Computer-Aided Design· 2026Q1
ParFEMWARP, the parallel finite element-based mesh warping algorithm: Algorithm design, analysis, and implementation
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- Q1SCImago
- 2026year
Short summary
A new parallel finite element-based mesh warping algorithm, ParFEMWARP, leverages MPI-3 shared memory and one-sided communication to efficiently warp large tetrahedral meshes, achieving strong scalability up to 32 cores and a 38.8% speedup increase with 256 cores.
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Key points
- ParFEMWARP is a parallel finite element-based mesh warping algorithm for tetrahedral meshes.
- It utilizes MPI-3 shared memory and one-sided communication for efficient parallel precomputation of neighbor lists.
- The algorithm solves a Poisson boundary value problem in parallel to deform the mesh.
- Strong scalability is observed up to 32 cores, with a 38.8% speedup increase using 256 cores across 64 nodes compared to a single node.
AI-generated from the title and abstract; the full text is not read.
Abstract
Warping large volume meshes has applications in biomechanics, aerodynamics, image processing, and cardiology. However, warping large, real-world meshes is computationally expensive. Existing parallel implementations of mesh warping algorithms do not take advantage of shared-memory and one-sided communication features available in the MPI-3 standard. We describe our parallelization of the finite element-based mesh warping algorithm for tetrahedral meshes. Our implementation is portable across shared and distributed memory architectures, as it takes advantage of shared memory and one-sided communication to precompute neighbor lists in parallel. We then deform a mesh by solving a Poisson boundary value problem and the resulting linear system, which has multiple right-hand sides, in parallel. Our results demonstrate strong scalability on up to 32 cores on a single node. Furthermore, we show a 38.8% increase in speedup with 256 cores distributed uniformly across 64 nodes versus our largest single node speedup while observing ideal speedups in the global stiffness matrix generation step and sublinear speedups overall.
The authors' abstract, as published at the source. Computer-Aided Design, 2026 · DOI ↗
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Computer Graphics and Computer-Aided DesignComputer Science