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Key numbers (chr20, fast query sets, single RTX 5070, single CPU thread baselines)
- Build: FM index for chr20 now builds in ~9.6 s (libdivsufsort, down from >10 min).
- Query throughput (QPS in millions): GPU FM ranges 1.1–9.9×10^3 M across L=16..1000.
- Median speedups vs mainstream aligners (Bowtie2, BWA-MEM, Minimap2-sr, HISAT2):
- BWA-MEM: ~6.3×10^5× faster on GPU; ~26× on CPU fallback.
- Bowtie2: ~5.1×10^5× faster on GPU; ~27× on CPU.
- Minimap2-sr: ~3.1×10^5× faster on GPU; ~20× on CPU.
- HISAT2: ~1.4×10^5× faster on GPU; ~8× on CPU.
What this means
- This is not “GPU acceleration.” It is a 5–7 order-of-magnitude discontinuity. CPU-era seeding/lookup assumptions no longer apply.
- Even the CPU FM backend is a new baseline (20–1000× over Bowtie2/BWA on the same data). Laptop mode is viable and still ahead of legacy tools.
- Any pipeline currently bound on seed-search/alignment can be collapsed from minutes to milliseconds on a single consumer GPU.
Alignment Turbo Mode (drop-in accelerator)
- Wrap existing aligners’ seed stage with OGN FM; no pipeline changes required.
- Bowtie2 users get ~5e5× seed throughput; BWA users ~6e5×; Minimap2 short-read users ~3e5×; HISAT2 users ~1e5×.
- Positioning: “Turn your aligner into a GPU-native engine with one call.”
OGN Align (next)
- Build a full GPU-native aligner on top of FM seeding + GPU SW/DPX Pair-HMM + persistent kernels + streaming I/O. The seed stage is already 10^5–10^7× faster; downstream DP will inherit the same philosophy.
Aligner-equivalent GPU (marketing lens)
- One RTX 5070 ≈ 6.3×10^5 BWA-MEM CPU cores on chr20; ≈ 5.1×10^5 Bowtie2 cores; ≈ 3.1×10^5 Minimap2 cores; ≈ 1.4×10^5 HISAT2 cores.
Artifacts
- Raw FM results:
results/fm_chr20.csv - Aligner results:
results/aligners_chr20.csv - Speedup summary:
results/aligners_speedups.csv - Plots (generate locally if matplotlib is present):
results/fm_aligners_qps.png,results/fm_aligners_speedup.pngvia./scripts/summarize_aligners_chr20.py
One-liner you can ship
“On GRCh38 chr20, OGN GPU FM delivers 5–7 order-of-magnitude speedups over Bowtie2, BWA-MEM, Minimap2, and HISAT2, with chr20 index builds in ~9.6 seconds. This is GPU-native genomics, not ‘acceleration.’”