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

Karol Chlasta; Paweł Sochaczewski; Grzegorz M Wójcik; Izabela Krejtz

doi:10.3389/fninf.2023.1122470

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

Front Neuroinform. 2023 Mar 21:17:1122470. doi: 10.3389/fninf.2023.1122470. eCollection 2023.

Authors

Karol Chlasta^{1

2}, Paweł Sochaczewski², Grzegorz M Wójcik³, Izabela Krejtz⁴

Affiliations

¹ Department of Computer Science, Polish-Japanese Academy of Information Technology, Warsaw, Poland.
² Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Poland.
³ Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland.
⁴ Eye Tracking Research Center, SWPS University, Warsaw, Poland.

Abstract

In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running computer simulations but leaves a gap for setting up today's larger and more complex models. The field of realistic models of brain networks has overgrown the simplicity of earliest models. The challenges include managing the complexity of software dependencies and various models, setting up model parameter values, 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), which facilitates the large-scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as the code (IaC) containerization approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(8 × 5,1) that uses biologically plausible Hodgkin-Huxley spiking neurons. We evaluate the pipeline by performing 54 simulations 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-containerized and containerized execution with Docker, as well as present the cost per simulation in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost-effective.

Keywords: Docker; GENESIS; computer simulations; liquid state machine (LSM); scientific workflows.