Mechanisms for store-wait-free multiprocessors
Bibliographic record
Abstract
Store misses cause significant delays in shared-memory multiprocessors because of limited store buffering and ordering constraints required for proper synchronization. Today, programmers must choose from a spectrum of memory consistency models that reduce store stalls at the cost of increased programming complexity. Prior research suggests that the performance gap among consistency models can be closed through speculation--enforcing order only when dynamically necessary. Unfortunately, past designs either provide insufficient buffering, replace all stores with read-modify-write operations, and/or recover from ordering violations via impractical fine-grained rollback mechanisms. We propose two mechanisms that, together, enable store-wait-free implementations of any memory consistency model. To eliminate buffer-capacity-related stalls, we propose the scalable store buffer, which places private/speculative values directly into the L1 cache, thereby eliminating the non-scalable associative search of conventional store buffers. To eliminate ordering-related stalls, we propose atomic sequence ordering, which enforces ordering constraints over coarse-grain access sequences while relaxing order among individual accesses. Using cycle-accurate full-system simulation of scientific and commercial applications, we demonstrate that these mechanisms allow the simplified programming of strict ordering while outperforming conventional implementations on average by 32% (sequential consistency), 22% (SPARC total store order) and 9% (SPARC relaxed memory order).
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".