Abstract
In this paper, we discuss strategies to parallelize selected deconvolution methods on a multi-GPU system. We provide a comparison of several approaches to split the deconvolution into subtasks while keeping the amount of costly data transfers as low as possible, and propose own implementation of three deconvolution methods which achieves up to 65× speedup over the CPU one. In the experimental part, we analyse how the individual stages of the computation contribute to the overall computation time as well as how the multi-GPU implementation scales in various setups. Finally, we identify bottlenecks of the system.
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Karas, P., Kuderjavý, M., Svoboda, D. (2013). Deconvolution of Huge 3-D Images: Parallelization Strategies on a Multi-GPU System. In: Kołodziej, J., Di Martino, B., Talia, D., Xiong, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8285. Springer, Cham. https://doi.org/10.1007/978-3-319-03859-9_24
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DOI: https://doi.org/10.1007/978-3-319-03859-9_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03858-2
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