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A scalable synthetic traffic model of Graph500 for computer networks analysis
Author(s) -
Fuentes Pablo,
Benito Mariano,
Vallejo Enrique,
Bosque José Luis,
Beivide Ramón,
Anghel Andreea,
Rodríguez Germán,
Gusat Mitch,
Minkenberg Cyriel,
Valero Mateo
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4231
Subject(s) - benchmark (surveying) , computer science , scalability , big data , computation , set (abstract data type) , distributed computing , interconnection , node (physics) , resource (disambiguation) , network simulation , data mining , computer network , database , geodesy , structural engineering , algorithm , engineering , programming language , geography
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.