Hastlayer (https://hastlayer.com/) by Lombiq Technologies is a .NET software developer-focused, easy-to-use high-level synthesis tool with the aim of accelerating applications. It converts standard .NET Common Intermediate Language (CIL) bytecode into equivalent Very High Speed Integrated Circuit Hardware Description Language (VHDL) constructs which can be implemented in hardware using FPGAs....
A new 5PF HPC is being built in Hungary, it will have more than 200 A100 GPUs. Dedicated partitions will be available for CPU-only jobs with almost 20 000 CPU cores, GPU partition with 200+ Nvidia A100 GPUs, Big Data partition with 9 TB RAM, AI partition with 8 GPU nodes.
This will be completed with most advanced HPC software and portal system open for both SMEs and Academia.
This talk...
GUARDYAN (GPU Assisted Reactor Dynamic Analysis) is a continuous energy Monte Carlo (MC) neutron transport code developed at Budapest University of Technology and Economics. It targets to solve time-dependent problems related to fission reactors with the main focus on simulating and analyzing short transients. The key idea of GUARDYAN is a massively parallel execution structure making use of...
Power grids are large complex networks whose dynamics, stability and vulnerability are intensively studied; new challenges arise with the increase of distributed renewable energy resources.
The dynamics of electrical grids is highly affected by desynchronization between nodes, which can start an avalanche-like cascade of line failures causing massive outages.
Modelling power systems in...
Recently Nanoplasmonic Laser Induced Fusion Experiments were proposed, as an improvement in achieving laser driven fusion [1]. This combines recent discoveries in heavy-ion collisions and optics. The existence of detonations with time-like normal on space-time hyper-surfaces combined with absorption adjustment using nanoantennas allows the possibility of heating the target in an opposing laser...
In this talk, we present work recently done by our group on the parallel solution of multiple tridiagonal linear systems that typically arise during the solution of discretised partial differential equations. We briefly introduce the established serial (Thomas) and parallel (Parallel Cyclic Reduction) algorithms for individual systems, then discuss how multiple systems are formed and solved in...
Designing spectroscopic follow-up observations in astronomy poses several challenges. Observing spectra is significantly more time consuming than photometric imaging observations yet, interesting objects need to be selected based on images taken with only a few broad-band filters. Hierarchical Bayesian Networks are often used to estimate physical parameters of photometrically observed stars, a...
Delay differential equations (DDE) appear in several branches of science and engineering. Possible applications include the modelling and forecasting of epidemics, the modelling of stability loss in control systems and many more. The delays in the differential equations can be caused by the incubation time of a virus in epidemic models or by the time which the computer needs to carry out the...
Mixing different precision of floating point arithmetics and number representations may be a highly effective tool to tackle some main challenges of exascale computing. By lowering precision, we can reduce memory and network traffic, decrease memory footprint, we can achieve more floating point operations per second by using less time to compute the same operations and we can also reduce...
The monoamine oxidase (MAO) is a flavoenzyme, which performs the oxidation of monoamine neurotransmitters such as serotonin, dopamine, norepinephrine, and their structurally related neuromodulator compounds, usually called "trace amines" (TAs) referring to their lower concentration compared to the main neurotransmitters. The latter group includes tryptamine (T), and phenylethylamine (PEA) as...
Modern proton Computed Tomography (pCT) images are usually reconstructed by the algebraic reconstruction techniques (ART). The Kaczmarz-method and its variations are among the most used methods, which are iterative solution techniques for linear problems with sparse matrices. One can ask, whether statistically-motivated iterations, which have been successfully used for emission tomography, can...
Transmembrane (TM) proteins are major drug targets, indicated by the high percentage of prescription drugs acting on them. For a rational drug design and an understanding of mutational effects on protein function, structural data at atomic resolution are required. However, hydrophobic TM proteins often resist experimental structure determination and in spite of the increasing number of cryo-EM...
Fairness in AI is a constantly evolving from a regulatory point of view, but the need of attention on this topic has been painstakingly clear after incidents in the past few years. In our presentation we are going to summarize examples of gender and racial bias in AI systems, as well as we are touching upon the latest regulatory trends including ALTAI. We are going to discuss some best...
As was shown by the pioneering work of Scott Aronson and Alex Arkhipov, bosonic systems are promising candidates to demonstrate quantum advantage.
Due to the nature of quantum states describing indistinguishable bosons, the exact simulation of particle number resolved bosonic systems is computationally very hard.
One of the main objectives of the Laboratory of Quantum Computer Simulators in...
Possibly the most influential achievements of modern computer science are the inventions of different machine learning algorithms, especially deep neural networks, which were able to solve problems that were previously intractable for computers, for example recognizing different animals on photos. On the other hand, in the last few decades we were witnessing an enormous improvement in quantum...
The complexity of the real world holds many challenges for autonomous driving including different lighting conditions and needs to focus on different tasks simultaneously. To address these challenges, we walk through two sets of complementary approaches: multi-task and multi-sensor systems based on deep learning that results in a superior world modeling capability around the car.
Astronomers and astrophysicists use more and more on Big Data from large sky surveys and simulations. It is thus inevitable that machine learning found its way into the process of exploitation of
these huge data sets. In the first part I'll review some recent applications of machine learning in
astronomy. The topics cover galaxy classification, asteroseismology, discovery...
