Bibliographic record
Abstract
Despite advances in hardware acceleration, ray tracing use in real-time rendering is limited and often lowers frame rates, leading users such as video game players to disable the feature entirely. Prior work has shown that dividing the BVH tree into smaller subtrees (treelets) and traversing all rays that visit a treelet before switching treelets can significantly reduce memory traffic on a specialized accelerator, but there are many challenges to applying treelets to GPUs. We find that a naive treelet implementation is ineffective and propose optimizations to improve performance. Virtualized Treelet Queues consist of two main components. Ray virtualization increases the number of concurrent rays in flight to create more cache reuse opportunities by terminating raygen shaders that have already issued their trace ray instruction, reclaiming CUDA cores and allowing more raygen shaders to be executed. To take advantage of the increased concurrent rays, we propose a dynamic treelet queue architecture that dynamically switches between traversal modes to increase efficiency. We also find that performing warp repacking boosts SIMT efficiency of warps in the RT unit which is crucial to achieving good traversal performance with treelet queues. Our simulations show virtualized treelet queues achieve on average 95% speedup compared to a baseline GPU with ray tracing acceleration across all scenes in LumiBench rendered with path tracing at one sample per pixel with three max bounces per ray.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".