Finding Common RNA Secondary Structures: A Case Study on the Dynamic Parallelization of a Data-driven Recurrence
Bibliographic record
Abstract
This paper presents the dynamic parallelization of a sequential algorithm for finding common RNA secondary structures that initially does not appear to be amenable to parallelization. A critical insight into the problem structure, which at first appears to be inherently top-down, leads to the development of a revised sequential algorithm that uses both bottom-up tabulation and top-down memoization. This novel combined approach proves well-suited for parallelization, overcoming the inherent difficulties in parallelizing the original top-down algorithm. The improved algorithm also eliminates two factors from the space complexity to fit into quadratic space, enabling the comparison of lengthy and complex RNA structures. Experimental results demonstrate that the parallel algorithm scales well, achieving speedup of up to 32X using 64 processors for contrived worst-case data containing structures having up to 1600 nested arcs. This algorithm illustrates the significant benefits that can be achieved by designing an underlying sequential dynamic programming algorithm with parallelizability in mind, instead of directly parallelizing an existing sequential algorithm. Our results also show the usefulness of combining both bottom-up and top-down perspectives when designing a parallel dynamic programming algorithm.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".