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Title: Reducing the Training Overhead of the HPC Compression Autoencoder via Dataset Proportioning
As the storage overhead of high-performance computing (HPC) data reaches into the petabyte or even exabyte scale, it could be useful to find new methods of compressing such data. The compression autoencoder (CAE) has recently been proposed to compress HPC data with a very high compression ratio; however, this machine learning-based method suffers from the major drawback of lengthy training times. In this paper, we attempt to mitigate this problem by proposing a proportioning scheme that reduces the amount of data that is used for training relative to the amount of data to be compressed. We show that this method drastically reduces the training time without, in most cases, significantly increasing the error. We further explain how this scheme can even improve the accuracy of the CAE on certain datasets. Finally, we provide some guidance on how to determine a suitable proportion of the training dataset to use in order to train the CAE for a given dataset.  more » « less
Award ID(s):
1828363 1813081
PAR ID:
10296692
Author(s) / Creator(s):
Date Published:
Journal Name:
Proc. Of the 15th IEEE International Conference on Networking, Architecture, and Storage (NAS)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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