A Python extension for the massively parallel multiphysics simulation framework waLBerla
Author(s): Bauer M, Schornbaum F, Godenschwager C, Markl M, Anderl D, Rüde U, Köstler H
Publisher: Academy & Industry Research Collaboration Center (AIRCC)
Publication year: 2015
Pages range: 1-14
We present a Python extension to the massively parallel HPC simulation toolkit waLBerla.waLBerla is a framework for stencil based algorithms operating on block-structured grids, with the main application field being fluid simulations in complex geometries using the lattice Boltzmann method. Careful performance engineering results in excellent node performance and good scalability to over 400,000 cores. To increase the usability and flexibility of the framework, a Python interface was developed. Python extensions are used at all stages of the simulation pipeline: they simplify and automate scenario setup, evaluation, and plotting. We show how our Python interface outperforms the existing text-file-based configuration mechanism, providing features like automatic nondimensionalization of physical quantities and handling of complex parameter dependencies. Furthermore, Python is used to process and evaluate results while the simulation is running, leading to smaller output files and the possibility to adjust parameters dependent on the current simulation state. C++ data structures are exported such that a seamless interfacing to other numerical Python libraries is possible. The expressive power of Python and the performance of C++ make development of efficient code with low time effort possible.
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APA: Bauer, M., Schornbaum, F., Godenschwager, C., Markl, M., Anderl, D., Rüde, U., & Köstler, H. (2015). A Python extension for the massively parallel multiphysics simulation framework waLBerla. International Journal of Distributed and Parallel Systems , 1-14. https://dx.doi.org/10.1080/17445760.2015.1118478
MLA: Bauer, Martin, et al. "A Python extension for the massively parallel multiphysics simulation framework waLBerla." International Journal of Distributed and Parallel Systems (2015): 1-14.