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 PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscTruth *done) 15 { 16 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 17 PetscErrorCode ierr; 18 PetscInt i,ishift; 19 20 PetscFunctionBegin; 21 *m = A->m; 22 if (!ia) PetscFunctionReturn(0); 23 ishift = 0; 24 if (symmetric && !A->structurally_symmetric) { 25 ierr = MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);CHKERRQ(ierr); 26 } else if (oshift == 1) { 27 PetscInt nz = a->i[A->m]; 28 /* malloc space and add 1 to i and j indices */ 29 ierr = PetscMalloc((A->m+1)*sizeof(PetscInt),ia);CHKERRQ(ierr); 30 ierr = PetscMalloc((nz+1)*sizeof(PetscInt),ja);CHKERRQ(ierr); 31 for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1; 32 for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1; 33 } else { 34 *ia = a->i; *ja = a->j; 35 } 36 PetscFunctionReturn(0); 37 } 38 39 #undef __FUNCT__ 40 #define __FUNCT__ "MatRestoreRowIJ_SeqAIJ" 41 PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done) 42 { 43 PetscErrorCode ierr; 44 45 PetscFunctionBegin; 46 if (!ia) PetscFunctionReturn(0); 47 if ((symmetric && !A->structurally_symmetric) || oshift == 1) { 48 ierr = PetscFree(*ia);CHKERRQ(ierr); 49 ierr = PetscFree(*ja);CHKERRQ(ierr); 50 } 51 PetscFunctionReturn(0); 52 } 53 54 #undef __FUNCT__ 55 #define __FUNCT__ "MatGetColumnIJ_SeqAIJ" 56 PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done) 57 { 58 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 59 PetscErrorCode ierr; 60 PetscInt i,*collengths,*cia,*cja,n = A->n,m = A->m; 61 PetscInt nz = a->i[m],row,*jj,mr,col; 62 63 PetscFunctionBegin; 64 *nn = A->n; 65 if (!ia) PetscFunctionReturn(0); 66 if (symmetric) { 67 ierr = MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,0,oshift,ia,ja);CHKERRQ(ierr); 68 } else { 69 ierr = PetscMalloc((n+1)*sizeof(PetscInt),&collengths);CHKERRQ(ierr); 70 ierr = PetscMemzero(collengths,n*sizeof(PetscInt));CHKERRQ(ierr); 71 ierr = PetscMalloc((n+1)*sizeof(PetscInt),&cia);CHKERRQ(ierr); 72 ierr = PetscMalloc((nz+1)*sizeof(PetscInt),&cja);CHKERRQ(ierr); 73 jj = a->j; 74 for (i=0; i<nz; i++) { 75 collengths[jj[i]]++; 76 } 77 cia[0] = oshift; 78 for (i=0; i<n; i++) { 79 cia[i+1] = cia[i] + collengths[i]; 80 } 81 ierr = PetscMemzero(collengths,n*sizeof(PetscInt));CHKERRQ(ierr); 82 jj = a->j; 83 for (row=0; row<m; row++) { 84 mr = a->i[row+1] - a->i[row]; 85 for (i=0; i<mr; i++) { 86 col = *jj++; 87 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 88 } 89 } 90 ierr = PetscFree(collengths);CHKERRQ(ierr); 91 *ia = cia; *ja = cja; 92 } 93 PetscFunctionReturn(0); 94 } 95 96 #undef __FUNCT__ 97 #define __FUNCT__ "MatRestoreColumnIJ_SeqAIJ" 98 PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done) 99 { 100 PetscErrorCode ierr; 101 102 PetscFunctionBegin; 103 if (!ia) PetscFunctionReturn(0); 104 105 ierr = PetscFree(*ia);CHKERRQ(ierr); 106 ierr = PetscFree(*ja);CHKERRQ(ierr); 107 108 PetscFunctionReturn(0); 109 } 110 111 #define CHUNKSIZE 15 112 113 #undef __FUNCT__ 114 #define __FUNCT__ "MatSetValues_SeqAIJ" 115 PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is) 116 { 117 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 118 PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,sorted = a->sorted; 119 PetscInt *imax = a->imax,*ai = a->i,*ailen = a->ilen; 120 PetscErrorCode ierr; 121 PetscInt *aj = a->j,nonew = a->nonew; 122 PetscScalar *ap,value,*aa = a->a; 123 PetscTruth ignorezeroentries = ((a->ignorezeroentries && is == ADD_VALUES) ? PETSC_TRUE:PETSC_FALSE); 124 PetscTruth roworiented = a->roworiented; 125 126 PetscFunctionBegin; 127 for (k=0; k<m; k++) { /* loop over added rows */ 128 row = im[k]; 129 if (row < 0) continue; 130 #if defined(PETSC_USE_BOPT_g) 131 if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->m-1); 132 #endif 133 rp = aj + ai[row]; ap = aa + ai[row]; 134 rmax = imax[row]; nrow = ailen[row]; 135 low = 0; 136 for (l=0; l<n; l++) { /* loop over added columns */ 137 if (in[l] < 0) continue; 138 #if defined(PETSC_USE_BOPT_g) 139 if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->n-1); 140 #endif 141 col = in[l]; 142 if (roworiented) { 143 value = v[l + k*n]; 144 } else { 145 value = v[k + l*m]; 146 } 147 if (value == 0.0 && ignorezeroentries) continue; 148 149 if (!sorted) low = 0; high = nrow; 150 while (high-low > 5) { 151 t = (low+high)/2; 152 if (rp[t] > col) high = t; 153 else low = t; 154 } 155 for (i=low; i<high; i++) { 156 if (rp[i] > col) break; 157 if (rp[i] == col) { 158 if (is == ADD_VALUES) ap[i] += value; 159 else ap[i] = value; 160 goto noinsert; 161 } 162 } 163 if (nonew == 1) goto noinsert; 164 else if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col); 165 if (nrow >= rmax) { 166 /* there is no extra room in row, therefore enlarge */ 167 PetscInt new_nz = ai[A->m] + CHUNKSIZE,*new_i,*new_j; 168 size_t len; 169 PetscScalar *new_a; 170 171 if (nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix requiring new malloc()",row,col); 172 173 /* malloc new storage space */ 174 len = ((size_t) new_nz)*(sizeof(PetscInt)+sizeof(PetscScalar))+(A->m+1)*sizeof(PetscInt); 175 ierr = PetscMalloc(len,&new_a);CHKERRQ(ierr); 176 new_j = (PetscInt*)(new_a + new_nz); 177 new_i = new_j + new_nz; 178 179 /* copy over old data into new slots */ 180 for (ii=0; ii<row+1; ii++) {new_i[ii] = ai[ii];} 181 for (ii=row+1; ii<A->m+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;} 182 ierr = PetscMemcpy(new_j,aj,(ai[row]+nrow)*sizeof(PetscInt));CHKERRQ(ierr); 183 len = (((size_t) new_nz) - CHUNKSIZE - ai[row] - nrow ); 184 ierr = PetscMemcpy(new_j+ai[row]+nrow+CHUNKSIZE,aj+ai[row]+nrow,len*sizeof(PetscInt));CHKERRQ(ierr); 185 ierr = PetscMemcpy(new_a,aa,(((size_t) ai[row])+nrow)*sizeof(PetscScalar));CHKERRQ(ierr); 186 ierr = PetscMemcpy(new_a+ai[row]+nrow+CHUNKSIZE,aa+ai[row]+nrow,len*sizeof(PetscScalar));CHKERRQ(ierr); 187 /* free up old matrix storage */ 188 ierr = PetscFree(a->a);CHKERRQ(ierr); 189 if (!a->singlemalloc) { 190 ierr = PetscFree(a->i);CHKERRQ(ierr); 191 ierr = PetscFree(a->j);CHKERRQ(ierr); 192 } 193 aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j; 194 a->singlemalloc = PETSC_TRUE; 195 196 rp = aj + ai[row]; ap = aa + ai[row] ; 197 rmax = imax[row] = imax[row] + CHUNKSIZE; 198 PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(PetscInt) + sizeof(PetscScalar))); 199 a->maxnz += CHUNKSIZE; 200 a->reallocs++; 201 } 202 N = nrow++ - 1; a->nz++; 203 /* shift up all the later entries in this row */ 204 for (ii=N; ii>=i; ii--) { 205 rp[ii+1] = rp[ii]; 206 ap[ii+1] = ap[ii]; 207 } 208 rp[i] = col; 209 ap[i] = value; 210 noinsert:; 211 low = i + 1; 212 } 213 ailen[row] = nrow; 214 } 215 PetscFunctionReturn(0); 216 } 217 218 #undef __FUNCT__ 219 #define __FUNCT__ "MatGetValues_SeqAIJ" 220 PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[]) 221 { 222 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 223 PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j; 224 PetscInt *ai = a->i,*ailen = a->ilen; 225 PetscScalar *ap,*aa = a->a,zero = 0.0; 226 227 PetscFunctionBegin; 228 for (k=0; k<m; k++) { /* loop over rows */ 229 row = im[k]; 230 if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); 231 if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->m-1); 232 rp = aj + ai[row]; ap = aa + ai[row]; 233 nrow = ailen[row]; 234 for (l=0; l<n; l++) { /* loop over columns */ 235 if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); 236 if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->n-1); 237 col = in[l] ; 238 high = nrow; low = 0; /* assume unsorted */ 239 while (high-low > 5) { 240 t = (low+high)/2; 241 if (rp[t] > col) high = t; 242 else low = t; 243 } 244 for (i=low; i<high; i++) { 245 if (rp[i] > col) break; 246 if (rp[i] == col) { 247 *v++ = ap[i]; 248 goto finished; 249 } 250 } 251 *v++ = zero; 252 finished:; 253 } 254 } 255 PetscFunctionReturn(0); 256 } 257 258 259 #undef __FUNCT__ 260 #define __FUNCT__ "MatView_SeqAIJ_Binary" 261 PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer) 262 { 263 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 264 PetscErrorCode ierr; 265 PetscInt i,*col_lens; 266 int fd; 267 268 PetscFunctionBegin; 269 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 270 ierr = PetscMalloc((4+A->m)*sizeof(PetscInt),&col_lens);CHKERRQ(ierr); 271 col_lens[0] = MAT_FILE_COOKIE; 272 col_lens[1] = A->m; 273 col_lens[2] = A->n; 274 col_lens[3] = a->nz; 275 276 /* store lengths of each row and write (including header) to file */ 277 for (i=0; i<A->m; i++) { 278 col_lens[4+i] = a->i[i+1] - a->i[i]; 279 } 280 ierr = PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,PETSC_TRUE);CHKERRQ(ierr); 281 ierr = PetscFree(col_lens);CHKERRQ(ierr); 282 283 /* store column indices (zero start index) */ 284 ierr = PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,PETSC_FALSE);CHKERRQ(ierr); 285 286 /* store nonzero values */ 287 ierr = PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 288 PetscFunctionReturn(0); 289 } 290 291 EXTERN PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer); 292 293 #undef __FUNCT__ 294 #define __FUNCT__ "MatView_SeqAIJ_ASCII" 295 PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer) 296 { 297 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 298 PetscErrorCode ierr; 299 PetscInt i,j,m = A->m,shift=0; 300 char *name; 301 PetscViewerFormat format; 302 303 PetscFunctionBegin; 304 ierr = PetscObjectGetName((PetscObject)A,&name);CHKERRQ(ierr); 305 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 306 if (format == PETSC_VIEWER_ASCII_INFO_DETAIL || format == PETSC_VIEWER_ASCII_INFO) { 307 if (a->inode.size) { 308 ierr = PetscViewerASCIIPrintf(viewer,"using I-node routines: found %D nodes, limit used is %D\n",a->inode.node_count,a->inode.limit);CHKERRQ(ierr); 309 } else { 310 ierr = PetscViewerASCIIPrintf(viewer,"not using I-node routines\n");CHKERRQ(ierr); 311 } 312 } else if (format == PETSC_VIEWER_ASCII_MATLAB) { 313 PetscInt nofinalvalue = 0; 314 if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->n-!shift)) { 315 nofinalvalue = 1; 316 } 317 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 318 ierr = PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->n);CHKERRQ(ierr); 319 ierr = PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);CHKERRQ(ierr); 320 ierr = PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);CHKERRQ(ierr); 321 ierr = PetscViewerASCIIPrintf(viewer,"zzz = [\n");CHKERRQ(ierr); 322 323 for (i=0; i<m; i++) { 324 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 325 #if defined(PETSC_USE_COMPLEX) 326 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); 327 #else 328 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);CHKERRQ(ierr); 329 #endif 330 } 331 } 332 if (nofinalvalue) { 333 ierr = PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->n,0.0);CHKERRQ(ierr); 334 } 335 ierr = PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);CHKERRQ(ierr); 336 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 337 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) { 338 PetscFunctionReturn(0); 339 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 340 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 341 for (i=0; i<m; i++) { 342 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 343 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 344 #if defined(PETSC_USE_COMPLEX) 345 if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) { 346 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 347 } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) { 348 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 349 } else if (PetscRealPart(a->a[j]) != 0.0) { 350 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));CHKERRQ(ierr); 351 } 352 #else 353 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,a->a[j]);CHKERRQ(ierr);} 354 #endif 355 } 356 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 357 } 358 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 359 } else if (format == PETSC_VIEWER_ASCII_SYMMODU) { 360 PetscInt nzd=0,fshift=1,*sptr; 361 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 362 ierr = PetscMalloc((m+1)*sizeof(PetscInt),&sptr);CHKERRQ(ierr); 363 for (i=0; i<m; i++) { 364 sptr[i] = nzd+1; 365 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 366 if (a->j[j] >= i) { 367 #if defined(PETSC_USE_COMPLEX) 368 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++; 369 #else 370 if (a->a[j] != 0.0) nzd++; 371 #endif 372 } 373 } 374 } 375 sptr[m] = nzd+1; 376 ierr = PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);CHKERRQ(ierr); 377 for (i=0; i<m+1; i+=6) { 378 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);} 379 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);} 380 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);} 381 else if (i+1<m) {ierr = PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);CHKERRQ(ierr);} 382 else if (i<m) {ierr = PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);CHKERRQ(ierr);} 383 else {ierr = PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);CHKERRQ(ierr);} 384 } 385 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 386 ierr = PetscFree(sptr);CHKERRQ(ierr); 387 for (i=0; i<m; i++) { 388 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 389 if (a->j[j] >= i) {ierr = PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);CHKERRQ(ierr);} 390 } 391 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 392 } 393 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 394 for (i=0; i<m; i++) { 395 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 396 if (a->j[j] >= i) { 397 #if defined(PETSC_USE_COMPLEX) 398 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) { 399 ierr = PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 400 } 401 #else 402 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);CHKERRQ(ierr);} 403 #endif 404 } 405 } 406 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 407 } 408 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 409 } else if (format == PETSC_VIEWER_ASCII_DENSE) { 410 PetscInt cnt = 0,jcnt; 411 PetscScalar value; 412 413 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 414 for (i=0; i<m; i++) { 415 jcnt = 0; 416 for (j=0; j<A->n; j++) { 417 if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) { 418 value = a->a[cnt++]; 419 jcnt++; 420 } else { 421 value = 0.0; 422 } 423 #if defined(PETSC_USE_COMPLEX) 424 ierr = PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));CHKERRQ(ierr); 425 #else 426 ierr = PetscViewerASCIIPrintf(viewer," %7.5e ",value);CHKERRQ(ierr); 427 #endif 428 } 429 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 430 } 431 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 432 } else { 433 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 434 for (i=0; i<m; i++) { 435 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 436 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 437 #if defined(PETSC_USE_COMPLEX) 438 if (PetscImaginaryPart(a->a[j]) > 0.0) { 439 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 440 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 441 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 442 } else { 443 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));CHKERRQ(ierr); 444 } 445 #else 446 ierr = PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,a->a[j]);CHKERRQ(ierr); 447 #endif 448 } 449 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 450 } 451 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 452 } 453 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 454 PetscFunctionReturn(0); 455 } 456 457 #undef __FUNCT__ 458 #define __FUNCT__ "MatView_SeqAIJ_Draw_Zoom" 459 PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa) 460 { 461 Mat A = (Mat) Aa; 462 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 463 PetscErrorCode ierr; 464 PetscInt i,j,m = A->m,color; 465 PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0; 466 PetscViewer viewer; 467 PetscViewerFormat format; 468 469 PetscFunctionBegin; 470 ierr = PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);CHKERRQ(ierr); 471 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 472 473 ierr = PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);CHKERRQ(ierr); 474 /* loop over matrix elements drawing boxes */ 475 476 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 477 /* Blue for negative, Cyan for zero and Red for positive */ 478 color = PETSC_DRAW_BLUE; 479 for (i=0; i<m; i++) { 480 y_l = m - i - 1.0; y_r = y_l + 1.0; 481 for (j=a->i[i]; j<a->i[i+1]; j++) { 482 x_l = a->j[j] ; x_r = x_l + 1.0; 483 #if defined(PETSC_USE_COMPLEX) 484 if (PetscRealPart(a->a[j]) >= 0.) continue; 485 #else 486 if (a->a[j] >= 0.) continue; 487 #endif 488 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 489 } 490 } 491 color = PETSC_DRAW_CYAN; 492 for (i=0; i<m; i++) { 493 y_l = m - i - 1.0; y_r = y_l + 1.0; 494 for (j=a->i[i]; j<a->i[i+1]; j++) { 495 x_l = a->j[j]; x_r = x_l + 1.0; 496 if (a->a[j] != 0.) continue; 497 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 498 } 499 } 500 color = PETSC_DRAW_RED; 501 for (i=0; i<m; i++) { 502 y_l = m - i - 1.0; y_r = y_l + 1.0; 503 for (j=a->i[i]; j<a->i[i+1]; j++) { 504 x_l = a->j[j]; x_r = x_l + 1.0; 505 #if defined(PETSC_USE_COMPLEX) 506 if (PetscRealPart(a->a[j]) <= 0.) continue; 507 #else 508 if (a->a[j] <= 0.) continue; 509 #endif 510 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 511 } 512 } 513 } else { 514 /* use contour shading to indicate magnitude of values */ 515 /* first determine max of all nonzero values */ 516 PetscInt nz = a->nz,count; 517 PetscDraw popup; 518 PetscReal scale; 519 520 for (i=0; i<nz; i++) { 521 if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]); 522 } 523 scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv; 524 ierr = PetscDrawGetPopup(draw,&popup);CHKERRQ(ierr); 525 if (popup) {ierr = PetscDrawScalePopup(popup,0.0,maxv);CHKERRQ(ierr);} 526 count = 0; 527 for (i=0; i<m; i++) { 528 y_l = m - i - 1.0; y_r = y_l + 1.0; 529 for (j=a->i[i]; j<a->i[i+1]; j++) { 530 x_l = a->j[j]; x_r = x_l + 1.0; 531 color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count])); 532 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 533 count++; 534 } 535 } 536 } 537 PetscFunctionReturn(0); 538 } 539 540 #undef __FUNCT__ 541 #define __FUNCT__ "MatView_SeqAIJ_Draw" 542 PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer) 543 { 544 PetscErrorCode ierr; 545 PetscDraw draw; 546 PetscReal xr,yr,xl,yl,h,w; 547 PetscTruth isnull; 548 549 PetscFunctionBegin; 550 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 551 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); 552 if (isnull) PetscFunctionReturn(0); 553 554 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);CHKERRQ(ierr); 555 xr = A->n; yr = A->m; h = yr/10.0; w = xr/10.0; 556 xr += w; yr += h; xl = -w; yl = -h; 557 ierr = PetscDrawSetCoordinates(draw,xl,yl,xr,yr);CHKERRQ(ierr); 558 ierr = PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);CHKERRQ(ierr); 559 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);CHKERRQ(ierr); 560 PetscFunctionReturn(0); 561 } 562 563 #undef __FUNCT__ 564 #define __FUNCT__ "MatView_SeqAIJ" 565 PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer) 566 { 567 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 568 PetscErrorCode ierr; 569 PetscTruth issocket,iascii,isbinary,isdraw; 570 571 PetscFunctionBegin; 572 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);CHKERRQ(ierr); 573 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);CHKERRQ(ierr); 574 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 575 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 576 if (issocket) { 577 ierr = PetscViewerSocketPutSparse_Private(viewer,A->m,A->n,a->nz,a->a,a->i,a->j);CHKERRQ(ierr); 578 } else if (iascii) { 579 ierr = MatView_SeqAIJ_ASCII(A,viewer);CHKERRQ(ierr); 580 } else if (isbinary) { 581 ierr = MatView_SeqAIJ_Binary(A,viewer);CHKERRQ(ierr); 582 } else if (isdraw) { 583 ierr = MatView_SeqAIJ_Draw(A,viewer);CHKERRQ(ierr); 584 } else { 585 SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name); 586 } 587 PetscFunctionReturn(0); 588 } 589 590 #undef __FUNCT__ 591 #define __FUNCT__ "MatAssemblyEnd_SeqAIJ" 592 PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode) 593 { 594 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 595 PetscErrorCode ierr; 596 PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax; 597 PetscInt m = A->m,*ip,N,*ailen = a->ilen,rmax = 0; 598 PetscScalar *aa = a->a,*ap; 599 600 PetscFunctionBegin; 601 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 602 603 if (m) rmax = ailen[0]; /* determine row with most nonzeros */ 604 for (i=1; i<m; i++) { 605 /* move each row back by the amount of empty slots (fshift) before it*/ 606 fshift += imax[i-1] - ailen[i-1]; 607 rmax = PetscMax(rmax,ailen[i]); 608 if (fshift) { 609 ip = aj + ai[i] ; 610 ap = aa + ai[i] ; 611 N = ailen[i]; 612 for (j=0; j<N; j++) { 613 ip[j-fshift] = ip[j]; 614 ap[j-fshift] = ap[j]; 615 } 616 } 617 ai[i] = ai[i-1] + ailen[i-1]; 618 } 619 if (m) { 620 fshift += imax[m-1] - ailen[m-1]; 621 ai[m] = ai[m-1] + ailen[m-1]; 622 } 623 /* reset ilen and imax for each row */ 624 for (i=0; i<m; i++) { 625 ailen[i] = imax[i] = ai[i+1] - ai[i]; 626 } 627 a->nz = ai[m]; 628 629 /* diagonals may have moved, so kill the diagonal pointers */ 630 if (fshift && a->diag) { 631 ierr = PetscFree(a->diag);CHKERRQ(ierr); 632 PetscLogObjectMemory(A,-(m+1)*sizeof(PetscInt)); 633 a->diag = 0; 634 } 635 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->n,fshift,a->nz); 636 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Number of mallocs during MatSetValues() is %D\n",a->reallocs); 637 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Most nonzeros in any row is %D\n",rmax); 638 a->reallocs = 0; 639 A->info.nz_unneeded = (double)fshift; 640 a->rmax = rmax; 641 642 /* check out for identical nodes. If found, use inode functions */ 643 ierr = Mat_AIJ_CheckInode(A,(PetscTruth)(!fshift));CHKERRQ(ierr); 644 645 PetscFunctionReturn(0); 646 } 647 648 #undef __FUNCT__ 649 #define __FUNCT__ "MatZeroEntries_SeqAIJ" 650 PetscErrorCode MatZeroEntries_SeqAIJ(Mat A) 651 { 652 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 653 PetscErrorCode ierr; 654 655 PetscFunctionBegin; 656 ierr = PetscMemzero(a->a,(a->i[A->m])*sizeof(PetscScalar));CHKERRQ(ierr); 657 PetscFunctionReturn(0); 658 } 659 660 #undef __FUNCT__ 661 #define __FUNCT__ "MatDestroy_SeqAIJ" 662 PetscErrorCode MatDestroy_SeqAIJ(Mat A) 663 { 664 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 665 PetscErrorCode ierr; 666 667 PetscFunctionBegin; 668 #if defined(PETSC_USE_LOG) 669 PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->m,A->n,a->nz); 670 #endif 671 if (a->freedata) { 672 ierr = PetscFree(a->a);CHKERRQ(ierr); 673 if (!a->singlemalloc) { 674 ierr = PetscFree(a->i);CHKERRQ(ierr); 675 ierr = PetscFree(a->j);CHKERRQ(ierr); 676 } 677 } 678 if (a->row) { 679 ierr = ISDestroy(a->row);CHKERRQ(ierr); 680 } 681 if (a->col) { 682 ierr = ISDestroy(a->col);CHKERRQ(ierr); 683 } 684 if (a->diag) {ierr = PetscFree(a->diag);CHKERRQ(ierr);} 685 if (a->ilen) {ierr = PetscFree(a->ilen);CHKERRQ(ierr);} 686 if (a->imax) {ierr = PetscFree(a->imax);CHKERRQ(ierr);} 687 if (a->idiag) {ierr = PetscFree(a->idiag);CHKERRQ(ierr);} 688 if (a->solve_work) {ierr = PetscFree(a->solve_work);CHKERRQ(ierr);} 689 if (a->inode.size) {ierr = PetscFree(a->inode.size);CHKERRQ(ierr);} 690 if (a->icol) {ierr = ISDestroy(a->icol);CHKERRQ(ierr);} 691 if (a->saved_values) {ierr = PetscFree(a->saved_values);CHKERRQ(ierr);} 692 if (a->coloring) {ierr = ISColoringDestroy(a->coloring);CHKERRQ(ierr);} 693 if (a->xtoy) {ierr = PetscFree(a->xtoy);CHKERRQ(ierr);} 694 695 ierr = PetscFree(a);CHKERRQ(ierr); 696 697 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);CHKERRQ(ierr); 698 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);CHKERRQ(ierr); 699 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);CHKERRQ(ierr); 700 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);CHKERRQ(ierr); 701 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);CHKERRQ(ierr); 702 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);CHKERRQ(ierr); 703 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);CHKERRQ(ierr); 704 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);CHKERRQ(ierr); 705 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatAdjustForInodes_C","",PETSC_NULL);CHKERRQ(ierr); 706 ierr = PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJGetInodeSizes_C","",PETSC_NULL);CHKERRQ(ierr); 707 PetscFunctionReturn(0); 708 } 709 710 #undef __FUNCT__ 711 #define __FUNCT__ "MatCompress_SeqAIJ" 712 PetscErrorCode MatCompress_SeqAIJ(Mat A) 713 { 714 PetscFunctionBegin; 715 PetscFunctionReturn(0); 716 } 717 718 #undef __FUNCT__ 719 #define __FUNCT__ "MatSetOption_SeqAIJ" 720 PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op) 721 { 722 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 723 724 PetscFunctionBegin; 725 switch (op) { 726 case MAT_ROW_ORIENTED: 727 a->roworiented = PETSC_TRUE; 728 break; 729 case MAT_KEEP_ZEROED_ROWS: 730 a->keepzeroedrows = PETSC_TRUE; 731 break; 732 case MAT_COLUMN_ORIENTED: 733 a->roworiented = PETSC_FALSE; 734 break; 735 case MAT_COLUMNS_SORTED: 736 a->sorted = PETSC_TRUE; 737 break; 738 case MAT_COLUMNS_UNSORTED: 739 a->sorted = PETSC_FALSE; 740 break; 741 case MAT_NO_NEW_NONZERO_LOCATIONS: 742 a->nonew = 1; 743 break; 744 case MAT_NEW_NONZERO_LOCATION_ERR: 745 a->nonew = -1; 746 break; 747 case MAT_NEW_NONZERO_ALLOCATION_ERR: 748 a->nonew = -2; 749 break; 750 case MAT_YES_NEW_NONZERO_LOCATIONS: 751 a->nonew = 0; 752 break; 753 case MAT_IGNORE_ZERO_ENTRIES: 754 a->ignorezeroentries = PETSC_TRUE; 755 break; 756 case MAT_USE_INODES: 757 a->inode.use = PETSC_TRUE; 758 break; 759 case MAT_DO_NOT_USE_INODES: 760 a->inode.use = PETSC_FALSE; 761 break; 762 case MAT_ROWS_SORTED: 763 case MAT_ROWS_UNSORTED: 764 case MAT_YES_NEW_DIAGONALS: 765 case MAT_IGNORE_OFF_PROC_ENTRIES: 766 case MAT_USE_HASH_TABLE: 767 PetscLogInfo(A,"MatSetOption_SeqAIJ:Option ignored\n"); 768 break; 769 case MAT_NO_NEW_DIAGONALS: 770 SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS"); 771 case MAT_INODE_LIMIT_1: 772 a->inode.limit = 1; 773 break; 774 case MAT_INODE_LIMIT_2: 775 a->inode.limit = 2; 776 break; 777 case MAT_INODE_LIMIT_3: 778 a->inode.limit = 3; 779 break; 780 case MAT_INODE_LIMIT_4: 781 a->inode.limit = 4; 782 break; 783 case MAT_INODE_LIMIT_5: 784 a->inode.limit = 5; 785 break; 786 case MAT_SYMMETRIC: 787 case MAT_STRUCTURALLY_SYMMETRIC: 788 case MAT_NOT_SYMMETRIC: 789 case MAT_NOT_STRUCTURALLY_SYMMETRIC: 790 case MAT_HERMITIAN: 791 case MAT_NOT_HERMITIAN: 792 case MAT_SYMMETRY_ETERNAL: 793 case MAT_NOT_SYMMETRY_ETERNAL: 794 break; 795 default: 796 SETERRQ(PETSC_ERR_SUP,"unknown option"); 797 } 798 PetscFunctionReturn(0); 799 } 800 801 #undef __FUNCT__ 802 #define __FUNCT__ "MatGetDiagonal_SeqAIJ" 803 PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v) 804 { 805 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 806 PetscErrorCode ierr; 807 PetscInt i,j,n; 808 PetscScalar *x,zero = 0.0; 809 810 PetscFunctionBegin; 811 ierr = VecSet(&zero,v);CHKERRQ(ierr); 812 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 813 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 814 if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 815 for (i=0; i<A->m; i++) { 816 for (j=a->i[i]; j<a->i[i+1]; j++) { 817 if (a->j[j] == i) { 818 x[i] = a->a[j]; 819 break; 820 } 821 } 822 } 823 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 824 PetscFunctionReturn(0); 825 } 826 827 828 #undef __FUNCT__ 829 #define __FUNCT__ "MatMultTransposeAdd_SeqAIJ" 830 PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy) 831 { 832 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 833 PetscScalar *x,*y; 834 PetscErrorCode ierr; 835 PetscInt m = A->m; 836 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 837 PetscScalar *v,alpha; 838 PetscInt n,i,*idx; 839 #endif 840 841 PetscFunctionBegin; 842 if (zz != yy) {ierr = VecCopy(zz,yy);CHKERRQ(ierr);} 843 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 844 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 845 846 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 847 fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y); 848 #else 849 for (i=0; i<m; i++) { 850 idx = a->j + a->i[i] ; 851 v = a->a + a->i[i] ; 852 n = a->i[i+1] - a->i[i]; 853 alpha = x[i]; 854 while (n-->0) {y[*idx++] += alpha * *v++;} 855 } 856 #endif 857 PetscLogFlops(2*a->nz); 858 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 859 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 860 PetscFunctionReturn(0); 861 } 862 863 #undef __FUNCT__ 864 #define __FUNCT__ "MatMultTranspose_SeqAIJ" 865 PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy) 866 { 867 PetscScalar zero = 0.0; 868 PetscErrorCode ierr; 869 870 PetscFunctionBegin; 871 ierr = VecSet(&zero,yy);CHKERRQ(ierr); 872 ierr = MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);CHKERRQ(ierr); 873 PetscFunctionReturn(0); 874 } 875 876 877 #undef __FUNCT__ 878 #define __FUNCT__ "MatMult_SeqAIJ" 879 PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy) 880 { 881 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 882 PetscScalar *x,*y,*v; 883 PetscErrorCode ierr; 884 PetscInt m = A->m,*idx,*ii; 885 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 886 PetscInt n,i,jrow,j; 887 PetscScalar sum; 888 #endif 889 890 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 891 #pragma disjoint(*x,*y,*v) 892 #endif 893 894 PetscFunctionBegin; 895 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 896 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 897 idx = a->j; 898 v = a->a; 899 ii = a->i; 900 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 901 fortranmultaij_(&m,x,ii,idx,v,y); 902 #else 903 for (i=0; i<m; i++) { 904 jrow = ii[i]; 905 n = ii[i+1] - jrow; 906 sum = 0.0; 907 for (j=0; j<n; j++) { 908 sum += v[jrow]*x[idx[jrow]]; jrow++; 909 } 910 y[i] = sum; 911 } 912 #endif 913 PetscLogFlops(2*a->nz - m); 914 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 915 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 916 PetscFunctionReturn(0); 917 } 918 919 #undef __FUNCT__ 920 #define __FUNCT__ "MatMultAdd_SeqAIJ" 921 PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz) 922 { 923 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 924 PetscScalar *x,*y,*z,*v; 925 PetscErrorCode ierr; 926 PetscInt m = A->m,*idx,*ii; 927 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 928 PetscInt n,i,jrow,j; 929 PetscScalar sum; 930 #endif 931 932 PetscFunctionBegin; 933 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 934 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 935 if (zz != yy) { 936 ierr = VecGetArray(zz,&z);CHKERRQ(ierr); 937 } else { 938 z = y; 939 } 940 941 idx = a->j; 942 v = a->a; 943 ii = a->i; 944 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 945 fortranmultaddaij_(&m,x,ii,idx,v,y,z); 946 #else 947 for (i=0; i<m; i++) { 948 jrow = ii[i]; 949 n = ii[i+1] - jrow; 950 sum = y[i]; 951 for (j=0; j<n; j++) { 952 sum += v[jrow]*x[idx[jrow]]; jrow++; 953 } 954 z[i] = sum; 955 } 956 #endif 957 PetscLogFlops(2*a->nz); 958 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 959 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 960 if (zz != yy) { 961 ierr = VecRestoreArray(zz,&z);CHKERRQ(ierr); 962 } 963 PetscFunctionReturn(0); 964 } 965 966 /* 967 Adds diagonal pointers to sparse matrix structure. 968 */ 969 #undef __FUNCT__ 970 #define __FUNCT__ "MatMarkDiagonal_SeqAIJ" 971 PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A) 972 { 973 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 974 PetscErrorCode ierr; 975 PetscInt i,j,*diag,m = A->m; 976 977 PetscFunctionBegin; 978 if (a->diag) PetscFunctionReturn(0); 979 980 ierr = PetscMalloc((m+1)*sizeof(PetscInt),&diag);CHKERRQ(ierr); 981 PetscLogObjectMemory(A,(m+1)*sizeof(PetscInt)); 982 for (i=0; i<A->m; i++) { 983 diag[i] = a->i[i+1]; 984 for (j=a->i[i]; j<a->i[i+1]; j++) { 985 if (a->j[j] == i) { 986 diag[i] = j; 987 break; 988 } 989 } 990 } 991 a->diag = diag; 992 PetscFunctionReturn(0); 993 } 994 995 /* 996 Checks for missing diagonals 997 */ 998 #undef __FUNCT__ 999 #define __FUNCT__ "MatMissingDiagonal_SeqAIJ" 1000 PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A) 1001 { 1002 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1003 PetscErrorCode ierr; 1004 PetscInt *diag,*jj = a->j,i; 1005 1006 PetscFunctionBegin; 1007 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1008 diag = a->diag; 1009 for (i=0; i<A->m; i++) { 1010 if (jj[diag[i]] != i) { 1011 SETERRQ1(PETSC_ERR_PLIB,"Matrix is missing diagonal number %D",i); 1012 } 1013 } 1014 PetscFunctionReturn(0); 1015 } 1016 1017 #undef __FUNCT__ 1018 #define __FUNCT__ "MatRelax_SeqAIJ" 1019 PetscErrorCode MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx) 1020 { 1021 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1022 PetscScalar *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag; 1023 const PetscScalar *v = a->a, *b, *bs,*xb, *ts; 1024 PetscErrorCode ierr; 1025 PetscInt n = A->n,m = A->m,i; 1026 const PetscInt *idx,*diag; 1027 1028 PetscFunctionBegin; 1029 its = its*lits; 1030 if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits); 1031 1032 if (!a->diag) {ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr);} 1033 diag = a->diag; 1034 if (!a->idiag) { 1035 ierr = PetscMalloc(3*m*sizeof(PetscScalar),&a->idiag);CHKERRQ(ierr); 1036 a->ssor = a->idiag + m; 1037 mdiag = a->ssor + m; 1038 1039 v = a->a; 1040 1041 /* this is wrong when fshift omega changes each iteration */ 1042 if (omega == 1.0 && !fshift) { 1043 for (i=0; i<m; i++) { 1044 mdiag[i] = v[diag[i]]; 1045 a->idiag[i] = 1.0/v[diag[i]]; 1046 } 1047 PetscLogFlops(m); 1048 } else { 1049 for (i=0; i<m; i++) { 1050 mdiag[i] = v[diag[i]]; 1051 a->idiag[i] = omega/(fshift + v[diag[i]]); 1052 } 1053 PetscLogFlops(2*m); 1054 } 1055 } 1056 t = a->ssor; 1057 idiag = a->idiag; 1058 mdiag = a->idiag + 2*m; 1059 1060 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 1061 if (xx != bb) { 1062 ierr = VecGetArray(bb,(PetscScalar**)&b);CHKERRQ(ierr); 1063 } else { 1064 b = x; 1065 } 1066 1067 /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */ 1068 xs = x; 1069 if (flag == SOR_APPLY_UPPER) { 1070 /* apply (U + D/omega) to the vector */ 1071 bs = b; 1072 for (i=0; i<m; i++) { 1073 d = fshift + a->a[diag[i]]; 1074 n = a->i[i+1] - diag[i] - 1; 1075 idx = a->j + diag[i] + 1; 1076 v = a->a + diag[i] + 1; 1077 sum = b[i]*d/omega; 1078 SPARSEDENSEDOT(sum,bs,v,idx,n); 1079 x[i] = sum; 1080 } 1081 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1082 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1083 PetscLogFlops(a->nz); 1084 PetscFunctionReturn(0); 1085 } 1086 1087 1088 /* Let A = L + U + D; where L is lower trianglar, 1089 U is upper triangular, E is diagonal; This routine applies 1090 1091 (L + E)^{-1} A (U + E)^{-1} 1092 1093 to a vector efficiently using Eisenstat's trick. This is for 1094 the case of SSOR preconditioner, so E is D/omega where omega 1095 is the relaxation factor. 1096 */ 1097 1098 if (flag == SOR_APPLY_LOWER) { 1099 SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented"); 1100 } else if (flag & SOR_EISENSTAT) { 1101 /* Let A = L + U + D; where L is lower trianglar, 1102 U is upper triangular, E is diagonal; This routine applies 1103 1104 (L + E)^{-1} A (U + E)^{-1} 1105 1106 to a vector efficiently using Eisenstat's trick. This is for 1107 the case of SSOR preconditioner, so E is D/omega where omega 1108 is the relaxation factor. 1109 */ 1110 scale = (2.0/omega) - 1.0; 1111 1112 /* x = (E + U)^{-1} b */ 1113 for (i=m-1; i>=0; i--) { 1114 n = a->i[i+1] - diag[i] - 1; 1115 idx = a->j + diag[i] + 1; 1116 v = a->a + diag[i] + 1; 1117 sum = b[i]; 1118 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1119 x[i] = sum*idiag[i]; 1120 } 1121 1122 /* t = b - (2*E - D)x */ 1123 v = a->a; 1124 for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; } 1125 1126 /* t = (E + L)^{-1}t */ 1127 ts = t; 1128 diag = a->diag; 1129 for (i=0; i<m; i++) { 1130 n = diag[i] - a->i[i]; 1131 idx = a->j + a->i[i]; 1132 v = a->a + a->i[i]; 1133 sum = t[i]; 1134 SPARSEDENSEMDOT(sum,ts,v,idx,n); 1135 t[i] = sum*idiag[i]; 1136 /* x = x + t */ 1137 x[i] += t[i]; 1138 } 1139 1140 PetscLogFlops(6*m-1 + 2*a->nz); 1141 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1142 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1143 PetscFunctionReturn(0); 1144 } 1145 if (flag & SOR_ZERO_INITIAL_GUESS) { 1146 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){ 1147 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1148 fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)b); 1149 #else 1150 for (i=0; i<m; i++) { 1151 n = diag[i] - a->i[i]; 1152 idx = a->j + a->i[i]; 1153 v = a->a + a->i[i]; 1154 sum = b[i]; 1155 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1156 x[i] = sum*idiag[i]; 1157 } 1158 #endif 1159 xb = x; 1160 PetscLogFlops(a->nz); 1161 } else xb = b; 1162 if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) && 1163 (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) { 1164 for (i=0; i<m; i++) { 1165 x[i] *= mdiag[i]; 1166 } 1167 PetscLogFlops(m); 1168 } 1169 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){ 1170 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1171 fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)xb); 1172 #else 1173 for (i=m-1; i>=0; i--) { 1174 n = a->i[i+1] - diag[i] - 1; 1175 idx = a->j + diag[i] + 1; 1176 v = a->a + diag[i] + 1; 1177 sum = xb[i]; 1178 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1179 x[i] = sum*idiag[i]; 1180 } 1181 #endif 1182 PetscLogFlops(a->nz); 1183 } 1184 its--; 1185 } 1186 while (its--) { 1187 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){ 1188 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1189 fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b); 1190 #else 1191 for (i=0; i<m; i++) { 1192 d = fshift + a->a[diag[i]]; 1193 n = a->i[i+1] - a->i[i]; 1194 idx = a->j + a->i[i]; 1195 v = a->a + a->i[i]; 1196 sum = b[i]; 1197 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1198 x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i]; 1199 } 1200 #endif 1201 PetscLogFlops(a->nz); 1202 } 1203 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){ 1204 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1205 fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b); 1206 #else 1207 for (i=m-1; i>=0; i--) { 1208 d = fshift + a->a[diag[i]]; 1209 n = a->i[i+1] - a->i[i]; 1210 idx = a->j + a->i[i]; 1211 v = a->a + a->i[i]; 1212 sum = b[i]; 1213 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1214 x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i]; 1215 } 1216 #endif 1217 PetscLogFlops(a->nz); 1218 } 1219 } 1220 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1221 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1222 PetscFunctionReturn(0); 1223 } 1224 1225 #undef __FUNCT__ 1226 #define __FUNCT__ "MatGetInfo_SeqAIJ" 1227 PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info) 1228 { 1229 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1230 1231 PetscFunctionBegin; 1232 info->rows_global = (double)A->m; 1233 info->columns_global = (double)A->n; 1234 info->rows_local = (double)A->m; 1235 info->columns_local = (double)A->n; 1236 info->block_size = 1.0; 1237 info->nz_allocated = (double)a->maxnz; 1238 info->nz_used = (double)a->nz; 1239 info->nz_unneeded = (double)(a->maxnz - a->nz); 1240 info->assemblies = (double)A->num_ass; 1241 info->mallocs = (double)a->reallocs; 1242 info->memory = A->mem; 1243 if (A->factor) { 1244 info->fill_ratio_given = A->info.fill_ratio_given; 1245 info->fill_ratio_needed = A->info.fill_ratio_needed; 1246 info->factor_mallocs = A->info.factor_mallocs; 1247 } else { 1248 info->fill_ratio_given = 0; 1249 info->fill_ratio_needed = 0; 1250 info->factor_mallocs = 0; 1251 } 1252 PetscFunctionReturn(0); 1253 } 1254 1255 #undef __FUNCT__ 1256 #define __FUNCT__ "MatZeroRows_SeqAIJ" 1257 PetscErrorCode MatZeroRows_SeqAIJ(Mat A,IS is,const PetscScalar *diag) 1258 { 1259 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1260 PetscErrorCode ierr; 1261 PetscInt i,N,*rows,m = A->m - 1; 1262 1263 PetscFunctionBegin; 1264 ierr = ISGetLocalSize(is,&N);CHKERRQ(ierr); 1265 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 1266 if (a->keepzeroedrows) { 1267 for (i=0; i<N; i++) { 1268 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1269 ierr = PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));CHKERRQ(ierr); 1270 } 1271 if (diag) { 1272 ierr = MatMissingDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1273 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1274 for (i=0; i<N; i++) { 1275 a->a[a->diag[rows[i]]] = *diag; 1276 } 1277 } 1278 } else { 1279 if (diag) { 1280 for (i=0; i<N; i++) { 1281 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1282 if (a->ilen[rows[i]] > 0) { 1283 a->ilen[rows[i]] = 1; 1284 a->a[a->i[rows[i]]] = *diag; 1285 a->j[a->i[rows[i]]] = rows[i]; 1286 } else { /* in case row was completely empty */ 1287 ierr = MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);CHKERRQ(ierr); 1288 } 1289 } 1290 } else { 1291 for (i=0; i<N; i++) { 1292 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]); 1293 a->ilen[rows[i]] = 0; 1294 } 1295 } 1296 } 1297 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 1298 ierr = MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1299 PetscFunctionReturn(0); 1300 } 1301 1302 #undef __FUNCT__ 1303 #define __FUNCT__ "MatGetRow_SeqAIJ" 1304 PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 1305 { 1306 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1307 PetscInt *itmp; 1308 1309 PetscFunctionBegin; 1310 if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row); 1311 1312 *nz = a->i[row+1] - a->i[row]; 1313 if (v) *v = a->a + a->i[row]; 1314 if (idx) { 1315 itmp = a->j + a->i[row]; 1316 if (*nz) { 1317 *idx = itmp; 1318 } 1319 else *idx = 0; 1320 } 1321 PetscFunctionReturn(0); 1322 } 1323 1324 /* remove this function? */ 1325 #undef __FUNCT__ 1326 #define __FUNCT__ "MatRestoreRow_SeqAIJ" 1327 PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v) 1328 { 1329 PetscFunctionBegin; 1330 PetscFunctionReturn(0); 1331 } 1332 1333 #undef __FUNCT__ 1334 #define __FUNCT__ "MatNorm_SeqAIJ" 1335 PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm) 1336 { 1337 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1338 PetscScalar *v = a->a; 1339 PetscReal sum = 0.0; 1340 PetscErrorCode ierr; 1341 PetscInt i,j; 1342 1343 PetscFunctionBegin; 1344 if (type == NORM_FROBENIUS) { 1345 for (i=0; i<a->nz; i++) { 1346 #if defined(PETSC_USE_COMPLEX) 1347 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1348 #else 1349 sum += (*v)*(*v); v++; 1350 #endif 1351 } 1352 *nrm = sqrt(sum); 1353 } else if (type == NORM_1) { 1354 PetscReal *tmp; 1355 PetscInt *jj = a->j; 1356 ierr = PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);CHKERRQ(ierr); 1357 ierr = PetscMemzero(tmp,A->n*sizeof(PetscReal));CHKERRQ(ierr); 1358 *nrm = 0.0; 1359 for (j=0; j<a->nz; j++) { 1360 tmp[*jj++] += PetscAbsScalar(*v); v++; 1361 } 1362 for (j=0; j<A->n; j++) { 1363 if (tmp[j] > *nrm) *nrm = tmp[j]; 1364 } 1365 ierr = PetscFree(tmp);CHKERRQ(ierr); 1366 } else if (type == NORM_INFINITY) { 1367 *nrm = 0.0; 1368 for (j=0; j<A->m; j++) { 1369 v = a->a + a->i[j]; 1370 sum = 0.0; 1371 for (i=0; i<a->i[j+1]-a->i[j]; i++) { 1372 sum += PetscAbsScalar(*v); v++; 1373 } 1374 if (sum > *nrm) *nrm = sum; 1375 } 1376 } else { 1377 SETERRQ(PETSC_ERR_SUP,"No support for two norm"); 1378 } 1379 PetscFunctionReturn(0); 1380 } 1381 1382 #undef __FUNCT__ 1383 #define __FUNCT__ "MatTranspose_SeqAIJ" 1384 PetscErrorCode MatTranspose_SeqAIJ(Mat A,Mat *B) 1385 { 1386 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1387 Mat C; 1388 PetscErrorCode ierr; 1389 PetscInt i,*aj = a->j,*ai = a->i,m = A->m,len,*col; 1390 PetscScalar *array = a->a; 1391 1392 PetscFunctionBegin; 1393 if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place"); 1394 ierr = PetscMalloc((1+A->n)*sizeof(PetscInt),&col);CHKERRQ(ierr); 1395 ierr = PetscMemzero(col,(1+A->n)*sizeof(PetscInt));CHKERRQ(ierr); 1396 1397 for (i=0; i<ai[m]; i++) col[aj[i]] += 1; 1398 ierr = MatCreate(A->comm,A->n,m,A->n,m,&C);CHKERRQ(ierr); 1399 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1400 ierr = MatSeqAIJSetPreallocation(C,0,col);CHKERRQ(ierr); 1401 ierr = PetscFree(col);CHKERRQ(ierr); 1402 for (i=0; i<m; i++) { 1403 len = ai[i+1]-ai[i]; 1404 ierr = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);CHKERRQ(ierr); 1405 array += len; 1406 aj += len; 1407 } 1408 1409 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1410 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1411 1412 if (B) { 1413 *B = C; 1414 } else { 1415 ierr = MatHeaderCopy(A,C);CHKERRQ(ierr); 1416 } 1417 PetscFunctionReturn(0); 1418 } 1419 1420 EXTERN_C_BEGIN 1421 #undef __FUNCT__ 1422 #define __FUNCT__ "MatIsTranspose_SeqAIJ" 1423 PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f) 1424 { 1425 Mat_SeqAIJ *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data; 1426 PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb; 1427 PetscErrorCode ierr; 1428 PetscInt ma,na,mb,nb, i; 1429 1430 PetscFunctionBegin; 1431 bij = (Mat_SeqAIJ *) B->data; 1432 1433 ierr = MatGetSize(A,&ma,&na);CHKERRQ(ierr); 1434 ierr = MatGetSize(B,&mb,&nb);CHKERRQ(ierr); 1435 if (ma!=nb || na!=mb){ 1436 *f = PETSC_FALSE; 1437 PetscFunctionReturn(0); 1438 } 1439 aii = aij->i; bii = bij->i; 1440 adx = aij->j; bdx = bij->j; 1441 va = aij->a; vb = bij->a; 1442 ierr = PetscMalloc(ma*sizeof(PetscInt),&aptr);CHKERRQ(ierr); 1443 ierr = PetscMalloc(mb*sizeof(PetscInt),&bptr);CHKERRQ(ierr); 1444 for (i=0; i<ma; i++) aptr[i] = aii[i]; 1445 for (i=0; i<mb; i++) bptr[i] = bii[i]; 1446 1447 *f = PETSC_TRUE; 1448 for (i=0; i<ma; i++) { 1449 while (aptr[i]<aii[i+1]) { 1450 PetscInt idc,idr; 1451 PetscScalar vc,vr; 1452 /* column/row index/value */ 1453 idc = adx[aptr[i]]; 1454 idr = bdx[bptr[idc]]; 1455 vc = va[aptr[i]]; 1456 vr = vb[bptr[idc]]; 1457 if (i!=idr || PetscAbsScalar(vc-vr) > tol) { 1458 *f = PETSC_FALSE; 1459 goto done; 1460 } else { 1461 aptr[i]++; 1462 if (B || i!=idc) bptr[idc]++; 1463 } 1464 } 1465 } 1466 done: 1467 ierr = PetscFree(aptr);CHKERRQ(ierr); 1468 if (B) { 1469 ierr = PetscFree(bptr);CHKERRQ(ierr); 1470 } 1471 PetscFunctionReturn(0); 1472 } 1473 EXTERN_C_END 1474 1475 #undef __FUNCT__ 1476 #define __FUNCT__ "MatIsSymmetric_SeqAIJ" 1477 PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f) 1478 { 1479 PetscErrorCode ierr; 1480 PetscFunctionBegin; 1481 ierr = MatIsTranspose_SeqAIJ(A,A,tol,f);CHKERRQ(ierr); 1482 PetscFunctionReturn(0); 1483 } 1484 1485 #undef __FUNCT__ 1486 #define __FUNCT__ "MatDiagonalScale_SeqAIJ" 1487 PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr) 1488 { 1489 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1490 PetscScalar *l,*r,x,*v; 1491 PetscErrorCode ierr; 1492 PetscInt i,j,m = A->m,n = A->n,M,nz = a->nz,*jj; 1493 1494 PetscFunctionBegin; 1495 if (ll) { 1496 /* The local size is used so that VecMPI can be passed to this routine 1497 by MatDiagonalScale_MPIAIJ */ 1498 ierr = VecGetLocalSize(ll,&m);CHKERRQ(ierr); 1499 if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length"); 1500 ierr = VecGetArray(ll,&l);CHKERRQ(ierr); 1501 v = a->a; 1502 for (i=0; i<m; i++) { 1503 x = l[i]; 1504 M = a->i[i+1] - a->i[i]; 1505 for (j=0; j<M; j++) { (*v++) *= x;} 1506 } 1507 ierr = VecRestoreArray(ll,&l);CHKERRQ(ierr); 1508 PetscLogFlops(nz); 1509 } 1510 if (rr) { 1511 ierr = VecGetLocalSize(rr,&n);CHKERRQ(ierr); 1512 if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length"); 1513 ierr = VecGetArray(rr,&r);CHKERRQ(ierr); 1514 v = a->a; jj = a->j; 1515 for (i=0; i<nz; i++) { 1516 (*v++) *= r[*jj++]; 1517 } 1518 ierr = VecRestoreArray(rr,&r);CHKERRQ(ierr); 1519 PetscLogFlops(nz); 1520 } 1521 PetscFunctionReturn(0); 1522 } 1523 1524 #undef __FUNCT__ 1525 #define __FUNCT__ "MatGetSubMatrix_SeqAIJ" 1526 PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B) 1527 { 1528 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c; 1529 PetscErrorCode ierr; 1530 PetscInt *smap,i,k,kstart,kend,oldcols = A->n,*lens; 1531 PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi; 1532 PetscInt *irow,*icol,nrows,ncols; 1533 PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen; 1534 PetscScalar *a_new,*mat_a; 1535 Mat C; 1536 PetscTruth stride; 1537 1538 PetscFunctionBegin; 1539 ierr = ISSorted(isrow,(PetscTruth*)&i);CHKERRQ(ierr); 1540 if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted"); 1541 ierr = ISSorted(iscol,(PetscTruth*)&i);CHKERRQ(ierr); 1542 if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted"); 1543 1544 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 1545 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 1546 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 1547 1548 ierr = ISStrideGetInfo(iscol,&first,&step);CHKERRQ(ierr); 1549 ierr = ISStride(iscol,&stride);CHKERRQ(ierr); 1550 if (stride && step == 1) { 1551 /* special case of contiguous rows */ 1552 ierr = PetscMalloc((2*nrows+1)*sizeof(PetscInt),&lens);CHKERRQ(ierr); 1553 starts = lens + nrows; 1554 /* loop over new rows determining lens and starting points */ 1555 for (i=0; i<nrows; i++) { 1556 kstart = ai[irow[i]]; 1557 kend = kstart + ailen[irow[i]]; 1558 for (k=kstart; k<kend; k++) { 1559 if (aj[k] >= first) { 1560 starts[i] = k; 1561 break; 1562 } 1563 } 1564 sum = 0; 1565 while (k < kend) { 1566 if (aj[k++] >= first+ncols) break; 1567 sum++; 1568 } 1569 lens[i] = sum; 1570 } 1571 /* create submatrix */ 1572 if (scall == MAT_REUSE_MATRIX) { 1573 PetscInt n_cols,n_rows; 1574 ierr = MatGetSize(*B,&n_rows,&n_cols);CHKERRQ(ierr); 1575 if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); 1576 ierr = MatZeroEntries(*B);CHKERRQ(ierr); 1577 C = *B; 1578 } else { 1579 ierr = MatCreate(A->comm,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE,&C);CHKERRQ(ierr); 1580 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1581 ierr = MatSeqAIJSetPreallocation(C,0,lens);CHKERRQ(ierr); 1582 } 1583 c = (Mat_SeqAIJ*)C->data; 1584 1585 /* loop over rows inserting into submatrix */ 1586 a_new = c->a; 1587 j_new = c->j; 1588 i_new = c->i; 1589 1590 for (i=0; i<nrows; i++) { 1591 ii = starts[i]; 1592 lensi = lens[i]; 1593 for (k=0; k<lensi; k++) { 1594 *j_new++ = aj[ii+k] - first; 1595 } 1596 ierr = PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));CHKERRQ(ierr); 1597 a_new += lensi; 1598 i_new[i+1] = i_new[i] + lensi; 1599 c->ilen[i] = lensi; 1600 } 1601 ierr = PetscFree(lens);CHKERRQ(ierr); 1602 } else { 1603 ierr = ISGetIndices(iscol,&icol);CHKERRQ(ierr); 1604 ierr = PetscMalloc((1+oldcols)*sizeof(PetscInt),&smap);CHKERRQ(ierr); 1605 1606 ierr = PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);CHKERRQ(ierr); 1607 ierr = PetscMemzero(smap,oldcols*sizeof(PetscInt));CHKERRQ(ierr); 1608 for (i=0; i<ncols; i++) smap[icol[i]] = i+1; 1609 /* determine lens of each row */ 1610 for (i=0; i<nrows; i++) { 1611 kstart = ai[irow[i]]; 1612 kend = kstart + a->ilen[irow[i]]; 1613 lens[i] = 0; 1614 for (k=kstart; k<kend; k++) { 1615 if (smap[aj[k]]) { 1616 lens[i]++; 1617 } 1618 } 1619 } 1620 /* Create and fill new matrix */ 1621 if (scall == MAT_REUSE_MATRIX) { 1622 PetscTruth equal; 1623 1624 c = (Mat_SeqAIJ *)((*B)->data); 1625 if ((*B)->m != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size"); 1626 ierr = PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(PetscInt),&equal);CHKERRQ(ierr); 1627 if (!equal) { 1628 SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros"); 1629 } 1630 ierr = PetscMemzero(c->ilen,(*B)->m*sizeof(PetscInt));CHKERRQ(ierr); 1631 C = *B; 1632 } else { 1633 ierr = MatCreate(A->comm,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE,&C);CHKERRQ(ierr); 1634 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1635 ierr = MatSeqAIJSetPreallocation(C,0,lens);CHKERRQ(ierr); 1636 } 1637 c = (Mat_SeqAIJ *)(C->data); 1638 for (i=0; i<nrows; i++) { 1639 row = irow[i]; 1640 kstart = ai[row]; 1641 kend = kstart + a->ilen[row]; 1642 mat_i = c->i[i]; 1643 mat_j = c->j + mat_i; 1644 mat_a = c->a + mat_i; 1645 mat_ilen = c->ilen + i; 1646 for (k=kstart; k<kend; k++) { 1647 if ((tcol=smap[a->j[k]])) { 1648 *mat_j++ = tcol - 1; 1649 *mat_a++ = a->a[k]; 1650 (*mat_ilen)++; 1651 1652 } 1653 } 1654 } 1655 /* Free work space */ 1656 ierr = ISRestoreIndices(iscol,&icol);CHKERRQ(ierr); 1657 ierr = PetscFree(smap);CHKERRQ(ierr); 1658 ierr = PetscFree(lens);CHKERRQ(ierr); 1659 } 1660 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1661 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1662 1663 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 1664 *B = C; 1665 PetscFunctionReturn(0); 1666 } 1667 1668 /* 1669 */ 1670 #undef __FUNCT__ 1671 #define __FUNCT__ "MatILUFactor_SeqAIJ" 1672 PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info) 1673 { 1674 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 1675 PetscErrorCode ierr; 1676 Mat outA; 1677 PetscTruth row_identity,col_identity; 1678 1679 PetscFunctionBegin; 1680 if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu"); 1681 ierr = ISIdentity(row,&row_identity);CHKERRQ(ierr); 1682 ierr = ISIdentity(col,&col_identity);CHKERRQ(ierr); 1683 if (!row_identity || !col_identity) { 1684 SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU"); 1685 } 1686 1687 outA = inA; 1688 inA->factor = FACTOR_LU; 1689 a->row = row; 1690 a->col = col; 1691 ierr = PetscObjectReference((PetscObject)row);CHKERRQ(ierr); 1692 ierr = PetscObjectReference((PetscObject)col);CHKERRQ(ierr); 1693 1694 /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */ 1695 if (a->icol) {ierr = ISDestroy(a->icol);CHKERRQ(ierr);} /* need to remove old one */ 1696 ierr = ISInvertPermutation(col,PETSC_DECIDE,&a->icol);CHKERRQ(ierr); 1697 PetscLogObjectParent(inA,a->icol); 1698 1699 if (!a->solve_work) { /* this matrix may have been factored before */ 1700 ierr = PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);CHKERRQ(ierr); 1701 } 1702 1703 if (!a->diag) { 1704 ierr = MatMarkDiagonal_SeqAIJ(inA);CHKERRQ(ierr); 1705 } 1706 ierr = MatLUFactorNumeric_SeqAIJ(inA,&outA);CHKERRQ(ierr); 1707 PetscFunctionReturn(0); 1708 } 1709 1710 #include "petscblaslapack.h" 1711 #undef __FUNCT__ 1712 #define __FUNCT__ "MatScale_SeqAIJ" 1713 PetscErrorCode MatScale_SeqAIJ(const PetscScalar *alpha,Mat inA) 1714 { 1715 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 1716 PetscBLASInt bnz = (PetscBLASInt)a->nz,one = 1; 1717 1718 PetscFunctionBegin; 1719 BLscal_(&bnz,(PetscScalar*)alpha,a->a,&one); 1720 PetscLogFlops(a->nz); 1721 PetscFunctionReturn(0); 1722 } 1723 1724 #undef __FUNCT__ 1725 #define __FUNCT__ "MatGetSubMatrices_SeqAIJ" 1726 PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[]) 1727 { 1728 PetscErrorCode ierr; 1729 PetscInt i; 1730 1731 PetscFunctionBegin; 1732 if (scall == MAT_INITIAL_MATRIX) { 1733 ierr = PetscMalloc((n+1)*sizeof(Mat),B);CHKERRQ(ierr); 1734 } 1735 1736 for (i=0; i<n; i++) { 1737 ierr = MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);CHKERRQ(ierr); 1738 } 1739 PetscFunctionReturn(0); 1740 } 1741 1742 #undef __FUNCT__ 1743 #define __FUNCT__ "MatGetBlockSize_SeqAIJ" 1744 PetscErrorCode MatGetBlockSize_SeqAIJ(Mat A,PetscInt *bs) 1745 { 1746 PetscFunctionBegin; 1747 *bs = 1; 1748 PetscFunctionReturn(0); 1749 } 1750 1751 #undef __FUNCT__ 1752 #define __FUNCT__ "MatIncreaseOverlap_SeqAIJ" 1753 PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov) 1754 { 1755 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1756 PetscErrorCode ierr; 1757 PetscInt row,i,j,k,l,m,n,*idx,*nidx,isz,val; 1758 PetscInt start,end,*ai,*aj; 1759 PetscBT table; 1760 1761 PetscFunctionBegin; 1762 m = A->m; 1763 ai = a->i; 1764 aj = a->j; 1765 1766 if (ov < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used"); 1767 1768 ierr = PetscMalloc((m+1)*sizeof(PetscInt),&nidx);CHKERRQ(ierr); 1769 ierr = PetscBTCreate(m,table);CHKERRQ(ierr); 1770 1771 for (i=0; i<is_max; i++) { 1772 /* Initialize the two local arrays */ 1773 isz = 0; 1774 ierr = PetscBTMemzero(m,table);CHKERRQ(ierr); 1775 1776 /* Extract the indices, assume there can be duplicate entries */ 1777 ierr = ISGetIndices(is[i],&idx);CHKERRQ(ierr); 1778 ierr = ISGetLocalSize(is[i],&n);CHKERRQ(ierr); 1779 1780 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 1781 for (j=0; j<n ; ++j){ 1782 if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];} 1783 } 1784 ierr = ISRestoreIndices(is[i],&idx);CHKERRQ(ierr); 1785 ierr = ISDestroy(is[i]);CHKERRQ(ierr); 1786 1787 k = 0; 1788 for (j=0; j<ov; j++){ /* for each overlap */ 1789 n = isz; 1790 for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */ 1791 row = nidx[k]; 1792 start = ai[row]; 1793 end = ai[row+1]; 1794 for (l = start; l<end ; l++){ 1795 val = aj[l] ; 1796 if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;} 1797 } 1798 } 1799 } 1800 ierr = ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));CHKERRQ(ierr); 1801 } 1802 ierr = PetscBTDestroy(table);CHKERRQ(ierr); 1803 ierr = PetscFree(nidx);CHKERRQ(ierr); 1804 PetscFunctionReturn(0); 1805 } 1806 1807 /* -------------------------------------------------------------- */ 1808 #undef __FUNCT__ 1809 #define __FUNCT__ "MatPermute_SeqAIJ" 1810 PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B) 1811 { 1812 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1813 PetscErrorCode ierr; 1814 PetscInt i,nz,m = A->m,n = A->n,*col; 1815 PetscInt *row,*cnew,j,*lens; 1816 IS icolp,irowp; 1817 PetscInt *cwork; 1818 PetscScalar *vwork; 1819 1820 PetscFunctionBegin; 1821 ierr = ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);CHKERRQ(ierr); 1822 ierr = ISGetIndices(irowp,&row);CHKERRQ(ierr); 1823 ierr = ISInvertPermutation(colp,PETSC_DECIDE,&icolp);CHKERRQ(ierr); 1824 ierr = ISGetIndices(icolp,&col);CHKERRQ(ierr); 1825 1826 /* determine lengths of permuted rows */ 1827 ierr = PetscMalloc((m+1)*sizeof(PetscInt),&lens);CHKERRQ(ierr); 1828 for (i=0; i<m; i++) { 1829 lens[row[i]] = a->i[i+1] - a->i[i]; 1830 } 1831 ierr = MatCreate(A->comm,m,n,m,n,B);CHKERRQ(ierr); 1832 ierr = MatSetType(*B,A->type_name);CHKERRQ(ierr); 1833 ierr = MatSeqAIJSetPreallocation(*B,0,lens);CHKERRQ(ierr); 1834 ierr = PetscFree(lens);CHKERRQ(ierr); 1835 1836 ierr = PetscMalloc(n*sizeof(PetscInt),&cnew);CHKERRQ(ierr); 1837 for (i=0; i<m; i++) { 1838 ierr = MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1839 for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];} 1840 ierr = MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);CHKERRQ(ierr); 1841 ierr = MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1842 } 1843 ierr = PetscFree(cnew);CHKERRQ(ierr); 1844 ierr = MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1845 ierr = MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1846 ierr = ISRestoreIndices(irowp,&row);CHKERRQ(ierr); 1847 ierr = ISRestoreIndices(icolp,&col);CHKERRQ(ierr); 1848 ierr = ISDestroy(irowp);CHKERRQ(ierr); 1849 ierr = ISDestroy(icolp);CHKERRQ(ierr); 1850 PetscFunctionReturn(0); 1851 } 1852 1853 #undef __FUNCT__ 1854 #define __FUNCT__ "MatPrintHelp_SeqAIJ" 1855 PetscErrorCode MatPrintHelp_SeqAIJ(Mat A) 1856 { 1857 static PetscTruth called = PETSC_FALSE; 1858 MPI_Comm comm = A->comm; 1859 PetscErrorCode ierr; 1860 1861 PetscFunctionBegin; 1862 if (called) {PetscFunctionReturn(0);} else called = PETSC_TRUE; 1863 ierr = (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):\n");CHKERRQ(ierr); 1864 ierr = (*PetscHelpPrintf)(comm," -mat_lu_pivotthreshold <threshold>: Set pivoting threshold\n");CHKERRQ(ierr); 1865 ierr = (*PetscHelpPrintf)(comm," -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.\n");CHKERRQ(ierr); 1866 ierr = (*PetscHelpPrintf)(comm," -mat_aij_no_inode: Do not use inodes\n");CHKERRQ(ierr); 1867 ierr = (*PetscHelpPrintf)(comm," -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)\n");CHKERRQ(ierr); 1868 PetscFunctionReturn(0); 1869 } 1870 1871 #undef __FUNCT__ 1872 #define __FUNCT__ "MatCopy_SeqAIJ" 1873 PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str) 1874 { 1875 PetscErrorCode ierr; 1876 1877 PetscFunctionBegin; 1878 /* If the two matrices have the same copy implementation, use fast copy. */ 1879 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 1880 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1881 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 1882 1883 if (a->i[A->m] != b->i[B->m]) { 1884 SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different"); 1885 } 1886 ierr = PetscMemcpy(b->a,a->a,(a->i[A->m])*sizeof(PetscScalar));CHKERRQ(ierr); 1887 } else { 1888 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1889 } 1890 PetscFunctionReturn(0); 1891 } 1892 1893 #undef __FUNCT__ 1894 #define __FUNCT__ "MatSetUpPreallocation_SeqAIJ" 1895 PetscErrorCode MatSetUpPreallocation_SeqAIJ(Mat A) 1896 { 1897 PetscErrorCode ierr; 1898 1899 PetscFunctionBegin; 1900 ierr = MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);CHKERRQ(ierr); 1901 PetscFunctionReturn(0); 1902 } 1903 1904 #undef __FUNCT__ 1905 #define __FUNCT__ "MatGetArray_SeqAIJ" 1906 PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[]) 1907 { 1908 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1909 PetscFunctionBegin; 1910 *array = a->a; 1911 PetscFunctionReturn(0); 1912 } 1913 1914 #undef __FUNCT__ 1915 #define __FUNCT__ "MatRestoreArray_SeqAIJ" 1916 PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[]) 1917 { 1918 PetscFunctionBegin; 1919 PetscFunctionReturn(0); 1920 } 1921 1922 #undef __FUNCT__ 1923 #define __FUNCT__ "MatFDColoringApply_SeqAIJ" 1924 PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx) 1925 { 1926 PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f; 1927 PetscErrorCode ierr; 1928 PetscInt k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2; 1929 PetscScalar dx,mone = -1.0,*y,*xx,*w3_array; 1930 PetscScalar *vscale_array; 1931 PetscReal epsilon = coloring->error_rel,umin = coloring->umin; 1932 Vec w1,w2,w3; 1933 void *fctx = coloring->fctx; 1934 PetscTruth flg; 1935 1936 PetscFunctionBegin; 1937 if (!coloring->w1) { 1938 ierr = VecDuplicate(x1,&coloring->w1);CHKERRQ(ierr); 1939 PetscLogObjectParent(coloring,coloring->w1); 1940 ierr = VecDuplicate(x1,&coloring->w2);CHKERRQ(ierr); 1941 PetscLogObjectParent(coloring,coloring->w2); 1942 ierr = VecDuplicate(x1,&coloring->w3);CHKERRQ(ierr); 1943 PetscLogObjectParent(coloring,coloring->w3); 1944 } 1945 w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3; 1946 1947 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 1948 ierr = PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);CHKERRQ(ierr); 1949 if (flg) { 1950 PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()\n"); 1951 } else { 1952 PetscTruth assembled; 1953 ierr = MatAssembled(J,&assembled);CHKERRQ(ierr); 1954 if (assembled) { 1955 ierr = MatZeroEntries(J);CHKERRQ(ierr); 1956 } 1957 } 1958 1959 ierr = VecGetOwnershipRange(x1,&start,&end);CHKERRQ(ierr); 1960 ierr = VecGetSize(x1,&N);CHKERRQ(ierr); 1961 1962 /* 1963 This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets 1964 coloring->F for the coarser grids from the finest 1965 */ 1966 if (coloring->F) { 1967 ierr = VecGetLocalSize(coloring->F,&m1);CHKERRQ(ierr); 1968 ierr = VecGetLocalSize(w1,&m2);CHKERRQ(ierr); 1969 if (m1 != m2) { 1970 coloring->F = 0; 1971 } 1972 } 1973 1974 if (coloring->F) { 1975 w1 = coloring->F; 1976 coloring->F = 0; 1977 } else { 1978 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1979 ierr = (*f)(sctx,x1,w1,fctx);CHKERRQ(ierr); 1980 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1981 } 1982 1983 /* 1984 Compute all the scale factors and share with other processors 1985 */ 1986 ierr = VecGetArray(x1,&xx);CHKERRQ(ierr);xx = xx - start; 1987 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr);vscale_array = vscale_array - start; 1988 for (k=0; k<coloring->ncolors; k++) { 1989 /* 1990 Loop over each column associated with color adding the 1991 perturbation to the vector w3. 1992 */ 1993 for (l=0; l<coloring->ncolumns[k]; l++) { 1994 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 1995 dx = xx[col]; 1996 if (dx == 0.0) dx = 1.0; 1997 #if !defined(PETSC_USE_COMPLEX) 1998 if (dx < umin && dx >= 0.0) dx = umin; 1999 else if (dx < 0.0 && dx > -umin) dx = -umin; 2000 #else 2001 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 2002 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 2003 #endif 2004 dx *= epsilon; 2005 vscale_array[col] = 1.0/dx; 2006 } 2007 } 2008 vscale_array = vscale_array + start;ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 2009 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 2010 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 2011 2012 /* ierr = VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD); 2013 ierr = VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/ 2014 2015 if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow; 2016 else vscaleforrow = coloring->columnsforrow; 2017 2018 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 2019 /* 2020 Loop over each color 2021 */ 2022 for (k=0; k<coloring->ncolors; k++) { 2023 coloring->currentcolor = k; 2024 ierr = VecCopy(x1,w3);CHKERRQ(ierr); 2025 ierr = VecGetArray(w3,&w3_array);CHKERRQ(ierr);w3_array = w3_array - start; 2026 /* 2027 Loop over each column associated with color adding the 2028 perturbation to the vector w3. 2029 */ 2030 for (l=0; l<coloring->ncolumns[k]; l++) { 2031 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 2032 dx = xx[col]; 2033 if (dx == 0.0) dx = 1.0; 2034 #if !defined(PETSC_USE_COMPLEX) 2035 if (dx < umin && dx >= 0.0) dx = umin; 2036 else if (dx < 0.0 && dx > -umin) dx = -umin; 2037 #else 2038 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 2039 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 2040 #endif 2041 dx *= epsilon; 2042 if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter"); 2043 w3_array[col] += dx; 2044 } 2045 w3_array = w3_array + start; ierr = VecRestoreArray(w3,&w3_array);CHKERRQ(ierr); 2046 2047 /* 2048 Evaluate function at x1 + dx (here dx is a vector of perturbations) 2049 */ 2050 2051 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2052 ierr = (*f)(sctx,w3,w2,fctx);CHKERRQ(ierr); 2053 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2054 ierr = VecAXPY(&mone,w1,w2);CHKERRQ(ierr); 2055 2056 /* 2057 Loop over rows of vector, putting results into Jacobian matrix 2058 */ 2059 ierr = VecGetArray(w2,&y);CHKERRQ(ierr); 2060 for (l=0; l<coloring->nrows[k]; l++) { 2061 row = coloring->rows[k][l]; 2062 col = coloring->columnsforrow[k][l]; 2063 y[row] *= vscale_array[vscaleforrow[k][l]]; 2064 srow = row + start; 2065 ierr = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 2066 } 2067 ierr = VecRestoreArray(w2,&y);CHKERRQ(ierr); 2068 } 2069 coloring->currentcolor = k; 2070 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 2071 xx = xx + start; ierr = VecRestoreArray(x1,&xx);CHKERRQ(ierr); 2072 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2073 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2074 PetscFunctionReturn(0); 2075 } 2076 2077 #include "petscblaslapack.h" 2078 #undef __FUNCT__ 2079 #define __FUNCT__ "MatAXPY_SeqAIJ" 2080 PetscErrorCode MatAXPY_SeqAIJ(const PetscScalar a[],Mat X,Mat Y,MatStructure str) 2081 { 2082 PetscErrorCode ierr; 2083 PetscInt i; 2084 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data; 2085 PetscBLASInt one=1,bnz = (PetscBLASInt)x->nz; 2086 2087 PetscFunctionBegin; 2088 if (str == SAME_NONZERO_PATTERN) { 2089 BLaxpy_(&bnz,(PetscScalar*)a,x->a,&one,y->a,&one); 2090 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2091 if (y->xtoy && y->XtoY != X) { 2092 ierr = PetscFree(y->xtoy);CHKERRQ(ierr); 2093 ierr = MatDestroy(y->XtoY);CHKERRQ(ierr); 2094 } 2095 if (!y->xtoy) { /* get xtoy */ 2096 ierr = MatAXPYGetxtoy_Private(X->m,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);CHKERRQ(ierr); 2097 y->XtoY = X; 2098 } 2099 for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += (*a)*(x->a[i]); 2100 PetscLogInfo(0,"MatAXPY_SeqAIJ: ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz); 2101 } else { 2102 ierr = MatAXPY_Basic(a,X,Y,str);CHKERRQ(ierr); 2103 } 2104 PetscFunctionReturn(0); 2105 } 2106 2107 /* -------------------------------------------------------------------*/ 2108 static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ, 2109 MatGetRow_SeqAIJ, 2110 MatRestoreRow_SeqAIJ, 2111 MatMult_SeqAIJ, 2112 /* 4*/ MatMultAdd_SeqAIJ, 2113 MatMultTranspose_SeqAIJ, 2114 MatMultTransposeAdd_SeqAIJ, 2115 MatSolve_SeqAIJ, 2116 MatSolveAdd_SeqAIJ, 2117 MatSolveTranspose_SeqAIJ, 2118 /*10*/ MatSolveTransposeAdd_SeqAIJ, 2119 MatLUFactor_SeqAIJ, 2120 0, 2121 MatRelax_SeqAIJ, 2122 MatTranspose_SeqAIJ, 2123 /*15*/ MatGetInfo_SeqAIJ, 2124 MatEqual_SeqAIJ, 2125 MatGetDiagonal_SeqAIJ, 2126 MatDiagonalScale_SeqAIJ, 2127 MatNorm_SeqAIJ, 2128 /*20*/ 0, 2129 MatAssemblyEnd_SeqAIJ, 2130 MatCompress_SeqAIJ, 2131 MatSetOption_SeqAIJ, 2132 MatZeroEntries_SeqAIJ, 2133 /*25*/ MatZeroRows_SeqAIJ, 2134 MatLUFactorSymbolic_SeqAIJ, 2135 MatLUFactorNumeric_SeqAIJ, 2136 MatCholeskyFactorSymbolic_SeqAIJ, 2137 MatCholeskyFactorNumeric_SeqAIJ, 2138 /*30*/ MatSetUpPreallocation_SeqAIJ, 2139 MatILUFactorSymbolic_SeqAIJ, 2140 MatICCFactorSymbolic_SeqAIJ, 2141 MatGetArray_SeqAIJ, 2142 MatRestoreArray_SeqAIJ, 2143 /*35*/ MatDuplicate_SeqAIJ, 2144 0, 2145 0, 2146 MatILUFactor_SeqAIJ, 2147 0, 2148 /*40*/ MatAXPY_SeqAIJ, 2149 MatGetSubMatrices_SeqAIJ, 2150 MatIncreaseOverlap_SeqAIJ, 2151 MatGetValues_SeqAIJ, 2152 MatCopy_SeqAIJ, 2153 /*45*/ MatPrintHelp_SeqAIJ, 2154 MatScale_SeqAIJ, 2155 0, 2156 0, 2157 MatILUDTFactor_SeqAIJ, 2158 /*50*/ MatGetBlockSize_SeqAIJ, 2159 MatGetRowIJ_SeqAIJ, 2160 MatRestoreRowIJ_SeqAIJ, 2161 MatGetColumnIJ_SeqAIJ, 2162 MatRestoreColumnIJ_SeqAIJ, 2163 /*55*/ MatFDColoringCreate_SeqAIJ, 2164 0, 2165 0, 2166 MatPermute_SeqAIJ, 2167 0, 2168 /*60*/ 0, 2169 MatDestroy_SeqAIJ, 2170 MatView_SeqAIJ, 2171 MatGetPetscMaps_Petsc, 2172 0, 2173 /*65*/ 0, 2174 0, 2175 0, 2176 0, 2177 0, 2178 /*70*/ 0, 2179 0, 2180 MatSetColoring_SeqAIJ, 2181 MatSetValuesAdic_SeqAIJ, 2182 MatSetValuesAdifor_SeqAIJ, 2183 /*75*/ MatFDColoringApply_SeqAIJ, 2184 0, 2185 0, 2186 0, 2187 0, 2188 /*80*/ 0, 2189 0, 2190 0, 2191 0, 2192 MatLoad_SeqAIJ, 2193 /*85*/ MatIsSymmetric_SeqAIJ, 2194 0, 2195 0, 2196 0, 2197 0, 2198 /*90*/ MatMatMult_SeqAIJ_SeqAIJ, 2199 MatMatMultSymbolic_SeqAIJ_SeqAIJ, 2200 MatMatMultNumeric_SeqAIJ_SeqAIJ, 2201 MatPtAP_SeqAIJ_SeqAIJ, 2202 MatPtAPSymbolic_SeqAIJ_SeqAIJ, 2203 /*95*/ MatPtAPNumeric_SeqAIJ_SeqAIJ, 2204 MatMatMultTranspose_SeqAIJ_SeqAIJ, 2205 MatMatMultTransposeSymbolic_SeqAIJ_SeqAIJ, 2206 MatMatMultTransposeNumeric_SeqAIJ_SeqAIJ, 2207 }; 2208 2209 EXTERN_C_BEGIN 2210 #undef __FUNCT__ 2211 #define __FUNCT__ "MatSeqAIJSetColumnIndices_SeqAIJ" 2212 PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices) 2213 { 2214 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2215 PetscInt i,nz,n; 2216 2217 PetscFunctionBegin; 2218 2219 nz = aij->maxnz; 2220 n = mat->n; 2221 for (i=0; i<nz; i++) { 2222 aij->j[i] = indices[i]; 2223 } 2224 aij->nz = nz; 2225 for (i=0; i<n; i++) { 2226 aij->ilen[i] = aij->imax[i]; 2227 } 2228 2229 PetscFunctionReturn(0); 2230 } 2231 EXTERN_C_END 2232 2233 #undef __FUNCT__ 2234 #define __FUNCT__ "MatSeqAIJSetColumnIndices" 2235 /*@ 2236 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 2237 in the matrix. 2238 2239 Input Parameters: 2240 + mat - the SeqAIJ matrix 2241 - indices - the column indices 2242 2243 Level: advanced 2244 2245 Notes: 2246 This can be called if you have precomputed the nonzero structure of the 2247 matrix and want to provide it to the matrix object to improve the performance 2248 of the MatSetValues() operation. 2249 2250 You MUST have set the correct numbers of nonzeros per row in the call to 2251 MatCreateSeqAIJ(). 2252 2253 MUST be called before any calls to MatSetValues(); 2254 2255 The indices should start with zero, not one. 2256 2257 @*/ 2258 PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices) 2259 { 2260 PetscErrorCode ierr,(*f)(Mat,PetscInt *); 2261 2262 PetscFunctionBegin; 2263 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2264 PetscValidPointer(indices,2); 2265 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);CHKERRQ(ierr); 2266 if (f) { 2267 ierr = (*f)(mat,indices);CHKERRQ(ierr); 2268 } else { 2269 SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices"); 2270 } 2271 PetscFunctionReturn(0); 2272 } 2273 2274 /* ----------------------------------------------------------------------------------------*/ 2275 2276 EXTERN_C_BEGIN 2277 #undef __FUNCT__ 2278 #define __FUNCT__ "MatStoreValues_SeqAIJ" 2279 PetscErrorCode MatStoreValues_SeqAIJ(Mat mat) 2280 { 2281 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2282 PetscErrorCode ierr; 2283 size_t nz = aij->i[mat->m]; 2284 2285 PetscFunctionBegin; 2286 if (aij->nonew != 1) { 2287 SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2288 } 2289 2290 /* allocate space for values if not already there */ 2291 if (!aij->saved_values) { 2292 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);CHKERRQ(ierr); 2293 } 2294 2295 /* copy values over */ 2296 ierr = PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2297 PetscFunctionReturn(0); 2298 } 2299 EXTERN_C_END 2300 2301 #undef __FUNCT__ 2302 #define __FUNCT__ "MatStoreValues" 2303 /*@ 2304 MatStoreValues - Stashes a copy of the matrix values; this allows, for 2305 example, reuse of the linear part of a Jacobian, while recomputing the 2306 nonlinear portion. 2307 2308 Collect on Mat 2309 2310 Input Parameters: 2311 . mat - the matrix (currently on AIJ matrices support this option) 2312 2313 Level: advanced 2314 2315 Common Usage, with SNESSolve(): 2316 $ Create Jacobian matrix 2317 $ Set linear terms into matrix 2318 $ Apply boundary conditions to matrix, at this time matrix must have 2319 $ final nonzero structure (i.e. setting the nonlinear terms and applying 2320 $ boundary conditions again will not change the nonzero structure 2321 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2322 $ ierr = MatStoreValues(mat); 2323 $ Call SNESSetJacobian() with matrix 2324 $ In your Jacobian routine 2325 $ ierr = MatRetrieveValues(mat); 2326 $ Set nonlinear terms in matrix 2327 2328 Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself: 2329 $ // build linear portion of Jacobian 2330 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2331 $ ierr = MatStoreValues(mat); 2332 $ loop over nonlinear iterations 2333 $ ierr = MatRetrieveValues(mat); 2334 $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian 2335 $ // call MatAssemblyBegin/End() on matrix 2336 $ Solve linear system with Jacobian 2337 $ endloop 2338 2339 Notes: 2340 Matrix must already be assemblied before calling this routine 2341 Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 2342 calling this routine. 2343 2344 .seealso: MatRetrieveValues() 2345 2346 @*/ 2347 PetscErrorCode MatStoreValues(Mat mat) 2348 { 2349 PetscErrorCode ierr,(*f)(Mat); 2350 2351 PetscFunctionBegin; 2352 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2353 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2354 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2355 2356 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2357 if (f) { 2358 ierr = (*f)(mat);CHKERRQ(ierr); 2359 } else { 2360 SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to store values"); 2361 } 2362 PetscFunctionReturn(0); 2363 } 2364 2365 EXTERN_C_BEGIN 2366 #undef __FUNCT__ 2367 #define __FUNCT__ "MatRetrieveValues_SeqAIJ" 2368 PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat) 2369 { 2370 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2371 PetscErrorCode ierr; 2372 PetscInt nz = aij->i[mat->m]; 2373 2374 PetscFunctionBegin; 2375 if (aij->nonew != 1) { 2376 SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2377 } 2378 if (!aij->saved_values) { 2379 SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first"); 2380 } 2381 /* copy values over */ 2382 ierr = PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2383 PetscFunctionReturn(0); 2384 } 2385 EXTERN_C_END 2386 2387 #undef __FUNCT__ 2388 #define __FUNCT__ "MatRetrieveValues" 2389 /*@ 2390 MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 2391 example, reuse of the linear part of a Jacobian, while recomputing the 2392 nonlinear portion. 2393 2394 Collect on Mat 2395 2396 Input Parameters: 2397 . mat - the matrix (currently on AIJ matrices support this option) 2398 2399 Level: advanced 2400 2401 .seealso: MatStoreValues() 2402 2403 @*/ 2404 PetscErrorCode MatRetrieveValues(Mat mat) 2405 { 2406 PetscErrorCode ierr,(*f)(Mat); 2407 2408 PetscFunctionBegin; 2409 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2410 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2411 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2412 2413 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2414 if (f) { 2415 ierr = (*f)(mat);CHKERRQ(ierr); 2416 } else { 2417 SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to retrieve values"); 2418 } 2419 PetscFunctionReturn(0); 2420 } 2421 2422 2423 /* --------------------------------------------------------------------------------*/ 2424 #undef __FUNCT__ 2425 #define __FUNCT__ "MatCreateSeqAIJ" 2426 /*@C 2427 MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format 2428 (the default parallel PETSc format). For good matrix assembly performance 2429 the user should preallocate the matrix storage by setting the parameter nz 2430 (or the array nnz). By setting these parameters accurately, performance 2431 during matrix assembly can be increased by more than a factor of 50. 2432 2433 Collective on MPI_Comm 2434 2435 Input Parameters: 2436 + comm - MPI communicator, set to PETSC_COMM_SELF 2437 . m - number of rows 2438 . n - number of columns 2439 . nz - number of nonzeros per row (same for all rows) 2440 - nnz - array containing the number of nonzeros in the various rows 2441 (possibly different for each row) or PETSC_NULL 2442 2443 Output Parameter: 2444 . A - the matrix 2445 2446 Notes: 2447 The AIJ format (also called the Yale sparse matrix format or 2448 compressed row storage), is fully compatible with standard Fortran 77 2449 storage. That is, the stored row and column indices can begin at 2450 either one (as in Fortran) or zero. See the users' manual for details. 2451 2452 Specify the preallocated storage with either nz or nnz (not both). 2453 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2454 allocation. For large problems you MUST preallocate memory or you 2455 will get TERRIBLE performance, see the users' manual chapter on matrices. 2456 2457 By default, this format uses inodes (identical nodes) when possible, to 2458 improve numerical efficiency of matrix-vector products and solves. We 2459 search for consecutive rows with the same nonzero structure, thereby 2460 reusing matrix information to achieve increased efficiency. 2461 2462 Options Database Keys: 2463 + -mat_aij_no_inode - Do not use inodes 2464 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2465 - -mat_aij_oneindex - Internally use indexing starting at 1 2466 rather than 0. Note that when calling MatSetValues(), 2467 the user still MUST index entries starting at 0! 2468 2469 Level: intermediate 2470 2471 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2472 2473 @*/ 2474 PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A) 2475 { 2476 PetscErrorCode ierr; 2477 2478 PetscFunctionBegin; 2479 ierr = MatCreate(comm,m,n,m,n,A);CHKERRQ(ierr); 2480 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 2481 ierr = MatSeqAIJSetPreallocation(*A,nz,nnz);CHKERRQ(ierr); 2482 PetscFunctionReturn(0); 2483 } 2484 2485 #define SKIP_ALLOCATION -4 2486 2487 #undef __FUNCT__ 2488 #define __FUNCT__ "MatSeqAIJSetPreallocation" 2489 /*@C 2490 MatSeqAIJSetPreallocation - For good matrix assembly performance 2491 the user should preallocate the matrix storage by setting the parameter nz 2492 (or the array nnz). By setting these parameters accurately, performance 2493 during matrix assembly can be increased by more than a factor of 50. 2494 2495 Collective on MPI_Comm 2496 2497 Input Parameters: 2498 + comm - MPI communicator, set to PETSC_COMM_SELF 2499 . m - number of rows 2500 . n - number of columns 2501 . nz - number of nonzeros per row (same for all rows) 2502 - nnz - array containing the number of nonzeros in the various rows 2503 (possibly different for each row) or PETSC_NULL 2504 2505 Output Parameter: 2506 . A - the matrix 2507 2508 Notes: 2509 The AIJ format (also called the Yale sparse matrix format or 2510 compressed row storage), is fully compatible with standard Fortran 77 2511 storage. That is, the stored row and column indices can begin at 2512 either one (as in Fortran) or zero. See the users' manual for details. 2513 2514 Specify the preallocated storage with either nz or nnz (not both). 2515 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2516 allocation. For large problems you MUST preallocate memory or you 2517 will get TERRIBLE performance, see the users' manual chapter on matrices. 2518 2519 By default, this format uses inodes (identical nodes) when possible, to 2520 improve numerical efficiency of matrix-vector products and solves. We 2521 search for consecutive rows with the same nonzero structure, thereby 2522 reusing matrix information to achieve increased efficiency. 2523 2524 Options Database Keys: 2525 + -mat_aij_no_inode - Do not use inodes 2526 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2527 - -mat_aij_oneindex - Internally use indexing starting at 1 2528 rather than 0. Note that when calling MatSetValues(), 2529 the user still MUST index entries starting at 0! 2530 2531 Level: intermediate 2532 2533 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2534 2535 @*/ 2536 PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[]) 2537 { 2538 PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[]); 2539 2540 PetscFunctionBegin; 2541 ierr = PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);CHKERRQ(ierr); 2542 if (f) { 2543 ierr = (*f)(B,nz,nnz);CHKERRQ(ierr); 2544 } 2545 PetscFunctionReturn(0); 2546 } 2547 2548 EXTERN_C_BEGIN 2549 #undef __FUNCT__ 2550 #define __FUNCT__ "MatSeqAIJSetPreallocation_SeqAIJ" 2551 PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,PetscInt *nnz) 2552 { 2553 Mat_SeqAIJ *b; 2554 size_t len = 0; 2555 PetscTruth skipallocation = PETSC_FALSE; 2556 PetscErrorCode ierr; 2557 PetscInt i; 2558 2559 PetscFunctionBegin; 2560 2561 if (nz == SKIP_ALLOCATION) { 2562 skipallocation = PETSC_TRUE; 2563 nz = 0; 2564 } 2565 2566 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 2567 if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz); 2568 if (nnz) { 2569 for (i=0; i<B->m; i++) { 2570 if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]); 2571 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); 2572 } 2573 } 2574 2575 B->preallocated = PETSC_TRUE; 2576 b = (Mat_SeqAIJ*)B->data; 2577 2578 ierr = PetscMalloc((B->m+1)*sizeof(PetscInt),&b->imax);CHKERRQ(ierr); 2579 if (!nnz) { 2580 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 2581 else if (nz <= 0) nz = 1; 2582 for (i=0; i<B->m; i++) b->imax[i] = nz; 2583 nz = nz*B->m; 2584 } else { 2585 nz = 0; 2586 for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];} 2587 } 2588 2589 if (!skipallocation) { 2590 /* allocate the matrix space */ 2591 len = ((size_t) nz)*(sizeof(PetscInt) + sizeof(PetscScalar)) + (B->m+1)*sizeof(PetscInt); 2592 ierr = PetscMalloc(len,&b->a);CHKERRQ(ierr); 2593 b->j = (PetscInt*)(b->a + nz); 2594 ierr = PetscMemzero(b->j,nz*sizeof(PetscInt));CHKERRQ(ierr); 2595 b->i = b->j + nz; 2596 b->i[0] = 0; 2597 for (i=1; i<B->m+1; i++) { 2598 b->i[i] = b->i[i-1] + b->imax[i-1]; 2599 } 2600 b->singlemalloc = PETSC_TRUE; 2601 b->freedata = PETSC_TRUE; 2602 } else { 2603 b->freedata = PETSC_FALSE; 2604 } 2605 2606 /* b->ilen will count nonzeros in each row so far. */ 2607 ierr = PetscMalloc((B->m+1)*sizeof(PetscInt),&b->ilen);CHKERRQ(ierr); 2608 PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(PetscInt)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2609 for (i=0; i<B->m; i++) { b->ilen[i] = 0;} 2610 2611 b->nz = 0; 2612 b->maxnz = nz; 2613 B->info.nz_unneeded = (double)b->maxnz; 2614 PetscFunctionReturn(0); 2615 } 2616 EXTERN_C_END 2617 2618 /*MC 2619 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 2620 based on compressed sparse row format. 2621 2622 Options Database Keys: 2623 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 2624 2625 Level: beginner 2626 2627 .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType 2628 M*/ 2629 2630 EXTERN_C_BEGIN 2631 #undef __FUNCT__ 2632 #define __FUNCT__ "MatCreate_SeqAIJ" 2633 PetscErrorCode MatCreate_SeqAIJ(Mat B) 2634 { 2635 Mat_SeqAIJ *b; 2636 PetscErrorCode ierr; 2637 PetscMPIInt size; 2638 2639 PetscFunctionBegin; 2640 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 2641 if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1"); 2642 2643 B->m = B->M = PetscMax(B->m,B->M); 2644 B->n = B->N = PetscMax(B->n,B->N); 2645 2646 ierr = PetscNew(Mat_SeqAIJ,&b);CHKERRQ(ierr); 2647 B->data = (void*)b; 2648 ierr = PetscMemzero(b,sizeof(Mat_SeqAIJ));CHKERRQ(ierr); 2649 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 2650 B->factor = 0; 2651 B->lupivotthreshold = 1.0; 2652 B->mapping = 0; 2653 ierr = PetscOptionsGetReal(B->prefix,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);CHKERRQ(ierr); 2654 ierr = PetscOptionsHasName(B->prefix,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);CHKERRQ(ierr); 2655 b->row = 0; 2656 b->col = 0; 2657 b->icol = 0; 2658 b->reallocs = 0; 2659 2660 ierr = PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);CHKERRQ(ierr); 2661 ierr = PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);CHKERRQ(ierr); 2662 2663 b->sorted = PETSC_FALSE; 2664 b->ignorezeroentries = PETSC_FALSE; 2665 b->roworiented = PETSC_TRUE; 2666 b->nonew = 0; 2667 b->diag = 0; 2668 b->solve_work = 0; 2669 B->spptr = 0; 2670 b->inode.use = PETSC_TRUE; 2671 b->inode.node_count = 0; 2672 b->inode.size = 0; 2673 b->inode.limit = 5; 2674 b->inode.max_limit = 5; 2675 b->saved_values = 0; 2676 b->idiag = 0; 2677 b->ssor = 0; 2678 b->keepzeroedrows = PETSC_FALSE; 2679 b->xtoy = 0; 2680 b->XtoY = 0; 2681 2682 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 2683 2684 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C", 2685 "MatSeqAIJSetColumnIndices_SeqAIJ", 2686 MatSeqAIJSetColumnIndices_SeqAIJ);CHKERRQ(ierr); 2687 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C", 2688 "MatStoreValues_SeqAIJ", 2689 MatStoreValues_SeqAIJ);CHKERRQ(ierr); 2690 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C", 2691 "MatRetrieveValues_SeqAIJ", 2692 MatRetrieveValues_SeqAIJ);CHKERRQ(ierr); 2693 #if !defined(PETSC_USE_64BIT_INT) 2694 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C", 2695 "MatConvert_SeqAIJ_SeqSBAIJ", 2696 MatConvert_SeqAIJ_SeqSBAIJ);CHKERRQ(ierr); 2697 #endif 2698 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C", 2699 "MatConvert_SeqAIJ_SeqBAIJ", 2700 MatConvert_SeqAIJ_SeqBAIJ);CHKERRQ(ierr); 2701 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C", 2702 "MatIsTranspose_SeqAIJ", 2703 MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 2704 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C", 2705 "MatSeqAIJSetPreallocation_SeqAIJ", 2706 MatSeqAIJSetPreallocation_SeqAIJ);CHKERRQ(ierr); 2707 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C", 2708 "MatReorderForNonzeroDiagonal_SeqAIJ", 2709 MatReorderForNonzeroDiagonal_SeqAIJ);CHKERRQ(ierr); 2710 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatAdjustForInodes_C", 2711 "MatAdjustForInodes_SeqAIJ", 2712 MatAdjustForInodes_SeqAIJ);CHKERRQ(ierr); 2713 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJGetInodeSizes_C", 2714 "MatSeqAIJGetInodeSizes_SeqAIJ", 2715 MatSeqAIJGetInodeSizes_SeqAIJ);CHKERRQ(ierr); 2716 PetscFunctionReturn(0); 2717 } 2718 EXTERN_C_END 2719 2720 #undef __FUNCT__ 2721 #define __FUNCT__ "MatDuplicate_SeqAIJ" 2722 PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B) 2723 { 2724 Mat C; 2725 Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data; 2726 PetscErrorCode ierr; 2727 PetscInt i,m = A->m; 2728 size_t len; 2729 2730 PetscFunctionBegin; 2731 *B = 0; 2732 ierr = MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);CHKERRQ(ierr); 2733 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 2734 ierr = PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));CHKERRQ(ierr); 2735 2736 c = (Mat_SeqAIJ*)C->data; 2737 2738 C->factor = A->factor; 2739 c->row = 0; 2740 c->col = 0; 2741 c->icol = 0; 2742 c->keepzeroedrows = a->keepzeroedrows; 2743 C->assembled = PETSC_TRUE; 2744 2745 C->M = A->m; 2746 C->N = A->n; 2747 2748 ierr = PetscMalloc((m+1)*sizeof(PetscInt),&c->imax);CHKERRQ(ierr); 2749 ierr = PetscMalloc((m+1)*sizeof(PetscInt),&c->ilen);CHKERRQ(ierr); 2750 for (i=0; i<m; i++) { 2751 c->imax[i] = a->imax[i]; 2752 c->ilen[i] = a->ilen[i]; 2753 } 2754 2755 /* allocate the matrix space */ 2756 c->singlemalloc = PETSC_TRUE; 2757 len = ((size_t) (m+1))*sizeof(PetscInt)+(a->i[m])*(sizeof(PetscScalar)+sizeof(PetscInt)); 2758 ierr = PetscMalloc(len,&c->a);CHKERRQ(ierr); 2759 c->j = (PetscInt*)(c->a + a->i[m] ); 2760 c->i = c->j + a->i[m]; 2761 ierr = PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 2762 if (m > 0) { 2763 ierr = PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));CHKERRQ(ierr); 2764 if (cpvalues == MAT_COPY_VALUES) { 2765 ierr = PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2766 } else { 2767 ierr = PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2768 } 2769 } 2770 2771 PetscLogObjectMemory(C,len+2*(m+1)*sizeof(PetscInt)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2772 c->sorted = a->sorted; 2773 c->roworiented = a->roworiented; 2774 c->nonew = a->nonew; 2775 c->ilu_preserve_row_sums = a->ilu_preserve_row_sums; 2776 c->saved_values = 0; 2777 c->idiag = 0; 2778 c->ssor = 0; 2779 c->ignorezeroentries = a->ignorezeroentries; 2780 c->freedata = PETSC_TRUE; 2781 2782 if (a->diag) { 2783 ierr = PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);CHKERRQ(ierr); 2784 PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt)); 2785 for (i=0; i<m; i++) { 2786 c->diag[i] = a->diag[i]; 2787 } 2788 } else c->diag = 0; 2789 c->inode.use = a->inode.use; 2790 c->inode.limit = a->inode.limit; 2791 c->inode.max_limit = a->inode.max_limit; 2792 if (a->inode.size){ 2793 ierr = PetscMalloc((m+1)*sizeof(PetscInt),&c->inode.size);CHKERRQ(ierr); 2794 c->inode.node_count = a->inode.node_count; 2795 ierr = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(PetscInt));CHKERRQ(ierr); 2796 } else { 2797 c->inode.size = 0; 2798 c->inode.node_count = 0; 2799 } 2800 c->nz = a->nz; 2801 c->maxnz = a->maxnz; 2802 c->solve_work = 0; 2803 C->preallocated = PETSC_TRUE; 2804 2805 *B = C; 2806 ierr = PetscFListDuplicate(A->qlist,&C->qlist);CHKERRQ(ierr); 2807 PetscFunctionReturn(0); 2808 } 2809 2810 #undef __FUNCT__ 2811 #define __FUNCT__ "MatLoad_SeqAIJ" 2812 PetscErrorCode MatLoad_SeqAIJ(PetscViewer viewer,const MatType type,Mat *A) 2813 { 2814 Mat_SeqAIJ *a; 2815 Mat B; 2816 PetscErrorCode ierr; 2817 PetscInt i,nz,header[4],*rowlengths = 0,M,N; 2818 int fd; 2819 PetscMPIInt size; 2820 MPI_Comm comm; 2821 2822 PetscFunctionBegin; 2823 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 2824 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2825 if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor"); 2826 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 2827 ierr = PetscBinaryRead(fd,header,4,PETSC_INT);CHKERRQ(ierr); 2828 if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file"); 2829 M = header[1]; N = header[2]; nz = header[3]; 2830 2831 if (nz < 0) { 2832 SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ"); 2833 } 2834 2835 /* read in row lengths */ 2836 ierr = PetscMalloc(M*sizeof(PetscInt),&rowlengths);CHKERRQ(ierr); 2837 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 2838 2839 /* create our matrix */ 2840 ierr = MatCreate(comm,PETSC_DECIDE,PETSC_DECIDE,M,N,&B);CHKERRQ(ierr); 2841 ierr = MatSetType(B,type);CHKERRQ(ierr); 2842 ierr = MatSeqAIJSetPreallocation(B,0,rowlengths);CHKERRQ(ierr); 2843 a = (Mat_SeqAIJ*)B->data; 2844 2845 /* read in column indices and adjust for Fortran indexing*/ 2846 ierr = PetscBinaryRead(fd,a->j,nz,PETSC_INT);CHKERRQ(ierr); 2847 2848 /* read in nonzero values */ 2849 ierr = PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);CHKERRQ(ierr); 2850 2851 /* set matrix "i" values */ 2852 a->i[0] = 0; 2853 for (i=1; i<= M; i++) { 2854 a->i[i] = a->i[i-1] + rowlengths[i-1]; 2855 a->ilen[i-1] = rowlengths[i-1]; 2856 } 2857 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 2858 2859 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2860 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2861 *A = B; 2862 PetscFunctionReturn(0); 2863 } 2864 2865 #undef __FUNCT__ 2866 #define __FUNCT__ "MatEqual_SeqAIJ" 2867 PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg) 2868 { 2869 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data; 2870 PetscErrorCode ierr; 2871 2872 PetscFunctionBegin; 2873 /* If the matrix dimensions are not equal,or no of nonzeros */ 2874 if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)) { 2875 *flg = PETSC_FALSE; 2876 PetscFunctionReturn(0); 2877 } 2878 2879 /* if the a->i are the same */ 2880 ierr = PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(PetscInt),flg);CHKERRQ(ierr); 2881 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2882 2883 /* if a->j are the same */ 2884 ierr = PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);CHKERRQ(ierr); 2885 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2886 2887 /* if a->a are the same */ 2888 ierr = PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);CHKERRQ(ierr); 2889 2890 PetscFunctionReturn(0); 2891 2892 } 2893 2894 #undef __FUNCT__ 2895 #define __FUNCT__ "MatCreateSeqAIJWithArrays" 2896 /*@C 2897 MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format) 2898 provided by the user. 2899 2900 Coolective on MPI_Comm 2901 2902 Input Parameters: 2903 + comm - must be an MPI communicator of size 1 2904 . m - number of rows 2905 . n - number of columns 2906 . i - row indices 2907 . j - column indices 2908 - a - matrix values 2909 2910 Output Parameter: 2911 . mat - the matrix 2912 2913 Level: intermediate 2914 2915 Notes: 2916 The i, j, and a arrays are not copied by this routine, the user must free these arrays 2917 once the matrix is destroyed 2918 2919 You cannot set new nonzero locations into this matrix, that will generate an error. 2920 2921 The i and j indices are 0 based 2922 2923 .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ() 2924 2925 @*/ 2926 PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat) 2927 { 2928 PetscErrorCode ierr; 2929 PetscInt ii; 2930 Mat_SeqAIJ *aij; 2931 2932 PetscFunctionBegin; 2933 ierr = MatCreate(comm,m,n,m,n,mat);CHKERRQ(ierr); 2934 ierr = MatSetType(*mat,MATSEQAIJ);CHKERRQ(ierr); 2935 ierr = MatSeqAIJSetPreallocation(*mat,SKIP_ALLOCATION,0);CHKERRQ(ierr); 2936 aij = (Mat_SeqAIJ*)(*mat)->data; 2937 2938 if (i[0] != 0) { 2939 SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0"); 2940 } 2941 aij->i = i; 2942 aij->j = j; 2943 aij->a = a; 2944 aij->singlemalloc = PETSC_FALSE; 2945 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 2946 aij->freedata = PETSC_FALSE; 2947 2948 for (ii=0; ii<m; ii++) { 2949 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii]; 2950 #if defined(PETSC_USE_BOPT_g) 2951 if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]); 2952 #endif 2953 } 2954 #if defined(PETSC_USE_BOPT_g) 2955 for (ii=0; ii<aij->i[m]; ii++) { 2956 if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]); 2957 if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]); 2958 } 2959 #endif 2960 2961 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2962 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2963 PetscFunctionReturn(0); 2964 } 2965 2966 #undef __FUNCT__ 2967 #define __FUNCT__ "MatSetColoring_SeqAIJ" 2968 PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring) 2969 { 2970 PetscErrorCode ierr; 2971 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2972 2973 PetscFunctionBegin; 2974 if (coloring->ctype == IS_COLORING_LOCAL) { 2975 ierr = ISColoringReference(coloring);CHKERRQ(ierr); 2976 a->coloring = coloring; 2977 } else if (coloring->ctype == IS_COLORING_GHOSTED) { 2978 PetscInt i,*larray; 2979 ISColoring ocoloring; 2980 ISColoringValue *colors; 2981 2982 /* set coloring for diagonal portion */ 2983 ierr = PetscMalloc((A->n+1)*sizeof(PetscInt),&larray);CHKERRQ(ierr); 2984 for (i=0; i<A->n; i++) { 2985 larray[i] = i; 2986 } 2987 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);CHKERRQ(ierr); 2988 ierr = PetscMalloc((A->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 2989 for (i=0; i<A->n; i++) { 2990 colors[i] = coloring->colors[larray[i]]; 2991 } 2992 ierr = PetscFree(larray);CHKERRQ(ierr); 2993 ierr = ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);CHKERRQ(ierr); 2994 a->coloring = ocoloring; 2995 } 2996 PetscFunctionReturn(0); 2997 } 2998 2999 #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 3000 EXTERN_C_BEGIN 3001 #include "adic/ad_utils.h" 3002 EXTERN_C_END 3003 3004 #undef __FUNCT__ 3005 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 3006 PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 3007 { 3008 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3009 PetscInt m = A->m,*ii = a->i,*jj = a->j,nz,i,j,nlen; 3010 PetscScalar *v = a->a,*values = ((PetscScalar*)advalues)+1; 3011 ISColoringValue *color; 3012 3013 PetscFunctionBegin; 3014 if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix"); 3015 nlen = PetscADGetDerivTypeSize()/sizeof(PetscScalar); 3016 color = a->coloring->colors; 3017 /* loop over rows */ 3018 for (i=0; i<m; i++) { 3019 nz = ii[i+1] - ii[i]; 3020 /* loop over columns putting computed value into matrix */ 3021 for (j=0; j<nz; j++) { 3022 *v++ = values[color[*jj++]]; 3023 } 3024 values += nlen; /* jump to next row of derivatives */ 3025 } 3026 PetscFunctionReturn(0); 3027 } 3028 3029 #else 3030 3031 #undef __FUNCT__ 3032 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 3033 PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 3034 { 3035 PetscFunctionBegin; 3036 SETERRQ(PETSC_ERR_SUP_SYS,"PETSc installed without ADIC"); 3037 } 3038 3039 #endif 3040 3041 #undef __FUNCT__ 3042 #define __FUNCT__ "MatSetValuesAdifor_SeqAIJ" 3043 PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues) 3044 { 3045 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3046 PetscInt m = A->m,*ii = a->i,*jj = a->j,nz,i,j; 3047 PetscScalar *v = a->a,*values = (PetscScalar *)advalues; 3048 ISColoringValue *color; 3049 3050 PetscFunctionBegin; 3051 if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix"); 3052 color = a->coloring->colors; 3053 /* loop over rows */ 3054 for (i=0; i<m; i++) { 3055 nz = ii[i+1] - ii[i]; 3056 /* loop over columns putting computed value into matrix */ 3057 for (j=0; j<nz; j++) { 3058 *v++ = values[color[*jj++]]; 3059 } 3060 values += nl; /* jump to next row of derivatives */ 3061 } 3062 PetscFunctionReturn(0); 3063 } 3064 3065