High Performance Computing: Applying Exascale Computing to Real-World Problems
This course, in partnership with IEEE Future Directions, will showcase high performance computing (HPC) as a technology that is addressing “grand challenges” in the exascale era of supercomputers. Developing exascale computing skills, tools and the ability to identify, formulate and leverage HPC systems, will deepen your appreciation of high performance computing as a converged platform for discovery based on theory, experiment, simulation, and observation. In the following four modules, we examine how supercomputing touches us every day by solving scientific equations, extracting analytic insights, and providing actionable predictions via the use of AI. We will learn how the converged platform of HPC was applied in a very real-world, life-or-death use case—the rapid-response, drug discovery during the COVID-19 pandemic.
What you will learn:
- Examine what is high performance computing including how it has evolved
- Review the exascale era of computing
- Consider how exascale era high performance computing has enabled the discovery of actionable insights
- Provide examples of “Grand Challenge” problems that need exascale high performance computing
- Review examples of high performance computing enabling faster and better insights for real-world problems including the Covid-19 drug/vaccine research
This course is part of the following course program:
High Performance Computing: Technologies, Solutions to Exascale Systems, and Beyond
Courses included in this program:
Instructor
Sreenivas Rangan Sukumar
Sreenivas Rangan Sukumar is a data science/artificial intelligence (AI) system architect with a unique 10-plus years of experience with high performance computing (HPC). Today, he works at Hewlett Packard Enterprise (HPE) as a distinguished technologist in the company’s esteemed Chief Technology Office. Before serving in a similar role at Cray, Inc., he was a scientist and group leader at Oak Ridge National Laboratory responsible for knowledge discovery and data science workflows on the world’s fastest supercomputers. He has a Ph.D. in artificial intelligence and over 100 publications describing edge-to-exascale pipelines of disparate data collection, organization, processing, integration, fusion, analysis, and inference. Currently, he is focused on architecting AI-first query systems and building next-generation supercomputers designed for data science and AI workloads. In this course, he will share unique experiences and perspectives as a user, customer, and architect of high performance computing systems.
Publication Year: 2023
ISBN: 978-1-7281-7827-1