pt - RISC2 Project https://www.risc2-project.eu Thu, 28 Sep 2023 10:21:06 +0000 en-US hourly 1 https://wordpress.org/?v=6.6.2 CARLA 2023: RISC2 Results Presented at Largest HPC Conference in Latin America https://www.risc2-project.eu/2023/09/28/carla-2023-risc2-results-presented-at-largest-hpc-conference-in-latin-america/ Thu, 28 Sep 2023 10:21:06 +0000 https://www.risc2-project.eu/?p=3033 From September 18 to 22, Cartagena de Indias, Colombia, hosted the Latin America High-Performance Computing Conference (CARLA), which brought together around 300 researchers in the field from around the world — with particular emphasis on the presence of young and female researchers . With a varied program, the event aimed to provide a discussion forum to […]

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From September 18 to 22, Cartagena de Indias, Colombia, hosted the Latin America High-Performance Computing Conference (CARLA), which brought together around 300 researchers in the field from around the world — with particular emphasis on the presence of young and female researchers . With a varied program, the event aimed to provide a discussion forum to encourage the growth and strengthening of the High-Performance Computing community in Latin America, focusing on the exchange and dissemination of ideas, techniques, and research, as well as their application.

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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RISC2’s partners gather in Brussels to reflect on three years of collaboration between EU and Latin America https://www.risc2-project.eu/2023/07/26/risc2s-partners-gather-in-brussels-to-reflect-on-three-years-of-collaboration-between-eu-and-latin-america/ Wed, 26 Jul 2023 12:03:56 +0000 https://www.risc2-project.eu/?p=2992 Over the past three years, the RISC2 project has established a network for the exchange of knowledge and experience that has enabled its European and Latin American partners to strengthen relations in HPC and take significant steps forward in this area. With the project quickly coming to an end, it was time to meet face-to-face […]

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Over the past three years, the RISC2 project has established a network for the exchange of knowledge and experience that has enabled its European and Latin American partners to strengthen relations in HPC and take significant steps forward in this area. With the project quickly coming to an end, it was time to meet face-to-face in Brussels to reflect on the progress and achievements, the goals set, the difficulties faced, and, above all, what can be expected for the future.

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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Scientific Machine Learning and HPC https://www.risc2-project.eu/2023/06/28/scientific-machine-learning-and-hpc/ Wed, 28 Jun 2023 08:24:28 +0000 https://www.risc2-project.eu/?p=2863 In recent years we have seen rapid growth in interest in artificial intelligence in general, and machine learning (ML) techniques, particularly in different branches of science and engineering. The rapid growth of the Scientific Machine Learning field derives from the combined development and use of efficient data analysis algorithms, the availability of data from scientific […]

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In recent years we have seen rapid growth in interest in artificial intelligence in general, and machine learning (ML) techniques, particularly in different branches of science and engineering. The rapid growth of the Scientific Machine Learning field derives from the combined development and use of efficient data analysis algorithms, the availability of data from scientific instruments and computer simulations, and advances in high-performance computing. On May 25 2023, COPPE/UFRJ organized a forum to discuss Artificial Intelligence developments and its impact on the society [*].

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.

[3] Karniadakis, George Em, et al. “Physics-informed machine learning.” Nature Reviews Physics 3.6 (2021): 422-440.

[4] Inria White Book on Artificial Intelligence: Current challenges and Inria’s engagement, 2nd edition, 2021. URL: https://www.inria.fr/en/white-paper-inria-artificial-intelligence

[5] Silva, Romulo, Umair bin Waheed, Alvaro Coutinho, and George Em Karniadakis. “Improving PINN-based Seismic Tomography by Respecting Physical Causality.” In AGU Fall Meeting Abstracts, vol. 2022, pp. S11C-09. 2022.

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Hypatia https://www.risc2-project.eu/2023/06/11/hypatia/ Sun, 11 Jun 2023 09:05:30 +0000 https://www.risc2-project.eu/?p=2207 Title: Hypatia System name: Hypatia Location: Universidad de los Andes Colombia – Data Center (Bogotá) Web OS: Linux CentOS 7 Country: Colombia Processor architecture: Master Node: 1 PowerEdge R640 Server: 2 x Intel® Xeon® Silver 4210R 2.4G, 10C/20T, 9.6GT/s, 13.75M Cache, Turbo, HT (100W) DDR4-2400. Mellanox ConnectX-6 Single Port HDR100 QSFP56 Infiniband Adapter Compute Node: […]

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  • Title: Hypatia
  • System name: Hypatia
  • Location: Universidad de los Andes Colombia – Data Center (Bogotá)
  • Web
  • OS: Linux CentOS 7
  • Country: Colombia
  • Processor architecture:
    • Master Node: 1 PowerEdge R640 Server: 2 x Intel® Xeon® Silver 4210R 2.4G, 10C/20T, 9.6GT/s, 13.75M Cache, Turbo, HT (100W) DDR4-2400. Mellanox ConnectX-6 Single Port HDR100 QSFP56 Infiniband Adapter
    • Compute Node:  
      • 10 PowerEdge R640 Server: 2 x Intel® Xeon® Gold 6242R 3.1G, 20C/40T, 10.4GT/s, 27.5M Cache, Turbo, HT (205W) DDR4-2933. Mellanox ConnectX-6 Single Port HDR100 QSFP56 Infiniband Adapter
      • 3 PowerEdge R6525 Server 256 GB: 2 x AMD EPYC 7402 2.80GHz, 24C/48T, 128M Cache (180W) DDR4-3200. Mellanox ConnectX-6 Single Port HDR100 QSFP56 Infiniband Adapter
      • 2 PowerEdge R6525 Server 512 GB: 2 x AMD EPYC 7402 2.80GHz, 24C/48T, 128M Cache (180W) DDR4-3200. Mellanox ConnectX-6 Single Port HDR100 QSFP56 Infiniband Adapter
      • 1 PowerEdge R6525 Server 1 TB: 2 x AMD EPYC 7402 2.80GHz, 24C/48T, 128M Cache (180W) DDR4-3200. Mellanox ConnectX-6 Single Port HDR100 QSFP56 Infiniband Adapter
      • 2 PowerEdge R740 Server: 3 x NVIDIA® Quadro® RTX6000 24 GB, 250W, Dual Slot, PCle x16 Passice Cooled, Full Height GPU. Intel® Xeon® Gold 6226R 2.9GHz, 16C/32T, 10.4GT/s, 22M Cache, Turbo, HT (150W) DDR4-2933. Mellanox ConnectX-6 Single Port HDR100 QSFP56 Infiniband Adapter
    • Storage:
      • 1Dell EMC ME4084 SAS OST – 84 X 4TB HDD 7.2K 512n SAS12 3.5
      • 1 Dell EMC ME4024 SAS MDT – 24 X 960 GB SSD SAS Read Intensive 12Gbps 512e 2.5in Hot-plug Drive, PM5-R, 1DWPD, 1752 TBW
      • 4 PowerEdge R740 Server: 2 x Intel® Xeon® Gold 6230R 2.1G, 26C/52T, 10.4GT/s, 35.75M Cache, Turbo, HT 150W) DDR4-2933. Mellanox ConnectX-6 Single Port HDR100 QSFP56 Infiniband Adapter
  • Vendor: DELL
  • Peak performance: TBC
  • Access Policy: TBC
  • Main research domains: TBC
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    Subsequent Progress And Challenges Concerning The México-UE Project ENERXICO: Supercomputing And Energy For México https://www.risc2-project.eu/2023/05/24/subsequent-progress-and-challenges-concerning-the-mexico-ue-project-enerxico-supercomputing-and-energy-for-mexico/ Wed, 24 May 2023 09:38:01 +0000 https://www.risc2-project.eu/?p=2824 In this short notice, we briefly describe some afterward advances and challenges with respect to two work packages developed in the ENERXICO Project. This opened the possibility of collaborating with colleagues from institutions that did not participate in the project, for example from the University of Santander in Colombia and from the University of Vigo […]

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    In this short notice, we briefly describe some afterward advances and challenges with respect to two work packages developed in the ENERXICO Project. This opened the possibility of collaborating with colleagues from institutions that did not participate in the project, for example from the University of Santander in Colombia and from the University of Vigo in Spain. This exemplifies the importance of the RISC2 project in the sense that strengthening collaboration and finding joint research areas and HPC applied ventures is of great benefit for both: our Latin American Countries and the EU. We are now initiating talks to target several Energy related topics with some of the RISC2 partners. 

    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)

     

     

     

     

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    Mapping human brain functions using HPC https://www.risc2-project.eu/2023/02/01/mapping-human-brain-functions-using-hpc/ Wed, 01 Feb 2023 13:17:19 +0000 https://www.risc2-project.eu/?p=2697 ContentMAP is the first Portuguese project in the field of Psychology and Cognitive Neuroscience to be awarded with European Research Council grant (ERC Starting Grant #802553). In this project one is mapping how the human brain represents object knowledge – for example, how one represents in the brain all one knows about a knife (that […]

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    ContentMAP is the first Portuguese project in the field of Psychology and Cognitive Neuroscience to be awarded with European Research Council grant (ERC Starting Grant #802553). In this project one is mapping how the human brain represents object knowledge – for example, how one represents in the brain all one knows about a knife (that it cuts, that it has a handle, that is made out of metal and plastic or metal and wood, that it has a serrated and sharp part, that it is smooth and cold, etc.)? To do this, the project collects numerous MRI images while participants see and interact with objects (fMRI). HPC (High Performance Computing) is of central importance for processing these images . The use of HPC has allowed to manipulate these data, perform analysis with machine learning and complex computing in a timely manner.

    Humans are particularly efficient at recognising objects – think about what surrounds us: one recognises the object where one is reading the text from as a screen, the place where one sits as a chair, the utensil in which one drinks coffee as a cup, and one does all of this extremely quickly and virtually automatically. One is able to do all this despite the fact that 1) one holds large amounts of information about each object (if one is asked to write down everything you know about a pen, you would certainly have a lot to say); and that 2) there are several exemplars of each object type (a glass can be tall, made out of glass, metal, paper or plastic, it can be different colours, etc. – but despite that, any of them would still be a glass). How does one do this? How one is able to store and process so much information in the process of recognising a glass, and generalise all the different instances of a glass to get the concept “glass”? The goal of the ContentMAP is to understand the processes that lead to successful object recognition.

    The answer to these question lies in better understanding of the organisational principles of information in the brain. It is, in fact, the efficient organisation of conceptual information and object representations in the brain that allows one to quickly and efficiently recognise the keyboard that is in front of each of us. To study the neuronal organisation of object knowledge, the project collects large sets of fMRI data from several participants, and then try to decode the organisational principles of information in the brain.

    Given the amount of data and the computational requirements of this type of data at the level of pre-processing and post processing, the use of HPC is essential to enable these studies to be conducted in a timely manner. For example, at the post-processing level, the project uses whole brain Support Vector Machine classification algorithms (searchlight procedures) that require hundreds of thousands of classifiers to be trained. Moreover, for each of these classifiers one needs to compute a sample distribution of the average, as well as test the various classifications of interest, and this has to be done per participant.

    Because of this, the use of HPC facilities of of the Advanced Computing Laboratory (LCA) at University of Coimbra is crucial. It allows us to actually perform these analyses in one to two weeks – something that on our 14-core computers would take a few months, which in pratice would mean, most probably, that the analysis would not be done. 

    By Faculty of Psychology and Educational Sciences, University of Coimbra

     

    Reference 

    ProAction Lab http://proactionlab.fpce.uc.pt/ 

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    LNCC’s HPC Summer School provided sessions related to HPC to their community https://www.risc2-project.eu/2023/01/30/lnccs-hpc-summer-school-provided-sessions-related-to-hpc-to-their-community/ Mon, 30 Jan 2023 11:31:48 +0000 https://www.risc2-project.eu/?p=2688 LNCC, one of the RISC2 Brazilian partners, organized the HPC Summer School “Escola Supercomputador Santos Dumont,” which took place between January 16 to 24, 2023, as part of the LNCC’s Summer Program. The School aimed to provide mini-courses and talks related to programming on high-performance computers, such as parallel programming models, profiling tools, and libraries […]

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    LNCC, one of the RISC2 Brazilian partners, organized the HPC Summer School “Escola Supercomputador Santos Dumont,” which took place between January 16 to 24, 2023, as part of the LNCC’s Summer Program.

    The School aimed to provide mini-courses and talks related to programming on high-performance computers, such as parallel programming models, profiling tools, and libraries for developing optimized parallel algorithms for the SDumont user community and the high-performance computing programming community.

    Due to the extensive territory of Brazil and the number of research projects, it is mandatory to provide regular HPC schools for the research community. According to Carla Osthoff, one of the organizers of this school, “SDumont is the only Brazilian supercomputer dedicated to the research community that is part of the TOP 500 list. The Brazilian Ministry of Science and Technology offers free access to all Brazilian research projects in the country and foreign collaborators. Currently, we have 238 research projects from 18 research areas.  This edition of the School received 350 registrations, but we also provided online YouTube access to the community.”

    The event happened remotely, and all the sessions are available on Youtube.

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    Webinar: Improving energy-efficiency of High-Performance Computing clusters https://www.risc2-project.eu/events/webinar-7-improving-energy-efficiency-of-high-performance-computing-clusters/ Thu, 26 Jan 2023 13:37:07 +0000 https://www.risc2-project.eu/?post_type=mec-events&p=2666 Date: April 26, 2023 | 3 p.m. (UTC+1) Speakers: Lubomir Riha and Ondřej Vysocký, IT4Innovations National Supercomputing Center Moderator: Esteban Mocskos, Universidad de Buenos Aires High-Performance Computing centers consume megawatts of electrical power, which is a limiting factor in building bigger systems on the path to exascale and post-exascale clusters. Such high power consumption leads to several challenges […]

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    Date: April 26, 2023 | 3 p.m. (UTC+1)

    Speakers: Lubomir Riha and Ondřej Vysocký, IT4Innovations National Supercomputing Center

    Moderator: Esteban Mocskos, Universidad de Buenos Aires

    High-Performance Computing centers consume megawatts of electrical power, which is a limiting factor in building bigger systems on the path to exascale and post-exascale clusters. Such high power consumption leads to several challenges including robust power supply and its network, enormous energy bills, or significant CO2 emissions. To increase power efficiency, vendors accommodate various heterogeneous hardware that must be fully utilized by users’ applications, to be used efficiently. Such requirements may be hard to fulfill, which open a possibility of limiting the available resources for additional power and energy savings with no or small performance penalty.

    The talk will present best practices on how to grant rights to control hardware parameters, how to measure the energy consumption of the hardware, and what can be expected from performing energy-saving activities based on hardware tuning.

    About the speakers:

    Lubomir Riha, Ph.D. is the Head of the Infrastructure Research Lab at IT4Innovations National Supercomputing Center. Previously he was a research scientist in the High-Performance Computing Lab at George Washington University, ECE Department. He received his Ph.D. degree in Electrical Engineering from the Czech Technical University in Prague, Czech Republic, and a Ph.D. degree in Computer Science from Bowie State University, USA. Currently, he is a local principal investigator of two EuroHPC Centers of Excellence: MAX and SPACE, and two EuroHPC projects: SCALABLE and EUPEX (designs a prototype of the European Exascale machine). Previously he was a local PI of the H2020 Center of Excellence POP2 and H2020-FET HPC READEX projects. His research interests are optimization of HPC applications, energy-efficient computing, acceleration of scientific and engineering applications using GPU and many-core accelerators, and parallel and distributed rendering.

    Ondrej Vysocky is a Ph.D. candidate at VSB – Technical University of Ostrava, Czech Republic and at the same time he works at IT4Innovations in Infrastructure Research Lab. His research is focused on energy efficiency in high-performance computing. He was an investigator of the Horizon 2020 READEX project which dealt with the energy efficiency of parallel applications using dynamic tuning. Since that time, he develops a MERIC library, a runtime system for energy measurement and hardware parameters tuning during a parallel application run. Using the library he is an investigator of several H2020 projects including Performance Optimisation and Productivity (POP2), or European Pilot for Exascale (EUPEX). He is also a member of the PowerStack initiative, which works on a holistic, extensible, and scalable approach of power management.

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    Webinar: Addressing the challenges of scientific visualization in the exascale age https://www.risc2-project.eu/events/webinar-addressing-the-challenges-of-scientific-visualization-in-the-exascale-age/ Tue, 24 Jan 2023 10:56:42 +0000 https://www.risc2-project.eu/?post_type=mec-events&p=2668 Date: May 31, 2023 | 4 p.m. (UTC+1) Speaker: João Barbosa, INESC TEC & MACC Moderator: Bernd Mohr, Jülich Supercomputing Centre (JSC) In the coming age of exascale computing, traditional post-hoc scientific visualization and analysis suffer similar challenges as numeric simulation. This talk will cover new methodologies of scientific visualization in high-performance computing systems specially designed for […]

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    Date: May 31, 2023 | 4 p.m. (UTC+1)

    In the coming age of exascale computing, traditional post-hoc scientific visualization and analysis suffer similar challenges as numeric simulation. This talk will cover new methodologies of scientific visualization in high-performance computing systems specially designed for large-scale scientific visualization that provides greater scalability, flexibility, and detail to overcome some of these challenges.

    About the speaker: João Barbosa joined the Minho Advanced Computing Center (MACC) in March 2020 as a full-time researcher in High-performance Computing, specializing in Scientific Visualization. Previously, he was part of the Texas Advanced Computing Center (TACC) Scalable Visualization team. As Research Associate at TACC, João has worked on several Scientific Visualization (SciVis) projects ranging from high-level applications such as Gas and Oil to low-level high-performance software packages in partnership with leading hardware and software companies. His current research focuses on high-performance real-time in-situ photo-realistic ray tracing for SciVis.

     

     

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    Webinar: Developing complex workflows that include HPC, Artificial Intelligence and Data Analytics https://www.risc2-project.eu/events/webinar-5-developing-complex-workflows-that-include-hpc-artificial-intelligence-and-data-analytics/ Tue, 24 Jan 2023 10:51:32 +0000 https://www.risc2-project.eu/?post_type=mec-events&p=2661 Date: February 22, 2023 | 4 p.m. (UTC) Speaker: Rosa M. Badia, Barcelona Supercomputing Center Moderator: Esteban Mocskos, Universidad de Buenos Aires The evolution of High-Performance Computing (HPC) systems towards every-time more complex machines is opening the opportunity of hosting larger and heterogeneous applications. In this sense, the demand for developing applications that are not purely HPC, but […]

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    Date: February 22, 2023 | 4 p.m. (UTC)

    Speaker: Rosa M. Badia, Barcelona Supercomputing Center

    Moderator: Esteban Mocskos, Universidad de Buenos Aires

    The evolution of High-Performance Computing (HPC) systems towards every-time more complex machines is opening the opportunity of hosting larger and heterogeneous applications. In this sense, the demand for developing applications that are not purely HPC, but that combine aspects of Artifical Intelligence and or Data analytics is becoming more common. However, there is a lack of environments that support the development of these complex workflows. The webinar will present PyCOMPSs, a parallel task-based programming in Python. Based on simple annotations, sequential Python programs can be executed in parallel in HPC-clusters and other distributed infrastructures.

    PyCOMPSs has been extended to support tasks that invoke HPC applications and can be combined with Artificial Intelligence and Data analytics frameworks.

    Some of these extensions are made in the framework of the eFlows4HPC project, which in addition is developing the HPC Workflows as a Service (HPCWaaS) methodology to make the development, deployment, execution and reuse of workflows easier. The webinar will present the current status of the PyCOMPSs programming model and how it is being extended in the eFlows4HPC project towards the project needs. Also, the HPCWaaS methodology will be introduced

    About the speaker: Rosa M. Badia holds a PhD on Computer Science (1994) from the Technical University of Catalonia (UPC).  She is the manager of the Workflows and Distributed Computing research group at the Barcelona Supercomputing Center (BSC).

    Her current research interests are programming models for complex platforms (from edge, fog, to Clouds and large HPC systems).  The group led by Dr. Badia has been developing StarSs programming model for more than 15 years, with a high success in adoption by application developers. Currently the group focuses its efforts in PyCOMPSs/COMPSs, an instance of the programming model for distributed computing including Cloud.

    Dr Badia has published nearly 200 papers in international conferences and journals in the topics of her research. Her group is very active in projects funded by the European Commission and in contracts with industry. Dr Badia is the PI of the eFlows4HPC project.

    Registrations are now closed.

     

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