Parallel discrete event simulation on the SpiNNaker engine

Abstract

The SpiNNaker engine is a multiprocessor system, designed with a scalable interconnection system to perform real-time neural network simulation. The scalable property of the SpiNNaker system has the potential of providing high computation power making it suitable for solving certain large scale systems, such as neural networks. In addition, biological neural systems are intrinsically non-deterministic, and there are a number of design axioms of SpiNNaker that made it ideally suited to the simulation of systems with such properties.

Interesting though they are, the non-deterministic attributes of SpiNNaker-based simulation are not the focus of this thesis. The high computational power available, coupled with the extremely low inter-chip communication cost, made SpiNNaker an attractive platform for other application areas in addition to its principal goal. One such problem is parallel discrete event simulation (PDES), which is the focus of this work.

Discrete event simulation is a simple yet powerful algorithmic technique. Parallel discrete event simulation, on the other hand, is much more complicated due to the increase in complexity arising from the need to keep simulation data synchronized in a distributed environment. This property of PDES makes it a suitable candidate for generic simulation evaluation. Based on this insight, this thesis carries out the evaluation of the generic simulation capability of the SpiNNaker platform using a specially built framework running on the conventional parallel processing cluster to model the actual SpiNNaker system. In addition, a novel load balancing technique was also introduced and evaluated in this project.

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