New course for Intersession 2017: Introduction to Scientific Computing

I am happy to announce that I will be offering a new course for Intersession 2017 at Johns Hopkins University, entitled Introduction to Scientific Computing (EN.530.391.13). The interactive two-credit course designed to teach upper-level undergraduate students and new graduate students about the true capabilities of their computers–beyond email and internet–will take place from 10 through 26 January 2017.

As with all Intersession courses, Introduction to Scientific Computing will be offered free of charge to students enrolled at Johns Hopkins University for the fall 2015 semester.

Registration for Intersession 2017 opens 6 December 2016 at 07:00. Have a question about the course? Submit a comment below or contact me.

Course description

The most important tool in science and engineering, computers find applications in all aspects of academia and industry alike. Though expected to employ this tool effectively, few scientists or engineers have been trained to harness the power at their fingertips, and most could benefit significantly from a high-level exposure to scientific computing methodology. This course will introduce many computational tools, tricks, and tips that would otherwise require years of trial and error to learn.

This course will be strongly focused on increasing user proficiency with computational resources, such as those provided by the Maryland Advanced Research Computing Center (MARCC) run by Johns Hopkins University.

Prerequisites: none

Schedule: 13:00-16:00 on Tuesday, Wednesday, and Friday 10-26 January 2017

Selected topics

  • Computer hardware basics
  • Computer software basics (compilers, linkers, libraries, etc.)
  • Using Linux (connecting, navigating, text editing, software installation, etc.)
  • Scripting (bash and python)
  • Code development best practices (style, modularity, etc.)
  • Code development tools (repositories, debuggers, profilers, etc.)
  • Introduction to high-performance computing (MARCC and other shared systems)
  • Parallelization (OpenMP, MPI, Cuda)

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