Actual source code: groppcg.c
1: #include <petsc/private/kspimpl.h>
3: /*
4: KSPSetUp_GROPPCG - Sets up the workspace needed by the GROPPCG method.
6: This is called once, usually automatically by KSPSolve() or KSPSetUp()
7: but can be called directly by KSPSetUp()
8: */
9: static PetscErrorCode KSPSetUp_GROPPCG(KSP ksp)
10: {
11: PetscFunctionBegin;
12: PetscCall(KSPSetWorkVecs(ksp, 6));
13: PetscFunctionReturn(PETSC_SUCCESS);
14: }
16: /*
17: KSPSolve_GROPPCG
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: */
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;
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);
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];
45: PetscCall(PCGetOperators(ksp->pc, &Amat, &Pmat));
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: }
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 */
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);
87: i = 0;
88: do {
89: ksp->its = i + 1;
90: i++;
92: PetscCall(VecDotBegin(p, s, &t));
93: PetscCall(PetscCommSplitReductionBegin(PetscObjectComm((PetscObject)p)));
95: PetscCall(KSP_PCApply(ksp, s, S)); /* S <- Bs */
97: PetscCall(VecDotEnd(p, s, &t));
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 */
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)));
112: PetscCall(KSP_MatMult(ksp, Amat, z, Z)); /* Z <- Az */
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));
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);
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 */
138: } while (i < ksp->max_it);
140: if (i >= ksp->max_it) ksp->reason = KSP_DIVERGED_ITS;
141: PetscFunctionReturn(PETSC_SUCCESS);
142: }
144: PETSC_INTERN PetscErrorCode KSPBuildResidual_CG(KSP, Vec, Vec, Vec *);
146: /*MC
147: KSPGROPPCG - A pipelined conjugate gradient method developed by Bill Gropp {cite}`eller2016scalable`. [](sec_pipelineksp)
149: Level: intermediate
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.
155: See also `KSPPIPECG`, which has only a single reduction that overlaps both the matrix-vector product and the preconditioner.
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)
160: Contributed by:
161: Pieter Ghysels, Universiteit Antwerpen, Intel Exascience lab Flanders
163: .seealso: [](ch_ksp), [](sec_pipelineksp), [](doc_faq_pipelined), `KSPCreate()`, `KSPPIPECG2()`, `KSPSetType()`, `KSPPIPECG`, `KSPPIPECR`, `KSPPGMRES`, `KSPCG`, `KSPCGUseSingleReduction()`
164: M*/
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));
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: }