Actual source code: superlu_dist.c

  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;

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

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

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

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

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

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

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

102: /*
103:    Performs MPI_Comm_free_keyval() on Petsc_Superlu_dist_keyval but keeps the global variable for
104:    users who do not destroy all PETSc objects before PetscFinalize().

106:    The value Petsc_Superlu_dist_keyval is retained so that Petsc_Superlu_dist_keyval_Delete_Fn()
107:    can still check that the keyval associated with the MPI communicator is correct when the MPI
108:    communicator is destroyed.

110:    This is called in PetscFinalize()
111: */
112: static PetscErrorCode Petsc_Superlu_dist_keyval_free(void)
113: {
115:   PetscMPIInt    Petsc_Superlu_dist_keyval_temp = Petsc_Superlu_dist_keyval;

118:   PetscInfo(NULL,"Freeing Petsc_Superlu_dist_keyval\n");
119:   MPI_Comm_free_keyval(&Petsc_Superlu_dist_keyval_temp);
120:   return(0);
121: }

123: static PetscErrorCode MatDestroy_SuperLU_DIST(Mat A)
124: {
125:   PetscErrorCode   ierr;
126:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;

129:   if (lu->CleanUpSuperLU_Dist) {
130:     /* Deallocate SuperLU_DIST storage */
131:     PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
132:     if (lu->options.SolveInitialized) {
133: #if defined(PETSC_USE_COMPLEX)
134:       PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
135: #else
136:       PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
137: #endif
138:     }
139:     PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->cmap->N, &lu->grid, &lu->LUstruct));
140:     PetscStackCall("SuperLU_DIST:ScalePermstructFree",ScalePermstructFree(&lu->ScalePermstruct));
141:     PetscStackCall("SuperLU_DIST:LUstructFree",LUstructFree(&lu->LUstruct));

143:     /* Release the SuperLU_DIST process grid. Only if the matrix has its own copy, this is it is not in the communicator context */
144:     if (lu->comm_superlu) {
145:       PetscStackCall("SuperLU_DIST:superlu_gridexit",superlu_gridexit(&lu->grid));
146:     }
147:   }
148:   /*
149:    * We always need to release the communicator that was created in MatGetFactor_aij_superlu_dist.
150:    * lu->CleanUpSuperLU_Dist was turned on in MatLUFactorSymbolic_SuperLU_DIST. There are some use
151:    * cases where we only create a matrix but do not solve mat. In these cases, lu->CleanUpSuperLU_Dist
152:    * is off, and the communicator was not released or marked as "not busy " in the old code.
153:    * Here we try to release comm regardless.
154:   */
155:   if (lu->comm_superlu) {
156:     MPI_Comm_free(&(lu->comm_superlu));
157:   } else {
158:     PetscSuperLU_DIST *context;
159:     MPI_Comm          comm;
160:     PetscMPIInt       flg;

162:     PetscObjectGetComm((PetscObject)A,&comm);
163:     MPI_Comm_get_attr(comm,Petsc_Superlu_dist_keyval,&context,&flg);
164:     if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Communicator does not have expected Petsc_Superlu_dist_keyval attribute");
165:     context->busy = PETSC_FALSE;
166:   }

168:   PetscFree(A->data);
169:   /* clear composed functions */
170:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
171:   PetscObjectComposeFunction((PetscObject)A,"MatSuperluDistGetDiagU_C",NULL);

173:   return(0);
174: }

176: static PetscErrorCode MatSolve_SuperLU_DIST(Mat A,Vec b_mpi,Vec x)
177: {
178:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
179:   PetscErrorCode   ierr;
180:   PetscInt         m=A->rmap->n;
181:   SuperLUStat_t    stat;
182:   double           berr[1];
183:   PetscScalar      *bptr=NULL;
184:   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
185:   static PetscBool cite = PETSC_FALSE;

188:   if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact must equal FACTORED");
189:   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);

191:   if (lu->options.SolveInitialized && !lu->matsolve_iscalled) {
192:     /* see comments in MatMatSolve() */
193: #if defined(PETSC_USE_COMPLEX)
194:     PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
195: #else
196:     PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
197: #endif
198:     lu->options.SolveInitialized = NO;
199:   }
200:   VecCopy(b_mpi,x);
201:   VecGetArray(x,&bptr);

203:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));        /* Initialize the statistics variables. */
204: #if defined(PETSC_USE_COMPLEX)
205:   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));
206: #else
207:   PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,1,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
208: #endif
209:   if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);

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

214:   VecRestoreArray(x,&bptr);
215:   lu->matsolve_iscalled    = PETSC_TRUE;
216:   lu->matmatsolve_iscalled = PETSC_FALSE;
217:   return(0);
218: }

220: static PetscErrorCode MatMatSolve_SuperLU_DIST(Mat A,Mat B_mpi,Mat X)
221: {
222:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
223:   PetscErrorCode   ierr;
224:   PetscInt         m=A->rmap->n,nrhs;
225:   SuperLUStat_t    stat;
226:   double           berr[1];
227:   PetscScalar      *bptr;
228:   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
229:   PetscBool        flg;

232:   if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact must equal FACTORED");
233:   PetscObjectTypeCompareAny((PetscObject)B_mpi,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
234:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
235:   if (X != B_mpi) {
236:     PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
237:     if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");
238:   }

240:   if (lu->options.SolveInitialized && !lu->matmatsolve_iscalled) {
241:     /* communication pattern of SOLVEstruct is unlikely created for matmatsolve,
242:        thus destroy it and create a new SOLVEstruct.
243:        Otherwise it may result in memory corruption or incorrect solution
244:        See src/mat/tests/ex125.c */
245: #if defined(PETSC_USE_COMPLEX)
246:     PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
247: #else
248:     PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
249: #endif
250:     lu->options.SolveInitialized = NO;
251:   }
252:   if (X != B_mpi) {
253:     MatCopy(B_mpi,X,SAME_NONZERO_PATTERN);
254:   }

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

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

261: #if defined(PETSC_USE_COMPLEX)
262:   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));
263: #else
264:   PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
265: #endif

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

270:   if (lu->options.PrintStat) PetscStackCall("SuperLU_DIST:PStatPrint",PStatPrint(&lu->options, &stat, &lu->grid));  /* Print the statistics. */
271:   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
272:   lu->matsolve_iscalled    = PETSC_FALSE;
273:   lu->matmatsolve_iscalled = PETSC_TRUE;
274:   return(0);
275: }

277: /*
278:   input:
279:    F:        numeric Cholesky factor
280:   output:
281:    nneg:     total number of negative pivots
282:    nzero:    total number of zero pivots
283:    npos:     (global dimension of F) - nneg - nzero
284: */
285: static PetscErrorCode MatGetInertia_SuperLU_DIST(Mat F,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
286: {
287:   PetscErrorCode   ierr;
288:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;
289:   PetscScalar      *diagU=NULL;
290:   PetscInt         M,i,neg=0,zero=0,pos=0;
291:   PetscReal        r;

294:   if (!F->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Matrix factor F is not assembled");
295:   if (lu->options.RowPerm != NOROWPERM) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Must set NOROWPERM");
296:   MatGetSize(F,&M,NULL);
297:   PetscMalloc1(M,&diagU);
298:   MatSuperluDistGetDiagU(F,diagU);
299:   for (i=0; i<M; i++) {
300: #if defined(PETSC_USE_COMPLEX)
301:     r = PetscImaginaryPart(diagU[i])/10.0;
302:     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);
303:     r = PetscRealPart(diagU[i]);
304: #else
305:     r = diagU[i];
306: #endif
307:     if (r > 0) {
308:       pos++;
309:     } else if (r < 0) {
310:       neg++;
311:     } else zero++;
312:   }

314:   PetscFree(diagU);
315:   if (nneg)  *nneg  = neg;
316:   if (nzero) *nzero = zero;
317:   if (npos)  *npos  = pos;
318:   return(0);
319: }

321: static PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat F,Mat A,const MatFactorInfo *info)
322: {
323:   Mat_SuperLU_DIST  *lu = (Mat_SuperLU_DIST*)F->data;
324:   Mat               Aloc;
325:   const PetscScalar *av;
326:   const PetscInt    *ai=NULL,*aj=NULL;
327:   PetscInt          nz,dummy;
328:   int               sinfo;   /* SuperLU_Dist info flag is always an int even with long long indices */
329:   SuperLUStat_t     stat;
330:   double            *berr=0;
331:   PetscBool         ismpiaij,isseqaij,flg;
332:   PetscErrorCode    ierr;

335:   PetscObjectBaseTypeCompare((PetscObject)A,MATSEQAIJ,&isseqaij);
336:   PetscObjectBaseTypeCompare((PetscObject)A,MATMPIAIJ,&ismpiaij);
337:   if (ismpiaij) {
338:     MatMPIAIJGetLocalMat(A,MAT_INITIAL_MATRIX,&Aloc);
339:   } else if (isseqaij) {
340:     PetscObjectReference((PetscObject)A);
341:     Aloc = A;
342:   } else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Not for type %s",((PetscObject)A)->type_name);

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

349:   /* Allocations for A_sup */
350:   if (lu->options.Fact == DOFACT) { /* first numeric factorization */
351: #if defined(PETSC_USE_COMPLEX)
352:     PetscStackCall("SuperLU_DIST:zallocateA_dist",zallocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
353: #else
354:     PetscStackCall("SuperLU_DIST:dallocateA_dist",dallocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
355: #endif
356:   } else { /* successive numeric factorization, sparsity pattern and perm_c are reused. */
357:     if (lu->FactPattern == SamePattern_SameRowPerm) {
358:       lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
359:     } else if (lu->FactPattern == SamePattern) {
360:       PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->rmap->N, &lu->grid, &lu->LUstruct)); /* Deallocate L and U matrices. */
361:       lu->options.Fact = SamePattern;
362:     } else if (lu->FactPattern == DOFACT) {
363:       PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
364:       PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->rmap->N, &lu->grid, &lu->LUstruct));
365:       lu->options.Fact = DOFACT;

367: #if defined(PETSC_USE_COMPLEX)
368:       PetscStackCall("SuperLU_DIST:zallocateA_dist",zallocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
369: #else
370:       PetscStackCall("SuperLU_DIST:dallocateA_dist",dallocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
371: #endif
372:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"options.Fact must be one of SamePattern SamePattern_SameRowPerm DOFACT");
373:   }

375:   /* Copy AIJ matrix to superlu_dist matrix */
376:   PetscArraycpy(lu->row,ai,Aloc->rmap->n+1);
377:   PetscArraycpy(lu->col,aj,nz);
378:   PetscArraycpy(lu->val,av,nz);
379:   MatRestoreRowIJ(Aloc,0,PETSC_FALSE,PETSC_FALSE,&dummy,&ai,&aj,&flg);
380:   if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"RestoreRowIJ failed");
381:   MatSeqAIJRestoreArrayRead(Aloc,&av);
382:   MatDestroy(&Aloc);

384:   /* Create and setup A_sup */
385:   if (lu->options.Fact == DOFACT) {
386: #if defined(PETSC_USE_COMPLEX)
387:     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));
388: #else
389:     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));
390: #endif
391:   }

393:   /* Factor the matrix. */
394:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));   /* Initialize the statistics variables. */
395: #if defined(PETSC_USE_COMPLEX)
396:   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));
397: #else
398:   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));
399: #endif

401:   if (sinfo > 0) {
402:     if (A->erroriffailure) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot in row %D",sinfo);
403:     else {
404:       if (sinfo <= lu->A_sup.ncol) {
405:         F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
406:         PetscInfo1(F,"U(i,i) is exactly zero, i= %D\n",sinfo);
407:       } else if (sinfo > lu->A_sup.ncol) {
408:         /*
409:          number of bytes allocated when memory allocation
410:          failure occurred, plus A->ncol.
411:          */
412:         F->factorerrortype = MAT_FACTOR_OUTMEMORY;
413:         PetscInfo1(F,"Number of bytes allocated when memory allocation fails %D\n",sinfo);
414:       }
415:     }
416:   } else if (sinfo < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB, "info = %D, argument in p*gssvx() had an illegal value", sinfo);

418:   if (lu->options.PrintStat) {
419:     PetscStackCall("SuperLU_DIST:PStatPrint",PStatPrint(&lu->options, &stat, &lu->grid));  /* Print the statistics. */
420:   }
421:   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
422:   F->assembled     = PETSC_TRUE;
423:   F->preallocated  = PETSC_TRUE;
424:   lu->options.Fact = FACTORED; /* The factored form of A is supplied. Local option used by this func. only */
425:   return(0);
426: }

428: /* Note the Petsc r and c permutations are ignored */
429: static PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
430: {
431:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;
432:   PetscInt         M   = A->rmap->N,N=A->cmap->N;

435:   /* Initialize ScalePermstruct and LUstruct. */
436:   PetscStackCall("SuperLU_DIST:ScalePermstructInit",ScalePermstructInit(M, N, &lu->ScalePermstruct));
437:   PetscStackCall("SuperLU_DIST:LUstructInit",LUstructInit(N, &lu->LUstruct));
438:   F->ops->lufactornumeric = MatLUFactorNumeric_SuperLU_DIST;
439:   F->ops->solve           = MatSolve_SuperLU_DIST;
440:   F->ops->matsolve        = MatMatSolve_SuperLU_DIST;
441:   F->ops->getinertia      = NULL;

443:   if (A->symmetric || A->hermitian) F->ops->getinertia = MatGetInertia_SuperLU_DIST;
444:   lu->CleanUpSuperLU_Dist = PETSC_TRUE;
445:   return(0);
446: }

448: static PetscErrorCode MatCholeskyFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,const MatFactorInfo *info)
449: {

453:   MatLUFactorSymbolic_SuperLU_DIST(F,A,r,r,info);
454:   F->ops->choleskyfactornumeric = MatLUFactorNumeric_SuperLU_DIST;
455:   return(0);
456: }

458: static PetscErrorCode MatFactorGetSolverType_aij_superlu_dist(Mat A,MatSolverType *type)
459: {
461:   *type = MATSOLVERSUPERLU_DIST;
462:   return(0);
463: }

465: static PetscErrorCode MatView_Info_SuperLU_DIST(Mat A,PetscViewer viewer)
466: {
467:   Mat_SuperLU_DIST       *lu=(Mat_SuperLU_DIST*)A->data;
468:   superlu_dist_options_t options;
469:   PetscErrorCode         ierr;

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

475:   options = lu->options;
476:   PetscViewerASCIIPrintf(viewer,"SuperLU_DIST run parameters:\n");
477:   PetscViewerASCIIPrintf(viewer,"  Process grid nprow %D x npcol %D \n",lu->nprow,lu->npcol);
478:   PetscViewerASCIIPrintf(viewer,"  Equilibrate matrix %s \n",PetscBools[options.Equil != NO]);
479:   PetscViewerASCIIPrintf(viewer,"  Replace tiny pivots %s \n",PetscBools[options.ReplaceTinyPivot != NO]);
480:   PetscViewerASCIIPrintf(viewer,"  Use iterative refinement %s \n",PetscBools[options.IterRefine == SLU_DOUBLE]);
481:   PetscViewerASCIIPrintf(viewer,"  Processors in row %d col partition %d \n",lu->nprow,lu->npcol);

483:   switch (options.RowPerm) {
484:   case NOROWPERM:
485:     PetscViewerASCIIPrintf(viewer,"  Row permutation NOROWPERM\n");
486:     break;
487:   case LargeDiag_MC64:
488:     PetscViewerASCIIPrintf(viewer,"  Row permutation LargeDiag_MC64\n");
489:     break;
490:   case LargeDiag_AWPM:
491:     PetscViewerASCIIPrintf(viewer,"  Row permutation LargeDiag_AWPM\n");
492:     break;
493:   case MY_PERMR:
494:     PetscViewerASCIIPrintf(viewer,"  Row permutation MY_PERMR\n");
495:     break;
496:   default:
497:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
498:   }

500:   switch (options.ColPerm) {
501:   case NATURAL:
502:     PetscViewerASCIIPrintf(viewer,"  Column permutation NATURAL\n");
503:     break;
504:   case MMD_AT_PLUS_A:
505:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_AT_PLUS_A\n");
506:     break;
507:   case MMD_ATA:
508:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_ATA\n");
509:     break;
510:   /*  Even though this is called METIS, the SuperLU_DIST code sets this by default if PARMETIS is defined, not METIS */
511:   case METIS_AT_PLUS_A:
512:     PetscViewerASCIIPrintf(viewer,"  Column permutation METIS_AT_PLUS_A\n");
513:     break;
514:   case PARMETIS:
515:     PetscViewerASCIIPrintf(viewer,"  Column permutation PARMETIS\n");
516:     break;
517:   default:
518:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
519:   }

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

523:   if (lu->FactPattern == SamePattern) {
524:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern\n");
525:   } else if (lu->FactPattern == SamePattern_SameRowPerm) {
526:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern_SameRowPerm\n");
527:   } else if (lu->FactPattern == DOFACT) {
528:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization DOFACT\n");
529:   } else {
530:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown factorization pattern");
531:   }
532:   return(0);
533: }

535: static PetscErrorCode MatView_SuperLU_DIST(Mat A,PetscViewer viewer)
536: {
537:   PetscErrorCode    ierr;
538:   PetscBool         iascii;
539:   PetscViewerFormat format;

542:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
543:   if (iascii) {
544:     PetscViewerGetFormat(viewer,&format);
545:     if (format == PETSC_VIEWER_ASCII_INFO) {
546:       MatView_Info_SuperLU_DIST(A,viewer);
547:     }
548:   }
549:   return(0);
550: }

552: static PetscErrorCode MatGetFactor_aij_superlu_dist(Mat A,MatFactorType ftype,Mat *F)
553: {
554:   Mat                    B;
555:   Mat_SuperLU_DIST       *lu;
556:   PetscErrorCode         ierr;
557:   PetscInt               M=A->rmap->N,N=A->cmap->N,indx;
558:   PetscMPIInt            size;
559:   superlu_dist_options_t options;
560:   PetscBool              flg;
561:   const char             *colperm[]     = {"NATURAL","MMD_AT_PLUS_A","MMD_ATA","METIS_AT_PLUS_A","PARMETIS"};
562:   const char             *rowperm[]     = {"NOROWPERM","LargeDiag_MC64","LargeDiag_AWPM","MY_PERMR"};
563:   const char             *factPattern[] = {"SamePattern","SamePattern_SameRowPerm","DOFACT"};
564:   PetscBool              set;

567:   /* Create the factorization matrix */
568:   MatCreate(PetscObjectComm((PetscObject)A),&B);
569:   MatSetSizes(B,A->rmap->n,A->cmap->n,M,N);
570:   PetscStrallocpy("superlu_dist",&((PetscObject)B)->type_name);
571:   MatSetUp(B);
572:   B->ops->getinfo = MatGetInfo_External;
573:   B->ops->view    = MatView_SuperLU_DIST;
574:   B->ops->destroy = MatDestroy_SuperLU_DIST;

576:   /* Set the default input options:
577:      options.Fact              = DOFACT;
578:      options.Equil             = YES;
579:      options.ParSymbFact       = NO;
580:      options.ColPerm           = METIS_AT_PLUS_A;
581:      options.RowPerm           = LargeDiag_MC64;
582:      options.ReplaceTinyPivot  = YES;
583:      options.IterRefine        = DOUBLE;
584:      options.Trans             = NOTRANS;
585:      options.SolveInitialized  = NO; -hold the communication pattern used MatSolve() and MatMatSolve()
586:      options.RefineInitialized = NO;
587:      options.PrintStat         = YES;
588:      options.SymPattern        = NO;
589:   */
590:   set_default_options_dist(&options);

592:   B->trivialsymbolic = PETSC_TRUE;
593:   if (ftype == MAT_FACTOR_LU) {
594:     B->factortype = MAT_FACTOR_LU;
595:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
596:   } else {
597:     B->factortype = MAT_FACTOR_CHOLESKY;
598:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SuperLU_DIST;
599:     options.SymPattern = YES;
600:   }

602:   /* set solvertype */
603:   PetscFree(B->solvertype);
604:   PetscStrallocpy(MATSOLVERSUPERLU_DIST,&B->solvertype);

606:   PetscNewLog(B,&lu);
607:   B->data = lu;
608:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);

610:   {
611:     PetscMPIInt       flg;
612:     MPI_Comm          comm;
613:     PetscSuperLU_DIST *context = NULL;

615:     PetscObjectGetComm((PetscObject)A,&comm);
616:     if (Petsc_Superlu_dist_keyval == MPI_KEYVAL_INVALID) {
617:       MPI_Comm_create_keyval(MPI_COMM_NULL_COPY_FN,Petsc_Superlu_dist_keyval_Delete_Fn,&Petsc_Superlu_dist_keyval,(void*)0);
618:       PetscRegisterFinalize(Petsc_Superlu_dist_keyval_free);
619:     }
620:     MPI_Comm_get_attr(comm,Petsc_Superlu_dist_keyval,&context,&flg);
621:     if (!flg || context->busy) {
622:       if (!flg) {
623:         PetscNew(&context);
624:         context->busy = PETSC_TRUE;
625:         MPI_Comm_dup(comm,&context->comm);
626:         MPI_Comm_set_attr(comm,Petsc_Superlu_dist_keyval,context);
627:       } else {
628:         MPI_Comm_dup(comm,&lu->comm_superlu);
629:       }

631:       /* Default num of process columns and rows */
632:       lu->nprow = (int_t) (0.5 + PetscSqrtReal((PetscReal)size));
633:       if (!lu->nprow) lu->nprow = 1;
634:       while (lu->nprow > 0) {
635:         lu->npcol = (int_t) (size/lu->nprow);
636:         if (size == lu->nprow * lu->npcol) break;
637:         lu->nprow--;
638:       }
639:       PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"SuperLU_Dist Options","Mat");
640:       PetscOptionsInt("-mat_superlu_dist_r","Number rows in processor partition","None",lu->nprow,(PetscInt*)&lu->nprow,NULL);
641:       PetscOptionsInt("-mat_superlu_dist_c","Number columns in processor partition","None",lu->npcol,(PetscInt*)&lu->npcol,NULL);
642:       PetscOptionsEnd();
643:       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);
644:       PetscStackCall("SuperLU_DIST:superlu_gridinit",superlu_gridinit(context ? context->comm : lu->comm_superlu, lu->nprow, lu->npcol, &lu->grid));
645:       if (context) context->grid = lu->grid;
646:       PetscInfo(NULL,"Duplicating a communicator for SuperLU_DIST and calling superlu_gridinit()\n");
647:       if (!flg) {
648:         PetscInfo(NULL,"Storing communicator and SuperLU_DIST grid in communicator attribute\n");
649:       } else {
650:         PetscInfo(NULL,"Communicator attribute already in use so not saving communicator and SuperLU_DIST grid in communicator attribute \n");
651:       }
652:     } else {
653:       PetscInfo(NULL,"Reusing communicator and superlu_gridinit() for SuperLU_DIST from communicator attribute.");
654:       context->busy = PETSC_TRUE;
655:       lu->grid      = context->grid;
656:     }
657:   }

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

663:   PetscOptionsEList("-mat_superlu_dist_rowperm","Row permutation","None",rowperm,4,rowperm[1],&indx,&flg);
664:   if (flg) {
665:     switch (indx) {
666:     case 0:
667:       options.RowPerm = NOROWPERM;
668:       break;
669:     case 1:
670:       options.RowPerm = LargeDiag_MC64;
671:       break;
672:     case 2:
673:       options.RowPerm = LargeDiag_AWPM;
674:       break;
675:     case 3:
676:       options.RowPerm = MY_PERMR;
677:       break;
678:     default:
679:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown row permutation");
680:     }
681:   }

683:   PetscOptionsEList("-mat_superlu_dist_colperm","Column permutation","None",colperm,5,colperm[3],&indx,&flg);
684:   if (flg) {
685:     switch (indx) {
686:     case 0:
687:       options.ColPerm = NATURAL;
688:       break;
689:     case 1:
690:       options.ColPerm = MMD_AT_PLUS_A;
691:       break;
692:     case 2:
693:       options.ColPerm = MMD_ATA;
694:       break;
695:     case 3:
696:       options.ColPerm = METIS_AT_PLUS_A;
697:       break;
698:     case 4:
699:       options.ColPerm = PARMETIS;   /* only works for np>1 */
700:       break;
701:     default:
702:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
703:     }
704:   }

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

710:   options.ParSymbFact = NO;
711:   PetscOptionsBool("-mat_superlu_dist_parsymbfact","Parallel symbolic factorization","None",PETSC_FALSE,&flg,&set);
712:   if (set && flg && size>1) {
713: #if defined(PETSC_HAVE_PARMETIS)
714:     options.ParSymbFact = YES;
715:     options.ColPerm     = PARMETIS;   /* in v2.2, PARMETIS is forced for ParSymbFact regardless of user ordering setting */
716: #else
717:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"parsymbfact needs PARMETIS");
718: #endif
719:   }

721:   lu->FactPattern = SamePattern;
722:   PetscOptionsEList("-mat_superlu_dist_fact","Sparsity pattern for repeated matrix factorization","None",factPattern,3,factPattern[0],&indx,&flg);
723:   if (flg) {
724:     switch (indx) {
725:     case 0:
726:       lu->FactPattern = SamePattern;
727:       break;
728:     case 1:
729:       lu->FactPattern = SamePattern_SameRowPerm;
730:       break;
731:     case 2:
732:       lu->FactPattern = DOFACT;
733:       break;
734:     }
735:   }

737:   options.IterRefine = NOREFINE;
738:   PetscOptionsBool("-mat_superlu_dist_iterrefine","Use iterative refinement","None",options.IterRefine == NOREFINE ? PETSC_FALSE : PETSC_TRUE ,&flg,&set);
739:   if (set) {
740:     if (flg) options.IterRefine = SLU_DOUBLE;
741:     else options.IterRefine = NOREFINE;
742:   }

744:   if (PetscLogPrintInfo) options.PrintStat = YES;
745:   else options.PrintStat = NO;
746:   PetscOptionsBool("-mat_superlu_dist_statprint","Print factorization information","None",(PetscBool)options.PrintStat,(PetscBool*)&options.PrintStat,NULL);
747:   PetscOptionsEnd();

749:   lu->options              = options;
750:   lu->options.Fact         = DOFACT;
751:   lu->matsolve_iscalled    = PETSC_FALSE;
752:   lu->matmatsolve_iscalled = PETSC_FALSE;

754:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_aij_superlu_dist);
755:   PetscObjectComposeFunction((PetscObject)B,"MatSuperluDistGetDiagU_C",MatSuperluDistGetDiagU_SuperLU_DIST);

757:   *F = B;
758:   return(0);
759: }

761: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_SuperLU_DIST(void)
762: {
765:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
766:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
767:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_superlu_dist);
768:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_superlu_dist);
769:   return(0);
770: }

772: /*MC
773:   MATSOLVERSUPERLU_DIST - Parallel direct solver package for LU factorization

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

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

779:    Works with AIJ matrices

781:   Options Database Keys:
782: + -mat_superlu_dist_r <n> - number of rows in processor partition
783: . -mat_superlu_dist_c <n> - number of columns in processor partition
784: . -mat_superlu_dist_equil - equilibrate the matrix
785: . -mat_superlu_dist_rowperm <NOROWPERM,LargeDiag_MC64,LargeDiag_AWPM,MY_PERMR> - row permutation
786: . -mat_superlu_dist_colperm <NATURAL,MMD_AT_PLUS_A,MMD_ATA,METIS_AT_PLUS_A,PARMETIS> - column permutation
787: . -mat_superlu_dist_replacetinypivot - replace tiny pivots
788: . -mat_superlu_dist_fact <SamePattern> - (choose one of) SamePattern SamePattern_SameRowPerm DOFACT
789: . -mat_superlu_dist_iterrefine - use iterative refinement
790: - -mat_superlu_dist_statprint - print factorization information

792:    Level: beginner

794: .seealso: PCLU

796: .seealso: PCFactorSetMatSolverType(), MatSolverType

798: M*/