLogPrintInfo) {
496: options.PrintStat = YES;
497: } else {
498: options.PrintStat = NO;
499: }
500: PetscOptionsTruth("-mat_superlu_dist_statprint","Print factorization information","None",
501: (PetscTruth)options.PrintStat,(PetscTruth*)&options.PrintStat,0);
502: PetscOptionsEnd();
504: /* Initialize the SuperLU process grid. */
505: superlu_gridinit(lu->comm_superlu, lu->nprow, lu->npcol, &lu->grid);
507: /* Initialize ScalePermstruct and LUstruct. */
508: ScalePermstructInit(M, N, &lu->ScalePermstruct);
509: LUstructInit(M, N, &lu->LUstruct);
511: lu->options = options;
512: lu->flg = DIFFERENT_NONZERO_PATTERN;
513: lu->CleanUpSuperLU_Dist = PETSC_TRUE;
514: *F = B;
516: return(0);
517: }
521: PetscErrorCode MatAssemblyEnd_SuperLU_DIST(Mat A,MatAssemblyType mode) {
522: PetscErrorCode ierr;
523: Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST*)(A->spptr);
526: (*lu->MatAssemblyEnd)(A,mode);
527: lu->MatLUFactorSymbolic = A->ops->lufactorsymbolic;
528: A->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
529: return(0);
530: }
534: PetscErrorCode MatFactorInfo_SuperLU_DIST(Mat A,PetscViewer viewer)
535: {
536: Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST*)A->spptr;
537: superlu_options_t options;
538: PetscErrorCode ierr;
541: /* check if matrix is superlu_dist type */
542: if (A->ops->solve != MatSolve_SuperLU_DIST) return(0);
544: options = lu->options;
545: PetscViewerASCIIPrintf(viewer,"SuperLU_DIST run parameters:\n");
546: PetscViewerASCIIPrintf(viewer," Equilibrate matrix %s \n",PetscTruths[options.Equil != NO]);
547: PetscViewerASCIIPrintf(viewer," Matrix input mode %d \n",lu->MatInputMode);
548: PetscViewerASCIIPrintf(viewer," Replace tiny pivots %s \n",PetscTruths[options.ReplaceTinyPivot != NO]);
549: PetscViewerASCIIPrintf(viewer," Use iterative refinement %s \n",PetscTruths[options.IterRefine == DOUBLE]);
550: PetscViewerASCIIPrintf(viewer," Processors in row %d col partition %d \n",lu->nprow,lu->npcol);
551: PetscViewerASCIIPrintf(viewer," Row permutation %s \n",(options.RowPerm == NOROWPERM) ? "NATURAL": "LargeDiag");
552: if (options.ColPerm == NATURAL) {
553: PetscViewerASCIIPrintf(viewer," Column permutation NATURAL\n");
554: } else if (options.ColPerm == MMD_AT_PLUS_A) {
555: PetscViewerASCIIPrintf(viewer," Column permutation MMD_AT_PLUS_A\n");
556: } else if (options.ColPerm == MMD_ATA) {
557: PetscViewerASCIIPrintf(viewer," Column permutation MMD_ATA\n");
558: } else {
559: SETERRQ(PETSC_ERR_ARG_WRONG,"Unknown column permutation");
560: }
561: return(0);
562: }
566: PetscErrorCode MatView_SuperLU_DIST(Mat A,PetscViewer viewer)
567: {
568: PetscErrorCode ierr;
569: PetscTruth iascii;
570: PetscViewerFormat format;
571: Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST*)(A->spptr);
574: (*lu->MatView)(A,viewer);
576: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
577: if (iascii) {
578: PetscViewerGetFormat(viewer,&format);
579: if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
580: MatFactorInfo_SuperLU_DIST(A,viewer);
581: }
582: }
583: return(0);
584: }
590: PetscErrorCode PETSCMAT_DLLEXPORT MatConvert_Base_SuperLU_DIST(Mat A,MatType type,MatReuse reuse,Mat *newmat)
591: {
592: /* This routine is only called to convert to MATSUPERLU_DIST */
593: /* from MATSEQAIJ if A has a single process communicator */
594: /* or MATMPIAIJ otherwise, so we will ignore 'MatType type'. */
595: PetscErrorCode ierr;
596: PetscMPIInt size;
597: MPI_Comm comm;
598: Mat B=*newmat;
599: Mat_SuperLU_DIST *lu;
602: if (reuse == MAT_INITIAL_MATRIX) {
603: MatDuplicate(A,MAT_COPY_VALUES,&B);
604: }
606: PetscObjectGetComm((PetscObject)A,&comm);
607: PetscNew(Mat_SuperLU_DIST,&lu);
609: lu->MatDuplicate = A->ops->duplicate;
610: lu->MatView = A->ops->view;
611: lu->MatAssemblyEnd = A->ops->assemblyend;
612: lu->MatLUFactorSymbolic = A->ops->lufactorsymbolic;
613: lu->MatDestroy = A->ops->destroy;
614: lu->CleanUpSuperLU_Dist = PETSC_FALSE;
616: B->spptr = (void*)lu;
617: B->ops->duplicate = MatDuplicate_SuperLU_DIST;
618: B->ops->view = MatView_SuperLU_DIST;
619: B->ops->assemblyend = MatAssemblyEnd_SuperLU_DIST;
620: B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
621: B->ops->destroy = MatDestroy_SuperLU_DIST;
622: MPI_Comm_size(comm,&size);
623: if (size == 1) {
624: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_superlu_dist_C",
625: "MatConvert_Base_SuperLU_DIST",MatConvert_Base_SuperLU_DIST);
626: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_superlu_dist_seqaij_C",
627: "MatConvert_SuperLU_DIST_Base",MatConvert_SuperLU_DIST_Base);
628: } else {
629: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_mpiaij_superlu_dist_C",
630: "MatConvert_Base_SuperLU_DIST",MatConvert_Base_SuperLU_DIST);
631: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_superlu_dist_mpiaij_C",
632: "MatConvert_SuperLU_DIST_Base",MatConvert_SuperLU_DIST_Base);
633: }
634: PetscInfo(0,"Using SuperLU_DIST for SeqAIJ LU factorization and solves.\n");
635: PetscObjectChangeTypeName((PetscObject)B,MATSUPERLU_DIST);
636: *newmat = B;
637: return(0);
638: }
643: PetscErrorCode MatDuplicate_SuperLU_DIST(Mat A, MatDuplicateOption op, Mat *M) {
644: PetscErrorCode ierr;
645: Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST *)A->spptr;
648: (*lu->MatDuplicate)(A,op,M);
649: PetscMemcpy((*M)->spptr,lu,sizeof(Mat_SuperLU_DIST));
650: return(0);
651: }
653: /*MC
654: MATSUPERLU_DIST - MATSUPERLU_DIST = "superlu_dist" - A matrix type providing direct solvers (LU) for parallel matrices
655: via the external package SuperLU_DIST.
657: If SuperLU_DIST is installed (see the manual for
658: instructions on how to declare the existence of external packages),
659: a matrix type can be constructed which invokes SuperLU_DIST solvers.
660: After calling MatCreate(...,A), simply call MatSetType(A,MATSUPERLU_DIST).
662: This matrix inherits from MATSEQAIJ when constructed with a single process communicator,
663: and from MATMPIAIJ otherwise. As a result, for single process communicators,
664: MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
665: for communicators controlling multiple processes. It is recommended that you call both of
666: the above preallocation routines for simplicity. One can also call MatConvert for an inplace
667: conversion to or from the MATSEQAIJ or MATMPIAIJ type (depending on the communicator size)
668: without data copy.
670: Options Database Keys:
671: + -mat_type superlu_dist - sets the matrix type to "superlu_dist" during a call to MatSetFromOptions()
672: . -mat_superlu_dist_r <n> - number of rows in processor partition
673: . -mat_superlu_dist_c <n> - number of columns in processor partition
674: . -mat_superlu_dist_matinput <0,1> - matrix input mode; 0=global, 1=distributed
675: . -mat_superlu_dist_equil - equilibrate the matrix
676: . -mat_superlu_dist_rowperm <LargeDiag,NATURAL> - row permutation
677: . -mat_superlu_dist_colperm <MMD_AT_PLUS_A,MMD_ATA,NATURAL> - column permutation
678: . -mat_superlu_dist_replacetinypivot - replace tiny pivots
679: . -mat_superlu_dist_iterrefine - use iterative refinement
680: - -mat_superlu_dist_statprint - print factorization information
682: Level: beginner
684: .seealso: PCLU
685: M*/
690: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SuperLU_DIST(Mat A)
691: {
693: PetscMPIInt size;
696: /* Change type name before calling MatSetType to force proper construction of SeqAIJ or MPIAIJ */
697: /* and SuperLU_DIST types */
698: PetscObjectChangeTypeName((PetscObject)A,MATSUPERLU_DIST);
699: MPI_Comm_size(A->comm,&size);
700: if (size == 1) {
701: MatSetType(A,MATSEQAIJ);
702: } else {
703: MatSetType(A,MATMPIAIJ);
704: /* A_diag = 0x0 ??? -- do we need it?
705: Mat A_diag = ((Mat_MPIAIJ *)A->data)->A;
706: MatConvert_Base_SuperLU_DIST(A_diag,MATSUPERLU_DIST,MAT_REUSE_MATRIX,&A_diag);
707: */
708: }
709: MatConvert_Base_SuperLU_DIST(A,MATSUPERLU_DIST,MAT_REUSE_MATRIX,&A);
710: return(0);
711: }