Multicore distributed dictionary learning: A microarray gene expression biclustering case study

Abstract

The increasing pervasion and scale of machine learning technologies is posing fundamental challenges for their realisation. In the main, current algorithms are centralised, with a large number of processing agents, distributed across parallel processing resources, accessing a single, very large data object. This creates bottlenecks as a result of limited memory access rates. Distributed learning has the potential to resolve this problem by employing networks of co-operating agents each operating on subsets of the data, but as yet their suitability for realisation on parallel architectures such as multicore are unknown. This paper presents the results of a case study deploying distributed dictionary learning for microarray gene expression bi-clustering on a 16-core Epiphany multicore. It shows that distributed learning approaches can enable near-linear speed-up with the number of processing resources and, via the use of DMA-based communication, a 50% increase in throughput can be enabled.