Abstract
In theory, branch predictors with more complicated algorithms and larger data structures provide more accurate predictions. Unfortunately, overly large structures and excessively complicated algorithms cannot be implemented because of their long access delay. To date, many strategies have been proposed to balance delay with accuracy, but none has completely solved the issue. The architecture for ahead branch prediction (A2BP) separates traditional predictors into two parts. First is a small table located at the front-end of the pipeline, which makes the prediction brief enough even for some aggressive processors. Second, operations on complicated algorithms and large data structures for accurate predictions are all moved to the back-end of the pipeline. An effective mechanism is introduced for ahead branch prediction in the back-end and small table update in the front. To substantially improve prediction accuracy, an indirect branch prediction algorithm based on branch history and target path (BHTP) is implemented in A2BP. Experiments with the standard performance evaluation corporation (SPEC) benchmarks on gem5/SimpleScalar simulators demonstrate that A2BP improves average performance by 2.92% compared with a commonly used branch target buffer-based predictor. In addition, indirect branch misses with the BHTP algorithm are reduced by an average of 28.98% compared with the traditional algorithm.
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Wenbing Jin received his BS from Beijing Institute of Technology in 1990, and his MS from Taiyuan University of Technology in 2006. He is a senior engineer with North Automatic Control Technology Institute, and is currently a PhD candidate of Beijing Institute of Technology. His research interests include computer architecture, parallel computing, and artificial intelligence. He is a member of ACM.
Feng Shi received his BE in Physics in 1983 from Peking University and received his PhD from Beijing Institute of Technology, in 1999. He is currently a professor with the School of Computer Science and Technology, Beijing Institute of Technology. His research focuses on parallel computing and computer architecture.
Qiugui Song received his BS from North University of China in 2002. He is a senior engineer with North Automatic Control Technology Institute. His research interests include computer architecture and embedded systems.
Yang Zhang is a lecturer with Hebei University of Science and Technology, and currently a PhD candidate of Beijing Institute of Technology. His research interests include computer architecture and high-performance computing.
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Jin, W., Shi, F., Song, Q. et al. A novel architecture for ahead branch prediction. Front. Comput. Sci. 7, 914–923 (2013). https://doi.org/10.1007/s11704-013-2260-x
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DOI: https://doi.org/10.1007/s11704-013-2260-x