# Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures

## TL;DR

Arbor is an open-source, high-performance library for simulating large neural networks on modern HPC architectures, offering significant speed improvements and scalability for neuroscience research.

## Contribution

It introduces a performance portable simulation library optimized for various HPC hardware, enabling faster and scalable neural network modeling.

## Key findings

- Arbor is an order of magnitude faster than comparable software.
- It achieves efficient weak scaling on large models.
- Supports multiple HPC architectures including x86, KNL, and GPUs.

## Abstract

We introduce Arbor, a performance portable library for simulation of large networks of multi-compartment neurons on HPC systems. Arbor is open source software, developed under the auspices of the HBP. The performance portability is by virtue of back-end specific optimizations for x86 multicore, Intel KNL, and NVIDIA GPUs. When coupled with low memory overheads, these optimizations make Arbor an order of magnitude faster than the most widely-used comparable simulation software. The single-node performance can be scaled out to run very large models at extreme scale with efficient weak scaling. HPC, GPU, neuroscience, neuron, software