A massive MPI parallel framework of smoothed particle hydrodynamics with optimized memory management for extreme mechanics problems

Jiahao Liu, Xiufeng Yang, Zhilang Zhang, Moubin Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

The dynamic failure process of structures under extreme loadings is very common in many fields of engineering and science. The smoothed particle hydrodynamics (SPH) method offers inherent benefits in dealing with complex interfaces and large material deformations in extreme mechanics problems. However, SPH simulations for 3D engineering applications are time-consuming. To address this issue, we introduce MPI (Message Passing Interface) in our SPH scheme to reduce computational time. Some optimizations are adopted to ensure the massive computation of the SPH method. In particular, an optimized memory management strategy is developed to control the memory footprint. With the present MPI-based massive parallelization of the SPH method, several validation examples are tested and analyzed. By comparing the present numerical results with the reference data, the dynamic failure process of complex structures subjected to extreme loadings like explosive and impact loadings can be well captured. A large number of particles, up to 2.04 billion, are adopted in the present simulations. The scaling tests show that the scalability of the massively parallel SPH program achieves a maximum parallel efficiency of 97% on 10020 CPU cores.

Original languageEnglish
Article number108970
JournalComputer Physics Communications
Volume295
DOIs
Publication statusPublished - Feb 2024

Keywords

  • Extreme mechanics problems
  • Massive high performance computing
  • Memory management
  • Message passing interface
  • Smoothed particle hydrodynamics

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Liu, J., Yang, X., Zhang, Z., & Liu, M. (2024). A massive MPI parallel framework of smoothed particle hydrodynamics with optimized memory management for extreme mechanics problems. Computer Physics Communications, 295, Article 108970. https://doi.org/10.1016/j.cpc.2023.108970