Comparing Multinomial and K-Means Clustering for SimPoint

Greg Hamerly, Erez Perelman, and Brad Calder

In Proceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS), March 2006


SimPoint is a technique used to pick what parts of the program's execution to simulate in order to have a complete picture of execution. SimPoint uses data clustering algorithms from machine learning to automatically find repetitive (similar) patterns in a program's execution, and it chooses one sample to represent each unique repetitive behavior. Together these samples represent an accurate picture of the complete execution of the program. SimPoint is based on the k-means clustering algorithm; recent work proposed using a different clustering method based on multinomial models, but only provided a preliminary comparison and analysis.

In this work we provide a detailed comparison of using $k$-means and multinomial clustering for SimPoint. We show that $k$-means performs better than the recently proposed multinomial clustering approach. We then propose two improvements to the prior multinomial clustering approach in the areas of feature reduction and the picking of simulation points which allow multinomial clustering to perform as well as k-means. We then conclude by examining how to potentially combine multinomial clustering with k-means.