The design of sparse matrix storage formats is essential to achieve high-performance sparse kernels in modern parallel architectures. The bitmap-based bmSparse format was designed with the SPGEMM operation as its main focus, but it shows potential for other operations as well when the sparse matrix has a convenient structure. In this paper, we propose a new SPMV kernel that greatly improves the load balance of previous open-source implementations and utilizes the GPU resources more efficiently. The results show speedups of up to $100 \times$ regarding other SPMV kernels for bmSparse. Finally, we leverage a hybrid implementation between the new and existing approach, selecting the kernel which will probably result in a better performance depending on each matrix characteristics.
High-performance computing (HPC) data centers are key for advancing research and industry, but their high energy consumption and environmental impact pose critical sustainability challenges. Few HPC centers currently use renewable energy sources, particularly hydrogen-based storage, or integrate waste heat reuse. Recently, Digital Twins (DTs) are proving crucial for advancing sustainable HPC centers, enabling the integration of advanced monitoring and energy optimization with renewable energy sources. They simulate operations, predict maintenance needs, balance workloads, and support renewable integration for enhanced efficiency. The paper first reviews current practices, focusing on the potential of DTs to improve HPC sustainability by dynamically managing resources and optimizing systems, including cooling, power distribution, and load balancing. Then, it illustrates the University of Turin’s Sustainable HPC4AI (S-HPC4AI) project, which aims to develop a low impact HPC center to support Artificial Intelligence (AI) research across diverse scientific fields. The facility will feature renewable energy sources, advanced self-cooling, and waste heat recovery, setting a benchmark for energy-efficient, low-carbon HPC infrastructure. Central to the project is the integration of hydrogen and solar energy, with photovoltaic systems providing clean power and hydrogen fuel cells serving as reliable backup sources, reducing reliance on fossil fuels. Waste heat can be transformed into a productive resource for a local automated phenotyping system, reducing energy consumption and environmental impact on the overall. Furthermore, a DT will be developed, integrating BIM with sensors data about performance, resource usage and operating conditions, enabling real-time monitoring and predictive analytics for managing power, cooling, and energy resources. The potential and challenges of the S-HPC4AI model are discussed, suggesting possible solutions.
The project aims to develop innovative data management solutions (database and file system archive) for the Spoke 3 division of the Italian National Center for HPC, Big Data, and Quantum Computing, rigorously applying the FAIR principles and Open Science methodologies. This system provides a robust and scalable framework designed to address big data challenges in astrophysics, which requires customized data models and management solutions. It focuses on managing observations from the Gaia and Fermi space missions, as well as simulations generated using the Pluto and Ramses codes, with the goal of supporting requests from the astrophysical Italian community on particular scientific cases.For Gaia data, we identified the HDF5 as a suitable format to improve data access and analysis outside the data reduction pipelines. The HDF5 format allows the nesting of primitive fields, arrays, and complex objects, thereby enhancing accessibility and computational efficiency. For the data management of the FT1 and FT2 data products from the Fermi Observatory, a common data model has been chosen to provide a unique point of access to the scientific data, employing logic for database queries and offering access to data products over a long observing period. Finally, to facilitate user interaction, a custom web application has been developed, providing a user-friendly interface for accessing and querying the archived metadata, additional “Cut & Merge” and “Transits” services, and supporting also secure authentication methods to ensure data privacy and compliance. These comprehensive resources enhance data accessibility and usability through the Spoke3 Archive Infrastructure, empowering the scientific community to explore and analyze astronomical data more effectively.
Celestial objects are known to be change in brightness over time, driven by a diverse combination of physical processes, whose time scales range from sub-milliseconds to billions of years. Stingray is an open-source Python package that brings advanced time series analysis techniques to the astronomical community, with a focus on high-energy astrophysics, but built on top of general-purpose classes and methods that are designed to be easily adapted and extended to other use cases. We describe the work being done to adapt Stingray to the analysis of large data archives. In particular, we measure the performance and scalability of Stingray and use parallel computing to speed up selected parts of the code.
The C++ language continually evolves through formal specifications established by its standards committee, proposing new features to maintain $\mathrm{C}++$ as a relevant programming language while improving usability, performance, and portability across platforms. With the addition of parallel Standard Template Library (STL) algorithms in C++17, programmers can now leverage parallel processing capabilities via vendor-neutral parallel execution policies. This study presents an adaptation of the NAS Parallel Benchmarks (NPB)—a well-established suite of applications for evaluating parallel architectures-by porting its sequential C-style code to use C++ STL abstractions and performance-portable parallelism features. Our goals are to (1) assess the suitability of C++ STL for scientific applications like the ones in the NPB and (2) provide a comparative performance and portability of STL algorithms’ parallel execution policies across different multicore architectures (x86 and AArch64). Results indicate that the performance of parallel STL algorithms is often close to that of optimized handwritten versions (OpenMP, Intel TBB, and FastFlow) on different architectures, with notable shortfalls. Across all NPB benchmarks, the STL algorithms’ geometric mean shows sequential execution times that are between 3.76% and $\mathrm{6. 9 \%}$ higher, while parallel executions may reach a geometric mean of up to $\mathrm{2 1. 2 1 \%}$ higher execution time.
