DNA sequence alignment is an important workload in computational genomics. Reference-guided DNA assembly involves aligning many read sequences against candidate locations in a long reference genome. To reduce the computational load of this alignment, candidate locations can be pre-filtered using simpler alignment algorithms like edit distance. Prior work has explored accelerating filtering on simulated compute-in-DRAM, due to the massive parallelism of compute-in-memory architectures. In this paper, we present work-in-progress on accelerating filtering using a commercial compute-in-SRAM accelerator. We leverage the recently released Gemini accelerator platform from GSI Technology, which is the first, to our knowledge, commercial-scale compute-in-SRAM system. We accelerate the Myers' bit-parallel edit distance algorithm, producing average speedups of 14.1x over single-core CPU performance. Individual query/candidate alignments produce speedups of up to 24.1x. These early results suggest this novel architecture is well-suited to accelerating the filtering step of sequence-to-sequence DNA alignment.

翻译：DNA序列比对是计算基因组学中的重要工作负载。参考序列辅助的DNA组装涉及将大量读段序列与长参考基因组中的候选位置进行比对。为减少比对的计算负担，可使用编辑距离等简单比对算法对候选位置进行预过滤。先前研究已在模拟的存内DRAM架构上探索过滤加速技术，这得益于存内计算架构的大规模并行性。本文呈现基于商用存内SRAM加速器实现过滤加速的进行中工作。我们采用GSI Technology近期发布的Gemini加速器平台——据我们所知，这是首个达到商用规模的存内SRAM系统。我们加速了迈尔斯比特并行编辑距离算法，相较于单核CPU性能实现平均14.1倍加速。单个查询/候选比对可获得最高24.1倍加速。这些初步结果表明，该新型架构非常适合加速序列到序列DNA比对的过滤步骤。