SimSDP, a Rapid Prototyping tool for Radio Astronomy: From NenuFAR Experiments to SKAO-Scale Simulation

The Square Kilometre Array Observatory (SKAO) will generate unprecedented volumes of data, requiring its Science Data Processor (SDP) to operate at terabyte rates under strict energy and performance constraints. Anticipating the computational cost of imaging algorithms is therefore critical for both astronomers and system designers. Yet, testing new algorithmic strategies directly on large HPC platforms is often impractical, due to cost, limited availability, and long development cycles. To address this, we present SimSDP, a rapid prototyping tool designed to support the development and optimization of radio-astronomy imaging pipelines for the future SKAO SDP. The tool enables early exploration of algorithmic choices and their impact on computational performance and energy consumption without requiring access to large-scale systems. In this work, we illustrate its use through three case studies: (1) modeling different strategies for spectral parallelism (joint vs. distributed deconvolution), (2) simulating pipeline execution on large-scale multi-core, multi-node systems with realistic observation sizes, and (3) prototyping pipelines with real-world data from the NenuFAR instrument. These experiments highlight how SimSDP can help astronomers and instrument designers anticipate the computational cost of imaging algorithms, compare different processing strategies, and better align algorithmic development with the capabilities of future HPC infrastructures. The tool is integrated into the PREESM framework and available as open-source, with future work aiming at extending models to GPU-based systems.