Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, SI 1000 Ljubljana, SI, Slovenia.
BMC Bioinformatics. 2014 Jun 25;15:216. doi: 10.1186/1471-2105-15-216.
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.
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.
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.
典型的全基因组关联研究（GWAS）中数据的广泛程度给基因-基因相互作用发现的软件工具带来了相当大的计算挑战。对数十万到数百万个单核苷酸多态性（SNP）之间的所有相互作用进行穷举评估可能需要数周甚至数月的计算时间。现代图形处理单元（GPU）和许多集成核（MIC）协处理器中的大规模并行硬件可以大大缩短运行时间。虽然 GPU 为基础的生物信息学实现的实用性已经得到了很好的研究，但 MIC 架构最近才被引入，并且可能提供了许多尚未被探索和测试的比较优势。
我们已经开发了一种异构的 GPU 和 Intel MIC 加速软件模块，用于 SNP-SNP 相互作用的发现，以取代交互式基于网络的数据探索程序 SNPsyn 中以前的单线程计算核心。我们报告了这两种现代大规模并行架构及其软件环境之间的差异。与单线程 CPU 实现相比，它们的实用性导致执行时间缩短了一个数量级。在单个 Nvidia Tesla K20 上的 GPU 实现速度是基于 Xeon Phi P5110 协处理器的 MIC 架构的两倍，但也需要相当多的编程工作。
通用 GPU 是一个成熟的平台，具有大量的计算能力，可以解决固有的并行问题，但对程序员来说可能要求很高。另一方面，新的 MIC 架构虽然在性能上有所欠缺，但减少了编程工作，并以更通用的架构弥补了它，使其更适合更广泛的问题。
