  Recently, research communities highlight the necessity 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, synchronization caching and skipping, and workload balancing, for
intra-, inter-, and beyond-iteration optimizations, respectively. Experiments
show that our middleware efficiently plugs accelerators to representative
distributed graph systems, e.g., GraphX and Powergraph, with up-to 20x
acceleration ratio.
