Bibliographic record
Abstract
Current shared-memory hardware is complex and inefficient. Prior work on the DeNovo coherence protocol showed that disciplined shared-memory programming models can enable more complexity-, performance-, and energy-efficient hardware than the state-of-the-art MESI protocol. DeNovo, however, severely restricted the synchronization constructs an application can support. This paper proposes DeNovoSync, a technique to support arbitrary synchronization in DeNovo. The key challenge is that DeNovo exploits race-freedom to use reader-initiated local self-invalidations (instead of conventional writer-initiated remote cache invalidations) to ensure coherence. Synchronization accesses are inherently racy and not directly amenable to self-invalidations. DeNovoSync addresses this challenge using a novel combination of registration of all synchronization reads with a judicious hardware backoff to limit unnecessary registrations. For a wide variety of synchronization constructs and applications, compared to MESI, DeNovoSync shows comparable or up to 22% lower execution time and up to 58% lower network traffic, enabling DeNovo's advantages for a much broader class of software than previously possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".