Trellis driver: distributing a java workflow across a network of workstations
Bibliographic record
Abstract
Some applications in science and engineering consist of a main job that invokes, or drives, other jobs. For example, a server process may receive a request, then invoke a workflow of stand-alone scripts or executables to handle the request, and then generate the final response. Java’s Runtime.exec() function allows jobs to be invoked from within a master Java program. However, these jobs are usually restricted to the same machine. If the number of jobs in the workflow is large, then it can be desirable to load balance the workload across different servers to maximize throughput. We describe the design and implementation of the Trellis Driver, a newly-developed Java module that runs jobs using TrellisDriver.exec() and allows jobs to be scheduled across clusters and metacomputers (i.e., aggregations of servers). Using a Java-based bioinformatics application as a case study, we evaluate the performance improvement Trellis Driver offers through workflow parallelism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".