Ray tracing can generate photorealistic images with more convincing visual effects compared to rasterization. Recent hardware advances have enabled ray tracing to be applied in real-time. Current GPUs feature a dedicated ray tracing acceleration unit, and game developers have started to make use of ray tracing APIs to bring more realistic graphics to their players. Industry cooperatively contributed to Vulkan, which recently introduced an open-standard API for ray tracing. However, little has been disclosed about the mapping of this API to hardware. In this paper, we introduce Vulkan-Sim, a detailed cycle-level simulator for enabling architecture research for ray tracing. We extend GPGPU-Sim, integrating it with Mesa, an open-source graphics library to support the Vulkan API, and add dedicated ray traversal and intersection units. We also demonstrate an explicit mapping of the Vulkan ray tracing pipeline to a modern GPU using a technique we call delayed intersection and any-hit execution. Additionally we evaluate several ray tracing workloads with Vulkan-Sim, identifying bottlenecks and inefficiencies of the ray tracing hardware we model. To demonstrate the utility of Vulkan-Sim we conduct two case studies evaluating techniques recently proposed or deployed by industry targeting enhanced ray tracing performance.

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%.