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UID:submissions.supercomputing.org_SC22_sess223_spostu107@linklings.com
SUMMARY:Comparing Effectiveness of Lossy and Lossless Reduction Techniques
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nComparing Effectiv
 eness of Lossy and Lossless Reduction Techniques\n\nNichols\n\nLarge data 
 sets tend to be very common in many areas of high-performance computing. O
 ften times, the size of these data sets are so extreme that they far excee
 d the storage capabilities of their system. This highlights an opportunity
  to employ compression methods in order to reduce the data set down to a m
 anageable size. Given that reduction methods operate on data in different 
 ways, it is important to compare these methods with the goal of determinin
 g the optimal approach for any given data set. This poster compares the ef
 fectiveness of different data reduction methods on image data from Los Ala
 mos 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 P
 SNR along with a very reasonable compression ratio.\n\nRegistration Catego
 ry: Tech Program Reg Pass, Exhibits Reg Pass
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