1 2 /* 3 Defines the basic matrix operations for the AIJ (compressed row) 4 matrix storage format. 5 */ 6 7 #include "src/mat/impls/aij/seq/aij.h" /*I "petscmat.h" I*/ 8 #include "src/inline/spops.h" 9 #include "src/inline/dot.h" 10 #include "petscbt.h" 11 12 #undef __FUNCT__ 13 #define __FUNCT__ "MatGetRowIJ_SeqAIJ" 14 int MatGetRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *m,int *ia[],int *ja[],PetscTruth *done) 15 { 16 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 17 int ierr,i,ishift; 18 19 PetscFunctionBegin; 20 *m = A->m; 21 if (!ia) PetscFunctionReturn(0); 22 ishift = 0; 23 if (symmetric && !A->structurally_symmetric) { 24 ierr = MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);CHKERRQ(ierr); 25 } else if (oshift == 1) { 26 int nz = a->i[A->m]; 27 /* malloc space and add 1 to i and j indices */ 28 ierr = PetscMalloc((A->m+1)*sizeof(int),ia);CHKERRQ(ierr); 29 ierr = PetscMalloc((nz+1)*sizeof(int),ja);CHKERRQ(ierr); 30 for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1; 31 for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1; 32 } else { 33 *ia = a->i; *ja = a->j; 34 } 35 PetscFunctionReturn(0); 36 } 37 38 #undef __FUNCT__ 39 #define __FUNCT__ "MatRestoreRowIJ_SeqAIJ" 40 int MatRestoreRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int *ia[],int *ja[],PetscTruth *done) 41 { 42 int ierr; 43 44 PetscFunctionBegin; 45 if (!ia) PetscFunctionReturn(0); 46 if ((symmetric && !A->structurally_symmetric) || oshift == 1) { 47 ierr = PetscFree(*ia);CHKERRQ(ierr); 48 ierr = PetscFree(*ja);CHKERRQ(ierr); 49 } 50 PetscFunctionReturn(0); 51 } 52 53 #undef __FUNCT__ 54 #define __FUNCT__ "MatGetColumnIJ_SeqAIJ" 55 int MatGetColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *nn,int *ia[],int *ja[],PetscTruth *done) 56 { 57 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 58 int ierr,i,*collengths,*cia,*cja,n = A->n,m = A->m; 59 int nz = a->i[m],row,*jj,mr,col; 60 61 PetscFunctionBegin; 62 *nn = A->n; 63 if (!ia) PetscFunctionReturn(0); 64 if (symmetric) { 65 ierr = MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,0,oshift,ia,ja);CHKERRQ(ierr); 66 } else { 67 ierr = PetscMalloc((n+1)*sizeof(int),&collengths);CHKERRQ(ierr); 68 ierr = PetscMemzero(collengths,n*sizeof(int));CHKERRQ(ierr); 69 ierr = PetscMalloc((n+1)*sizeof(int),&cia);CHKERRQ(ierr); 70 ierr = PetscMalloc((nz+1)*sizeof(int),&cja);CHKERRQ(ierr); 71 jj = a->j; 72 for (i=0; i<nz; i++) { 73 collengths[jj[i]]++; 74 } 75 cia[0] = oshift; 76 for (i=0; i<n; i++) { 77 cia[i+1] = cia[i] + collengths[i]; 78 } 79 ierr = PetscMemzero(collengths,n*sizeof(int));CHKERRQ(ierr); 80 jj = a->j; 81 for (row=0; row<m; row++) { 82 mr = a->i[row+1] - a->i[row]; 83 for (i=0; i<mr; i++) { 84 col = *jj++; 85 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 86 } 87 } 88 ierr = PetscFree(collengths);CHKERRQ(ierr); 89 *ia = cia; *ja = cja; 90 } 91 PetscFunctionReturn(0); 92 } 93 94 #undef __FUNCT__ 95 #define __FUNCT__ "MatRestoreColumnIJ_SeqAIJ" 96 int MatRestoreColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int *ia[],int *ja[],PetscTruth *done) 97 { 98 int ierr; 99 100 PetscFunctionBegin; 101 if (!ia) PetscFunctionReturn(0); 102 103 ierr = PetscFree(*ia);CHKERRQ(ierr); 104 ierr = PetscFree(*ja);CHKERRQ(ierr); 105 106 PetscFunctionReturn(0); 107 } 108 109 #define CHUNKSIZE 15 110 111 #undef __FUNCT__ 112 #define __FUNCT__ "MatSetValues_SeqAIJ" 113 int MatSetValues_SeqAIJ(Mat A,int m,const int im[],int n,const int in[],const PetscScalar v[],InsertMode is) 114 { 115 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 116 int *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,sorted = a->sorted; 117 int *imax = a->imax,*ai = a->i,*ailen = a->ilen; 118 int *aj = a->j,nonew = a->nonew,ierr; 119 PetscScalar *ap,value,*aa = a->a; 120 PetscTruth ignorezeroentries = ((a->ignorezeroentries && is == ADD_VALUES) ? PETSC_TRUE:PETSC_FALSE); 121 PetscTruth roworiented = a->roworiented; 122 123 PetscFunctionBegin; 124 for (k=0; k<m; k++) { /* loop over added rows */ 125 row = im[k]; 126 if (row < 0) continue; 127 #if defined(PETSC_USE_BOPT_g) 128 if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m-1); 129 #endif 130 rp = aj + ai[row]; ap = aa + ai[row]; 131 rmax = imax[row]; nrow = ailen[row]; 132 low = 0; 133 for (l=0; l<n; l++) { /* loop over added columns */ 134 if (in[l] < 0) continue; 135 #if defined(PETSC_USE_BOPT_g) 136 if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n-1); 137 #endif 138 col = in[l]; 139 if (roworiented) { 140 value = v[l + k*n]; 141 } else { 142 value = v[k + l*m]; 143 } 144 if (value == 0.0 && ignorezeroentries) continue; 145 146 if (!sorted) low = 0; high = nrow; 147 while (high-low > 5) { 148 t = (low+high)/2; 149 if (rp[t] > col) high = t; 150 else low = t; 151 } 152 for (i=low; i<high; i++) { 153 if (rp[i] > col) break; 154 if (rp[i] == col) { 155 if (is == ADD_VALUES) ap[i] += value; 156 else ap[i] = value; 157 goto noinsert; 158 } 159 } 160 if (nonew == 1) goto noinsert; 161 else if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix",row,col); 162 if (nrow >= rmax) { 163 /* there is no extra room in row, therefore enlarge */ 164 int new_nz = ai[A->m] + CHUNKSIZE,*new_i,*new_j; 165 size_t len; 166 PetscScalar *new_a; 167 168 if (nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix requiring new malloc()",row,col); 169 170 /* malloc new storage space */ 171 len = ((size_t) new_nz)*(sizeof(int)+sizeof(PetscScalar))+(A->m+1)*sizeof(int); 172 ierr = PetscMalloc(len,&new_a);CHKERRQ(ierr); 173 new_j = (int*)(new_a + new_nz); 174 new_i = new_j + new_nz; 175 176 /* copy over old data into new slots */ 177 for (ii=0; ii<row+1; ii++) {new_i[ii] = ai[ii];} 178 for (ii=row+1; ii<A->m+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;} 179 ierr = PetscMemcpy(new_j,aj,(ai[row]+nrow)*sizeof(int));CHKERRQ(ierr); 180 len = (((size_t) new_nz) - CHUNKSIZE - ai[row] - nrow ); 181 ierr = PetscMemcpy(new_j+ai[row]+nrow+CHUNKSIZE,aj+ai[row]+nrow,len*sizeof(int));CHKERRQ(ierr); 182 ierr = PetscMemcpy(new_a,aa,(((size_t) ai[row])+nrow)*sizeof(PetscScalar));CHKERRQ(ierr); 183 ierr = PetscMemcpy(new_a+ai[row]+nrow+CHUNKSIZE,aa+ai[row]+nrow,len*sizeof(PetscScalar));CHKERRQ(ierr); 184 /* free up old matrix storage */ 185 ierr = PetscFree(a->a);CHKERRQ(ierr); 186 if (!a->singlemalloc) { 187 ierr = PetscFree(a->i);CHKERRQ(ierr); 188 ierr = PetscFree(a->j);CHKERRQ(ierr); 189 } 190 aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j; 191 a->singlemalloc = PETSC_TRUE; 192 193 rp = aj + ai[row]; ap = aa + ai[row] ; 194 rmax = imax[row] = imax[row] + CHUNKSIZE; 195 PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + sizeof(PetscScalar))); 196 a->maxnz += CHUNKSIZE; 197 a->reallocs++; 198 } 199 N = nrow++ - 1; a->nz++; 200 /* shift up all the later entries in this row */ 201 for (ii=N; ii>=i; ii--) { 202 rp[ii+1] = rp[ii]; 203 ap[ii+1] = ap[ii]; 204 } 205 rp[i] = col; 206 ap[i] = value; 207 noinsert:; 208 low = i + 1; 209 } 210 ailen[row] = nrow; 211 } 212 PetscFunctionReturn(0); 213 } 214 215 #undef __FUNCT__ 216 #define __FUNCT__ "MatGetValues_SeqAIJ" 217 int MatGetValues_SeqAIJ(Mat A,int m,const int im[],int n,const int in[],PetscScalar v[]) 218 { 219 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 220 int *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j; 221 int *ai = a->i,*ailen = a->ilen; 222 PetscScalar *ap,*aa = a->a,zero = 0.0; 223 224 PetscFunctionBegin; 225 for (k=0; k<m; k++) { /* loop over rows */ 226 row = im[k]; 227 if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",row); 228 if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m-1); 229 rp = aj + ai[row]; ap = aa + ai[row]; 230 nrow = ailen[row]; 231 for (l=0; l<n; l++) { /* loop over columns */ 232 if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",in[l]); 233 if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n-1); 234 col = in[l] ; 235 high = nrow; low = 0; /* assume unsorted */ 236 while (high-low > 5) { 237 t = (low+high)/2; 238 if (rp[t] > col) high = t; 239 else low = t; 240 } 241 for (i=low; i<high; i++) { 242 if (rp[i] > col) break; 243 if (rp[i] == col) { 244 *v++ = ap[i]; 245 goto finished; 246 } 247 } 248 *v++ = zero; 249 finished:; 250 } 251 } 252 PetscFunctionReturn(0); 253 } 254 255 256 #undef __FUNCT__ 257 #define __FUNCT__ "MatView_SeqAIJ_Binary" 258 int MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer) 259 { 260 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 261 int i,fd,*col_lens,ierr; 262 263 PetscFunctionBegin; 264 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 265 ierr = PetscMalloc((4+A->m)*sizeof(int),&col_lens);CHKERRQ(ierr); 266 col_lens[0] = MAT_FILE_COOKIE; 267 col_lens[1] = A->m; 268 col_lens[2] = A->n; 269 col_lens[3] = a->nz; 270 271 /* store lengths of each row and write (including header) to file */ 272 for (i=0; i<A->m; i++) { 273 col_lens[4+i] = a->i[i+1] - a->i[i]; 274 } 275 ierr = PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,1);CHKERRQ(ierr); 276 ierr = PetscFree(col_lens);CHKERRQ(ierr); 277 278 /* store column indices (zero start index) */ 279 ierr = PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,0);CHKERRQ(ierr); 280 281 /* store nonzero values */ 282 ierr = PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,0);CHKERRQ(ierr); 283 PetscFunctionReturn(0); 284 } 285 286 extern int MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer); 287 288 #undef __FUNCT__ 289 #define __FUNCT__ "MatView_SeqAIJ_ASCII" 290 int MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer) 291 { 292 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 293 int ierr,i,j,m = A->m,shift=0; 294 char *name; 295 PetscViewerFormat format; 296 297 PetscFunctionBegin; 298 ierr = PetscObjectGetName((PetscObject)A,&name);CHKERRQ(ierr); 299 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 300 if (format == PETSC_VIEWER_ASCII_INFO_DETAIL || format == PETSC_VIEWER_ASCII_INFO) { 301 if (a->inode.size) { 302 ierr = PetscViewerASCIIPrintf(viewer,"using I-node routines: found %d nodes, limit used is %d\n",a->inode.node_count,a->inode.limit);CHKERRQ(ierr); 303 } else { 304 ierr = PetscViewerASCIIPrintf(viewer,"not using I-node routines\n");CHKERRQ(ierr); 305 } 306 } else if (format == PETSC_VIEWER_ASCII_MATLAB) { 307 int nofinalvalue = 0; 308 if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->n-!shift)) { 309 nofinalvalue = 1; 310 } 311 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 312 ierr = PetscViewerASCIIPrintf(viewer,"%% Size = %d %d \n",m,A->n);CHKERRQ(ierr); 313 ierr = PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %d \n",a->nz);CHKERRQ(ierr); 314 ierr = PetscViewerASCIIPrintf(viewer,"zzz = zeros(%d,3);\n",a->nz+nofinalvalue);CHKERRQ(ierr); 315 ierr = PetscViewerASCIIPrintf(viewer,"zzz = [\n");CHKERRQ(ierr); 316 317 for (i=0; i<m; i++) { 318 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 319 #if defined(PETSC_USE_COMPLEX) 320 ierr = PetscViewerASCIIPrintf(viewer,"%d %d %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 321 #else 322 ierr = PetscViewerASCIIPrintf(viewer,"%d %d %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);CHKERRQ(ierr); 323 #endif 324 } 325 } 326 if (nofinalvalue) { 327 ierr = PetscViewerASCIIPrintf(viewer,"%d %d %18.16e\n",m,A->n,0.0);CHKERRQ(ierr); 328 } 329 ierr = PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);CHKERRQ(ierr); 330 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 331 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) { 332 PetscFunctionReturn(0); 333 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 334 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 335 for (i=0; i<m; i++) { 336 ierr = PetscViewerASCIIPrintf(viewer,"row %d:",i);CHKERRQ(ierr); 337 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 338 #if defined(PETSC_USE_COMPLEX) 339 if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) { 340 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 341 } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) { 342 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 343 } else if (PetscRealPart(a->a[j]) != 0.0) { 344 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));CHKERRQ(ierr); 345 } 346 #else 347 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);CHKERRQ(ierr);} 348 #endif 349 } 350 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 351 } 352 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 353 } else if (format == PETSC_VIEWER_ASCII_SYMMODU) { 354 int nzd=0,fshift=1,*sptr; 355 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 356 ierr = PetscMalloc((m+1)*sizeof(int),&sptr);CHKERRQ(ierr); 357 for (i=0; i<m; i++) { 358 sptr[i] = nzd+1; 359 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 360 if (a->j[j] >= i) { 361 #if defined(PETSC_USE_COMPLEX) 362 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++; 363 #else 364 if (a->a[j] != 0.0) nzd++; 365 #endif 366 } 367 } 368 } 369 sptr[m] = nzd+1; 370 ierr = PetscViewerASCIIPrintf(viewer," %d %d\n\n",m,nzd);CHKERRQ(ierr); 371 for (i=0; i<m+1; i+=6) { 372 if (i+4<m) {ierr = 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]);CHKERRQ(ierr);} 373 else if (i+3<m) {ierr = PetscViewerASCIIPrintf(viewer," %d %d %d %d %d\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);CHKERRQ(ierr);} 374 else if (i+2<m) {ierr = PetscViewerASCIIPrintf(viewer," %d %d %d %d\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);CHKERRQ(ierr);} 375 else if (i+1<m) {ierr = PetscViewerASCIIPrintf(viewer," %d %d %d\n",sptr[i],sptr[i+1],sptr[i+2]);CHKERRQ(ierr);} 376 else if (i<m) {ierr = PetscViewerASCIIPrintf(viewer," %d %d\n",sptr[i],sptr[i+1]);CHKERRQ(ierr);} 377 else {ierr = PetscViewerASCIIPrintf(viewer," %d\n",sptr[i]);CHKERRQ(ierr);} 378 } 379 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 380 ierr = PetscFree(sptr);CHKERRQ(ierr); 381 for (i=0; i<m; i++) { 382 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 383 if (a->j[j] >= i) {ierr = PetscViewerASCIIPrintf(viewer," %d ",a->j[j]+fshift);CHKERRQ(ierr);} 384 } 385 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 386 } 387 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 388 for (i=0; i<m; i++) { 389 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 390 if (a->j[j] >= i) { 391 #if defined(PETSC_USE_COMPLEX) 392 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) { 393 ierr = PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 394 } 395 #else 396 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);CHKERRQ(ierr);} 397 #endif 398 } 399 } 400 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 401 } 402 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 403 } else if (format == PETSC_VIEWER_ASCII_DENSE) { 404 int cnt = 0,jcnt; 405 PetscScalar value; 406 407 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 408 for (i=0; i<m; i++) { 409 jcnt = 0; 410 for (j=0; j<A->n; j++) { 411 if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) { 412 value = a->a[cnt++]; 413 jcnt++; 414 } else { 415 value = 0.0; 416 } 417 #if defined(PETSC_USE_COMPLEX) 418 ierr = PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));CHKERRQ(ierr); 419 #else 420 ierr = PetscViewerASCIIPrintf(viewer," %7.5e ",value);CHKERRQ(ierr); 421 #endif 422 } 423 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 424 } 425 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 426 } else { 427 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 428 for (i=0; i<m; i++) { 429 ierr = PetscViewerASCIIPrintf(viewer,"row %d:",i);CHKERRQ(ierr); 430 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 431 #if defined(PETSC_USE_COMPLEX) 432 if (PetscImaginaryPart(a->a[j]) > 0.0) { 433 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 434 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 435 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 436 } else { 437 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));CHKERRQ(ierr); 438 } 439 #else 440 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);CHKERRQ(ierr); 441 #endif 442 } 443 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 444 } 445 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 446 } 447 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 448 PetscFunctionReturn(0); 449 } 450 451 #undef __FUNCT__ 452 #define __FUNCT__ "MatView_SeqAIJ_Draw_Zoom" 453 int MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa) 454 { 455 Mat A = (Mat) Aa; 456 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 457 int ierr,i,j,m = A->m,color; 458 PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0; 459 PetscViewer viewer; 460 PetscViewerFormat format; 461 462 PetscFunctionBegin; 463 ierr = PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);CHKERRQ(ierr); 464 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 465 466 ierr = PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);CHKERRQ(ierr); 467 /* loop over matrix elements drawing boxes */ 468 469 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 470 /* Blue for negative, Cyan for zero and Red for positive */ 471 color = PETSC_DRAW_BLUE; 472 for (i=0; i<m; i++) { 473 y_l = m - i - 1.0; y_r = y_l + 1.0; 474 for (j=a->i[i]; j<a->i[i+1]; j++) { 475 x_l = a->j[j] ; x_r = x_l + 1.0; 476 #if defined(PETSC_USE_COMPLEX) 477 if (PetscRealPart(a->a[j]) >= 0.) continue; 478 #else 479 if (a->a[j] >= 0.) continue; 480 #endif 481 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 482 } 483 } 484 color = PETSC_DRAW_CYAN; 485 for (i=0; i<m; i++) { 486 y_l = m - i - 1.0; y_r = y_l + 1.0; 487 for (j=a->i[i]; j<a->i[i+1]; j++) { 488 x_l = a->j[j]; x_r = x_l + 1.0; 489 if (a->a[j] != 0.) continue; 490 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 491 } 492 } 493 color = PETSC_DRAW_RED; 494 for (i=0; i<m; i++) { 495 y_l = m - i - 1.0; y_r = y_l + 1.0; 496 for (j=a->i[i]; j<a->i[i+1]; j++) { 497 x_l = a->j[j]; x_r = x_l + 1.0; 498 #if defined(PETSC_USE_COMPLEX) 499 if (PetscRealPart(a->a[j]) <= 0.) continue; 500 #else 501 if (a->a[j] <= 0.) continue; 502 #endif 503 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 504 } 505 } 506 } else { 507 /* use contour shading to indicate magnitude of values */ 508 /* first determine max of all nonzero values */ 509 int nz = a->nz,count; 510 PetscDraw popup; 511 PetscReal scale; 512 513 for (i=0; i<nz; i++) { 514 if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]); 515 } 516 scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv; 517 ierr = PetscDrawGetPopup(draw,&popup);CHKERRQ(ierr); 518 if (popup) {ierr = PetscDrawScalePopup(popup,0.0,maxv);CHKERRQ(ierr);} 519 count = 0; 520 for (i=0; i<m; i++) { 521 y_l = m - i - 1.0; y_r = y_l + 1.0; 522 for (j=a->i[i]; j<a->i[i+1]; j++) { 523 x_l = a->j[j]; x_r = x_l + 1.0; 524 color = PETSC_DRAW_BASIC_COLORS + (int)(scale*PetscAbsScalar(a->a[count])); 525 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 526 count++; 527 } 528 } 529 } 530 PetscFunctionReturn(0); 531 } 532 533 #undef __FUNCT__ 534 #define __FUNCT__ "MatView_SeqAIJ_Draw" 535 int MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer) 536 { 537 int ierr; 538 PetscDraw draw; 539 PetscReal xr,yr,xl,yl,h,w; 540 PetscTruth isnull; 541 542 PetscFunctionBegin; 543 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 544 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); 545 if (isnull) PetscFunctionReturn(0); 546 547 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);CHKERRQ(ierr); 548 xr = A->n; yr = A->m; h = yr/10.0; w = xr/10.0; 549 xr += w; yr += h; xl = -w; yl = -h; 550 ierr = PetscDrawSetCoordinates(draw,xl,yl,xr,yr);CHKERRQ(ierr); 551 ierr = PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);CHKERRQ(ierr); 552 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);CHKERRQ(ierr); 553 PetscFunctionReturn(0); 554 } 555 556 #undef __FUNCT__ 557 #define __FUNCT__ "MatView_SeqAIJ" 558 int MatView_SeqAIJ(Mat A,PetscViewer viewer) 559 { 560 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 561 int ierr; 562 PetscTruth issocket,iascii,isbinary,isdraw; 563 564 PetscFunctionBegin; 565 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);CHKERRQ(ierr); 566 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);CHKERRQ(ierr); 567 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 568 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 569 if (issocket) { 570 ierr = PetscViewerSocketPutSparse_Private(viewer,A->m,A->n,a->nz,a->a,a->i,a->j);CHKERRQ(ierr); 571 } else if (iascii) { 572 ierr = MatView_SeqAIJ_ASCII(A,viewer);CHKERRQ(ierr); 573 } else if (isbinary) { 574 ierr = MatView_SeqAIJ_Binary(A,viewer);CHKERRQ(ierr); 575 } else if (isdraw) { 576 ierr = MatView_SeqAIJ_Draw(A,viewer);CHKERRQ(ierr); 577 } else { 578 SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name); 579 } 580 PetscFunctionReturn(0); 581 } 582 583 #undef __FUNCT__ 584 #define __FUNCT__ "MatAssemblyEnd_SeqAIJ" 585 int MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode) 586 { 587 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 588 int fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax,ierr; 589 int m = A->m,*ip,N,*ailen = a->ilen,rmax = 0; 590 PetscScalar *aa = a->a,*ap; 591 592 PetscFunctionBegin; 593 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 594 595 if (m) rmax = ailen[0]; /* determine row with most nonzeros */ 596 for (i=1; i<m; i++) { 597 /* move each row back by the amount of empty slots (fshift) before it*/ 598 fshift += imax[i-1] - ailen[i-1]; 599 rmax = PetscMax(rmax,ailen[i]); 600 if (fshift) { 601 ip = aj + ai[i] ; 602 ap = aa + ai[i] ; 603 N = ailen[i]; 604 for (j=0; j<N; j++) { 605 ip[j-fshift] = ip[j]; 606 ap[j-fshift] = ap[j]; 607 } 608 } 609 ai[i] = ai[i-1] + ailen[i-1]; 610 } 611 if (m) { 612 fshift += imax[m-1] - ailen[m-1]; 613 ai[m] = ai[m-1] + ailen[m-1]; 614 } 615 /* reset ilen and imax for each row */ 616 for (i=0; i<m; i++) { 617 ailen[i] = imax[i] = ai[i+1] - ai[i]; 618 } 619 a->nz = ai[m]; 620 621 /* diagonals may have moved, so kill the diagonal pointers */ 622 if (fshift && a->diag) { 623 ierr = PetscFree(a->diag);CHKERRQ(ierr); 624 PetscLogObjectMemory(A,-(m+1)*sizeof(int)); 625 a->diag = 0; 626 } 627 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Matrix size: %d X %d; storage space: %d unneeded,%d used\n",m,A->n,fshift,a->nz); 628 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Number of mallocs during MatSetValues() is %d\n",a->reallocs); 629 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Most nonzeros in any row is %d\n",rmax); 630 a->reallocs = 0; 631 A->info.nz_unneeded = (double)fshift; 632 a->rmax = rmax; 633 634 /* check out for identical nodes. If found, use inode functions */ 635 ierr = Mat_AIJ_CheckInode(A,(PetscTruth)(!fshift));CHKERRQ(ierr); 636 637 PetscFunctionReturn(0); 638 } 639 640 #undef __FUNCT__ 641 #define __FUNCT__ "MatZeroEntries_SeqAIJ" 642 int MatZeroEntries_SeqAIJ(Mat A) 643 { 644 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 645 int ierr; 646 647 PetscFunctionBegin; 648 ierr = PetscMemzero(a->a,(a->i[A->m])*sizeof(PetscScalar));CHKERRQ(ierr); 649 PetscFunctionReturn(0); 650 } 651 652 #undef __FUNCT__ 653 #define __FUNCT__ "MatDestroy_SeqAIJ" 654 int MatDestroy_SeqAIJ(Mat A) 655 { 656 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 657 int ierr; 658 659 PetscFunctionBegin; 660 #if defined(PETSC_USE_LOG) 661 PetscLogObjectState((PetscObject)A,"Rows=%d, Cols=%d, NZ=%d",A->m,A->n,a->nz); 662 #endif 663 if (a->freedata) { 664 ierr = PetscFree(a->a);CHKERRQ(ierr); 665 if (!a->singlemalloc) { 666 ierr = PetscFree(a->i);CHKERRQ(ierr); 667 ierr = PetscFree(a->j);CHKERRQ(ierr); 668 } 669 } 670 if (a->row) { 671 ierr = ISDestroy(a->row);CHKERRQ(ierr); 672 } 673 if (a->col) { 674 ierr = ISDestroy(a->col);CHKERRQ(ierr); 675 } 676 if (a->diag) {ierr = PetscFree(a->diag);CHKERRQ(ierr);} 677 if (a->ilen) {ierr = PetscFree(a->ilen);CHKERRQ(ierr);} 678 if (a->imax) {ierr = PetscFree(a->imax);CHKERRQ(ierr);} 679 if (a->idiag) {ierr = PetscFree(a->idiag);CHKERRQ(ierr);} 680 if (a->solve_work) {ierr = PetscFree(a->solve_work);CHKERRQ(ierr);} 681 if (a->inode.size) {ierr = PetscFree(a->inode.size);CHKERRQ(ierr);} 682 if (a->icol) {ierr = ISDestroy(a->icol);CHKERRQ(ierr);} 683 if (a->saved_values) {ierr = PetscFree(a->saved_values);CHKERRQ(ierr);} 684 if (a->coloring) {ierr = ISColoringDestroy(a->coloring);CHKERRQ(ierr);} 685 if (a->xtoy) {ierr = PetscFree(a->xtoy);CHKERRQ(ierr);} 686 687 ierr = PetscFree(a);CHKERRQ(ierr); 688 PetscFunctionReturn(0); 689 } 690 691 #undef __FUNCT__ 692 #define __FUNCT__ "MatCompress_SeqAIJ" 693 int MatCompress_SeqAIJ(Mat A) 694 { 695 PetscFunctionBegin; 696 PetscFunctionReturn(0); 697 } 698 699 #undef __FUNCT__ 700 #define __FUNCT__ "MatSetOption_SeqAIJ" 701 int MatSetOption_SeqAIJ(Mat A,MatOption op) 702 { 703 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 704 705 PetscFunctionBegin; 706 switch (op) { 707 case MAT_ROW_ORIENTED: 708 a->roworiented = PETSC_TRUE; 709 break; 710 case MAT_KEEP_ZEROED_ROWS: 711 a->keepzeroedrows = PETSC_TRUE; 712 break; 713 case MAT_COLUMN_ORIENTED: 714 a->roworiented = PETSC_FALSE; 715 break; 716 case MAT_COLUMNS_SORTED: 717 a->sorted = PETSC_TRUE; 718 break; 719 case MAT_COLUMNS_UNSORTED: 720 a->sorted = PETSC_FALSE; 721 break; 722 case MAT_NO_NEW_NONZERO_LOCATIONS: 723 a->nonew = 1; 724 break; 725 case MAT_NEW_NONZERO_LOCATION_ERR: 726 a->nonew = -1; 727 break; 728 case MAT_NEW_NONZERO_ALLOCATION_ERR: 729 a->nonew = -2; 730 break; 731 case MAT_YES_NEW_NONZERO_LOCATIONS: 732 a->nonew = 0; 733 break; 734 case MAT_IGNORE_ZERO_ENTRIES: 735 a->ignorezeroentries = PETSC_TRUE; 736 break; 737 case MAT_USE_INODES: 738 a->inode.use = PETSC_TRUE; 739 break; 740 case MAT_DO_NOT_USE_INODES: 741 a->inode.use = PETSC_FALSE; 742 break; 743 case MAT_ROWS_SORTED: 744 case MAT_ROWS_UNSORTED: 745 case MAT_YES_NEW_DIAGONALS: 746 case MAT_IGNORE_OFF_PROC_ENTRIES: 747 case MAT_USE_HASH_TABLE: 748 PetscLogInfo(A,"MatSetOption_SeqAIJ:Option ignored\n"); 749 break; 750 case MAT_NO_NEW_DIAGONALS: 751 SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS"); 752 case MAT_INODE_LIMIT_1: 753 a->inode.limit = 1; 754 break; 755 case MAT_INODE_LIMIT_2: 756 a->inode.limit = 2; 757 break; 758 case MAT_INODE_LIMIT_3: 759 a->inode.limit = 3; 760 break; 761 case MAT_INODE_LIMIT_4: 762 a->inode.limit = 4; 763 break; 764 case MAT_INODE_LIMIT_5: 765 a->inode.limit = 5; 766 break; 767 case MAT_SYMMETRIC: 768 case MAT_STRUCTURALLY_SYMMETRIC: 769 case MAT_NOT_SYMMETRIC: 770 case MAT_NOT_STRUCTURALLY_SYMMETRIC: 771 case MAT_HERMITIAN: 772 case MAT_NOT_HERMITIAN: 773 case MAT_SYMMETRY_ETERNAL: 774 case MAT_NOT_SYMMETRY_ETERNAL: 775 break; 776 default: 777 SETERRQ(PETSC_ERR_SUP,"unknown option"); 778 } 779 PetscFunctionReturn(0); 780 } 781 782 #undef __FUNCT__ 783 #define __FUNCT__ "MatGetDiagonal_SeqAIJ" 784 int MatGetDiagonal_SeqAIJ(Mat A,Vec v) 785 { 786 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 787 int i,j,n,ierr; 788 PetscScalar *x,zero = 0.0; 789 790 PetscFunctionBegin; 791 ierr = VecSet(&zero,v);CHKERRQ(ierr); 792 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 793 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 794 if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 795 for (i=0; i<A->m; i++) { 796 for (j=a->i[i]; j<a->i[i+1]; j++) { 797 if (a->j[j] == i) { 798 x[i] = a->a[j]; 799 break; 800 } 801 } 802 } 803 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 804 PetscFunctionReturn(0); 805 } 806 807 808 #undef __FUNCT__ 809 #define __FUNCT__ "MatMultTransposeAdd_SeqAIJ" 810 int MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy) 811 { 812 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 813 PetscScalar *x,*y; 814 int ierr,m = A->m; 815 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 816 PetscScalar *v,alpha; 817 int n,i,*idx; 818 #endif 819 820 PetscFunctionBegin; 821 if (zz != yy) {ierr = VecCopy(zz,yy);CHKERRQ(ierr);} 822 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 823 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 824 825 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 826 fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y); 827 #else 828 for (i=0; i<m; i++) { 829 idx = a->j + a->i[i] ; 830 v = a->a + a->i[i] ; 831 n = a->i[i+1] - a->i[i]; 832 alpha = x[i]; 833 while (n-->0) {y[*idx++] += alpha * *v++;} 834 } 835 #endif 836 PetscLogFlops(2*a->nz); 837 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 838 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 839 PetscFunctionReturn(0); 840 } 841 842 #undef __FUNCT__ 843 #define __FUNCT__ "MatMultTranspose_SeqAIJ" 844 int MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy) 845 { 846 PetscScalar zero = 0.0; 847 int ierr; 848 849 PetscFunctionBegin; 850 ierr = VecSet(&zero,yy);CHKERRQ(ierr); 851 ierr = MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);CHKERRQ(ierr); 852 PetscFunctionReturn(0); 853 } 854 855 856 #undef __FUNCT__ 857 #define __FUNCT__ "MatMult_SeqAIJ" 858 int MatMult_SeqAIJ(Mat A,Vec xx,Vec yy) 859 { 860 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 861 PetscScalar *x,*y,*v; 862 int ierr,m = A->m,*idx,*ii; 863 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 864 int n,i,jrow,j; 865 PetscScalar sum; 866 #endif 867 868 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 869 #pragma disjoint(*x,*y,*v) 870 #endif 871 872 PetscFunctionBegin; 873 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 874 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 875 idx = a->j; 876 v = a->a; 877 ii = a->i; 878 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 879 fortranmultaij_(&m,x,ii,idx,v,y); 880 #else 881 for (i=0; i<m; i++) { 882 jrow = ii[i]; 883 n = ii[i+1] - jrow; 884 sum = 0.0; 885 for (j=0; j<n; j++) { 886 sum += v[jrow]*x[idx[jrow]]; jrow++; 887 } 888 y[i] = sum; 889 } 890 #endif 891 PetscLogFlops(2*a->nz - m); 892 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 893 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 894 PetscFunctionReturn(0); 895 } 896 897 #undef __FUNCT__ 898 #define __FUNCT__ "MatMultAdd_SeqAIJ" 899 int MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz) 900 { 901 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 902 PetscScalar *x,*y,*z,*v; 903 int ierr,m = A->m,*idx,*ii; 904 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 905 int n,i,jrow,j; 906 PetscScalar sum; 907 #endif 908 909 PetscFunctionBegin; 910 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 911 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 912 if (zz != yy) { 913 ierr = VecGetArray(zz,&z);CHKERRQ(ierr); 914 } else { 915 z = y; 916 } 917 918 idx = a->j; 919 v = a->a; 920 ii = a->i; 921 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 922 fortranmultaddaij_(&m,x,ii,idx,v,y,z); 923 #else 924 for (i=0; i<m; i++) { 925 jrow = ii[i]; 926 n = ii[i+1] - jrow; 927 sum = y[i]; 928 for (j=0; j<n; j++) { 929 sum += v[jrow]*x[idx[jrow]]; jrow++; 930 } 931 z[i] = sum; 932 } 933 #endif 934 PetscLogFlops(2*a->nz); 935 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 936 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 937 if (zz != yy) { 938 ierr = VecRestoreArray(zz,&z);CHKERRQ(ierr); 939 } 940 PetscFunctionReturn(0); 941 } 942 943 /* 944 Adds diagonal pointers to sparse matrix structure. 945 */ 946 #undef __FUNCT__ 947 #define __FUNCT__ "MatMarkDiagonal_SeqAIJ" 948 int MatMarkDiagonal_SeqAIJ(Mat A) 949 { 950 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 951 int i,j,*diag,m = A->m,ierr; 952 953 PetscFunctionBegin; 954 if (a->diag) PetscFunctionReturn(0); 955 956 ierr = PetscMalloc((m+1)*sizeof(int),&diag);CHKERRQ(ierr); 957 PetscLogObjectMemory(A,(m+1)*sizeof(int)); 958 for (i=0; i<A->m; i++) { 959 diag[i] = a->i[i+1]; 960 for (j=a->i[i]; j<a->i[i+1]; j++) { 961 if (a->j[j] == i) { 962 diag[i] = j; 963 break; 964 } 965 } 966 } 967 a->diag = diag; 968 PetscFunctionReturn(0); 969 } 970 971 /* 972 Checks for missing diagonals 973 */ 974 #undef __FUNCT__ 975 #define __FUNCT__ "MatMissingDiagonal_SeqAIJ" 976 int MatMissingDiagonal_SeqAIJ(Mat A) 977 { 978 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 979 int *diag,*jj = a->j,i,ierr; 980 981 PetscFunctionBegin; 982 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 983 diag = a->diag; 984 for (i=0; i<A->m; i++) { 985 if (jj[diag[i]] != i) { 986 SETERRQ1(PETSC_ERR_PLIB,"Matrix is missing diagonal number %d",i); 987 } 988 } 989 PetscFunctionReturn(0); 990 } 991 992 #undef __FUNCT__ 993 #define __FUNCT__ "MatRelax_SeqAIJ" 994 int MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx) 995 { 996 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 997 PetscScalar *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag; 998 const PetscScalar *v = a->a, *b, *bs,*xb, *ts; 999 int ierr,n = A->n,m = A->m,i; 1000 const int *idx,*diag; 1001 1002 PetscFunctionBegin; 1003 its = its*lits; 1004 if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits); 1005 1006 if (!a->diag) {ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr);} 1007 diag = a->diag; 1008 if (!a->idiag) { 1009 ierr = PetscMalloc(3*m*sizeof(PetscScalar),&a->idiag);CHKERRQ(ierr); 1010 a->ssor = a->idiag + m; 1011 mdiag = a->ssor + m; 1012 1013 v = a->a; 1014 1015 /* this is wrong when fshift omega changes each iteration */ 1016 if (omega == 1.0 && !fshift) { 1017 for (i=0; i<m; i++) { 1018 mdiag[i] = v[diag[i]]; 1019 a->idiag[i] = 1.0/v[diag[i]]; 1020 } 1021 PetscLogFlops(m); 1022 } else { 1023 for (i=0; i<m; i++) { 1024 mdiag[i] = v[diag[i]]; 1025 a->idiag[i] = omega/(fshift + v[diag[i]]); 1026 } 1027 PetscLogFlops(2*m); 1028 } 1029 } 1030 t = a->ssor; 1031 idiag = a->idiag; 1032 mdiag = a->idiag + 2*m; 1033 1034 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 1035 if (xx != bb) { 1036 ierr = VecGetArray(bb,(PetscScalar**)&b);CHKERRQ(ierr); 1037 } else { 1038 b = x; 1039 } 1040 1041 /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */ 1042 xs = x; 1043 if (flag == SOR_APPLY_UPPER) { 1044 /* apply (U + D/omega) to the vector */ 1045 bs = b; 1046 for (i=0; i<m; i++) { 1047 d = fshift + a->a[diag[i]]; 1048 n = a->i[i+1] - diag[i] - 1; 1049 idx = a->j + diag[i] + 1; 1050 v = a->a + diag[i] + 1; 1051 sum = b[i]*d/omega; 1052 SPARSEDENSEDOT(sum,bs,v,idx,n); 1053 x[i] = sum; 1054 } 1055 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1056 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1057 PetscLogFlops(a->nz); 1058 PetscFunctionReturn(0); 1059 } 1060 1061 1062 /* Let A = L + U + D; where L is lower trianglar, 1063 U is upper triangular, E is diagonal; This routine applies 1064 1065 (L + E)^{-1} A (U + E)^{-1} 1066 1067 to a vector efficiently using Eisenstat's trick. This is for 1068 the case of SSOR preconditioner, so E is D/omega where omega 1069 is the relaxation factor. 1070 */ 1071 1072 if (flag == SOR_APPLY_LOWER) { 1073 SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented"); 1074 } else if (flag & SOR_EISENSTAT) { 1075 /* Let A = L + U + D; where L is lower trianglar, 1076 U is upper triangular, E is diagonal; This routine applies 1077 1078 (L + E)^{-1} A (U + E)^{-1} 1079 1080 to a vector efficiently using Eisenstat's trick. This is for 1081 the case of SSOR preconditioner, so E is D/omega where omega 1082 is the relaxation factor. 1083 */ 1084 scale = (2.0/omega) - 1.0; 1085 1086 /* x = (E + U)^{-1} b */ 1087 for (i=m-1; i>=0; i--) { 1088 n = a->i[i+1] - diag[i] - 1; 1089 idx = a->j + diag[i] + 1; 1090 v = a->a + diag[i] + 1; 1091 sum = b[i]; 1092 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1093 x[i] = sum*idiag[i]; 1094 } 1095 1096 /* t = b - (2*E - D)x */ 1097 v = a->a; 1098 for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; } 1099 1100 /* t = (E + L)^{-1}t */ 1101 ts = t; 1102 diag = a->diag; 1103 for (i=0; i<m; i++) { 1104 n = diag[i] - a->i[i]; 1105 idx = a->j + a->i[i]; 1106 v = a->a + a->i[i]; 1107 sum = t[i]; 1108 SPARSEDENSEMDOT(sum,ts,v,idx,n); 1109 t[i] = sum*idiag[i]; 1110 /* x = x + t */ 1111 x[i] += t[i]; 1112 } 1113 1114 PetscLogFlops(6*m-1 + 2*a->nz); 1115 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1116 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1117 PetscFunctionReturn(0); 1118 } 1119 if (flag & SOR_ZERO_INITIAL_GUESS) { 1120 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){ 1121 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1122 fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(int*)diag,idiag,a->a,(void*)b); 1123 #else 1124 for (i=0; i<m; i++) { 1125 n = diag[i] - a->i[i]; 1126 idx = a->j + a->i[i]; 1127 v = a->a + a->i[i]; 1128 sum = b[i]; 1129 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1130 x[i] = sum*idiag[i]; 1131 } 1132 #endif 1133 xb = x; 1134 PetscLogFlops(a->nz); 1135 } else xb = b; 1136 if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) && 1137 (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) { 1138 for (i=0; i<m; i++) { 1139 x[i] *= mdiag[i]; 1140 } 1141 PetscLogFlops(m); 1142 } 1143 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){ 1144 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1145 fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(int*)diag,idiag,a->a,(void*)xb); 1146 #else 1147 for (i=m-1; i>=0; i--) { 1148 n = a->i[i+1] - diag[i] - 1; 1149 idx = a->j + diag[i] + 1; 1150 v = a->a + diag[i] + 1; 1151 sum = xb[i]; 1152 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1153 x[i] = sum*idiag[i]; 1154 } 1155 #endif 1156 PetscLogFlops(a->nz); 1157 } 1158 its--; 1159 } 1160 while (its--) { 1161 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){ 1162 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1163 fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(int*)diag,a->a,(void*)b); 1164 #else 1165 for (i=0; i<m; i++) { 1166 d = fshift + a->a[diag[i]]; 1167 n = a->i[i+1] - a->i[i]; 1168 idx = a->j + a->i[i]; 1169 v = a->a + a->i[i]; 1170 sum = b[i]; 1171 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1172 x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i]; 1173 } 1174 #endif 1175 PetscLogFlops(a->nz); 1176 } 1177 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){ 1178 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1179 fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(int*)diag,a->a,(void*)b); 1180 #else 1181 for (i=m-1; i>=0; i--) { 1182 d = fshift + a->a[diag[i]]; 1183 n = a->i[i+1] - a->i[i]; 1184 idx = a->j + a->i[i]; 1185 v = a->a + a->i[i]; 1186 sum = b[i]; 1187 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1188 x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i]; 1189 } 1190 #endif 1191 PetscLogFlops(a->nz); 1192 } 1193 } 1194 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1195 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1196 PetscFunctionReturn(0); 1197 } 1198 1199 #undef __FUNCT__ 1200 #define __FUNCT__ "MatGetInfo_SeqAIJ" 1201 int MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info) 1202 { 1203 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1204 1205 PetscFunctionBegin; 1206 info->rows_global = (double)A->m; 1207 info->columns_global = (double)A->n; 1208 info->rows_local = (double)A->m; 1209 info->columns_local = (double)A->n; 1210 info->block_size = 1.0; 1211 info->nz_allocated = (double)a->maxnz; 1212 info->nz_used = (double)a->nz; 1213 info->nz_unneeded = (double)(a->maxnz - a->nz); 1214 info->assemblies = (double)A->num_ass; 1215 info->mallocs = (double)a->reallocs; 1216 info->memory = A->mem; 1217 if (A->factor) { 1218 info->fill_ratio_given = A->info.fill_ratio_given; 1219 info->fill_ratio_needed = A->info.fill_ratio_needed; 1220 info->factor_mallocs = A->info.factor_mallocs; 1221 } else { 1222 info->fill_ratio_given = 0; 1223 info->fill_ratio_needed = 0; 1224 info->factor_mallocs = 0; 1225 } 1226 PetscFunctionReturn(0); 1227 } 1228 1229 #undef __FUNCT__ 1230 #define __FUNCT__ "MatZeroRows_SeqAIJ" 1231 int MatZeroRows_SeqAIJ(Mat A,IS is,const PetscScalar *diag) 1232 { 1233 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1234 int i,ierr,N,*rows,m = A->m - 1; 1235 1236 PetscFunctionBegin; 1237 ierr = ISGetLocalSize(is,&N);CHKERRQ(ierr); 1238 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 1239 if (a->keepzeroedrows) { 1240 for (i=0; i<N; i++) { 1241 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]); 1242 ierr = PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));CHKERRQ(ierr); 1243 } 1244 if (diag) { 1245 ierr = MatMissingDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1246 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1247 for (i=0; i<N; i++) { 1248 a->a[a->diag[rows[i]]] = *diag; 1249 } 1250 } 1251 } else { 1252 if (diag) { 1253 for (i=0; i<N; i++) { 1254 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]); 1255 if (a->ilen[rows[i]] > 0) { 1256 a->ilen[rows[i]] = 1; 1257 a->a[a->i[rows[i]]] = *diag; 1258 a->j[a->i[rows[i]]] = rows[i]; 1259 } else { /* in case row was completely empty */ 1260 ierr = MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);CHKERRQ(ierr); 1261 } 1262 } 1263 } else { 1264 for (i=0; i<N; i++) { 1265 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]); 1266 a->ilen[rows[i]] = 0; 1267 } 1268 } 1269 } 1270 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 1271 ierr = MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1272 PetscFunctionReturn(0); 1273 } 1274 1275 #undef __FUNCT__ 1276 #define __FUNCT__ "MatGetRow_SeqAIJ" 1277 int MatGetRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v) 1278 { 1279 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1280 int *itmp; 1281 1282 PetscFunctionBegin; 1283 if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %d out of range",row); 1284 1285 *nz = a->i[row+1] - a->i[row]; 1286 if (v) *v = a->a + a->i[row]; 1287 if (idx) { 1288 itmp = a->j + a->i[row]; 1289 if (*nz) { 1290 *idx = itmp; 1291 } 1292 else *idx = 0; 1293 } 1294 PetscFunctionReturn(0); 1295 } 1296 1297 /* remove this function? */ 1298 #undef __FUNCT__ 1299 #define __FUNCT__ "MatRestoreRow_SeqAIJ" 1300 int MatRestoreRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v) 1301 { 1302 /* Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1303 int ierr; */ 1304 1305 PetscFunctionBegin; 1306 /* if (idx) {if (*idx && a->indexshift) {ierr = PetscFree(*idx);CHKERRQ(ierr);}} */ 1307 PetscFunctionReturn(0); 1308 } 1309 1310 #undef __FUNCT__ 1311 #define __FUNCT__ "MatNorm_SeqAIJ" 1312 int MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm) 1313 { 1314 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1315 PetscScalar *v = a->a; 1316 PetscReal sum = 0.0; 1317 int i,j,ierr; 1318 1319 PetscFunctionBegin; 1320 if (type == NORM_FROBENIUS) { 1321 for (i=0; i<a->nz; i++) { 1322 #if defined(PETSC_USE_COMPLEX) 1323 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1324 #else 1325 sum += (*v)*(*v); v++; 1326 #endif 1327 } 1328 *nrm = sqrt(sum); 1329 } else if (type == NORM_1) { 1330 PetscReal *tmp; 1331 int *jj = a->j; 1332 ierr = PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);CHKERRQ(ierr); 1333 ierr = PetscMemzero(tmp,A->n*sizeof(PetscReal));CHKERRQ(ierr); 1334 *nrm = 0.0; 1335 for (j=0; j<a->nz; j++) { 1336 tmp[*jj++] += PetscAbsScalar(*v); v++; 1337 } 1338 for (j=0; j<A->n; j++) { 1339 if (tmp[j] > *nrm) *nrm = tmp[j]; 1340 } 1341 ierr = PetscFree(tmp);CHKERRQ(ierr); 1342 } else if (type == NORM_INFINITY) { 1343 *nrm = 0.0; 1344 for (j=0; j<A->m; j++) { 1345 v = a->a + a->i[j]; 1346 sum = 0.0; 1347 for (i=0; i<a->i[j+1]-a->i[j]; i++) { 1348 sum += PetscAbsScalar(*v); v++; 1349 } 1350 if (sum > *nrm) *nrm = sum; 1351 } 1352 } else { 1353 SETERRQ(PETSC_ERR_SUP,"No support for two norm"); 1354 } 1355 PetscFunctionReturn(0); 1356 } 1357 1358 #undef __FUNCT__ 1359 #define __FUNCT__ "MatTranspose_SeqAIJ" 1360 int MatTranspose_SeqAIJ(Mat A,Mat *B) 1361 { 1362 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1363 Mat C; 1364 int i,ierr,*aj = a->j,*ai = a->i,m = A->m,len,*col; 1365 PetscScalar *array = a->a; 1366 1367 PetscFunctionBegin; 1368 if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place"); 1369 ierr = PetscMalloc((1+A->n)*sizeof(int),&col);CHKERRQ(ierr); 1370 ierr = PetscMemzero(col,(1+A->n)*sizeof(int));CHKERRQ(ierr); 1371 1372 for (i=0; i<ai[m]; i++) col[aj[i]] += 1; 1373 ierr = MatCreate(A->comm,A->n,m,A->n,m,&C);CHKERRQ(ierr); 1374 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1375 ierr = MatSeqAIJSetPreallocation(C,0,col);CHKERRQ(ierr); 1376 ierr = PetscFree(col);CHKERRQ(ierr); 1377 for (i=0; i<m; i++) { 1378 len = ai[i+1]-ai[i]; 1379 ierr = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);CHKERRQ(ierr); 1380 array += len; 1381 aj += len; 1382 } 1383 1384 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1385 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1386 1387 if (B) { 1388 *B = C; 1389 } else { 1390 ierr = MatHeaderCopy(A,C);CHKERRQ(ierr); 1391 } 1392 PetscFunctionReturn(0); 1393 } 1394 1395 EXTERN_C_BEGIN 1396 #undef __FUNCT__ 1397 #define __FUNCT__ "MatIsTranspose_SeqAIJ" 1398 int MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f) 1399 { 1400 Mat_SeqAIJ *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data; 1401 int *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb; 1402 int ma,na,mb,nb, i,ierr; 1403 1404 PetscFunctionBegin; 1405 bij = (Mat_SeqAIJ *) B->data; 1406 1407 ierr = MatGetSize(A,&ma,&na);CHKERRQ(ierr); 1408 ierr = MatGetSize(B,&mb,&nb);CHKERRQ(ierr); 1409 if (ma!=nb || na!=mb){ 1410 *f = PETSC_FALSE; 1411 PetscFunctionReturn(0); 1412 } 1413 aii = aij->i; bii = bij->i; 1414 adx = aij->j; bdx = bij->j; 1415 va = aij->a; vb = bij->a; 1416 ierr = PetscMalloc(ma*sizeof(int),&aptr);CHKERRQ(ierr); 1417 ierr = PetscMalloc(mb*sizeof(int),&bptr);CHKERRQ(ierr); 1418 for (i=0; i<ma; i++) aptr[i] = aii[i]; 1419 for (i=0; i<mb; i++) bptr[i] = bii[i]; 1420 1421 *f = PETSC_TRUE; 1422 for (i=0; i<ma; i++) { 1423 /*printf("row %d spans %d--%d; we start @ %d\n", 1424 i,idx[ii[i]],idx[ii[i+1]-1],idx[aptr[i]]);*/ 1425 while (aptr[i]<aii[i+1]) { 1426 int idc,idr; 1427 PetscScalar vc,vr; 1428 /* column/row index/value */ 1429 idc = adx[aptr[i]]; 1430 idr = bdx[bptr[idc]]; 1431 vc = va[aptr[i]]; 1432 vr = vb[bptr[idc]]; 1433 /*printf("comparing %d: (%d,%d)=%e to (%d,%d)=%e\n", 1434 aptr[i],i,idc,vc,idc,idr,vr);*/ 1435 if (i!=idr || PetscAbsScalar(vc-vr) > tol) { 1436 *f = PETSC_FALSE; 1437 goto done; 1438 } else { 1439 aptr[i]++; 1440 if (B || i!=idc) bptr[idc]++; 1441 } 1442 } 1443 } 1444 done: 1445 ierr = PetscFree(aptr);CHKERRQ(ierr); 1446 if (B) { 1447 ierr = PetscFree(bptr);CHKERRQ(ierr); 1448 } 1449 PetscFunctionReturn(0); 1450 } 1451 EXTERN_C_END 1452 1453 #undef __FUNCT__ 1454 #define __FUNCT__ "MatIsSymmetric_SeqAIJ" 1455 int MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f) 1456 { 1457 int ierr; 1458 PetscFunctionBegin; 1459 ierr = MatIsTranspose_SeqAIJ(A,A,tol,f);CHKERRQ(ierr); 1460 PetscFunctionReturn(0); 1461 } 1462 1463 #undef __FUNCT__ 1464 #define __FUNCT__ "MatDiagonalScale_SeqAIJ" 1465 int MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr) 1466 { 1467 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1468 PetscScalar *l,*r,x,*v; 1469 int ierr,i,j,m = A->m,n = A->n,M,nz = a->nz,*jj; 1470 1471 PetscFunctionBegin; 1472 if (ll) { 1473 /* The local size is used so that VecMPI can be passed to this routine 1474 by MatDiagonalScale_MPIAIJ */ 1475 ierr = VecGetLocalSize(ll,&m);CHKERRQ(ierr); 1476 if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length"); 1477 ierr = VecGetArray(ll,&l);CHKERRQ(ierr); 1478 v = a->a; 1479 for (i=0; i<m; i++) { 1480 x = l[i]; 1481 M = a->i[i+1] - a->i[i]; 1482 for (j=0; j<M; j++) { (*v++) *= x;} 1483 } 1484 ierr = VecRestoreArray(ll,&l);CHKERRQ(ierr); 1485 PetscLogFlops(nz); 1486 } 1487 if (rr) { 1488 ierr = VecGetLocalSize(rr,&n);CHKERRQ(ierr); 1489 if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length"); 1490 ierr = VecGetArray(rr,&r);CHKERRQ(ierr); 1491 v = a->a; jj = a->j; 1492 for (i=0; i<nz; i++) { 1493 (*v++) *= r[*jj++]; 1494 } 1495 ierr = VecRestoreArray(rr,&r);CHKERRQ(ierr); 1496 PetscLogFlops(nz); 1497 } 1498 PetscFunctionReturn(0); 1499 } 1500 1501 #undef __FUNCT__ 1502 #define __FUNCT__ "MatGetSubMatrix_SeqAIJ" 1503 int MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,int csize,MatReuse scall,Mat *B) 1504 { 1505 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c; 1506 int *smap,i,k,kstart,kend,ierr,oldcols = A->n,*lens; 1507 int row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi; 1508 int *irow,*icol,nrows,ncols; 1509 int *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen; 1510 PetscScalar *a_new,*mat_a; 1511 Mat C; 1512 PetscTruth stride; 1513 1514 PetscFunctionBegin; 1515 ierr = ISSorted(isrow,(PetscTruth*)&i);CHKERRQ(ierr); 1516 if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted"); 1517 ierr = ISSorted(iscol,(PetscTruth*)&i);CHKERRQ(ierr); 1518 if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted"); 1519 1520 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 1521 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 1522 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 1523 1524 ierr = ISStrideGetInfo(iscol,&first,&step);CHKERRQ(ierr); 1525 ierr = ISStride(iscol,&stride);CHKERRQ(ierr); 1526 if (stride && step == 1) { 1527 /* special case of contiguous rows */ 1528 ierr = PetscMalloc((2*nrows+1)*sizeof(int),&lens);CHKERRQ(ierr); 1529 starts = lens + nrows; 1530 /* loop over new rows determining lens and starting points */ 1531 for (i=0; i<nrows; i++) { 1532 kstart = ai[irow[i]]; 1533 kend = kstart + ailen[irow[i]]; 1534 for (k=kstart; k<kend; k++) { 1535 if (aj[k] >= first) { 1536 starts[i] = k; 1537 break; 1538 } 1539 } 1540 sum = 0; 1541 while (k < kend) { 1542 if (aj[k++] >= first+ncols) break; 1543 sum++; 1544 } 1545 lens[i] = sum; 1546 } 1547 /* create submatrix */ 1548 if (scall == MAT_REUSE_MATRIX) { 1549 int n_cols,n_rows; 1550 ierr = MatGetSize(*B,&n_rows,&n_cols);CHKERRQ(ierr); 1551 if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); 1552 ierr = MatZeroEntries(*B);CHKERRQ(ierr); 1553 C = *B; 1554 } else { 1555 ierr = MatCreate(A->comm,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE,&C);CHKERRQ(ierr); 1556 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1557 ierr = MatSeqAIJSetPreallocation(C,0,lens);CHKERRQ(ierr); 1558 } 1559 c = (Mat_SeqAIJ*)C->data; 1560 1561 /* loop over rows inserting into submatrix */ 1562 a_new = c->a; 1563 j_new = c->j; 1564 i_new = c->i; 1565 1566 for (i=0; i<nrows; i++) { 1567 ii = starts[i]; 1568 lensi = lens[i]; 1569 for (k=0; k<lensi; k++) { 1570 *j_new++ = aj[ii+k] - first; 1571 } 1572 ierr = PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));CHKERRQ(ierr); 1573 a_new += lensi; 1574 i_new[i+1] = i_new[i] + lensi; 1575 c->ilen[i] = lensi; 1576 } 1577 ierr = PetscFree(lens);CHKERRQ(ierr); 1578 } else { 1579 ierr = ISGetIndices(iscol,&icol);CHKERRQ(ierr); 1580 ierr = PetscMalloc((1+oldcols)*sizeof(int),&smap);CHKERRQ(ierr); 1581 1582 ierr = PetscMalloc((1+nrows)*sizeof(int),&lens);CHKERRQ(ierr); 1583 ierr = PetscMemzero(smap,oldcols*sizeof(int));CHKERRQ(ierr); 1584 for (i=0; i<ncols; i++) smap[icol[i]] = i+1; 1585 /* determine lens of each row */ 1586 for (i=0; i<nrows; i++) { 1587 kstart = ai[irow[i]]; 1588 kend = kstart + a->ilen[irow[i]]; 1589 lens[i] = 0; 1590 for (k=kstart; k<kend; k++) { 1591 if (smap[aj[k]]) { 1592 lens[i]++; 1593 } 1594 } 1595 } 1596 /* Create and fill new matrix */ 1597 if (scall == MAT_REUSE_MATRIX) { 1598 PetscTruth equal; 1599 1600 c = (Mat_SeqAIJ *)((*B)->data); 1601 if ((*B)->m != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size"); 1602 ierr = PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(int),&equal);CHKERRQ(ierr); 1603 if (!equal) { 1604 SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros"); 1605 } 1606 ierr = PetscMemzero(c->ilen,(*B)->m*sizeof(int));CHKERRQ(ierr); 1607 C = *B; 1608 } else { 1609 ierr = MatCreate(A->comm,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE,&C);CHKERRQ(ierr); 1610 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1611 ierr = MatSeqAIJSetPreallocation(C,0,lens);CHKERRQ(ierr); 1612 } 1613 c = (Mat_SeqAIJ *)(C->data); 1614 for (i=0; i<nrows; i++) { 1615 row = irow[i]; 1616 kstart = ai[row]; 1617 kend = kstart + a->ilen[row]; 1618 mat_i = c->i[i]; 1619 mat_j = c->j + mat_i; 1620 mat_a = c->a + mat_i; 1621 mat_ilen = c->ilen + i; 1622 for (k=kstart; k<kend; k++) { 1623 if ((tcol=smap[a->j[k]])) { 1624 *mat_j++ = tcol - 1; 1625 *mat_a++ = a->a[k]; 1626 (*mat_ilen)++; 1627 1628 } 1629 } 1630 } 1631 /* Free work space */ 1632 ierr = ISRestoreIndices(iscol,&icol);CHKERRQ(ierr); 1633 ierr = PetscFree(smap);CHKERRQ(ierr); 1634 ierr = PetscFree(lens);CHKERRQ(ierr); 1635 } 1636 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1637 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1638 1639 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 1640 *B = C; 1641 PetscFunctionReturn(0); 1642 } 1643 1644 /* 1645 */ 1646 #undef __FUNCT__ 1647 #define __FUNCT__ "MatILUFactor_SeqAIJ" 1648 int MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info) 1649 { 1650 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 1651 int ierr; 1652 Mat outA; 1653 PetscTruth row_identity,col_identity; 1654 1655 PetscFunctionBegin; 1656 if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu"); 1657 ierr = ISIdentity(row,&row_identity);CHKERRQ(ierr); 1658 ierr = ISIdentity(col,&col_identity);CHKERRQ(ierr); 1659 if (!row_identity || !col_identity) { 1660 SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU"); 1661 } 1662 1663 outA = inA; 1664 inA->factor = FACTOR_LU; 1665 a->row = row; 1666 a->col = col; 1667 ierr = PetscObjectReference((PetscObject)row);CHKERRQ(ierr); 1668 ierr = PetscObjectReference((PetscObject)col);CHKERRQ(ierr); 1669 1670 /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */ 1671 if (a->icol) {ierr = ISDestroy(a->icol);CHKERRQ(ierr);} /* need to remove old one */ 1672 ierr = ISInvertPermutation(col,PETSC_DECIDE,&a->icol);CHKERRQ(ierr); 1673 PetscLogObjectParent(inA,a->icol); 1674 1675 if (!a->solve_work) { /* this matrix may have been factored before */ 1676 ierr = PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);CHKERRQ(ierr); 1677 } 1678 1679 if (!a->diag) { 1680 ierr = MatMarkDiagonal_SeqAIJ(inA);CHKERRQ(ierr); 1681 } 1682 ierr = MatLUFactorNumeric_SeqAIJ(inA,&outA);CHKERRQ(ierr); 1683 PetscFunctionReturn(0); 1684 } 1685 1686 #include "petscblaslapack.h" 1687 #undef __FUNCT__ 1688 #define __FUNCT__ "MatScale_SeqAIJ" 1689 int MatScale_SeqAIJ(const PetscScalar *alpha,Mat inA) 1690 { 1691 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 1692 int one = 1; 1693 1694 PetscFunctionBegin; 1695 BLscal_(&a->nz,(PetscScalar*)alpha,a->a,&one); 1696 PetscLogFlops(a->nz); 1697 PetscFunctionReturn(0); 1698 } 1699 1700 #undef __FUNCT__ 1701 #define __FUNCT__ "MatGetSubMatrices_SeqAIJ" 1702 int MatGetSubMatrices_SeqAIJ(Mat A,int n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[]) 1703 { 1704 int ierr,i; 1705 1706 PetscFunctionBegin; 1707 if (scall == MAT_INITIAL_MATRIX) { 1708 ierr = PetscMalloc((n+1)*sizeof(Mat),B);CHKERRQ(ierr); 1709 } 1710 1711 for (i=0; i<n; i++) { 1712 ierr = MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);CHKERRQ(ierr); 1713 } 1714 PetscFunctionReturn(0); 1715 } 1716 1717 #undef __FUNCT__ 1718 #define __FUNCT__ "MatGetBlockSize_SeqAIJ" 1719 int MatGetBlockSize_SeqAIJ(Mat A,int *bs) 1720 { 1721 PetscFunctionBegin; 1722 *bs = 1; 1723 PetscFunctionReturn(0); 1724 } 1725 1726 #undef __FUNCT__ 1727 #define __FUNCT__ "MatIncreaseOverlap_SeqAIJ" 1728 int MatIncreaseOverlap_SeqAIJ(Mat A,int is_max,IS is[],int ov) 1729 { 1730 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1731 int row,i,j,k,l,m,n,*idx,ierr,*nidx,isz,val; 1732 int start,end,*ai,*aj; 1733 PetscBT table; 1734 1735 PetscFunctionBegin; 1736 m = A->m; 1737 ai = a->i; 1738 aj = a->j; 1739 1740 if (ov < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used"); 1741 1742 ierr = PetscMalloc((m+1)*sizeof(int),&nidx);CHKERRQ(ierr); 1743 ierr = PetscBTCreate(m,table);CHKERRQ(ierr); 1744 1745 for (i=0; i<is_max; i++) { 1746 /* Initialize the two local arrays */ 1747 isz = 0; 1748 ierr = PetscBTMemzero(m,table);CHKERRQ(ierr); 1749 1750 /* Extract the indices, assume there can be duplicate entries */ 1751 ierr = ISGetIndices(is[i],&idx);CHKERRQ(ierr); 1752 ierr = ISGetLocalSize(is[i],&n);CHKERRQ(ierr); 1753 1754 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 1755 for (j=0; j<n ; ++j){ 1756 if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];} 1757 } 1758 ierr = ISRestoreIndices(is[i],&idx);CHKERRQ(ierr); 1759 ierr = ISDestroy(is[i]);CHKERRQ(ierr); 1760 1761 k = 0; 1762 for (j=0; j<ov; j++){ /* for each overlap */ 1763 n = isz; 1764 for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */ 1765 row = nidx[k]; 1766 start = ai[row]; 1767 end = ai[row+1]; 1768 for (l = start; l<end ; l++){ 1769 val = aj[l] ; 1770 if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;} 1771 } 1772 } 1773 } 1774 ierr = ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));CHKERRQ(ierr); 1775 } 1776 ierr = PetscBTDestroy(table);CHKERRQ(ierr); 1777 ierr = PetscFree(nidx);CHKERRQ(ierr); 1778 PetscFunctionReturn(0); 1779 } 1780 1781 /* -------------------------------------------------------------- */ 1782 #undef __FUNCT__ 1783 #define __FUNCT__ "MatPermute_SeqAIJ" 1784 int MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B) 1785 { 1786 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1787 int i,ierr,nz,m = A->m,n = A->n,*col; 1788 int *row,*cnew,j,*lens; 1789 IS icolp,irowp; 1790 int *cwork; 1791 PetscScalar *vwork; 1792 1793 PetscFunctionBegin; 1794 ierr = ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);CHKERRQ(ierr); 1795 ierr = ISGetIndices(irowp,&row);CHKERRQ(ierr); 1796 ierr = ISInvertPermutation(colp,PETSC_DECIDE,&icolp);CHKERRQ(ierr); 1797 ierr = ISGetIndices(icolp,&col);CHKERRQ(ierr); 1798 1799 /* determine lengths of permuted rows */ 1800 ierr = PetscMalloc((m+1)*sizeof(int),&lens);CHKERRQ(ierr); 1801 for (i=0; i<m; i++) { 1802 lens[row[i]] = a->i[i+1] - a->i[i]; 1803 } 1804 ierr = MatCreate(A->comm,m,n,m,n,B);CHKERRQ(ierr); 1805 ierr = MatSetType(*B,A->type_name);CHKERRQ(ierr); 1806 ierr = MatSeqAIJSetPreallocation(*B,0,lens);CHKERRQ(ierr); 1807 ierr = PetscFree(lens);CHKERRQ(ierr); 1808 1809 ierr = PetscMalloc(n*sizeof(int),&cnew);CHKERRQ(ierr); 1810 for (i=0; i<m; i++) { 1811 ierr = MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1812 for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];} 1813 ierr = MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);CHKERRQ(ierr); 1814 ierr = MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1815 } 1816 ierr = PetscFree(cnew);CHKERRQ(ierr); 1817 ierr = MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1818 ierr = MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1819 ierr = ISRestoreIndices(irowp,&row);CHKERRQ(ierr); 1820 ierr = ISRestoreIndices(icolp,&col);CHKERRQ(ierr); 1821 ierr = ISDestroy(irowp);CHKERRQ(ierr); 1822 ierr = ISDestroy(icolp);CHKERRQ(ierr); 1823 PetscFunctionReturn(0); 1824 } 1825 1826 #undef __FUNCT__ 1827 #define __FUNCT__ "MatPrintHelp_SeqAIJ" 1828 int MatPrintHelp_SeqAIJ(Mat A) 1829 { 1830 static PetscTruth called = PETSC_FALSE; 1831 MPI_Comm comm = A->comm; 1832 int ierr; 1833 1834 PetscFunctionBegin; 1835 if (called) {PetscFunctionReturn(0);} else called = PETSC_TRUE; 1836 ierr = (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):\n");CHKERRQ(ierr); 1837 ierr = (*PetscHelpPrintf)(comm," -mat_lu_pivotthreshold <threshold>: Set pivoting threshold\n");CHKERRQ(ierr); 1838 ierr = (*PetscHelpPrintf)(comm," -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.\n");CHKERRQ(ierr); 1839 ierr = (*PetscHelpPrintf)(comm," -mat_aij_no_inode: Do not use inodes\n");CHKERRQ(ierr); 1840 ierr = (*PetscHelpPrintf)(comm," -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)\n");CHKERRQ(ierr); 1841 PetscFunctionReturn(0); 1842 } 1843 1844 #undef __FUNCT__ 1845 #define __FUNCT__ "MatCopy_SeqAIJ" 1846 int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str) 1847 { 1848 int ierr; 1849 1850 PetscFunctionBegin; 1851 /* If the two matrices have the same copy implementation, use fast copy. */ 1852 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 1853 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1854 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 1855 1856 if (a->i[A->m] != b->i[B->m]) { 1857 SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different"); 1858 } 1859 ierr = PetscMemcpy(b->a,a->a,(a->i[A->m])*sizeof(PetscScalar));CHKERRQ(ierr); 1860 } else { 1861 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1862 } 1863 PetscFunctionReturn(0); 1864 } 1865 1866 #undef __FUNCT__ 1867 #define __FUNCT__ "MatSetUpPreallocation_SeqAIJ" 1868 int MatSetUpPreallocation_SeqAIJ(Mat A) 1869 { 1870 int ierr; 1871 1872 PetscFunctionBegin; 1873 ierr = MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);CHKERRQ(ierr); 1874 PetscFunctionReturn(0); 1875 } 1876 1877 #undef __FUNCT__ 1878 #define __FUNCT__ "MatGetArray_SeqAIJ" 1879 int MatGetArray_SeqAIJ(Mat A,PetscScalar *array[]) 1880 { 1881 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1882 PetscFunctionBegin; 1883 *array = a->a; 1884 PetscFunctionReturn(0); 1885 } 1886 1887 #undef __FUNCT__ 1888 #define __FUNCT__ "MatRestoreArray_SeqAIJ" 1889 int MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[]) 1890 { 1891 PetscFunctionBegin; 1892 PetscFunctionReturn(0); 1893 } 1894 1895 #undef __FUNCT__ 1896 #define __FUNCT__ "MatFDColoringApply_SeqAIJ" 1897 int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx) 1898 { 1899 int (*f)(void*,Vec,Vec,void*) = (int (*)(void*,Vec,Vec,void *))coloring->f; 1900 int k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2; 1901 PetscScalar dx,mone = -1.0,*y,*xx,*w3_array; 1902 PetscScalar *vscale_array; 1903 PetscReal epsilon = coloring->error_rel,umin = coloring->umin; 1904 Vec w1,w2,w3; 1905 void *fctx = coloring->fctx; 1906 PetscTruth flg; 1907 1908 PetscFunctionBegin; 1909 if (!coloring->w1) { 1910 ierr = VecDuplicate(x1,&coloring->w1);CHKERRQ(ierr); 1911 PetscLogObjectParent(coloring,coloring->w1); 1912 ierr = VecDuplicate(x1,&coloring->w2);CHKERRQ(ierr); 1913 PetscLogObjectParent(coloring,coloring->w2); 1914 ierr = VecDuplicate(x1,&coloring->w3);CHKERRQ(ierr); 1915 PetscLogObjectParent(coloring,coloring->w3); 1916 } 1917 w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3; 1918 1919 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 1920 ierr = PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);CHKERRQ(ierr); 1921 if (flg) { 1922 PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()\n"); 1923 } else { 1924 ierr = MatZeroEntries(J);CHKERRQ(ierr); 1925 } 1926 1927 ierr = VecGetOwnershipRange(x1,&start,&end);CHKERRQ(ierr); 1928 ierr = VecGetSize(x1,&N);CHKERRQ(ierr); 1929 1930 /* 1931 This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets 1932 coloring->F for the coarser grids from the finest 1933 */ 1934 if (coloring->F) { 1935 ierr = VecGetLocalSize(coloring->F,&m1);CHKERRQ(ierr); 1936 ierr = VecGetLocalSize(w1,&m2);CHKERRQ(ierr); 1937 if (m1 != m2) { 1938 coloring->F = 0; 1939 } 1940 } 1941 1942 if (coloring->F) { 1943 w1 = coloring->F; 1944 coloring->F = 0; 1945 } else { 1946 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1947 ierr = (*f)(sctx,x1,w1,fctx);CHKERRQ(ierr); 1948 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1949 } 1950 1951 /* 1952 Compute all the scale factors and share with other processors 1953 */ 1954 ierr = VecGetArray(x1,&xx);CHKERRQ(ierr);xx = xx - start; 1955 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr);vscale_array = vscale_array - start; 1956 for (k=0; k<coloring->ncolors; k++) { 1957 /* 1958 Loop over each column associated with color adding the 1959 perturbation to the vector w3. 1960 */ 1961 for (l=0; l<coloring->ncolumns[k]; l++) { 1962 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 1963 dx = xx[col]; 1964 if (dx == 0.0) dx = 1.0; 1965 #if !defined(PETSC_USE_COMPLEX) 1966 if (dx < umin && dx >= 0.0) dx = umin; 1967 else if (dx < 0.0 && dx > -umin) dx = -umin; 1968 #else 1969 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 1970 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 1971 #endif 1972 dx *= epsilon; 1973 vscale_array[col] = 1.0/dx; 1974 } 1975 } 1976 vscale_array = vscale_array + start;ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 1977 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1978 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1979 1980 /* ierr = VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD); 1981 ierr = VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/ 1982 1983 if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow; 1984 else vscaleforrow = coloring->columnsforrow; 1985 1986 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 1987 /* 1988 Loop over each color 1989 */ 1990 for (k=0; k<coloring->ncolors; k++) { 1991 coloring->currentcolor = k; 1992 ierr = VecCopy(x1,w3);CHKERRQ(ierr); 1993 ierr = VecGetArray(w3,&w3_array);CHKERRQ(ierr);w3_array = w3_array - start; 1994 /* 1995 Loop over each column associated with color adding the 1996 perturbation to the vector w3. 1997 */ 1998 for (l=0; l<coloring->ncolumns[k]; l++) { 1999 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 2000 dx = xx[col]; 2001 if (dx == 0.0) dx = 1.0; 2002 #if !defined(PETSC_USE_COMPLEX) 2003 if (dx < umin && dx >= 0.0) dx = umin; 2004 else if (dx < 0.0 && dx > -umin) dx = -umin; 2005 #else 2006 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 2007 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 2008 #endif 2009 dx *= epsilon; 2010 if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter"); 2011 w3_array[col] += dx; 2012 } 2013 w3_array = w3_array + start; ierr = VecRestoreArray(w3,&w3_array);CHKERRQ(ierr); 2014 2015 /* 2016 Evaluate function at x1 + dx (here dx is a vector of perturbations) 2017 */ 2018 2019 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2020 ierr = (*f)(sctx,w3,w2,fctx);CHKERRQ(ierr); 2021 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2022 ierr = VecAXPY(&mone,w1,w2);CHKERRQ(ierr); 2023 2024 /* 2025 Loop over rows of vector, putting results into Jacobian matrix 2026 */ 2027 ierr = VecGetArray(w2,&y);CHKERRQ(ierr); 2028 for (l=0; l<coloring->nrows[k]; l++) { 2029 row = coloring->rows[k][l]; 2030 col = coloring->columnsforrow[k][l]; 2031 y[row] *= vscale_array[vscaleforrow[k][l]]; 2032 srow = row + start; 2033 ierr = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 2034 } 2035 ierr = VecRestoreArray(w2,&y);CHKERRQ(ierr); 2036 } 2037 coloring->currentcolor = k; 2038 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 2039 xx = xx + start; ierr = VecRestoreArray(x1,&xx);CHKERRQ(ierr); 2040 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2041 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2042 PetscFunctionReturn(0); 2043 } 2044 2045 #include "petscblaslapack.h" 2046 #undef __FUNCT__ 2047 #define __FUNCT__ "MatAXPY_SeqAIJ" 2048 int MatAXPY_SeqAIJ(const PetscScalar a[],Mat X,Mat Y,MatStructure str) 2049 { 2050 int ierr,one=1,i; 2051 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data; 2052 2053 PetscFunctionBegin; 2054 if (str == SAME_NONZERO_PATTERN) { 2055 BLaxpy_(&x->nz,(PetscScalar*)a,x->a,&one,y->a,&one); 2056 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2057 if (y->xtoy && y->XtoY != X) { 2058 ierr = PetscFree(y->xtoy);CHKERRQ(ierr); 2059 ierr = MatDestroy(y->XtoY);CHKERRQ(ierr); 2060 } 2061 if (!y->xtoy) { /* get xtoy */ 2062 ierr = MatAXPYGetxtoy_Private(X->m,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);CHKERRQ(ierr); 2063 y->XtoY = X; 2064 } 2065 for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += (*a)*(x->a[i]); 2066 PetscLogInfo(0,"MatAXPY_SeqAIJ: ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz); 2067 } else { 2068 ierr = MatAXPY_Basic(a,X,Y,str);CHKERRQ(ierr); 2069 } 2070 PetscFunctionReturn(0); 2071 } 2072 2073 /* -------------------------------------------------------------------*/ 2074 static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ, 2075 MatGetRow_SeqAIJ, 2076 MatRestoreRow_SeqAIJ, 2077 MatMult_SeqAIJ, 2078 /* 4*/ MatMultAdd_SeqAIJ, 2079 MatMultTranspose_SeqAIJ, 2080 MatMultTransposeAdd_SeqAIJ, 2081 MatSolve_SeqAIJ, 2082 MatSolveAdd_SeqAIJ, 2083 MatSolveTranspose_SeqAIJ, 2084 /*10*/ MatSolveTransposeAdd_SeqAIJ, 2085 MatLUFactor_SeqAIJ, 2086 0, 2087 MatRelax_SeqAIJ, 2088 MatTranspose_SeqAIJ, 2089 /*15*/ MatGetInfo_SeqAIJ, 2090 MatEqual_SeqAIJ, 2091 MatGetDiagonal_SeqAIJ, 2092 MatDiagonalScale_SeqAIJ, 2093 MatNorm_SeqAIJ, 2094 /*20*/ 0, 2095 MatAssemblyEnd_SeqAIJ, 2096 MatCompress_SeqAIJ, 2097 MatSetOption_SeqAIJ, 2098 MatZeroEntries_SeqAIJ, 2099 /*25*/ MatZeroRows_SeqAIJ, 2100 MatLUFactorSymbolic_SeqAIJ, 2101 MatLUFactorNumeric_SeqAIJ, 2102 MatCholeskyFactorSymbolic_SeqAIJ, 2103 MatCholeskyFactorNumeric_SeqAIJ, 2104 /*30*/ MatSetUpPreallocation_SeqAIJ, 2105 MatILUFactorSymbolic_SeqAIJ, 2106 MatICCFactorSymbolic_SeqAIJ, 2107 MatGetArray_SeqAIJ, 2108 MatRestoreArray_SeqAIJ, 2109 /*35*/ MatDuplicate_SeqAIJ, 2110 0, 2111 0, 2112 MatILUFactor_SeqAIJ, 2113 0, 2114 /*40*/ MatAXPY_SeqAIJ, 2115 MatGetSubMatrices_SeqAIJ, 2116 MatIncreaseOverlap_SeqAIJ, 2117 MatGetValues_SeqAIJ, 2118 MatCopy_SeqAIJ, 2119 /*45*/ MatPrintHelp_SeqAIJ, 2120 MatScale_SeqAIJ, 2121 0, 2122 0, 2123 MatILUDTFactor_SeqAIJ, 2124 /*50*/ MatGetBlockSize_SeqAIJ, 2125 MatGetRowIJ_SeqAIJ, 2126 MatRestoreRowIJ_SeqAIJ, 2127 MatGetColumnIJ_SeqAIJ, 2128 MatRestoreColumnIJ_SeqAIJ, 2129 /*55*/ MatFDColoringCreate_SeqAIJ, 2130 0, 2131 0, 2132 MatPermute_SeqAIJ, 2133 0, 2134 /*60*/ 0, 2135 MatDestroy_SeqAIJ, 2136 MatView_SeqAIJ, 2137 MatGetPetscMaps_Petsc, 2138 0, 2139 /*65*/ 0, 2140 0, 2141 0, 2142 0, 2143 0, 2144 /*70*/ 0, 2145 0, 2146 MatSetColoring_SeqAIJ, 2147 MatSetValuesAdic_SeqAIJ, 2148 MatSetValuesAdifor_SeqAIJ, 2149 /*75*/ MatFDColoringApply_SeqAIJ, 2150 0, 2151 0, 2152 0, 2153 0, 2154 /*80*/ 0, 2155 0, 2156 0, 2157 0, 2158 /*85*/ MatLoad_SeqAIJ, 2159 MatIsSymmetric_SeqAIJ, 2160 0, 2161 0, 2162 0, 2163 /*90*/ 0, 2164 MatMatMult_SeqAIJ_SeqAIJ, 2165 MatMatMultSymbolic_SeqAIJ_SeqAIJ, 2166 MatMatMultNumeric_SeqAIJ_SeqAIJ, 2167 }; 2168 2169 EXTERN_C_BEGIN 2170 #undef __FUNCT__ 2171 #define __FUNCT__ "MatSeqAIJSetColumnIndices_SeqAIJ" 2172 int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices) 2173 { 2174 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2175 int i,nz,n; 2176 2177 PetscFunctionBegin; 2178 2179 nz = aij->maxnz; 2180 n = mat->n; 2181 for (i=0; i<nz; i++) { 2182 aij->j[i] = indices[i]; 2183 } 2184 aij->nz = nz; 2185 for (i=0; i<n; i++) { 2186 aij->ilen[i] = aij->imax[i]; 2187 } 2188 2189 PetscFunctionReturn(0); 2190 } 2191 EXTERN_C_END 2192 2193 #undef __FUNCT__ 2194 #define __FUNCT__ "MatSeqAIJSetColumnIndices" 2195 /*@ 2196 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 2197 in the matrix. 2198 2199 Input Parameters: 2200 + mat - the SeqAIJ matrix 2201 - indices - the column indices 2202 2203 Level: advanced 2204 2205 Notes: 2206 This can be called if you have precomputed the nonzero structure of the 2207 matrix and want to provide it to the matrix object to improve the performance 2208 of the MatSetValues() operation. 2209 2210 You MUST have set the correct numbers of nonzeros per row in the call to 2211 MatCreateSeqAIJ(). 2212 2213 MUST be called before any calls to MatSetValues(); 2214 2215 The indices should start with zero, not one. 2216 2217 @*/ 2218 int MatSeqAIJSetColumnIndices(Mat mat,int *indices) 2219 { 2220 int ierr,(*f)(Mat,int *); 2221 2222 PetscFunctionBegin; 2223 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2224 PetscValidPointer(indices,2); 2225 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);CHKERRQ(ierr); 2226 if (f) { 2227 ierr = (*f)(mat,indices);CHKERRQ(ierr); 2228 } else { 2229 SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices"); 2230 } 2231 PetscFunctionReturn(0); 2232 } 2233 2234 /* ----------------------------------------------------------------------------------------*/ 2235 2236 EXTERN_C_BEGIN 2237 #undef __FUNCT__ 2238 #define __FUNCT__ "MatStoreValues_SeqAIJ" 2239 int MatStoreValues_SeqAIJ(Mat mat) 2240 { 2241 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2242 size_t nz = aij->i[mat->m],ierr; 2243 2244 PetscFunctionBegin; 2245 if (aij->nonew != 1) { 2246 SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2247 } 2248 2249 /* allocate space for values if not already there */ 2250 if (!aij->saved_values) { 2251 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);CHKERRQ(ierr); 2252 } 2253 2254 /* copy values over */ 2255 ierr = PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2256 PetscFunctionReturn(0); 2257 } 2258 EXTERN_C_END 2259 2260 #undef __FUNCT__ 2261 #define __FUNCT__ "MatStoreValues" 2262 /*@ 2263 MatStoreValues - Stashes a copy of the matrix values; this allows, for 2264 example, reuse of the linear part of a Jacobian, while recomputing the 2265 nonlinear portion. 2266 2267 Collect on Mat 2268 2269 Input Parameters: 2270 . mat - the matrix (currently on AIJ matrices support this option) 2271 2272 Level: advanced 2273 2274 Common Usage, with SNESSolve(): 2275 $ Create Jacobian matrix 2276 $ Set linear terms into matrix 2277 $ Apply boundary conditions to matrix, at this time matrix must have 2278 $ final nonzero structure (i.e. setting the nonlinear terms and applying 2279 $ boundary conditions again will not change the nonzero structure 2280 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2281 $ ierr = MatStoreValues(mat); 2282 $ Call SNESSetJacobian() with matrix 2283 $ In your Jacobian routine 2284 $ ierr = MatRetrieveValues(mat); 2285 $ Set nonlinear terms in matrix 2286 2287 Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself: 2288 $ // build linear portion of Jacobian 2289 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2290 $ ierr = MatStoreValues(mat); 2291 $ loop over nonlinear iterations 2292 $ ierr = MatRetrieveValues(mat); 2293 $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian 2294 $ // call MatAssemblyBegin/End() on matrix 2295 $ Solve linear system with Jacobian 2296 $ endloop 2297 2298 Notes: 2299 Matrix must already be assemblied before calling this routine 2300 Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 2301 calling this routine. 2302 2303 .seealso: MatRetrieveValues() 2304 2305 @*/ 2306 int MatStoreValues(Mat mat) 2307 { 2308 int ierr,(*f)(Mat); 2309 2310 PetscFunctionBegin; 2311 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2312 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2313 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2314 2315 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2316 if (f) { 2317 ierr = (*f)(mat);CHKERRQ(ierr); 2318 } else { 2319 SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to store values"); 2320 } 2321 PetscFunctionReturn(0); 2322 } 2323 2324 EXTERN_C_BEGIN 2325 #undef __FUNCT__ 2326 #define __FUNCT__ "MatRetrieveValues_SeqAIJ" 2327 int MatRetrieveValues_SeqAIJ(Mat mat) 2328 { 2329 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2330 int nz = aij->i[mat->m],ierr; 2331 2332 PetscFunctionBegin; 2333 if (aij->nonew != 1) { 2334 SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2335 } 2336 if (!aij->saved_values) { 2337 SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first"); 2338 } 2339 /* copy values over */ 2340 ierr = PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2341 PetscFunctionReturn(0); 2342 } 2343 EXTERN_C_END 2344 2345 #undef __FUNCT__ 2346 #define __FUNCT__ "MatRetrieveValues" 2347 /*@ 2348 MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 2349 example, reuse of the linear part of a Jacobian, while recomputing the 2350 nonlinear portion. 2351 2352 Collect on Mat 2353 2354 Input Parameters: 2355 . mat - the matrix (currently on AIJ matrices support this option) 2356 2357 Level: advanced 2358 2359 .seealso: MatStoreValues() 2360 2361 @*/ 2362 int MatRetrieveValues(Mat mat) 2363 { 2364 int ierr,(*f)(Mat); 2365 2366 PetscFunctionBegin; 2367 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2368 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2369 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2370 2371 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2372 if (f) { 2373 ierr = (*f)(mat);CHKERRQ(ierr); 2374 } else { 2375 SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to retrieve values"); 2376 } 2377 PetscFunctionReturn(0); 2378 } 2379 2380 2381 /* --------------------------------------------------------------------------------*/ 2382 #undef __FUNCT__ 2383 #define __FUNCT__ "MatCreateSeqAIJ" 2384 /*@C 2385 MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format 2386 (the default parallel PETSc format). For good matrix assembly performance 2387 the user should preallocate the matrix storage by setting the parameter nz 2388 (or the array nnz). By setting these parameters accurately, performance 2389 during matrix assembly can be increased by more than a factor of 50. 2390 2391 Collective on MPI_Comm 2392 2393 Input Parameters: 2394 + comm - MPI communicator, set to PETSC_COMM_SELF 2395 . m - number of rows 2396 . n - number of columns 2397 . nz - number of nonzeros per row (same for all rows) 2398 - nnz - array containing the number of nonzeros in the various rows 2399 (possibly different for each row) or PETSC_NULL 2400 2401 Output Parameter: 2402 . A - the matrix 2403 2404 Notes: 2405 The AIJ format (also called the Yale sparse matrix format or 2406 compressed row storage), is fully compatible with standard Fortran 77 2407 storage. That is, the stored row and column indices can begin at 2408 either one (as in Fortran) or zero. See the users' manual for details. 2409 2410 Specify the preallocated storage with either nz or nnz (not both). 2411 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2412 allocation. For large problems you MUST preallocate memory or you 2413 will get TERRIBLE performance, see the users' manual chapter on matrices. 2414 2415 By default, this format uses inodes (identical nodes) when possible, to 2416 improve numerical efficiency of matrix-vector products and solves. We 2417 search for consecutive rows with the same nonzero structure, thereby 2418 reusing matrix information to achieve increased efficiency. 2419 2420 Options Database Keys: 2421 + -mat_aij_no_inode - Do not use inodes 2422 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2423 - -mat_aij_oneindex - Internally use indexing starting at 1 2424 rather than 0. Note that when calling MatSetValues(), 2425 the user still MUST index entries starting at 0! 2426 2427 Level: intermediate 2428 2429 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2430 2431 @*/ 2432 int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,const int nnz[],Mat *A) 2433 { 2434 int ierr; 2435 2436 PetscFunctionBegin; 2437 ierr = MatCreate(comm,m,n,m,n,A);CHKERRQ(ierr); 2438 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 2439 ierr = MatSeqAIJSetPreallocation(*A,nz,nnz);CHKERRQ(ierr); 2440 PetscFunctionReturn(0); 2441 } 2442 2443 #define SKIP_ALLOCATION -4 2444 2445 #undef __FUNCT__ 2446 #define __FUNCT__ "MatSeqAIJSetPreallocation" 2447 /*@C 2448 MatSeqAIJSetPreallocation - For good matrix assembly performance 2449 the user should preallocate the matrix storage by setting the parameter nz 2450 (or the array nnz). By setting these parameters accurately, performance 2451 during matrix assembly can be increased by more than a factor of 50. 2452 2453 Collective on MPI_Comm 2454 2455 Input Parameters: 2456 + comm - MPI communicator, set to PETSC_COMM_SELF 2457 . m - number of rows 2458 . n - number of columns 2459 . nz - number of nonzeros per row (same for all rows) 2460 - nnz - array containing the number of nonzeros in the various rows 2461 (possibly different for each row) or PETSC_NULL 2462 2463 Output Parameter: 2464 . A - the matrix 2465 2466 Notes: 2467 The AIJ format (also called the Yale sparse matrix format or 2468 compressed row storage), is fully compatible with standard Fortran 77 2469 storage. That is, the stored row and column indices can begin at 2470 either one (as in Fortran) or zero. See the users' manual for details. 2471 2472 Specify the preallocated storage with either nz or nnz (not both). 2473 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2474 allocation. For large problems you MUST preallocate memory or you 2475 will get TERRIBLE performance, see the users' manual chapter on matrices. 2476 2477 By default, this format uses inodes (identical nodes) when possible, to 2478 improve numerical efficiency of matrix-vector products and solves. We 2479 search for consecutive rows with the same nonzero structure, thereby 2480 reusing matrix information to achieve increased efficiency. 2481 2482 Options Database Keys: 2483 + -mat_aij_no_inode - Do not use inodes 2484 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2485 - -mat_aij_oneindex - Internally use indexing starting at 1 2486 rather than 0. Note that when calling MatSetValues(), 2487 the user still MUST index entries starting at 0! 2488 2489 Level: intermediate 2490 2491 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2492 2493 @*/ 2494 int MatSeqAIJSetPreallocation(Mat B,int nz,const int nnz[]) 2495 { 2496 int ierr,(*f)(Mat,int,const int[]); 2497 2498 PetscFunctionBegin; 2499 ierr = PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);CHKERRQ(ierr); 2500 if (f) { 2501 ierr = (*f)(B,nz,nnz);CHKERRQ(ierr); 2502 } 2503 PetscFunctionReturn(0); 2504 } 2505 2506 EXTERN_C_BEGIN 2507 #undef __FUNCT__ 2508 #define __FUNCT__ "MatSeqAIJSetPreallocation_SeqAIJ" 2509 int MatSeqAIJSetPreallocation_SeqAIJ(Mat B,int nz,int *nnz) 2510 { 2511 Mat_SeqAIJ *b; 2512 size_t len = 0; 2513 PetscTruth skipallocation = PETSC_FALSE; 2514 int i,ierr; 2515 2516 PetscFunctionBegin; 2517 2518 if (nz == SKIP_ALLOCATION) { 2519 skipallocation = PETSC_TRUE; 2520 nz = 0; 2521 } 2522 2523 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 2524 if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz); 2525 if (nnz) { 2526 for (i=0; i<B->m; i++) { 2527 if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]); 2528 if (nnz[i] > B->n) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->n); 2529 } 2530 } 2531 2532 B->preallocated = PETSC_TRUE; 2533 b = (Mat_SeqAIJ*)B->data; 2534 2535 ierr = PetscMalloc((B->m+1)*sizeof(int),&b->imax);CHKERRQ(ierr); 2536 if (!nnz) { 2537 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 2538 else if (nz <= 0) nz = 1; 2539 for (i=0; i<B->m; i++) b->imax[i] = nz; 2540 nz = nz*B->m; 2541 } else { 2542 nz = 0; 2543 for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];} 2544 } 2545 2546 if (!skipallocation) { 2547 /* allocate the matrix space */ 2548 len = ((size_t) nz)*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int); 2549 ierr = PetscMalloc(len,&b->a);CHKERRQ(ierr); 2550 b->j = (int*)(b->a + nz); 2551 ierr = PetscMemzero(b->j,nz*sizeof(int));CHKERRQ(ierr); 2552 b->i = b->j + nz; 2553 b->i[0] = 0; 2554 for (i=1; i<B->m+1; i++) { 2555 b->i[i] = b->i[i-1] + b->imax[i-1]; 2556 } 2557 b->singlemalloc = PETSC_TRUE; 2558 b->freedata = PETSC_TRUE; 2559 } else { 2560 b->freedata = PETSC_FALSE; 2561 } 2562 2563 /* b->ilen will count nonzeros in each row so far. */ 2564 ierr = PetscMalloc((B->m+1)*sizeof(int),&b->ilen);CHKERRQ(ierr); 2565 PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2566 for (i=0; i<B->m; i++) { b->ilen[i] = 0;} 2567 2568 b->nz = 0; 2569 b->maxnz = nz; 2570 B->info.nz_unneeded = (double)b->maxnz; 2571 PetscFunctionReturn(0); 2572 } 2573 EXTERN_C_END 2574 2575 /*MC 2576 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 2577 based on compressed sparse row format. 2578 2579 Options Database Keys: 2580 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 2581 2582 Level: beginner 2583 2584 .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType 2585 M*/ 2586 2587 EXTERN_C_BEGIN 2588 #undef __FUNCT__ 2589 #define __FUNCT__ "MatCreate_SeqAIJ" 2590 int MatCreate_SeqAIJ(Mat B) 2591 { 2592 Mat_SeqAIJ *b; 2593 int ierr,size; 2594 2595 PetscFunctionBegin; 2596 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 2597 if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1"); 2598 2599 B->m = B->M = PetscMax(B->m,B->M); 2600 B->n = B->N = PetscMax(B->n,B->N); 2601 2602 ierr = PetscNew(Mat_SeqAIJ,&b);CHKERRQ(ierr); 2603 B->data = (void*)b; 2604 ierr = PetscMemzero(b,sizeof(Mat_SeqAIJ));CHKERRQ(ierr); 2605 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 2606 B->factor = 0; 2607 B->lupivotthreshold = 1.0; 2608 B->mapping = 0; 2609 ierr = PetscOptionsGetReal(B->prefix,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);CHKERRQ(ierr); 2610 ierr = PetscOptionsHasName(B->prefix,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);CHKERRQ(ierr); 2611 b->row = 0; 2612 b->col = 0; 2613 b->icol = 0; 2614 b->reallocs = 0; 2615 2616 ierr = PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);CHKERRQ(ierr); 2617 ierr = PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);CHKERRQ(ierr); 2618 2619 b->sorted = PETSC_FALSE; 2620 b->ignorezeroentries = PETSC_FALSE; 2621 b->roworiented = PETSC_TRUE; 2622 b->nonew = 0; 2623 b->diag = 0; 2624 b->solve_work = 0; 2625 B->spptr = 0; 2626 b->inode.use = PETSC_TRUE; 2627 b->inode.node_count = 0; 2628 b->inode.size = 0; 2629 b->inode.limit = 5; 2630 b->inode.max_limit = 5; 2631 b->saved_values = 0; 2632 b->idiag = 0; 2633 b->ssor = 0; 2634 b->keepzeroedrows = PETSC_FALSE; 2635 b->xtoy = 0; 2636 b->XtoY = 0; 2637 2638 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 2639 2640 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C", 2641 "MatSeqAIJSetColumnIndices_SeqAIJ", 2642 MatSeqAIJSetColumnIndices_SeqAIJ);CHKERRQ(ierr); 2643 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C", 2644 "MatStoreValues_SeqAIJ", 2645 MatStoreValues_SeqAIJ);CHKERRQ(ierr); 2646 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C", 2647 "MatRetrieveValues_SeqAIJ", 2648 MatRetrieveValues_SeqAIJ);CHKERRQ(ierr); 2649 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C", 2650 "MatConvert_SeqAIJ_SeqSBAIJ", 2651 MatConvert_SeqAIJ_SeqSBAIJ);CHKERRQ(ierr); 2652 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C", 2653 "MatConvert_SeqAIJ_SeqBAIJ", 2654 MatConvert_SeqAIJ_SeqBAIJ);CHKERRQ(ierr); 2655 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C", 2656 "MatIsTranspose_SeqAIJ", 2657 MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 2658 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C", 2659 "MatSeqAIJSetPreallocation_SeqAIJ", 2660 MatSeqAIJSetPreallocation_SeqAIJ);CHKERRQ(ierr); 2661 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C", 2662 "MatReorderForNonzeroDiagonal_SeqAIJ", 2663 MatReorderForNonzeroDiagonal_SeqAIJ);CHKERRQ(ierr); 2664 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatAdjustForInodes_C", 2665 "MatAdjustForInodes_SeqAIJ", 2666 MatAdjustForInodes_SeqAIJ);CHKERRQ(ierr); 2667 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJGetInodeSizes_C", 2668 "MatSeqAIJGetInodeSizes_SeqAIJ", 2669 MatSeqAIJGetInodeSizes_SeqAIJ);CHKERRQ(ierr); 2670 ierr = RegisterApplyPtAPRoutines_Private(B);CHKERRQ(ierr); 2671 PetscFunctionReturn(0); 2672 } 2673 EXTERN_C_END 2674 2675 #undef __FUNCT__ 2676 #define __FUNCT__ "MatDuplicate_SeqAIJ" 2677 int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B) 2678 { 2679 Mat C; 2680 Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data; 2681 int i,m = A->m,ierr; 2682 size_t len; 2683 2684 PetscFunctionBegin; 2685 *B = 0; 2686 ierr = MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);CHKERRQ(ierr); 2687 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 2688 ierr = PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));CHKERRQ(ierr); 2689 2690 c = (Mat_SeqAIJ*)C->data; 2691 2692 C->factor = A->factor; 2693 c->row = 0; 2694 c->col = 0; 2695 c->icol = 0; 2696 c->keepzeroedrows = a->keepzeroedrows; 2697 C->assembled = PETSC_TRUE; 2698 2699 C->M = A->m; 2700 C->N = A->n; 2701 2702 ierr = PetscMalloc((m+1)*sizeof(int),&c->imax);CHKERRQ(ierr); 2703 ierr = PetscMalloc((m+1)*sizeof(int),&c->ilen);CHKERRQ(ierr); 2704 for (i=0; i<m; i++) { 2705 c->imax[i] = a->imax[i]; 2706 c->ilen[i] = a->ilen[i]; 2707 } 2708 2709 /* allocate the matrix space */ 2710 c->singlemalloc = PETSC_TRUE; 2711 len = ((size_t) (m+1))*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int)); 2712 ierr = PetscMalloc(len,&c->a);CHKERRQ(ierr); 2713 c->j = (int*)(c->a + a->i[m] ); 2714 c->i = c->j + a->i[m]; 2715 ierr = PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));CHKERRQ(ierr); 2716 if (m > 0) { 2717 ierr = PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(int));CHKERRQ(ierr); 2718 if (cpvalues == MAT_COPY_VALUES) { 2719 ierr = PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2720 } else { 2721 ierr = PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2722 } 2723 } 2724 2725 PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2726 c->sorted = a->sorted; 2727 c->roworiented = a->roworiented; 2728 c->nonew = a->nonew; 2729 c->ilu_preserve_row_sums = a->ilu_preserve_row_sums; 2730 c->saved_values = 0; 2731 c->idiag = 0; 2732 c->ssor = 0; 2733 c->ignorezeroentries = a->ignorezeroentries; 2734 c->freedata = PETSC_TRUE; 2735 2736 if (a->diag) { 2737 ierr = PetscMalloc((m+1)*sizeof(int),&c->diag);CHKERRQ(ierr); 2738 PetscLogObjectMemory(C,(m+1)*sizeof(int)); 2739 for (i=0; i<m; i++) { 2740 c->diag[i] = a->diag[i]; 2741 } 2742 } else c->diag = 0; 2743 c->inode.use = a->inode.use; 2744 c->inode.limit = a->inode.limit; 2745 c->inode.max_limit = a->inode.max_limit; 2746 if (a->inode.size){ 2747 ierr = PetscMalloc((m+1)*sizeof(int),&c->inode.size);CHKERRQ(ierr); 2748 c->inode.node_count = a->inode.node_count; 2749 ierr = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));CHKERRQ(ierr); 2750 } else { 2751 c->inode.size = 0; 2752 c->inode.node_count = 0; 2753 } 2754 c->nz = a->nz; 2755 c->maxnz = a->maxnz; 2756 c->solve_work = 0; 2757 C->preallocated = PETSC_TRUE; 2758 2759 *B = C; 2760 ierr = PetscFListDuplicate(A->qlist,&C->qlist);CHKERRQ(ierr); 2761 PetscFunctionReturn(0); 2762 } 2763 2764 #undef __FUNCT__ 2765 #define __FUNCT__ "MatLoad_SeqAIJ" 2766 int MatLoad_SeqAIJ(PetscViewer viewer,const MatType type,Mat *A) 2767 { 2768 Mat_SeqAIJ *a; 2769 Mat B; 2770 int i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N; 2771 MPI_Comm comm; 2772 2773 PetscFunctionBegin; 2774 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 2775 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2776 if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor"); 2777 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 2778 ierr = PetscBinaryRead(fd,header,4,PETSC_INT);CHKERRQ(ierr); 2779 if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file"); 2780 M = header[1]; N = header[2]; nz = header[3]; 2781 2782 if (nz < 0) { 2783 SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ"); 2784 } 2785 2786 /* read in row lengths */ 2787 ierr = PetscMalloc(M*sizeof(int),&rowlengths);CHKERRQ(ierr); 2788 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 2789 2790 /* create our matrix */ 2791 ierr = MatCreate(comm,PETSC_DECIDE,PETSC_DECIDE,M,N,&B);CHKERRQ(ierr); 2792 ierr = MatSetType(B,type);CHKERRQ(ierr); 2793 ierr = MatSeqAIJSetPreallocation(B,0,rowlengths);CHKERRQ(ierr); 2794 a = (Mat_SeqAIJ*)B->data; 2795 2796 /* read in column indices and adjust for Fortran indexing*/ 2797 ierr = PetscBinaryRead(fd,a->j,nz,PETSC_INT);CHKERRQ(ierr); 2798 2799 /* read in nonzero values */ 2800 ierr = PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);CHKERRQ(ierr); 2801 2802 /* set matrix "i" values */ 2803 a->i[0] = 0; 2804 for (i=1; i<= M; i++) { 2805 a->i[i] = a->i[i-1] + rowlengths[i-1]; 2806 a->ilen[i-1] = rowlengths[i-1]; 2807 } 2808 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 2809 2810 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2811 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2812 *A = B; 2813 PetscFunctionReturn(0); 2814 } 2815 2816 #undef __FUNCT__ 2817 #define __FUNCT__ "MatEqual_SeqAIJ" 2818 int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg) 2819 { 2820 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data; 2821 int ierr; 2822 2823 PetscFunctionBegin; 2824 /* If the matrix dimensions are not equal,or no of nonzeros */ 2825 if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)) { 2826 *flg = PETSC_FALSE; 2827 PetscFunctionReturn(0); 2828 } 2829 2830 /* if the a->i are the same */ 2831 ierr = PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);CHKERRQ(ierr); 2832 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2833 2834 /* if a->j are the same */ 2835 ierr = PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);CHKERRQ(ierr); 2836 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2837 2838 /* if a->a are the same */ 2839 ierr = PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);CHKERRQ(ierr); 2840 2841 PetscFunctionReturn(0); 2842 2843 } 2844 2845 #undef __FUNCT__ 2846 #define __FUNCT__ "MatCreateSeqAIJWithArrays" 2847 /*@C 2848 MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format) 2849 provided by the user. 2850 2851 Coolective on MPI_Comm 2852 2853 Input Parameters: 2854 + comm - must be an MPI communicator of size 1 2855 . m - number of rows 2856 . n - number of columns 2857 . i - row indices 2858 . j - column indices 2859 - a - matrix values 2860 2861 Output Parameter: 2862 . mat - the matrix 2863 2864 Level: intermediate 2865 2866 Notes: 2867 The i, j, and a arrays are not copied by this routine, the user must free these arrays 2868 once the matrix is destroyed 2869 2870 You cannot set new nonzero locations into this matrix, that will generate an error. 2871 2872 The i and j indices are 0 based 2873 2874 .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ() 2875 2876 @*/ 2877 int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat) 2878 { 2879 int ierr,ii; 2880 Mat_SeqAIJ *aij; 2881 2882 PetscFunctionBegin; 2883 ierr = MatCreate(comm,m,n,m,n,mat);CHKERRQ(ierr); 2884 ierr = MatSetType(*mat,MATSEQAIJ);CHKERRQ(ierr); 2885 ierr = MatSeqAIJSetPreallocation(*mat,SKIP_ALLOCATION,0);CHKERRQ(ierr); 2886 aij = (Mat_SeqAIJ*)(*mat)->data; 2887 2888 if (i[0] != 0) { 2889 SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0"); 2890 } 2891 aij->i = i; 2892 aij->j = j; 2893 aij->a = a; 2894 aij->singlemalloc = PETSC_FALSE; 2895 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 2896 aij->freedata = PETSC_FALSE; 2897 2898 for (ii=0; ii<m; ii++) { 2899 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii]; 2900 #if defined(PETSC_USE_BOPT_g) 2901 if (i[ii+1] - i[ii] < 0) SETERRQ2(1,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]); 2902 #endif 2903 } 2904 #if defined(PETSC_USE_BOPT_g) 2905 for (ii=0; ii<aij->i[m]; ii++) { 2906 if (j[ii] < 0) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]); 2907 if (j[ii] > n - 1) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]); 2908 } 2909 #endif 2910 2911 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2912 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2913 PetscFunctionReturn(0); 2914 } 2915 2916 #undef __FUNCT__ 2917 #define __FUNCT__ "MatSetColoring_SeqAIJ" 2918 int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring) 2919 { 2920 int ierr; 2921 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2922 2923 PetscFunctionBegin; 2924 if (coloring->ctype == IS_COLORING_LOCAL) { 2925 ierr = ISColoringReference(coloring);CHKERRQ(ierr); 2926 a->coloring = coloring; 2927 } else if (coloring->ctype == IS_COLORING_GHOSTED) { 2928 int i,*larray; 2929 ISColoring ocoloring; 2930 ISColoringValue *colors; 2931 2932 /* set coloring for diagonal portion */ 2933 ierr = PetscMalloc((A->n+1)*sizeof(int),&larray);CHKERRQ(ierr); 2934 for (i=0; i<A->n; i++) { 2935 larray[i] = i; 2936 } 2937 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);CHKERRQ(ierr); 2938 ierr = PetscMalloc((A->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 2939 for (i=0; i<A->n; i++) { 2940 colors[i] = coloring->colors[larray[i]]; 2941 } 2942 ierr = PetscFree(larray);CHKERRQ(ierr); 2943 ierr = ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);CHKERRQ(ierr); 2944 a->coloring = ocoloring; 2945 } 2946 PetscFunctionReturn(0); 2947 } 2948 2949 #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 2950 EXTERN_C_BEGIN 2951 #include "adic/ad_utils.h" 2952 EXTERN_C_END 2953 2954 #undef __FUNCT__ 2955 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 2956 int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 2957 { 2958 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2959 int m = A->m,*ii = a->i,*jj = a->j,nz,i,j,nlen; 2960 PetscScalar *v = a->a,*values = ((PetscScalar*)advalues)+1; 2961 ISColoringValue *color; 2962 2963 PetscFunctionBegin; 2964 if (!a->coloring) SETERRQ(1,"Coloring not set for matrix"); 2965 nlen = PetscADGetDerivTypeSize()/sizeof(PetscScalar); 2966 color = a->coloring->colors; 2967 /* loop over rows */ 2968 for (i=0; i<m; i++) { 2969 nz = ii[i+1] - ii[i]; 2970 /* loop over columns putting computed value into matrix */ 2971 for (j=0; j<nz; j++) { 2972 *v++ = values[color[*jj++]]; 2973 } 2974 values += nlen; /* jump to next row of derivatives */ 2975 } 2976 PetscFunctionReturn(0); 2977 } 2978 2979 #else 2980 2981 #undef __FUNCT__ 2982 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 2983 int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 2984 { 2985 PetscFunctionBegin; 2986 SETERRQ(1,"PETSc installed without ADIC"); 2987 } 2988 2989 #endif 2990 2991 #undef __FUNCT__ 2992 #define __FUNCT__ "MatSetValuesAdifor_SeqAIJ" 2993 int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues) 2994 { 2995 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2996 int m = A->m,*ii = a->i,*jj = a->j,nz,i,j; 2997 PetscScalar *v = a->a,*values = (PetscScalar *)advalues; 2998 ISColoringValue *color; 2999 3000 PetscFunctionBegin; 3001 if (!a->coloring) SETERRQ(1,"Coloring not set for matrix"); 3002 color = a->coloring->colors; 3003 /* loop over rows */ 3004 for (i=0; i<m; i++) { 3005 nz = ii[i+1] - ii[i]; 3006 /* loop over columns putting computed value into matrix */ 3007 for (j=0; j<nz; j++) { 3008 *v++ = values[color[*jj++]]; 3009 } 3010 values += nl; /* jump to next row of derivatives */ 3011 } 3012 PetscFunctionReturn(0); 3013 } 3014 3015