Modelling Application Cache Behavior using Regression Models

In this paper, we describe the creation of resource usage forecasts for applications with unknown execution characteristics, by evaluating different regression processes, including autoregressive, multivariate adaptive regression splines, exponential smoothing, etc. We utilize Performance Monitor Units (PMU) and generate hardware resource usage models for the L-2-cache and the L-3-cache using nine different regression processes. The measurement strategy and regression process methodology are general and applicable to any given hardware resource when performance counters are available. We use three benchmark applications: the SIFT feature detection algorithm, a standard matrix multiplication, and a version of Bubblesort. Our evaluation shows that Multi Adaptive Regressive Spline (MARS) models generate the best resource usage forecasts among the considered models, followed by Single Exponential Splines (SES) and Triple Exponential Splines (TES).