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
Cite
@article{arxiv.1901.07454,
title = {Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures},
author = {Nora Abi Akar and Ben Cumming and Vasileios Karakasis and Anne Küsters and Wouter Klijn and Alexander Peyser and Stuart Yates},
journal= {arXiv preprint arXiv:1901.07454},
year = {2019}
}
Comments
PDP 2019 27th Euromicro International Conference on Parallel, Distributed and Network-based Processing