Bibliographic record
Abstract
Discrete Event System Specification (DEVS) is a sound formalism to describe generic dynamic systems in a hierarchical and modular way. Cell-DEVS is a DEVS-based formalism intended to model compleX physical systems as cell spaces. This work presents new techniques for eXecuting DEVS and Cell-DEVS models in parallel and distributed environments based on the WARPED kernel, an implementation of the Time Warp protocol. The optimistic simulator PCD++, built as a new simulation engine for CD++, is a toolkit that implements the DEVS and Cell-DEVS formalisms. We redesign algorithms in CD++ to carry out optimistic simulations using a non-hierarchical approach that reduces the communication overhead. The message-passing organization is analyzed using a high-level abstraction referred to as wall clock time slice. We propose a two-level user-controlled state-saving mechanism to achieve efficient and fleXible state saving at runtime. Various optimization strategies are applied to PCD++ and their effects are analyzed quantitatively, including a risk-free message type-based state-saving strategy to reduce the number of states saved during the simulation significantly, and a one log file per node strategy to break the bottleneck caused by file I/O operations. It is shown that PCD++ markedly outperforms other alternatives and considerable speedups can be achieved in parallel and distributed simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".