Stratified sampling for even workload partitioning applied to single source shortest path algorithm
Bibliographic record
Abstract
An efficient implementation of large graph processing algorithms on distributed-memory machines requires a balanced partitioning of the graph across the machines. In a previous paper we presented an algorithm, named Workload Partitioning and Scheduling (WPS), that uses domain-specific knowledge to guide a sampling procedure in large implicitly-defined graphs. WPS's sampling procedure is used for partitioning the workload into parts of similar size which is then distributed amongst different machines. This article extends that earlier study and presents an investigation of the parallel and distributed implementation of Meyer's Δ-Stepping algorithm for solving the Single-Source Shortest Path (SSSP) problem for directed graphs. Our implementation leverages the WPS algorithm for evenly distributing the workload involved in processing the vertices of the input graph across distributed-memory machines. In contrast with the previous study, which focussed on implicitly-defined graphs, this work demonstrates that WPS is also equally applicable on explicitly-defined graphs. Empirical evidence shows that applying WPS to Meyer's SSSP algorithm yields significant performance benefits.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".