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Investigation of Code Optimization Strategies for Enhancing the Performance of Static Recrystallization Cellular Automata Models
Abstract:
Understanding and predicting static recrystallization (SRX) behavior is crucial for controlling the microstructure and mechanical properties of metals during thermomechanical processing. Among various numerical modelling approaches that can be used to support experimental studies on this topic is the cellular automata (CA) method. This approach gained significant attention due to its ability to simulate microstructural evolution at the mesoscale with high spatial resolution. However, the main limitation of CA models is their significant simulation time, especially for the 3D computational domains. Therefore, the paper focuses on enhancing the efficiency of CA SRX simulations to deliver results within an acceptable time frame. The goal is to minimize computation time and memory usage through code-level optimization, without altering the hardware or compiler settings. Optimization is performed on the sequential version of the validated CA SRX code. Initially, the source code was analyzed using a profiler tool to identify performance bottlenecks. The most inefficient parts of the code were then reimplemented to eliminate these bottlenecks. Optimization methods included eliminating redundant functions, modifying neighbor assignments in the automata space, reducing class data structures, enabling direct access to attributes, simplifying mathematical formulas, and removing unused objects. The obtained results are also validated against the output from the sequential version to ensure the model's predictive capabilities. The work clearly demonstrates that the optimization improved simulation efficiency across all tested variants, with only minor increases in memory usage.
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59-68
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April 2026
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