A parallel model for the heterogeneous computation of radio astronomy signal correlation

The computational requirements of scientific research are constantly growing. In the field of radio astronomy, observations have evolved from using single telescopes, to interferometer arrays of many telescopes, and there are currently arrays of massive scale under development. These interferometers use signal and image processing to produce data that is useful to radio astronomy, and the amount of processing required scales quadratically with the scale of the array. Traditional computational approaches are unable to meet this demand in the near future. This thesis explores the use of heterogeneous parallel processing to meet the computational demands of radio astronomy. In heterogeneous computing, multiple hardware architectures are used for processing. In this work, the Graphics Processing Unit (GPU) is used as a co-processor along with the Central Processing Unit (CPU) for the computation of signal processing algorithms. Specifically, the suitability of the GPU to accelerate the correlator algorithms used in radio astronomy is investigated. This work first implemented a FX correlator on the GPU, with a performance increase of one to two orders of magnitude over a serial CPU approach. The FX correlator algorithm combines pairs of telescope signals in the Fourier domain. Given N telescope signals from the interferometer array, N2 conjugate multiplications must be calculated in the algorithm. For extremely large arrays (N >> 30), this is a huge computational requirement. Testing will show that the GPU correlator produces results equivalent to that of a software correlator implemented on the CPU. However, the algorithm itself is adapted in order to take advantage of the processing power of the GPU. Research examined how correlator parameters, in particular the number of telescope signals and the Fast Fourier Transform (FFT) length, affected the results.