Efficient Memory Layout for Pre-Alignment Filtering of Long DNA Reads Using Racetrack Memory

Abstract

DNA sequence alignment is a fundamental and computationally expensive operation in bioinformatics. Researchers have developed pre-alignment filters that effectively reduce the amount of data consumed by the alignment process by discarding locations that result in a poor match. However, the filtering operation itself is memory-intensive for which the conventional Von-Neumann architectures perform poorly. Therefore, recent designs advocate compute near memory (CNM) accelerators based on stacked DRAM and more exotic memory technologies such as racetrack memories (RTM). However, these designs only support small DNA reads of circa 100 nucleotides, referred to as short reads . This letter proposes a CNM system for handling both long and short reads. It introduces a novel data-placement solution that significantly increases parallelism and reduces overhead. Evaluation results show substantial reductions in execution time ( $1.32\times$ ) and energy consumption (50%), compared to the state-of-the-art.