Python vs Julia - an example from machine learning

In Speeding up isotonic regression in scikit-learn, we dropped down into Cython to improve the performance of a regression algorithm. I thought it would be interesting to compare the performance of this (optimized) code in Python against the naive Julia implementation.

This article continues on from the previous one, so it may be worth reading that before continuing here to obtain the necessary background information.

We'll implement both of the algorithms for the previous article, and compare their performance in Julia against Python.

Linear PAVA

The Cython code is available on GitHub at scikit-learn, and the Julia code is available on GitHub at Isotonic.jl

The Julia implementation is straightforward implementation of PAVA, without any bells and whistles. The @inbounds macro was used to compare fairly with the Cython implementation, which turns off bounds checking as well.

Active Set

The active set implementation is approximately the same number of lines as the Cython implementation, and is perhaps more cleanly structured (via an explicit composite type ActiveState) that maintains a given active dual variable's parameters. It is also trivial to break repeated code into separated functions that can be trivially inlined by LLVM, while this is difficult for arbitrary arguments in Cython.

One-based indexing in Julia also made the algorithm somewhat cleaner.


We see that exactly the same algorithm in Julia is uniformly faster when compared to an equivalent Cython implementation.

For the active set implementations, Julia is anywhere between 5x and 300x faster on equivalent regression problems.

For the linear PAVA implementation, Julia is between 1.1x and 4x faster.

This certainly indicates Julia is a very attractive choice for performance-critical machine learning applications.

See the iJulia notebook for more information on how these performance measurements were obtained.

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