Towards Efficient Graph Traversal using a Multi-GPU Cluster

Graph processing has always been a challenge, as there are inherent complexities in it. These include scalability to larger data sets and clusters, dependencies between vertices in the graph, irregular memory accesses during processing and traversals, minimal locality of reference, etc. In literature, there are several implementations for parallel graph processing on single GPU systems but only few for single and multi-node multiGPU systems. In this paper, the prospects of improvement in large graph traversals by utilizing multi-GPU cluster for Breadth First Search algorithm has been studied. In this regard, a DiGPU, a CUDA-based implementation for graph traversal in shared memory multi-GPU and distributed memory multi-GPU systems has been proposed. In this work, an open source software module has also been developed and verified through set of experiments. Further, evaluations have been demonstrated on local cluster as well as on CDER cluster. Finally, experimental analysis has been performed on several graph data sets using different system configurations to study the impact of load distribution with respect to GPU specification on performance of our implementation. Keywords—Graph processing; GPU cluster; distributed graph traversal API; CUDA; BFS; MPI