Distributed-thread scheduling methods for reducing page-thrashing
Why this work is in the frame
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Bibliographic record
Abstract
Although distributed threads on distributed shared memory (DSM) provide an easy programming model for distributed computer systems, it is not easy to build a high performance system with them, because a software DSM system is prone to page-thrashing. One way to reduce page-thrashing is to utilize thread migration, which leads to changes in page access patterns on DSM. In this paper, we propose thread scheduling methods based upon page access information and discuss an analytical model for evaluating this information. Then, we describe our implementation of distributed threads, PARSEC (Parallel software environment for workstation cluster). Using user-level threads, PARSEC implements thread migration and thread scheduling based upon the page access information. We also measure the performance of some applications with these thread scheduling methods. These measurements indicate that the thread scheduling methods greatly reduce page-thrashing and improve total system performance.
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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 | 0.001 | 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