Analyzing and Improving Clustering Based Sampling for Microprocessor Simulation

Abstract

We propose a set of statistical metrics for making a comprehensive, fair, and insightful evaluation of features, clustering algorithms, and distance measures in representative sampling techniques for microprocessor simulation. Our evaluation of clustering algorithms using these metrics shows that CLARANS clustering algorithm produces better quality clusters in the feature space and more homogeneous phases for CPI compared to the popular k-means algorithm. We also propose a new micro-architecture independent data locality based feature, reuse distance distribution (RDD), for finding phases in programs, and show that the RDD feature consistently results in more homogeneous phases than basic block vector (BBV) for many SPEC CPU2000 benchmark programs.