TrioSeq: A Novel Approach to Accelerate Triplet Sequence Alignment on GPUs

2026-05-27

AI Summary

This work addresses the inefficiency of existing GPU-based methods for exact triplet sequence alignment, which are often closed-source, vendor-specific, and unable to leverage emerging hardware features. To overcome these limitations, the authors propose TrioSeq, the first open-source, cross-vendor GPU framework for triplet alignment. TrioSeq introduces fine-grained parallelism, inter-thread synchronization mechanisms, and an optimized triplet dynamic programming algorithm that fully exploits modern GPU hardware capabilities—such as cross-thread intrinsics—for the first time. Evaluated on simulated genomic datasets, TrioSeq achieves at least a 20% performance improvement over state-of-the-art GPU progressive alignment methods on both NVIDIA and AMD GPUs, thereby surpassing the constraints inherent in traditional pairwise sequence alignment approaches.

Abstract

State-of-the-art multiple sequence alignment (MSA) algorithms are based on progressive approaches that rely on pairwise sequence alignment (PSA) to generate guide trees to align all sequences. Given an evidenced explosion in genomic data availability, research efforts have focused on accelerating PSA on massively-parallel architectures (e.g., GPUs) and specialized hardware (e.g., FPGAs). However, there is increasing evidence that starting from exact 3-way alignments could provide more robust, accurate MSAs, and improve genomic analysis. While the current literature has shown that PSA algorithms can be extended to align sequence triplets, the existent state-of-the-art on hardware acceleration of exact 3-way alignments is still scarce. In particular, current GPU methods are still inefficient due to lacking support for novel hardware features (e.g., cross-thread intrinsics), while being closed-source and vendor-specific. In this paper, TrioSeq is proposed as a fine-grained strategy to efficiently implement 3-way alignments on GPUs, leveraging novel levels of GPU parallelism and synchronization to achieve high throughput in aligning sequence triplets. Evaluation on NVIDIA and AMD GPUs shows that TrioSeq outperforms state-of-the-art GPU progressive methods on 3-way alignment by at least 20% on simulated genomic datasets.

Problem

• triplet sequence alignment
• GPU acceleration
• multiple sequence alignment
• hardware acceleration
• genomic data

Innovation

• TrioSeq
• triplet sequence alignment
• GPU acceleration
• fine-grained parallelism
• cross-thread intrinsics