| June,2024 |
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| 10 Jun 9:00 am 12:00 pmThis 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. Level: Introductory Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Basic Python (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 10 Jun 9:00 am 12:00 pmSome popular Python libraries for data analytics, like Numpy, Pandas, Scikit-Learn, etc., usually work well if the dataset fits into the RAM on a single machine. When dealing with large datasets, it could be a challenge to work around memory constraints. This course introduces scalable and accelerated data analytics with Dask and RAPIDS. Dask provides a framework and libraries that can handle large datasets on a single multi-core machine or across multiple machines on a cluster. RAPIDS, on the other hand, can accelerate your data analytics by offloading analytics workloads to GPUs with less effort in code changes. Level: Introductory Length: Two 3-Hour Sessions (2 Days) Format: Lecture + Hands-on Prerequisites: Alliance Account Basic Python and Linux command line experience. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 10 Jun 1:30 pm 4:30 pmThis course is intended to help learners with a basic understanding of fluid dynamics and CFD bridge the knowledge gap towards the effective utilization of CFD on modern HPC architectures. This course will take an end-user approach to CFD tools on HPC systems (no coding) and, despite some prerequisites, will be given at an introductory/intermediate level (we will not cover advanced topics such as GPU or dynamic load-balancing). At the end of the course, the learner will be able to: Develop a systematic approach to estimate the HPC cost of a CFD problem. Explain the impact of modelling assumptions on HPC cost. Optimize the parameters and simulations for effective HPC usage. The course will use an entirely open source suite of CFD toolsets to mesh (Gmsh), simulate (OpenFoam/SU2), and visualize (Visit/Paraview). It should be noted that this is not a CFD course; therefore, undergraduate-level knowledge of CFD and numerical methods is expected, as well as a basic understanding of the Compute Ontario HPC system. The focus is on the effective use of CFD tools in modern HPC systems. Level: Intermediate, Length: Three 1-Hour Sessions (3 Days), Format: Lecture + Hands-on, Prerequisites: Undergraduate-level knowledge of fluid dynamics (ideally with some knowledge of turbulence), CFD, and numerical methods. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 10 Jun 1:30 pm 4:30 pmHave you ever tried to run someone else’s code and it just didn’t work? Have you ever been lost interpreting your colleague’s data? This hands-on session will provide researchers with tools and techniques to make their research process more transparent and reusable in remote computing environments. You’ll be using platforms like JupyterHub and command-line tools like Bash and Docker in a Linux environment to interact with the material through various exercises and examples. In this workshop, you’ll learn about: organizing your file directories writing readable metadata with README files automating your workflow with scripts capture and share your computational environment Level: Introductory Length: 3 hours Format: Lecture + Hands-on Prerequisites: Initial familiarity with command line tools and/or a Linux environment may be beneficial but not mandatory (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 11 Jun 9:00 am 12:00 pmThis is an introduction to the intermediate level OpenMP hand-on course. OpenMP is a standard parallel programming API that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran. This one-day course will cover the principles of OpenMP compiler directives, library routines, and environment variables with step-by-step hand-on examples. Case studies include various approaches for loop parallelism. We will also talk about the Task constructs for irregular programs, and the Target constructs for accelerators such as GPU. Participants will have hand-on programming experience with OpenMP as well as how to compile and run Multi-thread OpenMP code on different alliance clusters. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on (Hands-on portion is CPU only.) Prerequisites: Basic knowledge of C, C++, or Fortran (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 11 Jun 9:00 am 12:00 pmThis course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts: Part I covers the fundamentals of machine learning, and, Part II demonstrates the applications of various machine methods in solving a real world problem. 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. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration. Level: Introductory to Intermediate Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: Alliance Account Data preparation or equivalent knowledge. Basic Python knowledge and experience. Knowledge and experience with Tensorflow and Scikit-learn would also be helpful. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 11 Jun 1:30 pm 4:30 pmThis course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts: Part I covers the fundamentals of machine learning, and, Part II demonstrates the applications of various machine methods in solving a real world problem. 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. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration. Level: Introductory to Intermediate Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: Alliance Account Data preparation or equivalent knowledge. Basic Python knowledge and experience. Knowledge and experience with Tensorflow and Scikit-learn would also be helpful. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 11 Jun 1:30 pm 4:30 pmThis is an introduction to the intermediate level OpenMP hand-on course. OpenMP is a standard parallel programming API that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran. This one-day course will cover the principles of OpenMP compiler directives, library routines, and environment variables with step-by-step hand-on examples. Case studies include various approaches for loop parallelism. We will also talk about the Task constructs for irregular programs, and the Target constructs for accelerators such as GPU. Participants will have hand-on programming experience with OpenMP as well as how to compile and run Multi-thread OpenMP code on different alliance clusters. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on (Hands-on portion is CPU only.) Prerequisites: Basic knowledge of C, C++, or Fortran (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 12 Jun 9:00 am 12:00 pmNOTE: This course is divided into four (4) parts over three (3) days. Part I and Part II Description: Introduction of neural network programming concepts, theory, and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate concepts. (The Keras neural network framework will be used for neural network programming but no experience with Keras will be expected.) Part III Description: This part will continue the development of neural network programming approaches from Parts I and II. This part will focus on generative methods used to create images: variational auto-encoders, generative adversarial networks, and diffusion networks. Part IV Description: This part will continue the development of neural network programming approaches from Parts I through III. This part will focus on methods used to generate sequences: LSTM networks, sequence-to-sequence networks, and transformers. Level: Intermediate Length: Four 3-Hour Sessions (3 Days) Format: Lecture + Hands-on Prerequisites: Experience with Python (version 3.10) is assumed. Each part assumes what was covered in the previous parts of this course. Parts III and IV assume experience with neural network programming, per the first two neural network programming sessions in this course. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
| 12 Jun 9:00 am 12:00 pmDuring this hands-on workshop, we will introduce parallel and distributed computing in MATLAB with a focus on speeding up application codes and offloading compute. By working through common scenarios and workflows using hands-on demos, you will gain a detailed understanding of the parallel constructs in MATLAB, their capabilities, and some of the common hurdles that you'll encounter when using them. Users will learn: Multithreading vs multiprocessing When to use parfor vs parfeval constructs Creating data queues for data transfer Leveraging NVIDIA GPUs Parallelizing Simulink models Working with large data Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Working knowledge of MATLAB (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
