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This is a Quantitative Study study.

October 8, 2025Open Access

Comparing the Performance of MC/DC's on-GPU Event-based Processing Methods in Multigroup and Continuous-energy Problems

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Authors

BCBraxton CuneoSeattle University JMJoanna Piper MorganOregon State University IVIlham VariansyahOregon State University

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Implication

Analysis reveals asynchronous method outperforms stack-based method in MC/DC neutron transport benchmarks, indicating notable efficiency differences in GPU execution.

Key Points

Asynchronous method shows enhanced early scaling in the 3D C5G7 k-eigenvalue benchmark problem.
Performance varies in continuous-energy problems based on tally resolution and particle count.
MC/DC leverages Harmonize for efficient GPU resource management in neutron transport scenarios.
Four NVIDIA Tesla V100s were used to compare both methods under specific benchmark conditions.

Cite This Study

Cuneo et al. (2025) studied this question.

synapsesocial.com/papers/68e6bc5f38ca8e474d54a051 https://doi.org/10.13182/mc25-47174

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Implication

Analysis reveals asynchronous method outperforms stack-based method in MC/DC neutron transport benchmarks, indicating notable efficiency differences in GPU execution.

Comparing the Performance of MC/DC's on-GPU Event-based Processing Methods in Multigroup and Continuous-energy Problems

Discussion

Member takes

Implication

Key Points

Cite This Study

Comparing the Performance of MC/DC's on-GPU Event-based Processing Methods in Multigroup and Continuous-energy Problems

Discussion

Member takes

Implication

Key Points

Cite This Study

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