Efficient strategies to reduce the memory footprint and CPU usage in parallel Large Eddy Simulations of turbulent combustion with large flamelet-based chemistry tables

Prof. Dr. Christian Hasse, Technische Universität Bergakademie Freiberg

15 Dec 2015, 17:00–18:30; Location: S4|10-1

Fast chemical reactions in combustion take place in thin layers, which are usually called flamelets. The flamelet regime is the most prominent one in technical applications. Thus, flamelet-based models have been used very successfully and are actively developed for the simulation of turbulent combustion by the scientific community. Instead of solving these flamelets during the simulation, the flamelet structures are computed beforehand and stored in a look-up table. Then, the  thermochemical state (temperature, species composition etc.) is retrieved during the simulation. One drawback of this approach is that the size of these tables can become quite large due to the large number of look-up parameters (dimension of the table leading to multi-dimensional interpolation during look-up) and the number of stored solution variables (thermochemical state). Especially for complex configurations and fuels, both the dimension and the solution size can increase significantly.

In an MPI-parallelized application, each process needs an individual copy of the table in RAM. Considering the increasing table size explained above and the general hardware trend of decreasing RAM/core, the size of the table has become a limiting factor in parallel simulations of turbulent combustion.

This problem will be addressed on two accounts in this talk. First, a novel memory management strategy with an additional abstraction level is presented, which only loads the necessary parts into physical memory and allows for in-memory compression and stripping. Various parallel extensions using the new MPI-3 standard and the system call MMAP are discussed. The second strategy decomposes the local structure of the table and fits each solution variable to multidimensional polynomials of adaptive degree. After merging the different fits across all solution variables, automatic source code generation is employed and finally, instead of the table itself, the compiled fitting functions are stored in a shared library. During the simulation, the interpolation is replaced by the retrieval of an ID from a simple region database, which is associated with a function pointer in the library, which is then called for the final calculation of the thermochemical state.

The application of both methods is discussed with respect to both memory usage and CPU requirements for the LES of turbulent combustion. Finally, it is shown that the proposed strategy can also be applied for other physical problems and an example from radiation modeling is shown.

Category: CE Seminar


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