A Hybrid OpenMP-MPI Parallelization of Structure Software
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
Big Data has an increasing impact on the use of bioinformatics software. One way to deal with this challenge is through parallel computing. Using the program Structure as a case study, this paper investigates ways in which to counteract the challenges created by the growing datasets. This paper proposes an OpenMP-MPI hybrid parallelization of the MCMC steps, which are an integral part of Structure, and analyses the performance under various scenarios. The results indicate that the parallelization produce significant speedups over the serial version in all scenarios tested. This allows for the use of the hardware in a more efficient manner, by adapting the program to the parallel architecture. This is important because not only does it reduce the time required to perform existing analyses, but also opens the door to the analysis of previously impractically large datasets.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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