LMCC: Lazy Message and Centralized Cache for Asynchronous Graph Computing.

Graph has been widely used in complex network applications modeling, and the asynchronous graph processing model is superceding the BSP model because of its better convergence speed. However, the asynchronous GAS model proposed by PowerGraph usually results in irregular and unpredictable communication patterns as well as vertex-scale barriers, so it is difficult for programmers to optimize codes. To address these challenges, we propose LMCC, an improved message management approach including lazy pull-message model and vertex-oriented centralized cache, which can reduce communication cost in terms of message quantity, and reduce the number of computation iterations in turn, without compromising the accuracy of application results. Based on the deep investigation of the GAS phases, LMCC is designed to be totally transparent to user applications. Experimental results show that LMCC can deliver speedup for various types of graph computing benchmarks ranging from 129% to 271%.