Legacy Simulator FunctionΒΆ

Introduction || Standardized Simulator (gest-api) || Legacy Simulator Function

def my_simulation(Input, persis_info, sim_specs, libE_info):
    batch_size = sim_specs["user"]["batch_size"]

    Output = np.zeros(batch_size, sim_specs["out"])
    # ...
    Output["f"], persis_info = do_a_simulation(Input["x"], persis_info)

    return Output, persis_info

Most sim_f function definitions written by users resemble:

def my_simulation(Input, persis_info, sim_specs, libE_info):

where:

  • Input is a selection of the History array, a NumPy structured array.

  • persis_info is a dictionary containing state information.

  • sim_specs is a dictionary of simulation parameters.

  • libE_info is a dictionary containing libEnsemble-specific entries.

Valid simulator functions can accept a subset of the above parameters. So a very simple simulator function can start:

def my_simulation(Input):

If sim_specs was initially defined:

sim_specs = SimSpecs(
    sim_f=my_simulation,
    inputs=["x"],
    outputs=["f", float, (1,)],
    user={"batch_size": 128},
)

Then user parameters and a local array of outputs may be obtained/initialized like:

batch_size = sim_specs["user"]["batch_size"]
Output = np.zeros(batch_size, dtype=sim_specs["out"])

This array should be populated with output values from the simulation:

Output["f"], persis_info = do_a_simulation(Input["x"], persis_info)

Then return the array and persis_info to libEnsemble:

return Output, persis_info

Between the Output definition and the return, any computation can be performed. Users can try an executor to submit applications to parallel resources, or plug in components from other libraries to serve their needs.