A Parallel External-Memory Frontier Breadth-First Traversal Algorithm for Clusters of Workstations
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Bibliographic record
Abstract
This paper presents a parallel external-memory algorithm for performing a breadth-first traversal of an implicit graph on a cluster of workstations. The algorithm is a parallel version of the sorting-based external-memory frontier breadth-first traversal with delayed duplicate detection algorithm. The algorithm distributes the workload according to intervals that are computed at runtime via a sampling-based process. We present an experimental evaluation of the algorithm where we compare its performance to that of its sequential counterpart on the implicit graphs of two classic planning problems. The speedups attained by the algorithm over its sequential counterpart are consistently near linear and frequently above linear. Analysis reveals that the algorithm is proficient at distributing the workload and that increasing the number of samples obtained by the sampling-based process improves workload distribution. Analysis also reveals that the algorithm benefits from the caching of external memory in internal memory that is done by the operating system
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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 | 0.000 | 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 it