mm2-gb: GPU Accelerated Minimap2 for Long Read DNA Mapping
Bibliographic record
Abstract
Abstract Long-read DNA sequencing is becoming increasingly popular for genetic diagnostics. Minimap2 is the state-of-the-art long-read aligner. However, Minimap2’s chaining step is slow on the CPU and takes 40-68% of the time especially for long DNA reads. Prior works in accelerating Minimap2 either lose mapping accuracy, are closed source (and not updated) or deliver inconsistent speedups for longer reads. We introduce mm2-gb which accelerates the chaining step of Minimap2 on GPU without compromising mapping accuracy. In addition to intra- and inter-read parallelism exploited by prior works, mm2-gb exploits finer levels of parallelism by breaking down high latency large workloads into smaller independent segments that can be run in parallel and leverages several strategies for better workload balancing including split-kernels and prioritized scheduling of segments based on sorted size. We show that mm2-gb on an AMD Instinct™ MI210 GPU achieves 2.57-5.33x performance improvement on long nanopore reads (10kb-100kb), and up to 1.87x performance gain on super long reads (100kb-300kb) compared to SIMD accelerated mm2-fast. mm2-gb is open-sourced and available at https://github.com/Minimap2onGPU/mm2-gb .
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".