High-Performance Computing (HPC) through the LT Hub seeks to help students master complex problem-solving skills in the fields of data analytics, statistical modelling, and machine learning. Starting as a one-year pilot project, our HPC initiative for undergraduate teaching and learning supports research activities, while developing an understanding on how these HPC services might be sustained in the future.
Opportunities to Engage
As part of the High-Performance Computing (HPC) pilot, the following options will be available to you for studies in undergraduate courses.
JupyterHub Access
JupyterHub brings the power of HPC to notebooks, giving people access to computational environments and resources without burdening them with installation and maintenance. You and your students can get HPC work done in your own workspaces using shared resources, which can be managed efficiently by system administrators. In other words, our large-scale JupyterHub instance will more easily enable using Juptyer notebooks in course activities, similar to Compute Canada and UBC’s ubc.syzygy.ca site.
Teaching Support
Whether High-Performance Computing is the topic or a tool in the pursuit of the subject matter, we are here to assist with provisioning and providing access to HPC technical resources. This resourcing may mean enabling access to vendors offering online solutions or setting up an online environment of your own (e.g., using Amazon AWS, Google GCP, and Microsoft Azure).
Student Undergraduate Projects
Come to us with a proposal that includes the requirements and estimated costs for an undergraduate academic project, and we’ll work with you to determine how we can help.
How to Request Support
Requests for High Performance Computing support can be sent to LT.hub@ubc.ca. Please include as much of the following information as possible in your request:
- the course number for the undergraduate course that will use the support,
- a short description of the needs for the course,
- the timing and duration,
- details of the computing resources needed (e.g., servers, Graphics Processing Units, memory, storage, Jupyter notebooks),
- an estimated overall cost of the resources, and
- [if applicable] your preferred cloud solution (i.e., Amazon AWS, Google GCP, or Microsoft Azure).
We will review the request and let you know what support we can provide.