Regression-Based Multi-Model Prediction of Data Reuse Signature

As a locality metric, the distance of data reuses has been used in designing compiler, architecture, and file systems. Recently, Ding and Zhong described a method that predicts reuse distance histograms across all inputs of a program. In this paper we extend their method in two ways. First, we consider more than two training inputs using regression analysis. Second, we use a method called multi-model prediction to overcome the limitation due to small training inputs or coarse-grain data collection. Compared to Ding and Zhong’s method, the new locality prediction can reduce about half of the prediction error, remove 95% of space cost, and use much smaller inputs and faster data collection in training.