A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations

TL;DR

This paper introduces a modular, standardized workflow and an open-source framework called beNNch for benchmarking neuronal network simulations, enhancing reproducibility and guiding performance improvements on high-performance computing systems.

Contribution

It proposes a generic, modular benchmarking workflow and provides beNNch, a software tool for standardized, reproducible performance assessment of neuronal network simulators.

Findings

Performance bottlenecks identified in NEST simulator versions

Benchmarking across different network complexities on HPC systems

Framework facilitates reproducibility and comparison of simulation performance

Abstract

Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availability of detailed anatomical data on brain connectivity. Large-scale models that study interactions between multiple brain areas with intricate connectivity and investigate phenomena on long time scales such as system-level learning require progress in simulation speed. The corresponding development of state-of-the-art simulation engines relies on information provided by benchmark simulations which assess the time-to-solution for scientifically relevant, complementary network models using various combinations of hardware and software revisions. However, maintaining comparability of benchmark results is difficult due to a lack of standardized specifications for measuring the scaling performance of simulators on high-performance computing (HPC) systems. Motivated by the challenging complexity of benchmarking, we define a generic workflow that decomposes the endeavor into unique segments consisting of separate modules. As a reference implementation for the conceptual workflow, we develop beNNch: an open-source software framework for the configuration, execution, and analysis of benchmarks for neuronal network simulations. The framework records benchmarking data and metadata in a unified way to foster reproducibility. For illustration, we measure the performance of various versions of the NEST simulator across network models with different levels of complexity on a contemporary HPC system, demonstrating how performance bottlenecks can be identified, ultimately guiding the development toward more efficient simulation technology.