A Lifetime Reliability-Constrained Runtime Mapping for Throughput Optimization in Many-Core Systems
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Bibliographic record
Abstract
Due to technology scaling, lifetime reliability is becoming one of the major design constraints in the performance optimization of future many-core systems. Given a lifetime reliability constraint, the existing lifetime-constrained runtime mapping schemes often lead to low throughput because of the requirement to map all applications to compact regions. In this paper, we propose a runtime application mapping scheme that exploits a borrowing strategy to improve the throughput of many-core systems given a lifetime constraint. First, we propose using different strategies for mapping communication-intensive applications and computation-intensive applications. The lifetime reliability constraint can be relaxed in the local time scale when the communication requirement is high. The throughput is improved because the communication distance of communication-intensive applications is optimized while the waiting time of computation-intensive application is reduced. Then, we propose a method to effectively classify applications depending on the communication-to-computation ratio. A dynamic threshold is determined according to the current locations of available cores. Finally, we propose an improved neighborhood allocation scheme to reduce the communication cost in the task mapping. The experimental results show that compared to the state-of-the-art lifetime-constrained mapping, the proposed mapping scheme improves the throughput of many-core systems by 26% on average for synthetic task graphs and by 20% on average for realistic task graphs while the lifetime reliability is maintained within a constraint.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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