6.895 Theory of Parallel Systems Partial Parallelization of Graph Partitioning Algorithm METIS Term Project
Bibliographic record
Abstract
The METIS graph partitioning algorithm has three stages. The first stage, coarsening, takes a large graph, with vertices |V | and edges |E|, and creates successively smaller graphs that are good representations of the original graph. The second stage partitions the small graph. The third stage, refinement, projects and refines the partition of the smaller graph onto the original graph. We present implementation details of a parallel coarsening algorithm that adds �(|V |) parallelizable work to the original algorithm. we add �(|E|) serial work for optimal performance that limits our program to being 2.72 times faster on 8 processors than 1 processor, and 1.22 times faster on 8 processors than the original algorithm. We present issues with parallelizing refinement along with suggestions towards dealing with the issues. 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".