Actual source code: pmetis.c

petsc-master 2020-07-01
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  2:  #include <../src/mat/impls/adj/mpi/mpiadj.h>

  4: /*
  5:    Currently using ParMetis-4.0.2
  6: */

  8: #include <parmetis.h>

 10: /*
 11:       The first 5 elements of this structure are the input control array to Metis
 12: */
 13: typedef struct {
 14:   PetscInt  cuts;         /* number of cuts made (output) */
 15:   PetscInt  foldfactor;
 16:   PetscInt  parallel;     /* use parallel partitioner for coarse problem */
 17:   PetscInt  indexing;     /* 0 indicates C indexing, 1 Fortran */
 18:   PetscInt  printout;     /* indicates if one wishes Metis to print info */
 19:   PetscBool repartition;
 20: } MatPartitioning_Parmetis;

 22: #define CHKERRQPARMETIS(n,func)                                             \
 23:   if (n == METIS_ERROR_INPUT) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ParMETIS error due to wrong inputs and/or options for %s",func); \
 24:   else if (n == METIS_ERROR_MEMORY) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ParMETIS error due to insufficient memory in %s",func); \
 25:   else if (n == METIS_ERROR) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ParMETIS general error in %s",func); \

 27: #define PetscStackCallParmetis(func,args) do {PetscStackPush(#func);int status = func args;PetscStackPop; CHKERRQPARMETIS(status,#func);} while (0)

 29: static PetscErrorCode MatPartitioningApply_Parmetis_Private(MatPartitioning part, PetscBool useND, PetscBool isImprove, IS *partitioning)
 30: {
 31:   MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;
 32:   PetscErrorCode           ierr;
 33:   PetscInt                 *locals = NULL;
 34:   Mat                      mat     = part->adj,amat,pmat;
 35:   PetscBool                flg;
 36:   PetscInt                 bs = 1;

 41:   PetscObjectTypeCompare((PetscObject)mat,MATMPIADJ,&flg);
 42:   if (flg) {
 43:     amat = mat;
 44:     PetscObjectReference((PetscObject)amat);
 45:   } else {
 46:     /* bs indicates if the converted matrix is "reduced" from the original and hence the
 47:        resulting partition results need to be stretched to match the original matrix */
 48:     MatConvert(mat,MATMPIADJ,MAT_INITIAL_MATRIX,&amat);
 49:     if (amat->rmap->n > 0) bs = mat->rmap->n/amat->rmap->n;
 50:   }
 51:   MatMPIAdjCreateNonemptySubcommMat(amat,&pmat);
 52:   MPI_Barrier(PetscObjectComm((PetscObject)part));

 54:   if (pmat) {
 55:     MPI_Comm   pcomm,comm;
 56:     Mat_MPIAdj *adj     = (Mat_MPIAdj*)pmat->data;
 57:     PetscInt   *vtxdist = pmat->rmap->range;
 58:     PetscInt   *xadj    = adj->i;
 59:     PetscInt   *adjncy  = adj->j;
 60:     PetscInt   *NDorder = NULL;
 61:     PetscInt   itmp     = 0,wgtflag=0, numflag=0, ncon=1, nparts=part->n, options[24], i, j;
 62:     real_t     *tpwgts,*ubvec,itr=0.1;

 64:     PetscObjectGetComm((PetscObject)pmat,&pcomm);
 65:     if (PetscDefined(USE_DEBUG)) {
 66:       /* check that matrix has no diagonal entries */
 67:       PetscInt rstart;
 68:       MatGetOwnershipRange(pmat,&rstart,NULL);
 69:       for (i=0; i<pmat->rmap->n; i++) {
 70:         for (j=xadj[i]; j<xadj[i+1]; j++) {
 71:           if (adjncy[j] == i+rstart) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Row %D has diagonal entry; Parmetis forbids diagonal entry",i+rstart);
 72:         }
 73:       }
 74:     }

 76:     PetscMalloc1(pmat->rmap->n,&locals);

 78:     if (isImprove) {
 79:       PetscInt       i;
 80:       const PetscInt *part_indices;
 82:       ISGetIndices(*partitioning,&part_indices);
 83:       for (i=0; i<pmat->rmap->n; i++) locals[i] = part_indices[i*bs];
 84:       ISRestoreIndices(*partitioning,&part_indices);
 85:       ISDestroy(partitioning);
 86:     }

 88:     if (adj->values && part->use_edge_weights && !part->vertex_weights) wgtflag = 1;
 89:     if (part->vertex_weights && !adj->values) wgtflag = 2;
 90:     if (part->vertex_weights && adj->values && part->use_edge_weights) wgtflag = 3;

 92:     if (PetscLogPrintInfo) {itmp = pmetis->printout; pmetis->printout = 127;}
 93:     PetscMalloc1(ncon*nparts,&tpwgts);
 94:     for (i=0; i<ncon; i++) {
 95:       for (j=0; j<nparts; j++) {
 96:         if (part->part_weights) {
 97:           tpwgts[i*nparts+j] = part->part_weights[i*nparts+j];
 98:         } else {
 99:           tpwgts[i*nparts+j] = 1./nparts;
100:         }
101:       }
102:     }
103:     PetscMalloc1(ncon,&ubvec);
104:     for (i=0; i<ncon; i++) ubvec[i] = 1.05;
105:     /* This sets the defaults */
106:     options[0] = 0;
107:     for (i=1; i<24; i++) options[i] = -1;
108:     /* Duplicate the communicator to be sure that ParMETIS attribute caching does not interfere with PETSc. */
109:     MPI_Comm_dup(pcomm,&comm);
110:     if (useND) {
111:       PetscInt    *sizes, *seps, log2size, subd, *level;
112:       PetscMPIInt size;
113:       idx_t       mtype = PARMETIS_MTYPE_GLOBAL, rtype = PARMETIS_SRTYPE_2PHASE, p_nseps = 1, s_nseps = 1;
114:       real_t      ubfrac = 1.05;

116:       MPI_Comm_size(comm,&size);
117:       PetscMalloc1(pmat->rmap->n,&NDorder);
118:       PetscMalloc3(2*size,&sizes,4*size,&seps,size,&level);
119:       PetscStackCallParmetis(ParMETIS_V32_NodeND,((idx_t*)vtxdist,(idx_t*)xadj,(idx_t*)adjncy,(idx_t*)part->vertex_weights,(idx_t*)&numflag,&mtype,&rtype,&p_nseps,&s_nseps,&ubfrac,NULL/* seed */,NULL/* dbglvl */,(idx_t*)NDorder,(idx_t*)(sizes),&comm));
120:       log2size = PetscLog2Real(size);
121:       subd = PetscPowInt(2,log2size);
122:       MatPartitioningSizesToSep_Private(subd,sizes,seps,level);
123:       for (i=0;i<pmat->rmap->n;i++) {
124:         PetscInt loc;

126:         PetscFindInt(NDorder[i],2*subd,seps,&loc);
127:         if (loc < 0) {
128:           loc = -(loc+1);
129:           if (loc%2) { /* part of subdomain */
130:             locals[i] = loc/2;
131:           } else {
132:             PetscFindInt(NDorder[i],2*(subd-1),seps+2*subd,&loc);
133:             loc = loc < 0 ? -(loc+1)/2 : loc/2;
134:             locals[i] = level[loc];
135:           }
136:         } else locals[i] = loc/2;
137:       }
138:       PetscFree3(sizes,seps,level);
139:     } else {
140:       if (pmetis->repartition) {
141:         PetscStackCallParmetis(ParMETIS_V3_AdaptiveRepart,((idx_t*)vtxdist,(idx_t*)xadj,(idx_t*)adjncy,(idx_t*)part->vertex_weights,(idx_t*)part->vertex_weights,(idx_t*)adj->values,(idx_t*)&wgtflag,(idx_t*)&numflag,(idx_t*)&ncon,(idx_t*)&nparts,tpwgts,ubvec,&itr,(idx_t*)options,(idx_t*)&pmetis->cuts,(idx_t*)locals,&comm));
142:       } else if (isImprove) {
143:         PetscStackCallParmetis(ParMETIS_V3_RefineKway,((idx_t*)vtxdist,(idx_t*)xadj,(idx_t*)adjncy,(idx_t*)part->vertex_weights,(idx_t*)adj->values,(idx_t*)&wgtflag,(idx_t*)&numflag,(idx_t*)&ncon,(idx_t*)&nparts,tpwgts,ubvec,(idx_t*)options,(idx_t*)&pmetis->cuts,(idx_t*)locals,&comm));
144:       } else {
145:         PetscStackCallParmetis(ParMETIS_V3_PartKway,((idx_t*)vtxdist,(idx_t*)xadj,(idx_t*)adjncy,(idx_t*)part->vertex_weights,(idx_t*)adj->values,(idx_t*)&wgtflag,(idx_t*)&numflag,(idx_t*)&ncon,(idx_t*)&nparts,tpwgts,ubvec,(idx_t*)options,(idx_t*)&pmetis->cuts,(idx_t*)locals,&comm));
146:       }
147:     }
148:     MPI_Comm_free(&comm);

150:     PetscFree(tpwgts);
151:     PetscFree(ubvec);
152:     if (PetscLogPrintInfo) pmetis->printout = itmp;

154:     if (bs > 1) {
155:       PetscInt i,j,*newlocals;
156:       PetscMalloc1(bs*pmat->rmap->n,&newlocals);
157:       for (i=0; i<pmat->rmap->n; i++) {
158:         for (j=0; j<bs; j++) {
159:           newlocals[bs*i + j] = locals[i];
160:         }
161:       }
162:       PetscFree(locals);
163:       ISCreateGeneral(PetscObjectComm((PetscObject)part),bs*pmat->rmap->n,newlocals,PETSC_OWN_POINTER,partitioning);
164:     } else {
165:       ISCreateGeneral(PetscObjectComm((PetscObject)part),pmat->rmap->n,locals,PETSC_OWN_POINTER,partitioning);
166:     }
167:     if (useND) {
168:       IS ndis;

170:       if (bs > 1) {
171:         ISCreateBlock(PetscObjectComm((PetscObject)part),bs,pmat->rmap->n,NDorder,PETSC_OWN_POINTER,&ndis);
172:       } else {
173:         ISCreateGeneral(PetscObjectComm((PetscObject)part),pmat->rmap->n,NDorder,PETSC_OWN_POINTER,&ndis);
174:       }
175:       ISSetPermutation(ndis);
176:       PetscObjectCompose((PetscObject)(*partitioning),"_petsc_matpartitioning_ndorder",(PetscObject)ndis);
177:       ISDestroy(&ndis);
178:     }
179:   } else {
180:     ISCreateGeneral(PetscObjectComm((PetscObject)part),0,NULL,PETSC_COPY_VALUES,partitioning);
181:     if (useND) {
182:       IS ndis;

184:       if (bs > 1) {
185:         ISCreateBlock(PetscObjectComm((PetscObject)part),bs,0,NULL,PETSC_COPY_VALUES,&ndis);
186:       } else {
187:         ISCreateGeneral(PetscObjectComm((PetscObject)part),0,NULL,PETSC_COPY_VALUES,&ndis);
188:       }
189:       ISSetPermutation(ndis);
190:       PetscObjectCompose((PetscObject)(*partitioning),"_petsc_matpartitioning_ndorder",(PetscObject)ndis);
191:       ISDestroy(&ndis);
192:     }
193:   }
194:   MatDestroy(&pmat);
195:   MatDestroy(&amat);
196:   return(0);
197: }

199: /*
200:    Uses the ParMETIS parallel matrix partitioner to compute a nested dissection ordering of the matrix in parallel
201: */
202: static PetscErrorCode MatPartitioningApplyND_Parmetis(MatPartitioning part, IS *partitioning)
203: {

207:   MatPartitioningApply_Parmetis_Private(part, PETSC_TRUE, PETSC_FALSE, partitioning);
208:   return(0);
209: }

211: /*
212:    Uses the ParMETIS parallel matrix partitioner to partition the matrix in parallel
213: */
214: static PetscErrorCode MatPartitioningApply_Parmetis(MatPartitioning part, IS *partitioning)
215: {

219:   MatPartitioningApply_Parmetis_Private(part, PETSC_FALSE, PETSC_FALSE, partitioning);
220:   return(0);
221: }

223: /*
224:    Uses the ParMETIS to improve the quality  of a partition
225: */
226: static PetscErrorCode MatPartitioningImprove_Parmetis(MatPartitioning part, IS *partitioning)
227: {

231:   MatPartitioningApply_Parmetis_Private(part, PETSC_FALSE, PETSC_TRUE, partitioning);
232:   return(0);
233: }

235: PetscErrorCode MatPartitioningView_Parmetis(MatPartitioning part,PetscViewer viewer)
236: {
237:   MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;
238:   PetscErrorCode           ierr;
239:   PetscMPIInt              rank;
240:   PetscBool                iascii;

243:   MPI_Comm_rank(PetscObjectComm((PetscObject)part),&rank);
244:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
245:   if (iascii) {
246:     if (pmetis->parallel == 2) {
247:       PetscViewerASCIIPrintf(viewer,"  Using parallel coarse grid partitioner\n");
248:     } else {
249:       PetscViewerASCIIPrintf(viewer,"  Using sequential coarse grid partitioner\n");
250:     }
251:     PetscViewerASCIIPrintf(viewer,"  Using %D fold factor\n",pmetis->foldfactor);
252:     PetscViewerASCIIPushSynchronized(viewer);
253:     PetscViewerASCIISynchronizedPrintf(viewer,"  [%d]Number of cuts found %D\n",rank,pmetis->cuts);
254:     PetscViewerFlush(viewer);
255:     PetscViewerASCIIPopSynchronized(viewer);
256:   }
257:   return(0);
258: }

260: /*@
261:      MatPartitioningParmetisSetCoarseSequential - Use the sequential code to
262:          do the partitioning of the coarse grid.

264:   Logically Collective on MatPartitioning

266:   Input Parameter:
267: .  part - the partitioning context

269:    Level: advanced

271: @*/
272: PetscErrorCode  MatPartitioningParmetisSetCoarseSequential(MatPartitioning part)
273: {
274:   MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;

277:   pmetis->parallel = 1;
278:   return(0);
279: }

281: /*@
282:      MatPartitioningParmetisSetRepartition - Repartition
283:      current mesh to rebalance computation.

285:   Logically Collective on MatPartitioning

287:   Input Parameter:
288: .  part - the partitioning context

290:    Level: advanced

292: @*/
293: PetscErrorCode  MatPartitioningParmetisSetRepartition(MatPartitioning part)
294: {
295:   MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;

298:   pmetis->repartition = PETSC_TRUE;
299:   return(0);
300: }

302: /*@
303:   MatPartitioningParmetisGetEdgeCut - Returns the number of edge cuts in the vertex partition.

305:   Input Parameter:
306: . part - the partitioning context

308:   Output Parameter:
309: . cut - the edge cut

311:    Level: advanced

313: @*/
314: PetscErrorCode  MatPartitioningParmetisGetEdgeCut(MatPartitioning part, PetscInt *cut)
315: {
316:   MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*) part->data;

319:   *cut = pmetis->cuts;
320:   return(0);
321: }

323: PetscErrorCode MatPartitioningSetFromOptions_Parmetis(PetscOptionItems *PetscOptionsObject,MatPartitioning part)
324: {
326:   PetscBool      flag = PETSC_FALSE;

329:   PetscOptionsHead(PetscOptionsObject,"Set ParMeTiS partitioning options");
330:   PetscOptionsBool("-mat_partitioning_parmetis_coarse_sequential","Use sequential coarse partitioner","MatPartitioningParmetisSetCoarseSequential",flag,&flag,NULL);
331:   if (flag) {
332:     MatPartitioningParmetisSetCoarseSequential(part);
333:   }
334:   PetscOptionsBool("-mat_partitioning_parmetis_repartition","","MatPartitioningParmetisSetRepartition",flag,&flag,NULL);
335:   if(flag){
336:      MatPartitioningParmetisSetRepartition(part);
337:   }
338:   PetscOptionsTail();
339:   return(0);
340: }


343: PetscErrorCode MatPartitioningDestroy_Parmetis(MatPartitioning part)
344: {
345:   MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;
346:   PetscErrorCode           ierr;

349:   PetscFree(pmetis);
350:   return(0);
351: }


354: /*MC
355:    MATPARTITIONINGPARMETIS - Creates a partitioning context via the external package PARMETIS.

357:    Collective

359:    Input Parameter:
360: .  part - the partitioning context

362:    Options Database Keys:
363: .  -mat_partitioning_parmetis_coarse_sequential - use sequential PARMETIS coarse partitioner

365:    Level: beginner

367:    Notes:
368:     See https://www-users.cs.umn.edu/~karypis/metis/

370: .seealso: MatPartitioningSetType(), MatPartitioningType

372: M*/

374: PETSC_EXTERN PetscErrorCode MatPartitioningCreate_Parmetis(MatPartitioning part)
375: {
376:   PetscErrorCode           ierr;
377:   MatPartitioning_Parmetis *pmetis;

380:   PetscNewLog(part,&pmetis);
381:   part->data = (void*)pmetis;

383:   pmetis->cuts       = 0;   /* output variable */
384:   pmetis->foldfactor = 150; /*folding factor */
385:   pmetis->parallel   = 2;   /* use parallel partitioner for coarse grid */
386:   pmetis->indexing   = 0;   /* index numbering starts from 0 */
387:   pmetis->printout   = 0;   /* print no output while running */
388:   pmetis->repartition      = PETSC_FALSE;

390:   part->ops->apply          = MatPartitioningApply_Parmetis;
391:   part->ops->applynd        = MatPartitioningApplyND_Parmetis;
392:   part->ops->improve        = MatPartitioningImprove_Parmetis;
393:   part->ops->view           = MatPartitioningView_Parmetis;
394:   part->ops->destroy        = MatPartitioningDestroy_Parmetis;
395:   part->ops->setfromoptions = MatPartitioningSetFromOptions_Parmetis;
396:   return(0);
397: }

399: /*@
400:  MatMeshToVertexGraph -   This routine does not exist because ParMETIS does not provide the functionality.  Uses the ParMETIS package to
401:                        convert a Mat that represents a mesh to a Mat the represents the graph of the coupling
402:                        between vertices of the cells and is suitable for partitioning with the MatPartitioning object. Use this to partition
403:                        vertices of a mesh. More likely you should use MatMeshToCellGraph()

405:    Collective on Mat

407:    Input Parameter:
408: +     mesh - the graph that represents the mesh
409: -     ncommonnodes - mesh elements that share this number of common nodes are considered neighbors, use 2 for triangles and
410:                      quadrilaterials, 3 for tetrahedrals and 4 for hexahedrals

412:    Output Parameter:
413: .     dual - the dual graph

415:    Notes:
416:      Currently requires ParMetis to be installed and uses ParMETIS_V3_Mesh2Dual()

418:      The columns of each row of the Mat mesh are the global vertex numbers of the vertices of that rows cell. The number of rows in mesh is
419:      number of cells, the number of columns is the number of vertices.

421:    Level: advanced

423: .seealso: MatMeshToCellGraph(), MatCreateMPIAdj(), MatPartitioningCreate()

425: @*/
426: PetscErrorCode MatMeshToVertexGraph(Mat mesh,PetscInt ncommonnodes,Mat *dual)
427: {
429:   SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"ParMETIS does not provide this functionality");
430:   return(0);
431: }

433: /*@
434:      MatMeshToCellGraph -   Uses the ParMETIS package to convert a Mat that represents a mesh to a Mat the represents the graph of the coupling
435:                        between cells (the "dual" graph) and is suitable for partitioning with the MatPartitioning object. Use this to partition
436:                        cells of a mesh.

438:    Collective on Mat

440:    Input Parameter:
441: +     mesh - the graph that represents the mesh
442: -     ncommonnodes - mesh elements that share this number of common nodes are considered neighbors, use 2 for triangles and
443:                      quadrilaterials, 3 for tetrahedrals and 4 for hexahedrals

445:    Output Parameter:
446: .     dual - the dual graph

448:    Notes:
449:      Currently requires ParMetis to be installed and uses ParMETIS_V3_Mesh2Dual()

451: $     Each row of the mesh object represents a single cell in the mesh. For triangles it has 3 entries, quadrilaterials 4 entries,
452: $         tetrahedrals 4 entries and hexahedrals 8 entries. You can mix triangles and quadrilaterals in the same mesh, but cannot
453: $         mix  tetrahedrals and hexahedrals
454: $     The columns of each row of the Mat mesh are the global vertex numbers of the vertices of that row's cell.
455: $     The number of rows in mesh is number of cells, the number of columns is the number of vertices.


458:    Level: advanced

460: .seealso: MatMeshToVertexGraph(), MatCreateMPIAdj(), MatPartitioningCreate()


463: @*/
464: PetscErrorCode MatMeshToCellGraph(Mat mesh,PetscInt ncommonnodes,Mat *dual)
465: {
467:   PetscInt       *newxadj,*newadjncy;
468:   PetscInt       numflag=0;
469:   Mat_MPIAdj     *adj   = (Mat_MPIAdj*)mesh->data,*newadj;
470:   PetscBool      flg;
471:   MPI_Comm       comm;

474:   PetscObjectTypeCompare((PetscObject)mesh,MATMPIADJ,&flg);
475:   if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Must use MPIAdj matrix type");

477:   PetscObjectGetComm((PetscObject)mesh,&comm);
478:   PetscStackCallParmetis(ParMETIS_V3_Mesh2Dual,((idx_t*)mesh->rmap->range,(idx_t*)adj->i,(idx_t*)adj->j,(idx_t*)&numflag,(idx_t*)&ncommonnodes,(idx_t**)&newxadj,(idx_t**)&newadjncy,&comm));
479:   MatCreateMPIAdj(PetscObjectComm((PetscObject)mesh),mesh->rmap->n,mesh->rmap->N,newxadj,newadjncy,NULL,dual);
480:   newadj = (Mat_MPIAdj*)(*dual)->data;

482:   newadj->freeaijwithfree = PETSC_TRUE; /* signal the matrix should be freed with system free since space was allocated by ParMETIS */
483:   return(0);
484: }