A hierarchical processor scheduling policy for distributed-memory multicomputer systems
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Bibliographic record
Abstract
Processor scheduling policies for distributed memory systems can be divided into space sharing or time sharing policies. In space sharing, the set of processors in the system is partitioned and each partition is assigned for the exclusive use of a job. In time sharing policies, on the other hand, none of the processors is given exclusively to jobs; instead, several jobs share the processors (for example, in a round robin fashion). There are advantages and disadvantages associated with each type of policy. Typically, space sharing policies are good at low to moderate system loads and when job parallelism does not vary much. However, at high system loads and widely varying job parallelism, time sharing policies provide a better performance. We propose a new policy that is based on a hierarchical organization that incorporates the merits of these two types of policies. The new policy is a hybrid policy that uses both space sharing as well as time sharing to achieve better performance. We demonstrate that, at most system loads of interest, the proposed policy outperforms both space sharing and time sharing policies by a wide margin.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it