PnP: Pruning and Prediction for Point-To-Point Iterative Graph Analytics

Frequently used parallel iterative graph analytics algorithms are computationally expensive. However, researchers have observed that applications often require point-to-point versions of these analytics algorithms that are less demanding. In this paper we introduce the PnP parallel framework for iterative graph analytics that processes a stream of point-to-point queries with each involving a single source and destination vertex pair. The efficiency of our framework is derived from the following two novel features: online Pruning of graph exploration that eliminates propagation from vertices that are determined to not contribute to a query's final solution; and dynamic direction Prediction for solving the query in either forward (from source) or backward (from destination) direction as their costs can differ greatly. PnP employs a two-phase algorithm where, Phase 1 briefly traverses the graph in both directions to predict the faster direction and enable pruning; then Phase 2 completes query evaluation by running the algorithm for the chosen direction till it converges. Our experiments show that PnP responds to queries rapidly because of accurate direction selection and effective pruning that often offsets the runtime overhead of direction prediction. PnP substantially outperforms Quegel, the only other point-to-point query evaluation framework. Our experiments on multiple benchmarks and graphs show that PnP on a single machine is 8.2x to 3116x faster than Quegel on a cluster of four machines.