Lecture series

Supercomputing in science

The National competence centre together with the Computing Centre and the Computer Museum of the Centre of operations, SAS, organizes a popularization lecture series about supercomputing and HPC tools in various scientific fields. A part of the series covers applications using artificial intelligence and machine learning techniques. Listen to Slovak experts and join the discussion in a friendly and open setting.

We aim to introduce application possibilities of HPC in scientific research conducted by academic teams of SAS and Slovak universities, taking into account global advances and development. Even if using a commercial software packages for HPC calculations the knowledge of modeling and simulation methods, operation systems, computer architecture and basic programming skills are essential. Using HPC tools in science is without a doubt a multidisciplinary issue. This lecture series might in a way compensate the lack of such content in the Slovak universities curricula.

If you have a particular topic that you wish to hear about, please, let us know!

Join us for tea or coffee and discussions with invited speakers, which will take place every two weeks.

We are very pleased that our invitation was accepted by interesting speakers and we are looking forward to inspiring lectures and stimulating discussions in a relaxed atmosphere with coffee and refreshments!

HOW to follow the lectures:

Lectures are organized in hybrid format and it is possible to attend in person in the Computing centre building in VR (vaccinated & recovered) mode. Wearing an FFP2 respirator is mandatory. Please leave your contact email and phone number while registering on site.

  • in person in the building of the Computing centre in the Patronka campus please register to attend!
  • live on Facebook of the Centre of Operations, SAV. No registration needed.
  • watch the recordings on Facebook or YouTube of the Centre of operations, SAV.

Due to the constantly changing pandemic situation and preventive measures the format of individual lectures will be published 5 days prior to a specific event. Information can be found on our website and social networks. If you are registered for F2F lectures we will contact you directly by email.

Lecture series

15 Feb
Modelling of 2D systems properties using the Quantum Monte Carlo methods
Ivan Štich
Institute of Physics SAS

Prof. Štich is a partner of the TREX CoE - the European Center of Excellence in Exascale Computing (Horizon 2020 project). The lecture will focus on quantum Monte Carlo computational methods, for which the Nobel Prize in Physics was awarded in 2020 and which are well suited for the exascale computing. You will see a demonstration of calculations that required 30 million core hours to run.
1 Mar
Use of HPC in operational weather forecast in Slovakia
Slovak hydrometeorological institute

SHMU has been operating HPC since 2004. The primary application is the ALADIN model, developed within the international consortia ALADIN and currently ACCORD. This application contains hydride parallelism (combination of OpenMP, MPI) with the ability to set the length of the vector of internal cycles, so it is suitable for vector and scalar architectures. In the presentation we will provide an overview of the development of HPC in the field of meteorology in Slovakia. We will point out how SHMÚ compares them to other meteorological services in the EU. We will explain the specific requirements for HPC in terms of operational operation of the ALADIN model. Finally, we present our ideas about the future use of HPC in meteorology in Slovakia.
15 Mar
Introduction to artificial neural networks and evolutionary algorithms
Department of Robotics and Cybernetics, FEI STU

The presentation defines the terms biological and mathematical neuron, neural networks and their learning, selected architectures of artificial neural networks (UNS) and UNS applications, neuromorphic computing technologies, evolutionary algorithms (EA), genetic algorithm, bio-inspired algorithms, parallelization of EA and HPC and briefly explain their principles.
29 Mar
Deep learning and its basic applications for computer vision
Andrej Lúčny
Department of Computer Science FMFI UK

When we use some model of deep learning, it is almost impossible to understand how and why it works based on its architecture. To do this, we need to know the history of how and through what versions the researchers worked on the given architecture. In the lecture we will summarize the basic milestones of this history, starting with the perceptron and ending with the first successful deep neural networks. In doing so, we present the basic ideas behind these milestones: from a universal approximator through convolutional networks, autoencoders, fully convolutional networks to deep networks with typical building blocks (dropout, ReLU, batch normalization, residual connections), metric error functions and transformers. We will explain how deep neural networks differ from classical ones. We will also mention the role that better computing capabilities have played in their development.
12 Apr
Introduction to applications of complex calculations in medicine
Zuzana Černeková, Peter Bluska
Department of Computer Graphics and Vision, Department of Applied Informatics FMFI UK, Rádiológia s.r.o.

In medicine, in recent years, observing the exponential growth of data and their processing requires increasingly sophisticated systems. The first part of the presentation will outline the needs and problems of modern medicine with emphasis on the field of radiology, what tools are already available using artificial intelligence and what is the system of collecting annotated data for the needs of neural networks. In the second part we will get acquainted with the way of using neural networks to develop algorithms recognizing diagnoses in radiological images.
26 Apr
Use of ML / AI for applications in chemistry - potential drugs on Covid-19
Marián Gall
COO SAS - Computing Centre

How can we transform the structure of molecules into a form that neural networks understand? In this lecture, we want to summarize our efforts to replace the computationally expensive "docking" of molecules into the cavity of a target protein by machine learning and neural network methods. The target protein under study is 3CLpro SARS-CoV-2 (6WQF), which plays a key role in SARS-CoV-2 virus replication. How successful are these methods in preselecting large drug databases to select potential drugs for covid-19?
10 May
Current problems of reinforcedment learning
Michal Chovanec

Reinforcement learning - what it is and how it works, including demonstrations. The main points of the lecture will be:
  • Key achievements: Atari, GO, Hide and seek and demonstrations of deep reinforcement learning and AlphaGO.
  • The problem of exploitation: rare rewards, such as in roles: Montezuma’s revenge, Hide and seek. We examine the state space as "effective."
  • Mistakes and dubious publications - a summary of trials and mistakes as information for those who will work in this area to avoid them.
  • Conclusion - we will show what is currently being addressed, links to books and verified materials.
24 May
Natural and artificial intelligence
TOMÁŠ HromÁdka
Institute of Neuroimonulogy SAS
What are the similarities and differences between the intelligence of the biological system itself and the artificial intelligence? The effort to model the processes taking place in the brain was one of the inspirations for the development of artificial intelligence. We will talk about how information is processed in the living brain, the extent to which such artificial intelligence inspiration is usable and justifiable, and where the two types of intelligence differ.