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| March,2024 | |
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19 Mar 10:00 am 12:00 pmEES1137: Lecture 19In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024 |
19 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
20 Mar 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
21 Mar 11:00 am 12:00 pmEES1137: Lecture 20In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024 |
21 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
25 Mar 1:00 pm 4:00 pmParallel Debugging with DDTDebugging is an important step in developing a new code, or porting an old one to a new machine. In this session, we will discuss the debugging of frequently encountered bugs in serial code and debugging of parallel (MPI and threaded) codes using DDT. Virtual | HPC245 - Mar 2024 |
26 Mar 10:00 am 12:00 pmEES1137: Lecture 21In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024 |
26 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
27 Mar 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
28 Mar 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
28 Mar 11:00 am 12:00 pmEES1137: Lecture 22In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024 |
| April,2024 | |
2 Apr 10:00 am 12:00 pmEES1137: Lecture 23In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: AA207 | EES1137 - Winter 2024 |
2 Apr 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
4 Apr 11:00 am 12:00 pmEES1137: Lecture 24In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI. UTSC: IC120 | EES1137 - Winter 2024 |
4 Apr 11:00 am 12:00 pmPHY1610 Scientific Computing LectureThis course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...).This is a graduate course that can be taken for graduate credit by UofT PhD and MSc students. Students that wish to do so, should enrol using ACORN/ROSI.This is an in-person course. | PHY1610 - Winter 2024 |
10 Apr 11:00 am 12:00 pmIntro to Python for BiochemistryIn this course students will be instructed in how to program in Python. Ultimately students will learn how to use Python to analyze, process and visualize data. This course is designed for students with little to no experience in programming. This is a graduate course that can be taken for by UofT Biochemistry graduate students. Those students should enrol using ACORN/ROSI. | BCH2203 - Winter 2024 |
10 Apr 1:00 pm 2:30 pmIntro to NiagaraIn 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.Format: Virtual Virtual | HPC105 - Apr 2024 |
15 Apr 1:00 pm 4:00 pmShell ScriptingLearn how to write bash scripts, use environment variables, how to control process, and much more. Requires some Linux basic command line experience.Note: this event has been moved from April 8th to April 15th.Format: Virtual Virtual | SCMP201 - Apr 2024 |
17 Apr 12:00 pm 1:00 pmCO Colloquium "How to Buy a Supercomputer for Scientific Computing"Buying a new supercomputer that both maximises total performance, given our budget, and whose architecture suits our users' workloads is a very difficult balancing act. There are a wide range of decisions to be made, such as: CPU architecture; node count; memory size/bandwidth; GPU count; interconnect type; storage size; filesystem type/bandwidth; cooling type and power budget to name but a few. In order to balance all of these constraints we need to come up with a scoring system to compare potential candidate supercomputers. In this talk we describe the Scalable System Improvement (SSI) metric and apply it to the system refresh of Niagara & Mist. Virtual | COCO - 17 Apr 2024 |
23 Apr 11:00 am 12:00 pmDAT112: Lecture 1Introduction to neural network programming, lecture 1 | DAT112 - Apr 2024 |
25 Apr 11:00 am 12:00 pmDAT112: Lecture 2Introduction to neural network programming, lecture 2 | DAT112 - Apr 2024 |
30 Apr 11:00 am 12:00 pmDAT112: Lecture 3Introduction to neural network programming, lecture 3 | DAT112 - Apr 2024 |
| May,2024 | |
2 May 11:00 am 12:00 pmDAT112: Lecture 4Introduction to neural network programming, lecture 4 | DAT112 - Apr 2024 |
7 May 11:00 am 12:00 pmDAT112: Lecture 5Introduction to neural network programming, lecture 5 | DAT112 - Apr 2024 |
8 May 1:00 pm 2:30 pmIntro to NiagaraIn 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.Format: Virtual Virtual | HPC105 - May 2024 |
9 May 11:00 am 12:00 pmDAT112: Lecture 6Introduction to neural network programming, lecture 6 | DAT112 - Apr 2024 |
13 May 1:00 pm 4:00 pmRelational DatabasesPrinciples and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.Prerequisites: Some Linux command line experience. Python experience is strongly advised. Format: Virtual Virtual | SCMP231 - May 2024 |
14 May 11:00 am 12:00 pmDAT112: Lecture 7Introduction to neural network programming, lecture 7 | DAT112 - Apr 2024 |
16 May 11:00 am 12:00 pmDAT112: Lecture 8Introduction to neural network programming, lecture 8 | DAT112 - Apr 2024 |
21 May 11:00 am 12:00 pmDAT112: Lecture 9Introduction to neural network programming, lecture 9 | DAT112 - Apr 2024 |
23 May 11:00 am 12:00 pmDAT112: Lecture 10Introduction to neural network programming, lecture 10 | DAT112 - Apr 2024 |
27 May 1:00 pm 2:30 pmBash idioms, awk, etc.This workshop explores various concise and useful constructs for working with bash shell. The goal is to improve your shell skills. Attending this class requires some basic GNU/Linux command line experience.Format: Virtual | SCMP281 - May 2024 |
28 May 11:00 am 12:00 pmDAT112: Lecture 11Introduction to neural network programming, lecture 11 | DAT112 - Apr 2024 |
30 May 11:00 am 12:00 pmDAT112: Lecture 12Introduction to neural network programming, lecture 12 | DAT112 - Apr 2024 |
| June,2024 | |
3 Jun 9:00 am 12:00 pmCO Summer School S1: Introduction to Linux shell (morning session)Running programs on the supercomputers is done via the BASH shell. This course is two three hour live demos on using bash. No prior familiarity with bash is assumed. In addition to the basics of getting around, globbing, regular expressions, redirection, pipes, and scripting will be covered. A series of exercises are required to be done in order to complete the course. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
3 Jun 9:00 am 12:00 pmCO Summer School S2: Data PreparationThis course provides you with essential knowledge and skills to effectively prepare data for analysis. Starting with an overview of the Data Analytics pipeline and processes, the course explores various statistical and visualization techniques used in Exploratory and Descriptive Analytics to understand historical data. You will then delve into the art of Data Preparation, gaining expertise in data cleaning, handling missing values, detecting, and handling outliers, as well as transforming and engineering features. By the end of the course, you will be equipped with the necessary tools to ensure data quality and integrity, enabling you to make informed decisions and derive valuable insights from their data. Level: Introductory Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Basic Python (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
3 Jun 1:30 pm 4:30 pmCO Summer School S2: Data SecurityBe aware. Stay secure. Join us to learn more about the tools you can use to prevent the theft of your data and possibly of your identity. Other topics of discussion will include common hacking attempts, how to recognize them, and how to avoid having your data compromised, stolen, or destroyed. We will also talk about data encryption and provide tips for when travelling with electronic devices. Level: Introductory Length: 3 hours Format: Lecture Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
3 Jun 1:30 pm 4:30 pmCO Summer School S1: Introduction to Linux shell (afternoon session)Running programs on the supercomputers is done via the BASH shell. This course is two three hour live demos on using bash. No prior familiarity with bash is assumed. In addition to the basics of getting around, globbing, regular expressions, redirection, pipes, and scripting will be covered. A series of exercises are required to be done in order to complete the course. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
4 Jun 9:00 am 12:00 pmCO Summer School S1: Introduction to Advanced Research ComputingThis workshop is a primer for those largely new to supercomputing, i.e., to computing on shared, remote resources. It is intended to demystify the somewhat intimidating term "High-Performance Computing" (HPC), and to serve as a foundation upon which to build over the coming days. Topics will include motivation for HPC, available resources, essential issues, and a high level overview of parallel programming models commonly used on these systems. Level: Introductory Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Basic Linux e.g. "Introduction to Linux Shell" (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
4 Jun 9:00 am 12:00 pmCO Summer School S2: Bioinformatics: Analysis of RNA-sequencing dataRNA-Seq refers to high throughput sequencing methods that probes the entire transcriptomic landscape of a given tissue or sample of interest. The data acquired from such experiments can be used to explore the overall RNA profile of a sample as well as comparing samples under various conditions. While extremely powerful, RNA-Seq is susceptible to numerous experimental pitfalls and requires intimate knowledge of the experimental procedures and data analysis methods. When conducted properly RNA-Seq can reveal information about gene/transcript expression, splicing and the effects of mutations. In this session we will take a thorough look at a comprehensive RNA-Seq pipeline, from sample processing methods to final differential expression analysis. Relevant R / BioConductor packages will be introduced. We will have the opportunity to investigate numerous quality control metrics, perform genomic alignment, differential expression and pathway enrichment analysis. We will cover several “gotcha”s and common mistakes in experimental design and data analysis. Basic familiarity with R and Linux command line will be beneficial but not required. All necessary commands and parameters will be explained during the class. Participants will be offered hands-on practice in which they will use RStudio to run R/BioConductor scripts for data analysis as well as the Integrative Genomic Viewer (IGV) software to visualize genomic data on their laptops Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Basic R and Linux beneficial but not required (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
4 Jun 1:30 pm 4:30 pmCO Summer School S1: Introduction to Version Control with GitUsing version control for your scripts, codes, documents, papers, and even data, allows you to track changes, keep backups, and facilitate collaboration. This introductory workshop will teach you the basics of version control with the popular distributed version control software GIT. This workshop assumes that students have an understanding of basic Linux shell commands. Level: Introductory Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Basic Linux (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
4 Jun 1:30 pm 4:30 pmCO Summer School S2: Bioinformatics: Long-read sequencing applicationsLong-read sequencing technologies enable the sequencing of DNA fragments 10KB and longer. This read length greatly improves sequence mappability and assembly, providing an advantage over short-read sequences that are difficult to map uniquely to repetitive and GC-rich regions. Long-read sequencing has applications in a number of fields including genome assembly, diagnosis of genetic diseases, and metagenomics. In this workshop, we will focus on PacBio HiFi sequences and introduce you to tools for haplotyping, calling and visualizing structural variants and repeat expansions, visualizing read methylation, and detecting novel isoforms from PacBio Iso-Seq data. Participants will be offered hands-on practice in which they will use RStudio to run R/BioConductor scripts for data analysis as well as the Integrative Genomic Viewer (IGV) software to visualize genomic data on their laptops Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Basic R (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
5 Jun 9:00 am 10:20 amCO Summer School S2: Research Data Management: Rationale for ReproducibilityThe role of good research data management practices in supporting research reproducibility is becoming increasingly well known. The literature is replete, however, with examples of poor methodology, lack of transparency, mistakes, and misconduct leading to bad science and an inability to reproduce results. This introductory session will provide real-world, illustrative examples of each of these, along with practical suggestions on how to avoid them. Level: Introductory Length: 1.5 Hours Format: Lecture Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
5 Jun 9:00 am 12:00 pmCO Summer School S1: Introduction to Python (morning session)This course is designed to provide you with a solid foundation in Python programming language. Through a comprehensive curriculum and hands-on coding exercises, participants will learn the fundamentals of Python syntax, data types, functions, and file handling. By the end of the course, you will have gained the essential skills to write Python programs, solve problems, and build the foundation for more advanced Python development. Whether you are a beginner or have some programming experience, this course will equip you with the necessary tools to start your journey in Python programming. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
5 Jun 10:30 am 11:50 amCO Summer School S2: From the I-Ching to ChatGPT: A Brief History of AI and Some Historical and Current ApplicationsGoogle's 2017 research paper "Attention Is All You Need" described the transformer, a new machine learning technique. From that paper the modern Large Language Model was born, and we're now living in the thick of a new era brought on by companies like OpenAI, Mistral and Anthropic. But where does this cutting-edge technology come from? What are its roots? What are its problems? This talk explores the history of procedural generation in text and games, from the I-Ching to tranformer-based language models and beyond. The talk will emphasize current state of the art in text-based language models, and include demonstrations on how to run language models locally on your own hardware. Level: Introductory Length: 1.5 Hours Format: Lecture Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
5 Jun 1:30 pm 2:50 pmCO Summer School S2: Using generative AI tools for research data managementIn this workshop, we will explore the potential uses of generative artificial intelligence tools in research data management (RDM) with a focus on specific use cases. For example, can AI tools be used to write Data Management Plans, summarize funder requirements, assist with data analysis, or suggest file naming conventions and folder structures? This workshop will be interactive, and participants will be welcome to practice using AI tools along with the presenters using real-world data and prompts. We will also discuss the ethical considerations, including benefits and risks, of using AI tools in research and whether it is possible to use AI for RDM practices in an ethical manner. Level: Introductory Length: 1.5 Hours Format: Lecture + Hands-on Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
5 Jun 1:30 pm 4:30 pmCO Summer School S1: Introduction to Python (afternoon session)This course is designed to provide you with a solid foundation in Python programming language. Through a comprehensive curriculum and hands-on coding exercises, participants will learn the fundamentals of Python syntax, data types, functions, and file handling. By the end of the course, you will have gained the essential skills to write Python programs, solve problems, and build the foundation for more advanced Python development. Whether you are a beginner or have some programming experience, this course will equip you with the necessary tools to start your journey in Python programming. Level: Introductory Length: Two 3-Hour Sessions Format: Lecture + Hands-on Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
5 Jun 3:00 pm 4:20 pmCO Summer School S2: Introduction to Alliance RDM ServicesThis session provides an overview of the Research Data Management (RDM) Services offered by the Digital Research Alliance of Canada, including the DMP Assistant, a national, bilingual platform for the creation and management of data management plans (DMPs), the Federated Research Data Repository (FRDR), a bilingual publishing platform for sharing and preserving Canadian research data, and Lunaris, Canada’s national discovery service for multidisciplinary data from over 90 academic, government, and research repositories across the country. This session will introduce participants to these platforms and provide an overview of how they support the research lifecycle. Attendees will gain valuable insights into the benefits of these tools and how they can help researchers to streamline their data management workflows. Level: Introductory Length: 1.5 Hours Format: Lecture Prerequisites: None (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
6 Jun 9:00 am 12:00 pmCO Summer School S1: AI ShowcaseThis course introduces Artificial Intelligence (AI), a science focusing on developing intelligent systems capable of autonomous behavior. In this course, we explore the exciting world of AI, introducing its definition and history. We discuss the advantages and challenges of AI in the present time, along with various applications and projects that demonstrate its capabilities. Throughout the session, participants will gain insights into different types of AI, learn about running predefined projects, and discover AI showcases on various platforms. By the end of the course, participants will have the knowledge and resources to start their own AI projects with their data, exploring the latest AI advancements in our clusters. Level: Introductory Length: 3 Hours Format: Lecture Prerequisites: Basic Python beneficial but not required (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |
6 Jun 9:00 am 12:00 pmCO Summer School S2: High-Performance I/O and StorageThis workshop will help you understand the relation between storage systems and application-level performance. We will survey the design of storage found on national systems, and consider their performance implications. A range of different IO techniques, data formats, and libraries will be considered. Ideally, participants should have an account on the National Platform (DRI). Level: intermediate, examples/exercises will be in Python; having a DRAC account will be helpful. Level: Intermediate Length: 3 Hours Format: Lecture + Hands-on Prerequisites: Alliance Account, Python Experience (part of the 2024 Compute Ontario Summer School) Virtual | COSS2024 |

