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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:Workshop\nData Management, Hot Topics, Scientific Computing, W
 orkshop Reg Pass\n\nIntroduction - The 4th International Workshop on Data 
 Reduction for Big Scientific Data (DRBSD-4)\n\nKlasky, Liu, Foster, Ainswo
 rth\n\nAs the speed gap between compute and storage continues to exist and
  widen, the increasing data volume and velocity pose major challenges for 
 big data applications in terms of storage and analysis. This demands new r
 esearch and software tools that can further reduce data by several orders 
 of magnitud...\n\n---------------------\nExploring Best Lossy Compression 
 Strategy By Combining SZ with Spatiotemporal Decimation\n\nLiang, Di, Li, 
 Tao, Chen...\n\nIn today's extreme-scale scientific simulations, vast volu
 mes of data are being produced such that the data cannot be accommodated b
 y the parallel file system or the data writing/reading performance will be
  fairly low because of limited I/O bandwidth. In the past decade, many sna
 pshot-based (or spac...\n\n---------------------\nA Statistical Analysis o
 f Compressed Climate Model Data\n\nPoppick, Nardi, Feldman, Baker, Hammerl
 ing\n\nThe data storage burden resulting from large climate model simulati
 ons continues to grow. While lossy data compression methods can alleviate 
 this burden, they introduce the possibility that key climate variables cou
 ld be altered to the point of affecting scientific conclusions. Therefore,
  developing...\n\n---------------------\nSynthetic Data Generation for Eva
 luating Parallel I/O Compression Performance and Scalability\n\nZiegeler, 
 Stone\n\nCompression is one of the most common forms of data reduction and
  is typically the least invasive. As compute capability continues to outpa
 ce I/O bandwidths, compression becomes that much more attractive. This pap
 er explores the scalable performance of parallel compression and presents 
 an in-depth a...\n\n---------------------\nAmplitude-Aware Lossy Compressi
 on for Quantum Circuit Simulation\n\nWu, Di, Cappello, Finkel, Alexeev...\
 n\nClassical simulation of quantum circuits is crucial for evaluating and 
 validating the design of new quantum algorithms. However, the number of qu
 antum state amplitudes increases exponentially with the number of qubits, 
 leading to the exponential growth of the memory requirement for the simula
 tions. ...\n\n---------------------\nA Study on Checkpoints Compression fo
 r Adjoint Computation\n\nHou, Narayanan, Goldberg, Kukreja, Nicolae...\n\n
 When we want to understand the sensitivity of a simulation model with resp
 ect to an input value or to optimize an objective function, the gradient u
 sually provides a good hint. The adjoint state method is a widely used num
 erical method to compute the gradient of a function. It decomposes functio
 ns i...\n\n---------------------\nFeature-Relevant Data Reduction for In S
 itu Workflows\n\nFox, Wolf, Logan, Choi, Klasky...\n\nAs the amount of dat
 a produced by HPC simulations continues to grow and I/O throughput fails t
 o keep up, in situ data reduction is becoming an increasingly necessary co
 mponent of HPC workflows. Application scientists, however, prefer to avoid
  reduction in order to preserve data fidelity for post-hoc...\n\n---------
 ------------\nData Reduction Challenges in Coordinated Simulation and Expe
 rimental Fusion Science\n\nDettrick\n\n---------------------\nWorkshop Aft
 ernoon Break\n\nLiu\n\n---------------------\nPerspectives on Data Reducti
 on from ASCR\n\nBiven, Nowell\n
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