Actual source code: fdaij.c
petsc-3.13.6 2020-09-29
1: #include <../src/mat/impls/aij/seq/aij.h>
2: #include <../src/mat/impls/baij/seq/baij.h>
3: #include <../src/mat/impls/sell/seq/sell.h>
4: #include <petsc/private/isimpl.h>
6: /*
7: This routine is shared by SeqAIJ and SeqBAIJ matrices,
8: since it operators only on the nonzero structure of the elements or blocks.
9: */
10: PetscErrorCode MatFDColoringCreate_SeqXAIJ(Mat mat,ISColoring iscoloring,MatFDColoring c)
11: {
13: PetscInt bs,nis=iscoloring->n,m=mat->rmap->n;
14: PetscBool isBAIJ,isSELL;
17: /* set default brows and bcols for speedup inserting the dense matrix into sparse Jacobian */
18: MatGetBlockSize(mat,&bs);
19: PetscObjectBaseTypeCompare((PetscObject)mat,MATSEQBAIJ,&isBAIJ);
20: PetscObjectTypeCompare((PetscObject)mat,MATSEQSELL,&isSELL);
21: if (isBAIJ) {
22: c->brows = m;
23: c->bcols = 1;
24: } else { /* seqaij matrix */
25: /* bcols is chosen s.t. dy-array takes 50% of memory space as mat */
26: PetscReal mem;
27: PetscInt nz,brows,bcols;
28: if (isSELL) {
29: Mat_SeqSELL *spA = (Mat_SeqSELL*)mat->data;
30: nz = spA->nz;
31: } else {
32: Mat_SeqAIJ *spA = (Mat_SeqAIJ*)mat->data;
33: nz = spA->nz;
34: }
36: bs = 1; /* only bs=1 is supported for SeqAIJ matrix */
37: mem = nz*(sizeof(PetscScalar) + sizeof(PetscInt)) + 3*m*sizeof(PetscInt);
38: bcols = (PetscInt)(0.5*mem /(m*sizeof(PetscScalar)));
39: brows = 1000/bcols;
40: if (bcols > nis) bcols = nis;
41: if (brows == 0 || brows > m) brows = m;
42: c->brows = brows;
43: c->bcols = bcols;
44: }
46: c->M = mat->rmap->N/bs; /* set total rows, columns and local rows */
47: c->N = mat->cmap->N/bs;
48: c->m = mat->rmap->N/bs;
49: c->rstart = 0;
50: c->ncolors = nis;
51: c->ctype = iscoloring->ctype;
52: return(0);
53: }
55: /*
56: Reorder Jentry such that blocked brows*bols of entries from dense matrix are inserted into Jacobian for improved cache performance
57: Input Parameters:
58: + mat - the matrix containing the nonzero structure of the Jacobian
59: . color - the coloring context
60: - nz - number of local non-zeros in mat
61: */
62: PetscErrorCode MatFDColoringSetUpBlocked_AIJ_Private(Mat mat,MatFDColoring c,PetscInt nz)
63: {
65: PetscInt i,j,nrows,nbcols,brows=c->brows,bcols=c->bcols,mbs=c->m,nis=c->ncolors;
66: PetscInt *color_start,*row_start,*nrows_new,nz_new,row_end;
69: if (brows < 1 || brows > mbs) brows = mbs;
70: PetscMalloc2(bcols+1,&color_start,bcols,&row_start);
71: PetscCalloc1(nis,&nrows_new);
72: PetscMalloc1(bcols*mat->rmap->n,&c->dy);
73: PetscLogObjectMemory((PetscObject)c,bcols*mat->rmap->n*sizeof(PetscScalar));
75: nz_new = 0;
76: nbcols = 0;
77: color_start[bcols] = 0;
79: if (c->htype[0] == 'd') { /* ---- c->htype == 'ds', use MatEntry --------*/
80: MatEntry *Jentry_new,*Jentry=c->matentry;
82: PetscMalloc1(nz,&Jentry_new);
83: for (i=0; i<nis; i+=bcols) { /* loop over colors */
84: if (i + bcols > nis) {
85: color_start[nis - i] = color_start[bcols];
86: bcols = nis - i;
87: }
89: color_start[0] = color_start[bcols];
90: for (j=0; j<bcols; j++) {
91: color_start[j+1] = c->nrows[i+j] + color_start[j];
92: row_start[j] = 0;
93: }
95: row_end = brows;
96: if (row_end > mbs) row_end = mbs;
98: while (row_end <= mbs) { /* loop over block rows */
99: for (j=0; j<bcols; j++) { /* loop over block columns */
100: nrows = c->nrows[i+j];
101: nz = color_start[j];
102: while (row_start[j] < nrows) {
103: if (Jentry[nz].row >= row_end) {
104: color_start[j] = nz;
105: break;
106: } else { /* copy Jentry[nz] to Jentry_new[nz_new] */
107: Jentry_new[nz_new].row = Jentry[nz].row + j*mbs; /* index in dy-array */
108: Jentry_new[nz_new].col = Jentry[nz].col;
109: Jentry_new[nz_new].valaddr = Jentry[nz].valaddr;
110: nz_new++; nz++; row_start[j]++;
111: }
112: }
113: }
114: if (row_end == mbs) break;
115: row_end += brows;
116: if (row_end > mbs) row_end = mbs;
117: }
118: nrows_new[nbcols++] = nz_new;
119: }
120: PetscFree(Jentry);
121: c->matentry = Jentry_new;
122: } else { /* --------- c->htype == 'wp', use MatEntry2 ------------------*/
123: MatEntry2 *Jentry2_new,*Jentry2=c->matentry2;
125: PetscMalloc1(nz,&Jentry2_new);
126: for (i=0; i<nis; i+=bcols) { /* loop over colors */
127: if (i + bcols > nis) {
128: color_start[nis - i] = color_start[bcols];
129: bcols = nis - i;
130: }
132: color_start[0] = color_start[bcols];
133: for (j=0; j<bcols; j++) {
134: color_start[j+1] = c->nrows[i+j] + color_start[j];
135: row_start[j] = 0;
136: }
138: row_end = brows;
139: if (row_end > mbs) row_end = mbs;
141: while (row_end <= mbs) { /* loop over block rows */
142: for (j=0; j<bcols; j++) { /* loop over block columns */
143: nrows = c->nrows[i+j];
144: nz = color_start[j];
145: while (row_start[j] < nrows) {
146: if (Jentry2[nz].row >= row_end) {
147: color_start[j] = nz;
148: break;
149: } else { /* copy Jentry2[nz] to Jentry2_new[nz_new] */
150: Jentry2_new[nz_new].row = Jentry2[nz].row + j*mbs; /* index in dy-array */
151: Jentry2_new[nz_new].valaddr = Jentry2[nz].valaddr;
152: nz_new++; nz++; row_start[j]++;
153: }
154: }
155: }
156: if (row_end == mbs) break;
157: row_end += brows;
158: if (row_end > mbs) row_end = mbs;
159: }
160: nrows_new[nbcols++] = nz_new;
161: }
162: PetscFree(Jentry2);
163: c->matentry2 = Jentry2_new;
164: } /* ---------------------------------------------*/
166: PetscFree2(color_start,row_start);
168: for (i=nbcols-1; i>0; i--) nrows_new[i] -= nrows_new[i-1];
169: PetscFree(c->nrows);
170: c->nrows = nrows_new;
171: return(0);
172: }
174: PetscErrorCode MatFDColoringSetUp_SeqXAIJ(Mat mat,ISColoring iscoloring,MatFDColoring c)
175: {
176: PetscErrorCode ierr;
177: PetscInt i,n,nrows,mbs=c->m,j,k,m,ncols,col,nis=iscoloring->n,*rowhit,bs,bs2,*spidx,nz,tmp;
178: const PetscInt *is,*row,*ci,*cj;
179: PetscBool isBAIJ,isSELL;
180: const PetscScalar *A_val;
181: PetscScalar **valaddrhit;
182: MatEntry *Jentry;
183: MatEntry2 *Jentry2;
186: ISColoringGetIS(iscoloring,PETSC_OWN_POINTER,PETSC_IGNORE,&c->isa);
188: MatGetBlockSize(mat,&bs);
189: PetscObjectBaseTypeCompare((PetscObject)mat,MATSEQBAIJ,&isBAIJ);
190: PetscObjectTypeCompare((PetscObject)mat,MATSEQSELL,&isSELL);
191: if (isBAIJ) {
192: Mat_SeqBAIJ *spA = (Mat_SeqBAIJ*)mat->data;
194: A_val = spA->a;
195: nz = spA->nz;
196: } else if (isSELL) {
197: Mat_SeqSELL *spA = (Mat_SeqSELL*)mat->data;
199: A_val = spA->val;
200: nz = spA->nz;
201: bs = 1; /* only bs=1 is supported for SeqSELL matrix */
202: } else {
203: Mat_SeqAIJ *spA = (Mat_SeqAIJ*)mat->data;
205: A_val = spA->a;
206: nz = spA->nz;
207: bs = 1; /* only bs=1 is supported for SeqAIJ matrix */
208: }
210: PetscMalloc2(nis,&c->ncolumns,nis,&c->columns);
211: PetscMalloc1(nis,&c->nrows); /* nrows is freeed separately from ncolumns and columns */
212: PetscLogObjectMemory((PetscObject)c,3*nis*sizeof(PetscInt));
214: if (c->htype[0] == 'd') {
215: PetscMalloc1(nz,&Jentry);
216: PetscLogObjectMemory((PetscObject)c,nz*sizeof(MatEntry));
217: c->matentry = Jentry;
218: } else if (c->htype[0] == 'w') {
219: PetscMalloc1(nz,&Jentry2);
220: PetscLogObjectMemory((PetscObject)c,nz*sizeof(MatEntry2));
221: c->matentry2 = Jentry2;
222: } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"htype is not supported");
224: if (isBAIJ) {
225: MatGetColumnIJ_SeqBAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
226: } else if (isSELL) {
227: MatGetColumnIJ_SeqSELL_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
228: } else {
229: MatGetColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
230: }
232: PetscCalloc1(c->m,&rowhit);
233: PetscMalloc1(c->m,&valaddrhit);
235: nz = 0;
236: for (i=0; i<nis; i++) { /* loop over colors */
237: ISGetLocalSize(c->isa[i],&n);
238: ISGetIndices(c->isa[i],&is);
240: c->ncolumns[i] = n;
241: c->columns[i] = (PetscInt*)is;
242: /* note: we know that c->isa is going to be around as long at the c->columns values */
243: ISRestoreIndices(c->isa[i],&is);
245: /* fast, crude version requires O(N*N) work */
246: bs2 = bs*bs;
247: nrows = 0;
248: for (j=0; j<n; j++) { /* loop over columns */
249: col = is[j];
250: tmp = ci[col];
251: row = cj + tmp;
252: m = ci[col+1] - tmp;
253: nrows += m;
254: for (k=0; k<m; k++) { /* loop over columns marking them in rowhit */
255: rowhit[*row] = col + 1;
256: valaddrhit[*row++] = (PetscScalar*)&A_val[bs2*spidx[tmp + k]];
257: }
258: }
259: c->nrows[i] = nrows; /* total num of rows for this color */
261: if (c->htype[0] == 'd') {
262: for (j=0; j<mbs; j++) { /* loop over rows */
263: if (rowhit[j]) {
264: Jentry[nz].row = j; /* local row index */
265: Jentry[nz].col = rowhit[j] - 1; /* local column index */
266: Jentry[nz].valaddr = valaddrhit[j]; /* address of mat value for this entry */
267: nz++;
268: rowhit[j] = 0.0; /* zero rowhit for reuse */
269: }
270: }
271: } else { /* c->htype == 'wp' */
272: for (j=0; j<mbs; j++) { /* loop over rows */
273: if (rowhit[j]) {
274: Jentry2[nz].row = j; /* local row index */
275: Jentry2[nz].valaddr = valaddrhit[j]; /* address of mat value for this entry */
276: nz++;
277: rowhit[j] = 0.0; /* zero rowhit for reuse */
278: }
279: }
280: }
281: }
283: if (c->bcols > 1) { /* reorder Jentry for faster MatFDColoringApply() */
284: MatFDColoringSetUpBlocked_AIJ_Private(mat,c,nz);
285: }
287: if (isBAIJ) {
288: MatRestoreColumnIJ_SeqBAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
289: PetscMalloc1(bs*mat->rmap->n,&c->dy);
290: PetscLogObjectMemory((PetscObject)c,bs*mat->rmap->n*sizeof(PetscScalar));
291: } else if (isSELL) {
292: MatRestoreColumnIJ_SeqSELL_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
293: } else {
294: MatRestoreColumnIJ_SeqAIJ_Color(mat,0,PETSC_FALSE,PETSC_FALSE,&ncols,&ci,&cj,&spidx,NULL);
295: }
296: PetscFree(rowhit);
297: PetscFree(valaddrhit);
298: ISColoringRestoreIS(iscoloring,PETSC_OWN_POINTER,&c->isa);
300: VecCreateGhost(PetscObjectComm((PetscObject)mat),mat->rmap->n,PETSC_DETERMINE,0,NULL,&c->vscale);
301: PetscInfo3(c,"ncolors %D, brows %D and bcols %D are used.\n",c->ncolors,c->brows,c->bcols);
302: return(0);
303: }