Abstract
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.
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Acknowledgments
This work is partially conducted in the context of the joint ASTRON and IBM DOME project and is funded by the Netherlands Organisation for Scientific Research (NWO), the Dutch Ministry of EL&I, and the Province of Drenthe. We would like to thank the Barcelona Supercomputing Center for providing support and access to the MareNostrum III supercomputing cluster.
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Anghel, A., Rodriguez, G., Prisacari, B., Minkenberg, C., Dittmann, G. (2015). Quantifying Communication in Graph Analytics. In: Kunkel, J., Ludwig, T. (eds) High Performance Computing. ISC High Performance 2015. Lecture Notes in Computer Science(), vol 9137. Springer, Cham. https://doi.org/10.1007/978-3-319-20119-1_33
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DOI: https://doi.org/10.1007/978-3-319-20119-1_33
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