  Searching for sources of electromagnetic emission in spectral-line radio
astronomy interferometric data is a computationally intensive process. Parallel
programming techniques and High Performance Computing hardware may be used to
improve the computational performance of a source finding program. However, it
is desirable to further reduce the processing time of source finding in order
to decrease the computational resources required for the task. GPU acceleration
is a method that may achieve significant increases in performance for some
source finding algorithms, particularly for filtering image data. This work
considers the application of GPU acceleration to the task of source finding and
the techniques used to achieve the best performance, such as memory management.
We also examine the changes in performance, where the algorithms that were GPU
accelerated achieved a speedup of around 3.2 times the 12 core per node
CPU-only performance, while the program as a whole experienced a speedup of 2.0
times.
