TrioSeq: A Novel Approach to Accelerate Triplet Sequence Alignment on GPUs
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917540433756160 |
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| author | Graça, Miguel Ilic, Aleksandar |
| author_facet | Graça, Miguel Ilic, Aleksandar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_28400 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TrioSeq: A Novel Approach to Accelerate Triplet Sequence Alignment on GPUs Graça, Miguel Ilic, Aleksandar Distributed, Parallel, and Cluster Computing 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. |
| title | TrioSeq: A Novel Approach to Accelerate Triplet Sequence Alignment on GPUs |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2605.28400 |