Multi-dimensional imaging techniques involve the processing of high resolution images commonly used in medical, civil and remote-sensing applications. A barrier commonly encountered in this class of applications is the time required to carry out repetitive operations on large matrices. Partitioning these large datasets can help improve performance, and lends the data to more efficient parallel execution.

In this paper we describe our experience exploring two novel parallelization technologies: 1) a graphical processor unit (GPU)-based approach which utilizes 128 cores on a single GPU accelerator card, and 2) a middleware approach for semi-automatic parallelization on a cluster of multiple multi-core processors. We investigate these two platforms and describe their strengths and limitations. In addition, we provide some guidance to the programmer on which platform to use when porting multi-dimensional imaging applications. Using a 3-D application taken from a clinical image reconstruction algorithm, we demonstrate the degree of speedup we can obtain from these two approaches.