Improving edge finite element assembly for geophysical electromagnetic modelling on shared-memory architectures

This work presents a set of node-level optimizations to perform the assembly of edge finite element matrices that arise in 3D geophysical electromagnetic modelling on shared-memory architectures. Firstly, we describe the traditional and sequential assembly approach. Secondly, we depict our vectorized and shared-memory strategy which does not require any low level instructions because it is based on an interpreted programming language, namely, Python. As a result, we obtained a simple parallel-vectorized algorithm whose runtime performance is considerably better than sequential version. The set of optimizations have been included to the work-flow of the Parallel Edge-based Tool for Geophysical Electromagnetic Modelling (PETGEM) which is developed as open-source at the Barcelona Supercomputing Center. Finally, we present numerical results for a set of tests in order to illustrate the performance of our strategy.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 644202. The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant agreement No. 689772. Authors gratefully acknowledge the support from the Mexican National Council for Science and Technology (CONACYT). All numerical tests were performed on the MareNostrum supercomputer of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (www.bsc.es).Peer ReviewedPostprint (author's final draft

ETeach3D: Designing a 3D virtual environment for evaluating the digital competence of preservice teachers

The acquisition of teacher digital competence is a key aspect in the initial training of teachers. However, most existing evaluation instruments do not provide sufficient evidence of this teaching competence. In this study, we describe the design and development process of a three-dimensional (3D) virtual environment for evaluating the teacher digital competence of future teachers, through a performance-based, collaborative and contextual evaluation. This environment, named ETeach3D, has been constructed using the educational design research approach. It is based on successive iterative cycles and is in accordance with the criteria of usefulness, validity, and effectiveness. In addition to the research team responsible for the project, participating in this study were 187 Spanish undergraduate students of Education and 22 experts in the field of educational technology. Results show that these environments, in addition to other characteristics, should (a) function smoothly and have simple interfaces, realistic scenes, and interactive activities and (b) follow a systematic evaluation procedure that integrates several strategies and levels of complexity. This research helps to improve the initial training of preservice teachers and contributes to the growing number of educational design research studies that focus in the field of evaluation of the curriculum domain.This research is funded by the Ministry of Economy and Competitiveness of Spain, SIMUL@B (ref: EDU2013-42223-P), and supported by the Secretary of Universities and Research, Department of Economy and Knowledge, Government of Catalonia. The project is coordinated by the ARGET research group (ref: 2014SGR1399)

Toward an automatic full-wave inversion: Synthetic study cases

Full-waveform inversion (FWI) in seismic scenarios continues to be a complex procedure for subsurface imaging that might require extensive human interaction in terms of model setup, constraints, and data preconditioning. The underlying reason is the strong nonlinearity of the problem that forces the addition of a priori knowledge (or bias) in order to obtain geologically sound results. In particular, when the use of a long-offset receiver is not possible or may not favor the reconstruction of the fine structure of the model, one needs to rely on reflection data. As a consequence, the inversion process is more prone to becoming stuck in local minima. Nevertheless, misfit functionals can be devised that can either cope with missing long-wavenumber features of initial models (e.g., cross-correlation-based misfit) or invert reflection-dominated data whenever the models are sufficiently good (e.g., normalized offset-limited least-squares misfit). By combining both, high-frequency data content with poor initial models can be successfully inverted. If one can figure out simple parameterizations for such functionals, the amount of uncertainty and manual work related to tuning FWI would be substantially reduced. Thus, FWI might become a semiautomatized imaging tool.We want to thank Repsol for funding this research by means of the Aurora project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 644202. Additionally, the research leading to these results has received funding from the European Union’s Horizon 2020 Programme (2014-2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant agreement No 689772. We acknowledge Chevron for the dataset that was used in our second example.Peer ReviewedPostprint (author's final draft

Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators

In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications’ output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.This work was supported by the STIC-AmSud/CAPES scientific cooperation program under the EnergySFE research project grant 99999.007556/2015-02, EU H2020 Programme, and MCTI/RNP-Brazil under the HPC4E Project, grant agreement n° 689772. Tested K40 boards were donated thanks to Steve Keckler, Timothy Tsai, and Siva Hari from NVIDIA.Postprint (author's final draft

The largest European theropod dinosaurs: remains of a gigantic megalosaurid and giant theropod tracks from the Kimmeridgian of Asturias, Spain

The Kimmeridgian Vega, Terenes and Lastres formations of Asturias have yielded a rich vertebrate fauna, represented by both abundant tracks and osteological remains. However, skeletal remains of theropod dinosaurs are rare, and the diversity of theropod tracks has only partially been documented in the literature. Here we describe the only non-dental osteological theropod remain recovered so far, an isolated anterior caudal vertebra, as well as the largest theropod tracks found. The caudal vertebra can be shown to represent a megalosaurine megalosaurid and represents the largest theropod skeletal remain described from Europe so far. The tracks are also amongst the largest theropod footprints reported from any setting and can be assigned to two different morphotypes, one being characterized by its robustness and a weak mesaxony, and the other characterized by a strong mesaxony, representing a more gracile trackmaker. We discuss the recently proposed distinction between robust and gracile large to giant theropod tracks and their possible trackmakers during the Late Jurassic-Berriasian. In the absence of complete pedal skeletons of most basal tetanurans, the identity of the maker of Jurassic giant theropod tracks is difficult to establish. However, the notable robustness of megalosaurine megalosaurids fits well with the described robust morphotypes, whereas more slender large theropod tracks might have been made by a variety of basal tetanurans, including allosaurids, metriocanthosaurids or afrovenatorine megalosaurids, or even exceptionally large ceratosaurs. Concerning osteological remains of large theropods from the Late Jurassic of Europe, megalosaurids seem to be more abundant than previously recognized and occur in basically all Jurassic deposits where theropod remains have been found, whereas allosauroids seem to be represented by allosaurids in Western Europe and metriacanthosaurids in more eastern areas. Short-term fluctuations in sea level might have allowed exchange of large theropods between the islands that constituted Europe during the Late Jurassic

Applying future Exascale HPC methodologies in the energy sector

The appliance of new exascale HPC techniques to energy industry simulations is absolutely needed nowadays. In this sense, the common procedure is to customize these techniques to the specific energy sector they are of interest in order to go beyond the state-of-the-art in the required HPC exascale simulations. With this aim, the HPC4E project is developing new exascale methodologies to three different energy sources that are the present and the future of energy: wind energy production and design, efficient combustion systems for biomass-derived fuels (biogas), and exploration geophysics for hydrocarbon reservoirs. In this work, the general exascale advances proposed as part of HPC4E and its outcome to specific results in different domains are presented.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imaging.Postprint (author's final draft

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Three-Dimensional CSEM Modelling on Unstructured Tetrahedral Meshes Using Edge Finite Elements

The last decade has been a period of rapid growth for electromagnetic methods (EM) in geophysics, mostly because of their industrial adoption. In particular, the marine controlled-source electromagnetic method (CSEM) has become an important technique for reducing ambiguities in data interpretation in hydrocarbon exploration. In order to be able to predict the EM signature of a given geological structure, modelling tools provide us with synthetic results which we can then compare to real data. On the other hand and among the modelling methods for EM based upon 3D unstructured meshes, the Nédélec Edge Finite Element Method (EFEM) offers a good trade-off between accuracy and number of degrees of freedom, i.e. size of the problem. Furthermore, its divergence-free basis is very well suited for solving Maxwell’s equation. On top of that, we present the numerical formulation and results of 3D CSEM modelling using the Parallel Edge-based Tool for Geophysical Electromagnetic Modelling (PETGEM) on unstructured tetrahedral meshes. We validated our experiments against quasi-analytical results in canonical models.Authors of this work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie-Sklodowska Curie grant agreement No. 644202. The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant agreement No. 689772. Authors gratefully acknowledge the support from the Mexican National Council for Science and Technology (CONACyT). Numerical tests in this work were performed on the MareNostrum supercomputer of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (www.bsc.es).Peer Reviewe

Improving edge finite element assembly for geophysical electromagnetic modelling on shared-memory architectures

This work presents a set of node-level optimizations to perform the assembly of edge finite element matrices that arise in 3D geophysical electromagnetic modelling on shared-memory architectures. Firstly, we describe the traditional and sequential assembly approach. Secondly, we depict our vectorized and shared-memory strategy which does not require any low level instructions because it is based on an interpreted programming language, namely, Python. As a result, we obtained a simple parallel-vectorized algorithm whose runtime performance is considerably better than sequential version. The set of optimizations have been included to the work-flow of the Parallel Edge-based Tool for Geophysical Electromagnetic Modelling (PETGEM) which is developed as open-source at the Barcelona Supercomputing Center. Finally, we present numerical results for a set of tests in order to illustrate the performance of our strategy.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 644202. The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant agreement No. 689772. Authors gratefully acknowledge the support from the Mexican National Council for Science and Technology (CONACYT). All numerical tests were performed on the MareNostrum supercomputer of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (www.bsc.es).Peer Reviewe