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: #define CASTDOUBLECOMPLEX (doublecomplex*)
 12: #include <superlu_zdefs.h>
 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: #define SolveFinalize zSolveFinalize
 22: #define pgssvx pzgssvx
 23: #define Create_CompRowLoc_Matrix_dist zCreate_CompRowLoc_Matrix_dist
 24: #define pGetDiagU pzGetDiagU
 25: #define allocateA_dist zallocateA_dist
 26: #define SLU_ZD SLU_Z
 27: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
 28: #define DeAllocLlu_3d zDeAllocLlu_3d
 29: #define DeAllocGlu_3d zDeAllocGlu_3d
 30: #define Destroy_A3d_gathered_on_2d zDestroy_A3d_gathered_on_2d
 31: #define pgssvx3d pzgssvx3d
 32: #endif
 33: #else
 34: #define CASTDOUBLECOMPLEX
 35: #include <superlu_ddefs.h>
 36: #define LUstructInit dLUstructInit
 37: #define ScalePermstructInit dScalePermstructInit
 38: #define ScalePermstructFree dScalePermstructFree
 39: #define LUstructFree dLUstructFree
 40: #define Destroy_LU dDestroy_LU
 41: #define ScalePermstruct_t dScalePermstruct_t
 42: #define LUstruct_t dLUstruct_t
 43: #define SOLVEstruct_t dSOLVEstruct_t
 44: #define SolveFinalize dSolveFinalize
 45: #define pgssvx pdgssvx
 46: #define Create_CompRowLoc_Matrix_dist dCreate_CompRowLoc_Matrix_dist
 47: #define pGetDiagU pdGetDiagU
 48: #define allocateA_dist dallocateA_dist
 49: #define SLU_ZD SLU_D
 50: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
 51: #define DeAllocLlu_3d dDeAllocLlu_3d
 52: #define DeAllocGlu_3d dDeAllocGlu_3d
 53: #define Destroy_A3d_gathered_on_2d dDestroy_A3d_gathered_on_2d
 54: #define pgssvx3d pdgssvx3d
 55: #endif
 56: #endif
 57: EXTERN_C_END

 59: typedef struct {
 60:   int_t                  nprow,npcol,*row,*col;
 61:   gridinfo_t             grid;
 62: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
 63:   PetscBool              use3d;
 64:   int_t                  npdep; /* replication factor, must be power of two */
 65:   gridinfo3d_t           grid3d;
 66: #endif
 67:   superlu_dist_options_t options;
 68:   SuperMatrix            A_sup;
 69:   ScalePermstruct_t      ScalePermstruct;
 70:   LUstruct_t             LUstruct;
 71:   int                    StatPrint;
 72:   SOLVEstruct_t          SOLVEstruct;
 73:   fact_t                 FactPattern;
 74:   MPI_Comm               comm_superlu;
 75: #if defined(PETSC_USE_COMPLEX)
 76:   doublecomplex          *val;
 77: #else
 78:   double                 *val;
 79: #endif
 80:   PetscBool              matsolve_iscalled,matmatsolve_iscalled;
 81:   PetscBool              CleanUpSuperLU_Dist;  /* Flag to clean up (non-global) SuperLU objects during Destroy */
 82: } Mat_SuperLU_DIST;

 84: PetscErrorCode MatSuperluDistGetDiagU_SuperLU_DIST(Mat F,PetscScalar *diagU)
 85: {
 86:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;

 88:   PetscStackCall("SuperLU_DIST:pGetDiagU",pGetDiagU(F->rmap->N,&lu->LUstruct,&lu->grid,CASTDOUBLECOMPLEX diagU));
 89:   return 0;
 90: }

 92: PetscErrorCode MatSuperluDistGetDiagU(Mat F,PetscScalar *diagU)
 93: {
 95:   PetscTryMethod(F,"MatSuperluDistGetDiagU_C",(Mat,PetscScalar*),(F,diagU));
 96:   return 0;
 97: }

 99: /*  This allows reusing the Superlu_DIST communicator and grid when only a single SuperLU_DIST matrix is used at a time */
100: typedef struct {
101:   MPI_Comm     comm;
102:   PetscBool    busy;
103:   gridinfo_t   grid;
104: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
105:   PetscBool    use3d;
106:   gridinfo3d_t grid3d;
107: #endif
108: } PetscSuperLU_DIST;
109: static PetscMPIInt Petsc_Superlu_dist_keyval = MPI_KEYVAL_INVALID;

111: PETSC_EXTERN PetscMPIInt MPIAPI Petsc_Superlu_dist_keyval_Delete_Fn(MPI_Comm comm,PetscMPIInt keyval,void *attr_val,void *extra_state)
112: {
113:   PetscSuperLU_DIST *context = (PetscSuperLU_DIST *) attr_val;

115:   if (keyval != Petsc_Superlu_dist_keyval) SETERRMPI(PETSC_COMM_SELF,PETSC_ERR_ARG_CORRUPT,"Unexpected keyval");
116:   PetscInfo(NULL,"Removing Petsc_Superlu_dist_keyval attribute from communicator that is being freed\n");
117: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
118:   if (context->use3d) {
119:     PetscStackCall("SuperLU_DIST:superlu_gridexit3d",superlu_gridexit3d(&context->grid3d));
120:   } else
121: #endif
122:     PetscStackCall("SuperLU_DIST:superlu_gridexit",superlu_gridexit(&context->grid));
123:   MPI_Comm_free(&context->comm);
124:   PetscFree(context);
125:   return MPI_SUCCESS;
126: }

128: /*
129:    Performs MPI_Comm_free_keyval() on Petsc_Superlu_dist_keyval but keeps the global variable for
130:    users who do not destroy all PETSc objects before PetscFinalize().

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

136:    This is called in PetscFinalize()
137: */
138: static PetscErrorCode Petsc_Superlu_dist_keyval_free(void)
139: {
140:   PetscMPIInt    Petsc_Superlu_dist_keyval_temp = Petsc_Superlu_dist_keyval;

142:   PetscInfo(NULL,"Freeing Petsc_Superlu_dist_keyval\n");
143:   MPI_Comm_free_keyval(&Petsc_Superlu_dist_keyval_temp);
144:   return 0;
145: }

147: static PetscErrorCode MatDestroy_SuperLU_DIST(Mat A)
148: {
149:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;

151:   if (lu->CleanUpSuperLU_Dist) {
152:     /* Deallocate SuperLU_DIST storage */
153:     PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
154:     if (lu->options.SolveInitialized) {
155:       PetscStackCall("SuperLU_DIST:SolveFinalize",SolveFinalize(&lu->options, &lu->SOLVEstruct));
156:     }
157: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
158:     if (lu->use3d) {
159:       if (lu->grid3d.zscp.Iam == 0) {
160:         PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->cmap->N, &lu->grid3d.grid2d, &lu->LUstruct));
161:       } else {
162:         PetscStackCall("SuperLU_DIST:DeAllocLlu_3d",DeAllocLlu_3d(lu->A_sup.ncol, &lu->LUstruct, &lu->grid3d));
163:         PetscStackCall("SuperLU_DIST:DeAllocGlu_3d",DeAllocGlu_3d(&lu->LUstruct));
164:       }
165:       PetscStackCall("SuperLU_DIST:Destroy_A3d_gathered_on_2d",Destroy_A3d_gathered_on_2d(&lu->SOLVEstruct, &lu->grid3d));
166:     } else
167: #endif
168:       PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->cmap->N, &lu->grid, &lu->LUstruct));
169:     PetscStackCall("SuperLU_DIST:ScalePermstructFree",ScalePermstructFree(&lu->ScalePermstruct));
170:     PetscStackCall("SuperLU_DIST:LUstructFree",LUstructFree(&lu->LUstruct));

172:     /* Release the SuperLU_DIST process grid only if the matrix has its own copy, that is it is not in the communicator context */
173:     if (lu->comm_superlu) {
174: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
175:       if (lu->use3d) {
176:         PetscStackCall("SuperLU_DIST:superlu_gridexit3d",superlu_gridexit3d(&lu->grid3d));
177:       } else
178: #endif
179:         PetscStackCall("SuperLU_DIST:superlu_gridexit",superlu_gridexit(&lu->grid));
180:     }
181:   }
182:   /*
183:    * We always need to release the communicator that was created in MatGetFactor_aij_superlu_dist.
184:    * lu->CleanUpSuperLU_Dist was turned on in MatLUFactorSymbolic_SuperLU_DIST. There are some use
185:    * cases where we only create a matrix but do not solve mat. In these cases, lu->CleanUpSuperLU_Dist
186:    * is off, and the communicator was not released or marked as "not busy " in the old code.
187:    * Here we try to release comm regardless.
188:   */
189:   if (lu->comm_superlu) {
190:     PetscCommRestoreComm(PetscObjectComm((PetscObject)A),&lu->comm_superlu);
191:   } else {
192:     PetscSuperLU_DIST *context;
193:     MPI_Comm          comm;
194:     PetscMPIInt       flg;

196:     PetscObjectGetComm((PetscObject)A,&comm);
197:     MPI_Comm_get_attr(comm,Petsc_Superlu_dist_keyval,&context,&flg);
199:     context->busy = PETSC_FALSE;
200:   }

202:   PetscFree(A->data);
203:   /* clear composed functions */
204:   PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
205:   PetscObjectComposeFunction((PetscObject)A,"MatSuperluDistGetDiagU_C",NULL);

207:   return 0;
208: }

210: static PetscErrorCode MatSolve_SuperLU_DIST(Mat A,Vec b_mpi,Vec x)
211: {
212:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
213:   PetscInt         m=A->rmap->n;
214:   SuperLUStat_t    stat;
215:   double           berr[1];
216:   PetscScalar      *bptr=NULL;
217:   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
218:   static PetscBool cite = PETSC_FALSE;

221:   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);

223:   if (lu->options.SolveInitialized && !lu->matsolve_iscalled) {
224:     /* see comments in MatMatSolve() */
225:     PetscStackCall("SuperLU_DIST:SolveFinalize",SolveFinalize(&lu->options, &lu->SOLVEstruct));
226:     lu->options.SolveInitialized = NO;
227:   }
228:   VecCopy(b_mpi,x);
229:   VecGetArray(x,&bptr);

231:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));        /* Initialize the statistics variables. */
232: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0) && !PetscDefined(MISSING_GETLINE)
233:   if (lu->use3d)
234:     PetscStackCall("SuperLU_DIST:pgssvx3d",pgssvx3d(&lu->options,&lu->A_sup,&lu->ScalePermstruct,CASTDOUBLECOMPLEX bptr,m,1,&lu->grid3d,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
235:   else
236: #endif
237:     PetscStackCall("SuperLU_DIST:pgssvx",pgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,CASTDOUBLECOMPLEX bptr,m,1,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));

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

243:   VecRestoreArray(x,&bptr);
244:   lu->matsolve_iscalled    = PETSC_TRUE;
245:   lu->matmatsolve_iscalled = PETSC_FALSE;
246:   return 0;
247: }

249: static PetscErrorCode MatMatSolve_SuperLU_DIST(Mat A,Mat B_mpi,Mat X)
250: {
251:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->data;
252:   PetscInt         m=A->rmap->n,nrhs;
253:   SuperLUStat_t    stat;
254:   double           berr[1];
255:   PetscScalar      *bptr;
256:   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
257:   PetscBool        flg;

260:   PetscObjectTypeCompareAny((PetscObject)B_mpi,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
262:   if (X != B_mpi) {
263:     PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
265:   }

267:   if (lu->options.SolveInitialized && !lu->matmatsolve_iscalled) {
268:     /* communication pattern of SOLVEstruct is unlikely created for matmatsolve,
269:        thus destroy it and create a new SOLVEstruct.
270:        Otherwise it may result in memory corruption or incorrect solution
271:        See src/mat/tests/ex125.c */
272:     PetscStackCall("SuperLU_DIST:SolveFinalize",SolveFinalize(&lu->options, &lu->SOLVEstruct));
273:     lu->options.SolveInitialized = NO;
274:   }
275:   if (X != B_mpi) {
276:     MatCopy(B_mpi,X,SAME_NONZERO_PATTERN);
277:   }

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

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

284: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0) && !PetscDefined(MISSING_GETLINE)
285:   if (lu->use3d)
286:     PetscStackCall("SuperLU_DIST:pgssvx3d",pgssvx3d(&lu->options,&lu->A_sup,&lu->ScalePermstruct,CASTDOUBLECOMPLEX bptr,m,nrhs,&lu->grid3d,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
287:   else
288: #endif
289:     PetscStackCall("SuperLU_DIST:pgssvx",pgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,CASTDOUBLECOMPLEX bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));

292:   MatDenseRestoreArray(X,&bptr);

294:   if (lu->options.PrintStat) PetscStackCall("SuperLU_DIST:PStatPrint",PStatPrint(&lu->options, &stat, &lu->grid));  /* Print the statistics. */
295:   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
296:   lu->matsolve_iscalled    = PETSC_FALSE;
297:   lu->matmatsolve_iscalled = PETSC_TRUE;
298:   return 0;
299: }

301: /*
302:   input:
303:    F:        numeric Cholesky factor
304:   output:
305:    nneg:     total number of negative pivots
306:    nzero:    total number of zero pivots
307:    npos:     (global dimension of F) - nneg - nzero
308: */
309: static PetscErrorCode MatGetInertia_SuperLU_DIST(Mat F,PetscInt *nneg,PetscInt *nzero,PetscInt *npos)
310: {
311:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;
312:   PetscScalar      *diagU=NULL;
313:   PetscInt         M,i,neg=0,zero=0,pos=0;
314:   PetscReal        r;

318:   MatGetSize(F,&M,NULL);
319:   PetscMalloc1(M,&diagU);
320:   MatSuperluDistGetDiagU(F,diagU);
321:   for (i=0; i<M; i++) {
322: #if defined(PETSC_USE_COMPLEX)
323:     r = PetscImaginaryPart(diagU[i])/10.0;
325:     r = PetscRealPart(diagU[i]);
326: #else
327:     r = diagU[i];
328: #endif
329:     if (r > 0) {
330:       pos++;
331:     } else if (r < 0) {
332:       neg++;
333:     } else zero++;
334:   }

336:   PetscFree(diagU);
337:   if (nneg)  *nneg  = neg;
338:   if (nzero) *nzero = zero;
339:   if (npos)  *npos  = pos;
340:   return 0;
341: }

343: static PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat F,Mat A,const MatFactorInfo *info)
344: {
345:   Mat_SuperLU_DIST  *lu = (Mat_SuperLU_DIST*)F->data;
346:   Mat               Aloc;
347:   const PetscScalar *av;
348:   const PetscInt    *ai=NULL,*aj=NULL;
349:   PetscInt          nz,dummy;
350:   int               sinfo;   /* SuperLU_Dist info flag is always an int even with long long indices */
351:   SuperLUStat_t     stat;
352:   double            *berr=0;
353:   PetscBool         ismpiaij,isseqaij,flg;

355:   PetscObjectBaseTypeCompare((PetscObject)A,MATSEQAIJ,&isseqaij);
356:   PetscObjectBaseTypeCompare((PetscObject)A,MATMPIAIJ,&ismpiaij);
357:   if (ismpiaij) {
358:     MatMPIAIJGetLocalMat(A,MAT_INITIAL_MATRIX,&Aloc);
359:   } else if (isseqaij) {
360:     PetscObjectReference((PetscObject)A);
361:     Aloc = A;
362:   } else SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Not for type %s",((PetscObject)A)->type_name);

364:   MatGetRowIJ(Aloc,0,PETSC_FALSE,PETSC_FALSE,&dummy,&ai,&aj,&flg);
366:   MatSeqAIJGetArrayRead(Aloc,&av);
367:   nz   = ai[Aloc->rmap->n];

369:   /* Allocations for A_sup */
370:   if (lu->options.Fact == DOFACT) { /* first numeric factorization */
371:     PetscStackCall("SuperLU_DIST:allocateA_dist",allocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
372:   } else { /* successive numeric factorization, sparsity pattern and perm_c are reused. */
373:     if (lu->FactPattern == SamePattern_SameRowPerm) {
374:       lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
375:     } else if (lu->FactPattern == SamePattern) {
376: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
377:       if (lu->use3d) {
378:         if (lu->grid3d.zscp.Iam == 0) {
379:           PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->cmap->N, &lu->grid3d.grid2d, &lu->LUstruct));
380:           PetscStackCall("SuperLU_DIST:SolveFinalize",SolveFinalize(&lu->options, &lu->SOLVEstruct));
381:         } else {
382:           PetscStackCall("SuperLU_DIST:DeAllocLlu_3d",DeAllocLlu_3d(lu->A_sup.ncol, &lu->LUstruct, &lu->grid3d));
383:           PetscStackCall("SuperLU_DIST:DeAllocGlu_3d",DeAllocGlu_3d(&lu->LUstruct));
384:         }
385:       } else
386: #endif
387:         PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->rmap->N, &lu->grid, &lu->LUstruct));
388:       lu->options.Fact = SamePattern;
389:     } else if (lu->FactPattern == DOFACT) {
390:       PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
391:       PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->rmap->N, &lu->grid, &lu->LUstruct));
392:       lu->options.Fact = DOFACT;
393:       PetscStackCall("SuperLU_DIST:allocateA_dist",allocateA_dist(Aloc->rmap->n, nz, &lu->val, &lu->col, &lu->row));
394:     } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"options.Fact must be one of SamePattern SamePattern_SameRowPerm DOFACT");
395:   }

397:   /* Copy AIJ matrix to superlu_dist matrix */
398:   PetscArraycpy(lu->row,ai,Aloc->rmap->n+1);
399:   PetscArraycpy(lu->col,aj,nz);
400:   PetscArraycpy(lu->val,av,nz);
401:   MatRestoreRowIJ(Aloc,0,PETSC_FALSE,PETSC_FALSE,&dummy,&ai,&aj,&flg);
403:   MatSeqAIJRestoreArrayRead(Aloc,&av);
404:   MatDestroy(&Aloc);

406:   /* Create and setup A_sup */
407:   if (lu->options.Fact == DOFACT) {
408:     PetscStackCall("SuperLU_DIST:Create_CompRowLoc_Matrix_dist",Create_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_ZD, SLU_GE));
409:   }

411:   /* Factor the matrix. */
412:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));   /* Initialize the statistics variables. */
413: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0) && !PetscDefined(MISSING_GETLINE)
414:   if (lu->use3d) {
415:     PetscStackCall("SuperLU_DIST:pgssvx3d",pgssvx3d(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, A->rmap->n, 0, &lu->grid3d, &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
416:   } else
417: #endif
418:     PetscStackCall("SuperLU_DIST:pgssvx",pgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, A->rmap->n, 0, &lu->grid, &lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
419:   if (sinfo > 0) {
421:     else {
422:       if (sinfo <= lu->A_sup.ncol) {
423:         F->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
424:         PetscInfo(F,"U(i,i) is exactly zero, i= %d\n",sinfo);
425:       } else if (sinfo > lu->A_sup.ncol) {
426:         /*
427:          number of bytes allocated when memory allocation
428:          failure occurred, plus A->ncol.
429:          */
430:         F->factorerrortype = MAT_FACTOR_OUTMEMORY;
431:         PetscInfo(F,"Number of bytes allocated when memory allocation fails %d\n",sinfo);
432:       }
433:     }

436:   if (lu->options.PrintStat) {
437:     PetscStackCall("SuperLU_DIST:PStatPrint",PStatPrint(&lu->options, &stat, &lu->grid));  /* Print the statistics. */
438:   }
439:   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
440:   F->assembled     = PETSC_TRUE;
441:   F->preallocated  = PETSC_TRUE;
442:   lu->options.Fact = FACTORED; /* The factored form of A is supplied. Local option used by this func. only */
443:   return 0;
444: }

446: /* Note the Petsc r and c permutations are ignored */
447: static PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
448: {
449:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->data;
450:   PetscInt         M   = A->rmap->N,N=A->cmap->N;

452:   /* Initialize ScalePermstruct and LUstruct. */
453:   PetscStackCall("SuperLU_DIST:ScalePermstructInit",ScalePermstructInit(M, N, &lu->ScalePermstruct));
454:   PetscStackCall("SuperLU_DIST:LUstructInit",LUstructInit(N, &lu->LUstruct));
455:   F->ops->lufactornumeric = MatLUFactorNumeric_SuperLU_DIST;
456:   F->ops->solve           = MatSolve_SuperLU_DIST;
457:   F->ops->matsolve        = MatMatSolve_SuperLU_DIST;
458:   F->ops->getinertia      = NULL;

460:   if (A->symmetric || A->hermitian) F->ops->getinertia = MatGetInertia_SuperLU_DIST;
461:   lu->CleanUpSuperLU_Dist = PETSC_TRUE;
462:   return 0;
463: }

465: static PetscErrorCode MatCholeskyFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,const MatFactorInfo *info)
466: {
467:   MatLUFactorSymbolic_SuperLU_DIST(F,A,r,r,info);
468:   F->ops->choleskyfactornumeric = MatLUFactorNumeric_SuperLU_DIST;
469:   return 0;
470: }

472: static PetscErrorCode MatFactorGetSolverType_aij_superlu_dist(Mat A,MatSolverType *type)
473: {
474:   *type = MATSOLVERSUPERLU_DIST;
475:   return 0;
476: }

478: static PetscErrorCode MatView_Info_SuperLU_DIST(Mat A,PetscViewer viewer)
479: {
480:   Mat_SuperLU_DIST       *lu=(Mat_SuperLU_DIST*)A->data;
481:   superlu_dist_options_t options;

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

486:   options = lu->options;
487:   PetscViewerASCIIPrintf(viewer,"SuperLU_DIST run parameters:\n");
488:   /* would love to use superlu 'IFMT' macro but it looks like it's inconsistently applied, the
489:    * format spec for int64_t is set to %d for whatever reason */
490:   PetscViewerASCIIPrintf(viewer,"  Process grid nprow %lld x npcol %lld \n",(long long)lu->nprow,(long long)lu->npcol);
491: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
492:   if (lu->use3d) {
493:     PetscViewerASCIIPrintf(viewer,"  Using 3d decomposition with npdep %lld \n",(long long)lu->npdep);
494:   }
495: #endif

497:   PetscViewerASCIIPrintf(viewer,"  Equilibrate matrix %s \n",PetscBools[options.Equil != NO]);
498:   PetscViewerASCIIPrintf(viewer,"  Replace tiny pivots %s \n",PetscBools[options.ReplaceTinyPivot != NO]);
499:   PetscViewerASCIIPrintf(viewer,"  Use iterative refinement %s \n",PetscBools[options.IterRefine == SLU_DOUBLE]);
500:   PetscViewerASCIIPrintf(viewer,"  Processors in row %lld col partition %lld \n",(long long)lu->nprow,(long long)lu->npcol);

502:   switch (options.RowPerm) {
503:   case NOROWPERM:
504:     PetscViewerASCIIPrintf(viewer,"  Row permutation NOROWPERM\n");
505:     break;
506:   case LargeDiag_MC64:
507:     PetscViewerASCIIPrintf(viewer,"  Row permutation LargeDiag_MC64\n");
508:     break;
509:   case LargeDiag_AWPM:
510:     PetscViewerASCIIPrintf(viewer,"  Row permutation LargeDiag_AWPM\n");
511:     break;
512:   case MY_PERMR:
513:     PetscViewerASCIIPrintf(viewer,"  Row permutation MY_PERMR\n");
514:     break;
515:   default:
516:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
517:   }

519:   switch (options.ColPerm) {
520:   case NATURAL:
521:     PetscViewerASCIIPrintf(viewer,"  Column permutation NATURAL\n");
522:     break;
523:   case MMD_AT_PLUS_A:
524:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_AT_PLUS_A\n");
525:     break;
526:   case MMD_ATA:
527:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_ATA\n");
528:     break;
529:   /*  Even though this is called METIS, the SuperLU_DIST code sets this by default if PARMETIS is defined, not METIS */
530:   case METIS_AT_PLUS_A:
531:     PetscViewerASCIIPrintf(viewer,"  Column permutation METIS_AT_PLUS_A\n");
532:     break;
533:   case PARMETIS:
534:     PetscViewerASCIIPrintf(viewer,"  Column permutation PARMETIS\n");
535:     break;
536:   default:
537:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
538:   }

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

542:   if (lu->FactPattern == SamePattern) {
543:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern\n");
544:   } else if (lu->FactPattern == SamePattern_SameRowPerm) {
545:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern_SameRowPerm\n");
546:   } else if (lu->FactPattern == DOFACT) {
547:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization DOFACT\n");
548:   } else {
549:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown factorization pattern");
550:   }
551:   return 0;
552: }

554: static PetscErrorCode MatView_SuperLU_DIST(Mat A,PetscViewer viewer)
555: {
556:   PetscBool         iascii;
557:   PetscViewerFormat format;

559:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
560:   if (iascii) {
561:     PetscViewerGetFormat(viewer,&format);
562:     if (format == PETSC_VIEWER_ASCII_INFO) {
563:       MatView_Info_SuperLU_DIST(A,viewer);
564:     }
565:   }
566:   return 0;
567: }

569: static PetscErrorCode MatGetFactor_aij_superlu_dist(Mat A,MatFactorType ftype,Mat *F)
570: {
571:   Mat                    B;
572:   Mat_SuperLU_DIST       *lu;
573:   PetscErrorCode         ierr;
574:   PetscInt               M=A->rmap->N,N=A->cmap->N,indx;
575:   PetscMPIInt            size;
576:   superlu_dist_options_t options;
577:   PetscBool              flg;
578:   const char             *colperm[]     = {"NATURAL","MMD_AT_PLUS_A","MMD_ATA","METIS_AT_PLUS_A","PARMETIS"};
579:   const char             *rowperm[]     = {"NOROWPERM","LargeDiag_MC64","LargeDiag_AWPM","MY_PERMR"};
580:   const char             *factPattern[] = {"SamePattern","SamePattern_SameRowPerm","DOFACT"};
581:   PetscBool              set;

583:   /* Create the factorization matrix */
584:   MatCreate(PetscObjectComm((PetscObject)A),&B);
585:   MatSetSizes(B,A->rmap->n,A->cmap->n,M,N);
586:   PetscStrallocpy("superlu_dist",&((PetscObject)B)->type_name);
587:   MatSetUp(B);
588:   B->ops->getinfo = MatGetInfo_External;
589:   B->ops->view    = MatView_SuperLU_DIST;
590:   B->ops->destroy = MatDestroy_SuperLU_DIST;

592:   /* Set the default input options:
593:      options.Fact              = DOFACT;
594:      options.Equil             = YES;
595:      options.ParSymbFact       = NO;
596:      options.ColPerm           = METIS_AT_PLUS_A;
597:      options.RowPerm           = LargeDiag_MC64;
598:      options.ReplaceTinyPivot  = YES;
599:      options.IterRefine        = DOUBLE;
600:      options.Trans             = NOTRANS;
601:      options.SolveInitialized  = NO; -hold the communication pattern used MatSolve() and MatMatSolve()
602:      options.RefineInitialized = NO;
603:      options.PrintStat         = YES;
604:      options.SymPattern        = NO;
605:   */
606:   set_default_options_dist(&options);

608:   B->trivialsymbolic = PETSC_TRUE;
609:   if (ftype == MAT_FACTOR_LU) {
610:     B->factortype = MAT_FACTOR_LU;
611:     B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
612:   } else {
613:     B->factortype = MAT_FACTOR_CHOLESKY;
614:     B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SuperLU_DIST;
615:     options.SymPattern = YES;
616:   }

618:   /* set solvertype */
619:   PetscFree(B->solvertype);
620:   PetscStrallocpy(MATSOLVERSUPERLU_DIST,&B->solvertype);

622:   PetscNewLog(B,&lu);
623:   B->data = lu;
624:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);

626:   {
627:     PetscMPIInt       flg;
628:     MPI_Comm          comm;
629:     PetscSuperLU_DIST *context = NULL;

631:     PetscObjectGetComm((PetscObject)A,&comm);
632:     if (Petsc_Superlu_dist_keyval == MPI_KEYVAL_INVALID) {
633:       MPI_Comm_create_keyval(MPI_COMM_NULL_COPY_FN,Petsc_Superlu_dist_keyval_Delete_Fn,&Petsc_Superlu_dist_keyval,(void*)0);
634:       PetscRegisterFinalize(Petsc_Superlu_dist_keyval_free);
635:     }
636:     MPI_Comm_get_attr(comm,Petsc_Superlu_dist_keyval,&context,&flg);
637:     if (!flg || context->busy) {
638:       if (!flg) {
639:         PetscNew(&context);
640:         context->busy = PETSC_TRUE;
641:         MPI_Comm_dup(comm,&context->comm);
642:         MPI_Comm_set_attr(comm,Petsc_Superlu_dist_keyval,context);
643:       } else {
644:         PetscCommGetComm(PetscObjectComm((PetscObject)A),&lu->comm_superlu);
645:       }

647:       /* Default number of process columns and rows */
648:       lu->nprow = (int_t) (0.5 + PetscSqrtReal((PetscReal)size));
649:       if (!lu->nprow) lu->nprow = 1;
650:       while (lu->nprow > 0) {
651:         lu->npcol = (int_t) (size/lu->nprow);
652:         if (size == lu->nprow * lu->npcol) break;
653:         lu->nprow--;
654:       }
655: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
656:       lu->use3d = PETSC_FALSE;
657:       lu->npdep = 1;
658: #endif
659:       PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"SuperLU_Dist Options","Mat");
660: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
661:       PetscOptionsBool("-mat_superlu_dist_3d","Use SuperLU_DIST 3D distribution","None",lu->use3d,&lu->use3d,NULL);
663:       if (lu->use3d) {
664:         PetscInt t;
665:         PetscOptionsInt("-mat_superlu_dist_d","Number of z entries in processor partition","None",lu->npdep,(PetscInt*)&lu->npdep,NULL);
666:         t = (PetscInt) PetscLog2Real((PetscReal)lu->npdep);
668:         if (lu->npdep > 1) {
669:           lu->nprow = (int_t) (0.5 + PetscSqrtReal((PetscReal)(size/lu->npdep)));
670:           if (!lu->nprow) lu->nprow = 1;
671:           while (lu->nprow > 0) {
672:             lu->npcol = (int_t) (size/(lu->npdep*lu->nprow));
673:             if (size == lu->nprow * lu->npcol * lu->npdep) break;
674:             lu->nprow--;
675:           }
676:         }
677:       }
678: #endif
679:       PetscOptionsInt("-mat_superlu_dist_r","Number rows in processor partition","None",lu->nprow,(PetscInt*)&lu->nprow,NULL);
680:       PetscOptionsInt("-mat_superlu_dist_c","Number columns in processor partition","None",lu->npcol,(PetscInt*)&lu->npcol,NULL);
681: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
683: #else
685: #endif
686:       PetscOptionsEnd();
687: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
688:       if (lu->use3d) {
689:         PetscStackCall("SuperLU_DIST:superlu_gridinit3d",superlu_gridinit3d(context ? context->comm : lu->comm_superlu, lu->nprow, lu->npcol,lu->npdep, &lu->grid3d));
690:         if (context) {context->grid3d = lu->grid3d; context->use3d = lu->use3d;}
691:       } else {
692: #endif
693:         PetscStackCall("SuperLU_DIST:superlu_gridinit",superlu_gridinit(context ? context->comm : lu->comm_superlu, lu->nprow, lu->npcol, &lu->grid));
694:         if (context) context->grid = lu->grid;
695: #if PETSC_PKG_SUPERLU_DIST_VERSION_GE(7,2,0)
696:       }
697: #endif
698:       PetscInfo(NULL,"Duplicating a communicator for SuperLU_DIST and calling superlu_gridinit()\n");
699:       if (flg) {
700:         PetscInfo(NULL,"Communicator attribute already in use so not saving communicator and SuperLU_DIST grid in communicator attribute \n");
701:       } else {
702:         PetscInfo(NULL,"Storing communicator and SuperLU_DIST grid in communicator attribute\n");
703:       }
704:     } else {
705:       PetscInfo(NULL,"Reusing communicator and superlu_gridinit() for SuperLU_DIST from communicator attribute.");
706:       context->busy = PETSC_TRUE;
707:       lu->grid      = context->grid;
708:     }
709:   }

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

715:   PetscOptionsEList("-mat_superlu_dist_rowperm","Row permutation","None",rowperm,4,rowperm[1],&indx,&flg);
716:   if (flg) {
717:     switch (indx) {
718:     case 0:
719:       options.RowPerm = NOROWPERM;
720:       break;
721:     case 1:
722:       options.RowPerm = LargeDiag_MC64;
723:       break;
724:     case 2:
725:       options.RowPerm = LargeDiag_AWPM;
726:       break;
727:     case 3:
728:       options.RowPerm = MY_PERMR;
729:       break;
730:     default:
731:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown row permutation");
732:     }
733:   }

735:   PetscOptionsEList("-mat_superlu_dist_colperm","Column permutation","None",colperm,5,colperm[3],&indx,&flg);
736:   if (flg) {
737:     switch (indx) {
738:     case 0:
739:       options.ColPerm = NATURAL;
740:       break;
741:     case 1:
742:       options.ColPerm = MMD_AT_PLUS_A;
743:       break;
744:     case 2:
745:       options.ColPerm = MMD_ATA;
746:       break;
747:     case 3:
748:       options.ColPerm = METIS_AT_PLUS_A;
749:       break;
750:     case 4:
751:       options.ColPerm = PARMETIS;   /* only works for np>1 */
752:       break;
753:     default:
754:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
755:     }
756:   }

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

762:   options.ParSymbFact = NO;
763:   PetscOptionsBool("-mat_superlu_dist_parsymbfact","Parallel symbolic factorization","None",PETSC_FALSE,&flg,&set);
764:   if (set && flg && size>1) {
765: #if defined(PETSC_HAVE_PARMETIS)
766:     options.ParSymbFact = YES;
767:     options.ColPerm     = PARMETIS;   /* in v2.2, PARMETIS is forced for ParSymbFact regardless of user ordering setting */
768: #else
769:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"parsymbfact needs PARMETIS");
770: #endif
771:   }

773:   lu->FactPattern = SamePattern;
774:   PetscOptionsEList("-mat_superlu_dist_fact","Sparsity pattern for repeated matrix factorization","None",factPattern,3,factPattern[0],&indx,&flg);
775:   if (flg) {
776:     switch (indx) {
777:     case 0:
778:       lu->FactPattern = SamePattern;
779:       break;
780:     case 1:
781:       lu->FactPattern = SamePattern_SameRowPerm;
782:       break;
783:     case 2:
784:       lu->FactPattern = DOFACT;
785:       break;
786:     }
787:   }

789:   options.IterRefine = NOREFINE;
790:   PetscOptionsBool("-mat_superlu_dist_iterrefine","Use iterative refinement","None",options.IterRefine == NOREFINE ? PETSC_FALSE : PETSC_TRUE ,&flg,&set);
791:   if (set) {
792:     if (flg) options.IterRefine = SLU_DOUBLE;
793:     else options.IterRefine = NOREFINE;
794:   }

796:   if (PetscLogPrintInfo) options.PrintStat = YES;
797:   else options.PrintStat = NO;
798:   PetscOptionsBool("-mat_superlu_dist_statprint","Print factorization information","None",(PetscBool)options.PrintStat,(PetscBool*)&options.PrintStat,NULL);
799:   PetscOptionsEnd();

801:   lu->options              = options;
802:   lu->options.Fact         = DOFACT;
803:   lu->matsolve_iscalled    = PETSC_FALSE;
804:   lu->matmatsolve_iscalled = PETSC_FALSE;

806:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_aij_superlu_dist);
807:   PetscObjectComposeFunction((PetscObject)B,"MatSuperluDistGetDiagU_C",MatSuperluDistGetDiagU_SuperLU_DIST);

809:   *F = B;
810:   return 0;
811: }

813: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_SuperLU_DIST(void)
814: {
815:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
816:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
817:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_superlu_dist);
818:   MatSolverTypeRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ,MAT_FACTOR_CHOLESKY,MatGetFactor_aij_superlu_dist);
819:   return 0;
820: }

822: /*MC
823:   MATSOLVERSUPERLU_DIST - Parallel direct solver package for LU factorization

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

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

829:    Works with AIJ matrices

831:   Options Database Keys:
832: + -mat_superlu_dist_r <n> - number of rows in processor partition
833: . -mat_superlu_dist_c <n> - number of columns in processor partition
834: . -mat_superlu_dist_3d - use 3d partition, requires SuperLU_DIST 7.2 or later
835: . -mat_superlu_dist_d <n> - depth in 3d partition (valid only if -mat_superlu_dist_3d) is provided
836: . -mat_superlu_dist_equil - equilibrate the matrix
837: . -mat_superlu_dist_rowperm <NOROWPERM,LargeDiag_MC64,LargeDiag_AWPM,MY_PERMR> - row permutation
838: . -mat_superlu_dist_colperm <NATURAL,MMD_AT_PLUS_A,MMD_ATA,METIS_AT_PLUS_A,PARMETIS> - column permutation
839: . -mat_superlu_dist_replacetinypivot - replace tiny pivots
840: . -mat_superlu_dist_fact <SamePattern> - (choose one of) SamePattern SamePattern_SameRowPerm DOFACT
841: . -mat_superlu_dist_iterrefine - use iterative refinement
842: - -mat_superlu_dist_statprint - print factorization information

844:   Notes:
845:     If PETSc was configured with --with-cuda than this solver will automatically use the GPUs.

847:   Level: beginner

849: .seealso: PCLU

851: .seealso: PCFactorSetMatSolverType(), MatSolverType

853: M*/