GitHub

Scientific Computing

Notes related to my learning and teaching interests in several fields related to scientific computing (mostly applied mathematics and machine learning) and related applications. This home page is the entry point and those interested in more content and interesting links can use the sidebar with an index to the materials.

It can also be used as a general guide for introducing scientific computing as it tries to introduce the minimal skill set any scientific computing engineer or scientist should have:

  • Version control comes first, everything else is worthless without it, currently that means Git.
  • Next comes software documentation with Doxygen, Sphinx, and/or Documenter.jl.
  • A low(er) level programming language among C, C++, and Fortran, preferably all of them.
  • Scripting languages, as of 2024, Python is mandatory, Julia highly recommended.
  • Basic machine learning in one of the above scripts, everything is ML these days.
  • Shell automation, basis of both Bash/other UNIX shell and PowerShell are required.
  • Typesetting equations reports and presentations (beamer) in $\LaTeX$.
  • Domain specific skills related to the field of study (CFD, DFT, MD, ML, ...).

Some technologies have been mainstream or important in the past, but nowadays some of them have already died or are becoming too niche to be put in such a list. That is the case of SVN for version control. As for programming languages in science, that is the case of matlabish (MATLAB, Octave, Scilab) environments, which are still used by controls and automation people, but are mostly incompatible with good software practices and should be discouraged.

It is also worth getting familiar with high-performance computing (HPC); in the Top 500 page you can get to know the most powerful computers on Earth. The specification benchmarking page allows for the check of hardware specification, what is interesting when preparing investment in a computing structure. Lastly, when working in multi-user systems it is worth knowing about job management systems such as Slurm.

As a last word, I would like to remember that it is humanly impossible to master everything at once; even after more than 10 years in the field as of today I only have a grasp in the tools I do not use everyday. Software and methods evolve, and unless you keep using a specific tool you simply cannot afford to keep up to date with it. That should not be a roadblock for a scientist in the long term. As you get used to scientific software, getting back to a good level of some tool you used in the past is quick (but not extremely fast in some cases) and learning new tools for which you already know the science behind is trivial. Even exploring new fields become easy in some cases.