  Structural parameters are normally extracted from observed galaxies by
fitting analytic light profiles to the observations. Obtaining accurate fits to
high-resolution images is a computationally expensive task, requiring many
model evaluations and convolutions with the imaging point spread function.
While these algorithms contain high degrees of parallelism, current
implementations do not exploit this property. With evergrowing volumes of
observational data, an inability to make use of advances in computing power can
act as a constraint on scientific outcomes. This is the motivation behind our
work, which aims to implement the model-fitting procedure on a graphics
processing unit (GPU). We begin by analysing the algorithms involved in model
evaluation with respect to their suitability for modern many-core computing
architectures like GPUs, finding them to be well-placed to take advantage of
the high memory bandwidth offered by this hardware. Following our analysis, we
briefly describe a preliminary implementation of the model fitting procedure
using freely-available GPU libraries. Early results suggest a speed-up of
around 10x over a CPU implementation. We discuss the opportunities such a
speed-up could provide, including the ability to use more computationally
expensive but better-performing fitting routines to increase the quality and
robustness of fits.
