On Ray Reordering Techniques for Faster GPU Ray Tracing
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.