A Study of the Potential of Intel's Transactional Synchronization Extensions for Hybrid Transactional Memory
Bibliographic record
Abstract
Hardware Transactional Memory (TM) attempts to deliver on the promises made with Software Transactional Memory by enabling scalable parallel execution with low programming effort. Intel's TSX is a recent such effort to provide hardware support for Transactional Memory on commodity hardware. This dissertation studies the limitations of TSX and proposes both manual and automated techniques for improving application parallelism. We find that using only TSX may result in high abort rates and poor performance for certain workloads due to hardware resource limitations. Using the Moldyn benchmark, we present a series of manual optimizations on TSX to achieve performance comparable to fine-grained locking. We then translate the identified manual optimizations into automated techniques in the form of a software-hardware hybrid transactional memory library called HyTM. Using a realistic benchmark, SynQuake, we experimentally show that HyTM can significantly increase the fraction of transactions that take advantage of TSX and execute in hardware.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".