Actual source code: aij.c
petsc-3.9.4 2018-09-11
2: /*
3: Defines the basic matrix operations for the AIJ (compressed row)
4: matrix storage format.
5: */
8: #include <../src/mat/impls/aij/seq/aij.h>
9: #include <petscblaslapack.h>
10: #include <petscbt.h>
11: #include <petsc/private/kernels/blocktranspose.h>
13: PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
14: {
15: PetscErrorCode ierr;
16: PetscBool flg;
17: char type[256];
20: PetscObjectOptionsBegin((PetscObject)A);
21: PetscOptionsFList("-mat_seqaij_type","Matrix SeqAIJ type","MatSeqAIJSetType",MatSeqAIJList,"seqaij",type,256,&flg);
22: if (flg) {
23: MatSeqAIJSetType(A,type);
24: }
25: PetscOptionsEnd();
26: return(0);
27: }
29: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
30: {
32: PetscInt i,m,n;
33: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data;
36: MatGetSize(A,&m,&n);
37: PetscMemzero(norms,n*sizeof(PetscReal));
38: if (type == NORM_2) {
39: for (i=0; i<aij->i[m]; i++) {
40: norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
41: }
42: } else if (type == NORM_1) {
43: for (i=0; i<aij->i[m]; i++) {
44: norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
45: }
46: } else if (type == NORM_INFINITY) {
47: for (i=0; i<aij->i[m]; i++) {
48: norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
49: }
50: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");
52: if (type == NORM_2) {
53: for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
54: }
55: return(0);
56: }
58: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
59: {
60: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
61: PetscInt i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
62: const PetscInt *jj = a->j,*ii = a->i;
63: PetscInt *rows;
64: PetscErrorCode ierr;
67: for (i=0; i<m; i++) {
68: if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
69: cnt++;
70: }
71: }
72: PetscMalloc1(cnt,&rows);
73: cnt = 0;
74: for (i=0; i<m; i++) {
75: if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
76: rows[cnt] = i;
77: cnt++;
78: }
79: }
80: ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
81: return(0);
82: }
84: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
85: {
86: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
87: const MatScalar *aa = a->a;
88: PetscInt i,m=A->rmap->n,cnt = 0;
89: const PetscInt *ii = a->i,*jj = a->j,*diag;
90: PetscInt *rows;
91: PetscErrorCode ierr;
94: MatMarkDiagonal_SeqAIJ(A);
95: diag = a->diag;
96: for (i=0; i<m; i++) {
97: if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
98: cnt++;
99: }
100: }
101: PetscMalloc1(cnt,&rows);
102: cnt = 0;
103: for (i=0; i<m; i++) {
104: if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
105: rows[cnt++] = i;
106: }
107: }
108: *nrows = cnt;
109: *zrows = rows;
110: return(0);
111: }
113: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
114: {
115: PetscInt nrows,*rows;
119: *zrows = NULL;
120: MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
121: ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
122: return(0);
123: }
125: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
126: {
127: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
128: const MatScalar *aa;
129: PetscInt m=A->rmap->n,cnt = 0;
130: const PetscInt *ii;
131: PetscInt n,i,j,*rows;
132: PetscErrorCode ierr;
135: *keptrows = 0;
136: ii = a->i;
137: for (i=0; i<m; i++) {
138: n = ii[i+1] - ii[i];
139: if (!n) {
140: cnt++;
141: goto ok1;
142: }
143: aa = a->a + ii[i];
144: for (j=0; j<n; j++) {
145: if (aa[j] != 0.0) goto ok1;
146: }
147: cnt++;
148: ok1:;
149: }
150: if (!cnt) return(0);
151: PetscMalloc1(A->rmap->n-cnt,&rows);
152: cnt = 0;
153: for (i=0; i<m; i++) {
154: n = ii[i+1] - ii[i];
155: if (!n) continue;
156: aa = a->a + ii[i];
157: for (j=0; j<n; j++) {
158: if (aa[j] != 0.0) {
159: rows[cnt++] = i;
160: break;
161: }
162: }
163: }
164: ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
165: return(0);
166: }
168: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
169: {
170: PetscErrorCode ierr;
171: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) Y->data;
172: PetscInt i,m = Y->rmap->n;
173: const PetscInt *diag;
174: MatScalar *aa = aij->a;
175: const PetscScalar *v;
176: PetscBool missing;
179: if (Y->assembled) {
180: MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
181: if (!missing) {
182: diag = aij->diag;
183: VecGetArrayRead(D,&v);
184: if (is == INSERT_VALUES) {
185: for (i=0; i<m; i++) {
186: aa[diag[i]] = v[i];
187: }
188: } else {
189: for (i=0; i<m; i++) {
190: aa[diag[i]] += v[i];
191: }
192: }
193: VecRestoreArrayRead(D,&v);
194: return(0);
195: }
196: MatSeqAIJInvalidateDiagonal(Y);
197: }
198: MatDiagonalSet_Default(Y,D,is);
199: return(0);
200: }
202: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
203: {
204: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
206: PetscInt i,ishift;
209: *m = A->rmap->n;
210: if (!ia) return(0);
211: ishift = 0;
212: if (symmetric && !A->structurally_symmetric) {
213: MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
214: } else if (oshift == 1) {
215: PetscInt *tia;
216: PetscInt nz = a->i[A->rmap->n];
217: /* malloc space and add 1 to i and j indices */
218: PetscMalloc1(A->rmap->n+1,&tia);
219: for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
220: *ia = tia;
221: if (ja) {
222: PetscInt *tja;
223: PetscMalloc1(nz+1,&tja);
224: for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
225: *ja = tja;
226: }
227: } else {
228: *ia = a->i;
229: if (ja) *ja = a->j;
230: }
231: return(0);
232: }
234: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
235: {
239: if (!ia) return(0);
240: if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
241: PetscFree(*ia);
242: if (ja) {PetscFree(*ja);}
243: }
244: return(0);
245: }
247: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
248: {
249: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
251: PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
252: PetscInt nz = a->i[m],row,*jj,mr,col;
255: *nn = n;
256: if (!ia) return(0);
257: if (symmetric) {
258: MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
259: } else {
260: PetscCalloc1(n+1,&collengths);
261: PetscMalloc1(n+1,&cia);
262: PetscMalloc1(nz+1,&cja);
263: jj = a->j;
264: for (i=0; i<nz; i++) {
265: collengths[jj[i]]++;
266: }
267: cia[0] = oshift;
268: for (i=0; i<n; i++) {
269: cia[i+1] = cia[i] + collengths[i];
270: }
271: PetscMemzero(collengths,n*sizeof(PetscInt));
272: jj = a->j;
273: for (row=0; row<m; row++) {
274: mr = a->i[row+1] - a->i[row];
275: for (i=0; i<mr; i++) {
276: col = *jj++;
278: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
279: }
280: }
281: PetscFree(collengths);
282: *ia = cia; *ja = cja;
283: }
284: return(0);
285: }
287: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
288: {
292: if (!ia) return(0);
294: PetscFree(*ia);
295: PetscFree(*ja);
296: return(0);
297: }
299: /*
300: MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
301: MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
302: spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
303: */
304: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done)
305: {
306: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
308: PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
309: PetscInt nz = a->i[m],row,*jj,mr,col;
310: PetscInt *cspidx;
313: *nn = n;
314: if (!ia) return(0);
316: PetscCalloc1(n+1,&collengths);
317: PetscMalloc1(n+1,&cia);
318: PetscMalloc1(nz+1,&cja);
319: PetscMalloc1(nz+1,&cspidx);
320: jj = a->j;
321: for (i=0; i<nz; i++) {
322: collengths[jj[i]]++;
323: }
324: cia[0] = oshift;
325: for (i=0; i<n; i++) {
326: cia[i+1] = cia[i] + collengths[i];
327: }
328: PetscMemzero(collengths,n*sizeof(PetscInt));
329: jj = a->j;
330: for (row=0; row<m; row++) {
331: mr = a->i[row+1] - a->i[row];
332: for (i=0; i<mr; i++) {
333: col = *jj++;
334: cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */
335: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
336: }
337: }
338: PetscFree(collengths);
339: *ia = cia; *ja = cja;
340: *spidx = cspidx;
341: return(0);
342: }
344: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done)
345: {
349: MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
350: PetscFree(*spidx);
351: return(0);
352: }
354: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
355: {
356: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
357: PetscInt *ai = a->i;
361: PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
362: return(0);
363: }
365: /*
366: MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions
368: - a single row of values is set with each call
369: - no row or column indices are negative or (in error) larger than the number of rows or columns
370: - the values are always added to the matrix, not set
371: - no new locations are introduced in the nonzero structure of the matrix
373: This does NOT assume the global column indices are sorted
375: */
377: #include <petsc/private/isimpl.h>
378: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
379: {
380: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
381: PetscInt low,high,t,row,nrow,i,col,l;
382: const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
383: PetscInt lastcol = -1;
384: MatScalar *ap,value,*aa = a->a;
385: const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;
387: row = ridx[im[0]];
388: rp = aj + ai[row];
389: ap = aa + ai[row];
390: nrow = ailen[row];
391: low = 0;
392: high = nrow;
393: for (l=0; l<n; l++) { /* loop over added columns */
394: col = cidx[in[l]];
395: value = v[l];
397: if (col <= lastcol) low = 0;
398: else high = nrow;
399: lastcol = col;
400: while (high-low > 5) {
401: t = (low+high)/2;
402: if (rp[t] > col) high = t;
403: else low = t;
404: }
405: for (i=low; i<high; i++) {
406: if (rp[i] == col) {
407: ap[i] += value;
408: low = i + 1;
409: break;
410: }
411: }
412: }
413: return 0;
414: }
416: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
417: {
418: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
419: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
420: PetscInt *imax = a->imax,*ai = a->i,*ailen = a->ilen;
422: PetscInt *aj = a->j,nonew = a->nonew,lastcol = -1;
423: MatScalar *ap=NULL,value=0.0,*aa = a->a;
424: PetscBool ignorezeroentries = a->ignorezeroentries;
425: PetscBool roworiented = a->roworiented;
428: for (k=0; k<m; k++) { /* loop over added rows */
429: row = im[k];
430: if (row < 0) continue;
431: #if defined(PETSC_USE_DEBUG)
432: if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
433: #endif
434: rp = aj + ai[row];
435: if (!A->structure_only) ap = aa + ai[row];
436: rmax = imax[row]; nrow = ailen[row];
437: low = 0;
438: high = nrow;
439: for (l=0; l<n; l++) { /* loop over added columns */
440: if (in[l] < 0) continue;
441: #if defined(PETSC_USE_DEBUG)
442: if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
443: #endif
444: col = in[l];
445: if (!A->structure_only) {
446: if (roworiented) {
447: value = v[l + k*n];
448: } else {
449: value = v[k + l*m];
450: }
451: } else { /* A->structure_only */
452: value = 1; /* avoid 'continue' below? */
453: }
454: if ((value == 0.0 && ignorezeroentries) && (is == ADD_VALUES) && row != col) continue;
456: if (col <= lastcol) low = 0;
457: else high = nrow;
458: lastcol = col;
459: while (high-low > 5) {
460: t = (low+high)/2;
461: if (rp[t] > col) high = t;
462: else low = t;
463: }
464: for (i=low; i<high; i++) {
465: if (rp[i] > col) break;
466: if (rp[i] == col) {
467: if (!A->structure_only) {
468: if (is == ADD_VALUES) ap[i] += value;
469: else ap[i] = value;
470: }
471: low = i + 1;
472: goto noinsert;
473: }
474: }
475: if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
476: if (nonew == 1) goto noinsert;
477: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
478: if (A->structure_only) {
479: MatSeqXAIJReallocateAIJ_structure_only(A,A->rmap->n,1,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
480: } else {
481: MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
482: }
483: N = nrow++ - 1; a->nz++; high++;
484: /* shift up all the later entries in this row */
485: for (ii=N; ii>=i; ii--) {
486: rp[ii+1] = rp[ii];
487: if (!A->structure_only) ap[ii+1] = ap[ii];
488: }
489: rp[i] = col;
490: if (!A->structure_only) ap[i] = value;
491: low = i + 1;
492: A->nonzerostate++;
493: noinsert:;
494: }
495: ailen[row] = nrow;
496: }
497: return(0);
498: }
501: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
502: {
503: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
504: PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
505: PetscInt *ai = a->i,*ailen = a->ilen;
506: MatScalar *ap,*aa = a->a;
509: for (k=0; k<m; k++) { /* loop over rows */
510: row = im[k];
511: if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
512: if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
513: rp = aj + ai[row]; ap = aa + ai[row];
514: nrow = ailen[row];
515: for (l=0; l<n; l++) { /* loop over columns */
516: if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
517: if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
518: col = in[l];
519: high = nrow; low = 0; /* assume unsorted */
520: while (high-low > 5) {
521: t = (low+high)/2;
522: if (rp[t] > col) high = t;
523: else low = t;
524: }
525: for (i=low; i<high; i++) {
526: if (rp[i] > col) break;
527: if (rp[i] == col) {
528: *v++ = ap[i];
529: goto finished;
530: }
531: }
532: *v++ = 0.0;
533: finished:;
534: }
535: }
536: return(0);
537: }
540: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
541: {
542: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
544: PetscInt i,*col_lens;
545: int fd;
546: FILE *file;
549: PetscViewerBinaryGetDescriptor(viewer,&fd);
550: PetscMalloc1(4+A->rmap->n,&col_lens);
552: col_lens[0] = MAT_FILE_CLASSID;
553: col_lens[1] = A->rmap->n;
554: col_lens[2] = A->cmap->n;
555: col_lens[3] = a->nz;
557: /* store lengths of each row and write (including header) to file */
558: for (i=0; i<A->rmap->n; i++) {
559: col_lens[4+i] = a->i[i+1] - a->i[i];
560: }
561: PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
562: PetscFree(col_lens);
564: /* store column indices (zero start index) */
565: PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,PETSC_FALSE);
567: /* store nonzero values */
568: PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
570: PetscViewerBinaryGetInfoPointer(viewer,&file);
571: if (file) {
572: fprintf(file,"-matload_block_size %d\n",(int)PetscAbs(A->rmap->bs));
573: }
574: return(0);
575: }
577: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
578: {
580: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
581: PetscInt i,k,m=A->rmap->N;
584: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
585: for (i=0; i<m; i++) {
586: PetscViewerASCIIPrintf(viewer,"row %D:",i);
587: for (k=a->i[i]; k<a->i[i+1]; k++) {
588: PetscViewerASCIIPrintf(viewer," (%D) ",a->j[k]);
589: }
590: PetscViewerASCIIPrintf(viewer,"\n");
591: }
592: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
593: return(0);
594: }
596: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);
598: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
599: {
600: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
601: PetscErrorCode ierr;
602: PetscInt i,j,m = A->rmap->n;
603: const char *name;
604: PetscViewerFormat format;
607: if (A->structure_only) {
608: MatView_SeqAIJ_ASCII_structonly(A,viewer);
609: return(0);
610: }
612: PetscViewerGetFormat(viewer,&format);
613: if (format == PETSC_VIEWER_ASCII_MATLAB) {
614: PetscInt nofinalvalue = 0;
615: if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
616: /* Need a dummy value to ensure the dimension of the matrix. */
617: nofinalvalue = 1;
618: }
619: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
620: PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
621: PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
622: #if defined(PETSC_USE_COMPLEX)
623: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
624: #else
625: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
626: #endif
627: PetscViewerASCIIPrintf(viewer,"zzz = [\n");
629: for (i=0; i<m; i++) {
630: for (j=a->i[i]; j<a->i[i+1]; j++) {
631: #if defined(PETSC_USE_COMPLEX)
632: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",i+1,a->j[j]+1,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
633: #else
634: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
635: #endif
636: }
637: }
638: if (nofinalvalue) {
639: #if defined(PETSC_USE_COMPLEX)
640: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
641: #else
642: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->cmap->n,0.0);
643: #endif
644: }
645: PetscObjectGetName((PetscObject)A,&name);
646: PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
647: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
648: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
649: return(0);
650: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
651: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
652: for (i=0; i<m; i++) {
653: PetscViewerASCIIPrintf(viewer,"row %D:",i);
654: for (j=a->i[i]; j<a->i[i+1]; j++) {
655: #if defined(PETSC_USE_COMPLEX)
656: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
657: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
658: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
659: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
660: } else if (PetscRealPart(a->a[j]) != 0.0) {
661: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
662: }
663: #else
664: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
665: #endif
666: }
667: PetscViewerASCIIPrintf(viewer,"\n");
668: }
669: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
670: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
671: PetscInt nzd=0,fshift=1,*sptr;
672: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
673: PetscMalloc1(m+1,&sptr);
674: for (i=0; i<m; i++) {
675: sptr[i] = nzd+1;
676: for (j=a->i[i]; j<a->i[i+1]; j++) {
677: if (a->j[j] >= i) {
678: #if defined(PETSC_USE_COMPLEX)
679: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
680: #else
681: if (a->a[j] != 0.0) nzd++;
682: #endif
683: }
684: }
685: }
686: sptr[m] = nzd+1;
687: PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
688: for (i=0; i<m+1; i+=6) {
689: if (i+4<m) {
690: PetscViewerASCIIPrintf(viewer," %D %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);
691: } else if (i+3<m) {
692: PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
693: } else if (i+2<m) {
694: PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
695: } else if (i+1<m) {
696: PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
697: } else if (i<m) {
698: PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
699: } else {
700: PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
701: }
702: }
703: PetscViewerASCIIPrintf(viewer,"\n");
704: PetscFree(sptr);
705: for (i=0; i<m; i++) {
706: for (j=a->i[i]; j<a->i[i+1]; j++) {
707: if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
708: }
709: PetscViewerASCIIPrintf(viewer,"\n");
710: }
711: PetscViewerASCIIPrintf(viewer,"\n");
712: for (i=0; i<m; i++) {
713: for (j=a->i[i]; j<a->i[i+1]; j++) {
714: if (a->j[j] >= i) {
715: #if defined(PETSC_USE_COMPLEX)
716: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
717: PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
718: }
719: #else
720: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
721: #endif
722: }
723: }
724: PetscViewerASCIIPrintf(viewer,"\n");
725: }
726: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
727: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
728: PetscInt cnt = 0,jcnt;
729: PetscScalar value;
730: #if defined(PETSC_USE_COMPLEX)
731: PetscBool realonly = PETSC_TRUE;
733: for (i=0; i<a->i[m]; i++) {
734: if (PetscImaginaryPart(a->a[i]) != 0.0) {
735: realonly = PETSC_FALSE;
736: break;
737: }
738: }
739: #endif
741: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
742: for (i=0; i<m; i++) {
743: jcnt = 0;
744: for (j=0; j<A->cmap->n; j++) {
745: if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
746: value = a->a[cnt++];
747: jcnt++;
748: } else {
749: value = 0.0;
750: }
751: #if defined(PETSC_USE_COMPLEX)
752: if (realonly) {
753: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
754: } else {
755: PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
756: }
757: #else
758: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
759: #endif
760: }
761: PetscViewerASCIIPrintf(viewer,"\n");
762: }
763: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
764: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
765: PetscInt fshift=1;
766: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
767: #if defined(PETSC_USE_COMPLEX)
768: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
769: #else
770: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
771: #endif
772: PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
773: for (i=0; i<m; i++) {
774: for (j=a->i[i]; j<a->i[i+1]; j++) {
775: #if defined(PETSC_USE_COMPLEX)
776: PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
777: #else
778: PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
779: #endif
780: }
781: }
782: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
783: } else {
784: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
785: if (A->factortype) {
786: for (i=0; i<m; i++) {
787: PetscViewerASCIIPrintf(viewer,"row %D:",i);
788: /* L part */
789: for (j=a->i[i]; j<a->i[i+1]; j++) {
790: #if defined(PETSC_USE_COMPLEX)
791: if (PetscImaginaryPart(a->a[j]) > 0.0) {
792: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
793: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
794: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
795: } else {
796: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
797: }
798: #else
799: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
800: #endif
801: }
802: /* diagonal */
803: j = a->diag[i];
804: #if defined(PETSC_USE_COMPLEX)
805: if (PetscImaginaryPart(a->a[j]) > 0.0) {
806: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
807: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
808: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
809: } else {
810: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
811: }
812: #else
813: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
814: #endif
816: /* U part */
817: for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
818: #if defined(PETSC_USE_COMPLEX)
819: if (PetscImaginaryPart(a->a[j]) > 0.0) {
820: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
821: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
822: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
823: } else {
824: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
825: }
826: #else
827: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
828: #endif
829: }
830: PetscViewerASCIIPrintf(viewer,"\n");
831: }
832: } else {
833: for (i=0; i<m; i++) {
834: PetscViewerASCIIPrintf(viewer,"row %D:",i);
835: for (j=a->i[i]; j<a->i[i+1]; j++) {
836: #if defined(PETSC_USE_COMPLEX)
837: if (PetscImaginaryPart(a->a[j]) > 0.0) {
838: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
839: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
840: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
841: } else {
842: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
843: }
844: #else
845: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
846: #endif
847: }
848: PetscViewerASCIIPrintf(viewer,"\n");
849: }
850: }
851: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
852: }
853: PetscViewerFlush(viewer);
854: return(0);
855: }
857: #include <petscdraw.h>
858: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
859: {
860: Mat A = (Mat) Aa;
861: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
862: PetscErrorCode ierr;
863: PetscInt i,j,m = A->rmap->n;
864: int color;
865: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r;
866: PetscViewer viewer;
867: PetscViewerFormat format;
870: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
871: PetscViewerGetFormat(viewer,&format);
872: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
874: /* loop over matrix elements drawing boxes */
876: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
877: PetscDrawCollectiveBegin(draw);
878: /* Blue for negative, Cyan for zero and Red for positive */
879: color = PETSC_DRAW_BLUE;
880: for (i=0; i<m; i++) {
881: y_l = m - i - 1.0; y_r = y_l + 1.0;
882: for (j=a->i[i]; j<a->i[i+1]; j++) {
883: x_l = a->j[j]; x_r = x_l + 1.0;
884: if (PetscRealPart(a->a[j]) >= 0.) continue;
885: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
886: }
887: }
888: color = PETSC_DRAW_CYAN;
889: for (i=0; i<m; i++) {
890: y_l = m - i - 1.0; y_r = y_l + 1.0;
891: for (j=a->i[i]; j<a->i[i+1]; j++) {
892: x_l = a->j[j]; x_r = x_l + 1.0;
893: if (a->a[j] != 0.) continue;
894: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
895: }
896: }
897: color = PETSC_DRAW_RED;
898: for (i=0; i<m; i++) {
899: y_l = m - i - 1.0; y_r = y_l + 1.0;
900: for (j=a->i[i]; j<a->i[i+1]; j++) {
901: x_l = a->j[j]; x_r = x_l + 1.0;
902: if (PetscRealPart(a->a[j]) <= 0.) continue;
903: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
904: }
905: }
906: PetscDrawCollectiveEnd(draw);
907: } else {
908: /* use contour shading to indicate magnitude of values */
909: /* first determine max of all nonzero values */
910: PetscReal minv = 0.0, maxv = 0.0;
911: PetscInt nz = a->nz, count = 0;
912: PetscDraw popup;
914: for (i=0; i<nz; i++) {
915: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
916: }
917: if (minv >= maxv) maxv = minv + PETSC_SMALL;
918: PetscDrawGetPopup(draw,&popup);
919: PetscDrawScalePopup(popup,minv,maxv);
921: PetscDrawCollectiveBegin(draw);
922: for (i=0; i<m; i++) {
923: y_l = m - i - 1.0;
924: y_r = y_l + 1.0;
925: for (j=a->i[i]; j<a->i[i+1]; j++) {
926: x_l = a->j[j];
927: x_r = x_l + 1.0;
928: color = PetscDrawRealToColor(PetscAbsScalar(a->a[count]),minv,maxv);
929: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
930: count++;
931: }
932: }
933: PetscDrawCollectiveEnd(draw);
934: }
935: return(0);
936: }
938: #include <petscdraw.h>
939: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
940: {
942: PetscDraw draw;
943: PetscReal xr,yr,xl,yl,h,w;
944: PetscBool isnull;
947: PetscViewerDrawGetDraw(viewer,0,&draw);
948: PetscDrawIsNull(draw,&isnull);
949: if (isnull) return(0);
951: xr = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
952: xr += w; yr += h; xl = -w; yl = -h;
953: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
954: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
955: PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
956: PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
957: PetscDrawSave(draw);
958: return(0);
959: }
961: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
962: {
964: PetscBool iascii,isbinary,isdraw;
967: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
968: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
969: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
970: if (iascii) {
971: MatView_SeqAIJ_ASCII(A,viewer);
972: } else if (isbinary) {
973: MatView_SeqAIJ_Binary(A,viewer);
974: } else if (isdraw) {
975: MatView_SeqAIJ_Draw(A,viewer);
976: }
977: MatView_SeqAIJ_Inode(A,viewer);
978: return(0);
979: }
981: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
982: {
983: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
985: PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
986: PetscInt m = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
987: MatScalar *aa = a->a,*ap;
988: PetscReal ratio = 0.6;
991: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
993: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
994: for (i=1; i<m; i++) {
995: /* move each row back by the amount of empty slots (fshift) before it*/
996: fshift += imax[i-1] - ailen[i-1];
997: rmax = PetscMax(rmax,ailen[i]);
998: if (fshift) {
999: ip = aj + ai[i];
1000: ap = aa + ai[i];
1001: N = ailen[i];
1002: for (j=0; j<N; j++) {
1003: ip[j-fshift] = ip[j];
1004: if (!A->structure_only) ap[j-fshift] = ap[j];
1005: }
1006: }
1007: ai[i] = ai[i-1] + ailen[i-1];
1008: }
1009: if (m) {
1010: fshift += imax[m-1] - ailen[m-1];
1011: ai[m] = ai[m-1] + ailen[m-1];
1012: }
1014: /* reset ilen and imax for each row */
1015: a->nonzerorowcnt = 0;
1016: if (A->structure_only) {
1017: PetscFree2(a->imax,a->ilen);
1018: } else { /* !A->structure_only */
1019: for (i=0; i<m; i++) {
1020: ailen[i] = imax[i] = ai[i+1] - ai[i];
1021: a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
1022: }
1023: }
1024: a->nz = ai[m];
1025: if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);
1027: MatMarkDiagonal_SeqAIJ(A);
1028: PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
1029: PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1030: PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);
1032: A->info.mallocs += a->reallocs;
1033: a->reallocs = 0;
1034: A->info.nz_unneeded = (PetscReal)fshift;
1035: a->rmax = rmax;
1037: if (!A->structure_only) {
1038: MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
1039: }
1040: MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1041: MatSeqAIJInvalidateDiagonal(A);
1042: return(0);
1043: }
1045: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1046: {
1047: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1048: PetscInt i,nz = a->nz;
1049: MatScalar *aa = a->a;
1053: for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1054: MatSeqAIJInvalidateDiagonal(A);
1055: return(0);
1056: }
1058: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1059: {
1060: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1061: PetscInt i,nz = a->nz;
1062: MatScalar *aa = a->a;
1066: for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1067: MatSeqAIJInvalidateDiagonal(A);
1068: return(0);
1069: }
1071: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1072: {
1073: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1077: PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
1078: MatSeqAIJInvalidateDiagonal(A);
1079: return(0);
1080: }
1082: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1083: {
1084: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1088: #if defined(PETSC_USE_LOG)
1089: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
1090: #endif
1091: MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1092: ISDestroy(&a->row);
1093: ISDestroy(&a->col);
1094: PetscFree(a->diag);
1095: PetscFree(a->ibdiag);
1096: PetscFree2(a->imax,a->ilen);
1097: PetscFree(a->ipre);
1098: PetscFree3(a->idiag,a->mdiag,a->ssor_work);
1099: PetscFree(a->solve_work);
1100: ISDestroy(&a->icol);
1101: PetscFree(a->saved_values);
1102: ISColoringDestroy(&a->coloring);
1103: PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
1104: PetscFree(a->matmult_abdense);
1106: MatDestroy_SeqAIJ_Inode(A);
1107: PetscFree(A->data);
1109: PetscObjectChangeTypeName((PetscObject)A,0);
1110: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
1111: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1112: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1113: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
1114: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
1115: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
1116: #if defined(PETSC_HAVE_ELEMENTAL)
1117: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
1118: #endif
1119: #if defined(PETSC_HAVE_HYPRE)
1120: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
1121: PetscObjectComposeFunction((PetscObject)A,"MatMatMatMult_transpose_seqaij_seqaij_C",NULL);
1122: #endif
1123: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
1124: PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1125: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1126: PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
1127: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1128: PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1129: return(0);
1130: }
1132: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1133: {
1134: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1138: switch (op) {
1139: case MAT_ROW_ORIENTED:
1140: a->roworiented = flg;
1141: break;
1142: case MAT_KEEP_NONZERO_PATTERN:
1143: a->keepnonzeropattern = flg;
1144: break;
1145: case MAT_NEW_NONZERO_LOCATIONS:
1146: a->nonew = (flg ? 0 : 1);
1147: break;
1148: case MAT_NEW_NONZERO_LOCATION_ERR:
1149: a->nonew = (flg ? -1 : 0);
1150: break;
1151: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1152: a->nonew = (flg ? -2 : 0);
1153: break;
1154: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1155: a->nounused = (flg ? -1 : 0);
1156: break;
1157: case MAT_IGNORE_ZERO_ENTRIES:
1158: a->ignorezeroentries = flg;
1159: break;
1160: case MAT_SPD:
1161: case MAT_SYMMETRIC:
1162: case MAT_STRUCTURALLY_SYMMETRIC:
1163: case MAT_HERMITIAN:
1164: case MAT_SYMMETRY_ETERNAL:
1165: case MAT_STRUCTURE_ONLY:
1166: /* These options are handled directly by MatSetOption() */
1167: break;
1168: case MAT_NEW_DIAGONALS:
1169: case MAT_IGNORE_OFF_PROC_ENTRIES:
1170: case MAT_USE_HASH_TABLE:
1171: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1172: break;
1173: case MAT_USE_INODES:
1174: /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1175: break;
1176: case MAT_SUBMAT_SINGLEIS:
1177: A->submat_singleis = flg;
1178: break;
1179: default:
1180: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1181: }
1182: MatSetOption_SeqAIJ_Inode(A,op,flg);
1183: return(0);
1184: }
1186: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1187: {
1188: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1190: PetscInt i,j,n,*ai=a->i,*aj=a->j,nz;
1191: PetscScalar *aa=a->a,*x,zero=0.0;
1194: VecGetLocalSize(v,&n);
1195: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1197: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1198: PetscInt *diag=a->diag;
1199: VecGetArray(v,&x);
1200: for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1201: VecRestoreArray(v,&x);
1202: return(0);
1203: }
1205: VecSet(v,zero);
1206: VecGetArray(v,&x);
1207: for (i=0; i<n; i++) {
1208: nz = ai[i+1] - ai[i];
1209: if (!nz) x[i] = 0.0;
1210: for (j=ai[i]; j<ai[i+1]; j++) {
1211: if (aj[j] == i) {
1212: x[i] = aa[j];
1213: break;
1214: }
1215: }
1216: }
1217: VecRestoreArray(v,&x);
1218: return(0);
1219: }
1221: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1222: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1223: {
1224: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1225: PetscScalar *y;
1226: const PetscScalar *x;
1227: PetscErrorCode ierr;
1228: PetscInt m = A->rmap->n;
1229: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1230: const MatScalar *v;
1231: PetscScalar alpha;
1232: PetscInt n,i,j;
1233: const PetscInt *idx,*ii,*ridx=NULL;
1234: Mat_CompressedRow cprow = a->compressedrow;
1235: PetscBool usecprow = cprow.use;
1236: #endif
1239: if (zz != yy) {VecCopy(zz,yy);}
1240: VecGetArrayRead(xx,&x);
1241: VecGetArray(yy,&y);
1243: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1244: fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1245: #else
1246: if (usecprow) {
1247: m = cprow.nrows;
1248: ii = cprow.i;
1249: ridx = cprow.rindex;
1250: } else {
1251: ii = a->i;
1252: }
1253: for (i=0; i<m; i++) {
1254: idx = a->j + ii[i];
1255: v = a->a + ii[i];
1256: n = ii[i+1] - ii[i];
1257: if (usecprow) {
1258: alpha = x[ridx[i]];
1259: } else {
1260: alpha = x[i];
1261: }
1262: for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1263: }
1264: #endif
1265: PetscLogFlops(2.0*a->nz);
1266: VecRestoreArrayRead(xx,&x);
1267: VecRestoreArray(yy,&y);
1268: return(0);
1269: }
1271: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1272: {
1276: VecSet(yy,0.0);
1277: MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1278: return(0);
1279: }
1281: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1283: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1284: {
1285: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1286: PetscScalar *y;
1287: const PetscScalar *x;
1288: const MatScalar *aa;
1289: PetscErrorCode ierr;
1290: PetscInt m=A->rmap->n;
1291: const PetscInt *aj,*ii,*ridx=NULL;
1292: PetscInt n,i;
1293: PetscScalar sum;
1294: PetscBool usecprow=a->compressedrow.use;
1296: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1297: #pragma disjoint(*x,*y,*aa)
1298: #endif
1301: VecGetArrayRead(xx,&x);
1302: VecGetArray(yy,&y);
1303: ii = a->i;
1304: if (usecprow) { /* use compressed row format */
1305: PetscMemzero(y,m*sizeof(PetscScalar));
1306: m = a->compressedrow.nrows;
1307: ii = a->compressedrow.i;
1308: ridx = a->compressedrow.rindex;
1309: for (i=0; i<m; i++) {
1310: n = ii[i+1] - ii[i];
1311: aj = a->j + ii[i];
1312: aa = a->a + ii[i];
1313: sum = 0.0;
1314: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1315: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1316: y[*ridx++] = sum;
1317: }
1318: } else { /* do not use compressed row format */
1319: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1320: aj = a->j;
1321: aa = a->a;
1322: fortranmultaij_(&m,x,ii,aj,aa,y);
1323: #else
1324: for (i=0; i<m; i++) {
1325: n = ii[i+1] - ii[i];
1326: aj = a->j + ii[i];
1327: aa = a->a + ii[i];
1328: sum = 0.0;
1329: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1330: y[i] = sum;
1331: }
1332: #endif
1333: }
1334: PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1335: VecRestoreArrayRead(xx,&x);
1336: VecRestoreArray(yy,&y);
1337: return(0);
1338: }
1340: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1341: {
1342: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1343: PetscScalar *y;
1344: const PetscScalar *x;
1345: const MatScalar *aa;
1346: PetscErrorCode ierr;
1347: PetscInt m=A->rmap->n;
1348: const PetscInt *aj,*ii,*ridx=NULL;
1349: PetscInt n,i,nonzerorow=0;
1350: PetscScalar sum;
1351: PetscBool usecprow=a->compressedrow.use;
1353: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1354: #pragma disjoint(*x,*y,*aa)
1355: #endif
1358: VecGetArrayRead(xx,&x);
1359: VecGetArray(yy,&y);
1360: if (usecprow) { /* use compressed row format */
1361: m = a->compressedrow.nrows;
1362: ii = a->compressedrow.i;
1363: ridx = a->compressedrow.rindex;
1364: for (i=0; i<m; i++) {
1365: n = ii[i+1] - ii[i];
1366: aj = a->j + ii[i];
1367: aa = a->a + ii[i];
1368: sum = 0.0;
1369: nonzerorow += (n>0);
1370: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1371: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1372: y[*ridx++] = sum;
1373: }
1374: } else { /* do not use compressed row format */
1375: ii = a->i;
1376: for (i=0; i<m; i++) {
1377: n = ii[i+1] - ii[i];
1378: aj = a->j + ii[i];
1379: aa = a->a + ii[i];
1380: sum = 0.0;
1381: nonzerorow += (n>0);
1382: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1383: y[i] = sum;
1384: }
1385: }
1386: PetscLogFlops(2.0*a->nz - nonzerorow);
1387: VecRestoreArrayRead(xx,&x);
1388: VecRestoreArray(yy,&y);
1389: return(0);
1390: }
1392: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1393: {
1394: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1395: PetscScalar *y,*z;
1396: const PetscScalar *x;
1397: const MatScalar *aa;
1398: PetscErrorCode ierr;
1399: PetscInt m = A->rmap->n,*aj,*ii;
1400: PetscInt n,i,*ridx=NULL;
1401: PetscScalar sum;
1402: PetscBool usecprow=a->compressedrow.use;
1405: VecGetArrayRead(xx,&x);
1406: VecGetArrayPair(yy,zz,&y,&z);
1407: if (usecprow) { /* use compressed row format */
1408: if (zz != yy) {
1409: PetscMemcpy(z,y,m*sizeof(PetscScalar));
1410: }
1411: m = a->compressedrow.nrows;
1412: ii = a->compressedrow.i;
1413: ridx = a->compressedrow.rindex;
1414: for (i=0; i<m; i++) {
1415: n = ii[i+1] - ii[i];
1416: aj = a->j + ii[i];
1417: aa = a->a + ii[i];
1418: sum = y[*ridx];
1419: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1420: z[*ridx++] = sum;
1421: }
1422: } else { /* do not use compressed row format */
1423: ii = a->i;
1424: for (i=0; i<m; i++) {
1425: n = ii[i+1] - ii[i];
1426: aj = a->j + ii[i];
1427: aa = a->a + ii[i];
1428: sum = y[i];
1429: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1430: z[i] = sum;
1431: }
1432: }
1433: PetscLogFlops(2.0*a->nz);
1434: VecRestoreArrayRead(xx,&x);
1435: VecRestoreArrayPair(yy,zz,&y,&z);
1436: return(0);
1437: }
1439: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1440: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1441: {
1442: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1443: PetscScalar *y,*z;
1444: const PetscScalar *x;
1445: const MatScalar *aa;
1446: PetscErrorCode ierr;
1447: const PetscInt *aj,*ii,*ridx=NULL;
1448: PetscInt m = A->rmap->n,n,i;
1449: PetscScalar sum;
1450: PetscBool usecprow=a->compressedrow.use;
1453: VecGetArrayRead(xx,&x);
1454: VecGetArrayPair(yy,zz,&y,&z);
1455: if (usecprow) { /* use compressed row format */
1456: if (zz != yy) {
1457: PetscMemcpy(z,y,m*sizeof(PetscScalar));
1458: }
1459: m = a->compressedrow.nrows;
1460: ii = a->compressedrow.i;
1461: ridx = a->compressedrow.rindex;
1462: for (i=0; i<m; i++) {
1463: n = ii[i+1] - ii[i];
1464: aj = a->j + ii[i];
1465: aa = a->a + ii[i];
1466: sum = y[*ridx];
1467: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1468: z[*ridx++] = sum;
1469: }
1470: } else { /* do not use compressed row format */
1471: ii = a->i;
1472: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1473: aj = a->j;
1474: aa = a->a;
1475: fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1476: #else
1477: for (i=0; i<m; i++) {
1478: n = ii[i+1] - ii[i];
1479: aj = a->j + ii[i];
1480: aa = a->a + ii[i];
1481: sum = y[i];
1482: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1483: z[i] = sum;
1484: }
1485: #endif
1486: }
1487: PetscLogFlops(2.0*a->nz);
1488: VecRestoreArrayRead(xx,&x);
1489: VecRestoreArrayPair(yy,zz,&y,&z);
1490: return(0);
1491: }
1493: /*
1494: Adds diagonal pointers to sparse matrix structure.
1495: */
1496: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1497: {
1498: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1500: PetscInt i,j,m = A->rmap->n;
1503: if (!a->diag) {
1504: PetscMalloc1(m,&a->diag);
1505: PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1506: }
1507: for (i=0; i<A->rmap->n; i++) {
1508: a->diag[i] = a->i[i+1];
1509: for (j=a->i[i]; j<a->i[i+1]; j++) {
1510: if (a->j[j] == i) {
1511: a->diag[i] = j;
1512: break;
1513: }
1514: }
1515: }
1516: return(0);
1517: }
1519: /*
1520: Checks for missing diagonals
1521: */
1522: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool *missing,PetscInt *d)
1523: {
1524: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1525: PetscInt *diag,*ii = a->i,i;
1528: *missing = PETSC_FALSE;
1529: if (A->rmap->n > 0 && !ii) {
1530: *missing = PETSC_TRUE;
1531: if (d) *d = 0;
1532: PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1533: } else {
1534: diag = a->diag;
1535: for (i=0; i<A->rmap->n; i++) {
1536: if (diag[i] >= ii[i+1]) {
1537: *missing = PETSC_TRUE;
1538: if (d) *d = i;
1539: PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1540: break;
1541: }
1542: }
1543: }
1544: return(0);
1545: }
1547: /*
1548: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1549: */
1550: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1551: {
1552: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
1554: PetscInt i,*diag,m = A->rmap->n;
1555: MatScalar *v = a->a;
1556: PetscScalar *idiag,*mdiag;
1559: if (a->idiagvalid) return(0);
1560: MatMarkDiagonal_SeqAIJ(A);
1561: diag = a->diag;
1562: if (!a->idiag) {
1563: PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1564: PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
1565: v = a->a;
1566: }
1567: mdiag = a->mdiag;
1568: idiag = a->idiag;
1570: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1571: for (i=0; i<m; i++) {
1572: mdiag[i] = v[diag[i]];
1573: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1574: if (PetscRealPart(fshift)) {
1575: PetscInfo1(A,"Zero diagonal on row %D\n",i);
1576: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1577: A->factorerror_zeropivot_value = 0.0;
1578: A->factorerror_zeropivot_row = i;
1579: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1580: }
1581: idiag[i] = 1.0/v[diag[i]];
1582: }
1583: PetscLogFlops(m);
1584: } else {
1585: for (i=0; i<m; i++) {
1586: mdiag[i] = v[diag[i]];
1587: idiag[i] = omega/(fshift + v[diag[i]]);
1588: }
1589: PetscLogFlops(2.0*m);
1590: }
1591: a->idiagvalid = PETSC_TRUE;
1592: return(0);
1593: }
1595: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1596: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1597: {
1598: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1599: PetscScalar *x,d,sum,*t,scale;
1600: const MatScalar *v,*idiag=0,*mdiag;
1601: const PetscScalar *b, *bs,*xb, *ts;
1602: PetscErrorCode ierr;
1603: PetscInt n,m = A->rmap->n,i;
1604: const PetscInt *idx,*diag;
1607: its = its*lits;
1609: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1610: if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1611: a->fshift = fshift;
1612: a->omega = omega;
1614: diag = a->diag;
1615: t = a->ssor_work;
1616: idiag = a->idiag;
1617: mdiag = a->mdiag;
1619: VecGetArray(xx,&x);
1620: VecGetArrayRead(bb,&b);
1621: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1622: if (flag == SOR_APPLY_UPPER) {
1623: /* apply (U + D/omega) to the vector */
1624: bs = b;
1625: for (i=0; i<m; i++) {
1626: d = fshift + mdiag[i];
1627: n = a->i[i+1] - diag[i] - 1;
1628: idx = a->j + diag[i] + 1;
1629: v = a->a + diag[i] + 1;
1630: sum = b[i]*d/omega;
1631: PetscSparseDensePlusDot(sum,bs,v,idx,n);
1632: x[i] = sum;
1633: }
1634: VecRestoreArray(xx,&x);
1635: VecRestoreArrayRead(bb,&b);
1636: PetscLogFlops(a->nz);
1637: return(0);
1638: }
1640: if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1641: else if (flag & SOR_EISENSTAT) {
1642: /* Let A = L + U + D; where L is lower trianglar,
1643: U is upper triangular, E = D/omega; This routine applies
1645: (L + E)^{-1} A (U + E)^{-1}
1647: to a vector efficiently using Eisenstat's trick.
1648: */
1649: scale = (2.0/omega) - 1.0;
1651: /* x = (E + U)^{-1} b */
1652: for (i=m-1; i>=0; i--) {
1653: n = a->i[i+1] - diag[i] - 1;
1654: idx = a->j + diag[i] + 1;
1655: v = a->a + diag[i] + 1;
1656: sum = b[i];
1657: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1658: x[i] = sum*idiag[i];
1659: }
1661: /* t = b - (2*E - D)x */
1662: v = a->a;
1663: for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];
1665: /* t = (E + L)^{-1}t */
1666: ts = t;
1667: diag = a->diag;
1668: for (i=0; i<m; i++) {
1669: n = diag[i] - a->i[i];
1670: idx = a->j + a->i[i];
1671: v = a->a + a->i[i];
1672: sum = t[i];
1673: PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1674: t[i] = sum*idiag[i];
1675: /* x = x + t */
1676: x[i] += t[i];
1677: }
1679: PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1680: VecRestoreArray(xx,&x);
1681: VecRestoreArrayRead(bb,&b);
1682: return(0);
1683: }
1684: if (flag & SOR_ZERO_INITIAL_GUESS) {
1685: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1686: for (i=0; i<m; i++) {
1687: n = diag[i] - a->i[i];
1688: idx = a->j + a->i[i];
1689: v = a->a + a->i[i];
1690: sum = b[i];
1691: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1692: t[i] = sum;
1693: x[i] = sum*idiag[i];
1694: }
1695: xb = t;
1696: PetscLogFlops(a->nz);
1697: } else xb = b;
1698: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1699: for (i=m-1; i>=0; i--) {
1700: n = a->i[i+1] - diag[i] - 1;
1701: idx = a->j + diag[i] + 1;
1702: v = a->a + diag[i] + 1;
1703: sum = xb[i];
1704: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1705: if (xb == b) {
1706: x[i] = sum*idiag[i];
1707: } else {
1708: x[i] = (1-omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1709: }
1710: }
1711: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1712: }
1713: its--;
1714: }
1715: while (its--) {
1716: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1717: for (i=0; i<m; i++) {
1718: /* lower */
1719: n = diag[i] - a->i[i];
1720: idx = a->j + a->i[i];
1721: v = a->a + a->i[i];
1722: sum = b[i];
1723: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1724: t[i] = sum; /* save application of the lower-triangular part */
1725: /* upper */
1726: n = a->i[i+1] - diag[i] - 1;
1727: idx = a->j + diag[i] + 1;
1728: v = a->a + diag[i] + 1;
1729: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1730: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1731: }
1732: xb = t;
1733: PetscLogFlops(2.0*a->nz);
1734: } else xb = b;
1735: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1736: for (i=m-1; i>=0; i--) {
1737: sum = xb[i];
1738: if (xb == b) {
1739: /* whole matrix (no checkpointing available) */
1740: n = a->i[i+1] - a->i[i];
1741: idx = a->j + a->i[i];
1742: v = a->a + a->i[i];
1743: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1744: x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1745: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
1746: n = a->i[i+1] - diag[i] - 1;
1747: idx = a->j + diag[i] + 1;
1748: v = a->a + diag[i] + 1;
1749: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1750: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1751: }
1752: }
1753: if (xb == b) {
1754: PetscLogFlops(2.0*a->nz);
1755: } else {
1756: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1757: }
1758: }
1759: }
1760: VecRestoreArray(xx,&x);
1761: VecRestoreArrayRead(bb,&b);
1762: return(0);
1763: }
1766: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1767: {
1768: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1771: info->block_size = 1.0;
1772: info->nz_allocated = (double)a->maxnz;
1773: info->nz_used = (double)a->nz;
1774: info->nz_unneeded = (double)(a->maxnz - a->nz);
1775: info->assemblies = (double)A->num_ass;
1776: info->mallocs = (double)A->info.mallocs;
1777: info->memory = ((PetscObject)A)->mem;
1778: if (A->factortype) {
1779: info->fill_ratio_given = A->info.fill_ratio_given;
1780: info->fill_ratio_needed = A->info.fill_ratio_needed;
1781: info->factor_mallocs = A->info.factor_mallocs;
1782: } else {
1783: info->fill_ratio_given = 0;
1784: info->fill_ratio_needed = 0;
1785: info->factor_mallocs = 0;
1786: }
1787: return(0);
1788: }
1790: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1791: {
1792: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1793: PetscInt i,m = A->rmap->n - 1;
1794: PetscErrorCode ierr;
1795: const PetscScalar *xx;
1796: PetscScalar *bb;
1797: PetscInt d = 0;
1800: if (x && b) {
1801: VecGetArrayRead(x,&xx);
1802: VecGetArray(b,&bb);
1803: for (i=0; i<N; i++) {
1804: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1805: bb[rows[i]] = diag*xx[rows[i]];
1806: }
1807: VecRestoreArrayRead(x,&xx);
1808: VecRestoreArray(b,&bb);
1809: }
1811: if (a->keepnonzeropattern) {
1812: for (i=0; i<N; i++) {
1813: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1814: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1815: }
1816: if (diag != 0.0) {
1817: for (i=0; i<N; i++) {
1818: d = rows[i];
1819: if (a->diag[d] >= a->i[d+1]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in the zeroed row %D",d);
1820: }
1821: for (i=0; i<N; i++) {
1822: a->a[a->diag[rows[i]]] = diag;
1823: }
1824: }
1825: } else {
1826: if (diag != 0.0) {
1827: for (i=0; i<N; i++) {
1828: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1829: if (a->ilen[rows[i]] > 0) {
1830: a->ilen[rows[i]] = 1;
1831: a->a[a->i[rows[i]]] = diag;
1832: a->j[a->i[rows[i]]] = rows[i];
1833: } else { /* in case row was completely empty */
1834: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1835: }
1836: }
1837: } else {
1838: for (i=0; i<N; i++) {
1839: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1840: a->ilen[rows[i]] = 0;
1841: }
1842: }
1843: A->nonzerostate++;
1844: }
1845: (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
1846: return(0);
1847: }
1849: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1850: {
1851: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1852: PetscInt i,j,m = A->rmap->n - 1,d = 0;
1853: PetscErrorCode ierr;
1854: PetscBool missing,*zeroed,vecs = PETSC_FALSE;
1855: const PetscScalar *xx;
1856: PetscScalar *bb;
1859: if (x && b) {
1860: VecGetArrayRead(x,&xx);
1861: VecGetArray(b,&bb);
1862: vecs = PETSC_TRUE;
1863: }
1864: PetscCalloc1(A->rmap->n,&zeroed);
1865: for (i=0; i<N; i++) {
1866: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1867: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1869: zeroed[rows[i]] = PETSC_TRUE;
1870: }
1871: for (i=0; i<A->rmap->n; i++) {
1872: if (!zeroed[i]) {
1873: for (j=a->i[i]; j<a->i[i+1]; j++) {
1874: if (zeroed[a->j[j]]) {
1875: if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
1876: a->a[j] = 0.0;
1877: }
1878: }
1879: } else if (vecs) bb[i] = diag*xx[i];
1880: }
1881: if (x && b) {
1882: VecRestoreArrayRead(x,&xx);
1883: VecRestoreArray(b,&bb);
1884: }
1885: PetscFree(zeroed);
1886: if (diag != 0.0) {
1887: MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1888: if (missing) {
1889: if (a->nonew) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1890: else {
1891: for (i=0; i<N; i++) {
1892: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1893: }
1894: }
1895: } else {
1896: for (i=0; i<N; i++) {
1897: a->a[a->diag[rows[i]]] = diag;
1898: }
1899: }
1900: }
1901: (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
1902: return(0);
1903: }
1905: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1906: {
1907: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1908: PetscInt *itmp;
1911: if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);
1913: *nz = a->i[row+1] - a->i[row];
1914: if (v) *v = a->a + a->i[row];
1915: if (idx) {
1916: itmp = a->j + a->i[row];
1917: if (*nz) *idx = itmp;
1918: else *idx = 0;
1919: }
1920: return(0);
1921: }
1923: /* remove this function? */
1924: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1925: {
1927: return(0);
1928: }
1930: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1931: {
1932: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1933: MatScalar *v = a->a;
1934: PetscReal sum = 0.0;
1936: PetscInt i,j;
1939: if (type == NORM_FROBENIUS) {
1940: #if defined(PETSC_USE_REAL___FP16)
1941: PetscBLASInt one = 1,nz = a->nz;
1942: *nrm = BLASnrm2_(&nz,v,&one);
1943: #else
1944: for (i=0; i<a->nz; i++) {
1945: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1946: }
1947: *nrm = PetscSqrtReal(sum);
1948: #endif
1949: PetscLogFlops(2*a->nz);
1950: } else if (type == NORM_1) {
1951: PetscReal *tmp;
1952: PetscInt *jj = a->j;
1953: PetscCalloc1(A->cmap->n+1,&tmp);
1954: *nrm = 0.0;
1955: for (j=0; j<a->nz; j++) {
1956: tmp[*jj++] += PetscAbsScalar(*v); v++;
1957: }
1958: for (j=0; j<A->cmap->n; j++) {
1959: if (tmp[j] > *nrm) *nrm = tmp[j];
1960: }
1961: PetscFree(tmp);
1962: PetscLogFlops(PetscMax(a->nz-1,0));
1963: } else if (type == NORM_INFINITY) {
1964: *nrm = 0.0;
1965: for (j=0; j<A->rmap->n; j++) {
1966: v = a->a + a->i[j];
1967: sum = 0.0;
1968: for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1969: sum += PetscAbsScalar(*v); v++;
1970: }
1971: if (sum > *nrm) *nrm = sum;
1972: }
1973: PetscLogFlops(PetscMax(a->nz-1,0));
1974: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
1975: return(0);
1976: }
1978: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
1979: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
1980: {
1982: PetscInt i,j,anzj;
1983: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b;
1984: PetscInt an=A->cmap->N,am=A->rmap->N;
1985: PetscInt *ati,*atj,*atfill,*ai=a->i,*aj=a->j;
1988: /* Allocate space for symbolic transpose info and work array */
1989: PetscCalloc1(an+1,&ati);
1990: PetscMalloc1(ai[am],&atj);
1991: PetscMalloc1(an,&atfill);
1993: /* Walk through aj and count ## of non-zeros in each row of A^T. */
1994: /* Note: offset by 1 for fast conversion into csr format. */
1995: for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
1996: /* Form ati for csr format of A^T. */
1997: for (i=0;i<an;i++) ati[i+1] += ati[i];
1999: /* Copy ati into atfill so we have locations of the next free space in atj */
2000: PetscMemcpy(atfill,ati,an*sizeof(PetscInt));
2002: /* Walk through A row-wise and mark nonzero entries of A^T. */
2003: for (i=0;i<am;i++) {
2004: anzj = ai[i+1] - ai[i];
2005: for (j=0;j<anzj;j++) {
2006: atj[atfill[*aj]] = i;
2007: atfill[*aj++] += 1;
2008: }
2009: }
2011: /* Clean up temporary space and complete requests. */
2012: PetscFree(atfill);
2013: MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2014: MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2016: b = (Mat_SeqAIJ*)((*B)->data);
2017: b->free_a = PETSC_FALSE;
2018: b->free_ij = PETSC_TRUE;
2019: b->nonew = 0;
2020: return(0);
2021: }
2023: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
2024: {
2025: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2026: Mat C;
2028: PetscInt i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
2029: MatScalar *array = a->a;
2032: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
2033: PetscCalloc1(1+A->cmap->n,&col);
2035: for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
2036: MatCreate(PetscObjectComm((PetscObject)A),&C);
2037: MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
2038: MatSetBlockSizes(C,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2039: MatSetType(C,((PetscObject)A)->type_name);
2040: MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
2041: PetscFree(col);
2042: } else {
2043: C = *B;
2044: }
2046: for (i=0; i<m; i++) {
2047: len = ai[i+1]-ai[i];
2048: MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
2049: array += len;
2050: aj += len;
2051: }
2052: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2053: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2055: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
2056: *B = C;
2057: } else {
2058: MatHeaderMerge(A,&C);
2059: }
2060: return(0);
2061: }
2063: PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2064: {
2065: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2066: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2067: MatScalar *va,*vb;
2069: PetscInt ma,na,mb,nb, i;
2072: MatGetSize(A,&ma,&na);
2073: MatGetSize(B,&mb,&nb);
2074: if (ma!=nb || na!=mb) {
2075: *f = PETSC_FALSE;
2076: return(0);
2077: }
2078: aii = aij->i; bii = bij->i;
2079: adx = aij->j; bdx = bij->j;
2080: va = aij->a; vb = bij->a;
2081: PetscMalloc1(ma,&aptr);
2082: PetscMalloc1(mb,&bptr);
2083: for (i=0; i<ma; i++) aptr[i] = aii[i];
2084: for (i=0; i<mb; i++) bptr[i] = bii[i];
2086: *f = PETSC_TRUE;
2087: for (i=0; i<ma; i++) {
2088: while (aptr[i]<aii[i+1]) {
2089: PetscInt idc,idr;
2090: PetscScalar vc,vr;
2091: /* column/row index/value */
2092: idc = adx[aptr[i]];
2093: idr = bdx[bptr[idc]];
2094: vc = va[aptr[i]];
2095: vr = vb[bptr[idc]];
2096: if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2097: *f = PETSC_FALSE;
2098: goto done;
2099: } else {
2100: aptr[i]++;
2101: if (B || i!=idc) bptr[idc]++;
2102: }
2103: }
2104: }
2105: done:
2106: PetscFree(aptr);
2107: PetscFree(bptr);
2108: return(0);
2109: }
2111: PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2112: {
2113: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2114: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2115: MatScalar *va,*vb;
2117: PetscInt ma,na,mb,nb, i;
2120: MatGetSize(A,&ma,&na);
2121: MatGetSize(B,&mb,&nb);
2122: if (ma!=nb || na!=mb) {
2123: *f = PETSC_FALSE;
2124: return(0);
2125: }
2126: aii = aij->i; bii = bij->i;
2127: adx = aij->j; bdx = bij->j;
2128: va = aij->a; vb = bij->a;
2129: PetscMalloc1(ma,&aptr);
2130: PetscMalloc1(mb,&bptr);
2131: for (i=0; i<ma; i++) aptr[i] = aii[i];
2132: for (i=0; i<mb; i++) bptr[i] = bii[i];
2134: *f = PETSC_TRUE;
2135: for (i=0; i<ma; i++) {
2136: while (aptr[i]<aii[i+1]) {
2137: PetscInt idc,idr;
2138: PetscScalar vc,vr;
2139: /* column/row index/value */
2140: idc = adx[aptr[i]];
2141: idr = bdx[bptr[idc]];
2142: vc = va[aptr[i]];
2143: vr = vb[bptr[idc]];
2144: if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2145: *f = PETSC_FALSE;
2146: goto done;
2147: } else {
2148: aptr[i]++;
2149: if (B || i!=idc) bptr[idc]++;
2150: }
2151: }
2152: }
2153: done:
2154: PetscFree(aptr);
2155: PetscFree(bptr);
2156: return(0);
2157: }
2159: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2160: {
2164: MatIsTranspose_SeqAIJ(A,A,tol,f);
2165: return(0);
2166: }
2168: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2169: {
2173: MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2174: return(0);
2175: }
2177: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2178: {
2179: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2180: const PetscScalar *l,*r;
2181: PetscScalar x;
2182: MatScalar *v;
2183: PetscErrorCode ierr;
2184: PetscInt i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
2185: const PetscInt *jj;
2188: if (ll) {
2189: /* The local size is used so that VecMPI can be passed to this routine
2190: by MatDiagonalScale_MPIAIJ */
2191: VecGetLocalSize(ll,&m);
2192: if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2193: VecGetArrayRead(ll,&l);
2194: v = a->a;
2195: for (i=0; i<m; i++) {
2196: x = l[i];
2197: M = a->i[i+1] - a->i[i];
2198: for (j=0; j<M; j++) (*v++) *= x;
2199: }
2200: VecRestoreArrayRead(ll,&l);
2201: PetscLogFlops(nz);
2202: }
2203: if (rr) {
2204: VecGetLocalSize(rr,&n);
2205: if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2206: VecGetArrayRead(rr,&r);
2207: v = a->a; jj = a->j;
2208: for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2209: VecRestoreArrayRead(rr,&r);
2210: PetscLogFlops(nz);
2211: }
2212: MatSeqAIJInvalidateDiagonal(A);
2213: return(0);
2214: }
2216: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2217: {
2218: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c;
2220: PetscInt *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2221: PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2222: const PetscInt *irow,*icol;
2223: PetscInt nrows,ncols;
2224: PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2225: MatScalar *a_new,*mat_a;
2226: Mat C;
2227: PetscBool stride;
2231: ISGetIndices(isrow,&irow);
2232: ISGetLocalSize(isrow,&nrows);
2233: ISGetLocalSize(iscol,&ncols);
2235: PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2236: if (stride) {
2237: ISStrideGetInfo(iscol,&first,&step);
2238: } else {
2239: first = 0;
2240: step = 0;
2241: }
2242: if (stride && step == 1) {
2243: /* special case of contiguous rows */
2244: PetscMalloc2(nrows,&lens,nrows,&starts);
2245: /* loop over new rows determining lens and starting points */
2246: for (i=0; i<nrows; i++) {
2247: kstart = ai[irow[i]];
2248: kend = kstart + ailen[irow[i]];
2249: starts[i] = kstart;
2250: for (k=kstart; k<kend; k++) {
2251: if (aj[k] >= first) {
2252: starts[i] = k;
2253: break;
2254: }
2255: }
2256: sum = 0;
2257: while (k < kend) {
2258: if (aj[k++] >= first+ncols) break;
2259: sum++;
2260: }
2261: lens[i] = sum;
2262: }
2263: /* create submatrix */
2264: if (scall == MAT_REUSE_MATRIX) {
2265: PetscInt n_cols,n_rows;
2266: MatGetSize(*B,&n_rows,&n_cols);
2267: if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2268: MatZeroEntries(*B);
2269: C = *B;
2270: } else {
2271: PetscInt rbs,cbs;
2272: MatCreate(PetscObjectComm((PetscObject)A),&C);
2273: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2274: ISGetBlockSize(isrow,&rbs);
2275: ISGetBlockSize(iscol,&cbs);
2276: MatSetBlockSizes(C,rbs,cbs);
2277: MatSetType(C,((PetscObject)A)->type_name);
2278: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2279: }
2280: c = (Mat_SeqAIJ*)C->data;
2282: /* loop over rows inserting into submatrix */
2283: a_new = c->a;
2284: j_new = c->j;
2285: i_new = c->i;
2287: for (i=0; i<nrows; i++) {
2288: ii = starts[i];
2289: lensi = lens[i];
2290: for (k=0; k<lensi; k++) {
2291: *j_new++ = aj[ii+k] - first;
2292: }
2293: PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2294: a_new += lensi;
2295: i_new[i+1] = i_new[i] + lensi;
2296: c->ilen[i] = lensi;
2297: }
2298: PetscFree2(lens,starts);
2299: } else {
2300: ISGetIndices(iscol,&icol);
2301: PetscCalloc1(oldcols,&smap);
2302: PetscMalloc1(1+nrows,&lens);
2303: for (i=0; i<ncols; i++) {
2304: #if defined(PETSC_USE_DEBUG)
2305: if (icol[i] >= oldcols) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D <= A->cmap->n %D",i,icol[i],oldcols);
2306: #endif
2307: smap[icol[i]] = i+1;
2308: }
2310: /* determine lens of each row */
2311: for (i=0; i<nrows; i++) {
2312: kstart = ai[irow[i]];
2313: kend = kstart + a->ilen[irow[i]];
2314: lens[i] = 0;
2315: for (k=kstart; k<kend; k++) {
2316: if (smap[aj[k]]) {
2317: lens[i]++;
2318: }
2319: }
2320: }
2321: /* Create and fill new matrix */
2322: if (scall == MAT_REUSE_MATRIX) {
2323: PetscBool equal;
2325: c = (Mat_SeqAIJ*)((*B)->data);
2326: if ((*B)->rmap->n != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2327: PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2328: if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2329: PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2330: C = *B;
2331: } else {
2332: PetscInt rbs,cbs;
2333: MatCreate(PetscObjectComm((PetscObject)A),&C);
2334: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2335: ISGetBlockSize(isrow,&rbs);
2336: ISGetBlockSize(iscol,&cbs);
2337: MatSetBlockSizes(C,rbs,cbs);
2338: MatSetType(C,((PetscObject)A)->type_name);
2339: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2340: }
2341: c = (Mat_SeqAIJ*)(C->data);
2342: for (i=0; i<nrows; i++) {
2343: row = irow[i];
2344: kstart = ai[row];
2345: kend = kstart + a->ilen[row];
2346: mat_i = c->i[i];
2347: mat_j = c->j + mat_i;
2348: mat_a = c->a + mat_i;
2349: mat_ilen = c->ilen + i;
2350: for (k=kstart; k<kend; k++) {
2351: if ((tcol=smap[a->j[k]])) {
2352: *mat_j++ = tcol - 1;
2353: *mat_a++ = a->a[k];
2354: (*mat_ilen)++;
2356: }
2357: }
2358: }
2359: /* Free work space */
2360: ISRestoreIndices(iscol,&icol);
2361: PetscFree(smap);
2362: PetscFree(lens);
2363: /* sort */
2364: for (i = 0; i < nrows; i++) {
2365: PetscInt ilen;
2367: mat_i = c->i[i];
2368: mat_j = c->j + mat_i;
2369: mat_a = c->a + mat_i;
2370: ilen = c->ilen[i];
2371: PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
2372: }
2373: }
2374: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2375: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2377: ISRestoreIndices(isrow,&irow);
2378: *B = C;
2379: return(0);
2380: }
2382: PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2383: {
2385: Mat B;
2388: if (scall == MAT_INITIAL_MATRIX) {
2389: MatCreate(subComm,&B);
2390: MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2391: MatSetBlockSizesFromMats(B,mat,mat);
2392: MatSetType(B,MATSEQAIJ);
2393: MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2394: *subMat = B;
2395: } else {
2396: MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2397: }
2398: return(0);
2399: }
2401: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2402: {
2403: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2405: Mat outA;
2406: PetscBool row_identity,col_identity;
2409: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
2411: ISIdentity(row,&row_identity);
2412: ISIdentity(col,&col_identity);
2414: outA = inA;
2415: outA->factortype = MAT_FACTOR_LU;
2416: PetscFree(inA->solvertype);
2417: PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);
2419: PetscObjectReference((PetscObject)row);
2420: ISDestroy(&a->row);
2422: a->row = row;
2424: PetscObjectReference((PetscObject)col);
2425: ISDestroy(&a->col);
2427: a->col = col;
2429: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2430: ISDestroy(&a->icol);
2431: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2432: PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);
2434: if (!a->solve_work) { /* this matrix may have been factored before */
2435: PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2436: PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2437: }
2439: MatMarkDiagonal_SeqAIJ(inA);
2440: if (row_identity && col_identity) {
2441: MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2442: } else {
2443: MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2444: }
2445: return(0);
2446: }
2448: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2449: {
2450: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2451: PetscScalar oalpha = alpha;
2453: PetscBLASInt one = 1,bnz;
2456: PetscBLASIntCast(a->nz,&bnz);
2457: PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2458: PetscLogFlops(a->nz);
2459: MatSeqAIJInvalidateDiagonal(inA);
2460: return(0);
2461: }
2463: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2464: {
2466: PetscInt i;
2469: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2470: PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);
2472: for (i=0; i<submatj->nrqr; ++i) {
2473: PetscFree(submatj->sbuf2[i]);
2474: }
2475: PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);
2477: if (submatj->rbuf1) {
2478: PetscFree(submatj->rbuf1[0]);
2479: PetscFree(submatj->rbuf1);
2480: }
2482: for (i=0; i<submatj->nrqs; ++i) {
2483: PetscFree(submatj->rbuf3[i]);
2484: }
2485: PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
2486: PetscFree(submatj->pa);
2487: }
2489: #if defined(PETSC_USE_CTABLE)
2490: PetscTableDestroy((PetscTable*)&submatj->rmap);
2491: if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
2492: PetscFree(submatj->rmap_loc);
2493: #else
2494: PetscFree(submatj->rmap);
2495: #endif
2497: if (!submatj->allcolumns) {
2498: #if defined(PETSC_USE_CTABLE)
2499: PetscTableDestroy((PetscTable*)&submatj->cmap);
2500: #else
2501: PetscFree(submatj->cmap);
2502: #endif
2503: }
2504: PetscFree(submatj->row2proc);
2506: PetscFree(submatj);
2507: return(0);
2508: }
2510: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2511: {
2513: Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
2514: Mat_SubSppt *submatj = c->submatis1;
2517: submatj->destroy(C);
2518: MatDestroySubMatrix_Private(submatj);
2519: return(0);
2520: }
2522: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
2523: {
2525: PetscInt i;
2526: Mat C;
2527: Mat_SeqAIJ *c;
2528: Mat_SubSppt *submatj;
2531: for (i=0; i<n; i++) {
2532: C = (*mat)[i];
2533: c = (Mat_SeqAIJ*)C->data;
2534: submatj = c->submatis1;
2535: if (submatj) {
2536: if (--((PetscObject)C)->refct <= 0) {
2537: (submatj->destroy)(C);
2538: MatDestroySubMatrix_Private(submatj);
2539: PetscLayoutDestroy(&C->rmap);
2540: PetscLayoutDestroy(&C->cmap);
2541: PetscHeaderDestroy(&C);
2542: }
2543: } else {
2544: MatDestroy(&C);
2545: }
2546: }
2548: PetscFree(*mat);
2549: return(0);
2550: }
2552: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2553: {
2555: PetscInt i;
2558: if (scall == MAT_INITIAL_MATRIX) {
2559: PetscCalloc1(n+1,B);
2560: }
2562: for (i=0; i<n; i++) {
2563: MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2564: }
2565: return(0);
2566: }
2568: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2569: {
2570: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2572: PetscInt row,i,j,k,l,m,n,*nidx,isz,val;
2573: const PetscInt *idx;
2574: PetscInt start,end,*ai,*aj;
2575: PetscBT table;
2578: m = A->rmap->n;
2579: ai = a->i;
2580: aj = a->j;
2582: if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");
2584: PetscMalloc1(m+1,&nidx);
2585: PetscBTCreate(m,&table);
2587: for (i=0; i<is_max; i++) {
2588: /* Initialize the two local arrays */
2589: isz = 0;
2590: PetscBTMemzero(m,table);
2592: /* Extract the indices, assume there can be duplicate entries */
2593: ISGetIndices(is[i],&idx);
2594: ISGetLocalSize(is[i],&n);
2596: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2597: for (j=0; j<n; ++j) {
2598: if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2599: }
2600: ISRestoreIndices(is[i],&idx);
2601: ISDestroy(&is[i]);
2603: k = 0;
2604: for (j=0; j<ov; j++) { /* for each overlap */
2605: n = isz;
2606: for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2607: row = nidx[k];
2608: start = ai[row];
2609: end = ai[row+1];
2610: for (l = start; l<end; l++) {
2611: val = aj[l];
2612: if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2613: }
2614: }
2615: }
2616: ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2617: }
2618: PetscBTDestroy(&table);
2619: PetscFree(nidx);
2620: return(0);
2621: }
2623: /* -------------------------------------------------------------- */
2624: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2625: {
2626: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2628: PetscInt i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2629: const PetscInt *row,*col;
2630: PetscInt *cnew,j,*lens;
2631: IS icolp,irowp;
2632: PetscInt *cwork = NULL;
2633: PetscScalar *vwork = NULL;
2636: ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2637: ISGetIndices(irowp,&row);
2638: ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2639: ISGetIndices(icolp,&col);
2641: /* determine lengths of permuted rows */
2642: PetscMalloc1(m+1,&lens);
2643: for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2644: MatCreate(PetscObjectComm((PetscObject)A),B);
2645: MatSetSizes(*B,m,n,m,n);
2646: MatSetBlockSizesFromMats(*B,A,A);
2647: MatSetType(*B,((PetscObject)A)->type_name);
2648: MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2649: PetscFree(lens);
2651: PetscMalloc1(n,&cnew);
2652: for (i=0; i<m; i++) {
2653: MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2654: for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2655: MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2656: MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2657: }
2658: PetscFree(cnew);
2660: (*B)->assembled = PETSC_FALSE;
2662: MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2663: MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2664: ISRestoreIndices(irowp,&row);
2665: ISRestoreIndices(icolp,&col);
2666: ISDestroy(&irowp);
2667: ISDestroy(&icolp);
2668: return(0);
2669: }
2671: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2672: {
2676: /* If the two matrices have the same copy implementation, use fast copy. */
2677: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2678: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2679: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
2681: if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
2682: PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2683: PetscObjectStateIncrease((PetscObject)B);
2684: } else {
2685: MatCopy_Basic(A,B,str);
2686: }
2687: return(0);
2688: }
2690: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2691: {
2695: MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2696: return(0);
2697: }
2699: PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2700: {
2701: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2704: *array = a->a;
2705: return(0);
2706: }
2708: PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2709: {
2711: return(0);
2712: }
2714: /*
2715: Computes the number of nonzeros per row needed for preallocation when X and Y
2716: have different nonzero structure.
2717: */
2718: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
2719: {
2720: PetscInt i,j,k,nzx,nzy;
2723: /* Set the number of nonzeros in the new matrix */
2724: for (i=0; i<m; i++) {
2725: const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
2726: nzx = xi[i+1] - xi[i];
2727: nzy = yi[i+1] - yi[i];
2728: nnz[i] = 0;
2729: for (j=0,k=0; j<nzx; j++) { /* Point in X */
2730: for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
2731: if (k<nzy && yjj[k]==xjj[j]) k++; /* Skip duplicate */
2732: nnz[i]++;
2733: }
2734: for (; k<nzy; k++) nnz[i]++;
2735: }
2736: return(0);
2737: }
2739: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
2740: {
2741: PetscInt m = Y->rmap->N;
2742: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data;
2743: Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data;
2747: /* Set the number of nonzeros in the new matrix */
2748: MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
2749: return(0);
2750: }
2752: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2753: {
2755: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
2756: PetscBLASInt one=1,bnz;
2759: PetscBLASIntCast(x->nz,&bnz);
2760: if (str == SAME_NONZERO_PATTERN) {
2761: PetscScalar alpha = a;
2762: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2763: MatSeqAIJInvalidateDiagonal(Y);
2764: PetscObjectStateIncrease((PetscObject)Y);
2765: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2766: MatAXPY_Basic(Y,a,X,str);
2767: } else {
2768: Mat B;
2769: PetscInt *nnz;
2770: PetscMalloc1(Y->rmap->N,&nnz);
2771: MatCreate(PetscObjectComm((PetscObject)Y),&B);
2772: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2773: MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2774: MatSetBlockSizesFromMats(B,Y,Y);
2775: MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2776: MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2777: MatSeqAIJSetPreallocation(B,0,nnz);
2778: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2779: MatHeaderReplace(Y,&B);
2780: PetscFree(nnz);
2781: }
2782: return(0);
2783: }
2785: PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
2786: {
2787: #if defined(PETSC_USE_COMPLEX)
2788: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2789: PetscInt i,nz;
2790: PetscScalar *a;
2793: nz = aij->nz;
2794: a = aij->a;
2795: for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
2796: #else
2798: #endif
2799: return(0);
2800: }
2802: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2803: {
2804: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2806: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2807: PetscReal atmp;
2808: PetscScalar *x;
2809: MatScalar *aa;
2812: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2813: aa = a->a;
2814: ai = a->i;
2815: aj = a->j;
2817: VecSet(v,0.0);
2818: VecGetArray(v,&x);
2819: VecGetLocalSize(v,&n);
2820: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2821: for (i=0; i<m; i++) {
2822: ncols = ai[1] - ai[0]; ai++;
2823: x[i] = 0.0;
2824: for (j=0; j<ncols; j++) {
2825: atmp = PetscAbsScalar(*aa);
2826: if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2827: aa++; aj++;
2828: }
2829: }
2830: VecRestoreArray(v,&x);
2831: return(0);
2832: }
2834: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2835: {
2836: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2838: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2839: PetscScalar *x;
2840: MatScalar *aa;
2843: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2844: aa = a->a;
2845: ai = a->i;
2846: aj = a->j;
2848: VecSet(v,0.0);
2849: VecGetArray(v,&x);
2850: VecGetLocalSize(v,&n);
2851: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2852: for (i=0; i<m; i++) {
2853: ncols = ai[1] - ai[0]; ai++;
2854: if (ncols == A->cmap->n) { /* row is dense */
2855: x[i] = *aa; if (idx) idx[i] = 0;
2856: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
2857: x[i] = 0.0;
2858: if (idx) {
2859: idx[i] = 0; /* in case ncols is zero */
2860: for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2861: if (aj[j] > j) {
2862: idx[i] = j;
2863: break;
2864: }
2865: }
2866: }
2867: }
2868: for (j=0; j<ncols; j++) {
2869: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2870: aa++; aj++;
2871: }
2872: }
2873: VecRestoreArray(v,&x);
2874: return(0);
2875: }
2877: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2878: {
2879: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2881: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2882: PetscReal atmp;
2883: PetscScalar *x;
2884: MatScalar *aa;
2887: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2888: aa = a->a;
2889: ai = a->i;
2890: aj = a->j;
2892: VecSet(v,0.0);
2893: VecGetArray(v,&x);
2894: VecGetLocalSize(v,&n);
2895: if (n != A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", A->rmap->n, n);
2896: for (i=0; i<m; i++) {
2897: ncols = ai[1] - ai[0]; ai++;
2898: if (ncols) {
2899: /* Get first nonzero */
2900: for (j = 0; j < ncols; j++) {
2901: atmp = PetscAbsScalar(aa[j]);
2902: if (atmp > 1.0e-12) {
2903: x[i] = atmp;
2904: if (idx) idx[i] = aj[j];
2905: break;
2906: }
2907: }
2908: if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
2909: } else {
2910: x[i] = 0.0; if (idx) idx[i] = 0;
2911: }
2912: for (j = 0; j < ncols; j++) {
2913: atmp = PetscAbsScalar(*aa);
2914: if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2915: aa++; aj++;
2916: }
2917: }
2918: VecRestoreArray(v,&x);
2919: return(0);
2920: }
2922: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2923: {
2924: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2925: PetscErrorCode ierr;
2926: PetscInt i,j,m = A->rmap->n,ncols,n;
2927: const PetscInt *ai,*aj;
2928: PetscScalar *x;
2929: const MatScalar *aa;
2932: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2933: aa = a->a;
2934: ai = a->i;
2935: aj = a->j;
2937: VecSet(v,0.0);
2938: VecGetArray(v,&x);
2939: VecGetLocalSize(v,&n);
2940: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2941: for (i=0; i<m; i++) {
2942: ncols = ai[1] - ai[0]; ai++;
2943: if (ncols == A->cmap->n) { /* row is dense */
2944: x[i] = *aa; if (idx) idx[i] = 0;
2945: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
2946: x[i] = 0.0;
2947: if (idx) { /* find first implicit 0.0 in the row */
2948: idx[i] = 0; /* in case ncols is zero */
2949: for (j=0; j<ncols; j++) {
2950: if (aj[j] > j) {
2951: idx[i] = j;
2952: break;
2953: }
2954: }
2955: }
2956: }
2957: for (j=0; j<ncols; j++) {
2958: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2959: aa++; aj++;
2960: }
2961: }
2962: VecRestoreArray(v,&x);
2963: return(0);
2964: }
2966: #include <petscblaslapack.h>
2967: #include <petsc/private/kernels/blockinvert.h>
2969: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
2970: {
2971: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
2973: PetscInt i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
2974: MatScalar *diag,work[25],*v_work;
2975: PetscReal shift = 0.0;
2976: PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;
2979: allowzeropivot = PetscNot(A->erroriffailure);
2980: if (a->ibdiagvalid) {
2981: if (values) *values = a->ibdiag;
2982: return(0);
2983: }
2984: MatMarkDiagonal_SeqAIJ(A);
2985: if (!a->ibdiag) {
2986: PetscMalloc1(bs2*mbs,&a->ibdiag);
2987: PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
2988: }
2989: diag = a->ibdiag;
2990: if (values) *values = a->ibdiag;
2991: /* factor and invert each block */
2992: switch (bs) {
2993: case 1:
2994: for (i=0; i<mbs; i++) {
2995: MatGetValues(A,1,&i,1,&i,diag+i);
2996: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
2997: if (allowzeropivot) {
2998: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
2999: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3000: A->factorerror_zeropivot_row = i;
3001: PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3002: } else SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_MAT_LU_ZRPVT,"Zero pivot, row %D pivot %g tolerance %g",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3003: }
3004: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3005: }
3006: break;
3007: case 2:
3008: for (i=0; i<mbs; i++) {
3009: ij[0] = 2*i; ij[1] = 2*i + 1;
3010: MatGetValues(A,2,ij,2,ij,diag);
3011: PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
3012: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3013: PetscKernel_A_gets_transpose_A_2(diag);
3014: diag += 4;
3015: }
3016: break;
3017: case 3:
3018: for (i=0; i<mbs; i++) {
3019: ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
3020: MatGetValues(A,3,ij,3,ij,diag);
3021: PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
3022: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3023: PetscKernel_A_gets_transpose_A_3(diag);
3024: diag += 9;
3025: }
3026: break;
3027: case 4:
3028: for (i=0; i<mbs; i++) {
3029: ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
3030: MatGetValues(A,4,ij,4,ij,diag);
3031: PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
3032: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3033: PetscKernel_A_gets_transpose_A_4(diag);
3034: diag += 16;
3035: }
3036: break;
3037: case 5:
3038: for (i=0; i<mbs; i++) {
3039: ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
3040: MatGetValues(A,5,ij,5,ij,diag);
3041: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
3042: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3043: PetscKernel_A_gets_transpose_A_5(diag);
3044: diag += 25;
3045: }
3046: break;
3047: case 6:
3048: for (i=0; i<mbs; i++) {
3049: ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5;
3050: MatGetValues(A,6,ij,6,ij,diag);
3051: PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
3052: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3053: PetscKernel_A_gets_transpose_A_6(diag);
3054: diag += 36;
3055: }
3056: break;
3057: case 7:
3058: for (i=0; i<mbs; i++) {
3059: ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6;
3060: MatGetValues(A,7,ij,7,ij,diag);
3061: PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
3062: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3063: PetscKernel_A_gets_transpose_A_7(diag);
3064: diag += 49;
3065: }
3066: break;
3067: default:
3068: PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3069: for (i=0; i<mbs; i++) {
3070: for (j=0; j<bs; j++) {
3071: IJ[j] = bs*i + j;
3072: }
3073: MatGetValues(A,bs,IJ,bs,IJ,diag);
3074: PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
3075: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3076: PetscKernel_A_gets_transpose_A_N(diag,bs);
3077: diag += bs2;
3078: }
3079: PetscFree3(v_work,v_pivots,IJ);
3080: }
3081: a->ibdiagvalid = PETSC_TRUE;
3082: return(0);
3083: }
3085: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3086: {
3088: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data;
3089: PetscScalar a;
3090: PetscInt m,n,i,j,col;
3093: if (!x->assembled) {
3094: MatGetSize(x,&m,&n);
3095: for (i=0; i<m; i++) {
3096: for (j=0; j<aij->imax[i]; j++) {
3097: PetscRandomGetValue(rctx,&a);
3098: col = (PetscInt)(n*PetscRealPart(a));
3099: MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3100: }
3101: }
3102: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not yet coded");
3103: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3104: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3105: return(0);
3106: }
3108: PetscErrorCode MatShift_SeqAIJ(Mat Y,PetscScalar a)
3109: {
3111: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)Y->data;
3114: if (!Y->preallocated || !aij->nz) {
3115: MatSeqAIJSetPreallocation(Y,1,NULL);
3116: }
3117: MatShift_Basic(Y,a);
3118: return(0);
3119: }
3121: /* -------------------------------------------------------------------*/
3122: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3123: MatGetRow_SeqAIJ,
3124: MatRestoreRow_SeqAIJ,
3125: MatMult_SeqAIJ,
3126: /* 4*/ MatMultAdd_SeqAIJ,
3127: MatMultTranspose_SeqAIJ,
3128: MatMultTransposeAdd_SeqAIJ,
3129: 0,
3130: 0,
3131: 0,
3132: /* 10*/ 0,
3133: MatLUFactor_SeqAIJ,
3134: 0,
3135: MatSOR_SeqAIJ,
3136: MatTranspose_SeqAIJ,
3137: /*1 5*/ MatGetInfo_SeqAIJ,
3138: MatEqual_SeqAIJ,
3139: MatGetDiagonal_SeqAIJ,
3140: MatDiagonalScale_SeqAIJ,
3141: MatNorm_SeqAIJ,
3142: /* 20*/ 0,
3143: MatAssemblyEnd_SeqAIJ,
3144: MatSetOption_SeqAIJ,
3145: MatZeroEntries_SeqAIJ,
3146: /* 24*/ MatZeroRows_SeqAIJ,
3147: 0,
3148: 0,
3149: 0,
3150: 0,
3151: /* 29*/ MatSetUp_SeqAIJ,
3152: 0,
3153: 0,
3154: 0,
3155: 0,
3156: /* 34*/ MatDuplicate_SeqAIJ,
3157: 0,
3158: 0,
3159: MatILUFactor_SeqAIJ,
3160: 0,
3161: /* 39*/ MatAXPY_SeqAIJ,
3162: MatCreateSubMatrices_SeqAIJ,
3163: MatIncreaseOverlap_SeqAIJ,
3164: MatGetValues_SeqAIJ,
3165: MatCopy_SeqAIJ,
3166: /* 44*/ MatGetRowMax_SeqAIJ,
3167: MatScale_SeqAIJ,
3168: MatShift_SeqAIJ,
3169: MatDiagonalSet_SeqAIJ,
3170: MatZeroRowsColumns_SeqAIJ,
3171: /* 49*/ MatSetRandom_SeqAIJ,
3172: MatGetRowIJ_SeqAIJ,
3173: MatRestoreRowIJ_SeqAIJ,
3174: MatGetColumnIJ_SeqAIJ,
3175: MatRestoreColumnIJ_SeqAIJ,
3176: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3177: 0,
3178: 0,
3179: MatPermute_SeqAIJ,
3180: 0,
3181: /* 59*/ 0,
3182: MatDestroy_SeqAIJ,
3183: MatView_SeqAIJ,
3184: 0,
3185: MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ,
3186: /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ,
3187: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3188: 0,
3189: 0,
3190: 0,
3191: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3192: MatGetRowMinAbs_SeqAIJ,
3193: 0,
3194: 0,
3195: 0,
3196: /* 74*/ 0,
3197: MatFDColoringApply_AIJ,
3198: 0,
3199: 0,
3200: 0,
3201: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3202: 0,
3203: 0,
3204: 0,
3205: MatLoad_SeqAIJ,
3206: /* 84*/ MatIsSymmetric_SeqAIJ,
3207: MatIsHermitian_SeqAIJ,
3208: 0,
3209: 0,
3210: 0,
3211: /* 89*/ MatMatMult_SeqAIJ_SeqAIJ,
3212: MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3213: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3214: MatPtAP_SeqAIJ_SeqAIJ,
3215: MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy,
3216: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3217: MatMatTransposeMult_SeqAIJ_SeqAIJ,
3218: MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
3219: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3220: 0,
3221: /* 99*/ 0,
3222: 0,
3223: 0,
3224: MatConjugate_SeqAIJ,
3225: 0,
3226: /*104*/ MatSetValuesRow_SeqAIJ,
3227: MatRealPart_SeqAIJ,
3228: MatImaginaryPart_SeqAIJ,
3229: 0,
3230: 0,
3231: /*109*/ MatMatSolve_SeqAIJ,
3232: 0,
3233: MatGetRowMin_SeqAIJ,
3234: 0,
3235: MatMissingDiagonal_SeqAIJ,
3236: /*114*/ 0,
3237: 0,
3238: 0,
3239: 0,
3240: 0,
3241: /*119*/ 0,
3242: 0,
3243: 0,
3244: 0,
3245: MatGetMultiProcBlock_SeqAIJ,
3246: /*124*/ MatFindNonzeroRows_SeqAIJ,
3247: MatGetColumnNorms_SeqAIJ,
3248: MatInvertBlockDiagonal_SeqAIJ,
3249: 0,
3250: 0,
3251: /*129*/ 0,
3252: MatTransposeMatMult_SeqAIJ_SeqAIJ,
3253: MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
3254: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3255: MatTransposeColoringCreate_SeqAIJ,
3256: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3257: MatTransColoringApplyDenToSp_SeqAIJ,
3258: MatRARt_SeqAIJ_SeqAIJ,
3259: MatRARtSymbolic_SeqAIJ_SeqAIJ,
3260: MatRARtNumeric_SeqAIJ_SeqAIJ,
3261: /*139*/0,
3262: 0,
3263: 0,
3264: MatFDColoringSetUp_SeqXAIJ,
3265: MatFindOffBlockDiagonalEntries_SeqAIJ,
3266: /*144*/MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3267: MatDestroySubMatrices_SeqAIJ
3268: };
3270: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3271: {
3272: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3273: PetscInt i,nz,n;
3276: nz = aij->maxnz;
3277: n = mat->rmap->n;
3278: for (i=0; i<nz; i++) {
3279: aij->j[i] = indices[i];
3280: }
3281: aij->nz = nz;
3282: for (i=0; i<n; i++) {
3283: aij->ilen[i] = aij->imax[i];
3284: }
3285: return(0);
3286: }
3288: /*@
3289: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3290: in the matrix.
3292: Input Parameters:
3293: + mat - the SeqAIJ matrix
3294: - indices - the column indices
3296: Level: advanced
3298: Notes:
3299: This can be called if you have precomputed the nonzero structure of the
3300: matrix and want to provide it to the matrix object to improve the performance
3301: of the MatSetValues() operation.
3303: You MUST have set the correct numbers of nonzeros per row in the call to
3304: MatCreateSeqAIJ(), and the columns indices MUST be sorted.
3306: MUST be called before any calls to MatSetValues();
3308: The indices should start with zero, not one.
3310: @*/
3311: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3312: {
3318: PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3319: return(0);
3320: }
3322: /* ----------------------------------------------------------------------------------------*/
3324: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3325: {
3326: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3328: size_t nz = aij->i[mat->rmap->n];
3331: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3333: /* allocate space for values if not already there */
3334: if (!aij->saved_values) {
3335: PetscMalloc1(nz+1,&aij->saved_values);
3336: PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3337: }
3339: /* copy values over */
3340: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3341: return(0);
3342: }
3344: /*@
3345: MatStoreValues - Stashes a copy of the matrix values; this allows, for
3346: example, reuse of the linear part of a Jacobian, while recomputing the
3347: nonlinear portion.
3349: Collect on Mat
3351: Input Parameters:
3352: . mat - the matrix (currently only AIJ matrices support this option)
3354: Level: advanced
3356: Common Usage, with SNESSolve():
3357: $ Create Jacobian matrix
3358: $ Set linear terms into matrix
3359: $ Apply boundary conditions to matrix, at this time matrix must have
3360: $ final nonzero structure (i.e. setting the nonlinear terms and applying
3361: $ boundary conditions again will not change the nonzero structure
3362: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3363: $ MatStoreValues(mat);
3364: $ Call SNESSetJacobian() with matrix
3365: $ In your Jacobian routine
3366: $ MatRetrieveValues(mat);
3367: $ Set nonlinear terms in matrix
3369: Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3370: $ // build linear portion of Jacobian
3371: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3372: $ MatStoreValues(mat);
3373: $ loop over nonlinear iterations
3374: $ MatRetrieveValues(mat);
3375: $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3376: $ // call MatAssemblyBegin/End() on matrix
3377: $ Solve linear system with Jacobian
3378: $ endloop
3380: Notes:
3381: Matrix must already be assemblied before calling this routine
3382: Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3383: calling this routine.
3385: When this is called multiple times it overwrites the previous set of stored values
3386: and does not allocated additional space.
3388: .seealso: MatRetrieveValues()
3390: @*/
3391: PetscErrorCode MatStoreValues(Mat mat)
3392: {
3397: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3398: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3399: PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3400: return(0);
3401: }
3403: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3404: {
3405: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3407: PetscInt nz = aij->i[mat->rmap->n];
3410: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3411: if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3412: /* copy values over */
3413: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3414: return(0);
3415: }
3417: /*@
3418: MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3419: example, reuse of the linear part of a Jacobian, while recomputing the
3420: nonlinear portion.
3422: Collect on Mat
3424: Input Parameters:
3425: . mat - the matrix (currently only AIJ matrices support this option)
3427: Level: advanced
3429: .seealso: MatStoreValues()
3431: @*/
3432: PetscErrorCode MatRetrieveValues(Mat mat)
3433: {
3438: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3439: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3440: PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3441: return(0);
3442: }
3445: /* --------------------------------------------------------------------------------*/
3446: /*@C
3447: MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3448: (the default parallel PETSc format). For good matrix assembly performance
3449: the user should preallocate the matrix storage by setting the parameter nz
3450: (or the array nnz). By setting these parameters accurately, performance
3451: during matrix assembly can be increased by more than a factor of 50.
3453: Collective on MPI_Comm
3455: Input Parameters:
3456: + comm - MPI communicator, set to PETSC_COMM_SELF
3457: . m - number of rows
3458: . n - number of columns
3459: . nz - number of nonzeros per row (same for all rows)
3460: - nnz - array containing the number of nonzeros in the various rows
3461: (possibly different for each row) or NULL
3463: Output Parameter:
3464: . A - the matrix
3466: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3467: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3468: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3470: Notes:
3471: If nnz is given then nz is ignored
3473: The AIJ format (also called the Yale sparse matrix format or
3474: compressed row storage), is fully compatible with standard Fortran 77
3475: storage. That is, the stored row and column indices can begin at
3476: either one (as in Fortran) or zero. See the users' manual for details.
3478: Specify the preallocated storage with either nz or nnz (not both).
3479: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3480: allocation. For large problems you MUST preallocate memory or you
3481: will get TERRIBLE performance, see the users' manual chapter on matrices.
3483: By default, this format uses inodes (identical nodes) when possible, to
3484: improve numerical efficiency of matrix-vector products and solves. We
3485: search for consecutive rows with the same nonzero structure, thereby
3486: reusing matrix information to achieve increased efficiency.
3488: Options Database Keys:
3489: + -mat_no_inode - Do not use inodes
3490: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3492: Level: intermediate
3494: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()
3496: @*/
3497: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3498: {
3502: MatCreate(comm,A);
3503: MatSetSizes(*A,m,n,m,n);
3504: MatSetType(*A,MATSEQAIJ);
3505: MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3506: return(0);
3507: }
3509: /*@C
3510: MatSeqAIJSetPreallocation - For good matrix assembly performance
3511: the user should preallocate the matrix storage by setting the parameter nz
3512: (or the array nnz). By setting these parameters accurately, performance
3513: during matrix assembly can be increased by more than a factor of 50.
3515: Collective on MPI_Comm
3517: Input Parameters:
3518: + B - The matrix
3519: . nz - number of nonzeros per row (same for all rows)
3520: - nnz - array containing the number of nonzeros in the various rows
3521: (possibly different for each row) or NULL
3523: Notes:
3524: If nnz is given then nz is ignored
3526: The AIJ format (also called the Yale sparse matrix format or
3527: compressed row storage), is fully compatible with standard Fortran 77
3528: storage. That is, the stored row and column indices can begin at
3529: either one (as in Fortran) or zero. See the users' manual for details.
3531: Specify the preallocated storage with either nz or nnz (not both).
3532: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3533: allocation. For large problems you MUST preallocate memory or you
3534: will get TERRIBLE performance, see the users' manual chapter on matrices.
3536: You can call MatGetInfo() to get information on how effective the preallocation was;
3537: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3538: You can also run with the option -info and look for messages with the string
3539: malloc in them to see if additional memory allocation was needed.
3541: Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3542: entries or columns indices
3544: By default, this format uses inodes (identical nodes) when possible, to
3545: improve numerical efficiency of matrix-vector products and solves. We
3546: search for consecutive rows with the same nonzero structure, thereby
3547: reusing matrix information to achieve increased efficiency.
3549: Options Database Keys:
3550: + -mat_no_inode - Do not use inodes
3551: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3553: Level: intermediate
3555: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()
3557: @*/
3558: PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3559: {
3565: PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3566: return(0);
3567: }
3569: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3570: {
3571: Mat_SeqAIJ *b;
3572: PetscBool skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3574: PetscInt i;
3577: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3578: if (nz == MAT_SKIP_ALLOCATION) {
3579: skipallocation = PETSC_TRUE;
3580: nz = 0;
3581: }
3582: PetscLayoutSetUp(B->rmap);
3583: PetscLayoutSetUp(B->cmap);
3585: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3586: if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
3587: if (nnz) {
3588: for (i=0; i<B->rmap->n; i++) {
3589: if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
3590: if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %D value %d rowlength %D",i,nnz[i],B->cmap->n);
3591: }
3592: }
3594: B->preallocated = PETSC_TRUE;
3596: b = (Mat_SeqAIJ*)B->data;
3598: if (!skipallocation) {
3599: if (!b->imax) {
3600: PetscMalloc2(B->rmap->n,&b->imax,B->rmap->n,&b->ilen);
3601: PetscLogObjectMemory((PetscObject)B,2*B->rmap->n*sizeof(PetscInt));
3602: }
3603: if (!b->ipre) {
3604: PetscMalloc1(B->rmap->n,&b->ipre);
3605: PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
3606: }
3607: if (!nnz) {
3608: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3609: else if (nz < 0) nz = 1;
3610: for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3611: nz = nz*B->rmap->n;
3612: } else {
3613: nz = 0;
3614: for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3615: }
3616: /* b->ilen will count nonzeros in each row so far. */
3617: for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0;
3619: /* allocate the matrix space */
3620: /* FIXME: should B's old memory be unlogged? */
3621: MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3622: if (B->structure_only) {
3623: PetscMalloc1(nz,&b->j);
3624: PetscMalloc1(B->rmap->n+1,&b->i);
3625: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
3626: } else {
3627: PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
3628: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3629: }
3630: b->i[0] = 0;
3631: for (i=1; i<B->rmap->n+1; i++) {
3632: b->i[i] = b->i[i-1] + b->imax[i-1];
3633: }
3634: if (B->structure_only) {
3635: b->singlemalloc = PETSC_FALSE;
3636: b->free_a = PETSC_FALSE;
3637: } else {
3638: b->singlemalloc = PETSC_TRUE;
3639: b->free_a = PETSC_TRUE;
3640: }
3641: b->free_ij = PETSC_TRUE;
3642: } else {
3643: b->free_a = PETSC_FALSE;
3644: b->free_ij = PETSC_FALSE;
3645: }
3647: if (b->ipre && nnz != b->ipre && b->imax) {
3648: /* reserve user-requested sparsity */
3649: PetscMemcpy(b->ipre,b->imax,B->rmap->n*sizeof(PetscInt));
3650: }
3653: b->nz = 0;
3654: b->maxnz = nz;
3655: B->info.nz_unneeded = (double)b->maxnz;
3656: if (realalloc) {
3657: MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
3658: }
3659: B->was_assembled = PETSC_FALSE;
3660: B->assembled = PETSC_FALSE;
3661: return(0);
3662: }
3665: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
3666: {
3667: Mat_SeqAIJ *a;
3668: PetscInt i;
3673: a = (Mat_SeqAIJ*)A->data;
3674: /* if no saved info, we error out */
3675: if (!a->ipre) SETERRQ(PETSC_COMM_WORLD,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");
3677: if (!a->i || !a->j || !a->a || !a->imax || !a->ilen) SETERRQ(PETSC_COMM_WORLD,PETSC_ERR_ARG_NULL,"Memory info is incomplete, and can not reset preallocation \n");
3679: PetscMemcpy(a->imax,a->ipre,A->rmap->n*sizeof(PetscInt));
3680: PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
3681: a->i[0] = 0;
3682: for (i=1; i<A->rmap->n+1; i++) {
3683: a->i[i] = a->i[i-1] + a->imax[i-1];
3684: }
3685: A->preallocated = PETSC_TRUE;
3686: a->nz = 0;
3687: a->maxnz = a->i[A->rmap->n];
3688: A->info.nz_unneeded = (double)a->maxnz;
3689: A->was_assembled = PETSC_FALSE;
3690: A->assembled = PETSC_FALSE;
3691: return(0);
3692: }
3694: /*@
3695: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.
3697: Input Parameters:
3698: + B - the matrix
3699: . i - the indices into j for the start of each row (starts with zero)
3700: . j - the column indices for each row (starts with zero) these must be sorted for each row
3701: - v - optional values in the matrix
3703: Level: developer
3705: The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()
3707: .keywords: matrix, aij, compressed row, sparse, sequential
3709: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ
3710: @*/
3711: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3712: {
3718: PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3719: return(0);
3720: }
3722: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3723: {
3724: PetscInt i;
3725: PetscInt m,n;
3726: PetscInt nz;
3727: PetscInt *nnz, nz_max = 0;
3728: PetscScalar *values;
3732: if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
3734: PetscLayoutSetUp(B->rmap);
3735: PetscLayoutSetUp(B->cmap);
3737: MatGetSize(B, &m, &n);
3738: PetscMalloc1(m+1, &nnz);
3739: for (i = 0; i < m; i++) {
3740: nz = Ii[i+1]- Ii[i];
3741: nz_max = PetscMax(nz_max, nz);
3742: if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3743: nnz[i] = nz;
3744: }
3745: MatSeqAIJSetPreallocation(B, 0, nnz);
3746: PetscFree(nnz);
3748: if (v) {
3749: values = (PetscScalar*) v;
3750: } else {
3751: PetscCalloc1(nz_max, &values);
3752: }
3754: for (i = 0; i < m; i++) {
3755: nz = Ii[i+1] - Ii[i];
3756: MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3757: }
3759: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3760: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3762: if (!v) {
3763: PetscFree(values);
3764: }
3765: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3766: return(0);
3767: }
3769: #include <../src/mat/impls/dense/seq/dense.h>
3770: #include <petsc/private/kernels/petscaxpy.h>
3772: /*
3773: Computes (B'*A')' since computing B*A directly is untenable
3775: n p p
3776: ( ) ( ) ( )
3777: m ( A ) * n ( B ) = m ( C )
3778: ( ) ( ) ( )
3780: */
3781: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
3782: {
3783: PetscErrorCode ierr;
3784: Mat_SeqDense *sub_a = (Mat_SeqDense*)A->data;
3785: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ*)B->data;
3786: Mat_SeqDense *sub_c = (Mat_SeqDense*)C->data;
3787: PetscInt i,n,m,q,p;
3788: const PetscInt *ii,*idx;
3789: const PetscScalar *b,*a,*a_q;
3790: PetscScalar *c,*c_q;
3793: m = A->rmap->n;
3794: n = A->cmap->n;
3795: p = B->cmap->n;
3796: a = sub_a->v;
3797: b = sub_b->a;
3798: c = sub_c->v;
3799: PetscMemzero(c,m*p*sizeof(PetscScalar));
3801: ii = sub_b->i;
3802: idx = sub_b->j;
3803: for (i=0; i<n; i++) {
3804: q = ii[i+1] - ii[i];
3805: while (q-->0) {
3806: c_q = c + m*(*idx);
3807: a_q = a + m*i;
3808: PetscKernelAXPY(c_q,*b,a_q,m);
3809: idx++;
3810: b++;
3811: }
3812: }
3813: return(0);
3814: }
3816: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
3817: {
3819: PetscInt m=A->rmap->n,n=B->cmap->n;
3820: Mat Cmat;
3823: if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %D != B->rmap->n %D\n",A->cmap->n,B->rmap->n);
3824: MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
3825: MatSetSizes(Cmat,m,n,m,n);
3826: MatSetBlockSizesFromMats(Cmat,A,B);
3827: MatSetType(Cmat,MATSEQDENSE);
3828: MatSeqDenseSetPreallocation(Cmat,NULL);
3830: Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
3832: *C = Cmat;
3833: return(0);
3834: }
3836: /* ----------------------------------------------------------------*/
3837: PETSC_INTERN PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
3838: {
3842: if (scall == MAT_INITIAL_MATRIX) {
3843: PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
3844: MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
3845: PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
3846: }
3847: PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
3848: MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
3849: PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
3850: return(0);
3851: }
3854: /*MC
3855: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
3856: based on compressed sparse row format.
3858: Options Database Keys:
3859: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
3861: Level: beginner
3863: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3864: M*/
3866: /*MC
3867: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
3869: This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
3870: and MATMPIAIJ otherwise. As a result, for single process communicators,
3871: MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
3872: for communicators controlling multiple processes. It is recommended that you call both of
3873: the above preallocation routines for simplicity.
3875: Options Database Keys:
3876: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()
3878: Developer Notes: Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
3879: enough exist.
3881: Level: beginner
3883: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
3884: M*/
3886: /*MC
3887: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
3889: This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
3890: and MATMPIAIJCRL otherwise. As a result, for single process communicators,
3891: MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
3892: for communicators controlling multiple processes. It is recommended that you call both of
3893: the above preallocation routines for simplicity.
3895: Options Database Keys:
3896: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()
3898: Level: beginner
3900: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
3901: M*/
3903: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
3904: #if defined(PETSC_HAVE_ELEMENTAL)
3905: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
3906: #endif
3907: #if defined(PETSC_HAVE_HYPRE)
3908: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
3909: PETSC_INTERN PetscErrorCode MatMatMatMult_Transpose_AIJ_AIJ(Mat,Mat,Mat,MatReuse,PetscReal,Mat*);
3910: #endif
3911: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*);
3913: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3914: PETSC_EXTERN PetscErrorCode MatlabEnginePut_SeqAIJ(PetscObject,void*);
3915: PETSC_EXTERN PetscErrorCode MatlabEngineGet_SeqAIJ(PetscObject,void*);
3916: #endif
3918: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
3920: /*@C
3921: MatSeqAIJGetArray - gives access to the array where the data for a MATSEQAIJ matrix is stored
3923: Not Collective
3925: Input Parameter:
3926: . mat - a MATSEQAIJ matrix
3928: Output Parameter:
3929: . array - pointer to the data
3931: Level: intermediate
3933: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3934: @*/
3935: PetscErrorCode MatSeqAIJGetArray(Mat A,PetscScalar **array)
3936: {
3940: PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
3941: return(0);
3942: }
3944: /*@C
3945: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
3947: Not Collective
3949: Input Parameter:
3950: . mat - a MATSEQAIJ matrix
3952: Output Parameter:
3953: . nz - the maximum number of nonzeros in any row
3955: Level: intermediate
3957: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3958: @*/
3959: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
3960: {
3961: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data;
3964: *nz = aij->rmax;
3965: return(0);
3966: }
3968: /*@C
3969: MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()
3971: Not Collective
3973: Input Parameters:
3974: . mat - a MATSEQAIJ matrix
3975: . array - pointer to the data
3977: Level: intermediate
3979: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
3980: @*/
3981: PetscErrorCode MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
3982: {
3986: PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
3987: return(0);
3988: }
3990: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
3991: {
3992: Mat_SeqAIJ *b;
3994: PetscMPIInt size;
3997: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
3998: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
4000: PetscNewLog(B,&b);
4002: B->data = (void*)b;
4004: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4006: b->row = 0;
4007: b->col = 0;
4008: b->icol = 0;
4009: b->reallocs = 0;
4010: b->ignorezeroentries = PETSC_FALSE;
4011: b->roworiented = PETSC_TRUE;
4012: b->nonew = 0;
4013: b->diag = 0;
4014: b->solve_work = 0;
4015: B->spptr = 0;
4016: b->saved_values = 0;
4017: b->idiag = 0;
4018: b->mdiag = 0;
4019: b->ssor_work = 0;
4020: b->omega = 1.0;
4021: b->fshift = 0.0;
4022: b->idiagvalid = PETSC_FALSE;
4023: b->ibdiagvalid = PETSC_FALSE;
4024: b->keepnonzeropattern = PETSC_FALSE;
4026: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4027: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
4028: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);
4030: #if defined(PETSC_HAVE_MATLAB_ENGINE)
4031: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
4032: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
4033: #endif
4035: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
4036: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
4037: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
4038: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
4039: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
4040: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
4041: #if defined(PETSC_HAVE_MKL_SPARSE)
4042: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
4043: #endif
4044: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
4045: #if defined(PETSC_HAVE_ELEMENTAL)
4046: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
4047: #endif
4048: #if defined(PETSC_HAVE_HYPRE)
4049: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
4050: PetscObjectComposeFunction((PetscObject)B,"MatMatMatMult_transpose_seqaij_seqaij_C",MatMatMatMult_Transpose_AIJ_AIJ);
4051: #endif
4052: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
4053: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
4054: PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
4055: PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
4056: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
4057: PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
4058: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
4059: PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
4060: PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);
4061: PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);
4062: PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);
4063: MatCreate_SeqAIJ_Inode(B);
4064: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4065: MatSeqAIJSetTypeFromOptions(B); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4066: return(0);
4067: }
4069: /*
4070: Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4071: */
4072: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4073: {
4074: Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
4076: PetscInt i,m = A->rmap->n;
4079: c = (Mat_SeqAIJ*)C->data;
4081: C->factortype = A->factortype;
4082: c->row = 0;
4083: c->col = 0;
4084: c->icol = 0;
4085: c->reallocs = 0;
4087: C->assembled = PETSC_TRUE;
4089: PetscLayoutReference(A->rmap,&C->rmap);
4090: PetscLayoutReference(A->cmap,&C->cmap);
4092: PetscMalloc2(m,&c->imax,m,&c->ilen);
4093: PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));
4094: for (i=0; i<m; i++) {
4095: c->imax[i] = a->imax[i];
4096: c->ilen[i] = a->ilen[i];
4097: }
4099: /* allocate the matrix space */
4100: if (mallocmatspace) {
4101: PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4102: PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
4104: c->singlemalloc = PETSC_TRUE;
4106: PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
4107: if (m > 0) {
4108: PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
4109: if (cpvalues == MAT_COPY_VALUES) {
4110: PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
4111: } else {
4112: PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
4113: }
4114: }
4115: }
4117: c->ignorezeroentries = a->ignorezeroentries;
4118: c->roworiented = a->roworiented;
4119: c->nonew = a->nonew;
4120: if (a->diag) {
4121: PetscMalloc1(m+1,&c->diag);
4122: PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4123: for (i=0; i<m; i++) {
4124: c->diag[i] = a->diag[i];
4125: }
4126: } else c->diag = 0;
4128: c->solve_work = 0;
4129: c->saved_values = 0;
4130: c->idiag = 0;
4131: c->ssor_work = 0;
4132: c->keepnonzeropattern = a->keepnonzeropattern;
4133: c->free_a = PETSC_TRUE;
4134: c->free_ij = PETSC_TRUE;
4136: c->rmax = a->rmax;
4137: c->nz = a->nz;
4138: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
4139: C->preallocated = PETSC_TRUE;
4141: c->compressedrow.use = a->compressedrow.use;
4142: c->compressedrow.nrows = a->compressedrow.nrows;
4143: if (a->compressedrow.use) {
4144: i = a->compressedrow.nrows;
4145: PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4146: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
4147: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
4148: } else {
4149: c->compressedrow.use = PETSC_FALSE;
4150: c->compressedrow.i = NULL;
4151: c->compressedrow.rindex = NULL;
4152: }
4153: c->nonzerorowcnt = a->nonzerorowcnt;
4154: C->nonzerostate = A->nonzerostate;
4156: MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4157: PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4158: return(0);
4159: }
4161: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4162: {
4166: MatCreate(PetscObjectComm((PetscObject)A),B);
4167: MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4168: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4169: MatSetBlockSizesFromMats(*B,A,A);
4170: }
4171: MatSetType(*B,((PetscObject)A)->type_name);
4172: MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4173: return(0);
4174: }
4176: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4177: {
4178: Mat_SeqAIJ *a;
4180: PetscInt i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
4181: int fd;
4182: PetscMPIInt size;
4183: MPI_Comm comm;
4184: PetscInt bs = newMat->rmap->bs;
4187: /* force binary viewer to load .info file if it has not yet done so */
4188: PetscViewerSetUp(viewer);
4189: PetscObjectGetComm((PetscObject)viewer,&comm);
4190: MPI_Comm_size(comm,&size);
4191: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");
4193: PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
4194: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
4195: PetscOptionsEnd();
4196: if (bs < 0) bs = 1;
4197: MatSetBlockSize(newMat,bs);
4199: PetscViewerBinaryGetDescriptor(viewer,&fd);
4200: PetscBinaryRead(fd,header,4,PETSC_INT);
4201: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
4202: M = header[1]; N = header[2]; nz = header[3];
4204: if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
4206: /* read in row lengths */
4207: PetscMalloc1(M,&rowlengths);
4208: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
4210: /* check if sum of rowlengths is same as nz */
4211: for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
4212: if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %dD, sum-row-lengths = %D\n",nz,sum);
4214: /* set global size if not set already*/
4215: if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
4216: MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
4217: } else {
4218: /* if sizes and type are already set, check if the matrix global sizes are correct */
4219: MatGetSize(newMat,&rows,&cols);
4220: if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
4221: MatGetLocalSize(newMat,&rows,&cols);
4222: }
4223: if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);
4224: }
4225: MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
4226: a = (Mat_SeqAIJ*)newMat->data;
4228: PetscBinaryRead(fd,a->j,nz,PETSC_INT);
4230: /* read in nonzero values */
4231: PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);
4233: /* set matrix "i" values */
4234: a->i[0] = 0;
4235: for (i=1; i<= M; i++) {
4236: a->i[i] = a->i[i-1] + rowlengths[i-1];
4237: a->ilen[i-1] = rowlengths[i-1];
4238: }
4239: PetscFree(rowlengths);
4241: MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
4242: MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
4243: return(0);
4244: }
4246: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4247: {
4248: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4250: #if defined(PETSC_USE_COMPLEX)
4251: PetscInt k;
4252: #endif
4255: /* If the matrix dimensions are not equal,or no of nonzeros */
4256: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4257: *flg = PETSC_FALSE;
4258: return(0);
4259: }
4261: /* if the a->i are the same */
4262: PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
4263: if (!*flg) return(0);
4265: /* if a->j are the same */
4266: PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
4267: if (!*flg) return(0);
4269: /* if a->a are the same */
4270: #if defined(PETSC_USE_COMPLEX)
4271: for (k=0; k<a->nz; k++) {
4272: if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4273: *flg = PETSC_FALSE;
4274: return(0);
4275: }
4276: }
4277: #else
4278: PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
4279: #endif
4280: return(0);
4281: }
4283: /*@
4284: MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4285: provided by the user.
4287: Collective on MPI_Comm
4289: Input Parameters:
4290: + comm - must be an MPI communicator of size 1
4291: . m - number of rows
4292: . n - number of columns
4293: . i - row indices
4294: . j - column indices
4295: - a - matrix values
4297: Output Parameter:
4298: . mat - the matrix
4300: Level: intermediate
4302: Notes:
4303: The i, j, and a arrays are not copied by this routine, the user must free these arrays
4304: once the matrix is destroyed and not before
4306: You cannot set new nonzero locations into this matrix, that will generate an error.
4308: The i and j indices are 0 based
4310: The format which is used for the sparse matrix input, is equivalent to a
4311: row-major ordering.. i.e for the following matrix, the input data expected is
4312: as shown
4314: $ 1 0 0
4315: $ 2 0 3
4316: $ 4 5 6
4317: $
4318: $ i = {0,1,3,6} [size = nrow+1 = 3+1]
4319: $ j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
4320: $ v = {1,2,3,4,5,6} [size = 6]
4323: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4325: @*/
4326: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
4327: {
4329: PetscInt ii;
4330: Mat_SeqAIJ *aij;
4331: #if defined(PETSC_USE_DEBUG)
4332: PetscInt jj;
4333: #endif
4336: if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4337: MatCreate(comm,mat);
4338: MatSetSizes(*mat,m,n,m,n);
4339: /* MatSetBlockSizes(*mat,,); */
4340: MatSetType(*mat,MATSEQAIJ);
4341: MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4342: aij = (Mat_SeqAIJ*)(*mat)->data;
4343: PetscMalloc2(m,&aij->imax,m,&aij->ilen);
4345: aij->i = i;
4346: aij->j = j;
4347: aij->a = a;
4348: aij->singlemalloc = PETSC_FALSE;
4349: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4350: aij->free_a = PETSC_FALSE;
4351: aij->free_ij = PETSC_FALSE;
4353: for (ii=0; ii<m; ii++) {
4354: aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4355: #if defined(PETSC_USE_DEBUG)
4356: if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %D length = %D",ii,i[ii+1] - i[ii]);
4357: for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4358: if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
4359: if (j[jj] == j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
4360: }
4361: #endif
4362: }
4363: #if defined(PETSC_USE_DEBUG)
4364: for (ii=0; ii<aij->i[m]; ii++) {
4365: if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4366: if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %D index = %D",ii,j[ii]);
4367: }
4368: #endif
4370: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4371: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4372: return(0);
4373: }
4374: /*@C
4375: MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4376: provided by the user.
4378: Collective on MPI_Comm
4380: Input Parameters:
4381: + comm - must be an MPI communicator of size 1
4382: . m - number of rows
4383: . n - number of columns
4384: . i - row indices
4385: . j - column indices
4386: . a - matrix values
4387: . nz - number of nonzeros
4388: - idx - 0 or 1 based
4390: Output Parameter:
4391: . mat - the matrix
4393: Level: intermediate
4395: Notes:
4396: The i and j indices are 0 based
4398: The format which is used for the sparse matrix input, is equivalent to a
4399: row-major ordering.. i.e for the following matrix, the input data expected is
4400: as shown:
4402: 1 0 0
4403: 2 0 3
4404: 4 5 6
4406: i = {0,1,1,2,2,2}
4407: j = {0,0,2,0,1,2}
4408: v = {1,2,3,4,5,6}
4411: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4413: @*/
4414: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
4415: {
4417: PetscInt ii, *nnz, one = 1,row,col;
4421: PetscCalloc1(m,&nnz);
4422: for (ii = 0; ii < nz; ii++) {
4423: nnz[i[ii] - !!idx] += 1;
4424: }
4425: MatCreate(comm,mat);
4426: MatSetSizes(*mat,m,n,m,n);
4427: MatSetType(*mat,MATSEQAIJ);
4428: MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4429: for (ii = 0; ii < nz; ii++) {
4430: if (idx) {
4431: row = i[ii] - 1;
4432: col = j[ii] - 1;
4433: } else {
4434: row = i[ii];
4435: col = j[ii];
4436: }
4437: MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4438: }
4439: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4440: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4441: PetscFree(nnz);
4442: return(0);
4443: }
4445: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4446: {
4447: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data;
4451: a->idiagvalid = PETSC_FALSE;
4452: a->ibdiagvalid = PETSC_FALSE;
4454: MatSeqAIJInvalidateDiagonal_Inode(A);
4455: return(0);
4456: }
4458: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4459: {
4461: PetscMPIInt size;
4464: MPI_Comm_size(comm,&size);
4465: if (size == 1) {
4466: if (scall == MAT_INITIAL_MATRIX) {
4467: MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
4468: } else {
4469: MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
4470: }
4471: } else {
4472: MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
4473: }
4474: return(0);
4475: }
4477: /*
4478: Permute A into C's *local* index space using rowemb,colemb.
4479: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
4480: of [0,m), colemb is in [0,n).
4481: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
4482: */
4483: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
4484: {
4485: /* If making this function public, change the error returned in this function away from _PLIB. */
4487: Mat_SeqAIJ *Baij;
4488: PetscBool seqaij;
4489: PetscInt m,n,*nz,i,j,count;
4490: PetscScalar v;
4491: const PetscInt *rowindices,*colindices;
4494: if (!B) return(0);
4495: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
4496: PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
4497: if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
4498: if (rowemb) {
4499: ISGetLocalSize(rowemb,&m);
4500: if (m != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Row IS of size %D is incompatible with matrix row size %D",m,B->rmap->n);
4501: } else {
4502: if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
4503: }
4504: if (colemb) {
4505: ISGetLocalSize(colemb,&n);
4506: if (n != B->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Diag col IS of size %D is incompatible with input matrix col size %D",n,B->cmap->n);
4507: } else {
4508: if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
4509: }
4511: Baij = (Mat_SeqAIJ*)(B->data);
4512: if (pattern == DIFFERENT_NONZERO_PATTERN) {
4513: PetscMalloc1(B->rmap->n,&nz);
4514: for (i=0; i<B->rmap->n; i++) {
4515: nz[i] = Baij->i[i+1] - Baij->i[i];
4516: }
4517: MatSeqAIJSetPreallocation(C,0,nz);
4518: PetscFree(nz);
4519: }
4520: if (pattern == SUBSET_NONZERO_PATTERN) {
4521: MatZeroEntries(C);
4522: }
4523: count = 0;
4524: rowindices = NULL;
4525: colindices = NULL;
4526: if (rowemb) {
4527: ISGetIndices(rowemb,&rowindices);
4528: }
4529: if (colemb) {
4530: ISGetIndices(colemb,&colindices);
4531: }
4532: for (i=0; i<B->rmap->n; i++) {
4533: PetscInt row;
4534: row = i;
4535: if (rowindices) row = rowindices[i];
4536: for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
4537: PetscInt col;
4538: col = Baij->j[count];
4539: if (colindices) col = colindices[col];
4540: v = Baij->a[count];
4541: MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
4542: ++count;
4543: }
4544: }
4545: /* FIXME: set C's nonzerostate correctly. */
4546: /* Assembly for C is necessary. */
4547: C->preallocated = PETSC_TRUE;
4548: C->assembled = PETSC_TRUE;
4549: C->was_assembled = PETSC_FALSE;
4550: return(0);
4551: }
4553: PetscFunctionList MatSeqAIJList = NULL;
4555: /*@C
4556: MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype
4558: Collective on Mat
4560: Input Parameters:
4561: + mat - the matrix object
4562: - matype - matrix type
4564: Options Database Key:
4565: . -mat_seqai_type <method> - for example seqaijcrl
4568: Level: intermediate
4570: .keywords: Mat, MatType, set, method
4572: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
4573: @*/
4574: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
4575: {
4576: PetscErrorCode ierr,(*r)(Mat,const MatType,MatReuse,Mat*);
4577: PetscBool sametype;
4581: PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
4582: if (sametype) return(0);
4584: PetscFunctionListFind(MatSeqAIJList,matype,&r);
4585: if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
4586: (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
4587: return(0);
4588: }
4591: /*@C
4592: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential AIJ matrices
4594: Not Collective
4596: Input Parameters:
4597: + name - name of a new user-defined matrix type, for example MATSEQAIJCRL
4598: - function - routine to convert to subtype
4600: Notes:
4601: MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.
4604: Then, your matrix can be chosen with the procedural interface at runtime via the option
4605: $ -mat_seqaij_type my_mat
4607: Level: advanced
4609: .keywords: Mat, register
4611: .seealso: MatSeqAIJRegisterAll()
4614: Level: advanced
4615: @*/
4616: PetscErrorCode MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
4617: {
4621: PetscFunctionListAdd(&MatSeqAIJList,sname,function);
4622: return(0);
4623: }
4625: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
4627: /*@C
4628: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ
4630: Not Collective
4632: Level: advanced
4634: Developers Note: CUSP and CUSPARSE do not yet support the MatConvert_SeqAIJ..() paradigm and thus cannot be registered here
4636: .keywords: KSP, register, all
4638: .seealso: MatRegisterAll(), MatSeqAIJRegister()
4639: @*/
4640: PetscErrorCode MatSeqAIJRegisterAll(void)
4641: {
4645: if (MatSeqAIJRegisterAllCalled) return(0);
4646: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
4648: MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL);
4649: MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM);
4650: #if defined(PETSC_HAVE_MKL_SPARSE)
4651: MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL);
4652: #endif
4653: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
4654: MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
4655: #endif
4656: return(0);
4657: }
4659: /*
4660: Special version for direct calls from Fortran
4661: */
4662: #include <petsc/private/fortranimpl.h>
4663: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4664: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4665: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4666: #define matsetvaluesseqaij_ matsetvaluesseqaij
4667: #endif
4669: /* Change these macros so can be used in void function */
4670: #undef CHKERRQ
4671: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
4672: #undef SETERRQ2
4673: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
4674: #undef SETERRQ3
4675: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)
4677: PETSC_EXTERN void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
4678: {
4679: Mat A = *AA;
4680: PetscInt m = *mm, n = *nn;
4681: InsertMode is = *isis;
4682: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
4683: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4684: PetscInt *imax,*ai,*ailen;
4686: PetscInt *aj,nonew = a->nonew,lastcol = -1;
4687: MatScalar *ap,value,*aa;
4688: PetscBool ignorezeroentries = a->ignorezeroentries;
4689: PetscBool roworiented = a->roworiented;
4692: MatCheckPreallocated(A,1);
4693: imax = a->imax;
4694: ai = a->i;
4695: ailen = a->ilen;
4696: aj = a->j;
4697: aa = a->a;
4699: for (k=0; k<m; k++) { /* loop over added rows */
4700: row = im[k];
4701: if (row < 0) continue;
4702: #if defined(PETSC_USE_DEBUG)
4703: if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4704: #endif
4705: rp = aj + ai[row]; ap = aa + ai[row];
4706: rmax = imax[row]; nrow = ailen[row];
4707: low = 0;
4708: high = nrow;
4709: for (l=0; l<n; l++) { /* loop over added columns */
4710: if (in[l] < 0) continue;
4711: #if defined(PETSC_USE_DEBUG)
4712: if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4713: #endif
4714: col = in[l];
4715: if (roworiented) value = v[l + k*n];
4716: else value = v[k + l*m];
4718: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
4720: if (col <= lastcol) low = 0;
4721: else high = nrow;
4722: lastcol = col;
4723: while (high-low > 5) {
4724: t = (low+high)/2;
4725: if (rp[t] > col) high = t;
4726: else low = t;
4727: }
4728: for (i=low; i<high; i++) {
4729: if (rp[i] > col) break;
4730: if (rp[i] == col) {
4731: if (is == ADD_VALUES) ap[i] += value;
4732: else ap[i] = value;
4733: goto noinsert;
4734: }
4735: }
4736: if (value == 0.0 && ignorezeroentries) goto noinsert;
4737: if (nonew == 1) goto noinsert;
4738: if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
4739: MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
4740: N = nrow++ - 1; a->nz++; high++;
4741: /* shift up all the later entries in this row */
4742: for (ii=N; ii>=i; ii--) {
4743: rp[ii+1] = rp[ii];
4744: ap[ii+1] = ap[ii];
4745: }
4746: rp[i] = col;
4747: ap[i] = value;
4748: A->nonzerostate++;
4749: noinsert:;
4750: low = i + 1;
4751: }
4752: ailen[row] = nrow;
4753: }
4754: PetscFunctionReturnVoid();
4755: }