Memory management techniques for time warp on a distributed memory machine
Bibliographic record
Abstract
This paper examines memory management issues associated with time warp synchronized parallel simulation on distributed memory machines. The paper begins with a summary of the techniques which have been previously proposed for memory management on various parallel processor memory structures. It then concentrates the discussion on parallel simulation executing on a distributed memory computer-a system comprised of separate computers, interconnected by a communications network. An important characteristic of the software developed for such systems is the fact that the dynamic memory is allocated from a pool of memory that is shared by all of the processes at a given processor. This paper presents a new memory management protocol, pruneback, which recovers space by discarding previous states. This is different from all previous schemes such as artificial rollback and cancelback which recover memory space by causing one or more logical processes to roll back to an earlier simulation time. The paper includes an empirical study of a parallel simulation of a closed stochastic queueing network showing the relationship between simulation execution time and amount of memory available. The results indicate that using pruneback is significantly more effective than artificial rollback (adapted for a distributed memory computer) for this problem. In the study, varying the memory limits over a 2:1 range resulted in a 1:2 change in artificial rollback execution time and almost no change in pruneback execution time.>
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".