Minimizing Ray Tracing Memory Traffic through Quantized Structures and Ray Stream Tracing (2505.24653v1)

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.

• The paper introduces a novel memory reduction technique using 8-bit quantization of BVHs and triangle data to significantly lower ray tracing memory traffic.
• The approach achieves notable efficiency with over 57% reduction in node size and memory traffic dropping to as low as 18% compared to uncompressed methods.
• Integrating ray stream tracing minimizes traversal stack overhead, enabling fixed-point operations without decompression delays for efficient processing.

This paper, "Minimizing Ray Tracing Memory Traffic through Quantized Structures and Ray Stream Tracing" (2505.24653), addresses the significant memory bandwidth bottleneck in ray tracing, particularly for complex scenes where computational capabilities outpace memory access speeds. The authors propose a memory-efficient methodology that combines compressed data structures with ray stream tracing to substantially reduce memory traffic.

The core of the approach involves two main components:

1. Compressed Data Structures: Both Bounding Volume Hierarchies (BVHs) and triangle geometry are represented using compressed formats. This is achieved by employing 8-bit fixed-point representations within local coordinate systems defined for each BVH node.
   • Local Coordinate Systems: Each node defines an origin (full-precision integer) and power-of-two scale factors (8-bit exponents) for each axis.
   • Quantization of Bounds: Child node bounding box coordinates are quantized to 8-bit fixed-point values relative to the parent node's local origin and scale factors. Conservative rounding (down for min, up for max) ensures hierarchical containment.
   • Quantization of Triangles: Triangle vertices in leaf nodes are also quantized to 8-bit fixed-point using the leaf node's local coordinate system. This reduces a triangle's vertex data storage from 36 bytes (with 32-bit floats) to just 9 bytes. Other vertex attributes are stored separately.
   • Hole-Free Meshes: To avoid geometric holes between adjacent triangles residing in different leaf nodes with potentially different quantization scales, the largest scale factors among all leaf nodes are propagated upwards and broadcast to all leaf nodes. This ensures a consistent minimum precision across the entire geometry, although it means scenes with large triangles might force coarser quantization overall.
   • Memory Layout: A standard 8-wide BVH node using 32-bit floats might require 228 bytes. The compressed node structure, storing 8-bit bounds, 32-bit child/primitive indices, a 32-bit origin, and 8-bit scale exponents, is reduced to 96 bytes (over 57% reduction). Smaller node widths (2-wide, 4-wide) also show significant reductions (approx. 44% and 52% respectively), with efficiency improving with higher branching factors.
   • Ray Quantization: Rays themselves are also quantized to reduce memory footprint during ray stream processing. The ray direction is encoded using an octahedral mapping technique, compressing it to a 4-byte unsigned integer. Ray origin uses a fixed-point format. A ray occupies 32 bytes (16 bytes intersection record + 12 bytes origin + 4 bytes compressed direction).
2. Fixed-Point Traversal and Intersection: Ray traversal and intersection tests are performed directly on these quantized fixed-point data structures without decompression overhead.
   • Ray-Box Intersection: An adapted slabs method is used, operating on quantized bounds with fixed-point arithmetic. Explicit handling for zero ray direction components (common in fixed-point due to limited precision) is required.
   • Ray-Triangle Intersection: An edge-function based algorithm is adapted for fixed-point arithmetic. Intermediate results for cross and dot products are maintained in full fixed-point precision until the intersection decision point to avoid numerical issues and guarantee watertightness along shared edges. A precision analysis shows that maintaining intermediate results might theoretically require up to 64 bits plus a sign bit, although practical requirements are often lower.
3. Ray Stream Tracing Integration: The compressed data structures are integrated with ray stream tracing techniques. Ray streams group multiple rays processed together against the BVH. This approach reduces traversal stack memory usage by sharing traversal state across rays and amortizes node fetch costs. When combined with the compressed node and ray representations, this further reduces memory traffic.

The authors evaluate their approach using diverse scenes with varying complexity and compare configurations across 2-wide, 4-wide, and 8-wide BVHs, with and without compression, and using single-ray versus ray stream traversal.

Evaluation and Results:

• Memory Reduction: The compressed triangle representation reduces geometry storage significantly (e.g., from 135 MiB to 34 MiB for the Viking scene). Compressed BVH nodes also show substantial size reductions (e.g., 8-wide BVH for Viking scene reduced by 160 MiB).
• Memory Traffic: Memory traffic was measured for different components (bounds, triangles, ray lists, traversal stacks). The configurations combining ray stream tracing with compression (BVH4-RS-C and BVH8-RS-C) consistently achieved the lowest total memory traffic across scenes. For the BVH8-RS-C configuration, the memory traffic was reduced to as low as 18% of the respective uncompressed single-ray version (BVH8-SR-U). The advantage of BVH4-RS-C and BVH8-RS-C is more pronounced for scenes with higher triangle counts.
• Visual Quality: Quantization introduces subtle geometric distortions (discretized edges, small displacements). These artifacts are more visible at lower ray precision settings. The accuracy depends on the leaf-level precision, which is limited by the largest triangles in the scene due to the precision propagation mechanism. Pre-subdividing meshes with large triangles can mitigate this. Despite artifacts, the fixed-point approach avoids geometric holes common in floating-point methods due to careful precision handling and conservative rounding.

Limitations and Future Work:

• The visual quality is impacted by the quantization granularity, which is constrained by the largest triangles in the scene. Mesh preprocessing (subdivision) could improve this.
• BVH construction time is not evaluated; the approach uses a post-processing step on Embree BVHs.
• The evaluation is a CPU simulation; performance on actual specialized hardware units designed for fixed-point operations needs to be assessed.
• The fixed-point intersection units might require high bit widths for intermediate calculations (up to 64 bits). Transforming ray origins to low-precision leaf node space before intersection could potentially reduce hardware complexity.

Conclusion:

The paper demonstrates that a combination of quantized BVHs and triangles with ray stream tracing significantly reduces memory traffic in ray tracing, offering memory savings crucial for bandwidth-constrained environments. The proposed fixed-point approach avoids geometric holes, and while visual artifacts exist, they can be managed with appropriate precision settings or mesh preprocessing. The results suggest that future hardware or API designs could leverage these techniques for more efficient ray tracing.