Exploring Novel Parallelization Technologies for 3-D Imaging Applications

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 the authors describe the experience exploring two novel parallelization technologies: A Graphical Processor Unit (GPU)-based approach which utilizes 128 cores on a single GPU accelerator card, and a middleware approach for semi-automatic parallelization on a cluster of multiple multi-core processors.