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dc.contributor.authorZheng, Shuai
dc.contributor.authorShae, Zon Yin
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorJamjoom, Hani T.
dc.contributor.authorFong, Liana
dc.date.accessioned2015-08-04T06:24:17Z
dc.date.available2015-08-04T06:24:17Z
dc.date.issued2011
dc.identifier.isbn9783642233999
dc.identifier.issn03029743
dc.identifier.doi10.1007/978-3-642-23400-2_19
dc.identifier.urihttp://hdl.handle.net/10754/564342
dc.description.abstractSocial influence among users (e.g., collaboration on a project) creates bursty behavior in the underlying high performance computing (HPC) workloads. Using representative HPC and cluster workload logs, this paper identifies, analyzes, and quantifies the level of social influence across HPC users. We show the existence of a social graph that is characterized by a pattern of dominant users and followers. This pattern also follows a power-law distribution, which is consistent with those observed in mainstream social networks. Given its potential impact on HPC workloads prediction and scheduling, we propose a fast-converging, computationally-efficient online learning algorithm for identifying social groups. Extensive evaluation shows that our online algorithm can (1) quickly identify the social relationships by using a small portion of incoming jobs and (2) can efficiently track group evolution over time. © 2011 Springer-Verlag.
dc.publisherSpringer Nature
dc.titleAnalysis and modeling of social influence in high performance computing workloads
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.identifier.journalLecture Notes in Computer Science
dc.conference.date29 August 2011 through 2 September 2011
dc.conference.name17th International Conference on Parallel Processing, Euro-Par 2011
dc.conference.locationBordeaux
dc.contributor.institutionIBM T. J. Watson Research Center, Hawthorne, NY, United States
dc.identifier.arxividarXiv:1610.04676
kaust.personZheng, Shuai
kaust.personZhang, Xiangliang


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