# Performance of naive convolution against Python Numpy

**URL:** <https://discourse.julialang.org/t/performance-of-naive-convolution-against-python-numpy/75603>\
**Category:** New to Julia\
**Tags:** performance, loopvectorization\
**Created:** [February 1, 2022, 8:33pm UTC](https://discourse.julialang.org/t/performance-of-naive-convolution-against-python-numpy/75603 "2022-02-01T20:33:59Z")\
**Posts on this page:** 1\
**Showing post:** 11

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**Author:** ![Eben60](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/eben60/32/13475_2.png) [@Eben60](https://discourse.julialang.org/u/Eben60)\
**Post date:** [February 1, 2022, 10:20pm UTC](https://discourse.julialang.org/t/performance-of-naive-convolution-against-python-numpy/75603/11 "2022-02-01T22:20:18Z")

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[romainvieme](https://discourse.julialang.org/u/romainvieme), without actually checking your code, may I point you to a discussion which followed this post:

> [@General questions from Python user](https://discourse.julialang.org/t/general-questions-from-python-user/55475/44):
>
> In the course of my weekend procrastinations I contrieved an example to check my statement: some home-made implementation of convolution in both languages. Make up some signal and kernel data in Python (unimportant part which is not benchmarked): import numpy as np def lorentz(x, gamma, mu=0): return (gamma\*\*2/(((x-mu)\*\*2)+gamma\*\*2))/(np.pi\*gamma) def signal(x): return lorentz(x, 0.1, -0.3) + lorentz(x, 0.03, 0.1) + lorentz(x, 0.2, 0.25) def gauss(x, sigma, mu=0.0): return np.e…

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