Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm

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...

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Bibliographic Details
Published in:Electronics (Basel) 2019-11, Vol.8 (11), p.1342
Main Authors: Urgese, Gianvito, Barchi, Francesco, Parisi, Emanuele, Forno, Evelina, Acquaviva, Andrea, Macii, Enrico
Format: Article
Language:English
Subjects:
Algorithms
Brain research
Communication
Computer architecture
Computer simulation
Design
Energy efficiency
Graph representations
Interfaces
Matching
Microprocessors
Network latency
Neural networks
Neurons
Packet transmission
Platforms
Simulation
Software
Synchronism
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics8111342