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LOCATION:Track 1
DTSTART;TZID=America/New_York:20201112T100000
DTEND;TZID=America/New_York:20201112T183000
UID:submissions.supercomputing.org_SC20_sess207@linklings.com
SUMMARY:DRBSD-6: The 6th International Workshop on Data Analysis and Reduc
 tion for Big Scientific Data
DESCRIPTION:Workshop\n\nDRBSD-6 – Break\n\n\n\n---------------------\nDRBS
 D-6 – Introduction: The 6th International Workshop on Data Analysis and Re
 duction for Big Scientific Data\n\nKlasky, Liu, Foster, Ainsworth\n\nA gro
 wing disparity between simulation speeds and I/O rates makes it increasing
 ly infeasible for applications to save all results for analysis. In this n
 ew world, applications must increasingly perform online data analysis and 
 reduction; tasks that introduce algorithmic, implementation and programmi.
 ..\n\n---------------------\nAI for Science: Some Big Data Challenges\n\nS
 tevens\n\n---------------------\nDRBSD-6 – Closing Remarks\n\nKlasky\n\n--
 -------------------\nCombining Spatial and Temporal Properties for Improve
 ments in Data Reduction\n\nHickman Fulp, Biswas, Calhoun\n\nDue to I/O ban
 dwidth limitations, intelligent in situ data reduction methods are needed 
 to enable post-hoc workflows. Current state-of-the-art sampling methods sa
 ve data points if their region is deemed spatially or temporally important
 . By analyzing the properties of the data values at each time-st...\n\n---
 ------------------\nToward a Framework for Policy-Driven Adaptive In Situ 
 Workflows\n\nMehta, Singhal, Wolf, Podhorszki, Logan...\n\nAs we move towa
 rd exascale, managing vast volumes of data and extracting knowledge from i
 t in a timely way has become a challenge for science. In situ analysis of 
 data is a viable alternative to processing large data volumes post-hoc. Ho
 wever, composing and orchestrating in situ workflows remains ch...\n\n----
 -----------------\nData Compression with Deep Learning Based Generative Mo
 deling\n\nChoi, Pugmire, Klasky\n\nWe have been developing a VAE-based dat
 a compression method, called VAe Physics Optimized Reduction (VAPOR), with
  a dataset from XGC, a fusion simulation code. VAPOR is based on Vector Qu
 antized Variational Auto Encoder (VQ-VAE), focusing on compressing XGC 5D 
 distribution data as well as preserving...\n\n---------------------\nDRBSD
 -6 – Welcome and Introduction\n\nKlasky\n\n---------------------\nDynamic,
  Adaptive Resource Management for Scientific Workflows\n\nSussman\n\n-----
 ----------------\nDRBSD-6 – Break\n\n\n\n---------------------\nData Analy
 tics for Scientific Data Compression\n\nArchibald\n\n---------------------
 \nDRBSD-6 – Break\n\n\n\n---------------------\nStreaming Data – The Trans
 formation of HPC Systems into Discovery Machines\n\nBussmann\n\n----------
 -----------\nIntelligent Data Management for Extreme-Scales In-Situ Workfl
 ows\n\nParashar\n\nExtreme-scale scientific workflows continue to be chall
 enged by the massive amounts of data that needs to be exchanged between wo
 rkflow components and the associated costs. While in-situ workflow formula
 tions are addressing some of these challenges and are enabling the creatio
 n of extreme scale coup...\n\n---------------------\nA Survey of Resource 
 Constrained Scheduling for In Situ Analysis\n\nMunson\n\n-----------------
 ----\nThe Square Kilometre Array and Exascale Challenges for Future Astron
 omy Facilities\n\nQuinn\n\n---------------------\nInvited Talk: Sanjay Ran
 ka\n\nRanka\n\n---------------------\nMachine learning for science with a 
 deadline: a focus on the scientist\n\nChurchill\n\n\nRegistration Category
 : Workshop Reg Pass
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