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Computational Science Education Publications by SciNet

January 16, 2019 in blog, blog-general, for_educators, for_press, for_researchers, frontpage, news, Uncategorized

This year starts with the publication of three papers by SciNet analysts in the Journal of Computational Science Education that are a reflection of SciNet’s ten years of training and educating academic researchers in the practical and scalable use of high performance computing.

  1. Bridging the Educational Gap between Emerging and Established Scientific Computing Disciplines
    Marcelo Ponce, Erik Spence, Ramses van Zon, and Daniel Gruner, Journal of Computational Science Education Vol 10 (1) 4-11 (2019)
    https://doi.org/10.22369/issn.2153-4136/10/1/1

    In this paper, we describe our experience in developing curriculum courses aimed at graduate students in emerging computational fields, including biology and medical science. We focus primarily on computational data analysis and statistical analysis, while at the same time teaching students best practices in coding and software development. Our approach combines a theoretical background and practical applications of concepts. The outcomes and feedback we have obtained so far have revealed several issues: students in these particular areas lack instruction like this although they would tremendously benefit from it; we have detected several weaknesses in the formation of students, in particular in the statistical foundations but also in analytical thinking skills. We present here the tools, techniques and methodology we employ while teaching and developing this type of courses. We also show several outcomes from this initiative, including potential pathways for fruitful multi- disciplinary collaboration.

  2. Scientific Computing, High-Performance Computing and Data Science in Higher Education
    Marcelo Ponce, Erik Spence, Ramses van Zon, and Daniel Gruner, Journal of Computational Science Education Vol 10 (1), 24-31 (2019)
    https://doi.org/10.22369/issn.2153-4136/10/1/5

    We present an overview of current academic curricula for Scientific Computing, High-Performance Computing and Data Science. After a survey of current academic and non-academic programs across the globe, we focus on Canadian programs and specifically on the education program of the SciNet HPC Consortium, using its detailed enrollment and course statistics for the past six to seven years. Not only do these data display a steady and rapid increase in the demand for research-computing instruction, they also show a clear shift from traditional (high performance) computing to data- oriented methods. It is argued that this growing demand warrants specialized research computing degrees.

  3. Trends in Scientific Computing Training Delivered by a High-Performance Computing Center
    Ramses van Zon, Marcelo Ponce, Erik Spence, and Daniel Gruner, Journal of Computational Science Education Vol 10 (1), 53-60 (2019)
    https://doi.org/10.22369/issn.2153-4136/10/1/9

    We analyze the changes in the training and educational efforts of the SciNet HPC Consortium, a Canadian academic High Performance Computing center, in the areas of Scientific Computing and High-Performance Computing, over the last six years. Initially, SciNet offered isolated training events on how to use HPC systems and write parallel code, but the training program now consists of a broad range of workshops and courses that users can take toward certificates in scientific computing, data science, or high-performance computing. Using data on enrollment, attendence, and certificate numbers from SciNet’s education website, used by almost 1800 users so far, we extract trends on the growth, demand, and breadth of SciNet’s training program. Among the results are a steady overall growth, a sharp and steady increase in the demand for data science training, and a wider participation of ‘non-traditional’ computing disciplines, which has motivated an increasingly broad spectrum of training offerings. Of interest is also that many of the training initiatives have evolved into courses that can be taken as part of the graduate curriculum at the University of Toronto.

We also recently gave a webinar on these topics.

Other publications by SciNet people can be found at here.

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SciNet News January 2019

January 4, 2019 in for_researchers, for_users, newsletter

Summary

  • Full datacentre maintenance shutdown on January 15 and 16, 2019.
  • Winter training and education schedule open for registration.
  • Application for International HPC Summer School in Japan in July are still open until February 4th 2019.

Details can be found below and are also available on the SciNet education website courses.scinet.utoronto.ca and the SciNet wiki docs.scinet.utoronto.ca.

System News: Maintenance Shutdown

The SciNet datacentre will undergo a two-day maintenance shutdown on January 15th and 16th 2019, starting at 7 am EST on the 15th. There will be no access to any of the SciNet systems (Niagara, P7, P8, BGQ, SGC, HPSS, Teach cluster, or the filesystems) during this time.

This is in preparation for the upcoming installation of an emergency power generator and a larger UPS, which will result in increased resilience to power glitches and outages.

It is expected that the system will be available to users late on Wednesday January 16th, 2019

The status of the Niagara cluster can be checked on status.computecanada.ca. For up-to-date and more detailed information on the status of all the SciNet systems, check https://docs.scinet.utoronto.ca.

Events Coming Up

Unless stated otherwise, all events listed below take place at the SciNet Teaching Room or Boardroom at our offices on the eleventh floor of the MaRS West Tower, suite 1140A (661 University Avenue, Toronto ON M5G 1M1).

Most events are recorded and some are broadcast, but only some of the courses can be taken remotely for SciNet certificate credits.

Registration for SciNet courses should be done by logging into https://courses.scinet.utoronto.ca with your Compute Canada account (the same one that you use to log into Niagara).

  • INTRO TO SCINET AND NIAGARA Wednesday Jan 16, 2019, 10:00 am – 11:30 am SciNet Boardroom (suite 1140, 661 University Avenue, Toronto ON M5G 1M1).

    This is a class of approximately 60-90 minutes to introduce SciNet and the new supercomputer Niagara and teach you how to use Niagara.

    Participation counts towards the SciNet HPC Certificate.

    For more information and (free) registration, go to https://courses.scinet.utoronto.ca/418

    Further sessions of this Intro are planned for February 13, March 13, April 10 and May 8, 2019.

  • SCINET USER GROUP MEETING Wednesday Jan 16, 2019, 12:00 noon – 1:00 pm SciNet Boardroom (suite 1140, 661 University Avenue, Toronto ON M5G 1M1).

    Pizza, user discussion, and a tech talk TDB

    For sign up and more information, see https://courses.scinet.utoronto.ca/423

    Further SNUG sessions are planned for: February 13, March 13, April 10, and May 8, 2019.

  • INTRODUCTION TO THE LINUX SHELL Wednesday January 16, 10:00 am – 12:00 noon SciNet Teaching Room

    Learn the basics of how to use the unix shell in two hours. Very useful for new users of SciNet that have little or no experience with unix or linux.

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/428

    Further “Intro to the Linux shell” sessions are planned for: March 13 and May 8, 2019.

  • APPLICATION DEADLINE FOR INTERNATIONAL HPC SUMMER SCHOOL Applications due: February 4, 2019 Event dates: July 7-12, 2019 Location: Kobe, Japan

    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.

    SciNet invites students and early-career post-docs in science, engineering, and other fields at Canadian post-secondary institutions to apply for one of the eight spots allocated to Canada. Travel, lodging and meal expenses of the selected candidates will be covered.

    For more information and application see https://ss19.ihpcss.org.

  • ADVANCED LINUX SHELL PROGRAMMING Wednesday February 13, 1:00 pm – 4:00 pm SciNet Teaching Room

    Learn how to write bash script, use environment variables, how to control process, and much more. Requires some linux basic command line experience.

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/431

    Another “Advanced Shell Programming” session is planned for April 10, 2019.

  • SCIENTIFIC COMPUTING FOR PHYSICISTS (PHY1610) Winter 2019, starting January 8 SciNet Teaching Room

    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 course is part of the physics graduate program. Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.

    For more information, see https://courses.scinet.utoronto.ca/398

  • QUANTITATIVE APPLICATIONS FOR DATA ANALYSIS Winter 2019, starting January 9 University of Toronto Scarborough Campus

    In this course data analysis techniques utilizing Python and 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: Python and 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 EES graduate program and to be taught at the UTSc campus.

    For more information, see https://courses.scinet.utoronto.ca/403

  • INTRODUCTION TO NEURAL NETWORK PROGRAMMING Starting April 23, 2019, 6 weeks, Tuesdays and Thursdays 11:00 am – 12:00 noon SciNet Teaching Room

    This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.

    Participation counts towards the SciNet Data Science Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/417

  • RELATIONAL DATABASE BASICS Wednesday May 1, 1:00 pm – 4:00 pm SciNet Teaching Room

    Principles and uses of relational databases with practical examples using python and sqlite.

    Participation counts towards the SciNet Data Science Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/433

  • COMPUTE ONTARIO SUMMER SCHOOL Summer 2019

    The Compute Ontario Summer School on Scientific and High Performance Computing is an annual educational event for graduate/undergraduate students, postdocs and researchers to learn and share knowledge and experience in high performance and technical computing on modern HPC platforms.

    As in previous years, the 2019 Summer School is expected to have three installments, “West”, “Central”, and “East”. Each will be a week-long event with multiple parallel streams.

    More details will be announced later in 2019.

  • SCALING TO PETASCALE INSTITUTE August 19-23, 2019

    This will be a free virtual advanced HPC summer school, organized by a number of the US XSEDE sites, also hosted at SciNet. Topics will likely include OpenMP, MPI, CUDA, and OpenACC.

    More details will be announced later in 2019.

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2019 International Summer School on HPC Challenges in Computational Sciences, Japan, July 7-12

December 7, 2018 in blog, for_educators, for_press, for_researchers, for_users, frontpage, news

Graduate students and postdoctoral scholars from institutions in Canada, Europe, Japan and the United States are invited to apply for the tenth International Summer School on HPC Challenges in Computational Sciences, to be held from July 7 to 12, 2019, at the campus of the RIKEN Center for Computational Science (R-CCS) in Kobe, Japan.

Applications are due Feb 4, 2019. The summer school is organized by the Extreme Science and Engineering Discovery Environment (XSEDE), the Partnership for Advanced Computing in Europe (PRACE), the R-CCS, and the SciNet HPC Consortium.

Leading computational scientists and HPC technologists from the U.S., Japan, Europe and Canada will offer instructions on a variety of topics as well as dedicated mentoring. Topics include:

  • HPC challenges by discipline
  • HPC programming proficiency
  • Performance analysis & profiling
  • Scientific visualization
  • Big Data Analytics
  • Machine Learning
  • Canadian, EU, Japanese and U.S. HPC-infrastructures

The expense-paid program will benefit scholars from Canadian, European, Japanese and U.S. institutions who use advanced computing in their research. The ideal candidate will have many of the following qualities, however this list is not meant to be a “checklist” for applicants to meet all criteria:

  • Familiar with HPC, not necessarily an HPC expert, but rather a scholar who could benefit from including advanced computing tools and methods into their existing computational work
  • A graduate student with a strong research plan or a postdoctoral fellow in the early stages of their research careers
  • Regular practice with parallel programming (i.e., student utilizes parallel programming generally on a monthly basis or more)
  • May have a science or engineering background, however, applicants from other disciplines are welcome, provided their research activities include computational work.

Students from underrepresented groups in computing are highly encouraged to apply (i.e., women, racial/ethnic minorities, persons with disabilities, etc.). If you have any questions regarding your eligibility or how this program may benefit you or your research group, please do not hesitate to contact the individual associated with your region below.

Interested students should apply by February 4, 2019. School fees, meals and housing will be covered for all accepted applicants, as well as intercontinental flight costs.
Further information and application: http://ss19.ihpcss.org.

Contacts:

SciNet HPC Consortium
Ramses van Zon
SciNet, Univ. of Toronto, Canada
Email: rzon at scinet.utoronto.ca

PRACE:
Hermann Lederer
Max Planck Computing and Data Facility, Germany
Email: lederer at mpcdf.mpg.de

Simon Wong
ICHEC, Ireland
Email: simon.wong at ichec.ie

RIKEN:
Toshiyuki Imamura
CCS, RIKEN
Email: Imamura.toshiyuki at riken.jp

XSEDE:
Jay Alameda
NCSA, University of Illinois at Urbana-Champaign, United States
Email: alameda at illinois.edu

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SciNet Receives HPCwire Award

December 6, 2018 in blog, for_press, for_researchers, for_users, frontpage, in_the_news, news, Road_to_Niagara, success_story

We are very proud that SciNet has received the 2018 HPCwire Editor’s Award for Best Use of HPC in Physical Sciences. The award was announced at the 2018 International Conference for High Performance Computing, Networking, Storage and Analysis (SC18), in Dallas, Texas.

SciNet used Lenovo and Mellanox technologies on the new Niagara cluster to create spatial resolution models of the Pacific Ocean, helping to validate ocean waves movement and to assist in global warming calculations. These calculations were performed by a team of scientists involving University of Toronto’s Prof. W. Richard Peltier, University of Michigan oceanographer Prof. Brian Arbic, and NASA JPL’s Dr. Dimitris Menemenlis. More on this calculation can be found here.

This calculation was part of the “early science” program of the Niagara supercomputer at the SciNet HPC Consortion. In this short period in March of 2018, a number of scientists were given the opportunity to perform “heroic” calculations. These large scale calculation were essential to test, to tune and to get Niagara ready for use as a Canada’s fastest national academic supercomputer.

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SciNet News December 2018

December 4, 2018 in for_researchers, for_users, newsletter

Summary

  • The full datacentre shutdown that was originally scheduled for December 18, 2018, is postponed to January 15 and 16, 2019, and combined with annual maintenance.
  • While the University of Toronto will be closed from Dec 22 to Jan 6. SciNet support will be on a “best effort” basis.
  • Time limits of jobs on Niagara are now 24 hours for all users.
  • Winter training and education schedule announced.
  • Application for International HPC Summer School in Japan in July is open.

Details can be found below and are also available on the SciNet education website courses.scinet.utoronto.ca and the SciNet wiki docs.scinet.utoronto.ca.

System News

  • The SciNet datacentre will undergo a two-day maintenance shutdown on January 15th and 16th 2019, starting at 7 am EST on the 15th. There will be no access to any of the SciNet systems (Niagara, P7, P8, BGQ, SGC, HPSS, Teach cluster, or the filesystems) during this time.

    This is in preparation for the upcoming installation of an emergency power generator and a larger UPS, which will result in increased resilience to power glitches and outages.

    The status of the Niagara cluster can be checked on status.computecanada.ca. For up-to-date and more detailed information on the status of all the SciNet systems, you can always check https://docs.scinet.utoronto.ca.

  • The walltime limit of jobs on Niagara is now 24 hours for all users.
  • While the University of Toronto is closed from December 22, 2018 to January 6, 2019, the SciNet systems will remain available. However, support will be on a “best effort” basis during the break.

Events Coming Up

Unless stated otherwise, all events listed below take place at the SciNet Teaching Room at our offices on the eleventh floor of the MaRS West Tower, suite 1140A (661 University Avenue, Toronto ON M5G 1M1).

Most events will be recorded and some are broadcast, but only some of the courses can be taken remotely for SciNet certificate credits, as indicated below.

Registration for SciNet courses is done by logging into https://support.scinet.utoronto.ca/education with your Compute Canada account (the same one that you use to log into Niagara).

  • INTRO TO SCINET AND NIAGARA Wednesday Jan 16, 2019, 10:00 am – 11:30 am SciNet Boardroom (suite 1140, 661 University Avenue, Toronto ON M5G 1M1).

    This is a class of approximately 60-90 minutes to introduce SciNet and the new supercomputer Niagara and teach you how to use Niagara.

    Participation counts towards the SciNet HPC Certificate.

    For more information and (free) registration, go to https://courses.scinet.utoronto.ca/418

    Further sessions of this Intro are planned for February 13, March 13, April 10 and May 8, 2019.

  • SCINET USER GROUP MEETING Wednesday Jan 16, 2019, 12:00 noon – 1:00 pm SciNet Boardroom (suite 1140, 661 University Avenue, Toronto ON M5G 1M1).

    Pizza, user discussion, and a tech talk TDB

    For sign up and more information, see https://courses.scinet.utoronto.ca/423

    Further SNUG sessions are planned for: February 13, March 13, April 10, and May 8, 2019.

  • INTRODUCTION TO THE LINUX SHELL Wednesday January 16, 10:00 am – 12:00 noon SciNet Teaching Room

    Learn the basics of how to use the unix shell in two hours. Very useful for new users of SciNet that have little or no experience with unix or linux.

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/428

    Further “Intro to the Linux shell” sessions are planned for: March 13 and May 8, 2019.

  • ADVANCED LINUX SHELL PROGRAMMING Wednesday February 13, 1:00 pm – 4:00 pm SciNet Teaching Room

    Learn how to write bash script, use environment variables, how to control process, and much more. Requires some linux basic command line experience.

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/431

    Another “Advanced Shell Programming” session is planned for April 10, 2019.

  • SCIENTIFIC COMPUTING FOR PHYSICISTS (PHY1610) Winter 2019, starting January 8 SciNet Teaching Room

    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 course is part of the physics graduate program. Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.

    For more information, see https://courses.scinet.utoronto.ca/398

  • QUANTITATIVE APPLICATIONS FOR DATA ANALYSIS Winter 2019, starting January 9 University of Toronto Scarborough Campus

    In this course data analysis techniques utilizing Python and 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: Python and 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 EES graduate program and to be taught at the UTSc campus.

    For more information, see https://courses.scinet.utoronto.ca/403

  • INTRODUCTION TO NEURAL NETWORK PROGRAMMING Starting April 23, 2019, 6 weeks, Tuesdays and Thursdays 11:00 am – 12:00 noon SciNet Teaching Room

    This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.

    Participation counts towards the SciNet Data Science Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/417

  • RELATIONAL DATABASE BASICS Wednesday May 1, 1:00 pm – 4:00 pm SciNet Teaching Room

    Principles and uses of relational databases with practical examples using python and sqlite.

    Participation counts towards the SciNet Data Science Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/433

  • INTERNATIONAL HPC SUMMER SCHOOL July 7-12, 2019 Kobe, Japan

    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.

    SciNet invites students and early-career post-docs in science, engineering, and other fields at Canadian post-secondary institutions to apply for one of the eight spots allocated to Canada. Travel, lodging and meal expenses of the selected candidates will be covered.

    Applications are due February 4, 2019.

    For more information and application see https://ss19.ihpcss.org.

  • COMPUTE ONTARIO SUMMER SCHOOL Summer 2019

    The Compute Ontario Summer School on Scientific and High Performance Computing is an annual educational event for graduate/undergraduate students, postdocs and researchers to learn and share knowledge and experience in high performance and technical computing on modern HPC platforms.

    As in previous years, the 2019 Summer School is expected to have three installments, “West”, “Central”, and “East”. Each will be a week-long event with multiple parallel streams.

    More details will be announced later in 2019.

  • SCALING TO PETASCALE INSTITUTE Summer 2019

    This will be a free virtual advanced HPC summer school, organized by a number of the US XSEDE sites, also hosted at SciNet. Topics will likely include OpenMP, MPI, CUDA, and OpenACC.

    More details will be announced later in 2019.

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SciNet News September 2018

September 14, 2018 in for_researchers, for_users, newsletter

SUMMARY

  • Niagara takes the number 53 spot in the June 2018 TOP500 list of supercomputers (https://www.top500.org/list/2018/06).
  • Scratch purging policy on Niagara is in effect.
  • Burst buffer available on demand.
  • Various SciNet courses and events to start next week, including a “Intro to Niagara/SciNet” session and a TechTalk on “Machine Learning Cosmic Structure Formation” on Sept 12.
  • SciNet’s Jupyterhub with access to files on Niagara is online.
  • my.SciNet website with access to your Niagara jobs records is online.
  • Courses website now accessible with your Compute Canada password.

SYSTEM NEWS

EVENTS COMING UP

Registration for SciNet courses is done by logging into https://courses.scinet.utoronto.ca with your Compute Canada username
and password.

Many of the events are at the teaching room or boardroom in the SciNet offices on the eleventh floor of the MaRS West Tower, suite 1140A (661 University Avenue, Toronto ON M5G 1M1). SciNet events are often recorded and broadcast (see the courses site for links).

  • INTRO TO SCINET AND NIAGARA
    Wednesday Sept 12, 2018, 10:00 am – 11:30 am
    SciNet Boardroom (suite 1140, 661 University Avenue, Toronto).

    This is a class of approximately 90 minutes to introduce SciNet and the new supercomputer Niagara and teach you how to use Niagara.

    Participation counts towards the SciNet HPC Certificate.

    For more information and (free) registration, go to https://courses.scinet.utoronto.ca/404

  • SCINET USER GROUP MEETING
    Wednesday Sept 12, 2018, 12:00 noon – 1:00 pm
    SciNet Boardroom (suite 1140, 661 University Avenue, Toronto).

    The SciNet Users Group (SNUG) meetings are every month on the second Wednesday (except during the summer), and involve pizza, user discussion, feedback, and a half-hour talk on topics or technologies of interest to the SciNet community.

    The TechTalk will be on

    MACHINE LEARNING COSMIC STRUCTURE FORMATION

    by George Stein (Dept. of Astronomy-UofT, CITA).

    Abstract: In modern astrophysics and cosmology, accurate simulations of the large scale structure of the universe are necessary. Usually, this is accomplished by so called N-body simulations, which calculate the full gravitational collapse of a region of the universe over its 14 billion year history. Instead of calculating this costly gravitational evolution, we trained a three-dimensional deep Convolutional Neural Network (CNN) to identify dark matter proto-haloes directly from the cosmological initial conditions, and showed that a CNN of this type can be a viable alternative in some cases. In this talk I will discuss current cosmological simulations and the invasion of machine learning techniques, with a focus on our work. For more information see https://arxiv.org/abs/1805.04537.

    For sign up and more information, see https://courses.scinet.utoronto.ca/410

  • INTRODUCTION TO COMPUTATIONAL BIOSTATISTICS WITH R (MSC1090)
    Tuesdays and Thursday, 11 am – 12 noon
    Twelve weeks starting Sept 12.

    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 to be taught at the UofT St. George campus (i.e., not in the SciNet classroom). Contact us if you wish to audit the course without credit.

    This course will be recorded, but not broadcast.

    For more information, see https://courses.scinet.utoronto.ca/399

  • INTRODUCTION TO THE LINUX SHELL
    Wednesday Sept 19, 1:00 pm – 4:00 pm
    Teaching Room 1140A (MaRS West Tower, 661 University Ave, Toronto)

    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. It could be a great boon for your productivity!

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/407

  • INTRODUCTION TO PROGRAMMING
    Tuesdays and Thursdays, 1:00 pm – 2:00 pm
    Four weeks starting Oct 2.
    Teaching Room 1140A (MaRS West Tower, 661 University Ave, Toronto)

    New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises.

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/401

  • INTRO TO SCINET AND NIAGARA
    Wednesday Oct 10, 2018, 10:00 am – 11:30 am
    SciNet Boardroom (suite 1140, 661 University Avenue, Toronto).

    This is a class of approximately 90 minutes to introduce SciNet and the new supercomputer Niagara and teach you how to use Niagara.

    Participation counts towards the SciNet HPC Certificate.

    For more information and (free) registration, go to https://courses.scinet.utoronto.ca/405

  • SCINET USER GROUP MEETING
    Wednesday Oct 10, 2018, 12:00 noon – 1:00 pm
    SciNet Boardroom (suite 1140, 661 University Avenue, Toronto).

    The SciNet Users Group (SNUG) meetings are every month on the second Wednesday (except during the summer), and involve pizza, user
    discussion, feedback, and a half-hour talk (TBA) on topics or technologies of interest to the SciNet community. We’ll likely
    discuss the upcoming Resource Allocation Competition.

    For sign up and more information, see https://courses.scinet.utoronto.ca/411

  • ADVANCED SHELL PROGRAMMING
    Wednesday Oct 17, 2018, 1:00 pm – 4:00 pm
    Teaching Room 1140A (MaRS West Tower, 661 University Ave, Toronto)

    Learn how to write bash script, use environment variables, how to control process, and much more. Requires some linux basic command line experience.

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/409

  • NUMERICAL COMPUTING WITH PYTHON
    Tuesdays and Thursdays, 1:00 pm – 2:00 pm
    Four weeks, starting Nov 6, 2017 (skipping the week of Nov 12-16)
    Teaching Room 1140A (MaRS West Tower, 661 University Ave, Toronto)

    Learn about research computing even with little programming experience. Covers programming in python, best practices and
    visualization. Some experience with python is required. Four home work sets will be the basic of the evaluation.

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/402

  • INTRO TO SCINET AND NIAGARA
    Wednesday Nov 14, 2018, 10:00 am – 11:30 am
    SciNet Boardroom (suite 1140, 661 University Avenue, Toronto)

    This is a class of approximately 90 minutes to introduce SciNet and the new supercomputer Niagara and teach you how to use Niagara.

    Participation counts towards the SciNet HPC Certificate.

    For more information and (free) registration, go to https://courses.scinet.utoronto.ca/406

  • INTRODUCTION TO THE LINUX SHELL
    Wednesday Nov 21, 1:00 pm – 4:00 pm
    Teaching Room 1140A (MaRS West Tower, 661 University Ave, Toronto)

    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. It could be a great boon for your productivity!

    Participation counts towards the SciNet Scientific Computing Certificate.

    For sign up and more information, see https://courses.scinet.utoronto.ca/407

  • SCIENTIFIC COMPUTING FOR PHYSICISTS (PHY1610)
    Winter 2019
    Teaching Room 1140A (MaRS West Tower, 661 University Ave, Toronto)

    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 course is part of the physics graduate program. Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.

    For more information, see https://courses.scinet.utoronto.ca/398

  • QUANTITATIVE APPLICATIONS FOR DATA ANALYSIS
    Winter 2019
    University of Toronto Scarborough Campus

    In this course data analysis techniques utilizing Python and 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: Python and 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 EES graduate program and to be taught at the UTSc campus.

    For more information, see https://courses.scinet.utoronto.ca/403

As always, further details can be found below on the SciNet courses siteand the SciNet wiki.

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SciNet concludes its biggest-ever training week in scientific, high performance, and data science computing

June 19, 2018 in blog-general, for_educators, for_press, for_researchers, for_users, frontpage, news, success_story

When summer arrives, it means the return of Ontario’s Summer School on Scientific and High Performance Computing. This annual set of educational events bring together graduate students, undergraduate students, postdocs and researchers and gives them an opportunity to learn and share knowledge and experience in technical computing, data science and biomedical computing on modern high-performance computing platforms.

The summer school has been given at three locations for the last seven years and is a collaborative effort of the three High-Performance Consortia in Ontario: SHARCNET, CAC, and SciNet. This year, the first was from May 28 to June 1 in London, while the second one was hosted by SciNet on the St. George campus of the University of Toronto from June 11 to 15, and the third will take place in Kingston from July 30 to August 3.

HPC Python session at the 2018 summer school

High Performance Computing with Python (“Parallel Python”) Session at the 2018 Ontario HPC Summer School in Toronto

The format of the school is that of a five-day intensive workshop with mixed lectures and hands-on sessions on a number of selected topics. The topics slightly vary by location, but may include shared memory programming, distributed memory programming and general purpose graphics processing unit programming, and Python, R, machine learning, neural networks, visualization, debugging, bioinformatics and bioimaging. To make all these topics fit in one week, the lectures are organized in different streams. In Toronto, there was a high performance computing stream, a data science stream, and a biomedical stream, given by instructors from SciNet, SHARCNET, and CAMH.

group photo HPC Summer School in Toronto

Group Photo of the 2018 Ontario HPC Summer School in Toronto

This year was SciNet’s biggest summer school yet. For the first time, there were more than two hundred participants, from widely varying academic background like Business Administration, Dentistry, Biology, Medicine, Economics, Health Sciences, Phyics, Chemistry, Engineering, and Astronomy. Those who attended at least three days received a certificate of attendance.

To get a sense of the tremendous growth of this event, consider that in 2012, one of the first years of the summer school, in Toronto, we had 35 participants in the summer school, issued 20 certificates, and a total of 400 contact-hours were delivered by a handful of instructors. In 2018, these numbers are 7 to 10 times larger: there were 211 participants, 135 certificates were issues, and 3800 contact-hours were delivered by 17 instructors. If that is not a clear enough indication of the demand for this kind of training, consider this: within one day of opening the registration, there were over 100 registrations, and just one week later, the 200 registration mark was crossed. There were people on the waiting list, although we made an effort to try and accommodate most people in the end.

The school is offered for free, but without support for travel, lodging or meals. It is therefore not surprising that most participants are from the Toronto area, but there was a sizeable number of attendants from outside Toronto (60), from outside of Ontario (15) and even from outside Canada (5).

The participants seem to really appreciate the event, as borne out by the preliminary results from the post-event survey. From the survey, we got suggestions for improvements for the future, such as (even) more hands-on and better explanation of prerequisites and software for the sessions. Nonetheless, 94% of the respondents was very satisfied with the summer school, and thought the overall value of the summer school was high. 92% would recommend the event to their peers. Interestingly, the answers to the question “What was the most important thing you learned or did during the summer school?” varied a lot, indicating, perhaps, that the broad range of offered topics allowed participants to pick what suits them best.

Organizing the school was a tremendous experience, made possible by 8 instructors and staff members from SciNet, 9 from CAMH, and 2 from SHARCNET. The University of Toronto provided the space for the session, while Compute Ontario sponsored the coffee-and-cookie breaks.

If you missed it this time, look for the announcement next spring of the 2019 Ontario Summer School on High Performance Computing.

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2018 Compute Ontario Summer School Central

May 14, 2018 in blog, for_educators, for_researchers, for_users, news

The Compute Ontario Summer School on Scientific and High Performance Computing is an annual educational event for graduate/undergraduate students, postdocs and researchers to learn and share knowledge and experience in high performance and technical computing on modern HPC platforms.

Apart from the Compute Ontario Summer School Central, which will be hosted in Toronto by SciNet from June 11 to June 15, 2018, there are two other instances of the 2018 Compute Ontario summer school: One hosted by SHARCNET at Western University (May 28-Jun 1) one hosted by CAC at Queen’s University (Jul 30-Aug 3). Each of the three site has a slightly different list of courses, but all include both in-class lectures and hands-on sessions. Those who attend at least three full days cumulatively will receive an official certificate in HPC training.

The Toronto summer school (“Compute Ontario Summer School Central”), hosted by SciNet, will have the following three streams: Stream 1: High Performance computing; Stream 2: Data Science; Stream 3: Biomedical. Instructors are from SciNet, SHARCNET, and CAMH.

Location

Wilson Hall – New College
University of Toronto
St. George Campus
40 Willcocks St.
Toronto, ON M5S 1C6

Rooms: 524, 1016, 1017, 2006 (check the sessions for the room assignments)

Registration

Step 1: Log into the SciNet education site with your SciNet account, select the Compute Ontario Summer School in “Browse Courses”, and click on the “Register Me” link on the right. OR: if you do not have a SciNet account, register at tinyurl.com/toss2018reg, enter the required information such as login, password and email. In the latter case, you will receive an email with a link to confirm your email.

Step 2: Make your session selection (see program below). You can alter your selection at any time, but note that seats are limited.

Cost

The event is free of charge, though meals and lodging are at the participant’s own expense. We therefore thank the organizations who are providing the instructors (which they do free of
charge). This event is furthermore sponsored by Compute Ontario, the umbrella organization for Academic Advanced Research Computing in Ontario.

Program

HPC Stream Data Science Stream Biomedical Stream
Mon, Jun 11
Morning: 09:00-12:30
Welcome and Introduction to HPC and SciNet Welcome and Introduction to HPC and SciNet Welcome and Introduction to HPC and SciNet
Afternoon: 13:30-16:30
Programming Clusters with Message Passing Interface Introduction to the Linux Shell Python for MRI analysis
Tue, Jun 12
Morning: 09:30-12:30
Programming Clusters with Message Passing Interface Introduction to R Image Analysis at Scale
Afternoon: 13:30-16:30
Programming Clusters with Message Passing Interface Introduction to Python Machine Learning for Neuroimaging
Wed, Jun 13
Morning: 09:30-12:30
Programming GPUs with CUDA Parallel Python PLINK
Afternoon: 13:30-16:30
Programming GPUs with CUDA Machine Learning with Python Next Generation Sequencing
Thu, Jun 14
Morning: 09:30-12:30
Programming GPUs with CUDA Neural Networks with Python RNASeq Analysis
Afternoon: 13:30-16:30
Programming GPUs with CUDA Scientific Visualization Suites R for MRI analysis
Fri, Jun 15
Morning: 09:30-12:30
Shared Memory Parallel Programming with OpenMP Debugging, Profiling and Bring-Your-Own-Code Lab Public Datasets for Neuroimaging
Afternoon: 13:30-16:30
Shared Memory Parallel Programming with OpenMP Debugging, Profiling and Bring-Your-Own-Code Lab HCP with HPC: Surface Based Neuroimaging Analysis

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Launch of the Niagara Supercomputer at SciNet

March 5, 2018 in for_educators, for_press, for_researchers, for_users, frontpage, in_the_news, news, Road_to_Niagara

The Niagara supercomputer was officially launched on March 5th, 2018. We were honoured by the presence and remarks of Reza Moridi (Ontario Minister of Research, Innovation and Science), Nizar Ladak (Compute Ontario President and CEO), Dr. Roseann O’Reilly Runte (CFI President and CEO), Prof. Vivek Goel (Vice-president of Research and Innovation at the University of Toronto), and Prof. W. Richard Peltier (Scientific Director of SciNet).

SciNet’s CTO Daniel Gruner gave an overview of the new system:

Niagara is located at University of Toronto and operated by the university’s high-performance computing centre SciNet, but the system is open to all Canadian university researchers.

Niagara is the fastest computer system in the country and is able to run a single job across all 60,000 cores thanks to a high-performance network which interconnects all the nodes. For more information on the configuration, see here.

A time-lapse of the building of Niagara is available (part of SciNet’s YouTube channel):

This system is jointly funded by the Canada Foundation for Innovation, the Government of Ontario, and the University of Toronto.

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Road to Niagara 3: Hardware setup

March 5, 2018 in blog-technical, for_press, for_researchers, for_users, news, Road_to_Niagara, Uncategorized

This is the fourth of a series of posts on the transition to SciNet’s new supercomputer called “Niagara”, which will replace the General Purpose Cluster (GPC) and Tightly Coupled Cluster (TCS). The transition to Niagara will take place in the fall of 2017, and the system is planned to be available to users in early 2018.

The University of Toronto has awarded the contract for Niagara to Lenovo, and some of the details of the hardware specifications of the Niagara system have been released:

The system will have the following hardware components:

  • 1,500 nodes.
  • Each node will have 40 Intel Skylake cores (making a total of 60,000 cores) at 2.4 GHz.
  • Each node will have 200 GB (188 GiB)of DDR4 memory.
  • The interconnect between the nodes will be Mellanox EDR Infiniband in a Dragonfly+ topology.
  • A ~9PB usable shared parallel filesystem (GPFS) will be mounted on all nodes.
  • A 256TB Excelero burst buffer (NVMe fabric, up to 160 GB/s) will be available for fast I/O.
  • Peak theoretical speed: 4.61 PetaFLOPS

Niagara is estimated to be installed and operational towards in March 2018, and ready for users not too long after.

Even before official ready-date, there will a period in which select users can try out and port their codes to Niagara.

After the friendly-user period, all current users of the GPC (and former users of the TCS) will get access to Niagara.

The large core count, ample memory per core, and fast interconnect support Niagara’s intended purpose to enable large parallel compute jobs of 512 cores or more.

The software setup will also be tailored to large parallel computations. Nonetheless, there will still be a fair amount of backfill opportunity for smaller jobs.

The setup of Niagara is intended to be similar in spirit to the GPC, but different in form: scheduling per node, a home, scratch and possibly project directory defined in environment variables, a module system, and access to our team of analyst to help you get your codes running, and running well.

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