AUTHOR=Chlasta Karol , Sochaczewski Paweł , Wójcik Grzegorz M. , Krejtz Izabela 

TITLE=Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2023.1122470

DOI=10.3389/fninf.2023.1122470

ISSN=1662-5196

ABSTRACT=We explore the relationship between experiment setup and simulation run in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, or system-level model. GENESIS supports developing and running computer simulations, but leaves a gap for setting up and executing the experiments; that includes managing software dependencies, setting up model parameter values, different stimuli, storing the input parameters alongside the results, and providing execution statistics. Moreover, in the High Performance Computing (HPC) context, public cloud resources are becoming an alternative to the expensive on-premises clusters. We present Neural Simulation Pipeline (NSP), that facilitates the large scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as 
code (IaC) containerisation approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(4x8,1) that uses biologically plausible Hodgkin–Huxley spiking neurons. We evaluate the pipeline by performing 54 experiments executed on-premise, at the Hasso Plattner Institute's (HPI) Future Service-Oriented Computing (SOC) Lab, and through the Amazon Web Services (AWS), the biggest public cloud service provider in the world. We report on  the non-containerised and containerised execution with Docker, as well as present the cost per experiment in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost effective.