| The New Vacuum Solar Telescope(N VST), 1 meter large aperture ground-based solar telescope, has been built at the Full-shine Solar Observatory(FSO) of the Yunnan observatories in China in 2010. Its main scientific goal is to observe the fine structures in both the photosphere and the chromosphere. Multi-channel high resolution imaging system, with two photosphere channels and one chromosphere channel, has been installed and come into use at NVST. The band for observing the chromosphere is Halpha(6563?), and the band for observing the photosphere is TiO(7058?) and G-band(4300?), respectively. Because of its channel separatio n, the imaging system simultaneously acquires data using three detectors, capable of generating 2560*2160 or 1024*1024 pixels image data at a frame rate of around 10 images per second. When observing several hours per day, imaging system will produce at least 7TB of raw observational data. Although the storage cost is dropping continually, such huge data volume still becomes difficult to transfer and distribute.In order to improve spatial resolution of solar observational images, high resolution reconstruction techniques become indispensable. The raw data from NVST are reduced by lucky imaging(Level1) and speckle masking(Level1+) reconstruction. Both Level1 and Leve1l+ reconstruction techniques use at least 100 short exposure raw images to reconstruct one image statistically. Under an Interactive Data Language(IDL) implementation on a high-end computer, Level1 reconstruction of single channel data in a day took about one day, and Level1+ reconstruction took about at least three months. The processing speed was too slow to meet the urgent need of real-time high resolution imaging observations.To deal with both massive speckle data and large amount of computation in near real-time, it is indispensable to parallelize and accelerate high resolution reconstruction. Obviously, the(near) real-time data processing can improve the efficiency of the telescope, because not only the data volume is reduced by a factor of 100 rapidly, but also the time between the observations and data analyses is dramatically shorted. The rapid development of computer technology, e.g., the high performance computing technology, makes it possible to reconstruct massive speckle data in real-time on site. The thesis studies the key technologies of real-time image reconstruction through the distributed and parallel computing. The main contents of this thesis are as follows.(1) Study high resolution reconstruction algorithm and design method of parallel computing, investigate current popular several parallel computers and parallel programming models, and dig suitable parallel computers and parallel programming models for NVST.(2) Propose a parallel algorithm for Level1 reconstruction based on MPI(Message Passing Interface). Given massive speckle data and large amount of computation for Level1 reconstruction, we utilize high performance cluster and MPI technology to accelerate Level1 reconstruction. We systematically design and implement parallel algorithm for Level1 reconstruction based on MPI, and build a parallel processing pipeline for Level1 reconstruction to meet the urgent demand of real-time reconstruction. For reconstruction of one 1024*1024 pixels Halpha image, the whole data processing speed is about 23 times faster than that of IDL implementation. We analyze the speedup results between various modules to propose the technology and method for further optimizing parallel algorithm. We also test the scalability of parallel algorithm, and the results provide an excellent performance on scalability.(3) Propose a parallel algorithm for Level1+ reconstruction based on MPI. Given massive speckle data and huge amount of computation for Level1+ reconstruction, we take advantage of achievements and key technology of research work 2 to systematically design and implement parallel algorithm for Level1+ reconstruction based on MPI, and build a parallel processing pipeline for Level1+ reconstruction to meet the urgent demand of real-time reconstruction. The real-time performance is among the world leading level at present. For reconstruction of one 2560*2160 pixels TiO image, the whole data processing speed is about 122 times faster than that of IDL implementation. We analyze the speedup results between various modules to propose the technology and method for further optimizing parallel algorithm. We also test the scalability of parallel algorithm, and the results provide an excellent performance on scalability.(4) Propose a parallel algorithm for subimage reconstruction based on OpenMP. Although real-time performance of Leve1l+ reconstruction based on MPI has been achieved, considerable proportion of the computing time is spent in the reconstruction of all solar subimages because of the limitation of computing resources. We use OpenMP technology to accelerate subimage reconstruction, and systematically design and implement parallel algorithm for subimage reconstruction based on OpenMP. The time consumption of OpenMP-based implementation is Research on Technologies of M assive Data Parallel Processing for Solar Telescop e compared with that of the single thread CPU implementation, and a significant speedup of around 2.5 is achieved to reconstruct one 256*256 pixels subimage. The parallel algorithm shows increased speedup performance with the increase of data size, thus becomes more efficient for large scale data processing. We also test the scalability of parallel algorithm, and the results provide an excellent performance on scalability.(5) Propose a parallel algorithm for Level1 reconstruction based on MPI+OpenMP. Combined with the achievements of research work 3 and research work 4, we systematically design and implement parallel algorithm based on MPI+OpenMP to further improve the speedup performance for Level1+ reconstruction. The time consumption of MPI+OpenMP-based implementation is compared with that of the MPI implementation. The results show that under sufficient hardware computing resources the speed of MPI+OpenMP-based implementation is faster than that of the MPI implementation.(6) Propose a parallel algorithm for subimage reconstruction based on Compute Unified Device Architecture(CUDA). We systematically design and implement a new parallel method for speckle masking reconstruction of solar subimage on General Purpose Graphics Processing Units(GPGPU) to accelerate subimage reconstruction. The time consumption of CUDA-based implementation is compared with that of the single thread CPU implementation, and a significant speedup of around 6 is achieved to reconstruct one 256*256 pixels subimage. The parallel algorithm shows increased speedup performance with the increase of data size, thus becomes more efficient for large scale data processing.The thesis systematically designs and implements multiple parallel algorithms for high resolution reconstruction for NVST, and significantly improves the processing speed. On this basis, we successfully build multiple parallel processing pipelines and meet the urgent demand of real-time reconstruction of massive data for NVST. The real-time performance of reconstruction for NVST exceeds the present near real-time performance for NST(New Solar Telescope). I n addition, we optimize parallel algorithms for high resolution reconstruction for NVST, and further improve the processing speed. The research results of the thesis not only improve the efficiency and scientific output of the telescope, but also provide a better technical reference for the observation data handling system of next generation solar telescope(e.g., CGST, etc.). |
PDF Full Text Request