The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase

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High-frequency global wavefields for local 3-D structures by wavefield injection and extrapolation

Earth structure is multiscale, and seismology remains the primary means of deciphering signatures from small structures over large distances. To enable this at the highest resolution, we present a flexible injection and extrapolation type hybrid framework that couples wavefields from a pre-computed global database of accurate Green's functions for 1-D models with a local 3-Dmethod of choice (e.g. a spectral element or a finite-difference solver). The interface allows to embed a full 3-D domain in a spherically symmetric Earth model, tackling large-scale wave propagation with focus on localized heterogeneous complex structures. Thanks to reasonable computational costs (10k CPU hours) and storage requirements (a few TB for 1 Hzwaveforms) of databases of global Green's functions, the method provides coupling of 3-D wavefields that can reach the highest observable body-wave frequencies in the 1-4 Hz range. The framework is highly flexible and adaptable; alterations in source properties (radiation patterns and source-time function), in the source-receiver geometry, and in local domain dimensions and location can be introduced without re-running the global simulation. The once-and-for-all database approach reduces the overall computational cost by a factor of 5000-100 000 relative to a full 3-D run, provided that the local domain is of the order of tens of wavelengths in size. In this paper, we present the details of the method and its implementation, show benchmarks with a 3-D spectral element solver, discuss its setup-dependent performance and explore possible wave-propagation applications.ISSN:0956-540XISSN:1365-246

DT-GEO Deliverable: D7.1 Early definition of requirements and specifications DTC-E

Copyright Members of DT-GEO collaboration, 2022/2025.Early definition of requirements and specifications for work package 7 DTC-Es: requirements, specifications and interoperability of earthquake components. Collects and harmonizes the requirements and specifications for DTC-E and its individual modules developed in tasks 7.2 to 7.7.Project funded by Horizon Europe under the grant agreement No 101058129.Peer reviewe

A novel C-terminal mutation in Gsdma3 (C+/H−) leads to alopecia and corneal inflammatory response in mice

Purpose: Mutations in the gene encoding Gasdermin A3 (Gsdma3) have been described to cause severe skin phenotypes, including loss of sebaceous glands and alopecia, in mice. We discovered a novel C-terminal mutation in Gsdma3 in a new mouse line and characterized a less frequently reported corneal phenotype, likely caused by degeneration of Meibomian glands of the inner eyelid. Methods: We used histologic methods to evaluate the effects of the C+/H- mutation on sebaceous gland and skin morphology as well as Meibomian glands of the inner eyelid and corneal tissue. Chromosomal aberrations were excluded by karyogram analyses. The mutation was identified by Sanger sequencing of candidate genes. Results: Analyses of skin samples from affected mice confirmed the frequently reported phenotypes associated with mutations in Gsdma3: Degeneration of sebaceous glands and complete loss of pelage. Immunologic staining of corneal samples suggested an inflammatory response with signs of neovascularization in half of the affected older mice. While the corneal phenotype was observed at irregular time points, mainly after 6 months, its appearance coincided with a degeneration of Meibomian glands in the eyelids of affected animals. Conclusions: The mutation described herein is associated with inflammation and neovascularization of corneal tissue. Simultaneous degeneration of Meibomian glands in affected animals suggested a change in tear-film composition as the underlying cause for the corneal phenotype. Our data further support that different pathogenic mechanisms underlie some of the reported mutations in Gsdma3

The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase

The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018–2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023–2026).ISSN:0167-739XISSN:1872-711

Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project

Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project