Interactive Distributed Rendering of 3D Scenes on Multiple Xbox 360 Systems and Personal Computers
Bibliographic record
Abstract
In interactive visualization environments which use a multiple screen setup, every output device has to be supplied frequently with video information. Such virtual environments often use large projection screens, which require high resolution video data. When increasing scene complexity, it can become a challenge to equip a single computer with graphics hardware powerful enough for the task. An efficient approach is to distribute the workload to multiple low-cost computer systems such as game consoles. Nowadays' game consoles are very powerful and specialized for interactive graphics applications, therefore they are suitable to being used for rendering purposes. A framework has been developed that builds on Microsoft's XNA Game Studio. It enables for interactive distributed rendering on multiple Xbox 360 systems and PCs. Tasks such as game logic synchronization, network session management are fully handled by the framework. A game built with it can focus mostly on its own implementation. The framework's structure follows that of the XNA Game Studio, which allows porting existing game projects quickly. Our evaluation showed that the framework is a lightweight solution which leaves almost the full CPU time to the actual game.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".