Read mapping is a fundamental, yet computationally-expensive step in many genomics applications. It is used to identify potential matches and differences between fragments (called reads) of a sequenced genome and an already known genome (called a reference genome). To address the computational challenges in genome analysis, many prior works propose various approaches such as filters that select the reads that must undergo expensive computation, efficient heuristics, and hardware acceleration. While effective at reducing the computation overhead, all such approaches still require the costly movement of a large amount of data from storage to the rest of the system, which can significantly lower the end-to-end performance of read mapping in conventional and emerging genomics systems. We propose GenStore, the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequence analysis by exploiting low-cost and accurate in-storage filters. GenStore leverages hardware/software co-design to address the challenges of in-storage processing, supporting reads with 1) different read lengths and error rates, and 2) different degrees of genetic variation. Through rigorous analysis of read mapping processes, we meticulously design low-cost hardware accelerators and data/computation flows inside a NAND flash-based SSD. Our evaluation using a wide range of real genomic datasets shows that GenStore, when implemented in three modern SSDs, significantly improves the read mapping performance of state-of-the-art software (hardware) baselines by 2.07-6.05$\times$ (1.52-3.32$\times$) for read sets with high similarity to the reference genome and 1.45-33.63$\times$ (2.70-19.2$\times$) for read sets with low similarity to the reference genome.

翻译：读映射是众多基因组学应用中一项基础但计算密集的步骤。它用于识别测序基因组片段（称为读段）与已知基因组（称为参考基因组）之间的潜在匹配和差异。为应对基因组分析中的计算挑战，此前许多研究提出了多种方法，例如筛选需要执行昂贵计算的读段的过滤器、高效启发式算法以及硬件加速。尽管这些方法能有效降低计算开销，但它们仍需要将大量数据从存储设备传输到系统其他部分，这一过程成本高昂，会显著降低传统及新兴基因组学系统中读映射的端到端性能。我们提出了GenStore，这是首个专为基因组序列分析设计的存储内处理系统，通过利用低成本且精准的存储内过滤器，大幅减少了基因组序列分析中的数据移动和计算开销。GenStore采用硬件/软件协同设计来应对存储内处理的挑战，支持具有1)不同读段长度与错误率，以及2)不同程度遗传变异的读段。通过对读映射过程的严格分析，我们在基于NAND闪存的SSD内部精心设计了低成本的硬件加速器及数据/计算流程。使用多种真实基因组数据集的评估表明，当GenStore在三种现代SSD中实现时，对于与参考基因组高度相似的读段集，其读映射性能相比最先进的软件（硬件）基线提升了2.07-6.05倍（1.52-3.32倍）；对于与参考基因组相似度低的读段集，该提升幅度达1.45-33.63倍（2.70-19.2倍）。