Course Parallel and GPU programming in Python

What will you learn?

In this course, you will learn about parallel programming using Python, a language that has become more and more popular among researchers for its simplicity and the availability of specific programming libraries. In large computing systems, it is essential to exploit heterogeneous architectures correctly, and here you will understand the different challenges and how to overcome them with different Python features for CPU and GPU platforms that have direct application for scientific computing.

In this course you will:

• Understand the limits and merits of parallel programming and its use with Python
• Implement code using different libraries for parallel programming on CPU and GPU, including numba, PyCUDA or mpi4py.
• Experience how to achieve high performance with Python using the supercomputing facilities at SURF

For whom?

Everyone who is interested in learning how to get high performance for Python codes

Requirements:

• Basic knowledge of Linux
• Basic knowledge of Python and use of Jupyter notebooks
• Your own laptop with an up-to-date browser and a terminal emulator. The use of the operating systems Linux and macOS is preferred, but not mandatory. For Windows users, we recommend to download MobaXterm (portable version) as a terminal emulator.

This course will be taught in English.