Actual source code: sbaijcholmod.c

petsc-dev 2014-08-19
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  2: /*
  3:    Provides an interface to the CHOLMOD sparse solver available through SuiteSparse version 4.2.1

  5:    When build with PETSC_USE_64BIT_INDICES this will use UF_Long as the
  6:    integer type in UMFPACK, otherwise it will use int. This means
  7:    all integers in this file as simply declared as PetscInt. Also it means
  8:    that UMFPACK UL_Long version MUST be built with 64 bit integers when used.

 10: */

 12: #include <../src/mat/impls/sbaij/seq/sbaij.h>
 13: #include <../src/mat/impls/sbaij/seq/cholmod/cholmodimpl.h>

 15: /*
 16:    This is a terrible hack, but it allows the error handler to retain a context.
 17:    Note that this hack really cannot be made both reentrant and concurrent.
 18: */
 19: static Mat static_F;

 23: static void CholmodErrorHandler(int status,const char *file,int line,const char *message)
 24: {

 27:   if (status > CHOLMOD_OK) {
 28:     PetscInfo4(static_F,"CHOLMOD warning %d at %s:%d: %s",status,file,line,message);
 29:   } else if (status == CHOLMOD_OK) { /* Documentation says this can happen, but why? */
 30:     PetscInfo3(static_F,"CHOLMOD OK at %s:%d: %s",file,line,message);
 31:   } else {
 32:     PetscErrorPrintf("CHOLMOD error %d at %s:%d: %s\n",status,file,line,message);
 33:   }
 34:   PetscFunctionReturnVoid();
 35: }

 39: PetscErrorCode  CholmodStart(Mat F)
 40: {
 42:   Mat_CHOLMOD    *chol=(Mat_CHOLMOD*)F->spptr;
 43:   cholmod_common *c;
 44:   PetscBool      flg;

 47:   if (chol->common) return(0);
 48:   PetscMalloc(sizeof(*chol->common),&chol->common);
 49:   !cholmod_X_start(chol->common);

 51:   c                = chol->common;
 52:   c->error_handler = CholmodErrorHandler;

 54: #define CHOLMOD_OPTION_DOUBLE(name,help) do {                            \
 55:     PetscReal tmp = (PetscReal)c->name;                                  \
 56:     PetscOptionsReal("-mat_cholmod_" #name,help,"None",tmp,&tmp,0); \
 57:     c->name = (double)tmp;                                               \
 58: } while (0)

 60: #define CHOLMOD_OPTION_INT(name,help) do {                               \
 61:     PetscInt tmp = (PetscInt)c->name;                                    \
 62:     PetscOptionsInt("-mat_cholmod_" #name,help,"None",tmp,&tmp,0); \
 63:     c->name = (int)tmp;                                                  \
 64: } while (0)

 66: #define CHOLMOD_OPTION_SIZE_T(name,help) do {                            \
 67:     PetscInt tmp = (PetscInt)c->name;                                    \
 68:     PetscOptionsInt("-mat_cholmod_" #name,help,"None",tmp,&tmp,0); \
 69:     if (tmp < 0) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_OUTOFRANGE,"value must be positive"); \
 70:     c->name = (size_t)tmp;                                               \
 71: } while (0)

 73: #define CHOLMOD_OPTION_TRUTH(name,help) do {                             \
 74:     PetscBool tmp = (PetscBool) !!c->name;                              \
 75:     PetscOptionsBool("-mat_cholmod_" #name,help,"None",tmp,&tmp,0); \
 76:     c->name = (int)tmp;                                                  \
 77: } while (0)

 79:   PetscOptionsBegin(PetscObjectComm((PetscObject)F),((PetscObject)F)->prefix,"CHOLMOD Options","Mat");
 80:   /* CHOLMOD handles first-time packing and refactor-packing separately, but we usually want them to be the same. */
 81:   chol->pack = (PetscBool)c->final_pack;

 83:   PetscOptionsBool("-mat_cholmod_pack","Pack factors after factorization [disable for frequent repeat factorization]","None",chol->pack,&chol->pack,0);
 84:   c->final_pack = (int)chol->pack;

 86:   CHOLMOD_OPTION_DOUBLE(dbound,"Minimum absolute value of diagonal entries of D");
 87:   CHOLMOD_OPTION_DOUBLE(grow0,"Global growth ratio when factors are modified");
 88:   CHOLMOD_OPTION_DOUBLE(grow1,"Column growth ratio when factors are modified");
 89:   CHOLMOD_OPTION_SIZE_T(grow2,"Affine column growth constant when factors are modified");
 90:   CHOLMOD_OPTION_SIZE_T(maxrank,"Max rank of update, larger values are faster but use more memory [2,4,8]");
 91:   {
 92:     static const char *const list[] = {"SIMPLICIAL","AUTO","SUPERNODAL","MatCholmodFactorType","MAT_CHOLMOD_FACTOR_",0};
 93:     PetscEnum                choice = (PetscEnum)c->supernodal;

 95:     PetscOptionsEnum("-mat_cholmod_factor","Factorization method","None",list,(PetscEnum)c->supernodal,&choice,0);
 96:     c->supernodal = (int)choice;
 97:   }
 98:   if (c->supernodal) CHOLMOD_OPTION_DOUBLE(supernodal_switch,"flop/nnz_L threshold for switching to supernodal factorization");
 99:   CHOLMOD_OPTION_TRUTH(final_asis,"Leave factors \"as is\"");
100:   CHOLMOD_OPTION_TRUTH(final_pack,"Pack the columns when finished (use FALSE if the factors will be updated later)");
101:   if (!c->final_asis) {
102:     CHOLMOD_OPTION_TRUTH(final_super,"Leave supernodal factors instead of converting to simplicial");
103:     CHOLMOD_OPTION_TRUTH(final_ll,"Turn LDL' factorization into LL'");
104:     CHOLMOD_OPTION_TRUTH(final_monotonic,"Ensure columns are monotonic when done");
105:     CHOLMOD_OPTION_TRUTH(final_resymbol,"Remove numerically zero values resulting from relaxed supernodal amalgamation");
106:   }
107:   {
108:     PetscReal tmp[] = {(PetscReal)c->zrelax[0],(PetscReal)c->zrelax[1],(PetscReal)c->zrelax[2]};
109:     PetscInt  n     = 3;
110:     PetscOptionsRealArray("-mat_cholmod_zrelax","3 real supernodal relaxed amalgamation parameters","None",tmp,&n,&flg);
111:     if (flg && n != 3) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_OUTOFRANGE,"must provide exactly 3 parameters to -mat_cholmod_zrelax");
112:     if (flg) while (n--) c->zrelax[n] = (double)tmp[n];
113:   }
114:   {
115:     PetscInt n,tmp[] = {(PetscInt)c->nrelax[0],(PetscInt)c->nrelax[1],(PetscInt)c->nrelax[2]};
116:     PetscOptionsIntArray("-mat_cholmod_nrelax","3 size_t supernodal relaxed amalgamation parameters","None",tmp,&n,&flg);
117:     if (flg && n != 3) SETERRQ(PetscObjectComm((PetscObject)F),PETSC_ERR_ARG_OUTOFRANGE,"must provide exactly 3 parameters to -mat_cholmod_nrelax");
118:     if (flg) while (n--) c->nrelax[n] = (size_t)tmp[n];
119:   }
120:   CHOLMOD_OPTION_TRUTH(prefer_upper,"Work with upper triangular form [faster when using fill-reducing ordering, slower in natural ordering]");
121:   CHOLMOD_OPTION_TRUTH(default_nesdis,"Use NESDIS instead of METIS for nested dissection");
122:   CHOLMOD_OPTION_INT(print,"Verbosity level");
123:   PetscOptionsEnd();
124:   return(0);
125: }

129: static PetscErrorCode MatWrapCholmod_seqsbaij(Mat A,PetscBool values,cholmod_sparse *C,PetscBool  *aijalloc)
130: {
131:   Mat_SeqSBAIJ   *sbaij = (Mat_SeqSBAIJ*)A->data;

135:   PetscMemzero(C,sizeof(*C));
136:   /* CHOLMOD uses column alignment, SBAIJ stores the upper factor, so we pass it on as a lower factor, swapping the meaning of row and column */
137:   C->nrow   = (size_t)A->cmap->n;
138:   C->ncol   = (size_t)A->rmap->n;
139:   C->nzmax  = (size_t)sbaij->maxnz;
140:   C->p      = sbaij->i;
141:   C->i      = sbaij->j;
142:   C->x      = sbaij->a;
143:   C->stype  = -1;
144:   C->itype  = CHOLMOD_INT_TYPE;
145:   C->xtype  = CHOLMOD_SCALAR_TYPE;
146:   C->dtype  = CHOLMOD_DOUBLE;
147:   C->sorted = 1;
148:   C->packed = 1;
149:   *aijalloc = PETSC_FALSE;
150:   return(0);
151: }

155: static PetscErrorCode VecWrapCholmod(Vec X,cholmod_dense *Y)
156: {
158:   PetscScalar    *x;
159:   PetscInt       n;

162:   PetscMemzero(Y,sizeof(*Y));
163:   VecGetArray(X,&x);
164:   VecGetSize(X,&n);

166:   Y->x     = (double*)x;
167:   Y->nrow  = n;
168:   Y->ncol  = 1;
169:   Y->nzmax = n;
170:   Y->d     = n;
171:   Y->x     = (double*)x;
172:   Y->xtype = CHOLMOD_SCALAR_TYPE;
173:   Y->dtype = CHOLMOD_DOUBLE;
174:   return(0);
175: }

179: PetscErrorCode  MatDestroy_CHOLMOD(Mat F)
180: {
182:   Mat_CHOLMOD    *chol=(Mat_CHOLMOD*)F->spptr;

185:   if (chol) {
186:     !cholmod_X_free_factor(&chol->factor,chol->common);
187:     !cholmod_X_finish(chol->common);
188:     PetscFree(chol->common);
189:     PetscFree(chol->matrix);
190:     (*chol->Destroy)(F);
191:   }
192:   PetscFree(F->spptr);
193:   return(0);
194: }

196: static PetscErrorCode MatSolve_CHOLMOD(Mat,Vec,Vec);

198: /*static const char *const CholmodOrderingMethods[] = {"User","AMD","METIS","NESDIS(default)","Natural","NESDIS(small=20000)","NESDIS(small=4,no constrained)","NESDIS()"};*/

202: static PetscErrorCode MatFactorInfo_CHOLMOD(Mat F,PetscViewer viewer)
203: {
204:   Mat_CHOLMOD          *chol = (Mat_CHOLMOD*)F->spptr;
205:   const cholmod_common *c    = chol->common;
206:   PetscErrorCode       ierr;
207:   PetscInt             i;

210:   if (F->ops->solve != MatSolve_CHOLMOD) return(0);
211:   PetscViewerASCIIPrintf(viewer,"CHOLMOD run parameters:\n");
212:   PetscViewerASCIIPushTab(viewer);
213:   PetscViewerASCIIPrintf(viewer,"Pack factors after symbolic factorization: %s\n",chol->pack ? "TRUE" : "FALSE");
214:   PetscViewerASCIIPrintf(viewer,"Common.dbound            %g  (Smallest absolute value of diagonal entries of D)\n",c->dbound);
215:   PetscViewerASCIIPrintf(viewer,"Common.grow0             %g\n",c->grow0);
216:   PetscViewerASCIIPrintf(viewer,"Common.grow1             %g\n",c->grow1);
217:   PetscViewerASCIIPrintf(viewer,"Common.grow2             %u\n",(unsigned)c->grow2);
218:   PetscViewerASCIIPrintf(viewer,"Common.maxrank           %u\n",(unsigned)c->maxrank);
219:   PetscViewerASCIIPrintf(viewer,"Common.supernodal_switch %g\n",c->supernodal_switch);
220:   PetscViewerASCIIPrintf(viewer,"Common.supernodal        %d\n",c->supernodal);
221:   PetscViewerASCIIPrintf(viewer,"Common.final_asis        %d\n",c->final_asis);
222:   PetscViewerASCIIPrintf(viewer,"Common.final_super       %d\n",c->final_super);
223:   PetscViewerASCIIPrintf(viewer,"Common.final_ll          %d\n",c->final_ll);
224:   PetscViewerASCIIPrintf(viewer,"Common.final_pack        %d\n",c->final_pack);
225:   PetscViewerASCIIPrintf(viewer,"Common.final_monotonic   %d\n",c->final_monotonic);
226:   PetscViewerASCIIPrintf(viewer,"Common.final_resymbol    %d\n",c->final_resymbol);
227:   PetscViewerASCIIPrintf(viewer,"Common.zrelax            [%g,%g,%g]\n",c->zrelax[0],c->zrelax[1],c->zrelax[2]);
228:   PetscViewerASCIIPrintf(viewer,"Common.nrelax            [%u,%u,%u]\n",(unsigned)c->nrelax[0],(unsigned)c->nrelax[1],(unsigned)c->nrelax[2]);
229:   PetscViewerASCIIPrintf(viewer,"Common.prefer_upper      %d\n",c->prefer_upper);
230:   PetscViewerASCIIPrintf(viewer,"Common.print             %d\n",c->print);
231:   for (i=0; i<c->nmethods; i++) {
232:     PetscViewerASCIIPrintf(viewer,"Ordering method %D%s:\n",i,i==c->selected ? " [SELECTED]" : "");
233:     PetscViewerASCIIPrintf(viewer,"  lnz %g, fl %g, prune_dense %g, prune_dense2 %g\n",
234:                                   c->method[i].lnz,c->method[i].fl,c->method[i].prune_dense,c->method[i].prune_dense2);
235:   }
236:   PetscViewerASCIIPrintf(viewer,"Common.postorder         %d\n",c->postorder);
237:   PetscViewerASCIIPrintf(viewer,"Common.default_nesdis    %d (use NESDIS instead of METIS for nested dissection)\n",c->default_nesdis);
238:   /* Statistics */
239:   PetscViewerASCIIPrintf(viewer,"Common.fl                %g (flop count from most recent analysis)\n",c->fl);
240:   PetscViewerASCIIPrintf(viewer,"Common.lnz               %g (fundamental nz in L)\n",c->lnz);
241:   PetscViewerASCIIPrintf(viewer,"Common.anz               %g\n",c->anz);
242:   PetscViewerASCIIPrintf(viewer,"Common.modfl             %g (flop count from most recent update)\n",c->modfl);
243:   PetscViewerASCIIPrintf(viewer,"Common.malloc_count      %g (number of live objects)\n",(double)c->malloc_count);
244:   PetscViewerASCIIPrintf(viewer,"Common.memory_usage      %g (peak memory usage in bytes)\n",(double)c->memory_usage);
245:   PetscViewerASCIIPrintf(viewer,"Common.memory_inuse      %g (current memory usage in bytes)\n",(double)c->memory_inuse);
246:   PetscViewerASCIIPrintf(viewer,"Common.nrealloc_col      %g (number of column reallocations)\n",c->nrealloc_col);
247:   PetscViewerASCIIPrintf(viewer,"Common.nrealloc_factor   %g (number of factor reallocations due to column reallocations)\n",c->nrealloc_factor);
248:   PetscViewerASCIIPrintf(viewer,"Common.ndbounds_hit      %g (number of times diagonal was modified by dbound)\n",c->ndbounds_hit);
249:   PetscViewerASCIIPrintf(viewer,"Common.rowfacfl          %g (number of flops in last call to cholmod_rowfac)\n",c->rowfacfl);
250:   PetscViewerASCIIPrintf(viewer,"Common.aatfl             %g (number of flops to compute A(:,f)*A(:,f)')\n",c->aatfl);
251:   PetscViewerASCIIPopTab(viewer);
252:   return(0);
253: }

257: PetscErrorCode  MatView_CHOLMOD(Mat F,PetscViewer viewer)
258: {
259:   PetscErrorCode    ierr;
260:   PetscBool         iascii;
261:   PetscViewerFormat format;

264:   MatView_SeqSBAIJ(F,viewer);
265:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
266:   if (iascii) {
267:     PetscViewerGetFormat(viewer,&format);
268:     if (format == PETSC_VIEWER_ASCII_INFO) {
269:       MatFactorInfo_CHOLMOD(F,viewer);
270:     }
271:   }
272:   return(0);
273: }

277: static PetscErrorCode MatSolve_CHOLMOD(Mat F,Vec B,Vec X)
278: {
279:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->spptr;
280:   cholmod_dense  cholB,*cholX;
281:   PetscScalar    *x;

285:   VecWrapCholmod(B,&cholB);
286:   static_F = F;
287:   cholX    = cholmod_X_solve(CHOLMOD_A,chol->factor,&cholB,chol->common);
288:   if (!cholX) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_LIB,"CHOLMOD failed");
289:   VecGetArray(X,&x);
290:   PetscMemcpy(x,cholX->x,cholX->nrow*sizeof(*x));
291:   !cholmod_X_free_dense(&cholX,chol->common);
292:   VecRestoreArray(X,&x);
293:   return(0);
294: }

298: static PetscErrorCode MatCholeskyFactorNumeric_CHOLMOD(Mat F,Mat A,const MatFactorInfo *info)
299: {
300:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->spptr;
301:   cholmod_sparse cholA;
302:   PetscBool      aijalloc;

306:   (*chol->Wrap)(A,PETSC_TRUE,&cholA,&aijalloc);
307:   static_F = F;
308:   !cholmod_X_factorize(&cholA,chol->factor,chol->common);
309:   if (ierr) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD factorization failed with status %d",chol->common->status);
310:   if (chol->common->status == CHOLMOD_NOT_POSDEF) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_MAT_CH_ZRPVT,"CHOLMOD detected that the matrix is not positive definite, failure at column %u",(unsigned)chol->factor->minor);

312:   if (aijalloc) {PetscFree3(cholA.p,cholA.i,cholA.x);}

314:   F->ops->solve          = MatSolve_CHOLMOD;
315:   F->ops->solvetranspose = MatSolve_CHOLMOD;
316:   return(0);
317: }

321: PetscErrorCode  MatCholeskyFactorSymbolic_CHOLMOD(Mat F,Mat A,IS perm,const MatFactorInfo *info)
322: {
323:   Mat_CHOLMOD    *chol = (Mat_CHOLMOD*)F->spptr;
325:   cholmod_sparse cholA;
326:   PetscBool      aijalloc;
327:   PetscInt       *fset = 0;
328:   size_t         fsize = 0;

331:   (*chol->Wrap)(A,PETSC_FALSE,&cholA,&aijalloc);
332:   static_F = F;
333:   if (chol->factor) {
334:     !cholmod_X_resymbol(&cholA,fset,fsize,(int)chol->pack,chol->factor,chol->common);
335:     if (ierr) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
336:   } else if (perm) {
337:     const PetscInt *ip;
338:     ISGetIndices(perm,&ip);
339:     chol->factor = cholmod_X_analyze_p(&cholA,(PetscInt*)ip,fset,fsize,chol->common);
340:     if (!chol->factor) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
341:     ISRestoreIndices(perm,&ip);
342:   } else {
343:     chol->factor = cholmod_X_analyze(&cholA,chol->common);
344:     if (!chol->factor) SETERRQ1(PetscObjectComm((PetscObject)F),PETSC_ERR_LIB,"CHOLMOD analysis failed with status %d",chol->common->status);
345:   }

347:   if (aijalloc) {PetscFree3(cholA.p,cholA.i,cholA.x);}

349:   F->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_CHOLMOD;
350:   return(0);
351: }

355: PetscErrorCode MatFactorGetSolverPackage_seqsbaij_cholmod(Mat A,const MatSolverPackage *type)
356: {
358:   *type = MATSOLVERCHOLMOD;
359:   return(0);
360: }

362: /*MC
363:   MATSOLVERCHOLMOD = "cholmod" - A matrix type providing direct solvers (Cholesky) for sequential matrices
364:   via the external package CHOLMOD.

366:   ./configure --download-suitesparse to install PETSc to use CHOLMOD

368:   Consult CHOLMOD documentation for more information about the Common parameters
369:   which correspond to the options database keys below.

371:   Options Database Keys:
372: + -mat_cholmod_dbound <0>          - Minimum absolute value of diagonal entries of D (None)
373: . -mat_cholmod_grow0 <1.2>         - Global growth ratio when factors are modified (None)
374: . -mat_cholmod_grow1 <1.2>         - Column growth ratio when factors are modified (None)
375: . -mat_cholmod_grow2 <5>           - Affine column growth constant when factors are modified (None)
376: . -mat_cholmod_maxrank <8>         - Max rank of update, larger values are faster but use more memory [2,4,8] (None)
377: . -mat_cholmod_factor <AUTO>       - (choose one of) SIMPLICIAL AUTO SUPERNODAL
378: . -mat_cholmod_supernodal_switch <40> - flop/nnz_L threshold for switching to supernodal factorization (None)
379: . -mat_cholmod_final_asis <TRUE>   - Leave factors "as is" (None)
380: . -mat_cholmod_final_pack <TRUE>   - Pack the columns when finished (use FALSE if the factors will be updated later) (None)
381: . -mat_cholmod_zrelax <0.8>        - 3 real supernodal relaxed amalgamation parameters (None)
382: . -mat_cholmod_nrelax <4>          - 3 size_t supernodal relaxed amalgamation parameters (None)
383: . -mat_cholmod_prefer_upper <TRUE> - Work with upper triangular form (faster when using fill-reducing ordering, slower in natural ordering) (None)
384: - -mat_cholmod_print <3>           - Verbosity level (None)

386:    Level: beginner

388: .seealso: PCCHOLESKY, PCFactorSetMatSolverPackage(), MatSolverPackage
389: M*/

393: PETSC_EXTERN PetscErrorCode MatGetFactor_seqsbaij_cholmod(Mat A,MatFactorType ftype,Mat *F)
394: {
395:   Mat            B;
396:   Mat_CHOLMOD    *chol;
398:   PetscInt       m=A->rmap->n,n=A->cmap->n,bs;

401:   if (ftype != MAT_FACTOR_CHOLESKY) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_SUP,"CHOLMOD cannot do %s factorization with SBAIJ, only %s",
402:                                              MatFactorTypes[ftype],MatFactorTypes[MAT_FACTOR_CHOLESKY]);
403:   MatGetBlockSize(A,&bs);
404:   if (bs != 1) SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_SUP,"CHOLMOD only supports block size=1, given %D",bs);
405:   /* Create the factorization matrix F */
406:   MatCreate(PetscObjectComm((PetscObject)A),&B);
407:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,m,n);
408:   MatSetType(B,((PetscObject)A)->type_name);
409:   MatSeqSBAIJSetPreallocation(B,1,0,NULL);
410:   PetscNewLog(B,&chol);

412:   chol->Wrap    = MatWrapCholmod_seqsbaij;
413:   chol->Destroy = MatDestroy_SeqSBAIJ;
414:   B->spptr      = chol;

416:   B->ops->view                   = MatView_CHOLMOD;
417:   B->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_CHOLMOD;
418:   B->ops->destroy                = MatDestroy_CHOLMOD;
419:   PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverPackage_C",MatFactorGetSolverPackage_seqsbaij_cholmod);
420:   B->factortype                  = MAT_FACTOR_CHOLESKY;
421:   B->assembled                   = PETSC_TRUE; /* required by -ksp_view */
422:   B->preallocated                = PETSC_TRUE;

424:   CholmodStart(B);
425:   *F   = B;
426:   return(0);
427: }