Actual source code: baij.c
1: #define PETSCMAT_DLL
3: /*
4: Defines the basic matrix operations for the BAIJ (compressed row)
5: matrix storage format.
6: */
7: #include src/mat/impls/baij/seq/baij.h
8: #include src/inline/spops.h
9: #include petscsys.h
11: #include src/inline/ilu.h
15: /*@C
16: MatSeqBAIJInvertBlockDiagonal - Inverts the block diagonal entries.
18: Collective on Mat
20: Input Parameters:
21: . mat - the matrix
23: Level: advanced
24: @*/
25: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJInvertBlockDiagonal(Mat mat)
26: {
27: PetscErrorCode ierr,(*f)(Mat);
31: if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
32: if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
34: PetscObjectQueryFunction((PetscObject)mat,"MatSeqBAIJInvertBlockDiagonal_C",(void (**)(void))&f);
35: if (f) {
36: (*f)(mat);
37: } else {
38: SETERRQ(PETSC_ERR_SUP,"Currently only implemented for SeqBAIJ.");
39: }
40: return(0);
41: }
46: PetscErrorCode PETSCMAT_DLLEXPORT MatInvertBlockDiagonal_SeqBAIJ(Mat A)
47: {
48: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*) A->data;
50: PetscInt *diag_offset,i,bs = A->rmap.bs,mbs = a->mbs;
51: PetscScalar *v = a->a,*odiag,*diag,*mdiag;
54: if (a->idiagvalid) return(0);
55: MatMarkDiagonal_SeqBAIJ(A);
56: diag_offset = a->diag;
57: if (!a->idiag) {
58: PetscMalloc(2*bs*bs*mbs*sizeof(PetscScalar),&a->idiag);
59: }
60: diag = a->idiag;
61: mdiag = a->idiag+bs*bs*mbs;
62: /* factor and invert each block */
63: switch (bs){
64: case 2:
65: for (i=0; i<mbs; i++) {
66: odiag = v + 4*diag_offset[i];
67: diag[0] = odiag[0]; diag[1] = odiag[1]; diag[2] = odiag[2]; diag[3] = odiag[3];
68: mdiag[0] = odiag[0]; mdiag[1] = odiag[1]; mdiag[2] = odiag[2]; mdiag[3] = odiag[3];
69: Kernel_A_gets_inverse_A_2(diag);
70: diag += 4;
71: mdiag += 4;
72: }
73: break;
74: case 3:
75: for (i=0; i<mbs; i++) {
76: odiag = v + 9*diag_offset[i];
77: diag[0] = odiag[0]; diag[1] = odiag[1]; diag[2] = odiag[2]; diag[3] = odiag[3];
78: diag[4] = odiag[4]; diag[5] = odiag[5]; diag[6] = odiag[6]; diag[7] = odiag[7];
79: diag[8] = odiag[8];
80: mdiag[0] = odiag[0]; mdiag[1] = odiag[1]; mdiag[2] = odiag[2]; mdiag[3] = odiag[3];
81: mdiag[4] = odiag[4]; mdiag[5] = odiag[5]; mdiag[6] = odiag[6]; mdiag[7] = odiag[7];
82: mdiag[8] = odiag[8];
83: Kernel_A_gets_inverse_A_3(diag);
84: diag += 9;
85: mdiag += 9;
86: }
87: break;
88: case 4:
89: for (i=0; i<mbs; i++) {
90: odiag = v + 16*diag_offset[i];
91: PetscMemcpy(diag,odiag,16*sizeof(PetscScalar));
92: PetscMemcpy(mdiag,odiag,16*sizeof(PetscScalar));
93: Kernel_A_gets_inverse_A_4(diag);
94: diag += 16;
95: mdiag += 16;
96: }
97: break;
98: case 5:
99: for (i=0; i<mbs; i++) {
100: odiag = v + 25*diag_offset[i];
101: PetscMemcpy(diag,odiag,25*sizeof(PetscScalar));
102: PetscMemcpy(mdiag,odiag,25*sizeof(PetscScalar));
103: Kernel_A_gets_inverse_A_5(diag);
104: diag += 25;
105: mdiag += 25;
106: }
107: break;
108: default:
109: SETERRQ1(PETSC_ERR_SUP,"not supported for block size %D",bs);
110: }
111: a->idiagvalid = PETSC_TRUE;
112: return(0);
113: }
118: PetscErrorCode MatPBRelax_SeqBAIJ_2(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
119: {
120: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
121: PetscScalar *x,x1,x2,s1,s2;
122: const PetscScalar *v,*aa = a->a, *b, *idiag,*mdiag;
123: PetscErrorCode ierr;
124: PetscInt m = a->mbs,i,i2,nz,idx;
125: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
128: its = its*lits;
129: if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
130: if (fshift) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
131: if (omega != 1.0) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
132: if ((flag & SOR_EISENSTAT) ||(flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER) ) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for Eisenstat trick");
133: if (its > 1) SETERRQ(PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
135: if (!a->idiagvalid){MatInvertBlockDiagonal_SeqBAIJ(A);}
137: diag = a->diag;
138: idiag = a->idiag;
139: VecGetArray(xx,&x);
140: VecGetArray(bb,(PetscScalar**)&b);
142: if (flag & SOR_ZERO_INITIAL_GUESS) {
143: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
144: x[0] = b[0]*idiag[0] + b[1]*idiag[2];
145: x[1] = b[0]*idiag[1] + b[1]*idiag[3];
146: i2 = 2;
147: idiag += 4;
148: for (i=1; i<m; i++) {
149: v = aa + 4*ai[i];
150: vi = aj + ai[i];
151: nz = diag[i] - ai[i];
152: s1 = b[i2]; s2 = b[i2+1];
153: while (nz--) {
154: idx = 2*(*vi++);
155: x1 = x[idx]; x2 = x[1+idx];
156: s1 -= v[0]*x1 + v[2]*x2;
157: s2 -= v[1]*x1 + v[3]*x2;
158: v += 4;
159: }
160: x[i2] = idiag[0]*s1 + idiag[2]*s2;
161: x[i2+1] = idiag[1]*s1 + idiag[3]*s2;
162: idiag += 4;
163: i2 += 2;
164: }
165: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
166: PetscLogFlops(4*(a->nz));
167: }
168: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
169: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
170: i2 = 0;
171: mdiag = a->idiag+4*a->mbs;
172: for (i=0; i<m; i++) {
173: x1 = x[i2]; x2 = x[i2+1];
174: x[i2] = mdiag[0]*x1 + mdiag[2]*x2;
175: x[i2+1] = mdiag[1]*x1 + mdiag[3]*x2;
176: mdiag += 4;
177: i2 += 2;
178: }
179: PetscLogFlops(6*m);
180: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
181: PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
182: }
183: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
184: idiag = a->idiag+4*a->mbs - 4;
185: i2 = 2*m - 2;
186: x1 = x[i2]; x2 = x[i2+1];
187: x[i2] = idiag[0]*x1 + idiag[2]*x2;
188: x[i2+1] = idiag[1]*x1 + idiag[3]*x2;
189: idiag -= 4;
190: i2 -= 2;
191: for (i=m-2; i>=0; i--) {
192: v = aa + 4*(diag[i]+1);
193: vi = aj + diag[i] + 1;
194: nz = ai[i+1] - diag[i] - 1;
195: s1 = x[i2]; s2 = x[i2+1];
196: while (nz--) {
197: idx = 2*(*vi++);
198: x1 = x[idx]; x2 = x[1+idx];
199: s1 -= v[0]*x1 + v[2]*x2;
200: s2 -= v[1]*x1 + v[3]*x2;
201: v += 4;
202: }
203: x[i2] = idiag[0]*s1 + idiag[2]*s2;
204: x[i2+1] = idiag[1]*s1 + idiag[3]*s2;
205: idiag -= 4;
206: i2 -= 2;
207: }
208: PetscLogFlops(4*(a->nz));
209: }
210: } else {
211: SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
212: }
213: VecRestoreArray(xx,&x);
214: VecRestoreArray(bb,(PetscScalar**)&b);
215: return(0);
216: }
220: PetscErrorCode MatPBRelax_SeqBAIJ_3(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,x3,s1,s2,s3;
224: const PetscScalar *v,*aa = a->a, *b, *idiag,*mdiag;
225: PetscErrorCode ierr;
226: PetscInt m = a->mbs,i,i2,nz,idx;
227: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
230: its = its*lits;
231: if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
232: if (fshift) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
233: if (omega != 1.0) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
234: if ((flag & SOR_EISENSTAT) ||(flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER) ) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for Eisenstat trick");
235: if (its > 1) SETERRQ(PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
237: if (!a->idiagvalid){MatInvertBlockDiagonal_SeqBAIJ(A);}
239: diag = a->diag;
240: idiag = a->idiag;
241: VecGetArray(xx,&x);
242: VecGetArray(bb,(PetscScalar**)&b);
244: if (flag & SOR_ZERO_INITIAL_GUESS) {
245: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
246: x[0] = b[0]*idiag[0] + b[1]*idiag[3] + b[2]*idiag[6];
247: x[1] = b[0]*idiag[1] + b[1]*idiag[4] + b[2]*idiag[7];
248: x[2] = b[0]*idiag[2] + b[1]*idiag[5] + b[2]*idiag[8];
249: i2 = 3;
250: idiag += 9;
251: for (i=1; i<m; i++) {
252: v = aa + 9*ai[i];
253: vi = aj + ai[i];
254: nz = diag[i] - ai[i];
255: s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2];
256: while (nz--) {
257: idx = 3*(*vi++);
258: x1 = x[idx]; x2 = x[1+idx];x3 = x[2+idx];
259: s1 -= v[0]*x1 + v[3]*x2 + v[6]*x3;
260: s2 -= v[1]*x1 + v[4]*x2 + v[7]*x3;
261: s3 -= v[2]*x1 + v[5]*x2 + v[8]*x3;
262: v += 9;
263: }
264: x[i2] = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
265: x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
266: x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;
267: idiag += 9;
268: i2 += 3;
269: }
270: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
271: PetscLogFlops(9*(a->nz));
272: }
273: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
274: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
275: i2 = 0;
276: mdiag = a->idiag+9*a->mbs;
277: for (i=0; i<m; i++) {
278: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
279: x[i2] = mdiag[0]*x1 + mdiag[3]*x2 + mdiag[6]*x3;
280: x[i2+1] = mdiag[1]*x1 + mdiag[4]*x2 + mdiag[7]*x3;
281: x[i2+2] = mdiag[2]*x1 + mdiag[5]*x2 + mdiag[8]*x3;
282: mdiag += 9;
283: i2 += 3;
284: }
285: PetscLogFlops(15*m);
286: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
287: PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
288: }
289: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
290: idiag = a->idiag+9*a->mbs - 9;
291: i2 = 3*m - 3;
292: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2];
293: x[i2] = idiag[0]*x1 + idiag[3]*x2 + idiag[6]*x3;
294: x[i2+1] = idiag[1]*x1 + idiag[4]*x2 + idiag[7]*x3;
295: x[i2+2] = idiag[2]*x1 + idiag[5]*x2 + idiag[8]*x3;
296: idiag -= 9;
297: i2 -= 3;
298: for (i=m-2; i>=0; i--) {
299: v = aa + 9*(diag[i]+1);
300: vi = aj + diag[i] + 1;
301: nz = ai[i+1] - diag[i] - 1;
302: s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2];
303: while (nz--) {
304: idx = 3*(*vi++);
305: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx];
306: s1 -= v[0]*x1 + v[3]*x2 + v[6]*x3;
307: s2 -= v[1]*x1 + v[4]*x2 + v[7]*x3;
308: s3 -= v[2]*x1 + v[5]*x2 + v[8]*x3;
309: v += 9;
310: }
311: x[i2] = idiag[0]*s1 + idiag[3]*s2 + idiag[6]*s3;
312: x[i2+1] = idiag[1]*s1 + idiag[4]*s2 + idiag[7]*s3;
313: x[i2+2] = idiag[2]*s1 + idiag[5]*s2 + idiag[8]*s3;
314: idiag -= 9;
315: i2 -= 3;
316: }
317: PetscLogFlops(9*(a->nz));
318: }
319: } else {
320: SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
321: }
322: VecRestoreArray(xx,&x);
323: VecRestoreArray(bb,(PetscScalar**)&b);
324: return(0);
325: }
329: PetscErrorCode MatPBRelax_SeqBAIJ_4(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
330: {
331: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
332: PetscScalar *x,x1,x2,x3,x4,s1,s2,s3,s4;
333: const PetscScalar *v,*aa = a->a, *b, *idiag,*mdiag;
334: PetscErrorCode ierr;
335: PetscInt m = a->mbs,i,i2,nz,idx;
336: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
339: its = its*lits;
340: if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
341: if (fshift) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
342: if (omega != 1.0) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
343: if ((flag & SOR_EISENSTAT) ||(flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER) ) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for Eisenstat trick");
344: if (its > 1) SETERRQ(PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
346: if (!a->idiagvalid){MatInvertBlockDiagonal_SeqBAIJ(A);}
348: diag = a->diag;
349: idiag = a->idiag;
350: VecGetArray(xx,&x);
351: VecGetArray(bb,(PetscScalar**)&b);
353: if (flag & SOR_ZERO_INITIAL_GUESS) {
354: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
355: x[0] = b[0]*idiag[0] + b[1]*idiag[4] + b[2]*idiag[8] + b[3]*idiag[12];
356: x[1] = b[0]*idiag[1] + b[1]*idiag[5] + b[2]*idiag[9] + b[3]*idiag[13];
357: x[2] = b[0]*idiag[2] + b[1]*idiag[6] + b[2]*idiag[10] + b[3]*idiag[14];
358: x[3] = b[0]*idiag[3] + b[1]*idiag[7] + b[2]*idiag[11] + b[3]*idiag[15];
359: i2 = 4;
360: idiag += 16;
361: for (i=1; i<m; i++) {
362: v = aa + 16*ai[i];
363: vi = aj + ai[i];
364: nz = diag[i] - ai[i];
365: s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3];
366: while (nz--) {
367: idx = 4*(*vi++);
368: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx];
369: s1 -= v[0]*x1 + v[4]*x2 + v[8]*x3 + v[12]*x4;
370: s2 -= v[1]*x1 + v[5]*x2 + v[9]*x3 + v[13]*x4;
371: s3 -= v[2]*x1 + v[6]*x2 + v[10]*x3 + v[14]*x4;
372: s4 -= v[3]*x1 + v[7]*x2 + v[11]*x3 + v[15]*x4;
373: v += 16;
374: }
375: x[i2] = idiag[0]*s1 + idiag[4]*s2 + idiag[8]*s3 + idiag[12]*s4;
376: x[i2+1] = idiag[1]*s1 + idiag[5]*s2 + idiag[9]*s3 + idiag[13]*s4;
377: x[i2+2] = idiag[2]*s1 + idiag[6]*s2 + idiag[10]*s3 + idiag[14]*s4;
378: x[i2+3] = idiag[3]*s1 + idiag[7]*s2 + idiag[11]*s3 + idiag[15]*s4;
379: idiag += 16;
380: i2 += 4;
381: }
382: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
383: PetscLogFlops(16*(a->nz));
384: }
385: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
386: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
387: i2 = 0;
388: mdiag = a->idiag+16*a->mbs;
389: for (i=0; i<m; i++) {
390: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3];
391: x[i2] = mdiag[0]*x1 + mdiag[4]*x2 + mdiag[8]*x3 + mdiag[12]*x4;
392: x[i2+1] = mdiag[1]*x1 + mdiag[5]*x2 + mdiag[9]*x3 + mdiag[13]*x4;
393: x[i2+2] = mdiag[2]*x1 + mdiag[6]*x2 + mdiag[10]*x3 + mdiag[14]*x4;
394: x[i2+3] = mdiag[3]*x1 + mdiag[7]*x2 + mdiag[11]*x3 + mdiag[15]*x4;
395: mdiag += 16;
396: i2 += 4;
397: }
398: PetscLogFlops(28*m);
399: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
400: PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
401: }
402: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
403: idiag = a->idiag+16*a->mbs - 16;
404: i2 = 4*m - 4;
405: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3];
406: x[i2] = idiag[0]*x1 + idiag[4]*x2 + idiag[8]*x3 + idiag[12]*x4;
407: x[i2+1] = idiag[1]*x1 + idiag[5]*x2 + idiag[9]*x3 + idiag[13]*x4;
408: x[i2+2] = idiag[2]*x1 + idiag[6]*x2 + idiag[10]*x3 + idiag[14]*x4;
409: x[i2+3] = idiag[3]*x1 + idiag[7]*x2 + idiag[11]*x3 + idiag[15]*x4;
410: idiag -= 16;
411: i2 -= 4;
412: for (i=m-2; i>=0; i--) {
413: v = aa + 16*(diag[i]+1);
414: vi = aj + diag[i] + 1;
415: nz = ai[i+1] - diag[i] - 1;
416: s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3];
417: while (nz--) {
418: idx = 4*(*vi++);
419: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx];
420: s1 -= v[0]*x1 + v[4]*x2 + v[8]*x3 + v[12]*x4;
421: s2 -= v[1]*x1 + v[5]*x2 + v[9]*x3 + v[13]*x4;
422: s3 -= v[2]*x1 + v[6]*x2 + v[10]*x3 + v[14]*x4;
423: s4 -= v[3]*x1 + v[7]*x2 + v[11]*x3 + v[15]*x4;
424: v += 16;
425: }
426: x[i2] = idiag[0]*s1 + idiag[4]*s2 + idiag[8]*s3 + idiag[12]*s4;
427: x[i2+1] = idiag[1]*s1 + idiag[5]*s2 + idiag[9]*s3 + idiag[13]*s4;
428: x[i2+2] = idiag[2]*s1 + idiag[6]*s2 + idiag[10]*s3 + idiag[14]*s4;
429: x[i2+3] = idiag[3]*s1 + idiag[7]*s2 + idiag[11]*s3 + idiag[15]*s4;
430: idiag -= 16;
431: i2 -= 4;
432: }
433: PetscLogFlops(16*(a->nz));
434: }
435: } else {
436: SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
437: }
438: VecRestoreArray(xx,&x);
439: VecRestoreArray(bb,(PetscScalar**)&b);
440: return(0);
441: }
445: PetscErrorCode MatPBRelax_SeqBAIJ_5(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
446: {
447: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
448: PetscScalar *x,x1,x2,x3,x4,x5,s1,s2,s3,s4,s5;
449: const PetscScalar *v,*aa = a->a, *b, *idiag,*mdiag;
450: PetscErrorCode ierr;
451: PetscInt m = a->mbs,i,i2,nz,idx;
452: const PetscInt *diag,*ai = a->i,*aj = a->j,*vi;
455: its = its*lits;
456: if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);
457: if (fshift) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for diagonal shift");
458: if (omega != 1.0) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for non-trivial relaxation factor");
459: if ((flag & SOR_EISENSTAT) ||(flag & SOR_APPLY_UPPER) || (flag & SOR_APPLY_LOWER) ) SETERRQ(PETSC_ERR_SUP,"Sorry, no support for Eisenstat trick");
460: if (its > 1) SETERRQ(PETSC_ERR_SUP,"Sorry, no support yet for multiple point block SOR iterations");
462: if (!a->idiagvalid){MatInvertBlockDiagonal_SeqBAIJ(A);}
464: diag = a->diag;
465: idiag = a->idiag;
466: VecGetArray(xx,&x);
467: VecGetArray(bb,(PetscScalar**)&b);
469: if (flag & SOR_ZERO_INITIAL_GUESS) {
470: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
471: x[0] = b[0]*idiag[0] + b[1]*idiag[5] + b[2]*idiag[10] + b[3]*idiag[15] + b[4]*idiag[20];
472: x[1] = b[0]*idiag[1] + b[1]*idiag[6] + b[2]*idiag[11] + b[3]*idiag[16] + b[4]*idiag[21];
473: x[2] = b[0]*idiag[2] + b[1]*idiag[7] + b[2]*idiag[12] + b[3]*idiag[17] + b[4]*idiag[22];
474: x[3] = b[0]*idiag[3] + b[1]*idiag[8] + b[2]*idiag[13] + b[3]*idiag[18] + b[4]*idiag[23];
475: x[4] = b[0]*idiag[4] + b[1]*idiag[9] + b[2]*idiag[14] + b[3]*idiag[19] + b[4]*idiag[24];
476: i2 = 5;
477: idiag += 25;
478: for (i=1; i<m; i++) {
479: v = aa + 25*ai[i];
480: vi = aj + ai[i];
481: nz = diag[i] - ai[i];
482: s1 = b[i2]; s2 = b[i2+1]; s3 = b[i2+2]; s4 = b[i2+3]; s5 = b[i2+4];
483: while (nz--) {
484: idx = 5*(*vi++);
485: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx];
486: s1 -= v[0]*x1 + v[5]*x2 + v[10]*x3 + v[15]*x4 + v[20]*x5;
487: s2 -= v[1]*x1 + v[6]*x2 + v[11]*x3 + v[16]*x4 + v[21]*x5;
488: s3 -= v[2]*x1 + v[7]*x2 + v[12]*x3 + v[17]*x4 + v[22]*x5;
489: s4 -= v[3]*x1 + v[8]*x2 + v[13]*x3 + v[18]*x4 + v[23]*x5;
490: s5 -= v[4]*x1 + v[9]*x2 + v[14]*x3 + v[19]*x4 + v[24]*x5;
491: v += 25;
492: }
493: x[i2] = idiag[0]*s1 + idiag[5]*s2 + idiag[10]*s3 + idiag[15]*s4 + idiag[20]*s5;
494: x[i2+1] = idiag[1]*s1 + idiag[6]*s2 + idiag[11]*s3 + idiag[16]*s4 + idiag[21]*s5;
495: x[i2+2] = idiag[2]*s1 + idiag[7]*s2 + idiag[12]*s3 + idiag[17]*s4 + idiag[22]*s5;
496: x[i2+3] = idiag[3]*s1 + idiag[8]*s2 + idiag[13]*s3 + idiag[18]*s4 + idiag[23]*s5;
497: x[i2+4] = idiag[4]*s1 + idiag[9]*s2 + idiag[14]*s3 + idiag[19]*s4 + idiag[24]*s5;
498: idiag += 25;
499: i2 += 5;
500: }
501: /* for logging purposes assume number of nonzero in lower half is 1/2 of total */
502: PetscLogFlops(25*(a->nz));
503: }
504: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
505: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
506: i2 = 0;
507: mdiag = a->idiag+25*a->mbs;
508: for (i=0; i<m; i++) {
509: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4];
510: x[i2] = mdiag[0]*x1 + mdiag[5]*x2 + mdiag[10]*x3 + mdiag[15]*x4 + mdiag[20]*x5;
511: x[i2+1] = mdiag[1]*x1 + mdiag[6]*x2 + mdiag[11]*x3 + mdiag[16]*x4 + mdiag[21]*x5;
512: x[i2+2] = mdiag[2]*x1 + mdiag[7]*x2 + mdiag[12]*x3 + mdiag[17]*x4 + mdiag[22]*x5;
513: x[i2+3] = mdiag[3]*x1 + mdiag[8]*x2 + mdiag[13]*x3 + mdiag[18]*x4 + mdiag[23]*x5;
514: x[i2+4] = mdiag[4]*x1 + mdiag[9]*x2 + mdiag[14]*x3 + mdiag[19]*x4 + mdiag[24]*x5;
515: mdiag += 25;
516: i2 += 5;
517: }
518: PetscLogFlops(45*m);
519: } else if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
520: PetscMemcpy(x,b,A->rmap.N*sizeof(PetscScalar));
521: }
522: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
523: idiag = a->idiag+25*a->mbs - 25;
524: i2 = 5*m - 5;
525: x1 = x[i2]; x2 = x[i2+1]; x3 = x[i2+2]; x4 = x[i2+3]; x5 = x[i2+4];
526: x[i2] = idiag[0]*x1 + idiag[5]*x2 + idiag[10]*x3 + idiag[15]*x4 + idiag[20]*x5;
527: x[i2+1] = idiag[1]*x1 + idiag[6]*x2 + idiag[11]*x3 + idiag[16]*x4 + idiag[21]*x5;
528: x[i2+2] = idiag[2]*x1 + idiag[7]*x2 + idiag[12]*x3 + idiag[17]*x4 + idiag[22]*x5;
529: x[i2+3] = idiag[3]*x1 + idiag[8]*x2 + idiag[13]*x3 + idiag[18]*x4 + idiag[23]*x5;
530: x[i2+4] = idiag[4]*x1 + idiag[9]*x2 + idiag[14]*x3 + idiag[19]*x4 + idiag[24]*x5;
531: idiag -= 25;
532: i2 -= 5;
533: for (i=m-2; i>=0; i--) {
534: v = aa + 25*(diag[i]+1);
535: vi = aj + diag[i] + 1;
536: nz = ai[i+1] - diag[i] - 1;
537: s1 = x[i2]; s2 = x[i2+1]; s3 = x[i2+2]; s4 = x[i2+3]; s5 = x[i2+4];
538: while (nz--) {
539: idx = 5*(*vi++);
540: x1 = x[idx]; x2 = x[1+idx]; x3 = x[2+idx]; x4 = x[3+idx]; x5 = x[4+idx];
541: s1 -= v[0]*x1 + v[5]*x2 + v[10]*x3 + v[15]*x4 + v[20]*x5;
542: s2 -= v[1]*x1 + v[6]*x2 + v[11]*x3 + v[16]*x4 + v[21]*x5;
543: s3 -= v[2]*x1 + v[7]*x2 + v[12]*x3 + v[17]*x4 + v[22]*x5;
544: s4 -= v[3]*x1 + v[8]*x2 + v[13]*x3 + v[18]*x4 + v[23]*x5;
545: s5 -= v[4]*x1 + v[9]*x2 + v[14]*x3 + v[19]*x4 + v[24]*x5;
546: v += 25;
547: }
548: x[i2] = idiag[0]*s1 + idiag[5]*s2 + idiag[10]*s3 + idiag[15]*s4 + idiag[20]*s5;
549: x[i2+1] = idiag[1]*s1 + idiag[6]*s2 + idiag[11]*s3 + idiag[16]*s4 + idiag[21]*s5;
550: x[i2+2] = idiag[2]*s1 + idiag[7]*s2 + idiag[12]*s3 + idiag[17]*s4 + idiag[22]*s5;
551: x[i2+3] = idiag[3]*s1 + idiag[8]*s2 + idiag[13]*s3 + idiag[18]*s4 + idiag[23]*s5;
552: x[i2+4] = idiag[4]*s1 + idiag[9]*s2 + idiag[14]*s3 + idiag[19]*s4 + idiag[24]*s5;
553: idiag -= 25;
554: i2 -= 5;
555: }
556: PetscLogFlops(25*(a->nz));
557: }
558: } else {
559: SETERRQ(PETSC_ERR_SUP,"Only supports point block SOR with zero initial guess");
560: }
561: VecRestoreArray(xx,&x);
562: VecRestoreArray(bb,(PetscScalar**)&b);
563: return(0);
564: }
566: /*
567: Special version for direct calls from Fortran (Used in PETSc-fun3d)
568: */
569: #if defined(PETSC_HAVE_FORTRAN_CAPS)
570: #define matsetvaluesblocked4_ MATSETVALUESBLOCKED4
571: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
572: #define matsetvaluesblocked4_ matsetvaluesblocked4
573: #endif
578: void PETSCMAT_DLLEXPORT matsetvaluesblocked4_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[])
579: {
580: Mat A = *AA;
581: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
582: PetscInt *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,N,m = *mm,n = *nn;
583: PetscInt *ai=a->i,*ailen=a->ilen;
584: PetscInt *aj=a->j,stepval,lastcol = -1;
585: const PetscScalar *value = v;
586: MatScalar *ap,*aa = a->a,*bap;
589: if (A->rmap.bs != 4) SETERRABORT(A->comm,PETSC_ERR_ARG_WRONG,"Can only be called with a block size of 4");
590: stepval = (n-1)*4;
591: for (k=0; k<m; k++) { /* loop over added rows */
592: row = im[k];
593: rp = aj + ai[row];
594: ap = aa + 16*ai[row];
595: nrow = ailen[row];
596: low = 0;
597: high = nrow;
598: for (l=0; l<n; l++) { /* loop over added columns */
599: col = in[l];
600: if (col <= lastcol) low = 0; else high = nrow;
601: lastcol = col;
602: value = v + k*(stepval+4)*4 + l*4;
603: while (high-low > 7) {
604: t = (low+high)/2;
605: if (rp[t] > col) high = t;
606: else low = t;
607: }
608: for (i=low; i<high; i++) {
609: if (rp[i] > col) break;
610: if (rp[i] == col) {
611: bap = ap + 16*i;
612: for (ii=0; ii<4; ii++,value+=stepval) {
613: for (jj=ii; jj<16; jj+=4) {
614: bap[jj] += *value++;
615: }
616: }
617: goto noinsert2;
618: }
619: }
620: N = nrow++ - 1;
621: high++; /* added new column index thus must search to one higher than before */
622: /* shift up all the later entries in this row */
623: for (ii=N; ii>=i; ii--) {
624: rp[ii+1] = rp[ii];
625: PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
626: }
627: if (N >= i) {
628: PetscMemzero(ap+16*i,16*sizeof(MatScalar));
629: }
630: rp[i] = col;
631: bap = ap + 16*i;
632: for (ii=0; ii<4; ii++,value+=stepval) {
633: for (jj=ii; jj<16; jj+=4) {
634: bap[jj] = *value++;
635: }
636: }
637: noinsert2:;
638: low = i;
639: }
640: ailen[row] = nrow;
641: }
642: PetscFunctionReturnVoid();
643: }
646: #if defined(PETSC_HAVE_FORTRAN_CAPS)
647: #define matsetvalues4_ MATSETVALUES4
648: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
649: #define matsetvalues4_ matsetvalues4
650: #endif
655: void PETSCMAT_DLLEXPORT matsetvalues4_(Mat *AA,PetscInt *mm,PetscInt *im,PetscInt *nn,PetscInt *in,PetscScalar *v)
656: {
657: Mat A = *AA;
658: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
659: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,N,n = *nn,m = *mm;
660: PetscInt *ai=a->i,*ailen=a->ilen;
661: PetscInt *aj=a->j,brow,bcol;
662: PetscInt ridx,cidx,lastcol = -1;
663: MatScalar *ap,value,*aa=a->a,*bap;
664:
666: for (k=0; k<m; k++) { /* loop over added rows */
667: row = im[k]; brow = row/4;
668: rp = aj + ai[brow];
669: ap = aa + 16*ai[brow];
670: nrow = ailen[brow];
671: low = 0;
672: high = nrow;
673: for (l=0; l<n; l++) { /* loop over added columns */
674: col = in[l]; bcol = col/4;
675: ridx = row % 4; cidx = col % 4;
676: value = v[l + k*n];
677: if (col <= lastcol) low = 0; else high = nrow;
678: lastcol = col;
679: while (high-low > 7) {
680: t = (low+high)/2;
681: if (rp[t] > bcol) high = t;
682: else low = t;
683: }
684: for (i=low; i<high; i++) {
685: if (rp[i] > bcol) break;
686: if (rp[i] == bcol) {
687: bap = ap + 16*i + 4*cidx + ridx;
688: *bap += value;
689: goto noinsert1;
690: }
691: }
692: N = nrow++ - 1;
693: high++; /* added new column thus must search to one higher than before */
694: /* shift up all the later entries in this row */
695: for (ii=N; ii>=i; ii--) {
696: rp[ii+1] = rp[ii];
697: PetscMemcpy(ap+16*(ii+1),ap+16*(ii),16*sizeof(MatScalar));
698: }
699: if (N>=i) {
700: PetscMemzero(ap+16*i,16*sizeof(MatScalar));
701: }
702: rp[i] = bcol;
703: ap[16*i + 4*cidx + ridx] = value;
704: noinsert1:;
705: low = i;
706: }
707: ailen[brow] = nrow;
708: }
709: PetscFunctionReturnVoid();
710: }
713: /* UGLY, ugly, ugly
714: When MatScalar == PetscScalar the function MatSetValuesBlocked_SeqBAIJ_MatScalar() does
715: not exist. Otherwise ..._MatScalar() takes matrix dlements in single precision and
716: inserts them into the single precision data structure. The function MatSetValuesBlocked_SeqBAIJ()
717: converts the entries into single precision and then calls ..._MatScalar() to put them
718: into the single precision data structures.
719: */
720: #if defined(PETSC_USE_MAT_SINGLE)
721: EXTERN PetscErrorCode MatSetValuesBlocked_SeqBAIJ_MatScalar(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const MatScalar[],InsertMode);
722: #else
723: #define MatSetValuesBlocked_SeqBAIJ_MatScalar MatSetValuesBlocked_SeqBAIJ
724: #endif
726: #define CHUNKSIZE 10
728: /*
729: Checks for missing diagonals
730: */
733: PetscErrorCode MatMissingDiagonal_SeqBAIJ(Mat A)
734: {
735: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
737: PetscInt *diag,*jj = a->j,i;
740: MatMarkDiagonal_SeqBAIJ(A);
741: diag = a->diag;
742: for (i=0; i<a->mbs; i++) {
743: if (jj[diag[i]] != i) {
744: SETERRQ1(PETSC_ERR_PLIB,"Matrix is missing diagonal number %D",i);
745: }
746: }
747: return(0);
748: }
752: PetscErrorCode MatMarkDiagonal_SeqBAIJ(Mat A)
753: {
754: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
756: PetscInt i,j,*diag,m = a->mbs;
759: if (a->diag) return(0);
761: PetscMalloc((m+1)*sizeof(PetscInt),&diag);
762: PetscLogObjectMemory(A,(m+1)*sizeof(PetscInt));
763: for (i=0; i<m; i++) {
764: diag[i] = a->i[i+1];
765: for (j=a->i[i]; j<a->i[i+1]; j++) {
766: if (a->j[j] == i) {
767: diag[i] = j;
768: break;
769: }
770: }
771: }
772: a->diag = diag;
773: return(0);
774: }
777: EXTERN PetscErrorCode MatToSymmetricIJ_SeqAIJ(PetscInt,PetscInt*,PetscInt*,PetscInt,PetscInt,PetscInt**,PetscInt**);
781: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
782: {
783: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
785: PetscInt n = a->mbs,i;
788: *nn = n;
789: if (!ia) return(0);
790: if (symmetric) {
791: MatToSymmetricIJ_SeqAIJ(n,a->i,a->j,0,oshift,ia,ja);
792: } else if (oshift == 1) {
793: /* temporarily add 1 to i and j indices */
794: PetscInt nz = a->i[n];
795: for (i=0; i<nz; i++) a->j[i]++;
796: for (i=0; i<n+1; i++) a->i[i]++;
797: *ia = a->i; *ja = a->j;
798: } else {
799: *ia = a->i; *ja = a->j;
800: }
802: return(0);
803: }
807: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
808: {
809: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
811: PetscInt i,n = a->mbs;
814: if (!ia) return(0);
815: if (symmetric) {
816: PetscFree(*ia);
817: PetscFree(*ja);
818: } else if (oshift == 1) {
819: PetscInt nz = a->i[n]-1;
820: for (i=0; i<nz; i++) a->j[i]--;
821: for (i=0; i<n+1; i++) a->i[i]--;
822: }
823: return(0);
824: }
828: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
829: {
830: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
834: #if defined(PETSC_USE_LOG)
835: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap.N,A->cmap.n,a->nz);
836: #endif
837: MatSeqXAIJFreeAIJ(a->singlemalloc,&a->a,&a->j,&a->i);
838: if (a->row) {
839: ISDestroy(a->row);
840: }
841: if (a->col) {
842: ISDestroy(a->col);
843: }
844: PetscFree(a->diag);
845: PetscFree(a->idiag);
846: PetscFree2(a->imax,a->ilen);
847: PetscFree(a->solve_work);
848: PetscFree(a->mult_work);
849: if (a->icol) {ISDestroy(a->icol);}
850: PetscFree(a->saved_values);
851: #if defined(PETSC_USE_MAT_SINGLE)
852: PetscFree(a->setvaluescopy);
853: #endif
854: PetscFree(a->xtoy);
855: if (a->compressedrow.use){PetscFree(a->compressedrow.i);}
857: PetscFree(a);
859: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJInvertBlockDiagonal_C","",PETSC_NULL);
860: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
861: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
862: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetColumnIndices_C","",PETSC_NULL);
863: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqaij_C","",PETSC_NULL);
864: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqsbaij_C","",PETSC_NULL);
865: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetPreallocation_C","",PETSC_NULL);
866: return(0);
867: }
871: PetscErrorCode MatSetOption_SeqBAIJ(Mat A,MatOption op)
872: {
873: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
877: switch (op) {
878: case MAT_ROW_ORIENTED:
879: a->roworiented = PETSC_TRUE;
880: break;
881: case MAT_COLUMN_ORIENTED:
882: a->roworiented = PETSC_FALSE;
883: break;
884: case MAT_COLUMNS_SORTED:
885: a->sorted = PETSC_TRUE;
886: break;
887: case MAT_COLUMNS_UNSORTED:
888: a->sorted = PETSC_FALSE;
889: break;
890: case MAT_KEEP_ZEROED_ROWS:
891: a->keepzeroedrows = PETSC_TRUE;
892: break;
893: case MAT_NO_NEW_NONZERO_LOCATIONS:
894: a->nonew = 1;
895: break;
896: case MAT_NEW_NONZERO_LOCATION_ERR:
897: a->nonew = -1;
898: break;
899: case MAT_NEW_NONZERO_ALLOCATION_ERR:
900: a->nonew = -2;
901: break;
902: case MAT_YES_NEW_NONZERO_LOCATIONS:
903: a->nonew = 0;
904: break;
905: case MAT_ROWS_SORTED:
906: case MAT_ROWS_UNSORTED:
907: case MAT_YES_NEW_DIAGONALS:
908: case MAT_IGNORE_OFF_PROC_ENTRIES:
909: case MAT_USE_HASH_TABLE:
910: PetscInfo(A,"Option ignored\n");
911: break;
912: case MAT_NO_NEW_DIAGONALS:
913: SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
914: case MAT_SYMMETRIC:
915: case MAT_STRUCTURALLY_SYMMETRIC:
916: case MAT_NOT_SYMMETRIC:
917: case MAT_NOT_STRUCTURALLY_SYMMETRIC:
918: case MAT_HERMITIAN:
919: case MAT_NOT_HERMITIAN:
920: case MAT_SYMMETRY_ETERNAL:
921: case MAT_NOT_SYMMETRY_ETERNAL:
922: break;
923: default:
924: SETERRQ(PETSC_ERR_SUP,"unknown option");
925: }
926: return(0);
927: }
931: PetscErrorCode MatGetRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
932: {
933: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
935: PetscInt itmp,i,j,k,M,*ai,*aj,bs,bn,bp,*idx_i,bs2;
936: MatScalar *aa,*aa_i;
937: PetscScalar *v_i;
940: bs = A->rmap.bs;
941: ai = a->i;
942: aj = a->j;
943: aa = a->a;
944: bs2 = a->bs2;
945:
946: if (row < 0 || row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range", row);
947:
948: bn = row/bs; /* Block number */
949: bp = row % bs; /* Block Position */
950: M = ai[bn+1] - ai[bn];
951: *nz = bs*M;
952:
953: if (v) {
954: *v = 0;
955: if (*nz) {
956: PetscMalloc((*nz)*sizeof(PetscScalar),v);
957: for (i=0; i<M; i++) { /* for each block in the block row */
958: v_i = *v + i*bs;
959: aa_i = aa + bs2*(ai[bn] + i);
960: for (j=bp,k=0; j<bs2; j+=bs,k++) {v_i[k] = aa_i[j];}
961: }
962: }
963: }
965: if (idx) {
966: *idx = 0;
967: if (*nz) {
968: PetscMalloc((*nz)*sizeof(PetscInt),idx);
969: for (i=0; i<M; i++) { /* for each block in the block row */
970: idx_i = *idx + i*bs;
971: itmp = bs*aj[ai[bn] + i];
972: for (j=0; j<bs; j++) {idx_i[j] = itmp++;}
973: }
974: }
975: }
976: return(0);
977: }
981: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
982: {
986: if (idx) {PetscFree(*idx);}
987: if (v) {PetscFree(*v);}
988: return(0);
989: }
993: PetscErrorCode MatTranspose_SeqBAIJ(Mat A,Mat *B)
994: {
995: Mat_SeqBAIJ *a=(Mat_SeqBAIJ *)A->data;
996: Mat C;
998: PetscInt i,j,k,*aj=a->j,*ai=a->i,bs=A->rmap.bs,mbs=a->mbs,nbs=a->nbs,len,*col;
999: PetscInt *rows,*cols,bs2=a->bs2;
1000: PetscScalar *array;
1003: if (!B && mbs!=nbs) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Square matrix only for in-place");
1004: PetscMalloc((1+nbs)*sizeof(PetscInt),&col);
1005: PetscMemzero(col,(1+nbs)*sizeof(PetscInt));
1007: #if defined(PETSC_USE_MAT_SINGLE)
1008: PetscMalloc(a->bs2*a->nz*sizeof(PetscScalar),&array);
1009: for (i=0; i<a->bs2*a->nz; i++) array[i] = (PetscScalar)a->a[i];
1010: #else
1011: array = a->a;
1012: #endif
1014: for (i=0; i<ai[mbs]; i++) col[aj[i]] += 1;
1015: MatCreate(A->comm,&C);
1016: MatSetSizes(C,A->cmap.n,A->rmap.N,A->cmap.n,A->rmap.N);
1017: MatSetType(C,A->type_name);
1018: MatSeqBAIJSetPreallocation_SeqBAIJ(C,bs,PETSC_NULL,col);
1019: PetscFree(col);
1020: PetscMalloc(2*bs*sizeof(PetscInt),&rows);
1021: cols = rows + bs;
1022: for (i=0; i<mbs; i++) {
1023: cols[0] = i*bs;
1024: for (k=1; k<bs; k++) cols[k] = cols[k-1] + 1;
1025: len = ai[i+1] - ai[i];
1026: for (j=0; j<len; j++) {
1027: rows[0] = (*aj++)*bs;
1028: for (k=1; k<bs; k++) rows[k] = rows[k-1] + 1;
1029: MatSetValues(C,bs,rows,bs,cols,array,INSERT_VALUES);
1030: array += bs2;
1031: }
1032: }
1033: PetscFree(rows);
1034: #if defined(PETSC_USE_MAT_SINGLE)
1035: PetscFree(array);
1036: #endif
1037:
1038: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1039: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1040:
1041: if (B) {
1042: *B = C;
1043: } else {
1044: MatHeaderCopy(A,C);
1045: }
1046: return(0);
1047: }
1051: static PetscErrorCode MatView_SeqBAIJ_Binary(Mat A,PetscViewer viewer)
1052: {
1053: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1055: PetscInt i,*col_lens,bs = A->rmap.bs,count,*jj,j,k,l,bs2=a->bs2;
1056: int fd;
1057: PetscScalar *aa;
1058: FILE *file;
1061: PetscViewerBinaryGetDescriptor(viewer,&fd);
1062: PetscMalloc((4+A->rmap.N)*sizeof(PetscInt),&col_lens);
1063: col_lens[0] = MAT_FILE_COOKIE;
1065: col_lens[1] = A->rmap.N;
1066: col_lens[2] = A->cmap.n;
1067: col_lens[3] = a->nz*bs2;
1069: /* store lengths of each row and write (including header) to file */
1070: count = 0;
1071: for (i=0; i<a->mbs; i++) {
1072: for (j=0; j<bs; j++) {
1073: col_lens[4+count++] = bs*(a->i[i+1] - a->i[i]);
1074: }
1075: }
1076: PetscBinaryWrite(fd,col_lens,4+A->rmap.N,PETSC_INT,PETSC_TRUE);
1077: PetscFree(col_lens);
1079: /* store column indices (zero start index) */
1080: PetscMalloc((a->nz+1)*bs2*sizeof(PetscInt),&jj);
1081: count = 0;
1082: for (i=0; i<a->mbs; i++) {
1083: for (j=0; j<bs; j++) {
1084: for (k=a->i[i]; k<a->i[i+1]; k++) {
1085: for (l=0; l<bs; l++) {
1086: jj[count++] = bs*a->j[k] + l;
1087: }
1088: }
1089: }
1090: }
1091: PetscBinaryWrite(fd,jj,bs2*a->nz,PETSC_INT,PETSC_FALSE);
1092: PetscFree(jj);
1094: /* store nonzero values */
1095: PetscMalloc((a->nz+1)*bs2*sizeof(PetscScalar),&aa);
1096: count = 0;
1097: for (i=0; i<a->mbs; i++) {
1098: for (j=0; j<bs; j++) {
1099: for (k=a->i[i]; k<a->i[i+1]; k++) {
1100: for (l=0; l<bs; l++) {
1101: aa[count++] = a->a[bs2*k + l*bs + j];
1102: }
1103: }
1104: }
1105: }
1106: PetscBinaryWrite(fd,aa,bs2*a->nz,PETSC_SCALAR,PETSC_FALSE);
1107: PetscFree(aa);
1109: PetscViewerBinaryGetInfoPointer(viewer,&file);
1110: if (file) {
1111: fprintf(file,"-matload_block_size %d\n",(int)A->rmap.bs);
1112: }
1113: return(0);
1114: }
1118: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A,PetscViewer viewer)
1119: {
1120: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1121: PetscErrorCode ierr;
1122: PetscInt i,j,bs = A->rmap.bs,k,l,bs2=a->bs2;
1123: PetscViewerFormat format;
1126: PetscViewerGetFormat(viewer,&format);
1127: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1128: PetscViewerASCIIPrintf(viewer," block size is %D\n",bs);
1129: } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1130: Mat aij;
1131: MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&aij);
1132: MatView(aij,viewer);
1133: MatDestroy(aij);
1134: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1135: return(0);
1136: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1137: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
1138: for (i=0; i<a->mbs; i++) {
1139: for (j=0; j<bs; j++) {
1140: PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1141: for (k=a->i[i]; k<a->i[i+1]; k++) {
1142: for (l=0; l<bs; l++) {
1143: #if defined(PETSC_USE_COMPLEX)
1144: if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1145: PetscViewerASCIIPrintf(viewer," (%D, %G + %Gi) ",bs*a->j[k]+l,
1146: PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1147: } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1148: PetscViewerASCIIPrintf(viewer," (%D, %G - %Gi) ",bs*a->j[k]+l,
1149: PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1150: } else if (PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1151: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1152: }
1153: #else
1154: if (a->a[bs2*k + l*bs + j] != 0.0) {
1155: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1156: }
1157: #endif
1158: }
1159: }
1160: PetscViewerASCIIPrintf(viewer,"\n");
1161: }
1162: }
1163: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
1164: } else {
1165: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
1166: for (i=0; i<a->mbs; i++) {
1167: for (j=0; j<bs; j++) {
1168: PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1169: for (k=a->i[i]; k<a->i[i+1]; k++) {
1170: for (l=0; l<bs; l++) {
1171: #if defined(PETSC_USE_COMPLEX)
1172: if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0) {
1173: PetscViewerASCIIPrintf(viewer," (%D, %G + %G i) ",bs*a->j[k]+l,
1174: PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1175: } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0) {
1176: PetscViewerASCIIPrintf(viewer," (%D, %G - %G i) ",bs*a->j[k]+l,
1177: PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1178: } else {
1179: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1180: }
1181: #else
1182: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1183: #endif
1184: }
1185: }
1186: PetscViewerASCIIPrintf(viewer,"\n");
1187: }
1188: }
1189: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
1190: }
1191: PetscViewerFlush(viewer);
1192: return(0);
1193: }
1197: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1198: {
1199: Mat A = (Mat) Aa;
1200: Mat_SeqBAIJ *a=(Mat_SeqBAIJ*)A->data;
1202: PetscInt row,i,j,k,l,mbs=a->mbs,color,bs=A->rmap.bs,bs2=a->bs2;
1203: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1204: MatScalar *aa;
1205: PetscViewer viewer;
1209: /* still need to add support for contour plot of nonzeros; see MatView_SeqAIJ_Draw_Zoom()*/
1210: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1212: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
1214: /* loop over matrix elements drawing boxes */
1215: color = PETSC_DRAW_BLUE;
1216: for (i=0,row=0; i<mbs; i++,row+=bs) {
1217: for (j=a->i[i]; j<a->i[i+1]; j++) {
1218: y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1219: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1220: aa = a->a + j*bs2;
1221: for (k=0; k<bs; k++) {
1222: for (l=0; l<bs; l++) {
1223: if (PetscRealPart(*aa++) >= 0.) continue;
1224: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1225: }
1226: }
1227: }
1228: }
1229: color = PETSC_DRAW_CYAN;
1230: for (i=0,row=0; i<mbs; i++,row+=bs) {
1231: for (j=a->i[i]; j<a->i[i+1]; j++) {
1232: y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1233: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1234: aa = a->a + j*bs2;
1235: for (k=0; k<bs; k++) {
1236: for (l=0; l<bs; l++) {
1237: if (PetscRealPart(*aa++) != 0.) continue;
1238: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1239: }
1240: }
1241: }
1242: }
1244: color = PETSC_DRAW_RED;
1245: for (i=0,row=0; i<mbs; i++,row+=bs) {
1246: for (j=a->i[i]; j<a->i[i+1]; j++) {
1247: y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1248: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1249: aa = a->a + j*bs2;
1250: for (k=0; k<bs; k++) {
1251: for (l=0; l<bs; l++) {
1252: if (PetscRealPart(*aa++) <= 0.) continue;
1253: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1254: }
1255: }
1256: }
1257: }
1258: return(0);
1259: }
1263: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A,PetscViewer viewer)
1264: {
1266: PetscReal xl,yl,xr,yr,w,h;
1267: PetscDraw draw;
1268: PetscTruth isnull;
1272: PetscViewerDrawGetDraw(viewer,0,&draw);
1273: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1275: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1276: xr = A->cmap.n; yr = A->rmap.N; h = yr/10.0; w = xr/10.0;
1277: xr += w; yr += h; xl = -w; yl = -h;
1278: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1279: PetscDrawZoom(draw,MatView_SeqBAIJ_Draw_Zoom,A);
1280: PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
1281: return(0);
1282: }
1286: PetscErrorCode MatView_SeqBAIJ(Mat A,PetscViewer viewer)
1287: {
1289: PetscTruth iascii,isbinary,isdraw;
1292: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1293: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1294: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1295: if (iascii){
1296: MatView_SeqBAIJ_ASCII(A,viewer);
1297: } else if (isbinary) {
1298: MatView_SeqBAIJ_Binary(A,viewer);
1299: } else if (isdraw) {
1300: MatView_SeqBAIJ_Draw(A,viewer);
1301: } else {
1302: Mat B;
1303: MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&B);
1304: MatView(B,viewer);
1305: MatDestroy(B);
1306: }
1307: return(0);
1308: }
1313: PetscErrorCode MatGetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
1314: {
1315: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1316: PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
1317: PetscInt *ai = a->i,*ailen = a->ilen;
1318: PetscInt brow,bcol,ridx,cidx,bs=A->rmap.bs,bs2=a->bs2;
1319: MatScalar *ap,*aa = a->a,zero = 0.0;
1322: for (k=0; k<m; k++) { /* loop over rows */
1323: row = im[k]; brow = row/bs;
1324: if (row < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
1325: if (row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D too large", row);
1326: rp = aj + ai[brow] ; ap = aa + bs2*ai[brow] ;
1327: nrow = ailen[brow];
1328: for (l=0; l<n; l++) { /* loop over columns */
1329: if (in[l] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column");
1330: if (in[l] >= A->cmap.n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Column %D too large", in[l]);
1331: col = in[l] ;
1332: bcol = col/bs;
1333: cidx = col%bs;
1334: ridx = row%bs;
1335: high = nrow;
1336: low = 0; /* assume unsorted */
1337: while (high-low > 5) {
1338: t = (low+high)/2;
1339: if (rp[t] > bcol) high = t;
1340: else low = t;
1341: }
1342: for (i=low; i<high; i++) {
1343: if (rp[i] > bcol) break;
1344: if (rp[i] == bcol) {
1345: *v++ = ap[bs2*i+bs*cidx+ridx];
1346: goto finished;
1347: }
1348: }
1349: *v++ = zero;
1350: finished:;
1351: }
1352: }
1353: return(0);
1354: }
1356: #if defined(PETSC_USE_MAT_SINGLE)
1359: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
1360: {
1361: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)mat->data;
1363: PetscInt i,N = m*n*b->bs2;
1364: MatScalar *vsingle;
1367: if (N > b->setvalueslen) {
1368: PetscFree(b->setvaluescopy);
1369: PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
1370: b->setvalueslen = N;
1371: }
1372: vsingle = b->setvaluescopy;
1373: for (i=0; i<N; i++) {
1374: vsingle[i] = v[i];
1375: }
1376: MatSetValuesBlocked_SeqBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
1377: return(0);
1378: }
1379: #endif
1384: PetscErrorCode MatSetValuesBlocked_SeqBAIJ_MatScalar(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode is)
1385: {
1386: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1387: PetscInt *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,rmax,N,lastcol = -1;
1388: PetscInt *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1389: PetscErrorCode ierr;
1390: PetscInt *aj=a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap.bs,stepval;
1391: PetscTruth roworiented=a->roworiented;
1392: const MatScalar *value = v;
1393: MatScalar *ap,*aa = a->a,*bap;
1396: if (roworiented) {
1397: stepval = (n-1)*bs;
1398: } else {
1399: stepval = (m-1)*bs;
1400: }
1401: for (k=0; k<m; k++) { /* loop over added rows */
1402: row = im[k];
1403: if (row < 0) continue;
1404: #if defined(PETSC_USE_DEBUG)
1405: if (row >= a->mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,a->mbs-1);
1406: #endif
1407: rp = aj + ai[row];
1408: ap = aa + bs2*ai[row];
1409: rmax = imax[row];
1410: nrow = ailen[row];
1411: low = 0;
1412: high = nrow;
1413: for (l=0; l<n; l++) { /* loop over added columns */
1414: if (in[l] < 0) continue;
1415: #if defined(PETSC_USE_DEBUG)
1416: if (in[l] >= a->nbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],a->nbs-1);
1417: #endif
1418: col = in[l];
1419: if (roworiented) {
1420: value = v + k*(stepval+bs)*bs + l*bs;
1421: } else {
1422: value = v + l*(stepval+bs)*bs + k*bs;
1423: }
1424: if (col <= lastcol) low = 0; else high = nrow;
1425: lastcol = col;
1426: while (high-low > 7) {
1427: t = (low+high)/2;
1428: if (rp[t] > col) high = t;
1429: else low = t;
1430: }
1431: for (i=low; i<high; i++) {
1432: if (rp[i] > col) break;
1433: if (rp[i] == col) {
1434: bap = ap + bs2*i;
1435: if (roworiented) {
1436: if (is == ADD_VALUES) {
1437: for (ii=0; ii<bs; ii++,value+=stepval) {
1438: for (jj=ii; jj<bs2; jj+=bs) {
1439: bap[jj] += *value++;
1440: }
1441: }
1442: } else {
1443: for (ii=0; ii<bs; ii++,value+=stepval) {
1444: for (jj=ii; jj<bs2; jj+=bs) {
1445: bap[jj] = *value++;
1446: }
1447: }
1448: }
1449: } else {
1450: if (is == ADD_VALUES) {
1451: for (ii=0; ii<bs; ii++,value+=stepval) {
1452: for (jj=0; jj<bs; jj++) {
1453: *bap++ += *value++;
1454: }
1455: }
1456: } else {
1457: for (ii=0; ii<bs; ii++,value+=stepval) {
1458: for (jj=0; jj<bs; jj++) {
1459: *bap++ = *value++;
1460: }
1461: }
1462: }
1463: }
1464: goto noinsert2;
1465: }
1466: }
1467: if (nonew == 1) goto noinsert2;
1468: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1469: MatSeqXAIJReallocateAIJ(a,bs2,nrow,row,col,rmax,aa,ai,aj,a->mbs,rp,ap,imax,nonew);
1470: N = nrow++ - 1; high++;
1471: /* shift up all the later entries in this row */
1472: for (ii=N; ii>=i; ii--) {
1473: rp[ii+1] = rp[ii];
1474: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1475: }
1476: if (N >= i) {
1477: PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1478: }
1479: rp[i] = col;
1480: bap = ap + bs2*i;
1481: if (roworiented) {
1482: for (ii=0; ii<bs; ii++,value+=stepval) {
1483: for (jj=ii; jj<bs2; jj+=bs) {
1484: bap[jj] = *value++;
1485: }
1486: }
1487: } else {
1488: for (ii=0; ii<bs; ii++,value+=stepval) {
1489: for (jj=0; jj<bs; jj++) {
1490: *bap++ = *value++;
1491: }
1492: }
1493: }
1494: noinsert2:;
1495: low = i;
1496: }
1497: ailen[row] = nrow;
1498: }
1499: return(0);
1500: }
1504: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A,MatAssemblyType mode)
1505: {
1506: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1507: PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
1508: PetscInt m = A->rmap.N,*ip,N,*ailen = a->ilen;
1510: PetscInt mbs = a->mbs,bs2 = a->bs2,rmax = 0;
1511: MatScalar *aa = a->a,*ap;
1512: PetscReal ratio=0.6;
1515: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1517: if (m) rmax = ailen[0];
1518: for (i=1; i<mbs; i++) {
1519: /* move each row back by the amount of empty slots (fshift) before it*/
1520: fshift += imax[i-1] - ailen[i-1];
1521: rmax = PetscMax(rmax,ailen[i]);
1522: if (fshift) {
1523: ip = aj + ai[i]; ap = aa + bs2*ai[i];
1524: N = ailen[i];
1525: for (j=0; j<N; j++) {
1526: ip[j-fshift] = ip[j];
1527: PetscMemcpy(ap+(j-fshift)*bs2,ap+j*bs2,bs2*sizeof(MatScalar));
1528: }
1529: }
1530: ai[i] = ai[i-1] + ailen[i-1];
1531: }
1532: if (mbs) {
1533: fshift += imax[mbs-1] - ailen[mbs-1];
1534: ai[mbs] = ai[mbs-1] + ailen[mbs-1];
1535: }
1536: /* reset ilen and imax for each row */
1537: for (i=0; i<mbs; i++) {
1538: ailen[i] = imax[i] = ai[i+1] - ai[i];
1539: }
1540: a->nz = ai[mbs];
1542: /* diagonals may have moved, so kill the diagonal pointers */
1543: a->idiagvalid = PETSC_FALSE;
1544: if (fshift && a->diag) {
1545: PetscFree(a->diag);
1546: PetscLogObjectMemory(A,-(mbs+1)*sizeof(PetscInt));
1547: a->diag = 0;
1548: }
1549: 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);
1550: PetscInfo1(A,"Number of mallocs during MatSetValues is %D\n",a->reallocs);
1551: PetscInfo1(A,"Most nonzeros blocks in any row is %D\n",rmax);
1552: a->reallocs = 0;
1553: A->info.nz_unneeded = (PetscReal)fshift*bs2;
1555: /* check for zero rows. If found a large number of zero rows, use CompressedRow functions */
1556: if (a->compressedrow.use){
1557: Mat_CheckCompressedRow(A,&a->compressedrow,a->i,mbs,ratio);
1558: }
1560: A->same_nonzero = PETSC_TRUE;
1561: return(0);
1562: }
1564: /*
1565: This function returns an array of flags which indicate the locations of contiguous
1566: blocks that should be zeroed. for eg: if bs = 3 and is = [0,1,2,3,5,6,7,8,9]
1567: then the resulting sizes = [3,1,1,3,1] correspondig to sets [(0,1,2),(3),(5),(6,7,8),(9)]
1568: Assume: sizes should be long enough to hold all the values.
1569: */
1572: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[],PetscInt n,PetscInt bs,PetscInt sizes[], PetscInt *bs_max)
1573: {
1574: PetscInt i,j,k,row;
1575: PetscTruth flg;
1578: for (i=0,j=0; i<n; j++) {
1579: row = idx[i];
1580: if (row%bs!=0) { /* Not the begining of a block */
1581: sizes[j] = 1;
1582: i++;
1583: } else if (i+bs > n) { /* complete block doesn't exist (at idx end) */
1584: sizes[j] = 1; /* Also makes sure atleast 'bs' values exist for next else */
1585: i++;
1586: } else { /* Begining of the block, so check if the complete block exists */
1587: flg = PETSC_TRUE;
1588: for (k=1; k<bs; k++) {
1589: if (row+k != idx[i+k]) { /* break in the block */
1590: flg = PETSC_FALSE;
1591: break;
1592: }
1593: }
1594: if (flg) { /* No break in the bs */
1595: sizes[j] = bs;
1596: i+= bs;
1597: } else {
1598: sizes[j] = 1;
1599: i++;
1600: }
1601: }
1602: }
1603: *bs_max = j;
1604: return(0);
1605: }
1606:
1609: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A,PetscInt is_n,const PetscInt is_idx[],PetscScalar diag)
1610: {
1611: Mat_SeqBAIJ *baij=(Mat_SeqBAIJ*)A->data;
1613: PetscInt i,j,k,count,*rows;
1614: PetscInt bs=A->rmap.bs,bs2=baij->bs2,*sizes,row,bs_max;
1615: PetscScalar zero = 0.0;
1616: MatScalar *aa;
1619: /* Make a copy of the IS and sort it */
1620: /* allocate memory for rows,sizes */
1621: PetscMalloc((3*is_n+1)*sizeof(PetscInt),&rows);
1622: sizes = rows + is_n;
1624: /* copy IS values to rows, and sort them */
1625: for (i=0; i<is_n; i++) { rows[i] = is_idx[i]; }
1626: PetscSortInt(is_n,rows);
1627: if (baij->keepzeroedrows) {
1628: for (i=0; i<is_n; i++) { sizes[i] = 1; }
1629: bs_max = is_n;
1630: A->same_nonzero = PETSC_TRUE;
1631: } else {
1632: MatZeroRows_SeqBAIJ_Check_Blocks(rows,is_n,bs,sizes,&bs_max);
1633: A->same_nonzero = PETSC_FALSE;
1634: }
1636: for (i=0,j=0; i<bs_max; j+=sizes[i],i++) {
1637: row = rows[j];
1638: if (row < 0 || row > A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range",row);
1639: count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
1640: aa = baij->a + baij->i[row/bs]*bs2 + (row%bs);
1641: if (sizes[i] == bs && !baij->keepzeroedrows) {
1642: if (diag != 0.0) {
1643: if (baij->ilen[row/bs] > 0) {
1644: baij->ilen[row/bs] = 1;
1645: baij->j[baij->i[row/bs]] = row/bs;
1646: PetscMemzero(aa,count*bs*sizeof(MatScalar));
1647: }
1648: /* Now insert all the diagonal values for this bs */
1649: for (k=0; k<bs; k++) {
1650: (*A->ops->setvalues)(A,1,rows+j+k,1,rows+j+k,&diag,INSERT_VALUES);
1651: }
1652: } else { /* (diag == 0.0) */
1653: baij->ilen[row/bs] = 0;
1654: } /* end (diag == 0.0) */
1655: } else { /* (sizes[i] != bs) */
1656: #if defined (PETSC_USE_DEBUG)
1657: if (sizes[i] != 1) SETERRQ(PETSC_ERR_PLIB,"Internal Error. Value should be 1");
1658: #endif
1659: for (k=0; k<count; k++) {
1660: aa[0] = zero;
1661: aa += bs;
1662: }
1663: if (diag != 0.0) {
1664: (*A->ops->setvalues)(A,1,rows+j,1,rows+j,&diag,INSERT_VALUES);
1665: }
1666: }
1667: }
1669: PetscFree(rows);
1670: MatAssemblyEnd_SeqBAIJ(A,MAT_FINAL_ASSEMBLY);
1671: return(0);
1672: }
1676: PetscErrorCode MatSetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
1677: {
1678: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1679: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,lastcol = -1;
1680: PetscInt *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1681: PetscInt *aj=a->j,nonew=a->nonew,bs=A->rmap.bs,brow,bcol;
1683: PetscInt ridx,cidx,bs2=a->bs2;
1684: PetscTruth roworiented=a->roworiented;
1685: MatScalar *ap,value,*aa=a->a,*bap;
1688: for (k=0; k<m; k++) { /* loop over added rows */
1689: row = im[k];
1690: brow = row/bs;
1691: if (row < 0) continue;
1692: #if defined(PETSC_USE_DEBUG)
1693: if (row >= A->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.N-1);
1694: #endif
1695: rp = aj + ai[brow];
1696: ap = aa + bs2*ai[brow];
1697: rmax = imax[brow];
1698: nrow = ailen[brow];
1699: low = 0;
1700: high = nrow;
1701: for (l=0; l<n; l++) { /* loop over added columns */
1702: if (in[l] < 0) continue;
1703: #if defined(PETSC_USE_DEBUG)
1704: if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
1705: #endif
1706: col = in[l]; bcol = col/bs;
1707: ridx = row % bs; cidx = col % bs;
1708: if (roworiented) {
1709: value = v[l + k*n];
1710: } else {
1711: value = v[k + l*m];
1712: }
1713: if (col <= lastcol) low = 0; else high = nrow;
1714: lastcol = col;
1715: while (high-low > 7) {
1716: t = (low+high)/2;
1717: if (rp[t] > bcol) high = t;
1718: else low = t;
1719: }
1720: for (i=low; i<high; i++) {
1721: if (rp[i] > bcol) break;
1722: if (rp[i] == bcol) {
1723: bap = ap + bs2*i + bs*cidx + ridx;
1724: if (is == ADD_VALUES) *bap += value;
1725: else *bap = value;
1726: goto noinsert1;
1727: }
1728: }
1729: if (nonew == 1) goto noinsert1;
1730: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1731: MatSeqXAIJReallocateAIJ(a,bs2,nrow,brow,bcol,rmax,aa,ai,aj,a->mbs,rp,ap,imax,nonew);
1732: N = nrow++ - 1; high++;
1733: /* shift up all the later entries in this row */
1734: for (ii=N; ii>=i; ii--) {
1735: rp[ii+1] = rp[ii];
1736: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1737: }
1738: if (N>=i) {
1739: PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1740: }
1741: rp[i] = bcol;
1742: ap[bs2*i + bs*cidx + ridx] = value;
1743: a->nz++;
1744: noinsert1:;
1745: low = i;
1746: }
1747: ailen[brow] = nrow;
1748: }
1749: A->same_nonzero = PETSC_FALSE;
1750: return(0);
1751: }
1756: PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1757: {
1758: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)inA->data;
1759: Mat outA;
1761: PetscTruth row_identity,col_identity;
1764: if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels = 0 supported for in-place ILU");
1765: ISIdentity(row,&row_identity);
1766: ISIdentity(col,&col_identity);
1767: if (!row_identity || !col_identity) {
1768: SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU");
1769: }
1771: outA = inA;
1772: inA->factor = FACTOR_LU;
1774: if (!a->diag) {
1775: MatMarkDiagonal_SeqBAIJ(inA);
1776: }
1778: a->row = row;
1779: a->col = col;
1780: PetscObjectReference((PetscObject)row);
1781: PetscObjectReference((PetscObject)col);
1782:
1783: /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
1784: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1785: PetscLogObjectParent(inA,a->icol);
1786:
1787: /*
1788: Blocksize 2, 3, 4, 5, 6 and 7 have a special faster factorization/solver
1789: for ILU(0) factorization with natural ordering
1790: */
1791: if (inA->rmap.bs < 8) {
1792: MatSeqBAIJ_UpdateFactorNumeric_NaturalOrdering(inA);
1793: } else {
1794: if (!a->solve_work) {
1795: PetscMalloc((inA->rmap.N+inA->rmap.bs)*sizeof(PetscScalar),&a->solve_work);
1796: PetscLogObjectMemory(inA,(inA->rmap.N+inA->rmap.bs)*sizeof(PetscScalar));
1797: }
1798: }
1800: MatLUFactorNumeric(inA,info,&outA);
1802: return(0);
1803: }
1806: PetscErrorCode MatPrintHelp_SeqBAIJ(Mat A)
1807: {
1808: static PetscTruth called = PETSC_FALSE;
1809: MPI_Comm comm = A->comm;
1810: PetscErrorCode ierr;
1813: if (called) {return(0);} else called = PETSC_TRUE;
1814: (*PetscHelpPrintf)(comm," Options for MATSEQBAIJ and MATMPIBAIJ matrix formats (the defaults):\n");
1815: (*PetscHelpPrintf)(comm," -mat_block_size <block_size>\n");
1816: return(0);
1817: }
1822: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat,PetscInt *indices)
1823: {
1824: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data;
1825: PetscInt i,nz,nbs;
1828: nz = baij->maxnz/baij->bs2;
1829: nbs = baij->nbs;
1830: for (i=0; i<nz; i++) {
1831: baij->j[i] = indices[i];
1832: }
1833: baij->nz = nz;
1834: for (i=0; i<nbs; i++) {
1835: baij->ilen[i] = baij->imax[i];
1836: }
1838: return(0);
1839: }
1844: /*@
1845: MatSeqBAIJSetColumnIndices - Set the column indices for all the rows
1846: in the matrix.
1848: Input Parameters:
1849: + mat - the SeqBAIJ matrix
1850: - indices - the column indices
1852: Level: advanced
1854: Notes:
1855: This can be called if you have precomputed the nonzero structure of the
1856: matrix and want to provide it to the matrix object to improve the performance
1857: of the MatSetValues() operation.
1859: You MUST have set the correct numbers of nonzeros per row in the call to
1860: MatCreateSeqBAIJ(), and the columns indices MUST be sorted.
1862: MUST be called before any calls to MatSetValues();
1864: @*/
1865: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJSetColumnIndices(Mat mat,PetscInt *indices)
1866: {
1867: PetscErrorCode ierr,(*f)(Mat,PetscInt *);
1872: PetscObjectQueryFunction((PetscObject)mat,"MatSeqBAIJSetColumnIndices_C",(void (**)(void))&f);
1873: if (f) {
1874: (*f)(mat,indices);
1875: } else {
1876: SETERRQ(PETSC_ERR_ARG_WRONG,"Wrong type of matrix to set column indices");
1877: }
1878: return(0);
1879: }
1883: PetscErrorCode MatGetRowMax_SeqBAIJ(Mat A,Vec v)
1884: {
1885: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1887: PetscInt i,j,n,row,bs,*ai,*aj,mbs;
1888: PetscReal atmp;
1889: PetscScalar *x,zero = 0.0;
1890: MatScalar *aa;
1891: PetscInt ncols,brow,krow,kcol;
1894: if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1895: bs = A->rmap.bs;
1896: aa = a->a;
1897: ai = a->i;
1898: aj = a->j;
1899: mbs = a->mbs;
1901: VecSet(v,zero);
1902: VecGetArray(v,&x);
1903: VecGetLocalSize(v,&n);
1904: if (n != A->rmap.N) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1905: for (i=0; i<mbs; i++) {
1906: ncols = ai[1] - ai[0]; ai++;
1907: brow = bs*i;
1908: for (j=0; j<ncols; j++){
1909: /* bcol = bs*(*aj); */
1910: for (kcol=0; kcol<bs; kcol++){
1911: for (krow=0; krow<bs; krow++){
1912: atmp = PetscAbsScalar(*aa); aa++;
1913: row = brow + krow; /* row index */
1914: /* printf("val[%d,%d]: %G\n",row,bcol+kcol,atmp); */
1915: if (PetscAbsScalar(x[row]) < atmp) x[row] = atmp;
1916: }
1917: }
1918: aj++;
1919: }
1920: }
1921: VecRestoreArray(v,&x);
1922: return(0);
1923: }
1927: PetscErrorCode MatCopy_SeqBAIJ(Mat A,Mat B,MatStructure str)
1928: {
1932: /* If the two matrices have the same copy implementation, use fast copy. */
1933: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1934: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1935: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)B->data;
1937: if (a->i[A->rmap.N] != b->i[B->rmap.N]) {
1938: SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1939: }
1940: PetscMemcpy(b->a,a->a,(a->i[A->rmap.N])*sizeof(PetscScalar));
1941: } else {
1942: MatCopy_Basic(A,B,str);
1943: }
1944: return(0);
1945: }
1949: PetscErrorCode MatSetUpPreallocation_SeqBAIJ(Mat A)
1950: {
1954: MatSeqBAIJSetPreallocation_SeqBAIJ(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0);
1955: return(0);
1956: }
1960: PetscErrorCode MatGetArray_SeqBAIJ(Mat A,PetscScalar *array[])
1961: {
1962: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1964: *array = a->a;
1965: return(0);
1966: }
1970: PetscErrorCode MatRestoreArray_SeqBAIJ(Mat A,PetscScalar *array[])
1971: {
1973: return(0);
1974: }
1976: #include petscblaslapack.h
1979: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1980: {
1981: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data,*y = (Mat_SeqBAIJ *)Y->data;
1983: PetscInt i,bs=Y->rmap.bs,j,bs2;
1984: PetscBLASInt one=1,bnz = (PetscBLASInt)x->nz;
1987: if (str == SAME_NONZERO_PATTERN) {
1988: PetscScalar alpha = a;
1989: BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1990: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1991: if (y->xtoy && y->XtoY != X) {
1992: PetscFree(y->xtoy);
1993: MatDestroy(y->XtoY);
1994: }
1995: if (!y->xtoy) { /* get xtoy */
1996: MatAXPYGetxtoy_Private(x->mbs,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
1997: y->XtoY = X;
1998: }
1999: bs2 = bs*bs;
2000: for (i=0; i<x->nz; i++) {
2001: j = 0;
2002: while (j < bs2){
2003: y->a[bs2*y->xtoy[i]+j] += a*(x->a[bs2*i+j]);
2004: j++;
2005: }
2006: }
2007: PetscInfo3(0,"ratio of nnz(X)/nnz(Y): %D/%D = %G\n",bs2*x->nz,bs2*y->nz,(PetscReal)(bs2*x->nz)/(bs2*y->nz));
2008: } else {
2009: MatAXPY_Basic(Y,a,X,str);
2010: }
2011: return(0);
2012: }
2016: PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
2017: {
2018: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2019: PetscInt i,nz = a->bs2*a->i[a->mbs];
2020: PetscScalar *aa = a->a;
2023: for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
2024: return(0);
2025: }
2029: PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2030: {
2031: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2032: PetscInt i,nz = a->bs2*a->i[a->mbs];
2033: PetscScalar *aa = a->a;
2036: for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2037: return(0);
2038: }
2041: /* -------------------------------------------------------------------*/
2042: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
2043: MatGetRow_SeqBAIJ,
2044: MatRestoreRow_SeqBAIJ,
2045: MatMult_SeqBAIJ_N,
2046: /* 4*/ MatMultAdd_SeqBAIJ_N,
2047: MatMultTranspose_SeqBAIJ,
2048: MatMultTransposeAdd_SeqBAIJ,
2049: MatSolve_SeqBAIJ_N,
2050: 0,
2051: 0,
2052: /*10*/ 0,
2053: MatLUFactor_SeqBAIJ,
2054: 0,
2055: 0,
2056: MatTranspose_SeqBAIJ,
2057: /*15*/ MatGetInfo_SeqBAIJ,
2058: MatEqual_SeqBAIJ,
2059: MatGetDiagonal_SeqBAIJ,
2060: MatDiagonalScale_SeqBAIJ,
2061: MatNorm_SeqBAIJ,
2062: /*20*/ 0,
2063: MatAssemblyEnd_SeqBAIJ,
2064: 0,
2065: MatSetOption_SeqBAIJ,
2066: MatZeroEntries_SeqBAIJ,
2067: /*25*/ MatZeroRows_SeqBAIJ,
2068: MatLUFactorSymbolic_SeqBAIJ,
2069: MatLUFactorNumeric_SeqBAIJ_N,
2070: MatCholeskyFactorSymbolic_SeqBAIJ,
2071: MatCholeskyFactorNumeric_SeqBAIJ_N,
2072: /*30*/ MatSetUpPreallocation_SeqBAIJ,
2073: MatILUFactorSymbolic_SeqBAIJ,
2074: MatICCFactorSymbolic_SeqBAIJ,
2075: MatGetArray_SeqBAIJ,
2076: MatRestoreArray_SeqBAIJ,
2077: /*35*/ MatDuplicate_SeqBAIJ,
2078: 0,
2079: 0,
2080: MatILUFactor_SeqBAIJ,
2081: 0,
2082: /*40*/ MatAXPY_SeqBAIJ,
2083: MatGetSubMatrices_SeqBAIJ,
2084: MatIncreaseOverlap_SeqBAIJ,
2085: MatGetValues_SeqBAIJ,
2086: MatCopy_SeqBAIJ,
2087: /*45*/ MatPrintHelp_SeqBAIJ,
2088: MatScale_SeqBAIJ,
2089: 0,
2090: 0,
2091: 0,
2092: /*50*/ 0,
2093: MatGetRowIJ_SeqBAIJ,
2094: MatRestoreRowIJ_SeqBAIJ,
2095: 0,
2096: 0,
2097: /*55*/ 0,
2098: 0,
2099: 0,
2100: 0,
2101: MatSetValuesBlocked_SeqBAIJ,
2102: /*60*/ MatGetSubMatrix_SeqBAIJ,
2103: MatDestroy_SeqBAIJ,
2104: MatView_SeqBAIJ,
2105: 0,
2106: 0,
2107: /*65*/ 0,
2108: 0,
2109: 0,
2110: 0,
2111: 0,
2112: /*70*/ MatGetRowMax_SeqBAIJ,
2113: MatConvert_Basic,
2114: 0,
2115: 0,
2116: 0,
2117: /*75*/ 0,
2118: 0,
2119: 0,
2120: 0,
2121: 0,
2122: /*80*/ 0,
2123: 0,
2124: 0,
2125: 0,
2126: MatLoad_SeqBAIJ,
2127: /*85*/ 0,
2128: 0,
2129: 0,
2130: 0,
2131: 0,
2132: /*90*/ 0,
2133: 0,
2134: 0,
2135: 0,
2136: 0,
2137: /*95*/ 0,
2138: 0,
2139: 0,
2140: 0,
2141: 0,
2142: /*100*/0,
2143: 0,
2144: 0,
2145: 0,
2146: 0,
2147: /*105*/0,
2148: MatRealPart_SeqBAIJ,
2149: MatImaginaryPart_SeqBAIJ
2150: };
2155: PetscErrorCode PETSCMAT_DLLEXPORT MatStoreValues_SeqBAIJ(Mat mat)
2156: {
2157: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
2158: PetscInt nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;
2162: if (aij->nonew != 1) {
2163: SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2164: }
2166: /* allocate space for values if not already there */
2167: if (!aij->saved_values) {
2168: PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2169: }
2171: /* copy values over */
2172: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2173: return(0);
2174: }
2180: PetscErrorCode PETSCMAT_DLLEXPORT MatRetrieveValues_SeqBAIJ(Mat mat)
2181: {
2182: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
2184: PetscInt nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;
2187: if (aij->nonew != 1) {
2188: SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2189: }
2190: if (!aij->saved_values) {
2191: SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2192: }
2194: /* copy values over */
2195: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2196: return(0);
2197: }
2208: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B,PetscInt bs,PetscInt nz,PetscInt *nnz)
2209: {
2210: Mat_SeqBAIJ *b;
2212: PetscInt i,mbs,nbs,bs2,newbs = bs;
2213: PetscTruth flg,skipallocation = PETSC_FALSE;
2217: if (nz == MAT_SKIP_ALLOCATION) {
2218: skipallocation = PETSC_TRUE;
2219: nz = 0;
2220: }
2221: PetscOptionsGetInt(B->prefix,"-mat_block_size",&newbs,PETSC_NULL);
2222: if (nnz && newbs != bs) {
2223: SETERRQ(PETSC_ERR_ARG_WRONG,"Cannot change blocksize from command line if setting nnz");
2224: }
2225: bs = newbs;
2227: B->rmap.bs = B->cmap.bs = bs;
2228: PetscMapInitialize(B->comm,&B->rmap);
2229: PetscMapInitialize(B->comm,&B->cmap);
2231: B->preallocated = PETSC_TRUE;
2233: mbs = B->rmap.n/bs;
2234: nbs = B->cmap.n/bs;
2235: bs2 = bs*bs;
2237: if (mbs*bs!=B->rmap.n || nbs*bs!=B->cmap.n) {
2238: SETERRQ3(PETSC_ERR_ARG_SIZ,"Number rows %D, cols %D must be divisible by blocksize %D",B->rmap.N,B->cmap.n,bs);
2239: }
2241: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2242: if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
2243: if (nnz) {
2244: for (i=0; i<mbs; i++) {
2245: if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
2246: if (nnz[i] > nbs) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than block row length: local row %D value %D rowlength %D",i,nnz[i],nbs);
2247: }
2248: }
2250: b = (Mat_SeqBAIJ*)B->data;
2251: PetscOptionsHasName(PETSC_NULL,"-mat_no_unroll",&flg);
2252: B->ops->solve = MatSolve_SeqBAIJ_Update;
2253: B->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_Update;
2254: if (!flg) {
2255: switch (bs) {
2256: case 1:
2257: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_1;
2258: B->ops->mult = MatMult_SeqBAIJ_1;
2259: B->ops->multadd = MatMultAdd_SeqBAIJ_1;
2260: break;
2261: case 2:
2262: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_2;
2263: B->ops->mult = MatMult_SeqBAIJ_2;
2264: B->ops->multadd = MatMultAdd_SeqBAIJ_2;
2265: B->ops->pbrelax = MatPBRelax_SeqBAIJ_2;
2266: break;
2267: case 3:
2268: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_3;
2269: B->ops->mult = MatMult_SeqBAIJ_3;
2270: B->ops->multadd = MatMultAdd_SeqBAIJ_3;
2271: B->ops->pbrelax = MatPBRelax_SeqBAIJ_3;
2272: break;
2273: case 4:
2274: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_4;
2275: B->ops->mult = MatMult_SeqBAIJ_4;
2276: B->ops->multadd = MatMultAdd_SeqBAIJ_4;
2277: B->ops->pbrelax = MatPBRelax_SeqBAIJ_4;
2278: break;
2279: case 5:
2280: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_5;
2281: B->ops->mult = MatMult_SeqBAIJ_5;
2282: B->ops->multadd = MatMultAdd_SeqBAIJ_5;
2283: B->ops->pbrelax = MatPBRelax_SeqBAIJ_5;
2284: break;
2285: case 6:
2286: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_6;
2287: B->ops->mult = MatMult_SeqBAIJ_6;
2288: B->ops->multadd = MatMultAdd_SeqBAIJ_6;
2289: break;
2290: case 7:
2291: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_7;
2292: B->ops->mult = MatMult_SeqBAIJ_7;
2293: B->ops->multadd = MatMultAdd_SeqBAIJ_7;
2294: break;
2295: default:
2296: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_N;
2297: B->ops->mult = MatMult_SeqBAIJ_N;
2298: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
2299: break;
2300: }
2301: }
2302: B->rmap.bs = bs;
2303: b->mbs = mbs;
2304: b->nbs = nbs;
2305: if (!skipallocation) {
2306: PetscMalloc2(mbs,PetscInt,&b->imax,mbs,PetscInt,&b->ilen);
2307: /* b->ilen will count nonzeros in each block row so far. */
2308: for (i=0; i<mbs; i++) { b->ilen[i] = 0;}
2309: if (!nnz) {
2310: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2311: else if (nz <= 0) nz = 1;
2312: for (i=0; i<mbs; i++) b->imax[i] = nz;
2313: nz = nz*mbs;
2314: } else {
2315: nz = 0;
2316: for (i=0; i<mbs; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2317: }
2319: /* allocate the matrix space */
2320: PetscMalloc3(bs2*nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap.N+1,PetscInt,&b->i);
2321: PetscMemzero(b->a,nz*bs2*sizeof(MatScalar));
2322: PetscMemzero(b->j,nz*sizeof(PetscInt));
2323: b->singlemalloc = PETSC_TRUE;
2325: b->i[0] = 0;
2326: for (i=1; i<mbs+1; i++) {
2327: b->i[i] = b->i[i-1] + b->imax[i-1];
2328: }
2329: }
2331: B->rmap.bs = bs;
2332: b->bs2 = bs2;
2333: b->mbs = mbs;
2334: b->nz = 0;
2335: b->maxnz = nz*bs2;
2336: B->info.nz_unneeded = (PetscReal)b->maxnz;
2337: return(0);
2338: }
2341: /*MC
2342: MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on
2343: block sparse compressed row format.
2345: Options Database Keys:
2346: . -mat_type seqbaij - sets the matrix type to "seqbaij" during a call to MatSetFromOptions()
2348: Level: beginner
2350: .seealso: MatCreateSeqBAIJ()
2351: M*/
2356: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SeqBAIJ(Mat B)
2357: {
2359: PetscMPIInt size;
2360: Mat_SeqBAIJ *b;
2363: MPI_Comm_size(B->comm,&size);
2364: if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"Comm must be of size 1");
2366: PetscNew(Mat_SeqBAIJ,&b);
2367: B->data = (void*)b;
2368: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2369: B->factor = 0;
2370: B->mapping = 0;
2371: b->row = 0;
2372: b->col = 0;
2373: b->icol = 0;
2374: b->reallocs = 0;
2375: b->saved_values = 0;
2376: #if defined(PETSC_USE_MAT_SINGLE)
2377: b->setvalueslen = 0;
2378: b->setvaluescopy = PETSC_NULL;
2379: #endif
2381: b->sorted = PETSC_FALSE;
2382: b->roworiented = PETSC_TRUE;
2383: b->nonew = 0;
2384: b->diag = 0;
2385: b->solve_work = 0;
2386: b->mult_work = 0;
2387: B->spptr = 0;
2388: B->info.nz_unneeded = (PetscReal)b->maxnz;
2389: b->keepzeroedrows = PETSC_FALSE;
2390: b->xtoy = 0;
2391: b->XtoY = 0;
2392: b->compressedrow.use = PETSC_FALSE;
2393: b->compressedrow.nrows = 0;
2394: b->compressedrow.i = PETSC_NULL;
2395: b->compressedrow.rindex = PETSC_NULL;
2396: b->compressedrow.checked = PETSC_FALSE;
2397: B->same_nonzero = PETSC_FALSE;
2399: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJInvertBlockDiagonal_C",
2400: "MatInvertBlockDiagonal_SeqBAIJ",
2401: MatInvertBlockDiagonal_SeqBAIJ);
2402: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2403: "MatStoreValues_SeqBAIJ",
2404: MatStoreValues_SeqBAIJ);
2405: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2406: "MatRetrieveValues_SeqBAIJ",
2407: MatRetrieveValues_SeqBAIJ);
2408: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetColumnIndices_C",
2409: "MatSeqBAIJSetColumnIndices_SeqBAIJ",
2410: MatSeqBAIJSetColumnIndices_SeqBAIJ);
2411: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqaij_C",
2412: "MatConvert_SeqBAIJ_SeqAIJ",
2413: MatConvert_SeqBAIJ_SeqAIJ);
2414: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqsbaij_C",
2415: "MatConvert_SeqBAIJ_SeqSBAIJ",
2416: MatConvert_SeqBAIJ_SeqSBAIJ);
2417: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetPreallocation_C",
2418: "MatSeqBAIJSetPreallocation_SeqBAIJ",
2419: MatSeqBAIJSetPreallocation_SeqBAIJ);
2420: return(0);
2421: }
2426: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2427: {
2428: Mat C;
2429: Mat_SeqBAIJ *c,*a = (Mat_SeqBAIJ*)A->data;
2431: PetscInt i,mbs = a->mbs,nz = a->nz,bs2 = a->bs2;
2434: if (a->i[mbs] != nz) SETERRQ(PETSC_ERR_PLIB,"Corrupt matrix");
2436: *B = 0;
2437: MatCreate(A->comm,&C);
2438: MatSetSizes(C,A->rmap.N,A->cmap.n,A->rmap.N,A->cmap.n);
2439: MatSetType(C,A->type_name);
2440: PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
2441: c = (Mat_SeqBAIJ*)C->data;
2443: C->rmap.N = A->rmap.N;
2444: C->cmap.N = A->cmap.N;
2445: C->rmap.bs = A->rmap.bs;
2446: c->bs2 = a->bs2;
2447: c->mbs = a->mbs;
2448: c->nbs = a->nbs;
2450: PetscMalloc2(mbs,PetscInt,&c->imax,mbs,PetscInt,&c->ilen);
2451: for (i=0; i<mbs; i++) {
2452: c->imax[i] = a->imax[i];
2453: c->ilen[i] = a->ilen[i];
2454: }
2456: /* allocate the matrix space */
2457: PetscMalloc3(bs2*nz,PetscScalar,&c->a,nz,PetscInt,&c->j,mbs+1,PetscInt,&c->i);
2458: c->singlemalloc = PETSC_TRUE;
2459: PetscMemcpy(c->i,a->i,(mbs+1)*sizeof(PetscInt));
2460: if (mbs > 0) {
2461: PetscMemcpy(c->j,a->j,nz*sizeof(PetscInt));
2462: if (cpvalues == MAT_COPY_VALUES) {
2463: PetscMemcpy(c->a,a->a,bs2*nz*sizeof(MatScalar));
2464: } else {
2465: PetscMemzero(c->a,bs2*nz*sizeof(MatScalar));
2466: }
2467: }
2468: c->sorted = a->sorted;
2469: c->roworiented = a->roworiented;
2470: c->nonew = a->nonew;
2472: if (a->diag) {
2473: PetscMalloc((mbs+1)*sizeof(PetscInt),&c->diag);
2474: PetscLogObjectMemory(C,(mbs+1)*sizeof(PetscInt));
2475: for (i=0; i<mbs; i++) {
2476: c->diag[i] = a->diag[i];
2477: }
2478: } else c->diag = 0;
2479: c->nz = a->nz;
2480: c->maxnz = a->maxnz;
2481: c->solve_work = 0;
2482: c->mult_work = 0;
2483: C->preallocated = PETSC_TRUE;
2484: C->assembled = PETSC_TRUE;
2486: c->compressedrow.use = a->compressedrow.use;
2487: c->compressedrow.nrows = a->compressedrow.nrows;
2488: c->compressedrow.checked = a->compressedrow.checked;
2489: if ( a->compressedrow.checked && a->compressedrow.use){
2490: i = a->compressedrow.nrows;
2491: PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
2492: c->compressedrow.rindex = c->compressedrow.i + i + 1;
2493: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
2494: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
2495: } else {
2496: c->compressedrow.use = PETSC_FALSE;
2497: c->compressedrow.i = PETSC_NULL;
2498: c->compressedrow.rindex = PETSC_NULL;
2499: }
2500: C->same_nonzero = A->same_nonzero;
2501: *B = C;
2502: PetscFListDuplicate(A->qlist,&C->qlist);
2503: return(0);
2504: }
2508: PetscErrorCode MatLoad_SeqBAIJ(PetscViewer viewer, MatType type,Mat *A)
2509: {
2510: Mat_SeqBAIJ *a;
2511: Mat B;
2513: PetscInt i,nz,header[4],*rowlengths=0,M,N,bs=1;
2514: PetscInt *mask,mbs,*jj,j,rowcount,nzcount,k,*browlengths,maskcount;
2515: PetscInt kmax,jcount,block,idx,point,nzcountb,extra_rows;
2516: PetscInt *masked,nmask,tmp,bs2,ishift;
2517: PetscMPIInt size;
2518: int fd;
2519: PetscScalar *aa;
2520: MPI_Comm comm = ((PetscObject)viewer)->comm;
2523: PetscOptionsGetInt(PETSC_NULL,"-matload_block_size",&bs,PETSC_NULL);
2524: bs2 = bs*bs;
2526: MPI_Comm_size(comm,&size);
2527: if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"view must have one processor");
2528: PetscViewerBinaryGetDescriptor(viewer,&fd);
2529: PetscBinaryRead(fd,header,4,PETSC_INT);
2530: if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not Mat object");
2531: M = header[1]; N = header[2]; nz = header[3];
2533: if (header[3] < 0) {
2534: SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as SeqBAIJ");
2535: }
2537: if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");
2539: /*
2540: This code adds extra rows to make sure the number of rows is
2541: divisible by the blocksize
2542: */
2543: mbs = M/bs;
2544: extra_rows = bs - M + bs*(mbs);
2545: if (extra_rows == bs) extra_rows = 0;
2546: else mbs++;
2547: if (extra_rows) {
2548: PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2549: }
2551: /* read in row lengths */
2552: PetscMalloc((M+extra_rows)*sizeof(PetscInt),&rowlengths);
2553: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2554: for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2556: /* read in column indices */
2557: PetscMalloc((nz+extra_rows)*sizeof(PetscInt),&jj);
2558: PetscBinaryRead(fd,jj,nz,PETSC_INT);
2559: for (i=0; i<extra_rows; i++) jj[nz+i] = M+i;
2561: /* loop over row lengths determining block row lengths */
2562: PetscMalloc(mbs*sizeof(PetscInt),&browlengths);
2563: PetscMemzero(browlengths,mbs*sizeof(PetscInt));
2564: PetscMalloc(2*mbs*sizeof(PetscInt),&mask);
2565: PetscMemzero(mask,mbs*sizeof(PetscInt));
2566: masked = mask + mbs;
2567: rowcount = 0; nzcount = 0;
2568: for (i=0; i<mbs; i++) {
2569: nmask = 0;
2570: for (j=0; j<bs; j++) {
2571: kmax = rowlengths[rowcount];
2572: for (k=0; k<kmax; k++) {
2573: tmp = jj[nzcount++]/bs;
2574: if (!mask[tmp]) {masked[nmask++] = tmp; mask[tmp] = 1;}
2575: }
2576: rowcount++;
2577: }
2578: browlengths[i] += nmask;
2579: /* zero out the mask elements we set */
2580: for (j=0; j<nmask; j++) mask[masked[j]] = 0;
2581: }
2583: /* create our matrix */
2584: MatCreate(comm,&B);
2585: MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M+extra_rows,N+extra_rows);
2586: MatSetType(B,type);
2587: MatSeqBAIJSetPreallocation_SeqBAIJ(B,bs,0,browlengths);
2588: a = (Mat_SeqBAIJ*)B->data;
2590: /* set matrix "i" values */
2591: a->i[0] = 0;
2592: for (i=1; i<= mbs; i++) {
2593: a->i[i] = a->i[i-1] + browlengths[i-1];
2594: a->ilen[i-1] = browlengths[i-1];
2595: }
2596: a->nz = 0;
2597: for (i=0; i<mbs; i++) a->nz += browlengths[i];
2599: /* read in nonzero values */
2600: PetscMalloc((nz+extra_rows)*sizeof(PetscScalar),&aa);
2601: PetscBinaryRead(fd,aa,nz,PETSC_SCALAR);
2602: for (i=0; i<extra_rows; i++) aa[nz+i] = 1.0;
2604: /* set "a" and "j" values into matrix */
2605: nzcount = 0; jcount = 0;
2606: for (i=0; i<mbs; i++) {
2607: nzcountb = nzcount;
2608: nmask = 0;
2609: for (j=0; j<bs; j++) {
2610: kmax = rowlengths[i*bs+j];
2611: for (k=0; k<kmax; k++) {
2612: tmp = jj[nzcount++]/bs;
2613: if (!mask[tmp]) { masked[nmask++] = tmp; mask[tmp] = 1;}
2614: }
2615: }
2616: /* sort the masked values */
2617: PetscSortInt(nmask,masked);
2619: /* set "j" values into matrix */
2620: maskcount = 1;
2621: for (j=0; j<nmask; j++) {
2622: a->j[jcount++] = masked[j];
2623: mask[masked[j]] = maskcount++;
2624: }
2625: /* set "a" values into matrix */
2626: ishift = bs2*a->i[i];
2627: for (j=0; j<bs; j++) {
2628: kmax = rowlengths[i*bs+j];
2629: for (k=0; k<kmax; k++) {
2630: tmp = jj[nzcountb]/bs ;
2631: block = mask[tmp] - 1;
2632: point = jj[nzcountb] - bs*tmp;
2633: idx = ishift + bs2*block + j + bs*point;
2634: a->a[idx] = (MatScalar)aa[nzcountb++];
2635: }
2636: }
2637: /* zero out the mask elements we set */
2638: for (j=0; j<nmask; j++) mask[masked[j]] = 0;
2639: }
2640: if (jcount != a->nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Bad binary matrix");
2642: PetscFree(rowlengths);
2643: PetscFree(browlengths);
2644: PetscFree(aa);
2645: PetscFree(jj);
2646: PetscFree(mask);
2648: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2649: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2650: MatView_Private(B);
2652: *A = B;
2653: return(0);
2654: }
2658: /*@C
2659: MatCreateSeqBAIJ - Creates a sparse matrix in block AIJ (block
2660: compressed row) format. For good matrix assembly performance the
2661: user should preallocate the matrix storage by setting the parameter nz
2662: (or the array nnz). By setting these parameters accurately, performance
2663: during matrix assembly can be increased by more than a factor of 50.
2665: Collective on MPI_Comm
2667: Input Parameters:
2668: + comm - MPI communicator, set to PETSC_COMM_SELF
2669: . bs - size of block
2670: . m - number of rows
2671: . n - number of columns
2672: . nz - number of nonzero blocks per block row (same for all rows)
2673: - nnz - array containing the number of nonzero blocks in the various block rows
2674: (possibly different for each block row) or PETSC_NULL
2676: Output Parameter:
2677: . A - the matrix
2679: Options Database Keys:
2680: . -mat_no_unroll - uses code that does not unroll the loops in the
2681: block calculations (much slower)
2682: . -mat_block_size - size of the blocks to use
2684: Level: intermediate
2686: Notes:
2687: The number of rows and columns must be divisible by blocksize.
2689: If the nnz parameter is given then the nz parameter is ignored
2691: A nonzero block is any block that as 1 or more nonzeros in it
2693: The block AIJ format is fully compatible with standard Fortran 77
2694: storage. That is, the stored row and column indices can begin at
2695: either one (as in Fortran) or zero. See the users' manual for details.
2697: Specify the preallocated storage with either nz or nnz (not both).
2698: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
2699: allocation. For additional details, see the users manual chapter on
2700: matrices.
2702: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2703: @*/
2704: PetscErrorCode PETSCMAT_DLLEXPORT MatCreateSeqBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2705: {
2707:
2709: MatCreate(comm,A);
2710: MatSetSizes(*A,m,n,m,n);
2711: MatSetType(*A,MATSEQBAIJ);
2712: MatSeqBAIJSetPreallocation_SeqBAIJ(*A,bs,nz,(PetscInt*)nnz);
2713: return(0);
2714: }
2718: /*@C
2719: MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
2720: per row in the matrix. For good matrix assembly performance the
2721: user should preallocate the matrix storage by setting the parameter nz
2722: (or the array nnz). By setting these parameters accurately, performance
2723: during matrix assembly can be increased by more than a factor of 50.
2725: Collective on MPI_Comm
2727: Input Parameters:
2728: + A - the matrix
2729: . bs - size of block
2730: . nz - number of block nonzeros per block row (same for all rows)
2731: - nnz - array containing the number of block nonzeros in the various block rows
2732: (possibly different for each block row) or PETSC_NULL
2734: Options Database Keys:
2735: . -mat_no_unroll - uses code that does not unroll the loops in the
2736: block calculations (much slower)
2737: . -mat_block_size - size of the blocks to use
2739: Level: intermediate
2741: Notes:
2742: If the nnz parameter is given then the nz parameter is ignored
2744: The block AIJ format is fully compatible with standard Fortran 77
2745: storage. That is, the stored row and column indices can begin at
2746: either one (as in Fortran) or zero. See the users' manual for details.
2748: Specify the preallocated storage with either nz or nnz (not both).
2749: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
2750: allocation. For additional details, see the users manual chapter on
2751: matrices.
2753: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2754: @*/
2755: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt nz,const PetscInt nnz[])
2756: {
2757: PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[]);
2760: PetscObjectQueryFunction((PetscObject)B,"MatSeqBAIJSetPreallocation_C",(void (**)(void))&f);
2761: if (f) {
2762: (*f)(B,bs,nz,nnz);
2763: }
2764: return(0);
2765: }