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Useful resources
Several members of our collaboration completed their PhDs whilst working on GRChombo, and their theses are a useful source of information about the code and how it is used in research:
- Numerical Simulations of Instabilities in GR by M. Kunesch
- Applications of Numerical Relativity Beyond Astrophysics by S. Tunyasuvunakool
- Scalar fields in Numerical Relativity by K. Clough
- Exotic Compact Objects in Numerical Relativity by T. Helfer
- The role of strong gravity in the early universe by J. Aurrekoetxea
- Numerical modelling of gravitational wave sources in general relativity by M. Radia
The collaboration meets semi-regularly for a catch up and to plan future work. Once a year this includes a "Learn the code" day where more experienced members give training sessions on key aspects of the code. Some slides from previous meetings are provided below:
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Introduction by K Clough
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Parameters by A Drew
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AMR by M Radia
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AH Finder by T Franca
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Building, Profiling and Debugging by M Radia (Google Slides with animations here)
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Load Balancing by T França
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Visualisation by C Joana
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Introduction by K Clough
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Parameters by A Drew
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Building, Profiling and Debugging by M Radia (Google Slides with animations here)
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Load Balancing by D Traykova
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Tagging by E de Jong
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AH Finder by T Franca
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Visualisation VisIt by C Joana
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Visualisation Paraview by T Evstafyeva
(Note that some of the advice in these may now be out of date, so in cases of conflict with the wiki, you should consider the wiki the most accurate.)
There are many useful introductions to NR, but we recommend in particular the following open source resources:
- Living Reviews in Relativity has a dedicated section on Numerical Relativity.
- Eric Gourgoulhon's notes on NR give a detailed introduction to the ADM decomposition, with a focus on the geometric interpretation.
The textbooks of Alcubierre, Baumgarte & Shapiro, and Shibata are also bookcase essentials for anyone working in NR.
Style guide for C++ https://google.github.io/styleguide/cppguide.html
Basic guide to C++ https://www.tutorialspoint.com/cplusplus/index.htm
One should review in particular the sections on classes and templating, which are both used extensively in the code.
All these places offer courses relevant for scientific programming, best check them regularly
- https://www.archer.ac.uk/training/
- https://training.prace-ri.eu/
- https://extremecomputingtraining.anl.gov/ ( ATPESC - yearly course, typically around a week. Recommended for more advanced students)
- https://learn.tacc.utexas.edu/
- https://portal.xsede.org/training/overview
Most machines have their own webpages which give a guide to usage, and tell you things particular to each machine, like compilers available, the job submission system, where to store your data etc.
The Rosetta stone of job submission commands is here: https://slurm.schedmd.com/rosetta.pdf
Some useful commands:
- 'passwd' can be used to change your login password.
- 'grep' to search for text in your files
A comprehensive guide to git:
https://www.tutorialspoint.com/git/git_quick_guide.htm
in particular, a useful guide to "rebase a branch to master" is
The VisIt wiki is here:
http://www.visitusers.org/index.php?title=Main_Page
and the user manual is here:
http://visit-sphinx-user-manual.readthedocs.io/en/latest/
The yt package is also useful for analysing data, and has an excellent cookbook containing examples:
http://yt-project.org/doc/index.html
A very useful GUI tool to compare two directories is Meld:
Copyright GRChombo 2018. Contact us for further details.