mannetroll-cupyxturbo 0.1.0

2D Turbulence (SciPy + CuPy)

cupyxturbo — 2D Turbulence Simulation (SciPy / CuPy)

scipyturbo is a Direct Numerical Simulation (DNS) code for 2D Homogeneous Turbulence

It supports:

• SciPy for CPU runs
• CuPy (optional) for GPU acceleration on CUDA devices (e.g. RTX 3090)

The solver contains:

• PAO-style random-field initialization
• 3/2 de-aliasing in spectral space
• Crank–Nicolson time integration
• CFL-based adaptive time-stepping

Installation

Using uv

From the project root:

$ uv sync
$ uv run python -m scipyturbo.turbo_main

The DNS with SciPy (384 x 384)

Full CLI

$ python -m scipyturbo.turbo_simulator N Re K0 STEPS CFL BACKEND

Where:

• N — grid size (e.g. 256, 512)
• Re — Reynolds number (e.g. 10000)
• K0 — peak wavenumber of the energy spectrum
• STEPS — number of time steps
• CFL — target CFL number (e.g. 0.75)
• BACKEND — "cpu", "gpu", or "auto"

Examples:

# CPU run (SciPy with 4 workers)
$ python -m scipyturbo.turbo_simulator 256 10000 10 1001 0.75 cpu

# Auto-select backend (GPU if CuPy + CUDA are available)
$ python -m scipyturbo.turbo_simulator 256 10000 10 1001 0.75 auto

Enabling GPU with CuPy (CUDA 13)

On a CUDA machine (e.g. RTX 3090):

1. Check that the driver/CUDA are available:

   $ nvidia-smi | head -n 3
   

2. Install CuPy into the uv environment:

   $ uv sync
   $ uv pip install cupy
   

3. Verify that CuPy sees the GPU:

   $ uv run python -c "import cupy as cp; x = cp.arange(5); print(x, x.device)"
   

4. Run in GPU mode:

   $ uv run python -m scipyturbo.turbo_simulator 256 10000 10 1001 0.75 gpu
   

Or let the backend auto-detect:

   $ uv run python -m scipyturbo.turbo_simulator 256 10000 10 1001 0.75 auto

The DNS with CuPy (4096 x 4096)

Profiling

cProfile (CPU)

$ python -m cProfile -o turbo_simulator.prof -m scipyturbo.turbo_simulator    

Inspect the results:

$ python -m pstats turbo_simulator.prof
# inside pstats:
turbo_simulator.prof% sort time
turbo_simulator.prof% stats 20

GUI profiling with SnakeViz

Install SnakeViz:

$ uv pip install snakeviz

Visualize the profile:

$ snakeviz turbo_simulator.prof

Memory & CPU profiling with Scalene (GUI)

Install Scalene:

$ uv pip install scalene

Run with GUI report:

$ scalene -m scipyturbo.turbo_simulator 256 10000 10 201 0.75 cpu

Memory & CPU profiling with Scalene (CLI only)

For a terminal-only summary:

$ scalene --cli --cpu -m scipyturbo.turbo_simulator 256 10000 10 201 0.75 cpu

Project layout (key modules)

• scipyturbo/turbo_main.py
  PyQt6 GUI viewer; displays DNS fields (U, V, ω, kinetic) in real time.

• scipyturbo/turbo_simulator.py
  Headless CLI DNS solver:

  • PAO initialization (dns_pao_host_init)
  • FFT helpers (vfft_full_*)
  • STEP2A, STEP2B, STEP3
  • CFL-based time-step control (compute_cflm, next_dt)

• scipyturbo/turbo_wrapper.py
  Thin wrapper for programmatic use.

one-liner

$ curl -LsSf https://astral.sh/uv/install.sh | sh
$ uv cache clean mannetroll-cupyxturbo
$ uv run --python 3.13 --with mannetroll-cupyxturbo==0.1.0 python -m scipyturbo.turbo_main

License

Copyright © Mannetroll See the project repository for license details.