DCM: A Python-based middleware for parallel processing applications on small scale devices
Bibliographic record
Abstract
Parallel programming has been an active area of research in computer science and software engineering for many years. Parallel programming should ideally provide a linear speedup to computational problems. In reality, this is rarely the case. While there are some algorithms that cannot be parallelized, many that can, still fail to provide the ideal linear speedup. For algorithms that can benefit from parallelization, it is often much more difficult to develop the parallel code than it is to write a sequential, single-threaded program. The existence of this gap between ideal parallel computing and parallel computing on real hardware and software has caused many developers to create new solutions in an attempt to move real parallel computing closer to its idealized model. While many of these solutions provide a great performance benefit on large-scale systems, they often lag behind when deployed on small-scale systems. In this paper, we introduce the design and implementation of DCM (Distributed Computing Middleware) - a Python-based middleware for writing parallel processing applications for execution on clusters of small-scale devices. Evaluation results show the feasibility of DCM. Our middleware and its test cases are publicly available on GitHub.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".