Local DNA sequence alignment in a cluster of workstations: Algorithms and tools
Bibliographic record
Abstract
Abstract Distributed Shared Memory systems allow the use of the shared memory programming paradigm in distributed architectures where no physically shared memory exist. Scope consistent software DSMs provide a relaxed memory model that reduces the coherence overhead by ensuring consistency only at synchronization operations, on a per-lock basis. Much of the work in DSM systems is validated by benchmarks and there are only a few examples of real parallel applications running on DSM systems. Sequence comparison is a basic operation in DNA sequencing projects, and most of sequence comparison methods used are based on heuristics, that are faster but do not produce optimal alignments. Recently, many organisms had their DNA entirely sequenced, and this reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. In this article, we present and evaluate a parallelization strategy for implementing a sequence alignment algorithm for long sequences. This strategy was implemented in JIAJIA, a scope consistent software DSM system. Our results on an eight-machine cluster presented good speedups, showing that our parallelization strategy and programming support were appropriate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".