Bibliographic record
Abstract
This thesis evaluates new parallel approaches for simulated annealing-based placement, and also leverages recent processor features such as hardware transactional memory (TM) and thread-level speculation (TLS) that aim to make parallel programming easier. Our contributions include a quantitative comparison of the speedup and quality-of-results obtained with various parallel algorithmic and programming approaches. We find that while TM and TLS simplify parallel programming, neither can achieve a compelling combination of speedup and placement quality. Our best algorithms require more programming effort than TM or TLS, but outperform prior approaches: without loss of placement quality, we can reach 5.9x speedup with a deterministic algorithm and 34x speedup with a non-deterministic one. We also evaluate the impact of hardware platforms on placement time. We find that while the greatest speedups occur on systems with many (57) simple cores, the fastest execution is achieved by systems with fewer (16) more complex cores.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".