Abstract
The solution of higher-dimensional problems, such as the simulation of plasma turbulence in a fusion device as described by the five-dimensional gyrokinetic equations, is a grand challenge for current and future high-performance computing. The sparse grid combination technique is a promising approach to the solution of these problems on large-scale distributed memory systems. The combination technique numerically decomposes a single large problem into multiple moderately-sized partial problems that can be computed in parallel, independently and asynchronously of each other. The ability to efficiently combine the individual partial solutions to a common sparse grid solution is a key to the overall performance of such large-scale computations. In this work, we present new algorithms for the recombination of distributed component grids and demonstrate their scalability to 180, 225 cores on the supercomputer Hazel Hen.
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Acknowledgements
This work was supported by the German Research Foundation (DFG) through the Priority Program 1648 “Software for Exascale Computing” (SPPEXA).
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Heene, M., Pflüger, D. (2016). Scalable Algorithms for the Solution of Higher-Dimensional PDEs. In: Bungartz, HJ., Neumann, P., Nagel, W. (eds) Software for Exascale Computing - SPPEXA 2013-2015. Lecture Notes in Computational Science and Engineering, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-40528-5_8
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DOI: https://doi.org/10.1007/978-3-319-40528-5_8
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