Spring 2017: Introduction to Scientific Computing

Computers, one of the most important tools in science and engineering, find applications in all aspects of academia and industry alike. Though expected to employ this tool effectively, few scientist and 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 tutorial series will introduce many computational tools, tricks, and tips that would otherwise take years of trial and error to learn.

Schedule

During the spring semester, we will offer the tutorial series at the Johns Hopkins University Homewood campus:

  • Mondays from 3:00 pm to 5:00 pm in Malone G33, beginning 30 January and ending 1 May
  • Tuesdays from 3:00 pm to 5:00 pm in Bloomberg 462, beginning 31 January and ending 2 May

During the summer, we will repeat the tutorial series at the Johns Hopkins University School of Medicine: details to be announced

Prerequisites

No prior experience is required. Please bring a laptop to participate in the tutorials.

Attendance policy

The tutorial series is designed to build on itself as it progresses and we strongly discourage skipping tutorials. For a smaller time commitment, consider attending one of our training workshops.

Topics

These are the topics that we plan to cover:

  • Week 1: Computer hardware basics (single-user, cluster)
  • Week 2: Connecting to a remote system
  • Week 3: Linux basics (navigation, Bash, Vim, file transfer)
  • Week 4: MARCC environment (lmod, Slurm)
  • Week 5: Code repositories (Git)
  • Week 6: C basics (compiling, linking, debugging, profiling)
  • Week 7: Installing packages/libraries
  • Week 8: Python basics
  • Week 9: Python data analysis
  • Week 10: Data visualization (ParaView)
  • Week 11: Parallelization (OpenMP, MPI, Cuda)
  • Week 12: Data management plans
  • Week 13: LaTeX basics

We reserve the right to modify this schedule as the tutorial series progresses.

Questions?

Contact us at marcc-help@marcc.jhu.edu

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