图分区质量极大程度上影响着计算机之间的通信开销和负载平衡, 这对于大规模并行图计算的性能是至关重要的. 然而, 随着图数据规模的越来越大, 图分区算法的执行时间成为了一个不可避免的问题. 因此, 研究如何优化图分区算法的执行效率是有必要的. 本文提出了一个基于广度优先遍历加权图生成的启发式图分割方法, 该方法在实现较低的通信代价和较好负载平衡的同时, 只引入了少量的预处理时间开销. 实验结果表明, 本文的划分方法减少了复制因子, 降低通信开销, 并且引入的时间开销较小.
The quality of graph partitioning greatly affects the communication overhead and load balance among computers, which is crucial for the performance of large-scale parallel graph computation. However, as the scale of graph data continues to increase, the execution time and memory overhead of graph partitioning algorithms have become inevitable. Therefore, it is necessary to study how to optimize the execution efficiency of graph partitioning algorithms. This study proposes a heuristic graph partitioning method based on weighted graph generation by breadth-first traversal, which introduces only a small amount of preprocessing time overhead while achieving lower communication overhead and better load balance. Experimental results show that our partitioning method reduces replication factors, lowers communication overhead, and only introduces a small amount of time overhead.