Presentation
SIGN IN TO VIEW THIS PRESENTATION Sign In
Scalable Graph Analytics and HPC Operational Enhancement: Parallel Computing and ML/DL Innovations
DescriptionParallel 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 sociology, biology, online media, and recommendation systems. In this era, Machine Learning (ML) and Deep Learning (DL) emerge as indispensable tools, underpinning remarkable technological achievements. Within this dynamic landscape, my research revolves around the advancement of parallel algorithms tailored for large-scale graph operations. To achieve this, I harness the power of cutting-edge technologies including OpenMP, MPI, HIP, and CUDA, on the High-Performance Computing (HPC) platforms to unlock optimal performance. I also apply ML/DL techniques to HPC operational data, to streamline the monitoring and maintenance of supercomputers, alleviating the complexities 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.
Author/Presenter
Event Type
Workshop
TimeMonday, 13 November 20232:12pm - 2:15pm MST
Location505
State of the Practice
W