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Automating NEURON Simulation Deployment in Cloud Resources.

David B Stockton1, Fidel Santamaria2

  • 1Department of Biomedical Engineering, The University of Texas at San Antonio, San Antonio, TX, 78249, USA. david.stockton@utsa.edu.

Neuroinformatics
|September 23, 2016
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Summary
This summary is machine-generated.

Cloud computing offers a viable alternative to High Performance Computing (HPC) for neuroscience simulations. This study demonstrates deploying the NEURON simulator across various cloud platforms, enhancing computational accessibility for researchers.

Keywords:
Cloud computingComputational neuroscienceComputer simulationGrid computingNEURON (RRID:SCR_005393)NeuroManager

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Area of Science:

  • Computational Neuroscience
  • Cloud Computing in Research

Background:

  • Neuroscience simulations traditionally rely on local servers or High Performance Computing (HPC) facilities.
  • Cloud computing presents a new, accessible platform for large-scale scientific computation.

Purpose of the Study:

  • To compare High Performance Computing (HPC) and cloud resources for neuroscience simulations.
  • To deploy and evaluate the NEURON simulator on multiple cloud platforms.
  • To extend the NeuroManager software for unified access to diverse computational resources.

Main Methods:

  • Deployment of the NEURON simulator on Chameleon Cloud, Rackspace, and Amazon Elastic Cloud Computing.
  • Automation of cloud operations and integration with the NeuroManager software.
  • Comparative analysis of speedup, efficiency, session time, and cost across platforms.

Main Results:

  • Successful deployment of NEURON across private and public clouds.
  • Demonstrated ability to automate and manage simulations via an integrated interface.
  • Evaluation of performance metrics including speedup, efficiency, and cost-effectiveness.

Conclusions:

  • Cloud computing is a practical and scalable resource for neuroscience simulations.
  • The enhanced NeuroManager software simplifies the use of hybrid computational environments.
  • This approach expands accessibility and efficiency for computational neuroscience research.