Synchronization via scheduling: managing shared state in video games
Bibliographic record
Abstract
Video games are a performance hungry application domain with a complexity that often rivals operating systems. These performance and complexity issues in combination with tight development times and large teams means that consistent, specialized and pervasive support for parallelism is of paramount importance. The Cascade project is focused on designing solutions to support this application domain. In this paper we describe how the Cascade runtime extends the industry standard job/task graph execution model with a new approach for managing shared state. Traditional task graph models dictate that tasks making conflicting accesses to shared state must be linked by a dependency, even if there is no explicit logical ordering on their execution. In cases where it is difficult to understand if such implicit dependencies exist, the programer would create more dependencies than needed, which results in constrained graphs with large monolithic tasks and limited parallelism. By using the results of off-line code analysis and information exposed at runtime, the Cascade runtime automatically determines scenarios where implicit dependencies exist and schedules tasks to avoid data races. This technique is called Synchronization via Scheduling (SvS) and we present its two implementations. The first implementation uses Bloom filter based ‘signatures ’ and the second relies on automatic data partitioning which has optimization potential independent of SvS. Our experiments show that SvS succeeds in achieving a high degree of parallelism and allows for finer grained tasks. However, we find that one consequence of sufficiently [Copyright notice will appear here once ’preprint ’ option is removed.] fine-grained tasks is that the time to dispatch them exceeds their execution time, even using a highly optimized scheduler/manager. Fine-grained tasks, however, are a necessary condition for sufficient parallelism and overall performance gains, so this finding motivates further inquiry into how tasks are managed. 1.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".