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AMRaCut: Scalable Partitioning for Adaptive Mesh Refinement

Budvin Edippuliarachchi,D. V. Van Komen,Hari Sundar

Published 2025 in International Conference on Software Composition

ABSTRACT

Mesh partitioning is critical for scalable distributed PDE solvers. Traditional methods like spatial ordering and multi-level graph partitioning have significant tradeoffs between partition quality and parallel scalability. We present AMRaCut, a distributed-parallel mesh partitioner that bridges this gap using parallel label propagation and graph diffusion. It operates mostly locally on initial partitions, limiting inter-process communications to neighboring processes. This locality is especially effective in AMR, where mesh evolves dynamically with mostly local changes. AMRaCut achieves 5-10x speedups over multi-level partitioners (ParMETIS, PT-Scotch) while producing partitions of comparable quality and minimized boundaries. Its efficiency is comparable to sorting-based methods like space-filling curves. AMRaCut maintains maximum partition load within 2x of optimal, sufficient for distributed scalability. We verify that AMRaCut is effective in downstream tasks by evaluating a Finite Element Model SpMV operation. Despite the 2x imbalance, AMRaCut partitions perform on par with parMETIS/PT-Scotch partitions, outperforming spatially ordered partitions.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    International Conference on Software Composition

  • Publication date

    2025-11-15

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1145/3712285.3759863
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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