Actual source code: misk.c
1: #include <petsc/private/matimpl.h>
2: #include <../src/mat/impls/aij/seq/aij.h>
3: #include <../src/mat/impls/aij/mpi/mpiaij.h>
4: #include <petscsf.h>
6: #define MIS_NOT_DONE -2
7: #define MIS_DELETED -1
8: #define MIS_REMOVED -3
9: #define MIS_IS_SELECTED(s) (s >= 0)
11: /* edge for priority queue */
12: typedef struct edge_tag {
13: PetscReal weight;
14: PetscInt lid0, gid1, cpid1;
15: } Edge;
17: static PetscErrorCode PetscCoarsenDataView_private(PetscCoarsenData *agg_lists, PetscViewer viewer)
18: {
19: PetscCDIntNd *pos, *pos2;
21: PetscFunctionBegin;
22: for (PetscInt kk = 0; kk < agg_lists->size; kk++) {
23: PetscCall(PetscCDGetHeadPos(agg_lists, kk, &pos));
24: if ((pos2 = pos)) PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "selected local %" PetscInt_FMT ": ", kk));
25: while (pos) {
26: PetscInt gid1;
27: PetscCall(PetscCDIntNdGetID(pos, &gid1));
28: PetscCall(PetscCDGetNextPos(agg_lists, kk, &pos));
29: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " %" PetscInt_FMT " ", gid1));
30: }
31: if (pos2) PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "\n"));
32: }
33: PetscFunctionReturn(PETSC_SUCCESS);
34: }
36: /*
37: MatCoarsenApply_MISK_private - parallel heavy edge matching
39: Input Parameter:
40: . perm - permutation
41: . Gmat - global matrix of graph (data not defined)
43: Output Parameter:
44: . a_locals_llist - array of list of local nodes rooted at local node
45: */
46: static PetscErrorCode MatCoarsenApply_MISK_private(IS perm, const PetscInt misk, Mat Gmat, PetscCoarsenData **a_locals_llist)
47: {
48: PetscBool isMPI;
49: MPI_Comm comm;
50: PetscMPIInt rank, size;
51: Mat cMat, Prols[5], Rtot;
52: PetscScalar one = 1;
54: PetscFunctionBegin;
57: PetscAssertPointer(a_locals_llist, 4);
58: PetscCheck(misk < 5 && misk > 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "too many/few levels: %" PetscInt_FMT, misk);
59: PetscCall(PetscObjectBaseTypeCompare((PetscObject)Gmat, MATMPIAIJ, &isMPI));
60: PetscCall(PetscObjectGetComm((PetscObject)Gmat, &comm));
61: PetscCallMPI(MPI_Comm_rank(comm, &rank));
62: PetscCallMPI(MPI_Comm_size(comm, &size));
63: PetscCall(PetscInfo(Gmat, "misk %" PetscInt_FMT "\n", misk));
64: /* make a copy of the graph, this gets destroyed in iterates */
65: if (misk > 1) PetscCall(MatDuplicate(Gmat, MAT_COPY_VALUES, &cMat));
66: else cMat = Gmat;
67: for (PetscInt iterIdx = 0; iterIdx < misk; iterIdx++) {
68: Mat_SeqAIJ *matA, *matB = NULL;
69: Mat_MPIAIJ *mpimat = NULL;
70: const PetscInt *perm_ix;
71: const PetscInt nloc_inner = cMat->rmap->n;
72: PetscCoarsenData *agg_lists;
73: PetscInt *cpcol_gid = NULL, *cpcol_state, *lid_cprowID, *lid_state, *lid_parent_gid = NULL;
74: PetscInt num_fine_ghosts, kk, n, ix, j, *idx, *ai, Iend, my0, nremoved, gid, cpid, lidj, sgid, t1, t2, slid, nDone, nselected = 0, state;
75: PetscBool *lid_removed, isOK;
76: PetscSF sf;
78: if (isMPI) {
79: mpimat = (Mat_MPIAIJ *)cMat->data;
80: matA = (Mat_SeqAIJ *)mpimat->A->data;
81: matB = (Mat_SeqAIJ *)mpimat->B->data;
82: /* force compressed storage of B */
83: PetscCall(MatCheckCompressedRow(mpimat->B, matB->nonzerorowcnt, &matB->compressedrow, matB->i, cMat->rmap->n, -1.0));
84: } else {
85: PetscBool isAIJ;
87: matA = (Mat_SeqAIJ *)cMat->data;
88: PetscCall(PetscObjectBaseTypeCompare((PetscObject)cMat, MATSEQAIJ, &isAIJ));
89: PetscCheck(isAIJ, PETSC_COMM_SELF, PETSC_ERR_USER, "Require AIJ matrix.");
90: }
91: PetscCall(MatGetOwnershipRange(cMat, &my0, &Iend));
92: if (isMPI) {
93: PetscInt *lid_gid;
95: PetscCall(PetscMalloc1(nloc_inner, &lid_gid)); /* explicit array needed */
96: for (kk = 0, gid = my0; kk < nloc_inner; kk++, gid++) lid_gid[kk] = gid;
97: PetscCall(VecGetLocalSize(mpimat->lvec, &num_fine_ghosts));
98: PetscCall(PetscMalloc2(num_fine_ghosts, &cpcol_gid, num_fine_ghosts, &cpcol_state));
99: PetscCall(MatGetMultPetscSF(cMat, &sf));
100: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, lid_gid, cpcol_gid, MPI_REPLACE));
101: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, lid_gid, cpcol_gid, MPI_REPLACE));
102: for (kk = 0; kk < num_fine_ghosts; kk++) cpcol_state[kk] = MIS_NOT_DONE;
103: PetscCall(PetscFree(lid_gid));
104: } else num_fine_ghosts = 0;
106: PetscCall(PetscMalloc4(nloc_inner, &lid_cprowID, nloc_inner, &lid_removed, nloc_inner, &lid_parent_gid, nloc_inner, &lid_state));
107: PetscCall(PetscCDCreate(nloc_inner, &agg_lists));
108: /* need an inverse map - locals */
109: for (kk = 0; kk < nloc_inner; kk++) {
110: lid_cprowID[kk] = -1;
111: lid_removed[kk] = PETSC_FALSE;
112: lid_parent_gid[kk] = -1.0;
113: lid_state[kk] = MIS_NOT_DONE;
114: }
115: /* set index into cmpressed row 'lid_cprowID' */
116: if (matB) {
117: for (ix = 0; ix < matB->compressedrow.nrows; ix++) {
118: const PetscInt lid = matB->compressedrow.rindex[ix];
119: if (lid >= 0) lid_cprowID[lid] = ix;
120: }
121: }
122: /* MIS */
123: nremoved = nDone = 0;
124: if (!iterIdx) PetscCall(ISGetIndices(perm, &perm_ix)); // use permutation on first MIS
125: else perm_ix = NULL;
126: while (nDone < nloc_inner || PETSC_TRUE) { /* asynchronous not implemented */
127: /* check all vertices */
128: for (kk = 0; kk < nloc_inner; kk++) {
129: const PetscInt lid = perm_ix ? perm_ix[kk] : kk;
130: state = lid_state[lid];
131: if (iterIdx == 0 && lid_removed[lid]) continue;
132: if (state == MIS_NOT_DONE) {
133: /* parallel test, delete if selected ghost */
134: isOK = PETSC_TRUE;
135: /* parallel test */
136: if ((ix = lid_cprowID[lid]) != -1) { /* if I have any ghost neighbors */
137: ai = matB->compressedrow.i;
138: n = ai[ix + 1] - ai[ix];
139: idx = matB->j + ai[ix];
140: for (j = 0; j < n; j++) {
141: cpid = idx[j]; /* compressed row ID in B mat */
142: gid = cpcol_gid[cpid];
143: if (cpcol_state[cpid] == MIS_NOT_DONE && gid >= Iend) { /* or pe>rank */
144: isOK = PETSC_FALSE; /* can not delete */
145: break;
146: }
147: }
148: }
149: if (isOK) { /* select or remove this vertex if it is a true singleton like a BC */
150: nDone++;
151: /* check for singleton */
152: ai = matA->i;
153: n = ai[lid + 1] - ai[lid];
154: if (n < 2) {
155: /* if I have any ghost adj then not a singleton */
156: ix = lid_cprowID[lid];
157: if (ix == -1 || !(matB->compressedrow.i[ix + 1] - matB->compressedrow.i[ix])) {
158: if (iterIdx == 0) {
159: lid_removed[lid] = PETSC_TRUE;
160: nremoved++; // let it get selected
161: }
162: // PetscCall(PetscCDAppendID(agg_lists, lid, lid + my0));
163: // lid_state[lid] = nselected; // >= 0 is selected, cache for ordering coarse grid
164: /* should select this because it is technically in the MIS but lets not */
165: continue; /* one local adj (me) and no ghost - singleton */
166: }
167: }
168: /* SELECTED state encoded with global index */
169: lid_state[lid] = nselected; // >= 0 is selected, cache for ordering coarse grid
170: nselected++;
171: PetscCall(PetscCDAppendID(agg_lists, lid, lid + my0));
172: /* delete local adj */
173: idx = matA->j + ai[lid];
174: for (j = 0; j < n; j++) {
175: lidj = idx[j];
176: if (lid_state[lidj] == MIS_NOT_DONE) {
177: nDone++;
178: PetscCall(PetscCDAppendID(agg_lists, lid, lidj + my0));
179: lid_state[lidj] = MIS_DELETED; /* delete this */
180: }
181: }
182: } /* selected */
183: } /* not done vertex */
184: } /* vertex loop */
186: /* update ghost states and count todos */
187: if (isMPI) {
188: /* scatter states, check for done */
189: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, lid_state, cpcol_state, MPI_REPLACE));
190: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, lid_state, cpcol_state, MPI_REPLACE));
191: ai = matB->compressedrow.i;
192: for (ix = 0; ix < matB->compressedrow.nrows; ix++) {
193: const PetscInt lidj = matB->compressedrow.rindex[ix]; /* local boundary node */
194: state = lid_state[lidj];
195: if (state == MIS_NOT_DONE) {
196: /* look at ghosts */
197: n = ai[ix + 1] - ai[ix];
198: idx = matB->j + ai[ix];
199: for (j = 0; j < n; j++) {
200: cpid = idx[j]; /* compressed row ID in B mat */
201: if (MIS_IS_SELECTED(cpcol_state[cpid])) { /* lid is now deleted by ghost */
202: nDone++;
203: lid_state[lidj] = MIS_DELETED; /* delete this */
204: sgid = cpcol_gid[cpid];
205: lid_parent_gid[lidj] = sgid; /* keep track of proc that I belong to */
206: break;
207: }
208: }
209: }
210: }
211: /* all done? */
212: t1 = nloc_inner - nDone;
213: PetscCallMPI(MPIU_Allreduce(&t1, &t2, 1, MPIU_INT, MPI_SUM, comm)); /* synchronous version */
214: if (!t2) break;
215: } else break; /* no mpi - all done */
216: } /* outer parallel MIS loop */
217: if (!iterIdx) PetscCall(ISRestoreIndices(perm, &perm_ix));
218: PetscCall(PetscInfo(Gmat, "\t removed %" PetscInt_FMT " of %" PetscInt_FMT " vertices. %" PetscInt_FMT " selected.\n", nremoved, nloc_inner, nselected));
220: /* tell adj who my lid_parent_gid vertices belong to - fill in agg_lists selected ghost lists */
221: if (matB) {
222: PetscInt *cpcol_sel_gid, *icpcol_gid;
224: /* need to copy this to free buffer -- should do this globally */
225: PetscCall(PetscMalloc2(num_fine_ghosts, &icpcol_gid, num_fine_ghosts, &cpcol_sel_gid));
226: for (cpid = 0; cpid < num_fine_ghosts; cpid++) icpcol_gid[cpid] = cpcol_gid[cpid];
227: /* get proc of deleted ghost */
228: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, lid_parent_gid, cpcol_sel_gid, MPI_REPLACE));
229: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, lid_parent_gid, cpcol_sel_gid, MPI_REPLACE));
230: for (cpid = 0; cpid < num_fine_ghosts; cpid++) {
231: sgid = cpcol_sel_gid[cpid];
232: gid = icpcol_gid[cpid];
233: if (sgid >= my0 && sgid < Iend) { /* I own this deleted */
234: slid = sgid - my0;
235: PetscCall(PetscCDAppendID(agg_lists, slid, gid));
236: }
237: }
238: // done - cleanup
239: PetscCall(PetscFree2(icpcol_gid, cpcol_sel_gid));
240: PetscCall(PetscFree2(cpcol_gid, cpcol_state));
241: }
242: PetscCall(PetscFree4(lid_cprowID, lid_removed, lid_parent_gid, lid_state));
244: /* MIS done - make projection matrix - P */
245: MatType jtype;
246: PetscCall(MatGetType(Gmat, &jtype));
247: PetscCall(MatCreate(comm, &Prols[iterIdx]));
248: PetscCall(MatSetType(Prols[iterIdx], jtype));
249: PetscCall(MatSetSizes(Prols[iterIdx], nloc_inner, nselected, PETSC_DETERMINE, PETSC_DETERMINE));
250: PetscCall(MatSeqAIJSetPreallocation(Prols[iterIdx], 1, NULL));
251: PetscCall(MatMPIAIJSetPreallocation(Prols[iterIdx], 1, NULL, 1, NULL));
252: {
253: PetscCDIntNd *pos, *pos2;
254: PetscInt colIndex, Iend, fgid;
256: PetscCall(MatGetOwnershipRangeColumn(Prols[iterIdx], &colIndex, &Iend));
257: // TODO - order with permutation in lid_selected (reversed)
258: for (PetscInt lid = 0; lid < agg_lists->size; lid++) {
259: PetscCall(PetscCDGetHeadPos(agg_lists, lid, &pos));
260: pos2 = pos;
261: while (pos) {
262: PetscCall(PetscCDIntNdGetID(pos, &fgid));
263: PetscCall(PetscCDGetNextPos(agg_lists, lid, &pos));
264: PetscCall(MatSetValues(Prols[iterIdx], 1, &fgid, 1, &colIndex, &one, INSERT_VALUES));
265: }
266: if (pos2) colIndex++;
267: }
268: PetscCheck(Iend == colIndex, PETSC_COMM_SELF, PETSC_ERR_SUP, "Iend!=colIndex: %" PetscInt_FMT " %" PetscInt_FMT, Iend, colIndex);
269: }
270: PetscCall(MatAssemblyBegin(Prols[iterIdx], MAT_FINAL_ASSEMBLY));
271: PetscCall(MatAssemblyEnd(Prols[iterIdx], MAT_FINAL_ASSEMBLY));
272: /* project to make new graph for next MIS, skip if last */
273: if (iterIdx < misk - 1) {
274: Mat new_mat;
275: PetscCall(MatPtAP(cMat, Prols[iterIdx], MAT_INITIAL_MATRIX, PETSC_DETERMINE, &new_mat));
276: PetscCall(MatDestroy(&cMat));
277: cMat = new_mat; // next iter
278: } else if (cMat != Gmat) PetscCall(MatDestroy(&cMat));
279: // cleanup
280: PetscCall(PetscCDDestroy(agg_lists));
281: } /* MIS-k iteration */
282: /* make total prolongator Rtot = P_0 * P_1 * ... */
283: Rtot = Prols[misk - 1]; // compose P then transpose to get R
284: for (PetscInt iterIdx = misk - 1; iterIdx > 0; iterIdx--) {
285: Mat P;
287: PetscCall(MatMatMult(Prols[iterIdx - 1], Rtot, MAT_INITIAL_MATRIX, PETSC_CURRENT, &P));
288: PetscCall(MatDestroy(&Prols[iterIdx - 1]));
289: PetscCall(MatDestroy(&Rtot));
290: Rtot = P;
291: }
292: PetscCall(MatTranspose(Rtot, MAT_INPLACE_MATRIX, &Rtot)); // R now
293: PetscCall(MatViewFromOptions(Rtot, NULL, "-misk_aggregation_view"));
294: /* make aggregates with Rtot - could use Rtot directly in theory but have to go through the aggregate list data structure */
295: {
296: PetscInt Istart, Iend, ncols, NN, MM, jj = 0, max_osz = 0;
297: const PetscInt nloc = Gmat->rmap->n;
298: PetscCoarsenData *agg_lists;
299: Mat mat;
301: PetscCall(PetscCDCreate(nloc, &agg_lists));
302: *a_locals_llist = agg_lists; // return
303: PetscCall(MatGetOwnershipRange(Rtot, &Istart, &Iend));
304: for (PetscInt grow = Istart, lid = 0; grow < Iend; grow++, lid++) {
305: const PetscInt *idx;
307: PetscCall(MatGetRow(Rtot, grow, &ncols, &idx, NULL));
308: for (PetscInt jj = 0; jj < ncols; jj++) {
309: PetscInt gcol = idx[jj];
311: PetscCall(PetscCDAppendID(agg_lists, lid, gcol)); // local row, global column
312: }
313: PetscCall(MatRestoreRow(Rtot, grow, &ncols, &idx, NULL));
314: }
315: PetscCall(MatDestroy(&Rtot));
317: /* make fake matrix, get largest nnz */
318: for (PetscInt lid = 0; lid < nloc; lid++) {
319: PetscCall(PetscCDCountAt(agg_lists, lid, &jj));
320: if (jj > max_osz) max_osz = jj;
321: }
322: PetscCall(MatGetSize(Gmat, &MM, &NN));
323: if (max_osz > MM - nloc) max_osz = MM - nloc;
324: PetscCall(MatGetOwnershipRange(Gmat, &Istart, NULL));
325: /* matrix of ghost adj for square graph */
326: PetscCall(MatCreateAIJ(comm, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE, 0, NULL, max_osz, NULL, &mat));
327: for (PetscInt lid = 0, gidi = Istart; lid < nloc; lid++, gidi++) {
328: PetscCDIntNd *pos;
330: PetscCall(PetscCDGetHeadPos(agg_lists, lid, &pos));
331: while (pos) {
332: PetscInt gidj;
334: PetscCall(PetscCDIntNdGetID(pos, &gidj));
335: PetscCall(PetscCDGetNextPos(agg_lists, lid, &pos));
336: if (gidj < Istart || gidj >= Istart + nloc) PetscCall(MatSetValues(mat, 1, &gidi, 1, &gidj, &one, ADD_VALUES));
337: }
338: }
339: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
340: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
341: PetscCall(PetscCDSetMat(agg_lists, mat));
342: }
343: PetscFunctionReturn(PETSC_SUCCESS);
344: }
346: /*
347: Distance k MIS. k is in 'subctx'
348: */
349: static PetscErrorCode MatCoarsenApply_MISK(MatCoarsen coarse)
350: {
351: Mat mat = coarse->graph;
352: PetscInt k;
354: PetscFunctionBegin;
355: PetscCall(MatCoarsenMISKGetDistance(coarse, &k));
356: PetscCheck(k > 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "too few levels: %" PetscInt_FMT, k);
357: if (!coarse->perm) {
358: IS perm;
359: PetscInt n, m;
361: PetscCall(MatGetLocalSize(mat, &m, &n));
362: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), m, 0, 1, &perm));
363: PetscCall(MatCoarsenApply_MISK_private(perm, k, mat, &coarse->agg_lists));
364: PetscCall(ISDestroy(&perm));
365: } else {
366: PetscCall(MatCoarsenApply_MISK_private(coarse->perm, k, mat, &coarse->agg_lists));
367: }
368: PetscFunctionReturn(PETSC_SUCCESS);
369: }
371: static PetscErrorCode MatCoarsenView_MISK(MatCoarsen coarse, PetscViewer viewer)
372: {
373: PetscMPIInt rank;
374: PetscBool isascii;
375: PetscViewerFormat format;
377: PetscFunctionBegin;
378: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)coarse), &rank));
379: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
380: PetscCall(PetscViewerGetFormat(viewer, &format));
381: if (isascii && format == PETSC_VIEWER_ASCII_INFO_DETAIL && coarse->agg_lists) {
382: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
383: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] MISK aggregator\n", rank));
384: if (!rank) PetscCall(PetscCoarsenDataView_private(coarse->agg_lists, viewer));
385: PetscCall(PetscViewerFlush(viewer));
386: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
387: }
388: PetscFunctionReturn(PETSC_SUCCESS);
389: }
391: static PetscErrorCode MatCoarsenSetFromOptions_MISK(MatCoarsen coarse, PetscOptionItems PetscOptionsObject)
392: {
393: PetscInt k = 1;
394: PetscBool flg;
396: PetscFunctionBegin;
397: PetscOptionsHeadBegin(PetscOptionsObject, "MatCoarsen-MISk options");
398: PetscCall(PetscOptionsInt("-mat_coarsen_misk_distance", "k distance for MIS", "", k, &k, &flg));
399: if (flg) coarse->subctx = (void *)(size_t)k;
400: PetscOptionsHeadEnd();
401: PetscFunctionReturn(PETSC_SUCCESS);
402: }
404: /*MC
405: MATCOARSENMISK - A coarsener that uses MISK, a simple greedy coarsener
407: Level: beginner
409: Options Database Key:
410: . -mat_coarsen_misk_distance k - distance for MIS
412: Note:
413: When the coarsening is used inside `PCGAMG` then the options database key is `-pc_gamg_mat_coarsen_misk_distance`
415: .seealso: `MatCoarsen`, `MatCoarsenMISKSetDistance()`, `MatCoarsenApply()`, `MatCoarsenSetType()`, `MatCoarsenType`, `MatCoarsenCreate()`, `MATCOARSENHEM`, `MATCOARSENMIS`
416: M*/
418: PETSC_EXTERN PetscErrorCode MatCoarsenCreate_MISK(MatCoarsen coarse)
419: {
420: PetscFunctionBegin;
421: coarse->ops->apply = MatCoarsenApply_MISK;
422: coarse->ops->view = MatCoarsenView_MISK;
423: coarse->subctx = (void *)(size_t)1;
424: coarse->ops->setfromoptions = MatCoarsenSetFromOptions_MISK;
425: PetscFunctionReturn(PETSC_SUCCESS);
426: }
428: /*@
429: MatCoarsenMISKSetDistance - the distance to be used by MISK
431: Collective
433: Input Parameters:
434: + crs - the coarsen
435: - k - the distance
437: Options Database Key:
438: . -mat_coarsen_misk_distance k - distance for MIS
440: Level: advanced
442: Note:
443: When the coarsening is used inside `PCGAMG` then the options database key is `-pc_gamg_mat_coarsen_misk_distance`
445: .seealso: `MATCOARSENMISK`, `MatCoarsen`, `MatCoarsenSetFromOptions()`, `MatCoarsenSetType()`, `MatCoarsenRegister()`, `MatCoarsenCreate()`,
446: `MatCoarsenDestroy()`, `MatCoarsenSetAdjacency()`, `MatCoarsenMISKGetDistance()`,
447: `MatCoarsenGetData()`
448: @*/
449: PetscErrorCode MatCoarsenMISKSetDistance(MatCoarsen crs, PetscInt k)
450: {
451: PetscFunctionBegin;
452: crs->subctx = (void *)(size_t)k;
453: PetscFunctionReturn(PETSC_SUCCESS);
454: }
456: /*@
457: MatCoarsenMISKGetDistance - gets the distance to be used by MISK
459: Collective
461: Input Parameter:
462: . crs - the coarsen
464: Output Parameter:
465: . k - the distance
467: Level: advanced
469: .seealso: `MATCOARSENMISK`, `MatCoarsen`, `MatCoarsenSetFromOptions()`, `MatCoarsenSetType()`,
470: `MatCoarsenRegister()`, `MatCoarsenCreate()`, `MatCoarsenDestroy()`,
471: `MatCoarsenSetAdjacency()`, `MatCoarsenGetData()`
472: @*/
473: PetscErrorCode MatCoarsenMISKGetDistance(MatCoarsen crs, PetscInt *k)
474: {
475: PetscFunctionBegin;
476: *k = (PetscInt)(size_t)crs->subctx;
477: PetscFunctionReturn(PETSC_SUCCESS);
478: }