Improving performance of transactional memory through machine learning
Bibliographic record
Abstract
Summary Transactional memory (TM) is a programming paradigm that facilitates parallel programming for multi‐core processors. In the last few years, some chip manufacturers provided hardware support for TM to reduce runtime overhead of Software Transactional Memory (STM). In this work, we offer two optimization techniques for TMs. The first technique focuses on Restricted Transactional Memory (RTM) in Intel's Haswell processor and shows that while in some applications, RTM improves performance over STM, in some others, it falls behind STM. We exploit this variability and propose an adaptive technique that switches between RTM and STM, statically. The second technique focuses on the overhead of TM and enhances the speed of the adaptive system. In particular, we focus on the size of transactions and improve performance by changing the transaction size. Optimizing the transaction size manually is a time‐consuming process and requires significant software engineering effort. We use a combination of Linear Regression (LR) and decision tree to decide on the transaction size, automatically. We evaluate our optimization techniques using a set of benchmarks from NAS, DiscoPoP, and STAMP benchmark suites. Our experimental results reveal that our optimization techniques are able to improve the performance of TM programs by 9% and energy‐delay by 15%, on average.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".