A Customized Memory-aware Architecture for Biological Sequence Alignment (2507.22221v1)

Abstract: Sequence alignment is a fundamental process in computational biology which identifies regions of similarity in biological sequences. With the exponential growth in the volume of data in bioinformatics databases, the time, processing power, and memory bandwidth for comparing a query sequence with the available databases grows proportionally. The sequence alignment algorithms often involve simple arithmetic operations and feature high degrees of inherent fine-grained and coarse-grained parallelism. These features can be potentially exploited by a massive parallel processor, such as a GPU, to increase throughput. In this paper, we show that the excessive memory bandwidth demand of the sequence alignment algorithms prevents exploiting the maximum achievable throughput on conventional parallel machines. We then propose a memory-aware architecture to reduce the bandwidth demand of the sequence alignment algorithms, effectively pushing the memory wall to extract higher throughput. The design is integrated at the logic layer of an emerging 3D DRAM as a processing-in-memory architecture to further increase the available bandwidth. The experimental results show that the proposed architecture results in up to 2.4x speedup over a GPU-based design. Moreover, by moving the computation closer to the memory, power consumption is reduced by 37%, on average.