Automated Code Generation for Maximizing Performance of Detailed Chemistry Calculations in OpenFOAM

Thorsten Zirwes (), Feichi Zhang (), Jordan A. Denev, Peter Habisreuther and Henning Bockhorn
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Thorsten Zirwes: Karlsruhe Institute of Technology, Engler-Bunte-Institute/Combustion Technology
Feichi Zhang: Karlsruhe Institute of Technology, Engler-Bunte-Institute/Combustion Technology
Jordan A. Denev: Karlsruhe Institute of Technology, Engler-Bunte-Institute/Combustion Technology
Peter Habisreuther: Karlsruhe Institute of Technology, Engler-Bunte-Institute/Combustion Technology
Henning Bockhorn: Karlsruhe Institute of Technology, Engler-Bunte-Institute/Combustion Technology

A chapter in High Performance Computing in Science and Engineering ' 17, 2018, pp 189-204 from Springer

Abstract: Abstract In direct numerical simulation of turbulent combustion, the majority of the total simulation time is often spent on evaluating chemical reaction rates from detailed reaction mechanisms. In this work, an optimization method is presented for speeding up the calculation of chemical reaction rates significantly, which has been implemented into the open-source CFD code OpenFOAM. A converter tool has been developed, which translates any input file containing chemical reaction mechanisms into C++ source code. The automatically generated code allows to restructure the reaction mechanisms for efficient computation and enables more compiler optimizations. Additional performance improvements are achieved by generating densely packed data and linear access patterns that can be vectorized in order to exploit the maximum performance on HPC systems. The generated source code compiles to an OpenFOAM library, which can directly be used in simulations through OpenFOAM’s runtime selection mechanism. The optimization concept has been applied to a realistic combustion case simulated on two peta-scale supercomputers, among them the fastest HPC cluster Hazel Hen (Cray XC40) in Germany. The optimized code leads to a decrease of total simulation time of up to 40% and this improvement increases with the complexity of the involved chemical reactions. Moreover, the optimized code yields good parallel performance on up to 28,800 CPU cores.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-68394-2_11

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DOI: 10.1007/978-3-319-68394-2_11

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