High Performance Computing based simulations are crucial in Astrophysics and Cosmology, helping scientists investigate and understand complex astrophysical phenomena. Taking advantage of Exascale computing capabilities is essential for these efforts. However, the unprecedented architectural complexity of exascale systems impacts simulation codes. The SPACE Center of Excellence aims to re-engineer key astrophysical codes to adapt to these new computational challenges by adopting innovative programming paradigms and software solutions. Through co-design activities, SPACE brings together scientists, code developers, HPC experts, hardware manufacturers, and software developers. This collaboration enhances exascale astrophysics and cosmology applications, promoting the use of exascale and post-exascale computing capabilities. Additionally, SPACE addresses high-performance data analysis for the massive data outputs from exascale simulations, using machine learning and visualization tools. The project facilitates application deployment across platforms by focusing on code repositories and data sharing, integrating European astrophysical communities around exascale computing with standardized software and data protocols. In this paper, we present the SPACE Center of Excellence and the preliminary results achieved by the project.
Radio astronomy is currently facing a significant challenge due to the massive data volumes generated by modern radio-interferometers, which will be further exacerbated by the upcoming Square Kilometre Array. Efficient data processing at this scale necessitates advanced High-Performance Computing (HPC) resources. Our work focuses on developing a novel approach to implement the w-stacking algorithm on state-of-the-art HPC systems, specifically targeting heterogeneous architectures comprising both CPUs and GPUs. We introduce the RICK (Radio Imaging Code Kernels) code, designed to efficiently process radio-interferometric data by leveraging the parallelism and computational power of modern HPC nodes. This study demonstrates the effectiveness of RICK on a single computing node, showcasing significant performance improvements over traditional methods. The paper outlines the methodology, the algorithmic innovations, and the parallelization strategy, along with performance benchmarks on various CPU/GPU configurations, highlighting the potential of RICK for future large-scale radio astronomy projects.
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