Task Mapping Strategies for Electric Power System Simulations on Heterogeneous Clusters
Bibliographic record
Abstract
In this work, we propose improved task mapping strategies for real-time electric power system simulations on heterogeneous computing clusters, considering both heterogeneous communication links and processing capacities, with a focus on bottleneck objectives. We approach the problem through two complementary models: the bottleneck quadratic semi-assignment problem (BQSAP), which optimizes task configuration for a fixed number of computing nodes while minimizing communication and computation costs; and the variable-size bin packing problem with quadratic communication constraints (Q-VSBPP), which minimizes the required number of computing nodes, valuable for resource provisioning scenarios. We extend the PuLP library to solve approximately both problems, explicitly including communication costs and processing constraints, and formalize the nomenclature and definitions for bottleneck objectives in graph partitioning. This formalization fills a gap in the existing literature and provides a framework for the rigorous analysis and application of task mapping techniques to real-time electric power system simulation. Finally, we provide a quantitative study and benchmark the extended PuLP library with the SCOTCH partitioning library in the context of real-time electromagnetic transient (EMT) simulation task mapping.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".