(Presenting on Wed. 10/30/2019)
Abstract:
Computational genomics has proven its great potential to support precise and customized health care. However, with the wide adoption of the Next Generation Sequencing (NGS) technology, `DNA Alignment', as the crucial step in computational genomics, is becoming more and more challenging due to the booming bio-data. Consequently, various hardware approaches have been explored to accelerate DNA seeding - the core and most time consuming step in DNA alignment.
Most previous hardware approaches leverage multi-core, GPU, and FPGA to accelerate DNA seeding. However, DNA seeding is bounded by memory and above hardware approaches focus on computation. For this reason, Near Data Processing (NDP) is a better solution for DNA seeding. Unfortunately, existing NDP accelerators for DNA seeding face two grand challenges, i.e., fine-grained random memory access and scalability demand for booming bio-data. To address those challenges, we propose a practical, energy efficient, Dual-Inline Memory Module (DIMM) based, NDP Accelerator for DNA Seeding Algorithm (MEDAL), which is based on off-the-shelf DRAM components. For small databases that can be fitted within a single DRAM rank, we propose the intra-rank design, together with an algorithm-specific address mapping, bandwidth-aware data mapping, and Individual Chip Select (ICS) to address the challenge of fine-grained random memory access, improving parallelism and bandwidth utilization. Furthermore, to tackle the challenge of scalability for large databases, we propose three inter-rank designs (polling-based communication, interrupt-based communication, and Non-Volatile DIMM (NVDIMM)-based solution). In addition, we propose an algorithm-specific data compression technique to reduce memory footprint, introduce more space for the data mapping, and reduce the communication overhead. Experimental results show that for three proposed designs, on average, MEDAL can achieve 30.50x/8.37x/3.43x speedup and 289.91x/6.47x/2.89x energy reduction when compared with a 16-thread CPU baseline and two state-of-the-art NDP accelerators, respectively.
Bio of
Wenqin Huangfu:
Wenqin Huangfu is a fourth year Ph.D.
student in University of California at Santa Barbara. His advisor is Professor.
Yuan Xie.
Wenqin’s
research interests include domain-specific accelerator, Processing-In-Memory
(PIM), Near-Data-Processing (NDP), and emerging memory technology. Currently,
Wenqin is focusing on hardware acceleration of bioinformatics with PIM and NDP
technology.
