A scalable synthetic traffic model of Graph500 for computer networks analysis

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hdl:2117/113505
Document typeArticle
Defense date2017-12-25
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Abstract
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
CitationFuentes, P., Benito, M., Vallejo, E., Bosque, J., Beivide, R., Anghel, A., Rodríguez, G., Gusat, M., Minkenberg, C., Valero, M. A scalable synthetic traffic model of Graph500 for computer networks analysis. "Concurrency and computation. Practice and experience", 25 Desembre 2017, vol. 29, núm. 24, p. 1-15.
ISSN1532-0626
Publisher versionhttp://onlinelibrary.wiley.com/resolve/doi?DOI=10.1002/cpe.4231
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