Shader-driven compilation of rendering assets
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
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.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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