|
| 1 | +.. _nest_benchmark_results: |
| 2 | + |
| 3 | +NEST performance benchmarks |
| 4 | +=========================== |
| 5 | + |
| 6 | + |
| 7 | +NEST performance is continuously monitored and improved across various network sizes. |
| 8 | +Here we show benchmarking results for NEST version 3.8 on Jureca-DC. |
| 9 | + |
| 10 | + |
| 11 | +Strong scaling experiment of the Microcircuit model [1]_ |
| 12 | +--------------------------------------------------------- |
| 13 | + |
| 14 | +.. grid:: 1 1 1 1 |
| 15 | + |
| 16 | + .. grid-item:: |
| 17 | + :columns: 10 |
| 18 | + :class: sd-align-major-center |
| 19 | + |
| 20 | + .. image:: /static/img/mc_benchmark.png |
| 21 | + |
| 22 | +.. grid:: 1 1 1 1 |
| 23 | + |
| 24 | + .. grid-item:: |
| 25 | + :columns: 10 |
| 26 | + :class: sd-align-minor-center |
| 27 | + |
| 28 | + |
| 29 | + * The model has ~80 000 neurons and ~300 million synapses |
| 30 | + * Increasing number of computing resources decrease simulation time |
| 31 | + * The model runs faster than real time |
| 32 | + |
| 33 | + |
| 34 | + |
| 35 | + |
| 36 | +Strong scaling experiment of the Multi-area-model [2]_ |
| 37 | +------------------------------------------------------- |
| 38 | + |
| 39 | +.. grid:: 1 1 1 1 |
| 40 | + |
| 41 | + .. grid-item:: |
| 42 | + :class: sd-align-major-center |
| 43 | + :columns: 10 |
| 44 | + |
| 45 | + .. image:: /static/img/mam_benchmark.png |
| 46 | + |
| 47 | + |
| 48 | +.. grid:: 1 1 1 1 |
| 49 | + |
| 50 | + .. grid-item:: |
| 51 | + :columns: 10 |
| 52 | + :class: sd-align-minor-center |
| 53 | + |
| 54 | + * The model has ~4.1 million neurons and ~24 billion synapses |
| 55 | + * Steady decrease of run time with additional compute resources |
| 56 | + |
| 57 | + |
| 58 | + |
| 59 | + |
| 60 | +Weak scaling experiment of the HPC benchmark model [3]_ |
| 61 | +-------------------------------------------------------- |
| 62 | + |
| 63 | +.. grid:: 1 1 1 1 |
| 64 | + |
| 65 | + .. grid-item:: |
| 66 | + :columns: 10 |
| 67 | + :class: sd-align-major-center |
| 68 | + |
| 69 | + .. image:: /static/img/hpc_benchmark.png |
| 70 | + |
| 71 | + |
| 72 | +.. grid:: 1 1 1 1 |
| 73 | + |
| 74 | + .. grid-item:: |
| 75 | + :columns: 10 |
| 76 | + :class: sd-align-minor-center |
| 77 | + |
| 78 | + |
| 79 | + * The size of network scales proportionally with the computational resources used |
| 80 | + * Largest network size in this diagram: ~5.8 million neurons and ~65 billion synapses |
| 81 | + * The figure shows that NEST can handle massive networks and simulate them efficiently |
| 82 | + |
| 83 | + |
| 84 | +.. seealso:: |
| 85 | + |
| 86 | + Example networks: |
| 87 | + |
| 88 | + * :doc:`/auto_examples/Potjans_2014/index` |
| 89 | + * `Multi-area model <https://inm-6.github.io/multi-area-model/>`_ |
| 90 | + * :doc:`/auto_examples/hpc_benchmark` |
| 91 | + |
| 92 | +References |
| 93 | +---------- |
| 94 | + |
| 95 | +.. [1] Potjans TC. and Diesmann M. 2014. The cell-type specific cortical |
| 96 | + microcircuit: relating structure and activity in a full-scale spiking |
| 97 | + network model. Cerebral Cortex. 24(3):785–806. DOI: `10.1093/cercor/bhs358 <https://doi.org/10.1093/cercor/bhs358>`__. |
| 98 | +
|
| 99 | +
|
| 100 | +.. [2] Schmidt M, Bakker R, Hilgetag CC, Diesmann M and van Albada SJ. 2018. Multi-scale |
| 101 | + account of the network structure of macaque visual cortex. Brain Structure |
| 102 | + and Function. 223: 1409 https://doi.org/10.1007/s00429-017-1554-4 |
| 103 | +
|
| 104 | +.. [3] Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S. 2018. |
| 105 | + Extremely scalable spiking neuronal network simulation code: From laptops to exacale computers. |
| 106 | + Frontiers in Neuroinformatics. 12. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00002 |
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