Thanos: High-Performance CPU-GPU Based Balanced Graph Partitioning Using Cross-Decomposition

Abstract

As graphs become larger and more complex, it is becoming nearly impossible to process them without graph partitioning. Graph partitioning creates many subgraphs which can be processed in parallel thus delivering high-speed computation results. However, graph partitioning is a difficult task. In this work, we introduce Thanos, a fast graph partitioning tool which uses the cross-decomposition algorithm that iteratively partitions a graph. It also produces balanced loads of partitions. The algorithm is well suited for parallel GPU programming which leads to fast and high-quality graph partitioning solutions. Experimental results show that we have achieved 30x speedup and 35% better edge cut reduction compared to the CPU version of the popular graph partitioner, METIS, on average.