Summer School Courses

Courses taught in the past SHARCNET Summer Schools

• Introductory courses
  • Getting Started: 2020 (1d)
  • Shell / Linux: 2007 (0.5d), 2011 (0.5d; Tyson Whitehead), 2012 (0.5d), 2015 (1d; Isaac Ye), 2017 (0.5d; Isaac Ye)
  • Introduction to high performance computing: 2010 (0.5d), 2011 (0.5d; Sergey Mashchenko), 2012 (0.5d; Tyson Whitehead), 2013 (1d; Tyson Whitehead), 2019 (1d; Isaac Ye)
  • HPC Best Practices: Minimizing your time to results: 2010 (0.5d), 2011 (0.5d), 2013 (0.5d; Ge Baolai)
  • Scientific Computing: Languages, Packages and Libraries: 2011 (1d)
• Parallel programming
  • Intro to parallel computing: 2009 (1d), 2010 (1d; Sergey Mashchenko)
  • Interprocess Communication, Message Passing and MPI Basics: 2009 (1.5d)
  • MPI: 2007 (2d), 2008 (1d)+(1d; Tyson Whitehead), 2009 (1.5d), 2010 (2d), 2011 (2d), 2012 (2d), 2013 (2d; Ge Baolai), 2014 (2d; Ge Baolai), 2015 (2d), 2016 (2d), 2017 (2d; Jemmy Hu, Fei Mao), 2018 (2d; Jemmy Hu, Ge Baolai), 2019 (2d; Jemmy Hu, Ge Baolai), 2020 (3d)
  • GPU programming: 2008 (0.5d)
    • CUDA: 2009 (0.5d), 2010 (1d), 2012 (2d; Pawel Pomorski, Sergey Mashchenko), 2013 (2d; Pawel Pomorski, Sergey Mashchenko), 2014 (2d; Pawel Pomorski, Sergey Mashchenko), 2015 (2d; Pawel Pomorski, Sergey Mashchenko), 2016 (2d; Pawel Pomorski, Sergey Mashchenko), 2017 (2d; Pawel Pomorski, Sergey Mashchenko), 2018 (2d; Pawel Pomorski, Sergey Mashchenko), 2019 (2d; Pawel Pomorski, Sergey Mashchenko), 2020 (3d; Pawel Pomorski, Sergey Mashchenko)
    • OpenCL: 2010 (0.5d; Pawel Pomorski), 2011 (1d; Pawel Pomorski)
  • OpenMP: 2007 (1d), 2008 (1d), 2009 (1d), 2010 (2d), 2011 (2d), 2015 (1d; Jemmy Hu), 2018 (1d; Jemmy Hu), 2019 (1d; Jemmy Hu)
  • Pthreads: 2007 (1d), 2008 (1d), 2017 (0.5d; Ed Armstrong)
  • Unified Parallel C: 2008 (0.5d)
  • Cell BE Programming: 2009 (0.5d)
  • Computing with Intel Xeon Phi Co-processor: 2015 (1d; Fei Mao)
  • Parallel Programming Using the Pilot Library: 2010 (0.5d), 2011 (0.5d)
• Programming languages
  • Julia: 2020 (3d)
  • Fortran 90:
    • Fortran for Scientific and High Performance Computing: 2019 (1d; Ge Baolai)
    • Array Processing and Polymorphism: 2007 (0.5d)
    • Why write in Fortran: 2015 (0.5d; Ge Baolai)
    • Parallel Programming in Fortran: 2016 (0.5d; Ge Baolai), 2017 (0.5d; Ge Baolai)
    • Fortran for HPC: 2018 (1d; Ge Baolai)
  • C++:
    • Using C++'s Parallel Algorithms: 2019 (1d; Paul Preney)
    • C++: 2020 (2d)
    • How C++ Maps onto The Hardware and What That Means for Your Code: 2009 (1d; Tyson Whitehead)
    • Exploiting C++: Cache and Memory Layout, Copies, Moves, Threads and Random Numbers: 2015 (0.5d; Paul Preney)
    • Multithreading in C: 2016 (0.5d; Ed Armstrong)
    • C++ for High Performance Computing: 2017 (0.5d; Paul Preney), [https://www.sharcnet.ca/summerschool/2018/?page=ol_cxx&site=west 2018 (1d; Paul Preney))
  • MATLAB / Octave:
    • PRE and POST production with Octave: 2019 (1d; James Desjardins)
    • Using Octave on Graham: 2018 (1d; James Desjardins)
    • MATLAB and Distributed Computing Toolbox: 2008 (1d)
    • Parallel Computing in MATLAB: 2009 (0.5d), 2010 (0.5d; Jemmy Hu)
    • Octave: Core Loops in Native Code: 2009 (0.5d; Tyson Whitehead), 2016 (1d; James Desjardins)
    • Profiling Function Vectorization in Octave (Matlab): 2017 (0.5d; James Desjardins)
  • Python: 2015 (0.5d; Pawel Pomorski), 2016 (1d; Pawel Pomorski), 2017 (0.5d; Pawel Pomorski), 2018 (1d; Pawel Pomorski), 2019 (1d; Pawel Pomorski), 2020 (2d)
  • R for Data Analytic: 2017 (0.5d), 2018 (1d; Marcelo Ponce)
  • Java:
    • Thread Based Parallel Programming in Java: 2015 (0.5d; Ed Armstrong)
• Debugging / profiling / visualization / code development
  • Slurm Scheduling on Graham: 2020 (1d)
  • Using Graham Before and After Job Scheduling: 2020 (1d)
  • Parallel debugging: 2007 (0.5d), 2008 (0.5d), 2009 (0.5d), 2010 (0.5d), 2016 (0.5d; Sergey Mashchenko), 2017 (0.5d; Sergey Mashchenko), 2018 (1d; Sergey Mashchenko)
  • Profiling: 2009 (0.5d)
    • Low Level Issues in HPC: 2008 (0.5d)
    • Writing Native Code for High Level Environments: 2009 (0.5d)
  • Visualization: 2010 (0.5d), 2011 (0.5d), 2013 (0.5d), 2014 (1d), 2019 (1d; Tyson Whitehead)
    • VTK: 2007 (0.5d)
    • Paraview: 2016 (0.5d; Weiguang Guan), 2017 (0.5d; Tyson Whitehead), 2018 (1d; Tyson Whitehead, Weiguang Guan)
  • Development Environment: 2008 (1d), 2010 (0.5d)
  • Git: 2015 (0.5d; Tyson Whitehead)
• Big Data / Cloud / Deep Learning
  • Cloud / Singularity: 2018 (1d; Ed Armstrong)
  • Machine Learning: (1d; Weiguang Guan), 2020 (2d)
  • Machine Learning with sklearn and Tensorflow: 2018 (1d; Weiguang Guan, José Nandez)
  • Deep Learning for Beginners: 2017 (0.5d; Weiguang Guan)
  • Big Data: 2015 (0.5d)
  • Big Data modeling: 2017 (0.5d; Jose Nandez)
  • Data Science with Apache Spark: 2019 (1d; Jinhui Qin)
  • Introduction to Cloud Computing: 2016 (0.5d; Jose Nandez)
• Domain specific:
  • Hands-on session on metagenome assembly and binning: 2018 (1d; Armin Sobhani), 2019 (1d; Armin Sobhani)
  • Effective use of Computational chemistry packages on SHARCNET: 2016 (0.5d; Jemmy Hu)
  • Bioinformatics: 2020 (2d)
  • Bioinformatics Tools at SHARCNET: 2017 (0.5d; Armin Sobhani)