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 = VecGetArrayFast(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 = VecRestoreArrayFast(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 = VecGetArrayFast(xx,&x);CHKERRQ(ierr); 826 ierr = VecGetArrayFast(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 = VecRestoreArrayFast(xx,&x);CHKERRQ(ierr); 841 ierr = VecRestoreArrayFast(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 = VecGetArrayFast(xx,&x);CHKERRQ(ierr); 877 ierr = VecGetArrayFast(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 = VecRestoreArrayFast(xx,&x);CHKERRQ(ierr); 896 ierr = VecRestoreArrayFast(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 = VecGetArrayFast(xx,&x);CHKERRQ(ierr); 914 ierr = VecGetArrayFast(yy,&y);CHKERRQ(ierr); 915 if (zz != yy) { 916 ierr = VecGetArrayFast(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 = VecRestoreArrayFast(xx,&x);CHKERRQ(ierr); 939 ierr = VecRestoreArrayFast(yy,&y);CHKERRQ(ierr); 940 if (zz != yy) { 941 ierr = VecRestoreArrayFast(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 = VecGetArrayFast(xx,&x);CHKERRQ(ierr); 1038 if (xx != bb) { 1039 ierr = VecGetArrayFast(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 = VecRestoreArrayFast(xx,&x);CHKERRQ(ierr); 1059 if (bb != xx) {ierr = VecRestoreArrayFast(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 = VecRestoreArrayFast(xx,&x);CHKERRQ(ierr); 1119 if (bb != xx) {ierr = VecRestoreArrayFast(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 = VecRestoreArrayFast(xx,&x);CHKERRQ(ierr); 1198 if (bb != xx) {ierr = VecRestoreArrayFast(bb,(PetscScalar**)&b);CHKERRQ(ierr);} 1199 PetscFunctionReturn(0); 1200 } 1201 1202 #undef __FUNCT__ 1203 #define __FUNCT__ "MatGetInfo_SeqAIJ" 1204 int MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info) 1205 { 1206 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1207 1208 PetscFunctionBegin; 1209 info->rows_global = (double)A->m; 1210 info->columns_global = (double)A->n; 1211 info->rows_local = (double)A->m; 1212 info->columns_local = (double)A->n; 1213 info->block_size = 1.0; 1214 info->nz_allocated = (double)a->maxnz; 1215 info->nz_used = (double)a->nz; 1216 info->nz_unneeded = (double)(a->maxnz - a->nz); 1217 info->assemblies = (double)A->num_ass; 1218 info->mallocs = (double)a->reallocs; 1219 info->memory = A->mem; 1220 if (A->factor) { 1221 info->fill_ratio_given = A->info.fill_ratio_given; 1222 info->fill_ratio_needed = A->info.fill_ratio_needed; 1223 info->factor_mallocs = A->info.factor_mallocs; 1224 } else { 1225 info->fill_ratio_given = 0; 1226 info->fill_ratio_needed = 0; 1227 info->factor_mallocs = 0; 1228 } 1229 PetscFunctionReturn(0); 1230 } 1231 1232 #undef __FUNCT__ 1233 #define __FUNCT__ "MatZeroRows_SeqAIJ" 1234 int MatZeroRows_SeqAIJ(Mat A,IS is,const PetscScalar *diag) 1235 { 1236 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1237 int i,ierr,N,*rows,m = A->m - 1; 1238 1239 PetscFunctionBegin; 1240 ierr = ISGetLocalSize(is,&N);CHKERRQ(ierr); 1241 ierr = ISGetIndices(is,&rows);CHKERRQ(ierr); 1242 if (a->keepzeroedrows) { 1243 for (i=0; i<N; i++) { 1244 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]); 1245 ierr = PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));CHKERRQ(ierr); 1246 } 1247 if (diag) { 1248 ierr = MatMissingDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1249 ierr = MatMarkDiagonal_SeqAIJ(A);CHKERRQ(ierr); 1250 for (i=0; i<N; i++) { 1251 a->a[a->diag[rows[i]]] = *diag; 1252 } 1253 } 1254 } else { 1255 if (diag) { 1256 for (i=0; i<N; i++) { 1257 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]); 1258 if (a->ilen[rows[i]] > 0) { 1259 a->ilen[rows[i]] = 1; 1260 a->a[a->i[rows[i]]] = *diag; 1261 a->j[a->i[rows[i]]] = rows[i]; 1262 } else { /* in case row was completely empty */ 1263 ierr = MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);CHKERRQ(ierr); 1264 } 1265 } 1266 } else { 1267 for (i=0; i<N; i++) { 1268 if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]); 1269 a->ilen[rows[i]] = 0; 1270 } 1271 } 1272 } 1273 ierr = ISRestoreIndices(is,&rows);CHKERRQ(ierr); 1274 ierr = MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1275 PetscFunctionReturn(0); 1276 } 1277 1278 #undef __FUNCT__ 1279 #define __FUNCT__ "MatGetRow_SeqAIJ" 1280 int MatGetRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v) 1281 { 1282 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1283 int *itmp; 1284 1285 PetscFunctionBegin; 1286 if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %d out of range",row); 1287 1288 *nz = a->i[row+1] - a->i[row]; 1289 if (v) *v = a->a + a->i[row]; 1290 if (idx) { 1291 itmp = a->j + a->i[row]; 1292 if (*nz) { 1293 *idx = itmp; 1294 } 1295 else *idx = 0; 1296 } 1297 PetscFunctionReturn(0); 1298 } 1299 1300 /* remove this function? */ 1301 #undef __FUNCT__ 1302 #define __FUNCT__ "MatRestoreRow_SeqAIJ" 1303 int MatRestoreRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v) 1304 { 1305 /* Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1306 int ierr; */ 1307 1308 PetscFunctionBegin; 1309 /* if (idx) {if (*idx && a->indexshift) {ierr = PetscFree(*idx);CHKERRQ(ierr);}} */ 1310 PetscFunctionReturn(0); 1311 } 1312 1313 #undef __FUNCT__ 1314 #define __FUNCT__ "MatNorm_SeqAIJ" 1315 int MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm) 1316 { 1317 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1318 PetscScalar *v = a->a; 1319 PetscReal sum = 0.0; 1320 int i,j,ierr; 1321 1322 PetscFunctionBegin; 1323 if (type == NORM_FROBENIUS) { 1324 for (i=0; i<a->nz; i++) { 1325 #if defined(PETSC_USE_COMPLEX) 1326 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1327 #else 1328 sum += (*v)*(*v); v++; 1329 #endif 1330 } 1331 *nrm = sqrt(sum); 1332 } else if (type == NORM_1) { 1333 PetscReal *tmp; 1334 int *jj = a->j; 1335 ierr = PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);CHKERRQ(ierr); 1336 ierr = PetscMemzero(tmp,A->n*sizeof(PetscReal));CHKERRQ(ierr); 1337 *nrm = 0.0; 1338 for (j=0; j<a->nz; j++) { 1339 tmp[*jj++] += PetscAbsScalar(*v); v++; 1340 } 1341 for (j=0; j<A->n; j++) { 1342 if (tmp[j] > *nrm) *nrm = tmp[j]; 1343 } 1344 ierr = PetscFree(tmp);CHKERRQ(ierr); 1345 } else if (type == NORM_INFINITY) { 1346 *nrm = 0.0; 1347 for (j=0; j<A->m; j++) { 1348 v = a->a + a->i[j]; 1349 sum = 0.0; 1350 for (i=0; i<a->i[j+1]-a->i[j]; i++) { 1351 sum += PetscAbsScalar(*v); v++; 1352 } 1353 if (sum > *nrm) *nrm = sum; 1354 } 1355 } else { 1356 SETERRQ(PETSC_ERR_SUP,"No support for two norm"); 1357 } 1358 PetscFunctionReturn(0); 1359 } 1360 1361 #undef __FUNCT__ 1362 #define __FUNCT__ "MatTranspose_SeqAIJ" 1363 int MatTranspose_SeqAIJ(Mat A,Mat *B) 1364 { 1365 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1366 Mat C; 1367 int i,ierr,*aj = a->j,*ai = a->i,m = A->m,len,*col; 1368 PetscScalar *array = a->a; 1369 1370 PetscFunctionBegin; 1371 if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place"); 1372 ierr = PetscMalloc((1+A->n)*sizeof(int),&col);CHKERRQ(ierr); 1373 ierr = PetscMemzero(col,(1+A->n)*sizeof(int));CHKERRQ(ierr); 1374 1375 for (i=0; i<ai[m]; i++) col[aj[i]] += 1; 1376 ierr = MatCreateSeqAIJ(A->comm,A->n,m,0,col,&C);CHKERRQ(ierr); 1377 ierr = PetscFree(col);CHKERRQ(ierr); 1378 for (i=0; i<m; i++) { 1379 len = ai[i+1]-ai[i]; 1380 ierr = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);CHKERRQ(ierr); 1381 array += len; 1382 aj += len; 1383 } 1384 1385 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1386 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1387 1388 if (B) { 1389 *B = C; 1390 } else { 1391 ierr = MatHeaderCopy(A,C);CHKERRQ(ierr); 1392 } 1393 PetscFunctionReturn(0); 1394 } 1395 1396 EXTERN_C_BEGIN 1397 #undef __FUNCT__ 1398 #define __FUNCT__ "MatIsTranspose_SeqAIJ" 1399 int MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscTruth *f) 1400 { 1401 Mat_SeqAIJ *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data; 1402 int *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb; 1403 int ma,na,mb,nb, i,ierr; 1404 1405 PetscFunctionBegin; 1406 bij = (Mat_SeqAIJ *) B->data; 1407 1408 ierr = MatGetSize(A,&ma,&na); CHKERRQ(ierr); 1409 ierr = MatGetSize(B,&mb,&nb); CHKERRQ(ierr); 1410 if (ma!=nb || na!=mb) 1411 SETERRQ(1,"Incompatible A/B sizes for symmetry test"); 1412 aii = aij->i; bii = bij->i; 1413 adx = aij->j; bdx = bij->j; 1414 va = aij->a; vb = bij->a; 1415 ierr = PetscMalloc(ma*sizeof(int),&aptr); CHKERRQ(ierr); 1416 ierr = PetscMalloc(mb*sizeof(int),&bptr); CHKERRQ(ierr); 1417 for (i=0; i<ma; i++) aptr[i] = aii[i]; 1418 for (i=0; i<mb; i++) bptr[i] = bii[i]; 1419 1420 *f = PETSC_TRUE; 1421 for (i=0; i<ma; i++) { 1422 /*printf("row %d spans %d--%d; we start @ %d\n", 1423 i,idx[ii[i]],idx[ii[i+1]-1],idx[aptr[i]]);*/ 1424 while (aptr[i]<aii[i+1]) { 1425 int idc,idr; PetscScalar vc,vr; 1426 /* column/row index/value */ 1427 idc = adx[aptr[i]]; idr = bdx[bptr[idc]]; 1428 vc = va[aptr[i]]; vr = vb[bptr[idc]]; 1429 /*printf("comparing %d: (%d,%d)=%e to (%d,%d)=%e\n", 1430 aptr[i],i,idc,vc,idc,idr,vr);*/ 1431 if (i!=idr || vc!=vr) { 1432 *f = PETSC_FALSE; goto done; 1433 } else { 1434 aptr[i]++; if (B || i!=idc) bptr[idc]++; 1435 } 1436 } 1437 } 1438 done: 1439 ierr = PetscFree(aptr); CHKERRQ(ierr); 1440 if (B) { 1441 ierr = PetscFree(bptr); CHKERRQ(ierr); 1442 } 1443 1444 PetscFunctionReturn(0); 1445 } 1446 EXTERN_C_END 1447 1448 #undef __FUNCT__ 1449 #define __FUNCT__ "MatDiagonalScale_SeqAIJ" 1450 int MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr) 1451 { 1452 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1453 PetscScalar *l,*r,x,*v; 1454 int ierr,i,j,m = A->m,n = A->n,M,nz = a->nz,*jj; 1455 1456 PetscFunctionBegin; 1457 if (ll) { 1458 /* The local size is used so that VecMPI can be passed to this routine 1459 by MatDiagonalScale_MPIAIJ */ 1460 ierr = VecGetLocalSize(ll,&m);CHKERRQ(ierr); 1461 if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length"); 1462 ierr = VecGetArrayFast(ll,&l);CHKERRQ(ierr); 1463 v = a->a; 1464 for (i=0; i<m; i++) { 1465 x = l[i]; 1466 M = a->i[i+1] - a->i[i]; 1467 for (j=0; j<M; j++) { (*v++) *= x;} 1468 } 1469 ierr = VecRestoreArrayFast(ll,&l);CHKERRQ(ierr); 1470 PetscLogFlops(nz); 1471 } 1472 if (rr) { 1473 ierr = VecGetLocalSize(rr,&n);CHKERRQ(ierr); 1474 if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length"); 1475 ierr = VecGetArrayFast(rr,&r);CHKERRQ(ierr); 1476 v = a->a; jj = a->j; 1477 for (i=0; i<nz; i++) { 1478 (*v++) *= r[*jj++]; 1479 } 1480 ierr = VecRestoreArrayFast(rr,&r);CHKERRQ(ierr); 1481 PetscLogFlops(nz); 1482 } 1483 PetscFunctionReturn(0); 1484 } 1485 1486 #undef __FUNCT__ 1487 #define __FUNCT__ "MatGetSubMatrix_SeqAIJ" 1488 int MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,int csize,MatReuse scall,Mat *B) 1489 { 1490 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c; 1491 int *smap,i,k,kstart,kend,ierr,oldcols = A->n,*lens; 1492 int row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi; 1493 int *irow,*icol,nrows,ncols; 1494 int *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen; 1495 PetscScalar *a_new,*mat_a; 1496 Mat C; 1497 PetscTruth stride; 1498 1499 PetscFunctionBegin; 1500 ierr = ISSorted(isrow,(PetscTruth*)&i);CHKERRQ(ierr); 1501 if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted"); 1502 ierr = ISSorted(iscol,(PetscTruth*)&i);CHKERRQ(ierr); 1503 if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted"); 1504 1505 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 1506 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 1507 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 1508 1509 ierr = ISStrideGetInfo(iscol,&first,&step);CHKERRQ(ierr); 1510 ierr = ISStride(iscol,&stride);CHKERRQ(ierr); 1511 if (stride && step == 1) { 1512 /* special case of contiguous rows */ 1513 ierr = PetscMalloc((2*nrows+1)*sizeof(int),&lens);CHKERRQ(ierr); 1514 starts = lens + nrows; 1515 /* loop over new rows determining lens and starting points */ 1516 for (i=0; i<nrows; i++) { 1517 kstart = ai[irow[i]]; 1518 kend = kstart + ailen[irow[i]]; 1519 for (k=kstart; k<kend; k++) { 1520 if (aj[k] >= first) { 1521 starts[i] = k; 1522 break; 1523 } 1524 } 1525 sum = 0; 1526 while (k < kend) { 1527 if (aj[k++] >= first+ncols) break; 1528 sum++; 1529 } 1530 lens[i] = sum; 1531 } 1532 /* create submatrix */ 1533 if (scall == MAT_REUSE_MATRIX) { 1534 int n_cols,n_rows; 1535 ierr = MatGetSize(*B,&n_rows,&n_cols);CHKERRQ(ierr); 1536 if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size"); 1537 ierr = MatZeroEntries(*B);CHKERRQ(ierr); 1538 C = *B; 1539 } else { 1540 ierr = MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);CHKERRQ(ierr); 1541 } 1542 c = (Mat_SeqAIJ*)C->data; 1543 1544 /* loop over rows inserting into submatrix */ 1545 a_new = c->a; 1546 j_new = c->j; 1547 i_new = c->i; 1548 1549 for (i=0; i<nrows; i++) { 1550 ii = starts[i]; 1551 lensi = lens[i]; 1552 for (k=0; k<lensi; k++) { 1553 *j_new++ = aj[ii+k] - first; 1554 } 1555 ierr = PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));CHKERRQ(ierr); 1556 a_new += lensi; 1557 i_new[i+1] = i_new[i] + lensi; 1558 c->ilen[i] = lensi; 1559 } 1560 ierr = PetscFree(lens);CHKERRQ(ierr); 1561 } else { 1562 ierr = ISGetIndices(iscol,&icol);CHKERRQ(ierr); 1563 ierr = PetscMalloc((1+oldcols)*sizeof(int),&smap);CHKERRQ(ierr); 1564 1565 ierr = PetscMalloc((1+nrows)*sizeof(int),&lens);CHKERRQ(ierr); 1566 ierr = PetscMemzero(smap,oldcols*sizeof(int));CHKERRQ(ierr); 1567 for (i=0; i<ncols; i++) smap[icol[i]] = i+1; 1568 /* determine lens of each row */ 1569 for (i=0; i<nrows; i++) { 1570 kstart = ai[irow[i]]; 1571 kend = kstart + a->ilen[irow[i]]; 1572 lens[i] = 0; 1573 for (k=kstart; k<kend; k++) { 1574 if (smap[aj[k]]) { 1575 lens[i]++; 1576 } 1577 } 1578 } 1579 /* Create and fill new matrix */ 1580 if (scall == MAT_REUSE_MATRIX) { 1581 PetscTruth equal; 1582 1583 c = (Mat_SeqAIJ *)((*B)->data); 1584 if ((*B)->m != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size"); 1585 ierr = PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(int),&equal);CHKERRQ(ierr); 1586 if (!equal) { 1587 SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros"); 1588 } 1589 ierr = PetscMemzero(c->ilen,(*B)->m*sizeof(int));CHKERRQ(ierr); 1590 C = *B; 1591 } else { 1592 ierr = MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);CHKERRQ(ierr); 1593 } 1594 c = (Mat_SeqAIJ *)(C->data); 1595 for (i=0; i<nrows; i++) { 1596 row = irow[i]; 1597 kstart = ai[row]; 1598 kend = kstart + a->ilen[row]; 1599 mat_i = c->i[i]; 1600 mat_j = c->j + mat_i; 1601 mat_a = c->a + mat_i; 1602 mat_ilen = c->ilen + i; 1603 for (k=kstart; k<kend; k++) { 1604 if ((tcol=smap[a->j[k]])) { 1605 *mat_j++ = tcol - 1; 1606 *mat_a++ = a->a[k]; 1607 (*mat_ilen)++; 1608 1609 } 1610 } 1611 } 1612 /* Free work space */ 1613 ierr = ISRestoreIndices(iscol,&icol);CHKERRQ(ierr); 1614 ierr = PetscFree(smap);CHKERRQ(ierr); 1615 ierr = PetscFree(lens);CHKERRQ(ierr); 1616 } 1617 ierr = MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1618 ierr = MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1619 1620 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 1621 *B = C; 1622 PetscFunctionReturn(0); 1623 } 1624 1625 /* 1626 */ 1627 #undef __FUNCT__ 1628 #define __FUNCT__ "MatILUFactor_SeqAIJ" 1629 int MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info) 1630 { 1631 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 1632 int ierr; 1633 Mat outA; 1634 PetscTruth row_identity,col_identity; 1635 1636 PetscFunctionBegin; 1637 if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu"); 1638 ierr = ISIdentity(row,&row_identity);CHKERRQ(ierr); 1639 ierr = ISIdentity(col,&col_identity);CHKERRQ(ierr); 1640 if (!row_identity || !col_identity) { 1641 SETERRQ(1,"Row and column permutations must be identity for in-place ILU"); 1642 } 1643 1644 outA = inA; 1645 inA->factor = FACTOR_LU; 1646 a->row = row; 1647 a->col = col; 1648 ierr = PetscObjectReference((PetscObject)row);CHKERRQ(ierr); 1649 ierr = PetscObjectReference((PetscObject)col);CHKERRQ(ierr); 1650 1651 /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */ 1652 if (a->icol) {ierr = ISDestroy(a->icol);CHKERRQ(ierr);} /* need to remove old one */ 1653 ierr = ISInvertPermutation(col,PETSC_DECIDE,&a->icol);CHKERRQ(ierr); 1654 PetscLogObjectParent(inA,a->icol); 1655 1656 if (!a->solve_work) { /* this matrix may have been factored before */ 1657 ierr = PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);CHKERRQ(ierr); 1658 } 1659 1660 if (!a->diag) { 1661 ierr = MatMarkDiagonal_SeqAIJ(inA);CHKERRQ(ierr); 1662 } 1663 ierr = MatLUFactorNumeric_SeqAIJ(inA,&outA);CHKERRQ(ierr); 1664 PetscFunctionReturn(0); 1665 } 1666 1667 #include "petscblaslapack.h" 1668 #undef __FUNCT__ 1669 #define __FUNCT__ "MatScale_SeqAIJ" 1670 int MatScale_SeqAIJ(const PetscScalar *alpha,Mat inA) 1671 { 1672 Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data; 1673 int one = 1; 1674 1675 PetscFunctionBegin; 1676 BLscal_(&a->nz,(PetscScalar*)alpha,a->a,&one); 1677 PetscLogFlops(a->nz); 1678 PetscFunctionReturn(0); 1679 } 1680 1681 #undef __FUNCT__ 1682 #define __FUNCT__ "MatGetSubMatrices_SeqAIJ" 1683 int MatGetSubMatrices_SeqAIJ(Mat A,int n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[]) 1684 { 1685 int ierr,i; 1686 1687 PetscFunctionBegin; 1688 if (scall == MAT_INITIAL_MATRIX) { 1689 ierr = PetscMalloc((n+1)*sizeof(Mat),B);CHKERRQ(ierr); 1690 } 1691 1692 for (i=0; i<n; i++) { 1693 ierr = MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);CHKERRQ(ierr); 1694 } 1695 PetscFunctionReturn(0); 1696 } 1697 1698 #undef __FUNCT__ 1699 #define __FUNCT__ "MatGetBlockSize_SeqAIJ" 1700 int MatGetBlockSize_SeqAIJ(Mat A,int *bs) 1701 { 1702 PetscFunctionBegin; 1703 *bs = 1; 1704 PetscFunctionReturn(0); 1705 } 1706 1707 #undef __FUNCT__ 1708 #define __FUNCT__ "MatIncreaseOverlap_SeqAIJ" 1709 int MatIncreaseOverlap_SeqAIJ(Mat A,int is_max,IS is[],int ov) 1710 { 1711 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1712 int row,i,j,k,l,m,n,*idx,ierr,*nidx,isz,val; 1713 int start,end,*ai,*aj; 1714 PetscBT table; 1715 1716 PetscFunctionBegin; 1717 m = A->m; 1718 ai = a->i; 1719 aj = a->j; 1720 1721 if (ov < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used"); 1722 1723 ierr = PetscMalloc((m+1)*sizeof(int),&nidx);CHKERRQ(ierr); 1724 ierr = PetscBTCreate(m,table);CHKERRQ(ierr); 1725 1726 for (i=0; i<is_max; i++) { 1727 /* Initialize the two local arrays */ 1728 isz = 0; 1729 ierr = PetscBTMemzero(m,table);CHKERRQ(ierr); 1730 1731 /* Extract the indices, assume there can be duplicate entries */ 1732 ierr = ISGetIndices(is[i],&idx);CHKERRQ(ierr); 1733 ierr = ISGetLocalSize(is[i],&n);CHKERRQ(ierr); 1734 1735 /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */ 1736 for (j=0; j<n ; ++j){ 1737 if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];} 1738 } 1739 ierr = ISRestoreIndices(is[i],&idx);CHKERRQ(ierr); 1740 ierr = ISDestroy(is[i]);CHKERRQ(ierr); 1741 1742 k = 0; 1743 for (j=0; j<ov; j++){ /* for each overlap */ 1744 n = isz; 1745 for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */ 1746 row = nidx[k]; 1747 start = ai[row]; 1748 end = ai[row+1]; 1749 for (l = start; l<end ; l++){ 1750 val = aj[l] ; 1751 if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;} 1752 } 1753 } 1754 } 1755 ierr = ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));CHKERRQ(ierr); 1756 } 1757 ierr = PetscBTDestroy(table);CHKERRQ(ierr); 1758 ierr = PetscFree(nidx);CHKERRQ(ierr); 1759 PetscFunctionReturn(0); 1760 } 1761 1762 /* -------------------------------------------------------------- */ 1763 #undef __FUNCT__ 1764 #define __FUNCT__ "MatPermute_SeqAIJ" 1765 int MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B) 1766 { 1767 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1768 PetscScalar *vwork; 1769 int i,ierr,nz,m = A->m,n = A->n,*cwork; 1770 int *row,*col,*cnew,j,*lens; 1771 IS icolp,irowp; 1772 1773 PetscFunctionBegin; 1774 ierr = ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);CHKERRQ(ierr); 1775 ierr = ISGetIndices(irowp,&row);CHKERRQ(ierr); 1776 ierr = ISInvertPermutation(colp,PETSC_DECIDE,&icolp);CHKERRQ(ierr); 1777 ierr = ISGetIndices(icolp,&col);CHKERRQ(ierr); 1778 1779 /* determine lengths of permuted rows */ 1780 ierr = PetscMalloc((m+1)*sizeof(int),&lens);CHKERRQ(ierr); 1781 for (i=0; i<m; i++) { 1782 lens[row[i]] = a->i[i+1] - a->i[i]; 1783 } 1784 ierr = MatCreateSeqAIJ(A->comm,m,n,0,lens,B);CHKERRQ(ierr); 1785 ierr = PetscFree(lens);CHKERRQ(ierr); 1786 1787 ierr = PetscMalloc(n*sizeof(int),&cnew);CHKERRQ(ierr); 1788 for (i=0; i<m; i++) { 1789 ierr = MatGetRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1790 for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];} 1791 ierr = MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);CHKERRQ(ierr); 1792 ierr = MatRestoreRow(A,i,&nz,&cwork,&vwork);CHKERRQ(ierr); 1793 } 1794 ierr = PetscFree(cnew);CHKERRQ(ierr); 1795 ierr = MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1796 ierr = MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1797 ierr = ISRestoreIndices(irowp,&row);CHKERRQ(ierr); 1798 ierr = ISRestoreIndices(icolp,&col);CHKERRQ(ierr); 1799 ierr = ISDestroy(irowp);CHKERRQ(ierr); 1800 ierr = ISDestroy(icolp);CHKERRQ(ierr); 1801 PetscFunctionReturn(0); 1802 } 1803 1804 #undef __FUNCT__ 1805 #define __FUNCT__ "MatPrintHelp_SeqAIJ" 1806 int MatPrintHelp_SeqAIJ(Mat A) 1807 { 1808 static PetscTruth called = PETSC_FALSE; 1809 MPI_Comm comm = A->comm; 1810 int ierr; 1811 1812 PetscFunctionBegin; 1813 if (called) {PetscFunctionReturn(0);} else called = PETSC_TRUE; 1814 ierr = (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):\n");CHKERRQ(ierr); 1815 ierr = (*PetscHelpPrintf)(comm," -mat_lu_pivotthreshold <threshold>: Set pivoting threshold\n");CHKERRQ(ierr); 1816 ierr = (*PetscHelpPrintf)(comm," -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.\n");CHKERRQ(ierr); 1817 ierr = (*PetscHelpPrintf)(comm," -mat_aij_no_inode: Do not use inodes\n");CHKERRQ(ierr); 1818 ierr = (*PetscHelpPrintf)(comm," -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)\n");CHKERRQ(ierr); 1819 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 1820 ierr = (*PetscHelpPrintf)(comm," -mat_aij_matlab: Use Matlab engine sparse LU factorization and solve.\n");CHKERRQ(ierr); 1821 #endif 1822 PetscFunctionReturn(0); 1823 } 1824 1825 #undef __FUNCT__ 1826 #define __FUNCT__ "MatCopy_SeqAIJ" 1827 int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str) 1828 { 1829 int ierr; 1830 1831 PetscFunctionBegin; 1832 /* If the two matrices have the same copy implementation, use fast copy. */ 1833 if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) { 1834 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1835 Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data; 1836 1837 if (a->i[A->m] != b->i[B->m]) { 1838 SETERRQ(1,"Number of nonzeros in two matrices are different"); 1839 } 1840 ierr = PetscMemcpy(b->a,a->a,(a->i[A->m])*sizeof(PetscScalar));CHKERRQ(ierr); 1841 } else { 1842 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1843 } 1844 PetscFunctionReturn(0); 1845 } 1846 1847 #undef __FUNCT__ 1848 #define __FUNCT__ "MatSetUpPreallocation_SeqAIJ" 1849 int MatSetUpPreallocation_SeqAIJ(Mat A) 1850 { 1851 int ierr; 1852 1853 PetscFunctionBegin; 1854 ierr = MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);CHKERRQ(ierr); 1855 PetscFunctionReturn(0); 1856 } 1857 1858 #undef __FUNCT__ 1859 #define __FUNCT__ "MatGetArray_SeqAIJ" 1860 int MatGetArray_SeqAIJ(Mat A,PetscScalar *array[]) 1861 { 1862 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 1863 PetscFunctionBegin; 1864 *array = a->a; 1865 PetscFunctionReturn(0); 1866 } 1867 1868 #undef __FUNCT__ 1869 #define __FUNCT__ "MatRestoreArray_SeqAIJ" 1870 int MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[]) 1871 { 1872 PetscFunctionBegin; 1873 PetscFunctionReturn(0); 1874 } 1875 1876 #undef __FUNCT__ 1877 #define __FUNCT__ "MatFDColoringApply_SeqAIJ" 1878 int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx) 1879 { 1880 int (*f)(void *,Vec,Vec,void*) = (int (*)(void *,Vec,Vec,void *))coloring->f; 1881 int k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2; 1882 PetscScalar dx,mone = -1.0,*y,*xx,*w3_array; 1883 PetscScalar *vscale_array; 1884 PetscReal epsilon = coloring->error_rel,umin = coloring->umin; 1885 Vec w1,w2,w3; 1886 void *fctx = coloring->fctx; 1887 PetscTruth flg; 1888 1889 PetscFunctionBegin; 1890 if (!coloring->w1) { 1891 ierr = VecDuplicate(x1,&coloring->w1);CHKERRQ(ierr); 1892 PetscLogObjectParent(coloring,coloring->w1); 1893 ierr = VecDuplicate(x1,&coloring->w2);CHKERRQ(ierr); 1894 PetscLogObjectParent(coloring,coloring->w2); 1895 ierr = VecDuplicate(x1,&coloring->w3);CHKERRQ(ierr); 1896 PetscLogObjectParent(coloring,coloring->w3); 1897 } 1898 w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3; 1899 1900 ierr = MatSetUnfactored(J);CHKERRQ(ierr); 1901 ierr = PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);CHKERRQ(ierr); 1902 if (flg) { 1903 PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()\n"); 1904 } else { 1905 ierr = MatZeroEntries(J);CHKERRQ(ierr); 1906 } 1907 1908 ierr = VecGetOwnershipRange(x1,&start,&end);CHKERRQ(ierr); 1909 ierr = VecGetSize(x1,&N);CHKERRQ(ierr); 1910 1911 /* 1912 This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets 1913 coloring->F for the coarser grids from the finest 1914 */ 1915 if (coloring->F) { 1916 ierr = VecGetLocalSize(coloring->F,&m1);CHKERRQ(ierr); 1917 ierr = VecGetLocalSize(w1,&m2);CHKERRQ(ierr); 1918 if (m1 != m2) { 1919 coloring->F = 0; 1920 } 1921 } 1922 1923 if (coloring->F) { 1924 w1 = coloring->F; 1925 coloring->F = 0; 1926 } else { 1927 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1928 ierr = (*f)(sctx,x1,w1,fctx);CHKERRQ(ierr); 1929 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 1930 } 1931 1932 /* 1933 Compute all the scale factors and share with other processors 1934 */ 1935 ierr = VecGetArrayFast(x1,&xx);CHKERRQ(ierr);xx = xx - start; 1936 ierr = VecGetArrayFast(coloring->vscale,&vscale_array);CHKERRQ(ierr);vscale_array = vscale_array - start; 1937 for (k=0; k<coloring->ncolors; k++) { 1938 /* 1939 Loop over each column associated with color adding the 1940 perturbation to the vector w3. 1941 */ 1942 for (l=0; l<coloring->ncolumns[k]; l++) { 1943 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 1944 dx = xx[col]; 1945 if (dx == 0.0) dx = 1.0; 1946 #if !defined(PETSC_USE_COMPLEX) 1947 if (dx < umin && dx >= 0.0) dx = umin; 1948 else if (dx < 0.0 && dx > -umin) dx = -umin; 1949 #else 1950 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 1951 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 1952 #endif 1953 dx *= epsilon; 1954 vscale_array[col] = 1.0/dx; 1955 } 1956 } 1957 vscale_array = vscale_array + start;ierr = VecRestoreArrayFast(coloring->vscale,&vscale_array);CHKERRQ(ierr); 1958 ierr = VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1959 ierr = VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);CHKERRQ(ierr); 1960 1961 /* ierr = VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD); 1962 ierr = VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/ 1963 1964 if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow; 1965 else vscaleforrow = coloring->columnsforrow; 1966 1967 ierr = VecGetArrayFast(coloring->vscale,&vscale_array);CHKERRQ(ierr); 1968 /* 1969 Loop over each color 1970 */ 1971 for (k=0; k<coloring->ncolors; k++) { 1972 coloring->currentcolor = k; 1973 ierr = VecCopy(x1,w3);CHKERRQ(ierr); 1974 ierr = VecGetArrayFast(w3,&w3_array);CHKERRQ(ierr);w3_array = w3_array - start; 1975 /* 1976 Loop over each column associated with color adding the 1977 perturbation to the vector w3. 1978 */ 1979 for (l=0; l<coloring->ncolumns[k]; l++) { 1980 col = coloring->columns[k][l]; /* column of the matrix we are probing for */ 1981 dx = xx[col]; 1982 if (dx == 0.0) dx = 1.0; 1983 #if !defined(PETSC_USE_COMPLEX) 1984 if (dx < umin && dx >= 0.0) dx = umin; 1985 else if (dx < 0.0 && dx > -umin) dx = -umin; 1986 #else 1987 if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin; 1988 else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin; 1989 #endif 1990 dx *= epsilon; 1991 if (!PetscAbsScalar(dx)) SETERRQ(1,"Computed 0 differencing parameter"); 1992 w3_array[col] += dx; 1993 } 1994 w3_array = w3_array + start; ierr = VecRestoreArrayFast(w3,&w3_array);CHKERRQ(ierr); 1995 1996 /* 1997 Evaluate function at x1 + dx (here dx is a vector of perturbations) 1998 */ 1999 2000 ierr = PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2001 ierr = (*f)(sctx,w3,w2,fctx);CHKERRQ(ierr); 2002 ierr = PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);CHKERRQ(ierr); 2003 ierr = VecAXPY(&mone,w1,w2);CHKERRQ(ierr); 2004 2005 /* 2006 Loop over rows of vector, putting results into Jacobian matrix 2007 */ 2008 ierr = VecGetArrayFast(w2,&y);CHKERRQ(ierr); 2009 for (l=0; l<coloring->nrows[k]; l++) { 2010 row = coloring->rows[k][l]; 2011 col = coloring->columnsforrow[k][l]; 2012 y[row] *= vscale_array[vscaleforrow[k][l]]; 2013 srow = row + start; 2014 ierr = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);CHKERRQ(ierr); 2015 } 2016 ierr = VecRestoreArrayFast(w2,&y);CHKERRQ(ierr); 2017 } 2018 coloring->currentcolor = k; 2019 ierr = VecRestoreArrayFast(coloring->vscale,&vscale_array);CHKERRQ(ierr); 2020 xx = xx + start; ierr = VecRestoreArrayFast(x1,&xx);CHKERRQ(ierr); 2021 ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2022 ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2023 PetscFunctionReturn(0); 2024 } 2025 2026 #include "petscblaslapack.h" 2027 #undef __FUNCT__ 2028 #define __FUNCT__ "MatAXPY_SeqAIJ" 2029 int MatAXPY_SeqAIJ(const PetscScalar a[],Mat X,Mat Y,MatStructure str) 2030 { 2031 int ierr,one=1,i; 2032 Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data; 2033 2034 PetscFunctionBegin; 2035 if (str == SAME_NONZERO_PATTERN) { 2036 BLaxpy_(&x->nz,(PetscScalar*)a,x->a,&one,y->a,&one); 2037 } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */ 2038 if (y->xtoy && y->XtoY != X) { 2039 ierr = PetscFree(y->xtoy);CHKERRQ(ierr); 2040 ierr = MatDestroy(y->XtoY);CHKERRQ(ierr); 2041 } 2042 if (!y->xtoy) { /* get xtoy */ 2043 ierr = MatAXPYGetxtoy_Private(X->m,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);CHKERRQ(ierr); 2044 y->XtoY = X; 2045 } 2046 for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += (*a)*(x->a[i]); 2047 PetscLogInfo(0,"MatAXPY_SeqAIJ: ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz); 2048 } else { 2049 ierr = MatAXPY_Basic(a,X,Y,str);CHKERRQ(ierr); 2050 } 2051 PetscFunctionReturn(0); 2052 } 2053 2054 /* -------------------------------------------------------------------*/ 2055 static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ, 2056 MatGetRow_SeqAIJ, 2057 MatRestoreRow_SeqAIJ, 2058 MatMult_SeqAIJ, 2059 /* 4*/ MatMultAdd_SeqAIJ, 2060 MatMultTranspose_SeqAIJ, 2061 MatMultTransposeAdd_SeqAIJ, 2062 MatSolve_SeqAIJ, 2063 MatSolveAdd_SeqAIJ, 2064 MatSolveTranspose_SeqAIJ, 2065 /*10*/ MatSolveTransposeAdd_SeqAIJ, 2066 MatLUFactor_SeqAIJ, 2067 0, 2068 MatRelax_SeqAIJ, 2069 MatTranspose_SeqAIJ, 2070 /*15*/ MatGetInfo_SeqAIJ, 2071 MatEqual_SeqAIJ, 2072 MatGetDiagonal_SeqAIJ, 2073 MatDiagonalScale_SeqAIJ, 2074 MatNorm_SeqAIJ, 2075 /*20*/ 0, 2076 MatAssemblyEnd_SeqAIJ, 2077 MatCompress_SeqAIJ, 2078 MatSetOption_SeqAIJ, 2079 MatZeroEntries_SeqAIJ, 2080 /*25*/ MatZeroRows_SeqAIJ, 2081 MatLUFactorSymbolic_SeqAIJ, 2082 MatLUFactorNumeric_SeqAIJ, 2083 MatCholeskyFactorSymbolic_SeqAIJ, 2084 MatCholeskyFactorNumeric_SeqAIJ, 2085 /*30*/ MatSetUpPreallocation_SeqAIJ, 2086 MatILUFactorSymbolic_SeqAIJ, 2087 MatICCFactorSymbolic_SeqAIJ, 2088 MatGetArray_SeqAIJ, 2089 MatRestoreArray_SeqAIJ, 2090 /*35*/ MatDuplicate_SeqAIJ, 2091 0, 2092 0, 2093 MatILUFactor_SeqAIJ, 2094 0, 2095 /*40*/ MatAXPY_SeqAIJ, 2096 MatGetSubMatrices_SeqAIJ, 2097 MatIncreaseOverlap_SeqAIJ, 2098 MatGetValues_SeqAIJ, 2099 MatCopy_SeqAIJ, 2100 /*45*/ MatPrintHelp_SeqAIJ, 2101 MatScale_SeqAIJ, 2102 0, 2103 0, 2104 MatILUDTFactor_SeqAIJ, 2105 /*50*/ MatGetBlockSize_SeqAIJ, 2106 MatGetRowIJ_SeqAIJ, 2107 MatRestoreRowIJ_SeqAIJ, 2108 MatGetColumnIJ_SeqAIJ, 2109 MatRestoreColumnIJ_SeqAIJ, 2110 /*55*/ MatFDColoringCreate_SeqAIJ, 2111 0, 2112 0, 2113 MatPermute_SeqAIJ, 2114 0, 2115 /*60*/ 0, 2116 MatDestroy_SeqAIJ, 2117 MatView_SeqAIJ, 2118 MatGetPetscMaps_Petsc, 2119 0, 2120 /*65*/ 0, 2121 0, 2122 0, 2123 0, 2124 0, 2125 /*70*/ 0, 2126 0, 2127 MatSetColoring_SeqAIJ, 2128 MatSetValuesAdic_SeqAIJ, 2129 MatSetValuesAdifor_SeqAIJ, 2130 /*75*/ MatFDColoringApply_SeqAIJ, 2131 0, 2132 0, 2133 0, 2134 0, 2135 /*80*/ 0, 2136 0, 2137 0, 2138 0, 2139 /*85*/ MatLoad_SeqAIJ}; 2140 2141 EXTERN_C_BEGIN 2142 #undef __FUNCT__ 2143 #define __FUNCT__ "MatSeqAIJSetColumnIndices_SeqAIJ" 2144 2145 int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices) 2146 { 2147 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2148 int i,nz,n; 2149 2150 PetscFunctionBegin; 2151 2152 nz = aij->maxnz; 2153 n = mat->n; 2154 for (i=0; i<nz; i++) { 2155 aij->j[i] = indices[i]; 2156 } 2157 aij->nz = nz; 2158 for (i=0; i<n; i++) { 2159 aij->ilen[i] = aij->imax[i]; 2160 } 2161 2162 PetscFunctionReturn(0); 2163 } 2164 EXTERN_C_END 2165 2166 #undef __FUNCT__ 2167 #define __FUNCT__ "MatSeqAIJSetColumnIndices" 2168 /*@ 2169 MatSeqAIJSetColumnIndices - Set the column indices for all the rows 2170 in the matrix. 2171 2172 Input Parameters: 2173 + mat - the SeqAIJ matrix 2174 - indices - the column indices 2175 2176 Level: advanced 2177 2178 Notes: 2179 This can be called if you have precomputed the nonzero structure of the 2180 matrix and want to provide it to the matrix object to improve the performance 2181 of the MatSetValues() operation. 2182 2183 You MUST have set the correct numbers of nonzeros per row in the call to 2184 MatCreateSeqAIJ(). 2185 2186 MUST be called before any calls to MatSetValues(); 2187 2188 The indices should start with zero, not one. 2189 2190 @*/ 2191 int MatSeqAIJSetColumnIndices(Mat mat,int *indices) 2192 { 2193 int ierr,(*f)(Mat,int *); 2194 2195 PetscFunctionBegin; 2196 PetscValidHeaderSpecific(mat,MAT_COOKIE); 2197 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);CHKERRQ(ierr); 2198 if (f) { 2199 ierr = (*f)(mat,indices);CHKERRQ(ierr); 2200 } else { 2201 SETERRQ(1,"Wrong type of matrix to set column indices"); 2202 } 2203 PetscFunctionReturn(0); 2204 } 2205 2206 /* ----------------------------------------------------------------------------------------*/ 2207 2208 EXTERN_C_BEGIN 2209 #undef __FUNCT__ 2210 #define __FUNCT__ "MatStoreValues_SeqAIJ" 2211 int MatStoreValues_SeqAIJ(Mat mat) 2212 { 2213 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2214 size_t nz = aij->i[mat->m],ierr; 2215 2216 PetscFunctionBegin; 2217 if (aij->nonew != 1) { 2218 SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2219 } 2220 2221 /* allocate space for values if not already there */ 2222 if (!aij->saved_values) { 2223 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);CHKERRQ(ierr); 2224 } 2225 2226 /* copy values over */ 2227 ierr = PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2228 PetscFunctionReturn(0); 2229 } 2230 EXTERN_C_END 2231 2232 #undef __FUNCT__ 2233 #define __FUNCT__ "MatStoreValues" 2234 /*@ 2235 MatStoreValues - Stashes a copy of the matrix values; this allows, for 2236 example, reuse of the linear part of a Jacobian, while recomputing the 2237 nonlinear portion. 2238 2239 Collect on Mat 2240 2241 Input Parameters: 2242 . mat - the matrix (currently on AIJ matrices support this option) 2243 2244 Level: advanced 2245 2246 Common Usage, with SNESSolve(): 2247 $ Create Jacobian matrix 2248 $ Set linear terms into matrix 2249 $ Apply boundary conditions to matrix, at this time matrix must have 2250 $ final nonzero structure (i.e. setting the nonlinear terms and applying 2251 $ boundary conditions again will not change the nonzero structure 2252 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2253 $ ierr = MatStoreValues(mat); 2254 $ Call SNESSetJacobian() with matrix 2255 $ In your Jacobian routine 2256 $ ierr = MatRetrieveValues(mat); 2257 $ Set nonlinear terms in matrix 2258 2259 Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself: 2260 $ // build linear portion of Jacobian 2261 $ ierr = MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); 2262 $ ierr = MatStoreValues(mat); 2263 $ loop over nonlinear iterations 2264 $ ierr = MatRetrieveValues(mat); 2265 $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian 2266 $ // call MatAssemblyBegin/End() on matrix 2267 $ Solve linear system with Jacobian 2268 $ endloop 2269 2270 Notes: 2271 Matrix must already be assemblied before calling this routine 2272 Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 2273 calling this routine. 2274 2275 .seealso: MatRetrieveValues() 2276 2277 @*/ 2278 int MatStoreValues(Mat mat) 2279 { 2280 int ierr,(*f)(Mat); 2281 2282 PetscFunctionBegin; 2283 PetscValidHeaderSpecific(mat,MAT_COOKIE); 2284 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2285 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2286 2287 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2288 if (f) { 2289 ierr = (*f)(mat);CHKERRQ(ierr); 2290 } else { 2291 SETERRQ(1,"Wrong type of matrix to store values"); 2292 } 2293 PetscFunctionReturn(0); 2294 } 2295 2296 EXTERN_C_BEGIN 2297 #undef __FUNCT__ 2298 #define __FUNCT__ "MatRetrieveValues_SeqAIJ" 2299 int MatRetrieveValues_SeqAIJ(Mat mat) 2300 { 2301 Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data; 2302 int nz = aij->i[mat->m],ierr; 2303 2304 PetscFunctionBegin; 2305 if (aij->nonew != 1) { 2306 SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first"); 2307 } 2308 if (!aij->saved_values) { 2309 SETERRQ(1,"Must call MatStoreValues(A);first"); 2310 } 2311 2312 /* copy values over */ 2313 ierr = PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));CHKERRQ(ierr); 2314 PetscFunctionReturn(0); 2315 } 2316 EXTERN_C_END 2317 2318 #undef __FUNCT__ 2319 #define __FUNCT__ "MatRetrieveValues" 2320 /*@ 2321 MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 2322 example, reuse of the linear part of a Jacobian, while recomputing the 2323 nonlinear portion. 2324 2325 Collect on Mat 2326 2327 Input Parameters: 2328 . mat - the matrix (currently on AIJ matrices support this option) 2329 2330 Level: advanced 2331 2332 .seealso: MatStoreValues() 2333 2334 @*/ 2335 int MatRetrieveValues(Mat mat) 2336 { 2337 int ierr,(*f)(Mat); 2338 2339 PetscFunctionBegin; 2340 PetscValidHeaderSpecific(mat,MAT_COOKIE); 2341 if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix"); 2342 if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix"); 2343 2344 ierr = PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);CHKERRQ(ierr); 2345 if (f) { 2346 ierr = (*f)(mat);CHKERRQ(ierr); 2347 } else { 2348 SETERRQ(1,"Wrong type of matrix to retrieve values"); 2349 } 2350 PetscFunctionReturn(0); 2351 } 2352 2353 /* 2354 This allows SeqAIJ matrices to be passed to the matlab engine 2355 */ 2356 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 2357 #include "engine.h" /* Matlab include file */ 2358 #include "mex.h" /* Matlab include file */ 2359 EXTERN_C_BEGIN 2360 #undef __FUNCT__ 2361 #define __FUNCT__ "MatMatlabEnginePut_SeqAIJ" 2362 int MatMatlabEnginePut_SeqAIJ(PetscObject obj,void *mengine) 2363 { 2364 int ierr; 2365 Mat B = (Mat)obj; 2366 mxArray *mat; 2367 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)B->data; 2368 2369 PetscFunctionBegin; 2370 mat = mxCreateSparse(B->n,B->m,aij->nz,mxREAL); 2371 ierr = PetscMemcpy(mxGetPr(mat),aij->a,aij->nz*sizeof(PetscScalar));CHKERRQ(ierr); 2372 /* Matlab stores by column, not row so we pass in the transpose of the matrix */ 2373 ierr = PetscMemcpy(mxGetIr(mat),aij->j,aij->nz*sizeof(int));CHKERRQ(ierr); 2374 ierr = PetscMemcpy(mxGetJc(mat),aij->i,(B->m+1)*sizeof(int));CHKERRQ(ierr); 2375 2376 /* Matlab indices start at 0 for sparse (what a surprise) */ 2377 2378 ierr = PetscObjectName(obj);CHKERRQ(ierr); 2379 engPutVariable((Engine *)mengine,obj->name,mat); 2380 PetscFunctionReturn(0); 2381 } 2382 EXTERN_C_END 2383 2384 EXTERN_C_BEGIN 2385 #undef __FUNCT__ 2386 #define __FUNCT__ "MatMatlabEngineGet_SeqAIJ" 2387 int MatMatlabEngineGet_SeqAIJ(PetscObject obj,void *mengine) 2388 { 2389 int ierr,ii; 2390 Mat mat = (Mat)obj; 2391 Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data; 2392 mxArray *mmat; 2393 2394 PetscFunctionBegin; 2395 ierr = PetscFree(aij->a);CHKERRQ(ierr); 2396 2397 mmat = engGetVariable((Engine *)mengine,obj->name); 2398 2399 aij->nz = (mxGetJc(mmat))[mat->m]; 2400 ierr = PetscMalloc(((size_t) aij->nz)*(sizeof(int)+sizeof(PetscScalar))+(mat->m+1)*sizeof(int),&aij->a);CHKERRQ(ierr); 2401 aij->j = (int*)(aij->a + aij->nz); 2402 aij->i = aij->j + aij->nz; 2403 aij->singlemalloc = PETSC_TRUE; 2404 aij->freedata = PETSC_TRUE; 2405 2406 ierr = PetscMemcpy(aij->a,mxGetPr(mmat),aij->nz*sizeof(PetscScalar));CHKERRQ(ierr); 2407 /* Matlab stores by column, not row so we pass in the transpose of the matrix */ 2408 ierr = PetscMemcpy(aij->j,mxGetIr(mmat),aij->nz*sizeof(int));CHKERRQ(ierr); 2409 ierr = PetscMemcpy(aij->i,mxGetJc(mmat),(mat->m+1)*sizeof(int));CHKERRQ(ierr); 2410 2411 for (ii=0; ii<mat->m; ii++) { 2412 aij->ilen[ii] = aij->imax[ii] = aij->i[ii+1] - aij->i[ii]; 2413 } 2414 2415 ierr = MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2416 ierr = MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2417 2418 PetscFunctionReturn(0); 2419 } 2420 EXTERN_C_END 2421 #endif 2422 2423 /* --------------------------------------------------------------------------------*/ 2424 #undef __FUNCT__ 2425 #define __FUNCT__ "MatCreateSeqAIJ" 2426 /*@C 2427 MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format 2428 (the default parallel PETSc format). For good matrix assembly performance 2429 the user should preallocate the matrix storage by setting the parameter nz 2430 (or the array nnz). By setting these parameters accurately, performance 2431 during matrix assembly can be increased by more than a factor of 50. 2432 2433 Collective on MPI_Comm 2434 2435 Input Parameters: 2436 + comm - MPI communicator, set to PETSC_COMM_SELF 2437 . m - number of rows 2438 . n - number of columns 2439 . nz - number of nonzeros per row (same for all rows) 2440 - nnz - array containing the number of nonzeros in the various rows 2441 (possibly different for each row) or PETSC_NULL 2442 2443 Output Parameter: 2444 . A - the matrix 2445 2446 Notes: 2447 The AIJ format (also called the Yale sparse matrix format or 2448 compressed row storage), is fully compatible with standard Fortran 77 2449 storage. That is, the stored row and column indices can begin at 2450 either one (as in Fortran) or zero. See the users' manual for details. 2451 2452 Specify the preallocated storage with either nz or nnz (not both). 2453 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2454 allocation. For large problems you MUST preallocate memory or you 2455 will get TERRIBLE performance, see the users' manual chapter on matrices. 2456 2457 By default, this format uses inodes (identical nodes) when possible, to 2458 improve numerical efficiency of matrix-vector products and solves. We 2459 search for consecutive rows with the same nonzero structure, thereby 2460 reusing matrix information to achieve increased efficiency. 2461 2462 Options Database Keys: 2463 + -mat_aij_no_inode - Do not use inodes 2464 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2465 - -mat_aij_oneindex - Internally use indexing starting at 1 2466 rather than 0. Note that when calling MatSetValues(), 2467 the user still MUST index entries starting at 0! 2468 2469 Level: intermediate 2470 2471 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2472 2473 @*/ 2474 int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,const int nnz[],Mat *A) 2475 { 2476 int ierr; 2477 2478 PetscFunctionBegin; 2479 ierr = MatCreate(comm,m,n,m,n,A);CHKERRQ(ierr); 2480 ierr = MatSetType(*A,MATSEQAIJ);CHKERRQ(ierr); 2481 ierr = MatSeqAIJSetPreallocation(*A,nz,nnz);CHKERRQ(ierr); 2482 PetscFunctionReturn(0); 2483 } 2484 2485 #define SKIP_ALLOCATION -4 2486 2487 #undef __FUNCT__ 2488 #define __FUNCT__ "MatSeqAIJSetPreallocation" 2489 /*@C 2490 MatSeqAIJSetPreallocation - For good matrix assembly performance 2491 the user should preallocate the matrix storage by setting the parameter nz 2492 (or the array nnz). By setting these parameters accurately, performance 2493 during matrix assembly can be increased by more than a factor of 50. 2494 2495 Collective on MPI_Comm 2496 2497 Input Parameters: 2498 + comm - MPI communicator, set to PETSC_COMM_SELF 2499 . m - number of rows 2500 . n - number of columns 2501 . nz - number of nonzeros per row (same for all rows) 2502 - nnz - array containing the number of nonzeros in the various rows 2503 (possibly different for each row) or PETSC_NULL 2504 2505 Output Parameter: 2506 . A - the matrix 2507 2508 Notes: 2509 The AIJ format (also called the Yale sparse matrix format or 2510 compressed row storage), is fully compatible with standard Fortran 77 2511 storage. That is, the stored row and column indices can begin at 2512 either one (as in Fortran) or zero. See the users' manual for details. 2513 2514 Specify the preallocated storage with either nz or nnz (not both). 2515 Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 2516 allocation. For large problems you MUST preallocate memory or you 2517 will get TERRIBLE performance, see the users' manual chapter on matrices. 2518 2519 By default, this format uses inodes (identical nodes) when possible, to 2520 improve numerical efficiency of matrix-vector products and solves. We 2521 search for consecutive rows with the same nonzero structure, thereby 2522 reusing matrix information to achieve increased efficiency. 2523 2524 Options Database Keys: 2525 + -mat_aij_no_inode - Do not use inodes 2526 . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5) 2527 - -mat_aij_oneindex - Internally use indexing starting at 1 2528 rather than 0. Note that when calling MatSetValues(), 2529 the user still MUST index entries starting at 0! 2530 2531 Level: intermediate 2532 2533 .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays() 2534 2535 @*/ 2536 int MatSeqAIJSetPreallocation(Mat B,int nz,const int nnz[]) 2537 { 2538 int ierr,(*f)(Mat,int,const int[]); 2539 2540 PetscFunctionBegin; 2541 ierr = PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);CHKERRQ(ierr); 2542 if (f) { 2543 ierr = (*f)(B,nz,nnz);CHKERRQ(ierr); 2544 } 2545 PetscFunctionReturn(0); 2546 } 2547 2548 EXTERN_C_BEGIN 2549 #undef __FUNCT__ 2550 #define __FUNCT__ "MatSeqAIJSetPreallocation_SeqAIJ" 2551 int MatSeqAIJSetPreallocation_SeqAIJ(Mat B,int nz,int *nnz) 2552 { 2553 Mat_SeqAIJ *b; 2554 size_t len = 0; 2555 PetscTruth skipallocation = PETSC_FALSE; 2556 int i,ierr; 2557 2558 PetscFunctionBegin; 2559 2560 if (nz == SKIP_ALLOCATION) { 2561 skipallocation = PETSC_TRUE; 2562 nz = 0; 2563 } 2564 2565 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5; 2566 if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz); 2567 if (nnz) { 2568 for (i=0; i<B->m; i++) { 2569 if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]); 2570 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); 2571 } 2572 } 2573 2574 B->preallocated = PETSC_TRUE; 2575 b = (Mat_SeqAIJ*)B->data; 2576 2577 ierr = PetscMalloc((B->m+1)*sizeof(int),&b->imax);CHKERRQ(ierr); 2578 if (!nnz) { 2579 if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10; 2580 else if (nz <= 0) nz = 1; 2581 for (i=0; i<B->m; i++) b->imax[i] = nz; 2582 nz = nz*B->m; 2583 } else { 2584 nz = 0; 2585 for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];} 2586 } 2587 2588 if (!skipallocation) { 2589 /* allocate the matrix space */ 2590 len = ((size_t) nz)*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int); 2591 ierr = PetscMalloc(len,&b->a);CHKERRQ(ierr); 2592 b->j = (int*)(b->a + nz); 2593 ierr = PetscMemzero(b->j,nz*sizeof(int));CHKERRQ(ierr); 2594 b->i = b->j + nz; 2595 b->i[0] = 0; 2596 for (i=1; i<B->m+1; i++) { 2597 b->i[i] = b->i[i-1] + b->imax[i-1]; 2598 } 2599 b->singlemalloc = PETSC_TRUE; 2600 b->freedata = PETSC_TRUE; 2601 } else { 2602 b->freedata = PETSC_FALSE; 2603 } 2604 2605 /* b->ilen will count nonzeros in each row so far. */ 2606 ierr = PetscMalloc((B->m+1)*sizeof(int),&b->ilen);CHKERRQ(ierr); 2607 PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2608 for (i=0; i<B->m; i++) { b->ilen[i] = 0;} 2609 2610 b->nz = 0; 2611 b->maxnz = nz; 2612 B->info.nz_unneeded = (double)b->maxnz; 2613 PetscFunctionReturn(0); 2614 } 2615 EXTERN_C_END 2616 2617 /*MC 2618 MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 2619 based on compressed sparse row format. 2620 2621 Options Database Keys: 2622 . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions() 2623 2624 Level: beginner 2625 2626 .seealso: MatCreateSeqAIJ 2627 M*/ 2628 2629 EXTERN_C_BEGIN 2630 #undef __FUNCT__ 2631 #define __FUNCT__ "MatCreate_SeqAIJ" 2632 int MatCreate_SeqAIJ(Mat B) 2633 { 2634 Mat_SeqAIJ *b; 2635 int ierr,size; 2636 PetscTruth flg; 2637 2638 PetscFunctionBegin; 2639 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 2640 if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1"); 2641 2642 B->m = B->M = PetscMax(B->m,B->M); 2643 B->n = B->N = PetscMax(B->n,B->N); 2644 2645 ierr = PetscNew(Mat_SeqAIJ,&b);CHKERRQ(ierr); 2646 B->data = (void*)b; 2647 ierr = PetscMemzero(b,sizeof(Mat_SeqAIJ));CHKERRQ(ierr); 2648 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 2649 B->factor = 0; 2650 B->lupivotthreshold = 1.0; 2651 B->mapping = 0; 2652 ierr = PetscOptionsGetReal(B->prefix,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);CHKERRQ(ierr); 2653 ierr = PetscOptionsHasName(B->prefix,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);CHKERRQ(ierr); 2654 b->row = 0; 2655 b->col = 0; 2656 b->icol = 0; 2657 b->reallocs = 0; 2658 2659 ierr = PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);CHKERRQ(ierr); 2660 ierr = PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);CHKERRQ(ierr); 2661 2662 b->sorted = PETSC_FALSE; 2663 b->ignorezeroentries = PETSC_FALSE; 2664 b->roworiented = PETSC_TRUE; 2665 b->nonew = 0; 2666 b->diag = 0; 2667 b->solve_work = 0; 2668 B->spptr = 0; 2669 b->inode.use = PETSC_TRUE; 2670 b->inode.node_count = 0; 2671 b->inode.size = 0; 2672 b->inode.limit = 5; 2673 b->inode.max_limit = 5; 2674 b->saved_values = 0; 2675 b->idiag = 0; 2676 b->ssor = 0; 2677 b->keepzeroedrows = PETSC_FALSE; 2678 b->xtoy = 0; 2679 b->XtoY = 0; 2680 2681 ierr = PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);CHKERRQ(ierr); 2682 2683 ierr = PetscOptionsHasName(B->prefix,"-mat_aij_matlab",&flg);CHKERRQ(ierr); 2684 if (flg) {ierr = MatUseMatlab_SeqAIJ(B);CHKERRQ(ierr);} 2685 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C", 2686 "MatSeqAIJSetColumnIndices_SeqAIJ", 2687 MatSeqAIJSetColumnIndices_SeqAIJ);CHKERRQ(ierr); 2688 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C", 2689 "MatStoreValues_SeqAIJ", 2690 MatStoreValues_SeqAIJ);CHKERRQ(ierr); 2691 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C", 2692 "MatRetrieveValues_SeqAIJ", 2693 MatRetrieveValues_SeqAIJ);CHKERRQ(ierr); 2694 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C", 2695 "MatConvert_SeqAIJ_SeqSBAIJ", 2696 MatConvert_SeqAIJ_SeqSBAIJ);CHKERRQ(ierr); 2697 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C", 2698 "MatConvert_SeqAIJ_SeqBAIJ", 2699 MatConvert_SeqAIJ_SeqBAIJ);CHKERRQ(ierr); 2700 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C", 2701 "MatIsTranspose_SeqAIJ", 2702 MatIsTranspose_SeqAIJ);CHKERRQ(ierr); 2703 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C", 2704 "MatSeqAIJSetPreallocation_SeqAIJ", 2705 MatSeqAIJSetPreallocation_SeqAIJ);CHKERRQ(ierr); 2706 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C", 2707 "MatReorderForNonzeroDiagonal_SeqAIJ", 2708 MatReorderForNonzeroDiagonal_SeqAIJ);CHKERRQ(ierr); 2709 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatAdjustForInodes_C", 2710 "MatAdjustForInodes_SeqAIJ", 2711 MatAdjustForInodes_SeqAIJ);CHKERRQ(ierr); 2712 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJGetInodeSizes_C", 2713 "MatSeqAIJGetInodeSizes_SeqAIJ", 2714 MatSeqAIJGetInodeSizes_SeqAIJ);CHKERRQ(ierr); 2715 #if defined(PETSC_HAVE_MATLAB) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 2716 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatMatlabEnginePut_SeqAIJ",MatMatlabEnginePut_SeqAIJ);CHKERRQ(ierr); 2717 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatMatlabEngineGet_SeqAIJ",MatMatlabEngineGet_SeqAIJ);CHKERRQ(ierr); 2718 #endif 2719 ierr = RegisterApplyPtAPRoutines_Private(B);CHKERRQ(ierr); 2720 PetscFunctionReturn(0); 2721 } 2722 EXTERN_C_END 2723 2724 #undef __FUNCT__ 2725 #define __FUNCT__ "MatDuplicate_SeqAIJ" 2726 int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B) 2727 { 2728 Mat C; 2729 Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data; 2730 int i,m = A->m,ierr; 2731 size_t len; 2732 2733 PetscFunctionBegin; 2734 *B = 0; 2735 ierr = MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);CHKERRQ(ierr); 2736 ierr = MatSetType(C,MATSEQAIJ);CHKERRQ(ierr); 2737 c = (Mat_SeqAIJ*)C->data; 2738 2739 C->factor = A->factor; 2740 c->row = 0; 2741 c->col = 0; 2742 c->icol = 0; 2743 c->keepzeroedrows = a->keepzeroedrows; 2744 C->assembled = PETSC_TRUE; 2745 2746 C->M = A->m; 2747 C->N = A->n; 2748 2749 ierr = PetscMalloc((m+1)*sizeof(int),&c->imax);CHKERRQ(ierr); 2750 ierr = PetscMalloc((m+1)*sizeof(int),&c->ilen);CHKERRQ(ierr); 2751 for (i=0; i<m; i++) { 2752 c->imax[i] = a->imax[i]; 2753 c->ilen[i] = a->ilen[i]; 2754 } 2755 2756 /* allocate the matrix space */ 2757 c->singlemalloc = PETSC_TRUE; 2758 len = ((size_t) (m+1))*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int)); 2759 ierr = PetscMalloc(len,&c->a);CHKERRQ(ierr); 2760 c->j = (int*)(c->a + a->i[m] ); 2761 c->i = c->j + a->i[m]; 2762 ierr = PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));CHKERRQ(ierr); 2763 if (m > 0) { 2764 ierr = PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(int));CHKERRQ(ierr); 2765 if (cpvalues == MAT_COPY_VALUES) { 2766 ierr = PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2767 } else { 2768 ierr = PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));CHKERRQ(ierr); 2769 } 2770 } 2771 2772 PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ)); 2773 c->sorted = a->sorted; 2774 c->roworiented = a->roworiented; 2775 c->nonew = a->nonew; 2776 c->ilu_preserve_row_sums = a->ilu_preserve_row_sums; 2777 c->saved_values = 0; 2778 c->idiag = 0; 2779 c->ssor = 0; 2780 c->ignorezeroentries = a->ignorezeroentries; 2781 c->freedata = PETSC_TRUE; 2782 2783 if (a->diag) { 2784 ierr = PetscMalloc((m+1)*sizeof(int),&c->diag);CHKERRQ(ierr); 2785 PetscLogObjectMemory(C,(m+1)*sizeof(int)); 2786 for (i=0; i<m; i++) { 2787 c->diag[i] = a->diag[i]; 2788 } 2789 } else c->diag = 0; 2790 c->inode.use = a->inode.use; 2791 c->inode.limit = a->inode.limit; 2792 c->inode.max_limit = a->inode.max_limit; 2793 if (a->inode.size){ 2794 ierr = PetscMalloc((m+1)*sizeof(int),&c->inode.size);CHKERRQ(ierr); 2795 c->inode.node_count = a->inode.node_count; 2796 ierr = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));CHKERRQ(ierr); 2797 } else { 2798 c->inode.size = 0; 2799 c->inode.node_count = 0; 2800 } 2801 c->nz = a->nz; 2802 c->maxnz = a->maxnz; 2803 c->solve_work = 0; 2804 C->spptr = 0; /* Dangerous -I'm throwing away a->spptr */ 2805 C->preallocated = PETSC_TRUE; 2806 2807 *B = C; 2808 ierr = PetscFListDuplicate(A->qlist,&C->qlist);CHKERRQ(ierr); 2809 PetscFunctionReturn(0); 2810 } 2811 2812 #undef __FUNCT__ 2813 #define __FUNCT__ "MatLoad_SeqAIJ" 2814 int MatLoad_SeqAIJ(PetscViewer viewer,const MatType type,Mat *A) 2815 { 2816 Mat_SeqAIJ *a; 2817 Mat B; 2818 int i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N; 2819 MPI_Comm comm; 2820 2821 PetscFunctionBegin; 2822 ierr = PetscObjectGetComm((PetscObject)viewer,&comm);CHKERRQ(ierr); 2823 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 2824 if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor"); 2825 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 2826 ierr = PetscBinaryRead(fd,header,4,PETSC_INT);CHKERRQ(ierr); 2827 if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file"); 2828 M = header[1]; N = header[2]; nz = header[3]; 2829 2830 if (nz < 0) { 2831 SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ"); 2832 } 2833 2834 /* read in row lengths */ 2835 ierr = PetscMalloc(M*sizeof(int),&rowlengths);CHKERRQ(ierr); 2836 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 2837 2838 /* create our matrix */ 2839 ierr = MatCreate(comm,PETSC_DECIDE,PETSC_DECIDE,M,N,&B);CHKERRQ(ierr); 2840 ierr = MatSetType(B,type);CHKERRQ(ierr); 2841 ierr = MatSeqAIJSetPreallocation(B,0,rowlengths);CHKERRQ(ierr); 2842 a = (Mat_SeqAIJ*)B->data; 2843 2844 /* read in column indices and adjust for Fortran indexing*/ 2845 ierr = PetscBinaryRead(fd,a->j,nz,PETSC_INT);CHKERRQ(ierr); 2846 2847 /* read in nonzero values */ 2848 ierr = PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);CHKERRQ(ierr); 2849 2850 /* set matrix "i" values */ 2851 a->i[0] = 0; 2852 for (i=1; i<= M; i++) { 2853 a->i[i] = a->i[i-1] + rowlengths[i-1]; 2854 a->ilen[i-1] = rowlengths[i-1]; 2855 } 2856 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 2857 2858 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2859 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2860 *A = B; 2861 PetscFunctionReturn(0); 2862 } 2863 2864 #undef __FUNCT__ 2865 #define __FUNCT__ "MatEqual_SeqAIJ" 2866 int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg) 2867 { 2868 Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data; 2869 int ierr; 2870 2871 PetscFunctionBegin; 2872 /* If the matrix dimensions are not equal,or no of nonzeros */ 2873 if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)) { 2874 *flg = PETSC_FALSE; 2875 PetscFunctionReturn(0); 2876 } 2877 2878 /* if the a->i are the same */ 2879 ierr = PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);CHKERRQ(ierr); 2880 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2881 2882 /* if a->j are the same */ 2883 ierr = PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);CHKERRQ(ierr); 2884 if (*flg == PETSC_FALSE) PetscFunctionReturn(0); 2885 2886 /* if a->a are the same */ 2887 ierr = PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);CHKERRQ(ierr); 2888 2889 PetscFunctionReturn(0); 2890 2891 } 2892 2893 #undef __FUNCT__ 2894 #define __FUNCT__ "MatCreateSeqAIJWithArrays" 2895 /*@C 2896 MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format) 2897 provided by the user. 2898 2899 Coolective on MPI_Comm 2900 2901 Input Parameters: 2902 + comm - must be an MPI communicator of size 1 2903 . m - number of rows 2904 . n - number of columns 2905 . i - row indices 2906 . j - column indices 2907 - a - matrix values 2908 2909 Output Parameter: 2910 . mat - the matrix 2911 2912 Level: intermediate 2913 2914 Notes: 2915 The i, j, and a arrays are not copied by this routine, the user must free these arrays 2916 once the matrix is destroyed 2917 2918 You cannot set new nonzero locations into this matrix, that will generate an error. 2919 2920 The i and j indices are 0 based 2921 2922 .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ() 2923 2924 @*/ 2925 int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat) 2926 { 2927 int ierr,ii; 2928 Mat_SeqAIJ *aij; 2929 2930 PetscFunctionBegin; 2931 ierr = MatCreateSeqAIJ(comm,m,n,SKIP_ALLOCATION,0,mat);CHKERRQ(ierr); 2932 aij = (Mat_SeqAIJ*)(*mat)->data; 2933 2934 if (i[0] != 0) { 2935 SETERRQ(1,"i (row indices) must start with 0"); 2936 } 2937 aij->i = i; 2938 aij->j = j; 2939 aij->a = a; 2940 aij->singlemalloc = PETSC_FALSE; 2941 aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/ 2942 aij->freedata = PETSC_FALSE; 2943 2944 for (ii=0; ii<m; ii++) { 2945 aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii]; 2946 #if defined(PETSC_USE_BOPT_g) 2947 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]); 2948 #endif 2949 } 2950 #if defined(PETSC_USE_BOPT_g) 2951 for (ii=0; ii<aij->i[m]; ii++) { 2952 if (j[ii] < 0) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]); 2953 if (j[ii] > n - 1) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]); 2954 } 2955 #endif 2956 2957 ierr = MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2958 ierr = MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 2959 PetscFunctionReturn(0); 2960 } 2961 2962 #undef __FUNCT__ 2963 #define __FUNCT__ "MatSetColoring_SeqAIJ" 2964 int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring) 2965 { 2966 int ierr; 2967 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 2968 2969 PetscFunctionBegin; 2970 if (coloring->ctype == IS_COLORING_LOCAL) { 2971 ierr = ISColoringReference(coloring);CHKERRQ(ierr); 2972 a->coloring = coloring; 2973 } else if (coloring->ctype == IS_COLORING_GHOSTED) { 2974 int i,*larray; 2975 ISColoring ocoloring; 2976 ISColoringValue *colors; 2977 2978 /* set coloring for diagonal portion */ 2979 ierr = PetscMalloc((A->n+1)*sizeof(int),&larray);CHKERRQ(ierr); 2980 for (i=0; i<A->n; i++) { 2981 larray[i] = i; 2982 } 2983 ierr = ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);CHKERRQ(ierr); 2984 ierr = PetscMalloc((A->n+1)*sizeof(ISColoringValue),&colors);CHKERRQ(ierr); 2985 for (i=0; i<A->n; i++) { 2986 colors[i] = coloring->colors[larray[i]]; 2987 } 2988 ierr = PetscFree(larray);CHKERRQ(ierr); 2989 ierr = ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);CHKERRQ(ierr); 2990 a->coloring = ocoloring; 2991 } 2992 PetscFunctionReturn(0); 2993 } 2994 2995 #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE) 2996 EXTERN_C_BEGIN 2997 #include "adic/ad_utils.h" 2998 EXTERN_C_END 2999 3000 #undef __FUNCT__ 3001 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 3002 int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 3003 { 3004 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3005 int m = A->m,*ii = a->i,*jj = a->j,nz,i,j,nlen; 3006 PetscScalar *v = a->a,*values = ((PetscScalar*)advalues)+1; 3007 ISColoringValue *color; 3008 3009 PetscFunctionBegin; 3010 if (!a->coloring) SETERRQ(1,"Coloring not set for matrix"); 3011 nlen = PetscADGetDerivTypeSize()/sizeof(PetscScalar); 3012 color = a->coloring->colors; 3013 /* loop over rows */ 3014 for (i=0; i<m; i++) { 3015 nz = ii[i+1] - ii[i]; 3016 /* loop over columns putting computed value into matrix */ 3017 for (j=0; j<nz; j++) { 3018 *v++ = values[color[*jj++]]; 3019 } 3020 values += nlen; /* jump to next row of derivatives */ 3021 } 3022 PetscFunctionReturn(0); 3023 } 3024 3025 #else 3026 3027 #undef __FUNCT__ 3028 #define __FUNCT__ "MatSetValuesAdic_SeqAIJ" 3029 int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues) 3030 { 3031 PetscFunctionBegin; 3032 SETERRQ(1,"PETSc installed without ADIC"); 3033 } 3034 3035 #endif 3036 3037 #undef __FUNCT__ 3038 #define __FUNCT__ "MatSetValuesAdifor_SeqAIJ" 3039 int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues) 3040 { 3041 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 3042 int m = A->m,*ii = a->i,*jj = a->j,nz,i,j; 3043 PetscScalar *v = a->a,*values = (PetscScalar *)advalues; 3044 ISColoringValue *color; 3045 3046 PetscFunctionBegin; 3047 if (!a->coloring) SETERRQ(1,"Coloring not set for matrix"); 3048 color = a->coloring->colors; 3049 /* loop over rows */ 3050 for (i=0; i<m; i++) { 3051 nz = ii[i+1] - ii[i]; 3052 /* loop over columns putting computed value into matrix */ 3053 for (j=0; j<nz; j++) { 3054 *v++ = values[color[*jj++]]; 3055 } 3056 values += nl; /* jump to next row of derivatives */ 3057 } 3058 PetscFunctionReturn(0); 3059 } 3060 3061