Name
Abstract | ||
|---|---|---|
Predictors essentially predicts the most recent events based on the record of past events, history. It is obvious that prediction performance largely relies on regularity-randomness level of the history. This paper concentrates on extracting effective information from branch history, and discusses expected performance of branch predictors. For this purpose, this paper introduces entropy point-of-views for quantitative characterization of both program behavior and prediction mechanism. This paper defines four new entropies from different viewpoints; two of them are independent of prediction methods and the others are dependent on predictor organization. These new entropies are useful tools for analyzing upper-bound of prediction performance. This paper shows some evaluation results of typical predictors. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-78153-0_21 | ARCS |
Keywords | Field | DocType |
entropy point-of-views,branch history,entropy viewpoint,evaluation result,branch predictor,new entropy,prediction method,prediction performance,different viewpoint,prediction mechanism,effective information,upper bound | Data mining,Computer science,Program behavior,Viewpoints,Program counter,Entropy (information theory) | Conference |
Volume | ISSN | ISBN |
4934 | 0302-9743 | 3-540-78152-8 |
Citations | PageRank | References |
3 | 0.48 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Yokota | 1 | 41 | 21.70 |
| Kanemitsu Ootsu | 2 | 44 | 23.90 |
| Takanobu Baba | 3 | 71 | 27.53 |