Actual source code: mpisell.c
1: #include <../src/mat/impls/aij/mpi/mpiaij.h>
2: #include <../src/mat/impls/sell/mpi/mpisell.h>
3: #include <petsc/private/vecimpl.h>
4: #include <petsc/private/isimpl.h>
5: #include <petscblaslapack.h>
6: #include <petscsf.h>
8: /*MC
9: MATSELL - MATSELL = "sell" - A matrix type to be used for sparse matrices.
11: This matrix type is identical to `MATSEQSELL` when constructed with a single process communicator,
12: and `MATMPISELL` otherwise. As a result, for single process communicators,
13: `MatSeqSELLSetPreallocation()` is supported, and similarly `MatMPISELLSetPreallocation()` is supported
14: for communicators controlling multiple processes. It is recommended that you call both of
15: the above preallocation routines for simplicity.
17: Options Database Keys:
18: . -mat_type sell - sets the matrix type to `MATSELL` during a call to `MatSetFromOptions()`
20: Level: beginner
22: .seealso: `Mat`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSELL()`, `MatCreateSeqSELL()`, `MATSEQSELL`, `MATMPISELL`
23: M*/
25: static PetscErrorCode MatDiagonalSet_MPISELL(Mat Y, Vec D, InsertMode is)
26: {
27: Mat_MPISELL *sell = (Mat_MPISELL *)Y->data;
29: PetscFunctionBegin;
30: if (Y->assembled && Y->rmap->rstart == Y->cmap->rstart && Y->rmap->rend == Y->cmap->rend) {
31: PetscCall(MatDiagonalSet(sell->A, D, is));
32: } else {
33: PetscCall(MatDiagonalSet_Default(Y, D, is));
34: }
35: PetscFunctionReturn(PETSC_SUCCESS);
36: }
38: /*
39: Local utility routine that creates a mapping from the global column
40: number to the local number in the off-diagonal part of the local
41: storage of the matrix. When PETSC_USE_CTABLE is used this is scalable at
42: a slightly higher hash table cost; without it it is not scalable (each processor
43: has an order N integer array but is fast to access.
44: */
45: PetscErrorCode MatCreateColmap_MPISELL_Private(Mat mat)
46: {
47: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
48: PetscInt n = sell->B->cmap->n, i;
50: PetscFunctionBegin;
51: PetscCheck(sell->garray, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MPISELL Matrix was assembled but is missing garray");
52: #if defined(PETSC_USE_CTABLE)
53: PetscCall(PetscHMapICreateWithSize(n, &sell->colmap));
54: for (i = 0; i < n; i++) PetscCall(PetscHMapISet(sell->colmap, sell->garray[i] + 1, i + 1));
55: #else
56: PetscCall(PetscCalloc1(mat->cmap->N + 1, &sell->colmap));
57: for (i = 0; i < n; i++) sell->colmap[sell->garray[i]] = i + 1;
58: #endif
59: PetscFunctionReturn(PETSC_SUCCESS);
60: }
62: #define MatSetValues_SeqSELL_A_Private(row, col, value, addv, orow, ocol) \
63: { \
64: if (col <= lastcol1) low1 = 0; \
65: else high1 = nrow1; \
66: lastcol1 = col; \
67: while (high1 - low1 > 5) { \
68: t = (low1 + high1) / 2; \
69: if (cp1[sliceheight * t] > col) high1 = t; \
70: else low1 = t; \
71: } \
72: for (_i = low1; _i < high1; _i++) { \
73: if (cp1[sliceheight * _i] > col) break; \
74: if (cp1[sliceheight * _i] == col) { \
75: if (addv == ADD_VALUES) vp1[sliceheight * _i] += value; \
76: else vp1[sliceheight * _i] = value; \
77: inserted = PETSC_TRUE; \
78: goto a_noinsert; \
79: } \
80: } \
81: if (value == 0.0 && ignorezeroentries) { \
82: low1 = 0; \
83: high1 = nrow1; \
84: goto a_noinsert; \
85: } \
86: if (nonew == 1) { \
87: low1 = 0; \
88: high1 = nrow1; \
89: goto a_noinsert; \
90: } \
91: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
92: MatSeqXSELLReallocateSELL(A, am, 1, nrow1, a->sliidx, a->sliceheight, row / sliceheight, row, col, a->colidx, a->val, cp1, vp1, nonew, MatScalar); \
93: /* shift up all the later entries in this row */ \
94: for (ii = nrow1 - 1; ii >= _i; ii--) { \
95: cp1[sliceheight * (ii + 1)] = cp1[sliceheight * ii]; \
96: vp1[sliceheight * (ii + 1)] = vp1[sliceheight * ii]; \
97: } \
98: cp1[sliceheight * _i] = col; \
99: vp1[sliceheight * _i] = value; \
100: a->nz++; \
101: nrow1++; \
102: a_noinsert:; \
103: a->rlen[row] = nrow1; \
104: }
106: #define MatSetValues_SeqSELL_B_Private(row, col, value, addv, orow, ocol) \
107: { \
108: if (col <= lastcol2) low2 = 0; \
109: else high2 = nrow2; \
110: lastcol2 = col; \
111: while (high2 - low2 > 5) { \
112: t = (low2 + high2) / 2; \
113: if (cp2[sliceheight * t] > col) high2 = t; \
114: else low2 = t; \
115: } \
116: for (_i = low2; _i < high2; _i++) { \
117: if (cp2[sliceheight * _i] > col) break; \
118: if (cp2[sliceheight * _i] == col) { \
119: if (addv == ADD_VALUES) vp2[sliceheight * _i] += value; \
120: else vp2[sliceheight * _i] = value; \
121: inserted = PETSC_TRUE; \
122: goto b_noinsert; \
123: } \
124: } \
125: if (value == 0.0 && ignorezeroentries) { \
126: low2 = 0; \
127: high2 = nrow2; \
128: goto b_noinsert; \
129: } \
130: if (nonew == 1) { \
131: low2 = 0; \
132: high2 = nrow2; \
133: goto b_noinsert; \
134: } \
135: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", orow, ocol); \
136: MatSeqXSELLReallocateSELL(B, bm, 1, nrow2, b->sliidx, b->sliceheight, row / sliceheight, row, col, b->colidx, b->val, cp2, vp2, nonew, MatScalar); \
137: /* shift up all the later entries in this row */ \
138: for (ii = nrow2 - 1; ii >= _i; ii--) { \
139: cp2[sliceheight * (ii + 1)] = cp2[sliceheight * ii]; \
140: vp2[sliceheight * (ii + 1)] = vp2[sliceheight * ii]; \
141: } \
142: cp2[sliceheight * _i] = col; \
143: vp2[sliceheight * _i] = value; \
144: b->nz++; \
145: nrow2++; \
146: b_noinsert:; \
147: b->rlen[row] = nrow2; \
148: }
150: static PetscErrorCode MatSetValues_MPISELL(Mat mat, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode addv)
151: {
152: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
153: PetscScalar value;
154: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend, shift1, shift2;
155: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
156: PetscBool roworiented = sell->roworiented;
158: /* Some Variables required in the macro */
159: Mat A = sell->A;
160: Mat_SeqSELL *a = (Mat_SeqSELL *)A->data;
161: PetscBool ignorezeroentries = a->ignorezeroentries, found;
162: Mat B = sell->B;
163: Mat_SeqSELL *b = (Mat_SeqSELL *)B->data;
164: PetscInt *cp1, *cp2, ii, _i, nrow1, nrow2, low1, high1, low2, high2, t, lastcol1, lastcol2, sliceheight = a->sliceheight;
165: MatScalar *vp1, *vp2;
167: PetscFunctionBegin;
168: for (i = 0; i < m; i++) {
169: if (im[i] < 0) continue;
170: PetscCheck(im[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, im[i], mat->rmap->N - 1);
171: if (im[i] >= rstart && im[i] < rend) {
172: row = im[i] - rstart;
173: lastcol1 = -1;
174: shift1 = a->sliidx[row / sliceheight] + (row % sliceheight); /* starting index of the row */
175: cp1 = PetscSafePointerPlusOffset(a->colidx, shift1);
176: vp1 = PetscSafePointerPlusOffset(a->val, shift1);
177: nrow1 = a->rlen[row];
178: low1 = 0;
179: high1 = nrow1;
180: lastcol2 = -1;
181: shift2 = b->sliidx[row / sliceheight] + (row % sliceheight); /* starting index of the row */
182: cp2 = PetscSafePointerPlusOffset(b->colidx, shift2);
183: vp2 = PetscSafePointerPlusOffset(b->val, shift2);
184: nrow2 = b->rlen[row];
185: low2 = 0;
186: high2 = nrow2;
188: for (j = 0; j < n; j++) {
189: if (roworiented) value = v[i * n + j];
190: else value = v[i + j * m];
191: if (ignorezeroentries && value == 0.0 && (addv == ADD_VALUES)) continue;
192: if (in[j] >= cstart && in[j] < cend) {
193: col = in[j] - cstart;
194: MatSetValue_SeqSELL_Private(A, row, col, value, addv, im[i], in[j], cp1, vp1, lastcol1, low1, high1); /* set one value */
195: #if defined(PETSC_HAVE_CUDA)
196: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && found) A->offloadmask = PETSC_OFFLOAD_CPU;
197: #endif
198: } else if (in[j] < 0) {
199: continue;
200: } else {
201: PetscCheck(in[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[j], mat->cmap->N - 1);
202: if (mat->was_assembled) {
203: if (!sell->colmap) PetscCall(MatCreateColmap_MPISELL_Private(mat));
204: #if defined(PETSC_USE_CTABLE)
205: PetscCall(PetscHMapIGetWithDefault(sell->colmap, in[j] + 1, 0, &col));
206: col--;
207: #else
208: col = sell->colmap[in[j]] - 1;
209: #endif
210: if (col < 0 && !((Mat_SeqSELL *)sell->B->data)->nonew) {
211: PetscCall(MatDisAssemble_MPISELL(mat));
212: col = in[j];
213: /* Reinitialize the variables required by MatSetValues_SeqSELL_B_Private() */
214: B = sell->B;
215: b = (Mat_SeqSELL *)B->data;
216: shift2 = b->sliidx[row / sliceheight] + (row % sliceheight); /* starting index of the row */
217: cp2 = b->colidx + shift2;
218: vp2 = b->val + shift2;
219: nrow2 = b->rlen[row];
220: low2 = 0;
221: high2 = nrow2;
222: found = PETSC_FALSE;
223: } else {
224: PetscCheck(col >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at global row/column (%" PetscInt_FMT ", %" PetscInt_FMT ") into matrix", im[i], in[j]);
225: }
226: } else col = in[j];
227: MatSetValue_SeqSELL_Private(B, row, col, value, addv, im[i], in[j], cp2, vp2, lastcol2, low2, high2); /* set one value */
228: #if defined(PETSC_HAVE_CUDA)
229: if (B->offloadmask != PETSC_OFFLOAD_UNALLOCATED && found) B->offloadmask = PETSC_OFFLOAD_CPU;
230: #endif
231: }
232: }
233: } else {
234: PetscCheck(!mat->nooffprocentries, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Setting off process row %" PetscInt_FMT " even though MatSetOption(,MAT_NO_OFF_PROC_ENTRIES,PETSC_TRUE) was set", im[i]);
235: if (!sell->donotstash) {
236: mat->assembled = PETSC_FALSE;
237: if (roworiented) {
238: PetscCall(MatStashValuesRow_Private(&mat->stash, im[i], n, in, v + i * n, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
239: } else {
240: PetscCall(MatStashValuesCol_Private(&mat->stash, im[i], n, in, v + i, m, (PetscBool)(ignorezeroentries && (addv == ADD_VALUES))));
241: }
242: }
243: }
244: }
245: PetscFunctionReturn(PETSC_SUCCESS);
246: }
248: static PetscErrorCode MatGetValues_MPISELL(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
249: {
250: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
251: PetscInt i, j, rstart = mat->rmap->rstart, rend = mat->rmap->rend;
252: PetscInt cstart = mat->cmap->rstart, cend = mat->cmap->rend, row, col;
254: PetscFunctionBegin;
255: for (i = 0; i < m; i++) {
256: if (idxm[i] < 0) continue; /* negative row */
257: PetscCheck(idxm[i] < mat->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, idxm[i], mat->rmap->N - 1);
258: if (idxm[i] >= rstart && idxm[i] < rend) {
259: row = idxm[i] - rstart;
260: for (j = 0; j < n; j++) {
261: if (idxn[j] < 0) continue; /* negative column */
262: PetscCheck(idxn[j] < mat->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, idxn[j], mat->cmap->N - 1);
263: if (idxn[j] >= cstart && idxn[j] < cend) {
264: col = idxn[j] - cstart;
265: PetscCall(MatGetValues(sell->A, 1, &row, 1, &col, v + i * n + j));
266: } else {
267: if (!sell->colmap) PetscCall(MatCreateColmap_MPISELL_Private(mat));
268: #if defined(PETSC_USE_CTABLE)
269: PetscCall(PetscHMapIGetWithDefault(sell->colmap, idxn[j] + 1, 0, &col));
270: col--;
271: #else
272: col = sell->colmap[idxn[j]] - 1;
273: #endif
274: if ((col < 0) || (sell->garray[col] != idxn[j])) *(v + i * n + j) = 0.0;
275: else PetscCall(MatGetValues(sell->B, 1, &row, 1, &col, v + i * n + j));
276: }
277: }
278: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Only local values currently supported");
279: }
280: PetscFunctionReturn(PETSC_SUCCESS);
281: }
283: static PetscErrorCode MatAssemblyBegin_MPISELL(Mat mat, MatAssemblyType mode)
284: {
285: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
286: PetscInt nstash, reallocs;
288: PetscFunctionBegin;
289: if (sell->donotstash || mat->nooffprocentries) PetscFunctionReturn(PETSC_SUCCESS);
291: PetscCall(MatStashScatterBegin_Private(mat, &mat->stash, mat->rmap->range));
292: PetscCall(MatStashGetInfo_Private(&mat->stash, &nstash, &reallocs));
293: PetscCall(PetscInfo(sell->A, "Stash has %" PetscInt_FMT " entries, uses %" PetscInt_FMT " mallocs.\n", nstash, reallocs));
294: PetscFunctionReturn(PETSC_SUCCESS);
295: }
297: PetscErrorCode MatAssemblyEnd_MPISELL(Mat mat, MatAssemblyType mode)
298: {
299: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
300: PetscMPIInt n;
301: PetscInt i, flg;
302: PetscInt *row, *col;
303: PetscScalar *val;
304: PetscBool other_disassembled;
305: /* do not use 'b = (Mat_SeqSELL*)sell->B->data' as B can be reset in disassembly */
306: PetscFunctionBegin;
307: if (!sell->donotstash && !mat->nooffprocentries) {
308: while (1) {
309: PetscCall(MatStashScatterGetMesg_Private(&mat->stash, &n, &row, &col, &val, &flg));
310: if (!flg) break;
312: for (i = 0; i < n; i++) { /* assemble one by one */
313: PetscCall(MatSetValues_MPISELL(mat, 1, row + i, 1, col + i, val + i, mat->insertmode));
314: }
315: }
316: PetscCall(MatStashScatterEnd_Private(&mat->stash));
317: }
318: #if defined(PETSC_HAVE_CUDA)
319: if (mat->offloadmask == PETSC_OFFLOAD_CPU) sell->A->offloadmask = PETSC_OFFLOAD_CPU;
320: #endif
321: PetscCall(MatAssemblyBegin(sell->A, mode));
322: PetscCall(MatAssemblyEnd(sell->A, mode));
324: /*
325: determine if any processor has disassembled, if so we must
326: also disassemble ourselves, in order that we may reassemble.
327: */
328: /*
329: if nonzero structure of submatrix B cannot change then we know that
330: no processor disassembled thus we can skip this stuff
331: */
332: if (!((Mat_SeqSELL *)sell->B->data)->nonew) {
333: PetscCallMPI(MPIU_Allreduce(&mat->was_assembled, &other_disassembled, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)mat)));
334: if (mat->was_assembled && !other_disassembled) PetscCall(MatDisAssemble_MPISELL(mat));
335: }
336: if (!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) PetscCall(MatSetUpMultiply_MPISELL(mat));
337: #if defined(PETSC_HAVE_CUDA)
338: if (mat->offloadmask == PETSC_OFFLOAD_CPU && sell->B->offloadmask != PETSC_OFFLOAD_UNALLOCATED) sell->B->offloadmask = PETSC_OFFLOAD_CPU;
339: #endif
340: PetscCall(MatAssemblyBegin(sell->B, mode));
341: PetscCall(MatAssemblyEnd(sell->B, mode));
342: PetscCall(PetscFree2(sell->rowvalues, sell->rowindices));
343: sell->rowvalues = NULL;
344: PetscCall(VecDestroy(&sell->diag));
346: /* if no new nonzero locations are allowed in matrix then only set the matrix state the first time through */
347: if ((!mat->was_assembled && mode == MAT_FINAL_ASSEMBLY) || !((Mat_SeqSELL *)sell->A->data)->nonew) {
348: PetscObjectState state = sell->A->nonzerostate + sell->B->nonzerostate;
349: PetscCallMPI(MPIU_Allreduce(&state, &mat->nonzerostate, 1, MPIU_INT64, MPI_SUM, PetscObjectComm((PetscObject)mat)));
350: }
351: #if defined(PETSC_HAVE_CUDA)
352: mat->offloadmask = PETSC_OFFLOAD_BOTH;
353: #endif
354: PetscFunctionReturn(PETSC_SUCCESS);
355: }
357: static PetscErrorCode MatZeroEntries_MPISELL(Mat A)
358: {
359: Mat_MPISELL *l = (Mat_MPISELL *)A->data;
361: PetscFunctionBegin;
362: PetscCall(MatZeroEntries(l->A));
363: PetscCall(MatZeroEntries(l->B));
364: PetscFunctionReturn(PETSC_SUCCESS);
365: }
367: static PetscErrorCode MatMult_MPISELL(Mat A, Vec xx, Vec yy)
368: {
369: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
370: PetscInt nt;
372: PetscFunctionBegin;
373: PetscCall(VecGetLocalSize(xx, &nt));
374: PetscCheck(nt == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of A (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", A->cmap->n, nt);
375: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
376: PetscCall((*a->A->ops->mult)(a->A, xx, yy));
377: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
378: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, yy, yy));
379: PetscFunctionReturn(PETSC_SUCCESS);
380: }
382: static PetscErrorCode MatMultDiagonalBlock_MPISELL(Mat A, Vec bb, Vec xx)
383: {
384: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
386: PetscFunctionBegin;
387: PetscCall(MatMultDiagonalBlock(a->A, bb, xx));
388: PetscFunctionReturn(PETSC_SUCCESS);
389: }
391: static PetscErrorCode MatMultAdd_MPISELL(Mat A, Vec xx, Vec yy, Vec zz)
392: {
393: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
395: PetscFunctionBegin;
396: PetscCall(VecScatterBegin(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
397: PetscCall((*a->A->ops->multadd)(a->A, xx, yy, zz));
398: PetscCall(VecScatterEnd(a->Mvctx, xx, a->lvec, INSERT_VALUES, SCATTER_FORWARD));
399: PetscCall((*a->B->ops->multadd)(a->B, a->lvec, zz, zz));
400: PetscFunctionReturn(PETSC_SUCCESS);
401: }
403: static PetscErrorCode MatMultTranspose_MPISELL(Mat A, Vec xx, Vec yy)
404: {
405: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
407: PetscFunctionBegin;
408: /* do nondiagonal part */
409: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
410: /* do local part */
411: PetscCall((*a->A->ops->multtranspose)(a->A, xx, yy));
412: /* add partial results together */
413: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
414: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
415: PetscFunctionReturn(PETSC_SUCCESS);
416: }
418: static PetscErrorCode MatIsTranspose_MPISELL(Mat Amat, Mat Bmat, PetscReal tol, PetscBool *f)
419: {
420: MPI_Comm comm;
421: Mat_MPISELL *Asell = (Mat_MPISELL *)Amat->data, *Bsell;
422: Mat Adia = Asell->A, Bdia, Aoff, Boff, *Aoffs, *Boffs;
423: IS Me, Notme;
424: PetscInt M, N, first, last, *notme, i;
425: PetscMPIInt size;
427: PetscFunctionBegin;
428: /* Easy test: symmetric diagonal block */
429: Bsell = (Mat_MPISELL *)Bmat->data;
430: Bdia = Bsell->A;
431: PetscCall(MatIsTranspose(Adia, Bdia, tol, f));
432: if (!*f) PetscFunctionReturn(PETSC_SUCCESS);
433: PetscCall(PetscObjectGetComm((PetscObject)Amat, &comm));
434: PetscCallMPI(MPI_Comm_size(comm, &size));
435: if (size == 1) PetscFunctionReturn(PETSC_SUCCESS);
437: /* Hard test: off-diagonal block. This takes a MatCreateSubMatrix. */
438: PetscCall(MatGetSize(Amat, &M, &N));
439: PetscCall(MatGetOwnershipRange(Amat, &first, &last));
440: PetscCall(PetscMalloc1(N - last + first, ¬me));
441: for (i = 0; i < first; i++) notme[i] = i;
442: for (i = last; i < M; i++) notme[i - last + first] = i;
443: PetscCall(ISCreateGeneral(MPI_COMM_SELF, N - last + first, notme, PETSC_COPY_VALUES, &Notme));
444: PetscCall(ISCreateStride(MPI_COMM_SELF, last - first, first, 1, &Me));
445: PetscCall(MatCreateSubMatrices(Amat, 1, &Me, &Notme, MAT_INITIAL_MATRIX, &Aoffs));
446: Aoff = Aoffs[0];
447: PetscCall(MatCreateSubMatrices(Bmat, 1, &Notme, &Me, MAT_INITIAL_MATRIX, &Boffs));
448: Boff = Boffs[0];
449: PetscCall(MatIsTranspose(Aoff, Boff, tol, f));
450: PetscCall(MatDestroyMatrices(1, &Aoffs));
451: PetscCall(MatDestroyMatrices(1, &Boffs));
452: PetscCall(ISDestroy(&Me));
453: PetscCall(ISDestroy(&Notme));
454: PetscCall(PetscFree(notme));
455: PetscFunctionReturn(PETSC_SUCCESS);
456: }
458: static PetscErrorCode MatMultTransposeAdd_MPISELL(Mat A, Vec xx, Vec yy, Vec zz)
459: {
460: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
462: PetscFunctionBegin;
463: /* do nondiagonal part */
464: PetscCall((*a->B->ops->multtranspose)(a->B, xx, a->lvec));
465: /* do local part */
466: PetscCall((*a->A->ops->multtransposeadd)(a->A, xx, yy, zz));
467: /* add partial results together */
468: PetscCall(VecScatterBegin(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
469: PetscCall(VecScatterEnd(a->Mvctx, a->lvec, zz, ADD_VALUES, SCATTER_REVERSE));
470: PetscFunctionReturn(PETSC_SUCCESS);
471: }
473: /*
474: This only works correctly for square matrices where the subblock A->A is the
475: diagonal block
476: */
477: static PetscErrorCode MatGetDiagonal_MPISELL(Mat A, Vec v)
478: {
479: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
481: PetscFunctionBegin;
482: PetscCheck(A->rmap->N == A->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Supports only square matrix where A->A is diag block");
483: PetscCheck(A->rmap->rstart == A->cmap->rstart && A->rmap->rend == A->cmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "row partition must equal col partition");
484: PetscCall(MatGetDiagonal(a->A, v));
485: PetscFunctionReturn(PETSC_SUCCESS);
486: }
488: static PetscErrorCode MatScale_MPISELL(Mat A, PetscScalar aa)
489: {
490: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
492: PetscFunctionBegin;
493: PetscCall(MatScale(a->A, aa));
494: PetscCall(MatScale(a->B, aa));
495: PetscFunctionReturn(PETSC_SUCCESS);
496: }
498: PetscErrorCode MatDestroy_MPISELL(Mat mat)
499: {
500: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
502: PetscFunctionBegin;
503: PetscCall(PetscLogObjectState((PetscObject)mat, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT, mat->rmap->N, mat->cmap->N));
504: PetscCall(MatStashDestroy_Private(&mat->stash));
505: PetscCall(VecDestroy(&sell->diag));
506: PetscCall(MatDestroy(&sell->A));
507: PetscCall(MatDestroy(&sell->B));
508: #if defined(PETSC_USE_CTABLE)
509: PetscCall(PetscHMapIDestroy(&sell->colmap));
510: #else
511: PetscCall(PetscFree(sell->colmap));
512: #endif
513: PetscCall(PetscFree(sell->garray));
514: PetscCall(VecDestroy(&sell->lvec));
515: PetscCall(VecScatterDestroy(&sell->Mvctx));
516: PetscCall(PetscFree2(sell->rowvalues, sell->rowindices));
517: PetscCall(PetscFree(sell->ld));
518: PetscCall(PetscFree(mat->data));
520: PetscCall(PetscObjectChangeTypeName((PetscObject)mat, NULL));
521: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatStoreValues_C", NULL));
522: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatRetrieveValues_C", NULL));
523: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatIsTranspose_C", NULL));
524: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatMPISELLSetPreallocation_C", NULL));
525: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpisell_mpiaij_C", NULL));
526: #if defined(PETSC_HAVE_CUDA)
527: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatConvert_mpisell_mpisellcuda_C", NULL));
528: #endif
529: PetscCall(PetscObjectComposeFunction((PetscObject)mat, "MatDiagonalScaleLocal_C", NULL));
530: PetscFunctionReturn(PETSC_SUCCESS);
531: }
533: #include <petscdraw.h>
534: static PetscErrorCode MatView_MPISELL_ASCIIorDraworSocket(Mat mat, PetscViewer viewer)
535: {
536: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
537: PetscMPIInt rank = sell->rank, size = sell->size;
538: PetscBool isdraw, iascii, isbinary;
539: PetscViewer sviewer;
540: PetscViewerFormat format;
542: PetscFunctionBegin;
543: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
544: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
545: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
546: if (iascii) {
547: PetscCall(PetscViewerGetFormat(viewer, &format));
548: if (format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
549: MatInfo info;
550: PetscInt *inodes;
552: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)mat), &rank));
553: PetscCall(MatGetInfo(mat, MAT_LOCAL, &info));
554: PetscCall(MatInodeGetInodeSizes(sell->A, NULL, &inodes, NULL));
555: PetscCall(PetscViewerASCIIPushSynchronized(viewer));
556: if (!inodes) {
557: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %" PetscInt_FMT ", not using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used,
558: (PetscInt)info.nz_allocated, (PetscInt)info.memory));
559: } else {
560: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] Local rows %" PetscInt_FMT " nz %" PetscInt_FMT " nz alloced %" PetscInt_FMT " mem %" PetscInt_FMT ", using I-node routines\n", rank, mat->rmap->n, (PetscInt)info.nz_used,
561: (PetscInt)info.nz_allocated, (PetscInt)info.memory));
562: }
563: PetscCall(MatGetInfo(sell->A, MAT_LOCAL, &info));
564: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] on-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
565: PetscCall(MatGetInfo(sell->B, MAT_LOCAL, &info));
566: PetscCall(PetscViewerASCIISynchronizedPrintf(viewer, "[%d] off-diagonal part: nz %" PetscInt_FMT " \n", rank, (PetscInt)info.nz_used));
567: PetscCall(PetscViewerFlush(viewer));
568: PetscCall(PetscViewerASCIIPopSynchronized(viewer));
569: PetscCall(PetscViewerASCIIPrintf(viewer, "Information on VecScatter used in matrix-vector product: \n"));
570: PetscCall(VecScatterView(sell->Mvctx, viewer));
571: PetscFunctionReturn(PETSC_SUCCESS);
572: } else if (format == PETSC_VIEWER_ASCII_INFO) {
573: PetscInt inodecount, inodelimit, *inodes;
574: PetscCall(MatInodeGetInodeSizes(sell->A, &inodecount, &inodes, &inodelimit));
575: if (inodes) {
576: PetscCall(PetscViewerASCIIPrintf(viewer, "using I-node (on process 0) routines: found %" PetscInt_FMT " nodes, limit used is %" PetscInt_FMT "\n", inodecount, inodelimit));
577: } else {
578: PetscCall(PetscViewerASCIIPrintf(viewer, "not using I-node (on process 0) routines\n"));
579: }
580: PetscFunctionReturn(PETSC_SUCCESS);
581: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
582: PetscFunctionReturn(PETSC_SUCCESS);
583: }
584: } else if (isbinary) {
585: if (size == 1) {
586: PetscCall(PetscObjectSetName((PetscObject)sell->A, ((PetscObject)mat)->name));
587: PetscCall(MatView(sell->A, viewer));
588: } else {
589: /* PetscCall(MatView_MPISELL_Binary(mat,viewer)); */
590: }
591: PetscFunctionReturn(PETSC_SUCCESS);
592: } else if (isdraw) {
593: PetscDraw draw;
594: PetscBool isnull;
595: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
596: PetscCall(PetscDrawIsNull(draw, &isnull));
597: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
598: }
600: {
601: /* assemble the entire matrix onto first processor. */
602: Mat A;
603: Mat_SeqSELL *Aloc;
604: PetscInt M = mat->rmap->N, N = mat->cmap->N, *acolidx, row, col, i, j;
605: MatScalar *aval;
606: PetscBool isnonzero;
608: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &A));
609: if (rank == 0) {
610: PetscCall(MatSetSizes(A, M, N, M, N));
611: } else {
612: PetscCall(MatSetSizes(A, 0, 0, M, N));
613: }
614: /* This is just a temporary matrix, so explicitly using MATMPISELL is probably best */
615: PetscCall(MatSetType(A, MATMPISELL));
616: PetscCall(MatMPISELLSetPreallocation(A, 0, NULL, 0, NULL));
617: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE));
619: /* copy over the A part */
620: Aloc = (Mat_SeqSELL *)sell->A->data;
621: acolidx = Aloc->colidx;
622: aval = Aloc->val;
623: for (i = 0; i < Aloc->totalslices; i++) { /* loop over slices */
624: for (j = Aloc->sliidx[i]; j < Aloc->sliidx[i + 1]; j++) {
625: isnonzero = (PetscBool)((j - Aloc->sliidx[i]) / Aloc->sliceheight < Aloc->rlen[i * Aloc->sliceheight + j % Aloc->sliceheight]);
626: if (isnonzero) { /* check the mask bit */
627: row = i * Aloc->sliceheight + j % Aloc->sliceheight + mat->rmap->rstart;
628: col = *acolidx + mat->rmap->rstart;
629: PetscCall(MatSetValues(A, 1, &row, 1, &col, aval, INSERT_VALUES));
630: }
631: aval++;
632: acolidx++;
633: }
634: }
636: /* copy over the B part */
637: Aloc = (Mat_SeqSELL *)sell->B->data;
638: acolidx = Aloc->colidx;
639: aval = Aloc->val;
640: for (i = 0; i < Aloc->totalslices; i++) {
641: for (j = Aloc->sliidx[i]; j < Aloc->sliidx[i + 1]; j++) {
642: isnonzero = (PetscBool)((j - Aloc->sliidx[i]) / Aloc->sliceheight < Aloc->rlen[i * Aloc->sliceheight + j % Aloc->sliceheight]);
643: if (isnonzero) {
644: row = i * Aloc->sliceheight + j % Aloc->sliceheight + mat->rmap->rstart;
645: col = sell->garray[*acolidx];
646: PetscCall(MatSetValues(A, 1, &row, 1, &col, aval, INSERT_VALUES));
647: }
648: aval++;
649: acolidx++;
650: }
651: }
653: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
654: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
655: /*
656: Everyone has to call to draw the matrix since the graphics waits are
657: synchronized across all processors that share the PetscDraw object
658: */
659: PetscCall(PetscViewerGetSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
660: if (rank == 0) {
661: PetscCall(PetscObjectSetName((PetscObject)((Mat_MPISELL *)A->data)->A, ((PetscObject)mat)->name));
662: PetscCall(MatView_SeqSELL(((Mat_MPISELL *)A->data)->A, sviewer));
663: }
664: PetscCall(PetscViewerRestoreSubViewer(viewer, PETSC_COMM_SELF, &sviewer));
665: PetscCall(MatDestroy(&A));
666: }
667: PetscFunctionReturn(PETSC_SUCCESS);
668: }
670: static PetscErrorCode MatView_MPISELL(Mat mat, PetscViewer viewer)
671: {
672: PetscBool iascii, isdraw, issocket, isbinary;
674: PetscFunctionBegin;
675: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
676: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
677: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
678: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSOCKET, &issocket));
679: if (iascii || isdraw || isbinary || issocket) PetscCall(MatView_MPISELL_ASCIIorDraworSocket(mat, viewer));
680: PetscFunctionReturn(PETSC_SUCCESS);
681: }
683: static PetscErrorCode MatGetGhosts_MPISELL(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
684: {
685: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
687: PetscFunctionBegin;
688: PetscCall(MatGetSize(sell->B, NULL, nghosts));
689: if (ghosts) *ghosts = sell->garray;
690: PetscFunctionReturn(PETSC_SUCCESS);
691: }
693: static PetscErrorCode MatGetInfo_MPISELL(Mat matin, MatInfoType flag, MatInfo *info)
694: {
695: Mat_MPISELL *mat = (Mat_MPISELL *)matin->data;
696: Mat A = mat->A, B = mat->B;
697: PetscLogDouble isend[5], irecv[5];
699: PetscFunctionBegin;
700: info->block_size = 1.0;
701: PetscCall(MatGetInfo(A, MAT_LOCAL, info));
703: isend[0] = info->nz_used;
704: isend[1] = info->nz_allocated;
705: isend[2] = info->nz_unneeded;
706: isend[3] = info->memory;
707: isend[4] = info->mallocs;
709: PetscCall(MatGetInfo(B, MAT_LOCAL, info));
711: isend[0] += info->nz_used;
712: isend[1] += info->nz_allocated;
713: isend[2] += info->nz_unneeded;
714: isend[3] += info->memory;
715: isend[4] += info->mallocs;
716: if (flag == MAT_LOCAL) {
717: info->nz_used = isend[0];
718: info->nz_allocated = isend[1];
719: info->nz_unneeded = isend[2];
720: info->memory = isend[3];
721: info->mallocs = isend[4];
722: } else if (flag == MAT_GLOBAL_MAX) {
723: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_MAX, PetscObjectComm((PetscObject)matin)));
725: info->nz_used = irecv[0];
726: info->nz_allocated = irecv[1];
727: info->nz_unneeded = irecv[2];
728: info->memory = irecv[3];
729: info->mallocs = irecv[4];
730: } else if (flag == MAT_GLOBAL_SUM) {
731: PetscCallMPI(MPIU_Allreduce(isend, irecv, 5, MPIU_PETSCLOGDOUBLE, MPI_SUM, PetscObjectComm((PetscObject)matin)));
733: info->nz_used = irecv[0];
734: info->nz_allocated = irecv[1];
735: info->nz_unneeded = irecv[2];
736: info->memory = irecv[3];
737: info->mallocs = irecv[4];
738: }
739: info->fill_ratio_given = 0; /* no parallel LU/ILU/Cholesky */
740: info->fill_ratio_needed = 0;
741: info->factor_mallocs = 0;
742: PetscFunctionReturn(PETSC_SUCCESS);
743: }
745: static PetscErrorCode MatSetOption_MPISELL(Mat A, MatOption op, PetscBool flg)
746: {
747: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
749: PetscFunctionBegin;
750: switch (op) {
751: case MAT_NEW_NONZERO_LOCATIONS:
752: case MAT_NEW_NONZERO_ALLOCATION_ERR:
753: case MAT_UNUSED_NONZERO_LOCATION_ERR:
754: case MAT_KEEP_NONZERO_PATTERN:
755: case MAT_NEW_NONZERO_LOCATION_ERR:
756: case MAT_USE_INODES:
757: case MAT_IGNORE_ZERO_ENTRIES:
758: MatCheckPreallocated(A, 1);
759: PetscCall(MatSetOption(a->A, op, flg));
760: PetscCall(MatSetOption(a->B, op, flg));
761: break;
762: case MAT_ROW_ORIENTED:
763: MatCheckPreallocated(A, 1);
764: a->roworiented = flg;
766: PetscCall(MatSetOption(a->A, op, flg));
767: PetscCall(MatSetOption(a->B, op, flg));
768: break;
769: case MAT_FORCE_DIAGONAL_ENTRIES:
770: case MAT_SORTED_FULL:
771: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
772: break;
773: case MAT_IGNORE_OFF_PROC_ENTRIES:
774: a->donotstash = flg;
775: break;
776: case MAT_SPD:
777: case MAT_SPD_ETERNAL:
778: break;
779: case MAT_SYMMETRIC:
780: MatCheckPreallocated(A, 1);
781: PetscCall(MatSetOption(a->A, op, flg));
782: break;
783: case MAT_STRUCTURALLY_SYMMETRIC:
784: MatCheckPreallocated(A, 1);
785: PetscCall(MatSetOption(a->A, op, flg));
786: break;
787: case MAT_HERMITIAN:
788: MatCheckPreallocated(A, 1);
789: PetscCall(MatSetOption(a->A, op, flg));
790: break;
791: case MAT_SYMMETRY_ETERNAL:
792: MatCheckPreallocated(A, 1);
793: PetscCall(MatSetOption(a->A, op, flg));
794: break;
795: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
796: MatCheckPreallocated(A, 1);
797: PetscCall(MatSetOption(a->A, op, flg));
798: break;
799: default:
800: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
801: }
802: PetscFunctionReturn(PETSC_SUCCESS);
803: }
805: static PetscErrorCode MatDiagonalScale_MPISELL(Mat mat, Vec ll, Vec rr)
806: {
807: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
808: Mat a = sell->A, b = sell->B;
809: PetscInt s1, s2, s3;
811: PetscFunctionBegin;
812: PetscCall(MatGetLocalSize(mat, &s2, &s3));
813: if (rr) {
814: PetscCall(VecGetLocalSize(rr, &s1));
815: PetscCheck(s1 == s3, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "right vector non-conforming local size");
816: /* Overlap communication with computation. */
817: PetscCall(VecScatterBegin(sell->Mvctx, rr, sell->lvec, INSERT_VALUES, SCATTER_FORWARD));
818: }
819: if (ll) {
820: PetscCall(VecGetLocalSize(ll, &s1));
821: PetscCheck(s1 == s2, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "left vector non-conforming local size");
822: PetscUseTypeMethod(b, diagonalscale, ll, NULL);
823: }
824: /* scale the diagonal block */
825: PetscUseTypeMethod(a, diagonalscale, ll, rr);
827: if (rr) {
828: /* Do a scatter end and then right scale the off-diagonal block */
829: PetscCall(VecScatterEnd(sell->Mvctx, rr, sell->lvec, INSERT_VALUES, SCATTER_FORWARD));
830: PetscUseTypeMethod(b, diagonalscale, NULL, sell->lvec);
831: }
832: PetscFunctionReturn(PETSC_SUCCESS);
833: }
835: static PetscErrorCode MatSetUnfactored_MPISELL(Mat A)
836: {
837: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
839: PetscFunctionBegin;
840: PetscCall(MatSetUnfactored(a->A));
841: PetscFunctionReturn(PETSC_SUCCESS);
842: }
844: static PetscErrorCode MatEqual_MPISELL(Mat A, Mat B, PetscBool *flag)
845: {
846: Mat_MPISELL *matB = (Mat_MPISELL *)B->data, *matA = (Mat_MPISELL *)A->data;
847: Mat a, b, c, d;
848: PetscBool flg;
850: PetscFunctionBegin;
851: a = matA->A;
852: b = matA->B;
853: c = matB->A;
854: d = matB->B;
856: PetscCall(MatEqual(a, c, &flg));
857: if (flg) PetscCall(MatEqual(b, d, &flg));
858: PetscCallMPI(MPIU_Allreduce(&flg, flag, 1, MPIU_BOOL, MPI_LAND, PetscObjectComm((PetscObject)A)));
859: PetscFunctionReturn(PETSC_SUCCESS);
860: }
862: static PetscErrorCode MatCopy_MPISELL(Mat A, Mat B, MatStructure str)
863: {
864: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
865: Mat_MPISELL *b = (Mat_MPISELL *)B->data;
867: PetscFunctionBegin;
868: /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */
869: if ((str != SAME_NONZERO_PATTERN) || (A->ops->copy != B->ops->copy)) {
870: /* because of the column compression in the off-processor part of the matrix a->B,
871: the number of columns in a->B and b->B may be different, hence we cannot call
872: the MatCopy() directly on the two parts. If need be, we can provide a more
873: efficient copy than the MatCopy_Basic() by first uncompressing the a->B matrices
874: then copying the submatrices */
875: PetscCall(MatCopy_Basic(A, B, str));
876: } else {
877: PetscCall(MatCopy(a->A, b->A, str));
878: PetscCall(MatCopy(a->B, b->B, str));
879: }
880: PetscFunctionReturn(PETSC_SUCCESS);
881: }
883: static PetscErrorCode MatSetUp_MPISELL(Mat A)
884: {
885: PetscFunctionBegin;
886: PetscCall(MatMPISELLSetPreallocation(A, PETSC_DEFAULT, NULL, PETSC_DEFAULT, NULL));
887: PetscFunctionReturn(PETSC_SUCCESS);
888: }
890: static PetscErrorCode MatConjugate_MPISELL(Mat mat)
891: {
892: PetscFunctionBegin;
893: if (PetscDefined(USE_COMPLEX)) {
894: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
896: PetscCall(MatConjugate_SeqSELL(sell->A));
897: PetscCall(MatConjugate_SeqSELL(sell->B));
898: }
899: PetscFunctionReturn(PETSC_SUCCESS);
900: }
902: static PetscErrorCode MatRealPart_MPISELL(Mat A)
903: {
904: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
906: PetscFunctionBegin;
907: PetscCall(MatRealPart(a->A));
908: PetscCall(MatRealPart(a->B));
909: PetscFunctionReturn(PETSC_SUCCESS);
910: }
912: static PetscErrorCode MatImaginaryPart_MPISELL(Mat A)
913: {
914: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
916: PetscFunctionBegin;
917: PetscCall(MatImaginaryPart(a->A));
918: PetscCall(MatImaginaryPart(a->B));
919: PetscFunctionReturn(PETSC_SUCCESS);
920: }
922: static PetscErrorCode MatInvertBlockDiagonal_MPISELL(Mat A, const PetscScalar **values)
923: {
924: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
926: PetscFunctionBegin;
927: PetscCall(MatInvertBlockDiagonal(a->A, values));
928: A->factorerrortype = a->A->factorerrortype;
929: PetscFunctionReturn(PETSC_SUCCESS);
930: }
932: static PetscErrorCode MatSetRandom_MPISELL(Mat x, PetscRandom rctx)
933: {
934: Mat_MPISELL *sell = (Mat_MPISELL *)x->data;
936: PetscFunctionBegin;
937: PetscCall(MatSetRandom(sell->A, rctx));
938: PetscCall(MatSetRandom(sell->B, rctx));
939: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
940: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
941: PetscFunctionReturn(PETSC_SUCCESS);
942: }
944: static PetscErrorCode MatSetFromOptions_MPISELL(Mat A, PetscOptionItems *PetscOptionsObject)
945: {
946: PetscFunctionBegin;
947: PetscOptionsHeadBegin(PetscOptionsObject, "MPISELL options");
948: PetscOptionsHeadEnd();
949: PetscFunctionReturn(PETSC_SUCCESS);
950: }
952: static PetscErrorCode MatShift_MPISELL(Mat Y, PetscScalar a)
953: {
954: Mat_MPISELL *msell = (Mat_MPISELL *)Y->data;
955: Mat_SeqSELL *sell = (Mat_SeqSELL *)msell->A->data;
957: PetscFunctionBegin;
958: if (!Y->preallocated) {
959: PetscCall(MatMPISELLSetPreallocation(Y, 1, NULL, 0, NULL));
960: } else if (!sell->nz) {
961: PetscInt nonew = sell->nonew;
962: PetscCall(MatSeqSELLSetPreallocation(msell->A, 1, NULL));
963: sell->nonew = nonew;
964: }
965: PetscCall(MatShift_Basic(Y, a));
966: PetscFunctionReturn(PETSC_SUCCESS);
967: }
969: static PetscErrorCode MatMissingDiagonal_MPISELL(Mat A, PetscBool *missing, PetscInt *d)
970: {
971: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
973: PetscFunctionBegin;
974: PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only works for square matrices");
975: PetscCall(MatMissingDiagonal(a->A, missing, d));
976: if (d) {
977: PetscInt rstart;
978: PetscCall(MatGetOwnershipRange(A, &rstart, NULL));
979: *d += rstart;
980: }
981: PetscFunctionReturn(PETSC_SUCCESS);
982: }
984: static PetscErrorCode MatGetDiagonalBlock_MPISELL(Mat A, Mat *a)
985: {
986: PetscFunctionBegin;
987: *a = ((Mat_MPISELL *)A->data)->A;
988: PetscFunctionReturn(PETSC_SUCCESS);
989: }
991: static PetscErrorCode MatStoreValues_MPISELL(Mat mat)
992: {
993: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
995: PetscFunctionBegin;
996: PetscCall(MatStoreValues(sell->A));
997: PetscCall(MatStoreValues(sell->B));
998: PetscFunctionReturn(PETSC_SUCCESS);
999: }
1001: static PetscErrorCode MatRetrieveValues_MPISELL(Mat mat)
1002: {
1003: Mat_MPISELL *sell = (Mat_MPISELL *)mat->data;
1005: PetscFunctionBegin;
1006: PetscCall(MatRetrieveValues(sell->A));
1007: PetscCall(MatRetrieveValues(sell->B));
1008: PetscFunctionReturn(PETSC_SUCCESS);
1009: }
1011: static PetscErrorCode MatMPISELLSetPreallocation_MPISELL(Mat B, PetscInt d_rlenmax, const PetscInt d_rlen[], PetscInt o_rlenmax, const PetscInt o_rlen[])
1012: {
1013: Mat_MPISELL *b;
1015: PetscFunctionBegin;
1016: PetscCall(PetscLayoutSetUp(B->rmap));
1017: PetscCall(PetscLayoutSetUp(B->cmap));
1018: b = (Mat_MPISELL *)B->data;
1020: if (!B->preallocated) {
1021: /* Explicitly create 2 MATSEQSELL matrices. */
1022: PetscCall(MatCreate(PETSC_COMM_SELF, &b->A));
1023: PetscCall(MatSetSizes(b->A, B->rmap->n, B->cmap->n, B->rmap->n, B->cmap->n));
1024: PetscCall(MatSetBlockSizesFromMats(b->A, B, B));
1025: PetscCall(MatSetType(b->A, MATSEQSELL));
1026: PetscCall(MatCreate(PETSC_COMM_SELF, &b->B));
1027: PetscCall(MatSetSizes(b->B, B->rmap->n, B->cmap->N, B->rmap->n, B->cmap->N));
1028: PetscCall(MatSetBlockSizesFromMats(b->B, B, B));
1029: PetscCall(MatSetType(b->B, MATSEQSELL));
1030: }
1032: PetscCall(MatSeqSELLSetPreallocation(b->A, d_rlenmax, d_rlen));
1033: PetscCall(MatSeqSELLSetPreallocation(b->B, o_rlenmax, o_rlen));
1034: B->preallocated = PETSC_TRUE;
1035: B->was_assembled = PETSC_FALSE;
1036: /*
1037: critical for MatAssemblyEnd to work.
1038: MatAssemblyBegin checks it to set up was_assembled
1039: and MatAssemblyEnd checks was_assembled to determine whether to build garray
1040: */
1041: B->assembled = PETSC_FALSE;
1042: PetscFunctionReturn(PETSC_SUCCESS);
1043: }
1045: static PetscErrorCode MatDuplicate_MPISELL(Mat matin, MatDuplicateOption cpvalues, Mat *newmat)
1046: {
1047: Mat mat;
1048: Mat_MPISELL *a, *oldmat = (Mat_MPISELL *)matin->data;
1050: PetscFunctionBegin;
1051: *newmat = NULL;
1052: PetscCall(MatCreate(PetscObjectComm((PetscObject)matin), &mat));
1053: PetscCall(MatSetSizes(mat, matin->rmap->n, matin->cmap->n, matin->rmap->N, matin->cmap->N));
1054: PetscCall(MatSetBlockSizesFromMats(mat, matin, matin));
1055: PetscCall(MatSetType(mat, ((PetscObject)matin)->type_name));
1056: a = (Mat_MPISELL *)mat->data;
1058: mat->factortype = matin->factortype;
1059: mat->assembled = PETSC_TRUE;
1060: mat->insertmode = NOT_SET_VALUES;
1061: mat->preallocated = PETSC_TRUE;
1063: a->size = oldmat->size;
1064: a->rank = oldmat->rank;
1065: a->donotstash = oldmat->donotstash;
1066: a->roworiented = oldmat->roworiented;
1067: a->rowindices = NULL;
1068: a->rowvalues = NULL;
1069: a->getrowactive = PETSC_FALSE;
1071: PetscCall(PetscLayoutReference(matin->rmap, &mat->rmap));
1072: PetscCall(PetscLayoutReference(matin->cmap, &mat->cmap));
1074: if (oldmat->colmap) {
1075: #if defined(PETSC_USE_CTABLE)
1076: PetscCall(PetscHMapIDuplicate(oldmat->colmap, &a->colmap));
1077: #else
1078: PetscCall(PetscMalloc1(mat->cmap->N, &a->colmap));
1079: PetscCall(PetscArraycpy(a->colmap, oldmat->colmap, mat->cmap->N));
1080: #endif
1081: } else a->colmap = NULL;
1082: if (oldmat->garray) {
1083: PetscInt len;
1084: len = oldmat->B->cmap->n;
1085: PetscCall(PetscMalloc1(len + 1, &a->garray));
1086: if (len) PetscCall(PetscArraycpy(a->garray, oldmat->garray, len));
1087: } else a->garray = NULL;
1089: PetscCall(VecDuplicate(oldmat->lvec, &a->lvec));
1090: PetscCall(VecScatterCopy(oldmat->Mvctx, &a->Mvctx));
1091: PetscCall(MatDuplicate(oldmat->A, cpvalues, &a->A));
1092: PetscCall(MatDuplicate(oldmat->B, cpvalues, &a->B));
1093: PetscCall(PetscFunctionListDuplicate(((PetscObject)matin)->qlist, &((PetscObject)mat)->qlist));
1094: *newmat = mat;
1095: PetscFunctionReturn(PETSC_SUCCESS);
1096: }
1098: static const struct _MatOps MatOps_Values = {MatSetValues_MPISELL,
1099: NULL,
1100: NULL,
1101: MatMult_MPISELL,
1102: /* 4*/ MatMultAdd_MPISELL,
1103: MatMultTranspose_MPISELL,
1104: MatMultTransposeAdd_MPISELL,
1105: NULL,
1106: NULL,
1107: NULL,
1108: /*10*/ NULL,
1109: NULL,
1110: NULL,
1111: MatSOR_MPISELL,
1112: NULL,
1113: /*15*/ MatGetInfo_MPISELL,
1114: MatEqual_MPISELL,
1115: MatGetDiagonal_MPISELL,
1116: MatDiagonalScale_MPISELL,
1117: NULL,
1118: /*20*/ MatAssemblyBegin_MPISELL,
1119: MatAssemblyEnd_MPISELL,
1120: MatSetOption_MPISELL,
1121: MatZeroEntries_MPISELL,
1122: /*24*/ NULL,
1123: NULL,
1124: NULL,
1125: NULL,
1126: NULL,
1127: /*29*/ MatSetUp_MPISELL,
1128: NULL,
1129: NULL,
1130: MatGetDiagonalBlock_MPISELL,
1131: NULL,
1132: /*34*/ MatDuplicate_MPISELL,
1133: NULL,
1134: NULL,
1135: NULL,
1136: NULL,
1137: /*39*/ NULL,
1138: NULL,
1139: NULL,
1140: MatGetValues_MPISELL,
1141: MatCopy_MPISELL,
1142: /*44*/ NULL,
1143: MatScale_MPISELL,
1144: MatShift_MPISELL,
1145: MatDiagonalSet_MPISELL,
1146: NULL,
1147: /*49*/ MatSetRandom_MPISELL,
1148: NULL,
1149: NULL,
1150: NULL,
1151: NULL,
1152: /*54*/ MatFDColoringCreate_MPIXAIJ,
1153: NULL,
1154: MatSetUnfactored_MPISELL,
1155: NULL,
1156: NULL,
1157: /*59*/ NULL,
1158: MatDestroy_MPISELL,
1159: MatView_MPISELL,
1160: NULL,
1161: NULL,
1162: /*64*/ NULL,
1163: NULL,
1164: NULL,
1165: NULL,
1166: NULL,
1167: /*69*/ NULL,
1168: NULL,
1169: NULL,
1170: NULL,
1171: NULL,
1172: NULL,
1173: /*75*/ MatFDColoringApply_AIJ, /* reuse AIJ function */
1174: MatSetFromOptions_MPISELL,
1175: NULL,
1176: NULL,
1177: NULL,
1178: /*80*/ NULL,
1179: NULL,
1180: NULL,
1181: /*83*/ NULL,
1182: NULL,
1183: NULL,
1184: NULL,
1185: NULL,
1186: NULL,
1187: /*89*/ NULL,
1188: NULL,
1189: NULL,
1190: NULL,
1191: NULL,
1192: /*94*/ NULL,
1193: NULL,
1194: NULL,
1195: NULL,
1196: NULL,
1197: /*99*/ NULL,
1198: NULL,
1199: NULL,
1200: MatConjugate_MPISELL,
1201: NULL,
1202: /*104*/ NULL,
1203: MatRealPart_MPISELL,
1204: MatImaginaryPart_MPISELL,
1205: NULL,
1206: NULL,
1207: /*109*/ NULL,
1208: NULL,
1209: NULL,
1210: NULL,
1211: MatMissingDiagonal_MPISELL,
1212: /*114*/ NULL,
1213: NULL,
1214: MatGetGhosts_MPISELL,
1215: NULL,
1216: NULL,
1217: /*119*/ MatMultDiagonalBlock_MPISELL,
1218: NULL,
1219: NULL,
1220: NULL,
1221: NULL,
1222: /*124*/ NULL,
1223: NULL,
1224: MatInvertBlockDiagonal_MPISELL,
1225: NULL,
1226: NULL,
1227: /*129*/ NULL,
1228: NULL,
1229: NULL,
1230: NULL,
1231: NULL,
1232: /*134*/ NULL,
1233: NULL,
1234: NULL,
1235: NULL,
1236: NULL,
1237: /*139*/ NULL,
1238: NULL,
1239: NULL,
1240: MatFDColoringSetUp_MPIXAIJ,
1241: NULL,
1242: /*144*/ NULL,
1243: NULL,
1244: NULL,
1245: NULL,
1246: NULL,
1247: NULL,
1248: /*150*/ NULL,
1249: NULL,
1250: NULL,
1251: NULL,
1252: NULL,
1253: /*155*/ NULL,
1254: NULL};
1256: /*@C
1257: MatMPISELLSetPreallocation - Preallocates memory for a `MATMPISELL` sparse parallel matrix in sell format.
1258: For good matrix assembly performance the user should preallocate the matrix storage by
1259: setting the parameters `d_nz` (or `d_nnz`) and `o_nz` (or `o_nnz`).
1261: Collective
1263: Input Parameters:
1264: + B - the matrix
1265: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
1266: (same value is used for all local rows)
1267: . d_nnz - array containing the number of nonzeros in the various rows of the
1268: DIAGONAL portion of the local submatrix (possibly different for each row)
1269: or NULL (`PETSC_NULL_INTEGER` in Fortran), if `d_nz` is used to specify the nonzero structure.
1270: The size of this array is equal to the number of local rows, i.e 'm'.
1271: For matrices that will be factored, you must leave room for (and set)
1272: the diagonal entry even if it is zero.
1273: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
1274: submatrix (same value is used for all local rows).
1275: - o_nnz - array containing the number of nonzeros in the various rows of the
1276: OFF-DIAGONAL portion of the local submatrix (possibly different for
1277: each row) or NULL (`PETSC_NULL_INTEGER` in Fortran), if `o_nz` is used to specify the nonzero
1278: structure. The size of this array is equal to the number
1279: of local rows, i.e 'm'.
1281: Example usage:
1282: Consider the following 8x8 matrix with 34 non-zero values, that is
1283: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
1284: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
1285: as follows
1287: .vb
1288: 1 2 0 | 0 3 0 | 0 4
1289: Proc0 0 5 6 | 7 0 0 | 8 0
1290: 9 0 10 | 11 0 0 | 12 0
1291: -------------------------------------
1292: 13 0 14 | 15 16 17 | 0 0
1293: Proc1 0 18 0 | 19 20 21 | 0 0
1294: 0 0 0 | 22 23 0 | 24 0
1295: -------------------------------------
1296: Proc2 25 26 27 | 0 0 28 | 29 0
1297: 30 0 0 | 31 32 33 | 0 34
1298: .ve
1300: This can be represented as a collection of submatrices as
1302: .vb
1303: A B C
1304: D E F
1305: G H I
1306: .ve
1308: Where the submatrices A,B,C are owned by proc0, D,E,F are
1309: owned by proc1, G,H,I are owned by proc2.
1311: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
1312: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
1313: The 'M','N' parameters are 8,8, and have the same values on all procs.
1315: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
1316: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
1317: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
1318: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
1319: part as `MATSEQSELL` matrices. For example, proc1 will store [E] as a `MATSEQSELL`
1320: matrix, ans [DF] as another SeqSELL matrix.
1322: When `d_nz`, `o_nz` parameters are specified, `d_nz` storage elements are
1323: allocated for every row of the local diagonal submatrix, and o_nz
1324: storage locations are allocated for every row of the OFF-DIAGONAL submat.
1325: One way to choose `d_nz` and `o_nz` is to use the max nonzerors per local
1326: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
1327: In this case, the values of d_nz,o_nz are
1328: .vb
1329: proc0 dnz = 2, o_nz = 2
1330: proc1 dnz = 3, o_nz = 2
1331: proc2 dnz = 1, o_nz = 4
1332: .ve
1333: We are allocating m*(d_nz+o_nz) storage locations for every proc. This
1334: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
1335: for proc3. i.e we are using 12+15+10=37 storage locations to store
1336: 34 values.
1338: When `d_nnz`, `o_nnz` parameters are specified, the storage is specified
1339: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
1340: In the above case the values for d_nnz,o_nnz are
1341: .vb
1342: proc0 d_nnz = [2,2,2] and o_nnz = [2,2,2]
1343: proc1 d_nnz = [3,3,2] and o_nnz = [2,1,1]
1344: proc2 d_nnz = [1,1] and o_nnz = [4,4]
1345: .ve
1346: Here the space allocated is according to nz (or maximum values in the nnz
1347: if nnz is provided) for DIAGONAL and OFF-DIAGONAL submatrices, i.e (2+2+3+2)*3+(1+4)*2=37
1349: Level: intermediate
1351: Notes:
1352: If the *_nnz parameter is given then the *_nz parameter is ignored
1354: The stored row and column indices begin with zero.
1356: The parallel matrix is partitioned such that the first m0 rows belong to
1357: process 0, the next m1 rows belong to process 1, the next m2 rows belong
1358: to process 2 etc.. where m0,m1,m2... are the input parameter 'm'.
1360: The DIAGONAL portion of the local submatrix of a processor can be defined
1361: as the submatrix which is obtained by extraction the part corresponding to
1362: the rows r1-r2 and columns c1-c2 of the global matrix, where r1 is the
1363: first row that belongs to the processor, r2 is the last row belonging to
1364: the this processor, and c1-c2 is range of indices of the local part of a
1365: vector suitable for applying the matrix to. This is an mxn matrix. In the
1366: common case of a square matrix, the row and column ranges are the same and
1367: the DIAGONAL part is also square. The remaining portion of the local
1368: submatrix (mxN) constitute the OFF-DIAGONAL portion.
1370: If `o_nnz`, `d_nnz` are specified, then `o_nz`, and `d_nz` are ignored.
1372: You can call `MatGetInfo()` to get information on how effective the preallocation was;
1373: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
1374: You can also run with the option -info and look for messages with the string
1375: malloc in them to see if additional memory allocation was needed.
1377: .seealso: `Mat`, `MatCreate()`, `MatCreateSeqSELL()`, `MatSetValues()`, `MatCreateSELL()`,
1378: `MATMPISELL`, `MatGetInfo()`, `PetscSplitOwnership()`, `MATSELL`
1379: @*/
1380: PetscErrorCode MatMPISELLSetPreallocation(Mat B, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
1381: {
1382: PetscFunctionBegin;
1385: PetscTryMethod(B, "MatMPISELLSetPreallocation_C", (Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[]), (B, d_nz, d_nnz, o_nz, o_nnz));
1386: PetscFunctionReturn(PETSC_SUCCESS);
1387: }
1389: /*MC
1390: MATMPISELL - MATMPISELL = "mpisell" - A matrix type to be used for MPI sparse matrices,
1391: based on the sliced Ellpack format
1393: Options Database Key:
1394: . -mat_type sell - sets the matrix type to `MATSELL` during a call to `MatSetFromOptions()`
1396: Level: beginner
1398: .seealso: `Mat`, `MatCreateSELL()`, `MATSEQSELL`, `MATSELL`, `MATSEQAIJ`, `MATAIJ`, `MATMPIAIJ`
1399: M*/
1401: /*@C
1402: MatCreateSELL - Creates a sparse parallel matrix in `MATSELL` format.
1404: Collective
1406: Input Parameters:
1407: + comm - MPI communicator
1408: . m - number of local rows (or `PETSC_DECIDE` to have calculated if M is given)
1409: This value should be the same as the local size used in creating the
1410: y vector for the matrix-vector product y = Ax.
1411: . n - This value should be the same as the local size used in creating the
1412: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
1413: calculated if `N` is given) For square matrices n is almost always `m`.
1414: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
1415: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
1416: . d_rlenmax - max number of nonzeros per row in DIAGONAL portion of local submatrix
1417: (same value is used for all local rows)
1418: . d_rlen - array containing the number of nonzeros in the various rows of the
1419: DIAGONAL portion of the local submatrix (possibly different for each row)
1420: or `NULL`, if d_rlenmax is used to specify the nonzero structure.
1421: The size of this array is equal to the number of local rows, i.e `m`.
1422: . o_rlenmax - max number of nonzeros per row in the OFF-DIAGONAL portion of local
1423: submatrix (same value is used for all local rows).
1424: - o_rlen - array containing the number of nonzeros in the various rows of the
1425: OFF-DIAGONAL portion of the local submatrix (possibly different for
1426: each row) or `NULL`, if `o_rlenmax` is used to specify the nonzero
1427: structure. The size of this array is equal to the number
1428: of local rows, i.e `m`.
1430: Output Parameter:
1431: . A - the matrix
1433: Options Database Key:
1434: . -mat_sell_oneindex - Internally use indexing starting at 1
1435: rather than 0. When calling `MatSetValues()`,
1436: the user still MUST index entries starting at 0!
1438: Example:
1439: Consider the following 8x8 matrix with 34 non-zero values, that is
1440: assembled across 3 processors. Lets assume that proc0 owns 3 rows,
1441: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
1442: as follows
1444: .vb
1445: 1 2 0 | 0 3 0 | 0 4
1446: Proc0 0 5 6 | 7 0 0 | 8 0
1447: 9 0 10 | 11 0 0 | 12 0
1448: -------------------------------------
1449: 13 0 14 | 15 16 17 | 0 0
1450: Proc1 0 18 0 | 19 20 21 | 0 0
1451: 0 0 0 | 22 23 0 | 24 0
1452: -------------------------------------
1453: Proc2 25 26 27 | 0 0 28 | 29 0
1454: 30 0 0 | 31 32 33 | 0 34
1455: .ve
1457: This can be represented as a collection of submatrices as
1458: .vb
1459: A B C
1460: D E F
1461: G H I
1462: .ve
1464: Where the submatrices A,B,C are owned by proc0, D,E,F are
1465: owned by proc1, G,H,I are owned by proc2.
1467: The 'm' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
1468: The 'n' parameters for proc0,proc1,proc2 are 3,3,2 respectively.
1469: The 'M','N' parameters are 8,8, and have the same values on all procs.
1471: The DIAGONAL submatrices corresponding to proc0,proc1,proc2 are
1472: submatrices [A], [E], [I] respectively. The OFF-DIAGONAL submatrices
1473: corresponding to proc0,proc1,proc2 are [BC], [DF], [GH] respectively.
1474: Internally, each processor stores the DIAGONAL part, and the OFF-DIAGONAL
1475: part as `MATSEQSELL` matrices. For example, proc1 will store [E] as a `MATSEQSELL`
1476: matrix, ans [DF] as another `MATSEQSELL` matrix.
1478: When d_rlenmax, o_rlenmax parameters are specified, d_rlenmax storage elements are
1479: allocated for every row of the local diagonal submatrix, and o_rlenmax
1480: storage locations are allocated for every row of the OFF-DIAGONAL submat.
1481: One way to choose d_rlenmax and o_rlenmax is to use the max nonzerors per local
1482: rows for each of the local DIAGONAL, and the OFF-DIAGONAL submatrices.
1483: In this case, the values of d_rlenmax,o_rlenmax are
1484: .vb
1485: proc0 - d_rlenmax = 2, o_rlenmax = 2
1486: proc1 - d_rlenmax = 3, o_rlenmax = 2
1487: proc2 - d_rlenmax = 1, o_rlenmax = 4
1488: .ve
1489: We are allocating m*(d_rlenmax+o_rlenmax) storage locations for every proc. This
1490: translates to 3*(2+2)=12 for proc0, 3*(3+2)=15 for proc1, 2*(1+4)=10
1491: for proc3. i.e we are using 12+15+10=37 storage locations to store
1492: 34 values.
1494: When `d_rlen`, `o_rlen` parameters are specified, the storage is specified
1495: for every row, corresponding to both DIAGONAL and OFF-DIAGONAL submatrices.
1496: In the above case the values for `d_nnz`, `o_nnz` are
1497: .vb
1498: proc0 - d_nnz = [2,2,2] and o_nnz = [2,2,2]
1499: proc1 - d_nnz = [3,3,2] and o_nnz = [2,1,1]
1500: proc2 - d_nnz = [1,1] and o_nnz = [4,4]
1501: .ve
1502: Here the space allocated is still 37 though there are 34 nonzeros because
1503: the allocation is always done according to rlenmax.
1505: Level: intermediate
1507: Notes:
1508: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
1509: MatXXXXSetPreallocation() paradigm instead of this routine directly.
1510: [MatXXXXSetPreallocation() is, for example, `MatSeqSELLSetPreallocation()`]
1512: If the *_rlen parameter is given then the *_rlenmax parameter is ignored
1514: `m`, `n`, `M`, `N` parameters specify the size of the matrix, and its partitioning across
1515: processors, while `d_rlenmax`, `d_rlen`, `o_rlenmax` , `o_rlen` parameters specify the approximate
1516: storage requirements for this matrix.
1518: If `PETSC_DECIDE` or `PETSC_DETERMINE` is used for a particular argument on one
1519: processor than it must be used on all processors that share the object for
1520: that argument.
1522: The user MUST specify either the local or global matrix dimensions
1523: (possibly both).
1525: The parallel matrix is partitioned across processors such that the
1526: first m0 rows belong to process 0, the next m1 rows belong to
1527: process 1, the next m2 rows belong to process 2 etc.. where
1528: m0,m1,m2,.. are the input parameter 'm'. i.e each processor stores
1529: values corresponding to [`m` x `N`] submatrix.
1531: The columns are logically partitioned with the n0 columns belonging
1532: to 0th partition, the next n1 columns belonging to the next
1533: partition etc.. where n0,n1,n2... are the input parameter `n`.
1535: The DIAGONAL portion of the local submatrix on any given processor
1536: is the submatrix corresponding to the rows and columns `m`, `n`
1537: corresponding to the given processor. i.e diagonal matrix on
1538: process 0 is [m0 x n0], diagonal matrix on process 1 is [m1 x n1]
1539: etc. The remaining portion of the local submatrix [m x (N-n)]
1540: constitute the OFF-DIAGONAL portion. The example below better
1541: illustrates this concept.
1543: For a square global matrix we define each processor's diagonal portion
1544: to be its local rows and the corresponding columns (a square submatrix);
1545: each processor's off-diagonal portion encompasses the remainder of the
1546: local matrix (a rectangular submatrix).
1548: If `o_rlen`, `d_rlen` are specified, then `o_rlenmax`, and `d_rlenmax` are ignored.
1550: When calling this routine with a single process communicator, a matrix of
1551: type `MATSEQSELL` is returned. If a matrix of type `MATMPISELL` is desired for this
1552: type of communicator, use the construction mechanism
1553: .vb
1554: MatCreate(...,&A);
1555: MatSetType(A,MATMPISELL);
1556: MatSetSizes(A, m,n,M,N);
1557: MatMPISELLSetPreallocation(A,...);
1558: .ve
1560: .seealso: `Mat`, `MATSELL`, `MatCreate()`, `MatCreateSeqSELL()`, `MatSetValues()`, `MatMPISELLSetPreallocation()`, `MATMPISELL`
1561: @*/
1562: PetscErrorCode MatCreateSELL(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_rlenmax, const PetscInt d_rlen[], PetscInt o_rlenmax, const PetscInt o_rlen[], Mat *A)
1563: {
1564: PetscMPIInt size;
1566: PetscFunctionBegin;
1567: PetscCall(MatCreate(comm, A));
1568: PetscCall(MatSetSizes(*A, m, n, M, N));
1569: PetscCallMPI(MPI_Comm_size(comm, &size));
1570: if (size > 1) {
1571: PetscCall(MatSetType(*A, MATMPISELL));
1572: PetscCall(MatMPISELLSetPreallocation(*A, d_rlenmax, d_rlen, o_rlenmax, o_rlen));
1573: } else {
1574: PetscCall(MatSetType(*A, MATSEQSELL));
1575: PetscCall(MatSeqSELLSetPreallocation(*A, d_rlenmax, d_rlen));
1576: }
1577: PetscFunctionReturn(PETSC_SUCCESS);
1578: }
1580: /*@C
1581: MatMPISELLGetSeqSELL - Returns the local pieces of this distributed matrix
1583: Not Collective
1585: Input Parameter:
1586: . A - the `MATMPISELL` matrix
1588: Output Parameters:
1589: + Ad - The diagonal portion of `A`
1590: . Ao - The off-diagonal portion of `A`
1591: - colmap - An array mapping local column numbers of `Ao` to global column numbers of the parallel matrix
1593: Level: advanced
1595: .seealso: `Mat`, `MATSEQSELL`, `MATMPISELL`
1596: @*/
1597: PetscErrorCode MatMPISELLGetSeqSELL(Mat A, Mat *Ad, Mat *Ao, const PetscInt *colmap[])
1598: {
1599: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
1600: PetscBool flg;
1602: PetscFunctionBegin;
1603: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPISELL, &flg));
1604: PetscCheck(flg, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "This function requires a MATMPISELL matrix as input");
1605: if (Ad) *Ad = a->A;
1606: if (Ao) *Ao = a->B;
1607: if (colmap) *colmap = a->garray;
1608: PetscFunctionReturn(PETSC_SUCCESS);
1609: }
1611: /*@C
1612: MatMPISELLGetLocalMatCondensed - Creates a `MATSEQSELL` matrix from an `MATMPISELL` matrix by
1613: taking all its local rows and NON-ZERO columns
1615: Not Collective
1617: Input Parameters:
1618: + A - the matrix
1619: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
1620: . row - index sets of rows to extract (or `NULL`)
1621: - col - index sets of columns to extract (or `NULL`)
1623: Output Parameter:
1624: . A_loc - the local sequential matrix generated
1626: Level: advanced
1628: .seealso: `Mat`, `MATSEQSELL`, `MATMPISELL`, `MatGetOwnershipRange()`, `MatMPISELLGetLocalMat()`
1629: @*/
1630: PetscErrorCode MatMPISELLGetLocalMatCondensed(Mat A, MatReuse scall, IS *row, IS *col, Mat *A_loc)
1631: {
1632: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
1633: PetscInt i, start, end, ncols, nzA, nzB, *cmap, imark, *idx;
1634: IS isrowa, iscola;
1635: Mat *aloc;
1636: PetscBool match;
1638: PetscFunctionBegin;
1639: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPISELL, &match));
1640: PetscCheck(match, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Requires MATMPISELL matrix as input");
1641: PetscCall(PetscLogEventBegin(MAT_Getlocalmatcondensed, A, 0, 0, 0));
1642: if (!row) {
1643: start = A->rmap->rstart;
1644: end = A->rmap->rend;
1645: PetscCall(ISCreateStride(PETSC_COMM_SELF, end - start, start, 1, &isrowa));
1646: } else {
1647: isrowa = *row;
1648: }
1649: if (!col) {
1650: start = A->cmap->rstart;
1651: cmap = a->garray;
1652: nzA = a->A->cmap->n;
1653: nzB = a->B->cmap->n;
1654: PetscCall(PetscMalloc1(nzA + nzB, &idx));
1655: ncols = 0;
1656: for (i = 0; i < nzB; i++) {
1657: if (cmap[i] < start) idx[ncols++] = cmap[i];
1658: else break;
1659: }
1660: imark = i;
1661: for (i = 0; i < nzA; i++) idx[ncols++] = start + i;
1662: for (i = imark; i < nzB; i++) idx[ncols++] = cmap[i];
1663: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, ncols, idx, PETSC_OWN_POINTER, &iscola));
1664: } else {
1665: iscola = *col;
1666: }
1667: if (scall != MAT_INITIAL_MATRIX) {
1668: PetscCall(PetscMalloc1(1, &aloc));
1669: aloc[0] = *A_loc;
1670: }
1671: PetscCall(MatCreateSubMatrices(A, 1, &isrowa, &iscola, scall, &aloc));
1672: *A_loc = aloc[0];
1673: PetscCall(PetscFree(aloc));
1674: if (!row) PetscCall(ISDestroy(&isrowa));
1675: if (!col) PetscCall(ISDestroy(&iscola));
1676: PetscCall(PetscLogEventEnd(MAT_Getlocalmatcondensed, A, 0, 0, 0));
1677: PetscFunctionReturn(PETSC_SUCCESS);
1678: }
1680: #include <../src/mat/impls/aij/mpi/mpiaij.h>
1682: PetscErrorCode MatConvert_MPISELL_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
1683: {
1684: Mat_MPISELL *a = (Mat_MPISELL *)A->data;
1685: Mat B;
1686: Mat_MPIAIJ *b;
1688: PetscFunctionBegin;
1689: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled");
1691: if (reuse == MAT_REUSE_MATRIX) {
1692: B = *newmat;
1693: } else {
1694: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1695: PetscCall(MatSetType(B, MATMPIAIJ));
1696: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
1697: PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs));
1698: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
1699: PetscCall(MatMPIAIJSetPreallocation(B, 0, NULL, 0, NULL));
1700: }
1701: b = (Mat_MPIAIJ *)B->data;
1703: if (reuse == MAT_REUSE_MATRIX) {
1704: PetscCall(MatConvert_SeqSELL_SeqAIJ(a->A, MATSEQAIJ, MAT_REUSE_MATRIX, &b->A));
1705: PetscCall(MatConvert_SeqSELL_SeqAIJ(a->B, MATSEQAIJ, MAT_REUSE_MATRIX, &b->B));
1706: } else {
1707: PetscCall(MatDestroy(&b->A));
1708: PetscCall(MatDestroy(&b->B));
1709: PetscCall(MatDisAssemble_MPISELL(A));
1710: PetscCall(MatConvert_SeqSELL_SeqAIJ(a->A, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->A));
1711: PetscCall(MatConvert_SeqSELL_SeqAIJ(a->B, MATSEQAIJ, MAT_INITIAL_MATRIX, &b->B));
1712: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1713: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1714: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1715: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1716: }
1718: if (reuse == MAT_INPLACE_MATRIX) {
1719: PetscCall(MatHeaderReplace(A, &B));
1720: } else {
1721: *newmat = B;
1722: }
1723: PetscFunctionReturn(PETSC_SUCCESS);
1724: }
1726: PetscErrorCode MatConvert_MPIAIJ_MPISELL(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
1727: {
1728: Mat_MPIAIJ *a = (Mat_MPIAIJ *)A->data;
1729: Mat B;
1730: Mat_MPISELL *b;
1732: PetscFunctionBegin;
1733: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Matrix must be assembled");
1735: if (reuse == MAT_REUSE_MATRIX) {
1736: B = *newmat;
1737: } else {
1738: Mat_SeqAIJ *Aa = (Mat_SeqAIJ *)a->A->data, *Ba = (Mat_SeqAIJ *)a->B->data;
1739: PetscInt i, d_nz = 0, o_nz = 0, m = A->rmap->N, n = A->cmap->N, lm = A->rmap->n, ln = A->cmap->n;
1740: PetscInt *d_nnz, *o_nnz;
1741: PetscCall(PetscMalloc2(lm, &d_nnz, lm, &o_nnz));
1742: for (i = 0; i < lm; i++) {
1743: d_nnz[i] = Aa->i[i + 1] - Aa->i[i];
1744: o_nnz[i] = Ba->i[i + 1] - Ba->i[i];
1745: if (d_nnz[i] > d_nz) d_nz = d_nnz[i];
1746: if (o_nnz[i] > o_nz) o_nz = o_nnz[i];
1747: }
1748: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1749: PetscCall(MatSetType(B, MATMPISELL));
1750: PetscCall(MatSetSizes(B, lm, ln, m, n));
1751: PetscCall(MatSetBlockSizes(B, A->rmap->bs, A->cmap->bs));
1752: PetscCall(MatSeqSELLSetPreallocation(B, d_nz, d_nnz));
1753: PetscCall(MatMPISELLSetPreallocation(B, d_nz, d_nnz, o_nz, o_nnz));
1754: PetscCall(PetscFree2(d_nnz, o_nnz));
1755: }
1756: b = (Mat_MPISELL *)B->data;
1758: if (reuse == MAT_REUSE_MATRIX) {
1759: PetscCall(MatConvert_SeqAIJ_SeqSELL(a->A, MATSEQSELL, MAT_REUSE_MATRIX, &b->A));
1760: PetscCall(MatConvert_SeqAIJ_SeqSELL(a->B, MATSEQSELL, MAT_REUSE_MATRIX, &b->B));
1761: } else {
1762: PetscCall(MatDestroy(&b->A));
1763: PetscCall(MatDestroy(&b->B));
1764: PetscCall(MatConvert_SeqAIJ_SeqSELL(a->A, MATSEQSELL, MAT_INITIAL_MATRIX, &b->A));
1765: PetscCall(MatConvert_SeqAIJ_SeqSELL(a->B, MATSEQSELL, MAT_INITIAL_MATRIX, &b->B));
1766: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1767: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1768: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
1769: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
1770: }
1772: if (reuse == MAT_INPLACE_MATRIX) {
1773: PetscCall(MatHeaderReplace(A, &B));
1774: } else {
1775: *newmat = B;
1776: }
1777: PetscFunctionReturn(PETSC_SUCCESS);
1778: }
1780: PetscErrorCode MatSOR_MPISELL(Mat matin, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1781: {
1782: Mat_MPISELL *mat = (Mat_MPISELL *)matin->data;
1783: Vec bb1 = NULL;
1785: PetscFunctionBegin;
1786: if (flag == SOR_APPLY_UPPER) {
1787: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1788: PetscFunctionReturn(PETSC_SUCCESS);
1789: }
1791: if (its > 1 || ~flag & SOR_ZERO_INITIAL_GUESS || flag & SOR_EISENSTAT) PetscCall(VecDuplicate(bb, &bb1));
1793: if ((flag & SOR_LOCAL_SYMMETRIC_SWEEP) == SOR_LOCAL_SYMMETRIC_SWEEP) {
1794: if (flag & SOR_ZERO_INITIAL_GUESS) {
1795: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1796: its--;
1797: }
1799: while (its--) {
1800: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1801: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1803: /* update rhs: bb1 = bb - B*x */
1804: PetscCall(VecScale(mat->lvec, -1.0));
1805: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1807: /* local sweep */
1808: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_SYMMETRIC_SWEEP, fshift, lits, 1, xx));
1809: }
1810: } else if (flag & SOR_LOCAL_FORWARD_SWEEP) {
1811: if (flag & SOR_ZERO_INITIAL_GUESS) {
1812: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1813: its--;
1814: }
1815: while (its--) {
1816: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1817: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1819: /* update rhs: bb1 = bb - B*x */
1820: PetscCall(VecScale(mat->lvec, -1.0));
1821: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1823: /* local sweep */
1824: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_FORWARD_SWEEP, fshift, lits, 1, xx));
1825: }
1826: } else if (flag & SOR_LOCAL_BACKWARD_SWEEP) {
1827: if (flag & SOR_ZERO_INITIAL_GUESS) {
1828: PetscCall((*mat->A->ops->sor)(mat->A, bb, omega, flag, fshift, lits, 1, xx));
1829: its--;
1830: }
1831: while (its--) {
1832: PetscCall(VecScatterBegin(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1833: PetscCall(VecScatterEnd(mat->Mvctx, xx, mat->lvec, INSERT_VALUES, SCATTER_FORWARD));
1835: /* update rhs: bb1 = bb - B*x */
1836: PetscCall(VecScale(mat->lvec, -1.0));
1837: PetscCall((*mat->B->ops->multadd)(mat->B, mat->lvec, bb, bb1));
1839: /* local sweep */
1840: PetscCall((*mat->A->ops->sor)(mat->A, bb1, omega, SOR_BACKWARD_SWEEP, fshift, lits, 1, xx));
1841: }
1842: } else SETERRQ(PetscObjectComm((PetscObject)matin), PETSC_ERR_SUP, "Parallel SOR not supported");
1844: PetscCall(VecDestroy(&bb1));
1846: matin->factorerrortype = mat->A->factorerrortype;
1847: PetscFunctionReturn(PETSC_SUCCESS);
1848: }
1850: #if defined(PETSC_HAVE_CUDA)
1851: PETSC_INTERN PetscErrorCode MatConvert_MPISELL_MPISELLCUDA(Mat, MatType, MatReuse, Mat *);
1852: #endif
1854: /*MC
1855: MATMPISELL - MATMPISELL = "MPISELL" - A matrix type to be used for parallel sparse matrices.
1857: Options Database Keys:
1858: . -mat_type mpisell - sets the matrix type to `MATMPISELL` during a call to `MatSetFromOptions()`
1860: Level: beginner
1862: .seealso: `Mat`, `MATSELL`, `MATSEQSELL` `MatCreateSELL()`
1863: M*/
1864: PETSC_EXTERN PetscErrorCode MatCreate_MPISELL(Mat B)
1865: {
1866: Mat_MPISELL *b;
1867: PetscMPIInt size;
1869: PetscFunctionBegin;
1870: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
1871: PetscCall(PetscNew(&b));
1872: B->data = (void *)b;
1873: B->ops[0] = MatOps_Values;
1874: B->assembled = PETSC_FALSE;
1875: B->insertmode = NOT_SET_VALUES;
1876: b->size = size;
1877: PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)B), &b->rank));
1878: /* build cache for off array entries formed */
1879: PetscCall(MatStashCreate_Private(PetscObjectComm((PetscObject)B), 1, &B->stash));
1881: b->donotstash = PETSC_FALSE;
1882: b->colmap = NULL;
1883: b->garray = NULL;
1884: b->roworiented = PETSC_TRUE;
1886: /* stuff used for matrix vector multiply */
1887: b->lvec = NULL;
1888: b->Mvctx = NULL;
1890: /* stuff for MatGetRow() */
1891: b->rowindices = NULL;
1892: b->rowvalues = NULL;
1893: b->getrowactive = PETSC_FALSE;
1895: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_MPISELL));
1896: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_MPISELL));
1897: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_MPISELL));
1898: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPISELLSetPreallocation_C", MatMPISELLSetPreallocation_MPISELL));
1899: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpisell_mpiaij_C", MatConvert_MPISELL_MPIAIJ));
1900: #if defined(PETSC_HAVE_CUDA)
1901: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpisell_mpisellcuda_C", MatConvert_MPISELL_MPISELLCUDA));
1902: #endif
1903: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatDiagonalScaleLocal_C", MatDiagonalScaleLocal_MPISELL));
1904: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPISELL));
1905: PetscFunctionReturn(PETSC_SUCCESS);
1906: }