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DTSTART:19700308T020000
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DTSTAMP:20200129T163556Z
LOCATION:506
DTSTART;TZID=America/Denver:20191118T155500
DTEND;TZID=America/Denver:20191118T162000
UID:submissions.supercomputing.org_SC19_sess137_ws_xloop102@linklings.com
SUMMARY:Sparse Data Management in HDF5
DESCRIPTION:Workshop\n\nSparse Data Management in HDF5\n\nMainzer, Fortner
 , Heber, Pourmal, Quincey...\n\nThe importance of sparse data management i
 s growing with data produced by large-scale experimental and observational
  facilities that contain small amounts of non-zero values. In this documen
 t, we explore different design options to support sparse data in HDF5, one
  of the most popular high-performance I/O libraries and file formats used 
 for scientific data. We discuss several use cases and requirements. Our ma
 in hard design constraint is that any changes to the HDF5 dataset API woul
 d be a burden on users and not acceptable. The remaining options are discu
 ssed below. We provide a rationale for what we believe is the strongest ca
 ndidate and describe how its potential benefits can be simulated with the 
 existing HDF5 library. We have conducted a set of computational experiment
 s the results of which are reported here. They show that our candidate mee
 ts all relevant requirements and gives us a certain degree of confidence f
 or an HDF5 library-native implementation.\n\nTag: Workshop Reg Pass, Exper
 imental Datasets, High-fidelity Simulations, Scalable Computing\n\nRegistr
 ation Category: Workshop Reg Pass, Experimental Datasets, High-fidelity Si
 mulations, Scalable Computing
URL:https://sc19.supercomputing.org/presentation/?id=ws_xloop102&sess=sess
 137
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