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

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.

Making the Most of Scarce Input Data in Deep Learning-Based Source Code Classification for Heterogeneous Device Mapping

“…We use Needleman-Wunsch (NW), a global alignment algorithm belong to family of algorithms used in compilers in order to reduce the program size choosing how to fuse functions [35]. It is also used in other fields: from optimizing the configuration time of neuromorphic hardware, [36], [37], [38] to genetic sequence and amino acid sequence alignment [39], [40] in bioinformatics. We apply NW using naive scores for Match, Mismatch and Insertion or Deletion cases.…”

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