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DTSTAMP:20181221T160905Z
LOCATION:D168
DTSTART;TZID=America/Chicago:20181112T090000
DTEND;TZID=America/Chicago:20181112T173000
UID:submissions.supercomputing.org_SC18_sess140@linklings.com
SUMMARY:ISAV 2018: In Situ Infrastructures for Enabling Extreme-Scale Anal
 ysis and Visualization
DESCRIPTION:Workshop\nData Analytics, Data Management, Visualization, Work
 shop Reg Pass\n\nBest Paper Awards\n\n\n\n---------------------\nIn Situ D
 ata-Driven Adaptive Sampling for Large-Scale Simulation Data Summarization
 \n\nBiswas, Dutta, Pulido, Ahrens\n\nRecent advancements in the high-perfo
 rmance computing have enabled the scientists to model various scientific p
 henomena in great detail. However, the analysis and visualization of the o
 utput data from such large-scale simulations are posing significant challe
 nges due to the excessive size of output ...\n\n---------------------\nlib
 IS: A Lightweight Library for Flexible In Transit Visualization\n\nUsher, 
 Rizzi, Wald, Amstutz, Insley...\n\nAs simulations grow in scale, the need 
 for in situ analysis methods to handle the large data produced grows corre
 spondingly. One desirable approach to in situ visualization is in transit 
 visualization.  By decoupling the simulation and visualization code, in tr
 ansit approaches alleviate common diffi...\n\n---------------------\nIntro
 duction - ISAV 2018: In Situ Infrastructures for Enabling Extreme-Scale An
 alysis and Visualization\n\nWeber, O'Leary, Ferrier, Wolf\n\nThe considera
 ble interest in the HPC community regarding in situ analysis and visualiza
 tion is due to several factors. First is an I/O cost savings, where data i
 s analyzed/visualized while being generated, without first storing to a fi
 lesystem. Second is the potential for increased  accuracy, where ...\n\n--
 -------------------\nScheduling for In-machine Analytics: Data Size Is Imp
 ortant\n\nHonore, Aupy, Goglin\n\nWith the goal of performing exascale com
 puting, the importance of I/O management becomes increasingly critical to 
 maintain system performance.  While the computing capacities of machines a
 re getting higher, the I/O capabilities of systems do not follow the same 
 trend.  To address this issue, the HPC...\n\n---------------------\nPython
 -Based In Situ Analysis and Visualization\n\nLoring, Myers, Camp, Bethel\n
 \nThis work focuses on enabling the use of Python-based methods for the pu
 rpose of performing in situ analysis and visualization. This approach faci
 litates access to and use of a rapidly growing collection of Python-based,
  third-party libraries for analysis and visualization, as well as lowering
  the b...\n\n---------------------\nUnPanel on the State of the In Situ Co
 mmunity\n\n\n\nCome join our moderators as we engage in a community discus
 sion about gaps, fundamental research opportunities, and the maturity of t
 he community infrastructure.\n\n---------------------\nPaDaWAn: a Python I
 nfrastructure for Loosely Coupled In Situ Workflows\n\nCapul, Morais, Leki
 en, Perache\n\nThis paper presents PaDaWAn, an infrastructure written in P
 ython to provide loosely coupled in situ capabilities to accelerate file-b
 ased simulation workflows.  It provides services for in-memory data exchan
 ge between applications and a simple configuration model to switch from a 
 file-based workflow...\n\n---------------------\nISAV 2018 Wrap Up\n\n\n\n
 ---------------------\nA Flexible System For In Situ Triggers\n\nLarsen, W
 oods, Marsaglia, Biswas, Dutta...\n\nTriggers are an important mechanism f
 or adapting I/O and visualization actions as a simulation runs. We describ
 e the system for triggers in the Ascent in situ infrastructure. This syste
 m splits a trigger into two components, when to perform an action and what
  actions to perform. The decision for whe...\n\n---------------------\nIn-
 Transit Molecular Dynamics Analysis with Apache Flink\n\nColao, Raffin, Mu
 res, Padrón\n\nIn this paper, an on-line parallel analytics framework is p
 roposed to process and store in transit all the data being generated by a 
 Molecular Dynamics (MD) simulation run using staging nodes in the same clu
 ster executing the simulation. The implementation and deployment of such a
  parallel workflow ...\n\n---------------------\nLeveraging Scalable Event
  Distribution to Enable Data-Driven In Situ Scientific Workflows\n\nWang, 
 Simonet, Subedi, Davis, Parashar\n\nNovel event-driven workflow systems ha
 ve been effectively used to increase the performance of large-scale scient
 ific applications by removing most of the implicit synchronization require
 d to orchestrate distributed tasks. However, these event-driven workflow s
 ystems, by focusing only on events relat...\n\n---------------------\nKeyn
 ote: Perspectives on In Situ\n\nBiven\n\n---------------------\nLightning 
 Round Questions\n\n\n\n---------------------\nWorkshop Afternoon Break\n\n
 \n\n---------------------\nInvited Talk: Data Science Meets CFD\n\nLegensk
 y\n\n---------------------\nWorkshop Morning Break\n\n\n\n----------------
 -----\nWorkshop Lunch (on your own)\n\n\n
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