{"ID":2849186,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24545","arxiv_id":"2510.24545","title":"Exascale In-situ visualization for Astronomy \u0026 Cosmology","abstract":"Modern simulations and observations in Astronomy \u0026 Cosmology (A\u0026C) produce massively large data volumes, posing significant challenges for storage, access and data analysis. A long-standing bottleneck in high-performance computing, especially now in the exascale era, has been the requirement to write these large datasets to disks, which limits the performance. A promising solution to this challenge is in-situ processing, where analysis and visualization are performed concurrently with the simulation itself, bypassing the storage of the simulation data. In this work, we present new results from an approach for in-situ processing based on Hecuba, a framework that provides a highly distributed database for streaming A\u0026C simulation data directly into the visualization pipeline to make possible on-line visualization. By integrating Hecuba with the high-performance cosmological simulator ChaNGa, we enable real-time, in-situ visualization of N-body simulation results using tools such as ParaView and VisIVO.","short_abstract":"Modern simulations and observations in Astronomy \u0026 Cosmology (A\u0026C) produce massively large data volumes, posing significant challenges for storage, access and data analysis. A long-standing bottleneck in high-performance computing, especially now in the exascale era, has been the requirement to write these large datase...","url_abs":"https://arxiv.org/abs/2510.24545","url_pdf":"https://arxiv.org/pdf/2510.24545v1","authors":"[\"Nicola Tuccari\",\"Eva Sciacca\",\"Yolanda Becerra\",\"Enric Sosa Cintero\",\"Emiliano Tramontana\"]","published":"2025-10-28T15:44:57Z","proceeding":"astro-ph.IM","tasks":"[\"astro-ph.IM\",\"cs.DC\"]","methods":"[]","has_code":false}