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    Efficient Sparse Collective Communication and its application to Accelerate Distributed Deep Learning

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    Description:
    Technical report
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    Type
    Technical Report
    Authors
    Fei, Jiawei
    Ho, Chen-Yu cc
    Sahu, Atal Narayan
    Canini, Marco cc
    Sapio, Amedeo
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-09-30
    Permanent link to this record
    http://hdl.handle.net/10754/665369
    
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    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 8.2×. Even at 100 Gbps, we assess that OmniReduce delivers 1.2-2.6× better performance for network-bottlenecked DNNs.
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    Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Technical Reports

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