Minimizing Ray Tracing Memory Traffic through Quantized Structures and Ray Stream Tracing

TL;DR

This paper introduces a memory-efficient ray tracing method combining compressed data structures and ray stream techniques, significantly reducing memory traffic and enabling better performance on bandwidth-limited hardware.

Contribution

It presents a novel integrated approach using quantized BVH and ray stream tracing with fixed-point arithmetic to reduce memory traffic and improve numerical stability.

Findings

Memory traffic reduced to 18% of traditional methods

Quantized BVH enables efficient ray tracing on bandwidth-limited hardware

Fixed-point arithmetic avoids geometric holes despite reduced precision

Abstract

Memory bandwidth constraints continue to be a significant limiting factor in ray tracing performance, particularly as scene complexity grows and computational capabilities outpace memory access speeds. This paper presents a memory-efficient ray tracing methodology that integrates compressed data structures with ray stream techniques to reduce memory traffic. The approach implements compressed BVH and triangle representations to minimize acceleration structure size in combination with ray stream tracing to reduce traversal stack memory traffic. The technique employs fixed-point arithmetic for intersection tests for prospective hardware with tailored integer operations. Despite using reduced precision, geometric holes are avoided by leveraging fixed-point arithmetic instead of encountering the floating-point rounding errors common in traditional approaches. Quantitative analysis demonstrates significant memory traffic reduction across various scene complexities and BVH configurations. The presented 8-wide BVH ray stream implementation reduces memory traffic to only 18% of traditional approaches by using 8-bit quantization for box and triangle coordinates and directly ray tracing these quantized structures. These reductions are especially beneficial for bandwidth-constrained hardware environments such as mobile devices. This integrated approach addresses both memory bandwidth limitations and numerical precision challenges inherent to modern ray tracing applications.