Eva Sciacca
Eva Sciacca (Ph.D. in Mathematics for Technology) is a Computer Scientist and Information Technology researcher with over a decade of experience, working at the Istituto Nazionale di Astrofisica (INAF) since 2012. She has been extensively involved in cutting-edge research activities in the field of big-data, visual analytics, and machine learning. She has been instrumental in facilitating astrophysical data processing on distributed computing infrastructures, with a special focus on High-Performance Computing (HPC) and Cloud Computing. Over the past five years, Eva has played a pivotal role in several European-funded projects, including VIALACTEA, INDIGO-DataCloud, AENEAS, EOSC-Pilot, NEANIAS, and SPACE. She has been at the forefront of harnessing the potential of the European Open Science Cloud (EOSC) and the European High-Performance Computing Joint Undertaking (EuroHPC JU) to advance scientific research, and she is actively involved in the IT activities of the Square Kilometre Array (SKA) Regional Centres.
Session
Petabyte-scale data volumes are generated by observations and simulations in modern astronomy and astrophysics. Storage, access, and data analysis are significantly hampered by such data volumes and are leading to the development of a new generation of software tools. The Visualization Interface for the Virtual Observatory (VisIVO) has been designed, developed and maintained by INAF since 2005 to perform multi-dimensional data analysis and knowledge discovery in multivariate astrophysical datasets. Utilizing containerization and virtualization technologies, VisIVO has already been used to exploit distributed computing infrastructures including the European Open Science Cloud (EOSC).
We intend to adapt VisIVO solutions for high performance visualization of data generated on the (pre-)Exascale systems by HPC applications in Astrophysics and Cosmology (A&C), including GADGET (GAlaxies with Dark matter and Gas) and PLUTO simulations, thanks to the collaboration within the SPACE Center of Excellence, the H2020 EUPEX Project, and the ICSC National Research Centre. In this work, we outline the evolution's course as well as the execution strategies designed to achieve the following goals: enhance the portability of the VisIVO modular applications and their resource requirements; foster reproducibility and maintainability; take advantage of a more flexible resource exploitation over heterogeneous HPC facilities; and, finally, minimize data-movement overheads and improve I/O performances.
Acknowledgements: This work is funded by the European High Performance Computing Joint Undertaking (JU) and Belgium, Czech Republic, France, Germany, Greece, Italy, Norway, and Spain under grant agreement No 101093441 and it is supported by the spoke "FutureHPC & BigData” of the ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing – and hosting entity, funded by European Union – NextGenerationEU”