Evaluation of Python-based tools for distributed computing on the Raspberry Pi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the results of comparing the efficiency of four Python-based modules and libraries with regards to their respective ability to run distributed tasks across a given cluster of Raspberry Pi's. The four Python-based tools considered are the following: PyRO v4.45, DCM v1.0.0, PP v1.6.4.4, and Mpi4py v2.0.0. Three tests are used to compare run time efficiency for distributed tasks: The first test proposed is prime factorization of composite values n, where n is the product of two prime numbers, and p and q are the respective prime factors. The second test proposed is the approximation of π to a desired point of precision in a set number of iterations. The third test proposed is the approximation of π in a fixed number of iterations with variation in the cluster size used. The overall purpose of these tests is to provide a basis of comparison in regards to the efficiency of each Python-based tool.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it