Introduction to ACENET and High Performance Computing (HPC)

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  • Instructor:  TBD
  • Level: Beginner
  • Duration: 1.5 hours
  • Helpers: TBD
  • Date:  May 6, 2025 | 10:00 - 11:30 am (Atlantic)
  • Prerequisite: None
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COURSE DESCRIPTION

What is High Performance Computing (HPC) and what can it do for me? How can ACENET help? Used by researchers across many disciplines to tackle analyses too large or complex for a desktop, or to achieve improved efficiency over a desktop, this session takes participants through the preliminary stages of learning about high performance computing (HPC) and computing clusters, and how to get started with this type of computing. It then reviews software packages available for applications, data analysis, software development and compiling code. Finally, participants will be introduced to the concept of parallel computing to achieve much faster results in analysis. This session is designed for those with no prior experience in HPC, and are looking for an introduction and overview.

This session will be delivered online.

To get the most from ACENET basics, please register for a Digital Research Alliance of Canada (the Alliance) account. To register contact your supervising professor, ask for their CCRI, then visit https://ccdb.alliancecan.ca/account_application. If your professor is not registered with the Alliance, please have them register, then follow up with you. In addition to an Alliance account, you will want a computer with Windows, MacOS X, or a Unix-based operating system (not a ChromeBook), and a stable internet connection. A registered account is not mandatory, just recommended to get the most out of our lessons. You can attend the first session to see how ACENET can assist in accelerating your computational research before you decide to obtain an account, if you wish.

SETUP REQUIREMENTS
  • None

Meet your teaching team

Tannia Chevez

Host

Digital Training Specialist
BSc Computational Chemistry, Memorial University

Tannia joined ACENET in 2023 and is based in St. John’s. She has held positions as a research assistant in various departments, with responsibilities ranging from developing algorithms for an online animal sound repository, to crafting chemical composite films. Proficient in Python, Java, and JavaScript, she has focused on spectral data analysis, SEM image-based nanoparticle detection, and software development for data analysis. Tannia contributed significantly to the publication of a research paper by analyzing potential environmental toxicants, generating millions of chemical structure IDs, and conducting data extraction and analysis using Python, R, and JavaScript, as well as enhancing algorithms for simulating potential environmental toxicants’ behavior in water, soil, and air environments. Tannia’s teaching experience includes a Leader Instructor at Brilliant Labs where she taught a range of digital topics, and a Digital Literacy Instructor for the Association for New Canadians.