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DTSTAMP:20211207T055342Z
LOCATION:220-221
DTSTART;TZID=America/Chicago:20211118T153000
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UID:submissions.supercomputing.org_SC21_sess173@linklings.com
SUMMARY:Data Compression and Workflows
DESCRIPTION:Paper\n\nndzip-gpu: Efficient Lossless Compression of Scientif
 ic Floating-Point Data on GPUs\n\nKnorr, Thoman, Fahringer\n\nLossless dat
 a compression is a promising software approach for reducing the bandwidth 
 requirements of scientific applications on accelerator clusters without in
 troducing approximation errors.  Suitable compressors must be able to effe
 ctively compact floating-point data while saturating the system in...\n\n-
 --------------------\nResilient Error-Bounded Lossy Compressor for Data Tr
 ansfer\n\nLi, Di, Zhao, Liang, Chen...\n\nToday's exascale scientific appl
 ications or advanced instruments are producing vast volumes of data, which
  need to be shared/transferred through the network/devices with relatively
  low bandwidth (e.g., WAN). Lossy compression is an important strategy to 
 resolve the big data issue, however, little wo...\n\n---------------------
 \nProductivity, Portability, Performance: Data-Centric Python\n\nZiogas, S
 chneider, Ben-Nun, Calotoiu, De Matteis...\n\nPython has become the de fac
 to language for scientific computing. Programming in Python is highly prod
 uctive, mainly due to its rich science-oriented software ecosystem built a
 round the NumPy module. As a result, the demand for Python support in high
 -performance computing (HPC) has skyrocketed. The ...\n\n\nTag: Algorithms
 , Cloud and Distributed Computing, Data Management, Parallel Programming L
 anguages and Models\n\nRegistration Category: Tech Program Reg Pass
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