Genomic sequence alignment is the most critical and time-consuming step in genomic analysis. Alignment algorithms generally follow a seed-and-extend model. Acceleration of the extension phase for sequence alignment has been well explored in computing-centric architectures on field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), and graphics processing unit (GPU) (e.g., the Smith-Waterman algorithm). Compared with the extension phase, the seeding phase is more critical and essential. However, the seeding phase is bounded by memory, i.e., fine-grained random memory access and limited parallelism on conventional system. In this paper, we argue that the processing-in-memory (PIM) concept could be a viable solution to address these problems. This paper describes \PIM-Align"|an application-driven near-data processing architecture for sequence alignment. In order to achieve memory-capacity proportional performance by taking advantage of 3D-stacked dynamic random access memory (DRAM) technology, we propose a lightweight message mechanism between different memory partitions, and a specialized hardware prefetcher for memory access patterns of sequence alignment. Our evaluation shows that the proposed architecture can achieve 20x and 1 820x speedup when compared with the best available ASIC implementation and the software running on 32-thread CPU, respectively.

Key words: accelerator design; genomic sequence alignment; near-memory computing;