Learn how to write bash scripts, use environment variables, how to control process, and much more. Requires some linux basic command line experience. Location: SciNet Online

In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online

This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online

This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online

This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online

This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online

This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online

A quick introduction how to use SciNet and the Niagara and Mist supercomputers. Location: SciNet Online

This course is to introduce graduate students to the programming language Python in a biochemistry context. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis, and how to submitting analyses to the Teach cluster at SciNet, to which they will have access during the course. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture.
Enrollment for this course is closed. Part of Introduction to Programming in Python for Biochemistry, Location: SciNet Online

In this workshop, spread out over three days within one week, will cover parallel profiling, performance analysis, and tuning of applications. Location: SciNet Online

In this workshop, spread out over three days within one week, will cover parallel profiling, performance analysis, and tuning of applications. Location: SciNet Online

In this workshop, spread out over three days within one week, will cover parallel profiling, performance analysis, and tuning of applications. Location: SciNet Online

In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online

In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online

In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online

Monthly user meeting at SciNet, now virtual, with user discussion and a TechTalk. The topic on 11 Nov 2020 is TBA. Location: SciNet Online

In this workshop, spread out over three days within one week, you will learn advanced MPI techniques such as MPI Datatypes, MPI-IO and one-sided communications. Location: SciNet Online

In this workshop, spread out over three days within one week, you will learn advanced MPI techniques such as MPI Datatypes, MPI-IO and one-sided communications. Location: SciNet Online

In this workshop, spread out over three days within one week, you will learn advanced MPI techniques such as MPI Datatypes, MPI-IO and one-sided communications. Location: SciNet Online

In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform 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: R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the IMS graduate program and due to be current CoViD19 pandemic, it will be taught fully online. Location: SciNet Online

Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer. Location: SciNet Online

A quick introduction how to use SciNet and the Niagara and Mist supercomputers. Location: SciNet Online

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, ...). Part of Scientific Computing for Physicists, Location: SciNet Online

Monthly user meeting at SciNet, now virtual, with user discussion and a TechTalk. The topic on 13 Jan 2021 is TBA. Location: SciNet Online

This summer school will familiarize the best students in computational sciences with major state-of-the-art aspects of HPC for a variety of scientific disciplines, catalyze the formation of networks, provide advanced mentoring, facilitate international exchange and open up further career options. Leading Canadian, European, Japanese and American computational scientists and HPC technologists will offer instruction in parallel sessions on a variety of topics as: HPC challenges in major scientific disciplines, HPC programming proficiencies, Performance analysis and profiling, Software engineering, Numerical libraries, Big data analysis and analytics, Machine learning, Scientific visualization, and Canadian, European, Japanese and US HPC infrastructure. This events was originally planned to take place in July 2020 but has been moved to July 2021 instead.