Bibliographic record
Abstract
We present an algorithm which integrates flow control and dynamic load balancing in Time Warp. The algorithm is intended for use in a distributed memory environment. Our flow control algorithm makes use of stochastic learning automata and is similar to the leaky-bucket flow control algorithm used in computer networks. It regulates the flow of messages between processors continously throughout the course of the simulation, while the dynamic load balancing algorithm is invoked only when a load imbalance is detected. We compare the perfomance of the flow control algorithm, the dynamic load balancing algorithm and the integrated algorithm with that of a simulation without these controls. We simulated large shuffle ring networks with and without hot spots and a PCS network on an SGI Origin 2000 system. Our results indicate that the flow control scheme alone succeeds in greatly reducing the number and length of rollbacks as well as the number of anti-messages, thereby increasing the number of non-rolledback messages processed per second. It results in a large reduction in the amount of memory used and outperforms the dynamic load balancing algorithm for these measures. The integrated scheme produces even better results for all of these measures and results in reduced execution times.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".