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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems
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Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems

机译：FPGA和GPU加速计算系统上的GWAS中的并行上位检测

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摘要

High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g., processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderately-sized datasets and to a few hours for large-scale datasets.
机译：高通量基因分型技术（例如SNP阵列）可快速收集多达数百万个个体的遗传标记。在基因组广泛关联研究中检测上位性（基于2-SNP相互作用）是一项重要但耗时的操作，因为必须对每对被测标记物进行统计计算。因此，用于检测上位性的计算方法的运行时间过长。例如，使用标准3 GHz CPU上的最新工具处理由500,000个SNP和5,000个样本组成的中等大小的数据集需要几天。在本文中，我们演示了如何在两个不同的计算系统上结合使用细粒度和粗粒度并行机制来加速此任务。第一种架构基于可重配置硬件（FPGA），而第二种架构则使用连接至同一主机的多个GPU。我们证明，与顺序实现相比，这两个系统都可以实现大约四个数量级的加速。对于中等大小的数据集，这将大大减少用于检测上位性的运行时间，而对于大型数据集，则减少了数小时。

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