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 %d: ", (int)kk));
25: while (pos) {
26: PetscInt gid1;
27: PetscCall(PetscCDIntNdGetID(pos, &gid1));
28: PetscCall(PetscCDGetNextPos(agg_lists, kk, &pos));
29: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " %d ", (int)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: %d", (int)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 %d\n", (int)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: PetscLayout layout;
77: PetscSF sf;
79: if (isMPI) {
80: mpimat = (Mat_MPIAIJ *)cMat->data;
81: matA = (Mat_SeqAIJ *)mpimat->A->data;
82: matB = (Mat_SeqAIJ *)mpimat->B->data;
83: /* force compressed storage of B */
84: PetscCall(MatCheckCompressedRow(mpimat->B, matB->nonzerorowcnt, &matB->compressedrow, matB->i, cMat->rmap->n, -1.0));
85: } else {
86: PetscBool isAIJ;
87: PetscCall(PetscObjectBaseTypeCompare((PetscObject)cMat, MATSEQAIJ, &isAIJ));
88: PetscCheck(isAIJ, PETSC_COMM_SELF, PETSC_ERR_USER, "Require AIJ matrix.");
89: matA = (Mat_SeqAIJ *)cMat->data;
90: }
91: PetscCall(MatGetOwnershipRange(cMat, &my0, &Iend));
92: if (mpimat) {
93: PetscInt *lid_gid;
94: PetscCall(PetscMalloc1(nloc_inner, &lid_gid)); /* explicit array needed */
95: for (kk = 0, gid = my0; kk < nloc_inner; kk++, gid++) lid_gid[kk] = gid;
96: PetscCall(VecGetLocalSize(mpimat->lvec, &num_fine_ghosts));
97: PetscCall(PetscMalloc1(num_fine_ghosts, &cpcol_gid));
98: PetscCall(PetscMalloc1(num_fine_ghosts, &cpcol_state));
99: PetscCall(PetscSFCreate(PetscObjectComm((PetscObject)cMat), &sf));
100: PetscCall(MatGetLayouts(cMat, &layout, NULL));
101: PetscCall(PetscSFSetGraphLayout(sf, layout, num_fine_ghosts, NULL, PETSC_COPY_VALUES, mpimat->garray));
102: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, lid_gid, cpcol_gid, MPI_REPLACE));
103: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, lid_gid, cpcol_gid, MPI_REPLACE));
104: for (kk = 0; kk < num_fine_ghosts; kk++) cpcol_state[kk] = MIS_NOT_DONE;
105: PetscCall(PetscFree(lid_gid));
106: } else num_fine_ghosts = 0;
108: PetscCall(PetscMalloc1(nloc_inner, &lid_cprowID));
109: PetscCall(PetscMalloc1(nloc_inner, &lid_removed)); /* explicit array needed */
110: PetscCall(PetscMalloc1(nloc_inner, &lid_parent_gid));
111: PetscCall(PetscMalloc1(nloc_inner, &lid_state));
113: /* the data structure */
114: PetscCall(PetscCDCreate(nloc_inner, &agg_lists));
115: /* need an inverse map - locals */
116: for (kk = 0; kk < nloc_inner; kk++) {
117: lid_cprowID[kk] = -1;
118: lid_removed[kk] = PETSC_FALSE;
119: lid_parent_gid[kk] = -1.0;
120: lid_state[kk] = MIS_NOT_DONE;
121: }
122: /* set index into cmpressed row 'lid_cprowID' */
123: if (matB) {
124: for (ix = 0; ix < matB->compressedrow.nrows; ix++) {
125: const PetscInt lid = matB->compressedrow.rindex[ix];
126: if (lid >= 0) lid_cprowID[lid] = ix;
127: }
128: }
129: /* MIS */
130: nremoved = nDone = 0;
131: if (!iterIdx) PetscCall(ISGetIndices(perm, &perm_ix)); // use permutation on first MIS
132: else perm_ix = NULL;
133: while (nDone < nloc_inner || PETSC_TRUE) { /* asynchronous not implemented */
134: /* check all vertices */
135: for (kk = 0; kk < nloc_inner; kk++) {
136: const PetscInt lid = perm_ix ? perm_ix[kk] : kk;
137: state = lid_state[lid];
138: if (iterIdx == 0 && lid_removed[lid]) continue;
139: if (state == MIS_NOT_DONE) {
140: /* parallel test, delete if selected ghost */
141: isOK = PETSC_TRUE;
142: /* parallel test */
143: if ((ix = lid_cprowID[lid]) != -1) { /* if I have any ghost neighbors */
144: ai = matB->compressedrow.i;
145: n = ai[ix + 1] - ai[ix];
146: idx = matB->j + ai[ix];
147: for (j = 0; j < n; j++) {
148: cpid = idx[j]; /* compressed row ID in B mat */
149: gid = cpcol_gid[cpid];
150: if (cpcol_state[cpid] == MIS_NOT_DONE && gid >= Iend) { /* or pe>rank */
151: isOK = PETSC_FALSE; /* can not delete */
152: break;
153: }
154: }
155: }
156: if (isOK) { /* select or remove this vertex if it is a true singleton like a BC */
157: nDone++;
158: /* check for singleton */
159: ai = matA->i;
160: n = ai[lid + 1] - ai[lid];
161: if (n < 2) {
162: /* if I have any ghost adj then not a singleton */
163: ix = lid_cprowID[lid];
164: if (ix == -1 || !(matB->compressedrow.i[ix + 1] - matB->compressedrow.i[ix])) {
165: if (iterIdx == 0) {
166: lid_removed[lid] = PETSC_TRUE;
167: nremoved++; // let it get selected
168: }
169: // PetscCall(PetscCDAppendID(agg_lists, lid, lid + my0));
170: // lid_state[lid] = nselected; // >= 0 is selected, cache for ordering coarse grid
171: /* should select this because it is technically in the MIS but lets not */
172: continue; /* one local adj (me) and no ghost - singleton */
173: }
174: }
175: /* SELECTED state encoded with global index */
176: lid_state[lid] = nselected; // >= 0 is selected, cache for ordering coarse grid
177: nselected++;
178: PetscCall(PetscCDAppendID(agg_lists, lid, lid + my0));
179: /* delete local adj */
180: idx = matA->j + ai[lid];
181: for (j = 0; j < n; j++) {
182: lidj = idx[j];
183: if (lid_state[lidj] == MIS_NOT_DONE) {
184: nDone++;
185: PetscCall(PetscCDAppendID(agg_lists, lid, lidj + my0));
186: lid_state[lidj] = MIS_DELETED; /* delete this */
187: }
188: }
189: } /* selected */
190: } /* not done vertex */
191: } /* vertex loop */
193: /* update ghost states and count todos */
194: if (mpimat) {
195: /* scatter states, check for done */
196: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, lid_state, cpcol_state, MPI_REPLACE));
197: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, lid_state, cpcol_state, MPI_REPLACE));
198: ai = matB->compressedrow.i;
199: for (ix = 0; ix < matB->compressedrow.nrows; ix++) {
200: const int lidj = matB->compressedrow.rindex[ix]; /* local boundary node */
201: state = lid_state[lidj];
202: if (state == MIS_NOT_DONE) {
203: /* look at ghosts */
204: n = ai[ix + 1] - ai[ix];
205: idx = matB->j + ai[ix];
206: for (j = 0; j < n; j++) {
207: cpid = idx[j]; /* compressed row ID in B mat */
208: if (MIS_IS_SELECTED(cpcol_state[cpid])) { /* lid is now deleted by ghost */
209: nDone++;
210: lid_state[lidj] = MIS_DELETED; /* delete this */
211: sgid = cpcol_gid[cpid];
212: lid_parent_gid[lidj] = sgid; /* keep track of proc that I belong to */
213: break;
214: }
215: }
216: }
217: }
218: /* all done? */
219: t1 = nloc_inner - nDone;
220: PetscCall(MPIU_Allreduce(&t1, &t2, 1, MPIU_INT, MPI_SUM, comm)); /* synchronous version */
221: if (!t2) break;
222: } else break; /* no mpi - all done */
223: } /* outer parallel MIS loop */
224: if (!iterIdx) PetscCall(ISRestoreIndices(perm, &perm_ix));
225: PetscCall(PetscInfo(Gmat, "\t removed %" PetscInt_FMT " of %" PetscInt_FMT " vertices. %" PetscInt_FMT " selected.\n", nremoved, nloc_inner, nselected));
227: /* tell adj who my lid_parent_gid vertices belong to - fill in agg_lists selected ghost lists */
228: if (matB) {
229: PetscInt *cpcol_sel_gid, *icpcol_gid;
230: /* need to copy this to free buffer -- should do this globally */
231: PetscCall(PetscMalloc1(num_fine_ghosts, &cpcol_sel_gid));
232: PetscCall(PetscMalloc1(num_fine_ghosts, &icpcol_gid));
233: for (cpid = 0; cpid < num_fine_ghosts; cpid++) icpcol_gid[cpid] = cpcol_gid[cpid];
234: /* get proc of deleted ghost */
235: PetscCall(PetscSFBcastBegin(sf, MPIU_INT, lid_parent_gid, cpcol_sel_gid, MPI_REPLACE));
236: PetscCall(PetscSFBcastEnd(sf, MPIU_INT, lid_parent_gid, cpcol_sel_gid, MPI_REPLACE));
237: for (cpid = 0; cpid < num_fine_ghosts; cpid++) {
238: sgid = cpcol_sel_gid[cpid];
239: gid = icpcol_gid[cpid];
240: if (sgid >= my0 && sgid < Iend) { /* I own this deleted */
241: slid = sgid - my0;
242: PetscCall(PetscCDAppendID(agg_lists, slid, gid));
243: }
244: }
245: // done - cleanup
246: PetscCall(PetscFree(icpcol_gid));
247: PetscCall(PetscFree(cpcol_sel_gid));
248: PetscCall(PetscSFDestroy(&sf));
249: PetscCall(PetscFree(cpcol_gid));
250: PetscCall(PetscFree(cpcol_state));
251: }
252: PetscCall(PetscFree(lid_cprowID));
253: PetscCall(PetscFree(lid_removed));
254: PetscCall(PetscFree(lid_parent_gid));
255: PetscCall(PetscFree(lid_state));
257: /* MIS done - make projection matrix - P */
258: MatType jtype;
259: PetscCall(MatGetType(Gmat, &jtype));
260: PetscCall(MatCreate(comm, &Prols[iterIdx]));
261: PetscCall(MatSetType(Prols[iterIdx], jtype));
262: PetscCall(MatSetSizes(Prols[iterIdx], nloc_inner, nselected, PETSC_DETERMINE, PETSC_DETERMINE));
263: PetscCall(MatSeqAIJSetPreallocation(Prols[iterIdx], 1, NULL));
264: PetscCall(MatMPIAIJSetPreallocation(Prols[iterIdx], 1, NULL, 1, NULL));
265: {
266: PetscCDIntNd *pos, *pos2;
267: PetscInt colIndex, Iend, fgid;
268: PetscCall(MatGetOwnershipRangeColumn(Prols[iterIdx], &colIndex, &Iend));
269: // TODO - order with permutation in lid_selected (reversed)
270: for (PetscInt lid = 0; lid < agg_lists->size; lid++) {
271: PetscCall(PetscCDGetHeadPos(agg_lists, lid, &pos));
272: pos2 = pos;
273: while (pos) {
274: PetscCall(PetscCDIntNdGetID(pos, &fgid));
275: PetscCall(PetscCDGetNextPos(agg_lists, lid, &pos));
276: PetscCall(MatSetValues(Prols[iterIdx], 1, &fgid, 1, &colIndex, &one, INSERT_VALUES));
277: }
278: if (pos2) colIndex++;
279: }
280: PetscCheck(Iend == colIndex, PETSC_COMM_SELF, PETSC_ERR_SUP, "Iend!=colIndex: %d %d", (int)Iend, (int)colIndex);
281: }
282: PetscCall(MatAssemblyBegin(Prols[iterIdx], MAT_FINAL_ASSEMBLY));
283: PetscCall(MatAssemblyEnd(Prols[iterIdx], MAT_FINAL_ASSEMBLY));
284: /* project to make new graph for next MIS, skip if last */
285: if (iterIdx < misk - 1) {
286: Mat new_mat;
287: PetscCall(MatPtAP(cMat, Prols[iterIdx], MAT_INITIAL_MATRIX, PETSC_DEFAULT, &new_mat));
288: PetscCall(MatDestroy(&cMat));
289: cMat = new_mat; // next iter
290: } else if (cMat != Gmat) PetscCall(MatDestroy(&cMat));
291: // cleanup
292: PetscCall(PetscCDDestroy(agg_lists));
293: } /* MIS-k iteration */
294: /* make total prolongator Rtot = P_0 * P_1 * ... */
295: Rtot = Prols[misk - 1]; // compose P then transpose to get R
296: for (PetscInt iterIdx = misk - 1; iterIdx > 0; iterIdx--) {
297: Mat P;
298: PetscCall(MatMatMult(Prols[iterIdx - 1], Rtot, MAT_INITIAL_MATRIX, PETSC_DEFAULT, &P));
299: PetscCall(MatDestroy(&Prols[iterIdx - 1]));
300: PetscCall(MatDestroy(&Rtot));
301: Rtot = P;
302: }
303: PetscCall(MatTranspose(Rtot, MAT_INPLACE_MATRIX, &Rtot)); // R now
304: PetscCall(MatViewFromOptions(Rtot, NULL, "-misk_aggregation_view"));
305: /* make aggregates with Rtot - could use Rtot directly in theory but have to go through the aggregate list data structure */
306: {
307: PetscInt Istart, Iend, ncols, NN, MM, jj = 0, max_osz = 0;
308: const PetscInt nloc = Gmat->rmap->n;
309: PetscCoarsenData *agg_lists;
310: Mat mat;
311: PetscCall(PetscCDCreate(nloc, &agg_lists));
312: *a_locals_llist = agg_lists; // return
313: PetscCall(MatGetOwnershipRange(Rtot, &Istart, &Iend));
314: for (int grow = Istart, lid = 0; grow < Iend; grow++, lid++) {
315: const PetscInt *idx;
316: PetscCall(MatGetRow(Rtot, grow, &ncols, &idx, NULL));
317: for (int jj = 0; jj < ncols; jj++) {
318: PetscInt gcol = idx[jj];
319: PetscCall(PetscCDAppendID(agg_lists, lid, gcol)); // local row, global column
320: }
321: PetscCall(MatRestoreRow(Rtot, grow, &ncols, &idx, NULL));
322: }
323: PetscCall(MatDestroy(&Rtot));
325: /* make fake matrix, get largest nnz */
326: for (int lid = 0; lid < nloc; lid++) {
327: PetscCall(PetscCDCountAt(agg_lists, lid, &jj));
328: if (jj > max_osz) max_osz = jj;
329: }
330: PetscCall(MatGetSize(Gmat, &MM, &NN));
331: if (max_osz > MM - nloc) max_osz = MM - nloc;
332: PetscCall(MatGetOwnershipRange(Gmat, &Istart, NULL));
333: /* matrix of ghost adj for square graph */
334: PetscCall(MatCreateAIJ(comm, nloc, nloc, PETSC_DETERMINE, PETSC_DETERMINE, 0, NULL, max_osz, NULL, &mat));
335: for (PetscInt lid = 0, gidi = Istart; lid < nloc; lid++, gidi++) {
336: PetscCDIntNd *pos;
337: PetscCall(PetscCDGetHeadPos(agg_lists, lid, &pos));
338: while (pos) {
339: PetscInt gidj;
340: PetscCall(PetscCDIntNdGetID(pos, &gidj));
341: PetscCall(PetscCDGetNextPos(agg_lists, lid, &pos));
342: if (gidj < Istart || gidj >= Istart + nloc) PetscCall(MatSetValues(mat, 1, &gidi, 1, &gidj, &one, ADD_VALUES));
343: }
344: }
345: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
346: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
347: PetscCall(PetscCDSetMat(agg_lists, mat));
348: }
349: PetscFunctionReturn(PETSC_SUCCESS);
350: }
352: /*
353: Distance k MIS. k is in 'subctx'
354: */
355: static PetscErrorCode MatCoarsenApply_MISK(MatCoarsen coarse)
356: {
357: Mat mat = coarse->graph;
358: PetscInt k;
360: PetscFunctionBegin;
361: PetscCall(MatCoarsenMISKGetDistance(coarse, &k));
362: PetscCheck(k > 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "too few levels: %d", (int)k);
363: if (!coarse->perm) {
364: IS perm;
365: PetscInt n, m;
367: PetscCall(MatGetLocalSize(mat, &m, &n));
368: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), m, 0, 1, &perm));
369: PetscCall(MatCoarsenApply_MISK_private(perm, (PetscInt)k, mat, &coarse->agg_lists));
370: PetscCall(ISDestroy(&perm));
371: } else {
372: PetscCall(MatCoarsenApply_MISK_private(coarse->perm, (PetscInt)k, mat, &coarse->agg_lists));
373: }
374: PetscFunctionReturn(PETSC_SUCCESS);
375: }
377: static PetscErrorCode MatCoarsenView_MISK(MatCoarsen coarse, PetscViewer viewer)
378: {
379: PetscMPIInt rank;
380: PetscBool iascii;
382: PetscFunctionBegin;
383: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)coarse), &rank));
384: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
385: if (iascii) {
386: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
387: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, " [%d] MISK aggregator\n", rank));
388: if (!rank) PetscCall(PetscCoarsenDataView_private(coarse->agg_lists, viewer));
389: PetscCall(PetscViewerFlush(viewer));
390: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
391: }
392: PetscFunctionReturn(PETSC_SUCCESS);
393: }
395: static PetscErrorCode MatCoarsenSetFromOptions_MISK(MatCoarsen coarse, PetscOptionItems *PetscOptionsObject)
396: {
397: PetscInt k = 1;
398: PetscBool flg;
400: PetscFunctionBegin;
401: PetscOptionsHeadBegin(PetscOptionsObject, "MatCoarsen-MISk options");
402: PetscCall(PetscOptionsInt("-mat_coarsen_misk_distance", "k distance for MIS", "", k, &k, &flg));
403: if (flg) coarse->subctx = (void *)(size_t)k;
404: PetscOptionsHeadEnd();
405: PetscFunctionReturn(PETSC_SUCCESS);
406: }
408: /*MC
409: MATCOARSENMISK - A coarsener that uses MISK, a simple greedy coarsener
411: Level: beginner
413: Options Database Key:
414: . -mat_coarsen_misk_distance <k> - distance for MIS
416: .seealso: `MatCoarsen`, `MatCoarsenMISKSetDistance()`, `MatCoarsenApply()`, `MatCoarsenSetType()`, `MatCoarsenType`, `MatCoarsenCreate()`
417: M*/
419: PETSC_EXTERN PetscErrorCode MatCoarsenCreate_MISK(MatCoarsen coarse)
420: {
421: PetscFunctionBegin;
422: coarse->ops->apply = MatCoarsenApply_MISK;
423: coarse->ops->view = MatCoarsenView_MISK;
424: coarse->subctx = (void *)(size_t)1;
425: coarse->ops->setfromoptions = MatCoarsenSetFromOptions_MISK;
426: PetscFunctionReturn(PETSC_SUCCESS);
427: }
429: /*@
430: MatCoarsenMISKSetDistance - the distance to be used by MISK
432: Collective
434: Input Parameters:
435: + crs - the coarsen
436: - k - the distance
438: Options Database Key:
439: . -mat_coarsen_misk_distance <k> - distance for MIS
441: Level: advanced
443: .seealso: `MATCOARSENMISK`, `MatCoarsen`, `MatCoarsenSetFromOptions()`, `MatCoarsenSetType()`, `MatCoarsenRegister()`, `MatCoarsenCreate()`,
444: `MatCoarsenDestroy()`, `MatCoarsenSetAdjacency()`, `MatCoarsenMISKGetDistance()`
445: `MatCoarsenGetData()`
446: @*/
447: PetscErrorCode MatCoarsenMISKSetDistance(MatCoarsen crs, PetscInt k)
448: {
449: PetscFunctionBegin;
450: crs->subctx = (void *)(size_t)k;
451: PetscFunctionReturn(PETSC_SUCCESS);
452: }
454: /*@
455: MatCoarsenMISKGetDistance - gets the distance to be used by MISK
457: Collective
459: Input Parameter:
460: . crs - the coarsen
462: Output Parameter:
463: . k - the distance
465: Level: advanced
467: .seealso: `MATCOARSENMISK`, `MatCoarsen`, `MatCoarsenSetFromOptions()`, `MatCoarsenSetType()`,
468: `MatCoarsenRegister()`, `MatCoarsenCreate()`, `MatCoarsenDestroy()`,
469: `MatCoarsenSetAdjacency()`, `MatCoarsenGetData()`
470: @*/
471: PetscErrorCode MatCoarsenMISKGetDistance(MatCoarsen crs, PetscInt *k)
472: {
473: PetscFunctionBegin;
474: *k = (PetscInt)(size_t)crs->subctx;
475: PetscFunctionReturn(PETSC_SUCCESS);
476: }