Towards Portable Online Prediction of Network Utilization Using MPI-Level Monitoring

TitleTowards Portable Online Prediction of Network Utilization Using MPI-Level Monitoring
Publication TypeConference Paper
Year of Publication2019
AuthorsTseng, S-M., B. Nicolae, G. Bosilca, E. Jeannot, A. Chandramowlishwaran, and F. Cappello
Conference Name2019 European Conference on Parallel Processing (Euro-Par 2019)
Date Published2019-08
Conference LocationGöttingen, Germany

Stealing network bandwidth helps a variety of HPC runtimes and services to run additional operations in the background without negatively affecting the applications. A key ingredient to make this possible is an accurate prediction of the future network utilization, enabling the runtime to plan the background operations in advance, such as to avoid competing with the application for network bandwidth. In this paper, we propose a portable deep learning predictor that only uses the information available through MPI introspection to construct a recurrent sequence-to-sequence neural network capable of forecasting network utilization. We leverage the fact that most HPC applications exhibit periodic behaviors to enable predictions far into the future (at least the length of a period). Our online approach does not have an initial training phase, it continuously improves itself during application execution without incurring significant computational overhead. Experimental results show better accuracy and lower computational overhead compared with the state-of-the-art on two representative applications.

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