NEST: DIMM based Near-Data-Processing Accelerator for K-mer Counting

With the ability to help wildlife conservation, precise medical care, and disease understanding, genomics analysis is becoming more and moe important. Recently, with the development and wide adoption of the Next-Generation Sequencing (NGS) technology, bio-data grows exponentially, putting forward great challenges for k-mer counting - a widely used application in genomics analysis. Many hardware approaches have been explored to accelerate k-mer counting. Most of those approaches are compute-centric, i.e., based on CPU/GPU/FPGA. However, the space for performance improvement is limited for compute-centric accelerators, because k-mer counting is a memory-bound application. By integrating memory and computation close together and embracing higher memory bandwidth, Near-Data-Processing (NDP) is a good candidate to accelerate k-mer counting. Unfortunately, due to challenges of communication, bandwidth utilization, workload balance, and redundant memory accesses, previous NDP accelerators for k-mer counting cannot fully unleash the power of NDP. To build a practical, scalable, high-performance, and energy-efficient NDP accelerator for k-mer counting, we perform hardware/software co-design and propose the DIMM based Near-Data-Processing Accelerator for k-mer counting (NEST). To fully unleash the potential of NEST architecture, we modify the k-mer counting algorithm and propose a dedicated workflow to support efficient parallelism. Moreover, the proposed algorithm and workflow are able to reduce unnecessary inter-DIMM communication. To improve memory bandwidth utilization, we propose a novel address mapping scheme. The challenge of workload balance is addressed with the proposed task scheduling technique. In addition, scattered memory access and task switching are proposed to eliminate redundant memory access. Experimental results show that NEST provides 677.33x/27.24x/6.02x performance improvement and 1076.14x/62.26x/4.30x energy reduction, compared with a 48-thread CPU, a CPU/GPU hybrid approach, and a state-of-the-art NDP accelerator, respectively.