June,2024 |
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![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
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![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
12 Jun 1:00 pm 2:30 pmIn about 90 minutes, learn how to use the SciNet systems Niagara and Mist, from securely logging in to running computations on the supercomputer. Experienced users may still pick up some valuable pointers.THIS EVENT HAS BEEN CANCELED. Virtual | HPC105 - Jun 2024![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
12 Jun 1:30 pm 4:30 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![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
13 Jun 1:30 pm 4:30 pmApptainer 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. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: Experience using Alliance compute clusters, e.g., using the BASH shell and submitting jobs. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
14 Jun 9:00 am 12:00 pmMachine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. In this hands-on introductory workshop, you will learn how to apply Machine Learning, and get familiar with the basics of Deep Learning. MATLAB provides an environment to apply advanced techniques without requiring extensive coding nor experience in machine learning. Learn the fundamentals of machine learning (supervised learning, feature extraction, and hyperparameter tuning) Explore pre-processing and powerful visualization techniques Build and evaluate machine learning models for classification and regression of various data formats (signals, images, text) Perform hyperparameter tuning and feature selection to optimize model performance Discuss interoperability with other platforms Learn how to deploy Machine Learning models Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Working knowledge of MATLABMachine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. In this hands-on introductory workshop, you will learn how to apply Machine Learning, and get familiar with the basics of Deep Learning. MATLAB provides an environment to apply advanced techniques without requiring extensive coding nor experience in machine learning. Learn the fundamentals of machine learning (supervised learning, feature extraction, and hyperparameter tuning) Explore pre-processing and powerful visualization techniques Build and evaluate machine learning models for classification and regression of various data formats (signals, images, text) Perform hyperparameter tuning and feature selection to optimize model performance Discuss interoperability with other platforms Learn how to deploy Machine Learning models Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Working knowledge of MATLAB (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
14 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![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
14 Jun 1:30 pm 4:30 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![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
14 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![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |
17 Jun 9:00 am 12:00 pmLearn how to improve the performance and use parallel programming in Python. We will cover profiling, subprocess, numexpr, multiprocessing, MPI, and other performance enhancing techniques. Level: Intermediate Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisite: Some Python and Linux command line experience. (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024![Show in Google map](https://www.scinethpc.ca/wp-content/plugins/amr-ical-events-list/images/map_16.png) |