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

2026-01-27

AI Summary

This work addresses the performance bottleneck in genomic analysis caused by the computationally intensive dynamic programming required for paired-end sequencing read alignment. Existing filtering methods struggle to effectively handle read pairs, limiting their efficiency. To overcome this challenge, the authors propose an algorithm-hardware co-designed accelerator that introduces a novel dual-end joint filtering mechanism coupled with a lightweight alignment strategy, significantly reducing the number of dynamic programming invocations. The accompanying custom hardware architecture maintains high alignment accuracy while achieving a 1,575× improvement in throughput per watt over mainstream CPUs and a 1.43× gain compared to state-of-the-art accelerators.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Privacy-enhancing technologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• paired-end read mapping
• computational bottleneck
• read alignment
• filtering efficiency
• genome sequencing

Innovation

• hardware-algorithm co-design
• paired-end read mapping
• joint filtering
• lightweight alignment
• genome sequencing accelerator