Performance Optimization of Non-Equilibrium Ionization Simulations from MapReduce and GPU Acceleration
We propose a two-stage optimization strategy to accelerate non-equilibrium ionization (NEI) calculation that is crucial to various high energy astrophysical phenomena, by using methods of MapReduce modeling and GPU acceleration. First, we construct a parallel pipeline based on the MapReduce model that processes massive particles trajectories on a separate mesh decoupled from that has been used by other equations in the multiphysics simulations. Second, we accelerate the calculation of massive NEI equations by taking full advantage of heterogeneous multicore architecture of GPUs. The approach has been prototyped and tested in simulations using FLASH code and AtomDB atomic database. Our results show that the method can improve the end-to-end performance by 3-fold with less computing resources and reduce the overhead significantly. For standalone tests, the GPU-accelerated NEI solver can achieve a maximum 212-fold speedup compared to the CPU-based solver. With the capability to support nonintrusive simulation-time data analysis, our approach can be also applied to other multiphysics processes such as reactive flow simulations.
Xiao, Jian; Long, Min; Yu, Ce; Zhou, Xin; and Ji, Li. (2020). "Performance Optimization of Non-Equilibrium Ionization Simulations from MapReduce and GPU Acceleration". Parallel Computing, 98, 102682. https://doi.org/10.1016/j.parco.2020.102682