| Title |
A Wide-Window Superscalar Microprocessor Profiling Performance Model Using Multiple Branch Prediction |
| Keywords |
Wide-window superscalar microprocessor ; Multiple branch predictor ; Profiling performance model ; Statistical simulation |
| 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. |