Python is perhaps the most versatile programming language in existence, and sees widespread use in every field of modern computing. This tutorial focuses on Python for high-performance computing applications, and will include topics on performance optimization, parallel programming, and pipelining. The afternoon session will focus on using Python to (easily) write and scale massively parallel data analysis pipelines across a cluster. Prerequisites Please come to the course with some variant of Python 3 installed as well as your favorite text editor / IDE.

This is an introductory course covering programming and computing on GPUs — graphics processing units — which are an increasingly common presence in massively parallel computing architectures. The basics of GPU programming will be covered, and students will work through a number of hands on examples. The structuring of data and computations that makes full use of the GPU will be discussed in detail. This year the course will expand to cover the new features available on the GPUs installed on the Graham supercomputer at the University of Waterloo.

The MPI (Message Passing Interface) is widely used for programming parallel computers ranging from shared-memory servers to large clusters. This workshop is directed at current or prospective users of parallel computers who want to significantly improve the performance of their programs by “parallelizing” the code on a wide range of platforms. The content of the course covers the basics of MPI programming. After a brief introduction to MPI, we talk about MPI fundamentals involving about a dozen MPI routines that are enough to familiarize users with the basic concepts of MPI programming.