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A CUDA-based implementation of an improved SPH method on GPU

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  • Antonelli, L.
  • Francomano, E.
  • Gregoretti, F.

Abstract

We present a CUDA-based parallel implementation on GPU architecture of a modified version of the Smoothed Particle Hydrodynamics (SPH) method. This modified formulation exploits a strategy based on the Taylor series expansion, which simultaneously improves the approximation of a function and its derivatives with respect to the standard formulation. The improvement in accuracy comes at the cost of an additional computational effort. The computational demand becomes increasingly crucial as problem size increases but can be addressed by employing fast summations in a parallel computational scheme. The experimental analysis showed that our parallel implementation significantly reduces the runtime, when compared to the CPU-based implementation.

Suggested Citation

  • Antonelli, L. & Francomano, E. & Gregoretti, F., 2021. "A CUDA-based implementation of an improved SPH method on GPU," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    • Handle: RePEc:eee:apmaco:v:409:y:2021:i:c:s0096300320304410
    DOI: 10.1016/j.amc.2020.125482
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    References listed on IDEAS

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    1. Francomano, E. & Paliaga, M., 2018. "Highlighting numerical insights of an efficient SPH method," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 899-915.
    2. Francomano, Elisa & Hilker, Frank M. & Paliaga, Marta & Venturino, Ezio, 2018. "Separatrix reconstruction to identify tipping points in an eco-epidemiological model," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 80-91.
    3. Ala, Guido & Fasshauer, Gregory E. & Francomano, Elisa & Ganci, Salvatore & McCourt, Michael J., 2017. "An augmented MFS approach for brain activity reconstruction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 141(C), pages 3-15.
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