research-article

A Study of Sorting Algorithms on Approximate Memory

Authors:

Shunning Jiang,

Xueyan TangAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 647 - 662

https://doi.org/10.1145/2882903.2882908

Published: 14 June 2016 Publication History

Abstract

Hardware evolution has been one of the driving factors for the redesign of database systems. Recently, approximate storage emerges in the area of computer architecture. It trades off precision for better performance and/or energy consumption. Previous studies have demonstrated the benefits of approximate storage for applications that are tolerant to imprecision such as image processing. However, it is still an open question whether and how approximate storage can be used for applications that do not expose such intrinsic tolerance. In this paper, we study one of the most basic operations in database--sorting on a hybrid storage system with both precise storage and approximate storage. Particularly, we start with a study of three common sorting algorithms on approximate storage. Experimental results show that a 95% sorted sequence can be obtained with up to 40% reduction in total write latencies. Thus, we propose an approx-refine execution mechanism to improve the performance of sorting algorithms on the hybrid storage system to produce precise results. Our optimization gains the performance benefits by offloading the sorting operation to approximate storage, followed by an efficient refinement to resolve the unsortedness on the output of the approximate storage. Our experiments show that our approx-refine can reduce the total memory access time by up to 11%. These studies shed light on the potential of approximate hardware for improving the performance of applications that require precise results.

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Cited By

Ho YWu CYang MChen TChang Y(2019)Replanting Your Forest: NVM-friendly Bagging Strategy for Random Forest2019 IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)10.1109/NVMSA.2019.8863525(1-6)Online publication date: Aug-2019
https://doi.org/10.1109/NVMSA.2019.8863525
Li KLi G(2018)Approximate Query Processing: What is New and Where to Go?Data Science and Engineering10.1007/s41019-018-0074-43:4(379-397)Online publication date: 14-Sep-2018
https://doi.org/10.1007/s41019-018-0074-4
Sun JLi JGao H(2017)Efficient Batch Grouping in Relational DatasetsDatabase Systems for Advanced Applications10.1007/978-3-319-55753-3_24(376-390)Online publication date: 22-Mar-2017
https://doi.org/10.1007/978-3-319-55753-3_24

Index Terms

A Study of Sorting Algorithms on Approximate Memory
1. Hardware
  1. Emerging technologies
    1. Analysis and design of emerging devices and systems
2. Information systems
  1. Data management systems
    1. Database administration
      1. Database performance evaluation

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cover image ACM Conferences

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, USA

Copyright © 2016 ACM.

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Published: 14 June 2016

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SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

California, San Francisco, USA

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Cited By

Ho YWu CYang MChen TChang Y(2019)Replanting Your Forest: NVM-friendly Bagging Strategy for Random Forest2019 IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)10.1109/NVMSA.2019.8863525(1-6)Online publication date: Aug-2019
https://doi.org/10.1109/NVMSA.2019.8863525
Li KLi G(2018)Approximate Query Processing: What is New and Where to Go?Data Science and Engineering10.1007/s41019-018-0074-43:4(379-397)Online publication date: 14-Sep-2018
https://doi.org/10.1007/s41019-018-0074-4
Sun JLi JGao H(2017)Efficient Batch Grouping in Relational DatasetsDatabase Systems for Advanced Applications10.1007/978-3-319-55753-3_24(376-390)Online publication date: 22-Mar-2017
https://doi.org/10.1007/978-3-319-55753-3_24

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