user3191569 - 1 year ago 93
Python Question

# Multiprocessing python function for numerical calculations

Hoping to get some help here with parallelising my python code, I've been struggling with it for a while and come up with several errors in whichever way I try, currently running the code will take about 2-3 hours to complete, The code is given below;

``````import numpy as np
from scipy.constants import Boltzmann, elementary_charge as kb, e
import multiprocessing
from functools import partial
Tc = 9.2
x = []
g= []
def Delta(T):
'''
Delta(T) takes a temperature as an input and calculates a
temperature dependent variable based on Tc which is defined as a
global parameter
'''
d0 = (pi/1.78)*kb*Tc
D0 = d0*(np.sqrt(1-(T**2/Tc**2)))
return D0

def element_in_sum(T, n, phi):
D = Delta(T)
matsubara_frequency = (np.pi * kb * T) * (2*n + 1)
factor_d = np.sqrt((D**2 * cos(phi/2)**2) + matsubara_frequency**2)
element = ((2 * D * np.cos(phi/2))/ factor_d) * np.arctan((D * np.sin(phi/2))/factor_d)
return element

def sum_elements(T, M, phi):
'''
sum_elements(T,M,phi) is the most computationally heavy part
of the calculations, the larger the M value the more accurate the
results are.
T: temperature
M: number of steps for matrix calculation the larger the more accurate the calculation
phi: The phase of the system can be between 0- pi
'''
X = list(np.arange(0,M,1))
Y = [element_in_sum(T, n, phi) for n in X]
return sum(Y)

def KO_1(M, T, phi):
Iko1Rn = (2 * np.pi * kb * T /e) * sum_elements(T, M, phi)
return Iko1Rn

def main():
for j in range(1, 92):
T = 0.1*j
for i in range(1, 314):
phi = 0.01*i
pool = multiprocessing.Pool()
result = pool.apply_async(KO_1,args=(26000, T, phi,))
g.append(result)
pool.close()
pool.join()
A = max(g);
x.append(A)
del g[:]
``````

My approach was to try and send the KO1 function into a multiprocessing pool but I either get a
`Pickling`
error or a
`too many files open`
, Any help is greatly appreciated, and if multiprocessing is the wrong approach I would love any guide.

I haven't tested your code, but you can do several things to improve it.

First of all, don't create arrays unnecessarily. `sum_elements` creates three array-like objects when it can use just one generator. First, `np.arange` creates a numpy array, then the `list` function creates a list object and and then the list comprehension creates another list. The function does 4 times the work it should.

The correct way to implement it (in python3) would be:

``````def sum_elements(T, M, phi):
return sum(element_in_sum(T, n, phi) for n in range(0, M, 1))
``````

If you use python2, replace `range` with `xrange`. This tip will probably help you in any python script you'll write.

Also, try to utilize multiprocessing better. It seems what you need to do is to create a `multiprocessing.Pool` object once, and use the `pool.map` function.

The main function should look like this:

``````def job(args):
i, j = args
T = 0.1*j
phi = 0.01*i
return K0_1(26000, T, phi)

def main():
pool = multiprocessing.Pool(processes=4) # You can change this number
x = [max(pool.imap(job, ((i, j) for i in range(1, 314)) for j in range(1, 92)]
``````

Notice that I used a tuple in order to pass multiple arguments to job.

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