Learn how to analyse Python programmes and identify performance barriers to help you work more efficiently. Speed up Python programs using optimisation and parallelisation techniques.
The Python programming language is popular in scientific computing because of the benefits it offers for fast code development. The performance of pure Python programs is often suboptimal, but there are ways to make them faster and more efficient.
On this course, you’ll find out how to identify performance bottlenecks, perform numerical computations efficiently, and extend Python with compiled code. You’ll learn various ways to optimise and parallelise Python programs, particularly in the context of scientific and high performance computing.
What topics will you cover?
– Performance challenges of Python programming language
– Performance analysis of Python programs
– Efficient numerical calculations with NumPy
– Using compiled code with Python
– Interfacing Python to libraries written in other programming languages
– Parallel programming with Python
What will you achieve?
By the end of the course, you’ll be able to…
– Improve performance of Python programs
– Apply parallelisation techniques using Python
– Calculate numerical expressions efficiently in Python
– Identify performance bottlenecks in Python programs
– Develop higher performance extensions to Python
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