A global-shared and low-exchange parallel method of high resolution solar image reconstruction

Abstract

The high resolution image reconstruction takes an important place in the solar physics research, but the solar high resolution observation has been hindered severely for a long time due to huge observation data volume, slow reconstruction speed and other factors. In order to treat the huge volume of quasi real-time solar observation data and cope with the computing burden of the same magnitude for high resolution reconstruction of solar image, a number of advanced ground-based solar telescopes at home and abroad have adopted the speckle masking, a method of reconstruction algorithm that can be parallely realized, to reconstruct the high resolution image. Good treatment results are obtained from this method. However, it is still hard to meet the demand for solar observation data treatment at the current efficiency, since the volume of solar telescope observation data is increasing. This paper is directed at the demands of China′s new solar telescopes such as the NVST (The 1 m New Vacuum Solar Telescope) and ONSET (Optical and Near-Infrared Solar Eruption Tracer). By analyzing the computing time of each module with the Triple-Spectral method through actual measurement, the paper reaches a conclusion that the data exchange performance is a key bottleneck affecting the reconstruction effect. On this basis, the paper puts forward a universal method of global-shared low-exchange parallel high resolution image reconstruction. This method takes the Triple-Spectral as the core algorithm for image reconstruction and uses the message passing interface (MPI) and shared memory mechanism, allowing the reconstruction computing process to read and write the data in the shared memory at high speed after algorithm optimization. The shared memory, which is created for storage of image data and image reconstruction results respectively according to the size of the image, will be subsequently mapped into each process, giving access to read the processing data and store the reconstruction results independently. While computing the image reconstruction, each child process will not apply the MPI communication to obtain the sub-block image data. Instead, it will read the associated data from the shared memory according to the sub-block image numbering. After the sub-block image reconstruction is done, each child process will store the reconstruction results directly into the shared memory according to the sub-block image numbering, instead of sending the results to the host process via MPI communication. In this way the communication between processes is reduced, the comunication time saved and the data exchange efficiency improved. The experiment results show that on one 16-core PC server, it takes only about 12.4 s for reconstructing the 100-frame ONSET 1660×1660 pixel image and 5.6 s for the 100-frame NVST 1024×1024 pixel image under this method. Good efficiency is achieved in the reconstruction of solar telescope data for ONSET and NVST with different apertures, proving that this method has certain efficiency and universality. The parallel combination of the Triple-Spectral and K-T has greatly reduced the communication process and data exchange in the course of image reconstruction, saved the communication time and improved the reconstruction efficiency. To be further mentioned, the achievement of good efficiency on one server would bring the method into flexible application and save the equipment cost substantially. With the aid of the research outcome of the paper, it is expected to tackle the puzzles remained in the high resolution reconstruction of NVST and ONSET, and bring down the data storage burden. Its fulfillment of real-time high resolution reconstruction has laid a solid foundation for the follow-up researches.