PIM-Align:A Processing-in-Memory Architecture for FM-Index Search Algorithm

• 作者: Xue-Qi Li Guang-Ming Tan Ning-Hui Sun
• 来源期刊: 计算机科学技术学报（英文版）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: Special Section on Memory-Centric System Research for High-Performance Computing
• 页码范围: 56-70
• 页数: 15页
• 语种: 英文

摘要

Genomic sequence alignment is the most critical and time-consuming step in genomic analysis.Alignment al-gorithms 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 se-quence alignment.Our evaluation shows that the proposed architecture can achieve 20x and 1820x speedup when compared with the best available ASIC implementation and the software running on 32-thread CPU,respectively.