Abstract The conversion between visibilities and images is a fundamental yet computationally expensive operation in radio interferometric imaging. Although existing algorithms combining high-precision convolution kernels with w-stacking have achieved imaging precision on the order of 10 −12 , the efficient processing of massive datasets from next-generation arrays such as the SKA telescope remains a formidable challenge, constrained by the hardware limitations of heterogeneous platforms. In this work, we introduce the baseline separation paradigm (BSP), an innovative imaging framework designed to optimize workload distribution. The core strategy of BSP is to partition the dataset into two regimes: utilizing the GPU for the massive volume of short-baseline data to bypass the memory bottleneck, while leveraging the CPU for long baselines to preserve high-resolution details, with both regimes utilizing grids strictly minimized to their respective spatial frequency limits. This strategy effectively resolves the conflict between the limited memory capacity of accelerators and the large grid sizes required for wide-field imaging. We implemented a prototype to evaluate the algorithm performance. Experimental results show that, for SKA1-Low-scale simulations, BSP achieves a precision of 10 −11 , comparable to ducc0.wgridder, while providing a substantial speedup. This approach provides a scalable solution for future exascale astronomical data processing.
Xie et al. (2026) studied this question.