Li, Shijian and Walls, Robert J and Guo, Tian

Cloud GPU servers have become the de facto way for deep learning practitioners to train complex models on large-scale datasets. However, it is challenging to determine the appropriate cluster configuration-e.g., server type and number-for different training workloads while balancing the trade-offs in training time, cost, and model accuracy. Adding to the complexity is the potential to reduce the monetary cost by using cheaper, but revocable, transient GPU servers.In this work, we analyze distributed training performance under diverse cluster configurations using CM-DARE, a cloud-based measurement and training framework. Our empirical datasets include measurements from three GPU types, six geographic regions, twenty convolutional neural networks, and thousands of Google Cloud servers. We also demonstrate the feasibility of predicting training speed and overhead using regression-based models. Finally, we discuss potential use cases of our performance modeling such as detecting and mitigating performance bottlenecks.

@inproceedings{Li2020-lm,
  title = {Characterizing and Modeling Distributed Training with Transient
                 Cloud {GPU} Servers},
  booktitle = {2020 {IEEE} 40th International Conference on Distributed
                 Computing Systems ({ICDCS})},
  author = {Li, Shijian and Walls, Robert J and Guo, Tian},
  pages = {943--953},
  month = nov,
  year = {2020},
  keywords = {Training;Computational modeling;Graphics processing
                 units;Predictive models;Internet;Servers;Transient
                 analysis;distributed training;measurement;modeling}
}