Job operations are not parallelizable

まず、報告されている multiprocessing.Pool の例を exec_job で実行し、次に SimulatedBifurcationMachine が使用する solver.submit() と job.get_results() のエントリーポイントを追跡します。両方の worker のタイムスタンプを比較し、送信または結果取得がどこで直列化されるのかを特定します。独立したジョブを、観測された数分間の遅延なしに同時に送信して処理できれば完了です。

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.