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EP8 - Prof Jack Dongarra - High Performance Computing (HPC) Pioneer

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Manage episode 424273885 series 3572969
Contenu fourni par Neil Ashton. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Neil Ashton ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

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

  continue reading

10 episodes

Artwork
iconPartager
 
Manage episode 424273885 series 3572969
Contenu fourni par Neil Ashton. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Neil Ashton ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

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

  continue reading

10 episodes

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