Bibliographic record
Abstract
Rendering performance of consumer graphics hardware benefits from pre-processing geometric data into a form targeted to the un-derlying API and hardware. The various elements of geometric data are then coupled with a shading program at runtime to draw the as-set. In this paper we describe a system in which pre-processing is done in a compilation process in which the geometric data are pro-cessed with knowledge of their shading programs. The data are converted into structures targeted directly to the hardware, and a code stream is assembled that describes the manipulations required to render these data structures. Our compiler is structured like a traditional code compiler, with a front end that reads the geomet-ric data and attributes (hereafter referred to as an art asset) output from a 3D modeling package and shaders in a platform indepen-dent form and performs platform-independent optimizations, and a back end that performs platform-specific optimizations and gener-ates platform-targeted data structures and code streams. Our compiler back-end has been targeted to four platforms, three of which are radically different from one another. On all platforms the rendering performance of our compiled assets, used in real sit-uations, is well above that of hand-coded assets.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".