Paralelni vypocet dat pro matplotlib pomoci multiprocessing.Pool()

matplotlib_multiprocessing.py

"""
Paralelni vypocet dat pro matplotlib pomoci multiprocessing.Pool()

"""

from __future__ import print_function

import multiprocessing
import os
from timeit import default_timer as timer
import time
import numpy as np
import matplotlib.pyplot as plt


def f(a, b, c, d, e):
    time.sleep(0.001)  # ... komplikovany vypocet ...
    return a + b + c + d + e


def main():
    # vykresli konturu f(...)
    # a, b - promenne
    # c, d, e - konstanty

    c = 1
    d = 2
    e = 3

    delta = 0.1
    a_values = np.arange(-3.0, 3.0, delta)
    b_values = np.arange(0, 10.0, delta)

    X, Y = np.meshgrid(a_values, b_values)   # osy X, Y = a, b


    # varianta 1 - paralelne

    Z1 = []
    N = os.cpu_count()  # pocet procesu - tohle je default
    t0 = timer()
    with multiprocessing.Pool(N) as pool:
        for a_row, b_row in zip(X, Y):
            args = [(a, b, c, d, e) for a, b in zip(a_row, b_row)]
            values = pool.starmap(f, args)  # -> [f(*args[0]), f(*args[1]), ...]
            Z1.append(values)

    print("multiprocessing.Pool({}) took {:.3f} s".format(N, timer() - t0))

    # varianta 2 - bez paralelizace

    Z2 = []
    t0 = timer()
    for a_row, b_row in zip(X, Y):
        args = [(a, b, c, d, e) for a, b in zip(a_row, b_row)]
        values = [f(*args[i]) for i in range(len(args))]
        Z2.append(values)

    print("serial execution took {:.3f} s".format(timer() - t0))

    assert Z1 == Z2
    plt.figure()
    plt.contour(X, Y, Z1)
    plt.show()


# toto je pri pouzivani `multiprocessing` nutne
if __name__ == "__main__":
    main()