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DTSTART;TZID=America/Denver:20231112T162600
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UID:submissions.supercomputing.org_SC23_sess439_ws_worksp104@linklings.com
SUMMARY:Accelerating Data-Intensive Seismic Research Through Parallel Work
 flow Optimization and Federated Cyberinfrastructure
DESCRIPTION:Marcus Adair, Ivan Rodero, and Manish Parashar (University of 
 Utah; University of Utah, Scientific Computing and Imaging Institute (SCI)
 ) and Diego Melgar (University of Oregon)\n\nEarthquake early warning syst
 ems use synthetic data from simulation frameworks like MudPy to train mode
 ls for predicting the magnitudes of large earthquakes. MudPy, although pow
 erful, has limitations: a lengthy simulation time to generate the required
  data, lack of user-friendliness, and no platform for discovering and shar
 ing its data. We introduce FakeQuakes DAGMan Workflow (FDW), which utilize
 s Open Science Grid (OSG) for parallel computations to accelerate and stre
 amline MudPy simulations. FDW significantly reduces runtime and increases 
 throughput compared to a single-machine setup. Using FDW, we also explore 
 partitioned parallel HTCondor DAGMan workflows to enhance OSG efficiency. 
 Additionally, we investigate leveraging cyberinfrastructure, such as Virtu
 al Data Collaboratory (VDC), for enhancing MudPy and OSG. Specifically, we
  simulate using Cloud bursting policies to enforce FDW job-offloading to V
 DC during OSG peak demand, addressing shared resource issues and user goal
 s; we also discuss VDC’s value in facilitating a platform for broad access
  to MudPy products.\n\nTag: Applications, Cloud Computing, Distributed Com
 puting, Edge Computing, Large Scale Systems\n\nRegistration Category: Work
 shop Reg Pass\n\nSession Chairs: Silvina Caino-Lores (National Institute f
 or Research in Digital Science and Technology (Inria)) and Anirban Mandal 
 (Renaissance Computing Institute (RENCI), University of North Carolina at 
 Chapel Hill)\n\n
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