Simple Parallel programe

import os, sys, time, math

import numpy as np

import matplotlib.pyplot as plt

import matplotlib

import multiprocessing as mul

from functools import partial

import multiprocessing.pool

def func2(x, E):

f=1/np.sqrt(abs(E-2*x**(2.0)))

return f

def simp(ifunc, a, b, N, E):

h=abs((b-a)/N)

sum = 0

for i in range (1, N, 2):

sum = sum + 4*ifunc(a+i*h,E)

for i in range (2, N, 2):

sum = sum + 2*ifunc(a+i*h,E)

result = (h/3) * (ifunc(a+h/2,E) + ifunc(b-h/2,E) + sum)

return result

def trap(ifunc, a, b, N, E):

h=abs((b-a)/N)

sum = 0

for i in range (1, N, 1):

sum = sum + ifunc(a+i*h,E)

result = (h/2) * (ifunc(a+h/2,E) + ifunc(b-h/2,E) + 2.0*sum)

return result

i=1024*4

num = 1000

#serial version

start = time.time()

S=np.zeros(num)

E=np.zeros(num)

T=np.zeros(num)

for j in range (1, num):

E[j]=0.0001*j

S[j]=simp(func2,-np.sqrt(E[j]/2.),np.sqrt(E[j]/2.),i,E[j])

#T[j]=trap(func2,-np.sqrt(E[j]/2.),np.sqrt(E[j]/2.),i,E[j])

end = time.time()

print(f"serial part took={end - start}")

#parallel version:

start = time.time()

Nthreads = 4 #number of threads to be used

num_array = list(range(1,num+1))#np.linspace(1, num, num=num, endpoint=True)

#create an array on the forloop iterator

def par_simp(k, simp, ii): #redefine the fucntion so that for loop iterator is an arg.

EE=0.0001*k

return (simp(func2,-np.sqrt(EE/2.),np.sqrt(EE/2.),ii,EE))

psimp = partial(par_simp, simp = simp, ii = i)

#using partial module to make a function of only one argument

pool = mul.Pool(processes=Nthreads) #number of threads to use

Sp = np.array(pool.map(psimp, num_array)) ## pool.map returns a list over the forloop variable

pool.close()

pool.join()

end = time.time()

print(f"parallel part took={end - start}")

plt.plot(2*np.sqrt(E/2.), S)

plt.plot(2*np.sqrt(E/2.), Sp)

plt.show()