On the scalability and dynamic load balancing of parallel Verilog simulations
Bibliographic record
Abstract
As a consequence of Moore’s law, the size of integrated circuits has grown extensively, resulting in simulation becoming the major bottleneck in the circuit design process. In this paper, we examine the performance of a parallel Verilog simulator on large, real designs. As previous work has made use of either relatively small benchmarks or synthetic circuits, the use of these circuits is far more realistic. We develop a parser for Verilog files enabling us to simulate in parallel all synthesizable Verilog circuits. We utilize four circuits as our test benches; the LEON Processor, the OpenSparc T2 processor and two Viterbi decoder circuits. We observed 4,000,000 events per second on 32 processors for the Viterbi decoder with 800k gates. A dynamic load balancing approach is also developed which uses a combination of centralized and distributed control in order to accommodate its use for large circuits. 1
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".