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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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.

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This event had four main courses: “Foundations of Parallel Programming”, “Large scale data processing and machine learning”, “New architectures and specific computing platforms”, and “Administrations techniques for large-scale computing facilities”.

More than 100 students actively participated in the event who traveled from different part of the country. 30 students received financial support to participate (traveling and living) provided by the National HPC System (SNCAD) dependent of the Argentina’s Ministry of Science.

Esteban Mocskos, one of the organizers of the event, believes “this kind of events should be organized regularly to sustain the flux of students in the area of HPC”. In his opinion, “a lot of students from Argentina get their first contact with HPC topics. As such a large country, impacting a distant region also means impacting the neighboring countries. Those students will bring their experience to other students in their places”. According to Mocskos, initiatives like the “HPC, Data & Architecture Week” spark a lot of collaborations.

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The Alliance’s aim is to foster the development of secure, resilient and human-centric digital infrastructures on the basis of a values-based framework, ensuring a democratic and transparent enabling environment and putting a strong emphasis on privacy and digital rights. It is the first intercontinental digital partnership agreed between both regions under Global Gateway investment strategy, the EU’s offer for trusted and sustainable connections with partner countries.

More information here.

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LNCC, represented by Carla Osthoff, Kary Ocaña and Ana Karl, presented theoretical and practical courses in Bioinformatics, HPC, Mathematical and Chemical Computing.

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Building on the success of previous editions, the seventh installment of the Costa Rica High Performance Computing School (CRHPCS) aims at preparing students and researchers to introduce HPC tools in their workflows. A selected team of international experts taught sessions on shared-memory programming, distributed-memory programming, accelerator programming, and high performance computing.  This edition had instructors Alessandro Marani and Nitin Shukla from CINECA, which greatly helped in bringing a vibrant environment to the sessions.

Bernd Mohr, from Jülich Supercomputing Centre, was the Keynote Speaker of this year’s edition of the event.  A well-known figure in the HPC community at large, Bernd presented the talk Parallel Performance Analysis at Scale: From Single Node to one Million HPC Cores. In an amazing voyage through different architecture setups, Bernd highlighted the importance and challenges of performance analysis.

For Esteban Meneses, Costa Rica HPC School General Chair, the School is a key element in building a stronger and more connected HPC community in the region. This year, thanks to the RISC2 project, we were able to gather participants from Guatemala, El Salvador, and Colombia. Creating these ties is fundamental for later developing more complex initiatives. We aim at preparing future scientists that will develop groundbreaking computer applications that tackle the most pressing problems of our region.

More information here. 

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