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Towards Portable Online Prediction of Network Utilization using MPI-level Monitoring
Abstract : 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 on-line 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.
Cited literature [26 references]
https://hal.inria.fr/hal-02184204
Contributor : Bogdan Nicolae Connect in order to contact the contributor
Submitted on : Monday, July 15, 2019 - 6:28:00 PM
Last modification on : Friday, January 21, 2022 - 3:20:41 AM
Contributor : Bogdan Nicolae Connect in order to contact the contributor
Submitted on : Monday, July 15, 2019 - 6:28:00 PM
Last modification on : Friday, January 21, 2022 - 3:20:41 AM
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