A scalable synthetic traffic model of Graph500 for computer networks analysis. (5th July 2017)

• Record Type: Journal Article
• Authors: Fuentes, Pablo; Benito, Mariano; Vallejo, Enrique; Bosque, José Luis; Beivide, Ramón; Anghel, Andreea; Rodríguez, Germán; Gusat, Mitch; Minkenberg, Cyriel; Valero, Mateo
• Other Names: Carretero Jesus guestEditor.; Garcia‐Blas Javier guestEditor.; Nakano Koji guestEditor.; Mueller Peter guestEditor.; Grosu Daniel guestEditor.; Zheng Sheng guestEditor.; Xu Li guestEditor.; Xu Zheng guestEditor.; Yen Neil guestEditor.; Sugumaran Vijayan guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 24(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-05
• Subjects: Graph500 benchmark -- network evaluation -- scalable simulation tools -- system interconnect; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4231
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The Graph500 benchmark attempts to steer the design of High‐Performance Computing systems to maximize the performance under memory‐constricted application workloads. A realistic simulation of such benchmarks for architectural research is challenging due to size and detail limitations. By contrast, synthetic traffic workloads constitute one of the least resource‐consuming methods to evaluate the performance. In this work, we provide a simulation tool for network architects that need to evaluate the suitability of their interconnect for BigData applications. Our development is a low computation‐ and memory‐demanding synthetic traffic model that emulates the behavior of the Graph500 communications and is publicly available in an open‐source network simulator. The characterization of network traffic is inferred from a profile of several executions of the benchmark with different input parameters. We verify the validity of the equations in our model against an execution of the benchmark with a different set of parameters. Furthermore, we identify the impact of the node computation capabilities and network characteristics in the execution time of the model in a Dragonfly network.