Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-Wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Abstract We present a software system that enables path‐traced rendering of complex scenes. The system consists of two primary components: an application layer that implements the basic rendering algorithm, and an out‐of‐core scheduling and data‐management layer designed to assist the application layer in exploiting hybrid computational resources (e.g., CPUs and GPUs) simultaneously. We describe the basic system architecture, discuss design decisions of the system's data‐management layer, and outline an efficient implementation of a path tracer application, where GPUs perform functions such as ray tracing, shadow tracing, importance‐driven light sampling, and surface shading. The use of GPUs speeds up the runtime of these components by factors ranging from two to twenty, resulting in a substantial overall increase in rendering speed. The path tracer scales well with respect to CPUs, GPUs and memory per node as well as scaling with the number of nodes. The result is a system that can render large complex scenes with strong performance and scalability.

Abstract We present two novel parallel algorithms for rapidly constructing bounding volume hierarchies on manycore GPUs. The first uses a linear ordering derived from spatial Morton codes to build hierarchies extremely quickly and with high parallel scalability. The second is a top‐down approach that uses the surface area heuristic (SAH) to build hierarchies optimized for fast ray tracing. Both algorithms are combined into a hybrid algorithm that removes existing bottlenecks in the algorithm for GPU construction performance and scalability leading to significantly decreased build time. The resulting hierarchies are close in to optimized SAH hierarchies, but the construction process is substantially faster, leading to a significant net benefit when both construction and traversal cost are accounted for. Our preliminary results show that current GPU architectures can compete with CPU implementations of hierarchy construction running on multicore systems. In practice, we can construct hierarchies of models with up to several million triangles and use them for fast ray tracing or other applications.

We demonstrate an efficient data-parallel algorithm for building large hash tables of millions of elements in real-time. We consider two parallel algorithms for the construction: a classical sparse perfect hashing approach, and cuckoo hashing, which packs elements densely by allowing an element to be stored in one of multiple possible locations. Our construction is a hybrid approach that uses both algorithms. We measure the construction time, access time, and memory usage of our implementations and demonstrate real-time performance on large datasets: for 5 million key-value pairs, we construct a hash table in 35.7 ms using 1.42 times as much memory as the input data itself, and we can access all the elements in that hash table in 15.3 ms. For comparison, sorting the same data requires 36.6 ms, but accessing all the elements via binary search requires 79.5 ms. Furthermore, we show how our hashing methods can be applied to two graphics applications: 3D surface intersection for moving data and geometric hashing for image matching.

This article presents resolution-matched shadow maps (RMSM), a modified adaptive shadow map (ASM) algorithm, that is practical for interactive rendering of dynamic scenes. Adaptive shadow maps, which build a quadtree of shadow samples to match the projected resolution of each shadow texel in eye space, offer a robust solution to projective and perspective aliasing in shadow maps. However, their use for interactive dynamic scenes is plagued by an expensive iterative edge-finding algorithm that takes a highly variable amount of time per frame and is not guaranteed to converge to a correct solution. This article introduces a simplified algorithm that is up to ten times faster than ASMs, has more predictable performance, and delivers more accurate shadows. Our main contribution is the observation that it is more efficient to forgo the iterative refinement analysis in favor of generating all shadow texels requested by the pixels in the eye-space image. The practicality of this approach is based on the insight that, for surfaces continuously visible from the eye, adjacent eye-space pixels map to adjacent shadow texels in quadtree shadow space. This means that the number of contiguous regions of shadow texels (which can be efficiently generated with a rasterizer) is proportional to the number of continuously visible surfaces in the scene. Moreover, these regions can be coalesced to further reduce the number of render passes required to shadow an image. The secondary contribution of this paper is demonstrating the design and use of data-parallel algorithms inseparably mixed with traditional graphics programming to implement a novel interactive rendering algorithm. For the scenes described in this paper, we achieve 60--80 frames per second on static scenes and 20--60 frames per second on dynamic scenes for 512 2 and 1024 2 images with a maximum effective shadow resolution of 32,768 2 texels.

This article presents Glift, an abstraction and generic template library for defining complex, random-access graphics processor (GPU) data structures. Like modern CPU data structure libraries, Glift enables GPU programmers to separate algorithms from data structure definitions; thereby greatly simplifying algorithmic development and enabling reusable and interchangeable data structures. We characterize a large body of previously published GPU data structures in terms of our abstraction and present several new GPU data structures. The structures, a stack, quadtree, and octree, are explained using simple Glift concepts and implemented using reusable Glift components. We also describe two applications of these structures not previously demonstrated on GPUs: adaptive shadow maps and octree three-dimensional paint. Last, we show that our example Glift data structures perform comparably to handwritten implementations while requiring only a fraction of the programming effort.