[Incf-ocns-software-wg] INCF/OCNS SoftwareWG Dev session: Denis Alevi: Brian2CUDA: November 3, 2022, 1600 UTC over Zoom

[Ankur Sinha]

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