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Exploring Novel Parallelization Technologies for 3-D Imaging Applications

Diego Rivera, Dana Schaa, Micha Moffie, David Kaeli
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA
19th International Symposium on Computer Architecture and High Performance Computing, 2007. SBAC-PAD 2007

@article{rivera2007exploring,

   title={Exploring Novel Parallelization Technologies for 3-D Imaging Applications},

   author={Rivera, D. and Schaa, D. and Moffie, M. and Kaeli, D.},

   year={2007},

   publisher={IEEE Computer Society}

}

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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.
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