Scaling to 1024 software processes and hardware cores of the distributed simulation of a spiking neural network including up to 20G synapses

TL;DR

This paper demonstrates the scalability of a distributed spiking neural network simulator up to 1024 processes and 20 billion synapses, enabling large-scale brain simulations on high-performance computing platforms.

Contribution

It extends previous scalability analysis of the DPSNN simulator to 1024 processes, supporting large neural networks with up to 20 billion synapses and 11 million neurons.

Findings

Achieved strong and weak scaling up to 1024 processes.

Simulated large-scale neural networks with 20 billion synapses.

Demonstrated effective use of high-performance computing infrastructure.

Abstract

This short report describes the scaling, up to 1024 software processes and hardware cores, of a distributed simulator of plastic spiking neural networks. A previous report demonstrated good scalability of the simulator up to 128 processes. Herein we extend the speed-up measurements and strong and weak scaling analysis of the simulator to the range between 1 and 1024 software processes and hardware cores. We simulated two-dimensional grids of cortical columns including up to ~20G synapses connecting ~11M neurons. The neural network was distributed over a set of MPI processes and the simulations were run on a server platform composed of up to 64 dual-socket nodes, each socket equipped with Intel Haswell E5-2630 v3 processors (8 cores @ 2.4 GHz clock). All nodes are interconned through an InfiniBand network. The DPSNN simulator has been developed by INFN in the framework of EURETILE and CORTICONIC European FET Project and will be used by the WaveScalEW tem in the framework of the Human Brain Project (HBP), SubProject 2 - Cognitive and Systems Neuroscience. This report lays the groundwork for a more thorough comparison with the neural simulation tool NEST.