Actual source code: vpbjacobi.c
2: /*
3: Include files needed for the variable size block PBJacobi preconditioner:
4: pcimpl.h - private include file intended for use by all preconditioners
5: */
7: #include <petsc/private/pcimpl.h>
9: /*
10: Private context (data structure) for the VPBJacobi preconditioner.
11: */
12: typedef struct {
13: MatScalar *diag;
14: } PC_VPBJacobi;
16: static PetscErrorCode PCApply_VPBJacobi(PC pc,Vec x,Vec y)
17: {
18: PC_VPBJacobi *jac = (PC_VPBJacobi*)pc->data;
19: PetscErrorCode ierr;
20: PetscInt i,ncnt = 0;
21: const MatScalar *diag = jac->diag;
22: PetscInt ib,jb,bs;
23: const PetscScalar *xx;
24: PetscScalar *yy,x0,x1,x2,x3,x4,x5,x6;
25: PetscInt nblocks;
26: const PetscInt *bsizes;
29: MatGetVariableBlockSizes(pc->pmat,&nblocks,&bsizes);
30: VecGetArrayRead(x,&xx);
31: VecGetArray(y,&yy);
32: for (i=0; i<nblocks; i++) {
33: bs = bsizes[i];
34: switch (bs) {
35: case 1:
36: yy[ncnt] = *diag*xx[ncnt];
37: break;
38: case 2:
39: x0 = xx[ncnt]; x1 = xx[ncnt+1];
40: yy[ncnt] = diag[0]*x0 + diag[2]*x1;
41: yy[ncnt+1] = diag[1]*x0 + diag[3]*x1;
42: break;
43: case 3:
44: x0 = xx[ncnt]; x1 = xx[ncnt+1]; x2 = xx[ncnt+2];
45: yy[ncnt] = diag[0]*x0 + diag[3]*x1 + diag[6]*x2;
46: yy[ncnt+1] = diag[1]*x0 + diag[4]*x1 + diag[7]*x2;
47: yy[ncnt+2] = diag[2]*x0 + diag[5]*x1 + diag[8]*x2;
48: break;
49: case 4:
50: x0 = xx[ncnt]; x1 = xx[ncnt+1]; x2 = xx[ncnt+2]; x3 = xx[ncnt+3];
51: yy[ncnt] = diag[0]*x0 + diag[4]*x1 + diag[8]*x2 + diag[12]*x3;
52: yy[ncnt+1] = diag[1]*x0 + diag[5]*x1 + diag[9]*x2 + diag[13]*x3;
53: yy[ncnt+2] = diag[2]*x0 + diag[6]*x1 + diag[10]*x2 + diag[14]*x3;
54: yy[ncnt+3] = diag[3]*x0 + diag[7]*x1 + diag[11]*x2 + diag[15]*x3;
55: break;
56: case 5:
57: x0 = xx[ncnt]; x1 = xx[ncnt+1]; x2 = xx[ncnt+2]; x3 = xx[ncnt+3]; x4 = xx[ncnt+4];
58: yy[ncnt] = diag[0]*x0 + diag[5]*x1 + diag[10]*x2 + diag[15]*x3 + diag[20]*x4;
59: yy[ncnt+1] = diag[1]*x0 + diag[6]*x1 + diag[11]*x2 + diag[16]*x3 + diag[21]*x4;
60: yy[ncnt+2] = diag[2]*x0 + diag[7]*x1 + diag[12]*x2 + diag[17]*x3 + diag[22]*x4;
61: yy[ncnt+3] = diag[3]*x0 + diag[8]*x1 + diag[13]*x2 + diag[18]*x3 + diag[23]*x4;
62: yy[ncnt+4] = diag[4]*x0 + diag[9]*x1 + diag[14]*x2 + diag[19]*x3 + diag[24]*x4;
63: break;
64: case 6:
65: x0 = xx[ncnt]; x1 = xx[ncnt+1]; x2 = xx[ncnt+2]; x3 = xx[ncnt+3]; x4 = xx[ncnt+4]; x5 = xx[ncnt+5];
66: yy[ncnt] = diag[0]*x0 + diag[6]*x1 + diag[12]*x2 + diag[18]*x3 + diag[24]*x4 + diag[30]*x5;
67: yy[ncnt+1] = diag[1]*x0 + diag[7]*x1 + diag[13]*x2 + diag[19]*x3 + diag[25]*x4 + diag[31]*x5;
68: yy[ncnt+2] = diag[2]*x0 + diag[8]*x1 + diag[14]*x2 + diag[20]*x3 + diag[26]*x4 + diag[32]*x5;
69: yy[ncnt+3] = diag[3]*x0 + diag[9]*x1 + diag[15]*x2 + diag[21]*x3 + diag[27]*x4 + diag[33]*x5;
70: yy[ncnt+4] = diag[4]*x0 + diag[10]*x1 + diag[16]*x2 + diag[22]*x3 + diag[28]*x4 + diag[34]*x5;
71: yy[ncnt+5] = diag[5]*x0 + diag[11]*x1 + diag[17]*x2 + diag[23]*x3 + diag[29]*x4 + diag[35]*x5;
72: break;
73: case 7:
74: x0 = xx[ncnt]; x1 = xx[ncnt+1]; x2 = xx[ncnt+2]; x3 = xx[ncnt+3]; x4 = xx[ncnt+4]; x5 = xx[ncnt+5]; x6 = xx[ncnt+6];
75: yy[ncnt] = diag[0]*x0 + diag[7]*x1 + diag[14]*x2 + diag[21]*x3 + diag[28]*x4 + diag[35]*x5 + diag[42]*x6;
76: yy[ncnt+1] = diag[1]*x0 + diag[8]*x1 + diag[15]*x2 + diag[22]*x3 + diag[29]*x4 + diag[36]*x5 + diag[43]*x6;
77: yy[ncnt+2] = diag[2]*x0 + diag[9]*x1 + diag[16]*x2 + diag[23]*x3 + diag[30]*x4 + diag[37]*x5 + diag[44]*x6;
78: yy[ncnt+3] = diag[3]*x0 + diag[10]*x1 + diag[17]*x2 + diag[24]*x3 + diag[31]*x4 + diag[38]*x5 + diag[45]*x6;
79: yy[ncnt+4] = diag[4]*x0 + diag[11]*x1 + diag[18]*x2 + diag[25]*x3 + diag[32]*x4 + diag[39]*x5 + diag[46]*x6;
80: yy[ncnt+5] = diag[5]*x0 + diag[12]*x1 + diag[19]*x2 + diag[26]*x3 + diag[33]*x4 + diag[40]*x5 + diag[47]*x6;
81: yy[ncnt+6] = diag[6]*x0 + diag[13]*x1 + diag[20]*x2 + diag[27]*x3 + diag[34]*x4 + diag[41]*x5 + diag[48]*x6;
82: break;
83: default:
84: for (ib=0; ib<bs; ib++) {
85: PetscScalar rowsum = 0;
86: for (jb=0; jb<bs; jb++) {
87: rowsum += diag[ib+jb*bs] * xx[ncnt+jb];
88: }
89: yy[ncnt+ib] = rowsum;
90: }
91: }
92: ncnt += bsizes[i];
93: diag += bsizes[i]*bsizes[i];
94: }
95: VecRestoreArrayRead(x,&xx);
96: VecRestoreArray(y,&yy);
97: return(0);
98: }
100: /* -------------------------------------------------------------------------- */
101: static PetscErrorCode PCSetUp_VPBJacobi(PC pc)
102: {
103: PC_VPBJacobi *jac = (PC_VPBJacobi*)pc->data;
105: Mat A = pc->pmat;
106: MatFactorError err;
107: PetscInt i,nsize = 0,nlocal;
108: PetscInt nblocks;
109: const PetscInt *bsizes;
112: MatGetVariableBlockSizes(pc->pmat,&nblocks,&bsizes);
113: MatGetLocalSize(pc->pmat,&nlocal,NULL);
114: if (nlocal && !nblocks) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call MatSetVariableBlockSizes() before using PCVPBJACOBI");
115: if (!jac->diag) {
116: for (i=0; i<nblocks; i++) nsize += bsizes[i]*bsizes[i];
117: PetscMalloc1(nsize,&jac->diag);
118: }
119: MatInvertVariableBlockDiagonal(A,nblocks,bsizes,jac->diag);
120: MatFactorGetError(A,&err);
121: if (err) pc->failedreason = (PCFailedReason)err;
122: pc->ops->apply = PCApply_VPBJacobi;
123: return(0);
124: }
125: /* -------------------------------------------------------------------------- */
126: static PetscErrorCode PCDestroy_VPBJacobi(PC pc)
127: {
128: PC_VPBJacobi *jac = (PC_VPBJacobi*)pc->data;
132: /*
133: Free the private data structure that was hanging off the PC
134: */
135: PetscFree(jac->diag);
136: PetscFree(pc->data);
137: return(0);
138: }
140: /* -------------------------------------------------------------------------- */
141: /*MC
142: PCVPBJACOBI - Variable size point block Jacobi preconditioner
144: Notes:
145: See PCJACOBI for point Jacobi preconditioning, PCPBJACOBI for fixed point block size, and PCBJACOBI for large size blocks
147: This works for AIJ matrices
149: Uses dense LU factorization with partial pivoting to invert the blocks; if a zero pivot
150: is detected a PETSc error is generated.
152: One must call MatSetVariableBlockSizes() to use this preconditioner
154: Developer Notes:
155: This should support the PCSetErrorIfFailure() flag set to PETSC_TRUE to allow
156: the factorization to continue even after a zero pivot is found resulting in a Nan and hence
157: terminating KSP with a KSP_DIVERGED_NANORIF allowing
158: a nonlinear solver/ODE integrator to recover without stopping the program as currently happens.
160: Perhaps should provide an option that allows generation of a valid preconditioner
161: even if a block is singular as the PCJACOBI does.
163: Level: beginner
165: .seealso: MatSetVariableBlockSizes(), PCCreate(), PCSetType(), PCType (for list of available types), PC, PCJACOBI, PCPBJACOBI, PCBJACOBI
167: M*/
169: PETSC_EXTERN PetscErrorCode PCCreate_VPBJacobi(PC pc)
170: {
171: PC_VPBJacobi *jac;
175: /*
176: Creates the private data structure for this preconditioner and
177: attach it to the PC object.
178: */
179: PetscNewLog(pc,&jac);
180: pc->data = (void*)jac;
182: /*
183: Initialize the pointers to vectors to ZERO; these will be used to store
184: diagonal entries of the matrix for fast preconditioner application.
185: */
186: jac->diag = NULL;
188: /*
189: Set the pointers for the functions that are provided above.
190: Now when the user-level routines (such as PCApply(), PCDestroy(), etc.)
191: are called, they will automatically call these functions. Note we
192: choose not to provide a couple of these functions since they are
193: not needed.
194: */
195: pc->ops->apply = PCApply_VPBJacobi;
196: pc->ops->applytranspose = NULL;
197: pc->ops->setup = PCSetUp_VPBJacobi;
198: pc->ops->destroy = PCDestroy_VPBJacobi;
199: pc->ops->setfromoptions = NULL;
200: pc->ops->applyrichardson = NULL;
201: pc->ops->applysymmetricleft = NULL;
202: pc->ops->applysymmetricright = NULL;
203: return(0);
204: }