Sequence alignment is a fundamental building block for critical applications across multiple fields, such as computational biology and information retrieval.The rapid advancement of genome sequencing technologies and breakthrough generative AI tools, like AlphaFold, has driven an exponential increase in sequencedata production, creating a pressing need for fast and efficient sequence alignment tools to analyze ever-growing biological sequence databases.Notwithstanding the numerous accelerators proposed, from general-purpose architectures (CPUs and GPUs) to domainspecific designs (FPGAs and ASICs), the most efficient solutions suffer from over-specialization and fail to adapt to the wide variety of irregular use cases demanded by practical sequence alignment applications.Thus, it remains a challenge to design an architecture that can balance efficiency and flexibility to meet the demands of real-world alignment applications.This work introduces SMX, a heterogeneous architecture designed for high-performance sequence alignment that supports various configurations for different sequence types (DNA, protein, and ASCII text) and alignment models (including weighted gaps and substitution matrices).SMX integrates an ISA extension (SMX-1D) for irregular and sequential tasks and a specialized coprocessor (SMX-2D) to accelerate regular and parallel tasks, both orchestrated by the general-purpose core to enable seamless integration with state-of-the-art sequence alignment

MOTIVATION: Pairwise sequence alignment is a core component of multiple sequencing-data analysis tools. Recent advancements in sequencing technologies have enabled the generation of longer sequences at a much lower price. Thus, long-read sequencing technologies have become increasingly popular in sequencing-based studies. However, classical sequence analysis algorithms face significant scalability challenges when aligning long sequences. As a result, several heuristic methods have been developed to improve performance at the expense of accuracy, as they often fail to produce the optimal alignment. RESULTS: This paper introduces QuickEd, a sequence alignment algorithm based on a bound-and-align strategy. First, QuickEd effectively bounds the maximum alignment-score using efficient heuristic strategies. Then, QuickEd utilizes this bound to reduce the computations required to produce the optimal alignment. Compared to O(n2) complexity of traditional dynamic programming algorithms, QuickEd's bound-and-align strategy achieves O(ns^) complexity, where n is the sequence length and s^ is an estimated upper bound of the alignment-score between the sequences. As a result, QuickEd is consistently faster than other state-of-the-art implementations, such as Edlib and BiWFA, achieving performance speedups of 4.2-5.9× and 3.8-4.4×, respectively, aligning long and noisy datasets. In addition, QuickEd maintains a stable memory footprint below 35 MB while aligning sequences up to 1 Mbp. AVAILABILITY AND IMPLEMENTATION: QuickEd code and documentation are publicly available at https://github.com/maxdoblas/QuickEd.

Sequence alignment remains a fundamental problem in computer science with practical applications ranging from pattern matching to computational biology. The ever-increasing volumes of genomic data produced by modern DNA sequencers motivate improved software and hardware sequence alignment accelerators that scale with longer sequence lengths and high error rates without losing accuracy. Furthermore, the wide variety of use cases requiring sequence alignment demands flexible and efficient solutions that can match or even outperform expensive application-specific accelerators.