BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:America/Chicago X-LIC-LOCATION:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20211207T055401Z LOCATION:225 DTSTART;TZID=America/Chicago:20211115T090000 DTEND;TZID=America/Chicago:20211115T173000 UID:submissions.supercomputing.org_SC21_sess349@linklings.com SUMMARY:ISAV21: In Situ Infrastructures for Enabling Extreme Scale Analysi s and Visualization DESCRIPTION:Workshop\n\nISAV21: Opening\n\nRizzi\n\n--------------------- \nISAV21: Best Paper Award\n\nZiegeler\n\n---------------------\nISAV21: Lunch Break (12:30-2)\n\n\n\n---------------------\nLOOM: Interweaving Tig htly Coupled Visualization and Numeric Simulation Framework\n\nBarbosa, Na vratil, University of Minho, Portugal, Fussell\n\nPost-hoc high-fidelity s cientific visualization (HSV) of numerical simulations (NS) requires multi ple I/O check-pointing to inspect the simulation progress. The costs of th ese I/O is high and can grow exponentially with increasing problem sizes. \nIn situ HSV dispenses with costly I/O, but requires a...\n\n------------ ---------\nISAV21: Morning Break (10-10:30)\n\n\n\n---------------------\ nAutomated In Situ Computational Steering Using Ascent’s Capable Yes-No Ma chine\n\nLawson, Harrison, Brugger, Skinner, Larsen\n\nThe life of a multi -physics code user is complicated. Simulation crashes, efficient resource utilization, and simulation parameter choices are time consuming workflow issues that in- crease a user’s iteration time. Simulations often don’t pr ovide general tools to support automatically adapting workf...\n\n-------- -------------\nLightweight Interface for In Situ Analysis and Visualizatio n of Particle Data\n\nGrosset, Ahrens\n\nAs simulations get larger and pro duce more data, domain scientists are increasingly using in situ methods t o analyze and visualize data as they are being produced. Most tools for in situ, which allow domain scientists to monitor their simulations, such as ParaView Catalyst and VisIt Libsim, tend to ...\n\n---------------------\ nISAV21: Afternoon Break (3-3:30)\n\n\n\n---------------------\nDevelopin g and Evaluating In Situ Visualization Algorithms Using Containers\n\nWill , Wofford, Patchett, Rogers, Lukasczyk...\n\nFundamental research into in situ visualization and analysis techniques is difficult to access for visu alization researchers due to the overwhelming complexity and effort of con structing and managing in situ software stacks that allow reproducible eva luation of novel in situ visualization and analys...\n\n------------------ ---\nIn Situ Data-Driven Analysis and Learning of Turbulence Closures at S cale\n\nBalin\n\nLarge eddy simulation, which requires modeling of the sub -grid stress (SGS) tensor, offers a compromise between accuracy and effici ency of computations of turbulence flows. Data-driven approaches, such as neural networks (NN), have recently emerged and present encouraging result s for improved predic...\n\n---------------------\nISAV21: Introduction – In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualiza tion\n\nZiegeler\n\n---------------------\nISAV21: Closing\n\nZiegeler\n\ n---------------------\nISAV21: Panel – The Near-Future of In Situ Analys is\n\nZiegeler\n\nWhile panels often attempt to prognosticate the long-ter m future, we propose to address the shorter-term in this discussion. Our moderator will interview the panelists and the audience, both in person an d virtual, on the near-term future of in situ analysis. We wish to explor e how in situ analysis...\n\n---------------------\nIn Situ Climate Modeli ng for Analyzing Extreme Weather Events\n\nDutta, Klein, Tang, Wolfe, Roek el...\n\nThe study of many extreme weather events requires simulations wit h high spatiotemporal data that can grow in size quickly. Storing the raw data from such a large-scale simulation for traditional post hoc analyses is soon going to be prohibitive as the data generation speed is outpacing the data stor...\n\n---------------------\nISAV21: Invited Speaker –3D CF D In-Situ Co-Processing for Turbomachinery Design\n\nGerstenberger\n\nRepl acing gas turbine engine and rig tests with virtual simulation-based testi ng requires more accurate physical and mathematical models as well as more detailed geometry and realistic boundary conditions. However, the improve d accuracy is achieved at the cost of ever increasing demands on the HPC s ...\n\n---------------------\nIn-Situ Spatial Inference on Climate Simulat ions with Sparse Gaussian Processes\n\nGrosskopf, Lawrence, Biswas, Tang, Rumsey...\n\nAs extreme-scale physics simulation becomes increasingly memo ry and storage expensive, the ability to access full simulation data for s tatistical analysis is becoming increasingly limited. The capability to pe rform in-situ statistical inference of state variables is becoming increas ingly important f...\n\n---------------------\nISAV21: In Situ Infrastruct ures for Enabling Extreme Scale Analysis and Visualization\n\nRizzi, Pugmi re, Ziegeler, Larsen, Duque...\n\nAs HPC platforms increase in size and co mplexity, one significant challenge is the widening gap between computatio nal capacity and our ability to store data for subsequent analysis. One pr omising approach is to perform as much analysis as possible while computed data is still resident in memory, an ...\n\n\nTag: Applications, Big Data , Visualization\n\nRegistration Category: Workshop Reg Pass END:VEVENT END:VCALENDAR