High Performance Computing: Accelerating Application Performance Using Hardware and Software
This course, in partnership with IEEE Future Directions, illustrates the result of the never-ending appetite of these motivating applications that demand better accuracy, higher fidelity, and the timely delivery of actionable insights and predictions, all at the lowest possible cost. This course starts with the end in mind by answering the question, how can we satisfy the ever-increasing demand driven by the perpetually hungrier models and higher volumes of data? The answer is by building computers that can solve these continually growing scientific and business requirements by accelerating HPC application performance via the utilization of hardware and software organized in appropriate architectures.
What you will learn:
- Consider expectations of HPC hardware and software components
- Examine ways that HPC accelerates application performance
- Review component choices that contribute to performance
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
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: 978-1-7281-7828-8