1. Nonvolatile memory(e.g., Phase Change Memory) blurs the boundary between memory and storage and it could greatly facilitate the construction of in-memory durable data structures. Data structures can be processed and stored directly in NVRAM. To XXX, YYY is a widely adopted mechanism. However, XXXXXXXX. By leveraging the XXXXX, we can YYYYYY. We tested our YYYYYY. Experiment results show that ZZZZZZZ, which can help extend the lifetime of NVRAM and improve performance.

2. By enabling efficient XXXX, YYY serve as the foundation for ZZZ.  For example: By enabling efficient insertions, point lookups, and range queries, key-value stores serve as the foundation for this growing group of important applications.

3. For write-intensive workloads, key-value stores based on Log-Structured Merge-Trees(LSM-trees)[1] have become the state of the art. Various distributed and local stores built on LSM-trees are widely deployed in large-scale production environments, such as BigTable [2] and LevelDB [3] at Google, Cassandra [4], HBase [5] and RocksDB [6] at Facebook, and Riak [7] at Basho. The main advantage of LSM-trees over other indexing structures (such as B-trees) is that they maintain sequential access patterns for writes. Small updates on B-trees may involve many random writes, and are hence not efficient on either solid-state storage devices or hard-disk drives.

-----

Reference

[1] Patrick ONeil, Edward Cheng, Dieter Gawlick, and Elizabeth ONeil. The Log-Structured MergeTree (LSM-tree). Acta Informatica, 33(4):351–385, 1996.

[2] Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Michael Burrows, Tushar Chandra, Andrew Fikes, and Robert Gruber. Bigtable: A Distributed Storage System for Structured Data. In Proceedings of the 7th Symposium on Operating Systems Design and Implementation (OSDI ’06), pages 205–218, Seattle, Washington, November 2006.

[3] Sanjay Ghemawat and Jeff Dean. LevelDB. http://code.google.com/p/leveldb, 2011.

[4] Avinash Lakshman and Prashant Malik. Cassandra – A Decentralized Structured Storage System. In The 3rd ACM SIGOPS International Workshop on Large Scale Distributed Systems and Middleware, Big Sky Resort, Montana, Oct 2009.

[5] Tyler Harter, Dhruba Borthakur, Siying Dong, Amitanand Aiyer, Liyin Tang, Andrea C. ArpaciDusseau, and Remzi H. Arpaci-Dusseau. Analysis of HDFS Under HBase: A Facebook Messages Case Study. In Proceedings of the 12th
USENIX Symposium on File and Storage Technologies (FAST ’14), Santa Clara, California, February 2014.

[6] Facebook. RocksDB. http://rocksdb.org/, 2013.

[7] Riak. http://docs.basho.com/riak/, 2015.

[8]

SSD论文优秀句子的更多相关文章

  1. 深度学习 目标检测算法 SSD 论文简介

    深度学习 目标检测算法 SSD 论文简介 一.论文简介: ECCV-2016 Paper:https://arxiv.org/pdf/1512.02325v5.pdf  Slides:http://w ...

  2. SSD论文理解

    SSD论文贡献: 1. 引入了一种单阶段的检测器,比以前的算法YOLO更准更快,并没有使用RPN和Pooling操作: 2. 使用一个小的卷积滤波器应用在不同的feature map层从而预测BB的类 ...

  3. 翻译SSD论文(Single Shot MultiBox Detector)

    转自http://lib.csdn.net/article/deeplearning/53059 作者:Ai_Smith 本文翻译而来,如有侵权,请联系博主删除.未经博主允许,请勿转载.每晚泡脚,闲来 ...

  4. 深度学习笔记(七)SSD 论文阅读笔记简化

    一. 算法概述 本文提出的SSD算法是一种直接预测目标类别和bounding box的多目标检测算法.与faster rcnn相比,该算法没有生成 proposal 的过程,这就极大提高了检测速度.针 ...

  5. 深度学习笔记(七)SSD 论文阅读笔记

    一. 算法概述 本文提出的SSD算法是一种直接预测目标类别和bounding box的多目标检测算法.与faster rcnn相比,该算法没有生成 proposal 的过程,这就极大提高了检测速度.针 ...

  6. 转 SSD论文解读

    版权声明:本文为博主原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接和本声明. 本文链接:https://blog.csdn.net/u010167269/article/det ...

  7. SSD论文学习

    SSD: Single Shot MultiBox Detector——目标检测 参考https://blog.csdn.net/u010167269/article/details/52563573 ...

  8. ssd论文解读

    https://www.sohu.com/a/168738025_717210 https://www.cnblogs.com/lillylin/p/6207292.html https://blog ...

  9. ssd算法论文理解

    这篇博客主要是讲下我在阅读ssd论文时对论文的理解,并且自行使用pytorch实现了下论文的内容,并测试可以用. 开篇放下论文地址https://arxiv.org/abs/1512.02325,可以 ...

随机推荐

  1. 找回丢失的SQL Server性能计数器

    There was one time when I was delivering a Service using a tool that gathers performance data throug ...

  2. 【WPF】 打开本地的文件或者文件夹

    问题描述: 我做的程序中需要添加帮助文档,我将文档生成了CHM格式,在用户点击帮助按钮时候 弹出帮助文档. 实现方法: System.Diagnostics.Process.Start(AppDoma ...

  3. 理解shared_ptr<T> ---2

    1.引用计数字段不能放在资源管理类中.我们的解决办法是,把引用计数和资源绑在一起,进行二次封装.但是这样存在一个大问题,不同类型的资源管理类不能兼容.也就是说,shared_ptr<Dog> ...

  4. JavaScript中的Partial Application和Currying

    这篇文章是一篇学习笔记,记录我在JS学习中的一个知识点及我对它的理解,知识点和技巧本身并不是我原创的.(引用或参考到的文章来源在文末) 先不解释Partial Application(偏函数应用)和C ...

  5. 用HTML5 Canvas 做擦除及扩散效果

    2013年的时候曾经使用canvas实现了一个擦除效果的需求,即模拟用户在模糊的玻璃上擦除水雾看到清晰景色的交互效果.好在2012年的时候学习HTML5的时候研究过canvas了,所以在比较短的时间内 ...

  6. Oracle Hints具体解释

    在向大家具体介绍Oracle Hints之前,首先让大家了解下Oracle Hints是什么,然后全面介绍Oracle Hints,希望对大家实用.基于代价的优化器是非常聪明的,在绝大多数情况下它会选 ...

  7. 使用日志服务LogHub替换Kafka

    https://yq.aliyun.com/articles/35979#index_section

  8. Android_AsyncTask_DownloadImg

    layout.xml <LinearLayout xmlns:android="http://schemas.android.com/apk/res/android" xml ...

  9. 修改整个app的字体

    在项目开发中  有时候为了一些好的UI效果  可能需要自定义字体  app导入字体库的教程网上有很多 导进去 修改plist文件  然后如何将整个app的字体都换成自定义的字体呢  一个个去写太麻烦了 ...

  10. nfs文件系统挂载失败解决方法

    nfs文件系统挂载失败解决方法 */--> nfs文件系统挂载失败解决方法 Table of Contents 1. 错误提示 2. 我的配置 1 错误提示 bootserver=255.255 ...