A joint management middleware to improve training performance of deep recommendation systems with SSDs

Chun Feng Wu, Carole Jean Wu, Gu Yeon Wei, David Brooks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the sizes and variety of training data scale over time, data preprocessing is becoming an important performance bottleneck for training deep recommendation systems. This challenge becomes more serious when training data is stored in Solid-State Drives (SSDs). Due to the access behavior gap between recommendation systems and SSDs, unused training data may be read and filtered out during preprocessing. This work advocates a joint management middleware to avoid reading unused data by bridging the access behavior gap. The evaluation results show that our middleware can effectively improve the performance of the data preprocessing phase so as to boost training performance.

Original languageEnglish (US)
Title of host publicationProceedings of the 59th ACM/IEEE Design Automation Conference, DAC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages157-162
Number of pages6
ISBN (Electronic)9781450391429
DOIs
StatePublished - Jul 10 2022
Event59th ACM/IEEE Design Automation Conference, DAC 2022 - San Francisco, United States
Duration: Jul 10 2022Jul 14 2022

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference59th ACM/IEEE Design Automation Conference, DAC 2022
Country/TerritoryUnited States
CitySan Francisco
Period7/10/227/14/22

Keywords

  • data arranger
  • data preprocessing
  • deep recommendation systems
  • hardware/software co-design
  • log-structured merge (LSM)
  • solid-state drives (SSDs)
  • training performance

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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