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Parallel medical image reconstruction: from graphics processing units (GPU) to Grids

Maraike Schellmann, Sergei Gorlatch, Dominik Meilander, Thomas Kosters, Klaus Schafers, Frank Wubbeling, Martin Burger
Institut fur Informatik, Universitat Munster, Einsteinstr. 62,48149 Munster, Germany
The Journal of Supercomputing (5 March 2010)

@article{schellmannparallel,

   title={Parallel medical image reconstruction: from graphics processing units (GPU) to Grids},

   author={Schellmann, M. and Gorlatch, S. and Meil{\”a}nder, D. and K{\”o}sters, T. and Sch{\”a}fers, K. and W{\”u}bbeling, F. and Burger, M.},

   journal={The Journal of Supercomputing},

   pages={1–10},

   issn={0920-8542},

   publisher={Springer}

}

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We present and compare a variety of parallelization approaches for a real-world case study on modern parallel and distributed computer architectures. Our case study is a production-quality, time-intensive algorithm for medical image reconstruction used in computer tomography (PET). We parallelize this algorithm for the main kinds of contemporary parallel architectures: shared-memory multiprocessors, distributed-memory clusters, graphics processing units (GPU) using the CUDA framework, the Cell processor and, finally, how various architectures can be accessed in a distributed Grid environment. The main contribution of the paper, besides the parallelization approaches, is their systematic comparison regarding four important criteria: performance, programming comfort, accessibility, and cost-effectiveness. We report results of experiments on particular parallel machines of different architectures that confirm the findings of our systematic comparison.
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