This is an online "New User" self-paced course introducing what is Digital Research Alliance of Canada, its partners (including SHARCNET), its systems and services, where to find information, and how to obtain help. If you are new to Alliance and SHARCNET, have been away for while, or would like to review/refresh you memory about any of these items, feel free to enrol in this course. Upon the completion of the course and successfully passing the quiz at the end of the course, the Certificate of Course Completion will be issued.
Prerequisites: none
Estimated time: one hour
- Teacher: Sergey Mashchenko
This is a self-directed online learning course focused on advanced research computing literacy, designed for graduate students who are preparing to utilize high-performance computers in their research. The course does not assume any prerequisites, but prior knowledge in scientific computing and some exposure to computers would be beneficial.
Comprising multiple sections, this course covers a broad range of topics at an introductory level. It begins with an overview of what constitutes a supercomputer and introduces the available supercomputing resources across Canada. Subsequent sections delve into the fundamentals of modern computer architecture, scientific computing, parallel computing, GPU utilization, and extend to topics such as relational databases and visualization techniques. Each section serves as an introduction and guide, directing learners to related in-depth courses for further exploration.
As a self-paced course. There are no live classes, but there will be announced office hours for participants to ask questions. There is also a discussion forum where people may post questions and get answers.
To complete the course, the participants are expected to read the course materials and complete quizzes and assignments with a minimum grade of 80%.
Expected study time: 10 to 15 hours
- Teacher: Baolai Ge
- Teacher: Paul Preney
- Teacher: Tyson Whitehead
- Teacher: Tyson Whitehead
This self-paced online course is an introduction to GPU programming, focusing on OpenACC. The course consists of two parts, with each part containing three sections. We will start by discussing GPU architectures and basic principles of programming GPUs. Then we will introduce basic OpenACC constructs using simple code examples: SAXPY, Julia set, reduction, and Jacobi solver. A significant attention will be given to making the code efficient. The course has a programming assignment at the end. Submitted assignments will be graded by the instructors. If your assignment fared well, you will get a certificate for the course completion.
The course was developed and is being maintained by SHARCNET staff Pawel Pomorski and Sergey Mashchenko.
Prerequisites: some experience with C/C++ programming.
Estimated time: two hours for the theory, one to two hours for practical exercises.
- Teacher: Sergey Mashchenko
- Teacher: Pawel Pomorski
This course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. By the end of this course, the student will have an initial understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.
Prerequisites:
- Basic Python knowledge and experience.
- Using Alliance compute clusters.
- Knowledge and experience with Tensorflow and Scikit-learn would also be helpful.
- Teacher: Weiguang Guan
This self-paced course describes how to run programs on supercomputers using the BASH shell. In addition to the basic BASH shell usage, globbing, regular expressions, redirection, pipes, and scripting will also be covered.
Prerequisites: None.
- Teacher: Tyson Whitehead