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Previous research works include efforts to characterize and classify the memory behaviors of programs to predict the performance. Such knowledge could be useful to create workloads to perform performance studies on multi-core processors. It could also be utilized to form policies at system level to mitigate the interference between co-running programs due to use of shared resources. In this work, machine learning techniques are used to predict the performance on multi-core processors. The main contribution of the study is enumeration of solo-run program attributes, which can be used to predict concurrent-run performance despite change in the number of co-running programs sharing the resources. The concurrent-run involves the interference between co-running programs due to use of shared resources.<\/p>","DOI":"10.4018\/jghpc.2011100102","type":"journal-article","created":{"date-parts":[[2011,10,19]],"date-time":"2011-10-19T12:24:55Z","timestamp":1319027095000},"page":"14-28","source":"Crossref","is-referenced-by-count":2,"title":["Using Machine Learning Techniques for Performance Prediction on Multi-Cores"],"prefix":"10.4018","volume":"3","author":[{"given":"Jitendra Kumar","family":"Rai","sequence":"first","affiliation":[{"name":"ANURAG, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atul","family":"Negi","sequence":"additional","affiliation":[{"name":"University of Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajeev","family":"Wankar","sequence":"additional","affiliation":[{"name":"University of Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jghpc.2011100102-0","doi-asserted-by":"publisher","DOI":"10.1007\/BF00153759"},{"key":"jghpc.2011100102-1","author":"L.Breiman","year":"1984","journal-title":"Classification and regression trees"},{"key":"jghpc.2011100102-2","doi-asserted-by":"crossref","unstructured":"Chandra, D., Guo, F., Kim, S., & Solihin, Y. 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