Reducing OLTP instruction misses with thread migration
Bibliographic record
Abstract
During an instruction miss a processor is unable to fetch instructions. The more frequent instruction misses are the less able a modern processor is to find useful work to do and thus performance suffers. Online transaction processing (OLTP) suffers from high instruction miss rates since the instruction footprint of OLTP transactions does not fit in today's L1-I caches. However, modern many-core chips have ample aggregate L1 cache capacity across multiple cores. Looking at the code paths concurrently executing transactions follow, we observe a high degree of repetition both within and across transactions. This work presents TMi a technique that uses thread migration to reduce instruction misses by spreading the footprint of a transaction over multiple L1 caches. TMi is a software-transparent, hardware technique; TMi requires no code instrumentation, and efficiently utilizes available cache capacity. This work evaluates TMi's potential and shows that it may reduce instruction misses by 51% on average. This work discusses the underlying tradeoffs and challenges, such as an increase in data misses, and points to potential solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".