Actual source code: lusol.c

  1: #define PETSCMAT_DLL

  3: /* 
  4:         Provides an interface to the LUSOL package of ....

  6: */
 7:  #include src/mat/impls/aij/seq/aij.h

  9: #if defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
 10: #define LU1FAC   lu1fac_
 11: #define LU6SOL   lu6sol_
 12: #define M1PAGE   m1page_
 13: #define M5SETX   m5setx_
 14: #define M6RDEL   m6rdel_
 15: #elif !defined(PETSC_HAVE_FORTRAN_CAPS)
 16: #define LU1FAC   lu1fac
 17: #define LU6SOL   lu6sol
 18: #define M1PAGE   m1page
 19: #define M5SETX   m5setx
 20: #define M6RDEL   m6rdel
 21: #endif

 24: /*
 25:     Dummy symbols that the MINOS files mi25bfac.f and mi15blas.f may require
 26: */
 27: void PETSC_STDCALL M1PAGE() {
 28:   ;
 29: }
 30: void PETSC_STDCALL M5SETX() {
 31:   ;
 32: }

 34: void PETSC_STDCALL M6RDEL() {
 35:   ;
 36: }

 39:                         double *parmlu, double *data, int *indc, int *indr,
 40:                         int *rowperm, int *colperm, int *collen, int *rowlen,
 41:                         int *colstart, int *rowstart, int *rploc, int *cploc,
 42:                         int *rpinv, int *cpinv, double *w, int *inform);

 45:                         int *size, int *luparm, double *parmlu, double *data,
 46:                         int *indc, int *indr, int *rowperm, int *colperm,
 47:                         int *collen, int *rowlen, int *colstart, int *rowstart,
 48:                         int *inform);

 51: EXTERN PetscErrorCode MatDuplicate_LUSOL(Mat,MatDuplicateOption,Mat*);

 53: typedef struct  {
 54:   double *data;
 55:   int *indc;
 56:   int *indr;

 58:   int *ip;
 59:   int *iq;
 60:   int *lenc;
 61:   int *lenr;
 62:   int *locc;
 63:   int *locr;
 64:   int *iploc;
 65:   int *iqloc;
 66:   int *ipinv;
 67:   int *iqinv;
 68:   double *mnsw;
 69:   double *mnsv;

 71:   double elbowroom;
 72:   double luroom;                /* Extra space allocated when factor fails   */
 73:   double parmlu[30];                /* Input/output to LUSOL                     */

 75:   int n;                        /* Number of rows/columns in matrix          */
 76:   int nz;                        /* Number of nonzeros                        */
 77:   int nnz;                        /* Number of nonzeros allocated for factors  */
 78:   int luparm[30];                /* Input/output to LUSOL                     */

 80:   PetscErrorCode (*MatDuplicate)(Mat,MatDuplicateOption,Mat*);
 81:   PetscErrorCode (*MatLUFactorSymbolic)(Mat,IS,IS,MatFactorInfo*,Mat*);
 82:   PetscErrorCode (*MatDestroy)(Mat);
 83:   PetscTruth CleanUpLUSOL;

 85: } Mat_LUSOL;

 87: /*  LUSOL input/Output Parameters (Description uses C-style indexes
 88:  *
 89:  *  Input parameters                                        Typical value
 90:  *
 91:  *  luparm(0) = nout     File number for printed messages.         6
 92:  *  luparm(1) = lprint   Print level.                              0
 93:  *                    < 0 suppresses output.
 94:  *                    = 0 gives error messages.
 95:  *                    = 1 gives debug output from some of the
 96:  *                        other routines in LUSOL.
 97:  *                   >= 2 gives the pivot row and column and the
 98:  *                        no. of rows and columns involved at
 99:  *                        each elimination step in lu1fac.
100:  *  luparm(2) = maxcol   lu1fac: maximum number of columns         5
101:  *                        searched allowed in a Markowitz-type
102:  *                        search for the next pivot element.
103:  *                        For some of the factorization, the
104:  *                        number of rows searched is
105:  *                        maxrow = maxcol - 1.
106:  *
107:  *
108:  *  Output parameters
109:  *
110:  *  luparm(9) = inform   Return code from last call to any LU routine.
111:  *  luparm(10) = nsing    No. of singularities marked in the
112:  *                        output array w(*).
113:  *  luparm(11) = jsing    Column index of last singularity.
114:  *  luparm(12) = minlen   Minimum recommended value for  lena.
115:  *  luparm(13) = maxlen   ?
116:  *  luparm(14) = nupdat   No. of updates performed by the lu8 routines.
117:  *  luparm(15) = nrank    No. of nonempty rows of U.
118:  *  luparm(16) = ndens1   No. of columns remaining when the density of
119:  *                        the matrix being factorized reached dens1.
120:  *  luparm(17) = ndens2   No. of columns remaining when the density of
121:  *                        the matrix being factorized reached dens2.
122:  *  luparm(18) = jumin    The column index associated with dumin.
123:  *  luparm(19) = numl0    No. of columns in initial  L.
124:  *  luparm(20) = lenl0    Size of initial  L  (no. of nonzeros).
125:  *  luparm(21) = lenu0    Size of initial  U.
126:  *  luparm(22) = lenl     Size of current  L.
127:  *  luparm(23) = lenu     Size of current  U.
128:  *  luparm(24) = lrow     Length of row file.
129:  *  luparm(25) = ncp      No. of compressions of LU data structures.
130:  *  luparm(26) = mersum   lu1fac: sum of Markowitz merit counts.
131:  *  luparm(27) = nutri    lu1fac: triangular rows in U.
132:  *  luparm(28) = nltri    lu1fac: triangular rows in L.
133:  *  luparm(29) =
134:  *
135:  *
136:  *  Input parameters                                        Typical value
137:  *
138:  *  parmlu(0) = elmax1   Max multiplier allowed in  L           10.0
139:  *                        during factor.
140:  *  parmlu(1) = elmax2   Max multiplier allowed in  L           10.0
141:  *                        during updates.
142:  *  parmlu(2) = small    Absolute tolerance for             eps**0.8
143:  *                        treating reals as zero.     IBM double: 3.0d-13
144:  *  parmlu(3) = utol1    Absolute tol for flagging          eps**0.66667
145:  *                        small diagonals of U.       IBM double: 3.7d-11
146:  *  parmlu(4) = utol2    Relative tol for flagging          eps**0.66667
147:  *                        small diagonals of U.       IBM double: 3.7d-11
148:  *  parmlu(5) = uspace   Factor limiting waste space in  U.      3.0
149:  *                        In lu1fac, the row or column lists
150:  *                        are compressed if their length
151:  *                        exceeds uspace times the length of
152:  *                        either file after the last compression.
153:  *  parmlu(6) = dens1    The density at which the Markowitz      0.3
154:  *                        strategy should search maxcol columns
155:  *                        and no rows.
156:  *  parmlu(7) = dens2    the density at which the Markowitz      0.6
157:  *                        strategy should search only 1 column
158:  *                        or (preferably) use a dense LU for
159:  *                        all the remaining rows and columns.
160:  *
161:  *
162:  *  Output parameters
163:  *
164:  *  parmlu(9) = amax     Maximum element in  A.
165:  *  parmlu(10) = elmax    Maximum multiplier in current  L.
166:  *  parmlu(11) = umax     Maximum element in current  U.
167:  *  parmlu(12) = dumax    Maximum diagonal in  U.
168:  *  parmlu(13) = dumin    Minimum diagonal in  U.
169:  *  parmlu(14) =
170:  *  parmlu(15) =
171:  *  parmlu(16) =
172:  *  parmlu(17) =
173:  *  parmlu(18) =
174:  *  parmlu(19) = resid    lu6sol: residual after solve with U or U'.
175:  *  ...
176:  *  parmlu(29) =
177:  */

179: #define Factorization_Tolerance       1e-1
180: #define Factorization_Pivot_Tolerance pow(2.2204460492503131E-16, 2.0 / 3.0) 
181: #define Factorization_Small_Tolerance 1e-15 /* pow(DBL_EPSILON, 0.8) */

186: PetscErrorCode PETSCMAT_DLLEXPORT MatConvert_LUSOL_SeqAIJ(Mat A,const MatType type,MatReuse reuse,Mat *newmat)
187: {
188:   /* This routine is only called to convert an unfactored PETSc-LUSOL matrix */
189:   /* to its base PETSc type, so we will ignore 'MatType type'. */
191:   Mat            B=*newmat;
192:   Mat_LUSOL      *lusol=(Mat_LUSOL *)A->spptr;

195:   if (reuse == MAT_INITIAL_MATRIX) {
196:     MatDuplicate(A,MAT_COPY_VALUES,&B);
197:   }
198:   B->ops->duplicate        = lusol->MatDuplicate;
199:   B->ops->lufactorsymbolic = lusol->MatLUFactorSymbolic;
200:   B->ops->destroy          = lusol->MatDestroy;
201: 
202:   PetscFree(lusol);

204:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_lusol_C","",PETSC_NULL);
205:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_lusol_seqaij_C","",PETSC_NULL);

207:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
208:   *newmat = B;
209:   return(0);
210: }

215: PetscErrorCode MatDestroy_LUSOL(Mat A)
216: {
218:   Mat_LUSOL *lusol=(Mat_LUSOL *)A->spptr;

221:   if (lusol->CleanUpLUSOL) {
222:     PetscFree(lusol->ip);
223:     PetscFree(lusol->iq);
224:     PetscFree(lusol->lenc);
225:     PetscFree(lusol->lenr);
226:     PetscFree(lusol->locc);
227:     PetscFree(lusol->locr);
228:     PetscFree(lusol->iploc);
229:     PetscFree(lusol->iqloc);
230:     PetscFree(lusol->ipinv);
231:     PetscFree(lusol->iqinv);
232:     PetscFree(lusol->mnsw);
233:     PetscFree(lusol->mnsv);
234: 
235:     PetscFree(lusol->indc);
236:   }

238:   MatConvert_LUSOL_SeqAIJ(A,MATSEQAIJ,MAT_REUSE_MATRIX,&A);
239:   (*A->ops->destroy)(A);
240:   return(0);
241: }

245: PetscErrorCode MatSolve_LUSOL(Mat A,Vec b,Vec x)
246: {
247:   Mat_LUSOL *lusol=(Mat_LUSOL*)A->spptr;
248:   double    *bb,*xx;
249:   int       mode=5;
251:   int       i,m,n,nnz,status;

254:   VecGetArray(x, &xx);
255:   VecGetArray(b, &bb);

257:   m = n = lusol->n;
258:   nnz = lusol->nnz;

260:   for (i = 0; i < m; i++)
261:     {
262:       lusol->mnsv[i] = bb[i];
263:     }

265:   LU6SOL(&mode, &m, &n, lusol->mnsv, xx, &nnz,
266:          lusol->luparm, lusol->parmlu, lusol->data,
267:          lusol->indc, lusol->indr, lusol->ip, lusol->iq,
268:          lusol->lenc, lusol->lenr, lusol->locc, lusol->locr, &status);

270:   if (status != 0)
271:     {
272:       SETERRQ(PETSC_ERR_ARG_SIZ,"solve failed");
273:     }

275:   VecRestoreArray(x, &xx);
276:   VecRestoreArray(b, &bb);
277:   return(0);
278: }

282: PetscErrorCode MatLUFactorNumeric_LUSOL(Mat A,MatFactorInfo *info,Mat *F)
283: {
284:   Mat_SeqAIJ     *a;
285:   Mat_LUSOL      *lusol = (Mat_LUSOL*)(*F)->spptr;
287:   int            m, n, nz, nnz, status;
288:   int            i, rs, re;
289:   int            factorizations;

292:   MatGetSize(A,&m,&n);
293:   a = (Mat_SeqAIJ *)A->data;

295:   if (m != lusol->n) {
296:     SETERRQ(PETSC_ERR_ARG_SIZ,"factorization struct inconsistent");
297:   }

299:   factorizations = 0;
300:   do
301:     {
302:       /*******************************************************************/
303:       /* Check the workspace allocation.                                 */
304:       /*******************************************************************/

306:       nz = a->nz;
307:       nnz = PetscMax(lusol->nnz, (int)(lusol->elbowroom*nz));
308:       nnz = PetscMax(nnz, 5*n);

310:       if (nnz < lusol->luparm[12]){
311:         nnz = (int)(lusol->luroom * lusol->luparm[12]);
312:       } else if ((factorizations > 0) && (lusol->luroom < 6)){
313:         lusol->luroom += 0.1;
314:       }

316:       nnz = PetscMax(nnz, (int)(lusol->luroom*(lusol->luparm[22] + lusol->luparm[23])));

318:       if (nnz > lusol->nnz){
319:         PetscFree(lusol->indc);
320:         PetscMalloc((sizeof(double)+2*sizeof(int))*nnz,&lusol->indc);
321:         lusol->indr = lusol->indc + nnz;
322:         lusol->data = (double *)(lusol->indr + nnz);
323:         lusol->nnz  = nnz;
324:       }

326:       /*******************************************************************/
327:       /* Fill in the data for the problem.      (1-based Fortran style)  */
328:       /*******************************************************************/

330:       nz = 0;
331:       for (i = 0; i < n; i++)
332:         {
333:           rs = a->i[i];
334:           re = a->i[i+1];

336:           while (rs < re)
337:             {
338:               if (a->a[rs] != 0.0)
339:                 {
340:                   lusol->indc[nz] = i + 1;
341:                   lusol->indr[nz] = a->j[rs] + 1;
342:                   lusol->data[nz] = a->a[rs];
343:                   nz++;
344:                 }
345:               rs++;
346:             }
347:         }

349:       /*******************************************************************/
350:       /* Do the factorization.                                           */
351:       /*******************************************************************/

353:       LU1FAC(&m, &n, &nz, &nnz,
354:              lusol->luparm, lusol->parmlu, lusol->data,
355:              lusol->indc, lusol->indr, lusol->ip, lusol->iq,
356:              lusol->lenc, lusol->lenr, lusol->locc, lusol->locr,
357:              lusol->iploc, lusol->iqloc, lusol->ipinv,
358:              lusol->iqinv, lusol->mnsw, &status);
359: 
360:       switch(status)
361:         {
362:         case 0:                /* factored */
363:           break;

365:         case 7:                /* insufficient memory */
366:           break;

368:         case 1:
369:         case -1:                /* singular */
370:           SETERRQ(PETSC_ERR_LIB,"Singular matrix");

372:         case 3:
373:         case 4:                /* error conditions */
374:           SETERRQ(PETSC_ERR_LIB,"matrix error");

376:         default:                /* unknown condition */
377:           SETERRQ(PETSC_ERR_LIB,"matrix unknown return code");
378:         }

380:       factorizations++;
381:     } while (status == 7);
382:   (*F)->assembled = PETSC_TRUE;
383:   return(0);
384: }

388: PetscErrorCode MatLUFactorSymbolic_LUSOL(Mat A, IS r, IS c,MatFactorInfo *info, Mat *F) {
389:   /************************************************************************/
390:   /* Input                                                                */
391:   /*     A  - matrix to factor                                            */
392:   /*     r  - row permutation (ignored)                                   */
393:   /*     c  - column permutation (ignored)                                */
394:   /*                                                                      */
395:   /* Output                                                               */
396:   /*     F  - matrix storing the factorization;                           */
397:   /************************************************************************/
398:   Mat       B;
399:   Mat_LUSOL *lusol;
401:   int        i, m, n, nz, nnz;

404: 
405:   /************************************************************************/
406:   /* Check the arguments.                                                 */
407:   /************************************************************************/

409:   MatGetSize(A, &m, &n);
410:   nz = ((Mat_SeqAIJ *)A->data)->nz;

412:   /************************************************************************/
413:   /* Create the factorization.                                            */
414:   /************************************************************************/

416:   MatCreate(A->comm,&B);
417:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,m,n);
418:   MatSetType(B,A->type_name);
419:   MatSeqAIJSetPreallocation(B,0,PETSC_NULL);

421:   B->ops->lufactornumeric = MatLUFactorNumeric_LUSOL;
422:   B->ops->solve           = MatSolve_LUSOL;
423:   B->factor               = FACTOR_LU;
424:   lusol                   = (Mat_LUSOL*)(B->spptr);

426:   /************************************************************************/
427:   /* Initialize parameters                                                */
428:   /************************************************************************/

430:   for (i = 0; i < 30; i++)
431:     {
432:       lusol->luparm[i] = 0;
433:       lusol->parmlu[i] = 0;
434:     }

436:   lusol->luparm[1] = -1;
437:   lusol->luparm[2] = 5;
438:   lusol->luparm[7] = 1;

440:   lusol->parmlu[0] = 1 / Factorization_Tolerance;
441:   lusol->parmlu[1] = 1 / Factorization_Tolerance;
442:   lusol->parmlu[2] = Factorization_Small_Tolerance;
443:   lusol->parmlu[3] = Factorization_Pivot_Tolerance;
444:   lusol->parmlu[4] = Factorization_Pivot_Tolerance;
445:   lusol->parmlu[5] = 3.0;
446:   lusol->parmlu[6] = 0.3;
447:   lusol->parmlu[7] = 0.6;

449:   /************************************************************************/
450:   /* Allocate the workspace needed by LUSOL.                              */
451:   /************************************************************************/

453:   lusol->elbowroom = PetscMax(lusol->elbowroom, info->fill);
454:   nnz = PetscMax((int)(lusol->elbowroom*nz), 5*n);
455: 
456:   lusol->n = n;
457:   lusol->nz = nz;
458:   lusol->nnz = nnz;
459:   lusol->luroom = 1.75;

461:   PetscMalloc(sizeof(int)*n,&lusol->ip);
462:   PetscMalloc(sizeof(int)*n,&lusol->iq);
463:   PetscMalloc(sizeof(int)*n,&lusol->lenc);
464:   PetscMalloc(sizeof(int)*n,&lusol->lenr);
465:   PetscMalloc(sizeof(int)*n,&lusol->locc);
466:   PetscMalloc(sizeof(int)*n,&lusol->locr);
467:   PetscMalloc(sizeof(int)*n,&lusol->iploc);
468:   PetscMalloc(sizeof(int)*n,&lusol->iqloc);
469:   PetscMalloc(sizeof(int)*n,&lusol->ipinv);
470:   PetscMalloc(sizeof(int)*n,&lusol->iqinv);
471:   PetscMalloc(sizeof(double)*n,&lusol->mnsw);
472:   PetscMalloc(sizeof(double)*n,&lusol->mnsv);

474:   PetscMalloc((sizeof(double)+2*sizeof(int))*nnz,&lusol->indc);
475:   lusol->indr = lusol->indc + nnz;
476:   lusol->data = (double *)(lusol->indr + nnz);
477:   lusol->CleanUpLUSOL = PETSC_TRUE;
478:   *F = B;
479:   return(0);
480: }

485: PetscErrorCode PETSCMAT_DLLEXPORT MatConvert_SeqAIJ_LUSOL(Mat A,const MatType type,MatReuse reuse,Mat *newmat)
486: {
488:   PetscInt       m, n;
489:   Mat_LUSOL      *lusol;
490:   Mat            B=*newmat;

493:   MatGetSize(A, &m, &n);
494:   if (m != n) {
495:     SETERRQ(PETSC_ERR_ARG_SIZ,"matrix must be square");
496:   }
497:   if (reuse == MAT_INITIAL_MATRIX) {
498:     MatDuplicate(A,MAT_COPY_VALUES,&B);
499:   }
500: 
501:   PetscNew(Mat_LUSOL,&lusol);
502:   lusol->MatDuplicate        = A->ops->duplicate;
503:   lusol->MatLUFactorSymbolic = A->ops->lufactorsymbolic;
504:   lusol->MatDestroy          = A->ops->destroy;
505:   lusol->CleanUpLUSOL        = PETSC_FALSE;

507:   B->spptr                   = (void*)lusol;
508:   B->ops->duplicate          = MatDuplicate_LUSOL;
509:   B->ops->lufactorsymbolic   = MatLUFactorSymbolic_LUSOL;
510:   B->ops->destroy            = MatDestroy_LUSOL;

512:   PetscLogInfo((0,"MatConvert_SeqAIJ_LUSOL:Using LUSOL for LU factorization and solves.\n"));
513:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_lusol_C",
514:                                            "MatConvert_SeqAIJ_LUSOL",MatConvert_SeqAIJ_LUSOL);
515:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_lusol_seqaij_C",
516:                                            "MatConvert_LUSOL_SeqAIJ",MatConvert_LUSOL_SeqAIJ);
517:   PetscObjectChangeTypeName((PetscObject)B,type);
518:   *newmat = B;
519:   return(0);
520: }

525: PetscErrorCode MatDuplicate_LUSOL(Mat A, MatDuplicateOption op, Mat *M) {
527:   Mat_LUSOL *lu=(Mat_LUSOL *)A->spptr;
529:   (*lu->MatDuplicate)(A,op,M);
530:   PetscMemcpy((*M)->spptr,lu,sizeof(Mat_LUSOL));
531:   return(0);
532: }

534: /*MC
535:   MATLUSOL - MATLUSOL = "lusol" - A matrix type providing direct solvers (LU) for sequential matrices 
536:   via the external package LUSOL.

538:   If LUSOL is installed (see the manual for
539:   instructions on how to declare the existence of external packages),
540:   a matrix type can be constructed which invokes LUSOL solvers.
541:   After calling MatCreate(...,A), simply call MatSetType(A,MATLUSOL).
542:   This matrix type is only supported for double precision real.

544:   This matrix inherits from MATSEQAIJ.  As a result, MatSeqAIJSetPreallocation is 
545:   supported for this matrix type.  MatConvert can be called for a fast inplace conversion
546:   to and from the MATSEQAIJ matrix type.

548:   Options Database Keys:
549: . -mat_type lusol - sets the matrix type to "lusol" during a call to MatSetFromOptions()

551:    Level: beginner

553: .seealso: PCLU
554: M*/

559: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_LUSOL(Mat A)
560: {

564:   /* Change type name before calling MatSetType to force proper construction of SeqAIJ and LUSOL types */
565:   PetscObjectChangeTypeName((PetscObject)A,MATLUSOL);
566:   MatSetType(A,MATSEQAIJ);
567:   MatConvert_SeqAIJ_LUSOL(A,MATLUSOL,MAT_REUSE_MATRIX,&A);
568:   return(0);
569: }