MCCHE Precision Convergence Webinar Series with Dr. Dhabaleswar K. (DK) Panda
Scalable Distributed Deep/Machine Learning: Challenges and Opportunities in Handling Societal Problems
Dr. Dhabaleswar K. (DK) Panda
With a high-level panel of leaders in science, technology, on-the-ground action, investment, and policy
The talk begins by motivating the need for Deep/Machine Learning (DL/ML) to address various societal problems including digital pathology, digital agriculture, smart foodsheds, and animal ecology. Next, it focuses on challenges and opportunities emerging over the years (past, present, and future) in designing scalable distributed DL/ML solutions. I will focus on MPI-driven solutions for the DL/ML domains to extract performance and scalability for popular Deep Learning frameworks, large out-of-core models, GPUs, and DPUs. The applicability of the solutions to train large WSI pathology images will be presented next. After that, I will provide an overview of the newly established NSF-AI Institute ICICLE (https://icicle.osu.edu/) to address challenges in designing future high-performance edge-to-HPC/cloud middleware for AI-driven data-intensive applications. The role of DL/ML solutions in the context of the ICICLE institute on three user-inspired science cases (digital agriculture, smart foodsheds, and animal ecology) will be highlighted.
About the speaker
DK Panda is a Professor and University Distinguished Scholar of Computer Science and Engineering at the Ohio State University. He has published over 500 papers. The MVAPICH2 MPI libraries, designed and developed by his research group (http://mvapich.cse.ohio-state.edu), are currently being used by more than 3,200 organizations worldwide (in 89 countries). More than 1.56M downloads of this software have taken place from the project's site. This software is empowering many clusters in the TOP500 list. High-performance and scalable solutions for Deep Learning frameworks and Machine Learning applications are available from https://hidl.cse.ohio-state.edu. Prof. Panda is an IEEE Fellow and recipient of the 2022 IEEE Charles Babbage Award. More details about Prof. Panda are available at http://www.cse.ohio-state.edu/~panda.
About the series
The Precision Convergence series is launched to catalyze unique synergy between, on the one hand, novel partnerships across sciences, sectors and jurisdictions around targeted domains of real-world solutions, and on the other hand, a next generation convergence of AI with advanced research computing and other data and digital architectures such as PSC’s Bridges-2, and supporting data sharing frameworks such as HuBMAP, informing in a real time as possible the design, deployment and monitoring of solutions for adaptive real-world behavior and context.
The Precision Convergence Webinar Series is co-hosted by The McGill Centre for the Convergence of Health and Economics (MCCHE) at McGill University and The Pittsburgh Supercomputing Center, a joint computational research center between Carnegie Mellon University and the University of Pittsburgh.