Parallel shared-memory simulator performance for large ATM networks
Bibliographic record
Abstract
A performance comparison between an optimistic and a conservative parallel simulation kernel is presented. Performance of the parallel kernels is also compared to a central-event-list sequential kernel. A spectrum of ATM network and traffic scenarios representative of those used by ATM networking researchers are used for the comparison. Experiments are conducted with a cell-level ATM network simulator and an 18-processor SGI PowerChallenge shared-memory multiprocessor. The results show the performance advantages of parallel simulation ove r sequential simulation for ATM networks. Speedups of 4-5 relative to a fast sequential kernel are achieved on 16 processors for several large irregular ATM benchmark scenarios and the optimistic kernel achieves 2 to 5 times speedup on all 7 benchmarks. However, the relative performance of the two parallel simulation kernels is dependent on the size of the ATM network, the number of traffic sources, and the traffic source types used in the simulation. For some benchmarks the best single point performance is provided by the conservative kernel even on a single processor. Unfortunately, the conservative kernel performance is susceptible to small changes in the modeling code and is outperformed by the optimistic kernel on 5 of the 7 benchmarks. The optimistic parallel simulation kernel thus provides most robust performance, but its speedup is limited by the overheads of its implementation, which make it approximately half the speed of the sequential kernel on one processor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".