GenASM：一个高性能、低功耗的基因组序列近似字符串匹配加速框架
GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis
摘要：
基因组序列分析使医学和科学领域取得了重大进展，如个性化医疗，疫情追踪和对进化的理解。不幸的是，它目前受到现有系统的计算能力和内存带宽限制的制约，因为基因组序列分析中的许多步骤必须处理大量数据。造成此瓶颈的主要原因是近似字符串匹配（ASM）。我们提出了GenASM，第一个ASM加速框架的基因组序列分析。我们修改底层ASM算法（Bitap），以显着提高其并行性，减少其内存占用，我们设计的第一个硬件加速器Bitap。我们的硬件加速器由专门的计算单元和片上SRAM组成，旨在将计算速率与内存容量和带宽相匹配。我们证明了GenASM是一个灵活、高性能和低功耗的框架，它为基因组序列分析中的三种不同用例提供了显著的性能和功耗优势：1）GenASM加速了长读段和短读段的读段比对。对于长时间读取，GenASM的性能分别是最先进的软件和硬件加速器的116倍和3.9倍，而功耗则分别降低37倍和2.7倍。对于短读取，GenASM的性能比最先进的软件和硬件加速器高出111倍和1.9倍。2)GenASM加速了短读段的预比对过滤，性能是最先进的预比对过滤器的3.7倍，同时功耗降低了1.7倍，并显著提高了过滤精度。3)GenASM加速了编辑距离计算，与最先进的软件库和基于FPGA的加速器相比，分别具有22- 12501倍和9.3- 400倍的加速比，同时功耗降低了548- 582倍和67倍。
英文摘要：
Genome sequence analysis has enabled significant advancements in medical and scientific areas such as personalized medicine, outbreak tracing, and the understanding of evolution. Unfortunately, it 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). We propose GenASM, the first ASM acceleration framework for genome sequence analysis. We modify the underlying ASM algorithm (Bitap) to significantly increase its parallelism and reduce its memory footprint, and we design the first hardware accelerator for Bitap. Our hardware accelerator consists of specialized compute units and on-chip SRAMs that are designed to match the rate of computation with memory capacity and bandwidth. We demonstrate that GenASM is a flexible, high-performance, and low-power framework, which provides significant performance and power benefits for three different use cases in genome sequence analysis: 1) 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 116x and 3.9x, respectively, while consuming 37x and 2.7x less power. For short reads, GenASM outperforms state-of-the-art software and hardware accelerators by 111x and 1.9x. 2) GenASM accelerates pre-alignment filtering for short reads, with 3.7x the performance of a state-of-the-art pre-alignment filter, while consuming 1.7x less power and significantly improving the filtering accuracy. 3) GenASM accelerates edit distance calculation, with 22-12501x and 9.3-400x speedups over the state-of-the-art software library and FPGA-based accelerator, respectively, while consuming 548-582x and 67x less power.
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