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An Asynchronous Many-Task Algorithm for Unstructured SN Transport on Shared Memory Systems.

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Title: An Asynchronous Many-Task Algorithm for Unstructured SN Transport on Shared Memory Systems.
Authors: Elwood, Alex1 (AUTHOR) alex.elwood@bristol.ac.uk, Deakin, Tom1 (AUTHOR), Lovegrove, Justin2 (AUTHOR), Nelson, Chris3 (AUTHOR)
Source: Journal of Computational & Theoretical Transport. 2026, Vol. 55 Issue 4, p311-334. 24p.
Subjects: Parallel programming, Multicore processors, Neutron transport theory, Numerical calculations, Multiprocessors
Abstract: Discrete ordinates S N transport solvers on unstructured meshes pose a challenge to scale due to complex data dependencies, memory access patterns and a high-dimensional domain. In this paper, we review the performance bottlenecks within the shared memory parallelization scheme of an existing transport solver on modern many-core architectures with high core counts. With this analysis, we then survey the performance of this solver across a variety of compute hardware. We then present a new Asynchronous Many-Task (AMT) algorithm for shared memory parallelism, present results showing an increase in computational performance over the existing method, and evaluate why performance is improved. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computational & Theoretical Transport is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: An Asynchronous Many-Task Algorithm for Unstructured S<subscript>N</subscript> Transport on Shared Memory Systems.
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  Data: <searchLink fieldCode="AR" term="%22Elwood%2C+Alex%22">Elwood, Alex</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alex.elwood@bristol.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Deakin%2C+Tom%22">Deakin, Tom</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lovegrove%2C+Justin%22">Lovegrove, Justin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nelson%2C+Chris%22">Nelson, Chris</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+%26+Theoretical+Transport%22">Journal of Computational & Theoretical Transport</searchLink>. 2026, Vol. 55 Issue 4, p311-334. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Multicore+processors%22">Multicore processors</searchLink><br /><searchLink fieldCode="DE" term="%22Neutron+transport+theory%22">Neutron transport theory</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+calculations%22">Numerical calculations</searchLink><br /><searchLink fieldCode="DE" term="%22Multiprocessors%22">Multiprocessors</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Discrete ordinates S N transport solvers on unstructured meshes pose a challenge to scale due to complex data dependencies, memory access patterns and a high-dimensional domain. In this paper, we review the performance bottlenecks within the shared memory parallelization scheme of an existing transport solver on modern many-core architectures with high core counts. With this analysis, we then survey the performance of this solver across a variety of compute hardware. We then present a new Asynchronous Many-Task (AMT) algorithm for shared memory parallelism, present results showing an increase in computational performance over the existing method, and evaluate why performance is improved. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Computational & Theoretical Transport is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/23324309.2026.2641013
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        Text: English
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        Type: general
      – SubjectFull: Multicore processors
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      – SubjectFull: Neutron transport theory
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              M: 07
              Text: 2026
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