Task-Based Programming for Adaptive Mesh Refinement in Compressible Flow Simulations
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911095380246528 |
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| author | Wei, Anjiang Song, Hang Hidayetoglu, Mert Slaughter, Elliott Lele, Sanjiva K. Aiken, Alex |
| author_facet | Wei, Anjiang Song, Hang Hidayetoglu, Mert Slaughter, Elliott Lele, Sanjiva K. Aiken, Alex |
| contents | High-order solvers for compressible flows are vital in scientific applications. Adaptive mesh refinement (AMR) is a key technique for reducing computational cost by concentrating resolution in regions of interest. In this work, we develop an AMR-based numerical solver using Regent, a high-level programming language for the Legion programming model. We address several challenges associated with implementing AMR in Regent. These include dynamic data structures for patch refinement/coarsening, mesh validity enforcement, and reducing task launch overhead via task fusion. Experimental results show that task fusion achieves 18x speedup, while automated GPU kernel generation via simple annotations yields 9.7x speedup for the targeted kernel. We demonstrate our approach through simulations of two canonical compressible flow problems governed by the Euler equations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05020 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Task-Based Programming for Adaptive Mesh Refinement in Compressible Flow Simulations Wei, Anjiang Song, Hang Hidayetoglu, Mert Slaughter, Elliott Lele, Sanjiva K. Aiken, Alex Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science Mathematical Software High-order solvers for compressible flows are vital in scientific applications. Adaptive mesh refinement (AMR) is a key technique for reducing computational cost by concentrating resolution in regions of interest. In this work, we develop an AMR-based numerical solver using Regent, a high-level programming language for the Legion programming model. We address several challenges associated with implementing AMR in Regent. These include dynamic data structures for patch refinement/coarsening, mesh validity enforcement, and reducing task launch overhead via task fusion. Experimental results show that task fusion achieves 18x speedup, while automated GPU kernel generation via simple annotations yields 9.7x speedup for the targeted kernel. We demonstrate our approach through simulations of two canonical compressible flow problems governed by the Euler equations. |
| title | Task-Based Programming for Adaptive Mesh Refinement in Compressible Flow Simulations |
| topic | Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science Mathematical Software |
| url | https://arxiv.org/abs/2508.05020 |