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GPU Parallelization of Astronomical Image Subtraction

Gustav Arneving, Hugo Wilhelmsson
Linköping University, Department of Electrical Engineering
Linköping University, 2024

@misc{arneving2024gpu,

   title={GPU Parallelization of Astronomical Image Subtraction},

   author={Arneving, Gustav and Wilhelmsson, Hugo},

   year={2024}

}

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Astronomical image subtraction is a method for generating a difference image from two images, which covers the same area but taken at different times, in order to see changes over time. Due to the images being taken at different times, one of the images has to be convolved, to match the atmospheric conditions of the other image. HOTPANTS is an open source software used for astronomical image subtraction. The problem is that HOTPANTS is written in serial C and therefore does not scale well with growing image sizes. There have been previous efforts to parallelize HOTPANTS, which include PHOTPANTS and GBAISP. However, these projects are outdated or unavailable, respectively. The latest effort, BACH, is a reimplementation of HOTPANTS in C++, where the convolution and subtraction parts have been parallelized on a GPU using OpenCL. This thesis project is a continuation of BACH, called X-BACH, which aims to parallelize the remaining parts of the HOTPANTS algorithm using OpenCL. The results show that some parts of the HOTPANTS algorithm, excluding convolution and subtraction, are highly suitable for the GPU while other parts are not suitable for the GPU. It is believed that some parts which are not suitable for the GPU are highly suitable for CPU parallelization. Overall, running on an external GPU, X-BACH achieves a relative speed of 1 to 2 compared to BACH, and a relative of 0.8 to 2.5 compared to HOTPANTS. When running on an integrated GPU, X-BACH achieves a relative speed of 0.5 to 1.2 compared to BACH, and a relative speed of 0.3 to 2 compared to HOTPANTS. Some parts of the algorithm achieves a speedup of up to 10 times when parallelized on a GPU. In terms of accuracy, X-BACH generally obtains a maximum relative error in order of magnitude ranging from 10^−7 to 10^−1. However, on certain test images, the algorithm has been observed to be unstable.
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