Quantifying Communication in Graph Analytics

Data analytics require complex processing, often taking the shape of parallel graph-based workloads. In ensuring a high level of efficiency for these applications, understanding where the bottlenecks lie is key, particularly understanding to which extent their performance is computation or communication-bound. In this work, we analyze a reference workload in graph-based analytics, the Graph 500 benchmark. We conduct a wide array of tests on a high-performance computing system, the MareNostrum III supercomputer, using a custom high-precision profiling methodology. We show that the application performance is communication-bound, with up to 80% of the execution time being spent enabling communication. We equally show that, with the increase in scale and concurrency that is expected in future big data systems and applications, the importance of communication increases. Finally, we characterize this representative data-analytics workload and show that the dominating data exchange is uniform all-to-all communication, opening avenues for workload and network optimization.