Accurate and efficient processor performance prediction via regression tree based modeling
Document Type
Article
Publication Date
10-1-2009
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. © 2009 Elsevier B.V. All rights reserved.
Publication Source (Journal or Book title)
Journal of Systems Architecture
First Page
457
Last Page
467
Recommended Citation
Li, B., Peng, L., & Ramadass, B. (2009). Accurate and efficient processor performance prediction via regression tree based modeling. Journal of Systems Architecture, 55 (10-12), 457-467. https://doi.org/10.1016/j.sysarc.2009.09.004