4.75(4)
#IT-03-27 Python in High Performance Computing
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IT - Duration
11h - Total Enrolled
4 - Last Update
November 2, 2020
About Course
Partnership for Advanced Computing in Europe (PRACE)
Description
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
Who will you learn with?
Expert on high-performance computing working at CSC.fi
Senior application scientist at the Finnish national supercomputing center CSC – IT Center for Science.
Who developed the course?
The Partnership for Advanced Computing in Europe (PRACE) is an international non-profit association with its seat in Brussels.
What Will I Learn?
- 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
Topics for this course
Welcome to the course?
Welcome to Python in High Performance Computing00:01:37
Prerequisities and structure of the course
Setting up the programming environment
Performance challenges in Python?
Outline of Python performance issues00:00:54
Why are Python programs slow?
Experiences about performance of Python programs
Performance analysis?
Where program spends time?
Using applications own timers
Measuring small code snippets with timeit
Using cProfile00:05:57
Introducing heat equation
Hands-on: Performance analysis of heat equation solver
Pros and cons of various performance analysis approaches
Student Feedback
4.8
Total 4 Ratings
7 days ago
It was a good course for me to learn how to identify performance bottlenecks, perform numerical calculations efficiently, and extend Python with compiled code.
7 days ago
Jussi Enkovaara is so interesting, I am deeply in love with this lecturer, the course is very interesting.
7 days ago
In this course I learned various ways to optimize and parallel Python programs, especially in the context of science and high performance computing. Very good course. I suggest you take a look at it if you’re learning programming.
1 week ago
The Python programming language is popular in scientific computing, and I’ve always wanted to learn, well, the wired programming courses on this platform.
Material Includes
- Official Certificate
Login
Requirements
- The software needed is in the virtual machine that you will need to download and run to complete this course. You will also need a local machine with 15GB free disk space and 2GB RAM.
- Optionally, you can receive instructions to install the Python environment utilised in the course (Python, Numpy, Cython, mpi4py).
Target Audience
- The course is designed for Python programmers who want to speed up their codes. You should be familiar with the basics of the Python programming language.
