Cython not fast enough

Question

I rewrote my python loop in cython expecting a large improvement in speed. I only get about a factor of four. Am I doing something wrong? This is the code without cython:

import numpy as np
import itertools as itr
import math

def Pk (b, f, mu, k): # k is in Mpc
    isoPk = 200*math.exp(-(k-0.02)**2/2/0.01**2) # Isotropic power spectrum
    power = (b+mu**2*f)**2*isoPk
    return power

def Gendk (N, kvec, Pk, b, f, deltak3d):
    Nhalf = int(N/2)
    for xx, yy, zz in itr.product(range(0,N), range(0,N), range(0,Nhalf+1)):
        kx = kvec[xx]
        ky = kvec[yy]
        kz = kvec[zz]
        kk = math.sqrt(kx**2+ky**2+kz**2)
        if kk == 0:
            continue
        mu = kz/kk
        power = Pk(b, f, mu, kk)
        if power==0:
            deltaRe = 0 
            deltaIm = 0
        else:
            deltaRe = np.random.normal(0, power/2.0)
            if (xx==0 or xx==Nhalf) and (yy==0 or yy==Nhalf) and (zz==0 or zz==Nhalf):
                deltaIm = 0
            else:
                deltaIm = np.random.normal(0, power/2.0)
        x_conj = (2*N-xx)%N
        y_conj = (2*N-yy)%N
        z_conj = (2*N-zz)%N
        deltak3d[xx,yy,zz] = deltaRe + deltaIm*1j
        deltak3d[x_conj,y_conj,z_conj] = deltaRe - deltaIm*1j 

Ntot = 300000
L = 1000 
N = 128 
Nhalf = int(N/2)
kmax = 5.0 
dk = kmax/N
kvec = np.fft.fftfreq(N, L/N)
dL = L/N
deltak3d = np.zeros((N,N,N), dtype=complex)
deltak3d[0,0,0] = Ntot
Gendk(N, kvec, Pk, 2, 1, deltak3d)

This is the version with cython:

import numpy as np
import pyximport; pyximport.install(setup_args={"include_dirs":np.get_include()})
import testGauss as tG

Ntot = 300000
L = 1000 
N = 128 
Nhalf = int(N/2)
kmax = 5.0 
dk = kmax/N
kvec = np.fft.fftfreq(N, L/N)
dL = L/N 
deltak3d = np.zeros((N,N,N), dtype=complex)
deltak3d[0,0,0] = Ntot
tG.Gendk(N, kvec, tG.Pk, 2, 1, deltak3d)

and the testGauss.pyx file is:

import math
import numpy as np
cimport numpy as np
import itertools as itr

def Pk (double b, double f, double mu, double k): # k is in Mpc
    cdef double isoPk, power
    isoPk = 200*math.exp(-(k-0.02)**2/2/0.01**2) # Isotropic power spectrum
    power = (b+mu**2*f)**2*isoPk
    return power

def Gendk (int N, np.ndarray[np.float64_t,ndim=1] kvec, Pk, double b, double f, np.ndarray[np.complex128_t,ndim=3] deltak3d):
    cdef int Nhalf = int(N/2)
    cdef int xx, yy, zz
    cdef int x_conj, y_conj, z_conj
    cdef double kx, ky, kz, kk
    cdef mu
    cdef power
    cdef deltaRe, deltaIm
    for xx, yy, zz in itr.product(range(0,N), range(0,N), range(0,Nhalf+1)):
        kx = kvec[xx]
        ky = kvec[yy]
        kz = kvec[zz]
        kk = math.sqrt(kx**2+ky**2+kz**2)
        if kk == 0:
            continue
        mu = kz/kk
        power = Pk(b, f, mu, kk)
        if power==0:
            deltaRe = 0 
            deltaIm = 0
        else:
            deltaRe = np.random.normal(0, power/2.0)
            if (xx==0 or xx==Nhalf) and (yy==0 or yy==Nhalf) and (zz==0 or zz==Nhalf):
                deltaIm = 0
            else:
                deltaIm = np.random.normal(0, power/2.0)
        x_conj = (2*N-xx)%N
        y_conj = (2*N-yy)%N
        z_conj = (2*N-zz)%N
        deltak3d[xx,yy,zz] = deltaRe + deltaIm*1j
        deltak3d[x_conj,y_conj,z_conj] = deltaRe - deltaIm*1j 

Thank you very much in advance!

Answer 1

Use cProfile to profile your Python code. Maybe the most CPU intensive tasks are in NumPy already. Then there is not so much to gain from Cython.

Answer 2

You could get some speedup by replacing

import math

with

from libc cimport math

That will avoid a python function call when you do sqrt and exp, replacing it with a direct c call (which should be a lot faster).

I'm also slightly concerned at the calls to np.random.normal inside your loop, which add a reasonable python overhead each time. It might well be quicker to call this before the loop to generate a large array of random numbers (with the overhead of a single python call) then overwrite them with 0 if they aren't needed inside the loop.

The general advice for optimising Cython still applies: run

cython -a your_file.pyx

Look at the HTML, and worry about bits highlighted yellow (but only if they're called often)

Answer 3

Turning your code (slightly modified) in a native module with Pythran gives me a x50 speedup.

import numpy as np
import itertools as itr
import math
from random import gauss as normal

def Pk (b, f, mu, k): # k is in Mpc
    isoPk = 200*math.exp(-(k-0.02)**2/2/0.01**2) # Isotropic power spectrum
    power = (b+mu**2*f)**2*isoPk
    return power

#pythran export Gendk(int, float[], int, int, complex[][][])
def Gendk (N, kvec, b, f, deltak3d):
    Nhalf = int(N/2)
    for xx, yy, zz in itr.product(range(0, N), range(0, N), range(0, Nhalf+1)):
        kx = kvec[xx]
        ky = kvec[yy]
        kz = kvec[zz]
        kk = math.sqrt(kx**2+ky**2+kz**2)
        if kk == 0:
            continue
        mu = kz/kk
        power = Pk(b, f, mu, kk)
        if power == 0:
            deltaRe = 0 
            deltaIm = 0
        else:
            # deltaRe = np.random.normal(0, power/2.0)
            deltaRe = normal(0, power/2.0)
            if (xx == 0 or xx == Nhalf) and (yy == 0 or yy == Nhalf) and (zz == 0 or zz == Nhalf):
                deltaIm = 0
            else:
                #deltaIm = np.random.normal(0, power/2.0)
                deltaIm = normal(0, power/2.0)
        x_conj = (2*N-xx)%N
        y_conj = (2*N-yy)%N
        z_conj = (2*N-zz)%N
        deltak3d[xx, yy, zz] = deltaRe + deltaIm*1j
        deltak3d[x_conj, y_conj, z_conj] = deltaRe - deltaIm*1j 

Compiled with:

$ pythran tg.py

And tested with:

$ python -m timeit -s 'import numpy as np; Ntot = 30000; L = 1000; N = 12; Nhalf = int(N/2); kmax = 5.0; dk = kmax/N; kvec = np.fft.fftfreq(N, L/N); dL = L/N; deltak3d = np.zeros((N, N, N), dtype=complex); deltak3d[0, 0, 0] = Ntot; from tg import Gendk' 'Gendk(N, kvec, 2, 1, deltak3d)'

I get 10 loops, best of 3: 29.4 msec per loop for the CPython run and 1000 loops, best of 3: 587 usec per loop for the Pythran run.

Disclaimer: I'm a Pythran dev .