Comparison of array generation

test run: julia

julia> func(0,.1,.1); @time func(0,.1,10000)
elapsed time: 1.43696256 seconds (800264048 bytes allocated)

julia> func3(0,.1,.1); @time func3(0,.1,10000)
elapsed time: 0.838369673 seconds (800264048 bytes allocated)

julia> func5(0,.1,.1); @time func5(0,.1,10000)
elapsed time: 0.409506126 seconds (1000312 bytes allocated)

test run: python

In [2]: func()
(0.40718698501586914, 'elapsed')

test_array_create.jl

# Array creation from range
function func(a, inc, b)
    for i=1:1000
        [a:inc:b]
    end
end

# Assignment with array creation from range
function func3(a, inc, b)
    for i=1:1000
        A = [a:inc:b]
    end
end


# Preallocation
function func5(a, inc, b)
    A = Array(Float64, length(a:inc:b))
    for i=1:1000
        A[:] = a:inc:b
    end
end


func(0,.1,.1); @time func(0,.1,10000)
func3(0,.1,.1); @time func3(0,.1,10000)
func5(0,.1,.1); @time func5(0,.1,10000)

test_array_create.py

import numpy as np
from time import time

def func():
    a = 0
    b = 10000
    inc = 0.1
    t0 = time()
    for i in range(1000):
        np.arange(a, b, inc)
    print(time()-t0, "elapsed")

here is a c++ version:
https://gist.github.com/skariel/7410160

in my machine it is ~20% faster than func3