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A Wide-Window Superscalar Microprocessor Profiling Performance Model Using Multiple Branch Prediction  

Lee, Jong-Bok (한성대 공대 정보통신학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.58, no.7, 2009 , pp. 1443-1449 More about this Journal
Abstract
This paper presents a profiling model of a wide-window superscalar microprocessor using multiple branch prediction. The key idea is to apply statistical profiling technique to the superscalar microprocessor with a wide instruction window and a multiple branch predictor. The statistical profiling data are used to obtain a synthetical instruction trace, and the consecutive multiple branch prediction rates are utilized for running trace-driven simulation on the synthesized instruction trace. We describe our design and evaluate it with the SPEC 2000 integer benchmarks. Our performance model can achieve accuracy of 8.5 % on the average.
Keywords
Wide-window superscalar microprocessor; Multiple branch predictor; Profiling performance model; Statistical simulation;
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