Automating NEURON Simulation Deployment in Cloud Resources

Abstract

Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the Open-Stack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon’s proprietary software. We describe the manual procedures and how to automate cloud operations. We describe extending our simulation automation software called NeuroManager (Stockton and Santamaria, Frontiers in Neuroinformatics, 2015), so that the user is capable of recruiting private cloud, public cloud, HPC, and local servers simultaneously with a simple common interface. We conclude by performing several studies in which we examine speedup, efficiency, total session time, and cost for sets of simulations of a published NEURON model.

Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow

“…Recently, the Neuroscience Gateway (NSG, 2017a) also added RESTful support (NSG, 2017b). Although we used RESTful interactions minimally in the present project (instead we used the ABI SDK), we used them extensively in a previous work for accessing cloud resources (Stockton and Santamaria, 2016), and neuro-science databases may show increased public usage should they provide these types of interaction facilities.…”

“…Our group has developed an object–oriented MATLAB program called NeuroManager which automates the workflow of simulation job submissions when using multiple heterogeneous computational resources (Stockton and Santamaria, 2017, 2016, 2015). Simulation characteristics are embodied in a Simulator, which has as its SimCore a simulator engine such as NEURON, MCell, or custom simulators written in any language (Teka et al, 2014, 2016).…”

“…Users must generate code to translate the data for their research environment and develop ad hoc strategies for the workflow of database access, download, cataloging and analysis, thus raising the barrier to database use and increasing the difficulty of sharing with other researchers. We have argued that automation of the workflow in computational neuro-science investigation can increase the efficiency of research, the preservation of provenance and the reproducibility of results (Stockton and Santamaria, 2016, 2015). …”

“…We provide infrastructure to generate a local, specialized MySQL (My Structured Query Language) database of the electrophysiological data and extracted features; the user then can employ Python or MATLAB utilities to access the local database. We have also extended our workflow automation software (Stockton and Santamaria, 2016, 2015) to integrate the local database into the modeling/data analysis cycle within a single script. To support the investigation, we use a second MySQL database that associates input parameters with simulations, analyses, and comparisons, thus providing provenance information and post-investigation search capabilities.…”

INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures

“…INDIGO has contributed to the networking parts of the OCCI standard, as well as to the improvement of the TOSCA support in the upstream OpenStack components: the Heat Translator and TOSCA parser [21]. Our solution makes the execution of dynamic workflows [22][23][24] possible, in a more consistent way across hybrid Clouds [25].…”