BEGIN:VCALENDAR X-WR-TIMEZONE:America/Chicago PRODID:-//University of Iowa//Events 1.0//EN VERSION:2.0 CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20240328T175000Z DTSTART:20221128T143000 DTEND:20221128T153000 SUMMARY:Nuclear and Particle Physics Seminar (618 VAN) - Gürkan Karaman\; University of Iowa DESCRIPTION:Machine Learning and High-Performance Computing for LArTPC\n\nGürkan Karaman\; University of Iowa\n\nLiquid Argon Time Projection Chamber (LArTPC) has been used in many neutrino experiments and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). Convolutional Neural Network (CNN) algorithms are becoming performant and prevalent choice to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. The need to process billions of neutrino events with many machine learning algorithms creates computing challenge. In this talk\, we will investigate Graphical Processor Units(GPUs) for accelerating time-consuming computing tasks. \n\n\nhttps://events.uiowa.edu/75134 LOCATION:Van Allen Hall\, 618\, 30 North Dubuque Street\, Iowa City\, IA 52242 UID:edu.uiowa.events-prod-75134 X-ALT-DESC;FMTTYPE=text/html:
Liquid Argon Time Projection Chamber (LArTPC) has been used in many neutrino experiments and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). Convolutional Neural Network (CNN) algorithms are becoming performant and prevalent choice to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. The need to process billions of neutrino events with many machine learning algorithms creates computing challenge. In this talk\, we will investigate Graphical Processor Units(GPUs) for accelerating time-consuming computing tasks.
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