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

Particle 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 pr...

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Veröffentlicht in:Computer graphics forum Jg. 44; H. 3
Hauptverfasser: Wang, Zhe, Moreland, Kenneth, Larsen, Matthew, Kress, James, Childs, Hank, Li, Guan, Shan, Guihua, Pugmire, David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.06.2025
Schlagworte:
Advection
Algorithms
CCS Concepts
Computing methodologies → Massively parallel algorithms
Parallel processing
Workflow
Workload
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Zusammenfassung:Particle 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.70108