This work seeks to leverage Processing-with-storage-technology (PWST) to accelerate a key bioinformatics kernel called k-mer counting, which involves processing large files of sequence data on the disk to build a histogram of fixed-size genome sequence substrings and thereby entails prohibitively high I/O overhead. In particular, this work proposes a set of accelerator designs called Abakus that offer varying degrees of tradeoffs in terms of performance, efficiency, and hardware implementation complexity. The key to these designs is a set of domain-specific hardware extensions to accelerate the key operations for k-mer counting at various levels of the SSD hierarchy, with the goal of enhancing the limited computing capabilities of conventional SSDs, while exploiting the parallelism of the multi-channel, multi-way SSDs. Our evaluation suggests that Abakus can achieve 8.42×, 6.91×, and 2.32× speedup over the CPU-, GPU-, and near-data processing solutions.

这项工作旨在利用存储技术处理 (PWST) 来加速称为k聚体计数的关键生物信息学内核，其中涉及处理磁盘上的大型序列数据文件，以构建固定大小的基因组序列子串的直方图和因此需要极高的 I/O 开销。特别是，这项工作提出了一组称为 Abakus 的加速器设计，它在性能、效率和硬件实现复杂性方面提供了不同程度的权衡。这些设计的关键是一组特定领域的硬件扩展，以加速SSD 层次结构各个级别的k聚体计数的关键操作，其目标是增强传统 SSD 有限的计算能力，同时利用多通道、多路 SSD。我们的评估表明，相对于 CPU、GPU 和近数据处理解决方案，Abakus 可以实现 8.42 倍、6.91 倍和 2.32 倍的加速。