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Behavior Gaps and Relations between Operating System and Applications on Accessing DRAM

机译:行为差异以及操作系统和应用程序在访问DRAM上的关系

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Detailed analyses of the behaviors of operating system and applications are significant for taking full advantage of the precious hardware resources and improving performance. This paper focus on their DRAM access behaviors based on access proportion and row-buffer miss ratio (RBM). The access proportions of Kernel and User vary greatly in different stages throughout the lifetime of a process. Most of the row-buffer misses are caused by the one having higher access proportion. By analyzing the RBM series through ARMA model, we found that User's DRAM accesses only have short-term influences on its behavior, while the Kernel's influences are relatively deeper. The ARMA model for the RBM series is able to predict the future RBMs, which are profound basis to schedule the DRAM access commands. The results of Gaussian Fitting show that Kernel and User are tightly correlated on accessing DRAM, especially in the steady stage and the end stage of a process's life cycle. Based on this close relation, it is possible to estimate the DRAM access behaviors of the other one according to the one whose behaviors have been known. System-calls that obviously affect the access proportions and RBMs are also revealed in this paper.
机译:对操作系统和应用程序行为的详细分析对于充分利用宝贵的硬件资源并提高性能非常重要。本文重点介绍基于访问比例和行缓冲区未命中率(RBM)的DRAM访问行为。在整个过程的生命周期中,内核和用户的访问比例在不同阶段有很大的不同。大多数行缓冲区未命中是由具有较高访问比例的行缓冲区未命中引起的。通过基于ARMA模型的RBM系列分析,我们发现用户的DRAM访问对其行为仅具有短期影响,而内核的影响则相对较深。 RBM系列的ARMA模型能够预测未来的RBM,这是安排DRAM访问命令的深刻基础。高斯拟合的结果表明,内核和用户在访问DRAM时紧密相关,尤其是在过程生命周期的稳定阶段和结束阶段。基于这种紧密关系,可以根据已知行为的另一者来估计另一者的DRAM访问行为。本文还揭示了明显影响访问比例和RBM的系统调用。

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