Efficient sparse collective communication and its application to accelerate distributed deep learning
KAUST DepartmentComputer Science
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant NumberOSR-CRG2020-4382
Online Publication Date2021-08-09
Print Publication Date2021-08-09
Permanent link to this recordhttp://hdl.handle.net/10754/665369
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AbstractEfficient 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.
CitationFei, J., Ho, C.-Y., Sahu, A. N., Canini, M., & Sapio, A. (2021). Efficient sparse collective communication and its application to accelerate distributed deep learning. Proceedings of the 2021 ACM SIGCOMM 2021 Conference. doi:10.1145/3452296.3472904
SponsorsWe 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.
Conference/Event nameSIGCOMM '21: Proceedings of the 2021 ACM SIGCOMM 2021 Conference