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Benjamin Cumming authored
A big update that wraps over 90% of the public C++ API, with enough functionality to let Python users to perform useful modelling. Key features - wrapping of cable cell functionality - user-defined explicit compartmentalisation not supported - `single_cell_model` abstraction unique to the Python wrapper that simplifies developing and testing single cell models - one-dimensional cell builder for Python wrapper that simplfies building cells that - in and of itself limited in scope, but a very useful example of mapping a richer morphology builder onto `sample_tree`s. - parsing of region and location expressions from strings - implements a generic s-expression parser that we can use for other purposes later
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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.