index.rst

Arbor

What is Arbor?

Arbor is a high-performance library for computational neuroscience simulations.

The development team is from from high-performance computing (HPC) centers:

• Swiss National Supercomputing Center (CSCS), Jülich and BSC in work package 7.5.4 of the HBP.
• Aim to prepare neuroscience users for new HPC architectures;

Arbor is designed from the ground up for many core architectures:

• Written in C++11 and CUDA;
• Distributed parallelism using MPI;
• Multithreading with TBB and C++11 threads;
• Open source and open development;
• Sound development practices: unit testing, continuous Integration, and validation.

Features

We are actively developing Arbor, improving performance and adding features. Some key features include:

• Optimized back end for CUDA
• Optimized vector back ends for Intel (KNL, AVX, AVX2) and Arm (ARMv8-A NEON) intrinsics.
• Asynchronous spike exchange that overlaps compute and communication.
• Efficient sampling of voltage and current on all back ends.
• Efficient implementation of all features on GPU.
• Reporting of memory and energy consumption (when available on platform).
• An API for addition of new cell types, e.g. LIF and Poisson spike generators.
• Validation tests against numeric/analytic models and NEURON.

Citing Arbor

Specific versions of Arbor can be cited via Zenodo:

• v0.2:
• v0.1:

The following BibTeX can be used to cite Arbor:

@INPROCEEDINGS{
    paper:arbor2019,
    author={N. A. {Akar} and B. {Cumming} and V. {Karakasis} and A. {Küsters} and W. {Klijn} and A. {Peyser} and S. {Yates}},
    booktitle={2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
    title={{Arbor --- A Morphologically-Detailed Neural Network Simulation Library for Contemporary High-Performance Computing Architectures}},
    year={2019}, month={feb}, volume={}, number={},
    pages={274--282},
    doi={10.1109/EMPDP.2019.8671560},
    ISSN={2377-5750}}

Alternative citation formats for the paper can be downloaded here, and a preprint is available at arXiv.