Scientific Computing Course
This wiki page concerns the 2013 installment of SciNet's Scientific Computing course. Material from the previous installment can be found on Scientific Software Development Course, Numerical Tools for Physical Scientists (course), and High Performance Scientific Computing
Syllabus
About the course
- Whole-term graduate course
- Prerequisite: basic C, C++ or Fortran experience.
- Will use `C++ light' and Python
- Topics include: Scientific computing and programming skills, Parallel programming, and Hybrid programming.
There are three parts to this course:
- Scientific Software Development: Jan/Feb 2013
python, C++, git, make, modular programming, debugging - Numerical Tools for Physical Scientists: Feb/Mar 2013
modelling, floating point, Monte Carlo, ODE, linear algebra,fft - High Performance Scientific Computing: Mar/Apr 2013
openmp, mpi and hybrid programming
Each part consists of eight one-hour lectures, two per week.
These can be taken separately by astrophysics graduate students at the University of Toronto as mini-courses, and by physics graduate students at the University of Toronto as modular courses.
The first two parts count towards the SciNet Certificate in Scientific Computing, while the third part can count towards the SciNet HPC Certificate. For more info about the SciNet Certificates, see http://www.scinethpc.ca/2012/12/scinet-hpc-certificate-program.
Location and Times
SciNet HeadQuarters
256 McCaul Street, Toronto, ON
Room 229 (Conference Room)
Tuesdays 11:00 am - 12:00 noon
Thursdays 11:00 am - 12:00 noon
Instructors and office hours
- Ramses van Zon - 256 McCaul Street, Rm 228 - Mondays 3-4pm
- L. Jonathan Dursi - 256 McCaul Street, Rm 216 - Wednesdays 3-4pm
Grading scheme
Attendence to lectures.
Four home work sets (i.e., one per week), to be returned by email by 9:00 am the next Thursday.
Sign up
Sign up for this graduate course goes through SciNet's course website.
The direct link is https://support.scinet.utoronto.ca/courses/?q=node/99.
If you do not have a SciNet account but wish to register for this course, please email support@scinet.utoronto.ca .
Sign up is closed.
Part 1: Scientific Software Development
Prerequisites
Some programming experience. Some unix prompt experience.
Software that you'll need:
A unix-like environment with the GNU compiler suite (e.g. Cygwin), and Python (Enthought) installed on your laptop.
Dates
January 15, 17, 22, 24, 29, and 31, 2013
February 5 and 7, 2013
Topics (with lecture slides and recordings)
Lecture 1 C++ introduction
Lecture 2 More C++, build and version control
Lecture 3 Python and visualization
Lecture 4 Modular programming, refactoring, testing
Lecture 5 Object oriented programming
Lecture 6 ODE, interpolation
Lecture 7 Development tools: debugging and profiling
Lecture 8 Objects in Python, linking C++ and Python
Homework assignments
HW1
Multi-file C++ program to create a data file
We’ve learned programming in basic C++, use of make and Makefiles to build projects, and local use of git for version control. In this first assignment, you’ll use these to make a multi-file C++ program, built with make, which computes and outputs a data file.
- Start a git repository, and begin writing a C++ program to
- Get an array size and a standard deviation from user input,
- Allocate a 2d array,
- Store a 2d Gaussian with a maximum at the centre of the array and given standard deviation (in units of grid points),
- Outputs that array to a text file,
- Free the array, and exit.
- The output text file should contain just the data in text format, with a row of the file corresponding to a row of the array and with whitespace between the numbers.
- The 2d array creation/freeing routines should be in one file (with an associated header file), the gaussian calculation be in another (ditto), and the output routine be in a third, with the main program calling each of these.
- Use a makefile to build your code (add it to the repository).
- You can start with everything in one file, with hardcoded values for sizes and standard deviation and a static array, then refactor things into multiple files, adding the other features.
- As a test, use the ipython executable that came with your Enthought python distribution to read your data and plot it.
If your data file is named ‘data.txt’, running the following:
$ ipython --pylab
In [1]: data = numpy.genfromtxt('data.txt')
In [2]: contour(data)
should give a nice contour plot of a 2-dimensional gaussian.
- Email in your source code and the git log file of all your commits by email by next Thursday at 9:00 am.
Part 2: Numerical Tools for Physical Scientists
Prerequisites
Part 1 or solid c++ programming skills, including make and unix/linux prompt experience.
Software that you'll need
A unix-like environment with the GNU compiler suite (e.g. Cygwin), and Python (Enthought) installed on your laptop.
Dates
February 12, 14, 26, and 28, 2013
March 5, 7, 12, and 14, 2013
Topics
Lecture 9 Numerics
Lecture 10 Random numbers
Lecture 11 Numerical integration and ODEs
Lecture 12 Molecular Dynamics
Lecture 13 Linear Algebra part I
Lecture 14 Linear Algebra part II and PDEs
Lecture 15 Fast Fourier Transform
Lecture 16 FFT for real and multidimensional data
Part 3: High Performance Scientific Computing
Prerequisites
Part 1 or good c++ programming skills, including make and unix/linux prompt experience.
Software that you'll need
You will need to bring a laptop with a ssh facility. Hands-on parts will be done on SciNet's GPC cluster.
For those who don't have a SciNet account yet, the instructions can be found at http://wiki.scinethpc.ca/wiki/index.php/Essentials\#Accounts
Dates
March 19, 21, 26, and 28, 2013
April 2, 4, 9, and 11, 2013
Topics
Lecture 17 Intro to Parallel Computing
Lecture 18 Parallel Computing Paradigms
Lecture 19 Shared Memory Programming with OpenMP, part 1
Lecture 20 Shared Memory Programming with OpenMP part 2
Lecture 21 Distributed Parallel Programming with MPI, part 1
Lecture 22 Distributed Parallel Programming with MPI, part 2
Lecture 23 Distributed Parallel Programming with MPI, part 3
Lecture 24 Hybrid OpenMPI+MPI Programming