While there are mature performance monitoring, profiling and instrumentation tools to help understanding the dynamic behaviour of general-purpose GPU applications, the abstract programming models of graphics applications have limited the development of such tools for graphics. This paper introduces Vulkan Vision (V- Vision), a framework for collecting detailed GPU execution data from Vulkan applications to guide hardware-informed improvements. A core contribution of V- Vision is providing out-of-the-box data collection for capturing complete dynamic warp and thread execution traces. V- Vision also provides analyses for the follow purposes: identifying and visualizing application hotspots to guide optimization, characterizing application behaviour and estimating the effect of architectural modifications. This paper demonstrates the potential for these analyses in applications that utilize the recent ray-tracing extension in Vulkan and describes new insights about the applications and the underlying hardware.

Ray tracing has been used for years in motion picture to generate photorealistic images while faster raster-based shading techniques have been preferred for video games to meet real-time requirements. However, recent Graphics Processing Units (GPUs) incorporate hardware accelerator units designed for ray tracing. These accelerator units target the process of traversing hierarchical tree data structures used to test for ray-object intersections. Distinct rays following similar paths through these structures execute many redundant ray-box intersection tests. We propose a ray intersection predictor that speculatively elides redundant operations during this process and proceeds directly to test primitives that the ray is likely to intersect. A key aspect of our predictor strategy involves identifying hash functions that preserve enough spatial information to identify redundant traversals. We explore how to integrate our ray prediction strategy into existing GPU pipelines along with improving the predictor effectiveness by predicting nodes higher in the tree as well as regrouping and scheduling traversal operations in a low cost, judicious manner. On a mobile class GPU with a ray tracing accelerator unit, we find the addition of a 5.5KB predictor per streaming multiprocessor improves performance for ambient occlusion workloads by a geometric mean of 26%.

In response to recent studies on enrollment trends and our own assessment results, we have significantly modified our Computer Science major to include not only a traditional major in computer science but also to include concentrations in Business Information Systems and Graphic Design and Implementation. As we are a small liberal arts college with three faculty members and a small budget, we have partnered with other disciplines on campus to provide options for our majors to apply concepts and principles of computer science to other areas. We present here our plans of study for the three concentrations, our rationale for making these additions, and favorable responses from students, faculty, and administration.