LOMARC — Lookahead Matchmaking for Multi-resource Coscheduling
Bibliographic record
Abstract
Job scheduling typically focuses on the CPU with little work existing to include I/O or memory. Time-shared execution provides the chance to hide I/O and long-communication latencies though potentially creating a memory conflict. We consider two different cases: standard local CPU scheduling and coscheduling on hyperthreaded CPUs. The latter supports coscheduling without any context switches and provides additional options for CPU-internal resource sharing. We present an approach that includes all possible resources into the schedule optimization and improves utilization by coscheduling two jobs if feasible. Our LOMARC approach partially reorders the queue by lookahead to increase the potential to find good matches. In simulations based on the workload model of [12], we have obtained improvements of about 50% in both response times and relative bounded response times on hyperthreaded CPUs (i.e. cut times by half) and of about 25% on standard CPUs for our LOMARC scheduling approach. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".