A flexible simulation framework for processor scheduling algorithms in multicore systems.
Bibliographic record
Abstract
In traditional uniprocessor systems, processor scheduling is the responsibility of the operating system. In high performance computing (HPC) domains that largely involve parallel processors, the responsibility of scheduling is usually left to the applications. So far, parallel computing has been confined to a small group of specialized HPC users. In this context, the hardware, operating system, and the applications have been mostly designed independently with minimal interactions. As the multicore processors are becoming the norm, parallel programming is expected to emerge as the mainstream software development approach. This new trend poses several challenges including performance, power management, system utilization, and predictable response. Such a demand is hard to meet without the cooperation from hardware, operating system, and applications. Particularly, an efficient scheduling of cores to the application threads is fundamentally important in assuring the above mentioned characteristics. We believe, operating system requires to take a larger responsibility in ensuring efficient multicore scheduling of application threads. To study the performance of a new scheduling algorithm for the future multicore systems with hundreds and thousands of cores, we need a flexible scheduling simulation testbed. Designing such a multicore scheduling simulation testbed and illustrating its functionality by studying some well known scheduling algorithms Linux and Solaris are the main contributions of this thesis. In addition to studying Linux and Solaris scheduling algorithms, we demonstrate the power, flexibility, and use of the proposed scheduling testbed by simulating two popular gang scheduling algorithms - adaptive first-come-first-served (AFCFS) and largest gang first served (LGFS). As a result of this performance study, we designed a new gang scheduling algorithm and we compared its performance with AFCFS. The proposed scheduling simulation testbed is developed using Java and expected to be released for public use.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".