Technical Perspective: Toward a Generalized Accelerator for Genome Sequence Analysis

Dynamic programming (DP), first introduced by Richard Bellman in the 1950s, is a fundamental algorithmic technique in computer science with broad applicability across numerous domains. In computational genomics, it has proven particularly effective for sequence-comparison tasks, including those involving DNA, RNA, and proteins.

Over the past two decades, advancements in sequencing technologies have massively reduced costs and increased throughput, leading to rapid growth in genomic data. This growth has supported various advances in biology and medicine, including studies on species evolution and the genetic basis of human diseases. At the same time, it has motivated computer architects to develop hardware accelerators, such as application-specific integrated circuits (ASICs), to address the computational demands of genomics applications, such as base-calling, genome assembly, and pathogen detection.

Due to their computational intensity and centrality to genome sequence analysis, DP algorithms have been a major focus of these accelerators, including commercial solutions. For example, Nvidia recently introduced DPX instructions on Hopper GPUs to optimize these workloads. However, many previous ASIC solutions were narrowly tailored to specific tasks, limiting their applicability to other computational problems even within the DP domain. This inflexibility creates challenges for broader commercial adoption due to the high costs and risks associated with ASIC chip development.

Motivated by this observation, “GenDP: A Framework of Dynamic Programming Acceleration for Genome Sequencing Analysis” by Gu et al. aims to improve the adaptability of accelerators in genome sequence analysis through a novel framework called GenDP. The authors make an insightful observation that DP kernels used in genomics exhibit limited structural variation, characterized by three key parameters:

1. The inter-cell dependency pattern (for example, 1-D or 2-D DP tables) as described in the recursion equations.

2. The objective function, which defines the solution space and scoring criteria.

3. Numerical types and precision requirements, which vary across DP algorithms

Their proposed GenDP framework comprises two main components: DPAx, a programmable DP accelerator, and DPMap, a graph-partitioning algorithm for mapping high-level DP kernel specifications to DPAx hardware.

The DPAx accelerator uses linear systolic arrays of programmable compute units, supporting diverse objective functions and multi-precision arithmetic while leveraging the wavefront parallelism inherent in DP tables. The programmability of these compute units is enabled by a custom GenDP instruction set architecture (ISA), comprising control and compute instructions specifically designed for DP kernels. The compute units within systolic arrays have flexible interconnections to allow for different dependency patterns in DP algorithms. The DPMap algorithm serves as a compiler for the DPAx accelerator, processing high-level DP specifications represented as data-flow graphs—where nodes denote operations and edges define dependencies—and generating compute instructions for the computational units (control instructions were manually generated in this work). Benchmark results show that GenDP delivers significant improvements in throughput per area compared to general-purpose CPUs and GPUs, although the increased flexibility also results in approximately three-fold slowdown compared to fixed-function ASICs. Nevertheless, this trade-off between specialization and generality may provide a more practical balance for dynamic programming acceleration.

While DP algorithms continue to hold high relevance in genome sequence analysis, the field also includes other methods, such as non-DP approaches, that play a critical role in tasks such as taxonomic classification and protein structure prediction. The diversity of computational requirements makes designing a universal accelerator challenging. Nevertheless, GenDP offers an important step toward addressing this need. Future work could focus on expanding support for the emerging need for comparing arbitrarily long sequences and incorporating non-DP algorithms into a more unified framework. Such developments might eventually lead to a domain-specific processor for bioinformatics, which could help unlock new opportunities for genomics research and beyond.