Reproducible GPU Validation with cuQuantum Integration: 95%+ Utilization on H100
The Problem
When building GPU-accelerated prototypes with cuQuantum, you often face:
- Isolated examples: cuQuantum tutorials show single-use cases, not integrated workflows
- Unreproducible metrics: Benchmarks run differently each time, making it hard to validate improvements
- Dependency conflicts: Multiple prototypes require different versions of the same library
- Setup friction: Each prototype has its own installation process
I set out to prove that three GPU-first prototypes can run together in one environment with reproducible benchmarks, zero conflicts, and real cuQuantum integration.
The Solution
I built QuantumFlow: three prototypes that share dependencies, run in one Python environment, and produce reproducible JSON artifacts.
Key Results (NVIDIA H100 PCIe)
| Component | What it proves | Key metric |
|---|---|---|
| Team3 Innovation | cuQuantum contraction + sustained soak | NVML GPU util avg 95.47% + cuquantum_used=true |
| Team1 Quantum | Tensor Core-heavy screening workload | NVML GPU util avg 95.19% |
| Team2 Energy | Differentiable thermo + grid optimization | NVML GPU util avg 95.44% |
Quick Start (cuQuantum)
python -m pip install -U pip
python -m pip install -r prototypes/requirements.gpu-cu12-cuquantum.txt
python -c "from cuquantum import cutensornet, custatevec; print('OK')"
DEVICE=cuda python prototypes/ecosystem_smoke.py
What Makes This Different
- cuQuantum integration: Team3 demonstrates real cuQuantum usage (
cutensornet/tensornetcontractions +custatevec) in a complete workflow - Ecosystem compatibility: Three prototypes, one environment, zero conflicts
- Reproducible metrics: JSON artifacts (
latest.json) provide authoritative benchmarks - CPU fallback: Test on laptops, deploy on GPUs — same code, different performance
NVIDIA Technologies Used
- cuQuantum (Team3):
cutensornet/tensornetcontractions +custatevec - CUDA 12.x (PyTorch CUDA)
- Tensor Cores (BF16/FP16 matmul soak)
- NVML (
nvidia-ml-py) for utilization/memory metrics
Try It Yourself
# Clone the repository
git clone https://github.com/Corusant-world/quantumflow-prototypes.git
cd quantumflow-prototypes
# Install cuQuantum dependencies
python -m pip install -r prototypes/requirements.gpu-cu12-cuquantum.txt
# Run ecosystem smoke test
DEVICE=cuda python prototypes/ecosystem_smoke.py
# Run Team3 Innovation benchmark
python prototypes/team3_innovation/benchmarks/run_benchmarks.py
Links
- Main Post (CUDA Programming and Performance): Reproducible GPU Validation: 95%+ Utilization on H100 with Ecosystem Compatibility
- GitHub Repository: GitHub - Corusant-world/quantumflow-prototypes: GPU-accelerated prototypes ecosystem achieving 95%+ GPU utilization on NVIDIA H100 with reproducible benchmarks, cuQuantum integration, and ecosystem compatibility proof.
- Documentation: quantumflow-prototypes/README.md at main · Corusant-world/quantumflow-prototypes · GitHub
- Release Notes: Release QuantumFlow v0.1.1 — Initial Release · Corusant-world/quantumflow-prototypes · GitHub
This is just the beginning
This is the beginning of a quantum-accelerated tools ecosystem. I’m building not just prototypes, but a foundation for reproducible GPU development workflows that can scale from research to production.
Built on NVIDIA CUDA platform. I’m pushing the boundaries of GPU-accelerated computing, demonstrating reproducible development practices and ecosystem compatibility at scale.