Actual source code: aij.c
petsc-3.11.3 2019-06-26
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: diag = a->diag;
1605: for (i=0; i<A->rmap->n; i++) {
1606: if (diag[i] >= ii[i+1]) {
1607: *missing = PETSC_TRUE;
1608: if (d) *d = i;
1609: PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1610: break;
1611: }
1612: }
1613: }
1614: return(0);
1615: }
1617: #include <petscblaslapack.h>
1618: #include <petsc/private/kernels/blockinvert.h>
1620: /*
1621: Note that values is allocated externally by the PC and then passed into this routine
1622: */
1623: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
1624: {
1625: PetscErrorCode ierr;
1626: PetscInt n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
1627: PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;
1628: const PetscReal shift = 0.0;
1629: PetscInt ipvt[5];
1630: PetscScalar work[25],*v_work;
1633: allowzeropivot = PetscNot(A->erroriffailure);
1634: for (i=0; i<nblocks; i++) ncnt += bsizes[i];
1635: if (ncnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Total blocksizes %D doesn't match number matrix rows %D",ncnt,n);
1636: for (i=0; i<nblocks; i++) {
1637: bsizemax = PetscMax(bsizemax,bsizes[i]);
1638: }
1639: PetscMalloc1(bsizemax,&indx);
1640: if (bsizemax > 7) {
1641: PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
1642: }
1643: ncnt = 0;
1644: for (i=0; i<nblocks; i++) {
1645: for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
1646: MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
1647: switch (bsizes[i]) {
1648: case 1:
1649: *diag = 1.0/(*diag);
1650: break;
1651: case 2:
1652: PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
1653: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1654: PetscKernel_A_gets_transpose_A_2(diag);
1655: break;
1656: case 3:
1657: PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
1658: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1659: PetscKernel_A_gets_transpose_A_3(diag);
1660: break;
1661: case 4:
1662: PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
1663: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1664: PetscKernel_A_gets_transpose_A_4(diag);
1665: break;
1666: case 5:
1667: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
1668: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1669: PetscKernel_A_gets_transpose_A_5(diag);
1670: break;
1671: case 6:
1672: PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
1673: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1674: PetscKernel_A_gets_transpose_A_6(diag);
1675: break;
1676: case 7:
1677: PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
1678: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1679: PetscKernel_A_gets_transpose_A_7(diag);
1680: break;
1681: default:
1682: PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
1683: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1684: PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
1685: }
1686: ncnt += bsizes[i];
1687: diag += bsizes[i]*bsizes[i];
1688: }
1689: if (bsizemax > 7) {
1690: PetscFree2(v_work,v_pivots);
1691: }
1692: PetscFree(indx);
1693: return(0);
1694: }
1696: /*
1697: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1698: */
1699: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1700: {
1701: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
1703: PetscInt i,*diag,m = A->rmap->n;
1704: MatScalar *v = a->a;
1705: PetscScalar *idiag,*mdiag;
1708: if (a->idiagvalid) return(0);
1709: MatMarkDiagonal_SeqAIJ(A);
1710: diag = a->diag;
1711: if (!a->idiag) {
1712: PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1713: PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
1714: v = a->a;
1715: }
1716: mdiag = a->mdiag;
1717: idiag = a->idiag;
1719: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1720: for (i=0; i<m; i++) {
1721: mdiag[i] = v[diag[i]];
1722: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1723: if (PetscRealPart(fshift)) {
1724: PetscInfo1(A,"Zero diagonal on row %D\n",i);
1725: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1726: A->factorerror_zeropivot_value = 0.0;
1727: A->factorerror_zeropivot_row = i;
1728: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1729: }
1730: idiag[i] = 1.0/v[diag[i]];
1731: }
1732: PetscLogFlops(m);
1733: } else {
1734: for (i=0; i<m; i++) {
1735: mdiag[i] = v[diag[i]];
1736: idiag[i] = omega/(fshift + v[diag[i]]);
1737: }
1738: PetscLogFlops(2.0*m);
1739: }
1740: a->idiagvalid = PETSC_TRUE;
1741: return(0);
1742: }
1744: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1745: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1746: {
1747: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1748: PetscScalar *x,d,sum,*t,scale;
1749: const MatScalar *v,*idiag=0,*mdiag;
1750: const PetscScalar *b, *bs,*xb, *ts;
1751: PetscErrorCode ierr;
1752: PetscInt n,m = A->rmap->n,i;
1753: const PetscInt *idx,*diag;
1756: its = its*lits;
1758: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1759: if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1760: a->fshift = fshift;
1761: a->omega = omega;
1763: diag = a->diag;
1764: t = a->ssor_work;
1765: idiag = a->idiag;
1766: mdiag = a->mdiag;
1768: VecGetArray(xx,&x);
1769: VecGetArrayRead(bb,&b);
1770: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1771: if (flag == SOR_APPLY_UPPER) {
1772: /* apply (U + D/omega) to the vector */
1773: bs = b;
1774: for (i=0; i<m; i++) {
1775: d = fshift + mdiag[i];
1776: n = a->i[i+1] - diag[i] - 1;
1777: idx = a->j + diag[i] + 1;
1778: v = a->a + diag[i] + 1;
1779: sum = b[i]*d/omega;
1780: PetscSparseDensePlusDot(sum,bs,v,idx,n);
1781: x[i] = sum;
1782: }
1783: VecRestoreArray(xx,&x);
1784: VecRestoreArrayRead(bb,&b);
1785: PetscLogFlops(a->nz);
1786: return(0);
1787: }
1789: if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1790: else if (flag & SOR_EISENSTAT) {
1791: /* Let A = L + U + D; where L is lower trianglar,
1792: U is upper triangular, E = D/omega; This routine applies
1794: (L + E)^{-1} A (U + E)^{-1}
1796: to a vector efficiently using Eisenstat's trick.
1797: */
1798: scale = (2.0/omega) - 1.0;
1800: /* x = (E + U)^{-1} b */
1801: for (i=m-1; i>=0; i--) {
1802: n = a->i[i+1] - diag[i] - 1;
1803: idx = a->j + diag[i] + 1;
1804: v = a->a + diag[i] + 1;
1805: sum = b[i];
1806: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1807: x[i] = sum*idiag[i];
1808: }
1810: /* t = b - (2*E - D)x */
1811: v = a->a;
1812: for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];
1814: /* t = (E + L)^{-1}t */
1815: ts = t;
1816: diag = a->diag;
1817: for (i=0; i<m; i++) {
1818: n = diag[i] - a->i[i];
1819: idx = a->j + a->i[i];
1820: v = a->a + a->i[i];
1821: sum = t[i];
1822: PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1823: t[i] = sum*idiag[i];
1824: /* x = x + t */
1825: x[i] += t[i];
1826: }
1828: PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1829: VecRestoreArray(xx,&x);
1830: VecRestoreArrayRead(bb,&b);
1831: return(0);
1832: }
1833: if (flag & SOR_ZERO_INITIAL_GUESS) {
1834: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1835: for (i=0; i<m; i++) {
1836: n = diag[i] - a->i[i];
1837: idx = a->j + a->i[i];
1838: v = a->a + a->i[i];
1839: sum = b[i];
1840: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1841: t[i] = sum;
1842: x[i] = sum*idiag[i];
1843: }
1844: xb = t;
1845: PetscLogFlops(a->nz);
1846: } else xb = b;
1847: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1848: for (i=m-1; i>=0; i--) {
1849: n = a->i[i+1] - diag[i] - 1;
1850: idx = a->j + diag[i] + 1;
1851: v = a->a + diag[i] + 1;
1852: sum = xb[i];
1853: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1854: if (xb == b) {
1855: x[i] = sum*idiag[i];
1856: } else {
1857: x[i] = (1-omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1858: }
1859: }
1860: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1861: }
1862: its--;
1863: }
1864: while (its--) {
1865: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1866: for (i=0; i<m; i++) {
1867: /* lower */
1868: n = diag[i] - a->i[i];
1869: idx = a->j + a->i[i];
1870: v = a->a + a->i[i];
1871: sum = b[i];
1872: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1873: t[i] = sum; /* save application of the lower-triangular part */
1874: /* upper */
1875: n = a->i[i+1] - diag[i] - 1;
1876: idx = a->j + diag[i] + 1;
1877: v = a->a + diag[i] + 1;
1878: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1879: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1880: }
1881: xb = t;
1882: PetscLogFlops(2.0*a->nz);
1883: } else xb = b;
1884: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1885: for (i=m-1; i>=0; i--) {
1886: sum = xb[i];
1887: if (xb == b) {
1888: /* whole matrix (no checkpointing available) */
1889: n = a->i[i+1] - a->i[i];
1890: idx = a->j + a->i[i];
1891: v = a->a + a->i[i];
1892: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1893: x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1894: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
1895: n = a->i[i+1] - diag[i] - 1;
1896: idx = a->j + diag[i] + 1;
1897: v = a->a + diag[i] + 1;
1898: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1899: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1900: }
1901: }
1902: if (xb == b) {
1903: PetscLogFlops(2.0*a->nz);
1904: } else {
1905: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1906: }
1907: }
1908: }
1909: VecRestoreArray(xx,&x);
1910: VecRestoreArrayRead(bb,&b);
1911: return(0);
1912: }
1915: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1916: {
1917: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1920: info->block_size = 1.0;
1921: info->nz_allocated = (double)a->maxnz;
1922: info->nz_used = (double)a->nz;
1923: info->nz_unneeded = (double)(a->maxnz - a->nz);
1924: info->assemblies = (double)A->num_ass;
1925: info->mallocs = (double)A->info.mallocs;
1926: info->memory = ((PetscObject)A)->mem;
1927: if (A->factortype) {
1928: info->fill_ratio_given = A->info.fill_ratio_given;
1929: info->fill_ratio_needed = A->info.fill_ratio_needed;
1930: info->factor_mallocs = A->info.factor_mallocs;
1931: } else {
1932: info->fill_ratio_given = 0;
1933: info->fill_ratio_needed = 0;
1934: info->factor_mallocs = 0;
1935: }
1936: return(0);
1937: }
1939: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1940: {
1941: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1942: PetscInt i,m = A->rmap->n - 1;
1943: PetscErrorCode ierr;
1944: const PetscScalar *xx;
1945: PetscScalar *bb;
1946: PetscInt d = 0;
1949: if (x && b) {
1950: VecGetArrayRead(x,&xx);
1951: VecGetArray(b,&bb);
1952: for (i=0; i<N; i++) {
1953: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1954: if (rows[i] >= A->cmap->n) continue;
1955: bb[rows[i]] = diag*xx[rows[i]];
1956: }
1957: VecRestoreArrayRead(x,&xx);
1958: VecRestoreArray(b,&bb);
1959: }
1961: if (a->keepnonzeropattern) {
1962: for (i=0; i<N; i++) {
1963: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1964: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1965: }
1966: if (diag != 0.0) {
1967: for (i=0; i<N; i++) {
1968: d = rows[i];
1969: if (rows[i] >= A->cmap->n) continue;
1970: 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);
1971: }
1972: for (i=0; i<N; i++) {
1973: if (rows[i] >= A->cmap->n) continue;
1974: a->a[a->diag[rows[i]]] = diag;
1975: }
1976: }
1977: } else {
1978: if (diag != 0.0) {
1979: for (i=0; i<N; i++) {
1980: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1981: if (a->ilen[rows[i]] > 0) {
1982: if (rows[i] >= A->cmap->n) {
1983: a->ilen[rows[i]] = 0;
1984: } else {
1985: a->ilen[rows[i]] = 1;
1986: a->a[a->i[rows[i]]] = diag;
1987: a->j[a->i[rows[i]]] = rows[i];
1988: }
1989: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
1990: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1991: }
1992: }
1993: } else {
1994: for (i=0; i<N; i++) {
1995: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1996: a->ilen[rows[i]] = 0;
1997: }
1998: }
1999: A->nonzerostate++;
2000: }
2001: (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2002: return(0);
2003: }
2005: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2006: {
2007: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2008: PetscInt i,j,m = A->rmap->n - 1,d = 0;
2009: PetscErrorCode ierr;
2010: PetscBool missing,*zeroed,vecs = PETSC_FALSE;
2011: const PetscScalar *xx;
2012: PetscScalar *bb;
2015: if (x && b) {
2016: VecGetArrayRead(x,&xx);
2017: VecGetArray(b,&bb);
2018: vecs = PETSC_TRUE;
2019: }
2020: PetscCalloc1(A->rmap->n,&zeroed);
2021: for (i=0; i<N; i++) {
2022: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2023: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
2025: zeroed[rows[i]] = PETSC_TRUE;
2026: }
2027: for (i=0; i<A->rmap->n; i++) {
2028: if (!zeroed[i]) {
2029: for (j=a->i[i]; j<a->i[i+1]; j++) {
2030: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2031: if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
2032: a->a[j] = 0.0;
2033: }
2034: }
2035: } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
2036: }
2037: if (x && b) {
2038: VecRestoreArrayRead(x,&xx);
2039: VecRestoreArray(b,&bb);
2040: }
2041: PetscFree(zeroed);
2042: if (diag != 0.0) {
2043: MatMissingDiagonal_SeqAIJ(A,&missing,&d);
2044: if (missing) {
2045: for (i=0; i<N; i++) {
2046: if (rows[i] >= A->cmap->N) continue;
2047: 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]);
2048: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2049: }
2050: } else {
2051: for (i=0; i<N; i++) {
2052: a->a[a->diag[rows[i]]] = diag;
2053: }
2054: }
2055: }
2056: (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2057: return(0);
2058: }
2060: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2061: {
2062: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2063: PetscInt *itmp;
2066: if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);
2068: *nz = a->i[row+1] - a->i[row];
2069: if (v) *v = a->a + a->i[row];
2070: if (idx) {
2071: itmp = a->j + a->i[row];
2072: if (*nz) *idx = itmp;
2073: else *idx = 0;
2074: }
2075: return(0);
2076: }
2078: /* remove this function? */
2079: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2080: {
2082: return(0);
2083: }
2085: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
2086: {
2087: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2088: MatScalar *v = a->a;
2089: PetscReal sum = 0.0;
2091: PetscInt i,j;
2094: if (type == NORM_FROBENIUS) {
2095: #if defined(PETSC_USE_REAL___FP16)
2096: PetscBLASInt one = 1,nz = a->nz;
2097: *nrm = BLASnrm2_(&nz,v,&one);
2098: #else
2099: for (i=0; i<a->nz; i++) {
2100: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
2101: }
2102: *nrm = PetscSqrtReal(sum);
2103: #endif
2104: PetscLogFlops(2*a->nz);
2105: } else if (type == NORM_1) {
2106: PetscReal *tmp;
2107: PetscInt *jj = a->j;
2108: PetscCalloc1(A->cmap->n+1,&tmp);
2109: *nrm = 0.0;
2110: for (j=0; j<a->nz; j++) {
2111: tmp[*jj++] += PetscAbsScalar(*v); v++;
2112: }
2113: for (j=0; j<A->cmap->n; j++) {
2114: if (tmp[j] > *nrm) *nrm = tmp[j];
2115: }
2116: PetscFree(tmp);
2117: PetscLogFlops(PetscMax(a->nz-1,0));
2118: } else if (type == NORM_INFINITY) {
2119: *nrm = 0.0;
2120: for (j=0; j<A->rmap->n; j++) {
2121: v = a->a + a->i[j];
2122: sum = 0.0;
2123: for (i=0; i<a->i[j+1]-a->i[j]; i++) {
2124: sum += PetscAbsScalar(*v); v++;
2125: }
2126: if (sum > *nrm) *nrm = sum;
2127: }
2128: PetscLogFlops(PetscMax(a->nz-1,0));
2129: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
2130: return(0);
2131: }
2133: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
2134: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
2135: {
2137: PetscInt i,j,anzj;
2138: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b;
2139: PetscInt an=A->cmap->N,am=A->rmap->N;
2140: PetscInt *ati,*atj,*atfill,*ai=a->i,*aj=a->j;
2143: /* Allocate space for symbolic transpose info and work array */
2144: PetscCalloc1(an+1,&ati);
2145: PetscMalloc1(ai[am],&atj);
2146: PetscMalloc1(an,&atfill);
2148: /* Walk through aj and count ## of non-zeros in each row of A^T. */
2149: /* Note: offset by 1 for fast conversion into csr format. */
2150: for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
2151: /* Form ati for csr format of A^T. */
2152: for (i=0;i<an;i++) ati[i+1] += ati[i];
2154: /* Copy ati into atfill so we have locations of the next free space in atj */
2155: PetscMemcpy(atfill,ati,an*sizeof(PetscInt));
2157: /* Walk through A row-wise and mark nonzero entries of A^T. */
2158: for (i=0;i<am;i++) {
2159: anzj = ai[i+1] - ai[i];
2160: for (j=0;j<anzj;j++) {
2161: atj[atfill[*aj]] = i;
2162: atfill[*aj++] += 1;
2163: }
2164: }
2166: /* Clean up temporary space and complete requests. */
2167: PetscFree(atfill);
2168: MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2169: MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2171: b = (Mat_SeqAIJ*)((*B)->data);
2172: b->free_a = PETSC_FALSE;
2173: b->free_ij = PETSC_TRUE;
2174: b->nonew = 0;
2175: return(0);
2176: }
2178: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
2179: {
2180: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2181: Mat C;
2183: PetscInt i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
2184: MatScalar *array = a->a;
2187: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_INPLACE_MATRIX) {
2188: PetscCalloc1(1+A->cmap->n,&col);
2190: for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
2191: MatCreate(PetscObjectComm((PetscObject)A),&C);
2192: MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
2193: MatSetBlockSizes(C,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2194: MatSetType(C,((PetscObject)A)->type_name);
2195: MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
2196: PetscFree(col);
2197: } else {
2198: C = *B;
2199: }
2201: for (i=0; i<m; i++) {
2202: len = ai[i+1]-ai[i];
2203: MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
2204: array += len;
2205: aj += len;
2206: }
2207: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2208: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2210: if (reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX) {
2211: *B = C;
2212: } else {
2213: MatHeaderMerge(A,&C);
2214: }
2215: return(0);
2216: }
2218: PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2219: {
2220: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2221: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2222: MatScalar *va,*vb;
2224: PetscInt ma,na,mb,nb, i;
2227: MatGetSize(A,&ma,&na);
2228: MatGetSize(B,&mb,&nb);
2229: if (ma!=nb || na!=mb) {
2230: *f = PETSC_FALSE;
2231: return(0);
2232: }
2233: aii = aij->i; bii = bij->i;
2234: adx = aij->j; bdx = bij->j;
2235: va = aij->a; vb = bij->a;
2236: PetscMalloc1(ma,&aptr);
2237: PetscMalloc1(mb,&bptr);
2238: for (i=0; i<ma; i++) aptr[i] = aii[i];
2239: for (i=0; i<mb; i++) bptr[i] = bii[i];
2241: *f = PETSC_TRUE;
2242: for (i=0; i<ma; i++) {
2243: while (aptr[i]<aii[i+1]) {
2244: PetscInt idc,idr;
2245: PetscScalar vc,vr;
2246: /* column/row index/value */
2247: idc = adx[aptr[i]];
2248: idr = bdx[bptr[idc]];
2249: vc = va[aptr[i]];
2250: vr = vb[bptr[idc]];
2251: if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2252: *f = PETSC_FALSE;
2253: goto done;
2254: } else {
2255: aptr[i]++;
2256: if (B || i!=idc) bptr[idc]++;
2257: }
2258: }
2259: }
2260: done:
2261: PetscFree(aptr);
2262: PetscFree(bptr);
2263: return(0);
2264: }
2266: PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2267: {
2268: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2269: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2270: MatScalar *va,*vb;
2272: PetscInt ma,na,mb,nb, i;
2275: MatGetSize(A,&ma,&na);
2276: MatGetSize(B,&mb,&nb);
2277: if (ma!=nb || na!=mb) {
2278: *f = PETSC_FALSE;
2279: return(0);
2280: }
2281: aii = aij->i; bii = bij->i;
2282: adx = aij->j; bdx = bij->j;
2283: va = aij->a; vb = bij->a;
2284: PetscMalloc1(ma,&aptr);
2285: PetscMalloc1(mb,&bptr);
2286: for (i=0; i<ma; i++) aptr[i] = aii[i];
2287: for (i=0; i<mb; i++) bptr[i] = bii[i];
2289: *f = PETSC_TRUE;
2290: for (i=0; i<ma; i++) {
2291: while (aptr[i]<aii[i+1]) {
2292: PetscInt idc,idr;
2293: PetscScalar vc,vr;
2294: /* column/row index/value */
2295: idc = adx[aptr[i]];
2296: idr = bdx[bptr[idc]];
2297: vc = va[aptr[i]];
2298: vr = vb[bptr[idc]];
2299: if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2300: *f = PETSC_FALSE;
2301: goto done;
2302: } else {
2303: aptr[i]++;
2304: if (B || i!=idc) bptr[idc]++;
2305: }
2306: }
2307: }
2308: done:
2309: PetscFree(aptr);
2310: PetscFree(bptr);
2311: return(0);
2312: }
2314: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2315: {
2319: MatIsTranspose_SeqAIJ(A,A,tol,f);
2320: return(0);
2321: }
2323: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2324: {
2328: MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2329: return(0);
2330: }
2332: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2333: {
2334: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2335: const PetscScalar *l,*r;
2336: PetscScalar x;
2337: MatScalar *v;
2338: PetscErrorCode ierr;
2339: PetscInt i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
2340: const PetscInt *jj;
2343: if (ll) {
2344: /* The local size is used so that VecMPI can be passed to this routine
2345: by MatDiagonalScale_MPIAIJ */
2346: VecGetLocalSize(ll,&m);
2347: if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2348: VecGetArrayRead(ll,&l);
2349: v = a->a;
2350: for (i=0; i<m; i++) {
2351: x = l[i];
2352: M = a->i[i+1] - a->i[i];
2353: for (j=0; j<M; j++) (*v++) *= x;
2354: }
2355: VecRestoreArrayRead(ll,&l);
2356: PetscLogFlops(nz);
2357: }
2358: if (rr) {
2359: VecGetLocalSize(rr,&n);
2360: if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2361: VecGetArrayRead(rr,&r);
2362: v = a->a; jj = a->j;
2363: for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2364: VecRestoreArrayRead(rr,&r);
2365: PetscLogFlops(nz);
2366: }
2367: MatSeqAIJInvalidateDiagonal(A);
2368: return(0);
2369: }
2371: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2372: {
2373: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c;
2375: PetscInt *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2376: PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2377: const PetscInt *irow,*icol;
2378: PetscInt nrows,ncols;
2379: PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2380: MatScalar *a_new,*mat_a;
2381: Mat C;
2382: PetscBool stride;
2386: ISGetIndices(isrow,&irow);
2387: ISGetLocalSize(isrow,&nrows);
2388: ISGetLocalSize(iscol,&ncols);
2390: PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2391: if (stride) {
2392: ISStrideGetInfo(iscol,&first,&step);
2393: } else {
2394: first = 0;
2395: step = 0;
2396: }
2397: if (stride && step == 1) {
2398: /* special case of contiguous rows */
2399: PetscMalloc2(nrows,&lens,nrows,&starts);
2400: /* loop over new rows determining lens and starting points */
2401: for (i=0; i<nrows; i++) {
2402: kstart = ai[irow[i]];
2403: kend = kstart + ailen[irow[i]];
2404: starts[i] = kstart;
2405: for (k=kstart; k<kend; k++) {
2406: if (aj[k] >= first) {
2407: starts[i] = k;
2408: break;
2409: }
2410: }
2411: sum = 0;
2412: while (k < kend) {
2413: if (aj[k++] >= first+ncols) break;
2414: sum++;
2415: }
2416: lens[i] = sum;
2417: }
2418: /* create submatrix */
2419: if (scall == MAT_REUSE_MATRIX) {
2420: PetscInt n_cols,n_rows;
2421: MatGetSize(*B,&n_rows,&n_cols);
2422: if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2423: MatZeroEntries(*B);
2424: C = *B;
2425: } else {
2426: PetscInt rbs,cbs;
2427: MatCreate(PetscObjectComm((PetscObject)A),&C);
2428: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2429: ISGetBlockSize(isrow,&rbs);
2430: ISGetBlockSize(iscol,&cbs);
2431: MatSetBlockSizes(C,rbs,cbs);
2432: MatSetType(C,((PetscObject)A)->type_name);
2433: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2434: }
2435: c = (Mat_SeqAIJ*)C->data;
2437: /* loop over rows inserting into submatrix */
2438: a_new = c->a;
2439: j_new = c->j;
2440: i_new = c->i;
2442: for (i=0; i<nrows; i++) {
2443: ii = starts[i];
2444: lensi = lens[i];
2445: for (k=0; k<lensi; k++) {
2446: *j_new++ = aj[ii+k] - first;
2447: }
2448: PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2449: a_new += lensi;
2450: i_new[i+1] = i_new[i] + lensi;
2451: c->ilen[i] = lensi;
2452: }
2453: PetscFree2(lens,starts);
2454: } else {
2455: ISGetIndices(iscol,&icol);
2456: PetscCalloc1(oldcols,&smap);
2457: PetscMalloc1(1+nrows,&lens);
2458: for (i=0; i<ncols; i++) {
2459: #if defined(PETSC_USE_DEBUG)
2460: 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);
2461: #endif
2462: smap[icol[i]] = i+1;
2463: }
2465: /* determine lens of each row */
2466: for (i=0; i<nrows; i++) {
2467: kstart = ai[irow[i]];
2468: kend = kstart + a->ilen[irow[i]];
2469: lens[i] = 0;
2470: for (k=kstart; k<kend; k++) {
2471: if (smap[aj[k]]) {
2472: lens[i]++;
2473: }
2474: }
2475: }
2476: /* Create and fill new matrix */
2477: if (scall == MAT_REUSE_MATRIX) {
2478: PetscBool equal;
2480: c = (Mat_SeqAIJ*)((*B)->data);
2481: if ((*B)->rmap->n != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2482: PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2483: if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2484: PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2485: C = *B;
2486: } else {
2487: PetscInt rbs,cbs;
2488: MatCreate(PetscObjectComm((PetscObject)A),&C);
2489: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2490: ISGetBlockSize(isrow,&rbs);
2491: ISGetBlockSize(iscol,&cbs);
2492: MatSetBlockSizes(C,rbs,cbs);
2493: MatSetType(C,((PetscObject)A)->type_name);
2494: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2495: }
2496: c = (Mat_SeqAIJ*)(C->data);
2497: for (i=0; i<nrows; i++) {
2498: row = irow[i];
2499: kstart = ai[row];
2500: kend = kstart + a->ilen[row];
2501: mat_i = c->i[i];
2502: mat_j = c->j + mat_i;
2503: mat_a = c->a + mat_i;
2504: mat_ilen = c->ilen + i;
2505: for (k=kstart; k<kend; k++) {
2506: if ((tcol=smap[a->j[k]])) {
2507: *mat_j++ = tcol - 1;
2508: *mat_a++ = a->a[k];
2509: (*mat_ilen)++;
2511: }
2512: }
2513: }
2514: /* Free work space */
2515: ISRestoreIndices(iscol,&icol);
2516: PetscFree(smap);
2517: PetscFree(lens);
2518: /* sort */
2519: for (i = 0; i < nrows; i++) {
2520: PetscInt ilen;
2522: mat_i = c->i[i];
2523: mat_j = c->j + mat_i;
2524: mat_a = c->a + mat_i;
2525: ilen = c->ilen[i];
2526: PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
2527: }
2528: }
2529: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2530: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2532: ISRestoreIndices(isrow,&irow);
2533: *B = C;
2534: return(0);
2535: }
2537: PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2538: {
2540: Mat B;
2543: if (scall == MAT_INITIAL_MATRIX) {
2544: MatCreate(subComm,&B);
2545: MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2546: MatSetBlockSizesFromMats(B,mat,mat);
2547: MatSetType(B,MATSEQAIJ);
2548: MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2549: *subMat = B;
2550: } else {
2551: MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2552: }
2553: return(0);
2554: }
2556: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2557: {
2558: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2560: Mat outA;
2561: PetscBool row_identity,col_identity;
2564: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
2566: ISIdentity(row,&row_identity);
2567: ISIdentity(col,&col_identity);
2569: outA = inA;
2570: outA->factortype = MAT_FACTOR_LU;
2571: PetscFree(inA->solvertype);
2572: PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);
2574: PetscObjectReference((PetscObject)row);
2575: ISDestroy(&a->row);
2577: a->row = row;
2579: PetscObjectReference((PetscObject)col);
2580: ISDestroy(&a->col);
2582: a->col = col;
2584: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2585: ISDestroy(&a->icol);
2586: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2587: PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);
2589: if (!a->solve_work) { /* this matrix may have been factored before */
2590: PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2591: PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2592: }
2594: MatMarkDiagonal_SeqAIJ(inA);
2595: if (row_identity && col_identity) {
2596: MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2597: } else {
2598: MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2599: }
2600: return(0);
2601: }
2603: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2604: {
2605: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2606: PetscScalar oalpha = alpha;
2608: PetscBLASInt one = 1,bnz;
2611: PetscBLASIntCast(a->nz,&bnz);
2612: PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2613: PetscLogFlops(a->nz);
2614: MatSeqAIJInvalidateDiagonal(inA);
2615: return(0);
2616: }
2618: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2619: {
2621: PetscInt i;
2624: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2625: PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);
2627: for (i=0; i<submatj->nrqr; ++i) {
2628: PetscFree(submatj->sbuf2[i]);
2629: }
2630: PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);
2632: if (submatj->rbuf1) {
2633: PetscFree(submatj->rbuf1[0]);
2634: PetscFree(submatj->rbuf1);
2635: }
2637: for (i=0; i<submatj->nrqs; ++i) {
2638: PetscFree(submatj->rbuf3[i]);
2639: }
2640: PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
2641: PetscFree(submatj->pa);
2642: }
2644: #if defined(PETSC_USE_CTABLE)
2645: PetscTableDestroy((PetscTable*)&submatj->rmap);
2646: if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
2647: PetscFree(submatj->rmap_loc);
2648: #else
2649: PetscFree(submatj->rmap);
2650: #endif
2652: if (!submatj->allcolumns) {
2653: #if defined(PETSC_USE_CTABLE)
2654: PetscTableDestroy((PetscTable*)&submatj->cmap);
2655: #else
2656: PetscFree(submatj->cmap);
2657: #endif
2658: }
2659: PetscFree(submatj->row2proc);
2661: PetscFree(submatj);
2662: return(0);
2663: }
2665: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2666: {
2668: Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
2669: Mat_SubSppt *submatj = c->submatis1;
2672: (*submatj->destroy)(C);
2673: MatDestroySubMatrix_Private(submatj);
2674: return(0);
2675: }
2677: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
2678: {
2680: PetscInt i;
2681: Mat C;
2682: Mat_SeqAIJ *c;
2683: Mat_SubSppt *submatj;
2686: for (i=0; i<n; i++) {
2687: C = (*mat)[i];
2688: c = (Mat_SeqAIJ*)C->data;
2689: submatj = c->submatis1;
2690: if (submatj) {
2691: if (--((PetscObject)C)->refct <= 0) {
2692: (*submatj->destroy)(C);
2693: MatDestroySubMatrix_Private(submatj);
2694: PetscFree(C->defaultvectype);
2695: PetscLayoutDestroy(&C->rmap);
2696: PetscLayoutDestroy(&C->cmap);
2697: PetscHeaderDestroy(&C);
2698: }
2699: } else {
2700: MatDestroy(&C);
2701: }
2702: }
2704: /* Destroy Dummy submatrices created for reuse */
2705: MatDestroySubMatrices_Dummy(n,mat);
2707: PetscFree(*mat);
2708: return(0);
2709: }
2711: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2712: {
2714: PetscInt i;
2717: if (scall == MAT_INITIAL_MATRIX) {
2718: PetscCalloc1(n+1,B);
2719: }
2721: for (i=0; i<n; i++) {
2722: MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2723: }
2724: return(0);
2725: }
2727: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2728: {
2729: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2731: PetscInt row,i,j,k,l,m,n,*nidx,isz,val;
2732: const PetscInt *idx;
2733: PetscInt start,end,*ai,*aj;
2734: PetscBT table;
2737: m = A->rmap->n;
2738: ai = a->i;
2739: aj = a->j;
2741: if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");
2743: PetscMalloc1(m+1,&nidx);
2744: PetscBTCreate(m,&table);
2746: for (i=0; i<is_max; i++) {
2747: /* Initialize the two local arrays */
2748: isz = 0;
2749: PetscBTMemzero(m,table);
2751: /* Extract the indices, assume there can be duplicate entries */
2752: ISGetIndices(is[i],&idx);
2753: ISGetLocalSize(is[i],&n);
2755: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2756: for (j=0; j<n; ++j) {
2757: if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2758: }
2759: ISRestoreIndices(is[i],&idx);
2760: ISDestroy(&is[i]);
2762: k = 0;
2763: for (j=0; j<ov; j++) { /* for each overlap */
2764: n = isz;
2765: for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2766: row = nidx[k];
2767: start = ai[row];
2768: end = ai[row+1];
2769: for (l = start; l<end; l++) {
2770: val = aj[l];
2771: if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2772: }
2773: }
2774: }
2775: ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2776: }
2777: PetscBTDestroy(&table);
2778: PetscFree(nidx);
2779: return(0);
2780: }
2782: /* -------------------------------------------------------------- */
2783: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2784: {
2785: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2787: PetscInt i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2788: const PetscInt *row,*col;
2789: PetscInt *cnew,j,*lens;
2790: IS icolp,irowp;
2791: PetscInt *cwork = NULL;
2792: PetscScalar *vwork = NULL;
2795: ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2796: ISGetIndices(irowp,&row);
2797: ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2798: ISGetIndices(icolp,&col);
2800: /* determine lengths of permuted rows */
2801: PetscMalloc1(m+1,&lens);
2802: for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2803: MatCreate(PetscObjectComm((PetscObject)A),B);
2804: MatSetSizes(*B,m,n,m,n);
2805: MatSetBlockSizesFromMats(*B,A,A);
2806: MatSetType(*B,((PetscObject)A)->type_name);
2807: MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2808: PetscFree(lens);
2810: PetscMalloc1(n,&cnew);
2811: for (i=0; i<m; i++) {
2812: MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2813: for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2814: MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2815: MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2816: }
2817: PetscFree(cnew);
2819: (*B)->assembled = PETSC_FALSE;
2821: MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2822: MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2823: ISRestoreIndices(irowp,&row);
2824: ISRestoreIndices(icolp,&col);
2825: ISDestroy(&irowp);
2826: ISDestroy(&icolp);
2827: return(0);
2828: }
2830: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2831: {
2835: /* If the two matrices have the same copy implementation, use fast copy. */
2836: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2837: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2838: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
2840: 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");
2841: PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2842: PetscObjectStateIncrease((PetscObject)B);
2843: } else {
2844: MatCopy_Basic(A,B,str);
2845: }
2846: return(0);
2847: }
2849: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2850: {
2854: MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2855: return(0);
2856: }
2858: PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2859: {
2860: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2863: *array = a->a;
2864: return(0);
2865: }
2867: PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2868: {
2870: return(0);
2871: }
2873: /*
2874: Computes the number of nonzeros per row needed for preallocation when X and Y
2875: have different nonzero structure.
2876: */
2877: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
2878: {
2879: PetscInt i,j,k,nzx,nzy;
2882: /* Set the number of nonzeros in the new matrix */
2883: for (i=0; i<m; i++) {
2884: const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
2885: nzx = xi[i+1] - xi[i];
2886: nzy = yi[i+1] - yi[i];
2887: nnz[i] = 0;
2888: for (j=0,k=0; j<nzx; j++) { /* Point in X */
2889: for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
2890: if (k<nzy && yjj[k]==xjj[j]) k++; /* Skip duplicate */
2891: nnz[i]++;
2892: }
2893: for (; k<nzy; k++) nnz[i]++;
2894: }
2895: return(0);
2896: }
2898: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
2899: {
2900: PetscInt m = Y->rmap->N;
2901: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data;
2902: Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data;
2906: /* Set the number of nonzeros in the new matrix */
2907: MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
2908: return(0);
2909: }
2911: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2912: {
2914: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
2915: PetscBLASInt one=1,bnz;
2918: PetscBLASIntCast(x->nz,&bnz);
2919: if (str == SAME_NONZERO_PATTERN) {
2920: PetscScalar alpha = a;
2921: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2922: MatSeqAIJInvalidateDiagonal(Y);
2923: PetscObjectStateIncrease((PetscObject)Y);
2924: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2925: MatAXPY_Basic(Y,a,X,str);
2926: } else {
2927: Mat B;
2928: PetscInt *nnz;
2929: PetscMalloc1(Y->rmap->N,&nnz);
2930: MatCreate(PetscObjectComm((PetscObject)Y),&B);
2931: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2932: MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2933: MatSetBlockSizesFromMats(B,Y,Y);
2934: MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2935: MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2936: MatSeqAIJSetPreallocation(B,0,nnz);
2937: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2938: MatHeaderReplace(Y,&B);
2939: PetscFree(nnz);
2940: }
2941: return(0);
2942: }
2944: PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
2945: {
2946: #if defined(PETSC_USE_COMPLEX)
2947: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2948: PetscInt i,nz;
2949: PetscScalar *a;
2952: nz = aij->nz;
2953: a = aij->a;
2954: for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
2955: #else
2957: #endif
2958: return(0);
2959: }
2961: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2962: {
2963: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2965: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2966: PetscReal atmp;
2967: PetscScalar *x;
2968: MatScalar *aa;
2971: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2972: aa = a->a;
2973: ai = a->i;
2974: aj = a->j;
2976: VecSet(v,0.0);
2977: VecGetArray(v,&x);
2978: VecGetLocalSize(v,&n);
2979: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2980: for (i=0; i<m; i++) {
2981: ncols = ai[1] - ai[0]; ai++;
2982: x[i] = 0.0;
2983: for (j=0; j<ncols; j++) {
2984: atmp = PetscAbsScalar(*aa);
2985: if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2986: aa++; aj++;
2987: }
2988: }
2989: VecRestoreArray(v,&x);
2990: return(0);
2991: }
2993: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2994: {
2995: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2997: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2998: PetscScalar *x;
2999: MatScalar *aa;
3002: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3003: aa = a->a;
3004: ai = a->i;
3005: aj = a->j;
3007: VecSet(v,0.0);
3008: VecGetArray(v,&x);
3009: VecGetLocalSize(v,&n);
3010: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3011: for (i=0; i<m; i++) {
3012: ncols = ai[1] - ai[0]; ai++;
3013: if (ncols == A->cmap->n) { /* row is dense */
3014: x[i] = *aa; if (idx) idx[i] = 0;
3015: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3016: x[i] = 0.0;
3017: if (idx) {
3018: idx[i] = 0; /* in case ncols is zero */
3019: for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
3020: if (aj[j] > j) {
3021: idx[i] = j;
3022: break;
3023: }
3024: }
3025: }
3026: }
3027: for (j=0; j<ncols; j++) {
3028: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3029: aa++; aj++;
3030: }
3031: }
3032: VecRestoreArray(v,&x);
3033: return(0);
3034: }
3036: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3037: {
3038: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3040: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3041: PetscReal atmp;
3042: PetscScalar *x;
3043: MatScalar *aa;
3046: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3047: aa = a->a;
3048: ai = a->i;
3049: aj = a->j;
3051: VecSet(v,0.0);
3052: VecGetArray(v,&x);
3053: VecGetLocalSize(v,&n);
3054: 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);
3055: for (i=0; i<m; i++) {
3056: ncols = ai[1] - ai[0]; ai++;
3057: if (ncols) {
3058: /* Get first nonzero */
3059: for (j = 0; j < ncols; j++) {
3060: atmp = PetscAbsScalar(aa[j]);
3061: if (atmp > 1.0e-12) {
3062: x[i] = atmp;
3063: if (idx) idx[i] = aj[j];
3064: break;
3065: }
3066: }
3067: if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
3068: } else {
3069: x[i] = 0.0; if (idx) idx[i] = 0;
3070: }
3071: for (j = 0; j < ncols; j++) {
3072: atmp = PetscAbsScalar(*aa);
3073: if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
3074: aa++; aj++;
3075: }
3076: }
3077: VecRestoreArray(v,&x);
3078: return(0);
3079: }
3081: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3082: {
3083: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3084: PetscErrorCode ierr;
3085: PetscInt i,j,m = A->rmap->n,ncols,n;
3086: const PetscInt *ai,*aj;
3087: PetscScalar *x;
3088: const MatScalar *aa;
3091: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3092: aa = a->a;
3093: ai = a->i;
3094: aj = a->j;
3096: VecSet(v,0.0);
3097: VecGetArray(v,&x);
3098: VecGetLocalSize(v,&n);
3099: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3100: for (i=0; i<m; i++) {
3101: ncols = ai[1] - ai[0]; ai++;
3102: if (ncols == A->cmap->n) { /* row is dense */
3103: x[i] = *aa; if (idx) idx[i] = 0;
3104: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3105: x[i] = 0.0;
3106: if (idx) { /* find first implicit 0.0 in the row */
3107: idx[i] = 0; /* in case ncols is zero */
3108: for (j=0; j<ncols; j++) {
3109: if (aj[j] > j) {
3110: idx[i] = j;
3111: break;
3112: }
3113: }
3114: }
3115: }
3116: for (j=0; j<ncols; j++) {
3117: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3118: aa++; aj++;
3119: }
3120: }
3121: VecRestoreArray(v,&x);
3122: return(0);
3123: }
3125: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
3126: {
3127: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
3128: PetscErrorCode ierr;
3129: PetscInt i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
3130: MatScalar *diag,work[25],*v_work;
3131: const PetscReal shift = 0.0;
3132: PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;
3135: allowzeropivot = PetscNot(A->erroriffailure);
3136: if (a->ibdiagvalid) {
3137: if (values) *values = a->ibdiag;
3138: return(0);
3139: }
3140: MatMarkDiagonal_SeqAIJ(A);
3141: if (!a->ibdiag) {
3142: PetscMalloc1(bs2*mbs,&a->ibdiag);
3143: PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
3144: }
3145: diag = a->ibdiag;
3146: if (values) *values = a->ibdiag;
3147: /* factor and invert each block */
3148: switch (bs) {
3149: case 1:
3150: for (i=0; i<mbs; i++) {
3151: MatGetValues(A,1,&i,1,&i,diag+i);
3152: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3153: if (allowzeropivot) {
3154: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3155: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3156: A->factorerror_zeropivot_row = i;
3157: PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3158: } 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);
3159: }
3160: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3161: }
3162: break;
3163: case 2:
3164: for (i=0; i<mbs; i++) {
3165: ij[0] = 2*i; ij[1] = 2*i + 1;
3166: MatGetValues(A,2,ij,2,ij,diag);
3167: PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
3168: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3169: PetscKernel_A_gets_transpose_A_2(diag);
3170: diag += 4;
3171: }
3172: break;
3173: case 3:
3174: for (i=0; i<mbs; i++) {
3175: ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
3176: MatGetValues(A,3,ij,3,ij,diag);
3177: PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
3178: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3179: PetscKernel_A_gets_transpose_A_3(diag);
3180: diag += 9;
3181: }
3182: break;
3183: case 4:
3184: for (i=0; i<mbs; i++) {
3185: ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
3186: MatGetValues(A,4,ij,4,ij,diag);
3187: PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
3188: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3189: PetscKernel_A_gets_transpose_A_4(diag);
3190: diag += 16;
3191: }
3192: break;
3193: case 5:
3194: for (i=0; i<mbs; i++) {
3195: ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
3196: MatGetValues(A,5,ij,5,ij,diag);
3197: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
3198: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3199: PetscKernel_A_gets_transpose_A_5(diag);
3200: diag += 25;
3201: }
3202: break;
3203: case 6:
3204: for (i=0; i<mbs; i++) {
3205: 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;
3206: MatGetValues(A,6,ij,6,ij,diag);
3207: PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
3208: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3209: PetscKernel_A_gets_transpose_A_6(diag);
3210: diag += 36;
3211: }
3212: break;
3213: case 7:
3214: for (i=0; i<mbs; i++) {
3215: 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;
3216: MatGetValues(A,7,ij,7,ij,diag);
3217: PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
3218: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3219: PetscKernel_A_gets_transpose_A_7(diag);
3220: diag += 49;
3221: }
3222: break;
3223: default:
3224: PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3225: for (i=0; i<mbs; i++) {
3226: for (j=0; j<bs; j++) {
3227: IJ[j] = bs*i + j;
3228: }
3229: MatGetValues(A,bs,IJ,bs,IJ,diag);
3230: PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
3231: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3232: PetscKernel_A_gets_transpose_A_N(diag,bs);
3233: diag += bs2;
3234: }
3235: PetscFree3(v_work,v_pivots,IJ);
3236: }
3237: a->ibdiagvalid = PETSC_TRUE;
3238: return(0);
3239: }
3241: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3242: {
3244: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data;
3245: PetscScalar a;
3246: PetscInt m,n,i,j,col;
3249: if (!x->assembled) {
3250: MatGetSize(x,&m,&n);
3251: for (i=0; i<m; i++) {
3252: for (j=0; j<aij->imax[i]; j++) {
3253: PetscRandomGetValue(rctx,&a);
3254: col = (PetscInt)(n*PetscRealPart(a));
3255: MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3256: }
3257: }
3258: } else {
3259: for (i=0; i<aij->nz; i++) {PetscRandomGetValue(rctx,aij->a+i);}
3260: }
3261: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3262: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3263: return(0);
3264: }
3266: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3267: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
3268: {
3270: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data;
3271: PetscScalar a;
3272: PetscInt m,n,i,j,col,nskip;
3275: nskip = high - low;
3276: MatGetSize(x,&m,&n);
3277: n -= nskip; /* shrink number of columns where nonzeros can be set */
3278: for (i=0; i<m; i++) {
3279: for (j=0; j<aij->imax[i]; j++) {
3280: PetscRandomGetValue(rctx,&a);
3281: col = (PetscInt)(n*PetscRealPart(a));
3282: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3283: MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3284: }
3285: }
3286: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3287: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3288: return(0);
3289: }
3292: /* -------------------------------------------------------------------*/
3293: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3294: MatGetRow_SeqAIJ,
3295: MatRestoreRow_SeqAIJ,
3296: MatMult_SeqAIJ,
3297: /* 4*/ MatMultAdd_SeqAIJ,
3298: MatMultTranspose_SeqAIJ,
3299: MatMultTransposeAdd_SeqAIJ,
3300: 0,
3301: 0,
3302: 0,
3303: /* 10*/ 0,
3304: MatLUFactor_SeqAIJ,
3305: 0,
3306: MatSOR_SeqAIJ,
3307: MatTranspose_SeqAIJ_FAST,
3308: /*1 5*/ MatGetInfo_SeqAIJ,
3309: MatEqual_SeqAIJ,
3310: MatGetDiagonal_SeqAIJ,
3311: MatDiagonalScale_SeqAIJ,
3312: MatNorm_SeqAIJ,
3313: /* 20*/ 0,
3314: MatAssemblyEnd_SeqAIJ,
3315: MatSetOption_SeqAIJ,
3316: MatZeroEntries_SeqAIJ,
3317: /* 24*/ MatZeroRows_SeqAIJ,
3318: 0,
3319: 0,
3320: 0,
3321: 0,
3322: /* 29*/ MatSetUp_SeqAIJ,
3323: 0,
3324: 0,
3325: 0,
3326: 0,
3327: /* 34*/ MatDuplicate_SeqAIJ,
3328: 0,
3329: 0,
3330: MatILUFactor_SeqAIJ,
3331: 0,
3332: /* 39*/ MatAXPY_SeqAIJ,
3333: MatCreateSubMatrices_SeqAIJ,
3334: MatIncreaseOverlap_SeqAIJ,
3335: MatGetValues_SeqAIJ,
3336: MatCopy_SeqAIJ,
3337: /* 44*/ MatGetRowMax_SeqAIJ,
3338: MatScale_SeqAIJ,
3339: MatShift_SeqAIJ,
3340: MatDiagonalSet_SeqAIJ,
3341: MatZeroRowsColumns_SeqAIJ,
3342: /* 49*/ MatSetRandom_SeqAIJ,
3343: MatGetRowIJ_SeqAIJ,
3344: MatRestoreRowIJ_SeqAIJ,
3345: MatGetColumnIJ_SeqAIJ,
3346: MatRestoreColumnIJ_SeqAIJ,
3347: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3348: 0,
3349: 0,
3350: MatPermute_SeqAIJ,
3351: 0,
3352: /* 59*/ 0,
3353: MatDestroy_SeqAIJ,
3354: MatView_SeqAIJ,
3355: 0,
3356: MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ,
3357: /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ,
3358: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3359: 0,
3360: 0,
3361: 0,
3362: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3363: MatGetRowMinAbs_SeqAIJ,
3364: 0,
3365: 0,
3366: 0,
3367: /* 74*/ 0,
3368: MatFDColoringApply_AIJ,
3369: 0,
3370: 0,
3371: 0,
3372: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3373: 0,
3374: 0,
3375: 0,
3376: MatLoad_SeqAIJ,
3377: /* 84*/ MatIsSymmetric_SeqAIJ,
3378: MatIsHermitian_SeqAIJ,
3379: 0,
3380: 0,
3381: 0,
3382: /* 89*/ MatMatMult_SeqAIJ_SeqAIJ,
3383: MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3384: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3385: MatPtAP_SeqAIJ_SeqAIJ,
3386: MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy,
3387: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3388: MatMatTransposeMult_SeqAIJ_SeqAIJ,
3389: MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
3390: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3391: 0,
3392: /* 99*/ 0,
3393: 0,
3394: 0,
3395: MatConjugate_SeqAIJ,
3396: 0,
3397: /*104*/ MatSetValuesRow_SeqAIJ,
3398: MatRealPart_SeqAIJ,
3399: MatImaginaryPart_SeqAIJ,
3400: 0,
3401: 0,
3402: /*109*/ MatMatSolve_SeqAIJ,
3403: 0,
3404: MatGetRowMin_SeqAIJ,
3405: 0,
3406: MatMissingDiagonal_SeqAIJ,
3407: /*114*/ 0,
3408: 0,
3409: 0,
3410: 0,
3411: 0,
3412: /*119*/ 0,
3413: 0,
3414: 0,
3415: 0,
3416: MatGetMultiProcBlock_SeqAIJ,
3417: /*124*/ MatFindNonzeroRows_SeqAIJ,
3418: MatGetColumnNorms_SeqAIJ,
3419: MatInvertBlockDiagonal_SeqAIJ,
3420: MatInvertVariableBlockDiagonal_SeqAIJ,
3421: 0,
3422: /*129*/ 0,
3423: MatTransposeMatMult_SeqAIJ_SeqAIJ,
3424: MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
3425: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3426: MatTransposeColoringCreate_SeqAIJ,
3427: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3428: MatTransColoringApplyDenToSp_SeqAIJ,
3429: MatRARt_SeqAIJ_SeqAIJ,
3430: MatRARtSymbolic_SeqAIJ_SeqAIJ,
3431: MatRARtNumeric_SeqAIJ_SeqAIJ,
3432: /*139*/0,
3433: 0,
3434: 0,
3435: MatFDColoringSetUp_SeqXAIJ,
3436: MatFindOffBlockDiagonalEntries_SeqAIJ,
3437: /*144*/MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3438: MatDestroySubMatrices_SeqAIJ
3439: };
3441: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3442: {
3443: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3444: PetscInt i,nz,n;
3447: nz = aij->maxnz;
3448: n = mat->rmap->n;
3449: for (i=0; i<nz; i++) {
3450: aij->j[i] = indices[i];
3451: }
3452: aij->nz = nz;
3453: for (i=0; i<n; i++) {
3454: aij->ilen[i] = aij->imax[i];
3455: }
3456: return(0);
3457: }
3459: /*
3460: * When a sparse matrix has many zero columns, we should compact them out to save the space
3461: * This happens in MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3462: * */
3463: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3464: {
3465: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3466: PetscTable gid1_lid1;
3467: PetscTablePosition tpos;
3468: PetscInt gid,lid,i,j,ncols,ec;
3469: PetscInt *garray;
3470: PetscErrorCode ierr;
3475: /* use a table */
3476: PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
3477: ec = 0;
3478: for (i=0; i<mat->rmap->n; i++) {
3479: ncols = aij->i[i+1] - aij->i[i];
3480: for (j=0; j<ncols; j++) {
3481: PetscInt data,gid1 = aij->j[aij->i[i] + j] + 1;
3482: PetscTableFind(gid1_lid1,gid1,&data);
3483: if (!data) {
3484: /* one based table */
3485: PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
3486: }
3487: }
3488: }
3489: /* form array of columns we need */
3490: PetscMalloc1(ec+1,&garray);
3491: PetscTableGetHeadPosition(gid1_lid1,&tpos);
3492: while (tpos) {
3493: PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
3494: gid--;
3495: lid--;
3496: garray[lid] = gid;
3497: }
3498: PetscSortInt(ec,garray); /* sort, and rebuild */
3499: PetscTableRemoveAll(gid1_lid1);
3500: for (i=0; i<ec; i++) {
3501: PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
3502: }
3503: /* compact out the extra columns in B */
3504: for (i=0; i<mat->rmap->n; i++) {
3505: ncols = aij->i[i+1] - aij->i[i];
3506: for (j=0; j<ncols; j++) {
3507: PetscInt gid1 = aij->j[aij->i[i] + j] + 1;
3508: PetscTableFind(gid1_lid1,gid1,&lid);
3509: lid--;
3510: aij->j[aij->i[i] + j] = lid;
3511: }
3512: }
3513: mat->cmap->n = mat->cmap->N = ec;
3514: mat->cmap->bs = 1;
3516: PetscTableDestroy(&gid1_lid1);
3517: PetscLayoutSetUp((mat->cmap));
3518: ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
3519: ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
3520: return(0);
3521: }
3523: /*@
3524: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3525: in the matrix.
3527: Input Parameters:
3528: + mat - the SeqAIJ matrix
3529: - indices - the column indices
3531: Level: advanced
3533: Notes:
3534: This can be called if you have precomputed the nonzero structure of the
3535: matrix and want to provide it to the matrix object to improve the performance
3536: of the MatSetValues() operation.
3538: You MUST have set the correct numbers of nonzeros per row in the call to
3539: MatCreateSeqAIJ(), and the columns indices MUST be sorted.
3541: MUST be called before any calls to MatSetValues();
3543: The indices should start with zero, not one.
3545: @*/
3546: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3547: {
3553: PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3554: return(0);
3555: }
3557: /* ----------------------------------------------------------------------------------------*/
3559: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3560: {
3561: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3563: size_t nz = aij->i[mat->rmap->n];
3566: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3568: /* allocate space for values if not already there */
3569: if (!aij->saved_values) {
3570: PetscMalloc1(nz+1,&aij->saved_values);
3571: PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3572: }
3574: /* copy values over */
3575: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3576: return(0);
3577: }
3579: /*@
3580: MatStoreValues - Stashes a copy of the matrix values; this allows, for
3581: example, reuse of the linear part of a Jacobian, while recomputing the
3582: nonlinear portion.
3584: Collect on Mat
3586: Input Parameters:
3587: . mat - the matrix (currently only AIJ matrices support this option)
3589: Level: advanced
3591: Common Usage, with SNESSolve():
3592: $ Create Jacobian matrix
3593: $ Set linear terms into matrix
3594: $ Apply boundary conditions to matrix, at this time matrix must have
3595: $ final nonzero structure (i.e. setting the nonlinear terms and applying
3596: $ boundary conditions again will not change the nonzero structure
3597: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3598: $ MatStoreValues(mat);
3599: $ Call SNESSetJacobian() with matrix
3600: $ In your Jacobian routine
3601: $ MatRetrieveValues(mat);
3602: $ Set nonlinear terms in matrix
3604: Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3605: $ // build linear portion of Jacobian
3606: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3607: $ MatStoreValues(mat);
3608: $ loop over nonlinear iterations
3609: $ MatRetrieveValues(mat);
3610: $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3611: $ // call MatAssemblyBegin/End() on matrix
3612: $ Solve linear system with Jacobian
3613: $ endloop
3615: Notes:
3616: Matrix must already be assemblied before calling this routine
3617: Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3618: calling this routine.
3620: When this is called multiple times it overwrites the previous set of stored values
3621: and does not allocated additional space.
3623: .seealso: MatRetrieveValues()
3625: @*/
3626: PetscErrorCode MatStoreValues(Mat mat)
3627: {
3632: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3633: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3634: PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3635: return(0);
3636: }
3638: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3639: {
3640: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3642: PetscInt nz = aij->i[mat->rmap->n];
3645: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3646: if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3647: /* copy values over */
3648: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3649: return(0);
3650: }
3652: /*@
3653: MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3654: example, reuse of the linear part of a Jacobian, while recomputing the
3655: nonlinear portion.
3657: Collect on Mat
3659: Input Parameters:
3660: . mat - the matrix (currently only AIJ matrices support this option)
3662: Level: advanced
3664: .seealso: MatStoreValues()
3666: @*/
3667: PetscErrorCode MatRetrieveValues(Mat mat)
3668: {
3673: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3674: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3675: PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3676: return(0);
3677: }
3680: /* --------------------------------------------------------------------------------*/
3681: /*@C
3682: MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3683: (the default parallel PETSc format). For good matrix assembly performance
3684: the user should preallocate the matrix storage by setting the parameter nz
3685: (or the array nnz). By setting these parameters accurately, performance
3686: during matrix assembly can be increased by more than a factor of 50.
3688: Collective on MPI_Comm
3690: Input Parameters:
3691: + comm - MPI communicator, set to PETSC_COMM_SELF
3692: . m - number of rows
3693: . n - number of columns
3694: . nz - number of nonzeros per row (same for all rows)
3695: - nnz - array containing the number of nonzeros in the various rows
3696: (possibly different for each row) or NULL
3698: Output Parameter:
3699: . A - the matrix
3701: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3702: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3703: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3705: Notes:
3706: If nnz is given then nz is ignored
3708: The AIJ format (also called the Yale sparse matrix format or
3709: compressed row storage), is fully compatible with standard Fortran 77
3710: storage. That is, the stored row and column indices can begin at
3711: either one (as in Fortran) or zero. See the users' manual for details.
3713: Specify the preallocated storage with either nz or nnz (not both).
3714: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3715: allocation. For large problems you MUST preallocate memory or you
3716: will get TERRIBLE performance, see the users' manual chapter on matrices.
3718: By default, this format uses inodes (identical nodes) when possible, to
3719: improve numerical efficiency of matrix-vector products and solves. We
3720: search for consecutive rows with the same nonzero structure, thereby
3721: reusing matrix information to achieve increased efficiency.
3723: Options Database Keys:
3724: + -mat_no_inode - Do not use inodes
3725: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3727: Level: intermediate
3729: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()
3731: @*/
3732: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3733: {
3737: MatCreate(comm,A);
3738: MatSetSizes(*A,m,n,m,n);
3739: MatSetType(*A,MATSEQAIJ);
3740: MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3741: return(0);
3742: }
3744: /*@C
3745: MatSeqAIJSetPreallocation - For good matrix assembly performance
3746: the user should preallocate the matrix storage by setting the parameter nz
3747: (or the array nnz). By setting these parameters accurately, performance
3748: during matrix assembly can be increased by more than a factor of 50.
3750: Collective on MPI_Comm
3752: Input Parameters:
3753: + B - The matrix
3754: . nz - number of nonzeros per row (same for all rows)
3755: - nnz - array containing the number of nonzeros in the various rows
3756: (possibly different for each row) or NULL
3758: Notes:
3759: If nnz is given then nz is ignored
3761: The AIJ format (also called the Yale sparse matrix format or
3762: compressed row storage), is fully compatible with standard Fortran 77
3763: storage. That is, the stored row and column indices can begin at
3764: either one (as in Fortran) or zero. See the users' manual for details.
3766: Specify the preallocated storage with either nz or nnz (not both).
3767: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3768: allocation. For large problems you MUST preallocate memory or you
3769: will get TERRIBLE performance, see the users' manual chapter on matrices.
3771: You can call MatGetInfo() to get information on how effective the preallocation was;
3772: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3773: You can also run with the option -info and look for messages with the string
3774: malloc in them to see if additional memory allocation was needed.
3776: Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3777: entries or columns indices
3779: By default, this format uses inodes (identical nodes) when possible, to
3780: improve numerical efficiency of matrix-vector products and solves. We
3781: search for consecutive rows with the same nonzero structure, thereby
3782: reusing matrix information to achieve increased efficiency.
3784: Options Database Keys:
3785: + -mat_no_inode - Do not use inodes
3786: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3788: Level: intermediate
3790: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()
3792: @*/
3793: PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3794: {
3800: PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3801: return(0);
3802: }
3804: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3805: {
3806: Mat_SeqAIJ *b;
3807: PetscBool skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3809: PetscInt i;
3812: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3813: if (nz == MAT_SKIP_ALLOCATION) {
3814: skipallocation = PETSC_TRUE;
3815: nz = 0;
3816: }
3817: PetscLayoutSetUp(B->rmap);
3818: PetscLayoutSetUp(B->cmap);
3820: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3821: if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
3822: if (nnz) {
3823: for (i=0; i<B->rmap->n; i++) {
3824: 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]);
3825: 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);
3826: }
3827: }
3829: B->preallocated = PETSC_TRUE;
3831: b = (Mat_SeqAIJ*)B->data;
3833: if (!skipallocation) {
3834: if (!b->imax) {
3835: PetscMalloc2(B->rmap->n,&b->imax,B->rmap->n,&b->ilen);
3836: PetscLogObjectMemory((PetscObject)B,2*B->rmap->n*sizeof(PetscInt));
3837: }
3838: if (!b->ipre) {
3839: PetscMalloc1(B->rmap->n,&b->ipre);
3840: PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
3841: }
3842: if (!nnz) {
3843: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3844: else if (nz < 0) nz = 1;
3845: nz = PetscMin(nz,B->cmap->n);
3846: for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3847: nz = nz*B->rmap->n;
3848: } else {
3849: nz = 0;
3850: for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3851: }
3852: /* b->ilen will count nonzeros in each row so far. */
3853: for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0;
3855: /* allocate the matrix space */
3856: /* FIXME: should B's old memory be unlogged? */
3857: MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3858: if (B->structure_only) {
3859: PetscMalloc1(nz,&b->j);
3860: PetscMalloc1(B->rmap->n+1,&b->i);
3861: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
3862: } else {
3863: PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
3864: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3865: }
3866: b->i[0] = 0;
3867: for (i=1; i<B->rmap->n+1; i++) {
3868: b->i[i] = b->i[i-1] + b->imax[i-1];
3869: }
3870: if (B->structure_only) {
3871: b->singlemalloc = PETSC_FALSE;
3872: b->free_a = PETSC_FALSE;
3873: } else {
3874: b->singlemalloc = PETSC_TRUE;
3875: b->free_a = PETSC_TRUE;
3876: }
3877: b->free_ij = PETSC_TRUE;
3878: } else {
3879: b->free_a = PETSC_FALSE;
3880: b->free_ij = PETSC_FALSE;
3881: }
3883: if (b->ipre && nnz != b->ipre && b->imax) {
3884: /* reserve user-requested sparsity */
3885: PetscMemcpy(b->ipre,b->imax,B->rmap->n*sizeof(PetscInt));
3886: }
3889: b->nz = 0;
3890: b->maxnz = nz;
3891: B->info.nz_unneeded = (double)b->maxnz;
3892: if (realalloc) {
3893: MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
3894: }
3895: B->was_assembled = PETSC_FALSE;
3896: B->assembled = PETSC_FALSE;
3897: return(0);
3898: }
3901: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
3902: {
3903: Mat_SeqAIJ *a;
3904: PetscInt i;
3909: a = (Mat_SeqAIJ*)A->data;
3910: /* if no saved info, we error out */
3911: if (!a->ipre) SETERRQ(PETSC_COMM_WORLD,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");
3913: 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");
3915: PetscMemcpy(a->imax,a->ipre,A->rmap->n*sizeof(PetscInt));
3916: PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
3917: a->i[0] = 0;
3918: for (i=1; i<A->rmap->n+1; i++) {
3919: a->i[i] = a->i[i-1] + a->imax[i-1];
3920: }
3921: A->preallocated = PETSC_TRUE;
3922: a->nz = 0;
3923: a->maxnz = a->i[A->rmap->n];
3924: A->info.nz_unneeded = (double)a->maxnz;
3925: A->was_assembled = PETSC_FALSE;
3926: A->assembled = PETSC_FALSE;
3927: return(0);
3928: }
3930: /*@
3931: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.
3933: Input Parameters:
3934: + B - the matrix
3935: . i - the indices into j for the start of each row (starts with zero)
3936: . j - the column indices for each row (starts with zero) these must be sorted for each row
3937: - v - optional values in the matrix
3939: Level: developer
3941: The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()
3943: .keywords: matrix, aij, compressed row, sparse, sequential
3945: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ
3946: @*/
3947: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3948: {
3954: PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3955: return(0);
3956: }
3958: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3959: {
3960: PetscInt i;
3961: PetscInt m,n;
3962: PetscInt nz;
3963: PetscInt *nnz, nz_max = 0;
3964: PetscScalar *values;
3968: if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
3970: PetscLayoutSetUp(B->rmap);
3971: PetscLayoutSetUp(B->cmap);
3973: MatGetSize(B, &m, &n);
3974: PetscMalloc1(m+1, &nnz);
3975: for (i = 0; i < m; i++) {
3976: nz = Ii[i+1]- Ii[i];
3977: nz_max = PetscMax(nz_max, nz);
3978: if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3979: nnz[i] = nz;
3980: }
3981: MatSeqAIJSetPreallocation(B, 0, nnz);
3982: PetscFree(nnz);
3984: if (v) {
3985: values = (PetscScalar*) v;
3986: } else {
3987: PetscCalloc1(nz_max, &values);
3988: }
3990: for (i = 0; i < m; i++) {
3991: nz = Ii[i+1] - Ii[i];
3992: MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3993: }
3995: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3996: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3998: if (!v) {
3999: PetscFree(values);
4000: }
4001: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
4002: return(0);
4003: }
4005: #include <../src/mat/impls/dense/seq/dense.h>
4006: #include <petsc/private/kernels/petscaxpy.h>
4008: /*
4009: Computes (B'*A')' since computing B*A directly is untenable
4011: n p p
4012: ( ) ( ) ( )
4013: m ( A ) * n ( B ) = m ( C )
4014: ( ) ( ) ( )
4016: */
4017: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
4018: {
4019: PetscErrorCode ierr;
4020: Mat_SeqDense *sub_a = (Mat_SeqDense*)A->data;
4021: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ*)B->data;
4022: Mat_SeqDense *sub_c = (Mat_SeqDense*)C->data;
4023: PetscInt i,n,m,q,p;
4024: const PetscInt *ii,*idx;
4025: const PetscScalar *b,*a,*a_q;
4026: PetscScalar *c,*c_q;
4029: m = A->rmap->n;
4030: n = A->cmap->n;
4031: p = B->cmap->n;
4032: a = sub_a->v;
4033: b = sub_b->a;
4034: c = sub_c->v;
4035: PetscMemzero(c,m*p*sizeof(PetscScalar));
4037: ii = sub_b->i;
4038: idx = sub_b->j;
4039: for (i=0; i<n; i++) {
4040: q = ii[i+1] - ii[i];
4041: while (q-->0) {
4042: c_q = c + m*(*idx);
4043: a_q = a + m*i;
4044: PetscKernelAXPY(c_q,*b,a_q,m);
4045: idx++;
4046: b++;
4047: }
4048: }
4049: return(0);
4050: }
4052: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
4053: {
4055: PetscInt m=A->rmap->n,n=B->cmap->n;
4056: Mat Cmat;
4059: 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);
4060: MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
4061: MatSetSizes(Cmat,m,n,m,n);
4062: MatSetBlockSizesFromMats(Cmat,A,B);
4063: MatSetType(Cmat,MATSEQDENSE);
4064: MatSeqDenseSetPreallocation(Cmat,NULL);
4066: Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4068: *C = Cmat;
4069: return(0);
4070: }
4072: /* ----------------------------------------------------------------*/
4073: PETSC_INTERN PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
4074: {
4078: if (scall == MAT_INITIAL_MATRIX) {
4079: PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
4080: MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
4081: PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
4082: }
4083: PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
4084: MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
4085: PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
4086: return(0);
4087: }
4090: /*MC
4091: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4092: based on compressed sparse row format.
4094: Options Database Keys:
4095: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4097: Level: beginner
4099: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
4100: M*/
4102: /*MC
4103: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4105: This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
4106: and MATMPIAIJ otherwise. As a result, for single process communicators,
4107: MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
4108: for communicators controlling multiple processes. It is recommended that you call both of
4109: the above preallocation routines for simplicity.
4111: Options Database Keys:
4112: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()
4114: Developer Notes:
4115: Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
4116: enough exist.
4118: Level: beginner
4120: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
4121: M*/
4123: /*MC
4124: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4126: This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
4127: and MATMPIAIJCRL otherwise. As a result, for single process communicators,
4128: MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
4129: for communicators controlling multiple processes. It is recommended that you call both of
4130: the above preallocation routines for simplicity.
4132: Options Database Keys:
4133: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()
4135: Level: beginner
4137: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
4138: M*/
4140: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
4141: #if defined(PETSC_HAVE_ELEMENTAL)
4142: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
4143: #endif
4144: #if defined(PETSC_HAVE_HYPRE)
4145: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
4146: PETSC_INTERN PetscErrorCode MatMatMatMult_Transpose_AIJ_AIJ(Mat,Mat,Mat,MatReuse,PetscReal,Mat*);
4147: #endif
4148: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*);
4150: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
4151: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
4152: PETSC_INTERN PetscErrorCode MatPtAP_IS_XAIJ(Mat,Mat,MatReuse,PetscReal,Mat*);
4154: /*@C
4155: MatSeqAIJGetArray - gives access to the array where the data for a MATSEQAIJ matrix is stored
4157: Not Collective
4159: Input Parameter:
4160: . mat - a MATSEQAIJ matrix
4162: Output Parameter:
4163: . array - pointer to the data
4165: Level: intermediate
4167: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4168: @*/
4169: PetscErrorCode MatSeqAIJGetArray(Mat A,PetscScalar **array)
4170: {
4174: PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
4175: return(0);
4176: }
4178: /*@C
4179: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4181: Not Collective
4183: Input Parameter:
4184: . mat - a MATSEQAIJ matrix
4186: Output Parameter:
4187: . nz - the maximum number of nonzeros in any row
4189: Level: intermediate
4191: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4192: @*/
4193: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
4194: {
4195: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data;
4198: *nz = aij->rmax;
4199: return(0);
4200: }
4202: /*@C
4203: MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()
4205: Not Collective
4207: Input Parameters:
4208: . mat - a MATSEQAIJ matrix
4209: . array - pointer to the data
4211: Level: intermediate
4213: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
4214: @*/
4215: PetscErrorCode MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
4216: {
4220: PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
4221: return(0);
4222: }
4224: #if defined(PETSC_HAVE_CUDA)
4225: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat);
4226: #endif
4228: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4229: {
4230: Mat_SeqAIJ *b;
4232: PetscMPIInt size;
4235: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
4236: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
4238: PetscNewLog(B,&b);
4240: B->data = (void*)b;
4242: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4244: b->row = 0;
4245: b->col = 0;
4246: b->icol = 0;
4247: b->reallocs = 0;
4248: b->ignorezeroentries = PETSC_FALSE;
4249: b->roworiented = PETSC_TRUE;
4250: b->nonew = 0;
4251: b->diag = 0;
4252: b->solve_work = 0;
4253: B->spptr = 0;
4254: b->saved_values = 0;
4255: b->idiag = 0;
4256: b->mdiag = 0;
4257: b->ssor_work = 0;
4258: b->omega = 1.0;
4259: b->fshift = 0.0;
4260: b->idiagvalid = PETSC_FALSE;
4261: b->ibdiagvalid = PETSC_FALSE;
4262: b->keepnonzeropattern = PETSC_FALSE;
4264: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4265: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
4266: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);
4268: #if defined(PETSC_HAVE_MATLAB_ENGINE)
4269: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
4270: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
4271: #endif
4273: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
4274: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
4275: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
4276: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
4277: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
4278: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
4279: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
4280: #if defined(PETSC_HAVE_MKL_SPARSE)
4281: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
4282: #endif
4283: #if defined(PETSC_HAVE_CUDA)
4284: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
4285: #endif
4286: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
4287: #if defined(PETSC_HAVE_ELEMENTAL)
4288: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
4289: #endif
4290: #if defined(PETSC_HAVE_HYPRE)
4291: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
4292: PetscObjectComposeFunction((PetscObject)B,"MatMatMatMult_transpose_seqaij_seqaij_C",MatMatMatMult_Transpose_AIJ_AIJ);
4293: #endif
4294: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
4295: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
4296: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
4297: PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
4298: PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
4299: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
4300: PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
4301: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
4302: PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
4303: PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);
4304: PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);
4305: PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);
4306: PetscObjectComposeFunction((PetscObject)B,"MatPtAP_is_seqaij_C",MatPtAP_IS_XAIJ);
4307: MatCreate_SeqAIJ_Inode(B);
4308: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4309: MatSeqAIJSetTypeFromOptions(B); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4310: return(0);
4311: }
4313: /*
4314: Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4315: */
4316: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4317: {
4318: Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
4320: PetscInt i,m = A->rmap->n;
4323: c = (Mat_SeqAIJ*)C->data;
4325: C->factortype = A->factortype;
4326: c->row = 0;
4327: c->col = 0;
4328: c->icol = 0;
4329: c->reallocs = 0;
4331: C->assembled = PETSC_TRUE;
4333: PetscLayoutReference(A->rmap,&C->rmap);
4334: PetscLayoutReference(A->cmap,&C->cmap);
4336: PetscMalloc2(m,&c->imax,m,&c->ilen);
4337: PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));
4338: for (i=0; i<m; i++) {
4339: c->imax[i] = a->imax[i];
4340: c->ilen[i] = a->ilen[i];
4341: }
4343: /* allocate the matrix space */
4344: if (mallocmatspace) {
4345: PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4346: PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
4348: c->singlemalloc = PETSC_TRUE;
4350: PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
4351: if (m > 0) {
4352: PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
4353: if (cpvalues == MAT_COPY_VALUES) {
4354: PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
4355: } else {
4356: PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
4357: }
4358: }
4359: }
4361: c->ignorezeroentries = a->ignorezeroentries;
4362: c->roworiented = a->roworiented;
4363: c->nonew = a->nonew;
4364: if (a->diag) {
4365: PetscMalloc1(m+1,&c->diag);
4366: PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4367: for (i=0; i<m; i++) {
4368: c->diag[i] = a->diag[i];
4369: }
4370: } else c->diag = 0;
4372: c->solve_work = 0;
4373: c->saved_values = 0;
4374: c->idiag = 0;
4375: c->ssor_work = 0;
4376: c->keepnonzeropattern = a->keepnonzeropattern;
4377: c->free_a = PETSC_TRUE;
4378: c->free_ij = PETSC_TRUE;
4380: c->rmax = a->rmax;
4381: c->nz = a->nz;
4382: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
4383: C->preallocated = PETSC_TRUE;
4385: c->compressedrow.use = a->compressedrow.use;
4386: c->compressedrow.nrows = a->compressedrow.nrows;
4387: if (a->compressedrow.use) {
4388: i = a->compressedrow.nrows;
4389: PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4390: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
4391: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
4392: } else {
4393: c->compressedrow.use = PETSC_FALSE;
4394: c->compressedrow.i = NULL;
4395: c->compressedrow.rindex = NULL;
4396: }
4397: c->nonzerorowcnt = a->nonzerorowcnt;
4398: C->nonzerostate = A->nonzerostate;
4400: MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4401: PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4402: return(0);
4403: }
4405: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4406: {
4410: MatCreate(PetscObjectComm((PetscObject)A),B);
4411: MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4412: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4413: MatSetBlockSizesFromMats(*B,A,A);
4414: }
4415: MatSetType(*B,((PetscObject)A)->type_name);
4416: MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4417: return(0);
4418: }
4420: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4421: {
4422: PetscBool isbinary, ishdf5;
4428: /* force binary viewer to load .info file if it has not yet done so */
4429: PetscViewerSetUp(viewer);
4430: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
4431: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5, &ishdf5);
4432: if (isbinary) {
4433: MatLoad_SeqAIJ_Binary(newMat,viewer);
4434: } else if (ishdf5) {
4435: #if defined(PETSC_HAVE_HDF5)
4436: MatLoad_AIJ_HDF5(newMat,viewer);
4437: #else
4438: SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4439: #endif
4440: } else {
4441: 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);
4442: }
4443: return(0);
4444: }
4446: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat newMat, PetscViewer viewer)
4447: {
4448: Mat_SeqAIJ *a;
4450: PetscInt i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
4451: int fd;
4452: PetscMPIInt size;
4453: MPI_Comm comm;
4454: PetscInt bs = newMat->rmap->bs;
4457: PetscObjectGetComm((PetscObject)viewer,&comm);
4458: MPI_Comm_size(comm,&size);
4459: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");
4461: PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
4462: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
4463: PetscOptionsEnd();
4464: if (bs < 0) bs = 1;
4465: MatSetBlockSize(newMat,bs);
4467: PetscViewerBinaryGetDescriptor(viewer,&fd);
4468: PetscBinaryRead(fd,header,4,PETSC_INT);
4469: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
4470: M = header[1]; N = header[2]; nz = header[3];
4472: if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
4474: /* read in row lengths */
4475: PetscMalloc1(M,&rowlengths);
4476: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
4478: /* check if sum of rowlengths is same as nz */
4479: for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
4480: 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);
4482: /* set global size if not set already*/
4483: if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
4484: MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
4485: } else {
4486: /* if sizes and type are already set, check if the matrix global sizes are correct */
4487: MatGetSize(newMat,&rows,&cols);
4488: if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
4489: MatGetLocalSize(newMat,&rows,&cols);
4490: }
4491: 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);
4492: }
4493: MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
4494: a = (Mat_SeqAIJ*)newMat->data;
4496: PetscBinaryRead(fd,a->j,nz,PETSC_INT);
4498: /* read in nonzero values */
4499: PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);
4501: /* set matrix "i" values */
4502: a->i[0] = 0;
4503: for (i=1; i<= M; i++) {
4504: a->i[i] = a->i[i-1] + rowlengths[i-1];
4505: a->ilen[i-1] = rowlengths[i-1];
4506: }
4507: PetscFree(rowlengths);
4509: MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
4510: MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
4511: return(0);
4512: }
4514: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4515: {
4516: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4518: #if defined(PETSC_USE_COMPLEX)
4519: PetscInt k;
4520: #endif
4523: /* If the matrix dimensions are not equal,or no of nonzeros */
4524: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4525: *flg = PETSC_FALSE;
4526: return(0);
4527: }
4529: /* if the a->i are the same */
4530: PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
4531: if (!*flg) return(0);
4533: /* if a->j are the same */
4534: PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
4535: if (!*flg) return(0);
4537: /* if a->a are the same */
4538: #if defined(PETSC_USE_COMPLEX)
4539: for (k=0; k<a->nz; k++) {
4540: if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4541: *flg = PETSC_FALSE;
4542: return(0);
4543: }
4544: }
4545: #else
4546: PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
4547: #endif
4548: return(0);
4549: }
4551: /*@
4552: MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4553: provided by the user.
4555: Collective on MPI_Comm
4557: Input Parameters:
4558: + comm - must be an MPI communicator of size 1
4559: . m - number of rows
4560: . n - number of columns
4561: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4562: . j - column indices
4563: - a - matrix values
4565: Output Parameter:
4566: . mat - the matrix
4568: Level: intermediate
4570: Notes:
4571: The i, j, and a arrays are not copied by this routine, the user must free these arrays
4572: once the matrix is destroyed and not before
4574: You cannot set new nonzero locations into this matrix, that will generate an error.
4576: The i and j indices are 0 based
4578: The format which is used for the sparse matrix input, is equivalent to a
4579: row-major ordering.. i.e for the following matrix, the input data expected is
4580: as shown
4582: $ 1 0 0
4583: $ 2 0 3
4584: $ 4 5 6
4585: $
4586: $ i = {0,1,3,6} [size = nrow+1 = 3+1]
4587: $ j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
4588: $ v = {1,2,3,4,5,6} [size = 6]
4591: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4593: @*/
4594: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
4595: {
4597: PetscInt ii;
4598: Mat_SeqAIJ *aij;
4599: #if defined(PETSC_USE_DEBUG)
4600: PetscInt jj;
4601: #endif
4604: if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4605: MatCreate(comm,mat);
4606: MatSetSizes(*mat,m,n,m,n);
4607: /* MatSetBlockSizes(*mat,,); */
4608: MatSetType(*mat,MATSEQAIJ);
4609: MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4610: aij = (Mat_SeqAIJ*)(*mat)->data;
4611: PetscMalloc2(m,&aij->imax,m,&aij->ilen);
4613: aij->i = i;
4614: aij->j = j;
4615: aij->a = a;
4616: aij->singlemalloc = PETSC_FALSE;
4617: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4618: aij->free_a = PETSC_FALSE;
4619: aij->free_ij = PETSC_FALSE;
4621: for (ii=0; ii<m; ii++) {
4622: aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4623: #if defined(PETSC_USE_DEBUG)
4624: 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]);
4625: for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4626: 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);
4627: 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);
4628: }
4629: #endif
4630: }
4631: #if defined(PETSC_USE_DEBUG)
4632: for (ii=0; ii<aij->i[m]; ii++) {
4633: if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4634: 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]);
4635: }
4636: #endif
4638: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4639: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4640: return(0);
4641: }
4642: /*@C
4643: MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4644: provided by the user.
4646: Collective on MPI_Comm
4648: Input Parameters:
4649: + comm - must be an MPI communicator of size 1
4650: . m - number of rows
4651: . n - number of columns
4652: . i - row indices
4653: . j - column indices
4654: . a - matrix values
4655: . nz - number of nonzeros
4656: - idx - 0 or 1 based
4658: Output Parameter:
4659: . mat - the matrix
4661: Level: intermediate
4663: Notes:
4664: The i and j indices are 0 based
4666: The format which is used for the sparse matrix input, is equivalent to a
4667: row-major ordering.. i.e for the following matrix, the input data expected is
4668: as shown:
4670: 1 0 0
4671: 2 0 3
4672: 4 5 6
4674: i = {0,1,1,2,2,2}
4675: j = {0,0,2,0,1,2}
4676: v = {1,2,3,4,5,6}
4679: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4681: @*/
4682: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
4683: {
4685: PetscInt ii, *nnz, one = 1,row,col;
4689: PetscCalloc1(m,&nnz);
4690: for (ii = 0; ii < nz; ii++) {
4691: nnz[i[ii] - !!idx] += 1;
4692: }
4693: MatCreate(comm,mat);
4694: MatSetSizes(*mat,m,n,m,n);
4695: MatSetType(*mat,MATSEQAIJ);
4696: MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4697: for (ii = 0; ii < nz; ii++) {
4698: if (idx) {
4699: row = i[ii] - 1;
4700: col = j[ii] - 1;
4701: } else {
4702: row = i[ii];
4703: col = j[ii];
4704: }
4705: MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4706: }
4707: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4708: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4709: PetscFree(nnz);
4710: return(0);
4711: }
4713: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4714: {
4715: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data;
4719: a->idiagvalid = PETSC_FALSE;
4720: a->ibdiagvalid = PETSC_FALSE;
4722: MatSeqAIJInvalidateDiagonal_Inode(A);
4723: return(0);
4724: }
4726: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4727: {
4729: PetscMPIInt size;
4732: MPI_Comm_size(comm,&size);
4733: if (size == 1) {
4734: if (scall == MAT_INITIAL_MATRIX) {
4735: MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
4736: } else {
4737: MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
4738: }
4739: } else {
4740: MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
4741: }
4742: return(0);
4743: }
4745: /*
4746: Permute A into C's *local* index space using rowemb,colemb.
4747: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
4748: of [0,m), colemb is in [0,n).
4749: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
4750: */
4751: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
4752: {
4753: /* If making this function public, change the error returned in this function away from _PLIB. */
4755: Mat_SeqAIJ *Baij;
4756: PetscBool seqaij;
4757: PetscInt m,n,*nz,i,j,count;
4758: PetscScalar v;
4759: const PetscInt *rowindices,*colindices;
4762: if (!B) return(0);
4763: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
4764: PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
4765: if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
4766: if (rowemb) {
4767: ISGetLocalSize(rowemb,&m);
4768: 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);
4769: } else {
4770: if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
4771: }
4772: if (colemb) {
4773: ISGetLocalSize(colemb,&n);
4774: 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);
4775: } else {
4776: if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
4777: }
4779: Baij = (Mat_SeqAIJ*)(B->data);
4780: if (pattern == DIFFERENT_NONZERO_PATTERN) {
4781: PetscMalloc1(B->rmap->n,&nz);
4782: for (i=0; i<B->rmap->n; i++) {
4783: nz[i] = Baij->i[i+1] - Baij->i[i];
4784: }
4785: MatSeqAIJSetPreallocation(C,0,nz);
4786: PetscFree(nz);
4787: }
4788: if (pattern == SUBSET_NONZERO_PATTERN) {
4789: MatZeroEntries(C);
4790: }
4791: count = 0;
4792: rowindices = NULL;
4793: colindices = NULL;
4794: if (rowemb) {
4795: ISGetIndices(rowemb,&rowindices);
4796: }
4797: if (colemb) {
4798: ISGetIndices(colemb,&colindices);
4799: }
4800: for (i=0; i<B->rmap->n; i++) {
4801: PetscInt row;
4802: row = i;
4803: if (rowindices) row = rowindices[i];
4804: for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
4805: PetscInt col;
4806: col = Baij->j[count];
4807: if (colindices) col = colindices[col];
4808: v = Baij->a[count];
4809: MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
4810: ++count;
4811: }
4812: }
4813: /* FIXME: set C's nonzerostate correctly. */
4814: /* Assembly for C is necessary. */
4815: C->preallocated = PETSC_TRUE;
4816: C->assembled = PETSC_TRUE;
4817: C->was_assembled = PETSC_FALSE;
4818: return(0);
4819: }
4821: PetscFunctionList MatSeqAIJList = NULL;
4823: /*@C
4824: MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype
4826: Collective on Mat
4828: Input Parameters:
4829: + mat - the matrix object
4830: - matype - matrix type
4832: Options Database Key:
4833: . -mat_seqai_type <method> - for example seqaijcrl
4836: Level: intermediate
4838: .keywords: Mat, MatType, set, method
4840: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
4841: @*/
4842: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
4843: {
4844: PetscErrorCode ierr,(*r)(Mat,MatType,MatReuse,Mat*);
4845: PetscBool sametype;
4849: PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
4850: if (sametype) return(0);
4852: PetscFunctionListFind(MatSeqAIJList,matype,&r);
4853: if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
4854: (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
4855: return(0);
4856: }
4859: /*@C
4860: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential AIJ matrices
4862: Not Collective
4864: Input Parameters:
4865: + name - name of a new user-defined matrix type, for example MATSEQAIJCRL
4866: - function - routine to convert to subtype
4868: Notes:
4869: MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.
4872: Then, your matrix can be chosen with the procedural interface at runtime via the option
4873: $ -mat_seqaij_type my_mat
4875: Level: advanced
4877: .keywords: Mat, register
4879: .seealso: MatSeqAIJRegisterAll()
4882: Level: advanced
4883: @*/
4884: PetscErrorCode MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
4885: {
4889: MatInitializePackage();
4890: PetscFunctionListAdd(&MatSeqAIJList,sname,function);
4891: return(0);
4892: }
4894: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
4896: /*@C
4897: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ
4899: Not Collective
4901: Level: advanced
4903: Developers Note: CUSP and CUSPARSE do not yet support the MatConvert_SeqAIJ..() paradigm and thus cannot be registered here
4905: .keywords: KSP, register, all
4907: .seealso: MatRegisterAll(), MatSeqAIJRegister()
4908: @*/
4909: PetscErrorCode MatSeqAIJRegisterAll(void)
4910: {
4914: if (MatSeqAIJRegisterAllCalled) return(0);
4915: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
4917: MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL);
4918: MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM);
4919: MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL);
4920: #if defined(PETSC_HAVE_MKL_SPARSE)
4921: MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL);
4922: #endif
4923: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
4924: MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
4925: #endif
4926: return(0);
4927: }
4929: /*
4930: Special version for direct calls from Fortran
4931: */
4932: #include <petsc/private/fortranimpl.h>
4933: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4934: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4935: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4936: #define matsetvaluesseqaij_ matsetvaluesseqaij
4937: #endif
4939: /* Change these macros so can be used in void function */
4940: #undef CHKERRQ
4941: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
4942: #undef SETERRQ2
4943: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
4944: #undef SETERRQ3
4945: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)
4947: 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)
4948: {
4949: Mat A = *AA;
4950: PetscInt m = *mm, n = *nn;
4951: InsertMode is = *isis;
4952: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
4953: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4954: PetscInt *imax,*ai,*ailen;
4956: PetscInt *aj,nonew = a->nonew,lastcol = -1;
4957: MatScalar *ap,value,*aa;
4958: PetscBool ignorezeroentries = a->ignorezeroentries;
4959: PetscBool roworiented = a->roworiented;
4962: MatCheckPreallocated(A,1);
4963: imax = a->imax;
4964: ai = a->i;
4965: ailen = a->ilen;
4966: aj = a->j;
4967: aa = a->a;
4969: for (k=0; k<m; k++) { /* loop over added rows */
4970: row = im[k];
4971: if (row < 0) continue;
4972: #if defined(PETSC_USE_DEBUG)
4973: if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4974: #endif
4975: rp = aj + ai[row]; ap = aa + ai[row];
4976: rmax = imax[row]; nrow = ailen[row];
4977: low = 0;
4978: high = nrow;
4979: for (l=0; l<n; l++) { /* loop over added columns */
4980: if (in[l] < 0) continue;
4981: #if defined(PETSC_USE_DEBUG)
4982: if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4983: #endif
4984: col = in[l];
4985: if (roworiented) value = v[l + k*n];
4986: else value = v[k + l*m];
4988: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
4990: if (col <= lastcol) low = 0;
4991: else high = nrow;
4992: lastcol = col;
4993: while (high-low > 5) {
4994: t = (low+high)/2;
4995: if (rp[t] > col) high = t;
4996: else low = t;
4997: }
4998: for (i=low; i<high; i++) {
4999: if (rp[i] > col) break;
5000: if (rp[i] == col) {
5001: if (is == ADD_VALUES) ap[i] += value;
5002: else ap[i] = value;
5003: goto noinsert;
5004: }
5005: }
5006: if (value == 0.0 && ignorezeroentries) goto noinsert;
5007: if (nonew == 1) goto noinsert;
5008: if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
5009: MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
5010: N = nrow++ - 1; a->nz++; high++;
5011: /* shift up all the later entries in this row */
5012: for (ii=N; ii>=i; ii--) {
5013: rp[ii+1] = rp[ii];
5014: ap[ii+1] = ap[ii];
5015: }
5016: rp[i] = col;
5017: ap[i] = value;
5018: A->nonzerostate++;
5019: noinsert:;
5020: low = i + 1;
5021: }
5022: ailen[row] = nrow;
5023: }
5024: PetscFunctionReturnVoid();
5025: }