Devana: Call for Standard HPC access projects 1/24
The Computing Center of the Slovak Academy of Sciences and the National Supercomputing Center are opening the first Call for Projects for Standard Access to HPC 1/24. Projects are possible continuously, while there are 3 closing dates as standard during the year, after which the evaluation will take place until the submitted applications. It is possible to apply for access through the register.nscc.sk user portal register.nscc.sk .
Standard access to high-performance computing resources is open to all areas of science and research, especially for larger-scale projects. These projects should demonstrate excellence in the respective fields and a clear potential to bring innovative solutions to current social and technological challenges. In the application, it is necessary to demonstrate the efficiency and scalability of the proposed calculation strategies and methods in the HPC environment. The necessary data on the performance and parameters of the considered algorithms and applications can be obtained within the Testing Access.
Allocations are awarded for one (1) year with the option to apply for extension, if necessary. Access is free of charge, provided that all requirements defined in the Terms of reference are met. Submitted projects are evaluated from a technical point of view by the internal team of CC SAS and SK NSCC, and the quality of the scientific and research part is always evaluated by two independent external reviewers.
Call opening date: 4.1.2024 Call closing date: 31.1. 2024, 17:00 CET Communication of allocation decision: Up to 2 weeks from Call closing. Start of the allocation perion for awarded projects: no later than 15. 2. 2024
Eligible Researchers Scientists and researchers from Slovak public universities and the Slovak Academy of Sciences, as well as from public and state administration organizations and private enterprises registered in the Slovak Republic, can apply for standard access to HPC. Access is provided exclusively for civil and non-commercial open-science research and development. Interested parties from private companies should first contact the National Competence Centre for HPC.
Final report within 2 months from the end of the project.
Peer-review and other publications in domestic and foreign scientific periodicals with acknowledgments in the pre-defined wording, reported through the user portal.
Active participation in the Slovak HPC conference organized by the coordinator of this call (poster, other contribution).
Participation in dissemination activities of the coordinator (interview, article in the HPC magazine, etc.).
Are you a scientist or developer dealing with HPC applications? Are you interested in developing, optimizing or testing applications on EuroHPC Pre-exascale and/or Petascale systems? Take advantage of the great opportunity to gain access to Pre-exascale or Petascale systems. Applications can be submitted at any time as the call is open continuously.
Access is guaranteed within two weeks of the application deadline (application deadlines are usually set once a month). The evaluation of submitted projects will take place on a fixed date at monthly intervals. The objectives of the individual modes of access within this proposal are:
Benchmark calls are designed for code scalability tests, the outcome of which is to be included in the proposal in a future EuroHPC Extreme Scale and Regular call. Users receive a limited number of node hours; the maximum allocation period is three (3) months.
Development calls are designed for projects focusing on code and algorithm development and optimisation. This can be in the context of research projects from academia or industry, or as part of large public or private funded initiatives as for instance Centres of Excellence or Competence Centres. Users will typically be allocated a small number of node hours; the allocation period is one (1) year and is renewable up to 2 times.
Various systems will be available in each cut-off for the different Benchmark & Development Access modes. The exact amount of available node hours is subject to the EuroHPC systems participating in a given call and will be announced prior to the cut-off dates. For the first cut-off period the VEGA system resources (Slovenia) are available.
Anomaly Detection in Time Series Data:
Gambling prevention using Deep Learning
Gambling prevention of online casino players is a challenging ambition with positive impacts both on player’s
well-being, and for casino providers aiming for responsible gambling. To facilitate this, we propose an
unsupervised deep learning method with an objective to identify players showing signs of problem gambling
based on available data in a form of time series. We compare the transformer-based autoencoder architecture for
anomaly detection proposed by us with recurrent neural network and convolutional neural network autoencoder
architectures and highlight its advantages. Due to the fact that the players’ clinical diagnosis was not part of
the data at hand, we evaluated the outcome of our study by analyzing correlation of anomaly scores obtained
from the autoencoder and several proxy indicators associated with the problem gambling reported in the
literature.
illustrative image
Gambling prevention of players with problem
or pathological gambling, currently conceptualized
as a behavioural pattern where individuals
stake an object of value (typically money) on the
uncertain prospect of a larger reward [1], [2], is
of high societal importance. Research over the
past decade has revealed multiple similarities between
pathological gambling and the substance
use disorders [3]. With the high accessibility of
the Internet, the incidence of pathological gambling
has increased. This disorder can result
in significant negative consequences for the affected
individual and his/her family too. Therefore
detecting early warning signs of problem
gambling is crucial for maintaining player’s wellbeing. This work is a joint effort of Slovak National
Competence Center for High-performance
Computing, DOXXbet, ltd. – sports betting
and online casino, and Codium, ltd. – software
developer of the DOXXbet sports betting and
iGaming platform, with the goal to enhance
customer service and players’ engagement
via identification and prevention of gambling
behaviour. This proof of concept is a foundation for future tools, which will help casino mitigate
negative consequences for players, even for a
price of less provision for the provider, as in
line with European trends in risk management
related to problem gambling.
In our study we propose a completely
unsupervised deep learning approach using
transformer-based AE architecture to detect
anomalies in the dataset - players with anomalous
behaviour. The dataset at hand does not
comprehend the clinical diagnosis, and amongst
other proxy indicators mentioned before only
few are available - requests to increase spending
limits, chasing losses by gambling more (referred
to as chasing episodes later in this article), usage
of multiple payment methods, frequent withdrawals
of small amount of money and other
mentioned later in the text. Clearly, not all the
anomalous users must necessarily have problem
gambling, hence the proxy indicators are
used in combination with AE results, namely the
anomaly score. The foundation of our approach
rests on the idea that a compulsive gambler is an
anomaly within the active casino players, with
the literature mentioning their fraction amongst
all players being between 0.5% to 5% for chancebased
games.
Data
The data acquired for this research consist of sequences
of data points collected over time, tracking
multiple aspects of player’s behaviour such
as frequency and timing of their gaming activities,
frequency and amount of cash deposits,
payment methods used when depositing cash,
information about the bets, wins, losses, withdrawals
and requests for change of deposit limit.
Feature engineering resulted in 19 features in
a form of time series (TS), so that each feature consists of multiple time stamps. These features
can be classified into three categories - ”time”,
”money” and ”despair”, as inspired by Seth et
al. [7]. Table 1 summarizes the full set of TS
features with a short explanation.
Each feature is a sequence of N values, where
each value stands for one out of N consecutive
time windows. This value was produced by
aggregating daily data in the respective time
window, with the time window length being
specified in the Table 1 together with the information
about the time window being sliding or
not. Hence, for each sample we needed a history
of N time windows. Feature engineering procedure
is displayed in Figure 1 and the final data
shape is depicted in Figure 2.
Figure 1: Visualization of the data aggregation from daily basis into time windows, and eventually to TS features.
t1, …, t450 represent time stamps for daily data x1, ..., x450. Daily data points from a time window are aggregated into a
single value zi for all i ∈ (1, . . . , 8).1, …, x450. Denné záznamy z časového okna sú agregované do jednej hodnoty zi pre všetky i ∈ (1, . . . , 8).
Figure 2: Final data shape obtained after feature engineering.
Each sample is represented by 19 features consisting
of 8 time windows.
AE models comparison
Autoencoder is a "self-supervised" deep learning method suitable for anomaly detection in the Czech Republic. The idea behind using this type of neural network for anomaly detection is based on the model's reconstruction capability. AE learns to reconstruct the data in the training set and since the training set should ideally only contain "normal" observations, the model learns to reconstruct only such observations correctly. Therefore, when the input observation is anomalous, the trained AE model cannot reconstruct this input sufficiently correctly, resulting in a high reconstruction error. This reconstruction error can be used as an anomaly score for the given observation, where a higher score means a higher probability that the observation deviates from the general trend.
In the study, we trained an AE model based on transformers, where both the encoder and decoder contain a layer called "Multi Head Attention" with four "heads" and 32-dimensional key and value vectors. This layer is followed by a classical neural network with so-called "dropout" layers and residual connections. The entire AE model has just over 100k trainable parameters.
Reconstruction loss and Prediction ability
We performed a 3-fold
cross-validation by splitting the data into training,
validation, and test sets, and trained the
models for each split to assess their stability. Resulting
average loss values and their variances
are displayed in the Table 3. The average reconstruction
error of Transformer model is significantly
lower than all the other models. LSTM B
model comes second in the reconstruction performance
and CNN model seems to have the
worst prediction performance. Generally, the
test loss is observed to be always higher than
train and validation losses. The reason for this
is that those 211 data points that were removed
from the training set in the data cleaning process,
were moved to the test set. Without moving these samples, the test loss for transformer-based
model would be as low as 0.012, for CNN model
0.33, for LSTM A model 0.27, and for LSTM B
model 0.13. More detailed overview of the models’ performance
is displayed on the Figure 6 as histograms
of loss values of the test set. All histograms have
heavy right tail, which is expected for datasets
containing anomalies.
Figure 3: Reconstruction error histograms of the transformer-based AE model for the test set. On the x-axis is the value of the anomaly score and on the y-axis is the frequency of the corresponding value.
To demonstrate the quality of the CR reconstruction, the original (blue line) and predicted (red line) values for a randomly selected anomalous observation of one player are shown in Figure 4. The value of the anomaly score for the respective models is given in the caption of the graphs.
Figure 4: Comparison of the predictive ability of AE models. All models reconstructed the same observation coming from the test set. Predictive ability: the blue line represents the input data, the red reconstruction obtained using the transformer-based AE model. The number shown in the graph header represents the anomaly score for that data sample.
Results
Since clinical diagnosis was not part of the data we had, we can only rely on auxiliary indicators to identify players with potentially problem gambling. We approached this task by detecting anomalies in the data, but we are aware that not all anomalies necessarily indicate a gambling problem. Therefore, we will correlate the results of the AE model with the following auxiliary indicators:
Mean number of logins in a time window.
Mean number of withdrawals in a time window.
Mean number of small and frequent withdrawals
in a time window.
Mean number of requests for the change of
the deposit limit in a time window.
Sum of the chasing episodes in the time slot
of N time window
Figure 5 depicts the correlation of the anomaly
score with the proxy indicators. Each subplot contains 10 bars, each bar representing one
decile of the data samples (i.e. each bar represents
10% of data samples sorted by anomaly
score). The bar colors represent the category
value of the respective proxy indicator.
(a)
(b)
(c)
(d)
(e) Figure 5: Each bar in the graphs represents one decile of the anomaly score (MSE). The colors represent the categories of the relevant auxiliary indicators, with category values specified in the legend.
A distinctive pattern in players’ behavior can
be observed, where players with larger anomaly
scores tend to exhibit high values for all the indicators
evaluated. Higher frequency of logins
is proportionate to higher anomaly score with
more than half of the players in the last decile of
reconstruction error having a mean number of logins
in a time window greater than 50. The same
applies for mean number of cash withdrawals
in a time window. Players with low anomaly
score have almost none or very few withdrawals,
whilst more than one fourth of players in the
last anomaly score decile have two or more withdrawals
in a time window on average. Another
secondary indicator we utilize is the number of
small and frequent withdrawals. Most of the
players with at least one of these events is in
10% of players with the highest MSE. When analyzing
another indicator, namely the number
of requests for a deposit limit change, we observe
a more subtle pattern. It is evident that
players in the first five deciles generally have no
requests for a limit change (with very few exceptions),
while as the anomaly score increases,
the frequency of limit change requests also tends
to rise. The last proxy indicator depicted is the
number of chasing episodes. A rising frequency
of these events proportionate to their anomaly
score can be observed. More than half of the
players in the last decile have at least one chasing
episode in the time window.
If these plots are overlapped in order to identify
the portion of players fulfilling multiple
proxy indicators, following observations result:
in the last five percentiles of the anomaly scores
98.6% of players satisfy at least one proxy indicator,
and 77.3% satisfy at least three indicators.
As for the last two percentiles, so 2% of players
with the highest reconstruction error, almost
90% of them satisfy at least three indicators. The
thresholds used to calculate these proportion are
>= 1 chasing episode, >= 1 limit change, >= 1
small and frequent withdrawal, >= 31 logins
and >= 1.25 withdrawal on average per time
window.
Conclusion
In this work, we successfully applied a
transformer-based autoencoder (AE) to detect
anomalies in the dataset of online casino players.
The aim was to detect problem gamblers
in dataset at hand in an unsupervised manner.
19 features were derived from the raw time series
(TS) data reflecting players’ behavior in the
context of time, money and despair.
We compared the performance of this architecture
with three other AE architectures based on
LSTM and convolutional layers and found that
the transformer-based AE achieved the best results
amongst the four models in terms of reconstruction capability. This model also showcases
high correlation with proxy indicators such as
the number of logins, number of player’s withdrawals,
number of chasing episodes and other,
that are commonly mentioned in literature in relation
to the gambling disorder. This alignment of AE’s anomaly score with proxy indicators
enables us to gain insights into prediction’s effectiveness
in identifying players with potential
problem gambling.
Even though these proxy indicators were also
used as predictors, we suggest to use them as a secondary check when detecting players with potential
problem gambling in order to avoid false
positives, as not all anomalies must be linked to
the condition of gambling disorder.
Our findings demonstrate the potential
of transformer-based AEs for unsupervised
anomaly detection tasks in TS data, particularly
in the context of online casino player behavior.
[1] Alex Blaszczynski and Lia Nower. “A Pathways Model of Problem and Pathological Gambling”. In: Addiction (Abingdon, England) 97 (June 2002), pp. 487–99. doi: 10.1046/j.1360-0443.2002.00015.x.
[2] National Research Council. Pathological Gambling: A Critical Review. Washington, DC: The National Academies Press, 1999. isbn: 978-0-309-06571-9. doi: 10 . 17226 / 6329. url: https ://nap .nationalacademies.org/catalog/6329/pathological – gambling – a – critical -review.
[3] Luke Clark et al. “Pathological Choice: The Neuroscience of Gambling and Gambling Addiction”. In: Journal of Neuroscience 33.45 (2013), pp. 17617–17623. issn: 0270-6474. doi: 0.1523/JNEUROSCI.3231-13.2013.eprint: https : / / www . jneurosci . org /content / 33 / 45 / 17617 . full . pdf. url: https://www.jneurosci.org/content/33/45/17617.
[4] Deepanshi Seth et al. “A Deep Learning Framework for Ensuring Responsible Play in Skill-based Cash Gaming”. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) (2020), pp. 454–459.
Intent Classification for Bank Chatbots through LLM Fine-Tuning12 Sep-Tento článok hodnotí použitie veľkých jazykových modelov na klasifikáciu intentov v chatbote s preddefinovanými odpoveďami, určenom pre webové stránky bankového sektora. Zameriavame sa na efektivitu modelu SlovakBERT a porovnávame ho s použitím multilingválnych generatívnych modelov, ako sú Llama 8b instruct a Gemma 7b instruct, v ich predtrénovaných aj fine-tunovaných verziách. Výsledky naznačujú, že SlovakBERT dosahuje lepšie výsledky než ostatné modely, a to v presnosti klasifikácie ako aj v miere falošne pozitívnych predikcií.
Leveraging LLMs for Efficient Religious Text Analysis5 Aug-The analysis and research of texts with religious themes have historically been the domain of philosophers, theologians, and other social sciences specialists. With the advent of artificial intelligence, such as the large language models (LLMs), this task takes on new dimensions. These technologies can be leveraged to reveal various insights and nuances contained in religious texts — interpreting their symbolism and uncovering their meanings. This acceleration of the analytical process allows researchers to focus on specific aspects of texts relevant to their studies.
Mapping Tree Positions and Heights Using PointCloud Data Obtained Using LiDAR Technology25 Jul-Cieľom spolupráce medzi Národným superpočítačovým centrom (NSCC) a firmou SKYMOVE, v rámci projektu Národného kompetenčného centra pre HPC, bol návrh a implementácia pilotného softvérového riešenia pre spracovanie dát získaných technológiou LiDAR (Light Detection and Ranging) umiestnených na dronoch.
On June 12, representatives from national competence centres for HPC situated in the central Europe region met during the first Central European NCC working group.. The event was organized by NCC Slovenia and NCC Austria in Maribor, Slovenia and online.
3 sessions were on the agenda, covering the topics of interaction with industry, training and communication. Participants worked in small groups, discussing specific points and sharing best practices within the topics, such as SME approach strategies, service portfolio and how to deal with the state aid issues, rules of engagement with a private company.
Training event formats were discussed, considering post-pandemic trends; experiences with NCC-CoE collaborations were highlighted and different strategies of attracting SME participants to training courses were shared.
The communication session focused on promotion and communication channels, build-up of an audience for different topics and organization of theme-specific webinar series. In each section, opportunities and invitations for collaboration were discussed, as well.
The event was a great opportunity for getting to know colleagues from neighboring NCCs, informal networking, fostering collaboration and finding new inspiration. Great thanks to the organizers and we are looking forward to the next working group meeting!
Odborná konferencia Superpočítač a Slovensko v Bratislave15 Nov-Dňa 14. novembra 2024 sa v hoteli Devín v Bratislave uskutočnila odborná konferencia s názvom Superpočítač a Slovensko, ktorú zorganizovalo Ministerstvo investícií, regionálneho rozvoja a informatizácie SR. Konferencia sa zameriavala na aktuálne trendy a vývoj v oblasti vysokorýchlostného počítania na Slovensku. Súčasťou podujatia bola prezentácia L. Demovičovej z Národného kompetenčného centra pre HPC.
Konferencia vysokovýkonného počítania v Portugalsku12 Nov-V poradí 4. Stretnutie vysokovýkonného počítania 2024, ktoré sa konalo 5. a 6. novembra na Univerzite Beira Interior v Covilhã, sa etablovalo ako kľúčové stretnutie používateľov, technikov a partnerov ekosystému vysokovýkonného počítania v Portugalsku.
REGISTRÁCIA OTVORENÁ: Nová séria populárno-náučných prednášok o zaujímavých HPC aplikáciách6 Oct-Otvorili sme registráciu na sériu prednášok v zimnom semestri 2024, kde sa budeme venovať fascinujúcim témam, v ktorých vysokovýkonné počítanie zohráva kľúčovú úlohu. Tento semester sa zameriame na oblasti ako meteorológia, klimatológia, chémia, veľké jazykové modely a mnoho ďalších.
TREX CoE & NCCs collab / Code tuning for the exascale
TREX centre of excellence together with 3 national competence centres delivered a three-day workshop „Code tuning for the exascale“. NCC Slovakia, Austria and Czech Republic teamed up to bring an interesting programme including advanced parallel programming, energy efficiency analysis and HPC application optimization. The workshop took place in Bratislava, Slovakia during June 5 – 7, 2023 and was organized in a face-to-face format.
This was a nice example of collaboration and support between the NCCs and a CoE. Three complementary topics were covered by the agenda. During the first day the team from Austrian NCC presented advanced parallel programming techniques (MPI, OpenMP), followed by the presentation of a trending topic of energy efficiency in HPC by the Czech NCC on the second day. The third day belonged to the TREX CoE, which presented a tool for HPC applications performance analysis and optimization called MAQAO. Organization, access to HPC and technical HPC support was given by the Slovak NCC.
We hope the workshop fulfilled its‘ purpose to equip the participants with information, tools, skills and competence to help the develop and run efficiently their own software utilizing modern HPC infrastructures.
Odborná konferencia Superpočítač a Slovensko v Bratislave15 Nov-Dňa 14. novembra 2024 sa v hoteli Devín v Bratislave uskutočnila odborná konferencia s názvom Superpočítač a Slovensko, ktorú zorganizovalo Ministerstvo investícií, regionálneho rozvoja a informatizácie SR. Konferencia sa zameriavala na aktuálne trendy a vývoj v oblasti vysokorýchlostného počítania na Slovensku. Súčasťou podujatia bola prezentácia L. Demovičovej z Národného kompetenčného centra pre HPC.
Konferencia vysokovýkonného počítania v Portugalsku12 Nov-V poradí 4. Stretnutie vysokovýkonného počítania 2024, ktoré sa konalo 5. a 6. novembra na Univerzite Beira Interior v Covilhã, sa etablovalo ako kľúčové stretnutie používateľov, technikov a partnerov ekosystému vysokovýkonného počítania v Portugalsku.
REGISTRÁCIA OTVORENÁ: Nová séria populárno-náučných prednášok o zaujímavých HPC aplikáciách6 Oct-Otvorili sme registráciu na sériu prednášok v zimnom semestri 2024, kde sa budeme venovať fascinujúcim témam, v ktorých vysokovýkonné počítanie zohráva kľúčovú úlohu. Tento semester sa zameriame na oblasti ako meteorológia, klimatológia, chémia, veľké jazykové modely a mnoho ďalších.
10. novembra 2021 sa uskutočnila už štvrtá prednáška série Superpočítanie vo vede. Tentokrát sme privítali Dr. Dušana Bernáta z Fakulty matematiky, fyziky a informatiky Univerzity Komenského so zaujímavou prednáškou na tému Operačné systémy v multiprocesorových klastroch.
Účastníci získali prehľad o základných pojmoch a definíciách, ako operačný systém umožňuje a zabezpečuje prístup aplikácií a procesov k prostriedkom a ako tieto prostriedky spravuje. Dozvedeli sme sa viac o stavoch a zmenách stavu jednotlivých procesov. Zaujímavé boli aj informácie o tom, ako vyzerá politika a réžia plánovača (scheduler), ktorý prístup na CPU procesom prideľuje.
Obr. 1: Diagram stavu procesov
Z hľadiska vysokovýkonných výpočtových prostriedkov nás zaujíma, ako funguje OS v prostredí s mnohými procesormi. Zvyšovanie počtu procesorov je prirodzenou odpoveďou na rýchly nárast požiadaviek aplikácií a súčasne limitov zvyšovania výkonu jediného CPU. Operačný systém teda môže úlohy rozdeľovať medzi viaceré fyzické procesory (alebo jadrá), pričom tieto procesy sú nezávislé a môžu bežať súbežne. Programátori môžu využiť výhody viacerých procesorov a svoje úlohy rozdeliť na viacero súbežných podúloh. Tieto už ale nie sú nezávislé a väčšinou je potrebné, aby medzi sebou navzájom komunikovali. Jedna úloha – proces môže mať teda viacero samostatných tokov riadenia, ktoré nazývame vlákna (threads) a ktoré zdieľajú väčšinu prostriedkov tohto procesu, vrátane pamäte. Z pohľadu architektúry to môže vyzerať ako na obrázku ilustrujúcom schému symetrického multiprocesorového systému (Obr. 2), kde je jedna pamäť zdieľaná viacerými rovnocennými procesormi (architektúra UMA – Uniform Memory Access). Tu je potrebné ošetriť synchronizáciu prístupu k tejto spoločnej pamäti, čo je možné urobiť viacerými spôsobmi. Prístup SMP – symetrického multiprocesingu má však nevýhody ako zlá škálovateľnosť, čakanie pri synchronizácii, tzv. cache trashing.
Obr. 2: Symetrický multiprocesorový systém
Ak fyzickú pamäť rozdelíme na viaceré moduly, dosiahneme menšie zaťaženie zbernice, pretože procesory budú najviac využívať vlastnú lokálnu pamäť. Tento prístup poznáme ako NUMA – Non-Uniform Memory Access. Tu je najvýhodnejšie, ak OS alokuje úlohe pamäť pre dáta čo najbližšie k procesoru, na ktorom úloha beží. Na obrázku vidíme príklad prepojenia 4 procesorov:
Obr. 3: Ukážka 4 procesorového point-to-point prepojenia, konfigurácie typickej pre NUMA
Prednáška pokryla aj tému správy pamäte, virtuálnu pamäť a jej alokáciu – vrátane konceptu overcommit a tému súborového systému a jeho hierarchie.
Operačné systémy a ich fungovanie v HPC prostredí by si určite zaslúžili aj viac priestoru, ako náš formát môže poskytnúť. Ak vás téma zaujala, na Fakulte matematiky, fyziky a informatiky UK na túto tému prednáša práve Dr. Dušan Bernát – a ak ste našu prednášku nestihli, môžete si ju pozrieť na Facebook or YouTube.
Odborná konferencia Superpočítač a Slovensko v Bratislave15 Nov-Dňa 14. novembra 2024 sa v hoteli Devín v Bratislave uskutočnila odborná konferencia s názvom Superpočítač a Slovensko, ktorú zorganizovalo Ministerstvo investícií, regionálneho rozvoja a informatizácie SR. Konferencia sa zameriavala na aktuálne trendy a vývoj v oblasti vysokorýchlostného počítania na Slovensku. Súčasťou podujatia bola prezentácia L. Demovičovej z Národného kompetenčného centra pre HPC.
Konferencia vysokovýkonného počítania v Portugalsku12 Nov-V poradí 4. Stretnutie vysokovýkonného počítania 2024, ktoré sa konalo 5. a 6. novembra na Univerzite Beira Interior v Covilhã, sa etablovalo ako kľúčové stretnutie používateľov, technikov a partnerov ekosystému vysokovýkonného počítania v Portugalsku.
REGISTRÁCIA OTVORENÁ: Nová séria populárno-náučných prednášok o zaujímavých HPC aplikáciách6 Oct-Otvorili sme registráciu na sériu prednášok v zimnom semestri 2024, kde sa budeme venovať fascinujúcim témam, v ktorých vysokovýkonné počítanie zohráva kľúčovú úlohu. Tento semester sa zameriame na oblasti ako meteorológia, klimatológia, chémia, veľké jazykové modely a mnoho ďalších.
Benefit from the expertise and knowledge developed within the top-class PRACE Research Infrastructure.
The Fourteenth Call for Applications to SHAPE (SME HPC Adoption Programme in Europe) invites applications from European SMEs with an interesting idea that would benefit from High Performance Computing for increasing their competitiveness. SHAPE aims to work with selected SMEs to introduce HPC-based tools and techniques into their business, operational, or production environment. The selected solutions should bring a potential tangible Return on Investment to the SME’s business.
The SHAPE process is as follows:
The SME can send the completed application by e-mail to shape-application[at]prace-ri.eu The form in Microsoft Word format can be found HERE If you have any questions do not hesitate to contact us: eurocc@nscc.sk
The applications are then reviewed and rated, based principally on the strength of the business case and technical feasibility of the proposed work. The successful SMEs can then receive machine time on a PRACE system, and most importantly effort from a PRACE expert to work alongside the SME in evaluating and/or developing the HPC-based solution. In return the SME provides in-kind effort, publicity of their participation in SHAPE, and together with the PRACE expert, produces a public white paper on the work achieved in the project at its conclusion.
PRACE experts will work with the selected SMEs in order to develop their solutions, providing the participating SMEs with knowledge that will allow them to make an informed decision on the selected HPC solution.
For more information and to apply please visit PRACE SHAPE.
Join us to talk about supercomputing in science in an informal atmosphere with coffee and refreshments! We have prepared a series of interesting lectures on high-performance computing – there is no registration fee, but the registration is required.
National Competence Centre for HPC organizes a series of lectures on why scientists need supercomputers and high-performance computing. This is a joint activity of the NCC and the Computer museum of the Centre of Operations, SAS.
Every user realizes that the use of HPC in various disciplines is a multidisciplinary issue. Not only is it necessary to have expertise in a specific scientific field, but further knowledge of modeling and simulation methods, programming basics, operating systems and computer architecture is required. The first lectures will therefore cover mostly HPC basics, preparing the participants for later presentations, in which experts from the SAS and the Slovak universities will speak in more detail about the use of HPC in various scientific disciplines. Our goal is to compensate to some extent what is often missing in the curricula of the universities and to try to deliver comprehensive information in the context of the global development.
The lectures are suitable for students, but also for people who want to learn more about HPC technologies, their development and utilization in science. Open, informal discussions with the invited speakers will be an important part of the events taking place in the SAS lecture hall within the Patrónka area. It will also be possible to watch the lectures online via YouTube. Attendance is free, but all hygiene measures and rules in connection with the Covid-19 pandemic have to be observed. For in-person attendance it is also necessary to register on the website http://itkurzy.sav.sk/prednasky.
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!
September 29, 17:00 -Current trends and vision of HPC development in Slovakia Filip Holka (COO SAS), Lukáš Demovič (COO SAS)
12. októbra o 17:00 – Computer simulations and calculations as an essential tool in science Pavel Neogrády (Faculty of Natural Sciences, Comenius University)
26. októbra o 17:00 – Development of technology and computer architecture – from 1-processor serial computers to supercomputers Martin Šperka (Computer Museum, COO SAS)
If you are interested in a lecture on a specific topic, but did not find it in our schedule, do not hesitate to contact us!
Don't miss out on the other National Competence Centre for HPC activities(lectures, free IT courses, hackathons, events) - follow us on social networks and subscribe to ournewsletter.
Odborná konferencia Superpočítač a Slovensko v Bratislave15 Nov-Dňa 14. novembra 2024 sa v hoteli Devín v Bratislave uskutočnila odborná konferencia s názvom Superpočítač a Slovensko, ktorú zorganizovalo Ministerstvo investícií, regionálneho rozvoja a informatizácie SR. Konferencia sa zameriavala na aktuálne trendy a vývoj v oblasti vysokorýchlostného počítania na Slovensku. Súčasťou podujatia bola prezentácia L. Demovičovej z Národného kompetenčného centra pre HPC.
Konferencia vysokovýkonného počítania v Portugalsku12 Nov-V poradí 4. Stretnutie vysokovýkonného počítania 2024, ktoré sa konalo 5. a 6. novembra na Univerzite Beira Interior v Covilhã, sa etablovalo ako kľúčové stretnutie používateľov, technikov a partnerov ekosystému vysokovýkonného počítania v Portugalsku.
REGISTRÁCIA OTVORENÁ: Nová séria populárno-náučných prednášok o zaujímavých HPC aplikáciách6 Oct-Otvorili sme registráciu na sériu prednášok v zimnom semestri 2024, kde sa budeme venovať fascinujúcim témam, v ktorých vysokovýkonné počítanie zohráva kľúčovú úlohu. Tento semester sa zameriame na oblasti ako meteorológia, klimatológia, chémia, veľké jazykové modely a mnoho ďalších.
Applicants’ reply to scientific reviews: Mid-January 2022
Communication of allocation decision: End of March 2022
Allocation period for awarded proposals: 01/04/2022 – 31/03/2023
Type of Access (*): Single-year Project Access and Multi-year Project Access
(*) All proposals consist of 2 parts: An online form and the ‘Project scope and plan’. Please note that if you wish to continue work on a project that has finished or is ongoing, a new proposal (i.e. a continuation proposal) needs to be submitted via the platform in addition to a final/progress report.
Industry Access: Call 24 offers Principal Investigators from industry the possibility to apply for Single-year access to a special Industry Track which prioritises 10% of the total resources available.
The computer systems (called Tier-0 systems) and their operations that are accessible through PRACE are provided for this 23rd call by 5 PRACE hosting members: BSC representing Spain, CINECA representing Italy, ETH Zurich/CSCS representing Switzerland, GCS representing Germany and GENCI representing France.
Scientists and researchers can apply for access to PRACE resources. Industrial users can apply if they have their head offices or substantial R&D activity in Europe.
Proposals can be based on a 12-months schedule (Single-year Projects), or, on a 24- or 36-months schedule (Multi-year Projects). The allocation of awarded resources is made 1 year at a time with provisional allocations awarded for the 2nd and 3rd year.
You can find more information and submit your application HERE.
The resources in these calls are from the Fenix Research Infrastructure, funded by the European ICEI project (https://fenix-ri.eu/).
These calls for proposals are for European researchers from academia, research institutes in need of scalable computing resources, interactive computing services, VM services and data storage to carry on their research.
Submission dates
Calls are organised quarterly calls according to the following timeline (calls will be open until resources are available):
Call opening
Call closure
Allocation
Call #6
31/05/2021
09/07/2021
From 01/10/2021
Call #7
20/09/2021
29/10/2021
From 01/01/2022
Dates for calls in 2022 will be announced later in 2021.
The allocations are limited to 12 months.
The PRACE ICEI program is open to all European researchers, research organizations needing resource allocations regardless of funding sources.
You are eligible to apply for the call for proposals only if you need one or more:
Scalable computing services
Interactive computing services
Virtual machine services
Archival data repository
Active data repository
PRACE ICEI Proposal Requirements
Short description of the scientific goals and objectives including progress beyond state of the art and scientific impact
Type of resources required i.e. scalable computing resource, interactive computing resources, VM services, archival and active data repositories
Resources requests based on limits defined above (including information on scalability for scalable compute resources requests)
Description of the software and services needed to successfully complete the project
Description of the research methods (including a project workplan), algorithms, and code parallelization approach (including memory requirements)
Information on Data Management
Description of special needs (if any)
More information on resources available and the submission process can be found HERE.
