Benchmarking of RC2 v3.2.0 with extraneous timers optimization after calendars optimization about simod

[iharsuvorau]

• Governmental_Agency finishes with ValueError: probabilities are not non-negative reported by Prosimos (this log failed during the previous benchmarking because the time ran out, this time I use another machine for benchmarking, not HPC)

Still running most of the other logs.

[iharsuvorau]

Also, this time I'm facing a new issue with most of the runs. Before, I ran experiments via SLURM on HPC. Now, it's a simple powerful Linux machine in UT network, and I run experiments with a Python script executing a Simod's Docker container on a Simod configuration file via Python's subprocess.run.

I'm having the following issue now:

"""
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/multiprocessing/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/usr/local/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
    return list(map(*args))
  File "/usr/src/Simod/src/simod/simulation/prosimos.py", line 252, in _evaluate_logs_using_metrics
    value = compute_metric(metric, validation_log, validation_log_ids, simulated_log, simulated_log_ids)
  File "/usr/src/Simod/src/simod/metrics/metrics.py", line 31, in compute_metric
    result = get_dl(event_log_1, event_log_1_ids, event_log_2, event_log_2_ids)
  File "/usr/src/Simod/src/simod/metrics/metrics.py", line 106, in get_dl
    return evaluator.measure_distance()
  File "/usr/src/Simod/src/simod/metrics/tsd_evaluator.py", line 64, in measure_distance
    distance = self._evaluate_seq_distance(self.log_data, self.simulation_data)
  File "/usr/src/Simod/src/simod/metrics/tsd_evaluator.py", line 86, in _evaluate_seq_distance
    pool = Pool(processes=cpu_count)
  File "/usr/local/lib/python3.10/multiprocessing/context.py", line 119, in Pool
    return Pool(processes, initializer, initargs, maxtasksperchild,
  File "/usr/local/lib/python3.10/multiprocessing/pool.py", line 212, in __init__
    self._repopulate_pool()
  File "/usr/local/lib/python3.10/multiprocessing/pool.py", line 303, in _repopulate_pool
    return self._repopulate_pool_static(self._ctx, self.Process,
  File "/usr/local/lib/python3.10/multiprocessing/pool.py", line 326, in _repopulate_pool_static
    w.start()
  File "/usr/local/lib/python3.10/multiprocessing/process.py", line 118, in start
    assert not _current_process._config.get('daemon'), \
AssertionError: daemonic processes are not allowed to have children
"""

With this explanation https://stackoverflow.com/questions/51485212/multiprocessing-gives-assertionerror-daemonic-processes-are-not-allowed-to-have, it seems that because of the multithreaded evaluation which wants to run also multithreaded TSD (DL) metric computation it can't, because a thread cannot start another pool of threads.

There's a solution to this, https://stackoverflow.com/questions/6974695/python-process-pool-non-daemonic. But it still doesn't seem correct for a thread to run another pool of threads. It can actually slow down the metric computation because of a race for resources.

[iharsuvorau]

Hi @david-chapela. I'm still running benchmarking, but I've just taken BPIC 2012 to peak into the results.

With timers, graphs look way off. However, average cycle time seems to be closer to the original times.

[iharsuvorau]

These are the results after the bug is fixed (we've already reviewed them, so this is just for the history):

Production-timers_evaluation_20221222_160233267754.csv

Production-no-timers_evaluation_20221130_180242945366.csv

[iharsuvorau]

We decided:

• To compare the results when optimizing for extraneous timers BEFORE the calendar optimization
• To use absolute EMD instead of cycle time EMD in the extraneous package as an optimization metric