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Performance prediction model for distributed applications on multicore clusters

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dc.contributor.author Khanyile, NP
dc.contributor.author Tapamo, J-R
dc.contributor.author Dube, E
dc.date.accessioned 2012-09-21T10:04:26Z
dc.date.available 2012-09-21T10:04:26Z
dc.date.issued 2012-07
dc.identifier.citation Khanyile, NP, Tapamo, J-R and Dube, E. Performance prediction model for distributed applications on multicore clusters. World Congress on Engineering, London, United Kingdom, 4-6 July 2012 en_US
dc.identifier.isbn 978-988-19252-1-3
dc.identifier.uri http://www.iaeng.org/publication/WCE2012/WCE2012_pp1119-1124.pdf
dc.identifier.uri http://hdl.handle.net/10204/6102
dc.description World Congress on Engineering, London, United Kingdom, 4-6 July 2012 en_US
dc.description.abstract Distributed processing offers a way of successfully dealing with computationally demanding applications such as scientific problems. Over the years, researchers have investigated ways to predict the performance of parallel algorithms. Amdahl’s law states that the speedup of any parallel program has an upper bound which is determined by the amount of time spent on the sequential fraction of the program, no matter how small and regardless of the number of processing nodes used. This research discusses some of the short comings of this law in the current age. We propose a theoretical model for predicting the behavior of a distributed algorithm given the network restrictions of the cluster used. The paper focuses on the impact of latency and bandwidth which affect the cost of interprocessor communication and the number of processing nodes used to predict the performance. The model shows good accuracy in comparison to Amdahl’s law. en_US
dc.language.iso en en_US
dc.publisher IAENG en_US
dc.relation.ispartofseries Workflow;9542
dc.subject Amdahl’s law en_US
dc.subject Propagation delay en_US
dc.subject Multicore Clusters en_US
dc.subject Algorithms en_US
dc.subject Performance prediction model en_US
dc.subject Bandwith en_US
dc.title Performance prediction model for distributed applications on multicore clusters en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Khanyile, N., Tapamo, J., & Dube, E. (2012). Performance prediction model for distributed applications on multicore clusters. IAENG. http://hdl.handle.net/10204/6102 en_ZA
dc.identifier.chicagocitation Khanyile, NP, J-R Tapamo, and E Dube. "Performance prediction model for distributed applications on multicore clusters." (2012): http://hdl.handle.net/10204/6102 en_ZA
dc.identifier.vancouvercitation Khanyile N, Tapamo J, Dube E, Performance prediction model for distributed applications on multicore clusters; IAENG; 2012. http://hdl.handle.net/10204/6102 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Khanyile, NP AU - Tapamo, J-R AU - Dube, E AB - Distributed processing offers a way of successfully dealing with computationally demanding applications such as scientific problems. Over the years, researchers have investigated ways to predict the performance of parallel algorithms. Amdahl’s law states that the speedup of any parallel program has an upper bound which is determined by the amount of time spent on the sequential fraction of the program, no matter how small and regardless of the number of processing nodes used. This research discusses some of the short comings of this law in the current age. We propose a theoretical model for predicting the behavior of a distributed algorithm given the network restrictions of the cluster used. The paper focuses on the impact of latency and bandwidth which affect the cost of interprocessor communication and the number of processing nodes used to predict the performance. The model shows good accuracy in comparison to Amdahl’s law. DA - 2012-07 DB - ResearchSpace DP - CSIR KW - Amdahl’s law KW - Propagation delay KW - Multicore Clusters KW - Algorithms KW - Performance prediction model KW - Bandwith LK - https://researchspace.csir.co.za PY - 2012 SM - 978-988-19252-1-3 T1 - Performance prediction model for distributed applications on multicore clusters TI - Performance prediction model for distributed applications on multicore clusters UR - http://hdl.handle.net/10204/6102 ER - en_ZA


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