Organizer

  • Structural Division, Department of Civil Engineering, University of Pretoria
  • Professor George Markou - This email address is being protected from spambots. You need JavaScript enabled to view it.

Presenters

  • Dr Nikolaos Bakas - This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Professor Vagelis Harmandaris - This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Dr Jacob Finkenrath - This email address is being protected from spambots. You need JavaScript enabled to view it.

Date: 19th May 2022, 12:30- 13:45 (GMT+2)

Type of event: Online

Link to Event: https://eu.bbcollab.com/guest/61939675d7e94d9c9a4145201ced0c9b No passwords required)

You are cordially invited to participate in the upcoming event that will take place online featuring three distinct researchers that will present their recent work on Artificial Intelligence, Multi-Scale Modeling, and the use of High-Performance Computing in the field of Engineering. The respective abstracts of the three presentation are provided in this invitation, where a short bio is also included of our guest lecturers.

Event Program

Short Bio of the Guest Lecturers

Dr Nikolaos Bakas

Dr Nikolaos Bakas is an Associate Research Scientist at The Cyprus Institute. He has been a Lecturer in Computational Mechanics at Neapolis University Pafos and holds a PhD degree in Engineering Optimization. Currently, he works in research and industrial applications of Artificial Intelligence and Machine Learning. Particularly, he has published more than 50 research works, in journals and conferences. A prevalent work was his monograph in Research - A Science Partner Journal, where he gave a generic solution for the mathematical problem of extrapolating values from a given signal. Furthermore, he has been working on a variety of industrial applications of AI, with complex datasets, aiming to develop predictive models that generalize well on noisy datasets with unknown, chaotic distributions. He has taught at the National Technical University of Athens. and at Neapolis University from 2012 to 2019, and participated in a variety of research projects, at the National Technical University of Athens and at Neapolis University Pafos, as well as in Software Engineering companies in Greece. Finally, he funded Engineering Intelligence, a lifelong training organization for Engineers, with more than 5.000 professional Engineers trainees.

Professor Vagelis Harmandaris

Prof. Vagelis Harmandaris holds the ERA Chair in “Modeling and Simulation for Engineering Applications” (SimEA) at CaSToRC of The Cyprus Institute. He holds a PhD in Chemical Engineering from the University of Patras, Greece. He heads the SimEA group, consisting of about 15 members, focusing on the development of mathematical and computational methodologies for complex molecular systems, with important applications in nano/bio technology. Before joining the Cyprus Institute he was full Professor in the Department of Applied Mathematics at the University of Crete (currently on leave). He is also affiliated researcher at the Institute of Applied and Computational Mathematics of the Foundation of Research and Technology – Hellas (FORTH). In the past, he was researcher at the Max Planck Institute for Polymer Research, Mainz, Germany. He was involved in many projects funded by the European Union (Horizon and FP7 programmes) and via national grants in Germany (DFG) and Greece (ELIDEK); in several of them as coordinator. He has been a Reviewer for a large number of International Journals, for the European Union, and for various institutions among which the National Science Foundation (USA), the European Science Foundation (ESF), and the Partnership for Advanced Computing in Europe (PRACE). He has also been the organizer and co-organizer of more than 30 International workshops and conferences, and has served on a number of international advisory committees and bodies. He has been the author of more than 90 papers in refereed journals, 1 book, 3 chapters in books, and about 100 in refereed, and non-refereed, conference proceedings. His work has been presented more than 150 times (70 invited) in international conferences and academic and industrial institutions. As of December 2021, his articles have received 3692 citations in Web of Science (h-index is 31) and 4870 in Google Scholar.

Dr Jacob Finkenrath

Dr. Jacob Finkenrath is a Computational Scientist at the Computation-based Science and Technology Research Center (CaSToRC) at the Cyprus Institute (CyI) currently holding a position as a Research Scientist. He is leading the High Level Support Team (HLST) for academic projects of the National Competence Centre in High Performance Computing of Cyprus, which is getting implemented within the EuroHPC-JU project EuroCC. He received his PhD at the University of Wuppertal in 2015 in theoretical particle physics on stochastic methods for the calculation of the fermion determinant. Since 2015 he is working at CaSToRC as a Computational Scientist and is member of the Extended Twisted Mass collaboartion. Within this collaboration he is leading the simulation effort by tuning and generating cutting-edge lattice Quantum Chromodyanamics gauge ensembles at physical light, strange and charm quark masses. This large scale Markov Chain Monte Carlo simulations are utilizing large computer time allocations on HPC systems around the world like SuperMUC (LRZ, Germany), Hawk (HLRS, Germany) or Frontera (TACC, US).

Abstracts

Artificial Intelligence and High-Performance Computing in Engineering Simulations

In this short talk, we will demonstrate an overall approach for analysing tabular datasets and creating predictive models. Particularly, we will utilize Structural Engineering Datasets, for the prediction of response variables, based on geometric and material characteristics. The datasets have been developed utilizing the RECONAN software, and comprise the final results, in an input–output format. We will demonstrate descriptive statistics polts, and various predictive modelling methodologies with machine and deep learning algorithms and compare the performance of each. Finally, we will present the power of sensitivity analysis for the investigation of the behaviour of the machine learning model, which is many times considered a “black-box” due to its vastly complex nature.

Multiscale Simulations of Materials: from Atoms to Macroscopic Properties

Nowadays, computαtional approaches can be used in order to provide a direct insight at the properties of complex polymer-based materials across multiple spatiotemporal scales. Molecular simulations in particular have the advantage of accurately describing the chemistry of the systems under study, and of predicting their behavior at the molecular level. However, the study of macromolecular systems, such as polymers and proteins, via molecular simulations is a very challenging field, due to the broad spectrum of the underlying length and time scales. Here, we present a hierarchical multi-scale methodology for predicting the macroscopic properties of polymer-based nanostructured systems, that involves atomistic and coarse-grained simulations. The coarse-grained (CG) models are derived through a “bottom-up” data-driven strategy, using information from the detailed atomistic scale, for the given chemistry. The systematic linking between the atomistic and the chemistry-specific CG scale, allows the study of a broad range of molecular weights, for specific polymers, without any adjustable parameter [1-3]. At the same time, machine learning (ML) algorithms have been developed to re-introduce atomic detail in the CG scale, and thus obtaining atomistic configurations of high molecular weight polymers [4]. We apply the entire methodology to (a) polymer melts [5], and (b) polymer-based silica nanocomposites [1,6]. For both systems we provide a detailed study of their dynamical and rheological macroscopic properties. For the polymer melts, we report predictions about the self-diffusion coefficient of polymer chains, the relaxation modulus and the zero shear-rate viscosity, as a function of molecular length probing the transition from oligomers, to Rouse-like, up to the well-entangled systems. Concerning the polymer nanocomposites, we examine the structure and the dynamics of polymer chains at the polymer/nanoparticle interphase, by probing directly the density and the conformations of polymer chains, as well as and the segmental and terminal dynamics of the adsorbed, “bound” layer. In all cases the results are compared against experimental data and theoretical predictions. [1] H. Reda, A. Chazirakis, A.F. Behbahani, N. Savva, V. Harmandaris, “Mechanical properties of glassy polymer nanocomposites via atomistic and continuum models: The role of Interphases”, Comput. Methods Appl. Mech. Engrg. 2022, 395, 114905, https://doi.org/10.1016/j.cma.2022.114905 [2] E. Kalligiannaki, et al. Parametrizing coarse grained models for molecular systems at equilibrium”, Europ. Phys. J. Special Topics, 2016, 225, 1347–1372. http://dx.doi.org/10.1140/epjst/e2016-60145-x [3] Harmandaris, V.; Kremer, K. Dynamics of polystyrene melts through hierarchical multiscale simulations”. Macromolecules, 2009, 42, 791-802, https://doi.org/10.1021/ma8018624 [4] Lei, W. et al. Backmapping coarse-grained macromolecules: an efficient and versatile machine-learning approach. J. Chem. Phys., 2020, 153, 041101, https://doi.org/10.1063/5.0012320. [5] Behbahani, A.F. et al. Dynamics and Rheology of Polymer Melts via Hierarchical Atomistic, Coarse-grained, and Slip-spring Simulations, Macromolecules, 2021, 54, 6, 2740–2762, http://dx.doi.org/10.1021/acs.macromol.0c02583. [6] Behbahani, A.F. et al. Conformations and dynamics of polymer chains in cis and trans Poly(butadiene)/Silica nanocomposites through atomistic simulations: From the un-entangled to the entangled regime., Macromolecules, 2020, 53, 6173–6189, https://dx.doi.org/10.1021/acs.macromol.0c01030.

High Performance Computing

In the talk, we will give a short introduction on High Performance Computing (HPC). This will include a short discussion on the concept and on how to utilise HPC resources. Moreover a quick overview on the local system at The Cyprus Institute, Cyclone, short outlook on the European roadmap for HPC and an overview on the support for the Cypriot HPC community under the project EuroCC will be given.

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Date

Tuesday 21 - Thursday 23 June 2022

Location

The Cyprus Institute

Register for Training Event Here

Please visit our events platform for more details:

https://www.meetup.com/high-performance-computing-cyprus/events/285774446/

Description

This event will build on the knowledge and competences of the HPC Beginner Training Event and first HPC Intermediate Training Event and the focus of the event will be "GPU and Machine Learning".

It will teach more advanced aspects of HPC and will include GPU programming using CUDA, Python, Julia, Python and Data Science and TensorFlow/Keras for text processing.

This event is part of the EuroCC project and the National Competence Center activities.

Pre-requisites

Attendees should be familiar with at least one programming language, such as C and Python. Hands on exercises are part of the training and will be provided in C and Python

Attendees should ideally have attended previous training events. If not, interested attendees are advised to follow these via the following links:

Beginner Training Eventhttp://castorc.cyi.ac.cy/events/hpc-beginner-training-event-02-2021/

First HPC Intermediate Training Eventhttps://castorc.cyi.ac.cy/events/hpc-intermediate-training-event-04-2021

HPC Advanced Training Eventhttps://castorc.cyi.ac.cy/events/hpc-advanced-training-event-09-2021

Requirements

All attendees will need their own desktop or laptop with the following software installed:

  • Web browser - e.g. Firefox or Chrome
  • PDF viewer - e.g. Firefox, Adobe Acrobat
  • ssh client - Terminal for Mac or Linux is fine. For Windows Putty should be fine

Git Repository

The Git Repository with all material of the training event - including presentations and code, can be found at the following link:
THIS WILL APPEAR SOON

Agenda

Day 1 - Tuesday 21st June 2022

GPU programming using CUDA

  • 09:30 - 10:00: Welcome and Participant Introductions
  • 10:00-11:30: GPU programming using CUDA I
  • 11:30-12:00: Break
  • 12:00-13:00: GPU programming using CUDA I
  • 13:00-14:00: Lunch Break
  • 14:00-15:30: GPU programming using CUDA II
  • 15:30-16:00: Break
  • 16:00-17:00: GPU programming using CUDA II

Day 2 - Wednesday 22nd June 2022

GPU programming in Python and Introduction to MIT’s Julia Language

  • 10:00-11:30: GPU programming in Python
  • 11:30-12:00: Break
  • 12:00-13:00: GPU programming in Python
  • 13:00-14:00: Lunch Break
  • 14:00-15:30: Introduction to MIT’s Julia Language
  • 15:30-16:00: Break
  • 16:00-17:00: Introduction to MIT’s Julia Language

Day 3 - Thursday 23rd June 2022

Python in Data Science and TensorFlow/Keras for Text Processing

  • 10:00-11:30: Python in Data Science
  • 11:30-12:00: Break
  • 12:00-13:00: Python in Data Science
  • 13:00-14:00: Lunch Break
  • 14:00-15:30: TensorFlow/Keras for Text Processing
  • 15:30-16:00: Break
  • 16:00-17:00: Open Session - Questions and Discussion

GPU programming using CUDA

Prerequisites: Trainees should be comfortable in programming using C and should have covered the introductory CUDA training course covered here.

Description: The session will begin with a review of the GPU programming model and CUDA in particular, followed by a presentation of practical aspects to consider for achieving performance. The hands-on component will include practical examples to demonstrate how considerations such as data-layout, use of shared memory, and GPU thread distribution affect GPU kernel performance.

GPU programming in Python

Description: Following the introduction to CUDA on the first day, we will explore how similar results can be achieved directly via Python. First, we will introduce the equivalent of Numpy, i.e. Cupy and test its “out-of-the-box” performance. Then we will look into performance improvements compiling dedicated kernels for various examples first using tools natively available in Cupy and then Numba for CUDA. All sessions will be presented via examples and hands-on sessions.

Introduction to MIT’s Julia Language

Description: In this short session we will present the basic syntax structure of Julia Language. Julia is appropriate for Scientific Computing and Machine Learning, with a High-Performance compiler and simple syntax. Accordingly, we will demonstrate examples from Linear Algebra and BLAS, Statistics, and Machine Learning. Furthermore, the CUDA interface will be presented, along with commands for Multithreading and Vectorised computations. The examples will run in a hands-on session using Google Colab, where the participants may try the discussed concepts interactively.

Python in Data Science

Prerequisites: Trainees should be comfortable with writing simple Python code.

Description: In this session we will present a simple Data Science project lifecycle, using the Python programming language. That is, Python will be used in order to load, clean and explore a real-world dataset. A predictive model will be then implemented and evaluated. The examples will be presented in an interactive Google Colab Notebook, and trainees will have the opportunity to become familiar with some well-established Data Science tools and libraries, such as Numpy, Matplotlib, Pandas and Scikit-learn.

TensorFlow/Keras for Text Processing

Prerequisites: Attendees should be comfortable with the Python programming language. Keras/Tensorflow is not required but it makes the course easier to follow. Please view the following YouTube link for a similar course for computer vision tasks:
https://www.youtube.com/watch?v=jQgYuThPZVM

Description: In this short session we will use Keras/Tensorflow to solve a Natural Language Processing task, namely sentiment analysis.

EuroCCLogo

The Computation-based Science and Technology Research Center (CaSToRC) of the Cyprus Institute (CyI) has been inaugurated as the National Competence Center (NCC) in High Performance Computing (HPC) of Cyprus on 10th of September 2020, under the auspices of the Deputy Minister for Research, Innovation & Digital Policy Dr. Kyriakos Kokkinos. The inauguration of the CaSToRC as NCC in HPC is part of the EuroCC Competence Centre activity, one of the activities of the pan-European EuroHPC Joint Undertaking, a joint initiative between the EU, European countries and private partners to develop a World Class Supercomputing Ecosystem in Europe.

The EuroCC Competence Center activity brings together the necessary expertise to set up a network of NCCs in HPC across Europe to provide a broad service portfolio tailored to the respective national needs of industry, academia and public administration promoting innovation and competitiveness.

As part of its NCC responsibilities, CaSToRC seeks to help companies in technology transfer and business development activities. CaSToRC aims to achieve this through collaborative industrial projects where CaSToRC personnel can work together with company employees, train them in new technologies, which they can then implement in their company’s work process for its development and to make them more efficient and competitive. An example of such a collaboration is CaSToRC’s project with RetailZoom Ltd.

                                        retailzoom thin 

 

 

RetailZoom is a data analytics company operating in Europe, the Middle East and Africa, aiming at providing analytical insights and custom software solutions to medium and large businesses and organizations, with services revolving around statistical analysis and data automation.

The objective of the project is to create recommendation algorithms using machine learning / AI methodologies and HPC. These types of algorithms are used to gain complex insights into the customer and product based on past consumer interactions and make suggestions to consumers in a personalised way. With these algorithms, RetailZoom can help retailers provide a more personalised experience to their customers and at the same time the retailers can improve their sales.

The project started on the 1st of June and will run for 6 months.

The Computation-based Science and Technology Research Center (CaSToRC) of the Cyprus Institute (CyI) has been inaugurated as the National Competence Center (NCC) in High Performance Computing (HPC) of Cyprus on 10th of September 2020, under the auspices of the Deputy Minister for Research, Innovation & Digital Policy Dr. Kyriakos Kokkinos. The inauguration of the CaSToRC as NCC in HPC is part of the EuroCC Competence Centre activity, one of the activities of the pan-European EuroHPC Joint Undertaking, a joint initiative between the EU, European countries and private partners to develop a World Class Supercomputing Ecosystem in Europe.

The EuroCC Competence Center activity brings together the necessary expertise to set up a network of NCCs in HPC across Europe to provide a broad service portfolio tailored to the respective national needs of industry, academia and public administration promoting innovation and competitiveness.

As part of its NCC responsibilities, CaSToRC seeks to help companies in technology transfer and business development activities. CaSToRC aims to achieve this through collaborative industrial/governmental projects where CaSToRC personnel can work together with company employees, train them in new technologies, which they can then implement in their company’s work process for its development and to make them more efficient and competitive.

CaSToRC brings together industry and government with this collaboration between two private companies - Axia Surveyors and Real Geosolutions- and the Department of Lands and Surveys (DLS).

          

   axia logo

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dls   

      

 

AXIA CHARTERED SURVEYORS™ is a company registered under the name T.Dimopoulos & Associates Property Valuers L.L.C. The company operates throughout Cyprus and has offices in both Nicosia and Larnaca. ΑΧΙΑ specialises in property valuations and provides a wide range of other professional property related services such as: development appraisals, property management, planning & development advice, market research and investment consultancy.

Real-Geosolutions Ltd is the first real estate software firm in Cyprus who designed and developed a unique holistic Real Estate software with full Geographical Information Systems (GIS) Integration. The software covers Asset and Client Database Management, Land Cadastral Integration, GIS Integration, Valuation Analysis (complete analysis of comparable transactions with incorporation of Adjustment Matrix), Analysis Report Export and Statistical Analysis Export.

Department of Lands and Surveys (DLS) is solely responsible for the provision of services in connection with all the rights relating to immovable property - registration, valuation, general assessment (for tax purposes), tenure, surveying, cartography, geodesy and hydrography, as well as the management of all property belonging to Cyprus. It is also the main source of data relating to real property.

In this collaboration we develop large-scale Machine Learning algorithms for automated, mass real-estate appraisals in Cyprus. This will help bring more transparency to the market as valuations will be readily available to the general public, professional valuers and financial and banking institutions for control purposes.

The project started on the 1st of April and will run for 6 months

Cyclone - The Cyprus Institute

Opening Date: Monday 28th June 2021

Closing Date: Saturday 31st July 2021

Allocation Date: Friday 1st October 2021

Allocation Period: 1 year

Type of Access: Production Access

Introduction

The Cyprus Institute allows proposals with a Principal Investigator based in Cyprus to apply for production time on the HPC system “Cyclone” administered by the National Competence Center, CaSToRC of The Cyprus Institute.

This call is the 14th Cyclone call for Production Access inviting proposals for computing resources.

The deadline for submission of proposals is the end of Saturday 31st July 2021.

Available HPC Resources

The available resources on the HPC system “Cyclone” for this call are:

  • 2.5 million core hours
  • 225,000 GPU hours

The HPC System “Cyclone” is a hybrid CPU and GPU system with a theoretical peak performance of 600 TFlop/s. All nodes are equipped with two Intel Xeon Gold 6248 CPUs and 96GB of memory. The available GPUs are NVIDIA Tesla V100-SXM2 with 32GB each.

Allocation of resources will be for one year.

Eligibility

The Principal Investigator should be a senior researcher (must hold a PhD or have at least 3 years research experience) and be employed by a Cyprus based organisation that carries out research, innovation and development. The employment contract of the project Principal Investigator with their organisation must be valid to at least 3 months after the end of the allocation period.

Collaboration among organisations based in Cyprus from academia, industry and government, as well as organisations based in countries from the region and worldwide is strongly encouraged.

Projects that are led by or include non-academic institutions are welcome to apply provided they make their results public. Such applications should describe their intent to do so and sign the relative Cyprus Institute agreement document.

The Cyprus Institute may have further restrictions on who is eligible to use the system, for example, due to US export rules. It is the responsibility of the applicant to ensure that they are eligible to use the system.

Scope of the Call

Project access is intended for production-ready projects where significant amounts of computing resources are required.

All proposals are assessed by technical evaluation on the suitability and compatibility of the project with the requested computing resources.

Success in such a call will be based on the applicants providing a substantiated proposal for effectively using the requested resources and especially on the scientific or innovation merit of the project. We aim to support large-scale projects that require at least:

  • 500,000 CPU core hours
  • 50,000 GPU hours
  • A combination of the above

Projects requesting less resources than the ones mentioned above will not be considered.

How to Apply

All proposals must be submitted using the online tool found online at: https://ssl.linklings.net/applications/Cyclone/

All proposals should also use the Detailed Description Template (Version 14) which can be found at: https://castorc.cyi.ac.cy/images/Documents/CallsforProposals/CfP14-ProjectDetailedDescription_v14.docx

Mandatory fields, shown on the on-line form in red, must all be filled in before the proposal form can be submitted. After the form has been saved, applicants can continue to access it and make updates before they finally submit it. Applicants must ensure that details of the applications form and detailed description template are consistent between them, and that all required details are provided.

Assessment procedure

All eligible proposals for production access undergo administrative, technical and scientific project reviews.

The technical review is carried out by technical experts at The Cyprus and scientific project reviews are carried out by internationally recognized experts in the field of the proposal. During the project review, an increase or decrease in the requested amount of allocation time can be suggested due to recommendations received during the review process or general constraints such as the limited availability of resources.

The applicants will have the right to reply to the comments of both the technical and the project reviewers.

All proposals will finally be assessed by the Resource Allocation Committee, which will take into account the technical and scientific reviews of each proposal as well as the applicant’s right to reply, and allocate resources to successful proposals solely on merit.

Contact

For any queries concerning the application procedure, please contact: This email address is being protected from spambots. You need JavaScript enabled to view it. with email subject “14th Call for Proposals for Production Access”.

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