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Scalable Graph Analytics and HPC Operational Enhancement: Parallel Computing and ML/DL Innovations...

by Naw Safrin Sattar
Publication Type
Conference Paper
Book Title
WHPC@SC23: 16th International Women in HPC Workshop
Publication Date
Page Numbers
1 to 3
Conference Name
16th Annual WHPC@SC23 Workshop
Conference Location
Denver, Colorado, United States of America
Conference Sponsor
Conference Date

Parallel computing plays a pivotal role in the efficient processing of large-scale graphs. Complex network analysis stands as a capti- vating research frontier, holding promise across diverse scientific domains such as sociology, biology, online media, and recommenda- tion systems. In this era, Machine Learning (ML) and Deep Learning (DL) have emerged as indispensable tools, underpinning remarkable technological achievements. Within this dynamic landscape, my research revolves around advancing 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 syn- ergy between parallel computing, large-scale graph analysis, and ML/DL, improving computational efficiency and user experience.