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DTSTAMP:20260422T000604Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231115T100000
DTEND;TZID=America/Denver:20231115T170000
UID:submissions.supercomputing.org_SC23_sess303_rpost143@linklings.com
SUMMARY:Characterizing GPU Effectiveness on NRP for IceCube fp32 Compute
DESCRIPTION:David Schultz (University of Wisconsin, Madison); Igor Sfiligo
 i (University of California, San Diego (UCSD)); Benedikt Riedel (Universit
 y of Wisconsin, Madison); and Frank Würthwein (University of California, S
 an Diego (UCSD))\n\nThe IceCube Neutrino Observatory is a cubic kilometer 
 neutrino telescope located at the geographic South Pole. Understanding det
 ector systematic effects is a continuous process. This requires the Monte 
 Carlo simulation to be updated periodically to quantify potential changes 
 and improvements in science results with more detailed modelling of the sy
 stematic effects. IceCube’s largest systematic effect comes from the optic
 al properties of the ice the detector is embedded in. Over the last few ye
 ars there have been considerable improvements in the understanding of the 
 ice, which require a significant processing campaign to update the simulat
 ion. In winter 2023, the NRP project offered to provide the needed GPU com
 pute to IceCube in support of this activity. Given the mostly uniform natu
 re of such a simulation campaign, we thus have enough statistics to proper
 ly characterize the relative performance of the dozen GPU models present i
 n the NRP in the context of IceCube.\n\nTag: Artificial Intelligence/Machi
 ne Learning, Architecture and Networks, Heterogeneous Computing, I/O and F
 ile Systems, Performance Measurement, Modeling, and Tools, Post-Moore Comp
 uting, Programming Frameworks and System Software, Quantum Computing\n\nRe
 gistration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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