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version: 2 | ||
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build: | ||
os: ubuntu-22.04 | ||
tools: | ||
python: "3.8" | ||
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sphinx: | ||
builder: html | ||
configuration: doc/htmldoc/conf.py | ||
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python: | ||
version: "3.8" | ||
install: | ||
- requirements: doc/requirements.txt | ||
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build: | ||
image: latest |
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.. _benchmark: | ||
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Benchmarking NEST | ||
================= | ||
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beNNch | ||
~~~~~~ | ||
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Computational efficiency is essential to simulate complex neuronal networks and study long-term effects such as learning. | ||
The scaling performance of neuronal network simulators on high-performance computing systems can be assessed with benchmark simulations. | ||
However, maintaining comparability of benchmark results across different systems, software environments, network models, and researchers from potentially different labs poses a challenge. | ||
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The software framework `beNNch <https://github.com/INM-6/beNNch>`_ tackles this challenge by implementing a unified, modular workflow for configuring, executing, and analyzing such benchmarks. | ||
beNNch builds around the `JUBE Benchmarking Environment <https://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html>`_, installs simulation software, provides an interface to benchmark models, automates data and metadata annotation, and accounts for storage and presentation of results. | ||
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For more details on the conceptual ideas behind beNNch, refer to Albers et al. (2022) [1]_. | ||
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.. figure:: ../static/img/multi-area-model_5faa0e9c.png | ||
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Example ``beNNch`` output (Figure 5C of [1]_) | ||
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Strong-scaling performance of the `multi-area model <https://github.com/INM-6/multi-area-model>`_ simulated with NEST on JURECA-DC. | ||
The left graph shows the absolute wall-clock time measured with Python-level timers for both network construction and state propagation. | ||
Error bars indicate variability across three simulation repeats with different random seeds. | ||
The top right graph displays the real-time factor defined as wall-clock time normalized by the model time. | ||
Built-in timers resolve four different phases of the state propagation: update, collocation, communication, and delivery. | ||
Pink error bars show the same variability of state propagation as the left graph. | ||
The lower right graph shows the relative contribution of these phases to the state-propagation time. | ||
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.. seealso:: | ||
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For further details, see the accompanying `beNNch GitHub Page <https://inm-6.github.io/beNNch>`_. | ||
And for a detailed step-by-step walk though see `Walk through guide <https://inm-6.github.io/beNNch/walk-through.html>`_. | ||
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Example PyNEST script: :doc:`../auto_examples/hpc_benchmark` | ||
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References | ||
---------- | ||
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.. [1] Albers J., et al (2022). A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations. | ||
Frontiers in Neuroinformatics(16):837549. https://doi.org/10.3389/fninf.2022.837549 | ||
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