Machine learning based performance prediction for multi-core simulation

dc.contributor.author Rai, Jitendra Kumar
dc.contributor.author Negi, Atul
dc.contributor.author Wankar, Rajeev
dc.date.accessioned 2022-03-27T00:16:31Z
dc.date.available 2022-03-27T00:16:31Z
dc.date.issued 2011-12-26
dc.description.abstract Programs co-running on cores share resources on multi-core processor systems. It is now well known that interference between the programs arising from the sharing may result in severe performance degradations. It is the objective of recent research in system scheduling to be aware of shared resource requirements of the running programs (threads). To this end AKULA is a toolset recently developed that provides a platform for experiments and developing thread scheduling algorithms on multi-core processors. In AKULA a bootstrapping module works on the basis of previously collected performance data of programs to simulate program execution on multi-cores. In this paper we describe a different approach where that augments such a bootstrapping module with a model built using machine learning techniques. The proposed model will extend the bootstrapping module's ability to predict degradation in performance due to sharing where previous performance data is not available for pairing/co-scheduling of applications. Also the proposed approach allows greater scalability for variable number of processor cores sharing the resources. © 2011 Springer-Verlag.
dc.identifier.citation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). v.7080 LNAI
dc.identifier.issn 03029743
dc.identifier.uri 10.1007/978-3-642-25725-4_21
dc.identifier.uri http://link.springer.com/10.1007/978-3-642-25725-4_21
dc.identifier.uri https://dspace.uohyd.ac.in/handle/1/3155
dc.subject AKULA toolset
dc.subject Co-runner interference
dc.subject CPU Scheduling
dc.subject Machine learning techniques
dc.subject Multi-core simulation
dc.subject Performance prediction
dc.title Machine learning based performance prediction for multi-core simulation
dc.type Book Series. Conference Paper
dspace.entity.type
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