论文标题
GX-PLUG：用于插入加速器以分布式图形处理的中间件
GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Processing
论文作者
论文摘要
最近，研究社区强调了为大型图形处理制定可扩展性连续性的必要性，该处理能够从分布式图系统中获得扩展利益，而扩大范围则受益于高性能加速器。为此，我们提出了一种称为GX-Plug的中间件，以便于两者的优点。作为中间件，GX插头在支持不同的运行时环境，计算模型和编程模型方面具有多功能性。更重要的是，为了提高中间件性能，我们研究了一系列技术，包括管道洗牌，同步缓存和跳过，以及工作负载平衡，分别用于内部，间和超越idirate的优化。实验表明，我们的中间件有效地将加速器插入了代表性的分布式图系统，例如GraphX和PowerGraph，具有最高的加速度比率。
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.
