Leveraging HPC accelerator architectures with modern techniques - hydrologic modeling on GPUs with ParFlow

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 resultedin 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 theproject. 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 drawbacks.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.

Automated summary

The rapid changes in heterogeneous supercomputer architectures pose great challenges to scientific communities trying to leverage the latest technology in high-performance computing. Many existing projects with long development histories have resulted in a large amount of code that is not directly compatible with the latest accelerator architectures. In order to adapt to modern accelerator architectures, many projects rely on directive-based programming models or third-party domain-specific languages or libraries, introducing external dependencies out of the project's control. The paper proposes a lightweight application-side adaptor layer to address these issues, enabling versatile and inexpensive adaptation of new accelerator architectures without significant drawbacks.