ePrints.FRI - University of Ljubljana, Faculty of Computer and Information Science

Heterogeneous computing architecture for fast detection of SNP-SNP interactions

Davor Sluga and Tomaz Curk and Uros Lotric and Blaz Zupan Heterogeneous computing architecture for fast detection of SNP-SNP interactions. BMC Bioinformatics, 15 . p. 216.

Full text not available from this repository.


BACKGROUND. The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested. RESULTS. We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort. CONCLUSIONS. General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

Item Type: Article
Keywords: SNP-SNP interactions; Genome-wide association studies; Graphic processing unit; Many Integrated Core coprocessor; Intel Xeon Phi; CUDA
Institution: University of Ljubljana
Department: Faculty of Computer and Information Science
Divisions: Faculty of Computer and Information Science > Bioinformatics Laboratory
Faculty of Computer and Information Science > Laboratory for Adaptive Systems and Parallel Processing
Item ID: 2619
Date Deposited: 25 Jul 2014 02:35
Last Modified: 25 Jul 2014 02:37
URI: http://eprints.fri.uni-lj.si/id/eprint/2619

Actions (login required)

View Item