Pipette: Efficient Fine-Grained Reads for SSDs

Shuhan Bai; Hu Wan; Yun Huang; Xuan Sun; Fei Wu; Changsheng Xie; Hung-Chih Hsieh; Tei-Wei Kuo; Chun Jason Xue

doi:10.1145/3489517.3530467

Pipette: Efficient Fine-Grained Reads for SSDs

Shuhan Bai, Hu Wan, Yun Huang, Xuan Sun, Fei Wu, Changsheng Xie, Hung-Chih Hsieh, Tei-Wei Kuo, Chun Jason Xue

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

244 Downloads (CityUHK Scholars)

Abstract

Big data applications, such as recommendation systems and social networks, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices tend to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, a fine-grained read framework, Pipette, is proposed in this paper, as an extension to the traditional I/O framework. With an adaptive caching design, the proposed Pipette framework offers tremendous reduction in I/O traffics as well as achieves significant performance gain. A Pipette prototype was implemented with Ext4 file system on an SSD for two real-world applications, where the I/O throughput is improved by 31.6% and 33.5%, and the I/O traffic is reduced by 95.6% and 93.6%, respectively.

Original language	English
Title of host publication	DAC '22
Subtitle of host publication	Proceedings of the 59th ACM/IEEE Design Automation Conference
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Pages	385–390
ISBN (Print)	978-1-4503-9142-9
DOIs	https://doi.org/10.1145/3489517.3530467
Publication status	Published - Jul 2022
Event	59th Design Automation Conference, DAC 2022 - Moscone West Center, San Francisco, United States Duration: 10 Jul 2022 → 14 Jul 2022 https://www.dac.com/ https://ieeexplore.ieee.org/xpl/conhome/1000196/all-proceedings

Conference

Conference	59th Design Automation Conference, DAC 2022
Abbreviated title	DAC
Place	United States
City	San Francisco
Period	10/07/22 → 14/07/22
Internet address	https://www.dac.com/ https://ieeexplore.ieee.org/xpl/conhome/1000196/all-proceedings

Bibliographical note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61821003, No. U2001203, No. 61872413, No. 61902137, and by the Research Grants Council of the Hong Kong Special Administrative Region, China under Grant No. CityU 11217020, No. CityU 11218720.

Research Keywords

file system
solid-state drive
fine-grained reads

Publisher's Copyright Statement

COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: ©ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference, https://doi.org/10.1145/3489517.3530467

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10.1145/3489517.3530467

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Cite this

@inproceedings{ba61ab62f4b540e79a7da52cf76485d0,

title = "Pipette: Efficient Fine-Grained Reads for SSDs",

abstract = "Big data applications, such as recommendation systems and social networks, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices tend to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, a fine-grained read framework, Pipette, is proposed in this paper, as an extension to the traditional I/O framework. With an adaptive caching design, the proposed Pipette framework offers tremendous reduction in I/O traffics as well as achieves significant performance gain. A Pipette prototype was implemented with Ext4 file system on an SSD for two real-world applications, where the I/O throughput is improved by 31.6% and 33.5%, and the I/O traffic is reduced by 95.6% and 93.6%, respectively.",

keywords = "file system, solid-state drive, fine-grained reads",

author = "Shuhan Bai and Hu Wan and Yun Huang and Xuan Sun and Fei Wu and Changsheng Xie and Hung-Chih Hsieh and Tei-Wei Kuo and Xue, {Chun Jason}",

note = "Research Unit(s) information for this publication is provided by the author(s) concerned.; 59th Design Automation Conference, DAC 2022, DAC ; Conference date: 10-07-2022 Through 14-07-2022",

year = "2022",

month = jul,

doi = "10.1145/3489517.3530467",

language = "English",

isbn = "978-1-4503-9142-9",

pages = "385–390",

booktitle = "DAC '22",

publisher = "Association for Computing Machinery",

address = "United States",

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}

Bai, S , Wan, H , Huang, Y , Sun, X, Wu, F, Xie, C, Hsieh, H-C, Kuo, T-W & Xue, CJ 2022, Pipette: Efficient Fine-Grained Reads for SSDs. in DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference. Association for Computing Machinery, New York, NY, pp. 385–390, 59th Design Automation Conference, DAC 2022, San Francisco, California, United States, 10/07/22. https://doi.org/10.1145/3489517.3530467

Pipette: Efficient Fine-Grained Reads for SSDs. / Bai, Shuhan ; Wan, Hu ; Huang, Yun et al.
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference. New York, NY: Association for Computing Machinery, 2022. p. 385–390.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Pipette

T2 - 59th Design Automation Conference, DAC 2022

AU - Bai, Shuhan

AU - Wan, Hu

AU - Huang, Yun

AU - Sun, Xuan

AU - Wu, Fei

AU - Xie, Changsheng

AU - Hsieh, Hung-Chih

AU - Kuo, Tei-Wei

AU - Xue, Chun Jason

N1 - Research Unit(s) information for this publication is provided by the author(s) concerned.

PY - 2022/7

Y1 - 2022/7

N2 - Big data applications, such as recommendation systems and social networks, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices tend to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, a fine-grained read framework, Pipette, is proposed in this paper, as an extension to the traditional I/O framework. With an adaptive caching design, the proposed Pipette framework offers tremendous reduction in I/O traffics as well as achieves significant performance gain. A Pipette prototype was implemented with Ext4 file system on an SSD for two real-world applications, where the I/O throughput is improved by 31.6% and 33.5%, and the I/O traffic is reduced by 95.6% and 93.6%, respectively.

AB - Big data applications, such as recommendation systems and social networks, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices tend to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, a fine-grained read framework, Pipette, is proposed in this paper, as an extension to the traditional I/O framework. With an adaptive caching design, the proposed Pipette framework offers tremendous reduction in I/O traffics as well as achieves significant performance gain. A Pipette prototype was implemented with Ext4 file system on an SSD for two real-world applications, where the I/O throughput is improved by 31.6% and 33.5%, and the I/O traffic is reduced by 95.6% and 93.6%, respectively.

KW - file system

KW - solid-state drive

KW - fine-grained reads

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M3 - RGC 32 - Refereed conference paper (with host publication)

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SP - 385

EP - 390

BT - DAC '22

PB - Association for Computing Machinery

CY - New York, NY

Y2 - 10 July 2022 through 14 July 2022

ER -

Pipette: Efficient Fine-Grained Reads for SSDs

Abstract

Conference

Bibliographical note

Funding

Research Keywords

Publisher's Copyright Statement

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

GRF: Cutting the Tail Latency for Low-Cost High-Density Server SSDs with Reliability Considerations

GRF: How to Utilize a Huge Number of Flash Chips to Meet the Performance and Reliability Requirements via Self-Healing and Partitioning

Accelerating Large-Scale Recommender Systems

Cite this

Pipette: Efficient Fine-Grained Reads for SSDs

Abstract

Conference

Bibliographical note

Funding

Research Keywords

Publisher's Copyright Statement

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: Cutting the Tail Latency for Low-Cost High-Density Server SSDs with Reliability Considerations

GRF: How to Utilize a Huge Number of Flash Chips to Meet the Performance and Reliability Requirements via Self-Healing and Partitioning

Student theses

Accelerating Large-Scale Recommender Systems

Cite this