Grid Framework for Parallel Investigations of Spiking Neural Microcircuits Ioan Lucian Muntean, Marius Joldos Computer Science Department Technical University of Cluj-Napoca Romania Email: {Ioan.Lucian.Muntean, Marius.Joldos}@cs.utcluj.ro Abstract Simulation of spiking neural networks is computa- tionally expensive and the employment of multicore processors can boost the performance of such simu- lations. Designing parallelization strategies that work well for different characteristics of the microcircuits entails expensive computations, leading to increased development times. To speed up the design of mul- ticore software for computational neuroscience, we have developed a framework that exploits multicore systems available in grid computing environments. Due to the use of GridSFEA plugins, common operations such as evaluation of parallelization strategies can be undertaken with very little effort. We evaluated the plugins for the development of a synchronous multicore spiking neural simulator. This uses the spike response model combined with the phe- nomenological model of spike time dependent synapse plasticity. The parallelization uses OpenMP, the micro- circuits have small world topologies and count up to 10 4 neurons and 10 7 synapses with biological details. With this novel framework more complex investigations in computational neuroscience such as analysis of the dynamics of neural microcircuits could be tackled. 1. Motivation The computer simulation of biological neural micro- circuits has become a necessary instrument for modern computational neuroscientists. The more realistic the neuronal models and the interconnecting microcircuit topologies are, the more computationally expensive their simulation gets. Their rapid evolution, increasing availability, and potential for parallel processing make multicore and manycore technologies attractive to the simulation codes of neuroscientists. The adoption of these novel parallelization tech- nologies in the computational neuroscience software is rather slow, especially in the area of the simulation of biological neural microcircuits. The main reason for this reticence is that such neural systems exhibit a highly dynamic behavior, close to chaos [1]. Their simulation is very sensitive to any changes of the en- vironment (initial conditions, numerical accuracy [2], microcircuit topology [3], order of the neuronal pro- cessing etc). Thus, the effects introduced by the paral- lelization techniques and technologies need first to be investigated. To speed up the design of multicore software for computational neuroscience, we proposed in [4] a framework for the analysis of the dynamic behavior of biological neural microcircuits simulated with multi- core and manycore technologies. In [4], the OpenMP- based simulation of the Hodgkin-Huxley model was compared with similar scenarios computed with Neu- ron [5]. In this paper, in order to foster the exploitation of multicore systems available in distributed comput- ing environments we extend the framework with grid computing capabilities. The extensions are based on the concept of application plugins described in [6]. We used the plugins during the development of a synchronous multicore simulator based on the Spike Response Model (SRM) as presented in [7], combined with the phenomenological model of Spike Time De- pendent synapse Plasticity (STDP). The parallelization is carried out with OpenMP, the microcircuits have small world topologies and count up to 10 4 neurons and more than 10 7 synapses with biological details. Thus, the framework takes specifications of the simulation scenario, starts corresponding grid jobs, and handles the user interaction with the jobs and with their results. As such, common operations such as the evaluation of parallelization strategies, entailing the computation of thousands of scenario configurations, can be defined and undertaken with very little manage- ment effort. With this novel framework, more complex investigations such as the analysis of the dynamics of 2012 11th International Symposium on Parallel and Distributed Computing 978-0-7695-4805-0/12 $26.00 © 2012 IEEE DOI 10.1109/ISPDC.2012.37 219