Digital Nova Scotia

High Performance Computing Microcredential

September 15 - October 27, 2026

6-week program, 2 X 2-hour sessions/week | 24 hours total training

Valued at $1,225.00 + HST Eligible DNS members pay $310 + HST

Hosted by acenet

[button link="https://digitalnovascotia.com/programs/next-level-skills-programs/" label="More Courses"][button link="#application" label="Apply Now"]This course combines virtual classroom teaching and an independent study project. Participants will learn the fundamentals of high-performance computing and apply those skills on ACENET's computing cluster.High-Performance Computing (HPC) refers to the use of supercomputers with thousands of processing units to solve tasks that are too large and/or complex for a personal laptop or desktop. Breaking a large problem into tens, hundreds, or even thousands of smaller problems to be solved in parallel reduces processing time to a fraction of what it would be on a desktop or workstation.HPC is widely used to scale up code and optimize performance in large data mining, training of AI models, engineering and scientific simulations, remote sensing, land and ocean mapping, and many other fields.

What will you learn? (More information in the FAQ section)
  • How to securely access and work within high-performance computing (HPC) environments.
  • How to run and manage large-scale computing workloads using batch scheduling tools.
  • How to profile and optimize code performance for faster, more efficient computation.
  • How to build parallel programs with OpenMP and CUDA.
  • How to deploy and manage HPC clusters on cloud platforms.
  • How to leverage ACENET's supercomputing infrastructure and connect with Atlantic Canada's tech ecosystem.
Who's right for this course?

This intermediate level course is designed for mid-career IT professionals. This course will target individuals with the following prerequisites:

  • Proficiency with programming and IT development
  • Proficiency with Linux
  • Familiarity with Github (asynchronous learning material will provided prior to course commencement)
  • Basic familiarity with the C programming language (either a single university-level course, or self-taught are sufficient).
High Performance Computing Microcredential

About the Instructor

Ross Dickson

Based in Nova Scotia, Ross joined ACENET in 2007 and works on education, documentation, and client support in relation to high-performance computing. As a senior digital research consultant, he works with users in a range of disciplines, including chemistry, physics, biology, oceanography, neuroscience, several engineering disciplines, philosophy, and management studies. Following his doctoral and postdoctoral studies, Ross worked in software development for Hypercube Inc., makers of HyperChem for Windows, and for Molecular Mining Corporation where he helped specify some of the earliest software for analyzing high-throughput gene expression data.

Sarah Clarke

Sarah, based in Nova Scotia, joined ACENET in 2023. She has a range of teaching experience and held regular teaching assistant positions. Passionate about scientific literacy, Sarah has developed teaching materials and taught programming and robotics to youth in St. John's with Brilliant Labs. She has also led professional development workshops for teachers, focusing on digital skills. Most recently, Sarah was one of the principals who developed and taught ACENET’s 100-hour online Microcredential in Advanced Computing.

Serguei Vassiliev

Serguei joined ACENET in 2019 as a Research Consultant at UNB, with a background is in experimental and computational biophysics. Following his doctoral and postdoctoral studies in the biophysics of photosynthesis, Serguei was the Alexander von Humboldt Research Fellowship recipient at the Technical University of Berlin, where he studied energy conversion in natural and artificial photosynthetic systems using ultrafast laser spectroscopy. More recently he was working as a research associate at Brock University with Dr. Douglas Bruce, where he was engaged in inter-disciplinary research of photosynthesis, integrating biology, computational chemistry, physics and data science. Working at Brock University, Serguei developed specialized programs for global and target analysis of time-resolved spectroscopic data, and analysis of molecular dynamics trajectories. Serguei’s research interests include molecular modelling, simulations of water and oxygen transport in proteins, and computation of the properties of cofactors in protein complexes.

Funding

To apply your organization must

  • Be a Digital Nova Scotia member prior to the program start date.
  • Be a legally registered business, industry association, sector council, or non-profit organization operating in Nova Scotia.

Funding

  • Members are eligible for funding for up to 75% of direct training costs
  • Up to a maximum of $10,000, any amount over $10,000 is cost-shared at 50%.
  • The cost for this program with the funding support would be $310+HST

Course FAQ

What will the training cover?

Course Curriculum Overview:

  • Session 1: Description of HPC; accessing remote resources; batch scheduling (Learning Objectives 1-4)
  • Session 2: Performance profiling I (Learning Objective 5)
  • Session 3: Mathematical models of strong scaling and weak scaling (Learning Objective 6)
  • Session 4: OpenMP programming I (Learning Objective 7)
  • Session 5: OpenMP programming II (Learning Objective 7 continued)
  • Session 6: GPU programming I (Learning Objectives 9, 10)
  • Session 7: GPU programming II (Learning Objective 10 continued)
  • Session 8: GPU programming III (Learning Objective 11)
  • Session 9: HPC on cloud services (Learning Objective 12)
  • Session 10: Performance profiling II (Learning Objective 5)
  • Session 11: Independent study project presentations
  • Session 12: Independent study project presentations
Program Outcomes

By the end of this course, participants will understand how:

  1. an HPC cluster is different from personal computers and other "cloud computing" resources.
  2. to use SSH keys to access HPC resources securely.
  3. to use a batch scheduler to share HPC resources.
  4. hardware affects parallelism and how to make programming decisions.
  5. to profile a program to measure speed and identify hot spots or bottlenecks.
  6. to apply the concepts of strong and weak scaling, and the mathematical models describing them.
  7. to make an existing C language program multi-threaded using OpenMP.
  8. to identify data dependencies, race conditions, and other hazards of parallel programming.
  9. a GPU differs from a CPU.
  10. to use CUDA to program a GPU.
  11. to port an existing CPU program to use a GPU.
  12. to create an HPC cluster on a commercial cloud service (AWS, Google Cloud, Azure).

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