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.

我们提出了一种两阶段优化策略，以通过使用MapReduce建模和GPU加速的方法来加速对各种高能天文学现象至关重要的非平衡电离（NEI）计算。首先，我们基于MapReduce模型构造一条并行管道，该模型在多物理场模拟中的其他方程式已使用的独立网格上处理大量粒子轨迹，而该网格已解耦。其次，我们充分利用GPU的异构多核架构，加快了大规模NEI方程的计算。该方法已在使用FLASH代码和AtomDB的仿真中进行了原型设计和测试原子数据库。我们的结果表明，该方法可以以更少的计算资源将端到端性能提高3倍，并显着减少开销。对于独立测试，与基于CPU的求解器相比，GPU加速的NEI求解器可实现最大212倍的加速。凭借支持非侵入式仿真时数据分析的能力，我们的方法也可以应用于其他多物理场过程，例如反应流仿真。