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Comparing Multinomial and K-Means Clustering for SimPoint
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. These samples when taken
together represent an accurate picture of the complete execution of the
program. SimPoint is based on the k-means clustering algorithm, and recent
work has 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, in the
areas of feature reduction and the picking of simulation points, to the prior
multinomial clustering approach, which allows multinomial clustering to perform
as well as k-means. We then conclude by examining how to potentially combine
multinomial clustering with k-means.Pre-2018 CSE ID: CS2005-084
Comparing Multinomial and K-Means Clustering for SimPoint
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