Student: Coleman Nichols (Clemson University)
Supervisor: Jon Calhoun (Clemson University)
Abstract: Large data sets tend to be very common in many areas of high-performance computing. Often times, the size of these data sets are so extreme that they far exceed the storage capabilities of their system. This highlights an opportunity to employ compression methods in order to reduce the data set down to a manageable size. Given that reduction methods operate on data in different ways, it is important to compare these methods with the goal of determining the optimal approach for any given data set. This poster compares the effectiveness of different data reduction methods on image data from Los Alamos National Labs based on three major parameters: PSNR, compression ratio, and compression rate. Our analysis indicated the SZ lossy compressor was the most effective for this data set, given that it offered the highest PSNR along with a very reasonable compression ratio.
ACM-SRC Semi-Finalist: no
Poster Summary: PDF
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