Performance issue in python with nested loop

I'm attempted to re-create the conditions that the question was interested in, but first a smaller test case to illustrate a strategy. First the author's original implementation:

import numpy as np
import numba as nb
import numpy

def func(re, ws, a, l, r):

    for x1 in range(a**l):
        for x2 in range(a**l):
            for x3 in range(a**l):
                f11 = 0
                cv1 = numpy.ndarray.sum(
                numpy.absolute(numpy.subtract(ws[x1], ws[x2])))
                cv2 = numpy.ndarray.sum(
                numpy.absolute(numpy.subtract(ws[x1], ws[x3])))
                if cv1 == 0:
                    f11 += 1
                if cv2 == 0:
                    f11 += 1
                re[x1][x2][x3] = 1.0*r/(a**l-2)*(numpy.product(numpy.absolute(
                            numpy.subtract((2*ws[x1]+ws[x2]+ws[x3]), 2)))-f11)
                f11 *= 1.0*(1-r)/2
                re[x1][x2][x3] += f11

Now with a simple translation to Numba, which is really well suited to these types of deeply nested looping problems when you're dealing with numpy arrays and numerical calculations:

@nb.njit
def func2(re, ws, a, l, r):
    for x1 in range(a**l):
        for x2 in range(a**l):
            for x3 in range(a**l):
                f11 = 0.0
                cv1 = np.sum(np.abs(ws[x1] - ws[x2]))
                cv2 = np.sum(np.abs(ws[x1] - ws[x3]))

                if cv1 == 0:
                    f11 += 1
                if cv2 == 0:
                    f11 += 1
                y = np.prod(np.abs(2*ws[x1]+ws[x2]+ws[x3] -  2)) - f11
                re[x1,x2,x3] = 1.0*r/(a**l-2)*y
                f11 *= 1.0*(1-r)/2
                re[x1,x2,x3] += f11

and then with some further optimizations to get rid of temporary array creation:

@nb.njit
def func3(re, ws, a, l, r):
    for x1 in range(a**l):
        for x2 in range(a**l):
            for x3 in range(a**l):
                f11 = 0.0
                cv1 = 0.0
                cv2 = 0.0
                for i in range(ws.shape[1]):
                    cv1 += np.abs(ws[x1,i] - ws[x2,i])
                    cv2 += np.abs(ws[x1,i] - ws[x3,i])

                if cv1 == 0:
                    f11 += 1
                if cv2 == 0:
                    f11 += 1
                y = 1.0
                for i in range(ws.shape[1]):
                    y *= np.abs(2.0*ws[x1,i] + ws[x2,i] + ws[x3,i] - 2)
                y -= f11
                re[x1,x2,x3] = 1.0*r/(a**l-2)*y
                f11 *= 1.0*(1-r)/2
                re[x1,x2,x3] += f11

So some simple test data:

a = 2
l = 5
r = 0.2
wp = (numpy.arange(2**l)[:,None] >> numpy.arange(l)[::-1]) & 1
wp = numpy.hstack([wp.sum(1,keepdims=True), wp])
ws = wp[:, 3:l+3]
re = numpy.zeros((a**l, a**l, a**l))

and now let's check that all three functions produce the same result:

re = numpy.zeros((a**l, a**l, a**l))
func(re, ws, a, l, r)

re2 = numpy.zeros((a**l, a**l, a**l))
func2(re2, ws, a, l, r)

re3 = numpy.zeros((a**l, a**l, a**l))
func3(re3, ws, a, l, r)

print np.allclose(re, re2)  # True
print np.allclose(re, re3)  # True

And some initial timings using the jupyter notebook %timeit magic:

%timeit func(re, ws, a, l, r)
%timeit func2(re2, ws, a, l, r)
%timeit func3(re3, ws, a, l, r)

1 loop, best of 3: 404 ms per loop
100 loops, best of 3: 14.2 ms per loop
1000 loops, best of 3: 605 µs per loop

func2 is ~28x times faster than the original implementation. func3 is ~680x faster. Note that I'm running on a Macbook laptop with an i7 processor, 16 GB of RAM and using Numba 0.25.0.

Ok, so now let's time the a=2 l=10 case that everyone is wringing their hands about:

a = 2
l = 10
r = 0.2
wp = (numpy.arange(2**l)[:,None] >> numpy.arange(l)[::-1]) & 1
wp = numpy.hstack([wp.sum(1,keepdims=True), wp])
ws = wp[:, 3:l+3]
re = numpy.zeros((a**l, a**l, a**l))
print 'setup complete'

%timeit -n 1 -r 1 func3(re, ws, a, l, r)

# setup complete
# 1 loop, best of 1: 45.4 s per loop

So this took 45 seconds on my machine single threaded, which seems reasonable if you aren't then doing this one calculation too many times.