Comments (2)
Note that using clip and numpy methods in-place doesn't reduce the run time so much
in some case it is make it worse, cause the per call time has tiny effect
from tsnn.
parallelization technique in the simulate_iteration
doesn't gain much performance
num_cores = multiprocessing.cpu_count()
def iteration_parallel(net_obj, timestep):
net_obj.iteration = self.iteration
net_obj.behaviour[timestep].new_iteration(net_obj)
with Parallel(n_jobs=num_cores) as parallel:
for timestep in self.behaviour_timesteps:
parallel(
delayed(iteration_parallel)(net_obj, timestep)
for net_obj in self.all_objects()
if (
timestep in net_obj.behaviour
and net_obj.behaviour[timestep].behaviour_enabled
)
)
from tsnn.
Related Issues (2)
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from tsnn.