The solver module of the Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) pipeline aims to find the astrometric parameters of $\sim 10^{8}$ stars in the Milky Way, the attitude and instrumental settings of the Gaia satellite, and the parametrized post Newtonian parameter $\gamma$ with a resolution of 1 0 - 100 micro – arc seconds. To perform this task, the code, which runs in production on Leonardo CINECA infrastructure, solves a system of linear equations with the iterative LSQR algorithm, where the coefficient matrix is large (10-50 TB) and sparse and the iterations stop when least square convergence is reached. The solver was ported to GPU with CUDA, obtaining a $\sim 14 x$ acceleration factor over an original version CPU-parallelized with OpenMP. This work concentrates on a code section dedicated to covariances calculation, representing an important scientific task for Gaia mission, since the problems unknowns present strong correlations. Given the number of unknowns at mission end, the variances-covariances matrix is expected to occupy $\sim 1$ EB, which represents a substantial “Big Data” issue. To compute a subset of the total covariances, we defined an I/Obased pipeline made of two jobs. The first job, the LSQR, writes the files every $i \operatorname{tnCov} C P$ iterations, and the second job reads them and calculates the corresponding covariances. The two jobs can be launched either in sequence or concurrently. Previous studies demonstrated that the covariances calculation does not significantly slowdown the AVU-GSR production up to $\sim 3 \times 10^{7}$ covariances. Here we investigate the performance of the covariances pipeline as a function of $i t n \operatorname{Cov} C P$. The results show that writing smaller files more frequently or writing larger files less frequently does not affect the global performance of the solver, whose speed only depends on the number of covariances to calculate and of system unknowns.

The HaMMon project is the outcome of an industrial partnership that includes many Italian research institutions and private companies. It is led by UnipolSai and Leitha, and funded by the ICSC, the Italian National Research Center for High Performance Computing, Big Data and Quantum Computing.The ambition of HaMMon is to build a flexible and scalable platform to analyze the hydrogeological and atmospheric balance of the Italian territory. The project aims to expand the current knowledge in hazard mapping, monitoring, and forecasting from an industrial perspective by leveraging innovative technologies and the interdisciplinary activities carried out by the ICSC.In this work, we present the cloud-HPC infrastructure deployed in the High-Performance Computing for Artificial Intelligence (HPC4AI) green data center of the University of Turin which supports the testing and development of HaMMon’s applications and services. We describe the current activities and preliminary results related to the integration of Photogrammetry techniques, Data Visualization and Artificial Intelligence technologies, applied on aerial images, to assess extreme natural events and evaluate their impact on risk-exposed assets.

As the Square Kilometre Array (SKA) radio telescopes approach full operation, they are set to generate hundreds of petabytes of data annually, offering unprecedented resolution and detail in astrophysical observations. This massive influx of data presents significant challenges in terms of storage, processing, analysis and visualization. Scientific visualization becomes essential in this context, enabling researchers to interpret complex, high-dimensional datasets and extract valuable insights. This paper presents the latest developments of VisIVO Visual Analytics, an interactive visualization tool that is evolving to meet the needs of the SKA and similar large-scale astrophysical projects such as the James Webb Space Telescope and the Atacama Large Millimeter/submillimeter Array (ALMA). Originally designed as a desktop application, VisIVO is transitioning to a client-server architecture, allowing it to efficiently run remote visualization pipelines on remote servers. This shift significantly enhances the tool’s ability to handle large scale datasets, facilitating real-time, interactive analysis of complex data. This work also explores how VisIVO Visual Analytics can distribute computational workloads across multiple nodes in high-performance computing (HPC) clusters for parallel visualization pipelines, enabling advanced and interactive techniques to derive meaningful insights from the vast volumes of data generated by next-generation observatories.

Modern scientific experiments produce ever-increasing amounts of data, soon requiring ExaFLOPs computing capacities for analysis. Reaching such performance requires purpose-built supercomputers with$O(10^{3})$nodes, each hosting multicore CPUs and multiple GPUs, and applications designed to exploit this hardware optimally. Given that each supercomputer is generally a one-off project, the need for computing frameworks portable across diverse CPU and GPU architectures without performance losses is increasingly compelling. We investigate the performance portability (ȹ) of a real-world application: the solver module of the AVU–GSR pipeline for the ESA Gaia mission. This code finds the astrometric parameters of$\sim$$10^{8}$stars in the Milky Way using the LSQR iterative algorithm. LSQR is widely used to solve linear systems of equations across a wide range of high-performance computing applications, elevating the study beyond its astrophysical relevance. The code is memory-bound, with six main compute kernels implementing sparse matrix-by-vector products. We optimize the previous CUDA implementation and port the code to further six GPU-acceleration frameworks: C++ PSTL, SYCL, OpenMP, HIP, KOKKOS, and OpenACC. We evaluate each framework's performance portability across multiple GPUs (NVIDIA and AMD) and problem sizes in terms of application and architectural efficiency. Architectural efficiency is estimated through the roofline model of the six most computationally expensive GPU kernels. Our results show that C++ library-based (C++ PSTL and KOKKOS), pragma-based (OpenMP and OpenACC), and language-specific (CUDA, HIP, and SYCL) frameworks achieve increasingly better performance portability across the supported platforms with larger problem sizes providing better ȹ scores due to higher GPU occupancies.

Abstract The computing capacity needed to process the data generated in modern scientific experiments is approaching ExaFLOPs. Currently, achieving such performances is only feasible through GPU-accelerated supercomputers. Different languages were developed to program GPUs at different levels of abstraction. Typically, the more abstract the languages, the more portable they are across different GPUs. However, the less abstract and co-designed with the hardware, the more room for code optimization and, eventually, the more performance. In the HPC context, portability and performance are a fairly traditional dichotomy. The current C++ Parallel Standard Template Library (PSTL) has the potential to go beyond this dichotomy. In this work, we analyze the main performance benefits and limitations of PSTL using as a use-case the Gaia Astrometric Verification Unit-Global Sphere Reconstruction parallel solver developed by the European Space Agency Gaia mission. The code aims to find the astrometric parameters of $$\sim10^8$$ ∼ 10 8 stars in the Milky Way by iteratively solving a linear system of equations with the LSQR algorithm, originally GPU-ported with the CUDA language. We show that the performance obtained with the PSTL version, which is intrinsically more portable than CUDA, is comparable to the CUDA one on NVIDIA GPU architecture.

Abstract NEANIAS is a research and innovation action project funded by the European Union under the Horizon 2020 program. The project addresses the challenge of prototyping novel solutions for the underwater, atmospheric and space research communities, creating a collaborative research ecosystem, and contributing to the effective materialization of the European Open Science Cloud (EOSC). NEANIAS drives the co-design, implementation, delivery, and integration into EOSC of innovative thematic and core services, derived from state-of-the-art assets and practices in the target scientific communities. We present the overall NEANIAS ecosystem architecture, with an emphasis on its core visualization services, detailing their specifications and software development plan, and focusing on the underpinning service-oriented architecture for their delivery. We report on the underlying ideas and guiding principles for designing such visualization services, outlining their current release status and future development roadmaps towards Technological Readiness Level (TRL) 8 maturity and EOSC integration.

This paper presents a novel immersive Virtual Reality platform, named ARGO3D, tailored for improving research and teaching activities in Earth Sciences.The platform facilitates the exploration of geological environments and the assessment of geo-hazards, allowing reaching key sites of interest (some of them impossible to be reached in person) and thus to take measurements and collect data as it can be done in the real field.The target audience of ARGO3D encompasses students, teachers and early career scientists, as well as civil planning organisations and non-academics.The overall workflow for real ambient reconstruction, processing and rendering of the virtual ambient is presented, as well as a detailed description of the VR software tools and hardware devices employed.

Summary The availability of large‐scale digital surveys offers tremendous opportunities for advancing scientific knowledge in the astrophysics community. Nevertheless, the analysis of these data often requires very powerful computational resources. Science gateway technologies offer Web‐based environments to run applications with little concern for learning and managing the underlying infrastructures that execute them. This paper focuses on the issues related to the development of a science gateway customized for the needs of the astrophysics community. The VisIVO Science Gateway is wrapped around a WS‐PGRADE/grid User Support Environment portal integrating services for processing and visualizing large‐scale multidimensional astrophysical data sets on distributed computing infrastructures. We discuss the core tools and services supported including an application for mobile access to the gateway. We report our experiences in supporting specialized astrophysical communities requiring development of complex workflows for visualization and numerical simulations. Further, available platforms are discussed for sharing workflows in collaborative environments. Finally, we outline our vision for creating a federation of science gateways to benefit astrophysical communities by sharing a set of services for authentication, computing infrastructure access and data/workflow repositories. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Nowadays, collaborative applications are valuable tools for scientists to share their studies and experiences, for example, by interacting simultaneously with their data and outcomes giving feedback to other colleagues on how the data are processed. This paper presents a mobile application connected to a workflow‐enabled framework to perform visualization and data analysis of large‐scale, multi‐dimensional datasets on distributed computing infrastructures. In particular, the usage of workflow‐driven applications, through science gateway technologies, allows the scientist to share heavy data exploration tasks as workflows and the relative results in a transparent and user‐friendly way. Copyright © 2015 John Wiley & Sons, Ltd.

Workflow management has been widely adopted by scientific communities as a valuable tool to carry out complex experiments. It allows for the possibility to perform computations for data analysis and simulations, whereas hiding details of the complex infrastructures underneath. There are many workflow management systems that offer a large variety of generic services to coordinate the execution of workflows. Nowadays, there is a trend to extend the functionality of workflow management systems to cover all possible requirements that may arise from a user community. However, there are multiple scenarios for usage of workflow systems, involving various actors that require different services to be supported by these systems. In this paper we reflect about the usage scenarios of scientific workflow management based on the practical experience of heavy users of workflow technology from communities in three scientific domains: Astrophysics, Heliophysics and Biomedicine. We discuss the requirements regarding services and information to be provided by the workflow management system for each usage profile, and illustrate how these requirements are fulfilled by the tools these communities currently adopt. This paper contributes to the understanding of properties of future workflow management systems that are important to increase their adoption in a large variety of usage scenarios.

Even though the Italian Grid Infrastructure (IGI) is a general purpose distributed platform, in the past it has been used mainly for serial computations. Parallel applications have been typically executed on supercomputer facilities or, in case of ``not high-end'' HPC applications, on local commodity parallel clusters. Nowadays, with the availability of multiple cores processors, Grid computing is becoming very attractive also for parallel applications but some problems exist in supporting of HPC applications on Grid environment. Here we describe the work made to set up a HPC testbed for ``not high-end'' HPC applications, based on IGI Grid technologies, to find solutions to those problems. Participating sites have been selected among the ones running HPC clusters in Grid environment. Each of them contributed with their specific HPC experience and their available resources to the present test, which encompasses an unprecedented large set of applications from different disciplines in the fields of astronomy, astrophysics, chemistry, climatology, material science and oceanography. In addition to computing resources sharing, the main contribution of each participant was the identification of the real requirements of his application also related to the current middleware limitations and then the realization of a test platform enhanced with additional HPC solutions and configurations developed in a tight collaboration between HPC administrators, users and IGI managers. The main work was on computational resources selection, data management and the definition, the deployment and the documentation of the software execution environment. The outcoming results of the testbed represent the basis of the HPC support in the IGI production infrastructure.

Nowadays visualization-based knowledge discovery can play an important role in astrophysics. Collaborative visualization can enable multiple users to share visualization experiences, e.g. by interacting simultaneously with astrophysical datasets giving feedback on what other participants are doing/seeing. Further, workflow-driven applications allow reproduction of specific visualization results, a challenging task as selecting suitable visualization parameters may not be a straightforward process. This paper presents VisIVO Science Gateway, a web-based workflow-enabled framework integrating large-scale, multidimensional datasets and applications for visualization and data filtering on Distributed Computing Infrastructures (DCIs). Advanced users are able to create, change, invoke, and monitor workflows while standard users are provided with easy-to-use customised web interfaces hiding all technical aspects of the visualization algorithms and DCI configurations.