PAGE: A Partition Aware Graph Computation Engine

Graph partitioning is one of the key components in parallel graph computation, and the partition quality significantly affects the overall computing performance. In the existing graph computing systems, "good" partition schemes are preferred as they have smaller edge cut ratio and hence reduce the communication cost among working nodes. However, in an empirical study on Giraph[1], we found that the performance over well partitioned graph might be even two times worse than simple partitions. The cause is that the local message processing cost in graph computing systems may surpass the communication cost in several cases. In this paper, we analyse the cost of parallel graph computing systems as well as the relationship between the cost and underlying graph partitioning. Based on these observation, we propose a novel Partition Aware Graph computation Engine named PAGE. PAGE is equipped with two newly designed modules, i.e., the communication module with a dual concurrent message processor, and a partition aware one to monitor the system's status. The monitored information can be utilized to dynamically adjust the concurrency of dual concurrent message processor with a novel Dynamic Concurrency Control Model (DCCM). The DCCM applies several heuristic rules to determine the optimal concurrency for the message processor. We have implemented a prototype of PAGE and conducted extensive studies on a moderate size of cluster. The experimental results clearly demonstrate the PAGE's robustness under different graph partition qualities and show its advantages over existing systems with up to 59% improvement.