Export to Refworks Export to EndNote Email
Report an Issue

Toward improved branch prediction through data mining [electronic resource].

Published:
Washington, D.C. : United States. Dept. of Energy, 2009.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description:
14 pages : digital, PDF file
Additional Creators:
Sandia National Laboratories, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
Access Online

Availability

I Want It

Finding items...

Restrictions on Access:
Free-to-read Unrestricted online access
Summary:
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.
Report Numbers:
E 1.99:sand2009-6009
sand2009-6009
Subject(s):
Other Subject(s):
Note:
Published through SciTech Connect.
09/01/2009.
"sand2009-6009"
Hemmert, K. Scott; Johnson, D. Eric.
Funding Information:
AC04-94AL85000
View MARC record | catkey: 14654243