Fast parallel search capabilities on large datasets are required across multiple application domains. One such domain is genome analysis, which requires high-performance k mer matching in large genome databases. Recently proposed solutions implemented k mer matching in DRAM, utilizing its sheer capacity and parallelism. However, their operation is essentially bit-serial, which ultimately limits the performance, especially when matching long strings, as customary in genome analysis pipelines. The proposed solution, MajorK, enables bit-parallel majority based k mer matching in an unmodified commodity DRAM. MajorK employs multiple DRAM row activation, where the search patterns (query k mers) are coded into DRAM addresses. We evaluate MajorK on viral genome k mer matching and show that it can achieve up to 2.7 $ \times $ higher performance while providing a better matching accuracy compared to state-of-the-art DRAM based k mer matching accelerators.

中文翻译：

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MajorK：商品 DRAM 中基于多数的 kmer 匹配

跨多个应用程序域需要对大型数据集的快速并行搜索功能。基因组分析就是这样的领域之一，它需要 在大型基因组数据库中进行高性能的k mer 匹配。最近提出的解决方案 利用其巨大的容量和并行性在 DRAM 中实现了 kmer 匹配。然而，它们的操作本质上是位串行的，这最终限制了性能，特别是在匹配长字符串时，正如基因组分析管道中的惯例。所提出的解决方案 MajorK 可 在未修改的商品 DRAM 中实现基于位并行多数的k mer 匹配。 MajorK 采用多 DRAM 行激活，其中搜索模式（查询k 聚体）被编码到 DRAM 地址中。我们在病毒基因组k mer 匹配上评估 MajorK ，结果表明它可以达到 2.7 $\次$与最先进的基于 DRAM 的k mer 匹配加速器相比，它具有更高的性能，同时提供更好的匹配精度。