GenPairX: A Hardware-Algorithm Co-Designed Accelerator for Paired-End Read Mapping

TL;DR

GenPairX is a hardware-accelerated system that significantly speeds up paired-end read mapping in genome sequencing by using a novel filtering algorithm and specialized hardware, improving throughput and efficiency without losing accuracy.

Contribution

It introduces a co-designed hardware-algorithm approach with a new filtering method that jointly considers read pairs, enhancing performance in genome analysis.

Findings

Achieves 1575x higher throughput per watt than CPU-based solutions.

Delivers 1.43x higher throughput per watt compared to existing accelerators.

Maintains accuracy while significantly improving performance.

Abstract

Genome sequencing has become a central focus in computational biology. A genome study typically begins with sequencing, which produces millions to billions of short DNA fragments known as reads. Read mapping aligns these reads to a reference genome. Read mapping for short reads comes in two forms: single-end and paired-end, with the latter being more prevalent due to its higher accuracy and support for advanced analysis. Read mapping remains a major performance bottleneck in genome analysis due to expensive dynamic programming. Prior efforts have attempted to mitigate this cost by employing filters to identify and potentially discard computationally expensive matches and leveraging hardware accelerators to speed up the computations. While partially effective, these approaches have limitations. In particular, existing filters are often ineffective for paired-end reads, as they evaluate each read independently and exhibit relatively low filtering ratios. In this work, we propose GenPairX, a hardware-algorithm co-designed accelerator that efficiently minimizes the computational load of paired-end read mapping while enhancing the throughput of memory-intensive operations. GenPairX introduces: (1) a novel filtering algorithm that jointly considers both reads in a pair to improve filtering effectiveness, and a lightweight alignment algorithm to replace most of the computationally expensive dynamic programming operations, and (2) two specialized hardware mechanisms to support the proposed algorithms. Our evaluations show that GenPairX delivers substantial performance improvements over state-of-the-art solutions, achieving 1575x and 1.43x higher throughput per watt compared to leading CPU-based and accelerator-based read mappers, respectively, all without compromising accuracy.