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http://dx.doi.org/10.3745/KIPSTA.2005.12A.3.243

Data Communication Prediction Model in Multiprocessors based on Robust Estimation  

Jun Janghwan (동국대학교 정보통신공학과)
Lee Kangwoo (동국대학교 정보통신공학과)
Publication Information
The KIPS Transactions:PartA / v.12A, no.3, 2005 , pp. 243-252 More about this Journal
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.
Keywords
Modeling; Prediction Model; Data Communication; Statistical Estimation Methods;
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