Rapidly changing heterogeneous supercomputer architectures pose a great challenge to many scientific communities trying to leverage the latest technology in high-performance computing. Many existing projects with a long development history have resulted in a large amount of code that is not directly compatible with the latest accelerator architectures. Furthermore, due to limited resources of scientific institutions, developing and maintaining architecture-specific ports is generally unsustainable. In order to adapt to modern accelerator architectures, many projects rely on directive-based programming models or build the codebase tightly around a third-party domain-specific language or library. This introduces external dependencies out of control of the project. The presented paper tackles the issue by proposing a lightweight application-side adaptor layer for compute kernels and memory management resulting in a versatile and inexpensive adaptation of new accelerator architectures with little draw backs. A widely used hydrologic model demonstrates that such an approach pursued more than 20 years ago is still paying off with modern accelerator architectures as demonstrated by a very significant performance gain from NVIDIA A100 GPUs, high developer productivity, and minimally invasive implementation; all while the codebase is kept well maintainable in the long-term.

快速变化的异构超级计算机架构对许多试图利用高性能计算最新技术的科学界提出了巨大挑战。许多具有悠久开发历史的现有项目导致大量代码与最新的加速器架构不直接兼容。此外，由于科学机构的资源有限，开发和维护特定于架构的端口通常是不可持续的。为了适应现代加速器架构，许多项目依赖于基于指令的编程模型或紧密围绕第三方领域特定语言或库构建代码库。这引入了项目无法控制的外部依赖项。本文通过为计算内核和内存管理提出一个轻量级的应用程序端适配器层来解决这个问题，从而对新的加速器架构进行通用且廉价的适配，几乎没有什么缺点。一个广泛使用的水文模型表明，20 多年前采用的这种方法仍然可以通过现代加速器架构获得回报，这体现在 NVIDIA A100 GPU 的非常显着的性能提升、高开发人员生产力和微创实施；同时代码库长期保持良好的可维护性。一个广泛使用的水文模型表明，20 多年前采用的这种方法仍然可以通过现代加速器架构获得回报，这体现在 NVIDIA A100 GPU 的非常显着的性能提升、高开发人员生产力和微创实施；同时代码库长期保持良好的可维护性。一个广泛使用的水文模型表明，20 多年前采用的这种方法仍然可以通过现代加速器架构获得回报，这体现在 NVIDIA A100 GPU 的非常显着的性能提升、高开发人员生产力和微创实施；同时代码库长期保持良好的可维护性。