Thermometer: profile-guided btb replacement for data center applications
Thermometer: profile-guided btb replacement for data center applications
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DOI：
10.1145/3470496.3527430
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发表时间：
2022-06
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通讯作者：
Shixin Song;Tanvir Ahmed Khan;Sara Mahdizadeh-Shahri;Akshitha Sriraman;N. Soundararajan;S. Subramoney;Daniel A. Jiménez;Heiner Litz;Baris Kasikci
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作者：
Shixin Song;Tanvir Ahmed Khan;Sara Mahdizadeh-Shahri;Akshitha Sriraman;N. Soundararajan;S. Subramoney;Daniel A. Jiménez;Heiner Litz;Baris Kasikci
现代处理器采用脱钩前端，并通过提取指示的预摘要（FDIP）避免在数据中心应用程序中的前端摊位。在所有前端摊位中，我们发现最先进的BTB优化技术（例如，BTB预取和替换机制）无法消除这些错过，因为它们对数据中心应用程序中的分支重复使用不足，我们首先对数据中心应用程序的分支行为进行全面表征，并确定确定最佳的BTB替换决策需要考虑瞬态和整体（即在整个执行中）的分支行为。通过剖面指导的分析实现整体分支的技术，温度计生成了有用的BTB替换提示，即基础硬件可以利用13个广泛的数据中心。平均速度为8.7％（0.4％-64.9％），而最先进的BTB替代技术的速度则超过5.6倍（平均而言，表现最好的先前工作达到了1.5％的速度）。绩效加速平均是最佳BTB替换政策实现的速度的83.6％。
Modern processors employ a decoupled frontend with Fetch Directed Instruction Prefetching (FDIP) to avoid frontend stalls in data center applications. However, the large branch footprint of data center applications precipitates frequent Branch Target Buffer (BTB) misses that prohibit FDIP from eliminating more than 40% of all frontend stalls. We find that the state-of-the-art BTB optimization techniques (e.g., BTB prefetching and replacement mechanisms) cannot eliminate these misses due to their inadequate understanding of branch reuse behavior in data center applications. In this paper, we first perform a comprehensive characterization of the branch behavior of data center applications, and determine that identifying optimal BTB replacement decisions requires considering both transient and holistic (i.e., across the entire execution) branch behavior. We then present Thermometer, a novel BTB replacement technique that realizes the holistic branch behavior via a profile-guided analysis. Based on the collected profile, Thermometer generates useful BTB replacement hints that the underlying hardware can leverage. We evaluate Thermometer using 13 widely-used data center applications and demonstrate that it provides an average speedup of 8.7% (0.4%-64.9%) while outperforming the state-of-the-art BTB replacement techniques by 5.6× (on average, the best performing prior work achieves 1.5% speedup). We also demonstrate that Thermometer achieves a performance speedup that is, on average, 83.6% of the speedup achieved by the optimal BTB replacement policy.