BC-BSP: A BSP-Based System with Disk Cache for Large-Scale Graph Processing

Many applications in real life can be modeled by Graph, and the data scale is very large in many fields. People have paid more attention to large-scale graph processing. A BSP-based system with disk cache for large-scale graph processing is proposed in this paper. The system has the ability to expand the functions and strategies (such as adjusting the parameters according to the volume of data and supporting multiple aggregation functions at the same time), to process large-scale data, to balance load, and to run clustering or classification algorithms on metric datasets. Some experiments are done to evaluate the scalability of the system implemented in the paper, and the comparison between BC-BSP-based applications and MapReduce-based ones are made. The experimental results show that BSP-based applications have higher efficiency than the MapReduce-based applications when the volume of data can be put in the memory during the course of processing; on the contrary the latter is better than the former.