A Workload Partitioner for Heterogeneous Grids
Bibliographic record
Abstract
This chapter contains sections titled: Introduction Preliminaries Partion Graph Configuration Graph Partitioning Graph Metrics System Load Metrics System Metrics The MinEX Partitioner MinEX Data Structures Contraction Partitioning Reassignment Filter Refinement Latency Tolerance N-body Application Tree Creation Partition Graph Construction Graph Modifications for METIS Experimental Study Multiple Time Step Test Scalability Test Partitioner Speed Comparsions Partitioner Quality Comparsions Conclusions Reference An important characteristic of distributed grids is that they allow geographically separated multicomputers to be tied together in a transparent virtual environment to solve large-scale computational problems. However, many of these applications require effective runtime load balancing for the resulting solutions to be viable. This chapter presents MinEX, a novel latency tolerant partitioner that dynamically balances processor workloads while minimizing data movement and runtime communication for applications that are executed in a parallel distributed grid environment. The chapter also presents comparisons between the performance of MinEX to that of METIS, a popular multilevel family of partitioners. These comparisons were obtained using simulated heterogeneous grid configurations. A solver for the classical N-body problem is implemented to provide a framework for the comparisons. Experimental results show that the proposed MinEX partitioner provides superior quality partitions while being competitive to METIS in terms of execution speed.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".