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Xue-Qi Li, Student, Guang-Ming Tan, Ning-Hui Sun. PIM-Align: A Processing-in-Memory Architecture for FM-Index Search Algorithm[J]. Journal of Computer Science and Technology, 2021, 36(1): 56-70. DOI: 10.1007/s11390-020-0825-3
Citation: Xue-Qi Li, Student, Guang-Ming Tan, Ning-Hui Sun. PIM-Align: A Processing-in-Memory Architecture for FM-Index Search Algorithm[J]. Journal of Computer Science and Technology, 2021, 36(1): 56-70. DOI: 10.1007/s11390-020-0825-3

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

  • 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.
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