Scientific HPC in the pre-Exascale era (3rd edition)
Abstract
In this epoch of technological evolution, data concerning problems of different scientific areas are steeply increasing in volume, requiring hundreds of PB of storage (Big Data). Specifically, these data are extensive in both single file size and number of files in many fields, such as astronomy, cosmology, biology, and meteorology. Maintaining a proper performance trend towards pre-Exascale systems requires a specific codesign between hardware and software, exploiting High-Performance Computing (HPC) techniques. On the hardware side, increasingly heterogeneous architectures with multiple nodes and accelerators linked with high-bandwidth bridges to the single node are required. On the software side, applications have to be written with programming languages that allow portability among diverse architectures while not losing performance and minimizing the time required by the programmer to adapt the application. Other important aspects are the maintainability of the numerical stability of a problem solution while increasing the system size and the number of computational resources and the requirement of a "green" solution, that is, the ability to build infrastructures and applications to compute operations with Big Data volumes without excessively increasing the energetic consumptions.
Topics of interest
Contributions concerning the following areas of interest are welcome:
- Scientific applications toward (pre-)Exascale computing (mainly in the astrophysics and cosmology contexts but also in other scientific areas)
- Performance portability
- Scientific code scalability
- Numerical stability
- Green computing
- Big Data management
- Machine Learning
- Parallel and distributed computing
- Programming on heterogeneous architectures
- Programming on accelerators
- I/O techniques
Important Dates
- Title, abstract, and paper (preliminary) due: September 1st 2026
- Notifications to authors: September 15th 2026
- Camera-ready paper due: October 15th 2026
- Workshop date: TBD
Chairs
Valentina Cesare
Valentina Cesare is a fixed-term technologist at INAF - IRA, where she is working on real-time optimization of GPU-ported pipelines for Fast Radio Bursts detection within NGCroce project. From 01/12/2020 to 14/05/2025, she worked at INAF - OACT, firstly as fellowship student, then as research associate, and at last as fixed-term technologist, within a project about GPU porting of scientific applications related to the Gaia space mission, within the framework of the ICSC - National Center for Research in HPC, Big Data, and Quantum; Computing (PNRR - Future Computing initiative). A future involvement in the Euclid Consortium is planned. She received her Ph.D. in Physics and Astrophysics in March 2021 from the Physics Department of the University of Turin, with a thesis focused on galaxy dynamics in the framework of the modified gravity theory Refracted Gravity.
Gianluca Mittone
Gianluca Mittone is a postdoctoral researcher in computer science at the University of Turin, and his research is focused on the convergence between High-Performance Computing (HPC) and Artificial Intelligence (AI) techniques. In less than 5 years of research activity, he achieved 16 scientific publications and an H-index of 9(source Google Scholar). His works are mainly related to the use of AI in medicine and Federated Learning (FL). Specifically, he is currently investigating the deployment of cross-HPC FL workloads through workflow-based approaches; and the use of FL as a tool to allow AI-based computation to scale efficiently for HPC benchmarking purposes. He is currently co-principal investigator in a joint research effort between the University of Turin and Telecom Italia (TIM) to develop an FL-as-a-Service platform for the "TIM Edge & Cloud Continuum" IPCEI European Project. His achievements rewarded him with an HPC-Europa3 scholarship and an EuroPar foundation studentship, together with the 'Best PhD Symposium Award' during the 2023 edition of the conference. His PRAISE Score, an AI-based diagnostic tool, has been awarded as an officially recommended diagnostic software" by the European Society of Cardiology in their 2023 guidelines.
Bruno Casella
Bruno Casella is a postdoctoral researcher at the Computer Science Department of the University of Turin. His research focuses on federated, distributed, and theoretically grounded learning methods. In less than four years of research activity, he has achieved 18 scientific publications (in conference and workshop proceedings, and in journals) and an H-index of 8 (source: Google Scholar). He received his PhD in Modeling and Data Science at UniTO on the 26.06.2025 with a thesis entitled “Advancing Federated Learning. Towards Decentralization and Personalized Models“. He graduated in Computer Engineering in 2020 with a thesis on the performance of AlphaZero in different scenarios. He also received the Master’s Degree in Data Science for management in 2021 with a thesis on Federated Transfer Learning.
Giovanni Naldi
Giovanni Naldi received the BSc and MSc degrees in Telecommunications Engineering from the University of Bologna, Italy, in 2003 and 2006, respectively. In 2010 he joined the Institute of Radioastronomy of Bologna (INAF), as a Research Fellow, working on the design of analog receivers for low frequency aperture arrays. Since 2013, he has been working as a Technologist on the design of digital architectures for data acquisition and signal processing with FPGA-based platforms. He has a deep knowledge of the principal elaboration techniques of digital data for radio astronomical applications like filtering, channelization, correlation, beamforming, corner turning, I/O interfaces, etc… Also, he has experience with experimental observation activities for instrument commissioning. His other research interests include testing procedures of digital circuits and numerical simulations of antenna arrays systems. He is currently leading the development activities for the digital acquisition and computing system of the “Next Generation Croce del Nord” project (PNRR).