Accurate and efficient processor performance prediction via regression tree based modeling

Authors:
Bin Li;Lu Peng;Balachandran Ramadass
Affiliations:
Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA 70803, USA;Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA;Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
Venue:
Journal of Systems Architecture: the EUROMICRO Journal
Year:
2009

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Abstract

Computer architects usually evaluate new designs using cycle-accurate processor simulation. This approach provides a detailed insight into processor performance, power consumption and complexity. However, only configurations in a subspace can be simulated in practice due to long simulation time and limited resource, leading to suboptimal conclusions which might not be applied to a larger design space. In this paper, we propose a performance prediction approach which employs state-of-the-art techniques from experiment design, machine learning and data mining. According to our experiments on single and multi-core processors, our prediction model generates highly accurate estimations for unsampled points in the design space and show the robustness for the worst-case prediction. Moreover, the model provides quantitative interpretation tools that help investigators to efficiently tune design parameters and remove performance bottlenecks.