Dynamic Load Balancing for High-Performance Graph Processing on Hybrid CPU-GPU Platforms.

Graph analysis is becoming increasingly important in many research fields - biology, social sciences, data mining - and daily applications - path finding, product recommendation. Many different large-scale graph-processing systems have been proposed for different platforms. However, little effort has been placed on designing systems for hybrid CPU-GPU platforms. In this work, we present HyGraph, a novel graph-processing systems for hybrid platforms which delivers performance by using CPUs and GPUs concurrently. Its core feature is a specialized data structure which enables dynamic scheduling of jobs onto both the CPU and the GPUs, thus (1) supersedes the need for static workload distribution, (2) provides load balancing, and (3) minimizes inter-process communication overhead by overlapping computation and communication. Our preliminary results demonstrate that HyGraph outperforms CPU-only and GPU-only solutions, delivering close-to-optimal performance on the hybrid system. Moreover, it supports large-scale graphs which do not fit into GPU memory, and it is competitive against state-of-the-art systems.