Leveraging Core Specialization via OS Scheduling to Improve Performance on Asymmetric Multicore Systems
Bibliographic record
Abstract
Asymmetric multicore processors (AMPs) consist of cores with the same ISA (instruction-set architecture), but different microarchitectural features, speed, and power consumption. Because cores with more complex features and higher speed typically use more area and consume more energy relative to simpler and slower cores, we must use these cores for running applications that experience significant performance improvements from using those features. Having cores of different types in a single system allows optimizing the performance/energy trade-off. To deliver this potential to unmodified applications, the OS scheduler must map threads to cores in consideration of the properties of both. Our work describes a Comprehensive scheduler for Asymmetric Multicore Processors (CAMP) that addresses shortcomings of previous asymmetry-aware schedulers. First, previous schedulers catered to only one kind of workload properties that are crucial for scheduling on AMPs; either efficiency or thread-level parallelism (TLP), but not both. CAMP overcomes this limitation showing how using both efficiency and TLP in synergy in a single scheduling algorithm can improve performance. Second, most existing schedulers relying on models for estimating how much faster a thread executes on a “fast” vs. “slow” core (i.e., the speedup factor ) were specifically designed for AMP systems where cores differ only in clock frequency. However, more realistic AMP systems include cores that differ more significantly in their features. To demonstrate the effectiveness of CAMP on more realistic scenarios, we augmented the CAMP scheduler with a model that predicts the speedup factor on a real AMP prototype that closely matches future asymmetric systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".