Graph analysis is a powerful method in data analysis. Although several frameworks have been proposed for processing large graph instances in distributed environments, their performance is much lower than using efficient single-machine implementations provided with enough memory. In this paper, we present a fast distributed graph processing system, namely PGX.D. We show that PGX.D outperforms other distributed graph systems like GraphLab significantly (3x -- 90x). Furthermore, PGX.D on 4 to 16 machines is also faster than an implementation optimized for single-machine execution. Using a fast cooperative context-switching mechanism, we implement PGX.D as a low-overhead, bandwidth-efficient communication framework that supports remote data-pulling patterns. Moreover, PGX.D achieves large traffic reduction and good workload balance by applying selective ghost nodes, edge partitioning, and edge chunking transparently to the user. Our analysis confirms that each of these features is indeed crucial for overall performance of certain kinds of graph algorithms. Finally, we advocate the use of balanced beefy clusters where the sustained random DRAM-access bandwidth in aggregate is matched with the bandwidth of the underlying interconnection fabric.

Due to increasingly large datasets, graph analytics - traversals, all-pairs shortest path computations, centrality measures, etc. - are becoming the focus of high-performance computing (HPC). Because HPC is currently dominated by many-core architectures (both CPUs and GPUs), new graph processing solutions have to be defined to efficiently use such computing resources. Prior work focuses on platform-specific performance studies and on platform-specific algorithm development, successfully proving that algorithms highly tuned to GPUs or multi-core CPUs can provide high performance graph analytics. However, the portability of such algorithms remains an important concern for many users, especially the many companies without the resources to invest in HPC or concerned about lock-in in single-use parallel techniques.