On Determining How Many Computers to Use in Parallel VLSI Simulation
Bibliographic record
Abstract
Parallel discrete event simulation has been established as a technique which has great potential to speed up the execution of gate level circuit simulation. A fundamental problem posed by a parallel environment is the decision of whether it is best to simulate a particular circuit sequentially or on a parallel platform. Furthermore, in the event that a circuit should be simulated on a parallel platform, it is necessary to decide how many computing nodes should be used on the given platform. In this paper we propose a machine learning algorithm as an aid in making these decisions. The algorithm is based on the well-known K-Nearest Neighbor algorithm. After an extensive training regime, it was shown to make a correct prediction 99% of the time on whether to use a parallel or sequential simulator. The predicted number of nodes to use on a parallel platform was shown to produce an average execution time which was not more than 12% of the smallest execution time. The configuration which resulted in the minimal execution time was picked 61% of the time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".