Actual source code: aij.c
petsc-main 2021-04-20
1: /*
2: Defines the basic matrix operations for the AIJ (compressed row)
3: matrix storage format.
4: */
7: #include <../src/mat/impls/aij/seq/aij.h>
8: #include <petscblaslapack.h>
9: #include <petscbt.h>
10: #include <petsc/private/kernels/blocktranspose.h>
12: PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
13: {
14: PetscErrorCode ierr;
15: PetscBool flg;
16: char type[256];
19: PetscObjectOptionsBegin((PetscObject)A);
20: PetscOptionsFList("-mat_seqaij_type","Matrix SeqAIJ type","MatSeqAIJSetType",MatSeqAIJList,"seqaij",type,256,&flg);
21: if (flg) {
22: MatSeqAIJSetType(A,type);
23: }
24: PetscOptionsEnd();
25: return(0);
26: }
28: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
29: {
31: PetscInt i,m,n;
32: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data;
35: MatGetSize(A,&m,&n);
36: PetscArrayzero(norms,n);
37: if (type == NORM_2) {
38: for (i=0; i<aij->i[m]; i++) {
39: norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
40: }
41: } else if (type == NORM_1) {
42: for (i=0; i<aij->i[m]; i++) {
43: norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
44: }
45: } else if (type == NORM_INFINITY) {
46: for (i=0; i<aij->i[m]; i++) {
47: norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
48: }
49: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");
51: if (type == NORM_2) {
52: for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
53: }
54: return(0);
55: }
57: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
58: {
59: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
60: PetscInt i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
61: const PetscInt *jj = a->j,*ii = a->i;
62: PetscInt *rows;
63: PetscErrorCode ierr;
66: for (i=0; i<m; i++) {
67: if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
68: cnt++;
69: }
70: }
71: PetscMalloc1(cnt,&rows);
72: cnt = 0;
73: for (i=0; i<m; i++) {
74: if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
75: rows[cnt] = i;
76: cnt++;
77: }
78: }
79: ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
80: return(0);
81: }
83: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
84: {
85: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
86: const MatScalar *aa = a->a;
87: PetscInt i,m=A->rmap->n,cnt = 0;
88: const PetscInt *ii = a->i,*jj = a->j,*diag;
89: PetscInt *rows;
90: PetscErrorCode ierr;
93: MatMarkDiagonal_SeqAIJ(A);
94: diag = a->diag;
95: for (i=0; i<m; i++) {
96: if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
97: cnt++;
98: }
99: }
100: PetscMalloc1(cnt,&rows);
101: cnt = 0;
102: for (i=0; i<m; i++) {
103: if ((diag[i] >= ii[i+1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
104: rows[cnt++] = i;
105: }
106: }
107: *nrows = cnt;
108: *zrows = rows;
109: return(0);
110: }
112: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
113: {
114: PetscInt nrows,*rows;
118: *zrows = NULL;
119: MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
120: ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
121: return(0);
122: }
124: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
125: {
126: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
127: const MatScalar *aa;
128: PetscInt m=A->rmap->n,cnt = 0;
129: const PetscInt *ii;
130: PetscInt n,i,j,*rows;
131: PetscErrorCode ierr;
134: MatSeqAIJGetArrayRead(A,&aa);
135: *keptrows = NULL;
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: for (j=ii[i]; j<ii[i+1]; j++) {
144: if (aa[j] != 0.0) goto ok1;
145: }
146: cnt++;
147: ok1:;
148: }
149: if (!cnt) {
150: MatSeqAIJRestoreArrayRead(A,&aa);
151: return(0);
152: }
153: PetscMalloc1(A->rmap->n-cnt,&rows);
154: cnt = 0;
155: for (i=0; i<m; i++) {
156: n = ii[i+1] - ii[i];
157: if (!n) continue;
158: for (j=ii[i]; j<ii[i+1]; j++) {
159: if (aa[j] != 0.0) {
160: rows[cnt++] = i;
161: break;
162: }
163: }
164: }
165: MatSeqAIJRestoreArrayRead(A,&aa);
166: ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
167: return(0);
168: }
170: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
171: {
172: PetscErrorCode ierr;
173: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) Y->data;
174: PetscInt i,m = Y->rmap->n;
175: const PetscInt *diag;
176: MatScalar *aa;
177: const PetscScalar *v;
178: PetscBool missing;
179: #if defined(PETSC_HAVE_DEVICE)
180: PetscBool inserted = PETSC_FALSE;
181: #endif
184: if (Y->assembled) {
185: MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
186: if (!missing) {
187: diag = aij->diag;
188: VecGetArrayRead(D,&v);
189: MatSeqAIJGetArray(Y,&aa);
190: if (is == INSERT_VALUES) {
191: #if defined(PETSC_HAVE_DEVICE)
192: inserted = PETSC_TRUE;
193: #endif
194: for (i=0; i<m; i++) {
195: aa[diag[i]] = v[i];
196: }
197: } else {
198: for (i=0; i<m; i++) {
199: #if defined(PETSC_HAVE_DEVICE)
200: if (v[i] != 0.0) inserted = PETSC_TRUE;
201: #endif
202: aa[diag[i]] += v[i];
203: }
204: }
205: MatSeqAIJRestoreArray(Y,&aa);
206: #if defined(PETSC_HAVE_DEVICE)
207: if (inserted) Y->offloadmask = PETSC_OFFLOAD_CPU;
208: #endif
209: VecRestoreArrayRead(D,&v);
210: return(0);
211: }
212: MatSeqAIJInvalidateDiagonal(Y);
213: }
214: MatDiagonalSet_Default(Y,D,is);
215: return(0);
216: }
218: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
219: {
220: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
222: PetscInt i,ishift;
225: *m = A->rmap->n;
226: if (!ia) return(0);
227: ishift = 0;
228: if (symmetric && !A->structurally_symmetric) {
229: MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
230: } else if (oshift == 1) {
231: PetscInt *tia;
232: PetscInt nz = a->i[A->rmap->n];
233: /* malloc space and add 1 to i and j indices */
234: PetscMalloc1(A->rmap->n+1,&tia);
235: for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
236: *ia = tia;
237: if (ja) {
238: PetscInt *tja;
239: PetscMalloc1(nz+1,&tja);
240: for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
241: *ja = tja;
242: }
243: } else {
244: *ia = a->i;
245: if (ja) *ja = a->j;
246: }
247: return(0);
248: }
250: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
251: {
255: if (!ia) return(0);
256: if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
257: PetscFree(*ia);
258: if (ja) {PetscFree(*ja);}
259: }
260: return(0);
261: }
263: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
264: {
265: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
267: PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
268: PetscInt nz = a->i[m],row,*jj,mr,col;
271: *nn = n;
272: if (!ia) return(0);
273: if (symmetric) {
274: MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,PETSC_TRUE,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
275: } else {
276: PetscCalloc1(n,&collengths);
277: PetscMalloc1(n+1,&cia);
278: PetscMalloc1(nz,&cja);
279: jj = a->j;
280: for (i=0; i<nz; i++) {
281: collengths[jj[i]]++;
282: }
283: cia[0] = oshift;
284: for (i=0; i<n; i++) {
285: cia[i+1] = cia[i] + collengths[i];
286: }
287: PetscArrayzero(collengths,n);
288: jj = a->j;
289: for (row=0; row<m; row++) {
290: mr = a->i[row+1] - a->i[row];
291: for (i=0; i<mr; i++) {
292: col = *jj++;
294: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
295: }
296: }
297: PetscFree(collengths);
298: *ia = cia; *ja = cja;
299: }
300: return(0);
301: }
303: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
304: {
308: if (!ia) return(0);
310: PetscFree(*ia);
311: PetscFree(*ja);
312: return(0);
313: }
315: /*
316: MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
317: MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
318: spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
319: */
320: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done)
321: {
322: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
324: PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
325: PetscInt nz = a->i[m],row,mr,col,tmp;
326: PetscInt *cspidx;
327: const PetscInt *jj;
330: *nn = n;
331: if (!ia) return(0);
333: PetscCalloc1(n,&collengths);
334: PetscMalloc1(n+1,&cia);
335: PetscMalloc1(nz,&cja);
336: PetscMalloc1(nz,&cspidx);
337: jj = a->j;
338: for (i=0; i<nz; i++) {
339: collengths[jj[i]]++;
340: }
341: cia[0] = oshift;
342: for (i=0; i<n; i++) {
343: cia[i+1] = cia[i] + collengths[i];
344: }
345: PetscArrayzero(collengths,n);
346: jj = a->j;
347: for (row=0; row<m; row++) {
348: mr = a->i[row+1] - a->i[row];
349: for (i=0; i<mr; i++) {
350: col = *jj++;
351: tmp = cia[col] + collengths[col]++ - oshift;
352: cspidx[tmp] = a->i[row] + i; /* index of a->j */
353: cja[tmp] = row + oshift;
354: }
355: }
356: PetscFree(collengths);
357: *ia = cia;
358: *ja = cja;
359: *spidx = cspidx;
360: return(0);
361: }
363: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done)
364: {
368: MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
369: PetscFree(*spidx);
370: return(0);
371: }
373: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
374: {
375: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
376: PetscInt *ai = a->i;
380: PetscArraycpy(a->a+ai[row],v,ai[row+1]-ai[row]);
381: #if defined(PETSC_HAVE_DEVICE)
382: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && ai[row+1]-ai[row]) A->offloadmask = PETSC_OFFLOAD_CPU;
383: #endif
384: return(0);
385: }
387: /*
388: MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions
390: - a single row of values is set with each call
391: - no row or column indices are negative or (in error) larger than the number of rows or columns
392: - the values are always added to the matrix, not set
393: - no new locations are introduced in the nonzero structure of the matrix
395: This does NOT assume the global column indices are sorted
397: */
399: #include <petsc/private/isimpl.h>
400: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
401: {
402: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
403: PetscInt low,high,t,row,nrow,i,col,l;
404: const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
405: PetscInt lastcol = -1;
406: MatScalar *ap,value,*aa = a->a;
407: const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;
409: row = ridx[im[0]];
410: rp = aj + ai[row];
411: ap = aa + ai[row];
412: nrow = ailen[row];
413: low = 0;
414: high = nrow;
415: for (l=0; l<n; l++) { /* loop over added columns */
416: col = cidx[in[l]];
417: value = v[l];
419: if (col <= lastcol) low = 0;
420: else high = nrow;
421: lastcol = col;
422: while (high-low > 5) {
423: t = (low+high)/2;
424: if (rp[t] > col) high = t;
425: else low = t;
426: }
427: for (i=low; i<high; i++) {
428: if (rp[i] == col) {
429: ap[i] += value;
430: low = i + 1;
431: break;
432: }
433: }
434: }
435: #if defined(PETSC_HAVE_DEVICE)
436: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
437: #endif
438: return 0;
439: }
441: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
442: {
443: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
444: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
445: PetscInt *imax = a->imax,*ai = a->i,*ailen = a->ilen;
447: PetscInt *aj = a->j,nonew = a->nonew,lastcol = -1;
448: MatScalar *ap=NULL,value=0.0,*aa;
449: PetscBool ignorezeroentries = a->ignorezeroentries;
450: PetscBool roworiented = a->roworiented;
451: #if defined(PETSC_HAVE_DEVICE)
452: PetscBool inserted = PETSC_FALSE;
453: #endif
456: #if defined(PETSC_HAVE_DEVICE)
457: if (A->offloadmask == PETSC_OFFLOAD_GPU) {
458: const PetscScalar *dummy;
459: MatSeqAIJGetArrayRead(A,&dummy);
460: MatSeqAIJRestoreArrayRead(A,&dummy);
461: }
462: #endif
463: aa = a->a;
464: for (k=0; k<m; k++) { /* loop over added rows */
465: row = im[k];
466: if (row < 0) continue;
467: if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
468: rp = aj + ai[row];
469: if (!A->structure_only) ap = aa + ai[row];
470: rmax = imax[row]; nrow = ailen[row];
471: low = 0;
472: high = nrow;
473: for (l=0; l<n; l++) { /* loop over added columns */
474: if (in[l] < 0) continue;
475: if (PetscUnlikelyDebug(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);
476: col = in[l];
477: if (v && !A->structure_only) value = roworiented ? v[l + k*n] : v[k + l*m];
478: if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;
480: if (col <= lastcol) low = 0;
481: else high = nrow;
482: lastcol = col;
483: while (high-low > 5) {
484: t = (low+high)/2;
485: if (rp[t] > col) high = t;
486: else low = t;
487: }
488: for (i=low; i<high; i++) {
489: if (rp[i] > col) break;
490: if (rp[i] == col) {
491: if (!A->structure_only) {
492: if (is == ADD_VALUES) {
493: ap[i] += value;
494: (void)PetscLogFlops(1.0);
495: }
496: else ap[i] = value;
497: #if defined(PETSC_HAVE_DEVICE)
498: inserted = PETSC_TRUE;
499: #endif
500: }
501: low = i + 1;
502: goto noinsert;
503: }
504: }
505: if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
506: if (nonew == 1) goto noinsert;
507: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
508: if (A->structure_only) {
509: MatSeqXAIJReallocateAIJ_structure_only(A,A->rmap->n,1,nrow,row,col,rmax,ai,aj,rp,imax,nonew,MatScalar);
510: } else {
511: MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
512: }
513: N = nrow++ - 1; a->nz++; high++;
514: /* shift up all the later entries in this row */
515: PetscArraymove(rp+i+1,rp+i,N-i+1);
516: rp[i] = col;
517: if (!A->structure_only){
518: PetscArraymove(ap+i+1,ap+i,N-i+1);
519: ap[i] = value;
520: }
521: low = i + 1;
522: A->nonzerostate++;
523: #if defined(PETSC_HAVE_DEVICE)
524: inserted = PETSC_TRUE;
525: #endif
526: noinsert:;
527: }
528: ailen[row] = nrow;
529: }
530: #if defined(PETSC_HAVE_DEVICE)
531: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && inserted) A->offloadmask = PETSC_OFFLOAD_CPU;
532: #endif
533: return(0);
534: }
537: PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
538: {
539: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
540: PetscInt *rp,k,row;
541: PetscInt *ai = a->i;
543: PetscInt *aj = a->j;
544: MatScalar *aa = a->a,*ap;
547: if (A->was_assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot call on assembled matrix.");
548: if (m*n+a->nz > a->maxnz) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Number of entries in matrix will be larger than maximum nonzeros allocated for %D in MatSeqAIJSetTotalPreallocation()",a->maxnz);
549: for (k=0; k<m; k++) { /* loop over added rows */
550: row = im[k];
551: rp = aj + ai[row];
552: ap = aa + ai[row];
554: PetscMemcpy(rp,in,n*sizeof(PetscInt));
555: if (!A->structure_only) {
556: if (v) {
557: PetscMemcpy(ap,v,n*sizeof(PetscScalar));
558: v += n;
559: } else {
560: PetscMemzero(ap,n*sizeof(PetscScalar));
561: }
562: }
563: a->ilen[row] = n;
564: a->imax[row] = n;
565: a->i[row+1] = a->i[row]+n;
566: a->nz += n;
567: }
568: #if defined(PETSC_HAVE_DEVICE)
569: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
570: #endif
571: return(0);
572: }
574: /*@
575: MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.
577: Input Parameters:
578: + A - the SeqAIJ matrix
579: - nztotal - bound on the number of nonzeros
581: Level: advanced
583: Notes:
584: This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
585: Simply call MatSetValues() after this call to provide the matrix entries in the usual manner. This matrix may be used
586: as always with multiple matrix assemblies.
588: .seealso: MatSetOption(), MAT_SORTED_FULL, MatSetValues(), MatSeqAIJSetPreallocation()
589: @*/
591: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A,PetscInt nztotal)
592: {
594: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
597: PetscLayoutSetUp(A->rmap);
598: PetscLayoutSetUp(A->cmap);
599: a->maxnz = nztotal;
600: if (!a->imax) {
601: PetscMalloc1(A->rmap->n,&a->imax);
602: PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
603: }
604: if (!a->ilen) {
605: PetscMalloc1(A->rmap->n,&a->ilen);
606: PetscLogObjectMemory((PetscObject)A,A->rmap->n*sizeof(PetscInt));
607: } else {
608: PetscMemzero(a->ilen,A->rmap->n*sizeof(PetscInt));
609: }
611: /* allocate the matrix space */
612: if (A->structure_only) {
613: PetscMalloc1(nztotal,&a->j);
614: PetscMalloc1(A->rmap->n+1,&a->i);
615: PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*sizeof(PetscInt));
616: } else {
617: PetscMalloc3(nztotal,&a->a,nztotal,&a->j,A->rmap->n+1,&a->i);
618: PetscLogObjectMemory((PetscObject)A,(A->rmap->n+1)*sizeof(PetscInt)+nztotal*(sizeof(PetscScalar)+sizeof(PetscInt)));
619: }
620: a->i[0] = 0;
621: if (A->structure_only) {
622: a->singlemalloc = PETSC_FALSE;
623: a->free_a = PETSC_FALSE;
624: } else {
625: a->singlemalloc = PETSC_TRUE;
626: a->free_a = PETSC_TRUE;
627: }
628: a->free_ij = PETSC_TRUE;
629: A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
630: A->preallocated = PETSC_TRUE;
631: return(0);
632: }
634: PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
635: {
636: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
637: PetscInt *rp,k,row;
638: PetscInt *ai = a->i,*ailen = a->ilen;
640: PetscInt *aj = a->j;
641: MatScalar *aa = a->a,*ap;
644: for (k=0; k<m; k++) { /* loop over added rows */
645: row = im[k];
646: if (PetscUnlikelyDebug(n > a->imax[row])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Preallocation for row %D does not match number of columns provided",n);
647: rp = aj + ai[row];
648: ap = aa + ai[row];
649: if (!A->was_assembled) {
650: PetscMemcpy(rp,in,n*sizeof(PetscInt));
651: }
652: if (!A->structure_only) {
653: if (v) {
654: PetscMemcpy(ap,v,n*sizeof(PetscScalar));
655: v += n;
656: } else {
657: PetscMemzero(ap,n*sizeof(PetscScalar));
658: }
659: }
660: ailen[row] = n;
661: a->nz += n;
662: }
663: #if defined(PETSC_HAVE_DEVICE)
664: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED && m && n) A->offloadmask = PETSC_OFFLOAD_CPU;
665: #endif
666: return(0);
667: }
670: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
671: {
672: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
673: PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
674: PetscInt *ai = a->i,*ailen = a->ilen;
675: MatScalar *ap,*aa = a->a;
678: for (k=0; k<m; k++) { /* loop over rows */
679: row = im[k];
680: if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
681: 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);
682: rp = aj + ai[row]; ap = aa + ai[row];
683: nrow = ailen[row];
684: for (l=0; l<n; l++) { /* loop over columns */
685: if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
686: 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);
687: col = in[l];
688: high = nrow; low = 0; /* assume unsorted */
689: while (high-low > 5) {
690: t = (low+high)/2;
691: if (rp[t] > col) high = t;
692: else low = t;
693: }
694: for (i=low; i<high; i++) {
695: if (rp[i] > col) break;
696: if (rp[i] == col) {
697: *v++ = ap[i];
698: goto finished;
699: }
700: }
701: *v++ = 0.0;
702: finished:;
703: }
704: }
705: return(0);
706: }
708: PetscErrorCode MatView_SeqAIJ_Binary(Mat mat,PetscViewer viewer)
709: {
710: Mat_SeqAIJ *A = (Mat_SeqAIJ*)mat->data;
711: const PetscScalar *av;
712: PetscInt header[4],M,N,m,nz,i;
713: PetscInt *rowlens;
714: PetscErrorCode ierr;
717: PetscViewerSetUp(viewer);
719: M = mat->rmap->N;
720: N = mat->cmap->N;
721: m = mat->rmap->n;
722: nz = A->nz;
724: /* write matrix header */
725: header[0] = MAT_FILE_CLASSID;
726: header[1] = M; header[2] = N; header[3] = nz;
727: PetscViewerBinaryWrite(viewer,header,4,PETSC_INT);
729: /* fill in and store row lengths */
730: PetscMalloc1(m,&rowlens);
731: for (i=0; i<m; i++) rowlens[i] = A->i[i+1] - A->i[i];
732: PetscViewerBinaryWrite(viewer,rowlens,m,PETSC_INT);
733: PetscFree(rowlens);
734: /* store column indices */
735: PetscViewerBinaryWrite(viewer,A->j,nz,PETSC_INT);
736: /* store nonzero values */
737: MatSeqAIJGetArrayRead(mat,&av);
738: PetscViewerBinaryWrite(viewer,av,nz,PETSC_SCALAR);
739: MatSeqAIJRestoreArrayRead(mat,&av);
741: /* write block size option to the viewer's .info file */
742: MatView_Binary_BlockSizes(mat,viewer);
743: return(0);
744: }
746: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A,PetscViewer viewer)
747: {
749: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
750: PetscInt i,k,m=A->rmap->N;
753: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
754: for (i=0; i<m; i++) {
755: PetscViewerASCIIPrintf(viewer,"row %D:",i);
756: for (k=a->i[i]; k<a->i[i+1]; k++) {
757: PetscViewerASCIIPrintf(viewer," (%D) ",a->j[k]);
758: }
759: PetscViewerASCIIPrintf(viewer,"\n");
760: }
761: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
762: return(0);
763: }
765: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);
767: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
768: {
769: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
770: const PetscScalar *av;
771: PetscErrorCode ierr;
772: PetscInt i,j,m = A->rmap->n;
773: const char *name;
774: PetscViewerFormat format;
777: if (A->structure_only) {
778: MatView_SeqAIJ_ASCII_structonly(A,viewer);
779: return(0);
780: }
782: PetscViewerGetFormat(viewer,&format);
783: if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) return(0);
785: /* trigger copy to CPU if needed */
786: MatSeqAIJGetArrayRead(A,&av);
787: MatSeqAIJRestoreArrayRead(A,&av);
788: if (format == PETSC_VIEWER_ASCII_MATLAB) {
789: PetscInt nofinalvalue = 0;
790: if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
791: /* Need a dummy value to ensure the dimension of the matrix. */
792: nofinalvalue = 1;
793: }
794: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
795: PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
796: PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
797: #if defined(PETSC_USE_COMPLEX)
798: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
799: #else
800: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
801: #endif
802: PetscViewerASCIIPrintf(viewer,"zzz = [\n");
804: for (i=0; i<m; i++) {
805: for (j=a->i[i]; j<a->i[i+1]; j++) {
806: #if defined(PETSC_USE_COMPLEX)
807: 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]));
808: #else
809: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
810: #endif
811: }
812: }
813: if (nofinalvalue) {
814: #if defined(PETSC_USE_COMPLEX)
815: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
816: #else
817: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->cmap->n,0.0);
818: #endif
819: }
820: PetscObjectGetName((PetscObject)A,&name);
821: PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
822: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
823: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
824: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
825: for (i=0; i<m; i++) {
826: PetscViewerASCIIPrintf(viewer,"row %D:",i);
827: for (j=a->i[i]; j<a->i[i+1]; j++) {
828: #if defined(PETSC_USE_COMPLEX)
829: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
830: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
831: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
832: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
833: } else if (PetscRealPart(a->a[j]) != 0.0) {
834: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
835: }
836: #else
837: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
838: #endif
839: }
840: PetscViewerASCIIPrintf(viewer,"\n");
841: }
842: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
843: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
844: PetscInt nzd=0,fshift=1,*sptr;
845: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
846: PetscMalloc1(m+1,&sptr);
847: for (i=0; i<m; i++) {
848: sptr[i] = nzd+1;
849: for (j=a->i[i]; j<a->i[i+1]; j++) {
850: if (a->j[j] >= i) {
851: #if defined(PETSC_USE_COMPLEX)
852: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
853: #else
854: if (a->a[j] != 0.0) nzd++;
855: #endif
856: }
857: }
858: }
859: sptr[m] = nzd+1;
860: PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
861: for (i=0; i<m+1; i+=6) {
862: if (i+4<m) {
863: 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]);
864: } else if (i+3<m) {
865: PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
866: } else if (i+2<m) {
867: PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
868: } else if (i+1<m) {
869: PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
870: } else if (i<m) {
871: PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
872: } else {
873: PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
874: }
875: }
876: PetscViewerASCIIPrintf(viewer,"\n");
877: PetscFree(sptr);
878: for (i=0; i<m; i++) {
879: for (j=a->i[i]; j<a->i[i+1]; j++) {
880: if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
881: }
882: PetscViewerASCIIPrintf(viewer,"\n");
883: }
884: PetscViewerASCIIPrintf(viewer,"\n");
885: for (i=0; i<m; i++) {
886: for (j=a->i[i]; j<a->i[i+1]; j++) {
887: if (a->j[j] >= i) {
888: #if defined(PETSC_USE_COMPLEX)
889: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
890: PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
891: }
892: #else
893: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
894: #endif
895: }
896: }
897: PetscViewerASCIIPrintf(viewer,"\n");
898: }
899: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
900: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
901: PetscInt cnt = 0,jcnt;
902: PetscScalar value;
903: #if defined(PETSC_USE_COMPLEX)
904: PetscBool realonly = PETSC_TRUE;
906: for (i=0; i<a->i[m]; i++) {
907: if (PetscImaginaryPart(a->a[i]) != 0.0) {
908: realonly = PETSC_FALSE;
909: break;
910: }
911: }
912: #endif
914: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
915: for (i=0; i<m; i++) {
916: jcnt = 0;
917: for (j=0; j<A->cmap->n; j++) {
918: if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
919: value = a->a[cnt++];
920: jcnt++;
921: } else {
922: value = 0.0;
923: }
924: #if defined(PETSC_USE_COMPLEX)
925: if (realonly) {
926: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
927: } else {
928: PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
929: }
930: #else
931: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
932: #endif
933: }
934: PetscViewerASCIIPrintf(viewer,"\n");
935: }
936: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
937: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
938: PetscInt fshift=1;
939: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
940: #if defined(PETSC_USE_COMPLEX)
941: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
942: #else
943: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
944: #endif
945: PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
946: for (i=0; i<m; i++) {
947: for (j=a->i[i]; j<a->i[i+1]; j++) {
948: #if defined(PETSC_USE_COMPLEX)
949: PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
950: #else
951: PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
952: #endif
953: }
954: }
955: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
956: } else {
957: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
958: if (A->factortype) {
959: for (i=0; i<m; i++) {
960: PetscViewerASCIIPrintf(viewer,"row %D:",i);
961: /* L part */
962: for (j=a->i[i]; j<a->i[i+1]; j++) {
963: #if defined(PETSC_USE_COMPLEX)
964: if (PetscImaginaryPart(a->a[j]) > 0.0) {
965: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
966: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
967: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
968: } else {
969: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
970: }
971: #else
972: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
973: #endif
974: }
975: /* diagonal */
976: j = a->diag[i];
977: #if defined(PETSC_USE_COMPLEX)
978: if (PetscImaginaryPart(a->a[j]) > 0.0) {
979: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
980: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
981: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
982: } else {
983: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
984: }
985: #else
986: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
987: #endif
989: /* U part */
990: for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
991: #if defined(PETSC_USE_COMPLEX)
992: if (PetscImaginaryPart(a->a[j]) > 0.0) {
993: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
994: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
995: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
996: } else {
997: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
998: }
999: #else
1000: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
1001: #endif
1002: }
1003: PetscViewerASCIIPrintf(viewer,"\n");
1004: }
1005: } else {
1006: for (i=0; i<m; i++) {
1007: PetscViewerASCIIPrintf(viewer,"row %D:",i);
1008: for (j=a->i[i]; j<a->i[i+1]; j++) {
1009: #if defined(PETSC_USE_COMPLEX)
1010: if (PetscImaginaryPart(a->a[j]) > 0.0) {
1011: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
1012: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
1013: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
1014: } else {
1015: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
1016: }
1017: #else
1018: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
1019: #endif
1020: }
1021: PetscViewerASCIIPrintf(viewer,"\n");
1022: }
1023: }
1024: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1025: }
1026: PetscViewerFlush(viewer);
1027: return(0);
1028: }
1030: #include <petscdraw.h>
1031: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
1032: {
1033: Mat A = (Mat) Aa;
1034: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1035: PetscErrorCode ierr;
1036: PetscInt i,j,m = A->rmap->n;
1037: int color;
1038: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1039: PetscViewer viewer;
1040: PetscViewerFormat format;
1043: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1044: PetscViewerGetFormat(viewer,&format);
1045: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
1047: /* loop over matrix elements drawing boxes */
1049: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1050: PetscDrawCollectiveBegin(draw);
1051: /* Blue for negative, Cyan for zero and Red for positive */
1052: color = PETSC_DRAW_BLUE;
1053: for (i=0; i<m; i++) {
1054: y_l = m - i - 1.0; y_r = y_l + 1.0;
1055: for (j=a->i[i]; j<a->i[i+1]; j++) {
1056: x_l = a->j[j]; x_r = x_l + 1.0;
1057: if (PetscRealPart(a->a[j]) >= 0.) continue;
1058: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1059: }
1060: }
1061: color = PETSC_DRAW_CYAN;
1062: for (i=0; i<m; i++) {
1063: y_l = m - i - 1.0; y_r = y_l + 1.0;
1064: for (j=a->i[i]; j<a->i[i+1]; j++) {
1065: x_l = a->j[j]; x_r = x_l + 1.0;
1066: if (a->a[j] != 0.) continue;
1067: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1068: }
1069: }
1070: color = PETSC_DRAW_RED;
1071: for (i=0; i<m; i++) {
1072: y_l = m - i - 1.0; y_r = y_l + 1.0;
1073: for (j=a->i[i]; j<a->i[i+1]; j++) {
1074: x_l = a->j[j]; x_r = x_l + 1.0;
1075: if (PetscRealPart(a->a[j]) <= 0.) continue;
1076: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1077: }
1078: }
1079: PetscDrawCollectiveEnd(draw);
1080: } else {
1081: /* use contour shading to indicate magnitude of values */
1082: /* first determine max of all nonzero values */
1083: PetscReal minv = 0.0, maxv = 0.0;
1084: PetscInt nz = a->nz, count = 0;
1085: PetscDraw popup;
1087: for (i=0; i<nz; i++) {
1088: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
1089: }
1090: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1091: PetscDrawGetPopup(draw,&popup);
1092: PetscDrawScalePopup(popup,minv,maxv);
1094: PetscDrawCollectiveBegin(draw);
1095: for (i=0; i<m; i++) {
1096: y_l = m - i - 1.0;
1097: y_r = y_l + 1.0;
1098: for (j=a->i[i]; j<a->i[i+1]; j++) {
1099: x_l = a->j[j];
1100: x_r = x_l + 1.0;
1101: color = PetscDrawRealToColor(PetscAbsScalar(a->a[count]),minv,maxv);
1102: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1103: count++;
1104: }
1105: }
1106: PetscDrawCollectiveEnd(draw);
1107: }
1108: return(0);
1109: }
1111: #include <petscdraw.h>
1112: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
1113: {
1115: PetscDraw draw;
1116: PetscReal xr,yr,xl,yl,h,w;
1117: PetscBool isnull;
1120: PetscViewerDrawGetDraw(viewer,0,&draw);
1121: PetscDrawIsNull(draw,&isnull);
1122: if (isnull) return(0);
1124: xr = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
1125: xr += w; yr += h; xl = -w; yl = -h;
1126: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1127: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1128: PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
1129: PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
1130: PetscDrawSave(draw);
1131: return(0);
1132: }
1134: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
1135: {
1137: PetscBool iascii,isbinary,isdraw;
1140: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1141: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1142: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1143: if (iascii) {
1144: MatView_SeqAIJ_ASCII(A,viewer);
1145: } else if (isbinary) {
1146: MatView_SeqAIJ_Binary(A,viewer);
1147: } else if (isdraw) {
1148: MatView_SeqAIJ_Draw(A,viewer);
1149: }
1150: MatView_SeqAIJ_Inode(A,viewer);
1151: return(0);
1152: }
1154: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
1155: {
1156: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1158: PetscInt fshift = 0,i,*ai = a->i,*aj = a->j,*imax = a->imax;
1159: PetscInt m = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
1160: MatScalar *aa = a->a,*ap;
1161: PetscReal ratio = 0.6;
1164: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1165: MatSeqAIJInvalidateDiagonal(A);
1166: if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1167: /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
1168: MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1169: return(0);
1170: }
1172: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1173: for (i=1; i<m; i++) {
1174: /* move each row back by the amount of empty slots (fshift) before it*/
1175: fshift += imax[i-1] - ailen[i-1];
1176: rmax = PetscMax(rmax,ailen[i]);
1177: if (fshift) {
1178: ip = aj + ai[i];
1179: ap = aa + ai[i];
1180: N = ailen[i];
1181: PetscArraymove(ip-fshift,ip,N);
1182: if (!A->structure_only) {
1183: PetscArraymove(ap-fshift,ap,N);
1184: }
1185: }
1186: ai[i] = ai[i-1] + ailen[i-1];
1187: }
1188: if (m) {
1189: fshift += imax[m-1] - ailen[m-1];
1190: ai[m] = ai[m-1] + ailen[m-1];
1191: }
1193: /* reset ilen and imax for each row */
1194: a->nonzerorowcnt = 0;
1195: if (A->structure_only) {
1196: PetscFree(a->imax);
1197: PetscFree(a->ilen);
1198: } else { /* !A->structure_only */
1199: for (i=0; i<m; i++) {
1200: ailen[i] = imax[i] = ai[i+1] - ai[i];
1201: a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
1202: }
1203: }
1204: a->nz = ai[m];
1205: 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);
1207: MatMarkDiagonal_SeqAIJ(A);
1208: PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
1209: PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1210: PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);
1212: A->info.mallocs += a->reallocs;
1213: a->reallocs = 0;
1214: A->info.nz_unneeded = (PetscReal)fshift;
1215: a->rmax = rmax;
1217: if (!A->structure_only) {
1218: MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
1219: }
1220: MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1221: return(0);
1222: }
1224: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1225: {
1226: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1227: PetscInt i,nz = a->nz;
1228: MatScalar *aa;
1232: MatSeqAIJGetArray(A,&aa);
1233: for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1234: MatSeqAIJRestoreArray(A,&aa);
1235: MatSeqAIJInvalidateDiagonal(A);
1236: #if defined(PETSC_HAVE_DEVICE)
1237: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1238: #endif
1239: return(0);
1240: }
1242: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1243: {
1244: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1245: PetscInt i,nz = a->nz;
1246: MatScalar *aa;
1250: MatSeqAIJGetArray(A,&aa);
1251: for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1252: MatSeqAIJRestoreArray(A,&aa);
1253: MatSeqAIJInvalidateDiagonal(A);
1254: #if defined(PETSC_HAVE_DEVICE)
1255: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1256: #endif
1257: return(0);
1258: }
1260: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1261: {
1262: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1266: PetscArrayzero(a->a,a->i[A->rmap->n]);
1267: MatSeqAIJInvalidateDiagonal(A);
1268: #if defined(PETSC_HAVE_DEVICE)
1269: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
1270: #endif
1271: return(0);
1272: }
1274: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1275: {
1276: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1280: #if defined(PETSC_USE_LOG)
1281: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
1282: #endif
1283: MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1284: ISDestroy(&a->row);
1285: ISDestroy(&a->col);
1286: PetscFree(a->diag);
1287: PetscFree(a->ibdiag);
1288: PetscFree(a->imax);
1289: PetscFree(a->ilen);
1290: PetscFree(a->ipre);
1291: PetscFree3(a->idiag,a->mdiag,a->ssor_work);
1292: PetscFree(a->solve_work);
1293: ISDestroy(&a->icol);
1294: PetscFree(a->saved_values);
1295: PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
1297: MatDestroy_SeqAIJ_Inode(A);
1298: PetscFree(A->data);
1300: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1301: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1302: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1303: users reusing the matrix object with different data to incur in obscure segmentation faults
1304: due to different matrix sizes */
1305: PetscObjectCompose((PetscObject)A,"__PETSc__ab_dense",NULL);
1307: PetscObjectChangeTypeName((PetscObject)A,NULL);
1308: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
1309: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1310: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1311: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
1312: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
1313: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
1314: #if defined(PETSC_HAVE_CUDA)
1315: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcusparse_C",NULL);
1316: PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",NULL);
1317: PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",NULL);
1318: #endif
1319: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1320: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijkokkos_C",NULL);
1321: #endif
1322: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijcrl_C",NULL);
1323: #if defined(PETSC_HAVE_ELEMENTAL)
1324: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
1325: #endif
1326: #if defined(PETSC_HAVE_SCALAPACK)
1327: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_scalapack_C",NULL);
1328: #endif
1329: #if defined(PETSC_HAVE_HYPRE)
1330: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_hypre_C",NULL);
1331: PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",NULL);
1332: #endif
1333: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
1334: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsell_C",NULL);
1335: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_is_C",NULL);
1336: PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1337: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1338: PetscObjectComposeFunction((PetscObject)A,"MatResetPreallocation_C",NULL);
1339: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1340: PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1341: PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_is_seqaij_C",NULL);
1342: PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqdense_seqaij_C",NULL);
1343: PetscObjectComposeFunction((PetscObject)A,"MatProductSetFromOptions_seqaij_seqaij_C",NULL);
1344: return(0);
1345: }
1347: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1348: {
1349: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1353: switch (op) {
1354: case MAT_ROW_ORIENTED:
1355: a->roworiented = flg;
1356: break;
1357: case MAT_KEEP_NONZERO_PATTERN:
1358: a->keepnonzeropattern = flg;
1359: break;
1360: case MAT_NEW_NONZERO_LOCATIONS:
1361: a->nonew = (flg ? 0 : 1);
1362: break;
1363: case MAT_NEW_NONZERO_LOCATION_ERR:
1364: a->nonew = (flg ? -1 : 0);
1365: break;
1366: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1367: a->nonew = (flg ? -2 : 0);
1368: break;
1369: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1370: a->nounused = (flg ? -1 : 0);
1371: break;
1372: case MAT_IGNORE_ZERO_ENTRIES:
1373: a->ignorezeroentries = flg;
1374: break;
1375: case MAT_SPD:
1376: case MAT_SYMMETRIC:
1377: case MAT_STRUCTURALLY_SYMMETRIC:
1378: case MAT_HERMITIAN:
1379: case MAT_SYMMETRY_ETERNAL:
1380: case MAT_STRUCTURE_ONLY:
1381: /* These options are handled directly by MatSetOption() */
1382: break;
1383: case MAT_FORCE_DIAGONAL_ENTRIES:
1384: case MAT_IGNORE_OFF_PROC_ENTRIES:
1385: case MAT_USE_HASH_TABLE:
1386: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1387: break;
1388: case MAT_USE_INODES:
1389: MatSetOption_SeqAIJ_Inode(A,MAT_USE_INODES,flg);
1390: break;
1391: case MAT_SUBMAT_SINGLEIS:
1392: A->submat_singleis = flg;
1393: break;
1394: case MAT_SORTED_FULL:
1395: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1396: else A->ops->setvalues = MatSetValues_SeqAIJ;
1397: break;
1398: case MAT_FORM_EXPLICIT_TRANSPOSE:
1399: A->form_explicit_transpose = flg;
1400: break;
1401: default:
1402: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1403: }
1404: return(0);
1405: }
1407: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1408: {
1409: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1410: PetscErrorCode ierr;
1411: PetscInt i,j,n,*ai=a->i,*aj=a->j;
1412: PetscScalar *x;
1413: const PetscScalar *aa;
1416: VecGetLocalSize(v,&n);
1417: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1418: MatSeqAIJGetArrayRead(A,&aa);
1419: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1420: PetscInt *diag=a->diag;
1421: VecGetArrayWrite(v,&x);
1422: for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1423: VecRestoreArrayWrite(v,&x);
1424: MatSeqAIJRestoreArrayRead(A,&aa);
1425: return(0);
1426: }
1428: VecGetArrayWrite(v,&x);
1429: for (i=0; i<n; i++) {
1430: x[i] = 0.0;
1431: for (j=ai[i]; j<ai[i+1]; j++) {
1432: if (aj[j] == i) {
1433: x[i] = aa[j];
1434: break;
1435: }
1436: }
1437: }
1438: VecRestoreArrayWrite(v,&x);
1439: MatSeqAIJRestoreArrayRead(A,&aa);
1440: return(0);
1441: }
1443: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1444: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1445: {
1446: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1447: PetscScalar *y;
1448: const PetscScalar *x;
1449: PetscErrorCode ierr;
1450: PetscInt m = A->rmap->n;
1451: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1452: const MatScalar *v;
1453: PetscScalar alpha;
1454: PetscInt n,i,j;
1455: const PetscInt *idx,*ii,*ridx=NULL;
1456: Mat_CompressedRow cprow = a->compressedrow;
1457: PetscBool usecprow = cprow.use;
1458: #endif
1461: if (zz != yy) {VecCopy(zz,yy);}
1462: VecGetArrayRead(xx,&x);
1463: VecGetArray(yy,&y);
1465: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1466: fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1467: #else
1468: if (usecprow) {
1469: m = cprow.nrows;
1470: ii = cprow.i;
1471: ridx = cprow.rindex;
1472: } else {
1473: ii = a->i;
1474: }
1475: for (i=0; i<m; i++) {
1476: idx = a->j + ii[i];
1477: v = a->a + ii[i];
1478: n = ii[i+1] - ii[i];
1479: if (usecprow) {
1480: alpha = x[ridx[i]];
1481: } else {
1482: alpha = x[i];
1483: }
1484: for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1485: }
1486: #endif
1487: PetscLogFlops(2.0*a->nz);
1488: VecRestoreArrayRead(xx,&x);
1489: VecRestoreArray(yy,&y);
1490: return(0);
1491: }
1493: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1494: {
1498: VecSet(yy,0.0);
1499: MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1500: return(0);
1501: }
1503: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1505: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1506: {
1507: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1508: PetscScalar *y;
1509: const PetscScalar *x;
1510: const MatScalar *aa;
1511: PetscErrorCode ierr;
1512: PetscInt m=A->rmap->n;
1513: const PetscInt *aj,*ii,*ridx=NULL;
1514: PetscInt n,i;
1515: PetscScalar sum;
1516: PetscBool usecprow=a->compressedrow.use;
1518: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1519: #pragma disjoint(*x,*y,*aa)
1520: #endif
1523: if (a->inode.use && a->inode.checked) {
1524: MatMult_SeqAIJ_Inode(A,xx,yy);
1525: return(0);
1526: }
1527: VecGetArrayRead(xx,&x);
1528: VecGetArray(yy,&y);
1529: ii = a->i;
1530: if (usecprow) { /* use compressed row format */
1531: PetscArrayzero(y,m);
1532: m = a->compressedrow.nrows;
1533: ii = a->compressedrow.i;
1534: ridx = a->compressedrow.rindex;
1535: for (i=0; i<m; i++) {
1536: n = ii[i+1] - ii[i];
1537: aj = a->j + ii[i];
1538: aa = a->a + ii[i];
1539: sum = 0.0;
1540: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1541: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1542: y[*ridx++] = sum;
1543: }
1544: } else { /* do not use compressed row format */
1545: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1546: aj = a->j;
1547: aa = a->a;
1548: fortranmultaij_(&m,x,ii,aj,aa,y);
1549: #else
1550: for (i=0; i<m; i++) {
1551: n = ii[i+1] - ii[i];
1552: aj = a->j + ii[i];
1553: aa = a->a + ii[i];
1554: sum = 0.0;
1555: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1556: y[i] = sum;
1557: }
1558: #endif
1559: }
1560: PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1561: VecRestoreArrayRead(xx,&x);
1562: VecRestoreArray(yy,&y);
1563: return(0);
1564: }
1566: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1567: {
1568: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1569: PetscScalar *y;
1570: const PetscScalar *x;
1571: const MatScalar *aa;
1572: PetscErrorCode ierr;
1573: PetscInt m=A->rmap->n;
1574: const PetscInt *aj,*ii,*ridx=NULL;
1575: PetscInt n,i,nonzerorow=0;
1576: PetscScalar sum;
1577: PetscBool usecprow=a->compressedrow.use;
1579: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1580: #pragma disjoint(*x,*y,*aa)
1581: #endif
1584: VecGetArrayRead(xx,&x);
1585: VecGetArray(yy,&y);
1586: if (usecprow) { /* use compressed row format */
1587: m = a->compressedrow.nrows;
1588: ii = a->compressedrow.i;
1589: ridx = a->compressedrow.rindex;
1590: for (i=0; i<m; i++) {
1591: n = ii[i+1] - ii[i];
1592: aj = a->j + ii[i];
1593: aa = a->a + ii[i];
1594: sum = 0.0;
1595: nonzerorow += (n>0);
1596: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1597: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1598: y[*ridx++] = sum;
1599: }
1600: } else { /* do not use compressed row format */
1601: ii = a->i;
1602: for (i=0; i<m; i++) {
1603: n = ii[i+1] - ii[i];
1604: aj = a->j + ii[i];
1605: aa = a->a + ii[i];
1606: sum = 0.0;
1607: nonzerorow += (n>0);
1608: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1609: y[i] = sum;
1610: }
1611: }
1612: PetscLogFlops(2.0*a->nz - nonzerorow);
1613: VecRestoreArrayRead(xx,&x);
1614: VecRestoreArray(yy,&y);
1615: return(0);
1616: }
1618: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1619: {
1620: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1621: PetscScalar *y,*z;
1622: const PetscScalar *x;
1623: const MatScalar *aa;
1624: PetscErrorCode ierr;
1625: PetscInt m = A->rmap->n,*aj,*ii;
1626: PetscInt n,i,*ridx=NULL;
1627: PetscScalar sum;
1628: PetscBool usecprow=a->compressedrow.use;
1631: VecGetArrayRead(xx,&x);
1632: VecGetArrayPair(yy,zz,&y,&z);
1633: if (usecprow) { /* use compressed row format */
1634: if (zz != yy) {
1635: PetscArraycpy(z,y,m);
1636: }
1637: m = a->compressedrow.nrows;
1638: ii = a->compressedrow.i;
1639: ridx = a->compressedrow.rindex;
1640: for (i=0; i<m; i++) {
1641: n = ii[i+1] - ii[i];
1642: aj = a->j + ii[i];
1643: aa = a->a + ii[i];
1644: sum = y[*ridx];
1645: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1646: z[*ridx++] = sum;
1647: }
1648: } else { /* do not use compressed row format */
1649: ii = a->i;
1650: for (i=0; i<m; i++) {
1651: n = ii[i+1] - ii[i];
1652: aj = a->j + ii[i];
1653: aa = a->a + ii[i];
1654: sum = y[i];
1655: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1656: z[i] = sum;
1657: }
1658: }
1659: PetscLogFlops(2.0*a->nz);
1660: VecRestoreArrayRead(xx,&x);
1661: VecRestoreArrayPair(yy,zz,&y,&z);
1662: return(0);
1663: }
1665: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1666: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1667: {
1668: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1669: PetscScalar *y,*z;
1670: const PetscScalar *x;
1671: const MatScalar *aa;
1672: PetscErrorCode ierr;
1673: const PetscInt *aj,*ii,*ridx=NULL;
1674: PetscInt m = A->rmap->n,n,i;
1675: PetscScalar sum;
1676: PetscBool usecprow=a->compressedrow.use;
1679: if (a->inode.use && a->inode.checked) {
1680: MatMultAdd_SeqAIJ_Inode(A,xx,yy,zz);
1681: return(0);
1682: }
1683: VecGetArrayRead(xx,&x);
1684: VecGetArrayPair(yy,zz,&y,&z);
1685: if (usecprow) { /* use compressed row format */
1686: if (zz != yy) {
1687: PetscArraycpy(z,y,m);
1688: }
1689: m = a->compressedrow.nrows;
1690: ii = a->compressedrow.i;
1691: ridx = a->compressedrow.rindex;
1692: for (i=0; i<m; i++) {
1693: n = ii[i+1] - ii[i];
1694: aj = a->j + ii[i];
1695: aa = a->a + ii[i];
1696: sum = y[*ridx];
1697: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1698: z[*ridx++] = sum;
1699: }
1700: } else { /* do not use compressed row format */
1701: ii = a->i;
1702: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1703: aj = a->j;
1704: aa = a->a;
1705: fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1706: #else
1707: for (i=0; i<m; i++) {
1708: n = ii[i+1] - ii[i];
1709: aj = a->j + ii[i];
1710: aa = a->a + ii[i];
1711: sum = y[i];
1712: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1713: z[i] = sum;
1714: }
1715: #endif
1716: }
1717: PetscLogFlops(2.0*a->nz);
1718: VecRestoreArrayRead(xx,&x);
1719: VecRestoreArrayPair(yy,zz,&y,&z);
1720: return(0);
1721: }
1723: /*
1724: Adds diagonal pointers to sparse matrix structure.
1725: */
1726: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1727: {
1728: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1730: PetscInt i,j,m = A->rmap->n;
1733: if (!a->diag) {
1734: PetscMalloc1(m,&a->diag);
1735: PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1736: }
1737: for (i=0; i<A->rmap->n; i++) {
1738: a->diag[i] = a->i[i+1];
1739: for (j=a->i[i]; j<a->i[i+1]; j++) {
1740: if (a->j[j] == i) {
1741: a->diag[i] = j;
1742: break;
1743: }
1744: }
1745: }
1746: return(0);
1747: }
1749: PetscErrorCode MatShift_SeqAIJ(Mat A,PetscScalar v)
1750: {
1751: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1752: const PetscInt *diag = (const PetscInt*)a->diag;
1753: const PetscInt *ii = (const PetscInt*) a->i;
1754: PetscInt i,*mdiag = NULL;
1755: PetscErrorCode ierr;
1756: PetscInt cnt = 0; /* how many diagonals are missing */
1759: if (!A->preallocated || !a->nz) {
1760: MatSeqAIJSetPreallocation(A,1,NULL);
1761: MatShift_Basic(A,v);
1762: return(0);
1763: }
1765: if (a->diagonaldense) {
1766: cnt = 0;
1767: } else {
1768: PetscCalloc1(A->rmap->n,&mdiag);
1769: for (i=0; i<A->rmap->n; i++) {
1770: if (diag[i] >= ii[i+1]) {
1771: cnt++;
1772: mdiag[i] = 1;
1773: }
1774: }
1775: }
1776: if (!cnt) {
1777: MatShift_Basic(A,v);
1778: } else {
1779: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1780: PetscInt *oldj = a->j, *oldi = a->i;
1781: PetscBool singlemalloc = a->singlemalloc,free_a = a->free_a,free_ij = a->free_ij;
1783: a->a = NULL;
1784: a->j = NULL;
1785: a->i = NULL;
1786: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1787: for (i=0; i<A->rmap->n; i++) {
1788: a->imax[i] += mdiag[i];
1789: a->imax[i] = PetscMin(a->imax[i],A->cmap->n);
1790: }
1791: MatSeqAIJSetPreallocation_SeqAIJ(A,0,a->imax);
1793: /* copy old values into new matrix data structure */
1794: for (i=0; i<A->rmap->n; i++) {
1795: MatSetValues(A,1,&i,a->imax[i] - mdiag[i],&oldj[oldi[i]],&olda[oldi[i]],ADD_VALUES);
1796: if (i < A->cmap->n) {
1797: MatSetValue(A,i,i,v,ADD_VALUES);
1798: }
1799: }
1800: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
1801: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
1802: if (singlemalloc) {
1803: PetscFree3(olda,oldj,oldi);
1804: } else {
1805: if (free_a) {PetscFree(olda);}
1806: if (free_ij) {PetscFree(oldj);}
1807: if (free_ij) {PetscFree(oldi);}
1808: }
1809: }
1810: PetscFree(mdiag);
1811: a->diagonaldense = PETSC_TRUE;
1812: return(0);
1813: }
1815: /*
1816: Checks for missing diagonals
1817: */
1818: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool *missing,PetscInt *d)
1819: {
1820: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1821: PetscInt *diag,*ii = a->i,i;
1825: *missing = PETSC_FALSE;
1826: if (A->rmap->n > 0 && !ii) {
1827: *missing = PETSC_TRUE;
1828: if (d) *d = 0;
1829: PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1830: } else {
1831: PetscInt n;
1832: n = PetscMin(A->rmap->n, A->cmap->n);
1833: diag = a->diag;
1834: for (i=0; i<n; i++) {
1835: if (diag[i] >= ii[i+1]) {
1836: *missing = PETSC_TRUE;
1837: if (d) *d = i;
1838: PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1839: break;
1840: }
1841: }
1842: }
1843: return(0);
1844: }
1846: #include <petscblaslapack.h>
1847: #include <petsc/private/kernels/blockinvert.h>
1849: /*
1850: Note that values is allocated externally by the PC and then passed into this routine
1851: */
1852: PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A,PetscInt nblocks,const PetscInt *bsizes,PetscScalar *diag)
1853: {
1854: PetscErrorCode ierr;
1855: PetscInt n = A->rmap->n, i, ncnt = 0, *indx,j,bsizemax = 0,*v_pivots;
1856: PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;
1857: const PetscReal shift = 0.0;
1858: PetscInt ipvt[5];
1859: PetscScalar work[25],*v_work;
1862: allowzeropivot = PetscNot(A->erroriffailure);
1863: for (i=0; i<nblocks; i++) ncnt += bsizes[i];
1864: if (ncnt != n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Total blocksizes %D doesn't match number matrix rows %D",ncnt,n);
1865: for (i=0; i<nblocks; i++) {
1866: bsizemax = PetscMax(bsizemax,bsizes[i]);
1867: }
1868: PetscMalloc1(bsizemax,&indx);
1869: if (bsizemax > 7) {
1870: PetscMalloc2(bsizemax,&v_work,bsizemax,&v_pivots);
1871: }
1872: ncnt = 0;
1873: for (i=0; i<nblocks; i++) {
1874: for (j=0; j<bsizes[i]; j++) indx[j] = ncnt+j;
1875: MatGetValues(A,bsizes[i],indx,bsizes[i],indx,diag);
1876: switch (bsizes[i]) {
1877: case 1:
1878: *diag = 1.0/(*diag);
1879: break;
1880: case 2:
1881: PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
1882: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1883: PetscKernel_A_gets_transpose_A_2(diag);
1884: break;
1885: case 3:
1886: PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
1887: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1888: PetscKernel_A_gets_transpose_A_3(diag);
1889: break;
1890: case 4:
1891: PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
1892: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1893: PetscKernel_A_gets_transpose_A_4(diag);
1894: break;
1895: case 5:
1896: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
1897: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1898: PetscKernel_A_gets_transpose_A_5(diag);
1899: break;
1900: case 6:
1901: PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
1902: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1903: PetscKernel_A_gets_transpose_A_6(diag);
1904: break;
1905: case 7:
1906: PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
1907: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1908: PetscKernel_A_gets_transpose_A_7(diag);
1909: break;
1910: default:
1911: PetscKernel_A_gets_inverse_A(bsizes[i],diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
1912: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1913: PetscKernel_A_gets_transpose_A_N(diag,bsizes[i]);
1914: }
1915: ncnt += bsizes[i];
1916: diag += bsizes[i]*bsizes[i];
1917: }
1918: if (bsizemax > 7) {
1919: PetscFree2(v_work,v_pivots);
1920: }
1921: PetscFree(indx);
1922: return(0);
1923: }
1925: /*
1926: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1927: */
1928: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1929: {
1930: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
1931: PetscErrorCode ierr;
1932: PetscInt i,*diag,m = A->rmap->n;
1933: const MatScalar *v;
1934: PetscScalar *idiag,*mdiag;
1937: if (a->idiagvalid) return(0);
1938: MatMarkDiagonal_SeqAIJ(A);
1939: diag = a->diag;
1940: if (!a->idiag) {
1941: PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1942: PetscLogObjectMemory((PetscObject)A,3*m*sizeof(PetscScalar));
1943: }
1945: mdiag = a->mdiag;
1946: idiag = a->idiag;
1947: MatSeqAIJGetArrayRead(A,&v);
1948: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1949: for (i=0; i<m; i++) {
1950: mdiag[i] = v[diag[i]];
1951: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1952: if (PetscRealPart(fshift)) {
1953: PetscInfo1(A,"Zero diagonal on row %D\n",i);
1954: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1955: A->factorerror_zeropivot_value = 0.0;
1956: A->factorerror_zeropivot_row = i;
1957: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1958: }
1959: idiag[i] = 1.0/v[diag[i]];
1960: }
1961: PetscLogFlops(m);
1962: } else {
1963: for (i=0; i<m; i++) {
1964: mdiag[i] = v[diag[i]];
1965: idiag[i] = omega/(fshift + v[diag[i]]);
1966: }
1967: PetscLogFlops(2.0*m);
1968: }
1969: a->idiagvalid = PETSC_TRUE;
1970: MatSeqAIJRestoreArrayRead(A,&v);
1971: return(0);
1972: }
1974: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1975: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1976: {
1977: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1978: PetscScalar *x,d,sum,*t,scale;
1979: const MatScalar *v,*idiag=NULL,*mdiag,*aa;
1980: const PetscScalar *b, *bs,*xb, *ts;
1981: PetscErrorCode ierr;
1982: PetscInt n,m = A->rmap->n,i;
1983: const PetscInt *idx,*diag;
1986: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1987: MatSOR_SeqAIJ_Inode(A,bb,omega,flag,fshift,its,lits,xx);
1988: return(0);
1989: }
1990: its = its*lits;
1992: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1993: if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1994: a->fshift = fshift;
1995: a->omega = omega;
1997: diag = a->diag;
1998: t = a->ssor_work;
1999: idiag = a->idiag;
2000: mdiag = a->mdiag;
2002: MatSeqAIJGetArrayRead(A,&aa);
2003: VecGetArray(xx,&x);
2004: VecGetArrayRead(bb,&b);
2005: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
2006: if (flag == SOR_APPLY_UPPER) {
2007: /* apply (U + D/omega) to the vector */
2008: bs = b;
2009: for (i=0; i<m; i++) {
2010: d = fshift + mdiag[i];
2011: n = a->i[i+1] - diag[i] - 1;
2012: idx = a->j + diag[i] + 1;
2013: v = aa + diag[i] + 1;
2014: sum = b[i]*d/omega;
2015: PetscSparseDensePlusDot(sum,bs,v,idx,n);
2016: x[i] = sum;
2017: }
2018: VecRestoreArray(xx,&x);
2019: VecRestoreArrayRead(bb,&b);
2020: MatSeqAIJRestoreArrayRead(A,&aa);
2021: PetscLogFlops(a->nz);
2022: return(0);
2023: }
2025: if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
2026: else if (flag & SOR_EISENSTAT) {
2027: /* Let A = L + U + D; where L is lower triangular,
2028: U is upper triangular, E = D/omega; This routine applies
2030: (L + E)^{-1} A (U + E)^{-1}
2032: to a vector efficiently using Eisenstat's trick.
2033: */
2034: scale = (2.0/omega) - 1.0;
2036: /* x = (E + U)^{-1} b */
2037: for (i=m-1; i>=0; i--) {
2038: n = a->i[i+1] - diag[i] - 1;
2039: idx = a->j + diag[i] + 1;
2040: v = aa + diag[i] + 1;
2041: sum = b[i];
2042: PetscSparseDenseMinusDot(sum,x,v,idx,n);
2043: x[i] = sum*idiag[i];
2044: }
2046: /* t = b - (2*E - D)x */
2047: v = aa;
2048: for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];
2050: /* t = (E + L)^{-1}t */
2051: ts = t;
2052: diag = a->diag;
2053: for (i=0; i<m; i++) {
2054: n = diag[i] - a->i[i];
2055: idx = a->j + a->i[i];
2056: v = aa + a->i[i];
2057: sum = t[i];
2058: PetscSparseDenseMinusDot(sum,ts,v,idx,n);
2059: t[i] = sum*idiag[i];
2060: /* x = x + t */
2061: x[i] += t[i];
2062: }
2064: PetscLogFlops(6.0*m-1 + 2.0*a->nz);
2065: VecRestoreArray(xx,&x);
2066: VecRestoreArrayRead(bb,&b);
2067: return(0);
2068: }
2069: if (flag & SOR_ZERO_INITIAL_GUESS) {
2070: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2071: for (i=0; i<m; i++) {
2072: n = diag[i] - a->i[i];
2073: idx = a->j + a->i[i];
2074: v = aa + a->i[i];
2075: sum = b[i];
2076: PetscSparseDenseMinusDot(sum,x,v,idx,n);
2077: t[i] = sum;
2078: x[i] = sum*idiag[i];
2079: }
2080: xb = t;
2081: PetscLogFlops(a->nz);
2082: } else xb = b;
2083: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2084: for (i=m-1; i>=0; i--) {
2085: n = a->i[i+1] - diag[i] - 1;
2086: idx = a->j + diag[i] + 1;
2087: v = aa + diag[i] + 1;
2088: sum = xb[i];
2089: PetscSparseDenseMinusDot(sum,x,v,idx,n);
2090: if (xb == b) {
2091: x[i] = sum*idiag[i];
2092: } else {
2093: x[i] = (1-omega)*x[i] + sum*idiag[i]; /* omega in idiag */
2094: }
2095: }
2096: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
2097: }
2098: its--;
2099: }
2100: while (its--) {
2101: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2102: for (i=0; i<m; i++) {
2103: /* lower */
2104: n = diag[i] - a->i[i];
2105: idx = a->j + a->i[i];
2106: v = aa + a->i[i];
2107: sum = b[i];
2108: PetscSparseDenseMinusDot(sum,x,v,idx,n);
2109: t[i] = sum; /* save application of the lower-triangular part */
2110: /* upper */
2111: n = a->i[i+1] - diag[i] - 1;
2112: idx = a->j + diag[i] + 1;
2113: v = aa + diag[i] + 1;
2114: PetscSparseDenseMinusDot(sum,x,v,idx,n);
2115: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
2116: }
2117: xb = t;
2118: PetscLogFlops(2.0*a->nz);
2119: } else xb = b;
2120: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2121: for (i=m-1; i>=0; i--) {
2122: sum = xb[i];
2123: if (xb == b) {
2124: /* whole matrix (no checkpointing available) */
2125: n = a->i[i+1] - a->i[i];
2126: idx = a->j + a->i[i];
2127: v = aa + a->i[i];
2128: PetscSparseDenseMinusDot(sum,x,v,idx,n);
2129: x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
2130: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2131: n = a->i[i+1] - diag[i] - 1;
2132: idx = a->j + diag[i] + 1;
2133: v = aa + diag[i] + 1;
2134: PetscSparseDenseMinusDot(sum,x,v,idx,n);
2135: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
2136: }
2137: }
2138: if (xb == b) {
2139: PetscLogFlops(2.0*a->nz);
2140: } else {
2141: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
2142: }
2143: }
2144: }
2145: MatSeqAIJRestoreArrayRead(A,&aa);
2146: VecRestoreArray(xx,&x);
2147: VecRestoreArrayRead(bb,&b);
2148: return(0);
2149: }
2152: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
2153: {
2154: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2157: info->block_size = 1.0;
2158: info->nz_allocated = a->maxnz;
2159: info->nz_used = a->nz;
2160: info->nz_unneeded = (a->maxnz - a->nz);
2161: info->assemblies = A->num_ass;
2162: info->mallocs = A->info.mallocs;
2163: info->memory = ((PetscObject)A)->mem;
2164: if (A->factortype) {
2165: info->fill_ratio_given = A->info.fill_ratio_given;
2166: info->fill_ratio_needed = A->info.fill_ratio_needed;
2167: info->factor_mallocs = A->info.factor_mallocs;
2168: } else {
2169: info->fill_ratio_given = 0;
2170: info->fill_ratio_needed = 0;
2171: info->factor_mallocs = 0;
2172: }
2173: return(0);
2174: }
2176: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2177: {
2178: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2179: PetscInt i,m = A->rmap->n - 1;
2180: PetscErrorCode ierr;
2181: const PetscScalar *xx;
2182: PetscScalar *bb,*aa;
2183: PetscInt d = 0;
2186: if (x && b) {
2187: VecGetArrayRead(x,&xx);
2188: VecGetArray(b,&bb);
2189: for (i=0; i<N; i++) {
2190: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2191: if (rows[i] >= A->cmap->n) continue;
2192: bb[rows[i]] = diag*xx[rows[i]];
2193: }
2194: VecRestoreArrayRead(x,&xx);
2195: VecRestoreArray(b,&bb);
2196: }
2198: MatSeqAIJGetArray(A,&aa);
2199: if (a->keepnonzeropattern) {
2200: for (i=0; i<N; i++) {
2201: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2202: PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);
2203: }
2204: if (diag != 0.0) {
2205: for (i=0; i<N; i++) {
2206: d = rows[i];
2207: if (rows[i] >= A->cmap->n) continue;
2208: 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);
2209: }
2210: for (i=0; i<N; i++) {
2211: if (rows[i] >= A->cmap->n) continue;
2212: aa[a->diag[rows[i]]] = diag;
2213: }
2214: }
2215: } else {
2216: if (diag != 0.0) {
2217: for (i=0; i<N; i++) {
2218: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2219: if (a->ilen[rows[i]] > 0) {
2220: if (rows[i] >= A->cmap->n) {
2221: a->ilen[rows[i]] = 0;
2222: } else {
2223: a->ilen[rows[i]] = 1;
2224: aa[a->i[rows[i]]] = diag;
2225: a->j[a->i[rows[i]]] = rows[i];
2226: }
2227: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2228: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2229: }
2230: }
2231: } else {
2232: for (i=0; i<N; i++) {
2233: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2234: a->ilen[rows[i]] = 0;
2235: }
2236: }
2237: A->nonzerostate++;
2238: }
2239: MatSeqAIJRestoreArray(A,&aa);
2240: #if defined(PETSC_HAVE_DEVICE)
2241: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2242: #endif
2243: (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2244: return(0);
2245: }
2247: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
2248: {
2249: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2250: PetscInt i,j,m = A->rmap->n - 1,d = 0;
2251: PetscErrorCode ierr;
2252: PetscBool missing,*zeroed,vecs = PETSC_FALSE;
2253: const PetscScalar *xx;
2254: PetscScalar *bb,*aa;
2257: if (!N) return(0);
2258: MatSeqAIJGetArray(A,&aa);
2259: if (x && b) {
2260: VecGetArrayRead(x,&xx);
2261: VecGetArray(b,&bb);
2262: vecs = PETSC_TRUE;
2263: }
2264: PetscCalloc1(A->rmap->n,&zeroed);
2265: for (i=0; i<N; i++) {
2266: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
2267: PetscArrayzero(&aa[a->i[rows[i]]],a->ilen[rows[i]]);
2269: zeroed[rows[i]] = PETSC_TRUE;
2270: }
2271: for (i=0; i<A->rmap->n; i++) {
2272: if (!zeroed[i]) {
2273: for (j=a->i[i]; j<a->i[i+1]; j++) {
2274: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2275: if (vecs) bb[i] -= aa[j]*xx[a->j[j]];
2276: aa[j] = 0.0;
2277: }
2278: }
2279: } else if (vecs && i < A->cmap->N) bb[i] = diag*xx[i];
2280: }
2281: if (x && b) {
2282: VecRestoreArrayRead(x,&xx);
2283: VecRestoreArray(b,&bb);
2284: }
2285: PetscFree(zeroed);
2286: if (diag != 0.0) {
2287: MatMissingDiagonal_SeqAIJ(A,&missing,&d);
2288: if (missing) {
2289: for (i=0; i<N; i++) {
2290: if (rows[i] >= A->cmap->N) continue;
2291: 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]);
2292: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
2293: }
2294: } else {
2295: for (i=0; i<N; i++) {
2296: aa[a->diag[rows[i]]] = diag;
2297: }
2298: }
2299: }
2300: MatSeqAIJRestoreArray(A,&aa);
2301: #if defined(PETSC_HAVE_DEVICE)
2302: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2303: #endif
2304: (*A->ops->assemblyend)(A,MAT_FINAL_ASSEMBLY);
2305: return(0);
2306: }
2308: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2309: {
2310: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2311: const PetscScalar *aa = a->a;
2312: PetscInt *itmp;
2313: #if defined(PETSC_HAVE_DEVICE)
2314: PetscErrorCode ierr;
2315: PetscBool rest = PETSC_FALSE;
2316: #endif
2319: #if defined(PETSC_HAVE_DEVICE)
2320: if (v && A->offloadmask == PETSC_OFFLOAD_GPU) {
2321: /* triggers copy to CPU */
2322: rest = PETSC_TRUE;
2323: MatSeqAIJGetArrayRead(A,&aa);
2324: } else aa = a->a;
2325: #endif
2326: *nz = a->i[row+1] - a->i[row];
2327: if (v) *v = (PetscScalar*)(aa + a->i[row]);
2328: if (idx) {
2329: itmp = a->j + a->i[row];
2330: if (*nz) *idx = itmp;
2331: else *idx = NULL;
2332: }
2333: #if defined(PETSC_HAVE_DEVICE)
2334: if (rest) {
2335: MatSeqAIJRestoreArrayRead(A,&aa);
2336: }
2337: #endif
2338: return(0);
2339: }
2341: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
2342: {
2344: if (nz) *nz = 0;
2345: if (idx) *idx = NULL;
2346: if (v) *v = NULL;
2347: return(0);
2348: }
2350: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
2351: {
2352: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2353: const MatScalar *v;
2354: PetscReal sum = 0.0;
2355: PetscErrorCode ierr;
2356: PetscInt i,j;
2359: MatSeqAIJGetArrayRead(A,&v);
2360: if (type == NORM_FROBENIUS) {
2361: #if defined(PETSC_USE_REAL___FP16)
2362: PetscBLASInt one = 1,nz = a->nz;
2363: PetscStackCallBLAS("BLASnrm2",*nrm = BLASnrm2_(&nz,v,&one));
2364: #else
2365: for (i=0; i<a->nz; i++) {
2366: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
2367: }
2368: *nrm = PetscSqrtReal(sum);
2369: #endif
2370: PetscLogFlops(2.0*a->nz);
2371: } else if (type == NORM_1) {
2372: PetscReal *tmp;
2373: PetscInt *jj = a->j;
2374: PetscCalloc1(A->cmap->n+1,&tmp);
2375: *nrm = 0.0;
2376: for (j=0; j<a->nz; j++) {
2377: tmp[*jj++] += PetscAbsScalar(*v); v++;
2378: }
2379: for (j=0; j<A->cmap->n; j++) {
2380: if (tmp[j] > *nrm) *nrm = tmp[j];
2381: }
2382: PetscFree(tmp);
2383: PetscLogFlops(PetscMax(a->nz-1,0));
2384: } else if (type == NORM_INFINITY) {
2385: *nrm = 0.0;
2386: for (j=0; j<A->rmap->n; j++) {
2387: const PetscScalar *v2 = v + a->i[j];
2388: sum = 0.0;
2389: for (i=0; i<a->i[j+1]-a->i[j]; i++) {
2390: sum += PetscAbsScalar(*v2); v2++;
2391: }
2392: if (sum > *nrm) *nrm = sum;
2393: }
2394: PetscLogFlops(PetscMax(a->nz-1,0));
2395: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
2396: MatSeqAIJRestoreArrayRead(A,&v);
2397: return(0);
2398: }
2400: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
2401: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
2402: {
2404: PetscInt i,j,anzj;
2405: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b;
2406: PetscInt an=A->cmap->N,am=A->rmap->N;
2407: PetscInt *ati,*atj,*atfill,*ai=a->i,*aj=a->j;
2410: /* Allocate space for symbolic transpose info and work array */
2411: PetscCalloc1(an+1,&ati);
2412: PetscMalloc1(ai[am],&atj);
2413: PetscMalloc1(an,&atfill);
2415: /* Walk through aj and count ## of non-zeros in each row of A^T. */
2416: /* Note: offset by 1 for fast conversion into csr format. */
2417: for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
2418: /* Form ati for csr format of A^T. */
2419: for (i=0;i<an;i++) ati[i+1] += ati[i];
2421: /* Copy ati into atfill so we have locations of the next free space in atj */
2422: PetscArraycpy(atfill,ati,an);
2424: /* Walk through A row-wise and mark nonzero entries of A^T. */
2425: for (i=0;i<am;i++) {
2426: anzj = ai[i+1] - ai[i];
2427: for (j=0;j<anzj;j++) {
2428: atj[atfill[*aj]] = i;
2429: atfill[*aj++] += 1;
2430: }
2431: }
2433: /* Clean up temporary space and complete requests. */
2434: PetscFree(atfill);
2435: MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2436: MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2437: MatSetType(*B,((PetscObject)A)->type_name);
2439: b = (Mat_SeqAIJ*)((*B)->data);
2440: b->free_a = PETSC_FALSE;
2441: b->free_ij = PETSC_TRUE;
2442: b->nonew = 0;
2443: return(0);
2444: }
2446: PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2447: {
2448: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2449: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2450: const MatScalar *va,*vb;
2451: PetscErrorCode ierr;
2452: PetscInt ma,na,mb,nb, i;
2455: MatGetSize(A,&ma,&na);
2456: MatGetSize(B,&mb,&nb);
2457: if (ma!=nb || na!=mb) {
2458: *f = PETSC_FALSE;
2459: return(0);
2460: }
2461: MatSeqAIJGetArrayRead(A,&va);
2462: MatSeqAIJGetArrayRead(B,&vb);
2463: aii = aij->i; bii = bij->i;
2464: adx = aij->j; bdx = bij->j;
2465: PetscMalloc1(ma,&aptr);
2466: PetscMalloc1(mb,&bptr);
2467: for (i=0; i<ma; i++) aptr[i] = aii[i];
2468: for (i=0; i<mb; i++) bptr[i] = bii[i];
2470: *f = PETSC_TRUE;
2471: for (i=0; i<ma; i++) {
2472: while (aptr[i]<aii[i+1]) {
2473: PetscInt idc,idr;
2474: PetscScalar vc,vr;
2475: /* column/row index/value */
2476: idc = adx[aptr[i]];
2477: idr = bdx[bptr[idc]];
2478: vc = va[aptr[i]];
2479: vr = vb[bptr[idc]];
2480: if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2481: *f = PETSC_FALSE;
2482: goto done;
2483: } else {
2484: aptr[i]++;
2485: if (B || i!=idc) bptr[idc]++;
2486: }
2487: }
2488: }
2489: done:
2490: PetscFree(aptr);
2491: PetscFree(bptr);
2492: MatSeqAIJRestoreArrayRead(A,&va);
2493: MatSeqAIJRestoreArrayRead(B,&vb);
2494: return(0);
2495: }
2497: PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2498: {
2499: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) B->data;
2500: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2501: MatScalar *va,*vb;
2503: PetscInt ma,na,mb,nb, i;
2506: MatGetSize(A,&ma,&na);
2507: MatGetSize(B,&mb,&nb);
2508: if (ma!=nb || na!=mb) {
2509: *f = PETSC_FALSE;
2510: return(0);
2511: }
2512: aii = aij->i; bii = bij->i;
2513: adx = aij->j; bdx = bij->j;
2514: va = aij->a; vb = bij->a;
2515: PetscMalloc1(ma,&aptr);
2516: PetscMalloc1(mb,&bptr);
2517: for (i=0; i<ma; i++) aptr[i] = aii[i];
2518: for (i=0; i<mb; i++) bptr[i] = bii[i];
2520: *f = PETSC_TRUE;
2521: for (i=0; i<ma; i++) {
2522: while (aptr[i]<aii[i+1]) {
2523: PetscInt idc,idr;
2524: PetscScalar vc,vr;
2525: /* column/row index/value */
2526: idc = adx[aptr[i]];
2527: idr = bdx[bptr[idc]];
2528: vc = va[aptr[i]];
2529: vr = vb[bptr[idc]];
2530: if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2531: *f = PETSC_FALSE;
2532: goto done;
2533: } else {
2534: aptr[i]++;
2535: if (B || i!=idc) bptr[idc]++;
2536: }
2537: }
2538: }
2539: done:
2540: PetscFree(aptr);
2541: PetscFree(bptr);
2542: return(0);
2543: }
2545: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2546: {
2550: MatIsTranspose_SeqAIJ(A,A,tol,f);
2551: return(0);
2552: }
2554: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2555: {
2559: MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2560: return(0);
2561: }
2563: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2564: {
2565: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2566: const PetscScalar *l,*r;
2567: PetscScalar x;
2568: MatScalar *v;
2569: PetscErrorCode ierr;
2570: PetscInt i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz;
2571: const PetscInt *jj;
2574: if (ll) {
2575: /* The local size is used so that VecMPI can be passed to this routine
2576: by MatDiagonalScale_MPIAIJ */
2577: VecGetLocalSize(ll,&m);
2578: if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2579: VecGetArrayRead(ll,&l);
2580: MatSeqAIJGetArray(A,&v);
2581: for (i=0; i<m; i++) {
2582: x = l[i];
2583: M = a->i[i+1] - a->i[i];
2584: for (j=0; j<M; j++) (*v++) *= x;
2585: }
2586: VecRestoreArrayRead(ll,&l);
2587: PetscLogFlops(nz);
2588: MatSeqAIJRestoreArray(A,&v);
2589: }
2590: if (rr) {
2591: VecGetLocalSize(rr,&n);
2592: if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2593: VecGetArrayRead(rr,&r);
2594: MatSeqAIJGetArray(A,&v);
2595: jj = a->j;
2596: for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2597: MatSeqAIJRestoreArray(A,&v);
2598: VecRestoreArrayRead(rr,&r);
2599: PetscLogFlops(nz);
2600: }
2601: MatSeqAIJInvalidateDiagonal(A);
2602: #if defined(PETSC_HAVE_DEVICE)
2603: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
2604: #endif
2605: return(0);
2606: }
2608: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2609: {
2610: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c;
2611: PetscErrorCode ierr;
2612: PetscInt *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2613: PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2614: const PetscInt *irow,*icol;
2615: const PetscScalar *aa;
2616: PetscInt nrows,ncols;
2617: PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2618: MatScalar *a_new,*mat_a;
2619: Mat C;
2620: PetscBool stride;
2623: ISGetIndices(isrow,&irow);
2624: ISGetLocalSize(isrow,&nrows);
2625: ISGetLocalSize(iscol,&ncols);
2627: PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2628: if (stride) {
2629: ISStrideGetInfo(iscol,&first,&step);
2630: } else {
2631: first = 0;
2632: step = 0;
2633: }
2634: if (stride && step == 1) {
2635: /* special case of contiguous rows */
2636: PetscMalloc2(nrows,&lens,nrows,&starts);
2637: /* loop over new rows determining lens and starting points */
2638: for (i=0; i<nrows; i++) {
2639: kstart = ai[irow[i]];
2640: kend = kstart + ailen[irow[i]];
2641: starts[i] = kstart;
2642: for (k=kstart; k<kend; k++) {
2643: if (aj[k] >= first) {
2644: starts[i] = k;
2645: break;
2646: }
2647: }
2648: sum = 0;
2649: while (k < kend) {
2650: if (aj[k++] >= first+ncols) break;
2651: sum++;
2652: }
2653: lens[i] = sum;
2654: }
2655: /* create submatrix */
2656: if (scall == MAT_REUSE_MATRIX) {
2657: PetscInt n_cols,n_rows;
2658: MatGetSize(*B,&n_rows,&n_cols);
2659: if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2660: MatZeroEntries(*B);
2661: C = *B;
2662: } else {
2663: PetscInt rbs,cbs;
2664: MatCreate(PetscObjectComm((PetscObject)A),&C);
2665: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2666: ISGetBlockSize(isrow,&rbs);
2667: ISGetBlockSize(iscol,&cbs);
2668: MatSetBlockSizes(C,rbs,cbs);
2669: MatSetType(C,((PetscObject)A)->type_name);
2670: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2671: }
2672: c = (Mat_SeqAIJ*)C->data;
2674: /* loop over rows inserting into submatrix */
2675: a_new = c->a;
2676: j_new = c->j;
2677: i_new = c->i;
2678: MatSeqAIJGetArrayRead(A,&aa);
2679: for (i=0; i<nrows; i++) {
2680: ii = starts[i];
2681: lensi = lens[i];
2682: for (k=0; k<lensi; k++) {
2683: *j_new++ = aj[ii+k] - first;
2684: }
2685: PetscArraycpy(a_new,aa + starts[i],lensi);
2686: a_new += lensi;
2687: i_new[i+1] = i_new[i] + lensi;
2688: c->ilen[i] = lensi;
2689: }
2690: MatSeqAIJRestoreArrayRead(A,&aa);
2691: PetscFree2(lens,starts);
2692: } else {
2693: ISGetIndices(iscol,&icol);
2694: PetscCalloc1(oldcols,&smap);
2695: PetscMalloc1(1+nrows,&lens);
2696: for (i=0; i<ncols; i++) {
2697: if (PetscUnlikelyDebug(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);
2698: smap[icol[i]] = i+1;
2699: }
2701: /* determine lens of each row */
2702: for (i=0; i<nrows; i++) {
2703: kstart = ai[irow[i]];
2704: kend = kstart + a->ilen[irow[i]];
2705: lens[i] = 0;
2706: for (k=kstart; k<kend; k++) {
2707: if (smap[aj[k]]) {
2708: lens[i]++;
2709: }
2710: }
2711: }
2712: /* Create and fill new matrix */
2713: if (scall == MAT_REUSE_MATRIX) {
2714: PetscBool equal;
2716: c = (Mat_SeqAIJ*)((*B)->data);
2717: if ((*B)->rmap->n != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2718: PetscArraycmp(c->ilen,lens,(*B)->rmap->n,&equal);
2719: if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2720: PetscArrayzero(c->ilen,(*B)->rmap->n);
2721: C = *B;
2722: } else {
2723: PetscInt rbs,cbs;
2724: MatCreate(PetscObjectComm((PetscObject)A),&C);
2725: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2726: ISGetBlockSize(isrow,&rbs);
2727: ISGetBlockSize(iscol,&cbs);
2728: MatSetBlockSizes(C,rbs,cbs);
2729: MatSetType(C,((PetscObject)A)->type_name);
2730: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2731: }
2732: MatSeqAIJGetArrayRead(A,&aa);
2733: c = (Mat_SeqAIJ*)(C->data);
2734: for (i=0; i<nrows; i++) {
2735: row = irow[i];
2736: kstart = ai[row];
2737: kend = kstart + a->ilen[row];
2738: mat_i = c->i[i];
2739: mat_j = c->j + mat_i;
2740: mat_a = c->a + mat_i;
2741: mat_ilen = c->ilen + i;
2742: for (k=kstart; k<kend; k++) {
2743: if ((tcol=smap[a->j[k]])) {
2744: *mat_j++ = tcol - 1;
2745: *mat_a++ = aa[k];
2746: (*mat_ilen)++;
2748: }
2749: }
2750: }
2751: MatSeqAIJRestoreArrayRead(A,&aa);
2752: /* Free work space */
2753: ISRestoreIndices(iscol,&icol);
2754: PetscFree(smap);
2755: PetscFree(lens);
2756: /* sort */
2757: for (i = 0; i < nrows; i++) {
2758: PetscInt ilen;
2760: mat_i = c->i[i];
2761: mat_j = c->j + mat_i;
2762: mat_a = c->a + mat_i;
2763: ilen = c->ilen[i];
2764: PetscSortIntWithScalarArray(ilen,mat_j,mat_a);
2765: }
2766: }
2767: #if defined(PETSC_HAVE_DEVICE)
2768: MatBindToCPU(C,A->boundtocpu);
2769: #endif
2770: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2771: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2773: ISRestoreIndices(isrow,&irow);
2774: *B = C;
2775: return(0);
2776: }
2778: PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2779: {
2781: Mat B;
2784: if (scall == MAT_INITIAL_MATRIX) {
2785: MatCreate(subComm,&B);
2786: MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2787: MatSetBlockSizesFromMats(B,mat,mat);
2788: MatSetType(B,MATSEQAIJ);
2789: MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2790: *subMat = B;
2791: } else {
2792: MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2793: }
2794: return(0);
2795: }
2797: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2798: {
2799: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2801: Mat outA;
2802: PetscBool row_identity,col_identity;
2805: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
2807: ISIdentity(row,&row_identity);
2808: ISIdentity(col,&col_identity);
2810: outA = inA;
2811: outA->factortype = MAT_FACTOR_LU;
2812: PetscFree(inA->solvertype);
2813: PetscStrallocpy(MATSOLVERPETSC,&inA->solvertype);
2815: PetscObjectReference((PetscObject)row);
2816: ISDestroy(&a->row);
2818: a->row = row;
2820: PetscObjectReference((PetscObject)col);
2821: ISDestroy(&a->col);
2823: a->col = col;
2825: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2826: ISDestroy(&a->icol);
2827: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2828: PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);
2830: if (!a->solve_work) { /* this matrix may have been factored before */
2831: PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2832: PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2833: }
2835: MatMarkDiagonal_SeqAIJ(inA);
2836: if (row_identity && col_identity) {
2837: MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2838: } else {
2839: MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2840: }
2841: return(0);
2842: }
2844: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2845: {
2846: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2847: PetscScalar *v;
2849: PetscBLASInt one = 1,bnz;
2852: MatSeqAIJGetArray(inA,&v);
2853: PetscBLASIntCast(a->nz,&bnz);
2854: PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&alpha,v,&one));
2855: PetscLogFlops(a->nz);
2856: MatSeqAIJRestoreArray(inA,&v);
2857: MatSeqAIJInvalidateDiagonal(inA);
2858: return(0);
2859: }
2861: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2862: {
2864: PetscInt i;
2867: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2868: PetscFree4(submatj->sbuf1,submatj->ptr,submatj->tmp,submatj->ctr);
2870: for (i=0; i<submatj->nrqr; ++i) {
2871: PetscFree(submatj->sbuf2[i]);
2872: }
2873: PetscFree3(submatj->sbuf2,submatj->req_size,submatj->req_source1);
2875: if (submatj->rbuf1) {
2876: PetscFree(submatj->rbuf1[0]);
2877: PetscFree(submatj->rbuf1);
2878: }
2880: for (i=0; i<submatj->nrqs; ++i) {
2881: PetscFree(submatj->rbuf3[i]);
2882: }
2883: PetscFree3(submatj->req_source2,submatj->rbuf2,submatj->rbuf3);
2884: PetscFree(submatj->pa);
2885: }
2887: #if defined(PETSC_USE_CTABLE)
2888: PetscTableDestroy((PetscTable*)&submatj->rmap);
2889: if (submatj->cmap_loc) {PetscFree(submatj->cmap_loc);}
2890: PetscFree(submatj->rmap_loc);
2891: #else
2892: PetscFree(submatj->rmap);
2893: #endif
2895: if (!submatj->allcolumns) {
2896: #if defined(PETSC_USE_CTABLE)
2897: PetscTableDestroy((PetscTable*)&submatj->cmap);
2898: #else
2899: PetscFree(submatj->cmap);
2900: #endif
2901: }
2902: PetscFree(submatj->row2proc);
2904: PetscFree(submatj);
2905: return(0);
2906: }
2908: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2909: {
2911: Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data;
2912: Mat_SubSppt *submatj = c->submatis1;
2915: (*submatj->destroy)(C);
2916: MatDestroySubMatrix_Private(submatj);
2917: return(0);
2918: }
2920: PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n,Mat *mat[])
2921: {
2923: PetscInt i;
2924: Mat C;
2925: Mat_SeqAIJ *c;
2926: Mat_SubSppt *submatj;
2929: for (i=0; i<n; i++) {
2930: C = (*mat)[i];
2931: c = (Mat_SeqAIJ*)C->data;
2932: submatj = c->submatis1;
2933: if (submatj) {
2934: if (--((PetscObject)C)->refct <= 0) {
2935: (*submatj->destroy)(C);
2936: MatDestroySubMatrix_Private(submatj);
2937: PetscFree(C->defaultvectype);
2938: PetscLayoutDestroy(&C->rmap);
2939: PetscLayoutDestroy(&C->cmap);
2940: PetscHeaderDestroy(&C);
2941: }
2942: } else {
2943: MatDestroy(&C);
2944: }
2945: }
2947: /* Destroy Dummy submatrices created for reuse */
2948: MatDestroySubMatrices_Dummy(n,mat);
2950: PetscFree(*mat);
2951: return(0);
2952: }
2954: PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2955: {
2957: PetscInt i;
2960: if (scall == MAT_INITIAL_MATRIX) {
2961: PetscCalloc1(n+1,B);
2962: }
2964: for (i=0; i<n; i++) {
2965: MatCreateSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2966: }
2967: return(0);
2968: }
2970: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2971: {
2972: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2974: PetscInt row,i,j,k,l,m,n,*nidx,isz,val;
2975: const PetscInt *idx;
2976: PetscInt start,end,*ai,*aj;
2977: PetscBT table;
2980: m = A->rmap->n;
2981: ai = a->i;
2982: aj = a->j;
2984: if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");
2986: PetscMalloc1(m+1,&nidx);
2987: PetscBTCreate(m,&table);
2989: for (i=0; i<is_max; i++) {
2990: /* Initialize the two local arrays */
2991: isz = 0;
2992: PetscBTMemzero(m,table);
2994: /* Extract the indices, assume there can be duplicate entries */
2995: ISGetIndices(is[i],&idx);
2996: ISGetLocalSize(is[i],&n);
2998: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2999: for (j=0; j<n; ++j) {
3000: if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
3001: }
3002: ISRestoreIndices(is[i],&idx);
3003: ISDestroy(&is[i]);
3005: k = 0;
3006: for (j=0; j<ov; j++) { /* for each overlap */
3007: n = isz;
3008: for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
3009: row = nidx[k];
3010: start = ai[row];
3011: end = ai[row+1];
3012: for (l = start; l<end; l++) {
3013: val = aj[l];
3014: if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
3015: }
3016: }
3017: }
3018: ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
3019: }
3020: PetscBTDestroy(&table);
3021: PetscFree(nidx);
3022: return(0);
3023: }
3025: /* -------------------------------------------------------------- */
3026: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
3027: {
3028: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3030: PetscInt i,nz = 0,m = A->rmap->n,n = A->cmap->n;
3031: const PetscInt *row,*col;
3032: PetscInt *cnew,j,*lens;
3033: IS icolp,irowp;
3034: PetscInt *cwork = NULL;
3035: PetscScalar *vwork = NULL;
3038: ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
3039: ISGetIndices(irowp,&row);
3040: ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
3041: ISGetIndices(icolp,&col);
3043: /* determine lengths of permuted rows */
3044: PetscMalloc1(m+1,&lens);
3045: for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
3046: MatCreate(PetscObjectComm((PetscObject)A),B);
3047: MatSetSizes(*B,m,n,m,n);
3048: MatSetBlockSizesFromMats(*B,A,A);
3049: MatSetType(*B,((PetscObject)A)->type_name);
3050: MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
3051: PetscFree(lens);
3053: PetscMalloc1(n,&cnew);
3054: for (i=0; i<m; i++) {
3055: MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
3056: for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
3057: MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
3058: MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
3059: }
3060: PetscFree(cnew);
3062: (*B)->assembled = PETSC_FALSE;
3064: #if defined(PETSC_HAVE_DEVICE)
3065: MatBindToCPU(*B,A->boundtocpu);
3066: #endif
3067: MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
3068: MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
3069: ISRestoreIndices(irowp,&row);
3070: ISRestoreIndices(icolp,&col);
3071: ISDestroy(&irowp);
3072: ISDestroy(&icolp);
3073: if (rowp == colp) {
3074: MatPropagateSymmetryOptions(A,*B);
3075: }
3076: return(0);
3077: }
3079: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
3080: {
3084: /* If the two matrices have the same copy implementation, use fast copy. */
3085: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
3086: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3087: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
3088: const PetscScalar *aa;
3090: MatSeqAIJGetArrayRead(A,&aa);
3091: if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different %D != %D",a->i[A->rmap->n],b->i[B->rmap->n]);
3092: PetscArraycpy(b->a,aa,a->i[A->rmap->n]);
3093: PetscObjectStateIncrease((PetscObject)B);
3094: MatSeqAIJRestoreArrayRead(A,&aa);
3095: } else {
3096: MatCopy_Basic(A,B,str);
3097: }
3098: return(0);
3099: }
3101: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
3102: {
3106: MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,NULL);
3107: return(0);
3108: }
3110: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
3111: {
3112: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3115: *array = a->a;
3116: return(0);
3117: }
3119: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
3120: {
3122: *array = NULL;
3123: return(0);
3124: }
3126: /*
3127: Computes the number of nonzeros per row needed for preallocation when X and Y
3128: have different nonzero structure.
3129: */
3130: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
3131: {
3132: PetscInt i,j,k,nzx,nzy;
3135: /* Set the number of nonzeros in the new matrix */
3136: for (i=0; i<m; i++) {
3137: const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
3138: nzx = xi[i+1] - xi[i];
3139: nzy = yi[i+1] - yi[i];
3140: nnz[i] = 0;
3141: for (j=0,k=0; j<nzx; j++) { /* Point in X */
3142: for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
3143: if (k<nzy && yjj[k]==xjj[j]) k++; /* Skip duplicate */
3144: nnz[i]++;
3145: }
3146: for (; k<nzy; k++) nnz[i]++;
3147: }
3148: return(0);
3149: }
3151: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
3152: {
3153: PetscInt m = Y->rmap->N;
3154: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data;
3155: Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data;
3159: /* Set the number of nonzeros in the new matrix */
3160: MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
3161: return(0);
3162: }
3164: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
3165: {
3167: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
3170: if (str == UNKNOWN_NONZERO_PATTERN && x->nz == y->nz) {
3171: PetscBool e;
3172: PetscArraycmp(x->i,y->i,Y->rmap->n+1,&e);
3173: if (e) {
3174: PetscArraycmp(x->j,y->j,y->nz,&e);
3175: if (e) {
3176: str = SAME_NONZERO_PATTERN;
3177: }
3178: }
3179: }
3180: if (str == SAME_NONZERO_PATTERN) {
3181: const PetscScalar *xa;
3182: PetscScalar *ya,alpha = a;
3183: PetscBLASInt one = 1,bnz;
3185: PetscBLASIntCast(x->nz,&bnz);
3186: MatSeqAIJGetArray(Y,&ya);
3187: MatSeqAIJGetArrayRead(X,&xa);
3188: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,xa,&one,ya,&one));
3189: MatSeqAIJRestoreArrayRead(X,&xa);
3190: MatSeqAIJRestoreArray(Y,&ya);
3191: PetscLogFlops(2.0*bnz);
3192: MatSeqAIJInvalidateDiagonal(Y);
3193: PetscObjectStateIncrease((PetscObject)Y);
3194: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3195: MatAXPY_Basic(Y,a,X,str);
3196: } else {
3197: Mat B;
3198: PetscInt *nnz;
3199: PetscMalloc1(Y->rmap->N,&nnz);
3200: MatCreate(PetscObjectComm((PetscObject)Y),&B);
3201: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
3202: MatSetLayouts(B,Y->rmap,Y->cmap);
3203: MatSetType(B,((PetscObject)Y)->type_name);
3204: MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
3205: MatSeqAIJSetPreallocation(B,0,nnz);
3206: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
3207: MatHeaderReplace(Y,&B);
3208: PetscFree(nnz);
3209: }
3210: return(0);
3211: }
3213: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3214: {
3215: #if defined(PETSC_USE_COMPLEX)
3216: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3217: PetscInt i,nz;
3218: PetscScalar *a;
3222: nz = aij->nz;
3223: MatSeqAIJGetArray(mat,&a);
3224: for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
3225: MatSeqAIJRestoreArray(mat,&a);
3226: #else
3228: #endif
3229: return(0);
3230: }
3232: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3233: {
3234: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3235: PetscErrorCode ierr;
3236: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3237: PetscReal atmp;
3238: PetscScalar *x;
3239: const MatScalar *aa,*av;
3242: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3243: MatSeqAIJGetArrayRead(A,&av);
3244: aa = av;
3245: ai = a->i;
3246: aj = a->j;
3248: VecSet(v,0.0);
3249: VecGetArrayWrite(v,&x);
3250: VecGetLocalSize(v,&n);
3251: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3252: for (i=0; i<m; i++) {
3253: ncols = ai[1] - ai[0]; ai++;
3254: for (j=0; j<ncols; j++) {
3255: atmp = PetscAbsScalar(*aa);
3256: if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
3257: aa++; aj++;
3258: }
3259: }
3260: VecRestoreArrayWrite(v,&x);
3261: MatSeqAIJRestoreArrayRead(A,&av);
3262: return(0);
3263: }
3265: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3266: {
3267: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3268: PetscErrorCode ierr;
3269: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3270: PetscScalar *x;
3271: const MatScalar *aa,*av;
3274: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3275: MatSeqAIJGetArrayRead(A,&av);
3276: aa = av;
3277: ai = a->i;
3278: aj = a->j;
3280: VecSet(v,0.0);
3281: VecGetArrayWrite(v,&x);
3282: VecGetLocalSize(v,&n);
3283: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3284: for (i=0; i<m; i++) {
3285: ncols = ai[1] - ai[0]; ai++;
3286: if (ncols == A->cmap->n) { /* row is dense */
3287: x[i] = *aa; if (idx) idx[i] = 0;
3288: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3289: x[i] = 0.0;
3290: if (idx) {
3291: for (j=0; j<ncols; j++) { /* find first implicit 0.0 in the row */
3292: if (aj[j] > j) {
3293: idx[i] = j;
3294: break;
3295: }
3296: }
3297: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3298: if (j==ncols && j < A->cmap->n) idx[i] = j;
3299: }
3300: }
3301: for (j=0; j<ncols; j++) {
3302: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3303: aa++; aj++;
3304: }
3305: }
3306: VecRestoreArrayWrite(v,&x);
3307: MatSeqAIJRestoreArrayRead(A,&av);
3308: return(0);
3309: }
3311: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3312: {
3313: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3314: PetscErrorCode ierr;
3315: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
3316: PetscScalar *x;
3317: const MatScalar *aa,*av;
3320: MatSeqAIJGetArrayRead(A,&av);
3321: aa = av;
3322: ai = a->i;
3323: aj = a->j;
3325: VecSet(v,0.0);
3326: VecGetArrayWrite(v,&x);
3327: VecGetLocalSize(v,&n);
3328: if (n != m) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", m, n);
3329: for (i=0; i<m; i++) {
3330: ncols = ai[1] - ai[0]; ai++;
3331: if (ncols == A->cmap->n) { /* row is dense */
3332: x[i] = *aa; if (idx) idx[i] = 0;
3333: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3334: x[i] = 0.0;
3335: if (idx) { /* find first implicit 0.0 in the row */
3336: for (j=0; j<ncols; j++) {
3337: if (aj[j] > j) {
3338: idx[i] = j;
3339: break;
3340: }
3341: }
3342: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3343: if (j==ncols && j < A->cmap->n) idx[i] = j;
3344: }
3345: }
3346: for (j=0; j<ncols; j++) {
3347: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3348: aa++; aj++;
3349: }
3350: }
3351: VecRestoreArrayWrite(v,&x);
3352: MatSeqAIJRestoreArrayRead(A,&av);
3353: return(0);
3354: }
3356: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
3357: {
3358: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3359: PetscErrorCode ierr;
3360: PetscInt i,j,m = A->rmap->n,ncols,n;
3361: const PetscInt *ai,*aj;
3362: PetscScalar *x;
3363: const MatScalar *aa,*av;
3366: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3367: MatSeqAIJGetArrayRead(A,&av);
3368: aa = av;
3369: ai = a->i;
3370: aj = a->j;
3372: VecSet(v,0.0);
3373: VecGetArrayWrite(v,&x);
3374: VecGetLocalSize(v,&n);
3375: if (n != m) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
3376: for (i=0; i<m; i++) {
3377: ncols = ai[1] - ai[0]; ai++;
3378: if (ncols == A->cmap->n) { /* row is dense */
3379: x[i] = *aa; if (idx) idx[i] = 0;
3380: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3381: x[i] = 0.0;
3382: if (idx) { /* find first implicit 0.0 in the row */
3383: for (j=0; j<ncols; j++) {
3384: if (aj[j] > j) {
3385: idx[i] = j;
3386: break;
3387: }
3388: }
3389: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3390: if (j==ncols && j < A->cmap->n) idx[i] = j;
3391: }
3392: }
3393: for (j=0; j<ncols; j++) {
3394: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
3395: aa++; aj++;
3396: }
3397: }
3398: VecRestoreArrayWrite(v,&x);
3399: MatSeqAIJRestoreArrayRead(A,&av);
3400: return(0);
3401: }
3403: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
3404: {
3405: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
3406: PetscErrorCode ierr;
3407: PetscInt i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
3408: MatScalar *diag,work[25],*v_work;
3409: const PetscReal shift = 0.0;
3410: PetscBool allowzeropivot,zeropivotdetected=PETSC_FALSE;
3413: allowzeropivot = PetscNot(A->erroriffailure);
3414: if (a->ibdiagvalid) {
3415: if (values) *values = a->ibdiag;
3416: return(0);
3417: }
3418: MatMarkDiagonal_SeqAIJ(A);
3419: if (!a->ibdiag) {
3420: PetscMalloc1(bs2*mbs,&a->ibdiag);
3421: PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
3422: }
3423: diag = a->ibdiag;
3424: if (values) *values = a->ibdiag;
3425: /* factor and invert each block */
3426: switch (bs) {
3427: case 1:
3428: for (i=0; i<mbs; i++) {
3429: MatGetValues(A,1,&i,1,&i,diag+i);
3430: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3431: if (allowzeropivot) {
3432: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3433: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3434: A->factorerror_zeropivot_row = i;
3435: PetscInfo3(A,"Zero pivot, row %D pivot %g tolerance %g\n",i,(double)PetscAbsScalar(diag[i]),(double)PETSC_MACHINE_EPSILON);
3436: } 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);
3437: }
3438: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3439: }
3440: break;
3441: case 2:
3442: for (i=0; i<mbs; i++) {
3443: ij[0] = 2*i; ij[1] = 2*i + 1;
3444: MatGetValues(A,2,ij,2,ij,diag);
3445: PetscKernel_A_gets_inverse_A_2(diag,shift,allowzeropivot,&zeropivotdetected);
3446: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3447: PetscKernel_A_gets_transpose_A_2(diag);
3448: diag += 4;
3449: }
3450: break;
3451: case 3:
3452: for (i=0; i<mbs; i++) {
3453: ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
3454: MatGetValues(A,3,ij,3,ij,diag);
3455: PetscKernel_A_gets_inverse_A_3(diag,shift,allowzeropivot,&zeropivotdetected);
3456: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3457: PetscKernel_A_gets_transpose_A_3(diag);
3458: diag += 9;
3459: }
3460: break;
3461: case 4:
3462: for (i=0; i<mbs; i++) {
3463: ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
3464: MatGetValues(A,4,ij,4,ij,diag);
3465: PetscKernel_A_gets_inverse_A_4(diag,shift,allowzeropivot,&zeropivotdetected);
3466: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3467: PetscKernel_A_gets_transpose_A_4(diag);
3468: diag += 16;
3469: }
3470: break;
3471: case 5:
3472: for (i=0; i<mbs; i++) {
3473: ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
3474: MatGetValues(A,5,ij,5,ij,diag);
3475: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift,allowzeropivot,&zeropivotdetected);
3476: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3477: PetscKernel_A_gets_transpose_A_5(diag);
3478: diag += 25;
3479: }
3480: break;
3481: case 6:
3482: for (i=0; i<mbs; i++) {
3483: 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;
3484: MatGetValues(A,6,ij,6,ij,diag);
3485: PetscKernel_A_gets_inverse_A_6(diag,shift,allowzeropivot,&zeropivotdetected);
3486: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3487: PetscKernel_A_gets_transpose_A_6(diag);
3488: diag += 36;
3489: }
3490: break;
3491: case 7:
3492: for (i=0; i<mbs; i++) {
3493: 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;
3494: MatGetValues(A,7,ij,7,ij,diag);
3495: PetscKernel_A_gets_inverse_A_7(diag,shift,allowzeropivot,&zeropivotdetected);
3496: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3497: PetscKernel_A_gets_transpose_A_7(diag);
3498: diag += 49;
3499: }
3500: break;
3501: default:
3502: PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3503: for (i=0; i<mbs; i++) {
3504: for (j=0; j<bs; j++) {
3505: IJ[j] = bs*i + j;
3506: }
3507: MatGetValues(A,bs,IJ,bs,IJ,diag);
3508: PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work,allowzeropivot,&zeropivotdetected);
3509: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3510: PetscKernel_A_gets_transpose_A_N(diag,bs);
3511: diag += bs2;
3512: }
3513: PetscFree3(v_work,v_pivots,IJ);
3514: }
3515: a->ibdiagvalid = PETSC_TRUE;
3516: return(0);
3517: }
3519: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3520: {
3522: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data;
3523: PetscScalar a;
3524: PetscInt m,n,i,j,col;
3527: if (!x->assembled) {
3528: MatGetSize(x,&m,&n);
3529: for (i=0; i<m; i++) {
3530: for (j=0; j<aij->imax[i]; j++) {
3531: PetscRandomGetValue(rctx,&a);
3532: col = (PetscInt)(n*PetscRealPart(a));
3533: MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3534: }
3535: }
3536: } else {
3537: for (i=0; i<aij->nz; i++) {PetscRandomGetValue(rctx,aij->a+i);}
3538: }
3539: #if defined(PETSC_HAVE_DEVICE)
3540: if (x->offloadmask != PETSC_OFFLOAD_UNALLOCATED) x->offloadmask = PETSC_OFFLOAD_CPU;
3541: #endif
3542: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3543: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3544: return(0);
3545: }
3547: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3548: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x,PetscInt low,PetscInt high,PetscRandom rctx)
3549: {
3551: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data;
3552: PetscScalar a;
3553: PetscInt m,n,i,j,col,nskip;
3556: nskip = high - low;
3557: MatGetSize(x,&m,&n);
3558: n -= nskip; /* shrink number of columns where nonzeros can be set */
3559: for (i=0; i<m; i++) {
3560: for (j=0; j<aij->imax[i]; j++) {
3561: PetscRandomGetValue(rctx,&a);
3562: col = (PetscInt)(n*PetscRealPart(a));
3563: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3564: MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3565: }
3566: }
3567: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3568: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3569: return(0);
3570: }
3573: /* -------------------------------------------------------------------*/
3574: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3575: MatGetRow_SeqAIJ,
3576: MatRestoreRow_SeqAIJ,
3577: MatMult_SeqAIJ,
3578: /* 4*/ MatMultAdd_SeqAIJ,
3579: MatMultTranspose_SeqAIJ,
3580: MatMultTransposeAdd_SeqAIJ,
3581: NULL,
3582: NULL,
3583: NULL,
3584: /* 10*/ NULL,
3585: MatLUFactor_SeqAIJ,
3586: NULL,
3587: MatSOR_SeqAIJ,
3588: MatTranspose_SeqAIJ,
3589: /*1 5*/ MatGetInfo_SeqAIJ,
3590: MatEqual_SeqAIJ,
3591: MatGetDiagonal_SeqAIJ,
3592: MatDiagonalScale_SeqAIJ,
3593: MatNorm_SeqAIJ,
3594: /* 20*/ NULL,
3595: MatAssemblyEnd_SeqAIJ,
3596: MatSetOption_SeqAIJ,
3597: MatZeroEntries_SeqAIJ,
3598: /* 24*/ MatZeroRows_SeqAIJ,
3599: NULL,
3600: NULL,
3601: NULL,
3602: NULL,
3603: /* 29*/ MatSetUp_SeqAIJ,
3604: NULL,
3605: NULL,
3606: NULL,
3607: NULL,
3608: /* 34*/ MatDuplicate_SeqAIJ,
3609: NULL,
3610: NULL,
3611: MatILUFactor_SeqAIJ,
3612: NULL,
3613: /* 39*/ MatAXPY_SeqAIJ,
3614: MatCreateSubMatrices_SeqAIJ,
3615: MatIncreaseOverlap_SeqAIJ,
3616: MatGetValues_SeqAIJ,
3617: MatCopy_SeqAIJ,
3618: /* 44*/ MatGetRowMax_SeqAIJ,
3619: MatScale_SeqAIJ,
3620: MatShift_SeqAIJ,
3621: MatDiagonalSet_SeqAIJ,
3622: MatZeroRowsColumns_SeqAIJ,
3623: /* 49*/ MatSetRandom_SeqAIJ,
3624: MatGetRowIJ_SeqAIJ,
3625: MatRestoreRowIJ_SeqAIJ,
3626: MatGetColumnIJ_SeqAIJ,
3627: MatRestoreColumnIJ_SeqAIJ,
3628: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3629: NULL,
3630: NULL,
3631: MatPermute_SeqAIJ,
3632: NULL,
3633: /* 59*/ NULL,
3634: MatDestroy_SeqAIJ,
3635: MatView_SeqAIJ,
3636: NULL,
3637: NULL,
3638: /* 64*/ NULL,
3639: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3640: NULL,
3641: NULL,
3642: NULL,
3643: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3644: MatGetRowMinAbs_SeqAIJ,
3645: NULL,
3646: NULL,
3647: NULL,
3648: /* 74*/ NULL,
3649: MatFDColoringApply_AIJ,
3650: NULL,
3651: NULL,
3652: NULL,
3653: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3654: NULL,
3655: NULL,
3656: NULL,
3657: MatLoad_SeqAIJ,
3658: /* 84*/ MatIsSymmetric_SeqAIJ,
3659: MatIsHermitian_SeqAIJ,
3660: NULL,
3661: NULL,
3662: NULL,
3663: /* 89*/ NULL,
3664: NULL,
3665: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3666: NULL,
3667: NULL,
3668: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3669: NULL,
3670: NULL,
3671: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3672: NULL,
3673: /* 99*/ MatProductSetFromOptions_SeqAIJ,
3674: NULL,
3675: NULL,
3676: MatConjugate_SeqAIJ,
3677: NULL,
3678: /*104*/ MatSetValuesRow_SeqAIJ,
3679: MatRealPart_SeqAIJ,
3680: MatImaginaryPart_SeqAIJ,
3681: NULL,
3682: NULL,
3683: /*109*/ MatMatSolve_SeqAIJ,
3684: NULL,
3685: MatGetRowMin_SeqAIJ,
3686: NULL,
3687: MatMissingDiagonal_SeqAIJ,
3688: /*114*/ NULL,
3689: NULL,
3690: NULL,
3691: NULL,
3692: NULL,
3693: /*119*/ NULL,
3694: NULL,
3695: NULL,
3696: NULL,
3697: MatGetMultiProcBlock_SeqAIJ,
3698: /*124*/ MatFindNonzeroRows_SeqAIJ,
3699: MatGetColumnNorms_SeqAIJ,
3700: MatInvertBlockDiagonal_SeqAIJ,
3701: MatInvertVariableBlockDiagonal_SeqAIJ,
3702: NULL,
3703: /*129*/ NULL,
3704: NULL,
3705: NULL,
3706: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3707: MatTransposeColoringCreate_SeqAIJ,
3708: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3709: MatTransColoringApplyDenToSp_SeqAIJ,
3710: NULL,
3711: NULL,
3712: MatRARtNumeric_SeqAIJ_SeqAIJ,
3713: /*139*/NULL,
3714: NULL,
3715: NULL,
3716: MatFDColoringSetUp_SeqXAIJ,
3717: MatFindOffBlockDiagonalEntries_SeqAIJ,
3718: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3719: /*145*/MatDestroySubMatrices_SeqAIJ,
3720: NULL,
3721: NULL
3722: };
3724: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3725: {
3726: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3727: PetscInt i,nz,n;
3730: nz = aij->maxnz;
3731: n = mat->rmap->n;
3732: for (i=0; i<nz; i++) {
3733: aij->j[i] = indices[i];
3734: }
3735: aij->nz = nz;
3736: for (i=0; i<n; i++) {
3737: aij->ilen[i] = aij->imax[i];
3738: }
3739: return(0);
3740: }
3742: /*
3743: * When a sparse matrix has many zero columns, we should compact them out to save the space
3744: * This happens in MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3745: * */
3746: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3747: {
3748: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3749: PetscTable gid1_lid1;
3750: PetscTablePosition tpos;
3751: PetscInt gid,lid,i,ec,nz = aij->nz;
3752: PetscInt *garray,*jj = aij->j;
3753: PetscErrorCode ierr;
3758: /* use a table */
3759: PetscTableCreate(mat->rmap->n,mat->cmap->N+1,&gid1_lid1);
3760: ec = 0;
3761: for (i=0; i<nz; i++) {
3762: PetscInt data,gid1 = jj[i] + 1;
3763: PetscTableFind(gid1_lid1,gid1,&data);
3764: if (!data) {
3765: /* one based table */
3766: PetscTableAdd(gid1_lid1,gid1,++ec,INSERT_VALUES);
3767: }
3768: }
3769: /* form array of columns we need */
3770: PetscMalloc1(ec+1,&garray);
3771: PetscTableGetHeadPosition(gid1_lid1,&tpos);
3772: while (tpos) {
3773: PetscTableGetNext(gid1_lid1,&tpos,&gid,&lid);
3774: gid--;
3775: lid--;
3776: garray[lid] = gid;
3777: }
3778: PetscSortInt(ec,garray); /* sort, and rebuild */
3779: PetscTableRemoveAll(gid1_lid1);
3780: for (i=0; i<ec; i++) {
3781: PetscTableAdd(gid1_lid1,garray[i]+1,i+1,INSERT_VALUES);
3782: }
3783: /* compact out the extra columns in B */
3784: for (i=0; i<nz; i++) {
3785: PetscInt gid1 = jj[i] + 1;
3786: PetscTableFind(gid1_lid1,gid1,&lid);
3787: lid--;
3788: jj[i] = lid;
3789: }
3790: PetscLayoutDestroy(&mat->cmap);
3791: PetscTableDestroy(&gid1_lid1);
3792: PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat),ec,ec,1,&mat->cmap);
3793: ISLocalToGlobalMappingCreate(PETSC_COMM_SELF,mat->cmap->bs,mat->cmap->n,garray,PETSC_OWN_POINTER,mapping);
3794: ISLocalToGlobalMappingSetType(*mapping,ISLOCALTOGLOBALMAPPINGHASH);
3795: return(0);
3796: }
3798: /*@
3799: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3800: in the matrix.
3802: Input Parameters:
3803: + mat - the SeqAIJ matrix
3804: - indices - the column indices
3806: Level: advanced
3808: Notes:
3809: This can be called if you have precomputed the nonzero structure of the
3810: matrix and want to provide it to the matrix object to improve the performance
3811: of the MatSetValues() operation.
3813: You MUST have set the correct numbers of nonzeros per row in the call to
3814: MatCreateSeqAIJ(), and the columns indices MUST be sorted.
3816: MUST be called before any calls to MatSetValues();
3818: The indices should start with zero, not one.
3820: @*/
3821: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3822: {
3828: PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3829: return(0);
3830: }
3832: /* ----------------------------------------------------------------------------------------*/
3834: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3835: {
3836: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3838: size_t nz = aij->i[mat->rmap->n];
3841: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3843: /* allocate space for values if not already there */
3844: if (!aij->saved_values) {
3845: PetscMalloc1(nz+1,&aij->saved_values);
3846: PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3847: }
3849: /* copy values over */
3850: PetscArraycpy(aij->saved_values,aij->a,nz);
3851: return(0);
3852: }
3854: /*@
3855: MatStoreValues - Stashes a copy of the matrix values; this allows, for
3856: example, reuse of the linear part of a Jacobian, while recomputing the
3857: nonlinear portion.
3859: Collect on Mat
3861: Input Parameters:
3862: . mat - the matrix (currently only AIJ matrices support this option)
3864: Level: advanced
3866: Common Usage, with SNESSolve():
3867: $ Create Jacobian matrix
3868: $ Set linear terms into matrix
3869: $ Apply boundary conditions to matrix, at this time matrix must have
3870: $ final nonzero structure (i.e. setting the nonlinear terms and applying
3871: $ boundary conditions again will not change the nonzero structure
3872: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3873: $ MatStoreValues(mat);
3874: $ Call SNESSetJacobian() with matrix
3875: $ In your Jacobian routine
3876: $ MatRetrieveValues(mat);
3877: $ Set nonlinear terms in matrix
3879: Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3880: $ // build linear portion of Jacobian
3881: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3882: $ MatStoreValues(mat);
3883: $ loop over nonlinear iterations
3884: $ MatRetrieveValues(mat);
3885: $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3886: $ // call MatAssemblyBegin/End() on matrix
3887: $ Solve linear system with Jacobian
3888: $ endloop
3890: Notes:
3891: Matrix must already be assemblied before calling this routine
3892: Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3893: calling this routine.
3895: When this is called multiple times it overwrites the previous set of stored values
3896: and does not allocated additional space.
3898: .seealso: MatRetrieveValues()
3900: @*/
3901: PetscErrorCode MatStoreValues(Mat mat)
3902: {
3907: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3908: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3909: PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3910: return(0);
3911: }
3913: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3914: {
3915: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3917: PetscInt nz = aij->i[mat->rmap->n];
3920: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3921: if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3922: /* copy values over */
3923: PetscArraycpy(aij->a,aij->saved_values,nz);
3924: return(0);
3925: }
3927: /*@
3928: MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3929: example, reuse of the linear part of a Jacobian, while recomputing the
3930: nonlinear portion.
3932: Collect on Mat
3934: Input Parameters:
3935: . mat - the matrix (currently only AIJ matrices support this option)
3937: Level: advanced
3939: .seealso: MatStoreValues()
3941: @*/
3942: PetscErrorCode MatRetrieveValues(Mat mat)
3943: {
3948: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3949: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3950: PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3951: return(0);
3952: }
3955: /* --------------------------------------------------------------------------------*/
3956: /*@C
3957: MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3958: (the default parallel PETSc format). For good matrix assembly performance
3959: the user should preallocate the matrix storage by setting the parameter nz
3960: (or the array nnz). By setting these parameters accurately, performance
3961: during matrix assembly can be increased by more than a factor of 50.
3963: Collective
3965: Input Parameters:
3966: + comm - MPI communicator, set to PETSC_COMM_SELF
3967: . m - number of rows
3968: . n - number of columns
3969: . nz - number of nonzeros per row (same for all rows)
3970: - nnz - array containing the number of nonzeros in the various rows
3971: (possibly different for each row) or NULL
3973: Output Parameter:
3974: . A - the matrix
3976: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3977: MatXXXXSetPreallocation() paradigm instead of this routine directly.
3978: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3980: Notes:
3981: If nnz is given then nz is ignored
3983: The AIJ format (also called the Yale sparse matrix format or
3984: compressed row storage), is fully compatible with standard Fortran 77
3985: storage. That is, the stored row and column indices can begin at
3986: either one (as in Fortran) or zero. See the users' manual for details.
3988: Specify the preallocated storage with either nz or nnz (not both).
3989: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3990: allocation. For large problems you MUST preallocate memory or you
3991: will get TERRIBLE performance, see the users' manual chapter on matrices.
3993: By default, this format uses inodes (identical nodes) when possible, to
3994: improve numerical efficiency of matrix-vector products and solves. We
3995: search for consecutive rows with the same nonzero structure, thereby
3996: reusing matrix information to achieve increased efficiency.
3998: Options Database Keys:
3999: + -mat_no_inode - Do not use inodes
4000: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4002: Level: intermediate
4004: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()
4006: @*/
4007: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
4008: {
4012: MatCreate(comm,A);
4013: MatSetSizes(*A,m,n,m,n);
4014: MatSetType(*A,MATSEQAIJ);
4015: MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
4016: return(0);
4017: }
4019: /*@C
4020: MatSeqAIJSetPreallocation - For good matrix assembly performance
4021: the user should preallocate the matrix storage by setting the parameter nz
4022: (or the array nnz). By setting these parameters accurately, performance
4023: during matrix assembly can be increased by more than a factor of 50.
4025: Collective
4027: Input Parameters:
4028: + B - The matrix
4029: . nz - number of nonzeros per row (same for all rows)
4030: - nnz - array containing the number of nonzeros in the various rows
4031: (possibly different for each row) or NULL
4033: Notes:
4034: If nnz is given then nz is ignored
4036: The AIJ format (also called the Yale sparse matrix format or
4037: compressed row storage), is fully compatible with standard Fortran 77
4038: storage. That is, the stored row and column indices can begin at
4039: either one (as in Fortran) or zero. See the users' manual for details.
4041: Specify the preallocated storage with either nz or nnz (not both).
4042: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
4043: allocation. For large problems you MUST preallocate memory or you
4044: will get TERRIBLE performance, see the users' manual chapter on matrices.
4046: You can call MatGetInfo() to get information on how effective the preallocation was;
4047: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
4048: You can also run with the option -info and look for messages with the string
4049: malloc in them to see if additional memory allocation was needed.
4051: Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
4052: entries or columns indices
4054: By default, this format uses inodes (identical nodes) when possible, to
4055: improve numerical efficiency of matrix-vector products and solves. We
4056: search for consecutive rows with the same nonzero structure, thereby
4057: reusing matrix information to achieve increased efficiency.
4059: Options Database Keys:
4060: + -mat_no_inode - Do not use inodes
4061: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
4063: Level: intermediate
4065: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo(),
4066: MatSeqAIJSetTotalPreallocation()
4068: @*/
4069: PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
4070: {
4076: PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
4077: return(0);
4078: }
4080: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
4081: {
4082: Mat_SeqAIJ *b;
4083: PetscBool skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
4085: PetscInt i;
4088: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
4089: if (nz == MAT_SKIP_ALLOCATION) {
4090: skipallocation = PETSC_TRUE;
4091: nz = 0;
4092: }
4093: PetscLayoutSetUp(B->rmap);
4094: PetscLayoutSetUp(B->cmap);
4096: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
4097: if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
4098: if (PetscUnlikelyDebug(nnz)) {
4099: for (i=0; i<B->rmap->n; i++) {
4100: 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]);
4101: 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);
4102: }
4103: }
4105: B->preallocated = PETSC_TRUE;
4107: b = (Mat_SeqAIJ*)B->data;
4109: if (!skipallocation) {
4110: if (!b->imax) {
4111: PetscMalloc1(B->rmap->n,&b->imax);
4112: PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4113: }
4114: if (!b->ilen) {
4115: /* b->ilen will count nonzeros in each row so far. */
4116: PetscCalloc1(B->rmap->n,&b->ilen);
4117: PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4118: } else {
4119: PetscMemzero(b->ilen,B->rmap->n*sizeof(PetscInt));
4120: }
4121: if (!b->ipre) {
4122: PetscMalloc1(B->rmap->n,&b->ipre);
4123: PetscLogObjectMemory((PetscObject)B,B->rmap->n*sizeof(PetscInt));
4124: }
4125: if (!nnz) {
4126: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4127: else if (nz < 0) nz = 1;
4128: nz = PetscMin(nz,B->cmap->n);
4129: for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
4130: nz = nz*B->rmap->n;
4131: } else {
4132: PetscInt64 nz64 = 0;
4133: for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz64 += nnz[i];}
4134: PetscIntCast(nz64,&nz);
4135: }
4137: /* allocate the matrix space */
4138: /* FIXME: should B's old memory be unlogged? */
4139: MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
4140: if (B->structure_only) {
4141: PetscMalloc1(nz,&b->j);
4142: PetscMalloc1(B->rmap->n+1,&b->i);
4143: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*sizeof(PetscInt));
4144: } else {
4145: PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
4146: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
4147: }
4148: b->i[0] = 0;
4149: for (i=1; i<B->rmap->n+1; i++) {
4150: b->i[i] = b->i[i-1] + b->imax[i-1];
4151: }
4152: if (B->structure_only) {
4153: b->singlemalloc = PETSC_FALSE;
4154: b->free_a = PETSC_FALSE;
4155: } else {
4156: b->singlemalloc = PETSC_TRUE;
4157: b->free_a = PETSC_TRUE;
4158: }
4159: b->free_ij = PETSC_TRUE;
4160: } else {
4161: b->free_a = PETSC_FALSE;
4162: b->free_ij = PETSC_FALSE;
4163: }
4165: if (b->ipre && nnz != b->ipre && b->imax) {
4166: /* reserve user-requested sparsity */
4167: PetscArraycpy(b->ipre,b->imax,B->rmap->n);
4168: }
4171: b->nz = 0;
4172: b->maxnz = nz;
4173: B->info.nz_unneeded = (double)b->maxnz;
4174: if (realalloc) {
4175: MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
4176: }
4177: B->was_assembled = PETSC_FALSE;
4178: B->assembled = PETSC_FALSE;
4179: return(0);
4180: }
4183: PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4184: {
4185: Mat_SeqAIJ *a;
4186: PetscInt i;
4192: /* Check local size. If zero, then return */
4193: if (!A->rmap->n) return(0);
4195: a = (Mat_SeqAIJ*)A->data;
4196: /* if no saved info, we error out */
4197: if (!a->ipre) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"No saved preallocation info \n");
4199: if (!a->i || !a->j || !a->a || !a->imax || !a->ilen) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_NULL,"Memory info is incomplete, and can not reset preallocation \n");
4201: PetscArraycpy(a->imax,a->ipre,A->rmap->n);
4202: PetscArrayzero(a->ilen,A->rmap->n);
4203: a->i[0] = 0;
4204: for (i=1; i<A->rmap->n+1; i++) {
4205: a->i[i] = a->i[i-1] + a->imax[i-1];
4206: }
4207: A->preallocated = PETSC_TRUE;
4208: a->nz = 0;
4209: a->maxnz = a->i[A->rmap->n];
4210: A->info.nz_unneeded = (double)a->maxnz;
4211: A->was_assembled = PETSC_FALSE;
4212: A->assembled = PETSC_FALSE;
4213: return(0);
4214: }
4216: /*@
4217: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.
4219: Input Parameters:
4220: + B - the matrix
4221: . i - the indices into j for the start of each row (starts with zero)
4222: . j - the column indices for each row (starts with zero) these must be sorted for each row
4223: - v - optional values in the matrix
4225: Level: developer
4227: Notes:
4228: The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()
4230: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4231: structure will be the union of all the previous nonzero structures.
4233: Developer Notes:
4234: An optimization could be added to the implementation where it checks if the i, and j are identical to the current i and j and
4235: then just copies the v values directly with PetscMemcpy().
4237: This routine could also take a PetscCopyMode argument to allow sharing the values instead of always copying them.
4239: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), MATSEQAIJ, MatResetPreallocation()
4240: @*/
4241: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
4242: {
4248: PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
4249: return(0);
4250: }
4252: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
4253: {
4254: PetscInt i;
4255: PetscInt m,n;
4256: PetscInt nz;
4257: PetscInt *nnz;
4261: if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
4263: PetscLayoutSetUp(B->rmap);
4264: PetscLayoutSetUp(B->cmap);
4266: MatGetSize(B, &m, &n);
4267: PetscMalloc1(m+1, &nnz);
4268: for (i = 0; i < m; i++) {
4269: nz = Ii[i+1]- Ii[i];
4270: if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
4271: nnz[i] = nz;
4272: }
4273: MatSeqAIJSetPreallocation(B, 0, nnz);
4274: PetscFree(nnz);
4276: for (i = 0; i < m; i++) {
4277: MatSetValues_SeqAIJ(B, 1, &i, Ii[i+1] - Ii[i], J+Ii[i], v ? v + Ii[i] : NULL, INSERT_VALUES);
4278: }
4280: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
4281: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
4283: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
4284: return(0);
4285: }
4287: #include <../src/mat/impls/dense/seq/dense.h>
4288: #include <petsc/private/kernels/petscaxpy.h>
4290: /*
4291: Computes (B'*A')' since computing B*A directly is untenable
4293: n p p
4294: [ ] [ ] [ ]
4295: m [ A ] * n [ B ] = m [ C ]
4296: [ ] [ ] [ ]
4298: */
4299: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
4300: {
4301: PetscErrorCode ierr;
4302: Mat_SeqDense *sub_a = (Mat_SeqDense*)A->data;
4303: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ*)B->data;
4304: Mat_SeqDense *sub_c = (Mat_SeqDense*)C->data;
4305: PetscInt i,j,n,m,q,p;
4306: const PetscInt *ii,*idx;
4307: const PetscScalar *b,*a,*a_q;
4308: PetscScalar *c,*c_q;
4309: PetscInt clda = sub_c->lda;
4310: PetscInt alda = sub_a->lda;
4313: m = A->rmap->n;
4314: n = A->cmap->n;
4315: p = B->cmap->n;
4316: a = sub_a->v;
4317: b = sub_b->a;
4318: c = sub_c->v;
4319: if (clda == m) {
4320: PetscArrayzero(c,m*p);
4321: } else {
4322: for (j=0;j<p;j++)
4323: for (i=0;i<m;i++)
4324: c[j*clda + i] = 0.0;
4325: }
4326: ii = sub_b->i;
4327: idx = sub_b->j;
4328: for (i=0; i<n; i++) {
4329: q = ii[i+1] - ii[i];
4330: while (q-->0) {
4331: c_q = c + clda*(*idx);
4332: a_q = a + alda*i;
4333: PetscKernelAXPY(c_q,*b,a_q,m);
4334: idx++;
4335: b++;
4336: }
4337: }
4338: return(0);
4339: }
4341: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat C)
4342: {
4344: PetscInt m=A->rmap->n,n=B->cmap->n;
4345: PetscBool cisdense;
4348: 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);
4349: MatSetSizes(C,m,n,m,n);
4350: MatSetBlockSizesFromMats(C,A,B);
4351: PetscObjectTypeCompareAny((PetscObject)C,&cisdense,MATSEQDENSE,MATSEQDENSECUDA,"");
4352: if (!cisdense) {
4353: MatSetType(C,MATDENSE);
4354: }
4355: MatSetUp(C);
4357: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4358: return(0);
4359: }
4361: /* ----------------------------------------------------------------*/
4362: /*MC
4363: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4364: based on compressed sparse row format.
4366: Options Database Keys:
4367: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4369: Level: beginner
4371: Notes:
4372: MatSetValues() may be called for this matrix type with a NULL argument for the numerical values,
4373: in this case the values associated with the rows and columns one passes in are set to zero
4374: in the matrix
4376: MatSetOptions(,MAT_STRUCTURE_ONLY,PETSC_TRUE) may be called for this matrix type. In this no
4377: space is allocated for the nonzero entries and any entries passed with MatSetValues() are ignored
4379: Developer Notes:
4380: It would be nice if all matrix formats supported passing NULL in for the numerical values
4382: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
4383: M*/
4385: /*MC
4386: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4388: This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
4389: and MATMPIAIJ otherwise. As a result, for single process communicators,
4390: MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation() is supported
4391: for communicators controlling multiple processes. It is recommended that you call both of
4392: the above preallocation routines for simplicity.
4394: Options Database Keys:
4395: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()
4397: Developer Notes:
4398: Subclasses include MATAIJCUSPARSE, MATAIJPERM, MATAIJSELL, MATAIJMKL, MATAIJCRL, and also automatically switches over to use inodes when
4399: enough exist.
4401: Level: beginner
4403: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
4404: M*/
4406: /*MC
4407: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4409: This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
4410: and MATMPIAIJCRL otherwise. As a result, for single process communicators,
4411: MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
4412: for communicators controlling multiple processes. It is recommended that you call both of
4413: the above preallocation routines for simplicity.
4415: Options Database Keys:
4416: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()
4418: Level: beginner
4420: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
4421: M*/
4423: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
4424: #if defined(PETSC_HAVE_ELEMENTAL)
4425: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
4426: #endif
4427: #if defined(PETSC_HAVE_SCALAPACK)
4428: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat,MatType,MatReuse,Mat*);
4429: #endif
4430: #if defined(PETSC_HAVE_HYPRE)
4431: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A,MatType,MatReuse,Mat*);
4432: #endif
4434: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat,MatType,MatReuse,Mat*);
4435: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat,MatType,MatReuse,Mat*);
4436: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4438: /*@C
4439: MatSeqAIJGetArray - gives read/write access to the array where the data for a MATSEQAIJ matrix is stored
4441: Not Collective
4443: Input Parameter:
4444: . mat - a MATSEQAIJ matrix
4446: Output Parameter:
4447: . array - pointer to the data
4449: Level: intermediate
4451: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4452: @*/
4453: PetscErrorCode MatSeqAIJGetArray(Mat A,PetscScalar **array)
4454: {
4458: PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
4459: #if defined(PETSC_HAVE_DEVICE)
4460: if (A->offloadmask != PETSC_OFFLOAD_UNALLOCATED) A->offloadmask = PETSC_OFFLOAD_CPU;
4461: #endif
4462: return(0);
4463: }
4465: /*@C
4466: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a MATSEQAIJ matrix is stored
4468: Not Collective
4470: Input Parameter:
4471: . mat - a MATSEQAIJ matrix
4473: Output Parameter:
4474: . array - pointer to the data
4476: Level: intermediate
4478: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayRead()
4479: @*/
4480: PetscErrorCode MatSeqAIJGetArrayRead(Mat A,const PetscScalar **array)
4481: {
4482: #if defined(PETSC_HAVE_DEVICE)
4483: PetscOffloadMask oval;
4484: #endif
4488: #if defined(PETSC_HAVE_DEVICE)
4489: oval = A->offloadmask;
4490: #endif
4491: MatSeqAIJGetArray(A,(PetscScalar**)array);
4492: #if defined(PETSC_HAVE_DEVICE)
4493: if (oval == PETSC_OFFLOAD_GPU || oval == PETSC_OFFLOAD_BOTH) A->offloadmask = PETSC_OFFLOAD_BOTH;
4494: #endif
4495: return(0);
4496: }
4498: /*@C
4499: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4501: Not Collective
4503: Input Parameter:
4504: . mat - a MATSEQAIJ matrix
4506: Output Parameter:
4507: . array - pointer to the data
4509: Level: intermediate
4511: .seealso: MatSeqAIJGetArray(), MatSeqAIJGetArrayRead()
4512: @*/
4513: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A,const PetscScalar **array)
4514: {
4515: #if defined(PETSC_HAVE_DEVICE)
4516: PetscOffloadMask oval;
4517: #endif
4521: #if defined(PETSC_HAVE_DEVICE)
4522: oval = A->offloadmask;
4523: #endif
4524: MatSeqAIJRestoreArray(A,(PetscScalar**)array);
4525: #if defined(PETSC_HAVE_DEVICE)
4526: A->offloadmask = oval;
4527: #endif
4528: return(0);
4529: }
4531: /*@C
4532: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4534: Not Collective
4536: Input Parameter:
4537: . mat - a MATSEQAIJ matrix
4539: Output Parameter:
4540: . nz - the maximum number of nonzeros in any row
4542: Level: intermediate
4544: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
4545: @*/
4546: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
4547: {
4548: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data;
4551: *nz = aij->rmax;
4552: return(0);
4553: }
4555: /*@C
4556: MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()
4558: Not Collective
4560: Input Parameters:
4561: + mat - a MATSEQAIJ matrix
4562: - array - pointer to the data
4564: Level: intermediate
4566: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
4567: @*/
4568: PetscErrorCode MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
4569: {
4573: PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
4574: return(0);
4575: }
4577: #if defined(PETSC_HAVE_CUDA)
4578: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat);
4579: #endif
4580: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4581: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat);
4582: #endif
4584: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4585: {
4586: Mat_SeqAIJ *b;
4588: PetscMPIInt size;
4591: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
4592: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
4594: PetscNewLog(B,&b);
4596: B->data = (void*)b;
4598: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
4599: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4601: b->row = NULL;
4602: b->col = NULL;
4603: b->icol = NULL;
4604: b->reallocs = 0;
4605: b->ignorezeroentries = PETSC_FALSE;
4606: b->roworiented = PETSC_TRUE;
4607: b->nonew = 0;
4608: b->diag = NULL;
4609: b->solve_work = NULL;
4610: B->spptr = NULL;
4611: b->saved_values = NULL;
4612: b->idiag = NULL;
4613: b->mdiag = NULL;
4614: b->ssor_work = NULL;
4615: b->omega = 1.0;
4616: b->fshift = 0.0;
4617: b->idiagvalid = PETSC_FALSE;
4618: b->ibdiagvalid = PETSC_FALSE;
4619: b->keepnonzeropattern = PETSC_FALSE;
4621: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4622: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
4623: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);
4625: #if defined(PETSC_HAVE_MATLAB_ENGINE)
4626: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
4627: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
4628: #endif
4630: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
4631: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
4632: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
4633: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
4634: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
4635: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
4636: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijsell_C",MatConvert_SeqAIJ_SeqAIJSELL);
4637: #if defined(PETSC_HAVE_MKL_SPARSE)
4638: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijmkl_C",MatConvert_SeqAIJ_SeqAIJMKL);
4639: #endif
4640: #if defined(PETSC_HAVE_CUDA)
4641: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcusparse_C",MatConvert_SeqAIJ_SeqAIJCUSPARSE);
4642: PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaijcusparse_seqaij_C",MatProductSetFromOptions_SeqAIJ);
4643: PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaijcusparse_C",MatProductSetFromOptions_SeqAIJ);
4644: #endif
4645: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4646: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijkokkos_C",MatConvert_SeqAIJ_SeqAIJKokkos);
4647: #endif
4648: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
4649: #if defined(PETSC_HAVE_ELEMENTAL)
4650: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
4651: #endif
4652: #if defined(PETSC_HAVE_SCALAPACK)
4653: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_scalapack_C",MatConvert_AIJ_ScaLAPACK);
4654: #endif
4655: #if defined(PETSC_HAVE_HYPRE)
4656: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_hypre_C",MatConvert_AIJ_HYPRE);
4657: PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_transpose_seqaij_seqaij_C",MatProductSetFromOptions_Transpose_AIJ_AIJ);
4658: #endif
4659: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
4660: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsell_C",MatConvert_SeqAIJ_SeqSELL);
4661: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_is_C",MatConvert_XAIJ_IS);
4662: PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
4663: PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
4664: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
4665: PetscObjectComposeFunction((PetscObject)B,"MatResetPreallocation_C",MatResetPreallocation_SeqAIJ);
4666: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
4667: PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
4668: PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_is_seqaij_C",MatProductSetFromOptions_IS_XAIJ);
4669: PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqdense_seqaij_C",MatProductSetFromOptions_SeqDense_SeqAIJ);
4670: PetscObjectComposeFunction((PetscObject)B,"MatProductSetFromOptions_seqaij_seqaij_C",MatProductSetFromOptions_SeqAIJ);
4671: MatCreate_SeqAIJ_Inode(B);
4672: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
4673: MatSeqAIJSetTypeFromOptions(B); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4674: return(0);
4675: }
4677: /*
4678: Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4679: */
4680: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4681: {
4682: Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data,*a = (Mat_SeqAIJ*)A->data;
4684: PetscInt m = A->rmap->n,i;
4687: if (!A->assembled && cpvalues!=MAT_DO_NOT_COPY_VALUES) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Cannot duplicate unassembled matrix");
4689: C->factortype = A->factortype;
4690: c->row = NULL;
4691: c->col = NULL;
4692: c->icol = NULL;
4693: c->reallocs = 0;
4695: C->assembled = PETSC_TRUE;
4697: PetscLayoutReference(A->rmap,&C->rmap);
4698: PetscLayoutReference(A->cmap,&C->cmap);
4700: PetscMalloc1(m,&c->imax);
4701: PetscMemcpy(c->imax,a->imax,m*sizeof(PetscInt));
4702: PetscMalloc1(m,&c->ilen);
4703: PetscMemcpy(c->ilen,a->ilen,m*sizeof(PetscInt));
4704: PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));
4706: /* allocate the matrix space */
4707: if (mallocmatspace) {
4708: PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4709: PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
4711: c->singlemalloc = PETSC_TRUE;
4713: PetscArraycpy(c->i,a->i,m+1);
4714: if (m > 0) {
4715: PetscArraycpy(c->j,a->j,a->i[m]);
4716: if (cpvalues == MAT_COPY_VALUES) {
4717: const PetscScalar *aa;
4719: MatSeqAIJGetArrayRead(A,&aa);
4720: PetscArraycpy(c->a,aa,a->i[m]);
4721: MatSeqAIJGetArrayRead(A,&aa);
4722: } else {
4723: PetscArrayzero(c->a,a->i[m]);
4724: }
4725: }
4726: }
4728: c->ignorezeroentries = a->ignorezeroentries;
4729: c->roworiented = a->roworiented;
4730: c->nonew = a->nonew;
4731: if (a->diag) {
4732: PetscMalloc1(m+1,&c->diag);
4733: PetscMemcpy(c->diag,a->diag,m*sizeof(PetscInt));
4734: PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4735: } else c->diag = NULL;
4737: c->solve_work = NULL;
4738: c->saved_values = NULL;
4739: c->idiag = NULL;
4740: c->ssor_work = NULL;
4741: c->keepnonzeropattern = a->keepnonzeropattern;
4742: c->free_a = PETSC_TRUE;
4743: c->free_ij = PETSC_TRUE;
4745: c->rmax = a->rmax;
4746: c->nz = a->nz;
4747: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
4748: C->preallocated = PETSC_TRUE;
4750: c->compressedrow.use = a->compressedrow.use;
4751: c->compressedrow.nrows = a->compressedrow.nrows;
4752: if (a->compressedrow.use) {
4753: i = a->compressedrow.nrows;
4754: PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4755: PetscArraycpy(c->compressedrow.i,a->compressedrow.i,i+1);
4756: PetscArraycpy(c->compressedrow.rindex,a->compressedrow.rindex,i);
4757: } else {
4758: c->compressedrow.use = PETSC_FALSE;
4759: c->compressedrow.i = NULL;
4760: c->compressedrow.rindex = NULL;
4761: }
4762: c->nonzerorowcnt = a->nonzerorowcnt;
4763: C->nonzerostate = A->nonzerostate;
4765: MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4766: PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4767: return(0);
4768: }
4770: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4771: {
4775: MatCreate(PetscObjectComm((PetscObject)A),B);
4776: MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4777: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4778: MatSetBlockSizesFromMats(*B,A,A);
4779: }
4780: MatSetType(*B,((PetscObject)A)->type_name);
4781: MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4782: return(0);
4783: }
4785: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4786: {
4787: PetscBool isbinary, ishdf5;
4793: /* force binary viewer to load .info file if it has not yet done so */
4794: PetscViewerSetUp(viewer);
4795: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
4796: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERHDF5, &ishdf5);
4797: if (isbinary) {
4798: MatLoad_SeqAIJ_Binary(newMat,viewer);
4799: } else if (ishdf5) {
4800: #if defined(PETSC_HAVE_HDF5)
4801: MatLoad_AIJ_HDF5(newMat,viewer);
4802: #else
4803: SETERRQ(PetscObjectComm((PetscObject)newMat),PETSC_ERR_SUP,"HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
4804: #endif
4805: } else {
4806: 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);
4807: }
4808: return(0);
4809: }
4811: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
4812: {
4813: Mat_SeqAIJ *a = (Mat_SeqAIJ*)mat->data;
4815: PetscInt header[4],*rowlens,M,N,nz,sum,rows,cols,i;
4818: PetscViewerSetUp(viewer);
4820: /* read in matrix header */
4821: PetscViewerBinaryRead(viewer,header,4,NULL,PETSC_INT);
4822: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Not a matrix object in file");
4823: M = header[1]; N = header[2]; nz = header[3];
4824: if (M < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix row size (%D) in file is negative",M);
4825: if (N < 0) SETERRQ1(PetscObjectComm((PetscObject)viewer),PETSC_ERR_FILE_UNEXPECTED,"Matrix column size (%D) in file is negative",N);
4826: if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk, cannot load as SeqAIJ");
4828: /* set block sizes from the viewer's .info file */
4829: MatLoad_Binary_BlockSizes(mat,viewer);
4830: /* set local and global sizes if not set already */
4831: if (mat->rmap->n < 0) mat->rmap->n = M;
4832: if (mat->cmap->n < 0) mat->cmap->n = N;
4833: if (mat->rmap->N < 0) mat->rmap->N = M;
4834: if (mat->cmap->N < 0) mat->cmap->N = N;
4835: PetscLayoutSetUp(mat->rmap);
4836: PetscLayoutSetUp(mat->cmap);
4838: /* check if the matrix sizes are correct */
4839: MatGetSize(mat,&rows,&cols);
4840: if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);
4842: /* read in row lengths */
4843: PetscMalloc1(M,&rowlens);
4844: PetscViewerBinaryRead(viewer,rowlens,M,NULL,PETSC_INT);
4845: /* check if sum(rowlens) is same as nz */
4846: sum = 0; for (i=0; i<M; i++) sum += rowlens[i];
4847: if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Inconsistent matrix data in file: nonzeros = %D, sum-row-lengths = %D\n",nz,sum);
4848: /* preallocate and check sizes */
4849: MatSeqAIJSetPreallocation_SeqAIJ(mat,0,rowlens);
4850: MatGetSize(mat,&rows,&cols);
4851: 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);
4852: /* store row lengths */
4853: PetscArraycpy(a->ilen,rowlens,M);
4854: PetscFree(rowlens);
4856: /* fill in "i" row pointers */
4857: a->i[0] = 0; for (i=0; i<M; i++) a->i[i+1] = a->i[i] + a->ilen[i];
4858: /* read in "j" column indices */
4859: PetscViewerBinaryRead(viewer,a->j,nz,NULL,PETSC_INT);
4860: /* read in "a" nonzero values */
4861: PetscViewerBinaryRead(viewer,a->a,nz,NULL,PETSC_SCALAR);
4863: MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
4864: MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
4865: return(0);
4866: }
4868: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4869: {
4870: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4872: #if defined(PETSC_USE_COMPLEX)
4873: PetscInt k;
4874: #endif
4877: /* If the matrix dimensions are not equal,or no of nonzeros */
4878: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4879: *flg = PETSC_FALSE;
4880: return(0);
4881: }
4883: /* if the a->i are the same */
4884: PetscArraycmp(a->i,b->i,A->rmap->n+1,flg);
4885: if (!*flg) return(0);
4887: /* if a->j are the same */
4888: PetscArraycmp(a->j,b->j,a->nz,flg);
4889: if (!*flg) return(0);
4891: /* if a->a are the same */
4892: #if defined(PETSC_USE_COMPLEX)
4893: for (k=0; k<a->nz; k++) {
4894: if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4895: *flg = PETSC_FALSE;
4896: return(0);
4897: }
4898: }
4899: #else
4900: PetscArraycmp(a->a,b->a,a->nz,flg);
4901: #endif
4902: return(0);
4903: }
4905: /*@
4906: MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4907: provided by the user.
4909: Collective
4911: Input Parameters:
4912: + comm - must be an MPI communicator of size 1
4913: . m - number of rows
4914: . n - number of columns
4915: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
4916: . j - column indices
4917: - a - matrix values
4919: Output Parameter:
4920: . mat - the matrix
4922: Level: intermediate
4924: Notes:
4925: The i, j, and a arrays are not copied by this routine, the user must free these arrays
4926: once the matrix is destroyed and not before
4928: You cannot set new nonzero locations into this matrix, that will generate an error.
4930: The i and j indices are 0 based
4932: The format which is used for the sparse matrix input, is equivalent to a
4933: row-major ordering.. i.e for the following matrix, the input data expected is
4934: as shown
4936: $ 1 0 0
4937: $ 2 0 3
4938: $ 4 5 6
4939: $
4940: $ i = {0,1,3,6} [size = nrow+1 = 3+1]
4941: $ j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
4942: $ v = {1,2,3,4,5,6} [size = 6]
4945: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4947: @*/
4948: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat)
4949: {
4951: PetscInt ii;
4952: Mat_SeqAIJ *aij;
4953: PetscInt jj;
4956: if (m > 0 && i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4957: MatCreate(comm,mat);
4958: MatSetSizes(*mat,m,n,m,n);
4959: /* MatSetBlockSizes(*mat,,); */
4960: MatSetType(*mat,MATSEQAIJ);
4961: MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,NULL);
4962: aij = (Mat_SeqAIJ*)(*mat)->data;
4963: PetscMalloc1(m,&aij->imax);
4964: PetscMalloc1(m,&aij->ilen);
4966: aij->i = i;
4967: aij->j = j;
4968: aij->a = a;
4969: aij->singlemalloc = PETSC_FALSE;
4970: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4971: aij->free_a = PETSC_FALSE;
4972: aij->free_ij = PETSC_FALSE;
4974: for (ii=0; ii<m; ii++) {
4975: aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4976: if (PetscDefined(USE_DEBUG)) {
4977: 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]);
4978: for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4979: 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);
4980: 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);
4981: }
4982: }
4983: }
4984: if (PetscDefined(USE_DEBUG)) {
4985: for (ii=0; ii<aij->i[m]; ii++) {
4986: if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4987: 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]);
4988: }
4989: }
4991: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4992: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4993: return(0);
4994: }
4995: /*@C
4996: MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4997: provided by the user.
4999: Collective
5001: Input Parameters:
5002: + comm - must be an MPI communicator of size 1
5003: . m - number of rows
5004: . n - number of columns
5005: . i - row indices
5006: . j - column indices
5007: . a - matrix values
5008: . nz - number of nonzeros
5009: - idx - 0 or 1 based
5011: Output Parameter:
5012: . mat - the matrix
5014: Level: intermediate
5016: Notes:
5017: The i and j indices are 0 based
5019: The format which is used for the sparse matrix input, is equivalent to a
5020: row-major ordering.. i.e for the following matrix, the input data expected is
5021: as shown:
5023: 1 0 0
5024: 2 0 3
5025: 4 5 6
5027: i = {0,1,1,2,2,2}
5028: j = {0,0,2,0,1,2}
5029: v = {1,2,3,4,5,6}
5032: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
5034: @*/
5035: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt i[],PetscInt j[],PetscScalar a[],Mat *mat,PetscInt nz,PetscBool idx)
5036: {
5038: PetscInt ii, *nnz, one = 1,row,col;
5042: PetscCalloc1(m,&nnz);
5043: for (ii = 0; ii < nz; ii++) {
5044: nnz[i[ii] - !!idx] += 1;
5045: }
5046: MatCreate(comm,mat);
5047: MatSetSizes(*mat,m,n,m,n);
5048: MatSetType(*mat,MATSEQAIJ);
5049: MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
5050: for (ii = 0; ii < nz; ii++) {
5051: if (idx) {
5052: row = i[ii] - 1;
5053: col = j[ii] - 1;
5054: } else {
5055: row = i[ii];
5056: col = j[ii];
5057: }
5058: MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
5059: }
5060: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
5061: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
5062: PetscFree(nnz);
5063: return(0);
5064: }
5066: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5067: {
5068: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data;
5072: a->idiagvalid = PETSC_FALSE;
5073: a->ibdiagvalid = PETSC_FALSE;
5075: MatSeqAIJInvalidateDiagonal_Inode(A);
5076: return(0);
5077: }
5079: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
5080: {
5082: PetscMPIInt size;
5085: MPI_Comm_size(comm,&size);
5086: if (size == 1) {
5087: if (scall == MAT_INITIAL_MATRIX) {
5088: MatDuplicate(inmat,MAT_COPY_VALUES,outmat);
5089: } else {
5090: MatCopy(inmat,*outmat,SAME_NONZERO_PATTERN);
5091: }
5092: } else {
5093: MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
5094: }
5095: return(0);
5096: }
5098: /*
5099: Permute A into C's *local* index space using rowemb,colemb.
5100: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5101: of [0,m), colemb is in [0,n).
5102: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5103: */
5104: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
5105: {
5106: /* If making this function public, change the error returned in this function away from _PLIB. */
5108: Mat_SeqAIJ *Baij;
5109: PetscBool seqaij;
5110: PetscInt m,n,*nz,i,j,count;
5111: PetscScalar v;
5112: const PetscInt *rowindices,*colindices;
5115: if (!B) return(0);
5116: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5117: PetscObjectBaseTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
5118: if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
5119: if (rowemb) {
5120: ISGetLocalSize(rowemb,&m);
5121: 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);
5122: } else {
5123: if (C->rmap->n != B->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
5124: }
5125: if (colemb) {
5126: ISGetLocalSize(colemb,&n);
5127: 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);
5128: } else {
5129: if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
5130: }
5132: Baij = (Mat_SeqAIJ*)(B->data);
5133: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5134: PetscMalloc1(B->rmap->n,&nz);
5135: for (i=0; i<B->rmap->n; i++) {
5136: nz[i] = Baij->i[i+1] - Baij->i[i];
5137: }
5138: MatSeqAIJSetPreallocation(C,0,nz);
5139: PetscFree(nz);
5140: }
5141: if (pattern == SUBSET_NONZERO_PATTERN) {
5142: MatZeroEntries(C);
5143: }
5144: count = 0;
5145: rowindices = NULL;
5146: colindices = NULL;
5147: if (rowemb) {
5148: ISGetIndices(rowemb,&rowindices);
5149: }
5150: if (colemb) {
5151: ISGetIndices(colemb,&colindices);
5152: }
5153: for (i=0; i<B->rmap->n; i++) {
5154: PetscInt row;
5155: row = i;
5156: if (rowindices) row = rowindices[i];
5157: for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
5158: PetscInt col;
5159: col = Baij->j[count];
5160: if (colindices) col = colindices[col];
5161: v = Baij->a[count];
5162: MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
5163: ++count;
5164: }
5165: }
5166: /* FIXME: set C's nonzerostate correctly. */
5167: /* Assembly for C is necessary. */
5168: C->preallocated = PETSC_TRUE;
5169: C->assembled = PETSC_TRUE;
5170: C->was_assembled = PETSC_FALSE;
5171: return(0);
5172: }
5174: PetscFunctionList MatSeqAIJList = NULL;
5176: /*@C
5177: MatSeqAIJSetType - Converts a MATSEQAIJ matrix to a subtype
5179: Collective on Mat
5181: Input Parameters:
5182: + mat - the matrix object
5183: - matype - matrix type
5185: Options Database Key:
5186: . -mat_seqai_type <method> - for example seqaijcrl
5189: Level: intermediate
5191: .seealso: PCSetType(), VecSetType(), MatCreate(), MatType, Mat
5192: @*/
5193: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5194: {
5195: PetscErrorCode ierr,(*r)(Mat,MatType,MatReuse,Mat*);
5196: PetscBool sametype;
5200: PetscObjectTypeCompare((PetscObject)mat,matype,&sametype);
5201: if (sametype) return(0);
5203: PetscFunctionListFind(MatSeqAIJList,matype,&r);
5204: if (!r) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unknown Mat type given: %s",matype);
5205: (*r)(mat,matype,MAT_INPLACE_MATRIX,&mat);
5206: return(0);
5207: }
5210: /*@C
5211: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential AIJ matrices
5213: Not Collective
5215: Input Parameters:
5216: + name - name of a new user-defined matrix type, for example MATSEQAIJCRL
5217: - function - routine to convert to subtype
5219: Notes:
5220: MatSeqAIJRegister() may be called multiple times to add several user-defined solvers.
5223: Then, your matrix can be chosen with the procedural interface at runtime via the option
5224: $ -mat_seqaij_type my_mat
5226: Level: advanced
5228: .seealso: MatSeqAIJRegisterAll()
5231: Level: advanced
5232: @*/
5233: PetscErrorCode MatSeqAIJRegister(const char sname[],PetscErrorCode (*function)(Mat,MatType,MatReuse,Mat *))
5234: {
5238: MatInitializePackage();
5239: PetscFunctionListAdd(&MatSeqAIJList,sname,function);
5240: return(0);
5241: }
5243: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5245: /*@C
5246: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of SeqAIJ
5248: Not Collective
5250: Level: advanced
5252: Developers Note: CUSPARSE does not yet support the MatConvert_SeqAIJ..() paradigm and thus cannot be registered here
5254: .seealso: MatRegisterAll(), MatSeqAIJRegister()
5255: @*/
5256: PetscErrorCode MatSeqAIJRegisterAll(void)
5257: {
5261: if (MatSeqAIJRegisterAllCalled) return(0);
5262: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5264: MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL);
5265: MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM);
5266: MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL);
5267: #if defined(PETSC_HAVE_MKL_SPARSE)
5268: MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL);
5269: #endif
5270: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5271: MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL);
5272: #endif
5273: return(0);
5274: }
5276: /*
5277: Special version for direct calls from Fortran
5278: */
5279: #include <petsc/private/fortranimpl.h>
5280: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5281: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5282: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5283: #define matsetvaluesseqaij_ matsetvaluesseqaij
5284: #endif
5286: /* Change these macros so can be used in void function */
5287: #undef CHKERRQ
5288: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
5289: #undef SETERRQ2
5290: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
5291: #undef SETERRQ3
5292: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)
5294: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
5295: {
5296: Mat A = *AA;
5297: PetscInt m = *mm, n = *nn;
5298: InsertMode is = *isis;
5299: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
5300: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
5301: PetscInt *imax,*ai,*ailen;
5303: PetscInt *aj,nonew = a->nonew,lastcol = -1;
5304: MatScalar *ap,value,*aa;
5305: PetscBool ignorezeroentries = a->ignorezeroentries;
5306: PetscBool roworiented = a->roworiented;
5309: MatCheckPreallocated(A,1);
5310: imax = a->imax;
5311: ai = a->i;
5312: ailen = a->ilen;
5313: aj = a->j;
5314: aa = a->a;
5316: for (k=0; k<m; k++) { /* loop over added rows */
5317: row = im[k];
5318: if (row < 0) continue;
5319: if (PetscUnlikelyDebug(row >= A->rmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
5320: rp = aj + ai[row]; ap = aa + ai[row];
5321: rmax = imax[row]; nrow = ailen[row];
5322: low = 0;
5323: high = nrow;
5324: for (l=0; l<n; l++) { /* loop over added columns */
5325: if (in[l] < 0) continue;
5326: if (PetscUnlikelyDebug(in[l] >= A->cmap->n)) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
5327: col = in[l];
5328: if (roworiented) value = v[l + k*n];
5329: else value = v[k + l*m];
5331: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5333: if (col <= lastcol) low = 0;
5334: else high = nrow;
5335: lastcol = col;
5336: while (high-low > 5) {
5337: t = (low+high)/2;
5338: if (rp[t] > col) high = t;
5339: else low = t;
5340: }
5341: for (i=low; i<high; i++) {
5342: if (rp[i] > col) break;
5343: if (rp[i] == col) {
5344: if (is == ADD_VALUES) ap[i] += value;
5345: else ap[i] = value;
5346: goto noinsert;
5347: }
5348: }
5349: if (value == 0.0 && ignorezeroentries) goto noinsert;
5350: if (nonew == 1) goto noinsert;
5351: if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
5352: MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
5353: N = nrow++ - 1; a->nz++; high++;
5354: /* shift up all the later entries in this row */
5355: for (ii=N; ii>=i; ii--) {
5356: rp[ii+1] = rp[ii];
5357: ap[ii+1] = ap[ii];
5358: }
5359: rp[i] = col;
5360: ap[i] = value;
5361: A->nonzerostate++;
5362: noinsert:;
5363: low = i + 1;
5364: }
5365: ailen[row] = nrow;
5366: }
5367: PetscFunctionReturnVoid();
5368: }