GenPairX: A Hardware-Algorithm Co-Designed Accelerator for Paired-End Read Mapping
Julien Eudine, Chu Li, Zhuo Cheng, Renzo Andri, Can Firtina, Mohammad Sadrosadati, Nika Mansouri Ghiasi, Konstantina Koliogeorgi, Anirban Nag, Arash Tavakkol, Haiyu Mao, Onur Mutlu, Shai Bergman, Ji Zhang

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…
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Taxonomy
TopicsGenomics and Phylogenetic Studies · Algorithms and Data Compression · Network Packet Processing and Optimization
