Statistical Prediction of Lossy Compression Ratios for 3D Scientific Data
DescriptionIn the fields of science and engineering, lossy compression plays a growing role in running scientific simulations, as output data is on the scale of terabytes. Using error bounded lossy compression reduces the amount of storage for each simulation; however, there is no known bound for the upper limit of lossy compressibility. Data correlation structures, compressors and error bounds are factors allowing larger compression ratios and improved quality metrics. This provides one direction towards quantifying lossy compressibility. Our previous work explored 2D statistical methods to characterize the data correlation structures and their relationships, through functional models, to compression ratios and quality metrics for 2D scientific data. In this poster, we explore the expansion of our statistical methods to 3D scientific data. The method was comparable to 2D. Our work is the next step towards evaluating the theoretical limits of lossy compressibility used to predict compression performance and optimally adapt compressors.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
TimeWednesday, 16 November 20224:45pm - 5pm CST
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