def solve_petsc(self, solver_data_list, rhs_list, nu, *args, **kwargs):
#try catch for the petsc4py use in multiple rhs solve
try:
import petsc4py
petsc4py.init(sys.argv)
from petsc4py import PETSc
from pysit.util.wrappers.petsc import PetscWrapper
except ImportError:
raise ImportError(
'petsc4py is not installed, please install it and try again')
flag = 'petsc' in kwargs
if flag == 0:
petsc = None
else:
petsc = kwargs['petsc']
if petsc is not None:
if len(solver_data_list) != len(rhs_list):
raise ValueError(
'solver and right hand side list must be the same size')
else:
#Building the Helmholtz operator for petsc
H = self._build_helmholtz_operator(nu)
ndof = H.shape[1]
nshot = len(rhs_list)
# creating the B rhs Matrix
B = PETSc.Mat().createDense([ndof, nshot])
B.setUp()
for i in range(nshot):
# added by zhilong [0:ndof]
B.setValues(list(range(0, ndof)), [i], rhs_list[i][0:ndof])
B.assemblyBegin()
B.assemblyEnd()
# call the wrapper to solve the system
wrapper = PetscWrapper()
try:
linear_solver = wrapper.factorize(H, petsc,
PETSc.COMM_WORLD)
Uhat = linear_solver(B.getDenseArray())
except:
raise SyntaxError(
'petsc = ' + str(petsc) +
' is not a correct solver you can only use \'superlu_dist\', \'mumps\' or \'mkl_pardiso\' '
)
numb = 0
for solver_data in solver_data_list:
u = Uhat[:, numb]
u = np.append(
u, np.zeros(2 * ndof)
) # added by zhilong, assume that we do not need the auxiliary wavefields
u.shape = solver_data.k.data.shape
solver_data.k.data = u
numb += 1
def solve_petsc_uhat(self,
solver,
rhs_list,
frequency,
petsc='mumps',
*args,
**kwargs):
#try catch for the petsc4py use in multiple rhs solve
#use only in the data generation where we do not need to compute
#the system for the whole auxiliary fields
try:
import petsc4py
petsc4py.init(sys.argv)
from petsc4py import PETSc
from pysit.util.wrappers.petsc import PetscWrapper
except ImportError:
raise ImportError(
'petsc4py is not installed, please install it and try again')
#Building the Helmholtz operator for petsc
H = self._build_helmholtz_operator(frequency)
ndof = H.shape[1]
nshot = len(rhs_list)
# if the compact operator is used we do not need to slice the solution
if self.compact:
usize = ndof
else:
nwfield = len(self.WavefieldVector.aux_names) + 1
usize = ndof / nwfield
# creating the B rhs Matrix
B = PETSc.Mat().createDense([ndof, nshot])
B.setUp()
for i in range(nshot):
B.setValues(list(range(0, ndof)), [i],
rhs_list[i][0:ndof]) # added by zhilong [0:ndof]
B.assemblyBegin()
B.assemblyEnd()
# call the wrapper to solve the system
wrapper = PetscWrapper()
try:
linear_solver = wrapper.factorize(H, petsc, PETSc.COMM_WORLD)
Uhat = linear_solver(B.getDenseArray())
except:
raise SyntaxError(
'petsc = ' + str(petsc) +
' is not a correct solver you can only use \'superlu_dist\', \'mumps\' or \'mkl_pardiso\' '
)
Uhat = Uhat[range(usize), :]
return Uhat
def solve_petsc(self, solver_data_list, rhs_list, nu, *args, **kwargs ):
#try catch for the petsc4py use in multiple rhs solve
try:
import petsc4py
petsc4py.init(sys.argv)
from petsc4py import PETSc
from pysit.util.wrappers.petsc import PetscWrapper
except ImportError:
raise ImportError('petsc4py is not installed, please install it and try again')
petsc = kwargs['petsc']
if petsc is not None:
if len(solver_data_list) != len(rhs_list):
raise ValueError('solver and right hand side list must be the same size')
else:
#Building the Helmholtz operator for petsc
H = self._build_helmholtz_operator(nu)
ndof = H.shape[1]
nshot = len(rhs_list)
# creating the B rhs Matrix
B = PETSc.Mat().createDense([ndof, nshot])
B.setUp()
for i in range(nshot):
# added by zhilong [0:ndof]
B.setValues(list(range(0, ndof)), [i], rhs_list[i][0:ndof])
B.assemblyBegin()
B.assemblyEnd()
# call the wrapper to solve the system
wrapper = PetscWrapper()
try:
linear_solver = wrapper.factorize(H, petsc, PETSc.COMM_WORLD)
Uhat = linear_solver(B.getDenseArray())
except:
raise SyntaxError('petsc = '+str(petsc)+' is not a correct solver you can only use \'superlu_dist\', \'mumps\' or \'mkl_pardiso\' ')
numb = 0
for solver_data in solver_data_list:
u = Uhat[:,numb]
u = np.append(u, np.zeros(2*ndof)) # added by zhilong, assume that we do not need the auxiliary wavefields
u.shape = solver_data.k.data.shape
solver_data.k.data = u
numb += 1
def solve_petsc_uhat(self, solver, rhs_list, frequency, petsc='mumps', *args, **kwargs):
#try catch for the petsc4py use in multiple rhs solve
#use only in the data generation where we do not need to compute
#the system for the whole auxiliary fields
try:
import petsc4py
petsc4py.init(sys.argv)
from petsc4py import PETSc
from pysit.util.wrappers.petsc import PetscWrapper
except ImportError:
raise ImportError('petsc4py is not installed, please install it and try again')
#Building the Helmholtz operator for petsc
H = self._build_helmholtz_operator(frequency)
ndof = H.shape[1]
nshot = len(rhs_list)
# if the compact operator is used we do not need to slice the solution
if self.compact:
usize = ndof
else:
nwfield = len(self.WavefieldVector.aux_names) + 1
usize = ndof/nwfield
# creating the B rhs Matrix
B = PETSc.Mat().createDense([ndof, nshot])
B.setUp()
for i in range(nshot):
B.setValues(range(0, ndof), [i], rhs_list[i])
B.assemblyBegin()
B.assemblyEnd()
# call the wrapper to solve the system
wrapper = PetscWrapper()
try:
linear_solver = wrapper.factorize(H, petsc, PETSc.COMM_WORLD)
Uhat = linear_solver(B.getDenseArray())
except:
raise SyntaxError('petsc = '+str(petsc)+' is not a correct solver you can only use \'superlu_dist\', \'mumps\' or \'mkl_pardiso\' ')
Uhat = Uhat[xrange(usize),:]
return Uhat
def factorize_at_freq(self, freq):
#This function follows the same idea as the 'factorized' routine of scipy.sparse.linalg.dsolve.linsolve
if self.petscdict is None: #umfdict contains the umf objects for every frequency.
self.petscdict = dict()
#The matrix
A = self.solver.linear_operators[freq]
print "Factorizing matrix"
wrapper = PetscWrapper()
linear_solver = wrapper.factorize(A, self.petsc, PETSc.COMM_WORLD)
self.petscdict[freq] = linear_solver
def solve(b):
warnings.warn(
'Right now only solving for one shot at a time. Petsc can take many RHS at the same time. Need to change code...'
)
#b is a complex128 vector. The solve routine of umfpack expects a vector with real and imaginary part (two float64 vectors).
if b.dtype != "complex128":
raise Exception(
"Efficient only for complex128 systems. Need to write more optimal routines for other dtypes."
)
ndof = b.size #Assuming b is length of single shot RHS vector
# creating the B rhs Matrix. Borrowed from the standard CDA solver, its petsc solve routine
B = PETSc.Mat().createDense(
[ndof,
1]) #right now 1 refers to us passing only 1 shot at a time
B.setUp()
B.setValues(range(0, ndof), [0], b) #only set up one shot for now
B.assemblyBegin()
B.assemblyEnd()
x = self.petscdict[freq](B.getDenseArray())
return x.flatten(
) #NOW THAT I AM USING ONE SHOT AT A TIME I AM STILL EXPECTING A FLAT RETURN VECTOR
return solve
def _build_petsc_mkl_pardiso_solver(self, nu):
dummy_wrapper = PetscWrapper()
return dummy_wrapper.factorize(self.linear_operators[nu],
'mkl_pardiso')
def _build_petsc_superlu_dist_solver(self, nu):
dummy_wrapper = PetscWrapper()
return dummy_wrapper.factorize(self.linear_operators[nu],
'superlu_dist')
def _build_petsc_mumps_solver(self, nu):
dummy_wrapper = PetscWrapper()
return dummy_wrapper.factorize(self.linear_operators[nu], 'mumps')
def _build_petsc_mkl_pardiso_solver(self, nu):
dummy_wrapper = PetscWrapper()
return dummy_wrapper.factorize(self.linear_operators[nu], 'mkl_pardiso')
def _build_petsc_superlu_dist_solver(self, nu):
dummy_wrapper = PetscWrapper()
return dummy_wrapper.factorize(self.linear_operators[nu], 'superlu_dist')
def _build_petsc_mumps_solver(self, nu):
dummy_wrapper = PetscWrapper()
return dummy_wrapper.factorize(self.linear_operators[nu], 'mumps')
