Performance Benchmarking of Parallel Hyperparameter Tuning for Deep Learning based Tornado Predictions
MetadataShow full item record
Type of Work35 pages
journal articles preprints
Citation of Original PublicationJonathan N. Basalyga et al., Performance Benchmarking of Parallel Hyperparameter Tuning for Deep Learning based Tornado Predictions, http://hpcf-files.umbc.edu/research/papers/Barajas_BDR2020.pdf
RightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic weather patterns much more rapidly, thus allowing for more timely alerts to the public. To deal with class imbalance challenges in machine learning, different data augmentation approaches have been proposed. In this work, we examine the wall time difference between live data augmentation methods versus the use of preaugmented data when they are used in a convolutional neural network based training for tornado prediction. We also compare CPU and GPU based training over varying sizes of augmented data sets. Additionally we examine what impact varying the number of GPUs used for training will produce given a convolutional neural network on wall time and accuracy. We conclude that using multiple GPUs to train a single network has no significant advantage over using a single GPU. The number of GPUs used during training should be kept as small as possible for maximum search throughput as the native Keras multi-GPU model provides little speedup with optimal learning parameters.