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arXiv · 2512.19851

An Adaptive Distributed Stencil Abstraction for GPUs

Aditya Bhosale·Laxmikant Kale
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Abstract

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.

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Aditya Bhosale, Laxmikant Kale. 2025-12-22. An Adaptive Distributed Stencil Abstraction for GPUs. https://doi.org/10.1109/sci68648.2025.11333865

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