“The RISC2 project has proven to be a team effort in which European and Latin American partners worked together to drive HPC collaboration forward. We have been able to create a lively and active community across the Atlantic to stimulate dialogue and boost cooperation that won’t die with RISC2’s formal end”, says Fabrizio Gagliardi, manager director of RISC2.

Since 2021, this knowledge-sharing network has organised webinars, summer schools, meetings with policymakers and participated in conferences and dissemination events on both sides of the Atlantic. The project also resulted in the HPC Observatory Repository — a collection of documents and training materials produced as part of the project – and the White Paper on HPC R&I Collaboration Opportunities, a document that reviews the key socio-economic and environmental factors and trends that influence HPC needs.

These were two of the issues highlighted by European Commission officials and experts during the final evaluation of the project, which could provide continuity to the work carried out by the consortium over the last three years, in line with the wishes of the partners and the advice of the evaluators. “Beyond RISC2, we should keep the momentum and leverage the importance of Latin America in the frame of the Green Deal actions: HPC stakeholders should encourage policymakers to build bilateral agreements and offer open calls focused on HPC collaboration“, reflects Fabrizio Gagliardi.

The post Three years of building bridges in HPC research between Europe and Latin America: RISC2 project comes to an end first appeared on RISC2 Project.

As the coordinator of the High Performance Computing Center (Nacad) at COPPE/UFRJ, Alvaro Coutinho, presented advances in AI in Engineering and the importance of multidisciplinary research networks to address current issues in Scientific Machine Learning. Alvaro took the opportunity to highlight the need for Brazil to invest in high performance computing capacity.

The country’s sovereignty needs autonomy in producing ML advances, which depends on HPC support at the Universities and Research Centers. Brazil has nine machines in the Top 500 list of the most powerful computer systems in the world, but almost all at Petrobras company, and Universities need much more. ML is well-known to require HPC, when combined to scientific computer simulations it becomes essential.

The conventional notion of ML involves training an algorithm to automatically discover patterns, signals, or structures that may be hidden in huge databases and whose exact nature is unknown and therefore cannot be explicitly programmed. This field may face two major drawbacks: the need for a significant volume of (labelled) expensive to acquire data and limitations for extrapolating (making predictions beyond scenarios contained in the trained data difficult).

Considering that an algorithm’s predictive ability is a learning skill, current challenges must be addressed to improve the analytical and predictive capacity of Scientific ML algorithms, for example, to maximize its impact in applications of renewable energy. References [1-5] illustrate recent advances in Scientific Machine Learning in different areas of engineering and computer science.

References:

[*] https://www.coppe.ufrj.br/pt-br/planeta-coppe-noticias/noticias/coppe-e-sociedade-especialistas-debatem-os-reflexos-da-inteligencia

[1] Baker, Nathan, Steven L. Brunton, J. Nathan Kutz, Krithika Manohar, Aleksandr Y. Aravkin, Kristi Morgansen, Jennifer Klemisch, Nicholas Goebel, James Buttrick, Jeffrey Poskin, Agnes Blom-Schieber, Thomas Hogan, Darren McDonaldAlexander, Frank, Bremer, Timo, Hagberg, Aric, Kevrekidis, Yannis, Najm, Habib, Parashar, Manish, Patra, Abani, Sethian, James, Wild, Stefan, Willcox, Karen, and Lee, Steven. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. United States: N. p., 2019. Web. doi:10.2172/1478744.

[2] Brunton, Steven L., Bernd R. Noack, and Petros Koumoutsakos. “Machine learning for fluid mechanics.” Annual Review of Fluid Mechanics 52 (2020): 477-508.

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Several factors contributed to the catastrophic outcomes of the Black Death. The crises was boosted by the lack of two important components: knowledge and technology. There was no clue about the spread dynamics of the disease, and containment policies were desperately based on assumptions or beliefs. Some opted for self-isolation to get away from the “bad air” that was believed to be the cause of the illness [2]. Others thought the plague was a divine punishment and persecuted the heretics in order to “appease the heavens” [3]. Though the first of these two strategies was actually very effective, the second one only increased the tragedy of that scenario. 

The bubonic plague of the 14th century is a great example of how unfortunate ignorance can be in the context of epidemics. If the transmission mechanisms are not well-understood, we are not able to design productive measures against them. We may end up −such as our medieval predecessors− making things much more worse. Fortunately, the advances in science and technology have provided humanity with powerful tools to comprehend infectious diseases and rapidly develop response plans. In this particular matter, epidemic models and simulations have become crucial. 

In the recent COVID-19 events, many public health authorities relied on the outcomes of models, so as to determine the most probable paths of the epidemic and make informed decisions regarding sanitary measures [4]. Epidemic models have been around for a long time, and have become more and more sophisticated. One reason is the fact that they feed on data that has to be collected and processed, and which has increased in quantity and variety.  

Data contains interesting patterns that give hints about the influence of apparently non-epidemiological factors such as mobility and interaction type [5]. This is how, in the 19th century, John Snow managed to discover the cause of a cholera epidemic in Soho. He plotted the registered cholera cases in a map and saw they clustered around a water pump that he presumed was contaminated [6]. Thanks to Dr. Snow’s findings, water quality started to be considered as an important component of public health. 

As models grow in intricacy, the demand for more powerful computing systems also increases. In advanced approaches such as agent-based [7] and network (graph) models [8], every person is represented inside a complex framework in which the infection spreads according to specific rules. These rules could be related to the nature of the relations between individuals, their number of contacts, the places they visit, disease characteristics, and even stochastic influences. Frameworks are commonly composed of millions of individuals too, because we often want to analyze countrywide effects. 

In brief, to unravel epidemic dynamics we need to process and produce a lot of accurate information, and we need to do it fast. High-performance computing (HPC) systems provide high-spec hardware and support advanced techniques such as parallel computing, which accelerate calculation by using several resources at a time to perform one or different tasks concurrently. This is an advantage for stochastic epidemic models that require hundreds of independent executions to deliver reliable outputs. Frameworks with millions of nodes or agents need several GB of memory to be processed, which is a requirement that can be met only by HPC systems. 

Based on the work of Cruz et al. [9], we developed a model that represents the spread dynamics of COVID-19 in Costa Rica [10]. This model consists of a contact network of five million nodes, in which every Costa Rican citizen has a family, school, work, or random connection with their neighbors. These relations impact the probability of getting infected, as well as the “infection status” of the neighbors. The infection status varies with time, as people evolve from not having symptoms to have mild, severe, or critical conditions. People may be asymptomatic as well. The model also addresses variations in location, school and workplace sizes, age, mobility, and vaccination rates. In addition, some of these inputs are stochastic. 

Such model takes only a few hours to be simulated in an HPC cluster, when normal systems would require much more time. We managed to evaluate scenarios in which different sanitary measures were changed or eliminated. This analysis brought interesting results, such as that going to a meeting with our family or friends could be as harmful as attending a concert with dozens of strangers, in terms of the additional infections that these activities would generate. Such findings are valuable inputs for health authorities, because they demonstrate that preventing certain behaviors in the population can delay the peak of infections and give them more time to save lives. 

Even though HPC has been fundamental in computational epidemiology to give key insights into epidemic dynamics, we still have to leverage this technology in some contexts. For example, we must first strengthen health and information systems in developing countries to get the maximum advantage of HPC and epidemic models. The above can be achieved through inter–institutional and international collaboration, but also through national policies that support research and development. If we encourage the study of infectious diseases, we benefit from this knowledge in a way that we can approach other pandemics better in the future. 

References

[1] Encyclopedia Britannica. n.d. Crisis, recovery, and resilience: Did the Middle Ages end?. [online] Available at: <https://www.britannica.com/topic/history-of-Europe/Crisis-recovery-and-resilience-Did-the-Middle-Ages-end> [Accessed 13 September 2022]. 

[2] Mellinger, J., 2006. Fourteenth-Century England, Medical Ethics, and the Plague. AMA Journal of Ethics, 8(4), pp.256-260. 

[3] Carr, H., 2020. Black Death Quarantine: How Did We Try To Contain The Deadly Disease?. [online] Historyextra.com. Available at: <https://www.historyextra.com/period/medieval/plague-black-death-quarantine-history-how-stop-spread/> [Accessed 13 September 2022]. 

[4] McBryde, E., Meehan, M., Adegboye, O., Adekunle, A., Caldwell, J., Pak, A., Rojas, D., Williams, B. and Trauer, J., 2020. Role of modelling in COVID-19 policy development. Paediatric Respiratory Reviews, 35, pp.57-60. 

[5] Pasha, D., Lundeen, A., Yeasmin, D. and Pasha, M., 2021. An analysis to identify the important variables for the spread of COVID-19 using numerical techniques and data science. Case Studies in Chemical and Environmental Engineering, 3, p.100067. 

[6] Bbc.co.uk. 2014. Historic Figures: John Snow (1813 – 1858). [online] Available at: <https://www.bbc.co.uk/history/historic_figures/snow_john.shtml> [Accessed 13 September 2022]. 

[7] Publichealth.columbia.edu. 2022. Agent-Based Modeling. [online] Available at: <https://www.publichealth.columbia.edu/research/population-health-methods/agent-based-modeling> [Accessed 13 September 2022]. 

[8] Keeling, M. and Eames, K., 2005. Networks and epidemic models. Journal of The Royal Society Interface, 2(4), pp.295-307. 

[9] Cruz, E., Maciel, J., Clozato, C., Serpa, M., Navaux, P., Meneses, E., Abdalah, M. and Diener, M., 2021. Simulation-based evaluation of school reopening strategies during COVID-19: A case study of São Paulo, Brazil. Epidemiology and Infection, 149. 

[10] Abdalah, M., Soto, C., Arce, M., Cruz, E., Maciel, J., Clozato, C. and Meneses, E., 2022. Understanding COVID-19 Epidemic in Costa Rica Through Network-Based Modeling. Communications in Computer and Information Science, pp.61-75. 

By CeNAT

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The RISC2 consortium presented the work done so far, demonstrating achievements, and outputs, such as the creation of a strong collaborative network between the European and the Latin American research and industrial communities on advanced HPC application development, the HPC Observatory, the promotion of science, technology, and innovation to overcome different challenges, the project website and the White Paper, among other.

The European Commission was represented by Josiane Xavier Parreira, Laura Tosoratto and Lidia Yamamoto. The reviewers recognized and acknowledged the work done so far and provided advice and suggestions for future improvement. Official result of the review will be available in about one month.

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