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The volume of data and the velocity with which it is being generated by com-putational experiments on high performance computing (HPC) systems is quicklyoutpacing our ability to effectively store this information in its fullfidelity. There- fore, it is critically important to identify and studycompression methodologies that retain as much information as possible,particularly in the most salient regions of the simulation space. In thispaper, we cast this in terms of a general decision-theoretic problem anddiscuss a wavelet-based compression strategy for its solution. We pro- vide aheuristic argument as justification and illustrate our methodology on severalexamples. Finally, we will discuss how our proposed methodology may be utilizedin an HPC environment on large-scale computational experiments.

Prioritized Data Compression using Wavelets