A highly efficient runtime and graph library for large scale graph analytics

Graph analytics on big data is currently a very active area of research in both industry and academia. To support graph analytics efficiently a large number of graph processing sys- Tems have emerged targeting various perspectives of a graph application such as in memory and on disk representations, persistent storage, database capability, runtimes and execu- Tion models for exploiting parallelism, etc. In this paper we discuss a novel graph processing system called System G Native Store which allows for efficient graph data organization and processing on modern computing ar- chitectures. In particular we describe a runtime designed to exploit multiple levels of parallelism and a generic infras- Tructure that allows users to express graphs with various in memory and persistent storage properties. We experi- mentally show the efficiency of System G Native Store for processing graph queries on state-of-the-art platforms. © 2014 ACM.