Skip to content

Update benchmark result docs #3259

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jul 23, 2024
Merged

Update benchmark result docs #3259

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
50 changes: 34 additions & 16 deletions doc/htmldoc/benchmark_results.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,10 @@ NEST performance benchmarks


NEST performance is continuously monitored and improved across various network sizes.
Here we show benchmarking results for NEST version 3.8 on Jureca-DC.
Here we show benchmarking results for NEST version 3.8 on Jureca-DC [1]_.
The benchmarking framework and the structure of the graphs is described in [2]_.


Strong scaling experiment of the Microcircuit model [1]_
Strong scaling experiment of the Microcircuit model [3]_
---------------------------------------------------------

.. grid:: 1 1 1 1
Expand All @@ -26,14 +26,16 @@ Strong scaling experiment of the Microcircuit model [1]_
:class: sd-align-minor-center


* The model has ~80 000 neurons and ~300 million synapses
* The model has ~80 000 neurons and ~300 million synapses, minimal delay 0.1 ms
* 2 MPI processes per node, 64 threads per MPI process
* Increasing number of computing resources decrease simulation time
* The model runs faster than real time
* Data averaged over 3 runs with different seeds, error bars indicate standard deviation
* The model runs faster than real time [4]_




Strong scaling experiment of the Multi-area-model [2]_
Strong scaling experiment of the Multi-area-model [5]_
-------------------------------------------------------

.. grid:: 1 1 1 1
Expand All @@ -51,13 +53,15 @@ Strong scaling experiment of the Multi-area-model [2]_
:columns: 10
:class: sd-align-minor-center

* The model has ~4.1 million neurons and ~24 billion synapses
* The model has ~4.1 million neurons and ~24 billion synapses, minimal delay 0.1 ms
* 2 MPI processes per node, 64 threads per MPI process
* Steady decrease of run time with additional compute resources
* Data averaged over 3 runs with different seeds, error bars indicate standard deviation




Weak scaling experiment of the HPC benchmark model [3]_
Weak scaling experiment of the HPC benchmark model [6]_
--------------------------------------------------------

.. grid:: 1 1 1 1
Expand All @@ -77,8 +81,10 @@ Weak scaling experiment of the HPC benchmark model [3]_


* The size of network scales proportionally with the computational resources used
* Largest network size in this diagram: ~5.8 million neurons and ~65 billion synapses
* Largest network size in this diagram: ~5.8 million neurons and ~65 billion synapses, minimal delay 1.5 ms
* 2 MPI processes per node, 64 threads per MPI process
* The figure shows that NEST can handle massive networks and simulate them efficiently
* Data averaged over 3 runs with different seeds, error bars indicate standard deviation


.. seealso::
Expand All @@ -92,15 +98,27 @@ Weak scaling experiment of the HPC benchmark model [3]_
References
----------

.. [1] Potjans TC. and Diesmann M. 2014. The cell-type specific cortical
.. [1] Juelich Supercomputing Centre. 2021. JURECA: Data Centric and Booster Modules implementing the Modular
Supercomputing Architecture at Jülich Supercomputing Centre. Journal of large-scale research facilities,
7, A182. DOI: http://dx.doi.org/10.17815/jlsrf-7-182


.. [2] Albers J, Pronold J, Kurth AC, Vennemo SB, Haghighi Mood K, Patronis A, Terhorst D, Jordan J, Kunkel S,
Tetzlaff T, Diesmann M and Senk J (2022). A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.
Frontiers in Neuroinformatics(16):837549. https://doi.org/10.3389/fninf.2022.837549

.. [3] Potjans TC. and Diesmann M. 2014. The cell-type specific cortical
microcircuit: relating structure and activity in a full-scale spiking
network model. Cerebral Cortex. 24(3):785–806. DOI: `10.1093/cercor/bhs358 <https://doi.org/10.1093/cercor/bhs358>`__.

.. [4] Kurth AC, Senk J, Terhorst D, Finnerty J, Diesmann M. 2022. Sub-realtime simulation of a neuronal network of natural density.
Neuromorphic computing and engineering 2(2), 021001
https://iopscience.iop.org/article/10.1088/2634-4386/ac55fc/meta

.. [2] Schmidt M, Bakker R, Hilgetag CC, Diesmann M and van Albada SJ. 2018. Multi-scale
account of the network structure of macaque visual cortex. Brain Structure
and Function. 223: 1409 https://doi.org/10.1007/s00429-017-1554-4
.. [5] Schmidt M, Bakker R, Hilgetag CC, Diesmann M and van Albada SJ. 2018. Multi-scale
account of the network structure of macaque visual cortex. Brain Structure
and Function. 223: 1409 https://doi.org/10.1007/s00429-017-1554-4

.. [3] Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S. 2018.
Extremely scalable spiking neuronal network simulation code: From laptops to exacale computers.
Frontiers in Neuroinformatics. 12. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00002
.. [6] Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S. 2018.
Extremely scalable spiking neuronal network simulation code: From laptops to exacale computers.
Frontiers in Neuroinformatics. 12. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00002
Loading