Actual source code: superlu_dist.c

petsc-master 2020-12-02
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  1: /*
  2:         Provides an interface to the SuperLU_DIST sparse solver
  3: */

  5: #include <../src/mat/impls/aij/seq/aij.h>
  6: #include <../src/mat/impls/aij/mpi/mpiaij.h>
  7: #include <petscpkg_version.h>

  9: EXTERN_C_BEGIN
 10: #if defined(PETSC_USE_COMPLEX)
 11: #include <superlu_zdefs.h>
 12: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(6,3,0)
 13: #define LUstructInit zLUstructInit
 14: #define ScalePermstructInit zScalePermstructInit
 15: #define ScalePermstructFree zScalePermstructFree
 16: #define LUstructFree zLUstructFree
 17: #define Destroy_LU zDestroy_LU
 18: #define ScalePermstruct_t zScalePermstruct_t
 19: #define LUstruct_t zLUstruct_t
 20: #define SOLVEstruct_t zSOLVEstruct_t
 21: #endif
 22: #else
 23: #include <superlu_ddefs.h>
 24: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(6,3,0)
 25: #define LUstructInit dLUstructInit
 26: #define ScalePermstructInit dScalePermstructInit
 27: #define ScalePermstructFree dScalePermstructFree
 28: #define LUstructFree dLUstructFree
 29: #define Destroy_LU dDestroy_LU
 30: #define ScalePermstruct_t dScalePermstruct_t
 31: #define LUstruct_t dLUstruct_t
 32: #define SOLVEstruct_t dSOLVEstruct_t
 33: #endif
 34: #endif
 35: EXTERN_C_END

 37: typedef struct {
 38:   int_t                  nprow,npcol,*row,*col;
 39:   gridinfo_t             grid;
 40:   superlu_dist_options_t options;
 41:   SuperMatrix            A_sup;
 42:   ScalePermstruct_t      ScalePermstruct;
 43:   LUstruct_t             LUstruct;
 44:   int                    StatPrint;
 45:   SOLVEstruct_t          SOLVEstruct;
 46:   fact_t                 FactPattern;
 47:   MPI_Comm               comm_superlu;
 48: #if defined(PETSC_USE_COMPLEX)
 49:   doublecomplex          *val;
 50: #else
 51:   double                 *val;
 52: #endif
 53:   PetscBool              matsolve_iscalled,matmatsolve_iscalled;
 54:   PetscBool              CleanUpSuperLU_Dist;  /* Flag to clean up (non-global) SuperLU objects during Destroy */
 55: } Mat_SuperLU_DIST;


 58: PetscErrorCode MatSuperluDistGetDiagU_SuperLU_DIST(Mat F,PetscScalar *diagU)
 59: {
 60:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;

 63: #if defined(PETSC_USE_COMPLEX)
 64:   PetscStackCall("SuperLU_DIST:pzGetDiagU",pzGetDiagU(F->rmap->N,&lu->LUstruct,&lu->grid,(doublecomplex*)diagU));
 65: #else
 66:   PetscStackCall("SuperLU_DIST:pdGetDiagU",pdGetDiagU(F->rmap->N,&lu->LUstruct,&lu->grid,diagU));
 67: #endif
 68:   return(0);
 69: }

 71: PetscErrorCode MatSuperluDistGetDiagU(Mat F,PetscScalar *diagU)
 72: {

 77:   PetscTryMethod(F,"MatSuperluDistGetDiagU_C",(Mat,PetscScalar*),(F,diagU));
 78:   return(0);
 79: }

 81: /*  This allows reusing the Superlu_DIST communicator and grid when only a single SuperLU_DIST matrix is used at a time */
 82: typedef struct {
 83:   MPI_Comm   comm;
 84:   PetscBool  busy;
 85:   gridinfo_t grid;
 86: } PetscSuperLU_DIST;
 87: static PetscMPIInt Petsc_Superlu_dist_keyval = MPI_KEYVAL_INVALID;

 89: PETSC_EXTERN PetscMPIInt MPIAPI Petsc_Superlu_dist_keyval_Delete_Fn(MPI_Comm comm,PetscMPIInt keyval,void *attr_val,void *extra_state)
 90: {
 91:   PetscErrorCode    ierr;
 92:   PetscSuperLU_DIST *context = (PetscSuperLU_DIST *) attr_val;

 95:   if (keyval != Petsc_Superlu_dist_keyval) SETERRMPI(PETSC_COMM_SELF,PETSC_ERR_ARG_CORRUPT,"Unexpected keyval");
 96:   PetscInfo(NULL,"Removing Petsc_Superlu_dist_keyval attribute from communicator that is being freed\n");
 97:   PetscStackCall("SuperLU_DIST:superlu_gridexit",superlu_gridexit(&context->grid));
 98:   MPI_Comm_free(&context->comm);
 99:   PetscFree(context);
100:   PetscFunctionReturn(MPI_SUCCESS);
101: }

103: static PetscErrorCode Petsc_Superlu_dist_keyval_free(void)
104: {

108:   PetscInfo(NULL,"Freeing Petsc_Superlu_dist_keyval\n");
109:   MPI_Comm_free_keyval(&Petsc_Superlu_dist_keyval);
110:   return(0);
111: }

113: static PetscErrorCode MatDestroy_SuperLU_DIST(Mat A)
114: {
115:   PetscErrorCode   ierr;
116:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;

119:   if (lu->CleanUpSuperLU_Dist) {
120:     /* Deallocate SuperLU_DIST storage */
121:     PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
122:     if (lu->options.SolveInitialized) {
123: #if defined(PETSC_USE_COMPLEX)
124:       PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
125: #else
126:       PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
127: #endif
128:     }
129:     PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->cmap->N, &lu->grid, &lu->LUstruct));
130:     PetscStackCall("SuperLU_DIST:ScalePermstructFree",ScalePermstructFree(&lu->ScalePermstruct));
131:     PetscStackCall("SuperLU_DIST:LUstructFree",LUstructFree(&lu->LUstruct));

133:     /* Release the SuperLU_DIST process grid. Only if the matrix has its own copy, this is it is not in the communicator context */
134:     if (lu->comm_superlu) {
135:       PetscStackCall("SuperLU_DIST:superlu_gridexit",superlu_gridexit(&lu->grid));
136:       MPI_Comm_free(&(lu->comm_superlu));
137:     } else {
138:       PetscSuperLU_DIST *context;
139:       MPI_Comm          comm;
140:       PetscMPIInt       flg;

142:       PetscObjectGetComm((PetscObject)A,&comm);
143:       MPI_Comm_get_attr(comm,Petsc_Superlu_dist_keyval,&context,&flg);
144:       if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Communicator does not have expected Petsc_Superlu_dist_keyval attribute");
145:       context->busy = PETSC_FALSE;
146:     }
147:   }
148:   PetscFree(A->data);
149:   /* clear composed functions */
150:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
151:   PetscObjectComposeFunction((PetscObject)A,"MatSuperluDistGetDiagU_C",NULL);

153:   return(0);
154: }

156: static PetscErrorCode MatSolve_SuperLU_DIST(Mat A,Vec b_mpi,Vec x)
157: {
158:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
159:   PetscErrorCode   ierr;
160:   PetscInt         m=A->rmap->n;
161:   SuperLUStat_t    stat;
162:   double           berr[1];
163:   PetscScalar      *bptr=NULL;
164:   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
165:   static PetscBool cite = PETSC_FALSE;

168:   if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact must equal FACTORED");
169:   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);

171:   if (lu->options.SolveInitialized && !lu->matsolve_iscalled) {
172:     /* see comments in MatMatSolve() */
173: #if defined(PETSC_USE_COMPLEX)
174:     PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
175: #else
176:     PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
177: #endif
178:     lu->options.SolveInitialized = NO;
179:   }
180:   VecCopy(b_mpi,x);
181:   VecGetArray(x,&bptr);

183:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));        /* Initialize the statistics variables. */
184: #if defined(PETSC_USE_COMPLEX)
185:   PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,(doublecomplex*)bptr,m,1,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
186: #else
187:   PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,1,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
188: #endif
189:   if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);

191:   if (lu->options.PrintStat) PetscStackCall("SuperLU_DIST:PStatPrint",PStatPrint(&lu->options, &stat, &lu->grid));  /* Print the statistics. */
192:   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));

194:   VecRestoreArray(x,&bptr);
195:   lu->matsolve_iscalled    = PETSC_TRUE;
196:   lu->matmatsolve_iscalled = PETSC_FALSE;
197:   return(0);
198: }

200: static PetscErrorCode MatMatSolve_SuperLU_DIST(Mat A,Mat B_mpi,Mat X)
201: {
202:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
203:   PetscErrorCode   ierr;
204:   PetscInt         m=A->rmap->n,nrhs;
205:   SuperLUStat_t    stat;
206:   double           berr[1];
207:   PetscScalar      *bptr;
208:   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
209:   PetscBool        flg;

212:   if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact must equal FACTORED");
213:   PetscObjectTypeCompareAny((PetscObject)B_mpi,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
214:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
215:   if (X != B_mpi) {
216:     PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
217:     if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
218:   }

220:   if (lu->options.SolveInitialized && !lu->matmatsolve_iscalled) {
221:     /* communication pattern of SOLVEstruct is unlikely created for matmatsolve,
222:        thus destroy it and create a new SOLVEstruct.
223:        Otherwise it may result in memory corruption or incorrect solution
224:        See src/mat/tests/ex125.c */
225: #if defined(PETSC_USE_COMPLEX)
226:     PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
227: #else
228:     PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
229: #endif
230:     lu->options.SolveInitialized = NO;
231:   }
232:   if (X != B_mpi) {
233:     MatCopy(B_mpi,X,SAME_NONZERO_PATTERN);
234:   }

236:   MatGetSize(B_mpi,NULL,&nrhs);

238:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));        /* Initialize the statistics variables. */
239:   MatDenseGetArray(X,&bptr);

241: #if defined(PETSC_USE_COMPLEX)
242:   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));
243: #else
244:   PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
245: #endif

247:   if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
248:   MatDenseRestoreArray(X,&bptr);

250:   if (lu->options.PrintStat) PetscStackCall("SuperLU_DIST:PStatPrint",PStatPrint(&lu->options, &stat, &lu->grid));  /* Print the statistics. */
251:   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
252:   lu->matsolve_iscalled    = PETSC_FALSE;
253:   lu->matmatsolve_iscalled = PETSC_TRUE;
254:   return(0);
255: }

257: /*
258:   input:
259:    F:        numeric Cholesky factor
260:   output:
261:    nneg:     total number of negative pivots
262:    nzero:    total number of zero pivots
263:    npos:     (global dimension of F) - nneg - nzero
264: */
265: static PetscErrorCode MatGetInertia_SuperLU_DIST(Mat F,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
266: {
267:   PetscErrorCode   ierr;
268:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;
269:   PetscScalar      *diagU=NULL;
270:   PetscInt         M,i,neg=0,zero=0,pos=0;
271:   PetscReal        r;

274:   if (!F->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Matrix factor F is not assembled");
275:   if (lu->options.RowPerm != NOROWPERM) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Must set NOROWPERM");
276:   MatGetSize(F,&M,NULL);
277:   PetscMalloc1(M,&diagU);
278:   MatSuperluDistGetDiagU(F,diagU);
279:   for (i=0; i<M; i++) {
280: #if defined(PETSC_USE_COMPLEX)
281:     r = PetscImaginaryPart(diagU[i])/10.0;
282:     if (r< -PETSC_MACHINE_EPSILON || r>PETSC_MACHINE_EPSILON) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"diagU[%d]=%g + i %g is non-real",i,PetscRealPart(diagU[i]),r*10.0);
283:     r = PetscRealPart(diagU[i]);
284: #else
285:     r = diagU[i];
286: #endif
287:     if (r > 0) {
288:       pos++;
289:     } else if (r < 0) {
290:       neg++;
291:     } else zero++;
292:   }

294:   PetscFree(diagU);
295:   if (nneg)  *nneg  = neg;
296:   if (nzero) *nzero = zero;
297:   if (npos)  *npos  = pos;
298:   return(0);
299: }

301: static PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat F,Mat A,const MatFactorInfo *info)
302: {
303:   Mat_SuperLU_DIST  *lu = (Mat_SuperLU_DIST*)F->data;
304:   Mat               Aloc;
305:   const PetscScalar *av;
306:   const PetscInt    *ai=NULL,*aj=NULL;
307:   PetscInt          nz,dummy;
308:   int               sinfo;   /* SuperLU_Dist info flag is always an int even with long long indices */
309:   SuperLUStat_t     stat;
310:   double            *berr=0;
311:   PetscBool         ismpiaij,isseqaij,flg;
312:   PetscErrorCode    ierr;

315:   PetscObjectBaseTypeCompare((PetscObject)A,MATSEQAIJ,&isseqaij);
316:   PetscObjectBaseTypeCompare((PetscObject)A,MATMPIAIJ,&ismpiaij);
317:   if (ismpiaij) {
318:     MatMPIAIJGetLocalMat(A,MAT_INITIAL_MATRIX,&Aloc);
319:   } else if (isseqaij) {
320:     PetscObjectReference((PetscObject)A);
321:     Aloc = A;
322:   } else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Not for type %s",((PetscObject)A)->type_name);

324:   MatGetRowIJ(Aloc,0,PETSC_FALSE,PETSC_FALSE,&dummy,&ai,&aj,&flg);
325:   if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"GetRowIJ failed");
326:   MatSeqAIJGetArrayRead(Aloc,&av);
327:   nz   = ai[Aloc->rmap->n];

329:   /* Allocations for A_sup */
330:   if (lu->options.Fact == DOFACT) { /* first numeric factorization */
331: #if defined(PETSC_USE_COMPLEX)
332:     PetscStackCall("SuperLU_DIST:zallocateA_dist",zallocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
333: #else
334:     PetscStackCall("SuperLU_DIST:dallocateA_dist",dallocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
335: #endif
336:   } else { /* successive numeric factorization, sparsity pattern and perm_c are reused. */
337:     if (lu->FactPattern == SamePattern_SameRowPerm) {
338:       lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
339:     } else if (lu->FactPattern == SamePattern) {
340:       PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->rmap->N, &lu->grid, &lu->LUstruct)); /* Deallocate L and U matrices. */
341:       lu->options.Fact = SamePattern;
342:     } else if (lu->FactPattern == DOFACT) {
343:       PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
344:       PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->rmap->N, &lu->grid, &lu->LUstruct));
345:       lu->options.Fact = DOFACT;

347: #if defined(PETSC_USE_COMPLEX)
348:       PetscStackCall("SuperLU_DIST:zallocateA_dist",zallocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
349: #else
350:       PetscStackCall("SuperLU_DIST:dallocateA_dist",dallocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
351: #endif
352:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"options.Fact must be one of SamePattern SamePattern_SameRowPerm DOFACT");
353:   }

355:   /* Copy AIJ matrix to superlu_dist matrix */
356:   PetscArraycpy(lu->row,ai,Aloc->rmap->n+1);
357:   PetscArraycpy(lu->col,aj,nz);
358:   PetscArraycpy(lu->val,av,nz);
359:   MatRestoreRowIJ(Aloc,0,PETSC_FALSE,PETSC_FALSE,&dummy,&ai,&aj,&flg);
360:   if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"RestoreRowIJ failed");
361:   MatSeqAIJRestoreArrayRead(Aloc,&av);
362:   MatDestroy(&Aloc);

364:   /* Create and setup A_sup */
365:   if (lu->options.Fact == DOFACT) {
366: #if defined(PETSC_USE_COMPLEX)
367:     PetscStackCall("SuperLU_DIST:zCreate_CompRowLoc_Matrix_dist",zCreate_CompRowLoc_Matrix_dist(&lu->A_sup, A->rmap->N, A->cmap->N, nz, A->rmap->n, A->rmap->rstart, lu->val, lu->col, lu->row, SLU_NR_loc, SLU_Z, SLU_GE));
368: #else
369:     PetscStackCall("SuperLU_DIST:dCreate_CompRowLoc_Matrix_dist",dCreate_CompRowLoc_Matrix_dist(&lu->A_sup, A->rmap->N, A->cmap->N, nz, A->rmap->n, A->rmap->rstart, lu->val, lu->col, lu->row, SLU_NR_loc, SLU_D, SLU_GE));
370: #endif
371:   }

373:   /* Factor the matrix. */
374:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));   /* Initialize the statistics variables. */
375: #if defined(PETSC_USE_COMPLEX)
376:   PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, A->rmap->n, 0, &lu->grid, &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
377: #else
378:   PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, A->rmap->n, 0, &lu->grid, &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
379: #endif

381:   if (sinfo > 0) {
382:     if (A->erroriffailure) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot in row %D",sinfo);
383:     else {
384:       if (sinfo <= lu->A_sup.ncol) {
385:         F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
386:         PetscInfo1(F,"U(i,i) is exactly zero, i= %D\n",sinfo);
387:       } else if (sinfo > lu->A_sup.ncol) {
388:         /*
389:          number of bytes allocated when memory allocation
390:          failure occurred, plus A->ncol.
391:          */
392:         F->factorerrortype = MAT_FACTOR_OUTMEMORY;
393:         PetscInfo1(F,"Number of bytes allocated when memory allocation fails %D\n",sinfo);
394:       }
395:     }
396:   } else if (sinfo < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB, "info = %D, argument in p*gssvx() had an illegal value", sinfo);

398:   if (lu->options.PrintStat) {
399:     PetscStackCall("SuperLU_DIST:PStatPrint",PStatPrint(&lu->options, &stat, &lu->grid));  /* Print the statistics. */
400:   }
401:   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
402:   F->assembled     = PETSC_TRUE;
403:   F->preallocated  = PETSC_TRUE;
404:   lu->options.Fact = FACTORED; /* The factored form of A is supplied. Local option used by this func. only */
405:   return(0);
406: }

408: /* Note the Petsc r and c permutations are ignored */
409: static PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
410: {
411:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;
412:   PetscInt         M   = A->rmap->N,N=A->cmap->N;

415:   /* Initialize ScalePermstruct and LUstruct. */
416:   PetscStackCall("SuperLU_DIST:ScalePermstructInit",ScalePermstructInit(M, N, &lu->ScalePermstruct));
417:   PetscStackCall("SuperLU_DIST:LUstructInit",LUstructInit(N, &lu->LUstruct));
418:   F->ops->lufactornumeric = MatLUFactorNumeric_SuperLU_DIST;
419:   F->ops->solve           = MatSolve_SuperLU_DIST;
420:   F->ops->matsolve        = MatMatSolve_SuperLU_DIST;
421:   F->ops->getinertia      = NULL;

423:   if (A->symmetric || A->hermitian) F->ops->getinertia = MatGetInertia_SuperLU_DIST;
424:   lu->CleanUpSuperLU_Dist = PETSC_TRUE;
425:   return(0);
426: }

428: static PetscErrorCode MatCholeskyFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,const MatFactorInfo *info)
429: {

433:   MatLUFactorSymbolic_SuperLU_DIST(F,A,r,r,info);
434:   F->ops->choleskyfactornumeric = MatLUFactorNumeric_SuperLU_DIST;
435:   return(0);
436: }

438: static PetscErrorCode MatFactorGetSolverType_aij_superlu_dist(Mat A,MatSolverType *type)
439: {
441:   *type = MATSOLVERSUPERLU_DIST;
442:   return(0);
443: }

445: static PetscErrorCode MatView_Info_SuperLU_DIST(Mat A,PetscViewer viewer)
446: {
447:   Mat_SuperLU_DIST       *lu=(Mat_SuperLU_DIST*)A->data;
448:   superlu_dist_options_t options;
449:   PetscErrorCode         ierr;

452:   /* check if matrix is superlu_dist type */
453:   if (A->ops->solve != MatSolve_SuperLU_DIST) return(0);

455:   options = lu->options;
456:   PetscViewerASCIIPrintf(viewer,"SuperLU_DIST run parameters:\n");
457:   PetscViewerASCIIPrintf(viewer,"  Process grid nprow %D x npcol %D \n",lu->nprow,lu->npcol);
458:   PetscViewerASCIIPrintf(viewer,"  Equilibrate matrix %s \n",PetscBools[options.Equil != NO]);
459:   PetscViewerASCIIPrintf(viewer,"  Replace tiny pivots %s \n",PetscBools[options.ReplaceTinyPivot != NO]);
460:   PetscViewerASCIIPrintf(viewer,"  Use iterative refinement %s \n",PetscBools[options.IterRefine == SLU_DOUBLE]);
461:   PetscViewerASCIIPrintf(viewer,"  Processors in row %d col partition %d \n",lu->nprow,lu->npcol);

463:   switch (options.RowPerm) {
464:   case NOROWPERM:
465:     PetscViewerASCIIPrintf(viewer,"  Row permutation NOROWPERM\n");
466:     break;
467:   case LargeDiag_MC64:
468:     PetscViewerASCIIPrintf(viewer,"  Row permutation LargeDiag_MC64\n");
469:     break;
470:   case LargeDiag_AWPM:
471:     PetscViewerASCIIPrintf(viewer,"  Row permutation LargeDiag_AWPM\n");
472:     break;
473:   case MY_PERMR:
474:     PetscViewerASCIIPrintf(viewer,"  Row permutation MY_PERMR\n");
475:     break;
476:   default:
477:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
478:   }

480:   switch (options.ColPerm) {
481:   case NATURAL:
482:     PetscViewerASCIIPrintf(viewer,"  Column permutation NATURAL\n");
483:     break;
484:   case MMD_AT_PLUS_A:
485:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_AT_PLUS_A\n");
486:     break;
487:   case MMD_ATA:
488:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_ATA\n");
489:     break;
490:   /*  Even though this is called METIS, the SuperLU_DIST code sets this by default if PARMETIS is defined, not METIS */
491:   case METIS_AT_PLUS_A:
492:     PetscViewerASCIIPrintf(viewer,"  Column permutation METIS_AT_PLUS_A\n");
493:     break;
494:   case PARMETIS:
495:     PetscViewerASCIIPrintf(viewer,"  Column permutation PARMETIS\n");
496:     break;
497:   default:
498:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
499:   }

501:   PetscViewerASCIIPrintf(viewer,"  Parallel symbolic factorization %s \n",PetscBools[options.ParSymbFact != NO]);

503:   if (lu->FactPattern == SamePattern) {
504:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern\n");
505:   } else if (lu->FactPattern == SamePattern_SameRowPerm) {
506:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern_SameRowPerm\n");
507:   } else if (lu->FactPattern == DOFACT) {
508:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization DOFACT\n");
509:   } else {
510:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown factorization pattern");
511:   }
512:   return(0);
513: }

515: static PetscErrorCode MatView_SuperLU_DIST(Mat A,PetscViewer viewer)
516: {
517:   PetscErrorCode    ierr;
518:   PetscBool         iascii;
519:   PetscViewerFormat format;

522:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
523:   if (iascii) {
524:     PetscViewerGetFormat(viewer,&format);
525:     if (format == PETSC_VIEWER_ASCII_INFO) {
526:       MatView_Info_SuperLU_DIST(A,viewer);
527:     }
528:   }
529:   return(0);
530: }

532: static PetscErrorCode MatGetFactor_aij_superlu_dist(Mat A,MatFactorType ftype,Mat *F)
533: {
534:   Mat                    B;
535:   Mat_SuperLU_DIST       *lu;
536:   PetscErrorCode         ierr;
537:   PetscInt               M=A->rmap->N,N=A->cmap->N,indx;
538:   PetscMPIInt            size;
539:   superlu_dist_options_t options;
540:   PetscBool              flg;
541:   const char             *colperm[]     = {"NATURAL","MMD_AT_PLUS_A","MMD_ATA","METIS_AT_PLUS_A","PARMETIS"};
542:   const char             *rowperm[]     = {"NOROWPERM","LargeDiag_MC64","LargeDiag_AWPM","MY_PERMR"};
543:   const char             *factPattern[] = {"SamePattern","SamePattern_SameRowPerm","DOFACT"};
544:   PetscBool              set;

547:   /* Create the factorization matrix */
548:   MatCreate(PetscObjectComm((PetscObject)A),&B);
549:   MatSetSizes(B,A->rmap->n,A->cmap->n,M,N);
550:   PetscStrallocpy("superlu_dist",&((PetscObject)B)->type_name);
551:   MatSetUp(B);
552:   B->ops->getinfo = MatGetInfo_External;
553:   B->ops->view    = MatView_SuperLU_DIST;
554:   B->ops->destroy = MatDestroy_SuperLU_DIST;

556:   /* Set the default input options:
557:      options.Fact              = DOFACT;
558:      options.Equil             = YES;
559:      options.ParSymbFact       = NO;
560:      options.ColPerm           = METIS_AT_PLUS_A;
561:      options.RowPerm           = LargeDiag_MC64;
562:      options.ReplaceTinyPivot  = YES;
563:      options.IterRefine        = DOUBLE;
564:      options.Trans             = NOTRANS;
565:      options.SolveInitialized  = NO; -hold the communication pattern used MatSolve() and MatMatSolve()
566:      options.RefineInitialized = NO;
567:      options.PrintStat         = YES;
568:      options.SymPattern        = NO;
569:   */
570:   set_default_options_dist(&options);

572:   if (ftype == MAT_FACTOR_LU) {
573:     B->factortype = MAT_FACTOR_LU;
574:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
575:   } else {
576:     B->factortype = MAT_FACTOR_CHOLESKY;
577:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SuperLU_DIST;
578:     options.SymPattern = YES;
579:   }

581:   /* set solvertype */
582:   PetscFree(B->solvertype);
583:   PetscStrallocpy(MATSOLVERSUPERLU_DIST,&B->solvertype);

585:   PetscNewLog(B,&lu);
586:   B->data = lu;
587:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);

589:   {
590:     PetscMPIInt       flg;
591:     MPI_Comm          comm;
592:     PetscSuperLU_DIST *context = NULL;

594:     PetscObjectGetComm((PetscObject)A,&comm);
595:     if (Petsc_Superlu_dist_keyval == MPI_KEYVAL_INVALID) {
596:       MPI_Comm_create_keyval(MPI_COMM_NULL_COPY_FN,Petsc_Superlu_dist_keyval_Delete_Fn,&Petsc_Superlu_dist_keyval,(void*)0);
597:       PetscRegisterFinalize(Petsc_Superlu_dist_keyval_free);
598:     }
599:     MPI_Comm_get_attr(comm,Petsc_Superlu_dist_keyval,&context,&flg);
600:     if (!flg || context->busy) {
601:       if (!flg) {
602:         PetscNew(&context);
603:         context->busy = PETSC_TRUE;
604:         MPI_Comm_dup(comm,&context->comm);
605:         MPI_Comm_set_attr(comm,Petsc_Superlu_dist_keyval,context);
606:       } else {
607:         MPI_Comm_dup(comm,&lu->comm_superlu);
608:       }

610:       /* Default num of process columns and rows */
611:       lu->nprow = (int_t) (0.5 + PetscSqrtReal((PetscReal)size));
612:       if (!lu->nprow) lu->nprow = 1;
613:       while (lu->nprow > 0) {
614:         lu->npcol = (int_t) (size/lu->nprow);
615:         if (size == lu->nprow * lu->npcol) break;
616:         lu->nprow--;
617:       }
618:       PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"SuperLU_Dist Options","Mat");
619:       PetscOptionsInt("-mat_superlu_dist_r","Number rows in processor partition","None",lu->nprow,(PetscInt*)&lu->nprow,NULL);
620:       PetscOptionsInt("-mat_superlu_dist_c","Number columns in processor partition","None",lu->npcol,(PetscInt*)&lu->npcol,NULL);
621:       PetscOptionsEnd();
622:       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);
623:       PetscStackCall("SuperLU_DIST:superlu_gridinit",superlu_gridinit(context ? context->comm : lu->comm_superlu, lu->nprow, lu->npcol, &lu->grid));
624:       if (context) context->grid = lu->grid;
625:       PetscInfo(NULL,"Duplicating a communicator for SuperLU_DIST and calling superlu_gridinit()\n");
626:       if (!flg) {
627:         PetscInfo(NULL,"Storing communicator and SuperLU_DIST grid in communicator attribute\n");
628:       } else {
629:         PetscInfo(NULL,"Communicator attribute already in use so not saving communicator and SuperLU_DIST grid in communicator attribute \n");
630:       }
631:     } else {
632:       PetscInfo(NULL,"Reusing communicator and superlu_gridinit() for SuperLU_DIST from communicator attribute.");
633:       context->busy = PETSC_TRUE;
634:       lu->grid      = context->grid;
635:     }
636:   }

638:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"SuperLU_Dist Options","Mat");
639:   PetscOptionsBool("-mat_superlu_dist_equil","Equilibrate matrix","None",options.Equil ? PETSC_TRUE : PETSC_FALSE,&flg,&set);
640:   if (set && !flg) options.Equil = NO;

642:   PetscOptionsEList("-mat_superlu_dist_rowperm","Row permutation","None",rowperm,4,rowperm[1],&indx,&flg);
643:   if (flg) {
644:     switch (indx) {
645:     case 0:
646:       options.RowPerm = NOROWPERM;
647:       break;
648:     case 1:
649:       options.RowPerm = LargeDiag_MC64;
650:       break;
651:     case 2:
652:       options.RowPerm = LargeDiag_AWPM;
653:       break;
654:     case 3:
655:       options.RowPerm = MY_PERMR;
656:       break;
657:     default:
658:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown row permutation");
659:     }
660:   }

662:   PetscOptionsEList("-mat_superlu_dist_colperm","Column permutation","None",colperm,5,colperm[3],&indx,&flg);
663:   if (flg) {
664:     switch (indx) {
665:     case 0:
666:       options.ColPerm = NATURAL;
667:       break;
668:     case 1:
669:       options.ColPerm = MMD_AT_PLUS_A;
670:       break;
671:     case 2:
672:       options.ColPerm = MMD_ATA;
673:       break;
674:     case 3:
675:       options.ColPerm = METIS_AT_PLUS_A;
676:       break;
677:     case 4:
678:       options.ColPerm = PARMETIS;   /* only works for np>1 */
679:       break;
680:     default:
681:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
682:     }
683:   }

685:   options.ReplaceTinyPivot = NO;
686:   PetscOptionsBool("-mat_superlu_dist_replacetinypivot","Replace tiny pivots","None",options.ReplaceTinyPivot ? PETSC_TRUE : PETSC_FALSE,&flg,&set);
687:   if (set && flg) options.ReplaceTinyPivot = YES;

689:   options.ParSymbFact = NO;
690:   PetscOptionsBool("-mat_superlu_dist_parsymbfact","Parallel symbolic factorization","None",PETSC_FALSE,&flg,&set);
691:   if (set && flg && size>1) {
692: #if defined(PETSC_HAVE_PARMETIS)
693:     options.ParSymbFact = YES;
694:     options.ColPerm     = PARMETIS;   /* in v2.2, PARMETIS is forced for ParSymbFact regardless of user ordering setting */
695: #else
696:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"parsymbfact needs PARMETIS");
697: #endif
698:   }

700:   lu->FactPattern = SamePattern;
701:   PetscOptionsEList("-mat_superlu_dist_fact","Sparsity pattern for repeated matrix factorization","None",factPattern,3,factPattern[0],&indx,&flg);
702:   if (flg) {
703:     switch (indx) {
704:     case 0:
705:       lu->FactPattern = SamePattern;
706:       break;
707:     case 1:
708:       lu->FactPattern = SamePattern_SameRowPerm;
709:       break;
710:     case 2:
711:       lu->FactPattern = DOFACT;
712:       break;
713:     }
714:   }

716:   options.IterRefine = NOREFINE;
717:   PetscOptionsBool("-mat_superlu_dist_iterrefine","Use iterative refinement","None",options.IterRefine == NOREFINE ? PETSC_FALSE : PETSC_TRUE ,&flg,&set);
718:   if (set) {
719:     if (flg) options.IterRefine = SLU_DOUBLE;
720:     else options.IterRefine = NOREFINE;
721:   }

723:   if (PetscLogPrintInfo) options.PrintStat = YES;
724:   else options.PrintStat = NO;
725:   PetscOptionsBool("-mat_superlu_dist_statprint","Print factorization information","None",(PetscBool)options.PrintStat,(PetscBool*)&options.PrintStat,NULL);
726:   PetscOptionsEnd();

728:   lu->options              = options;
729:   lu->options.Fact         = DOFACT;
730:   lu->matsolve_iscalled    = PETSC_FALSE;
731:   lu->matmatsolve_iscalled = PETSC_FALSE;

733:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_aij_superlu_dist);
734:   PetscObjectComposeFunction((PetscObject)B,"MatSuperluDistGetDiagU_C",MatSuperluDistGetDiagU_SuperLU_DIST);

736:   *F = B;
737:   return(0);
738: }

740: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_SuperLU_DIST(void)
741: {
744:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
745:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
746:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_superlu_dist);
747:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_superlu_dist);
748:   return(0);
749: }

751: /*MC
752:   MATSOLVERSUPERLU_DIST - Parallel direct solver package for LU factorization

754:   Use ./configure --download-superlu_dist --download-parmetis --download-metis --download-ptscotch  to have PETSc installed with SuperLU_DIST

756:   Use -pc_type lu -pc_factor_mat_solver_type superlu_dist to use this direct solver

758:    Works with AIJ matrices

760:   Options Database Keys:
761: + -mat_superlu_dist_r <n> - number of rows in processor partition
762: . -mat_superlu_dist_c <n> - number of columns in processor partition
763: . -mat_superlu_dist_equil - equilibrate the matrix
764: . -mat_superlu_dist_rowperm <NOROWPERM,LargeDiag_MC64,LargeDiag_AWPM,MY_PERMR> - row permutation
765: . -mat_superlu_dist_colperm <NATURAL,MMD_AT_PLUS_A,MMD_ATA,METIS_AT_PLUS_A,PARMETIS> - column permutation
766: . -mat_superlu_dist_replacetinypivot - replace tiny pivots
767: . -mat_superlu_dist_fact <SamePattern> - (choose one of) SamePattern SamePattern_SameRowPerm DOFACT
768: . -mat_superlu_dist_iterrefine - use iterative refinement
769: - -mat_superlu_dist_statprint - print factorization information

771:    Level: beginner

773: .seealso: PCLU

775: .seealso: PCFactorSetMatSolverType(), MatSolverType

777: M*/