Reproducible GPU Validation: 95%+ Utilization on H100 with Ecosystem Compatibility

Accelerated Computing CUDA CUDA Programming and Performance
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Reproducible GPU Validation with Ecosystem Compatibility: Three Prototypes, One Environment, 95%+ Utilization

The Problem

When building GPU-accelerated prototypes, you often face:

  • Dependency conflicts: Multiple prototypes require different versions of the same library
  • Unreproducible metrics: Benchmarks run differently each time, making it hard to validate improvements
  • Isolated examples: cuQuantum tutorials show single-use cases, not integrated workflows
  • 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 and zero conflicts.

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
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%
Team3 Innovation cuQuantum contraction + sustained soak NVML GPU util avg 95.47% + cuquantum_used=true

Quick Start

One-Command Installation (GPU, CUDA 12)

python -m pip install -U pip
python -m pip install -r prototypes/requirements.gpu-cu12.txt
DEVICE=cuda python prototypes/ecosystem_smoke.py

With cuQuantum (Team3 acceleration)

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

  1. Ecosystem compatibility: Three prototypes, one environment, zero conflicts
  2. Reproducible metrics: JSON artifacts (latest.json) provide authoritative benchmarks
  3. CPU fallback: Test on laptops, deploy on GPUs — same code, different performance
  4. cuQuantum integration: Team3 demonstrates real cuQuantum usage in a complete workflow

NVIDIA Technologies Used

  • CUDA 12.x (PyTorch CUDA)
  • Tensor Cores (BF16/FP16 matmul soak)
  • NVML (nvidia-ml-py) for utilization/memory metrics
  • cuQuantum (Team3): cutensornet / tensornet contractions + custatevec

Try It Yourself

# Clone the repository
git clone https://github.com/Corusant-world/quantumflow-prototypes.git
cd quantumflow-prototypes

# Install dependencies
python -m pip install -r prototypes/requirements.gpu-cu12.txt

# Run ecosystem smoke test
DEVICE=cuda python prototypes/ecosystem_smoke.py

# Run individual benchmarks
python prototypes/team1_quantum/benchmarks/run_benchmarks.py
python prototypes/team2_energy/benchmarks/run_benchmarks.py
python prototypes/team3_innovation/benchmarks/run_benchmarks.py

Links

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.

cuQuantum Integration: 95%+ GPU Utilization on H100 (QuantumFlow)