Kilo TM Correctness: ABA Tolerance and Validation-Commit Indivisibility
Bibliographic record
Abstract
Kilo TM is a hardware transactional memory (TM) system proposed for GPU architec-tures [1]. In Kilo TM, each transaction detects the existence of conflicts with other trans-actions via value-based conflict detection [2, 3]. With value-based conflict detection, each transaction buffers its writes to memory in a write-log and saves the values of its reads from memory in a read-log during execution. Upon its completion, the transaction compares the saved values of its read-set with the latest values in memory before it commits. We refer this comparison as validation. Any difference between the saved value and the latest value in memory indicates the existence of a conflict. Kilo TM uses value-based conflict detection because it avoids direct communication between transactions, and it does not require any essential on-chip storage (Kilo TM uses on-chip storage to improve commit parallelism). More details regarding the design of Kilo TM are available in our paper for the 44th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO 2011) [1]. A general concern for the correctness of value-based conflict detection is the possibility of subtle bugs due to the ABA problem. We show that value-based conflict detection can tolerate the ABA problem, and like NOrec [3], we can create a logical order for Kilo TM in
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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 | 0.004 | 0.028 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".