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The design emphasis for supercomputing systems has moved from raw performance to performance-per-watt, and as a result, supercomputing architectures are converging on processor with wide vector units and many processing cores per chip. Such processors are capable of performant image rendering purely in software. This improved capability is fortuitous, since the prevailing homogeneous system designs lack dedicated, hardware-accelerated rendering subsystems for use in data visualization. Reliance on this ”software-defined” rendering capability will grow in importance since, due to growing data sizes, visualizations must be performed on the same machine where the data is produced. Further, as data sizes outgrow disk I/O capacity, visualization will be increasingly incorporated into the simulation code itself (in situ visualization).
Fortunately, a number of recently-developed software packages capture the improved rendering performance of general-purpose processors and expose that performance through common visualization toolkits. Among these packages are those that render via rasterization, the algorithm used in hardware accelerators, as well as those that render via ray tracing, an algorithm that physically simulates the travel of light. To produce insightful visualizations most efficiently, the visualization scientist should understand the capabilities, performance characteristics and precision limits of each rendering method.
In this half-day introductory tutorial, we present a primer on rasterization and ray tracing and an overview of (mostly) open-source software packages available to the open-science community, as well as hands-on experience with the fundamental techniques. We begin with a brief background of terms and concepts to ensure that all participants have a working knowledge of the material covered in the remainder of the tutorial. We then motivate the concepts through three application lightning talks that demonstrate the use of rasterization and ray tracing in actual domain applications. Finally, participants will apply the concepts in guided hands-on visualization labs using the TACC XSEDE resources Stampede and Maverick.
