Towards scalable and transparent parallelization of multiplayer games using transactional memory support
Bibliographic record
Abstract
This work addresses the problem of parallelizing multiplayer games using software Transactional Memory (STM) support. Using a realistic high impact application, we show that STM provides not only ease of programming, but also better performance than that achievable with state-of-the-art lock-based programming. Towards this goal, we use SynQuake, a game benchmark which extracts the main data structures and the essential features of the popular multiplayer game Quake, but can be driven with a synthetic workload generator that flexibly emulates client game actions and various hot-spot scenarios in the game world. We implement, evaluate and compare the STM version of SynQuake with a state-of-the-art lock-based parallelization of Quake, which we ported to SynQuake. While in STM-SynQuake support for maintaining the consistency of each potentially complex game action is automatic, conservative locking of surrounding objects within a bounding box for the duration of the game action is inherently needed in lock-based SynQuake. This leads to a higher scalability factor of STM-SynQuake versus lock-based SynQuake, due to a higher degree of false sharing in the latter.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 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".