# Analytic Performance Modeling and Analysis of Detailed Neuron Simulations

## TL;DR

This paper applies the Execution-Cache-Memory performance model to analyze and predict the runtime of detailed neuron simulations, providing insights into performance bottlenecks and guiding optimization and hardware co-design.

## Contribution

It introduces a novel application of the ECM performance model to morphologically detailed neuron simulations, enabling accurate performance predictions and deeper understanding of computational bottlenecks.

## Key findings

- ECM model accurately predicts kernel runtimes
- Identifies key performance bottlenecks in neuron simulations
- Provides insights for software optimization and hardware co-design

## Abstract

Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state of the art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering and modeling methods to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive co-design efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory (ECM) performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually co-design of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.