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-inSRAM system. We accelerate the Myers’ bit-parallel edit distance algorithm, producing average speedups of 14.1× over single-core CPU performance. Individual query/candidate alignments produce speedups of up to 24.1×. These early results suggest this novel architecture is well-suited to accelerating the filtering step of sequence-to-sequence DNA alignment.

Appears in the 5th Workshop on Accelerator Architecture in
Computational Biology and Bioinformatics (AACBB)
June 2023

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