We give an overview of design features of the NEural Simulation Tool (NEST) that enable simulations of spiking point neurons to be scaled to hundreds of thousands of processors. Recent years have seen rapid development of the necessary simulation technology. They enable the investigation of integrative models to arrive at a multi-scale picture of brain dynamics relating macroscopic imaging measures to the microscopic dynamics. Large-scale simulations of neuronal networks provide a unique view onto brain dynamics, complementing experiments, small-scale simulations, and theory. The application of Business Intelligence techniques to support the proposed method is demonstrated, as well as the obtained results are shown and discussed. The usage of business process model metrics and thresholds to formalize process modeling guidelines is outlined, as well as the procedure of business process model analysis and improvement is shown. Proposed method is based on best practices in business process modeling, process model metrics, and corresponding thresholds. Existing approaches to business process model analysis are reviewed. The lifecycle of Business Process Management from business process modeling to applying the Business Intelligence and process mining techniques is considered. Therefore, this article proposes a method for business process model analysis and improvement. Since business process modeling is considered as the foundation of Business Process Management, it is required to design understandable and modifiable process models used to analyze and improve depicted business processes. 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. 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. 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). Moreover, the Python/C API evolves with each new version of Python, which results in a proliferation of version-dependent code branches. This is partly because adding new features requires writing low-level C++ code intermixed with calls to the Python/C API, which is unrewarding. However, developing and maintaining PyNEST has become increasingly difficult over time. 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. The introduction of PyNEST (Eppler et al., 2008), the Python interface for NEST, enabled users to control simulations using Python. 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). NEST is a simulator for large-scale networks of spiking point neuron models (Gewaltig and Diesmann, 2007). Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers.
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