[Incf-ocns-software-wg] INCF/OCNS SoftwareWG Dev session: Denis Alevi: Brian2CUDA: November 3, 2022, 1600 UTC over Zoom
Ankur Sinha
sanjay.ankur at gmail.com
Tue Nov 1 11:19:27 CET 2022
Dear all,
A reminder of this week's dev session on Brian2CUDA. It promises to be a
great session, and we hope to have a discussion on GPU usage for
simulation too. Please do come along.
Calendar invite also attached.
On Tue, Oct 25, 2022 12:26:05 +0100, Ankur Sinha wrote:
> Dear all,
>
> Please join us for our next dev session on Brian2CUDA:
>
> https://ocns.github.io/SoftwareWG/2022/10/18/dev-session-denis-alevi-brian2cuda.html
>
> Denis Alevi will introduce the Brian2CUDA tool in this session, and
> discuss its development. We will also have a discussion on GPU based
> simulation in neuroscience after the presentation.
>
> - Date: Thursday, November 3, 2022, 1600 UTC (Click here to see your local time: https://www.timeanddate.com/worldclock/fixedtime.html?msg=Dev+session%3A+Denis+Alevi+Brian2CUDA&iso=20221103T16&p1=136&ah=1).
> - Location (Zoom, login required): https://ucl.zoom.us/j/95692778384?pwd=VldIQ3hPTU1zczNpYjQxSSt4Z25xdz09
>
> The abstract for the talk is below:
>
> Graphics processing units (GPUs) are widely available and have been used
> with great success to accelerate scientific computing in the last
> decade. These advances, however, are often not available to researchers
> interested in simulating spiking neural networks, but lacking the
> technical knowledge to write the necessary low-level code. Writing
> low-level code is not necessary when using the popular Brian simulator,
> which provides a framework to generate efficient CPU code from
> high-level model definitions in Python. Here, we present Brian2CUDA, an
> open-source software that extends the Brian simulator with a GPU
> backend. Our implementation generates efficient code for the numerical
> integration of neuronal states and for the propagation of synaptic
> events on GPUs, making use of their massively parallel arithmetic
> capabilities. We benchmark the performance improvements of our software
> for several model types and find that it can accelerate simulations by
> up to three orders of magnitude compared to Brian’s CPU backend.
> Currently, Brian2CUDA is the only package that supports Brian’s full
> feature set on GPUs, including arbitrary neuron and synapse models,
> plasticity rules, and heterogeneous delays. When comparing its
> performance with Brian2GeNN, another GPU-based backend for the Brian
> simulator with fewer features, we find that Brian2CUDA gives comparable
> speedups, while being typically slower for small and faster for large
> networks. By combining the flexibility of the Brian simulator with the
> simulation speed of GPUs, Brian2CUDA enables researchers to efficiently
> simulate spiking neural networks with minimal effort and thereby makes
> the advancements of GPU computing available to a larger audience of
> neuroscientists.
>
> References:
>
> - Publication: Brian2CUDA: flexible and efficient simulation of spiking neural network models on GPUs: https://www.frontiersin.org/articles/10.3389/fninf.2022.883700/abstract
> - Documentation: https://brian2cuda.readthedocs.io/en/latest/
> - Source code: https://github.com/brian-team/brian2cuda
>
> We hope to see you there.
> On behalf of the INCF/OCNS Software working group,
--
Thanks,
Regards,
Ankur Sinha (He / Him / His) | https://ankursinha.in
Research Fellow at the Silver Lab, University College London | http://silverlab.org/
Free/Open source community volunteer at the NeuroFedora project | https://neuro.fedoraproject.org
Time zone: Europe/London
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