Writing your own code
Programming Resources
Table of Contents: Parallel Libraries • Debugging and memory debugging • Julia • Chapel • OpenMP • MATLAB
Parallel Libraries
“Working with the Python DASK library”
Webinar (2019-Oct-16) by Chris Want
- Python notebook with an interactive slide deck on GitHub and as a gzipped file
“A Brief Introduction to the Boost MPI Library”
Webinar (2018-May-09) by Patrick Mann
Debugging and memory debugging
“Memory debugging with Valgrind”
Webinar (2019-Feb-20) by Tyson Whitehead
Julia
(You can also browse all our materials on Julia programming here.)
“ThreadsX.jl: easier multithreading in Julia”
Webinar (2022-Feb-02) by Alex Razoumov
“Easier parallel Julia workflow with Dagger.jl”
Webinar (2021-Oct-27) by Marie-Hélène Burle
Designed specifically for HPC and inspired by the Python library Dask, Dagger is a distributed framework with a scheduler built on top of Distributed.jl for efficient parallel and out-of-core execution of tasks represented by a directed acyclic graph (DAG). Dask supports computing with multiple threads, multiple processes, and on GPUs. Checkpoints are easy to create if you need to interrupt and resume computations. Finally, Dagger provides some debugging and runtime profiling tools.
“Parallel programming in Julia”
Webinar (2021-Mar-17) by Alex Razoumov and Marie-Hélène Burle
In this webinar, we start with a quick review of Julia’s multi-threading features but focus primarily on Distributed standard library and its large array of tools. We show parallelization using three problems: a slowly converging series, a Julia set, and an N-body solver. We run the examples on a multi-core laptop and an HPC cluster.
“High-performance research computing with Julia”
Webinar (2020-Mar-04) by Marie-Hélène Burle
Chapel
“Intro to Parallel Programming in Chapel”
Webinar series by Alex Razoumov
In this three-part online webinar series, we introduce the main concepts of the Chapel parallel programming language. Chapel is a relatively new language for both shared- and distributed-memory programming, with easy-to-use, high-level features that make it ideal for learning parallel programming for a novice HPC user.
Unlike other high-level data-processing languages and workflows, the primary application of Chapel is numerical modelling and simulation codes, so this workshop is ideal for anyone who wants to learn how to write efficient large-scale numerical codes.
Part 1: Basic language features (2018-Feb-28)
Part 2: Task parallelism in Chapel (2018-Mar-07)
Part 3: Data parallelism in Chapel (2018-Mar-14)
“Computing graphs on an HPC cluster: working with distributed unstructured data in Chapel”
Webinar (2019-Apr-17) by Alex Razoumov
- ZIP file with slides and sample codes
“Working with data files and external C libraries in Chapel”
Webinar (2020-Mar-18) by Alex Razoumov
HPC Carpentry Course
As part of their contribution to HPC Carpentry, WestGrid staff authored a Parallel programming in Chapel course. The materials and exercises presented in this course can be presented as a full-day workshop. If you have questions about the materials, please contact Alex Razoumov - alex.razoumov@westgrid.ca.
OpenMP
Intro to Parallel Programming for Shared Memory Machines
Webinar (2019-Oct) by Ali Kerrache
This online workshop explores how to use OpenMP to improve the speed of serial jobs on multi-core machines. We review how to add OpenMP constructs to a serial program in order to run it using multiple cores. Viewers are led through a series of hands-on, interactive examples, focusing on multi-threading parallel programming.
The topics covered include:
- Basic OpenMP operations
- Loops
- Reduction variables
MATLAB
“Data Analytics and Machine Learning with MATLAB”
Webinar (2018-Oct-31) by Sumit Tandon