GX-Plug：一个支持加速器的分布式图形处理中间件
GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Processing
摘要：
最近，研究界强调了制定大规模图处理的可扩展性连续体的必要性，它可以从分布式图系统中获得横向扩展的好处，并从高性能加速器中获得纵向扩展的好处。为此，我们提出了一个中间件，称为GX插件，便于集成两者的优点。作为一个中间件，GX插件在支持不同的运行时环境、计算模型和编程模型方面是通用的。此外，为了提高中间件的性能，我们研究了一系列的技术，包括流水线洗牌，同步缓存和跳过，和工作负载平衡，分别为内，间，和超越迭代优化。实验表明，我们的中间件有效地插件加速器代表分布式图形系统，例如，GraphX和Powergraph，加速比高达20倍。
英文摘要：
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.
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