Two - Level Adaptive Multiple Branch Prediction Using a Global History

We propose an enhanced multiple branch predictor using per-primary address branch histories. Using this scheme, the interferences among diierent branches are reduced, enhancing the average prediction accuracy. Also, since the branches predicted each cycle do not suuer from successive dependencies, predictions are generated in parallel. This scheme results in higher average branch prediction accuracy than the previous global history scheme under the same implementation cost. For two branch predictions, the prediction accuracy of integer benchmarks varies between 92.0 and 96.9 percent. For oating point benchmarks including nasa7, the accuracy is between 94.8 and 95.8 percent .