research-article

RecSSD: near data processing for solid state drive based recommendation inference

Authors:

Mark Wilkening,

Caroline Trippel,

Carole-Jean Wu,

Gu-Yeon WeiAuthors Info & Claims

ASPLOS '21: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

Pages 717 - 729

https://doi.org/10.1145/3445814.3446763

Published: 17 April 2021 Publication History

Abstract

Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment. State-of-the-art models comprise large embedding tables that have billions of parameters requiring large memory capacities. Unfortunately, large and fast DRAM-based memories levy high infrastructure costs. Conventional SSD-based storage solutions offer an order of magnitude larger capacity, but have worse read latency and bandwidth, degrading inference performance. RecSSD is a near data processing based SSD memory system customized for neural recommendation inference that reduces end-to-end model inference latency by 2× compared to using COTS SSDs across eight industry-representative models.

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Cited By

Chen CYen JLai YLin YYang C(2024)RecTS: A Temporal-Aware Memory System Optimization for Training Deep Learning Recommendation ModelsProceedings of the 17th ACM International Systems and Storage Conference10.1145/3688351.3689155(104-117)Online publication date: 16-Sep-2024
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Index Terms

RecSSD: near data processing for solid state drive based recommendation inference
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Hardware
  1. Communication hardware, interfaces and storage
    1. External storage

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cover image ACM Conferences

ASPLOS '21: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

April 2021

1090 pages

ISBN:9781450383172

DOI:10.1145/3445814

General Chair:
Tim Sherwood
University of California at Santa Barbara, USA
,
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Emery Berger
University of Massachusetts at Amherst, USA
,
Christos Kozyrakis
Stanford University, USA

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Published: 17 April 2021

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Cited By

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https://dl.acm.org/doi/10.1145/3688351.3689155
Chun MKim MLee DPark JKim J(2024)ReadGuard: Integrated SSD Management for Priority-Aware Read Performance DifferentiationACM Transactions on Storage10.1145/367688420:4(1-39)Online publication date: 25-Jul-2024
https://dl.acm.org/doi/10.1145/3676884
Teguia DChen JBitchebe SBalmau OTchana ASchiavoni VEdinger JCao JJin Z(2024)vPIM: Processing-in-Memory VirtualizationProceedings of the 25th International Middleware Conference10.1145/3652892.3700782(417-430)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3652892.3700782
Liu SZheng NKang HSimmons XZhang JLanger MZhu WLee MWang Z(2024)Embedding Optimization for Training Large-scale Deep Learning Recommendation Systems with EMBarkProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688111(622-632)Online publication date: 8-Oct-2024
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Lim JKim YChung SKoushanfar FKong J(2024)Near-Memory Computing With Compressed Embedding Table for Personalized RecommendationIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.334587012:3(938-951)Online publication date: Jul-2024
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Li JShen ZLiu DChen XZhong KZeng ZTan Y(2024)LightFS: A Lightweight Host-CSD Coordinated File System Optimizing for Heavy Small File AccessesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344301043:11(3527-3538)Online publication date: Nov-2024
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