Job operations are not parallelizable

Inizia eseguendo l’esempio segnalato di multiprocessing.Pool con exec_job, quindi traccia i punti di ingresso solver.submit() e job.get_results() utilizzati da SimulatedBifurcationMachine. Confronta i timestamp di entrambi i worker e identifica dove l’invio o il recupero dei risultati viene serializzato. Il lavoro è completato quando i job indipendenti possono essere inviati ed elaborati contemporaneamente senza il ritardo osservato di diversi minuti.

I'm trying to submit and process results of multiple jobs in parallel but I'm noticing that, in many cases, jobs are submitted sequentially.

import datetime
import imp
from multiprocessing import Pool
from azure.quantum import Workspace
from azure.quantum.optimization import Problem, ProblemType, Term
from azure.quantum.target.toshiba import SimulatedBifurcationMachine
import logging


def create_problem():
    problem = Problem('Test_Problem', problem_type=ProblemType.pubo)
    problem.add_terms([
        Term(c=1, indices=[]),
        Term(c=2, indices=[1, 2]),
        Term(c=-3, indices=[1, 2]),
        Term(c=1, indices=[0, 2])
    ])
    return problem


def exec_job(no):
    workspace = Workspace (
        resource_id = "<workspace-id>",
        location= "<location>"
    )

    problem = create_problem()
    solver = SimulatedBifurcationMachine(workspace, loops=0, timeout=10)
    print(str(datetime.datetime.utcnow()), "\t[%d] solver.submit() start" % no)
    job = solver.submit(problem)
    print(str(datetime.datetime.utcnow()), "\t[%d] job.id=%s" % (no, str(job.id)))

    print(str(datetime.datetime.utcnow()), "\t[%d] job.get_results() start" % no)
    result = job.get_results()
    print(str(datetime.datetime.utcnow()), "\t[%d] job.details=%s" % (no, str(job.details)))


if __name__ == '__main__':
    logging.basicConfig(level=logging.DEBUG)

    njobs = 2
    with Pool(processes=njobs) as pool:
        pool.map(func=exec_job, iterable=range(njobs))

For the above, output is:
2022-04-21 18:40:23.466530 [0] solver.submit() start
2022-04-21 18:40:23.542727 [1] solver.submit() start
2022-04-21 18:40:28.752803 [1] job.id=810bc3bb-c1a2-11ec-baa1-b831b575aea6
2022-04-21 18:40:28.752803 [1] job.get_results() start
..........2022-04-21 18:40:52.826348 [1] job.details={..........}
2022-04-21 18:44:19.108243 [0] job.id=81018a8e-c1a2-11ec-adf4-b831b575aea6
2022-04-21 18:44:19.109211 [0] job.get_results() start
..........2022-04-21 18:44:42.799796 [0] job.details={..............}

You can see that job 1 was submitted and awaited while job 0 hasn't been submitted at the same time and was done 4 mins after the first job.