An efficient work-distribution strategy for gridding radio-telescope data on GPUs

This paper presents a novel work-distribution strategy for GPUs, that efficiently convolves radio-telescope data onto a grid, one of the most time-consuming processing steps to create a sky image. Unlike existing work-distribution strategies, this strategy keeps the number of device-memory accesses low, without incurring the overhead from sorting or searching within telescope data. Performance measurements show that the strategy is an order of magnitude faster than existing accelerator-based gridders. We compare CUDA and OpenCL performance for multiple platforms. Also, we report very good multi-GPU scaling properties on a system with eight GPUs, and show that our prototype implementation is highly energy efficient. Finally, we describe how a unique property of GPUs, fast texture interpolation, can be used as a potential way to improve image quality.