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Flashy prefetching for high-performance flash drives

机译:高性能Flash驱动器的Flashy预取

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摘要

While hard drives hold on to the capacity advantage, flash-based solid-state drives (SSD) with high bandwidth and low latency have become good alternatives for I/O-intensive applications. Traditional data prefetching has been primarily designed to improve I/O performance on hard drives. The same techniques, if applied unchanged on flash drives, are likely to either fail to fully utilize SSDs, or interfere with application I/O requests, both of which could result in undesirable application performance. In this work, we demonstrate that data prefetching, when effectively harnessing the high performance of SSDs, can provide significant performance benefits for a wide range of data-intensive applications. The new technique, flashy prefetching, consists of accurate prediction of application needs in runtime and adaptive feedback-directed prefetching that scales with application needs, while being considerate to underlying storage devices. We have implemented a real system in Linux and evaluated it on four different SSDs. The results show 65–70% prefetching accuracy and an average 20% speedup on LFS, web search engine traces, BLAST, and TPC-H like benchmarks across various storage drives.
机译:尽管硬盘驱动器保持了容量优势,但具有高带宽和低延迟的基于闪存的固态驱动器(SSD)已成为I / O密集型应用程序的不错选择。传统的数据预取主要是为了提高硬盘驱动器的I / O性能而设计的。如果相同的技术在闪存驱动器上保持不变,则可能无法完全利用SSD,或者可能干扰应用程序I / O请求,这两种技术均可能导致不良的应用程序性能。在这项工作中,我们证明了数据预取在有效利用SSD的高性能时,可以为各种数据密集型应用程序提供显着的性能优势。闪存预取这一新技术包括在运行时准确预测应用程序需求,以及根据应用程序需求扩展规模的自适应反馈定向预取,同时考虑底层存储设备。我们在Linux中实现了一个真实的系统,并在四个不同的SSD上对其进行了评估。结果表明,在各种存储驱动器上,LFS,Web搜索引擎跟踪,BLAST和TPC-H之类的基准,预取精度为65-70%,平均提速20%。

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