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.

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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A country that does not have the computational capacity to solve its own problems will have no alternative but to try to acquire solutions provided by others. One of the most important aspects of sovereignty in the 21st century is the ability to produce mathematical models and to have the capacity to solve them. Today, the availability of computing power commensurate with one’s wealth exponentially increases a country’s capacity to produce knowledge. in the developed world, it is estimated that for every dollar invested in supercomputing, the return to society is of the order of US$ 44(1) and to the academic world US$ 30(2). For these reasons, HPC occupies an important place on the political and diplomatic agendas of developed countries. 

In Latin America, investment in HPC is very low compared to what’s the US, Asia and Europe are doing. In order to quantify this difference, we present the tables below, which show the accumulated computing capacity in the ranking of the 500 most powerful supercomputers in the world – the TOP500(3) – (Table 1), and the local reality (Table 2). Other data are also included, such as the population (in millions), the number of researchers per 1,000 inhabitants (Res/1000), the computing capacity per researcher (Gflops/Res) and the computing capacity per US$ million of GPD. In Table 1, we have grouped the countries by geographical area. America appears as the area with the highest computing capacity, essentially due to the USA, which has almost 45% of the world’s computing capacity in the TOP500. It if followed by Asia and then Europe. Tis TOP500 list includes mainly academic research centres, but also industry ones, typically those used in applied research (many private ones do not wish to publish such information for obvious reasons). For example, in Brazil – which shows good computing capacity with 88,175 TFlops – the vast majority is in the hands of the oil industry and only about 3,000 TFlops are used for basic research. Countries listed in the TOP500 invest in HPC from a few TFlops per million GDP (Belgium 5, Spain 7, Bulgaria 8), through countries investing in the order of hundreds (Italy 176, Japan 151, USA 138), to even thousands, as is the case in Finland with 1,478. For those countries where we were able to find data on the number of researchers, these range from a few Gflops per researcher (Belgium 19, Spain 24, Hungary 52) to close to 1,000 GFlops, i.e. 1 TFlop (USA 970, Italy 966), with Finland surpassing this barrier with 4,647. Note that, unlike what happens locally, countries with a certain degree of development invest every 3-4 years in supercomputing, so the data we are showing will soon be updated and there will be variations in the list. For example, this year a new supercomputer will come into operation in Spain(4), which, with an investment of some 150 million euros, will give Spain one of the most powerful supercomputers in Europe – and the world.

Table 1. HPC availability per researcher and relative to GDP in the TOP500 countries (includes HPC in industry).

The local reality is far from this data. Table 2 shows data from Argentina, Brazil, Chile and Mexico. In Chile, the availability of computing power is 2-3 times less per researcher than in countries with less computing power in the OECD and up to 100 times less than a researcher in the US. In Chile, our investment measured in TFlops per million US$ of GDP is 166 times less than in the US; with respect to European countries that invest less in HPC it is 9 times less, and with respect to the European average (including Finland) it is 80 times less, i.e. the difference is considerable. It is clear that we need to close this gap. An investment go about 5 million dollars in HPC infrastructure in the next 5 years would close this gap by a factor of almost 20 times our computational capacity. However, returning to the example of Spain, the supercomputer it will have this year will offer 23 times more computing power than at present and, therefore, we will only maintain our relative distance. If we do not invest, the dap will increase by at least 23 times and will end up being huge. Therefore, we do not only need a one-time investment, but we need to ensure a regular investment. Some neighbouring countries are already investing significantly in supercomputing. This is the case in Argentina, where they are investing 7 million dollars (2 million for the datacenter and 5 million to buy a new supercomputer), which will increase their current capacities by almost 40 times(5).

Table 2. HPC availability per researcher and relative to GDP in the region (*only HPC capacity in academia is considered in this table).

For the above reasons, we are working to convince the Chilean authorities that we must have greater funding and, more crucially, permanent state funding in HPC. In relation to this, on July 6 we signed a collaboration agreement between 44 institutions with the support of the Ministry of Science to work on the creation of the National Supercomputing Laboratory(6). The agreement recognised that supercomputers are a critical infrastructure for Chile’s development, that it is necessary to centralise the requirements/resources at the national level, obtain permanent funding from the State and create a new institutional framework to provide governance. In an unprecedented inter-institutional collaboration in Chile, the competition for HPC resources at the national level is eliminated ad the possibility of direct funding from the State is opened up without generating controversy.

Undoubtedly, supercomputing is a fundamental pillar for the development of any country, where increasing investment provides a strategic advantage, and in Latin America we should not be left behind.

By NLHPC

References

(1) Hyperion Research HPC Investments Bring High Returns

(2) EESI-2 Special Study To Measure And Model How Investments In HPC Can Create Financial ROI And Scientific Innovation In Europe 

(3) https://top500.org/ 

(4) https://www.lavanguardia.com/ciencia/20230129/8713515/llega-superordenador-marenostrum-5-bsc-barcelona.html

(5) https://www.hpcwire.com/2022/12/15/argentina-announces-new-supercomputer-for-national-science/

(6) https://uchile.cl/noticias/187955/44-instituciones-crearan-el-laboratorio-nacional-de-supercomputacion

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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/ 

The post Mapping human brain functions using HPC first appeared on RISC2 Project.

The computer named JUPITER (short for “Joint Undertaking Pioneer for Innovative and Transformative Exascale Research”) will be installed 2023/2024 on the campus of Forschungszentrum Jülich. It is intended that the system will be operated by the Jülich Supercomputing Centre (JSC), whose supercomputers JUWELS and JURECA currently rank among the most powerful in the world. JSC has participated in the application procedure for a high-end supercomputer as a member of the Gauss Centre for Supercomputing (GCS), an association of the three German national supercomputing centres JSC in Jülich, High Performance Computing Stuttgart (HLRS), and Leibniz Computing Centre (LRZ) in Garching. The competition was organized by the European supercomputing initiative EuroHPC JU, which was formed by the European Union together with European countries and private companies. 

JUPITER is now set to become the first European supercomputer to make the leap into the exascale class. In terms of computing power, it will be more powerful that 5 million modern laptops of PCs. Just like Jülich’s current supercomputer JUWELS, JUPITER will be based on a dynamic, modular supercomputing architecture, which Forschungszentrum Jülich developed together with European and international partners in the EU’s DEEP research projects.

In a modular supercomputer, various computing modules are coupled together. This enables program parts of complex simulations to be distributed over several modules, ensuring that the various hardware properties can be optimally utilized in each case. Its modular construction also means that the system is well prepared for integrating future technologies such as quantum computing or neurotrophic modules, which emulate the neural structure of a biological brain.

Figure Modular Supercomputing Architecture: Computing and storage modules of the exascale computer in its basis configuration (blue) as well as optional modules (green) and modules for future technologies (purple) as possible extensions. 

In its basis configuration, JUPITER will have and enormously powerful booster module with highly efficient GPU-based computation accelerators. Massively parallel applications are accelerated by this booster in a similar way to a turbocharger, for example to calculate high-resolution climate models, develop new materials, simulate complex cell processes and energy systems, advanced basic research, or train next-generation, computationally intensive machine-learning algorithms.

One major challenge is the energy that is required for such large computing power. The average power is anticipated to be up to 15 megawatts. JUPITER has been designed as a “green” supercomputer and will be powered by green electricity. The envisaged warm water cooling system should help to ensure that JUPITER achieves the highest efficiency values. At the same time, the cooling technology opens up the possibility of intelligently using the waste heat  that is produced. For example, just like its predecessor system JUWELS, JUPITER will be connected to the new low-temperature network on the Forschungszentrum Jülich campus. Further potential applications for the waste heat from JUPITER are currently being investigated by Forschungszentrum Jülich.

By Jülich Supercomputing Centre (JSC)

The first image is JUWELS: Germany’s fastest supercomputer JUWELS at Forschungszentrum Jülich, which is funded in equal parts by the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the State of North Rhine-Westphalia (MKW NRW) via the Gauss Centre for Supercomputing (GCS). (Copyright: Forschungszentrum Jülich / Sascha Kreklau)

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In the European and Latin American (EuroLatAm) HPC ecosystems, it tis possible to identify actors in different domains: industry, academy, research, society, and government. Each of them, at different levels, has a group of demands or interactions, depending on the interests. I.e., the industry demands capabilities to have HPC solutions for productivity and wants skills from the academy to perform development actors to build applications to use solutions. Another example could be the relationship between research and the government. In the HPC Ecosystem, collaborations allow synergies to face common interests. Still, it demands policies and coordinated roadmaps to support long-term projects and activities with a clear impact on society.

Of course, a historical relationship exists between Latin America and Europe from colonial history. In the case of advanced computing projects, it is possible to identify, from the first EuroLatAm Grid Computing projects more than twenty years ago until the real supercomputing projects such as RISC and RISC2. Still, now, more with shared interests and the different EuroLatAm HPC projects improve competitiveness and collaboration. Competitiveness for industrial and productive business, partnership (and competitiveness) in science and education goals, and human wellness. So paraphrasing Mateo Valero “who does not compute does not compete”, I would add “who does not collaborate does not survive”.

Taking collaboration and competitiveness, the RISC2 project allows identifying sustainability elements and sustainable workflows for different projects. The impressive interaction between the actors of the HPC EuroLatAm ecosystem has not only given scientific results but also policies, recommendations, best practices, and new questions. For these outcomes, in the past 2022 Supercomputing Conference, RISC2 was awarded the 2022 HPCWire Editors’ Choice Award as the Best HPC Collaboration.

Sustainable advanced computing ecosystems and their growth are evident with the knowledge of the results of projects such as RISC2. Collaboration, interaction, and competitiveness build human development and guarantee development, technological diversification, and peer-to-peer relationships to attack common interests and problems. So, RISC2 is a crucial step to advance to a RISC3 as it was at the time of the previous RISC.

By Universidad Industrial de Santander

The post Advanced Computing Collaboration to Growth Sustainable Ecosystems first appeared on RISC2 Project.

However, to realize the full potential of this synergy, ML models (or models for short) must be built, combined and ensembled, which can be very complex as there can be many models to select from. Furthermore, they should be shared and reused, in particular, in different execution environments such as HPC or Spark clusters.

To address this problem, we proposed Gypscie [Porto 2022, Zorrilla 2022], a new framework that supports the entire ML lifecycle and enables model reuse and import from other frameworks. The approach behind Gypscie is to combine several rich capabilities for model and data management, and model execution, which are typically provided by different tools, in a unique framework. Overall, Gypscie provides: a platform for supporting the complete model life-cycle, from model building to deployment, monitoring and policies enforcement; an environment for casual users to find ready-to-use models that best fit a particular prediction problem, an environment to optimize ML task scheduling and execution; an easy way for developers to benchmark their models against other competitive models and improve them; a central point of access to assess models’ compliance to policies and ethics and obtain and curate observational and predictive data; provenance information and model explainability. Finally, Gypscie interfaces with multiple execution environments to run ML tasks, e.g., an HPC system such as the Santos Dumont supercomputer at LNCC or a Spark cluster. 

Gypscie comes with SAVIME [Silva 2020], a multidimensional array in-memory database system for importing, storing and querying model (tensor) data. The SAVIME open-source system has been developed to support analytical queries over scientific data. Its offers an extremely efficient ingestion procedure, which practically eliminates the waiting time to analyze incoming data. It also supports dense and sparse arrays and non-integer dimension indexing. It offers a functional query language processed by a query optimiser that generates efficient query execution plans.

References

[Porto 2022] Fabio Porto, Patrick Valduriez: Data and Machine Learning Model Management with Gypscie. CARLA 2022 – Workshop on HPC and Data Sciences meet Scientific Computing, SCALAC, Sep 2022, Porto Alegre, Brazil. pp.1-2. 

[Zorrilla 2022] Rocío Zorrilla, Eduardo Ogasawara, Patrick Valduriez, Fabio Porto: A Data-Driven Model Selection Approach to Spatio-Temporal Prediction. SBBD 2022 – Brazilian Symposium on Databases, SBBD, Sep 2022, Buzios, Brazil. pp.1-12. 

[Silva 2020] A.C. Silva, H. Lourenço, D. Ramos, F. Porto, P. Valduriez. Savime: An Array DBMS for Simulation Analysis and Prediction. Journal of Information Data Management 11(3), 2020. 

By LNCC and Inria 

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