The scale of real-world graphs is constantly growing. To deal with large-scale graphs, distributed graph processing has attracted much research efforts. Existing distributed graph processing systems are commonly built on traditional TCP/IP communication stack, which leads to network bottleneck because of low bandwidth and heavy kernel stack operations. Meanwhile, in real power-law graphs, the average number of mirror vertices after graph partitioning is very large, resulting in significant communication overhead among nodes. The emerging high-performance Remote Direct Memory Access (RDMA) network has the features of low latency, high bandwidth, and low CPU overhead, which brings new opportunities for distributed graph processing systems. Existing RDMA-assisted graph processing systems focus on synchronous execution, which imposes barriers between consecutive iterations. Synchronous execution transfers bulk data among nodes and thus only needs a small number of network transfers. However, synchronous execution is usually less efficient than asynchronous execution because of bulk synchronization. Asynchronous execution accelerates graph processing by eliminating barriers, which in turn requires to transfer a large amount of small size data. In this paper, we propose RGraph, an RDMA-assisted asynchronous distributed graph processing system. RGraph distributes edges into two parts to isolate master and mirror vertices. RGraph exploits the asymmetry of RDMA to accelerate the one-to-many communication between master and mirror vertices. We implement RGraph on top of PowerGraph and conduct comprehensive experiments with large-scale real graphs to evaluate its performance. Results show that compared to existing designs, RGraph reduces the execution time by up to 81%.

中文翻译：

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RGraph：基于远程直接内存访问不对称的异步图处理

真实世界图的规模不断增长。为了处理大规模图，分布式图处理吸引了很多研究工作。现有的分布式图处理系统通常建立在传统的 TCP/IP 通信栈上，由于带宽低、内核栈操作繁重而导致网络瓶颈。同时，在实际幂律图中，图分割后的平均镜像顶点数非常大，导致节点之间的通信开销很大。新兴的高性能远程直接内存访问(RDMA)网络具有低延迟、高带宽和低CPU开销的特点，为分布式图处理系统带来了新的机遇。现有的 RDMA 辅助图形处理系统专注于同步执行，这在连续迭代之间设置了障碍。同步执行在节点之间传输大量数据，因此只需要少量的网络传输。但是，由于批量同步，同步执行的效率通常低于异步执行。异步执行通过消除障碍来加速图形处理，这反过来又需要传输大量的小尺寸数据。在本文中，我们提出了 RGraph，一种 RDMA 辅助的异步分布式图处理系统。RGraph 将边分成两部分以隔离主顶点和镜像顶点。RGraph 利用 RDMA 的不对称性来加速主节点和镜像节点之间的一对多通信。我们在 PowerGraph 之上实现 RGraph，并使用大规模真实图进行综合实验以评估其性能。结果表明，与现有设计相比，RGraph 将执行时间减少了高达 81%。