1 #include <petsc/private/kspimpl.h>
2
3 /*
4 KSPSetUp_GROPPCG - Sets up the workspace needed by the GROPPCG method.
5
6 This is called once, usually automatically by KSPSolve() or KSPSetUp()
7 but can be called directly by KSPSetUp()
8 */
KSPSetUp_GROPPCG(KSP ksp)9 static PetscErrorCode KSPSetUp_GROPPCG(KSP ksp)
10 {
11 PetscFunctionBegin;
12 PetscCall(KSPSetWorkVecs(ksp, 6));
13 PetscFunctionReturn(PETSC_SUCCESS);
14 }
15
16 /*
17 KSPSolve_GROPPCG
18
19 Input Parameter:
20 . ksp - the Krylov space object that was set to use conjugate gradient, by, for
21 example, KSPCreate(MPI_Comm,KSP *ksp); KSPSetType(ksp,KSPCG);
22 */
KSPSolve_GROPPCG(KSP ksp)23 static PetscErrorCode KSPSolve_GROPPCG(KSP ksp)
24 {
25 PetscInt i;
26 PetscScalar alpha, beta = 0.0, gamma, gammaNew, t;
27 PetscReal dp = 0.0;
28 Vec x, b, r, p, s, S, z, Z;
29 Mat Amat, Pmat;
30 PetscBool diagonalscale;
31
32 PetscFunctionBegin;
33 PetscCall(PCGetDiagonalScale(ksp->pc, &diagonalscale));
34 PetscCheck(!diagonalscale, PetscObjectComm((PetscObject)ksp), PETSC_ERR_SUP, "Krylov method %s does not support diagonal scaling", ((PetscObject)ksp)->type_name);
35
36 x = ksp->vec_sol;
37 b = ksp->vec_rhs;
38 r = ksp->work[0];
39 p = ksp->work[1];
40 s = ksp->work[2];
41 S = ksp->work[3];
42 z = ksp->work[4];
43 Z = ksp->work[5];
44
45 PetscCall(PCGetOperators(ksp->pc, &Amat, &Pmat));
46
47 ksp->its = 0;
48 if (!ksp->guess_zero) {
49 PetscCall(KSP_MatMult(ksp, Amat, x, r)); /* r <- b - Ax */
50 PetscCall(VecAYPX(r, -1.0, b));
51 } else {
52 PetscCall(VecCopy(b, r)); /* r <- b (x is 0) */
53 }
54
55 PetscCall(KSP_PCApply(ksp, r, z)); /* z <- Br */
56 PetscCall(VecCopy(z, p)); /* p <- z */
57 PetscCall(VecDotBegin(r, z, &gamma)); /* gamma <- z'*r */
58 PetscCall(PetscCommSplitReductionBegin(PetscObjectComm((PetscObject)r)));
59 PetscCall(KSP_MatMult(ksp, Amat, p, s)); /* s <- Ap */
60 PetscCall(VecDotEnd(r, z, &gamma)); /* gamma <- z'*r */
61
62 switch (ksp->normtype) {
63 case KSP_NORM_PRECONDITIONED:
64 /* This could be merged with the computation of gamma above */
65 PetscCall(VecNorm(z, NORM_2, &dp)); /* dp <- z'*z = e'*A'*B'*B*A'*e' */
66 break;
67 case KSP_NORM_UNPRECONDITIONED:
68 /* This could be merged with the computation of gamma above */
69 PetscCall(VecNorm(r, NORM_2, &dp)); /* dp <- r'*r = e'*A'*A*e */
70 break;
71 case KSP_NORM_NATURAL:
72 KSPCheckDot(ksp, gamma);
73 dp = PetscSqrtReal(PetscAbsScalar(gamma)); /* dp <- r'*z = r'*B*r = e'*A'*B*A*e */
74 break;
75 case KSP_NORM_NONE:
76 dp = 0.0;
77 break;
78 default:
79 SETERRQ(PetscObjectComm((PetscObject)ksp), PETSC_ERR_SUP, "%s", KSPNormTypes[ksp->normtype]);
80 }
81 PetscCall(KSPLogResidualHistory(ksp, dp));
82 PetscCall(KSPMonitor(ksp, 0, dp));
83 ksp->rnorm = dp;
84 PetscCall((*ksp->converged)(ksp, 0, dp, &ksp->reason, ksp->cnvP)); /* test for convergence */
85 if (ksp->reason) PetscFunctionReturn(PETSC_SUCCESS);
86
87 i = 0;
88 do {
89 ksp->its = i + 1;
90 i++;
91
92 PetscCall(VecDotBegin(p, s, &t));
93 PetscCall(PetscCommSplitReductionBegin(PetscObjectComm((PetscObject)p)));
94
95 PetscCall(KSP_PCApply(ksp, s, S)); /* S <- Bs */
96
97 PetscCall(VecDotEnd(p, s, &t));
98
99 alpha = gamma / t;
100 PetscCall(VecAXPY(x, alpha, p)); /* x <- x + alpha * p */
101 PetscCall(VecAXPY(r, -alpha, s)); /* r <- r - alpha * s */
102 PetscCall(VecAXPY(z, -alpha, S)); /* z <- z - alpha * S */
103
104 if (ksp->normtype == KSP_NORM_UNPRECONDITIONED) {
105 PetscCall(VecNormBegin(r, NORM_2, &dp));
106 } else if (ksp->normtype == KSP_NORM_PRECONDITIONED) {
107 PetscCall(VecNormBegin(z, NORM_2, &dp));
108 }
109 PetscCall(VecDotBegin(r, z, &gammaNew));
110 PetscCall(PetscCommSplitReductionBegin(PetscObjectComm((PetscObject)r)));
111
112 PetscCall(KSP_MatMult(ksp, Amat, z, Z)); /* Z <- Az */
113
114 if (ksp->normtype == KSP_NORM_UNPRECONDITIONED) {
115 PetscCall(VecNormEnd(r, NORM_2, &dp));
116 } else if (ksp->normtype == KSP_NORM_PRECONDITIONED) {
117 PetscCall(VecNormEnd(z, NORM_2, &dp));
118 }
119 PetscCall(VecDotEnd(r, z, &gammaNew));
120
121 if (ksp->normtype == KSP_NORM_NATURAL) {
122 KSPCheckDot(ksp, gammaNew);
123 dp = PetscSqrtReal(PetscAbsScalar(gammaNew)); /* dp <- r'*z = r'*B*r = e'*A'*B*A*e */
124 } else if (ksp->normtype == KSP_NORM_NONE) {
125 dp = 0.0;
126 }
127 ksp->rnorm = dp;
128 PetscCall(KSPLogResidualHistory(ksp, dp));
129 PetscCall(KSPMonitor(ksp, i, dp));
130 PetscCall((*ksp->converged)(ksp, i, dp, &ksp->reason, ksp->cnvP));
131 if (ksp->reason) PetscFunctionReturn(PETSC_SUCCESS);
132
133 beta = gammaNew / gamma;
134 gamma = gammaNew;
135 PetscCall(VecAYPX(p, beta, z)); /* p <- z + beta * p */
136 PetscCall(VecAYPX(s, beta, Z)); /* s <- Z + beta * s */
137
138 } while (i < ksp->max_it);
139
140 if (i >= ksp->max_it) ksp->reason = KSP_DIVERGED_ITS;
141 PetscFunctionReturn(PETSC_SUCCESS);
142 }
143
144 PETSC_INTERN PetscErrorCode KSPBuildResidual_CG(KSP, Vec, Vec, Vec *);
145
146 /*MC
147 KSPGROPPCG - A pipelined conjugate gradient method developed by Bill Gropp {cite}`eller2016scalable`. [](sec_pipelineksp)
148
149 Level: intermediate
150
151 Notes:
152 This method has two reductions, one of which is overlapped with the matrix-vector product and one of which is
153 overlapped with the preconditioner.
154
155 See also `KSPPIPECG`, which has only a single reduction that overlaps both the matrix-vector product and the preconditioner.
156
157 MPI configuration may be necessary for reductions to make asynchronous progress, which is important for performance of pipelined methods.
158 See [](doc_faq_pipelined)
159
160 Contributed by:
161 Pieter Ghysels, Universiteit Antwerpen, Intel Exascience lab Flanders
162
163 .seealso: [](ch_ksp), [](sec_pipelineksp), [](doc_faq_pipelined), `KSPCreate()`, `KSPPIPECG2()`, `KSPSetType()`, `KSPPIPECG`, `KSPPIPECR`, `KSPPGMRES`, `KSPCG`, `KSPCGUseSingleReduction()`
164 M*/
165
KSPCreate_GROPPCG(KSP ksp)166 PETSC_EXTERN PetscErrorCode KSPCreate_GROPPCG(KSP ksp)
167 {
168 PetscFunctionBegin;
169 PetscCall(KSPSetSupportedNorm(ksp, KSP_NORM_UNPRECONDITIONED, PC_LEFT, 2));
170 PetscCall(KSPSetSupportedNorm(ksp, KSP_NORM_PRECONDITIONED, PC_LEFT, 2));
171 PetscCall(KSPSetSupportedNorm(ksp, KSP_NORM_NATURAL, PC_LEFT, 2));
172 PetscCall(KSPSetSupportedNorm(ksp, KSP_NORM_NONE, PC_LEFT, 1));
173
174 ksp->ops->setup = KSPSetUp_GROPPCG;
175 ksp->ops->solve = KSPSolve_GROPPCG;
176 ksp->ops->destroy = KSPDestroyDefault;
177 ksp->ops->view = NULL;
178 ksp->ops->setfromoptions = NULL;
179 ksp->ops->buildsolution = KSPBuildSolutionDefault;
180 ksp->ops->buildresidual = KSPBuildResidual_CG;
181 PetscFunctionReturn(PETSC_SUCCESS);
182 }
183