Stratified Sampling for Even Workload Partitioning Applied to IDA* and Delaunay Algorithms
Bibliographic record
Abstract
This work presents Workload Partitioning and Scheduling (WPS), a novel algorithm for evenly partitioning the computational workload of large implicitly-defined work-list-based applications on distributed/shared-memory systems. In WPS, a stratified sampling technique estimates the number of work items that will be processed in each step of the target application. Then WPS uses this estimation to evenly partition and distribute the computational workload. An empirical evaluation on large applications -- Iterative-Deepening A* (IDA*) applied to (4 × 4)- and (5 × 5)-Sliding-Tile Puzzles, Delaunay Mesh Generation, and Delaunay Mesh Refinement -- shows that WPS is applicable to a range of applications. A coordination between WPS and existing work-stealing schedulers for intra-node load balancing yields additional speedups in the range of 18% to 40% compared to that achieved with the existing work-stealing schedulers alone. Such a coordination also outperforms an existing workload-partitioning scheme intended specifically for IDA* algorithms by 17% to 36%.
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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 | 0.006 |
| 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 | 0.001 |
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".