Zatel: Sample Complexity-Aware Scale-Model Simulation for Ray Tracing
Bibliographic record
Abstract
Ray tracing is a computationally intensive rendering technique that simulates the behavior of light rays as they interact with objects in a scene. It is becoming increasingly popular in video games and is already the de facto standard for animated movies. However, current hardware still struggles to efficiently ray trace complex scenes and requires further research. To evaluate early-stage hardware proposals that accelerate ray tracing for G PU s, one either uses cycle-accurate simulators, which are highly accurate and flexible but slow, or other models that are an order of magnitude faster but provide limited output with high error margins. In this paper, we propose Zatel, a prediction methodology for evaluating GPU performance on ray tracing workloads. We observe that the desired metrics can be estimated with reasonable accuracy by only tracing a representative subset of pixels. Furthermore, the parallel nature of GPUs allows us to split the scene into chunks, which lets Zatel execute faster using downscaled GPU configurations. We incorporate these two optimization steps into Zatel and evaluate it on a benchmark suite for ray tracing using Vulkan-Sim, a cycle-accurate simulator. By relying on Vulkan-Sim, architectural changes are captured through the simulator, and Zatel does not need to be updated to support each change. Zatel records less than 1 % error with 10 x simulation time speedup for measuring simulation cycles on a mobile G PU.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".