A Generic Execution Management Framework for Scientific Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Managing the execution of scientific applications in a heterogeneous grid computing environment can be a daunting task, particularly for long running jobs. Increasing fault tolerance by checkpointing and migrating jobs between resources requires expertise and time of the scientist. Automation of such tasks can allow the scientist to focus more on the scientific results and less on the technical details. In this paper a generic framework for managing and automating the execution of jobs is presented. It uses of a variety of information models describing systems, policies, and application details/requirements to make suitable decisions on where and how to run, checkpoint, migrate and reconfigure jobs as needed. To demonstrate the utility of the framework, it is used as part of a simulation study to assess the impact availability of application memory usage information has on meeting the QoS objectives of job submitters and on overall utilization of resources. The study shows that with greater availability of memory usage information, the execution management framework is able to better meet user objectives and improve utilization of resources, particularly when the objective is to make more efficient use of resources.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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