Bibliographic record
Abstract
The MCS lock is one of the most prevalent queuing locks. It provides fair scheduling and high performance on massively parallel systems. However, the MCS lock mandates a bring-your-own-context policy: each lock user must provide an additional context (i.e., a queue node) to interact with the lock. This paper proposes MCSg, a variant of the MCS lock that relaxes this restriction. Our key observation is that not all lock users are created equal . We analyzed how locks are used in massively-parallel modern systems, such as NUMA-aware operating systems and databases. We found that such systems often have a small number of "regular" code paths that enter the lock very frequently. Such code paths are the primary beneficiary of the high scalability of MCS locks. However, there are also many "guest" code paths that infrequently enter the lock and do not need the same degree of fairness to access the lock (e.g., background tasks that only run periodically with lower priority). These guest users, which are typically spread out in various modules of the software, prefer context-free locks, such as ticket locks. MCSg provides these guests a context-free interface while regular users still enjoy the benefits provided by MCS. It can also be used as a drop-in replacement of MCS for more advanced locks, such as cohort locking. We also propose MCSg++, an extended version of MCSg, which avoids guest starvation and non-FIFO behaviors that might happen with MCSg. Our evaluation using microbenchmarks and the TPC-C database benchmark on a 16-socket, 240-core server shows that both MCSg and MCSg++ preserve the benefits of MCS for regular users while providing a context-free interface for guests.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.423 | 0.276 |
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".