Running Molecular Dynamics Simulations in a Grid Environment
Bibliographic record
Abstract
Grid computing enables resources in different administrative domains to be shared. Researchers are able to collaborate more easily and can gain access to more computing power, enabling more studies to be run and larger problems to be considered. A common middleware employed by grid projects is the Globus Toolkit. Recently, a new version of the Globus Toolkit (GT4), based on standards that build on Web Services, has been released. However, most grid projects using the Globus Toolkit still employ GT2. This paper presents an approach for running molecular dynamics simulations in a mixed GT2/GT4 grid environment. Simulations are automatically checkpointed and migrated between sites as needed, increasing fault tolerance should a site fail. Users do not need to manually discover what resources are available and do not need to learn the potentially different approaches for submitting jobs at different sites. All jobs are submitted to a metascheduler which handles the site specific details. Simulations have been successfully run in a grid environment comprised of resources from across Canada, including resources from the Grid Research Centre at the University of Calgary, WestGrid and ACEnet.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".