Two-Stage In-Storage Processing and Scheduling for Pattern Matching Applications

Joohyeong Yoon; Yoonjin Lee; Won Seob Jeong; Won Woo Ro

doi:10.1109/ACCESS.2021.3093764

Two-Stage In-Storage Processing and Scheduling for Pattern Matching Applications

Joohyeong Yoon, Yoonjin Lee, Won Seob Jeong, Won Woo Ro

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

In-storage processing technology allows applications to run on embedded processors and accelerators inside solid-state drives (SSDs) for efficient computing distribution. Especially, in pattern matching applications, in-storage computing can be processed quickly due to low data access latency, and the number of I/Os can be reduced by returning only a small amount of results to the host system after processing. Previously proposed in-storage processing is separated into three phases: Command decoding, data access, and data processing. In this case, data processing is strictly isolated from data access, and the isolation constraints the utilization of storage. Merging data access and data processing among the phases can enhance the utilization of storage. To efficiently merge them, we propose two-stage in-storage processing and scheduling, especially for the pattern matching application. The first stage processing during data access reduces the second stage processing latency. Also, leveraging the pattern matching results of the first stage processing, our scheduler prioritizes key requests that should return the results to the host system so that they are completed earlier than non-key requests. The proposed scheduling reduces the response time of in-storage processing requests by 52.6 % on average.

Original language	English
Article number	9468607
Pages (from-to)	95702-95715
Number of pages	14
Journal	IEEE Access
Volume	9
DOIs	https://doi.org/10.1109/ACCESS.2021.3093764
Publication status	Published - 2021

Bibliographical note

Funding Information:
This work was supported in part by the Ministry of Trade, Industry and Energy (MOTIE) under Grant 10080674 (Development of Reconfigurable Artificial Neural Network Accelerator and Instruction Set Architecture) and Korea Semiconductor Research Consortium (KSRC) Support Program for the Development of the Future Semiconductor Device, in part by Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) under Grant 2021-0-00853 (Developing Software Platform for Programming of PIM, 50%) and Samsung Electronics Company Ltd., under Grant IO201210-07936-01.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science(all)
Materials Science(all)
Engineering(all)
Electrical and Electronic Engineering

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10.1109/ACCESS.2021.3093764

Cite this

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abstract = "In-storage processing technology allows applications to run on embedded processors and accelerators inside solid-state drives (SSDs) for efficient computing distribution. Especially, in pattern matching applications, in-storage computing can be processed quickly due to low data access latency, and the number of I/Os can be reduced by returning only a small amount of results to the host system after processing. Previously proposed in-storage processing is separated into three phases: Command decoding, data access, and data processing. In this case, data processing is strictly isolated from data access, and the isolation constraints the utilization of storage. Merging data access and data processing among the phases can enhance the utilization of storage. To efficiently merge them, we propose two-stage in-storage processing and scheduling, especially for the pattern matching application. The first stage processing during data access reduces the second stage processing latency. Also, leveraging the pattern matching results of the first stage processing, our scheduler prioritizes key requests that should return the results to the host system so that they are completed earlier than non-key requests. The proposed scheduling reduces the response time of in-storage processing requests by 52.6 % on average.",

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Two-Stage In-Storage Processing and Scheduling for Pattern Matching Applications

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