Combined adaptive load balancing algorithm was tested on the HPC provider computing resources. Algorithm is based on domain decomposition and master-slave algorithms. Its core scheduling adaptive mechanism handles load redistribution according obtained and analyzed data. Selection of distribution algorithm, based on collected parameters and previously defined conditions, proved to deliver increased performances and reduced imbalance. Results of simulations confirm better performance of proposed algorithms compared to the standard algorithms reviewed in this paper.
Experimenting with combined adaptive load balancing for parallel application
Simulations on up to 224 CPU cores proved its validity and better efficiency than standard domain decomposition and master slave algorithms. In addition, simulations have shown that there are no large losses due to the increase in the number of cores on which the simulation is performed. More information on the experiment goals and the algorithm is available in the following reference: L. Filipovic, B. Krstajic, and T. Popovic, “Combined adaptive load balancing algorithm for parallel applications”, 8th International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2021.
The day-ahead energy market lets market participants commit to buy or sell wholesale electricity one day before the operating day, to help avoid price volatility. Forecasting day-ahead electricity prices and loads creates basis for decision making in this process. Mr. Milutin Pavićević, a young researcher from the University of Donja Gorica , explored the possibility to utilize artificial neural networks in order to improve the forecasting day-ahead electricity prices and loads based on the historical data. This was the topic of his Master thesis research work done under supervision of professor Tomo Popovic, which finally resulted in a scientific article published in MDPI journal Sensors. The paper is titled ”Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks” within the Special Issue Complex Data Processing Systems and Computing Algorithms: New Concepts and Applications.
During this research effort the researchers engaged the domain experts which provided us with generous help in obtaining datasets and understanding the problem of day-ahead consumption, spot price prediction, and the electricity market. The results show the promising efficiency of AI and machine learning for the task of short-term prediction of electricity metrics. With the support of EuroCC Montenegro, the future work will include experimenting on the HPC infrastructure and creation of an industry pilot demonstration for the energy sector.
Forecasting Day-Ahead Electricity Metrics with Machine Learning
ABSTRACT – As artificial neural network architectures grow increasingly more efficient in time-series prediction tasks, their use for day-ahead electricity price and demand prediction, a task with very specific rules and highly volatile dataset values, grows more attractive. Without a standardized way to compare the efficiency of algorithms and methods for forecasting electricity metrics, it is hard to have a good sense of the strengths and weaknesses of each approach. In this paper, we create models in several neural network architectures for predicting the electricity price on the HUPX market and electricity load in Montenegro and compare them to multiple neural network models on the same basis (using the same dataset and metrics). The results show the promising efficiency of neural networks in general for the task of short-term prediction in the field, with methods combining fully connected layers and recurrent neural or temporal convolutional layers performing the best. The feature extraction power of convolutional layers shows very promising results and recommends the further exploration of temporal convolutional networks in the field.
The paper can be accessed at the Sensors website at the following link.
On Thursday, 20 January, from 2pm – 5:30pm CET, representatives from NCC Montenegro took part in the Training Best Practice Seminar organized by CASTIEL project. The topics for the seminar were: How to organize an event such as an HPC hackathon or a winter-school and how to attract (new) attendees. Dr. Tomo Popovic and Dr. Luka Filipovic gave a presentation on How to organise an international conference, and discussed the experiences on organizing a HPC/HPDA/AI workshop within IEEE Information Technology IT2021 conference that was held last year.
Presentation on how to organise a training workshop within an conferenceAround 50 people attended the presentation
Registration is open for the upcoming EuroCC Training Event – 2nd High Performance Computing, High Performance Data Analytics, and Artificial Intelligence Workshop. This event is organized in alignment with the 26th International Information Technology IEEE Conference IT 2022, traditionally taking place in Žabljak, Montenegro. However, the IT2022 is organized as a virtual online event this year. Depending on the epidemic situation in Podgorica, the EuroCC training may be offered in a hybrid (virtual and live at the UDG). You can access the finalized version of the agenda here.
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You can register for the event by clicking on the image below or at this link.
Please click on the image to open the registration form
FF4EuroHPC experiment update: How to use AI and ML in agriculture sector? The idea of this experiment is to monitor the number of chickens, their weight and other parameters with the help of AI and a camera in which AI models will be integrated, and placed on poultry farms. Find more information here (based on FF4EuroHPC post on LinkedIn).
Currently developing AI prediction models for object detection and object segmentation
