Two-level adaptive training branch prediction
Two-level adaptive training branch prediction
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DOI：
10.1145/123465.123475
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发表时间：
1991-09
期刊：
影响因子：
--
通讯作者：
Tse-Yu Yeh;Y. Patt
中科院分区：
文献类型：
--
作者：
Tse-Yu Yeh;Y. Patt
在其他结构中，高性能微体系结构使用深流水线来帮助加速执行。在有条件分支存在的情况下，良好的分支预测器对于深层管道的有效性的重要性是众所周知的。事实上，文献中包含了许多分支预测方案的建议。有些是静态的，因为它们使用操作码信息和概要统计信息来进行预测。另一些则是动态的，因为它们使用运行时执行历史来进行预测。本文提出了一种新的动态分支预测器--两级自适应绘制方案，它根据运行时收集的信息来改变分支预测算法。介绍了两级自适应训练分支预测器的几种结构，并进行了仿真，并与其他已知静态和动态分支预测方案的仿真进行了比较。两级自适应训练分支预测在十个SPEC基准中的九个上达到了97%的准确率，而其他方案的准确率不到93%。由于预测未命中需要刷新已在进行中的推测性执行，因此相关的度量是未命中率。两级自适应训练方案的错失率为3%，而其他方案的错失率为7%(最好的情况)。这意味着在减少所需的管道屏蔽数量方面提高了100%以上。
High-performance microarchitectures use, among other structures, deep pipelines to help speed up exe- cution. The importance of a good branch predictor to the effectiveness of a deep pipeline in the presence of condi- tional branches is well-known. In fact, the literature contains proposals for a number of branch prediction schemes. Some are static in that they use opcode information and profiling statistics to make predictions. Others are dynamic in that they use run-time execution history to make predictions. This paper proposes a new dynamic branch predictor, the Two-Level Adaptive Paining scheme, which alters the branch prediction algorithm on the basis of information collected at run-time. Several configurations of the Two-Level Adaptive Training Branch Predictor are introduced, simulated, and compared to simulations of other known static and dynamic branch prediction schemes. Two-Level Adaptive Training Branch Prediction achieves 97 percent accuracy on nine of the ten SPEC benchmarks, compared to less than 93 percent for other schemes. Since a prediction miss requires flushing of the speculative execution already in progress, the relevant metric is the miss rate. The miss rate is 3 percent for the Two-Level Adaptive Training scheme vs. 7 percent (best case) for the other schemes. This represents more than a 100 percent improvement in reducing the number of pipeline hushes required.