Events

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March,2024
25 Mar 1:00 pm 4:00 pm

Parallel Debugging with DDT

Debugging 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 2024Show in Google map
26 Mar 10:00 am 12:00 pm

EES1137: Lecture 21

In 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 2024Show in Google map
26 Mar 11:00 am 12:00 pm

PHY1610 Scientific Computing Lecture

This 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 pm

Intro to Python for Biochemistry

In 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 pm

EES1137: Lecture 22

In 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 2024Show in Google map
28 Mar 11:00 am 12:00 pm

PHY1610 Scientific Computing Lecture

This 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
April,2024
2 Apr 10:00 am 12:00 pm

EES1137: Lecture 23

In 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 2024Show in Google map
2 Apr 11:00 am 12:00 pm

PHY1610 Scientific Computing Lecture

This 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 pm

EES1137: Lecture 24

In 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 2024Show in Google map
4 Apr 11:00 am 12:00 pm

PHY1610 Scientific Computing Lecture

This 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 pm

Intro to Python for Biochemistry

In 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 pm

Intro to Niagara

In 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 2024Show in Google map
15 Apr 1:00 pm 4:00 pm

Shell Scripting

Learn 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 2024Show in Google map
17 Apr 12:00 pm 1:00 pm

CO 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 2024Show in Google map
23 Apr 11:00 am 12:00 pm

DAT112: Lecture 1

Introduction to neural network programming, lecture 1
DAT112 - Apr 2024
25 Apr 11:00 am 12:00 pm

DAT112: Lecture 2

Introduction to neural network programming, lecture 2
DAT112 - Apr 2024
30 Apr 11:00 am 12:00 pm

DAT112: Lecture 3

Introduction to neural network programming, lecture 3
DAT112 - Apr 2024
May,2024
2 May 11:00 am 12:00 pm

DAT112: Lecture 4

Introduction to neural network programming, lecture 4
DAT112 - Apr 2024
7 May 11:00 am 12:00 pm

DAT112: Lecture 5

Introduction to neural network programming, lecture 5
DAT112 - Apr 2024
8 May 1:00 pm 2:30 pm

Intro to Niagara

In 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 2024Show in Google map
9 May 11:00 am 12:00 pm

DAT112: Lecture 6

Introduction to neural network programming, lecture 6
DAT112 - Apr 2024
13 May 1:00 pm 4:00 pm

Relational Databases

Principles 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 2024Show in Google map
14 May 11:00 am 12:00 pm

DAT112: Lecture 7

Introduction to neural network programming, lecture 7
DAT112 - Apr 2024
16 May 11:00 am 12:00 pm

DAT112: Lecture 8

Introduction to neural network programming, lecture 8
DAT112 - Apr 2024
21 May 11:00 am 12:00 pm

DAT112: Lecture 9

Introduction to neural network programming, lecture 9
DAT112 - Apr 2024
23 May 11:00 am 12:00 pm

DAT112: Lecture 10

Introduction to neural network programming, lecture 10
DAT112 - Apr 2024
27 May 1:00 pm 4:00 pm

Bash 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 pm

DAT112: Lecture 11

Introduction to neural network programming, lecture 11
DAT112 - Apr 2024