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Searching for sources of electromagnetic emission in spectral-line radioastronomy interferometric data is a computationally intensive process. Parallelprogramming techniques and High Performance Computing hardware may be used toimprove the computational performance of a source finding program. However, itis desirable to further reduce the processing time of source finding in orderto decrease the computational resources required for the task. GPU accelerationis a method that may achieve significant increases in performance for somesource finding algorithms, particularly for filtering image data. This workconsiders the application of GPU acceleration to the task of source finding andthe techniques used to achieve the best performance, such as memory management.We also examine the changes in performance, where the algorithms that were GPUaccelerated achieved a speedup of around 3.2 times the 12 core per nodeCPU-only performance, while the program as a whole experienced a speedup of 2.0times.

Performance Analysis of GPU-Accelerated Filter-Based Source Finding for HI Spectral Line Image Data