Neuroscientific Software Management

Gentoo

We tackle the advanced software management needs faced by neuroscience by leveraging the manifold capabilities of the Gentoo metadistribution and the Portage package manager. This task materializes chiefly in writing ebuilds for the most popular neuroscientifc packages and their dependencies, integrating these into the Gentoo ebuild repositing model, and testing the resulting environment in present research scenarios.

Ebuilds are reposited in directory trees called repositories, which can be enabled by the addition of a simple text file defining a small number of parameters (such as name, location, and priority) to the package manager configuration directory. In addition to the main Gentoo repository, containing just under 20.000 packages, a number of other repositories enjoy official status, and their users can rely on support from the entire Gentoo community. Of these we distribute neuroscience ebuilds via the Gentoo Science overlay (Lecher et al. 2015), which now provides just under 1000 highly specialized scientific packages. Thus we ensure our ebuilds receive support from the broader Gentoo community, and attain a higher flexibility and responsiveness compared to the main Gentoo ebuild repository.

We have contributed and are maintaining ebuilds for about 40 neuroscientifically relevant software packages to the Gentoo Science overlay. This set encompasses highly specialized software, as well as a few more general scientific packages, and a number of dependencies not previously available for Gentoo. The ebuilds for dependencies not directly related to scientific applications are scheduled for migration to the main Gentoo repository.

Our contributed ebuild set (Table 1) incorporates the top-level packages of a full-fledged neuroscientific software environment, and is decisevly broader than what a neuroscientist would commonly require for one particular research project. We use this set to seed dependency graphs in Fig. 1. These graphs depic all of the software packages a Gentoo system would contain after the package manager is prompted to install the full contributed ebuild set. Compared to other GNU/Linux distributions, these graphs are notably small, showcasing Gentoo's capacity to create powerful, fully featured, but lightweight systems.

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b

Figure 1.

Dependency graphs with hierarchical edge bundling, depicting packages as vertices and dependency relationships as edges. The graphs are seeded by the ~40 packages which we maintain and have contributed to the Portage environment primarily for neuroscience use. Graph (a) covers the set's entire non-optional dependency stack, and totals ~550 packages. Graph (b) covers the set's entire dependency stack, including all optional dependencies, and totals ~3500 packages. The seed packages and their dependency relationships are highlighted in green. Dependencies provided by the Gentoo Science repository and their dependency relationships are colored purple. Dependencies provided by the main Gentoo repository and their dependency relationships are colored purple-tinted gray. The graph shows a tight clustering of neuroscientific Python packages, indicating the infrastructure cohesiveness and application diversity of scientific Python. The graph shows that Portage neuroscience packages make use of ~20 lower-level packages from Gentoo Science - illustrating the benefit of integrating scientific software management across disciplines. It is also notable that this graph includes deep Haskell and TeX dependency stacks - which are pulled in by DataLad (Halchenko et al. 2016) and PythonTeX respectively. Both of these packages are very optional; PythonTeX in particular would only be required if the system were designed to support re-executable publications (Poore 2015). This is a theoretical system make-up, and in practice a Gentoo neuroscience data analysis system may be even more lightweight. The figures were generated with DeGraVi (Christian 2017), which makes considerable use of the graph-tool module (Peixoto 2014).

a: Minimal (excluding all optional features) dependency graph of the contributed neuroscience package set.  
b: Maximal (including all optional features) dependency graph of the contributed neuroscience package set.  

Neuroscientific research and teaching was performed on Gentoo platforms using our ebuilds on at least 4 physical machines and over 100 virtual machines by at least 40 students and researchers at least at 4 academic institutions. The testing process demonstrated the usability of our software management solution, and illustrated areas which could most benefit from improvement, notably the ease of distribution for base Gentoo systems.

We have made a comprehensive set of neuroscientific software packages available for the wide family of Gentoo distributions and derivatives. Via Gentoo-prefix, these neuroscientific software packages are, in fact, also accessible to users of many other operating systems.

Having demonstrated the feasibility of Gentoo for neuroscientific research we seek to further improve the system, by augmenting packaging with outstanding issues, and compiling a detailed overview of the easiest ways to obtain a base Gentoo distribution - tailored to popular research usage scenarios.

We thank the Gentoo community and developers, in particular Ted Rodgers, Andrew Savchenko, and Benda Xu. We thank the Gentoo Science Project members, in particular Justin Lercher and François Bissey. We thank the brainhack organizers and attendees, and the NeuroDebian and DataLad developer, Yaroslav Halchenko.

Institute for Biomedical Engineering, ETH and University of Zürich