Profiling and Visualizing GPU Memory Access and Cache Behavior of Ray Tracers

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dc.contributor.authorBuelow, Max vonen_US
dc.contributor.authorRiemann, Kaien_US
dc.contributor.authorGuthe, Stefanen_US
dc.contributor.authorFellner, Dieter W.en_US
dc.contributor.editorBujack, Roxanaen_US
dc.contributor.editorTierny, Julienen_US
dc.contributor.editorSadlo, Filipen_US
dc.date.accessioned2022-06-02T14:36:49Z
dc.date.available2022-06-02T14:36:49Z
dc.date.issued2022
dc.description.abstractGraphical processing units (GPUs) have gained popularity in recent years due to their efficiency in running massively parallel applications. Recent developments have also adapted ray-tracing algorithms to the GPU, where the bottleneck in the overall performance is usually given by the memory bandwidth. In this paper, we present an interactive, web-based visualization tool for GPU memory traces that provides visual insight into the memory and cache behavior of our reference ray tracer, by mapping internal GPU state back onto 3D objects. In order to visualize cache behavior, we use reuse distances on both GPU cache layers that are calculated on the basis of memory traces extracted from a real GPU using binary instrumentation. An advantage of our system is that it runs independently of the ray-tracing program. We further show visualizations of our GPU ray tracer and compare the visualizations of several ray-tracing approaches. We find our work to act as a convenient toolset to gather insights on which data structures and mesh regions can be cached efficiently, and how ray-tracing acceleration structures behave on various input meshes, bounding volume hierarchies, memory layouts, frame buffer resolutions, and work distribution techniques.en_US
dc.description.sectionheadersRendering and Simulation
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20221061
dc.identifier.isbn978-3-03868-175-5
dc.identifier.issn1727-348X
dc.identifier.pages7-17
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.2312/pgv.20221061
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20221061
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Graphics processors; Theory of computation --> Program analysis
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectComputing methodologies
dc.subjectGraphics processors
dc.subjectTheory of computation
dc.subjectProgram analysis
dc.titleProfiling and Visualizing GPU Memory Access and Cache Behavior of Ray Tracersen_US
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