Saudi HPC/AI Conference 2022:
Using HPC & AI to accelerate and improve medical research
(September 27-29, 2022)
KEYNOTE SESSIONS
Jean-Laurent Philippe
HPC EMEA Director, Intel
Talks Title
HPC & AI with Intel in the New Era of Supercomputing
Short Description
HPC, AI, and Analytics users ask more of their HPC-AI systems than ever before. High Performance Computing is the foundation of research and discovery. Artificial Intelligence is adding to it. Intel’s deep investments in developer ecosystems, tools, technology and an open platform are clearing the path forward to scale artificial intelligence everywhere. Intel has made AI more accessible and scalable for developers through extensive optimizations of popular libraries and frameworks on Intel® Xeon® Scalable processors. Intel’s investment in multiple AI architectures to meet diverse customer requirements, using an open standards-based programming model, makes it easier for developers to run more AI workloads in more use cases. Let’s look at Intel HPC-AI strategy and new innovations including the latest Intel® Xeon® Scalable processors, data center GPUs and powerful software tools. Together, let’s accelerate the next era of innovation in HPC-AI.
Paul Calleja
Director, The Cambridge Centre for Data-Driven Discovery, UK
Talks Title
TBA
Short Description
.
Dhabaleswar K. (DK) Panda
Professor and University Distinguished Scholar, The Ohio State University
Talks Title
High-Performance Deep Learning, Machine Learning, and Data Science on Modern HPC Systems
Short Description
This talk will start with an overview of challenges being faced by the AI community to achieve high-performance Deep Learning (DL), Machine Learning (ML), and Data Science on Modern HPC systems with both scale-up and scale-out strategies. Next, we will focus on a range of solutions to address these challenges: 1) MPI-driven Deep Learning on CPU and GPU-based systems, 2) Out-of-core DNN training and exploiting Hybrid (Data and Model) parallelism for training large models and data, 3) High-performance MPI Runtime for cuML to support GPU-accelerated ML applications, and 4) High-Performance Dask for supporting data science applications. Case studies to accelerate DL, ML, and data science applications on modern HPC systems will be presented.