1 /*$Id: aij.c,v 1.385 2001/09/07 20:09:22 bsmith Exp $*/ 2 /* 3 Defines the basic matrix operations for the AIJ (compressed row) 4 matrix storage format. 5 */ 6 7 #include "src/mat/impls/aij/seq/aij.h" /*I "petscmat.h" I*/ 8 #include "src/inline/spops.h" 9 #include "src/inline/dot.h" 10 #include "petscbt.h" 11 12 #undef __FUNCT__ 13 #define __FUNCT__ "MatGetRowIJ_SeqAIJ" 14 int MatGetRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *m,int *ia[],int *ja[],PetscTruth *done) 15 { 16 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 17 int ierr,i,ishift; 18 19 PetscFunctionBegin; 20 *m = A->m; 21 if (!ia) PetscFunctionReturn(0); 22 ishift = 0; 23 if (symmetric && !A->structurally_symmetric) { 24 ierr = MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);CHKERRQ(ierr); 25 } else if (oshift == 1) { 26 int nz = a->i[A->m]; 27 /* malloc space and add 1 to i and j indices */ 28 ierr = PetscMalloc((A->m+1)*sizeof(int),ia);CHKERRQ(ierr); 29 ierr = PetscMalloc((nz+1)*sizeof(int),ja);CHKERRQ(ierr); 30 for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1; 31 for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1; 32 } else { 33 *ia = a->i; *ja = a->j; 34 } 35 PetscFunctionReturn(0); 36 } 37 38 #undef __FUNCT__ 39 #define __FUNCT__ "MatRestoreRowIJ_SeqAIJ" 40 int MatRestoreRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int *ia[],int *ja[],PetscTruth *done) 41 { 42 int ierr; 43 44 PetscFunctionBegin; 45 if (!ia) PetscFunctionReturn(0); 46 if ((symmetric && !A->structurally_symmetric) || oshift == 1) { 47 ierr = PetscFree(*ia);CHKERRQ(ierr); 48 ierr = PetscFree(*ja);CHKERRQ(ierr); 49 } 50 PetscFunctionReturn(0); 51 } 52 53 #undef __FUNCT__ 54 #define __FUNCT__ "MatGetColumnIJ_SeqAIJ" 55 int MatGetColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *nn,int *ia[],int *ja[],PetscTruth *done) 56 { 57 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 58 int ierr,i,*collengths,*cia,*cja,n = A->n,m = A->m; 59 int nz = a->i[m],row,*jj,mr,col; 60 61 PetscFunctionBegin; 62 *nn = A->n; 63 if (!ia) PetscFunctionReturn(0); 64 if (symmetric) { 65 ierr = MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,0,oshift,ia,ja);CHKERRQ(ierr); 66 } else { 67 ierr = PetscMalloc((n+1)*sizeof(int),&collengths);CHKERRQ(ierr); 68 ierr = PetscMemzero(collengths,n*sizeof(int));CHKERRQ(ierr); 69 ierr = PetscMalloc((n+1)*sizeof(int),&cia);CHKERRQ(ierr); 70 ierr = PetscMalloc((nz+1)*sizeof(int),&cja);CHKERRQ(ierr); 71 jj = a->j; 72 for (i=0; i<nz; i++) { 73 collengths[jj[i]]++; 74 } 75 cia[0] = oshift; 76 for (i=0; i<n; i++) { 77 cia[i+1] = cia[i] + collengths[i]; 78 } 79 ierr = PetscMemzero(collengths,n*sizeof(int));CHKERRQ(ierr); 80 jj = a->j; 81 for (row=0; row<m; row++) { 82 mr = a->i[row+1] - a->i[row]; 83 for (i=0; i<mr; i++) { 84 col = *jj++; 85 cja[cia[col] + collengths[col]++ - oshift] = row + oshift; 86 } 87 } 88 ierr = PetscFree(collengths);CHKERRQ(ierr); 89 *ia = cia; *ja = cja; 90 } 91 PetscFunctionReturn(0); 92 } 93 94 #undef __FUNCT__ 95 #define __FUNCT__ "MatRestoreColumnIJ_SeqAIJ" 96 int MatRestoreColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int *ia[],int *ja[],PetscTruth *done) 97 { 98 int ierr; 99 100 PetscFunctionBegin; 101 if (!ia) PetscFunctionReturn(0); 102 103 ierr = PetscFree(*ia);CHKERRQ(ierr); 104 ierr = PetscFree(*ja);CHKERRQ(ierr); 105 106 PetscFunctionReturn(0); 107 } 108 109 #define CHUNKSIZE 15 110 111 #undef __FUNCT__ 112 #define __FUNCT__ "MatSetValues_SeqAIJ" 113 int MatSetValues_SeqAIJ(Mat A,int m,const int im[],int n,const int in[],const PetscScalar v[],InsertMode is) 114 { 115 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 116 int *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,sorted = a->sorted; 117 int *imax = a->imax,*ai = a->i,*ailen = a->ilen; 118 int *aj = a->j,nonew = a->nonew,ierr; 119 PetscScalar *ap,value,*aa = a->a; 120 PetscTruth ignorezeroentries = ((a->ignorezeroentries && is == ADD_VALUES) ? PETSC_TRUE:PETSC_FALSE); 121 PetscTruth roworiented = a->roworiented; 122 123 PetscFunctionBegin; 124 for (k=0; k<m; k++) { /* loop over added rows */ 125 row = im[k]; 126 if (row < 0) continue; 127 #if defined(PETSC_USE_BOPT_g) 128 if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m-1); 129 #endif 130 rp = aj + ai[row]; ap = aa + ai[row]; 131 rmax = imax[row]; nrow = ailen[row]; 132 low = 0; 133 for (l=0; l<n; l++) { /* loop over added columns */ 134 if (in[l] < 0) continue; 135 #if defined(PETSC_USE_BOPT_g) 136 if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n-1); 137 #endif 138 col = in[l]; 139 if (roworiented) { 140 value = v[l + k*n]; 141 } else { 142 value = v[k + l*m]; 143 } 144 if (value == 0.0 && ignorezeroentries) continue; 145 146 if (!sorted) low = 0; high = nrow; 147 while (high-low > 5) { 148 t = (low+high)/2; 149 if (rp[t] > col) high = t; 150 else low = t; 151 } 152 for (i=low; i<high; i++) { 153 if (rp[i] > col) break; 154 if (rp[i] == col) { 155 if (is == ADD_VALUES) ap[i] += value; 156 else ap[i] = value; 157 goto noinsert; 158 } 159 } 160 if (nonew == 1) goto noinsert; 161 else if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix",row,col); 162 if (nrow >= rmax) { 163 /* there is no extra room in row, therefore enlarge */ 164 int new_nz = ai[A->m] + CHUNKSIZE,*new_i,*new_j; 165 size_t len; 166 PetscScalar *new_a; 167 168 if (nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix requiring new malloc()",row,col); 169 170 /* malloc new storage space */ 171 len = ((size_t) new_nz)*(sizeof(int)+sizeof(PetscScalar))+(A->m+1)*sizeof(int); 172 ierr = PetscMalloc(len,&new_a);CHKERRQ(ierr); 173 new_j = (int*)(new_a + new_nz); 174 new_i = new_j + new_nz; 175 176 /* copy over old data into new slots */ 177 for (ii=0; ii<row+1; ii++) {new_i[ii] = ai[ii];} 178 for (ii=row+1; ii<A->m+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;} 179 ierr = PetscMemcpy(new_j,aj,(ai[row]+nrow)*sizeof(int));CHKERRQ(ierr); 180 len = (((size_t) new_nz) - CHUNKSIZE - ai[row] - nrow ); 181 ierr = PetscMemcpy(new_j+ai[row]+nrow+CHUNKSIZE,aj+ai[row]+nrow,len*sizeof(int));CHKERRQ(ierr); 182 ierr = PetscMemcpy(new_a,aa,(((size_t) ai[row])+nrow)*sizeof(PetscScalar));CHKERRQ(ierr); 183 ierr = PetscMemcpy(new_a+ai[row]+nrow+CHUNKSIZE,aa+ai[row]+nrow,len*sizeof(PetscScalar));CHKERRQ(ierr); 184 /* free up old matrix storage */ 185 ierr = PetscFree(a->a);CHKERRQ(ierr); 186 if (!a->singlemalloc) { 187 ierr = PetscFree(a->i);CHKERRQ(ierr); 188 ierr = PetscFree(a->j);CHKERRQ(ierr); 189 } 190 aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j; 191 a->singlemalloc = PETSC_TRUE; 192 193 rp = aj + ai[row]; ap = aa + ai[row] ; 194 rmax = imax[row] = imax[row] + CHUNKSIZE; 195 PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + sizeof(PetscScalar))); 196 a->maxnz += CHUNKSIZE; 197 a->reallocs++; 198 } 199 N = nrow++ - 1; a->nz++; 200 /* shift up all the later entries in this row */ 201 for (ii=N; ii>=i; ii--) { 202 rp[ii+1] = rp[ii]; 203 ap[ii+1] = ap[ii]; 204 } 205 rp[i] = col; 206 ap[i] = value; 207 noinsert:; 208 low = i + 1; 209 } 210 ailen[row] = nrow; 211 } 212 PetscFunctionReturn(0); 213 } 214 215 #undef __FUNCT__ 216 #define __FUNCT__ "MatGetValues_SeqAIJ" 217 int MatGetValues_SeqAIJ(Mat A,int m,const int im[],int n,const int in[],PetscScalar v[]) 218 { 219 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 220 int *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j; 221 int *ai = a->i,*ailen = a->ilen; 222 PetscScalar *ap,*aa = a->a,zero = 0.0; 223 224 PetscFunctionBegin; 225 for (k=0; k<m; k++) { /* loop over rows */ 226 row = im[k]; 227 if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",row); 228 if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m-1); 229 rp = aj + ai[row]; ap = aa + ai[row]; 230 nrow = ailen[row]; 231 for (l=0; l<n; l++) { /* loop over columns */ 232 if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",in[l]); 233 if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n-1); 234 col = in[l] ; 235 high = nrow; low = 0; /* assume unsorted */ 236 while (high-low > 5) { 237 t = (low+high)/2; 238 if (rp[t] > col) high = t; 239 else low = t; 240 } 241 for (i=low; i<high; i++) { 242 if (rp[i] > col) break; 243 if (rp[i] == col) { 244 *v++ = ap[i]; 245 goto finished; 246 } 247 } 248 *v++ = zero; 249 finished:; 250 } 251 } 252 PetscFunctionReturn(0); 253 } 254 255 256 #undef __FUNCT__ 257 #define __FUNCT__ "MatView_SeqAIJ_Binary" 258 int MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer) 259 { 260 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 261 int i,fd,*col_lens,ierr; 262 263 PetscFunctionBegin; 264 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 265 ierr = PetscMalloc((4+A->m)*sizeof(int),&col_lens);CHKERRQ(ierr); 266 col_lens[0] = MAT_FILE_COOKIE; 267 col_lens[1] = A->m; 268 col_lens[2] = A->n; 269 col_lens[3] = a->nz; 270 271 /* store lengths of each row and write (including header) to file */ 272 for (i=0; i<A->m; i++) { 273 col_lens[4+i] = a->i[i+1] - a->i[i]; 274 } 275 ierr = PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,1);CHKERRQ(ierr); 276 ierr = PetscFree(col_lens);CHKERRQ(ierr); 277 278 /* store column indices (zero start index) */ 279 ierr = PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,0);CHKERRQ(ierr); 280 281 /* store nonzero values */ 282 ierr = PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,0);CHKERRQ(ierr); 283 PetscFunctionReturn(0); 284 } 285 286 extern int MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer); 287 288 #undef __FUNCT__ 289 #define __FUNCT__ "MatView_SeqAIJ_ASCII" 290 int MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer) 291 { 292 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 293 int ierr,i,j,m = A->m,shift=0; 294 char *name; 295 PetscViewerFormat format; 296 297 PetscFunctionBegin; 298 ierr = PetscObjectGetName((PetscObject)A,&name);CHKERRQ(ierr); 299 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 300 if (format == PETSC_VIEWER_ASCII_INFO_DETAIL || format == PETSC_VIEWER_ASCII_INFO) { 301 if (a->inode.size) { 302 ierr = PetscViewerASCIIPrintf(viewer,"using I-node routines: found %d nodes, limit used is %d\n",a->inode.node_count,a->inode.limit);CHKERRQ(ierr); 303 } else { 304 ierr = PetscViewerASCIIPrintf(viewer,"not using I-node routines\n");CHKERRQ(ierr); 305 } 306 } else if (format == PETSC_VIEWER_ASCII_MATLAB) { 307 int nofinalvalue = 0; 308 if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->n-!shift)) { 309 nofinalvalue = 1; 310 } 311 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 312 ierr = PetscViewerASCIIPrintf(viewer,"%% Size = %d %d \n",m,A->n);CHKERRQ(ierr); 313 ierr = PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %d \n",a->nz);CHKERRQ(ierr); 314 ierr = PetscViewerASCIIPrintf(viewer,"zzz = zeros(%d,3);\n",a->nz+nofinalvalue);CHKERRQ(ierr); 315 ierr = PetscViewerASCIIPrintf(viewer,"zzz = [\n");CHKERRQ(ierr); 316 317 for (i=0; i<m; i++) { 318 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 319 #if defined(PETSC_USE_COMPLEX) 320 ierr = PetscViewerASCIIPrintf(viewer,"%d %d %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 321 #else 322 ierr = PetscViewerASCIIPrintf(viewer,"%d %d %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);CHKERRQ(ierr); 323 #endif 324 } 325 } 326 if (nofinalvalue) { 327 ierr = PetscViewerASCIIPrintf(viewer,"%d %d %18.16e\n",m,A->n,0.0);CHKERRQ(ierr); 328 } 329 ierr = PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);CHKERRQ(ierr); 330 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 331 } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) { 332 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX) 333 ierr = MatSeqAIJFactorInfo_Matlab(A,viewer);CHKERRQ(ierr); 334 #endif 335 PetscFunctionReturn(0); 336 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 337 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 338 for (i=0; i<m; i++) { 339 ierr = PetscViewerASCIIPrintf(viewer,"row %d:",i);CHKERRQ(ierr); 340 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 341 #if defined(PETSC_USE_COMPLEX) 342 if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) { 343 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 344 } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) { 345 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 346 } else if (PetscRealPart(a->a[j]) != 0.0) { 347 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));CHKERRQ(ierr); 348 } 349 #else 350 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);CHKERRQ(ierr);} 351 #endif 352 } 353 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 354 } 355 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 356 } else if (format == PETSC_VIEWER_ASCII_SYMMODU) { 357 int nzd=0,fshift=1,*sptr; 358 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 359 ierr = PetscMalloc((m+1)*sizeof(int),&sptr);CHKERRQ(ierr); 360 for (i=0; i<m; i++) { 361 sptr[i] = nzd+1; 362 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 363 if (a->j[j] >= i) { 364 #if defined(PETSC_USE_COMPLEX) 365 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++; 366 #else 367 if (a->a[j] != 0.0) nzd++; 368 #endif 369 } 370 } 371 } 372 sptr[m] = nzd+1; 373 ierr = PetscViewerASCIIPrintf(viewer," %d %d\n\n",m,nzd);CHKERRQ(ierr); 374 for (i=0; i<m+1; i+=6) { 375 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);} 376 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);} 377 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);} 378 else if (i+1<m) {ierr = PetscViewerASCIIPrintf(viewer," %d %d %d\n",sptr[i],sptr[i+1],sptr[i+2]);CHKERRQ(ierr);} 379 else if (i<m) {ierr = PetscViewerASCIIPrintf(viewer," %d %d\n",sptr[i],sptr[i+1]);CHKERRQ(ierr);} 380 else {ierr = PetscViewerASCIIPrintf(viewer," %d\n",sptr[i]);CHKERRQ(ierr);} 381 } 382 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 383 ierr = PetscFree(sptr);CHKERRQ(ierr); 384 for (i=0; i<m; i++) { 385 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 386 if (a->j[j] >= i) {ierr = PetscViewerASCIIPrintf(viewer," %d ",a->j[j]+fshift);CHKERRQ(ierr);} 387 } 388 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 389 } 390 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 391 for (i=0; i<m; i++) { 392 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 393 if (a->j[j] >= i) { 394 #if defined(PETSC_USE_COMPLEX) 395 if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) { 396 ierr = PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 397 } 398 #else 399 if (a->a[j] != 0.0) {ierr = PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);CHKERRQ(ierr);} 400 #endif 401 } 402 } 403 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 404 } 405 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 406 } else if (format == PETSC_VIEWER_ASCII_DENSE) { 407 int cnt = 0,jcnt; 408 PetscScalar value; 409 410 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 411 for (i=0; i<m; i++) { 412 jcnt = 0; 413 for (j=0; j<A->n; j++) { 414 if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) { 415 value = a->a[cnt++]; 416 jcnt++; 417 } else { 418 value = 0.0; 419 } 420 #if defined(PETSC_USE_COMPLEX) 421 ierr = PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));CHKERRQ(ierr); 422 #else 423 ierr = PetscViewerASCIIPrintf(viewer," %7.5e ",value);CHKERRQ(ierr); 424 #endif 425 } 426 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 427 } 428 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 429 } else { 430 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 431 for (i=0; i<m; i++) { 432 ierr = PetscViewerASCIIPrintf(viewer,"row %d:",i);CHKERRQ(ierr); 433 for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) { 434 #if defined(PETSC_USE_COMPLEX) 435 if (PetscImaginaryPart(a->a[j]) > 0.0) { 436 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 437 } else if (PetscImaginaryPart(a->a[j]) < 0.0) { 438 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));CHKERRQ(ierr); 439 } else { 440 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));CHKERRQ(ierr); 441 } 442 #else 443 ierr = PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);CHKERRQ(ierr); 444 #endif 445 } 446 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 447 } 448 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 449 } 450 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 451 PetscFunctionReturn(0); 452 } 453 454 #undef __FUNCT__ 455 #define __FUNCT__ "MatView_SeqAIJ_Draw_Zoom" 456 int MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa) 457 { 458 Mat A = (Mat) Aa; 459 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 460 int ierr,i,j,m = A->m,color; 461 PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0; 462 PetscViewer viewer; 463 PetscViewerFormat format; 464 465 PetscFunctionBegin; 466 ierr = PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);CHKERRQ(ierr); 467 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 468 469 ierr = PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);CHKERRQ(ierr); 470 /* loop over matrix elements drawing boxes */ 471 472 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 473 /* Blue for negative, Cyan for zero and Red for positive */ 474 color = PETSC_DRAW_BLUE; 475 for (i=0; i<m; i++) { 476 y_l = m - i - 1.0; y_r = y_l + 1.0; 477 for (j=a->i[i]; j<a->i[i+1]; j++) { 478 x_l = a->j[j] ; x_r = x_l + 1.0; 479 #if defined(PETSC_USE_COMPLEX) 480 if (PetscRealPart(a->a[j]) >= 0.) continue; 481 #else 482 if (a->a[j] >= 0.) continue; 483 #endif 484 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 485 } 486 } 487 color = PETSC_DRAW_CYAN; 488 for (i=0; i<m; i++) { 489 y_l = m - i - 1.0; y_r = y_l + 1.0; 490 for (j=a->i[i]; j<a->i[i+1]; j++) { 491 x_l = a->j[j]; x_r = x_l + 1.0; 492 if (a->a[j] != 0.) continue; 493 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 494 } 495 } 496 color = PETSC_DRAW_RED; 497 for (i=0; i<m; i++) { 498 y_l = m - i - 1.0; y_r = y_l + 1.0; 499 for (j=a->i[i]; j<a->i[i+1]; j++) { 500 x_l = a->j[j]; x_r = x_l + 1.0; 501 #if defined(PETSC_USE_COMPLEX) 502 if (PetscRealPart(a->a[j]) <= 0.) continue; 503 #else 504 if (a->a[j] <= 0.) continue; 505 #endif 506 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 507 } 508 } 509 } else { 510 /* use contour shading to indicate magnitude of values */ 511 /* first determine max of all nonzero values */ 512 int nz = a->nz,count; 513 PetscDraw popup; 514 PetscReal scale; 515 516 for (i=0; i<nz; i++) { 517 if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]); 518 } 519 scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv; 520 ierr = PetscDrawGetPopup(draw,&popup);CHKERRQ(ierr); 521 if (popup) {ierr = PetscDrawScalePopup(popup,0.0,maxv);CHKERRQ(ierr);} 522 count = 0; 523 for (i=0; i<m; i++) { 524 y_l = m - i - 1.0; y_r = y_l + 1.0; 525 for (j=a->i[i]; j<a->i[i+1]; j++) { 526 x_l = a->j[j]; x_r = x_l + 1.0; 527 color = PETSC_DRAW_BASIC_COLORS + (int)(scale*PetscAbsScalar(a->a[count])); 528 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 529 count++; 530 } 531 } 532 } 533 PetscFunctionReturn(0); 534 } 535 536 #undef __FUNCT__ 537 #define __FUNCT__ "MatView_SeqAIJ_Draw" 538 int MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer) 539 { 540 int ierr; 541 PetscDraw draw; 542 PetscReal xr,yr,xl,yl,h,w; 543 PetscTruth isnull; 544 545 PetscFunctionBegin; 546 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 547 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); 548 if (isnull) PetscFunctionReturn(0); 549 550 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);CHKERRQ(ierr); 551 xr = A->n; yr = A->m; h = yr/10.0; w = xr/10.0; 552 xr += w; yr += h; xl = -w; yl = -h; 553 ierr = PetscDrawSetCoordinates(draw,xl,yl,xr,yr);CHKERRQ(ierr); 554 ierr = PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);CHKERRQ(ierr); 555 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);CHKERRQ(ierr); 556 PetscFunctionReturn(0); 557 } 558 559 #undef __FUNCT__ 560 #define __FUNCT__ "MatView_SeqAIJ" 561 int MatView_SeqAIJ(Mat A,PetscViewer viewer) 562 { 563 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 564 int ierr; 565 PetscTruth issocket,isascii,isbinary,isdraw; 566 567 PetscFunctionBegin; 568 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);CHKERRQ(ierr); 569 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);CHKERRQ(ierr); 570 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 571 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 572 if (issocket) { 573 ierr = PetscViewerSocketPutSparse_Private(viewer,A->m,A->n,a->nz,a->a,a->i,a->j);CHKERRQ(ierr); 574 } else if (isascii) { 575 ierr = MatView_SeqAIJ_ASCII(A,viewer);CHKERRQ(ierr); 576 } else if (isbinary) { 577 ierr = MatView_SeqAIJ_Binary(A,viewer);CHKERRQ(ierr); 578 } else if (isdraw) { 579 ierr = MatView_SeqAIJ_Draw(A,viewer);CHKERRQ(ierr); 580 } else { 581 SETERRQ1(1,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name); 582 } 583 PetscFunctionReturn(0); 584 } 585 586 #undef __FUNCT__ 587 #define __FUNCT__ "MatAssemblyEnd_SeqAIJ" 588 int MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode) 589 { 590 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 591 int fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax,ierr; 592 int m = A->m,*ip,N,*ailen = a->ilen,rmax = 0; 593 PetscScalar *aa = a->a,*ap; 594 595 PetscFunctionBegin; 596 if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(0); 597 598 if (m) rmax = ailen[0]; /* determine row with most nonzeros */ 599 for (i=1; i<m; i++) { 600 /* move each row back by the amount of empty slots (fshift) before it*/ 601 fshift += imax[i-1] - ailen[i-1]; 602 rmax = PetscMax(rmax,ailen[i]); 603 if (fshift) { 604 ip = aj + ai[i] ; 605 ap = aa + ai[i] ; 606 N = ailen[i]; 607 for (j=0; j<N; j++) { 608 ip[j-fshift] = ip[j]; 609 ap[j-fshift] = ap[j]; 610 } 611 } 612 ai[i] = ai[i-1] + ailen[i-1]; 613 } 614 if (m) { 615 fshift += imax[m-1] - ailen[m-1]; 616 ai[m] = ai[m-1] + ailen[m-1]; 617 } 618 /* reset ilen and imax for each row */ 619 for (i=0; i<m; i++) { 620 ailen[i] = imax[i] = ai[i+1] - ai[i]; 621 } 622 a->nz = ai[m]; 623 624 /* diagonals may have moved, so kill the diagonal pointers */ 625 if (fshift && a->diag) { 626 ierr = PetscFree(a->diag);CHKERRQ(ierr); 627 PetscLogObjectMemory(A,-(m+1)*sizeof(int)); 628 a->diag = 0; 629 } 630 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Matrix size: %d X %d; storage space: %d unneeded,%d used\n",m,A->n,fshift,a->nz); 631 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Number of mallocs during MatSetValues() is %d\n",a->reallocs); 632 PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Most nonzeros in any row is %d\n",rmax); 633 a->reallocs = 0; 634 A->info.nz_unneeded = (double)fshift; 635 a->rmax = rmax; 636 637 /* check out for identical nodes. If found, use inode functions */ 638 ierr = Mat_AIJ_CheckInode(A,(PetscTruth)(!fshift));CHKERRQ(ierr); 639 640 PetscFunctionReturn(0); 641 } 642 643 #undef __FUNCT__ 644 #define __FUNCT__ "MatZeroEntries_SeqAIJ" 645 int MatZeroEntries_SeqAIJ(Mat A) 646 { 647 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 648 int ierr; 649 650 PetscFunctionBegin; 651 ierr = PetscMemzero(a->a,(a->i[A->m])*sizeof(PetscScalar));CHKERRQ(ierr); 652 PetscFunctionReturn(0); 653 } 654 655 #undef __FUNCT__ 656 #define __FUNCT__ "MatDestroy_SeqAIJ" 657 int MatDestroy_SeqAIJ(Mat A) 658 { 659 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 660 int ierr; 661 662 PetscFunctionBegin; 663 #if defined(PETSC_USE_LOG) 664 PetscLogObjectState((PetscObject)A,"Rows=%d, Cols=%d, NZ=%d",A->m,A->n,a->nz); 665 #endif 666 if (a->freedata) { 667 ierr = PetscFree(a->a);CHKERRQ(ierr); 668 if (!a->singlemalloc) { 669 ierr = PetscFree(a->i);CHKERRQ(ierr); 670 ierr = PetscFree(a->j);CHKERRQ(ierr); 671 } 672 } 673 if (a->row) { 674 ierr = ISDestroy(a->row);CHKERRQ(ierr); 675 } 676 if (a->col) { 677 ierr = ISDestroy(a->col);CHKERRQ(ierr); 678 } 679 if (a->diag) {ierr = PetscFree(a->diag);CHKERRQ(ierr);} 680 if (a->ilen) {ierr = PetscFree(a->ilen);CHKERRQ(ierr);} 681 if (a->imax) {ierr = PetscFree(a->imax);CHKERRQ(ierr);} 682 if (a->idiag) {ierr = PetscFree(a->idiag);CHKERRQ(ierr);} 683 if (a->solve_work) {ierr = PetscFree(a->solve_work);CHKERRQ(ierr);} 684 if (a->inode.size) {ierr = PetscFree(a->inode.size);CHKERRQ(ierr);} 685 if (a->icol) {ierr = ISDestroy(a->icol);CHKERRQ(ierr);} 686 if (a->saved_values) {ierr = PetscFree(a->saved_values);CHKERRQ(ierr);} 687 if (a->coloring) {ierr = ISColoringDestroy(a->coloring);CHKERRQ(ierr);} 688 if (a->xtoy) {ierr = PetscFree(a->xtoy);CHKERRQ(ierr);} 689 690 ierr = PetscFree(a);CHKERRQ(ierr); 691 PetscFunctionReturn(0); 692 } 693 694 #undef __FUNCT__ 695 #define __FUNCT__ "MatCompress_SeqAIJ" 696 int MatCompress_SeqAIJ(Mat A) 697 { 698 PetscFunctionBegin; 699 PetscFunctionReturn(0); 700 } 701 702 #undef __FUNCT__ 703 #define __FUNCT__ "MatSetOption_SeqAIJ" 704 int MatSetOption_SeqAIJ(Mat A,MatOption op) 705 { 706 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 707 708 PetscFunctionBegin; 709 switch (op) { 710 case MAT_ROW_ORIENTED: 711 a->roworiented = PETSC_TRUE; 712 break; 713 case MAT_KEEP_ZEROED_ROWS: 714 a->keepzeroedrows = PETSC_TRUE; 715 break; 716 case MAT_COLUMN_ORIENTED: 717 a->roworiented = PETSC_FALSE; 718 break; 719 case MAT_COLUMNS_SORTED: 720 a->sorted = PETSC_TRUE; 721 break; 722 case MAT_COLUMNS_UNSORTED: 723 a->sorted = PETSC_FALSE; 724 break; 725 case MAT_NO_NEW_NONZERO_LOCATIONS: 726 a->nonew = 1; 727 break; 728 case MAT_NEW_NONZERO_LOCATION_ERR: 729 a->nonew = -1; 730 break; 731 case MAT_NEW_NONZERO_ALLOCATION_ERR: 732 a->nonew = -2; 733 break; 734 case MAT_YES_NEW_NONZERO_LOCATIONS: 735 a->nonew = 0; 736 break; 737 case MAT_IGNORE_ZERO_ENTRIES: 738 a->ignorezeroentries = PETSC_TRUE; 739 break; 740 case MAT_USE_INODES: 741 a->inode.use = PETSC_TRUE; 742 break; 743 case MAT_DO_NOT_USE_INODES: 744 a->inode.use = PETSC_FALSE; 745 break; 746 case MAT_ROWS_SORTED: 747 case MAT_ROWS_UNSORTED: 748 case MAT_YES_NEW_DIAGONALS: 749 case MAT_IGNORE_OFF_PROC_ENTRIES: 750 case MAT_USE_HASH_TABLE: 751 PetscLogInfo(A,"MatSetOption_SeqAIJ:Option ignored\n"); 752 break; 753 case MAT_NO_NEW_DIAGONALS: 754 SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS"); 755 case MAT_INODE_LIMIT_1: 756 a->inode.limit = 1; 757 break; 758 case MAT_INODE_LIMIT_2: 759 a->inode.limit = 2; 760 break; 761 case MAT_INODE_LIMIT_3: 762 a->inode.limit = 3; 763 break; 764 case MAT_INODE_LIMIT_4: 765 a->inode.limit = 4; 766 break; 767 case MAT_INODE_LIMIT_5: 768 a->inode.limit = 5; 769 break; 770 case MAT_SYMMETRIC: 771 case MAT_STRUCTURALLY_SYMMETRIC: 772 case MAT_NOT_SYMMETRIC: 773 case MAT_NOT_STRUCTURALLY_SYMMETRIC: 774 case MAT_HERMITIAN: 775 case MAT_NOT_HERMITIAN: 776 case MAT_SYMMETRY_ETERNAL: 777 case MAT_NOT_SYMMETRY_ETERNAL: 778 break; 779 default: 780 SETERRQ(PETSC_ERR_SUP,"unknown option"); 781 } 782 PetscFunctionReturn(0); 783 } 784 785 #undef __FUNCT__ 786 #define __FUNCT__ "MatGetDiagonal_SeqAIJ" 787 int MatGetDiagonal_SeqAIJ(Mat A,Vec v) 788 { 789 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 790 int i,j,n,ierr; 791 PetscScalar *x,zero = 0.0; 792 793 PetscFunctionBegin; 794 ierr = VecSet(&zero,v);CHKERRQ(ierr); 795 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 796 ierr = VecGetLocalSize(v,&n);CHKERRQ(ierr); 797 if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector"); 798 for (i=0; i<A->m; i++) { 799 for (j=a->i[i]; j<a->i[i+1]; j++) { 800 if (a->j[j] == i) { 801 x[i] = a->a[j]; 802 break; 803 } 804 } 805 } 806 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 807 PetscFunctionReturn(0); 808 } 809 810 811 #undef __FUNCT__ 812 #define __FUNCT__ "MatMultTransposeAdd_SeqAIJ" 813 int MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy) 814 { 815 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 816 PetscScalar *x,*y; 817 int ierr,m = A->m; 818 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 819 PetscScalar *v,alpha; 820 int n,i,*idx; 821 #endif 822 823 PetscFunctionBegin; 824 if (zz != yy) {ierr = VecCopy(zz,yy);CHKERRQ(ierr);} 825 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 826 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 827 828 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ) 829 fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y); 830 #else 831 for (i=0; i<m; i++) { 832 idx = a->j + a->i[i] ; 833 v = a->a + a->i[i] ; 834 n = a->i[i+1] - a->i[i]; 835 alpha = x[i]; 836 while (n-->0) {y[*idx++] += alpha * *v++;} 837 } 838 #endif 839 PetscLogFlops(2*a->nz); 840 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 841 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 842 PetscFunctionReturn(0); 843 } 844 845 #undef __FUNCT__ 846 #define __FUNCT__ "MatMultTranspose_SeqAIJ" 847 int MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy) 848 { 849 PetscScalar zero = 0.0; 850 int ierr; 851 852 PetscFunctionBegin; 853 ierr = VecSet(&zero,yy);CHKERRQ(ierr); 854 ierr = MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);CHKERRQ(ierr); 855 PetscFunctionReturn(0); 856 } 857 858 859 #undef __FUNCT__ 860 #define __FUNCT__ "MatMult_SeqAIJ" 861 int MatMult_SeqAIJ(Mat A,Vec xx,Vec yy) 862 { 863 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 864 PetscScalar *x,*y,*v; 865 int ierr,m = A->m,*idx,*ii; 866 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 867 int n,i,jrow,j; 868 PetscScalar sum; 869 #endif 870 871 #if defined(PETSC_HAVE_PRAGMA_DISJOINT) 872 #pragma disjoint(*x,*y,*v) 873 #endif 874 875 PetscFunctionBegin; 876 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 877 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 878 idx = a->j; 879 v = a->a; 880 ii = a->i; 881 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ) 882 fortranmultaij_(&m,x,ii,idx,v,y); 883 #else 884 for (i=0; i<m; i++) { 885 jrow = ii[i]; 886 n = ii[i+1] - jrow; 887 sum = 0.0; 888 for (j=0; j<n; j++) { 889 sum += v[jrow]*x[idx[jrow]]; jrow++; 890 } 891 y[i] = sum; 892 } 893 #endif 894 PetscLogFlops(2*a->nz - m); 895 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 896 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 897 PetscFunctionReturn(0); 898 } 899 900 #undef __FUNCT__ 901 #define __FUNCT__ "MatMultAdd_SeqAIJ" 902 int MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz) 903 { 904 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 905 PetscScalar *x,*y,*z,*v; 906 int ierr,m = A->m,*idx,*ii; 907 #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 908 int n,i,jrow,j; 909 PetscScalar sum; 910 #endif 911 912 PetscFunctionBegin; 913 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 914 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 915 if (zz != yy) { 916 ierr = VecGetArray(zz,&z);CHKERRQ(ierr); 917 } else { 918 z = y; 919 } 920 921 idx = a->j; 922 v = a->a; 923 ii = a->i; 924 #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ) 925 fortranmultaddaij_(&m,x,ii,idx,v,y,z); 926 #else 927 for (i=0; i<m; i++) { 928 jrow = ii[i]; 929 n = ii[i+1] - jrow; 930 sum = y[i]; 931 for (j=0; j<n; j++) { 932 sum += v[jrow]*x[idx[jrow]]; jrow++; 933 } 934 z[i] = sum; 935 } 936 #endif 937 PetscLogFlops(2*a->nz); 938 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 939 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 940 if (zz != yy) { 941 ierr = VecRestoreArray(zz,&z);CHKERRQ(ierr); 942 } 943 PetscFunctionReturn(0); 944 } 945 946 /* 947 Adds diagonal pointers to sparse matrix structure. 948 */ 949 #undef __FUNCT__ 950 #define __FUNCT__ "MatMarkDiagonal_SeqAIJ" 951 int MatMarkDiagonal_SeqAIJ(Mat A) 952 { 953 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 954 int i,j,*diag,m = A->m,ierr; 955 956 PetscFunctionBegin; 957 if (a->diag) PetscFunctionReturn(0); 958 959 ierr = PetscMalloc((m+1)*sizeof(int),&diag);CHKERRQ(ierr); 960 PetscLogObjectMemory(A,(m+1)*sizeof(int)); 961 for (i=0; i<A->m; i++) { 962 diag[i] = a->i[i+1]; 963 for (j=a->i[i]; j<a->i[i+1]; j++) { 964 if (a->j[j] == i) { 965 diag[i] = j; 966 break; 967 } 968 } 969 } 970 a->diag = diag; 971 PetscFunctionReturn(0); 972 } 973 974 /* 975 Checks for missing diagonals 976 */ 977 #undef __FUNCT__ 978 #define __FUNCT__ "MatMissingDiagonal_SeqAIJ" 979 int MatMissingDiagonal_SeqAIJ(Mat A) 980 { 981 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 982 int *diag,*jj = a->j,i,ierr; 983 984 PetscFunctionBegin; 985 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 986 diag = a->diag; 987 for (i=0; i<A->m; i++) { 988 if (jj[diag[i]] != i) { 989 SETERRQ1(1,"Matrix is missing diagonal number %d",i); 990 } 991 } 992 PetscFunctionReturn(0); 993 } 994 995 #undef __FUNCT__ 996 #define __FUNCT__ "MatRelax_SeqAIJ" 997 int MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx) 998 { 999 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1000 PetscScalar *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag; 1001 const PetscScalar *v = a->a, *b, *bs,*xb, *ts; 1002 int ierr,n = A->n,m = A->m,i; 1003 const int *idx,*diag; 1004 1005 PetscFunctionBegin; 1006 its = its*lits; 1007 if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits); 1008 1009 if (!a->diag) {ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr);} 1010 diag = a->diag; 1011 if (!a->idiag) { 1012 ierr = PetscMalloc(3*m*sizeof(PetscScalar),&a->idiag);CHKERRQ(ierr); 1013 a->ssor = a->idiag + m; 1014 mdiag = a->ssor + m; 1015 1016 v = a->a; 1017 1018 /* this is wrong when fshift omega changes each iteration */ 1019 if (omega == 1.0 && fshift == 0.0) { 1020 for (i=0; i<m; i++) { 1021 mdiag[i] = v[diag[i]]; 1022 a->idiag[i] = 1.0/v[diag[i]]; 1023 } 1024 PetscLogFlops(m); 1025 } else { 1026 for (i=0; i<m; i++) { 1027 mdiag[i] = v[diag[i]]; 1028 a->idiag[i] = omega/(fshift + v[diag[i]]); 1029 } 1030 PetscLogFlops(2*m); 1031 } 1032 } 1033 t = a->ssor; 1034 idiag = a->idiag; 1035 mdiag = a->idiag + 2*m; 1036 1037 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 1038 if (xx != bb) { 1039 ierr = VecGetArray(bb,(PetscScalar**)&b);CHKERRQ(ierr); 1040 } else { 1041 b = x; 1042 } 1043 1044 /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */ 1045 xs = x; 1046 if (flag == SOR_APPLY_UPPER) { 1047 /* apply (U + D/omega) to the vector */ 1048 bs = b; 1049 for (i=0; i<m; i++) { 1050 d = fshift + a->a[diag[i]]; 1051 n = a->i[i+1] - diag[i] - 1; 1052 idx = a->j + diag[i] + 1; 1053 v = a->a + diag[i] + 1; 1054 sum = b[i]*d/omega; 1055 SPARSEDENSEDOT(sum,bs,v,idx,n); 1056 x[i] = sum; 1057 } 1058 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1059 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1060 PetscLogFlops(a->nz); 1061 PetscFunctionReturn(0); 1062 } 1063 1064 1065 /* Let A = L + U + D; where L is lower trianglar, 1066 U is upper triangular, E is diagonal; This routine applies 1067 1068 (L + E)^{-1} A (U + E)^{-1} 1069 1070 to a vector efficiently using Eisenstat's trick. This is for 1071 the case of SSOR preconditioner, so E is D/omega where omega 1072 is the relaxation factor. 1073 */ 1074 1075 if (flag == SOR_APPLY_LOWER) { 1076 SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented"); 1077 } else if (flag & SOR_EISENSTAT) { 1078 /* Let A = L + U + D; where L is lower trianglar, 1079 U is upper triangular, E is diagonal; This routine applies 1080 1081 (L + E)^{-1} A (U + E)^{-1} 1082 1083 to a vector efficiently using Eisenstat's trick. This is for 1084 the case of SSOR preconditioner, so E is D/omega where omega 1085 is the relaxation factor. 1086 */ 1087 scale = (2.0/omega) - 1.0; 1088 1089 /* x = (E + U)^{-1} b */ 1090 for (i=m-1; i>=0; i--) { 1091 n = a->i[i+1] - diag[i] - 1; 1092 idx = a->j + diag[i] + 1; 1093 v = a->a + diag[i] + 1; 1094 sum = b[i]; 1095 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1096 x[i] = sum*idiag[i]; 1097 } 1098 1099 /* t = b - (2*E - D)x */ 1100 v = a->a; 1101 for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; } 1102 1103 /* t = (E + L)^{-1}t */ 1104 ts = t; 1105 diag = a->diag; 1106 for (i=0; i<m; i++) { 1107 n = diag[i] - a->i[i]; 1108 idx = a->j + a->i[i]; 1109 v = a->a + a->i[i]; 1110 sum = t[i]; 1111 SPARSEDENSEMDOT(sum,ts,v,idx,n); 1112 t[i] = sum*idiag[i]; 1113 /* x = x + t */ 1114 x[i] += t[i]; 1115 } 1116 1117 PetscLogFlops(6*m-1 + 2*a->nz); 1118 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1119 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1120 PetscFunctionReturn(0); 1121 } 1122 if (flag & SOR_ZERO_INITIAL_GUESS) { 1123 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){ 1124 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1125 fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(int *)diag,idiag,a->a,(void*)b); 1126 #else 1127 for (i=0; i<m; i++) { 1128 n = diag[i] - a->i[i]; 1129 idx = a->j + a->i[i]; 1130 v = a->a + a->i[i]; 1131 sum = b[i]; 1132 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1133 x[i] = sum*idiag[i]; 1134 } 1135 #endif 1136 xb = x; 1137 PetscLogFlops(a->nz); 1138 } else xb = b; 1139 if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) && 1140 (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) { 1141 for (i=0; i<m; i++) { 1142 x[i] *= mdiag[i]; 1143 } 1144 PetscLogFlops(m); 1145 } 1146 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){ 1147 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1148 fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(int*)diag,idiag,a->a,(void*)xb); 1149 #else 1150 for (i=m-1; i>=0; i--) { 1151 n = a->i[i+1] - diag[i] - 1; 1152 idx = a->j + diag[i] + 1; 1153 v = a->a + diag[i] + 1; 1154 sum = xb[i]; 1155 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1156 x[i] = sum*idiag[i]; 1157 } 1158 #endif 1159 PetscLogFlops(a->nz); 1160 } 1161 its--; 1162 } 1163 while (its--) { 1164 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){ 1165 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1166 fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(int*)diag,a->a,(void*)b); 1167 #else 1168 for (i=0; i<m; i++) { 1169 d = fshift + a->a[diag[i]]; 1170 n = a->i[i+1] - a->i[i]; 1171 idx = a->j + a->i[i]; 1172 v = a->a + a->i[i]; 1173 sum = b[i]; 1174 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1175 x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i]; 1176 } 1177 #endif 1178 PetscLogFlops(a->nz); 1179 } 1180 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){ 1181 #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ) 1182 fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(int*)diag,a->a,(void*)b); 1183 #else 1184 for (i=m-1; i>=0; i--) { 1185 d = fshift + a->a[diag[i]]; 1186 n = a->i[i+1] - a->i[i]; 1187 idx = a->j + a->i[i]; 1188 v = a->a + a->i[i]; 1189 sum = b[i]; 1190 SPARSEDENSEMDOT(sum,xs,v,idx,n); 1191 x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i]; 1192 } 1193 #endif 1194 PetscLogFlops(a->nz); 1195 } 1196 } 1197 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 1198 if (bb != xx) {ierr = VecRestoreArray(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1199 PetscFunctionReturn(0); 1200 } 1201 1202 #undef __FUNCT__ 1203 #define __FUNCT__ "MatGetInfo_SeqAIJ" 1204 int MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info) 1205 { 1206 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1207 1208 PetscFunctionBegin; 1209 info->rows_global = (double)A->m; 1210 info->columns_global = (double)A->n; 1211 info->rows_local = (double)A->m; 1212 info->columns_local = (double)A->n; 1213 info->block_size = 1.0; 1214 info->nz_allocated = (double)a->maxnz; 1215 info->nz_used = (double)a->nz; 1216 info->nz_unneeded = (double)(a->maxnz - a->nz); 1217 info->assemblies = (double)A->num_ass; 1218 info->mallocs = (double)a->reallocs; 1219 info->memory = A->mem; 1220 if (A->factor) { 1221 info->fill_ratio_given = A->info.fill_ratio_given; 1222 info->fill_ratio_needed = A->info.fill_ratio_needed; 1223 info->factor_mallocs = A->info.factor_mallocs; 1224 } else { 1225 info->fill_ratio_given = 0; 1226 info->fill_ratio_needed = 0; 1227 info->factor_mallocs = 0; 1228 } 1229 PetscFunctionReturn(0); 1230 } 1231 1232 #undef __FUNCT__ 1233 #define __FUNCT__ "MatZeroRows_SeqAIJ" 1234 int MatZeroRows_SeqAIJ(Mat A,IS is,const PetscScalar *diag) 1235 { 1236 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1237 int i,ierr,N,*rows,m = A->m - 1; 1238 1239 PetscFunctionBegin; 1240 ierr = ISGetLocalSize(is,&N);CHKERRQ(ierr); 1241 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 1242 if (a->keepzeroedrows) { 1243 for (i=0; i<N; i++) { 1244 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]); 1245 ierr = PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));CHKERRQ(ierr); 1246 } 1247 if (diag) { 1248 ierr = MatMissingDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1249 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1250 for (i=0; i<N; i++) { 1251 a->a[a->diag[rows[i]]] = *diag; 1252 } 1253 } 1254 } else { 1255 if (diag) { 1256 for (i=0; i<N; i++) { 1257 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]); 1258 if (a->ilen[rows[i]] > 0) { 1259 a->ilen[rows[i]] = 1; 1260 a->a[a->i[rows[i]]] = *diag; 1261 a->j[a->i[rows[i]]] = rows[i]; 1262 } else { /* in case row was completely empty */ 1263 ierr = MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);CHKERRQ(ierr); 1264 } 1265 } 1266 } else { 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 a->ilen[rows[i]] = 0; 1270 } 1271 } 1272 } 1273 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 1274 ierr = MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1275 PetscFunctionReturn(0); 1276 } 1277 1278 #undef __FUNCT__ 1279 #define __FUNCT__ "MatGetRow_SeqAIJ" 1280 int MatGetRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v) 1281 { 1282 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1283 int *itmp; 1284 1285 PetscFunctionBegin; 1286 if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %d out of range",row); 1287 1288 *nz = a->i[row+1] - a->i[row]; 1289 if (v) *v = a->a + a->i[row]; 1290 if (idx) { 1291 itmp = a->j + a->i[row]; 1292 if (*nz) { 1293 *idx = itmp; 1294 } 1295 else *idx = 0; 1296 } 1297 PetscFunctionReturn(0); 1298 } 1299 1300 /* remove this function? */ 1301 #undef __FUNCT__ 1302 #define __FUNCT__ "MatRestoreRow_SeqAIJ" 1303 int MatRestoreRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v) 1304 { 1305 /* Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1306 int ierr; */ 1307 1308 PetscFunctionBegin; 1309 /* if (idx) {if (*idx && a->indexshift) {ierr = PetscFree(*idx);CHKERRQ(ierr);}} */ 1310 PetscFunctionReturn(0); 1311 } 1312 1313 #undef __FUNCT__ 1314 #define __FUNCT__ "MatNorm_SeqAIJ" 1315 int MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm) 1316 { 1317 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1318 PetscScalar *v = a->a; 1319 PetscReal sum = 0.0; 1320 int i,j,ierr; 1321 1322 PetscFunctionBegin; 1323 if (type == NORM_FROBENIUS) { 1324 for (i=0; i<a->nz; i++) { 1325 #if defined(PETSC_USE_COMPLEX) 1326 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1327 #else 1328 sum += (*v)*(*v); v++; 1329 #endif 1330 } 1331 *nrm = sqrt(sum); 1332 } else if (type == NORM_1) { 1333 PetscReal *tmp; 1334 int *jj = a->j; 1335 ierr = PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);CHKERRQ(ierr); 1336 ierr = PetscMemzero(tmp,A->n*sizeof(PetscReal));CHKERRQ(ierr); 1337 *nrm = 0.0; 1338 for (j=0; j<a->nz; j++) { 1339 tmp[*jj++] += PetscAbsScalar(*v); v++; 1340 } 1341 for (j=0; j<A->n; j++) { 1342 if (tmp[j] > *nrm) *nrm = tmp[j]; 1343 } 1344 ierr = PetscFree(tmp);CHKERRQ(ierr); 1345 } else if (type == NORM_INFINITY) { 1346 *nrm = 0.0; 1347 for (j=0; j<A->m; j++) { 1348 v = a->a + a->i[j]; 1349 sum = 0.0; 1350 for (i=0; i<a->i[j+1]-a->i[j]; i++) { 1351 sum += PetscAbsScalar(*v); v++; 1352 } 1353 if (sum > *nrm) *nrm = sum; 1354 } 1355 } else { 1356 SETERRQ(PETSC_ERR_SUP,"No support for two norm"); 1357 } 1358 PetscFunctionReturn(0); 1359 } 1360 1361 #undef __FUNCT__ 1362 #define __FUNCT__ "MatTranspose_SeqAIJ" 1363 int MatTranspose_SeqAIJ(Mat A,Mat *B) 1364 { 1365 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1366 Mat C; 1367 int i,ierr,*aj = a->j,*ai = a->i,m = A->m,len,*col; 1368 PetscScalar *array = a->a; 1369 1370 PetscFunctionBegin; 1371 if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place"); 1372 ierr = PetscMalloc((1+A->n)*sizeof(int),&col);CHKERRQ(ierr); 1373 ierr = PetscMemzero(col,(1+A->n)*sizeof(int));CHKERRQ(ierr); 1374 1375 for (i=0; i<ai[m]; i++) col[aj[i]] += 1; 1376 ierr = MatCreateSeqAIJ(A->comm,A->n,m,0,col,&C);CHKERRQ(ierr); 1377 ierr = PetscFree(col);CHKERRQ(ierr); 1378 for (i=0; i<m; i++) { 1379 len = ai[i+1]-ai[i]; 1380 ierr = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);CHKERRQ(ierr); 1381 array += len; 1382 aj += len; 1383 } 1384 1385 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1386 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1387 1388 if (B) { 1389 *B = C; 1390 } else { 1391 ierr = MatHeaderCopy(A,C);CHKERRQ(ierr); 1392 } 1393 PetscFunctionReturn(0); 1394 } 1395 1396 EXTERN_C_BEGIN 1397 #undef __FUNCT__ 1398 #define __FUNCT__ "MatIsTranspose_SeqAIJ" 1399 int MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscTruth *f) 1400 { 1401 Mat_SeqAIJ *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data; 1402 int *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb; 1403 int ma,na,mb,nb, i,ierr; 1404 1405 PetscFunctionBegin; 1406 bij = (Mat_SeqAIJ *) B->data; 1407 1408 ierr = MatGetSize(A,&ma,&na); CHKERRQ(ierr); 1409 ierr = MatGetSize(B,&mb,&nb); CHKERRQ(ierr); 1410 if (ma!=nb || na!=mb) 1411 SETERRQ(1,"Incompatible A/B sizes for symmetry test"); 1412 aii = aij->i; bii = bij->i; 1413 adx = aij->j; bdx = bij->j; 1414 va = aij->a; vb = bij->a; 1415 ierr = PetscMalloc(ma*sizeof(int),&aptr); CHKERRQ(ierr); 1416 ierr = PetscMalloc(mb*sizeof(int),&bptr); CHKERRQ(ierr); 1417 for (i=0; i<ma; i++) aptr[i] = aii[i]; 1418 for (i=0; i<mb; i++) bptr[i] = bii[i]; 1419 1420 *f = PETSC_TRUE; 1421 for (i=0; i<ma; i++) { 1422 /*printf("row %d spans %d--%d; we start @ %d\n", 1423 i,idx[ii[i]],idx[ii[i+1]-1],idx[aptr[i]]);*/ 1424 while (aptr[i]<aii[i+1]) { 1425 int idc,idr; PetscScalar vc,vr; 1426 /* column/row index/value */ 1427 idc = adx[aptr[i]]; idr = bdx[bptr[idc]]; 1428 vc = va[aptr[i]]; vr = vb[bptr[idc]]; 1429 /*printf("comparing %d: (%d,%d)=%e to (%d,%d)=%e\n", 1430 aptr[i],i,idc,vc,idc,idr,vr);*/ 1431 if (i!=idr || vc!=vr) { 1432 *f = PETSC_FALSE; goto done; 1433 } else { 1434 aptr[i]++; if (B || i!=idc) bptr[idc]++; 1435 } 1436 } 1437 } 1438 done: 1439 ierr = PetscFree(aptr); CHKERRQ(ierr); 1440 if (B) { 1441 ierr = PetscFree(bptr); CHKERRQ(ierr); 1442 } 1443 1444 PetscFunctionReturn(0); 1445 } 1446 EXTERN_C_END 1447 1448 EXTERN_C_BEGIN 1449 #undef __FUNCT__ 1450 #define __FUNCT__ "MatIsSymmetric_SeqAIJ" 1451 int MatIsSymmetric_SeqAIJ(Mat A,PetscTruth *f) 1452 { 1453 int ierr; 1454 PetscFunctionBegin; 1455 ierr = MatIsTranspose_SeqAIJ(A,A,f); CHKERRQ(ierr); 1456 PetscFunctionReturn(0); 1457 } 1458 EXTERN_C_END 1459 1460 #undef __FUNCT__ 1461 #define __FUNCT__ "MatDiagonalScale_SeqAIJ" 1462 int MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr) 1463 { 1464 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1465 PetscScalar *l,*r,x,*v; 1466 int ierr,i,j,m = A->m,n = A->n,M,nz = a->nz,*jj; 1467 1468 PetscFunctionBegin; 1469 if (ll) { 1470 /* The local size is used so that VecMPI can be passed to this routine 1471 by MatDiagonalScale_MPIAIJ */ 1472 ierr = VecGetLocalSize(ll,&m);CHKERRQ(ierr); 1473 if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length"); 1474 ierr = VecGetArray(ll,&l);CHKERRQ(ierr); 1475 v = a->a; 1476 for (i=0; i<m; i++) { 1477 x = l[i]; 1478 M = a->i[i+1] - a->i[i]; 1479 for (j=0; j<M; j++) { (*v++) *= x;} 1480 } 1481 ierr = VecRestoreArray(ll,&l);CHKERRQ(ierr); 1482 PetscLogFlops(nz); 1483 } 1484 if (rr) { 1485 ierr = VecGetLocalSize(rr,&n);CHKERRQ(ierr); 1486 if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length"); 1487 ierr = VecGetArray(rr,&r);CHKERRQ(ierr); 1488 v = a->a; jj = a->j; 1489 for (i=0; i<nz; i++) { 1490 (*v++) *= r[*jj++]; 1491 } 1492 ierr = VecRestoreArray(rr,&r);CHKERRQ(ierr); 1493 PetscLogFlops(nz); 1494 } 1495 PetscFunctionReturn(0); 1496 } 1497 1498 #undef __FUNCT__ 1499 #define __FUNCT__ "MatGetSubMatrix_SeqAIJ" 1500 int MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,int csize,MatReuse scall,Mat *B) 1501 { 1502 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c; 1503 int *smap,i,k,kstart,kend,ierr,oldcols = A->n,*lens; 1504 int row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi; 1505 int *irow,*icol,nrows,ncols; 1506 int *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen; 1507 PetscScalar *a_new,*mat_a; 1508 Mat C; 1509 PetscTruth stride; 1510 1511 PetscFunctionBegin; 1512 ierr = ISSorted(isrow,(PetscTruth*)&i);CHKERRQ(ierr); 1513 if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted"); 1514 ierr = ISSorted(iscol,(PetscTruth*)&i);CHKERRQ(ierr); 1515 if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted"); 1516 1517 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 1518 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 1519 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 1520 1521 ierr = ISStrideGetInfo(iscol,&first,&step);CHKERRQ(ierr); 1522 ierr = ISStride(iscol,&stride);CHKERRQ(ierr); 1523 if (stride && step == 1) { 1524 /* special case of contiguous rows */ 1525 ierr = PetscMalloc((2*nrows+1)*sizeof(int),&lens);CHKERRQ(ierr); 1526 starts = lens + nrows; 1527 /* loop over new rows determining lens and starting points */ 1528 for (i=0; i<nrows; i++) { 1529 kstart = ai[irow[i]]; 1530 kend = kstart + ailen[irow[i]]; 1531 for (k=kstart; k<kend; k++) { 1532 if (aj[k] >= first) { 1533 starts[i] = k; 1534 break; 1535 } 1536 } 1537 sum = 0; 1538 while (k < kend) { 1539 if (aj[k++] >= first+ncols) break; 1540 sum++; 1541 } 1542 lens[i] = sum; 1543 } 1544 /* create submatrix */ 1545 if (scall == MAT_REUSE_MATRIX) { 1546 int n_cols,n_rows; 1547 ierr = MatGetSize(*B,&n_rows,&n_cols);CHKERRQ(ierr); 1548 if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); 1549 ierr = MatZeroEntries(*B);CHKERRQ(ierr); 1550 C = *B; 1551 } else { 1552 ierr = MatCreate(A->comm,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE,&C);CHKERRQ(ierr); 1553 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1554 ierr = MatSeqAIJSetPreallocation(C,0,lens);CHKERRQ(ierr); 1555 } 1556 c = (Mat_SeqAIJ*)C->data; 1557 1558 /* loop over rows inserting into submatrix */ 1559 a_new = c->a; 1560 j_new = c->j; 1561 i_new = c->i; 1562 1563 for (i=0; i<nrows; i++) { 1564 ii = starts[i]; 1565 lensi = lens[i]; 1566 for (k=0; k<lensi; k++) { 1567 *j_new++ = aj[ii+k] - first; 1568 } 1569 ierr = PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));CHKERRQ(ierr); 1570 a_new += lensi; 1571 i_new[i+1] = i_new[i] + lensi; 1572 c->ilen[i] = lensi; 1573 } 1574 ierr = PetscFree(lens);CHKERRQ(ierr); 1575 } else { 1576 ierr = ISGetIndices(iscol,&icol);CHKERRQ(ierr); 1577 ierr = PetscMalloc((1+oldcols)*sizeof(int),&smap);CHKERRQ(ierr); 1578 1579 ierr = PetscMalloc((1+nrows)*sizeof(int),&lens);CHKERRQ(ierr); 1580 ierr = PetscMemzero(smap,oldcols*sizeof(int));CHKERRQ(ierr); 1581 for (i=0; i<ncols; i++) smap[icol[i]] = i+1; 1582 /* determine lens of each row */ 1583 for (i=0; i<nrows; i++) { 1584 kstart = ai[irow[i]]; 1585 kend = kstart + a->ilen[irow[i]]; 1586 lens[i] = 0; 1587 for (k=kstart; k<kend; k++) { 1588 if (smap[aj[k]]) { 1589 lens[i]++; 1590 } 1591 } 1592 } 1593 /* Create and fill new matrix */ 1594 if (scall == MAT_REUSE_MATRIX) { 1595 PetscTruth equal; 1596 1597 c = (Mat_SeqAIJ *)((*B)->data); 1598 if ((*B)->m != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size"); 1599 ierr = PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(int),&equal);CHKERRQ(ierr); 1600 if (!equal) { 1601 SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros"); 1602 } 1603 ierr = PetscMemzero(c->ilen,(*B)->m*sizeof(int));CHKERRQ(ierr); 1604 C = *B; 1605 } else { 1606 ierr = MatCreate(A->comm,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE,&C);CHKERRQ(ierr); 1607 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1608 ierr = MatSeqAIJSetPreallocation(C,0,lens);CHKERRQ(ierr); 1609 } 1610 c = (Mat_SeqAIJ *)(C->data); 1611 for (i=0; i<nrows; i++) { 1612 row = irow[i]; 1613 kstart = ai[row]; 1614 kend = kstart + a->ilen[row]; 1615 mat_i = c->i[i]; 1616 mat_j = c->j + mat_i; 1617 mat_a = c->a + mat_i; 1618 mat_ilen = c->ilen + i; 1619 for (k=kstart; k<kend; k++) { 1620 if ((tcol=smap[a->j[k]])) { 1621 *mat_j++ = tcol - 1; 1622 *mat_a++ = a->a[k]; 1623 (*mat_ilen)++; 1624 1625 } 1626 } 1627 } 1628 /* Free work space */ 1629 ierr = ISRestoreIndices(iscol,&icol);CHKERRQ(ierr); 1630 ierr = PetscFree(smap);CHKERRQ(ierr); 1631 ierr = PetscFree(lens);CHKERRQ(ierr); 1632 } 1633 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1634 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1635 1636 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 1637 *B = C; 1638 PetscFunctionReturn(0); 1639 } 1640 1641 /* 1642 */ 1643 #undef __FUNCT__ 1644 #define __FUNCT__ "MatILUFactor_SeqAIJ" 1645 int MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info) 1646 { 1647 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 1648 int ierr; 1649 Mat outA; 1650 PetscTruth row_identity,col_identity; 1651 1652 PetscFunctionBegin; 1653 if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu"); 1654 ierr = ISIdentity(row,&row_identity);CHKERRQ(ierr); 1655 ierr = ISIdentity(col,&col_identity);CHKERRQ(ierr); 1656 if (!row_identity || !col_identity) { 1657 SETERRQ(1,"Row and column permutations must be identity for in-place ILU"); 1658 } 1659 1660 outA = inA; 1661 inA->factor = FACTOR_LU; 1662 a->row = row; 1663 a->col = col; 1664 ierr = PetscObjectReference((PetscObject)row);CHKERRQ(ierr); 1665 ierr = PetscObjectReference((PetscObject)col);CHKERRQ(ierr); 1666 1667 /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */ 1668 if (a->icol) {ierr = ISDestroy(a->icol);CHKERRQ(ierr);} /* need to remove old one */ 1669 ierr = ISInvertPermutation(col,PETSC_DECIDE,&a->icol);CHKERRQ(ierr); 1670 PetscLogObjectParent(inA,a->icol); 1671 1672 if (!a->solve_work) { /* this matrix may have been factored before */ 1673 ierr = PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);CHKERRQ(ierr); 1674 } 1675 1676 if (!a->diag) { 1677 ierr = MatMarkDiagonal_SeqAIJ(inA);CHKERRQ(ierr); 1678 } 1679 ierr = MatLUFactorNumeric_SeqAIJ(inA,&outA);CHKERRQ(ierr); 1680 PetscFunctionReturn(0); 1681 } 1682 1683 #include "petscblaslapack.h" 1684 #undef __FUNCT__ 1685 #define __FUNCT__ "MatScale_SeqAIJ" 1686 int MatScale_SeqAIJ(const PetscScalar *alpha,Mat inA) 1687 { 1688 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 1689 int one = 1; 1690 1691 PetscFunctionBegin; 1692 BLscal_(&a->nz,(PetscScalar*)alpha,a->a,&one); 1693 PetscLogFlops(a->nz); 1694 PetscFunctionReturn(0); 1695 } 1696 1697 #undef __FUNCT__ 1698 #define __FUNCT__ "MatGetSubMatrices_SeqAIJ" 1699 int MatGetSubMatrices_SeqAIJ(Mat A,int n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[]) 1700 { 1701 int ierr,i; 1702 1703 PetscFunctionBegin; 1704 if (scall == MAT_INITIAL_MATRIX) { 1705 ierr = PetscMalloc((n+1)*sizeof(Mat),B);CHKERRQ(ierr); 1706 } 1707 1708 for (i=0; i<n; i++) { 1709 ierr = MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);CHKERRQ(ierr); 1710 } 1711 PetscFunctionReturn(0); 1712 } 1713 1714 #undef __FUNCT__ 1715 #define __FUNCT__ "MatGetBlockSize_SeqAIJ" 1716 int MatGetBlockSize_SeqAIJ(Mat A,int *bs) 1717 { 1718 PetscFunctionBegin; 1719 *bs = 1; 1720 PetscFunctionReturn(0); 1721 } 1722 1723 #undef __FUNCT__ 1724 #define __FUNCT__ "MatIncreaseOverlap_SeqAIJ" 1725 int MatIncreaseOverlap_SeqAIJ(Mat A,int is_max,IS is[],int ov) 1726 { 1727 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1728 int row,i,j,k,l,m,n,*idx,ierr,*nidx,isz,val; 1729 int start,end,*ai,*aj; 1730 PetscBT table; 1731 1732 PetscFunctionBegin; 1733 m = A->m; 1734 ai = a->i; 1735 aj = a->j; 1736 1737 if (ov < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used"); 1738 1739 ierr = PetscMalloc((m+1)*sizeof(int),&nidx);CHKERRQ(ierr); 1740 ierr = PetscBTCreate(m,table);CHKERRQ(ierr); 1741 1742 for (i=0; i<is_max; i++) { 1743 /* Initialize the two local arrays */ 1744 isz = 0; 1745 ierr = PetscBTMemzero(m,table);CHKERRQ(ierr); 1746 1747 /* Extract the indices, assume there can be duplicate entries */ 1748 ierr = ISGetIndices(is[i],&idx);CHKERRQ(ierr); 1749 ierr = ISGetLocalSize(is[i],&n);CHKERRQ(ierr); 1750 1751 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 1752 for (j=0; j<n ; ++j){ 1753 if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];} 1754 } 1755 ierr = ISRestoreIndices(is[i],&idx);CHKERRQ(ierr); 1756 ierr = ISDestroy(is[i]);CHKERRQ(ierr); 1757 1758 k = 0; 1759 for (j=0; j<ov; j++){ /* for each overlap */ 1760 n = isz; 1761 for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */ 1762 row = nidx[k]; 1763 start = ai[row]; 1764 end = ai[row+1]; 1765 for (l = start; l<end ; l++){ 1766 val = aj[l] ; 1767 if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;} 1768 } 1769 } 1770 } 1771 ierr = ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));CHKERRQ(ierr); 1772 } 1773 ierr = PetscBTDestroy(table);CHKERRQ(ierr); 1774 ierr = PetscFree(nidx);CHKERRQ(ierr); 1775 PetscFunctionReturn(0); 1776 } 1777 1778 /* -------------------------------------------------------------- */ 1779 #undef __FUNCT__ 1780 #define __FUNCT__ "MatPermute_SeqAIJ" 1781 int MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B) 1782 { 1783 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1784 PetscScalar *vwork; 1785 int i,ierr,nz,m = A->m,n = A->n,*cwork; 1786 int *row,*col,*cnew,j,*lens; 1787 IS icolp,irowp; 1788 1789 PetscFunctionBegin; 1790 ierr = ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);CHKERRQ(ierr); 1791 ierr = ISGetIndices(irowp,&row);CHKERRQ(ierr); 1792 ierr = ISInvertPermutation(colp,PETSC_DECIDE,&icolp);CHKERRQ(ierr); 1793 ierr = ISGetIndices(icolp,&col);CHKERRQ(ierr); 1794 1795 /* determine lengths of permuted rows */ 1796 ierr = PetscMalloc((m+1)*sizeof(int),&lens);CHKERRQ(ierr); 1797 for (i=0; i<m; i++) { 1798 lens[row[i]] = a->i[i+1] - a->i[i]; 1799 } 1800 ierr = MatCreateSeqAIJ(A->comm,m,n,0,lens,B);CHKERRQ(ierr); 1801 ierr = PetscFree(lens);CHKERRQ(ierr); 1802 1803 ierr = PetscMalloc(n*sizeof(int),&cnew);CHKERRQ(ierr); 1804 for (i=0; i<m; i++) { 1805 ierr = MatGetRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1806 for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];} 1807 ierr = MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);CHKERRQ(ierr); 1808 ierr = MatRestoreRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1809 } 1810 ierr = PetscFree(cnew);CHKERRQ(ierr); 1811 ierr = MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1812 ierr = MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1813 ierr = ISRestoreIndices(irowp,&row);CHKERRQ(ierr); 1814 ierr = ISRestoreIndices(icolp,&col);CHKERRQ(ierr); 1815 ierr = ISDestroy(irowp);CHKERRQ(ierr); 1816 ierr = ISDestroy(icolp);CHKERRQ(ierr); 1817 PetscFunctionReturn(0); 1818 } 1819 1820 #undef __FUNCT__ 1821 #define __FUNCT__ "MatPrintHelp_SeqAIJ" 1822 int MatPrintHelp_SeqAIJ(Mat A) 1823 { 1824 static PetscTruth called = PETSC_FALSE; 1825 MPI_Comm comm = A->comm; 1826 int ierr; 1827 1828 PetscFunctionBegin; 1829 if (called) {PetscFunctionReturn(0);} else called = PETSC_TRUE; 1830 ierr = (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):\n");CHKERRQ(ierr); 1831 ierr = (*PetscHelpPrintf)(comm," -mat_lu_pivotthreshold <threshold>: Set pivoting threshold\n");CHKERRQ(ierr); 1832 ierr = (*PetscHelpPrintf)(comm," -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.\n");CHKERRQ(ierr); 1833 ierr = (*PetscHelpPrintf)(comm," -mat_aij_no_inode: Do not use inodes\n");CHKERRQ(ierr); 1834 ierr = (*PetscHelpPrintf)(comm," -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)\n");CHKERRQ(ierr); 1835 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 1836 ierr = (*PetscHelpPrintf)(comm," -mat_aij_matlab: Use Matlab engine sparse LU factorization and solve.\n");CHKERRQ(ierr); 1837 #endif 1838 PetscFunctionReturn(0); 1839 } 1840 1841 #undef __FUNCT__ 1842 #define __FUNCT__ "MatCopy_SeqAIJ" 1843 int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str) 1844 { 1845 int ierr; 1846 1847 PetscFunctionBegin; 1848 /* If the two matrices have the same copy implementation, use fast copy. */ 1849 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 1850 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1851 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 1852 1853 if (a->i[A->m] != b->i[B->m]) { 1854 SETERRQ(1,"Number of nonzeros in two matrices are different"); 1855 } 1856 ierr = PetscMemcpy(b->a,a->a,(a->i[A->m])*sizeof(PetscScalar));CHKERRQ(ierr); 1857 } else { 1858 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1859 } 1860 PetscFunctionReturn(0); 1861 } 1862 1863 #undef __FUNCT__ 1864 #define __FUNCT__ "MatSetUpPreallocation_SeqAIJ" 1865 int MatSetUpPreallocation_SeqAIJ(Mat A) 1866 { 1867 int ierr; 1868 1869 PetscFunctionBegin; 1870 ierr = MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);CHKERRQ(ierr); 1871 PetscFunctionReturn(0); 1872 } 1873 1874 #undef __FUNCT__ 1875 #define __FUNCT__ "MatGetArray_SeqAIJ" 1876 int MatGetArray_SeqAIJ(Mat A,PetscScalar *array[]) 1877 { 1878 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1879 PetscFunctionBegin; 1880 *array = a->a; 1881 PetscFunctionReturn(0); 1882 } 1883 1884 #undef __FUNCT__ 1885 #define __FUNCT__ "MatRestoreArray_SeqAIJ" 1886 int MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[]) 1887 { 1888 PetscFunctionBegin; 1889 PetscFunctionReturn(0); 1890 } 1891 1892 #undef __FUNCT__ 1893 #define __FUNCT__ "MatFDColoringApply_SeqAIJ" 1894 int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx) 1895 { 1896 int (*f)(void *,Vec,Vec,void*) = (int (*)(void *,Vec,Vec,void *))coloring->f; 1897 int k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2; 1898 PetscScalar dx,mone = -1.0,*y,*xx,*w3_array; 1899 PetscScalar *vscale_array; 1900 PetscReal epsilon = coloring->error_rel,umin = coloring->umin; 1901 Vec w1,w2,w3; 1902 void *fctx = coloring->fctx; 1903 PetscTruth flg; 1904 1905 PetscFunctionBegin; 1906 if (!coloring->w1) { 1907 ierr = VecDuplicate(x1,&coloring->w1);CHKERRQ(ierr); 1908 PetscLogObjectParent(coloring,coloring->w1); 1909 ierr = VecDuplicate(x1,&coloring->w2);CHKERRQ(ierr); 1910 PetscLogObjectParent(coloring,coloring->w2); 1911 ierr = VecDuplicate(x1,&coloring->w3);CHKERRQ(ierr); 1912 PetscLogObjectParent(coloring,coloring->w3); 1913 } 1914 w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3; 1915 1916 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 1917 ierr = PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);CHKERRQ(ierr); 1918 if (flg) { 1919 PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()\n"); 1920 } else { 1921 ierr = MatZeroEntries(J);CHKERRQ(ierr); 1922 } 1923 1924 ierr = VecGetOwnershipRange(x1,&start,&end);CHKERRQ(ierr); 1925 ierr = VecGetSize(x1,&N);CHKERRQ(ierr); 1926 1927 /* 1928 This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets 1929 coloring->F for the coarser grids from the finest 1930 */ 1931 if (coloring->F) { 1932 ierr = VecGetLocalSize(coloring->F,&m1);CHKERRQ(ierr); 1933 ierr = VecGetLocalSize(w1,&m2);CHKERRQ(ierr); 1934 if (m1 != m2) { 1935 coloring->F = 0; 1936 } 1937 } 1938 1939 if (coloring->F) { 1940 w1 = coloring->F; 1941 coloring->F = 0; 1942 } else { 1943 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1944 ierr = (*f)(sctx,x1,w1,fctx);CHKERRQ(ierr); 1945 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1946 } 1947 1948 /* 1949 Compute all the scale factors and share with other processors 1950 */ 1951 ierr = VecGetArray(x1,&xx);CHKERRQ(ierr);xx = xx - start; 1952 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr);vscale_array = vscale_array - start; 1953 for (k=0; k<coloring->ncolors; k++) { 1954 /* 1955 Loop over each column associated with color adding the 1956 perturbation to the vector w3. 1957 */ 1958 for (l=0; l<coloring->ncolumns[k]; l++) { 1959 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 1960 dx = xx[col]; 1961 if (dx == 0.0) dx = 1.0; 1962 #if !defined(PETSC_USE_COMPLEX) 1963 if (dx < umin && dx >= 0.0) dx = umin; 1964 else if (dx < 0.0 && dx > -umin) dx = -umin; 1965 #else 1966 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 1967 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 1968 #endif 1969 dx *= epsilon; 1970 vscale_array[col] = 1.0/dx; 1971 } 1972 } 1973 vscale_array = vscale_array + start;ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 1974 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1975 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1976 1977 /* ierr = VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD); 1978 ierr = VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/ 1979 1980 if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow; 1981 else vscaleforrow = coloring->columnsforrow; 1982 1983 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 1984 /* 1985 Loop over each color 1986 */ 1987 for (k=0; k<coloring->ncolors; k++) { 1988 coloring->currentcolor = k; 1989 ierr = VecCopy(x1,w3);CHKERRQ(ierr); 1990 ierr = VecGetArray(w3,&w3_array);CHKERRQ(ierr);w3_array = w3_array - start; 1991 /* 1992 Loop over each column associated with color adding the 1993 perturbation to the vector w3. 1994 */ 1995 for (l=0; l<coloring->ncolumns[k]; l++) { 1996 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 1997 dx = xx[col]; 1998 if (dx == 0.0) dx = 1.0; 1999 #if !defined(PETSC_USE_COMPLEX) 2000 if (dx < umin && dx >= 0.0) dx = umin; 2001 else if (dx < 0.0 && dx > -umin) dx = -umin; 2002 #else 2003 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 2004 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 2005 #endif 2006 dx *= epsilon; 2007 if (!PetscAbsScalar(dx)) SETERRQ(1,"Computed 0 differencing parameter"); 2008 w3_array[col] += dx; 2009 } 2010 w3_array = w3_array + start; ierr = VecRestoreArray(w3,&w3_array);CHKERRQ(ierr); 2011 2012 /* 2013 Evaluate function at x1 + dx (here dx is a vector of perturbations) 2014 */ 2015 2016 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2017 ierr = (*f)(sctx,w3,w2,fctx);CHKERRQ(ierr); 2018 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2019 ierr = VecAXPY(&mone,w1,w2);CHKERRQ(ierr); 2020 2021 /* 2022 Loop over rows of vector, putting results into Jacobian matrix 2023 */ 2024 ierr = VecGetArray(w2,&y);CHKERRQ(ierr); 2025 for (l=0; l<coloring->nrows[k]; l++) { 2026 row = coloring->rows[k][l]; 2027 col = coloring->columnsforrow[k][l]; 2028 y[row] *= vscale_array[vscaleforrow[k][l]]; 2029 srow = row + start; 2030 ierr = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 2031 } 2032 ierr = VecRestoreArray(w2,&y);CHKERRQ(ierr); 2033 } 2034 coloring->currentcolor = k; 2035 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 2036 xx = xx + start; ierr = VecRestoreArray(x1,&xx);CHKERRQ(ierr); 2037 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2038 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2039 PetscFunctionReturn(0); 2040 } 2041 2042 #include "petscblaslapack.h" 2043 #undef __FUNCT__ 2044 #define __FUNCT__ "MatAXPY_SeqAIJ" 2045 int MatAXPY_SeqAIJ(const PetscScalar a[],Mat X,Mat Y,MatStructure str) 2046 { 2047 int ierr,one=1,i; 2048 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data; 2049 2050 PetscFunctionBegin; 2051 if (str == SAME_NONZERO_PATTERN) { 2052 BLaxpy_(&x->nz,(PetscScalar*)a,x->a,&one,y->a,&one); 2053 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2054 if (y->xtoy && y->XtoY != X) { 2055 ierr = PetscFree(y->xtoy);CHKERRQ(ierr); 2056 ierr = MatDestroy(y->XtoY);CHKERRQ(ierr); 2057 } 2058 if (!y->xtoy) { /* get xtoy */ 2059 ierr = MatAXPYGetxtoy_Private(X->m,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);CHKERRQ(ierr); 2060 y->XtoY = X; 2061 } 2062 for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += (*a)*(x->a[i]); 2063 PetscLogInfo(0,"MatAXPY_SeqAIJ: ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz); 2064 } else { 2065 ierr = MatAXPY_Basic(a,X,Y,str);CHKERRQ(ierr); 2066 } 2067 PetscFunctionReturn(0); 2068 } 2069 2070 /* -------------------------------------------------------------------*/ 2071 static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ, 2072 MatGetRow_SeqAIJ, 2073 MatRestoreRow_SeqAIJ, 2074 MatMult_SeqAIJ, 2075 /* 4*/ MatMultAdd_SeqAIJ, 2076 MatMultTranspose_SeqAIJ, 2077 MatMultTransposeAdd_SeqAIJ, 2078 MatSolve_SeqAIJ, 2079 MatSolveAdd_SeqAIJ, 2080 MatSolveTranspose_SeqAIJ, 2081 /*10*/ MatSolveTransposeAdd_SeqAIJ, 2082 MatLUFactor_SeqAIJ, 2083 0, 2084 MatRelax_SeqAIJ, 2085 MatTranspose_SeqAIJ, 2086 /*15*/ MatGetInfo_SeqAIJ, 2087 MatEqual_SeqAIJ, 2088 MatGetDiagonal_SeqAIJ, 2089 MatDiagonalScale_SeqAIJ, 2090 MatNorm_SeqAIJ, 2091 /*20*/ 0, 2092 MatAssemblyEnd_SeqAIJ, 2093 MatCompress_SeqAIJ, 2094 MatSetOption_SeqAIJ, 2095 MatZeroEntries_SeqAIJ, 2096 /*25*/ MatZeroRows_SeqAIJ, 2097 MatLUFactorSymbolic_SeqAIJ, 2098 MatLUFactorNumeric_SeqAIJ, 2099 MatCholeskyFactorSymbolic_SeqAIJ, 2100 MatCholeskyFactorNumeric_SeqAIJ, 2101 /*30*/ MatSetUpPreallocation_SeqAIJ, 2102 MatILUFactorSymbolic_SeqAIJ, 2103 MatICCFactorSymbolic_SeqAIJ, 2104 MatGetArray_SeqAIJ, 2105 MatRestoreArray_SeqAIJ, 2106 /*35*/ MatDuplicate_SeqAIJ, 2107 0, 2108 0, 2109 MatILUFactor_SeqAIJ, 2110 0, 2111 /*40*/ MatAXPY_SeqAIJ, 2112 MatGetSubMatrices_SeqAIJ, 2113 MatIncreaseOverlap_SeqAIJ, 2114 MatGetValues_SeqAIJ, 2115 MatCopy_SeqAIJ, 2116 /*45*/ MatPrintHelp_SeqAIJ, 2117 MatScale_SeqAIJ, 2118 0, 2119 0, 2120 MatILUDTFactor_SeqAIJ, 2121 /*50*/ MatGetBlockSize_SeqAIJ, 2122 MatGetRowIJ_SeqAIJ, 2123 MatRestoreRowIJ_SeqAIJ, 2124 MatGetColumnIJ_SeqAIJ, 2125 MatRestoreColumnIJ_SeqAIJ, 2126 /*55*/ MatFDColoringCreate_SeqAIJ, 2127 0, 2128 0, 2129 MatPermute_SeqAIJ, 2130 0, 2131 /*60*/ 0, 2132 MatDestroy_SeqAIJ, 2133 MatView_SeqAIJ, 2134 MatGetPetscMaps_Petsc, 2135 0, 2136 /*65*/ 0, 2137 0, 2138 0, 2139 0, 2140 0, 2141 /*70*/ 0, 2142 0, 2143 MatSetColoring_SeqAIJ, 2144 MatSetValuesAdic_SeqAIJ, 2145 MatSetValuesAdifor_SeqAIJ, 2146 /*75*/ MatFDColoringApply_SeqAIJ, 2147 0, 2148 0, 2149 0, 2150 0, 2151 /*80*/ 0, 2152 0, 2153 0, 2154 0, 2155 /*85*/ MatLoad_SeqAIJ, 2156 MatIsSymmetric_SeqAIJ, 2157 }; 2158 2159 EXTERN_C_BEGIN 2160 #undef __FUNCT__ 2161 #define __FUNCT__ "MatSeqAIJSetColumnIndices_SeqAIJ" 2162 2163 int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices) 2164 { 2165 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2166 int i,nz,n; 2167 2168 PetscFunctionBegin; 2169 2170 nz = aij->maxnz; 2171 n = mat->n; 2172 for (i=0; i<nz; i++) { 2173 aij->j[i] = indices[i]; 2174 } 2175 aij->nz = nz; 2176 for (i=0; i<n; i++) { 2177 aij->ilen[i] = aij->imax[i]; 2178 } 2179 2180 PetscFunctionReturn(0); 2181 } 2182 EXTERN_C_END 2183 2184 #undef __FUNCT__ 2185 #define __FUNCT__ "MatSeqAIJSetColumnIndices" 2186 /*@ 2187 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 2188 in the matrix. 2189 2190 Input Parameters: 2191 + mat - the SeqAIJ matrix 2192 - indices - the column indices 2193 2194 Level: advanced 2195 2196 Notes: 2197 This can be called if you have precomputed the nonzero structure of the 2198 matrix and want to provide it to the matrix object to improve the performance 2199 of the MatSetValues() operation. 2200 2201 You MUST have set the correct numbers of nonzeros per row in the call to 2202 MatCreateSeqAIJ(). 2203 2204 MUST be called before any calls to MatSetValues(); 2205 2206 The indices should start with zero, not one. 2207 2208 @*/ 2209 int MatSeqAIJSetColumnIndices(Mat mat,int *indices) 2210 { 2211 int ierr,(*f)(Mat,int *); 2212 2213 PetscFunctionBegin; 2214 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2215 PetscValidPointer(indices,2); 2216 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);CHKERRQ(ierr); 2217 if (f) { 2218 ierr = (*f)(mat,indices);CHKERRQ(ierr); 2219 } else { 2220 SETERRQ(1,"Wrong type of matrix to set column indices"); 2221 } 2222 PetscFunctionReturn(0); 2223 } 2224 2225 /* ----------------------------------------------------------------------------------------*/ 2226 2227 EXTERN_C_BEGIN 2228 #undef __FUNCT__ 2229 #define __FUNCT__ "MatStoreValues_SeqAIJ" 2230 int MatStoreValues_SeqAIJ(Mat mat) 2231 { 2232 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2233 size_t nz = aij->i[mat->m],ierr; 2234 2235 PetscFunctionBegin; 2236 if (aij->nonew != 1) { 2237 SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2238 } 2239 2240 /* allocate space for values if not already there */ 2241 if (!aij->saved_values) { 2242 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);CHKERRQ(ierr); 2243 } 2244 2245 /* copy values over */ 2246 ierr = PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2247 PetscFunctionReturn(0); 2248 } 2249 EXTERN_C_END 2250 2251 #undef __FUNCT__ 2252 #define __FUNCT__ "MatStoreValues" 2253 /*@ 2254 MatStoreValues - Stashes a copy of the matrix values; this allows, for 2255 example, reuse of the linear part of a Jacobian, while recomputing the 2256 nonlinear portion. 2257 2258 Collect on Mat 2259 2260 Input Parameters: 2261 . mat - the matrix (currently on AIJ matrices support this option) 2262 2263 Level: advanced 2264 2265 Common Usage, with SNESSolve(): 2266 $ Create Jacobian matrix 2267 $ Set linear terms into matrix 2268 $ Apply boundary conditions to matrix, at this time matrix must have 2269 $ final nonzero structure (i.e. setting the nonlinear terms and applying 2270 $ boundary conditions again will not change the nonzero structure 2271 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2272 $ ierr = MatStoreValues(mat); 2273 $ Call SNESSetJacobian() with matrix 2274 $ In your Jacobian routine 2275 $ ierr = MatRetrieveValues(mat); 2276 $ Set nonlinear terms in matrix 2277 2278 Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself: 2279 $ // build linear portion of Jacobian 2280 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2281 $ ierr = MatStoreValues(mat); 2282 $ loop over nonlinear iterations 2283 $ ierr = MatRetrieveValues(mat); 2284 $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian 2285 $ // call MatAssemblyBegin/End() on matrix 2286 $ Solve linear system with Jacobian 2287 $ endloop 2288 2289 Notes: 2290 Matrix must already be assemblied before calling this routine 2291 Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 2292 calling this routine. 2293 2294 .seealso: MatRetrieveValues() 2295 2296 @*/ 2297 int MatStoreValues(Mat mat) 2298 { 2299 int ierr,(*f)(Mat); 2300 2301 PetscFunctionBegin; 2302 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2303 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2304 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2305 2306 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2307 if (f) { 2308 ierr = (*f)(mat);CHKERRQ(ierr); 2309 } else { 2310 SETERRQ(1,"Wrong type of matrix to store values"); 2311 } 2312 PetscFunctionReturn(0); 2313 } 2314 2315 EXTERN_C_BEGIN 2316 #undef __FUNCT__ 2317 #define __FUNCT__ "MatRetrieveValues_SeqAIJ" 2318 int MatRetrieveValues_SeqAIJ(Mat mat) 2319 { 2320 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2321 int nz = aij->i[mat->m],ierr; 2322 2323 PetscFunctionBegin; 2324 if (aij->nonew != 1) { 2325 SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2326 } 2327 if (!aij->saved_values) { 2328 SETERRQ(1,"Must call MatStoreValues(A);first"); 2329 } 2330 2331 /* copy values over */ 2332 ierr = PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2333 PetscFunctionReturn(0); 2334 } 2335 EXTERN_C_END 2336 2337 #undef __FUNCT__ 2338 #define __FUNCT__ "MatRetrieveValues" 2339 /*@ 2340 MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 2341 example, reuse of the linear part of a Jacobian, while recomputing the 2342 nonlinear portion. 2343 2344 Collect on Mat 2345 2346 Input Parameters: 2347 . mat - the matrix (currently on AIJ matrices support this option) 2348 2349 Level: advanced 2350 2351 .seealso: MatStoreValues() 2352 2353 @*/ 2354 int MatRetrieveValues(Mat mat) 2355 { 2356 int ierr,(*f)(Mat); 2357 2358 PetscFunctionBegin; 2359 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2360 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2361 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2362 2363 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2364 if (f) { 2365 ierr = (*f)(mat);CHKERRQ(ierr); 2366 } else { 2367 SETERRQ(1,"Wrong type of matrix to retrieve values"); 2368 } 2369 PetscFunctionReturn(0); 2370 } 2371 2372 /* 2373 This allows SeqAIJ matrices to be passed to the matlab engine 2374 */ 2375 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 2376 #include "engine.h" /* Matlab include file */ 2377 #include "mex.h" /* Matlab include file */ 2378 EXTERN_C_BEGIN 2379 #undef __FUNCT__ 2380 #define __FUNCT__ "MatMatlabEnginePut_SeqAIJ" 2381 int MatMatlabEnginePut_SeqAIJ(PetscObject obj,void *mengine) 2382 { 2383 int ierr; 2384 Mat B = (Mat)obj; 2385 mxArray *mat; 2386 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)B->data; 2387 2388 PetscFunctionBegin; 2389 mat = mxCreateSparse(B->n,B->m,aij->nz,mxREAL); 2390 ierr = PetscMemcpy(mxGetPr(mat),aij->a,aij->nz*sizeof(PetscScalar));CHKERRQ(ierr); 2391 /* Matlab stores by column, not row so we pass in the transpose of the matrix */ 2392 ierr = PetscMemcpy(mxGetIr(mat),aij->j,aij->nz*sizeof(int));CHKERRQ(ierr); 2393 ierr = PetscMemcpy(mxGetJc(mat),aij->i,(B->m+1)*sizeof(int));CHKERRQ(ierr); 2394 2395 /* Matlab indices start at 0 for sparse (what a surprise) */ 2396 2397 ierr = PetscObjectName(obj);CHKERRQ(ierr); 2398 engPutVariable((Engine *)mengine,obj->name,mat); 2399 PetscFunctionReturn(0); 2400 } 2401 EXTERN_C_END 2402 2403 EXTERN_C_BEGIN 2404 #undef __FUNCT__ 2405 #define __FUNCT__ "MatMatlabEngineGet_SeqAIJ" 2406 int MatMatlabEngineGet_SeqAIJ(PetscObject obj,void *mengine) 2407 { 2408 int ierr,ii; 2409 Mat mat = (Mat)obj; 2410 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 2411 mxArray *mmat; 2412 2413 PetscFunctionBegin; 2414 ierr = PetscFree(aij->a);CHKERRQ(ierr); 2415 2416 mmat = engGetVariable((Engine *)mengine,obj->name); 2417 2418 aij->nz = (mxGetJc(mmat))[mat->m]; 2419 ierr = PetscMalloc(((size_t) aij->nz)*(sizeof(int)+sizeof(PetscScalar))+(mat->m+1)*sizeof(int),&aij->a);CHKERRQ(ierr); 2420 aij->j = (int*)(aij->a + aij->nz); 2421 aij->i = aij->j + aij->nz; 2422 aij->singlemalloc = PETSC_TRUE; 2423 aij->freedata = PETSC_TRUE; 2424 2425 ierr = PetscMemcpy(aij->a,mxGetPr(mmat),aij->nz*sizeof(PetscScalar));CHKERRQ(ierr); 2426 /* Matlab stores by column, not row so we pass in the transpose of the matrix */ 2427 ierr = PetscMemcpy(aij->j,mxGetIr(mmat),aij->nz*sizeof(int));CHKERRQ(ierr); 2428 ierr = PetscMemcpy(aij->i,mxGetJc(mmat),(mat->m+1)*sizeof(int));CHKERRQ(ierr); 2429 2430 for (ii=0; ii<mat->m; ii++) { 2431 aij->ilen[ii] = aij->imax[ii] = aij->i[ii+1] - aij->i[ii]; 2432 } 2433 2434 ierr = MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2435 ierr = MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2436 2437 PetscFunctionReturn(0); 2438 } 2439 EXTERN_C_END 2440 #endif 2441 2442 /* --------------------------------------------------------------------------------*/ 2443 #undef __FUNCT__ 2444 #define __FUNCT__ "MatCreateSeqAIJ" 2445 /*@C 2446 MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format 2447 (the default parallel PETSc format). For good matrix assembly performance 2448 the user should preallocate the matrix storage by setting the parameter nz 2449 (or the array nnz). By setting these parameters accurately, performance 2450 during matrix assembly can be increased by more than a factor of 50. 2451 2452 Collective on MPI_Comm 2453 2454 Input Parameters: 2455 + comm - MPI communicator, set to PETSC_COMM_SELF 2456 . m - number of rows 2457 . n - number of columns 2458 . nz - number of nonzeros per row (same for all rows) 2459 - nnz - array containing the number of nonzeros in the various rows 2460 (possibly different for each row) or PETSC_NULL 2461 2462 Output Parameter: 2463 . A - the matrix 2464 2465 Notes: 2466 The AIJ format (also called the Yale sparse matrix format or 2467 compressed row storage), is fully compatible with standard Fortran 77 2468 storage. That is, the stored row and column indices can begin at 2469 either one (as in Fortran) or zero. See the users' manual for details. 2470 2471 Specify the preallocated storage with either nz or nnz (not both). 2472 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2473 allocation. For large problems you MUST preallocate memory or you 2474 will get TERRIBLE performance, see the users' manual chapter on matrices. 2475 2476 By default, this format uses inodes (identical nodes) when possible, to 2477 improve numerical efficiency of matrix-vector products and solves. We 2478 search for consecutive rows with the same nonzero structure, thereby 2479 reusing matrix information to achieve increased efficiency. 2480 2481 Options Database Keys: 2482 + -mat_aij_no_inode - Do not use inodes 2483 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2484 - -mat_aij_oneindex - Internally use indexing starting at 1 2485 rather than 0. Note that when calling MatSetValues(), 2486 the user still MUST index entries starting at 0! 2487 2488 Level: intermediate 2489 2490 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2491 2492 @*/ 2493 int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,const int nnz[],Mat *A) 2494 { 2495 int ierr; 2496 2497 PetscFunctionBegin; 2498 ierr = MatCreate(comm,m,n,m,n,A);CHKERRQ(ierr); 2499 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 2500 ierr = MatSeqAIJSetPreallocation(*A,nz,nnz);CHKERRQ(ierr); 2501 PetscFunctionReturn(0); 2502 } 2503 2504 #define SKIP_ALLOCATION -4 2505 2506 #undef __FUNCT__ 2507 #define __FUNCT__ "MatSeqAIJSetPreallocation" 2508 /*@C 2509 MatSeqAIJSetPreallocation - For good matrix assembly performance 2510 the user should preallocate the matrix storage by setting the parameter nz 2511 (or the array nnz). By setting these parameters accurately, performance 2512 during matrix assembly can be increased by more than a factor of 50. 2513 2514 Collective on MPI_Comm 2515 2516 Input Parameters: 2517 + comm - MPI communicator, set to PETSC_COMM_SELF 2518 . m - number of rows 2519 . n - number of columns 2520 . nz - number of nonzeros per row (same for all rows) 2521 - nnz - array containing the number of nonzeros in the various rows 2522 (possibly different for each row) or PETSC_NULL 2523 2524 Output Parameter: 2525 . A - the matrix 2526 2527 Notes: 2528 The AIJ format (also called the Yale sparse matrix format or 2529 compressed row storage), is fully compatible with standard Fortran 77 2530 storage. That is, the stored row and column indices can begin at 2531 either one (as in Fortran) or zero. See the users' manual for details. 2532 2533 Specify the preallocated storage with either nz or nnz (not both). 2534 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2535 allocation. For large problems you MUST preallocate memory or you 2536 will get TERRIBLE performance, see the users' manual chapter on matrices. 2537 2538 By default, this format uses inodes (identical nodes) when possible, to 2539 improve numerical efficiency of matrix-vector products and solves. We 2540 search for consecutive rows with the same nonzero structure, thereby 2541 reusing matrix information to achieve increased efficiency. 2542 2543 Options Database Keys: 2544 + -mat_aij_no_inode - Do not use inodes 2545 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2546 - -mat_aij_oneindex - Internally use indexing starting at 1 2547 rather than 0. Note that when calling MatSetValues(), 2548 the user still MUST index entries starting at 0! 2549 2550 Level: intermediate 2551 2552 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2553 2554 @*/ 2555 int MatSeqAIJSetPreallocation(Mat B,int nz,const int nnz[]) 2556 { 2557 int ierr,(*f)(Mat,int,const int[]); 2558 2559 PetscFunctionBegin; 2560 ierr = PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);CHKERRQ(ierr); 2561 if (f) { 2562 ierr = (*f)(B,nz,nnz);CHKERRQ(ierr); 2563 } 2564 PetscFunctionReturn(0); 2565 } 2566 2567 EXTERN_C_BEGIN 2568 #undef __FUNCT__ 2569 #define __FUNCT__ "MatSeqAIJSetPreallocation_SeqAIJ" 2570 int MatSeqAIJSetPreallocation_SeqAIJ(Mat B,int nz,int *nnz) 2571 { 2572 Mat_SeqAIJ *b; 2573 size_t len = 0; 2574 PetscTruth skipallocation = PETSC_FALSE; 2575 int i,ierr; 2576 2577 PetscFunctionBegin; 2578 2579 if (nz == SKIP_ALLOCATION) { 2580 skipallocation = PETSC_TRUE; 2581 nz = 0; 2582 } 2583 2584 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 2585 if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz); 2586 if (nnz) { 2587 for (i=0; i<B->m; i++) { 2588 if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]); 2589 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); 2590 } 2591 } 2592 2593 B->preallocated = PETSC_TRUE; 2594 b = (Mat_SeqAIJ*)B->data; 2595 2596 ierr = PetscMalloc((B->m+1)*sizeof(int),&b->imax);CHKERRQ(ierr); 2597 if (!nnz) { 2598 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 2599 else if (nz <= 0) nz = 1; 2600 for (i=0; i<B->m; i++) b->imax[i] = nz; 2601 nz = nz*B->m; 2602 } else { 2603 nz = 0; 2604 for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];} 2605 } 2606 2607 if (!skipallocation) { 2608 /* allocate the matrix space */ 2609 len = ((size_t) nz)*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int); 2610 ierr = PetscMalloc(len,&b->a);CHKERRQ(ierr); 2611 b->j = (int*)(b->a + nz); 2612 ierr = PetscMemzero(b->j,nz*sizeof(int));CHKERRQ(ierr); 2613 b->i = b->j + nz; 2614 b->i[0] = 0; 2615 for (i=1; i<B->m+1; i++) { 2616 b->i[i] = b->i[i-1] + b->imax[i-1]; 2617 } 2618 b->singlemalloc = PETSC_TRUE; 2619 b->freedata = PETSC_TRUE; 2620 } else { 2621 b->freedata = PETSC_FALSE; 2622 } 2623 2624 /* b->ilen will count nonzeros in each row so far. */ 2625 ierr = PetscMalloc((B->m+1)*sizeof(int),&b->ilen);CHKERRQ(ierr); 2626 PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2627 for (i=0; i<B->m; i++) { b->ilen[i] = 0;} 2628 2629 b->nz = 0; 2630 b->maxnz = nz; 2631 B->info.nz_unneeded = (double)b->maxnz; 2632 PetscFunctionReturn(0); 2633 } 2634 EXTERN_C_END 2635 2636 /*MC 2637 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 2638 based on compressed sparse row format. 2639 2640 Options Database Keys: 2641 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 2642 2643 Level: beginner 2644 2645 .seealso: MatCreateSeqAIJ 2646 M*/ 2647 2648 EXTERN_C_BEGIN 2649 #undef __FUNCT__ 2650 #define __FUNCT__ "MatCreate_SeqAIJ" 2651 int MatCreate_SeqAIJ(Mat B) 2652 { 2653 Mat_SeqAIJ *b; 2654 int ierr,size; 2655 PetscTruth flg; 2656 2657 PetscFunctionBegin; 2658 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 2659 if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1"); 2660 2661 B->m = B->M = PetscMax(B->m,B->M); 2662 B->n = B->N = PetscMax(B->n,B->N); 2663 2664 ierr = PetscNew(Mat_SeqAIJ,&b);CHKERRQ(ierr); 2665 B->data = (void*)b; 2666 ierr = PetscMemzero(b,sizeof(Mat_SeqAIJ));CHKERRQ(ierr); 2667 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 2668 B->factor = 0; 2669 B->lupivotthreshold = 1.0; 2670 B->mapping = 0; 2671 ierr = PetscOptionsGetReal(B->prefix,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);CHKERRQ(ierr); 2672 ierr = PetscOptionsHasName(B->prefix,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);CHKERRQ(ierr); 2673 b->row = 0; 2674 b->col = 0; 2675 b->icol = 0; 2676 b->reallocs = 0; 2677 2678 ierr = PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);CHKERRQ(ierr); 2679 ierr = PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);CHKERRQ(ierr); 2680 2681 b->sorted = PETSC_FALSE; 2682 b->ignorezeroentries = PETSC_FALSE; 2683 b->roworiented = PETSC_TRUE; 2684 b->nonew = 0; 2685 b->diag = 0; 2686 b->solve_work = 0; 2687 B->spptr = 0; 2688 b->inode.use = PETSC_TRUE; 2689 b->inode.node_count = 0; 2690 b->inode.size = 0; 2691 b->inode.limit = 5; 2692 b->inode.max_limit = 5; 2693 b->saved_values = 0; 2694 b->idiag = 0; 2695 b->ssor = 0; 2696 b->keepzeroedrows = PETSC_FALSE; 2697 b->xtoy = 0; 2698 b->XtoY = 0; 2699 2700 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 2701 2702 ierr = PetscOptionsHasName(B->prefix,"-mat_aij_matlab",&flg);CHKERRQ(ierr); 2703 if (flg) {ierr = MatUseMatlab_SeqAIJ(B);CHKERRQ(ierr);} 2704 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C", 2705 "MatSeqAIJSetColumnIndices_SeqAIJ", 2706 MatSeqAIJSetColumnIndices_SeqAIJ);CHKERRQ(ierr); 2707 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C", 2708 "MatStoreValues_SeqAIJ", 2709 MatStoreValues_SeqAIJ);CHKERRQ(ierr); 2710 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C", 2711 "MatRetrieveValues_SeqAIJ", 2712 MatRetrieveValues_SeqAIJ);CHKERRQ(ierr); 2713 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C", 2714 "MatConvert_SeqAIJ_SeqSBAIJ", 2715 MatConvert_SeqAIJ_SeqSBAIJ);CHKERRQ(ierr); 2716 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C", 2717 "MatConvert_SeqAIJ_SeqBAIJ", 2718 MatConvert_SeqAIJ_SeqBAIJ);CHKERRQ(ierr); 2719 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C", 2720 "MatIsTranspose_SeqAIJ", 2721 MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 2722 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C", 2723 "MatSeqAIJSetPreallocation_SeqAIJ", 2724 MatSeqAIJSetPreallocation_SeqAIJ);CHKERRQ(ierr); 2725 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C", 2726 "MatReorderForNonzeroDiagonal_SeqAIJ", 2727 MatReorderForNonzeroDiagonal_SeqAIJ);CHKERRQ(ierr); 2728 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatAdjustForInodes_C", 2729 "MatAdjustForInodes_SeqAIJ", 2730 MatAdjustForInodes_SeqAIJ);CHKERRQ(ierr); 2731 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJGetInodeSizes_C", 2732 "MatSeqAIJGetInodeSizes_SeqAIJ", 2733 MatSeqAIJGetInodeSizes_SeqAIJ);CHKERRQ(ierr); 2734 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 2735 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatMatlabEnginePut_SeqAIJ",MatMatlabEnginePut_SeqAIJ);CHKERRQ(ierr); 2736 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatMatlabEngineGet_SeqAIJ",MatMatlabEngineGet_SeqAIJ);CHKERRQ(ierr); 2737 #endif 2738 ierr = RegisterApplyPtAPRoutines_Private(B);CHKERRQ(ierr); 2739 PetscFunctionReturn(0); 2740 } 2741 EXTERN_C_END 2742 2743 #undef __FUNCT__ 2744 #define __FUNCT__ "MatDuplicate_SeqAIJ" 2745 int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B) 2746 { 2747 Mat C; 2748 Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data; 2749 int i,m = A->m,ierr; 2750 size_t len; 2751 2752 PetscFunctionBegin; 2753 *B = 0; 2754 ierr = MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);CHKERRQ(ierr); 2755 ierr = MatSetType(C,MATSEQAIJ);CHKERRQ(ierr); 2756 c = (Mat_SeqAIJ*)C->data; 2757 2758 C->factor = A->factor; 2759 c->row = 0; 2760 c->col = 0; 2761 c->icol = 0; 2762 c->keepzeroedrows = a->keepzeroedrows; 2763 C->assembled = PETSC_TRUE; 2764 2765 C->M = A->m; 2766 C->N = A->n; 2767 2768 ierr = PetscMalloc((m+1)*sizeof(int),&c->imax);CHKERRQ(ierr); 2769 ierr = PetscMalloc((m+1)*sizeof(int),&c->ilen);CHKERRQ(ierr); 2770 for (i=0; i<m; i++) { 2771 c->imax[i] = a->imax[i]; 2772 c->ilen[i] = a->ilen[i]; 2773 } 2774 2775 /* allocate the matrix space */ 2776 c->singlemalloc = PETSC_TRUE; 2777 len = ((size_t) (m+1))*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int)); 2778 ierr = PetscMalloc(len,&c->a);CHKERRQ(ierr); 2779 c->j = (int*)(c->a + a->i[m] ); 2780 c->i = c->j + a->i[m]; 2781 ierr = PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));CHKERRQ(ierr); 2782 if (m > 0) { 2783 ierr = PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(int));CHKERRQ(ierr); 2784 if (cpvalues == MAT_COPY_VALUES) { 2785 ierr = PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2786 } else { 2787 ierr = PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2788 } 2789 } 2790 2791 PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2792 c->sorted = a->sorted; 2793 c->roworiented = a->roworiented; 2794 c->nonew = a->nonew; 2795 c->ilu_preserve_row_sums = a->ilu_preserve_row_sums; 2796 c->saved_values = 0; 2797 c->idiag = 0; 2798 c->ssor = 0; 2799 c->ignorezeroentries = a->ignorezeroentries; 2800 c->freedata = PETSC_TRUE; 2801 2802 if (a->diag) { 2803 ierr = PetscMalloc((m+1)*sizeof(int),&c->diag);CHKERRQ(ierr); 2804 PetscLogObjectMemory(C,(m+1)*sizeof(int)); 2805 for (i=0; i<m; i++) { 2806 c->diag[i] = a->diag[i]; 2807 } 2808 } else c->diag = 0; 2809 c->inode.use = a->inode.use; 2810 c->inode.limit = a->inode.limit; 2811 c->inode.max_limit = a->inode.max_limit; 2812 if (a->inode.size){ 2813 ierr = PetscMalloc((m+1)*sizeof(int),&c->inode.size);CHKERRQ(ierr); 2814 c->inode.node_count = a->inode.node_count; 2815 ierr = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));CHKERRQ(ierr); 2816 } else { 2817 c->inode.size = 0; 2818 c->inode.node_count = 0; 2819 } 2820 c->nz = a->nz; 2821 c->maxnz = a->maxnz; 2822 c->solve_work = 0; 2823 C->spptr = 0; /* Dangerous -I'm throwing away a->spptr */ 2824 C->preallocated = PETSC_TRUE; 2825 2826 *B = C; 2827 ierr = PetscFListDuplicate(A->qlist,&C->qlist);CHKERRQ(ierr); 2828 PetscFunctionReturn(0); 2829 } 2830 2831 #undef __FUNCT__ 2832 #define __FUNCT__ "MatLoad_SeqAIJ" 2833 int MatLoad_SeqAIJ(PetscViewer viewer,const MatType type,Mat *A) 2834 { 2835 Mat_SeqAIJ *a; 2836 Mat B; 2837 int i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N; 2838 MPI_Comm comm; 2839 2840 PetscFunctionBegin; 2841 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 2842 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2843 if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor"); 2844 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 2845 ierr = PetscBinaryRead(fd,header,4,PETSC_INT);CHKERRQ(ierr); 2846 if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file"); 2847 M = header[1]; N = header[2]; nz = header[3]; 2848 2849 if (nz < 0) { 2850 SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ"); 2851 } 2852 2853 /* read in row lengths */ 2854 ierr = PetscMalloc(M*sizeof(int),&rowlengths);CHKERRQ(ierr); 2855 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 2856 2857 /* create our matrix */ 2858 ierr = MatCreate(comm,PETSC_DECIDE,PETSC_DECIDE,M,N,&B);CHKERRQ(ierr); 2859 ierr = MatSetType(B,type);CHKERRQ(ierr); 2860 ierr = MatSeqAIJSetPreallocation(B,0,rowlengths);CHKERRQ(ierr); 2861 a = (Mat_SeqAIJ*)B->data; 2862 2863 /* read in column indices and adjust for Fortran indexing*/ 2864 ierr = PetscBinaryRead(fd,a->j,nz,PETSC_INT);CHKERRQ(ierr); 2865 2866 /* read in nonzero values */ 2867 ierr = PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);CHKERRQ(ierr); 2868 2869 /* set matrix "i" values */ 2870 a->i[0] = 0; 2871 for (i=1; i<= M; i++) { 2872 a->i[i] = a->i[i-1] + rowlengths[i-1]; 2873 a->ilen[i-1] = rowlengths[i-1]; 2874 } 2875 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 2876 2877 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2878 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2879 *A = B; 2880 PetscFunctionReturn(0); 2881 } 2882 2883 #undef __FUNCT__ 2884 #define __FUNCT__ "MatEqual_SeqAIJ" 2885 int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg) 2886 { 2887 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data; 2888 int ierr; 2889 2890 PetscFunctionBegin; 2891 /* If the matrix dimensions are not equal,or no of nonzeros */ 2892 if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)) { 2893 *flg = PETSC_FALSE; 2894 PetscFunctionReturn(0); 2895 } 2896 2897 /* if the a->i are the same */ 2898 ierr = PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);CHKERRQ(ierr); 2899 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2900 2901 /* if a->j are the same */ 2902 ierr = PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);CHKERRQ(ierr); 2903 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2904 2905 /* if a->a are the same */ 2906 ierr = PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);CHKERRQ(ierr); 2907 2908 PetscFunctionReturn(0); 2909 2910 } 2911 2912 #undef __FUNCT__ 2913 #define __FUNCT__ "MatCreateSeqAIJWithArrays" 2914 /*@C 2915 MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format) 2916 provided by the user. 2917 2918 Coolective on MPI_Comm 2919 2920 Input Parameters: 2921 + comm - must be an MPI communicator of size 1 2922 . m - number of rows 2923 . n - number of columns 2924 . i - row indices 2925 . j - column indices 2926 - a - matrix values 2927 2928 Output Parameter: 2929 . mat - the matrix 2930 2931 Level: intermediate 2932 2933 Notes: 2934 The i, j, and a arrays are not copied by this routine, the user must free these arrays 2935 once the matrix is destroyed 2936 2937 You cannot set new nonzero locations into this matrix, that will generate an error. 2938 2939 The i and j indices are 0 based 2940 2941 .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ() 2942 2943 @*/ 2944 int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat) 2945 { 2946 int ierr,ii; 2947 Mat_SeqAIJ *aij; 2948 2949 PetscFunctionBegin; 2950 ierr = MatCreateSeqAIJ(comm,m,n,SKIP_ALLOCATION,0,mat);CHKERRQ(ierr); 2951 aij = (Mat_SeqAIJ*)(*mat)->data; 2952 2953 if (i[0] != 0) { 2954 SETERRQ(1,"i (row indices) must start with 0"); 2955 } 2956 aij->i = i; 2957 aij->j = j; 2958 aij->a = a; 2959 aij->singlemalloc = PETSC_FALSE; 2960 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 2961 aij->freedata = PETSC_FALSE; 2962 2963 for (ii=0; ii<m; ii++) { 2964 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii]; 2965 #if defined(PETSC_USE_BOPT_g) 2966 if (i[ii+1] - i[ii] < 0) SETERRQ2(1,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]); 2967 #endif 2968 } 2969 #if defined(PETSC_USE_BOPT_g) 2970 for (ii=0; ii<aij->i[m]; ii++) { 2971 if (j[ii] < 0) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]); 2972 if (j[ii] > n - 1) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]); 2973 } 2974 #endif 2975 2976 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2977 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2978 PetscFunctionReturn(0); 2979 } 2980 2981 #undef __FUNCT__ 2982 #define __FUNCT__ "MatSetColoring_SeqAIJ" 2983 int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring) 2984 { 2985 int ierr; 2986 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2987 2988 PetscFunctionBegin; 2989 if (coloring->ctype == IS_COLORING_LOCAL) { 2990 ierr = ISColoringReference(coloring);CHKERRQ(ierr); 2991 a->coloring = coloring; 2992 } else if (coloring->ctype == IS_COLORING_GHOSTED) { 2993 int i,*larray; 2994 ISColoring ocoloring; 2995 ISColoringValue *colors; 2996 2997 /* set coloring for diagonal portion */ 2998 ierr = PetscMalloc((A->n+1)*sizeof(int),&larray);CHKERRQ(ierr); 2999 for (i=0; i<A->n; i++) { 3000 larray[i] = i; 3001 } 3002 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);CHKERRQ(ierr); 3003 ierr = PetscMalloc((A->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 3004 for (i=0; i<A->n; i++) { 3005 colors[i] = coloring->colors[larray[i]]; 3006 } 3007 ierr = PetscFree(larray);CHKERRQ(ierr); 3008 ierr = ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);CHKERRQ(ierr); 3009 a->coloring = ocoloring; 3010 } 3011 PetscFunctionReturn(0); 3012 } 3013 3014 #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 3015 EXTERN_C_BEGIN 3016 #include "adic/ad_utils.h" 3017 EXTERN_C_END 3018 3019 #undef __FUNCT__ 3020 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 3021 int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 3022 { 3023 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3024 int m = A->m,*ii = a->i,*jj = a->j,nz,i,j,nlen; 3025 PetscScalar *v = a->a,*values = ((PetscScalar*)advalues)+1; 3026 ISColoringValue *color; 3027 3028 PetscFunctionBegin; 3029 if (!a->coloring) SETERRQ(1,"Coloring not set for matrix"); 3030 nlen = PetscADGetDerivTypeSize()/sizeof(PetscScalar); 3031 color = a->coloring->colors; 3032 /* loop over rows */ 3033 for (i=0; i<m; i++) { 3034 nz = ii[i+1] - ii[i]; 3035 /* loop over columns putting computed value into matrix */ 3036 for (j=0; j<nz; j++) { 3037 *v++ = values[color[*jj++]]; 3038 } 3039 values += nlen; /* jump to next row of derivatives */ 3040 } 3041 PetscFunctionReturn(0); 3042 } 3043 3044 #else 3045 3046 #undef __FUNCT__ 3047 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 3048 int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 3049 { 3050 PetscFunctionBegin; 3051 SETERRQ(1,"PETSc installed without ADIC"); 3052 } 3053 3054 #endif 3055 3056 #undef __FUNCT__ 3057 #define __FUNCT__ "MatSetValuesAdifor_SeqAIJ" 3058 int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues) 3059 { 3060 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3061 int m = A->m,*ii = a->i,*jj = a->j,nz,i,j; 3062 PetscScalar *v = a->a,*values = (PetscScalar *)advalues; 3063 ISColoringValue *color; 3064 3065 PetscFunctionBegin; 3066 if (!a->coloring) SETERRQ(1,"Coloring not set for matrix"); 3067 color = a->coloring->colors; 3068 /* loop over rows */ 3069 for (i=0; i<m; i++) { 3070 nz = ii[i+1] - ii[i]; 3071 /* loop over columns putting computed value into matrix */ 3072 for (j=0; j<nz; j++) { 3073 *v++ = values[color[*jj++]]; 3074 } 3075 values += nl; /* jump to next row of derivatives */ 3076 } 3077 PetscFunctionReturn(0); 3078 } 3079 3080