On the benefits of a workflow-aware file system in high-performance computing systems
Bibliographic record
Abstract
Traditional high-performance computing (HPC) systems have independent job schedulers and file systems that do not interact in substantial ways. We make the case that some integration of scheduler and file system can have three main benefits. First, the dataflow dependencies between the jobs in a workflow can be inferred by combining the scheduler's knowledge of the jobs (and possibly control-flow) and the file system's knowledge of the files accessed. Second, the dataflow information can be used to improve workflow instance concurrency when there are (potential) filename conflicts. Third, when workflows need to be re-computed, only the affected jobs need to be re-executed. We present the design and a simulation study of the Workflow-Aware File System (WaFS). Our design layers a namespace manager (NM) on top of existing file systems to provide, for example, a dataflow engine and a versioned file system. Our simulation study (with a specific set of application parameters) shows that a combined WaFS-aware file system and scheduler can significantly improve makespans for intensive workloads and be efficient in the re-computation of jobs.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".