DeNovoND: Efficient Hardware for Disciplined Nondeterminism
Bibliographic record
Abstract
Recent research in disciplined shared-memory programming models presents a unique opportunity for rethinking the multicore memory hierarchy for better efficiency in terms of complexity, performance, and energy. The DeNovo hardware system showed that for deterministic programs written using such disciplined models, hardware can be much more efficient than the current state of the art. For DeNovo to be adopted by commercial systems, however, it is necessary to extend it to support nondeterministic applications as well; for example, applications using lock synchronization. This article proposes DeNovoND, a system that provides support for disciplined nondeterministic codes with locks while retaining the simplicity, performance, and energy benefits of DeNovo. The authors designed and implemented simple memory consistency semantics for safe nondeterminism using distributed queue-based locks and access signatures. The resulting protocol avoids transient states, invalidation traffic, directory sharer-lists, and false sharing, which are all significant sources of inefficiency in existing protocols. Their experiments showed that DeNovoND provides comparable or better execution time for applications designed for lock synchronization. In addition, it incurs 33 percent less network traffic on average relative to a state-of-the-art invalidation-based protocol, which directly translates into energy savings.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".