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 = MatCreate(A->comm,A->n,m,A->n,m,&C);CHKERRQ(ierr); 1377 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 1378 ierr = MatSeqAIJSetPreallocation(C,0,col);CHKERRQ(ierr); 1379 ierr = PetscFree(col);CHKERRQ(ierr); 1380 for (i=0; i<m; i++) { 1381 len = ai[i+1]-ai[i]; 1382 ierr = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);CHKERRQ(ierr); 1383 array += len; 1384 aj += len; 1385 } 1386 1387 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1388 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1389 1390 if (B) { 1391 *B = C; 1392 } else { 1393 ierr = MatHeaderCopy(A,C);CHKERRQ(ierr); 1394 } 1395 PetscFunctionReturn(0); 1396 } 1397 1398 EXTERN_C_BEGIN 1399 #undef __FUNCT__ 1400 #define __FUNCT__ "MatIsTranspose_SeqAIJ" 1401 int MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscTruth *f) 1402 { 1403 Mat_SeqAIJ *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data; 1404 int *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb; 1405 int ma,na,mb,nb, i,ierr; 1406 1407 PetscFunctionBegin; 1408 bij = (Mat_SeqAIJ *) B->data; 1409 1410 ierr = MatGetSize(A,&ma,&na); CHKERRQ(ierr); 1411 ierr = MatGetSize(B,&mb,&nb); CHKERRQ(ierr); 1412 if (ma!=nb || na!=mb) 1413 SETERRQ(1,"Incompatible A/B sizes for symmetry test"); 1414 aii = aij->i; bii = bij->i; 1415 adx = aij->j; bdx = bij->j; 1416 va = aij->a; vb = bij->a; 1417 ierr = PetscMalloc(ma*sizeof(int),&aptr); CHKERRQ(ierr); 1418 ierr = PetscMalloc(mb*sizeof(int),&bptr); CHKERRQ(ierr); 1419 for (i=0; i<ma; i++) aptr[i] = aii[i]; 1420 for (i=0; i<mb; i++) bptr[i] = bii[i]; 1421 1422 *f = PETSC_TRUE; 1423 for (i=0; i<ma; i++) { 1424 /*printf("row %d spans %d--%d; we start @ %d\n", 1425 i,idx[ii[i]],idx[ii[i+1]-1],idx[aptr[i]]);*/ 1426 while (aptr[i]<aii[i+1]) { 1427 int idc,idr; PetscScalar vc,vr; 1428 /* column/row index/value */ 1429 idc = adx[aptr[i]]; idr = bdx[bptr[idc]]; 1430 vc = va[aptr[i]]; vr = vb[bptr[idc]]; 1431 /*printf("comparing %d: (%d,%d)=%e to (%d,%d)=%e\n", 1432 aptr[i],i,idc,vc,idc,idr,vr);*/ 1433 if (i!=idr || vc!=vr) { 1434 *f = PETSC_FALSE; goto done; 1435 } else { 1436 aptr[i]++; if (B || i!=idc) bptr[idc]++; 1437 } 1438 } 1439 } 1440 done: 1441 ierr = PetscFree(aptr); CHKERRQ(ierr); 1442 if (B) { 1443 ierr = PetscFree(bptr); CHKERRQ(ierr); 1444 } 1445 1446 PetscFunctionReturn(0); 1447 } 1448 EXTERN_C_END 1449 1450 #undef __FUNCT__ 1451 #define __FUNCT__ "MatIsSymmetric_SeqAIJ" 1452 int MatIsSymmetric_SeqAIJ(Mat A,PetscTruth *f) 1453 { 1454 int ierr; 1455 PetscFunctionBegin; 1456 ierr = MatIsTranspose_SeqAIJ(A,A,f); CHKERRQ(ierr); 1457 PetscFunctionReturn(0); 1458 } 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 = MatCreate(A->comm,m,n,m,n,B);CHKERRQ(ierr); 1801 ierr = MatSetType(*B,A->type_name);CHKERRQ(ierr); 1802 ierr = MatSeqAIJSetPreallocation(*B,0,lens);CHKERRQ(ierr); 1803 ierr = PetscFree(lens);CHKERRQ(ierr); 1804 1805 ierr = PetscMalloc(n*sizeof(int),&cnew);CHKERRQ(ierr); 1806 for (i=0; i<m; i++) { 1807 ierr = MatGetRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1808 for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];} 1809 ierr = MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);CHKERRQ(ierr); 1810 ierr = MatRestoreRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1811 } 1812 ierr = PetscFree(cnew);CHKERRQ(ierr); 1813 ierr = MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1814 ierr = MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1815 ierr = ISRestoreIndices(irowp,&row);CHKERRQ(ierr); 1816 ierr = ISRestoreIndices(icolp,&col);CHKERRQ(ierr); 1817 ierr = ISDestroy(irowp);CHKERRQ(ierr); 1818 ierr = ISDestroy(icolp);CHKERRQ(ierr); 1819 PetscFunctionReturn(0); 1820 } 1821 1822 #undef __FUNCT__ 1823 #define __FUNCT__ "MatPrintHelp_SeqAIJ" 1824 int MatPrintHelp_SeqAIJ(Mat A) 1825 { 1826 static PetscTruth called = PETSC_FALSE; 1827 MPI_Comm comm = A->comm; 1828 int ierr; 1829 1830 PetscFunctionBegin; 1831 if (called) {PetscFunctionReturn(0);} else called = PETSC_TRUE; 1832 ierr = (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):\n");CHKERRQ(ierr); 1833 ierr = (*PetscHelpPrintf)(comm," -mat_lu_pivotthreshold <threshold>: Set pivoting threshold\n");CHKERRQ(ierr); 1834 ierr = (*PetscHelpPrintf)(comm," -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.\n");CHKERRQ(ierr); 1835 ierr = (*PetscHelpPrintf)(comm," -mat_aij_no_inode: Do not use inodes\n");CHKERRQ(ierr); 1836 ierr = (*PetscHelpPrintf)(comm," -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)\n");CHKERRQ(ierr); 1837 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 1838 ierr = (*PetscHelpPrintf)(comm," -mat_aij_matlab: Use Matlab engine sparse LU factorization and solve.\n");CHKERRQ(ierr); 1839 #endif 1840 PetscFunctionReturn(0); 1841 } 1842 1843 #undef __FUNCT__ 1844 #define __FUNCT__ "MatCopy_SeqAIJ" 1845 int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str) 1846 { 1847 int ierr; 1848 1849 PetscFunctionBegin; 1850 /* If the two matrices have the same copy implementation, use fast copy. */ 1851 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 1852 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1853 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 1854 1855 if (a->i[A->m] != b->i[B->m]) { 1856 SETERRQ(1,"Number of nonzeros in two matrices are different"); 1857 } 1858 ierr = PetscMemcpy(b->a,a->a,(a->i[A->m])*sizeof(PetscScalar));CHKERRQ(ierr); 1859 } else { 1860 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1861 } 1862 PetscFunctionReturn(0); 1863 } 1864 1865 #undef __FUNCT__ 1866 #define __FUNCT__ "MatSetUpPreallocation_SeqAIJ" 1867 int MatSetUpPreallocation_SeqAIJ(Mat A) 1868 { 1869 int ierr; 1870 1871 PetscFunctionBegin; 1872 ierr = MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);CHKERRQ(ierr); 1873 PetscFunctionReturn(0); 1874 } 1875 1876 #undef __FUNCT__ 1877 #define __FUNCT__ "MatGetArray_SeqAIJ" 1878 int MatGetArray_SeqAIJ(Mat A,PetscScalar *array[]) 1879 { 1880 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1881 PetscFunctionBegin; 1882 *array = a->a; 1883 PetscFunctionReturn(0); 1884 } 1885 1886 #undef __FUNCT__ 1887 #define __FUNCT__ "MatRestoreArray_SeqAIJ" 1888 int MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[]) 1889 { 1890 PetscFunctionBegin; 1891 PetscFunctionReturn(0); 1892 } 1893 1894 #undef __FUNCT__ 1895 #define __FUNCT__ "MatFDColoringApply_SeqAIJ" 1896 int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx) 1897 { 1898 int (*f)(void *,Vec,Vec,void*) = (int (*)(void *,Vec,Vec,void *))coloring->f; 1899 int k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2; 1900 PetscScalar dx,mone = -1.0,*y,*xx,*w3_array; 1901 PetscScalar *vscale_array; 1902 PetscReal epsilon = coloring->error_rel,umin = coloring->umin; 1903 Vec w1,w2,w3; 1904 void *fctx = coloring->fctx; 1905 PetscTruth flg; 1906 1907 PetscFunctionBegin; 1908 if (!coloring->w1) { 1909 ierr = VecDuplicate(x1,&coloring->w1);CHKERRQ(ierr); 1910 PetscLogObjectParent(coloring,coloring->w1); 1911 ierr = VecDuplicate(x1,&coloring->w2);CHKERRQ(ierr); 1912 PetscLogObjectParent(coloring,coloring->w2); 1913 ierr = VecDuplicate(x1,&coloring->w3);CHKERRQ(ierr); 1914 PetscLogObjectParent(coloring,coloring->w3); 1915 } 1916 w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3; 1917 1918 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 1919 ierr = PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);CHKERRQ(ierr); 1920 if (flg) { 1921 PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()\n"); 1922 } else { 1923 ierr = MatZeroEntries(J);CHKERRQ(ierr); 1924 } 1925 1926 ierr = VecGetOwnershipRange(x1,&start,&end);CHKERRQ(ierr); 1927 ierr = VecGetSize(x1,&N);CHKERRQ(ierr); 1928 1929 /* 1930 This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets 1931 coloring->F for the coarser grids from the finest 1932 */ 1933 if (coloring->F) { 1934 ierr = VecGetLocalSize(coloring->F,&m1);CHKERRQ(ierr); 1935 ierr = VecGetLocalSize(w1,&m2);CHKERRQ(ierr); 1936 if (m1 != m2) { 1937 coloring->F = 0; 1938 } 1939 } 1940 1941 if (coloring->F) { 1942 w1 = coloring->F; 1943 coloring->F = 0; 1944 } else { 1945 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1946 ierr = (*f)(sctx,x1,w1,fctx);CHKERRQ(ierr); 1947 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1948 } 1949 1950 /* 1951 Compute all the scale factors and share with other processors 1952 */ 1953 ierr = VecGetArray(x1,&xx);CHKERRQ(ierr);xx = xx - start; 1954 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr);vscale_array = vscale_array - start; 1955 for (k=0; k<coloring->ncolors; k++) { 1956 /* 1957 Loop over each column associated with color adding the 1958 perturbation to the vector w3. 1959 */ 1960 for (l=0; l<coloring->ncolumns[k]; l++) { 1961 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 1962 dx = xx[col]; 1963 if (dx == 0.0) dx = 1.0; 1964 #if !defined(PETSC_USE_COMPLEX) 1965 if (dx < umin && dx >= 0.0) dx = umin; 1966 else if (dx < 0.0 && dx > -umin) dx = -umin; 1967 #else 1968 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 1969 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 1970 #endif 1971 dx *= epsilon; 1972 vscale_array[col] = 1.0/dx; 1973 } 1974 } 1975 vscale_array = vscale_array + start;ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 1976 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1977 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1978 1979 /* ierr = VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD); 1980 ierr = VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/ 1981 1982 if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow; 1983 else vscaleforrow = coloring->columnsforrow; 1984 1985 ierr = VecGetArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 1986 /* 1987 Loop over each color 1988 */ 1989 for (k=0; k<coloring->ncolors; k++) { 1990 coloring->currentcolor = k; 1991 ierr = VecCopy(x1,w3);CHKERRQ(ierr); 1992 ierr = VecGetArray(w3,&w3_array);CHKERRQ(ierr);w3_array = w3_array - start; 1993 /* 1994 Loop over each column associated with color adding the 1995 perturbation to the vector w3. 1996 */ 1997 for (l=0; l<coloring->ncolumns[k]; l++) { 1998 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 1999 dx = xx[col]; 2000 if (dx == 0.0) dx = 1.0; 2001 #if !defined(PETSC_USE_COMPLEX) 2002 if (dx < umin && dx >= 0.0) dx = umin; 2003 else if (dx < 0.0 && dx > -umin) dx = -umin; 2004 #else 2005 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 2006 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 2007 #endif 2008 dx *= epsilon; 2009 if (!PetscAbsScalar(dx)) SETERRQ(1,"Computed 0 differencing parameter"); 2010 w3_array[col] += dx; 2011 } 2012 w3_array = w3_array + start; ierr = VecRestoreArray(w3,&w3_array);CHKERRQ(ierr); 2013 2014 /* 2015 Evaluate function at x1 + dx (here dx is a vector of perturbations) 2016 */ 2017 2018 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2019 ierr = (*f)(sctx,w3,w2,fctx);CHKERRQ(ierr); 2020 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2021 ierr = VecAXPY(&mone,w1,w2);CHKERRQ(ierr); 2022 2023 /* 2024 Loop over rows of vector, putting results into Jacobian matrix 2025 */ 2026 ierr = VecGetArray(w2,&y);CHKERRQ(ierr); 2027 for (l=0; l<coloring->nrows[k]; l++) { 2028 row = coloring->rows[k][l]; 2029 col = coloring->columnsforrow[k][l]; 2030 y[row] *= vscale_array[vscaleforrow[k][l]]; 2031 srow = row + start; 2032 ierr = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 2033 } 2034 ierr = VecRestoreArray(w2,&y);CHKERRQ(ierr); 2035 } 2036 coloring->currentcolor = k; 2037 ierr = VecRestoreArray(coloring->vscale,&vscale_array);CHKERRQ(ierr); 2038 xx = xx + start; ierr = VecRestoreArray(x1,&xx);CHKERRQ(ierr); 2039 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2040 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2041 PetscFunctionReturn(0); 2042 } 2043 2044 #include "petscblaslapack.h" 2045 #undef __FUNCT__ 2046 #define __FUNCT__ "MatAXPY_SeqAIJ" 2047 int MatAXPY_SeqAIJ(const PetscScalar a[],Mat X,Mat Y,MatStructure str) 2048 { 2049 int ierr,one=1,i; 2050 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data; 2051 2052 PetscFunctionBegin; 2053 if (str == SAME_NONZERO_PATTERN) { 2054 BLaxpy_(&x->nz,(PetscScalar*)a,x->a,&one,y->a,&one); 2055 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2056 if (y->xtoy && y->XtoY != X) { 2057 ierr = PetscFree(y->xtoy);CHKERRQ(ierr); 2058 ierr = MatDestroy(y->XtoY);CHKERRQ(ierr); 2059 } 2060 if (!y->xtoy) { /* get xtoy */ 2061 ierr = MatAXPYGetxtoy_Private(X->m,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);CHKERRQ(ierr); 2062 y->XtoY = X; 2063 } 2064 for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += (*a)*(x->a[i]); 2065 PetscLogInfo(0,"MatAXPY_SeqAIJ: ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz); 2066 } else { 2067 ierr = MatAXPY_Basic(a,X,Y,str);CHKERRQ(ierr); 2068 } 2069 PetscFunctionReturn(0); 2070 } 2071 2072 /* -------------------------------------------------------------------*/ 2073 static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ, 2074 MatGetRow_SeqAIJ, 2075 MatRestoreRow_SeqAIJ, 2076 MatMult_SeqAIJ, 2077 /* 4*/ MatMultAdd_SeqAIJ, 2078 MatMultTranspose_SeqAIJ, 2079 MatMultTransposeAdd_SeqAIJ, 2080 MatSolve_SeqAIJ, 2081 MatSolveAdd_SeqAIJ, 2082 MatSolveTranspose_SeqAIJ, 2083 /*10*/ MatSolveTransposeAdd_SeqAIJ, 2084 MatLUFactor_SeqAIJ, 2085 0, 2086 MatRelax_SeqAIJ, 2087 MatTranspose_SeqAIJ, 2088 /*15*/ MatGetInfo_SeqAIJ, 2089 MatEqual_SeqAIJ, 2090 MatGetDiagonal_SeqAIJ, 2091 MatDiagonalScale_SeqAIJ, 2092 MatNorm_SeqAIJ, 2093 /*20*/ 0, 2094 MatAssemblyEnd_SeqAIJ, 2095 MatCompress_SeqAIJ, 2096 MatSetOption_SeqAIJ, 2097 MatZeroEntries_SeqAIJ, 2098 /*25*/ MatZeroRows_SeqAIJ, 2099 MatLUFactorSymbolic_SeqAIJ, 2100 MatLUFactorNumeric_SeqAIJ, 2101 MatCholeskyFactorSymbolic_SeqAIJ, 2102 MatCholeskyFactorNumeric_SeqAIJ, 2103 /*30*/ MatSetUpPreallocation_SeqAIJ, 2104 MatILUFactorSymbolic_SeqAIJ, 2105 MatICCFactorSymbolic_SeqAIJ, 2106 MatGetArray_SeqAIJ, 2107 MatRestoreArray_SeqAIJ, 2108 /*35*/ MatDuplicate_SeqAIJ, 2109 0, 2110 0, 2111 MatILUFactor_SeqAIJ, 2112 0, 2113 /*40*/ MatAXPY_SeqAIJ, 2114 MatGetSubMatrices_SeqAIJ, 2115 MatIncreaseOverlap_SeqAIJ, 2116 MatGetValues_SeqAIJ, 2117 MatCopy_SeqAIJ, 2118 /*45*/ MatPrintHelp_SeqAIJ, 2119 MatScale_SeqAIJ, 2120 0, 2121 0, 2122 MatILUDTFactor_SeqAIJ, 2123 /*50*/ MatGetBlockSize_SeqAIJ, 2124 MatGetRowIJ_SeqAIJ, 2125 MatRestoreRowIJ_SeqAIJ, 2126 MatGetColumnIJ_SeqAIJ, 2127 MatRestoreColumnIJ_SeqAIJ, 2128 /*55*/ MatFDColoringCreate_SeqAIJ, 2129 0, 2130 0, 2131 MatPermute_SeqAIJ, 2132 0, 2133 /*60*/ 0, 2134 MatDestroy_SeqAIJ, 2135 MatView_SeqAIJ, 2136 MatGetPetscMaps_Petsc, 2137 0, 2138 /*65*/ 0, 2139 0, 2140 0, 2141 0, 2142 0, 2143 /*70*/ 0, 2144 0, 2145 MatSetColoring_SeqAIJ, 2146 MatSetValuesAdic_SeqAIJ, 2147 MatSetValuesAdifor_SeqAIJ, 2148 /*75*/ MatFDColoringApply_SeqAIJ, 2149 0, 2150 0, 2151 0, 2152 0, 2153 /*80*/ 0, 2154 0, 2155 0, 2156 0, 2157 /*85*/ MatLoad_SeqAIJ, 2158 MatIsSymmetric_SeqAIJ, 2159 }; 2160 2161 EXTERN_C_BEGIN 2162 #undef __FUNCT__ 2163 #define __FUNCT__ "MatSeqAIJSetColumnIndices_SeqAIJ" 2164 int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices) 2165 { 2166 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2167 int i,nz,n; 2168 2169 PetscFunctionBegin; 2170 2171 nz = aij->maxnz; 2172 n = mat->n; 2173 for (i=0; i<nz; i++) { 2174 aij->j[i] = indices[i]; 2175 } 2176 aij->nz = nz; 2177 for (i=0; i<n; i++) { 2178 aij->ilen[i] = aij->imax[i]; 2179 } 2180 2181 PetscFunctionReturn(0); 2182 } 2183 EXTERN_C_END 2184 2185 #undef __FUNCT__ 2186 #define __FUNCT__ "MatSeqAIJSetColumnIndices" 2187 /*@ 2188 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 2189 in the matrix. 2190 2191 Input Parameters: 2192 + mat - the SeqAIJ matrix 2193 - indices - the column indices 2194 2195 Level: advanced 2196 2197 Notes: 2198 This can be called if you have precomputed the nonzero structure of the 2199 matrix and want to provide it to the matrix object to improve the performance 2200 of the MatSetValues() operation. 2201 2202 You MUST have set the correct numbers of nonzeros per row in the call to 2203 MatCreateSeqAIJ(). 2204 2205 MUST be called before any calls to MatSetValues(); 2206 2207 The indices should start with zero, not one. 2208 2209 @*/ 2210 int MatSeqAIJSetColumnIndices(Mat mat,int *indices) 2211 { 2212 int ierr,(*f)(Mat,int *); 2213 2214 PetscFunctionBegin; 2215 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2216 PetscValidPointer(indices,2); 2217 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);CHKERRQ(ierr); 2218 if (f) { 2219 ierr = (*f)(mat,indices);CHKERRQ(ierr); 2220 } else { 2221 SETERRQ(1,"Wrong type of matrix to set column indices"); 2222 } 2223 PetscFunctionReturn(0); 2224 } 2225 2226 /* ----------------------------------------------------------------------------------------*/ 2227 2228 EXTERN_C_BEGIN 2229 #undef __FUNCT__ 2230 #define __FUNCT__ "MatStoreValues_SeqAIJ" 2231 int MatStoreValues_SeqAIJ(Mat mat) 2232 { 2233 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2234 size_t nz = aij->i[mat->m],ierr; 2235 2236 PetscFunctionBegin; 2237 if (aij->nonew != 1) { 2238 SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2239 } 2240 2241 /* allocate space for values if not already there */ 2242 if (!aij->saved_values) { 2243 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);CHKERRQ(ierr); 2244 } 2245 2246 /* copy values over */ 2247 ierr = PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2248 PetscFunctionReturn(0); 2249 } 2250 EXTERN_C_END 2251 2252 #undef __FUNCT__ 2253 #define __FUNCT__ "MatStoreValues" 2254 /*@ 2255 MatStoreValues - Stashes a copy of the matrix values; this allows, for 2256 example, reuse of the linear part of a Jacobian, while recomputing the 2257 nonlinear portion. 2258 2259 Collect on Mat 2260 2261 Input Parameters: 2262 . mat - the matrix (currently on AIJ matrices support this option) 2263 2264 Level: advanced 2265 2266 Common Usage, with SNESSolve(): 2267 $ Create Jacobian matrix 2268 $ Set linear terms into matrix 2269 $ Apply boundary conditions to matrix, at this time matrix must have 2270 $ final nonzero structure (i.e. setting the nonlinear terms and applying 2271 $ boundary conditions again will not change the nonzero structure 2272 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2273 $ ierr = MatStoreValues(mat); 2274 $ Call SNESSetJacobian() with matrix 2275 $ In your Jacobian routine 2276 $ ierr = MatRetrieveValues(mat); 2277 $ Set nonlinear terms in matrix 2278 2279 Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself: 2280 $ // build linear portion of Jacobian 2281 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2282 $ ierr = MatStoreValues(mat); 2283 $ loop over nonlinear iterations 2284 $ ierr = MatRetrieveValues(mat); 2285 $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian 2286 $ // call MatAssemblyBegin/End() on matrix 2287 $ Solve linear system with Jacobian 2288 $ endloop 2289 2290 Notes: 2291 Matrix must already be assemblied before calling this routine 2292 Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 2293 calling this routine. 2294 2295 .seealso: MatRetrieveValues() 2296 2297 @*/ 2298 int MatStoreValues(Mat mat) 2299 { 2300 int ierr,(*f)(Mat); 2301 2302 PetscFunctionBegin; 2303 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2304 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2305 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2306 2307 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2308 if (f) { 2309 ierr = (*f)(mat);CHKERRQ(ierr); 2310 } else { 2311 SETERRQ(1,"Wrong type of matrix to store values"); 2312 } 2313 PetscFunctionReturn(0); 2314 } 2315 2316 EXTERN_C_BEGIN 2317 #undef __FUNCT__ 2318 #define __FUNCT__ "MatRetrieveValues_SeqAIJ" 2319 int MatRetrieveValues_SeqAIJ(Mat mat) 2320 { 2321 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2322 int nz = aij->i[mat->m],ierr; 2323 2324 PetscFunctionBegin; 2325 if (aij->nonew != 1) { 2326 SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2327 } 2328 if (!aij->saved_values) { 2329 SETERRQ(1,"Must call MatStoreValues(A);first"); 2330 } 2331 2332 /* copy values over */ 2333 ierr = PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2334 PetscFunctionReturn(0); 2335 } 2336 EXTERN_C_END 2337 2338 #undef __FUNCT__ 2339 #define __FUNCT__ "MatRetrieveValues" 2340 /*@ 2341 MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 2342 example, reuse of the linear part of a Jacobian, while recomputing the 2343 nonlinear portion. 2344 2345 Collect on Mat 2346 2347 Input Parameters: 2348 . mat - the matrix (currently on AIJ matrices support this option) 2349 2350 Level: advanced 2351 2352 .seealso: MatStoreValues() 2353 2354 @*/ 2355 int MatRetrieveValues(Mat mat) 2356 { 2357 int ierr,(*f)(Mat); 2358 2359 PetscFunctionBegin; 2360 PetscValidHeaderSpecific(mat,MAT_COOKIE,1); 2361 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2362 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2363 2364 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2365 if (f) { 2366 ierr = (*f)(mat);CHKERRQ(ierr); 2367 } else { 2368 SETERRQ(1,"Wrong type of matrix to retrieve values"); 2369 } 2370 PetscFunctionReturn(0); 2371 } 2372 2373 /* 2374 This allows SeqAIJ matrices to be passed to the matlab engine 2375 */ 2376 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 2377 #include "engine.h" /* Matlab include file */ 2378 #include "mex.h" /* Matlab include file */ 2379 EXTERN_C_BEGIN 2380 #undef __FUNCT__ 2381 #define __FUNCT__ "MatMatlabEnginePut_SeqAIJ" 2382 int MatMatlabEnginePut_SeqAIJ(PetscObject obj,void *mengine) 2383 { 2384 int ierr; 2385 Mat B = (Mat)obj; 2386 mxArray *mat; 2387 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)B->data; 2388 2389 PetscFunctionBegin; 2390 mat = mxCreateSparse(B->n,B->m,aij->nz,mxREAL); 2391 ierr = PetscMemcpy(mxGetPr(mat),aij->a,aij->nz*sizeof(PetscScalar));CHKERRQ(ierr); 2392 /* Matlab stores by column, not row so we pass in the transpose of the matrix */ 2393 ierr = PetscMemcpy(mxGetIr(mat),aij->j,aij->nz*sizeof(int));CHKERRQ(ierr); 2394 ierr = PetscMemcpy(mxGetJc(mat),aij->i,(B->m+1)*sizeof(int));CHKERRQ(ierr); 2395 2396 /* Matlab indices start at 0 for sparse (what a surprise) */ 2397 2398 ierr = PetscObjectName(obj);CHKERRQ(ierr); 2399 engPutVariable((Engine *)mengine,obj->name,mat); 2400 PetscFunctionReturn(0); 2401 } 2402 EXTERN_C_END 2403 2404 EXTERN_C_BEGIN 2405 #undef __FUNCT__ 2406 #define __FUNCT__ "MatMatlabEngineGet_SeqAIJ" 2407 int MatMatlabEngineGet_SeqAIJ(PetscObject obj,void *mengine) 2408 { 2409 int ierr,ii; 2410 Mat mat = (Mat)obj; 2411 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 2412 mxArray *mmat; 2413 2414 PetscFunctionBegin; 2415 ierr = PetscFree(aij->a);CHKERRQ(ierr); 2416 2417 mmat = engGetVariable((Engine *)mengine,obj->name); 2418 2419 aij->nz = (mxGetJc(mmat))[mat->m]; 2420 ierr = PetscMalloc(((size_t) aij->nz)*(sizeof(int)+sizeof(PetscScalar))+(mat->m+1)*sizeof(int),&aij->a);CHKERRQ(ierr); 2421 aij->j = (int*)(aij->a + aij->nz); 2422 aij->i = aij->j + aij->nz; 2423 aij->singlemalloc = PETSC_TRUE; 2424 aij->freedata = PETSC_TRUE; 2425 2426 ierr = PetscMemcpy(aij->a,mxGetPr(mmat),aij->nz*sizeof(PetscScalar));CHKERRQ(ierr); 2427 /* Matlab stores by column, not row so we pass in the transpose of the matrix */ 2428 ierr = PetscMemcpy(aij->j,mxGetIr(mmat),aij->nz*sizeof(int));CHKERRQ(ierr); 2429 ierr = PetscMemcpy(aij->i,mxGetJc(mmat),(mat->m+1)*sizeof(int));CHKERRQ(ierr); 2430 2431 for (ii=0; ii<mat->m; ii++) { 2432 aij->ilen[ii] = aij->imax[ii] = aij->i[ii+1] - aij->i[ii]; 2433 } 2434 2435 ierr = MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2436 ierr = MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2437 2438 PetscFunctionReturn(0); 2439 } 2440 EXTERN_C_END 2441 #endif 2442 2443 /* --------------------------------------------------------------------------------*/ 2444 #undef __FUNCT__ 2445 #define __FUNCT__ "MatCreateSeqAIJ" 2446 /*@C 2447 MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format 2448 (the default parallel PETSc format). For good matrix assembly performance 2449 the user should preallocate the matrix storage by setting the parameter nz 2450 (or the array nnz). By setting these parameters accurately, performance 2451 during matrix assembly can be increased by more than a factor of 50. 2452 2453 Collective on MPI_Comm 2454 2455 Input Parameters: 2456 + comm - MPI communicator, set to PETSC_COMM_SELF 2457 . m - number of rows 2458 . n - number of columns 2459 . nz - number of nonzeros per row (same for all rows) 2460 - nnz - array containing the number of nonzeros in the various rows 2461 (possibly different for each row) or PETSC_NULL 2462 2463 Output Parameter: 2464 . A - the matrix 2465 2466 Notes: 2467 The AIJ format (also called the Yale sparse matrix format or 2468 compressed row storage), is fully compatible with standard Fortran 77 2469 storage. That is, the stored row and column indices can begin at 2470 either one (as in Fortran) or zero. See the users' manual for details. 2471 2472 Specify the preallocated storage with either nz or nnz (not both). 2473 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2474 allocation. For large problems you MUST preallocate memory or you 2475 will get TERRIBLE performance, see the users' manual chapter on matrices. 2476 2477 By default, this format uses inodes (identical nodes) when possible, to 2478 improve numerical efficiency of matrix-vector products and solves. We 2479 search for consecutive rows with the same nonzero structure, thereby 2480 reusing matrix information to achieve increased efficiency. 2481 2482 Options Database Keys: 2483 + -mat_aij_no_inode - Do not use inodes 2484 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2485 - -mat_aij_oneindex - Internally use indexing starting at 1 2486 rather than 0. Note that when calling MatSetValues(), 2487 the user still MUST index entries starting at 0! 2488 2489 Level: intermediate 2490 2491 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2492 2493 @*/ 2494 int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,const int nnz[],Mat *A) 2495 { 2496 int ierr; 2497 2498 PetscFunctionBegin; 2499 ierr = MatCreate(comm,m,n,m,n,A);CHKERRQ(ierr); 2500 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 2501 ierr = MatSeqAIJSetPreallocation(*A,nz,nnz);CHKERRQ(ierr); 2502 PetscFunctionReturn(0); 2503 } 2504 2505 #define SKIP_ALLOCATION -4 2506 2507 #undef __FUNCT__ 2508 #define __FUNCT__ "MatSeqAIJSetPreallocation" 2509 /*@C 2510 MatSeqAIJSetPreallocation - For good matrix assembly performance 2511 the user should preallocate the matrix storage by setting the parameter nz 2512 (or the array nnz). By setting these parameters accurately, performance 2513 during matrix assembly can be increased by more than a factor of 50. 2514 2515 Collective on MPI_Comm 2516 2517 Input Parameters: 2518 + comm - MPI communicator, set to PETSC_COMM_SELF 2519 . m - number of rows 2520 . n - number of columns 2521 . nz - number of nonzeros per row (same for all rows) 2522 - nnz - array containing the number of nonzeros in the various rows 2523 (possibly different for each row) or PETSC_NULL 2524 2525 Output Parameter: 2526 . A - the matrix 2527 2528 Notes: 2529 The AIJ format (also called the Yale sparse matrix format or 2530 compressed row storage), is fully compatible with standard Fortran 77 2531 storage. That is, the stored row and column indices can begin at 2532 either one (as in Fortran) or zero. See the users' manual for details. 2533 2534 Specify the preallocated storage with either nz or nnz (not both). 2535 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2536 allocation. For large problems you MUST preallocate memory or you 2537 will get TERRIBLE performance, see the users' manual chapter on matrices. 2538 2539 By default, this format uses inodes (identical nodes) when possible, to 2540 improve numerical efficiency of matrix-vector products and solves. We 2541 search for consecutive rows with the same nonzero structure, thereby 2542 reusing matrix information to achieve increased efficiency. 2543 2544 Options Database Keys: 2545 + -mat_aij_no_inode - Do not use inodes 2546 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2547 - -mat_aij_oneindex - Internally use indexing starting at 1 2548 rather than 0. Note that when calling MatSetValues(), 2549 the user still MUST index entries starting at 0! 2550 2551 Level: intermediate 2552 2553 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2554 2555 @*/ 2556 int MatSeqAIJSetPreallocation(Mat B,int nz,const int nnz[]) 2557 { 2558 int ierr,(*f)(Mat,int,const int[]); 2559 2560 PetscFunctionBegin; 2561 ierr = PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);CHKERRQ(ierr); 2562 if (f) { 2563 ierr = (*f)(B,nz,nnz);CHKERRQ(ierr); 2564 } 2565 PetscFunctionReturn(0); 2566 } 2567 2568 EXTERN_C_BEGIN 2569 #undef __FUNCT__ 2570 #define __FUNCT__ "MatSeqAIJSetPreallocation_SeqAIJ" 2571 int MatSeqAIJSetPreallocation_SeqAIJ(Mat B,int nz,int *nnz) 2572 { 2573 Mat_SeqAIJ *b; 2574 size_t len = 0; 2575 PetscTruth skipallocation = PETSC_FALSE; 2576 int i,ierr; 2577 2578 PetscFunctionBegin; 2579 2580 if (nz == SKIP_ALLOCATION) { 2581 skipallocation = PETSC_TRUE; 2582 nz = 0; 2583 } 2584 2585 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 2586 if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz); 2587 if (nnz) { 2588 for (i=0; i<B->m; i++) { 2589 if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]); 2590 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); 2591 } 2592 } 2593 2594 B->preallocated = PETSC_TRUE; 2595 b = (Mat_SeqAIJ*)B->data; 2596 2597 ierr = PetscMalloc((B->m+1)*sizeof(int),&b->imax);CHKERRQ(ierr); 2598 if (!nnz) { 2599 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 2600 else if (nz <= 0) nz = 1; 2601 for (i=0; i<B->m; i++) b->imax[i] = nz; 2602 nz = nz*B->m; 2603 } else { 2604 nz = 0; 2605 for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];} 2606 } 2607 2608 if (!skipallocation) { 2609 /* allocate the matrix space */ 2610 len = ((size_t) nz)*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int); 2611 ierr = PetscMalloc(len,&b->a);CHKERRQ(ierr); 2612 b->j = (int*)(b->a + nz); 2613 ierr = PetscMemzero(b->j,nz*sizeof(int));CHKERRQ(ierr); 2614 b->i = b->j + nz; 2615 b->i[0] = 0; 2616 for (i=1; i<B->m+1; i++) { 2617 b->i[i] = b->i[i-1] + b->imax[i-1]; 2618 } 2619 b->singlemalloc = PETSC_TRUE; 2620 b->freedata = PETSC_TRUE; 2621 } else { 2622 b->freedata = PETSC_FALSE; 2623 } 2624 2625 /* b->ilen will count nonzeros in each row so far. */ 2626 ierr = PetscMalloc((B->m+1)*sizeof(int),&b->ilen);CHKERRQ(ierr); 2627 PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2628 for (i=0; i<B->m; i++) { b->ilen[i] = 0;} 2629 2630 b->nz = 0; 2631 b->maxnz = nz; 2632 B->info.nz_unneeded = (double)b->maxnz; 2633 PetscFunctionReturn(0); 2634 } 2635 EXTERN_C_END 2636 2637 /*MC 2638 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 2639 based on compressed sparse row format. 2640 2641 Options Database Keys: 2642 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 2643 2644 Level: beginner 2645 2646 .seealso: MatCreateSeqAIJ 2647 M*/ 2648 2649 EXTERN_C_BEGIN 2650 #undef __FUNCT__ 2651 #define __FUNCT__ "MatCreate_SeqAIJ" 2652 int MatCreate_SeqAIJ(Mat B) 2653 { 2654 Mat_SeqAIJ *b; 2655 int ierr,size; 2656 PetscTruth flg; 2657 2658 PetscFunctionBegin; 2659 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 2660 if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1"); 2661 2662 B->m = B->M = PetscMax(B->m,B->M); 2663 B->n = B->N = PetscMax(B->n,B->N); 2664 2665 ierr = PetscNew(Mat_SeqAIJ,&b);CHKERRQ(ierr); 2666 B->data = (void*)b; 2667 ierr = PetscMemzero(b,sizeof(Mat_SeqAIJ));CHKERRQ(ierr); 2668 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 2669 B->factor = 0; 2670 B->lupivotthreshold = 1.0; 2671 B->mapping = 0; 2672 ierr = PetscOptionsGetReal(B->prefix,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);CHKERRQ(ierr); 2673 ierr = PetscOptionsHasName(B->prefix,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);CHKERRQ(ierr); 2674 b->row = 0; 2675 b->col = 0; 2676 b->icol = 0; 2677 b->reallocs = 0; 2678 2679 ierr = PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);CHKERRQ(ierr); 2680 ierr = PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);CHKERRQ(ierr); 2681 2682 b->sorted = PETSC_FALSE; 2683 b->ignorezeroentries = PETSC_FALSE; 2684 b->roworiented = PETSC_TRUE; 2685 b->nonew = 0; 2686 b->diag = 0; 2687 b->solve_work = 0; 2688 B->spptr = 0; 2689 b->inode.use = PETSC_TRUE; 2690 b->inode.node_count = 0; 2691 b->inode.size = 0; 2692 b->inode.limit = 5; 2693 b->inode.max_limit = 5; 2694 b->saved_values = 0; 2695 b->idiag = 0; 2696 b->ssor = 0; 2697 b->keepzeroedrows = PETSC_FALSE; 2698 b->xtoy = 0; 2699 b->XtoY = 0; 2700 2701 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 2702 2703 ierr = PetscOptionsHasName(B->prefix,"-mat_aij_matlab",&flg);CHKERRQ(ierr); 2704 if (flg) {ierr = MatUseMatlab_SeqAIJ(B);CHKERRQ(ierr);} 2705 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C", 2706 "MatSeqAIJSetColumnIndices_SeqAIJ", 2707 MatSeqAIJSetColumnIndices_SeqAIJ);CHKERRQ(ierr); 2708 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C", 2709 "MatStoreValues_SeqAIJ", 2710 MatStoreValues_SeqAIJ);CHKERRQ(ierr); 2711 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C", 2712 "MatRetrieveValues_SeqAIJ", 2713 MatRetrieveValues_SeqAIJ);CHKERRQ(ierr); 2714 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C", 2715 "MatConvert_SeqAIJ_SeqSBAIJ", 2716 MatConvert_SeqAIJ_SeqSBAIJ);CHKERRQ(ierr); 2717 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C", 2718 "MatConvert_SeqAIJ_SeqBAIJ", 2719 MatConvert_SeqAIJ_SeqBAIJ);CHKERRQ(ierr); 2720 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C", 2721 "MatIsTranspose_SeqAIJ", 2722 MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 2723 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C", 2724 "MatSeqAIJSetPreallocation_SeqAIJ", 2725 MatSeqAIJSetPreallocation_SeqAIJ);CHKERRQ(ierr); 2726 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C", 2727 "MatReorderForNonzeroDiagonal_SeqAIJ", 2728 MatReorderForNonzeroDiagonal_SeqAIJ);CHKERRQ(ierr); 2729 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatAdjustForInodes_C", 2730 "MatAdjustForInodes_SeqAIJ", 2731 MatAdjustForInodes_SeqAIJ);CHKERRQ(ierr); 2732 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJGetInodeSizes_C", 2733 "MatSeqAIJGetInodeSizes_SeqAIJ", 2734 MatSeqAIJGetInodeSizes_SeqAIJ);CHKERRQ(ierr); 2735 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 2736 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatMatlabEnginePut_SeqAIJ",MatMatlabEnginePut_SeqAIJ);CHKERRQ(ierr); 2737 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatMatlabEngineGet_SeqAIJ",MatMatlabEngineGet_SeqAIJ);CHKERRQ(ierr); 2738 #endif 2739 ierr = RegisterApplyPtAPRoutines_Private(B);CHKERRQ(ierr); 2740 PetscFunctionReturn(0); 2741 } 2742 EXTERN_C_END 2743 2744 #undef __FUNCT__ 2745 #define __FUNCT__ "MatDuplicate_SeqAIJ" 2746 int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B) 2747 { 2748 Mat C; 2749 Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data; 2750 int i,m = A->m,ierr; 2751 size_t len; 2752 2753 PetscFunctionBegin; 2754 *B = 0; 2755 ierr = MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);CHKERRQ(ierr); 2756 ierr = MatSetType(C,A->type_name);CHKERRQ(ierr); 2757 c = (Mat_SeqAIJ*)C->data; 2758 2759 C->factor = A->factor; 2760 c->row = 0; 2761 c->col = 0; 2762 c->icol = 0; 2763 c->keepzeroedrows = a->keepzeroedrows; 2764 C->assembled = PETSC_TRUE; 2765 2766 C->M = A->m; 2767 C->N = A->n; 2768 2769 ierr = PetscMalloc((m+1)*sizeof(int),&c->imax);CHKERRQ(ierr); 2770 ierr = PetscMalloc((m+1)*sizeof(int),&c->ilen);CHKERRQ(ierr); 2771 for (i=0; i<m; i++) { 2772 c->imax[i] = a->imax[i]; 2773 c->ilen[i] = a->ilen[i]; 2774 } 2775 2776 /* allocate the matrix space */ 2777 c->singlemalloc = PETSC_TRUE; 2778 len = ((size_t) (m+1))*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int)); 2779 ierr = PetscMalloc(len,&c->a);CHKERRQ(ierr); 2780 c->j = (int*)(c->a + a->i[m] ); 2781 c->i = c->j + a->i[m]; 2782 ierr = PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));CHKERRQ(ierr); 2783 if (m > 0) { 2784 ierr = PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(int));CHKERRQ(ierr); 2785 if (cpvalues == MAT_COPY_VALUES) { 2786 ierr = PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2787 } else { 2788 ierr = PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2789 } 2790 } 2791 2792 PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2793 c->sorted = a->sorted; 2794 c->roworiented = a->roworiented; 2795 c->nonew = a->nonew; 2796 c->ilu_preserve_row_sums = a->ilu_preserve_row_sums; 2797 c->saved_values = 0; 2798 c->idiag = 0; 2799 c->ssor = 0; 2800 c->ignorezeroentries = a->ignorezeroentries; 2801 c->freedata = PETSC_TRUE; 2802 2803 if (a->diag) { 2804 ierr = PetscMalloc((m+1)*sizeof(int),&c->diag);CHKERRQ(ierr); 2805 PetscLogObjectMemory(C,(m+1)*sizeof(int)); 2806 for (i=0; i<m; i++) { 2807 c->diag[i] = a->diag[i]; 2808 } 2809 } else c->diag = 0; 2810 c->inode.use = a->inode.use; 2811 c->inode.limit = a->inode.limit; 2812 c->inode.max_limit = a->inode.max_limit; 2813 if (a->inode.size){ 2814 ierr = PetscMalloc((m+1)*sizeof(int),&c->inode.size);CHKERRQ(ierr); 2815 c->inode.node_count = a->inode.node_count; 2816 ierr = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));CHKERRQ(ierr); 2817 } else { 2818 c->inode.size = 0; 2819 c->inode.node_count = 0; 2820 } 2821 c->nz = a->nz; 2822 c->maxnz = a->maxnz; 2823 c->solve_work = 0; 2824 C->spptr = 0; /* Dangerous -I'm throwing away a->spptr */ 2825 C->preallocated = PETSC_TRUE; 2826 2827 *B = C; 2828 ierr = PetscFListDuplicate(A->qlist,&C->qlist);CHKERRQ(ierr); 2829 PetscFunctionReturn(0); 2830 } 2831 2832 #undef __FUNCT__ 2833 #define __FUNCT__ "MatLoad_SeqAIJ" 2834 int MatLoad_SeqAIJ(PetscViewer viewer,const MatType type,Mat *A) 2835 { 2836 Mat_SeqAIJ *a; 2837 Mat B; 2838 int i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N; 2839 MPI_Comm comm; 2840 2841 PetscFunctionBegin; 2842 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 2843 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2844 if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor"); 2845 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 2846 ierr = PetscBinaryRead(fd,header,4,PETSC_INT);CHKERRQ(ierr); 2847 if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file"); 2848 M = header[1]; N = header[2]; nz = header[3]; 2849 2850 if (nz < 0) { 2851 SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ"); 2852 } 2853 2854 /* read in row lengths */ 2855 ierr = PetscMalloc(M*sizeof(int),&rowlengths);CHKERRQ(ierr); 2856 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 2857 2858 /* create our matrix */ 2859 ierr = MatCreate(comm,PETSC_DECIDE,PETSC_DECIDE,M,N,&B);CHKERRQ(ierr); 2860 ierr = MatSetType(B,type);CHKERRQ(ierr); 2861 ierr = MatSeqAIJSetPreallocation(B,0,rowlengths);CHKERRQ(ierr); 2862 a = (Mat_SeqAIJ*)B->data; 2863 2864 /* read in column indices and adjust for Fortran indexing*/ 2865 ierr = PetscBinaryRead(fd,a->j,nz,PETSC_INT);CHKERRQ(ierr); 2866 2867 /* read in nonzero values */ 2868 ierr = PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);CHKERRQ(ierr); 2869 2870 /* set matrix "i" values */ 2871 a->i[0] = 0; 2872 for (i=1; i<= M; i++) { 2873 a->i[i] = a->i[i-1] + rowlengths[i-1]; 2874 a->ilen[i-1] = rowlengths[i-1]; 2875 } 2876 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 2877 2878 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2879 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2880 *A = B; 2881 PetscFunctionReturn(0); 2882 } 2883 2884 #undef __FUNCT__ 2885 #define __FUNCT__ "MatEqual_SeqAIJ" 2886 int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg) 2887 { 2888 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data; 2889 int ierr; 2890 2891 PetscFunctionBegin; 2892 /* If the matrix dimensions are not equal,or no of nonzeros */ 2893 if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)) { 2894 *flg = PETSC_FALSE; 2895 PetscFunctionReturn(0); 2896 } 2897 2898 /* if the a->i are the same */ 2899 ierr = PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);CHKERRQ(ierr); 2900 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2901 2902 /* if a->j are the same */ 2903 ierr = PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);CHKERRQ(ierr); 2904 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2905 2906 /* if a->a are the same */ 2907 ierr = PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);CHKERRQ(ierr); 2908 2909 PetscFunctionReturn(0); 2910 2911 } 2912 2913 #undef __FUNCT__ 2914 #define __FUNCT__ "MatCreateSeqAIJWithArrays" 2915 /*@C 2916 MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format) 2917 provided by the user. 2918 2919 Coolective on MPI_Comm 2920 2921 Input Parameters: 2922 + comm - must be an MPI communicator of size 1 2923 . m - number of rows 2924 . n - number of columns 2925 . i - row indices 2926 . j - column indices 2927 - a - matrix values 2928 2929 Output Parameter: 2930 . mat - the matrix 2931 2932 Level: intermediate 2933 2934 Notes: 2935 The i, j, and a arrays are not copied by this routine, the user must free these arrays 2936 once the matrix is destroyed 2937 2938 You cannot set new nonzero locations into this matrix, that will generate an error. 2939 2940 The i and j indices are 0 based 2941 2942 .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ() 2943 2944 @*/ 2945 int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat) 2946 { 2947 int ierr,ii; 2948 Mat_SeqAIJ *aij; 2949 2950 PetscFunctionBegin; 2951 ierr = MatCreate(comm,m,n,m,n,mat);CHKERRQ(ierr); 2952 ierr = MatSetType(*mat,MATSEQAIJ);CHKERRQ(ierr); 2953 ierr = MatSeqAIJSetPreallocation(*mat,SKIP_ALLOCATION,0);CHKERRQ(ierr); 2954 aij = (Mat_SeqAIJ*)(*mat)->data; 2955 2956 if (i[0] != 0) { 2957 SETERRQ(1,"i (row indices) must start with 0"); 2958 } 2959 aij->i = i; 2960 aij->j = j; 2961 aij->a = a; 2962 aij->singlemalloc = PETSC_FALSE; 2963 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 2964 aij->freedata = PETSC_FALSE; 2965 2966 for (ii=0; ii<m; ii++) { 2967 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii]; 2968 #if defined(PETSC_USE_BOPT_g) 2969 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]); 2970 #endif 2971 } 2972 #if defined(PETSC_USE_BOPT_g) 2973 for (ii=0; ii<aij->i[m]; ii++) { 2974 if (j[ii] < 0) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]); 2975 if (j[ii] > n - 1) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]); 2976 } 2977 #endif 2978 2979 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2980 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2981 PetscFunctionReturn(0); 2982 } 2983 2984 #undef __FUNCT__ 2985 #define __FUNCT__ "MatSetColoring_SeqAIJ" 2986 int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring) 2987 { 2988 int ierr; 2989 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2990 2991 PetscFunctionBegin; 2992 if (coloring->ctype == IS_COLORING_LOCAL) { 2993 ierr = ISColoringReference(coloring);CHKERRQ(ierr); 2994 a->coloring = coloring; 2995 } else if (coloring->ctype == IS_COLORING_GHOSTED) { 2996 int i,*larray; 2997 ISColoring ocoloring; 2998 ISColoringValue *colors; 2999 3000 /* set coloring for diagonal portion */ 3001 ierr = PetscMalloc((A->n+1)*sizeof(int),&larray);CHKERRQ(ierr); 3002 for (i=0; i<A->n; i++) { 3003 larray[i] = i; 3004 } 3005 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);CHKERRQ(ierr); 3006 ierr = PetscMalloc((A->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 3007 for (i=0; i<A->n; i++) { 3008 colors[i] = coloring->colors[larray[i]]; 3009 } 3010 ierr = PetscFree(larray);CHKERRQ(ierr); 3011 ierr = ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);CHKERRQ(ierr); 3012 a->coloring = ocoloring; 3013 } 3014 PetscFunctionReturn(0); 3015 } 3016 3017 #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 3018 EXTERN_C_BEGIN 3019 #include "adic/ad_utils.h" 3020 EXTERN_C_END 3021 3022 #undef __FUNCT__ 3023 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 3024 int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 3025 { 3026 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3027 int m = A->m,*ii = a->i,*jj = a->j,nz,i,j,nlen; 3028 PetscScalar *v = a->a,*values = ((PetscScalar*)advalues)+1; 3029 ISColoringValue *color; 3030 3031 PetscFunctionBegin; 3032 if (!a->coloring) SETERRQ(1,"Coloring not set for matrix"); 3033 nlen = PetscADGetDerivTypeSize()/sizeof(PetscScalar); 3034 color = a->coloring->colors; 3035 /* loop over rows */ 3036 for (i=0; i<m; i++) { 3037 nz = ii[i+1] - ii[i]; 3038 /* loop over columns putting computed value into matrix */ 3039 for (j=0; j<nz; j++) { 3040 *v++ = values[color[*jj++]]; 3041 } 3042 values += nlen; /* jump to next row of derivatives */ 3043 } 3044 PetscFunctionReturn(0); 3045 } 3046 3047 #else 3048 3049 #undef __FUNCT__ 3050 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 3051 int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 3052 { 3053 PetscFunctionBegin; 3054 SETERRQ(1,"PETSc installed without ADIC"); 3055 } 3056 3057 #endif 3058 3059 #undef __FUNCT__ 3060 #define __FUNCT__ "MatSetValuesAdifor_SeqAIJ" 3061 int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues) 3062 { 3063 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3064 int m = A->m,*ii = a->i,*jj = a->j,nz,i,j; 3065 PetscScalar *v = a->a,*values = (PetscScalar *)advalues; 3066 ISColoringValue *color; 3067 3068 PetscFunctionBegin; 3069 if (!a->coloring) SETERRQ(1,"Coloring not set for matrix"); 3070 color = a->coloring->colors; 3071 /* loop over rows */ 3072 for (i=0; i<m; i++) { 3073 nz = ii[i+1] - ii[i]; 3074 /* loop over columns putting computed value into matrix */ 3075 for (j=0; j<nz; j++) { 3076 *v++ = values[color[*jj++]]; 3077 } 3078 values += nl; /* jump to next row of derivatives */ 3079 } 3080 PetscFunctionReturn(0); 3081 } 3082 3083