xref: /petsc/src/mat/impls/aij/mpi/superlu_dist/superlu_dist.c (revision 03e6d32953ff6d123393c3df7e15df579e2665b4)
1 
2 /*
3         Provides an interface to the SuperLU_DIST sparse solver
4 */
5 
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
11 
12 #if defined(PETSC_USE_64BIT_INDICES)
13 /* ugly SuperLU_Dist variable telling it to use long long int */
14 #define _LONGINT
15 #endif
16 
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
24 
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};
31 
32 typedef struct {
33   int_t                  nprow,npcol,*row,*col;
34   gridinfo_t             grid;
35   superlu_dist_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;
52 
53 
54 PetscErrorCode MatSuperluDistGetDiagU_SuperLU_DIST(Mat F,PetscScalar *diagU)
55 {
56   Mat_SuperLU_DIST  *lu= (Mat_SuperLU_DIST*)F->data;
57 
58   PetscFunctionBegin;
59 #if defined(PETSC_USE_COMPLEX)
60   PetscStackCall("SuperLU_DIST:pzGetDiagU",pzGetDiagU(F->rmap->N,&lu->LUstruct,&lu->grid,(doublecomplex*)diagU));
61 #else
62   PetscStackCall("SuperLU_DIST:pdGetDiagU",pdGetDiagU(F->rmap->N,&lu->LUstruct,&lu->grid,diagU));
63 #endif
64   PetscFunctionReturn(0);
65 }
66 
67 PetscErrorCode MatSuperluDistGetDiagU(Mat F,PetscScalar *diagU)
68 {
69   PetscErrorCode    ierr;
70 
71   PetscFunctionBegin;
72   PetscValidHeaderSpecific(F,MAT_CLASSID,1);
73   ierr = PetscTryMethod(F,"MatSuperluDistGetDiagU_C",(Mat,PetscScalar*),(F,diagU));CHKERRQ(ierr);
74   PetscFunctionReturn(0);
75 }
76 
77 static PetscErrorCode MatDestroy_SuperLU_DIST(Mat A)
78 {
79   PetscErrorCode   ierr;
80   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
81 
82   PetscFunctionBegin;
83   if (lu->CleanUpSuperLU_Dist) {
84     /* Deallocate SuperLU_DIST storage */
85     if (lu->MatInputMode == GLOBAL) {
86       PetscStackCall("SuperLU_DIST:Destroy_CompCol_Matrix_dist",Destroy_CompCol_Matrix_dist(&lu->A_sup));
87     } else {
88       PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
89       if (lu->options.SolveInitialized) {
90 #if defined(PETSC_USE_COMPLEX)
91         PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
92 #else
93         PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
94 #endif
95       }
96     }
97     PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->cmap->N, &lu->grid, &lu->LUstruct));
98     PetscStackCall("SuperLU_DIST:ScalePermstructFree",ScalePermstructFree(&lu->ScalePermstruct));
99     PetscStackCall("SuperLU_DIST:LUstructFree",LUstructFree(&lu->LUstruct));
100 
101     /* Release the SuperLU_DIST process grid. */
102     PetscStackCall("SuperLU_DIST:superlu_gridexit",superlu_gridexit(&lu->grid));
103     ierr = MPI_Comm_free(&(lu->comm_superlu));CHKERRQ(ierr);
104   }
105   ierr = PetscFree(A->data);CHKERRQ(ierr);
106   /* clear composed functions */
107   ierr = PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverPackage_C",NULL);CHKERRQ(ierr);
108   ierr = PetscObjectComposeFunction((PetscObject)A,"MatSuperluDistGetDiagU_C",NULL);CHKERRQ(ierr);
109 
110   PetscFunctionReturn(0);
111 }
112 
113 static PetscErrorCode MatSolve_SuperLU_DIST(Mat A,Vec b_mpi,Vec x)
114 {
115   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
116   PetscErrorCode   ierr;
117   PetscMPIInt      size;
118   PetscInt         m=A->rmap->n,M=A->rmap->N,N=A->cmap->N;
119   SuperLUStat_t    stat;
120   double           berr[1];
121   PetscScalar      *bptr;
122   PetscInt         nrhs=1;
123   Vec              x_seq;
124   IS               iden;
125   VecScatter       scat;
126   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
127   static PetscBool cite = PETSC_FALSE;
128 
129   PetscFunctionBegin;
130   ierr = 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);CHKERRQ(ierr);
131   ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);CHKERRQ(ierr);
132   if (size > 1 && lu->MatInputMode == GLOBAL) {
133     /* global mat input, convert b to x_seq */
134     ierr = VecCreateSeq(PETSC_COMM_SELF,N,&x_seq);CHKERRQ(ierr);
135     ierr = ISCreateStride(PETSC_COMM_SELF,N,0,1,&iden);CHKERRQ(ierr);
136     ierr = VecScatterCreate(b_mpi,iden,x_seq,iden,&scat);CHKERRQ(ierr);
137     ierr = ISDestroy(&iden);CHKERRQ(ierr);
138 
139     ierr = VecScatterBegin(scat,b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr);
140     ierr = VecScatterEnd(scat,b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr);
141     ierr = VecGetArray(x_seq,&bptr);CHKERRQ(ierr);
142   } else { /* size==1 || distributed mat input */
143     if (lu->options.SolveInitialized && !lu->matsolve_iscalled) {
144       /* see comments in MatMatSolve() */
145 #if defined(PETSC_USE_COMPLEX)
146       PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
147 #else
148       PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
149 #endif
150       lu->options.SolveInitialized = NO;
151     }
152     ierr = VecCopy(b_mpi,x);CHKERRQ(ierr);
153     ierr = VecGetArray(x,&bptr);CHKERRQ(ierr);
154   }
155 
156   if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact mush equal FACTORED");
157 
158   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));        /* Initialize the statistics variables. */
159   if (lu->MatInputMode == GLOBAL) {
160 #if defined(PETSC_USE_COMPLEX)
161     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));
162 #else
163     PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr,M,nrhs,&lu->grid,&lu->LUstruct,berr,&stat,&info));
164 #endif
165   } else { /* distributed mat input */
166 #if defined(PETSC_USE_COMPLEX)
167     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));
168 #else
169     PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
170 #endif
171   }
172   if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
173 
174   if (lu->options.PrintStat) PStatPrint(&lu->options, &stat, &lu->grid);      /* Print the statistics. */
175   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
176 
177   if (size > 1 && lu->MatInputMode == GLOBAL) {
178     /* convert seq x to mpi x */
179     ierr = VecRestoreArray(x_seq,&bptr);CHKERRQ(ierr);
180     ierr = VecScatterBegin(scat,x_seq,x,INSERT_VALUES,SCATTER_REVERSE);CHKERRQ(ierr);
181     ierr = VecScatterEnd(scat,x_seq,x,INSERT_VALUES,SCATTER_REVERSE);CHKERRQ(ierr);
182     ierr = VecScatterDestroy(&scat);CHKERRQ(ierr);
183     ierr = VecDestroy(&x_seq);CHKERRQ(ierr);
184   } else {
185     ierr = VecRestoreArray(x,&bptr);CHKERRQ(ierr);
186 
187     lu->matsolve_iscalled    = PETSC_TRUE;
188     lu->matmatsolve_iscalled = PETSC_FALSE;
189   }
190   PetscFunctionReturn(0);
191 }
192 
193 static PetscErrorCode MatMatSolve_SuperLU_DIST(Mat A,Mat B_mpi,Mat X)
194 {
195   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
196   PetscErrorCode   ierr;
197   PetscMPIInt      size;
198   PetscInt         M=A->rmap->N,m=A->rmap->n,nrhs;
199   SuperLUStat_t    stat;
200   double           berr[1];
201   PetscScalar      *bptr;
202   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
203   PetscBool        flg;
204 
205   PetscFunctionBegin;
206   if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact mush equal FACTORED");
207   ierr = PetscObjectTypeCompareAny((PetscObject)B_mpi,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr);
208   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
209   ierr = PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);CHKERRQ(ierr);
210   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
211 
212   ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);CHKERRQ(ierr);
213   if (size > 1 && lu->MatInputMode == GLOBAL) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatInputMode=GLOBAL for nproc %d>1 is not supported",size);
214   /* size==1 or distributed mat input */
215   if (lu->options.SolveInitialized && !lu->matmatsolve_iscalled) {
216     /* communication pattern of SOLVEstruct is unlikely created for matmatsolve,
217        thus destroy it and create a new SOLVEstruct.
218        Otherwise it may result in memory corruption or incorrect solution
219        See src/mat/examples/tests/ex125.c */
220 #if defined(PETSC_USE_COMPLEX)
221     PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
222 #else
223     PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
224 #endif
225     lu->options.SolveInitialized = NO;
226   }
227   ierr = MatCopy(B_mpi,X,SAME_NONZERO_PATTERN);CHKERRQ(ierr);
228 
229   ierr = MatGetSize(B_mpi,NULL,&nrhs);CHKERRQ(ierr);
230 
231   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));        /* Initialize the statistics variables. */
232   ierr = MatDenseGetArray(X,&bptr);CHKERRQ(ierr);
233   if (lu->MatInputMode == GLOBAL) { /* size == 1 */
234 #if defined(PETSC_USE_COMPLEX)
235     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));
236 #else
237     PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr, M, nrhs, &lu->grid, &lu->LUstruct, berr, &stat, &info));
238 #endif
239   } else { /* distributed mat input */
240 #if defined(PETSC_USE_COMPLEX)
241     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));
242 #else
243     PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
244 #endif
245   }
246   if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
247   ierr = MatDenseRestoreArray(X,&bptr);CHKERRQ(ierr);
248 
249   if (lu->options.PrintStat) PStatPrint(&lu->options, &stat, &lu->grid); /* Print the statistics. */
250   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
251   lu->matsolve_iscalled    = PETSC_FALSE;
252   lu->matmatsolve_iscalled = PETSC_TRUE;
253   PetscFunctionReturn(0);
254 }
255 
256 
257 static PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat F,Mat A,const MatFactorInfo *info)
258 {
259   Mat              *tseq,A_seq = NULL;
260   Mat_SeqAIJ       *aa,*bb;
261   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)(F)->data;
262   PetscErrorCode   ierr;
263   PetscInt         M=A->rmap->N,N=A->cmap->N,i,*ai,*aj,*bi,*bj,nz,rstart,*garray,
264                    m=A->rmap->n, colA_start,j,jcol,jB,countA,countB,*bjj,*ajj;
265   int              sinfo;   /* SuperLU_Dist info flag is always an int even with long long indices */
266   PetscMPIInt      size;
267   SuperLUStat_t    stat;
268   double           *berr=0;
269   IS               isrow;
270 #if defined(PETSC_USE_COMPLEX)
271   doublecomplex    *av, *bv;
272 #else
273   double           *av, *bv;
274 #endif
275 
276   PetscFunctionBegin;
277   ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);CHKERRQ(ierr);
278 
279   if (lu->MatInputMode == GLOBAL) { /* global mat input */
280     if (size > 1) { /* convert mpi A to seq mat A */
281       ierr = ISCreateStride(PETSC_COMM_SELF,M,0,1,&isrow);CHKERRQ(ierr);
282       ierr = MatGetSubMatrices(A,1,&isrow,&isrow,MAT_INITIAL_MATRIX,&tseq);CHKERRQ(ierr);
283       ierr = ISDestroy(&isrow);CHKERRQ(ierr);
284 
285       A_seq = *tseq;
286       ierr  = PetscFree(tseq);CHKERRQ(ierr);
287       aa    = (Mat_SeqAIJ*)A_seq->data;
288     } else {
289       PetscBool flg;
290       ierr = PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&flg);CHKERRQ(ierr);
291       if (flg) {
292         Mat_MPIAIJ *At = (Mat_MPIAIJ*)A->data;
293         A = At->A;
294       }
295       aa =  (Mat_SeqAIJ*)A->data;
296     }
297 
298     /* Convert Petsc NR matrix to SuperLU_DIST NC.
299        Note: memories of lu->val, col and row are allocated by CompRow_to_CompCol_dist()! */
300     if (lu->options.Fact != DOFACT) {/* successive numeric factorization, sparsity pattern is reused. */
301       PetscStackCall("SuperLU_DIST:Destroy_CompCol_Matrix_dist",Destroy_CompCol_Matrix_dist(&lu->A_sup));
302       if (lu->FactPattern == SamePattern_SameRowPerm) {
303         lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
304       } else { /* lu->FactPattern == SamePattern */
305         PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(N, &lu->grid, &lu->LUstruct));
306         lu->options.Fact = SamePattern;
307       }
308     }
309 #if defined(PETSC_USE_COMPLEX)
310     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));
311 #else
312     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));
313 #endif
314 
315     /* Create compressed column matrix A_sup. */
316 #if defined(PETSC_USE_COMPLEX)
317     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));
318 #else
319     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));
320 #endif
321   } else { /* distributed mat input */
322     Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
323     aa=(Mat_SeqAIJ*)(mat->A)->data;
324     bb=(Mat_SeqAIJ*)(mat->B)->data;
325     ai=aa->i; aj=aa->j;
326     bi=bb->i; bj=bb->j;
327 #if defined(PETSC_USE_COMPLEX)
328     av=(doublecomplex*)aa->a;
329     bv=(doublecomplex*)bb->a;
330 #else
331     av=aa->a;
332     bv=bb->a;
333 #endif
334     rstart = A->rmap->rstart;
335     nz     = aa->nz + bb->nz;
336     garray = mat->garray;
337 
338     if (lu->options.Fact == DOFACT) { /* first numeric factorization */
339 #if defined(PETSC_USE_COMPLEX)
340       PetscStackCall("SuperLU_DIST:zallocateA_dist",zallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row));
341 #else
342       PetscStackCall("SuperLU_DIST:dallocateA_dist",dallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row));
343 #endif
344     } else { /* successive numeric factorization, sparsity pattern and perm_c are reused. */
345       if (lu->FactPattern == SamePattern_SameRowPerm) {
346         lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
347       } else if (lu->FactPattern == SamePattern) {
348         PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(N, &lu->grid, &lu->LUstruct)); /* Deallocate storage associated with the L and U matrices. */
349         lu->options.Fact = SamePattern;
350       } else {
351         SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"options.Fact must be one of SamePattern SamePattern_SameRowPerm");
352       }
353     }
354     nz = 0;
355     for (i=0; i<m; i++) {
356       lu->row[i] = nz;
357       countA     = ai[i+1] - ai[i];
358       countB     = bi[i+1] - bi[i];
359       if (aj) {
360         ajj = aj + ai[i]; /* ptr to the beginning of this row */
361       } else {
362         ajj = NULL;
363       }
364       bjj = bj + bi[i];
365 
366       /* B part, smaller col index */
367       if (aj) {
368         colA_start = rstart + ajj[0]; /* the smallest global col index of A */
369       } else { /* superlu_dist does not require matrix has diagonal entries, thus aj=NULL would work */
370         colA_start = rstart;
371       }
372       jB         = 0;
373       for (j=0; j<countB; j++) {
374         jcol = garray[bjj[j]];
375         if (jcol > colA_start) {
376           jB = j;
377           break;
378         }
379         lu->col[nz]   = jcol;
380         lu->val[nz++] = *bv++;
381         if (j==countB-1) jB = countB;
382       }
383 
384       /* A part */
385       for (j=0; j<countA; j++) {
386         lu->col[nz]   = rstart + ajj[j];
387         lu->val[nz++] = *av++;
388       }
389 
390       /* B part, larger col index */
391       for (j=jB; j<countB; j++) {
392         lu->col[nz]   = garray[bjj[j]];
393         lu->val[nz++] = *bv++;
394       }
395     }
396     lu->row[m] = nz;
397 
398     if (lu->options.Fact == DOFACT) {
399 #if defined(PETSC_USE_COMPLEX)
400       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));
401 #else
402       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));
403 #endif
404     }
405   }
406 
407   /* Factor the matrix. */
408   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));   /* Initialize the statistics variables. */
409   if (lu->MatInputMode == GLOBAL) { /* global mat input */
410 #if defined(PETSC_USE_COMPLEX)
411     PetscStackCall("SuperLU_DIST:pzgssvx_ABglobal",pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,&lu->grid, &lu->LUstruct, berr, &stat, &sinfo));
412 #else
413     PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,&lu->grid, &lu->LUstruct, berr, &stat, &sinfo));
414 #endif
415   } else { /* distributed mat input */
416 #if defined(PETSC_USE_COMPLEX)
417     PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, m, 0, &lu->grid,&lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
418 #else
419     PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, m, 0, &lu->grid,&lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
420 #endif
421   }
422 
423   if (sinfo > 0) {
424     if (A->erroriffailure) {
425       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot in row %D",sinfo);
426     } else {
427       if (sinfo <= lu->A_sup.ncol) {
428         F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
429         ierr = PetscInfo1(F,"U(i,i) is exactly zero, i= %D\n",sinfo);CHKERRQ(ierr);
430       } else if (sinfo > lu->A_sup.ncol) {
431         /*
432          number of bytes allocated when memory allocation
433          failure occurred, plus A->ncol.
434          */
435         F->factorerrortype = MAT_FACTOR_OUTMEMORY;
436         ierr = PetscInfo1(F,"Number of bytes allocated when memory allocation fails %D\n",sinfo);CHKERRQ(ierr);
437       }
438     }
439   } else if (sinfo < 0) {
440     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB, "info = %D, argument in p*gssvx() had an illegal value", sinfo);
441   }
442 
443   if (lu->MatInputMode == GLOBAL && size > 1) {
444     ierr = MatDestroy(&A_seq);CHKERRQ(ierr);
445   }
446 
447   if (lu->options.PrintStat) {
448     PStatPrint(&lu->options, &stat, &lu->grid);  /* Print the statistics. */
449   }
450   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
451   (F)->assembled    = PETSC_TRUE;
452   (F)->preallocated = PETSC_TRUE;
453   lu->options.Fact  = FACTORED; /* The factored form of A is supplied. Local option used by this func. only */
454   PetscFunctionReturn(0);
455 }
456 
457 /* Note the Petsc r and c permutations are ignored */
458 static PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
459 {
460   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;
461   PetscInt         M   = A->rmap->N,N=A->cmap->N;
462 
463   PetscFunctionBegin;
464   /* Initialize the SuperLU process grid. */
465   PetscStackCall("SuperLU_DIST:superlu_gridinit",superlu_gridinit(lu->comm_superlu, lu->nprow, lu->npcol, &lu->grid));
466 
467   /* Initialize ScalePermstruct and LUstruct. */
468   PetscStackCall("SuperLU_DIST:ScalePermstructInit",ScalePermstructInit(M, N, &lu->ScalePermstruct));
469   PetscStackCall("SuperLU_DIST:LUstructInit",LUstructInit(N, &lu->LUstruct));
470   F->ops->lufactornumeric = MatLUFactorNumeric_SuperLU_DIST;
471   F->ops->solve           = MatSolve_SuperLU_DIST;
472   F->ops->matsolve        = MatMatSolve_SuperLU_DIST;
473   lu->CleanUpSuperLU_Dist = PETSC_TRUE;
474   PetscFunctionReturn(0);
475 }
476 
477 static PetscErrorCode MatFactorGetSolverPackage_aij_superlu_dist(Mat A,const MatSolverPackage *type)
478 {
479   PetscFunctionBegin;
480   *type = MATSOLVERSUPERLU_DIST;
481   PetscFunctionReturn(0);
482 }
483 
484 static PetscErrorCode MatFactorInfo_SuperLU_DIST(Mat A,PetscViewer viewer)
485 {
486   Mat_SuperLU_DIST       *lu=(Mat_SuperLU_DIST*)A->data;
487   superlu_dist_options_t options;
488   PetscErrorCode         ierr;
489 
490   PetscFunctionBegin;
491   /* check if matrix is superlu_dist type */
492   if (A->ops->solve != MatSolve_SuperLU_DIST) PetscFunctionReturn(0);
493 
494   options = lu->options;
495   ierr    = PetscViewerASCIIPrintf(viewer,"SuperLU_DIST run parameters:\n");CHKERRQ(ierr);
496   ierr    = PetscViewerASCIIPrintf(viewer,"  Process grid nprow %D x npcol %D \n",lu->nprow,lu->npcol);CHKERRQ(ierr);
497   ierr    = PetscViewerASCIIPrintf(viewer,"  Equilibrate matrix %s \n",PetscBools[options.Equil != NO]);CHKERRQ(ierr);
498   ierr    = PetscViewerASCIIPrintf(viewer,"  Matrix input mode %d \n",lu->MatInputMode);CHKERRQ(ierr);
499   ierr    = PetscViewerASCIIPrintf(viewer,"  Replace tiny pivots %s \n",PetscBools[options.ReplaceTinyPivot != NO]);CHKERRQ(ierr);
500   ierr    = PetscViewerASCIIPrintf(viewer,"  Use iterative refinement %s \n",PetscBools[options.IterRefine == SLU_DOUBLE]);CHKERRQ(ierr);
501   ierr    = PetscViewerASCIIPrintf(viewer,"  Processors in row %d col partition %d \n",lu->nprow,lu->npcol);CHKERRQ(ierr);
502   ierr    = PetscViewerASCIIPrintf(viewer,"  Row permutation %s \n",(options.RowPerm == NOROWPERM) ? "NATURAL" : "LargeDiag");CHKERRQ(ierr);
503 
504   switch (options.ColPerm) {
505   case NATURAL:
506     ierr = PetscViewerASCIIPrintf(viewer,"  Column permutation NATURAL\n");CHKERRQ(ierr);
507     break;
508   case MMD_AT_PLUS_A:
509     ierr = PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_AT_PLUS_A\n");CHKERRQ(ierr);
510     break;
511   case MMD_ATA:
512     ierr = PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_ATA\n");CHKERRQ(ierr);
513     break;
514   case METIS_AT_PLUS_A:
515     ierr = PetscViewerASCIIPrintf(viewer,"  Column permutation METIS_AT_PLUS_A\n");CHKERRQ(ierr);
516     break;
517   case PARMETIS:
518     ierr = PetscViewerASCIIPrintf(viewer,"  Column permutation PARMETIS\n");CHKERRQ(ierr);
519     break;
520   default:
521     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
522   }
523 
524   ierr = PetscViewerASCIIPrintf(viewer,"  Parallel symbolic factorization %s \n",PetscBools[options.ParSymbFact != NO]);CHKERRQ(ierr);
525 
526   if (lu->FactPattern == SamePattern) {
527     ierr = PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern\n");CHKERRQ(ierr);
528   } else {
529     ierr = PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern_SameRowPerm\n");CHKERRQ(ierr);
530   }
531   PetscFunctionReturn(0);
532 }
533 
534 static PetscErrorCode MatView_SuperLU_DIST(Mat A,PetscViewer viewer)
535 {
536   PetscErrorCode    ierr;
537   PetscBool         iascii;
538   PetscViewerFormat format;
539 
540   PetscFunctionBegin;
541   ierr = PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);CHKERRQ(ierr);
542   if (iascii) {
543     ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr);
544     if (format == PETSC_VIEWER_ASCII_INFO) {
545       ierr = MatFactorInfo_SuperLU_DIST(A,viewer);CHKERRQ(ierr);
546     }
547   }
548   PetscFunctionReturn(0);
549 }
550 
551 static PetscErrorCode MatGetFactor_aij_superlu_dist(Mat A,MatFactorType ftype,Mat *F)
552 {
553   Mat                    B;
554   Mat_SuperLU_DIST       *lu;
555   PetscErrorCode         ierr;
556   PetscInt               M=A->rmap->N,N=A->cmap->N,indx;
557   PetscMPIInt            size;
558   superlu_dist_options_t options;
559   PetscBool              flg;
560   const char             *colperm[]     = {"NATURAL","MMD_AT_PLUS_A","MMD_ATA","METIS_AT_PLUS_A","PARMETIS"};
561   const char             *rowperm[]     = {"LargeDiag","NATURAL"};
562   const char             *factPattern[] = {"SamePattern","SamePattern_SameRowPerm"};
563   PetscBool              set;
564 
565   PetscFunctionBegin;
566   /* Create the factorization matrix */
567   ierr = MatCreate(PetscObjectComm((PetscObject)A),&B);CHKERRQ(ierr);
568   ierr = MatSetSizes(B,A->rmap->n,A->cmap->n,M,N);CHKERRQ(ierr);
569   ierr = PetscStrallocpy("superlu_dist",&((PetscObject)B)->type_name);CHKERRQ(ierr);
570   ierr = MatSetUp(B);CHKERRQ(ierr);
571   B->ops->getinfo          = MatGetInfo_External;
572   B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
573   B->ops->view             = MatView_SuperLU_DIST;
574   B->ops->destroy          = MatDestroy_SuperLU_DIST;
575 
576   B->factortype = MAT_FACTOR_LU;
577 
578   /* set solvertype */
579   ierr = PetscFree(B->solvertype);CHKERRQ(ierr);
580   ierr = PetscStrallocpy(MATSOLVERSUPERLU_DIST,&B->solvertype);CHKERRQ(ierr);
581 
582   ierr     = PetscNewLog(B,&lu);CHKERRQ(ierr);
583   B->data = lu;
584 
585   /* Set the default input options:
586      options.Fact              = DOFACT;
587      options.Equil             = YES;
588      options.ParSymbFact       = NO;
589      options.ColPerm           = METIS_AT_PLUS_A;
590      options.RowPerm           = LargeDiag;
591      options.ReplaceTinyPivot  = YES;
592      options.IterRefine        = DOUBLE;
593      options.Trans             = NOTRANS;
594      options.SolveInitialized  = NO; -hold the communication pattern used MatSolve() and MatMatSolve()
595      options.RefineInitialized = NO;
596      options.PrintStat         = YES;
597   */
598   set_default_options_dist(&options);
599 
600   ierr = MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(lu->comm_superlu));CHKERRQ(ierr);
601   ierr = MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);CHKERRQ(ierr);
602   /* Default num of process columns and rows */
603   lu->npcol = (int_t) (0.5 + PetscSqrtReal((PetscReal)size));
604   if (!lu->npcol) lu->npcol = 1;
605   while (lu->npcol > 0) {
606     lu->nprow = (int_t) (size/lu->npcol);
607     if (size == lu->nprow * lu->npcol) break;
608     lu->npcol--;
609   }
610 
611   ierr = PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"SuperLU_Dist Options","Mat");CHKERRQ(ierr);
612   ierr = PetscOptionsInt("-mat_superlu_dist_r","Number rows in processor partition","None",lu->nprow,(PetscInt*)&lu->nprow,NULL);CHKERRQ(ierr);
613   ierr = PetscOptionsInt("-mat_superlu_dist_c","Number columns in processor partition","None",lu->npcol,(PetscInt*)&lu->npcol,NULL);CHKERRQ(ierr);
614   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);
615 
616   lu->MatInputMode = DISTRIBUTED;
617 
618   ierr = PetscOptionsEnum("-mat_superlu_dist_matinput","Matrix input mode (global or distributed)","None",SuperLU_MatInputModes,(PetscEnum)lu->MatInputMode,(PetscEnum*)&lu->MatInputMode,NULL);CHKERRQ(ierr);
619   if (lu->MatInputMode == DISTRIBUTED && size == 1) lu->MatInputMode = GLOBAL;
620 
621   ierr = PetscOptionsBool("-mat_superlu_dist_equil","Equilibrate matrix","None",options.Equil ? PETSC_TRUE : PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
622   if (set && !flg) options.Equil = NO;
623 
624   ierr = PetscOptionsEList("-mat_superlu_dist_rowperm","Row permutation","None",rowperm,2,rowperm[0],&indx,&flg);CHKERRQ(ierr);
625   if (flg) {
626     switch (indx) {
627     case 0:
628       options.RowPerm = LargeDiag;
629       break;
630     case 1:
631       options.RowPerm = NOROWPERM;
632       break;
633     }
634   }
635 
636   ierr = PetscOptionsEList("-mat_superlu_dist_colperm","Column permutation","None",colperm,5,colperm[3],&indx,&flg);CHKERRQ(ierr);
637   if (flg) {
638     switch (indx) {
639     case 0:
640       options.ColPerm = NATURAL;
641       break;
642     case 1:
643       options.ColPerm = MMD_AT_PLUS_A;
644       break;
645     case 2:
646       options.ColPerm = MMD_ATA;
647       break;
648     case 3:
649       options.ColPerm = METIS_AT_PLUS_A;
650       break;
651     case 4:
652       options.ColPerm = PARMETIS;   /* only works for np>1 */
653       break;
654     default:
655       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
656     }
657   }
658 
659   options.ReplaceTinyPivot = NO;
660   ierr = PetscOptionsBool("-mat_superlu_dist_replacetinypivot","Replace tiny pivots","None",options.ReplaceTinyPivot ? PETSC_TRUE : PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
661   if (set && flg) options.ReplaceTinyPivot = YES;
662 
663   options.ParSymbFact = NO;
664   ierr = PetscOptionsBool("-mat_superlu_dist_parsymbfact","Parallel symbolic factorization","None",PETSC_FALSE,&flg,&set);CHKERRQ(ierr);
665   if (set && flg && size>1) {
666     if (lu->MatInputMode == GLOBAL) {
667 #if defined(PETSC_USE_INFO)
668       ierr = PetscInfo(A,"Warning: '-mat_superlu_dist_parsymbfact' is ignored because MatInputMode=GLOBAL\n");CHKERRQ(ierr);
669 #endif
670     } else {
671 #if defined(PETSC_HAVE_PARMETIS)
672       options.ParSymbFact = YES;
673       options.ColPerm     = PARMETIS;   /* in v2.2, PARMETIS is forced for ParSymbFact regardless of user ordering setting */
674 #else
675       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"parsymbfact needs PARMETIS");
676 #endif
677     }
678   }
679 
680   lu->FactPattern = SamePattern_SameRowPerm;
681   ierr = PetscOptionsEList("-mat_superlu_dist_fact","Sparsity pattern for repeated matrix factorization","None",factPattern,2,factPattern[0],&indx,&flg);CHKERRQ(ierr);
682   if (flg) {
683     switch (indx) {
684     case 0:
685       lu->FactPattern = SamePattern;
686       break;
687     case 1:
688       lu->FactPattern = SamePattern_SameRowPerm;
689       break;
690     }
691   }
692 
693   options.IterRefine = NOREFINE;
694   ierr               = PetscOptionsBool("-mat_superlu_dist_iterrefine","Use iterative refinement","None",options.IterRefine == NOREFINE ? PETSC_FALSE : PETSC_TRUE ,&flg,&set);CHKERRQ(ierr);
695   if (set) {
696     if (flg) options.IterRefine = SLU_DOUBLE;
697     else options.IterRefine = NOREFINE;
698   }
699 
700   if (PetscLogPrintInfo) options.PrintStat = YES;
701   else options.PrintStat = NO;
702   ierr = PetscOptionsBool("-mat_superlu_dist_statprint","Print factorization information","None",(PetscBool)options.PrintStat,(PetscBool*)&options.PrintStat,NULL);CHKERRQ(ierr);
703   ierr = PetscOptionsEnd();CHKERRQ(ierr);
704 
705   lu->options              = options;
706   lu->options.Fact         = DOFACT;
707   lu->matsolve_iscalled    = PETSC_FALSE;
708   lu->matmatsolve_iscalled = PETSC_FALSE;
709 
710   ierr = PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_aij_superlu_dist);CHKERRQ(ierr);
711   ierr = PetscObjectComposeFunction((PetscObject)B,"MatSuperluDistGetDiagU_C",MatSuperluDistGetDiagU_SuperLU_DIST);CHKERRQ(ierr);
712 
713   *F = B;
714   PetscFunctionReturn(0);
715 }
716 
717 PETSC_EXTERN PetscErrorCode MatSolverPackageRegister_SuperLU_DIST(void)
718 {
719   PetscErrorCode ierr;
720   PetscFunctionBegin;
721   ierr = MatSolverPackageRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ,  MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);CHKERRQ(ierr);
722   ierr = MatSolverPackageRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ,  MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);CHKERRQ(ierr);
723   PetscFunctionReturn(0);
724 }
725 
726 /*MC
727   MATSOLVERSUPERLU_DIST - Parallel direct solver package for LU factorization
728 
729   Use ./configure --download-superlu_dist --download-parmetis --download-metis --download-ptscotch  to have PETSc installed with SuperLU_DIST
730 
731   Use -pc_type lu -pc_factor_mat_solver_package superlu_dist to us this direct solver
732 
733    Works with AIJ matrices
734 
735   Options Database Keys:
736 + -mat_superlu_dist_r <n> - number of rows in processor partition
737 . -mat_superlu_dist_c <n> - number of columns in processor partition
738 . -mat_superlu_dist_matinput <0,1> - matrix input mode; 0=global, 1=distributed
739 . -mat_superlu_dist_equil - equilibrate the matrix
740 . -mat_superlu_dist_rowperm <LargeDiag,NATURAL> - row permutation
741 . -mat_superlu_dist_colperm <MMD_AT_PLUS_A,MMD_ATA,NATURAL> - column permutation
742 . -mat_superlu_dist_replacetinypivot - replace tiny pivots
743 . -mat_superlu_dist_fact <SamePattern> - (choose one of) SamePattern SamePattern_SameRowPerm
744 . -mat_superlu_dist_iterrefine - use iterative refinement
745 - -mat_superlu_dist_statprint - print factorization information
746 
747    Level: beginner
748 
749 .seealso: PCLU
750 
751 .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage
752 
753 M*/
754 
755