Collection-focused parallelism
Bibliographic record
Abstract
Constructing parallel software is, in essence, the process of associating ‘work ’ with computational units. The definition of work is dependent upon the model of parallelism used, and our choice of model can have profound effects on both programmer productivity and run-time efficiency. Given that the movement of data is responsible for the majority of parallelism overhead, and accessing data is responsible for the majority of parallelism errors, data items should be the basis for describing parallel work. As data items rarely exist in isolation and are instead parts of larger collections, we argue that subsets of collections should be the basic unit of parallelism. This requires a semantically rich method of referring to these sub-collections. Sub-collections are not guaranteed to be disjoint, and so an efficient run-time mechanism is required to maintain correctness. With a focus on complex systems, we present some of the challenges inherent in this approach and describe how we are extending Synchronization via Scheduling (SvS) and other techniques to overcome these difficulties. We discuss our experiences incorporating these techniques into a modern video game engine used in an in-development title. 1.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".