Toward improved branch prediction through data mining.

Technical Report · Tue Sep 01 00:00:00 EDT 2009

DOI:https://doi.org/10.2172/993886· OSTI ID:993886

Hemmert, K Scott; Johnson, D Eric ^[1]

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1. University of Texas at Austin

Data mining and machine learning techniques can be applied to computer system design to aid in optimizing design decisions, improving system runtime performance. Data mining techniques have been investigated in the context of branch prediction. Specifically, a comparison of traditional branch predictor performance has been made to data mining algorithms. Additionally, the possiblity of whether additional features available within the architectural state might serve to further improve branch prediction has been evaluated. Results show that data mining techniques indicate potential for improved branch prediction, especially when register file contents are included as a feature set.

• Research Organization: Sandia National Laboratories
• Sponsoring Organization: USDOE
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 993886
• Report Number(s): SAND2009-6009
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
ALGORITHMS
COMPUTERS
INFORMATION RETRIEVAL
INFORMATION SYSTEMS
PERFORMANCE