HyGraph: Accelerating Graph Processing with Hybrid Memory-centric Computing

Graph applications are challenging to run efficiently on conventional systems because of their large and irregular data. Several works have exploited near-data processing (NDP) based on emerging 3D-stacked memory to accelerate graph processing applications by offloading computations to massively parallel cores in the memory chip. Even though NDP can efficiently support parallel operations in a memory scalable way, it still requires data movement between memory and near-memory cores. Such data movement introduces large overhead because of the random data pattern in graph workloads. Furthermore, the parallelism provided by NDP systems is still insufficient for graph applications because of the limited number of processing cores. In this work, we tackle these challenges by integrating processing in-memory (PIM) technology in the NDP-based accelerator. We propose HyGraph, a software-hardware co-design for graph acceleration that exploits hybrid memory-centric computing technologies, including NDP and PIM. The design of HyGraph includes an optimization algorithm for hybrid memory layout, a run-time system combining both NDP and PIM processing flows, and customized hardware for efficiently enabling PIM functionality in NDP systems. Our experimental results show that HyGraph is up to 1.9× faster and 2.4× more energy-efficient than state-of-the-art memory-centric graph accelerators on several widely used graph algorithms with various real-world graphs.