Bibliographic record
Abstract
While Sequential Consistency (SC) is the most intuitive memory consistency model and the one most programmers likely assume, current multiprocessors do not support it. Instead, they support more relaxed models that deliver high performance. SC implementations are considered either too slow or -- when they can match the performance of relaxed models -- too difficult to implement. In this paper, we propose Bulk Enforcement of SC (BulkSC), anovel way of providing SC that is simple to implement and offers performance comparable to Release Consistency (RC). The idea is to dynamically group sets of consecutive instructions into chunks that appear to execute atomically and in isolation. The hardware enforces SC at the coarse grain of chunks which, to the program, appears as providing SC at the individual memory access level. BulkSC keeps the implementation simple by largely decoupling memory consistency enforcement from processor structures. Moreover, it delivers high performance by enabling full memory access reordering and overlapping within chunks and across chunks. We describe a complete system architecture that supports BulkSC and show that it delivers performance comparable to RC.
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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 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".