Mohamed Wahib is a senior scientist at AIST/TokyoTech Open Innovation Laboratory, Tokyo, Japan. Prior to that he worked as a researcher in RIKEN Center for Computational Science (RIKEN-CCS). He received his Ph.D. in Computer Science in 2012 from Hokkaido University, Japan. Prior to his graduate studies, he worked as a researcher at Texas Instruments (TI) R&D labs in Dallas, TX for four years. His research interests revolve around the central topic of “Performance-centric Software Development”, in the context of HPC. He is actively working on several projects including high-level frameworks for programming traditional scientific applications, as well as high-performance AI and data analytics.
Research papers covered during the seminar:
- ParDNN: An Oracle for Characterizing and Guiding Large-Scale Training of Deep Neural Networks, HPDC’21
- Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA. SC’20
- A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs. IPDPS’20