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A dynamic block activation framework for continuum models

Abstract

Efficient utilization of massively parallel computing resources is crucial for advancing scientific understanding through complex simulations. However, existing adaptive methods often face challenges in implementation complexity and scalability on modern parallel hardware. Here we present dynamic block activation (DBA), an acceleration framework that can be applied to a broad range of continuum simulations by strategically allocating resources on the basis of the dynamic features of the physical model. By exploiting the hierarchical structure of parallel hardware and dynamically activating and deactivating computation blocks, DBA optimizes performance while maintaining accuracy. We demonstrate DBA’s effectiveness through solving representative models spanning multiple scientific fields, including materials science, biophysics and fluid dynamics, achieving 216–816 central processing unit core-equivalent speedups on a single graphics processing unit (GPU), up to fivefold acceleration compared with highly optimized GPU code and nearly perfect scalability up to 32 GPUs. By addressing common challenges, such as divergent memory access, and reducing programming burden, DBA offers a promising approach to fully leverage massively parallel systems across multiple scientific computing domains.

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Fig. 1: Schematic overview of the DBA algorithm and key features of the study.
Fig. 2: Numerical simulations of solid nucleus growth in undercooled melts.
Fig. 3: Dendritic growth simulation results and performance from DBA.
Fig. 4: Phase-separation simulation results and performance from DBA.
Fig. 5: KHI simulation results from DBA.

Data availability

All of the experiments in this Resource are based on simulations, and there are no input data. Source data are provided with this paper.

Code availability

The code that supports the results within this Resource is available via GitHub at https://github.com/zhangruoyao68/DBA and via Zenodo at https://doi.org/10.5281/zenodo.14868458 (ref. 66).

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Acknowledgements

R.Z. was supported by the National Science Foundation (NSF) Materials Research Science and Engineering Center Program through the Princeton Center for Complex Materials (PCCM) (grant no. DMR-2011750). Y.X. was supported by the National Natural Science Foundation of China (grant no. 12204162). Useful discussions with M. P. Haataja, R. Teyssier, S. Cohen, J. Lalmansingh and Q. Cai are gratefully acknowledged. The simulations presented in this Resource were performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE) and Research Computing at Princeton University.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA

    Ruoyao Zhang

  2. College of Materials Science and Engineering, Hunan University, Changsha, People’s Republic of China

    Yang Xia

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R.Z. and Y.X. conceptualized the study, designed the computational framework, implemented the code, analyzed results, visualized simulations and drafted the paper.

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Yang Xia.

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Nature Computational Science thanks Cody Permann, Tatu Pinomaa, Nicolò Scapin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Supplementary Figs. 1 and 2, Supplementary model descriptions and Supplementary Tables 1 and 2.

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Source Data Fig. 3

Excel file for the source data used in Fig. 3.

Source Data Fig. 4

Excel file for the source data used in Fig. 4.

Source Data Fig. 5

Excel file for the source data used in Fig. 5.

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Zhang, R., Xia, Y. A dynamic block activation framework for continuum models.
Nat Comput Sci (2025). https://doi.org/10.1038/s43588-025-00780-2

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  • Received: 30 August 2024

  • Accepted: 19 February 2025

  • Published: 17 March 2025

  • DOI: https://doi.org/10.1038/s43588-025-00780-2

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