The potential for variable-granularity access tracking for optimistic parallelism
Bibliographic record
Abstract
Support for optimistic parallelism such as thread-level speculation (TLS) and transactional memory (TM) has been proposed to ease the task of parallelizing software to exploit the new abundance of multicores. A key requirement for such support is the mechanism for tracking memory accesses so that conflicts between speculative threads or transactions can be detected; existing schemes mainly track accesses at a single fixed granularity---i.e., at the word level, cache-line level, or page level. In this paper we demonstrate, for a hardware implementation of TLS and corresponding speculatively-parallelized SpecINT benchmarks, that the coarsest access tracking granularity that does not incur false violations varies significantly across applications, within applications, and across ranges of memory---from word-size to page size. These results motivate a variable-granularity approach to access tracking, and we show that such an approach can reduce the number of memory ranges that must be tracked and compared to detect conflicts can be reduced by an order of magnitude compared to word-level tracking, without increasing false violations. We are currently developing variable-granularity implementations of both a hardware-based TLS system and an STM system.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".