Parallelizing Depth-First Search for Pathway Finding: A Comprehensive Investigation
Bibliographic record
Abstract
Search algorithms are integral to numerous applications in computer science.With the prevalence of multi-core processors in contemporary computing devices, the parallelization of search algorithms has surfaced as a viable strategy for achieving significant performance enhancements.This paper offers a detailed examination of the performance improvements garnered through the parallelization of search procedures, with a particular emphasis on the Depth-First Search (DFS) algorithm as it pertains to pathway discovery in binary trees.The primary aim of this study was to contrast the performance of the conventional sequential DFS approach with a novel parallel strategy designed to exploit the computational capabilities of multi-core processors.By capitalizing on the resources available in modern desktop and laptop computers, it was intended to markedly diminish the processing time necessary for examining all possible pathways in both symmetrical and asymmetrical binary trees.A meticulous experimental evaluation was conducted using a varied assortment of binary trees, spanning perfectly balanced to highly skewed structures, to ensure a thorough assessment of the effectiveness of both strategies.The primary metric employed for performance evaluation was the total processing time, a crucial consideration for time-critical applications.The experimental results confirmed the superiority of the parallelized method over the conventional sequential DFS approach.The parallel technique demonstrated significantly lower processing times for pathway discovery in all binary tree scenarios tested.These performance enhancements were particularly noticeable in larger and more complex trees, underscoring the potential of parallelization for managing computationally demanding tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".