Parallel computing in Julia

Tuesday, June 8
9 am–5 pm Pacific Time

This course will start at 9am Pacific Time and will run until 5pm Pacific Time. Its format will be a combination of several interactive Zoom sessions and the reading materials in-between the Zoom sessions. Course materials will be added here shortly before the start of the course.

Julia is a high-level programming language well suited for scientific computing and data science. Just-in-time compilation, among other things, makes Julia really fast yet interactive. For heavy computations, Julia supports multi-threaded and multi-process parallelism, both natively and via a number of external packages. It also supports memory arrays distributed across multiple processes either on the same or different nodes. In this hands-on workshop, we will start with a quick review of Julia’s multi-threading features but will focus primarily on Distributed standard library and its large array of tools. We will demo parallelization using three problems: a slowly converging series, a Julia set, and an N-body solver. We will run examples on a multi-core laptop and an HPC cluster.

Instructor: Alex Razoumov (WestGrid)

Prerequisites: working knowledge of serial Julia (covered in our Julia course) and familiarity with Compute Canada’s HPC cluster environment, in particular, with the Slurm scheduler (covered in our HPC course).

Software: All attendees will need a remote secure shell (SSH) client installed on their computer in order to participate in the course exercises. On Windows we recommend the free Home Edition of MobaXterm. On Mac and Linux computers SSH is usually pre-installed (try typing ssh in a terminal to make sure it is there). No need to install Julia on your computer.

Zoom   9:00am-12:00pm Pacific
Introduction to parallel Julia
Base.Threads (part 1)
Slow series
Base.Threads (part 2)
Distributed.jl (part 1)
Distributed.jl (part 2)

In the afternoon Zoom session you’ll be working on one of two projects: parallelizing Julia set (I recommend to do this with distributed arrays) and parallelizing the N-body code (I recommend to do this with shared arrays). Note: we will guide you through the process and answer questions, but we will not share the final solutions with you today; the goal is to build your own!

Zoom   1:00pm-4:00pm Pacific
DistributedArrays.jl
Parallelizing Julia set
SharedArrays.jl
Parallelizing N-body