Optimizing the GPU based method calculating energy deposition of beams coupling with discrete materials in dynamical and thermal simulations for higher computing efficiency

The particle counting part in the unified gas-kinetic wave–particle method on graphics processing unit (GPU) devices is computationally intensive. This paper introduces a piecewise-hierarchical (P-H) particle counting strategy tailored for the Single Instruction Multiple Threads architecture, which leverages GPU memory hierarchy to reduce access conflicts. The strategy was evaluated based on throughput, roofline performance, and computation time metrics. Compared to the global counting strategy, the P-H approach achieved a 3.37× speedup for the particle counting kernel, and the overall program experienced a performance boost of more than 30%.

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Optimizing the GPU based method calculating energy deposition of beams coupling with discrete materials in dynamical and thermal simulations for higher computing efficiency

Cited by 3 publications

References 12 publications

Mesoscopic CUDA 3D MRT-LBM Simulation of Natural Convection of Power-Law Fluids in a Differentially Heated Cubic Cavity with a Machine Learning Cross-Validation

Mesoscopic CUDA 3D MRT-LBM Simulation of Natural Convection of Power-Law Fluids in a Differentially Heated Cubic Cavity with a Machine Learning Cross-Validation

Thermal study of a scanning beam in granular flow target

A piecewise-hierarchical particle count method suitable for the implementation of the unified gas-kinetic wave–particle method on graphics processing unit devices

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