Actual source code: partition.c
petsc-3.11.4 2019-09-28
2: #include <petsc/private/matimpl.h>
4: /* Logging support */
5: PetscClassId MAT_PARTITIONING_CLASSID;
7: /*
8: Simplest partitioning, keeps the current partitioning.
9: */
10: static PetscErrorCode MatPartitioningApply_Current(MatPartitioning part,IS *partitioning)
11: {
13: PetscInt m;
14: PetscMPIInt rank,size;
17: MPI_Comm_size(PetscObjectComm((PetscObject)part),&size);
18: if (part->n != size) {
19: const char *prefix;
20: PetscObjectGetOptionsPrefix((PetscObject)part,&prefix);
21: SETERRQ1(PetscObjectComm((PetscObject)part),PETSC_ERR_SUP,"This is the DEFAULT NO-OP partitioner, it currently only supports one domain per processor\nuse -%smat_partitioning_type parmetis or chaco or ptscotch for more than one subdomain per processor",prefix ? prefix : "");
22: }
23: MPI_Comm_rank(PetscObjectComm((PetscObject)part),&rank);
25: MatGetLocalSize(part->adj,&m,NULL);
26: ISCreateStride(PetscObjectComm((PetscObject)part),m,rank,0,partitioning);
27: return(0);
28: }
30: /*
31: partition an index to rebalance the computation
32: */
33: static PetscErrorCode MatPartitioningApply_Average(MatPartitioning part,IS *partitioning)
34: {
36: PetscInt m,M,nparts,*indices,r,d,*parts,i,start,end,loc;
39: MatGetSize(part->adj,&M,NULL);
40: MatGetLocalSize(part->adj,&m,NULL);
41: nparts = part->n;
42: PetscCalloc1(nparts,&parts);
43: d = M/nparts;
44: for (i=0; i<nparts; i++) parts[i] = d;
45: r = M%nparts;
46: for (i=0; i<r; i++) parts[i] += 1;
47: for (i=1; i<nparts; i++) parts[i] += parts[i-1];
48: PetscCalloc1(m,&indices);
49: MatGetOwnershipRange(part->adj,&start,&end);
50: for (i=start; i<end; i++) {
51: PetscFindInt(i,nparts,parts,&loc);
52: if (loc<0) loc = -(loc+1);
53: else loc = loc+1;
54: indices[i-start] = loc;
55: }
56: PetscFree(parts);
57: ISCreateGeneral(PetscObjectComm((PetscObject)part),m,indices,PETSC_OWN_POINTER,partitioning);
58: return(0);
59: }
61: static PetscErrorCode MatPartitioningApply_Square(MatPartitioning part,IS *partitioning)
62: {
64: PetscInt cell,n,N,p,rstart,rend,*color;
65: PetscMPIInt size;
68: MPI_Comm_size(PetscObjectComm((PetscObject)part),&size);
69: if (part->n != size) SETERRQ(PetscObjectComm((PetscObject)part),PETSC_ERR_SUP,"Currently only supports one domain per processor");
70: p = (PetscInt)PetscSqrtReal((PetscReal)part->n);
71: if (p*p != part->n) SETERRQ(PetscObjectComm((PetscObject)part),PETSC_ERR_SUP,"Square partitioning requires \"perfect square\" number of domains");
73: MatGetSize(part->adj,&N,NULL);
74: n = (PetscInt)PetscSqrtReal((PetscReal)N);
75: if (n*n != N) SETERRQ(PetscObjectComm((PetscObject)part),PETSC_ERR_SUP,"Square partitioning requires square domain");
76: if (n%p != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Square partitioning requires p to divide n");
77: MatGetOwnershipRange(part->adj,&rstart,&rend);
78: PetscMalloc1(rend-rstart,&color);
79: /* for (int cell=rstart; cell<rend; cell++) { color[cell-rstart] = ((cell%n) < (n/2)) + 2 * ((cell/n) < (n/2)); } */
80: for (cell=rstart; cell<rend; cell++) {
81: color[cell-rstart] = ((cell%n) / (n/p)) + p * ((cell/n) / (n/p));
82: }
83: ISCreateGeneral(PetscObjectComm((PetscObject)part),rend-rstart,color,PETSC_OWN_POINTER,partitioning);
84: return(0);
85: }
87: PETSC_EXTERN PetscErrorCode MatPartitioningCreate_Current(MatPartitioning part)
88: {
90: part->ops->apply = MatPartitioningApply_Current;
91: part->ops->view = 0;
92: part->ops->destroy = 0;
93: return(0);
94: }
96: PETSC_EXTERN PetscErrorCode MatPartitioningCreate_Average(MatPartitioning part)
97: {
99: part->ops->apply = MatPartitioningApply_Average;
100: part->ops->view = 0;
101: part->ops->destroy = 0;
102: return(0);
103: }
105: PETSC_EXTERN PetscErrorCode MatPartitioningCreate_Square(MatPartitioning part)
106: {
108: part->ops->apply = MatPartitioningApply_Square;
109: part->ops->view = 0;
110: part->ops->destroy = 0;
111: return(0);
112: }
115: /* gets as input the "sizes" array computed by ParMetis_*_NodeND and returns
116: seps[ 0 : 2*p) : the start and end node of each subdomain
117: seps[2*p : 2*p+2*(p-1)) : the start and end node of each separator
118: levels[ 0 : p-1) : level in the tree for each separator (-1 root, -2 and -3 first level and so on)
119: The arrays must be large enough
120: */
121: PETSC_INTERN PetscErrorCode MatPartitioningSizesToSep_Private(PetscInt p, PetscInt sizes[], PetscInt seps[], PetscInt level[])
122: {
123: PetscInt l2p,i,pTree,pStartTree;
127: l2p = PetscLog2Real(p);
128: if (l2p - (PetscInt)PetscLog2Real(p)) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"%D is not a power of 2",p);
129: if (!p) return(0);
130: PetscMemzero(seps,(2*p-2)*sizeof(PetscInt));
131: PetscMemzero(level,(p-1)*sizeof(PetscInt));
132: seps[2*p-2] = sizes[2*p-2];
133: pTree = p;
134: pStartTree = 0;
135: while (pTree != 1) {
136: for (i = pStartTree; i < pStartTree + pTree; i++) {
137: seps[i] += sizes[i];
138: seps[pStartTree + pTree + (i-pStartTree)/2] += seps[i];
139: }
140: pStartTree += pTree;
141: pTree = pTree/2;
142: }
143: seps[2*p-2] -= sizes[2*p-2];
145: pStartTree = 2*p-2;
146: pTree = 1;
147: while (pStartTree > 0) {
148: for (i = pStartTree; i < pStartTree + pTree; i++) {
149: PetscInt k = 2*i - (pStartTree +2*pTree);
150: PetscInt n = seps[k+1];
152: seps[k+1] = seps[i] - sizes[k+1];
153: seps[k] = seps[k+1] + sizes[k+1] - n - sizes[k];
154: level[i-p] = -pTree - i + pStartTree;
155: }
156: pTree *= 2;
157: pStartTree -= pTree;
158: }
159: /* I know there should be a formula */
160: PetscSortIntWithArrayPair(p-1,seps+p,sizes+p,level);
161: for (i=2*p-2;i>=0;i--) { seps[2*i] = seps[i]; seps[2*i+1] = seps[i] + sizes[i] - 1; }
162: return(0);
163: }
165: /* ===========================================================================================*/
167: PetscFunctionList MatPartitioningList = 0;
168: PetscBool MatPartitioningRegisterAllCalled = PETSC_FALSE;
171: /*@C
172: MatPartitioningRegister - Adds a new sparse matrix partitioning to the matrix package.
174: Not Collective
176: Input Parameters:
177: + sname - name of partitioning (for example MATPARTITIONINGCURRENT) or parmetis
178: - function - function pointer that creates the partitioning type
180: Level: developer
182: Sample usage:
183: .vb
184: MatPartitioningRegister("my_part",MyPartCreate);
185: .ve
187: Then, your partitioner can be chosen with the procedural interface via
188: $ MatPartitioningSetType(part,"my_part")
189: or at runtime via the option
190: $ -mat_partitioning_type my_part
192: .keywords: matrix, partitioning, register
194: .seealso: MatPartitioningRegisterDestroy(), MatPartitioningRegisterAll()
195: @*/
196: PetscErrorCode MatPartitioningRegister(const char sname[],PetscErrorCode (*function)(MatPartitioning))
197: {
201: MatInitializePackage();
202: PetscFunctionListAdd(&MatPartitioningList,sname,function);
203: return(0);
204: }
206: /*@C
207: MatPartitioningGetType - Gets the Partitioning method type and name (as a string)
208: from the partitioning context.
210: Not collective
212: Input Parameter:
213: . partitioning - the partitioning context
215: Output Parameter:
216: . type - partitioner type
218: Level: intermediate
220: Not Collective
222: .keywords: Partitioning, get, method, name, type
223: @*/
224: PetscErrorCode MatPartitioningGetType(MatPartitioning partitioning,MatPartitioningType *type)
225: {
229: *type = ((PetscObject)partitioning)->type_name;
230: return(0);
231: }
233: /*@C
234: MatPartitioningSetNParts - Set how many partitions need to be created;
235: by default this is one per processor. Certain partitioning schemes may
236: in fact only support that option.
238: Not collective
240: Input Parameter:
241: . partitioning - the partitioning context
242: . n - the number of partitions
244: Level: intermediate
246: Not Collective
248: .keywords: Partitioning, set
250: .seealso: MatPartitioningCreate(), MatPartitioningApply()
251: @*/
252: PetscErrorCode MatPartitioningSetNParts(MatPartitioning part,PetscInt n)
253: {
255: part->n = n;
256: return(0);
257: }
259: /*@
260: MatPartitioningApplyND - Gets a nested dissection partitioning for a matrix.
262: Collective on Mat
264: Input Parameters:
265: . matp - the matrix partitioning object
267: Output Parameters:
268: . partitioning - the partitioning. For each local node, a positive value indicates the processor
269: number the node has been assigned to. Negative x values indicate the separator level -(x+1).
271: Level: beginner
273: The user can define additional partitionings; see MatPartitioningRegister().
275: .keywords: matrix, get, partitioning
277: .seealso: MatPartitioningRegister(), MatPartitioningCreate(),
278: MatPartitioningDestroy(), MatPartitioningSetAdjacency(), ISPartitioningToNumbering(),
279: ISPartitioningCount()
280: @*/
281: PetscErrorCode MatPartitioningApplyND(MatPartitioning matp,IS *partitioning)
282: {
288: if (!matp->adj->assembled) SETERRQ(PetscObjectComm((PetscObject)matp),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
289: if (matp->adj->factortype) SETERRQ(PetscObjectComm((PetscObject)matp),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
290: if (!matp->ops->applynd) SETERRQ1(PetscObjectComm((PetscObject)matp),PETSC_ERR_SUP,"Nested dissection not provided by MatPartitioningType %s",((PetscObject)matp)->type_name);
291: PetscLogEventBegin(MAT_PartitioningND,matp,0,0,0);
292: (*matp->ops->applynd)(matp,partitioning);
293: PetscLogEventEnd(MAT_PartitioningND,matp,0,0,0);
295: MatPartitioningViewFromOptions(matp,NULL,"-mat_partitioning_view");
296: ISViewFromOptions(*partitioning,NULL,"-mat_partitioning_view");
297: return(0);
298: }
300: /*@
301: MatPartitioningApply - Gets a partitioning for a matrix.
303: Collective on Mat
305: Input Parameters:
306: . matp - the matrix partitioning object
308: Output Parameters:
309: . partitioning - the partitioning. For each local node this tells the processor
310: number that that node is assigned to.
312: Options Database Keys:
313: To specify the partitioning through the options database, use one of
314: the following
315: $ -mat_partitioning_type parmetis, -mat_partitioning current
316: To see the partitioning result
317: $ -mat_partitioning_view
319: Level: beginner
321: The user can define additional partitionings; see MatPartitioningRegister().
323: .keywords: matrix, get, partitioning
325: .seealso: MatPartitioningRegister(), MatPartitioningCreate(),
326: MatPartitioningDestroy(), MatPartitioningSetAdjacency(), ISPartitioningToNumbering(),
327: ISPartitioningCount()
328: @*/
329: PetscErrorCode MatPartitioningApply(MatPartitioning matp,IS *partitioning)
330: {
332: PetscBool viewbalance,improve;
337: if (!matp->adj->assembled) SETERRQ(PetscObjectComm((PetscObject)matp),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
338: if (matp->adj->factortype) SETERRQ(PetscObjectComm((PetscObject)matp),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
339: if (!matp->ops->apply) SETERRQ(PetscObjectComm((PetscObject)matp),PETSC_ERR_ARG_WRONGSTATE,"Must set type with MatPartitioningSetFromOptions() or MatPartitioningSetType()");
340: PetscLogEventBegin(MAT_Partitioning,matp,0,0,0);
341: (*matp->ops->apply)(matp,partitioning);
342: PetscLogEventEnd(MAT_Partitioning,matp,0,0,0);
344: MatPartitioningViewFromOptions(matp,NULL,"-mat_partitioning_view");
345: ISViewFromOptions(*partitioning,NULL,"-mat_partitioning_view");
347: PetscObjectOptionsBegin((PetscObject)matp);
348: viewbalance = PETSC_FALSE;
349: PetscOptionsBool("-mat_partitioning_view_imbalance","Display imbalance information of a partition",NULL,PETSC_FALSE,&viewbalance,NULL);
350: improve = PETSC_FALSE;
351: PetscOptionsBool("-mat_partitioning_improve","Improve the quality of a partition",NULL,PETSC_FALSE,&improve,NULL);
352: PetscOptionsEnd();
354: if (improve) {
355: MatPartitioningImprove(matp,partitioning);
356: }
358: if (viewbalance) {
359: MatPartitioningViewImbalance(matp,*partitioning);
360: }
361: return(0);
362: }
365: /*@
366: MatPartitioningImprove - Improves the quality of a given partition.
368: Collective on Mat
370: Input Parameters:
371: . matp - the matrix partitioning object
372: . partitioning - the partitioning. For each local node this tells the processor
373: number that that node is assigned to.
375: Output Parameters:
376: . partitioning - the partitioning. For each local node this tells the processor
377: number that that node is assigned to.
379: Options Database Keys:
380: To improve the quality of the partition
381: $ -mat_partitioning_improve
383: Level: beginner
386: .keywords: matrix, improve, partitioning
388: .seealso: MatPartitioningApply(), MatPartitioningCreate(),
389: MatPartitioningDestroy(), MatPartitioningSetAdjacency(), ISPartitioningToNumbering(),
390: ISPartitioningCount()
391: @*/
392: PetscErrorCode MatPartitioningImprove(MatPartitioning matp,IS *partitioning)
393: {
399: if (!matp->adj->assembled) SETERRQ(PetscObjectComm((PetscObject)matp),PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
400: if (matp->adj->factortype) SETERRQ(PetscObjectComm((PetscObject)matp),PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
401: PetscLogEventBegin(MAT_Partitioning,matp,0,0,0);
402: if (matp->ops->improve) {
403: (*matp->ops->improve)(matp,partitioning);
404: }
405: PetscLogEventEnd(MAT_Partitioning,matp,0,0,0);
406: return(0);
407: }
409: /*@
410: MatPartitioningViewImbalance - Display partitioning imbalance information.
412: Collective on MatPartitioning
414: Input Parameters:
415: . matp - the matrix partitioning object
416: . partitioning - the partitioning. For each local node this tells the processor
417: number that that node is assigned to.
419: Options Database Keys:
420: To see the partitioning imbalance information
421: $ -mat_partitioning_view_balance
423: Level: beginner
425: .keywords: matrix, imbalance, partitioning
427: .seealso: MatPartitioningApply(), MatPartitioningView()
428: @*/
429: PetscErrorCode MatPartitioningViewImbalance(MatPartitioning matp, IS partitioning)
430: {
431: PetscErrorCode ierr;
432: PetscInt nparts,*subdomainsizes,*subdomainsizes_tmp,nlocal,i,maxsub,minsub,avgsub;
433: const PetscInt *indices;
434: PetscViewer viewer;
439: nparts = matp->n;
440: PetscCalloc2(nparts,&subdomainsizes,nparts,&subdomainsizes_tmp);
441: ISGetLocalSize(partitioning,&nlocal);
442: ISGetIndices(partitioning,&indices);
443: for (i=0;i<nlocal;i++) {
444: subdomainsizes_tmp[indices[i]] += matp->vertex_weights? matp->vertex_weights[i]:1;
445: }
446: MPI_Allreduce(subdomainsizes_tmp,subdomainsizes,nparts,MPIU_INT,MPI_SUM, PetscObjectComm((PetscObject)matp));
447: ISRestoreIndices(partitioning,&indices);
448: minsub = PETSC_MAX_INT, maxsub = PETSC_MIN_INT, avgsub=0;
449: for (i=0; i<nparts; i++) {
450: minsub = PetscMin(minsub,subdomainsizes[i]);
451: maxsub = PetscMax(maxsub,subdomainsizes[i]);
452: avgsub += subdomainsizes[i];
453: }
454: avgsub /=nparts;
455: PetscFree2(subdomainsizes,subdomainsizes_tmp);
456: PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)matp),&viewer);
457: MatPartitioningView(matp,viewer);
458: PetscViewerASCIIPrintf(viewer,"Partitioning Imbalance Info: Max %D, Min %D, Avg %D, R %g\n",maxsub, minsub, avgsub, (double)(maxsub/(PetscReal)minsub));
459: return(0);
460: }
462: /*@
463: MatPartitioningSetAdjacency - Sets the adjacency graph (matrix) of the thing to be
464: partitioned.
466: Collective on MatPartitioning and Mat
468: Input Parameters:
469: + part - the partitioning context
470: - adj - the adjacency matrix
472: Level: beginner
474: .keywords: Partitioning, adjacency
476: .seealso: MatPartitioningCreate()
477: @*/
478: PetscErrorCode MatPartitioningSetAdjacency(MatPartitioning part,Mat adj)
479: {
483: part->adj = adj;
484: return(0);
485: }
487: /*@
488: MatPartitioningDestroy - Destroys the partitioning context.
490: Collective on Partitioning
492: Input Parameters:
493: . part - the partitioning context
495: Level: beginner
497: .keywords: Partitioning, destroy, context
499: .seealso: MatPartitioningCreate()
500: @*/
501: PetscErrorCode MatPartitioningDestroy(MatPartitioning *part)
502: {
506: if (!*part) return(0);
508: if (--((PetscObject)(*part))->refct > 0) {*part = 0; return(0);}
510: if ((*part)->ops->destroy) {
511: (*(*part)->ops->destroy)((*part));
512: }
513: PetscFree((*part)->vertex_weights);
514: PetscFree((*part)->part_weights);
515: PetscHeaderDestroy(part);
516: return(0);
517: }
519: /*@C
520: MatPartitioningSetVertexWeights - Sets the weights for vertices for a partitioning.
522: Logically Collective on Partitioning
524: Input Parameters:
525: + part - the partitioning context
526: - weights - the weights, on each process this array must have the same size as the number of local rows
528: Level: beginner
530: Notes:
531: The array weights is freed by PETSc so the user should not free the array. In C/C++
532: the array must be obtained with a call to PetscMalloc(), not malloc().
534: .keywords: Partitioning, destroy, context
536: .seealso: MatPartitioningCreate(), MatPartitioningSetType(), MatPartitioningSetPartitionWeights()
537: @*/
538: PetscErrorCode MatPartitioningSetVertexWeights(MatPartitioning part,const PetscInt weights[])
539: {
545: PetscFree(part->vertex_weights);
547: part->vertex_weights = (PetscInt*)weights;
548: return(0);
549: }
551: /*@C
552: MatPartitioningSetPartitionWeights - Sets the weights for each partition.
554: Logically Collective on Partitioning
556: Input Parameters:
557: + part - the partitioning context
558: - weights - An array of size nparts that is used to specify the fraction of
559: vertex weight that should be distributed to each sub-domain for
560: the balance constraint. If all of the sub-domains are to be of
561: the same size, then each of the nparts elements should be set
562: to a value of 1/nparts. Note that the sum of all of the weights
563: should be one.
565: Level: beginner
567: Notes:
568: The array weights is freed by PETSc so the user should not free the array. In C/C++
569: the array must be obtained with a call to PetscMalloc(), not malloc().
571: .keywords: Partitioning, destroy, context
573: .seealso: MatPartitioningCreate(), MatPartitioningSetType(), MatPartitioningSetVertexWeights()
574: @*/
575: PetscErrorCode MatPartitioningSetPartitionWeights(MatPartitioning part,const PetscReal weights[])
576: {
582: PetscFree(part->part_weights);
584: part->part_weights = (PetscReal*)weights;
585: return(0);
586: }
588: /*@
589: MatPartitioningCreate - Creates a partitioning context.
591: Collective on MPI_Comm
593: Input Parameter:
594: . comm - MPI communicator
596: Output Parameter:
597: . newp - location to put the context
599: Level: beginner
601: .keywords: Partitioning, create, context
603: .seealso: MatPartitioningSetType(), MatPartitioningApply(), MatPartitioningDestroy(),
604: MatPartitioningSetAdjacency()
606: @*/
607: PetscErrorCode MatPartitioningCreate(MPI_Comm comm,MatPartitioning *newp)
608: {
609: MatPartitioning part;
610: PetscErrorCode ierr;
611: PetscMPIInt size;
614: *newp = 0;
616: MatInitializePackage();
617: PetscHeaderCreate(part,MAT_PARTITIONING_CLASSID,"MatPartitioning","Matrix/graph partitioning","MatOrderings",comm,MatPartitioningDestroy,MatPartitioningView);
618: part->vertex_weights = NULL;
619: part->part_weights = NULL;
621: MPI_Comm_size(comm,&size);
622: part->n = (PetscInt)size;
624: *newp = part;
625: return(0);
626: }
628: /*@C
629: MatPartitioningView - Prints the partitioning data structure.
631: Collective on MatPartitioning
633: Input Parameters:
634: . part - the partitioning context
635: . viewer - optional visualization context
637: Level: intermediate
639: Note:
640: The available visualization contexts include
641: + PETSC_VIEWER_STDOUT_SELF - standard output (default)
642: - PETSC_VIEWER_STDOUT_WORLD - synchronized standard
643: output where only the first processor opens
644: the file. All other processors send their
645: data to the first processor to print.
647: The user can open alternative visualization contexts with
648: . PetscViewerASCIIOpen() - output to a specified file
650: .keywords: Partitioning, view
652: .seealso: PetscViewerASCIIOpen()
653: @*/
654: PetscErrorCode MatPartitioningView(MatPartitioning part,PetscViewer viewer)
655: {
657: PetscBool iascii;
661: if (!viewer) {
662: PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)part),&viewer);
663: }
667: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
668: if (iascii) {
669: PetscObjectPrintClassNamePrefixType((PetscObject)part,viewer);
670: if (part->vertex_weights) {
671: PetscViewerASCIIPrintf(viewer," Using vertex weights\n");
672: }
673: }
674: if (part->ops->view) {
675: PetscViewerASCIIPushTab(viewer);
676: (*part->ops->view)(part,viewer);
677: PetscViewerASCIIPopTab(viewer);
678: }
679: return(0);
680: }
682: /*@C
683: MatPartitioningSetType - Sets the type of partitioner to use
685: Collective on MatPartitioning
687: Input Parameter:
688: . part - the partitioning context.
689: . type - a known method
691: Options Database Command:
692: $ -mat_partitioning_type <type>
693: $ Use -help for a list of available methods
694: $ (for instance, parmetis)
696: Level: intermediate
698: .keywords: partitioning, set, method, type
700: .seealso: MatPartitioningCreate(), MatPartitioningApply(), MatPartitioningType
702: @*/
703: PetscErrorCode MatPartitioningSetType(MatPartitioning part,MatPartitioningType type)
704: {
705: PetscErrorCode ierr,(*r)(MatPartitioning);
706: PetscBool match;
712: PetscObjectTypeCompare((PetscObject)part,type,&match);
713: if (match) return(0);
715: if (part->ops->destroy) {
716: (*part->ops->destroy)(part);
717: part->ops->destroy = NULL;
718: }
719: part->setupcalled = 0;
720: part->data = 0;
721: PetscMemzero(part->ops,sizeof(struct _MatPartitioningOps));
723: PetscFunctionListFind(MatPartitioningList,type,&r);
724: if (!r) SETERRQ1(PetscObjectComm((PetscObject)part),PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown partitioning type %s",type);
726: (*r)(part);
728: PetscFree(((PetscObject)part)->type_name);
729: PetscStrallocpy(type,&((PetscObject)part)->type_name);
730: return(0);
731: }
733: /*@
734: MatPartitioningSetFromOptions - Sets various partitioning options from the
735: options database.
737: Collective on MatPartitioning
739: Input Parameter:
740: . part - the partitioning context.
742: Options Database Command:
743: $ -mat_partitioning_type <type>
744: $ Use -help for a list of available methods
745: $ (for instance, parmetis)
746: $ -mat_partitioning_nparts - number of subgraphs
749: Notes:
750: If the partitioner has not been set by the user it uses one of the installed partitioner such as ParMetis. If there are
751: no installed partitioners it uses current which means no repartioning.
753: Level: beginner
755: .keywords: partitioning, set, method, type
756: @*/
757: PetscErrorCode MatPartitioningSetFromOptions(MatPartitioning part)
758: {
760: PetscBool flag;
761: char type[256];
762: const char *def;
765: PetscObjectOptionsBegin((PetscObject)part);
766: if (!((PetscObject)part)->type_name) {
767: #if defined(PETSC_HAVE_PARMETIS)
768: def = MATPARTITIONINGPARMETIS;
769: #elif defined(PETSC_HAVE_CHACO)
770: def = MATPARTITIONINGCHACO;
771: #elif defined(PETSC_HAVE_PARTY)
772: def = MATPARTITIONINGPARTY;
773: #elif defined(PETSC_HAVE_PTSCOTCH)
774: def = MATPARTITIONINGPTSCOTCH;
775: #else
776: def = MATPARTITIONINGCURRENT;
777: #endif
778: } else {
779: def = ((PetscObject)part)->type_name;
780: }
781: PetscOptionsFList("-mat_partitioning_type","Type of partitioner","MatPartitioningSetType",MatPartitioningList,def,type,256,&flag);
782: if (flag) {
783: MatPartitioningSetType(part,type);
784: }
786: PetscOptionsInt("-mat_partitioning_nparts","number of fine parts",NULL,part->n,& part->n,&flag);
788: /*
789: Set the type if it was never set.
790: */
791: if (!((PetscObject)part)->type_name) {
792: MatPartitioningSetType(part,def);
793: }
795: if (part->ops->setfromoptions) {
796: (*part->ops->setfromoptions)(PetscOptionsObject,part);
797: }
798: PetscOptionsEnd();
799: return(0);
800: }