An Adaptive Distributed Stencil Abstraction for GPUs
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914394557906944 |
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| author | Bhosale, Aditya Kale, Laxmikant |
| author_facet | Bhosale, Aditya Kale, Laxmikant |
| contents | The scientific computing ecosystem in Python is largely confined to single-node parallelism, creating a gap between high-level prototyping in NumPy and high-performance execution on modern supercomputers. The increasing prevalence of hardware accelerators and the need for energy efficiency have made resource adaptivity a critical requirement, yet traditional HPC abstractions remain rigid. To address these challenges, we present an adaptive, distributed abstraction for stencil computations on multi-node GPUs. This abstraction is built using CharmTyles, a framework based on the adaptive Charm++ runtime, and features a familiar NumPy-like syntax to minimize the porting effort from prototype to production code. We showcase the resource elasticity of our abstraction by dynamically rescaling a running application across a different number of nodes and present a performance analysis of the associated overheads. Furthermore, we demonstrate that our abstraction achieves significant performance improvements over both a specialized, high-performance stencil DSL and a generalized NumPy replacement. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_19851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Adaptive Distributed Stencil Abstraction for GPUs Bhosale, Aditya Kale, Laxmikant Distributed, Parallel, and Cluster Computing The scientific computing ecosystem in Python is largely confined to single-node parallelism, creating a gap between high-level prototyping in NumPy and high-performance execution on modern supercomputers. The increasing prevalence of hardware accelerators and the need for energy efficiency have made resource adaptivity a critical requirement, yet traditional HPC abstractions remain rigid. To address these challenges, we present an adaptive, distributed abstraction for stencil computations on multi-node GPUs. This abstraction is built using CharmTyles, a framework based on the adaptive Charm++ runtime, and features a familiar NumPy-like syntax to minimize the porting effort from prototype to production code. We showcase the resource elasticity of our abstraction by dynamically rescaling a running application across a different number of nodes and present a performance analysis of the associated overheads. Furthermore, we demonstrate that our abstraction achieves significant performance improvements over both a specialized, high-performance stencil DSL and a generalized NumPy replacement. |
| title | An Adaptive Distributed Stencil Abstraction for GPUs |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2512.19851 |