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Front Neuroinform. 2014 Mar 14;8:23. doi: 10.3389/fninf.2014.00023. eCollection 2014.

CyNEST: a maintainable Cython-based interface for the NEST simulator.

Author information

1
Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center Jülich, Germany ; Faculty of Biology, Albert-Ludwig University of Freiburg Freiburg im Breisgau, Germany.
2
Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center Jülich, Germany ; Institute for Advanced Simulation (IAS-6), Theoretical Neuroscience and Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research Center and JARA Jülich, Germany ; Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum Bochum, Germany.

Abstract

NEST is a simulator for large-scale networks of spiking point neuron models (Gewaltig and Diesmann, 2007). Originally, simulations were controlled via the Simulation Language Interpreter (SLI), a built-in scripting facility implementing a language derived from PostScript (Adobe Systems, Inc., 1999). The introduction of PyNEST (Eppler et al., 2008), the Python interface for NEST, enabled users to control simulations using Python. As the majority of NEST users found PyNEST easier to use and to combine with other applications, it immediately displaced SLI as the default NEST interface. However, developing and maintaining PyNEST has become increasingly difficult over time. This is partly because adding new features requires writing low-level C++ code intermixed with calls to the Python/C API, which is unrewarding. Moreover, the Python/C API evolves with each new version of Python, which results in a proliferation of version-dependent code branches. In this contribution we present the re-implementation of PyNEST in the Cython language, a superset of Python that additionally supports the declaration of C/C++ types for variables and class attributes, and provides a convenient foreign function interface (FFI) for invoking C/C++ routines (Behnel et al., 2011). Code generation via Cython allows the production of smaller and more maintainable bindings, including increased compatibility with all supported Python releases without additional burden for NEST developers. Furthermore, this novel approach opens up the possibility to support alternative implementations of the Python language at no cost given a functional Cython back-end for the corresponding implementation, and also enables cross-compilation of Python bindings for embedded systems and supercomputers alike.

KEYWORDS:

HPC; Python language; maintainability; neural simulator; technical debt

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