Memory-Optimised Parallel Processing of Hi-C Data

—This paper presents the optimisation efforts on the creation of a graph-based mapping representation of gene adjacency. The method is based on the Hi-C process, starting from Next Generation Sequencing data, and it analyses a huge amount of static data in order to produce maps for one or more genes. Straightforward parallelisation of this scheme does not yield acceptable performance on multicore architectures since the scalability is rather limited due to the memory bound nature of the problem. This work focuses on the memory optimisations that can be applied to the graph construction algorithm and its (complex) data structures to derive a cache-oblivious algorithm and eventually to improve the memory bandwidth utilisation. We used as running example NuChart-II, a tool for annotation and statistic analysis of Hi-C data that creates a gene-centric neighborhood graph. The proposed approach, which is exemplified for Hi-C, addresses several common issue in the parallelisation of memory...

• Type: Journal
• Year: 2015
• Where: PDP
• Authors: Maurizio Drocco, Claudia Misale, Guilherme Peretti Pezzi, Fabio Tordini, Marco Aldinucci
• Keywords: Distributed And Parallel Computing, PDP 2015