We investigate how graph partitioning adversely affects the performance of graph analytics. We demonstrate that graph partitioning induces extra work during graph traversal and that graph partitions have markedly different connectivity than the original graph. By consequence, increasing the number of partitions reaches a tipping point after which overheads quickly dominate performance gains. Moreover, we show that the heuristic to balance CPU load between graph partitions by balancing the number of edges is inappropriate for a range of graph analyses. However, even when it is appropriate, it is sub-optimal due to the skewed degree distribution of social networks. Based on these observations, we propose GraphGrind, a new graph analytics system that addresses the limitations incurred by graph partitioning. We moreover propose a NUMA-aware extension to the Cilk programming language and obtain a scale-free yet NUMA-aware parallel programming environment which underpins NUMA-aware scheduling in GraphGrind. We demonstrate that GraphGrind outperforms state-of-the-art graph analytics systems for shared memory including Ligra, Polymer and Galois.
GraphGrind REF analyzes the impact of graph partitioning on graph processing performance, and finds that graph partitioning causes extra graph traversal cost and load imbalance.
GraphGrind: addressing load imbalance of graph partitioning
ICS '17
Computer Science