“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.

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Given the nature of the project, RISC2 could not fail to be represented through its partners and with a strong presence in the event’s program. Carlos J. Barrios, researcher from the Universidad Industrial de Santander and RISC2 partner was responsible for opening CARLA, with his address setting the tone for the conference, emphasizing the importance of collaborative efforts and knowledge sharing in furthering the frontiers of HPC.

Fabrizio Gagliardi, the coordinator of RISC2, also took center stage with a special talk that introduced the audience to the mission and objectives of the RISC2 project. The presentation shed light on the pivotal role that RISC2 plays in advancing HPC research and development of the cooperation between the two continents in this field. Gagliardi participated in the EuroHPCLatam panel: Policy and Global Actions, which included representatives from Red Clara, CAF and the Ministry of CyT Colombia. This panel explored the policies and global actions required to propel HPC forward in Latin America, emphasizing collaboration between key stakeholders.

Another highlight of CARLA 2023 was the tribute to Mateo Valero, one of the promoters of RISC2. Valero’s dedication and contributions to the field were celebrated through an award with his name and one he was the first recepient, underscoring the lasting impact of his work on the entire HPC community.

This event was particularly important as it coincided with the end of the RISC2 project and the presentation of its results. Over the course of three years, the initiative has strengthened contacts and promoted the exchange of knowledge between researchers from Latin America and Europe through the organization of nine webinars, the support of several schools, workshops, and other training events in the field for young students and researchers. During this period, RISC2 partners also participated in several conferences and ceremonies with policymakers to raise awareness of the importance of continuing to support and prioritize this area of research in the future.

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The session began with a welcome and introduction by Mateo Valero (BSC), one of the main drivers of this cooperation and a leading name in the field of HPC. This intervention was later complemented by Fabrizio Gagliardi (BSC). Afterward, Elsa Carvalho (INESC TEC) presented the work done in terms of communication by the RISC2 team, an important segment for all the news and achievements to reach all the partners and countries involved.

Carlos J. Barrios Hernandez then presented the work done within the HPC Observatory, a relevant source of information that European and Latin American research organizations can address with HPC and/or AI issues.

The session closed with an important and pertinent debate on how to strengthen cooperation in HPC between the European Union and Latin America, in which all participants contributed and gave their opinion, committing to efforts so that the work developed within the framework of RISC2 is continued.

What our partners had to say about the meeting?

Rafael Mayo Garcia, CIEMAT:

“The policy event organized by RISC2 in Brussels was of utmost importance for the development of HPC and digital capabilities for a shared infrastructure between EU and LAC. Even more, it has had crucial contributions to international entities such as CYTED, the Ibero-American Programme for the Development of Science and Technology. On the CIEMAT side, it has been a new step beyond for building and participating in a HPC shared ecosystem.”

Esteban Meneses, CeNAT:

“In Costa Rica, CeNAT plays a critical role in fostering technological change. To achieve that goal, it is fundamental to synchronize our efforts with other key players, particularly government institutions. The event policy in Brussels was a great opportunity to get closer to our science and technology ministry and start a dialogue on the importance of HPC, data science, and artificial intelligence for bringing about the societal changes we aim for.”

Esteban Mocskos, UBA:

“The Policy Event recently held in Brussels and organized by the RISC2 project had several remarkable points. The gathering of experts in HPC research and management in Latin America and Europe served to plan the next steps in the joint endeavor to deepen the collaboration in this field. The advance in management policies, application optimization, and user engagement are fundamental topics treated during the main sessions and also during the point-to-point talks in every corner of the meeting room.
I can say that this meeting will also spawn different paths in these collaboration efforts that we’ll surely see their results during the following years with a positive impact on both sides of this fruitful relationship: Latin America and Europe.”

Sergio Nesmachnow, Universidad de la República:

“The National Supercomputing Center (Uruguay) and Universidad de la República have led the development of HPC strategies and technologies and their application to relevant problems in Uruguay. Specific meetings such as the policy event organized by RISC2 in Brussels are key to present and disseminate the current developments and achievements to relevant political and technological leaders in our country, so that they gain knowledge about the usefulness of HPC technologies and infrastructure to foster the development of national scientific research in capital areas such as sustainability, energy, and social development. It was very important to present the network of collaborators in Latin America and Europe and to show the involvement of institutional and government agencies.

Within the contacts and talks during the organization of the meeting, we introduced the projecto to national authorities, including the National Director of Science and Technology, Ministry of Education and Culture, and the President of the National Agency for Research and Innovation, as well as the Uruguayan Agency for International Cooperation and academic authorities from all institutions involved in the National Supercomputing Center initiative. We hope the established contacts can result in productive joint efforts to foster the development of HPC and related scientific areas in our country and the region.”

Carla Osthoff, LNCC:

“In Brazil, LNCC is critical in providing High Performance Computing Resources for the Research Community and training Human Resources and fostering new technologies. The policy event organized by RISC2 in Brussels was fundamental to synchronizing LNCC efforts with other government institutions and international  entities. On the LNCC side, it has been a new step beyond building and participating in an HPC-shared ecosystem.

Specific meetings such as the policy event organized by RISC2 in Brussels  were very important to present the network of collaborators in Latin America and Europe and to show the involvement of institutional and government agencies.

As a result of joint activities in research and development in the areas of information and communication technologies (ICT), artificial intelligence, applied mathematics, and computational modelling, with emphasis on the areas of scientific computing and data science, a Memorandum of Understanding (MoU) have been signed between LNCC and Inria/France. As a  result of new joint activities, LNCC and INESC TEC/Portugal are starting  collaboration through INESC TEC International Visiting Researcher Programme 2023.”

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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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Read more here.

Check the agenda:

The post Future of EU-LATAM collaboration on HPC first appeared on RISC2 Project.

Last month, students from the field of Computer Sciences, Engineering and others related to HPC (Physics & Material Sciences, Biology/Bioinformatics, Finance, etc), gathered for six days in Cartagena de Indias, Colombia, for the SC-Camp, where they had the chance to learn more about Super Computing and Distributed Systems. The week was filled with courses with focus on practical sessions, keynotes and a collaborative project.

As such, the aim of the SC-Camp was to give undergraduate and master students state-of-the-art lectures and programming practical sessions about High Performance and Distributed Computing topics.

SC-Camp is an itinerant school, which means that every year t will take HPC knowledge to a different place. This was the first time, since the Covid-19 pandemic, the SC-Camp took place in person.

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The ENERXICO Project focused on developing advanced simulation software solutions for oil & gas, wind energy and transportation powertrain industries.  The institutions that collaborated in the project are for México: ININ (Institution responsible for México), Centro de Investigación y de Estudios Avanzados del IPN (Cinvestav), Universidad Nacional Autónoma de México (UNAM IINGEN, FCUNAM), Universidad Autónoma Metropolitana-Azcapotzalco, Instituto Mexicano del Petróleo, Instituto Politécnico Nacional (IPN) and Pemex, and for the European Union: Centro de Supercómputo de Barcelona (Institution responsible for the EU), Technische Universitäts München, Alemania (TUM), Universidad de Grenoble Alpes, Francia (UGA), CIEMAT, España, Repsol, Iberdrola, Bull, Francia e Universidad Politécnica de Valencia, España.  

The Project contemplated four working packages (WP): 

WP1 Exascale Enabling: This was a cross-cutting work package that focused on assessing performance bottlenecks and improving the efficiency of the HPC codes proposed in vertical WP (UE Coordinator: BULL, MEX Coordinator: CINVESTAV-COMPUTACIÓN); 

WP2 Renewable energies:  This WP deployed new applications required to design, optimize and forecast the production of wind farms (UE Coordinator: IBR, MEX Coordinator: ININ); 

WP3 Oil and gas energies: This WP addressed the impact of HPC on the entire oil industry chain (UE Coordinator: REPSOL, MEX Coordinator: ININ); 

WP4 Biofuels for transport: This WP displayed advanced numerical simulations of biofuels under conditions similar to those of an engine (UE Coordinator: UPV-CMT, MEX Coordinator: UNAM); 

For WP1 the following codes were optimized for exascale computers: Alya, Bsit, DualSPHysics, ExaHyPE, Seossol, SEM46 and WRF.   

As an example, we present some of the results for the DualPHYysics code. We evaluated two architectures: The first set of hardware used were identical nodes, each equipped with 2 ”Intel Xeon Gold 6248 Processors”, clocking at 2.5 GHz with about 192 GB of system memory. Each node contained 4 Nvidia V100 Tesla GPUs with 32 GB of main memory each. The second set of hardware used were identical nodes, each equipped with 2 ”AMD Milan 7763 Processors”, clocking at 2.45 GHz with about 512 GB of system memory. Each node contained 4 Nvidia V100 Ampere GPUs with 40 GB of main memory each. The code was compiled and linked with CUDA 10.2 and OpenMPI 4. The application was executed using one GPU per MPI rank. 

In Figures 1 and 2 we show the scalability of the code for the strong and weak scaling tests that indicate that the scaling is very good. Motivated by these excellent results, we are in the process of performing in the LUMI supercomputer new SPH simulations with up to 26,834 million particles that will be run with up to 500 GPUs, which is 53.7 million particles per GPU. These simulations will be done initially for a Wave Energy Converter (WEC) Farm (see Figure 3), and later for turbulent models. 

Figure 1. Strong scaling test with a fix number of particles but increasing number of GPUs.

Figure 2. Weak scaling test with increasing number of particles and GPUs.

Figure 3. Wave Energy Converter (WEC) Farm (taken from https://corpowerocean.com/)

As part of WP3, ENERXICO developed a first version of a computer code called Black Hole (or BH code) for the numerical simulation of oil reservoirs, based on the numerical technique known as Smoothed Particle Hydrodynamics or SPH. This new code is an extension of the DualSPHysics code (https://dual.sphysics.org/) and is the first SPH based code that has been developed for the numerical simulation of oil reservoirs and has important benefits versus commercial codes based on other numerical techniques.  

The BH code is a large-scale massively parallel reservoir simulator capable of performing simulations with billions of “particles” or fluid elements that represent the system under study. It contains improved multi-physics modules that automatically combine the effects of interrelated physical and chemical phenomena to accurately simulate in-situ recovery processes. This has led to the development of a graphical user interface, considered as a multiple-platform application for code execution and visualization, and for carrying out simulations with data provided by industrial partners and performing comparisons with available commercial packages.  

Furthermore, a considerable effort is presently being made to simplify the process of setting up the input for reservoir simulations from exploration data by means of a workflow fully integrated in our industrial partners’ software environment.  A crucial part of the numerical simulations is the equation of state.  We have developed an equation of state based on crude oil data (the so-called PVT) in two forms, the first as a subroutine that is integrated into the code, and the second as an interpolation subroutine of properties’ tables that are generated from the equation of state subroutine.  

An oil reservoir is composed of a porous medium with a multiphase fluid made of oil, gas, rock and other solids. The aim of the code is to simulate fluid flow in a porous medium, as well as the behaviour of the system at different pressures and temperatures.  The tool should allow the reduction of uncertainties in the predictions that are carried out. For example, it may answer questions about the benefits of injecting a solvent, which could be CO2, nitrogen, combustion gases, methane, etc. into a reservoir, and the times of eruption of the gases in the production wells. With these estimates, it can take the necessary measures to mitigate their presence, calculate the expense, the pressure to be injected, the injection volumes and most importantly, where and for how long. The same happens with more complex processes such as those where fluids, air or steam are injected, which interact with the rock, oil, water and gas present in the reservoir. The simulator should be capable of monitoring and preparing measurement plans. 

In order to be able to perform a simulation of a reservoir oil field, an initial model needs to be created.  Using geophysical forward and inverse numerical techniques, the ENERXICO project evaluated novel, high-performance simulation packages for challenging seismic exploration cases that are characterized by extreme geometric complexity. Now, we are undergoing an exploration of high-order methods based upon fully unstructured tetrahedral meshes and also tree-structured Cartesian meshes with adaptive mesh refinement (AMR) for better spatial resolution. Using this methodology, our packages (and some commercial packages) together with seismic and geophysical data of naturally fractured reservoir oil fields, are able to create the geometry (see Figure 4), and exhibit basic properties of the oil reservoir field we want to study.  A number of numerical simulations are performed and from these oil fields exploitation scenarios are generated.

Figure 4. A detail of the initial model for a SPH simulation of a porous medium.

More information about the ENERXICO Project can be found in: https://enerxico-project.eu/

By: Jaime Klapp (ININ, México) and Isidoro Gitler (Cinvestav, México)

The post Subsequent Progress And Challenges Concerning The México-UE Project ENERXICO: Supercomputing And Energy For México first appeared on RISC2 Project.

The goal of the investigation is to provide users with simplified access to computing structures through scientific solutions that represent significant developments in their fields. In the case of this project, it is intended to develop intelligent green scientific solutions for BioinfoPortal (a multiuser Brazilian infrastructure)supported by High-Performance Computing environments.

Technologically, it includes areas such as scientific workflows, data mining, machine learning, and deep learning. The outlook, in case of success, is the analysis and interpretation of Big Data allowing new paths in molecular biology, genetics, biomedicine, and health— so it becomes necessary tools capable of digesting the amount of information, efficiently, which can come.

The team performed several large-scale bioinformatics experiments that are considered to be computationally intensive. Currently, artificial intelligence is being used to generate models to analyze computational and bioinformatics metadata to understand how automatic learning can predict computational resources efficiently. The workshop was held from April 10th to 11th, and took place in the University of Sao Paulo.

RISC2 Project, which aims to explore the HPC impact in the economies of Latin America and Europe, relies on the interaction between researchers and policymakers in both regions. It also includes 16 academic partners such as the University of Buenos Aires, National Laboratory for High Performance Computing of Chile, Julich Supercomputing Centre, Barcelona Supercomputing Center (the leader of the consortium), among others.

The post Latin American researchers present greener gateways for Big Data in INRIA Brazil Workshop first appeared on RISC2 Project.

By:

Carneiro, B. Fagundes, C. Osthoff, G. Freire, K. Ocaña, L. Cruz, L. Gadelha, M. Coelho, M. Galheigo, and R. Terra are with the National Laboratory of Scientific Computing, Rio de Janeiro, Brazil.

Carvalho is with the Federal Center for Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil.

Douglas Cardoso is with the Polytechnic Institute of Tomar, Portugal.

Boito and L, Teylo is with the University of Bordeaux, CNRS, Bordeaux INP, INRIA, LaBRI, Talence, France.

Navaux is with the Informatics Institute, the Federal University of Rio Grande do Sul, and Rio Grande do Sul, Brazil.

References:

Ocaña, K. A. C. S.; Galheigo, M.; Osthoff, C.; Gadelha, L. M. R.; Porto, F.; Gomes, A. T. A.; Oliveira, D.; Vasconcelos, A. T. BioinfoPortal: A scientific gateway for integrating bioinformatics applications on the Brazilian national high-performance computing network. Future Generation Computer Systems, v. 107, p. 192-214, 2020.

Mondelli, M. L.; Magalhães, T.; Loss, G.; Wilde, M.; Foster, I.; Mattoso, M. L. Q.; Katz, D. S.; Barbosa, H. J. C.; Vasconcelos, A. T. R.; Ocaña, K. A. C. S; Gadelha, L. BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments. PeerJ, v. 1, p. 1, 2018.

Coelho, M.; Freire, G.; Ocaña, K.; Osthoff, C.; Galheigo, M.; Carneiro, A. R.; Boito, F.; Navaux, P.; Cardoso, D. O. Desenvolvimento de um Framework de Aprendizado de Máquina no Apoio a Gateways Científicos Verdes, Inteligentes e Eficientes: BioinfoPortal como Caso de Estudo Brasileiro In: XXIII Simpósio em Sistemas Computacionais de Alto Desempenho – WSCAD 2022 (https://wscad.ufsc.br/), 2022.

Terra, R.; Ocaña, K.; Osthoff, C.; Cruz, L.; Boito, F.; Navaux, P.; Carvalho, D. Framework para a Construção de Redes Filogenéticas em Ambiente de Computação de Alto Desempenho. In: XXIII Simpósio em Sistemas Computacionais de Alto Desempenho – WSCAD 2022 (https://wscad.ufsc.br/), 2022.

Ocaña, K.; Cruz, L.; Coelho, M.; Terra, R.; Galheigo, M.; Carneiro, A.; Carvalho, D.; Gadelha, L.; Boito, F.; Navaux, P.; Osthoff, C. ParslRNA-Seq: an efficient and scalable RNAseq analysis workflow for studies of differentiated gene expression. In: Latin America High-Performance Computing Conference (CARLA), 2022, Rio Grande do Sul, Brazil. Proceedings of the Latin American High-Performance Computing Conference – CARLA 2022 (http://www.carla22.org/), 2022.

^[1] https://bioinfo.lncc.br/

^[2] https://git.tecgraf.puc-rio.br/csbase-dev/csgrid/-/tree/CSGRID-2.3-LNCC

^[3] https://https://sdumont.lncc.br

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