Workshop 1 : Data Science on HPC platforms
Methods in Machine and Deep Learning are being investigated to either augment or replace traditional simulation. Contemporary trends in hardware and software have enabled convergence of HPC and AI. In this workshop we survey these trends and explore what is available on system level and in software to ease the transition to developing data science workloads. The talks include a walk through the current hardware and system resources available to accelerate the Data science workflows. It is followed by the cataloging of software tools and frameworks available to develop ML/DL models, accelerate their training process and handle the associated data requirements at scale. Also presented will be some examples where communities (e.g. CFD and Geoscience), who traditionally rely on classical HPC simulations, leverage Data Science methods to accelerate their scientific investigations. Attendees are assumed to have no prior experience with ML/DL workloads.
Sr. Computational Scientist Lead, KAUST
Saber Feki leads the computational and data science and engineering at the KAUST Supercomputing Core Laboratory, providing support, training, advanced services and research collaborations with users of the leadership supercomputer Shaheen II Cray XC40 and a heterogeneous cluster “Ibex” with over 600 GPUs.
Saber is passionate about technology, and enjoys working with users and technology vendors to plan and execute refreshes to KAUST HPC and AI infrastructure with the latest hardware and software technologies. He is leveraging his expertise to support and consult for several similar deployments for local and regional organizations such as the American University of Sharjah, and the National Center of Meteorology of Saudi Arabia.
Saber received his MSc and Ph.D. degrees in computer science from the University of Houston in 2008 and 2010, respectively. He then joined the oil and gas company TOTAL in 2011 as an HPC Research Scientist. Saber has been working at KAUST since 2012.
Didier Barradas Bautista
Staff Scientist, KAUST
Speaker(s) Profile:Didier Barrradas-Bautista is a staff scientist at the KAUST Visualization Lab in Saudi Arabia. He supports the ongoing research on machine learning and AI at the University by providing training to use computational resources and data science.
Didier has worked in bioinformatics and mathematical modeling using machine learning and high-performance computing. He is enthusiastic about the latest trends in machine learning and Artificial intelligence's impact on the world. He enjoys the insight provided by data science combined with the power supplied by HPC systems. Didier received this Ph.D. degree from the University of Barcelona in collaboration with the Barcelona Supercomputing Center in 2017.
Mohsin Ahmed Shaikh is a Computational Scientist at King Abdullah University of Science and Technology.
Data Science on HPC platforms
Rooh Khurram is working as a Staff Scientist at KAUST Supercomputer Lab at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He has conducted research in finite element methods, high performance computing, multiscale methods, fluid structure interaction, detached eddy simulations, in-flight icing, and computational wind engineering. He has over 20 years of industrial and academic experience in CFD. He specializes in developing custom made computational codes for industrial and academic applications. His industrial collaborators include: Boeing, Bombardier, Bell Helicopter, and Newmerical Technologies Inc. Before joining KAUST in 2012, Rooh worked at the CFD Lab at McGill University and the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. Rooh received his Ph.D. from the University of Illinois at Chicago in 2005. In addition to a Ph.D. in Civil Engineering, Rooh has degrees in Mechanical Engineering, Nuclear Engineering, and Aerospace Engineering.
Workshop 2 : DPC++ Training Workshop
Workshop introducing the DPC++ technology, targeting educating the audience about the ambitions behind it, how to write simple programs using it and some of the advanced features it offers. Data Parallel C++ is a high-level language designed for data-parallel programming. The intent is to provide developers with a higher-level language to use other than OpenCL and other languages, making programs portable across different architectures while keeping the ability to write hardware-specific kernels to optimize performance on different platforms.
Amr Mohamed Nasreldin Elsayed
HPC software engineer, Brightskies Technologies
Amr Elsayed is an HPC software engineer who’s been working with Brightskies for 4 years. Originally a graduate from Alexandria University with a Bachelor in computer engineering, during his time with Brightskies contributed in multiple projects including multiple collaborations with Intel regarding leveraging DAOS filesystem for oil and gas workflows, as well as contributing in open-sourcing the first seismic imaging code based on DPC++ as well as assisting customers on-site offering consultancy on software design and optimization.
Workshop 3 : Enabling and democratizing MLops in Healthcare
The implementation of AI-based systems is having increasing success in the Healthcare Industry, enabling technological advances for both diagnosis and treatment of clinical conditions, as well as for the optimization and improvement of the efficiency of healthcare facility management.
ML-based systems’ R&D and deployment have seen the emergence of the so-called MLOps, a framework that aims to solve many of the organizational challenges related to the training and deployment phases.
The implementation of MLops framework also requires the development of SW platforms, which provide tools for development teams to simplify and optimize workflows by reducing potential bottlenecks due, for example, to the management and use of a complex HW and SW infrastructure for the prototyping and development of AI models.
Dr Valerio Rizzo
AI Lead & Solution Architect, Lenovo
Valerio is the AI Lead & Solution Architect for Lenovo, he is key member of an expert team of Artificial Intelligence, Machine Learning and Deep Learning specialists operating within the EMEA field sales organization and its business development team. He is a recognized expert in the fields of neuroscience and neurophysiology with 10 years of track record in brain research made between Italy and USA.
Workshop 4 : Power, Application Efficiency and Challenges for a HPC Vendor
This workshop consists of two modules.
Module 1: HPC drives innovation & discovery, but increasing demands for more performance are driving up the heat of next-gen HPC processors and accelerators. In this workshop we will learn about current and future cooling approaches and the challenges of balancing with sustainability requirements
Module 2: With all the advances in massively parallel and multi-core computing with CPUs and domain specific accelerators, it is often overlooked whether the computational work is being done in an efficient manner. This efficiency is largely determined at the application level and therefore puts the responsibility of sustaining a certain performance trajectory into the hands of the user. It is observed that the adoption rate of new hardware capabilities is decreasing and lead to a feeling of diminishing returns. At the same time, the well-known laws of parallel performance are limiting the perspective of a system builder. The presentation tries gives an overview of these challenges and what can be done to overcome them. It will also offer a CPU centric view of application performance at a very low level. The overview will be amended by a few case studies and optimization strategies on real applications.
AI Lead & Solution Architect,Dell
An Enterprise Technologist specialising in Data Centers, specifically Power & Cooling with long term experience of all types of Computer Hardware & environments. With a background in electrical, mechanical & electronic engineering & also a member of the BCS (was the British Computer Society)
Jim has worked in the IT industry for 41+ years, predominately in Professional Services for a variety of major companies, including Banks, Finance Houses, Telecomm Companies & hardware manufacturers. During this time, he delivered multiple projects involving data center builds & refurbishments as both Technical Authority & Project Manager. Has now been with Dell for over 16 years, He undertakes consultancy & audit on Data Centre matters for Dell’s customers as well as supporting Dell’s sales force. Another area is partner support working with Dell’s data centre infrastructure partners.
He has also completed & passed the ITIL Practioner exam, EU Code of Conduct for Data Centres exam & DCDs Energy Efficiency Best Practice certification. Together with being Dell’s only certified EU CoC for Data Centre assessor. He has also authored articles that have appeared in various trade journals as well as appearing as a speaker at various trade shows. Jim has undertaken media training internally to enable him to both speak publicly & on social media on Dell’s behalf.
HPC application specialist, Dell
Martin joined Dell Technologies in 2011, after having worked as an HPC application specialist for 12 years at SGI and IBM. In 2019, he joined AMD as a senior manager and worked on porting and optimizing the major HPC applications to the “Rome” microarchitecture. Martin returned to Dell Technologies in May 2020 as the HPC performance lead and Distinguished Member of Technical Staff in Dell ISG. He owns a master’s degree in physical chemistry, obtained at the VU University of Amsterdam.
Workshop 5 : Cray AI Development Software Environment for HPE SUPERCOMPUTING Workshop
He HPE Cray AI Development Environment is a machine learning training platform that makes building machine learning models fast and easy.
The software platform enables Machine Learning Engineers and researchers to:
- Train models faster using state-of-the-art distributed training: by provisioning machines, setting up networking, optimizing communication between machines, efficient distributed data loading, and fault tolerance.
- Automatically find high-quality models with advanced hyperparameter tuning: including state-of-theart algorithms developed by the creators of Hyperband1 and ASHA2
- Efficiently utilize different accelerators (e.g. GPUs): with intelligent and configurable resource management.
- Track, reproduce, and collaborate on experiments: with automatic experiment tracking that works outof-the-box, covering code versions, metrics, checkpoints, and hyperparameters.
As an end-to-end training platform, the system integrates these features into an easy-to-use, high performance Machine Learning and Deep Learning environment that can be deployed on bare meta.
Kubernetes, or the cloud, supporting the largest providers such as AWS, Azure, and GCP”””
AI Solutions Engineer, Hewlett Packard Enterprise
Andrea is a presales solution engineer for the AI Strategy and Solution Group.