1 2 /* 3 Defines projective product routines where A is a SeqAIJ matrix 4 C = R * A * R^T 5 */ 6 7 #include <../src/mat/impls/aij/seq/aij.h> 8 #include <../src/mat/utils/freespace.h> 9 #include <../src/mat/impls/dense/seq/dense.h> /*I "petscmat.h" I*/ 10 11 PetscErrorCode MatDestroy_SeqAIJ_RARt(Mat A) 12 { 13 PetscErrorCode ierr; 14 Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data; 15 Mat_RARt *rart = a->rart; 16 17 PetscFunctionBegin; 18 ierr = MatTransposeColoringDestroy(&rart->matcoloring);CHKERRQ(ierr); 19 ierr = MatDestroy(&rart->Rt);CHKERRQ(ierr); 20 ierr = MatDestroy(&rart->RARt);CHKERRQ(ierr); 21 ierr = MatDestroy(&rart->ARt);CHKERRQ(ierr); 22 ierr = PetscFree(rart->work);CHKERRQ(ierr); 23 24 A->ops->destroy = rart->destroy; 25 if (A->ops->destroy) { 26 ierr = (*A->ops->destroy)(A);CHKERRQ(ierr); 27 } 28 ierr = PetscFree(rart);CHKERRQ(ierr); 29 PetscFunctionReturn(0); 30 } 31 32 PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ_colorrart(Mat A,Mat R,PetscReal fill,Mat *C) 33 { 34 PetscErrorCode ierr; 35 Mat P; 36 PetscInt *rti,*rtj; 37 Mat_RARt *rart; 38 MatColoring coloring; 39 MatTransposeColoring matcoloring; 40 ISColoring iscoloring; 41 Mat Rt_dense,RARt_dense; 42 Mat_SeqAIJ *c; 43 44 PetscFunctionBegin; 45 /* create symbolic P=Rt */ 46 ierr = MatGetSymbolicTranspose_SeqAIJ(R,&rti,&rtj);CHKERRQ(ierr); 47 ierr = MatCreateSeqAIJWithArrays(PETSC_COMM_SELF,R->cmap->n,R->rmap->n,rti,rtj,NULL,&P);CHKERRQ(ierr); 48 49 /* get symbolic C=Pt*A*P */ 50 ierr = MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy(A,P,fill,C);CHKERRQ(ierr); 51 ierr = MatSetBlockSizes(*C,PetscAbs(R->rmap->bs),PetscAbs(R->rmap->bs));CHKERRQ(ierr); 52 (*C)->ops->rartnumeric = MatRARtNumeric_SeqAIJ_SeqAIJ_colorrart; 53 54 /* create a supporting struct */ 55 ierr = PetscNew(&rart);CHKERRQ(ierr); 56 c = (Mat_SeqAIJ*)(*C)->data; 57 c->rart = rart; 58 59 /* ------ Use coloring ---------- */ 60 /* inode causes memory problem, don't know why */ 61 if (c->inode.use) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"MAT_USE_INODES is not supported. Use '-mat_no_inode'"); 62 63 /* Create MatTransposeColoring from symbolic C=R*A*R^T */ 64 ierr = MatColoringCreate(*C,&coloring);CHKERRQ(ierr); 65 ierr = MatColoringSetDistance(coloring,2);CHKERRQ(ierr); 66 ierr = MatColoringSetType(coloring,MATCOLORINGSL);CHKERRQ(ierr); 67 ierr = MatColoringSetFromOptions(coloring);CHKERRQ(ierr); 68 ierr = MatColoringApply(coloring,&iscoloring);CHKERRQ(ierr); 69 ierr = MatColoringDestroy(&coloring);CHKERRQ(ierr); 70 ierr = MatTransposeColoringCreate(*C,iscoloring,&matcoloring);CHKERRQ(ierr); 71 72 rart->matcoloring = matcoloring; 73 ierr = ISColoringDestroy(&iscoloring);CHKERRQ(ierr); 74 75 /* Create Rt_dense */ 76 ierr = MatCreate(PETSC_COMM_SELF,&Rt_dense);CHKERRQ(ierr); 77 ierr = MatSetSizes(Rt_dense,A->cmap->n,matcoloring->ncolors,A->cmap->n,matcoloring->ncolors);CHKERRQ(ierr); 78 ierr = MatSetType(Rt_dense,MATSEQDENSE);CHKERRQ(ierr); 79 ierr = MatSeqDenseSetPreallocation(Rt_dense,NULL);CHKERRQ(ierr); 80 81 Rt_dense->assembled = PETSC_TRUE; 82 rart->Rt = Rt_dense; 83 84 /* Create RARt_dense = R*A*Rt_dense */ 85 ierr = MatCreate(PETSC_COMM_SELF,&RARt_dense);CHKERRQ(ierr); 86 ierr = MatSetSizes(RARt_dense,(*C)->rmap->n,matcoloring->ncolors,(*C)->rmap->n,matcoloring->ncolors);CHKERRQ(ierr); 87 ierr = MatSetType(RARt_dense,MATSEQDENSE);CHKERRQ(ierr); 88 ierr = MatSeqDenseSetPreallocation(RARt_dense,NULL);CHKERRQ(ierr); 89 90 rart->RARt = RARt_dense; 91 92 /* Allocate work array to store columns of A*R^T used in MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqDense() */ 93 ierr = PetscMalloc1(A->rmap->n*4,&rart->work);CHKERRQ(ierr); 94 95 rart->destroy = (*C)->ops->destroy; 96 (*C)->ops->destroy = MatDestroy_SeqAIJ_RARt; 97 98 /* clean up */ 99 ierr = MatRestoreSymbolicTranspose_SeqAIJ(R,&rti,&rtj);CHKERRQ(ierr); 100 ierr = MatDestroy(&P);CHKERRQ(ierr); 101 102 #if defined(PETSC_USE_INFO) 103 { 104 PetscReal density= (PetscReal)(c->nz)/(RARt_dense->rmap->n*RARt_dense->cmap->n); 105 ierr = PetscInfo(*C,"C=R*(A*Rt) via coloring C - use sparse-dense inner products\n");CHKERRQ(ierr); 106 ierr = PetscInfo6(*C,"RARt_den %D %D; Rt %D %D (RARt->nz %D)/(m*ncolors)=%g\n",RARt_dense->rmap->n,RARt_dense->cmap->n,R->cmap->n,R->rmap->n,c->nz,density);CHKERRQ(ierr); 107 } 108 #endif 109 PetscFunctionReturn(0); 110 } 111 112 /* 113 RAB = R * A * B, R and A in seqaij format, B in dense format; 114 */ 115 PetscErrorCode MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqDense(Mat R,Mat A,Mat B,Mat RAB,PetscScalar *work) 116 { 117 Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*r=(Mat_SeqAIJ*)R->data; 118 PetscErrorCode ierr; 119 PetscScalar *b,r1,r2,r3,r4,*b1,*b2,*b3,*b4; 120 MatScalar *aa,*ra; 121 PetscInt cn =B->cmap->n,bm=B->rmap->n,col,i,j,n,*ai=a->i,*aj,am=A->rmap->n; 122 PetscInt am2=2*am,am3=3*am,bm4=4*bm; 123 PetscScalar *d,*c,*c2,*c3,*c4; 124 PetscInt *rj,rm=R->rmap->n,dm=RAB->rmap->n,dn=RAB->cmap->n; 125 PetscInt rm2=2*rm,rm3=3*rm,colrm; 126 127 PetscFunctionBegin; 128 if (!dm || !dn) PetscFunctionReturn(0); 129 if (bm != A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number columns in A %D not equal rows in B %D\n",A->cmap->n,bm); 130 if (am != R->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number columns in R %D not equal rows in A %D\n",R->cmap->n,am); 131 if (R->rmap->n != RAB->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number rows in RAB %D not equal rows in R %D\n",RAB->rmap->n,R->rmap->n); 132 if (B->cmap->n != RAB->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Number columns in RAB %D not equal columns in B %D\n",RAB->cmap->n,B->cmap->n); 133 134 { /* 135 This approach is not as good as original ones (will be removed later), but it reveals that 136 AB_den=A*B takes almost all execution time in R*A*B for src/ksp/ksp/examples/tutorials/ex56.c 137 */ 138 PetscBool via_matmatmult=PETSC_FALSE; 139 ierr = PetscOptionsGetBool(NULL,NULL,"-matrart_via_matmatmult",&via_matmatmult,NULL);CHKERRQ(ierr); 140 if (via_matmatmult) { 141 Mat AB_den; 142 ierr = MatMatMultSymbolic_SeqAIJ_SeqDense(A,B,0.0,&AB_den);CHKERRQ(ierr); 143 ierr = MatMatMultNumeric_SeqAIJ_SeqDense(A,B,AB_den);CHKERRQ(ierr); 144 ierr = MatMatMultNumeric_SeqAIJ_SeqDense(R,AB_den,RAB);CHKERRQ(ierr); 145 ierr = MatDestroy(&AB_den);CHKERRQ(ierr); 146 PetscFunctionReturn(0); 147 } 148 } 149 150 ierr = MatDenseGetArray(B,&b);CHKERRQ(ierr); 151 ierr = MatDenseGetArray(RAB,&d);CHKERRQ(ierr); 152 b1 = b; b2 = b1 + bm; b3 = b2 + bm; b4 = b3 + bm; 153 c = work; c2 = c + am; c3 = c2 + am; c4 = c3 + am; 154 for (col=0; col<cn-4; col += 4) { /* over columns of C */ 155 for (i=0; i<am; i++) { /* over rows of A in those columns */ 156 r1 = r2 = r3 = r4 = 0.0; 157 n = ai[i+1] - ai[i]; 158 aj = a->j + ai[i]; 159 aa = a->a + ai[i]; 160 for (j=0; j<n; j++) { 161 r1 += (*aa)*b1[*aj]; 162 r2 += (*aa)*b2[*aj]; 163 r3 += (*aa)*b3[*aj]; 164 r4 += (*aa++)*b4[*aj++]; 165 } 166 c[i] = r1; 167 c[am + i] = r2; 168 c[am2 + i] = r3; 169 c[am3 + i] = r4; 170 } 171 b1 += bm4; 172 b2 += bm4; 173 b3 += bm4; 174 b4 += bm4; 175 176 /* RAB[:,col] = R*C[:,col] */ 177 colrm = col*rm; 178 for (i=0; i<rm; i++) { /* over rows of R in those columns */ 179 r1 = r2 = r3 = r4 = 0.0; 180 n = r->i[i+1] - r->i[i]; 181 rj = r->j + r->i[i]; 182 ra = r->a + r->i[i]; 183 for (j=0; j<n; j++) { 184 r1 += (*ra)*c[*rj]; 185 r2 += (*ra)*c2[*rj]; 186 r3 += (*ra)*c3[*rj]; 187 r4 += (*ra++)*c4[*rj++]; 188 } 189 d[colrm + i] = r1; 190 d[colrm + rm + i] = r2; 191 d[colrm + rm2 + i] = r3; 192 d[colrm + rm3 + i] = r4; 193 } 194 } 195 for (; col<cn; col++) { /* over extra columns of C */ 196 for (i=0; i<am; i++) { /* over rows of A in those columns */ 197 r1 = 0.0; 198 n = a->i[i+1] - a->i[i]; 199 aj = a->j + a->i[i]; 200 aa = a->a + a->i[i]; 201 for (j=0; j<n; j++) { 202 r1 += (*aa++)*b1[*aj++]; 203 } 204 c[i] = r1; 205 } 206 b1 += bm; 207 208 for (i=0; i<rm; i++) { /* over rows of R in those columns */ 209 r1 = 0.0; 210 n = r->i[i+1] - r->i[i]; 211 rj = r->j + r->i[i]; 212 ra = r->a + r->i[i]; 213 for (j=0; j<n; j++) { 214 r1 += (*ra++)*c[*rj++]; 215 } 216 d[col*rm + i] = r1; 217 } 218 } 219 ierr = PetscLogFlops(cn*2.0*(a->nz + r->nz));CHKERRQ(ierr); 220 221 ierr = MatDenseRestoreArray(B,&b);CHKERRQ(ierr); 222 ierr = MatDenseRestoreArray(RAB,&d);CHKERRQ(ierr); 223 ierr = MatAssemblyBegin(RAB,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 224 ierr = MatAssemblyEnd(RAB,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr); 225 PetscFunctionReturn(0); 226 } 227 228 PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ_colorrart(Mat A,Mat R,Mat C) 229 { 230 PetscErrorCode ierr; 231 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 232 Mat_RARt *rart=c->rart; 233 MatTransposeColoring matcoloring; 234 Mat Rt,RARt; 235 236 PetscFunctionBegin; 237 /* Get dense Rt by Apply MatTransposeColoring to R */ 238 matcoloring = rart->matcoloring; 239 Rt = rart->Rt; 240 ierr = MatTransColoringApplySpToDen(matcoloring,R,Rt);CHKERRQ(ierr); 241 242 /* Get dense RARt = R*A*Rt -- dominates! */ 243 RARt = rart->RARt; 244 ierr = MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqDense(R,A,Rt,RARt,rart->work);CHKERRQ(ierr); 245 246 /* Recover C from C_dense */ 247 ierr = MatTransColoringApplyDenToSp(matcoloring,RARt,C);CHKERRQ(ierr); 248 PetscFunctionReturn(0); 249 } 250 251 PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ_matmattransposemult(Mat A,Mat R,PetscReal fill,Mat *C) 252 { 253 PetscErrorCode ierr; 254 Mat ARt,RARt; 255 Mat_SeqAIJ *c; 256 Mat_RARt *rart; 257 258 PetscFunctionBegin; 259 /* must use '-mat_no_inode' with '-matmattransmult_color 1' - do not knwo why? */ 260 ierr = MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(A,R,fill,&ARt);CHKERRQ(ierr); 261 ierr = MatMatMultSymbolic_SeqAIJ_SeqAIJ(R,ARt,fill,&RARt);CHKERRQ(ierr); 262 *C = RARt; 263 RARt->ops->rartnumeric = MatRARtNumeric_SeqAIJ_SeqAIJ_matmattransposemult; 264 265 ierr = PetscNew(&rart);CHKERRQ(ierr); 266 c = (Mat_SeqAIJ*)(*C)->data; 267 c->rart = rart; 268 rart->ARt = ARt; 269 rart->destroy = RARt->ops->destroy; 270 RARt->ops->destroy = MatDestroy_SeqAIJ_RARt; 271 #if defined(PETSC_USE_INFO) 272 ierr = PetscInfo(*C,"Use ARt=A*R^T, C=R*ARt via MatMatTransposeMult(). Coloring can be applied to A*R^T.\n");CHKERRQ(ierr); 273 #endif 274 PetscFunctionReturn(0); 275 } 276 277 PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ_matmattransposemult(Mat A,Mat R,Mat C) 278 { 279 PetscErrorCode ierr; 280 Mat_SeqAIJ *c=(Mat_SeqAIJ*)C->data; 281 Mat_RARt *rart=c->rart; 282 Mat ARt=rart->ARt; 283 284 PetscFunctionBegin; 285 ierr = MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(A,R,ARt);CHKERRQ(ierr); /* dominate! */ 286 ierr = MatMatMultNumeric_SeqAIJ_SeqAIJ(R,ARt,C);CHKERRQ(ierr); 287 PetscFunctionReturn(0); 288 } 289 290 PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ(Mat A,Mat R,PetscReal fill,Mat *C) 291 { 292 PetscErrorCode ierr; 293 Mat Rt; 294 Mat_SeqAIJ *c; 295 Mat_RARt *rart; 296 297 PetscFunctionBegin; 298 ierr = MatTranspose_SeqAIJ(R,MAT_INITIAL_MATRIX,&Rt);CHKERRQ(ierr); 299 ierr = MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ(R,A,Rt,fill,C);CHKERRQ(ierr); 300 301 ierr = PetscNew(&rart);CHKERRQ(ierr); 302 rart->Rt = Rt; 303 c = (Mat_SeqAIJ*)(*C)->data; 304 c->rart = rart; 305 rart->destroy = (*C)->ops->destroy; 306 (*C)->ops->destroy = MatDestroy_SeqAIJ_RARt; 307 (*C)->ops->rartnumeric = MatRARtNumeric_SeqAIJ_SeqAIJ; 308 #if defined(PETSC_USE_INFO) 309 ierr = PetscInfo(*C,"Use Rt=R^T and C=R*A*Rt via MatMatMatMult() to avoid sparse inner products\n");CHKERRQ(ierr); 310 #endif 311 PetscFunctionReturn(0); 312 } 313 314 PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ(Mat A,Mat R,Mat C) 315 { 316 PetscErrorCode ierr; 317 Mat_SeqAIJ *c = (Mat_SeqAIJ*)C->data; 318 Mat_RARt *rart = c->rart; 319 Mat Rt = rart->Rt; 320 321 PetscFunctionBegin; 322 ierr = MatTranspose_SeqAIJ(R,MAT_REUSE_MATRIX,&Rt);CHKERRQ(ierr); 323 ierr = MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ(R,A,Rt,C);CHKERRQ(ierr); 324 PetscFunctionReturn(0); 325 } 326 327 PetscErrorCode MatRARt_SeqAIJ_SeqAIJ(Mat A,Mat R,MatReuse scall,PetscReal fill,Mat *C) 328 { 329 PetscErrorCode ierr; 330 const char *algTypes[3] = {"matmatmatmult","matmattransposemult","coloring_rart"}; 331 PetscInt alg=0; /* set default algorithm */ 332 333 PetscFunctionBegin; 334 if (scall == MAT_INITIAL_MATRIX) { 335 ierr = PetscObjectOptionsBegin((PetscObject)A);CHKERRQ(ierr); 336 PetscOptionsObject->alreadyprinted = PETSC_FALSE; /* a hack to ensure the option shows in '-help' */ 337 ierr = PetscOptionsEList("-matrart_via","Algorithmic approach","MatRARt",algTypes,3,algTypes[0],&alg,NULL);CHKERRQ(ierr); 338 ierr = PetscOptionsEnd();CHKERRQ(ierr); 339 340 ierr = PetscLogEventBegin(MAT_RARtSymbolic,A,R,0,0);CHKERRQ(ierr); 341 switch (alg) { 342 case 1: 343 /* via matmattransposemult: ARt=A*R^T, C=R*ARt - matrix coloring can be applied to A*R^T */ 344 ierr = MatRARtSymbolic_SeqAIJ_SeqAIJ_matmattransposemult(A,R,fill,C);CHKERRQ(ierr); 345 break; 346 case 2: 347 /* via coloring_rart: apply coloring C = R*A*R^T */ 348 ierr = MatRARtSymbolic_SeqAIJ_SeqAIJ_colorrart(A,R,fill,C);CHKERRQ(ierr); 349 break; 350 default: 351 /* via matmatmatmult: Rt=R^T, C=R*A*Rt - avoid inefficient sparse inner products */ 352 ierr = MatRARtSymbolic_SeqAIJ_SeqAIJ(A,R,fill,C);CHKERRQ(ierr); 353 break; 354 } 355 ierr = PetscLogEventEnd(MAT_RARtSymbolic,A,R,0,0);CHKERRQ(ierr); 356 } 357 358 ierr = PetscLogEventBegin(MAT_RARtNumeric,A,R,0,0);CHKERRQ(ierr); 359 ierr = (*(*C)->ops->rartnumeric)(A,R,*C);CHKERRQ(ierr); 360 ierr = PetscLogEventEnd(MAT_RARtNumeric,A,R,0,0);CHKERRQ(ierr); 361 PetscFunctionReturn(0); 362 } 363