Actual source code: superlu_dist.c
petsc-3.6.4 2016-04-12
2: /*
3: Provides an interface to the SuperLU_DIST_2.2 sparse solver
4: */
6: #include <../src/mat/impls/aij/seq/aij.h>
7: #include <../src/mat/impls/aij/mpi/mpiaij.h>
8: #if defined(PETSC_HAVE_STDLIB_H) /* This is to get around weird problem with SuperLU on cray */
9: #include <stdlib.h>
10: #endif
12: #if defined(PETSC_USE_64BIT_INDICES)
13: /* ugly SuperLU_Dist variable telling it to use long long int */
14: #define _LONGINT
15: #endif
17: EXTERN_C_BEGIN
18: #if defined(PETSC_USE_COMPLEX)
19: #include <superlu_zdefs.h>
20: #else
21: #include <superlu_ddefs.h>
22: #endif
23: EXTERN_C_END
25: /*
26: GLOBAL - The sparse matrix and right hand side are all stored initially on process 0. Should be called centralized
27: DISTRIBUTED - The sparse matrix and right hand size are initially stored across the entire MPI communicator.
28: */
29: typedef enum {GLOBAL,DISTRIBUTED} SuperLU_MatInputMode;
30: const char *SuperLU_MatInputModes[] = {"GLOBAL","DISTRIBUTED","SuperLU_MatInputMode","PETSC_",0};
32: typedef struct {
33: int_t nprow,npcol,*row,*col;
34: gridinfo_t grid;
35: superlu_options_t options;
36: SuperMatrix A_sup;
37: ScalePermstruct_t ScalePermstruct;
38: LUstruct_t LUstruct;
39: int StatPrint;
40: SuperLU_MatInputMode MatInputMode;
41: SOLVEstruct_t SOLVEstruct;
42: fact_t FactPattern;
43: MPI_Comm comm_superlu;
44: #if defined(PETSC_USE_COMPLEX)
45: doublecomplex *val;
46: #else
47: double *val;
48: #endif
49: PetscBool matsolve_iscalled,matmatsolve_iscalled;
50: PetscBool CleanUpSuperLU_Dist; /* Flag to clean up (non-global) SuperLU objects during Destroy */
51: } Mat_SuperLU_DIST;
53: extern PetscErrorCode MatFactorInfo_SuperLU_DIST(Mat,PetscViewer);
54: extern PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat,Mat,const MatFactorInfo*);
55: extern PetscErrorCode MatDestroy_SuperLU_DIST(Mat);
56: extern PetscErrorCode MatView_SuperLU_DIST(Mat,PetscViewer);
57: extern PetscErrorCode MatSolve_SuperLU_DIST(Mat,Vec,Vec);
58: extern PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat,Mat,IS,IS,const MatFactorInfo*);
59: extern PetscErrorCode MatDestroy_MPIAIJ(Mat);
63: PetscErrorCode MatGetDiagonal_SuperLU_DIST(Mat A,Vec v)
64: {
66: SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type: SuperLU_DIST factor");
67: return(0);
68: }
72: PetscErrorCode MatDestroy_SuperLU_DIST(Mat A)
73: {
74: PetscErrorCode ierr;
75: Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
76: PetscBool flg;
79: if (lu && lu->CleanUpSuperLU_Dist) {
80: /* Deallocate SuperLU_DIST storage */
81: if (lu->MatInputMode == GLOBAL) {
82: PetscStackCall("SuperLU_DIST:Destroy_CompCol_Matrix_dist",Destroy_CompCol_Matrix_dist(&lu->A_sup));
83: } else {
84: PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
85: if (lu->options.SolveInitialized) {
86: #if defined(PETSC_USE_COMPLEX)
87: PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
88: #else
89: PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
90: #endif
91: }
92: }
93: PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->cmap->N, &lu->grid, &lu->LUstruct));
94: PetscStackCall("SuperLU_DIST:ScalePermstructFree",ScalePermstructFree(&lu->ScalePermstruct));
95: PetscStackCall("SuperLU_DIST:LUstructFree",LUstructFree(&lu->LUstruct));
97: /* Release the SuperLU_DIST process grid. */
98: PetscStackCall("SuperLU_DIST:superlu_gridexit",superlu_gridexit(&lu->grid));
99: MPI_Comm_free(&(lu->comm_superlu));
100: }
101: PetscFree(A->spptr);
103: PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&flg);
104: if (flg) {
105: MatDestroy_SeqAIJ(A);
106: } else {
107: MatDestroy_MPIAIJ(A);
108: }
109: return(0);
110: }
114: PetscErrorCode MatSolve_SuperLU_DIST(Mat A,Vec b_mpi,Vec x)
115: {
116: Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
117: PetscErrorCode ierr;
118: PetscMPIInt size;
119: PetscInt m=A->rmap->n,M=A->rmap->N,N=A->cmap->N;
120: SuperLUStat_t stat;
121: double berr[1];
122: PetscScalar *bptr;
123: PetscInt nrhs=1;
124: Vec x_seq;
125: IS iden;
126: VecScatter scat;
127: int info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
128: static PetscBool cite = PETSC_FALSE;
131: PetscCitationsRegister("@article{lidemmel03,\n author = {Xiaoye S. Li and James W. Demmel},\n title = {{SuperLU_DIST}: A Scalable Distributed-Memory Sparse Direct\n Solver for Unsymmetric Linear Systems},\n journal = {ACM Trans. Mathematical Software},\n volume = {29},\n number = {2},\n pages = {110-140},\n year = 2003\n}\n",&cite);
132: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
133: if (size > 1 && lu->MatInputMode == GLOBAL) {
134: /* global mat input, convert b to x_seq */
135: VecCreateSeq(PETSC_COMM_SELF,N,&x_seq);
136: ISCreateStride(PETSC_COMM_SELF,N,0,1,&iden);
137: VecScatterCreate(b_mpi,iden,x_seq,iden,&scat);
138: ISDestroy(&iden);
140: VecScatterBegin(scat,b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD);
141: VecScatterEnd(scat,b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD);
142: VecGetArray(x_seq,&bptr);
143: } else { /* size==1 || distributed mat input */
144: if (lu->options.SolveInitialized && !lu->matsolve_iscalled) {
145: /* see comments in MatMatSolve() */
146: #if defined(PETSC_USE_COMPLEX)
147: PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
148: #else
149: PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
150: #endif
151: lu->options.SolveInitialized = NO;
152: }
153: VecCopy(b_mpi,x);
154: VecGetArray(x,&bptr);
155: }
157: if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact mush equal FACTORED");
159: PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat)); /* Initialize the statistics variables. */
160: if (lu->MatInputMode == GLOBAL) {
161: #if defined(PETSC_USE_COMPLEX)
162: PetscStackCall("SuperLU_DIST:pzgssvx_ABglobal",pzgssvx_ABglobal(&lu->options,&lu->A_sup,&lu->ScalePermstruct,(doublecomplex*)bptr,M,nrhs,&lu->grid,&lu->LUstruct,berr,&stat,&info));
163: #else
164: PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr,M,nrhs,&lu->grid,&lu->LUstruct,berr,&stat,&info));
165: #endif
166: } else { /* distributed mat input */
167: #if defined(PETSC_USE_COMPLEX)
168: PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,(doublecomplex*)bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
169: #else
170: PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
171: #endif
172: }
173: if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
175: if (lu->options.PrintStat) PStatPrint(&lu->options, &stat, &lu->grid); /* Print the statistics. */
176: PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
178: if (size > 1 && lu->MatInputMode == GLOBAL) {
179: /* convert seq x to mpi x */
180: VecRestoreArray(x_seq,&bptr);
181: VecScatterBegin(scat,x_seq,x,INSERT_VALUES,SCATTER_REVERSE);
182: VecScatterEnd(scat,x_seq,x,INSERT_VALUES,SCATTER_REVERSE);
183: VecScatterDestroy(&scat);
184: VecDestroy(&x_seq);
185: } else {
186: VecRestoreArray(x,&bptr);
188: lu->matsolve_iscalled = PETSC_TRUE;
189: lu->matmatsolve_iscalled = PETSC_FALSE;
190: }
191: return(0);
192: }
196: PetscErrorCode MatMatSolve_SuperLU_DIST(Mat A,Mat B_mpi,Mat X)
197: {
198: Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
199: PetscErrorCode ierr;
200: PetscMPIInt size;
201: PetscInt M=A->rmap->N,m=A->rmap->n,nrhs;
202: SuperLUStat_t stat;
203: double berr[1];
204: PetscScalar *bptr;
205: int info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
206: PetscBool flg;
209: if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact mush equal FACTORED");
210: PetscObjectTypeCompareAny((PetscObject)B_mpi,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
211: if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
212: PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
213: if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
215: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
216: if (size > 1 && lu->MatInputMode == GLOBAL) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatInputMode=GLOBAL for nproc %d>1 is not supported",size);
217: /* size==1 or distributed mat input */
218: if (lu->options.SolveInitialized && !lu->matmatsolve_iscalled) {
219: /* communication pattern of SOLVEstruct is unlikely created for matmatsolve,
220: thus destroy it and create a new SOLVEstruct.
221: Otherwise it may result in memory corruption or incorrect solution
222: See src/mat/examples/tests/ex125.c */
223: #if defined(PETSC_USE_COMPLEX)
224: PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
225: #else
226: PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
227: #endif
228: lu->options.SolveInitialized = NO;
229: }
230: MatCopy(B_mpi,X,SAME_NONZERO_PATTERN);
232: MatGetSize(B_mpi,NULL,&nrhs);
234: PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat)); /* Initialize the statistics variables. */
235: MatDenseGetArray(X,&bptr);
236: if (lu->MatInputMode == GLOBAL) { /* size == 1 */
237: #if defined(PETSC_USE_COMPLEX)
238: PetscStackCall("SuperLU_DIST:pzgssvx_ABglobal",pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,(doublecomplex*)bptr, M, nrhs,&lu->grid, &lu->LUstruct, berr, &stat, &info));
239: #else
240: PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr, M, nrhs, &lu->grid, &lu->LUstruct, berr, &stat, &info));
241: #endif
242: } else { /* distributed mat input */
243: #if defined(PETSC_USE_COMPLEX)
244: PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,(doublecomplex*)bptr,m,nrhs,&lu->grid, &lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
245: #else
246: PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
247: #endif
248: }
249: if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
250: MatDenseRestoreArray(X,&bptr);
252: if (lu->options.PrintStat) PStatPrint(&lu->options, &stat, &lu->grid); /* Print the statistics. */
253: PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
254: lu->matsolve_iscalled = PETSC_FALSE;
255: lu->matmatsolve_iscalled = PETSC_TRUE;
256: return(0);
257: }
262: PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat F,Mat A,const MatFactorInfo *info)
263: {
264: Mat *tseq,A_seq = NULL;
265: Mat_SeqAIJ *aa,*bb;
266: Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)(F)->spptr;
267: PetscErrorCode ierr;
268: PetscInt M=A->rmap->N,N=A->cmap->N,i,*ai,*aj,*bi,*bj,nz,rstart,*garray,
269: m=A->rmap->n, colA_start,j,jcol,jB,countA,countB,*bjj,*ajj;
270: int sinfo; /* SuperLU_Dist info flag is always an int even with long long indices */
271: PetscMPIInt size;
272: SuperLUStat_t stat;
273: double *berr=0;
274: IS isrow;
275: Mat F_diag=NULL;
276: #if defined(PETSC_USE_COMPLEX)
277: doublecomplex *av, *bv;
278: #else
279: double *av, *bv;
280: #endif
283: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
285: if (lu->MatInputMode == GLOBAL) { /* global mat input */
286: if (size > 1) { /* convert mpi A to seq mat A */
287: ISCreateStride(PETSC_COMM_SELF,M,0,1,&isrow);
288: MatGetSubMatrices(A,1,&isrow,&isrow,MAT_INITIAL_MATRIX,&tseq);
289: ISDestroy(&isrow);
291: A_seq = *tseq;
292: PetscFree(tseq);
293: aa = (Mat_SeqAIJ*)A_seq->data;
294: } else {
295: PetscBool flg;
296: PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&flg);
297: if (flg) {
298: Mat_MPIAIJ *At = (Mat_MPIAIJ*)A->data;
299: A = At->A;
300: }
301: aa = (Mat_SeqAIJ*)A->data;
302: }
304: /* Convert Petsc NR matrix to SuperLU_DIST NC.
305: Note: memories of lu->val, col and row are allocated by CompRow_to_CompCol_dist()! */
306: if (lu->options.Fact != DOFACT) {/* successive numeric factorization, sparsity pattern is reused. */
307: PetscStackCall("SuperLU_DIST:Destroy_CompCol_Matrix_dist",Destroy_CompCol_Matrix_dist(&lu->A_sup));
308: if (lu->FactPattern == SamePattern_SameRowPerm) {
309: lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
310: } else { /* lu->FactPattern == SamePattern */
311: PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(N, &lu->grid, &lu->LUstruct));
312: lu->options.Fact = SamePattern;
313: }
314: }
315: #if defined(PETSC_USE_COMPLEX)
316: PetscStackCall("SuperLU_DIST:zCompRow_to_CompCol_dist",zCompRow_to_CompCol_dist(M,N,aa->nz,(doublecomplex*)aa->a,(int_t*)aa->j,(int_t*)aa->i,&lu->val,&lu->col, &lu->row));
317: #else
318: PetscStackCall("SuperLU_DIST:dCompRow_to_CompCol_dist",dCompRow_to_CompCol_dist(M,N,aa->nz,aa->a,(int_t*)aa->j,(int_t*)aa->i,&lu->val, &lu->col, &lu->row));
319: #endif
321: /* Create compressed column matrix A_sup. */
322: #if defined(PETSC_USE_COMPLEX)
323: PetscStackCall("SuperLU_DIST:zCreate_CompCol_Matrix_dist",zCreate_CompCol_Matrix_dist(&lu->A_sup, M, N, aa->nz, lu->val, lu->col, lu->row, SLU_NC, SLU_Z, SLU_GE));
324: #else
325: PetscStackCall("SuperLU_DIST:dCreate_CompCol_Matrix_dist",dCreate_CompCol_Matrix_dist(&lu->A_sup, M, N, aa->nz, lu->val, lu->col, lu->row, SLU_NC, SLU_D, SLU_GE));
326: #endif
327: } else { /* distributed mat input */
328: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
329: aa=(Mat_SeqAIJ*)(mat->A)->data;
330: bb=(Mat_SeqAIJ*)(mat->B)->data;
331: ai=aa->i; aj=aa->j;
332: bi=bb->i; bj=bb->j;
333: #if defined(PETSC_USE_COMPLEX)
334: av=(doublecomplex*)aa->a;
335: bv=(doublecomplex*)bb->a;
336: #else
337: av=aa->a;
338: bv=bb->a;
339: #endif
340: rstart = A->rmap->rstart;
341: nz = aa->nz + bb->nz;
342: garray = mat->garray;
344: if (lu->options.Fact == DOFACT) { /* first numeric factorization */
345: #if defined(PETSC_USE_COMPLEX)
346: PetscStackCall("SuperLU_DIST:zallocateA_dist",zallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row));
347: #else
348: PetscStackCall("SuperLU_DIST:dallocateA_dist",dallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row));
349: #endif
350: } else { /* successive numeric factorization, sparsity pattern and perm_c are reused. */
351: /* Destroy_CompRowLoc_Matrix_dist(&lu->A_sup); */ /* this leads to crash! However, see SuperLU_DIST_2.5/EXAMPLE/pzdrive2.c */
352: if (lu->FactPattern == SamePattern_SameRowPerm) {
353: lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
354: } else {
355: PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(N, &lu->grid, &lu->LUstruct)); /* Deallocate storage associated with the L and U matrices. */
356: lu->options.Fact = SamePattern;
357: }
358: }
359: nz = 0;
360: for (i=0; i<m; i++) {
361: lu->row[i] = nz;
362: countA = ai[i+1] - ai[i];
363: countB = bi[i+1] - bi[i];
364: if (aj) {
365: ajj = aj + ai[i]; /* ptr to the beginning of this row */
366: } else {
367: ajj = NULL;
368: }
369: bjj = bj + bi[i];
371: /* B part, smaller col index */
372: if (aj) {
373: colA_start = rstart + ajj[0]; /* the smallest global col index of A */
374: } else { /* superlu_dist does not require matrix has diagonal entries, thus aj=NULL would work */
375: colA_start = rstart;
376: }
377: jB = 0;
378: for (j=0; j<countB; j++) {
379: jcol = garray[bjj[j]];
380: if (jcol > colA_start) {
381: jB = j;
382: break;
383: }
384: lu->col[nz] = jcol;
385: lu->val[nz++] = *bv++;
386: if (j==countB-1) jB = countB;
387: }
389: /* A part */
390: for (j=0; j<countA; j++) {
391: lu->col[nz] = rstart + ajj[j];
392: lu->val[nz++] = *av++;
393: }
394:
395: /* B part, larger col index */
396: for (j=jB; j<countB; j++) {
397: lu->col[nz] = garray[bjj[j]];
398: lu->val[nz++] = *bv++;
399: }
400: }
401: lu->row[m] = nz;
402: #if defined(PETSC_USE_COMPLEX)
403: PetscStackCall("SuperLU_DIST:zCreate_CompRowLoc_Matrix_dist",zCreate_CompRowLoc_Matrix_dist(&lu->A_sup, M, N, nz, m, rstart,lu->val, lu->col, lu->row, SLU_NR_loc, SLU_Z, SLU_GE));
404: #else
405: PetscStackCall("SuperLU_DIST:dCreate_CompRowLoc_Matrix_dist",dCreate_CompRowLoc_Matrix_dist(&lu->A_sup, M, N, nz, m, rstart,lu->val, lu->col, lu->row, SLU_NR_loc, SLU_D, SLU_GE));
406: #endif
407: }
409: /* Factor the matrix. */
410: PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat)); /* Initialize the statistics variables. */
411: if (lu->MatInputMode == GLOBAL) { /* global mat input */
412: #if defined(PETSC_USE_COMPLEX)
413: PetscStackCall("SuperLU_DIST:pzgssvx_ABglobal",pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,&lu->grid, &lu->LUstruct, berr, &stat, &sinfo));
414: #else
415: PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,&lu->grid, &lu->LUstruct, berr, &stat, &sinfo));
416: #endif
417: } else { /* distributed mat input */
418: #if defined(PETSC_USE_COMPLEX)
419: PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, m, 0, &lu->grid,&lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
420: if (sinfo) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pzgssvx fails, info: %d\n",sinfo);
421: #else
422: PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, m, 0, &lu->grid,&lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
423: if (sinfo) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",sinfo);
424: #endif
425: }
427: if (lu->MatInputMode == GLOBAL && size > 1) {
428: MatDestroy(&A_seq);
429: }
431: if (lu->options.PrintStat) {
432: PStatPrint(&lu->options, &stat, &lu->grid); /* Print the statistics. */
433: }
434: PStatFree(&stat);
435: if (size > 1) {
436: F_diag = ((Mat_MPIAIJ*)(F)->data)->A;
437: F_diag->assembled = PETSC_TRUE;
438: }
439: (F)->assembled = PETSC_TRUE;
440: (F)->preallocated = PETSC_TRUE;
441: lu->options.Fact = FACTORED; /* The factored form of A is supplied. Local option used by this func. only */
442: return(0);
443: }
445: /* Note the Petsc r and c permutations are ignored */
448: PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
449: {
450: Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->spptr;
451: PetscInt M = A->rmap->N,N=A->cmap->N;
454: /* Initialize the SuperLU process grid. */
455: PetscStackCall("SuperLU_DIST:superlu_gridinit",superlu_gridinit(lu->comm_superlu, lu->nprow, lu->npcol, &lu->grid));
457: /* Initialize ScalePermstruct and LUstruct. */
458: PetscStackCall("SuperLU_DIST:ScalePermstructInit",ScalePermstructInit(M, N, &lu->ScalePermstruct));
459: PetscStackCall("SuperLU_DIST:LUstructInit",LUstructInit(N, &lu->LUstruct));
460: F->ops->lufactornumeric = MatLUFactorNumeric_SuperLU_DIST;
461: F->ops->solve = MatSolve_SuperLU_DIST;
462: F->ops->matsolve = MatMatSolve_SuperLU_DIST;
463: lu->CleanUpSuperLU_Dist = PETSC_TRUE;
464: return(0);
465: }
469: PetscErrorCode MatFactorGetSolverPackage_aij_superlu_dist(Mat A,const MatSolverPackage *type)
470: {
472: *type = MATSOLVERSUPERLU_DIST;
473: return(0);
474: }
478: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_superlu_dist(Mat A,MatFactorType ftype,Mat *F)
479: {
480: Mat B;
481: Mat_SuperLU_DIST *lu;
482: PetscErrorCode ierr;
483: PetscInt M=A->rmap->N,N=A->cmap->N,indx;
484: PetscMPIInt size;
485: superlu_options_t options;
486: PetscBool flg;
487: const char *colperm[] = {"NATURAL","MMD_AT_PLUS_A","MMD_ATA","METIS_AT_PLUS_A","PARMETIS"};
488: const char *rowperm[] = {"LargeDiag","NATURAL"};
489: const char *factPattern[] = {"SamePattern","SamePattern_SameRowPerm"};
490: PetscBool set;
493: /* Create the factorization matrix */
494: MatCreate(PetscObjectComm((PetscObject)A),&B);
495: MatSetSizes(B,A->rmap->n,A->cmap->n,M,N);
496: MatSetType(B,((PetscObject)A)->type_name);
497: MatSeqAIJSetPreallocation(B,0,NULL);
498: MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);
500: B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
501: B->ops->view = MatView_SuperLU_DIST;
502: B->ops->destroy = MatDestroy_SuperLU_DIST;
503: B->ops->getdiagonal = MatGetDiagonal_SuperLU_DIST;
505: PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_aij_superlu_dist);
507: B->factortype = MAT_FACTOR_LU;
509: PetscNewLog(B,&lu);
510: B->spptr = lu;
512: /* Set the default input options:
513: options.Fact = DOFACT;
514: options.Equil = YES;
515: options.ParSymbFact = NO;
516: options.ColPerm = METIS_AT_PLUS_A;
517: options.RowPerm = LargeDiag;
518: options.ReplaceTinyPivot = YES;
519: options.IterRefine = DOUBLE;
520: options.Trans = NOTRANS;
521: options.SolveInitialized = NO; -hold the communication pattern used MatSolve() and MatMatSolve()
522: options.RefineInitialized = NO;
523: options.PrintStat = YES;
524: */
525: set_default_options_dist(&options);
527: MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(lu->comm_superlu));
528: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
529: /* Default num of process columns and rows */
530: lu->npcol = (int_t) (0.5 + PetscSqrtReal((PetscReal)size));
531: if (!lu->npcol) lu->npcol = 1;
532: while (lu->npcol > 0) {
533: lu->nprow = (int_t) (size/lu->npcol);
534: if (size == lu->nprow * lu->npcol) break;
535: lu->npcol--;
536: }
538: PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"SuperLU_Dist Options","Mat");
539: PetscOptionsInt("-mat_superlu_dist_r","Number rows in processor partition","None",lu->nprow,(PetscInt*)&lu->nprow,NULL);
540: PetscOptionsInt("-mat_superlu_dist_c","Number columns in processor partition","None",lu->npcol,(PetscInt*)&lu->npcol,NULL);
541: if (size != lu->nprow * lu->npcol) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number of processes %d must equal to nprow %d * npcol %d",size,lu->nprow,lu->npcol);
543: lu->MatInputMode = DISTRIBUTED;
545: PetscOptionsEnum("-mat_superlu_dist_matinput","Matrix input mode (global or distributed)","None",SuperLU_MatInputModes,(PetscEnum)lu->MatInputMode,(PetscEnum*)&lu->MatInputMode,NULL);
546: if (lu->MatInputMode == DISTRIBUTED && size == 1) lu->MatInputMode = GLOBAL;
548: PetscOptionsBool("-mat_superlu_dist_equil","Equilibrate matrix","None",options.Equil ? PETSC_TRUE : PETSC_FALSE,&flg,&set);
549: if (set && !flg) options.Equil = NO;
551: PetscOptionsEList("-mat_superlu_dist_rowperm","Row permutation","None",rowperm,2,rowperm[0],&indx,&flg);
552: if (flg) {
553: switch (indx) {
554: case 0:
555: options.RowPerm = LargeDiag;
556: break;
557: case 1:
558: options.RowPerm = NOROWPERM;
559: break;
560: }
561: }
563: PetscOptionsEList("-mat_superlu_dist_colperm","Column permutation","None",colperm,5,colperm[3],&indx,&flg);
564: if (flg) {
565: switch (indx) {
566: case 0:
567: options.ColPerm = NATURAL;
568: break;
569: case 1:
570: options.ColPerm = MMD_AT_PLUS_A;
571: break;
572: case 2:
573: options.ColPerm = MMD_ATA;
574: break;
575: case 3:
576: options.ColPerm = METIS_AT_PLUS_A;
577: break;
578: case 4:
579: options.ColPerm = PARMETIS; /* only works for np>1 */
580: break;
581: default:
582: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
583: }
584: }
586: PetscOptionsBool("-mat_superlu_dist_replacetinypivot","Replace tiny pivots","None",options.ReplaceTinyPivot ? PETSC_TRUE : PETSC_FALSE,&flg,&set);
587: if (set && !flg) options.ReplaceTinyPivot = NO;
589: options.ParSymbFact = NO;
591: PetscOptionsBool("-mat_superlu_dist_parsymbfact","Parallel symbolic factorization","None",PETSC_FALSE,&flg,&set);
592: if (set && flg && size>1) {
593: if (lu->MatInputMode == GLOBAL) {
594: #if defined(PETSC_USE_INFO)
595: PetscInfo(A,"Warning: '-mat_superlu_dist_parsymbfact' is ignored because MatInputMode=GLOBAL\n");
596: #endif
597: } else {
598: #if defined(PETSC_HAVE_PARMETIS)
599: options.ParSymbFact = YES;
600: options.ColPerm = PARMETIS; /* in v2.2, PARMETIS is forced for ParSymbFact regardless of user ordering setting */
601: #else
602: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"parsymbfact needs PARMETIS");
603: #endif
604: }
605: }
607: lu->FactPattern = SamePattern_SameRowPerm;
609: PetscOptionsEList("-mat_superlu_dist_fact","Sparsity pattern for repeated matrix factorization","None",factPattern,2,factPattern[1],&indx,&flg);
610: if (flg) {
611: switch (indx) {
612: case 0:
613: lu->FactPattern = SamePattern;
614: break;
615: case 1:
616: lu->FactPattern = SamePattern_SameRowPerm;
617: break;
618: }
619: }
621: options.IterRefine = NOREFINE;
622: PetscOptionsBool("-mat_superlu_dist_iterrefine","Use iterative refinement","None",options.IterRefine == NOREFINE ? PETSC_FALSE : PETSC_TRUE ,&flg,&set);
623: if (set) {
624: if (flg) options.IterRefine = SLU_DOUBLE;
625: else options.IterRefine = NOREFINE;
626: }
628: if (PetscLogPrintInfo) options.PrintStat = YES;
629: else options.PrintStat = NO;
630: PetscOptionsBool("-mat_superlu_dist_statprint","Print factorization information","None",(PetscBool)options.PrintStat,(PetscBool*)&options.PrintStat,NULL);
631: PetscOptionsEnd();
633: lu->options = options;
634: lu->options.Fact = DOFACT;
635: lu->matsolve_iscalled = PETSC_FALSE;
636: lu->matmatsolve_iscalled = PETSC_FALSE;
638: *F = B;
639: return(0);
640: }
644: PETSC_EXTERN PetscErrorCode MatSolverPackageRegister_SuperLU_DIST(void)
645: {
648: MatSolverPackageRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ, MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
649: MatSolverPackageRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ, MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
650: return(0);
651: }
655: PetscErrorCode MatFactorInfo_SuperLU_DIST(Mat A,PetscViewer viewer)
656: {
657: Mat_SuperLU_DIST *lu=(Mat_SuperLU_DIST*)A->spptr;
658: superlu_options_t options;
659: PetscErrorCode ierr;
662: /* check if matrix is superlu_dist type */
663: if (A->ops->solve != MatSolve_SuperLU_DIST) return(0);
665: options = lu->options;
666: PetscViewerASCIIPrintf(viewer,"SuperLU_DIST run parameters:\n");
667: PetscViewerASCIIPrintf(viewer," Process grid nprow %D x npcol %D \n",lu->nprow,lu->npcol);
668: PetscViewerASCIIPrintf(viewer," Equilibrate matrix %s \n",PetscBools[options.Equil != NO]);
669: PetscViewerASCIIPrintf(viewer," Matrix input mode %d \n",lu->MatInputMode);
670: PetscViewerASCIIPrintf(viewer," Replace tiny pivots %s \n",PetscBools[options.ReplaceTinyPivot != NO]);
671: PetscViewerASCIIPrintf(viewer," Use iterative refinement %s \n",PetscBools[options.IterRefine == SLU_DOUBLE]);
672: PetscViewerASCIIPrintf(viewer," Processors in row %d col partition %d \n",lu->nprow,lu->npcol);
673: PetscViewerASCIIPrintf(viewer," Row permutation %s \n",(options.RowPerm == NOROWPERM) ? "NATURAL" : "LargeDiag");
675: switch (options.ColPerm) {
676: case NATURAL:
677: PetscViewerASCIIPrintf(viewer," Column permutation NATURAL\n");
678: break;
679: case MMD_AT_PLUS_A:
680: PetscViewerASCIIPrintf(viewer," Column permutation MMD_AT_PLUS_A\n");
681: break;
682: case MMD_ATA:
683: PetscViewerASCIIPrintf(viewer," Column permutation MMD_ATA\n");
684: break;
685: case METIS_AT_PLUS_A:
686: PetscViewerASCIIPrintf(viewer," Column permutation METIS_AT_PLUS_A\n");
687: break;
688: case PARMETIS:
689: PetscViewerASCIIPrintf(viewer," Column permutation PARMETIS\n");
690: break;
691: default:
692: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
693: }
695: PetscViewerASCIIPrintf(viewer," Parallel symbolic factorization %s \n",PetscBools[options.ParSymbFact != NO]);
697: if (lu->FactPattern == SamePattern) {
698: PetscViewerASCIIPrintf(viewer," Repeated factorization SamePattern\n");
699: } else {
700: PetscViewerASCIIPrintf(viewer," Repeated factorization SamePattern_SameRowPerm\n");
701: }
702: return(0);
703: }
707: PetscErrorCode MatView_SuperLU_DIST(Mat A,PetscViewer viewer)
708: {
709: PetscErrorCode ierr;
710: PetscBool iascii;
711: PetscViewerFormat format;
714: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
715: if (iascii) {
716: PetscViewerGetFormat(viewer,&format);
717: if (format == PETSC_VIEWER_ASCII_INFO) {
718: MatFactorInfo_SuperLU_DIST(A,viewer);
719: }
720: }
721: return(0);
722: }
725: /*MC
726: MATSOLVERSUPERLU_DIST - Parallel direct solver package for LU factorization
728: Use ./configure --download-superlu_dist --download-parmetis --download-metis --download-ptscotch to have PETSc installed with SuperLU_DIST
730: Use -pc_type lu -pc_factor_mat_solver_package superlu_dist to us this direct solver
732: Works with AIJ matrices
734: Options Database Keys:
735: + -mat_superlu_dist_r <n> - number of rows in processor partition
736: . -mat_superlu_dist_c <n> - number of columns in processor partition
737: . -mat_superlu_dist_matinput <0,1> - matrix input mode; 0=global, 1=distributed
738: . -mat_superlu_dist_equil - equilibrate the matrix
739: . -mat_superlu_dist_rowperm <LargeDiag,NATURAL> - row permutation
740: . -mat_superlu_dist_colperm <MMD_AT_PLUS_A,MMD_ATA,NATURAL> - column permutation
741: . -mat_superlu_dist_replacetinypivot - replace tiny pivots
742: . -mat_superlu_dist_fact <SamePattern> - (choose one of) SamePattern SamePattern_SameRowPerm
743: . -mat_superlu_dist_iterrefine - use iterative refinement
744: - -mat_superlu_dist_statprint - print factorization information
746: Level: beginner
748: .seealso: PCLU
750: .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage
752: M*/