Actual source code: pmetis.c
petsc-3.13.6 2020-09-29
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 defined(PETSC_USE_DEBUG)
66: /* check that matrix has no diagonal entries */
67: {
68: PetscInt rstart;
69: MatGetOwnershipRange(pmat,&rstart,NULL);
70: for (i=0; i<pmat->rmap->n; i++) {
71: for (j=xadj[i]; j<xadj[i+1]; j++) {
72: if (adjncy[j] == i+rstart) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Row %D has diagonal entry; Parmetis forbids diagonal entry",i+rstart);
73: }
74: }
75: }
76: #endif
78: PetscMalloc1(pmat->rmap->n,&locals);
80: if (isImprove) {
81: PetscInt i;
82: const PetscInt *part_indices;
84: ISGetIndices(*partitioning,&part_indices);
85: for (i=0; i<pmat->rmap->n; i++) locals[i] = part_indices[i*bs];
86: ISRestoreIndices(*partitioning,&part_indices);
87: ISDestroy(partitioning);
88: }
90: if (adj->values && part->use_edge_weights && !part->vertex_weights) wgtflag = 1;
91: if (part->vertex_weights && !adj->values) wgtflag = 2;
92: if (part->vertex_weights && adj->values && part->use_edge_weights) wgtflag = 3;
94: if (PetscLogPrintInfo) {itmp = pmetis->printout; pmetis->printout = 127;}
95: PetscMalloc1(ncon*nparts,&tpwgts);
96: for (i=0; i<ncon; i++) {
97: for (j=0; j<nparts; j++) {
98: if (part->part_weights) {
99: tpwgts[i*nparts+j] = part->part_weights[i*nparts+j];
100: } else {
101: tpwgts[i*nparts+j] = 1./nparts;
102: }
103: }
104: }
105: PetscMalloc1(ncon,&ubvec);
106: for (i=0; i<ncon; i++) ubvec[i] = 1.05;
107: /* This sets the defaults */
108: options[0] = 0;
109: for (i=1; i<24; i++) options[i] = -1;
110: /* Duplicate the communicator to be sure that ParMETIS attribute caching does not interfere with PETSc. */
111: MPI_Comm_dup(pcomm,&comm);
112: if (useND) {
113: PetscInt *sizes, *seps, log2size, subd, *level;
114: PetscMPIInt size;
115: idx_t mtype = PARMETIS_MTYPE_GLOBAL, rtype = PARMETIS_SRTYPE_2PHASE, p_nseps = 1, s_nseps = 1;
116: real_t ubfrac = 1.05;
118: MPI_Comm_size(comm,&size);
119: PetscMalloc1(pmat->rmap->n,&NDorder);
120: PetscMalloc3(2*size,&sizes,4*size,&seps,size,&level);
121: 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));
122: log2size = PetscLog2Real(size);
123: subd = PetscPowInt(2,log2size);
124: MatPartitioningSizesToSep_Private(subd,sizes,seps,level);
125: for (i=0;i<pmat->rmap->n;i++) {
126: PetscInt loc;
128: PetscFindInt(NDorder[i],2*subd,seps,&loc);
129: if (loc < 0) {
130: loc = -(loc+1);
131: if (loc%2) { /* part of subdomain */
132: locals[i] = loc/2;
133: } else {
134: PetscFindInt(NDorder[i],2*(subd-1),seps+2*subd,&loc);
135: loc = loc < 0 ? -(loc+1)/2 : loc/2;
136: locals[i] = level[loc];
137: }
138: } else locals[i] = loc/2;
139: }
140: PetscFree3(sizes,seps,level);
141: } else {
142: if (pmetis->repartition) {
143: 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));
144: } else if (isImprove) {
145: 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));
146: } else {
147: 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));
148: }
149: }
150: MPI_Comm_free(&comm);
152: PetscFree(tpwgts);
153: PetscFree(ubvec);
154: if (PetscLogPrintInfo) pmetis->printout = itmp;
156: if (bs > 1) {
157: PetscInt i,j,*newlocals;
158: PetscMalloc1(bs*pmat->rmap->n,&newlocals);
159: for (i=0; i<pmat->rmap->n; i++) {
160: for (j=0; j<bs; j++) {
161: newlocals[bs*i + j] = locals[i];
162: }
163: }
164: PetscFree(locals);
165: ISCreateGeneral(PetscObjectComm((PetscObject)part),bs*pmat->rmap->n,newlocals,PETSC_OWN_POINTER,partitioning);
166: } else {
167: ISCreateGeneral(PetscObjectComm((PetscObject)part),pmat->rmap->n,locals,PETSC_OWN_POINTER,partitioning);
168: }
169: if (useND) {
170: IS ndis;
172: if (bs > 1) {
173: ISCreateBlock(PetscObjectComm((PetscObject)part),bs,pmat->rmap->n,NDorder,PETSC_OWN_POINTER,&ndis);
174: } else {
175: ISCreateGeneral(PetscObjectComm((PetscObject)part),pmat->rmap->n,NDorder,PETSC_OWN_POINTER,&ndis);
176: }
177: ISSetPermutation(ndis);
178: PetscObjectCompose((PetscObject)(*partitioning),"_petsc_matpartitioning_ndorder",(PetscObject)ndis);
179: ISDestroy(&ndis);
180: }
181: } else {
182: ISCreateGeneral(PetscObjectComm((PetscObject)part),0,NULL,PETSC_COPY_VALUES,partitioning);
183: if (useND) {
184: IS ndis;
186: if (bs > 1) {
187: ISCreateBlock(PetscObjectComm((PetscObject)part),bs,0,NULL,PETSC_COPY_VALUES,&ndis);
188: } else {
189: ISCreateGeneral(PetscObjectComm((PetscObject)part),0,NULL,PETSC_COPY_VALUES,&ndis);
190: }
191: ISSetPermutation(ndis);
192: PetscObjectCompose((PetscObject)(*partitioning),"_petsc_matpartitioning_ndorder",(PetscObject)ndis);
193: ISDestroy(&ndis);
194: }
195: }
196: MatDestroy(&pmat);
197: MatDestroy(&amat);
198: return(0);
199: }
201: /*
202: Uses the ParMETIS parallel matrix partitioner to compute a nested dissection ordering of the matrix in parallel
203: */
204: static PetscErrorCode MatPartitioningApplyND_Parmetis(MatPartitioning part, IS *partitioning)
205: {
209: MatPartitioningApply_Parmetis_Private(part, PETSC_TRUE, PETSC_FALSE, partitioning);
210: return(0);
211: }
213: /*
214: Uses the ParMETIS parallel matrix partitioner to partition the matrix in parallel
215: */
216: static PetscErrorCode MatPartitioningApply_Parmetis(MatPartitioning part, IS *partitioning)
217: {
221: MatPartitioningApply_Parmetis_Private(part, PETSC_FALSE, PETSC_FALSE, partitioning);
222: return(0);
223: }
225: /*
226: Uses the ParMETIS to improve the quality of a partition
227: */
228: static PetscErrorCode MatPartitioningImprove_Parmetis(MatPartitioning part, IS *partitioning)
229: {
233: MatPartitioningApply_Parmetis_Private(part, PETSC_FALSE, PETSC_TRUE, partitioning);
234: return(0);
235: }
237: PetscErrorCode MatPartitioningView_Parmetis(MatPartitioning part,PetscViewer viewer)
238: {
239: MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;
240: PetscErrorCode ierr;
241: PetscMPIInt rank;
242: PetscBool iascii;
245: MPI_Comm_rank(PetscObjectComm((PetscObject)part),&rank);
246: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
247: if (iascii) {
248: if (pmetis->parallel == 2) {
249: PetscViewerASCIIPrintf(viewer," Using parallel coarse grid partitioner\n");
250: } else {
251: PetscViewerASCIIPrintf(viewer," Using sequential coarse grid partitioner\n");
252: }
253: PetscViewerASCIIPrintf(viewer," Using %D fold factor\n",pmetis->foldfactor);
254: PetscViewerASCIIPushSynchronized(viewer);
255: PetscViewerASCIISynchronizedPrintf(viewer," [%d]Number of cuts found %D\n",rank,pmetis->cuts);
256: PetscViewerFlush(viewer);
257: PetscViewerASCIIPopSynchronized(viewer);
258: }
259: return(0);
260: }
262: /*@
263: MatPartitioningParmetisSetCoarseSequential - Use the sequential code to
264: do the partitioning of the coarse grid.
266: Logically Collective on MatPartitioning
268: Input Parameter:
269: . part - the partitioning context
271: Level: advanced
273: @*/
274: PetscErrorCode MatPartitioningParmetisSetCoarseSequential(MatPartitioning part)
275: {
276: MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;
279: pmetis->parallel = 1;
280: return(0);
281: }
283: /*@
284: MatPartitioningParmetisSetRepartition - Repartition
285: current mesh to rebalance computation.
287: Logically Collective on MatPartitioning
289: Input Parameter:
290: . part - the partitioning context
292: Level: advanced
294: @*/
295: PetscErrorCode MatPartitioningParmetisSetRepartition(MatPartitioning part)
296: {
297: MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;
300: pmetis->repartition = PETSC_TRUE;
301: return(0);
302: }
304: /*@
305: MatPartitioningParmetisGetEdgeCut - Returns the number of edge cuts in the vertex partition.
307: Input Parameter:
308: . part - the partitioning context
310: Output Parameter:
311: . cut - the edge cut
313: Level: advanced
315: @*/
316: PetscErrorCode MatPartitioningParmetisGetEdgeCut(MatPartitioning part, PetscInt *cut)
317: {
318: MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*) part->data;
321: *cut = pmetis->cuts;
322: return(0);
323: }
325: PetscErrorCode MatPartitioningSetFromOptions_Parmetis(PetscOptionItems *PetscOptionsObject,MatPartitioning part)
326: {
328: PetscBool flag = PETSC_FALSE;
331: PetscOptionsHead(PetscOptionsObject,"Set ParMeTiS partitioning options");
332: PetscOptionsBool("-mat_partitioning_parmetis_coarse_sequential","Use sequential coarse partitioner","MatPartitioningParmetisSetCoarseSequential",flag,&flag,NULL);
333: if (flag) {
334: MatPartitioningParmetisSetCoarseSequential(part);
335: }
336: PetscOptionsBool("-mat_partitioning_parmetis_repartition","","MatPartitioningParmetisSetRepartition",flag,&flag,NULL);
337: if(flag){
338: MatPartitioningParmetisSetRepartition(part);
339: }
340: PetscOptionsTail();
341: return(0);
342: }
345: PetscErrorCode MatPartitioningDestroy_Parmetis(MatPartitioning part)
346: {
347: MatPartitioning_Parmetis *pmetis = (MatPartitioning_Parmetis*)part->data;
348: PetscErrorCode ierr;
351: PetscFree(pmetis);
352: return(0);
353: }
356: /*MC
357: MATPARTITIONINGPARMETIS - Creates a partitioning context via the external package PARMETIS.
359: Collective
361: Input Parameter:
362: . part - the partitioning context
364: Options Database Keys:
365: . -mat_partitioning_parmetis_coarse_sequential - use sequential PARMETIS coarse partitioner
367: Level: beginner
369: Notes:
370: See https://www-users.cs.umn.edu/~karypis/metis/
372: .seealso: MatPartitioningSetType(), MatPartitioningType
374: M*/
376: PETSC_EXTERN PetscErrorCode MatPartitioningCreate_Parmetis(MatPartitioning part)
377: {
378: PetscErrorCode ierr;
379: MatPartitioning_Parmetis *pmetis;
382: PetscNewLog(part,&pmetis);
383: part->data = (void*)pmetis;
385: pmetis->cuts = 0; /* output variable */
386: pmetis->foldfactor = 150; /*folding factor */
387: pmetis->parallel = 2; /* use parallel partitioner for coarse grid */
388: pmetis->indexing = 0; /* index numbering starts from 0 */
389: pmetis->printout = 0; /* print no output while running */
390: pmetis->repartition = PETSC_FALSE;
392: part->ops->apply = MatPartitioningApply_Parmetis;
393: part->ops->applynd = MatPartitioningApplyND_Parmetis;
394: part->ops->improve = MatPartitioningImprove_Parmetis;
395: part->ops->view = MatPartitioningView_Parmetis;
396: part->ops->destroy = MatPartitioningDestroy_Parmetis;
397: part->ops->setfromoptions = MatPartitioningSetFromOptions_Parmetis;
398: return(0);
399: }
401: /*@
402: MatMeshToVertexGraph - This routine does not exist because ParMETIS does not provide the functionality. Uses the ParMETIS package to
403: convert a Mat that represents a mesh to a Mat the represents the graph of the coupling
404: between vertices of the cells and is suitable for partitioning with the MatPartitioning object. Use this to partition
405: vertices of a mesh. More likely you should use MatMeshToCellGraph()
407: Collective on Mat
409: Input Parameter:
410: + mesh - the graph that represents the mesh
411: - ncommonnodes - mesh elements that share this number of common nodes are considered neighbors, use 2 for triangles and
412: quadrilaterials, 3 for tetrahedrals and 4 for hexahedrals
414: Output Parameter:
415: . dual - the dual graph
417: Notes:
418: Currently requires ParMetis to be installed and uses ParMETIS_V3_Mesh2Dual()
420: 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
421: number of cells, the number of columns is the number of vertices.
423: Level: advanced
425: .seealso: MatMeshToCellGraph(), MatCreateMPIAdj(), MatPartitioningCreate()
427: @*/
428: PetscErrorCode MatMeshToVertexGraph(Mat mesh,PetscInt ncommonnodes,Mat *dual)
429: {
431: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"ParMETIS does not provide this functionality");
432: return(0);
433: }
435: /*@
436: MatMeshToCellGraph - Uses the ParMETIS package to convert a Mat that represents a mesh to a Mat the represents the graph of the coupling
437: between cells (the "dual" graph) and is suitable for partitioning with the MatPartitioning object. Use this to partition
438: cells of a mesh.
440: Collective on Mat
442: Input Parameter:
443: + mesh - the graph that represents the mesh
444: - ncommonnodes - mesh elements that share this number of common nodes are considered neighbors, use 2 for triangles and
445: quadrilaterials, 3 for tetrahedrals and 4 for hexahedrals
447: Output Parameter:
448: . dual - the dual graph
450: Notes:
451: Currently requires ParMetis to be installed and uses ParMETIS_V3_Mesh2Dual()
453: $ Each row of the mesh object represents a single cell in the mesh. For triangles it has 3 entries, quadrilaterials 4 entries,
454: $ tetrahedrals 4 entries and hexahedrals 8 entries. You can mix triangles and quadrilaterals in the same mesh, but cannot
455: $ mix tetrahedrals and hexahedrals
456: $ The columns of each row of the Mat mesh are the global vertex numbers of the vertices of that row's cell.
457: $ The number of rows in mesh is number of cells, the number of columns is the number of vertices.
460: Level: advanced
462: .seealso: MatMeshToVertexGraph(), MatCreateMPIAdj(), MatPartitioningCreate()
465: @*/
466: PetscErrorCode MatMeshToCellGraph(Mat mesh,PetscInt ncommonnodes,Mat *dual)
467: {
469: PetscInt *newxadj,*newadjncy;
470: PetscInt numflag=0;
471: Mat_MPIAdj *adj = (Mat_MPIAdj*)mesh->data,*newadj;
472: PetscBool flg;
473: MPI_Comm comm;
476: PetscObjectTypeCompare((PetscObject)mesh,MATMPIADJ,&flg);
477: if (!flg) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Must use MPIAdj matrix type");
479: PetscObjectGetComm((PetscObject)mesh,&comm);
480: 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));
481: MatCreateMPIAdj(PetscObjectComm((PetscObject)mesh),mesh->rmap->n,mesh->rmap->N,newxadj,newadjncy,NULL,dual);
482: newadj = (Mat_MPIAdj*)(*dual)->data;
484: newadj->freeaijwithfree = PETSC_TRUE; /* signal the matrix should be freed with system free since space was allocated by ParMETIS */
485: return(0);
486: }