BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260708T214339Z UID:68e3020c-0d87-4206-8cc0-3c720707c0bd DTSTART:20200309T223000Z DTEND:20200311T205000Z DESCRIPTION:Machine Learning in HPC\n\n10 - 11 March 2020\n\nDescription\n\ nAfter the course the participants should be able to understand basic prin ciples in Machine Learning and apply basic machine learning methods.\n\nLe arn how to efficiently use HPC infrastructures to get\nthe best performanc e out of different machine learning tools. How to use these machine learni ng frameworks like: Tensorflow\, PyTorch\, Keras\, Horovood with hands-on sessions.\n\nLearn using multiple GPUs to significantly shorten the time r equired to train lots of data\, making solving complex problems feasible.\ n\nLearn best-practices to avoid common mistakes to efficiently use the HP C infrastracture and overcome the scalability challenges when using parall el computing techniques\n\nPrerequisites\n\nThe course addresses participa nts who are familiar with the Python programming language and have working experience with the Linux operating system and the use of the command lin e. Experience with parallel programming or gpu programming is not required . Knowledge of mathematical basics in linear algebra\, and notions of mach ine learning will be helpful.\n\nBring your own laptop in order to be able to participate in the training hands on. Hands on work will be done in pa irs so if you don’t have a laptop you might work with a colleague.\n\nCo urse language is English.\n\nRegistration\n\nThe maximum number of partici pants is 30. \n\nRegistrations will be evaluated on a first-come\, first-s erved basis. GRNET is responsible for the selection of the participants on the basis of the training requirements and the technical skills of the ca ndidates. GRNET will also seek to guarantee the maximum possible geograph ical coverage with the participation of candidates from many countries.\n\ nVenue\n\nGRNET headquarters\n\nAddress: 2nd  Floor\, 7\, Kifisias Av. GR 115 23 Athens\n\nInformation on how to reach GRNET headquarters ia availa ble on GRNET website: https://grnet.gr/en/contact-us/  \n\nAccommodation options near GRNET can be found at: https://grnet.gr/wp-content/uploads/s ites/13/2015/11/Hotels-near-GRNET-en.pdf\n\nARIS - System Information\n\nA RIS is the name of the Greek supercomputer\, deployed and operated by GRNE T (Greek Research and Technology Network) in Athens. ARIS consists of 532 computational nodes seperated in five “islands” as listed here:\n\n\n \n 426 thin nodes: Regular compute nodes without accelerator.\n \n \n 44 g pu nodes: “2 x NVIDIA Tesla k40m” accelerated nodes.\n \n \n 18 phi no des: “2 x INTEL Xeon Phi 7120p” accelerated nodes.\n \n \n 44 fat node s: Fat compute nodes have larger number of cores and memory per core than a thin node.\n \n \n 1 ml node: Machine Learning node consisting of 1 serv er\, containing 2 Intel E5-2698v4 processors\, 512 GB of central memory an d 8 NVIDIA V100 GPU card.\n \n\n\nAll the nodes are connected via Infiniba nd network and share 2PB GPFS storage.The infrastructure also has an IBM T S3500 library of maximum storage capacity of about 6 PB. Access to the sys tem is provided by two login nodes. \n\nAbout Tutors\n\nDr. 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 (2003-200 5). 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 si tes administrator (HG-02\, GR-06)\, NGI_GRNET support staff (2008-2014). I n PRACE 1IP/2IP/3IP/4IP/5IP/6IP he was involved in benchmarking tasks eith er 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 activit ies related to user consultations\, porting\, optimization and running HPC applications at national and international resources.\n\n\nPanos Louridas (Male) is an Associate Professor at the Department of Management Science a nd Technology of the Athens University of Economics and Business. His rese arch interests include software systems\, practical cryptography\, busines s analytics\, data science\, and software analysis and design. He is the a uthor of the well-received book “Real-World Algorithms: A Beginner’s G uide”\, published by the MIT Press\, and translated in Russian\, Korean and Chinese. Panos Louridas has published widely in software engineering a nd data science\; he is an active data scientist\, and a seasoned software practitioner with over 25 years of professional practice. As a practition er\, 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 Engin eering from the University of Manchester\, and a Diploma in Computer Scien ce from the University of Athens.\n\nVasiliki Kougia (Female) She is curre ntly a research assistant at Athens University of Economics and Business ( AUEB) and a member of the Natural Language Processing group of AUEB. I rec eived my M.Sc. degree in Computer Science from AUEB (2018-2019) and gradua ted from the Department of Informatics of the same university (2012-2018). She is a teaching assistant in the Practical Data Science and Text Analyt ics courses of the M.Sc. in Data Science and in the Natural Language Proce ssing course of the M.Sc. in Computer Science\, of AUEB (2019-2020). Her m ain research interest is Artificial intelligence and especially machine le arning and deep learning methods for Natural Language Processing and Compu ter Vision.\n\nKonstantina Dritsa (Female) is a PhD candidate in the Busin ess Analytics Laboratory of the Athens University of Economics &\; Busi ness. Her research interests include all aspects of machine learning\, wit h a focus on applications for predictions of source code properties. She h olds a Bachelor from the Department of Management Science &\; Technolog y and an MSc in Information Systems\, both by the Athens University of Eco nomics and Business. She is a member of the Hellenic IT Museum\, at the po sition of the administrative assistant of the Board of Advisors. She has p reviously worked in the travel industry as a Python developer and content editor.\n\nAbout GRNET\n\nGRNET – National Infrastructures for Research and Technology\, is the national network\, cloud computing and IT e-Infras tructure and services provider. It supports hundreds of thousands of users in the key areas of Research\, Education\, Health and Culture.\n\nGRNET p rovides an integrated environment of cutting-edge technologies integrating a country-wide dark fiber network\, data centers\, a high performance com puting system and Internet\, cloud computing\, high-performance computing\ , authentication and authorization services\, security services\, as well as audio\, voice and video services.\n\nGRNET scientific and advisory duti es address the areas of information technology\, digital technologies\, co mmunications\, e-government\, new technologies and their applications\, re search and development\, education\, as well as the promotion of Digital T ransformation.\n\nThrough international partnerships and the coordination of EC co-funded projects\, it creates opportunities for know-how developme nt and exploitation\, and contributes\, in a decisive manner\, to the deve lopment of Research and Science in Greece and abroad.\n\nNational Infrastr uctures for Research and Technology – Networking Research and Education \n\nwww.grnet.gr\, hpc.grnet.gr\n\n \nhttps://events.prace-ri.eu/event/99 4/ SUMMARY:Machine Learning in HPC @GRNET URL;VALUE=URI:https://events.prace-ri.eu/event/994/ END:VEVENT END:VCALENDAR