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  • 05.05. Mathematical geophysics  (1)
  • Distributed computing  (1)
  • 1
    Publication Date: 2023-03-20
    Description: 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.
    Description: Published
    Description: 414-429
    Description: 6T. Studi di pericolosità sismica e da maremoto
    Description: 8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
    Description: 4V. Processi pre-eruttivi
    Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio
    Description: 3IT. Calcolo scientifico
    Description: JCR Journal
    Keywords: High performance computing ; Distributed computing ; Parallel programming ; HPC-DA-AI convergence ; Workflow development ; Workflow orchestration
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
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  • 2
    Publication Date: 2023-12-27
    Description: In recent years, there has been a growing inter- est in ensemble approaches for modelling the atmospheric transport of volcanic aerosol, ash, and lapilli (tephra). The development of such techniques enables the exploration of novel methods for incorporating real observations into tephra dispersal models. However, traditional data assimilation al- gorithms, including ensemble Kalman filter (EnKF) meth- ods, can yield suboptimal state estimates for positive-definite variables such as those related to volcanic aerosols and tephra deposits. This study proposes two new ensemble- based data assimilation techniques for semi-positive-definite variables with highly skewed uncertainty distributions, in- cluding aerosol concentrations and tephra deposit mass load- ing: the Gaussian with non-negative constraints (GNC) and gamma inverse-gamma (GIG) methods. The proposed meth- ods are applied to reconstruct the tephra fallout deposit re- sulting from the 2015 Calbuco eruption using an ensemble of 256 runs performed with the FALL3D dispersal model. An assessment of the methodologies is conducted consider- ing two independent datasets of deposit thickness measure- ments: an assimilation dataset and a validation dataset. Dif- ferent evaluation metrics (e.g. RMSE, MBE, and SMAPE) are computed for the validation dataset, and the results are compared to two references: the ensemble prior mean and the EnKF analysis. Results show that the assimilation leads to a significant improvement over the first-guess results ob- tained from the simple ensemble forecast. The evidence from this study suggests that the GNC method was the most skilful approach and represents a promising alternative for assimila- tion of volcanic fallout data. The spatial distributions of the tephra fallout deposit thickness and volume according to the GNC analysis are in good agreement with estimations based on field measurements and isopach maps reported in previ- ous studies. On the other hand, although it is an interesting approach, the GIG method failed to improve the EnKF analysis.
    Description: EU
    Description: Published
    Description: 3459–3478
    Description: OSV3: Sviluppo di nuovi sistemi osservazionali e di analisi ad alta sensibilità
    Description: JCR Journal
    Keywords: Data Assimilation ; Tephra deposits ; 05.05. Mathematical geophysics ; 01.01. Atmosphere ; 04.08. Volcanology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
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