Gem5Pred: Predictive Approaches For Gem5 Simulation Time
Tian Yan Xueyang Li Sifat Ut Taki Saeid Mehrdad
Gem5, an open-source, flexible, and cost-effective simulator, is widely
recognized and utilized in both academic and industry fields for hardware
simulation. However, the typically time-consuming nature of simulating programs
on Gem5 underscores the need for a predictive model that can estimate
simulation time. As of now, no such dataset or model exists. In response to
this gap, this paper makes a novel contribution by introducing a unique dataset
specifically created for this purpose. We also conducted analysis of the
effects of different instruction types on the simulation time in Gem5. After
this, we employ three distinct models leveraging CodeBERT to execute the
prediction task based on the developed dataset. Our superior regression model
achieves a Mean Absolute Error (MAE) of 0.546, while our top-performing
classification model records an Accuracy of 0.696. Our models establish a
foundation for future investigations on this topic, serving as benchmarks
against which subsequent models can be compared. We hope that our contribution
can simulate further research in this field. The dataset we used is available
at https://github.com/XueyangLiOSU/Gem5Pred.