Mostly-optimistic concurrency control for highly contended dynamic workloads on a thousand cores
Bibliographic record
Abstract
Future servers will be equipped with thousands of CPU cores and deep memory hierarchies. Traditional concurrency control (CC) schemes---both optimistic and pessimistic---slow down orders of magnitude in such environments for highly contended workloads. Optimistic CC (OCC) scales the best for workloads with few conflicts, but suffers from clobbered reads for high conflict workloads. Although pessimistic locking can protect reads, it floods cache-coherence backbones in deep memory hierarchies and can also cause numerous deadlock aborts. This paper proposes a new CC scheme, mostly-optimistic concurrency control (MOCC), to address these problems. MOCC achieves orders of magnitude higher performance for dynamic workloads on modern servers. The key objective of MOCC is to avoid clobbered reads for high conflict workloads, without any centralized mechanisms or heavyweight interthread communication. To satisfy such needs, we devise a native, cancellable reader-writer spinlock and a serializable protocol that can acquire, release and re-acquire locks in any order without expensive interthread communication. For low conflict workloads, MOCC maintains OCC's high performance without taking read locks. Our experiments with high conflict YCSB workloads on a 288-core server reveal that MOCC performs 8× and 23× faster than OCC and pessimistic locking, respectively. It achieves 17 million TPS for TPC-C and more than 110 million TPS for YCSB without conflicts, 170× faster than pessimistic methods.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".