Actual source code: maij.c
1: #include <../src/mat/impls/maij/maij.h>
2: #include <../src/mat/utils/freespace.h>
4: /*@
5: MatMAIJGetAIJ - Get the `MATAIJ` matrix describing the blockwise action of the `MATMAIJ` matrix
7: Not Collective, but if the `MATMAIJ` matrix is parallel, the `MATAIJ` matrix is also parallel
9: Input Parameter:
10: . A - the `MATMAIJ` matrix
12: Output Parameter:
13: . B - the `MATAIJ` matrix
15: Level: advanced
17: Note:
18: The reference count on the `MATAIJ` matrix is not increased so you should not destroy it.
20: .seealso: [](ch_matrices), `Mat`, `MATMAIJ`, `MATAIJ`, `MatCreateMAIJ()`
21: @*/
22: PetscErrorCode MatMAIJGetAIJ(Mat A, Mat *B)
23: {
24: PetscBool ismpimaij, isseqmaij;
26: PetscFunctionBegin;
27: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATMPIMAIJ, &ismpimaij));
28: PetscCall(PetscObjectTypeCompare((PetscObject)A, MATSEQMAIJ, &isseqmaij));
29: if (ismpimaij) {
30: Mat_MPIMAIJ *b = (Mat_MPIMAIJ *)A->data;
32: *B = b->A;
33: } else if (isseqmaij) {
34: Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
36: *B = b->AIJ;
37: } else {
38: *B = A;
39: }
40: PetscFunctionReturn(PETSC_SUCCESS);
41: }
43: /*@
44: MatMAIJRedimension - Get a new `MATMAIJ` matrix with the same action, but for a different block size
46: Logically Collective
48: Input Parameters:
49: + A - the `MATMAIJ` matrix
50: - dof - the block size for the new matrix
52: Output Parameter:
53: . B - the new `MATMAIJ` matrix
55: Level: advanced
57: .seealso: [](ch_matrices), `Mat`, `MATMAIJ`, `MatCreateMAIJ()`
58: @*/
59: PetscErrorCode MatMAIJRedimension(Mat A, PetscInt dof, Mat *B)
60: {
61: Mat Aij = NULL;
63: PetscFunctionBegin;
65: PetscCall(MatMAIJGetAIJ(A, &Aij));
66: PetscCall(MatCreateMAIJ(Aij, dof, B));
67: PetscFunctionReturn(PETSC_SUCCESS);
68: }
70: static PetscErrorCode MatDestroy_SeqMAIJ(Mat A)
71: {
72: Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
74: PetscFunctionBegin;
75: PetscCall(MatDestroy(&b->AIJ));
76: PetscCall(PetscFree(A->data));
77: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqmaij_seqaijcusparse_C", NULL));
78: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqmaij_seqaij_C", NULL));
79: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqmaij_C", NULL));
80: PetscFunctionReturn(PETSC_SUCCESS);
81: }
83: static PetscErrorCode MatSetUp_MAIJ(Mat A)
84: {
85: PetscFunctionBegin;
86: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Must use MatCreateMAIJ() to create MAIJ matrices");
87: }
89: static PetscErrorCode MatView_SeqMAIJ(Mat A, PetscViewer viewer)
90: {
91: Mat B;
93: PetscFunctionBegin;
94: PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &B));
95: PetscCall(MatView(B, viewer));
96: PetscCall(MatDestroy(&B));
97: PetscFunctionReturn(PETSC_SUCCESS);
98: }
100: static PetscErrorCode MatView_MPIMAIJ(Mat A, PetscViewer viewer)
101: {
102: Mat B;
104: PetscFunctionBegin;
105: PetscCall(MatConvert(A, MATMPIAIJ, MAT_INITIAL_MATRIX, &B));
106: PetscCall(MatView(B, viewer));
107: PetscCall(MatDestroy(&B));
108: PetscFunctionReturn(PETSC_SUCCESS);
109: }
111: static PetscErrorCode MatDestroy_MPIMAIJ(Mat A)
112: {
113: Mat_MPIMAIJ *b = (Mat_MPIMAIJ *)A->data;
115: PetscFunctionBegin;
116: PetscCall(MatDestroy(&b->AIJ));
117: PetscCall(MatDestroy(&b->OAIJ));
118: PetscCall(MatDestroy(&b->A));
119: PetscCall(VecScatterDestroy(&b->ctx));
120: PetscCall(VecDestroy(&b->w));
121: PetscCall(PetscFree(A->data));
122: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_mpimaij_mpiaijcusparse_C", NULL));
123: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_mpimaij_mpiaij_C", NULL));
124: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_mpiaij_mpimaij_C", NULL));
125: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
126: PetscFunctionReturn(PETSC_SUCCESS);
127: }
129: /*MC
130: MATMAIJ - MATMAIJ = "maij" - A matrix type to be used for restriction and interpolation operations for
131: multicomponent problems, interpolating or restricting each component the same way independently.
132: The matrix type is based on `MATSEQAIJ` for sequential matrices, and `MATMPIAIJ` for distributed matrices.
134: Operations provided:
135: .vb
136: MatMult()
137: MatMultTranspose()
138: MatMultAdd()
139: MatMultTransposeAdd()
140: .ve
142: Level: advanced
144: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatMAIJGetAIJ()`, `MatMAIJRedimension()`, `MatCreateMAIJ()`
145: M*/
147: PETSC_EXTERN PetscErrorCode MatCreate_MAIJ(Mat A)
148: {
149: Mat_MPIMAIJ *b;
150: PetscMPIInt size;
152: PetscFunctionBegin;
153: PetscCall(PetscNew(&b));
154: A->data = (void *)b;
156: PetscCall(PetscMemzero(A->ops, sizeof(struct _MatOps)));
158: A->ops->setup = MatSetUp_MAIJ;
160: b->AIJ = NULL;
161: b->dof = 0;
162: b->OAIJ = NULL;
163: b->ctx = NULL;
164: b->w = NULL;
165: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
166: if (size == 1) {
167: PetscCall(PetscObjectChangeTypeName((PetscObject)A, MATSEQMAIJ));
168: } else {
169: PetscCall(PetscObjectChangeTypeName((PetscObject)A, MATMPIMAIJ));
170: }
171: A->preallocated = PETSC_TRUE;
172: A->assembled = PETSC_TRUE;
173: PetscFunctionReturn(PETSC_SUCCESS);
174: }
176: #if PetscHasAttribute(always_inline)
177: #define PETSC_FORCE_INLINE __attribute__((always_inline))
178: #else
179: #define PETSC_FORCE_INLINE
180: #endif
182: #if defined(__clang__)
183: #define PETSC_PRAGMA_UNROLL _Pragma("unroll")
184: #else
185: #define PETSC_PRAGMA_UNROLL
186: #endif
188: enum {
189: MAT_SEQMAIJ_MAX_TEMPLATE_SIZE = 18
190: };
192: // try as hard as possible to get these "template"s inlined, GCC apparently does take 'inline'
193: // keyword into account for these...
194: PETSC_FORCE_INLINE static inline PetscErrorCode MatMult_MatMultAdd_SeqMAIJ_Template(Mat A, Vec xx, Vec yy, Vec zz, int N)
195: {
196: const PetscBool mult_add = yy == NULL ? PETSC_FALSE : PETSC_TRUE;
197: const Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
198: const Mat baij = b->AIJ;
199: const Mat_SeqAIJ *a = (Mat_SeqAIJ *)baij->data;
200: const PetscInt m = baij->rmap->n;
201: const PetscInt nz = a->nz;
202: const PetscInt *idx = a->j;
203: const PetscInt *ii = a->i;
204: const PetscScalar *v = a->a;
205: PetscInt nonzerorow = 0;
206: const PetscScalar *x;
207: PetscScalar *z;
209: PetscFunctionBegin;
210: PetscAssert(N <= MAT_SEQMAIJ_MAX_TEMPLATE_SIZE, PETSC_COMM_SELF, PETSC_ERR_PLIB, "%s() called with N = %d > max size %d", PETSC_FUNCTION_NAME, N, MAT_SEQMAIJ_MAX_TEMPLATE_SIZE);
211: if (mult_add && yy != zz) PetscCall(VecCopy(yy, zz));
212: PetscCall(VecGetArrayRead(xx, &x));
213: if (mult_add) {
214: PetscCall(VecGetArray(zz, &z));
215: } else {
216: PetscCall(VecGetArrayWrite(zz, &z));
217: }
219: for (PetscInt i = 0; i < m; ++i) {
220: PetscInt jrow = ii[i];
221: const PetscInt n = ii[i + 1] - jrow;
222: // leave a line so clang-format does not align these decls
223: PetscScalar sum[MAT_SEQMAIJ_MAX_TEMPLATE_SIZE] = {0};
225: nonzerorow += n > 0;
226: for (PetscInt j = 0; j < n; ++j, ++jrow) {
227: const PetscScalar v_jrow = v[jrow];
228: const PetscInt N_idx_jrow = N * idx[jrow];
230: PETSC_PRAGMA_UNROLL
231: for (int k = 0; k < N; ++k) sum[k] += v_jrow * x[N_idx_jrow + k];
232: }
234: PETSC_PRAGMA_UNROLL
235: for (int k = 0; k < N; ++k) {
236: const PetscInt z_idx = N * i + k;
238: if (mult_add) {
239: z[z_idx] += sum[k];
240: } else {
241: z[z_idx] = sum[k];
242: }
243: }
244: }
245: PetscCall(PetscLogFlops(2 * N * nz - (mult_add ? 0 : (N * nonzerorow))));
246: PetscCall(VecRestoreArrayRead(xx, &x));
247: if (mult_add) {
248: PetscCall(VecRestoreArray(zz, &z));
249: } else {
250: PetscCall(VecRestoreArrayWrite(zz, &z));
251: }
252: PetscFunctionReturn(PETSC_SUCCESS);
253: }
255: PETSC_FORCE_INLINE static inline PetscErrorCode MatMultTranspose_MatMultTransposeAdd_SeqMAIJ_Template(Mat A, Vec xx, Vec yy, Vec zz, int N)
256: {
257: const PetscBool mult_add = yy == NULL ? PETSC_FALSE : PETSC_TRUE;
258: const Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
259: const Mat baij = b->AIJ;
260: const Mat_SeqAIJ *a = (Mat_SeqAIJ *)baij->data;
261: const PetscInt m = baij->rmap->n;
262: const PetscInt nz = a->nz;
263: const PetscInt *a_j = a->j;
264: const PetscInt *a_i = a->i;
265: const PetscScalar *a_a = a->a;
266: const PetscScalar *x;
267: PetscScalar *z;
269: PetscFunctionBegin;
270: PetscAssert(N <= MAT_SEQMAIJ_MAX_TEMPLATE_SIZE, PETSC_COMM_SELF, PETSC_ERR_PLIB, "%s() called with N = %d > max size %d", PETSC_FUNCTION_NAME, N, MAT_SEQMAIJ_MAX_TEMPLATE_SIZE);
271: if (mult_add) {
272: if (yy != zz) PetscCall(VecCopy(yy, zz));
273: } else {
274: PetscCall(VecSet(zz, 0.0));
275: }
276: PetscCall(VecGetArrayRead(xx, &x));
277: PetscCall(VecGetArray(zz, &z));
279: for (PetscInt i = 0; i < m; i++) {
280: const PetscInt a_ii = a_i[i];
281: const PetscInt *idx = PetscSafePointerPlusOffset(a_j, a_ii);
282: const PetscScalar *v = PetscSafePointerPlusOffset(a_a, a_ii);
283: const PetscInt n = a_i[i + 1] - a_ii;
284: PetscScalar alpha[MAT_SEQMAIJ_MAX_TEMPLATE_SIZE];
286: PETSC_PRAGMA_UNROLL
287: for (int k = 0; k < N; ++k) alpha[k] = x[N * i + k];
288: for (PetscInt j = 0; j < n; ++j) {
289: const PetscInt N_idx_j = N * idx[j];
290: const PetscScalar v_j = v[j];
292: PETSC_PRAGMA_UNROLL
293: for (int k = 0; k < N; ++k) z[N_idx_j + k] += alpha[k] * v_j;
294: }
295: }
297: PetscCall(PetscLogFlops(2 * N * nz));
298: PetscCall(VecRestoreArrayRead(xx, &x));
299: PetscCall(VecRestoreArray(zz, &z));
300: PetscFunctionReturn(PETSC_SUCCESS);
301: }
303: #define MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(N) \
304: static PetscErrorCode PetscConcat(MatMult_SeqMAIJ_, N)(Mat A, Vec xx, Vec yy) \
305: { \
306: PetscFunctionBegin; \
307: PetscCall(MatMult_MatMultAdd_SeqMAIJ_Template(A, xx, NULL, yy, N)); \
308: PetscFunctionReturn(PETSC_SUCCESS); \
309: } \
310: static PetscErrorCode PetscConcat(MatMultTranspose_SeqMAIJ_, N)(Mat A, Vec xx, Vec yy) \
311: { \
312: PetscFunctionBegin; \
313: PetscCall(MatMultTranspose_MatMultTransposeAdd_SeqMAIJ_Template(A, xx, NULL, yy, N)); \
314: PetscFunctionReturn(PETSC_SUCCESS); \
315: } \
316: static PetscErrorCode PetscConcat(MatMultAdd_SeqMAIJ_, N)(Mat A, Vec xx, Vec yy, Vec zz) \
317: { \
318: PetscFunctionBegin; \
319: PetscCall(MatMult_MatMultAdd_SeqMAIJ_Template(A, xx, yy, zz, N)); \
320: PetscFunctionReturn(PETSC_SUCCESS); \
321: } \
322: static PetscErrorCode PetscConcat(MatMultTransposeAdd_SeqMAIJ_, N)(Mat A, Vec xx, Vec yy, Vec zz) \
323: { \
324: PetscFunctionBegin; \
325: PetscCall(MatMultTranspose_MatMultTransposeAdd_SeqMAIJ_Template(A, xx, yy, zz, N)); \
326: PetscFunctionReturn(PETSC_SUCCESS); \
327: }
329: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(2)
330: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(3)
331: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(4)
332: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(5)
333: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(6)
334: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(7)
335: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(8)
336: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(9)
337: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(10)
338: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(11)
339: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(16)
340: MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE(18)
342: #undef MAT_SEQ_MAIJ_INSTANTIATE_MATMULT_MATMULTADD_TEMPLATE
344: static PetscErrorCode MatMult_SeqMAIJ_N(Mat A, Vec xx, Vec yy)
345: {
346: Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
347: Mat_SeqAIJ *a = (Mat_SeqAIJ *)b->AIJ->data;
348: const PetscScalar *x, *v;
349: PetscScalar *y, *sums;
350: const PetscInt m = b->AIJ->rmap->n, *idx, *ii;
351: PetscInt n, i, jrow, j, dof = b->dof, k;
353: PetscFunctionBegin;
354: PetscCall(VecGetArrayRead(xx, &x));
355: PetscCall(VecSet(yy, 0.0));
356: PetscCall(VecGetArray(yy, &y));
357: idx = a->j;
358: v = a->a;
359: ii = a->i;
361: for (i = 0; i < m; i++) {
362: jrow = ii[i];
363: n = ii[i + 1] - jrow;
364: sums = y + dof * i;
365: for (j = 0; j < n; j++) {
366: for (k = 0; k < dof; k++) sums[k] += v[jrow] * x[dof * idx[jrow] + k];
367: jrow++;
368: }
369: }
371: PetscCall(PetscLogFlops(2.0 * dof * a->nz));
372: PetscCall(VecRestoreArrayRead(xx, &x));
373: PetscCall(VecRestoreArray(yy, &y));
374: PetscFunctionReturn(PETSC_SUCCESS);
375: }
377: static PetscErrorCode MatMultAdd_SeqMAIJ_N(Mat A, Vec xx, Vec yy, Vec zz)
378: {
379: Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
380: Mat_SeqAIJ *a = (Mat_SeqAIJ *)b->AIJ->data;
381: const PetscScalar *x, *v;
382: PetscScalar *y, *sums;
383: const PetscInt m = b->AIJ->rmap->n, *idx, *ii;
384: PetscInt n, i, jrow, j, dof = b->dof, k;
386: PetscFunctionBegin;
387: if (yy != zz) PetscCall(VecCopy(yy, zz));
388: PetscCall(VecGetArrayRead(xx, &x));
389: PetscCall(VecGetArray(zz, &y));
390: idx = a->j;
391: v = a->a;
392: ii = a->i;
394: for (i = 0; i < m; i++) {
395: jrow = ii[i];
396: n = ii[i + 1] - jrow;
397: sums = y + dof * i;
398: for (j = 0; j < n; j++) {
399: for (k = 0; k < dof; k++) sums[k] += v[jrow] * x[dof * idx[jrow] + k];
400: jrow++;
401: }
402: }
404: PetscCall(PetscLogFlops(2.0 * dof * a->nz));
405: PetscCall(VecRestoreArrayRead(xx, &x));
406: PetscCall(VecRestoreArray(zz, &y));
407: PetscFunctionReturn(PETSC_SUCCESS);
408: }
410: static PetscErrorCode MatMultTranspose_SeqMAIJ_N(Mat A, Vec xx, Vec yy)
411: {
412: Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
413: Mat_SeqAIJ *a = (Mat_SeqAIJ *)b->AIJ->data;
414: const PetscScalar *x, *v, *alpha;
415: PetscScalar *y;
416: const PetscInt m = b->AIJ->rmap->n, *idx, dof = b->dof;
417: PetscInt n, i, k;
419: PetscFunctionBegin;
420: PetscCall(VecGetArrayRead(xx, &x));
421: PetscCall(VecSet(yy, 0.0));
422: PetscCall(VecGetArray(yy, &y));
423: for (i = 0; i < m; i++) {
424: idx = PetscSafePointerPlusOffset(a->j, a->i[i]);
425: v = PetscSafePointerPlusOffset(a->a, a->i[i]);
426: n = a->i[i + 1] - a->i[i];
427: alpha = x + dof * i;
428: while (n-- > 0) {
429: for (k = 0; k < dof; k++) y[dof * (*idx) + k] += alpha[k] * (*v);
430: idx++;
431: v++;
432: }
433: }
434: PetscCall(PetscLogFlops(2.0 * dof * a->nz));
435: PetscCall(VecRestoreArrayRead(xx, &x));
436: PetscCall(VecRestoreArray(yy, &y));
437: PetscFunctionReturn(PETSC_SUCCESS);
438: }
440: static PetscErrorCode MatMultTransposeAdd_SeqMAIJ_N(Mat A, Vec xx, Vec yy, Vec zz)
441: {
442: Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
443: Mat_SeqAIJ *a = (Mat_SeqAIJ *)b->AIJ->data;
444: const PetscScalar *x, *v, *alpha;
445: PetscScalar *y;
446: const PetscInt m = b->AIJ->rmap->n, *idx, dof = b->dof;
447: PetscInt n, i, k;
449: PetscFunctionBegin;
450: if (yy != zz) PetscCall(VecCopy(yy, zz));
451: PetscCall(VecGetArrayRead(xx, &x));
452: PetscCall(VecGetArray(zz, &y));
453: for (i = 0; i < m; i++) {
454: idx = a->j + a->i[i];
455: v = a->a + a->i[i];
456: n = a->i[i + 1] - a->i[i];
457: alpha = x + dof * i;
458: while (n-- > 0) {
459: for (k = 0; k < dof; k++) y[dof * (*idx) + k] += alpha[k] * (*v);
460: idx++;
461: v++;
462: }
463: }
464: PetscCall(PetscLogFlops(2.0 * dof * a->nz));
465: PetscCall(VecRestoreArrayRead(xx, &x));
466: PetscCall(VecRestoreArray(zz, &y));
467: PetscFunctionReturn(PETSC_SUCCESS);
468: }
470: static PetscErrorCode MatMult_MPIMAIJ_dof(Mat A, Vec xx, Vec yy)
471: {
472: Mat_MPIMAIJ *b = (Mat_MPIMAIJ *)A->data;
474: PetscFunctionBegin;
475: /* start the scatter */
476: PetscCall(VecScatterBegin(b->ctx, xx, b->w, INSERT_VALUES, SCATTER_FORWARD));
477: PetscCall((*b->AIJ->ops->mult)(b->AIJ, xx, yy));
478: PetscCall(VecScatterEnd(b->ctx, xx, b->w, INSERT_VALUES, SCATTER_FORWARD));
479: PetscCall((*b->OAIJ->ops->multadd)(b->OAIJ, b->w, yy, yy));
480: PetscFunctionReturn(PETSC_SUCCESS);
481: }
483: static PetscErrorCode MatMultTranspose_MPIMAIJ_dof(Mat A, Vec xx, Vec yy)
484: {
485: Mat_MPIMAIJ *b = (Mat_MPIMAIJ *)A->data;
487: PetscFunctionBegin;
488: PetscCall((*b->OAIJ->ops->multtranspose)(b->OAIJ, xx, b->w));
489: PetscCall((*b->AIJ->ops->multtranspose)(b->AIJ, xx, yy));
490: PetscCall(VecScatterBegin(b->ctx, b->w, yy, ADD_VALUES, SCATTER_REVERSE));
491: PetscCall(VecScatterEnd(b->ctx, b->w, yy, ADD_VALUES, SCATTER_REVERSE));
492: PetscFunctionReturn(PETSC_SUCCESS);
493: }
495: static PetscErrorCode MatMultAdd_MPIMAIJ_dof(Mat A, Vec xx, Vec yy, Vec zz)
496: {
497: Mat_MPIMAIJ *b = (Mat_MPIMAIJ *)A->data;
499: PetscFunctionBegin;
500: /* start the scatter */
501: PetscCall(VecScatterBegin(b->ctx, xx, b->w, INSERT_VALUES, SCATTER_FORWARD));
502: PetscCall((*b->AIJ->ops->multadd)(b->AIJ, xx, yy, zz));
503: PetscCall(VecScatterEnd(b->ctx, xx, b->w, INSERT_VALUES, SCATTER_FORWARD));
504: PetscCall((*b->OAIJ->ops->multadd)(b->OAIJ, b->w, zz, zz));
505: PetscFunctionReturn(PETSC_SUCCESS);
506: }
508: static PetscErrorCode MatMultTransposeAdd_MPIMAIJ_dof(Mat A, Vec xx, Vec yy, Vec zz)
509: {
510: Mat_MPIMAIJ *b = (Mat_MPIMAIJ *)A->data;
512: PetscFunctionBegin;
513: PetscCall((*b->OAIJ->ops->multtranspose)(b->OAIJ, xx, b->w));
514: PetscCall((*b->AIJ->ops->multtransposeadd)(b->AIJ, xx, yy, zz));
515: PetscCall(VecScatterBegin(b->ctx, b->w, zz, ADD_VALUES, SCATTER_REVERSE));
516: PetscCall(VecScatterEnd(b->ctx, b->w, zz, ADD_VALUES, SCATTER_REVERSE));
517: PetscFunctionReturn(PETSC_SUCCESS);
518: }
520: static PetscErrorCode MatProductSetFromOptions_SeqAIJ_SeqMAIJ(Mat C)
521: {
522: Mat_Product *product = C->product;
524: PetscFunctionBegin;
525: PetscCheck(product->type == MATPRODUCT_PtAP, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat Product type %s is not supported for SeqAIJ and SeqMAIJ matrices", MatProductTypes[product->type]);
526: C->ops->productsymbolic = MatProductSymbolic_PtAP_SeqAIJ_SeqMAIJ;
527: PetscFunctionReturn(PETSC_SUCCESS);
528: }
530: static PetscErrorCode MatProductSetFromOptions_MPIAIJ_MPIMAIJ(Mat C)
531: {
532: Mat_Product *product = C->product;
533: PetscBool flg = PETSC_FALSE;
534: Mat A = product->A, P = product->B;
535: PetscInt alg = 1; /* set default algorithm */
536: #if !defined(PETSC_HAVE_HYPRE)
537: const char *algTypes[4] = {"scalable", "nonscalable", "allatonce", "allatonce_merged"};
538: PetscInt nalg = 4;
539: #else
540: const char *algTypes[5] = {"scalable", "nonscalable", "allatonce", "allatonce_merged", "hypre"};
541: PetscInt nalg = 5;
542: #endif
544: PetscFunctionBegin;
545: PetscCheck(product->type == MATPRODUCT_PtAP, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat Product type %s is not supported for MPIAIJ and MPIMAIJ matrices", MatProductTypes[product->type]);
547: /* PtAP */
548: /* Check matrix local sizes */
549: PetscCheck(A->rmap->rstart == P->rmap->rstart && A->rmap->rend == P->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, Arow (%" PetscInt_FMT ", %" PetscInt_FMT ") != Prow (%" PetscInt_FMT ",%" PetscInt_FMT ")",
550: A->rmap->rstart, A->rmap->rend, P->rmap->rstart, P->rmap->rend);
551: PetscCheck(A->cmap->rstart == P->rmap->rstart && A->cmap->rend == P->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrix local dimensions are incompatible, Acol (%" PetscInt_FMT ", %" PetscInt_FMT ") != Prow (%" PetscInt_FMT ",%" PetscInt_FMT ")",
552: A->cmap->rstart, A->cmap->rend, P->rmap->rstart, P->rmap->rend);
554: /* Set the default algorithm */
555: PetscCall(PetscStrcmp(C->product->alg, "default", &flg));
556: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
558: /* Get runtime option */
559: PetscOptionsBegin(PetscObjectComm((PetscObject)C), ((PetscObject)C)->prefix, "MatProduct_PtAP", "Mat");
560: PetscCall(PetscOptionsEList("-mat_product_algorithm", "Algorithmic approach", "MatPtAP", algTypes, nalg, algTypes[alg], &alg, &flg));
561: if (flg) PetscCall(MatProductSetAlgorithm(C, algTypes[alg]));
562: PetscOptionsEnd();
564: PetscCall(PetscStrcmp(C->product->alg, "allatonce", &flg));
565: if (flg) {
566: C->ops->productsymbolic = MatProductSymbolic_PtAP_MPIAIJ_MPIMAIJ;
567: PetscFunctionReturn(PETSC_SUCCESS);
568: }
570: PetscCall(PetscStrcmp(C->product->alg, "allatonce_merged", &flg));
571: if (flg) {
572: C->ops->productsymbolic = MatProductSymbolic_PtAP_MPIAIJ_MPIMAIJ;
573: PetscFunctionReturn(PETSC_SUCCESS);
574: }
576: /* Convert P from MAIJ to AIJ matrix since implementation not available for MAIJ */
577: PetscCall(PetscInfo(A, "Converting from MAIJ to AIJ matrix since implementation not available for MAIJ\n"));
578: PetscCall(MatConvert(P, MATMPIAIJ, MAT_INPLACE_MATRIX, &P));
579: PetscCall(MatProductSetFromOptions(C));
580: PetscFunctionReturn(PETSC_SUCCESS);
581: }
583: static PetscErrorCode MatPtAPNumeric_SeqAIJ_SeqMAIJ(Mat A, Mat PP, Mat C)
584: {
585: /* This routine requires testing -- first draft only */
586: Mat_SeqMAIJ *pp = (Mat_SeqMAIJ *)PP->data;
587: Mat P = pp->AIJ;
588: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
589: Mat_SeqAIJ *p = (Mat_SeqAIJ *)P->data;
590: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
591: const PetscInt *ai = a->i, *aj = a->j, *pi = p->i, *pj = p->j, *pJ, *pjj;
592: const PetscInt *ci = c->i, *cj = c->j, *cjj;
593: const PetscInt am = A->rmap->N, cn = C->cmap->N, cm = C->rmap->N, ppdof = pp->dof;
594: PetscInt i, j, k, pshift, poffset, anzi, pnzi, apnzj, nextap, pnzj, prow, crow, *apj, *apjdense;
595: const MatScalar *aa = a->a, *pa = p->a, *pA, *paj;
596: MatScalar *ca = c->a, *caj, *apa;
598: PetscFunctionBegin;
599: /* Allocate temporary array for storage of one row of A*P */
600: PetscCall(PetscCalloc3(cn, &apa, cn, &apj, cn, &apjdense));
602: /* Clear old values in C */
603: PetscCall(PetscArrayzero(ca, ci[cm]));
605: for (i = 0; i < am; i++) {
606: /* Form sparse row of A*P */
607: anzi = ai[i + 1] - ai[i];
608: apnzj = 0;
609: for (j = 0; j < anzi; j++) {
610: /* Get offset within block of P */
611: pshift = *aj % ppdof;
612: /* Get block row of P */
613: prow = *aj++ / ppdof; /* integer division */
614: pnzj = pi[prow + 1] - pi[prow];
615: pjj = pj + pi[prow];
616: paj = pa + pi[prow];
617: for (k = 0; k < pnzj; k++) {
618: poffset = pjj[k] * ppdof + pshift;
619: if (!apjdense[poffset]) {
620: apjdense[poffset] = -1;
621: apj[apnzj++] = poffset;
622: }
623: apa[poffset] += (*aa) * paj[k];
624: }
625: PetscCall(PetscLogFlops(2.0 * pnzj));
626: aa++;
627: }
629: /* Sort the j index array for quick sparse axpy. */
630: /* Note: a array does not need sorting as it is in dense storage locations. */
631: PetscCall(PetscSortInt(apnzj, apj));
633: /* Compute P^T*A*P using outer product (P^T)[:,j]*(A*P)[j,:]. */
634: prow = i / ppdof; /* integer division */
635: pshift = i % ppdof;
636: poffset = pi[prow];
637: pnzi = pi[prow + 1] - poffset;
638: /* Reset pJ and pA so we can traverse the same row of P 'dof' times. */
639: pJ = pj + poffset;
640: pA = pa + poffset;
641: for (j = 0; j < pnzi; j++) {
642: crow = (*pJ) * ppdof + pshift;
643: cjj = cj + ci[crow];
644: caj = ca + ci[crow];
645: pJ++;
646: /* Perform sparse axpy operation. Note cjj includes apj. */
647: for (k = 0, nextap = 0; nextap < apnzj; k++) {
648: if (cjj[k] == apj[nextap]) caj[k] += (*pA) * apa[apj[nextap++]];
649: }
650: PetscCall(PetscLogFlops(2.0 * apnzj));
651: pA++;
652: }
654: /* Zero the current row info for A*P */
655: for (j = 0; j < apnzj; j++) {
656: apa[apj[j]] = 0.;
657: apjdense[apj[j]] = 0;
658: }
659: }
661: /* Assemble the final matrix and clean up */
662: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
663: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
664: PetscCall(PetscFree3(apa, apj, apjdense));
665: PetscFunctionReturn(PETSC_SUCCESS);
666: }
668: static PetscErrorCode MatPtAPSymbolic_SeqAIJ_SeqMAIJ(Mat A, Mat PP, PetscReal fill, Mat C)
669: {
670: PetscFreeSpaceList free_space = NULL, current_space = NULL;
671: Mat_SeqMAIJ *pp = (Mat_SeqMAIJ *)PP->data;
672: Mat P = pp->AIJ;
673: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *p = (Mat_SeqAIJ *)P->data, *c;
674: PetscInt *pti, *ptj, *ptJ;
675: PetscInt *ci, *cj, *ptadenserow, *ptasparserow, *denserow, *sparserow, *ptaj;
676: const PetscInt an = A->cmap->N, am = A->rmap->N, pn = P->cmap->N, pm = P->rmap->N, ppdof = pp->dof;
677: PetscInt i, j, k, dof, pshift, ptnzi, arow, anzj, ptanzi, prow, pnzj, cnzi, cn;
678: MatScalar *ca;
679: const PetscInt *pi = p->i, *pj = p->j, *pjj, *ai = a->i, *aj = a->j, *ajj;
681: PetscFunctionBegin;
682: /* Get ij structure of P^T */
683: PetscCall(MatGetSymbolicTranspose_SeqAIJ(P, &pti, &ptj));
685: cn = pn * ppdof;
686: /* Allocate ci array, arrays for fill computation and */
687: /* free space for accumulating nonzero column info */
688: PetscCall(PetscMalloc1(cn + 1, &ci));
689: ci[0] = 0;
691: /* Work arrays for rows of P^T*A */
692: PetscCall(PetscMalloc4(an, &ptadenserow, an, &ptasparserow, cn, &denserow, cn, &sparserow));
693: PetscCall(PetscArrayzero(ptadenserow, an));
694: PetscCall(PetscArrayzero(denserow, cn));
696: /* Set initial free space to be nnz(A) scaled by aspect ratio of P. */
697: /* This should be reasonable if sparsity of PtAP is similar to that of A. */
698: /* Note, aspect ratio of P is the same as the aspect ratio of SeqAIJ inside P */
699: PetscCall(PetscFreeSpaceGet(PetscIntMultTruncate(ai[am] / pm, pn), &free_space));
700: current_space = free_space;
702: /* Determine symbolic info for each row of C: */
703: for (i = 0; i < pn; i++) {
704: ptnzi = pti[i + 1] - pti[i];
705: ptJ = ptj + pti[i];
706: for (dof = 0; dof < ppdof; dof++) {
707: ptanzi = 0;
708: /* Determine symbolic row of PtA: */
709: for (j = 0; j < ptnzi; j++) {
710: /* Expand ptJ[j] by block size and shift by dof to get the right row of A */
711: arow = ptJ[j] * ppdof + dof;
712: /* Nonzeros of P^T*A will be in same locations as any element of A in that row */
713: anzj = ai[arow + 1] - ai[arow];
714: ajj = aj + ai[arow];
715: for (k = 0; k < anzj; k++) {
716: if (!ptadenserow[ajj[k]]) {
717: ptadenserow[ajj[k]] = -1;
718: ptasparserow[ptanzi++] = ajj[k];
719: }
720: }
721: }
722: /* Using symbolic info for row of PtA, determine symbolic info for row of C: */
723: ptaj = ptasparserow;
724: cnzi = 0;
725: for (j = 0; j < ptanzi; j++) {
726: /* Get offset within block of P */
727: pshift = *ptaj % ppdof;
728: /* Get block row of P */
729: prow = (*ptaj++) / ppdof; /* integer division */
730: /* P has same number of nonzeros per row as the compressed form */
731: pnzj = pi[prow + 1] - pi[prow];
732: pjj = pj + pi[prow];
733: for (k = 0; k < pnzj; k++) {
734: /* Locations in C are shifted by the offset within the block */
735: /* Note: we cannot use PetscLLAdd here because of the additional offset for the write location */
736: if (!denserow[pjj[k] * ppdof + pshift]) {
737: denserow[pjj[k] * ppdof + pshift] = -1;
738: sparserow[cnzi++] = pjj[k] * ppdof + pshift;
739: }
740: }
741: }
743: /* sort sparserow */
744: PetscCall(PetscSortInt(cnzi, sparserow));
746: /* If free space is not available, make more free space */
747: /* Double the amount of total space in the list */
748: if (current_space->local_remaining < cnzi) PetscCall(PetscFreeSpaceGet(PetscIntSumTruncate(cnzi, current_space->total_array_size), ¤t_space));
750: /* Copy data into free space, and zero out denserows */
751: PetscCall(PetscArraycpy(current_space->array, sparserow, cnzi));
753: current_space->array += cnzi;
754: current_space->local_used += cnzi;
755: current_space->local_remaining -= cnzi;
757: for (j = 0; j < ptanzi; j++) ptadenserow[ptasparserow[j]] = 0;
758: for (j = 0; j < cnzi; j++) denserow[sparserow[j]] = 0;
760: /* Aside: Perhaps we should save the pta info for the numerical factorization. */
761: /* For now, we will recompute what is needed. */
762: ci[i * ppdof + 1 + dof] = ci[i * ppdof + dof] + cnzi;
763: }
764: }
765: /* nnz is now stored in ci[ptm], column indices are in the list of free space */
766: /* Allocate space for cj, initialize cj, and */
767: /* destroy list of free space and other temporary array(s) */
768: PetscCall(PetscMalloc1(ci[cn] + 1, &cj));
769: PetscCall(PetscFreeSpaceContiguous(&free_space, cj));
770: PetscCall(PetscFree4(ptadenserow, ptasparserow, denserow, sparserow));
772: /* Allocate space for ca */
773: PetscCall(PetscCalloc1(ci[cn] + 1, &ca));
775: /* put together the new matrix */
776: PetscCall(MatSetSeqAIJWithArrays_private(PetscObjectComm((PetscObject)A), cn, cn, ci, cj, ca, NULL, C));
777: PetscCall(MatSetBlockSize(C, pp->dof));
779: /* MatCreateSeqAIJWithArrays flags matrix so PETSc doesn't free the user's arrays. */
780: /* Since these are PETSc arrays, change flags to free them as necessary. */
781: c = (Mat_SeqAIJ *)C->data;
782: c->free_a = PETSC_TRUE;
783: c->free_ij = PETSC_TRUE;
784: c->nonew = 0;
786: C->ops->ptapnumeric = MatPtAPNumeric_SeqAIJ_SeqMAIJ;
787: C->ops->productnumeric = MatProductNumeric_PtAP;
789: /* Clean up. */
790: PetscCall(MatRestoreSymbolicTranspose_SeqAIJ(P, &pti, &ptj));
791: PetscFunctionReturn(PETSC_SUCCESS);
792: }
794: PETSC_INTERN PetscErrorCode MatProductSymbolic_PtAP_SeqAIJ_SeqMAIJ(Mat C)
795: {
796: Mat_Product *product = C->product;
797: Mat A = product->A, P = product->B;
799: PetscFunctionBegin;
800: PetscCall(MatPtAPSymbolic_SeqAIJ_SeqMAIJ(A, P, product->fill, C));
801: PetscFunctionReturn(PETSC_SUCCESS);
802: }
804: PetscErrorCode MatPtAPNumeric_MPIAIJ_MPIXAIJ_allatonce(Mat, Mat, PetscInt, Mat);
806: PETSC_INTERN PetscErrorCode MatPtAPNumeric_MPIAIJ_MPIMAIJ_allatonce(Mat A, Mat P, Mat C)
807: {
808: Mat_MPIMAIJ *maij = (Mat_MPIMAIJ *)P->data;
810: PetscFunctionBegin;
811: PetscCall(MatPtAPNumeric_MPIAIJ_MPIXAIJ_allatonce(A, maij->A, maij->dof, C));
812: PetscFunctionReturn(PETSC_SUCCESS);
813: }
815: PetscErrorCode MatPtAPSymbolic_MPIAIJ_MPIXAIJ_allatonce(Mat, Mat, PetscInt, PetscReal, Mat);
817: PETSC_INTERN PetscErrorCode MatPtAPSymbolic_MPIAIJ_MPIMAIJ_allatonce(Mat A, Mat P, PetscReal fill, Mat C)
818: {
819: Mat_MPIMAIJ *maij = (Mat_MPIMAIJ *)P->data;
821: PetscFunctionBegin;
822: PetscCall(MatPtAPSymbolic_MPIAIJ_MPIXAIJ_allatonce(A, maij->A, maij->dof, fill, C));
823: C->ops->ptapnumeric = MatPtAPNumeric_MPIAIJ_MPIMAIJ_allatonce;
824: PetscFunctionReturn(PETSC_SUCCESS);
825: }
827: PetscErrorCode MatPtAPNumeric_MPIAIJ_MPIXAIJ_allatonce_merged(Mat, Mat, PetscInt, Mat);
829: PETSC_INTERN PetscErrorCode MatPtAPNumeric_MPIAIJ_MPIMAIJ_allatonce_merged(Mat A, Mat P, Mat C)
830: {
831: Mat_MPIMAIJ *maij = (Mat_MPIMAIJ *)P->data;
833: PetscFunctionBegin;
834: PetscCall(MatPtAPNumeric_MPIAIJ_MPIXAIJ_allatonce_merged(A, maij->A, maij->dof, C));
835: PetscFunctionReturn(PETSC_SUCCESS);
836: }
838: PetscErrorCode MatPtAPSymbolic_MPIAIJ_MPIXAIJ_allatonce_merged(Mat, Mat, PetscInt, PetscReal, Mat);
840: PETSC_INTERN PetscErrorCode MatPtAPSymbolic_MPIAIJ_MPIMAIJ_allatonce_merged(Mat A, Mat P, PetscReal fill, Mat C)
841: {
842: Mat_MPIMAIJ *maij = (Mat_MPIMAIJ *)P->data;
844: PetscFunctionBegin;
845: PetscCall(MatPtAPSymbolic_MPIAIJ_MPIXAIJ_allatonce_merged(A, maij->A, maij->dof, fill, C));
846: C->ops->ptapnumeric = MatPtAPNumeric_MPIAIJ_MPIMAIJ_allatonce_merged;
847: PetscFunctionReturn(PETSC_SUCCESS);
848: }
850: PETSC_INTERN PetscErrorCode MatProductSymbolic_PtAP_MPIAIJ_MPIMAIJ(Mat C)
851: {
852: Mat_Product *product = C->product;
853: Mat A = product->A, P = product->B;
854: PetscBool flg;
856: PetscFunctionBegin;
857: PetscCall(PetscStrcmp(product->alg, "allatonce", &flg));
858: if (flg) {
859: PetscCall(MatPtAPSymbolic_MPIAIJ_MPIMAIJ_allatonce(A, P, product->fill, C));
860: C->ops->productnumeric = MatProductNumeric_PtAP;
861: PetscFunctionReturn(PETSC_SUCCESS);
862: }
864: PetscCall(PetscStrcmp(product->alg, "allatonce_merged", &flg));
865: PetscCheck(flg, PetscObjectComm((PetscObject)C), PETSC_ERR_SUP, "Mat Product Algorithm is not supported");
866: PetscCall(MatPtAPSymbolic_MPIAIJ_MPIMAIJ_allatonce_merged(A, P, product->fill, C));
867: C->ops->productnumeric = MatProductNumeric_PtAP;
868: PetscFunctionReturn(PETSC_SUCCESS);
869: }
871: PETSC_INTERN PetscErrorCode MatConvert_SeqMAIJ_SeqAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
872: {
873: Mat_SeqMAIJ *b = (Mat_SeqMAIJ *)A->data;
874: Mat a = b->AIJ, B;
875: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)a->data;
876: PetscInt m, n, i, ncols, *ilen, nmax = 0, *icols, j, k, ii, dof = b->dof;
877: PetscInt *cols;
878: PetscScalar *vals;
880: PetscFunctionBegin;
881: PetscCall(MatGetSize(a, &m, &n));
882: PetscCall(PetscMalloc1(dof * m, &ilen));
883: for (i = 0; i < m; i++) {
884: nmax = PetscMax(nmax, aij->ilen[i]);
885: for (j = 0; j < dof; j++) ilen[dof * i + j] = aij->ilen[i];
886: }
887: PetscCall(MatCreate(PETSC_COMM_SELF, &B));
888: PetscCall(MatSetSizes(B, dof * m, dof * n, dof * m, dof * n));
889: PetscCall(MatSetType(B, newtype));
890: PetscCall(MatSeqAIJSetPreallocation(B, 0, ilen));
891: PetscCall(PetscFree(ilen));
892: PetscCall(PetscMalloc1(nmax, &icols));
893: ii = 0;
894: for (i = 0; i < m; i++) {
895: PetscCall(MatGetRow_SeqAIJ(a, i, &ncols, &cols, &vals));
896: for (j = 0; j < dof; j++) {
897: for (k = 0; k < ncols; k++) icols[k] = dof * cols[k] + j;
898: PetscCall(MatSetValues_SeqAIJ(B, 1, &ii, ncols, icols, vals, INSERT_VALUES));
899: ii++;
900: }
901: PetscCall(MatRestoreRow_SeqAIJ(a, i, &ncols, &cols, &vals));
902: }
903: PetscCall(PetscFree(icols));
904: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
905: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
907: if (reuse == MAT_INPLACE_MATRIX) {
908: PetscCall(MatHeaderReplace(A, &B));
909: } else {
910: *newmat = B;
911: }
912: PetscFunctionReturn(PETSC_SUCCESS);
913: }
915: #include <../src/mat/impls/aij/mpi/mpiaij.h>
917: PETSC_INTERN PetscErrorCode MatConvert_MPIMAIJ_MPIAIJ(Mat A, MatType newtype, MatReuse reuse, Mat *newmat)
918: {
919: Mat_MPIMAIJ *maij = (Mat_MPIMAIJ *)A->data;
920: Mat MatAIJ = ((Mat_SeqMAIJ *)maij->AIJ->data)->AIJ, B;
921: Mat MatOAIJ = ((Mat_SeqMAIJ *)maij->OAIJ->data)->AIJ;
922: Mat_SeqAIJ *AIJ = (Mat_SeqAIJ *)MatAIJ->data;
923: Mat_SeqAIJ *OAIJ = (Mat_SeqAIJ *)MatOAIJ->data;
924: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)maij->A->data;
925: PetscInt dof = maij->dof, i, j, *dnz = NULL, *onz = NULL, nmax = 0, onmax = 0;
926: PetscInt *oicols = NULL, *icols = NULL, ncols, *cols = NULL, oncols, *ocols = NULL;
927: PetscInt rstart, cstart, *garray, ii, k;
928: PetscScalar *vals, *ovals;
930: PetscFunctionBegin;
931: PetscCall(PetscMalloc2(A->rmap->n, &dnz, A->rmap->n, &onz));
932: for (i = 0; i < A->rmap->n / dof; i++) {
933: nmax = PetscMax(nmax, AIJ->ilen[i]);
934: onmax = PetscMax(onmax, OAIJ->ilen[i]);
935: for (j = 0; j < dof; j++) {
936: dnz[dof * i + j] = AIJ->ilen[i];
937: onz[dof * i + j] = OAIJ->ilen[i];
938: }
939: }
940: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
941: PetscCall(MatSetSizes(B, A->rmap->n, A->cmap->n, A->rmap->N, A->cmap->N));
942: PetscCall(MatSetType(B, newtype));
943: PetscCall(MatMPIAIJSetPreallocation(B, 0, dnz, 0, onz));
944: PetscCall(MatSetBlockSize(B, dof));
945: PetscCall(PetscFree2(dnz, onz));
947: PetscCall(PetscMalloc2(nmax, &icols, onmax, &oicols));
948: rstart = dof * maij->A->rmap->rstart;
949: cstart = dof * maij->A->cmap->rstart;
950: garray = mpiaij->garray;
952: ii = rstart;
953: for (i = 0; i < A->rmap->n / dof; i++) {
954: PetscCall(MatGetRow_SeqAIJ(MatAIJ, i, &ncols, &cols, &vals));
955: PetscCall(MatGetRow_SeqAIJ(MatOAIJ, i, &oncols, &ocols, &ovals));
956: for (j = 0; j < dof; j++) {
957: for (k = 0; k < ncols; k++) icols[k] = cstart + dof * cols[k] + j;
958: for (k = 0; k < oncols; k++) oicols[k] = dof * garray[ocols[k]] + j;
959: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, ncols, icols, vals, INSERT_VALUES));
960: PetscCall(MatSetValues_MPIAIJ(B, 1, &ii, oncols, oicols, ovals, INSERT_VALUES));
961: ii++;
962: }
963: PetscCall(MatRestoreRow_SeqAIJ(MatAIJ, i, &ncols, &cols, &vals));
964: PetscCall(MatRestoreRow_SeqAIJ(MatOAIJ, i, &oncols, &ocols, &ovals));
965: }
966: PetscCall(PetscFree2(icols, oicols));
968: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
969: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
971: if (reuse == MAT_INPLACE_MATRIX) {
972: PetscInt refct = ((PetscObject)A)->refct; /* save ((PetscObject)A)->refct */
973: ((PetscObject)A)->refct = 1;
975: PetscCall(MatHeaderReplace(A, &B));
977: ((PetscObject)A)->refct = refct; /* restore ((PetscObject)A)->refct */
978: } else {
979: *newmat = B;
980: }
981: PetscFunctionReturn(PETSC_SUCCESS);
982: }
984: static PetscErrorCode MatCreateSubMatrix_MAIJ(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
985: {
986: Mat A;
988: PetscFunctionBegin;
989: PetscCall(MatConvert(mat, MATAIJ, MAT_INITIAL_MATRIX, &A));
990: PetscCall(MatCreateSubMatrix(A, isrow, iscol, cll, newmat));
991: PetscCall(MatDestroy(&A));
992: PetscFunctionReturn(PETSC_SUCCESS);
993: }
995: static PetscErrorCode MatCreateSubMatrices_MAIJ(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
996: {
997: Mat A;
999: PetscFunctionBegin;
1000: PetscCall(MatConvert(mat, MATAIJ, MAT_INITIAL_MATRIX, &A));
1001: PetscCall(MatCreateSubMatrices(A, n, irow, icol, scall, submat));
1002: PetscCall(MatDestroy(&A));
1003: PetscFunctionReturn(PETSC_SUCCESS);
1004: }
1006: /*@
1007: MatCreateMAIJ - Creates a matrix type providing restriction and interpolation
1008: operations for multicomponent problems. It interpolates each component the same
1009: way independently. The matrix type is based on `MATSEQAIJ` for sequential matrices,
1010: and `MATMPIAIJ` for distributed matrices.
1012: Collective
1014: Input Parameters:
1015: + A - the `MATAIJ` matrix describing the action on blocks
1016: - dof - the block size (number of components per node)
1018: Output Parameter:
1019: . maij - the new `MATMAIJ` matrix
1021: Level: advanced
1023: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MATMAIJ`, `MatMAIJGetAIJ()`, `MatMAIJRedimension()`
1024: @*/
1025: PetscErrorCode MatCreateMAIJ(Mat A, PetscInt dof, Mat *maij)
1026: {
1027: PetscInt n;
1028: Mat B;
1029: PetscBool flg;
1030: #if defined(PETSC_HAVE_CUDA)
1031: /* hack to prevent conversion to AIJ format for CUDA when used inside a parallel MAIJ */
1032: PetscBool convert = dof < 0 ? PETSC_FALSE : PETSC_TRUE;
1033: #endif
1035: PetscFunctionBegin;
1036: dof = PetscAbs(dof);
1037: PetscCall(PetscObjectReference((PetscObject)A));
1039: if (dof == 1) *maij = A;
1040: else {
1041: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
1042: /* propagate vec type */
1043: PetscCall(MatSetVecType(B, A->defaultvectype));
1044: PetscCall(MatSetSizes(B, dof * A->rmap->n, dof * A->cmap->n, dof * A->rmap->N, dof * A->cmap->N));
1045: PetscCall(PetscLayoutSetBlockSize(B->rmap, dof));
1046: PetscCall(PetscLayoutSetBlockSize(B->cmap, dof));
1047: PetscCall(PetscLayoutSetUp(B->rmap));
1048: PetscCall(PetscLayoutSetUp(B->cmap));
1050: B->assembled = PETSC_TRUE;
1052: PetscCall(PetscObjectBaseTypeCompare((PetscObject)A, MATSEQAIJ, &flg));
1053: if (flg) {
1054: Mat_SeqMAIJ *b;
1056: PetscCall(MatSetType(B, MATSEQMAIJ));
1058: B->ops->setup = NULL;
1059: B->ops->destroy = MatDestroy_SeqMAIJ;
1060: B->ops->view = MatView_SeqMAIJ;
1062: b = (Mat_SeqMAIJ *)B->data;
1063: b->dof = dof;
1064: b->AIJ = A;
1066: if (dof == 2) {
1067: B->ops->mult = MatMult_SeqMAIJ_2;
1068: B->ops->multadd = MatMultAdd_SeqMAIJ_2;
1069: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_2;
1070: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_2;
1071: } else if (dof == 3) {
1072: B->ops->mult = MatMult_SeqMAIJ_3;
1073: B->ops->multadd = MatMultAdd_SeqMAIJ_3;
1074: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_3;
1075: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_3;
1076: } else if (dof == 4) {
1077: B->ops->mult = MatMult_SeqMAIJ_4;
1078: B->ops->multadd = MatMultAdd_SeqMAIJ_4;
1079: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_4;
1080: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_4;
1081: } else if (dof == 5) {
1082: B->ops->mult = MatMult_SeqMAIJ_5;
1083: B->ops->multadd = MatMultAdd_SeqMAIJ_5;
1084: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_5;
1085: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_5;
1086: } else if (dof == 6) {
1087: B->ops->mult = MatMult_SeqMAIJ_6;
1088: B->ops->multadd = MatMultAdd_SeqMAIJ_6;
1089: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_6;
1090: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_6;
1091: } else if (dof == 7) {
1092: B->ops->mult = MatMult_SeqMAIJ_7;
1093: B->ops->multadd = MatMultAdd_SeqMAIJ_7;
1094: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_7;
1095: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_7;
1096: } else if (dof == 8) {
1097: B->ops->mult = MatMult_SeqMAIJ_8;
1098: B->ops->multadd = MatMultAdd_SeqMAIJ_8;
1099: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_8;
1100: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_8;
1101: } else if (dof == 9) {
1102: B->ops->mult = MatMult_SeqMAIJ_9;
1103: B->ops->multadd = MatMultAdd_SeqMAIJ_9;
1104: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_9;
1105: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_9;
1106: } else if (dof == 10) {
1107: B->ops->mult = MatMult_SeqMAIJ_10;
1108: B->ops->multadd = MatMultAdd_SeqMAIJ_10;
1109: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_10;
1110: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_10;
1111: } else if (dof == 11) {
1112: B->ops->mult = MatMult_SeqMAIJ_11;
1113: B->ops->multadd = MatMultAdd_SeqMAIJ_11;
1114: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_11;
1115: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_11;
1116: } else if (dof == 16) {
1117: B->ops->mult = MatMult_SeqMAIJ_16;
1118: B->ops->multadd = MatMultAdd_SeqMAIJ_16;
1119: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_16;
1120: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_16;
1121: } else if (dof == 18) {
1122: B->ops->mult = MatMult_SeqMAIJ_18;
1123: B->ops->multadd = MatMultAdd_SeqMAIJ_18;
1124: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_18;
1125: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_18;
1126: } else {
1127: B->ops->mult = MatMult_SeqMAIJ_N;
1128: B->ops->multadd = MatMultAdd_SeqMAIJ_N;
1129: B->ops->multtranspose = MatMultTranspose_SeqMAIJ_N;
1130: B->ops->multtransposeadd = MatMultTransposeAdd_SeqMAIJ_N;
1131: }
1132: #if defined(PETSC_HAVE_CUDA)
1133: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqmaij_seqaijcusparse_C", MatConvert_SeqMAIJ_SeqAIJ));
1134: #endif
1135: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqmaij_seqaij_C", MatConvert_SeqMAIJ_SeqAIJ));
1136: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqmaij_C", MatProductSetFromOptions_SeqAIJ_SeqMAIJ));
1137: } else {
1138: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)A->data;
1139: Mat_MPIMAIJ *b;
1140: IS from, to;
1141: Vec gvec;
1143: PetscCall(MatSetType(B, MATMPIMAIJ));
1145: B->ops->setup = NULL;
1146: B->ops->destroy = MatDestroy_MPIMAIJ;
1147: B->ops->view = MatView_MPIMAIJ;
1149: b = (Mat_MPIMAIJ *)B->data;
1150: b->dof = dof;
1151: b->A = A;
1153: PetscCall(MatCreateMAIJ(mpiaij->A, -dof, &b->AIJ));
1154: PetscCall(MatCreateMAIJ(mpiaij->B, -dof, &b->OAIJ));
1156: PetscCall(VecGetSize(mpiaij->lvec, &n));
1157: PetscCall(VecCreate(PETSC_COMM_SELF, &b->w));
1158: PetscCall(VecSetSizes(b->w, n * dof, n * dof));
1159: PetscCall(VecSetBlockSize(b->w, dof));
1160: PetscCall(VecSetType(b->w, VECSEQ));
1162: /* create two temporary Index sets for build scatter gather */
1163: PetscCall(ISCreateBlock(PetscObjectComm((PetscObject)A), dof, n, mpiaij->garray, PETSC_COPY_VALUES, &from));
1164: PetscCall(ISCreateStride(PETSC_COMM_SELF, n * dof, 0, 1, &to));
1166: /* create temporary global vector to generate scatter context */
1167: PetscCall(VecCreateMPIWithArray(PetscObjectComm((PetscObject)A), dof, dof * A->cmap->n, dof * A->cmap->N, NULL, &gvec));
1169: /* generate the scatter context */
1170: PetscCall(VecScatterCreate(gvec, from, b->w, to, &b->ctx));
1172: PetscCall(ISDestroy(&from));
1173: PetscCall(ISDestroy(&to));
1174: PetscCall(VecDestroy(&gvec));
1176: B->ops->mult = MatMult_MPIMAIJ_dof;
1177: B->ops->multtranspose = MatMultTranspose_MPIMAIJ_dof;
1178: B->ops->multadd = MatMultAdd_MPIMAIJ_dof;
1179: B->ops->multtransposeadd = MatMultTransposeAdd_MPIMAIJ_dof;
1181: #if defined(PETSC_HAVE_CUDA)
1182: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpimaij_mpiaijcusparse_C", MatConvert_MPIMAIJ_MPIAIJ));
1183: #endif
1184: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_mpimaij_mpiaij_C", MatConvert_MPIMAIJ_MPIAIJ));
1185: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_mpiaij_mpimaij_C", MatProductSetFromOptions_MPIAIJ_MPIMAIJ));
1186: }
1187: B->ops->createsubmatrix = MatCreateSubMatrix_MAIJ;
1188: B->ops->createsubmatrices = MatCreateSubMatrices_MAIJ;
1189: PetscCall(MatSetUp(B));
1190: #if defined(PETSC_HAVE_CUDA)
1191: /* temporary until we have CUDA implementation of MAIJ */
1192: {
1193: PetscBool flg;
1194: if (convert) {
1195: PetscCall(PetscObjectTypeCompareAny((PetscObject)A, &flg, MATSEQAIJCUSPARSE, MATMPIAIJCUSPARSE, MATAIJCUSPARSE, ""));
1196: if (flg) PetscCall(MatConvert(B, ((PetscObject)A)->type_name, MAT_INPLACE_MATRIX, &B));
1197: }
1198: }
1199: #endif
1200: *maij = B;
1201: }
1202: PetscFunctionReturn(PETSC_SUCCESS);
1203: }