A Framework for Executing Long Running Jobs in Grid Environments
Bibliographic record
Abstract
Computational jobs that take days, weeks or months to run usually cannot be executed as a single job due to system failures and scheduling constraints. Instead the job must be split into a series of shorter jobs. Solutions for managing the execution of such jobs in grid environments must address many issues. Participating systems and their properties can change over time and therefore it is important to have dynamic resource discovery mechanisms. Data management tools are needed to manage and keep track of data that can be distributed across multiple sites. Fault tolerance is required to handle the many different errors and failures that can occur in such environments. Furthermore, support for job reconfiguration, in terms of the number of processors, run length, and memory required, is necessary to allow jobs to adapt to the heterogeneous resources they are submitted to. This paper presents a framework for executing long running jobs in grid environments that addresses the above issues. The framework automates checkpointing, migration and reconfiguration of jobs. It has been successfully tested with the GROMACS molecular dynamics simulation application in a GT4-based grid environment comprised of resources distributed across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".