Distributed machine learning (DML) technology makes it possible to train large neural networks in a reasonable amount of time. Meanwhile, as the computing power grows much faster than network capacity, network communication has gradually become the bottleneck of DML. Current multi-tenant GPU clusters face network contention caused by hash-collision problem which not only further increases the overhead of communication, but also creates unfairness and affects the user experience. In this paper, we firstly analyse how network contention affects the training time in a cluster with 32 NVIDIA V100 GPUs. Then we propose vClos to eliminate network contention by jointly optimizing network topology and communication pattern in distributed training. An OCS-vClos which introduces a layer of optical circuit switches (OCSs) in the leaf-spine network is also proposed to reduce potential network resource fragmentation caused by resource allocation strategy in vClos. Testbed experiments and real-trace-based large-scale simulations are conducted to demonstrate the superiority of vClos over existing network resource scheduling strategies.

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