Decoupling Data Layouts from Bounding Volume Hierarchies

TL;DR

This paper introduces Scion, a DSL and compiler that decouples data layout design from traversal algorithms in bounding volume hierarchies, enabling architecture-agnostic optimization.

Contribution

It presents a novel language and compiler that allow independent specification and optimization of data layouts for BVHs, improving performance portability.

Findings

Pareto-optimal layouts vary across algorithms and architectures.

Scion can express a broad spectrum of layout optimizations.

A new ray tracing layout achieves Pareto-optimality across diverse scenarios.

Abstract

Bounding volume hierarchies are ubiquitous acceleration structures in graphics, scientific computing, and data analytics. Their performance depends critically on data layout choices that affect cache utilization, memory bandwidth, and vectorization -- increasingly dominant factors in modern computing. Yet, in most programming systems, these layout choices are hopelessly entangled with the traversal logic. This entanglement prevents developers from independently optimizing data layouts and algorithms across different contexts, perpetuating a false dichotomy between performance and portability. We introduce Scion, a domain-specific language and compiler for specifying the data layouts of bounding volume hierarchies independent of tree traversal algorithms. We show that Scion can express a broad spectrum of layout optimizations used in high-performance computing while remaining architecture-agnostic. We demonstrate empirically that Pareto-optimal layouts (along performance and memory footprint axes) vary across algorithms, architectures, and workload characteristics. Through systematic design exploration, we also identify a novel ray tracing layout that combines optimization techniques from prior work, achieving Pareto-optimality across diverse architectures and scenes.