Calendar

DESCRIPTION: Apptainer is a secure container technology designed to be used on for high performance compute clusters. This workshop will focus on how to use Apptainer as well as how to make use of tools such as Conda and Spack within Apptainer. By the end of these sessions, one will have learnt about Apptainer and how it is installed and used on our computer clusters, how to build an Apptainer container image, how to install tools such as Conda/Spack from inside an Apptainer container shell, and,
how to use Apptainer containers within job submission scripts.

TEACHER: Paul Preney (SHARCNET, University of Windsor)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITE: Basic knowledge of Linux shell and how to run programs from the shell.

DESCRIPTION: The new H100 GPUs in Compute Ontario systems are up to 8 years newer and many times more powerful than some of the GPU that are being replaced. There are several new architectural features that are worth knowing about, and it's worth considering how to make full use of these much larger systems. In this session we'll cover: Differences from P100, V100, and A100; Advanced features; Taking advantage of the features with compilers and libraries that do much of the work for you; and the pros and cons of different approaches like MIG and MPS to stack multiple partly-accelerated runs on one GPU.

TEACHER: Jonathan Dursi (NVIDIA)

LEVEL: Intermediate

FORMAT: Lecture

CERTIFICATE: Attendance

PREREQUISITES: Experience with previous generations of GPUs (V100, A100)

DESCRIPTION: With the release of the Tri-Agency Research Data Management Policy all Canadian post-secondary institutions and research hospitals that administer Tri-Agency funding were required to develop and post institutional research data management (RDM) strategies by March 1, 2023. As institutions finalized their strategies, they began to consider what implementation would look like. To support inter-institutional, cross-functional dialogue around implementation, a two-day, SSHRC-supported workshop was hosted at the University of Waterloo in September 2023. Over 30 institutions of varying sizes and research intensities sent cohorts representing libraries, information technology, and research offices to participate in dialogues around challenges and collaborative solutions in RDM strategy implementation. The high-level recommendations from that workshop have been released as the report _Building an Inter-Institutional and Cross-Functional Research Data Management Community: From Strategy to Implementation_. In this short workshop, participants will discuss the recommendations in the report and how they can be implemented in their institutions.

TEACHERS: Jennifer Abel (University of Calgary) and Ian Milligan (University of Waterloo)

LEVEL: Intermediate

FORMAT: Workshop

CERTIFICATE: Attendance

PREREQUISITES:

* Participants should be involved in RDM-supporting work in their institution; e.g., in the library, research office, IT/research computing, ethics, etc.
* Participants should also read the executive summary of the report before the workshop.

DESCRIPTION: Apptainer is a secure container technology designed to be used on for high performance compute clusters. This workshop will focus on how to use Apptainer as well as how to make use of tools such as Conda and Spack within Apptainer. By the end of these sessions, one will have learnt about Apptainer and how it is installed and used on our computer clusters, how to build an Apptainer container image, how to install tools such as Conda/Spack from inside an Apptainer container shell, and,
how to use Apptainer containers within job submission scripts.

TEACHER: Paul Preney (SHARCNET, University of Windsor)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITE: Basic knowledge of Linux shell and how to run programs from the shell.

DESCRIPTION: This workshop introduces the topic of text mining and its applications. It covers different encoding mechanisms to convert text into numbers that algorithms can handle. It gives an overview of different text mining tasks, including de-identification, sentiment analysis and document clustering, and how they work with examples and live demos. There will also be references to state-of-the-art tools and libraries to conduct various text mining tasks.

TEACHER: Amal Khalil (CAC, Queen's University)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITES: Basic Python