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DTSTART;TZID=America/New_York:20241121T133000
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UID:submissions.supercomputing.org_SC24_sess381@linklings.com
SUMMARY:Computational Efficiency and Learning Techniques
DESCRIPTION:Accelerated Kinetic Monte Carlo Simulations of Atomistically-R
 esolved Resistive Memory Arrays\n\nSimulating emerging resistive switching
  memory devices, such as memristors, requires modeling frameworks that can
  treat the motion of point defects across nanoscale domains. Field-driven 
 Kinetic Monte Carlo (d-KMC) methods that simulate the discrete structural 
 evolution of atomic coordinates in the ...\n\n\nManasa Kaniselvan, Alexand
 er Maeder, Marko Mladenovic, Mathieu Luisier, and Alexandros Ziogas (ETH Z
 ürich)\n---------------------\nLarge Language Models for Anomaly Detection
  in Computational Workflows: from Supervised Fine-Tuning to In-Context Lea
 rning\n\nAnomaly detection in computational workflows is critical for ensu
 ring system reliability and security. However, traditional rule-based meth
 ods struggle to detect novel anomalies. This paper explores leveraging lar
 ge language models (LLMs) for workflow anomaly detection by exploiting the
 ir ability to...\n\n\nHongwei Jin (Argonne National Laboratory (ANL)), Geo
 rge Papadimitriou (University of Southern California (USC)), Krishnan Ragh
 avan (Argonne National Laboratory (ANL)), Pawel Zuk (University of Souther
 n California (USC)), Prasanna Balaprakash (Oak Ridge National Laboratory (
 ORNL)), Cong Wang and Anirban Mandal (Renaissance Computing Institute (REN
 CI)), and Ewa Deelman (University of Southern California (USC))\n---------
 ------------\nDesigning a GPU-Accelerated Communication Layer for Efficien
 t Fluid-Structure Interaction Computations on Heterogenous Systems\n\nAs b
 iological research demands simulations with increasingly larger cell count
 s, optimizing these models for large-scale deployment on heterogeneous sup
 ercomputing resources becomes crucial. This requires the redesign of fluid
 -structure interaction tasks written around distributed data structures bu
 ...\n\n\nAristotle Martin (Duke University), Geng Liu (Argonne National La
 boratory (ANL)), Balint Joo (Oak Ridge National Laboratory (ORNL)), Runxin
  Wu and Mohammed Shihab Kabir (Duke University), Erik Draeger (Lawrence Li
 vermore National Laboratory (LLNL)), and Amanda Randles (Duke University)\
 n\nTag: Accelerators, Applications and Application Frameworks, Artificial 
 Intelligence/Machine Learning, Modeling and Simulation, Numerical Methods\
 n\nRegistration Category: Tech Program Reg Pass\n\nSession Chair: Murali E
 mani (Argonne National Laboratory (ANL))
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