What does this research mean for the field?

Offloading highly parallel routines of a large eddy simulation solver to GPUs, particularly using the Kokkos programming model to retain data on the device between timesteps, yields at least a 5-fold execution speedup compared to multi-core CPUs. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to analyze approaches for accelerating a large eddy simulation solver by leveraging GPU capabilities.

May 17, 2026Open Access

Porting a large eddy simulation computational fluid dynamics solver to graphics processing units

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The aim is to analyze approaches for accelerating a large eddy simulation solver by leveraging GPU capabilities.
Porting code routines to GPUs to offload execution time-heavy sections.
Utilizing CUDA for programming NVIDIA GPUs and Kokkos for ease in handling 3D arrays in PETSc.
Maintaining data on the GPU between time steps to enhance performance.
Achieved at least 5 times speedup with older generation GPUs compared to CPU execution.
Performance gains increase significantly when data is retained on GPUs between timesteps.
Discussion includes benefits for highly parallel routines and outlines for future code porting strategies.

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Implication

Randomized trial evaluates performance optimization using GPUs in computational fluid dynamics, highlighting coding methodologies.

Cite This Study

Andrés Alberto Escobedo Campos (2026) studied this question.

synapsesocial.com/papers/6a095b3e7880e6d24efe0fb4 https://doi.org/10.14288/1.0452530

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Porting a large eddy simulation computational fluid dynamics solver to graphics processing units

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Discussion

Implication

Cite This Study

Porting a large eddy simulation computational fluid dynamics solver to graphics processing units

Key Points

Discussion

Implication

Cite This Study

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