def arguments(self, **kwargs):
mapper = {i.name: i for i in self.parameters}
local_grids_mapper = {
namespace['code-grid-name'](k): v
for k, v in self.yk_soln.local_grids.items()
}
# The user has the illusion to provide plain data objects to the
# generated kernels, but what we actually need and thus going to
# provide are pointers to the wrapped YASK grids.
toshare = {}
more_args = {}
for i in self.parameters:
grid_arg = mapper.get(namespace['code-grid-name'](i.name))
if grid_arg is not None:
assert i.provider.from_YASK is True
obj = kwargs.get(i.name, i.provider)
# Get the associated Data wrapper (scalars are a special case)
if np.isscalar(obj):
wrapper = DataScalar(obj)
toshare[i.provider] = wrapper
else:
wrapper = obj.data
toshare[obj] = wrapper
# Add C-level pointer to the YASK grids
more_args[grid_arg.name] = wrapper.rawpointer
elif i.name in local_grids_mapper:
# Add C-level pointer to the temporary YASK grids
more_args[i.name] = rawpointer(local_grids_mapper[i.name])
kwargs.update(more_args)
return super(Operator, self).arguments(**kwargs), toshare
def apply(self, **kwargs):
# Build the arguments list to invoke the kernel function
args = self.arguments(**kwargs)
# Map default Functions to runtime Functions; will be used for "var sharing"
toshare = {}
for i in self.input:
v = kwargs.get(i.name, i)
if np.isscalar(v):
toshare[i] = DataScalar(v)
elif i.from_YASK and (i.is_Constant or i.is_Function):
toshare[v] = v.data
for i in self.yk_solns.values():
i.pre_apply(toshare)
arg_values = [args[p.name] for p in self.parameters]
cfunction = self.cfunction
with self._profiler.timer_on('apply', comm=args.comm):
cfunction(*arg_values)
for i in self.yk_solns.values():
i.post_apply()
# Output summary of performance achieved
return self._emit_apply_profiling(args)
def apply(self, **kwargs):
# Build the arguments list to invoke the kernel function
arguments = self.arguments(**kwargs)
# Map default Functions to runtime Functions; will be used for "grid sharing"
toshare = {}
for i in self.input:
v = kwargs.get(i.name, i)
if np.isscalar(v):
toshare[i] = DataScalar(v)
elif i.from_YASK and (i.is_Constant or i.is_Function):
toshare[v] = v.data
log("Running YASK Operator through Devito...")
arg_values = [arguments[p.name] for p in self.parameters]
self.yk_soln.run(self.cfunction, arg_values, toshare)
log("YASK Operator successfully run!")
# Output summary of performance achieved
return self._profile_output(arguments)
def data(self, val):
self._value = DataScalar(val)
def __init__(self, *args, **kwargs):
value = kwargs.pop('value', 0.)
super(Constant, self).__init__(*args,
value=DataScalar(value),
**kwargs)
