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

Damla Senol Cali, Gurpreet S. Kalsi, Zulal Bingol, Can Firtina, Lavanya Subramanian, Jeremie S. Kim, Rachata Ausavarungnirun, Mohammed Alser, Juan Gomez-Luna, Amirali Boroumand, Anant Norion, Allison Scibisz (+4 others)
2020 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)  
Genome sequence analysis has enabled signi cant advancements in medical and scienti c areas such as personalized medicine, outbreak tracing, and the understanding of evolution. To perform genome sequencing, devices extract small random fragments of an organism's DNA sequence (known as reads). The rst step of genome sequence analysis is a computational process known as read mapping. In read mapping, each fragment is matched to its potential location in the reference genome with the goal of
more » ... fying the original location of each read in the genome. Unfortunately, rapid genome sequencing is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data. A major contributor to this bottleneck is approximate string matching (ASM), which is used at multiple points during the mapping process. ASM enables read mapping to account for sequencing errors and genetic variations in the reads. We propose GenASM, the rst ASM acceleration framework for genome sequence analysis. GenASM performs bitvectorbased ASM, which can e ciently accelerate multiple steps of genome sequence analysis. We modify the underlying ASM algorithm (Bitap) to signi cantly increase its parallelism and reduce its memory footprint. Using this modi ed algorithm, we design the rst hardware accelerator for Bitap. Our hardware accelerator consists of specialized systolic-array-based compute units and on-chip SRAMs that are designed to match the rate of computation with memory capacity and bandwidth, resulting in an e cient design whose performance scales linearly as we increase the number of compute units working in parallel. We demonstrate that GenASM provides signi cant performance and power bene ts for three di erent use cases in genome sequence analysis. First, GenASM accelerates read alignment for both long reads and short reads. For long reads, GenASM outperforms state-of-the-art software and hardware accelerators by 116× and 3.9×, respectively, while reducing power consumption by 37× and 2.7×. For short reads, GenASM outperforms state-of-the-art software and hardware accelerators by 111× and 1.9×. Second, GenASM accelerates pre-alignment ltering for short reads, with 3.7× the performance of a state-of-theart pre-alignment lter, while reducing power consumption by 1.7× and signi cantly improving the ltering accuracy. Third, GenASM accelerates edit distance calculation, with 22-12501× and 9.3-400× speedups over the state-of-the-art software library and FPGA-based accelerator, respectively, while reducing power consumption by 548-582× and 67×. We conclude that GenASM is a exible, high-performance, and low-power framework, and we brie y discuss four other use cases that can bene t from GenASM. 951 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)
doi:10.1109/micro50266.2020.00081 dblp:conf/micro/CaliKBFSKAAGBNS20 fatcat:q2mvhrltnfgczjso5q457go55i