def axb(A, B, X):
#Initial the X matrix
X.zeroEntries()
B.convert('dense')
X.convert('dense')
#Setup the precondition
pcr = PETSc.PC().create(comm=PETSc.COMM_WORLD)
pcr.setType('lu')
pcr.setFactorSolverType('superlu_dist')
pcr.setFactorShift(shift_type=1, amount=1e-10)
pcr.setFactorOrdering(ord_type='amd')
pcr.setFromOptions()
pcr.setOperators(A)
pcr.setUp()
F = pcr.getFactorMatrix()
#Solve the AX=B
F.matSolve(B, X)
X.assemblyBegin()
X.assemblyEnd()
X.convert('aij')
pcr.destroy()
return X
def setUp(self):
pc = self.pc = PETSc.PC()
ctx = PC_PYTHON_CLASS()
pc.createPython(ctx, comm=PETSc.COMM_SELF)
self.pc.prefix = self.PC_PREFIX
self.pc.setFromOptions()
assert self._getCtx().log['create'] == 1
assert self._getCtx().log['setFromOptions'] == 1
def help(args=None):
import sys, shlex
# program name
try:
prog = sys.argv[0]
except Exception:
prog = getattr(sys, 'executable', 'python')
# arguments
if args is None:
args = sys.argv[1:]
elif isinstance(args, str):
args = shlex.split(args)
else:
args = [str(a) for a in args]
# import and initialize
import petsc4py
petsc4py.init([prog, '-help'] + args)
from petsc4py import PETSc
# help dispatcher
COMM = PETSc.COMM_SELF
if 'vec' in args:
vec = PETSc.Vec().create(comm=COMM)
vec.setSizes(0)
vec.setFromOptions()
vec.destroy()
if 'mat' in args:
mat = PETSc.Mat().create(comm=COMM)
mat.setSizes([0, 0])
mat.setFromOptions()
mat.destroy()
if 'pc' in args:
pc = PETSc.PC().create(comm=COMM)
pc.setFromOptions()
pc.destroy()
if 'ksp' in args:
ksp = PETSc.KSP().create(comm=COMM)
ksp.setFromOptions()
ksp.destroy()
if 'snes' in args:
snes = PETSc.SNES().create(comm=COMM)
snes.setFromOptions()
snes.destroy()
if 'ts' in args:
ts = PETSc.TS().create(comm=COMM)
ts.setFromOptions()
ts.destroy()
if 'tao' in args:
tao = PETSc.TAO().create(comm=COMM)
tao.setFromOptions()
tao.destroy()
if 'dmda' in args:
dmda = PETSc.DMDA().create(comm=COMM)
dmda.setFromOptions()
dmda.destroy()
if 'dmplex' in args:
dmplex = PETSc.DMPlex().create(comm=COMM)
dmplex.setFromOptions()
dmplex.destroy()
def pc_setup(self):
self.pc = PETSc.PC().create(MPI.comm_world)
self.pc.setType("cholesky")
if hasattr(self.pc, 'setFactorSolverType'):
self.pc.setFactorSolverType("mumps")
elif hasattr(self.pc, 'setFactorSolverPackage'):
self.pc.setFactorSolverPackage('mumps')
else:
ColorPrint.print_warn('Could not configure preconditioner')
def solveLinear(self):
self.method_PETSc.solve(self.b_PETSc, self.x_PETSc)
convergedReason = self.method_PETSc.getConvergedReason()
if convergedReason < 0:
if self.rank == 0:
print "linear solver has not converged for reason", convergedReason
opt_init = self.method_PETSc.getPC().getType()
self.method_PETSc.setInitialGuessNonzero(0)
if convergedReason < 0:
# if self.rank==0: print "linear solver has not converged for reason", convergedReason
pc = _PETSc.PC()
pc.create(self.comm)
pc.setType('sor') #successive over relaxation
self.method_PETSc.setPC(pc)
self.method_PETSc.setOperators(self.A_PETSc)
if self.rank == 0:
print "change preconditionner to", self.method_PETSc.getPC(
).getType()
self.method_PETSc.solve(self.b_PETSc, self.x_PETSc)
convergedReason = self.method_PETSc.getConvergedReason()
self.method_PETSc.setInitialGuessNonzero(1)
if convergedReason < 0:
if self.rank == 0:
print "linear solver has not converged for reason", convergedReason
pc.setType('asm') #successive over relax
self.method_PETSc.setPC(pc)
self.method_PETSc.setOperators(self.A_PETSc)
if self.rank == 0:
print "change preconditionner to", self.method_PETSc.getPC(
).getType()
self.method_PETSc.solve(self.b_PETSc, self.x_PETSc)
convergedReason = self.method_PETSc.getConvergedReason()
if convergedReason < 0:
if self.rank == 0:
print "linear solver has not converged for reason", convergedReason
else:
if self.rank == 0: print "linear solver has finally converged"
self.method_PETSc.setInitialGuessNonzero(0)
#if self.rank==0: print "setInitialGuess back to non zero"
pc.setType(opt_init)
self.method_PETSc.setPC(pc)
self.method_PETSc.setOperators(self.A_PETSc)
if self.rank == 0:
print "change preconditionner back to", self.method_PETSc.getPC(
).getType()
return
def get_pc(self):
flag_3_way = self.pc_type in ("diagonal 3-way", "undrained 3-way")
ctx = PreconditionerCC(self.P.mat(), self.P_diff.mat(), self.index_map, flag_3_way, self.inner_ksp_type,
self.inner_pc_type, self.inner_rtol, self.inner_atol, self.inner_maxiter, self.inner_monitor, 1.0, 0.1, self.inner_accel_order, self.bcs_sub_pressure)
self.pc = PETSc.PC().create()
self.pc.setType('python')
self.pc.setPythonContext(ctx)
self.pc.setOperators(self.A.mat())
self.pc.setUp()
return self.pc
def solve_eigensystem(A, B, problem_type=SLEPc.EPS.ProblemType.GHEP):
# Create the results vectors
xr, tmp = A.getVecs()
xi, tmp = A.getVecs()
pc = PETSc.PC().create()
# pc.setType(pc.Type.HYPRE)
pc.setType(pc.Type.BJACOBI)
ksp = PETSc.KSP().create()
ksp.setType(ksp.Type.PREONLY)
ksp.setPC(pc)
F = SLEPc.ST().create()
F.setType(F.Type.PRECOND)
F.setKSP(ksp)
F.setShift(0)
# Setup the eigensolver
E = SLEPc.EPS().create()
E.setST(F)
E.setOperators(A, B)
E.setType(E.Type.LOBPCG)
E.setDimensions(10, PETSc.DECIDE)
E.setWhichEigenpairs(E.Which.SMALLEST_REAL)
E.setProblemType(problem_type)
E.setFromOptions()
# Solve the eigensystem
E.solve()
Print("")
its = E.getIterationNumber()
Print("Number of iterations of the method: %i" % its)
sol_type = E.getType()
Print("Solution method: %s" % sol_type)
nev, ncv, mpd = E.getDimensions()
Print("Number of requested eigenvalues: %i" % nev)
tol, maxit = E.getTolerances()
Print("Stopping condition: tol=%.4g, maxit=%d" % (tol, maxit))
nconv = E.getConverged()
Print("Number of converged eigenpairs: %d" % nconv)
if nconv > 0:
Print("")
Print(" k ||Ax-kx||/||kx|| ")
Print("----------------- ------------------")
for i in range(nconv):
k = E.getEigenpair(i, xr, xi)
error = E.computeError(i)
if k.imag != 0.0:
Print(" %9f%+9f j %12g" % (k.real, k.imag, error))
else:
Print(" %12f %12g" % (k.real, error))
Print("")
def __init__(self, mat, freedofs=None):
ngs.BaseMatrix.__init__(self)
self.ngsmat = mat
self.vecmap = VectorMapping(mat.row_pardofs, freedofs)
self.mat = CreatePETScMatrix(mat, freedofs)
self.precond = psc.PC().create()
self.precond.setType("gamg")
self.precond.setOperators(self.mat)
self.precond.setUp()
self.pscx, self.pscy = self.mat.createVecs()
def petsc_preconditioner(B, mat, A):
'''
Represent block_mat as a petsc matrix (by having corrent matrix
vector product
'''
pc = PETSc.PC().create()
pc.setType(PETSc.PC.Type.PYTHON)
pc.setOperators(mat) # To get the sizes right
pc.setPythonContext(WrapAction(B, A))
pc.setUp()
return pc
def setup_preconditioner(W, which, eps=None):
'''
This is a block diagonal preconditioner based on H1 x H^{-0.5}
'''
from xii.linalg.matrix_utils import as_petsc
from numpy import hstack
from petsc4py import PETSc
from hsmg import HsNorm
assert len(eps) == 2
mu_value, lmbda_value = eps
V, Q = W
# H1
u, v = TrialFunction(V), TestFunction(V)
b00 = inner(grad(u), grad(v)) * dx + inner(u, v) * dx
A = as_backend_type(assemble(b00))
# Attach rigid deformations to A
# Functions
Z = [
interpolate(Constant((1, 0)), V),
interpolate(Constant((0, 1)), V),
interpolate(Expression(('x[1]', '-x[0]'), degree=1), V)
]
# The basis
Z = VectorSpaceBasis([z.vector() for z in Z])
Z.orthonormalize()
A.set_nullspace(Z)
A.set_near_nullspace(Z)
A = as_petsc(A)
# Setup the preconditioner in petsc
pc = PETSc.PC().create()
pc.setType(PETSc.PC.Type.HYPRE)
pc.setOperators(A)
# Other options
opts = PETSc.Options()
opts.setValue('pc_hypre_boomeramg_cycle_type', 'V')
opts.setValue('pc_hypre_boomeramg_relax_type_all', 'symmetric-SOR/Jacobi')
opts.setValue('pc_hypre_boomeramg_coarsen_type', 'Falgout')
pc.setFromOptions()
# Wrap for cbc.block
B00 = BlockPC(pc)
# The Q norm via spectral
Qi = Q.sub(0).collapse()
B11 = inverse(VectorizedOperator(HsNorm(Qi, s=-0.5), Q))
return block_diag_mat([B00, B11])
def __init__(self, V, hdiv0=False, bc=None):
# FIXME: lift
assert V.ufl_element().family() == 'Raviart-Thomas'
assert V.ufl_element().degree() == 1
mesh = V.mesh()
assert mesh.geometry().dim() == 2
sigma, tau = TrialFunction(V), TestFunction(V)
a = inner(div(sigma), div(tau)) * dx
if not hdiv0:
a += inner(sigma, tau) * dx
f = Constant(np.zeros(V.ufl_element().value_shape()))
L = inner(tau, f) * dx
A, _ = assemble_system(a, L, bc)
# AMS setup
Q = FunctionSpace(mesh, 'CG', 1)
G = DiscreteOperators.build_gradient(V, Q)
pc = PETSc.PC().create(mesh.mpi_comm().tompi4py())
pc.setType('hypre')
pc.setHYPREType('ams')
# Attach gradient
pc.setHYPREDiscreteGradient(mat(G))
# Constant nullspace (in case not mass and bcs)
constants = [
vec(interpolate(c, V).vector())
for c in (Constant((1, 0)), Constant((0, 1)))
]
pc.setHYPRESetEdgeConstantVectors(*constants)
# NOTE: term mass term is accounted for automatically by Hypre
# unless pc.setPoissonBetaMatrix(None)
if hdiv0: pc.setHYPRESetBetaPoissonMatrix(None)
pc.setOperators(mat(A))
# FIXME: some defaults
pc.setFromOptions()
pc.setUp()
self.pc = pc
self.A = A # For creating vec
def __init__(self, A, prectype, parameters=None, pdes=1, nullspace=None):
from dolfin import info
from time import time
T = time()
Ad = A.down_cast().mat()
if nullspace:
from block.block_util import isscalar
ns = PETSc.NullSpace()
if isscalar(nullspace):
ns.create(constant=True)
else:
ns.create(constant=False,
vectors=[v.down_cast().vec() for v in nullspace])
try:
Ad.setNearNullSpace(ns)
except:
info(
'failed to set near null space (not supported in petsc4py version)'
)
self.A = A
self.petsc_prec = PETSc.PC()
self.petsc_prec.create()
self.petsc_prec.setType(prectype)
# self.petsc_prec.setOperators(Ad, Ad, PETSc.Mat.Structure.SAME_PRECONDITIONER)
self.petsc_prec.setOperators(Ad, Ad)
# Merge parameters into the options database
if parameters:
origOptions = PETSc.Options().getAll()
for key, val in iter(parameters.items()):
PETSc.Options().setValue(key, val)
# Create preconditioner based on the options database
self.petsc_prec.setFromOptions()
self.petsc_prec.setUp()
# Reset the options database
if parameters:
for key in iter(parameters.keys()):
PETSc.Options().delValue(key)
for key, val in iter(origOptions.items()):
PETSc.Options().setValue(key, val)
info('constructed %s preconditioner in %.2f s' %
(self.__class__.__name__, time() - T))
def setUp(self):
pc = self.pc = PETSc.PC()
pc.create(PETSc.COMM_SELF)
pc.setType(self.PC_TYPE)
module = __name__
factory = 'PC_PYTHON_CLASS'
self.pc.prefix = self.PC_PREFIX
OptDB = PETSc.Options(self.pc)
assert OptDB.prefix == self.pc.prefix
OptDB['pc_python_type'] = '%s.%s' % (module, factory)
self.pc.setFromOptions()
del OptDB['pc_python_type']
assert self._getCtx().log['create'] == 1
assert self._getCtx().log['setFromOptions'] == 1
ctx = self._getCtx()
self.assertEqual(getrefcount(ctx), 3)
def help(args=None):
import sys, shlex
# program name
try:
prog = sys.argv[0]
except Exception:
prog = getattr(sys, 'executable', 'python')
if args is None:
args = sys.argv[1:]
elif isinstance(args, str):
args = shlex.split(args)
else:
args = [str(a) for a in args]
import petsc4py
petsc4py.init([prog, '-help'] + args)
from petsc4py import PETSc
COMM = PETSc.COMM_SELF
if 'vec' in args:
vec = PETSc.Vec().create(comm=COMM)
vec.setSizes(0)
vec.setFromOptions()
del vec
if 'mat' in args:
mat = PETSc.Mat().create(comm=COMM)
mat.setSizes([0, 0])
mat.setFromOptions()
del mat
if 'ksp' in args:
ksp = PETSc.KSP().create(comm=COMM)
ksp.setFromOptions()
del ksp
if 'pc' in args:
pc = PETSc.PC().create(comm=COMM)
pc.setFromOptions()
del pc
if 'snes' in args:
snes = PETSc.SNES().create(comm=COMM)
snes.setFromOptions()
del snes
if 'ts' in args:
ts = PETSc.TS().create(comm=COMM)
ts.setFromOptions()
del ts
if 'da' in args:
da = PETSc.DA().create(comm=COMM)
da.setFromOptions()
del da
def pc_mat(pc, block):
'''Set pc for (non-block) operator'''
if isinstance(block, LU):
pc.setType('lu')
pc.setFactorPivot(1E-18)
return block.A
if isinstance(block, SUPERLU_LU):
pc.setType('lu')
pc.setFactorPivot(1E-18)
pc.setFactorSolverType('superlu')
return block.A
if isinstance(block, AMG):
pc.setType('hypre')
return block.A
if isinstance(block, Elasticity):
pc.setType('gamg')
return block.A
# FIXME: Very add hoc support for sum, this should recursive
if isinstance(block, block_add):
this, that = block.A, block.B
assert isinstance(this, precond) and isinstance(that, precond)
pc.setType('composite')
pc.setCompositeType(PETSc.PC.CompositeType.ADDITIVE)
for sub, op in enumerate((this, that)):
# Fake it
pc_ = PETSc.PC().create()
A = pc_mat(pc_, op)
pc.addCompositePC(pc_.getType())
pc_sub = pc.getCompositePC(sub)
# Make it
pc_mat(pc_sub, op)
pc_sub.setOperators(as_petsc(A))
mat = diagonal_matrix(op.A.size(0), 1)
return mat
assert False, type(block)
def testGetSetPC(self):
oldpc = self.ksp.getPC()
self.assertEqual(oldpc.getRefCount(), 2)
newpc = PETSc.PC()
newpc.create(self.ksp.getComm())
self.assertEqual(newpc.getRefCount(), 1)
self.ksp.setPC(newpc)
self.assertEqual(newpc.getRefCount(), 2)
self.assertEqual(oldpc.getRefCount(), 1)
oldpc.destroy()
self.assertFalse(bool(oldpc))
pc = self.ksp.getPC()
self.assertTrue(bool(pc))
self.assertEqual(pc, newpc)
self.assertEqual(pc.getRefCount(), 3)
newpc.destroy()
self.assertFalse(bool(newpc))
self.assertEqual(pc.getRefCount(), 2)
def setup_preconditioner(W, which, eps=None):
'''
This is a block diagonal preconditioner based on H1 x L2 x H^{-0.5}
'''
from block.algebraic.petsc import LumpedInvDiag
from xii.linalg.matrix_utils import as_petsc
from numpy import hstack
from petsc4py import PETSc
from hsmg import HsNorm
V, Q, Y = W
# H1
u, v = TrialFunction(V), TestFunction(V)
b00 = inner(grad(u), grad(v)) * dx + inner(u, v) * dx
# NOTE: since interpolation is broken with MINI I don't interpolate
# here the RM basis to attach the vectros to matrix
A = assemble(b00)
A = as_petsc(A)
# Setup the preconditioner in petsc
pc = PETSc.PC().create()
pc.setType(PETSc.PC.Type.HYPRE)
pc.setOperators(A)
# Other options
opts = PETSc.Options()
opts.setValue('pc_hypre_boomeramg_cycle_type', 'V')
opts.setValue('pc_hypre_boomeramg_relax_type_all', 'symmetric-SOR/Jacobi')
opts.setValue('pc_hypre_boomeramg_coarsen_type', 'Falgout')
pc.setFromOptions()
# Wrap for cbc.block
B00 = BlockPC(pc)
p, q = TrialFunction(Q), TestFunction(Q)
B11 = LumpedInvDiag(assemble(inner(p, q) * dx))
# The Y norm via spectral
Yi = Y.sub(0).collapse()
B22 = inverse(VectorizedOperator(HsNorm(Yi, s=-0.5), Y))
return block_diag_mat([B00, B11, B22])
def inertia_setup(self):
self.pc = PETSc.PC().create(MPI.comm_world)
prefix = "inertia_"
if prefix:
self.pc.setOptionsPrefix(prefix)
self.pc.setFromOptions()
for parameter, value in self.parameters['inertia'].items():
dolfin.PETScOptions.set(parameter, value)
log(
LogLevel.DEBUG, 'Setting up inertia solver: {}: {}'.format(
prefix + parameter, value))
dolfin.PETScOptions.set("inertia_ksp_type", "preonly")
dolfin.PETScOptions.set("inertia_pc_type", "cholesky")
dolfin.PETScOptions.set("inertia_pc_factor_mat_solver_type", "mumps")
dolfin.PETScOptions.set("inertia_mat_mumps_icntl_24", 1)
dolfin.PETScOptions.set("inertia_mat_mumps_icntl_13", 1)
self.pc.setFromOptions()
def compute_stability(self, mu, params, lb, ub, branch, z, v, w, Z, bcs,
J):
# trial = w
# test = v
test = TestFunction(Z)
comm = Z.mesh().mpi_comm()
# a dummy linear form, needed to construct the SystemAssembler
b = inner(
Function(Z), test
) * dx # a dummy linear form, needed to construct the SystemAssembler
# Build the LHS matrix
A = PETScMatrix(comm)
asm = SystemAssembler(J, b, bcs)
asm.assemble(A)
pc = PETSc.PC().create(comm)
pc.setOperators(A.mat())
pc.setType("cholesky")
if PETSc.Sys.getVersion()[0:2] < (3, 9):
pc.setFactorSolverPackage("mumps")
else:
pc.setFactorSolverType("mumps")
pc.setUp()
Factor = pc.getFactorMatrix()
(neg, zero, pos) = Factor.getInertia()
inertia = [neg, zero, pos]
expected_dim = 0
# Nocedal & Wright, theorem 16.3
if neg == expected_dim:
is_stable = True
else:
is_stable = False
d = {"stable": is_stable}
return inertia
def initialize(self, pc):
options_prefix = pc.getOptionsPrefix()
A, P = pc.getOperators()
dm = pc.getDM()
appctx = dm.getAppCtx()
F = appctx[0].F
V = F.arguments()[0].function_space()
# create vertically constant version of functionspace
mesh = V.mesh()
hcell, vcell = mesh.ufl_cell().sub_cells()
hele, _ = V.ufl_element().sub_elements()
vele = FiniteElement("R", vcell, 0)
ele = TensorProductElement(hele, vele)
V_1layer = FunctionSpace(mesh, ele)
# create interpolation matrix Prol from V_1layer to V
v = TestFunction(V_1layer)
interp = Interpolator(v, V)
Prol = interp.callable().handle
self.pc = PETSc.PC().create(comm=pc.comm)
self.pc.setOptionsPrefix(options_prefix + 'lumped_')
# hack: we actually want to call self.pc.setMGGalerkin()
# but there appears to be no petsc4py interface
options = PETSc.Options()
options[options_prefix + 'lumped_pc_mg_galerkin'] = 'both'
self.pc.setOperators(A, P)
self.pc.setType("mg")
self.pc.setMGLevels(2)
self.pc.setMGInterpolation(1, Prol)
self.pc.setFromOptions()
self.pc.setUp()
self.update(pc)
poisson_problem = poisson(dm, {"dx": 0.1, "dy": 0.1, "dz": 0.1})
ksp = PETSc.KSP().create()
ksp.setDM(dm)
ksp.setComputeRHS(poisson_problem.rhs)
ksp.setComputeOperators(poisson_problem.compute_operators)
ksp.setFromOptions()
if (comm.size <= 1):
if not(OptDB.hasName("ksp_type")):
ksp.setType(PETSc.KSP().Type.PREONLY)
if not(OptDB.hasName("pc_type")):
ksp.getPC().setType(PETSc.PC().Type.LU)
else:
if not(OptDB.hasName("ksp_type")):
ksp.setType(PETSc.KSP().Type.GMRES)
if not(OptDB.hasName("pc_type")):
ksp.getPC().setType(PETSc.PC().Type.BJACOBI)
field = dm.createGlobalVector()
sol = field.duplicate()
start = time.clock()
cProfile.run("ksp.solve(field,sol)", sort="time")
end = time.clock()
print(ksp.getSolution().getArray()[:])
def __init__(self, comm, cpu, rank, dim, maxit, relative, solver):
self.A_PETSc = _PETSc.Mat()
self.A_PETSc_c = _PETSc.Mat()
self.A_PETSc_p = _PETSc.Mat()
self.method_PETSc = _PETSc.KSP()
self.null_space = None
self.comm = comm
self.dim = dim
self.cpu = cpu
self.rank = rank
csr = (_np.zeros(self.dim + 1, dtype=_np.int32),
_np.zeros(0, dtype=_np.int32), _np.zeros(0))
self.A_PETSc.create(self.comm)
self.A_PETSc.setSizes([self.dim, self.dim])
self.A_PETSc.setType('mpiaij')
self.A_PETSc.setPreallocationCSR(csr)
self.A_PETSc_p.create(self.comm)
self.A_PETSc_p.setSizes([self.dim, self.dim])
self.A_PETSc_p.setType('mpiaij')
self.A_PETSc_p.setPreallocationCSR(csr)
self.A_PETSc_c.create(self.comm)
self.A_PETSc_c.setSizes([self.dim, self.dim])
self.A_PETSc_c.setType('mpiaij')
self.A_PETSc_c.setPreallocationCSR(csr)
self.x_PETSc, self.b_PETSc = self.A_PETSc_p.getVecs()
length_x = self.b_PETSc.getOwnershipRange(
)[1] - self.b_PETSc.getOwnershipRange()[0]
self.method_PETSc.create(self.comm)
if solver == "pressure":
self.method_PETSc.setType('gmres')
#self.method_PETSc.setType('bicg')
#self.method_PETSc.setType('bcgs')
#self.method_PETSc.setType('cg') #JADIM
#self.method_PETSc.setType('preonly')
pc = _PETSc.PC()
pc.create(self.comm)
pc.setType('asm') #additive schwarz
#pc.setType('pbjacobi') #JADIM
#pc.setType('sor')
#pc.setASMType(2)
# pc.setType('ilu')
self.method_PETSc.setPC(pc)
#print self.method_PETSc.getPCSide()
if solver == "velocity":
self.method_PETSc.setType('bcgs')
#self.method_PETSc.setType('gmres')
#self.method_PETSc.setType('cg') #JADIM
pc = _PETSc.PC()
pc.create(self.comm)
pc.setType('asm') #additive schwarz
#pc.setType('pbjacobi') #JADIM
#pc.setType('sor')
self.method_PETSc.setPC(pc)
pc = self.method_PETSc.getPC()
if self.rank == 0:
print solver, self.method_PETSc.getType(), pc.getType(
), relative, maxit
#self.method_PETSc.setInitialGuessNonzero(1)
self.method_PETSc.setInitialGuessNonzero(0)
self.method_PETSc.setTolerances(rtol=relative,
atol=1e-50,
divtol=1e5,
max_it=maxit)
a = inner(grad(u), grad(v)) * dx
L = f * v * dx + g * v * ds
# Compute solution
u = Function(V)
A, b = assemble_system(a, L, bc)
# Fetch underlying PETSc objects
A_petsc = as_backend_type(A).mat()
b_petsc = as_backend_type(b).vec()
x_petsc = as_backend_type(u.vector()).vec()
# Create solver, apply preconditioner and solve system
ksp = PETSc.KSP().create()
ksp.setOperators(A_petsc)
pc = PETSc.PC().create()
pc.setOperators(A_petsc)
pc.setType(pc.Type.JACOBI)
ksp.setPC(pc)
ksp.solve(b_petsc, x_petsc)
# Plot solution
plot(u, interactive=True)
# Save solution to file
file = File("poisson.pvd")
file << u
def build_preconditioner(self):
opts = PETSc.Options().getAll()
if 'snes_lag_preconditioner' in opts:
if opts['snes_lag_preconditioner'] == "-1" and hasattr(self, 'P'):
self.P.setReusePreconditioner(True)
return
self.P = PETSc.PC().create(self.comm)
# Set the operator appropriately, taking the right subsets
(Jmat, Pmat) = self.get_submat()
self._subJ = PETScMatrix(Jmat)
if self.nullsp is not None:
Jmat.setNearNullSpace(self.nullsp)
self.P.setOperators(Jmat, Pmat)
(self._ptmpvec1, self._ptmpvec2) = map(PETScVector, Jmat.createVecs())
self.P.setType("lu")
self.P.setFactorSolverPackage("mumps")
self.P.setOptionsPrefix(self.pc_prefix)
self.P.setFromOptions()
if self.P.getType() == "fieldsplit" and Jmat.getBlockSize(
) <= 1 and self.eqn_subindices is None:
if self.fieldsplit_is is None:
self.fieldsplit_is = []
for i in range(self.function_space.num_sub_spaces()):
subdofs = self.function_space.sub(i).dofmap().dofs()
iset = PETSc.IS().createGeneral(subdofs)
self.fieldsplit_is.append(("%s" % i, iset))
if self.inact_subindices is None:
self.P.setFieldSplitIS(*self.fieldsplit_is)
else:
# OK. Get the dofs from the inactive set and figure out which split they're from.
# Agh. VIs make everything so complicated.
fsets = [
set(field[1].getIndices()) for field in self.fieldsplit_is
]
inact_fieldsplit_is = {}
for field in self.fieldsplit_is:
inact_fieldsplit_is[int(field[0])] = []
# OK. Suppose you have a 6x6 matrix and the splits are [1, 2, 3, 4]; [5, 6].
# Suppose further that the inactive indices are [2, 3, 4, 5]. Then what we
# want to produce for the fieldsplit of the reduced problem is [1, 2, 3]; [4].
# In other words, we add the *index* of the inactive dof to the reduced split
# if the inactive dof is in the full split.
# We also need the offset of how many dofs all earlier processes own.
from mpi4py import MPI as MPI4
offset = MPI4.COMM_WORLD.exscan(
self.inact_subindices.getLocalSize())
if offset is None: offset = 0
for (i, idx) in enumerate(self.inact_subindices.getIndices()):
for (j, fset) in enumerate(fsets):
if idx in fset:
inact_fieldsplit_is[j].append(offset + i)
break
inact_input = []
for j in inact_fieldsplit_is:
iset = PETSc.IS().createGeneral(inact_fieldsplit_is[j],
comm=self.comm)
inact_input.append(("%s" % j, iset))
for (orig_data, vi_data) in zip(self.fieldsplit_is,
inact_input):
orig_iset = orig_data[1]
vi_iset = vi_data[1]
if orig_iset.getSizes() == vi_iset.getSizes():
nullsp = orig_iset.query("nearnullspace")
if nullsp is not None:
vi_iset.compose("nearnullspace", nullsp)
self.P.setFieldSplitIS(*inact_input)
if self.eqn_subindices is not None:
nullsp = self.eqn_subindices.query("nearnullspace")
if nullsp is not None:
op = self.P.getOperators()[0]
op.setNearNullSpace(nullsp)
self.preconditioner_hook() # argh, such a hack.
self.P.setUp()