Actual source code: aij.c
petsc-3.11.4 2019-09-28
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,"MatConvert_seqaij_seqsell_C",NULL);
1125: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_is_C",NULL);
1126: PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1127: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1128: PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
1129: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1130: PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1131: PetscObjectComposeFunction((PetscObject)A,"MatPtAP_is_seqaij_C",NULL);
1132: return(0);
1133: }
1135: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1136: {
1137: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1141: switch (op) {
1142: case MAT_ROW_ORIENTED:
1143: a->roworiented = flg;
1144: break;
1145: case MAT_KEEP_NONZERO_PATTERN:
1146: a->keepnonzeropattern = flg;
1147: break;
1148: case MAT_NEW_NONZERO_LOCATIONS:
1149: a->nonew = (flg ? 0 : 1);
1150: break;
1151: case MAT_NEW_NONZERO_LOCATION_ERR:
1152: a->nonew = (flg ? -1 : 0);
1153: break;
1154: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1155: a->nonew = (flg ? -2 : 0);
1156: break;
1157: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1158: a->nounused = (flg ? -1 : 0);
1159: break;
1160: case MAT_IGNORE_ZERO_ENTRIES:
1161: a->ignorezeroentries = flg;
1162: break;
1163: case MAT_SPD:
1164: case MAT_SYMMETRIC:
1165: case MAT_STRUCTURALLY_SYMMETRIC:
1166: case MAT_HERMITIAN:
1167: case MAT_SYMMETRY_ETERNAL:
1168: case MAT_STRUCTURE_ONLY:
1169: /* These options are handled directly by MatSetOption() */
1170: break;
1171: case MAT_NEW_DIAGONALS:
1172: case MAT_IGNORE_OFF_PROC_ENTRIES:
1173: case MAT_USE_HASH_TABLE:
1174: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1175: break;
1176: case MAT_USE_INODES:
1177: /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1178: break;
1179: case MAT_SUBMAT_SINGLEIS:
1180: A->submat_singleis = flg;
1181: break;
1182: default:
1183: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1184: }
1185: MatSetOption_SeqAIJ_Inode(A,op,flg);
1186: return(0);
1187: }
1189: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1190: {
1191: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1193: PetscInt i,j,n,*ai=a->i,*aj=a->j,nz;
1194: PetscScalar *aa=a->a,*x,zero=0.0;
1197: VecGetLocalSize(v,&n);
1198: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1200: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1201: PetscInt *diag=a->diag;
1202: VecGetArray(v,&x);
1203: for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1204: VecRestoreArray(v,&x);
1205: return(0);
1206: }
1208: VecSet(v,zero);
1209: VecGetArray(v,&x);
1210: for (i=0; i<n; i++) {
1211: nz = ai[i+1] - ai[i];
1212: if (!nz) x[i] = 0.0;
1213: for (j=ai[i]; j<ai[i+1]; j++) {
1214: if (aj[j] == i) {
1215: x[i] = aa[j];
1216: break;
1217: }
1218: }
1219: }
1220: VecRestoreArray(v,&x);
1221: return(0);
1222: }
1224: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1225: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1226: {
1227: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1228: PetscScalar *y;
1229: const PetscScalar *x;
1230: PetscErrorCode ierr;
1231: PetscInt m = A->rmap->n;
1232: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1233: const MatScalar *v;
1234: PetscScalar alpha;
1235: PetscInt n,i,j;
1236: const PetscInt *idx,*ii,*ridx=NULL;
1237: Mat_CompressedRow cprow = a->compressedrow;
1238: PetscBool usecprow = cprow.use;
1239: #endif
1242: if (zz != yy) {VecCopy(zz,yy);}
1243: VecGetArrayRead(xx,&x);
1244: VecGetArray(yy,&y);
1246: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1247: fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1248: #else
1249: if (usecprow) {
1250: m = cprow.nrows;
1251: ii = cprow.i;
1252: ridx = cprow.rindex;
1253: } else {
1254: ii = a->i;
1255: }
1256: for (i=0; i<m; i++) {
1257: idx = a->j + ii[i];
1258: v = a->a + ii[i];
1259: n = ii[i+1] - ii[i];
1260: if (usecprow) {
1261: alpha = x[ridx[i]];
1262: } else {
1263: alpha = x[i];
1264: }
1265: for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1266: }
1267: #endif
1268: PetscLogFlops(2.0*a->nz);
1269: VecRestoreArrayRead(xx,&x);
1270: VecRestoreArray(yy,&y);
1271: return(0);
1272: }
1274: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1275: {
1279: VecSet(yy,0.0);
1280: MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1281: return(0);
1282: }
1284: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1286: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1287: {
1288: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1289: PetscScalar *y;
1290: const PetscScalar *x;
1291: const MatScalar *aa;
1292: PetscErrorCode ierr;
1293: PetscInt m=A->rmap->n;
1294: const PetscInt *aj,*ii,*ridx=NULL;
1295: PetscInt n,i;
1296: PetscScalar sum;
1297: PetscBool usecprow=a->compressedrow.use;
1299: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1300: #pragma disjoint(*x,*y,*aa)
1301: #endif
1304: VecGetArrayRead(xx,&x);
1305: VecGetArray(yy,&y);
1306: ii = a->i;
1307: if (usecprow) { /* use compressed row format */
1308: PetscMemzero(y,m*sizeof(PetscScalar));
1309: m = a->compressedrow.nrows;
1310: ii = a->compressedrow.i;
1311: ridx = a->compressedrow.rindex;
1312: for (i=0; i<m; i++) {
1313: n = ii[i+1] - ii[i];
1314: aj = a->j + ii[i];
1315: aa = a->a + ii[i];
1316: sum = 0.0;
1317: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1318: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1319: y[*ridx++] = sum;
1320: }
1321: } else { /* do not use compressed row format */
1322: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1323: aj = a->j;
1324: aa = a->a;
1325: fortranmultaij_(&m,x,ii,aj,aa,y);
1326: #else
1327: for (i=0; i<m; i++) {
1328: n = ii[i+1] - ii[i];
1329: aj = a->j + ii[i];
1330: aa = a->a + ii[i];
1331: sum = 0.0;
1332: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1333: y[i] = sum;
1334: }
1335: #endif
1336: }
1337: PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1338: VecRestoreArrayRead(xx,&x);
1339: VecRestoreArray(yy,&y);
1340: return(0);
1341: }
1343: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1344: {
1345: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1346: PetscScalar *y;
1347: const PetscScalar *x;
1348: const MatScalar *aa;
1349: PetscErrorCode ierr;
1350: PetscInt m=A->rmap->n;
1351: const PetscInt *aj,*ii,*ridx=NULL;
1352: PetscInt n,i,nonzerorow=0;
1353: PetscScalar sum;
1354: PetscBool usecprow=a->compressedrow.use;
1356: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1357: #pragma disjoint(*x,*y,*aa)
1358: #endif
1361: VecGetArrayRead(xx,&x);
1362: VecGetArray(yy,&y);
1363: if (usecprow) { /* use compressed row format */
1364: m = a->compressedrow.nrows;
1365: ii = a->compressedrow.i;
1366: ridx = a->compressedrow.rindex;
1367: for (i=0; i<m; i++) {
1368: n = ii[i+1] - ii[i];
1369: aj = a->j + ii[i];
1370: aa = a->a + ii[i];
1371: sum = 0.0;
1372: nonzerorow += (n>0);
1373: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1374: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1375: y[*ridx++] = sum;
1376: }
1377: } else { /* do not use compressed row format */
1378: ii = a->i;
1379: for (i=0; i<m; i++) {
1380: n = ii[i+1] - ii[i];
1381: aj = a->j + ii[i];
1382: aa = a->a + ii[i];
1383: sum = 0.0;
1384: nonzerorow += (n>0);
1385: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1386: y[i] = sum;
1387: }
1388: }
1389: PetscLogFlops(2.0*a->nz - nonzerorow);
1390: VecRestoreArrayRead(xx,&x);
1391: VecRestoreArray(yy,&y);
1392: return(0);
1393: }
1395: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1396: {
1397: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1398: PetscScalar *y,*z;
1399: const PetscScalar *x;
1400: const MatScalar *aa;
1401: PetscErrorCode ierr;
1402: PetscInt m = A->rmap->n,*aj,*ii;
1403: PetscInt n,i,*ridx=NULL;
1404: PetscScalar sum;
1405: PetscBool usecprow=a->compressedrow.use;
1408: VecGetArrayRead(xx,&x);
1409: VecGetArrayPair(yy,zz,&y,&z);
1410: if (usecprow) { /* use compressed row format */
1411: if (zz != yy) {
1412: PetscMemcpy(z,y,m*sizeof(PetscScalar));
1413: }
1414: m = a->compressedrow.nrows;
1415: ii = a->compressedrow.i;
1416: ridx = a->compressedrow.rindex;
1417: for (i=0; i<m; i++) {
1418: n = ii[i+1] - ii[i];
1419: aj = a->j + ii[i];
1420: aa = a->a + ii[i];
1421: sum = y[*ridx];
1422: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1423: z[*ridx++] = sum;
1424: }
1425: } else { /* do not use compressed row format */
1426: ii = a->i;
1427: for (i=0; i<m; i++) {
1428: n = ii[i+1] - ii[i];
1429: aj = a->j + ii[i];
1430: aa = a->a + ii[i];
1431: sum = y[i];
1432: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1433: z[i] = sum;
1434: }
1435: }
1436: PetscLogFlops(2.0*a->nz);
1437: VecRestoreArrayRead(xx,&x);
1438: VecRestoreArrayPair(yy,zz,&y,&z);
1439: return(0);
1440: }
1442: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1443: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1444: {
1445: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1446: PetscScalar *y,*z;
1447: const PetscScalar *x;
1448: const MatScalar *aa;
1449: PetscErrorCode ierr;
1450: const PetscInt *aj,*ii,*ridx=NULL;
1451: PetscInt m = A->rmap->n,n,i;
1452: PetscScalar sum;
1453: PetscBool usecprow=a->compressedrow.use;
1456: VecGetArrayRead(xx,&x);
1457: VecGetArrayPair(yy,zz,&y,&z);
1458: if (usecprow) { /* use compressed row format */
1459: if (zz != yy) {
1460: PetscMemcpy(z,y,m*sizeof(PetscScalar));
1461: }
1462: m = a->compressedrow.nrows;
1463: ii = a->compressedrow.i;
1464: ridx = a->compressedrow.rindex;
1465: for (i=0; i<m; i++) {
1466: n = ii[i+1] - ii[i];
1467: aj = a->j + ii[i];
1468: aa = a->a + ii[i];
1469: sum = y[*ridx];
1470: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1471: z[*ridx++] = sum;
1472: }
1473: } else { /* do not use compressed row format */
1474: ii = a->i;
1475: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1476: aj = a->j;
1477: aa = a->a;
1478: fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1479: #else
1480: for (i=0; i<m; i++) {
1481: n = ii[i+1] - ii[i];
1482: aj = a->j + ii[i];
1483: aa = a->a + ii[i];
1484: sum = y[i];
1485: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1486: z[i] = sum;
1487: }
1488: #endif
1489: }
1490: PetscLogFlops(2.0*a->nz);
1491: VecRestoreArrayRead(xx,&x);
1492: VecRestoreArrayPair(yy,zz,&y,&z);
1493: return(0);
1494: }
1496: /*
1497: Adds diagonal pointers to sparse matrix structure.
1498: */
1499: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1500: {
1501: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1503: PetscInt i,j,m = A->rmap->n;
1506: if (!a->diag) {
1507: PetscMalloc1(m,&a->diag);
1508: PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1509: }
1510: for (i=0; i<A->rmap->n; i++) {
1511: a->diag[i] = a->i[i+1];
1512: for (j=a->i[i]; j<a->i[i+1]; j++) {
1513: if (a->j[j] == i) {
1514: a->diag[i] = j;
1515: break;
1516: }
1517: }
1518: }
1519: return(0);
1520: }
1522: PetscErrorCode MatShift_SeqAIJ(Mat A,PetscScalar v)
1523: {
1524: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1525: const PetscInt *diag = (const PetscInt*)a->diag;
1526: const PetscInt *ii = (const PetscInt*) a->i;
1527: PetscInt i,*mdiag = NULL;
1528: PetscErrorCode ierr;
1529: PetscInt cnt = 0; /* how many diagonals are missing */
1532: if (!A->preallocated || !a->nz) {
1533: MatSeqAIJSetPreallocation(A,1,NULL);
1534: MatShift_Basic(A,v);
1535: return(0);
1536: }
1538: if (a->diagonaldense) {
1539: cnt = 0;
1540: } else {
1541: PetscCalloc1(A->rmap->n,&mdiag);
1542: for (i=0; i<A->rmap->n; i++) {
1543: if (diag[i] >= ii[i+1]) {
1544: cnt++;
1545: mdiag[i] = 1;
1546: }
1547: }
1548: }
1549: if (!cnt) {
1550: MatShift_Basic(A,v);
1551: } else {
1552: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1553: PetscInt *oldj = a->j, *oldi = a->i;
1554: PetscBool singlemalloc = a->singlemalloc,free_a = a->free_a,free_ij = a->free_ij;
1556: a->a = NULL;
1557: a->j = NULL;
1558: a->i = NULL;
1559: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1560: for (i=0; i<A->rmap->n; i++) {
1561: a->imax[i] += mdiag[i];
1562: a->imax[i] = PetscMin(a->imax[i],A->cmap->n);
1563: }
1564: MatSeqAIJSetPreallocation_SeqAIJ(A,0,a->imax);
1566: /* copy old values into new matrix data structure */
1567: for (i=0; i<A->rmap->n; i++) {
1568: MatSetValues(A,1,&i,a->imax[i] - mdiag[i],&oldj[oldi[i]],&olda[oldi[i]],ADD_VALUES);
1569: if (i < A->cmap->n) {
1570: MatSetValue(A,i,i,v,ADD_VALUES);
1571: }
1572: }
1573: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1574: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1575: if (singlemalloc) {
1576: PetscFree3(olda,oldj,oldi);
1577: } else {
1578: if (free_a) {PetscFree(olda);}
1579: if (free_ij) {PetscFree(oldj);}
1580: if (free_ij) {PetscFree(oldi);}
1581: }
1582: }
1583: PetscFree(mdiag);
1584: a->diagonaldense = PETSC_TRUE;
1585: return(0);
1586: }
1588: /*
1589: Checks for missing diagonals
1590: */
1591: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool *missing,PetscInt *d)
1592: {
1593: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1594: PetscInt *diag,*ii = a->i,i;
1598: *missing = PETSC_FALSE;
1599: if (A->rmap->n > 0 && !ii) {
1600: *missing = PETSC_TRUE;
1601: if (d) *d = 0;
1602: PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1603: } else {
1604: PetscInt n;
1605: n = PetscMin(A->rmap->n, A->cmap->n);
1606: diag = a->diag;
1607: for (i=0; i<n; i++) {
1608: if (diag[i] >= ii[i+1]) {
1609: *missing = PETSC_TRUE;
1610: if (d) *d = i;
1611: PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1612: break;
1613: }
1614: }
1615: }
1616: return(0);
1617: }
1619: #include <petscblaslapack.h>
1620: #include <petsc/private/kernels/blockinvert.h>
1622: /*
1623: Note that values is allocated externally by the PC and then passed into this routine
1624: */
1625: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
1626: {
1627: PetscErrorCode ierr;
1628: PetscInt n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
1629: PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;
1630: const PetscReal shift = 0.0;
1631: PetscInt ipvt[5];
1632: PetscScalar work[25],*v_work;
1635: allowzeropivot = PetscNot(A->erroriffailure);
1636: for (i=0; i<nblocks; i++) ncnt += bsizes[i];
1637: if (ncnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Total blocksizes %D doesn't match number matrix rows %D",ncnt,n);
1638: for (i=0; i<nblocks; i++) {
1639: bsizemax = PetscMax(bsizemax,bsizes[i]);
1640: }
1641: PetscMalloc1(bsizemax,&indx);
1642: if (bsizemax > 7) {
1643: PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
1644: }
1645: ncnt = 0;
1646: for (i=0; i<nblocks; i++) {
1647: for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
1648: MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
1649: switch (bsizes[i]) {
1650: case 1:
1651: *diag = 1.0/(*diag);
1652: break;
1653: case 2:
1654: PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
1655: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1656: PetscKernel_A_gets_transpose_A_2(diag);
1657: break;
1658: case 3:
1659: PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
1660: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1661: PetscKernel_A_gets_transpose_A_3(diag);
1662: break;
1663: case 4:
1664: PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
1665: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1666: PetscKernel_A_gets_transpose_A_4(diag);
1667: break;
1668: case 5:
1669: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
1670: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1671: PetscKernel_A_gets_transpose_A_5(diag);
1672: break;
1673: case 6:
1674: PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
1675: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1676: PetscKernel_A_gets_transpose_A_6(diag);
1677: break;
1678: case 7:
1679: PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
1680: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1681: PetscKernel_A_gets_transpose_A_7(diag);
1682: break;
1683: default:
1684: PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
1685: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1686: PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
1687: }
1688: ncnt += bsizes[i];
1689: diag += bsizes[i]*bsizes[i];
1690: }
1691: if (bsizemax > 7) {
1692: PetscFree2(v_work,v_pivots);
1693: }
1694: PetscFree(indx);
1695: return(0);
1696: }
1698: /*
1699: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1700: */
1701: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1702: {
1703: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
1705: PetscInt i,*diag,m = A->rmap->n;
1706: MatScalar *v = a->a;
1707: PetscScalar *idiag,*mdiag;
1710: if (a->idiagvalid) return(0);
1711: MatMarkDiagonal_SeqAIJ(A);
1712: diag = a->diag;
1713: if (!a->idiag) {
1714: PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1715: PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
1716: v = a->a;
1717: }
1718: mdiag = a->mdiag;
1719: idiag = a->idiag;
1721: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1722: for (i=0; i<m; i++) {
1723: mdiag[i] = v[diag[i]];
1724: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1725: if (PetscRealPart(fshift)) {
1726: PetscInfo1(A,"Zero diagonal on row %D\n",i);
1727: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1728: A->factorerror_zeropivot_value = 0.0;
1729: A->factorerror_zeropivot_row = i;
1730: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1731: }
1732: idiag[i] = 1.0/v[diag[i]];
1733: }
1734: PetscLogFlops(m);
1735: } else {
1736: for (i=0; i<m; i++) {
1737: mdiag[i] = v[diag[i]];
1738: idiag[i] = omega/(fshift + v[diag[i]]);
1739: }
1740: PetscLogFlops(2.0*m);
1741: }
1742: a->idiagvalid = PETSC_TRUE;
1743: return(0);
1744: }
1746: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1747: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1748: {
1749: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1750: PetscScalar *x,d,sum,*t,scale;
1751: const MatScalar *v,*idiag=0,*mdiag;
1752: const PetscScalar *b, *bs,*xb, *ts;
1753: PetscErrorCode ierr;
1754: PetscInt n,m = A->rmap->n,i;
1755: const PetscInt *idx,*diag;
1758: its = its*lits;
1760: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1761: if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1762: a->fshift = fshift;
1763: a->omega = omega;
1765: diag = a->diag;
1766: t = a->ssor_work;
1767: idiag = a->idiag;
1768: mdiag = a->mdiag;
1770: VecGetArray(xx,&x);
1771: VecGetArrayRead(bb,&b);
1772: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1773: if (flag == SOR_APPLY_UPPER) {
1774: /* apply (U + D/omega) to the vector */
1775: bs = b;
1776: for (i=0; i<m; i++) {
1777: d = fshift + mdiag[i];
1778: n = a->i[i+1] - diag[i] - 1;
1779: idx = a->j + diag[i] + 1;
1780: v = a->a + diag[i] + 1;
1781: sum = b[i]*d/omega;
1782: PetscSparseDensePlusDot(sum,bs,v,idx,n);
1783: x[i] = sum;
1784: }
1785: VecRestoreArray(xx,&x);
1786: VecRestoreArrayRead(bb,&b);
1787: PetscLogFlops(a->nz);
1788: return(0);
1789: }
1791: if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1792: else if (flag & SOR_EISENSTAT) {
1793: /* Let A = L + U + D; where L is lower trianglar,
1794: U is upper triangular, E = D/omega; This routine applies
1796: (L + E)^{-1} A (U + E)^{-1}
1798: to a vector efficiently using Eisenstat's trick.
1799: */
1800: scale = (2.0/omega) - 1.0;
1802: /* x = (E + U)^{-1} b */
1803: for (i=m-1; i>=0; i--) {
1804: n = a->i[i+1] - diag[i] - 1;
1805: idx = a->j + diag[i] + 1;
1806: v = a->a + diag[i] + 1;
1807: sum = b[i];
1808: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1809: x[i] = sum*idiag[i];
1810: }
1812: /* t = b - (2*E - D)x */
1813: v = a->a;
1814: for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];
1816: /* t = (E + L)^{-1}t */
1817: ts = t;
1818: diag = a->diag;
1819: for (i=0; i<m; i++) {
1820: n = diag[i] - a->i[i];
1821: idx = a->j + a->i[i];
1822: v = a->a + a->i[i];
1823: sum = t[i];
1824: PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1825: t[i] = sum*idiag[i];
1826: /* x = x + t */
1827: x[i] += t[i];
1828: }
1830: PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1831: VecRestoreArray(xx,&x);
1832: VecRestoreArrayRead(bb,&b);
1833: return(0);
1834: }
1835: if (flag & SOR_ZERO_INITIAL_GUESS) {
1836: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1837: for (i=0; i<m; i++) {
1838: n = diag[i] - a->i[i];
1839: idx = a->j + a->i[i];
1840: v = a->a + a->i[i];
1841: sum = b[i];
1842: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1843: t[i] = sum;
1844: x[i] = sum*idiag[i];
1845: }
1846: xb = t;
1847: PetscLogFlops(a->nz);
1848: } else xb = b;
1849: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1850: for (i=m-1; i>=0; i--) {
1851: n = a->i[i+1] - diag[i] - 1;
1852: idx = a->j + diag[i] + 1;
1853: v = a->a + diag[i] + 1;
1854: sum = xb[i];
1855: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1856: if (xb == b) {
1857: x[i] = sum*idiag[i];
1858: } else {
1859: x[i] = (1-omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1860: }
1861: }
1862: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1863: }
1864: its--;
1865: }
1866: while (its--) {
1867: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1868: for (i=0; i<m; i++) {
1869: /* lower */
1870: n = diag[i] - a->i[i];
1871: idx = a->j + a->i[i];
1872: v = a->a + a->i[i];
1873: sum = b[i];
1874: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1875: t[i] = sum; /* save application of the lower-triangular part */
1876: /* upper */
1877: n = a->i[i+1] - diag[i] - 1;
1878: idx = a->j + diag[i] + 1;
1879: v = a->a + diag[i] + 1;
1880: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1881: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1882: }
1883: xb = t;
1884: PetscLogFlops(2.0*a->nz);
1885: } else xb = b;
1886: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1887: for (i=m-1; i>=0; i--) {
1888: sum = xb[i];
1889: if (xb == b) {
1890: /* whole matrix (no checkpointing available) */
1891: n = a->i[i+1] - a->i[i];
1892: idx = a->j + a->i[i];
1893: v = a->a + a->i[i];
1894: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1895: x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1896: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
1897: n = a->i[i+1] - diag[i] - 1;
1898: idx = a->j + diag[i] + 1;
1899: v = a->a + diag[i] + 1;
1900: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1901: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1902: }
1903: }
1904: if (xb == b) {
1905: PetscLogFlops(2.0*a->nz);
1906: } else {
1907: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1908: }
1909: }
1910: }
1911: VecRestoreArray(xx,&x);
1912: VecRestoreArrayRead(bb,&b);
1913: return(0);
1914: }
1917: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1918: {
1919: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1922: info->block_size = 1.0;
1923: info->nz_allocated = (double)a->maxnz;
1924: info->nz_used = (double)a->nz;
1925: info->nz_unneeded = (double)(a->maxnz - a->nz);
1926: info->assemblies = (double)A->num_ass;
1927: info->mallocs = (double)A->info.mallocs;
1928: info->memory = ((PetscObject)A)->mem;
1929: if (A->factortype) {
1930: info->fill_ratio_given = A->info.fill_ratio_given;
1931: info->fill_ratio_needed = A->info.fill_ratio_needed;
1932: info->factor_mallocs = A->info.factor_mallocs;
1933: } else {
1934: info->fill_ratio_given = 0;
1935: info->fill_ratio_needed = 0;
1936: info->factor_mallocs = 0;
1937: }
1938: return(0);
1939: }
1941: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1942: {
1943: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1944: PetscInt i,m = A->rmap->n - 1;
1945: PetscErrorCode ierr;
1946: const PetscScalar *xx;
1947: PetscScalar *bb;
1948: PetscInt d = 0;
1951: if (x && b) {
1952: VecGetArrayRead(x,&xx);
1953: VecGetArray(b,&bb);
1954: for (i=0; i<N; i++) {
1955: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1956: if (rows[i] >= A->cmap->n) continue;
1957: bb[rows[i]] = diag*xx[rows[i]];
1958: }
1959: VecRestoreArrayRead(x,&xx);
1960: VecRestoreArray(b,&bb);
1961: }
1963: if (a->keepnonzeropattern) {
1964: for (i=0; i<N; i++) {
1965: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1966: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1967: }
1968: if (diag != 0.0) {
1969: for (i=0; i<N; i++) {
1970: d = rows[i];
1971: if (rows[i] >= A->cmap->n) continue;
1972: 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);
1973: }
1974: for (i=0; i<N; i++) {
1975: if (rows[i] >= A->cmap->n) continue;
1976: a->a[a->diag[rows[i]]] = diag;
1977: }
1978: }
1979: } else {
1980: if (diag != 0.0) {
1981: for (i=0; i<N; i++) {
1982: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1983: if (a->ilen[rows[i]] > 0) {
1984: if (rows[i] >= A->cmap->n) {
1985: a->ilen[rows[i]] = 0;
1986: } else {
1987: a->ilen[rows[i]] = 1;
1988: a->a[a->i[rows[i]]] = diag;
1989: a->j[a->i[rows[i]]] = rows[i];
1990: }
1991: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
1992: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1993: }
1994: }
1995: } else {
1996: for (i=0; i<N; i++) {
1997: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1998: a->ilen[rows[i]] = 0;
1999: }
2000: }
2001: A->nonzerostate++;
2002: }
2003: (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2004: return(0);
2005: }
2007: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2008: {
2009: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2010: PetscInt i,j,m = A->rmap->n - 1,d = 0;
2011: PetscErrorCode ierr;
2012: PetscBool missing,*zeroed,vecs = PETSC_FALSE;
2013: const PetscScalar *xx;
2014: PetscScalar *bb;
2017: if (x && b) {
2018: VecGetArrayRead(x,&xx);
2019: VecGetArray(b,&bb);
2020: vecs = PETSC_TRUE;
2021: }
2022: PetscCalloc1(A->rmap->n,&zeroed);
2023: for (i=0; i<N; i++) {
2024: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2025: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
2027: zeroed[rows[i]] = PETSC_TRUE;
2028: }
2029: for (i=0; i<A->rmap->n; i++) {
2030: if (!zeroed[i]) {
2031: for (j=a->i[i]; j<a->i[i+1]; j++) {
2032: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2033: if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
2034: a->a[j] = 0.0;
2035: }
2036: }
2037: } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
2038: }
2039: if (x && b) {
2040: VecRestoreArrayRead(x,&xx);
2041: VecRestoreArray(b,&bb);
2042: }
2043: PetscFree(zeroed);
2044: if (diag != 0.0) {
2045: MatMissingDiagonal_SeqAIJ(A,&missing,&d);
2046: if (missing) {
2047: for (i=0; i<N; i++) {
2048: if (rows[i] >= A->cmap->N) continue;
2049: if (a->nonew && rows[i] >= d) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D (%D)",d,rows[i]);
2050: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2051: }
2052: } else {
2053: for (i=0; i<N; i++) {
2054: a->a[a->diag[rows[i]]] = diag;
2055: }
2056: }
2057: }
2058: (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2059: return(0);
2060: }
2062: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2063: {
2064: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2065: PetscInt *itmp;
2068: if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);
2070: *nz = a->i[row+1] - a->i[row];
2071: if (v) *v = a->a + a->i[row];
2072: if (idx) {
2073: itmp = a->j + a->i[row];
2074: if (*nz) *idx = itmp;
2075: else *idx = 0;
2076: }
2077: return(0);
2078: }
2080: /* remove this function? */
2081: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2082: {
2084: return(0);
2085: }
2087: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
2088: {
2089: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2090: MatScalar *v = a->a;
2091: PetscReal sum = 0.0;
2093: PetscInt i,j;
2096: if (type == NORM_FROBENIUS) {
2097: #if defined(PETSC_USE_REAL___FP16)
2098: PetscBLASInt one = 1,nz = a->nz;
2099: *nrm = BLASnrm2_(&nz,v,&one);
2100: #else
2101: for (i=0; i<a->nz; i++) {
2102: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
2103: }
2104: *nrm = PetscSqrtReal(sum);
2105: #endif
2106: PetscLogFlops(2*a->nz);
2107: } else if (type == NORM_1) {
2108: PetscReal *tmp;
2109: PetscInt *jj = a->j;
2110: PetscCalloc1(A->cmap->n+1,&tmp);
2111: *nrm = 0.0;
2112: for (j=0; j<a->nz; j++) {
2113: tmp[*jj++] += PetscAbsScalar(*v); v++;
2114: }
2115: for (j=0; j<A->cmap->n; j++) {
2116: if (tmp[j] > *nrm) *nrm = tmp[j];
2117: }
2118: PetscFree(tmp);
2119: PetscLogFlops(PetscMax(a->nz-1,0));
2120: } else if (type == NORM_INFINITY) {
2121: *nrm = 0.0;
2122: for (j=0; j<A->rmap->n; j++) {
2123: v = a->a + a->i[j];
2124: sum = 0.0;
2125: for (i=0; i<a->i[j+1]-a->i[j]; i++) {
2126: sum += PetscAbsScalar(*v); v++;
2127: }
2128: if (sum > *nrm) *nrm = sum;
2129: }
2130: PetscLogFlops(PetscMax(a->nz-1,0));
2131: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
2132: return(0);
2133: }
2135: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
2136: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
2137: {
2139: PetscInt i,j,anzj;
2140: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b;
2141: PetscInt an=A->cmap->N,am=A->rmap->N;
2142: PetscInt *ati,*atj,*atfill,*ai=a->i,*aj=a->j;
2145: /* Allocate space for symbolic transpose info and work array */
2146: PetscCalloc1(an+1,&ati);
2147: PetscMalloc1(ai[am],&atj);
2148: PetscMalloc1(an,&atfill);
2150: /* Walk through aj and count ## of non-zeros in each row of A^T. */
2151: /* Note: offset by 1 for fast conversion into csr format. */
2152: for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
2153: /* Form ati for csr format of A^T. */
2154: for (i=0;i<an;i++) ati[i+1] += ati[i];
2156: /* Copy ati into atfill so we have locations of the next free space in atj */
2157: PetscMemcpy(atfill,ati,an*sizeof(PetscInt));
2159: /* Walk through A row-wise and mark nonzero entries of A^T. */
2160: for (i=0;i<am;i++) {
2161: anzj = ai[i+1] - ai[i];
2162: for (j=0;j<anzj;j++) {
2163: atj[atfill[*aj]] = i;
2164: atfill[*aj++] += 1;
2165: }
2166: }
2168: /* Clean up temporary space and complete requests. */
2169: PetscFree(atfill);
2170: MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2171: MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2173: b = (Mat_SeqAIJ*)((*B)->data);
2174: b->free_a = PETSC_FALSE;
2175: b->free_ij = PETSC_TRUE;
2176: b->nonew = 0;
2177: return(0);
2178: }
2180: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
2181: {
2182: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2183: Mat C;
2185: PetscInt i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
2186: MatScalar *array = a->a;
2189: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
2190: PetscCalloc1(1+A->cmap->n,&col);
2192: for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
2193: MatCreate(PetscObjectComm((PetscObject)A),&C);
2194: MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
2195: MatSetBlockSizes(C,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2196: MatSetType(C,((PetscObject)A)->type_name);
2197: MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
2198: PetscFree(col);
2199: } else {
2200: C = *B;
2201: }
2203: for (i=0; i<m; i++) {
2204: len = ai[i+1]-ai[i];
2205: MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
2206: array += len;
2207: aj += len;
2208: }
2209: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2210: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2212: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
2213: *B = C;
2214: } else {
2215: MatHeaderMerge(A,&C);
2216: }
2217: return(0);
2218: }
2220: PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2221: {
2222: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2223: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2224: MatScalar *va,*vb;
2226: PetscInt ma,na,mb,nb, i;
2229: MatGetSize(A,&ma,&na);
2230: MatGetSize(B,&mb,&nb);
2231: if (ma!=nb || na!=mb) {
2232: *f = PETSC_FALSE;
2233: return(0);
2234: }
2235: aii = aij->i; bii = bij->i;
2236: adx = aij->j; bdx = bij->j;
2237: va = aij->a; vb = bij->a;
2238: PetscMalloc1(ma,&aptr);
2239: PetscMalloc1(mb,&bptr);
2240: for (i=0; i<ma; i++) aptr[i] = aii[i];
2241: for (i=0; i<mb; i++) bptr[i] = bii[i];
2243: *f = PETSC_TRUE;
2244: for (i=0; i<ma; i++) {
2245: while (aptr[i]<aii[i+1]) {
2246: PetscInt idc,idr;
2247: PetscScalar vc,vr;
2248: /* column/row index/value */
2249: idc = adx[aptr[i]];
2250: idr = bdx[bptr[idc]];
2251: vc = va[aptr[i]];
2252: vr = vb[bptr[idc]];
2253: if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2254: *f = PETSC_FALSE;
2255: goto done;
2256: } else {
2257: aptr[i]++;
2258: if (B || i!=idc) bptr[idc]++;
2259: }
2260: }
2261: }
2262: done:
2263: PetscFree(aptr);
2264: PetscFree(bptr);
2265: return(0);
2266: }
2268: PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2269: {
2270: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2271: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2272: MatScalar *va,*vb;
2274: PetscInt ma,na,mb,nb, i;
2277: MatGetSize(A,&ma,&na);
2278: MatGetSize(B,&mb,&nb);
2279: if (ma!=nb || na!=mb) {
2280: *f = PETSC_FALSE;
2281: return(0);
2282: }
2283: aii = aij->i; bii = bij->i;
2284: adx = aij->j; bdx = bij->j;
2285: va = aij->a; vb = bij->a;
2286: PetscMalloc1(ma,&aptr);
2287: PetscMalloc1(mb,&bptr);
2288: for (i=0; i<ma; i++) aptr[i] = aii[i];
2289: for (i=0; i<mb; i++) bptr[i] = bii[i];
2291: *f = PETSC_TRUE;
2292: for (i=0; i<ma; i++) {
2293: while (aptr[i]<aii[i+1]) {
2294: PetscInt idc,idr;
2295: PetscScalar vc,vr;
2296: /* column/row index/value */
2297: idc = adx[aptr[i]];
2298: idr = bdx[bptr[idc]];
2299: vc = va[aptr[i]];
2300: vr = vb[bptr[idc]];
2301: if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2302: *f = PETSC_FALSE;
2303: goto done;
2304: } else {
2305: aptr[i]++;
2306: if (B || i!=idc) bptr[idc]++;
2307: }
2308: }
2309: }
2310: done:
2311: PetscFree(aptr);
2312: PetscFree(bptr);
2313: return(0);
2314: }
2316: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2317: {
2321: MatIsTranspose_SeqAIJ(A,A,tol,f);
2322: return(0);
2323: }
2325: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2326: {
2330: MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2331: return(0);
2332: }
2334: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2335: {
2336: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2337: const PetscScalar *l,*r;
2338: PetscScalar x;
2339: MatScalar *v;
2340: PetscErrorCode ierr;
2341: PetscInt i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
2342: const PetscInt *jj;
2345: if (ll) {
2346: /* The local size is used so that VecMPI can be passed to this routine
2347: by MatDiagonalScale_MPIAIJ */
2348: VecGetLocalSize(ll,&m);
2349: if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2350: VecGetArrayRead(ll,&l);
2351: v = a->a;
2352: for (i=0; i<m; i++) {
2353: x = l[i];
2354: M = a->i[i+1] - a->i[i];
2355: for (j=0; j<M; j++) (*v++) *= x;
2356: }
2357: VecRestoreArrayRead(ll,&l);
2358: PetscLogFlops(nz);
2359: }
2360: if (rr) {
2361: VecGetLocalSize(rr,&n);
2362: if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2363: VecGetArrayRead(rr,&r);
2364: v = a->a; jj = a->j;
2365: for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2366: VecRestoreArrayRead(rr,&r);
2367: PetscLogFlops(nz);
2368: }
2369: MatSeqAIJInvalidateDiagonal(A);
2370: return(0);
2371: }
2373: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2374: {
2375: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c;
2377: PetscInt *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2378: PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2379: const PetscInt *irow,*icol;
2380: PetscInt nrows,ncols;
2381: PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2382: MatScalar *a_new,*mat_a;
2383: Mat C;
2384: PetscBool stride;
2388: ISGetIndices(isrow,&irow);
2389: ISGetLocalSize(isrow,&nrows);
2390: ISGetLocalSize(iscol,&ncols);
2392: PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2393: if (stride) {
2394: ISStrideGetInfo(iscol,&first,&step);
2395: } else {
2396: first = 0;
2397: step = 0;
2398: }
2399: if (stride && step == 1) {
2400: /* special case of contiguous rows */
2401: PetscMalloc2(nrows,&lens,nrows,&starts);
2402: /* loop over new rows determining lens and starting points */
2403: for (i=0; i<nrows; i++) {
2404: kstart = ai[irow[i]];
2405: kend = kstart + ailen[irow[i]];
2406: starts[i] = kstart;
2407: for (k=kstart; k<kend; k++) {
2408: if (aj[k] >= first) {
2409: starts[i] = k;
2410: break;
2411: }
2412: }
2413: sum = 0;
2414: while (k < kend) {
2415: if (aj[k++] >= first+ncols) break;
2416: sum++;
2417: }
2418: lens[i] = sum;
2419: }
2420: /* create submatrix */
2421: if (scall == MAT_REUSE_MATRIX) {
2422: PetscInt n_cols,n_rows;
2423: MatGetSize(*B,&n_rows,&n_cols);
2424: if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2425: MatZeroEntries(*B);
2426: C = *B;
2427: } else {
2428: PetscInt rbs,cbs;
2429: MatCreate(PetscObjectComm((PetscObject)A),&C);
2430: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2431: ISGetBlockSize(isrow,&rbs);
2432: ISGetBlockSize(iscol,&cbs);
2433: MatSetBlockSizes(C,rbs,cbs);
2434: MatSetType(C,((PetscObject)A)->type_name);
2435: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2436: }
2437: c = (Mat_SeqAIJ*)C->data;
2439: /* loop over rows inserting into submatrix */
2440: a_new = c->a;
2441: j_new = c->j;
2442: i_new = c->i;
2444: for (i=0; i<nrows; i++) {
2445: ii = starts[i];
2446: lensi = lens[i];
2447: for (k=0; k<lensi; k++) {
2448: *j_new++ = aj[ii+k] - first;
2449: }
2450: PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2451: a_new += lensi;
2452: i_new[i+1] = i_new[i] + lensi;
2453: c->ilen[i] = lensi;
2454: }
2455: PetscFree2(lens,starts);
2456: } else {
2457: ISGetIndices(iscol,&icol);
2458: PetscCalloc1(oldcols,&smap);
2459: PetscMalloc1(1+nrows,&lens);
2460: for (i=0; i<ncols; i++) {
2461: #if defined(PETSC_USE_DEBUG)
2462: 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);
2463: #endif
2464: smap[icol[i]] = i+1;
2465: }
2467: /* determine lens of each row */
2468: for (i=0; i<nrows; i++) {
2469: kstart = ai[irow[i]];
2470: kend = kstart + a->ilen[irow[i]];
2471: lens[i] = 0;
2472: for (k=kstart; k<kend; k++) {
2473: if (smap[aj[k]]) {
2474: lens[i]++;
2475: }
2476: }
2477: }
2478: /* Create and fill new matrix */
2479: if (scall == MAT_REUSE_MATRIX) {
2480: PetscBool equal;
2482: c = (Mat_SeqAIJ*)((*B)->data);
2483: if ((*B)->rmap->n != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2484: PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2485: if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2486: PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2487: C = *B;
2488: } else {
2489: PetscInt rbs,cbs;
2490: MatCreate(PetscObjectComm((PetscObject)A),&C);
2491: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2492: ISGetBlockSize(isrow,&rbs);
2493: ISGetBlockSize(iscol,&cbs);
2494: MatSetBlockSizes(C,rbs,cbs);
2495: MatSetType(C,((PetscObject)A)->type_name);
2496: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2497: }
2498: c = (Mat_SeqAIJ*)(C->data);
2499: for (i=0; i<nrows; i++) {
2500: row = irow[i];
2501: kstart = ai[row];
2502: kend = kstart + a->ilen[row];
2503: mat_i = c->i[i];
2504: mat_j = c->j + mat_i;
2505: mat_a = c->a + mat_i;
2506: mat_ilen = c->ilen + i;
2507: for (k=kstart; k<kend; k++) {
2508: if ((tcol=smap[a->j[k]])) {
2509: *mat_j++ = tcol - 1;
2510: *mat_a++ = a->a[k];
2511: (*mat_ilen)++;
2513: }
2514: }
2515: }
2516: /* Free work space */
2517: ISRestoreIndices(iscol,&icol);
2518: PetscFree(smap);
2519: PetscFree(lens);
2520: /* sort */
2521: for (i = 0; i < nrows; i++) {
2522: PetscInt ilen;
2524: mat_i = c->i[i];
2525: mat_j = c->j + mat_i;
2526: mat_a = c->a + mat_i;
2527: ilen = c->ilen[i];
2528: PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
2529: }
2530: }
2531: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2532: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2534: ISRestoreIndices(isrow,&irow);
2535: *B = C;
2536: return(0);
2537: }
2539: PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2540: {
2542: Mat B;
2545: if (scall == MAT_INITIAL_MATRIX) {
2546: MatCreate(subComm,&B);
2547: MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2548: MatSetBlockSizesFromMats(B,mat,mat);
2549: MatSetType(B,MATSEQAIJ);
2550: MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2551: *subMat = B;
2552: } else {
2553: MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2554: }
2555: return(0);
2556: }
2558: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2559: {
2560: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2562: Mat outA;
2563: PetscBool row_identity,col_identity;
2566: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
2568: ISIdentity(row,&row_identity);
2569: ISIdentity(col,&col_identity);
2571: outA = inA;
2572: outA->factortype = MAT_FACTOR_LU;
2573: PetscFree(inA->solvertype);
2574: PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);
2576: PetscObjectReference((PetscObject)row);
2577: ISDestroy(&a->row);
2579: a->row = row;
2581: PetscObjectReference((PetscObject)col);
2582: ISDestroy(&a->col);
2584: a->col = col;
2586: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2587: ISDestroy(&a->icol);
2588: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2589: PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);
2591: if (!a->solve_work) { /* this matrix may have been factored before */
2592: PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2593: PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2594: }
2596: MatMarkDiagonal_SeqAIJ(inA);
2597: if (row_identity && col_identity) {
2598: MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2599: } else {
2600: MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2601: }
2602: return(0);
2603: }
2605: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2606: {
2607: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2608: PetscScalar oalpha = alpha;
2610: PetscBLASInt one = 1,bnz;
2613: PetscBLASIntCast(a->nz,&bnz);
2614: PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2615: PetscLogFlops(a->nz);
2616: MatSeqAIJInvalidateDiagonal(inA);
2617: return(0);
2618: }
2620: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2621: {
2623: PetscInt i;
2626: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2627: PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);
2629: for (i=0; i<submatj->nrqr; ++i) {
2630: PetscFree(submatj->sbuf2[i]);
2631: }
2632: PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);
2634: if (submatj->rbuf1) {
2635: PetscFree(submatj->rbuf1[0]);
2636: PetscFree(submatj->rbuf1);
2637: }
2639: for (i=0; i<submatj->nrqs; ++i) {
2640: PetscFree(submatj->rbuf3[i]);
2641: }
2642: PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
2643: PetscFree(submatj->pa);
2644: }
2646: #if defined(PETSC_USE_CTABLE)
2647: PetscTableDestroy((PetscTable*)&submatj->rmap);
2648: if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
2649: PetscFree(submatj->rmap_loc);
2650: #else
2651: PetscFree(submatj->rmap);
2652: #endif
2654: if (!submatj->allcolumns) {
2655: #if defined(PETSC_USE_CTABLE)
2656: PetscTableDestroy((PetscTable*)&submatj->cmap);
2657: #else
2658: PetscFree(submatj->cmap);
2659: #endif
2660: }
2661: PetscFree(submatj->row2proc);
2663: PetscFree(submatj);
2664: return(0);
2665: }
2667: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2668: {
2670: Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
2671: Mat_SubSppt *submatj = c->submatis1;
2674: (*submatj->destroy)(C);
2675: MatDestroySubMatrix_Private(submatj);
2676: return(0);
2677: }
2679: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
2680: {
2682: PetscInt i;
2683: Mat C;
2684: Mat_SeqAIJ *c;
2685: Mat_SubSppt *submatj;
2688: for (i=0; i<n; i++) {
2689: C = (*mat)[i];
2690: c = (Mat_SeqAIJ*)C->data;
2691: submatj = c->submatis1;
2692: if (submatj) {
2693: if (--((PetscObject)C)->refct <= 0) {
2694: (*submatj->destroy)(C);
2695: MatDestroySubMatrix_Private(submatj);
2696: PetscFree(C->defaultvectype);
2697: PetscLayoutDestroy(&C->rmap);
2698: PetscLayoutDestroy(&C->cmap);
2699: PetscHeaderDestroy(&C);
2700: }
2701: } else {
2702: MatDestroy(&C);
2703: }
2704: }
2706: /* Destroy Dummy submatrices created for reuse */
2707: MatDestroySubMatrices_Dummy(n,mat);
2709: PetscFree(*mat);
2710: return(0);
2711: }
2713: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2714: {
2716: PetscInt i;
2719: if (scall == MAT_INITIAL_MATRIX) {
2720: PetscCalloc1(n+1,B);
2721: }
2723: for (i=0; i<n; i++) {
2724: MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2725: }
2726: return(0);
2727: }
2729: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2730: {
2731: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2733: PetscInt row,i,j,k,l,m,n,*nidx,isz,val;
2734: const PetscInt *idx;
2735: PetscInt start,end,*ai,*aj;
2736: PetscBT table;
2739: m = A->rmap->n;
2740: ai = a->i;
2741: aj = a->j;
2743: if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");
2745: PetscMalloc1(m+1,&nidx);
2746: PetscBTCreate(m,&table);
2748: for (i=0; i<is_max; i++) {
2749: /* Initialize the two local arrays */
2750: isz = 0;
2751: PetscBTMemzero(m,table);
2753: /* Extract the indices, assume there can be duplicate entries */
2754: ISGetIndices(is[i],&idx);
2755: ISGetLocalSize(is[i],&n);
2757: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2758: for (j=0; j<n; ++j) {
2759: if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2760: }
2761: ISRestoreIndices(is[i],&idx);
2762: ISDestroy(&is[i]);
2764: k = 0;
2765: for (j=0; j<ov; j++) { /* for each overlap */
2766: n = isz;
2767: for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2768: row = nidx[k];
2769: start = ai[row];
2770: end = ai[row+1];
2771: for (l = start; l<end; l++) {
2772: val = aj[l];
2773: if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2774: }
2775: }
2776: }
2777: ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2778: }
2779: PetscBTDestroy(&table);
2780: PetscFree(nidx);
2781: return(0);
2782: }
2784: /* -------------------------------------------------------------- */
2785: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2786: {
2787: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2789: PetscInt i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2790: const PetscInt *row,*col;
2791: PetscInt *cnew,j,*lens;
2792: IS icolp,irowp;
2793: PetscInt *cwork = NULL;
2794: PetscScalar *vwork = NULL;
2797: ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2798: ISGetIndices(irowp,&row);
2799: ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2800: ISGetIndices(icolp,&col);
2802: /* determine lengths of permuted rows */
2803: PetscMalloc1(m+1,&lens);
2804: for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2805: MatCreate(PetscObjectComm((PetscObject)A),B);
2806: MatSetSizes(*B,m,n,m,n);
2807: MatSetBlockSizesFromMats(*B,A,A);
2808: MatSetType(*B,((PetscObject)A)->type_name);
2809: MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2810: PetscFree(lens);
2812: PetscMalloc1(n,&cnew);
2813: for (i=0; i<m; i++) {
2814: MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2815: for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2816: MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2817: MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2818: }
2819: PetscFree(cnew);
2821: (*B)->assembled = PETSC_FALSE;
2823: MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2824: MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2825: ISRestoreIndices(irowp,&row);
2826: ISRestoreIndices(icolp,&col);
2827: ISDestroy(&irowp);
2828: ISDestroy(&icolp);
2829: return(0);
2830: }
2832: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2833: {
2837: /* If the two matrices have the same copy implementation, use fast copy. */
2838: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2839: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2840: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
2842: 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");
2843: PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2844: PetscObjectStateIncrease((PetscObject)B);
2845: } else {
2846: MatCopy_Basic(A,B,str);
2847: }
2848: return(0);
2849: }
2851: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2852: {
2856: MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2857: return(0);
2858: }
2860: PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2861: {
2862: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2865: *array = a->a;
2866: return(0);
2867: }
2869: PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2870: {
2872: return(0);
2873: }
2875: /*
2876: Computes the number of nonzeros per row needed for preallocation when X and Y
2877: have different nonzero structure.
2878: */
2879: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
2880: {
2881: PetscInt i,j,k,nzx,nzy;
2884: /* Set the number of nonzeros in the new matrix */
2885: for (i=0; i<m; i++) {
2886: const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
2887: nzx = xi[i+1] - xi[i];
2888: nzy = yi[i+1] - yi[i];
2889: nnz[i] = 0;
2890: for (j=0,k=0; j<nzx; j++) { /* Point in X */
2891: for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
2892: if (k<nzy && yjj[k]==xjj[j]) k++; /* Skip duplicate */
2893: nnz[i]++;
2894: }
2895: for (; k<nzy; k++) nnz[i]++;
2896: }
2897: return(0);
2898: }
2900: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
2901: {
2902: PetscInt m = Y->rmap->N;
2903: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data;
2904: Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data;
2908: /* Set the number of nonzeros in the new matrix */
2909: MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
2910: return(0);
2911: }
2913: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2914: {
2916: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
2917: PetscBLASInt one=1,bnz;
2920: PetscBLASIntCast(x->nz,&bnz);
2921: if (str == SAME_NONZERO_PATTERN) {
2922: PetscScalar alpha = a;
2923: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2924: MatSeqAIJInvalidateDiagonal(Y);
2925: PetscObjectStateIncrease((PetscObject)Y);
2926: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2927: MatAXPY_Basic(Y,a,X,str);
2928: } else {
2929: Mat B;
2930: PetscInt *nnz;
2931: PetscMalloc1(Y->rmap->N,&nnz);
2932: MatCreate(PetscObjectComm((PetscObject)Y),&B);
2933: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2934: MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2935: MatSetBlockSizesFromMats(B,Y,Y);
2936: MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2937: MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2938: MatSeqAIJSetPreallocation(B,0,nnz);
2939: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2940: MatHeaderReplace(Y,&B);
2941: PetscFree(nnz);
2942: }
2943: return(0);
2944: }
2946: PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
2947: {
2948: #if defined(PETSC_USE_COMPLEX)
2949: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2950: PetscInt i,nz;
2951: PetscScalar *a;
2954: nz = aij->nz;
2955: a = aij->a;
2956: for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
2957: #else
2959: #endif
2960: return(0);
2961: }
2963: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2964: {
2965: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2967: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2968: PetscReal atmp;
2969: PetscScalar *x;
2970: MatScalar *aa;
2973: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2974: aa = a->a;
2975: ai = a->i;
2976: aj = a->j;
2978: VecSet(v,0.0);
2979: VecGetArray(v,&x);
2980: VecGetLocalSize(v,&n);
2981: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2982: for (i=0; i<m; i++) {
2983: ncols = ai[1] - ai[0]; ai++;
2984: x[i] = 0.0;
2985: for (j=0; j<ncols; j++) {
2986: atmp = PetscAbsScalar(*aa);
2987: if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2988: aa++; aj++;
2989: }
2990: }
2991: VecRestoreArray(v,&x);
2992: return(0);
2993: }
2995: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2996: {
2997: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2999: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3000: PetscScalar *x;
3001: MatScalar *aa;
3004: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3005: aa = a->a;
3006: ai = a->i;
3007: aj = a->j;
3009: VecSet(v,0.0);
3010: VecGetArray(v,&x);
3011: VecGetLocalSize(v,&n);
3012: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3013: for (i=0; i<m; i++) {
3014: ncols = ai[1] - ai[0]; ai++;
3015: if (ncols == A->cmap->n) { /* row is dense */
3016: x[i] = *aa; if (idx) idx[i] = 0;
3017: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3018: x[i] = 0.0;
3019: if (idx) {
3020: idx[i] = 0; /* in case ncols is zero */
3021: for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
3022: if (aj[j] > j) {
3023: idx[i] = j;
3024: break;
3025: }
3026: }
3027: }
3028: }
3029: for (j=0; j<ncols; j++) {
3030: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3031: aa++; aj++;
3032: }
3033: }
3034: VecRestoreArray(v,&x);
3035: return(0);
3036: }
3038: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3039: {
3040: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3042: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3043: PetscReal atmp;
3044: PetscScalar *x;
3045: MatScalar *aa;
3048: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3049: aa = a->a;
3050: ai = a->i;
3051: aj = a->j;
3053: VecSet(v,0.0);
3054: VecGetArray(v,&x);
3055: VecGetLocalSize(v,&n);
3056: 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);
3057: for (i=0; i<m; i++) {
3058: ncols = ai[1] - ai[0]; ai++;
3059: if (ncols) {
3060: /* Get first nonzero */
3061: for (j = 0; j < ncols; j++) {
3062: atmp = PetscAbsScalar(aa[j]);
3063: if (atmp > 1.0e-12) {
3064: x[i] = atmp;
3065: if (idx) idx[i] = aj[j];
3066: break;
3067: }
3068: }
3069: if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
3070: } else {
3071: x[i] = 0.0; if (idx) idx[i] = 0;
3072: }
3073: for (j = 0; j < ncols; j++) {
3074: atmp = PetscAbsScalar(*aa);
3075: if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
3076: aa++; aj++;
3077: }
3078: }
3079: VecRestoreArray(v,&x);
3080: return(0);
3081: }
3083: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3084: {
3085: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3086: PetscErrorCode ierr;
3087: PetscInt i,j,m = A->rmap->n,ncols,n;
3088: const PetscInt *ai,*aj;
3089: PetscScalar *x;
3090: const MatScalar *aa;
3093: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3094: aa = a->a;
3095: ai = a->i;
3096: aj = a->j;
3098: VecSet(v,0.0);
3099: VecGetArray(v,&x);
3100: VecGetLocalSize(v,&n);
3101: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3102: for (i=0; i<m; i++) {
3103: ncols = ai[1] - ai[0]; ai++;
3104: if (ncols == A->cmap->n) { /* row is dense */
3105: x[i] = *aa; if (idx) idx[i] = 0;
3106: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3107: x[i] = 0.0;
3108: if (idx) { /* find first implicit 0.0 in the row */
3109: idx[i] = 0; /* in case ncols is zero */
3110: for (j=0; j<ncols; j++) {
3111: if (aj[j] > j) {
3112: idx[i] = j;
3113: break;
3114: }
3115: }
3116: }
3117: }
3118: for (j=0; j<ncols; j++) {
3119: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3120: aa++; aj++;
3121: }
3122: }
3123: VecRestoreArray(v,&x);
3124: return(0);
3125: }
3127: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
3128: {
3129: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
3130: PetscErrorCode ierr;
3131: PetscInt i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
3132: MatScalar *diag,work[25],*v_work;
3133: const PetscReal shift = 0.0;
3134: PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;
3137: allowzeropivot = PetscNot(A->erroriffailure);
3138: if (a->ibdiagvalid) {
3139: if (values) *values = a->ibdiag;
3140: return(0);
3141: }
3142: MatMarkDiagonal_SeqAIJ(A);
3143: if (!a->ibdiag) {
3144: PetscMalloc1(bs2*mbs,&a->ibdiag);
3145: PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
3146: }
3147: diag = a->ibdiag;
3148: if (values) *values = a->ibdiag;
3149: /* factor and invert each block */
3150: switch (bs) {
3151: case 1:
3152: for (i=0; i<mbs; i++) {
3153: MatGetValues(A,1,&i,1,&i,diag+i);
3154: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3155: if (allowzeropivot) {
3156: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3157: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3158: A->factorerror_zeropivot_row = i;
3159: PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3160: } 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);
3161: }
3162: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3163: }
3164: break;
3165: case 2:
3166: for (i=0; i<mbs; i++) {
3167: ij[0] = 2*i; ij[1] = 2*i + 1;
3168: MatGetValues(A,2,ij,2,ij,diag);
3169: PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
3170: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3171: PetscKernel_A_gets_transpose_A_2(diag);
3172: diag += 4;
3173: }
3174: break;
3175: case 3:
3176: for (i=0; i<mbs; i++) {
3177: ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
3178: MatGetValues(A,3,ij,3,ij,diag);
3179: PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
3180: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3181: PetscKernel_A_gets_transpose_A_3(diag);
3182: diag += 9;
3183: }
3184: break;
3185: case 4:
3186: for (i=0; i<mbs; i++) {
3187: ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
3188: MatGetValues(A,4,ij,4,ij,diag);
3189: PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
3190: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3191: PetscKernel_A_gets_transpose_A_4(diag);
3192: diag += 16;
3193: }
3194: break;
3195: case 5:
3196: for (i=0; i<mbs; i++) {
3197: ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
3198: MatGetValues(A,5,ij,5,ij,diag);
3199: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
3200: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3201: PetscKernel_A_gets_transpose_A_5(diag);
3202: diag += 25;
3203: }
3204: break;
3205: case 6:
3206: for (i=0; i<mbs; i++) {
3207: 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;
3208: MatGetValues(A,6,ij,6,ij,diag);
3209: PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
3210: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3211: PetscKernel_A_gets_transpose_A_6(diag);
3212: diag += 36;
3213: }
3214: break;
3215: case 7:
3216: for (i=0; i<mbs; i++) {
3217: 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;
3218: MatGetValues(A,7,ij,7,ij,diag);
3219: PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
3220: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3221: PetscKernel_A_gets_transpose_A_7(diag);
3222: diag += 49;
3223: }
3224: break;
3225: default:
3226: PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3227: for (i=0; i<mbs; i++) {
3228: for (j=0; j<bs; j++) {
3229: IJ[j] = bs*i + j;
3230: }
3231: MatGetValues(A,bs,IJ,bs,IJ,diag);
3232: PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
3233: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3234: PetscKernel_A_gets_transpose_A_N(diag,bs);
3235: diag += bs2;
3236: }
3237: PetscFree3(v_work,v_pivots,IJ);
3238: }
3239: a->ibdiagvalid = PETSC_TRUE;
3240: return(0);
3241: }
3243: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3244: {
3246: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data;
3247: PetscScalar a;
3248: PetscInt m,n,i,j,col;
3251: if (!x->assembled) {
3252: MatGetSize(x,&m,&n);
3253: for (i=0; i<m; i++) {
3254: for (j=0; j<aij->imax[i]; j++) {
3255: PetscRandomGetValue(rctx,&a);
3256: col = (PetscInt)(n*PetscRealPart(a));
3257: MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3258: }
3259: }
3260: } else {
3261: for (i=0; i<aij->nz; i++) {PetscRandomGetValue(rctx,aij->a+i);}
3262: }
3263: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3264: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3265: return(0);
3266: }
3268: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3269: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
3270: {
3272: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data;
3273: PetscScalar a;
3274: PetscInt m,n,i,j,col,nskip;
3277: nskip = high - low;
3278: MatGetSize(x,&m,&n);
3279: n -= nskip; /* shrink number of columns where nonzeros can be set */
3280: for (i=0; i<m; i++) {
3281: for (j=0; j<aij->imax[i]; j++) {
3282: PetscRandomGetValue(rctx,&a);
3283: col = (PetscInt)(n*PetscRealPart(a));
3284: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3285: MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3286: }
3287: }
3288: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3289: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3290: return(0);
3291: }
3294: /* -------------------------------------------------------------------*/
3295: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3296: MatGetRow_SeqAIJ,
3297: MatRestoreRow_SeqAIJ,
3298: MatMult_SeqAIJ,
3299: /* 4*/ MatMultAdd_SeqAIJ,
3300: MatMultTranspose_SeqAIJ,
3301: MatMultTransposeAdd_SeqAIJ,
3302: 0,
3303: 0,
3304: 0,
3305: /* 10*/ 0,
3306: MatLUFactor_SeqAIJ,
3307: 0,
3308: MatSOR_SeqAIJ,
3309: MatTranspose_SeqAIJ_FAST,
3310: /*1 5*/ MatGetInfo_SeqAIJ,
3311: MatEqual_SeqAIJ,
3312: MatGetDiagonal_SeqAIJ,
3313: MatDiagonalScale_SeqAIJ,
3314: MatNorm_SeqAIJ,
3315: /* 20*/ 0,
3316: MatAssemblyEnd_SeqAIJ,
3317: MatSetOption_SeqAIJ,
3318: MatZeroEntries_SeqAIJ,
3319: /* 24*/ MatZeroRows_SeqAIJ,
3320: 0,
3321: 0,
3322: 0,
3323: 0,
3324: /* 29*/ MatSetUp_SeqAIJ,
3325: 0,
3326: 0,
3327: 0,
3328: 0,
3329: /* 34*/ MatDuplicate_SeqAIJ,
3330: 0,
3331: 0,
3332: MatILUFactor_SeqAIJ,
3333: 0,
3334: /* 39*/ MatAXPY_SeqAIJ,
3335: MatCreateSubMatrices_SeqAIJ,
3336: MatIncreaseOverlap_SeqAIJ,
3337: MatGetValues_SeqAIJ,
3338: MatCopy_SeqAIJ,
3339: /* 44*/ MatGetRowMax_SeqAIJ,
3340: MatScale_SeqAIJ,
3341: MatShift_SeqAIJ,
3342: MatDiagonalSet_SeqAIJ,
3343: MatZeroRowsColumns_SeqAIJ,
3344: /* 49*/ MatSetRandom_SeqAIJ,
3345: MatGetRowIJ_SeqAIJ,
3346: MatRestoreRowIJ_SeqAIJ,
3347: MatGetColumnIJ_SeqAIJ,
3348: MatRestoreColumnIJ_SeqAIJ,
3349: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3350: 0,
3351: 0,
3352: MatPermute_SeqAIJ,
3353: 0,
3354: /* 59*/ 0,
3355: MatDestroy_SeqAIJ,
3356: MatView_SeqAIJ,
3357: 0,
3358: MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ,
3359: /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ,
3360: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3361: 0,
3362: 0,
3363: 0,
3364: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3365: MatGetRowMinAbs_SeqAIJ,
3366: 0,
3367: 0,
3368: 0,
3369: /* 74*/ 0,
3370: MatFDColoringApply_AIJ,
3371: 0,
3372: 0,
3373: 0,
3374: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3375: 0,
3376: 0,
3377: 0,
3378: MatLoad_SeqAIJ,
3379: /* 84*/ MatIsSymmetric_SeqAIJ,
3380: MatIsHermitian_SeqAIJ,
3381: 0,
3382: 0,
3383: 0,
3384: /* 89*/ MatMatMult_SeqAIJ_SeqAIJ,
3385: MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3386: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3387: MatPtAP_SeqAIJ_SeqAIJ,
3388: MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy,
3389: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3390: MatMatTransposeMult_SeqAIJ_SeqAIJ,
3391: MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
3392: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3393: 0,
3394: /* 99*/ 0,
3395: 0,
3396: 0,
3397: MatConjugate_SeqAIJ,
3398: 0,
3399: /*104*/ MatSetValuesRow_SeqAIJ,
3400: MatRealPart_SeqAIJ,
3401: MatImaginaryPart_SeqAIJ,
3402: 0,
3403: 0,
3404: /*109*/ MatMatSolve_SeqAIJ,
3405: 0,
3406: MatGetRowMin_SeqAIJ,
3407: 0,
3408: MatMissingDiagonal_SeqAIJ,
3409: /*114*/ 0,
3410: 0,
3411: 0,
3412: 0,
3413: 0,
3414: /*119*/ 0,
3415: 0,
3416: 0,
3417: 0,
3418: MatGetMultiProcBlock_SeqAIJ,
3419: /*124*/ MatFindNonzeroRows_SeqAIJ,
3420: MatGetColumnNorms_SeqAIJ,
3421: MatInvertBlockDiagonal_SeqAIJ,
3422: MatInvertVariableBlockDiagonal_SeqAIJ,
3423: 0,
3424: /*129*/ 0,
3425: MatTransposeMatMult_SeqAIJ_SeqAIJ,
3426: MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
3427: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3428: MatTransposeColoringCreate_SeqAIJ,
3429: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3430: MatTransColoringApplyDenToSp_SeqAIJ,
3431: MatRARt_SeqAIJ_SeqAIJ,
3432: MatRARtSymbolic_SeqAIJ_SeqAIJ,
3433: MatRARtNumeric_SeqAIJ_SeqAIJ,
3434: /*139*/0,
3435: 0,
3436: 0,
3437: MatFDColoringSetUp_SeqXAIJ,
3438: MatFindOffBlockDiagonalEntries_SeqAIJ,
3439: /*144*/MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3440: MatDestroySubMatrices_SeqAIJ
3441: };
3443: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3444: {
3445: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3446: PetscInt i,nz,n;
3449: nz = aij->maxnz;
3450: n = mat->rmap->n;
3451: for (i=0; i<nz; i++) {
3452: aij->j[i] = indices[i];
3453: }
3454: aij->nz = nz;
3455: for (i=0; i<n; i++) {
3456: aij->ilen[i] = aij->imax[i];
3457: }
3458: return(0);
3459: }
3461: /*
3462: * When a sparse matrix has many zero columns, we should compact them out to save the space
3463: * This happens in MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3464: * */
3465: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3466: {
3467: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3468: PetscTable gid1_lid1;
3469: PetscTablePosition tpos;
3470: PetscInt gid,lid,i,j,ncols,ec;
3471: PetscInt *garray;
3472: PetscErrorCode ierr;
3477: /* use a table */
3478: PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
3479: ec = 0;
3480: for (i=0; i<mat->rmap->n; i++) {
3481: ncols = aij->i[i+1] - aij->i[i];
3482: for (j=0; j<ncols; j++) {
3483: PetscInt data,gid1 = aij->j[aij->i[i] + j] + 1;
3484: PetscTableFind(gid1_lid1,gid1,&data);
3485: if (!data) {
3486: /* one based table */
3487: PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
3488: }
3489: }
3490: }
3491: /* form array of columns we need */
3492: PetscMalloc1(ec+1,&garray);
3493: PetscTableGetHeadPosition(gid1_lid1,&tpos);
3494: while (tpos) {
3495: PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
3496: gid--;
3497: lid--;
3498: garray[lid] = gid;
3499: }
3500: PetscSortInt(ec,garray); /* sort, and rebuild */
3501: PetscTableRemoveAll(gid1_lid1);
3502: for (i=0; i<ec; i++) {
3503: PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
3504: }
3505: /* compact out the extra columns in B */
3506: for (i=0; i<mat->rmap->n; i++) {
3507: ncols = aij->i[i+1] - aij->i[i];
3508: for (j=0; j<ncols; j++) {
3509: PetscInt gid1 = aij->j[aij->i[i] + j] + 1;
3510: PetscTableFind(gid1_lid1,gid1,&lid);
3511: lid--;
3512: aij->j[aij->i[i] + j] = lid;
3513: }
3514: }
3515: mat->cmap->n = mat->cmap->N = ec;
3516: mat->cmap->bs = 1;
3518: PetscTableDestroy(&gid1_lid1);
3519: PetscLayoutSetUp((mat->cmap));
3520: ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
3521: ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
3522: return(0);
3523: }
3525: /*@
3526: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3527: in the matrix.
3529: Input Parameters:
3530: + mat - the SeqAIJ matrix
3531: - indices - the column indices
3533: Level: advanced
3535: Notes:
3536: This can be called if you have precomputed the nonzero structure of the
3537: matrix and want to provide it to the matrix object to improve the performance
3538: of the MatSetValues() operation.
3540: You MUST have set the correct numbers of nonzeros per row in the call to
3541: MatCreateSeqAIJ(), and the columns indices MUST be sorted.
3543: MUST be called before any calls to MatSetValues();
3545: The indices should start with zero, not one.
3547: @*/
3548: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3549: {
3555: PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3556: return(0);
3557: }
3559: /* ----------------------------------------------------------------------------------------*/
3561: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3562: {
3563: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3565: size_t nz = aij->i[mat->rmap->n];
3568: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3570: /* allocate space for values if not already there */
3571: if (!aij->saved_values) {
3572: PetscMalloc1(nz+1,&aij->saved_values);
3573: PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3574: }
3576: /* copy values over */
3577: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3578: return(0);
3579: }
3581: /*@
3582: MatStoreValues - Stashes a copy of the matrix values; this allows, for
3583: example, reuse of the linear part of a Jacobian, while recomputing the
3584: nonlinear portion.
3586: Collect on Mat
3588: Input Parameters:
3589: . mat - the matrix (currently only AIJ matrices support this option)
3591: Level: advanced
3593: Common Usage, with SNESSolve():
3594: $ Create Jacobian matrix
3595: $ Set linear terms into matrix
3596: $ Apply boundary conditions to matrix, at this time matrix must have
3597: $ final nonzero structure (i.e. setting the nonlinear terms and applying
3598: $ boundary conditions again will not change the nonzero structure
3599: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3600: $ MatStoreValues(mat);
3601: $ Call SNESSetJacobian() with matrix
3602: $ In your Jacobian routine
3603: $ MatRetrieveValues(mat);
3604: $ Set nonlinear terms in matrix
3606: Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3607: $ // build linear portion of Jacobian
3608: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3609: $ MatStoreValues(mat);
3610: $ loop over nonlinear iterations
3611: $ MatRetrieveValues(mat);
3612: $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3613: $ // call MatAssemblyBegin/End() on matrix
3614: $ Solve linear system with Jacobian
3615: $ endloop
3617: Notes:
3618: Matrix must already be assemblied before calling this routine
3619: Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3620: calling this routine.
3622: When this is called multiple times it overwrites the previous set of stored values
3623: and does not allocated additional space.
3625: .seealso: MatRetrieveValues()
3627: @*/
3628: PetscErrorCode MatStoreValues(Mat mat)
3629: {
3634: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3635: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3636: PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3637: return(0);
3638: }
3640: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3641: {
3642: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3644: PetscInt nz = aij->i[mat->rmap->n];
3647: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3648: if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3649: /* copy values over */
3650: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3651: return(0);
3652: }
3654: /*@
3655: MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3656: example, reuse of the linear part of a Jacobian, while recomputing the
3657: nonlinear portion.
3659: Collect on Mat
3661: Input Parameters:
3662: . mat - the matrix (currently only AIJ matrices support this option)
3664: Level: advanced
3666: .seealso: MatStoreValues()
3668: @*/
3669: PetscErrorCode MatRetrieveValues(Mat mat)
3670: {
3675: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3676: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3677: PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3678: return(0);
3679: }
3682: /* --------------------------------------------------------------------------------*/
3683: /*@C
3684: MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3685: (the default parallel PETSc format). For good matrix assembly performance
3686: the user should preallocate the matrix storage by setting the parameter nz
3687: (or the array nnz). By setting these parameters accurately, performance
3688: during matrix assembly can be increased by more than a factor of 50.
3690: Collective on MPI_Comm
3692: Input Parameters:
3693: + comm - MPI communicator, set to PETSC_COMM_SELF
3694: . m - number of rows
3695: . n - number of columns
3696: . nz - number of nonzeros per row (same for all rows)
3697: - nnz - array containing the number of nonzeros in the various rows
3698: (possibly different for each row) or NULL
3700: Output Parameter:
3701: . A - the matrix
3703: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3704: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3705: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3707: Notes:
3708: If nnz is given then nz is ignored
3710: The AIJ format (also called the Yale sparse matrix format or
3711: compressed row storage), is fully compatible with standard Fortran 77
3712: storage. That is, the stored row and column indices can begin at
3713: either one (as in Fortran) or zero. See the users' manual for details.
3715: Specify the preallocated storage with either nz or nnz (not both).
3716: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3717: allocation. For large problems you MUST preallocate memory or you
3718: will get TERRIBLE performance, see the users' manual chapter on matrices.
3720: By default, this format uses inodes (identical nodes) when possible, to
3721: improve numerical efficiency of matrix-vector products and solves. We
3722: search for consecutive rows with the same nonzero structure, thereby
3723: reusing matrix information to achieve increased efficiency.
3725: Options Database Keys:
3726: + -mat_no_inode - Do not use inodes
3727: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3729: Level: intermediate
3731: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()
3733: @*/
3734: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3735: {
3739: MatCreate(comm,A);
3740: MatSetSizes(*A,m,n,m,n);
3741: MatSetType(*A,MATSEQAIJ);
3742: MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3743: return(0);
3744: }
3746: /*@C
3747: MatSeqAIJSetPreallocation - For good matrix assembly performance
3748: the user should preallocate the matrix storage by setting the parameter nz
3749: (or the array nnz). By setting these parameters accurately, performance
3750: during matrix assembly can be increased by more than a factor of 50.
3752: Collective on MPI_Comm
3754: Input Parameters:
3755: + B - The matrix
3756: . nz - number of nonzeros per row (same for all rows)
3757: - nnz - array containing the number of nonzeros in the various rows
3758: (possibly different for each row) or NULL
3760: Notes:
3761: If nnz is given then nz is ignored
3763: The AIJ format (also called the Yale sparse matrix format or
3764: compressed row storage), is fully compatible with standard Fortran 77
3765: storage. That is, the stored row and column indices can begin at
3766: either one (as in Fortran) or zero. See the users' manual for details.
3768: Specify the preallocated storage with either nz or nnz (not both).
3769: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3770: allocation. For large problems you MUST preallocate memory or you
3771: will get TERRIBLE performance, see the users' manual chapter on matrices.
3773: You can call MatGetInfo() to get information on how effective the preallocation was;
3774: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3775: You can also run with the option -info and look for messages with the string
3776: malloc in them to see if additional memory allocation was needed.
3778: Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3779: entries or columns indices
3781: By default, this format uses inodes (identical nodes) when possible, to
3782: improve numerical efficiency of matrix-vector products and solves. We
3783: search for consecutive rows with the same nonzero structure, thereby
3784: reusing matrix information to achieve increased efficiency.
3786: Options Database Keys:
3787: + -mat_no_inode - Do not use inodes
3788: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3790: Level: intermediate
3792: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()
3794: @*/
3795: PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3796: {
3802: PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3803: return(0);
3804: }
3806: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3807: {
3808: Mat_SeqAIJ *b;
3809: PetscBool skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3811: PetscInt i;
3814: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3815: if (nz == MAT_SKIP_ALLOCATION) {
3816: skipallocation = PETSC_TRUE;
3817: nz = 0;
3818: }
3819: PetscLayoutSetUp(B->rmap);
3820: PetscLayoutSetUp(B->cmap);
3822: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3823: if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
3824: if (nnz) {
3825: for (i=0; i<B->rmap->n; i++) {
3826: 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]);
3827: 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);
3828: }
3829: }
3831: B->preallocated = PETSC_TRUE;
3833: b = (Mat_SeqAIJ*)B->data;
3835: if (!skipallocation) {
3836: if (!b->imax) {
3837: PetscMalloc2(B->rmap->n,&b->imax,B->rmap->n,&b->ilen);
3838: PetscLogObjectMemory((PetscObject)B,2*B->rmap->n*sizeof(PetscInt));
3839: }
3840: if (!b->ipre) {
3841: PetscMalloc1(B->rmap->n,&b->ipre);
3842: PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
3843: }
3844: if (!nnz) {
3845: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3846: else if (nz < 0) nz = 1;
3847: nz = PetscMin(nz,B->cmap->n);
3848: for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3849: nz = nz*B->rmap->n;
3850: } else {
3851: nz = 0;
3852: for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3853: }
3854: /* b->ilen will count nonzeros in each row so far. */
3855: for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0;
3857: /* allocate the matrix space */
3858: /* FIXME: should B's old memory be unlogged? */
3859: MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3860: if (B->structure_only) {
3861: PetscMalloc1(nz,&b->j);
3862: PetscMalloc1(B->rmap->n+1,&b->i);
3863: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
3864: } else {
3865: PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
3866: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3867: }
3868: b->i[0] = 0;
3869: for (i=1; i<B->rmap->n+1; i++) {
3870: b->i[i] = b->i[i-1] + b->imax[i-1];
3871: }
3872: if (B->structure_only) {
3873: b->singlemalloc = PETSC_FALSE;
3874: b->free_a = PETSC_FALSE;
3875: } else {
3876: b->singlemalloc = PETSC_TRUE;
3877: b->free_a = PETSC_TRUE;
3878: }
3879: b->free_ij = PETSC_TRUE;
3880: } else {
3881: b->free_a = PETSC_FALSE;
3882: b->free_ij = PETSC_FALSE;
3883: }
3885: if (b->ipre && nnz != b->ipre && b->imax) {
3886: /* reserve user-requested sparsity */
3887: PetscMemcpy(b->ipre,b->imax,B->rmap->n*sizeof(PetscInt));
3888: }
3891: b->nz = 0;
3892: b->maxnz = nz;
3893: B->info.nz_unneeded = (double)b->maxnz;
3894: if (realalloc) {
3895: MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
3896: }
3897: B->was_assembled = PETSC_FALSE;
3898: B->assembled = PETSC_FALSE;
3899: return(0);
3900: }
3903: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
3904: {
3905: Mat_SeqAIJ *a;
3906: PetscInt i;
3911: a = (Mat_SeqAIJ*)A->data;
3912: /* if no saved info, we error out */
3913: if (!a->ipre) SETERRQ(PETSC_COMM_WORLD,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");
3915: 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");
3917: PetscMemcpy(a->imax,a->ipre,A->rmap->n*sizeof(PetscInt));
3918: PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
3919: a->i[0] = 0;
3920: for (i=1; i<A->rmap->n+1; i++) {
3921: a->i[i] = a->i[i-1] + a->imax[i-1];
3922: }
3923: A->preallocated = PETSC_TRUE;
3924: a->nz = 0;
3925: a->maxnz = a->i[A->rmap->n];
3926: A->info.nz_unneeded = (double)a->maxnz;
3927: A->was_assembled = PETSC_FALSE;
3928: A->assembled = PETSC_FALSE;
3929: return(0);
3930: }
3932: /*@
3933: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.
3935: Input Parameters:
3936: + B - the matrix
3937: . i - the indices into j for the start of each row (starts with zero)
3938: . j - the column indices for each row (starts with zero) these must be sorted for each row
3939: - v - optional values in the matrix
3941: Level: developer
3943: The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()
3945: .keywords: matrix, aij, compressed row, sparse, sequential
3947: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ
3948: @*/
3949: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3950: {
3956: PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3957: return(0);
3958: }
3960: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3961: {
3962: PetscInt i;
3963: PetscInt m,n;
3964: PetscInt nz;
3965: PetscInt *nnz, nz_max = 0;
3966: PetscScalar *values;
3970: if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
3972: PetscLayoutSetUp(B->rmap);
3973: PetscLayoutSetUp(B->cmap);
3975: MatGetSize(B, &m, &n);
3976: PetscMalloc1(m+1, &nnz);
3977: for (i = 0; i < m; i++) {
3978: nz = Ii[i+1]- Ii[i];
3979: nz_max = PetscMax(nz_max, nz);
3980: if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3981: nnz[i] = nz;
3982: }
3983: MatSeqAIJSetPreallocation(B, 0, nnz);
3984: PetscFree(nnz);
3986: if (v) {
3987: values = (PetscScalar*) v;
3988: } else {
3989: PetscCalloc1(nz_max, &values);
3990: }
3992: for (i = 0; i < m; i++) {
3993: nz = Ii[i+1] - Ii[i];
3994: MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3995: }
3997: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3998: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
4000: if (!v) {
4001: PetscFree(values);
4002: }
4003: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
4004: return(0);
4005: }
4007: #include <../src/mat/impls/dense/seq/dense.h>
4008: #include <petsc/private/kernels/petscaxpy.h>
4010: /*
4011: Computes (B'*A')' since computing B*A directly is untenable
4013: n p p
4014: ( ) ( ) ( )
4015: m ( A ) * n ( B ) = m ( C )
4016: ( ) ( ) ( )
4018: */
4019: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
4020: {
4021: PetscErrorCode ierr;
4022: Mat_SeqDense *sub_a = (Mat_SeqDense*)A->data;
4023: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ*)B->data;
4024: Mat_SeqDense *sub_c = (Mat_SeqDense*)C->data;
4025: PetscInt i,n,m,q,p;
4026: const PetscInt *ii,*idx;
4027: const PetscScalar *b,*a,*a_q;
4028: PetscScalar *c,*c_q;
4031: m = A->rmap->n;
4032: n = A->cmap->n;
4033: p = B->cmap->n;
4034: a = sub_a->v;
4035: b = sub_b->a;
4036: c = sub_c->v;
4037: PetscMemzero(c,m*p*sizeof(PetscScalar));
4039: ii = sub_b->i;
4040: idx = sub_b->j;
4041: for (i=0; i<n; i++) {
4042: q = ii[i+1] - ii[i];
4043: while (q-->0) {
4044: c_q = c + m*(*idx);
4045: a_q = a + m*i;
4046: PetscKernelAXPY(c_q,*b,a_q,m);
4047: idx++;
4048: b++;
4049: }
4050: }
4051: return(0);
4052: }
4054: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
4055: {
4057: PetscInt m=A->rmap->n,n=B->cmap->n;
4058: Mat Cmat;
4061: 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);
4062: MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
4063: MatSetSizes(Cmat,m,n,m,n);
4064: MatSetBlockSizesFromMats(Cmat,A,B);
4065: MatSetType(Cmat,MATSEQDENSE);
4066: MatSeqDenseSetPreallocation(Cmat,NULL);
4068: Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4070: *C = Cmat;
4071: return(0);
4072: }
4074: /* ----------------------------------------------------------------*/
4075: PETSC_INTERN PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
4076: {
4080: if (scall == MAT_INITIAL_MATRIX) {
4081: PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
4082: MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
4083: PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
4084: }
4085: PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
4086: MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
4087: PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
4088: return(0);
4089: }
4092: /*MC
4093: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4094: based on compressed sparse row format.
4096: Options Database Keys:
4097: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4099: Level: beginner
4101: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
4102: M*/
4104: /*MC
4105: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4107: This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
4108: and MATMPIAIJ otherwise. As a result, for single process communicators,
4109: MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
4110: for communicators controlling multiple processes. It is recommended that you call both of
4111: the above preallocation routines for simplicity.
4113: Options Database Keys:
4114: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()
4116: Developer Notes:
4117: Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
4118: enough exist.
4120: Level: beginner
4122: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
4123: M*/
4125: /*MC
4126: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4128: This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
4129: and MATMPIAIJCRL otherwise. As a result, for single process communicators,
4130: MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
4131: for communicators controlling multiple processes. It is recommended that you call both of
4132: the above preallocation routines for simplicity.
4134: Options Database Keys:
4135: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()
4137: Level: beginner
4139: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
4140: M*/
4142: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
4143: #if defined(PETSC_HAVE_ELEMENTAL)
4144: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
4145: #endif
4146: #if defined(PETSC_HAVE_HYPRE)
4147: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
4148: PETSC_INTERN PetscErrorCode MatMatMatMult_Transpose_AIJ_AIJ(Mat,Mat,Mat,MatReuse,PetscReal,Mat*);
4149: #endif
4150: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*);
4152: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
4153: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
4154: PETSC_INTERN PetscErrorCode MatPtAP_IS_XAIJ(Mat,Mat,MatReuse,PetscReal,Mat*);
4156: /*@C
4157: MatSeqAIJGetArray - gives access to the array where the data for a MATSEQAIJ matrix is stored
4159: Not Collective
4161: Input Parameter:
4162: . mat - a MATSEQAIJ matrix
4164: Output Parameter:
4165: . array - pointer to the data
4167: Level: intermediate
4169: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4170: @*/
4171: PetscErrorCode MatSeqAIJGetArray(Mat A,PetscScalar **array)
4172: {
4176: PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
4177: return(0);
4178: }
4180: /*@C
4181: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4183: Not Collective
4185: Input Parameter:
4186: . mat - a MATSEQAIJ matrix
4188: Output Parameter:
4189: . nz - the maximum number of nonzeros in any row
4191: Level: intermediate
4193: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4194: @*/
4195: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
4196: {
4197: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data;
4200: *nz = aij->rmax;
4201: return(0);
4202: }
4204: /*@C
4205: MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()
4207: Not Collective
4209: Input Parameters:
4210: . mat - a MATSEQAIJ matrix
4211: . array - pointer to the data
4213: Level: intermediate
4215: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
4216: @*/
4217: PetscErrorCode MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
4218: {
4222: PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
4223: return(0);
4224: }
4226: #if defined(PETSC_HAVE_CUDA)
4227: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat);
4228: #endif
4230: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4231: {
4232: Mat_SeqAIJ *b;
4234: PetscMPIInt size;
4237: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
4238: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
4240: PetscNewLog(B,&b);
4242: B->data = (void*)b;
4244: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4246: b->row = 0;
4247: b->col = 0;
4248: b->icol = 0;
4249: b->reallocs = 0;
4250: b->ignorezeroentries = PETSC_FALSE;
4251: b->roworiented = PETSC_TRUE;
4252: b->nonew = 0;
4253: b->diag = 0;
4254: b->solve_work = 0;
4255: B->spptr = 0;
4256: b->saved_values = 0;
4257: b->idiag = 0;
4258: b->mdiag = 0;
4259: b->ssor_work = 0;
4260: b->omega = 1.0;
4261: b->fshift = 0.0;
4262: b->idiagvalid = PETSC_FALSE;
4263: b->ibdiagvalid = PETSC_FALSE;
4264: b->keepnonzeropattern = PETSC_FALSE;
4266: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4267: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
4268: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);
4270: #if defined(PETSC_HAVE_MATLAB_ENGINE)
4271: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
4272: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
4273: #endif
4275: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
4276: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
4277: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
4278: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
4279: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
4280: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
4281: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
4282: #if defined(PETSC_HAVE_MKL_SPARSE)
4283: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
4284: #endif
4285: #if defined(PETSC_HAVE_CUDA)
4286: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
4287: #endif
4288: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
4289: #if defined(PETSC_HAVE_ELEMENTAL)
4290: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
4291: #endif
4292: #if defined(PETSC_HAVE_HYPRE)
4293: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
4294: PetscObjectComposeFunction((PetscObject)B,"MatMatMatMult_transpose_seqaij_seqaij_C",MatMatMatMult_Transpose_AIJ_AIJ);
4295: #endif
4296: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
4297: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
4298: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
4299: PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
4300: PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
4301: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
4302: PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
4303: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
4304: PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
4305: PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);
4306: PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);
4307: PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);
4308: PetscObjectComposeFunction((PetscObject)B,"MatPtAP_is_seqaij_C",MatPtAP_IS_XAIJ);
4309: MatCreate_SeqAIJ_Inode(B);
4310: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4311: MatSeqAIJSetTypeFromOptions(B); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4312: return(0);
4313: }
4315: /*
4316: Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4317: */
4318: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4319: {
4320: Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
4322: PetscInt i,m = A->rmap->n;
4325: c = (Mat_SeqAIJ*)C->data;
4327: C->factortype = A->factortype;
4328: c->row = 0;
4329: c->col = 0;
4330: c->icol = 0;
4331: c->reallocs = 0;
4333: C->assembled = PETSC_TRUE;
4335: PetscLayoutReference(A->rmap,&C->rmap);
4336: PetscLayoutReference(A->cmap,&C->cmap);
4338: PetscMalloc2(m,&c->imax,m,&c->ilen);
4339: PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));
4340: for (i=0; i<m; i++) {
4341: c->imax[i] = a->imax[i];
4342: c->ilen[i] = a->ilen[i];
4343: }
4345: /* allocate the matrix space */
4346: if (mallocmatspace) {
4347: PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4348: PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
4350: c->singlemalloc = PETSC_TRUE;
4352: PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
4353: if (m > 0) {
4354: PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
4355: if (cpvalues == MAT_COPY_VALUES) {
4356: PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
4357: } else {
4358: PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
4359: }
4360: }
4361: }
4363: c->ignorezeroentries = a->ignorezeroentries;
4364: c->roworiented = a->roworiented;
4365: c->nonew = a->nonew;
4366: if (a->diag) {
4367: PetscMalloc1(m+1,&c->diag);
4368: PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4369: for (i=0; i<m; i++) {
4370: c->diag[i] = a->diag[i];
4371: }
4372: } else c->diag = 0;
4374: c->solve_work = 0;
4375: c->saved_values = 0;
4376: c->idiag = 0;
4377: c->ssor_work = 0;
4378: c->keepnonzeropattern = a->keepnonzeropattern;
4379: c->free_a = PETSC_TRUE;
4380: c->free_ij = PETSC_TRUE;
4382: c->rmax = a->rmax;
4383: c->nz = a->nz;
4384: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
4385: C->preallocated = PETSC_TRUE;
4387: c->compressedrow.use = a->compressedrow.use;
4388: c->compressedrow.nrows = a->compressedrow.nrows;
4389: if (a->compressedrow.use) {
4390: i = a->compressedrow.nrows;
4391: PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4392: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
4393: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
4394: } else {
4395: c->compressedrow.use = PETSC_FALSE;
4396: c->compressedrow.i = NULL;
4397: c->compressedrow.rindex = NULL;
4398: }
4399: c->nonzerorowcnt = a->nonzerorowcnt;
4400: C->nonzerostate = A->nonzerostate;
4402: MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4403: PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4404: return(0);
4405: }
4407: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4408: {
4412: MatCreate(PetscObjectComm((PetscObject)A),B);
4413: MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4414: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4415: MatSetBlockSizesFromMats(*B,A,A);
4416: }
4417: MatSetType(*B,((PetscObject)A)->type_name);
4418: MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4419: return(0);
4420: }
4422: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4423: {
4424: PetscBool isbinary, ishdf5;
4430: /* force binary viewer to load .info file if it has not yet done so */
4431: PetscViewerSetUp(viewer);
4432: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
4433: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5, &ishdf5);
4434: if (isbinary) {
4435: MatLoad_SeqAIJ_Binary(newMat,viewer);
4436: } else if (ishdf5) {
4437: #if defined(PETSC_HAVE_HDF5)
4438: MatLoad_AIJ_HDF5(newMat,viewer);
4439: #else
4440: SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4441: #endif
4442: } else {
4443: SETERRQ2(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"Viewer type %s not yet supported for reading %s matrices",((PetscObject)viewer)->type_name,((PetscObject)newMat)->type_name);
4444: }
4445: return(0);
4446: }
4448: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat newMat, PetscViewer viewer)
4449: {
4450: Mat_SeqAIJ *a;
4452: PetscInt i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
4453: int fd;
4454: PetscMPIInt size;
4455: MPI_Comm comm;
4456: PetscInt bs = newMat->rmap->bs;
4459: PetscObjectGetComm((PetscObject)viewer,&comm);
4460: MPI_Comm_size(comm,&size);
4461: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");
4463: PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
4464: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
4465: PetscOptionsEnd();
4466: if (bs < 0) bs = 1;
4467: MatSetBlockSize(newMat,bs);
4469: PetscViewerBinaryGetDescriptor(viewer,&fd);
4470: PetscBinaryRead(fd,header,4,PETSC_INT);
4471: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
4472: M = header[1]; N = header[2]; nz = header[3];
4474: if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
4476: /* read in row lengths */
4477: PetscMalloc1(M,&rowlengths);
4478: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
4480: /* check if sum of rowlengths is same as nz */
4481: for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
4482: 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);
4484: /* set global size if not set already*/
4485: if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
4486: MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
4487: } else {
4488: /* if sizes and type are already set, check if the matrix global sizes are correct */
4489: MatGetSize(newMat,&rows,&cols);
4490: if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
4491: MatGetLocalSize(newMat,&rows,&cols);
4492: }
4493: 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);
4494: }
4495: MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
4496: a = (Mat_SeqAIJ*)newMat->data;
4498: PetscBinaryRead(fd,a->j,nz,PETSC_INT);
4500: /* read in nonzero values */
4501: PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);
4503: /* set matrix "i" values */
4504: a->i[0] = 0;
4505: for (i=1; i<= M; i++) {
4506: a->i[i] = a->i[i-1] + rowlengths[i-1];
4507: a->ilen[i-1] = rowlengths[i-1];
4508: }
4509: PetscFree(rowlengths);
4511: MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
4512: MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
4513: return(0);
4514: }
4516: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4517: {
4518: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4520: #if defined(PETSC_USE_COMPLEX)
4521: PetscInt k;
4522: #endif
4525: /* If the matrix dimensions are not equal,or no of nonzeros */
4526: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4527: *flg = PETSC_FALSE;
4528: return(0);
4529: }
4531: /* if the a->i are the same */
4532: PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
4533: if (!*flg) return(0);
4535: /* if a->j are the same */
4536: PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
4537: if (!*flg) return(0);
4539: /* if a->a are the same */
4540: #if defined(PETSC_USE_COMPLEX)
4541: for (k=0; k<a->nz; k++) {
4542: if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4543: *flg = PETSC_FALSE;
4544: return(0);
4545: }
4546: }
4547: #else
4548: PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
4549: #endif
4550: return(0);
4551: }
4553: /*@
4554: MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4555: provided by the user.
4557: Collective on MPI_Comm
4559: Input Parameters:
4560: + comm - must be an MPI communicator of size 1
4561: . m - number of rows
4562: . n - number of columns
4563: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4564: . j - column indices
4565: - a - matrix values
4567: Output Parameter:
4568: . mat - the matrix
4570: Level: intermediate
4572: Notes:
4573: The i, j, and a arrays are not copied by this routine, the user must free these arrays
4574: once the matrix is destroyed and not before
4576: You cannot set new nonzero locations into this matrix, that will generate an error.
4578: The i and j indices are 0 based
4580: The format which is used for the sparse matrix input, is equivalent to a
4581: row-major ordering.. i.e for the following matrix, the input data expected is
4582: as shown
4584: $ 1 0 0
4585: $ 2 0 3
4586: $ 4 5 6
4587: $
4588: $ i = {0,1,3,6} [size = nrow+1 = 3+1]
4589: $ j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
4590: $ v = {1,2,3,4,5,6} [size = 6]
4593: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4595: @*/
4596: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
4597: {
4599: PetscInt ii;
4600: Mat_SeqAIJ *aij;
4601: #if defined(PETSC_USE_DEBUG)
4602: PetscInt jj;
4603: #endif
4606: if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4607: MatCreate(comm,mat);
4608: MatSetSizes(*mat,m,n,m,n);
4609: /* MatSetBlockSizes(*mat,,); */
4610: MatSetType(*mat,MATSEQAIJ);
4611: MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4612: aij = (Mat_SeqAIJ*)(*mat)->data;
4613: PetscMalloc2(m,&aij->imax,m,&aij->ilen);
4615: aij->i = i;
4616: aij->j = j;
4617: aij->a = a;
4618: aij->singlemalloc = PETSC_FALSE;
4619: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4620: aij->free_a = PETSC_FALSE;
4621: aij->free_ij = PETSC_FALSE;
4623: for (ii=0; ii<m; ii++) {
4624: aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4625: #if defined(PETSC_USE_DEBUG)
4626: 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]);
4627: for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4628: if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
4629: if (j[jj] == j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual column %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
4630: }
4631: #endif
4632: }
4633: #if defined(PETSC_USE_DEBUG)
4634: for (ii=0; ii<aij->i[m]; ii++) {
4635: if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4636: 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]);
4637: }
4638: #endif
4640: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4641: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4642: return(0);
4643: }
4644: /*@C
4645: MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4646: provided by the user.
4648: Collective on MPI_Comm
4650: Input Parameters:
4651: + comm - must be an MPI communicator of size 1
4652: . m - number of rows
4653: . n - number of columns
4654: . i - row indices
4655: . j - column indices
4656: . a - matrix values
4657: . nz - number of nonzeros
4658: - idx - 0 or 1 based
4660: Output Parameter:
4661: . mat - the matrix
4663: Level: intermediate
4665: Notes:
4666: The i and j indices are 0 based
4668: The format which is used for the sparse matrix input, is equivalent to a
4669: row-major ordering.. i.e for the following matrix, the input data expected is
4670: as shown:
4672: 1 0 0
4673: 2 0 3
4674: 4 5 6
4676: i = {0,1,1,2,2,2}
4677: j = {0,0,2,0,1,2}
4678: v = {1,2,3,4,5,6}
4681: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4683: @*/
4684: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
4685: {
4687: PetscInt ii, *nnz, one = 1,row,col;
4691: PetscCalloc1(m,&nnz);
4692: for (ii = 0; ii < nz; ii++) {
4693: nnz[i[ii] - !!idx] += 1;
4694: }
4695: MatCreate(comm,mat);
4696: MatSetSizes(*mat,m,n,m,n);
4697: MatSetType(*mat,MATSEQAIJ);
4698: MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4699: for (ii = 0; ii < nz; ii++) {
4700: if (idx) {
4701: row = i[ii] - 1;
4702: col = j[ii] - 1;
4703: } else {
4704: row = i[ii];
4705: col = j[ii];
4706: }
4707: MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4708: }
4709: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4710: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4711: PetscFree(nnz);
4712: return(0);
4713: }
4715: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4716: {
4717: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data;
4721: a->idiagvalid = PETSC_FALSE;
4722: a->ibdiagvalid = PETSC_FALSE;
4724: MatSeqAIJInvalidateDiagonal_Inode(A);
4725: return(0);
4726: }
4728: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4729: {
4731: PetscMPIInt size;
4734: MPI_Comm_size(comm,&size);
4735: if (size == 1) {
4736: if (scall == MAT_INITIAL_MATRIX) {
4737: MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
4738: } else {
4739: MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
4740: }
4741: } else {
4742: MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
4743: }
4744: return(0);
4745: }
4747: /*
4748: Permute A into C's *local* index space using rowemb,colemb.
4749: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
4750: of [0,m), colemb is in [0,n).
4751: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
4752: */
4753: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
4754: {
4755: /* If making this function public, change the error returned in this function away from _PLIB. */
4757: Mat_SeqAIJ *Baij;
4758: PetscBool seqaij;
4759: PetscInt m,n,*nz,i,j,count;
4760: PetscScalar v;
4761: const PetscInt *rowindices,*colindices;
4764: if (!B) return(0);
4765: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
4766: PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
4767: if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
4768: if (rowemb) {
4769: ISGetLocalSize(rowemb,&m);
4770: 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);
4771: } else {
4772: if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
4773: }
4774: if (colemb) {
4775: ISGetLocalSize(colemb,&n);
4776: 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);
4777: } else {
4778: if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
4779: }
4781: Baij = (Mat_SeqAIJ*)(B->data);
4782: if (pattern == DIFFERENT_NONZERO_PATTERN) {
4783: PetscMalloc1(B->rmap->n,&nz);
4784: for (i=0; i<B->rmap->n; i++) {
4785: nz[i] = Baij->i[i+1] - Baij->i[i];
4786: }
4787: MatSeqAIJSetPreallocation(C,0,nz);
4788: PetscFree(nz);
4789: }
4790: if (pattern == SUBSET_NONZERO_PATTERN) {
4791: MatZeroEntries(C);
4792: }
4793: count = 0;
4794: rowindices = NULL;
4795: colindices = NULL;
4796: if (rowemb) {
4797: ISGetIndices(rowemb,&rowindices);
4798: }
4799: if (colemb) {
4800: ISGetIndices(colemb,&colindices);
4801: }
4802: for (i=0; i<B->rmap->n; i++) {
4803: PetscInt row;
4804: row = i;
4805: if (rowindices) row = rowindices[i];
4806: for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
4807: PetscInt col;
4808: col = Baij->j[count];
4809: if (colindices) col = colindices[col];
4810: v = Baij->a[count];
4811: MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
4812: ++count;
4813: }
4814: }
4815: /* FIXME: set C's nonzerostate correctly. */
4816: /* Assembly for C is necessary. */
4817: C->preallocated = PETSC_TRUE;
4818: C->assembled = PETSC_TRUE;
4819: C->was_assembled = PETSC_FALSE;
4820: return(0);
4821: }
4823: PetscFunctionList MatSeqAIJList = NULL;
4825: /*@C
4826: MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype
4828: Collective on Mat
4830: Input Parameters:
4831: + mat - the matrix object
4832: - matype - matrix type
4834: Options Database Key:
4835: . -mat_seqai_type <method> - for example seqaijcrl
4838: Level: intermediate
4840: .keywords: Mat, MatType, set, method
4842: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
4843: @*/
4844: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
4845: {
4846: PetscErrorCode ierr,(*r)(Mat,MatType,MatReuse,Mat*);
4847: PetscBool sametype;
4851: PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
4852: if (sametype) return(0);
4854: PetscFunctionListFind(MatSeqAIJList,matype,&r);
4855: if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
4856: (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
4857: return(0);
4858: }
4861: /*@C
4862: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential AIJ matrices
4864: Not Collective
4866: Input Parameters:
4867: + name - name of a new user-defined matrix type, for example MATSEQAIJCRL
4868: - function - routine to convert to subtype
4870: Notes:
4871: MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.
4874: Then, your matrix can be chosen with the procedural interface at runtime via the option
4875: $ -mat_seqaij_type my_mat
4877: Level: advanced
4879: .keywords: Mat, register
4881: .seealso: MatSeqAIJRegisterAll()
4884: Level: advanced
4885: @*/
4886: PetscErrorCode MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
4887: {
4891: MatInitializePackage();
4892: PetscFunctionListAdd(&MatSeqAIJList,sname,function);
4893: return(0);
4894: }
4896: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
4898: /*@C
4899: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ
4901: Not Collective
4903: Level: advanced
4905: Developers Note: CUSP and CUSPARSE do not yet support the MatConvert_SeqAIJ..() paradigm and thus cannot be registered here
4907: .keywords: KSP, register, all
4909: .seealso: MatRegisterAll(), MatSeqAIJRegister()
4910: @*/
4911: PetscErrorCode MatSeqAIJRegisterAll(void)
4912: {
4916: if (MatSeqAIJRegisterAllCalled) return(0);
4917: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
4919: MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL);
4920: MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM);
4921: MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL);
4922: #if defined(PETSC_HAVE_MKL_SPARSE)
4923: MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL);
4924: #endif
4925: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
4926: MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
4927: #endif
4928: return(0);
4929: }
4931: /*
4932: Special version for direct calls from Fortran
4933: */
4934: #include <petsc/private/fortranimpl.h>
4935: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4936: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4937: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4938: #define matsetvaluesseqaij_ matsetvaluesseqaij
4939: #endif
4941: /* Change these macros so can be used in void function */
4942: #undef CHKERRQ
4943: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
4944: #undef SETERRQ2
4945: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
4946: #undef SETERRQ3
4947: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)
4949: 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)
4950: {
4951: Mat A = *AA;
4952: PetscInt m = *mm, n = *nn;
4953: InsertMode is = *isis;
4954: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
4955: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4956: PetscInt *imax,*ai,*ailen;
4958: PetscInt *aj,nonew = a->nonew,lastcol = -1;
4959: MatScalar *ap,value,*aa;
4960: PetscBool ignorezeroentries = a->ignorezeroentries;
4961: PetscBool roworiented = a->roworiented;
4964: MatCheckPreallocated(A,1);
4965: imax = a->imax;
4966: ai = a->i;
4967: ailen = a->ilen;
4968: aj = a->j;
4969: aa = a->a;
4971: for (k=0; k<m; k++) { /* loop over added rows */
4972: row = im[k];
4973: if (row < 0) continue;
4974: #if defined(PETSC_USE_DEBUG)
4975: if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4976: #endif
4977: rp = aj + ai[row]; ap = aa + ai[row];
4978: rmax = imax[row]; nrow = ailen[row];
4979: low = 0;
4980: high = nrow;
4981: for (l=0; l<n; l++) { /* loop over added columns */
4982: if (in[l] < 0) continue;
4983: #if defined(PETSC_USE_DEBUG)
4984: if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4985: #endif
4986: col = in[l];
4987: if (roworiented) value = v[l + k*n];
4988: else value = v[k + l*m];
4990: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
4992: if (col <= lastcol) low = 0;
4993: else high = nrow;
4994: lastcol = col;
4995: while (high-low > 5) {
4996: t = (low+high)/2;
4997: if (rp[t] > col) high = t;
4998: else low = t;
4999: }
5000: for (i=low; i<high; i++) {
5001: if (rp[i] > col) break;
5002: if (rp[i] == col) {
5003: if (is == ADD_VALUES) ap[i] += value;
5004: else ap[i] = value;
5005: goto noinsert;
5006: }
5007: }
5008: if (value == 0.0 && ignorezeroentries) goto noinsert;
5009: if (nonew == 1) goto noinsert;
5010: if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
5011: MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
5012: N = nrow++ - 1; a->nz++; high++;
5013: /* shift up all the later entries in this row */
5014: for (ii=N; ii>=i; ii--) {
5015: rp[ii+1] = rp[ii];
5016: ap[ii+1] = ap[ii];
5017: }
5018: rp[i] = col;
5019: ap[i] = value;
5020: A->nonzerostate++;
5021: noinsert:;
5022: low = i + 1;
5023: }
5024: ailen[row] = nrow;
5025: }
5026: PetscFunctionReturnVoid();
5027: }