SW-actors: accelerating the Smith–Waterman algorithm via actors
Bibliographic record
Abstract
Abstract Motivation The Smith–Waterman (SW) algorithm is widely regarded as the gold standard for local sequence alignment. However, its time complexity in a serial implementation limits its practicality for large datasets. In this article, we introduce SW-actors, a parallel implementation of the SW algorithm that leverages the actor model of concurrent computation to optimize resource utilization by efficiently scheduling and managing independent alignment tasks across processors at both the interalignment and intraalignment levels. Results SW-actors is compared with the state-of-the-art implementations Parasail, SeqAn, and SWIPE using four datasets of varying sequence lengths ranging from 85 to 74778 nucleotides. In terms of wall-clock time, SW-actors is 1.33×, 2.00×, 2.49×, and 1.94× faster than the next best implementation for the different datasets. SW-actors is up to 22× faster than serial on 40 cores. The speedup is consistent for larger datasets and hence offers significant advantages for medium- to large-scale alignments. Availability and implementation The SW-actors source code and underlying data are available at https://git.cs.usask.ca/numerical_simulations_lab/actors/papers/sw-actors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".