Heterogeneous computing and exploiting integrated CPU-GPU architectures has become a clear current trend since the flattening of Moore's Law. In this work, we propose a numerical and algorithmic re-design of a p-adaptive quadrature-free discontinuous Galerkin method (DG) for the shallow water equations (SWE). Our new approach separates the computations of the non-adaptive (lower-order) and adaptive (higher-order) parts of the discretization form each other. Thereby, we can overlap computations of the lower-order and the higher-order DG solution components. Furthermore, we investigate execution times of main computational kernels and use automatic code generation to optimize their distribution between the CPU and GPU. Several setups, including a prototype of a tsunami simulation in a tide-driven flow scenario, are investigated, and the results show that significant performance improvements can be achieved in suitable setups.

翻译：异构计算与集成CPU-GPU架构的利用已成为当前摩尔定律趋缓趋势下的明确发展方向。本文针对浅水方程（SWE）提出了一种p自适应无求积间断伽辽金方法（DG）的数值与算法重构。新方法将离散格式中的非自适应（低阶）部分与自适应（高阶）部分进行分离计算，从而能够实现低阶与高阶DG解分量的计算重叠。此外，我们研究了主要计算核的执行时间，并通过自动代码生成优化其CPU与GPU间的任务分配。通过对包括潮汐驱动流场景下的海啸模拟原型在内的多种算例进行验证，结果表明在适当配置下可获得显著性能提升。