Buelow, Max vonRiemann, KaiGuthe, StefanFellner, Dieter W.Bujack, RoxanaTierny, JulienSadlo, Filip2022-06-022022-06-022022978-3-03868-175-51727-348Xhttps://doi.org/10.2312/pgv.20221061https://diglib.eg.org:443/handle/10.2312/pgv20221061Graphical 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.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Graphics processors; Theory of computation --> Program analysisHuman centered computingVisual analyticsComputing methodologiesGraphics processorsTheory of computationProgram analysis10.2312/pgv.202210617-1711 pages