A parallel Breadth-First Search using shared memory level-synchronization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breadth-first search (BFS) stands as a cornerstone in graph exploration techniques, enabling systematic traversal of a provided graph. As the digital era continues to burgeon, there has been a marked upswing in the need to process vast graph-based data sets. To harness the power of such data effectively, it becomes imperative to use computational tools efficiently. Parallelizing BFS emerges as a pivotal strategy in this regard, leveraging the expansive capabilities of multiprocessor systems to maximize efficiency. This manuscript introduces a level-synchronous parallel BFS that is predicated on the shared-memory model. Recognizing the potential pitfalls of such an approach, especially regarding overhead induced by implicit barriers and critical sections, meticulous optimization techniques are infused into the model. These strategies are not mere afterthoughts; they are woven into the fabric of the design, ensuring smooth operations even when scaled. To validate the efficacy of this model, a rigorous assessment is carried out using the Graph500 benchmark. This offers insights into the performance scale of the parallel BFS algorithm, especially focusing on its speedup in relation to the number of operational threads. Concluding this exploration, the paper delineates prospective avenues for refining and further enhancing the proposed parallel implementation, aiming for even greater efficiencies in future endeavors.
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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