In this episode, Neil speaks to Professor Jack Dongarra, a renowned figure in the supercomputing and high-performance computing (HPC) world. He is a Professor at University of Tennessee as well as a Distinguished Researcher at Oak Ridge National Laboratory (ORNL) and a Turing Fellow at the University of Manchester. He is the inventor of the LINPACK library that is still used today to benchmark the Top 500 list of the most powerful supercomputers and was one of the key people involved in the creation of Message-Passing-Inferface (MPI). They discuss what is HPC, the challenges and opportunities in the field, and the future of HPC. They also touch on the role of machine learning and AI in HPC, the competitiveness of the United States in the field, and potential future technologies in HPC. Professor Dongarra shares his insights and advice based on his extensive experience in the field.

As part of their discussion they discuss two papers from Prof Dongarra:

1) High-Performance Computing: Challenges and Opportunities: https://arxiv.org/abs/2203.02544
2) Can the United States Maintain Its Leadership in High-Performance Computing? - A report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR Office: https://www.osti.gov/biblio/1989107/

Chapters

00:00 Introduction
04:18 Defining HPC and its Impact
08:11 Challenges and Opportunities in HPC
28:20 The Competitiveness of the United States in HPC
44:31 The Future of HPC: Technologies and Innovations
49:30 Insights and Advice from Professor Jack Dongarra

In this episode, Neil interviews Pat Symonds, one of the most well known and respected engineers in Formula One. They discuss Pat's career in engineering, his time in Formula One, and the evolution of the sport. Pat shares insights into his early motivations, his work with different teams, and the challenges he faced. They also touch on the growth of Motorsport Valley in the UK and the potential for Formula One teams to be based in other countries. In this conversation, Pat discusses his experience in Formula One and the challenges of being a technical director. He emphasizes the importance of continuous learning and the ability to make compromises in order to achieve success. He shares insights into the culture at Williams and Benetton and how it impacted their success. Additionally, he discusses the future of Formula One, including the use of AI and ML, the potential shift towards sustainable fuels, and the role of motor manufacturers.

In this episode I speak to Prof Juan J. Alonso on his vision of the future of computational science as well as his journey from academia to entrepreneurship - founding Luminary Cloud. He reflects on the revolutions in computational science and the different ways of developing software throughout his career. Alonso emphasizes the importance of academia in creating and perpetuating knowledge, as well as the value of innovation and new ideas. He also discusses the changes in the CFD world, the emergence of new technologies like GPU computing and cloud computing, and the potential for advancements in computational simulations for analysis and design. We also touch on the transition of the aerospace industry towards commercial software and the potential for cloud computing to revolutionize CFD. The conversation concludes with a discussion on the progress made towards achieving the goals outlined in the 2030 CFD vision report and the role of machine learning and AI in simulation-driven workflows.

In this final part of the conversation, Juan discusses the potential applications of ML and AI in engineering. He identifies four main areas where these technologies can be beneficial, but emphasizes that these applications will always be based on high-fidelity simulations. He concludes by envisioning the future of computational-driven science and the continued innovation in the field.

You can check out Luminary Cloud at https://www.luminarycloud.com and Prof Alonso's Stanford research at: https://adl.stanford.edu


06:00 Introduction and Background
09:11 Early Interest in Aerospace Engineering
12:13 From Academia to Industry
15:11 Decision to Stay in Academia
17:11 Balancing Fundamental Science and Applied Research
22:14 Early Aims and Focus on High Performance Computing
29:18 Emergence of GPU Computing and Cloud Computing
32:23 Conditions for Innovation and Entrepreneurship
35:01 The Importance of the Bay Area
35:37 Challenges and Requirements in Developing Solvers
41:00 The Role of the Bay Area in Attracting Computational Science Talent
44:16 The Difficulty and Respect for Building High-Quality Commercial Software
47:03 The Transition of the Aerospace Industry towards Commercial Software
49:30 The Potential of Cloud Computing in Revolutionizing CFD
53:59 Progress towards the Goals of the 2030 CFD Vision Report
01:00:53 The Role of Machine Learning and AI in Simulation-Driven Workflows
01:04:01 Applications of ML and AI in Engineering
01:05:36 Optimization and Design Optimization with ML and AI
01:06:04 Outer Loops and Uncertainty Quantification
01:07:04 Digital Twin Frameworks and Constant Retraining
01:12:36 The Value of Open-Source Codes in Academia
01:16:19 Challenges of Integrating Commercial Tools with Research
01:25:20 The Future of Computational-Driven Science
01:29:01 Continued Innovation and Replacement of Physical Experimentation