Generalizing Ray Tracing Accelerators for Tree Traversals on GPUs
Bibliographic record
Abstract
Tree traversal is a fundamental operation in many applications, such as database indexing and physics simulations. Although tree traversals feature high parallelism, they are inherently divergent and irregular, leading to inefficient performance on GPUs. Tree traversals are also prevalent in ray tracing, which is executed on dedicated Ray-Tracing Accelerators (RTAs) in modern GPUs to mitigate inefficiencies such as control flow divergence and underutilization of memory bandwidth by irregular memory accesses. In this paper, we propose the Tree Traversal Accelerator (TTA) to replicate the success of RTAs in ray tracing for general tree traversal applications. TTAs extend RTAs to support tree structures and operations beyond those in ray tracing, such as B- Tree search and radius search algorithms, by modifying existing computing units. Despite TTAs' effectiveness, they still rely on fixed-function computations, making it challenging to support other tree-based applications such as N-Body simulation fully. Thus, we introduce TTA + as an alternative design, which modularizes the RTA computing units and makes them programmable, trading some efficiency for flexibility. With less than 1 % increase in RTA area, our proposals can achieve up to S.4x speedup for B-Tree search, 1.7x for N-Body simulation, and 1.2x for select ray-tracing applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".