MEDAL: Scalable DIMM based Near Data Processing Accelerator for DNA Seeding Algorithm
MEDAL: Scalable DIMM based Near Data Processing Accelerator for DNA Seeding Algorithm
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DOI：
10.1145/3352460.3358329
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发表时间：
2019-10
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影响因子：
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通讯作者：
Wenqin Huangfu;Xueqi Li;Shuangchen Li;Xing Hu;P. Gu;Yuan Xie
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作者：
Wenqin Huangfu;Xueqi Li;Shuangchen Li;Xing Hu;P. Gu;Yuan Xie
计算基因组学已证明其在支持精确和定制医疗保健方面的巨大潜力。然而，随着下一代测序（NGS）技术的广泛采用，“DNA比对”作为计算基因组学的关键步骤，由于生物数据的蓬勃发展而变得越来越具有挑战性。因此，人们已经探索了各种硬件方法来加速 DNA 播种——DNA 比对中的核心和最耗时的步骤。以前的大多数硬件方法都利用多核、GPU 和 FPGA 来加速 DNA 播种。然而，DNA 播种受到内存的限制，并且上述硬件方法侧重于计算。因此，近数据处理 (NDP) 是 DNA 播种的更好解决方案。不幸的是，现有的 DNA 播种 NDP 加速器面临两大挑战，即细粒度随机内存访问和蓬勃发展的生物数据的可扩展性需求。为了应对这些挑战，我们提出了一种实用、节能、基于双列直插内存模块 (DIMM) 的 DNA 播种算法 (MEDAL) 的 NDP 加速器，该加速器基于现成的 DRAM 组件。对于可以安装在单个 DRAM 列中的小型数据库，我们提出了列内设计，以及特定于算法的地址映射、带宽感知数据映射和单独芯片选择 (ICS)，以解决细粒度随机存储器访问的挑战，提高并行性和带宽利用率。此外，为了应对大型数据库的可扩展性挑战，我们提出了三种列间设计（基于轮询的通信、基于中断的通信和基于非易失性 DIMM (NVDIMM) 的解决方案）。此外，我们提出了一种特定于算法的数据压缩技术，以减少内存占用，为数据映射引入更多空间，并减少通信开销。实验结果表明，对于三种提出的设计，与 16 线程 CPU 基准和两个最先进的 NDP 加速器相比，MEDAL 平均可以分别实现 30.50x/8.37x/3.43x 的加速和 289.91x/6.47x/2.89x 的能耗降低。
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.