固态硬盘会加速您的生物信息学吗？深入剖析、性能分析等
Will solid-state drives accelerate your bioinformatics? In-depth profiling, performance analysis, and beyond
摘要：
在生物信息学中已经产生了各种各样的大规模数据。作为回应，并行计算部分满足了有效处理生物医学大数据的需求。然而，许多生物信息学程序的时间需求仍然很高，大规模的实际应用，由于并行化的因素，阻碍了加速。最近，出现了新一代的存储设备，例如基于NAND闪存的固态驱动器（SSD），并且随着对近数据处理的重新关注，它们越来越多地成为可以伴随并行处理的加速方法。在某些情况下，简单地将硬盘驱动器（HDD）替换为SSD会导致显着的加速。尽管SSD具有各种优势和不断降低的成本，但很少有人对重要但耗时的生物信息学程序的基于SSD的分析和性能探索进行回顾。为了提供信息，我们使用多种类型的设备对23个关键生物信息学程序进行了深入的分析和分析。基于我们从这项研究中获得的见解，我们进一步讨论了设计和优化生物信息学算法和管道以充分利用SSD的相关问题。我们的项目涵盖了传统和新兴的重要领域，如比对、组装、作图、表达分析、变异检测和宏基因组学。我们解释了如何通过并行化加速可以与SSD相结合以提高性能，以及如何使用SSD可以加快重要的生物信息学管道，例如基因组分析工具包（GATK）的变异检测和使用RNA测序（RNA-seq）的转录组分析。我们希望这篇评论可以提供有用的方向和提示，以配合未来的生物信息学算法设计程序，适当考虑新一代的强大的存储设备。
英文摘要：
A wide variety of large-scale data has been produced in bioinformatics. In response, the need for efficient handling of biomedical big data has been partly met by parallel computing. However, the time demand of many bioinformatics programs still remains high for large-scale practical uses due to factors that hinder acceleration by parallelization. Recently, new generations of storage devices have emerged, such as NAND flash-based solid-state drives (SSDs), and with the renewed interest in near-data processing, they are increasingly becoming acceleration methods that can accompany parallel processing. In certain cases, a simple drop-in replacement of hard disk drives (HDDs) by SSDs results in dramatic speedup. Despite the various advantages and continuous cost reduction of SSDs, there has been little review of SSD-based profiling and performance exploration of important but time-consuming bioinformatics programs. For an informative review, we perform in-depth profiling and analysis of 23 key bioinformatics programs using multiple types of devices. Based on the insight we obtain from this research, we further discuss issues related to design and optimize bioinformatics algorithms and pipelines to fully exploit SSDs. The programs we profile cover traditional and emerging areas of importance, such as alignment, assembly, mapping, expression analysis, variant calling, and metagenomics. We explain how acceleration by parallelization can be combined with SSDs for improved performance and also how using SSDs can expedite important bioinformatics pipelines, such as variant calling by the Genome Analysis Toolkit (GATK) and transcriptome analysis using RNA sequencing (RNA-seq). We hope that this review can provide useful directions and tips to accompany future bioinformatics algorithm design procedures that properly consider new generations of powerful storage devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
