Difference between revisions of "2016 Ontario Summer School for High Performance Computing Central"
| Line 49: | Line 49: | ||
''Time/date'' | ''Time/date'' | ||
| − | Monday July 11, 2016, 09:30 - 12:30 | + | Monday July 11, 2016, 13:30 - 16:30<br /> |
| + | Tuesday July 12, 2016, 09:30 - 12:30 | ||
''Location'' | ''Location'' | ||
| Line 74: | Line 75: | ||
====Abstract==== | ====Abstract==== | ||
| − | + | Working with many of the HPC systems in Ontario involves using the Linux/UNIX command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with this course. This hands on session will cover basic commands and scripting, as well as touching on some powerful constructs like regular expressions. It could be a great boon for your productivity! | |
| − | Working with many of the HPC systems in Ontario involves using the | ||
====Details==== | ====Details==== | ||
''Time/date'' | ''Time/date'' | ||
| − | Monday July 13 | + | Monday July 11, 13:30 - 16:30 |
''Location'' | ''Location'' | ||
| − | + | MC 254 | |
| − | + | 5 King's College Road | |
Toronto, ON M5S 1A4 | Toronto, ON M5S 1A4 | ||
| Line 98: | Line 98: | ||
====Slides and other material==== | ====Slides and other material==== | ||
| − | |||
| − | |||
---- | ---- | ||
| − | ===Data | + | ===R for Data Science=== |
=====Abstract===== | =====Abstract===== | ||
| Line 113: | Line 111: | ||
''Date/time'' | ''Date/time'' | ||
| − | + | Tuesday July 12, 2016, 09:30 - 12:30 | |
''Location'' | ''Location'' | ||
| − | + | MC 254 | |
| − | + | 5 King's College Road | |
Toronto, ON M5S 1A4 | Toronto, ON M5S 1A4 | ||
| Line 129: | Line 127: | ||
=====Prerequisites===== | =====Prerequisites===== | ||
| − | Some programming experience in any language. You should have used and written functions. Bring a laptop with | + | Some programming experience in any language. You should have used and written functions. Bring a laptop with R or Rstudio installed. |
=====Slides and other material===== | =====Slides and other material===== | ||
| − | |||
| Line 138: | Line 135: | ||
| − | === | + | ===Programming Clusters with MPI=== |
| − | =====Abstract===== | + | =====Abstract===== |
| − | + | In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of MPI programming. Examples and exercises will be based on parallelization of common scientific computing problems. | |
=====Details===== | =====Details===== | ||
| − | |||
''Time/date'' | ''Time/date'' | ||
| − | + | Tuesday July 12, 2016, 13:30 - 16:30<br /> | |
| + | Wednesday July 16, 2016, 09:30 - 12:30<br /> | ||
| + | Wednesday July 16, 2016, 13:30 - 16:30<br /> | ||
''Location'' | ''Location'' | ||
| − | + | MC 252 | |
| − | + | 5 King's College Road | |
Toronto, ON M5S 1A4 | Toronto, ON M5S 1A4 | ||
| Line 159: | Line 157: | ||
''Instructor'' | ''Instructor'' | ||
| − | Dr. | + | Dr. Scott Northrup (SciNet) |
=====Prerequisites===== | =====Prerequisites===== | ||
| − | + | C and/or Fortran scientific programming; experience editing and compiling code in a Linux environment. | |
=====Slides and other material===== | =====Slides and other material===== | ||
| − | |||
---- | ---- | ||
| − | + | ===Parallel R for Data Science=== | |
| − | |||
| − | === | ||
=====Abstract===== | =====Abstract===== | ||
| − | + | This half-day session will cover parallel programming in R, with a focus on parallel data analysis. Topics covered will include snow, parallel, and foreach/doparallel. | |
=====Details===== | =====Details===== | ||
| − | |||
| − | Tuesday July | + | ''Time/date'' |
| + | |||
| + | Tuesday July 12, 2016, 13:30 - 16:30 | ||
''Location'' | ''Location'' | ||
| − | + | MC 252 | |
| − | + | 5 King's College Road | |
Toronto, ON M5S 1A4 | Toronto, ON M5S 1A4 | ||
| Line 195: | Line 191: | ||
=====Prerequisites===== | =====Prerequisites===== | ||
| − | + | Experience with programming in R and a rudimentary understanding of parallel computing. | |
=====Slides and other material===== | =====Slides and other material===== | ||
| − | [https://wiki.scinet.utoronto.ca/wiki/images/ | + | [https://wiki.scinet.utoronto.ca/wiki/images/a/ac/Parallel-R.pdf Slides]. |
| Line 204: | Line 200: | ||
| − | ===Python | + | |
| + | ===Scientific Computing with Python=== | ||
=====Abstract===== | =====Abstract===== | ||
| − | + | In this session, you will be taught how to use python for research computing purposes. Topics include: the basics of python, automation, numpy, scipy, file i/o, and visualization. | |
=====Details===== | =====Details===== | ||
| − | ''Time/date'' | + | ''Time/date''' |
| − | Tuesday July 14, | + | Tuesday July 14, 9:30 - 12:30 |
''Location'' | ''Location'' | ||
| Line 227: | Line 224: | ||
=====Prerequisites===== | =====Prerequisites===== | ||
| − | + | A bit of coding experience in any programming or scripting language. Command-line experience in Linux is a plus. | |
=====Slides and other material===== | =====Slides and other material===== | ||
| − | [https://wiki.scinet.utoronto.ca/wiki/images/ | + | [https://wiki.scinet.utoronto.ca/wiki/images/f/f3/Intro-Python.pdf Slides]. |
| Line 236: | Line 233: | ||
| − | === | + | ===Python for High Performance Computing=== |
=====Abstract===== | =====Abstract===== | ||
| − | + | This session will cover parallel programming in python, with a focus on parallel data analysis. We will cover subprocess, multiprocessing, pypar and other parallel-enabling python packages. | |
=====Details===== | =====Details===== | ||
''Time/date'' | ''Time/date'' | ||
| − | + | Tuesday July 14, 13:30 - 16:30 | |
| − | |||
| − | |||
''Location'' | ''Location'' | ||
| − | Galbraith Building, Room | + | Galbraith Building, Room 405 |
35 St. George Street | 35 St. George Street | ||
| Line 258: | Line 253: | ||
''Instructor'' | ''Instructor'' | ||
| − | Dr. | + | Dr. Erik Spence (SciNet) |
=====Prerequisites===== | =====Prerequisites===== | ||
| − | + | Python programming experience and a rudimentary understanding of parallel computing. | |
=====Slides and other material===== | =====Slides and other material===== | ||
| − | + | [https://wiki.scinet.utoronto.ca/wiki/images/2/2b/Parallel-Python.pdf Slides]. | |
---- | ---- | ||
| + | |||
Revision as of 10:52, 6 July 2016
About
The 2016 Ontario Summer School on High Performance Computing, whose second installment of the year will be hosted by SciNet, provides attendees with opportunities to learn and share knowledge and experience in high performance and technical computing. The format: a five-day workshop with mixed lectures and hands-on sessions on a number of selected subjects, including shared memory programming, distributed memory programming, parallel data processing, and general purpose graphics processing unit programming.
Below are descriptions and course material for the Central version, given from July 11th to July 15th, 2016, in Toronto.
Introduction to HPC and SciNet
Abstract
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.
Details
Time/date
Monday July 11, 2016, 09:30 - 12:30
Location
MC 252
5 King's College Road
Toronto, ON M5S 1A4
Instructor
Dr. Mike Nolta (SciNet)
Prerequisites
Basic Linux skills are useful to follow along. If you do not have these, consider taking the "Intro to the Linux Shell" in the afternoon.
Slides and other material
Abstract
In these two half-day sessions, lectures and hands-on labs are interspersed, and the students will learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP's threads, memory, and performance, reductions and load balancing. We will also discuss extensions to heterogeneous systems such as offered by the OpenMP 4.0 and OpenACC standards.
Details
Time/date
Monday July 11, 2016, 13:30 - 16:30
Tuesday July 12, 2016, 09:30 - 12:30
Location
MC 252
5 King's College Road
Toronto, ON M5S 1A4
Instructor
Dr. Ramses van Zon (SciNet)
Prerequisites
C/C++ and/or Fortran scientific programming; Experience editing and compiling code in a Linux environment.
Slides and other material
Introduction to the Linux Shell
Abstract
Working with many of the HPC systems in Ontario involves using the Linux/UNIX command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with this course. This hands on session will cover basic commands and scripting, as well as touching on some powerful constructs like regular expressions. It could be a great boon for your productivity!
Details
Time/date
Monday July 11, 13:30 - 16:30
Location
MC 254
5 King's College Road
Toronto, ON M5S 1A4
Instructor
Dr. Mike Nolta (SciNet)
Prerequisites
A laptop with a linux-like environment.
Slides and other material
R for Data Science
Abstract
This session offers a brief introduction to R, with a focus on data analysis and statistics.
Details
Date/time
Tuesday July 12, 2016, 09:30 - 12:30
Location
MC 254
5 King's College Road
Toronto, ON M5S 1A4
Instructor
Dr. Erik Spence (SciNet)
Prerequisites
Some programming experience in any language. You should have used and written functions. Bring a laptop with R or Rstudio installed.
Slides and other material
Programming Clusters with MPI
Abstract
In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of MPI programming. Examples and exercises will be based on parallelization of common scientific computing problems.
Details
Time/date
Tuesday July 12, 2016, 13:30 - 16:30
Wednesday July 16, 2016, 09:30 - 12:30
Wednesday July 16, 2016, 13:30 - 16:30
Location
MC 252
5 King's College Road
Toronto, ON M5S 1A4
Instructor
Dr. Scott Northrup (SciNet)
Prerequisites
C and/or Fortran scientific programming; experience editing and compiling code in a Linux environment.
Slides and other material
Parallel R for Data Science
Abstract
This half-day session will cover parallel programming in R, with a focus on parallel data analysis. Topics covered will include snow, parallel, and foreach/doparallel.
Details
Time/date
Tuesday July 12, 2016, 13:30 - 16:30
Location
MC 252
5 King's College Road
Toronto, ON M5S 1A4
Instructor
Dr. Erik Spence (SciNet)
Prerequisites
Experience with programming in R and a rudimentary understanding of parallel computing.
Slides and other material
Scientific Computing with Python
Abstract
In this session, you will be taught how to use python for research computing purposes. Topics include: the basics of python, automation, numpy, scipy, file i/o, and visualization.
Details
Time/date'
Tuesday July 14, 9:30 - 12:30
Location
Galbraith Building, Room 405
35 St. George Street
Toronto, ON M5S 1A4
Instructor
Dr. Erik Spence (SciNet)
Prerequisites
A bit of coding experience in any programming or scripting language. Command-line experience in Linux is a plus.
Slides and other material
Python for High Performance Computing
Abstract
This session will cover parallel programming in python, with a focus on parallel data analysis. We will cover subprocess, multiprocessing, pypar and other parallel-enabling python packages.
Details
Time/date
Tuesday July 14, 13:30 - 16:30
Location
Galbraith Building, Room 405
35 St. George Street
Toronto, ON M5S 1A4
Instructor
Dr. Erik Spence (SciNet)
Prerequisites
Python programming experience and a rudimentary understanding of parallel computing.
Slides and other material
Programming GPUs with CUDA
Abstract
This 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.
Details
Time/date
Wednesday July 15, 9:30 - 16:30
Thursday July 16, 9:30 - 16:30
Location
Galbraith Building, Room 405
35 St. George Street
Toronto, ON M5S 1A4
Instructors Dr. Pawel Pomorski and Dr. Sergey Mashchenko (SHARCNET)
Prerequisites
C/C++ scientific programming, experience editing and compiling code in a Linux environment. Some experience with CUDA and/or OpenMP a plus.
Slides and other materials
Day 1:
See also http://ppomorsk.sharcnet.ca
Day 2:
Visualization
Abstract
The first part of this session will be a basic introduction to general-purpose scientific visualization tools such as gnuplot, grace(xmgr), remote tools (tunneling, VNC), python's matplotlib, and examples from other. The second part will be a mix of lecture and hands-on to introduce more specialized scientific visualization software such as vmd, paraview, and visit.
Details
Time/date
Friday July 17, 9:30 - 16:30
Location
Galbraith Building, Room 404
35 St. George Street
Toronto, ON M5S 1A4
Instructor Dr. Marcelo Ponce (SciNet)
Prerequisites
Software installed, for the morning session: gnuplot, ssh (graphic client), vnc client, python and matplotlib; afternoon session: ParaView & VisIt.
Slides and other material
Morning session: Visualization I
Afternoon session: Visualization II
Samples to use during the tutorial: Data sets
An example of a complete visualization session in VisIt: VisIt session (un-tar this file and load the session from the File/Menu in VisIt using "Restore session with sources...", then choose your local "noise.silo" file to be used within the session).
Debugging, Optimization, and Best Practices
Abstract
Debugging is an important step in developing a new code, or porting an old one to a new machine. We will discuss the debugging of frequently encountered bugs in serial code with gdb and the debugging of parallel (MPI and threaded) codes with DDT on live systems. Next, we will present general issues, common pitfalls and optimization strategies that are application to HPC systems. By using profiling tools available for parallel programs, we can see when the bottlenecks are. We can try to alleviate them by following common best practices, allowing one to obtain their results more quickly and at the same time minimize their burden on shared system resources. Walk-through examples and case studies will be presented during the session to illustrate the concepts.
Details
Date/time
Friday July 17, 9:30 - 16:30
Location
Galbraith Building, Room 405
35 St. George Street
Toronto, ON M5S 1A4
Instructors
Dr. Ramses van Zon and Dr. Mike Nolta (SciNet)
Prerequisites
Basic familiarity with a high-level language (C/C++/Fortran) as well as compiling and running programs via the command line environment on a HPC system.
Slides and other material
Group Photo
