HETEROGENEOUS DESIGN AND EFFICIENT CPU-GPU IMPLEMENTATION OF COLLISION DETECTION
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
Collison detection is a wide-ranging real-world application. It is one of the key components used in gaming, simulation and animation. Efficient algorithms are required for collision detection as it is repeatedly executed throughout the course of an application. Moreover, due to its computationally intensive nature researchers are investigating ways to reduce its execution time. This paper furthers those research works by devising a parallel CPU-GPU implementation of both broad and narrow phase collision detection with heterogenous workload sharing. An important aspect of co-scheduling is to determine an optimal CPU-GPU partition ratio. We also showcase a successive approximation approach for CPU-GPU implementation of collision detection. The paper demonstrates that the framework is not only applicable to CPU/GPU systems but to other system configuration obtaining a peak performance improvement in the range of 18%.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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