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DTSTART;TZID=America/Chicago:20181111T140000
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UID:submissions.supercomputing.org_SC18_sess143@linklings.com
SUMMARY:The 4th International Workshop on Data Reduction for Big Scientifi
 c Data (DRBSD-4)
DESCRIPTION:Data Reduction Challenges in Coordinated Simulation and Experi
 mental Fusion Science\n\nSean Dettrick (TAE Technologies)\n---------------
 ------\nExploring Best Lossy Compression Strategy By Combining SZ with Spa
 tiotemporal Decimation\n\nIn today's extreme-scale scientific simulations,
  vast volumes of data are being produced such that the data cannot be acco
 mmodated by the parallel file system or the data writing/reading performan
 ce will be fairly low because of limited I/O bandwidth. In the past decade
 , many snapshot-based (or spac...\n\n\nXin Liang (University of California
 , Riverside); Sheng Di (Argonne National Laboratory); Sihuan Li (Universit
 y of California, Riverside); Dingwen Tao (University of Alabama); Zizhong 
 Chen (University of California, Riverside); and Franck Cappello (Argonne N
 ational Laboratory)\n---------------------\nSynthetic Data Generation for 
 Evaluating Parallel I/O Compression Performance and Scalability\n\nCompres
 sion is one of the most common forms of data reduction and is typically th
 e least invasive. As compute capability continues to outpace I/O bandwidth
 s, compression becomes that much more attractive. This paper explores the 
 scalable performance of parallel compression and presents an in-depth a...
 \n\n\nSean B. Ziegeler and Christopher P. Stone (US Department of Defense 
 HPC Modernization Program, Engility Corporation)\n---------------------\nA
 mplitude-Aware Lossy Compression for Quantum Circuit Simulation\n\nClassic
 al simulation of quantum circuits is crucial for evaluating and validating
  the design of new quantum algorithms. However, the number of quantum stat
 e amplitudes increases exponentially with the number of qubits, leading to
  the exponential growth of the memory requirement for the simulations. ...
 \n\n\nXin-Chuan Wu (University of Chicago); Sheng Di, Franck Cappello, Hal
  Finkel, and Yuri Alexeev (Argonne National Laboratory); and Frederic T. C
 hong (University of Chicago)\n---------------------\nWorkshop Afternoon Br
 eak\n\nQing Liu (New Jersey Institute of Technology)\n--------------------
 -\nPerspectives on Data Reduction from ASCR\n\nLaura Biven and Lucy Nowell
  (US Department of Energy Office of Advanced Scientific Computing Research
 )\n---------------------\nFeature-Relevant Data Reduction for In Situ Work
 flows\n\nAs the amount of data produced by HPC simulations continues to gr
 ow and I/O throughput fails to keep up, in situ data reduction is becoming
  an increasingly necessary component of HPC workflows. Application scienti
 sts, however, prefer to avoid reduction in order to preserve data fidelity
  for post-hoc...\n\n\nWill Fox (Massachusetts Institute of Technology), Ma
 tthew Wolf (Oak Ridge National Laboratory), Jeremy Logan (University of Te
 nnessee), Jong Youl Choi and Scott Klasky (Oak Ridge National Laboratory),
  and Tahsin Kurc (Stony Brook University)\n---------------------\nA Study 
 on Checkpoints Compression for Adjoint Computation\n\nWhen we want to unde
 rstand the sensitivity of a simulation model with respect to an input valu
 e or to optimize an objective function, the gradient usually provides a go
 od hint. The adjoint state method is a widely used numerical method to com
 pute the gradient of a function. It decomposes functions i...\n\n\nKai-Yua
 n Hou (Northwestern University); Sri Hari Krishna Narayanan (Argonne Natio
 nal Laboratory); Daniel Goldberg (University of Edinburgh); Navjot Kukreja
  (Imperial College, London); and Bogdan Nicolae and Paul Hovland (Argonne 
 National Laboratory)\n---------------------\nIntroduction - The 4th Intern
 ational Workshop on Data Reduction for Big Scientific Data (DRBSD-4)\n\nAs
  the speed gap between compute and storage continues to exist and widen, t
 he increasing data volume and velocity pose major challenges for big data 
 applications in terms of storage and analysis. This demands new research a
 nd software tools that can further reduce data by several orders of magnit
 ud...\n\n\nScott Klasky (Oak Ridge National Laboratory), Qing Liu (New Jer
 sey Institute of Technology), Ian Foster (Argonne National Laboratory), an
 d Mark Ainsworth (Brown University)\n---------------------\nA Statistical 
 Analysis of Compressed Climate Model Data\n\nThe data storage burden resul
 ting from large climate model simulations continues to grow. While lossy d
 ata compression methods can alleviate this burden, they introduce the poss
 ibility that key climate variables could be altered to the point of affect
 ing scientific conclusions. Therefore, developing...\n\n\nAndrew Poppick, 
 Joseph Nardi, and Noah Feldman (Carleton College) and Allison Baker and Do
 rit Hammerling (National Center for Atmospheric Research)\n\nTag: Data Man
 agement, Hot Topics, Scientific Computing\n\nRegistration Category: Worksh
 op Reg Pass
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