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

Susanne Kunkel,Wolfram Schenck

doi:10.3389/fninf.2017.00040

Abstract

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. 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 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.

Highlights

The neuronal network simulator NEST (Gewaltig and Diesmann, 2007) has an active and growing community of developers and users
It enables simulations of spiking neuronal networks that differ greatly in size and complexity, and depending on the requirements of the models researchers can run the simulations on their laptops or they can make use of high-performance computing (HPC) facilities
We address the question if performance measurements that are carried out in dry-run mode are close to the values observed in real runs

Summary

Introduction

The neuronal network simulator NEST (Gewaltig and Diesmann, 2007) has an active and growing community of developers and users. Advances in computer hardware on the one hand and the requirements of novel computational models on the other hand push the development of new simulation technology and have made NEST a tool for a broad spectrum of applications. It enables simulations of spiking neuronal networks that differ greatly in size and complexity, and depending on the requirements of the models researchers can run the simulations on their laptops or they can make use of high-performance computing (HPC) facilities.

Methods

Results

Conclusion