Research on Parallel Algorithms for uv-faceting Imaging |
| |
| Institution: | 1. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030;2. Key Laboratory of Radio Astronomy, Chinese Academy of Sciences, Nanjing 210008;3. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004;1. School of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024;2. School of Astronomy and Space Science, Nanjing University, Nanjing 210034;1. Harold Vance Department of Petroleum Engineering, Texas A&M University, 3116 TAMU, College Station, TX 77843-3116, USA;2. Petroleum Engineering Program, Texas A&M Engineering Building, Education City, PO Box 23874, Doha, Qatar;1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210033;2. Key Laboratory of Planetary Sciences, Chinese Academy of Sciences, Nanjing 210033;3. School of Astronomy and Space Science, Nanjing University, Nanjing 210023;1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210008;2. University of Chinese Academy of Sciences, Beijing 100049;1. Institute of Physics, Silesian University in Opava, Faculty of Philosophy and Science, Bezru?ovo nám. 13, 746 01 Opava, Czech Republic;2. Oxford e-Research Centre, University of Oxford, 7 Keble Road, Oxford OX1 3QG, United Kingdom |
| |
| Abstract: | The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-Processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software Applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility. |
| |
| Keywords: | Instrumentation Interferometers—methods observational—techniques interferometric—techniques image processing—radio continuum general |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
