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Using Machine Learning Techniques for Performance Prediction on Multi-Cores

Using Machine Learning Techniques for Performance Prediction on Multi-Cores

Jitendra Kumar Rai, Atul Negi, Rajeev Wankar
Copyright: © 2011 |Volume: 3 |Issue: 4 |Pages: 15
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781613507247|DOI: 10.4018/jghpc.2011100102
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MLA

Rai, Jitendra Kumar, et al. "Using Machine Learning Techniques for Performance Prediction on Multi-Cores." IJGHPC vol.3, no.4 2011: pp.14-28. http://doi.org/10.4018/jghpc.2011100102

APA

Rai, J. K., Negi, A., & Wankar, R. (2011). Using Machine Learning Techniques for Performance Prediction on Multi-Cores. International Journal of Grid and High Performance Computing (IJGHPC), 3(4), 14-28. http://doi.org/10.4018/jghpc.2011100102

Chicago

Rai, Jitendra Kumar, Atul Negi, and Rajeev Wankar. "Using Machine Learning Techniques for Performance Prediction on Multi-Cores," International Journal of Grid and High Performance Computing (IJGHPC) 3, no.4: 14-28. http://doi.org/10.4018/jghpc.2011100102

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Abstract

Sharing of resources by the cores of multi-core processors brings performance issues for the system. Majority of the shared resources belong to memory hierarchy sub-system of the processors such as last level caches, prefetchers and memory buses. Programs co-running on the cores of a multi-core processor may interfere with each other due to usage of such shared resources. Such interference causes co-running programs to suffer with performance degradation. 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.

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