Actual source code: baij.c
petsc-3.9.4 2018-09-11
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: #if defined(PETSC_HAVE_HYPRE)
12: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat,MatType,MatReuse,Mat*);
13: #endif
15: #if defined(PETSC_HAVE_MKL_SPARSE_OPTIMIZE)
16: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat,const MatType,MatReuse,Mat*);
17: #endif
19: PetscErrorCode MatInvertBlockDiagonal_SeqBAIJ(Mat A,const PetscScalar **values)
20: {
21: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*) A->data;
23: PetscInt *diag_offset,i,bs = A->rmap->bs,mbs = a->mbs,ipvt[5],bs2 = bs*bs,*v_pivots;
24: MatScalar *v = a->a,*odiag,*diag,work[25],*v_work;
25: PetscReal shift = 0.0;
26: PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;
29: allowzeropivot = PetscNot(A->erroriffailure);
31: if (a->idiagvalid) {
32: if (values) *values = a->idiag;
33: return(0);
34: }
35: MatMarkDiagonal_SeqBAIJ(A);
36: diag_offset = a->diag;
37: if (!a->idiag) {
38: PetscMalloc1(bs2*mbs,&a->idiag);
39: PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
40: }
41: diag = a->idiag;
42: if (values) *values = a->idiag;
43: /* factor and invert each block */
44: switch (bs) {
45: case 1:
46: for (i=0; i<mbs; i++) {
47: odiag = v + 1*diag_offset[i];
48: diag[0] = odiag[0];
50: if (PetscAbsScalar(diag[0] + shift) < PETSC_MACHINE_EPSILON) {
51: if (allowzeropivot) {
52: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
53: A->factorerror_zeropivot_value = PetscAbsScalar(diag[0]);
54: A->factorerror_zeropivot_row = i;
55: PetscInfo1(A,"Zero pivot, row %D\n",i);
56: } else SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot, row %D pivot value %g tolerance %g",i,(double)PetscAbsScalar(diag[0]),(double)PETSC_MACHINE_EPSILON);
57: }
59: diag[0] = (PetscScalar)1.0 / (diag[0] + shift);
60: diag += 1;
61: }
62: break;
63: case 2:
64: for (i=0; i<mbs; i++) {
65: odiag = v + 4*diag_offset[i];
66: diag[0] = odiag[0]; diag[1] = odiag[1]; diag[2] = odiag[2]; diag[3] = odiag[3];
67: PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
68: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
69: diag += 4;
70: }
71: break;
72: case 3:
73: for (i=0; i<mbs; i++) {
74: odiag = v + 9*diag_offset[i];
75: diag[0] = odiag[0]; diag[1] = odiag[1]; diag[2] = odiag[2]; diag[3] = odiag[3];
76: diag[4] = odiag[4]; diag[5] = odiag[5]; diag[6] = odiag[6]; diag[7] = odiag[7];
77: diag[8] = odiag[8];
78: PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
79: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
80: diag += 9;
81: }
82: break;
83: case 4:
84: for (i=0; i<mbs; i++) {
85: odiag = v + 16*diag_offset[i];
86: PetscMemcpy(diag,odiag,16*sizeof(PetscScalar));
87: PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
88: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
89: diag += 16;
90: }
91: break;
92: case 5:
93: for (i=0; i<mbs; i++) {
94: odiag = v + 25*diag_offset[i];
95: PetscMemcpy(diag,odiag,25*sizeof(PetscScalar));
96: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
97: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
98: diag += 25;
99: }
100: break;
101: case 6:
102: for (i=0; i<mbs; i++) {
103: odiag = v + 36*diag_offset[i];
104: PetscMemcpy(diag,odiag,36*sizeof(PetscScalar));
105: PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
106: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
107: diag += 36;
108: }
109: break;
110: case 7:
111: for (i=0; i<mbs; i++) {
112: odiag = v + 49*diag_offset[i];
113: PetscMemcpy(diag,odiag,49*sizeof(PetscScalar));
114: PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
115: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
116: diag += 49;
117: }
118: break;
119: default:
120: PetscMalloc2(bs,&v_work,bs,&v_pivots);
121: for (i=0; i<mbs; i++) {
122: odiag = v + bs2*diag_offset[i];
123: PetscMemcpy(diag,odiag,bs2*sizeof(PetscScalar));
124: PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
125: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
126: diag += bs2;
127: }
128: PetscFree2(v_work,v_pivots);
129: }
130: a->idiagvalid = PETSC_TRUE;
131: return(0);
132: }
134: PetscErrorCode MatSOR_SeqBAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
135: {
136: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
137: PetscScalar *x,*work,*w,*workt,*t;
138: const MatScalar *v,*aa = a->a, *idiag;
139: const PetscScalar *b,*xb;
140: PetscScalar s[7], xw[7]={0}; /* avoid some compilers thinking xw is uninitialized */
141: PetscErrorCode ierr;
142: PetscInt m = a->mbs,i,i2,nz,bs = A->rmap->bs,bs2 = bs*bs,k,j,idx,it;
143: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
146: if (fshift == -1.0) fshift = 0.0; /* negative fshift indicates do not error on zero diagonal; this code never errors on zero diagonal */
147: its = its*lits;
148: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
149: if (its <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
150: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
151: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
152: if ((flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for applying upper or lower triangular parts");
154: if (!a->idiagvalid) {MatInvertBlockDiagonal(A,NULL);}
156: if (!m) return(0);
157: diag = a->diag;
158: idiag = a->idiag;
159: k = PetscMax(A->rmap->n,A->cmap->n);
160: if (!a->mult_work) {
161: PetscMalloc1(k+1,&a->mult_work);
162: }
163: if (!a->sor_workt) {
164: PetscMalloc1(k,&a->sor_workt);
165: }
166: if (!a->sor_work) {
167: PetscMalloc1(bs,&a->sor_work);
168: }
169: work = a->mult_work;
170: t = a->sor_workt;
171: w = a->sor_work;
173: VecGetArray(xx,&x);
174: VecGetArrayRead(bb,&b);
176: if (flag & SOR_ZERO_INITIAL_GUESS) {
177: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
178: switch (bs) {
179: case 1:
180: PetscKernel_v_gets_A_times_w_1(x,idiag,b);
181: t[0] = b[0];
182: i2 = 1;
183: idiag += 1;
184: for (i=1; i<m; i++) {
185: v = aa + ai[i];
186: vi = aj + ai[i];
187: nz = diag[i] - ai[i];
188: s[0] = b[i2];
189: for (j=0; j<nz; j++) {
190: xw[0] = x[vi[j]];
191: PetscKernel_v_gets_v_minus_A_times_w_1(s,(v+j),xw);
192: }
193: t[i2] = s[0];
194: PetscKernel_v_gets_A_times_w_1(xw,idiag,s);
195: x[i2] = xw[0];
196: idiag += 1;
197: i2 += 1;
198: }
199: break;
200: case 2:
201: PetscKernel_v_gets_A_times_w_2(x,idiag,b);
202: t[0] = b[0]; t[1] = b[1];
203: i2 = 2;
204: idiag += 4;
205: for (i=1; i<m; i++) {
206: v = aa + 4*ai[i];
207: vi = aj + ai[i];
208: nz = diag[i] - ai[i];
209: s[0] = b[i2]; s[1] = b[i2+1];
210: for (j=0; j<nz; j++) {
211: idx = 2*vi[j];
212: it = 4*j;
213: xw[0] = x[idx]; xw[1] = x[1+idx];
214: PetscKernel_v_gets_v_minus_A_times_w_2(s,(v+it),xw);
215: }
216: t[i2] = s[0]; t[i2+1] = s[1];
217: PetscKernel_v_gets_A_times_w_2(xw,idiag,s);
218: x[i2] = xw[0]; x[i2+1] = xw[1];
219: idiag += 4;
220: i2 += 2;
221: }
222: break;
223: case 3:
224: PetscKernel_v_gets_A_times_w_3(x,idiag,b);
225: t[0] = b[0]; t[1] = b[1]; t[2] = b[2];
226: i2 = 3;
227: idiag += 9;
228: for (i=1; i<m; i++) {
229: v = aa + 9*ai[i];
230: vi = aj + ai[i];
231: nz = diag[i] - ai[i];
232: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2];
233: while (nz--) {
234: idx = 3*(*vi++);
235: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
236: PetscKernel_v_gets_v_minus_A_times_w_3(s,v,xw);
237: v += 9;
238: }
239: t[i2] = s[0]; t[i2+1] = s[1]; t[i2+2] = s[2];
240: PetscKernel_v_gets_A_times_w_3(xw,idiag,s);
241: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2];
242: idiag += 9;
243: i2 += 3;
244: }
245: break;
246: case 4:
247: PetscKernel_v_gets_A_times_w_4(x,idiag,b);
248: t[0] = b[0]; t[1] = b[1]; t[2] = b[2]; t[3] = b[3];
249: i2 = 4;
250: idiag += 16;
251: for (i=1; i<m; i++) {
252: v = aa + 16*ai[i];
253: vi = aj + ai[i];
254: nz = diag[i] - ai[i];
255: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3];
256: while (nz--) {
257: idx = 4*(*vi++);
258: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx]; xw[3] = x[3+idx];
259: PetscKernel_v_gets_v_minus_A_times_w_4(s,v,xw);
260: v += 16;
261: }
262: t[i2] = s[0]; t[i2+1] = s[1]; t[i2+2] = s[2]; t[i2 + 3] = s[3];
263: PetscKernel_v_gets_A_times_w_4(xw,idiag,s);
264: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3];
265: idiag += 16;
266: i2 += 4;
267: }
268: break;
269: case 5:
270: PetscKernel_v_gets_A_times_w_5(x,idiag,b);
271: t[0] = b[0]; t[1] = b[1]; t[2] = b[2]; t[3] = b[3]; t[4] = b[4];
272: i2 = 5;
273: idiag += 25;
274: for (i=1; i<m; i++) {
275: v = aa + 25*ai[i];
276: vi = aj + ai[i];
277: nz = diag[i] - ai[i];
278: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3]; s[4] = b[i2+4];
279: while (nz--) {
280: idx = 5*(*vi++);
281: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx]; xw[3] = x[3+idx]; xw[4] = x[4+idx];
282: PetscKernel_v_gets_v_minus_A_times_w_5(s,v,xw);
283: v += 25;
284: }
285: t[i2] = s[0]; t[i2+1] = s[1]; t[i2+2] = s[2]; t[i2+3] = s[3]; t[i2+4] = s[4];
286: PetscKernel_v_gets_A_times_w_5(xw,idiag,s);
287: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3]; x[i2+4] = xw[4];
288: idiag += 25;
289: i2 += 5;
290: }
291: break;
292: case 6:
293: PetscKernel_v_gets_A_times_w_6(x,idiag,b);
294: t[0] = b[0]; t[1] = b[1]; t[2] = b[2]; t[3] = b[3]; t[4] = b[4]; t[5] = b[5];
295: i2 = 6;
296: idiag += 36;
297: for (i=1; i<m; i++) {
298: v = aa + 36*ai[i];
299: vi = aj + ai[i];
300: nz = diag[i] - ai[i];
301: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3]; s[4] = b[i2+4]; s[5] = b[i2+5];
302: while (nz--) {
303: idx = 6*(*vi++);
304: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
305: xw[3] = x[3+idx]; xw[4] = x[4+idx]; xw[5] = x[5+idx];
306: PetscKernel_v_gets_v_minus_A_times_w_6(s,v,xw);
307: v += 36;
308: }
309: t[i2] = s[0]; t[i2+1] = s[1]; t[i2+2] = s[2];
310: t[i2+3] = s[3]; t[i2+4] = s[4]; t[i2+5] = s[5];
311: PetscKernel_v_gets_A_times_w_6(xw,idiag,s);
312: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3]; x[i2+4] = xw[4]; x[i2+5] = xw[5];
313: idiag += 36;
314: i2 += 6;
315: }
316: break;
317: case 7:
318: PetscKernel_v_gets_A_times_w_7(x,idiag,b);
319: t[0] = b[0]; t[1] = b[1]; t[2] = b[2];
320: t[3] = b[3]; t[4] = b[4]; t[5] = b[5]; t[6] = b[6];
321: i2 = 7;
322: idiag += 49;
323: for (i=1; i<m; i++) {
324: v = aa + 49*ai[i];
325: vi = aj + ai[i];
326: nz = diag[i] - ai[i];
327: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2];
328: s[3] = b[i2+3]; s[4] = b[i2+4]; s[5] = b[i2+5]; s[6] = b[i2+6];
329: while (nz--) {
330: idx = 7*(*vi++);
331: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
332: xw[3] = x[3+idx]; xw[4] = x[4+idx]; xw[5] = x[5+idx]; xw[6] = x[6+idx];
333: PetscKernel_v_gets_v_minus_A_times_w_7(s,v,xw);
334: v += 49;
335: }
336: t[i2] = s[0]; t[i2+1] = s[1]; t[i2+2] = s[2];
337: t[i2+3] = s[3]; t[i2+4] = s[4]; t[i2+5] = s[5]; t[i2+6] = s[6];
338: PetscKernel_v_gets_A_times_w_7(xw,idiag,s);
339: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2];
340: x[i2+3] = xw[3]; x[i2+4] = xw[4]; x[i2+5] = xw[5]; x[i2+6] = xw[6];
341: idiag += 49;
342: i2 += 7;
343: }
344: break;
345: default:
346: PetscKernel_w_gets_Ar_times_v(bs,bs,b,idiag,x);
347: PetscMemcpy(t,b,bs*sizeof(PetscScalar));
348: i2 = bs;
349: idiag += bs2;
350: for (i=1; i<m; i++) {
351: v = aa + bs2*ai[i];
352: vi = aj + ai[i];
353: nz = diag[i] - ai[i];
355: PetscMemcpy(w,b+i2,bs*sizeof(PetscScalar));
356: /* copy all rows of x that are needed into contiguous space */
357: workt = work;
358: for (j=0; j<nz; j++) {
359: PetscMemcpy(workt,x + bs*(*vi++),bs*sizeof(PetscScalar));
360: workt += bs;
361: }
362: PetscKernel_w_gets_w_minus_Ar_times_v(bs,bs*nz,w,v,work);
363: PetscMemcpy(t+i2,w,bs*sizeof(PetscScalar));
364: PetscKernel_w_gets_Ar_times_v(bs,bs,w,idiag,x+i2);
366: idiag += bs2;
367: i2 += bs;
368: }
369: break;
370: }
371: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
372: PetscLogFlops(1.0*bs2*a->nz);
373: xb = t;
374: }
375: else xb = b;
376: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
377: idiag = a->idiag+bs2*(a->mbs-1);
378: i2 = bs * (m-1);
379: switch (bs) {
380: case 1:
381: s[0] = xb[i2];
382: PetscKernel_v_gets_A_times_w_1(xw,idiag,s);
383: x[i2] = xw[0];
384: i2 -= 1;
385: for (i=m-2; i>=0; i--) {
386: v = aa + (diag[i]+1);
387: vi = aj + diag[i] + 1;
388: nz = ai[i+1] - diag[i] - 1;
389: s[0] = xb[i2];
390: for (j=0; j<nz; j++) {
391: xw[0] = x[vi[j]];
392: PetscKernel_v_gets_v_minus_A_times_w_1(s,(v+j),xw);
393: }
394: PetscKernel_v_gets_A_times_w_1(xw,idiag,s);
395: x[i2] = xw[0];
396: idiag -= 1;
397: i2 -= 1;
398: }
399: break;
400: case 2:
401: s[0] = xb[i2]; s[1] = xb[i2+1];
402: PetscKernel_v_gets_A_times_w_2(xw,idiag,s);
403: x[i2] = xw[0]; x[i2+1] = xw[1];
404: i2 -= 2;
405: idiag -= 4;
406: for (i=m-2; i>=0; i--) {
407: v = aa + 4*(diag[i] + 1);
408: vi = aj + diag[i] + 1;
409: nz = ai[i+1] - diag[i] - 1;
410: s[0] = xb[i2]; s[1] = xb[i2+1];
411: for (j=0; j<nz; j++) {
412: idx = 2*vi[j];
413: it = 4*j;
414: xw[0] = x[idx]; xw[1] = x[1+idx];
415: PetscKernel_v_gets_v_minus_A_times_w_2(s,(v+it),xw);
416: }
417: PetscKernel_v_gets_A_times_w_2(xw,idiag,s);
418: x[i2] = xw[0]; x[i2+1] = xw[1];
419: idiag -= 4;
420: i2 -= 2;
421: }
422: break;
423: case 3:
424: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2];
425: PetscKernel_v_gets_A_times_w_3(xw,idiag,s);
426: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2];
427: i2 -= 3;
428: idiag -= 9;
429: for (i=m-2; i>=0; i--) {
430: v = aa + 9*(diag[i]+1);
431: vi = aj + diag[i] + 1;
432: nz = ai[i+1] - diag[i] - 1;
433: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2];
434: while (nz--) {
435: idx = 3*(*vi++);
436: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
437: PetscKernel_v_gets_v_minus_A_times_w_3(s,v,xw);
438: v += 9;
439: }
440: PetscKernel_v_gets_A_times_w_3(xw,idiag,s);
441: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2];
442: idiag -= 9;
443: i2 -= 3;
444: }
445: break;
446: case 4:
447: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2]; s[3] = xb[i2+3];
448: PetscKernel_v_gets_A_times_w_4(xw,idiag,s);
449: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3];
450: i2 -= 4;
451: idiag -= 16;
452: for (i=m-2; i>=0; i--) {
453: v = aa + 16*(diag[i]+1);
454: vi = aj + diag[i] + 1;
455: nz = ai[i+1] - diag[i] - 1;
456: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2]; s[3] = xb[i2+3];
457: while (nz--) {
458: idx = 4*(*vi++);
459: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx]; xw[3] = x[3+idx];
460: PetscKernel_v_gets_v_minus_A_times_w_4(s,v,xw);
461: v += 16;
462: }
463: PetscKernel_v_gets_A_times_w_4(xw,idiag,s);
464: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3];
465: idiag -= 16;
466: i2 -= 4;
467: }
468: break;
469: case 5:
470: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2]; s[3] = xb[i2+3]; s[4] = xb[i2+4];
471: PetscKernel_v_gets_A_times_w_5(xw,idiag,s);
472: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3]; x[i2+4] = xw[4];
473: i2 -= 5;
474: idiag -= 25;
475: for (i=m-2; i>=0; i--) {
476: v = aa + 25*(diag[i]+1);
477: vi = aj + diag[i] + 1;
478: nz = ai[i+1] - diag[i] - 1;
479: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2]; s[3] = xb[i2+3]; s[4] = xb[i2+4];
480: while (nz--) {
481: idx = 5*(*vi++);
482: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx]; xw[3] = x[3+idx]; xw[4] = x[4+idx];
483: PetscKernel_v_gets_v_minus_A_times_w_5(s,v,xw);
484: v += 25;
485: }
486: PetscKernel_v_gets_A_times_w_5(xw,idiag,s);
487: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3]; x[i2+4] = xw[4];
488: idiag -= 25;
489: i2 -= 5;
490: }
491: break;
492: case 6:
493: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2]; s[3] = xb[i2+3]; s[4] = xb[i2+4]; s[5] = xb[i2+5];
494: PetscKernel_v_gets_A_times_w_6(xw,idiag,s);
495: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3]; x[i2+4] = xw[4]; x[i2+5] = xw[5];
496: i2 -= 6;
497: idiag -= 36;
498: for (i=m-2; i>=0; i--) {
499: v = aa + 36*(diag[i]+1);
500: vi = aj + diag[i] + 1;
501: nz = ai[i+1] - diag[i] - 1;
502: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2]; s[3] = xb[i2+3]; s[4] = xb[i2+4]; s[5] = xb[i2+5];
503: while (nz--) {
504: idx = 6*(*vi++);
505: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
506: xw[3] = x[3+idx]; xw[4] = x[4+idx]; xw[5] = x[5+idx];
507: PetscKernel_v_gets_v_minus_A_times_w_6(s,v,xw);
508: v += 36;
509: }
510: PetscKernel_v_gets_A_times_w_6(xw,idiag,s);
511: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2]; x[i2+3] = xw[3]; x[i2+4] = xw[4]; x[i2+5] = xw[5];
512: idiag -= 36;
513: i2 -= 6;
514: }
515: break;
516: case 7:
517: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2];
518: s[3] = xb[i2+3]; s[4] = xb[i2+4]; s[5] = xb[i2+5]; s[6] = xb[i2+6];
519: PetscKernel_v_gets_A_times_w_7(x,idiag,b);
520: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2];
521: x[i2+3] = xw[3]; x[i2+4] = xw[4]; x[i2+5] = xw[5]; x[i2+6] = xw[6];
522: i2 -= 7;
523: idiag -= 49;
524: for (i=m-2; i>=0; i--) {
525: v = aa + 49*(diag[i]+1);
526: vi = aj + diag[i] + 1;
527: nz = ai[i+1] - diag[i] - 1;
528: s[0] = xb[i2]; s[1] = xb[i2+1]; s[2] = xb[i2+2];
529: s[3] = xb[i2+3]; s[4] = xb[i2+4]; s[5] = xb[i2+5]; s[6] = xb[i2+6];
530: while (nz--) {
531: idx = 7*(*vi++);
532: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
533: xw[3] = x[3+idx]; xw[4] = x[4+idx]; xw[5] = x[5+idx]; xw[6] = x[6+idx];
534: PetscKernel_v_gets_v_minus_A_times_w_7(s,v,xw);
535: v += 49;
536: }
537: PetscKernel_v_gets_A_times_w_7(xw,idiag,s);
538: x[i2] = xw[0]; x[i2+1] = xw[1]; x[i2+2] = xw[2];
539: x[i2+3] = xw[3]; x[i2+4] = xw[4]; x[i2+5] = xw[5]; x[i2+6] = xw[6];
540: idiag -= 49;
541: i2 -= 7;
542: }
543: break;
544: default:
545: PetscMemcpy(w,xb+i2,bs*sizeof(PetscScalar));
546: PetscKernel_w_gets_Ar_times_v(bs,bs,w,idiag,x+i2);
547: i2 -= bs;
548: idiag -= bs2;
549: for (i=m-2; i>=0; i--) {
550: v = aa + bs2*(diag[i]+1);
551: vi = aj + diag[i] + 1;
552: nz = ai[i+1] - diag[i] - 1;
554: PetscMemcpy(w,xb+i2,bs*sizeof(PetscScalar));
555: /* copy all rows of x that are needed into contiguous space */
556: workt = work;
557: for (j=0; j<nz; j++) {
558: PetscMemcpy(workt,x + bs*(*vi++),bs*sizeof(PetscScalar));
559: workt += bs;
560: }
561: PetscKernel_w_gets_w_minus_Ar_times_v(bs,bs*nz,w,v,work);
562: PetscKernel_w_gets_Ar_times_v(bs,bs,w,idiag,x+i2);
564: idiag -= bs2;
565: i2 -= bs;
566: }
567: break;
568: }
569: PetscLogFlops(1.0*bs2*(a->nz));
570: }
571: its--;
572: }
573: while (its--) {
574: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
575: idiag = a->idiag;
576: i2 = 0;
577: switch (bs) {
578: case 1:
579: for (i=0; i<m; i++) {
580: v = aa + ai[i];
581: vi = aj + ai[i];
582: nz = ai[i+1] - ai[i];
583: s[0] = b[i2];
584: for (j=0; j<nz; j++) {
585: xw[0] = x[vi[j]];
586: PetscKernel_v_gets_v_minus_A_times_w_1(s,(v+j),xw);
587: }
588: PetscKernel_v_gets_A_times_w_1(xw,idiag,s);
589: x[i2] += xw[0];
590: idiag += 1;
591: i2 += 1;
592: }
593: break;
594: case 2:
595: for (i=0; i<m; i++) {
596: v = aa + 4*ai[i];
597: vi = aj + ai[i];
598: nz = ai[i+1] - ai[i];
599: s[0] = b[i2]; s[1] = b[i2+1];
600: for (j=0; j<nz; j++) {
601: idx = 2*vi[j];
602: it = 4*j;
603: xw[0] = x[idx]; xw[1] = x[1+idx];
604: PetscKernel_v_gets_v_minus_A_times_w_2(s,(v+it),xw);
605: }
606: PetscKernel_v_gets_A_times_w_2(xw,idiag,s);
607: x[i2] += xw[0]; x[i2+1] += xw[1];
608: idiag += 4;
609: i2 += 2;
610: }
611: break;
612: case 3:
613: for (i=0; i<m; i++) {
614: v = aa + 9*ai[i];
615: vi = aj + ai[i];
616: nz = ai[i+1] - ai[i];
617: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2];
618: while (nz--) {
619: idx = 3*(*vi++);
620: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
621: PetscKernel_v_gets_v_minus_A_times_w_3(s,v,xw);
622: v += 9;
623: }
624: PetscKernel_v_gets_A_times_w_3(xw,idiag,s);
625: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2];
626: idiag += 9;
627: i2 += 3;
628: }
629: break;
630: case 4:
631: for (i=0; i<m; i++) {
632: v = aa + 16*ai[i];
633: vi = aj + ai[i];
634: nz = ai[i+1] - ai[i];
635: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3];
636: while (nz--) {
637: idx = 4*(*vi++);
638: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx]; xw[3] = x[3+idx];
639: PetscKernel_v_gets_v_minus_A_times_w_4(s,v,xw);
640: v += 16;
641: }
642: PetscKernel_v_gets_A_times_w_4(xw,idiag,s);
643: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2]; x[i2+3] += xw[3];
644: idiag += 16;
645: i2 += 4;
646: }
647: break;
648: case 5:
649: for (i=0; i<m; i++) {
650: v = aa + 25*ai[i];
651: vi = aj + ai[i];
652: nz = ai[i+1] - ai[i];
653: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3]; s[4] = b[i2+4];
654: while (nz--) {
655: idx = 5*(*vi++);
656: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx]; xw[3] = x[3+idx]; xw[4] = x[4+idx];
657: PetscKernel_v_gets_v_minus_A_times_w_5(s,v,xw);
658: v += 25;
659: }
660: PetscKernel_v_gets_A_times_w_5(xw,idiag,s);
661: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2]; x[i2+3] += xw[3]; x[i2+4] += xw[4];
662: idiag += 25;
663: i2 += 5;
664: }
665: break;
666: case 6:
667: for (i=0; i<m; i++) {
668: v = aa + 36*ai[i];
669: vi = aj + ai[i];
670: nz = ai[i+1] - ai[i];
671: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3]; s[4] = b[i2+4]; s[5] = b[i2+5];
672: while (nz--) {
673: idx = 6*(*vi++);
674: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
675: xw[3] = x[3+idx]; xw[4] = x[4+idx]; xw[5] = x[5+idx];
676: PetscKernel_v_gets_v_minus_A_times_w_6(s,v,xw);
677: v += 36;
678: }
679: PetscKernel_v_gets_A_times_w_6(xw,idiag,s);
680: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2];
681: x[i2+3] += xw[3]; x[i2+4] += xw[4]; x[i2+5] += xw[5];
682: idiag += 36;
683: i2 += 6;
684: }
685: break;
686: case 7:
687: for (i=0; i<m; i++) {
688: v = aa + 49*ai[i];
689: vi = aj + ai[i];
690: nz = ai[i+1] - ai[i];
691: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2];
692: s[3] = b[i2+3]; s[4] = b[i2+4]; s[5] = b[i2+5]; s[6] = b[i2+6];
693: while (nz--) {
694: idx = 7*(*vi++);
695: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
696: xw[3] = x[3+idx]; xw[4] = x[4+idx]; xw[5] = x[5+idx]; xw[6] = x[6+idx];
697: PetscKernel_v_gets_v_minus_A_times_w_7(s,v,xw);
698: v += 49;
699: }
700: PetscKernel_v_gets_A_times_w_7(xw,idiag,s);
701: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2];
702: x[i2+3] += xw[3]; x[i2+4] += xw[4]; x[i2+5] += xw[5]; x[i2+6] += xw[6];
703: idiag += 49;
704: i2 += 7;
705: }
706: break;
707: default:
708: for (i=0; i<m; i++) {
709: v = aa + bs2*ai[i];
710: vi = aj + ai[i];
711: nz = ai[i+1] - ai[i];
713: PetscMemcpy(w,b+i2,bs*sizeof(PetscScalar));
714: /* copy all rows of x that are needed into contiguous space */
715: workt = work;
716: for (j=0; j<nz; j++) {
717: PetscMemcpy(workt,x + bs*(*vi++),bs*sizeof(PetscScalar));
718: workt += bs;
719: }
720: PetscKernel_w_gets_w_minus_Ar_times_v(bs,bs*nz,w,v,work);
721: PetscKernel_w_gets_w_plus_Ar_times_v(bs,bs,w,idiag,x+i2);
723: idiag += bs2;
724: i2 += bs;
725: }
726: break;
727: }
728: PetscLogFlops(2.0*bs2*a->nz);
729: }
730: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
731: idiag = a->idiag+bs2*(a->mbs-1);
732: i2 = bs * (m-1);
733: switch (bs) {
734: case 1:
735: for (i=m-1; i>=0; i--) {
736: v = aa + ai[i];
737: vi = aj + ai[i];
738: nz = ai[i+1] - ai[i];
739: s[0] = b[i2];
740: for (j=0; j<nz; j++) {
741: xw[0] = x[vi[j]];
742: PetscKernel_v_gets_v_minus_A_times_w_1(s,(v+j),xw);
743: }
744: PetscKernel_v_gets_A_times_w_1(xw,idiag,s);
745: x[i2] += xw[0];
746: idiag -= 1;
747: i2 -= 1;
748: }
749: break;
750: case 2:
751: for (i=m-1; i>=0; i--) {
752: v = aa + 4*ai[i];
753: vi = aj + ai[i];
754: nz = ai[i+1] - ai[i];
755: s[0] = b[i2]; s[1] = b[i2+1];
756: for (j=0; j<nz; j++) {
757: idx = 2*vi[j];
758: it = 4*j;
759: xw[0] = x[idx]; xw[1] = x[1+idx];
760: PetscKernel_v_gets_v_minus_A_times_w_2(s,(v+it),xw);
761: }
762: PetscKernel_v_gets_A_times_w_2(xw,idiag,s);
763: x[i2] += xw[0]; x[i2+1] += xw[1];
764: idiag -= 4;
765: i2 -= 2;
766: }
767: break;
768: case 3:
769: for (i=m-1; i>=0; i--) {
770: v = aa + 9*ai[i];
771: vi = aj + ai[i];
772: nz = ai[i+1] - ai[i];
773: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2];
774: while (nz--) {
775: idx = 3*(*vi++);
776: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
777: PetscKernel_v_gets_v_minus_A_times_w_3(s,v,xw);
778: v += 9;
779: }
780: PetscKernel_v_gets_A_times_w_3(xw,idiag,s);
781: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2];
782: idiag -= 9;
783: i2 -= 3;
784: }
785: break;
786: case 4:
787: for (i=m-1; i>=0; i--) {
788: v = aa + 16*ai[i];
789: vi = aj + ai[i];
790: nz = ai[i+1] - ai[i];
791: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3];
792: while (nz--) {
793: idx = 4*(*vi++);
794: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx]; xw[3] = x[3+idx];
795: PetscKernel_v_gets_v_minus_A_times_w_4(s,v,xw);
796: v += 16;
797: }
798: PetscKernel_v_gets_A_times_w_4(xw,idiag,s);
799: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2]; x[i2+3] += xw[3];
800: idiag -= 16;
801: i2 -= 4;
802: }
803: break;
804: case 5:
805: for (i=m-1; i>=0; i--) {
806: v = aa + 25*ai[i];
807: vi = aj + ai[i];
808: nz = ai[i+1] - ai[i];
809: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3]; s[4] = b[i2+4];
810: while (nz--) {
811: idx = 5*(*vi++);
812: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx]; xw[3] = x[3+idx]; xw[4] = x[4+idx];
813: PetscKernel_v_gets_v_minus_A_times_w_5(s,v,xw);
814: v += 25;
815: }
816: PetscKernel_v_gets_A_times_w_5(xw,idiag,s);
817: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2]; x[i2+3] += xw[3]; x[i2+4] += xw[4];
818: idiag -= 25;
819: i2 -= 5;
820: }
821: break;
822: case 6:
823: for (i=m-1; i>=0; i--) {
824: v = aa + 36*ai[i];
825: vi = aj + ai[i];
826: nz = ai[i+1] - ai[i];
827: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2]; s[3] = b[i2+3]; s[4] = b[i2+4]; s[5] = b[i2+5];
828: while (nz--) {
829: idx = 6*(*vi++);
830: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
831: xw[3] = x[3+idx]; xw[4] = x[4+idx]; xw[5] = x[5+idx];
832: PetscKernel_v_gets_v_minus_A_times_w_6(s,v,xw);
833: v += 36;
834: }
835: PetscKernel_v_gets_A_times_w_6(xw,idiag,s);
836: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2];
837: x[i2+3] += xw[3]; x[i2+4] += xw[4]; x[i2+5] += xw[5];
838: idiag -= 36;
839: i2 -= 6;
840: }
841: break;
842: case 7:
843: for (i=m-1; i>=0; i--) {
844: v = aa + 49*ai[i];
845: vi = aj + ai[i];
846: nz = ai[i+1] - ai[i];
847: s[0] = b[i2]; s[1] = b[i2+1]; s[2] = b[i2+2];
848: s[3] = b[i2+3]; s[4] = b[i2+4]; s[5] = b[i2+5]; s[6] = b[i2+6];
849: while (nz--) {
850: idx = 7*(*vi++);
851: xw[0] = x[idx]; xw[1] = x[1+idx]; xw[2] = x[2+idx];
852: xw[3] = x[3+idx]; xw[4] = x[4+idx]; xw[5] = x[5+idx]; xw[6] = x[6+idx];
853: PetscKernel_v_gets_v_minus_A_times_w_7(s,v,xw);
854: v += 49;
855: }
856: PetscKernel_v_gets_A_times_w_7(xw,idiag,s);
857: x[i2] += xw[0]; x[i2+1] += xw[1]; x[i2+2] += xw[2];
858: x[i2+3] += xw[3]; x[i2+4] += xw[4]; x[i2+5] += xw[5]; x[i2+6] += xw[6];
859: idiag -= 49;
860: i2 -= 7;
861: }
862: break;
863: default:
864: for (i=m-1; i>=0; i--) {
865: v = aa + bs2*ai[i];
866: vi = aj + ai[i];
867: nz = ai[i+1] - ai[i];
869: PetscMemcpy(w,b+i2,bs*sizeof(PetscScalar));
870: /* copy all rows of x that are needed into contiguous space */
871: workt = work;
872: for (j=0; j<nz; j++) {
873: PetscMemcpy(workt,x + bs*(*vi++),bs*sizeof(PetscScalar));
874: workt += bs;
875: }
876: PetscKernel_w_gets_w_minus_Ar_times_v(bs,bs*nz,w,v,work);
877: PetscKernel_w_gets_w_plus_Ar_times_v(bs,bs,w,idiag,x+i2);
879: idiag -= bs2;
880: i2 -= bs;
881: }
882: break;
883: }
884: PetscLogFlops(2.0*bs2*(a->nz));
885: }
886: }
887: VecRestoreArray(xx,&x);
888: VecRestoreArrayRead(bb,&b);
889: return(0);
890: }
893: /*
894: Special version for direct calls from Fortran (Used in PETSc-fun3d)
895: */
896: #if defined(PETSC_HAVE_FORTRAN_CAPS)
897: #define matsetvaluesblocked4_ MATSETVALUESBLOCKED4
898: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
899: #define matsetvaluesblocked4_ matsetvaluesblocked4
900: #endif
902: PETSC_EXTERN void matsetvaluesblocked4_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[])
903: {
904: Mat A = *AA;
905: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
906: PetscInt *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,N,m = *mm,n = *nn;
907: PetscInt *ai =a->i,*ailen=a->ilen;
908: PetscInt *aj =a->j,stepval,lastcol = -1;
909: const PetscScalar *value = v;
910: MatScalar *ap,*aa = a->a,*bap;
913: if (A->rmap->bs != 4) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_WRONG,"Can only be called with a block size of 4");
914: stepval = (n-1)*4;
915: for (k=0; k<m; k++) { /* loop over added rows */
916: row = im[k];
917: rp = aj + ai[row];
918: ap = aa + 16*ai[row];
919: nrow = ailen[row];
920: low = 0;
921: high = nrow;
922: for (l=0; l<n; l++) { /* loop over added columns */
923: col = in[l];
924: if (col <= lastcol) low = 0;
925: else high = nrow;
926: lastcol = col;
927: value = v + k*(stepval+4 + l)*4;
928: while (high-low > 7) {
929: t = (low+high)/2;
930: if (rp[t] > col) high = t;
931: else low = t;
932: }
933: for (i=low; i<high; i++) {
934: if (rp[i] > col) break;
935: if (rp[i] == col) {
936: bap = ap + 16*i;
937: for (ii=0; ii<4; ii++,value+=stepval) {
938: for (jj=ii; jj<16; jj+=4) {
939: bap[jj] += *value++;
940: }
941: }
942: goto noinsert2;
943: }
944: }
945: N = nrow++ - 1;
946: high++; /* added new column index thus must search to one higher than before */
947: /* shift up all the later entries in this row */
948: for (ii=N; ii>=i; ii--) {
949: rp[ii+1] = rp[ii];
950: PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
951: }
952: if (N >= i) {
953: PetscMemzero(ap+16*i,16*sizeof(MatScalar));
954: }
955: rp[i] = col;
956: bap = ap + 16*i;
957: for (ii=0; ii<4; ii++,value+=stepval) {
958: for (jj=ii; jj<16; jj+=4) {
959: bap[jj] = *value++;
960: }
961: }
962: noinsert2:;
963: low = i;
964: }
965: ailen[row] = nrow;
966: }
967: PetscFunctionReturnVoid();
968: }
970: #if defined(PETSC_HAVE_FORTRAN_CAPS)
971: #define matsetvalues4_ MATSETVALUES4
972: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
973: #define matsetvalues4_ matsetvalues4
974: #endif
976: PETSC_EXTERN void matsetvalues4_(Mat *AA,PetscInt *mm,PetscInt *im,PetscInt *nn,PetscInt *in,PetscScalar *v)
977: {
978: Mat A = *AA;
979: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
980: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,N,n = *nn,m = *mm;
981: PetscInt *ai=a->i,*ailen=a->ilen;
982: PetscInt *aj=a->j,brow,bcol;
983: PetscInt ridx,cidx,lastcol = -1;
984: MatScalar *ap,value,*aa=a->a,*bap;
987: for (k=0; k<m; k++) { /* loop over added rows */
988: row = im[k]; brow = row/4;
989: rp = aj + ai[brow];
990: ap = aa + 16*ai[brow];
991: nrow = ailen[brow];
992: low = 0;
993: high = nrow;
994: for (l=0; l<n; l++) { /* loop over added columns */
995: col = in[l]; bcol = col/4;
996: ridx = row % 4; cidx = col % 4;
997: value = v[l + k*n];
998: if (col <= lastcol) low = 0;
999: else high = nrow;
1000: lastcol = col;
1001: while (high-low > 7) {
1002: t = (low+high)/2;
1003: if (rp[t] > bcol) high = t;
1004: else low = t;
1005: }
1006: for (i=low; i<high; i++) {
1007: if (rp[i] > bcol) break;
1008: if (rp[i] == bcol) {
1009: bap = ap + 16*i + 4*cidx + ridx;
1010: *bap += value;
1011: goto noinsert1;
1012: }
1013: }
1014: N = nrow++ - 1;
1015: high++; /* added new column thus must search to one higher than before */
1016: /* shift up all the later entries in this row */
1017: for (ii=N; ii>=i; ii--) {
1018: rp[ii+1] = rp[ii];
1019: PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
1020: }
1021: if (N>=i) {
1022: PetscMemzero(ap+16*i,16*sizeof(MatScalar));
1023: }
1024: rp[i] = bcol;
1025: ap[16*i + 4*cidx + ridx] = value;
1026: noinsert1:;
1027: low = i;
1028: }
1029: ailen[brow] = nrow;
1030: }
1031: PetscFunctionReturnVoid();
1032: }
1034: /*
1035: Checks for missing diagonals
1036: */
1037: PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A,PetscBool *missing,PetscInt *d)
1038: {
1039: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1041: PetscInt *diag,*ii = a->i,i;
1044: MatMarkDiagonal_SeqBAIJ(A);
1045: *missing = PETSC_FALSE;
1046: if (A->rmap->n > 0 && !ii) {
1047: *missing = PETSC_TRUE;
1048: if (d) *d = 0;
1049: PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1050: } else {
1051: diag = a->diag;
1052: for (i=0; i<a->mbs; i++) {
1053: if (diag[i] >= ii[i+1]) {
1054: *missing = PETSC_TRUE;
1055: if (d) *d = i;
1056: PetscInfo1(A,"Matrix is missing block diagonal number %D\n",i);
1057: break;
1058: }
1059: }
1060: }
1061: return(0);
1062: }
1064: PetscErrorCode MatMarkDiagonal_SeqBAIJ(Mat A)
1065: {
1066: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1068: PetscInt i,j,m = a->mbs;
1071: if (!a->diag) {
1072: PetscMalloc1(m,&a->diag);
1073: PetscLogObjectMemory((PetscObject)A,m*sizeof(PetscInt));
1074: a->free_diag = PETSC_TRUE;
1075: }
1076: for (i=0; i<m; i++) {
1077: a->diag[i] = a->i[i+1];
1078: for (j=a->i[i]; j<a->i[i+1]; j++) {
1079: if (a->j[j] == i) {
1080: a->diag[i] = j;
1081: break;
1082: }
1083: }
1084: }
1085: return(0);
1086: }
1089: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool blockcompressed,PetscInt *nn,const PetscInt *inia[],const PetscInt *inja[],PetscBool *done)
1090: {
1091: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1093: PetscInt i,j,n = a->mbs,nz = a->i[n],*tia,*tja,bs = A->rmap->bs,k,l,cnt;
1094: PetscInt **ia = (PetscInt**)inia,**ja = (PetscInt**)inja;
1097: *nn = n;
1098: if (!ia) return(0);
1099: if (symmetric) {
1100: MatToSymmetricIJ_SeqAIJ(n,a->i,a->j,PETSC_TRUE,0,0,&tia,&tja);
1101: nz = tia[n];
1102: } else {
1103: tia = a->i; tja = a->j;
1104: }
1106: if (!blockcompressed && bs > 1) {
1107: (*nn) *= bs;
1108: /* malloc & create the natural set of indices */
1109: PetscMalloc1((n+1)*bs,ia);
1110: if (n) {
1111: (*ia)[0] = oshift;
1112: for (j=1; j<bs; j++) {
1113: (*ia)[j] = (tia[1]-tia[0])*bs+(*ia)[j-1];
1114: }
1115: }
1117: for (i=1; i<n; i++) {
1118: (*ia)[i*bs] = (tia[i]-tia[i-1])*bs + (*ia)[i*bs-1];
1119: for (j=1; j<bs; j++) {
1120: (*ia)[i*bs+j] = (tia[i+1]-tia[i])*bs + (*ia)[i*bs+j-1];
1121: }
1122: }
1123: if (n) {
1124: (*ia)[n*bs] = (tia[n]-tia[n-1])*bs + (*ia)[n*bs-1];
1125: }
1127: if (inja) {
1128: PetscMalloc1(nz*bs*bs,ja);
1129: cnt = 0;
1130: for (i=0; i<n; i++) {
1131: for (j=0; j<bs; j++) {
1132: for (k=tia[i]; k<tia[i+1]; k++) {
1133: for (l=0; l<bs; l++) {
1134: (*ja)[cnt++] = bs*tja[k] + l;
1135: }
1136: }
1137: }
1138: }
1139: }
1141: if (symmetric) { /* deallocate memory allocated in MatToSymmetricIJ_SeqAIJ() */
1142: PetscFree(tia);
1143: PetscFree(tja);
1144: }
1145: } else if (oshift == 1) {
1146: if (symmetric) {
1147: nz = tia[A->rmap->n/bs];
1148: /* add 1 to i and j indices */
1149: for (i=0; i<A->rmap->n/bs+1; i++) tia[i] = tia[i] + 1;
1150: *ia = tia;
1151: if (ja) {
1152: for (i=0; i<nz; i++) tja[i] = tja[i] + 1;
1153: *ja = tja;
1154: }
1155: } else {
1156: nz = a->i[A->rmap->n/bs];
1157: /* malloc space and add 1 to i and j indices */
1158: PetscMalloc1(A->rmap->n/bs+1,ia);
1159: for (i=0; i<A->rmap->n/bs+1; i++) (*ia)[i] = a->i[i] + 1;
1160: if (ja) {
1161: PetscMalloc1(nz,ja);
1162: for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
1163: }
1164: }
1165: } else {
1166: *ia = tia;
1167: if (ja) *ja = tja;
1168: }
1169: return(0);
1170: }
1172: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool blockcompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
1173: {
1177: if (!ia) return(0);
1178: if ((!blockcompressed && A->rmap->bs > 1) || (symmetric || oshift == 1)) {
1179: PetscFree(*ia);
1180: if (ja) {PetscFree(*ja);}
1181: }
1182: return(0);
1183: }
1185: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
1186: {
1187: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1191: #if defined(PETSC_USE_LOG)
1192: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->N,A->cmap->n,a->nz);
1193: #endif
1194: MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1195: ISDestroy(&a->row);
1196: ISDestroy(&a->col);
1197: if (a->free_diag) {PetscFree(a->diag);}
1198: PetscFree(a->idiag);
1199: if (a->free_imax_ilen) {PetscFree2(a->imax,a->ilen);}
1200: PetscFree(a->solve_work);
1201: PetscFree(a->mult_work);
1202: PetscFree(a->sor_workt);
1203: PetscFree(a->sor_work);
1204: ISDestroy(&a->icol);
1205: PetscFree(a->saved_values);
1206: PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
1208: MatDestroy(&a->sbaijMat);
1209: MatDestroy(&a->parent);
1210: PetscFree(A->data);
1212: PetscObjectChangeTypeName((PetscObject)A,0);
1213: PetscObjectComposeFunction((PetscObject)A,"MatInvertBlockDiagonal_C",NULL);
1214: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1215: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1216: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetColumnIndices_C",NULL);
1217: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqaij_C",NULL);
1218: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqsbaij_C",NULL);
1219: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetPreallocation_C",NULL);
1220: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetPreallocationCSR_C",NULL);
1221: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqbstrm_C",NULL);
1222: PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1223: #if defined(PETSC_HAVE_HYPRE)
1224: PetscObjectComposeFunction((PetscObject)A,"MatConvert_mpiaij_hypre_C",NULL);
1225: #endif
1226: return(0);
1227: }
1229: PetscErrorCode MatSetOption_SeqBAIJ(Mat A,MatOption op,PetscBool flg)
1230: {
1231: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1235: switch (op) {
1236: case MAT_ROW_ORIENTED:
1237: a->roworiented = flg;
1238: break;
1239: case MAT_KEEP_NONZERO_PATTERN:
1240: a->keepnonzeropattern = flg;
1241: break;
1242: case MAT_NEW_NONZERO_LOCATIONS:
1243: a->nonew = (flg ? 0 : 1);
1244: break;
1245: case MAT_NEW_NONZERO_LOCATION_ERR:
1246: a->nonew = (flg ? -1 : 0);
1247: break;
1248: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1249: a->nonew = (flg ? -2 : 0);
1250: break;
1251: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1252: a->nounused = (flg ? -1 : 0);
1253: break;
1254: case MAT_NEW_DIAGONALS:
1255: case MAT_IGNORE_OFF_PROC_ENTRIES:
1256: case MAT_USE_HASH_TABLE:
1257: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1258: break;
1259: case MAT_SPD:
1260: case MAT_SYMMETRIC:
1261: case MAT_STRUCTURALLY_SYMMETRIC:
1262: case MAT_HERMITIAN:
1263: case MAT_SYMMETRY_ETERNAL:
1264: case MAT_SUBMAT_SINGLEIS:
1265: case MAT_STRUCTURE_ONLY:
1266: /* These options are handled directly by MatSetOption() */
1267: break;
1268: default:
1269: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1270: }
1271: return(0);
1272: }
1274: /* used for both SeqBAIJ and SeqSBAIJ matrices */
1275: PetscErrorCode MatGetRow_SeqBAIJ_private(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v,PetscInt *ai,PetscInt *aj,PetscScalar *aa)
1276: {
1278: PetscInt itmp,i,j,k,M,bn,bp,*idx_i,bs,bs2;
1279: MatScalar *aa_i;
1280: PetscScalar *v_i;
1283: bs = A->rmap->bs;
1284: bs2 = bs*bs;
1285: if (row < 0 || row >= A->rmap->N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range", row);
1287: bn = row/bs; /* Block number */
1288: bp = row % bs; /* Block Position */
1289: M = ai[bn+1] - ai[bn];
1290: *nz = bs*M;
1292: if (v) {
1293: *v = 0;
1294: if (*nz) {
1295: PetscMalloc1(*nz,v);
1296: for (i=0; i<M; i++) { /* for each block in the block row */
1297: v_i = *v + i*bs;
1298: aa_i = aa + bs2*(ai[bn] + i);
1299: for (j=bp,k=0; j<bs2; j+=bs,k++) v_i[k] = aa_i[j];
1300: }
1301: }
1302: }
1304: if (idx) {
1305: *idx = 0;
1306: if (*nz) {
1307: PetscMalloc1(*nz,idx);
1308: for (i=0; i<M; i++) { /* for each block in the block row */
1309: idx_i = *idx + i*bs;
1310: itmp = bs*aj[ai[bn] + i];
1311: for (j=0; j<bs; j++) idx_i[j] = itmp++;
1312: }
1313: }
1314: }
1315: return(0);
1316: }
1318: PetscErrorCode MatGetRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1319: {
1320: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1324: MatGetRow_SeqBAIJ_private(A,row,nz,idx,v,a->i,a->j,a->a);
1325: return(0);
1326: }
1328: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1329: {
1333: if (idx) {PetscFree(*idx);}
1334: if (v) {PetscFree(*v);}
1335: return(0);
1336: }
1338: PetscErrorCode MatTranspose_SeqBAIJ(Mat A,MatReuse reuse,Mat *B)
1339: {
1340: Mat_SeqBAIJ *a=(Mat_SeqBAIJ*)A->data;
1341: Mat C;
1343: PetscInt i,j,k,*aj=a->j,*ai=a->i,bs=A->rmap->bs,mbs=a->mbs,nbs=a->nbs,len,*col;
1344: PetscInt *rows,*cols,bs2=a->bs2;
1345: MatScalar *array;
1348: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
1349: PetscCalloc1(1+nbs,&col);
1351: for (i=0; i<ai[mbs]; i++) col[aj[i]] += 1;
1352: MatCreate(PetscObjectComm((PetscObject)A),&C);
1353: MatSetSizes(C,A->cmap->n,A->rmap->N,A->cmap->n,A->rmap->N);
1354: MatSetType(C,((PetscObject)A)->type_name);
1355: MatSeqBAIJSetPreallocation(C,bs,0,col);
1356: PetscFree(col);
1357: } else {
1358: C = *B;
1359: }
1361: array = a->a;
1362: PetscMalloc2(bs,&rows,bs,&cols);
1363: for (i=0; i<mbs; i++) {
1364: cols[0] = i*bs;
1365: for (k=1; k<bs; k++) cols[k] = cols[k-1] + 1;
1366: len = ai[i+1] - ai[i];
1367: for (j=0; j<len; j++) {
1368: rows[0] = (*aj++)*bs;
1369: for (k=1; k<bs; k++) rows[k] = rows[k-1] + 1;
1370: MatSetValues_SeqBAIJ(C,bs,rows,bs,cols,array,INSERT_VALUES);
1371: array += bs2;
1372: }
1373: }
1374: PetscFree2(rows,cols);
1376: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1377: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1379: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
1380: *B = C;
1381: } else {
1382: MatHeaderMerge(A,&C);
1383: }
1384: return(0);
1385: }
1387: PetscErrorCode MatIsTranspose_SeqBAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
1388: {
1390: Mat Btrans;
1393: *f = PETSC_FALSE;
1394: MatTranspose_SeqBAIJ(A,MAT_INITIAL_MATRIX,&Btrans);
1395: MatEqual_SeqBAIJ(B,Btrans,f);
1396: MatDestroy(&Btrans);
1397: return(0);
1398: }
1400: static PetscErrorCode MatView_SeqBAIJ_Binary(Mat A,PetscViewer viewer)
1401: {
1402: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1404: PetscInt i,*col_lens,bs = A->rmap->bs,count,*jj,j,k,l,bs2=a->bs2;
1405: int fd;
1406: PetscScalar *aa;
1407: FILE *file;
1410: PetscViewerBinaryGetDescriptor(viewer,&fd);
1411: PetscMalloc1(4+A->rmap->N,&col_lens);
1412: col_lens[0] = MAT_FILE_CLASSID;
1414: col_lens[1] = A->rmap->N;
1415: col_lens[2] = A->cmap->n;
1416: col_lens[3] = a->nz*bs2;
1418: /* store lengths of each row and write (including header) to file */
1419: count = 0;
1420: for (i=0; i<a->mbs; i++) {
1421: for (j=0; j<bs; j++) {
1422: col_lens[4+count++] = bs*(a->i[i+1] - a->i[i]);
1423: }
1424: }
1425: PetscBinaryWrite(fd,col_lens,4+A->rmap->N,PETSC_INT,PETSC_TRUE);
1426: PetscFree(col_lens);
1428: /* store column indices (zero start index) */
1429: PetscMalloc1((a->nz+1)*bs2,&jj);
1430: count = 0;
1431: for (i=0; i<a->mbs; i++) {
1432: for (j=0; j<bs; j++) {
1433: for (k=a->i[i]; k<a->i[i+1]; k++) {
1434: for (l=0; l<bs; l++) {
1435: jj[count++] = bs*a->j[k] + l;
1436: }
1437: }
1438: }
1439: }
1440: PetscBinaryWrite(fd,jj,bs2*a->nz,PETSC_INT,PETSC_FALSE);
1441: PetscFree(jj);
1443: /* store nonzero values */
1444: PetscMalloc1((a->nz+1)*bs2,&aa);
1445: count = 0;
1446: for (i=0; i<a->mbs; i++) {
1447: for (j=0; j<bs; j++) {
1448: for (k=a->i[i]; k<a->i[i+1]; k++) {
1449: for (l=0; l<bs; l++) {
1450: aa[count++] = a->a[bs2*k + l*bs + j];
1451: }
1452: }
1453: }
1454: }
1455: PetscBinaryWrite(fd,aa,bs2*a->nz,PETSC_SCALAR,PETSC_FALSE);
1456: PetscFree(aa);
1458: PetscViewerBinaryGetInfoPointer(viewer,&file);
1459: if (file) {
1460: fprintf(file,"-matload_block_size %d\n",(int)A->rmap->bs);
1461: }
1462: return(0);
1463: }
1465: static PetscErrorCode MatView_SeqBAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
1466: {
1468: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1469: PetscInt i,bs = A->rmap->bs,k;
1472: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1473: for (i=0; i<a->mbs; i++) {
1474: PetscViewerASCIIPrintf(viewer,"row %D-%D:",i*bs,i*bs+bs-1);
1475: for (k=a->i[i]; k<a->i[i+1]; k++) {
1476: PetscViewerASCIIPrintf(viewer," (%D-%D) ",bs*a->j[k],bs*a->j[k]+bs-1);
1477: }
1478: PetscViewerASCIIPrintf(viewer,"\n");
1479: }
1480: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1481: return(0);
1482: }
1484: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A,PetscViewer viewer)
1485: {
1486: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1487: PetscErrorCode ierr;
1488: PetscInt i,j,bs = A->rmap->bs,k,l,bs2=a->bs2;
1489: PetscViewerFormat format;
1492: if (A->structure_only) {
1493: MatView_SeqBAIJ_ASCII_structonly(A,viewer);
1494: return(0);
1495: }
1497: PetscViewerGetFormat(viewer,&format);
1498: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1499: PetscViewerASCIIPrintf(viewer," block size is %D\n",bs);
1500: } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1501: const char *matname;
1502: Mat aij;
1503: MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&aij);
1504: PetscObjectGetName((PetscObject)A,&matname);
1505: PetscObjectSetName((PetscObject)aij,matname);
1506: MatView(aij,viewer);
1507: MatDestroy(&aij);
1508: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1509: return(0);
1510: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1511: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1512: for (i=0; i<a->mbs; i++) {
1513: for (j=0; j<bs; j++) {
1514: PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1515: for (k=a->i[i]; k<a->i[i+1]; k++) {
1516: for (l=0; l<bs; l++) {
1517: #if defined(PETSC_USE_COMPLEX)
1518: if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1519: PetscViewerASCIIPrintf(viewer," (%D, %g + %gi) ",bs*a->j[k]+l,
1520: (double)PetscRealPart(a->a[bs2*k + l*bs + j]),(double)PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1521: } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1522: PetscViewerASCIIPrintf(viewer," (%D, %g - %gi) ",bs*a->j[k]+l,
1523: (double)PetscRealPart(a->a[bs2*k + l*bs + j]),-(double)PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1524: } else if (PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1525: PetscViewerASCIIPrintf(viewer," (%D, %g) ",bs*a->j[k]+l,(double)PetscRealPart(a->a[bs2*k + l*bs + j]));
1526: }
1527: #else
1528: if (a->a[bs2*k + l*bs + j] != 0.0) {
1529: PetscViewerASCIIPrintf(viewer," (%D, %g) ",bs*a->j[k]+l,(double)a->a[bs2*k + l*bs + j]);
1530: }
1531: #endif
1532: }
1533: }
1534: PetscViewerASCIIPrintf(viewer,"\n");
1535: }
1536: }
1537: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1538: } else {
1539: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1540: for (i=0; i<a->mbs; i++) {
1541: for (j=0; j<bs; j++) {
1542: PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1543: for (k=a->i[i]; k<a->i[i+1]; k++) {
1544: for (l=0; l<bs; l++) {
1545: #if defined(PETSC_USE_COMPLEX)
1546: if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0) {
1547: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i) ",bs*a->j[k]+l,
1548: (double)PetscRealPart(a->a[bs2*k + l*bs + j]),(double)PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1549: } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0) {
1550: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i) ",bs*a->j[k]+l,
1551: (double)PetscRealPart(a->a[bs2*k + l*bs + j]),-(double)PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1552: } else {
1553: PetscViewerASCIIPrintf(viewer," (%D, %g) ",bs*a->j[k]+l,(double)PetscRealPart(a->a[bs2*k + l*bs + j]));
1554: }
1555: #else
1556: PetscViewerASCIIPrintf(viewer," (%D, %g) ",bs*a->j[k]+l,(double)a->a[bs2*k + l*bs + j]);
1557: #endif
1558: }
1559: }
1560: PetscViewerASCIIPrintf(viewer,"\n");
1561: }
1562: }
1563: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1564: }
1565: PetscViewerFlush(viewer);
1566: return(0);
1567: }
1569: #include <petscdraw.h>
1570: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1571: {
1572: Mat A = (Mat) Aa;
1573: Mat_SeqBAIJ *a=(Mat_SeqBAIJ*)A->data;
1574: PetscErrorCode ierr;
1575: PetscInt row,i,j,k,l,mbs=a->mbs,color,bs=A->rmap->bs,bs2=a->bs2;
1576: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1577: MatScalar *aa;
1578: PetscViewer viewer;
1579: PetscViewerFormat format;
1582: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1583: PetscViewerGetFormat(viewer,&format);
1584: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
1586: /* loop over matrix elements drawing boxes */
1588: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1589: PetscDrawCollectiveBegin(draw);
1590: /* Blue for negative, Cyan for zero and Red for positive */
1591: color = PETSC_DRAW_BLUE;
1592: for (i=0,row=0; i<mbs; i++,row+=bs) {
1593: for (j=a->i[i]; j<a->i[i+1]; j++) {
1594: y_l = A->rmap->N - row - 1.0; y_r = y_l + 1.0;
1595: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1596: aa = a->a + j*bs2;
1597: for (k=0; k<bs; k++) {
1598: for (l=0; l<bs; l++) {
1599: if (PetscRealPart(*aa++) >= 0.) continue;
1600: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1601: }
1602: }
1603: }
1604: }
1605: color = PETSC_DRAW_CYAN;
1606: for (i=0,row=0; i<mbs; i++,row+=bs) {
1607: for (j=a->i[i]; j<a->i[i+1]; j++) {
1608: y_l = A->rmap->N - row - 1.0; y_r = y_l + 1.0;
1609: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1610: aa = a->a + j*bs2;
1611: for (k=0; k<bs; k++) {
1612: for (l=0; l<bs; l++) {
1613: if (PetscRealPart(*aa++) != 0.) continue;
1614: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1615: }
1616: }
1617: }
1618: }
1619: color = PETSC_DRAW_RED;
1620: for (i=0,row=0; i<mbs; i++,row+=bs) {
1621: for (j=a->i[i]; j<a->i[i+1]; j++) {
1622: y_l = A->rmap->N - row - 1.0; y_r = y_l + 1.0;
1623: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1624: aa = a->a + j*bs2;
1625: for (k=0; k<bs; k++) {
1626: for (l=0; l<bs; l++) {
1627: if (PetscRealPart(*aa++) <= 0.) continue;
1628: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1629: }
1630: }
1631: }
1632: }
1633: PetscDrawCollectiveEnd(draw);
1634: } else {
1635: /* use contour shading to indicate magnitude of values */
1636: /* first determine max of all nonzero values */
1637: PetscReal minv = 0.0, maxv = 0.0;
1638: PetscDraw popup;
1640: for (i=0; i<a->nz*a->bs2; i++) {
1641: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
1642: }
1643: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1644: PetscDrawGetPopup(draw,&popup);
1645: PetscDrawScalePopup(popup,0.0,maxv);
1647: PetscDrawCollectiveBegin(draw);
1648: for (i=0,row=0; i<mbs; i++,row+=bs) {
1649: for (j=a->i[i]; j<a->i[i+1]; j++) {
1650: y_l = A->rmap->N - row - 1.0; y_r = y_l + 1.0;
1651: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1652: aa = a->a + j*bs2;
1653: for (k=0; k<bs; k++) {
1654: for (l=0; l<bs; l++) {
1655: MatScalar v = *aa++;
1656: color = PetscDrawRealToColor(PetscAbsScalar(v),minv,maxv);
1657: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1658: }
1659: }
1660: }
1661: }
1662: PetscDrawCollectiveEnd(draw);
1663: }
1664: return(0);
1665: }
1667: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A,PetscViewer viewer)
1668: {
1670: PetscReal xl,yl,xr,yr,w,h;
1671: PetscDraw draw;
1672: PetscBool isnull;
1675: PetscViewerDrawGetDraw(viewer,0,&draw);
1676: PetscDrawIsNull(draw,&isnull);
1677: if (isnull) return(0);
1679: xr = A->cmap->n; yr = A->rmap->N; h = yr/10.0; w = xr/10.0;
1680: xr += w; yr += h; xl = -w; yl = -h;
1681: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1682: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1683: PetscDrawZoom(draw,MatView_SeqBAIJ_Draw_Zoom,A);
1684: PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
1685: PetscDrawSave(draw);
1686: return(0);
1687: }
1689: PetscErrorCode MatView_SeqBAIJ(Mat A,PetscViewer viewer)
1690: {
1692: PetscBool iascii,isbinary,isdraw;
1695: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1696: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1697: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1698: if (iascii) {
1699: MatView_SeqBAIJ_ASCII(A,viewer);
1700: } else if (isbinary) {
1701: MatView_SeqBAIJ_Binary(A,viewer);
1702: } else if (isdraw) {
1703: MatView_SeqBAIJ_Draw(A,viewer);
1704: } else {
1705: Mat B;
1706: MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&B);
1707: MatView(B,viewer);
1708: MatDestroy(&B);
1709: }
1710: return(0);
1711: }
1714: PetscErrorCode MatGetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
1715: {
1716: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1717: PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
1718: PetscInt *ai = a->i,*ailen = a->ilen;
1719: PetscInt brow,bcol,ridx,cidx,bs=A->rmap->bs,bs2=a->bs2;
1720: MatScalar *ap,*aa = a->a;
1723: for (k=0; k<m; k++) { /* loop over rows */
1724: row = im[k]; brow = row/bs;
1725: if (row < 0) {v += n; continue;} /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */
1726: if (row >= A->rmap->N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D too large", row);
1727: rp = aj + ai[brow]; ap = aa + bs2*ai[brow];
1728: nrow = ailen[brow];
1729: for (l=0; l<n; l++) { /* loop over columns */
1730: if (in[l] < 0) {v++; continue;} /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */
1731: if (in[l] >= A->cmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column %D too large", in[l]);
1732: col = in[l];
1733: bcol = col/bs;
1734: cidx = col%bs;
1735: ridx = row%bs;
1736: high = nrow;
1737: low = 0; /* assume unsorted */
1738: while (high-low > 5) {
1739: t = (low+high)/2;
1740: if (rp[t] > bcol) high = t;
1741: else low = t;
1742: }
1743: for (i=low; i<high; i++) {
1744: if (rp[i] > bcol) break;
1745: if (rp[i] == bcol) {
1746: *v++ = ap[bs2*i+bs*cidx+ridx];
1747: goto finished;
1748: }
1749: }
1750: *v++ = 0.0;
1751: finished:;
1752: }
1753: }
1754: return(0);
1755: }
1757: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
1758: {
1759: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1760: PetscInt *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,rmax,N,lastcol = -1;
1761: PetscInt *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1762: PetscErrorCode ierr;
1763: PetscInt *aj =a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap->bs,stepval;
1764: PetscBool roworiented=a->roworiented;
1765: const PetscScalar *value = v;
1766: MatScalar *ap=NULL,*aa = a->a,*bap;
1769: if (roworiented) {
1770: stepval = (n-1)*bs;
1771: } else {
1772: stepval = (m-1)*bs;
1773: }
1774: for (k=0; k<m; k++) { /* loop over added rows */
1775: row = im[k];
1776: if (row < 0) continue;
1777: #if defined(PETSC_USE_DEBUG)
1778: if (row >= a->mbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Block row index too large %D max %D",row,a->mbs-1);
1779: #endif
1780: rp = aj + ai[row];
1781: if (!A->structure_only) ap = aa + bs2*ai[row];
1782: rmax = imax[row];
1783: nrow = ailen[row];
1784: low = 0;
1785: high = nrow;
1786: for (l=0; l<n; l++) { /* loop over added columns */
1787: if (in[l] < 0) continue;
1788: #if defined(PETSC_USE_DEBUG)
1789: if (in[l] >= a->nbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Block column index too large %D max %D",in[l],a->nbs-1);
1790: #endif
1791: col = in[l];
1792: if (!A->structure_only) {
1793: if (roworiented) {
1794: value = v + (k*(stepval+bs) + l)*bs;
1795: } else {
1796: value = v + (l*(stepval+bs) + k)*bs;
1797: }
1798: }
1799: if (col <= lastcol) low = 0;
1800: else high = nrow;
1801: lastcol = col;
1802: while (high-low > 7) {
1803: t = (low+high)/2;
1804: if (rp[t] > col) high = t;
1805: else low = t;
1806: }
1807: for (i=low; i<high; i++) {
1808: if (rp[i] > col) break;
1809: if (rp[i] == col) {
1810: if (A->structure_only) goto noinsert2;
1811: bap = ap + bs2*i;
1812: if (roworiented) {
1813: if (is == ADD_VALUES) {
1814: for (ii=0; ii<bs; ii++,value+=stepval) {
1815: for (jj=ii; jj<bs2; jj+=bs) {
1816: bap[jj] += *value++;
1817: }
1818: }
1819: } else {
1820: for (ii=0; ii<bs; ii++,value+=stepval) {
1821: for (jj=ii; jj<bs2; jj+=bs) {
1822: bap[jj] = *value++;
1823: }
1824: }
1825: }
1826: } else {
1827: if (is == ADD_VALUES) {
1828: for (ii=0; ii<bs; ii++,value+=bs+stepval) {
1829: for (jj=0; jj<bs; jj++) {
1830: bap[jj] += value[jj];
1831: }
1832: bap += bs;
1833: }
1834: } else {
1835: for (ii=0; ii<bs; ii++,value+=bs+stepval) {
1836: for (jj=0; jj<bs; jj++) {
1837: bap[jj] = value[jj];
1838: }
1839: bap += bs;
1840: }
1841: }
1842: }
1843: goto noinsert2;
1844: }
1845: }
1846: if (nonew == 1) goto noinsert2;
1847: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new blocked index new nonzero block (%D, %D) in the matrix", row, col);
1848: if (A->structure_only) {
1849: MatSeqXAIJReallocateAIJ_structure_only(A,a->mbs,bs2,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
1850: } else {
1851: MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
1852: }
1853: N = nrow++ - 1; high++;
1854: /* shift up all the later entries in this row */
1855: for (ii=N; ii>=i; ii--) {
1856: rp[ii+1] = rp[ii];
1857: if (!A->structure_only) {
1858: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1859: }
1860: }
1861: if (N >= i && !A->structure_only) {
1862: PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1863: }
1865: rp[i] = col;
1866: if (!A->structure_only) {
1867: bap = ap + bs2*i;
1868: if (roworiented) {
1869: for (ii=0; ii<bs; ii++,value+=stepval) {
1870: for (jj=ii; jj<bs2; jj+=bs) {
1871: bap[jj] = *value++;
1872: }
1873: }
1874: } else {
1875: for (ii=0; ii<bs; ii++,value+=stepval) {
1876: for (jj=0; jj<bs; jj++) {
1877: *bap++ = *value++;
1878: }
1879: }
1880: }
1881: }
1882: noinsert2:;
1883: low = i;
1884: }
1885: ailen[row] = nrow;
1886: }
1887: return(0);
1888: }
1890: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A,MatAssemblyType mode)
1891: {
1892: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1893: PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
1894: PetscInt m = A->rmap->N,*ip,N,*ailen = a->ilen;
1896: PetscInt mbs = a->mbs,bs2 = a->bs2,rmax = 0;
1897: MatScalar *aa = a->a,*ap;
1898: PetscReal ratio=0.6;
1901: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1903: if (m) rmax = ailen[0];
1904: for (i=1; i<mbs; i++) {
1905: /* move each row back by the amount of empty slots (fshift) before it*/
1906: fshift += imax[i-1] - ailen[i-1];
1907: rmax = PetscMax(rmax,ailen[i]);
1908: if (fshift) {
1909: ip = aj + ai[i]; ap = aa + bs2*ai[i];
1910: N = ailen[i];
1911: for (j=0; j<N; j++) {
1912: ip[j-fshift] = ip[j];
1913: if (!A->structure_only) {
1914: PetscMemcpy(ap+(j-fshift)*bs2,ap+j*bs2,bs2*sizeof(MatScalar));
1915: }
1916: }
1917: }
1918: ai[i] = ai[i-1] + ailen[i-1];
1919: }
1920: if (mbs) {
1921: fshift += imax[mbs-1] - ailen[mbs-1];
1922: ai[mbs] = ai[mbs-1] + ailen[mbs-1];
1923: }
1925: /* reset ilen and imax for each row */
1926: a->nonzerorowcnt = 0;
1927: if (A->structure_only) {
1928: PetscFree2(a->imax,a->ilen);
1929: } else { /* !A->structure_only */
1930: for (i=0; i<mbs; i++) {
1931: ailen[i] = imax[i] = ai[i+1] - ai[i];
1932: a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
1933: }
1934: }
1935: a->nz = ai[mbs];
1937: /* diagonals may have moved, so kill the diagonal pointers */
1938: a->idiagvalid = PETSC_FALSE;
1939: if (fshift && a->diag) {
1940: PetscFree(a->diag);
1941: PetscLogObjectMemory((PetscObject)A,-(mbs+1)*sizeof(PetscInt));
1942: a->diag = 0;
1943: }
1944: if (fshift && a->nounused == -1) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D block size %D, %D unneeded", m, A->cmap->n, A->rmap->bs, fshift*bs2);
1945: PetscInfo5(A,"Matrix size: %D X %D, block size %D; storage space: %D unneeded, %D used\n",m,A->cmap->n,A->rmap->bs,fshift*bs2,a->nz*bs2);
1946: PetscInfo1(A,"Number of mallocs during MatSetValues is %D\n",a->reallocs);
1947: PetscInfo1(A,"Most nonzeros blocks in any row is %D\n",rmax);
1949: A->info.mallocs += a->reallocs;
1950: a->reallocs = 0;
1951: A->info.nz_unneeded = (PetscReal)fshift*bs2;
1952: a->rmax = rmax;
1954: if (!A->structure_only) {
1955: MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,mbs,ratio);
1956: }
1957: return(0);
1958: }
1960: /*
1961: This function returns an array of flags which indicate the locations of contiguous
1962: blocks that should be zeroed. for eg: if bs = 3 and is = [0,1,2,3,5,6,7,8,9]
1963: then the resulting sizes = [3,1,1,3,1] correspondig to sets [(0,1,2),(3),(5),(6,7,8),(9)]
1964: Assume: sizes should be long enough to hold all the values.
1965: */
1966: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[],PetscInt n,PetscInt bs,PetscInt sizes[], PetscInt *bs_max)
1967: {
1968: PetscInt i,j,k,row;
1969: PetscBool flg;
1972: for (i=0,j=0; i<n; j++) {
1973: row = idx[i];
1974: if (row%bs!=0) { /* Not the begining of a block */
1975: sizes[j] = 1;
1976: i++;
1977: } else if (i+bs > n) { /* complete block doesn't exist (at idx end) */
1978: sizes[j] = 1; /* Also makes sure atleast 'bs' values exist for next else */
1979: i++;
1980: } else { /* Begining of the block, so check if the complete block exists */
1981: flg = PETSC_TRUE;
1982: for (k=1; k<bs; k++) {
1983: if (row+k != idx[i+k]) { /* break in the block */
1984: flg = PETSC_FALSE;
1985: break;
1986: }
1987: }
1988: if (flg) { /* No break in the bs */
1989: sizes[j] = bs;
1990: i += bs;
1991: } else {
1992: sizes[j] = 1;
1993: i++;
1994: }
1995: }
1996: }
1997: *bs_max = j;
1998: return(0);
1999: }
2001: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A,PetscInt is_n,const PetscInt is_idx[],PetscScalar diag,Vec x, Vec b)
2002: {
2003: Mat_SeqBAIJ *baij=(Mat_SeqBAIJ*)A->data;
2004: PetscErrorCode ierr;
2005: PetscInt i,j,k,count,*rows;
2006: PetscInt bs=A->rmap->bs,bs2=baij->bs2,*sizes,row,bs_max;
2007: PetscScalar zero = 0.0;
2008: MatScalar *aa;
2009: const PetscScalar *xx;
2010: PetscScalar *bb;
2013: /* fix right hand side if needed */
2014: if (x && b) {
2015: VecGetArrayRead(x,&xx);
2016: VecGetArray(b,&bb);
2017: for (i=0; i<is_n; i++) {
2018: bb[is_idx[i]] = diag*xx[is_idx[i]];
2019: }
2020: VecRestoreArrayRead(x,&xx);
2021: VecRestoreArray(b,&bb);
2022: }
2024: /* Make a copy of the IS and sort it */
2025: /* allocate memory for rows,sizes */
2026: PetscMalloc2(is_n,&rows,2*is_n,&sizes);
2028: /* copy IS values to rows, and sort them */
2029: for (i=0; i<is_n; i++) rows[i] = is_idx[i];
2030: PetscSortInt(is_n,rows);
2032: if (baij->keepnonzeropattern) {
2033: for (i=0; i<is_n; i++) sizes[i] = 1;
2034: bs_max = is_n;
2035: } else {
2036: MatZeroRows_SeqBAIJ_Check_Blocks(rows,is_n,bs,sizes,&bs_max);
2037: A->nonzerostate++;
2038: }
2040: for (i=0,j=0; i<bs_max; j+=sizes[i],i++) {
2041: row = rows[j];
2042: if (row < 0 || row > A->rmap->N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range",row);
2043: count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
2044: aa = ((MatScalar*)(baij->a)) + baij->i[row/bs]*bs2 + (row%bs);
2045: if (sizes[i] == bs && !baij->keepnonzeropattern) {
2046: if (diag != (PetscScalar)0.0) {
2047: if (baij->ilen[row/bs] > 0) {
2048: baij->ilen[row/bs] = 1;
2049: baij->j[baij->i[row/bs]] = row/bs;
2051: PetscMemzero(aa,count*bs*sizeof(MatScalar));
2052: }
2053: /* Now insert all the diagonal values for this bs */
2054: for (k=0; k<bs; k++) {
2055: (*A->ops->setvalues)(A,1,rows+j+k,1,rows+j+k,&diag,INSERT_VALUES);
2056: }
2057: } else { /* (diag == 0.0) */
2058: baij->ilen[row/bs] = 0;
2059: } /* end (diag == 0.0) */
2060: } else { /* (sizes[i] != bs) */
2061: #if defined(PETSC_USE_DEBUG)
2062: if (sizes[i] != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Internal Error. Value should be 1");
2063: #endif
2064: for (k=0; k<count; k++) {
2065: aa[0] = zero;
2066: aa += bs;
2067: }
2068: if (diag != (PetscScalar)0.0) {
2069: (*A->ops->setvalues)(A,1,rows+j,1,rows+j,&diag,INSERT_VALUES);
2070: }
2071: }
2072: }
2074: PetscFree2(rows,sizes);
2075: MatAssemblyEnd_SeqBAIJ(A,MAT_FINAL_ASSEMBLY);
2076: return(0);
2077: }
2079: PetscErrorCode MatZeroRowsColumns_SeqBAIJ(Mat A,PetscInt is_n,const PetscInt is_idx[],PetscScalar diag,Vec x, Vec b)
2080: {
2081: Mat_SeqBAIJ *baij=(Mat_SeqBAIJ*)A->data;
2082: PetscErrorCode ierr;
2083: PetscInt i,j,k,count;
2084: PetscInt bs =A->rmap->bs,bs2=baij->bs2,row,col;
2085: PetscScalar zero = 0.0;
2086: MatScalar *aa;
2087: const PetscScalar *xx;
2088: PetscScalar *bb;
2089: PetscBool *zeroed,vecs = PETSC_FALSE;
2092: /* fix right hand side if needed */
2093: if (x && b) {
2094: VecGetArrayRead(x,&xx);
2095: VecGetArray(b,&bb);
2096: vecs = PETSC_TRUE;
2097: }
2099: /* zero the columns */
2100: PetscCalloc1(A->rmap->n,&zeroed);
2101: for (i=0; i<is_n; i++) {
2102: if (is_idx[i] < 0 || is_idx[i] >= A->rmap->N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range",is_idx[i]);
2103: zeroed[is_idx[i]] = PETSC_TRUE;
2104: }
2105: for (i=0; i<A->rmap->N; i++) {
2106: if (!zeroed[i]) {
2107: row = i/bs;
2108: for (j=baij->i[row]; j<baij->i[row+1]; j++) {
2109: for (k=0; k<bs; k++) {
2110: col = bs*baij->j[j] + k;
2111: if (zeroed[col]) {
2112: aa = ((MatScalar*)(baij->a)) + j*bs2 + (i%bs) + bs*k;
2113: if (vecs) bb[i] -= aa[0]*xx[col];
2114: aa[0] = 0.0;
2115: }
2116: }
2117: }
2118: } else if (vecs) bb[i] = diag*xx[i];
2119: }
2120: PetscFree(zeroed);
2121: if (vecs) {
2122: VecRestoreArrayRead(x,&xx);
2123: VecRestoreArray(b,&bb);
2124: }
2126: /* zero the rows */
2127: for (i=0; i<is_n; i++) {
2128: row = is_idx[i];
2129: count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
2130: aa = ((MatScalar*)(baij->a)) + baij->i[row/bs]*bs2 + (row%bs);
2131: for (k=0; k<count; k++) {
2132: aa[0] = zero;
2133: aa += bs;
2134: }
2135: if (diag != (PetscScalar)0.0) {
2136: (*A->ops->setvalues)(A,1,&row,1,&row,&diag,INSERT_VALUES);
2137: }
2138: }
2139: MatAssemblyEnd_SeqBAIJ(A,MAT_FINAL_ASSEMBLY);
2140: return(0);
2141: }
2143: PetscErrorCode MatSetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
2144: {
2145: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2146: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,lastcol = -1;
2147: PetscInt *imax=a->imax,*ai=a->i,*ailen=a->ilen;
2148: PetscInt *aj =a->j,nonew=a->nonew,bs=A->rmap->bs,brow,bcol;
2150: PetscInt ridx,cidx,bs2=a->bs2;
2151: PetscBool roworiented=a->roworiented;
2152: MatScalar *ap=NULL,value=0.0,*aa=a->a,*bap;
2155: for (k=0; k<m; k++) { /* loop over added rows */
2156: row = im[k];
2157: brow = row/bs;
2158: if (row < 0) continue;
2159: #if defined(PETSC_USE_DEBUG)
2160: if (row >= A->rmap->N) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->N-1);
2161: #endif
2162: rp = aj + ai[brow];
2163: if (!A->structure_only) ap = aa + bs2*ai[brow];
2164: rmax = imax[brow];
2165: nrow = ailen[brow];
2166: low = 0;
2167: high = nrow;
2168: for (l=0; l<n; l++) { /* loop over added columns */
2169: if (in[l] < 0) continue;
2170: #if defined(PETSC_USE_DEBUG)
2171: if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
2172: #endif
2173: col = in[l]; bcol = col/bs;
2174: ridx = row % bs; cidx = col % bs;
2175: if (!A->structure_only) {
2176: if (roworiented) {
2177: value = v[l + k*n];
2178: } else {
2179: value = v[k + l*m];
2180: }
2181: }
2182: if (col <= lastcol) low = 0; else high = nrow;
2183: lastcol = col;
2184: while (high-low > 7) {
2185: t = (low+high)/2;
2186: if (rp[t] > bcol) high = t;
2187: else low = t;
2188: }
2189: for (i=low; i<high; i++) {
2190: if (rp[i] > bcol) break;
2191: if (rp[i] == bcol) {
2192: bap = ap + bs2*i + bs*cidx + ridx;
2193: if (!A->structure_only) {
2194: if (is == ADD_VALUES) *bap += value;
2195: else *bap = value;
2196: }
2197: goto noinsert1;
2198: }
2199: }
2200: if (nonew == 1) goto noinsert1;
2201: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
2202: if (A->structure_only) {
2203: MatSeqXAIJReallocateAIJ_structure_only(A,a->mbs,bs2,nrow,brow,bcol,rmax,ai,aj,rp,imax,nonew,MatScalar);
2204: } else {
2205: MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
2206: }
2207: N = nrow++ - 1; high++;
2208: /* shift up all the later entries in this row */
2209: for (ii=N; ii>=i; ii--) {
2210: rp[ii+1] = rp[ii];
2211: if (!A->structure_only) {
2212: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
2213: }
2214: }
2215: if (N>=i && !A->structure_only) {
2216: PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
2217: }
2218: rp[i] = bcol;
2219: if (!A->structure_only) ap[bs2*i + bs*cidx + ridx] = value;
2220: a->nz++;
2221: A->nonzerostate++;
2222: noinsert1:;
2223: low = i;
2224: }
2225: ailen[brow] = nrow;
2226: }
2227: return(0);
2228: }
2230: PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2231: {
2232: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)inA->data;
2233: Mat outA;
2235: PetscBool row_identity,col_identity;
2238: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels = 0 supported for in-place ILU");
2239: ISIdentity(row,&row_identity);
2240: ISIdentity(col,&col_identity);
2241: if (!row_identity || !col_identity) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU");
2243: outA = inA;
2244: inA->factortype = MAT_FACTOR_LU;
2245: PetscFree(inA->solvertype);
2246: PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);
2248: MatMarkDiagonal_SeqBAIJ(inA);
2250: PetscObjectReference((PetscObject)row);
2251: ISDestroy(&a->row);
2252: a->row = row;
2253: PetscObjectReference((PetscObject)col);
2254: ISDestroy(&a->col);
2255: a->col = col;
2257: /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
2258: ISDestroy(&a->icol);
2259: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2260: PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);
2262: MatSeqBAIJSetNumericFactorization_inplace(inA,(PetscBool)(row_identity && col_identity));
2263: if (!a->solve_work) {
2264: PetscMalloc1(inA->rmap->N+inA->rmap->bs,&a->solve_work);
2265: PetscLogObjectMemory((PetscObject)inA,(inA->rmap->N+inA->rmap->bs)*sizeof(PetscScalar));
2266: }
2267: MatLUFactorNumeric(outA,inA,info);
2268: return(0);
2269: }
2271: PetscErrorCode MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat,PetscInt *indices)
2272: {
2273: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ*)mat->data;
2274: PetscInt i,nz,mbs;
2277: nz = baij->maxnz;
2278: mbs = baij->mbs;
2279: for (i=0; i<nz; i++) {
2280: baij->j[i] = indices[i];
2281: }
2282: baij->nz = nz;
2283: for (i=0; i<mbs; i++) {
2284: baij->ilen[i] = baij->imax[i];
2285: }
2286: return(0);
2287: }
2289: /*@
2290: MatSeqBAIJSetColumnIndices - Set the column indices for all the rows
2291: in the matrix.
2293: Input Parameters:
2294: + mat - the SeqBAIJ matrix
2295: - indices - the column indices
2297: Level: advanced
2299: Notes:
2300: This can be called if you have precomputed the nonzero structure of the
2301: matrix and want to provide it to the matrix object to improve the performance
2302: of the MatSetValues() operation.
2304: You MUST have set the correct numbers of nonzeros per row in the call to
2305: MatCreateSeqBAIJ(), and the columns indices MUST be sorted.
2307: MUST be called before any calls to MatSetValues();
2309: @*/
2310: PetscErrorCode MatSeqBAIJSetColumnIndices(Mat mat,PetscInt *indices)
2311: {
2317: PetscUseMethod(mat,"MatSeqBAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
2318: return(0);
2319: }
2321: PetscErrorCode MatGetRowMaxAbs_SeqBAIJ(Mat A,Vec v,PetscInt idx[])
2322: {
2323: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2325: PetscInt i,j,n,row,bs,*ai,*aj,mbs;
2326: PetscReal atmp;
2327: PetscScalar *x,zero = 0.0;
2328: MatScalar *aa;
2329: PetscInt ncols,brow,krow,kcol;
2332: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2333: bs = A->rmap->bs;
2334: aa = a->a;
2335: ai = a->i;
2336: aj = a->j;
2337: mbs = a->mbs;
2339: VecSet(v,zero);
2340: VecGetArray(v,&x);
2341: VecGetLocalSize(v,&n);
2342: if (n != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2343: for (i=0; i<mbs; i++) {
2344: ncols = ai[1] - ai[0]; ai++;
2345: brow = bs*i;
2346: for (j=0; j<ncols; j++) {
2347: for (kcol=0; kcol<bs; kcol++) {
2348: for (krow=0; krow<bs; krow++) {
2349: atmp = PetscAbsScalar(*aa);aa++;
2350: row = brow + krow; /* row index */
2351: if (PetscAbsScalar(x[row]) < atmp) {x[row] = atmp; if (idx) idx[row] = bs*(*aj) + kcol;}
2352: }
2353: }
2354: aj++;
2355: }
2356: }
2357: VecRestoreArray(v,&x);
2358: return(0);
2359: }
2361: PetscErrorCode MatCopy_SeqBAIJ(Mat A,Mat B,MatStructure str)
2362: {
2366: /* If the two matrices have the same copy implementation, use fast copy. */
2367: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2368: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2369: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)B->data;
2370: PetscInt ambs=a->mbs,bmbs=b->mbs,abs=A->rmap->bs,bbs=B->rmap->bs,bs2=abs*abs;
2372: if (a->i[ambs] != b->i[bmbs]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzero blocks in matrices A %D and B %D are different",a->i[ambs],b->i[bmbs]);
2373: if (abs != bbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Block size A %D and B %D are different",abs,bbs);
2374: PetscMemcpy(b->a,a->a,(bs2*a->i[ambs])*sizeof(PetscScalar));
2375: PetscObjectStateIncrease((PetscObject)B);
2376: } else {
2377: MatCopy_Basic(A,B,str);
2378: }
2379: return(0);
2380: }
2382: PetscErrorCode MatSetUp_SeqBAIJ(Mat A)
2383: {
2387: MatSeqBAIJSetPreallocation(A,A->rmap->bs,PETSC_DEFAULT,0);
2388: return(0);
2389: }
2391: PetscErrorCode MatSeqBAIJGetArray_SeqBAIJ(Mat A,PetscScalar *array[])
2392: {
2393: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2396: *array = a->a;
2397: return(0);
2398: }
2400: PetscErrorCode MatSeqBAIJRestoreArray_SeqBAIJ(Mat A,PetscScalar *array[])
2401: {
2403: return(0);
2404: }
2406: PetscErrorCode MatAXPYGetPreallocation_SeqBAIJ(Mat Y,Mat X,PetscInt *nnz)
2407: {
2408: PetscInt bs = Y->rmap->bs,mbs = Y->rmap->N/bs;
2409: Mat_SeqBAIJ *x = (Mat_SeqBAIJ*)X->data;
2410: Mat_SeqBAIJ *y = (Mat_SeqBAIJ*)Y->data;
2414: /* Set the number of nonzeros in the new matrix */
2415: MatAXPYGetPreallocation_SeqX_private(mbs,x->i,x->j,y->i,y->j,nnz);
2416: return(0);
2417: }
2419: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2420: {
2421: Mat_SeqBAIJ *x = (Mat_SeqBAIJ*)X->data,*y = (Mat_SeqBAIJ*)Y->data;
2423: PetscInt bs=Y->rmap->bs,bs2=bs*bs;
2424: PetscBLASInt one=1;
2427: if (str == SAME_NONZERO_PATTERN) {
2428: PetscScalar alpha = a;
2429: PetscBLASInt bnz;
2430: PetscBLASIntCast(x->nz*bs2,&bnz);
2431: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2432: PetscObjectStateIncrease((PetscObject)Y);
2433: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2434: MatAXPY_Basic(Y,a,X,str);
2435: } else {
2436: Mat B;
2437: PetscInt *nnz;
2438: if (bs != X->rmap->bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Matrices must have same block size");
2439: PetscMalloc1(Y->rmap->N,&nnz);
2440: MatCreate(PetscObjectComm((PetscObject)Y),&B);
2441: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2442: MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2443: MatSetBlockSizesFromMats(B,Y,Y);
2444: MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2445: MatAXPYGetPreallocation_SeqBAIJ(Y,X,nnz);
2446: MatSeqBAIJSetPreallocation(B,bs,0,nnz);
2447: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2448: MatHeaderReplace(Y,&B);
2449: PetscFree(nnz);
2450: }
2451: return(0);
2452: }
2454: PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
2455: {
2456: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2457: PetscInt i,nz = a->bs2*a->i[a->mbs];
2458: MatScalar *aa = a->a;
2461: for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
2462: return(0);
2463: }
2465: PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2466: {
2467: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2468: PetscInt i,nz = a->bs2*a->i[a->mbs];
2469: MatScalar *aa = a->a;
2472: for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2473: return(0);
2474: }
2476: /*
2477: Code almost idential to MatGetColumnIJ_SeqAIJ() should share common code
2478: */
2479: PetscErrorCode MatGetColumnIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
2480: {
2481: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2483: PetscInt bs = A->rmap->bs,i,*collengths,*cia,*cja,n = A->cmap->n/bs,m = A->rmap->n/bs;
2484: PetscInt nz = a->i[m],row,*jj,mr,col;
2487: *nn = n;
2488: if (!ia) return(0);
2489: if (symmetric) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not for BAIJ matrices");
2490: else {
2491: PetscCalloc1(n+1,&collengths);
2492: PetscMalloc1(n+1,&cia);
2493: PetscMalloc1(nz+1,&cja);
2494: jj = a->j;
2495: for (i=0; i<nz; i++) {
2496: collengths[jj[i]]++;
2497: }
2498: cia[0] = oshift;
2499: for (i=0; i<n; i++) {
2500: cia[i+1] = cia[i] + collengths[i];
2501: }
2502: PetscMemzero(collengths,n*sizeof(PetscInt));
2503: jj = a->j;
2504: for (row=0; row<m; row++) {
2505: mr = a->i[row+1] - a->i[row];
2506: for (i=0; i<mr; i++) {
2507: col = *jj++;
2509: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
2510: }
2511: }
2512: PetscFree(collengths);
2513: *ia = cia; *ja = cja;
2514: }
2515: return(0);
2516: }
2518: PetscErrorCode MatRestoreColumnIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
2519: {
2523: if (!ia) return(0);
2524: PetscFree(*ia);
2525: PetscFree(*ja);
2526: return(0);
2527: }
2529: /*
2530: MatGetColumnIJ_SeqBAIJ_Color() and MatRestoreColumnIJ_SeqBAIJ_Color() are customized from
2531: MatGetColumnIJ_SeqBAIJ() and MatRestoreColumnIJ_SeqBAIJ() by adding an output
2532: spidx[], index of a->a, to be used in MatTransposeColoringCreate() and MatFDColoringCreate()
2533: */
2534: PetscErrorCode MatGetColumnIJ_SeqBAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done)
2535: {
2536: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2538: PetscInt i,*collengths,*cia,*cja,n=a->nbs,m=a->mbs;
2539: PetscInt nz = a->i[m],row,*jj,mr,col;
2540: PetscInt *cspidx;
2543: *nn = n;
2544: if (!ia) return(0);
2546: PetscCalloc1(n+1,&collengths);
2547: PetscMalloc1(n+1,&cia);
2548: PetscMalloc1(nz+1,&cja);
2549: PetscMalloc1(nz+1,&cspidx);
2550: jj = a->j;
2551: for (i=0; i<nz; i++) {
2552: collengths[jj[i]]++;
2553: }
2554: cia[0] = oshift;
2555: for (i=0; i<n; i++) {
2556: cia[i+1] = cia[i] + collengths[i];
2557: }
2558: PetscMemzero(collengths,n*sizeof(PetscInt));
2559: jj = a->j;
2560: for (row=0; row<m; row++) {
2561: mr = a->i[row+1] - a->i[row];
2562: for (i=0; i<mr; i++) {
2563: col = *jj++;
2564: cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */
2565: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
2566: }
2567: }
2568: PetscFree(collengths);
2569: *ia = cia; *ja = cja;
2570: *spidx = cspidx;
2571: return(0);
2572: }
2574: PetscErrorCode MatRestoreColumnIJ_SeqBAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done)
2575: {
2579: MatRestoreColumnIJ_SeqBAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
2580: PetscFree(*spidx);
2581: return(0);
2582: }
2584: PetscErrorCode MatShift_SeqBAIJ(Mat Y,PetscScalar a)
2585: {
2587: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ*)Y->data;
2590: if (!Y->preallocated || !aij->nz) {
2591: MatSeqBAIJSetPreallocation(Y,Y->rmap->bs,1,NULL);
2592: }
2593: MatShift_Basic(Y,a);
2594: return(0);
2595: }
2597: /* -------------------------------------------------------------------*/
2598: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
2599: MatGetRow_SeqBAIJ,
2600: MatRestoreRow_SeqBAIJ,
2601: MatMult_SeqBAIJ_N,
2602: /* 4*/ MatMultAdd_SeqBAIJ_N,
2603: MatMultTranspose_SeqBAIJ,
2604: MatMultTransposeAdd_SeqBAIJ,
2605: 0,
2606: 0,
2607: 0,
2608: /* 10*/ 0,
2609: MatLUFactor_SeqBAIJ,
2610: 0,
2611: 0,
2612: MatTranspose_SeqBAIJ,
2613: /* 15*/ MatGetInfo_SeqBAIJ,
2614: MatEqual_SeqBAIJ,
2615: MatGetDiagonal_SeqBAIJ,
2616: MatDiagonalScale_SeqBAIJ,
2617: MatNorm_SeqBAIJ,
2618: /* 20*/ 0,
2619: MatAssemblyEnd_SeqBAIJ,
2620: MatSetOption_SeqBAIJ,
2621: MatZeroEntries_SeqBAIJ,
2622: /* 24*/ MatZeroRows_SeqBAIJ,
2623: 0,
2624: 0,
2625: 0,
2626: 0,
2627: /* 29*/ MatSetUp_SeqBAIJ,
2628: 0,
2629: 0,
2630: 0,
2631: 0,
2632: /* 34*/ MatDuplicate_SeqBAIJ,
2633: 0,
2634: 0,
2635: MatILUFactor_SeqBAIJ,
2636: 0,
2637: /* 39*/ MatAXPY_SeqBAIJ,
2638: MatCreateSubMatrices_SeqBAIJ,
2639: MatIncreaseOverlap_SeqBAIJ,
2640: MatGetValues_SeqBAIJ,
2641: MatCopy_SeqBAIJ,
2642: /* 44*/ 0,
2643: MatScale_SeqBAIJ,
2644: MatShift_SeqBAIJ,
2645: 0,
2646: MatZeroRowsColumns_SeqBAIJ,
2647: /* 49*/ 0,
2648: MatGetRowIJ_SeqBAIJ,
2649: MatRestoreRowIJ_SeqBAIJ,
2650: MatGetColumnIJ_SeqBAIJ,
2651: MatRestoreColumnIJ_SeqBAIJ,
2652: /* 54*/ MatFDColoringCreate_SeqXAIJ,
2653: 0,
2654: 0,
2655: 0,
2656: MatSetValuesBlocked_SeqBAIJ,
2657: /* 59*/ MatCreateSubMatrix_SeqBAIJ,
2658: MatDestroy_SeqBAIJ,
2659: MatView_SeqBAIJ,
2660: 0,
2661: 0,
2662: /* 64*/ 0,
2663: 0,
2664: 0,
2665: 0,
2666: 0,
2667: /* 69*/ MatGetRowMaxAbs_SeqBAIJ,
2668: 0,
2669: MatConvert_Basic,
2670: 0,
2671: 0,
2672: /* 74*/ 0,
2673: MatFDColoringApply_BAIJ,
2674: 0,
2675: 0,
2676: 0,
2677: /* 79*/ 0,
2678: 0,
2679: 0,
2680: 0,
2681: MatLoad_SeqBAIJ,
2682: /* 84*/ 0,
2683: 0,
2684: 0,
2685: 0,
2686: 0,
2687: /* 89*/ 0,
2688: 0,
2689: 0,
2690: 0,
2691: 0,
2692: /* 94*/ 0,
2693: 0,
2694: 0,
2695: 0,
2696: 0,
2697: /* 99*/ 0,
2698: 0,
2699: 0,
2700: 0,
2701: 0,
2702: /*104*/ 0,
2703: MatRealPart_SeqBAIJ,
2704: MatImaginaryPart_SeqBAIJ,
2705: 0,
2706: 0,
2707: /*109*/ 0,
2708: 0,
2709: 0,
2710: 0,
2711: MatMissingDiagonal_SeqBAIJ,
2712: /*114*/ 0,
2713: 0,
2714: 0,
2715: 0,
2716: 0,
2717: /*119*/ 0,
2718: 0,
2719: MatMultHermitianTranspose_SeqBAIJ,
2720: MatMultHermitianTransposeAdd_SeqBAIJ,
2721: 0,
2722: /*124*/ 0,
2723: 0,
2724: MatInvertBlockDiagonal_SeqBAIJ,
2725: 0,
2726: 0,
2727: /*129*/ 0,
2728: 0,
2729: 0,
2730: 0,
2731: 0,
2732: /*134*/ 0,
2733: 0,
2734: 0,
2735: 0,
2736: 0,
2737: /*139*/ MatSetBlockSizes_Default,
2738: 0,
2739: 0,
2740: MatFDColoringSetUp_SeqXAIJ,
2741: 0,
2742: /*144*/MatCreateMPIMatConcatenateSeqMat_SeqBAIJ,
2743: MatDestroySubMatrices_SeqBAIJ
2744: };
2746: PetscErrorCode MatStoreValues_SeqBAIJ(Mat mat)
2747: {
2748: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ*)mat->data;
2749: PetscInt nz = aij->i[aij->mbs]*aij->bs2;
2753: if (aij->nonew != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
2755: /* allocate space for values if not already there */
2756: if (!aij->saved_values) {
2757: PetscMalloc1(nz+1,&aij->saved_values);
2758: PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
2759: }
2761: /* copy values over */
2762: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2763: return(0);
2764: }
2766: PetscErrorCode MatRetrieveValues_SeqBAIJ(Mat mat)
2767: {
2768: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ*)mat->data;
2770: PetscInt nz = aij->i[aij->mbs]*aij->bs2;
2773: if (aij->nonew != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
2774: if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2776: /* copy values over */
2777: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2778: return(0);
2779: }
2781: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqAIJ(Mat, MatType,MatReuse,Mat*);
2782: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqSBAIJ(Mat, MatType,MatReuse,Mat*);
2784: PetscErrorCode MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B,PetscInt bs,PetscInt nz,PetscInt *nnz)
2785: {
2786: Mat_SeqBAIJ *b;
2788: PetscInt i,mbs,nbs,bs2;
2789: PetscBool flg = PETSC_FALSE,skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
2792: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
2793: if (nz == MAT_SKIP_ALLOCATION) {
2794: skipallocation = PETSC_TRUE;
2795: nz = 0;
2796: }
2798: MatSetBlockSize(B,PetscAbs(bs));
2799: PetscLayoutSetUp(B->rmap);
2800: PetscLayoutSetUp(B->cmap);
2801: PetscLayoutGetBlockSize(B->rmap,&bs);
2803: B->preallocated = PETSC_TRUE;
2805: mbs = B->rmap->n/bs;
2806: nbs = B->cmap->n/bs;
2807: bs2 = bs*bs;
2809: if (mbs*bs!=B->rmap->n || nbs*bs!=B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number rows %D, cols %D must be divisible by blocksize %D",B->rmap->N,B->cmap->n,bs);
2811: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2812: if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
2813: if (nnz) {
2814: for (i=0; i<mbs; i++) {
2815: if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
2816: if (nnz[i] > nbs) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than block row length: local row %D value %D rowlength %D",i,nnz[i],nbs);
2817: }
2818: }
2820: b = (Mat_SeqBAIJ*)B->data;
2821: PetscOptionsBegin(PetscObjectComm((PetscObject)B),NULL,"Optimize options for SEQBAIJ matrix 2 ","Mat");
2822: PetscOptionsBool("-mat_no_unroll","Do not optimize for block size (slow)",NULL,flg,&flg,NULL);
2823: PetscOptionsEnd();
2825: if (!flg) {
2826: switch (bs) {
2827: case 1:
2828: B->ops->mult = MatMult_SeqBAIJ_1;
2829: B->ops->multadd = MatMultAdd_SeqBAIJ_1;
2830: break;
2831: case 2:
2832: B->ops->mult = MatMult_SeqBAIJ_2;
2833: B->ops->multadd = MatMultAdd_SeqBAIJ_2;
2834: break;
2835: case 3:
2836: B->ops->mult = MatMult_SeqBAIJ_3;
2837: B->ops->multadd = MatMultAdd_SeqBAIJ_3;
2838: break;
2839: case 4:
2840: B->ops->mult = MatMult_SeqBAIJ_4;
2841: B->ops->multadd = MatMultAdd_SeqBAIJ_4;
2842: break;
2843: case 5:
2844: B->ops->mult = MatMult_SeqBAIJ_5;
2845: B->ops->multadd = MatMultAdd_SeqBAIJ_5;
2846: break;
2847: case 6:
2848: B->ops->mult = MatMult_SeqBAIJ_6;
2849: B->ops->multadd = MatMultAdd_SeqBAIJ_6;
2850: break;
2851: case 7:
2852: B->ops->mult = MatMult_SeqBAIJ_7;
2853: B->ops->multadd = MatMultAdd_SeqBAIJ_7;
2854: break;
2855: case 9:
2856: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
2857: B->ops->mult = MatMult_SeqBAIJ_9_AVX2;
2858: B->ops->multadd = MatMultAdd_SeqBAIJ_9_AVX2;
2859: #else
2860: B->ops->mult = MatMult_SeqBAIJ_N;
2861: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
2862: #endif
2863: break;
2864: case 11:
2865: B->ops->mult = MatMult_SeqBAIJ_11;
2866: B->ops->multadd = MatMultAdd_SeqBAIJ_11;
2867: break;
2868: case 15:
2869: B->ops->mult = MatMult_SeqBAIJ_15_ver1;
2870: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
2871: break;
2872: default:
2873: B->ops->mult = MatMult_SeqBAIJ_N;
2874: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
2875: break;
2876: }
2877: }
2878: B->ops->sor = MatSOR_SeqBAIJ;
2879: b->mbs = mbs;
2880: b->nbs = nbs;
2881: if (!skipallocation) {
2882: if (!b->imax) {
2883: PetscMalloc2(mbs,&b->imax,mbs,&b->ilen);
2884: PetscLogObjectMemory((PetscObject)B,2*mbs*sizeof(PetscInt));
2886: b->free_imax_ilen = PETSC_TRUE;
2887: }
2888: /* b->ilen will count nonzeros in each block row so far. */
2889: for (i=0; i<mbs; i++) b->ilen[i] = 0;
2890: if (!nnz) {
2891: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2892: else if (nz < 0) nz = 1;
2893: for (i=0; i<mbs; i++) b->imax[i] = nz;
2894: nz = nz*mbs;
2895: } else {
2896: nz = 0;
2897: for (i=0; i<mbs; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2898: }
2900: /* allocate the matrix space */
2901: MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
2902: if (B->structure_only) {
2903: PetscMalloc1(nz,&b->j);
2904: PetscMalloc1(B->rmap->N+1,&b->i);
2905: PetscLogObjectMemory((PetscObject)B,(B->rmap->N+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
2906: } else {
2907: PetscMalloc3(bs2*nz,&b->a,nz,&b->j,B->rmap->N+1,&b->i);
2908: PetscLogObjectMemory((PetscObject)B,(B->rmap->N+1)*sizeof(PetscInt)+nz*(bs2*sizeof(PetscScalar)+sizeof(PetscInt)));
2909: PetscMemzero(b->a,nz*bs2*sizeof(MatScalar));
2910: }
2911: PetscMemzero(b->j,nz*sizeof(PetscInt));
2913: if (B->structure_only) {
2914: b->singlemalloc = PETSC_FALSE;
2915: b->free_a = PETSC_FALSE;
2916: } else {
2917: b->singlemalloc = PETSC_TRUE;
2918: b->free_a = PETSC_TRUE;
2919: }
2920: b->free_ij = PETSC_TRUE;
2922: b->i[0] = 0;
2923: for (i=1; i<mbs+1; i++) {
2924: b->i[i] = b->i[i-1] + b->imax[i-1];
2925: }
2927: } else {
2928: b->free_a = PETSC_FALSE;
2929: b->free_ij = PETSC_FALSE;
2930: }
2932: b->bs2 = bs2;
2933: b->mbs = mbs;
2934: b->nz = 0;
2935: b->maxnz = nz;
2936: B->info.nz_unneeded = (PetscReal)b->maxnz*bs2;
2937: B->was_assembled = PETSC_FALSE;
2938: B->assembled = PETSC_FALSE;
2939: if (realalloc) {MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);}
2940: return(0);
2941: }
2943: PetscErrorCode MatSeqBAIJSetPreallocationCSR_SeqBAIJ(Mat B,PetscInt bs,const PetscInt ii[],const PetscInt jj[],const PetscScalar V[])
2944: {
2945: PetscInt i,m,nz,nz_max=0,*nnz;
2946: PetscScalar *values=0;
2947: PetscBool roworiented = ((Mat_SeqBAIJ*)B->data)->roworiented;
2951: if (bs < 1) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive but it is %D",bs);
2952: PetscLayoutSetBlockSize(B->rmap,bs);
2953: PetscLayoutSetBlockSize(B->cmap,bs);
2954: PetscLayoutSetUp(B->rmap);
2955: PetscLayoutSetUp(B->cmap);
2956: PetscLayoutGetBlockSize(B->rmap,&bs);
2957: m = B->rmap->n/bs;
2959: if (ii[0] != 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %D",ii[0]);
2960: PetscMalloc1(m+1, &nnz);
2961: for (i=0; i<m; i++) {
2962: nz = ii[i+1]- ii[i];
2963: if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D",i,nz);
2964: nz_max = PetscMax(nz_max, nz);
2965: nnz[i] = nz;
2966: }
2967: MatSeqBAIJSetPreallocation(B,bs,0,nnz);
2968: PetscFree(nnz);
2970: values = (PetscScalar*)V;
2971: if (!values) {
2972: PetscCalloc1(bs*bs*(nz_max+1),&values);
2973: }
2974: for (i=0; i<m; i++) {
2975: PetscInt ncols = ii[i+1] - ii[i];
2976: const PetscInt *icols = jj + ii[i];
2977: const PetscScalar *svals = values + (V ? (bs*bs*ii[i]) : 0);
2978: if (!roworiented) {
2979: MatSetValuesBlocked_SeqBAIJ(B,1,&i,ncols,icols,svals,INSERT_VALUES);
2980: } else {
2981: PetscInt j;
2982: for (j=0; j<ncols; j++) {
2983: const PetscScalar *svals = values + (V ? (bs*bs*(ii[i]+j)) : 0);
2984: MatSetValuesBlocked_SeqBAIJ(B,1,&i,1,&icols[j],svals,INSERT_VALUES);
2985: }
2986: }
2987: }
2988: if (!V) { PetscFree(values); }
2989: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2990: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2991: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
2992: return(0);
2993: }
2995: /*MC
2996: MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on
2997: block sparse compressed row format.
2999: Options Database Keys:
3000: . -mat_type seqbaij - sets the matrix type to "seqbaij" during a call to MatSetFromOptions()
3002: Level: beginner
3004: .seealso: MatCreateSeqBAIJ()
3005: M*/
3007: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBSTRM(Mat, MatType,MatReuse,Mat*);
3009: PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJ(Mat B)
3010: {
3012: PetscMPIInt size;
3013: Mat_SeqBAIJ *b;
3016: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
3017: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Comm must be of size 1");
3019: PetscNewLog(B,&b);
3020: B->data = (void*)b;
3021: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3023: b->row = 0;
3024: b->col = 0;
3025: b->icol = 0;
3026: b->reallocs = 0;
3027: b->saved_values = 0;
3029: b->roworiented = PETSC_TRUE;
3030: b->nonew = 0;
3031: b->diag = 0;
3032: B->spptr = 0;
3033: B->info.nz_unneeded = (PetscReal)b->maxnz*b->bs2;
3034: b->keepnonzeropattern = PETSC_FALSE;
3036: PetscObjectComposeFunction((PetscObject)B,"MatInvertBlockDiagonal_C",MatInvertBlockDiagonal_SeqBAIJ);
3037: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqBAIJ);
3038: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqBAIJ);
3039: PetscObjectComposeFunction((PetscObject)B,"MatSeqBAIJSetColumnIndices_C",MatSeqBAIJSetColumnIndices_SeqBAIJ);
3040: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqbaij_seqaij_C",MatConvert_SeqBAIJ_SeqAIJ);
3041: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqbaij_seqsbaij_C",MatConvert_SeqBAIJ_SeqSBAIJ);
3042: PetscObjectComposeFunction((PetscObject)B,"MatSeqBAIJSetPreallocation_C",MatSeqBAIJSetPreallocation_SeqBAIJ);
3043: PetscObjectComposeFunction((PetscObject)B,"MatSeqBAIJSetPreallocationCSR_C",MatSeqBAIJSetPreallocationCSR_SeqBAIJ);
3044: PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqBAIJ);
3045: #if defined(PETSC_HAVE_HYPRE)
3046: PetscObjectComposeFunction((PetscObject)B,"MatConvert_baij_hypre_C",MatConvert_AIJ_HYPRE);
3047: #endif
3048: PetscObjectChangeTypeName((PetscObject)B,MATSEQBAIJ);
3049: return(0);
3050: }
3052: PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
3053: {
3054: Mat_SeqBAIJ *c = (Mat_SeqBAIJ*)C->data,*a = (Mat_SeqBAIJ*)A->data;
3056: PetscInt i,mbs = a->mbs,nz = a->nz,bs2 = a->bs2;
3059: if (a->i[mbs] != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Corrupt matrix");
3061: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3062: c->imax = a->imax;
3063: c->ilen = a->ilen;
3064: c->free_imax_ilen = PETSC_FALSE;
3065: } else {
3066: PetscMalloc2(mbs,&c->imax,mbs,&c->ilen);
3067: PetscLogObjectMemory((PetscObject)C,2*mbs*sizeof(PetscInt));
3068: for (i=0; i<mbs; i++) {
3069: c->imax[i] = a->imax[i];
3070: c->ilen[i] = a->ilen[i];
3071: }
3072: c->free_imax_ilen = PETSC_TRUE;
3073: }
3075: /* allocate the matrix space */
3076: if (mallocmatspace) {
3077: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3078: PetscCalloc1(bs2*nz,&c->a);
3079: PetscLogObjectMemory((PetscObject)C,a->i[mbs]*bs2*sizeof(PetscScalar));
3081: c->i = a->i;
3082: c->j = a->j;
3083: c->singlemalloc = PETSC_FALSE;
3084: c->free_a = PETSC_TRUE;
3085: c->free_ij = PETSC_FALSE;
3086: c->parent = A;
3087: C->preallocated = PETSC_TRUE;
3088: C->assembled = PETSC_TRUE;
3090: PetscObjectReference((PetscObject)A);
3091: MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3092: MatSetOption(C,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3093: } else {
3094: PetscMalloc3(bs2*nz,&c->a,nz,&c->j,mbs+1,&c->i);
3095: PetscLogObjectMemory((PetscObject)C,a->i[mbs]*(bs2*sizeof(PetscScalar)+sizeof(PetscInt))+(mbs+1)*sizeof(PetscInt));
3097: c->singlemalloc = PETSC_TRUE;
3098: c->free_a = PETSC_TRUE;
3099: c->free_ij = PETSC_TRUE;
3101: PetscMemcpy(c->i,a->i,(mbs+1)*sizeof(PetscInt));
3102: if (mbs > 0) {
3103: PetscMemcpy(c->j,a->j,nz*sizeof(PetscInt));
3104: if (cpvalues == MAT_COPY_VALUES) {
3105: PetscMemcpy(c->a,a->a,bs2*nz*sizeof(MatScalar));
3106: } else {
3107: PetscMemzero(c->a,bs2*nz*sizeof(MatScalar));
3108: }
3109: }
3110: C->preallocated = PETSC_TRUE;
3111: C->assembled = PETSC_TRUE;
3112: }
3113: }
3115: c->roworiented = a->roworiented;
3116: c->nonew = a->nonew;
3118: PetscLayoutReference(A->rmap,&C->rmap);
3119: PetscLayoutReference(A->cmap,&C->cmap);
3121: c->bs2 = a->bs2;
3122: c->mbs = a->mbs;
3123: c->nbs = a->nbs;
3125: if (a->diag) {
3126: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3127: c->diag = a->diag;
3128: c->free_diag = PETSC_FALSE;
3129: } else {
3130: PetscMalloc1(mbs+1,&c->diag);
3131: PetscLogObjectMemory((PetscObject)C,(mbs+1)*sizeof(PetscInt));
3132: for (i=0; i<mbs; i++) c->diag[i] = a->diag[i];
3133: c->free_diag = PETSC_TRUE;
3134: }
3135: } else c->diag = 0;
3137: c->nz = a->nz;
3138: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
3139: c->solve_work = NULL;
3140: c->mult_work = NULL;
3141: c->sor_workt = NULL;
3142: c->sor_work = NULL;
3144: c->compressedrow.use = a->compressedrow.use;
3145: c->compressedrow.nrows = a->compressedrow.nrows;
3146: if (a->compressedrow.use) {
3147: i = a->compressedrow.nrows;
3148: PetscMalloc2(i+1,&c->compressedrow.i,i+1,&c->compressedrow.rindex);
3149: PetscLogObjectMemory((PetscObject)C,(2*i+1)*sizeof(PetscInt));
3150: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
3151: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
3152: } else {
3153: c->compressedrow.use = PETSC_FALSE;
3154: c->compressedrow.i = NULL;
3155: c->compressedrow.rindex = NULL;
3156: }
3157: C->nonzerostate = A->nonzerostate;
3159: PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
3160: return(0);
3161: }
3163: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
3164: {
3168: MatCreate(PetscObjectComm((PetscObject)A),B);
3169: MatSetSizes(*B,A->rmap->N,A->cmap->n,A->rmap->N,A->cmap->n);
3170: MatSetType(*B,MATSEQBAIJ);
3171: MatDuplicateNoCreate_SeqBAIJ(*B,A,cpvalues,PETSC_TRUE);
3172: return(0);
3173: }
3175: PetscErrorCode MatLoad_SeqBAIJ(Mat newmat,PetscViewer viewer)
3176: {
3177: Mat_SeqBAIJ *a;
3179: PetscInt i,nz,header[4],*rowlengths=0,M,N,bs = newmat->rmap->bs;
3180: PetscInt *mask,mbs,*jj,j,rowcount,nzcount,k,*browlengths,maskcount;
3181: PetscInt kmax,jcount,block,idx,point,nzcountb,extra_rows,rows,cols;
3182: PetscInt *masked,nmask,tmp,bs2,ishift;
3183: PetscMPIInt size;
3184: int fd;
3185: PetscScalar *aa;
3186: MPI_Comm comm;
3189: /* force binary viewer to load .info file if it has not yet done so */
3190: PetscViewerSetUp(viewer);
3191: PetscObjectGetComm((PetscObject)viewer,&comm);
3192: PetscOptionsBegin(comm,NULL,"Options for loading SEQBAIJ matrix","Mat");
3193: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
3194: PetscOptionsEnd();
3195: if (bs < 0) bs = 1;
3196: bs2 = bs*bs;
3198: MPI_Comm_size(comm,&size);
3199: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"view must have one processor");
3200: PetscViewerBinaryGetDescriptor(viewer,&fd);
3201: PetscBinaryRead(fd,header,4,PETSC_INT);
3202: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not Mat object");
3203: M = header[1]; N = header[2]; nz = header[3];
3205: if (header[3] < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as SeqBAIJ");
3206: if (M != N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Can only do square matrices");
3208: /*
3209: This code adds extra rows to make sure the number of rows is
3210: divisible by the blocksize
3211: */
3212: mbs = M/bs;
3213: extra_rows = bs - M + bs*(mbs);
3214: if (extra_rows == bs) extra_rows = 0;
3215: else mbs++;
3216: if (extra_rows) {
3217: PetscInfo(viewer,"Padding loaded matrix to match blocksize\n");
3218: }
3220: /* Set global sizes if not already set */
3221: if (newmat->rmap->n < 0 && newmat->rmap->N < 0 && newmat->cmap->n < 0 && newmat->cmap->N < 0) {
3222: MatSetSizes(newmat,PETSC_DECIDE,PETSC_DECIDE,M+extra_rows,N+extra_rows);
3223: } else { /* Check if the matrix global sizes are correct */
3224: MatGetSize(newmat,&rows,&cols);
3225: if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
3226: MatGetLocalSize(newmat,&rows,&cols);
3227: }
3228: if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix in file of different length (%d, %d) than the input matrix (%d, %d)",M,N,rows,cols);
3229: }
3231: /* read in row lengths */
3232: PetscMalloc1(M+extra_rows,&rowlengths);
3233: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
3234: for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
3236: /* read in column indices */
3237: PetscMalloc1(nz+extra_rows,&jj);
3238: PetscBinaryRead(fd,jj,nz,PETSC_INT);
3239: for (i=0; i<extra_rows; i++) jj[nz+i] = M+i;
3241: /* loop over row lengths determining block row lengths */
3242: PetscCalloc1(mbs,&browlengths);
3243: PetscMalloc2(mbs,&mask,mbs,&masked);
3244: PetscMemzero(mask,mbs*sizeof(PetscInt));
3245: rowcount = 0;
3246: nzcount = 0;
3247: for (i=0; i<mbs; i++) {
3248: nmask = 0;
3249: for (j=0; j<bs; j++) {
3250: kmax = rowlengths[rowcount];
3251: for (k=0; k<kmax; k++) {
3252: tmp = jj[nzcount++]/bs;
3253: if (!mask[tmp]) {masked[nmask++] = tmp; mask[tmp] = 1;}
3254: }
3255: rowcount++;
3256: }
3257: browlengths[i] += nmask;
3258: /* zero out the mask elements we set */
3259: for (j=0; j<nmask; j++) mask[masked[j]] = 0;
3260: }
3262: /* Do preallocation */
3263: MatSeqBAIJSetPreallocation(newmat,bs,0,browlengths);
3264: a = (Mat_SeqBAIJ*)newmat->data;
3266: /* set matrix "i" values */
3267: a->i[0] = 0;
3268: for (i=1; i<= mbs; i++) {
3269: a->i[i] = a->i[i-1] + browlengths[i-1];
3270: a->ilen[i-1] = browlengths[i-1];
3271: }
3272: a->nz = 0;
3273: for (i=0; i<mbs; i++) a->nz += browlengths[i];
3275: /* read in nonzero values */
3276: PetscMalloc1(nz+extra_rows,&aa);
3277: PetscBinaryRead(fd,aa,nz,PETSC_SCALAR);
3278: for (i=0; i<extra_rows; i++) aa[nz+i] = 1.0;
3280: /* set "a" and "j" values into matrix */
3281: nzcount = 0; jcount = 0;
3282: for (i=0; i<mbs; i++) {
3283: nzcountb = nzcount;
3284: nmask = 0;
3285: for (j=0; j<bs; j++) {
3286: kmax = rowlengths[i*bs+j];
3287: for (k=0; k<kmax; k++) {
3288: tmp = jj[nzcount++]/bs;
3289: if (!mask[tmp]) { masked[nmask++] = tmp; mask[tmp] = 1;}
3290: }
3291: }
3292: /* sort the masked values */
3293: PetscSortInt(nmask,masked);
3295: /* set "j" values into matrix */
3296: maskcount = 1;
3297: for (j=0; j<nmask; j++) {
3298: a->j[jcount++] = masked[j];
3299: mask[masked[j]] = maskcount++;
3300: }
3301: /* set "a" values into matrix */
3302: ishift = bs2*a->i[i];
3303: for (j=0; j<bs; j++) {
3304: kmax = rowlengths[i*bs+j];
3305: for (k=0; k<kmax; k++) {
3306: tmp = jj[nzcountb]/bs;
3307: block = mask[tmp] - 1;
3308: point = jj[nzcountb] - bs*tmp;
3309: idx = ishift + bs2*block + j + bs*point;
3310: a->a[idx] = (MatScalar)aa[nzcountb++];
3311: }
3312: }
3313: /* zero out the mask elements we set */
3314: for (j=0; j<nmask; j++) mask[masked[j]] = 0;
3315: }
3316: if (jcount != a->nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Bad binary matrix");
3318: PetscFree(rowlengths);
3319: PetscFree(browlengths);
3320: PetscFree(aa);
3321: PetscFree(jj);
3322: PetscFree2(mask,masked);
3324: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
3325: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
3326: return(0);
3327: }
3329: /*@C
3330: MatCreateSeqBAIJ - Creates a sparse matrix in block AIJ (block
3331: compressed row) format. For good matrix assembly performance the
3332: user should preallocate the matrix storage by setting the parameter nz
3333: (or the array nnz). By setting these parameters accurately, performance
3334: during matrix assembly can be increased by more than a factor of 50.
3336: Collective on MPI_Comm
3338: Input Parameters:
3339: + comm - MPI communicator, set to PETSC_COMM_SELF
3340: . bs - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row
3341: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs()
3342: . m - number of rows
3343: . n - number of columns
3344: . nz - number of nonzero blocks per block row (same for all rows)
3345: - nnz - array containing the number of nonzero blocks in the various block rows
3346: (possibly different for each block row) or NULL
3348: Output Parameter:
3349: . A - the matrix
3351: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3352: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3353: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3355: Options Database Keys:
3356: . -mat_no_unroll - uses code that does not unroll the loops in the
3357: block calculations (much slower)
3358: . -mat_block_size - size of the blocks to use
3360: Level: intermediate
3362: Notes:
3363: The number of rows and columns must be divisible by blocksize.
3365: If the nnz parameter is given then the nz parameter is ignored
3367: A nonzero block is any block that as 1 or more nonzeros in it
3369: The block AIJ format is fully compatible with standard Fortran 77
3370: storage. That is, the stored row and column indices can begin at
3371: either one (as in Fortran) or zero. See the users' manual for details.
3373: Specify the preallocated storage with either nz or nnz (not both).
3374: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3375: allocation. See Users-Manual: ch_mat for details.
3376: matrices.
3378: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateBAIJ()
3379: @*/
3380: PetscErrorCode MatCreateSeqBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3381: {
3385: MatCreate(comm,A);
3386: MatSetSizes(*A,m,n,m,n);
3387: MatSetType(*A,MATSEQBAIJ);
3388: MatSeqBAIJSetPreallocation(*A,bs,nz,(PetscInt*)nnz);
3389: return(0);
3390: }
3392: /*@C
3393: MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
3394: per row in the matrix. For good matrix assembly performance the
3395: user should preallocate the matrix storage by setting the parameter nz
3396: (or the array nnz). By setting these parameters accurately, performance
3397: during matrix assembly can be increased by more than a factor of 50.
3399: Collective on MPI_Comm
3401: Input Parameters:
3402: + B - the matrix
3403: . bs - size of block, the blocks are ALWAYS square. One can use MatSetBlockSizes() to set a different row and column blocksize but the row
3404: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with MatCreateVecs()
3405: . nz - number of block nonzeros per block row (same for all rows)
3406: - nnz - array containing the number of block nonzeros in the various block rows
3407: (possibly different for each block row) or NULL
3409: Options Database Keys:
3410: . -mat_no_unroll - uses code that does not unroll the loops in the
3411: block calculations (much slower)
3412: . -mat_block_size - size of the blocks to use
3414: Level: intermediate
3416: Notes:
3417: If the nnz parameter is given then the nz parameter is ignored
3419: You can call MatGetInfo() to get information on how effective the preallocation was;
3420: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3421: You can also run with the option -info and look for messages with the string
3422: malloc in them to see if additional memory allocation was needed.
3424: The block AIJ format is fully compatible with standard Fortran 77
3425: storage. That is, the stored row and column indices can begin at
3426: either one (as in Fortran) or zero. See the users' manual for details.
3428: Specify the preallocated storage with either nz or nnz (not both).
3429: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3430: allocation. See Users-Manual: ch_mat for details.
3432: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateBAIJ(), MatGetInfo()
3433: @*/
3434: PetscErrorCode MatSeqBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt nz,const PetscInt nnz[])
3435: {
3442: PetscTryMethod(B,"MatSeqBAIJSetPreallocation_C",(Mat,PetscInt,PetscInt,const PetscInt[]),(B,bs,nz,nnz));
3443: return(0);
3444: }
3446: /*@C
3447: MatSeqBAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format
3448: (the default sequential PETSc format).
3450: Collective on MPI_Comm
3452: Input Parameters:
3453: + B - the matrix
3454: . i - the indices into j for the start of each local row (starts with zero)
3455: . j - the column indices for each local row (starts with zero) these must be sorted for each row
3456: - v - optional values in the matrix
3458: Level: developer
3460: Notes:
3461: The order of the entries in values is specified by the MatOption MAT_ROW_ORIENTED. For example, C programs
3462: may want to use the default MAT_ROW_ORIENTED=PETSC_TRUE and use an array v[nnz][bs][bs] where the second index is
3463: over rows within a block and the last index is over columns within a block row. Fortran programs will likely set
3464: MAT_ROW_ORIENTED=PETSC_FALSE and use a Fortran array v(bs,bs,nnz) in which the first index is over rows within a
3465: block column and the second index is over columns within a block.
3467: .keywords: matrix, aij, compressed row, sparse
3469: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatSeqBAIJSetPreallocation(), MATSEQBAIJ
3470: @*/
3471: PetscErrorCode MatSeqBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
3472: {
3479: PetscTryMethod(B,"MatSeqBAIJSetPreallocationCSR_C",(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,bs,i,j,v));
3480: return(0);
3481: }
3484: /*@
3485: MatCreateSeqBAIJWithArrays - Creates an sequential BAIJ matrix using matrix elements provided by the user.
3487: Collective on MPI_Comm
3489: Input Parameters:
3490: + comm - must be an MPI communicator of size 1
3491: . bs - size of block
3492: . m - number of rows
3493: . n - number of columns
3494: . i - row indices
3495: . j - column indices
3496: - a - matrix values
3498: Output Parameter:
3499: . mat - the matrix
3501: Level: advanced
3503: Notes:
3504: The i, j, and a arrays are not copied by this routine, the user must free these arrays
3505: once the matrix is destroyed
3507: You cannot set new nonzero locations into this matrix, that will generate an error.
3509: The i and j indices are 0 based
3511: When block size is greater than 1 the matrix values must be stored using the BAIJ storage format (see the BAIJ code to determine this).
3513: The order of the entries in values is the same as the block compressed sparse row storage format; that is, it is
3514: the same as a three dimensional array in Fortran values(bs,bs,nnz) that contains the first column of the first
3515: block, followed by the second column of the first block etc etc. That is, the blocks are contiguous in memory
3516: with column-major ordering within blocks.
3518: .seealso: MatCreate(), MatCreateBAIJ(), MatCreateSeqBAIJ()
3520: @*/
3521: PetscErrorCode MatCreateSeqBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
3522: {
3524: PetscInt ii;
3525: Mat_SeqBAIJ *baij;
3528: if (bs != 1) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"block size %D > 1 is not supported yet",bs);
3529: if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3531: MatCreate(comm,mat);
3532: MatSetSizes(*mat,m,n,m,n);
3533: MatSetType(*mat,MATSEQBAIJ);
3534: MatSeqBAIJSetPreallocation(*mat,bs,MAT_SKIP_ALLOCATION,0);
3535: baij = (Mat_SeqBAIJ*)(*mat)->data;
3536: PetscMalloc2(m,&baij->imax,m,&baij->ilen);
3537: PetscLogObjectMemory((PetscObject)*mat,2*m*sizeof(PetscInt));
3539: baij->i = i;
3540: baij->j = j;
3541: baij->a = a;
3543: baij->singlemalloc = PETSC_FALSE;
3544: baij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3545: baij->free_a = PETSC_FALSE;
3546: baij->free_ij = PETSC_FALSE;
3548: for (ii=0; ii<m; ii++) {
3549: baij->ilen[ii] = baij->imax[ii] = i[ii+1] - i[ii];
3550: #if defined(PETSC_USE_DEBUG)
3551: if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
3552: #endif
3553: }
3554: #if defined(PETSC_USE_DEBUG)
3555: for (ii=0; ii<baij->i[m]; ii++) {
3556: if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
3557: if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
3558: }
3559: #endif
3561: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
3562: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
3563: return(0);
3564: }
3566: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqBAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
3567: {
3569: PetscMPIInt size;
3572: MPI_Comm_size(comm,&size);
3573: if (size == 1 && scall == MAT_REUSE_MATRIX) {
3574: MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
3575: } else {
3576: MatCreateMPIMatConcatenateSeqMat_MPIBAIJ(comm,inmat,n,scall,outmat);
3577: }
3578: return(0);
3579: }