Principal component and cluster analysis of SPEC CPUint2006 Benchmarks

dc.contributor.author	Kareem, Abdul P.	en
dc.contributor.author	Singh, R. A.	en
dc.date.accessioned	2015-01-28T21:15:44Z
dc.date.available	2015-01-28T21:15:44Z
dc.date.issued	2015-01-28
dc.identifier.uri	http://hdl.handle.net/11400/4987
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://e-jst.teiath.gr/	en
dc.subject	Processor
dc.subject	Επεξεργαστής
dc.subject	PCA
dc.subject	SPEC CPU2006
dc.subject	Processor Performance
dc.subject	Moore’s Law
dc.subject	Επίδοση
dc.subject	Νόμος του Moore
dc.title	Principal component and cluster analysis of SPEC CPUint2006 Benchmarks	en
heal.type	journalArticle
heal.secondaryTitle	input data set selection	en
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.language	en
heal.access	free
heal.publicationDate	2009
heal.bibliographicCitation	Kareem, A.P. and Singh, R.A. (2009). Principal component and cluster analysis of SPEC CPUint2006 Benchmarks: input data set selection. "e-Journal of Science & Technology". [Online] 4(3): 79-89. Available from: http://e-jst.teiath.gr/	en
heal.abstract	Technological scaling of processor parameters has a critical limit. The Scaling advanced CMOS technology to the next generation effects improves performance, increases transistor density, and reduces power consumption of the processor. In this paper we describe the statistical analysis of SPEC CPUint2006 benchmarks workload and input data selection for microarchitectural research. Today we need a processor which can provide high performance boost for a broad spectrum. We use statistical analysis techniques, Principal Component Analysis (PCA) and Cluster Analysis (CA) for the study of benchmark workload classification using recently published SPEC CPUint2006 performance numbers of thirty Intel’s commercial processors. We calculated five most significant PCs, which are retained for 85% of the variance, PC2, PC3, PC4 and PC5 covers 11.1%, 2.9%, 0.6% and 0.1% variance respectively. We classified the CINT benchmarks in two sub groups. We found that the benchmarks 471.omnetpp, 462.libquantum 403.gcc, and 429.mcf exhibits higher memory wait time. Our results and analysis can be used by performance engineers, scientists and developers to better understand the benchmark workload and select input dataset for better microarchitecture design of the processors.	en
heal.publisher	Νερατζής, Ηλίας	el
heal.publisher	Σιανούδης, Ιωάννης	el
heal.journalName	e-Journal of Science & Technology	en
heal.journalName	e-Περιοδικό Επιστήμης & Τεχνολογίας	el
heal.journalType	peer-reviewed
heal.fullTextAvailability	true