Graphics rendering applications increasingly leverage neural networks in tasks such as denoising, supersampling, and frame extrapolation to improve image quality while maintaining frame rates. The temporal coherence inherent in these tasks presents an opportunity to reuse intermediate results from previous frames and avoid redundant computations. Recent work has shown that caching intermediate features to be reused in subsequent inferences is an effective method to reduce latency in diffusion models. We extend this idea to real-time rendering and present ReFrame, which explores different caching policies to optimize trade-offs between quality and performance in rendering workloads. ReFrame can be applied to a variety of encoder-decoder style networks commonly found in rendering pipelines. Experimental results show that we achieve 1.4$\times$ speedup on average with negligible quality loss in three real-time rendering tasks. Code available: https://ubc-aamodt-group.github.io/reframe-layer-caching/

Tree traversal is a fundamental operation in many applications, such as database indexing and physics simulations. Although tree traversals feature high parallelism, they are inherently divergent and irregular, leading to inefficient performance on GPUs. Tree traversals are also prevalent in ray tracing, which is executed on dedicated Ray-Tracing Accelerators (RTAs) in modern GPUs to mitigate inefficiencies such as control flow divergence and underutilization of memory bandwidth by irregular memory accesses. In this paper, we propose the Tree Traversal Accelerator (TTA) to replicate the success of RTAs in ray tracing for general tree traversal applications. TTAs extend RTAs to support tree structures and operations beyond those in ray tracing, such as B- Tree search and radius search algorithms, by modifying existing computing units. Despite TTAs' effectiveness, they still rely on fixed-function computations, making it challenging to support other tree-based applications such as N-Body simulation fully. Thus, we introduce TTA + as an alternative design, which modularizes the RTA computing units and makes them programmable, trading some efficiency for flexibility. With less than 1 % increase in RTA area, our proposals can achieve up to S.4x speedup for B-Tree search, 1.7x for N-Body simulation, and 1.2x for select ray-tracing applications.

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.

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%.

A reduction in the time it takes to train machine learning models can be translated into improvements in accuracy. important factor that increases training time in deep neural networks (DNNs) is the need to store large amounts of temporary data during the back-propagation algorithm. To enable training very large models this temporary data can be offloaded from limited size GPU memory to CPU memory but this data movement incurs large performance overheads. We observe that in one important class of DNNs, convolutional neural networks (CNNs), there is spatial correlation in these temporary values. We propose JPEG for ACTivations (JPEGACT), a lossy activation offload accelerator for training CNNs that works by discarding redundant spatial information. JPEGACT adapts the well-known JPEG algorithm from 2D image compression to activation compression. We show how to optimize the JPEG algorithm so as to ensure convergence and maintain accuracy during training. JPEG-ACT achieves 2.4× higher training performance compared to prior offload accelerators, and 1.6× compared to prior activation compression methods. An efficient hardware implementation allows JPEG-ACT to consume less than 1% of the power and area of a modern GPU.