Toward improved branch prediction through data mining.

Hemmert, K. Scott ; Johnson, D. Eric (University of Texas at Austin)

Sandia National Laboratories

关键词: Information Systems;    Computers;    Information Retrieval;    Algorithms;    97 Mathematical Methods And Computing;   

DOI  :  10.2172/993886
RP-ID  :  SAND2009-6009
RP-ID  :  AC04-94AL85000
RP-ID  :  993886

美国|英语

来源: UNT Digital Library

【 摘 要 】

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.