A Study on Improving the Performance of Two-level Adaptive Training Branch Prediction by Dynamic Selecting Branch History

• Main Authors: Chung, Li-Wen, 鍾立文
• Other Authors: Huang, Tsung-Chuan
• Format: Others
• Language: zh-TW
• Published: 1998
• Online Access: http://ndltd.ncl.edu.tw/handle/68297042457673283291

碩士 === 國立中山大學 === 電機工程研究所 === 86 === 　　Predict the branch outcomes correctly can avoid pipeline bubbles and thus reduce the attendant loss in performance. The accuracy of branch prediction is especially importan in the future, since the more instruction level parallelism is exploited in high-performance microprocessors the induced penalty will get worse when we make error predition. In this paper, we propose a mechanism, called X-Prediction (eXcellent Prediction), to improve the prediction acculacy and the performance in Two-Level-Adaptive Training Branch Prediction. Because it is not feasible to have a big enough hardware to record the history of branch instruction, several branches must share the same buffer entries, and this causes the so-called interference problem. X-Prediction utilizes a simple hardware to look for the destructive interferences in Two-Level-Adaptive Training Branch Prediction, Then use the auxiliary predictor to make prediction. With simulation assistance, we determine the required parameters in implementing this mechanism. Through delicate simulation with six benchmarks from SPEC92 benchmark suite, we find that its performance is improved over that of Yeh and Patt's in average 2-3%