CODEWITHSUNDEEP
pythonnumpyparallel-processing
HighwayJohn
HighwayJohn

Reputation: 921

Parallel programming in python (nested loop)

I have the following code and since it is very slow for larger l i am asking myself if there is an easy possibility to parallelize this loop. I also tried to parallize it by my own, but because I never did it before I am not able to manage it. I would be happy if you can help me out

print('Create 2.0L%.3frec%.3f.npy' % (l, r))
for x1 in range(a**l):
    for x2 in range(a**l):
        for x3 in range(a**l):
            f11 = 0
            if numpy.ndarray.sum(numpy.absolute(numpy.subtract(ws[x1:x1+1], ws[x2:x2+1]))) == 0:
                f11 += 1
            if numpy.ndarray.sum(numpy.absolute(numpy.subtract(ws[x1:x1+1], ws[x3:x3+1]))) == 0:
                f11 += 1
            re[x1][x2][x3] = 1.0*r/(a**l-2)*(numpy.product(numpy.absolute(numpy.subtract((2*ws[x1:x1+1]+ws[x2:x2+1]+ws[x3:x3+1]), 2)))-f11)

            cv1 = numpy.ndarray.sum(numpy.absolute(numpy.subtract(ws[x1:x1+1], ws[x2:x2+1])))
            cv2 = numpy.ndarray.sum(numpy.absolute(numpy.subtract(ws[x1:x1+1], ws[x3:x3+1])))
            c2 = 0
            if cv1 == 0:
                c2 += 1
            if cv2 == 0:
                c2 += 1
            c2 *= 1.0*(1-r)/2
            re[x1][x2][x3] += c2
numpy.save('2.0L%.3frec%.3f' % (l, r), re)
print('Saved 2.0L%.3frec%.3f.npy' % (l, r))

So since all entries of re are independent of the others, there should be a way. I guess I would be helped if I know a solution how to parllize a python program like:

for x1 in range(a):
    for x2 in range(a):
        for x3 in range(a):
            re[x1][x2][x3] = 5*3

Upvotes: 2

Views: 261

Answers (1)

Forzaa
Forzaa

Reputation: 1545

I don't fully understand what you are trying to calculate, but I'll give it a shot. For your latter question, you can do this as follows:

re = np.empty([a]*3) 
x = np.indices([a]*3)
re[x] = 5*3

You can vectorize your code as follows: 

x = np.indices([a**l]*3)
cv1 = (ws[x[0]] == ws[x[1]]).astype(float)
cv2 = (ws[x[0]] == ws[x[2]]).astype(float)
f11 = cv1 + cv2
re = 1.0*r/(a**l-2)*(np.absolute(2*ws[x[0]]+ws[x[1]]+ws[x[2]]-2)-f11)    
re += f11*1.0*(1-r)/2

(I removed superfluous sums and products, and used the == operator to check for equal values, and used that c2 and f11 are the same thing). In theory this should do the same as your code.

Upvotes: 2

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