State-of-the-Art Report on Optimizing Particle Advection Performance

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dc.contributor.authorYenpure, Abhisheken_US
dc.contributor.authorSane, Sudhanshuen_US
dc.contributor.authorBinyahib, Robaen_US
dc.contributor.authorPugmire, Daviden_US
dc.contributor.authorGarth, Christophen_US
dc.contributor.authorChilds, Hanken_US
dc.contributor.editorBruckner, Stefanen_US
dc.contributor.editorRaidou, Renata G.en_US
dc.contributor.editorTurkay, Cagatayen_US
dc.date.accessioned2023-06-10T06:28:21Z
dc.date.available2023-06-10T06:28:21Z
dc.date.issued2023
dc.description.abstractThe computational work to perform particle advection-based flow visualization techniques varies based on many factors, including number of particles, duration, and mesh type. In many cases, the total work is significant, and total execution time (''performance'') is a critical issue. This state-of-the-art report considers existing optimizations for particle advection, using two high-level categories: algorithmic optimizations and hardware efficiency. The sub-categories for algorithmic optimizations include solvers, cell locators, I/O efficiency, and precomputation, while the sub-categories for hardware efficiency all involve parallelism: shared-memory, distributed-memory, and hybrid. Finally, this STAR concludes by identifying current gaps in our understanding of particle advection performance and its optimizations.en_US
dc.description.documenttypestar
dc.description.number3
dc.description.sectionheadersVolumes and Particles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14858
dc.identifier.issn1467-8659
dc.identifier.pages517-537
dc.identifier.pages21 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14858
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14858
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleState-of-the-Art Report on Optimizing Particle Advection Performanceen_US
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