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 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 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 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 + 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 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,m = a->mbs;
759: if (!a->diag) {
760: PetscMalloc(m*sizeof(PetscInt),&a->diag);
761: }
762: for (i=0; i<m; i++) {
763: a->diag[i] = a->i[i+1];
764: for (j=a->i[i]; j<a->i[i+1]; j++) {
765: if (a->j[j] == i) {
766: a->diag[i] = j;
767: break;
768: }
769: }
770: }
771: return(0);
772: }
775: EXTERN PetscErrorCode MatToSymmetricIJ_SeqAIJ(PetscInt,PetscInt*,PetscInt*,PetscInt,PetscInt,PetscInt**,PetscInt**);
779: static PetscErrorCode MatGetRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
780: {
781: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
783: PetscInt n = a->mbs,i;
786: *nn = n;
787: if (!ia) return(0);
788: if (symmetric) {
789: MatToSymmetricIJ_SeqAIJ(n,a->i,a->j,0,oshift,ia,ja);
790: } else if (oshift == 1) {
791: /* temporarily add 1 to i and j indices */
792: PetscInt nz = a->i[n];
793: for (i=0; i<nz; i++) a->j[i]++;
794: for (i=0; i<n+1; i++) a->i[i]++;
795: *ia = a->i; *ja = a->j;
796: } else {
797: *ia = a->i; *ja = a->j;
798: }
800: return(0);
801: }
805: static PetscErrorCode MatRestoreRowIJ_SeqBAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
806: {
807: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
809: PetscInt i,n = a->mbs;
812: if (!ia) return(0);
813: if (symmetric) {
814: PetscFree(*ia);
815: PetscFree(*ja);
816: } else if (oshift == 1) {
817: PetscInt nz = a->i[n]-1;
818: for (i=0; i<nz; i++) a->j[i]--;
819: for (i=0; i<n+1; i++) a->i[i]--;
820: }
821: return(0);
822: }
826: PetscErrorCode MatDestroy_SeqBAIJ(Mat A)
827: {
828: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
832: #if defined(PETSC_USE_LOG)
833: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap.N,A->cmap.n,a->nz);
834: #endif
835: MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
836: if (a->row) {
837: ISDestroy(a->row);
838: }
839: if (a->col) {
840: ISDestroy(a->col);
841: }
842: PetscFree(a->diag);
843: PetscFree(a->idiag);
844: PetscFree2(a->imax,a->ilen);
845: PetscFree(a->solve_work);
846: PetscFree(a->mult_work);
847: if (a->icol) {ISDestroy(a->icol);}
848: PetscFree(a->saved_values);
849: #if defined(PETSC_USE_MAT_SINGLE)
850: PetscFree(a->setvaluescopy);
851: #endif
852: PetscFree(a->xtoy);
853: if (a->compressedrow.use){PetscFree(a->compressedrow.i);}
855: PetscFree(a);
857: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJInvertBlockDiagonal_C","",PETSC_NULL);
858: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
859: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
860: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetColumnIndices_C","",PETSC_NULL);
861: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqaij_C","",PETSC_NULL);
862: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqbaij_seqsbaij_C","",PETSC_NULL);
863: PetscObjectComposeFunction((PetscObject)A,"MatSeqBAIJSetPreallocation_C","",PETSC_NULL);
864: return(0);
865: }
869: PetscErrorCode MatSetOption_SeqBAIJ(Mat A,MatOption op)
870: {
871: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
875: switch (op) {
876: case MAT_ROW_ORIENTED:
877: a->roworiented = PETSC_TRUE;
878: break;
879: case MAT_COLUMN_ORIENTED:
880: a->roworiented = PETSC_FALSE;
881: break;
882: case MAT_COLUMNS_SORTED:
883: a->sorted = PETSC_TRUE;
884: break;
885: case MAT_COLUMNS_UNSORTED:
886: a->sorted = PETSC_FALSE;
887: break;
888: case MAT_KEEP_ZEROED_ROWS:
889: a->keepzeroedrows = PETSC_TRUE;
890: break;
891: case MAT_NO_NEW_NONZERO_LOCATIONS:
892: a->nonew = 1;
893: break;
894: case MAT_NEW_NONZERO_LOCATION_ERR:
895: a->nonew = -1;
896: break;
897: case MAT_NEW_NONZERO_ALLOCATION_ERR:
898: a->nonew = -2;
899: break;
900: case MAT_YES_NEW_NONZERO_LOCATIONS:
901: a->nonew = 0;
902: break;
903: case MAT_ROWS_SORTED:
904: case MAT_ROWS_UNSORTED:
905: case MAT_YES_NEW_DIAGONALS:
906: case MAT_IGNORE_OFF_PROC_ENTRIES:
907: case MAT_USE_HASH_TABLE:
908: PetscInfo1(A,"Option %d ignored\n",op);
909: break;
910: case MAT_NO_NEW_DIAGONALS:
911: SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
912: case MAT_SYMMETRIC:
913: case MAT_STRUCTURALLY_SYMMETRIC:
914: case MAT_NOT_SYMMETRIC:
915: case MAT_NOT_STRUCTURALLY_SYMMETRIC:
916: case MAT_HERMITIAN:
917: case MAT_NOT_HERMITIAN:
918: case MAT_SYMMETRY_ETERNAL:
919: case MAT_NOT_SYMMETRY_ETERNAL:
920: break;
921: default:
922: SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
923: }
924: return(0);
925: }
929: PetscErrorCode MatGetRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
930: {
931: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
933: PetscInt itmp,i,j,k,M,*ai,*aj,bs,bn,bp,*idx_i,bs2;
934: MatScalar *aa,*aa_i;
935: PetscScalar *v_i;
938: bs = A->rmap.bs;
939: ai = a->i;
940: aj = a->j;
941: aa = a->a;
942: bs2 = a->bs2;
943:
944: if (row < 0 || row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range", row);
945:
946: bn = row/bs; /* Block number */
947: bp = row % bs; /* Block Position */
948: M = ai[bn+1] - ai[bn];
949: *nz = bs*M;
950:
951: if (v) {
952: *v = 0;
953: if (*nz) {
954: PetscMalloc((*nz)*sizeof(PetscScalar),v);
955: for (i=0; i<M; i++) { /* for each block in the block row */
956: v_i = *v + i*bs;
957: aa_i = aa + bs2*(ai[bn] + i);
958: for (j=bp,k=0; j<bs2; j+=bs,k++) {v_i[k] = aa_i[j];}
959: }
960: }
961: }
963: if (idx) {
964: *idx = 0;
965: if (*nz) {
966: PetscMalloc((*nz)*sizeof(PetscInt),idx);
967: for (i=0; i<M; i++) { /* for each block in the block row */
968: idx_i = *idx + i*bs;
969: itmp = bs*aj[ai[bn] + i];
970: for (j=0; j<bs; j++) {idx_i[j] = itmp++;}
971: }
972: }
973: }
974: return(0);
975: }
979: PetscErrorCode MatRestoreRow_SeqBAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
980: {
984: if (idx) {PetscFree(*idx);}
985: if (v) {PetscFree(*v);}
986: return(0);
987: }
991: PetscErrorCode MatTranspose_SeqBAIJ(Mat A,Mat *B)
992: {
993: Mat_SeqBAIJ *a=(Mat_SeqBAIJ *)A->data;
994: Mat C;
996: PetscInt i,j,k,*aj=a->j,*ai=a->i,bs=A->rmap.bs,mbs=a->mbs,nbs=a->nbs,len,*col;
997: PetscInt *rows,*cols,bs2=a->bs2;
998: PetscScalar *array;
1001: if (!B && mbs!=nbs) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Square matrix only for in-place");
1002: PetscMalloc((1+nbs)*sizeof(PetscInt),&col);
1003: PetscMemzero(col,(1+nbs)*sizeof(PetscInt));
1005: #if defined(PETSC_USE_MAT_SINGLE)
1006: PetscMalloc(a->bs2*a->nz*sizeof(PetscScalar),&array);
1007: for (i=0; i<a->bs2*a->nz; i++) array[i] = (PetscScalar)a->a[i];
1008: #else
1009: array = a->a;
1010: #endif
1012: for (i=0; i<ai[mbs]; i++) col[aj[i]] += 1;
1013: MatCreate(A->comm,&C);
1014: MatSetSizes(C,A->cmap.n,A->rmap.N,A->cmap.n,A->rmap.N);
1015: MatSetType(C,A->type_name);
1016: MatSeqBAIJSetPreallocation_SeqBAIJ(C,bs,PETSC_NULL,col);
1017: PetscFree(col);
1018: PetscMalloc(2*bs*sizeof(PetscInt),&rows);
1019: cols = rows + bs;
1020: for (i=0; i<mbs; i++) {
1021: cols[0] = i*bs;
1022: for (k=1; k<bs; k++) cols[k] = cols[k-1] + 1;
1023: len = ai[i+1] - ai[i];
1024: for (j=0; j<len; j++) {
1025: rows[0] = (*aj++)*bs;
1026: for (k=1; k<bs; k++) rows[k] = rows[k-1] + 1;
1027: MatSetValues(C,bs,rows,bs,cols,array,INSERT_VALUES);
1028: array += bs2;
1029: }
1030: }
1031: PetscFree(rows);
1032: #if defined(PETSC_USE_MAT_SINGLE)
1033: PetscFree(array);
1034: #endif
1035:
1036: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1037: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1038:
1039: if (B) {
1040: *B = C;
1041: } else {
1042: MatHeaderCopy(A,C);
1043: }
1044: return(0);
1045: }
1049: static PetscErrorCode MatView_SeqBAIJ_Binary(Mat A,PetscViewer viewer)
1050: {
1051: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1053: PetscInt i,*col_lens,bs = A->rmap.bs,count,*jj,j,k,l,bs2=a->bs2;
1054: int fd;
1055: PetscScalar *aa;
1056: FILE *file;
1059: PetscViewerBinaryGetDescriptor(viewer,&fd);
1060: PetscMalloc((4+A->rmap.N)*sizeof(PetscInt),&col_lens);
1061: col_lens[0] = MAT_FILE_COOKIE;
1063: col_lens[1] = A->rmap.N;
1064: col_lens[2] = A->cmap.n;
1065: col_lens[3] = a->nz*bs2;
1067: /* store lengths of each row and write (including header) to file */
1068: count = 0;
1069: for (i=0; i<a->mbs; i++) {
1070: for (j=0; j<bs; j++) {
1071: col_lens[4+count++] = bs*(a->i[i+1] - a->i[i]);
1072: }
1073: }
1074: PetscBinaryWrite(fd,col_lens,4+A->rmap.N,PETSC_INT,PETSC_TRUE);
1075: PetscFree(col_lens);
1077: /* store column indices (zero start index) */
1078: PetscMalloc((a->nz+1)*bs2*sizeof(PetscInt),&jj);
1079: count = 0;
1080: for (i=0; i<a->mbs; i++) {
1081: for (j=0; j<bs; j++) {
1082: for (k=a->i[i]; k<a->i[i+1]; k++) {
1083: for (l=0; l<bs; l++) {
1084: jj[count++] = bs*a->j[k] + l;
1085: }
1086: }
1087: }
1088: }
1089: PetscBinaryWrite(fd,jj,bs2*a->nz,PETSC_INT,PETSC_FALSE);
1090: PetscFree(jj);
1092: /* store nonzero values */
1093: PetscMalloc((a->nz+1)*bs2*sizeof(PetscScalar),&aa);
1094: count = 0;
1095: for (i=0; i<a->mbs; i++) {
1096: for (j=0; j<bs; j++) {
1097: for (k=a->i[i]; k<a->i[i+1]; k++) {
1098: for (l=0; l<bs; l++) {
1099: aa[count++] = a->a[bs2*k + l*bs + j];
1100: }
1101: }
1102: }
1103: }
1104: PetscBinaryWrite(fd,aa,bs2*a->nz,PETSC_SCALAR,PETSC_FALSE);
1105: PetscFree(aa);
1107: PetscViewerBinaryGetInfoPointer(viewer,&file);
1108: if (file) {
1109: fprintf(file,"-matload_block_size %d\n",(int)A->rmap.bs);
1110: }
1111: return(0);
1112: }
1116: static PetscErrorCode MatView_SeqBAIJ_ASCII(Mat A,PetscViewer viewer)
1117: {
1118: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1119: PetscErrorCode ierr;
1120: PetscInt i,j,bs = A->rmap.bs,k,l,bs2=a->bs2;
1121: PetscViewerFormat format;
1124: PetscViewerGetFormat(viewer,&format);
1125: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1126: PetscViewerASCIIPrintf(viewer," block size is %D\n",bs);
1127: } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
1128: Mat aij;
1129: MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&aij);
1130: MatView(aij,viewer);
1131: MatDestroy(aij);
1132: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
1133: return(0);
1134: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1135: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
1136: for (i=0; i<a->mbs; i++) {
1137: for (j=0; j<bs; j++) {
1138: PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1139: for (k=a->i[i]; k<a->i[i+1]; k++) {
1140: for (l=0; l<bs; l++) {
1141: #if defined(PETSC_USE_COMPLEX)
1142: if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1143: PetscViewerASCIIPrintf(viewer," (%D, %G + %Gi) ",bs*a->j[k]+l,
1144: PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1145: } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0 && PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1146: PetscViewerASCIIPrintf(viewer," (%D, %G - %Gi) ",bs*a->j[k]+l,
1147: PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1148: } else if (PetscRealPart(a->a[bs2*k + l*bs + j]) != 0.0) {
1149: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1150: }
1151: #else
1152: if (a->a[bs2*k + l*bs + j] != 0.0) {
1153: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1154: }
1155: #endif
1156: }
1157: }
1158: PetscViewerASCIIPrintf(viewer,"\n");
1159: }
1160: }
1161: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
1162: } else {
1163: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
1164: for (i=0; i<a->mbs; i++) {
1165: for (j=0; j<bs; j++) {
1166: PetscViewerASCIIPrintf(viewer,"row %D:",i*bs+j);
1167: for (k=a->i[i]; k<a->i[i+1]; k++) {
1168: for (l=0; l<bs; l++) {
1169: #if defined(PETSC_USE_COMPLEX)
1170: if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) > 0.0) {
1171: PetscViewerASCIIPrintf(viewer," (%D, %G + %G i) ",bs*a->j[k]+l,
1172: PetscRealPart(a->a[bs2*k + l*bs + j]),PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1173: } else if (PetscImaginaryPart(a->a[bs2*k + l*bs + j]) < 0.0) {
1174: PetscViewerASCIIPrintf(viewer," (%D, %G - %G i) ",bs*a->j[k]+l,
1175: PetscRealPart(a->a[bs2*k + l*bs + j]),-PetscImaginaryPart(a->a[bs2*k + l*bs + j]));
1176: } else {
1177: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,PetscRealPart(a->a[bs2*k + l*bs + j]));
1178: }
1179: #else
1180: PetscViewerASCIIPrintf(viewer," (%D, %G) ",bs*a->j[k]+l,a->a[bs2*k + l*bs + j]);
1181: #endif
1182: }
1183: }
1184: PetscViewerASCIIPrintf(viewer,"\n");
1185: }
1186: }
1187: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
1188: }
1189: PetscViewerFlush(viewer);
1190: return(0);
1191: }
1195: static PetscErrorCode MatView_SeqBAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1196: {
1197: Mat A = (Mat) Aa;
1198: Mat_SeqBAIJ *a=(Mat_SeqBAIJ*)A->data;
1200: PetscInt row,i,j,k,l,mbs=a->mbs,color,bs=A->rmap.bs,bs2=a->bs2;
1201: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1202: MatScalar *aa;
1203: PetscViewer viewer;
1207: /* still need to add support for contour plot of nonzeros; see MatView_SeqAIJ_Draw_Zoom()*/
1208: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1210: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
1212: /* loop over matrix elements drawing boxes */
1213: color = PETSC_DRAW_BLUE;
1214: for (i=0,row=0; i<mbs; i++,row+=bs) {
1215: for (j=a->i[i]; j<a->i[i+1]; j++) {
1216: y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1217: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1218: aa = a->a + j*bs2;
1219: for (k=0; k<bs; k++) {
1220: for (l=0; l<bs; l++) {
1221: if (PetscRealPart(*aa++) >= 0.) continue;
1222: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1223: }
1224: }
1225: }
1226: }
1227: color = PETSC_DRAW_CYAN;
1228: for (i=0,row=0; i<mbs; i++,row+=bs) {
1229: for (j=a->i[i]; j<a->i[i+1]; j++) {
1230: y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1231: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1232: aa = a->a + j*bs2;
1233: for (k=0; k<bs; k++) {
1234: for (l=0; l<bs; l++) {
1235: if (PetscRealPart(*aa++) != 0.) continue;
1236: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1237: }
1238: }
1239: }
1240: }
1242: color = PETSC_DRAW_RED;
1243: for (i=0,row=0; i<mbs; i++,row+=bs) {
1244: for (j=a->i[i]; j<a->i[i+1]; j++) {
1245: y_l = A->rmap.N - row - 1.0; y_r = y_l + 1.0;
1246: x_l = a->j[j]*bs; x_r = x_l + 1.0;
1247: aa = a->a + j*bs2;
1248: for (k=0; k<bs; k++) {
1249: for (l=0; l<bs; l++) {
1250: if (PetscRealPart(*aa++) <= 0.) continue;
1251: PetscDrawRectangle(draw,x_l+k,y_l-l,x_r+k,y_r-l,color,color,color,color);
1252: }
1253: }
1254: }
1255: }
1256: return(0);
1257: }
1261: static PetscErrorCode MatView_SeqBAIJ_Draw(Mat A,PetscViewer viewer)
1262: {
1264: PetscReal xl,yl,xr,yr,w,h;
1265: PetscDraw draw;
1266: PetscTruth isnull;
1270: PetscViewerDrawGetDraw(viewer,0,&draw);
1271: PetscDrawIsNull(draw,&isnull); if (isnull) return(0);
1273: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1274: xr = A->cmap.n; yr = A->rmap.N; h = yr/10.0; w = xr/10.0;
1275: xr += w; yr += h; xl = -w; yl = -h;
1276: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1277: PetscDrawZoom(draw,MatView_SeqBAIJ_Draw_Zoom,A);
1278: PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
1279: return(0);
1280: }
1284: PetscErrorCode MatView_SeqBAIJ(Mat A,PetscViewer viewer)
1285: {
1287: PetscTruth iascii,isbinary,isdraw;
1290: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
1291: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
1292: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
1293: if (iascii){
1294: MatView_SeqBAIJ_ASCII(A,viewer);
1295: } else if (isbinary) {
1296: MatView_SeqBAIJ_Binary(A,viewer);
1297: } else if (isdraw) {
1298: MatView_SeqBAIJ_Draw(A,viewer);
1299: } else {
1300: Mat B;
1301: MatConvert(A,MATSEQAIJ,MAT_INITIAL_MATRIX,&B);
1302: MatView(B,viewer);
1303: MatDestroy(B);
1304: }
1305: return(0);
1306: }
1311: PetscErrorCode MatGetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
1312: {
1313: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1314: PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
1315: PetscInt *ai = a->i,*ailen = a->ilen;
1316: PetscInt brow,bcol,ridx,cidx,bs=A->rmap.bs,bs2=a->bs2;
1317: MatScalar *ap,*aa = a->a,zero = 0.0;
1320: for (k=0; k<m; k++) { /* loop over rows */
1321: row = im[k]; brow = row/bs;
1322: if (row < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative row");
1323: if (row >= A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D too large", row);
1324: rp = aj + ai[brow] ; ap = aa + bs2*ai[brow] ;
1325: nrow = ailen[brow];
1326: for (l=0; l<n; l++) { /* loop over columns */
1327: if (in[l] < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Negative column");
1328: if (in[l] >= A->cmap.n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Column %D too large", in[l]);
1329: col = in[l] ;
1330: bcol = col/bs;
1331: cidx = col%bs;
1332: ridx = row%bs;
1333: high = nrow;
1334: low = 0; /* assume unsorted */
1335: while (high-low > 5) {
1336: t = (low+high)/2;
1337: if (rp[t] > bcol) high = t;
1338: else low = t;
1339: }
1340: for (i=low; i<high; i++) {
1341: if (rp[i] > bcol) break;
1342: if (rp[i] == bcol) {
1343: *v++ = ap[bs2*i+bs*cidx+ridx];
1344: goto finished;
1345: }
1346: }
1347: *v++ = zero;
1348: finished:;
1349: }
1350: }
1351: return(0);
1352: }
1354: #if defined(PETSC_USE_MAT_SINGLE)
1357: PetscErrorCode MatSetValuesBlocked_SeqBAIJ(Mat mat,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode addv)
1358: {
1359: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)mat->data;
1361: PetscInt i,N = m*n*b->bs2;
1362: MatScalar *vsingle;
1365: if (N > b->setvalueslen) {
1366: PetscFree(b->setvaluescopy);
1367: PetscMalloc(N*sizeof(MatScalar),&b->setvaluescopy);
1368: b->setvalueslen = N;
1369: }
1370: vsingle = b->setvaluescopy;
1371: for (i=0; i<N; i++) {
1372: vsingle[i] = v[i];
1373: }
1374: MatSetValuesBlocked_SeqBAIJ_MatScalar(mat,m,im,n,in,vsingle,addv);
1375: return(0);
1376: }
1377: #endif
1382: PetscErrorCode MatSetValuesBlocked_SeqBAIJ_MatScalar(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const MatScalar v[],InsertMode is)
1383: {
1384: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1385: PetscInt *rp,k,low,high,t,ii,jj,row,nrow,i,col,l,rmax,N,lastcol = -1;
1386: PetscInt *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1387: PetscErrorCode ierr;
1388: PetscInt *aj=a->j,nonew=a->nonew,bs2=a->bs2,bs=A->rmap.bs,stepval;
1389: PetscTruth roworiented=a->roworiented;
1390: const MatScalar *value = v;
1391: MatScalar *ap,*aa = a->a,*bap;
1394: if (roworiented) {
1395: stepval = (n-1)*bs;
1396: } else {
1397: stepval = (m-1)*bs;
1398: }
1399: for (k=0; k<m; k++) { /* loop over added rows */
1400: row = im[k];
1401: if (row < 0) continue;
1402: #if defined(PETSC_USE_DEBUG)
1403: if (row >= a->mbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,a->mbs-1);
1404: #endif
1405: rp = aj + ai[row];
1406: ap = aa + bs2*ai[row];
1407: rmax = imax[row];
1408: nrow = ailen[row];
1409: low = 0;
1410: high = nrow;
1411: for (l=0; l<n; l++) { /* loop over added columns */
1412: if (in[l] < 0) continue;
1413: #if defined(PETSC_USE_DEBUG)
1414: if (in[l] >= a->nbs) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],a->nbs-1);
1415: #endif
1416: col = in[l];
1417: if (roworiented) {
1418: value = v + k*(stepval+bs)*bs + l*bs;
1419: } else {
1420: value = v + l*(stepval+bs)*bs + k*bs;
1421: }
1422: if (col <= lastcol) low = 0; else high = nrow;
1423: lastcol = col;
1424: while (high-low > 7) {
1425: t = (low+high)/2;
1426: if (rp[t] > col) high = t;
1427: else low = t;
1428: }
1429: for (i=low; i<high; i++) {
1430: if (rp[i] > col) break;
1431: if (rp[i] == col) {
1432: bap = ap + bs2*i;
1433: if (roworiented) {
1434: if (is == ADD_VALUES) {
1435: for (ii=0; ii<bs; ii++,value+=stepval) {
1436: for (jj=ii; jj<bs2; jj+=bs) {
1437: bap[jj] += *value++;
1438: }
1439: }
1440: } else {
1441: for (ii=0; ii<bs; ii++,value+=stepval) {
1442: for (jj=ii; jj<bs2; jj+=bs) {
1443: bap[jj] = *value++;
1444: }
1445: }
1446: }
1447: } else {
1448: if (is == ADD_VALUES) {
1449: for (ii=0; ii<bs; ii++,value+=stepval) {
1450: for (jj=0; jj<bs; jj++) {
1451: *bap++ += *value++;
1452: }
1453: }
1454: } else {
1455: for (ii=0; ii<bs; ii++,value+=stepval) {
1456: for (jj=0; jj<bs; jj++) {
1457: *bap++ = *value++;
1458: }
1459: }
1460: }
1461: }
1462: goto noinsert2;
1463: }
1464: }
1465: if (nonew == 1) goto noinsert2;
1466: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1467: MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew);
1468: N = nrow++ - 1; high++;
1469: /* shift up all the later entries in this row */
1470: for (ii=N; ii>=i; ii--) {
1471: rp[ii+1] = rp[ii];
1472: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1473: }
1474: if (N >= i) {
1475: PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1476: }
1477: rp[i] = col;
1478: bap = ap + bs2*i;
1479: if (roworiented) {
1480: for (ii=0; ii<bs; ii++,value+=stepval) {
1481: for (jj=ii; jj<bs2; jj+=bs) {
1482: bap[jj] = *value++;
1483: }
1484: }
1485: } else {
1486: for (ii=0; ii<bs; ii++,value+=stepval) {
1487: for (jj=0; jj<bs; jj++) {
1488: *bap++ = *value++;
1489: }
1490: }
1491: }
1492: noinsert2:;
1493: low = i;
1494: }
1495: ailen[row] = nrow;
1496: }
1497: return(0);
1498: }
1502: PetscErrorCode MatAssemblyEnd_SeqBAIJ(Mat A,MatAssemblyType mode)
1503: {
1504: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1505: PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
1506: PetscInt m = A->rmap.N,*ip,N,*ailen = a->ilen;
1508: PetscInt mbs = a->mbs,bs2 = a->bs2,rmax = 0;
1509: MatScalar *aa = a->a,*ap;
1510: PetscReal ratio=0.6;
1513: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1515: if (m) rmax = ailen[0];
1516: for (i=1; i<mbs; i++) {
1517: /* move each row back by the amount of empty slots (fshift) before it*/
1518: fshift += imax[i-1] - ailen[i-1];
1519: rmax = PetscMax(rmax,ailen[i]);
1520: if (fshift) {
1521: ip = aj + ai[i]; ap = aa + bs2*ai[i];
1522: N = ailen[i];
1523: for (j=0; j<N; j++) {
1524: ip[j-fshift] = ip[j];
1525: PetscMemcpy(ap+(j-fshift)*bs2,ap+j*bs2,bs2*sizeof(MatScalar));
1526: }
1527: }
1528: ai[i] = ai[i-1] + ailen[i-1];
1529: }
1530: if (mbs) {
1531: fshift += imax[mbs-1] - ailen[mbs-1];
1532: ai[mbs] = ai[mbs-1] + ailen[mbs-1];
1533: }
1534: /* reset ilen and imax for each row */
1535: for (i=0; i<mbs; i++) {
1536: ailen[i] = imax[i] = ai[i+1] - ai[i];
1537: }
1538: a->nz = ai[mbs];
1540: /* diagonals may have moved, so kill the diagonal pointers */
1541: a->idiagvalid = PETSC_FALSE;
1542: if (fshift && a->diag) {
1543: PetscFree(a->diag);
1544: PetscLogObjectMemory(A,-(mbs+1)*sizeof(PetscInt));
1545: a->diag = 0;
1546: }
1547: 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);
1548: PetscInfo1(A,"Number of mallocs during MatSetValues is %D\n",a->reallocs);
1549: PetscInfo1(A,"Most nonzeros blocks in any row is %D\n",rmax);
1550: a->reallocs = 0;
1551: A->info.nz_unneeded = (PetscReal)fshift*bs2;
1553: /* check for zero rows. If found a large number of zero rows, use CompressedRow functions */
1554: if (a->compressedrow.use){
1555: Mat_CheckCompressedRow(A,&a->compressedrow,a->i,mbs,ratio);
1556: }
1558: A->same_nonzero = PETSC_TRUE;
1559: return(0);
1560: }
1562: /*
1563: This function returns an array of flags which indicate the locations of contiguous
1564: blocks that should be zeroed. for eg: if bs = 3 and is = [0,1,2,3,5,6,7,8,9]
1565: then the resulting sizes = [3,1,1,3,1] correspondig to sets [(0,1,2),(3),(5),(6,7,8),(9)]
1566: Assume: sizes should be long enough to hold all the values.
1567: */
1570: static PetscErrorCode MatZeroRows_SeqBAIJ_Check_Blocks(PetscInt idx[],PetscInt n,PetscInt bs,PetscInt sizes[], PetscInt *bs_max)
1571: {
1572: PetscInt i,j,k,row;
1573: PetscTruth flg;
1576: for (i=0,j=0; i<n; j++) {
1577: row = idx[i];
1578: if (row%bs!=0) { /* Not the begining of a block */
1579: sizes[j] = 1;
1580: i++;
1581: } else if (i+bs > n) { /* complete block doesn't exist (at idx end) */
1582: sizes[j] = 1; /* Also makes sure atleast 'bs' values exist for next else */
1583: i++;
1584: } else { /* Begining of the block, so check if the complete block exists */
1585: flg = PETSC_TRUE;
1586: for (k=1; k<bs; k++) {
1587: if (row+k != idx[i+k]) { /* break in the block */
1588: flg = PETSC_FALSE;
1589: break;
1590: }
1591: }
1592: if (flg) { /* No break in the bs */
1593: sizes[j] = bs;
1594: i+= bs;
1595: } else {
1596: sizes[j] = 1;
1597: i++;
1598: }
1599: }
1600: }
1601: *bs_max = j;
1602: return(0);
1603: }
1604:
1607: PetscErrorCode MatZeroRows_SeqBAIJ(Mat A,PetscInt is_n,const PetscInt is_idx[],PetscScalar diag)
1608: {
1609: Mat_SeqBAIJ *baij=(Mat_SeqBAIJ*)A->data;
1611: PetscInt i,j,k,count,*rows;
1612: PetscInt bs=A->rmap.bs,bs2=baij->bs2,*sizes,row,bs_max;
1613: PetscScalar zero = 0.0;
1614: MatScalar *aa;
1617: /* Make a copy of the IS and sort it */
1618: /* allocate memory for rows,sizes */
1619: PetscMalloc((3*is_n+1)*sizeof(PetscInt),&rows);
1620: sizes = rows + is_n;
1622: /* copy IS values to rows, and sort them */
1623: for (i=0; i<is_n; i++) { rows[i] = is_idx[i]; }
1624: PetscSortInt(is_n,rows);
1625: if (baij->keepzeroedrows) {
1626: for (i=0; i<is_n; i++) { sizes[i] = 1; }
1627: bs_max = is_n;
1628: A->same_nonzero = PETSC_TRUE;
1629: } else {
1630: MatZeroRows_SeqBAIJ_Check_Blocks(rows,is_n,bs,sizes,&bs_max);
1631: A->same_nonzero = PETSC_FALSE;
1632: }
1634: for (i=0,j=0; i<bs_max; j+=sizes[i],i++) {
1635: row = rows[j];
1636: if (row < 0 || row > A->rmap.N) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range",row);
1637: count = (baij->i[row/bs +1] - baij->i[row/bs])*bs;
1638: aa = baij->a + baij->i[row/bs]*bs2 + (row%bs);
1639: if (sizes[i] == bs && !baij->keepzeroedrows) {
1640: if (diag != 0.0) {
1641: if (baij->ilen[row/bs] > 0) {
1642: baij->ilen[row/bs] = 1;
1643: baij->j[baij->i[row/bs]] = row/bs;
1644: PetscMemzero(aa,count*bs*sizeof(MatScalar));
1645: }
1646: /* Now insert all the diagonal values for this bs */
1647: for (k=0; k<bs; k++) {
1648: (*A->ops->setvalues)(A,1,rows+j+k,1,rows+j+k,&diag,INSERT_VALUES);
1649: }
1650: } else { /* (diag == 0.0) */
1651: baij->ilen[row/bs] = 0;
1652: } /* end (diag == 0.0) */
1653: } else { /* (sizes[i] != bs) */
1654: #if defined (PETSC_USE_DEBUG)
1655: if (sizes[i] != 1) SETERRQ(PETSC_ERR_PLIB,"Internal Error. Value should be 1");
1656: #endif
1657: for (k=0; k<count; k++) {
1658: aa[0] = zero;
1659: aa += bs;
1660: }
1661: if (diag != 0.0) {
1662: (*A->ops->setvalues)(A,1,rows+j,1,rows+j,&diag,INSERT_VALUES);
1663: }
1664: }
1665: }
1667: PetscFree(rows);
1668: MatAssemblyEnd_SeqBAIJ(A,MAT_FINAL_ASSEMBLY);
1669: return(0);
1670: }
1674: PetscErrorCode MatSetValues_SeqBAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
1675: {
1676: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1677: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,lastcol = -1;
1678: PetscInt *imax=a->imax,*ai=a->i,*ailen=a->ilen;
1679: PetscInt *aj=a->j,nonew=a->nonew,bs=A->rmap.bs,brow,bcol;
1681: PetscInt ridx,cidx,bs2=a->bs2;
1682: PetscTruth roworiented=a->roworiented;
1683: MatScalar *ap,value,*aa=a->a,*bap;
1686: for (k=0; k<m; k++) { /* loop over added rows */
1687: row = im[k];
1688: brow = row/bs;
1689: if (row < 0) continue;
1690: #if defined(PETSC_USE_DEBUG)
1691: if (row >= A->rmap.N) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.N-1);
1692: #endif
1693: rp = aj + ai[brow];
1694: ap = aa + bs2*ai[brow];
1695: rmax = imax[brow];
1696: nrow = ailen[brow];
1697: low = 0;
1698: high = nrow;
1699: for (l=0; l<n; l++) { /* loop over added columns */
1700: if (in[l] < 0) continue;
1701: #if defined(PETSC_USE_DEBUG)
1702: if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
1703: #endif
1704: col = in[l]; bcol = col/bs;
1705: ridx = row % bs; cidx = col % bs;
1706: if (roworiented) {
1707: value = v[l + k*n];
1708: } else {
1709: value = v[k + l*m];
1710: }
1711: if (col <= lastcol) low = 0; else high = nrow;
1712: lastcol = col;
1713: while (high-low > 7) {
1714: t = (low+high)/2;
1715: if (rp[t] > bcol) high = t;
1716: else low = t;
1717: }
1718: for (i=low; i<high; i++) {
1719: if (rp[i] > bcol) break;
1720: if (rp[i] == bcol) {
1721: bap = ap + bs2*i + bs*cidx + ridx;
1722: if (is == ADD_VALUES) *bap += value;
1723: else *bap = value;
1724: goto noinsert1;
1725: }
1726: }
1727: if (nonew == 1) goto noinsert1;
1728: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1729: MatSeqXAIJReallocateAIJ(A,a->mbs,bs2,nrow,brow,bcol,rmax,aa,ai,aj,rp,ap,imax,nonew);
1730: N = nrow++ - 1; high++;
1731: /* shift up all the later entries in this row */
1732: for (ii=N; ii>=i; ii--) {
1733: rp[ii+1] = rp[ii];
1734: PetscMemcpy(ap+bs2*(ii+1),ap+bs2*(ii),bs2*sizeof(MatScalar));
1735: }
1736: if (N>=i) {
1737: PetscMemzero(ap+bs2*i,bs2*sizeof(MatScalar));
1738: }
1739: rp[i] = bcol;
1740: ap[bs2*i + bs*cidx + ridx] = value;
1741: a->nz++;
1742: noinsert1:;
1743: low = i;
1744: }
1745: ailen[brow] = nrow;
1746: }
1747: A->same_nonzero = PETSC_FALSE;
1748: return(0);
1749: }
1754: PetscErrorCode MatILUFactor_SeqBAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1755: {
1756: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)inA->data;
1757: Mat outA;
1759: PetscTruth row_identity,col_identity;
1762: if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels = 0 supported for in-place ILU");
1763: ISIdentity(row,&row_identity);
1764: ISIdentity(col,&col_identity);
1765: if (!row_identity || !col_identity) {
1766: SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU");
1767: }
1769: outA = inA;
1770: inA->factor = FACTOR_LU;
1772: MatMarkDiagonal_SeqBAIJ(inA);
1774: a->row = row;
1775: a->col = col;
1776: PetscObjectReference((PetscObject)row);
1777: PetscObjectReference((PetscObject)col);
1778:
1779: /* Create the invert permutation so that it can be used in MatLUFactorNumeric() */
1780: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1781: PetscLogObjectParent(inA,a->icol);
1782:
1783: /*
1784: Blocksize 2, 3, 4, 5, 6 and 7 have a special faster factorization/solver
1785: for ILU(0) factorization with natural ordering
1786: */
1787: if (inA->rmap.bs < 8) {
1788: MatSeqBAIJ_UpdateFactorNumeric_NaturalOrdering(inA);
1789: } else {
1790: if (!a->solve_work) {
1791: PetscMalloc((inA->rmap.N+inA->rmap.bs)*sizeof(PetscScalar),&a->solve_work);
1792: PetscLogObjectMemory(inA,(inA->rmap.N+inA->rmap.bs)*sizeof(PetscScalar));
1793: }
1794: }
1796: MatLUFactorNumeric(inA,info,&outA);
1798: return(0);
1799: }
1804: PetscErrorCode MatSeqBAIJSetColumnIndices_SeqBAIJ(Mat mat,PetscInt *indices)
1805: {
1806: Mat_SeqBAIJ *baij = (Mat_SeqBAIJ *)mat->data;
1807: PetscInt i,nz,nbs;
1810: nz = baij->maxnz/baij->bs2;
1811: nbs = baij->nbs;
1812: for (i=0; i<nz; i++) {
1813: baij->j[i] = indices[i];
1814: }
1815: baij->nz = nz;
1816: for (i=0; i<nbs; i++) {
1817: baij->ilen[i] = baij->imax[i];
1818: }
1820: return(0);
1821: }
1826: /*@
1827: MatSeqBAIJSetColumnIndices - Set the column indices for all the rows
1828: in the matrix.
1830: Input Parameters:
1831: + mat - the SeqBAIJ matrix
1832: - indices - the column indices
1834: Level: advanced
1836: Notes:
1837: This can be called if you have precomputed the nonzero structure of the
1838: matrix and want to provide it to the matrix object to improve the performance
1839: of the MatSetValues() operation.
1841: You MUST have set the correct numbers of nonzeros per row in the call to
1842: MatCreateSeqBAIJ(), and the columns indices MUST be sorted.
1844: MUST be called before any calls to MatSetValues();
1846: @*/
1847: PetscErrorCode MatSeqBAIJSetColumnIndices(Mat mat,PetscInt *indices)
1848: {
1849: PetscErrorCode ierr,(*f)(Mat,PetscInt *);
1854: PetscObjectQueryFunction((PetscObject)mat,"MatSeqBAIJSetColumnIndices_C",(void (**)(void))&f);
1855: if (f) {
1856: (*f)(mat,indices);
1857: } else {
1858: SETERRQ(PETSC_ERR_ARG_WRONG,"Wrong type of matrix to set column indices");
1859: }
1860: return(0);
1861: }
1865: PetscErrorCode MatGetRowMax_SeqBAIJ(Mat A,Vec v)
1866: {
1867: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1869: PetscInt i,j,n,row,bs,*ai,*aj,mbs;
1870: PetscReal atmp;
1871: PetscScalar *x,zero = 0.0;
1872: MatScalar *aa;
1873: PetscInt ncols,brow,krow,kcol;
1876: if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
1877: bs = A->rmap.bs;
1878: aa = a->a;
1879: ai = a->i;
1880: aj = a->j;
1881: mbs = a->mbs;
1883: VecSet(v,zero);
1884: VecGetArray(v,&x);
1885: VecGetLocalSize(v,&n);
1886: if (n != A->rmap.N) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1887: for (i=0; i<mbs; i++) {
1888: ncols = ai[1] - ai[0]; ai++;
1889: brow = bs*i;
1890: for (j=0; j<ncols; j++){
1891: /* bcol = bs*(*aj); */
1892: for (kcol=0; kcol<bs; kcol++){
1893: for (krow=0; krow<bs; krow++){
1894: atmp = PetscAbsScalar(*aa); aa++;
1895: row = brow + krow; /* row index */
1896: /* printf("val[%d,%d]: %G\n",row,bcol+kcol,atmp); */
1897: if (PetscAbsScalar(x[row]) < atmp) x[row] = atmp;
1898: }
1899: }
1900: aj++;
1901: }
1902: }
1903: VecRestoreArray(v,&x);
1904: return(0);
1905: }
1909: PetscErrorCode MatCopy_SeqBAIJ(Mat A,Mat B,MatStructure str)
1910: {
1914: /* If the two matrices have the same copy implementation, use fast copy. */
1915: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1916: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1917: Mat_SeqBAIJ *b = (Mat_SeqBAIJ*)B->data;
1919: if (a->i[A->rmap.N] != b->i[B->rmap.N]) {
1920: SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1921: }
1922: PetscMemcpy(b->a,a->a,(a->i[A->rmap.N])*sizeof(PetscScalar));
1923: } else {
1924: MatCopy_Basic(A,B,str);
1925: }
1926: return(0);
1927: }
1931: PetscErrorCode MatSetUpPreallocation_SeqBAIJ(Mat A)
1932: {
1936: MatSeqBAIJSetPreallocation_SeqBAIJ(A,PetscMax(A->rmap.bs,1),PETSC_DEFAULT,0);
1937: return(0);
1938: }
1942: PetscErrorCode MatGetArray_SeqBAIJ(Mat A,PetscScalar *array[])
1943: {
1944: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
1946: *array = a->a;
1947: return(0);
1948: }
1952: PetscErrorCode MatRestoreArray_SeqBAIJ(Mat A,PetscScalar *array[])
1953: {
1955: return(0);
1956: }
1958: #include petscblaslapack.h
1961: PetscErrorCode MatAXPY_SeqBAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
1962: {
1963: Mat_SeqBAIJ *x = (Mat_SeqBAIJ *)X->data,*y = (Mat_SeqBAIJ *)Y->data;
1965: PetscInt i,bs=Y->rmap.bs,j,bs2;
1966: PetscBLASInt one=1,bnz = (PetscBLASInt)x->nz;
1969: if (str == SAME_NONZERO_PATTERN) {
1970: PetscScalar alpha = a;
1971: BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
1972: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
1973: if (y->xtoy && y->XtoY != X) {
1974: PetscFree(y->xtoy);
1975: MatDestroy(y->XtoY);
1976: }
1977: if (!y->xtoy) { /* get xtoy */
1978: MatAXPYGetxtoy_Private(x->mbs,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
1979: y->XtoY = X;
1980: }
1981: bs2 = bs*bs;
1982: for (i=0; i<x->nz; i++) {
1983: j = 0;
1984: while (j < bs2){
1985: y->a[bs2*y->xtoy[i]+j] += a*(x->a[bs2*i+j]);
1986: j++;
1987: }
1988: }
1989: 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));
1990: } else {
1991: MatAXPY_Basic(Y,a,X,str);
1992: }
1993: return(0);
1994: }
1998: PetscErrorCode MatRealPart_SeqBAIJ(Mat A)
1999: {
2000: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2001: PetscInt i,nz = a->bs2*a->i[a->mbs];
2002: PetscScalar *aa = a->a;
2005: for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
2006: return(0);
2007: }
2011: PetscErrorCode MatImaginaryPart_SeqBAIJ(Mat A)
2012: {
2013: Mat_SeqBAIJ *a = (Mat_SeqBAIJ*)A->data;
2014: PetscInt i,nz = a->bs2*a->i[a->mbs];
2015: PetscScalar *aa = a->a;
2018: for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
2019: return(0);
2020: }
2023: /* -------------------------------------------------------------------*/
2024: static struct _MatOps MatOps_Values = {MatSetValues_SeqBAIJ,
2025: MatGetRow_SeqBAIJ,
2026: MatRestoreRow_SeqBAIJ,
2027: MatMult_SeqBAIJ_N,
2028: /* 4*/ MatMultAdd_SeqBAIJ_N,
2029: MatMultTranspose_SeqBAIJ,
2030: MatMultTransposeAdd_SeqBAIJ,
2031: MatSolve_SeqBAIJ_N,
2032: 0,
2033: 0,
2034: /*10*/ 0,
2035: MatLUFactor_SeqBAIJ,
2036: 0,
2037: 0,
2038: MatTranspose_SeqBAIJ,
2039: /*15*/ MatGetInfo_SeqBAIJ,
2040: MatEqual_SeqBAIJ,
2041: MatGetDiagonal_SeqBAIJ,
2042: MatDiagonalScale_SeqBAIJ,
2043: MatNorm_SeqBAIJ,
2044: /*20*/ 0,
2045: MatAssemblyEnd_SeqBAIJ,
2046: 0,
2047: MatSetOption_SeqBAIJ,
2048: MatZeroEntries_SeqBAIJ,
2049: /*25*/ MatZeroRows_SeqBAIJ,
2050: MatLUFactorSymbolic_SeqBAIJ,
2051: MatLUFactorNumeric_SeqBAIJ_N,
2052: MatCholeskyFactorSymbolic_SeqBAIJ,
2053: MatCholeskyFactorNumeric_SeqBAIJ_N,
2054: /*30*/ MatSetUpPreallocation_SeqBAIJ,
2055: MatILUFactorSymbolic_SeqBAIJ,
2056: MatICCFactorSymbolic_SeqBAIJ,
2057: MatGetArray_SeqBAIJ,
2058: MatRestoreArray_SeqBAIJ,
2059: /*35*/ MatDuplicate_SeqBAIJ,
2060: 0,
2061: 0,
2062: MatILUFactor_SeqBAIJ,
2063: 0,
2064: /*40*/ MatAXPY_SeqBAIJ,
2065: MatGetSubMatrices_SeqBAIJ,
2066: MatIncreaseOverlap_SeqBAIJ,
2067: MatGetValues_SeqBAIJ,
2068: MatCopy_SeqBAIJ,
2069: /*45*/ 0,
2070: MatScale_SeqBAIJ,
2071: 0,
2072: 0,
2073: 0,
2074: /*50*/ 0,
2075: MatGetRowIJ_SeqBAIJ,
2076: MatRestoreRowIJ_SeqBAIJ,
2077: 0,
2078: 0,
2079: /*55*/ 0,
2080: 0,
2081: 0,
2082: 0,
2083: MatSetValuesBlocked_SeqBAIJ,
2084: /*60*/ MatGetSubMatrix_SeqBAIJ,
2085: MatDestroy_SeqBAIJ,
2086: MatView_SeqBAIJ,
2087: 0,
2088: 0,
2089: /*65*/ 0,
2090: 0,
2091: 0,
2092: 0,
2093: 0,
2094: /*70*/ MatGetRowMax_SeqBAIJ,
2095: MatConvert_Basic,
2096: 0,
2097: 0,
2098: 0,
2099: /*75*/ 0,
2100: 0,
2101: 0,
2102: 0,
2103: 0,
2104: /*80*/ 0,
2105: 0,
2106: 0,
2107: 0,
2108: MatLoad_SeqBAIJ,
2109: /*85*/ 0,
2110: 0,
2111: 0,
2112: 0,
2113: 0,
2114: /*90*/ 0,
2115: 0,
2116: 0,
2117: 0,
2118: 0,
2119: /*95*/ 0,
2120: 0,
2121: 0,
2122: 0,
2123: 0,
2124: /*100*/0,
2125: 0,
2126: 0,
2127: 0,
2128: 0,
2129: /*105*/0,
2130: MatRealPart_SeqBAIJ,
2131: MatImaginaryPart_SeqBAIJ
2132: };
2137: PetscErrorCode MatStoreValues_SeqBAIJ(Mat mat)
2138: {
2139: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
2140: PetscInt nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;
2144: if (aij->nonew != 1) {
2145: SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2146: }
2148: /* allocate space for values if not already there */
2149: if (!aij->saved_values) {
2150: PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2151: }
2153: /* copy values over */
2154: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2155: return(0);
2156: }
2162: PetscErrorCode MatRetrieveValues_SeqBAIJ(Mat mat)
2163: {
2164: Mat_SeqBAIJ *aij = (Mat_SeqBAIJ *)mat->data;
2166: PetscInt nz = aij->i[mat->rmap.N]*mat->rmap.bs*aij->bs2;
2169: if (aij->nonew != 1) {
2170: SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2171: }
2172: if (!aij->saved_values) {
2173: SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2174: }
2176: /* copy values over */
2177: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2178: return(0);
2179: }
2190: PetscErrorCode MatSeqBAIJSetPreallocation_SeqBAIJ(Mat B,PetscInt bs,PetscInt nz,PetscInt *nnz)
2191: {
2192: Mat_SeqBAIJ *b;
2194: PetscInt i,mbs,nbs,bs2,newbs = bs;
2195: PetscTruth flg,skipallocation = PETSC_FALSE;
2199: if (nz == MAT_SKIP_ALLOCATION) {
2200: skipallocation = PETSC_TRUE;
2201: nz = 0;
2202: }
2204: PetscOptionsBegin(B->comm,B->prefix,"Options for SEQBAIJ matrix","Mat");
2205: PetscOptionsInt("-mat_block_size","Set the blocksize used to store the matrix","MatSeqBAIJSetPreallocation",bs,&newbs,PETSC_NULL);
2206: PetscOptionsEnd();
2208: if (nnz && newbs != bs) {
2209: SETERRQ(PETSC_ERR_ARG_WRONG,"Cannot change blocksize from command line if setting nnz");
2210: }
2211: bs = newbs;
2213: B->rmap.bs = B->cmap.bs = bs;
2214: PetscMapInitialize(B->comm,&B->rmap);
2215: PetscMapInitialize(B->comm,&B->cmap);
2217: B->preallocated = PETSC_TRUE;
2219: mbs = B->rmap.n/bs;
2220: nbs = B->cmap.n/bs;
2221: bs2 = bs*bs;
2223: if (mbs*bs!=B->rmap.n || nbs*bs!=B->cmap.n) {
2224: SETERRQ3(PETSC_ERR_ARG_SIZ,"Number rows %D, cols %D must be divisible by blocksize %D",B->rmap.N,B->cmap.n,bs);
2225: }
2227: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2228: if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
2229: if (nnz) {
2230: for (i=0; i<mbs; i++) {
2231: if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
2232: 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);
2233: }
2234: }
2236: b = (Mat_SeqBAIJ*)B->data;
2237: PetscOptionsBegin(B->comm,PETSC_NULL,"Options for SEQBAIJ matrix","Mat");
2238: PetscOptionsTruth("-mat_no_unroll","Do not optimize for block size (slow)",PETSC_NULL,PETSC_FALSE,&flg,PETSC_NULL);
2239: PetscOptionsEnd();
2241: B->ops->solve = MatSolve_SeqBAIJ_Update;
2242: B->ops->solvetranspose = MatSolveTranspose_SeqBAIJ_Update;
2243: if (!flg) {
2244: switch (bs) {
2245: case 1:
2246: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_1;
2247: B->ops->mult = MatMult_SeqBAIJ_1;
2248: B->ops->multadd = MatMultAdd_SeqBAIJ_1;
2249: break;
2250: case 2:
2251: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_2;
2252: B->ops->mult = MatMult_SeqBAIJ_2;
2253: B->ops->multadd = MatMultAdd_SeqBAIJ_2;
2254: B->ops->pbrelax = MatPBRelax_SeqBAIJ_2;
2255: break;
2256: case 3:
2257: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_3;
2258: B->ops->mult = MatMult_SeqBAIJ_3;
2259: B->ops->multadd = MatMultAdd_SeqBAIJ_3;
2260: B->ops->pbrelax = MatPBRelax_SeqBAIJ_3;
2261: break;
2262: case 4:
2263: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_4;
2264: B->ops->mult = MatMult_SeqBAIJ_4;
2265: B->ops->multadd = MatMultAdd_SeqBAIJ_4;
2266: B->ops->pbrelax = MatPBRelax_SeqBAIJ_4;
2267: break;
2268: case 5:
2269: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_5;
2270: B->ops->mult = MatMult_SeqBAIJ_5;
2271: B->ops->multadd = MatMultAdd_SeqBAIJ_5;
2272: B->ops->pbrelax = MatPBRelax_SeqBAIJ_5;
2273: break;
2274: case 6:
2275: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_6;
2276: B->ops->mult = MatMult_SeqBAIJ_6;
2277: B->ops->multadd = MatMultAdd_SeqBAIJ_6;
2278: break;
2279: case 7:
2280: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_7;
2281: B->ops->mult = MatMult_SeqBAIJ_7;
2282: B->ops->multadd = MatMultAdd_SeqBAIJ_7;
2283: break;
2284: default:
2285: B->ops->lufactornumeric = MatLUFactorNumeric_SeqBAIJ_N;
2286: B->ops->mult = MatMult_SeqBAIJ_N;
2287: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
2288: break;
2289: }
2290: }
2291: B->rmap.bs = bs;
2292: b->mbs = mbs;
2293: b->nbs = nbs;
2294: if (!skipallocation) {
2295: PetscMalloc2(mbs,PetscInt,&b->imax,mbs,PetscInt,&b->ilen);
2296: /* b->ilen will count nonzeros in each block row so far. */
2297: for (i=0; i<mbs; i++) { b->ilen[i] = 0;}
2298: if (!nnz) {
2299: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2300: else if (nz <= 0) nz = 1;
2301: for (i=0; i<mbs; i++) b->imax[i] = nz;
2302: nz = nz*mbs;
2303: } else {
2304: nz = 0;
2305: for (i=0; i<mbs; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2306: }
2308: /* allocate the matrix space */
2309: PetscMalloc3(bs2*nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap.N+1,PetscInt,&b->i);
2310: PetscMemzero(b->a,nz*bs2*sizeof(MatScalar));
2311: PetscMemzero(b->j,nz*sizeof(PetscInt));
2312: b->singlemalloc = PETSC_TRUE;
2314: b->i[0] = 0;
2315: for (i=1; i<mbs+1; i++) {
2316: b->i[i] = b->i[i-1] + b->imax[i-1];
2317: }
2318: b->free_a = PETSC_TRUE;
2319: b->free_ij = PETSC_TRUE;
2320: } else {
2321: b->free_a = PETSC_FALSE;
2322: b->free_ij = PETSC_FALSE;
2323: }
2325: B->rmap.bs = bs;
2326: b->bs2 = bs2;
2327: b->mbs = mbs;
2328: b->nz = 0;
2329: b->maxnz = nz*bs2;
2330: B->info.nz_unneeded = (PetscReal)b->maxnz;
2331: return(0);
2332: }
2335: /*MC
2336: MATSEQBAIJ - MATSEQBAIJ = "seqbaij" - A matrix type to be used for sequential block sparse matrices, based on
2337: block sparse compressed row format.
2339: Options Database Keys:
2340: . -mat_type seqbaij - sets the matrix type to "seqbaij" during a call to MatSetFromOptions()
2342: Level: beginner
2344: .seealso: MatCreateSeqBAIJ()
2345: M*/
2350: PetscErrorCode MatCreate_SeqBAIJ(Mat B)
2351: {
2353: PetscMPIInt size;
2354: Mat_SeqBAIJ *b;
2357: MPI_Comm_size(B->comm,&size);
2358: if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"Comm must be of size 1");
2360: PetscNew(Mat_SeqBAIJ,&b);
2361: B->data = (void*)b;
2362: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2363: B->factor = 0;
2364: B->mapping = 0;
2365: b->row = 0;
2366: b->col = 0;
2367: b->icol = 0;
2368: b->reallocs = 0;
2369: b->saved_values = 0;
2370: #if defined(PETSC_USE_MAT_SINGLE)
2371: b->setvalueslen = 0;
2372: b->setvaluescopy = PETSC_NULL;
2373: #endif
2375: b->sorted = PETSC_FALSE;
2376: b->roworiented = PETSC_TRUE;
2377: b->nonew = 0;
2378: b->diag = 0;
2379: b->solve_work = 0;
2380: b->mult_work = 0;
2381: B->spptr = 0;
2382: B->info.nz_unneeded = (PetscReal)b->maxnz;
2383: b->keepzeroedrows = PETSC_FALSE;
2384: b->xtoy = 0;
2385: b->XtoY = 0;
2386: b->compressedrow.use = PETSC_FALSE;
2387: b->compressedrow.nrows = 0;
2388: b->compressedrow.i = PETSC_NULL;
2389: b->compressedrow.rindex = PETSC_NULL;
2390: b->compressedrow.checked = PETSC_FALSE;
2391: B->same_nonzero = PETSC_FALSE;
2393: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJInvertBlockDiagonal_C",
2394: "MatInvertBlockDiagonal_SeqBAIJ",
2395: MatInvertBlockDiagonal_SeqBAIJ);
2396: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2397: "MatStoreValues_SeqBAIJ",
2398: MatStoreValues_SeqBAIJ);
2399: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2400: "MatRetrieveValues_SeqBAIJ",
2401: MatRetrieveValues_SeqBAIJ);
2402: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetColumnIndices_C",
2403: "MatSeqBAIJSetColumnIndices_SeqBAIJ",
2404: MatSeqBAIJSetColumnIndices_SeqBAIJ);
2405: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqaij_C",
2406: "MatConvert_SeqBAIJ_SeqAIJ",
2407: MatConvert_SeqBAIJ_SeqAIJ);
2408: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqbaij_seqsbaij_C",
2409: "MatConvert_SeqBAIJ_SeqSBAIJ",
2410: MatConvert_SeqBAIJ_SeqSBAIJ);
2411: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqBAIJSetPreallocation_C",
2412: "MatSeqBAIJSetPreallocation_SeqBAIJ",
2413: MatSeqBAIJSetPreallocation_SeqBAIJ);
2414: PetscObjectChangeTypeName((PetscObject)B,MATSEQBAIJ);
2415: return(0);
2416: }
2421: PetscErrorCode MatDuplicate_SeqBAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2422: {
2423: Mat C;
2424: Mat_SeqBAIJ *c,*a = (Mat_SeqBAIJ*)A->data;
2426: PetscInt i,mbs = a->mbs,nz = a->nz,bs2 = a->bs2;
2429: if (a->i[mbs] != nz) SETERRQ(PETSC_ERR_PLIB,"Corrupt matrix");
2431: *B = 0;
2432: MatCreate(A->comm,&C);
2433: MatSetSizes(C,A->rmap.N,A->cmap.n,A->rmap.N,A->cmap.n);
2434: MatSetType(C,A->type_name);
2435: PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
2436: c = (Mat_SeqBAIJ*)C->data;
2438: C->rmap.N = A->rmap.N;
2439: C->cmap.N = A->cmap.N;
2440: C->rmap.bs = A->rmap.bs;
2441: c->bs2 = a->bs2;
2442: c->mbs = a->mbs;
2443: c->nbs = a->nbs;
2445: PetscMalloc2(mbs,PetscInt,&c->imax,mbs,PetscInt,&c->ilen);
2446: for (i=0; i<mbs; i++) {
2447: c->imax[i] = a->imax[i];
2448: c->ilen[i] = a->ilen[i];
2449: }
2451: /* allocate the matrix space */
2452: PetscMalloc3(bs2*nz,PetscScalar,&c->a,nz,PetscInt,&c->j,mbs+1,PetscInt,&c->i);
2453: c->singlemalloc = PETSC_TRUE;
2454: PetscMemcpy(c->i,a->i,(mbs+1)*sizeof(PetscInt));
2455: if (mbs > 0) {
2456: PetscMemcpy(c->j,a->j,nz*sizeof(PetscInt));
2457: if (cpvalues == MAT_COPY_VALUES) {
2458: PetscMemcpy(c->a,a->a,bs2*nz*sizeof(MatScalar));
2459: } else {
2460: PetscMemzero(c->a,bs2*nz*sizeof(MatScalar));
2461: }
2462: }
2463: c->sorted = a->sorted;
2464: c->roworiented = a->roworiented;
2465: c->nonew = a->nonew;
2467: if (a->diag) {
2468: PetscMalloc((mbs+1)*sizeof(PetscInt),&c->diag);
2469: PetscLogObjectMemory(C,(mbs+1)*sizeof(PetscInt));
2470: for (i=0; i<mbs; i++) {
2471: c->diag[i] = a->diag[i];
2472: }
2473: } else c->diag = 0;
2474: c->nz = a->nz;
2475: c->maxnz = a->maxnz;
2476: c->solve_work = 0;
2477: c->mult_work = 0;
2478: c->free_a = PETSC_TRUE;
2479: c->free_ij = PETSC_TRUE;
2480: C->preallocated = PETSC_TRUE;
2481: C->assembled = PETSC_TRUE;
2483: c->compressedrow.use = a->compressedrow.use;
2484: c->compressedrow.nrows = a->compressedrow.nrows;
2485: c->compressedrow.checked = a->compressedrow.checked;
2486: if ( a->compressedrow.checked && a->compressedrow.use){
2487: i = a->compressedrow.nrows;
2488: PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
2489: c->compressedrow.rindex = c->compressedrow.i + i + 1;
2490: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
2491: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
2492: } else {
2493: c->compressedrow.use = PETSC_FALSE;
2494: c->compressedrow.i = PETSC_NULL;
2495: c->compressedrow.rindex = PETSC_NULL;
2496: }
2497: C->same_nonzero = A->same_nonzero;
2498: *B = C;
2499: PetscFListDuplicate(A->qlist,&C->qlist);
2500: return(0);
2501: }
2505: PetscErrorCode MatLoad_SeqBAIJ(PetscViewer viewer, MatType type,Mat *A)
2506: {
2507: Mat_SeqBAIJ *a;
2508: Mat B;
2510: PetscInt i,nz,header[4],*rowlengths=0,M,N,bs=1;
2511: PetscInt *mask,mbs,*jj,j,rowcount,nzcount,k,*browlengths,maskcount;
2512: PetscInt kmax,jcount,block,idx,point,nzcountb,extra_rows;
2513: PetscInt *masked,nmask,tmp,bs2,ishift;
2514: PetscMPIInt size;
2515: int fd;
2516: PetscScalar *aa;
2517: MPI_Comm comm = ((PetscObject)viewer)->comm;
2520: PetscOptionsBegin(comm,PETSC_NULL,"Options for loading SEQBAIJ matrix","Mat");
2521: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
2522: PetscOptionsEnd();
2523: bs2 = bs*bs;
2525: MPI_Comm_size(comm,&size);
2526: if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"view must have one processor");
2527: PetscViewerBinaryGetDescriptor(viewer,&fd);
2528: PetscBinaryRead(fd,header,4,PETSC_INT);
2529: if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not Mat object");
2530: M = header[1]; N = header[2]; nz = header[3];
2532: if (header[3] < 0) {
2533: SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format, cannot load as SeqBAIJ");
2534: }
2536: if (M != N) SETERRQ(PETSC_ERR_SUP,"Can only do square matrices");
2538: /*
2539: This code adds extra rows to make sure the number of rows is
2540: divisible by the blocksize
2541: */
2542: mbs = M/bs;
2543: extra_rows = bs - M + bs*(mbs);
2544: if (extra_rows == bs) extra_rows = 0;
2545: else mbs++;
2546: if (extra_rows) {
2547: PetscInfo(0,"Padding loaded matrix to match blocksize\n");
2548: }
2550: /* read in row lengths */
2551: PetscMalloc((M+extra_rows)*sizeof(PetscInt),&rowlengths);
2552: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2553: for (i=0; i<extra_rows; i++) rowlengths[M+i] = 1;
2555: /* read in column indices */
2556: PetscMalloc((nz+extra_rows)*sizeof(PetscInt),&jj);
2557: PetscBinaryRead(fd,jj,nz,PETSC_INT);
2558: for (i=0; i<extra_rows; i++) jj[nz+i] = M+i;
2560: /* loop over row lengths determining block row lengths */
2561: PetscMalloc(mbs*sizeof(PetscInt),&browlengths);
2562: PetscMemzero(browlengths,mbs*sizeof(PetscInt));
2563: PetscMalloc(2*mbs*sizeof(PetscInt),&mask);
2564: PetscMemzero(mask,mbs*sizeof(PetscInt));
2565: masked = mask + mbs;
2566: rowcount = 0; nzcount = 0;
2567: for (i=0; i<mbs; i++) {
2568: nmask = 0;
2569: for (j=0; j<bs; j++) {
2570: kmax = rowlengths[rowcount];
2571: for (k=0; k<kmax; k++) {
2572: tmp = jj[nzcount++]/bs;
2573: if (!mask[tmp]) {masked[nmask++] = tmp; mask[tmp] = 1;}
2574: }
2575: rowcount++;
2576: }
2577: browlengths[i] += nmask;
2578: /* zero out the mask elements we set */
2579: for (j=0; j<nmask; j++) mask[masked[j]] = 0;
2580: }
2582: /* create our matrix */
2583: MatCreate(comm,&B);
2584: MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M+extra_rows,N+extra_rows);
2585: MatSetType(B,type);
2586: MatSeqBAIJSetPreallocation_SeqBAIJ(B,bs,0,browlengths);
2587: a = (Mat_SeqBAIJ*)B->data;
2589: /* set matrix "i" values */
2590: a->i[0] = 0;
2591: for (i=1; i<= mbs; i++) {
2592: a->i[i] = a->i[i-1] + browlengths[i-1];
2593: a->ilen[i-1] = browlengths[i-1];
2594: }
2595: a->nz = 0;
2596: for (i=0; i<mbs; i++) a->nz += browlengths[i];
2598: /* read in nonzero values */
2599: PetscMalloc((nz+extra_rows)*sizeof(PetscScalar),&aa);
2600: PetscBinaryRead(fd,aa,nz,PETSC_SCALAR);
2601: for (i=0; i<extra_rows; i++) aa[nz+i] = 1.0;
2603: /* set "a" and "j" values into matrix */
2604: nzcount = 0; jcount = 0;
2605: for (i=0; i<mbs; i++) {
2606: nzcountb = nzcount;
2607: nmask = 0;
2608: for (j=0; j<bs; j++) {
2609: kmax = rowlengths[i*bs+j];
2610: for (k=0; k<kmax; k++) {
2611: tmp = jj[nzcount++]/bs;
2612: if (!mask[tmp]) { masked[nmask++] = tmp; mask[tmp] = 1;}
2613: }
2614: }
2615: /* sort the masked values */
2616: PetscSortInt(nmask,masked);
2618: /* set "j" values into matrix */
2619: maskcount = 1;
2620: for (j=0; j<nmask; j++) {
2621: a->j[jcount++] = masked[j];
2622: mask[masked[j]] = maskcount++;
2623: }
2624: /* set "a" values into matrix */
2625: ishift = bs2*a->i[i];
2626: for (j=0; j<bs; j++) {
2627: kmax = rowlengths[i*bs+j];
2628: for (k=0; k<kmax; k++) {
2629: tmp = jj[nzcountb]/bs ;
2630: block = mask[tmp] - 1;
2631: point = jj[nzcountb] - bs*tmp;
2632: idx = ishift + bs2*block + j + bs*point;
2633: a->a[idx] = (MatScalar)aa[nzcountb++];
2634: }
2635: }
2636: /* zero out the mask elements we set */
2637: for (j=0; j<nmask; j++) mask[masked[j]] = 0;
2638: }
2639: if (jcount != a->nz) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Bad binary matrix");
2641: PetscFree(rowlengths);
2642: PetscFree(browlengths);
2643: PetscFree(aa);
2644: PetscFree(jj);
2645: PetscFree(mask);
2647: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2648: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2649: MatView_Private(B);
2651: *A = B;
2652: return(0);
2653: }
2657: /*@C
2658: MatCreateSeqBAIJ - Creates a sparse matrix in block AIJ (block
2659: compressed row) format. For good matrix assembly performance the
2660: user should preallocate the matrix storage by setting the parameter nz
2661: (or the array nnz). By setting these parameters accurately, performance
2662: during matrix assembly can be increased by more than a factor of 50.
2664: Collective on MPI_Comm
2666: Input Parameters:
2667: + comm - MPI communicator, set to PETSC_COMM_SELF
2668: . bs - size of block
2669: . m - number of rows
2670: . n - number of columns
2671: . nz - number of nonzero blocks per block row (same for all rows)
2672: - nnz - array containing the number of nonzero blocks in the various block rows
2673: (possibly different for each block row) or PETSC_NULL
2675: Output Parameter:
2676: . A - the matrix
2678: Options Database Keys:
2679: . -mat_no_unroll - uses code that does not unroll the loops in the
2680: block calculations (much slower)
2681: . -mat_block_size - size of the blocks to use
2683: Level: intermediate
2685: Notes:
2686: The number of rows and columns must be divisible by blocksize.
2688: If the nnz parameter is given then the nz parameter is ignored
2690: A nonzero block is any block that as 1 or more nonzeros in it
2692: The block AIJ format is fully compatible with standard Fortran 77
2693: storage. That is, the stored row and column indices can begin at
2694: either one (as in Fortran) or zero. See the users' manual for details.
2696: Specify the preallocated storage with either nz or nnz (not both).
2697: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
2698: allocation. For additional details, see the users manual chapter on
2699: matrices.
2701: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2702: @*/
2703: PetscErrorCode MatCreateSeqBAIJ(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2704: {
2706:
2708: MatCreate(comm,A);
2709: MatSetSizes(*A,m,n,m,n);
2710: MatSetType(*A,MATSEQBAIJ);
2711: MatSeqBAIJSetPreallocation_SeqBAIJ(*A,bs,nz,(PetscInt*)nnz);
2712: return(0);
2713: }
2717: /*@C
2718: MatSeqBAIJSetPreallocation - Sets the block size and expected nonzeros
2719: per row in the matrix. For good matrix assembly performance the
2720: user should preallocate the matrix storage by setting the parameter nz
2721: (or the array nnz). By setting these parameters accurately, performance
2722: during matrix assembly can be increased by more than a factor of 50.
2724: Collective on MPI_Comm
2726: Input Parameters:
2727: + A - the matrix
2728: . bs - size of block
2729: . nz - number of block nonzeros per block row (same for all rows)
2730: - nnz - array containing the number of block nonzeros in the various block rows
2731: (possibly different for each block row) or PETSC_NULL
2733: Options Database Keys:
2734: . -mat_no_unroll - uses code that does not unroll the loops in the
2735: block calculations (much slower)
2736: . -mat_block_size - size of the blocks to use
2738: Level: intermediate
2740: Notes:
2741: If the nnz parameter is given then the nz parameter is ignored
2743: The block AIJ format is fully compatible with standard Fortran 77
2744: storage. That is, the stored row and column indices can begin at
2745: either one (as in Fortran) or zero. See the users' manual for details.
2747: Specify the preallocated storage with either nz or nnz (not both).
2748: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
2749: allocation. For additional details, see the users manual chapter on
2750: matrices.
2752: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatCreateMPIBAIJ()
2753: @*/
2754: PetscErrorCode MatSeqBAIJSetPreallocation(Mat B,PetscInt bs,PetscInt nz,const PetscInt nnz[])
2755: {
2756: PetscErrorCode ierr,(*f)(Mat,PetscInt,PetscInt,const PetscInt[]);
2759: PetscObjectQueryFunction((PetscObject)B,"MatSeqBAIJSetPreallocation_C",(void (**)(void))&f);
2760: if (f) {
2761: (*f)(B,bs,nz,nnz);
2762: }
2763: return(0);
2764: }
2768: /*@
2769: MatCreateSeqBAIJWithArrays - Creates an sequential BAIJ matrix using matrix elements
2770: (upper triangular entries in CSR format) provided by the user.
2772: Collective on MPI_Comm
2774: Input Parameters:
2775: + comm - must be an MPI communicator of size 1
2776: . bs - size of block
2777: . m - number of rows
2778: . n - number of columns
2779: . i - row indices
2780: . j - column indices
2781: - a - matrix values
2783: Output Parameter:
2784: . mat - the matrix
2786: Level: intermediate
2788: Notes:
2789: The i, j, and a arrays are not copied by this routine, the user must free these arrays
2790: once the matrix is destroyed
2792: You cannot set new nonzero locations into this matrix, that will generate an error.
2794: The i and j indices are 0 based
2796: .seealso: MatCreate(), MatCreateMPIBAIJ(), MatCreateSeqBAIJ()
2798: @*/
2799: PetscErrorCode MatCreateSeqBAIJWithArrays(MPI_Comm comm,PetscInt bs,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
2800: {
2802: PetscInt ii;
2803: Mat_SeqBAIJ *baij;
2806: if (bs != 1) SETERRQ1(PETSC_ERR_SUP,"block size %D > 1 is not supported yet",bs);
2807: if (i[0]) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
2808:
2809: MatCreate(comm,mat);
2810: MatSetSizes(*mat,m,n,m,n);
2811: MatSetType(*mat,MATSEQBAIJ);
2812: MatSeqBAIJSetPreallocation_SeqBAIJ(*mat,bs,MAT_SKIP_ALLOCATION,0);
2813: baij = (Mat_SeqBAIJ*)(*mat)->data;
2814: PetscMalloc2(m,PetscInt,&baij->imax,m,PetscInt,&baij->ilen);
2816: baij->i = i;
2817: baij->j = j;
2818: baij->a = a;
2819: baij->singlemalloc = PETSC_FALSE;
2820: baij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
2821: baij->free_a = PETSC_FALSE;
2822: baij->free_ij = PETSC_FALSE;
2824: for (ii=0; ii<m; ii++) {
2825: baij->ilen[ii] = baij->imax[ii] = i[ii+1] - i[ii];
2826: #if defined(PETSC_USE_DEBUG)
2827: if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
2828: #endif
2829: }
2830: #if defined(PETSC_USE_DEBUG)
2831: for (ii=0; ii<baij->i[m]; ii++) {
2832: if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
2833: if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
2834: }
2835: #endif
2837: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
2838: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
2839: return(0);
2840: }