Abstract
SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform.
Highlights
A neuromorphic system is a massively multi-core system composed of simple processing units and memory elements communicating by message exchanging [1]
We presented an implementation of an Message Passing Interface (MPI)-based DNA sequence matching algorithm for evaluating two critical aspects of using one of the more promising neuromorphic emerging technology
We benchmarking the SpiNNaker many-core neuromorphic platform and its MPI support, showing that the scaling performances are kept linear when an increasing number of cores is used during the computation
Summary
A neuromorphic system is a massively multi-core system composed of simple processing units and memory elements communicating by message exchanging [1]. This type of approach strives to simulate the behaviour of the brain using design principles based on biological nervous systems. Neuromorphic systems differ from traditional multi-core systems in the way in which memory and processing are organised In this case, memory is distributed with processing units rather than centralised and physically separated from the cores. The processing units remain in an idle state until an event is presented, triggering a reaction; after that, the units return to the idle state Using this feature, neuromorphic systems are much more energy-efficient than traditional multi-core systems.
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