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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BEING VIEWED BY OTHERS KOHN–SHAM TIME-DEPENDENT DENSITY FUNCTIONAL THEORY WITH TAMM–DANCOFF APPROXIMATION ON MASSIVELY PARALLEL GPUS Article Open access 26 May 2023 SHIFTING SANDS OF

HARDWARE AND SOFTWARE IN EXASCALE QUANTUM MECHANICAL SIMULATIONS Article 25 April 2025 THE CO-EVOLUTION OF COMPUTATIONAL PHYSICS AND HIGH-PERFORMANCE COMPUTING Article 23 August 2024 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

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Download references 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.

AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA Ruoyao Zhang * College of Materials Science and

Engineering, Hunan University, Changsha, People’s Republic of China Yang Xia Authors * Ruoyao Zhang View author publications You can also search for this author inPubMed Google Scholar *

Yang Xia View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS R.Z. and Y.X. conceptualized the study, designed the computational framework,

implemented the code, analyzed results, visualized simulations and drafted the paper. CORRESPONDING AUTHOR Correspondence to Yang Xia. ETHICS DECLARATIONS COMPETING INTERESTS The authors

declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Computational Science_ thanks Cody Permann, Tatu Pinomaa, Nicolò Scapin and the other, anonymous, reviewer(s) for

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activation framework for continuum models. _Nat Comput Sci_ 5, 345–354 (2025). https://doi.org/10.1038/s43588-025-00780-2 Download citation * Received: 30 August 2024 * Accepted: 19

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