Performance Analysis and Comparison of Distributed Machine Learning Systems
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Deep learning has permeated through many aspects of computing/processing systems in recent years. While distributed training architectures/frameworks are adopted for training large deep learning models quickly, there has not been a systematic study of the communication bottlenecks of these architectures and their effects on the computation cycle time and scalability. In order to analyze this problem for synchronous Stochastic Gradient Descent (SGD) training of deep learning models, we developed a performance model of computation time and communication latency under three different system architectures: Parameter Server (PS), peer-to-peer (P2P), and Ring allreduce (RA). To complement and corroborate our analytical models with quantitative results, we evaluated the computation and communication performance of these system architectures of the systems via experiments performed with Tensorflow and Horovod frameworks. We found that the system architecture has a very significant effect on the performance of training. RA-based systems achieve scalable performance as they successfully decouple network usage from the number of workers in the system. In contrast, 1PS systems suffer from low performance due to network congestion at the parameter server side. While P2P systems fare better than 1PS systems, they still suffer from significant network bottleneck. Finally, RA systems also excel by virtue of overlapping computation time and communication time, which PS and P2P architectures fail to achieve.
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