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Genome sequence analysis has enabled significant advancements in medical andscientific areas such as personalized medicine, outbreak tracing, and theunderstanding of evolution. Unfortunately, it is currently bottlenecked by thecomputational power and memory bandwidth limitations of existing systems, asmany of the steps in genome sequence analysis must process a large amount ofdata. A major contributor to this bottleneck is approximate string matching(ASM).  We propose GenASM, the first ASM acceleration framework for genome sequenceanalysis. We modify the underlying ASM algorithm (Bitap) to significantlyincrease its parallelism and reduce its memory footprint, and we design thefirst hardware accelerator for Bitap. Our hardware accelerator consists ofspecialized compute units and on-chip SRAMs that are designed to match the rateof computation with memory capacity and bandwidth.  We demonstrate that GenASM is a flexible, high-performance, and low-powerframework, which provides significant performance and power benefits for threedifferent use cases in genome sequence analysis: 1) GenASM accelerates readalignment for both long reads and short reads. For long reads, GenASMoutperforms state-of-the-art software and hardware accelerators by 116x and3.9x, respectively, while consuming 37x and 2.7x less power. For short reads,GenASM outperforms state-of-the-art software and hardware accelerators by 111xand 1.9x. 2) GenASM accelerates pre-alignment filtering for short reads, with3.7x the performance of a state-of-the-art pre-alignment filter, whileconsuming 1.7x less power and significantly improving the filtering accuracy.3) GenASM accelerates edit distance calculation, with 22-12501x and 9.3-400xspeedups over the state-of-the-art software library and FPGA-based accelerator,respectively, while consuming 548-582x and 67x less power.

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis