Abstract
We present a variety of possible parallelization approaches for a real-world case study using several modern parallel and distributed computer architectures. Our case study is a production-quality, time-intensive algorithm for medical image reconstruction used in computer tomography. We describe how this algorithm can be parallelized for the main kinds of contemporary parallel architectures: shared-memory multiprocessors, distributed-memory clusters, graphics processors, 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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Schellmann, M. et al. (2009). Parallel Medical Image Reconstruction: From Graphics Processors to Grids. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2009. Lecture Notes in Computer Science, vol 5698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03275-2_45
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DOI: https://doi.org/10.1007/978-3-642-03275-2_45
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