Vidya: Performing Code-Block I/O Characterization for Data Access Optimization
Vidya: Performing Code-Block I/O Characterization for Data Access Optimization
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DOI：
10.1109/hipc.2018.00036
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发表时间：
2018-12
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通讯作者：
H. Devarajan;Anthony Kougkas;Prajwal Challa;Xian-He Sun
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文献类型：
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作者：
H. Devarajan;Anthony Kougkas;Prajwal Challa;Xian-He Sun
在高性能计算系统中，理解、描述和调优科学应用程序的I/O行为是一个越来越复杂的过程。现有工具使用离线分析或在线分析来深入了解应用程序的I/O模式。然而，缺乏一个明确的公式来描述应用程序的I/O。此外，这些工具是特定于应用程序的，不考虑多租户系统。本文介绍了Vidya，一个I/O分析框架，它可以使用一个称为代码块I/O表征（CIOC）的新公式来预测应用程序的I/O强度。使用CIOC，开发人员和系统架构师可以调优应用程序的I/O行为，并更好地匹配底层存储系统，从而最大化性能。评估结果表明，Vidya可以预测应用程序的I/O强度，方差为0.05%。Vidya可以以98%的高精度分析应用程序，同时将分析时间缩短9倍。我们进一步展示了Vidya如何将应用程序的I/O时间优化3.7倍。
Understanding, characterizing and tuning scientific applications' I/O behavior is an increasingly complicated process in HPC systems. Existing tools use either offline profiling or online analysis to get insights into the applications' I/O patterns. However, there is lack of a clear formula to characterize applications' I/O. Moreover, these tools are application specific and do not account for multi-tenant systems. This paper presents Vidya, an I/O profiling framework which can predict application's I/O intensity using a new formula called Code-Block I/O Characterization (CIOC). Using CIOC, developers and system architects can tune an application's I/O behavior and better match the underlying storage system to maximize performance. Evaluation results show that Vidya can predict an application's I/O intensity with a variance of 0.05%. Vidya can profile applications with a high accuracy of 98% while reducing profiling time by 9x. We further show how Vidya can optimize an application's I/O time by 3.7x.