An integrated hardware-software approach to flexible transactional memory
Bibliographic record
Abstract
There has been considerable recent interest in both hardware andsoftware transactional memory (TM). We present an intermediateapproach, in which hardware serves to accelerate a TM implementation controlled fundamentally by software. Specifically, we describe an alert on update mechanism (AOU) that allows a thread to receive fast, asynchronous notification when previously-identified lines are written by other threads, and a programmable data isolation mechanism (PDI) that allows a thread to hide its speculative writes from other threads, ignoring conflicts, until software decides to make them visible. These mechanisms reduce bookkeeping, validation, and copying overheads without constraining software policy on a host of design decisions. We have used AOU and PDI to implement a hardwareacceleratedsoftware transactional memory system we call RTM. We have also used AOU alone to create a simpler "RTM-Lite". Across a range of microbenchmarks, RTM outperforms RSTM, a publicly available software transactional memory system, by as much as 8.7x (geometric mean of 3.5x) in single-thread mode. At 16 threads, it outperforms RSTM by as much as 5x, with an average speedup of 2x. Performance degrades gracefully when transactions overflow hardware structures. RTM-Lite is slightly faster than RTM for transactions that modify only small objects; full RTM is significantly faster when objects are large. In a strongargument for policy flexibility, we find that the choice between eager (first-access) and lazy (commit-time) conflict detection can lead to significant performance differences in both directions, depending on application characteristics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".