Critical Path Awareness Techniques for Large-Scale Graph Partitioning
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Bibliographic record
Abstract
Graph partitioning is one of the fundamental problems in many graph-based applications and systems. It enables the division of a graph into smaller sub-graphs for subsequent parallel processing, reducing the processing latency of the graph. The critical path of a graph is the logical path with the longest delay from input to output. The processing time of the graph mainly depends on the delay incurred by the critical path, independent of other paths with small delays. Therefore, it can reduce the processing time of the graph by protecting the critical path of the graph from partition. However, existing approaches to graph partitioning only focus on metrics such as minimum cut and partition balance. As a result, the critical paths of graphs may be destroyed in the partitioning procedure. To address this problem, we present a critical path awareness approach, namely path-metis, to protect the critical paths and alleviate the processing latency after graph partitioning. In path-metis, two efficient strategies, including Slack and critical path fix strategies, are introduced. The Slack strategy, which incorporates critical path information into the weights of DAG, is used as pre-processing before traditional multi-level partitioning methods, like Metis. Then, for the generated partitioning scheme, the critical path fix strategy is proposed to further protect critical paths from being cut. We demonstrate the effectiveness of our approach on both real and synthetic datasets. From the experimental results, compared to Metis, our method improves critical path performance by 17.70%.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 it