Tutorial session on "Scientific Visualization with VisIt" at CANHEIT-HPCS2-16 (canheit-hpcs.ualberta.ca) Location: SciNet Teaching Room MaRS 1140

Part of the 2016 Ontario Summer School, this half-day session will provide an introduction to basic concepts of high-performance computing. It is intended to be a high level primer for those largely new to HPC, and serve as a foundation upon which to build over the coming days. Topics will include motivation for HPC, essential issues, problem characteristics as they apply to parallelism and a high level overview of parallel programming models. Strategies of running large sets of serial processes using e.g. GNU parallel, will also be presented. Location: SciNet Conference Room (old location on McCaul Street)

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116

Part of the 2016 Ontario Summer School, this two-day session is an introductory course covering programming and computing on GPUs - graphics processing units - which are an increasingly common presence in massively parallel computing architectures. This session will cover both of the available C-like programming frameworks: NVIDIA’s CUDA C. The basics of GPU programming will be covered, and students will work through a number of hands on examples. Demonstrations of profiling and debugging applications running on the GPU will also be included. The structuring of data and computations that makes full use of the GPU will be discussed in detail. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. Location: Wallberg Building (UofT- St. George Campus), Rm 116

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 116

A week-long intensive workshop on high performance computing, parallel programming, visualization and big data. Up to 30 credits towards SciNet certificates. Part of Ontario Summer School - Central, Location: Wallberg Building (UofT- St. George Campus), Rm 119

As part of the "2016 Industrial Problem Solving Workshop" to be hosted at the Fields Institute, SciNet will provide an introduction to High Performance Computing resources, as well as support for participants who decide to tackle this problems numerical utilizing supercomputer resources. Location: SciNet Teaching Room MaRS 1140

pizza, user discussion, and a techtalk on "Overview of 3D Scientific Visualization Tools" Location: SciNet Teaching Room MaRS 1140

Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140

Live broadcast of the Q&A session for the 2016 Visualization Challenge: "Visualize this!/Faites-nous voir ça!" competition Part of Visualization Challenge -- live Q&A session, Location: SciNet Boardroom MaRS 1140

Information sessions for the 2017 Big Data Challenge for High Schools Students. Part of 2016-17 Big Data Challenge for High School Students -- Information Sessions, Location: Villanova College

Information sessions for the 2017 Big Data Challenge for High Schools Students. Part of 2016-17 Big Data Challenge for High School Students -- Information Sessions, Location: Earl Haig Secondary School

Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140

In about 60 to 90 minutes, you will learn how to use the SciNet systems. Experienced users may still pick up some valuable pointers. Location: SciNet Boardroom MaRS 1140

Explore use advanced examples of parallel computing in scientific research.This course can be taken as a mini/modular graduate course by Physics students. Location: SciNet Teaching Room MaRS 1140

The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108

Learn the basics of how to use the unix shell in two hours. Includes an mild introduction to bash scripting as well. Very useful for new users of SciNet that have little or no experience with unix or linux. Location: SciNet Teaching Room MaRS 1140

The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108

The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108

In about 60 to 90 minutes, you will learn how to use the SciNet systems. Experienced users may still pick up some valuable pointers. Location: SciNet Boardroom MaRS 1140

The goal of this course is to prepare 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.This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.The course can also be taken as a mini/modular graduate course by Physics students. Part of Introduction to Data Analysis with R, Location: Koffler House, KP108

Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205

Learn about research computing even with little programming experience. Covers basics of programming in python, best practices and visualization. The course will last 4 weeks with 2 lectures per week (mini/modular grad course).The course can be taken as a mini/modular graduate course by Physics students.This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. Location: Bissell Building, Rm 205