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UID:submissions.supercomputing.org_SC23_sess442_ws_whpc122@linklings.com
SUMMARY:Scalable Graph Analytics and HPC Operational Enhancement:  Paralle
 l Computing and ML/DL Innovations
DESCRIPTION:Naw Safrin Sattar (Oak Ridge National Laboratory (ORNL))\n\nPa
 rallel computing plays a pivotal role in the efficient processing of vast-
 scale graph. Complex network analysis stands as a captivating frontier of 
 research, holding promise across diverse scientific domains such as sociol
 ogy, biology, online media, and recommendation systems. In this era, Machi
 ne Learning (ML) and Deep Learning (DL) emerge as indispensable tools, und
 erpinning remarkable technological achievements. Within this dynamic lands
 cape, my research revolves around the advancement of parallel algorithms t
 ailored for large-scale graph operations. To achieve this, I harness the p
 ower of cutting-edge technologies including OpenMP, MPI, HIP, and CUDA, on
  the High-Performance Computing (HPC) platforms to unlock optimal performa
 nce. I also apply ML/DL techniques to HPC operational data, to streamline 
 the monitoring and maintenance of supercomputers, alleviating the complexi
 ties associated with their upkeep and enhancing user support. My research 
 echoes the synergy between parallel computing, large-scale graph analysis,
  and ML/DL, enhancing computational efficiency and user experience.\n\nTag
 : State of the Practice\n\nRegistration Category: Workshop Reg Pass\n\nSes
 sion Chairs: Elsa J. Gonsiorowski (Lawrence Livermore National Laboratory 
 (LLNL)) and Mozhgan Kabiri chimeh (NVIDIA Corporation)\n\n
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