Exploiting distributed version concurrency in a transactional memory cluster
Bibliographic record
Abstract
We investigate a transactional memory runtime system providing scaling and strong consistency, i.e., 1-copy serializability on commodity clusters for both distributed scientific applications and database applications. We introduce a novel page-level distributed concurrency control algorithm, called Distributed Multiversioning (DMV). DMV automatically detects and resolves conflicts caused by data races for distributed transactions accessing shared in-memory data structures. DMV's key novelty is in exploiting the distributed data versions that naturally come about in a replicated cluster in order to avoid read-write conflicts, hence provide scaling. DMV runs conflicting read-only and update transactions in parallel on different replicas instead of using different physical data copies within a single node as in classic multiversioning. In its most general form, DMV can be used to implement a software transactional memory system on a cluster for scaling C++ applications. DMV supports highly multithreaded database applications as well by centralizing updates on a master replica and creating the required page versions for read-only transactions lazily, on a set of slave replicas. We also show that a version-aware scheduling technique can distribute the read-only transactions across the slaves in such a way to minimize version conflicts.In our evaluation, we use DMV as a lightweight approach to scaling a hash table microbenchmark workload and the industry-standard e-commerce workload of the TPC-W benchmark on a commodity cluster. Our measurements show scaling for both benchmarks. In particular, we show near-linear scaling up to 8 transactional nodes for the most common e-commerce workload, the TPC-W shopping mix. We further show that our scaling for the TPC-W e-commerce benchmark compares favorably with that of an existing coarse-grained asynchronous replication technique.
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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 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".