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Abstract
: Python has become the dominant language in scientific computing thanks t
o its high-level syntax\, extensive ecosystem\, and ease of use. However\,
its performance often lags behind traditional compiled languages like C\,
C++\, and Fortran\, as well as newer contenders like Julia and Chapel. Th
is course is designed to help you speed up your Python workflows using a v
ariety of tools and techniques.
We’ll begin with classic optimiza
tion methods such as NumPy-based vectorization\, and explore just-in-time
compilation using Numba\, along with performance profiling techniques. Fro
m there\, we’ll delve into parallelization – starting with multithread
ing using external libraries like NumExpr and Python 3.13’s new free-thr
eading capabilities – but placing greater emphasis on multiprocessing.
Next\, we’ll dive into Ray\, a powerful and flexible framework for
scaling Python applications. While Ray is widely used in AI\, our focus w
ill be on its core capabilities for distributed computing and data process
ing. You’ll learn how to parallelize CPU-bound numerical workflows – w
ith and without reduction – as well as optimize I/O-bound tasks. We’ll
also explore combining Ray with Numba and will discuss coding tightly cou
pled parallel problems.