Brownlee, CarsonPatchett, JohnLo, Li-TaDeMarle, DavidMitchell, ChristopherAhrens, JamesHansen, Charles D.Hank Childs and Torsten Kuhlen and Fabio Marton2013-11-082013-11-082012978-3-905674-35-41727-348Xhttps://doi.org/10.2312/EGPGV/EGPGV12/051-060Large-scale analysis and visualization is becoming increasingly important as supercomputers and their simulations produce larger and larger data. These large data sizes are pushing the limits of traditional rendering algorithms and tools thus motivating a study exploring these limits and their possible resolutions through alternative rendering algorithms . In order to better understand real-world performance with large data, this paper presents a detailed timing study on a large cluster with the widely used visualization tools ParaView and VisIt. The software ray tracer Manta was integrated into these programs in order to show that improved performance could be attained with software ray tracing on a distributed memory, GPU enabled, parallel visualization resource. Using the Texas Advanced Computing Center's Longhorn cluster which has multi-core CPUs and GPUs with large-scale polygonal data, we find multi-core CPU ray tracing to be significantly faster than both software rasterization and hardware-accelerated rasterization in existing scientific visualization tools with large data.Categories and Subject Descriptors (according to ACM CCS): I.3.1 [Computer Graphics]: Graphics Systems- Distributed/network graphics