Abstract
The proliferation in the last years of many iterative algorithms for Computed Tomography is a result of the need of finding new ways for obtaining high quality images using low dose acquisition methods. These iterative algorithms are, in many cases, computationally much more expensive than traditional analytic ones. Based on the resolution of large linear systems, they normally make use of backprojection and projections operands in an iterative way reducing the performance of the algorithms compared to traditional ones. They are also algorithms that rely on a large quantity of memory because they need of working with large coefficient matrices. As the resolution of the available detectors increase, the size of these matrices starts to be unmanageable in standard workstations. In this work we propose a distributed solution of an iterative reconstruction algorithm with the help of the PETSc library. We show in our preliminary results the good scalability of the solution in one node (close to the ideal one) and the possibilities offered with a larger number of nodes. However, when increasing the number of nodes the performance degrades due to the poor scalability of some fundamental pieces of the algorithm as well as the increase of the time spend in both MPI communication and reduction.
E. Serrano—This work has been partially supported under the COST Action IC1305 “Network for Sustainable Ultrascale Computing Platforms” (NESUS), the grant TIN2013-41350-P, Scalable Data Management Techniques for High-End Computing Systems from the Spanish Ministry of Economy and Competitiveness, FPU14/03875 from the Spanish Ministry of Education, NECRA RTC-2014-3028-1, TEC2013-47270-R and RTC-2014-3028-1 project.
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Serrano, E., Vander Aa, T., Wuyts, R., Blas, J.G., Carretero, J., Abella, M. (2016). Exploring a Distributed Iterative Reconstructor Based on Split Bregman Using PETSc. In: Carretero, J., et al. Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10049. Springer, Cham. https://doi.org/10.1007/978-3-319-49956-7_15
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DOI: https://doi.org/10.1007/978-3-319-49956-7_15
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