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NVM Streaker: a fast and reconfigurable performance simulator for non-volatile memory-based memory architecture

机译:NVM Streaker:一种快速且可重新配置的性能模拟器,用于基于非易失性存储器的存储器体系结构

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摘要

The high density, low power consumption non-volatile memory (NVM) provides a promising DRAM alternative for the in-memory big-data processing applications, e.g., Spark, It is significant to simulate the behaviors when NVMs are deployed into the area of big-data processing before their widespread use in market. However, existing simulation approaches are not applicable for big-data processing due to two reasons. First, some approaches require complicated hardware and/or OS supports. Second, cycle-level or function-level simulations are too time-consuming to simulate the whole software stack of big-data processing. Therefore, the complexity and expensive time cost in NVM simulation have dramatically dragged down the integrated research of big data with NVM. This paper proposes a fast and reconfigurable simulation method, called NVM Streaker, which does not need complex hardware or OS supports. It simulates NVM access costs using disturbed DRAM accesses and commonly configurable hardware parameters. It is fast since we use DRAM accesses and change its access costs to simulate NVM access costs, thus enabling to simulate the whole software stack to run Spark applications. It is reconfigurable since we enable users to configure the disturbed memory access costs, in order to simulate different NVM access costs. The experimental results show that we can simulate Spark applications with almost negligible cost and high efficiency.
机译:高密度,低功耗的非易失性存储器(NVM)为内存中大数据处理应用程序(例如Spark)提供了一种有前途的DRAM替代品。对于将NVM部署到大容量区域时的行为仿真非常重要。数据处理在市场上广泛使用之前。但是,由于两个原因,现有的仿真方法不适用于大数据处理。首先,某些方法需要复杂的硬件和/或OS支持。其次,周期级别或功能级别的模拟过于费时,无法模拟整个软件堆栈的大数据处理。因此,NVM仿真的复杂性和昂贵的时间成本极大地拖累了NVM对大数据的集成研究。本文提出了一种快速且可重新配置的仿真方法,称为NVM Streaker,它不需要复杂的硬件或操作系统支持。它使用受干扰的DRAM访问和通常可配置的硬件参数来模拟NVM访问成本。由于我们使用DRAM访问并更改其访问成本来模拟NVM访问成本,因此速度很快,从而能够模拟整个软件堆栈来运行Spark应用程序。它是可重新配置的,因为我们使用户能够配置受干扰的内存访问成本,以便模拟不同的NVM访问成本。实验结果表明,我们可以以几乎可​​以忽略的成本和高效率来模拟Spark应用程序。

著录项

  • 来源
    《Journal of supercomputing》 |2018年第8期|3875-3903|共29页
  • 作者单位

    State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences,University of Chinese Academy of Sciences;

    State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences;

    State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences,University of Chinese Academy of Sciences;

    State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences,University of Chinese Academy of Sciences;

    State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences;

    State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences;

    State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences,University of Chinese Academy of Sciences;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    NVM; Reconfigurable; Fast simulation; Big data;

    机译:NVM;可重构;快速仿真;大数据;

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