# Accelerate

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Shrishti Shah](https://cerebralvalley.ai/u/Shrishti18)
- **GitHub:** https://github.com/Shrishti18/gpu-sceptre
- **Demo video:** https://youtu.be/8uAsuCAQXfc
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/298

gpu-sceptre is an independent, from-scratch GPU implementation of SCEPTRE (Barry et al., 2021), the calibrated conditional-resampling test for single-cell CRISPR (Perturb-seq) screens. We re-expressed both expensive stages — the per-gene negative-binomial GLM precompute and the per-pair resampling kernel — as batched float64 tensor operations, so the entire screen runs on a single GPU.

What we found: on SCEPTRE's own example dataset (526 genes × 45,919 cells), a single free Colab T4 ran the analysis ~29× faster than SCEPTRE on one CPU core and ~23× faster than on two — SCEPTRE parallelizes at only ~63% efficiency, so matching one GPU takes ~30+ cores. Results match an independent double-precision reference to machine precision (~1e-15 kernel, ~1e-10 precompute); null p-values stay calibrated and all planted knockdowns are recovered.

Why it matters: SCEPTRE's statistical rigor normally requires a CPU cluster. gpu-sceptre puts the gold-standard method on hardware any lab or student already has for free — and every number is reproducible from committed scripts. Saves a lot of time hence accelerate the research.

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