Two-phase load distribution for rendering large 3D models on a graphics cluster
Bibliographic record
Abstract
In this paper we address the problem of distributing rendering computations for real-time display of very large 3D models using a graphics cluster. With a programmable graphics processing unit (GPU) in each node, rendering computations are increasingly carried out in two phases using two separate GPU programs: a vertex shader program for vertex (geometry) processing and a fragment shader program for pixel (color) processing. With fragment shader programs becoming more and more time consuming for increased realism and special visual effects, distributing load solely based on geometry as is done in most contemporary systems can cause significant load imbalance. There is often only a weak correlation between geometry and pixel data distribution, due to multiple factors such as occlusion of objects behind, by objects in front. Clearly, load balancing for geometry processing or pixel processing alone is not optimal. In this paper, we present a novel in-frame two-phase load-balancing technique that distributes data first for geometry and then for pixel processing. The technique is implemented on a graphics cluster and experimental results demonstrate considerable improvements in rendering performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".