Data Communication Prediction Model in Multiprocessors based on Robust Estimation


KIPS Transactions on Computer and Communication Systems, Vol. 12, No. 3, pp. 243-252, Jun. 2005
10.3745/KIPSTA.2005.12.3.243,   PDF Download:

Abstract

This paper introduces a noble modeling technique to build data communication prediction models in multiprocessors, using Least-Squares and Robust Estimation methods. A set of sample communication rates are collected by using a few small input data sets into workload programs. By applying estimation methods to these samples, we can build analytic models that precisely estimate communication rates for huge input data sets. The primary advantage is that, since the models depend only on data set size not on the specifications of target systems or workloads, they can be utilized to various systems and applications. In addition, the fact that the algorithmic behavioral characteristics of workloads are reflected into the models entitles them to model diverse other performance metrics. In this paper, we built models for cache miss rates which are the main causes of data communication in shared memory multiprocessor systems. The results present excellent prediction error rates; below 1% for five cases out of 12, and about 3% for the rest cases.


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Cite this article
[IEEE Style]
J. H. Jun and K. W. Lee, "Data Communication Prediction Model in Multiprocessors based on Robust Estimation," KIPS Journal A (2001 ~ 2012) , vol. 12, no. 3, pp. 243-252, 2005. DOI: 10.3745/KIPSTA.2005.12.3.243.

[ACM Style]
Jang Hwan Jun and Kang Woo Lee. 2005. Data Communication Prediction Model in Multiprocessors based on Robust Estimation. KIPS Journal A (2001 ~ 2012) , 12, 3, (2005), 243-252. DOI: 10.3745/KIPSTA.2005.12.3.243.