Profiling Platform Storage Using IO500 and Mistral

Nolan D. Monnier, J. Lofstead, Margaret Lawson, M. Curry
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引用次数: 4

Abstract

This paper explores how we used IO500 and the Mistral tool from Ellexus to observe detailed performance characteristics to inform tuning IO performance on Astra, a ARM-based Sandia machine with an all flash, Lustre-based storage array. Through this case study, we demonstrate that IO500 serves as a meaningful storage benchmark, even for all flash storage. We also demonstrate that using fine-grained profiling tools, such as Mistral, is essential for revealing tuning requirement details. Overall, this paper demonstrates the value of a broad spectrum benchmark, like IO500, together with a fine grained performance analysis tool, such as Mistral, for understanding detailed storage system performance for better informed tuning.
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使用IO500和Mistral分析平台存储
本文探讨了我们如何使用IO500和Ellexus的Mistral工具来观察详细的性能特征,以便在Astra上调优IO性能。Astra是基于arm的Sandia机器,采用全闪存和基于lustret的存储阵列。通过本案例研究,我们证明IO500可以作为有意义的存储基准,甚至适用于所有闪存。我们还演示了使用细粒度分析工具,如Mistral,对于揭示调优需求细节是必不可少的。总的来说,本文展示了广谱基准测试(如IO500)和细粒度性能分析工具(如Mistral)的价值,可以了解详细的存储系统性能,以便更好地进行调优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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[Copyright notice] Applying Machine Learning to Understand Write Performance of Large-scale Parallel Filesystems Towards Physical Design Management in Storage Systems Profiling Platform Storage Using IO500 and Mistral Active Learning-based Automatic Tuning and Prediction of Parallel I/O Performance
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