Accurate and efficient processor performance prediction via regression tree based modeling
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.
modeling evaluation modeling techniques modeling of computer architecture measurement simulation of multiple-processor systems