Accurate and efficient regression modeling for microarchitectural performance and power prediction
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Accurate and efficient processor performance prediction via regression tree based modeling
Journal of Systems Architecture: the EUROMICRO Journal
Model guided adaptive design and analysis in computer experiment
Statistical Analysis and Data Mining
Microarchitectural design space exploration made fast
Microprocessors & Microsystems
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Computer architects usually evaluate new designs by cycle-accurate processor simulation. This approach provides 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 in a larger design space. In this paper, we propose an automated performance prediction approach which employs state-of-the-art techniques from experiment design, machine learning and data mining. Our method not only produces highly accurate estimations for unsampled points in the design space, but also provides interpretation tools that help investigators to understand performance bottlenecks. According to our experiments, by sampling only 0.02% of the full design space with about 15 millions points, the median percentage errors, based on 5000 independent test points, range from 0.32% to 3.12% in 12 benchmarks. Even for the worst-case performance, the percentage errors are within 7% for 10 out of 12 benchmarks. In addition, the proposed model can also help architects to find important design parameters and performance bottlenecks.