Dataflow-Based Scheduling for Scientific Workflows in HPC with Storage Constraints
Why this work is in the frame
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Bibliographic record
Abstract
In high-performance computing (HPC), workflow-based workloads are usually data intensive for exploratory analysis of a scientific computation problem that may involve a large parameter space. To achieve the best performance, storage resource constraint is always a pragmatic concern in reality as the potential problem space scale, especially in big data science, as well as its required dataset are ever growing to outpace any increasing rate of storage capacity. Therefore, the workflow computation in a HPC environment with finite storage resources is still a practical topic that is worthwhile studying. To this end, we propose a novel scheduling framework that enhances the scheduling policies of Versioned Name Space and Overwrite-Safe Concurrency, introduced in our earlier work, with abilities to handle the deadlock problem in workflow computation with finite storage constraints. We achieve this goal by leveraging the data dependency information of the workflow to integrate a collection of deadlock resolution algorithms into the workflow scheduler. With such integration, after extensive simulation-based studies we conclude that the enhanced scheduling policies can solve the deadlock problem introduced by the storage constraints caused by big data overflow. More interestingly, we demonstrate that our enhanced scheduling policies perform better than the cases where only pure deadlock algorithms are applied when storage is highly constrained in terms of makespan performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it