Impact of MCMC convergence behavior on MMC parallelization
Bibliographic record
Abstract
Multicanonical Monte Carlo (MMC) is a technique to accelerate simulations by using adaptive importance sampling (IS). Because the adaption algorithm is system independent, MMC is a practical, handy tool that can be used in many situations and in many fields of research. Combination of MMC with supercomputer infrastructures can support increasingly complex systems. A supercomputer works with parallelized algorithms. Efficient MMC relies on Markov Chain Monte Carlo (MCMC) as a key enabler in generating samples from biased distributions. While Monte Carlo simulations are embarrassingly parallelizable, MCMC is inherently serial in nature and a priori difficult to parallelize. In this article, we will examine three diverse systems to explore how MMC can benefit from parallelization. We will uncover some hints to parameterize the parallel algorithm to compromise between speed and accuracy.
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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) | 0.001 | 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".