Actual source code: ml.c
petsc-3.6.4 2016-04-12
2: /*
3: Provides an interface to the ML smoothed Aggregation
4: Note: Something non-obvious breaks -pc_mg_type ADDITIVE for parallel runs
5: Jed Brown, see [PETSC #18321, #18449].
6: */
7: #include <petsc/private/pcimpl.h> /*I "petscpc.h" I*/
8: #include <petsc/private/pcmgimpl.h> /*I "petscksp.h" I*/
9: #include <../src/mat/impls/aij/seq/aij.h>
10: #include <../src/mat/impls/aij/mpi/mpiaij.h>
11: #include <petscdm.h> /* for DMDestroy(&pc->mg) hack */
13: EXTERN_C_BEGIN
14: /* HAVE_CONFIG_H flag is required by ML include files */
15: #if !defined(HAVE_CONFIG_H)
16: #define HAVE_CONFIG_H
17: #endif
18: #include <ml_include.h>
19: #include <ml_viz_stats.h>
20: EXTERN_C_END
22: typedef enum {PCML_NULLSPACE_AUTO,PCML_NULLSPACE_USER,PCML_NULLSPACE_BLOCK,PCML_NULLSPACE_SCALAR} PCMLNullSpaceType;
23: static const char *const PCMLNullSpaceTypes[] = {"AUTO","USER","BLOCK","SCALAR","PCMLNullSpaceType","PCML_NULLSPACE_",0};
25: /* The context (data structure) at each grid level */
26: typedef struct {
27: Vec x,b,r; /* global vectors */
28: Mat A,P,R;
29: KSP ksp;
30: Vec coords; /* projected by ML, if PCSetCoordinates is called; values packed by node */
31: } GridCtx;
33: /* The context used to input PETSc matrix into ML at fine grid */
34: typedef struct {
35: Mat A; /* Petsc matrix in aij format */
36: Mat Aloc; /* local portion of A to be used by ML */
37: Vec x,y;
38: ML_Operator *mlmat;
39: PetscScalar *pwork; /* tmp array used by PetscML_comm() */
40: } FineGridCtx;
42: /* The context associates a ML matrix with a PETSc shell matrix */
43: typedef struct {
44: Mat A; /* PETSc shell matrix associated with mlmat */
45: ML_Operator *mlmat; /* ML matrix assorciated with A */
46: Vec y, work;
47: } Mat_MLShell;
49: /* Private context for the ML preconditioner */
50: typedef struct {
51: ML *ml_object;
52: ML_Aggregate *agg_object;
53: GridCtx *gridctx;
54: FineGridCtx *PetscMLdata;
55: PetscInt Nlevels,MaxNlevels,MaxCoarseSize,CoarsenScheme,EnergyMinimization,MinPerProc,PutOnSingleProc,RepartitionType,ZoltanScheme;
56: PetscReal Threshold,DampingFactor,EnergyMinimizationDropTol,MaxMinRatio,AuxThreshold;
57: PetscBool SpectralNormScheme_Anorm,BlockScaling,EnergyMinimizationCheap,Symmetrize,OldHierarchy,KeepAggInfo,Reusable,Repartition,Aux;
58: PetscBool reuse_interpolation;
59: PCMLNullSpaceType nulltype;
60: PetscMPIInt size; /* size of communicator for pc->pmat */
61: PetscInt dim; /* data from PCSetCoordinates(_ML) */
62: PetscInt nloc;
63: PetscReal *coords; /* ML has a grid object for each level: the finest grid will point into coords */
64: } PC_ML;
68: static int PetscML_getrow(ML_Operator *ML_data, int N_requested_rows, int requested_rows[],int allocated_space, int columns[], double values[], int row_lengths[])
69: {
71: PetscInt m,i,j,k=0,row,*aj;
72: PetscScalar *aa;
73: FineGridCtx *ml=(FineGridCtx*)ML_Get_MyGetrowData(ML_data);
74: Mat_SeqAIJ *a = (Mat_SeqAIJ*)ml->Aloc->data;
76: MatGetSize(ml->Aloc,&m,NULL); if (ierr) return(0);
77: for (i = 0; i<N_requested_rows; i++) {
78: row = requested_rows[i];
79: row_lengths[i] = a->ilen[row];
80: if (allocated_space < k+row_lengths[i]) return(0);
81: if ((row >= 0) || (row <= (m-1))) {
82: aj = a->j + a->i[row];
83: aa = a->a + a->i[row];
84: for (j=0; j<row_lengths[i]; j++) {
85: columns[k] = aj[j];
86: values[k++] = aa[j];
87: }
88: }
89: }
90: return(1);
91: }
95: static PetscErrorCode PetscML_comm(double p[],void *ML_data)
96: {
97: PetscErrorCode ierr;
98: FineGridCtx *ml = (FineGridCtx*)ML_data;
99: Mat A = ml->A;
100: Mat_MPIAIJ *a = (Mat_MPIAIJ*)A->data;
101: PetscMPIInt size;
102: PetscInt i,in_length=A->rmap->n,out_length=ml->Aloc->cmap->n;
103: const PetscScalar *array;
106: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
107: if (size == 1) return 0;
109: VecPlaceArray(ml->y,p);
110: VecScatterBegin(a->Mvctx,ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
111: VecScatterEnd(a->Mvctx,ml->y,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
112: VecResetArray(ml->y);
113: VecGetArrayRead(a->lvec,&array);
114: for (i=in_length; i<out_length; i++) p[i] = array[i-in_length];
115: VecRestoreArrayRead(a->lvec,&array);
116: return(0);
117: }
121: static int PetscML_matvec(ML_Operator *ML_data,int in_length,double p[],int out_length,double ap[])
122: {
124: FineGridCtx *ml = (FineGridCtx*)ML_Get_MyMatvecData(ML_data);
125: Mat A = ml->A, Aloc=ml->Aloc;
126: PetscMPIInt size;
127: PetscScalar *pwork=ml->pwork;
128: PetscInt i;
131: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
132: if (size == 1) {
133: VecPlaceArray(ml->x,p);
134: } else {
135: for (i=0; i<in_length; i++) pwork[i] = p[i];
136: PetscML_comm(pwork,ml);
137: VecPlaceArray(ml->x,pwork);
138: }
139: VecPlaceArray(ml->y,ap);
140: MatMult(Aloc,ml->x,ml->y);
141: VecResetArray(ml->x);
142: VecResetArray(ml->y);
143: return(0);
144: }
148: static PetscErrorCode MatMult_ML(Mat A,Vec x,Vec y)
149: {
150: PetscErrorCode ierr;
151: Mat_MLShell *shell;
152: PetscScalar *yarray;
153: const PetscScalar *xarray;
154: PetscInt x_length,y_length;
157: MatShellGetContext(A,(void**)&shell);
158: VecGetArrayRead(x,&xarray);
159: VecGetArray(y,&yarray);
160: x_length = shell->mlmat->invec_leng;
161: y_length = shell->mlmat->outvec_leng;
162: PetscStackCall("ML_Operator_Apply",ML_Operator_Apply(shell->mlmat,x_length,(PetscScalar*)xarray,y_length,yarray));
163: VecRestoreArrayRead(x,&xarray);
164: VecRestoreArray(y,&yarray);
165: return(0);
166: }
170: /* Computes y = w + A * x
171: It is possible that w == y, but not x == y
172: */
173: static PetscErrorCode MatMultAdd_ML(Mat A,Vec x,Vec w,Vec y)
174: {
175: Mat_MLShell *shell;
176: PetscScalar *yarray;
177: const PetscScalar *xarray;
178: PetscInt x_length,y_length;
179: PetscErrorCode ierr;
182: MatShellGetContext(A, (void**) &shell);
183: if (y == w) {
184: if (!shell->work) {
185: VecDuplicate(y, &shell->work);
186: }
187: VecGetArrayRead(x, &xarray);
188: VecGetArray(shell->work, &yarray);
189: x_length = shell->mlmat->invec_leng;
190: y_length = shell->mlmat->outvec_leng;
191: PetscStackCall("ML_Operator_Apply",ML_Operator_Apply(shell->mlmat, x_length, (PetscScalar*)xarray, y_length, yarray));
192: VecRestoreArrayRead(x, &xarray);
193: VecRestoreArray(shell->work, &yarray);
194: VecAXPY(y, 1.0, shell->work);
195: } else {
196: VecGetArrayRead(x, &xarray);
197: VecGetArray(y, &yarray);
198: x_length = shell->mlmat->invec_leng;
199: y_length = shell->mlmat->outvec_leng;
200: PetscStackCall("ML_Operator_Apply",ML_Operator_Apply(shell->mlmat, x_length, (PetscScalar *)xarray, y_length, yarray));
201: VecRestoreArrayRead(x, &xarray);
202: VecRestoreArray(y, &yarray);
203: VecAXPY(y, 1.0, w);
204: }
205: return(0);
206: }
208: /* newtype is ignored since only handles one case */
211: static PetscErrorCode MatConvert_MPIAIJ_ML(Mat A,MatType newtype,MatReuse scall,Mat *Aloc)
212: {
214: Mat_MPIAIJ *mpimat=(Mat_MPIAIJ*)A->data;
215: Mat_SeqAIJ *mat,*a=(Mat_SeqAIJ*)(mpimat->A)->data,*b=(Mat_SeqAIJ*)(mpimat->B)->data;
216: PetscInt *ai=a->i,*aj=a->j,*bi=b->i,*bj=b->j;
217: PetscScalar *aa=a->a,*ba=b->a,*ca;
218: PetscInt am =A->rmap->n,an=A->cmap->n,i,j,k;
219: PetscInt *ci,*cj,ncols;
222: if (am != an) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"A must have a square diagonal portion, am: %d != an: %d",am,an);
224: if (scall == MAT_INITIAL_MATRIX) {
225: PetscMalloc1(1+am,&ci);
226: ci[0] = 0;
227: for (i=0; i<am; i++) ci[i+1] = ci[i] + (ai[i+1] - ai[i]) + (bi[i+1] - bi[i]);
228: PetscMalloc1(1+ci[am],&cj);
229: PetscMalloc1(1+ci[am],&ca);
231: k = 0;
232: for (i=0; i<am; i++) {
233: /* diagonal portion of A */
234: ncols = ai[i+1] - ai[i];
235: for (j=0; j<ncols; j++) {
236: cj[k] = *aj++;
237: ca[k++] = *aa++;
238: }
239: /* off-diagonal portion of A */
240: ncols = bi[i+1] - bi[i];
241: for (j=0; j<ncols; j++) {
242: cj[k] = an + (*bj); bj++;
243: ca[k++] = *ba++;
244: }
245: }
246: if (k != ci[am]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"k: %d != ci[am]: %d",k,ci[am]);
248: /* put together the new matrix */
249: an = mpimat->A->cmap->n+mpimat->B->cmap->n;
250: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,am,an,ci,cj,ca,Aloc);
252: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
253: /* Since these are PETSc arrays, change flags to free them as necessary. */
254: mat = (Mat_SeqAIJ*)(*Aloc)->data;
255: mat->free_a = PETSC_TRUE;
256: mat->free_ij = PETSC_TRUE;
258: mat->nonew = 0;
259: } else if (scall == MAT_REUSE_MATRIX) {
260: mat=(Mat_SeqAIJ*)(*Aloc)->data;
261: ci = mat->i; cj = mat->j; ca = mat->a;
262: for (i=0; i<am; i++) {
263: /* diagonal portion of A */
264: ncols = ai[i+1] - ai[i];
265: for (j=0; j<ncols; j++) *ca++ = *aa++;
266: /* off-diagonal portion of A */
267: ncols = bi[i+1] - bi[i];
268: for (j=0; j<ncols; j++) *ca++ = *ba++;
269: }
270: } else SETERRQ1(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Invalid MatReuse %d",(int)scall);
271: return(0);
272: }
274: extern PetscErrorCode MatDestroy_Shell(Mat);
277: static PetscErrorCode MatDestroy_ML(Mat A)
278: {
280: Mat_MLShell *shell;
283: MatShellGetContext(A,(void**)&shell);
284: VecDestroy(&shell->y);
285: if (shell->work) {VecDestroy(&shell->work);}
286: PetscFree(shell);
287: MatDestroy_Shell(A);
288: PetscObjectChangeTypeName((PetscObject)A,0);
289: return(0);
290: }
294: static PetscErrorCode MatWrapML_SeqAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
295: {
296: struct ML_CSR_MSRdata *matdata = (struct ML_CSR_MSRdata*)mlmat->data;
297: PetscErrorCode ierr;
298: PetscInt m =mlmat->outvec_leng,n=mlmat->invec_leng,*nnz = NULL,nz_max;
299: PetscInt *ml_cols=matdata->columns,*ml_rowptr=matdata->rowptr,*aj,i;
300: PetscScalar *ml_vals=matdata->values,*aa;
303: if (!mlmat->getrow) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
304: if (m != n) { /* ML Pmat and Rmat are in CSR format. Pass array pointers into SeqAIJ matrix */
305: if (reuse) {
306: Mat_SeqAIJ *aij= (Mat_SeqAIJ*)(*newmat)->data;
307: aij->i = ml_rowptr;
308: aij->j = ml_cols;
309: aij->a = ml_vals;
310: } else {
311: /* sort ml_cols and ml_vals */
312: PetscMalloc1(m+1,&nnz);
313: for (i=0; i<m; i++) nnz[i] = ml_rowptr[i+1] - ml_rowptr[i];
314: aj = ml_cols; aa = ml_vals;
315: for (i=0; i<m; i++) {
316: PetscSortIntWithScalarArray(nnz[i],aj,aa);
317: aj += nnz[i]; aa += nnz[i];
318: }
319: MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,m,n,ml_rowptr,ml_cols,ml_vals,newmat);
320: PetscFree(nnz);
321: }
322: return(0);
323: }
325: nz_max = PetscMax(1,mlmat->max_nz_per_row);
326: PetscMalloc2(nz_max,&aa,nz_max,&aj);
327: if (!reuse) {
328: MatCreate(PETSC_COMM_SELF,newmat);
329: MatSetSizes(*newmat,m,n,PETSC_DECIDE,PETSC_DECIDE);
330: MatSetType(*newmat,MATSEQAIJ);
331: /* keep track of block size for A matrices */
332: MatSetBlockSize (*newmat, mlmat->num_PDEs);
334: PetscMalloc1(m,&nnz);
335: for (i=0; i<m; i++) {
336: PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&nnz[i]));
337: }
338: MatSeqAIJSetPreallocation(*newmat,0,nnz);
339: }
340: for (i=0; i<m; i++) {
341: PetscInt ncols;
343: PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&ncols));
344: MatSetValues(*newmat,1,&i,ncols,aj,aa,INSERT_VALUES);
345: }
346: MatAssemblyBegin(*newmat,MAT_FINAL_ASSEMBLY);
347: MatAssemblyEnd(*newmat,MAT_FINAL_ASSEMBLY);
349: PetscFree2(aa,aj);
350: PetscFree(nnz);
351: return(0);
352: }
356: static PetscErrorCode MatWrapML_SHELL(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
357: {
359: PetscInt m,n;
360: ML_Comm *MLcomm;
361: Mat_MLShell *shellctx;
364: m = mlmat->outvec_leng;
365: n = mlmat->invec_leng;
367: if (reuse) {
368: MatShellGetContext(*newmat,(void**)&shellctx);
369: shellctx->mlmat = mlmat;
370: return(0);
371: }
373: MLcomm = mlmat->comm;
375: PetscNew(&shellctx);
376: MatCreateShell(MLcomm->USR_comm,m,n,PETSC_DETERMINE,PETSC_DETERMINE,shellctx,newmat);
377: MatShellSetOperation(*newmat,MATOP_MULT,(void(*)(void))MatMult_ML);
378: MatShellSetOperation(*newmat,MATOP_MULT_ADD,(void(*)(void))MatMultAdd_ML);
380: shellctx->A = *newmat;
381: shellctx->mlmat = mlmat;
382: shellctx->work = NULL;
384: VecCreate(MLcomm->USR_comm,&shellctx->y);
385: VecSetSizes(shellctx->y,m,PETSC_DECIDE);
386: VecSetType(shellctx->y,VECSTANDARD);
388: (*newmat)->ops->destroy = MatDestroy_ML;
389: return(0);
390: }
394: static PetscErrorCode MatWrapML_MPIAIJ(ML_Operator *mlmat,MatReuse reuse,Mat *newmat)
395: {
396: PetscInt *aj;
397: PetscScalar *aa;
399: PetscInt i,j,*gordering;
400: PetscInt m=mlmat->outvec_leng,n,nz_max,row;
401: Mat A;
404: if (!mlmat->getrow) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"mlmat->getrow = NULL");
405: n = mlmat->invec_leng;
406: if (m != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"m %d must equal to n %d",m,n);
408: /* create global row numbering for a ML_Operator */
409: PetscStackCall("ML_build_global_numbering",ML_build_global_numbering(mlmat,&gordering,"rows"));
411: nz_max = PetscMax(1,mlmat->max_nz_per_row) + 1;
412: PetscMalloc2(nz_max,&aa,nz_max,&aj);
413: if (reuse) {
414: A = *newmat;
415: } else {
416: PetscInt *nnzA,*nnzB,*nnz;
417: PetscInt rstart;
418: MatCreate(mlmat->comm->USR_comm,&A);
419: MatSetSizes(A,m,n,PETSC_DECIDE,PETSC_DECIDE);
420: MatSetType(A,MATMPIAIJ);
421: /* keep track of block size for A matrices */
422: MatSetBlockSize (A,mlmat->num_PDEs);
423: PetscMalloc3(m,&nnzA,m,&nnzB,m,&nnz);
424: MPI_Scan(&m,&rstart,1,MPIU_INT,MPI_SUM,mlmat->comm->USR_comm);
425: rstart -= m;
427: for (i=0; i<m; i++) {
428: row = gordering[i] - rstart;
429: PetscStackCall("ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&nnz[i]));
430: nnzA[row] = 0;
431: for (j=0; j<nnz[i]; j++) {
432: if (aj[j] < m) nnzA[row]++;
433: }
434: nnzB[row] = nnz[i] - nnzA[row];
435: }
436: MatMPIAIJSetPreallocation(A,0,nnzA,0,nnzB);
437: PetscFree3(nnzA,nnzB,nnz);
438: }
439: for (i=0; i<m; i++) {
440: PetscInt ncols;
441: row = gordering[i];
443: PetscStackCall(",ML_Operator_Getrow",ML_Operator_Getrow(mlmat,1,&i,nz_max,aj,aa,&ncols));
444: for (j = 0; j < ncols; j++) aj[j] = gordering[aj[j]];
445: MatSetValues(A,1,&row,ncols,aj,aa,INSERT_VALUES);
446: }
447: PetscStackCall("ML_free",ML_free(gordering));
448: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
449: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
450: *newmat = A;
452: PetscFree2(aa,aj);
453: return(0);
454: }
456: /* -------------------------------------------------------------------------- */
457: /*
458: PCSetCoordinates_ML
460: Input Parameter:
461: . pc - the preconditioner context
462: */
465: static PetscErrorCode PCSetCoordinates_ML(PC pc, PetscInt ndm, PetscInt a_nloc, PetscReal *coords)
466: {
467: PC_MG *mg = (PC_MG*)pc->data;
468: PC_ML *pc_ml = (PC_ML*)mg->innerctx;
470: PetscInt arrsz,oldarrsz,bs,my0,kk,ii,nloc,Iend;
471: Mat Amat = pc->pmat;
473: /* this function copied and modified from PCSetCoordinates_GEO -TGI */
476: MatGetBlockSize(Amat, &bs);
478: MatGetOwnershipRange(Amat, &my0, &Iend);
479: nloc = (Iend-my0)/bs;
481: if (nloc!=a_nloc) SETERRQ2(PetscObjectComm((PetscObject)Amat),PETSC_ERR_ARG_WRONG, "Number of local blocks must locations = %d %d.",a_nloc,nloc);
482: if ((Iend-my0)%bs!=0) SETERRQ1(PetscObjectComm((PetscObject)Amat),PETSC_ERR_ARG_WRONG, "Bad local size %d.",nloc);
484: oldarrsz = pc_ml->dim * pc_ml->nloc;
485: pc_ml->dim = ndm;
486: pc_ml->nloc = a_nloc;
487: arrsz = ndm * a_nloc;
489: /* create data - syntactic sugar that should be refactored at some point */
490: if (pc_ml->coords==0 || (oldarrsz != arrsz)) {
491: PetscFree(pc_ml->coords);
492: PetscMalloc1(arrsz, &pc_ml->coords);
493: }
494: for (kk=0; kk<arrsz; kk++) pc_ml->coords[kk] = -999.;
495: /* copy data in - column oriented */
496: for (kk = 0; kk < nloc; kk++) {
497: for (ii = 0; ii < ndm; ii++) {
498: pc_ml->coords[ii*nloc + kk] = coords[kk*ndm + ii];
499: }
500: }
501: return(0);
502: }
504: /* -----------------------------------------------------------------------------*/
505: extern PetscErrorCode PCReset_MG(PC);
508: PetscErrorCode PCReset_ML(PC pc)
509: {
511: PC_MG *mg = (PC_MG*)pc->data;
512: PC_ML *pc_ml = (PC_ML*)mg->innerctx;
513: PetscInt level,fine_level=pc_ml->Nlevels-1,dim=pc_ml->dim;
516: if (dim) {
517: ML_Aggregate_Viz_Stats * grid_info = (ML_Aggregate_Viz_Stats*) pc_ml->ml_object->Grid[0].Grid;
519: for (level=0; level<=fine_level; level++) {
520: VecDestroy(&pc_ml->gridctx[level].coords);
521: }
523: grid_info->x = 0; /* do this so ML doesn't try to free coordinates */
524: grid_info->y = 0;
525: grid_info->z = 0;
527: PetscStackCall("ML_Operator_Getrow",ML_Aggregate_VizAndStats_Clean(pc_ml->ml_object));
528: }
529: PetscStackCall("ML_Aggregate_Destroy",ML_Aggregate_Destroy(&pc_ml->agg_object));
530: PetscStackCall("ML_Aggregate_Destroy",ML_Destroy(&pc_ml->ml_object));
532: if (pc_ml->PetscMLdata) {
533: PetscFree(pc_ml->PetscMLdata->pwork);
534: MatDestroy(&pc_ml->PetscMLdata->Aloc);
535: VecDestroy(&pc_ml->PetscMLdata->x);
536: VecDestroy(&pc_ml->PetscMLdata->y);
537: }
538: PetscFree(pc_ml->PetscMLdata);
540: if (pc_ml->gridctx) {
541: for (level=0; level<fine_level; level++) {
542: if (pc_ml->gridctx[level].A) {MatDestroy(&pc_ml->gridctx[level].A);}
543: if (pc_ml->gridctx[level].P) {MatDestroy(&pc_ml->gridctx[level].P);}
544: if (pc_ml->gridctx[level].R) {MatDestroy(&pc_ml->gridctx[level].R);}
545: if (pc_ml->gridctx[level].x) {VecDestroy(&pc_ml->gridctx[level].x);}
546: if (pc_ml->gridctx[level].b) {VecDestroy(&pc_ml->gridctx[level].b);}
547: if (pc_ml->gridctx[level+1].r) {VecDestroy(&pc_ml->gridctx[level+1].r);}
548: }
549: }
550: PetscFree(pc_ml->gridctx);
551: PetscFree(pc_ml->coords);
553: pc_ml->dim = 0;
554: pc_ml->nloc = 0;
555: PCReset_MG(pc);
556: return(0);
557: }
558: /* -------------------------------------------------------------------------- */
559: /*
560: PCSetUp_ML - Prepares for the use of the ML preconditioner
561: by setting data structures and options.
563: Input Parameter:
564: . pc - the preconditioner context
566: Application Interface Routine: PCSetUp()
568: Notes:
569: The interface routine PCSetUp() is not usually called directly by
570: the user, but instead is called by PCApply() if necessary.
571: */
572: extern PetscErrorCode PCSetFromOptions_MG(PetscOptions *PetscOptionsObject,PC);
573: extern PetscErrorCode PCReset_MG(PC);
577: PetscErrorCode PCSetUp_ML(PC pc)
578: {
579: PetscErrorCode ierr;
580: PetscMPIInt size;
581: FineGridCtx *PetscMLdata;
582: ML *ml_object;
583: ML_Aggregate *agg_object;
584: ML_Operator *mlmat;
585: PetscInt nlocal_allcols,Nlevels,mllevel,level,level1,m,fine_level,bs;
586: Mat A,Aloc;
587: GridCtx *gridctx;
588: PC_MG *mg = (PC_MG*)pc->data;
589: PC_ML *pc_ml = (PC_ML*)mg->innerctx;
590: PetscBool isSeq, isMPI;
591: KSP smoother;
592: PC subpc;
593: PetscInt mesh_level, old_mesh_level;
594: MatInfo info;
595: static PetscBool cite = PETSC_FALSE;
598: PetscCitationsRegister("@TechReport{ml_users_guide,\n author = {M. Sala and J.J. Hu and R.S. Tuminaro},\n title = {{ML}3.1 {S}moothed {A}ggregation {U}ser's {G}uide},\n institution = {Sandia National Laboratories},\n number = {SAND2004-4821},\n year = 2004\n}\n",&cite);
599: A = pc->pmat;
600: MPI_Comm_size(PetscObjectComm((PetscObject)A),&size);
602: if (pc->setupcalled) {
603: if (pc->flag == SAME_NONZERO_PATTERN && pc_ml->reuse_interpolation) {
604: /*
605: Reuse interpolaton instead of recomputing aggregates and updating the whole hierarchy. This is less expensive for
606: multiple solves in which the matrix is not changing too quickly.
607: */
608: ml_object = pc_ml->ml_object;
609: gridctx = pc_ml->gridctx;
610: Nlevels = pc_ml->Nlevels;
611: fine_level = Nlevels - 1;
612: gridctx[fine_level].A = A;
614: PetscObjectTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);
615: PetscObjectTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);
616: if (isMPI) {
617: MatConvert_MPIAIJ_ML(A,NULL,MAT_INITIAL_MATRIX,&Aloc);
618: } else if (isSeq) {
619: Aloc = A;
620: PetscObjectReference((PetscObject)Aloc);
621: } else SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONG, "Matrix type '%s' cannot be used with ML. ML can only handle AIJ matrices.",((PetscObject)A)->type_name);
623: MatGetSize(Aloc,&m,&nlocal_allcols);
624: PetscMLdata = pc_ml->PetscMLdata;
625: MatDestroy(&PetscMLdata->Aloc);
626: PetscMLdata->A = A;
627: PetscMLdata->Aloc = Aloc;
628: PetscStackCall("ML_Aggregate_Destroy",ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata));
629: PetscStackCall("ML_Set_Amatrix_Matvec",ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec));
631: mesh_level = ml_object->ML_finest_level;
632: while (ml_object->SingleLevel[mesh_level].Rmat->to) {
633: old_mesh_level = mesh_level;
634: mesh_level = ml_object->SingleLevel[mesh_level].Rmat->to->levelnum;
636: /* clean and regenerate A */
637: mlmat = &(ml_object->Amat[mesh_level]);
638: PetscStackCall("ML_Operator_Clean",ML_Operator_Clean(mlmat));
639: PetscStackCall("ML_Operator_Init",ML_Operator_Init(mlmat,ml_object->comm));
640: PetscStackCall("ML_Gen_AmatrixRAP",ML_Gen_AmatrixRAP(ml_object, old_mesh_level, mesh_level));
641: }
643: level = fine_level - 1;
644: if (size == 1) { /* convert ML P, R and A into seqaij format */
645: for (mllevel=1; mllevel<Nlevels; mllevel++) {
646: mlmat = &(ml_object->Amat[mllevel]);
647: MatWrapML_SeqAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);
648: level--;
649: }
650: } else { /* convert ML P and R into shell format, ML A into mpiaij format */
651: for (mllevel=1; mllevel<Nlevels; mllevel++) {
652: mlmat = &(ml_object->Amat[mllevel]);
653: MatWrapML_MPIAIJ(mlmat,MAT_REUSE_MATRIX,&gridctx[level].A);
654: level--;
655: }
656: }
658: for (level=0; level<fine_level; level++) {
659: if (level > 0) {
660: PCMGSetResidual(pc,level,PCMGResidualDefault,gridctx[level].A);
661: }
662: KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A);
663: }
664: PCMGSetResidual(pc,fine_level,PCMGResidualDefault,gridctx[fine_level].A);
665: KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A);
667: PCSetUp_MG(pc);
668: return(0);
669: } else {
670: /* since ML can change the size of vectors/matrices at any level we must destroy everything */
671: PCReset_ML(pc);
672: }
673: }
675: /* setup special features of PCML */
676: /*--------------------------------*/
677: /* covert A to Aloc to be used by ML at fine grid */
678: pc_ml->size = size;
679: PetscObjectTypeCompare((PetscObject) A, MATSEQAIJ, &isSeq);
680: PetscObjectTypeCompare((PetscObject) A, MATMPIAIJ, &isMPI);
681: if (isMPI) {
682: MatConvert_MPIAIJ_ML(A,NULL,MAT_INITIAL_MATRIX,&Aloc);
683: } else if (isSeq) {
684: Aloc = A;
685: PetscObjectReference((PetscObject)Aloc);
686: } else SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_WRONG, "Matrix type '%s' cannot be used with ML. ML can only handle AIJ matrices.",((PetscObject)A)->type_name);
688: /* create and initialize struct 'PetscMLdata' */
689: PetscNewLog(pc,&PetscMLdata);
690: pc_ml->PetscMLdata = PetscMLdata;
691: PetscMalloc1(Aloc->cmap->n+1,&PetscMLdata->pwork);
693: VecCreate(PETSC_COMM_SELF,&PetscMLdata->x);
694: VecSetSizes(PetscMLdata->x,Aloc->cmap->n,Aloc->cmap->n);
695: VecSetType(PetscMLdata->x,VECSEQ);
697: VecCreate(PETSC_COMM_SELF,&PetscMLdata->y);
698: VecSetSizes(PetscMLdata->y,A->rmap->n,PETSC_DECIDE);
699: VecSetType(PetscMLdata->y,VECSEQ);
700: PetscMLdata->A = A;
701: PetscMLdata->Aloc = Aloc;
702: if (pc_ml->dim) { /* create vecs around the coordinate data given */
703: PetscInt i,j,dim=pc_ml->dim;
704: PetscInt nloc = pc_ml->nloc,nlocghost;
705: PetscReal *ghostedcoords;
707: MatGetBlockSize(A,&bs);
708: nlocghost = Aloc->cmap->n / bs;
709: PetscMalloc1(dim*nlocghost,&ghostedcoords);
710: for (i = 0; i < dim; i++) {
711: /* copy coordinate values into first component of pwork */
712: for (j = 0; j < nloc; j++) {
713: PetscMLdata->pwork[bs * j] = pc_ml->coords[nloc * i + j];
714: }
715: /* get the ghost values */
716: PetscML_comm(PetscMLdata->pwork,PetscMLdata);
717: /* write into the vector */
718: for (j = 0; j < nlocghost; j++) {
719: ghostedcoords[i * nlocghost + j] = PetscMLdata->pwork[bs * j];
720: }
721: }
722: /* replace the original coords with the ghosted coords, because these are
723: * what ML needs */
724: PetscFree(pc_ml->coords);
725: pc_ml->coords = ghostedcoords;
726: }
728: /* create ML discretization matrix at fine grid */
729: /* ML requires input of fine-grid matrix. It determines nlevels. */
730: MatGetSize(Aloc,&m,&nlocal_allcols);
731: MatGetBlockSize(A,&bs);
732: PetscStackCall("ML_Create",ML_Create(&ml_object,pc_ml->MaxNlevels));
733: PetscStackCall("ML_Comm_Set_UsrComm",ML_Comm_Set_UsrComm(ml_object->comm,PetscObjectComm((PetscObject)A)));
734: pc_ml->ml_object = ml_object;
735: PetscStackCall("ML_Init_Amatrix",ML_Init_Amatrix(ml_object,0,m,m,PetscMLdata));
736: PetscStackCall("ML_Set_Amatrix_Getrow",ML_Set_Amatrix_Getrow(ml_object,0,PetscML_getrow,PetscML_comm,nlocal_allcols));
737: PetscStackCall("ML_Set_Amatrix_Matvec",ML_Set_Amatrix_Matvec(ml_object,0,PetscML_matvec));
739: PetscStackCall("ML_Set_Symmetrize",ML_Set_Symmetrize(ml_object,pc_ml->Symmetrize ? ML_YES : ML_NO));
741: /* aggregation */
742: PetscStackCall("ML_Aggregate_Create",ML_Aggregate_Create(&agg_object));
743: pc_ml->agg_object = agg_object;
745: {
746: MatNullSpace mnull;
747: MatGetNearNullSpace(A,&mnull);
748: if (pc_ml->nulltype == PCML_NULLSPACE_AUTO) {
749: if (mnull) pc_ml->nulltype = PCML_NULLSPACE_USER;
750: else if (bs > 1) pc_ml->nulltype = PCML_NULLSPACE_BLOCK;
751: else pc_ml->nulltype = PCML_NULLSPACE_SCALAR;
752: }
753: switch (pc_ml->nulltype) {
754: case PCML_NULLSPACE_USER: {
755: PetscScalar *nullvec;
756: const PetscScalar *v;
757: PetscBool has_const;
758: PetscInt i,j,mlocal,nvec,M;
759: const Vec *vecs;
761: if (!mnull) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"Must provide explicit null space using MatSetNearNullSpace() to use user-specified null space");
762: MatGetSize(A,&M,NULL);
763: MatGetLocalSize(Aloc,&mlocal,NULL);
764: MatNullSpaceGetVecs(mnull,&has_const,&nvec,&vecs);
765: PetscMalloc1((nvec+!!has_const)*mlocal,&nullvec);
766: if (has_const) for (i=0; i<mlocal; i++) nullvec[i] = 1.0/M;
767: for (i=0; i<nvec; i++) {
768: VecGetArrayRead(vecs[i],&v);
769: for (j=0; j<mlocal; j++) nullvec[(i+!!has_const)*mlocal + j] = v[j];
770: VecRestoreArrayRead(vecs[i],&v);
771: }
772: PetscStackCall("ML_Aggregate_Create",ML_Aggregate_Set_NullSpace(agg_object,bs,nvec+!!has_const,nullvec,mlocal);CHKERRQ(ierr));
773: PetscFree(nullvec);
774: } break;
775: case PCML_NULLSPACE_BLOCK:
776: PetscStackCall("ML_Aggregate_Set_NullSpace",ML_Aggregate_Set_NullSpace(agg_object,bs,bs,0,0);CHKERRQ(ierr));
777: break;
778: case PCML_NULLSPACE_SCALAR:
779: break;
780: default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_SUP,"Unknown null space type");
781: }
782: }
783: PetscStackCall("ML_Aggregate_Set_MaxCoarseSize",ML_Aggregate_Set_MaxCoarseSize(agg_object,pc_ml->MaxCoarseSize));
784: /* set options */
785: switch (pc_ml->CoarsenScheme) {
786: case 1:
787: PetscStackCall("ML_Aggregate_Set_CoarsenScheme_Coupled",ML_Aggregate_Set_CoarsenScheme_Coupled(agg_object));break;
788: case 2:
789: PetscStackCall("ML_Aggregate_Set_CoarsenScheme_MIS",ML_Aggregate_Set_CoarsenScheme_MIS(agg_object));break;
790: case 3:
791: PetscStackCall("ML_Aggregate_Set_CoarsenScheme_METIS",ML_Aggregate_Set_CoarsenScheme_METIS(agg_object));break;
792: }
793: PetscStackCall("ML_Aggregate_Set_Threshold",ML_Aggregate_Set_Threshold(agg_object,pc_ml->Threshold));
794: PetscStackCall("ML_Aggregate_Set_DampingFactor",ML_Aggregate_Set_DampingFactor(agg_object,pc_ml->DampingFactor));
795: if (pc_ml->SpectralNormScheme_Anorm) {
796: PetscStackCall("ML_Set_SpectralNormScheme_Anorm",ML_Set_SpectralNormScheme_Anorm(ml_object));
797: }
798: agg_object->keep_agg_information = (int)pc_ml->KeepAggInfo;
799: agg_object->keep_P_tentative = (int)pc_ml->Reusable;
800: agg_object->block_scaled_SA = (int)pc_ml->BlockScaling;
801: agg_object->minimizing_energy = (int)pc_ml->EnergyMinimization;
802: agg_object->minimizing_energy_droptol = (double)pc_ml->EnergyMinimizationDropTol;
803: agg_object->cheap_minimizing_energy = (int)pc_ml->EnergyMinimizationCheap;
805: if (pc_ml->Aux) {
806: if (!pc_ml->dim) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"Auxiliary matrix requires coordinates");
807: ml_object->Amat[0].aux_data->threshold = pc_ml->AuxThreshold;
808: ml_object->Amat[0].aux_data->enable = 1;
809: ml_object->Amat[0].aux_data->max_level = 10;
810: ml_object->Amat[0].num_PDEs = bs;
811: }
813: MatGetInfo(A,MAT_LOCAL,&info);
814: ml_object->Amat[0].N_nonzeros = (int) info.nz_used;
816: if (pc_ml->dim) {
817: PetscInt i,dim = pc_ml->dim;
818: ML_Aggregate_Viz_Stats *grid_info;
819: PetscInt nlocghost;
821: MatGetBlockSize(A,&bs);
822: nlocghost = Aloc->cmap->n / bs;
824: PetscStackCall("ML_Aggregate_VizAndStats_Setup(",ML_Aggregate_VizAndStats_Setup(ml_object)); /* create ml info for coords */
825: grid_info = (ML_Aggregate_Viz_Stats*) ml_object->Grid[0].Grid;
826: for (i = 0; i < dim; i++) {
827: /* set the finest level coordinates to point to the column-order array
828: * in pc_ml */
829: /* NOTE: must point away before VizAndStats_Clean so ML doesn't free */
830: switch (i) {
831: case 0: grid_info->x = pc_ml->coords + nlocghost * i; break;
832: case 1: grid_info->y = pc_ml->coords + nlocghost * i; break;
833: case 2: grid_info->z = pc_ml->coords + nlocghost * i; break;
834: default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_SIZ,"PCML coordinate dimension must be <= 3");
835: }
836: }
837: grid_info->Ndim = dim;
838: }
840: /* repartitioning */
841: if (pc_ml->Repartition) {
842: PetscStackCall("ML_Repartition_Activate",ML_Repartition_Activate(ml_object));
843: PetscStackCall("ML_Repartition_Set_LargestMinMaxRatio",ML_Repartition_Set_LargestMinMaxRatio(ml_object,pc_ml->MaxMinRatio));
844: PetscStackCall("ML_Repartition_Set_MinPerProc",ML_Repartition_Set_MinPerProc(ml_object,pc_ml->MinPerProc));
845: PetscStackCall("ML_Repartition_Set_PutOnSingleProc",ML_Repartition_Set_PutOnSingleProc(ml_object,pc_ml->PutOnSingleProc));
846: #if 0 /* Function not yet defined in ml-6.2 */
847: /* I'm not sure what compatibility issues might crop up if we partitioned
848: * on the finest level, so to be safe repartition starts on the next
849: * finest level (reflection default behavior in
850: * ml_MultiLevelPreconditioner) */
851: PetscStackCall("ML_Repartition_Set_StartLevel",ML_Repartition_Set_StartLevel(ml_object,1));
852: #endif
854: if (!pc_ml->RepartitionType) {
855: PetscInt i;
857: if (!pc_ml->dim) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_USER,"ML Zoltan repartitioning requires coordinates");
858: PetscStackCall("ML_Repartition_Set_Partitioner",ML_Repartition_Set_Partitioner(ml_object,ML_USEZOLTAN));
859: PetscStackCall("ML_Aggregate_Set_Dimensions",ML_Aggregate_Set_Dimensions(agg_object, pc_ml->dim));
861: for (i = 0; i < ml_object->ML_num_levels; i++) {
862: ML_Aggregate_Viz_Stats *grid_info = (ML_Aggregate_Viz_Stats*)ml_object->Grid[i].Grid;
863: grid_info->zoltan_type = pc_ml->ZoltanScheme + 1; /* ml numbers options 1, 2, 3 */
864: /* defaults from ml_agg_info.c */
865: grid_info->zoltan_estimated_its = 40; /* only relevant to hypergraph / fast hypergraph */
866: grid_info->zoltan_timers = 0;
867: grid_info->smoothing_steps = 4; /* only relevant to hypergraph / fast hypergraph */
868: }
869: } else {
870: PetscStackCall("ML_Repartition_Set_Partitioner",ML_Repartition_Set_Partitioner(ml_object,ML_USEPARMETIS));
871: }
872: }
874: if (pc_ml->OldHierarchy) {
875: PetscStackCall("ML_Gen_MGHierarchy_UsingAggregation",Nlevels = ML_Gen_MGHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object));
876: } else {
877: PetscStackCall("ML_Gen_MultiLevelHierarchy_UsingAggregation",Nlevels = ML_Gen_MultiLevelHierarchy_UsingAggregation(ml_object,0,ML_INCREASING,agg_object));
878: }
879: if (Nlevels<=0) SETERRQ1(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_OUTOFRANGE,"Nlevels %d must > 0",Nlevels);
880: pc_ml->Nlevels = Nlevels;
881: fine_level = Nlevels - 1;
883: PCMGSetLevels(pc,Nlevels,NULL);
884: /* set default smoothers */
885: for (level=1; level<=fine_level; level++) {
886: PCMGGetSmoother(pc,level,&smoother);
887: KSPSetType(smoother,KSPRICHARDSON);
888: KSPGetPC(smoother,&subpc);
889: PCSetType(subpc,PCSOR);
890: }
891: PetscObjectOptionsBegin((PetscObject)pc);
892: PCSetFromOptions_MG(PetscOptionsObject,pc); /* should be called in PCSetFromOptions_ML(), but cannot be called prior to PCMGSetLevels() */
893: PetscOptionsEnd();
895: PetscMalloc1(Nlevels,&gridctx);
897: pc_ml->gridctx = gridctx;
899: /* wrap ML matrices by PETSc shell matrices at coarsened grids.
900: Level 0 is the finest grid for ML, but coarsest for PETSc! */
901: gridctx[fine_level].A = A;
903: level = fine_level - 1;
904: if (size == 1) { /* convert ML P, R and A into seqaij format */
905: for (mllevel=1; mllevel<Nlevels; mllevel++) {
906: mlmat = &(ml_object->Pmat[mllevel]);
907: MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);
908: mlmat = &(ml_object->Rmat[mllevel-1]);
909: MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);
911: mlmat = &(ml_object->Amat[mllevel]);
912: MatWrapML_SeqAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
913: level--;
914: }
915: } else { /* convert ML P and R into shell format, ML A into mpiaij format */
916: for (mllevel=1; mllevel<Nlevels; mllevel++) {
917: mlmat = &(ml_object->Pmat[mllevel]);
918: MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].P);
919: mlmat = &(ml_object->Rmat[mllevel-1]);
920: MatWrapML_SHELL(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].R);
922: mlmat = &(ml_object->Amat[mllevel]);
923: MatWrapML_MPIAIJ(mlmat,MAT_INITIAL_MATRIX,&gridctx[level].A);
924: level--;
925: }
926: }
928: /* create vectors and ksp at all levels */
929: for (level=0; level<fine_level; level++) {
930: level1 = level + 1;
931: VecCreate(((PetscObject)gridctx[level].A)->comm,&gridctx[level].x);
932: VecSetSizes(gridctx[level].x,gridctx[level].A->cmap->n,PETSC_DECIDE);
933: VecSetType(gridctx[level].x,VECMPI);
934: PCMGSetX(pc,level,gridctx[level].x);
936: VecCreate(((PetscObject)gridctx[level].A)->comm,&gridctx[level].b);
937: VecSetSizes(gridctx[level].b,gridctx[level].A->rmap->n,PETSC_DECIDE);
938: VecSetType(gridctx[level].b,VECMPI);
939: PCMGSetRhs(pc,level,gridctx[level].b);
941: VecCreate(((PetscObject)gridctx[level1].A)->comm,&gridctx[level1].r);
942: VecSetSizes(gridctx[level1].r,gridctx[level1].A->rmap->n,PETSC_DECIDE);
943: VecSetType(gridctx[level1].r,VECMPI);
944: PCMGSetR(pc,level1,gridctx[level1].r);
946: if (level == 0) {
947: PCMGGetCoarseSolve(pc,&gridctx[level].ksp);
948: } else {
949: PCMGGetSmoother(pc,level,&gridctx[level].ksp);
950: }
951: }
952: PCMGGetSmoother(pc,fine_level,&gridctx[fine_level].ksp);
954: /* create coarse level and the interpolation between the levels */
955: for (level=0; level<fine_level; level++) {
956: level1 = level + 1;
957: PCMGSetInterpolation(pc,level1,gridctx[level].P);
958: PCMGSetRestriction(pc,level1,gridctx[level].R);
959: if (level > 0) {
960: PCMGSetResidual(pc,level,PCMGResidualDefault,gridctx[level].A);
961: }
962: KSPSetOperators(gridctx[level].ksp,gridctx[level].A,gridctx[level].A);
963: }
964: PCMGSetResidual(pc,fine_level,PCMGResidualDefault,gridctx[fine_level].A);
965: KSPSetOperators(gridctx[fine_level].ksp,gridctx[level].A,gridctx[fine_level].A);
967: /* put coordinate info in levels */
968: if (pc_ml->dim) {
969: PetscInt i,j,dim = pc_ml->dim;
970: PetscInt bs, nloc;
971: PC subpc;
972: PetscReal *array;
974: level = fine_level;
975: for (mllevel = 0; mllevel < Nlevels; mllevel++) {
976: ML_Aggregate_Viz_Stats *grid_info = (ML_Aggregate_Viz_Stats*)ml_object->Amat[mllevel].to->Grid->Grid;
977: MPI_Comm comm = ((PetscObject)gridctx[level].A)->comm;
979: MatGetBlockSize (gridctx[level].A, &bs);
980: MatGetLocalSize (gridctx[level].A, NULL, &nloc);
981: nloc /= bs; /* number of local nodes */
983: VecCreate(comm,&gridctx[level].coords);
984: VecSetSizes(gridctx[level].coords,dim * nloc,PETSC_DECIDE);
985: VecSetType(gridctx[level].coords,VECMPI);
986: VecGetArray(gridctx[level].coords,&array);
987: for (j = 0; j < nloc; j++) {
988: for (i = 0; i < dim; i++) {
989: switch (i) {
990: case 0: array[dim * j + i] = grid_info->x[j]; break;
991: case 1: array[dim * j + i] = grid_info->y[j]; break;
992: case 2: array[dim * j + i] = grid_info->z[j]; break;
993: default: SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_ARG_SIZ,"PCML coordinate dimension must be <= 3");
994: }
995: }
996: }
998: /* passing coordinates to smoothers/coarse solver, should they need them */
999: KSPGetPC(gridctx[level].ksp,&subpc);
1000: PCSetCoordinates(subpc,dim,nloc,array);
1001: VecRestoreArray(gridctx[level].coords,&array);
1002: level--;
1003: }
1004: }
1006: /* setupcalled is set to 0 so that MG is setup from scratch */
1007: pc->setupcalled = 0;
1008: PCSetUp_MG(pc);
1009: return(0);
1010: }
1012: /* -------------------------------------------------------------------------- */
1013: /*
1014: PCDestroy_ML - Destroys the private context for the ML preconditioner
1015: that was created with PCCreate_ML().
1017: Input Parameter:
1018: . pc - the preconditioner context
1020: Application Interface Routine: PCDestroy()
1021: */
1024: PetscErrorCode PCDestroy_ML(PC pc)
1025: {
1027: PC_MG *mg = (PC_MG*)pc->data;
1028: PC_ML *pc_ml= (PC_ML*)mg->innerctx;
1031: PCReset_ML(pc);
1032: PetscFree(pc_ml);
1033: PCDestroy_MG(pc);
1034: PetscObjectComposeFunction((PetscObject)pc,"PCSetCoordinates_C",NULL);
1035: return(0);
1036: }
1040: PetscErrorCode PCSetFromOptions_ML(PetscOptions *PetscOptionsObject,PC pc)
1041: {
1043: PetscInt indx,PrintLevel,partindx;
1044: const char *scheme[] = {"Uncoupled","Coupled","MIS","METIS"};
1045: const char *part[] = {"Zoltan","ParMETIS"};
1046: #if defined(HAVE_ML_ZOLTAN)
1047: PetscInt zidx;
1048: const char *zscheme[] = {"RCB","hypergraph","fast_hypergraph"};
1049: #endif
1050: PC_MG *mg = (PC_MG*)pc->data;
1051: PC_ML *pc_ml = (PC_ML*)mg->innerctx;
1052: PetscMPIInt size;
1053: MPI_Comm comm;
1056: PetscObjectGetComm((PetscObject)pc,&comm);
1057: MPI_Comm_size(comm,&size);
1058: PetscOptionsHead(PetscOptionsObject,"ML options");
1060: PrintLevel = 0;
1061: indx = 0;
1062: partindx = 0;
1064: PetscOptionsInt("-pc_ml_PrintLevel","Print level","ML_Set_PrintLevel",PrintLevel,&PrintLevel,NULL);
1065: PetscStackCall("ML_Set_PrintLeve",ML_Set_PrintLevel(PrintLevel));
1066: PetscOptionsInt("-pc_ml_maxNlevels","Maximum number of levels","None",pc_ml->MaxNlevels,&pc_ml->MaxNlevels,NULL);
1067: PetscOptionsInt("-pc_ml_maxCoarseSize","Maximum coarsest mesh size","ML_Aggregate_Set_MaxCoarseSize",pc_ml->MaxCoarseSize,&pc_ml->MaxCoarseSize,NULL);
1068: PetscOptionsEList("-pc_ml_CoarsenScheme","Aggregate Coarsen Scheme","ML_Aggregate_Set_CoarsenScheme_*",scheme,4,scheme[0],&indx,NULL);
1070: pc_ml->CoarsenScheme = indx;
1072: PetscOptionsReal("-pc_ml_DampingFactor","P damping factor","ML_Aggregate_Set_DampingFactor",pc_ml->DampingFactor,&pc_ml->DampingFactor,NULL);
1073: PetscOptionsReal("-pc_ml_Threshold","Smoother drop tol","ML_Aggregate_Set_Threshold",pc_ml->Threshold,&pc_ml->Threshold,NULL);
1074: PetscOptionsBool("-pc_ml_SpectralNormScheme_Anorm","Method used for estimating spectral radius","ML_Set_SpectralNormScheme_Anorm",pc_ml->SpectralNormScheme_Anorm,&pc_ml->SpectralNormScheme_Anorm,NULL);
1075: PetscOptionsBool("-pc_ml_Symmetrize","Symmetrize aggregation","ML_Set_Symmetrize",pc_ml->Symmetrize,&pc_ml->Symmetrize,NULL);
1076: PetscOptionsBool("-pc_ml_BlockScaling","Scale all dofs at each node together","None",pc_ml->BlockScaling,&pc_ml->BlockScaling,NULL);
1077: PetscOptionsEnum("-pc_ml_nullspace","Which type of null space information to use","None",PCMLNullSpaceTypes,(PetscEnum)pc_ml->nulltype,(PetscEnum*)&pc_ml->nulltype,NULL);
1078: PetscOptionsInt("-pc_ml_EnergyMinimization","Energy minimization norm type (0=no minimization; see ML manual for 1,2,3; -1 and 4 undocumented)","None",pc_ml->EnergyMinimization,&pc_ml->EnergyMinimization,NULL);
1079: PetscOptionsBool("-pc_ml_reuse_interpolation","Reuse the interpolation operators when possible (cheaper, weaker when matrix entries change a lot)","None",pc_ml->reuse_interpolation,&pc_ml->reuse_interpolation,NULL);
1080: /*
1081: The following checks a number of conditions. If we let this stuff slip by, then ML's error handling will take over.
1082: This is suboptimal because it amounts to calling exit(1) so we check for the most common conditions.
1084: We also try to set some sane defaults when energy minimization is activated, otherwise it's hard to find a working
1085: combination of options and ML's exit(1) explanations don't help matters.
1086: */
1087: if (pc_ml->EnergyMinimization < -1 || pc_ml->EnergyMinimization > 4) SETERRQ(comm,PETSC_ERR_ARG_OUTOFRANGE,"EnergyMinimization must be in range -1..4");
1088: if (pc_ml->EnergyMinimization == 4 && size > 1) SETERRQ(comm,PETSC_ERR_SUP,"Energy minimization type 4 does not work in parallel");
1089: if (pc_ml->EnergyMinimization == 4) {PetscInfo(pc,"Mandel's energy minimization scheme is experimental and broken in ML-6.2\n");}
1090: if (pc_ml->EnergyMinimization) {
1091: PetscOptionsReal("-pc_ml_EnergyMinimizationDropTol","Energy minimization drop tolerance","None",pc_ml->EnergyMinimizationDropTol,&pc_ml->EnergyMinimizationDropTol,NULL);
1092: }
1093: if (pc_ml->EnergyMinimization == 2) {
1094: /* According to ml_MultiLevelPreconditioner.cpp, this option is only meaningful for norm type (2) */
1095: PetscOptionsBool("-pc_ml_EnergyMinimizationCheap","Use cheaper variant of norm type 2","None",pc_ml->EnergyMinimizationCheap,&pc_ml->EnergyMinimizationCheap,NULL);
1096: }
1097: /* energy minimization sometimes breaks if this is turned off, the more classical stuff should be okay without it */
1098: if (pc_ml->EnergyMinimization) pc_ml->KeepAggInfo = PETSC_TRUE;
1099: PetscOptionsBool("-pc_ml_KeepAggInfo","Allows the preconditioner to be reused, or auxilliary matrices to be generated","None",pc_ml->KeepAggInfo,&pc_ml->KeepAggInfo,NULL);
1100: /* Option (-1) doesn't work at all (calls exit(1)) if the tentative restriction operator isn't stored. */
1101: if (pc_ml->EnergyMinimization == -1) pc_ml->Reusable = PETSC_TRUE;
1102: PetscOptionsBool("-pc_ml_Reusable","Store intermedaiate data structures so that the multilevel hierarchy is reusable","None",pc_ml->Reusable,&pc_ml->Reusable,NULL);
1103: /*
1104: ML's C API is severely underdocumented and lacks significant functionality. The C++ API calls
1105: ML_Gen_MultiLevelHierarchy_UsingAggregation() which is a modified copy (!?) of the documented function
1106: ML_Gen_MGHierarchy_UsingAggregation(). This modification, however, does not provide a strict superset of the
1107: functionality in the old function, so some users may still want to use it. Note that many options are ignored in
1108: this context, but ML doesn't provide a way to find out which ones.
1109: */
1110: PetscOptionsBool("-pc_ml_OldHierarchy","Use old routine to generate hierarchy","None",pc_ml->OldHierarchy,&pc_ml->OldHierarchy,NULL);
1111: PetscOptionsBool("-pc_ml_repartition", "Allow ML to repartition levels of the heirarchy","ML_Repartition_Activate",pc_ml->Repartition,&pc_ml->Repartition,NULL);
1112: if (pc_ml->Repartition) {
1113: PetscOptionsReal("-pc_ml_repartitionMaxMinRatio", "Acceptable ratio of repartitioned sizes","ML_Repartition_Set_LargestMinMaxRatio",pc_ml->MaxMinRatio,&pc_ml->MaxMinRatio,NULL);
1114: PetscOptionsInt("-pc_ml_repartitionMinPerProc", "Smallest repartitioned size","ML_Repartition_Set_MinPerProc",pc_ml->MinPerProc,&pc_ml->MinPerProc,NULL);
1115: PetscOptionsInt("-pc_ml_repartitionPutOnSingleProc", "Problem size automatically repartitioned to one processor","ML_Repartition_Set_PutOnSingleProc",pc_ml->PutOnSingleProc,&pc_ml->PutOnSingleProc,NULL);
1116: #if defined(HAVE_ML_ZOLTAN)
1117: partindx = 0;
1118: PetscOptionsEList("-pc_ml_repartitionType", "Repartitioning library to use","ML_Repartition_Set_Partitioner",part,2,part[0],&partindx,NULL);
1120: pc_ml->RepartitionType = partindx;
1121: if (!partindx) {
1122: PetscInt zindx = 0;
1124: PetscOptionsEList("-pc_ml_repartitionZoltanScheme", "Repartitioning scheme to use","None",zscheme,3,zscheme[0],&zindx,NULL);
1126: pc_ml->ZoltanScheme = zindx;
1127: }
1128: #else
1129: partindx = 1;
1130: PetscOptionsEList("-pc_ml_repartitionType", "Repartitioning library to use","ML_Repartition_Set_Partitioner",part,2,part[1],&partindx,NULL);
1131: pc_ml->RepartitionType = partindx;
1132: if (!partindx) SETERRQ(PetscObjectComm((PetscObject)pc),PETSC_ERR_SUP_SYS,"ML not compiled with Zoltan");
1133: #endif
1134: PetscOptionsBool("-pc_ml_Aux","Aggregate using auxiliary coordinate-based laplacian","None",pc_ml->Aux,&pc_ml->Aux,NULL);
1135: PetscOptionsReal("-pc_ml_AuxThreshold","Auxiliary smoother drop tol","None",pc_ml->AuxThreshold,&pc_ml->AuxThreshold,NULL);
1136: }
1137: PetscOptionsTail();
1138: return(0);
1139: }
1141: /* -------------------------------------------------------------------------- */
1142: /*
1143: PCCreate_ML - Creates a ML preconditioner context, PC_ML,
1144: and sets this as the private data within the generic preconditioning
1145: context, PC, that was created within PCCreate().
1147: Input Parameter:
1148: . pc - the preconditioner context
1150: Application Interface Routine: PCCreate()
1151: */
1153: /*MC
1154: PCML - Use algebraic multigrid preconditioning. This preconditioner requires you provide
1155: fine grid discretization matrix. The coarser grid matrices and restriction/interpolation
1156: operators are computed by ML, with the matrices coverted to PETSc matrices in aij format
1157: and the restriction/interpolation operators wrapped as PETSc shell matrices.
1159: Options Database Key:
1160: Multigrid options(inherited):
1161: + -pc_mg_cycles <1>: 1 for V cycle, 2 for W-cycle (MGSetCycles)
1162: . -pc_mg_smoothup <1>: Number of post-smoothing steps (MGSetNumberSmoothUp)
1163: . -pc_mg_smoothdown <1>: Number of pre-smoothing steps (MGSetNumberSmoothDown)
1164: - -pc_mg_type <multiplicative>: (one of) additive multiplicative full kascade
1165: ML options:
1166: + -pc_ml_PrintLevel <0>: Print level (ML_Set_PrintLevel)
1167: . -pc_ml_maxNlevels <10>: Maximum number of levels (None)
1168: . -pc_ml_maxCoarseSize <1>: Maximum coarsest mesh size (ML_Aggregate_Set_MaxCoarseSize)
1169: . -pc_ml_CoarsenScheme <Uncoupled>: (one of) Uncoupled Coupled MIS METIS
1170: . -pc_ml_DampingFactor <1.33333>: P damping factor (ML_Aggregate_Set_DampingFactor)
1171: . -pc_ml_Threshold <0>: Smoother drop tol (ML_Aggregate_Set_Threshold)
1172: . -pc_ml_SpectralNormScheme_Anorm <false>: Method used for estimating spectral radius (ML_Set_SpectralNormScheme_Anorm)
1173: . -pc_ml_repartition <false>: Allow ML to repartition levels of the heirarchy (ML_Repartition_Activate)
1174: . -pc_ml_repartitionMaxMinRatio <1.3>: Acceptable ratio of repartitioned sizes (ML_Repartition_Set_LargestMinMaxRatio)
1175: . -pc_ml_repartitionMinPerProc <512>: Smallest repartitioned size (ML_Repartition_Set_MinPerProc)
1176: . -pc_ml_repartitionPutOnSingleProc <5000>: Problem size automatically repartitioned to one processor (ML_Repartition_Set_PutOnSingleProc)
1177: . -pc_ml_repartitionType <Zoltan>: Repartitioning library to use (ML_Repartition_Set_Partitioner)
1178: . -pc_ml_repartitionZoltanScheme <RCB>: Repartitioning scheme to use (None)
1179: . -pc_ml_Aux <false>: Aggregate using auxiliary coordinate-based laplacian (None)
1180: - -pc_ml_AuxThreshold <0.0>: Auxiliary smoother drop tol (None)
1182: Level: intermediate
1184: Concepts: multigrid
1186: .seealso: PCCreate(), PCSetType(), PCType (for list of available types), PC, PCMGType,
1187: PCMGSetLevels(), PCMGGetLevels(), PCMGSetType(), MPSetCycles(), PCMGSetNumberSmoothDown(),
1188: PCMGSetNumberSmoothUp(), PCMGGetCoarseSolve(), PCMGSetResidual(), PCMGSetInterpolation(),
1189: PCMGSetRestriction(), PCMGGetSmoother(), PCMGGetSmootherUp(), PCMGGetSmootherDown(),
1190: PCMGSetCycleTypeOnLevel(), PCMGSetRhs(), PCMGSetX(), PCMGSetR()
1191: M*/
1195: PETSC_EXTERN PetscErrorCode PCCreate_ML(PC pc)
1196: {
1198: PC_ML *pc_ml;
1199: PC_MG *mg;
1202: /* PCML is an inherited class of PCMG. Initialize pc as PCMG */
1203: PCSetType(pc,PCMG); /* calls PCCreate_MG() and MGCreate_Private() */
1204: PetscObjectChangeTypeName((PetscObject)pc,PCML);
1205: /* Since PCMG tries to use DM assocated with PC must delete it */
1206: DMDestroy(&pc->dm);
1207: mg = (PC_MG*)pc->data;
1208: mg->galerkin = 2; /* Use Galerkin, but it is computed externally */
1210: /* create a supporting struct and attach it to pc */
1211: PetscNewLog(pc,&pc_ml);
1212: mg->innerctx = pc_ml;
1214: pc_ml->ml_object = 0;
1215: pc_ml->agg_object = 0;
1216: pc_ml->gridctx = 0;
1217: pc_ml->PetscMLdata = 0;
1218: pc_ml->Nlevels = -1;
1219: pc_ml->MaxNlevels = 10;
1220: pc_ml->MaxCoarseSize = 1;
1221: pc_ml->CoarsenScheme = 1;
1222: pc_ml->Threshold = 0.0;
1223: pc_ml->DampingFactor = 4.0/3.0;
1224: pc_ml->SpectralNormScheme_Anorm = PETSC_FALSE;
1225: pc_ml->size = 0;
1226: pc_ml->dim = 0;
1227: pc_ml->nloc = 0;
1228: pc_ml->coords = 0;
1229: pc_ml->Repartition = PETSC_FALSE;
1230: pc_ml->MaxMinRatio = 1.3;
1231: pc_ml->MinPerProc = 512;
1232: pc_ml->PutOnSingleProc = 5000;
1233: pc_ml->RepartitionType = 0;
1234: pc_ml->ZoltanScheme = 0;
1235: pc_ml->Aux = PETSC_FALSE;
1236: pc_ml->AuxThreshold = 0.0;
1238: /* allow for coordinates to be passed */
1239: PetscObjectComposeFunction((PetscObject)pc,"PCSetCoordinates_C",PCSetCoordinates_ML);
1241: /* overwrite the pointers of PCMG by the functions of PCML */
1242: pc->ops->setfromoptions = PCSetFromOptions_ML;
1243: pc->ops->setup = PCSetUp_ML;
1244: pc->ops->reset = PCReset_ML;
1245: pc->ops->destroy = PCDestroy_ML;
1246: return(0);
1247: }