-д хадгалсан:
| Үндсэн зохиолчид: | , , |
|---|---|
| Формат: | Recurso digital |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
Zenodo
2026
|
| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.5281/zenodo.19168954 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
|
Агуулга:
- <p dir="auto">Legacy CPU-based scientific codebases in the DOE’s exascale ecosystem represent decades of investment but create a critical interoperability bottleneck for massively threaded GPU accelerators. This paper presents a systematic refactoring framework based on Fowler et al. (1999) principles to port and optimize these codebases for GPU execution while preserving correctness and performance.</p> <p dir="auto">Targeted refactorings (Extract Method, Introduce Parameter Object, Replace Conditional with Polymorphism) eliminate monolithic structures and data clumps. Applied to mesoscopic materials simulations, the approach achieves >10× speedup on A100/H100 GPUs while maintaining numerical fidelity (relative error < 10^{-10}).</p> <p dir="auto">The refactored codebases integrate seamlessly with the NET4EXA BXIv3 European interconnect (scaling to 8M endpoints with native GPU zero-copy support), making them fully actionable for real-time engineering in the Genesis Mission without rewriting from scratch. The framework incorporates automated detection of refactoring opportunities via XGBoost + TF-IDF commit-message classification (100% accuracy, Al-Fraihat et al., 2024) and includes security and integrity validation based on large-scale empirical analysis (Iannone et al., 2023).</p> <p dir="auto">This work provides a practical, low-risk pathway for DOE scientific software teams to modernize legacy Fortran/C++ code for exascale GPU systems while preserving decades of domain knowledge.</p>