GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Processing - Zou, Kai; Xie, Xike; Li, Qi; Kong, Deyu (Data engineering, 2022)
Recently, research communities highlight the neces-sity of formulating a scalability continuum for large-scale graph processing, which gains the scale-out benefits from distributed graph systems, and the scale-up benefits from high-performance accelerators. To this end, we propose a middleware, called the GX-plug, for the ease of integrating the merits of both. As a middleware, the GX-plug is versatile in supporting different runtime environments, computation models, and programming models. More, for improving the middleware performance, we study a series of techniques, including pipeline shuffle, synchro-nization caching and skipping, and workload balancing, for intra-, inter-, and beyond-iteration optimizations, respectively. Exper-iments show that our middleware efficiently plugs accelerators to representative distributed graph systems, e.g., GraphX and Powergraph, with up-to 20x acceleration ratio.