The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code

The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code

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NEST is a simulator for spiking neuronal networks that commits to a bushranger awning 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.Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability.However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times.Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities.

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.We show that measurements of memory usage and runtime of neuronal network simulations closely puffy spa headband match the corresponding dry-run data.Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.