Our results show that the GPU-SExtractor outperforms the original SExtractor by a factor of 6, taking a merely 1.9 second to process a typical 4KX4K image ...
We start from SExtractor, an astronomical source extraction tool widely used in astronomy projects, and study its parallelization on the GPU.
Abstract—In astronomical observatory projects, raw images are processed so that information about the celestial objects in the images is extracted into ...
The results show that our GPU-SExtractor outperforms the original SExtractor by a factor of 6, taking a merely 1.9 second to process a typical 4KX4K image ...
To address this problem, we propose to use the GPU (Graphics Processing Unit) to accelerate source extraction. Specifically, we start from SExtractor, an ...
Accelerating astronomical source extraction with graphics processors
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In our GPU-SExtractor, we re-design and parallelize the three major steps of SExtractor: 1) Background Computation, 2) Multi-Threshold Object Detection and 3) ...
We thus transfer weight from the GPU mem- ory to the CPU memory and extract the centerline on the CPU by a simple backtracing which traverses the MST from the ...
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Parallelizing Astronomical Source Extraction on the GPU · Image reconstruction in speckle interferometry · A Survey on GPU Techniques in Astronomical Data ...
Parallelizing Astronomical Source Extraction on the GPU. In IEEE 9th International Conference on eScience, pages 88–97. IEEE, October 2013. [ bib | DOI ].
The proposed algorithm is optimized in parallel from the CUDA kernel level and CUDA stream level to speed up feature extraction execution.