Heterogeneous computing architecture for fast detection of SNP-SNP interactions

Abstract

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.

Figure 1

SNPsyn graphical user interface.a) A synergy versus information gain plot is used to select SNP-SNP pairs. b) Gene Ontology enrichment analysis for genes overlapping with selected SNP-SNP pairs. c) Synergy network of selected SNPs.

Figure 2

SNPsyn software architecture. Computation of SNP-SNP interaction is coded in C++ for the CPU, CUDA and MIC architectures. The scheduler that invokes the three heterogeneous implementations is written in Python.

Figure 3

CUDA code snippet. Variables threads and blocks store the thread configuration. Function cudaMemcpy feeds the data into the GPU and retrieves the results afterwards. Each of the preconfigured GPU threads independently executes the computeIGain function and scores the associated SNP pair.

Figure 4

MIC code snippet. The first pragma directive marks the start of a MIC code section. Keywords in and out indicate the data to be transferred to and from the Xeon Phi. The OpenMP clause omp parallel for launches all available threads in parallel, which execute the code in the body of the loop and score the SNP pairs.

Figure 5

Execution times and speedups achieved on various computing resources. Shown are execution times on each hardware configuration for different problem sizes (a) and speedups in comparison to a single CPU thread execution (b).