Actual source code: superlu_dist.c
petsc-3.7.1 2016-05-15
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      2: /*
      3:         Provides an interface to the SuperLU_DIST_2.2 sparse solver
      4: */

      6: #include <../src/mat/impls/aij/seq/aij.h>
      7: #include <../src/mat/impls/aij/mpi/mpiaij.h>
      8: #if defined(PETSC_HAVE_STDLIB_H) /* This is to get around weird problem with SuperLU on cray */
      9: #include <stdlib.h>
     10: #endif

     12: #if defined(PETSC_USE_64BIT_INDICES)
     13: /* ugly SuperLU_Dist variable telling it to use long long int */
     14: #define _LONGINT
     15: #endif

     17: EXTERN_C_BEGIN
     18: #if defined(PETSC_USE_COMPLEX)
     19: #include <superlu_zdefs.h>
     20: #else
     21: #include <superlu_ddefs.h>
     22: #endif
     23: EXTERN_C_END

     25: /*
     26:     GLOBAL - The sparse matrix and right hand side are all stored initially on process 0. Should be called centralized
     27:     DISTRIBUTED - The sparse matrix and right hand size are initially stored across the entire MPI communicator.
     28: */
     29: typedef enum {GLOBAL,DISTRIBUTED} SuperLU_MatInputMode;
     30: const char *SuperLU_MatInputModes[] = {"GLOBAL","DISTRIBUTED","SuperLU_MatInputMode","PETSC_",0};

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

     53: extern PetscErrorCode MatFactorInfo_SuperLU_DIST(Mat,PetscViewer);
     54: extern PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat,Mat,const MatFactorInfo*);
     55: extern PetscErrorCode MatDestroy_SuperLU_DIST(Mat);
     56: extern PetscErrorCode MatView_SuperLU_DIST(Mat,PetscViewer);
     57: extern PetscErrorCode MatSolve_SuperLU_DIST(Mat,Vec,Vec);
     58: extern PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat,Mat,IS,IS,const MatFactorInfo*);
     59: extern PetscErrorCode MatDestroy_MPIAIJ(Mat);

     63: PetscErrorCode MatGetDiagonal_SuperLU_DIST(Mat A,Vec v)
     64: {
     66:   SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"Mat type: SuperLU_DIST factor");
     67:   return(0);
     68: }

     72: PetscErrorCode MatDestroy_SuperLU_DIST(Mat A)
     73: {
     74:   PetscErrorCode   ierr;
     75:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
     76:   PetscBool        flg;

     79:   if (lu && lu->CleanUpSuperLU_Dist) {
     80:     /* Deallocate SuperLU_DIST storage */
     81:     if (lu->MatInputMode == GLOBAL) {
     82:       PetscStackCall("SuperLU_DIST:Destroy_CompCol_Matrix_dist",Destroy_CompCol_Matrix_dist(&lu->A_sup));
     83:     } else {
     84:       PetscStackCall("SuperLU_DIST:Destroy_CompRowLoc_Matrix_dist",Destroy_CompRowLoc_Matrix_dist(&lu->A_sup));
     85:       if (lu->options.SolveInitialized) {
     86: #if defined(PETSC_USE_COMPLEX)
     87:         PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
     88: #else
     89:         PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
     90: #endif
     91:       }
     92:     }
     93:     PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(A->cmap->N, &lu->grid, &lu->LUstruct));
     94:     PetscStackCall("SuperLU_DIST:ScalePermstructFree",ScalePermstructFree(&lu->ScalePermstruct));
     95:     PetscStackCall("SuperLU_DIST:LUstructFree",LUstructFree(&lu->LUstruct));

     97:     /* Release the SuperLU_DIST process grid. */
     98:     PetscStackCall("SuperLU_DIST:superlu_gridexit",superlu_gridexit(&lu->grid));
     99:     MPI_Comm_free(&(lu->comm_superlu));
    100:   }
    101:   PetscFree(A->spptr);

    103:   PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&flg);
    104:   if (flg) {
    105:     MatDestroy_SeqAIJ(A);
    106:   } else {
    107:     MatDestroy_MPIAIJ(A);
    108:   }
    109:   return(0);
    110: }

    114: PetscErrorCode MatSolve_SuperLU_DIST(Mat A,Vec b_mpi,Vec x)
    115: {
    116:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
    117:   PetscErrorCode   ierr;
    118:   PetscMPIInt      size;
    119:   PetscInt         m=A->rmap->n,M=A->rmap->N,N=A->cmap->N;
    120:   SuperLUStat_t    stat;
    121:   double           berr[1];
    122:   PetscScalar      *bptr;
    123:   PetscInt         nrhs=1;
    124:   Vec              x_seq;
    125:   IS               iden;
    126:   VecScatter       scat;
    127:   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
    128:   static PetscBool cite = PETSC_FALSE;

    131:   PetscCitationsRegister("@article{lidemmel03,\n  author = {Xiaoye S. Li and James W. Demmel},\n  title = {{SuperLU_DIST}: A Scalable Distributed-Memory Sparse Direct\n           Solver for Unsymmetric Linear Systems},\n  journal = {ACM Trans. Mathematical Software},\n  volume = {29},\n  number = {2},\n  pages = {110-140},\n  year = 2003\n}\n",&cite);
    132:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
    133:   if (size > 1 && lu->MatInputMode == GLOBAL) {
    134:     /* global mat input, convert b to x_seq */
    135:     VecCreateSeq(PETSC_COMM_SELF,N,&x_seq);
    136:     ISCreateStride(PETSC_COMM_SELF,N,0,1,&iden);
    137:     VecScatterCreate(b_mpi,iden,x_seq,iden,&scat);
    138:     ISDestroy(&iden);

    140:     VecScatterBegin(scat,b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD);
    141:     VecScatterEnd(scat,b_mpi,x_seq,INSERT_VALUES,SCATTER_FORWARD);
    142:     VecGetArray(x_seq,&bptr);
    143:   } else { /* size==1 || distributed mat input */
    144:     if (lu->options.SolveInitialized && !lu->matsolve_iscalled) {
    145:       /* see comments in MatMatSolve() */
    146: #if defined(PETSC_USE_COMPLEX)
    147:       PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
    148: #else
    149:       PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
    150: #endif
    151:       lu->options.SolveInitialized = NO;
    152:     }
    153:     VecCopy(b_mpi,x);
    154:     VecGetArray(x,&bptr);
    155:   }

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

    159:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));        /* Initialize the statistics variables. */
    160:   if (lu->MatInputMode == GLOBAL) {
    161: #if defined(PETSC_USE_COMPLEX)
    162:     PetscStackCall("SuperLU_DIST:pzgssvx_ABglobal",pzgssvx_ABglobal(&lu->options,&lu->A_sup,&lu->ScalePermstruct,(doublecomplex*)bptr,M,nrhs,&lu->grid,&lu->LUstruct,berr,&stat,&info));
    163: #else
    164:     PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr,M,nrhs,&lu->grid,&lu->LUstruct,berr,&stat,&info));
    165: #endif
    166:   } else { /* distributed mat input */
    167: #if defined(PETSC_USE_COMPLEX)
    168:     PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,(doublecomplex*)bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
    169: #else
    170:     PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
    171: #endif
    172:   }
    173:   if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);

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

    178:   if (size > 1 && lu->MatInputMode == GLOBAL) {
    179:     /* convert seq x to mpi x */
    180:     VecRestoreArray(x_seq,&bptr);
    181:     VecScatterBegin(scat,x_seq,x,INSERT_VALUES,SCATTER_REVERSE);
    182:     VecScatterEnd(scat,x_seq,x,INSERT_VALUES,SCATTER_REVERSE);
    183:     VecScatterDestroy(&scat);
    184:     VecDestroy(&x_seq);
    185:   } else {
    186:     VecRestoreArray(x,&bptr);

    188:     lu->matsolve_iscalled    = PETSC_TRUE;
    189:     lu->matmatsolve_iscalled = PETSC_FALSE;
    190:   }
    191:   return(0);
    192: }

    196: PetscErrorCode MatMatSolve_SuperLU_DIST(Mat A,Mat B_mpi,Mat X)
    197: {
    198:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)A->spptr;
    199:   PetscErrorCode   ierr;
    200:   PetscMPIInt      size;
    201:   PetscInt         M=A->rmap->N,m=A->rmap->n,nrhs;
    202:   SuperLUStat_t    stat;
    203:   double           berr[1];
    204:   PetscScalar      *bptr;
    205:   int              info; /* SuperLU_Dist info code is ALWAYS an int, even with long long indices */
    206:   PetscBool        flg;

    209:   if (lu->options.Fact != FACTORED) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"SuperLU_DIST options.Fact mush equal FACTORED");
    210:   PetscObjectTypeCompareAny((PetscObject)B_mpi,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
    211:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix B must be MATDENSE matrix");
    212:   PetscObjectTypeCompareAny((PetscObject)X,&flg,MATSEQDENSE,MATMPIDENSE,NULL);
    213:   if (!flg) SETERRQ(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Matrix X must be MATDENSE matrix");

    215:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
    216:   if (size > 1 && lu->MatInputMode == GLOBAL) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"MatInputMode=GLOBAL for nproc %d>1 is not supported",size);
    217:   /* size==1 or distributed mat input */
    218:   if (lu->options.SolveInitialized && !lu->matmatsolve_iscalled) {
    219:     /* communication pattern of SOLVEstruct is unlikely created for matmatsolve,
    220:        thus destroy it and create a new SOLVEstruct.
    221:        Otherwise it may result in memory corruption or incorrect solution
    222:        See src/mat/examples/tests/ex125.c */
    223: #if defined(PETSC_USE_COMPLEX)
    224:     PetscStackCall("SuperLU_DIST:zSolveFinalize",zSolveFinalize(&lu->options, &lu->SOLVEstruct));
    225: #else
    226:     PetscStackCall("SuperLU_DIST:dSolveFinalize",dSolveFinalize(&lu->options, &lu->SOLVEstruct));
    227: #endif
    228:     lu->options.SolveInitialized = NO;
    229:   }
    230:   MatCopy(B_mpi,X,SAME_NONZERO_PATTERN);

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

    234:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));        /* Initialize the statistics variables. */
    235:   MatDenseGetArray(X,&bptr);
    236:   if (lu->MatInputMode == GLOBAL) { /* size == 1 */
    237: #if defined(PETSC_USE_COMPLEX)
    238:     PetscStackCall("SuperLU_DIST:pzgssvx_ABglobal",pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,(doublecomplex*)bptr, M, nrhs,&lu->grid, &lu->LUstruct, berr, &stat, &info));
    239: #else
    240:     PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct,bptr, M, nrhs, &lu->grid, &lu->LUstruct, berr, &stat, &info));
    241: #endif
    242:   } else { /* distributed mat input */
    243: #if defined(PETSC_USE_COMPLEX)
    244:     PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,(doublecomplex*)bptr,m,nrhs,&lu->grid, &lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
    245: #else
    246:     PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options,&lu->A_sup,&lu->ScalePermstruct,bptr,m,nrhs,&lu->grid,&lu->LUstruct,&lu->SOLVEstruct,berr,&stat,&info));
    247: #endif
    248:   }
    249:   if (info) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"pdgssvx fails, info: %d\n",info);
    250:   MatDenseRestoreArray(X,&bptr);

    252:   if (lu->options.PrintStat) PStatPrint(&lu->options, &stat, &lu->grid); /* Print the statistics. */
    253:   PetscStackCall("SuperLU_DIST:PStatFree",PStatFree(&stat));
    254:   lu->matsolve_iscalled    = PETSC_FALSE;
    255:   lu->matmatsolve_iscalled = PETSC_TRUE;
    256:   return(0);
    257: }

    262: PetscErrorCode MatLUFactorNumeric_SuperLU_DIST(Mat F,Mat A,const MatFactorInfo *info)
    263: {
    264:   Mat              *tseq,A_seq = NULL;
    265:   Mat_SeqAIJ       *aa,*bb;
    266:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)(F)->spptr;
    267:   PetscErrorCode   ierr;
    268:   PetscInt         M=A->rmap->N,N=A->cmap->N,i,*ai,*aj,*bi,*bj,nz,rstart,*garray,
    269:                    m=A->rmap->n, colA_start,j,jcol,jB,countA,countB,*bjj,*ajj;
    270:   int              sinfo;   /* SuperLU_Dist info flag is always an int even with long long indices */
    271:   PetscMPIInt      size;
    272:   SuperLUStat_t    stat;
    273:   double           *berr=0;
    274:   IS               isrow;
    275:   Mat              F_diag=NULL;
    276: #if defined(PETSC_USE_COMPLEX)
    277:   doublecomplex    *av, *bv;
    278: #else
    279:   double           *av, *bv;
    280: #endif

    283:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);

    285:   if (lu->MatInputMode == GLOBAL) { /* global mat input */
    286:     if (size > 1) { /* convert mpi A to seq mat A */
    287:       ISCreateStride(PETSC_COMM_SELF,M,0,1,&isrow);
    288:       MatGetSubMatrices(A,1,&isrow,&isrow,MAT_INITIAL_MATRIX,&tseq);
    289:       ISDestroy(&isrow);

    291:       A_seq = *tseq;
    292:       PetscFree(tseq);
    293:       aa    = (Mat_SeqAIJ*)A_seq->data;
    294:     } else {
    295:       PetscBool flg;
    296:       PetscObjectTypeCompare((PetscObject)A,MATMPIAIJ,&flg);
    297:       if (flg) {
    298:         Mat_MPIAIJ *At = (Mat_MPIAIJ*)A->data;
    299:         A = At->A;
    300:       }
    301:       aa =  (Mat_SeqAIJ*)A->data;
    302:     }

    304:     /* Convert Petsc NR matrix to SuperLU_DIST NC.
    305:        Note: memories of lu->val, col and row are allocated by CompRow_to_CompCol_dist()! */
    306:     if (lu->options.Fact != DOFACT) {/* successive numeric factorization, sparsity pattern is reused. */
    307:       PetscStackCall("SuperLU_DIST:Destroy_CompCol_Matrix_dist",Destroy_CompCol_Matrix_dist(&lu->A_sup));
    308:       if (lu->FactPattern == SamePattern_SameRowPerm) {
    309:         lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
    310:       } else { /* lu->FactPattern == SamePattern */
    311:         PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(N, &lu->grid, &lu->LUstruct));
    312:         lu->options.Fact = SamePattern;
    313:       }
    314:     }
    315: #if defined(PETSC_USE_COMPLEX)
    316:     PetscStackCall("SuperLU_DIST:zCompRow_to_CompCol_dist",zCompRow_to_CompCol_dist(M,N,aa->nz,(doublecomplex*)aa->a,(int_t*)aa->j,(int_t*)aa->i,&lu->val,&lu->col, &lu->row));
    317: #else
    318:     PetscStackCall("SuperLU_DIST:dCompRow_to_CompCol_dist",dCompRow_to_CompCol_dist(M,N,aa->nz,aa->a,(int_t*)aa->j,(int_t*)aa->i,&lu->val, &lu->col, &lu->row));
    319: #endif

    321:     /* Create compressed column matrix A_sup. */
    322: #if defined(PETSC_USE_COMPLEX)
    323:     PetscStackCall("SuperLU_DIST:zCreate_CompCol_Matrix_dist",zCreate_CompCol_Matrix_dist(&lu->A_sup, M, N, aa->nz, lu->val, lu->col, lu->row, SLU_NC, SLU_Z, SLU_GE));
    324: #else
    325:     PetscStackCall("SuperLU_DIST:dCreate_CompCol_Matrix_dist",dCreate_CompCol_Matrix_dist(&lu->A_sup, M, N, aa->nz, lu->val, lu->col, lu->row, SLU_NC, SLU_D, SLU_GE));
    326: #endif
    327:   } else { /* distributed mat input */
    328:     Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
    329:     aa=(Mat_SeqAIJ*)(mat->A)->data;
    330:     bb=(Mat_SeqAIJ*)(mat->B)->data;
    331:     ai=aa->i; aj=aa->j;
    332:     bi=bb->i; bj=bb->j;
    333: #if defined(PETSC_USE_COMPLEX)
    334:     av=(doublecomplex*)aa->a;
    335:     bv=(doublecomplex*)bb->a;
    336: #else
    337:     av=aa->a;
    338:     bv=bb->a;
    339: #endif
    340:     rstart = A->rmap->rstart;
    341:     nz     = aa->nz + bb->nz;
    342:     garray = mat->garray;

    344:     if (lu->options.Fact == DOFACT) { /* first numeric factorization */
    345: #if defined(PETSC_USE_COMPLEX)
    346:       PetscStackCall("SuperLU_DIST:zallocateA_dist",zallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row));
    347: #else
    348:       PetscStackCall("SuperLU_DIST:dallocateA_dist",dallocateA_dist(m, nz, &lu->val, &lu->col, &lu->row));
    349: #endif
    350:     } else { /* successive numeric factorization, sparsity pattern and perm_c are reused. */
    351:       SUPERLU_FREE((void*)lu->A_sup.Store);
    352:       if (lu->FactPattern == SamePattern_SameRowPerm) {
    353:         lu->options.Fact = SamePattern_SameRowPerm; /* matrix has similar numerical values */
    354:       } else {
    355:         PetscStackCall("SuperLU_DIST:Destroy_LU",Destroy_LU(N, &lu->grid, &lu->LUstruct)); /* Deallocate storage associated with the L and U matrices. */
    356:         lu->options.Fact = SamePattern;
    357:       }
    358:     }
    359:     nz = 0;
    360:     for (i=0; i<m; i++) {
    361:       lu->row[i] = nz;
    362:       countA     = ai[i+1] - ai[i];
    363:       countB     = bi[i+1] - bi[i];
    364:       if (aj) {
    365:         ajj = aj + ai[i]; /* ptr to the beginning of this row */
    366:       } else {
    367:         ajj = NULL;
    368:       }
    369:       bjj = bj + bi[i];

    371:       /* B part, smaller col index */
    372:       if (aj) {
    373:         colA_start = rstart + ajj[0]; /* the smallest global col index of A */
    374:       } else { /* superlu_dist does not require matrix has diagonal entries, thus aj=NULL would work */
    375:         colA_start = rstart;
    376:       }
    377:       jB         = 0;
    378:       for (j=0; j<countB; j++) {
    379:         jcol = garray[bjj[j]];
    380:         if (jcol > colA_start) {
    381:           jB = j;
    382:           break;
    383:         }
    384:         lu->col[nz]   = jcol;
    385:         lu->val[nz++] = *bv++;
    386:         if (j==countB-1) jB = countB;
    387:       }

    389:       /* A part */
    390:       for (j=0; j<countA; j++) {
    391:         lu->col[nz]   = rstart + ajj[j];
    392:         lu->val[nz++] = *av++;
    393:       }
    394:
    395:       /* B part, larger col index */
    396:       for (j=jB; j<countB; j++) {
    397:         lu->col[nz]   = garray[bjj[j]];
    398:         lu->val[nz++] = *bv++;
    399:       }
    400:     }
    401:     lu->row[m] = nz;
    402: #if defined(PETSC_USE_COMPLEX)
    403:     PetscStackCall("SuperLU_DIST:zCreate_CompRowLoc_Matrix_dist",zCreate_CompRowLoc_Matrix_dist(&lu->A_sup, M, N, nz, m, rstart,lu->val, lu->col, lu->row, SLU_NR_loc, SLU_Z, SLU_GE));
    404: #else
    405:     PetscStackCall("SuperLU_DIST:dCreate_CompRowLoc_Matrix_dist",dCreate_CompRowLoc_Matrix_dist(&lu->A_sup, M, N, nz, m, rstart,lu->val, lu->col, lu->row, SLU_NR_loc, SLU_D, SLU_GE));
    406: #endif
    407:   }

    409:   /* Factor the matrix. */
    410:   PetscStackCall("SuperLU_DIST:PStatInit",PStatInit(&stat));   /* Initialize the statistics variables. */
    411:   if (lu->MatInputMode == GLOBAL) { /* global mat input */
    412: #if defined(PETSC_USE_COMPLEX)
    413:     PetscStackCall("SuperLU_DIST:pzgssvx_ABglobal",pzgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,&lu->grid, &lu->LUstruct, berr, &stat, &sinfo));
    414: #else
    415:     PetscStackCall("SuperLU_DIST:pdgssvx_ABglobal",pdgssvx_ABglobal(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, M, 0,&lu->grid, &lu->LUstruct, berr, &stat, &sinfo));
    416: #endif
    417:   } else { /* distributed mat input */
    418: #if defined(PETSC_USE_COMPLEX)
    419:     PetscStackCall("SuperLU_DIST:pzgssvx",pzgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, m, 0, &lu->grid,&lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
    420: #else
    421:     PetscStackCall("SuperLU_DIST:pdgssvx",pdgssvx(&lu->options, &lu->A_sup, &lu->ScalePermstruct, 0, m, 0, &lu->grid,&lu->LUstruct, &lu->SOLVEstruct, berr, &stat, &sinfo));
    422: #endif
    423:   }
    424:
    425:   if (sinfo > 0) {
    426:     if (A->erroriffailure) {
    427:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot in row %D",sinfo);
    428:     } else {
    429:       if (sinfo <= lu->A_sup.ncol) {
    430:         F->errortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
    431:         PetscInfo1(F,"U(i,i) is exactly zero, i= %D\n",sinfo);
    432:       } else if (sinfo > lu->A_sup.ncol) {
    433:         /*
    434:          number of bytes allocated when memory allocation
    435:          failure occurred, plus A->ncol.
    436:          */
    437:         F->errortype = MAT_FACTOR_OUTMEMORY;
    438:         PetscInfo1(F,"Number of bytes allocated when memory allocation fails %D\n",sinfo);
    439:       }
    440:     }
    441:   } else if (sinfo < 0) {
    442:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB, "info = %D, argument in p*gssvx() had an illegal value", sinfo);
    443:   }

    445:   if (lu->MatInputMode == GLOBAL && size > 1) {
    446:     MatDestroy(&A_seq);
    447:   }

    449:   if (lu->options.PrintStat) {
    450:     PStatPrint(&lu->options, &stat, &lu->grid);  /* Print the statistics. */
    451:   }
    452:   PStatFree(&stat);
    453:   if (size > 1) {
    454:     F_diag            = ((Mat_MPIAIJ*)(F)->data)->A;
    455:     F_diag->assembled = PETSC_TRUE;
    456:   }
    457:   (F)->assembled    = PETSC_TRUE;
    458:   (F)->preallocated = PETSC_TRUE;
    459:   lu->options.Fact  = FACTORED; /* The factored form of A is supplied. Local option used by this func. only */
    460:   return(0);
    461: }

    463: /* Note the Petsc r and c permutations are ignored */
    466: PetscErrorCode MatLUFactorSymbolic_SuperLU_DIST(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
    467: {
    468:   Mat_SuperLU_DIST *lu = (Mat_SuperLU_DIST*)F->spptr;
    469:   PetscInt         M   = A->rmap->N,N=A->cmap->N;

    472:   /* Initialize the SuperLU process grid. */
    473:   PetscStackCall("SuperLU_DIST:superlu_gridinit",superlu_gridinit(lu->comm_superlu, lu->nprow, lu->npcol, &lu->grid));

    475:   /* Initialize ScalePermstruct and LUstruct. */
    476:   PetscStackCall("SuperLU_DIST:ScalePermstructInit",ScalePermstructInit(M, N, &lu->ScalePermstruct));
    477:   PetscStackCall("SuperLU_DIST:LUstructInit",LUstructInit(N, &lu->LUstruct));
    478:   F->ops->lufactornumeric = MatLUFactorNumeric_SuperLU_DIST;
    479:   F->ops->solve           = MatSolve_SuperLU_DIST;
    480:   F->ops->matsolve        = MatMatSolve_SuperLU_DIST;
    481:   lu->CleanUpSuperLU_Dist = PETSC_TRUE;
    482:   return(0);
    483: }

    487: static PetscErrorCode MatFactorGetSolverPackage_aij_superlu_dist(Mat A,const MatSolverPackage *type)
    488: {
    490:   *type = MATSOLVERSUPERLU_DIST;
    491:   return(0);
    492: }

    496: static PetscErrorCode MatGetFactor_aij_superlu_dist(Mat A,MatFactorType ftype,Mat *F)
    497: {
    498:   Mat                    B;
    499:   Mat_SuperLU_DIST       *lu;
    500:   PetscErrorCode         ierr;
    501:   PetscInt               M=A->rmap->N,N=A->cmap->N,indx;
    502:   PetscMPIInt            size;
    503:   superlu_dist_options_t options;
    504:   PetscBool              flg;
    505:   const char             *colperm[]     = {"NATURAL","MMD_AT_PLUS_A","MMD_ATA","METIS_AT_PLUS_A","PARMETIS"};
    506:   const char             *rowperm[]     = {"LargeDiag","NATURAL"};
    507:   const char             *factPattern[] = {"SamePattern","SamePattern_SameRowPerm"};
    508:   PetscBool              set;

    511:   /* Create the factorization matrix */
    512:   MatCreate(PetscObjectComm((PetscObject)A),&B);
    513:   MatSetSizes(B,A->rmap->n,A->cmap->n,M,N);
    514:   MatSetType(B,((PetscObject)A)->type_name);
    515:   MatSeqAIJSetPreallocation(B,0,NULL);
    516:   MatMPIAIJSetPreallocation(B,0,NULL,0,NULL);

    518:   B->ops->lufactorsymbolic = MatLUFactorSymbolic_SuperLU_DIST;
    519:   B->ops->view             = MatView_SuperLU_DIST;
    520:   B->ops->destroy          = MatDestroy_SuperLU_DIST;
    521:   B->ops->getdiagonal      = MatGetDiagonal_SuperLU_DIST;

    523:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_aij_superlu_dist);

    525:   B->factortype = MAT_FACTOR_LU;

    527:   /* set solvertype */
    528:   PetscFree(B->solvertype);
    529:   PetscStrallocpy(MATSOLVERSUPERLU_DIST,&B->solvertype);

    531:   PetscNewLog(B,&lu);
    532:   B->spptr = lu;

    534:   /* Set the default input options:
    535:      options.Fact              = DOFACT;
    536:      options.Equil             = YES;
    537:      options.ParSymbFact       = NO;
    538:      options.ColPerm           = METIS_AT_PLUS_A;
    539:      options.RowPerm           = LargeDiag;
    540:      options.ReplaceTinyPivot  = YES;
    541:      options.IterRefine        = DOUBLE;
    542:      options.Trans             = NOTRANS;
    543:      options.SolveInitialized  = NO; -hold the communication pattern used MatSolve() and MatMatSolve()
    544:      options.RefineInitialized = NO;
    545:      options.PrintStat         = YES;
    546:   */
    547:   set_default_options_dist(&options);

    549:   MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(lu->comm_superlu));
    550:   MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
    551:   /* Default num of process columns and rows */
    552:   lu->npcol = (int_t) (0.5 + PetscSqrtReal((PetscReal)size));
    553:   if (!lu->npcol) lu->npcol = 1;
    554:   while (lu->npcol > 0) {
    555:     lu->nprow = (int_t) (size/lu->npcol);
    556:     if (size == lu->nprow * lu->npcol) break;
    557:     lu->npcol--;
    558:   }

    560:   PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"SuperLU_Dist Options","Mat");
    561:   PetscOptionsInt("-mat_superlu_dist_r","Number rows in processor partition","None",lu->nprow,(PetscInt*)&lu->nprow,NULL);
    562:   PetscOptionsInt("-mat_superlu_dist_c","Number columns in processor partition","None",lu->npcol,(PetscInt*)&lu->npcol,NULL);
    563:   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);

    565:   lu->MatInputMode = DISTRIBUTED;

    567:   PetscOptionsEnum("-mat_superlu_dist_matinput","Matrix input mode (global or distributed)","None",SuperLU_MatInputModes,(PetscEnum)lu->MatInputMode,(PetscEnum*)&lu->MatInputMode,NULL);
    568:   if (lu->MatInputMode == DISTRIBUTED && size == 1) lu->MatInputMode = GLOBAL;

    570:   PetscOptionsBool("-mat_superlu_dist_equil","Equilibrate matrix","None",options.Equil ? PETSC_TRUE : PETSC_FALSE,&flg,&set);
    571:   if (set && !flg) options.Equil = NO;

    573:   PetscOptionsEList("-mat_superlu_dist_rowperm","Row permutation","None",rowperm,2,rowperm[0],&indx,&flg);
    574:   if (flg) {
    575:     switch (indx) {
    576:     case 0:
    577:       options.RowPerm = LargeDiag;
    578:       break;
    579:     case 1:
    580:       options.RowPerm = NOROWPERM;
    581:       break;
    582:     }
    583:   }

    585:   PetscOptionsEList("-mat_superlu_dist_colperm","Column permutation","None",colperm,5,colperm[3],&indx,&flg);
    586:   if (flg) {
    587:     switch (indx) {
    588:     case 0:
    589:       options.ColPerm = NATURAL;
    590:       break;
    591:     case 1:
    592:       options.ColPerm = MMD_AT_PLUS_A;
    593:       break;
    594:     case 2:
    595:       options.ColPerm = MMD_ATA;
    596:       break;
    597:     case 3:
    598:       options.ColPerm = METIS_AT_PLUS_A;
    599:       break;
    600:     case 4:
    601:       options.ColPerm = PARMETIS;   /* only works for np>1 */
    602:       break;
    603:     default:
    604:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
    605:     }
    606:   }

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

    612:   options.ParSymbFact = NO;
    613:   PetscOptionsBool("-mat_superlu_dist_parsymbfact","Parallel symbolic factorization","None",PETSC_FALSE,&flg,&set);
    614:   if (set && flg && size>1) {
    615:     if (lu->MatInputMode == GLOBAL) {
    616: #if defined(PETSC_USE_INFO)
    617:       PetscInfo(A,"Warning: '-mat_superlu_dist_parsymbfact' is ignored because MatInputMode=GLOBAL\n");
    618: #endif
    619:     } else {
    620: #if defined(PETSC_HAVE_PARMETIS)
    621:       options.ParSymbFact = YES;
    622:       options.ColPerm     = PARMETIS;   /* in v2.2, PARMETIS is forced for ParSymbFact regardless of user ordering setting */
    623: #else
    624:       SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"parsymbfact needs PARMETIS");
    625: #endif
    626:     }
    627:   }

    629:   lu->FactPattern = SamePattern;
    630:   PetscOptionsEList("-mat_superlu_dist_fact","Sparsity pattern for repeated matrix factorization","None",factPattern,2,factPattern[0],&indx,&flg);
    631:   if (flg) {
    632:     switch (indx) {
    633:     case 0:
    634:       lu->FactPattern = SamePattern;
    635:       break;
    636:     case 1:
    637:       lu->FactPattern = SamePattern_SameRowPerm;
    638:       break;
    639:     }
    640:   }

    642:   options.IterRefine = NOREFINE;
    643:   PetscOptionsBool("-mat_superlu_dist_iterrefine","Use iterative refinement","None",options.IterRefine == NOREFINE ? PETSC_FALSE : PETSC_TRUE ,&flg,&set);
    644:   if (set) {
    645:     if (flg) options.IterRefine = SLU_DOUBLE;
    646:     else options.IterRefine = NOREFINE;
    647:   }

    649:   if (PetscLogPrintInfo) options.PrintStat = YES;
    650:   else options.PrintStat = NO;
    651:   PetscOptionsBool("-mat_superlu_dist_statprint","Print factorization information","None",(PetscBool)options.PrintStat,(PetscBool*)&options.PrintStat,NULL);
    652:   PetscOptionsEnd();

    654:   lu->options              = options;
    655:   lu->options.Fact         = DOFACT;
    656:   lu->matsolve_iscalled    = PETSC_FALSE;
    657:   lu->matmatsolve_iscalled = PETSC_FALSE;

    659:   *F = B;
    660:   return(0);
    661: }

    665: PETSC_EXTERN PetscErrorCode MatSolverPackageRegister_SuperLU_DIST(void)
    666: {
    669:   MatSolverPackageRegister(MATSOLVERSUPERLU_DIST,MATMPIAIJ,  MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
    670:   MatSolverPackageRegister(MATSOLVERSUPERLU_DIST,MATSEQAIJ,  MAT_FACTOR_LU,MatGetFactor_aij_superlu_dist);
    671:   return(0);
    672: }

    676: PetscErrorCode MatFactorInfo_SuperLU_DIST(Mat A,PetscViewer viewer)
    677: {
    678:   Mat_SuperLU_DIST       *lu=(Mat_SuperLU_DIST*)A->spptr;
    679:   superlu_dist_options_t options;
    680:   PetscErrorCode         ierr;

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

    686:   options = lu->options;
    687:   PetscViewerASCIIPrintf(viewer,"SuperLU_DIST run parameters:\n");
    688:   PetscViewerASCIIPrintf(viewer,"  Process grid nprow %D x npcol %D \n",lu->nprow,lu->npcol);
    689:   PetscViewerASCIIPrintf(viewer,"  Equilibrate matrix %s \n",PetscBools[options.Equil != NO]);
    690:   PetscViewerASCIIPrintf(viewer,"  Matrix input mode %d \n",lu->MatInputMode);
    691:   PetscViewerASCIIPrintf(viewer,"  Replace tiny pivots %s \n",PetscBools[options.ReplaceTinyPivot != NO]);
    692:   PetscViewerASCIIPrintf(viewer,"  Use iterative refinement %s \n",PetscBools[options.IterRefine == SLU_DOUBLE]);
    693:   PetscViewerASCIIPrintf(viewer,"  Processors in row %d col partition %d \n",lu->nprow,lu->npcol);
    694:   PetscViewerASCIIPrintf(viewer,"  Row permutation %s \n",(options.RowPerm == NOROWPERM) ? "NATURAL" : "LargeDiag");

    696:   switch (options.ColPerm) {
    697:   case NATURAL:
    698:     PetscViewerASCIIPrintf(viewer,"  Column permutation NATURAL\n");
    699:     break;
    700:   case MMD_AT_PLUS_A:
    701:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_AT_PLUS_A\n");
    702:     break;
    703:   case MMD_ATA:
    704:     PetscViewerASCIIPrintf(viewer,"  Column permutation MMD_ATA\n");
    705:     break;
    706:   case METIS_AT_PLUS_A:
    707:     PetscViewerASCIIPrintf(viewer,"  Column permutation METIS_AT_PLUS_A\n");
    708:     break;
    709:   case PARMETIS:
    710:     PetscViewerASCIIPrintf(viewer,"  Column permutation PARMETIS\n");
    711:     break;
    712:   default:
    713:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown column permutation");
    714:   }

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

    718:   if (lu->FactPattern == SamePattern) {
    719:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern\n");
    720:   } else {
    721:     PetscViewerASCIIPrintf(viewer,"  Repeated factorization SamePattern_SameRowPerm\n");
    722:   }
    723:   return(0);
    724: }

    728: PetscErrorCode MatView_SuperLU_DIST(Mat A,PetscViewer viewer)
    729: {
    730:   PetscErrorCode    ierr;
    731:   PetscBool         iascii;
    732:   PetscViewerFormat format;

    735:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    736:   if (iascii) {
    737:     PetscViewerGetFormat(viewer,&format);
    738:     if (format == PETSC_VIEWER_ASCII_INFO) {
    739:       MatFactorInfo_SuperLU_DIST(A,viewer);
    740:     }
    741:   }
    742:   return(0);
    743: }

    746: /*MC
    747:   MATSOLVERSUPERLU_DIST - Parallel direct solver package for LU factorization

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

    751:   Use -pc_type lu -pc_factor_mat_solver_package superlu_dist to us this direct solver

    753:    Works with AIJ matrices

    755:   Options Database Keys:
    756: + -mat_superlu_dist_r <n> - number of rows in processor partition
    757: . -mat_superlu_dist_c <n> - number of columns in processor partition
    758: . -mat_superlu_dist_matinput <0,1> - matrix input mode; 0=global, 1=distributed
    759: . -mat_superlu_dist_equil - equilibrate the matrix
    760: . -mat_superlu_dist_rowperm <LargeDiag,NATURAL> - row permutation
    761: . -mat_superlu_dist_colperm <MMD_AT_PLUS_A,MMD_ATA,NATURAL> - column permutation
    762: . -mat_superlu_dist_replacetinypivot - replace tiny pivots
    763: . -mat_superlu_dist_fact <SamePattern> - (choose one of) SamePattern SamePattern_SameRowPerm
    764: . -mat_superlu_dist_iterrefine - use iterative refinement
    765: - -mat_superlu_dist_statprint - print factorization information

    767:    Level: beginner

    769: .seealso: PCLU

    771: .seealso: PCFactorSetMatSolverPackage(), MatSolverPackage

    773: M*/