Machine Learning in HPC
10 - 11 March 2020
After the course the participants should be able to understand basic principles in Machine Learning and apply basic machine learning methods.
Learn how to efficiently use HPC infrastructures to get
the best performance out of different machine learning tools. How to use these machine learning frameworks like: Tensorflow, PyTorch, Keras, Horovood with hands-on sessions.
Learn using multiple GPUs to significantly shorten the time required to train lots of data, making solving complex problems feasible.
Learn best-practices to avoid common mistakes to efficiently use the HPC infrastracture and overcome the scalability challenges when using parallel computing techniques
The course addresses participants who are familiar with the Python programming language and have working experience with the Linux operating system and the use of the command line. Experience with parallel programming or gpu programming is not required. Knowledge of mathematical basics in linear algebra, and notions of machine learning will be helpful.
Bring your own laptop in order to be able to participate in the training hands on. Hands on work will be done in pairs so if you don’t have a laptop you might work with a colleague.
Course language is English.
The maximum number of participants is 30.
Registrations will be evaluated on a first-come, first-served basis. GRNET is responsible for the selection of the participants on the basis of the training requirements and the technical skills of the candidates. GRNET will also seek to guarantee the maximum possible geographical coverage with the participation of candidates from many countries.
Address: 2nd Floor, 7, Kifisias Av. GR 115 23 Athens
Information on how to reach GRNET headquarters ia available on GRNET website: https://grnet.gr/en/contact-us/
Accommodation options near GRNET can be found at: https://grnet.gr/wp-content/uploads/sites/13/2015/11/Hotels-near-GRNET-en.pdf
ARIS - System Information
ARIS is the name of the Greek supercomputer, deployed and operated by GRNET (Greek Research and Technology Network) in Athens. ARIS consists of 532 computational nodes seperated in five “islands” as listed here:
426 thin nodes: Regular compute nodes without accelerator.
44 gpu nodes: “2 x NVIDIA Tesla k40m” accelerated nodes.
18 phi nodes: “2 x INTEL Xeon Phi 7120p” accelerated nodes.
44 fat nodes: Fat compute nodes have larger number of cores and memory per core than a thin node.
1 ml node: Machine Learning node consisting of 1 server, containing 2 Intel E5-2698v4 processors, 512 GB of central memory and 8 NVIDIA V100 GPU card.
All the nodes are connected via Infiniband network and share 2PB GPFS storage.The infrastructure also has an IBM TS3500 library of maximum storage capacity of about 6 PB. Access to the system is provided by two login nodes.
Dr. Dellis (Male) holds a B.Sc. in Chemistry (1990) and PhD in Computational Chemistry (1995) from the National and Kapodistrian University of Athens, Greece. He has extensive HPC and grid computing experience. He was using HPC systems in computational chemistry research projects on fz-juelich machines (2003-2005). He received an HPC-Europa grant on BSC (2009). In EGEE/EGI projects he acted as application support and VO software manager for SEE VO, grid sites administrator (HG-02, GR-06), NGI_GRNET support staff (2008-2014). In PRACE 1IP/2IP/3IP/4IP/5IP/6IP he was involved in benchmarking tasks either as group member or as BCO (2010-2020). Currently he holds the position of “HPC Team leader” at GRNET S.A. where he is responsible for activities related to user consultations, porting, optimization and running HPC applications at national and international resources.
Panos Louridas(Male) is an Associate Professor at the Department of Management Science and Technology of the Athens University of Economics and Business. His research interests include software systems, practical cryptography, business analytics, data science, and software analysis and design. He is the author of the well-received book “Real-World Algorithms: A Beginner’s Guide”, published by the MIT Press, and translated in Russian, Korean and Chinese. Panos Louridas has published widely in software engineering and data science; he is an active data scientist, and a seasoned software practitioner with over 25 years of professional practice. As a practitioner, he has been in charge of the Okeanos cloud computing platform (https://okeanos.grnet.gr) and the Zeus e-voting system (https://zeus.grnet.gr), used by thousands of users in production. He is a member of the ACM, the IEEE, Usenix, and the AAAS. He holds a PhD and an MSc in Software Engineering from the University of Manchester, and a Diploma in Computer Science from the University of Athens.
Vasiliki Kougia (Female) She is currently a research assistant at Athens University of Economics and Business (AUEB) and a member of the Natural Language Processing group of AUEB. I received my M.Sc. degree in Computer Science from AUEB (2018-2019) and graduated from the Department of Informatics of the same university (2012-2018). She is a teaching assistant in the Practical Data Science and Text Analytics courses of the M.Sc. in Data Science and in the Natural Language Processing course of the M.Sc. in Computer Science, of AUEB (2019-2020). Her main research interest is Artificial intelligence and especially machine learning and deep learning methods for Natural Language Processing and Computer Vision.
Konstantina Dritsa (Female) is a PhD candidate in the Business Analytics Laboratory of the Athens University of Economics & Business. Her research interests include all aspects of machine learning, with a focus on applications for predictions of source code properties. She holds a Bachelor from the Department of Management Science & Technology and an MSc in Information Systems, both by the Athens University of Economics and Business. She is a member of the Hellenic IT Museum, at the position of the administrative assistant of the Board of Advisors. She has previously worked in the travel industry as a Python developer and content editor.
GRNET – National Infrastructures for Research and Technology, is the national network, cloud computing and IT e-Infrastructure and services provider. It supports hundreds of thousands of users in the key areas of Research, Education, Health and Culture.
GRNET provides an integrated environment of cutting-edge technologies integrating a country-wide dark fiber network, data centers, a high performance computing system and Internet, cloud computing, high-performance computing, authentication and authorization services, security services, as well as audio, voice and video services.
GRNET scientific and advisory duties address the areas of information technology, digital technologies, communications, e-government, new technologies and their applications, research and development, education, as well as the promotion of Digital Transformation.
Through international partnerships and the coordination of EC co-funded projects, it creates opportunities for know-how development and exploitation, and contributes, in a decisive manner, to the development of Research and Science in Greece and abroad.
National Infrastructures for Research and Technology – Networking Research and Education