With the recent move towards sequencing of accurate long reads, finding solutions that support efficient analysis of these reads becomes more necessary. The long execution time required for sequence alignment of long reads negatively affects genomic studies relying on sequence alignment. Although pre-alignment filtering as an extra step before alignment was recently introduced to mitigate sequence alignment for short reads, these filters do not work as efficiently for long reads. Moreover, even with efficient pre-alignment filters, the overall end-to-end (i.e., filtering + original alignment) execution time of alignment for long reads remains high, while the filtering step is now a major portion of the end-to-end execution time. Our paper makes three contributions. First, it identifies data movement of sequences between memory units and computing units as the main source of inefficiency for pre-alignment filters of long reads. This is because although filters reject many of these long sequencing pairs before they get to the alignment stage, they still require a huge cost regarding time and energy consumption for the large data transferred between memory and processor. Second, this paper introduces an adaptation of a short-read pre-alignment filtering algorithm suitable for long reads. We call this LongGeneGuardian. Finally, it presents Filter-Fuse as an architecture that supports LongGeneGuardian inside the memory. FilterFuse exploits the Computation-In-Memory computing paradigm, eliminating the cost of data movement in LongGeneGuardian. Our evaluations show that FilterFuse improves the execution time of filtering by 120.47x for long reads compared to State-of-the-Art (SoTA) filter, SneakySnake. FilterFuse also improves the end-to-end execution time of sequence alignment by up to 49.14x and 5207.63x compared to SneakySnake with SoTA aligner and only SoTA aligner, respectively.

翻译：随着近年来向准确长读段测序的推进，寻找支持高效分析这些读段的解决方案变得愈加必要。长读段序列比对所需的较长执行时间对依赖序列比对的基因组学研究产生了负面影响。尽管近期引入了预比对过滤作为比对前的额外步骤以缓解短读段的序列比对问题，但这些过滤器对长读段的效率并不高。此外，即使采用高效的预比对过滤器，长读段比对的整体端到端（即过滤+原始比对）执行时间仍然较高，而过滤步骤现在已成为端到端执行时间的主要部分。本文做出三项贡献：首先，它指出序列在内存单元与计算单元之间的数据移动是长读段预比对过滤低效的主要根源。这是因为尽管过滤器在比对阶段前拒绝了大量长测序配对，但内存与处理器之间传输的大量数据仍会导致巨大的时间和能耗成本。其次，本文提出了一种适用于长读段的短读段预比对过滤算法的改进版本，我们将其命名为LongGeneGuardian。最后，本文介绍了FilterFuse——一种在内存内部支持LongGeneGuardian的架构。FilterFuse利用了计算-内存一体化计算范式，消除了LongGeneGuardian中的数据移动成本。我们的评估表明，与最先进的过滤器SneakySnake相比，FilterFuse将长读段的过滤执行时间提升了120.47倍。与结合最先进比对器的SneakySnake及仅使用最先进比对器相比，FilterFuse还将序列比对的端到端执行时间分别提升了高达49.14倍和5207.63倍。