Parallel Computing on a Mobile Device
Bibliographic record
Abstract
The Message Passing Interface (MPI) was published as a standard in 1992. Since then, many implementations have been developed. The MPICH library is one of the most well-known and freely available implementations. These libraries allow for the simplification of parallel computing on clusters and parallel machines. The system provides the developer with an easy-to-use set of functions for point-to-point and global communications. The details of how the actual communication takes place are hidden from the programmers, allowing them to focus on the domain-specific problem at hand. Communication between nodes on such systems is carried out via high-speed cabled interconnects (Gigabit Ethernet and upwards). The world of mobile computing, especially mobile phones, is now a ubiquitous technology. Mobile devices do not have any facility to allow for connections using traditional high-speed cabling; therefore, it is necessary to make use of wireless communication mechanisms to achieve interdevice communication. The majority of medium- to high-end phones are Bluetooth-enabled as standard, allowing for wireless communication to take place. The Mobile Message Passing Interface (MMPI) provides the developer with an intuitive set of functions to allow for communications between nodes (mobile phones) across a Bluetooth network. This chapter looks at the MMPI library and how it may be used for parallel computing on mobile phones (Smartphones).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.021 |
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".