2022-2023 Academic Year Courses

Running programs on the supercomputers is done via the Bash shell. This course is three one hour lectures on using BASH. No prior familiarity with BASH is assumed. In addition to the basics of getting around, globbing, regular expressions, redirection, pipes, and scripting will be covered.

Live classes will take place on Mon., Oct. 3, Wed., Oct. 5, and Thurs., Oct. 6 from 2pm-3pm Eastern Time. Recordings and exercises are available for self-paced learning.

Oct. 6, 2022 update: Originally there was a single course called, "2022-2023 Introduction to Advanced Research Computing (ARC). Each topic/module in the original course has been moved to be in their own course rather than being a section in one large course. This course is one of those courses. (Information has been preserved concerning enrolment and attendance.) To see a listing of all courses see the course listing in the 2022-2023 Courses category.

Python has become one of the most popular programming languages in scientific computing. It is high level enough that learning it is easy, and coding with it is significantly faster than other programming languages. However, the performance of pure Python programs is often suboptimal which might hinder your research. This course will demonstrate some ways to identify performance bottlenecks, improve slow blocks of code, and how to extend Python with compiled code. You'll learn various ways to optimise and parallelise Python programs, particularly in the context of scientific and high performance computing.

Knowledge Prerequisites: know what classes and functions are, familiarity with Jupyter Notebook, basic console use; and comfortable with Python software carpentry material.

Live classes will take place on Mon., Oct. 17, Wed., Oct. 19, Thurs., Oct. 20 and Mon., Oct. 24 from 4pm-5pm. Recordings and exercises will be also available for self paced learning.

Despite the fact that Fortran is the the oldest computer language born in the 1950s, and numerous modern languages have evolved ever since, Fortran, as the way it is spelled today, is juvenilized and still used by many people, after undergoing significant modifications and improvement in the past two decades that are rarely seen in any other languages.

The modern Fortran has evolved to its current form significantly different from its ancestor with many new modern language features. Inherited from old FORTRAN standards and enhanced by the newer ones, Fortran is still the language of choice for scientific computing for its intrinsic support for real and complex floating point operations as well as the natural expression of multidimensional arrays. Its array handling, enhanced by index slicing, makes array processing similar or identical to its counterparts, such as MATLAB, hence superior to any other programming languages. The unified interface of intrinsic functions for all data types makes translating mathematical expressions to computer programmes a lot cleaner and easier. Further, since the standard 2008, the introduction of co-arrays has made writing parallel code that can run on multiple core computers and clusters trivial, one needs not to know the traditional message passing interface (MPI) library, nor threading. They are built into the syntax of the language itself.

This module is a companion to the Parallel Programming with C++ module, where you will find the features missing from Fortran that are more essential to general purpose computing than number crunching. We will show some of the practice of interlanguage programming, through which the shortcomings of Fortran could be complemented by interfacing with C/C++.

This module contains live classes and homework assignments.

The live sessions will take place on Wed., Nov. 9; Thurs., Nov. 10; Mon., Nov. 14; Wed., Nov. 16; Thurs., Nov. 17; and Mon., Nov. 21 from 4pm to 5pm Eastern Time.

Some common libraries for data science in Python, such as Numpy, Pandas, Scikit-Learn, etc. usually work well if the dataset fits into the RAM on a single machine. When dealing with large datasets, it could be a challenge to work around memory constraints. This course module provides an introduction to scalable and accelerated data science with Dask and RAPIDS. Dask provides a framework and libraries that can handle large datasets on a single multi-core machine or crossing multiple machines on a cluster; while RAPIDS can help to offload analytics workloads to GPUs to accelerate your data science and analytics toolchain with minimal code changes.

The live sessions will take place on Mon., Jan. 16; Wed., Jan. 18; and Thurs., Jan. 19 from 4pm to 5pm Eastern Time.

ParaView is a very powerful temporal 3D visualization program. The Graham VDI system is a remote desktop interface for doing graphical pre- and post-processing on files stored on graham. This course consists of a general introduction to the Graham VDI system and ParaView via a live demonstration that you can follow along. There will be small exercises where you get a chance to see if you have actually understood the concepts.

The live sessions will take place on Wed., Feb. 8 and Thurs., Feb. 9 from 4pm to 5pm Eastern Time.

Recordings and exercises will be also available for self paced learning.

NOTE: Due to a family emergency of one of the instructors, the three live classes for the Julia module next week are cancelled. This module will be rescheduled for the summer school this summer. We sincerely apologize for the inconvenience and we are looking forward to meeting you in the future.

Julia is becoming increasingly popular for scientific computing. One may use it for prototyping as Matlab, R and Python for productivity, while gaining the same performance as compiled languages such as C/C++ and Fortran. The language is designed for both prototyping and performance, as well as simplicity. This is an introductory module on julia. Students will be able to get started quickly with the basics, in comparison with other similar languages such as Matlab, R, Python and Fortran and move on to learn how to write code that can run in parallel on multi-core and cluster systems through examples.

The live sessions will take place on Mon., Feb. 27; Wed., Mar. 1; and Thurs., Mar. 2 from 4pm to 5pm Eastern Time.

This "course" is a type of commons area for all 2022-2023 SHARCNET Training courses. Its purposes include facilitating the posting of messages, etc. to all persons enrolled in any of these courses. Normally there will be little to no activity in this "course".