In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection

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dc.contributor.authorWang, Zheen_US
dc.contributor.authorMoreland, Kennethen_US
dc.contributor.authorLarsen, Matthewen_US
dc.contributor.authorKress, Jamesen_US
dc.contributor.authorChilds, Hanken_US
dc.contributor.authorLi, Guanen_US
dc.contributor.authorShan, Guihuaen_US
dc.contributor.authorPugmire, Daviden_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorAndrienko, Nataliaen_US
dc.contributor.editorWang, Beien_US
dc.date.accessioned2025-05-26T06:36:44Z
dc.date.available2025-05-26T06:36:44Z
dc.date.issued2025
dc.description.abstractParticle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization-Over-Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization-over-data algorithm. With tightly coupled workload estimation and load-balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round-robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.en_US
dc.description.sectionheadersFlow Vis
dc.description.seriesinformationComputer Graphics Forum
dc.identifier.doi10.1111/cgf.70108
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70108
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70108
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Massively parallel algorithms
dc.subjectComputing methodologies → Massively parallel algorithms
dc.titleIn Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advectionen_US
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