-
Sam Yates authored
* Collect cable cell parameter setting structures into `include/cable_cell_param.hpp`. * Restructure electrical specifications (axial resistance, membrane capacitance) and ionic concentrations and reversal potentials on cable cells, so that these can be specified with a global default, per-cell defaults, and per-segment values. * Allow reversal potentials to be set by a mechanism of a new kind 'revpot', which are prohibited from maintaining state or writing to any shared state other than ionic reversal potentials. * Specify reversal potential mechanisms as global defaults or per-cell. Reversal potential mechanisms may not be specified at the level of a segment in order to avoid non-linearities arising from the discretization. * Supply default cable cell parameter data that matches NEURON values (this is _not_ used by default). * Replace the d_lambda calculation with one that approximates more faithfully the effect of tapered segments, and which will use the electrical values inherited by cell or global defaults. * Supply a bundled mechanism 'nernst' that replaces the previous hard-coded Nernst potential calculation, for use (together with ion rebinding) in the new ion description scheme. * All ions used in a cable_cell model must be present in the top level global parameter table, together with their charge. * Extend semantics of external variables in modcc, to permit direct assignment (as opposed to just += or -=.) * Extend `util::binary_search_index` to allow for a projection functional analagous to other range utilites. * Add documentation on the cable cell API, sketching over many of the details, but describing in particular the interface changes for default parameters and reversal potential behaviour. * Adjust pyarb for new API * Hard code global cable cell properties in the python recipe shim to useneuron default parameters.
Unverifiedfd4f4def
index.rst 3.38 KiB
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.