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DTSTAMP:20260422T000604Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231115T100000
DTEND;TZID=America/Denver:20231115T170000
UID:submissions.supercomputing.org_SC23_sess299_spostg115@linklings.com
SUMMARY:Fast Operations on Compressed Arrays without Decompression
DESCRIPTION:Harvey Dam (University of Utah)\n\nIn modern scientific comput
 ing and machine learning systems, data movement has overtaken compute as t
 he performance bottleneck, thus motivating the wider adoption of lossy dat
 a compression. Unfortunately, state-of-the-art floating-point array compre
 ssors such as SZ and ZFP require decompression before operations can be pe
 rformed on the data. In this work, our contribution is to show that compre
 ssion methods can be designed to allow efficient operations on compressed 
 arrays without having to first decompress. In particular, compression meth
 ods that consist of only linear transformations and quantization allow cer
 tain operations on compressed arrays without decompression. We develop suc
 h a compression method, called PyBlaz, the first compression method we kno
 w that can compress arbitrary-dimensional arrays and directly operate on t
 he compressed representation, with all stages running on GPUs.\n\nIn the p
 oster session, I will provide details about each compression step, several
  compressed-spaced operations, and our ongoing performance and application
  experiments.\n\nTag: Artificial Intelligence/Machine Learning, Algorithms
 , Applications, Architecture and Networks, Cloud Computing, Distributed Co
 mputing, Data Analysis, Visualization, and Storage, Performance Measuremen
 t, Modeling, and Tools, Programming Frameworks and System Software\n\nRegi
 stration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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