Learn High Performance Scientific Computing

Sit up straight and pay attention

Starting March 14th, 2014 and continuing for 10 weeks, Dr. Randall J. LeVeque, University of Washington, will be teaching High Performance Scientific Computing on-line at Coursera. There is no cost to take the course.

From the course description: Programming-oriented course on effectively using modern computers to solve scientific computing problems arising in the physical/engineering sciences and other fields. Provides an introduction to efficient serial and parallel computing using Fortran 90, OpenMP, MPI, and Python, and software development tools such as version control, Makefiles, and debugging.
Workload: 5-10 hours/week
Taught In: English
Subtitles Available In: English

Course Sylabus

The use of a variety of languages and techniques will be integrated throughout the course as much as possible, rather than taught linearly. The topics below will be covered at an introductory level, with the goal of learning enough to feel comfortable starting to use them in your everyday work. Once you've reached that level, abundant resources are available on the web to learn the more advanced features that are most relevant for you.

  • Working at the command line in Unix-like shells (e.g. Linux or a Mac OSX terminal).
  • Version control systems, particularly git, and the use of Github and Bitbucket repositories.
  • Work habits for documentation of your code and reproducibility of your results.
  • Interactive Python using IPython, and the IPython Notebook.
  • Python scripting and its uses in scientific computing.
  • Subtleties of computer arithmetic that can affect program correctness.
  • How numbers are stored: binary vs. ASCII representations, efficient I/O.
  • Fortran 90, a compiled language that is widely used in scientific computing.
  • Makefiles for building software and checking dependencies.
  • The high cost of data communication. Registers, cache, main memory, and how this memory hierarchy affects code performance.
  • OpenMP on top of Fortran for parallel programming of shared memory computers, such as a multicore laptop.
  • MPI on top of Fortran for distributed memory parallel programming, such as on a cluster.
  • Parallel computing in IPython.
  • Debuggers, unit tests, regression tests, verification and validation of computer codes.
  • Graphics and visualization of computational results using Python.

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