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Accelerating Data Analysis in Simulation Neuroscience with Big Data Technologies

  • 2018
  • OriginalPaper
  • Chapter
Published in:
Authors
Judit Planas
Fabien Delalondre
Felix Schürmann
Published in
Computational Science – ICCS 2018
Publisher
Springer International Publishing

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Abstract

Important progress in computational sciences has been made possible recently thanks to the increasing computing power of high performance systems. Following this trend, larger scientific studies, like brain tissue simulations, will continue to grow in the future. In addition to the challenges of conducting these experiments, we foresee an explosion of the amount of data generated and the consequent unfeasibility of analyzing and understanding the results with the current techniques.
This paper proposes Neurolytics, a new data analysis framework, together with a new data layout. The implementation of Neurolytics is mainly focused on simulation neuroscience, although we believe that our design can be applied to other science domains. The framework relies on big data technologies, like Apache Spark, to enable fast, reliable and distributed analyses of brain simulation data in this case. Our experimental evaluation on a cluster of 100 nodes shows that Neurolytics gets up to 374x speed-up compared to a thread-parallel Python implementation and is on par with a highly optimized Spark code. This demonstrates the suitability of our proposal to help scientists structure and understand the results of their experiments in a fast and efficient way.

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previous chapter A Computational Model-Based Framework to Plan Clinical Experiments – An Application to Vascular Adaptation Biology
next chapter Spiral Wave Drift Induced by High-Frequency Forcing. Parallel Simulation in the Luo–Rudy Anisotropic Model of Cardiac Tissue
Title
Accelerating Data Analysis in Simulation Neuroscience with Big Data Technologies
Authors
Judit Planas
Fabien Delalondre
Felix Schürmann
Copyright Year
2018
Book
Computational Science – ICCS 2018

Print ISBN: 978-3-319-93697-0

Electronic ISBN: 978-3-319-93698-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93698-7_28
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