The full text of this article


Analysing and improving clustering based sampling for microprocessor simulation
by Yue Luo, Ajay Joshi, Aashish Phansalkar, Lizy John, Joydeep Ghosh
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 5, No. 4, 2008


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 different 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 the Basic Block Vector (BBV) feature for many SPEC CPU2000 benchmark programs.

Online publication date: Sat, 27-Dec-2008


is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN):
Login with your Inderscience username and password:


    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email