Dear Remy,
we have been assessing the performance of NEST for models of approximately the size you
are mentioning, see for example
here<https://iopscience.iop.org/article/10.1088/2634-4386/ac55fc/meta> and
here<https://www.frontiersin.org/articles/10.3389/fninf.2022.837549/full…ll>.
Generally, NEST does not profit so much from very high clock speeds.
As the dynamics of neuronal networks is a microscopically parallel problem NEST can
exploit parallel hardware:
A high number of cores with a big cache seems to be helpful getting very good
performance.
And indeed, internally conducted benchmarks indicate that even larger cache sizes (in
comparison to the nodes used in the cited paper) can still yield improvements in
performance.
Additionally, when simulating larger networks the speed of data transmission between nodes
becomes important for the performance.
Best
Anno
On 22.11.23 16:50, cagnol@ksvi.mff.cuni.cz<mailto:cagnol@ksvi.mff.cuni.cz> wrote:
Dear colleagues,
We are procuring a new small computing cluster on which we want to primarily run NEST
simulations.
Our models are medium in size (100s of thousands of neurons) but we run them for
relatively long biological times (100s of seconds), so the network setup duration is
mostly irrelevant for us.
What we would like to ask is if there is any insight into the current NEST simulator in
terms of what are the hardware bottlenecks of these types of simulations. Are we going to
do better with investing into more cores or higher clock speeds or more cache, or faster
RAM? I understand that this is a complex multi-factor optimization question, but any
insights or links to existing material that would
allow us to form an informed decision would be greatly appreciated.
Best regards,
Remy Cagnol.
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