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Abstract: Python has become the dominant language in
scientific computing thanks to its high-level syntax\, extensive ecosyste
m\, and ease of use. However\, its performance often lags behind tradition
al compiled languages like C\, C++\, and Fortran\, as well as newer conten
ders like Julia and Chapel. This course is designed to help you speed up y
our Python workflows using a variety of tools and techniques.
We’
ll begin with classic optimization methods such as NumPy-based vectorizati
on\, and explore just-in-time compilation using Numba\, along with perform
ance profiling techniques. From there\, we’ll delve into parallelization
– starting with multithreading using external libraries like NumExpr an
d Python 3.13’s new free-threading capabilities – but placing greater
emphasis on multiprocessing.
Next\, we’ll dive into Ray\, a power
ful and flexible framework for scaling Python applications. While Ray is w
idely used in AI\, our focus will be on its core capabilities for distribu
ted computing and data processing. You’ll learn how to parallelize CPU-b
ound numerical workflows – with and without reduction – as well as opt
imize I/O-bound tasks. We’ll also explore combining Ray with Numba and w
ill discuss coding tightly coupled parallel problems.