Speeding up your code

Some of the actual as opposed to imagined needs for concurrent computing.

Dr Climate

In today’s modern world of big data and high resolution numerical models, it’s pretty easy to write a data analysis script that would take days/weeks (or even longer) to run on your personal (or departmental) computer. With buzz words like high performance computing, cloud computing, vectorisation, supercomputing and parallel programming floating around, what’s not so easy is figuring out the best course of action for speeding up that code. This post is my attempt to make sense of all the options…

Step 1: Vectorisation

The first thing to do with any slow script is to use a profiling tool to locate exactly which part/s of the code are taking so long to run. All programming languages have profilers, and they’re normally pretty simple to use. If your code is written in a high-level language like Python, R or Matlab, then the bottleneck is most likely a loop of some sort…

View original post 1,435 more words

About ecoquant

See https://wordpress.com/view/667-per-cm.net/ Retired data scientist and statistician. Now working projects in quantitative ecology and, specifically, phenology of Bryophyta and technical methods for their study.
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