Rewriting a for loop in pure NumPy to decrease execution time

Question

I recently asked about trying to optimise a Python loop for a scientific application, and received an excellent, smart way of recoding it within NumPy which reduced execution time by a factor of around 100 for me!

However, calculation of the B value is actually nested within a few other loops, because it is evaluated at a regular grid of positions. Is there a similarly smart NumPy rewrite to shave time off this procedure?

I suspect the performance gain for this part would be less marked, and the disadvantages would presumably be that it would not be possible to report back to the user on the progress of the calculation, that the results could not be written to the output file until the end of the calculation, and possibly that doing this in one enormous step would have memory implications? Is it possible to circumvent any of these?

import numpy as np
import time

def reshape_vector(v):
    b = np.empty((3,1))
    for i in range(3):
        b[i][0] = v[i]
    return b

def unit_vectors(r):
     return r / np.sqrt((r*r).sum(0))

def calculate_dipole(mu, r_i, mom_i):
    relative = mu - r_i
    r_unit = unit_vectors(relative)
    A = 1e-7

    num = A*(3*np.sum(mom_i*r_unit, 0)*r_unit - mom_i)
    den = np.sqrt(np.sum(relative*relative, 0))**3
    B = np.sum(num/den, 1)
    return B

N = 20000 # number of dipoles
r_i = np.random.random((3,N)) # positions of dipoles
mom_i = np.random.random((3,N)) # moments of dipoles
a = np.random.random((3,3)) # three basis vectors for this crystal
n = [10,10,10] # points at which to evaluate sum
gamma_mu = 135.5 # a constant

t_start = time.clock()
for i in range(n[0]):
    r_frac_x = np.float(i)/np.float(n[0])
    r_test_x = r_frac_x * a[0]
    for j in range(n[1]):
        r_frac_y = np.float(j)/np.float(n[1])
        r_test_y = r_frac_y * a[1]
        for k in range(n[2]):
            r_frac_z = np.float(k)/np.float(n[2])
            r_test = r_test_x +r_test_y + r_frac_z * a[2]
            r_test_fast = reshape_vector(r_test)
            B = calculate_dipole(r_test_fast, r_i, mom_i)
            omega = gamma_mu*np.sqrt(np.dot(B,B))
            # write r_test, B and omega to a file
    frac_done = np.float(i+1)/(n[0]+1)
    t_elapsed = (time.clock()-t_start)
    t_remain = (1-frac_done)*t_elapsed/frac_done
    print frac_done*100,'% done in',t_elapsed/60.,'minutes...approximately',t_remain/60.,'minutes remaining'

Answer 1

If you profile your code, you'll see that 99% of the running time is in calculate_dipole so reducing the time for this looping really won't give a noticeable reduction in execution time. You still need to focus on calculate_dipole if you want to make this faster. I tried my Cython code for calculate_dipole on this and got a reduction by about a factor of 2 in the overall time. There might be other ways to improve the Cython code too.

Answer 2

One obvious thing you can do is replace the line

r_test_fast = reshape_vector(r_test)

with

r_test_fast = r_test.reshape((3,1))

Probably won't make any big difference in performance, but in any case it makes sense to use the numpy builtins instead of reinventing the wheel.

Generally speaking, as you probably have noticed by now, the trick with optimizing numpy is to express the algorithm with the help of numpy whole-array operations or at least with slices instead of iterating over each element in python code. What tends to prevent this kind of "vectorization" is so-called loop-carried dependencies, i.e. loops where each iteration is dependent on the result of a previous iteration. Looking briefly at your code, you have no such thing, and it should be possible to vectorize your code just fine.

EDIT: One solution

I haven't verified this is correct, but should give you an idea of how to approach it.

First, take the cartesian() function, which we'll use. Then

def calculate_dipole_vect(mus, r_i, mom_i):
    # Treat each mu sequentially
    Bs = []
    omega = []
    for mu in mus:
        rel = mu - r_i
        r_norm = np.sqrt((rel * rel).sum(1))
        r_unit =  rel / r_norm[:, np.newaxis]
        A = 1e-7

        num = A*(3*np.sum(mom_i * r_unit, 0)*r_unit - mom_i)
        den = r_norm ** 3
        B = np.sum(num / den[:, np.newaxis], 0)
        Bs.append(B)
        omega.append(gamma_mu * np.sqrt(np.dot(B, B)))
    return Bs, omega


# Transpose to get more "natural" ordering with row-major numpy
r_i = r_i.T
mom_i = mom_i.T

t_start = time.clock()
r_frac = cartesian((np.arange(n[0]) / float(n[0]),
                    np.arange(n[1]) / float(n[1]),
                    np.arange(n[2]) / float(n[2])))
r_test = np.dot(r_frac, a)
B, omega = calculate_dipole_vect(r_test, r_i, mom_i)

print 'Total time for vectorized: %f s' % (time.clock() - t_start)

Well, in my testing, this is in fact slightly slower than the loop-based approach I started from. The thing is, in the original version in the question, it was already vectorized with whole-array operations over arrays of shape (20000, 3), so any further vectorization doesn't really bring much further benefit. In fact, it may worsen the performance, as above, maybe due to big temporary arrays.