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Development of New GPU-Optimized Reactor Physics Monte Carlo Code GREAPMC.

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Title: Development of New GPU-Optimized Reactor Physics Monte Carlo Code GREAPMC.
Authors: Rizwan Ali, Muhammad1 (AUTHOR) deokjung@unist.ac.kr, Aygul, Murat Serdar1 (AUTHOR), Lee, Deokjung1,2 (AUTHOR)
Source: Nuclear Science & Engineering. 2026 Suppl 1, Vol. 200, pS754-S769. 16p.
Subject Terms: *Pressurized water reactors, *Optimization algorithms, *Nuclear reactors, *Neutron transport theory, *Monte Carlo method, *Mathematical optimization
Abstract: This article presents the novel algorithmic developments and performance analysis of the GPU-optimized REActor Physics Monte Carlo (GREAPMC) graphical processing unit (GPU)–accelerated multigroup Monte Carlo (MC) code tailored specifically for pressurized water reactor simulations. GREAPMC tackles the thread divergence issue inherent in history-based neutron tracking on GPUs by introducing two new optimization strategies. The first novel approach dynamically replaces inactive particles with new ones during the execution of the transport loop, while the second strategy enhances efficiency by capping the history length during active cycles at a predefined maximum number of interactions. Subsequently, it sorts and invokes the kernel with only the surviving neutrons. The maximum number of interactions is automatically adjusted while considering cycle time during inactive cycles. Both methods significantly accelerate computation compared to MCS, a high-fidelity MC code developed at Ulsan National Institute of Science and Technology, with the latter approach demonstrating the most substantial acceleration. GREAPMC further enhances efficiency by adopting cell-based geometry modeling. This approach eliminates cell search overhead, ensuring consistent execution times even as the number of cells increases. Overall, these algorithmic developments in GREAPMC achieve substantial computational acceleration against MCS. A single GPU card in this study demonstrates performance equivalent to approximately 570 cores from the specific CPU model used. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 192155927
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Development of New GPU-Optimized Reactor Physics Monte Carlo Code GREAPMC.
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  Data: <searchLink fieldCode="AR" term="%22Rizwan+Ali%2C+Muhammad%22">Rizwan Ali, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> deokjung@unist.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Aygul%2C+Murat+Serdar%22">Aygul, Murat Serdar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Deokjung%22">Lee, Deokjung</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Nuclear+Science+%26+Engineering%22">Nuclear Science & Engineering</searchLink>. 2026 Suppl 1, Vol. 200, pS754-S769. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Pressurized+water+reactors%22">Pressurized water reactors</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Nuclear+reactors%22">Nuclear reactors</searchLink><br />*<searchLink fieldCode="DE" term="%22Neutron+transport+theory%22">Neutron transport theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article presents the novel algorithmic developments and performance analysis of the GPU-optimized REActor Physics Monte Carlo (GREAPMC) graphical processing unit (GPU)–accelerated multigroup Monte Carlo (MC) code tailored specifically for pressurized water reactor simulations. GREAPMC tackles the thread divergence issue inherent in history-based neutron tracking on GPUs by introducing two new optimization strategies. The first novel approach dynamically replaces inactive particles with new ones during the execution of the transport loop, while the second strategy enhances efficiency by capping the history length during active cycles at a predefined maximum number of interactions. Subsequently, it sorts and invokes the kernel with only the surviving neutrons. The maximum number of interactions is automatically adjusted while considering cycle time during inactive cycles. Both methods significantly accelerate computation compared to MCS, a high-fidelity MC code developed at Ulsan National Institute of Science and Technology, with the latter approach demonstrating the most substantial acceleration. GREAPMC further enhances efficiency by adopting cell-based geometry modeling. This approach eliminates cell search overhead, ensuring consistent execution times even as the number of cells increases. Overall, these algorithmic developments in GREAPMC achieve substantial computational acceleration against MCS. A single GPU card in this study demonstrates performance equivalent to approximately 570 cores from the specific CPU model used. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/00295639.2025.2502714
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: S754
    Subjects:
      – SubjectFull: Pressurized water reactors
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Nuclear reactors
        Type: general
      – SubjectFull: Neutron transport theory
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
    Titles:
      – TitleFull: Development of New GPU-Optimized Reactor Physics Monte Carlo Code GREAPMC.
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            NameFull: Rizwan Ali, Muhammad
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            NameFull: Aygul, Murat Serdar
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            NameFull: Lee, Deokjung
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            – D: 02
              M: 02
              Text: 2026 Suppl 1
              Type: published
              Y: 2026
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            – Type: issn-print
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              Value: 200
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            – TitleFull: Nuclear Science & Engineering
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