diff --git a/examples/scalar_diffusion/scalar_diffusion.py b/examples/scalar_diffusion/scalar_diffusion.py
index 9ed569c4638a3a9221c11cd7e1b2516079823d15..e1db7dabc16712c598cf87c4fa9c36d8c34a110a 100755
--- a/examples/scalar_diffusion/scalar_diffusion.py
+++ b/examples/scalar_diffusion/scalar_diffusion.py
@@ -15,15 +15,16 @@ def compute(args):
from hysop.constants import Implementation, ComputeGranularity
from hysop.operator.directional.diffusion_dir import DirectionalDiffusion
+ from hysop.operator.diffusion import Diffusion
from hysop.methods import StrangOrder, TimeIntegrator, SpaceDiscretization
from hysop.numerics.splitting.strang import StrangSplitting
-
+
# Define domain
dim = args.ndim
npts = args.npts
box = Box(origin=args.box_origin, length=args.box_length, dim=dim)
-
+
# Get default MPI Parameters from domain (even for serial jobs)
mpi_params = MPIParams(comm=box.task_comm,
task_id=box.current_task())
@@ -32,75 +33,84 @@ def compute(args):
dt = ScalarParameter('dt', dtype=args.dtype)
nu = ScalarParameter('nu', initial_value=args.nu, const=True, dtype=args.dtype)
scalar = Field(domain=box, name='S0', nb_components=1, dtype=args.dtype)
-
+
# Diffusion operator discretization parameters
- method = {
+ method = {
ComputeGranularity: args.compute_granularity,
SpaceDiscretization: args.fd_order,
TimeIntegrator: args.time_integrator,
}
-
+
# Setup implementation specific variables
impl = args.impl
extra_op_kwds = { 'mpi_params': mpi_params }
- if (impl is Implementation.PYTHON):
- pass
+
+ # Create the problem we want to solve and insert our
+ # directional splitting subgraph.
+ # Add a writer of input field at given frequency.
+ problem = Problem(method=method)
+
+ if (impl is Implementation.FORTRAN):
+ # Build the directional diffusion operator
+ diffusion = Diffusion(implementation=impl,
+ name='diffusion', dt=dt,
+ Fin=scalar, nu=nu,
+ variables = {scalar: npts},
+ **extra_op_kwds)
+ problem.insert(diffusion)
elif (impl is Implementation.OPENCL):
# For the OpenCL implementation we need to setup the compute device
# and configure how the code is generated and compiled at runtime.
-
+
# Create an explicit OpenCL context from user parameters
from hysop.backend.device.opencl.opencl_tools import get_or_create_opencl_env
- cl_env = get_or_create_opencl_env(mpi_params=mpi_params, platform_id=args.cl_platform_id,
+ cl_env = get_or_create_opencl_env(mpi_params=mpi_params, platform_id=args.cl_platform_id,
device_id=args.cl_device_id)
-
+
# Configure OpenCL kernel generation and tuning (already done by HysopArgParser)
from hysop.methods import OpenClKernelConfig
method[OpenClKernelConfig] = args.opencl_kernel_config
-
+
# Setup opencl specific extra operator keyword arguments
extra_op_kwds['cl_env'] = cl_env
+
+ # Build the directional diffusion operator
+ diffusion = DirectionalDiffusion(implementation=impl,
+ name='diffusion', dt=dt,
+ fields=scalar, coeffs=nu,
+ variables = {scalar: npts},
+ **extra_op_kwds)
+
+ # Build the directional splitting operator graph
+ splitting = StrangSplitting(splitting_dim=dim, order=args.strang_order)
+ splitting.push_operators(diffusion)
+
+ problem.insert(splitting)
else:
msg='Unknown implementation \'{}\'.'.format(impl)
raise ValueError(msg)
-
- # Build the directional diffusion operator
- diffusion = DirectionalDiffusion(implementation=impl,
- name='diffusion', dt=dt,
- fields=scalar, coeffs=nu,
- variables = {scalar: npts},
- **extra_op_kwds)
-
- # Build the directional splitting operator graph
- splitting = StrangSplitting(splitting_dim=dim, order=args.strang_order)
- splitting.push_operators(diffusion)
-
- # Create the problem we want to solve and insert our
- # directional splitting subgraph.
- # Add a writer of input field at given frequency.
- problem = Problem(method=method)
- problem.insert(splitting)
+
problem.dump_inputs(fields=scalar, filename='S0', frequency=args.dump_freq, **extra_op_kwds)
problem.build(args)
-
+
# If a visu_rank was provided, and show_graph was set,
# display the graph on the given process rank.
if args.display_graph:
problem.display(args.visu_rank)
-
+
# Initialize discrete scalar field
problem.initialize_field(scalar, formula=init_scalar)
-
- # Create a simulation and solve the problem
+
+ # Create a simulation and solve the problem
# (do not forget to specify the dt parameter here)
- simu = Simulation(start=args.tstart, end=args.tend,
+ simu = Simulation(start=args.tstart, end=args.tend,
nb_iter=args.nb_iter, max_iter=args.max_iter,
times_of_interest=args.dump_times,
dt=dt, dt0=args.dt)
-
- # Finally solve the problem
+
+ # Finally solve the problem
problem.solve(simu, dry_run=args.dry_run)
-
+
# Finalize
problem.finalize()
@@ -117,7 +127,7 @@ if __name__=='__main__':
description+='Diffuse a scalar with a given diffusion coefficient. '
description+='\n\nThe diffusion operator is directionally splitted resulting '
description+='in the use of one or more diffusion operators per direction.'
-
+
super(ScalarDiffusionArgParser, self).__init__(
prog_name=prog_name,
description=description,
@@ -136,8 +146,8 @@ if __name__=='__main__':
parser = ScalarDiffusionArgParser()
- parser.set_defaults(box_origin=(0.0,), box_length=(1.0,),
- tstart=0.0, tend=1.0, nb_iter=500,
+ parser.set_defaults(box_origin=(0.0,), box_length=(1.0,),
+ tstart=0.0, tend=1.0, nb_iter=500,
dump_freq=5, nu=0.01, impl='cl')
parser.run(compute)