HelixAccel analyzes your pipelines and automatically selects the fastest, most cost-efficient execution across CPU and GPU — without changing your workflow.
Built for modern bioinformatics & pharma R&D teams
Modern datasets have outgrown the infrastructure they run on.
Single-cell and genomics workflows can take hours or days to complete, blocking iteration.
Scaling datasets leads to exponential infrastructure spend — without proportional results.
Teams burn engineering time tuning workflows across CPU, GPU, and tool combinations.
We analyze your dataset and workflow, then dynamically choose the most efficient execution strategy — across CPU, GPU, and optimized algorithms.
Automatically selects the best compute path based on dataset size and structure.
Uses GPUs where they help. Avoids them where they don't. No manual tuning.
Minimizes cloud spend while maintaining full scientific accuracy.
Four steps. No workflow changes required.
Profile your dataset — size, sparsity, complexity.
Choose optimal execution: CPU vs GPU, exact vs approximate.
Run the optimized pipeline across the right hardware.
Ensure biological outputs remain consistent and reproducible.
Benchmarked on a single-cell RNA-seq workflow (1.3M cells).
| Pipeline | Runtime | Cost | Speedup |
|---|---|---|---|
| Standard (CPU) | 120 min | $100 | 1.0× |
| GPU baseline | 35 min | $90 | 3.4× |
| HelixAccel | 20 min | $40 | 6.0× |
Same scientific output. Verified bit-for-bit on downstream clustering and DE results.
Accelerate clustering, PCA, and neighbor graph construction on large cell atlases.
Optimize alignment, variant calling, and downstream analysis end-to-end.
Handle large, complex, integrated datasets with consistent runtime.
HelixAccel integrates seamlessly with the tools your team already uses. No need to rewrite your workflows.
import scanpy as sc import helixaccel as hx adata = sc.read_h5ad("atlas.h5ad") # One line. No workflow changes. with hx.optimize(): sc.pp.neighbors(adata) sc.tl.umap(adata) sc.tl.leiden(adata)
We'll optimize one of your pipelines in under 2 weeks. No workflow changes required.