Actual source code: baij.c
2: /*
3: Defines the basic matrix operations for the BAIJ (compressed row)
4: matrix storage format.
5: */
6: #include <../src/mat/impls/baij/seq/baij.h> /*I "petscmat.h" I*/
7: #include <petscblaslapack.h>
8: #include <../src/mat/blockinvert.h>
13: PetscErrorCode MatInvertBlockDiagonal_SeqBAIJ(Mat A,PetscScalar **values)
14: {
15: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*) A->data;
17: PetscInt *diag_offset,i,bs = A->rmap->bs,mbs = a->mbs,ipvt[5],bs2 = bs*bs,*v_pivots;
18: MatScalar *v = a->a,*odiag,*diag,*mdiag,work[25],*v_work;
19: PetscReal shift = 0.0;
22: if (a->idiagvalid) {
23: if (values)*values = a->idiag;
24: return(0);
25: }
26: MatMarkDiagonal_SeqBAIJ(A);
27: diag_offset = a->diag;
28: if (!a->idiag) {
29: PetscMalloc(2*bs2*mbs*sizeof(PetscScalar),&a->idiag);
30: PetscLogObjectMemory(A,2*bs2*mbs*sizeof(PetscScalar));
31: }
32: diag = a->idiag;
33: mdiag = a->idiag+bs2*mbs;
34: if (values) *values = a->idiag;
35: /* factor and invert each block */
36: switch (bs){
37: case 1:
38: for (i=0; i<mbs; i++) {
39: odiag = v + 1*diag_offset[i];
40: diag[0] = odiag[0];
41: mdiag[0] = odiag[0];
42: diag[0] = (PetscScalar)1.0 / (diag[0] + shift);
43: diag += 1;
44: mdiag += 1;
45: }
46: break;
47: case 2:
48: for (i=0; i<mbs; i++) {
49: odiag = v + 4*diag_offset[i];
50: diag[0] = odiag[0]; diag[1] = odiag[1]; diag[2] = odiag[2]; diag[3] = odiag[3];
51: mdiag[0] = odiag[0]; mdiag[1] = odiag[1]; mdiag[2] = odiag[2]; mdiag[3] = odiag[3];
52: Kernel_A_gets_inverse_A_2(diag,shift);
53: diag += 4;
54: mdiag += 4;
55: }
56: break;
57: case 3:
58: for (i=0; i<mbs; i++) {
59: odiag = v + 9*diag_offset[i];
60: diag[0] = odiag[0]; diag[1] = odiag[1]; diag[2] = odiag[2]; diag[3] = odiag[3];
61: diag[4] = odiag[4]; diag[5] = odiag[5]; diag[6] = odiag[6]; diag[7] = odiag[7];
62: diag[8] = odiag[8];
63: mdiag[0] = odiag[0]; mdiag[1] = odiag[1]; mdiag[2] = odiag[2]; mdiag[3] = odiag[3];
64: mdiag[4] = odiag[4]; mdiag[5] = odiag[5]; mdiag[6] = odiag[6]; mdiag[7] = odiag[7];
65: mdiag[8] = odiag[8];
66: Kernel_A_gets_inverse_A_3(diag,shift);
67: diag += 9;
68: mdiag += 9;
69: }
70: break;
71: case 4:
72: for (i=0; i<mbs; i++) {
73: odiag = v + 16*diag_offset[i];
74: PetscMemcpy(diag,odiag,16*sizeof(PetscScalar));
75: PetscMemcpy(mdiag,odiag,16*sizeof(PetscScalar));
76: Kernel_A_gets_inverse_A_4(diag,shift);
77: diag += 16;
78: mdiag += 16;
79: }
80: break;
81: case 5:
82: for (i=0; i<mbs; i++) {
83: odiag = v + 25*diag_offset[i];
84: PetscMemcpy(diag,odiag,25*sizeof(PetscScalar));
85: PetscMemcpy(mdiag,odiag,25*sizeof(PetscScalar));
86: Kernel_A_gets_inverse_A_5(diag,ipvt,work,shift);
87: diag += 25;
88: mdiag += 25;
89: }
90: break;
91: case 6:
92: for (i=0; i<mbs; i++) {
93: odiag = v + 36*diag_offset[i];
94: PetscMemcpy(diag,odiag,36*sizeof(PetscScalar));
95: PetscMemcpy(mdiag,odiag,36*sizeof(PetscScalar));
96: Kernel_A_gets_inverse_A_6(diag,shift);
97: diag += 36;
98: mdiag += 36;
99: }
100: break;
101: case 7:
102: for (i=0; i<mbs; i++) {
103: odiag = v + 49*diag_offset[i];
104: PetscMemcpy(diag,odiag,49*sizeof(PetscScalar));
105: PetscMemcpy(mdiag,odiag,49*sizeof(PetscScalar));
106: Kernel_A_gets_inverse_A_7(diag,shift);
107: diag += 49;
108: mdiag += 49;
109: }
110: break;
111: default:
112: PetscMalloc2(bs,MatScalar,&v_work,bs,PetscInt,&v_pivots);
113: for (i=0; i<mbs; i++) {
114: odiag = v + bs2*diag_offset[i];
115: PetscMemcpy(diag,odiag,bs2*sizeof(PetscScalar));
116: PetscMemcpy(mdiag,odiag,bs2*sizeof(PetscScalar));
117: Kernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);
118: diag += bs2;
119: mdiag += bs2;
120: }
121: PetscFree2(v_work,v_pivots);
122: }
123: a->idiagvalid = PETSC_TRUE;
124: return(0);
125: }
129: PetscErrorCode MatSOR_SeqBAIJ_1(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
130: {
131: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
132: PetscScalar *x,x1,s1;
133: const PetscScalar *b;
134: const MatScalar *aa = a->a, *idiag,*mdiag,*v;
135: PetscErrorCode ierr;
136: PetscInt m = a->mbs,i,i2,nz,j;
137: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
140: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
141: its = its*lits;
142: if (its <= 0) SETERRQ2(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
143: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
144: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
145: 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");
146: if (its > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
148: if (!a->idiagvalid){MatInvertBlockDiagonal(A,PETSC_NULL);}
150: diag = a->diag;
151: idiag = a->idiag;
152: VecGetArray(xx,&x);
153: VecGetArrayRead(bb,&b);
155: if (flag & SOR_ZERO_INITIAL_GUESS) {
156: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
157: x[0] = b[0]*idiag[0];
158: i2 = 1;
159: idiag += 1;
160: for (i=1; i<m; i++) {
161: v = aa + ai[i];
162: vi = aj + ai[i];
163: nz = diag[i] - ai[i];
164: s1 = b[i2];
165: for (j=0; j<nz; j++) {
166: s1 -= v[j]*x[vi[j]];
167: }
168: x[i2] = idiag[0]*s1;
169: idiag += 1;
170: i2 += 1;
171: }
172: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
173: PetscLogFlops(a->nz);
174: }
175: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
176: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
177: i2 = 0;
178: mdiag = a->idiag+a->mbs;
179: for (i=0; i<m; i++) {
180: x1 = x[i2];
181: x[i2] = mdiag[0]*x1;
182: mdiag += 1;
183: i2 += 1;
184: }
185: PetscLogFlops(m);
186: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
187: PetscMemcpy(x,b,A->rmap->N*sizeof(PetscScalar));
188: }
189: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
190: idiag = a->idiag+a->mbs - 1;
191: i2 = m - 1;
192: x1 = x[i2];
193: x[i2] = idiag[0]*x1;
194: idiag -= 1;
195: i2 -= 1;
196: for (i=m-2; i>=0; i--) {
197: v = aa + (diag[i]+1);
198: vi = aj + diag[i] + 1;
199: nz = ai[i+1] - diag[i] - 1;
200: s1 = x[i2];
201: for (j=0; j<nz; j++) {
202: s1 -= v[j]*x[vi[j]];
203: }
204: x[i2] = idiag[0]*s1;
205: idiag -= 1;
206: i2 -= 1;
207: }
208: PetscLogFlops(a->nz);
209: }
210: } else {
211: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
212: }
213: VecRestoreArray(xx,&x);
214: VecRestoreArrayRead(bb,&b);
215: return(0);
216: }
220: PetscErrorCode MatSOR_SeqBAIJ_2(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
221: {
222: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
223: PetscScalar *x,x1,x2,s1,s2;
224: const PetscScalar *b;
225: const MatScalar *v,*aa = a->a, *idiag,*mdiag;
226: PetscErrorCode ierr;
227: PetscInt m = a->mbs,i,i2,nz,idx,j,it;
228: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
231: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
232: its = its*lits;
233: if (its <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
234: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
235: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
236: 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");
237: if (its > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
239: if (!a->idiagvalid){MatInvertBlockDiagonal(A,PETSC_NULL);}
241: diag = a->diag;
242: idiag = a->idiag;
243: VecGetArray(xx,&x);
244: VecGetArrayRead(bb,&b);
246: if (flag & SOR_ZERO_INITIAL_GUESS) {
247: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
248: x[0] = b[0]*idiag[0] + b[1]*idiag[2];
249: x[1] = b[0]*idiag[1] + b[1]*idiag[3];
250: i2 = 2;
251: idiag += 4;
252: for (i=1; i<m; i++) {
253: v = aa + 4*ai[i];
254: vi = aj + ai[i];
255: nz = diag[i] - ai[i];
256: s1 = b[i2]; s2 = b[i2+1];
257: for (j=0; j<nz; j++) {
258: idx = 2*vi[j];
259: it = 4*j;
260: x1 = x[idx]; x2 = x[1+idx];
261: s1 -= v[it]*x1 + v[it+2]*x2;
262: s2 -= v[it+1]*x1 + v[it+3]*x2;
263: }
264: x[i2] = idiag[0]*s1 + idiag[2]*s2;
265: x[i2+1] = idiag[1]*s1 + idiag[3]*s2;
266: idiag += 4;
267: i2 += 2;
268: }
269: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
270: PetscLogFlops(4.0*(a->nz));
271: }
272: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
273: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
274: i2 = 0;
275: mdiag = a->idiag+4*a->mbs;
276: for (i=0; i<m; i++) {
277: x1 = x[i2]; x2 = x[i2+1];
278: x[i2] = mdiag[0]*x1 + mdiag[2]*x2;
279: x[i2+1] = mdiag[1]*x1 + mdiag[3]*x2;
280: mdiag += 4;
281: i2 += 2;
282: }
283: PetscLogFlops(6.0*m);
284: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
285: PetscMemcpy(x,b,A->rmap->N*sizeof(PetscScalar));
286: }
287: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
288: idiag = a->idiag+4*a->mbs - 4;
289: i2 = 2*m - 2;
290: x1 = x[i2]; x2 = x[i2+1];
291: x[i2] = idiag[0]*x1 + idiag[2]*x2;
292: x[i2+1] = idiag[1]*x1 + idiag[3]*x2;
293: idiag -= 4;
294: i2 -= 2;
295: for (i=m-2; i>=0; i--) {
296: v = aa + 4*(diag[i]+1);
297: vi = aj + diag[i] + 1;
298: nz = ai[i+1] - diag[i] - 1;
299: s1 = x[i2]; s2 = x[i2+1];
300: for (j=0; j<nz; j++) {
301: idx = 2*vi[j];
302: it = 4*j;
303: x1 = x[idx]; x2 = x[1+idx];
304: s1 -= v[it]*x1 + v[it+2]*x2;
305: s2 -= v[it+1]*x1 + v[it+3]*x2;
306: }
307: x[i2] = idiag[0]*s1 + idiag[2]*s2;
308: x[i2+1] = idiag[1]*s1 + idiag[3]*s2;
309: idiag -= 4;
310: i2 -= 2;
311: }
312: PetscLogFlops(4.0*(a->nz));
313: }
314: } else {
315: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
316: }
317: VecRestoreArray(xx,&x);
318: VecRestoreArrayRead(bb,&b);
319: return(0);
320: }
324: PetscErrorCode MatSOR_SeqBAIJ_3(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
325: {
326: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
327: PetscScalar *x,x1,x2,x3,s1,s2,s3;
328: const MatScalar *v,*aa = a->a, *idiag,*mdiag;
329: const PetscScalar *b;
330: PetscErrorCode ierr;
331: PetscInt m = a->mbs,i,i2,nz,idx;
332: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
335: its = its*lits;
336: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
337: if (its <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
338: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
339: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
340: 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");
341: if (its > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
343: if (!a->idiagvalid){MatInvertBlockDiagonal(A,PETSC_NULL);}
345: diag = a->diag;
346: idiag = a->idiag;
347: VecGetArray(xx,&x);
348: VecGetArrayRead(bb,&b);
350: if (flag & SOR_ZERO_INITIAL_GUESS) {
351: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
352: x[0] = b[0]*idiag[0] + b[1]*idiag[3] + b[2]*idiag[6];
353: x[1] = b[0]*idiag[1] + b[1]*idiag[4] + b[2]*idiag[7];
354: x[2] = b[0]*idiag[2] + b[1]*idiag[5] + b[2]*idiag[8];
355: i2 = 3;
356: idiag += 9;
357: for (i=1; i<m; i++) {
358: v = aa + 9*ai[i];
359: vi = aj + ai[i];
360: nz = diag[i] - ai[i];
361: s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2];
362: while (nz--) {
363: idx = 3*(*vi++);
364: x1 = x[idx]; x2 = x[1+idx];x3 = x[2+idx];
365: s1 -= v[0]*x1 + v[3]*x2 + v[6]*x3;
366: s2 -= v[1]*x1 + v[4]*x2 + v[7]*x3;
367: s3 -= v[2]*x1 + v[5]*x2 + v[8]*x3;
368: v += 9;
369: }
370: x[i2] = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
371: x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
372: x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;
373: idiag += 9;
374: i2 += 3;
375: }
376: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
377: PetscLogFlops(9.0*(a->nz));
378: }
379: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
380: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
381: i2 = 0;
382: mdiag = a->idiag+9*a->mbs;
383: for (i=0; i<m; i++) {
384: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
385: x[i2] = mdiag[0]*x1 + mdiag[3]*x2 + mdiag[6]*x3;
386: x[i2+1] = mdiag[1]*x1 + mdiag[4]*x2 + mdiag[7]*x3;
387: x[i2+2] = mdiag[2]*x1 + mdiag[5]*x2 + mdiag[8]*x3;
388: mdiag += 9;
389: i2 += 3;
390: }
391: PetscLogFlops(15.0*m);
392: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
393: PetscMemcpy(x,b,A->rmap->N*sizeof(PetscScalar));
394: }
395: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
396: idiag = a->idiag+9*a->mbs - 9;
397: i2 = 3*m - 3;
398: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
399: x[i2] = idiag[0]*x1 + idiag[3]*x2 + idiag[6]*x3;
400: x[i2+1] = idiag[1]*x1 + idiag[4]*x2 + idiag[7]*x3;
401: x[i2+2] = idiag[2]*x1 + idiag[5]*x2 + idiag[8]*x3;
402: idiag -= 9;
403: i2 -= 3;
404: for (i=m-2; i>=0; i--) {
405: v = aa + 9*(diag[i]+1);
406: vi = aj + diag[i] + 1;
407: nz = ai[i+1] - diag[i] - 1;
408: s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2];
409: while (nz--) {
410: idx = 3*(*vi++);
411: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx];
412: s1 -= v[0]*x1 + v[3]*x2 + v[6]*x3;
413: s2 -= v[1]*x1 + v[4]*x2 + v[7]*x3;
414: s3 -= v[2]*x1 + v[5]*x2 + v[8]*x3;
415: v += 9;
416: }
417: x[i2] = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
418: x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
419: x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;
420: idiag -= 9;
421: i2 -= 3;
422: }
423: PetscLogFlops(9.0*(a->nz));
424: }
425: } else {
426: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
427: }
428: VecRestoreArray(xx,&x);
429: VecRestoreArrayRead(bb,&b);
430: return(0);
431: }
435: PetscErrorCode MatSOR_SeqBAIJ_4(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
436: {
437: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
438: PetscScalar *x,x1,x2,x3,x4,s1,s2,s3,s4;
439: const MatScalar *v,*aa = a->a, *idiag,*mdiag;
440: const PetscScalar *b;
441: PetscErrorCode ierr;
442: PetscInt m = a->mbs,i,i2,nz,idx;
443: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
446: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
447: its = its*lits;
448: if (its <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
449: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
450: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
451: 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");
452: if (its > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
454: if (!a->idiagvalid){MatInvertBlockDiagonal(A,PETSC_NULL);}
456: diag = a->diag;
457: idiag = a->idiag;
458: VecGetArray(xx,&x);
459: VecGetArrayRead(bb,&b);
461: if (flag & SOR_ZERO_INITIAL_GUESS) {
462: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
463: x[0] = b[0]*idiag[0] + b[1]*idiag[4] + b[2]*idiag[8] + b[3]*idiag[12];
464: x[1] = b[0]*idiag[1] + b[1]*idiag[5] + b[2]*idiag[9] + b[3]*idiag[13];
465: x[2] = b[0]*idiag[2] + b[1]*idiag[6] + b[2]*idiag[10] + b[3]*idiag[14];
466: x[3] = b[0]*idiag[3] + b[1]*idiag[7] + b[2]*idiag[11] + b[3]*idiag[15];
467: i2 = 4;
468: idiag += 16;
469: for (i=1; i<m; i++) {
470: v = aa + 16*ai[i];
471: vi = aj + ai[i];
472: nz = diag[i] - ai[i];
473: s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3];
474: while (nz--) {
475: idx = 4*(*vi++);
476: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx];
477: s1 -= v[0]*x1 + v[4]*x2 + v[8]*x3 + v[12]*x4;
478: s2 -= v[1]*x1 + v[5]*x2 + v[9]*x3 + v[13]*x4;
479: s3 -= v[2]*x1 + v[6]*x2 + v[10]*x3 + v[14]*x4;
480: s4 -= v[3]*x1 + v[7]*x2 + v[11]*x3 + v[15]*x4;
481: v += 16;
482: }
483: x[i2] = idiag[0]*s1 + idiag[4]*s2 + idiag[8]*s3 + idiag[12]*s4;
484: x[i2+1] = idiag[1]*s1 + idiag[5]*s2 + idiag[9]*s3 + idiag[13]*s4;
485: x[i2+2] = idiag[2]*s1 + idiag[6]*s2 + idiag[10]*s3 + idiag[14]*s4;
486: x[i2+3] = idiag[3]*s1 + idiag[7]*s2 + idiag[11]*s3 + idiag[15]*s4;
487: idiag += 16;
488: i2 += 4;
489: }
490: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
491: PetscLogFlops(16.0*(a->nz));
492: }
493: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
494: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
495: i2 = 0;
496: mdiag = a->idiag+16*a->mbs;
497: for (i=0; i<m; i++) {
498: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3];
499: x[i2] = mdiag[0]*x1 + mdiag[4]*x2 + mdiag[8]*x3 + mdiag[12]*x4;
500: x[i2+1] = mdiag[1]*x1 + mdiag[5]*x2 + mdiag[9]*x3 + mdiag[13]*x4;
501: x[i2+2] = mdiag[2]*x1 + mdiag[6]*x2 + mdiag[10]*x3 + mdiag[14]*x4;
502: x[i2+3] = mdiag[3]*x1 + mdiag[7]*x2 + mdiag[11]*x3 + mdiag[15]*x4;
503: mdiag += 16;
504: i2 += 4;
505: }
506: PetscLogFlops(28.0*m);
507: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
508: PetscMemcpy(x,b,A->rmap->N*sizeof(PetscScalar));
509: }
510: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
511: idiag = a->idiag+16*a->mbs - 16;
512: i2 = 4*m - 4;
513: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3];
514: x[i2] = idiag[0]*x1 + idiag[4]*x2 + idiag[8]*x3 + idiag[12]*x4;
515: x[i2+1] = idiag[1]*x1 + idiag[5]*x2 + idiag[9]*x3 + idiag[13]*x4;
516: x[i2+2] = idiag[2]*x1 + idiag[6]*x2 + idiag[10]*x3 + idiag[14]*x4;
517: x[i2+3] = idiag[3]*x1 + idiag[7]*x2 + idiag[11]*x3 + idiag[15]*x4;
518: idiag -= 16;
519: i2 -= 4;
520: for (i=m-2; i>=0; i--) {
521: v = aa + 16*(diag[i]+1);
522: vi = aj + diag[i] + 1;
523: nz = ai[i+1] - diag[i] - 1;
524: s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3];
525: while (nz--) {
526: idx = 4*(*vi++);
527: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx];
528: s1 -= v[0]*x1 + v[4]*x2 + v[8]*x3 + v[12]*x4;
529: s2 -= v[1]*x1 + v[5]*x2 + v[9]*x3 + v[13]*x4;
530: s3 -= v[2]*x1 + v[6]*x2 + v[10]*x3 + v[14]*x4;
531: s4 -= v[3]*x1 + v[7]*x2 + v[11]*x3 + v[15]*x4;
532: v += 16;
533: }
534: x[i2] = idiag[0]*s1 + idiag[4]*s2 + idiag[8]*s3 + idiag[12]*s4;
535: x[i2+1] = idiag[1]*s1 + idiag[5]*s2 + idiag[9]*s3 + idiag[13]*s4;
536: x[i2+2] = idiag[2]*s1 + idiag[6]*s2 + idiag[10]*s3 + idiag[14]*s4;
537: x[i2+3] = idiag[3]*s1 + idiag[7]*s2 + idiag[11]*s3 + idiag[15]*s4;
538: idiag -= 16;
539: i2 -= 4;
540: }
541: PetscLogFlops(16.0*(a->nz));
542: }
543: } else {
544: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
545: }
546: VecRestoreArray(xx,&x);
547: VecRestoreArrayRead(bb,&b);
548: return(0);
549: }
553: PetscErrorCode MatSOR_SeqBAIJ_5(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
554: {
555: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
556: PetscScalar *x,x1,x2,x3,x4,x5,s1,s2,s3,s4,s5;
557: const MatScalar *v,*aa = a->a, *idiag,*mdiag;
558: const PetscScalar *b;
559: PetscErrorCode ierr;
560: PetscInt m = a->mbs,i,i2,nz,idx;
561: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
564: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
565: its = its*lits;
566: if (its <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
567: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
568: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
569: 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");
570: if (its > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
572: if (!a->idiagvalid){MatInvertBlockDiagonal(A,PETSC_NULL);}
574: diag = a->diag;
575: idiag = a->idiag;
576: VecGetArray(xx,&x);
577: VecGetArrayRead(bb,&b);
579: if (flag & SOR_ZERO_INITIAL_GUESS) {
580: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
581: x[0] = b[0]*idiag[0] + b[1]*idiag[5] + b[2]*idiag[10] + b[3]*idiag[15] + b[4]*idiag[20];
582: x[1] = b[0]*idiag[1] + b[1]*idiag[6] + b[2]*idiag[11] + b[3]*idiag[16] + b[4]*idiag[21];
583: x[2] = b[0]*idiag[2] + b[1]*idiag[7] + b[2]*idiag[12] + b[3]*idiag[17] + b[4]*idiag[22];
584: x[3] = b[0]*idiag[3] + b[1]*idiag[8] + b[2]*idiag[13] + b[3]*idiag[18] + b[4]*idiag[23];
585: x[4] = b[0]*idiag[4] + b[1]*idiag[9] + b[2]*idiag[14] + b[3]*idiag[19] + b[4]*idiag[24];
586: i2 = 5;
587: idiag += 25;
588: for (i=1; i<m; i++) {
589: v = aa + 25*ai[i];
590: vi = aj + ai[i];
591: nz = diag[i] - ai[i];
592: s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3]; s5 = b[i2+4];
593: while (nz--) {
594: idx = 5*(*vi++);
595: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx];
596: s1 -= v[0]*x1 + v[5]*x2 + v[10]*x3 + v[15]*x4 + v[20]*x5;
597: s2 -= v[1]*x1 + v[6]*x2 + v[11]*x3 + v[16]*x4 + v[21]*x5;
598: s3 -= v[2]*x1 + v[7]*x2 + v[12]*x3 + v[17]*x4 + v[22]*x5;
599: s4 -= v[3]*x1 + v[8]*x2 + v[13]*x3 + v[18]*x4 + v[23]*x5;
600: s5 -= v[4]*x1 + v[9]*x2 + v[14]*x3 + v[19]*x4 + v[24]*x5;
601: v += 25;
602: }
603: x[i2] = idiag[0]*s1 + idiag[5]*s2 + idiag[10]*s3 + idiag[15]*s4 + idiag[20]*s5;
604: x[i2+1] = idiag[1]*s1 + idiag[6]*s2 + idiag[11]*s3 + idiag[16]*s4 + idiag[21]*s5;
605: x[i2+2] = idiag[2]*s1 + idiag[7]*s2 + idiag[12]*s3 + idiag[17]*s4 + idiag[22]*s5;
606: x[i2+3] = idiag[3]*s1 + idiag[8]*s2 + idiag[13]*s3 + idiag[18]*s4 + idiag[23]*s5;
607: x[i2+4] = idiag[4]*s1 + idiag[9]*s2 + idiag[14]*s3 + idiag[19]*s4 + idiag[24]*s5;
608: idiag += 25;
609: i2 += 5;
610: }
611: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
612: PetscLogFlops(25.0*(a->nz));
613: }
614: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
615: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
616: i2 = 0;
617: mdiag = a->idiag+25*a->mbs;
618: for (i=0; i<m; i++) {
619: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4];
620: x[i2] = mdiag[0]*x1 + mdiag[5]*x2 + mdiag[10]*x3 + mdiag[15]*x4 + mdiag[20]*x5;
621: x[i2+1] = mdiag[1]*x1 + mdiag[6]*x2 + mdiag[11]*x3 + mdiag[16]*x4 + mdiag[21]*x5;
622: x[i2+2] = mdiag[2]*x1 + mdiag[7]*x2 + mdiag[12]*x3 + mdiag[17]*x4 + mdiag[22]*x5;
623: x[i2+3] = mdiag[3]*x1 + mdiag[8]*x2 + mdiag[13]*x3 + mdiag[18]*x4 + mdiag[23]*x5;
624: x[i2+4] = mdiag[4]*x1 + mdiag[9]*x2 + mdiag[14]*x3 + mdiag[19]*x4 + mdiag[24]*x5;
625: mdiag += 25;
626: i2 += 5;
627: }
628: PetscLogFlops(45.0*m);
629: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
630: PetscMemcpy(x,b,A->rmap->N*sizeof(PetscScalar));
631: }
632: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
633: idiag = a->idiag+25*a->mbs - 25;
634: i2 = 5*m - 5;
635: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4];
636: x[i2] = idiag[0]*x1 + idiag[5]*x2 + idiag[10]*x3 + idiag[15]*x4 + idiag[20]*x5;
637: x[i2+1] = idiag[1]*x1 + idiag[6]*x2 + idiag[11]*x3 + idiag[16]*x4 + idiag[21]*x5;
638: x[i2+2] = idiag[2]*x1 + idiag[7]*x2 + idiag[12]*x3 + idiag[17]*x4 + idiag[22]*x5;
639: x[i2+3] = idiag[3]*x1 + idiag[8]*x2 + idiag[13]*x3 + idiag[18]*x4 + idiag[23]*x5;
640: x[i2+4] = idiag[4]*x1 + idiag[9]*x2 + idiag[14]*x3 + idiag[19]*x4 + idiag[24]*x5;
641: idiag -= 25;
642: i2 -= 5;
643: for (i=m-2; i>=0; i--) {
644: v = aa + 25*(diag[i]+1);
645: vi = aj + diag[i] + 1;
646: nz = ai[i+1] - diag[i] - 1;
647: s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3]; s5 = x[i2+4];
648: while (nz--) {
649: idx = 5*(*vi++);
650: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx];
651: s1 -= v[0]*x1 + v[5]*x2 + v[10]*x3 + v[15]*x4 + v[20]*x5;
652: s2 -= v[1]*x1 + v[6]*x2 + v[11]*x3 + v[16]*x4 + v[21]*x5;
653: s3 -= v[2]*x1 + v[7]*x2 + v[12]*x3 + v[17]*x4 + v[22]*x5;
654: s4 -= v[3]*x1 + v[8]*x2 + v[13]*x3 + v[18]*x4 + v[23]*x5;
655: s5 -= v[4]*x1 + v[9]*x2 + v[14]*x3 + v[19]*x4 + v[24]*x5;
656: v += 25;
657: }
658: x[i2] = idiag[0]*s1 + idiag[5]*s2 + idiag[10]*s3 + idiag[15]*s4 + idiag[20]*s5;
659: x[i2+1] = idiag[1]*s1 + idiag[6]*s2 + idiag[11]*s3 + idiag[16]*s4 + idiag[21]*s5;
660: x[i2+2] = idiag[2]*s1 + idiag[7]*s2 + idiag[12]*s3 + idiag[17]*s4 + idiag[22]*s5;
661: x[i2+3] = idiag[3]*s1 + idiag[8]*s2 + idiag[13]*s3 + idiag[18]*s4 + idiag[23]*s5;
662: x[i2+4] = idiag[4]*s1 + idiag[9]*s2 + idiag[14]*s3 + idiag[19]*s4 + idiag[24]*s5;
663: idiag -= 25;
664: i2 -= 5;
665: }
666: PetscLogFlops(25.0*(a->nz));
667: }
668: } else {
669: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
670: }
671: VecRestoreArray(xx,&x);
672: VecRestoreArrayRead(bb,&b);
673: return(0);
674: }
678: PetscErrorCode MatSOR_SeqBAIJ_6(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
679: {
680: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
681: PetscScalar *x,x1,x2,x3,x4,x5,x6,s1,s2,s3,s4,s5,s6;
682: const MatScalar *v,*aa = a->a, *idiag,*mdiag;
683: const PetscScalar *b;
684: PetscErrorCode ierr;
685: PetscInt m = a->mbs,i,i2,nz,idx;
686: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
689: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
690: its = its*lits;
691: if (its <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
692: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
693: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
694: 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");
695: if (its > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
697: if (!a->idiagvalid){MatInvertBlockDiagonal(A,PETSC_NULL);}
699: diag = a->diag;
700: idiag = a->idiag;
701: VecGetArray(xx,&x);
702: VecGetArrayRead(bb,&b);
704: if (flag & SOR_ZERO_INITIAL_GUESS) {
705: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
706: x[0] = b[0]*idiag[0] + b[1]*idiag[6] + b[2]*idiag[12] + b[3]*idiag[18] + b[4]*idiag[24] + b[5]*idiag[30];
707: x[1] = b[0]*idiag[1] + b[1]*idiag[7] + b[2]*idiag[13] + b[3]*idiag[19] + b[4]*idiag[25] + b[5]*idiag[31];
708: x[2] = b[0]*idiag[2] + b[1]*idiag[8] + b[2]*idiag[14] + b[3]*idiag[20] + b[4]*idiag[26] + b[5]*idiag[32];
709: x[3] = b[0]*idiag[3] + b[1]*idiag[9] + b[2]*idiag[15] + b[3]*idiag[21] + b[4]*idiag[27] + b[5]*idiag[33];
710: x[4] = b[0]*idiag[4] + b[1]*idiag[10] + b[2]*idiag[16] + b[3]*idiag[22] + b[4]*idiag[28] + b[5]*idiag[34];
711: x[5] = b[0]*idiag[5] + b[1]*idiag[11] + b[2]*idiag[17] + b[3]*idiag[23] + b[4]*idiag[29] + b[5]*idiag[35];
712: i2 = 6;
713: idiag += 36;
714: for (i=1; i<m; i++) {
715: v = aa + 36*ai[i];
716: vi = aj + ai[i];
717: nz = diag[i] - ai[i];
718: s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3]; s5 = b[i2+4]; s6 = b[i2+5];
719: while (nz--) {
720: idx = 6*(*vi++);
721: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx]; x6 = x[5+idx];
722: s1 -= v[0]*x1 + v[6]*x2 + v[12]*x3 + v[18]*x4 + v[24]*x5 + v[30]*x6;
723: s2 -= v[1]*x1 + v[7]*x2 + v[13]*x3 + v[19]*x4 + v[25]*x5 + v[31]*x6;
724: s3 -= v[2]*x1 + v[8]*x2 + v[14]*x3 + v[20]*x4 + v[26]*x5 + v[32]*x6;
725: s4 -= v[3]*x1 + v[9]*x2 + v[15]*x3 + v[21]*x4 + v[27]*x5 + v[33]*x6;
726: s5 -= v[4]*x1 + v[10]*x2 + v[16]*x3 + v[22]*x4 + v[28]*x5 + v[34]*x6;
727: s6 -= v[5]*x1 + v[11]*x2 + v[17]*x3 + v[23]*x4 + v[29]*x5 + v[35]*x6;
728: v += 36;
729: }
730: x[i2] = idiag[0]*s1 + idiag[6]*s2 + idiag[12]*s3 + idiag[18]*s4 + idiag[24]*s5 + idiag[30]*s6;
731: x[i2+1] = idiag[1]*s1 + idiag[7]*s2 + idiag[13]*s3 + idiag[19]*s4 + idiag[25]*s5 + idiag[31]*s6;
732: x[i2+2] = idiag[2]*s1 + idiag[8]*s2 + idiag[14]*s3 + idiag[20]*s4 + idiag[26]*s5 + idiag[32]*s6;
733: x[i2+3] = idiag[3]*s1 + idiag[9]*s2 + idiag[15]*s3 + idiag[21]*s4 + idiag[27]*s5 + idiag[33]*s6;
734: x[i2+4] = idiag[4]*s1 + idiag[10]*s2 + idiag[16]*s3 + idiag[22]*s4 + idiag[28]*s5 + idiag[34]*s6;
735: x[i2+5] = idiag[5]*s1 + idiag[11]*s2 + idiag[17]*s3 + idiag[23]*s4 + idiag[29]*s5 + idiag[35]*s6;
736: idiag += 36;
737: i2 += 6;
738: }
739: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
740: PetscLogFlops(36.0*(a->nz));
741: }
742: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
743: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
744: i2 = 0;
745: mdiag = a->idiag+36*a->mbs;
746: for (i=0; i<m; i++) {
747: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4]; x6 = x[i2+5];
748: x[i2] = mdiag[0]*x1 + mdiag[6]*x2 + mdiag[12]*x3 + mdiag[18]*x4 + mdiag[24]*x5 + mdiag[30]*x6;
749: x[i2+1] = mdiag[1]*x1 + mdiag[7]*x2 + mdiag[13]*x3 + mdiag[19]*x4 + mdiag[25]*x5 + mdiag[31]*x6;
750: x[i2+2] = mdiag[2]*x1 + mdiag[8]*x2 + mdiag[14]*x3 + mdiag[20]*x4 + mdiag[26]*x5 + mdiag[32]*x6;
751: x[i2+3] = mdiag[3]*x1 + mdiag[9]*x2 + mdiag[15]*x3 + mdiag[21]*x4 + mdiag[27]*x5 + mdiag[33]*x6;
752: x[i2+4] = mdiag[4]*x1 + mdiag[10]*x2 + mdiag[16]*x3 + mdiag[22]*x4 + mdiag[28]*x5 + mdiag[34]*x6;
753: x[i2+5] = mdiag[5]*x1 + mdiag[11]*x2 + mdiag[17]*x3 + mdiag[23]*x4 + mdiag[29]*x5 + mdiag[35]*x6;
754: mdiag += 36;
755: i2 += 6;
756: }
757: PetscLogFlops(60.0*m);
758: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
759: PetscMemcpy(x,b,A->rmap->N*sizeof(PetscScalar));
760: }
761: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
762: idiag = a->idiag+36*a->mbs - 36;
763: i2 = 6*m - 6;
764: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4]; x6 = x[i2+5];
765: x[i2] = idiag[0]*x1 + idiag[6]*x2 + idiag[12]*x3 + idiag[18]*x4 + idiag[24]*x5 + idiag[30]*x6;
766: x[i2+1] = idiag[1]*x1 + idiag[7]*x2 + idiag[13]*x3 + idiag[19]*x4 + idiag[25]*x5 + idiag[31]*x6;
767: x[i2+2] = idiag[2]*x1 + idiag[8]*x2 + idiag[14]*x3 + idiag[20]*x4 + idiag[26]*x5 + idiag[32]*x6;
768: x[i2+3] = idiag[3]*x1 + idiag[9]*x2 + idiag[15]*x3 + idiag[21]*x4 + idiag[27]*x5 + idiag[33]*x6;
769: x[i2+4] = idiag[4]*x1 + idiag[10]*x2 + idiag[16]*x3 + idiag[22]*x4 + idiag[28]*x5 + idiag[34]*x6;
770: x[i2+5] = idiag[5]*x1 + idiag[11]*x2 + idiag[17]*x3 + idiag[23]*x4 + idiag[29]*x5 + idiag[35]*x6;
771: idiag -= 36;
772: i2 -= 6;
773: for (i=m-2; i>=0; i--) {
774: v = aa + 36*(diag[i]+1);
775: vi = aj + diag[i] + 1;
776: nz = ai[i+1] - diag[i] - 1;
777: s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3]; s5 = x[i2+4]; s6 = x[i2+5];
778: while (nz--) {
779: idx = 6*(*vi++);
780: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx]; x6 = x[5+idx];
781: s1 -= v[0]*x1 + v[6]*x2 + v[12]*x3 + v[18]*x4 + v[24]*x5 + v[30]*x6;
782: s2 -= v[1]*x1 + v[7]*x2 + v[13]*x3 + v[19]*x4 + v[25]*x5 + v[31]*x6;
783: s3 -= v[2]*x1 + v[8]*x2 + v[14]*x3 + v[20]*x4 + v[26]*x5 + v[32]*x6;
784: s4 -= v[3]*x1 + v[9]*x2 + v[15]*x3 + v[21]*x4 + v[27]*x5 + v[33]*x6;
785: s5 -= v[4]*x1 + v[10]*x2 + v[16]*x3 + v[22]*x4 + v[28]*x5 + v[34]*x6;
786: s6 -= v[5]*x1 + v[11]*x2 + v[17]*x3 + v[23]*x4 + v[29]*x5 + v[35]*x6;
787: v += 36;
788: }
789: x[i2] = idiag[0]*s1 + idiag[6]*s2 + idiag[12]*s3 + idiag[18]*s4 + idiag[24]*s5 + idiag[30]*s6;
790: x[i2+1] = idiag[1]*s1 + idiag[7]*s2 + idiag[13]*s3 + idiag[19]*s4 + idiag[25]*s5 + idiag[31]*s6;
791: x[i2+2] = idiag[2]*s1 + idiag[8]*s2 + idiag[14]*s3 + idiag[20]*s4 + idiag[26]*s5 + idiag[32]*s6;
792: x[i2+3] = idiag[3]*s1 + idiag[9]*s2 + idiag[15]*s3 + idiag[21]*s4 + idiag[27]*s5 + idiag[33]*s6;
793: x[i2+4] = idiag[4]*s1 + idiag[10]*s2 + idiag[16]*s3 + idiag[22]*s4 + idiag[28]*s5 + idiag[34]*s6;
794: x[i2+5] = idiag[5]*s1 + idiag[11]*s2 + idiag[17]*s3 + idiag[23]*s4 + idiag[29]*s5 + idiag[35]*s6;
795: idiag -= 36;
796: i2 -= 6;
797: }
798: PetscLogFlops(36.0*(a->nz));
799: }
800: } else {
801: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
802: }
803: VecRestoreArray(xx,&x);
804: VecRestoreArrayRead(bb,&b);
805: return(0);
806: }
810: PetscErrorCode MatSOR_SeqBAIJ_7(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
811: {
812: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
813: PetscScalar *x,x1,x2,x3,x4,x5,x6,x7,s1,s2,s3,s4,s5,s6,s7;
814: const MatScalar *v,*aa = a->a, *idiag,*mdiag;
815: const PetscScalar *b;
816: PetscErrorCode ierr;
817: PetscInt m = a->mbs,i,i2,nz,idx;
818: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
821: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
822: its = its*lits;
823: if (its <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
824: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
825: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
826: 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");
827: if (its > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
829: if (!a->idiagvalid){MatInvertBlockDiagonal(A,PETSC_NULL);}
831: diag = a->diag;
832: idiag = a->idiag;
833: VecGetArray(xx,&x);
834: VecGetArrayRead(bb,&b);
836: if (flag & SOR_ZERO_INITIAL_GUESS) {
837: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
838: x[0] = b[0]*idiag[0] + b[1]*idiag[7] + b[2]*idiag[14] + b[3]*idiag[21] + b[4]*idiag[28] + b[5]*idiag[35] + b[6]*idiag[42];
839: x[1] = b[0]*idiag[1] + b[1]*idiag[8] + b[2]*idiag[15] + b[3]*idiag[22] + b[4]*idiag[29] + b[5]*idiag[36] + b[6]*idiag[43];
840: x[2] = b[0]*idiag[2] + b[1]*idiag[9] + b[2]*idiag[16] + b[3]*idiag[23] + b[4]*idiag[30] + b[5]*idiag[37] + b[6]*idiag[44];
841: x[3] = b[0]*idiag[3] + b[1]*idiag[10] + b[2]*idiag[17] + b[3]*idiag[24] + b[4]*idiag[31] + b[5]*idiag[38] + b[6]*idiag[45];
842: x[4] = b[0]*idiag[4] + b[1]*idiag[11] + b[2]*idiag[18] + b[3]*idiag[25] + b[4]*idiag[32] + b[5]*idiag[39] + b[6]*idiag[46];
843: x[5] = b[0]*idiag[5] + b[1]*idiag[12] + b[2]*idiag[19] + b[3]*idiag[26] + b[4]*idiag[33] + b[5]*idiag[40] + b[6]*idiag[47];
844: x[6] = b[0]*idiag[6] + b[1]*idiag[13] + b[2]*idiag[20] + b[3]*idiag[27] + b[4]*idiag[34] + b[5]*idiag[41] + b[6]*idiag[48];
845: i2 = 7;
846: idiag += 49;
847: for (i=1; i<m; i++) {
848: v = aa + 49*ai[i];
849: vi = aj + ai[i];
850: nz = diag[i] - ai[i];
851: s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3]; s5 = b[i2+4]; s6 = b[i2+5]; s7 = b[i2+6];
852: while (nz--) {
853: idx = 7*(*vi++);
854: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx]; x6 = x[5+idx]; x7 = x[6+idx];
855: s1 -= v[0]*x1 + v[7]*x2 + v[14]*x3 + v[21]*x4 + v[28]*x5 + v[35]*x6 + v[42]*x7;
856: s2 -= v[1]*x1 + v[8]*x2 + v[15]*x3 + v[22]*x4 + v[29]*x5 + v[36]*x6 + v[43]*x7;
857: s3 -= v[2]*x1 + v[9]*x2 + v[16]*x3 + v[23]*x4 + v[30]*x5 + v[37]*x6 + v[44]*x7;
858: s4 -= v[3]*x1 + v[10]*x2 + v[17]*x3 + v[24]*x4 + v[31]*x5 + v[38]*x6 + v[45]*x7;
859: s5 -= v[4]*x1 + v[11]*x2 + v[18]*x3 + v[25]*x4 + v[32]*x5 + v[39]*x6 + v[46]*x7;
860: s6 -= v[5]*x1 + v[12]*x2 + v[19]*x3 + v[26]*x4 + v[33]*x5 + v[40]*x6 + v[47]*x7;
861: s7 -= v[6]*x1 + v[13]*x2 + v[20]*x3 + v[27]*x4 + v[34]*x5 + v[41]*x6 + v[48]*x7;
862: v += 49;
863: }
864: x[i2] = idiag[0]*s1 + idiag[7]*s2 + idiag[14]*s3 + idiag[21]*s4 + idiag[28]*s5 + idiag[35]*s6 + idiag[42]*s7;
865: x[i2+1] = idiag[1]*s1 + idiag[8]*s2 + idiag[15]*s3 + idiag[22]*s4 + idiag[29]*s5 + idiag[36]*s6 + idiag[43]*s7;
866: x[i2+2] = idiag[2]*s1 + idiag[9]*s2 + idiag[16]*s3 + idiag[23]*s4 + idiag[30]*s5 + idiag[37]*s6 + idiag[44]*s7;
867: x[i2+3] = idiag[3]*s1 + idiag[10]*s2 + idiag[17]*s3 + idiag[24]*s4 + idiag[31]*s5 + idiag[38]*s6 + idiag[45]*s7;
868: x[i2+4] = idiag[4]*s1 + idiag[11]*s2 + idiag[18]*s3 + idiag[25]*s4 + idiag[32]*s5 + idiag[39]*s6 + idiag[46]*s7;
869: x[i2+5] = idiag[5]*s1 + idiag[12]*s2 + idiag[19]*s3 + idiag[26]*s4 + idiag[33]*s5 + idiag[40]*s6 + idiag[47]*s7;
870: x[i2+6] = idiag[6]*s1 + idiag[13]*s2 + idiag[20]*s3 + idiag[27]*s4 + idiag[34]*s5 + idiag[41]*s6 + idiag[48]*s7;
871: idiag += 49;
872: i2 += 7;
873: }
874: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
875: PetscLogFlops(49.0*(a->nz));
876: }
877: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
878: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
879: i2 = 0;
880: mdiag = a->idiag+49*a->mbs;
881: for (i=0; i<m; i++) {
882: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4]; x6 = x[i2+5]; x7 = x[i2+6];
883: x[i2] = mdiag[0]*x1 + mdiag[7]*x2 + mdiag[14]*x3 + mdiag[21]*x4 + mdiag[28]*x5 + mdiag[35]*x6 + mdiag[42]*x7;
884: x[i2+1] = mdiag[1]*x1 + mdiag[8]*x2 + mdiag[15]*x3 + mdiag[22]*x4 + mdiag[29]*x5 + mdiag[36]*x6 + mdiag[43]*x7;
885: x[i2+2] = mdiag[2]*x1 + mdiag[9]*x2 + mdiag[16]*x3 + mdiag[23]*x4 + mdiag[30]*x5 + mdiag[37]*x6 + mdiag[44]*x7;
886: x[i2+3] = mdiag[3]*x1 + mdiag[10]*x2 + mdiag[17]*x3 + mdiag[24]*x4 + mdiag[31]*x5 + mdiag[38]*x6 + mdiag[45]*x7;
887: x[i2+4] = mdiag[4]*x1 + mdiag[11]*x2 + mdiag[18]*x3 + mdiag[25]*x4 + mdiag[32]*x5 + mdiag[39]*x6 + mdiag[46]*x7;
888: x[i2+5] = mdiag[5]*x1 + mdiag[12]*x2 + mdiag[19]*x3 + mdiag[26]*x4 + mdiag[33]*x5 + mdiag[40]*x6 + mdiag[47]*x7;
889: x[i2+6] = mdiag[6]*x1 + mdiag[13]*x2 + mdiag[20]*x3 + mdiag[27]*x4 + mdiag[34]*x5 + mdiag[41]*x6 + mdiag[48]*x7;
890: mdiag += 49;
891: i2 += 7;
892: }
893: PetscLogFlops(93.0*m);
894: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
895: PetscMemcpy(x,b,A->rmap->N*sizeof(PetscScalar));
896: }
897: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
898: idiag = a->idiag+49*a->mbs - 49;
899: i2 = 7*m - 7;
900: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4]; x6 = x[i2+5]; x7 = x[i2+6];
901: x[i2] = idiag[0]*x1 + idiag[7]*x2 + idiag[14]*x3 + idiag[21]*x4 + idiag[28]*x5 + idiag[35]*x6 + idiag[42]*x7;
902: x[i2+1] = idiag[1]*x1 + idiag[8]*x2 + idiag[15]*x3 + idiag[22]*x4 + idiag[29]*x5 + idiag[36]*x6 + idiag[43]*x7;
903: x[i2+2] = idiag[2]*x1 + idiag[9]*x2 + idiag[16]*x3 + idiag[23]*x4 + idiag[30]*x5 + idiag[37]*x6 + idiag[44]*x7;
904: x[i2+3] = idiag[3]*x1 + idiag[10]*x2 + idiag[17]*x3 + idiag[24]*x4 + idiag[31]*x5 + idiag[38]*x6 + idiag[45]*x7;
905: x[i2+4] = idiag[4]*x1 + idiag[11]*x2 + idiag[18]*x3 + idiag[25]*x4 + idiag[32]*x5 + idiag[39]*x6 + idiag[46]*x7;
906: x[i2+5] = idiag[5]*x1 + idiag[12]*x2 + idiag[19]*x3 + idiag[26]*x4 + idiag[33]*x5 + idiag[40]*x6 + idiag[47]*x7;
907: x[i2+6] = idiag[6]*x1 + idiag[13]*x2 + idiag[20]*x3 + idiag[27]*x4 + idiag[34]*x5 + idiag[41]*x6 + idiag[48]*x7;
908: idiag -= 49;
909: i2 -= 7;
910: for (i=m-2; i>=0; i--) {
911: v = aa + 49*(diag[i]+1);
912: vi = aj + diag[i] + 1;
913: nz = ai[i+1] - diag[i] - 1;
914: s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3]; s5 = x[i2+4]; s6 = x[i2+5]; s7 = x[i2+6];
915: while (nz--) {
916: idx = 7*(*vi++);
917: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx]; x6 = x[5+idx]; x7 = x[6+idx];
918: s1 -= v[0]*x1 + v[7]*x2 + v[14]*x3 + v[21]*x4 + v[28]*x5 + v[35]*x6 + v[42]*x7;
919: s2 -= v[1]*x1 + v[8]*x2 + v[15]*x3 + v[22]*x4 + v[29]*x5 + v[36]*x6 + v[43]*x7;
920: s3 -= v[2]*x1 + v[9]*x2 + v[16]*x3 + v[23]*x4 + v[30]*x5 + v[37]*x6 + v[44]*x7;
921: s4 -= v[3]*x1 + v[10]*x2 + v[17]*x3 + v[24]*x4 + v[31]*x5 + v[38]*x6 + v[45]*x7;
922: s5 -= v[4]*x1 + v[11]*x2 + v[18]*x3 + v[25]*x4 + v[32]*x5 + v[39]*x6 + v[46]*x7;
923: s6 -= v[5]*x1 + v[12]*x2 + v[19]*x3 + v[26]*x4 + v[33]*x5 + v[40]*x6 + v[47]*x7;
924: s7 -= v[6]*x1 + v[13]*x2 + v[20]*x3 + v[27]*x4 + v[34]*x5 + v[41]*x6 + v[48]*x7;
925: v += 49;
926: }
927: x[i2] = idiag[0]*s1 + idiag[7]*s2 + idiag[14]*s3 + idiag[21]*s4 + idiag[28]*s5 + idiag[35]*s6 + idiag[42]*s7;
928: x[i2+1] = idiag[1]*s1 + idiag[8]*s2 + idiag[15]*s3 + idiag[22]*s4 + idiag[29]*s5 + idiag[36]*s6 + idiag[43]*s7;
929: x[i2+2] = idiag[2]*s1 + idiag[9]*s2 + idiag[16]*s3 + idiag[23]*s4 + idiag[30]*s5 + idiag[37]*s6 + idiag[44]*s7;
930: x[i2+3] = idiag[3]*s1 + idiag[10]*s2 + idiag[17]*s3 + idiag[24]*s4 + idiag[31]*s5 + idiag[38]*s6 + idiag[45]*s7;
931: x[i2+4] = idiag[4]*s1 + idiag[11]*s2 + idiag[18]*s3 + idiag[25]*s4 + idiag[32]*s5 + idiag[39]*s6 + idiag[46]*s7;
932: x[i2+5] = idiag[5]*s1 + idiag[12]*s2 + idiag[19]*s3 + idiag[26]*s4 + idiag[33]*s5 + idiag[40]*s6 + idiag[47]*s7;
933: x[i2+6] = idiag[6]*s1 + idiag[13]*s2 + idiag[20]*s3 + idiag[27]*s4 + idiag[34]*s5 + idiag[41]*s6 + idiag[48]*s7;
934: idiag -= 49;
935: i2 -= 7;
936: }
937: PetscLogFlops(49.0*(a->nz));
938: }
939: } else {
940: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
941: }
942: VecRestoreArray(xx,&x);
943: VecRestoreArrayRead(bb,&b);
944: return(0);
945: }
949: PetscErrorCode MatSOR_SeqBAIJ_N(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
950: {
951: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
952: PetscScalar *x,*work,*w,*workt;
953: const MatScalar *v,*aa = a->a, *idiag,*mdiag;
954: const PetscScalar *b;
955: PetscErrorCode ierr;
956: PetscInt m = a->mbs,i,i2,nz,bs = A->rmap->bs,bs2 = bs*bs,k,j;
957: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
960: its = its*lits;
961: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
962: if (its <= 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
963: if (fshift) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
964: if (omega != 1.0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
965: 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");
966: if (its > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
968: if (!a->idiagvalid){MatInvertBlockDiagonal(A,PETSC_NULL);}
970: diag = a->diag;
971: idiag = a->idiag;
972: if (!a->mult_work) {
973: k = PetscMax(A->rmap->n,A->cmap->n);
974: PetscMalloc((k+1)*sizeof(PetscScalar),&a->mult_work);
975: }
976: work = a->mult_work;
977: if (!a->sor_work) {
978: PetscMalloc(bs*sizeof(PetscScalar),&a->sor_work);
979: }
980: w = a->sor_work;
982: VecGetArray(xx,&x);
983: VecGetArrayRead(bb,&b);
985: if (flag & SOR_ZERO_INITIAL_GUESS) {
986: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
987: Kernel_w_gets_Ar_times_v(bs,bs,b,idiag,x);
988: /*x[0] = b[0]*idiag[0] + b[1]*idiag[3] + b[2]*idiag[6];
989: x[1] = b[0]*idiag[1] + b[1]*idiag[4] + b[2]*idiag[7];
990: x[2] = b[0]*idiag[2] + b[1]*idiag[5] + b[2]*idiag[8];*/
991: i2 = bs;
992: idiag += bs2;
993: for (i=1; i<m; i++) {
994: v = aa + bs2*ai[i];
995: vi = aj + ai[i];
996: nz = diag[i] - ai[i];
998: PetscMemcpy(w,b+i2,bs*sizeof(PetscScalar));
999: /* copy all rows of x that are needed into contiguous space */
1000: workt = work;
1001: for (j=0; j<nz; j++) {
1002: PetscMemcpy(workt,x + bs*(*vi++),bs*sizeof(PetscScalar));
1003: workt += bs;
1004: }
1005: Kernel_w_gets_w_minus_Ar_times_v(bs,bs*nz,w,v,work);
1006: /*s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2];
1007: while (nz--) {
1008: idx = N*(*vi++);
1009: x1 = x[idx]; x2 = x[1+idx];x3 = x[2+idx];
1010: s1 -= v[0]*x1 + v[3]*x2 + v[6]*x3;
1011: s2 -= v[1]*x1 + v[4]*x2 + v[7]*x3;
1012: s3 -= v[2]*x1 + v[5]*x2 + v[8]*x3;
1013: v += N2;
1014: } */
1016: Kernel_w_gets_Ar_times_v(bs,bs,w,idiag,x+i2);
1017: /* x[i2] = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
1018: x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
1019: x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;*/
1021: idiag += bs2;
1022: i2 += bs;
1023: }
1024: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
1025: PetscLogFlops(1.0*bs2*(a->nz));
1026: }
1027: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1028: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1029: i2 = 0;
1030: mdiag = a->idiag+bs2*a->mbs;
1031: PetscMemcpy(work,x,m*bs*sizeof(PetscScalar));
1032: for (i=0; i<m; i++) {
1033: Kernel_w_gets_Ar_times_v(bs,bs,work+i2,mdiag,x+i2);
1034: /* x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
1035: x[i2] = mdiag[0]*x1 + mdiag[3]*x2 + mdiag[6]*x3;
1036: x[i2+1] = mdiag[1]*x1 + mdiag[4]*x2 + mdiag[7]*x3;
1037: x[i2+2] = mdiag[2]*x1 + mdiag[5]*x2 + mdiag[8]*x3; */
1039: mdiag += bs2;
1040: i2 += bs;
1041: }
1042: PetscLogFlops(2.0*bs*(bs-1)*m);
1043: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1044: PetscMemcpy(x,b,A->rmap->N*sizeof(PetscScalar));
1045: }
1046: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1047: idiag = a->idiag+bs2*a->mbs - bs2;
1048: i2 = bs*m - bs;
1049: PetscMemcpy(w,x+i2,bs*sizeof(PetscScalar));
1050: Kernel_w_gets_Ar_times_v(bs,bs,w,idiag,x+i2);
1051: /*x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
1052: x[i2] = idiag[0]*x1 + idiag[3]*x2 + idiag[6]*x3;
1053: x[i2+1] = idiag[1]*x1 + idiag[4]*x2 + idiag[7]*x3;
1054: x[i2+2] = idiag[2]*x1 + idiag[5]*x2 + idiag[8]*x3;*/
1055: idiag -= bs2;
1056: i2 -= bs;
1057: for (i=m-2; i>=0; i--) {
1058: v = aa + bs2*(diag[i]+1);
1059: vi = aj + diag[i] + 1;
1060: nz = ai[i+1] - diag[i] - 1;
1062: PetscMemcpy(w,x+i2,bs*sizeof(PetscScalar));
1063: /* copy all rows of x that are needed into contiguous space */
1064: workt = work;
1065: for (j=0; j<nz; j++) {
1066: PetscMemcpy(workt,x + bs*(*vi++),bs*sizeof(PetscScalar));
1067: workt += bs;
1068: }
1069: Kernel_w_gets_w_minus_Ar_times_v(bs,bs*nz,w,v,work);
1070: /* s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2];
1071: while (nz--) {
1072: idx = N*(*vi++);
1073: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx];
1074: s1 -= v[0]*x1 + v[3]*x2 + v[6]*x3;
1075: s2 -= v[1]*x1 + v[4]*x2 + v[7]*x3;
1076: s3 -= v[2]*x1 + v[5]*x2 + v[8]*x3;
1077: v += N2;
1078: } */
1080: Kernel_w_gets_Ar_times_v(bs,bs,w,idiag,x+i2);
1081: /*x[i2] = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
1082: x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
1083: x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3; */
1084: idiag -= bs2;
1085: i2 -= bs;
1086: }
1087: PetscLogFlops(1.0*bs2*(a->nz));
1088: }
1089: } else {
1090: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
1091: }
1092: VecRestoreArray(xx,&x);
1093: VecRestoreArrayRead(bb,&b);
1094: return(0);
1095: }
1097: /*
1098: Special version for direct calls from Fortran (Used in PETSc-fun3d)
1099: */
1100: #if defined(PETSC_HAVE_FORTRAN_CAPS)
1101: #define matsetvaluesblocked4_ MATSETVALUESBLOCKED4
1102: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
1103: #define matsetvaluesblocked4_ matsetvaluesblocked4
1104: #endif
1109: void matsetvaluesblocked4_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[])
1110: {
1111: Mat A = *AA;
1112: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1113: PetscInt *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,N,m = *mm,n = *nn;
1114: PetscInt *ai=a->i,*ailen=a->ilen;
1115: PetscInt *aj=a->j,stepval,lastcol = -1;
1116: const PetscScalar *value = v;
1117: MatScalar *ap,*aa = a->a,*bap;
1120: if (A->rmap->bs != 4) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_WRONG,"Can only be called with a block size of 4");
1121: stepval = (n-1)*4;
1122: for (k=0; k<m; k++) { /* loop over added rows */
1123: row = im[k];
1124: rp = aj + ai[row];
1125: ap = aa + 16*ai[row];
1126: nrow = ailen[row];
1127: low = 0;
1128: high = nrow;
1129: for (l=0; l<n; l++) { /* loop over added columns */
1130: col = in[l];
1131: if (col <= lastcol) low = 0; else high = nrow;
1132: lastcol = col;
1133: value = v + k*(stepval+4 + l)*4;
1134: while (high-low > 7) {
1135: t = (low+high)/2;
1136: if (rp[t] > col) high = t;
1137: else low = t;
1138: }
1139: for (i=low; i<high; i++) {
1140: if (rp[i] > col) break;
1141: if (rp[i] == col) {
1142: bap = ap + 16*i;
1143: for (ii=0; ii<4; ii++,value+=stepval) {
1144: for (jj=ii; jj<16; jj+=4) {
1145: bap[jj] += *value++;
1146: }
1147: }
1148: goto noinsert2;
1149: }
1150: }
1151: N = nrow++ - 1;
1152: high++; /* added new column index thus must search to one higher than before */
1153: /* shift up all the later entries in this row */
1154: for (ii=N; ii>=i; ii--) {
1155: rp[ii+1] = rp[ii];
1156: PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
1157: }
1158: if (N >= i) {
1159: PetscMemzero(ap+16*i,16*sizeof(MatScalar));
1160: }
1161: rp[i] = col;
1162: bap = ap + 16*i;
1163: for (ii=0; ii<4; ii++,value+=stepval) {
1164: for (jj=ii; jj<16; jj+=4) {
1165: bap[jj] = *value++;
1166: }
1167: }
1168: noinsert2:;
1169: low = i;
1170: }
1171: ailen[row] = nrow;
1172: }
1173: PetscFunctionReturnVoid();
1174: }
1177: #if defined(PETSC_HAVE_FORTRAN_CAPS)
1178: #define matsetvalues4_ MATSETVALUES4
1179: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
1180: #define matsetvalues4_ matsetvalues4
1181: #endif
1186: void matsetvalues4_(Mat *AA,PetscInt *mm,PetscInt *im,PetscInt *nn,PetscInt *in,PetscScalar *v)
1187: {
1188: Mat A = *AA;
1189: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1190: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,N,n = *nn,m = *mm;
1191: PetscInt *ai=a->i,*ailen=a->ilen;
1192: PetscInt *aj=a->j,brow,bcol;
1193: PetscInt ridx,cidx,lastcol = -1;
1194: MatScalar *ap,value,*aa=a->a,*bap;
1195:
1197: for (k=0; k<m; k++) { /* loop over added rows */
1198: row = im[k]; brow = row/4;
1199: rp = aj + ai[brow];
1200: ap = aa + 16*ai[brow];
1201: nrow = ailen[brow];
1202: low = 0;
1203: high = nrow;
1204: for (l=0; l<n; l++) { /* loop over added columns */
1205: col = in[l]; bcol = col/4;
1206: ridx = row % 4; cidx = col % 4;
1207: value = v[l + k*n];
1208: if (col <= lastcol) low = 0; else high = nrow;
1209: lastcol = col;
1210: while (high-low > 7) {
1211: t = (low+high)/2;
1212: if (rp[t] > bcol) high = t;
1213: else low = t;
1214: }
1215: for (i=low; i<high; i++) {
1216: if (rp[i] > bcol) break;
1217: if (rp[i] == bcol) {
1218: bap = ap + 16*i + 4*cidx + ridx;
1219: *bap += value;
1220: goto noinsert1;
1221: }
1222: }
1223: N = nrow++ - 1;
1224: high++; /* added new column thus must search to one higher than before */
1225: /* shift up all the later entries in this row */
1226: for (ii=N; ii>=i; ii--) {
1227: rp[ii+1] = rp[ii];
1228: PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
1229: }
1230: if (N>=i) {
1231: PetscMemzero(ap+16*i,16*sizeof(MatScalar));
1232: }
1233: rp[i] = bcol;
1234: ap[16*i + 4*cidx + ridx] = value;
1235: noinsert1:;
1236: low = i;
1237: }
1238: ailen[brow] = nrow;
1239: }
1240: PetscFunctionReturnVoid();
1241: }
1244: /*
1245: Checks for missing diagonals
1246: */
1249: PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A,PetscBool *missing,PetscInt *d)
1250: {
1251: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1253: PetscInt *diag,*jj = a->j,i;
1256: MatMarkDiagonal_SeqBAIJ(A);
1257: *missing = PETSC_FALSE;
1258: if (A->rmap->n > 0 && !jj) {
1259: *missing = PETSC_TRUE;
1260: if (d) *d = 0;
1261: PetscInfo(A,"Matrix has no entries therefor is missing diagonal");
1262: } else {
1263: diag = a->diag;
1264: for (i=0; i<a->mbs; i++) {
1265: if (jj[diag[i]] != i) {
1266: *missing = PETSC_TRUE;
1267: if (d) *d = i;
1268: PetscInfo1(A,"Matrix is missing block diagonal number %D",i);
1269: }
1270: }
1271: }
1272: return(0);
1273: }
1277: PetscErrorCode MatMarkDiagonal_SeqBAIJ(Mat A)
1278: {
1279: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1281: PetscInt i,j,m = a->mbs;
1284: if (!a->diag) {
1285: PetscMalloc(m*sizeof(PetscInt),&a->diag);
1286: PetscLogObjectMemory(A,m*sizeof(PetscInt));
1287: a->free_diag = PETSC_TRUE;
1288: }
1289: for (i=0; i<m; i++) {
1290: a->diag[i] = a->i[i+1];
1291: for (j=a->i[i]; j<a->i[i+1]; j++) {
1292: if (a->j[j] == i) {
1293: a->diag[i] = j;
1294: break;
1295: }
1296: }
1297: }
1298: return(0);
1299: }
1306: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool blockcompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscBool *done)
1307: {
1308: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1310: PetscInt i,j,n = a->mbs,nz = a->i[n],bs = A->rmap->bs,k,l,cnt;
1311: PetscInt *tia, *tja;
1314: *nn = n;
1315: if (!ia) return(0);
1316: if (symmetric) {
1317: MatToSymmetricIJ_SeqAIJ(n,a->i,a->j,0,0,&tia,&tja);
1318: nz = tia[n];
1319: } else {
1320: tia = a->i; tja = a->j;
1321: }
1322:
1323: if (!blockcompressed && bs > 1) {
1324: (*nn) *= bs;
1325: /* malloc & create the natural set of indices */
1326: PetscMalloc((n+1)*bs*sizeof(PetscInt),ia);
1327: if (n) {
1328: (*ia)[0] = 0;
1329: for (j=1; j<bs; j++) {
1330: (*ia)[j] = (tia[1]-tia[0])*bs+(*ia)[j-1];
1331: }
1332: }
1334: for (i=1; i<n; i++) {
1335: (*ia)[i*bs] = (tia[i]-tia[i-1])*bs + (*ia)[i*bs-1];
1336: for (j=1; j<bs; j++) {
1337: (*ia)[i*bs+j] = (tia[i+1]-tia[i])*bs + (*ia)[i*bs+j-1];
1338: }
1339: }
1340: if (n) {
1341: (*ia)[n*bs] = (tia[n]-tia[n-1])*bs + (*ia)[n*bs-1];
1342: }
1344: if (ja) {
1345: PetscMalloc(nz*bs*bs*sizeof(PetscInt),ja);
1346: cnt = 0;
1347: for (i=0; i<n; i++) {
1348: for (j=0; j<bs; j++) {
1349: for (k=tia[i]; k<tia[i+1]; k++) {
1350: for (l=0; l<bs; l++) {
1351: (*ja)[cnt++] = bs*tja[k] + l;
1352: }
1353: }
1354: }
1355: }
1356: }
1358: n *= bs;
1359: nz *= bs*bs;
1360: if (symmetric) { /* deallocate memory allocated in MatToSymmetricIJ_SeqAIJ() */
1361: PetscFree(tia);
1362: PetscFree(tja);
1363: }
1364: } else if (oshift == 1) {
1365: if (symmetric) {
1366: PetscInt nz = tia[A->rmap->n/bs];
1367: /* add 1 to i and j indices */
1368: for (i=0; i<A->rmap->n/bs+1; i++) tia[i] = tia[i] + 1;
1369: *ia = tia;
1370: if (ja) {
1371: for (i=0; i<nz; i++) tja[i] = tja[i] + 1;
1372: *ja = tja;
1373: }
1374: } else {
1375: PetscInt nz = a->i[A->rmap->n/bs];
1376: /* malloc space and add 1 to i and j indices */
1377: PetscMalloc((A->rmap->n/bs+1)*sizeof(PetscInt),ia);
1378: for (i=0; i<A->rmap->n/bs+1; i++) (*ia)[i] = a->i[i] + 1;
1379: if (ja) {
1380: PetscMalloc(nz*sizeof(PetscInt),ja);
1381: for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
1382: }
1383: }
1384: } else {
1385: *ia = tia;
1386: if (ja) *ja = tja;
1387: }
1388:
1389: return(0);
1390: }
1394: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool blockcompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscBool *done)
1395: {
1399: if (!ia) return(0);
1400: if ((!blockcompressed && A->rmap->bs > 1) || (symmetric || oshift == 1)) {
1401: PetscFree(*ia);
1402: if (ja) {PetscFree(*ja);}
1403: }
1404: return(0);
1405: }
1409: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
1410: {
1411: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1415: #if defined(PETSC_USE_LOG)
1416: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->N,A->cmap->n,a->nz);
1417: #endif
1418: MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1419: ISDestroy(&a->row);
1420: ISDestroy(&a->col);
1421: if (a->free_diag) {PetscFree(a->diag);}
1422: PetscFree(a->idiag);
1423: if (a->free_imax_ilen) {PetscFree2(a->imax,a->ilen);}
1424: PetscFree(a->solve_work);
1425: PetscFree(a->mult_work);
1426: PetscFree(a->sor_work);
1427: ISDestroy(&a->icol);
1428: PetscFree(a->saved_values);
1429: PetscFree(a->xtoy);
1430: PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
1432: MatDestroy(&a->sbaijMat);
1433: MatDestroy(&a->parent);
1434: PetscFree(A->data);
1436: PetscObjectChangeTypeName((PetscObject)A,0);
1437: PetscObjectComposeFunction((PetscObject)A,"MatInvertBlockDiagonal_C","",PETSC_NULL);
1438: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
1439: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
1440: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetColumnIndices_C","",PETSC_NULL);
1441: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqaij_C","",PETSC_NULL);
1442: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqsbaij_C","",PETSC_NULL);
1443: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetPreallocation_C","",PETSC_NULL);
1444: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetPreallocationCSR_C","",PETSC_NULL);
1445: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqbstrm_C","",PETSC_NULL);
1446: PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
1447: return(0);
1448: }
1452: PetscErrorCode MatSetOption_SeqBAIJ(Mat A,MatOption op,PetscBool flg)
1453: {
1454: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1458: switch (op) {
1459: case MAT_ROW_ORIENTED:
1460: a->roworiented = flg;
1461: break;
1462: case MAT_KEEP_NONZERO_PATTERN:
1463: a->keepnonzeropattern = flg;
1464: break;
1465: case MAT_NEW_NONZERO_LOCATIONS:
1466: a->nonew = (flg ? 0 : 1);
1467: break;
1468: case MAT_NEW_NONZERO_LOCATION_ERR:
1469: a->nonew = (flg ? -1 : 0);
1470: break;
1471: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1472: a->nonew = (flg ? -2 : 0);
1473: break;
1474: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1475: a->nounused = (flg ? -1 : 0);
1476: break;
1477: case MAT_CHECK_COMPRESSED_ROW:
1478: a->compressedrow.check = flg;
1479: break;
1480: case MAT_NEW_DIAGONALS:
1481: case MAT_IGNORE_OFF_PROC_ENTRIES:
1482: case MAT_USE_HASH_TABLE:
1483: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1484: break;
1485: case MAT_SYMMETRIC:
1486: case MAT_STRUCTURALLY_SYMMETRIC:
1487: case MAT_HERMITIAN:
1488: case MAT_SYMMETRY_ETERNAL:
1489: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1490: break;
1491: default:
1492: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1493: }
1494: return(0);
1495: }
1499: PetscErrorCode MatGetRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1500: {
1501: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1503: PetscInt itmp,i,j,k,M,*ai,*aj,bs,bn,bp,*idx_i,bs2;
1504: MatScalar *aa,*aa_i;
1505: PetscScalar *v_i;
1508: bs = A->rmap->bs;
1509: ai = a->i;
1510: aj = a->j;
1511: aa = a->a;
1512: bs2 = a->bs2;
1513:
1514: if (row < 0 || row >= A->rmap->N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range", row);
1515:
1516: bn = row/bs; /* Block number */
1517: bp = row % bs; /* Block Position */
1518: M = ai[bn+1] - ai[bn];
1519: *nz = bs*M;
1520:
1521: if (v) {
1522: *v = 0;
1523: if (*nz) {
1524: PetscMalloc((*nz)*sizeof(PetscScalar),v);
1525: for (i=0; i<M; i++) { /* for each block in the block row */
1526: v_i = *v + i*bs;
1527: aa_i = aa + bs2*(ai[bn] + i);
1528: for (j=bp,k=0; j<bs2; j+=bs,k++) {v_i[k] = aa_i[j];}
1529: }
1530: }
1531: }
1533: if (idx) {
1534: *idx = 0;
1535: if (*nz) {
1536: PetscMalloc((*nz)*sizeof(PetscInt),idx);
1537: for (i=0; i<M; i++) { /* for each block in the block row */
1538: idx_i = *idx + i*bs;
1539: itmp = bs*aj[ai[bn] + i];
1540: for (j=0; j<bs; j++) {idx_i[j] = itmp++;}
1541: }
1542: }
1543: }
1544: return(0);
1545: }
1549: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1550: {
1554: if (idx) {PetscFree(*idx);}
1555: if (v) {PetscFree(*v);}
1556: return(0);
1557: }
1563: PetscErrorCode MatTranspose_SeqBAIJ(Mat A,MatReuse reuse,Mat *B)
1564: {
1565: Mat_SeqBAIJ *a=(Mat_SeqBAIJ *)A->data;
1566: Mat C;
1568: PetscInt i,j,k,*aj=a->j,*ai=a->i,bs=A->rmap->bs,mbs=a->mbs,nbs=a->nbs,len,*col;
1569: PetscInt *rows,*cols,bs2=a->bs2;
1570: MatScalar *array;
1573: if (reuse == MAT_REUSE_MATRIX && A == *B && mbs != nbs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1574: if (reuse == MAT_INITIAL_MATRIX || A == *B) {
1575: PetscMalloc((1+nbs)*sizeof(PetscInt),&col);
1576: PetscMemzero(col,(1+nbs)*sizeof(PetscInt));
1578: for (i=0; i<ai[mbs]; i++) col[aj[i]] += 1;
1579: MatCreate(((PetscObject)A)->comm,&C);
1580: MatSetSizes(C,A->cmap->n,A->rmap->N,A->cmap->n,A->rmap->N);
1581: MatSetType(C,((PetscObject)A)->type_name);
1582: MatSeqBAIJSetPreallocation_SeqBAIJ(C,bs,PETSC_NULL,col);
1583: PetscFree(col);
1584: } else {
1585: C = *B;
1586: }
1588: array = a->a;
1589: PetscMalloc2(bs,PetscInt,&rows,bs,PetscInt,&cols);
1590: for (i=0; i<mbs; i++) {
1591: cols[0] = i*bs;
1592: for (k=1; k<bs; k++) cols[k] = cols[k-1] + 1;
1593: len = ai[i+1] - ai[i];
1594: for (j=0; j<len; j++) {
1595: rows[0] = (*aj++)*bs;
1596: for (k=1; k<bs; k++) rows[k] = rows[k-1] + 1;
1597: MatSetValues_SeqBAIJ(C,bs,rows,bs,cols,array,INSERT_VALUES);
1598: array += bs2;
1599: }
1600: }
1601: PetscFree2(rows,cols);
1602:
1603: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1604: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1605:
1606: if (reuse == MAT_INITIAL_MATRIX || *B != A) {
1607: *B = C;
1608: } else {
1609: MatHeaderMerge(A,C);
1610: }
1611: return(0);
1612: }
1617: PetscErrorCode MatIsTranspose_SeqBAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
1618: {
1620: Mat Btrans;
1623: *f = PETSC_FALSE;
1624: MatTranspose_SeqBAIJ(A,MAT_INITIAL_MATRIX,&Btrans);
1625: MatEqual_SeqBAIJ(B,Btrans,f);
1626: MatDestroy(&Btrans);
1627: return(0);
1628: }
1633: static PetscErrorCode MatView_SeqBAIJ_Binary(Mat A,PetscViewer viewer)
1634: {
1635: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1637: PetscInt i,*col_lens,bs = A->rmap->bs,count,*jj,j,k,l,bs2=a->bs2;
1638: int fd;
1639: PetscScalar *aa;
1640: FILE *file;
1643: PetscViewerBinaryGetDescriptor(viewer,&fd);
1644: PetscMalloc((4+A->rmap->N)*sizeof(PetscInt),&col_lens);
1645: col_lens[0] = MAT_FILE_CLASSID;
1647: col_lens[1] = A->rmap->N;
1648: col_lens[2] = A->cmap->n;
1649: col_lens[3] = a->nz*bs2;
1651: /* store lengths of each row and write (including header) to file */
1652: count = 0;
1653: for (i=0; i<a->mbs; i++) {
1654: for (j=0; j<bs; j++) {
1655: col_lens[4+count++] = bs*(a->i[i+1] - a->i[i]);
1656: }
1657: }
1658: PetscBinaryWrite(fd,col_lens,4+A->rmap->N,PETSC_INT,PETSC_TRUE);
1659: PetscFree(col_lens);
1661: /* store column indices (zero start index) */
1662: PetscMalloc((a->nz+1)*bs2*sizeof(PetscInt),&jj);
1663: count = 0;
1664: for (i=0; i<a->mbs; i++) {
1665: for (j=0; j<bs; j++) {
1666: for (k=a->i[i]; k<a->i[i+1]; k++) {
1667: for (l=0; l<bs; l++) {
1668: jj[count++] = bs*a->j[k] + l;
1669: }
1670: }
1671: }
1672: }
1673: PetscBinaryWrite(fd,jj,bs2*a->nz,PETSC_INT,PETSC_FALSE);
1674: PetscFree(jj);
1676: /* store nonzero values */
1677: PetscMalloc((a->nz+1)*bs2*sizeof(PetscScalar),&aa);
1678: count = 0;
1679: for (i=0; i<a->mbs; i++) {
1680: for (j=0; j<bs; j++) {
1681: for (k=a->i[i]; k<a->i[i+1]; k++) {
1682: for (l=0; l<bs; l++) {
1683: aa[count++] = a->a[bs2*k + l*bs + j];
1684: }
1685: }
1686: }
1687: }
1688: PetscBinaryWrite(fd,aa,bs2*a->nz,PETSC_SCALAR,PETSC_FALSE);
1689: PetscFree(aa);
1691: PetscViewerBinaryGetInfoPointer(viewer,&file);
1692: if (file) {
1693: fprintf(file,"-matload_block_size %d\n",(int)A->rmap->bs);
1694: }
1695: return(0);
1696: }
1700: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A,PetscViewer viewer)
1701: {
1702: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1703: PetscErrorCode ierr;
1704: PetscInt i,j,bs = A->rmap->bs,k,l,bs2=a->bs2;
1705: PetscViewerFormat format;
1708: PetscViewerGetFormat(viewer,&format);
1709: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1710: PetscViewerASCIIPrintf(viewer," block size is %D\n",bs);
1711: } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1712: Mat aij;
1713: MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&aij);
1714: MatView(aij,viewer);
1715: MatDestroy(&aij);
1716: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1717: return(0);
1718: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1719: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1720: PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
1721: for (i=0; i<a->mbs; i++) {
1722: for (j=0; j<bs; j++) {
1723: PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1724: for (k=a->i[i]; k<a->i[i+1]; k++) {
1725: for (l=0; l<bs; l++) {
1726: #if defined(PETSC_USE_COMPLEX)
1727: if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1728: PetscViewerASCIIPrintf(viewer," (%D, %G + %Gi) ",bs*a->j[k]+l,
1729: PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1730: } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1731: PetscViewerASCIIPrintf(viewer," (%D, %G - %Gi) ",bs*a->j[k]+l,
1732: PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1733: } else if (PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1734: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1735: }
1736: #else
1737: if (a->a[bs2*k + l*bs + j] != 0.0) {
1738: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1739: }
1740: #endif
1741: }
1742: }
1743: PetscViewerASCIIPrintf(viewer,"\n");
1744: }
1745: }
1746: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1747: } else {
1748: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1749: PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
1750: for (i=0; i<a->mbs; i++) {
1751: for (j=0; j<bs; j++) {
1752: PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1753: for (k=a->i[i]; k<a->i[i+1]; k++) {
1754: for (l=0; l<bs; l++) {
1755: #if defined(PETSC_USE_COMPLEX)
1756: if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0) {
1757: PetscViewerASCIIPrintf(viewer," (%D, %G + %G i) ",bs*a->j[k]+l,
1758: PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1759: } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0) {
1760: PetscViewerASCIIPrintf(viewer," (%D, %G - %G i) ",bs*a->j[k]+l,
1761: PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1762: } else {
1763: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1764: }
1765: #else
1766: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1767: #endif
1768: }
1769: }
1770: PetscViewerASCIIPrintf(viewer,"\n");
1771: }
1772: }
1773: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1774: }
1775: PetscViewerFlush(viewer);
1776: return(0);
1777: }
1781: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1782: {
1783: Mat A = (Mat) Aa;
1784: Mat_SeqBAIJ *a=(Mat_SeqBAIJ*)A->data;
1786: PetscInt row,i,j,k,l,mbs=a->mbs,color,bs=A->rmap->bs,bs2=a->bs2;
1787: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1788: MatScalar *aa;
1789: PetscViewer viewer;
1790: PetscViewerFormat format;
1793: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1794: PetscViewerGetFormat(viewer,&format);
1796: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
1798: /* loop over matrix elements drawing boxes */
1800: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1801: color = PETSC_DRAW_BLUE;
1802: for (i=0,row=0; i<mbs; i++,row+=bs) {
1803: for (j=a->i[i]; j<a->i[i+1]; j++) {
1804: y_l = A->rmap->N - row - 1.0; y_r = y_l + 1.0;
1805: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1806: aa = a->a + j*bs2;
1807: for (k=0; k<bs; k++) {
1808: for (l=0; l<bs; l++) {
1809: if (PetscRealPart(*aa++) >= 0.) continue;
1810: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1811: }
1812: }
1813: }
1814: }
1815: color = PETSC_DRAW_CYAN;
1816: for (i=0,row=0; i<mbs; i++,row+=bs) {
1817: for (j=a->i[i]; j<a->i[i+1]; j++) {
1818: y_l = A->rmap->N - row - 1.0; y_r = y_l + 1.0;
1819: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1820: aa = a->a + j*bs2;
1821: for (k=0; k<bs; k++) {
1822: for (l=0; l<bs; l++) {
1823: if (PetscRealPart(*aa++) != 0.) continue;
1824: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1825: }
1826: }
1827: }
1828: }
1829: color = PETSC_DRAW_RED;
1830: for (i=0,row=0; i<mbs; i++,row+=bs) {
1831: for (j=a->i[i]; j<a->i[i+1]; j++) {
1832: y_l = A->rmap->N - row - 1.0; y_r = y_l + 1.0;
1833: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1834: aa = a->a + j*bs2;
1835: for (k=0; k<bs; k++) {
1836: for (l=0; l<bs; l++) {
1837: if (PetscRealPart(*aa++) <= 0.) continue;
1838: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1839: }
1840: }
1841: }
1842: }
1843: } else {
1844: /* use contour shading to indicate magnitude of values */
1845: /* first determine max of all nonzero values */
1846: PetscDraw popup;
1847: PetscReal scale,maxv = 0.0;
1849: for (i=0; i<a->nz*a->bs2; i++) {
1850: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
1851: }
1852: scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
1853: PetscDrawGetPopup(draw,&popup);
1854: if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
1855: for (i=0,row=0; i<mbs; i++,row+=bs) {
1856: for (j=a->i[i]; j<a->i[i+1]; j++) {
1857: y_l = A->rmap->N - row - 1.0; y_r = y_l + 1.0;
1858: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1859: aa = a->a + j*bs2;
1860: for (k=0; k<bs; k++) {
1861: for (l=0; l<bs; l++) {
1862: color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(*aa++));
1863: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1864: }
1865: }
1866: }
1867: }
1868: }
1869: return(0);
1870: }
1874: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A,PetscViewer viewer)
1875: {
1877: PetscReal xl,yl,xr,yr,w,h;
1878: PetscDraw draw;
1879: PetscBool isnull;
1883: PetscViewerDrawGetDraw(viewer,0,&draw);
1884: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1886: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1887: xr = A->cmap->n; yr = A->rmap->N; h = yr/10.0; w = xr/10.0;
1888: xr += w; yr += h; xl = -w; yl = -h;
1889: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1890: PetscDrawZoom(draw,MatView_SeqBAIJ_Draw_Zoom,A);
1891: PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
1892: return(0);
1893: }
1897: PetscErrorCode MatView_SeqBAIJ(Mat A,PetscViewer viewer)
1898: {
1900: PetscBool iascii,isbinary,isdraw;
1903: PetscTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1904: PetscTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1905: PetscTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1906: if (iascii){
1907: MatView_SeqBAIJ_ASCII(A,viewer);
1908: } else if (isbinary) {
1909: MatView_SeqBAIJ_Binary(A,viewer);
1910: } else if (isdraw) {
1911: MatView_SeqBAIJ_Draw(A,viewer);
1912: } else {
1913: Mat B;
1914: MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&B);
1915: MatView(B,viewer);
1916: MatDestroy(&B);
1917: }
1918: return(0);
1919: }
1924: PetscErrorCode MatGetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
1925: {
1926: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1927: PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
1928: PetscInt *ai = a->i,*ailen = a->ilen;
1929: PetscInt brow,bcol,ridx,cidx,bs=A->rmap->bs,bs2=a->bs2;
1930: MatScalar *ap,*aa = a->a;
1933: for (k=0; k<m; k++) { /* loop over rows */
1934: row = im[k]; brow = row/bs;
1935: if (row < 0) {v += n; continue;} /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row"); */
1936: if (row >= A->rmap->N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D too large", row);
1937: rp = aj + ai[brow] ; ap = aa + bs2*ai[brow] ;
1938: nrow = ailen[brow];
1939: for (l=0; l<n; l++) { /* loop over columns */
1940: if (in[l] < 0) {v++; continue;} /* SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column"); */
1941: if (in[l] >= A->cmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column %D too large", in[l]);
1942: col = in[l] ;
1943: bcol = col/bs;
1944: cidx = col%bs;
1945: ridx = row%bs;
1946: high = nrow;
1947: low = 0; /* assume unsorted */
1948: while (high-low > 5) {
1949: t = (low+high)/2;
1950: if (rp[t] > bcol) high = t;
1951: else low = t;
1952: }
1953: for (i=low; i<high; i++) {
1954: if (rp[i] > bcol) break;
1955: if (rp[i] == bcol) {
1956: *v++ = ap[bs2*i+bs*cidx+ridx];
1957: goto finished;
1958: }
1959: }
1960: *v++ = 0.0;
1961: finished:;
1962: }
1963: }
1964: return(0);
1965: }
1969: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
1970: {
1971: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1972: PetscInt *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,rmax,N,lastcol = -1;
1973: PetscInt *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1974: PetscErrorCode ierr;
1975: PetscInt *aj=a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap->bs,stepval;
1976: PetscBool roworiented=a->roworiented;
1977: const PetscScalar *value = v;
1978: MatScalar *ap,*aa = a->a,*bap;
1981: if (roworiented) {
1982: stepval = (n-1)*bs;
1983: } else {
1984: stepval = (m-1)*bs;
1985: }
1986: for (k=0; k<m; k++) { /* loop over added rows */
1987: row = im[k];
1988: if (row < 0) continue;
1989: #if defined(PETSC_USE_DEBUG)
1990: if (row >= a->mbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,a->mbs-1);
1991: #endif
1992: rp = aj + ai[row];
1993: ap = aa + bs2*ai[row];
1994: rmax = imax[row];
1995: nrow = ailen[row];
1996: low = 0;
1997: high = nrow;
1998: for (l=0; l<n; l++) { /* loop over added columns */
1999: if (in[l] < 0) continue;
2000: #if defined(PETSC_USE_DEBUG)
2001: if (in[l] >= a->nbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],a->nbs-1);
2002: #endif
2003: col = in[l];
2004: if (roworiented) {
2005: value = v + (k*(stepval+bs) + l)*bs;
2006: } else {
2007: value = v + (l*(stepval+bs) + k)*bs;
2008: }
2009: if (col <= lastcol) low = 0; else high = nrow;
2010: lastcol = col;
2011: while (high-low > 7) {
2012: t = (low+high)/2;
2013: if (rp[t] > col) high = t;
2014: else low = t;
2015: }
2016: for (i=low; i<high; i++) {
2017: if (rp[i] > col) break;
2018: if (rp[i] == col) {
2019: bap = ap + bs2*i;
2020: if (roworiented) {
2021: if (is == ADD_VALUES) {
2022: for (ii=0; ii<bs; ii++,value+=stepval) {
2023: for (jj=ii; jj<bs2; jj+=bs) {
2024: bap[jj] += *value++;
2025: }
2026: }
2027: } else {
2028: for (ii=0; ii<bs; ii++,value+=stepval) {
2029: for (jj=ii; jj<bs2; jj+=bs) {
2030: bap[jj] = *value++;
2031: }
2032: }
2033: }
2034: } else {
2035: if (is == ADD_VALUES) {
2036: for (ii=0; ii<bs; ii++,value+=bs+stepval) {
2037: for (jj=0; jj<bs; jj++) {
2038: bap[jj] += value[jj];
2039: }
2040: bap += bs;
2041: }
2042: } else {
2043: for (ii=0; ii<bs; ii++,value+=bs+stepval) {
2044: for (jj=0; jj<bs; jj++) {
2045: bap[jj] = value[jj];
2046: }
2047: bap += bs;
2048: }
2049: }
2050: }
2051: goto noinsert2;
2052: }
2053: }
2054: if (nonew == 1) goto noinsert2;
2055: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
2056: MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
2057: N = nrow++ - 1; high++;
2058: /* shift up all the later entries in this row */
2059: for (ii=N; ii>=i; ii--) {
2060: rp[ii+1] = rp[ii];
2061: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
2062: }
2063: if (N >= i) {
2064: PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
2065: }
2066: rp[i] = col;
2067: bap = ap + bs2*i;
2068: if (roworiented) {
2069: for (ii=0; ii<bs; ii++,value+=stepval) {
2070: for (jj=ii; jj<bs2; jj+=bs) {
2071: bap[jj] = *value++;
2072: }
2073: }
2074: } else {
2075: for (ii=0; ii<bs; ii++,value+=stepval) {
2076: for (jj=0; jj<bs; jj++) {
2077: *bap++ = *value++;
2078: }
2079: }
2080: }
2081: noinsert2:;
2082: low = i;
2083: }
2084: ailen[row] = nrow;
2085: }
2086: return(0);
2087: }
2091: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A,MatAssemblyType mode)
2092: {
2093: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2094: PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
2095: PetscInt m = A->rmap->N,*ip,N,*ailen = a->ilen;
2097: PetscInt mbs = a->mbs,bs2 = a->bs2,rmax = 0;
2098: MatScalar *aa = a->a,*ap;
2099: PetscReal ratio=0.6;
2102: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
2104: if (m) rmax = ailen[0];
2105: for (i=1; i<mbs; i++) {
2106: /* move each row back by the amount of empty slots (fshift) before it*/
2107: fshift += imax[i-1] - ailen[i-1];
2108: rmax = PetscMax(rmax,ailen[i]);
2109: if (fshift) {
2110: ip = aj + ai[i]; ap = aa + bs2*ai[i];
2111: N = ailen[i];
2112: for (j=0; j<N; j++) {
2113: ip[j-fshift] = ip[j];
2114: PetscMemcpy(ap+(j-fshift)*bs2,ap+j*bs2,bs2*sizeof(MatScalar));
2115: }
2116: }
2117: ai[i] = ai[i-1] + ailen[i-1];
2118: }
2119: if (mbs) {
2120: fshift += imax[mbs-1] - ailen[mbs-1];
2121: ai[mbs] = ai[mbs-1] + ailen[mbs-1];
2122: }
2123: /* reset ilen and imax for each row */
2124: for (i=0; i<mbs; i++) {
2125: ailen[i] = imax[i] = ai[i+1] - ai[i];
2126: }
2127: a->nz = ai[mbs];
2129: /* diagonals may have moved, so kill the diagonal pointers */
2130: a->idiagvalid = PETSC_FALSE;
2131: if (fshift && a->diag) {
2132: PetscFree(a->diag);
2133: PetscLogObjectMemory(A,-(mbs+1)*sizeof(PetscInt));
2134: a->diag = 0;
2135: }
2136: 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);
2137: 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);
2138: PetscInfo1(A,"Number of mallocs during MatSetValues is %D\n",a->reallocs);
2139: PetscInfo1(A,"Most nonzeros blocks in any row is %D\n",rmax);
2140: A->info.mallocs += a->reallocs;
2141: a->reallocs = 0;
2142: A->info.nz_unneeded = (PetscReal)fshift*bs2;
2144: MatCheckCompressedRow(A,&a->compressedrow,a->i,mbs,ratio);
2145: A->same_nonzero = PETSC_TRUE;
2146: return(0);
2147: }
2149: /*
2150: This function returns an array of flags which indicate the locations of contiguous
2151: blocks that should be zeroed. for eg: if bs = 3 and is = [0,1,2,3,5,6,7,8,9]
2152: then the resulting sizes = [3,1,1,3,1] correspondig to sets [(0,1,2),(3),(5),(6,7,8),(9)]
2153: Assume: sizes should be long enough to hold all the values.
2154: */
2157: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[],PetscInt n,PetscInt bs,PetscInt sizes[], PetscInt *bs_max)
2158: {
2159: PetscInt i,j,k,row;
2160: PetscBool flg;
2163: for (i=0,j=0; i<n; j++) {
2164: row = idx[i];
2165: if (row%bs!=0) { /* Not the begining of a block */
2166: sizes[j] = 1;
2167: i++;
2168: } else if (i+bs > n) { /* complete block doesn't exist (at idx end) */
2169: sizes[j] = 1; /* Also makes sure atleast 'bs' values exist for next else */
2170: i++;
2171: } else { /* Begining of the block, so check if the complete block exists */
2172: flg = PETSC_TRUE;
2173: for (k=1; k<bs; k++) {
2174: if (row+k != idx[i+k]) { /* break in the block */
2175: flg = PETSC_FALSE;
2176: break;
2177: }
2178: }
2179: if (flg) { /* No break in the bs */
2180: sizes[j] = bs;
2181: i+= bs;
2182: } else {
2183: sizes[j] = 1;
2184: i++;
2185: }
2186: }
2187: }
2188: *bs_max = j;
2189: return(0);
2190: }
2191:
2194: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A,PetscInt is_n,const PetscInt is_idx[],PetscScalar diag,Vec x, Vec b)
2195: {
2196: Mat_SeqBAIJ *baij=(Mat_SeqBAIJ*)A->data;
2197: PetscErrorCode ierr;
2198: PetscInt i,j,k,count,*rows;
2199: PetscInt bs=A->rmap->bs,bs2=baij->bs2,*sizes,row,bs_max;
2200: PetscScalar zero = 0.0;
2201: MatScalar *aa;
2202: const PetscScalar *xx;
2203: PetscScalar *bb;
2206: /* fix right hand side if needed */
2207: if (x && b) {
2208: VecGetArrayRead(x,&xx);
2209: VecGetArray(b,&bb);
2210: for (i=0; i<is_n; i++) {
2211: bb[is_idx[i]] = diag*xx[is_idx[i]];
2212: }
2213: VecRestoreArrayRead(x,&xx);
2214: VecRestoreArray(b,&bb);
2215: }
2217: /* Make a copy of the IS and sort it */
2218: /* allocate memory for rows,sizes */
2219: PetscMalloc2(is_n,PetscInt,&rows,2*is_n,PetscInt,&sizes);
2221: /* copy IS values to rows, and sort them */
2222: for (i=0; i<is_n; i++) { rows[i] = is_idx[i]; }
2223: PetscSortInt(is_n,rows);
2225: if (baij->keepnonzeropattern) {
2226: for (i=0; i<is_n; i++) { sizes[i] = 1; }
2227: bs_max = is_n;
2228: A->same_nonzero = PETSC_TRUE;
2229: } else {
2230: MatZeroRows_SeqBAIJ_Check_Blocks(rows,is_n,bs,sizes,&bs_max);
2231: A->same_nonzero = PETSC_FALSE;
2232: }
2234: for (i=0,j=0; i<bs_max; j+=sizes[i],i++) {
2235: row = rows[j];
2236: if (row < 0 || row > A->rmap->N) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range",row);
2237: count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
2238: aa = ((MatScalar*)(baij->a)) + baij->i[row/bs]*bs2 + (row%bs);
2239: if (sizes[i] == bs && !baij->keepnonzeropattern) {
2240: if (diag != (PetscScalar)0.0) {
2241: if (baij->ilen[row/bs] > 0) {
2242: baij->ilen[row/bs] = 1;
2243: baij->j[baij->i[row/bs]] = row/bs;
2244: PetscMemzero(aa,count*bs*sizeof(MatScalar));
2245: }
2246: /* Now insert all the diagonal values for this bs */
2247: for (k=0; k<bs; k++) {
2248: (*A->ops->setvalues)(A,1,rows+j+k,1,rows+j+k,&diag,INSERT_VALUES);
2249: }
2250: } else { /* (diag == 0.0) */
2251: baij->ilen[row/bs] = 0;
2252: } /* end (diag == 0.0) */
2253: } else { /* (sizes[i] != bs) */
2254: #if defined (PETSC_USE_DEBUG)
2255: if (sizes[i] != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Internal Error. Value should be 1");
2256: #endif
2257: for (k=0; k<count; k++) {
2258: aa[0] = zero;
2259: aa += bs;
2260: }
2261: if (diag != (PetscScalar)0.0) {
2262: (*A->ops->setvalues)(A,1,rows+j,1,rows+j,&diag,INSERT_VALUES);
2263: }
2264: }
2265: }
2267: PetscFree2(rows,sizes);
2268: MatAssemblyEnd_SeqBAIJ(A,MAT_FINAL_ASSEMBLY);
2269: return(0);
2270: }
2274: PetscErrorCode MatZeroRowsColumns_SeqBAIJ(Mat A,PetscInt is_n,const PetscInt is_idx[],PetscScalar diag,Vec x, Vec b)
2275: {
2276: Mat_SeqBAIJ *baij=(Mat_SeqBAIJ*)A->data;
2277: PetscErrorCode ierr;
2278: PetscInt i,j,k,count;
2279: PetscInt bs=A->rmap->bs,bs2=baij->bs2,row,col;
2280: PetscScalar zero = 0.0;
2281: MatScalar *aa;
2282: const PetscScalar *xx;
2283: PetscScalar *bb;
2284: PetscBool *zeroed,vecs = PETSC_FALSE;
2287: /* fix right hand side if needed */
2288: if (x && b) {
2289: VecGetArrayRead(x,&xx);
2290: VecGetArray(b,&bb);
2291: vecs = PETSC_TRUE;
2292: }
2293: A->same_nonzero = PETSC_TRUE;
2295: /* zero the columns */
2296: PetscMalloc(A->rmap->n*sizeof(PetscBool),&zeroed);
2297: PetscMemzero(zeroed,A->rmap->n*sizeof(PetscBool));
2298: for (i=0; i<is_n; i++) {
2299: 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]);
2300: zeroed[is_idx[i]] = PETSC_TRUE;
2301: }
2302: for (i=0; i<A->rmap->N; i++) {
2303: if (!zeroed[i]) {
2304: row = i/bs;
2305: for (j=baij->i[row]; j<baij->i[row+1]; j++) {
2306: for (k=0; k<bs; k++) {
2307: col = bs*baij->j[j] + k;
2308: if (zeroed[col]) {
2309: aa = ((MatScalar*)(baij->a)) + j*bs2 + (i%bs) + bs*k;
2310: if (vecs) bb[i] -= aa[0]*xx[col];
2311: aa[0] = 0.0;
2312: }
2313: }
2314: }
2315: } else if (vecs) bb[i] = diag*xx[i];
2316: }
2317: PetscFree(zeroed);
2318: if (vecs) {
2319: VecRestoreArrayRead(x,&xx);
2320: VecRestoreArray(b,&bb);
2321: }
2323: /* zero the rows */
2324: for (i=0; i<is_n; i++) {
2325: row = is_idx[i];
2326: count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
2327: aa = ((MatScalar*)(baij->a)) + baij->i[row/bs]*bs2 + (row%bs);
2328: for (k=0; k<count; k++) {
2329: aa[0] = zero;
2330: aa += bs;
2331: }
2332: if (diag != (PetscScalar)0.0) {
2333: (*A->ops->setvalues)(A,1,&row,1,&row,&diag,INSERT_VALUES);
2334: }
2335: }
2336: MatAssemblyEnd_SeqBAIJ(A,MAT_FINAL_ASSEMBLY);
2337: return(0);
2338: }
2342: PetscErrorCode MatSetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
2343: {
2344: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2345: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,lastcol = -1;
2346: PetscInt *imax=a->imax,*ai=a->i,*ailen=a->ilen;
2347: PetscInt *aj=a->j,nonew=a->nonew,bs=A->rmap->bs,brow,bcol;
2349: PetscInt ridx,cidx,bs2=a->bs2;
2350: PetscBool roworiented=a->roworiented;
2351: MatScalar *ap,value,*aa=a->a,*bap;
2355: for (k=0; k<m; k++) { /* loop over added rows */
2356: row = im[k];
2357: brow = row/bs;
2358: if (row < 0) continue;
2359: #if defined(PETSC_USE_DEBUG)
2360: 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);
2361: #endif
2362: rp = aj + ai[brow];
2363: ap = aa + bs2*ai[brow];
2364: rmax = imax[brow];
2365: nrow = ailen[brow];
2366: low = 0;
2367: high = nrow;
2368: for (l=0; l<n; l++) { /* loop over added columns */
2369: if (in[l] < 0) continue;
2370: #if defined(PETSC_USE_DEBUG)
2371: 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);
2372: #endif
2373: col = in[l]; bcol = col/bs;
2374: ridx = row % bs; cidx = col % bs;
2375: if (roworiented) {
2376: value = v[l + k*n];
2377: } else {
2378: value = v[k + l*m];
2379: }
2380: if (col <= lastcol) low = 0; else high = nrow;
2381: lastcol = col;
2382: while (high-low > 7) {
2383: t = (low+high)/2;
2384: if (rp[t] > bcol) high = t;
2385: else low = t;
2386: }
2387: for (i=low; i<high; i++) {
2388: if (rp[i] > bcol) break;
2389: if (rp[i] == bcol) {
2390: bap = ap + bs2*i + bs*cidx + ridx;
2391: if (is == ADD_VALUES) *bap += value;
2392: else *bap = value;
2393: goto noinsert1;
2394: }
2395: }
2396: if (nonew == 1) goto noinsert1;
2397: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
2398: MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
2399: N = nrow++ - 1; high++;
2400: /* shift up all the later entries in this row */
2401: for (ii=N; ii>=i; ii--) {
2402: rp[ii+1] = rp[ii];
2403: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
2404: }
2405: if (N>=i) {
2406: PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
2407: }
2408: rp[i] = bcol;
2409: ap[bs2*i + bs*cidx + ridx] = value;
2410: a->nz++;
2411: noinsert1:;
2412: low = i;
2413: }
2414: ailen[brow] = nrow;
2415: }
2416: A->same_nonzero = PETSC_FALSE;
2417: return(0);
2418: }
2422: PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2423: {
2424: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)inA->data;
2425: Mat outA;
2427: PetscBool row_identity,col_identity;
2430: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels = 0 supported for in-place ILU");
2431: ISIdentity(row,&row_identity);
2432: ISIdentity(col,&col_identity);
2433: if (!row_identity || !col_identity) {
2434: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU");
2435: }
2437: outA = inA;
2438: inA->factortype = MAT_FACTOR_LU;
2440: MatMarkDiagonal_SeqBAIJ(inA);
2442: PetscObjectReference((PetscObject)row);
2443: ISDestroy(&a->row);
2444: a->row = row;
2445: PetscObjectReference((PetscObject)col);
2446: ISDestroy(&a->col);
2447: a->col = col;
2448:
2449: /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
2450: ISDestroy(&a->icol);
2451: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2452: PetscLogObjectParent(inA,a->icol);
2453:
2454: MatSeqBAIJSetNumericFactorization_inplace(inA,(PetscBool)(row_identity && col_identity));
2455: if (!a->solve_work) {
2456: PetscMalloc((inA->rmap->N+inA->rmap->bs)*sizeof(PetscScalar),&a->solve_work);
2457: PetscLogObjectMemory(inA,(inA->rmap->N+inA->rmap->bs)*sizeof(PetscScalar));
2458: }
2459: MatLUFactorNumeric(outA,inA,info);
2461: return(0);
2462: }
2467: PetscErrorCode MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat,PetscInt *indices)
2468: {
2469: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data;
2470: PetscInt i,nz,mbs;
2473: nz = baij->maxnz;
2474: mbs = baij->mbs;
2475: for (i=0; i<nz; i++) {
2476: baij->j[i] = indices[i];
2477: }
2478: baij->nz = nz;
2479: for (i=0; i<mbs; i++) {
2480: baij->ilen[i] = baij->imax[i];
2481: }
2482: return(0);
2483: }
2488: /*@
2489: MatSeqBAIJSetColumnIndices - Set the column indices for all the rows
2490: in the matrix.
2492: Input Parameters:
2493: + mat - the SeqBAIJ matrix
2494: - indices - the column indices
2496: Level: advanced
2498: Notes:
2499: This can be called if you have precomputed the nonzero structure of the
2500: matrix and want to provide it to the matrix object to improve the performance
2501: of the MatSetValues() operation.
2503: You MUST have set the correct numbers of nonzeros per row in the call to
2504: MatCreateSeqBAIJ(), and the columns indices MUST be sorted.
2506: MUST be called before any calls to MatSetValues();
2508: @*/
2509: PetscErrorCode MatSeqBAIJSetColumnIndices(Mat mat,PetscInt *indices)
2510: {
2516: PetscUseMethod(mat,"MatSeqBAIJSetColumnIndices_C",(Mat,PetscInt *),(mat,indices));
2517: return(0);
2518: }
2522: PetscErrorCode MatGetRowMaxAbs_SeqBAIJ(Mat A,Vec v,PetscInt idx[])
2523: {
2524: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2526: PetscInt i,j,n,row,bs,*ai,*aj,mbs;
2527: PetscReal atmp;
2528: PetscScalar *x,zero = 0.0;
2529: MatScalar *aa;
2530: PetscInt ncols,brow,krow,kcol;
2533: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2534: bs = A->rmap->bs;
2535: aa = a->a;
2536: ai = a->i;
2537: aj = a->j;
2538: mbs = a->mbs;
2540: VecSet(v,zero);
2541: VecGetArray(v,&x);
2542: VecGetLocalSize(v,&n);
2543: if (n != A->rmap->N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2544: for (i=0; i<mbs; i++) {
2545: ncols = ai[1] - ai[0]; ai++;
2546: brow = bs*i;
2547: for (j=0; j<ncols; j++){
2548: for (kcol=0; kcol<bs; kcol++){
2549: for (krow=0; krow<bs; krow++){
2550: atmp = PetscAbsScalar(*aa);aa++;
2551: row = brow + krow; /* row index */
2552: /* printf("val[%d,%d]: %G\n",row,bcol+kcol,atmp); */
2553: if (PetscAbsScalar(x[row]) < atmp) {x[row] = atmp; if (idx) idx[row] = bs*(*aj) + kcol;}
2554: }
2555: }
2556: aj++;
2557: }
2558: }
2559: VecRestoreArray(v,&x);
2560: return(0);
2561: }
2565: PetscErrorCode MatCopy_SeqBAIJ(Mat A,Mat B,MatStructure str)
2566: {
2570: /* If the two matrices have the same copy implementation, use fast copy. */
2571: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2572: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2573: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)B->data;
2574: PetscInt ambs=a->mbs,bmbs=b->mbs,abs=A->rmap->bs,bbs=B->rmap->bs,bs2=abs*abs;
2576: 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]);
2577: if (abs != bbs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Block size A %D and B %D are different",abs,bbs);
2578: PetscMemcpy(b->a,a->a,(bs2*a->i[ambs])*sizeof(PetscScalar));
2579: } else {
2580: MatCopy_Basic(A,B,str);
2581: }
2582: return(0);
2583: }
2587: PetscErrorCode MatSetUpPreallocation_SeqBAIJ(Mat A)
2588: {
2592: MatSeqBAIJSetPreallocation_SeqBAIJ(A,-PetscMax(A->rmap->bs,1),PETSC_DEFAULT,0);
2593: return(0);
2594: }
2598: PetscErrorCode MatGetArray_SeqBAIJ(Mat A,PetscScalar *array[])
2599: {
2600: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2602: *array = a->a;
2603: return(0);
2604: }
2608: PetscErrorCode MatRestoreArray_SeqBAIJ(Mat A,PetscScalar *array[])
2609: {
2611: return(0);
2612: }
2616: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2617: {
2618: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data,*y = (Mat_SeqBAIJ *)Y->data;
2620: PetscInt i,bs=Y->rmap->bs,j,bs2=bs*bs;
2621: PetscBLASInt one=1;
2624: if (str == SAME_NONZERO_PATTERN) {
2625: PetscScalar alpha = a;
2626: PetscBLASInt bnz = PetscBLASIntCast(x->nz*bs2);
2627: BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2628: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2629: if (y->xtoy && y->XtoY != X) {
2630: PetscFree(y->xtoy);
2631: MatDestroy(&y->XtoY);
2632: }
2633: if (!y->xtoy) { /* get xtoy */
2634: MatAXPYGetxtoy_Private(x->mbs,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2635: y->XtoY = X;
2636: PetscObjectReference((PetscObject)X);
2637: }
2638: for (i=0; i<x->nz; i++) {
2639: j = 0;
2640: while (j < bs2){
2641: y->a[bs2*y->xtoy[i]+j] += a*(x->a[bs2*i+j]);
2642: j++;
2643: }
2644: }
2645: PetscInfo3(Y,"ratio of nnz(X)/nnz(Y): %D/%D = %G\n",bs2*x->nz,bs2*y->nz,(PetscReal)(bs2*x->nz)/(bs2*y->nz));
2646: } else {
2647: MatAXPY_Basic(Y,a,X,str);
2648: }
2649: return(0);
2650: }
2654: PetscErrorCode MatSetBlockSize_SeqBAIJ(Mat A,PetscInt bs)
2655: {
2656: PetscInt rbs,cbs;
2660: PetscLayoutGetBlockSize(A->rmap,&rbs);
2661: PetscLayoutGetBlockSize(A->cmap,&cbs);
2662: if (rbs != bs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Attempt to set block size %d with BAIJ %d",bs,rbs);
2663: if (cbs != bs) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Attempt to set block size %d with BAIJ %d",bs,cbs);
2664: return(0);
2665: }
2669: PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
2670: {
2671: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2672: PetscInt i,nz = a->bs2*a->i[a->mbs];
2673: MatScalar *aa = a->a;
2676: for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
2677: return(0);
2678: }
2682: PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2683: {
2684: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2685: PetscInt i,nz = a->bs2*a->i[a->mbs];
2686: MatScalar *aa = a->a;
2689: for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2690: return(0);
2691: }
2697: /*
2698: Code almost idential to MatGetColumnIJ_SeqAIJ() should share common code
2699: */
2700: PetscErrorCode MatGetColumnIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscBool *done)
2701: {
2702: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2704: PetscInt bs = A->rmap->bs,i,*collengths,*cia,*cja,n = A->cmap->n/bs,m = A->rmap->n/bs;
2705: PetscInt nz = a->i[m],row,*jj,mr,col;
2708: *nn = n;
2709: if (!ia) return(0);
2710: if (symmetric) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not for BAIJ matrices");
2711: else {
2712: PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
2713: PetscMemzero(collengths,n*sizeof(PetscInt));
2714: PetscMalloc((n+1)*sizeof(PetscInt),&cia);
2715: PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
2716: jj = a->j;
2717: for (i=0; i<nz; i++) {
2718: collengths[jj[i]]++;
2719: }
2720: cia[0] = oshift;
2721: for (i=0; i<n; i++) {
2722: cia[i+1] = cia[i] + collengths[i];
2723: }
2724: PetscMemzero(collengths,n*sizeof(PetscInt));
2725: jj = a->j;
2726: for (row=0; row<m; row++) {
2727: mr = a->i[row+1] - a->i[row];
2728: for (i=0; i<mr; i++) {
2729: col = *jj++;
2730: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
2731: }
2732: }
2733: PetscFree(collengths);
2734: *ia = cia; *ja = cja;
2735: }
2736: return(0);
2737: }
2741: PetscErrorCode MatRestoreColumnIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscBool *done)
2742: {
2746: if (!ia) return(0);
2747: PetscFree(*ia);
2748: PetscFree(*ja);
2749: return(0);
2750: }
2754: PetscErrorCode MatFDColoringApply_BAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
2755: {
2756: PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
2758: PetscInt bs = J->rmap->bs,i,j,k,start,end,l,row,col,*srows,**vscaleforrow,m1,m2;
2759: PetscScalar dx,*y,*xx,*w3_array;
2760: PetscScalar *vscale_array;
2761: PetscReal epsilon = coloring->error_rel,umin = coloring->umin,unorm;
2762: Vec w1=coloring->w1,w2=coloring->w2,w3;
2763: void *fctx = coloring->fctx;
2764: PetscBool flg = PETSC_FALSE;
2765: PetscInt ctype=coloring->ctype,N,col_start=0,col_end=0;
2766: Vec x1_tmp;
2772: if (!f) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call MatFDColoringSetFunction()");
2774: PetscLogEventBegin(MAT_FDColoringApply,coloring,J,x1,0);
2775: MatSetUnfactored(J);
2776: PetscOptionsGetBool(PETSC_NULL,"-mat_fd_coloring_dont_rezero",&flg,PETSC_NULL);
2777: if (flg) {
2778: PetscInfo(coloring,"Not calling MatZeroEntries()\n");
2779: } else {
2780: PetscBool assembled;
2781: MatAssembled(J,&assembled);
2782: if (assembled) {
2783: MatZeroEntries(J);
2784: }
2785: }
2787: x1_tmp = x1;
2788: if (!coloring->vscale){
2789: VecDuplicate(x1_tmp,&coloring->vscale);
2790: }
2791:
2792: /*
2793: This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
2794: coloring->F for the coarser grids from the finest
2795: */
2796: if (coloring->F) {
2797: VecGetLocalSize(coloring->F,&m1);
2798: VecGetLocalSize(w1,&m2);
2799: if (m1 != m2) {
2800: coloring->F = 0;
2801: }
2802: }
2804: if (coloring->htype[0] == 'w') { /* tacky test; need to make systematic if we add other approaches to computing h*/
2805: VecNorm(x1_tmp,NORM_2,&unorm);
2806: }
2807: VecGetOwnershipRange(w1,&start,&end); /* OwnershipRange is used by ghosted x! */
2808:
2809: /* Set w1 = F(x1) */
2810: if (coloring->F) {
2811: w1 = coloring->F; /* use already computed value of function */
2812: coloring->F = 0;
2813: } else {
2814: PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2815: (*f)(sctx,x1_tmp,w1,fctx);
2816: PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2817: }
2818:
2819: if (!coloring->w3) {
2820: VecDuplicate(x1_tmp,&coloring->w3);
2821: PetscLogObjectParent(coloring,coloring->w3);
2822: }
2823: w3 = coloring->w3;
2825: CHKMEMQ;
2826: /* Compute all the local scale factors, including ghost points */
2827: VecGetLocalSize(x1_tmp,&N);
2828: VecGetArray(x1_tmp,&xx);
2829: VecGetArray(coloring->vscale,&vscale_array);
2830: if (ctype == IS_COLORING_GHOSTED){
2831: col_start = 0; col_end = N;
2832: } else if (ctype == IS_COLORING_GLOBAL){
2833: xx = xx - start;
2834: vscale_array = vscale_array - start;
2835: col_start = start; col_end = N + start;
2836: } CHKMEMQ;
2837: for (col=col_start; col<col_end; col++){
2838: /* Loop over each local column, vscale[col] = 1./(epsilon*dx[col]) */
2839: if (coloring->htype[0] == 'w') {
2840: dx = 1.0 + unorm;
2841: } else {
2842: dx = xx[col];
2843: }
2844: if (dx == (PetscScalar)0.0) dx = 1.0;
2845: #if !defined(PETSC_USE_COMPLEX)
2846: if (dx < umin && dx >= 0.0) dx = umin;
2847: else if (dx < 0.0 && dx > -umin) dx = -umin;
2848: #else
2849: if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin;
2850: else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2851: #endif
2852: dx *= epsilon;
2853: vscale_array[col] = (PetscScalar)1.0/dx;
2854: } CHKMEMQ;
2855: if (ctype == IS_COLORING_GLOBAL) vscale_array = vscale_array + start;
2856: VecRestoreArray(coloring->vscale,&vscale_array);
2857: if (ctype == IS_COLORING_GLOBAL){
2858: VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2859: VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2860: }
2861: CHKMEMQ;
2862: if (coloring->vscaleforrow) {
2863: vscaleforrow = coloring->vscaleforrow;
2864: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"Null Object: coloring->vscaleforrow");
2866: PetscMalloc(bs*sizeof(PetscInt),&srows);
2867: /*
2868: Loop over each color
2869: */
2870: VecGetArray(coloring->vscale,&vscale_array);
2871: for (k=0; k<coloring->ncolors; k++) {
2872: coloring->currentcolor = k;
2873: for (i=0; i<bs; i++) {
2874: VecCopy(x1_tmp,w3);
2875: VecGetArray(w3,&w3_array);
2876: if (ctype == IS_COLORING_GLOBAL) w3_array = w3_array - start;
2877: /*
2878: Loop over each column associated with color
2879: adding the perturbation to the vector w3.
2880: */
2881: for (l=0; l<coloring->ncolumns[k]; l++) {
2882: col = i + bs*coloring->columns[k][l]; /* local column of the matrix we are probing for */
2883: if (coloring->htype[0] == 'w') {
2884: dx = 1.0 + unorm;
2885: } else {
2886: dx = xx[col];
2887: }
2888: if (dx == (PetscScalar)0.0) dx = 1.0;
2889: #if !defined(PETSC_USE_COMPLEX)
2890: if (dx < umin && dx >= 0.0) dx = umin;
2891: else if (dx < 0.0 && dx > -umin) dx = -umin;
2892: #else
2893: if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin;
2894: else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2895: #endif
2896: dx *= epsilon;
2897: if (!PetscAbsScalar(dx)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Computed 0 differencing parameter");
2898: w3_array[col] += dx;
2899: }
2900: if (ctype == IS_COLORING_GLOBAL) w3_array = w3_array + start;
2901: VecRestoreArray(w3,&w3_array);
2903: /*
2904: Evaluate function at w3 = x1 + dx (here dx is a vector of perturbations)
2905: w2 = F(x1 + dx) - F(x1)
2906: */
2907: PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2908: (*f)(sctx,w3,w2,fctx);
2909: PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2910: VecAXPY(w2,-1.0,w1);
2911:
2912: /*
2913: Loop over rows of vector, putting results into Jacobian matrix
2914: */
2915: VecGetArray(w2,&y);
2916: for (l=0; l<coloring->nrows[k]; l++) {
2917: row = bs*coloring->rows[k][l]; /* local row index */
2918: col = i + bs*coloring->columnsforrow[k][l]; /* global column index */
2919: for (j=0; j<bs; j++) {
2920: y[row+j] *= vscale_array[j+bs*vscaleforrow[k][l]];
2921: srows[j] = row + start + j;
2922: }
2923: MatSetValues(J,bs,srows,1,&col,y+row,INSERT_VALUES);
2924: }
2925: VecRestoreArray(w2,&y);
2926: }
2927: } /* endof for each color */
2928: if (ctype == IS_COLORING_GLOBAL) xx = xx + start;
2929: VecRestoreArray(coloring->vscale,&vscale_array);
2930: VecRestoreArray(x1_tmp,&xx);
2931: PetscFree(srows);
2932:
2933: coloring->currentcolor = -1;
2934: MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2935: MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2936: PetscLogEventEnd(MAT_FDColoringApply,coloring,J,x1,0);
2937: return(0);
2938: }
2940: /* -------------------------------------------------------------------*/
2941: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
2942: MatGetRow_SeqBAIJ,
2943: MatRestoreRow_SeqBAIJ,
2944: MatMult_SeqBAIJ_N,
2945: /* 4*/ MatMultAdd_SeqBAIJ_N,
2946: MatMultTranspose_SeqBAIJ,
2947: MatMultTransposeAdd_SeqBAIJ,
2948: 0,
2949: 0,
2950: 0,
2951: /*10*/ 0,
2952: MatLUFactor_SeqBAIJ,
2953: 0,
2954: 0,
2955: MatTranspose_SeqBAIJ,
2956: /*15*/ MatGetInfo_SeqBAIJ,
2957: MatEqual_SeqBAIJ,
2958: MatGetDiagonal_SeqBAIJ,
2959: MatDiagonalScale_SeqBAIJ,
2960: MatNorm_SeqBAIJ,
2961: /*20*/ 0,
2962: MatAssemblyEnd_SeqBAIJ,
2963: MatSetOption_SeqBAIJ,
2964: MatZeroEntries_SeqBAIJ,
2965: /*24*/ MatZeroRows_SeqBAIJ,
2966: 0,
2967: 0,
2968: 0,
2969: 0,
2970: /*29*/ MatSetUpPreallocation_SeqBAIJ,
2971: 0,
2972: 0,
2973: MatGetArray_SeqBAIJ,
2974: MatRestoreArray_SeqBAIJ,
2975: /*34*/ MatDuplicate_SeqBAIJ,
2976: 0,
2977: 0,
2978: MatILUFactor_SeqBAIJ,
2979: 0,
2980: /*39*/ MatAXPY_SeqBAIJ,
2981: MatGetSubMatrices_SeqBAIJ,
2982: MatIncreaseOverlap_SeqBAIJ,
2983: MatGetValues_SeqBAIJ,
2984: MatCopy_SeqBAIJ,
2985: /*44*/ 0,
2986: MatScale_SeqBAIJ,
2987: 0,
2988: 0,
2989: MatZeroRowsColumns_SeqBAIJ,
2990: /*49*/ MatSetBlockSize_SeqBAIJ,
2991: MatGetRowIJ_SeqBAIJ,
2992: MatRestoreRowIJ_SeqBAIJ,
2993: MatGetColumnIJ_SeqBAIJ,
2994: MatRestoreColumnIJ_SeqBAIJ,
2995: /*54*/ MatFDColoringCreate_SeqAIJ,
2996: 0,
2997: 0,
2998: 0,
2999: MatSetValuesBlocked_SeqBAIJ,
3000: /*59*/ MatGetSubMatrix_SeqBAIJ,
3001: MatDestroy_SeqBAIJ,
3002: MatView_SeqBAIJ,
3003: 0,
3004: 0,
3005: /*64*/ 0,
3006: 0,
3007: 0,
3008: 0,
3009: 0,
3010: /*69*/ MatGetRowMaxAbs_SeqBAIJ,
3011: 0,
3012: MatConvert_Basic,
3013: 0,
3014: 0,
3015: /*74*/ 0,
3016: MatFDColoringApply_BAIJ,
3017: 0,
3018: 0,
3019: 0,
3020: /*79*/ 0,
3021: 0,
3022: 0,
3023: 0,
3024: MatLoad_SeqBAIJ,
3025: /*84*/ 0,
3026: 0,
3027: 0,
3028: 0,
3029: 0,
3030: /*89*/ 0,
3031: 0,
3032: 0,
3033: 0,
3034: 0,
3035: /*94*/ 0,
3036: 0,
3037: 0,
3038: 0,
3039: 0,
3040: /*99*/0,
3041: 0,
3042: 0,
3043: 0,
3044: 0,
3045: /*104*/0,
3046: MatRealPart_SeqBAIJ,
3047: MatImaginaryPart_SeqBAIJ,
3048: 0,
3049: 0,
3050: /*109*/0,
3051: 0,
3052: 0,
3053: 0,
3054: MatMissingDiagonal_SeqBAIJ,
3055: /*114*/0,
3056: 0,
3057: 0,
3058: 0,
3059: 0,
3060: /*119*/0,
3061: 0,
3062: MatMultHermitianTranspose_SeqBAIJ,
3063: MatMultHermitianTransposeAdd_SeqBAIJ,
3064: 0,
3065: /*124*/0,
3066: 0,
3067: MatInvertBlockDiagonal_SeqBAIJ
3068: };
3073: PetscErrorCode MatStoreValues_SeqBAIJ(Mat mat)
3074: {
3075: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
3076: PetscInt nz = aij->i[mat->rmap->N]*mat->rmap->bs*aij->bs2;
3080: if (aij->nonew != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3082: /* allocate space for values if not already there */
3083: if (!aij->saved_values) {
3084: PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
3085: PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
3086: }
3088: /* copy values over */
3089: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3090: return(0);
3091: }
3097: PetscErrorCode MatRetrieveValues_SeqBAIJ(Mat mat)
3098: {
3099: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
3101: PetscInt nz = aij->i[mat->rmap->N]*mat->rmap->bs*aij->bs2;
3104: if (aij->nonew != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3105: if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3107: /* copy values over */
3108: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3109: return(0);
3110: }
3121: PetscErrorCode MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B,PetscInt bs,PetscInt nz,PetscInt *nnz)
3122: {
3123: Mat_SeqBAIJ *b;
3125: PetscInt i,mbs,nbs,bs2,newbs = PetscAbs(bs);
3126: PetscBool flg,skipallocation = PETSC_FALSE;
3130: if (nz == MAT_SKIP_ALLOCATION) {
3131: skipallocation = PETSC_TRUE;
3132: nz = 0;
3133: }
3135: if (bs < 0) {
3136: PetscOptionsBegin(((PetscObject)B)->comm,((PetscObject)B)->prefix,"Block options for SEQBAIJ matrix 1","Mat");
3137: PetscOptionsInt("-mat_block_size","Set the blocksize used to store the matrix","MatSeqBAIJSetPreallocation",newbs,&newbs,PETSC_NULL);
3138: PetscOptionsEnd();
3139: bs = PetscAbs(bs);
3140: }
3141: if (nnz && newbs != bs) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Cannot change blocksize from command line if setting nnz");
3142: bs = newbs;
3144: PetscLayoutSetBlockSize(B->rmap,bs);
3145: PetscLayoutSetBlockSize(B->cmap,bs);
3146: PetscLayoutSetUp(B->rmap);
3147: PetscLayoutSetUp(B->cmap);
3149: B->preallocated = PETSC_TRUE;
3151: mbs = B->rmap->n/bs;
3152: nbs = B->cmap->n/bs;
3153: bs2 = bs*bs;
3155: 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);
3157: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3158: if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
3159: if (nnz) {
3160: for (i=0; i<mbs; i++) {
3161: 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]);
3162: 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);
3163: }
3164: }
3166: b = (Mat_SeqBAIJ*)B->data;
3167: PetscOptionsBegin(((PetscObject)B)->comm,PETSC_NULL,"Optimize options for SEQBAIJ matrix 2 ","Mat");
3168: PetscOptionsBool("-mat_no_unroll","Do not optimize for block size (slow)",PETSC_NULL,PETSC_FALSE,&flg,PETSC_NULL);
3169: PetscOptionsEnd();
3171: if (!flg) {
3172: switch (bs) {
3173: case 1:
3174: B->ops->mult = MatMult_SeqBAIJ_1;
3175: B->ops->multadd = MatMultAdd_SeqBAIJ_1;
3176: B->ops->sor = MatSOR_SeqBAIJ_1;
3177: break;
3178: case 2:
3179: B->ops->mult = MatMult_SeqBAIJ_2;
3180: B->ops->multadd = MatMultAdd_SeqBAIJ_2;
3181: B->ops->sor = MatSOR_SeqBAIJ_2;
3182: break;
3183: case 3:
3184: B->ops->mult = MatMult_SeqBAIJ_3;
3185: B->ops->multadd = MatMultAdd_SeqBAIJ_3;
3186: B->ops->sor = MatSOR_SeqBAIJ_3;
3187: break;
3188: case 4:
3189: B->ops->mult = MatMult_SeqBAIJ_4;
3190: B->ops->multadd = MatMultAdd_SeqBAIJ_4;
3191: B->ops->sor = MatSOR_SeqBAIJ_4;
3192: break;
3193: case 5:
3194: B->ops->mult = MatMult_SeqBAIJ_5;
3195: B->ops->multadd = MatMultAdd_SeqBAIJ_5;
3196: B->ops->sor = MatSOR_SeqBAIJ_5;
3197: break;
3198: case 6:
3199: B->ops->mult = MatMult_SeqBAIJ_6;
3200: B->ops->multadd = MatMultAdd_SeqBAIJ_6;
3201: B->ops->sor = MatSOR_SeqBAIJ_6;
3202: break;
3203: case 7:
3204: B->ops->mult = MatMult_SeqBAIJ_7;
3205: B->ops->multadd = MatMultAdd_SeqBAIJ_7;
3206: B->ops->sor = MatSOR_SeqBAIJ_7;
3207: break;
3208: case 15:
3209: B->ops->mult = MatMult_SeqBAIJ_15_ver1;
3210: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3211: B->ops->sor = MatSOR_SeqBAIJ_N;
3212: break;
3213: default:
3214: B->ops->mult = MatMult_SeqBAIJ_N;
3215: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
3216: B->ops->sor = MatSOR_SeqBAIJ_N;
3217: break;
3218: }
3219: }
3220: B->rmap->bs = bs;
3221: b->mbs = mbs;
3222: b->nbs = nbs;
3223: if (!skipallocation) {
3224: if (!b->imax) {
3225: PetscMalloc2(mbs,PetscInt,&b->imax,mbs,PetscInt,&b->ilen);
3226: PetscLogObjectMemory(B,2*mbs*sizeof(PetscInt));
3227: b->free_imax_ilen = PETSC_TRUE;
3228: }
3229: /* b->ilen will count nonzeros in each block row so far. */
3230: for (i=0; i<mbs; i++) { b->ilen[i] = 0;}
3231: if (!nnz) {
3232: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3233: else if (nz < 0) nz = 1;
3234: for (i=0; i<mbs; i++) b->imax[i] = nz;
3235: nz = nz*mbs;
3236: } else {
3237: nz = 0;
3238: for (i=0; i<mbs; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3239: }
3241: /* allocate the matrix space */
3242: MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3243: PetscMalloc3(bs2*nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap->N+1,PetscInt,&b->i);
3244: PetscLogObjectMemory(B,(B->rmap->N+1)*sizeof(PetscInt)+nz*(bs2*sizeof(PetscScalar)+sizeof(PetscInt)));
3245: PetscMemzero(b->a,nz*bs2*sizeof(MatScalar));
3246: PetscMemzero(b->j,nz*sizeof(PetscInt));
3247: b->singlemalloc = PETSC_TRUE;
3248: b->i[0] = 0;
3249: for (i=1; i<mbs+1; i++) {
3250: b->i[i] = b->i[i-1] + b->imax[i-1];
3251: }
3252: b->free_a = PETSC_TRUE;
3253: b->free_ij = PETSC_TRUE;
3254: } else {
3255: b->free_a = PETSC_FALSE;
3256: b->free_ij = PETSC_FALSE;
3257: }
3259: B->rmap->bs = bs;
3260: b->bs2 = bs2;
3261: b->mbs = mbs;
3262: b->nz = 0;
3263: b->maxnz = nz;
3264: B->info.nz_unneeded = (PetscReal)b->maxnz*bs2;
3265: return(0);
3266: }
3272: PetscErrorCode MatSeqBAIJSetPreallocationCSR_SeqBAIJ(Mat B,PetscInt bs,const PetscInt ii[],const PetscInt jj[],const PetscScalar V[])
3273: {
3274: PetscInt i,m,nz,nz_max=0,*nnz;
3275: PetscScalar *values=0;
3279: if (bs < 1) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Invalid block size specified, must be positive but it is %D",bs);
3280: PetscLayoutSetBlockSize(B->rmap,bs);
3281: PetscLayoutSetBlockSize(B->cmap,bs);
3282: PetscLayoutSetUp(B->rmap);
3283: PetscLayoutSetUp(B->cmap);
3284: m = B->rmap->n/bs;
3286: if (ii[0] != 0) { SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "ii[0] must be 0 but it is %D",ii[0]); }
3287: PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
3288: for(i=0; i<m; i++) {
3289: nz = ii[i+1]- ii[i];
3290: if (nz < 0) { SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D",i,nz); }
3291: nz_max = PetscMax(nz_max, nz);
3292: nnz[i] = nz;
3293: }
3294: MatSeqBAIJSetPreallocation(B,bs,0,nnz);
3295: PetscFree(nnz);
3297: values = (PetscScalar*)V;
3298: if (!values) {
3299: PetscMalloc(bs*bs*(nz_max+1)*sizeof(PetscScalar),&values);
3300: PetscMemzero(values,bs*bs*nz_max*sizeof(PetscScalar));
3301: }
3302: for (i=0; i<m; i++) {
3303: PetscInt ncols = ii[i+1] - ii[i];
3304: const PetscInt *icols = jj + ii[i];
3305: const PetscScalar *svals = values + (V ? (bs*bs*ii[i]) : 0);
3306: MatSetValuesBlocked_SeqBAIJ(B,1,&i,ncols,icols,svals,INSERT_VALUES);
3307: }
3308: if (!V) { PetscFree(values); }
3309: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3310: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3312: return(0);
3313: }
3320: #if defined(PETSC_HAVE_MUMPS)
3322: #endif
3326: /*MC
3327: MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on
3328: block sparse compressed row format.
3330: Options Database Keys:
3331: . -mat_type seqbaij - sets the matrix type to "seqbaij" during a call to MatSetFromOptions()
3333: Level: beginner
3335: .seealso: MatCreateSeqBAIJ()
3336: M*/
3345: PetscErrorCode MatCreate_SeqBAIJ(Mat B)
3346: {
3348: PetscMPIInt size;
3349: Mat_SeqBAIJ *b;
3352: MPI_Comm_size(((PetscObject)B)->comm,&size);
3353: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Comm must be of size 1");
3355: PetscNewLog(B,Mat_SeqBAIJ,&b);
3356: B->data = (void*)b;
3357: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3358: b->row = 0;
3359: b->col = 0;
3360: b->icol = 0;
3361: b->reallocs = 0;
3362: b->saved_values = 0;
3364: b->roworiented = PETSC_TRUE;
3365: b->nonew = 0;
3366: b->diag = 0;
3367: b->solve_work = 0;
3368: b->mult_work = 0;
3369: B->spptr = 0;
3370: B->info.nz_unneeded = (PetscReal)b->maxnz*b->bs2;
3371: b->keepnonzeropattern = PETSC_FALSE;
3372: b->xtoy = 0;
3373: b->XtoY = 0;
3374: B->same_nonzero = PETSC_FALSE;
3376: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactorAvailable_petsc_C",
3377: "MatGetFactorAvailable_seqbaij_petsc",
3378: MatGetFactorAvailable_seqbaij_petsc);
3379: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_petsc_C",
3380: "MatGetFactor_seqbaij_petsc",
3381: MatGetFactor_seqbaij_petsc);
3382: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_bstrm_C",
3383: "MatGetFactor_seqbaij_bstrm",
3384: MatGetFactor_seqbaij_bstrm);
3385: #if defined(PETSC_HAVE_MUMPS)
3386: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mumps_C", "MatGetFactor_baij_mumps", MatGetFactor_baij_mumps);
3387: #endif
3388: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatInvertBlockDiagonal_C",
3389: "MatInvertBlockDiagonal_SeqBAIJ",
3390: MatInvertBlockDiagonal_SeqBAIJ);
3391: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
3392: "MatStoreValues_SeqBAIJ",
3393: MatStoreValues_SeqBAIJ);
3394: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
3395: "MatRetrieveValues_SeqBAIJ",
3396: MatRetrieveValues_SeqBAIJ);
3397: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetColumnIndices_C",
3398: "MatSeqBAIJSetColumnIndices_SeqBAIJ",
3399: MatSeqBAIJSetColumnIndices_SeqBAIJ);
3400: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqaij_C",
3401: "MatConvert_SeqBAIJ_SeqAIJ",
3402: MatConvert_SeqBAIJ_SeqAIJ);
3403: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqsbaij_C",
3404: "MatConvert_SeqBAIJ_SeqSBAIJ",
3405: MatConvert_SeqBAIJ_SeqSBAIJ);
3406: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetPreallocation_C",
3407: "MatSeqBAIJSetPreallocation_SeqBAIJ",
3408: MatSeqBAIJSetPreallocation_SeqBAIJ);
3409: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetPreallocationCSR_C",
3410: "MatSeqBAIJSetPreallocationCSR_SeqBAIJ",
3411: MatSeqBAIJSetPreallocationCSR_SeqBAIJ);
3412: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqbstrm_C",
3413: "MatConvert_SeqBAIJ_SeqBSTRM",
3414: MatConvert_SeqBAIJ_SeqBSTRM);
3415: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
3416: "MatIsTranspose_SeqBAIJ",
3417: MatIsTranspose_SeqBAIJ);
3418: PetscObjectChangeTypeName((PetscObject)B,MATSEQBAIJ);
3419: return(0);
3420: }
3425: PetscErrorCode MatDuplicateNoCreate_SeqBAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
3426: {
3427: Mat_SeqBAIJ *c = (Mat_SeqBAIJ*)C->data,*a = (Mat_SeqBAIJ*)A->data;
3429: PetscInt i,mbs = a->mbs,nz = a->nz,bs2 = a->bs2;
3432: if (a->i[mbs] != nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Corrupt matrix");
3434: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3435: c->imax = a->imax;
3436: c->ilen = a->ilen;
3437: c->free_imax_ilen = PETSC_FALSE;
3438: } else {
3439: PetscMalloc2(mbs,PetscInt,&c->imax,mbs,PetscInt,&c->ilen);
3440: PetscLogObjectMemory(C,2*mbs*sizeof(PetscInt));
3441: for (i=0; i<mbs; i++) {
3442: c->imax[i] = a->imax[i];
3443: c->ilen[i] = a->ilen[i];
3444: }
3445: c->free_imax_ilen = PETSC_TRUE;
3446: }
3448: /* allocate the matrix space */
3449: if (mallocmatspace){
3450: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3451: PetscMalloc(bs2*nz*sizeof(PetscScalar),&c->a);
3452: PetscLogObjectMemory(C,a->i[mbs]*bs2*sizeof(PetscScalar));
3453: c->singlemalloc = PETSC_FALSE;
3454: c->free_ij = PETSC_FALSE;
3455: c->i = a->i;
3456: c->j = a->j;
3457: c->parent = A;
3458: PetscObjectReference((PetscObject)A);
3459: MatSetOption(A,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3460: MatSetOption(C,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3461: } else {
3462: PetscMalloc3(bs2*nz,PetscScalar,&c->a,nz,PetscInt,&c->j,mbs+1,PetscInt,&c->i);
3463: PetscLogObjectMemory(C,a->i[mbs]*(bs2*sizeof(PetscScalar)+sizeof(PetscInt))+(mbs+1)*sizeof(PetscInt));
3464: c->singlemalloc = PETSC_TRUE;
3465: c->free_ij = PETSC_TRUE;
3466: PetscMemcpy(c->i,a->i,(mbs+1)*sizeof(PetscInt));
3467: if (mbs > 0) {
3468: PetscMemcpy(c->j,a->j,nz*sizeof(PetscInt));
3469: if (cpvalues == MAT_COPY_VALUES) {
3470: PetscMemcpy(c->a,a->a,bs2*nz*sizeof(MatScalar));
3471: } else {
3472: PetscMemzero(c->a,bs2*nz*sizeof(MatScalar));
3473: }
3474: }
3475: }
3476: }
3478: c->roworiented = a->roworiented;
3479: c->nonew = a->nonew;
3480: PetscLayoutReference(A->rmap,&C->rmap);
3481: PetscLayoutReference(A->cmap,&C->cmap);
3482: c->bs2 = a->bs2;
3483: c->mbs = a->mbs;
3484: c->nbs = a->nbs;
3486: if (a->diag) {
3487: if (cpvalues == MAT_SHARE_NONZERO_PATTERN) {
3488: c->diag = a->diag;
3489: c->free_diag = PETSC_FALSE;
3490: } else {
3491: PetscMalloc((mbs+1)*sizeof(PetscInt),&c->diag);
3492: PetscLogObjectMemory(C,(mbs+1)*sizeof(PetscInt));
3493: for (i=0; i<mbs; i++) {
3494: c->diag[i] = a->diag[i];
3495: }
3496: c->free_diag = PETSC_TRUE;
3497: }
3498: } else c->diag = 0;
3499: c->nz = a->nz;
3500: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
3501: c->solve_work = 0;
3502: c->mult_work = 0;
3503: c->free_a = PETSC_TRUE;
3504: c->free_ij = PETSC_TRUE;
3505: C->preallocated = PETSC_TRUE;
3506: C->assembled = PETSC_TRUE;
3508: c->compressedrow.use = a->compressedrow.use;
3509: c->compressedrow.nrows = a->compressedrow.nrows;
3510: c->compressedrow.check = a->compressedrow.check;
3511: if (a->compressedrow.use){
3512: i = a->compressedrow.nrows;
3513: PetscMalloc2(i+1,PetscInt,&c->compressedrow.i,i+1,PetscInt,&c->compressedrow.rindex);
3514: PetscLogObjectMemory(C,(2*i+1)*sizeof(PetscInt));
3515: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
3516: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
3517: } else {
3518: c->compressedrow.use = PETSC_FALSE;
3519: c->compressedrow.i = PETSC_NULL;
3520: c->compressedrow.rindex = PETSC_NULL;
3521: }
3522: C->same_nonzero = A->same_nonzero;
3523: PetscFListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
3524: PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
3525: return(0);
3526: }
3530: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
3531: {
3535: MatCreate(((PetscObject)A)->comm,B);
3536: MatSetSizes(*B,A->rmap->N,A->cmap->n,A->rmap->N,A->cmap->n);
3537: MatSetType(*B,MATSEQBAIJ);
3538: MatDuplicateNoCreate_SeqBAIJ(*B,A,cpvalues,PETSC_TRUE);
3539: return(0);
3540: }
3544: PetscErrorCode MatLoad_SeqBAIJ(Mat newmat,PetscViewer viewer)
3545: {
3546: Mat_SeqBAIJ *a;
3548: PetscInt i,nz,header[4],*rowlengths=0,M,N,bs=1;
3549: PetscInt *mask,mbs,*jj,j,rowcount,nzcount,k,*browlengths,maskcount;
3550: PetscInt kmax,jcount,block,idx,point,nzcountb,extra_rows,rows,cols;
3551: PetscInt *masked,nmask,tmp,bs2,ishift;
3552: PetscMPIInt size;
3553: int fd;
3554: PetscScalar *aa;
3555: MPI_Comm comm = ((PetscObject)viewer)->comm;
3558: PetscOptionsBegin(comm,PETSC_NULL,"Options for loading SEQBAIJ matrix","Mat");
3559: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
3560: PetscOptionsEnd();
3561: bs2 = bs*bs;
3563: MPI_Comm_size(comm,&size);
3564: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"view must have one processor");
3565: PetscViewerBinaryGetDescriptor(viewer,&fd);
3566: PetscBinaryRead(fd,header,4,PETSC_INT);
3567: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not Mat object");
3568: M = header[1]; N = header[2]; nz = header[3];
3570: if (header[3] < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as SeqBAIJ");
3571: if (M != N) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Can only do square matrices");
3573: /*
3574: This code adds extra rows to make sure the number of rows is
3575: divisible by the blocksize
3576: */
3577: mbs = M/bs;
3578: extra_rows = bs - M + bs*(mbs);
3579: if (extra_rows == bs) extra_rows = 0;
3580: else mbs++;
3581: if (extra_rows) {
3582: PetscInfo(viewer,"Padding loaded matrix to match blocksize\n");
3583: }
3585: /* Set global sizes if not already set */
3586: if (newmat->rmap->n < 0 && newmat->rmap->N < 0 && newmat->cmap->n < 0 && newmat->cmap->N < 0) {
3587: MatSetSizes(newmat,PETSC_DECIDE,PETSC_DECIDE,M+extra_rows,N+extra_rows);
3588: } else { /* Check if the matrix global sizes are correct */
3589: MatGetSize(newmat,&rows,&cols);
3590: if (rows < 0 && cols < 0){ /* user might provide local size instead of global size */
3591: MatGetLocalSize(newmat,&rows,&cols);
3592: }
3593: 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);
3594: }
3595:
3596: /* read in row lengths */
3597: PetscMalloc((M+extra_rows)*sizeof(PetscInt),&rowlengths);
3598: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
3599: for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
3601: /* read in column indices */
3602: PetscMalloc((nz+extra_rows)*sizeof(PetscInt),&jj);
3603: PetscBinaryRead(fd,jj,nz,PETSC_INT);
3604: for (i=0; i<extra_rows; i++) jj[nz+i] = M+i;
3606: /* loop over row lengths determining block row lengths */
3607: PetscMalloc(mbs*sizeof(PetscInt),&browlengths);
3608: PetscMemzero(browlengths,mbs*sizeof(PetscInt));
3609: PetscMalloc2(mbs,PetscInt,&mask,mbs,PetscInt,&masked);
3610: PetscMemzero(mask,mbs*sizeof(PetscInt));
3611: rowcount = 0;
3612: nzcount = 0;
3613: for (i=0; i<mbs; i++) {
3614: nmask = 0;
3615: for (j=0; j<bs; j++) {
3616: kmax = rowlengths[rowcount];
3617: for (k=0; k<kmax; k++) {
3618: tmp = jj[nzcount++]/bs;
3619: if (!mask[tmp]) {masked[nmask++] = tmp; mask[tmp] = 1;}
3620: }
3621: rowcount++;
3622: }
3623: browlengths[i] += nmask;
3624: /* zero out the mask elements we set */
3625: for (j=0; j<nmask; j++) mask[masked[j]] = 0;
3626: }
3628: /* Do preallocation */
3629: MatSeqBAIJSetPreallocation_SeqBAIJ(newmat,bs,0,browlengths);
3630: a = (Mat_SeqBAIJ*)newmat->data;
3632: /* set matrix "i" values */
3633: a->i[0] = 0;
3634: for (i=1; i<= mbs; i++) {
3635: a->i[i] = a->i[i-1] + browlengths[i-1];
3636: a->ilen[i-1] = browlengths[i-1];
3637: }
3638: a->nz = 0;
3639: for (i=0; i<mbs; i++) a->nz += browlengths[i];
3641: /* read in nonzero values */
3642: PetscMalloc((nz+extra_rows)*sizeof(PetscScalar),&aa);
3643: PetscBinaryRead(fd,aa,nz,PETSC_SCALAR);
3644: for (i=0; i<extra_rows; i++) aa[nz+i] = 1.0;
3646: /* set "a" and "j" values into matrix */
3647: nzcount = 0; jcount = 0;
3648: for (i=0; i<mbs; i++) {
3649: nzcountb = nzcount;
3650: nmask = 0;
3651: for (j=0; j<bs; j++) {
3652: kmax = rowlengths[i*bs+j];
3653: for (k=0; k<kmax; k++) {
3654: tmp = jj[nzcount++]/bs;
3655: if (!mask[tmp]) { masked[nmask++] = tmp; mask[tmp] = 1;}
3656: }
3657: }
3658: /* sort the masked values */
3659: PetscSortInt(nmask,masked);
3661: /* set "j" values into matrix */
3662: maskcount = 1;
3663: for (j=0; j<nmask; j++) {
3664: a->j[jcount++] = masked[j];
3665: mask[masked[j]] = maskcount++;
3666: }
3667: /* set "a" values into matrix */
3668: ishift = bs2*a->i[i];
3669: for (j=0; j<bs; j++) {
3670: kmax = rowlengths[i*bs+j];
3671: for (k=0; k<kmax; k++) {
3672: tmp = jj[nzcountb]/bs ;
3673: block = mask[tmp] - 1;
3674: point = jj[nzcountb] - bs*tmp;
3675: idx = ishift + bs2*block + j + bs*point;
3676: a->a[idx] = (MatScalar)aa[nzcountb++];
3677: }
3678: }
3679: /* zero out the mask elements we set */
3680: for (j=0; j<nmask; j++) mask[masked[j]] = 0;
3681: }
3682: if (jcount != a->nz) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Bad binary matrix");
3684: PetscFree(rowlengths);
3685: PetscFree(browlengths);
3686: PetscFree(aa);
3687: PetscFree(jj);
3688: PetscFree2(mask,masked);
3690: MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);
3691: MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);
3692: MatView_Private(newmat);
3693: return(0);
3694: }
3698: /*@C
3699: MatCreateSeqBAIJ - Creates a sparse matrix in block AIJ (block
3700: compressed row) format. For good matrix assembly performance the
3701: user should preallocate the matrix storage by setting the parameter nz
3702: (or the array nnz). By setting these parameters accurately, performance
3703: during matrix assembly can be increased by more than a factor of 50.
3705: Collective on MPI_Comm
3707: Input Parameters:
3708: + comm - MPI communicator, set to PETSC_COMM_SELF
3709: . bs - size of block
3710: . m - number of rows
3711: . n - number of columns
3712: . nz - number of nonzero blocks per block row (same for all rows)
3713: - nnz - array containing the number of nonzero blocks in the various block rows
3714: (possibly different for each block row) or PETSC_NULL
3716: Output Parameter:
3717: . A - the matrix
3719: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3720: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3721: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3723: Options Database Keys:
3724: . -mat_no_unroll - uses code that does not unroll the loops in the
3725: block calculations (much slower)
3726: . -mat_block_size - size of the blocks to use
3728: Level: intermediate
3730: Notes:
3731: The number of rows and columns must be divisible by blocksize.
3733: If the nnz parameter is given then the nz parameter is ignored
3735: A nonzero block is any block that as 1 or more nonzeros in it
3737: The block AIJ format is fully compatible with standard Fortran 77
3738: storage. That is, the stored row and column indices can begin at
3739: either one (as in Fortran) or zero. See the users' manual for details.
3741: Specify the preallocated storage with either nz or nnz (not both).
3742: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
3743: allocation. See the <A href="../../docs/manual.pdf#nameddest=ch_mat">Mat chapter of the users manual</A> for details.
3744: matrices.
3746: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
3747: @*/
3748: PetscErrorCode MatCreateSeqBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3749: {
3751:
3753: MatCreate(comm,A);
3754: MatSetSizes(*A,m,n,m,n);
3755: MatSetType(*A,MATSEQBAIJ);
3756: MatSeqBAIJSetPreallocation_SeqBAIJ(*A,bs,nz,(PetscInt*)nnz);
3757: return(0);
3758: }
3762: /*@C
3763: MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
3764: per row in the matrix. For good matrix assembly performance the
3765: user should preallocate the matrix storage by setting the parameter nz
3766: (or the array nnz). By setting these parameters accurately, performance
3767: during matrix assembly can be increased by more than a factor of 50.
3769: Collective on MPI_Comm
3771: Input Parameters:
3772: + A - the matrix
3773: . bs - size of block
3774: . nz - number of block nonzeros per block row (same for all rows)
3775: - nnz - array containing the number of block nonzeros in the various block rows
3776: (possibly different for each block row) or PETSC_NULL
3778: Options Database Keys:
3779: . -mat_no_unroll - uses code that does not unroll the loops in the
3780: block calculations (much slower)
3781: . -mat_block_size - size of the blocks to use
3783: Level: intermediate
3785: Notes:
3786: If the nnz parameter is given then the nz parameter is ignored
3788: You can call MatGetInfo() to get information on how effective the preallocation was;
3789: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3790: You can also run with the option -info and look for messages with the string
3791: malloc in them to see if additional memory allocation was needed.
3793: The block AIJ format is fully compatible with standard Fortran 77
3794: storage. That is, the stored row and column indices can begin at
3795: either one (as in Fortran) or zero. See the users' manual for details.
3797: Specify the preallocated storage with either nz or nnz (not both).
3798: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
3799: allocation. See the <A href="../../docs/manual.pdf#nameddest=ch_mat">Mat chapter of the users manual</A> for details.
3801: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ(), MatGetInfo()
3802: @*/
3803: PetscErrorCode MatSeqBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt nz,const PetscInt nnz[])
3804: {
3808: PetscTryMethod(B,"MatSeqBAIJSetPreallocation_C",(Mat,PetscInt,PetscInt,const PetscInt[]),(B,bs,nz,nnz));
3809: return(0);
3810: }
3814: /*@C
3815: MatSeqBAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format
3816: (the default sequential PETSc format).
3818: Collective on MPI_Comm
3820: Input Parameters:
3821: + A - the matrix
3822: . i - the indices into j for the start of each local row (starts with zero)
3823: . j - the column indices for each local row (starts with zero) these must be sorted for each row
3824: - v - optional values in the matrix
3826: Level: developer
3828: .keywords: matrix, aij, compressed row, sparse
3830: .seealso: MatCreate(), MatCreateSeqBAIJ(), MatSetValues(), MatSeqBAIJSetPreallocation(), MATSEQBAIJ
3831: @*/
3832: PetscErrorCode MatSeqBAIJSetPreallocationCSR(Mat B,PetscInt bs,const PetscInt i[],const PetscInt j[], const PetscScalar v[])
3833: {
3837: PetscTryMethod(B,"MatSeqBAIJSetPreallocationCSR_C",(Mat,PetscInt,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,bs,i,j,v));
3838: return(0);
3839: }
3844: /*@
3845: MatCreateSeqBAIJWithArrays - Creates an sequential BAIJ matrix using matrix elements provided by the user.
3847: Collective on MPI_Comm
3849: Input Parameters:
3850: + comm - must be an MPI communicator of size 1
3851: . bs - size of block
3852: . m - number of rows
3853: . n - number of columns
3854: . i - row indices
3855: . j - column indices
3856: - a - matrix values
3858: Output Parameter:
3859: . mat - the matrix
3861: Level: advanced
3863: Notes:
3864: The i, j, and a arrays are not copied by this routine, the user must free these arrays
3865: once the matrix is destroyed
3867: You cannot set new nonzero locations into this matrix, that will generate an error.
3869: The i and j indices are 0 based
3871: 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).
3874: .seealso: MatCreate(), MatCreateMPIBAIJ(), MatCreateSeqBAIJ()
3876: @*/
3877: PetscErrorCode MatCreateSeqBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
3878: {
3880: PetscInt ii;
3881: Mat_SeqBAIJ *baij;
3884: if (bs != 1) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"block size %D > 1 is not supported yet",bs);
3885: if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3886:
3887: MatCreate(comm,mat);
3888: MatSetSizes(*mat,m,n,m,n);
3889: MatSetType(*mat,MATSEQBAIJ);
3890: MatSeqBAIJSetPreallocation_SeqBAIJ(*mat,bs,MAT_SKIP_ALLOCATION,0);
3891: baij = (Mat_SeqBAIJ*)(*mat)->data;
3892: PetscMalloc2(m,PetscInt,&baij->imax,m,PetscInt,&baij->ilen);
3893: PetscLogObjectMemory(*mat,2*m*sizeof(PetscInt));
3895: baij->i = i;
3896: baij->j = j;
3897: baij->a = a;
3898: baij->singlemalloc = PETSC_FALSE;
3899: baij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3900: baij->free_a = PETSC_FALSE;
3901: baij->free_ij = PETSC_FALSE;
3903: for (ii=0; ii<m; ii++) {
3904: baij->ilen[ii] = baij->imax[ii] = i[ii+1] - i[ii];
3905: #if defined(PETSC_USE_DEBUG)
3906: 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]);
3907: #endif
3908: }
3909: #if defined(PETSC_USE_DEBUG)
3910: for (ii=0; ii<baij->i[m]; ii++) {
3911: if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
3912: 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]);
3913: }
3914: #endif
3916: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
3917: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
3918: return(0);
3919: }