Actual source code: fdmpiaij.c
petsc-3.13.6 2020-09-29
1: #include <../src/mat/impls/sell/mpi/mpisell.h>
2: #include <../src/mat/impls/aij/mpi/mpiaij.h>
3: #include <../src/mat/impls/baij/mpi/mpibaij.h>
4: #include <petsc/private/isimpl.h>
6: PetscErrorCode MatFDColoringApply_BAIJ(Mat J,MatFDColoring coloring,Vec x1,void *sctx)
7: {
8: PetscErrorCode (*f)(void*,Vec,Vec,void*)=(PetscErrorCode (*)(void*,Vec,Vec,void*))coloring->f;
9: PetscErrorCode ierr;
10: PetscInt k,cstart,cend,l,row,col,nz,spidx,i,j;
11: PetscScalar dx=0.0,*w3_array,*dy_i,*dy=coloring->dy;
12: PetscScalar *vscale_array;
13: const PetscScalar *xx;
14: PetscReal epsilon=coloring->error_rel,umin=coloring->umin,unorm;
15: Vec w1=coloring->w1,w2=coloring->w2,w3,vscale=coloring->vscale;
16: void *fctx=coloring->fctx;
17: PetscInt ctype=coloring->ctype,nxloc,nrows_k;
18: PetscScalar *valaddr;
19: MatEntry *Jentry=coloring->matentry;
20: MatEntry2 *Jentry2=coloring->matentry2;
21: const PetscInt ncolors=coloring->ncolors,*ncolumns=coloring->ncolumns,*nrows=coloring->nrows;
22: PetscInt bs=J->rmap->bs;
25: VecBindToCPU(x1,PETSC_TRUE);
26: /* (1) Set w1 = F(x1) */
27: if (!coloring->fset) {
28: PetscLogEventBegin(MAT_FDColoringFunction,coloring,0,0,0);
29: (*f)(sctx,x1,w1,fctx);
30: PetscLogEventEnd(MAT_FDColoringFunction,coloring,0,0,0);
31: } else {
32: coloring->fset = PETSC_FALSE;
33: }
35: /* (2) Compute vscale = 1./dx - the local scale factors, including ghost points */
36: VecGetLocalSize(x1,&nxloc);
37: if (coloring->htype[0] == 'w') {
38: /* vscale = dx is a constant scalar */
39: VecNorm(x1,NORM_2,&unorm);
40: dx = 1.0/(PetscSqrtReal(1.0 + unorm)*epsilon);
41: } else {
42: VecGetArrayRead(x1,&xx);
43: VecGetArray(vscale,&vscale_array);
44: for (col=0; col<nxloc; col++) {
45: dx = xx[col];
46: if (PetscAbsScalar(dx) < umin) {
47: if (PetscRealPart(dx) >= 0.0) dx = umin;
48: else if (PetscRealPart(dx) < 0.0 ) dx = -umin;
49: }
50: dx *= epsilon;
51: vscale_array[col] = 1.0/dx;
52: }
53: VecRestoreArrayRead(x1,&xx);
54: VecRestoreArray(vscale,&vscale_array);
55: }
56: if (ctype == IS_COLORING_GLOBAL && coloring->htype[0] == 'd') {
57: VecGhostUpdateBegin(vscale,INSERT_VALUES,SCATTER_FORWARD);
58: VecGhostUpdateEnd(vscale,INSERT_VALUES,SCATTER_FORWARD);
59: }
61: /* (3) Loop over each color */
62: if (!coloring->w3) {
63: VecDuplicate(x1,&coloring->w3);
64: /* Vec is used instensively in particular piece of scalar CPU code; won't benifit from bouncing back and forth to the GPU */
65: VecBindToCPU(coloring->w3,PETSC_TRUE);
66: PetscLogObjectParent((PetscObject)coloring,(PetscObject)coloring->w3);
67: }
68: w3 = coloring->w3;
70: VecGetOwnershipRange(x1,&cstart,&cend); /* used by ghosted vscale */
71: if (vscale) {
72: VecGetArray(vscale,&vscale_array);
73: }
74: nz = 0;
75: for (k=0; k<ncolors; k++) {
76: coloring->currentcolor = k;
78: /*
79: (3-1) Loop over each column associated with color
80: adding the perturbation to the vector w3 = x1 + dx.
81: */
82: VecCopy(x1,w3);
83: dy_i = dy;
84: for (i=0; i<bs; i++) { /* Loop over a block of columns */
85: VecGetArray(w3,&w3_array);
86: if (ctype == IS_COLORING_GLOBAL) w3_array -= cstart; /* shift pointer so global index can be used */
87: if (coloring->htype[0] == 'w') {
88: for (l=0; l<ncolumns[k]; l++) {
89: col = i + bs*coloring->columns[k][l]; /* local column (in global index!) of the matrix we are probing for */
90: w3_array[col] += 1.0/dx;
91: if (i) w3_array[col-1] -= 1.0/dx; /* resume original w3[col-1] */
92: }
93: } else { /* htype == 'ds' */
94: vscale_array -= cstart; /* shift pointer so global index can be used */
95: for (l=0; l<ncolumns[k]; l++) {
96: col = i + bs*coloring->columns[k][l]; /* local column (in global index!) of the matrix we are probing for */
97: w3_array[col] += 1.0/vscale_array[col];
98: if (i) w3_array[col-1] -= 1.0/vscale_array[col-1]; /* resume original w3[col-1] */
99: }
100: vscale_array += cstart;
101: }
102: if (ctype == IS_COLORING_GLOBAL) w3_array += cstart;
103: VecRestoreArray(w3,&w3_array);
105: /*
106: (3-2) Evaluate function at w3 = x1 + dx (here dx is a vector of perturbations)
107: w2 = F(x1 + dx) - F(x1)
108: */
109: PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
110: VecPlaceArray(w2,dy_i); /* place w2 to the array dy_i */
111: (*f)(sctx,w3,w2,fctx);
112: PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
113: VecAXPY(w2,-1.0,w1);
114: VecResetArray(w2);
115: dy_i += nxloc; /* points to dy+i*nxloc */
116: }
118: /*
119: (3-3) Loop over rows of vector, putting results into Jacobian matrix
120: */
121: nrows_k = nrows[k];
122: if (coloring->htype[0] == 'w') {
123: for (l=0; l<nrows_k; l++) {
124: row = bs*Jentry2[nz].row; /* local row index */
125: valaddr = Jentry2[nz++].valaddr;
126: spidx = 0;
127: dy_i = dy;
128: for (i=0; i<bs; i++) { /* column of the block */
129: for (j=0; j<bs; j++) { /* row of the block */
130: valaddr[spidx++] = dy_i[row+j]*dx;
131: }
132: dy_i += nxloc; /* points to dy+i*nxloc */
133: }
134: }
135: } else { /* htype == 'ds' */
136: for (l=0; l<nrows_k; l++) {
137: row = bs*Jentry[nz].row; /* local row index */
138: col = bs*Jentry[nz].col; /* local column index */
139: valaddr = Jentry[nz++].valaddr;
140: spidx = 0;
141: dy_i = dy;
142: for (i=0; i<bs; i++) { /* column of the block */
143: for (j=0; j<bs; j++) { /* row of the block */
144: valaddr[spidx++] = dy_i[row+j]*vscale_array[col+i];
145: }
146: dy_i += nxloc; /* points to dy+i*nxloc */
147: }
148: }
149: }
150: }
151: MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
152: MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
153: if (vscale) {
154: VecRestoreArray(vscale,&vscale_array);
155: }
157: coloring->currentcolor = -1;
158: VecBindToCPU(x1,PETSC_FALSE);
159: return(0);
160: }
162: /* this is declared PETSC_EXTERN because it is used by MatFDColoringUseDM() which is in the DM library */
163: PetscErrorCode MatFDColoringApply_AIJ(Mat J,MatFDColoring coloring,Vec x1,void *sctx)
164: {
165: PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void*))coloring->f;
166: PetscErrorCode ierr;
167: PetscInt k,cstart,cend,l,row,col,nz;
168: PetscScalar dx=0.0,*y,*w3_array;
169: const PetscScalar *xx;
170: PetscScalar *vscale_array;
171: PetscReal epsilon=coloring->error_rel,umin=coloring->umin,unorm;
172: Vec w1=coloring->w1,w2=coloring->w2,w3,vscale=coloring->vscale;
173: void *fctx=coloring->fctx;
174: ISColoringType ctype=coloring->ctype;
175: PetscInt nxloc,nrows_k;
176: MatEntry *Jentry=coloring->matentry;
177: MatEntry2 *Jentry2=coloring->matentry2;
178: const PetscInt ncolors=coloring->ncolors,*ncolumns=coloring->ncolumns,*nrows=coloring->nrows;
181: VecBindToCPU(x1,PETSC_TRUE);
182: if ((ctype == IS_COLORING_LOCAL) && (J->ops->fdcoloringapply == MatFDColoringApply_AIJ)) SETERRQ(PetscObjectComm((PetscObject)J),PETSC_ERR_SUP,"Must call MatColoringUseDM() with IS_COLORING_LOCAL");
183: /* (1) Set w1 = F(x1) */
184: if (!coloring->fset) {
185: PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
186: (*f)(sctx,x1,w1,fctx);
187: PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
188: } else {
189: coloring->fset = PETSC_FALSE;
190: }
192: /* (2) Compute vscale = 1./dx - the local scale factors, including ghost points */
193: if (coloring->htype[0] == 'w') {
194: /* vscale = 1./dx is a constant scalar */
195: VecNorm(x1,NORM_2,&unorm);
196: dx = 1.0/(PetscSqrtReal(1.0 + unorm)*epsilon);
197: } else {
198: VecGetLocalSize(x1,&nxloc);
199: VecGetArrayRead(x1,&xx);
200: VecGetArray(vscale,&vscale_array);
201: for (col=0; col<nxloc; col++) {
202: dx = xx[col];
203: if (PetscAbsScalar(dx) < umin) {
204: if (PetscRealPart(dx) >= 0.0) dx = umin;
205: else if (PetscRealPart(dx) < 0.0 ) dx = -umin;
206: }
207: dx *= epsilon;
208: vscale_array[col] = 1.0/dx;
209: }
210: VecRestoreArrayRead(x1,&xx);
211: VecRestoreArray(vscale,&vscale_array);
212: }
213: if (ctype == IS_COLORING_GLOBAL && coloring->htype[0] == 'd') {
214: VecGhostUpdateBegin(vscale,INSERT_VALUES,SCATTER_FORWARD);
215: VecGhostUpdateEnd(vscale,INSERT_VALUES,SCATTER_FORWARD);
216: }
218: /* (3) Loop over each color */
219: if (!coloring->w3) {
220: VecDuplicate(x1,&coloring->w3);
221: PetscLogObjectParent((PetscObject)coloring,(PetscObject)coloring->w3);
222: }
223: w3 = coloring->w3;
225: VecGetOwnershipRange(x1,&cstart,&cend); /* used by ghosted vscale */
226: if (vscale) {
227: VecGetArray(vscale,&vscale_array);
228: }
229: nz = 0;
231: if (coloring->bcols > 1) { /* use blocked insertion of Jentry */
232: PetscInt i,m=J->rmap->n,nbcols,bcols=coloring->bcols;
233: PetscScalar *dy=coloring->dy,*dy_k;
235: nbcols = 0;
236: for (k=0; k<ncolors; k+=bcols) {
238: /*
239: (3-1) Loop over each column associated with color
240: adding the perturbation to the vector w3 = x1 + dx.
241: */
243: dy_k = dy;
244: if (k + bcols > ncolors) bcols = ncolors - k;
245: for (i=0; i<bcols; i++) {
246: coloring->currentcolor = k+i;
248: VecCopy(x1,w3);
249: VecGetArray(w3,&w3_array);
250: if (ctype == IS_COLORING_GLOBAL) w3_array -= cstart; /* shift pointer so global index can be used */
251: if (coloring->htype[0] == 'w') {
252: for (l=0; l<ncolumns[k+i]; l++) {
253: col = coloring->columns[k+i][l]; /* local column (in global index!) of the matrix we are probing for */
254: w3_array[col] += 1.0/dx;
255: }
256: } else { /* htype == 'ds' */
257: vscale_array -= cstart; /* shift pointer so global index can be used */
258: for (l=0; l<ncolumns[k+i]; l++) {
259: col = coloring->columns[k+i][l]; /* local column (in global index!) of the matrix we are probing for */
260: w3_array[col] += 1.0/vscale_array[col];
261: }
262: vscale_array += cstart;
263: }
264: if (ctype == IS_COLORING_GLOBAL) w3_array += cstart;
265: VecRestoreArray(w3,&w3_array);
267: /*
268: (3-2) Evaluate function at w3 = x1 + dx (here dx is a vector of perturbations)
269: w2 = F(x1 + dx) - F(x1)
270: */
271: PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
272: VecPlaceArray(w2,dy_k); /* place w2 to the array dy_i */
273: (*f)(sctx,w3,w2,fctx);
274: PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
275: VecAXPY(w2,-1.0,w1);
276: VecResetArray(w2);
277: dy_k += m; /* points to dy+i*nxloc */
278: }
280: /*
281: (3-3) Loop over block rows of vector, putting results into Jacobian matrix
282: */
283: nrows_k = nrows[nbcols++];
285: if (coloring->htype[0] == 'w') {
286: for (l=0; l<nrows_k; l++) {
287: row = Jentry2[nz].row; /* local row index */
288: *(Jentry2[nz++].valaddr) = dy[row]*dx;
289: }
290: } else { /* htype == 'ds' */
291: for (l=0; l<nrows_k; l++) {
292: row = Jentry[nz].row; /* local row index */
293: *(Jentry[nz].valaddr) = dy[row]*vscale_array[Jentry[nz].col];
294: nz++;
295: }
296: }
297: }
298: } else { /* bcols == 1 */
299: for (k=0; k<ncolors; k++) {
300: coloring->currentcolor = k;
302: /*
303: (3-1) Loop over each column associated with color
304: adding the perturbation to the vector w3 = x1 + dx.
305: */
306: VecCopy(x1,w3);
307: VecGetArray(w3,&w3_array);
308: if (ctype == IS_COLORING_GLOBAL) w3_array -= cstart; /* shift pointer so global index can be used */
309: if (coloring->htype[0] == 'w') {
310: for (l=0; l<ncolumns[k]; l++) {
311: col = coloring->columns[k][l]; /* local column (in global index!) of the matrix we are probing for */
312: w3_array[col] += 1.0/dx;
313: }
314: } else { /* htype == 'ds' */
315: vscale_array -= cstart; /* shift pointer so global index can be used */
316: for (l=0; l<ncolumns[k]; l++) {
317: col = coloring->columns[k][l]; /* local column (in global index!) of the matrix we are probing for */
318: w3_array[col] += 1.0/vscale_array[col];
319: }
320: vscale_array += cstart;
321: }
322: if (ctype == IS_COLORING_GLOBAL) w3_array += cstart;
323: VecRestoreArray(w3,&w3_array);
325: /*
326: (3-2) Evaluate function at w3 = x1 + dx (here dx is a vector of perturbations)
327: w2 = F(x1 + dx) - F(x1)
328: */
329: PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
330: (*f)(sctx,w3,w2,fctx);
331: PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
332: VecAXPY(w2,-1.0,w1);
334: /*
335: (3-3) Loop over rows of vector, putting results into Jacobian matrix
336: */
337: nrows_k = nrows[k];
338: VecGetArray(w2,&y);
339: if (coloring->htype[0] == 'w') {
340: for (l=0; l<nrows_k; l++) {
341: row = Jentry2[nz].row; /* local row index */
342: *(Jentry2[nz++].valaddr) = y[row]*dx;
343: }
344: } else { /* htype == 'ds' */
345: for (l=0; l<nrows_k; l++) {
346: row = Jentry[nz].row; /* local row index */
347: *(Jentry[nz].valaddr) = y[row]*vscale_array[Jentry[nz].col];
348: nz++;
349: }
350: }
351: VecRestoreArray(w2,&y);
352: }
353: }
355: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA)
356: if (J->offloadmask != PETSC_OFFLOAD_UNALLOCATED) J->offloadmask = PETSC_OFFLOAD_CPU;
357: #endif
358: MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
359: MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
360: if (vscale) {
361: VecRestoreArray(vscale,&vscale_array);
362: }
363: coloring->currentcolor = -1;
364: VecBindToCPU(x1,PETSC_FALSE);
365: return(0);
366: }
368: PetscErrorCode MatFDColoringSetUp_MPIXAIJ(Mat mat,ISColoring iscoloring,MatFDColoring c)
369: {
370: PetscErrorCode ierr;
371: PetscMPIInt size,*ncolsonproc,*disp,nn;
372: PetscInt i,n,nrows,nrows_i,j,k,m,ncols,col,*rowhit,cstart,cend,colb;
373: const PetscInt *is,*A_ci,*A_cj,*B_ci,*B_cj,*row=NULL,*ltog=NULL;
374: PetscInt nis=iscoloring->n,nctot,*cols,tmp = 0;
375: ISLocalToGlobalMapping map=mat->cmap->mapping;
376: PetscInt ctype=c->ctype,*spidxA,*spidxB,nz,bs,bs2,spidx;
377: Mat A,B;
378: PetscScalar *A_val,*B_val,**valaddrhit;
379: MatEntry *Jentry;
380: MatEntry2 *Jentry2;
381: PetscBool isBAIJ,isSELL;
382: PetscInt bcols=c->bcols;
383: #if defined(PETSC_USE_CTABLE)
384: PetscTable colmap=NULL;
385: #else
386: PetscInt *colmap=NULL; /* local col number of off-diag col */
387: #endif
390: if (ctype == IS_COLORING_LOCAL) {
391: if (!map) SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_ARG_INCOMP,"When using ghosted differencing matrix must have local to global mapping provided with MatSetLocalToGlobalMapping");
392: ISLocalToGlobalMappingGetIndices(map,<og);
393: }
395: MatGetBlockSize(mat,&bs);
396: PetscObjectBaseTypeCompare((PetscObject)mat,MATMPIBAIJ,&isBAIJ);
397: PetscObjectTypeCompare((PetscObject)mat,MATMPISELL,&isSELL);
398: if (isBAIJ) {
399: Mat_MPIBAIJ *baij=(Mat_MPIBAIJ*)mat->data;
400: Mat_SeqBAIJ *spA,*spB;
401: A = baij->A; spA = (Mat_SeqBAIJ*)A->data; A_val = spA->a;
402: B = baij->B; spB = (Mat_SeqBAIJ*)B->data; B_val = spB->a;
403: nz = spA->nz + spB->nz; /* total nonzero entries of mat */
404: if (!baij->colmap) {
405: MatCreateColmap_MPIBAIJ_Private(mat);
406: }
407: colmap = baij->colmap;
408: MatGetColumnIJ_SeqBAIJ_Color(A,0,PETSC_FALSE,PETSC_FALSE,&ncols,&A_ci,&A_cj,&spidxA,NULL);
409: MatGetColumnIJ_SeqBAIJ_Color(B,0,PETSC_FALSE,PETSC_FALSE,&ncols,&B_ci,&B_cj,&spidxB,NULL);
411: if (ctype == IS_COLORING_GLOBAL && c->htype[0] == 'd') { /* create vscale for storing dx */
412: PetscInt *garray;
413: PetscMalloc1(B->cmap->n,&garray);
414: for (i=0; i<baij->B->cmap->n/bs; i++) {
415: for (j=0; j<bs; j++) {
416: garray[i*bs+j] = bs*baij->garray[i]+j;
417: }
418: }
419: VecCreateGhost(PetscObjectComm((PetscObject)mat),mat->cmap->n,PETSC_DETERMINE,B->cmap->n,garray,&c->vscale);
420: VecBindToCPU(c->vscale,PETSC_TRUE);
421: PetscFree(garray);
422: }
423: } else if (isSELL) {
424: Mat_MPISELL *sell=(Mat_MPISELL*)mat->data;
425: Mat_SeqSELL *spA,*spB;
426: A = sell->A; spA = (Mat_SeqSELL*)A->data; A_val = spA->val;
427: B = sell->B; spB = (Mat_SeqSELL*)B->data; B_val = spB->val;
428: nz = spA->nz + spB->nz; /* total nonzero entries of mat */
429: if (!sell->colmap) {
430: /* Allow access to data structures of local part of matrix
431: - creates aij->colmap which maps global column number to local number in part B */
432: MatCreateColmap_MPISELL_Private(mat);
433: }
434: colmap = sell->colmap;
435: MatGetColumnIJ_SeqSELL_Color(A,0,PETSC_FALSE,PETSC_FALSE,&ncols,&A_ci,&A_cj,&spidxA,NULL);
436: MatGetColumnIJ_SeqSELL_Color(B,0,PETSC_FALSE,PETSC_FALSE,&ncols,&B_ci,&B_cj,&spidxB,NULL);
438: bs = 1; /* only bs=1 is supported for non MPIBAIJ matrix */
440: if (ctype == IS_COLORING_GLOBAL && c->htype[0] == 'd') { /* create vscale for storing dx */
441: VecCreateGhost(PetscObjectComm((PetscObject)mat),mat->cmap->n,PETSC_DETERMINE,B->cmap->n,sell->garray,&c->vscale);
442: VecBindToCPU(c->vscale,PETSC_TRUE);
443: }
444: } else {
445: Mat_MPIAIJ *aij=(Mat_MPIAIJ*)mat->data;
446: Mat_SeqAIJ *spA,*spB;
447: A = aij->A; spA = (Mat_SeqAIJ*)A->data; A_val = spA->a;
448: B = aij->B; spB = (Mat_SeqAIJ*)B->data; B_val = spB->a;
449: nz = spA->nz + spB->nz; /* total nonzero entries of mat */
450: if (!aij->colmap) {
451: /* Allow access to data structures of local part of matrix
452: - creates aij->colmap which maps global column number to local number in part B */
453: MatCreateColmap_MPIAIJ_Private(mat);
454: }
455: colmap = aij->colmap;
456: MatGetColumnIJ_SeqAIJ_Color(A,0,PETSC_FALSE,PETSC_FALSE,&ncols,&A_ci,&A_cj,&spidxA,NULL);
457: MatGetColumnIJ_SeqAIJ_Color(B,0,PETSC_FALSE,PETSC_FALSE,&ncols,&B_ci,&B_cj,&spidxB,NULL);
459: bs = 1; /* only bs=1 is supported for non MPIBAIJ matrix */
461: if (ctype == IS_COLORING_GLOBAL && c->htype[0] == 'd') { /* create vscale for storing dx */
462: VecCreateGhost(PetscObjectComm((PetscObject)mat),mat->cmap->n,PETSC_DETERMINE,B->cmap->n,aij->garray,&c->vscale);
463: VecBindToCPU(c->vscale,PETSC_TRUE);
464: }
465: }
467: m = mat->rmap->n/bs;
468: cstart = mat->cmap->rstart/bs;
469: cend = mat->cmap->rend/bs;
471: PetscMalloc2(nis,&c->ncolumns,nis,&c->columns);
472: PetscMalloc1(nis,&c->nrows);
473: PetscLogObjectMemory((PetscObject)c,3*nis*sizeof(PetscInt));
475: if (c->htype[0] == 'd') {
476: PetscMalloc1(nz,&Jentry);
477: PetscLogObjectMemory((PetscObject)c,nz*sizeof(MatEntry));
478: c->matentry = Jentry;
479: } else if (c->htype[0] == 'w') {
480: PetscMalloc1(nz,&Jentry2);
481: PetscLogObjectMemory((PetscObject)c,nz*sizeof(MatEntry2));
482: c->matentry2 = Jentry2;
483: } else SETERRQ(PetscObjectComm((PetscObject)mat),PETSC_ERR_SUP,"htype is not supported");
485: PetscMalloc2(m+1,&rowhit,m+1,&valaddrhit);
486: nz = 0;
487: ISColoringGetIS(iscoloring,PETSC_OWN_POINTER, PETSC_IGNORE,&c->isa);
489: if (ctype == IS_COLORING_GLOBAL) {
490: MPI_Comm_size(PetscObjectComm((PetscObject)mat),&size);
491: PetscMalloc2(size,&ncolsonproc,size,&disp);
492: }
494: for (i=0; i<nis; i++) { /* for each local color */
495: ISGetLocalSize(c->isa[i],&n);
496: ISGetIndices(c->isa[i],&is);
498: c->ncolumns[i] = n; /* local number of columns of this color on this process */
499: c->columns[i] = (PetscInt*)is;
501: if (ctype == IS_COLORING_GLOBAL) {
502: /* Determine nctot, the total (parallel) number of columns of this color */
503: /* ncolsonproc[j]: local ncolumns on proc[j] of this color */
504: PetscMPIIntCast(n,&nn);
505: MPI_Allgather(&nn,1,MPI_INT,ncolsonproc,1,MPI_INT,PetscObjectComm((PetscObject)mat));
506: nctot = 0; for (j=0; j<size; j++) nctot += ncolsonproc[j];
507: if (!nctot) {
508: PetscInfo(mat,"Coloring of matrix has some unneeded colors with no corresponding rows\n");
509: }
511: disp[0] = 0;
512: for (j=1; j<size; j++) {
513: disp[j] = disp[j-1] + ncolsonproc[j-1];
514: }
516: /* Get cols, the complete list of columns for this color on each process */
517: PetscMalloc1(nctot+1,&cols);
518: MPI_Allgatherv((void*)is,n,MPIU_INT,cols,ncolsonproc,disp,MPIU_INT,PetscObjectComm((PetscObject)mat));
519: } else if (ctype == IS_COLORING_LOCAL) {
520: /* Determine local number of columns of this color on this process, including ghost points */
521: nctot = n;
522: cols = (PetscInt*)is;
523: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not provided for this MatFDColoring type");
525: /* Mark all rows affect by these columns */
526: PetscArrayzero(rowhit,m);
527: bs2 = bs*bs;
528: nrows_i = 0;
529: for (j=0; j<nctot; j++) { /* loop over columns*/
530: if (ctype == IS_COLORING_LOCAL) {
531: col = ltog[cols[j]];
532: } else {
533: col = cols[j];
534: }
535: if (col >= cstart && col < cend) { /* column is in A, diagonal block of mat */
536: tmp = A_ci[col-cstart];
537: row = A_cj + tmp;
538: nrows = A_ci[col-cstart+1] - tmp;
539: nrows_i += nrows;
541: /* loop over columns of A marking them in rowhit */
542: for (k=0; k<nrows; k++) {
543: /* set valaddrhit for part A */
544: spidx = bs2*spidxA[tmp + k];
545: valaddrhit[*row] = &A_val[spidx];
546: rowhit[*row++] = col - cstart + 1; /* local column index */
547: }
548: } else { /* column is in B, off-diagonal block of mat */
549: #if defined(PETSC_USE_CTABLE)
550: PetscTableFind(colmap,col+1,&colb);
551: colb--;
552: #else
553: colb = colmap[col] - 1; /* local column index */
554: #endif
555: if (colb == -1) {
556: nrows = 0;
557: } else {
558: colb = colb/bs;
559: tmp = B_ci[colb];
560: row = B_cj + tmp;
561: nrows = B_ci[colb+1] - tmp;
562: }
563: nrows_i += nrows;
564: /* loop over columns of B marking them in rowhit */
565: for (k=0; k<nrows; k++) {
566: /* set valaddrhit for part B */
567: spidx = bs2*spidxB[tmp + k];
568: valaddrhit[*row] = &B_val[spidx];
569: rowhit[*row++] = colb + 1 + cend - cstart; /* local column index */
570: }
571: }
572: }
573: c->nrows[i] = nrows_i;
575: if (c->htype[0] == 'd') {
576: for (j=0; j<m; j++) {
577: if (rowhit[j]) {
578: Jentry[nz].row = j; /* local row index */
579: Jentry[nz].col = rowhit[j] - 1; /* local column index */
580: Jentry[nz].valaddr = valaddrhit[j]; /* address of mat value for this entry */
581: nz++;
582: }
583: }
584: } else { /* c->htype == 'wp' */
585: for (j=0; j<m; j++) {
586: if (rowhit[j]) {
587: Jentry2[nz].row = j; /* local row index */
588: Jentry2[nz].valaddr = valaddrhit[j]; /* address of mat value for this entry */
589: nz++;
590: }
591: }
592: }
593: if (ctype == IS_COLORING_GLOBAL) {
594: PetscFree(cols);
595: }
596: }
597: if (ctype == IS_COLORING_GLOBAL) {
598: PetscFree2(ncolsonproc,disp);
599: }
601: if (bcols > 1) { /* reorder Jentry for faster MatFDColoringApply() */
602: MatFDColoringSetUpBlocked_AIJ_Private(mat,c,nz);
603: }
605: if (isBAIJ) {
606: MatRestoreColumnIJ_SeqBAIJ_Color(A,0,PETSC_FALSE,PETSC_FALSE,&ncols,&A_ci,&A_cj,&spidxA,NULL);
607: MatRestoreColumnIJ_SeqBAIJ_Color(B,0,PETSC_FALSE,PETSC_FALSE,&ncols,&B_ci,&B_cj,&spidxB,NULL);
608: PetscMalloc1(bs*mat->rmap->n,&c->dy);
609: } else if (isSELL) {
610: MatRestoreColumnIJ_SeqSELL_Color(A,0,PETSC_FALSE,PETSC_FALSE,&ncols,&A_ci,&A_cj,&spidxA,NULL);
611: MatRestoreColumnIJ_SeqSELL_Color(B,0,PETSC_FALSE,PETSC_FALSE,&ncols,&B_ci,&B_cj,&spidxB,NULL);
612: } else {
613: MatRestoreColumnIJ_SeqAIJ_Color(A,0,PETSC_FALSE,PETSC_FALSE,&ncols,&A_ci,&A_cj,&spidxA,NULL);
614: MatRestoreColumnIJ_SeqAIJ_Color(B,0,PETSC_FALSE,PETSC_FALSE,&ncols,&B_ci,&B_cj,&spidxB,NULL);
615: }
617: ISColoringRestoreIS(iscoloring,PETSC_OWN_POINTER,&c->isa);
618: PetscFree2(rowhit,valaddrhit);
620: if (ctype == IS_COLORING_LOCAL) {
621: ISLocalToGlobalMappingRestoreIndices(map,<og);
622: }
623: PetscInfo3(c,"ncolors %D, brows %D and bcols %D are used.\n",c->ncolors,c->brows,c->bcols);
624: return(0);
625: }
627: PetscErrorCode MatFDColoringCreate_MPIXAIJ(Mat mat,ISColoring iscoloring,MatFDColoring c)
628: {
630: PetscInt bs,nis=iscoloring->n,m=mat->rmap->n;
631: PetscBool isBAIJ,isSELL;
634: /* set default brows and bcols for speedup inserting the dense matrix into sparse Jacobian;
635: bcols is chosen s.t. dy-array takes 50% of memory space as mat */
636: MatGetBlockSize(mat,&bs);
637: PetscObjectBaseTypeCompare((PetscObject)mat,MATMPIBAIJ,&isBAIJ);
638: PetscObjectTypeCompare((PetscObject)mat,MATMPISELL,&isSELL);
639: if (isBAIJ || m == 0) {
640: c->brows = m;
641: c->bcols = 1;
642: } else if (isSELL) {
643: /* bcols is chosen s.t. dy-array takes 50% of local memory space as mat */
644: Mat_MPISELL *sell=(Mat_MPISELL*)mat->data;
645: Mat_SeqSELL *spA,*spB;
646: Mat A,B;
647: PetscInt nz,brows,bcols;
648: PetscReal mem;
650: bs = 1; /* only bs=1 is supported for MPISELL matrix */
652: A = sell->A; spA = (Mat_SeqSELL*)A->data;
653: B = sell->B; spB = (Mat_SeqSELL*)B->data;
654: nz = spA->nz + spB->nz; /* total local nonzero entries of mat */
655: mem = nz*(sizeof(PetscScalar) + sizeof(PetscInt)) + 3*m*sizeof(PetscInt);
656: bcols = (PetscInt)(0.5*mem /(m*sizeof(PetscScalar)));
657: brows = 1000/bcols;
658: if (bcols > nis) bcols = nis;
659: if (brows == 0 || brows > m) brows = m;
660: c->brows = brows;
661: c->bcols = bcols;
662: } else { /* mpiaij matrix */
663: /* bcols is chosen s.t. dy-array takes 50% of local memory space as mat */
664: Mat_MPIAIJ *aij=(Mat_MPIAIJ*)mat->data;
665: Mat_SeqAIJ *spA,*spB;
666: Mat A,B;
667: PetscInt nz,brows,bcols;
668: PetscReal mem;
670: bs = 1; /* only bs=1 is supported for MPIAIJ matrix */
672: A = aij->A; spA = (Mat_SeqAIJ*)A->data;
673: B = aij->B; spB = (Mat_SeqAIJ*)B->data;
674: nz = spA->nz + spB->nz; /* total local nonzero entries of mat */
675: mem = nz*(sizeof(PetscScalar) + sizeof(PetscInt)) + 3*m*sizeof(PetscInt);
676: bcols = (PetscInt)(0.5*mem /(m*sizeof(PetscScalar)));
677: brows = 1000/bcols;
678: if (bcols > nis) bcols = nis;
679: if (brows == 0 || brows > m) brows = m;
680: c->brows = brows;
681: c->bcols = bcols;
682: }
684: c->M = mat->rmap->N/bs; /* set the global rows and columns and local rows */
685: c->N = mat->cmap->N/bs;
686: c->m = mat->rmap->n/bs;
687: c->rstart = mat->rmap->rstart/bs;
688: c->ncolors = nis;
689: return(0);
690: }
692: /*@C
694: MatFDColoringSetValues - takes a matrix in compressed color format and enters the matrix into a PETSc Mat
696: Collective on J
698: Input Parameters:
699: + J - the sparse matrix
700: . coloring - created with MatFDColoringCreate() and a local coloring
701: - y - column major storage of matrix values with one color of values per column, the number of rows of y should match
702: the number of local rows of J and the number of columns is the number of colors.
704: Level: intermediate
706: Notes: the matrix in compressed color format may come from an AD code
708: The code will be slightly faster if MatFDColoringSetBlockSize(coloring,PETSC_DEFAULT,nc); is called immediately after creating the coloring
710: .seealso: MatFDColoringCreate(), ISColoring, ISColoringCreate(), ISColoringSetType(), IS_COLORING_LOCAL, MatFDColoringSetBlockSize()
712: @*/
713: PetscErrorCode MatFDColoringSetValues(Mat J,MatFDColoring coloring,const PetscScalar *y)
714: {
715: PetscErrorCode ierr;
716: MatEntry2 *Jentry2;
717: PetscInt row,i,nrows_k,l,ncolors,nz = 0,bcols,nbcols = 0;
718: const PetscInt *nrows;
719: PetscBool eq;
724: PetscObjectCompareId((PetscObject)J,coloring->matid,&eq);
725: if (!eq) SETERRQ(PetscObjectComm((PetscObject)J),PETSC_ERR_ARG_WRONG,"Matrix used with MatFDColoringSetValues() must be that used with MatFDColoringCreate()");
726: Jentry2 = coloring->matentry2;
727: nrows = coloring->nrows;
728: ncolors = coloring->ncolors;
729: bcols = coloring->bcols;
731: for (i=0; i<ncolors; i+=bcols) {
732: nrows_k = nrows[nbcols++];
733: for (l=0; l<nrows_k; l++) {
734: row = Jentry2[nz].row; /* local row index */
735: *(Jentry2[nz++].valaddr) = y[row];
736: }
737: y += bcols*coloring->m;
738: }
739: MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
740: MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
741: return(0);
742: }