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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.15967 |
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| _version_ | 1866911116430409728 |
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| author | Cianciosa, M. Batchelor, D. Elwasif, W. |
| author_facet | Cianciosa, M. Batchelor, D. Elwasif, W. |
| contents | Modern supercomputers are increasingly relying on Graphic Processing Units (GPUs) and other accelerators to achieve exa-scale performance at reasonable energy usage. The challenge of exploiting these accelerators is the incompatibility between different vendors. A scientific code written using CUDA will not operate on a AMD gpu. Frameworks that can abstract the physics from the accelerator kernel code are needed to exploit the current and future hardware. In the world of machine learning, several auto differentiation frameworks have been developed that have the promise of abstracting the math from the compute hardware. However in practice, these framework often lag in supporting non-CUDA platforms. Their reliance on python makes them challenging to embed within non python based applications. In this paper we present the development of a graph computation framework which compiles physics equations to optimized kernel code for the central processing unit (CPUs), Apple GPUs, and NVidia GPUs. The utility of this framework will be demonstrated for a Radio Frequency (RF) ray tracing problems in fusion energy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15967 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | graph framework: A Domain Specific Compiler for Building Physics Applications Cianciosa, M. Batchelor, D. Elwasif, W. Computational Physics Modern supercomputers are increasingly relying on Graphic Processing Units (GPUs) and other accelerators to achieve exa-scale performance at reasonable energy usage. The challenge of exploiting these accelerators is the incompatibility between different vendors. A scientific code written using CUDA will not operate on a AMD gpu. Frameworks that can abstract the physics from the accelerator kernel code are needed to exploit the current and future hardware. In the world of machine learning, several auto differentiation frameworks have been developed that have the promise of abstracting the math from the compute hardware. However in practice, these framework often lag in supporting non-CUDA platforms. Their reliance on python makes them challenging to embed within non python based applications. In this paper we present the development of a graph computation framework which compiles physics equations to optimized kernel code for the central processing unit (CPUs), Apple GPUs, and NVidia GPUs. The utility of this framework will be demonstrated for a Radio Frequency (RF) ray tracing problems in fusion energy. |
| title | graph framework: A Domain Specific Compiler for Building Physics Applications |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2508.15967 |