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Development of TEZip in PyTorch: Integrating New Prediction Models into an Existing Compression Framework

SessionResearch, ACM SRC, and Doctoral Showcase Posters

DescriptionIn high performance computing, researchers often work with extremely large time-series datasets. Compression techniques allow them to shrink their data down, allowing for quicker and less storage-intensive transfers. TEZip is a compression model that utilizes PredNet (a video prediction model) to predict each frame of a time-series dataset, subtracting these predictions from the actual frames and performing further encoding operations. However, TEZip is currently built using TensorFlow and only supports the PredNet model, which trains and predicts slowly. In this work, we rebuilt TEZip in order to accommodate for PyTorch models while also including functionality for PredNet as well as ConvLSTM, a simpler time-series prediction model. We found that our PyTorch version (specifically with the ConvLSTM model) results in faster compression and decompression times. This work is significant in extending the capabilities of TEZip and suggests that simple prediction models are worth exploring in the realm of prediction-based compression.

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