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It is important to scale out deep neural network (DNN) training for reducingmodel training time. The high communication overhead is one of the majorperformance bottlenecks for distributed DNN training across multiple GPUs. Ourinvestigations have shown that popular open-source DNN systems could onlyachieve 2.5 speedup ratio on 64 GPUs connected by 56 Gbps network. To addressthis problem, we propose a communication backend named GradientFlow fordistributed DNN training, and employ a set of network optimization techniques.First, we integrate ring-based allreduce, mixed-precision training, andcomputation/communication overlap into GradientFlow. Second, we propose lazyallreduce to improve network throughput by fusing multiple communicationoperations into a single one, and design coarse-grained sparse communication toreduce network traffic by only transmitting important gradient chunks. Whentraining ImageNet/AlexNet on 512 GPUs, our approach achieves 410.2 speedupratio and completes 95-epoch training in 1.5 minutes, which outperformsexisting approaches.

Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes