A unified design solution for heterogeneous explicit hyperbolic solvers is herein introduced. The proposed design is entirely cross-compatible between CPUs and GPUs, through an intuitive object-oriented approach. The advantages of a unified CPU+GPU development approach are discussed and exemplified, and a complete description of the data and code structures are provided and benchmarked. The benefits of different object-oriented designs are quantified under static and dynamic loads in terms of parallel performance and scalability. A fair comparison with graphics processors provides a realistic measure of achievable GPU implementation benefits. Both automatically and manually tuned GPU executions are compared and shown to also have a significant impact on the obtained performance. Overall, the proposed design combines a good sequential performance with a supra-linear scalability on modern CPUs. On GPUs, execution is shown to be up to 40 times faster than its single-threaded counterpart, opening a wider range of applicable model scales and resolutions.

本文介绍了用于异构显式双曲求解器的统一设计解决方案。通过一种直观的面向对象方法，所提出的设计在CPU和GPU之间是完全交叉兼容的。讨论并举例说明了统一的CPU + GPU开发方法的优点，并提供了数据和代码结构的完整描述并进行了基准测试。在并行性能和可伸缩性方面，在静态和动态负载下量化了不同的面向对象设计的好处。与图形处理器的合理比较提供了可实现的GPU实施优势的现实度量。对自动和手动调整的GPU执行进行了比较，并显示它们也对获得的性能产生重大影响。总体，提出的设计将良好的顺序性能与现代CPU的超线性可扩展性结合在一起。在GPU上，执行速度显示出比其单线程对应程序快40倍，从而打开了更广泛的适用模型比例和分辨率。