Difference between revisions of "Online Seminars"

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==General Interest Webinars==
==General Interest Webinars==
===2021===  
===2021===  
* 2021/02/24 - [https://www.youtube.com/watch?v=n_rNjvafkxY Generating interactive visualizations with Plotly on Graham], James Desjardins, [[Webinar 2021 Generating interactive visualizations with Plotly on Graham|Abstract]], [[Media:.pdf|slides]]
* 2021/02/10 - [https://www.youtube.com/watch?v=i-DaDcCPRLI Using multiple GPUs for Machine Learning], Isaac Ye, [[Webinar 2021 Using multiple GPUs for Machine Learning|Abstract]], [[Media:.pdf|slides]]
* 2021/02/10 - [https://www.youtube.com/watch?v=i-DaDcCPRLI Using multiple GPUs for Machine Learning], Isaac Ye, [[Webinar 2021 Using multiple GPUs for Machine Learning|Abstract]], [[Media:.pdf|slides]]
* 2021/02/03 - [https://www.youtube.com/watch?v=GHlls6bQZ5o Computing in arbitrary precision], Ge Baolai, [[Webinar 2021 Computing in arbitrary precision|Abstract]], [[Media:.pdf|slides]]
* 2021/02/03 - [https://www.youtube.com/watch?v=GHlls6bQZ5o Computing in arbitrary precision], Ge Baolai, [[Webinar 2021 Computing in arbitrary precision|Abstract]], [[Media:.pdf|slides]]

Revision as of 10:02, 16 March 2021

Recordings of most of our webinars can be found on SHARCNET youtube channel, http://youtube.sharcnet.ca .

General Interest Webinars

2021

2020

2019

2018

2018/05/09 - Summer School preview, Tyson Whitehead
From May 28th to June 1st SHARCNET will run its annual Summer School on Advanced Research Computing, this time at Western University. This summer school will be our largest yet: for the first time we will have three separate full streams, with SHARCNET staff providing instructions on 13 different courses ranging from traditional HPC topics (2-days in-depth courses on MPI and CUDA) to courses on machine learning, singularity and cloud computing. Each course is 1-2 days long, with plenty of hands on time. This webinar will briefly describe the courses which will be offered at the Summer School.

slides


2017

2016

2015

2015/04/29 - High Performance Computing with Python, Pawel Pomorski
Python has numerous advantages over traditional compiled languages like C and Fortran, and it is seeing increasing adoption among the scientific community. However, despite its advantages, there are challenges associated with using Python in a High Performance Computing (HPC) environment. First, a “vanilla” Python program is generally slower than an analogous compiled language program. Also, Python is relatively new to the HPC field, and many scientific programmers may not be aware of its parallel computing capabilities. This talk will discuss various strategies to make a serial Python code faster, for example using libraries like NumPy, or tools like Cython which compile Python code. The talk will also discuss the available tools for running Python in parallel, focusing on the mpi4py module which implements MPI (Message Passing Interface) in Python.

slides


2014

2014/11/12 - Linear Algebra on GPU, Pawel Pomorski
This seminar will provide an overview of how one can efficiently solve linear algebra problems using GPGPU (General Purpose Graphics Processing Unit) hardware and the associated CUDA software framework. The basic issues involved in developing efficient code for this type of computation will be discussed, followed by a demonstration of how to use three popular libraries relevant to the problem: CUBLAS, CULA and MAGMA.

slides


2014/02/19 - Profiling MPI codes with Allinea's MAP, Sergey Mashchenko
Recently SHARCNET acquired a powerful MPI profiler made by Allinea - MAP. It now comes bundled up with their other popular product, parallel debugger DDT, and is installed on our cluster orca. This tutorial will give a brief overview of the software, with a live demonstration of the profiling a realistic MPI code.

slides


2013

2013/12/18 - Why Would I Use GPUs?, Pawel Pomorski
GPUs (Graphics Processing Units) can provide a significant speedup for certain types of scientific computations. This talk will discuss which programs can benefit from this speedup, and how in certain cases it can be obtained without much effort using already existing packages and libraries. Simulation packages already accelerated for the GPU will be discussed, with focus on NAMD molecular dynamics package as a useful example. The use of GPU-enabled numerical libraries useful for common problems will be discussed. The use of these techniques will be demonstrated with example runs on SHARCNET’s new GPU cluster. While not the focus of this talk, a brief overview of available programming approaches for GPUs will be also provided.

Slides as PDF file


New User Seminars

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