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Implementation and Optimization of a 1D2V PIC Method for Nonlinear Kinetic Models on GPUs

机译:GPU上非线性动力学模型的1D2V PIC方法的实现和优化

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This paper considers the parallel numerical simulation of the time evolution of galaxies and globular clusters on GPUs. The model used is the Einstein–Vlasov system, which is designed, in particular, to study the formation of black holes and spacetime singularities in a general relativistic framework.First, a reference implementation is derived using NVIDIA CUDA as programming model, which is then optimized in several steps. Bottlenecks are identified by profiling, and different approaches, namely particle sort, improved treatment of atomic operations, and kernel fusion are investigated to overcome these bottlenecks. Each optimized variant is evaluated in relation to the other variants using detailed runtime experiments and profiling results. Using in the order of 107 to 108 particles, speedups between 1.84 and 2.38 w.r.t. the reference implementation have been observed.
机译:本文考虑了GPU上星系和球状星团时间演化的并行数值模拟。使用的模型是Einstein–Vlasov系统,该系统特别设计用于研究一般相对论框架中黑洞的形成和时空奇点。首先,使用NVIDIA CUDA作为编程模型推导参考实现,然后该参考实现分几步进行优化。可以通过性能分析来识别瓶颈,并研究了不同的方法,即颗粒分类,改进的原子操作处理和核融合来克服这些瓶颈。使用详细的运行时实验和概要分析结果,将评估每个优化的变体与其他变体的关系。使用顺序为10 7 至10 8 颗粒,加速比在1.84和2.38 w.r.t.已观察到参考实施。

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