Actual source code: baij.c
2: /*
3: Defines the basic matrix operations for the BAIJ (compressed row)
4: matrix storage format.
5: */
6: #include <../src/mat/impls/baij/seq/baij.h>
7: #include <petscblaslapack.h>
8: #include <petsc/private/kernels/blockinvert.h>
9: #include <petsc/private/kernels/blockmatmult.h>
11: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
12: #define TYPE BAIJ
13: #define TYPE_BS
14: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
15: #undef TYPE_BS
16: #define TYPE_BS _BS
17: #define TYPE_BS_ON
18: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
19: #undef TYPE_BS
20: #include "../src/mat/impls/aij/seq/seqhashmat.h"
21: #undef TYPE
22: #undef TYPE_BS_ON
24: #if defined(PETSC_HAVE_HYPRE)
25: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
26: #endif
28: #if defined(PETSC_HAVE_MKL_SPARSE_OPTIMIZE)
29: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat, MatType, MatReuse, Mat *);
30: #endif
31: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
33: static PetscErrorCode MatGetColumnReductions_SeqBAIJ(Mat A, PetscInt type, PetscReal *reductions)
34: {
35: Mat_SeqBAIJ *a_aij = (Mat_SeqBAIJ *)A->data;
36: PetscInt m, n, ib, jb, bs = A->rmap->bs;
37: MatScalar *a_val = a_aij->a;
39: PetscFunctionBegin;
40: PetscCall(MatGetSize(A, &m, &n));
41: PetscCall(PetscArrayzero(reductions, n));
42: if (type == NORM_2) {
43: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
44: for (jb = 0; jb < bs; jb++) {
45: for (ib = 0; ib < bs; ib++) {
46: reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val * *a_val);
47: a_val++;
48: }
49: }
50: }
51: } else if (type == NORM_1) {
52: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
53: for (jb = 0; jb < bs; jb++) {
54: for (ib = 0; ib < bs; ib++) {
55: reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscAbsScalar(*a_val);
56: a_val++;
57: }
58: }
59: }
60: } else if (type == NORM_INFINITY) {
61: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
62: for (jb = 0; jb < bs; jb++) {
63: for (ib = 0; ib < bs; ib++) {
64: int col = A->cmap->rstart + a_aij->j[i] * bs + jb;
65: reductions[col] = PetscMax(PetscAbsScalar(*a_val), reductions[col]);
66: a_val++;
67: }
68: }
69: }
70: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
71: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
72: for (jb = 0; jb < bs; jb++) {
73: for (ib = 0; ib < bs; ib++) {
74: reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscRealPart(*a_val);
75: a_val++;
76: }
77: }
78: }
79: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
80: for (PetscInt i = a_aij->i[0]; i < a_aij->i[A->rmap->n / bs]; i++) {
81: for (jb = 0; jb < bs; jb++) {
82: for (ib = 0; ib < bs; ib++) {
83: reductions[A->cmap->rstart + a_aij->j[i] * bs + jb] += PetscImaginaryPart(*a_val);
84: a_val++;
85: }
86: }
87: }
88: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
89: if (type == NORM_2) {
90: for (PetscInt i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
91: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
92: for (PetscInt i = 0; i < n; i++) reductions[i] /= m;
93: }
94: PetscFunctionReturn(PETSC_SUCCESS);
95: }
97: PetscErrorCode MatInvertBlockDiagonal_SeqBAIJ(Mat A, const PetscScalar **values)
98: {
99: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
100: PetscInt *diag_offset, i, bs = A->rmap->bs, mbs = a->mbs, ipvt[5], bs2 = bs * bs, *v_pivots;
101: MatScalar *v = a->a, *odiag, *diag, work[25], *v_work;
102: PetscReal shift = 0.0;
103: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
105: PetscFunctionBegin;
106: allowzeropivot = PetscNot(A->erroriffailure);
108: if (a->idiagvalid) {
109: if (values) *values = a->idiag;
110: PetscFunctionReturn(PETSC_SUCCESS);
111: }
112: PetscCall(MatMarkDiagonal_SeqBAIJ(A));
113: diag_offset = a->diag;
114: if (!a->idiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->idiag)); }
115: diag = a->idiag;
116: if (values) *values = a->idiag;
117: /* factor and invert each block */
118: switch (bs) {
119: case 1:
120: for (i = 0; i < mbs; i++) {
121: odiag = v + 1 * diag_offset[i];
122: diag[0] = odiag[0];
124: if (PetscAbsScalar(diag[0] + shift) < PETSC_MACHINE_EPSILON) {
125: if (allowzeropivot) {
126: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
127: A->factorerror_zeropivot_value = PetscAbsScalar(diag[0]);
128: A->factorerror_zeropivot_row = i;
129: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT "\n", i));
130: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot value %g tolerance %g", i, (double)PetscAbsScalar(diag[0]), (double)PETSC_MACHINE_EPSILON);
131: }
133: diag[0] = (PetscScalar)1.0 / (diag[0] + shift);
134: diag += 1;
135: }
136: break;
137: case 2:
138: for (i = 0; i < mbs; i++) {
139: odiag = v + 4 * diag_offset[i];
140: diag[0] = odiag[0];
141: diag[1] = odiag[1];
142: diag[2] = odiag[2];
143: diag[3] = odiag[3];
144: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
145: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
146: diag += 4;
147: }
148: break;
149: case 3:
150: for (i = 0; i < mbs; i++) {
151: odiag = v + 9 * diag_offset[i];
152: diag[0] = odiag[0];
153: diag[1] = odiag[1];
154: diag[2] = odiag[2];
155: diag[3] = odiag[3];
156: diag[4] = odiag[4];
157: diag[5] = odiag[5];
158: diag[6] = odiag[6];
159: diag[7] = odiag[7];
160: diag[8] = odiag[8];
161: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
162: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
163: diag += 9;
164: }
165: break;
166: case 4:
167: for (i = 0; i < mbs; i++) {
168: odiag = v + 16 * diag_offset[i];
169: PetscCall(PetscArraycpy(diag, odiag, 16));
170: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
171: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
172: diag += 16;
173: }
174: break;
175: case 5:
176: for (i = 0; i < mbs; i++) {
177: odiag = v + 25 * diag_offset[i];
178: PetscCall(PetscArraycpy(diag, odiag, 25));
179: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
180: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
181: diag += 25;
182: }
183: break;
184: case 6:
185: for (i = 0; i < mbs; i++) {
186: odiag = v + 36 * diag_offset[i];
187: PetscCall(PetscArraycpy(diag, odiag, 36));
188: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
189: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
190: diag += 36;
191: }
192: break;
193: case 7:
194: for (i = 0; i < mbs; i++) {
195: odiag = v + 49 * diag_offset[i];
196: PetscCall(PetscArraycpy(diag, odiag, 49));
197: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
198: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
199: diag += 49;
200: }
201: break;
202: default:
203: PetscCall(PetscMalloc2(bs, &v_work, bs, &v_pivots));
204: for (i = 0; i < mbs; i++) {
205: odiag = v + bs2 * diag_offset[i];
206: PetscCall(PetscArraycpy(diag, odiag, bs2));
207: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
208: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
209: diag += bs2;
210: }
211: PetscCall(PetscFree2(v_work, v_pivots));
212: }
213: a->idiagvalid = PETSC_TRUE;
214: PetscFunctionReturn(PETSC_SUCCESS);
215: }
217: PetscErrorCode MatSOR_SeqBAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
218: {
219: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
220: PetscScalar *x, *work, *w, *workt, *t;
221: const MatScalar *v, *aa = a->a, *idiag;
222: const PetscScalar *b, *xb;
223: PetscScalar s[7], xw[7] = {0}; /* avoid some compilers thinking xw is uninitialized */
224: PetscInt m = a->mbs, i, i2, nz, bs = A->rmap->bs, bs2 = bs * bs, k, j, idx, it;
225: const PetscInt *diag, *ai = a->i, *aj = a->j, *vi;
227: PetscFunctionBegin;
228: its = its * lits;
229: PetscCheck(!(flag & SOR_EISENSTAT), PETSC_COMM_SELF, PETSC_ERR_SUP, "No support yet for Eisenstat");
230: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " and local its %" PetscInt_FMT " both positive", its, lits);
231: PetscCheck(!fshift, PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for diagonal shift");
232: PetscCheck(omega == 1.0, PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for non-trivial relaxation factor");
233: PetscCheck(!(flag & SOR_APPLY_UPPER) && !(flag & SOR_APPLY_LOWER), PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for applying upper or lower triangular parts");
235: if (!a->idiagvalid) PetscCall(MatInvertBlockDiagonal(A, NULL));
237: if (!m) PetscFunctionReturn(PETSC_SUCCESS);
238: diag = a->diag;
239: idiag = a->idiag;
240: k = PetscMax(A->rmap->n, A->cmap->n);
241: if (!a->mult_work) PetscCall(PetscMalloc1(k + 1, &a->mult_work));
242: if (!a->sor_workt) PetscCall(PetscMalloc1(k, &a->sor_workt));
243: if (!a->sor_work) PetscCall(PetscMalloc1(bs, &a->sor_work));
244: work = a->mult_work;
245: t = a->sor_workt;
246: w = a->sor_work;
248: PetscCall(VecGetArray(xx, &x));
249: PetscCall(VecGetArrayRead(bb, &b));
251: if (flag & SOR_ZERO_INITIAL_GUESS) {
252: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
253: switch (bs) {
254: case 1:
255: PetscKernel_v_gets_A_times_w_1(x, idiag, b);
256: t[0] = b[0];
257: i2 = 1;
258: idiag += 1;
259: for (i = 1; i < m; i++) {
260: v = aa + ai[i];
261: vi = aj + ai[i];
262: nz = diag[i] - ai[i];
263: s[0] = b[i2];
264: for (j = 0; j < nz; j++) {
265: xw[0] = x[vi[j]];
266: PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
267: }
268: t[i2] = s[0];
269: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
270: x[i2] = xw[0];
271: idiag += 1;
272: i2 += 1;
273: }
274: break;
275: case 2:
276: PetscKernel_v_gets_A_times_w_2(x, idiag, b);
277: t[0] = b[0];
278: t[1] = b[1];
279: i2 = 2;
280: idiag += 4;
281: for (i = 1; i < m; i++) {
282: v = aa + 4 * ai[i];
283: vi = aj + ai[i];
284: nz = diag[i] - ai[i];
285: s[0] = b[i2];
286: s[1] = b[i2 + 1];
287: for (j = 0; j < nz; j++) {
288: idx = 2 * vi[j];
289: it = 4 * j;
290: xw[0] = x[idx];
291: xw[1] = x[1 + idx];
292: PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
293: }
294: t[i2] = s[0];
295: t[i2 + 1] = s[1];
296: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
297: x[i2] = xw[0];
298: x[i2 + 1] = xw[1];
299: idiag += 4;
300: i2 += 2;
301: }
302: break;
303: case 3:
304: PetscKernel_v_gets_A_times_w_3(x, idiag, b);
305: t[0] = b[0];
306: t[1] = b[1];
307: t[2] = b[2];
308: i2 = 3;
309: idiag += 9;
310: for (i = 1; i < m; i++) {
311: v = aa + 9 * ai[i];
312: vi = aj + ai[i];
313: nz = diag[i] - ai[i];
314: s[0] = b[i2];
315: s[1] = b[i2 + 1];
316: s[2] = b[i2 + 2];
317: while (nz--) {
318: idx = 3 * (*vi++);
319: xw[0] = x[idx];
320: xw[1] = x[1 + idx];
321: xw[2] = x[2 + idx];
322: PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
323: v += 9;
324: }
325: t[i2] = s[0];
326: t[i2 + 1] = s[1];
327: t[i2 + 2] = s[2];
328: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
329: x[i2] = xw[0];
330: x[i2 + 1] = xw[1];
331: x[i2 + 2] = xw[2];
332: idiag += 9;
333: i2 += 3;
334: }
335: break;
336: case 4:
337: PetscKernel_v_gets_A_times_w_4(x, idiag, b);
338: t[0] = b[0];
339: t[1] = b[1];
340: t[2] = b[2];
341: t[3] = b[3];
342: i2 = 4;
343: idiag += 16;
344: for (i = 1; i < m; i++) {
345: v = aa + 16 * ai[i];
346: vi = aj + ai[i];
347: nz = diag[i] - ai[i];
348: s[0] = b[i2];
349: s[1] = b[i2 + 1];
350: s[2] = b[i2 + 2];
351: s[3] = b[i2 + 3];
352: while (nz--) {
353: idx = 4 * (*vi++);
354: xw[0] = x[idx];
355: xw[1] = x[1 + idx];
356: xw[2] = x[2 + idx];
357: xw[3] = x[3 + idx];
358: PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
359: v += 16;
360: }
361: t[i2] = s[0];
362: t[i2 + 1] = s[1];
363: t[i2 + 2] = s[2];
364: t[i2 + 3] = s[3];
365: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
366: x[i2] = xw[0];
367: x[i2 + 1] = xw[1];
368: x[i2 + 2] = xw[2];
369: x[i2 + 3] = xw[3];
370: idiag += 16;
371: i2 += 4;
372: }
373: break;
374: case 5:
375: PetscKernel_v_gets_A_times_w_5(x, idiag, b);
376: t[0] = b[0];
377: t[1] = b[1];
378: t[2] = b[2];
379: t[3] = b[3];
380: t[4] = b[4];
381: i2 = 5;
382: idiag += 25;
383: for (i = 1; i < m; i++) {
384: v = aa + 25 * ai[i];
385: vi = aj + ai[i];
386: nz = diag[i] - ai[i];
387: s[0] = b[i2];
388: s[1] = b[i2 + 1];
389: s[2] = b[i2 + 2];
390: s[3] = b[i2 + 3];
391: s[4] = b[i2 + 4];
392: while (nz--) {
393: idx = 5 * (*vi++);
394: xw[0] = x[idx];
395: xw[1] = x[1 + idx];
396: xw[2] = x[2 + idx];
397: xw[3] = x[3 + idx];
398: xw[4] = x[4 + idx];
399: PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
400: v += 25;
401: }
402: t[i2] = s[0];
403: t[i2 + 1] = s[1];
404: t[i2 + 2] = s[2];
405: t[i2 + 3] = s[3];
406: t[i2 + 4] = s[4];
407: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
408: x[i2] = xw[0];
409: x[i2 + 1] = xw[1];
410: x[i2 + 2] = xw[2];
411: x[i2 + 3] = xw[3];
412: x[i2 + 4] = xw[4];
413: idiag += 25;
414: i2 += 5;
415: }
416: break;
417: case 6:
418: PetscKernel_v_gets_A_times_w_6(x, idiag, b);
419: t[0] = b[0];
420: t[1] = b[1];
421: t[2] = b[2];
422: t[3] = b[3];
423: t[4] = b[4];
424: t[5] = b[5];
425: i2 = 6;
426: idiag += 36;
427: for (i = 1; i < m; i++) {
428: v = aa + 36 * ai[i];
429: vi = aj + ai[i];
430: nz = diag[i] - ai[i];
431: s[0] = b[i2];
432: s[1] = b[i2 + 1];
433: s[2] = b[i2 + 2];
434: s[3] = b[i2 + 3];
435: s[4] = b[i2 + 4];
436: s[5] = b[i2 + 5];
437: while (nz--) {
438: idx = 6 * (*vi++);
439: xw[0] = x[idx];
440: xw[1] = x[1 + idx];
441: xw[2] = x[2 + idx];
442: xw[3] = x[3 + idx];
443: xw[4] = x[4 + idx];
444: xw[5] = x[5 + idx];
445: PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
446: v += 36;
447: }
448: t[i2] = s[0];
449: t[i2 + 1] = s[1];
450: t[i2 + 2] = s[2];
451: t[i2 + 3] = s[3];
452: t[i2 + 4] = s[4];
453: t[i2 + 5] = s[5];
454: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
455: x[i2] = xw[0];
456: x[i2 + 1] = xw[1];
457: x[i2 + 2] = xw[2];
458: x[i2 + 3] = xw[3];
459: x[i2 + 4] = xw[4];
460: x[i2 + 5] = xw[5];
461: idiag += 36;
462: i2 += 6;
463: }
464: break;
465: case 7:
466: PetscKernel_v_gets_A_times_w_7(x, idiag, b);
467: t[0] = b[0];
468: t[1] = b[1];
469: t[2] = b[2];
470: t[3] = b[3];
471: t[4] = b[4];
472: t[5] = b[5];
473: t[6] = b[6];
474: i2 = 7;
475: idiag += 49;
476: for (i = 1; i < m; i++) {
477: v = aa + 49 * ai[i];
478: vi = aj + ai[i];
479: nz = diag[i] - ai[i];
480: s[0] = b[i2];
481: s[1] = b[i2 + 1];
482: s[2] = b[i2 + 2];
483: s[3] = b[i2 + 3];
484: s[4] = b[i2 + 4];
485: s[5] = b[i2 + 5];
486: s[6] = b[i2 + 6];
487: while (nz--) {
488: idx = 7 * (*vi++);
489: xw[0] = x[idx];
490: xw[1] = x[1 + idx];
491: xw[2] = x[2 + idx];
492: xw[3] = x[3 + idx];
493: xw[4] = x[4 + idx];
494: xw[5] = x[5 + idx];
495: xw[6] = x[6 + idx];
496: PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
497: v += 49;
498: }
499: t[i2] = s[0];
500: t[i2 + 1] = s[1];
501: t[i2 + 2] = s[2];
502: t[i2 + 3] = s[3];
503: t[i2 + 4] = s[4];
504: t[i2 + 5] = s[5];
505: t[i2 + 6] = s[6];
506: PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
507: x[i2] = xw[0];
508: x[i2 + 1] = xw[1];
509: x[i2 + 2] = xw[2];
510: x[i2 + 3] = xw[3];
511: x[i2 + 4] = xw[4];
512: x[i2 + 5] = xw[5];
513: x[i2 + 6] = xw[6];
514: idiag += 49;
515: i2 += 7;
516: }
517: break;
518: default:
519: PetscKernel_w_gets_Ar_times_v(bs, bs, b, idiag, x);
520: PetscCall(PetscArraycpy(t, b, bs));
521: i2 = bs;
522: idiag += bs2;
523: for (i = 1; i < m; i++) {
524: v = aa + bs2 * ai[i];
525: vi = aj + ai[i];
526: nz = diag[i] - ai[i];
528: PetscCall(PetscArraycpy(w, b + i2, bs));
529: /* copy all rows of x that are needed into contiguous space */
530: workt = work;
531: for (j = 0; j < nz; j++) {
532: PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
533: workt += bs;
534: }
535: PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
536: PetscCall(PetscArraycpy(t + i2, w, bs));
537: PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);
539: idiag += bs2;
540: i2 += bs;
541: }
542: break;
543: }
544: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
545: PetscCall(PetscLogFlops(1.0 * bs2 * a->nz));
546: xb = t;
547: } else xb = b;
548: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
549: idiag = a->idiag + bs2 * (a->mbs - 1);
550: i2 = bs * (m - 1);
551: switch (bs) {
552: case 1:
553: s[0] = xb[i2];
554: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
555: x[i2] = xw[0];
556: i2 -= 1;
557: for (i = m - 2; i >= 0; i--) {
558: v = aa + (diag[i] + 1);
559: vi = aj + diag[i] + 1;
560: nz = ai[i + 1] - diag[i] - 1;
561: s[0] = xb[i2];
562: for (j = 0; j < nz; j++) {
563: xw[0] = x[vi[j]];
564: PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
565: }
566: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
567: x[i2] = xw[0];
568: idiag -= 1;
569: i2 -= 1;
570: }
571: break;
572: case 2:
573: s[0] = xb[i2];
574: s[1] = xb[i2 + 1];
575: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
576: x[i2] = xw[0];
577: x[i2 + 1] = xw[1];
578: i2 -= 2;
579: idiag -= 4;
580: for (i = m - 2; i >= 0; i--) {
581: v = aa + 4 * (diag[i] + 1);
582: vi = aj + diag[i] + 1;
583: nz = ai[i + 1] - diag[i] - 1;
584: s[0] = xb[i2];
585: s[1] = xb[i2 + 1];
586: for (j = 0; j < nz; j++) {
587: idx = 2 * vi[j];
588: it = 4 * j;
589: xw[0] = x[idx];
590: xw[1] = x[1 + idx];
591: PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
592: }
593: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
594: x[i2] = xw[0];
595: x[i2 + 1] = xw[1];
596: idiag -= 4;
597: i2 -= 2;
598: }
599: break;
600: case 3:
601: s[0] = xb[i2];
602: s[1] = xb[i2 + 1];
603: s[2] = xb[i2 + 2];
604: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
605: x[i2] = xw[0];
606: x[i2 + 1] = xw[1];
607: x[i2 + 2] = xw[2];
608: i2 -= 3;
609: idiag -= 9;
610: for (i = m - 2; i >= 0; i--) {
611: v = aa + 9 * (diag[i] + 1);
612: vi = aj + diag[i] + 1;
613: nz = ai[i + 1] - diag[i] - 1;
614: s[0] = xb[i2];
615: s[1] = xb[i2 + 1];
616: s[2] = xb[i2 + 2];
617: while (nz--) {
618: idx = 3 * (*vi++);
619: xw[0] = x[idx];
620: xw[1] = x[1 + idx];
621: xw[2] = x[2 + idx];
622: PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
623: v += 9;
624: }
625: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
626: x[i2] = xw[0];
627: x[i2 + 1] = xw[1];
628: x[i2 + 2] = xw[2];
629: idiag -= 9;
630: i2 -= 3;
631: }
632: break;
633: case 4:
634: s[0] = xb[i2];
635: s[1] = xb[i2 + 1];
636: s[2] = xb[i2 + 2];
637: s[3] = xb[i2 + 3];
638: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
639: x[i2] = xw[0];
640: x[i2 + 1] = xw[1];
641: x[i2 + 2] = xw[2];
642: x[i2 + 3] = xw[3];
643: i2 -= 4;
644: idiag -= 16;
645: for (i = m - 2; i >= 0; i--) {
646: v = aa + 16 * (diag[i] + 1);
647: vi = aj + diag[i] + 1;
648: nz = ai[i + 1] - diag[i] - 1;
649: s[0] = xb[i2];
650: s[1] = xb[i2 + 1];
651: s[2] = xb[i2 + 2];
652: s[3] = xb[i2 + 3];
653: while (nz--) {
654: idx = 4 * (*vi++);
655: xw[0] = x[idx];
656: xw[1] = x[1 + idx];
657: xw[2] = x[2 + idx];
658: xw[3] = x[3 + idx];
659: PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
660: v += 16;
661: }
662: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
663: x[i2] = xw[0];
664: x[i2 + 1] = xw[1];
665: x[i2 + 2] = xw[2];
666: x[i2 + 3] = xw[3];
667: idiag -= 16;
668: i2 -= 4;
669: }
670: break;
671: case 5:
672: s[0] = xb[i2];
673: s[1] = xb[i2 + 1];
674: s[2] = xb[i2 + 2];
675: s[3] = xb[i2 + 3];
676: s[4] = xb[i2 + 4];
677: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
678: x[i2] = xw[0];
679: x[i2 + 1] = xw[1];
680: x[i2 + 2] = xw[2];
681: x[i2 + 3] = xw[3];
682: x[i2 + 4] = xw[4];
683: i2 -= 5;
684: idiag -= 25;
685: for (i = m - 2; i >= 0; i--) {
686: v = aa + 25 * (diag[i] + 1);
687: vi = aj + diag[i] + 1;
688: nz = ai[i + 1] - diag[i] - 1;
689: s[0] = xb[i2];
690: s[1] = xb[i2 + 1];
691: s[2] = xb[i2 + 2];
692: s[3] = xb[i2 + 3];
693: s[4] = xb[i2 + 4];
694: while (nz--) {
695: idx = 5 * (*vi++);
696: xw[0] = x[idx];
697: xw[1] = x[1 + idx];
698: xw[2] = x[2 + idx];
699: xw[3] = x[3 + idx];
700: xw[4] = x[4 + idx];
701: PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
702: v += 25;
703: }
704: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
705: x[i2] = xw[0];
706: x[i2 + 1] = xw[1];
707: x[i2 + 2] = xw[2];
708: x[i2 + 3] = xw[3];
709: x[i2 + 4] = xw[4];
710: idiag -= 25;
711: i2 -= 5;
712: }
713: break;
714: case 6:
715: s[0] = xb[i2];
716: s[1] = xb[i2 + 1];
717: s[2] = xb[i2 + 2];
718: s[3] = xb[i2 + 3];
719: s[4] = xb[i2 + 4];
720: s[5] = xb[i2 + 5];
721: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
722: x[i2] = xw[0];
723: x[i2 + 1] = xw[1];
724: x[i2 + 2] = xw[2];
725: x[i2 + 3] = xw[3];
726: x[i2 + 4] = xw[4];
727: x[i2 + 5] = xw[5];
728: i2 -= 6;
729: idiag -= 36;
730: for (i = m - 2; i >= 0; i--) {
731: v = aa + 36 * (diag[i] + 1);
732: vi = aj + diag[i] + 1;
733: nz = ai[i + 1] - diag[i] - 1;
734: s[0] = xb[i2];
735: s[1] = xb[i2 + 1];
736: s[2] = xb[i2 + 2];
737: s[3] = xb[i2 + 3];
738: s[4] = xb[i2 + 4];
739: s[5] = xb[i2 + 5];
740: while (nz--) {
741: idx = 6 * (*vi++);
742: xw[0] = x[idx];
743: xw[1] = x[1 + idx];
744: xw[2] = x[2 + idx];
745: xw[3] = x[3 + idx];
746: xw[4] = x[4 + idx];
747: xw[5] = x[5 + idx];
748: PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
749: v += 36;
750: }
751: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
752: x[i2] = xw[0];
753: x[i2 + 1] = xw[1];
754: x[i2 + 2] = xw[2];
755: x[i2 + 3] = xw[3];
756: x[i2 + 4] = xw[4];
757: x[i2 + 5] = xw[5];
758: idiag -= 36;
759: i2 -= 6;
760: }
761: break;
762: case 7:
763: s[0] = xb[i2];
764: s[1] = xb[i2 + 1];
765: s[2] = xb[i2 + 2];
766: s[3] = xb[i2 + 3];
767: s[4] = xb[i2 + 4];
768: s[5] = xb[i2 + 5];
769: s[6] = xb[i2 + 6];
770: PetscKernel_v_gets_A_times_w_7(x, idiag, b);
771: x[i2] = xw[0];
772: x[i2 + 1] = xw[1];
773: x[i2 + 2] = xw[2];
774: x[i2 + 3] = xw[3];
775: x[i2 + 4] = xw[4];
776: x[i2 + 5] = xw[5];
777: x[i2 + 6] = xw[6];
778: i2 -= 7;
779: idiag -= 49;
780: for (i = m - 2; i >= 0; i--) {
781: v = aa + 49 * (diag[i] + 1);
782: vi = aj + diag[i] + 1;
783: nz = ai[i + 1] - diag[i] - 1;
784: s[0] = xb[i2];
785: s[1] = xb[i2 + 1];
786: s[2] = xb[i2 + 2];
787: s[3] = xb[i2 + 3];
788: s[4] = xb[i2 + 4];
789: s[5] = xb[i2 + 5];
790: s[6] = xb[i2 + 6];
791: while (nz--) {
792: idx = 7 * (*vi++);
793: xw[0] = x[idx];
794: xw[1] = x[1 + idx];
795: xw[2] = x[2 + idx];
796: xw[3] = x[3 + idx];
797: xw[4] = x[4 + idx];
798: xw[5] = x[5 + idx];
799: xw[6] = x[6 + idx];
800: PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
801: v += 49;
802: }
803: PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
804: x[i2] = xw[0];
805: x[i2 + 1] = xw[1];
806: x[i2 + 2] = xw[2];
807: x[i2 + 3] = xw[3];
808: x[i2 + 4] = xw[4];
809: x[i2 + 5] = xw[5];
810: x[i2 + 6] = xw[6];
811: idiag -= 49;
812: i2 -= 7;
813: }
814: break;
815: default:
816: PetscCall(PetscArraycpy(w, xb + i2, bs));
817: PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);
818: i2 -= bs;
819: idiag -= bs2;
820: for (i = m - 2; i >= 0; i--) {
821: v = aa + bs2 * (diag[i] + 1);
822: vi = aj + diag[i] + 1;
823: nz = ai[i + 1] - diag[i] - 1;
825: PetscCall(PetscArraycpy(w, xb + i2, bs));
826: /* copy all rows of x that are needed into contiguous space */
827: workt = work;
828: for (j = 0; j < nz; j++) {
829: PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
830: workt += bs;
831: }
832: PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
833: PetscKernel_w_gets_Ar_times_v(bs, bs, w, idiag, x + i2);
835: idiag -= bs2;
836: i2 -= bs;
837: }
838: break;
839: }
840: PetscCall(PetscLogFlops(1.0 * bs2 * (a->nz)));
841: }
842: its--;
843: }
844: while (its--) {
845: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
846: idiag = a->idiag;
847: i2 = 0;
848: switch (bs) {
849: case 1:
850: for (i = 0; i < m; i++) {
851: v = aa + ai[i];
852: vi = aj + ai[i];
853: nz = ai[i + 1] - ai[i];
854: s[0] = b[i2];
855: for (j = 0; j < nz; j++) {
856: xw[0] = x[vi[j]];
857: PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
858: }
859: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
860: x[i2] += xw[0];
861: idiag += 1;
862: i2 += 1;
863: }
864: break;
865: case 2:
866: for (i = 0; i < m; i++) {
867: v = aa + 4 * ai[i];
868: vi = aj + ai[i];
869: nz = ai[i + 1] - ai[i];
870: s[0] = b[i2];
871: s[1] = b[i2 + 1];
872: for (j = 0; j < nz; j++) {
873: idx = 2 * vi[j];
874: it = 4 * j;
875: xw[0] = x[idx];
876: xw[1] = x[1 + idx];
877: PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
878: }
879: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
880: x[i2] += xw[0];
881: x[i2 + 1] += xw[1];
882: idiag += 4;
883: i2 += 2;
884: }
885: break;
886: case 3:
887: for (i = 0; i < m; i++) {
888: v = aa + 9 * ai[i];
889: vi = aj + ai[i];
890: nz = ai[i + 1] - ai[i];
891: s[0] = b[i2];
892: s[1] = b[i2 + 1];
893: s[2] = b[i2 + 2];
894: while (nz--) {
895: idx = 3 * (*vi++);
896: xw[0] = x[idx];
897: xw[1] = x[1 + idx];
898: xw[2] = x[2 + idx];
899: PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
900: v += 9;
901: }
902: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
903: x[i2] += xw[0];
904: x[i2 + 1] += xw[1];
905: x[i2 + 2] += xw[2];
906: idiag += 9;
907: i2 += 3;
908: }
909: break;
910: case 4:
911: for (i = 0; i < m; i++) {
912: v = aa + 16 * ai[i];
913: vi = aj + ai[i];
914: nz = ai[i + 1] - ai[i];
915: s[0] = b[i2];
916: s[1] = b[i2 + 1];
917: s[2] = b[i2 + 2];
918: s[3] = b[i2 + 3];
919: while (nz--) {
920: idx = 4 * (*vi++);
921: xw[0] = x[idx];
922: xw[1] = x[1 + idx];
923: xw[2] = x[2 + idx];
924: xw[3] = x[3 + idx];
925: PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
926: v += 16;
927: }
928: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
929: x[i2] += xw[0];
930: x[i2 + 1] += xw[1];
931: x[i2 + 2] += xw[2];
932: x[i2 + 3] += xw[3];
933: idiag += 16;
934: i2 += 4;
935: }
936: break;
937: case 5:
938: for (i = 0; i < m; i++) {
939: v = aa + 25 * ai[i];
940: vi = aj + ai[i];
941: nz = ai[i + 1] - ai[i];
942: s[0] = b[i2];
943: s[1] = b[i2 + 1];
944: s[2] = b[i2 + 2];
945: s[3] = b[i2 + 3];
946: s[4] = b[i2 + 4];
947: while (nz--) {
948: idx = 5 * (*vi++);
949: xw[0] = x[idx];
950: xw[1] = x[1 + idx];
951: xw[2] = x[2 + idx];
952: xw[3] = x[3 + idx];
953: xw[4] = x[4 + idx];
954: PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
955: v += 25;
956: }
957: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
958: x[i2] += xw[0];
959: x[i2 + 1] += xw[1];
960: x[i2 + 2] += xw[2];
961: x[i2 + 3] += xw[3];
962: x[i2 + 4] += xw[4];
963: idiag += 25;
964: i2 += 5;
965: }
966: break;
967: case 6:
968: for (i = 0; i < m; i++) {
969: v = aa + 36 * ai[i];
970: vi = aj + ai[i];
971: nz = ai[i + 1] - ai[i];
972: s[0] = b[i2];
973: s[1] = b[i2 + 1];
974: s[2] = b[i2 + 2];
975: s[3] = b[i2 + 3];
976: s[4] = b[i2 + 4];
977: s[5] = b[i2 + 5];
978: while (nz--) {
979: idx = 6 * (*vi++);
980: xw[0] = x[idx];
981: xw[1] = x[1 + idx];
982: xw[2] = x[2 + idx];
983: xw[3] = x[3 + idx];
984: xw[4] = x[4 + idx];
985: xw[5] = x[5 + idx];
986: PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
987: v += 36;
988: }
989: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
990: x[i2] += xw[0];
991: x[i2 + 1] += xw[1];
992: x[i2 + 2] += xw[2];
993: x[i2 + 3] += xw[3];
994: x[i2 + 4] += xw[4];
995: x[i2 + 5] += xw[5];
996: idiag += 36;
997: i2 += 6;
998: }
999: break;
1000: case 7:
1001: for (i = 0; i < m; i++) {
1002: v = aa + 49 * ai[i];
1003: vi = aj + ai[i];
1004: nz = ai[i + 1] - ai[i];
1005: s[0] = b[i2];
1006: s[1] = b[i2 + 1];
1007: s[2] = b[i2 + 2];
1008: s[3] = b[i2 + 3];
1009: s[4] = b[i2 + 4];
1010: s[5] = b[i2 + 5];
1011: s[6] = b[i2 + 6];
1012: while (nz--) {
1013: idx = 7 * (*vi++);
1014: xw[0] = x[idx];
1015: xw[1] = x[1 + idx];
1016: xw[2] = x[2 + idx];
1017: xw[3] = x[3 + idx];
1018: xw[4] = x[4 + idx];
1019: xw[5] = x[5 + idx];
1020: xw[6] = x[6 + idx];
1021: PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
1022: v += 49;
1023: }
1024: PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
1025: x[i2] += xw[0];
1026: x[i2 + 1] += xw[1];
1027: x[i2 + 2] += xw[2];
1028: x[i2 + 3] += xw[3];
1029: x[i2 + 4] += xw[4];
1030: x[i2 + 5] += xw[5];
1031: x[i2 + 6] += xw[6];
1032: idiag += 49;
1033: i2 += 7;
1034: }
1035: break;
1036: default:
1037: for (i = 0; i < m; i++) {
1038: v = aa + bs2 * ai[i];
1039: vi = aj + ai[i];
1040: nz = ai[i + 1] - ai[i];
1042: PetscCall(PetscArraycpy(w, b + i2, bs));
1043: /* copy all rows of x that are needed into contiguous space */
1044: workt = work;
1045: for (j = 0; j < nz; j++) {
1046: PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
1047: workt += bs;
1048: }
1049: PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
1050: PetscKernel_w_gets_w_plus_Ar_times_v(bs, bs, w, idiag, x + i2);
1052: idiag += bs2;
1053: i2 += bs;
1054: }
1055: break;
1056: }
1057: PetscCall(PetscLogFlops(2.0 * bs2 * a->nz));
1058: }
1059: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1060: idiag = a->idiag + bs2 * (a->mbs - 1);
1061: i2 = bs * (m - 1);
1062: switch (bs) {
1063: case 1:
1064: for (i = m - 1; i >= 0; i--) {
1065: v = aa + ai[i];
1066: vi = aj + ai[i];
1067: nz = ai[i + 1] - ai[i];
1068: s[0] = b[i2];
1069: for (j = 0; j < nz; j++) {
1070: xw[0] = x[vi[j]];
1071: PetscKernel_v_gets_v_minus_A_times_w_1(s, (v + j), xw);
1072: }
1073: PetscKernel_v_gets_A_times_w_1(xw, idiag, s);
1074: x[i2] += xw[0];
1075: idiag -= 1;
1076: i2 -= 1;
1077: }
1078: break;
1079: case 2:
1080: for (i = m - 1; i >= 0; i--) {
1081: v = aa + 4 * ai[i];
1082: vi = aj + ai[i];
1083: nz = ai[i + 1] - ai[i];
1084: s[0] = b[i2];
1085: s[1] = b[i2 + 1];
1086: for (j = 0; j < nz; j++) {
1087: idx = 2 * vi[j];
1088: it = 4 * j;
1089: xw[0] = x[idx];
1090: xw[1] = x[1 + idx];
1091: PetscKernel_v_gets_v_minus_A_times_w_2(s, (v + it), xw);
1092: }
1093: PetscKernel_v_gets_A_times_w_2(xw, idiag, s);
1094: x[i2] += xw[0];
1095: x[i2 + 1] += xw[1];
1096: idiag -= 4;
1097: i2 -= 2;
1098: }
1099: break;
1100: case 3:
1101: for (i = m - 1; i >= 0; i--) {
1102: v = aa + 9 * ai[i];
1103: vi = aj + ai[i];
1104: nz = ai[i + 1] - ai[i];
1105: s[0] = b[i2];
1106: s[1] = b[i2 + 1];
1107: s[2] = b[i2 + 2];
1108: while (nz--) {
1109: idx = 3 * (*vi++);
1110: xw[0] = x[idx];
1111: xw[1] = x[1 + idx];
1112: xw[2] = x[2 + idx];
1113: PetscKernel_v_gets_v_minus_A_times_w_3(s, v, xw);
1114: v += 9;
1115: }
1116: PetscKernel_v_gets_A_times_w_3(xw, idiag, s);
1117: x[i2] += xw[0];
1118: x[i2 + 1] += xw[1];
1119: x[i2 + 2] += xw[2];
1120: idiag -= 9;
1121: i2 -= 3;
1122: }
1123: break;
1124: case 4:
1125: for (i = m - 1; i >= 0; i--) {
1126: v = aa + 16 * ai[i];
1127: vi = aj + ai[i];
1128: nz = ai[i + 1] - ai[i];
1129: s[0] = b[i2];
1130: s[1] = b[i2 + 1];
1131: s[2] = b[i2 + 2];
1132: s[3] = b[i2 + 3];
1133: while (nz--) {
1134: idx = 4 * (*vi++);
1135: xw[0] = x[idx];
1136: xw[1] = x[1 + idx];
1137: xw[2] = x[2 + idx];
1138: xw[3] = x[3 + idx];
1139: PetscKernel_v_gets_v_minus_A_times_w_4(s, v, xw);
1140: v += 16;
1141: }
1142: PetscKernel_v_gets_A_times_w_4(xw, idiag, s);
1143: x[i2] += xw[0];
1144: x[i2 + 1] += xw[1];
1145: x[i2 + 2] += xw[2];
1146: x[i2 + 3] += xw[3];
1147: idiag -= 16;
1148: i2 -= 4;
1149: }
1150: break;
1151: case 5:
1152: for (i = m - 1; i >= 0; i--) {
1153: v = aa + 25 * ai[i];
1154: vi = aj + ai[i];
1155: nz = ai[i + 1] - ai[i];
1156: s[0] = b[i2];
1157: s[1] = b[i2 + 1];
1158: s[2] = b[i2 + 2];
1159: s[3] = b[i2 + 3];
1160: s[4] = b[i2 + 4];
1161: while (nz--) {
1162: idx = 5 * (*vi++);
1163: xw[0] = x[idx];
1164: xw[1] = x[1 + idx];
1165: xw[2] = x[2 + idx];
1166: xw[3] = x[3 + idx];
1167: xw[4] = x[4 + idx];
1168: PetscKernel_v_gets_v_minus_A_times_w_5(s, v, xw);
1169: v += 25;
1170: }
1171: PetscKernel_v_gets_A_times_w_5(xw, idiag, s);
1172: x[i2] += xw[0];
1173: x[i2 + 1] += xw[1];
1174: x[i2 + 2] += xw[2];
1175: x[i2 + 3] += xw[3];
1176: x[i2 + 4] += xw[4];
1177: idiag -= 25;
1178: i2 -= 5;
1179: }
1180: break;
1181: case 6:
1182: for (i = m - 1; i >= 0; i--) {
1183: v = aa + 36 * ai[i];
1184: vi = aj + ai[i];
1185: nz = ai[i + 1] - ai[i];
1186: s[0] = b[i2];
1187: s[1] = b[i2 + 1];
1188: s[2] = b[i2 + 2];
1189: s[3] = b[i2 + 3];
1190: s[4] = b[i2 + 4];
1191: s[5] = b[i2 + 5];
1192: while (nz--) {
1193: idx = 6 * (*vi++);
1194: xw[0] = x[idx];
1195: xw[1] = x[1 + idx];
1196: xw[2] = x[2 + idx];
1197: xw[3] = x[3 + idx];
1198: xw[4] = x[4 + idx];
1199: xw[5] = x[5 + idx];
1200: PetscKernel_v_gets_v_minus_A_times_w_6(s, v, xw);
1201: v += 36;
1202: }
1203: PetscKernel_v_gets_A_times_w_6(xw, idiag, s);
1204: x[i2] += xw[0];
1205: x[i2 + 1] += xw[1];
1206: x[i2 + 2] += xw[2];
1207: x[i2 + 3] += xw[3];
1208: x[i2 + 4] += xw[4];
1209: x[i2 + 5] += xw[5];
1210: idiag -= 36;
1211: i2 -= 6;
1212: }
1213: break;
1214: case 7:
1215: for (i = m - 1; i >= 0; i--) {
1216: v = aa + 49 * ai[i];
1217: vi = aj + ai[i];
1218: nz = ai[i + 1] - ai[i];
1219: s[0] = b[i2];
1220: s[1] = b[i2 + 1];
1221: s[2] = b[i2 + 2];
1222: s[3] = b[i2 + 3];
1223: s[4] = b[i2 + 4];
1224: s[5] = b[i2 + 5];
1225: s[6] = b[i2 + 6];
1226: while (nz--) {
1227: idx = 7 * (*vi++);
1228: xw[0] = x[idx];
1229: xw[1] = x[1 + idx];
1230: xw[2] = x[2 + idx];
1231: xw[3] = x[3 + idx];
1232: xw[4] = x[4 + idx];
1233: xw[5] = x[5 + idx];
1234: xw[6] = x[6 + idx];
1235: PetscKernel_v_gets_v_minus_A_times_w_7(s, v, xw);
1236: v += 49;
1237: }
1238: PetscKernel_v_gets_A_times_w_7(xw, idiag, s);
1239: x[i2] += xw[0];
1240: x[i2 + 1] += xw[1];
1241: x[i2 + 2] += xw[2];
1242: x[i2 + 3] += xw[3];
1243: x[i2 + 4] += xw[4];
1244: x[i2 + 5] += xw[5];
1245: x[i2 + 6] += xw[6];
1246: idiag -= 49;
1247: i2 -= 7;
1248: }
1249: break;
1250: default:
1251: for (i = m - 1; i >= 0; i--) {
1252: v = aa + bs2 * ai[i];
1253: vi = aj + ai[i];
1254: nz = ai[i + 1] - ai[i];
1256: PetscCall(PetscArraycpy(w, b + i2, bs));
1257: /* copy all rows of x that are needed into contiguous space */
1258: workt = work;
1259: for (j = 0; j < nz; j++) {
1260: PetscCall(PetscArraycpy(workt, x + bs * (*vi++), bs));
1261: workt += bs;
1262: }
1263: PetscKernel_w_gets_w_minus_Ar_times_v(bs, bs * nz, w, v, work);
1264: PetscKernel_w_gets_w_plus_Ar_times_v(bs, bs, w, idiag, x + i2);
1266: idiag -= bs2;
1267: i2 -= bs;
1268: }
1269: break;
1270: }
1271: PetscCall(PetscLogFlops(2.0 * bs2 * (a->nz)));
1272: }
1273: }
1274: PetscCall(VecRestoreArray(xx, &x));
1275: PetscCall(VecRestoreArrayRead(bb, &b));
1276: PetscFunctionReturn(PETSC_SUCCESS);
1277: }
1279: /*
1280: Special version for direct calls from Fortran (Used in PETSc-fun3d)
1281: */
1282: #if defined(PETSC_HAVE_FORTRAN_CAPS)
1283: #define matsetvaluesblocked4_ MATSETVALUESBLOCKED4
1284: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
1285: #define matsetvaluesblocked4_ matsetvaluesblocked4
1286: #endif
1288: PETSC_EXTERN void matsetvaluesblocked4_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[])
1289: {
1290: Mat A = *AA;
1291: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1292: PetscInt *rp, k, low, high, t, ii, jj, row, nrow, i, col, l, N, m = *mm, n = *nn;
1293: PetscInt *ai = a->i, *ailen = a->ilen;
1294: PetscInt *aj = a->j, stepval, lastcol = -1;
1295: const PetscScalar *value = v;
1296: MatScalar *ap, *aa = a->a, *bap;
1298: PetscFunctionBegin;
1299: if (A->rmap->bs != 4) SETERRABORT(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Can only be called with a block size of 4");
1300: stepval = (n - 1) * 4;
1301: for (k = 0; k < m; k++) { /* loop over added rows */
1302: row = im[k];
1303: rp = aj + ai[row];
1304: ap = aa + 16 * ai[row];
1305: nrow = ailen[row];
1306: low = 0;
1307: high = nrow;
1308: for (l = 0; l < n; l++) { /* loop over added columns */
1309: col = in[l];
1310: if (col <= lastcol) low = 0;
1311: else high = nrow;
1312: lastcol = col;
1313: value = v + k * (stepval + 4 + l) * 4;
1314: while (high - low > 7) {
1315: t = (low + high) / 2;
1316: if (rp[t] > col) high = t;
1317: else low = t;
1318: }
1319: for (i = low; i < high; i++) {
1320: if (rp[i] > col) break;
1321: if (rp[i] == col) {
1322: bap = ap + 16 * i;
1323: for (ii = 0; ii < 4; ii++, value += stepval) {
1324: for (jj = ii; jj < 16; jj += 4) bap[jj] += *value++;
1325: }
1326: goto noinsert2;
1327: }
1328: }
1329: N = nrow++ - 1;
1330: high++; /* added new column index thus must search to one higher than before */
1331: /* shift up all the later entries in this row */
1332: for (ii = N; ii >= i; ii--) {
1333: rp[ii + 1] = rp[ii];
1334: PetscCallVoid(PetscArraycpy(ap + 16 * (ii + 1), ap + 16 * (ii), 16));
1335: }
1336: if (N >= i) PetscCallVoid(PetscArrayzero(ap + 16 * i, 16));
1337: rp[i] = col;
1338: bap = ap + 16 * i;
1339: for (ii = 0; ii < 4; ii++, value += stepval) {
1340: for (jj = ii; jj < 16; jj += 4) bap[jj] = *value++;
1341: }
1342: noinsert2:;
1343: low = i;
1344: }
1345: ailen[row] = nrow;
1346: }
1347: PetscFunctionReturnVoid();
1348: }
1350: #if defined(PETSC_HAVE_FORTRAN_CAPS)
1351: #define matsetvalues4_ MATSETVALUES4
1352: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
1353: #define matsetvalues4_ matsetvalues4
1354: #endif
1356: PETSC_EXTERN void matsetvalues4_(Mat *AA, PetscInt *mm, PetscInt *im, PetscInt *nn, PetscInt *in, PetscScalar *v)
1357: {
1358: Mat A = *AA;
1359: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1360: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, N, n = *nn, m = *mm;
1361: PetscInt *ai = a->i, *ailen = a->ilen;
1362: PetscInt *aj = a->j, brow, bcol;
1363: PetscInt ridx, cidx, lastcol = -1;
1364: MatScalar *ap, value, *aa = a->a, *bap;
1366: PetscFunctionBegin;
1367: for (k = 0; k < m; k++) { /* loop over added rows */
1368: row = im[k];
1369: brow = row / 4;
1370: rp = aj + ai[brow];
1371: ap = aa + 16 * ai[brow];
1372: nrow = ailen[brow];
1373: low = 0;
1374: high = nrow;
1375: for (l = 0; l < n; l++) { /* loop over added columns */
1376: col = in[l];
1377: bcol = col / 4;
1378: ridx = row % 4;
1379: cidx = col % 4;
1380: value = v[l + k * n];
1381: if (col <= lastcol) low = 0;
1382: else high = nrow;
1383: lastcol = col;
1384: while (high - low > 7) {
1385: t = (low + high) / 2;
1386: if (rp[t] > bcol) high = t;
1387: else low = t;
1388: }
1389: for (i = low; i < high; i++) {
1390: if (rp[i] > bcol) break;
1391: if (rp[i] == bcol) {
1392: bap = ap + 16 * i + 4 * cidx + ridx;
1393: *bap += value;
1394: goto noinsert1;
1395: }
1396: }
1397: N = nrow++ - 1;
1398: high++; /* added new column thus must search to one higher than before */
1399: /* shift up all the later entries in this row */
1400: PetscCallVoid(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
1401: PetscCallVoid(PetscArraymove(ap + 16 * i + 16, ap + 16 * i, 16 * (N - i + 1)));
1402: PetscCallVoid(PetscArrayzero(ap + 16 * i, 16));
1403: rp[i] = bcol;
1404: ap[16 * i + 4 * cidx + ridx] = value;
1405: noinsert1:;
1406: low = i;
1407: }
1408: ailen[brow] = nrow;
1409: }
1410: PetscFunctionReturnVoid();
1411: }
1413: /*
1414: Checks for missing diagonals
1415: */
1416: PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A, PetscBool *missing, PetscInt *d)
1417: {
1418: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1419: PetscInt *diag, *ii = a->i, i;
1421: PetscFunctionBegin;
1422: PetscCall(MatMarkDiagonal_SeqBAIJ(A));
1423: *missing = PETSC_FALSE;
1424: if (A->rmap->n > 0 && !ii) {
1425: *missing = PETSC_TRUE;
1426: if (d) *d = 0;
1427: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1428: } else {
1429: PetscInt n;
1430: n = PetscMin(a->mbs, a->nbs);
1431: diag = a->diag;
1432: for (i = 0; i < n; i++) {
1433: if (diag[i] >= ii[i + 1]) {
1434: *missing = PETSC_TRUE;
1435: if (d) *d = i;
1436: PetscCall(PetscInfo(A, "Matrix is missing block diagonal number %" PetscInt_FMT "\n", i));
1437: break;
1438: }
1439: }
1440: }
1441: PetscFunctionReturn(PETSC_SUCCESS);
1442: }
1444: PetscErrorCode MatMarkDiagonal_SeqBAIJ(Mat A)
1445: {
1446: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1447: PetscInt i, j, m = a->mbs;
1449: PetscFunctionBegin;
1450: if (!a->diag) {
1451: PetscCall(PetscMalloc1(m, &a->diag));
1452: a->free_diag = PETSC_TRUE;
1453: }
1454: for (i = 0; i < m; i++) {
1455: a->diag[i] = a->i[i + 1];
1456: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1457: if (a->j[j] == i) {
1458: a->diag[i] = j;
1459: break;
1460: }
1461: }
1462: }
1463: PetscFunctionReturn(PETSC_SUCCESS);
1464: }
1466: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool blockcompressed, PetscInt *nn, const PetscInt *inia[], const PetscInt *inja[], PetscBool *done)
1467: {
1468: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1469: PetscInt i, j, n = a->mbs, nz = a->i[n], *tia, *tja, bs = A->rmap->bs, k, l, cnt;
1470: PetscInt **ia = (PetscInt **)inia, **ja = (PetscInt **)inja;
1472: PetscFunctionBegin;
1473: *nn = n;
1474: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
1475: if (symmetric) {
1476: PetscCall(MatToSymmetricIJ_SeqAIJ(n, a->i, a->j, PETSC_TRUE, 0, 0, &tia, &tja));
1477: nz = tia[n];
1478: } else {
1479: tia = a->i;
1480: tja = a->j;
1481: }
1483: if (!blockcompressed && bs > 1) {
1484: (*nn) *= bs;
1485: /* malloc & create the natural set of indices */
1486: PetscCall(PetscMalloc1((n + 1) * bs, ia));
1487: if (n) {
1488: (*ia)[0] = oshift;
1489: for (j = 1; j < bs; j++) (*ia)[j] = (tia[1] - tia[0]) * bs + (*ia)[j - 1];
1490: }
1492: for (i = 1; i < n; i++) {
1493: (*ia)[i * bs] = (tia[i] - tia[i - 1]) * bs + (*ia)[i * bs - 1];
1494: for (j = 1; j < bs; j++) (*ia)[i * bs + j] = (tia[i + 1] - tia[i]) * bs + (*ia)[i * bs + j - 1];
1495: }
1496: if (n) (*ia)[n * bs] = (tia[n] - tia[n - 1]) * bs + (*ia)[n * bs - 1];
1498: if (inja) {
1499: PetscCall(PetscMalloc1(nz * bs * bs, ja));
1500: cnt = 0;
1501: for (i = 0; i < n; i++) {
1502: for (j = 0; j < bs; j++) {
1503: for (k = tia[i]; k < tia[i + 1]; k++) {
1504: for (l = 0; l < bs; l++) (*ja)[cnt++] = bs * tja[k] + l;
1505: }
1506: }
1507: }
1508: }
1510: if (symmetric) { /* deallocate memory allocated in MatToSymmetricIJ_SeqAIJ() */
1511: PetscCall(PetscFree(tia));
1512: PetscCall(PetscFree(tja));
1513: }
1514: } else if (oshift == 1) {
1515: if (symmetric) {
1516: nz = tia[A->rmap->n / bs];
1517: /* add 1 to i and j indices */
1518: for (i = 0; i < A->rmap->n / bs + 1; i++) tia[i] = tia[i] + 1;
1519: *ia = tia;
1520: if (ja) {
1521: for (i = 0; i < nz; i++) tja[i] = tja[i] + 1;
1522: *ja = tja;
1523: }
1524: } else {
1525: nz = a->i[A->rmap->n / bs];
1526: /* malloc space and add 1 to i and j indices */
1527: PetscCall(PetscMalloc1(A->rmap->n / bs + 1, ia));
1528: for (i = 0; i < A->rmap->n / bs + 1; i++) (*ia)[i] = a->i[i] + 1;
1529: if (ja) {
1530: PetscCall(PetscMalloc1(nz, ja));
1531: for (i = 0; i < nz; i++) (*ja)[i] = a->j[i] + 1;
1532: }
1533: }
1534: } else {
1535: *ia = tia;
1536: if (ja) *ja = tja;
1537: }
1538: PetscFunctionReturn(PETSC_SUCCESS);
1539: }
1541: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool blockcompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
1542: {
1543: PetscFunctionBegin;
1544: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
1545: if ((!blockcompressed && A->rmap->bs > 1) || (symmetric || oshift == 1)) {
1546: PetscCall(PetscFree(*ia));
1547: if (ja) PetscCall(PetscFree(*ja));
1548: }
1549: PetscFunctionReturn(PETSC_SUCCESS);
1550: }
1552: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
1553: {
1554: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1556: PetscFunctionBegin;
1557: #if defined(PETSC_USE_LOG)
1558: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->N, A->cmap->n, a->nz));
1559: #endif
1560: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1561: PetscCall(ISDestroy(&a->row));
1562: PetscCall(ISDestroy(&a->col));
1563: if (a->free_diag) PetscCall(PetscFree(a->diag));
1564: PetscCall(PetscFree(a->idiag));
1565: if (a->free_imax_ilen) PetscCall(PetscFree2(a->imax, a->ilen));
1566: PetscCall(PetscFree(a->solve_work));
1567: PetscCall(PetscFree(a->mult_work));
1568: PetscCall(PetscFree(a->sor_workt));
1569: PetscCall(PetscFree(a->sor_work));
1570: PetscCall(ISDestroy(&a->icol));
1571: PetscCall(PetscFree(a->saved_values));
1572: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1574: PetscCall(MatDestroy(&a->sbaijMat));
1575: PetscCall(MatDestroy(&a->parent));
1576: PetscCall(PetscFree(A->data));
1578: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1579: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJGetArray_C", NULL));
1580: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJRestoreArray_C", NULL));
1581: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1582: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1583: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetColumnIndices_C", NULL));
1584: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqaij_C", NULL));
1585: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqsbaij_C", NULL));
1586: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetPreallocation_C", NULL));
1587: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqBAIJSetPreallocationCSR_C", NULL));
1588: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_seqbstrm_C", NULL));
1589: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1590: #if defined(PETSC_HAVE_HYPRE)
1591: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_hypre_C", NULL));
1592: #endif
1593: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqbaij_is_C", NULL));
1594: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1595: PetscFunctionReturn(PETSC_SUCCESS);
1596: }
1598: PetscErrorCode MatSetOption_SeqBAIJ(Mat A, MatOption op, PetscBool flg)
1599: {
1600: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1602: PetscFunctionBegin;
1603: switch (op) {
1604: case MAT_ROW_ORIENTED:
1605: a->roworiented = flg;
1606: break;
1607: case MAT_KEEP_NONZERO_PATTERN:
1608: a->keepnonzeropattern = flg;
1609: break;
1610: case MAT_NEW_NONZERO_LOCATIONS:
1611: a->nonew = (flg ? 0 : 1);
1612: break;
1613: case MAT_NEW_NONZERO_LOCATION_ERR:
1614: a->nonew = (flg ? -1 : 0);
1615: break;
1616: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1617: a->nonew = (flg ? -2 : 0);
1618: break;
1619: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1620: a->nounused = (flg ? -1 : 0);
1621: break;
1622: case MAT_FORCE_DIAGONAL_ENTRIES:
1623: case MAT_IGNORE_OFF_PROC_ENTRIES:
1624: case MAT_USE_HASH_TABLE:
1625: case MAT_SORTED_FULL:
1626: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1627: break;
1628: case MAT_SPD:
1629: case MAT_SYMMETRIC:
1630: case MAT_STRUCTURALLY_SYMMETRIC:
1631: case MAT_HERMITIAN:
1632: case MAT_SYMMETRY_ETERNAL:
1633: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1634: case MAT_SUBMAT_SINGLEIS:
1635: case MAT_STRUCTURE_ONLY:
1636: case MAT_SPD_ETERNAL:
1637: /* if the diagonal matrix is square it inherits some of the properties above */
1638: break;
1639: default:
1640: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1641: }
1642: PetscFunctionReturn(PETSC_SUCCESS);
1643: }
1645: /* used for both SeqBAIJ and SeqSBAIJ matrices */
1646: PetscErrorCode MatGetRow_SeqBAIJ_private(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v, PetscInt *ai, PetscInt *aj, PetscScalar *aa)
1647: {
1648: PetscInt itmp, i, j, k, M, bn, bp, *idx_i, bs, bs2;
1649: MatScalar *aa_i;
1650: PetscScalar *v_i;
1652: PetscFunctionBegin;
1653: bs = A->rmap->bs;
1654: bs2 = bs * bs;
1655: PetscCheck(row >= 0 && row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " out of range", row);
1657: bn = row / bs; /* Block number */
1658: bp = row % bs; /* Block Position */
1659: M = ai[bn + 1] - ai[bn];
1660: *nz = bs * M;
1662: if (v) {
1663: *v = NULL;
1664: if (*nz) {
1665: PetscCall(PetscMalloc1(*nz, v));
1666: for (i = 0; i < M; i++) { /* for each block in the block row */
1667: v_i = *v + i * bs;
1668: aa_i = aa + bs2 * (ai[bn] + i);
1669: for (j = bp, k = 0; j < bs2; j += bs, k++) v_i[k] = aa_i[j];
1670: }
1671: }
1672: }
1674: if (idx) {
1675: *idx = NULL;
1676: if (*nz) {
1677: PetscCall(PetscMalloc1(*nz, idx));
1678: for (i = 0; i < M; i++) { /* for each block in the block row */
1679: idx_i = *idx + i * bs;
1680: itmp = bs * aj[ai[bn] + i];
1681: for (j = 0; j < bs; j++) idx_i[j] = itmp++;
1682: }
1683: }
1684: }
1685: PetscFunctionReturn(PETSC_SUCCESS);
1686: }
1688: PetscErrorCode MatGetRow_SeqBAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1689: {
1690: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1692: PetscFunctionBegin;
1693: PetscCall(MatGetRow_SeqBAIJ_private(A, row, nz, idx, v, a->i, a->j, a->a));
1694: PetscFunctionReturn(PETSC_SUCCESS);
1695: }
1697: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
1698: {
1699: PetscFunctionBegin;
1700: if (nz) *nz = 0;
1701: if (idx) PetscCall(PetscFree(*idx));
1702: if (v) PetscCall(PetscFree(*v));
1703: PetscFunctionReturn(PETSC_SUCCESS);
1704: }
1706: PetscErrorCode MatTranspose_SeqBAIJ(Mat A, MatReuse reuse, Mat *B)
1707: {
1708: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data, *at;
1709: Mat C;
1710: PetscInt i, j, k, *aj = a->j, *ai = a->i, bs = A->rmap->bs, mbs = a->mbs, nbs = a->nbs, *atfill;
1711: PetscInt bs2 = a->bs2, *ati, *atj, anzj, kr;
1712: MatScalar *ata, *aa = a->a;
1714: PetscFunctionBegin;
1715: if (reuse == MAT_REUSE_MATRIX) PetscCall(MatTransposeCheckNonzeroState_Private(A, *B));
1716: PetscCall(PetscCalloc1(1 + nbs, &atfill));
1717: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1718: for (i = 0; i < ai[mbs]; i++) atfill[aj[i]] += 1; /* count num of non-zeros in row aj[i] */
1720: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
1721: PetscCall(MatSetSizes(C, A->cmap->n, A->rmap->N, A->cmap->n, A->rmap->N));
1722: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
1723: PetscCall(MatSeqBAIJSetPreallocation(C, bs, 0, atfill));
1725: at = (Mat_SeqBAIJ *)C->data;
1726: ati = at->i;
1727: for (i = 0; i < nbs; i++) at->ilen[i] = at->imax[i] = ati[i + 1] - ati[i];
1728: } else {
1729: C = *B;
1730: at = (Mat_SeqBAIJ *)C->data;
1731: ati = at->i;
1732: }
1734: atj = at->j;
1735: ata = at->a;
1737: /* Copy ati into atfill so we have locations of the next free space in atj */
1738: PetscCall(PetscArraycpy(atfill, ati, nbs));
1740: /* Walk through A row-wise and mark nonzero entries of A^T. */
1741: for (i = 0; i < mbs; i++) {
1742: anzj = ai[i + 1] - ai[i];
1743: for (j = 0; j < anzj; j++) {
1744: atj[atfill[*aj]] = i;
1745: for (kr = 0; kr < bs; kr++) {
1746: for (k = 0; k < bs; k++) ata[bs2 * atfill[*aj] + k * bs + kr] = *aa++;
1747: }
1748: atfill[*aj++] += 1;
1749: }
1750: }
1751: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
1752: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
1754: /* Clean up temporary space and complete requests. */
1755: PetscCall(PetscFree(atfill));
1757: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1758: PetscCall(MatSetBlockSizes(C, PetscAbs(A->cmap->bs), PetscAbs(A->rmap->bs)));
1759: *B = C;
1760: } else {
1761: PetscCall(MatHeaderMerge(A, &C));
1762: }
1763: PetscFunctionReturn(PETSC_SUCCESS);
1764: }
1766: static PetscErrorCode MatIsTranspose_SeqBAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
1767: {
1768: Mat Btrans;
1770: PetscFunctionBegin;
1771: *f = PETSC_FALSE;
1772: PetscCall(MatTranspose(A, MAT_INITIAL_MATRIX, &Btrans));
1773: PetscCall(MatEqual_SeqBAIJ(B, Btrans, f));
1774: PetscCall(MatDestroy(&Btrans));
1775: PetscFunctionReturn(PETSC_SUCCESS);
1776: }
1778: /* Used for both SeqBAIJ and SeqSBAIJ matrices */
1779: PetscErrorCode MatView_SeqBAIJ_Binary(Mat mat, PetscViewer viewer)
1780: {
1781: Mat_SeqBAIJ *A = (Mat_SeqBAIJ *)mat->data;
1782: PetscInt header[4], M, N, m, bs, nz, cnt, i, j, k, l;
1783: PetscInt *rowlens, *colidxs;
1784: PetscScalar *matvals;
1786: PetscFunctionBegin;
1787: PetscCall(PetscViewerSetUp(viewer));
1789: M = mat->rmap->N;
1790: N = mat->cmap->N;
1791: m = mat->rmap->n;
1792: bs = mat->rmap->bs;
1793: nz = bs * bs * A->nz;
1795: /* write matrix header */
1796: header[0] = MAT_FILE_CLASSID;
1797: header[1] = M;
1798: header[2] = N;
1799: header[3] = nz;
1800: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
1802: /* store row lengths */
1803: PetscCall(PetscMalloc1(m, &rowlens));
1804: for (cnt = 0, i = 0; i < A->mbs; i++)
1805: for (j = 0; j < bs; j++) rowlens[cnt++] = bs * (A->i[i + 1] - A->i[i]);
1806: PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
1807: PetscCall(PetscFree(rowlens));
1809: /* store column indices */
1810: PetscCall(PetscMalloc1(nz, &colidxs));
1811: for (cnt = 0, i = 0; i < A->mbs; i++)
1812: for (k = 0; k < bs; k++)
1813: for (j = A->i[i]; j < A->i[i + 1]; j++)
1814: for (l = 0; l < bs; l++) colidxs[cnt++] = bs * A->j[j] + l;
1815: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz);
1816: PetscCall(PetscViewerBinaryWrite(viewer, colidxs, nz, PETSC_INT));
1817: PetscCall(PetscFree(colidxs));
1819: /* store nonzero values */
1820: PetscCall(PetscMalloc1(nz, &matvals));
1821: for (cnt = 0, i = 0; i < A->mbs; i++)
1822: for (k = 0; k < bs; k++)
1823: for (j = A->i[i]; j < A->i[i + 1]; j++)
1824: for (l = 0; l < bs; l++) matvals[cnt++] = A->a[bs * (bs * j + l) + k];
1825: PetscCheck(cnt == nz, PETSC_COMM_SELF, PETSC_ERR_LIB, "Internal PETSc error: cnt = %" PetscInt_FMT " nz = %" PetscInt_FMT, cnt, nz);
1826: PetscCall(PetscViewerBinaryWrite(viewer, matvals, nz, PETSC_SCALAR));
1827: PetscCall(PetscFree(matvals));
1829: /* write block size option to the viewer's .info file */
1830: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
1831: PetscFunctionReturn(PETSC_SUCCESS);
1832: }
1834: static PetscErrorCode MatView_SeqBAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
1835: {
1836: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1837: PetscInt i, bs = A->rmap->bs, k;
1839: PetscFunctionBegin;
1840: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1841: for (i = 0; i < a->mbs; i++) {
1842: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT "-%" PetscInt_FMT ":", i * bs, i * bs + bs - 1));
1843: for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT "-%" PetscInt_FMT ") ", bs * a->j[k], bs * a->j[k] + bs - 1));
1844: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1845: }
1846: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1847: PetscFunctionReturn(PETSC_SUCCESS);
1848: }
1850: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A, PetscViewer viewer)
1851: {
1852: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1853: PetscInt i, j, bs = A->rmap->bs, k, l, bs2 = a->bs2;
1854: PetscViewerFormat format;
1856: PetscFunctionBegin;
1857: if (A->structure_only) {
1858: PetscCall(MatView_SeqBAIJ_ASCII_structonly(A, viewer));
1859: PetscFunctionReturn(PETSC_SUCCESS);
1860: }
1862: PetscCall(PetscViewerGetFormat(viewer, &format));
1863: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1864: PetscCall(PetscViewerASCIIPrintf(viewer, " block size is %" PetscInt_FMT "\n", bs));
1865: } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1866: const char *matname;
1867: Mat aij;
1868: PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &aij));
1869: PetscCall(PetscObjectGetName((PetscObject)A, &matname));
1870: PetscCall(PetscObjectSetName((PetscObject)aij, matname));
1871: PetscCall(MatView(aij, viewer));
1872: PetscCall(MatDestroy(&aij));
1873: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1874: PetscFunctionReturn(PETSC_SUCCESS);
1875: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1876: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1877: for (i = 0; i < a->mbs; i++) {
1878: for (j = 0; j < bs; j++) {
1879: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i * bs + j));
1880: for (k = a->i[i]; k < a->i[i + 1]; k++) {
1881: for (l = 0; l < bs; l++) {
1882: #if defined(PETSC_USE_COMPLEX)
1883: if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) > 0.0 && PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1884: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %gi) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), (double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1885: } else if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) < 0.0 && PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1886: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %gi) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), -(double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1887: } else if (PetscRealPart(a->a[bs2 * k + l * bs + j]) != 0.0) {
1888: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j])));
1889: }
1890: #else
1891: if (a->a[bs2 * k + l * bs + j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)a->a[bs2 * k + l * bs + j]));
1892: #endif
1893: }
1894: }
1895: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1896: }
1897: }
1898: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1899: } else {
1900: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
1901: for (i = 0; i < a->mbs; i++) {
1902: for (j = 0; j < bs; j++) {
1903: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i * bs + j));
1904: for (k = a->i[i]; k < a->i[i + 1]; k++) {
1905: for (l = 0; l < bs; l++) {
1906: #if defined(PETSC_USE_COMPLEX)
1907: if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) > 0.0) {
1908: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), (double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1909: } else if (PetscImaginaryPart(a->a[bs2 * k + l * bs + j]) < 0.0) {
1910: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j]), -(double)PetscImaginaryPart(a->a[bs2 * k + l * bs + j])));
1911: } else {
1912: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)PetscRealPart(a->a[bs2 * k + l * bs + j])));
1913: }
1914: #else
1915: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", bs * a->j[k] + l, (double)a->a[bs2 * k + l * bs + j]));
1916: #endif
1917: }
1918: }
1919: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
1920: }
1921: }
1922: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
1923: }
1924: PetscCall(PetscViewerFlush(viewer));
1925: PetscFunctionReturn(PETSC_SUCCESS);
1926: }
1928: #include <petscdraw.h>
1929: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
1930: {
1931: Mat A = (Mat)Aa;
1932: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
1933: PetscInt row, i, j, k, l, mbs = a->mbs, color, bs = A->rmap->bs, bs2 = a->bs2;
1934: PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r;
1935: MatScalar *aa;
1936: PetscViewer viewer;
1937: PetscViewerFormat format;
1939: PetscFunctionBegin;
1940: PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
1941: PetscCall(PetscViewerGetFormat(viewer, &format));
1942: PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));
1944: /* loop over matrix elements drawing boxes */
1946: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1947: PetscDrawCollectiveBegin(draw);
1948: /* Blue for negative, Cyan for zero and Red for positive */
1949: color = PETSC_DRAW_BLUE;
1950: for (i = 0, row = 0; i < mbs; i++, row += bs) {
1951: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1952: y_l = A->rmap->N - row - 1.0;
1953: y_r = y_l + 1.0;
1954: x_l = a->j[j] * bs;
1955: x_r = x_l + 1.0;
1956: aa = a->a + j * bs2;
1957: for (k = 0; k < bs; k++) {
1958: for (l = 0; l < bs; l++) {
1959: if (PetscRealPart(*aa++) >= 0.) continue;
1960: PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
1961: }
1962: }
1963: }
1964: }
1965: color = PETSC_DRAW_CYAN;
1966: for (i = 0, row = 0; i < mbs; i++, row += bs) {
1967: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1968: y_l = A->rmap->N - row - 1.0;
1969: y_r = y_l + 1.0;
1970: x_l = a->j[j] * bs;
1971: x_r = x_l + 1.0;
1972: aa = a->a + j * bs2;
1973: for (k = 0; k < bs; k++) {
1974: for (l = 0; l < bs; l++) {
1975: if (PetscRealPart(*aa++) != 0.) continue;
1976: PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
1977: }
1978: }
1979: }
1980: }
1981: color = PETSC_DRAW_RED;
1982: for (i = 0, row = 0; i < mbs; i++, row += bs) {
1983: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1984: y_l = A->rmap->N - row - 1.0;
1985: y_r = y_l + 1.0;
1986: x_l = a->j[j] * bs;
1987: x_r = x_l + 1.0;
1988: aa = a->a + j * bs2;
1989: for (k = 0; k < bs; k++) {
1990: for (l = 0; l < bs; l++) {
1991: if (PetscRealPart(*aa++) <= 0.) continue;
1992: PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
1993: }
1994: }
1995: }
1996: }
1997: PetscDrawCollectiveEnd(draw);
1998: } else {
1999: /* use contour shading to indicate magnitude of values */
2000: /* first determine max of all nonzero values */
2001: PetscReal minv = 0.0, maxv = 0.0;
2002: PetscDraw popup;
2004: for (i = 0; i < a->nz * a->bs2; i++) {
2005: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
2006: }
2007: if (minv >= maxv) maxv = minv + PETSC_SMALL;
2008: PetscCall(PetscDrawGetPopup(draw, &popup));
2009: PetscCall(PetscDrawScalePopup(popup, 0.0, maxv));
2011: PetscDrawCollectiveBegin(draw);
2012: for (i = 0, row = 0; i < mbs; i++, row += bs) {
2013: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2014: y_l = A->rmap->N - row - 1.0;
2015: y_r = y_l + 1.0;
2016: x_l = a->j[j] * bs;
2017: x_r = x_l + 1.0;
2018: aa = a->a + j * bs2;
2019: for (k = 0; k < bs; k++) {
2020: for (l = 0; l < bs; l++) {
2021: MatScalar v = *aa++;
2022: color = PetscDrawRealToColor(PetscAbsScalar(v), minv, maxv);
2023: PetscCall(PetscDrawRectangle(draw, x_l + k, y_l - l, x_r + k, y_r - l, color, color, color, color));
2024: }
2025: }
2026: }
2027: }
2028: PetscDrawCollectiveEnd(draw);
2029: }
2030: PetscFunctionReturn(PETSC_SUCCESS);
2031: }
2033: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A, PetscViewer viewer)
2034: {
2035: PetscReal xl, yl, xr, yr, w, h;
2036: PetscDraw draw;
2037: PetscBool isnull;
2039: PetscFunctionBegin;
2040: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
2041: PetscCall(PetscDrawIsNull(draw, &isnull));
2042: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
2044: xr = A->cmap->n;
2045: yr = A->rmap->N;
2046: h = yr / 10.0;
2047: w = xr / 10.0;
2048: xr += w;
2049: yr += h;
2050: xl = -w;
2051: yl = -h;
2052: PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
2053: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
2054: PetscCall(PetscDrawZoom(draw, MatView_SeqBAIJ_Draw_Zoom, A));
2055: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
2056: PetscCall(PetscDrawSave(draw));
2057: PetscFunctionReturn(PETSC_SUCCESS);
2058: }
2060: PetscErrorCode MatView_SeqBAIJ(Mat A, PetscViewer viewer)
2061: {
2062: PetscBool iascii, isbinary, isdraw;
2064: PetscFunctionBegin;
2065: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
2066: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
2067: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
2068: if (iascii) {
2069: PetscCall(MatView_SeqBAIJ_ASCII(A, viewer));
2070: } else if (isbinary) {
2071: PetscCall(MatView_SeqBAIJ_Binary(A, viewer));
2072: } else if (isdraw) {
2073: PetscCall(MatView_SeqBAIJ_Draw(A, viewer));
2074: } else {
2075: Mat B;
2076: PetscCall(MatConvert(A, MATSEQAIJ, MAT_INITIAL_MATRIX, &B));
2077: PetscCall(MatView(B, viewer));
2078: PetscCall(MatDestroy(&B));
2079: }
2080: PetscFunctionReturn(PETSC_SUCCESS);
2081: }
2083: PetscErrorCode MatGetValues_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
2084: {
2085: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2086: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
2087: PetscInt *ai = a->i, *ailen = a->ilen;
2088: PetscInt brow, bcol, ridx, cidx, bs = A->rmap->bs, bs2 = a->bs2;
2089: MatScalar *ap, *aa = a->a;
2091: PetscFunctionBegin;
2092: for (k = 0; k < m; k++) { /* loop over rows */
2093: row = im[k];
2094: brow = row / bs;
2095: if (row < 0) {
2096: v += n;
2097: continue;
2098: } /* negative row */
2099: PetscCheck(row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row %" PetscInt_FMT " too large", row);
2100: rp = aj ? aj + ai[brow] : NULL; /* mustn't add to NULL, that is UB */
2101: ap = aa ? aa + bs2 * ai[brow] : NULL; /* mustn't add to NULL, that is UB */
2102: nrow = ailen[brow];
2103: for (l = 0; l < n; l++) { /* loop over columns */
2104: if (in[l] < 0) {
2105: v++;
2106: continue;
2107: } /* negative column */
2108: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column %" PetscInt_FMT " too large", in[l]);
2109: col = in[l];
2110: bcol = col / bs;
2111: cidx = col % bs;
2112: ridx = row % bs;
2113: high = nrow;
2114: low = 0; /* assume unsorted */
2115: while (high - low > 5) {
2116: t = (low + high) / 2;
2117: if (rp[t] > bcol) high = t;
2118: else low = t;
2119: }
2120: for (i = low; i < high; i++) {
2121: if (rp[i] > bcol) break;
2122: if (rp[i] == bcol) {
2123: *v++ = ap[bs2 * i + bs * cidx + ridx];
2124: goto finished;
2125: }
2126: }
2127: *v++ = 0.0;
2128: finished:;
2129: }
2130: }
2131: PetscFunctionReturn(PETSC_SUCCESS);
2132: }
2134: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
2135: {
2136: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2137: PetscInt *rp, k, low, high, t, ii, jj, row, nrow, i, col, l, rmax, N, lastcol = -1;
2138: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
2139: PetscInt *aj = a->j, nonew = a->nonew, bs2 = a->bs2, bs = A->rmap->bs, stepval;
2140: PetscBool roworiented = a->roworiented;
2141: const PetscScalar *value = v;
2142: MatScalar *ap = NULL, *aa = a->a, *bap;
2144: PetscFunctionBegin;
2145: if (roworiented) {
2146: stepval = (n - 1) * bs;
2147: } else {
2148: stepval = (m - 1) * bs;
2149: }
2150: for (k = 0; k < m; k++) { /* loop over added rows */
2151: row = im[k];
2152: if (row < 0) continue;
2153: PetscCheck(row < a->mbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block row index too large %" PetscInt_FMT " max %" PetscInt_FMT, row, a->mbs - 1);
2154: rp = aj + ai[row];
2155: if (!A->structure_only) ap = aa + bs2 * ai[row];
2156: rmax = imax[row];
2157: nrow = ailen[row];
2158: low = 0;
2159: high = nrow;
2160: for (l = 0; l < n; l++) { /* loop over added columns */
2161: if (in[l] < 0) continue;
2162: PetscCheck(in[l] < a->nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Block column index too large %" PetscInt_FMT " max %" PetscInt_FMT, in[l], a->nbs - 1);
2163: col = in[l];
2164: if (!A->structure_only) {
2165: if (roworiented) {
2166: value = v + (k * (stepval + bs) + l) * bs;
2167: } else {
2168: value = v + (l * (stepval + bs) + k) * bs;
2169: }
2170: }
2171: if (col <= lastcol) low = 0;
2172: else high = nrow;
2173: lastcol = col;
2174: while (high - low > 7) {
2175: t = (low + high) / 2;
2176: if (rp[t] > col) high = t;
2177: else low = t;
2178: }
2179: for (i = low; i < high; i++) {
2180: if (rp[i] > col) break;
2181: if (rp[i] == col) {
2182: if (A->structure_only) goto noinsert2;
2183: bap = ap + bs2 * i;
2184: if (roworiented) {
2185: if (is == ADD_VALUES) {
2186: for (ii = 0; ii < bs; ii++, value += stepval) {
2187: for (jj = ii; jj < bs2; jj += bs) bap[jj] += *value++;
2188: }
2189: } else {
2190: for (ii = 0; ii < bs; ii++, value += stepval) {
2191: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
2192: }
2193: }
2194: } else {
2195: if (is == ADD_VALUES) {
2196: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
2197: for (jj = 0; jj < bs; jj++) bap[jj] += value[jj];
2198: bap += bs;
2199: }
2200: } else {
2201: for (ii = 0; ii < bs; ii++, value += bs + stepval) {
2202: for (jj = 0; jj < bs; jj++) bap[jj] = value[jj];
2203: bap += bs;
2204: }
2205: }
2206: }
2207: goto noinsert2;
2208: }
2209: }
2210: if (nonew == 1) goto noinsert2;
2211: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new blocked index new nonzero block (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", row, col);
2212: if (A->structure_only) {
2213: MatSeqXAIJReallocateAIJ_structure_only(A, a->mbs, bs2, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar);
2214: } else {
2215: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
2216: }
2217: N = nrow++ - 1;
2218: high++;
2219: /* shift up all the later entries in this row */
2220: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
2221: rp[i] = col;
2222: if (!A->structure_only) {
2223: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
2224: bap = ap + bs2 * i;
2225: if (roworiented) {
2226: for (ii = 0; ii < bs; ii++, value += stepval) {
2227: for (jj = ii; jj < bs2; jj += bs) bap[jj] = *value++;
2228: }
2229: } else {
2230: for (ii = 0; ii < bs; ii++, value += stepval) {
2231: for (jj = 0; jj < bs; jj++) *bap++ = *value++;
2232: }
2233: }
2234: }
2235: noinsert2:;
2236: low = i;
2237: }
2238: ailen[row] = nrow;
2239: }
2240: PetscFunctionReturn(PETSC_SUCCESS);
2241: }
2243: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A, MatAssemblyType mode)
2244: {
2245: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2246: PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
2247: PetscInt m = A->rmap->N, *ip, N, *ailen = a->ilen;
2248: PetscInt mbs = a->mbs, bs2 = a->bs2, rmax = 0;
2249: MatScalar *aa = a->a, *ap;
2250: PetscReal ratio = 0.6;
2252: PetscFunctionBegin;
2253: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
2255: if (m) rmax = ailen[0];
2256: for (i = 1; i < mbs; i++) {
2257: /* move each row back by the amount of empty slots (fshift) before it*/
2258: fshift += imax[i - 1] - ailen[i - 1];
2259: rmax = PetscMax(rmax, ailen[i]);
2260: if (fshift) {
2261: ip = aj + ai[i];
2262: ap = aa + bs2 * ai[i];
2263: N = ailen[i];
2264: PetscCall(PetscArraymove(ip - fshift, ip, N));
2265: if (!A->structure_only) PetscCall(PetscArraymove(ap - bs2 * fshift, ap, bs2 * N));
2266: }
2267: ai[i] = ai[i - 1] + ailen[i - 1];
2268: }
2269: if (mbs) {
2270: fshift += imax[mbs - 1] - ailen[mbs - 1];
2271: ai[mbs] = ai[mbs - 1] + ailen[mbs - 1];
2272: }
2274: /* reset ilen and imax for each row */
2275: a->nonzerorowcnt = 0;
2276: if (A->structure_only) {
2277: PetscCall(PetscFree2(a->imax, a->ilen));
2278: } else { /* !A->structure_only */
2279: for (i = 0; i < mbs; i++) {
2280: ailen[i] = imax[i] = ai[i + 1] - ai[i];
2281: a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
2282: }
2283: }
2284: a->nz = ai[mbs];
2286: /* diagonals may have moved, so kill the diagonal pointers */
2287: a->idiagvalid = PETSC_FALSE;
2288: if (fshift && a->diag) PetscCall(PetscFree(a->diag));
2289: if (fshift) PetscCheck(a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT " block size %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, A->rmap->bs, fshift * bs2);
2290: PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT ", block size %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded, %" PetscInt_FMT " used\n", m, A->cmap->n, A->rmap->bs, fshift * bs2, a->nz * bs2));
2291: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues is %" PetscInt_FMT "\n", a->reallocs));
2292: PetscCall(PetscInfo(A, "Most nonzeros blocks in any row is %" PetscInt_FMT "\n", rmax));
2294: A->info.mallocs += a->reallocs;
2295: a->reallocs = 0;
2296: A->info.nz_unneeded = (PetscReal)fshift * bs2;
2297: a->rmax = rmax;
2299: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, mbs, ratio));
2300: PetscFunctionReturn(PETSC_SUCCESS);
2301: }
2303: /*
2304: This function returns an array of flags which indicate the locations of contiguous
2305: blocks that should be zeroed. for eg: if bs = 3 and is = [0,1,2,3,5,6,7,8,9]
2306: then the resulting sizes = [3,1,1,3,1] corresponding to sets [(0,1,2),(3),(5),(6,7,8),(9)]
2307: Assume: sizes should be long enough to hold all the values.
2308: */
2309: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[], PetscInt n, PetscInt bs, PetscInt sizes[], PetscInt *bs_max)
2310: {
2311: PetscInt j = 0;
2313: PetscFunctionBegin;
2314: for (PetscInt i = 0; i < n; j++) {
2315: PetscInt row = idx[i];
2316: if (row % bs != 0) { /* Not the beginning of a block */
2317: sizes[j] = 1;
2318: i++;
2319: } else if (i + bs > n) { /* complete block doesn't exist (at idx end) */
2320: sizes[j] = 1; /* Also makes sure at least 'bs' values exist for next else */
2321: i++;
2322: } else { /* Beginning of the block, so check if the complete block exists */
2323: PetscBool flg = PETSC_TRUE;
2324: for (PetscInt k = 1; k < bs; k++) {
2325: if (row + k != idx[i + k]) { /* break in the block */
2326: flg = PETSC_FALSE;
2327: break;
2328: }
2329: }
2330: if (flg) { /* No break in the bs */
2331: sizes[j] = bs;
2332: i += bs;
2333: } else {
2334: sizes[j] = 1;
2335: i++;
2336: }
2337: }
2338: }
2339: *bs_max = j;
2340: PetscFunctionReturn(PETSC_SUCCESS);
2341: }
2343: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A, PetscInt is_n, const PetscInt is_idx[], PetscScalar diag, Vec x, Vec b)
2344: {
2345: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)A->data;
2346: PetscInt i, j, k, count, *rows;
2347: PetscInt bs = A->rmap->bs, bs2 = baij->bs2, *sizes, row, bs_max;
2348: PetscScalar zero = 0.0;
2349: MatScalar *aa;
2350: const PetscScalar *xx;
2351: PetscScalar *bb;
2353: PetscFunctionBegin;
2354: /* fix right hand side if needed */
2355: if (x && b) {
2356: PetscCall(VecGetArrayRead(x, &xx));
2357: PetscCall(VecGetArray(b, &bb));
2358: for (i = 0; i < is_n; i++) bb[is_idx[i]] = diag * xx[is_idx[i]];
2359: PetscCall(VecRestoreArrayRead(x, &xx));
2360: PetscCall(VecRestoreArray(b, &bb));
2361: }
2363: /* Make a copy of the IS and sort it */
2364: /* allocate memory for rows,sizes */
2365: PetscCall(PetscMalloc2(is_n, &rows, 2 * is_n, &sizes));
2367: /* copy IS values to rows, and sort them */
2368: for (i = 0; i < is_n; i++) rows[i] = is_idx[i];
2369: PetscCall(PetscSortInt(is_n, rows));
2371: if (baij->keepnonzeropattern) {
2372: for (i = 0; i < is_n; i++) sizes[i] = 1;
2373: bs_max = is_n;
2374: } else {
2375: PetscCall(MatZeroRows_SeqBAIJ_Check_Blocks(rows, is_n, bs, sizes, &bs_max));
2376: A->nonzerostate++;
2377: }
2379: for (i = 0, j = 0; i < bs_max; j += sizes[i], i++) {
2380: row = rows[j];
2381: PetscCheck(row >= 0 && row <= A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", row);
2382: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
2383: aa = ((MatScalar *)(baij->a)) + baij->i[row / bs] * bs2 + (row % bs);
2384: if (sizes[i] == bs && !baij->keepnonzeropattern) {
2385: if (diag != (PetscScalar)0.0) {
2386: if (baij->ilen[row / bs] > 0) {
2387: baij->ilen[row / bs] = 1;
2388: baij->j[baij->i[row / bs]] = row / bs;
2390: PetscCall(PetscArrayzero(aa, count * bs));
2391: }
2392: /* Now insert all the diagonal values for this bs */
2393: for (k = 0; k < bs; k++) PetscCall((*A->ops->setvalues)(A, 1, rows + j + k, 1, rows + j + k, &diag, INSERT_VALUES));
2394: } else { /* (diag == 0.0) */
2395: baij->ilen[row / bs] = 0;
2396: } /* end (diag == 0.0) */
2397: } else { /* (sizes[i] != bs) */
2398: PetscAssert(sizes[i] == 1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Internal Error. Value should be 1");
2399: for (k = 0; k < count; k++) {
2400: aa[0] = zero;
2401: aa += bs;
2402: }
2403: if (diag != (PetscScalar)0.0) PetscCall((*A->ops->setvalues)(A, 1, rows + j, 1, rows + j, &diag, INSERT_VALUES));
2404: }
2405: }
2407: PetscCall(PetscFree2(rows, sizes));
2408: PetscCall(MatAssemblyEnd_SeqBAIJ(A, MAT_FINAL_ASSEMBLY));
2409: PetscFunctionReturn(PETSC_SUCCESS);
2410: }
2412: static PetscErrorCode MatZeroRowsColumns_SeqBAIJ(Mat A, PetscInt is_n, const PetscInt is_idx[], PetscScalar diag, Vec x, Vec b)
2413: {
2414: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)A->data;
2415: PetscInt i, j, k, count;
2416: PetscInt bs = A->rmap->bs, bs2 = baij->bs2, row, col;
2417: PetscScalar zero = 0.0;
2418: MatScalar *aa;
2419: const PetscScalar *xx;
2420: PetscScalar *bb;
2421: PetscBool *zeroed, vecs = PETSC_FALSE;
2423: PetscFunctionBegin;
2424: /* fix right hand side if needed */
2425: if (x && b) {
2426: PetscCall(VecGetArrayRead(x, &xx));
2427: PetscCall(VecGetArray(b, &bb));
2428: vecs = PETSC_TRUE;
2429: }
2431: /* zero the columns */
2432: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2433: for (i = 0; i < is_n; i++) {
2434: PetscCheck(is_idx[i] >= 0 && is_idx[i] < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", is_idx[i]);
2435: zeroed[is_idx[i]] = PETSC_TRUE;
2436: }
2437: for (i = 0; i < A->rmap->N; i++) {
2438: if (!zeroed[i]) {
2439: row = i / bs;
2440: for (j = baij->i[row]; j < baij->i[row + 1]; j++) {
2441: for (k = 0; k < bs; k++) {
2442: col = bs * baij->j[j] + k;
2443: if (zeroed[col]) {
2444: aa = ((MatScalar *)(baij->a)) + j * bs2 + (i % bs) + bs * k;
2445: if (vecs) bb[i] -= aa[0] * xx[col];
2446: aa[0] = 0.0;
2447: }
2448: }
2449: }
2450: } else if (vecs) bb[i] = diag * xx[i];
2451: }
2452: PetscCall(PetscFree(zeroed));
2453: if (vecs) {
2454: PetscCall(VecRestoreArrayRead(x, &xx));
2455: PetscCall(VecRestoreArray(b, &bb));
2456: }
2458: /* zero the rows */
2459: for (i = 0; i < is_n; i++) {
2460: row = is_idx[i];
2461: count = (baij->i[row / bs + 1] - baij->i[row / bs]) * bs;
2462: aa = ((MatScalar *)(baij->a)) + baij->i[row / bs] * bs2 + (row % bs);
2463: for (k = 0; k < count; k++) {
2464: aa[0] = zero;
2465: aa += bs;
2466: }
2467: if (diag != (PetscScalar)0.0) PetscUseTypeMethod(A, setvalues, 1, &row, 1, &row, &diag, INSERT_VALUES);
2468: }
2469: PetscCall(MatAssemblyEnd_SeqBAIJ(A, MAT_FINAL_ASSEMBLY));
2470: PetscFunctionReturn(PETSC_SUCCESS);
2471: }
2473: PetscErrorCode MatSetValues_SeqBAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
2474: {
2475: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2476: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N, lastcol = -1;
2477: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
2478: PetscInt *aj = a->j, nonew = a->nonew, bs = A->rmap->bs, brow, bcol;
2479: PetscInt ridx, cidx, bs2 = a->bs2;
2480: PetscBool roworiented = a->roworiented;
2481: MatScalar *ap = NULL, value = 0.0, *aa = a->a, *bap;
2483: PetscFunctionBegin;
2484: for (k = 0; k < m; k++) { /* loop over added rows */
2485: row = im[k];
2486: brow = row / bs;
2487: if (row < 0) continue;
2488: PetscCheck(row < A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->N - 1);
2489: rp = aj + ai[brow];
2490: if (!A->structure_only) ap = aa + bs2 * ai[brow];
2491: rmax = imax[brow];
2492: nrow = ailen[brow];
2493: low = 0;
2494: high = nrow;
2495: for (l = 0; l < n; l++) { /* loop over added columns */
2496: if (in[l] < 0) continue;
2497: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
2498: col = in[l];
2499: bcol = col / bs;
2500: ridx = row % bs;
2501: cidx = col % bs;
2502: if (!A->structure_only) {
2503: if (roworiented) {
2504: value = v[l + k * n];
2505: } else {
2506: value = v[k + l * m];
2507: }
2508: }
2509: if (col <= lastcol) low = 0;
2510: else high = nrow;
2511: lastcol = col;
2512: while (high - low > 7) {
2513: t = (low + high) / 2;
2514: if (rp[t] > bcol) high = t;
2515: else low = t;
2516: }
2517: for (i = low; i < high; i++) {
2518: if (rp[i] > bcol) break;
2519: if (rp[i] == bcol) {
2520: bap = ap + bs2 * i + bs * cidx + ridx;
2521: if (!A->structure_only) {
2522: if (is == ADD_VALUES) *bap += value;
2523: else *bap = value;
2524: }
2525: goto noinsert1;
2526: }
2527: }
2528: if (nonew == 1) goto noinsert1;
2529: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero (%" PetscInt_FMT ", %" PetscInt_FMT ") in the matrix", row, col);
2530: if (A->structure_only) {
2531: MatSeqXAIJReallocateAIJ_structure_only(A, a->mbs, bs2, nrow, brow, bcol, rmax, ai, aj, rp, imax, nonew, MatScalar);
2532: } else {
2533: MatSeqXAIJReallocateAIJ(A, a->mbs, bs2, nrow, brow, bcol, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
2534: }
2535: N = nrow++ - 1;
2536: high++;
2537: /* shift up all the later entries in this row */
2538: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
2539: rp[i] = bcol;
2540: if (!A->structure_only) {
2541: PetscCall(PetscArraymove(ap + bs2 * (i + 1), ap + bs2 * i, bs2 * (N - i + 1)));
2542: PetscCall(PetscArrayzero(ap + bs2 * i, bs2));
2543: ap[bs2 * i + bs * cidx + ridx] = value;
2544: }
2545: a->nz++;
2546: A->nonzerostate++;
2547: noinsert1:;
2548: low = i;
2549: }
2550: ailen[brow] = nrow;
2551: }
2552: PetscFunctionReturn(PETSC_SUCCESS);
2553: }
2555: static PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2556: {
2557: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)inA->data;
2558: Mat outA;
2559: PetscBool row_identity, col_identity;
2561: PetscFunctionBegin;
2562: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels = 0 supported for in-place ILU");
2563: PetscCall(ISIdentity(row, &row_identity));
2564: PetscCall(ISIdentity(col, &col_identity));
2565: PetscCheck(row_identity && col_identity, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Row and column permutations must be identity for in-place ILU");
2567: outA = inA;
2568: inA->factortype = MAT_FACTOR_LU;
2569: PetscCall(PetscFree(inA->solvertype));
2570: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2572: PetscCall(MatMarkDiagonal_SeqBAIJ(inA));
2574: PetscCall(PetscObjectReference((PetscObject)row));
2575: PetscCall(ISDestroy(&a->row));
2576: a->row = row;
2577: PetscCall(PetscObjectReference((PetscObject)col));
2578: PetscCall(ISDestroy(&a->col));
2579: a->col = col;
2581: /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
2582: PetscCall(ISDestroy(&a->icol));
2583: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2585: PetscCall(MatSeqBAIJSetNumericFactorization_inplace(inA, (PetscBool)(row_identity && col_identity)));
2586: if (!a->solve_work) PetscCall(PetscMalloc1(inA->rmap->N + inA->rmap->bs, &a->solve_work));
2587: PetscCall(MatLUFactorNumeric(outA, inA, info));
2588: PetscFunctionReturn(PETSC_SUCCESS);
2589: }
2591: static PetscErrorCode MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat, const PetscInt *indices)
2592: {
2593: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data;
2595: PetscFunctionBegin;
2596: baij->nz = baij->maxnz;
2597: PetscCall(PetscArraycpy(baij->j, indices, baij->nz));
2598: PetscCall(PetscArraycpy(baij->ilen, baij->imax, baij->mbs));
2599: PetscFunctionReturn(PETSC_SUCCESS);
2600: }
2602: /*@
2603: MatSeqBAIJSetColumnIndices - Set the column indices for all the rows in the matrix.
2605: Input Parameters:
2606: + mat - the `MATSEQBAIJ` matrix
2607: - indices - the column indices
2609: Level: advanced
2611: Notes:
2612: This can be called if you have precomputed the nonzero structure of the
2613: matrix and want to provide it to the matrix object to improve the performance
2614: of the `MatSetValues()` operation.
2616: You MUST have set the correct numbers of nonzeros per row in the call to
2617: `MatCreateSeqBAIJ()`, and the columns indices MUST be sorted.
2619: MUST be called before any calls to `MatSetValues()`
2621: .seealso: [](chapter_matrices), `Mat`, `MATSEQBAIJ`, `MatSetValues()`
2622: @*/
2623: PetscErrorCode MatSeqBAIJSetColumnIndices(Mat mat, PetscInt *indices)
2624: {
2625: PetscFunctionBegin;
2628: PetscUseMethod(mat, "MatSeqBAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
2629: PetscFunctionReturn(PETSC_SUCCESS);
2630: }
2632: PetscErrorCode MatGetRowMaxAbs_SeqBAIJ(Mat A, Vec v, PetscInt idx[])
2633: {
2634: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2635: PetscInt i, j, n, row, bs, *ai, *aj, mbs;
2636: PetscReal atmp;
2637: PetscScalar *x, zero = 0.0;
2638: MatScalar *aa;
2639: PetscInt ncols, brow, krow, kcol;
2641: PetscFunctionBegin;
2642: /* why is this not a macro???????????????????????????????????????????????????????????????? */
2643: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2644: bs = A->rmap->bs;
2645: aa = a->a;
2646: ai = a->i;
2647: aj = a->j;
2648: mbs = a->mbs;
2650: PetscCall(VecSet(v, zero));
2651: PetscCall(VecGetArray(v, &x));
2652: PetscCall(VecGetLocalSize(v, &n));
2653: PetscCheck(n == A->rmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
2654: for (i = 0; i < mbs; i++) {
2655: ncols = ai[1] - ai[0];
2656: ai++;
2657: brow = bs * i;
2658: for (j = 0; j < ncols; j++) {
2659: for (kcol = 0; kcol < bs; kcol++) {
2660: for (krow = 0; krow < bs; krow++) {
2661: atmp = PetscAbsScalar(*aa);
2662: aa++;
2663: row = brow + krow; /* row index */
2664: if (PetscAbsScalar(x[row]) < atmp) {
2665: x[row] = atmp;
2666: if (idx) idx[row] = bs * (*aj) + kcol;
2667: }
2668: }
2669: }
2670: aj++;
2671: }
2672: }
2673: PetscCall(VecRestoreArray(v, &x));
2674: PetscFunctionReturn(PETSC_SUCCESS);
2675: }
2677: PetscErrorCode MatCopy_SeqBAIJ(Mat A, Mat B, MatStructure str)
2678: {
2679: PetscFunctionBegin;
2680: /* If the two matrices have the same copy implementation, use fast copy. */
2681: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2682: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2683: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data;
2684: PetscInt ambs = a->mbs, bmbs = b->mbs, abs = A->rmap->bs, bbs = B->rmap->bs, bs2 = abs * abs;
2686: PetscCheck(a->i[ambs] == b->i[bmbs], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzero blocks in matrices A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", a->i[ambs], b->i[bmbs]);
2687: PetscCheck(abs == bbs, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Block size A %" PetscInt_FMT " and B %" PetscInt_FMT " are different", abs, bbs);
2688: PetscCall(PetscArraycpy(b->a, a->a, bs2 * a->i[ambs]));
2689: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2690: } else {
2691: PetscCall(MatCopy_Basic(A, B, str));
2692: }
2693: PetscFunctionReturn(PETSC_SUCCESS);
2694: }
2696: static PetscErrorCode MatSeqBAIJGetArray_SeqBAIJ(Mat A, PetscScalar *array[])
2697: {
2698: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2700: PetscFunctionBegin;
2701: *array = a->a;
2702: PetscFunctionReturn(PETSC_SUCCESS);
2703: }
2705: static PetscErrorCode MatSeqBAIJRestoreArray_SeqBAIJ(Mat A, PetscScalar *array[])
2706: {
2707: PetscFunctionBegin;
2708: *array = NULL;
2709: PetscFunctionReturn(PETSC_SUCCESS);
2710: }
2712: PetscErrorCode MatAXPYGetPreallocation_SeqBAIJ(Mat Y, Mat X, PetscInt *nnz)
2713: {
2714: PetscInt bs = Y->rmap->bs, mbs = Y->rmap->N / bs;
2715: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data;
2716: Mat_SeqBAIJ *y = (Mat_SeqBAIJ *)Y->data;
2718: PetscFunctionBegin;
2719: /* Set the number of nonzeros in the new matrix */
2720: PetscCall(MatAXPYGetPreallocation_SeqX_private(mbs, x->i, x->j, y->i, y->j, nnz));
2721: PetscFunctionReturn(PETSC_SUCCESS);
2722: }
2724: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
2725: {
2726: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data, *y = (Mat_SeqBAIJ *)Y->data;
2727: PetscInt bs = Y->rmap->bs, bs2 = bs * bs;
2728: PetscBLASInt one = 1;
2730: PetscFunctionBegin;
2731: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
2732: PetscBool e = x->nz == y->nz && x->mbs == y->mbs && bs == X->rmap->bs ? PETSC_TRUE : PETSC_FALSE;
2733: if (e) {
2734: PetscCall(PetscArraycmp(x->i, y->i, x->mbs + 1, &e));
2735: if (e) {
2736: PetscCall(PetscArraycmp(x->j, y->j, x->i[x->mbs], &e));
2737: if (e) str = SAME_NONZERO_PATTERN;
2738: }
2739: }
2740: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
2741: }
2742: if (str == SAME_NONZERO_PATTERN) {
2743: PetscScalar alpha = a;
2744: PetscBLASInt bnz;
2745: PetscCall(PetscBLASIntCast(x->nz * bs2, &bnz));
2746: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, x->a, &one, y->a, &one));
2747: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
2748: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2749: PetscCall(MatAXPY_Basic(Y, a, X, str));
2750: } else {
2751: Mat B;
2752: PetscInt *nnz;
2753: PetscCheck(bs == X->rmap->bs, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Matrices must have same block size");
2754: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
2755: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
2756: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
2757: PetscCall(MatSetSizes(B, Y->rmap->n, Y->cmap->n, Y->rmap->N, Y->cmap->N));
2758: PetscCall(MatSetBlockSizesFromMats(B, Y, Y));
2759: PetscCall(MatSetType(B, (MatType)((PetscObject)Y)->type_name));
2760: PetscCall(MatAXPYGetPreallocation_SeqBAIJ(Y, X, nnz));
2761: PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz));
2762: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
2763: PetscCall(MatHeaderMerge(Y, &B));
2764: PetscCall(PetscFree(nnz));
2765: }
2766: PetscFunctionReturn(PETSC_SUCCESS);
2767: }
2769: PETSC_INTERN PetscErrorCode MatConjugate_SeqBAIJ(Mat A)
2770: {
2771: #if PetscDefined(USE_COMPLEX)
2772: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2773: PetscInt i, nz = a->bs2 * a->i[a->mbs];
2774: MatScalar *aa = a->a;
2776: PetscFunctionBegin;
2777: for (i = 0; i < nz; i++) aa[i] = PetscConj(aa[i]);
2778: PetscFunctionReturn(PETSC_SUCCESS);
2779: #else
2780: (void)A;
2781: return PETSC_SUCCESS;
2782: #endif
2783: }
2785: static PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
2786: {
2787: #if PetscDefined(USE_COMPLEX)
2788: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2789: PetscInt i, nz = a->bs2 * a->i[a->mbs];
2790: MatScalar *aa = a->a;
2792: PetscFunctionBegin;
2793: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
2794: PetscFunctionReturn(PETSC_SUCCESS);
2795: #else
2796: (void)A;
2797: return PETSC_SUCCESS;
2798: #endif
2799: }
2801: static PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2802: {
2803: #if PetscDefined(USE_COMPLEX)
2804: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2805: PetscInt i, nz = a->bs2 * a->i[a->mbs];
2806: MatScalar *aa = a->a;
2808: PetscFunctionBegin;
2809: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2810: PetscFunctionReturn(PETSC_SUCCESS);
2811: #else
2812: (void)A;
2813: return PETSC_SUCCESS;
2814: #endif
2815: }
2817: /*
2818: Code almost identical to MatGetColumnIJ_SeqAIJ() should share common code
2819: */
2820: static PetscErrorCode MatGetColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
2821: {
2822: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2823: PetscInt bs = A->rmap->bs, i, *collengths, *cia, *cja, n = A->cmap->n / bs, m = A->rmap->n / bs;
2824: PetscInt nz = a->i[m], row, *jj, mr, col;
2826: PetscFunctionBegin;
2827: *nn = n;
2828: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
2829: PetscCheck(!symmetric, PETSC_COMM_SELF, PETSC_ERR_SUP, "Not for BAIJ matrices");
2830: PetscCall(PetscCalloc1(n, &collengths));
2831: PetscCall(PetscMalloc1(n + 1, &cia));
2832: PetscCall(PetscMalloc1(nz, &cja));
2833: jj = a->j;
2834: for (i = 0; i < nz; i++) collengths[jj[i]]++;
2835: cia[0] = oshift;
2836: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
2837: PetscCall(PetscArrayzero(collengths, n));
2838: jj = a->j;
2839: for (row = 0; row < m; row++) {
2840: mr = a->i[row + 1] - a->i[row];
2841: for (i = 0; i < mr; i++) {
2842: col = *jj++;
2844: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
2845: }
2846: }
2847: PetscCall(PetscFree(collengths));
2848: *ia = cia;
2849: *ja = cja;
2850: PetscFunctionReturn(PETSC_SUCCESS);
2851: }
2853: static PetscErrorCode MatRestoreColumnIJ_SeqBAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
2854: {
2855: PetscFunctionBegin;
2856: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
2857: PetscCall(PetscFree(*ia));
2858: PetscCall(PetscFree(*ja));
2859: PetscFunctionReturn(PETSC_SUCCESS);
2860: }
2862: /*
2863: MatGetColumnIJ_SeqBAIJ_Color() and MatRestoreColumnIJ_SeqBAIJ_Color() are customized from
2864: MatGetColumnIJ_SeqBAIJ() and MatRestoreColumnIJ_SeqBAIJ() by adding an output
2865: spidx[], index of a->a, to be used in MatTransposeColoringCreate() and MatFDColoringCreate()
2866: */
2867: PetscErrorCode MatGetColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
2868: {
2869: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
2870: PetscInt i, *collengths, *cia, *cja, n = a->nbs, m = a->mbs;
2871: PetscInt nz = a->i[m], row, *jj, mr, col;
2872: PetscInt *cspidx;
2874: PetscFunctionBegin;
2875: *nn = n;
2876: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
2878: PetscCall(PetscCalloc1(n, &collengths));
2879: PetscCall(PetscMalloc1(n + 1, &cia));
2880: PetscCall(PetscMalloc1(nz, &cja));
2881: PetscCall(PetscMalloc1(nz, &cspidx));
2882: jj = a->j;
2883: for (i = 0; i < nz; i++) collengths[jj[i]]++;
2884: cia[0] = oshift;
2885: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
2886: PetscCall(PetscArrayzero(collengths, n));
2887: jj = a->j;
2888: for (row = 0; row < m; row++) {
2889: mr = a->i[row + 1] - a->i[row];
2890: for (i = 0; i < mr; i++) {
2891: col = *jj++;
2892: cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */
2893: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
2894: }
2895: }
2896: PetscCall(PetscFree(collengths));
2897: *ia = cia;
2898: *ja = cja;
2899: *spidx = cspidx;
2900: PetscFunctionReturn(PETSC_SUCCESS);
2901: }
2903: PetscErrorCode MatRestoreColumnIJ_SeqBAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
2904: {
2905: PetscFunctionBegin;
2906: PetscCall(MatRestoreColumnIJ_SeqBAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
2907: PetscCall(PetscFree(*spidx));
2908: PetscFunctionReturn(PETSC_SUCCESS);
2909: }
2911: PetscErrorCode MatShift_SeqBAIJ(Mat Y, PetscScalar a)
2912: {
2913: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)Y->data;
2915: PetscFunctionBegin;
2916: if (!Y->preallocated || !aij->nz) PetscCall(MatSeqBAIJSetPreallocation(Y, Y->rmap->bs, 1, NULL));
2917: PetscCall(MatShift_Basic(Y, a));
2918: PetscFunctionReturn(PETSC_SUCCESS);
2919: }
2921: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
2922: MatGetRow_SeqBAIJ,
2923: MatRestoreRow_SeqBAIJ,
2924: MatMult_SeqBAIJ_N,
2925: /* 4*/ MatMultAdd_SeqBAIJ_N,
2926: MatMultTranspose_SeqBAIJ,
2927: MatMultTransposeAdd_SeqBAIJ,
2928: NULL,
2929: NULL,
2930: NULL,
2931: /* 10*/ NULL,
2932: MatLUFactor_SeqBAIJ,
2933: NULL,
2934: NULL,
2935: MatTranspose_SeqBAIJ,
2936: /* 15*/ MatGetInfo_SeqBAIJ,
2937: MatEqual_SeqBAIJ,
2938: MatGetDiagonal_SeqBAIJ,
2939: MatDiagonalScale_SeqBAIJ,
2940: MatNorm_SeqBAIJ,
2941: /* 20*/ NULL,
2942: MatAssemblyEnd_SeqBAIJ,
2943: MatSetOption_SeqBAIJ,
2944: MatZeroEntries_SeqBAIJ,
2945: /* 24*/ MatZeroRows_SeqBAIJ,
2946: NULL,
2947: NULL,
2948: NULL,
2949: NULL,
2950: /* 29*/ MatSetUp_Seq_Hash,
2951: NULL,
2952: NULL,
2953: NULL,
2954: NULL,
2955: /* 34*/ MatDuplicate_SeqBAIJ,
2956: NULL,
2957: NULL,
2958: MatILUFactor_SeqBAIJ,
2959: NULL,
2960: /* 39*/ MatAXPY_SeqBAIJ,
2961: MatCreateSubMatrices_SeqBAIJ,
2962: MatIncreaseOverlap_SeqBAIJ,
2963: MatGetValues_SeqBAIJ,
2964: MatCopy_SeqBAIJ,
2965: /* 44*/ NULL,
2966: MatScale_SeqBAIJ,
2967: MatShift_SeqBAIJ,
2968: NULL,
2969: MatZeroRowsColumns_SeqBAIJ,
2970: /* 49*/ NULL,
2971: MatGetRowIJ_SeqBAIJ,
2972: MatRestoreRowIJ_SeqBAIJ,
2973: MatGetColumnIJ_SeqBAIJ,
2974: MatRestoreColumnIJ_SeqBAIJ,
2975: /* 54*/ MatFDColoringCreate_SeqXAIJ,
2976: NULL,
2977: NULL,
2978: NULL,
2979: MatSetValuesBlocked_SeqBAIJ,
2980: /* 59*/ MatCreateSubMatrix_SeqBAIJ,
2981: MatDestroy_SeqBAIJ,
2982: MatView_SeqBAIJ,
2983: NULL,
2984: NULL,
2985: /* 64*/ NULL,
2986: NULL,
2987: NULL,
2988: NULL,
2989: NULL,
2990: /* 69*/ MatGetRowMaxAbs_SeqBAIJ,
2991: NULL,
2992: MatConvert_Basic,
2993: NULL,
2994: NULL,
2995: /* 74*/ NULL,
2996: MatFDColoringApply_BAIJ,
2997: NULL,
2998: NULL,
2999: NULL,
3000: /* 79*/ NULL,
3001: NULL,
3002: NULL,
3003: NULL,
3004: MatLoad_SeqBAIJ,
3005: /* 84*/ NULL,
3006: NULL,
3007: NULL,
3008: NULL,
3009: NULL,
3010: /* 89*/ NULL,
3011: NULL,
3012: NULL,
3013: NULL,
3014: NULL,
3015: /* 94*/ NULL,
3016: NULL,
3017: NULL,
3018: NULL,
3019: NULL,
3020: /* 99*/ NULL,
3021: NULL,
3022: NULL,
3023: MatConjugate_SeqBAIJ,
3024: NULL,
3025: /*104*/ NULL,
3026: MatRealPart_SeqBAIJ,
3027: MatImaginaryPart_SeqBAIJ,
3028: NULL,
3029: NULL,
3030: /*109*/ NULL,
3031: NULL,
3032: NULL,
3033: NULL,
3034: MatMissingDiagonal_SeqBAIJ,
3035: /*114*/ NULL,
3036: NULL,
3037: NULL,
3038: NULL,
3039: NULL,
3040: /*119*/ NULL,
3041: NULL,
3042: MatMultHermitianTranspose_SeqBAIJ,
3043: MatMultHermitianTransposeAdd_SeqBAIJ,
3044: NULL,
3045: /*124*/ NULL,
3046: MatGetColumnReductions_SeqBAIJ,
3047: MatInvertBlockDiagonal_SeqBAIJ,
3048: NULL,
3049: NULL,
3050: /*129*/ NULL,
3051: NULL,
3052: NULL,
3053: NULL,
3054: NULL,
3055: /*134*/ NULL,
3056: NULL,
3057: NULL,
3058: NULL,
3059: NULL,
3060: /*139*/ MatSetBlockSizes_Default,
3061: NULL,
3062: NULL,
3063: MatFDColoringSetUp_SeqXAIJ,
3064: NULL,
3065: /*144*/ MatCreateMPIMatConcatenateSeqMat_SeqBAIJ,
3066: MatDestroySubMatrices_SeqBAIJ,
3067: NULL,
3068: NULL,
3069: NULL,
3070: NULL,
3071: /*150*/ NULL,
3072: NULL};
3074: static PetscErrorCode MatStoreValues_SeqBAIJ(Mat mat)
3075: {
3076: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
3077: PetscInt nz = aij->i[aij->mbs] * aij->bs2;
3079: PetscFunctionBegin;
3080: PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3082: /* allocate space for values if not already there */
3083: if (!aij->saved_values) PetscCall(PetscMalloc1(nz + 1, &aij->saved_values));
3085: /* copy values over */
3086: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3087: PetscFunctionReturn(PETSC_SUCCESS);
3088: }
3090: static PetscErrorCode MatRetrieveValues_SeqBAIJ(Mat mat)
3091: {
3092: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
3093: PetscInt nz = aij->i[aij->mbs] * aij->bs2;
3095: PetscFunctionBegin;
3096: PetscCheck(aij->nonew == 1, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3097: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3099: /* copy values over */
3100: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3101: PetscFunctionReturn(PETSC_SUCCESS);
3102: }
3104: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType, MatReuse, Mat *);
3105: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqSBAIJ(Mat, MatType, MatReuse, Mat *);
3107: PetscErrorCode MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B, PetscInt bs, PetscInt nz, PetscInt *nnz)
3108: {
3109: Mat_SeqBAIJ *b = (Mat_SeqBAIJ *)B->data;
3110: PetscInt i, mbs, nbs, bs2;
3111: PetscBool flg = PETSC_FALSE, skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3113: PetscFunctionBegin;
3114: if (B->hash_active) {
3115: PetscInt bs;
3116: PetscCall(PetscMemcpy(&B->ops, &b->cops, sizeof(*(B->ops))));
3117: PetscCall(PetscHMapIJVDestroy(&b->ht));
3118: PetscCall(MatGetBlockSize(B, &bs));
3119: if (bs > 1) PetscCall(PetscHSetIJDestroy(&b->bht));
3120: PetscCall(PetscFree(b->dnz));
3121: PetscCall(PetscFree(b->bdnz));
3122: B->hash_active = PETSC_FALSE;
3123: }
3124: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3125: if (nz == MAT_SKIP_ALLOCATION) {
3126: skipallocation = PETSC_TRUE;
3127: nz = 0;
3128: }
3130: PetscCall(MatSetBlockSize(B, PetscAbs(bs)));
3131: PetscCall(PetscLayoutSetUp(B->rmap));
3132: PetscCall(PetscLayoutSetUp(B->cmap));
3133: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
3135: B->preallocated = PETSC_TRUE;
3137: mbs = B->rmap->n / bs;
3138: nbs = B->cmap->n / bs;
3139: bs2 = bs * bs;
3141: PetscCheck(mbs * bs == B->rmap->n && nbs * bs == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number rows %" PetscInt_FMT ", cols %" PetscInt_FMT " must be divisible by blocksize %" PetscInt_FMT, B->rmap->N, B->cmap->n, bs);
3143: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3144: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3145: if (nnz) {
3146: for (i = 0; i < mbs; i++) {
3147: PetscCheck(nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, nnz[i]);
3148: PetscCheck(nnz[i] <= nbs, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than block row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], nbs);
3149: }
3150: }
3152: PetscOptionsBegin(PetscObjectComm((PetscObject)B), NULL, "Optimize options for SEQBAIJ matrix 2 ", "Mat");
3153: PetscCall(PetscOptionsBool("-mat_no_unroll", "Do not optimize for block size (slow)", NULL, flg, &flg, NULL));
3154: PetscOptionsEnd();
3156: if (!flg) {
3157: switch (bs) {
3158: case 1:
3159: B->ops->mult = MatMult_SeqBAIJ_1;
3160: B->ops->multadd = MatMultAdd_SeqBAIJ_1;
3161: break;
3162: case 2:
3163: B->ops->mult = MatMult_SeqBAIJ_2;
3164: B->ops->multadd = MatMultAdd_SeqBAIJ_2;
3165: break;
3166: case 3:
3167: B->ops->mult = MatMult_SeqBAIJ_3;
3168: B->ops->multadd = MatMultAdd_SeqBAIJ_3;
3169: break;
3170: case 4:
3171: B->ops->mult = MatMult_SeqBAIJ_4;
3172: B->ops->multadd = MatMultAdd_SeqBAIJ_4;
3173: break;
3174: case 5:
3175: B->ops->mult = MatMult_SeqBAIJ_5;
3176: B->ops->multadd = MatMultAdd_SeqBAIJ_5;
3177: break;
3178: case 6:
3179: B->ops->mult = MatMult_SeqBAIJ_6;
3180: B->ops->multadd = MatMultAdd_SeqBAIJ_6;
3181: break;
3182: case 7:
3183: B->ops->mult = MatMult_SeqBAIJ_7;
3184: B->ops->multadd = MatMultAdd_SeqBAIJ_7;
3185: break;
3186: case 9: {
3187: PetscInt version = 1;
3188: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3189: switch (version) {
3190: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
3191: case 1:
3192: B->ops->mult = MatMult_SeqBAIJ_9_AVX2;
3193: B->ops->multadd = MatMultAdd_SeqBAIJ_9_AVX2;
3194: PetscCall(PetscInfo((PetscObject)B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3195: break;
3196: #endif
3197: default:
3198: B->ops->mult = MatMult_SeqBAIJ_N;
3199: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3200: PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3201: break;
3202: }
3203: break;
3204: }
3205: case 11:
3206: B->ops->mult = MatMult_SeqBAIJ_11;
3207: B->ops->multadd = MatMultAdd_SeqBAIJ_11;
3208: break;
3209: case 12: {
3210: PetscInt version = 1;
3211: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3212: switch (version) {
3213: case 1:
3214: B->ops->mult = MatMult_SeqBAIJ_12_ver1;
3215: B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1;
3216: PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3217: break;
3218: case 2:
3219: B->ops->mult = MatMult_SeqBAIJ_12_ver2;
3220: B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver2;
3221: PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3222: break;
3223: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
3224: case 3:
3225: B->ops->mult = MatMult_SeqBAIJ_12_AVX2;
3226: B->ops->multadd = MatMultAdd_SeqBAIJ_12_ver1;
3227: PetscCall(PetscInfo((PetscObject)B, "Using AVX2 for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3228: break;
3229: #endif
3230: default:
3231: B->ops->mult = MatMult_SeqBAIJ_N;
3232: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3233: PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3234: break;
3235: }
3236: break;
3237: }
3238: case 15: {
3239: PetscInt version = 1;
3240: PetscCall(PetscOptionsGetInt(NULL, ((PetscObject)B)->prefix, "-mat_baij_mult_version", &version, NULL));
3241: switch (version) {
3242: case 1:
3243: B->ops->mult = MatMult_SeqBAIJ_15_ver1;
3244: PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3245: break;
3246: case 2:
3247: B->ops->mult = MatMult_SeqBAIJ_15_ver2;
3248: PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3249: break;
3250: case 3:
3251: B->ops->mult = MatMult_SeqBAIJ_15_ver3;
3252: PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3253: break;
3254: case 4:
3255: B->ops->mult = MatMult_SeqBAIJ_15_ver4;
3256: PetscCall(PetscInfo((PetscObject)B, "Using version %" PetscInt_FMT " of MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", version, bs));
3257: break;
3258: default:
3259: B->ops->mult = MatMult_SeqBAIJ_N;
3260: PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3261: break;
3262: }
3263: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3264: break;
3265: }
3266: default:
3267: B->ops->mult = MatMult_SeqBAIJ_N;
3268: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3269: PetscCall(PetscInfo((PetscObject)B, "Using BLAS for MatMult for BAIJ for blocksize %" PetscInt_FMT "\n", bs));
3270: break;
3271: }
3272: }
3273: B->ops->sor = MatSOR_SeqBAIJ;
3274: b->mbs = mbs;
3275: b->nbs = nbs;
3276: if (!skipallocation) {
3277: if (!b->imax) {
3278: PetscCall(PetscMalloc2(mbs, &b->imax, mbs, &b->ilen));
3280: b->free_imax_ilen = PETSC_TRUE;
3281: }
3282: /* b->ilen will count nonzeros in each block row so far. */
3283: for (i = 0; i < mbs; i++) b->ilen[i] = 0;
3284: if (!nnz) {
3285: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3286: else if (nz < 0) nz = 1;
3287: nz = PetscMin(nz, nbs);
3288: for (i = 0; i < mbs; i++) b->imax[i] = nz;
3289: PetscCall(PetscIntMultError(nz, mbs, &nz));
3290: } else {
3291: PetscInt64 nz64 = 0;
3292: for (i = 0; i < mbs; i++) {
3293: b->imax[i] = nnz[i];
3294: nz64 += nnz[i];
3295: }
3296: PetscCall(PetscIntCast(nz64, &nz));
3297: }
3299: /* allocate the matrix space */
3300: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
3301: if (B->structure_only) {
3302: PetscCall(PetscMalloc1(nz, &b->j));
3303: PetscCall(PetscMalloc1(B->rmap->N + 1, &b->i));
3304: } else {
3305: PetscInt nzbs2 = 0;
3306: PetscCall(PetscIntMultError(nz, bs2, &nzbs2));
3307: PetscCall(PetscMalloc3(nzbs2, &b->a, nz, &b->j, B->rmap->N + 1, &b->i));
3308: PetscCall(PetscArrayzero(b->a, nz * bs2));
3309: }
3310: PetscCall(PetscArrayzero(b->j, nz));
3312: if (B->structure_only) {
3313: b->singlemalloc = PETSC_FALSE;
3314: b->free_a = PETSC_FALSE;
3315: } else {
3316: b->singlemalloc = PETSC_TRUE;
3317: b->free_a = PETSC_TRUE;
3318: }
3319: b->free_ij = PETSC_TRUE;
3321: b->i[0] = 0;
3322: for (i = 1; i < mbs + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
3324: } else {
3325: b->free_a = PETSC_FALSE;
3326: b->free_ij = PETSC_FALSE;
3327: }
3329: b->bs2 = bs2;
3330: b->mbs = mbs;
3331: b->nz = 0;
3332: b->maxnz = nz;
3333: B->info.nz_unneeded = (PetscReal)b->maxnz * bs2;
3334: B->was_assembled = PETSC_FALSE;
3335: B->assembled = PETSC_FALSE;
3336: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
3337: PetscFunctionReturn(PETSC_SUCCESS);
3338: }
3340: PetscErrorCode MatSeqBAIJSetPreallocationCSR_SeqBAIJ(Mat B, PetscInt bs, const PetscInt ii[], const PetscInt jj[], const PetscScalar V[])
3341: {
3342: PetscInt i, m, nz, nz_max = 0, *nnz;
3343: PetscScalar *values = NULL;
3344: PetscBool roworiented = ((Mat_SeqBAIJ *)B->data)->roworiented;
3346: PetscFunctionBegin;
3347: PetscCheck(bs >= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Invalid block size specified, must be positive but it is %" PetscInt_FMT, bs);
3348: PetscCall(PetscLayoutSetBlockSize(B->rmap, bs));
3349: PetscCall(PetscLayoutSetBlockSize(B->cmap, bs));
3350: PetscCall(PetscLayoutSetUp(B->rmap));
3351: PetscCall(PetscLayoutSetUp(B->cmap));
3352: PetscCall(PetscLayoutGetBlockSize(B->rmap, &bs));
3353: m = B->rmap->n / bs;
3355: PetscCheck(ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %" PetscInt_FMT, ii[0]);
3356: PetscCall(PetscMalloc1(m + 1, &nnz));
3357: for (i = 0; i < m; i++) {
3358: nz = ii[i + 1] - ii[i];
3359: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
3360: nz_max = PetscMax(nz_max, nz);
3361: nnz[i] = nz;
3362: }
3363: PetscCall(MatSeqBAIJSetPreallocation(B, bs, 0, nnz));
3364: PetscCall(PetscFree(nnz));
3366: values = (PetscScalar *)V;
3367: if (!values) PetscCall(PetscCalloc1(bs * bs * (nz_max + 1), &values));
3368: for (i = 0; i < m; i++) {
3369: PetscInt ncols = ii[i + 1] - ii[i];
3370: const PetscInt *icols = jj + ii[i];
3371: if (bs == 1 || !roworiented) {
3372: const PetscScalar *svals = values + (V ? (bs * bs * ii[i]) : 0);
3373: PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, ncols, icols, svals, INSERT_VALUES));
3374: } else {
3375: PetscInt j;
3376: for (j = 0; j < ncols; j++) {
3377: const PetscScalar *svals = values + (V ? (bs * bs * (ii[i] + j)) : 0);
3378: PetscCall(MatSetValuesBlocked_SeqBAIJ(B, 1, &i, 1, &icols[j], svals, INSERT_VALUES));
3379: }
3380: }
3381: }
3382: if (!V) PetscCall(PetscFree(values));
3383: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
3384: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
3385: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3386: PetscFunctionReturn(PETSC_SUCCESS);
3387: }
3389: /*@C
3390: MatSeqBAIJGetArray - gives read/write access to the array where the data for a `MATSEQBAIJ` matrix is stored
3392: Not Collective
3394: Input Parameter:
3395: . mat - a `MATSEQBAIJ` matrix
3397: Output Parameter:
3398: . array - pointer to the data
3400: Level: intermediate
3402: .seealso: [](chapter_matrices), `Mat`, `MATSEQBAIJ`, `MatSeqBAIJRestoreArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`
3403: @*/
3404: PetscErrorCode MatSeqBAIJGetArray(Mat A, PetscScalar **array)
3405: {
3406: PetscFunctionBegin;
3407: PetscUseMethod(A, "MatSeqBAIJGetArray_C", (Mat, PetscScalar **), (A, array));
3408: PetscFunctionReturn(PETSC_SUCCESS);
3409: }
3411: /*@C
3412: MatSeqBAIJRestoreArray - returns access to the array where the data for a `MATSEQBAIJ` matrix is stored obtained by `MatSeqBAIJGetArray()`
3414: Not Collective
3416: Input Parameters:
3417: + mat - a `MATSEQBAIJ` matrix
3418: - array - pointer to the data
3420: Level: intermediate
3422: .seealso: [](chapter_matrices), `Mat`, `MatSeqBAIJGetArray()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`
3423: @*/
3424: PetscErrorCode MatSeqBAIJRestoreArray(Mat A, PetscScalar **array)
3425: {
3426: PetscFunctionBegin;
3427: PetscUseMethod(A, "MatSeqBAIJRestoreArray_C", (Mat, PetscScalar **), (A, array));
3428: PetscFunctionReturn(PETSC_SUCCESS);
3429: }
3431: /*MC
3432: MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on
3433: block sparse compressed row format.
3435: Options Database Keys:
3436: + -mat_type seqbaij - sets the matrix type to `MATSEQBAIJ` during a call to `MatSetFromOptions()`
3437: - -mat_baij_mult_version version - indicate the version of the matrix-vector product to use (0 often indicates using BLAS)
3439: Level: beginner
3441: Notes:
3442: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
3443: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
3445: Run with `-info` to see what version of the matrix-vector product is being used
3447: .seealso: [](chapter_matrices), `Mat`, `MatCreateSeqBAIJ()`
3448: M*/
3450: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBSTRM(Mat, MatType, MatReuse, Mat *);
3452: PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJ(Mat B)
3453: {
3454: PetscMPIInt size;
3455: Mat_SeqBAIJ *b;
3457: PetscFunctionBegin;
3458: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
3459: PetscCheck(size == 1, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Comm must be of size 1");
3461: PetscCall(PetscNew(&b));
3462: B->data = (void *)b;
3463: PetscCall(PetscMemcpy(B->ops, &MatOps_Values, sizeof(struct _MatOps)));
3465: b->row = NULL;
3466: b->col = NULL;
3467: b->icol = NULL;
3468: b->reallocs = 0;
3469: b->saved_values = NULL;
3471: b->roworiented = PETSC_TRUE;
3472: b->nonew = 0;
3473: b->diag = NULL;
3474: B->spptr = NULL;
3475: B->info.nz_unneeded = (PetscReal)b->maxnz * b->bs2;
3476: b->keepnonzeropattern = PETSC_FALSE;
3478: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJGetArray_C", MatSeqBAIJGetArray_SeqBAIJ));
3479: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJRestoreArray_C", MatSeqBAIJRestoreArray_SeqBAIJ));
3480: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqBAIJ));
3481: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqBAIJ));
3482: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetColumnIndices_C", MatSeqBAIJSetColumnIndices_SeqBAIJ));
3483: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqaij_C", MatConvert_SeqBAIJ_SeqAIJ));
3484: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_seqsbaij_C", MatConvert_SeqBAIJ_SeqSBAIJ));
3485: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocation_C", MatSeqBAIJSetPreallocation_SeqBAIJ));
3486: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqBAIJSetPreallocationCSR_C", MatSeqBAIJSetPreallocationCSR_SeqBAIJ));
3487: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqBAIJ));
3488: #if defined(PETSC_HAVE_HYPRE)
3489: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_hypre_C", MatConvert_AIJ_HYPRE));
3490: #endif
3491: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaij_is_C", MatConvert_XAIJ_IS));
3492: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJ));
3493: PetscFunctionReturn(PETSC_SUCCESS);
3494: }
3496: PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
3497: {
3498: Mat_SeqBAIJ *c = (Mat_SeqBAIJ *)C->data, *a = (Mat_SeqBAIJ *)A->data;
3499: PetscInt i, mbs = a->mbs, nz = a->nz, bs2 = a->bs2;
3501: PetscFunctionBegin;
3502: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
3503: PetscCheck(a->i[mbs] == nz, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Corrupt matrix");
3505: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3506: c->imax = a->imax;
3507: c->ilen = a->ilen;
3508: c->free_imax_ilen = PETSC_FALSE;
3509: } else {
3510: PetscCall(PetscMalloc2(mbs, &c->imax, mbs, &c->ilen));
3511: for (i = 0; i < mbs; i++) {
3512: c->imax[i] = a->imax[i];
3513: c->ilen[i] = a->ilen[i];
3514: }
3515: c->free_imax_ilen = PETSC_TRUE;
3516: }
3518: /* allocate the matrix space */
3519: if (mallocmatspace) {
3520: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3521: PetscCall(PetscCalloc1(bs2 * nz, &c->a));
3523: c->i = a->i;
3524: c->j = a->j;
3525: c->singlemalloc = PETSC_FALSE;
3526: c->free_a = PETSC_TRUE;
3527: c->free_ij = PETSC_FALSE;
3528: c->parent = A;
3529: C->preallocated = PETSC_TRUE;
3530: C->assembled = PETSC_TRUE;
3532: PetscCall(PetscObjectReference((PetscObject)A));
3533: PetscCall(MatSetOption(A, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3534: PetscCall(MatSetOption(C, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
3535: } else {
3536: PetscCall(PetscMalloc3(bs2 * nz, &c->a, nz, &c->j, mbs + 1, &c->i));
3538: c->singlemalloc = PETSC_TRUE;
3539: c->free_a = PETSC_TRUE;
3540: c->free_ij = PETSC_TRUE;
3542: PetscCall(PetscArraycpy(c->i, a->i, mbs + 1));
3543: if (mbs > 0) {
3544: PetscCall(PetscArraycpy(c->j, a->j, nz));
3545: if (cpvalues == MAT_COPY_VALUES) {
3546: PetscCall(PetscArraycpy(c->a, a->a, bs2 * nz));
3547: } else {
3548: PetscCall(PetscArrayzero(c->a, bs2 * nz));
3549: }
3550: }
3551: C->preallocated = PETSC_TRUE;
3552: C->assembled = PETSC_TRUE;
3553: }
3554: }
3556: c->roworiented = a->roworiented;
3557: c->nonew = a->nonew;
3559: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
3560: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
3562: c->bs2 = a->bs2;
3563: c->mbs = a->mbs;
3564: c->nbs = a->nbs;
3566: if (a->diag) {
3567: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3568: c->diag = a->diag;
3569: c->free_diag = PETSC_FALSE;
3570: } else {
3571: PetscCall(PetscMalloc1(mbs + 1, &c->diag));
3572: for (i = 0; i < mbs; i++) c->diag[i] = a->diag[i];
3573: c->free_diag = PETSC_TRUE;
3574: }
3575: } else c->diag = NULL;
3577: c->nz = a->nz;
3578: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
3579: c->solve_work = NULL;
3580: c->mult_work = NULL;
3581: c->sor_workt = NULL;
3582: c->sor_work = NULL;
3584: c->compressedrow.use = a->compressedrow.use;
3585: c->compressedrow.nrows = a->compressedrow.nrows;
3586: if (a->compressedrow.use) {
3587: i = a->compressedrow.nrows;
3588: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i + 1, &c->compressedrow.rindex));
3589: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
3590: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
3591: } else {
3592: c->compressedrow.use = PETSC_FALSE;
3593: c->compressedrow.i = NULL;
3594: c->compressedrow.rindex = NULL;
3595: }
3596: C->nonzerostate = A->nonzerostate;
3598: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
3599: PetscFunctionReturn(PETSC_SUCCESS);
3600: }
3602: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
3603: {
3604: PetscFunctionBegin;
3605: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
3606: PetscCall(MatSetSizes(*B, A->rmap->N, A->cmap->n, A->rmap->N, A->cmap->n));
3607: PetscCall(MatSetType(*B, MATSEQBAIJ));
3608: PetscCall(MatDuplicateNoCreate_SeqBAIJ(*B, A, cpvalues, PETSC_TRUE));
3609: PetscFunctionReturn(PETSC_SUCCESS);
3610: }
3612: /* Used for both SeqBAIJ and SeqSBAIJ matrices */
3613: PetscErrorCode MatLoad_SeqBAIJ_Binary(Mat mat, PetscViewer viewer)
3614: {
3615: PetscInt header[4], M, N, nz, bs, m, n, mbs, nbs, rows, cols, sum, i, j, k;
3616: PetscInt *rowidxs, *colidxs;
3617: PetscScalar *matvals;
3619: PetscFunctionBegin;
3620: PetscCall(PetscViewerSetUp(viewer));
3622: /* read matrix header */
3623: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
3624: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
3625: M = header[1];
3626: N = header[2];
3627: nz = header[3];
3628: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
3629: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
3630: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqBAIJ");
3632: /* set block sizes from the viewer's .info file */
3633: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
3634: /* set local and global sizes if not set already */
3635: if (mat->rmap->n < 0) mat->rmap->n = M;
3636: if (mat->cmap->n < 0) mat->cmap->n = N;
3637: if (mat->rmap->N < 0) mat->rmap->N = M;
3638: if (mat->cmap->N < 0) mat->cmap->N = N;
3639: PetscCall(PetscLayoutSetUp(mat->rmap));
3640: PetscCall(PetscLayoutSetUp(mat->cmap));
3642: /* check if the matrix sizes are correct */
3643: PetscCall(MatGetSize(mat, &rows, &cols));
3644: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
3645: PetscCall(MatGetBlockSize(mat, &bs));
3646: PetscCall(MatGetLocalSize(mat, &m, &n));
3647: mbs = m / bs;
3648: nbs = n / bs;
3650: /* read in row lengths, column indices and nonzero values */
3651: PetscCall(PetscMalloc1(m + 1, &rowidxs));
3652: PetscCall(PetscViewerBinaryRead(viewer, rowidxs + 1, m, NULL, PETSC_INT));
3653: rowidxs[0] = 0;
3654: for (i = 0; i < m; i++) rowidxs[i + 1] += rowidxs[i];
3655: sum = rowidxs[m];
3656: PetscCheck(sum == nz, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
3658: /* read in column indices and nonzero values */
3659: PetscCall(PetscMalloc2(rowidxs[m], &colidxs, nz, &matvals));
3660: PetscCall(PetscViewerBinaryRead(viewer, colidxs, rowidxs[m], NULL, PETSC_INT));
3661: PetscCall(PetscViewerBinaryRead(viewer, matvals, rowidxs[m], NULL, PETSC_SCALAR));
3663: { /* preallocate matrix storage */
3664: PetscBT bt; /* helper bit set to count nonzeros */
3665: PetscInt *nnz;
3666: PetscBool sbaij;
3668: PetscCall(PetscBTCreate(nbs, &bt));
3669: PetscCall(PetscCalloc1(mbs, &nnz));
3670: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATSEQSBAIJ, &sbaij));
3671: for (i = 0; i < mbs; i++) {
3672: PetscCall(PetscBTMemzero(nbs, bt));
3673: for (k = 0; k < bs; k++) {
3674: PetscInt row = bs * i + k;
3675: for (j = rowidxs[row]; j < rowidxs[row + 1]; j++) {
3676: PetscInt col = colidxs[j];
3677: if (!sbaij || col >= row)
3678: if (!PetscBTLookupSet(bt, col / bs)) nnz[i]++;
3679: }
3680: }
3681: }
3682: PetscCall(PetscBTDestroy(&bt));
3683: PetscCall(MatSeqBAIJSetPreallocation(mat, bs, 0, nnz));
3684: PetscCall(MatSeqSBAIJSetPreallocation(mat, bs, 0, nnz));
3685: PetscCall(PetscFree(nnz));
3686: }
3688: /* store matrix values */
3689: for (i = 0; i < m; i++) {
3690: PetscInt row = i, s = rowidxs[i], e = rowidxs[i + 1];
3691: PetscCall((*mat->ops->setvalues)(mat, 1, &row, e - s, colidxs + s, matvals + s, INSERT_VALUES));
3692: }
3694: PetscCall(PetscFree(rowidxs));
3695: PetscCall(PetscFree2(colidxs, matvals));
3696: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
3697: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
3698: PetscFunctionReturn(PETSC_SUCCESS);
3699: }
3701: PetscErrorCode MatLoad_SeqBAIJ(Mat mat, PetscViewer viewer)
3702: {
3703: PetscBool isbinary;
3705: PetscFunctionBegin;
3706: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
3707: PetscCheck(isbinary, PetscObjectComm((PetscObject)viewer), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)mat)->type_name);
3708: PetscCall(MatLoad_SeqBAIJ_Binary(mat, viewer));
3709: PetscFunctionReturn(PETSC_SUCCESS);
3710: }
3712: /*@C
3713: MatCreateSeqBAIJ - Creates a sparse matrix in `MATSEQAIJ` (block
3714: compressed row) format. For good matrix assembly performance the
3715: user should preallocate the matrix storage by setting the parameter `nz`
3716: (or the array `nnz`).
3718: Collective
3720: Input Parameters:
3721: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3722: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3723: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3724: . m - number of rows
3725: . n - number of columns
3726: . nz - number of nonzero blocks per block row (same for all rows)
3727: - nnz - array containing the number of nonzero blocks in the various block rows
3728: (possibly different for each block row) or `NULL`
3730: Output Parameter:
3731: . A - the matrix
3733: Options Database Keys:
3734: + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower)
3735: - -mat_block_size - size of the blocks to use
3737: Level: intermediate
3739: Notes:
3740: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
3741: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3742: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
3744: The number of rows and columns must be divisible by blocksize.
3746: If the `nnz` parameter is given then the `nz` parameter is ignored
3748: A nonzero block is any block that as 1 or more nonzeros in it
3750: The `MATSEQBAIJ` format is fully compatible with standard Fortran
3751: storage. That is, the stored row and column indices can begin at
3752: either one (as in Fortran) or zero.
3754: Specify the preallocated storage with either `nz` or `nnz` (not both).
3755: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3756: allocation. See [Sparse Matrices](sec_matsparse) for details.
3757: matrices.
3759: .seealso: [](chapter_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`
3760: @*/
3761: PetscErrorCode MatCreateSeqBAIJ(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3762: {
3763: PetscFunctionBegin;
3764: PetscCall(MatCreate(comm, A));
3765: PetscCall(MatSetSizes(*A, m, n, m, n));
3766: PetscCall(MatSetType(*A, MATSEQBAIJ));
3767: PetscCall(MatSeqBAIJSetPreallocation(*A, bs, nz, (PetscInt *)nnz));
3768: PetscFunctionReturn(PETSC_SUCCESS);
3769: }
3771: /*@C
3772: MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
3773: per row in the matrix. For good matrix assembly performance the
3774: user should preallocate the matrix storage by setting the parameter `nz`
3775: (or the array `nnz`).
3777: Collective
3779: Input Parameters:
3780: + B - the matrix
3781: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
3782: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
3783: . nz - number of block nonzeros per block row (same for all rows)
3784: - nnz - array containing the number of block nonzeros in the various block rows
3785: (possibly different for each block row) or `NULL`
3787: Options Database Keys:
3788: + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower)
3789: - -mat_block_size - size of the blocks to use
3791: Level: intermediate
3793: Notes:
3794: If the `nnz` parameter is given then the `nz` parameter is ignored
3796: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3797: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3798: You can also run with the option `-info` and look for messages with the string
3799: malloc in them to see if additional memory allocation was needed.
3801: The `MATSEQBAIJ` format is fully compatible with standard Fortran
3802: storage. That is, the stored row and column indices can begin at
3803: either one (as in Fortran) or zero.
3805: Specify the preallocated storage with either nz or nnz (not both).
3806: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3807: allocation. See [Sparse Matrices](sec_matsparse) for details.
3809: .seealso: [](chapter_matrices), `Mat`, [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`, `MatGetInfo()`
3810: @*/
3811: PetscErrorCode MatSeqBAIJSetPreallocation(Mat B, PetscInt bs, PetscInt nz, const PetscInt nnz[])
3812: {
3813: PetscFunctionBegin;
3817: PetscTryMethod(B, "MatSeqBAIJSetPreallocation_C", (Mat, PetscInt, PetscInt, const PetscInt[]), (B, bs, nz, nnz));
3818: PetscFunctionReturn(PETSC_SUCCESS);
3819: }
3821: /*@C
3822: MatSeqBAIJSetPreallocationCSR - Creates a sparse sequential matrix in `MATSEQBAIJ` format using the given nonzero structure and (optional) numerical values
3824: Collective
3826: Input Parameters:
3827: + B - the matrix
3828: . bs - the blocksize
3829: . i - the indices into `j` for the start of each local row (starts with zero)
3830: . j - the column indices for each local row (starts with zero) these must be sorted for each row
3831: - v - optional values in the matrix
3833: Level: advanced
3835: Notes:
3836: The order of the entries in values is specified by the `MatOption` `MAT_ROW_ORIENTED`. For example, C programs
3837: may want to use the default `MAT_ROW_ORIENTED` of `PETSC_TRUE` and use an array v[nnz][bs][bs] where the second index is
3838: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
3839: `MAT_ROW_ORIENTED` of `PETSC_FALSE` and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a
3840: block column and the second index is over columns within a block.
3842: Though this routine has Preallocation() in the name it also sets the exact nonzero locations of the matrix entries and usually the numerical values as well
3844: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateSeqBAIJ()`, `MatSetValues()`, `MatSeqBAIJSetPreallocation()`, `MATSEQBAIJ`
3845: @*/
3846: PetscErrorCode MatSeqBAIJSetPreallocationCSR(Mat B, PetscInt bs, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
3847: {
3848: PetscFunctionBegin;
3852: PetscTryMethod(B, "MatSeqBAIJSetPreallocationCSR_C", (Mat, PetscInt, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, bs, i, j, v));
3853: PetscFunctionReturn(PETSC_SUCCESS);
3854: }
3856: /*@
3857: MatCreateSeqBAIJWithArrays - Creates a `MATSEQBAIJ` matrix using matrix elements provided by the user.
3859: Collective
3861: Input Parameters:
3862: + comm - must be an MPI communicator of size 1
3863: . bs - size of block
3864: . m - number of rows
3865: . n - number of columns
3866: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row block row of the matrix
3867: . j - column indices
3868: - a - matrix values
3870: Output Parameter:
3871: . mat - the matrix
3873: Level: advanced
3875: Notes:
3876: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
3877: once the matrix is destroyed
3879: You cannot set new nonzero locations into this matrix, that will generate an error.
3881: The `i` and `j` indices are 0 based
3883: When block size is greater than 1 the matrix values must be stored using the `MATSEQBAIJ` storage format
3885: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3886: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3887: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3888: with column-major ordering within blocks.
3890: .seealso: [](chapter_matrices), `Mat`, `MatCreate()`, `MatCreateBAIJ()`, `MatCreateSeqBAIJ()`
3891: @*/
3892: PetscErrorCode MatCreateSeqBAIJWithArrays(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
3893: {
3894: Mat_SeqBAIJ *baij;
3896: PetscFunctionBegin;
3897: PetscCheck(bs == 1, PETSC_COMM_SELF, PETSC_ERR_SUP, "block size %" PetscInt_FMT " > 1 is not supported yet", bs);
3898: if (m > 0) PetscCheck(i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
3900: PetscCall(MatCreate(comm, mat));
3901: PetscCall(MatSetSizes(*mat, m, n, m, n));
3902: PetscCall(MatSetType(*mat, MATSEQBAIJ));
3903: PetscCall(MatSeqBAIJSetPreallocation(*mat, bs, MAT_SKIP_ALLOCATION, NULL));
3904: baij = (Mat_SeqBAIJ *)(*mat)->data;
3905: PetscCall(PetscMalloc2(m, &baij->imax, m, &baij->ilen));
3907: baij->i = i;
3908: baij->j = j;
3909: baij->a = a;
3911: baij->singlemalloc = PETSC_FALSE;
3912: baij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3913: baij->free_a = PETSC_FALSE;
3914: baij->free_ij = PETSC_FALSE;
3915: baij->free_imax_ilen = PETSC_TRUE;
3917: for (PetscInt ii = 0; ii < m; ii++) {
3918: const PetscInt row_len = i[ii + 1] - i[ii];
3920: baij->ilen[ii] = baij->imax[ii] = row_len;
3921: PetscCheck(row_len >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, row_len);
3922: }
3923: if (PetscDefined(USE_DEBUG)) {
3924: for (PetscInt ii = 0; ii < baij->i[m]; ii++) {
3925: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
3926: PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
3927: }
3928: }
3930: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
3931: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
3932: PetscFunctionReturn(PETSC_SUCCESS);
3933: }
3935: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqBAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
3936: {
3937: PetscFunctionBegin;
3938: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(comm, inmat, n, scall, outmat));
3939: PetscFunctionReturn(PETSC_SUCCESS);
3940: }