Discovering patterns for architecture simulation by using sequence mining
Document Type
Article
Publication Date
2012
Department
Department of Computer Science
Abstract
The goal of computer architecture research is to design and build high performance systems that make effective use of resources such as space and power. The design process typically involves a detailed simulation of the proposed architecture followed by corrections and improvements based on the simulation results. Both simulator development and result analysis are very challenging tasks due to the inherent complexity of the underlying systems. The motivation of this work is to apply episode mining algorithms to a new domain, architecture simulation, and to prepare an environment to make predictions about the performance of programs in different architectures. We describe our tool called Episode Mining Tool (EMT), which includes three temporal sequence mining algorithms, a preprocessor, and a visual analyzer. We present empirical analysis of the episode rules that were mined from datasets obtained by running detailed micro-architectural simulations.
Publication Title
Pattern Discovery Using Sequence Data Mining: Applications and Studies
ISBN
9781613500569
Recommended Citation
Senkul, P.,
Onder, N.,
Onder, S.,
Maden, E.,
&
Nyew, H.
(2012).
Discovering patterns for architecture simulation by using sequence mining.
Pattern Discovery Using Sequence Data Mining: Applications and Studies, 212-236.
http://doi.org/10.4018/978-1-61350-056-9.ch013
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/880
Publisher's Statement
Copyright © 2012, IGI Global - All Rights Reserved. Publisher’s version of record: https://doi.org/10.4018/978-1-61350-056-9.ch013