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Empirical Study of Data Allocation in Heterogeneous Memory

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Smart Computing and Communication (SmartCom 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10699))

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Abstract

With the rapid development of data-driven technologies, implementing heterogeneous memories is an alternative for processing large-size data tasks or efficient computations while considering economic factors. Many previous studies have addressed the exploration of adopting heterogeneous memories in the field of the algorithm design. One of the vital components of using the heterogeneous memory is creating effective data allocation plans. However, it is challenge to discern the superiority of each method for generating data allocation plans due to various application scenarios and constraints. In this work, we have completed an empirical study focusing recent advanced data allocation mechanisms for heterogeneous memories. We use experimental evaluations to examine a number of representative strategies and the main findings of this work also include analyses and syntheses deriving from our evaluations.

This work is supported by the Basic and Frontier Technology Research of Henan Province Science and Technology Department (No. 162300410198).

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Author information

Authors and Affiliations

  1. Institute of Intelligent Network System, Henan University, Kaifeng, 475000, China

    Hui Zhao

  2. Department of Computer Science, Pace University, New York City, NY, 10038, USA

    Meikang Qiu

  3. Shenzhen University, Shenzhen, 518060, Guangdong, China

    Meikang Qiu

  4. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Keke Gai

Authors
  1. Hui Zhao
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  2. Meikang Qiu
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  3. Keke Gai
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Corresponding author

Correspondence to Keke Gai .

Editor information

Editors and Affiliations

  1. Columbia University, New York, New York, USA

    Meikang Qiu

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Zhao, H., Qiu, M., Gai, K. (2018). Empirical Study of Data Allocation in Heterogeneous Memory. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2017. Lecture Notes in Computer Science(), vol 10699. Springer, Cham. https://doi.org/10.1007/978-3-319-73830-7_38

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  • DOI: https://doi.org/10.1007/978-3-319-73830-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73829-1

  • Online ISBN: 978-3-319-73830-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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