描述分支错误预测惩罚

Stijn Eyerman, James E. Smith, L. Eeckhout
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引用次数: 59

摘要

尽管经过多年的研究,分支错误预测仍然是流水线超标量处理器中一个重要的性能障碍。通常,分支错误预测的损失可能比前端管道的长度要大得多(前端管道的长度通常等同于错误预测的损失)。我们确定并量化了分支错误预测惩罚的五个因素:(i)前端管道长度,(ii)自最后一次错过事件(分支错误预测,i -缓存错过,长d -缓存错过)以来的指令数量-这与错过事件的爆发有关,(iii)程序的固有ILP, (iv)功能单元延迟,以及(v)短(LI) d -缓存错过的数量。本文中所做的表征是由“区间分析”驱动的,这是一种将超标量处理器性能建模为间隔间隔序列的分析方法。
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Characterizing the branch misprediction penalty
Despite years of study, branch mispredictions remain as a significant performance impediment in pipelined superscalar processors. In general, the branch misprediction penalty can be substantially larger than the frontend pipeline length (which is often equated with the misprediction penalty). We identify and quantify five contributors to the branch misprediction penalty: (i) the frontend pipeline length, (ii) the number of instructions since the last miss event (branch misprediction, I-cache miss, long D-cache miss)-this is related to the burstiness of miss events, (iii) the inherent ILP of the program, (iv) the functional unit latencies, and (v) the number of short (LI) D-cache misses. The characterizations done in this paper are driven by 'interval analysis', an analytical approach that models superscalar processor performance as a sequence of inter-miss intervals.
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