Scalable sort-first parallel direct volume rendering with dynamic load balancing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We describe a sort-first algorithm for parallel direct volume rendering on GPUs, with the intent of high scalability in regards to both performance and data set size. We explore three novel techniques for estimating the computation time for rendering each pixel, so that we can guarantee a good load balancing regardless of the level of frame-to frame coherence. A bricking technique is used to subdivide the object space, thus allowing each rendering node to load only the bricks of data that are needed to render their respective portions of the image space. This enables us to render data sets larger than an individual GPU's texture memory. We cull bricks that do not contribute to the final image in order to reduce the data that is loaded and provide a coarse method of empty space leaping. We introduce a novel method of eliminating the overhead of generating vertices for the proxy geometry of each brick, by creating a single template of vertices that are used to render all bricks of the same size. Finally, detailed performance measurements document the various aspects of our algorithm.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it