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Virginia Tech

iLORE: Discovering a Lineage of Microprocessors

Abstract

dc:description.abstract

Researchers, benchmarking organizations, and hardware manufacturers maintain repositories of computer component and performance information. However, this data is split across many isolated sources and is stored in a form that is not conducive to analysis. A centralized repository of said data would arm stakeholders across industry and academia with a tool to more quantitatively understand the history of computing. We propose iLORE, a data model designed to represent intricate relationships between computer system benchmarks and computer components. We detail the methods we used to implement and populate the iLORE data model using data harvested from publicly available sources. Finally, we demonstrate the validity and utility of our iLORE implementation through an analysis of the characteristics and lineage of commercial microprocessors. We encourage the research community to interact with our data and visualizations at csgenome.org.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Computer Science and Applications
Department dc:contributor.department
Computer Science
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Furman, Samuel Lewis
Chair dc:contributor.committeechair
  • Cameron, Kirk W.
Committee members dc:contributor.committeemember
  • Back, Godmar V.
  • Ellis, Margaret O.

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:31519
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/104071

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Furman, Samuel Lewis. iLORE: Discovering a Lineage of Microprocessors. masters thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104071