class brainpy.running.cpu_ordered_parallel(func, arguments, num_process=None, num_task=None, **tqdm_kwargs)[source]#

Performs a parallel ordered map with a progress bar.


>>> import brainpy as bp
>>> import brainpy.math as bm
>>> import numpy as np
>>> def simulate(inp):
>>>   inp = bm.as_jax(inp)
>>>   hh = bp.dyn.HH(1)
>>>   runner = bp.DSRunner(hh, inputs=['input', inp],
>>>                        monitors=['V', 'spike'],
>>>                        progress_bar=False)
>>>   runner.run(100)
>>>   bm.clear_buffer_memory()  # clear all cached data and functions
>>>   return runner.mon.spike.sum()
>>> if __name__ == '__main__':  # This is important!
>>>   results = bp.running.cpu_unordered_parallel(simulate, [np.arange(1, 10, 100)], num_process=10)
>>>   print(results)
  • func (callable, function) – The function to apply to each element of the given Iterables.

  • arguments (sequence of Iterable, dict) – One or more Iterables containing the data to be mapped.

  • num_process (int, float) – Number of threads used for parallel running. If int, it is the number of threads to be used; if float, it is the fraction of total threads to be used for running.

  • num_task (int) – The total number of tasks in this parallel running.

  • tqdm_kwargs (Any) – The setting for the progress bar.


results – A list which will apply the function to each element of the given tasks.

Return type: