Bibliographic record
Abstract
Multicore processors have become commonplace in both desk-top and servers. A serious challenge with multicore processors is that cores share on and o chip resources such as caches, memory buses, and memory controllers. Competition for these shared resources between threads running on different cores can result in severe and unpredictable performance degradations. It has been shown in previous work that the OS scheduler can be made shared-resource-aware and can greatly reduce the negative e ects of resource contention. The search space of potential scheduling algorithms is huge considering the diversity of available multicore architectures, an almost infinite set of potential workloads, and a variety of conflicting performance goals. We believe the two biggest obstacles to developing new scheduling algorithms are the difficulty of implementation and the duration of testing. We address both of these challenges with our toolset AKULA which we introduce in this paper. AKULA provides an API that allows developers to implement and debug scheduling algorithms easily and quickly without the need to modify the kernel or use system calls. AKULA also provides a rapid evaluation module, based on a novel evaluation technique also introduced in this paper, which allows the created scheduling algorithm to be tested on a wide variety of work-loads in just a fraction of the time testing on real hardware would take. AKULA also facilitates running scheduling algorithms created with its API on real machines without the need for additional modifications. We use AKULA to develop and evaluate a variety of different contention-aware scheduling algorithms. We use the rapid evaluation module to test our algorithms on thousands of workloads and assess their scalability to futuristic massively multicore machines.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.344 | 0.336 |
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".