Design patterns for parallel computing using a network of processors
Bibliographic record
Abstract
High complexity of building parallel applications is often cited as one of the major impediments to the mainstream adoption of parallel computing. To deal with the complexity of software development, abstractions such as macros, functions, abstract data types, and objects are commonly employed by sequential as well as parallel programming models. This paper describes the concept of a design pattern for the development of parallel applications. A design pattern in our case describes a recurring parallel programming problem and a reusable solution to that problem. A design pattern is implemented as a reusable code skeleton for quick and reliable development of parallel applications. A parallel programming system, called DPnDP (Design Patterns and Distributed Processes), that employs such design patterns is described. In the past, parallel programming systems have allowed fast prototyping of parallel applications based on commonly occurring communication and synchronization structures. The uniqueness of our approach is in the use of a standard structure and interface for a design pattern. This has several important implications: first, design patterns can be defined and added to the system's library in an incremental manner without requiring any major modification of the system (extensibility). Second, customization of a parallel application is possible by mixing design patterns with low level parallel code resulting in a flexible and efficient parallel programming tool (flexibility). Also, a parallel design pattern can be parameterized to provide some variations in terms of structure and behavior.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".