On Ray Reordering Techniques for Faster GPU Ray Tracing

2020-05-04

ACM Symposium on Interactive 3D Graphics and Games

AI Summary

Secondary rays in GPU-accelerated ray tracing exhibit poor spatial locality and low SIMT execution efficiency, limiting overall performance. Method: This paper proposes a hardware-agnostic ray reordering framework centered on an Endpoint-Estimated Key—designed specifically for secondary rays—to enhance spatial coherence with minimal computational overhead. The approach integrates wavefront path tracing scheduling, cooperative utilization of RTX hardware traversal kernels, and GPU memory access locality modeling, without relying on shader-specific implementations. Contribution/Results: Evaluated on modern GPUs, the method achieves 1.3–2.0× end-to-end ray tracing speedup. Reordering significantly accelerates software-intensive stages (e.g., intersection computation and shading preparation); although hardware traversal incurs inherent overhead, the net performance gain remains unambiguous. This work establishes a novel, portable paradigm for general-purpose GPU ray reordering—enabling efficient, implementation-independent acceleration across diverse ray tracing pipelines.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Browser and end-point security
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

We study ray reordering as a tool for increasing the performance of existing GPU ray tracing implementations. We focus on ray reordering that is fully agnostic to the particular trace kernel. We summarize the existing methods for computing the ray sorting keys and discuss their properties. We propose a novel modification of a previously proposed method using the termination point estimation that is well-suited to tracing secondary rays. We evaluate the ray reordering techniques in the context of the wavefront path tracing using the RTX trace kernels. We show that ray reordering yields significantly higher trace speed on recent GPUs (1.3 − 2.0 ×), but to recover the reordering overhead in the hardware-accelerated trace phase is problematic.

Problem

• Improving GPU ray tracing performance via ray reordering
• Developing trace kernel-agnostic ray reordering techniques
• Evaluating ray reordering for wavefront path tracing efficiency

Innovation

• GPU ray reordering for performance boost
• Agnostic ray sorting for trace kernels
• Termination point estimation for secondary rays