1 #define PETSCMAT_DLL 2 3 /* 4 Defines the basic matrix operations for sequential dense. 5 */ 6 7 #include "src/mat/impls/dense/seq/dense.h" 8 #include "petscblaslapack.h" 9 10 #undef __FUNCT__ 11 #define __FUNCT__ "MatAXPY_SeqDense" 12 PetscErrorCode MatAXPY_SeqDense(Mat Y,PetscScalar alpha,Mat X,MatStructure str) 13 { 14 Mat_SeqDense *x = (Mat_SeqDense*)X->data,*y = (Mat_SeqDense*)Y->data; 15 PetscScalar oalpha = alpha; 16 PetscInt j; 17 PetscBLASInt N = (PetscBLASInt)X->rmap.n*X->cmap.n,m=(PetscBLASInt)X->rmap.n,ldax = x->lda,lday=y->lda,one = 1; 18 PetscErrorCode ierr; 19 20 PetscFunctionBegin; 21 if (ldax>m || lday>m) { 22 for (j=0; j<X->cmap.n; j++) { 23 BLASaxpy_(&m,&oalpha,x->v+j*ldax,&one,y->v+j*lday,&one); 24 } 25 } else { 26 BLASaxpy_(&N,&oalpha,x->v,&one,y->v,&one); 27 } 28 ierr = PetscLogFlops(2*N-1);CHKERRQ(ierr); 29 PetscFunctionReturn(0); 30 } 31 32 #undef __FUNCT__ 33 #define __FUNCT__ "MatGetInfo_SeqDense" 34 PetscErrorCode MatGetInfo_SeqDense(Mat A,MatInfoType flag,MatInfo *info) 35 { 36 PetscInt N = A->rmap.n*A->cmap.n; 37 38 PetscFunctionBegin; 39 info->rows_global = (double)A->rmap.n; 40 info->columns_global = (double)A->cmap.n; 41 info->rows_local = (double)A->rmap.n; 42 info->columns_local = (double)A->cmap.n; 43 info->block_size = 1.0; 44 info->nz_allocated = (double)N; 45 info->nz_used = (double)N; 46 info->nz_unneeded = (double)0; 47 info->assemblies = (double)A->num_ass; 48 info->mallocs = 0; 49 info->memory = A->mem; 50 info->fill_ratio_given = 0; 51 info->fill_ratio_needed = 0; 52 info->factor_mallocs = 0; 53 PetscFunctionReturn(0); 54 } 55 56 #undef __FUNCT__ 57 #define __FUNCT__ "MatScale_SeqDense" 58 PetscErrorCode MatScale_SeqDense(Mat A,PetscScalar alpha) 59 { 60 Mat_SeqDense *a = (Mat_SeqDense*)A->data; 61 PetscScalar oalpha = alpha; 62 PetscBLASInt one = 1,lda = a->lda,j,nz; 63 PetscErrorCode ierr; 64 65 PetscFunctionBegin; 66 if (lda>A->rmap.n) { 67 nz = (PetscBLASInt)A->rmap.n; 68 for (j=0; j<A->cmap.n; j++) { 69 BLASscal_(&nz,&oalpha,a->v+j*lda,&one); 70 } 71 } else { 72 nz = (PetscBLASInt)A->rmap.n*A->cmap.n; 73 BLASscal_(&nz,&oalpha,a->v,&one); 74 } 75 ierr = PetscLogFlops(nz);CHKERRQ(ierr); 76 PetscFunctionReturn(0); 77 } 78 79 /* ---------------------------------------------------------------*/ 80 /* COMMENT: I have chosen to hide row permutation in the pivots, 81 rather than put it in the Mat->row slot.*/ 82 #undef __FUNCT__ 83 #define __FUNCT__ "MatLUFactor_SeqDense" 84 PetscErrorCode MatLUFactor_SeqDense(Mat A,IS row,IS col,MatFactorInfo *minfo) 85 { 86 #if defined(PETSC_MISSING_LAPACK_GETRF) 87 PetscFunctionBegin; 88 SETERRQ(PETSC_ERR_SUP,"GETRF - Lapack routine is unavailable."); 89 #else 90 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 91 PetscErrorCode ierr; 92 PetscBLASInt n = (PetscBLASInt)A->cmap.n,m = (PetscBLASInt)A->rmap.n,info; 93 94 PetscFunctionBegin; 95 if (!mat->pivots) { 96 ierr = PetscMalloc((A->rmap.n+1)*sizeof(PetscBLASInt),&mat->pivots);CHKERRQ(ierr); 97 ierr = PetscLogObjectMemory(A,A->rmap.n*sizeof(PetscBLASInt));CHKERRQ(ierr); 98 } 99 A->factor = FACTOR_LU; 100 if (!A->rmap.n || !A->cmap.n) PetscFunctionReturn(0); 101 LAPACKgetrf_(&m,&n,mat->v,&mat->lda,mat->pivots,&info); 102 if (info<0) SETERRQ(PETSC_ERR_LIB,"Bad argument to LU factorization"); 103 if (info>0) SETERRQ(PETSC_ERR_MAT_LU_ZRPVT,"Bad LU factorization"); 104 ierr = PetscLogFlops((2*A->cmap.n*A->cmap.n*A->cmap.n)/3);CHKERRQ(ierr); 105 #endif 106 PetscFunctionReturn(0); 107 } 108 109 #undef __FUNCT__ 110 #define __FUNCT__ "MatDuplicate_SeqDense" 111 PetscErrorCode MatDuplicate_SeqDense(Mat A,MatDuplicateOption cpvalues,Mat *newmat) 112 { 113 Mat_SeqDense *mat = (Mat_SeqDense*)A->data,*l; 114 PetscErrorCode ierr; 115 PetscInt lda = (PetscInt)mat->lda,j,m; 116 Mat newi; 117 118 PetscFunctionBegin; 119 ierr = MatCreate(A->comm,&newi);CHKERRQ(ierr); 120 ierr = MatSetSizes(newi,A->rmap.n,A->cmap.n,A->rmap.n,A->cmap.n);CHKERRQ(ierr); 121 ierr = MatSetType(newi,A->type_name);CHKERRQ(ierr); 122 ierr = MatSeqDenseSetPreallocation(newi,PETSC_NULL);CHKERRQ(ierr); 123 if (cpvalues == MAT_COPY_VALUES) { 124 l = (Mat_SeqDense*)newi->data; 125 if (lda>A->rmap.n) { 126 m = A->rmap.n; 127 for (j=0; j<A->cmap.n; j++) { 128 ierr = PetscMemcpy(l->v+j*m,mat->v+j*lda,m*sizeof(PetscScalar));CHKERRQ(ierr); 129 } 130 } else { 131 ierr = PetscMemcpy(l->v,mat->v,A->rmap.n*A->cmap.n*sizeof(PetscScalar));CHKERRQ(ierr); 132 } 133 } 134 newi->assembled = PETSC_TRUE; 135 *newmat = newi; 136 PetscFunctionReturn(0); 137 } 138 139 #undef __FUNCT__ 140 #define __FUNCT__ "MatLUFactorSymbolic_SeqDense" 141 PetscErrorCode MatLUFactorSymbolic_SeqDense(Mat A,IS row,IS col,MatFactorInfo *info,Mat *fact) 142 { 143 PetscErrorCode ierr; 144 145 PetscFunctionBegin; 146 ierr = MatDuplicate_SeqDense(A,MAT_DO_NOT_COPY_VALUES,fact);CHKERRQ(ierr); 147 PetscFunctionReturn(0); 148 } 149 150 #undef __FUNCT__ 151 #define __FUNCT__ "MatLUFactorNumeric_SeqDense" 152 PetscErrorCode MatLUFactorNumeric_SeqDense(Mat A,MatFactorInfo *info_dummy,Mat *fact) 153 { 154 Mat_SeqDense *mat = (Mat_SeqDense*)A->data,*l = (Mat_SeqDense*)(*fact)->data; 155 PetscErrorCode ierr; 156 PetscInt lda1=mat->lda,lda2=l->lda, m=A->rmap.n,n=A->cmap.n, j; 157 MatFactorInfo info; 158 159 PetscFunctionBegin; 160 /* copy the numerical values */ 161 if (lda1>m || lda2>m ) { 162 for (j=0; j<n; j++) { 163 ierr = PetscMemcpy(l->v+j*lda2,mat->v+j*lda1,m*sizeof(PetscScalar));CHKERRQ(ierr); 164 } 165 } else { 166 ierr = PetscMemcpy(l->v,mat->v,A->rmap.n*A->cmap.n*sizeof(PetscScalar));CHKERRQ(ierr); 167 } 168 (*fact)->factor = 0; 169 ierr = MatLUFactor(*fact,0,0,&info);CHKERRQ(ierr); 170 PetscFunctionReturn(0); 171 } 172 173 #undef __FUNCT__ 174 #define __FUNCT__ "MatCholeskyFactorSymbolic_SeqDense" 175 PetscErrorCode MatCholeskyFactorSymbolic_SeqDense(Mat A,IS row,MatFactorInfo *info,Mat *fact) 176 { 177 PetscErrorCode ierr; 178 179 PetscFunctionBegin; 180 ierr = MatConvert(A,MATSAME,MAT_INITIAL_MATRIX,fact);CHKERRQ(ierr); 181 PetscFunctionReturn(0); 182 } 183 184 #undef __FUNCT__ 185 #define __FUNCT__ "MatCholeskyFactor_SeqDense" 186 PetscErrorCode MatCholeskyFactor_SeqDense(Mat A,IS perm,MatFactorInfo *factinfo) 187 { 188 #if defined(PETSC_MISSING_LAPACK_POTRF) 189 PetscFunctionBegin; 190 SETERRQ(PETSC_ERR_SUP,"POTRF - Lapack routine is unavailable."); 191 #else 192 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 193 PetscErrorCode ierr; 194 PetscBLASInt n = (PetscBLASInt)A->cmap.n,info; 195 196 PetscFunctionBegin; 197 ierr = PetscFree(mat->pivots);CHKERRQ(ierr); 198 mat->pivots = 0; 199 200 if (!A->rmap.n || !A->cmap.n) PetscFunctionReturn(0); 201 LAPACKpotrf_("L",&n,mat->v,&mat->lda,&info); 202 if (info) SETERRQ1(PETSC_ERR_MAT_CH_ZRPVT,"Bad factorization: zero pivot in row %D",(PetscInt)info-1); 203 A->factor = FACTOR_CHOLESKY; 204 ierr = PetscLogFlops((A->cmap.n*A->cmap.n*A->cmap.n)/3);CHKERRQ(ierr); 205 #endif 206 PetscFunctionReturn(0); 207 } 208 209 #undef __FUNCT__ 210 #define __FUNCT__ "MatCholeskyFactorNumeric_SeqDense" 211 PetscErrorCode MatCholeskyFactorNumeric_SeqDense(Mat A,MatFactorInfo *info_dummy,Mat *fact) 212 { 213 PetscErrorCode ierr; 214 MatFactorInfo info; 215 216 PetscFunctionBegin; 217 info.fill = 1.0; 218 ierr = MatCholeskyFactor_SeqDense(*fact,0,&info);CHKERRQ(ierr); 219 PetscFunctionReturn(0); 220 } 221 222 #undef __FUNCT__ 223 #define __FUNCT__ "MatSolve_SeqDense" 224 PetscErrorCode MatSolve_SeqDense(Mat A,Vec xx,Vec yy) 225 { 226 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 227 PetscErrorCode ierr; 228 PetscBLASInt m = (PetscBLASInt)A->rmap.n, one = 1,info; 229 PetscScalar *x,*y; 230 231 PetscFunctionBegin; 232 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 233 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 234 ierr = PetscMemcpy(y,x,A->rmap.n*sizeof(PetscScalar));CHKERRQ(ierr); 235 if (A->factor == FACTOR_LU) { 236 #if defined(PETSC_MISSING_LAPACK_GETRS) 237 SETERRQ(PETSC_ERR_SUP,"GETRS - Lapack routine is unavailable."); 238 #else 239 LAPACKgetrs_("N",&m,&one,mat->v,&mat->lda,mat->pivots,y,&m,&info); 240 if (info) SETERRQ(PETSC_ERR_LIB,"GETRS - Bad solve"); 241 #endif 242 } else if (A->factor == FACTOR_CHOLESKY){ 243 #if defined(PETSC_MISSING_LAPACK_POTRS) 244 SETERRQ(PETSC_ERR_SUP,"POTRS - Lapack routine is unavailable."); 245 #else 246 LAPACKpotrs_("L",&m,&one,mat->v,&mat->lda,y,&m,&info); 247 if (info) SETERRQ(PETSC_ERR_LIB,"POTRS Bad solve"); 248 #endif 249 } 250 else SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Matrix must be factored to solve"); 251 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 252 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 253 ierr = PetscLogFlops(2*A->cmap.n*A->cmap.n - A->cmap.n);CHKERRQ(ierr); 254 PetscFunctionReturn(0); 255 } 256 257 #undef __FUNCT__ 258 #define __FUNCT__ "MatSolveTranspose_SeqDense" 259 PetscErrorCode MatSolveTranspose_SeqDense(Mat A,Vec xx,Vec yy) 260 { 261 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 262 PetscErrorCode ierr; 263 PetscBLASInt m = (PetscBLASInt) A->rmap.n,one = 1,info; 264 PetscScalar *x,*y; 265 266 PetscFunctionBegin; 267 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 268 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 269 ierr = PetscMemcpy(y,x,A->rmap.n*sizeof(PetscScalar));CHKERRQ(ierr); 270 /* assume if pivots exist then use LU; else Cholesky */ 271 if (mat->pivots) { 272 #if defined(PETSC_MISSING_LAPACK_GETRS) 273 SETERRQ(PETSC_ERR_SUP,"GETRS - Lapack routine is unavailable."); 274 #else 275 LAPACKgetrs_("T",&m,&one,mat->v,&mat->lda,mat->pivots,y,&m,&info); 276 if (info) SETERRQ(PETSC_ERR_LIB,"POTRS - Bad solve"); 277 #endif 278 } else { 279 #if defined(PETSC_MISSING_LAPACK_POTRS) 280 SETERRQ(PETSC_ERR_SUP,"POTRS - Lapack routine is unavailable."); 281 #else 282 LAPACKpotrs_("L",&m,&one,mat->v,&mat->lda,y,&m,&info); 283 if (info) SETERRQ(PETSC_ERR_LIB,"POTRS - Bad solve"); 284 #endif 285 } 286 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 287 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 288 ierr = PetscLogFlops(2*A->cmap.n*A->cmap.n - A->cmap.n);CHKERRQ(ierr); 289 PetscFunctionReturn(0); 290 } 291 292 #undef __FUNCT__ 293 #define __FUNCT__ "MatSolveAdd_SeqDense" 294 PetscErrorCode MatSolveAdd_SeqDense(Mat A,Vec xx,Vec zz,Vec yy) 295 { 296 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 297 PetscErrorCode ierr; 298 PetscBLASInt m = (PetscBLASInt)A->rmap.n,one = 1,info; 299 PetscScalar *x,*y,sone = 1.0; 300 Vec tmp = 0; 301 302 PetscFunctionBegin; 303 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 304 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 305 if (!A->rmap.n || !A->cmap.n) PetscFunctionReturn(0); 306 if (yy == zz) { 307 ierr = VecDuplicate(yy,&tmp);CHKERRQ(ierr); 308 ierr = PetscLogObjectParent(A,tmp);CHKERRQ(ierr); 309 ierr = VecCopy(yy,tmp);CHKERRQ(ierr); 310 } 311 ierr = PetscMemcpy(y,x,A->rmap.n*sizeof(PetscScalar));CHKERRQ(ierr); 312 /* assume if pivots exist then use LU; else Cholesky */ 313 if (mat->pivots) { 314 #if defined(PETSC_MISSING_LAPACK_GETRS) 315 SETERRQ(PETSC_ERR_SUP,"GETRS - Lapack routine is unavailable."); 316 #else 317 LAPACKgetrs_("N",&m,&one,mat->v,&mat->lda,mat->pivots,y,&m,&info); 318 if (info) SETERRQ(PETSC_ERR_LIB,"Bad solve"); 319 #endif 320 } else { 321 #if defined(PETSC_MISSING_LAPACK_POTRS) 322 SETERRQ(PETSC_ERR_SUP,"POTRS - Lapack routine is unavailable."); 323 #else 324 LAPACKpotrs_("L",&m,&one,mat->v,&mat->lda,y,&m,&info); 325 if (info) SETERRQ(PETSC_ERR_LIB,"Bad solve"); 326 #endif 327 } 328 if (tmp) {ierr = VecAXPY(yy,sone,tmp);CHKERRQ(ierr); ierr = VecDestroy(tmp);CHKERRQ(ierr);} 329 else {ierr = VecAXPY(yy,sone,zz);CHKERRQ(ierr);} 330 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 331 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 332 ierr = PetscLogFlops(2*A->cmap.n*A->cmap.n);CHKERRQ(ierr); 333 PetscFunctionReturn(0); 334 } 335 336 #undef __FUNCT__ 337 #define __FUNCT__ "MatSolveTransposeAdd_SeqDense" 338 PetscErrorCode MatSolveTransposeAdd_SeqDense(Mat A,Vec xx,Vec zz,Vec yy) 339 { 340 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 341 PetscErrorCode ierr; 342 PetscBLASInt m = (PetscBLASInt)A->rmap.n,one = 1,info; 343 PetscScalar *x,*y,sone = 1.0; 344 Vec tmp; 345 346 PetscFunctionBegin; 347 if (!A->rmap.n || !A->cmap.n) PetscFunctionReturn(0); 348 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 349 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 350 if (yy == zz) { 351 ierr = VecDuplicate(yy,&tmp);CHKERRQ(ierr); 352 ierr = PetscLogObjectParent(A,tmp);CHKERRQ(ierr); 353 ierr = VecCopy(yy,tmp);CHKERRQ(ierr); 354 } 355 ierr = PetscMemcpy(y,x,A->rmap.n*sizeof(PetscScalar));CHKERRQ(ierr); 356 /* assume if pivots exist then use LU; else Cholesky */ 357 if (mat->pivots) { 358 #if defined(PETSC_MISSING_LAPACK_GETRS) 359 SETERRQ(PETSC_ERR_SUP,"GETRS - Lapack routine is unavailable."); 360 #else 361 LAPACKgetrs_("T",&m,&one,mat->v,&mat->lda,mat->pivots,y,&m,&info); 362 if (info) SETERRQ(PETSC_ERR_LIB,"Bad solve"); 363 #endif 364 } else { 365 #if defined(PETSC_MISSING_LAPACK_POTRS) 366 SETERRQ(PETSC_ERR_SUP,"POTRS - Lapack routine is unavailable."); 367 #else 368 LAPACKpotrs_("L",&m,&one,mat->v,&mat->lda,y,&m,&info); 369 if (info) SETERRQ(PETSC_ERR_LIB,"Bad solve"); 370 #endif 371 } 372 if (tmp) { 373 ierr = VecAXPY(yy,sone,tmp);CHKERRQ(ierr); 374 ierr = VecDestroy(tmp);CHKERRQ(ierr); 375 } else { 376 ierr = VecAXPY(yy,sone,zz);CHKERRQ(ierr); 377 } 378 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 379 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 380 ierr = PetscLogFlops(2*A->cmap.n*A->cmap.n);CHKERRQ(ierr); 381 PetscFunctionReturn(0); 382 } 383 /* ------------------------------------------------------------------*/ 384 #undef __FUNCT__ 385 #define __FUNCT__ "MatRelax_SeqDense" 386 PetscErrorCode MatRelax_SeqDense(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal shift,PetscInt its,PetscInt lits,Vec xx) 387 { 388 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 389 PetscScalar *x,*b,*v = mat->v,zero = 0.0,xt; 390 PetscErrorCode ierr; 391 PetscInt m = A->rmap.n,i; 392 #if !defined(PETSC_USE_COMPLEX) 393 PetscBLASInt bm = (PetscBLASInt)m, o = 1; 394 #endif 395 396 PetscFunctionBegin; 397 if (flag & SOR_ZERO_INITIAL_GUESS) { 398 /* this is a hack fix, should have another version without the second BLASdot */ 399 ierr = VecSet(xx,zero);CHKERRQ(ierr); 400 } 401 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 402 ierr = VecGetArray(bb,&b);CHKERRQ(ierr); 403 its = its*lits; 404 if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits); 405 while (its--) { 406 if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){ 407 for (i=0; i<m; i++) { 408 #if defined(PETSC_USE_COMPLEX) 409 /* cannot use BLAS dot for complex because compiler/linker is 410 not happy about returning a double complex */ 411 PetscInt _i; 412 PetscScalar sum = b[i]; 413 for (_i=0; _i<m; _i++) { 414 sum -= PetscConj(v[i+_i*m])*x[_i]; 415 } 416 xt = sum; 417 #else 418 xt = b[i] - BLASdot_(&bm,v+i,&bm,x,&o); 419 #endif 420 x[i] = (1. - omega)*x[i] + omega*(xt+v[i + i*m]*x[i])/(v[i + i*m]+shift); 421 } 422 } 423 if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){ 424 for (i=m-1; i>=0; i--) { 425 #if defined(PETSC_USE_COMPLEX) 426 /* cannot use BLAS dot for complex because compiler/linker is 427 not happy about returning a double complex */ 428 PetscInt _i; 429 PetscScalar sum = b[i]; 430 for (_i=0; _i<m; _i++) { 431 sum -= PetscConj(v[i+_i*m])*x[_i]; 432 } 433 xt = sum; 434 #else 435 xt = b[i] - BLASdot_(&bm,v+i,&bm,x,&o); 436 #endif 437 x[i] = (1. - omega)*x[i] + omega*(xt+v[i + i*m]*x[i])/(v[i + i*m]+shift); 438 } 439 } 440 } 441 ierr = VecRestoreArray(bb,&b);CHKERRQ(ierr); 442 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 443 PetscFunctionReturn(0); 444 } 445 446 /* -----------------------------------------------------------------*/ 447 #undef __FUNCT__ 448 #define __FUNCT__ "MatMultTranspose_SeqDense" 449 PetscErrorCode MatMultTranspose_SeqDense(Mat A,Vec xx,Vec yy) 450 { 451 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 452 PetscScalar *v = mat->v,*x,*y; 453 PetscErrorCode ierr; 454 PetscBLASInt m = (PetscBLASInt)A->rmap.n, n = (PetscBLASInt)A->cmap.n,_One=1; 455 PetscScalar _DOne=1.0,_DZero=0.0; 456 457 PetscFunctionBegin; 458 if (!A->rmap.n || !A->cmap.n) PetscFunctionReturn(0); 459 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 460 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 461 BLASgemv_("T",&m,&n,&_DOne,v,&mat->lda,x,&_One,&_DZero,y,&_One); 462 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 463 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 464 ierr = PetscLogFlops(2*A->rmap.n*A->cmap.n - A->cmap.n);CHKERRQ(ierr); 465 PetscFunctionReturn(0); 466 } 467 468 #undef __FUNCT__ 469 #define __FUNCT__ "MatMult_SeqDense" 470 PetscErrorCode MatMult_SeqDense(Mat A,Vec xx,Vec yy) 471 { 472 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 473 PetscScalar *v = mat->v,*x,*y,_DOne=1.0,_DZero=0.0; 474 PetscErrorCode ierr; 475 PetscBLASInt m = (PetscBLASInt)A->rmap.n, n = (PetscBLASInt)A->cmap.n, _One=1; 476 477 PetscFunctionBegin; 478 if (!A->rmap.n || !A->cmap.n) PetscFunctionReturn(0); 479 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 480 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 481 BLASgemv_("N",&m,&n,&_DOne,v,&(mat->lda),x,&_One,&_DZero,y,&_One); 482 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 483 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 484 ierr = PetscLogFlops(2*A->rmap.n*A->cmap.n - A->rmap.n);CHKERRQ(ierr); 485 PetscFunctionReturn(0); 486 } 487 488 #undef __FUNCT__ 489 #define __FUNCT__ "MatMultAdd_SeqDense" 490 PetscErrorCode MatMultAdd_SeqDense(Mat A,Vec xx,Vec zz,Vec yy) 491 { 492 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 493 PetscScalar *v = mat->v,*x,*y,_DOne=1.0; 494 PetscErrorCode ierr; 495 PetscBLASInt m = (PetscBLASInt)A->rmap.n, n = (PetscBLASInt)A->cmap.n, _One=1; 496 497 PetscFunctionBegin; 498 if (!A->rmap.n || !A->cmap.n) PetscFunctionReturn(0); 499 if (zz != yy) {ierr = VecCopy(zz,yy);CHKERRQ(ierr);} 500 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 501 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 502 BLASgemv_("N",&m,&n,&_DOne,v,&(mat->lda),x,&_One,&_DOne,y,&_One); 503 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 504 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 505 ierr = PetscLogFlops(2*A->rmap.n*A->cmap.n);CHKERRQ(ierr); 506 PetscFunctionReturn(0); 507 } 508 509 #undef __FUNCT__ 510 #define __FUNCT__ "MatMultTransposeAdd_SeqDense" 511 PetscErrorCode MatMultTransposeAdd_SeqDense(Mat A,Vec xx,Vec zz,Vec yy) 512 { 513 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 514 PetscScalar *v = mat->v,*x,*y; 515 PetscErrorCode ierr; 516 PetscBLASInt m = (PetscBLASInt)A->rmap.n, n = (PetscBLASInt)A->cmap.n, _One=1; 517 PetscScalar _DOne=1.0; 518 519 PetscFunctionBegin; 520 if (!A->rmap.n || !A->cmap.n) PetscFunctionReturn(0); 521 if (zz != yy) {ierr = VecCopy(zz,yy);CHKERRQ(ierr);} 522 ierr = VecGetArray(xx,&x);CHKERRQ(ierr); 523 ierr = VecGetArray(yy,&y);CHKERRQ(ierr); 524 BLASgemv_("T",&m,&n,&_DOne,v,&(mat->lda),x,&_One,&_DOne,y,&_One); 525 ierr = VecRestoreArray(xx,&x);CHKERRQ(ierr); 526 ierr = VecRestoreArray(yy,&y);CHKERRQ(ierr); 527 ierr = PetscLogFlops(2*A->rmap.n*A->cmap.n);CHKERRQ(ierr); 528 PetscFunctionReturn(0); 529 } 530 531 /* -----------------------------------------------------------------*/ 532 #undef __FUNCT__ 533 #define __FUNCT__ "MatGetRow_SeqDense" 534 PetscErrorCode MatGetRow_SeqDense(Mat A,PetscInt row,PetscInt *ncols,PetscInt **cols,PetscScalar **vals) 535 { 536 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 537 PetscScalar *v; 538 PetscErrorCode ierr; 539 PetscInt i; 540 541 PetscFunctionBegin; 542 *ncols = A->cmap.n; 543 if (cols) { 544 ierr = PetscMalloc((A->cmap.n+1)*sizeof(PetscInt),cols);CHKERRQ(ierr); 545 for (i=0; i<A->cmap.n; i++) (*cols)[i] = i; 546 } 547 if (vals) { 548 ierr = PetscMalloc((A->cmap.n+1)*sizeof(PetscScalar),vals);CHKERRQ(ierr); 549 v = mat->v + row; 550 for (i=0; i<A->cmap.n; i++) {(*vals)[i] = *v; v += mat->lda;} 551 } 552 PetscFunctionReturn(0); 553 } 554 555 #undef __FUNCT__ 556 #define __FUNCT__ "MatRestoreRow_SeqDense" 557 PetscErrorCode MatRestoreRow_SeqDense(Mat A,PetscInt row,PetscInt *ncols,PetscInt **cols,PetscScalar **vals) 558 { 559 PetscErrorCode ierr; 560 PetscFunctionBegin; 561 if (cols) {ierr = PetscFree(*cols);CHKERRQ(ierr);} 562 if (vals) {ierr = PetscFree(*vals);CHKERRQ(ierr); } 563 PetscFunctionReturn(0); 564 } 565 /* ----------------------------------------------------------------*/ 566 #undef __FUNCT__ 567 #define __FUNCT__ "MatSetValues_SeqDense" 568 PetscErrorCode MatSetValues_SeqDense(Mat A,PetscInt m,const PetscInt indexm[],PetscInt n,const PetscInt indexn[],const PetscScalar v[],InsertMode addv) 569 { 570 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 571 PetscInt i,j,idx=0; 572 573 PetscFunctionBegin; 574 if (!mat->roworiented) { 575 if (addv == INSERT_VALUES) { 576 for (j=0; j<n; j++) { 577 if (indexn[j] < 0) {idx += m; continue;} 578 #if defined(PETSC_USE_DEBUG) 579 if (indexn[j] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",indexn[j],A->cmap.n-1); 580 #endif 581 for (i=0; i<m; i++) { 582 if (indexm[i] < 0) {idx++; continue;} 583 #if defined(PETSC_USE_DEBUG) 584 if (indexm[i] >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",indexm[i],A->rmap.n-1); 585 #endif 586 mat->v[indexn[j]*mat->lda + indexm[i]] = v[idx++]; 587 } 588 } 589 } else { 590 for (j=0; j<n; j++) { 591 if (indexn[j] < 0) {idx += m; continue;} 592 #if defined(PETSC_USE_DEBUG) 593 if (indexn[j] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",indexn[j],A->cmap.n-1); 594 #endif 595 for (i=0; i<m; i++) { 596 if (indexm[i] < 0) {idx++; continue;} 597 #if defined(PETSC_USE_DEBUG) 598 if (indexm[i] >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",indexm[i],A->rmap.n-1); 599 #endif 600 mat->v[indexn[j]*mat->lda + indexm[i]] += v[idx++]; 601 } 602 } 603 } 604 } else { 605 if (addv == INSERT_VALUES) { 606 for (i=0; i<m; i++) { 607 if (indexm[i] < 0) { idx += n; continue;} 608 #if defined(PETSC_USE_DEBUG) 609 if (indexm[i] >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",indexm[i],A->rmap.n-1); 610 #endif 611 for (j=0; j<n; j++) { 612 if (indexn[j] < 0) { idx++; continue;} 613 #if defined(PETSC_USE_DEBUG) 614 if (indexn[j] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",indexn[j],A->cmap.n-1); 615 #endif 616 mat->v[indexn[j]*mat->lda + indexm[i]] = v[idx++]; 617 } 618 } 619 } else { 620 for (i=0; i<m; i++) { 621 if (indexm[i] < 0) { idx += n; continue;} 622 #if defined(PETSC_USE_DEBUG) 623 if (indexm[i] >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",indexm[i],A->rmap.n-1); 624 #endif 625 for (j=0; j<n; j++) { 626 if (indexn[j] < 0) { idx++; continue;} 627 #if defined(PETSC_USE_DEBUG) 628 if (indexn[j] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",indexn[j],A->cmap.n-1); 629 #endif 630 mat->v[indexn[j]*mat->lda + indexm[i]] += v[idx++]; 631 } 632 } 633 } 634 } 635 PetscFunctionReturn(0); 636 } 637 638 #undef __FUNCT__ 639 #define __FUNCT__ "MatGetValues_SeqDense" 640 PetscErrorCode MatGetValues_SeqDense(Mat A,PetscInt m,const PetscInt indexm[],PetscInt n,const PetscInt indexn[],PetscScalar v[]) 641 { 642 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 643 PetscInt i,j; 644 PetscScalar *vpt = v; 645 646 PetscFunctionBegin; 647 /* row-oriented output */ 648 for (i=0; i<m; i++) { 649 for (j=0; j<n; j++) { 650 *vpt++ = mat->v[indexn[j]*mat->lda + indexm[i]]; 651 } 652 } 653 PetscFunctionReturn(0); 654 } 655 656 /* -----------------------------------------------------------------*/ 657 658 #include "petscsys.h" 659 660 #undef __FUNCT__ 661 #define __FUNCT__ "MatLoad_SeqDense" 662 PetscErrorCode MatLoad_SeqDense(PetscViewer viewer, MatType type,Mat *A) 663 { 664 Mat_SeqDense *a; 665 Mat B; 666 PetscErrorCode ierr; 667 PetscInt *scols,i,j,nz,header[4]; 668 int fd; 669 PetscMPIInt size; 670 PetscInt *rowlengths = 0,M,N,*cols; 671 PetscScalar *vals,*svals,*v,*w; 672 MPI_Comm comm = ((PetscObject)viewer)->comm; 673 674 PetscFunctionBegin; 675 ierr = MPI_Comm_size(comm,&size);CHKERRQ(ierr); 676 if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"view must have one processor"); 677 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 678 ierr = PetscBinaryRead(fd,header,4,PETSC_INT);CHKERRQ(ierr); 679 if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Not matrix object"); 680 M = header[1]; N = header[2]; nz = header[3]; 681 682 if (nz == MATRIX_BINARY_FORMAT_DENSE) { /* matrix in file is dense */ 683 ierr = MatCreate(comm,A);CHKERRQ(ierr); 684 ierr = MatSetSizes(*A,M,N,M,N);CHKERRQ(ierr); 685 ierr = MatSetType(*A,type);CHKERRQ(ierr); 686 ierr = MatSeqDenseSetPreallocation(*A,PETSC_NULL);CHKERRQ(ierr); 687 B = *A; 688 a = (Mat_SeqDense*)B->data; 689 v = a->v; 690 /* Allocate some temp space to read in the values and then flip them 691 from row major to column major */ 692 ierr = PetscMalloc((M*N > 0 ? M*N : 1)*sizeof(PetscScalar),&w);CHKERRQ(ierr); 693 /* read in nonzero values */ 694 ierr = PetscBinaryRead(fd,w,M*N,PETSC_SCALAR);CHKERRQ(ierr); 695 /* now flip the values and store them in the matrix*/ 696 for (j=0; j<N; j++) { 697 for (i=0; i<M; i++) { 698 *v++ =w[i*N+j]; 699 } 700 } 701 ierr = PetscFree(w);CHKERRQ(ierr); 702 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 703 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 704 } else { 705 /* read row lengths */ 706 ierr = PetscMalloc((M+1)*sizeof(PetscInt),&rowlengths);CHKERRQ(ierr); 707 ierr = PetscBinaryRead(fd,rowlengths,M,PETSC_INT);CHKERRQ(ierr); 708 709 /* create our matrix */ 710 ierr = MatCreate(comm,A);CHKERRQ(ierr); 711 ierr = MatSetSizes(*A,M,N,M,N);CHKERRQ(ierr); 712 ierr = MatSetType(*A,type);CHKERRQ(ierr); 713 ierr = MatSeqDenseSetPreallocation(*A,PETSC_NULL);CHKERRQ(ierr); 714 B = *A; 715 a = (Mat_SeqDense*)B->data; 716 v = a->v; 717 718 /* read column indices and nonzeros */ 719 ierr = PetscMalloc((nz+1)*sizeof(PetscInt),&scols);CHKERRQ(ierr); 720 cols = scols; 721 ierr = PetscBinaryRead(fd,cols,nz,PETSC_INT);CHKERRQ(ierr); 722 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&svals);CHKERRQ(ierr); 723 vals = svals; 724 ierr = PetscBinaryRead(fd,vals,nz,PETSC_SCALAR);CHKERRQ(ierr); 725 726 /* insert into matrix */ 727 for (i=0; i<M; i++) { 728 for (j=0; j<rowlengths[i]; j++) v[i+M*scols[j]] = svals[j]; 729 svals += rowlengths[i]; scols += rowlengths[i]; 730 } 731 ierr = PetscFree(vals);CHKERRQ(ierr); 732 ierr = PetscFree(cols);CHKERRQ(ierr); 733 ierr = PetscFree(rowlengths);CHKERRQ(ierr); 734 735 ierr = MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 736 ierr = MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 737 } 738 PetscFunctionReturn(0); 739 } 740 741 #include "petscsys.h" 742 743 #undef __FUNCT__ 744 #define __FUNCT__ "MatView_SeqDense_ASCII" 745 static PetscErrorCode MatView_SeqDense_ASCII(Mat A,PetscViewer viewer) 746 { 747 Mat_SeqDense *a = (Mat_SeqDense*)A->data; 748 PetscErrorCode ierr; 749 PetscInt i,j; 750 const char *name; 751 PetscScalar *v; 752 PetscViewerFormat format; 753 754 PetscFunctionBegin; 755 ierr = PetscObjectGetName((PetscObject)A,&name);CHKERRQ(ierr); 756 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 757 if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) { 758 PetscFunctionReturn(0); /* do nothing for now */ 759 } else if (format == PETSC_VIEWER_ASCII_COMMON) { 760 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 761 for (i=0; i<A->rmap.n; i++) { 762 v = a->v + i; 763 ierr = PetscViewerASCIIPrintf(viewer,"row %D:",i);CHKERRQ(ierr); 764 for (j=0; j<A->cmap.n; j++) { 765 #if defined(PETSC_USE_COMPLEX) 766 if (PetscRealPart(*v) != 0.0 && PetscImaginaryPart(*v) != 0.0) { 767 ierr = PetscViewerASCIIPrintf(viewer," (%D, %G + %G i) ",j,PetscRealPart(*v),PetscImaginaryPart(*v));CHKERRQ(ierr); 768 } else if (PetscRealPart(*v)) { 769 ierr = PetscViewerASCIIPrintf(viewer," (%D, %G) ",j,PetscRealPart(*v));CHKERRQ(ierr); 770 } 771 #else 772 if (*v) { 773 ierr = PetscViewerASCIIPrintf(viewer," (%D, %G) ",j,*v);CHKERRQ(ierr); 774 } 775 #endif 776 v += a->lda; 777 } 778 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 779 } 780 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 781 } else { 782 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_NO);CHKERRQ(ierr); 783 #if defined(PETSC_USE_COMPLEX) 784 PetscTruth allreal = PETSC_TRUE; 785 /* determine if matrix has all real values */ 786 v = a->v; 787 for (i=0; i<A->rmap.n*A->cmap.n; i++) { 788 if (PetscImaginaryPart(v[i])) { allreal = PETSC_FALSE; break ;} 789 } 790 #endif 791 if (format == PETSC_VIEWER_ASCII_MATLAB) { 792 ierr = PetscObjectGetName((PetscObject)A,&name);CHKERRQ(ierr); 793 ierr = PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",A->rmap.n,A->cmap.n);CHKERRQ(ierr); 794 ierr = PetscViewerASCIIPrintf(viewer,"%s = zeros(%D,%D);\n",name,A->rmap.n,A->cmap.n);CHKERRQ(ierr); 795 ierr = PetscViewerASCIIPrintf(viewer,"%s = [\n",name);CHKERRQ(ierr); 796 } 797 798 for (i=0; i<A->rmap.n; i++) { 799 v = a->v + i; 800 for (j=0; j<A->cmap.n; j++) { 801 #if defined(PETSC_USE_COMPLEX) 802 if (allreal) { 803 ierr = PetscViewerASCIIPrintf(viewer,"%6.4e ",PetscRealPart(*v));CHKERRQ(ierr); 804 } else { 805 ierr = PetscViewerASCIIPrintf(viewer,"%6.4e + %6.4e i ",PetscRealPart(*v),PetscImaginaryPart(*v));CHKERRQ(ierr); 806 } 807 #else 808 ierr = PetscViewerASCIIPrintf(viewer,"%6.4e ",*v);CHKERRQ(ierr); 809 #endif 810 v += a->lda; 811 } 812 ierr = PetscViewerASCIIPrintf(viewer,"\n");CHKERRQ(ierr); 813 } 814 if (format == PETSC_VIEWER_ASCII_MATLAB) { 815 ierr = PetscViewerASCIIPrintf(viewer,"];\n");CHKERRQ(ierr); 816 } 817 ierr = PetscViewerASCIIUseTabs(viewer,PETSC_YES);CHKERRQ(ierr); 818 } 819 ierr = PetscViewerFlush(viewer);CHKERRQ(ierr); 820 PetscFunctionReturn(0); 821 } 822 823 #undef __FUNCT__ 824 #define __FUNCT__ "MatView_SeqDense_Binary" 825 static PetscErrorCode MatView_SeqDense_Binary(Mat A,PetscViewer viewer) 826 { 827 Mat_SeqDense *a = (Mat_SeqDense*)A->data; 828 PetscErrorCode ierr; 829 int fd; 830 PetscInt ict,j,n = A->cmap.n,m = A->rmap.n,i,*col_lens,nz = m*n; 831 PetscScalar *v,*anonz,*vals; 832 PetscViewerFormat format; 833 834 PetscFunctionBegin; 835 ierr = PetscViewerBinaryGetDescriptor(viewer,&fd);CHKERRQ(ierr); 836 837 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 838 if (format == PETSC_VIEWER_BINARY_NATIVE) { 839 /* store the matrix as a dense matrix */ 840 ierr = PetscMalloc(4*sizeof(PetscInt),&col_lens);CHKERRQ(ierr); 841 col_lens[0] = MAT_FILE_COOKIE; 842 col_lens[1] = m; 843 col_lens[2] = n; 844 col_lens[3] = MATRIX_BINARY_FORMAT_DENSE; 845 ierr = PetscBinaryWrite(fd,col_lens,4,PETSC_INT,PETSC_TRUE);CHKERRQ(ierr); 846 ierr = PetscFree(col_lens);CHKERRQ(ierr); 847 848 /* write out matrix, by rows */ 849 ierr = PetscMalloc((m*n+1)*sizeof(PetscScalar),&vals);CHKERRQ(ierr); 850 v = a->v; 851 for (i=0; i<m; i++) { 852 for (j=0; j<n; j++) { 853 vals[i + j*m] = *v++; 854 } 855 } 856 ierr = PetscBinaryWrite(fd,vals,n*m,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 857 ierr = PetscFree(vals);CHKERRQ(ierr); 858 } else { 859 ierr = PetscMalloc((4+nz)*sizeof(PetscInt),&col_lens);CHKERRQ(ierr); 860 col_lens[0] = MAT_FILE_COOKIE; 861 col_lens[1] = m; 862 col_lens[2] = n; 863 col_lens[3] = nz; 864 865 /* store lengths of each row and write (including header) to file */ 866 for (i=0; i<m; i++) col_lens[4+i] = n; 867 ierr = PetscBinaryWrite(fd,col_lens,4+m,PETSC_INT,PETSC_TRUE);CHKERRQ(ierr); 868 869 /* Possibly should write in smaller increments, not whole matrix at once? */ 870 /* store column indices (zero start index) */ 871 ict = 0; 872 for (i=0; i<m; i++) { 873 for (j=0; j<n; j++) col_lens[ict++] = j; 874 } 875 ierr = PetscBinaryWrite(fd,col_lens,nz,PETSC_INT,PETSC_FALSE);CHKERRQ(ierr); 876 ierr = PetscFree(col_lens);CHKERRQ(ierr); 877 878 /* store nonzero values */ 879 ierr = PetscMalloc((nz+1)*sizeof(PetscScalar),&anonz);CHKERRQ(ierr); 880 ict = 0; 881 for (i=0; i<m; i++) { 882 v = a->v + i; 883 for (j=0; j<n; j++) { 884 anonz[ict++] = *v; v += a->lda; 885 } 886 } 887 ierr = PetscBinaryWrite(fd,anonz,nz,PETSC_SCALAR,PETSC_FALSE);CHKERRQ(ierr); 888 ierr = PetscFree(anonz);CHKERRQ(ierr); 889 } 890 PetscFunctionReturn(0); 891 } 892 893 #undef __FUNCT__ 894 #define __FUNCT__ "MatView_SeqDense_Draw_Zoom" 895 PetscErrorCode MatView_SeqDense_Draw_Zoom(PetscDraw draw,void *Aa) 896 { 897 Mat A = (Mat) Aa; 898 Mat_SeqDense *a = (Mat_SeqDense*)A->data; 899 PetscErrorCode ierr; 900 PetscInt m = A->rmap.n,n = A->cmap.n,color,i,j; 901 PetscScalar *v = a->v; 902 PetscViewer viewer; 903 PetscDraw popup; 904 PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,scale,maxv = 0.0; 905 PetscViewerFormat format; 906 907 PetscFunctionBegin; 908 909 ierr = PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);CHKERRQ(ierr); 910 ierr = PetscViewerGetFormat(viewer,&format);CHKERRQ(ierr); 911 ierr = PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);CHKERRQ(ierr); 912 913 /* Loop over matrix elements drawing boxes */ 914 if (format != PETSC_VIEWER_DRAW_CONTOUR) { 915 /* Blue for negative and Red for positive */ 916 color = PETSC_DRAW_BLUE; 917 for(j = 0; j < n; j++) { 918 x_l = j; 919 x_r = x_l + 1.0; 920 for(i = 0; i < m; i++) { 921 y_l = m - i - 1.0; 922 y_r = y_l + 1.0; 923 #if defined(PETSC_USE_COMPLEX) 924 if (PetscRealPart(v[j*m+i]) > 0.) { 925 color = PETSC_DRAW_RED; 926 } else if (PetscRealPart(v[j*m+i]) < 0.) { 927 color = PETSC_DRAW_BLUE; 928 } else { 929 continue; 930 } 931 #else 932 if (v[j*m+i] > 0.) { 933 color = PETSC_DRAW_RED; 934 } else if (v[j*m+i] < 0.) { 935 color = PETSC_DRAW_BLUE; 936 } else { 937 continue; 938 } 939 #endif 940 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 941 } 942 } 943 } else { 944 /* use contour shading to indicate magnitude of values */ 945 /* first determine max of all nonzero values */ 946 for(i = 0; i < m*n; i++) { 947 if (PetscAbsScalar(v[i]) > maxv) maxv = PetscAbsScalar(v[i]); 948 } 949 scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv; 950 ierr = PetscDrawGetPopup(draw,&popup);CHKERRQ(ierr); 951 if (popup) {ierr = PetscDrawScalePopup(popup,0.0,maxv);CHKERRQ(ierr);} 952 for(j = 0; j < n; j++) { 953 x_l = j; 954 x_r = x_l + 1.0; 955 for(i = 0; i < m; i++) { 956 y_l = m - i - 1.0; 957 y_r = y_l + 1.0; 958 color = PETSC_DRAW_BASIC_COLORS + (int)(scale*PetscAbsScalar(v[j*m+i])); 959 ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);CHKERRQ(ierr); 960 } 961 } 962 } 963 PetscFunctionReturn(0); 964 } 965 966 #undef __FUNCT__ 967 #define __FUNCT__ "MatView_SeqDense_Draw" 968 PetscErrorCode MatView_SeqDense_Draw(Mat A,PetscViewer viewer) 969 { 970 PetscDraw draw; 971 PetscTruth isnull; 972 PetscReal xr,yr,xl,yl,h,w; 973 PetscErrorCode ierr; 974 975 PetscFunctionBegin; 976 ierr = PetscViewerDrawGetDraw(viewer,0,&draw);CHKERRQ(ierr); 977 ierr = PetscDrawIsNull(draw,&isnull);CHKERRQ(ierr); 978 if (isnull) PetscFunctionReturn(0); 979 980 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);CHKERRQ(ierr); 981 xr = A->cmap.n; yr = A->rmap.n; h = yr/10.0; w = xr/10.0; 982 xr += w; yr += h; xl = -w; yl = -h; 983 ierr = PetscDrawSetCoordinates(draw,xl,yl,xr,yr);CHKERRQ(ierr); 984 ierr = PetscDrawZoom(draw,MatView_SeqDense_Draw_Zoom,A);CHKERRQ(ierr); 985 ierr = PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);CHKERRQ(ierr); 986 PetscFunctionReturn(0); 987 } 988 989 #undef __FUNCT__ 990 #define __FUNCT__ "MatView_SeqDense" 991 PetscErrorCode MatView_SeqDense(Mat A,PetscViewer viewer) 992 { 993 PetscErrorCode ierr; 994 PetscTruth issocket,iascii,isbinary,isdraw; 995 996 PetscFunctionBegin; 997 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);CHKERRQ(ierr); 998 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);CHKERRQ(ierr); 999 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);CHKERRQ(ierr); 1000 ierr = PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);CHKERRQ(ierr); 1001 1002 if (iascii) { 1003 ierr = MatView_SeqDense_ASCII(A,viewer);CHKERRQ(ierr); 1004 #if defined(PETSC_USE_SOCKET_VIEWER) 1005 } else if (issocket) { 1006 Mat_SeqDense *a = (Mat_SeqDense*)A->data; 1007 if (a->lda>A->rmap.n) SETERRQ(PETSC_ERR_SUP,"Case can not handle LDA"); 1008 ierr = PetscViewerSocketPutScalar(viewer,A->rmap.n,A->cmap.n,a->v);CHKERRQ(ierr); 1009 #endif 1010 } else if (isbinary) { 1011 ierr = MatView_SeqDense_Binary(A,viewer);CHKERRQ(ierr); 1012 } else if (isdraw) { 1013 ierr = MatView_SeqDense_Draw(A,viewer);CHKERRQ(ierr); 1014 } else { 1015 SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by dense matrix",((PetscObject)viewer)->type_name); 1016 } 1017 PetscFunctionReturn(0); 1018 } 1019 1020 #undef __FUNCT__ 1021 #define __FUNCT__ "MatDestroy_SeqDense" 1022 PetscErrorCode MatDestroy_SeqDense(Mat mat) 1023 { 1024 Mat_SeqDense *l = (Mat_SeqDense*)mat->data; 1025 PetscErrorCode ierr; 1026 1027 PetscFunctionBegin; 1028 #if defined(PETSC_USE_LOG) 1029 PetscLogObjectState((PetscObject)mat,"Rows %D Cols %D",mat->rmap.n,mat->cmap.n); 1030 #endif 1031 ierr = PetscFree(l->pivots);CHKERRQ(ierr); 1032 if (!l->user_alloc) {ierr = PetscFree(l->v);CHKERRQ(ierr);} 1033 ierr = PetscFree(l);CHKERRQ(ierr); 1034 ierr = PetscObjectComposeFunctionDynamic((PetscObject)mat,"MatSeqDenseSetPreallocation_C","",PETSC_NULL);CHKERRQ(ierr); 1035 PetscFunctionReturn(0); 1036 } 1037 1038 #undef __FUNCT__ 1039 #define __FUNCT__ "MatTranspose_SeqDense" 1040 PetscErrorCode MatTranspose_SeqDense(Mat A,Mat *matout) 1041 { 1042 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 1043 PetscErrorCode ierr; 1044 PetscInt k,j,m,n,M; 1045 PetscScalar *v,tmp; 1046 1047 PetscFunctionBegin; 1048 v = mat->v; m = A->rmap.n; M = mat->lda; n = A->cmap.n; 1049 if (!matout) { /* in place transpose */ 1050 if (m != n) { 1051 SETERRQ(PETSC_ERR_SUP,"Can not transpose non-square matrix in place"); 1052 } else { 1053 for (j=0; j<m; j++) { 1054 for (k=0; k<j; k++) { 1055 tmp = v[j + k*M]; 1056 v[j + k*M] = v[k + j*M]; 1057 v[k + j*M] = tmp; 1058 } 1059 } 1060 } 1061 } else { /* out-of-place transpose */ 1062 Mat tmat; 1063 Mat_SeqDense *tmatd; 1064 PetscScalar *v2; 1065 1066 ierr = MatCreate(A->comm,&tmat);CHKERRQ(ierr); 1067 ierr = MatSetSizes(tmat,A->cmap.n,A->rmap.n,A->cmap.n,A->rmap.n);CHKERRQ(ierr); 1068 ierr = MatSetType(tmat,A->type_name);CHKERRQ(ierr); 1069 ierr = MatSeqDenseSetPreallocation(tmat,PETSC_NULL);CHKERRQ(ierr); 1070 tmatd = (Mat_SeqDense*)tmat->data; 1071 v = mat->v; v2 = tmatd->v; 1072 for (j=0; j<n; j++) { 1073 for (k=0; k<m; k++) v2[j + k*n] = v[k + j*M]; 1074 } 1075 ierr = MatAssemblyBegin(tmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1076 ierr = MatAssemblyEnd(tmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1077 *matout = tmat; 1078 } 1079 PetscFunctionReturn(0); 1080 } 1081 1082 #undef __FUNCT__ 1083 #define __FUNCT__ "MatEqual_SeqDense" 1084 PetscErrorCode MatEqual_SeqDense(Mat A1,Mat A2,PetscTruth *flg) 1085 { 1086 Mat_SeqDense *mat1 = (Mat_SeqDense*)A1->data; 1087 Mat_SeqDense *mat2 = (Mat_SeqDense*)A2->data; 1088 PetscInt i,j; 1089 PetscScalar *v1 = mat1->v,*v2 = mat2->v; 1090 1091 PetscFunctionBegin; 1092 if (A1->rmap.n != A2->rmap.n) {*flg = PETSC_FALSE; PetscFunctionReturn(0);} 1093 if (A1->cmap.n != A2->cmap.n) {*flg = PETSC_FALSE; PetscFunctionReturn(0);} 1094 for (i=0; i<A1->rmap.n; i++) { 1095 v1 = mat1->v+i; v2 = mat2->v+i; 1096 for (j=0; j<A1->cmap.n; j++) { 1097 if (*v1 != *v2) {*flg = PETSC_FALSE; PetscFunctionReturn(0);} 1098 v1 += mat1->lda; v2 += mat2->lda; 1099 } 1100 } 1101 *flg = PETSC_TRUE; 1102 PetscFunctionReturn(0); 1103 } 1104 1105 #undef __FUNCT__ 1106 #define __FUNCT__ "MatGetDiagonal_SeqDense" 1107 PetscErrorCode MatGetDiagonal_SeqDense(Mat A,Vec v) 1108 { 1109 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 1110 PetscErrorCode ierr; 1111 PetscInt i,n,len; 1112 PetscScalar *x,zero = 0.0; 1113 1114 PetscFunctionBegin; 1115 ierr = VecSet(v,zero);CHKERRQ(ierr); 1116 ierr = VecGetSize(v,&n);CHKERRQ(ierr); 1117 ierr = VecGetArray(v,&x);CHKERRQ(ierr); 1118 len = PetscMin(A->rmap.n,A->cmap.n); 1119 if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming mat and vec"); 1120 for (i=0; i<len; i++) { 1121 x[i] = mat->v[i*mat->lda + i]; 1122 } 1123 ierr = VecRestoreArray(v,&x);CHKERRQ(ierr); 1124 PetscFunctionReturn(0); 1125 } 1126 1127 #undef __FUNCT__ 1128 #define __FUNCT__ "MatDiagonalScale_SeqDense" 1129 PetscErrorCode MatDiagonalScale_SeqDense(Mat A,Vec ll,Vec rr) 1130 { 1131 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 1132 PetscScalar *l,*r,x,*v; 1133 PetscErrorCode ierr; 1134 PetscInt i,j,m = A->rmap.n,n = A->cmap.n; 1135 1136 PetscFunctionBegin; 1137 if (ll) { 1138 ierr = VecGetSize(ll,&m);CHKERRQ(ierr); 1139 ierr = VecGetArray(ll,&l);CHKERRQ(ierr); 1140 if (m != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vec wrong size"); 1141 for (i=0; i<m; i++) { 1142 x = l[i]; 1143 v = mat->v + i; 1144 for (j=0; j<n; j++) { (*v) *= x; v+= m;} 1145 } 1146 ierr = VecRestoreArray(ll,&l);CHKERRQ(ierr); 1147 ierr = PetscLogFlops(n*m);CHKERRQ(ierr); 1148 } 1149 if (rr) { 1150 ierr = VecGetSize(rr,&n);CHKERRQ(ierr); 1151 ierr = VecGetArray(rr,&r);CHKERRQ(ierr); 1152 if (n != A->cmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vec wrong size"); 1153 for (i=0; i<n; i++) { 1154 x = r[i]; 1155 v = mat->v + i*m; 1156 for (j=0; j<m; j++) { (*v++) *= x;} 1157 } 1158 ierr = VecRestoreArray(rr,&r);CHKERRQ(ierr); 1159 ierr = PetscLogFlops(n*m);CHKERRQ(ierr); 1160 } 1161 PetscFunctionReturn(0); 1162 } 1163 1164 #undef __FUNCT__ 1165 #define __FUNCT__ "MatNorm_SeqDense" 1166 PetscErrorCode MatNorm_SeqDense(Mat A,NormType type,PetscReal *nrm) 1167 { 1168 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 1169 PetscScalar *v = mat->v; 1170 PetscReal sum = 0.0; 1171 PetscInt lda=mat->lda,m=A->rmap.n,i,j; 1172 PetscErrorCode ierr; 1173 1174 PetscFunctionBegin; 1175 if (type == NORM_FROBENIUS) { 1176 if (lda>m) { 1177 for (j=0; j<A->cmap.n; j++) { 1178 v = mat->v+j*lda; 1179 for (i=0; i<m; i++) { 1180 #if defined(PETSC_USE_COMPLEX) 1181 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1182 #else 1183 sum += (*v)*(*v); v++; 1184 #endif 1185 } 1186 } 1187 } else { 1188 for (i=0; i<A->cmap.n*A->rmap.n; i++) { 1189 #if defined(PETSC_USE_COMPLEX) 1190 sum += PetscRealPart(PetscConj(*v)*(*v)); v++; 1191 #else 1192 sum += (*v)*(*v); v++; 1193 #endif 1194 } 1195 } 1196 *nrm = sqrt(sum); 1197 ierr = PetscLogFlops(2*A->cmap.n*A->rmap.n);CHKERRQ(ierr); 1198 } else if (type == NORM_1) { 1199 *nrm = 0.0; 1200 for (j=0; j<A->cmap.n; j++) { 1201 v = mat->v + j*mat->lda; 1202 sum = 0.0; 1203 for (i=0; i<A->rmap.n; i++) { 1204 sum += PetscAbsScalar(*v); v++; 1205 } 1206 if (sum > *nrm) *nrm = sum; 1207 } 1208 ierr = PetscLogFlops(A->cmap.n*A->rmap.n);CHKERRQ(ierr); 1209 } else if (type == NORM_INFINITY) { 1210 *nrm = 0.0; 1211 for (j=0; j<A->rmap.n; j++) { 1212 v = mat->v + j; 1213 sum = 0.0; 1214 for (i=0; i<A->cmap.n; i++) { 1215 sum += PetscAbsScalar(*v); v += mat->lda; 1216 } 1217 if (sum > *nrm) *nrm = sum; 1218 } 1219 ierr = PetscLogFlops(A->cmap.n*A->rmap.n);CHKERRQ(ierr); 1220 } else { 1221 SETERRQ(PETSC_ERR_SUP,"No two norm"); 1222 } 1223 PetscFunctionReturn(0); 1224 } 1225 1226 #undef __FUNCT__ 1227 #define __FUNCT__ "MatSetOption_SeqDense" 1228 PetscErrorCode MatSetOption_SeqDense(Mat A,MatOption op) 1229 { 1230 Mat_SeqDense *aij = (Mat_SeqDense*)A->data; 1231 PetscErrorCode ierr; 1232 1233 PetscFunctionBegin; 1234 switch (op) { 1235 case MAT_ROW_ORIENTED: 1236 aij->roworiented = PETSC_TRUE; 1237 break; 1238 case MAT_COLUMN_ORIENTED: 1239 aij->roworiented = PETSC_FALSE; 1240 break; 1241 case MAT_ROWS_SORTED: 1242 case MAT_ROWS_UNSORTED: 1243 case MAT_COLUMNS_SORTED: 1244 case MAT_COLUMNS_UNSORTED: 1245 case MAT_NO_NEW_NONZERO_LOCATIONS: 1246 case MAT_YES_NEW_NONZERO_LOCATIONS: 1247 case MAT_NEW_NONZERO_LOCATION_ERR: 1248 case MAT_NO_NEW_DIAGONALS: 1249 case MAT_YES_NEW_DIAGONALS: 1250 case MAT_IGNORE_OFF_PROC_ENTRIES: 1251 case MAT_USE_HASH_TABLE: 1252 ierr = PetscInfo1(A,"Option %d ignored\n",op);CHKERRQ(ierr); 1253 break; 1254 case MAT_SYMMETRIC: 1255 case MAT_STRUCTURALLY_SYMMETRIC: 1256 case MAT_NOT_SYMMETRIC: 1257 case MAT_NOT_STRUCTURALLY_SYMMETRIC: 1258 case MAT_HERMITIAN: 1259 case MAT_NOT_HERMITIAN: 1260 case MAT_SYMMETRY_ETERNAL: 1261 case MAT_NOT_SYMMETRY_ETERNAL: 1262 break; 1263 default: 1264 SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op); 1265 } 1266 PetscFunctionReturn(0); 1267 } 1268 1269 #undef __FUNCT__ 1270 #define __FUNCT__ "MatZeroEntries_SeqDense" 1271 PetscErrorCode MatZeroEntries_SeqDense(Mat A) 1272 { 1273 Mat_SeqDense *l = (Mat_SeqDense*)A->data; 1274 PetscErrorCode ierr; 1275 PetscInt lda=l->lda,m=A->rmap.n,j; 1276 1277 PetscFunctionBegin; 1278 if (lda>m) { 1279 for (j=0; j<A->cmap.n; j++) { 1280 ierr = PetscMemzero(l->v+j*lda,m*sizeof(PetscScalar));CHKERRQ(ierr); 1281 } 1282 } else { 1283 ierr = PetscMemzero(l->v,A->rmap.n*A->cmap.n*sizeof(PetscScalar));CHKERRQ(ierr); 1284 } 1285 PetscFunctionReturn(0); 1286 } 1287 1288 #undef __FUNCT__ 1289 #define __FUNCT__ "MatZeroRows_SeqDense" 1290 PetscErrorCode MatZeroRows_SeqDense(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag) 1291 { 1292 Mat_SeqDense *l = (Mat_SeqDense*)A->data; 1293 PetscInt n = A->cmap.n,i,j; 1294 PetscScalar *slot; 1295 1296 PetscFunctionBegin; 1297 for (i=0; i<N; i++) { 1298 slot = l->v + rows[i]; 1299 for (j=0; j<n; j++) { *slot = 0.0; slot += n;} 1300 } 1301 if (diag != 0.0) { 1302 for (i=0; i<N; i++) { 1303 slot = l->v + (n+1)*rows[i]; 1304 *slot = diag; 1305 } 1306 } 1307 PetscFunctionReturn(0); 1308 } 1309 1310 #undef __FUNCT__ 1311 #define __FUNCT__ "MatGetArray_SeqDense" 1312 PetscErrorCode MatGetArray_SeqDense(Mat A,PetscScalar *array[]) 1313 { 1314 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 1315 1316 PetscFunctionBegin; 1317 if (mat->lda != A->rmap.n) SETERRQ(PETSC_ERR_SUP,"Cannot get array for Dense matrices with LDA different from number of rows"); 1318 *array = mat->v; 1319 PetscFunctionReturn(0); 1320 } 1321 1322 #undef __FUNCT__ 1323 #define __FUNCT__ "MatRestoreArray_SeqDense" 1324 PetscErrorCode MatRestoreArray_SeqDense(Mat A,PetscScalar *array[]) 1325 { 1326 PetscFunctionBegin; 1327 *array = 0; /* user cannot accidently use the array later */ 1328 PetscFunctionReturn(0); 1329 } 1330 1331 #undef __FUNCT__ 1332 #define __FUNCT__ "MatGetSubMatrix_SeqDense" 1333 static PetscErrorCode MatGetSubMatrix_SeqDense(Mat A,IS isrow,IS iscol,PetscInt cs,MatReuse scall,Mat *B) 1334 { 1335 Mat_SeqDense *mat = (Mat_SeqDense*)A->data; 1336 PetscErrorCode ierr; 1337 PetscInt i,j,*irow,*icol,nrows,ncols; 1338 PetscScalar *av,*bv,*v = mat->v; 1339 Mat newmat; 1340 1341 PetscFunctionBegin; 1342 ierr = ISGetIndices(isrow,&irow);CHKERRQ(ierr); 1343 ierr = ISGetIndices(iscol,&icol);CHKERRQ(ierr); 1344 ierr = ISGetLocalSize(isrow,&nrows);CHKERRQ(ierr); 1345 ierr = ISGetLocalSize(iscol,&ncols);CHKERRQ(ierr); 1346 1347 /* Check submatrixcall */ 1348 if (scall == MAT_REUSE_MATRIX) { 1349 PetscInt n_cols,n_rows; 1350 ierr = MatGetSize(*B,&n_rows,&n_cols);CHKERRQ(ierr); 1351 if (n_rows != nrows || n_cols != ncols) { 1352 /* resize the result result matrix to match number of requested rows/columns */ 1353 ierr = MatSetSizes(*B,nrows,nrows,nrows,nrows);CHKERRQ(ierr); 1354 } 1355 newmat = *B; 1356 } else { 1357 /* Create and fill new matrix */ 1358 ierr = MatCreate(A->comm,&newmat);CHKERRQ(ierr); 1359 ierr = MatSetSizes(newmat,nrows,ncols,nrows,ncols);CHKERRQ(ierr); 1360 ierr = MatSetType(newmat,A->type_name);CHKERRQ(ierr); 1361 ierr = MatSeqDenseSetPreallocation(newmat,PETSC_NULL);CHKERRQ(ierr); 1362 } 1363 1364 /* Now extract the data pointers and do the copy,column at a time */ 1365 bv = ((Mat_SeqDense*)newmat->data)->v; 1366 1367 for (i=0; i<ncols; i++) { 1368 av = v + mat->lda*icol[i]; 1369 for (j=0; j<nrows; j++) { 1370 *bv++ = av[irow[j]]; 1371 } 1372 } 1373 1374 /* Assemble the matrices so that the correct flags are set */ 1375 ierr = MatAssemblyBegin(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1376 ierr = MatAssemblyEnd(newmat,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 1377 1378 /* Free work space */ 1379 ierr = ISRestoreIndices(isrow,&irow);CHKERRQ(ierr); 1380 ierr = ISRestoreIndices(iscol,&icol);CHKERRQ(ierr); 1381 *B = newmat; 1382 PetscFunctionReturn(0); 1383 } 1384 1385 #undef __FUNCT__ 1386 #define __FUNCT__ "MatGetSubMatrices_SeqDense" 1387 PetscErrorCode MatGetSubMatrices_SeqDense(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[]) 1388 { 1389 PetscErrorCode ierr; 1390 PetscInt i; 1391 1392 PetscFunctionBegin; 1393 if (scall == MAT_INITIAL_MATRIX) { 1394 ierr = PetscMalloc((n+1)*sizeof(Mat),B);CHKERRQ(ierr); 1395 } 1396 1397 for (i=0; i<n; i++) { 1398 ierr = MatGetSubMatrix_SeqDense(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);CHKERRQ(ierr); 1399 } 1400 PetscFunctionReturn(0); 1401 } 1402 1403 #undef __FUNCT__ 1404 #define __FUNCT__ "MatAssemblyBegin_SeqDense" 1405 PetscErrorCode MatAssemblyBegin_SeqDense(Mat mat,MatAssemblyType mode) 1406 { 1407 PetscFunctionBegin; 1408 PetscFunctionReturn(0); 1409 } 1410 1411 #undef __FUNCT__ 1412 #define __FUNCT__ "MatAssemblyEnd_SeqDense" 1413 PetscErrorCode MatAssemblyEnd_SeqDense(Mat mat,MatAssemblyType mode) 1414 { 1415 PetscFunctionBegin; 1416 PetscFunctionReturn(0); 1417 } 1418 1419 #undef __FUNCT__ 1420 #define __FUNCT__ "MatCopy_SeqDense" 1421 PetscErrorCode MatCopy_SeqDense(Mat A,Mat B,MatStructure str) 1422 { 1423 Mat_SeqDense *a = (Mat_SeqDense*)A->data,*b = (Mat_SeqDense *)B->data; 1424 PetscErrorCode ierr; 1425 PetscInt lda1=a->lda,lda2=b->lda, m=A->rmap.n,n=A->cmap.n, j; 1426 1427 PetscFunctionBegin; 1428 /* If the two matrices don't have the same copy implementation, they aren't compatible for fast copy. */ 1429 if (A->ops->copy != B->ops->copy) { 1430 ierr = MatCopy_Basic(A,B,str);CHKERRQ(ierr); 1431 PetscFunctionReturn(0); 1432 } 1433 if (m != B->rmap.n || n != B->cmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"size(B) != size(A)"); 1434 if (lda1>m || lda2>m) { 1435 for (j=0; j<n; j++) { 1436 ierr = PetscMemcpy(b->v+j*lda2,a->v+j*lda1,m*sizeof(PetscScalar));CHKERRQ(ierr); 1437 } 1438 } else { 1439 ierr = PetscMemcpy(b->v,a->v,A->rmap.n*A->cmap.n*sizeof(PetscScalar));CHKERRQ(ierr); 1440 } 1441 PetscFunctionReturn(0); 1442 } 1443 1444 #undef __FUNCT__ 1445 #define __FUNCT__ "MatSetUpPreallocation_SeqDense" 1446 PetscErrorCode MatSetUpPreallocation_SeqDense(Mat A) 1447 { 1448 PetscErrorCode ierr; 1449 1450 PetscFunctionBegin; 1451 ierr = MatSeqDenseSetPreallocation(A,0);CHKERRQ(ierr); 1452 PetscFunctionReturn(0); 1453 } 1454 1455 #undef __FUNCT__ 1456 #define __FUNCT__ "MatSetSizes_SeqDense" 1457 PetscErrorCode MatSetSizes_SeqDense(Mat A,PetscInt m,PetscInt n,PetscInt M,PetscInt N) 1458 { 1459 Mat_SeqDense *a = (Mat_SeqDense*)A->data; 1460 PetscErrorCode ierr; 1461 PetscFunctionBegin; 1462 /* this will not be called before lda, Mmax, and Nmax have been set */ 1463 m = PetscMax(m,M); 1464 n = PetscMax(n,N); 1465 if (m > a->Mmax) SETERRQ2(PETSC_ERR_SUP,"Cannot yet resize number rows of dense matrix larger then its initial size %d, requested %d",a->lda,(int)m); 1466 if (n > a->Nmax) SETERRQ2(PETSC_ERR_SUP,"Cannot yet resize number columns of dense matrix larger then its initial size %d, requested %d",a->Nmax,(int)n); 1467 A->rmap.n = A->rmap.n = m; 1468 A->cmap.n = A->cmap.N = n; 1469 if (a->changelda) a->lda = m; 1470 ierr = PetscMemzero(a->v,m*n*sizeof(PetscScalar));CHKERRQ(ierr); 1471 PetscFunctionReturn(0); 1472 } 1473 1474 /* ----------------------------------------------------------------*/ 1475 #undef __FUNCT__ 1476 #define __FUNCT__ "MatMatMult_SeqDense_SeqDense" 1477 PetscErrorCode MatMatMult_SeqDense_SeqDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 1478 { 1479 PetscErrorCode ierr; 1480 1481 PetscFunctionBegin; 1482 if (scall == MAT_INITIAL_MATRIX){ 1483 ierr = MatMatMultSymbolic_SeqDense_SeqDense(A,B,fill,C);CHKERRQ(ierr); 1484 } 1485 ierr = MatMatMultNumeric_SeqDense_SeqDense(A,B,*C);CHKERRQ(ierr); 1486 PetscFunctionReturn(0); 1487 } 1488 1489 #undef __FUNCT__ 1490 #define __FUNCT__ "MatMatMultSymbolic_SeqDense_SeqDense" 1491 PetscErrorCode MatMatMultSymbolic_SeqDense_SeqDense(Mat A,Mat B,PetscReal fill,Mat *C) 1492 { 1493 PetscErrorCode ierr; 1494 PetscInt m=A->rmap.n,n=B->cmap.n; 1495 Mat Cmat; 1496 1497 PetscFunctionBegin; 1498 if (A->cmap.n != B->rmap.n) SETERRQ2(PETSC_ERR_ARG_SIZ,"A->cmap.n %d != B->rmap.n %d\n",A->cmap.n,B->rmap.n); 1499 ierr = MatCreate(PETSC_COMM_SELF,&Cmat);CHKERRQ(ierr); 1500 ierr = MatSetSizes(Cmat,m,n,m,n);CHKERRQ(ierr); 1501 ierr = MatSetType(Cmat,MATSEQDENSE);CHKERRQ(ierr); 1502 ierr = MatSeqDenseSetPreallocation(Cmat,PETSC_NULL);CHKERRQ(ierr); 1503 Cmat->assembled = PETSC_TRUE; 1504 *C = Cmat; 1505 PetscFunctionReturn(0); 1506 } 1507 1508 #undef __FUNCT__ 1509 #define __FUNCT__ "MatMatMultNumeric_SeqDense_SeqDense" 1510 PetscErrorCode MatMatMultNumeric_SeqDense_SeqDense(Mat A,Mat B,Mat C) 1511 { 1512 Mat_SeqDense *a = (Mat_SeqDense*)A->data; 1513 Mat_SeqDense *b = (Mat_SeqDense*)B->data; 1514 Mat_SeqDense *c = (Mat_SeqDense*)C->data; 1515 PetscBLASInt m=(PetscBLASInt)A->rmap.n,n=(PetscBLASInt)B->cmap.n,k=(PetscBLASInt)A->cmap.n; 1516 PetscScalar _DOne=1.0,_DZero=0.0; 1517 1518 PetscFunctionBegin; 1519 BLASgemm_("N","N",&m,&n,&k,&_DOne,a->v,&a->lda,b->v,&b->lda,&_DZero,c->v,&c->lda); 1520 PetscFunctionReturn(0); 1521 } 1522 1523 #undef __FUNCT__ 1524 #define __FUNCT__ "MatMatMultTranspose_SeqDense_SeqDense" 1525 PetscErrorCode MatMatMultTranspose_SeqDense_SeqDense(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C) 1526 { 1527 PetscErrorCode ierr; 1528 1529 PetscFunctionBegin; 1530 if (scall == MAT_INITIAL_MATRIX){ 1531 ierr = MatMatMultTransposeSymbolic_SeqDense_SeqDense(A,B,fill,C);CHKERRQ(ierr); 1532 } 1533 ierr = MatMatMultTransposeNumeric_SeqDense_SeqDense(A,B,*C);CHKERRQ(ierr); 1534 PetscFunctionReturn(0); 1535 } 1536 1537 #undef __FUNCT__ 1538 #define __FUNCT__ "MatMatMultTransposeSymbolic_SeqDense_SeqDense" 1539 PetscErrorCode MatMatMultTransposeSymbolic_SeqDense_SeqDense(Mat A,Mat B,PetscReal fill,Mat *C) 1540 { 1541 PetscErrorCode ierr; 1542 PetscInt m=A->cmap.n,n=B->cmap.n; 1543 Mat Cmat; 1544 1545 PetscFunctionBegin; 1546 if (A->rmap.n != B->rmap.n) SETERRQ2(PETSC_ERR_ARG_SIZ,"A->rmap.n %d != B->rmap.n %d\n",A->rmap.n,B->rmap.n); 1547 ierr = MatCreate(PETSC_COMM_SELF,&Cmat);CHKERRQ(ierr); 1548 ierr = MatSetSizes(Cmat,m,n,m,n);CHKERRQ(ierr); 1549 ierr = MatSetType(Cmat,MATSEQDENSE);CHKERRQ(ierr); 1550 ierr = MatSeqDenseSetPreallocation(Cmat,PETSC_NULL);CHKERRQ(ierr); 1551 Cmat->assembled = PETSC_TRUE; 1552 *C = Cmat; 1553 PetscFunctionReturn(0); 1554 } 1555 1556 #undef __FUNCT__ 1557 #define __FUNCT__ "MatMatMultTransposeNumeric_SeqDense_SeqDense" 1558 PetscErrorCode MatMatMultTransposeNumeric_SeqDense_SeqDense(Mat A,Mat B,Mat C) 1559 { 1560 Mat_SeqDense *a = (Mat_SeqDense*)A->data; 1561 Mat_SeqDense *b = (Mat_SeqDense*)B->data; 1562 Mat_SeqDense *c = (Mat_SeqDense*)C->data; 1563 PetscBLASInt m=(PetscBLASInt)A->cmap.n,n=(PetscBLASInt)B->cmap.n,k=(PetscBLASInt)A->rmap.n; 1564 PetscScalar _DOne=1.0,_DZero=0.0; 1565 1566 PetscFunctionBegin; 1567 BLASgemm_("T","N",&m,&n,&k,&_DOne,a->v,&a->lda,b->v,&b->lda,&_DZero,c->v,&c->lda); 1568 PetscFunctionReturn(0); 1569 } 1570 /* -------------------------------------------------------------------*/ 1571 static struct _MatOps MatOps_Values = {MatSetValues_SeqDense, 1572 MatGetRow_SeqDense, 1573 MatRestoreRow_SeqDense, 1574 MatMult_SeqDense, 1575 /* 4*/ MatMultAdd_SeqDense, 1576 MatMultTranspose_SeqDense, 1577 MatMultTransposeAdd_SeqDense, 1578 MatSolve_SeqDense, 1579 MatSolveAdd_SeqDense, 1580 MatSolveTranspose_SeqDense, 1581 /*10*/ MatSolveTransposeAdd_SeqDense, 1582 MatLUFactor_SeqDense, 1583 MatCholeskyFactor_SeqDense, 1584 MatRelax_SeqDense, 1585 MatTranspose_SeqDense, 1586 /*15*/ MatGetInfo_SeqDense, 1587 MatEqual_SeqDense, 1588 MatGetDiagonal_SeqDense, 1589 MatDiagonalScale_SeqDense, 1590 MatNorm_SeqDense, 1591 /*20*/ MatAssemblyBegin_SeqDense, 1592 MatAssemblyEnd_SeqDense, 1593 0, 1594 MatSetOption_SeqDense, 1595 MatZeroEntries_SeqDense, 1596 /*25*/ MatZeroRows_SeqDense, 1597 MatLUFactorSymbolic_SeqDense, 1598 MatLUFactorNumeric_SeqDense, 1599 MatCholeskyFactorSymbolic_SeqDense, 1600 MatCholeskyFactorNumeric_SeqDense, 1601 /*30*/ MatSetUpPreallocation_SeqDense, 1602 0, 1603 0, 1604 MatGetArray_SeqDense, 1605 MatRestoreArray_SeqDense, 1606 /*35*/ MatDuplicate_SeqDense, 1607 0, 1608 0, 1609 0, 1610 0, 1611 /*40*/ MatAXPY_SeqDense, 1612 MatGetSubMatrices_SeqDense, 1613 0, 1614 MatGetValues_SeqDense, 1615 MatCopy_SeqDense, 1616 /*45*/ 0, 1617 MatScale_SeqDense, 1618 0, 1619 0, 1620 0, 1621 /*50*/ 0, 1622 0, 1623 0, 1624 0, 1625 0, 1626 /*55*/ 0, 1627 0, 1628 0, 1629 0, 1630 0, 1631 /*60*/ 0, 1632 MatDestroy_SeqDense, 1633 MatView_SeqDense, 1634 0, 1635 0, 1636 /*65*/ 0, 1637 0, 1638 0, 1639 0, 1640 0, 1641 /*70*/ 0, 1642 0, 1643 0, 1644 0, 1645 0, 1646 /*75*/ 0, 1647 0, 1648 0, 1649 0, 1650 0, 1651 /*80*/ 0, 1652 0, 1653 0, 1654 0, 1655 /*84*/ MatLoad_SeqDense, 1656 0, 1657 0, 1658 0, 1659 0, 1660 0, 1661 /*90*/ MatMatMult_SeqDense_SeqDense, 1662 MatMatMultSymbolic_SeqDense_SeqDense, 1663 MatMatMultNumeric_SeqDense_SeqDense, 1664 0, 1665 0, 1666 /*95*/ 0, 1667 MatMatMultTranspose_SeqDense_SeqDense, 1668 MatMatMultTransposeSymbolic_SeqDense_SeqDense, 1669 MatMatMultTransposeNumeric_SeqDense_SeqDense, 1670 0, 1671 /*100*/0, 1672 0, 1673 0, 1674 0, 1675 MatSetSizes_SeqDense}; 1676 1677 #undef __FUNCT__ 1678 #define __FUNCT__ "MatCreateSeqDense" 1679 /*@C 1680 MatCreateSeqDense - Creates a sequential dense matrix that 1681 is stored in column major order (the usual Fortran 77 manner). Many 1682 of the matrix operations use the BLAS and LAPACK routines. 1683 1684 Collective on MPI_Comm 1685 1686 Input Parameters: 1687 + comm - MPI communicator, set to PETSC_COMM_SELF 1688 . m - number of rows 1689 . n - number of columns 1690 - data - optional location of matrix data. Set data=PETSC_NULL for PETSc 1691 to control all matrix memory allocation. 1692 1693 Output Parameter: 1694 . A - the matrix 1695 1696 Notes: 1697 The data input variable is intended primarily for Fortran programmers 1698 who wish to allocate their own matrix memory space. Most users should 1699 set data=PETSC_NULL. 1700 1701 Level: intermediate 1702 1703 .keywords: dense, matrix, LAPACK, BLAS 1704 1705 .seealso: MatCreate(), MatCreateMPIDense(), MatSetValues() 1706 @*/ 1707 PetscErrorCode PETSCMAT_DLLEXPORT MatCreateSeqDense(MPI_Comm comm,PetscInt m,PetscInt n,PetscScalar *data,Mat *A) 1708 { 1709 PetscErrorCode ierr; 1710 1711 PetscFunctionBegin; 1712 ierr = MatCreate(comm,A);CHKERRQ(ierr); 1713 ierr = MatSetSizes(*A,m,n,m,n);CHKERRQ(ierr); 1714 ierr = MatSetType(*A,MATSEQDENSE);CHKERRQ(ierr); 1715 ierr = MatSeqDenseSetPreallocation(*A,data);CHKERRQ(ierr); 1716 PetscFunctionReturn(0); 1717 } 1718 1719 #undef __FUNCT__ 1720 #define __FUNCT__ "MatSeqDensePreallocation" 1721 /*@C 1722 MatSeqDenseSetPreallocation - Sets the array used for storing the matrix elements 1723 1724 Collective on MPI_Comm 1725 1726 Input Parameters: 1727 + A - the matrix 1728 - data - the array (or PETSC_NULL) 1729 1730 Notes: 1731 The data input variable is intended primarily for Fortran programmers 1732 who wish to allocate their own matrix memory space. Most users should 1733 need not call this routine. 1734 1735 Level: intermediate 1736 1737 .keywords: dense, matrix, LAPACK, BLAS 1738 1739 .seealso: MatCreate(), MatCreateMPIDense(), MatSetValues() 1740 @*/ 1741 PetscErrorCode PETSCMAT_DLLEXPORT MatSeqDenseSetPreallocation(Mat B,PetscScalar data[]) 1742 { 1743 PetscErrorCode ierr,(*f)(Mat,PetscScalar[]); 1744 1745 PetscFunctionBegin; 1746 ierr = PetscObjectQueryFunction((PetscObject)B,"MatSeqDenseSetPreallocation_C",(void (**)(void))&f);CHKERRQ(ierr); 1747 if (f) { 1748 ierr = (*f)(B,data);CHKERRQ(ierr); 1749 } 1750 PetscFunctionReturn(0); 1751 } 1752 1753 EXTERN_C_BEGIN 1754 #undef __FUNCT__ 1755 #define __FUNCT__ "MatSeqDensePreallocation_SeqDense" 1756 PetscErrorCode PETSCMAT_DLLEXPORT MatSeqDenseSetPreallocation_SeqDense(Mat B,PetscScalar *data) 1757 { 1758 Mat_SeqDense *b; 1759 PetscErrorCode ierr; 1760 1761 PetscFunctionBegin; 1762 B->preallocated = PETSC_TRUE; 1763 b = (Mat_SeqDense*)B->data; 1764 if (!data) { 1765 ierr = PetscMalloc((b->lda*b->Nmax+1)*sizeof(PetscScalar),&b->v);CHKERRQ(ierr); 1766 ierr = PetscMemzero(b->v,b->lda*b->Nmax*sizeof(PetscScalar));CHKERRQ(ierr); 1767 b->user_alloc = PETSC_FALSE; 1768 ierr = PetscLogObjectMemory(B,b->lda*b->Nmax*sizeof(PetscScalar));CHKERRQ(ierr); 1769 } else { /* user-allocated storage */ 1770 b->v = data; 1771 b->user_alloc = PETSC_TRUE; 1772 } 1773 PetscFunctionReturn(0); 1774 } 1775 EXTERN_C_END 1776 1777 #undef __FUNCT__ 1778 #define __FUNCT__ "MatSeqDenseSetLDA" 1779 /*@C 1780 MatSeqDenseSetLDA - Declare the leading dimension of the user-provided array 1781 1782 Input parameter: 1783 + A - the matrix 1784 - lda - the leading dimension 1785 1786 Notes: 1787 This routine is to be used in conjunction with MatSeqDenseSetPreallocation; 1788 it asserts that the preallocation has a leading dimension (the LDA parameter 1789 of Blas and Lapack fame) larger than M, the first dimension of the matrix. 1790 1791 Level: intermediate 1792 1793 .keywords: dense, matrix, LAPACK, BLAS 1794 1795 .seealso: MatCreate(), MatCreateSeqDense(), MatSeqDenseSetPreallocation(), MatSetMaximumSize() 1796 @*/ 1797 PetscErrorCode PETSCMAT_DLLEXPORT MatSeqDenseSetLDA(Mat B,PetscInt lda) 1798 { 1799 Mat_SeqDense *b = (Mat_SeqDense*)B->data; 1800 1801 PetscFunctionBegin; 1802 if (lda < B->rmap.n) SETERRQ2(PETSC_ERR_ARG_SIZ,"LDA %D must be at least matrix dimension %D",lda,B->rmap.n); 1803 b->lda = lda; 1804 b->changelda = PETSC_FALSE; 1805 b->Mmax = PetscMax(b->Mmax,lda); 1806 PetscFunctionReturn(0); 1807 } 1808 1809 /*MC 1810 MATSEQDENSE - MATSEQDENSE = "seqdense" - A matrix type to be used for sequential dense matrices. 1811 1812 Options Database Keys: 1813 . -mat_type seqdense - sets the matrix type to "seqdense" during a call to MatSetFromOptions() 1814 1815 Level: beginner 1816 1817 .seealso: MatCreateSeqDense() 1818 1819 M*/ 1820 1821 EXTERN_C_BEGIN 1822 #undef __FUNCT__ 1823 #define __FUNCT__ "MatCreate_SeqDense" 1824 PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SeqDense(Mat B) 1825 { 1826 Mat_SeqDense *b; 1827 PetscErrorCode ierr; 1828 PetscMPIInt size; 1829 1830 PetscFunctionBegin; 1831 ierr = MPI_Comm_size(B->comm,&size);CHKERRQ(ierr); 1832 if (size > 1) SETERRQ(PETSC_ERR_ARG_WRONG,"Comm must be of size 1"); 1833 1834 B->rmap.bs = B->cmap.bs = 1; 1835 ierr = PetscMapInitialize(B->comm,&B->rmap);CHKERRQ(ierr); 1836 ierr = PetscMapInitialize(B->comm,&B->cmap);CHKERRQ(ierr); 1837 1838 ierr = PetscNew(Mat_SeqDense,&b);CHKERRQ(ierr); 1839 ierr = PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));CHKERRQ(ierr); 1840 B->factor = 0; 1841 B->mapping = 0; 1842 ierr = PetscLogObjectMemory(B,sizeof(struct _p_Mat));CHKERRQ(ierr); 1843 B->data = (void*)b; 1844 1845 1846 b->pivots = 0; 1847 b->roworiented = PETSC_TRUE; 1848 b->v = 0; 1849 b->lda = B->rmap.n; 1850 b->changelda = PETSC_FALSE; 1851 b->Mmax = B->rmap.n; 1852 b->Nmax = B->cmap.n; 1853 1854 ierr = PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqDenseSetPreallocation_C", 1855 "MatSeqDenseSetPreallocation_SeqDense", 1856 MatSeqDenseSetPreallocation_SeqDense);CHKERRQ(ierr); 1857 PetscFunctionReturn(0); 1858 } 1859 1860 1861 EXTERN_C_END 1862