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. To address these challenges, we propose GMX, a set of ISA extensions that enable efficient sequence alignment computations based on dynamic programming (DP). GMX extensions provide the basic building-block operations to perform fast tile-wise computations of the DP matrix, reducing the memory footprint and allowing easy integration into widely-used algorithms and tools. Furthermore, we provide an efficient hardware implementation that integrates GMX extensions in a RISC-V-based edge system-on-chip (SoC). Compared to widely-used software implementations, our hardware-software co-design leveraging GMX extensions obtains speed-ups from 25–265 ×, scaling to megabyte-long sequences. Compared to domain-specific accelerators (DSA), we demonstrate that GMX-accelerated implementations demand significantly less memory bandwidth, requiring less area per processing element (PE). As a result, a single GMX-enabled core achieves a throughput per area between 0.35-0.52 × that of state-of-the-art DSAs while being more flexible and reusing the core’s resources. Post-place-and-route results for a GMX-enhanced SoC in 22nm technology shows that GMX extensions only account for 1.7% of the overall area while consuming just 8.47mW. We conclude that GMX extensions represent versatile and scalable ISA additions to improve the performance of genome analysis tools and other use cases that require fast and efficient sequence alignment.

The ever-increasing yields in genome sequence data production pose a computational challenge to current genome sequence analysis tools, jeopardizing the future of personalized medicine. Leveraging hardware accelerators (GPUs, FPGAs, and ASICs) to accelerate computationally-intensive algorithms like sequence alignment has become paramount. Recently, the wavefront alignment algorithm was introduced, significantly reducing the execution time to perform sequence alignment. This paper presents the first-ever ASIC accelerator of the WFA integrated into a RISC-V system-on-chip. Our designed chip greatly accelerates sequence alignment, delivering up to 1076 × better performance over the CPU implementation of the WFA running on the RISC-V core of the chip.

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.

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. Using QuickEd’s bound-and-align strategy, we reduce O ( n ² ) complexity of traditional dynamic programming algorithms to O ( nŝ ), where n is the sequence length and ŝ 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 1.6 − 7.3× and 2.1 − 2.5×, respectively, aligning long and noisy datasets. In addition, QuickEd maintains a stable memory footprint below 50 MB while aligning sequences up to 1 Mbp. Availability: QuickEd code and documentation are publicly available at https://github.com/maxdoblas/QuickEd . Contact: max.doblas@bsc.es