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Software-hardware co-design for fast and scalable training of deep learning recommendation models

ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

Pages 993 - 1011

https://doi.org/10.1145/3470496.3533727

Published: 11 June 2022 Publication History

Abstract

Deep learning recommendation models (DLRMs) have been used across many business-critical services at Meta and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper, we present Neo, a software-hardware co-designed system for high-performance distributed training of large-scale DLRMs. Neo employs a novel 4D parallelism strategy that combines table-wise, row-wise, column-wise, and data parallelism for training massive embedding operators in DLRMs. In addition, Neo enables extremely high-performance and memory-efficient embedding computations using a variety of critical systems optimizations, including hybrid kernel fusion, software-managed caching, and quality-preserving compression. Finally, Neo is paired with ZionEX, a new hardware platform co-designed with Neo's 4D parallelism for optimizing communications for large-scale DLRM training. Our evaluation on 128 GPUs using 16 ZionEX nodes shows that Neo outperforms existing systems by up to 40× for training 12-trillion-parameter DLRM models deployed in production.

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cover image ACM Conferences

ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture

June 2022

1097 pages

ISBN:9781450386104

DOI:10.1145/3470496

General Chairs:
Valentina Salapura
Google
,
Mohamed Zahran
New York University
,
Program Chairs:
Fred Chong
The University of Chicago
,
Lingjia Tang
The University of Michigan

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Cited By

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https://doi.org/10.14778/3685800.3685832
Zhang HZhao PMiao XShao YLiu ZYang TCui B(2024)Experimental Analysis of Large-Scale Learnable Vector Storage CompressionProceedings of the VLDB Endowment10.14778/3636218.363623417:4(808-822)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.14778/3636218.3636234
Wu ZYuan Dai LWang YWang SBergman K(2024)Flexible silicon photonic architecture for accelerating distributed deep learningJournal of Optical Communications and Networking10.1364/JOCN.49737216:2(A157)Online publication date: 9-Jan-2024
https://doi.org/10.1364/JOCN.497372
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