GraphPar: Efficient Workload-Aware Subgraph Matching System on Multiple GPUs.

Subgraph matching (SM) has witnessed tremendous progress in recent years, enabling a broad spectrum of big data applications. SM applications are extremely computeintensive since they require tremendous set operations, i.e., enumerating all the possible vertex pairs and counting the common neighbor of each pair. GPU is potentially promising hardware to accelerate SM applications due to its massive parallelism. However, SM applications achieve low efficiency and often fail to deliver high performance in multi-GPU systems owing to irregular edge distribution which exhausts the computing power and aggravates the load-imbalance problems. Although many existing frameworks have proffer numerous methods at high-level to improve the efficiency of GPU-based SM, e.g., assign matching order, early termination, and automorphismbreaking, the low-level issues on GPU architecture and system, e.g., thread mapping, graph partitions are not well addressed. In this work, we develop GraphPar, an efficient SM system targeting multi-GPUs. GraphPar proposes an effective workload- aware scheduling and an efficient set operation designing, which could successfully reduce the stragglers and significantly accelerate SM. Experiments on a V100 GPU cluster show that GraphPar is up to 4.21 × faster than the state-of-the-art GPU-based GPM system G ² Miner.