user3191569 - 11 months ago 56

Python Question

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`

`too many files open`

Answer Source

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.