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Related Experiment Video

Updated: May 20, 2026

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model
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Memory-Aware External Facelist Calculation: A Data-Parallel Atomic Hash Counting Approach.

Spiros Tsalikis, Will Schroeder, Daniel Szafir

    IEEE Transactions on Visualization and Computer Graphics
    |May 18, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a novel, memory-aware algorithm for calculating the external facelist of volumetric meshes, crucial for scientific visualization. The new method offers superior performance and reduced memory usage on both CPUs and GPUs.

    Area of Science:

    • Scientific Visualization
    • Computational Geometry
    • High-Performance Computing

    Background:

    • Unstructured volumetric meshes are vital for scientific simulations and analyses, particularly in finite element analysis.
    • Calculating the external facelist is a fundamental and performance-critical step in scientific visualization for rendering polygonal meshes.
    • Existing methods in libraries like VTK and Viskores have performance and memory limitations.

    Purpose of the Study:

    • To assess the performance and memory constraints of current external facelist calculation algorithms.
    • To develop and introduce a novel, memory-aware external facelist calculation algorithm.
    • To optimize the external facelist calculation for efficient execution on many-core architectures.

    Main Methods:

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  • Exploration of existing external facelist algorithms in VTK and Viskores.
  • Development of a new memory-aware algorithm utilizing an atomic hash counting approach.
  • Leveraging Viskores' data-parallel primitive operations for broad architectural compatibility.

    • Main Results:

    • The novel algorithm demonstrates the lowest GPU memory footprint and second-lowest CPU memory footprint among evaluated methods.
    • The proposed algorithm achieves the fastest execution performance on both CPU and GPU.
    • The new method is integrated into VTK and Viskores as an open-source solution.

    Conclusions:

    • The developed memory-aware external facelist algorithm significantly improves performance and memory efficiency.
    • This advancement benefits scientific visualization by accelerating mesh processing on modern hardware.
    • The open-source availability promotes wider adoption and further development in the scientific community.