This project contains the code for the parallel simulation engine I developed in the context of my master's thesis:
Modelling of an on-chip modelocked ring laser with a graphene-based saturable absorber
- author - Joris Borms
- promotor - prof. Nathalie Vermeulen (VUB - B-PHOT)
- external advisor - prof. Erwin Bente (TU/e)
The purpose of this framework is to speed up simulation work using PHIsim (https://sites.google.com/tue.nl/phisim/home). Note that this framework was designed specifically for PHIsimV3, and might require updates to work with different versions.
We achieve the parallelization by defining simulation objects that contain all necessary information for running a PHIsim instance; then we spin out these instances to separate processes. The framework also allows you to define hooks to collect information from a simulation, so some of the data processing can also be parallelized.
The framework was tested on computers with the Windows 10 and Windows 11 operating systems.
- A working installation of PHIsim, and a basic understanding of how it works. I would recommend that you at least try to run a few examples before trying to parallelize simulations.
- Have a recent Python version installed (I recommend 3.12), including a package manager such as pip. A couple of packages (listed in the comments in the
__init__file) are required to run the code. Some experience with packages such as numpy and matplotlib is useful, for processing and plotting results.- (Note: I realize that there are Python frameworks to formally specify which packages and which versions of those packages are used. At the time of writing my master's thesis, I considered this a low-priority task, and I didn't put any effort into it. You can blame my limited experience with Python dependency hell for that.)
Before you start using this framework, it's best to have a working PHIsim simulation with (at least) one variation of the setup you want to test. In other words, you manually design your device_input file such that you have a device file that you know will work. The format of the device_input file is easy to understand, so it should be straightforward to create the device_file by hand. Then, you can use the python scripts published on the PHIsim website to run your simulation and debug any potential issues. If this is your first rodeo with PHIsim, this will allow you to familiarize yourself with the concepts used by the simulation.
As a reminder, PHIsim actually consists out of 2 executables:
PHsim_input.exewhich converts a textual description (which I called thedevice_inputfile earlier on) of your structure into a detailed device filePHIsim.exewhich takes in the detailed device, along with a simulation parameters file and (optionally) an input signal file, and runs the actual simulation. After a simulation run, this executable writes 3 files: the optical output signals, and a carrier and photon density file. The former is generally what we're interested in, the latter 2 files can be used to continue the simulation with another run.
All of this is summarized in the following diagram (image courtesy of prof. Erwin Bente):
The parallelization framework aims to automate these tasks. There are a lot of degrees of freedom here, so there are quite a few things to set up before we have a fully contained simulation object. I'll go over them step by step in the next sections:
Starting from your working setup, create a function to write a parameterized device_file. Let's say that, for example, you have a simple setup with some waveguides and an SOA. The file could look like this:
IO_left__ 8 # this always needs to be in the input file
paswg_L__ 1 200 # passive waveguide on the left
soa_1____ 2 100 0 # 100 segm = amplifier 800 micron approx (100fs, 20wl segments)
paswg_R__ 1 200 # passive waveguide on the right
IO_right_ 9 # this always needs to be in the input file
-1 -1 # end of component list - start of connections list
IO_left__ R0 paswg_L__ L0 #
paswg_L__ R0 soa_1____ L0 #
soa_1____ R0 paswg_R__ L0 #
paswg_R__ R0 IO_right_ L0 #
xxx -1 xxx -1 # end of connections list - start of current source list
0 0.1 # current in soa_1 in amps 0.2 amps = 13kA/cm2
-1 -1 # end of current source list
From this point on the file can contain any text.
Let's say we want to have a parameterized waveguide, SOA length, and SOA driving current. The corresponding python function would then become (for example):
def initialize_device_input_file(wg_segments, soa_segments, soa_current, device_filename):
# build device with given parameters
device_input_content = """\
IO_left__ 8 # this always needs to be in the input file
paswg_L__ 1 {wg} # passive waveguide on the left
soa_1____ 2 {seg} 0 # 100 segm = amplifier 800 micron approx (100fs, 20wl segments)
paswg_R__ 1 {wg} # passive waveguide on the right
IO_right_ 9 # this always needs to be in the input file
-1 -1 # end of component list - start of connections list
IO_left__ R0 paswg_L__ L0 #
paswg_L__ R0 soa_1____ L0 #
soa_1____ R0 paswg_R__ L0 #
paswg_R__ R0 IO_right_ L0 #
xxx -1 xxx -1 # end of connections list - start of current source list
0 {cur} # current in soa_1 in amps 0.2 amps = 13kA/cm2
-1 -1 # end of current source list
From this point on the file can contain any text.
""".format(cur = soa_current, seg = soa_segments, wg = wg_segments)
# write device to file
with open(device_filename, 'w') as f:
f.write(device_input_content)You see we put the device code in a string and replaced a few constants with variables (to have a variable device file) using format to fill those in. Then, we open a file and write the content there.
The simulation has a lot of input parameters, which, broadly speaking, fall into two categories:
- The runtime parameters: how many cycles to run the simulation, the size of a segment, ...
- The physical parameters: material gain, losses, all sorts of coefficients to describe physical process, ...
Most of these parameters are contained within the parameter_input_file shown in the image. Since you may want to test some of these parameters in a simulation sweep, the parallel framework has some facilities to define them and write the parameter file. This is the class PHIsim_SimulationParams defined in the PHIsim_sim_params.py file, which also contains a variable PHIsim_params_InGaAsP_ridge with some useful physical constants, relevant for an InP ridge platform (YMMV). An example on how to use this could be:
import tools.PHIsim_sim_params as phip
# define a local copy, overwrite a few parameters
local_sim_params = phip.PHIsim_params_InGaAsP_ridge.copy(
n_wavelen_segment = 5,
nr_cycles = 20000,
# (for example) test with very low waveguide loss
other_loss_pwg = 0.001,
)
# create the parameter_input_file
local_sim_params.write_to_file() This may not be applicable in some cases, for example, if you're simulating a self-starting laser. In those cases, you can just leave the input signal file empty. If you do need an input signal, you will need to create a function to write that signal to a file. The framework provides some helper functions for a few common signal inputs (for example, a sech2 pulse) in the file PHIsim_signal_input_util.py. Some examples of how to use this can be found in the example simulation file PHIsim_SOA_only_w_dispatcher.py.
With everything above in place, we now need to tie all those elements together in a simulation object that contains all information on how to run a single PHIsim instance. This object will need to be a subclass of the abstract PHIsim_ConcurrentSetup class defined in PHIsim_dispatcher.py. You must initialize the base class with a PHIsim_SimulationParams instance and a local name, which will be used as the subfolder where simulations will be put. Then, each subclass must implement the initialize_input_files() method to write the required simulation input files in the assigned subfolder.
A skeleton of this implementation, using the running example of a SOA test, would look like:
from typing import override
import tools.PHIsim_sim_params as phip
import tools.PHIsim_dispatcher as phid
class TestSOASetup(phid.PHIsim_ConcurrentSetup):
def __init__(self, sim_params : phip.PHIsim_SimulationParams,
soa_len, soa_current, wg_len, signal_params):
self.soa_segments = sim_params.length_to_num_segments(soa_len)
self.wg_segments = sim_params.length_to_num_segments(wg_len)
self.soa_current = soa_current
self.signal_params = signal_params # assuming this is a structure containing signal power, type, duration, ...
# assumes that soa_len and soa_current are the variable parameters and wg_len is a fixed parameter
# embedding the variables in the work_folder name will make it easier to process the raw data later (if required)
work_folder = "soa_{sseg}_segments_{scur}_mA".format(sseg=self.soa_segments, scur=(int)self.soa_current*1000)
# initialize the base class
super().__init__(sim_params, work_folder)
@override
def initialize_input_files(self):
self.initialize_device_file()
self.initialize_sim_parameter_file()
self.initialize_signal_input_file()
## everything else defaults
self.default_initialize_carrierfile()
self.default_initialize_photond_file()
def initialize_device_file(self):
# use the function we defined earlier
initialize_device_input_file(self.soa_segments, self.wg_segments, self.soa_current,
self.signal_params.device_file)
def initialize_sim_parameter_file(self):
# this one's easy
self.sim_params.write_to_file()
def initialize_signal_input_file(self):
# use self.signal_params and signal utilities to generate a signal, and write to file
# ...Take a moment to read that and try to understand what's happening. It's a lot to take in, but should be relatively straightforward:
- We create new setup class
TestSOASetupwhich inherits from the base classPHIsim_ConcurrentSetup - This class has a constructor (the
__init__()method) which takes in all parameters for a simulation run. Here, that's the simulation paramaters, model parameters and signal parameters. The constructor stores all parameters, creates a unique work-folder name based on these parameters, and initializes the base class.- footnote 1: Of course, you can also pass and store other parameters not directly related to the simulation.This can be helpful later when processing the data.
- footnote 2: Here, I'm auto-generating the work-folder name. In some cases, if you have a lot of parameters to play around with that may be difficult, and it will make more sense to pass in a sensible work-folder name instead.
- We override the
initialize_input_files()function. Note that the@overrideannotation is optional, but I prefer having it to make it more obvious that this is an overriding method. We initialize the 5 different input files, but we can almost always use theself.default_initialize_xxx()for the carrierfile and photond file. So that leaves us with 3 files that need to be initialized: the device file, the simulations parameter file and the signal file. This is all done in this method.
We can now use the TestSOASetup class to define a parameter sweep and run the simulations. Let's say we want to run a set of simulations with SOA length between 100μm and 1mm with increments of 50μm, the code could look like this:
# include all previous imports
import numpy as np
executables = phid.PHIsim_Executables("C:/PHIsim") # where PHIsim executables are installed
work_folder = "C:/Simulation_results/single_SOA_simulations" # folder to drop results
# some fixed parameters - use SI units for all dimensions
waveguide_len = 1e-3
soa_current = 0.1
local_sim_params = phip.PHIsim_params_InGaAsP_ridge.copy(
# whatever we need to override here
)
signal_params = {} # parameters for the input signal, not implemented in this example
test_setups = []
for soa_len in np.linspace(1e-4, 1e-3, 19):
test_setups.append(
TestSOASetup(local_sim_params, soa_len, soa_current, waveguide_len, signal_params))
# run all simulations in parallel and get results
results = phid.PHIsim_run_concurrent(test_setups, work_folder, executables) More specifically, you want to create an Iterable of PHIsim_ConcurrentSetup objects and pass that to the PHIsim_run_concurrent() function. Here, I just created a list, which will be sufficient for most cases. You also need to tell the framework where to find the PHIsim executables, and where to put the results. When this is finished, the following folders will have been created (assuming, for example, that 100μm = approx 20 segments):
- C:/Simulation_results/single_SOA_simulations/soa_20_segments_100_mA
- C:/Simulation_results/single_SOA_simulations/soa_30_segments_100_mA
- C:/Simulation_results/single_SOA_simulations/soa_40_segments_100_mA
- ...
Each folder containing all input and output files of a single PHIsim run.
After a simulation has run, you can process the results. The results object returned by PHIsim_run_concurrent() is a dictionary that maps a test setup to a PHIsim_Result object (also defined in PHIsim_dispatcher.py), which contains the output optical power and phase at both the left (RL) and right (LR) outputs. Suppose, for example, you want to calculate the total energy output during a simulation, you could calculate
def energy_out(data, simulation_params, output_side):
if output_side == "left":
return np.sum(data.P_RL_out) * simulation_params.simulation_time_step()
elif output_side == "right":
return np.sum(data.P_LR_out) * simulation_params.simulation_time_step()
raise RuntimeError(f"unknown output side {output_side}, use 'left' or 'right'")and then get the energy output versus soa_length,
Eout_for_soa_len = {}
for (setup, result_data) in results.items():
# actual length may differ slightly from input length due to rounding
actual_soa_len = setup.sim_params.simulation_segment_length(setup.soa_segments)
output_energy_L = energy_out(result_data, setup.sim_params, "left") # function defined above
Eout_for_soa_len[actual_soa_len] = output_energy_LAfter this, the variable Eout_for_soa_len will contain the output energy (on the left side) for different SOA lengths with the same driving current. This can then be used for further processing, or perhaps plotting a curve.
Although this particular value is probably not very interesting, it serves as an example of how the result data can be processed. The example file PHIsim_SOA_only_w_dispatcher.py contains more complicated examples and some plotting using matplotlib.
You can also add some concurrent data processing to the simulation setup object. For this, you override the process_result() function which allows you to add more data to the results object. With the example specified above, we could parallelize the calculations of the output energy with:
class TestSOASetup(phid.PHIsim_ConcurrentSetup):
# ...
# assume all other code in the class stays the same as above
# ...
@override
def process_result(self, result_data: phid.PHIsim_Result):
result_data.output_energy_R = energy_out(result_data, self.sim_params, "right")
result_data.output_energy_L = energy_out(result_data, self.sim_params, "left")And then the data collection reduces to
Eout_for_soa_len = {}
for (setup, result_data) in results.items():
# actual length may differ slightly from input length due to rounding
actual_soa_len = setup.sim_params.simulation_segment_length(setup.soa_segments)
Eout_for_soa_len[actual_soa_len] = result_data.output_energy_LAnd with this, the calculation of the output energy is now fully parallelized. Of course, the performance gain in this example would be minimal. For more complex data processing, such as spectral analysis or curve fitting, the gains could be noticeable.
For longer simulations that cannot be completed in a single PHIsim run, we can run PHIsim several times in succession, using the carrier density and photon density output files of a previous run as inputs for the next. With the framework, this can easily be automated. When creating a test setup, we simply need to fill in the num_PHIsim_cycles parameter when initializing the PHIsim_ConcurrentSetup base class. For example:
class TestSOASetup(phid.PHIsim_ConcurrentSetup):
def __init__(self, sim_params : phip.PHIsim_SimulationParams,
soa_len, soa_current, wg_len, signal_params, num_cycles : int):
# ...
# assume other code in the constructor stays the same
# ...
# initialize the base class
super().__init__(sim_params, work_folder, num_cycles)You can then either pass in a fixed number, or calculate the desired number of cycles.
desired_time = 1e-6
# sim_params.simulation_total_time() returns the simulated time in one PHIsim run
num_cycles = (int) np.ceil(desired_time / sim_params.simulation_total_time())
# you probably want to guard yourself against typos in the desired_time variable
# (yes, I'm speaking out of experience)
if num_cycles > 100:
print(f"WARNING: num_cycles is {num_cycles}, are you sure about that?")