Shape Optimizing Load Distribution Heuristic for Parallel Adaptive
Bibliographic record
Abstract
Abstract. Load balancing plays an important role in parallel numerical simulations. To address this problem, some general purpose libraries as well as a number of more specific approaches have been developed. Many of them base on vertex exchange operations like the Kerninghan-Lin heuristic which, due to their sequential nature, are hard to parallelize. Furthermore, libraries like Metis and Jostle primarily minimize the edgecut and cannot obey constraints like connectivity and straight partition boundaries, which are important for some numerical solvers. In this paper we present a new approach to address the load balancing problem. In contrast to existing heuristics, we are able to guarantee connectivity and the resulting partitions are usually well shaped. Furthermore, our experiments indicate that we can outperform the two parallel state-of-the-art libraries Metis and Jostle also according to the classic metrics like edge-cut and boundary length. The proposed algorithm thereby contains a high degree of natural parallelism, while its drawback is the long run-time, especially if the parallelism is not exploited.
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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 | 0.001 | 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".