Benchmarking High Performance Graph Analysis Systems with Graph Mining and Pattern Matching Workloads

Abstract

The increases in volume and inter-connectivity of graph data result in the emergence of recent scalable and high perfor- mance graph analysis systems. Those systems provide dif- ferent graph representation models, a variety of querying interfaces and libraries, and several underlying computation models. As a consequence, such diversities complicate in- situ choices of best platforms for data scientists according to their desired graph analysis tasks. In this poster pre- sentation, we compare recent high performance and scalable graph analysis systems in distributed and supercomputer- based processing environments with two important graph analysis workloads: graph mining and graph pattern match- ing. We also compare those systems in terms of expressive- ness and suitability of their querying interfaces for the two distinct workloads.