AI for High Performance Computing: Experiences and Opportunities
HPC environment, complexity, writing applications, AI techniques, optimization, top performance, opportunities …
This talk focuses on how AI techniques can be used in the development of the HPC environment and tools. As larger HPC systems become more and more heterogeneous by adding GPUs and other devices for performance and energy efficiency, they also become more complex to write and optimize the HPC applications for. For instance, both CPU and GPUs have several types of memories and caches that codes need to be optimized for. We show how AI techniques can help us pick among 10s of thousands of parameters one ends up needing to optimize for the best possible performance of some given complex applications. Ideas for future opportunities will also be discussed.
Dr. Anne C. Elster is a Professor of HPC in Computer Science at NTNU and was the Co-founder and Co-director of the Norwegian University of Science and Technology’s Computational Science and Visualization program and also established the IDI/NTNU HPC-Lab, a well-respected research lab in heterogeneous computing that regularly receives international visitors. She is also a Visiting Scientist at the University of Texas at Austin. Her current research interests are in high-performance parallel computing, focusing on developing good models and tools for heterogeneous computing and parallel software environments. Methods include applying machine learning for code optimization and image processing, and developing parallel scientific codes that interact visually with the users by taking advantage of the powers in modern GPUs. Her novel fast linear bit-reversal algorithm is still noteworthy.
She works very closely with her graduate students and has so far supervised over 75 masters theses (several of which have received prizes), has supervised several PhD and Post Docs, and served on PhD committees internationally, including in Denmark, Italy, Saudi Arabia, Sweden, and the United States. She has published widely in the field of high-performance computing (HPC).