Shared-data or message passing computing models – A human factor in technical choices
Bibliographic record
Abstract
It is an ongoing debate in parallel processing whether shared-or distributed-memory computing models are better, whether shared-data or message passing is preferable.Recent research has shed some more light on the debate, showing that many applications can be supported well in either model (though potentially with some special tuning for the corresponding machine) but that for some applications with more extreme behavior the corresponding machine type and computing model are preferable or even the only feasible solution.Otherwise, what does make people choose one or the other?We investigate the human factor and propose the model that the personality type determines to a large extent personal preferences.The paper discusses the relationship between certain personality types and the programming model.To determine these aspects, we applied the psychological Myer-Briggs-Type-Indicator test on a group of students for whom both programming models were mostly new (i.e. who were not pre-occupied).The results give reasonable evidence for the validity of our proposed model and the relevance of the human factor in technical choices, i.e. that choices are not only/always a matter of which model is "better".
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".