Parallel implementation of w-projection wide-field imaging

w-Projection is a wide-field imaging technique that is widely used in radio synthesis arrays. Processing the wide-field big data generated by the future Square Kilometre Array(SKA) will require significant updates to current methods to significantly reduce the time consumed on data processing. Data loading and gridding are found to be two major time-consuming tasks in w-projection. In this paper, we investigate two parallel methods of accelerating w-projection processing on multiple nodes: the hybrid Message Passing Interface(MPI) and Open Multi-Processing(OpenMP) method based on multicore Central Processing Units(CPUs) and the hybrid MPI and Compute Unified Device Architecture(CUDA)method based on Graphics Processing Units(GPUs). Both methods are successfully employed and operated in various computational environments, confirming their robustness. The experimental results show that the total runtime of both MPI + OpenMP and MPI + CUDA methods is significantly shorter than that of single-thread processing. MPI + CUDA generally shows faster performance when running on multiple nodes than MPI + OpenMP, especially on large numbers of nodes. The single-precision GPU-based processing yields faster computation than the double-precision processing; while the single-and doubleprecision CPU-based processing shows consistent computational performance. The gridding time remarkably increases when the support size of the convolution kernel is larger than 8 and the image size is larger than 2,048 pixels. The present research offers useful guidance for developing SKA imaging pipelines.

Status and progress of China SKA Regional Centre prototype

The Square Kilometre Array(SKA)project consists of delivering two largest radio telescope arrays being built by the SKA Observatory(SKAO),which is an intergovernmental organization bringing together nations from around the world with China being one of the major member countries.The computing resources needed to process,distribute,curate and use the vast amount of data that will be generated by the SKA telescopes are too large for the SKAO to manage on its own.To address this challenge,the SKAO is working with the international community to create a shared,distributed data,computing and networking capability called the SKA Regional Centre Alliance.In this model,the SKAO will be supported by a global network of SKA Regional Centres(SRCs)distributed around the world in its member countries to build an end-to-end science data system that will provide astronomers with high-quality science products.SRCs undertake deep processing,scientific analysis,and long-term storage of the SKA data,as well as user support.China has been actively participating in and promoting the construction of SRCs.This paper introduces the international cooperation and ongoing prototyping of the global SRC network,the basis for the construction of the China SRC and describes in detail the progress of the China SRC prototype.The paper also presents examples of scientific applications of SKA precursor and pathfinder telescopes performed using resources from the China SRC prototype.Finally,the future prospects of the China SRC are presented.

人工智能用于天体普查:最前沿的技术遇见最古老的科学

Astronomy is the oldest natural science based on observation,and a census of objects in the sky map to create a catalog is the basis for further research.This effort is achieved through astronomical object detection,also known as"source finding",which aims to identify individual objects in an astronomical image and then retrieve the properties of those objects to form a catalog.The completeness,reliability,and accuracy of the resulting catalog has a profound impact on astrophysical research.