Statistical Analysis of Parallel Data Uploading using OpenGL

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dc.contributor.authorWiedemann, Markusen_US
dc.contributor.authorKranzlmüller, Dieteren_US
dc.contributor.editorChilds, Hank and Frey, Steffenen_US
dc.date.accessioned2019-06-02T18:26:08Z
dc.date.available2019-06-02T18:26:08Z
dc.date.issued2019
dc.description.abstractModern real-time visualizations of large-scale datasets require constant high frame rates while their datasets might exceed the available graphics memory. This requires sophisticated upload strategies from host memory to the memory of the graphics cards. A possible solution uses outsourcing of all data uploads onto concurrent threads and disconnecting prohibitive data dependencies. OpenGL provides a variety of functions and parameters but not all allow minimal interference on rendering. In this work, we present a thorough and statistically sound analysis of various effects introduced by choosing different input parameters, such as size, partitioning and number of threads for uploading, as well as combinations of buffer usage hints and uploading functions. This approach provides insight into the problem and offers a basis for future optimizations.en_US
dc.description.sectionheadersSession 4
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20191114
dc.identifier.isbn978-3-03868-079-6
dc.identifier.issn1727-348X
dc.identifier.pages101-108
dc.identifier.urihttps://doi.org/10.2312/pgv.20191114
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20191114
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectModel development and analysis
dc.subjectComputer graphics
dc.subjectParallel algorithms
dc.titleStatistical Analysis of Parallel Data Uploading using OpenGLen_US
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EGPGV19: Eurographics Symposium on Parallel Graphics and Visualization