Efficient sparse collective communication and its application to accelerate distributed deep learning

Jiawei Fei, Chen-Yu Ho, Atal N. Sahu, Marco Canini, Amedeo Sapio

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

10 Scopus citations

Abstract

Efficient collective communication is crucial to parallel-computing applications such as distributed training of large-scale recommendation systems and natural language processing models. Existing collective communication libraries focus on optimizing operations for dense inputs, resulting in transmissions of many zeros when inputs are sparse. This counters current trends that see increasing data sparsity in large models. We propose OmniReduce, an efficient streaming aggregation system that exploits sparsity to maximize effective bandwidth use by sending only non-zero data blocks. We demonstrate that this idea is beneficial and accelerates distributed training by up to 8.2x. Even at 100 Gbps, OmniReduce delivers 1.4--2.9x better performance for network-bottlenecked DNNs.
Original languageEnglish (US)
Title of host publicationProceedings of the 2021 ACM SIGCOMM 2021 Conference
PublisherACM
ISBN (Print)9781450383837
DOIs
StatePublished - Sep 30 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-08-12
Acknowledged KAUST grant number(s): OSR-CRG2020-4382
Acknowledgements: We are grateful to Arvind Krishnamurthy, Jacob Nelson and Dan R. K. Ports for their helpful suggestions. We are thankful to Meituan for granting us access to a multi-GPU server testbed. We thank our shepherd, Kate Lin, and the anonymous reviewers for their helpful feedback. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2020-4382. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST. The work of Jiawei Fei at KAUST is supported by a sponsorship from China Scholarship Council (CSC). This
work was partially supported by a gift in kind from Huawei.

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