Bibliographic record
Abstract
In this paper, we present a family of three algorithms which serve to perform checkpoints and to roll back time warp. These algorithms are primarily intended for use in simulations in which there are a large number of LPs and in which events have a small computational granularity. Important representatives of this class are VLSI and computer network simulations. In each of our algorithms, LPs are gathered into clusters via algorithms which are application dependent. In order to examine the performance of our algorithms and to compare them to Time Warp, we made use of two of the largest digital logic circuits available from the ISCAS89 benchmark series of combinational circuits. The execution time, number of states saved, and maximal memory consumption were compared to the same quantities for time warp. Our results indicated that each of the algorithms occupies a different point in the spectrum of possible trade-offs between memory usage and execution time, ranging from substantial memory savings (at a comparable cost in speed) to memory savings and a comparable speed to time warp. Hence, an important benefit of our algorithms is the ability to trade off memory requirements with 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 | 0.005 |
| 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) | 0.002 | 0.001 |
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".