Summer School Courses
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Jump to navigationJump to searchCourses 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)