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HPC-TRES |A national network to support young researchers using supercomputing to address Earth system modelling challenges. In 2015, OGS and Cineca entered into an agreement to implement a training programme in the field of High Performance Computing (HPC) for applications in Earth Sciences, called "HPC Training and Research for Earth Sciences" (HPC-TRES). The programme is co-funded by the Ministry of Universities and Research (MUR) through PRACE-Italy, the special contribution for Italy's participation in the activities related to the international PRACE infrastructure. The main objectives of the programme are capacity building, the enhancement of human capital, and advanced training in the fields of Digital Twin Earth, Earth System modelling (atmosphere, hydrosphere, lithosphere and biosphere) and numerical methods, the latter being considered a strategic transversal component for modelling. These objectives will be pursued through the use of national and European PRACE HPC infrastructures and services, the optimisation of algorithms and calculation codes, the management of large amounts of data ("Big Data") and graphic visualisation techniques for multidisciplinary applications in the Earth Sciences, also in the context of the "Blue Growth" strategy and, more generally, of the National Recovery and Resilience Plan. HPC-TRES envisages the establishment, funding and management of training and research awards (grants for expenditure on training and research activities established in accordance with the regulations in force governing scholarships, research grants, and PhDs) designed to contribute to the thematic research lines of the scientific plan of the HPC-TRES programme. In 2021, OGS, CINECA, CNR, INGV, CMCC, POLITO-DIATI, and ICTP signed the constitution of the Joint Research Unit HPC-TRES (JRU HPC-TRES), to further support the action of the HPC-TRES program. The 5th edition of the HPC-TRES workshop (related to year 2022) was held on 24 January 2023 at the DoveVivo Campus (Trieste). More information:https://www.ogs.it/en/high-performance-computing-laboratory-hpc-tres&nbsp

HPC-TRES | The Italian National Institute of Oceanography and Applied Geophysics - OGS and CINECA drew up an agreement to implement a training program focussed on High Performance Computing (HPC) applications for Earth Sciences, the “HPC Training and Research for Earth Sciences” (HPC-TRES). The program is co-sponsored by the Minister of University and Research (MUR) under its extraordinary contribute for the Italian participation to activities related to the international infrastructure PRACE – The Partnership for Advanced Computing in Europe. The major objectives of the program are: capacity building, enhancement of human resources, and advanced training in the fields of Earth System modelling (atmosphere, hydrosphere, lithosphere and biosphere) and numerical models, the latter considered as a strategic cross cutting element for modelling activities. These objectives will be pursued through the use of national and European HPC infrastructures and services in the framework of PRACE, the optimization of algorithms and numerical codes, the optimal management of “Earth Sciences Big Data”, and the graphical visualization techniques for multidisciplinary applications in the Earth Sciences, also in the frame of the “Blue Growth” strategy. Therefore, HPC-TRES agreement establishes, sponsor and oversees training and research awards (i.e. contributes for training and research activities set up according to current regulations that regulate scholarships – to be also used for masters and specialized courses in HPC -, research grants and PhDs) that will support the research lines scientific plan of the HPC-TRES program. Some Italian research groups and institutions (INGV-Pisa, CNR/ISAC, CNR/IGG, CMCC, ICTP ESP), where the external members of the HPC-TRES Steering Committee lead their research activities and personally contributed to define the scientific plan of the HPC-TRES program, have already subscribed the HPC-TRES initiative, offering logistic support and hospitality to the winners of the training and research awards sponsored by OGS. In 2021, OGS, CINECA, CNR, INGV, CMCC, POLITO-DIATI, and ICTP signed the constitution of the Joint Research Unit HPC-TRES (JRU HPC-TRES), to further support the action of the HPC-TRES program. More information: https://www.ogs.it/it/content/hpc-training-and-research-earth-sciences-hpc-tres, Editors: M. Zanenghi, V. Mosetti, S. Salon

Inferring the structure and operation of a computing model, given some observations of its behavior, is in general a desirable but daunting task. In this paper we try to solve a constrained version of this problem. We consider a P system Pi with active membranes and using cooperative rewriting, communication, and division rules and a collection of pairs of its consecutive configurations. Then, we feed this collection of configurations as input to a (mu+lambda) evolutionary algorithm that evolves a population of (initially random) P systems, each with its own rules, with the aim of obtaining an individual that approximates as well as possible. We discuss the results obtained on different benchmark problems, designed to test the ability to infer cooperative rewriting, communication, and membrane division rules. We will also provide a description of how fitness results are influenced by different setting of the hyperparameters of the evolutionary algorithm. The results show that the proposed approach is able to find correct solutions for small problems, and it is a promising research direction for the automatic synthesis of P systems.

Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all the evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use "old" generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP., Comment: 19 pages, 4 figures, 5 tables. Submitted to Applied Sciences